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  • Published: 03 August 2021

Macroscale patterns of oceanic zooplankton composition and size structure

  • Manoela C. Brandão 1 , 2   na1 ,
  • Fabio Benedetti 3   na1 ,
  • Séverine Martini 4 ,
  • Yawouvi Dodji Soviadan 1 ,
  • Jean-Olivier Irisson 1 ,
  • Jean-Baptiste Romagnan 5 ,
  • Amanda Elineau 1 ,
  • Corinne Desnos 1 ,
  • Laëtitia Jalabert 1 ,
  • Andrea S. Freire 6 ,
  • Marc Picheral 1 ,
  • Lionel Guidi 1 ,
  • Gabriel Gorsky 1 ,
  • Chris Bowler 7 , 8 ,
  • Lee Karp-Boss 9 ,
  • Nicolas Henry 8 , 10 ,
  • Colomban de Vargas 8 , 10 ,
  • Matthew B. Sullivan 11 ,
  • Tara Oceans Consortium Coordinators ,
  • Lars Stemmann 1 , 8 &
  • Fabien Lombard 1 , 8 , 12  

Scientific Reports volume  11 , Article number:  15714 ( 2021 ) Cite this article

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  • Ocean sciences

An Author Correction to this article was published on 06 October 2021

This article has been updated

Ocean plankton comprise organisms from viruses to fish larvae that are fundamental to ecosystem functioning and the provision of marine services such as fisheries and CO 2 sequestration. The latter services are partly governed by variations in plankton community composition and the expression of traits such as body size at community-level. While community assembly has been thoroughly studied for the smaller end of the plankton size spectrum, the larger end comprises ectotherms that are often studied at the species, or group-level, rather than as communities. The body size of marine ectotherms decreases with temperature, but controls on community-level traits remain elusive, hindering the predictability of marine services provision. Here, we leverage Tara Oceans datasets to determine how zooplankton community composition and size structure varies with latitude, temperature and productivity-related covariates in the global surface ocean. Zooplankton abundance and median size decreased towards warmer and less productive environments, as a result of changes in copepod composition. However, some clades displayed the opposite relationships, which may be ascribed to alternative feeding strategies. Given that climate models predict increasingly warmed and stratified oceans, our findings suggest that zooplankton communities will shift towards smaller organisms which might weaken their contribution to the biological carbon pump.

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Introduction.

Body size has been defined as a “master trait” for plankton as it is a morphological characteristic shared by organisms across taxonomy and that characterizes the functions performed by organisms in ecosystems 1 , 2 . It has a paramount effect on growth, reproduction, feeding strategies and mortality 3 . One of the oldest manifestations of the biogeography of traits was proposed over 170 years ago, namely Bergmann’s rule, in which field observations showed that larger species tend to be found at higher, colder latitudes 4 .

In the oceans, size is critical in determining trophic links in planktonic ecosystems and is thus a critical factor in regulating the efficiency of the biological carbon pump 5 . Body size is sensitive to changes in temperature due to the thermal dependence of physiological processes 6 . The plankton is mainly composed of ectotherms which are organisms that do not generate sufficient metabolic heat to elevate their body temperature, so their metabolic processes depends on external temperature 7 . Consequently, ectotherms grow more slowly and reach maturity at a larger body size in colder environments, which has long puzzled biologists because classic theories of life-history evolution predict smaller adult sizes in environments delaying growth 8 . This pattern of body size variation, known as the temperature-size rule (TSR 9 ), has been observed for a wide range of ectotherms, including single-celled and multicellular species, invertebrates and vertebrates 8 , 10 .

The processes underlying the inverse relationship between body size and temperature remain to be identified 8 . Despite temperature playing a major role in shaping latitudinal variations in organism size, these patterns may also rely on complex interactions between physical, chemical and biological factors. For instance, oxygen supply plays a central role in determining the magnitude of ectothermic temperature-size responses, but it is hard to disentangle the relative effects of oxygen and temperature from field data because these two variables are often strongly inter-related in the surface ocean 11 , 12 .

The major drivers of community-level plankton size structure (i.e. distribution of individual body size in a given community) must be identified to effectively perform the ecological predictions that are progressively requested in a context of climate change 13 . Global patterns of phytoplankton biomass, size and community composition have been extensively studied thanks to satellite sensors that can detect phytoplankton pigments from space. Satellite observations showed that larger phytoplankton dominate in upwelling regions and at high latitudes where seasonal mixing regimes elicit higher macronutrients availability 14 , 15 . In contrast, zooplankton size structure and composition remain challenging to study in situ and remain poorly constrained by observations. Body size variations of planktonic copepods have been derived from literature-based relationships and have been found to display latitudinal patterns driven by variations in temperature and primary production 16 . Previous studies showed that temperature, rather than food availability, is the dominant variable in explaining variations in copepod body size 17 . Body size can be altered experimentally in the laboratory 18 , 19 . However, how these species-specific-based and/or laboratory-based observations can be transferred to the size structure of natural communities remains unclear. Knowing how size structure and abundance scale with changing abiotic conditions at the community level is critical because these factors determine the production and the functioning of the entire ecosystem 20 .

Here, we use plankton samples homogeneously collected at a macroscale during the Tara Oceans expeditions (2009–2013) that were analyzed with the ZooScan imaging system 21 to document how zooplankton composition (i.e., the abundance of different groups) and size structure at the community level varies with latitude, temperature, oxygen, macronutrient concentrations, phytoplankton biomass and other ecosystem properties. We develop multivariate regression models to identify the underlying drivers of the global gradients of abundance and size structure for more than 30 zooplankton clades.

Latitudinal patterns of zooplankton abundance and composition

Based on the Zooscan analysis of the WP2 (200 µm mesh), Bongo (300 µm mesh) and Régent (680 µm mesh) net samples, we found that most of the 36 zooplankton groups retained displayed significant latitudinal patterns of abundance (Fig.  1 ). Here, we focused on the significant patterns observed for total zooplankton and those broad groups displaying the highest contributions to total abundance (i.e., Copepoda, Rhizaria, Cnidaria, Tunicata, Chaetognatha and Ostracoda plus Cladocera) based on WP2 net samples, as this net showed the broadest spatial coverage (Supplementary Fig. S1 ). The spatial patterns were nonetheless consistent across all three nets and for all other groups (Supplementary Fig. S2 ). Total zooplankton and its main constituting groups displayed non-monotonic gradients of abundance with peaks in the Arctic and/or near the equator, and depressions in the tropical gyres. Zooplankton abundance was highest in the Arctic (Fig.  1 a,b), north of 60°N, and decreased progressively towards the equator. A secondary peak was visible near the equator because of the relatively higher abundance in the eastern tropical Pacific Ocean. Zooplankton abundance decreased towards the Southern Ocean, whose few sampled stations displayed the lowest abundances.

figure 1

Maps and latitudinal patterns of the abundance (cubic-transformed ind m 3 ) of ( a , b ) Total zooplankton, ( c , d ) Copepoda, ( e , f ) Rhizaria, ( g , h ) Cnidaria, ( i , j ) Tunicata, ( k , l ) Chaetognatha, and ( m , n ) Ostracoda + Cladocera observed in samples collected by the WP2 net. The solid curves on the right-hand side plots illustrate the prediction from the Generalized Additive Model (GAM) fitting abundance against latitude. The explanatory power of the GAM (adjusted R 2 ), the number of samples used and the significance of the smooth term (p < 0.001 = ***, p < 0.01 = ***, p < 0.05 = *, p > 0.05 = ns) are reported on the plots. The grey ribbon illustrates the standard error of the GAM prediction.

Gradients in zooplankton abundance were clearly driven by copepods (Fig.  1 c,d) as those dominated community composition (74% of total abundance in WP2 samples, 73% and 82% in the Bongo and Régent samples, respectively; Supplementary Doc. S3 ). Copepods displayed the same abundance pattern as total zooplankton but showed a slightly weaker tropical peak. The latter was actually more marked for other groups, especially the Rhizaria (Fig.  1 e,f) that showed very low abundances towards the poles. Gelatinous groups displayed contrasted patterns. Latitudinal gradients were more marked for Tunicata (Fig.  1 i,j) and Chaetognatha (Fig.  1 k,l) than for total zooplankton as their abundance levels observed in tropical upwelling regions compete with those observed in the Arctic Ocean. Carnivorous jellyfishes (Cnidaria; Fig.  1 g,h) displayed a weakly significant latitudinal pattern that was driven by higher abundances in the western Arctic Ocean. Eumalacostraca (i.e., macrozooplankton such as euphausiids, amphipods and decapods) also showed strong bimodal gradient but only in the Régent data, and pteropods showed no distinguishable latitudinal abundance pattern (Supplementary Fig. S2 ).

Considering the dominance of copepods in terms of abundances in the communities sampled, we examined the underlying latitudinal gradients in copepod order and family composition (Fig.  2 ). All nets (Fig.  2 a–c) showed an increase in the relative contribution of calanoid families, and especially the large-bodied Calanidae, to the detriment of Cyclopoida (Oithonidae) and Poecilostomatoida (Oncaeidae, Corycaeidae and Sapphirinidae). The relative abundances of copepod families were more evenly distributed in the tropics than in the poles, reflecting gradients of decreasing copepod diversity with latitude (already documented by Ibarbalz et al. 22 ). The variations in copepod abundance were driven by the increase in calanoids (mainly Calanidae) and oithonids towards the Arctic Ocean (Supplementary Fig. S2 ). Conversely, the following families showed clear abundance peaks in tropical regions (gyres or upwelling): Augaptilidae, Candaciidae, Corycaeidae, Eucalanidae, Euchaetidae, Oncaeidae, Paracalanidae, Sapphirinidae and Temoridae. The WP2 data showed less marked variations (Fig.  2 a) as this net better samples the smaller Poecilostomatoida and Cyclopoida. The Régent net captured a lower quantity of unidentified Calanoida (Fig.  2 c) as the WP2 and Bongo nets (Fig.  2 b) as the relatively coarse mesh of this net is not able to retain smaller organisms, a pattern that was found across all zooplankton groups (Supplementary Doc. S4 ). The Bongo net showed lower zooplankton abundances than the WP2 net because of its coarser mesh (1.5 times coarser), yet the global patterns in abundances between these two nets showed relatively high positive correlations (rho > 0.4) for several of the main zooplankton groups (e.g. Total zooplankton, Copepoda, Rhizaria, Eumalacostraca, and Ostracoda + Cladocera; Supplementary Doc. S4 ). Differences between the WP2 samples and the Régent samples were more marked as the latter was equipped with a mesh 3.4 times larger than the former. Only the abundances of total zooplankton, Cnidaria and Eumalacostraca showed relatively high correlations to the WP2 data.

figure 2

Variations in Copepoda community composition across the tropical (0–30°), temperate (30°–60°) and polar (> 60°) latitudinal bands, depicted through the changes in relative abundances of the copepod Orders (Calanoida, Cyclopoida and Poecilostomatoida) and Families sampled by the ( a ) Bongo net, ( b ) WP2 net, and ( c ) Régent net. Taxa with lower than 1% are not shown. Unidentified categories correspond to those organisms that could be assigned to an Order but not to a Family because of the limited resolution of the imaging system.

Latitudinal patterns of zooplankton size structure

Variations in median Equivalent Spherical Diameter (ESD) were explored to examine latitudinal patterns in zooplankton size structure. The most consistent cross-net patterns of median ESD were found for the total zooplankton community, which was driven by the median ESD of calanoid copepods (Fig.  3 ; see Supplementary Fig. S5 for the other groups). The most prominent feature of the copepod median ESD pattern was a sharp decline from the Arctic to the equator, which was more marked in the Bongo (Fig.  3 b) and Régent data (Fig.  3 c) than in the WP2 (Fig.  3 a). In the southern hemisphere, patterns differed across nets: copepod median ESD sharply increased towards the Southern Ocean according to the Régent net, whereas it showed no variations or a slight decrease according to the WP2 and Bongo samples, respectively. Considering the relatively poor coverage of the Southern Ocean by Tara Oceans, these latter patterns should be interpreted with caution. According to the WP2, the net that best sampled the smaller Poecilostomatoida, the latter showed median ESD patterns that were opposite to the Calanoida: their median ESD clearly increased from the poles to the tropics and peaked in the southern hemisphere around 30°S (Supplementary Fig. S5 ).

figure 3

Maps and latitudinal patterns of the logged median Equivalent Spherical Diameter (ESD, µm) observed for Copepoda based on ( a , b ) WP2 samples (200 µm mesh), ( c , d ) Bongo samples (300 µm mesh) and ( e , f ) Régent samples (680 µm mesh). The major and minor axes of the best fitting ellipses were measured for each organism to estimate their ESD. Community-level size structure was determined through the median value of the ESD distribution at individual-level. The solid curves in the right-hand side plots illustrate the prediction from the Generalized Additive Model (GAM) fitting median ESD as a function of latitude. The explanatory power of the GAM (adjusted R2), the number of samples used and the significance of the smooth term (p < 0.001 = ***, p < 0.01 = ***, p < 0.05 = *, p > 0.05 = ns) are reported on the plots. The grey ribbon illustrates the standard error of the prediction. Only the stations where ESD was measured for at least 20 individuals were considered.

Contrary to abundances, a secondary tropical peak in median ESD was not observed for zooplankton (Supplementary Fig. S5 ). Abundance and median ESD were significantly positively correlated for total zooplankton in the WP2 and Régent data, and for the Copepoda and Calanoida in all nets (Supplementary Table S6 ).

Among non-copepod groups, the Cnidaria also showed a sharp decrease in median ESD from the Arctic Ocean to the equator in both WP2 and Régent samples (Supplementary Fig. S5 ). The median ESD of Rhizaria followed the opposite pattern according to the WP2 and Bongo samples as it peaked around 40°N and decreased towards lower latitudes. Our approach did not detect clear latitudinal gradients in median ESD for most of the other zooplankton groups (Supplementary Table S7 ), either because of insufficient observations or because median ESD is not controlled by factors that vary latitudinally. Therefore, we examined the potential environmental drivers of median ESD variations to help us explain why size structure estimates display less marked latitudinal patterns.

Relationships with environmental covariates

The strength of the linear covariance between the groups’ abundance, median ESD and environmental covariates was examined through non parametric correlation coefficients (Fig.  4 ; Supplementary Fig. S8 ). The median ESD of most zooplankton groups displayed similar significant correlation patterns across nets: the median ESD of total zooplankton, Copepoda, Calanoida, Cnidaria and Eumalacostraca decreased with temperature, salinity and picophytoplankton (%Pico), but increased weakly with oxygen, chlorophyll a, macronutrient concentrations, microphytoplankton (%Micro) and the intensity of particles backscattering (bbp470). Total zooplankton median ESD decreased significantly with Mixed Layer Depth (MLD) only in the WP2 samples (Fig.  4 a), a pattern driven by the Calanoida. The median ESD of the Poecilostomatoida increased with temperature, salinity, %Pico and Photosynthetically Active Radiation (PAR). We also found PAR to be the main covariate associated with a lower median ESD of Rhizaria according to the Régent data (Fig.  4 c). The Rhizaria showed less significant correlations but differed from the main pattern as their median ESD slightly increased with %Micro and decreases with %Pico and PAR. The only groups displaying a similar pattern were the Tunicata and to a lesser extent the Chaetognatha (Supplementary Fig. S8 ).

figure 4

Heatmaps of the Spearman’s rank correlation coefficients computed between the size structure (i.e., logged median Equivalent Spherical Diameter; ESD) of the main zooplankton groups and the selected 14 covariates depicting the environmental conditions in the global surface ocean as sampled by ( a ) WP2 net (200 µm mesh), ( b ) Bongo net (300 µm mesh) and ( c ) Régent net (680 µm). The significance of the Spearman’s rank correlation tests are reported in the tiles (p < 0.001 = ***, p < 0.01 = ***, p < 0.05 = *, p > 0.05 = ns). Only the zooplankton groups displaying significant correlation coefficients for more than one environmental covariate in at least one net parameter are shown (see Supplementary Fig. S8 for all groups). Only the stations where ESD was measured for at least 20 individuals of a group were considered when computing the correlation coefficients. Distance stands for distance to coast (in km).

Zooplankton abundances displayed stronger correlation patterns than median ESD (Supplementary Fig. S8 ) and seem to be more strongly linked to productivity-related covariates (i.e. chlorophyll a, bbp470, %Micro, %Pico and macronutrient concentrations) than physical ones (i.e. temperature and oxygen). The abundance of most groups increased significantly with chlorophyll a, macronutrient concentrations, %Micro and bbp47, but decreased with %Pico. The abundance of some groups presented correlation patterns that departed from the abovementioned trend as they increased with temperature and decreased with oxygen (Supplementary Fig. S8 ): Rhizaria (WP2 and Régent), Eumalacostraca (Régent mainly), Chaetognatha and Poecilostomatoida (WP2 only).

Nonlinear relationships between median ESD estimates and a subset of environmental covariates were explored through Generalized Additive Models (GAMs, see “ Methods ”) to identify and rank the drivers of size structure of zooplankton groups (Table 1 ). In total, 102 GAMs were fitted to median ESD estimates (n = 40 for the WP2 and Bongo data, n = 22 for the Régent; Supplementary Table S7 ). These GAMs showed reasonable to good fit as the median (± IQR) %Dev was 53.6% (± 33.4%). The GAMs based on the Régent observations displayed significantly higher %Dev (57.9% ± 24.7%) than those based on the WP2 (55.4% ± 31.3%) and Bongo (48.7% ± 34.9%) (Kruskal–Wallis test, Chi 2  = 143.6, p < 2.2 × 10 −16 ). The GAMs including temperature did not show higher %Dev than those including oxygen except with the Régent data but the difference was found to be marginal (Chi 2  = 19.1, p = 1.3 × 10 −5 ). Substantial variations in smooth term rankings were visible across nets and zooplankton groups (Table 1 ; Supplementary Fig. S9 ). Oxygen and temperature were the two top-ranking significant covariates, while the remaining eight covariates displayed lower median ranks (Supplementary Fig. S9 ) though some (e.g., salinity, MLD, chlorophyll a or %Micro) emerged as key covariates for modelling the median ESD of some groups (Table 1 ).

The smoothing curves of the GAMs displaying a %Dev > 50% were extracted to cluster the groups based on the shape of these curves along with each covariate (see Methods ). This way, we were able to identify clusters of zooplankton groups displaying similar functional responses to the covariates selected (i.e. zooplankton groups sharing similar drivers of global median ESD), and we could project their similarities in a two dimensional metric dimensional scaling (MDS) space to summarize the main trends. Four clusters were identified (Table 1 and Fig.  5 ). Cluster 1 comprised six models with a mix of Bongo and Régent observations: the median ESD of total zooplankton and the Calanoida (Bongo), Copepoda and Calanoida (Régent) and Cladocera + Ostracoda (Bongo). This cluster gathered groups whose median ESD showed linear increases with oxygen and PAR and no response to temperature (Supplementary Fig. S10 ). The smoothing curves modelled for the other covariates were either non-significant or highly variable between groups (Fig.  5 ; Supplementary Fig. S10 ). The smoothing curves of the zooplankton WP2 data and its main driving group (i.e. calanoid copepods) were clustered with the Tunicata and Eumalacostraca (WP2), the Copepoda (Bongo) and the Chaetognatha (Régent). Contrary to cluster 1, these groups displayed non linear decreases in median ESD with temperature and relatively strong non liner increases with oxygen. Cluster 3 was the largest as it comprised nine models from various groups and nets: Cyclopoida, Poecilostomatoida and the Rhizaria (all WP2), the Eumalacostraca (Bongo and Régent), the Cladocera + Ostracoda (Régent) and the gelatinous zooplankton (Cnidaria, Tunicata and Chaetognatha) sampled with the Bongo net. Because of the cluster’s larger size, the response curves modelled for these groups were diverse. The main trend was an overall non linear decrease in median ESD with oxygen concentration. Finally, Cluster 4 gathered a single model (Cnidaria, WP2) meaning it displayed an original combination of modelled response curves. The median ESD of the Cnidaria (WP2) decreased linearly with temperature and increased non linearly with oxygen, and it departed from the other groups because of its strong linear decreases with salinity, particles backscattering and distance to coast (Supplementary Fig. S10 ).

figure 5

Two dimensional metric dimensional scaling (MDS) plot illustrating the similarity between the responses of the groups’ median ESD to the environmental covariates selected. The smoothing curves from the Generalized Additive Models (GAMs) modelling the global gradients in log-transformed median Equivalent Spherical Diameter (ESD, µm) of the zooplankton groups (estimated for various plankton nets) as a function of ten environmental covariates and displaying a deviance explained > 40%. The smoothing curves were combined into a multivariate data series to compute Dynamic Time Warping (DTW) distances and perform partitioning around medoids (PAM) clustering. This way the GAMs were clustered into four clusters representing combinations of zooplankton groups and plankton nets that exhibit similar median ESD-covariate relationships.

The same approach was applied to investigate the drivers of global abundance patterns (Table 2 ; Supplementary Table S7 ). The median ESD-based GAMs display higher %Dev than the abundance-based GAMs (40.7% ± 27.3%; Chi 2  = 1697.3.6, p < 2.2 × 10 −16 ) whatever the net, despite the higher number of observations available for modelling abundances (i.e. 200 GAMs were fitted based on the transformed abundance data). The WP2-based GAMs presented slightly higher %Dev (Chi 2  = 62.9, p = 2.2 × 10 −14 ) than the ones based on the Régent and Bongo observations. The GAMs including temperature displayed a lower %Dev than those including oxygen (37.9 ± 25.8 versus 42.5 ± 27.2; Chi 2  = 144.1, p < 2.2 × 10 −16 ). Contrary to median ESD-GAMs, the inclusion of oxygen instead of temperature substantially increased the %Dev for total zooplankton and Calanoida, and Cyclopoida (Table 2 ), implying that oxygen could be a stronger driver than temperature for zooplankton abundances. Again, the smooth terms associated with temperature and oxygen emerged as the two most significant terms (Table 2 ; Supplementary Fig. S9 ). Substantial variations in smooth terms rankings were observed across nets and groups again (Table 2 ). However, NO 2 NO 3 concentrations, chlorophyll a and MLD showed higher significance rankings than in the ESD-based GAMs (Table 2 ), implying these covariates were more critical to include when modelling zooplankton abundance than size structure.

Again, the smooth curves of the GAMs displaying a %Dev > 40% were extracted to cluster the zooplankton groups based on the similarity of their responses to the covariates. Four clusters could be identified and these are more clearly delineated than those based on the median ESD response curves as evidenced by the relatively more scaterred positions of the groups in MDS space (Fig.  6 ; Supplementary Fig. S11 ). Cluster 1 gathered the smooth curves modelled for total zooplankton, Copepoda and Calanoida based on the Régent data. Their abundances showed: (i) a strong nonlinear decrease with temperature and increase with oxygen concentration, (ii) non linear decreases with salinity and distance to coast, and (iii) slight increases with PAR and NO 2 NO 3 concentrations. Cluster 2 was the largest clusters as it gathered the responses of diverse range of 17 different models based on various groups and nets. This implies that a relative broad ranges of abundances responses within this cluster (Supplementary Fig. S11 ), which is why it holds a relatively neutral central position in the MDS space (Fig.  6 ). Yet, nearly all groups showed null responses in abundances to temperature, except total zooplankton and Copepoda (Bongo data) which showed non linear decreases. Cluster 3 was also a smaller cluster composed of three models only: the Poecilostomatoida (both WP2 and Bongo) and the Tunicata (WP2 only). Contrary to clusters 1 and 2, these were characterized by non linear increases in abundance with temperature and NO 2 NO 3 concentrations but decreases with oxygen and MLD. This is why these groups are positioned on the negative side of MDS2. Finally, cluster 4 also comprised the same groups as cluster 1 but based on the WP2 abundance estimates instead of the Régent ones. Contrary to the latter, total zooplankton and calanoid copepods here showed null response to temperature. Yet, similar to cluster 1, they also showed strong abundances increase with oxygen concentrations, which explains why both clusters are positioned on the positive side of MDS2 (Fig.  6 ). This cluster also displayed original strong gaussian responses to chlorophyll a and particles backscattering.

figure 6

Two dimensional metric dimensional scaling (MDS) plot illustrating the similarity between the responses of the groups’ abundances to the environmental covariates selected. The smoothing curves from the Generalized Additive Models (GAMs) modelling the global gradients in cubic-transformed abundances (ind m 3 ) of the zooplankton groups (estimated for various plankton nets) as a function of ten environmental covariates and displaying a deviance explained > 40%. Smoothing curves span a 1–100 scale spanning the range of the covariates measured values. The smoothing curves were combined into a multivariate data series to compute Dynamic Time Warping (DTW) distances and perform partitioning around medoids (PAM) clustering. This way the GAMs were clustered into four clusters that represent combinations of zooplankton groups and plankton nets that exhibit similar abundance-covariate relationships.

Here, we provide a homogeneous dataset of zooplankton composition and size structure based on individual measurements of body size and document the shape of the relationships between community-level size structure and key environmental drivers on a macroecological scale. We find that zooplankton communities exhibit larger median size and abundance towards the poles and towards the tropical upwelling regions sampled (Supplementary Doc. S12 ), a pattern that is largely driven by copepods. The higher contributions of the large-bodied grazing Calanidae relative to the smaller omnivorous-carnivorous Cyclopoida and Poecilostomatoida (i.e. Oithonidae, Oncaeidae and Corycaeidae) drives the latitudinal increase in median size towards the poles, in addition to the observed negative scaling of body length with temperature which is in line with the TSR 9 , 17 . Indeed, our inspection of size structure-environment relationships show that zooplankton size decreases with temperature, salinity, MLD and the contribution of the smallest phytoplankton cells to phytoplankton biomass. Conversely, it increases with concentrations of oxygen, macronutrients, phytoplankton biomass and the contribution of large phytoplankton (e.g. diatoms) to said biomass. Using species body size estimates from the literature, Brun et al. 16 also found copepod mean body size to increase towards the poles, a pattern driven by a negative temperature-size relationship and a positive relationship between phytoplankton size and zooplankton size. Several explanations for increased body size towards the poles have been proposed, varying from the stimulating effects of temperature on ectotherm metabolism, the synergetic effects of the presence of larger prey, and the availability of oxygen as a function of temperature 2 , 23 . For metazoan ectotherms, the effects of temperature on somatic and gonad growth seem to be the most robust explanation 24 . The negative correlation between community-level size and temperature might stem from the positive effect of temperature on growth rates. At low latitudes, metabolic rates are higher and life cycles become shorter for the various species composing the community. Consequently community-level median size decreases because of warmer temperatures, and the body surface area to body volume ratio increases 25 . Despite decades of research, it is still uncertain whether the temperature-size rule is an adaptive response to temperature‐related physiological processes (i.e. enzyme activity) or ecological constraints (e.g. food availability, predation and other mortality causes), or a response to biological constraints operating at cellular level such as oxygen supply 12 . Arthropods and rotifers have been shown to reach smaller body sizes in poorly oxygenated waters 23 , 26 . The potential role of oxygen concentration on the onset of maturation and on size variations remains unclear and is mostly masked by its strong collinearity with surface temperature 12 .

In contrast to the decrease in zooplankton median size and abundance observed towards oligotrophic subtropical gyres, an increase was observed near the equatorial regions where the upwelling regime creates colder and more productive conditions. We found the main groups of the zooplankton communities sampled in the eastern boundary upwellings (EBUS) to display significantly higher abundances relative to communities sampled at comparable latitudes (Supplementary Doc. S12 ). However, the EBUS do not strongly affect the modelled latitudinal patterns of zooplankton abundance (Supplementary Doc. S12 ). Yet, the effects of the upwelling regime are more marked for abundances than for size structure. This could be linked to the way we estimated median ESD (e.g. aggregated distributions of body size estimated from particles images) compared to the more direct and less uncertain counting of abundance, or to the fact that fewer stations are available when studying size structure gradients (see “ Methods ”). Overall, abundances showed correlation patterns with the environmental covariates that are quite similar to median size for the total zooplankton community and its major constituting groups (Calanoida, but also Tunicata, Chaetognatha and Cnidaria). This suggests that zooplankton size structure and abundance respond similarly to environmental drivers. Temperature and/or oxygen concentration were found to be the two main covariates in explaining the quasi-global variations of both size structure and abundance. However, we found productivity-related covariates (i.e. Chlorophyll a, NO 2 NO 3 concentration, bbp470 and %Micro) to be of higher importance for modelling zooplankton groups abundance. This is an important factor to consider when defining the key parameters to model either zooplankton size or biomass. Our results support the view that temperature and oxygen are more important parameters than the available biomass of photoautotrophs in driving zooplankton community-level and individual-level body size variations 12 , 17 and therefore in controlling the expression of physiological traits that scale allometrically (e.g., growth, respiration).

Yet, the abundance of some zooplankton groups (Poecilostomatoida, Rhizaria and Chaetognatha, and Pteropoda to a lesser extent) show correlation patterns that are opposite to the general copepod-driven trend: their abundance actually increases with temperature, PAR and the contribution of small phytoplankton. These groups rely on feeding strategies that are very different from the filter-feeding Calanoida 16 , 27 , 28 . For instance, the Poecilostomatoida are cruise-feeding and ambush-feeding copepods displaying a broad omnivorous-carnivorous diet 27 , 28 , 29 . Similarly, chaetognaths are carnivorous ambush-feeders and many pteropods deploy mucus nets for feeding passively on particles fluxes 27 . Therefore, these groups are able to thrive in large phytoplankton-depleted conditions where mortality-risks and competition for food are more pronounced than in phytoplankton-replete conditions thanks to their alternative feeding strategies. If their growth and reproduction are less dependent on phytoplankton biomass while still promoted in warmer conditions, then spatial patterns driven by positive temperature-abundance relationships can emerge. Our results further support the view that zooplankton is not a homogeneous category whose size structure and biomass dynamics can be adequately modelled through a few size classes 1 , 30 .

We found the median ESD of large protists (i.e. Rhizaria, which mainly comprise Foraminifera and Radiolaria) to increase linearly with %Micro but to decrease with %Pico, PAR, and chlorophyll a to a lesser extent. Contrary to Copepoda, temperature and oxygen did not show clear effect on the size structure of those large protists as their median ESD shows contrasted responses to these two covariates across nets. Large protists abundance increased significantly with temperature, macronutrients concentrations, bbp470 and decreased significantly with oxygen. Therefore, the drivers underlying the patterns of Rhizaria abundance and size structure seem distinct, or even opposite, to those that govern copepod size structure and abundance patterns. Again, this could be ascribed to their notable difference in life strategies. Numerous species of Rhizaria are large single-celled mixotrophic protists that host obligate intracellular microalgal symbionts (photosymbionts 31 ). Spinose foraminifera show higher contents of chlorophyll a than the shorter non-spinose species 32 . The efficient photosynthesis performed by photosymbionts, promoted in conditions of higher irradiance and macronutrient concentrations, can lead to oxygen concentrations reaching nearly 200% of the oxygen saturation levels 33 , 34 , and potentially even more within their cytoplasm. Such high oxygen availability in the protist cells may weaken the usual temperature- and oxygen-driven constraints on their body size. High oxygen concentrations promote the formation of reactive oxygen species (ROS), which could significantly damage cell structures through the oxidation of DNA, cell membranes or proteins. Overproduction of ROS driven by temperature increase is suspected to trigger coral bleaching, either by symbiont expulsion or digestion 35 . Similar reactions may occur within protists 36 . We hypothesize that large protists attempt to prevent ROS accumulation by optimizing the distance between the photosymbionts and themselves. Indeed, most symbiont-bearing Foraminifera tend to display large spinose formation, as a support for the symbiont swarms located further away from the central shell 33 , 34 , 37 , but also enhance prey encounter rates 38 . Keeping larger sizes to enhance prey capture and avoid ROS could explain the observed stability in median size and abundance of these organisms in the warmer tropical conditions.

The heterogeneity of sampling strategies between surveys usually hinders global scale plankton studies that require the combination of data from multiple oceanographic cruises. The data collected from the Tara Oceans expeditions allow us to examine the in situ properties of plankton communities at a very large spatial scale, thanks to the uniform sampling strategy. However, it should be reminded that the one-time nature of such sampling impedes us from addressing the temporal variations of plankton community size structure across the different provinces studied. In addition, it is also worth to point out that the distribution of the sampling stations are unequal across latitudes (Supplementary Fig. S1 ). Notwithstanding, the latitudinal patterns we observe for copepod size structure are consistent with those of previous studies that resolved seasonal variations 16 , 17 , therefore providing some support for the temporal consistency of our results. The correlations we report between abundance, size structure and the environmental variables do not ascertain the ecological and biological processes through which the observed latitudinal patterns emerge. Nonetheless, correlative studies such as ours are key for identifying the major drivers of biological changes and pinpoint further studies to be performed under more controlled conditions that will seek to identify and test the precise biological processes underlying the patterns.

While the level of taxonomic identification of the ZooScan imaging system remains suitable for a size-based community-level study, it does not enable us to depict finer variations in species composition that could be important to further understand the assembly of plankton communities in response to environmental gradients. However, it allowed us to observe large scale patterns and to identify the shape of the relationships between environmental drivers and size structure that would have taken years to depict through non-automated methods. The observed latitudinal patterns in abundance and size structure are relatively consistent across the three nets used but some discrepancies were found (e.g., unidentified Copepoda, Cyclopoida, or Pteropoda; Supplementary Fig. S2 ). These were likely due to the relative coarse mesh of the Bongo and Régent nets, which underestimated the abundances of most groups (Supplementary Doc. S4 ). Therefore, these nets could have underestimated the strength of some latitudinal abundance and size structure patterns and their relationships to environmental covariates. Discrepancies between the WP2 data and the two other nets could also stem from differences in sampling depth and net tow, which are known to affect plankton community estimates. The potential effects of these sampling parameters remain difficult to describe here, as only the effects of the mesh size could be evaluated (Supplementary Doc. S4 ). While the WP2 net was towed vertically from 100 m depth to the surface while the Bongo and Régent nets were towed obliquely from 500 m to the surface. Although these to nets were equipped with coarser meshes, they were towed deeper so they could have captured the deeper living community better 39 . Nonetheless, considering that most of the zooplankton organisms are concentrated in the 0–200 m layer 39 , we are confident that the sampling design of the present Tara expeditions adequately captured the macroscale patterns of zooplankton community composition.

Our study follows a trait-based approach to examine the distribution of a “master trait” (i.e. body size) to better investigate how community composition relates to ecosystem functioning 1 . We report quasi-global size-latitude relationships in the size structure of major marine zooplankton groups, as well as their scaling with environmental covariates at the community-level. Larger zooplankton are known to enhance energy fluxes to higher trophic levels and to promote carbon export towards deeper layers 40 , 41 . Therefore, our observations bring further support to the view that ongoing global climate warming will elicit a decrease in zooplankton size and lower their contribution to the biological carbon pump 41 as well as to overall metabolic rates 3 . However, fully understanding and predicting such anticipated changes requires a precise parameterization of how environmental conditions impact marine organisms in marine ecosystem models. The representation of plankton diversity in mechanistic marine ecosystem models is improving as the latter may now include from ten 42 to hundreds of plankton functional types in the case of self-assembling traits-based models 43 , 44 . Yet vast inter-model discrepancies exist in terms of their parametrization 45 . Models often aim to validate their parameterization using emergent constraints 46 , 47 . The relationships observed between zooplankton community size structure and environmental covariates, or community biomass per size classes and environmental covariates provide such constraints for model validation and evaluation 45 , 46 , 47 but also shows that one single parametrization is not sufficient to fully capture the variety of the responses observed among plankton organisms. Therefore, our study allows a more precise parametrization of such models, and thus a more precise estimation of future climatic impact on zooplankton organisms abundance, size and by extension effect on the biological carbon pump. We call for closer collaborations between the fields of macroecology, biology, experimental physiology and adaptation to disentangle the roles of multiple drivers in shaping individual traits and the community-level response of marine ecosystems to current and future cumulative effects of stressors, through cell-to-ecosystem studies 48 .

Sample collection

Zooplankton samples and environmental data were collected at 168 stations across all major oceanic provinces during the Tara Oceans expeditions (2009–2013) (Supplementary Fig. S1 ). Zooplankton was collected with three different types of nets to cover the 200–680 µm size range, encompassing most of the organisms constituting the mesozooplankton. A WP2 net of 200 μm mesh size and 0.57 m 2 opening was towed vertically or obliquely from 100 m depth to the surface. A Bongo net and a Régent net, of 300 and 680 μm mesh size (0.57 and 1.12 m 2 opening), respectively, were towed obliquely from 500 m depth to the surface. Samples were preserved with buffered formaldehyde (4%) for later digitization and morphological analyses. The Tara Oceans expeditions sampling strategy and methodologies are fully described in Pesant et al. 49 .

Measurements of environmental covariates

To describe the abiotic habitat associated with each plankton sample, vertical profiles of physical and biogeochemical variables (thereinafter called environmental covariates) were measured by a conductivity temperature depth sensor/rosette (CTD) and Niskin bottles following a published sampling package 50 . A detailed description of each method used as well as all metadata used are available on PANGAEA 51 , 52 , 53 , 54 .

Temperature (°C), salinity (psu) and oxygen concentration (µmol kg −1 ) were measured at 10 m depth. Mixed Layer Depth (MLD, m) was estimated based on the 0.03 kg m −3 sigma differential density relative to the density at 10 m depth 55 . Chlorophyll a concentration was estimated from vertical CTD casts. The values derived from the fluorescence composite profiles were integrated from 0 to 200 m (or 100 m depending on seafloor depth), using the trapezoidal method. Nutrients concentrations [nitrite/nitrate (NO 2 NO 3 , µmol l −1 ), phosphate (PO 4 , µmol l −1 ) and silicate (SiO 2 , µmol l −1 )] were determined using segmented flow analysis 56 . For nutrient concentrations, the average of the median values corresponding to each integrated nets samples 53 was used as it is a better indicator of the overall conditions over the course of a sampling station.

The contribution of the three main phytoplankton size classes to total phytoplankton biomass, %Pico (< 2 µm), %Nano (2–20 µm), and %Micro (> 20 µm) were estimated based on HPLC analysis 57 . The measurements were integrated over the 0–200 water column.

Surface Photosynthetically Active Radiation (PAR, mol quanta m −2  day −1 ) was calculated from in situ sensor data, calibrated using factory settings. Surface backscattering coefficient of particles at 470 nm (bbp470, m −1 ) was calculated from in situ sensor data, corrected with in situ measurements in dark conditions. For both PAR and bbp470, we used the median value around the sampling date and location 51 , 52 , 53 , 54 .

Among all the contextual metadata provided by the TARA consortium 51 , 52 , 53 , 54 , the above-mentioned covariates were selected because: (i) they were the most complete across most sampling stations; (ii) presented the most normal-like distribution and because they were collinear with their alternative versions. Finally, distance to coast (km) was added a posteriori to the suite of covariates to help disentangling coastal samples from the open ocean ones and include this geographical effects in our statistical models. Distance to coast was computed as the shortest Haversine distance to 0 m isobath, on a 15 min resolution. The bathymetric data from the ETOPO1 database ( https://ngdc.noaa.gov/mgg/global/global.html ) were used and obtained through the marmap R package 58 .

Zooplankton abundance and size estimates

Zooplankton samples were analyzed using the ZooScan imaging system 21 . Zooplankton images classification was performed using an automatic recognition algorithm and validated into taxonomic groups by a posteriori expert inspection. Organisms were classified into coarse taxonomic groups on Ecotaxa 59 , generally at the class or order-level except for copepods which were identified down to the family level whenever possible. For our spatial analyses (see below), 36 taxonomic groups were retained, including total zooplankton, Copepoda, Chaetognatha, Cnidaria, Tunicata (mainly appendicularians, salps and doliolids, which present a range in size, due to change in clade), Eumalacostraca (mainly amphipods, decapods and euphausiids), Rhizaria (mainly foraminifers and radiolarians), Pteropoda and small crustacean grazers (Cladocera, Ostracoda and nauplii larvae). The Copepoda class was then broken down into its main five orders (Calanoida, Cyclopoida, Poecilostomatoida, Harpacticoida and Monstrilloida) which were also broken down to families whenever possible (i.e. to attain n >  = 20 individuals per clade per station; e.g. Oithonidae, Calanidae, Oncaeidae etc.). The last two groups gathered the unidentified Copepoda and the small unidentified Calanoida for which the resolution of the ZooScan did not permit a more precise classification. The list of living organisms identified on Ecotaxa as well as their final taxonomic classification is summarized in Supplementary Table S13 . Abundance values were standardized to the number of individuals per m 3 according to the volume of water filtered. The final mean abundances are the sum of all individuals divided by the volume of water filtered by each net and sample.

The major and minor axes of the best fitting ellipses were measured for each living organism to derive their equivalent spherical diameter (ESD) which is here used as a proxy of body size at the individual-level. Then, the community-level size structure of each of the 36 abovementioned groups was estimated through the median value of the ESD distribution at the individual level. When estimating both abundances and median ESD estimates of a group, all the individuals from the smaller nested groups were accounted for.

Numerical analyses

Three main steps were carried out: (i) spatial patterns of abundance and size structure were explored; (ii) the strength of their linear relationship with various environmental covariates was examined; and (iii) nonlinear statistical models were fitted based on the groups’ abundances and median ESD and the selected environmental covariates to examine the underlying drivers of global abundance and size structure.

First, the distribution of the groups’ abundance and median ESD were visually inspected for each of the 36 groups and four transformations (square-root, natural log, log to base 10 and cubic) were applied to examine which would you provide the distribution closest to a normal distribution (based on the p-value of Shapiro–Wilk normality tests). As a result, the groups’ abundances were cubic-transformed and the median ESD estimates were log-transformed.

To identify the groups displaying the most compelling global patterns of abundance and size structure, a first set of spatially-explicit generalized additive models (GAMs) 60 were fitted to the latitude of the sampling stations for each net separately. GAMs are generalized linear models that allow to incorporate flexible nonlinear responses through smoothing functions. GAMs allow us to identify and model nonlinear latitudinal patterns that might emerge because of species-environment relationships. Thin plate regression splines were applied and the smoothing parameters were determined through restricted maximum likelihood (REML) and a Gaussian link function. We insured that at least 30 stations, with each at least 20 individuals were available for fitting the GAMs and thus avoid focusing on groups presenting few observations. Variability in the groups’ abundance may bias the associated median ESD estimates. Some groups of finer taxonomic resolution (i.e. families) may display very low abundances and thus an insufficient number of individuals to derive a robust median ESD estimate from. Therefore, we ensured to consider only on those sampling stations that displayed a sufficient amount of individuals (n ≥ 20) for examining size structure patterns. The adjusted R 2 of the GAMs, as well as the p-values of the latitude smooth terms, were examined to identify the groups displaying significant latitudinal patterns.

Prior to examining the correlation between the groups’ abundance and median ESD and the selected environmental covariates, the latter were transformed (i.e. square-root, natural log, log to base 10 and cubic) and the normality of the distributions was tested. Macronutrients (NO 2 NO 3 , PO 4 and SiO 2 ) concentrations were cubic-transformed and chlorophyll a concentration was log-transformed. The values of the other covariates were kept as is. Spearman’s rank correlation coefficients (ρ) were computed between the groups’ abundance/median ESD and all covariates to examine the strength of their linear relationships and identify the main drivers of the spatial patterns. Then, the shape of specific abundance-environment and size-environment relationships were modelled through GAMs. A prior selection of covariates was carried out to discard those that would be too collinear 61 . Covariates collinearity is a sensitive issue when modelling biotic-abiotic relationships through regressive models as it may inflate parameters and errors estimates 61 . For each net data, pairwise Spearman’s correlation coefficients (ρ) were computed between covariates (Supplementary Fig. S14 ). When a pair of covariates displayed a |ρ|≥ 0.7, the covariate displaying the distribution closest to normality and the least amount of missing values was retained. As a result, %Pico was discarded to the advantage of %Micro (ρ = 0.85), and PO 4 and SiO 2 concentrations were discarded to the advantage of NO 2 NO 3 concentration (ρ = 0.95 and ρ = 0.87, respectively). NO 2 NO 3 was thus used to represent gradients in macronutrients concentration. Temperature and dissolved oxygen concentration also displayed high collinearity (ρ = − 0.95). Yet, we wanted to assess how important both these covariates could be in explaining abundance and size structure patterns. Therefore we ran the subsequent analyses by accounting for temperature and O 2 but separately.

Using the groups transformed abundance and median ESD as response variables, we fitted GAMs for each net data using the following ten covariates: temperature, salinity, MLD, PAR, NO 2 NO 3 , chlorophyll a, bbp470, %Micro, %Nano and distance to coast. A second set of GAMs was trained by replacing temperature by oxygen. Again, thin plate regression splines were applied and the parameters were determined through REML and a Gaussian link function. The dimensions of the basis of the smooth terms were adjusted by dividing the number of available observations by the number of covariates. Extra penalties were added to each smoothing term so the parameter estimation can completely remove terms estimated as insignificant. Model terms were then selected by backwards removal of insignificant variables. The percentage of explained deviance (%Dev) and the adjusted R 2 of the GAMs were retrieved to evaluate their performance. For each GAM, covariates significance was ranked according to their relative F statistic (Supplementary Fig. S9 ). We tested for significant variations in %Dev across net data or covariates sets through non-parametric variance analyses (Kruskal–Wallis test) followed by posthoc pairwise comparisons using Dunn’s test and Bonferroni’s method for adjusting p-values. Only those stations displaying more than 20 individuals when modelling the groups’ size structure estimates were considered. The significant smooth terms (p-values < 0.05) were identified and their smoothing function and standard error estimates were plotted on the scale of the corresponding covariate (covariate-specific smooth curve).

The covariate-specific smooth curves of the GAMs fitted on median ESD estimates displaying a %Dev ≥ 50% were then used to cluster the zooplankton groups based on the shapes of the modelled smooth curves. This way, we identified groups that respond similarly to environmental gradients. In short, the smooth curves were treated as independent data series and we used Dynamic Time Warping (DTW) 62 to compare them to each other. DTW is an algorithm that tries to find the optimum warping path between two univariate or multivariate data series. DTW stretches the data series locally to have one match the other(s) as much as possible. Then, the Euclidean distance between the data series is computed by summing the distances of the aligned data points. The modelled smooth curves were projected on a scale of 1 to 100 values scaling the range observed for each covariate. All the covariate-specific smooth curves were out together in a list that can be assimilated to a multivariate data series even in lengths for each retained GAM. DTW distances were then computed and partitioning around medoids (PAM) clustering 63 was performed to cluster the GAMs (i.e. a zooplankton group + a net type) into two to ten clusters. Five different indices (Calinski-Harabasz, Dunn’s, Silhouettes, classic and modified Davies Bouldin indices) were examined to choose the optimal number of cluster. Four clusters were retained for the median-ESD based GAMs, and four for the abundance-based ones (Supplementary Fig. S15 ). Hierarchical clustering approaches were also examined but they yielded unclear performance indices so PAM was preferred. Every modelled covariate-specific smooth curves used for the DTW clustering are reported in Supplementary Figs. S10 and S11 for the median ESD responses and the abundance models, respectively. To summarize such large amount of information and illustrate the similarities between the zooplankton groups, the inter-group distance matrix issued from the DTW algorithm was projected onto to a two dimensional space based on classical multidimensional scaling.

The analyses were performed with the R v3.5.2 64 environment and with MATLAB R2017a. All maps presented were plotted in R v3.5.2. The main packages used for data analyses and plotting were tidyverse 65 and HH 66 and FactoMineR 67 . GAMs were built using the mgcv 60 package. The partitional clustering of the zooplankton groups based on the shape of the smoothing curves issued from the GAMs was performed using the dtwclust 68 package.

Data availability

Median ESD and abundance values by zooplankton groups are available at https://doi.org/10.17632/nwvjwccgvh.1 . Zooplankton imaging datasets from the Tara Oceans expeditions are available through the collaborative web Ecotaxa application and repository under the addresses: https://ecotaxa.obs-vlfr.fr/prj/377 , https://ecotaxa.obs-vlfr.fr/prj/2245 , https://ecotaxa.obs-vlfr.fr/prj/378 for the WP2 net; https://ecotaxa.obs-vlfr.fr/prj/397 , https://ecotaxa.obs-vlfr.fr/prj/398 , https://ecotaxa.obs-vlfr.fr/prj/395 for the Bongo net; https://ecotaxa.obs-vlfr.fr/prj/415 , https://ecotaxa.obs-vlfr.fr/prj/409 , https://ecotaxa.obs-vlfr.fr/prj/408 , https://ecotaxa.obs-vlfr.fr/prj/411 , https://ecotaxa.obs-vlfr.fr/prj/412 for the Régent net. Contextual data from the Tara Oceans expedition, including those that are newly released from the Arctic Ocean, are available at https://doi.org/10.1594/PANGAEA.875582 .

Change history

06 october 2021.

A Correction to this paper has been published: https://doi.org/10.1038/s41598-021-99772-1

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Acknowledgements

Tara Oceans (which includes both the Tara Oceans and Tara Oceans Polar Circle expeditions) would not exist without the leadership of the Tara Expeditions Foundation and the continuous support of 23 institutes ( http://oceans.taraexpeditions.org ). We further thank the commitment of the following sponsors: CNRS (in particular Groupement de Recherche GDR3280 and the Research Federation for the study of Global Ocean Systems Ecology and Evolution, FR2022/ Tara Oceans-GOSEE), European Molecular Biology Laboratory (EMBL), Genoscope/CEA, The French Ministry of Research, and the French Government ‘Investissements d’Avenir’ programmes OCEANOMICS (ANR-11-BTBR-0008), FRANCE GENOMIQUE (ANR-10-INBS-09-08), MEMO LIFE (ANR-10-LABX-54), and PSL Research University (ANR-11-IDEX-0001-02). M.C.B. acknowledges postdoc fellowships from the Coordination for the Improvement of Higher Education Personnel of Brazil (CAPES) (99999.000487/2016-03) and the Fonds Français pour l'Environnement Mondial (FFEM). F.B. received support from ETH Zürich. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862923. This output reflects only the author’s view, and the European Union cannot be held responsible for any use that may be made of the information contained therein. We also thank the support and commitment of Agnès b. and Etienne Bourgois, the Prince Albert II de Monaco Foundation, the Veolia Foundation, Region Bretagne, Lorient Agglomeration, Serge Ferrari, World Courier, and KAUST. The global sampling effort was enabled by countless scientists and crew who sampled aboard the Tara from 2009-2013, and we thank MERCATOR-CORIOLIS and ACRI-ST for providing daily satellite data during the expeditions. We are also grateful to the countries who graciously granted sampling permissions. The authors declare that all data reported herein are fully and freely available from the date of publication, with no restrictions, and that all of the analyses, publications, and ownership of data are free from legal entanglement or restriction by the various nations whose waters the Tara Oceans expeditions sampled. The following people were involved in plankton image sorting: B. Serranito, N. Monferrer, C. Merland and F. Roullier. We thank the EMBRC platform PIQv for image analysis. This work was supported by EMBRC‐France, whose French state funds are managed by the ANR within the Investments of the Future program under reference ANR‐10‐INBS‐02. We are grateful to Meike Vogt for sharing her thoughts regarding the potential use of the present observations for zooplankton modelling. This article is contribution number 121 of Tara Oceans.

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These authors contributed equally: Manoela C. Brandão and Fabio Benedetti.

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Authors and Affiliations

Sorbonne Université, CNRS, Laboratoire d’Océanographie de Villefranche, 06230, Villefranche-sur-mer, France

Manoela C. Brandão, Yawouvi Dodji Soviadan, Jean-Olivier Irisson, Amanda Elineau, Corinne Desnos, Laëtitia Jalabert, Marc Picheral, Lionel Guidi, Gabriel Gorsky, Gabriel Gorsky, Lionel Guidi, Lars Stemmann, Lars Stemmann & Fabien Lombard

Ifremer, Centre Bretagne, Unité Dynamiques des Ecosystèmes Côtiers, 29280, Plouzané, France

Manoela C. Brandão

ETH Zürich, Institute of Biogeochemistry and Pollutant Dynamics, 8092, Zürich, Switzerland

Fabio Benedetti

Aix Marseille Univ., Université de Toulon, CNRS, IRD, MIO UM 110, 13288, Marseille, France

Séverine Martini & Pascal Hingamp

Ifremer, Centre Atlantique, Unité Ecologie et Modèles Pour l’Halieutique, 44311, Nantes, France

Jean-Baptiste Romagnan

Departamento de Ecologia e Zoologia, Universidade Federal de Santa Catarina, Florianópolis, 88010970, Brazil

Andrea S. Freire

Institut de Biologie de l’École Normale Supérieure (IBENS), CNRS, INSERM, PSL Université Paris, 75005, Paris, France

Chris Bowler, Chris Bowler & Eric Karsenti

Research Federation for the Study of Global Ocean Systems Ecology and Evolution, FR2022/Tara Oceans GOSEE, 75016, Paris, France

Chris Bowler, Nicolas Henry, Colomban de Vargas, Chris Bowler, Colomban de Vargas, Lars Stemmann & Fabien Lombard

School of Marine Sciences, University of Maine, Orono, 04469, USA

Lee Karp-Boss, Emmanuel Boss & Lee Karp-Boss

Sorbonne Université, CNRS, Station Biologique de Roscoff, AD2M, UMR 7144, 29680, Roscoff, France

Nicolas Henry, Colomban de Vargas, Colomban de Vargas & Fabrice Not

Department of Microbiology and Civil, Environmental, and Geodetic Engineering, The Ohio State University, Columbus, 43214, USA

Matthew B. Sullivan & Matthew B. Sullivan

Institut Universitaire de France, 75231, Paris, France

Fabien Lombard

Institute of Marine Sciences (ICM) – CSIC, Pg. Marítim de la Barceloneta, 37-49, 08003, Barcelona, Spain

  • Silvia G. Acinas

Takuvik Joint International Laboratory (UMI3376), Université Laval (Canada) – CNRS (France), Université Laval, Québec, QC, G1V 0A6, Canada

Marcel Babin

Structural and Computational Biology, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117, Heidelberg, Germany

Peer Bork & Stefanie Kandels

European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK

Guy Cochrane

CNRS Biologie Intégrative Des Organismes Marins (BIOM), UMR7232, 1 avenue Pierre Fabre, 66650, Banyuls-sur-Mer, France

Nigel Grimsley

Sorbonne Université, Observatoire Océanologique de Banyuls-Sur-Mer, 1 avenue Pierre Fabre, 66650, Banyuls-sur-Mer, France

Stazione Zoologica Anton Dohrn, Villa Comunale, 80121, Naples, Italy

Daniele Iudicone

Génomique Métabolique, Genoscope, Institut de Biologie François Jacob, Commissariat à l’Énergie Atomique (CEA), CNRS, Université Évry, Université Paris-Saclay, Évry, France

Olivier Jaillon & Patrick Wincker

Directors’ Research, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117, Heidelberg, Germany

Stefanie Kandels & Eric Karsenti

Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto, 611-001, Japan

Hiroyuki Ogata

Bigelow Laboratory for Ocean Sciences, East Boothbay, ME, 04544, USA

Nicole Poulton

PANGAEA, Data Publisher for Earth and Environmental Science, University of Bremen, Bremen, Germany

Stephane Pesant

MARUM, Center for Marine Environmental Sciences, University of Bremen, Bremen, Germany

Department of Microbiology and Immunology, Rega Institute, KU Leuven, Herestraat 49, 3000, Leuven, Belgium

Jeroen Raes

Center for the Biology of Disease, VIB, Herestraat 49, 3000, Leuven, Belgium

Department of Applied Biological Sciences, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium

Sorbonne Université, CNRS, UMR 7009 Biodev, Observatoire Océanologique, 06230, Villefranche-sur-mer, France

Christian Sardet

Department of Geosciences, Laboratoire de Météorologie Dynamique (LMD), Ecole Normale Supérieure, 24 rue Lhomond, 75231, Paris Cedex 05, France

Sabrina Speich

Laboratoire de Physique des Océans, UBO-IUEM, Place Copernic, 29820, Plouzané, France

Department of Biology, Institute of Microbiology and Swiss Institute of Bioinformatics, ETH Zürich, Vladimir-Prelog-Weg 4, 8093, Zürich, Switzerland

Shinichi Sunagawa

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  • , Marcel Babin
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  • , Emmanuel Boss
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  • , Guy Cochrane
  • , Colomban de Vargas
  • , Gabriel Gorsky
  • , Lionel Guidi
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  • , Stefanie Kandels
  • , Lee Karp-Boss
  • , Eric Karsenti
  • , Fabrice Not
  • , Hiroyuki Ogata
  • , Nicole Poulton
  • , Stephane Pesant
  • , Jeroen Raes
  • , Christian Sardet
  • , Sabrina Speich
  • , Lars Stemmann
  • , Matthew B. Sullivan
  • , Shinichi Sunagawa
  •  & Patrick Wincker

Contributions

M.C.B., F.B., L.S. and F.L. conceptualized the study and wrote the original draft. M.C.B., F.B., S.M., Y.D.S., J-B.R., A.E., C.D., L.J., M.P., L.G., G.G., L.S. and F.L. participated in data curation. M.C.B., F.B. and F.L. lead the preparation of the data and performed the numerical analyses. M.C.B., F.B., S.M., Y.D.S., J.-O.I., L.S. and F.L. helped interpret the data. L.S. and F.L. supervised the study. All authors reviewed the manuscript.

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Correspondence to Manoela C. Brandão or Fabio Benedetti .

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The original online version of this Article was revised: The original version of this Article contained an error in the Acknowledgements section, where, “F.B. received support from ETH Zürich and from the European Union’s Horizon 2020 research and innovation program under grant agreement n°SEP-210591007.” now reads: “F.B. received support from ETH Zürich. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862923. This output reflects only the author’s view, and the European Union cannot be held responsible for any use that may be made of the information contained therein.”

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Brandão, M.C., Benedetti, F., Martini, S. et al. Macroscale patterns of oceanic zooplankton composition and size structure. Sci Rep 11 , 15714 (2021). https://doi.org/10.1038/s41598-021-94615-5

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literature review on zooplankton

What drives zooplankton taxonomic and functional β diversity? A review of Brazilian rivers

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  • Published: 24 October 2023
  • Volume 851 , pages 1305–1318, ( 2024 )

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literature review on zooplankton

  • Gleice de Souza Santos 1 ,
  • Leidiane Pereira Diniz   ORCID: orcid.org/0000-0002-7516-6879 1 ,
  • Edissa Emi Cortez Silva 1 ,
  • Tayenne Luna Tomé de Paula 1 ,
  • Paula Cristine Silva Gomes 1 ,
  • Raquel Xavier Calvi 1 ,
  • Bruna Lana Delfim 1 ,
  • Nadson Ressyé Simões 2 &
  • Eneida Maria Eskinazi-Sant’Anna   ORCID: orcid.org/0000-0001-6409-7129 1  

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We carried out a literature review to investigate the taxonomic and functional β diversity of zooplankton and its species replacement (β repl ) and richness difference (β rich ) components in Brazilian rivers. In addition, the taxonomic (LCBD-t) and functional ecological uniqueness (LCBD-f) were also measured. We tested the following hypotheses: (i) The β repl component is the main driver of taxonomic β diversity, while β rich is most important for functional β diversity, due to functional simplification; (ii) Sites with lower taxonomic and functional richness are the ones that most contribute to LCBD. Contrary to expectations, β rich drove the taxonomic and functional β diversity. This may have occurred due to environmental constraints and geographic distance, thus causing a loss or gain of species and traits between hydrographic regions. For just two regions, the sites with the lowest functional richness were the ones that most contributed to LCBD-f. This reinforces that sites with lower richness can support species that perform unique functions in the ecosystem. As studies involving zooplankton from lotic systems are still limited in Brazil, we suggest that future research should consider the patterns of β diversity in these dynamic, diverse, and threatened aquatic ecosystems.

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literature review on zooplankton

Ecological uniqueness and species richness of zooplankton in subtropical floodplain lakes

literature review on zooplankton

Environmental filter drives the taxonomic and functional β-diversity of zooplankton in tropical shallow lakes

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de Souza Santos, G., Diniz, L.P., Silva, E.E.C. et al. What drives zooplankton taxonomic and functional β diversity? A review of Brazilian rivers. Hydrobiologia 851 , 1305–1318 (2024). https://doi.org/10.1007/s10750-023-05394-1

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Which physicochemical variables should zooplankton ecologists measure when they conduct field studies?

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Derek K Gray, Mariam Elmarsafy, Jasmina M Vucic, Matthew Teillet, Thomas J Pretty, Rachel S Cohen, Mercedes Huynh, Which physicochemical variables should zooplankton ecologists measure when they conduct field studies?, Journal of Plankton Research , Volume 43, Issue 2, March/April 2021, Pages 180–198, https://doi.org/10.1093/plankt/fbab003

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Over the last century, a rich literature has developed describing how the physical and chemical environment influences zooplankton communities, but there is little guidance on the suite of limnological variables that should be measured by zooplankton ecologists. We performed a literature review to assess (i) which variables were measured most often by zooplankton ecologists, (ii) which of these variables were consistently related to zooplankton abundance and richness and (iii) whether key variables were overlooked by investigators. Our results show that there is a core group of nine limnological variables that are measured most frequently, including lake surface area, pH, phosphorus, nitrogen, dissolved oxygen, conductivity, chlorophyll- a , maximum depth and temperature. These variables were among those most often associated with variation in zooplankton, but several others, including dissolved organic carbon, alkalinity and nitrate, were sampled infrequently, despite showing promise as important explanatory variables. The selection of variables in past studies did not correlate with how often those variables were significant in the literature, but instead, might have been related to their ease of measurement. Neglecting to measure important variables could have implications for fundamental and applied studies that aim to understand the factors structuring zooplankton communities and their response to environmental change.

Over the past century, limnology and ecology journals have been filled with correlative and mechanistic studies examining how the physical and chemical environment influences zooplankton communities ( Robert, 1923 ; Moore, 1978 ; Tessier and Horwitz, 1990 ; Swadling et al. , 2000 ; Schell et al. , 2001 ; Dodson et al. , 2005 ; Tavernini et al. , 2009 ; Vogt and Beisner, 2011 ; Banerjee et al. , 2019 ; Loewen et al. , 2019 ). Freshwater ecologists have speculated on and studied the factors that control the distribution and abundance of zooplankton since at least the late 1800s when Forbes published his famous treatise, The Lake as a Microcosm ( Forbes, 2012 ). Despite the rich literature on the subject, there are still no clear guidelines on the suite of physical and chemical variables that should be measured by zooplankton ecologists when they visit a lake or pond. Often, the approach may be to measure as many variables as is practical. However, collecting data for a large number of physical and chemical variables may be difficult if the lakes of interest are remote ( O’Brien et al. , 2004 ; Lyons and Vinebrooke, 2016 ). In addition, it can be expensive and time consuming to collect bathymetric data and perform the entire suite of possible water chemistry analyses (e.g. trace elements, organic carbon, nutrients). If a limnologist is planning a survey of lakes with the hopes of explaining differences in zooplankton communities among those lakes, which physicochemical variables should they measure?

An obvious starting point for a zooplankton ecologist looking to choose physicochemical variables to incorporate in a survey is the rich literature examining the drivers of zooplankton community structure. In general, there are two types of studies: (i) correlative studies that assess relationships between physicochemical variables and zooplankton communities using field survey data (e.g. Dodson, 1992 ) and (ii) mechanistic studies based on controlled experiments that are able to more directly link variation in physicochemical variables to zooplankton survivorship, abundance and richness (e.g. Locke, 1991 ). While mechanistic studies can provide stronger evidence for the effects of individual physicochemical variables, some variables are difficult to manipulate in an experimental context, such as lake surface area, mean depth and latitude. Examining all the potential interactions among physicochemical variables can also be challenging in an experimental setting, as variables may act synergistically or antagonistically (e.g. Thompson and Shurin, 2012 ). Some processes also need to be studied at larger scales, such as dispersal, which is often measured by examining spatial autocorrelation in zooplankton communities across lakes in a region (Beisner et al. , 2006; Shurin et al. , 2009). Therefore, both large-scale correlative and smaller-scale mechanistic studies have a role to play in elucidating the physicochemical variables responsible for shaping zooplankton communities.

While there is a wealth of literature on the subject of zooplankton–environment relationships, one must only read a few studies to realize that there is little consistency in the list of physicochemical variables measured by different research teams (e.g. Özkan et al. , 2014 vs. Mac Leod et al. , 2018 ). Why is dissolved organic carbon measured in one study, but total organic carbon is measured in another? Why does one study include alkalinity, while another does not measure this variable at all? We can think of four possible reasons for the lack of consistency among studies. First, the purpose or questions driving a study may call for the measurement of a particular variable. For example, alkalinity might be of interest in a region impacted by acidification ( Confer et al. , 1983 ), and landscape variables might be a consideration if the goal of the study is to assess the impact of shoreline development ( Marburg et al. , 2006 ; Alexander et al. , 2008 ). Second, investigators may be limited by the analytical equipment available or by the logistics of collecting the data in the field. For example, constructing bathymetric maps is time consuming, and the equipment can be expensive ( Gao, 2009 ), so mean depth may be excluded from the list of variables measured. Third, the selection of variables could be somewhat idiosyncratic, depending on the training and experiences of a particular investigator. For example, if there was a tradition of collecting Secchi disc measurements in the research group where an investigator did their training, they might be more likely to continue to do so when planning future surveys. Finally, if a sampling program has been continuing for a long period of time, investigators might decide that consistency in the variables measured through time is an important goal.

In addition to variation in the suite of physical and chemical variables collected by limnologists, there is also variability in the types of statistical analyses ecologists use to examine relationships between environmental variables and zooplankton communities. Common analyses in the literature can be divided into two general categories: univariate and multivariate. Univariate tests, such as the t -test, analysis of variance, linear regression and correlation, involve only one dependent variable. The dependent variable is often species richness, diversity, biomass or abundance (e.g. Confer et al. , 1983 ). In contrast, multivariate ordination techniques work with two or more dependent variables, which are often represented by the abundance of each species found in a lake ( Dodson et al. , 2005 ). Examples commonly used in ecology include principal component analysis (PCA), non-metric multidimensional scaling, redundancy analysis (RDA) and canonical correspondence analysis (CCA) ( James and McCulloch, 1990 ). The selection of a particular statistical test for a study is likely related to the question of interest, as well as to the types of tests the investigators are familiar with, but we are unaware of any review that has considered how often different types of tests are used by zooplankton ecologists. This type of information may be helpful for new investigators (e.g. students) as they search for the appropriate tests to use as they plan their own studies. Regardless of the type of test selected, the power to detect relationships between zooplankton communities and environmental variables will depend on the strength of that relationship (effect size) as well as the sample size (e.g. number of lakes studied).

For this study, we performed a literature review to determine (i) which variables were measured most often by zooplankton ecologists, (ii) which of these variables were consistently related to zooplankton abundance and richness and (iii) whether key variables were often overlooked by investigators. We also used our data set to explore some of the potential reasons for investigators choices of variables in past studies and examine the types of statistical analyses they used to look for associations between environmental variables and zooplankton communities. Based on our analyses, we provide recommendations on the environmental variables that zooplankton ecologists should consider measuring when planning future surveys.

We performed a literature review to identify studies that investigated the environmental variables associated with zooplankton community structure in lakes or ponds using Google Scholar. For the purposes of our study, we defined community structure as either univariate metrics used to describe communities (richness, diversity, total abundance) or multivariate analyses such as PCA, which summarizes relative species abundance. We considered studies published between 1970 and 2019 and used the following search terms to identify relevant studies (i) zooplankton distribution and abundance, (ii) zooplankton abundance environmental factors, (iii) zooplankton diversity environmental factors, (iv) zooplankton richness environmental factors and (v) zooplankton lakes. The start date of 1970 was chosen based on our inability to find many studies published before that year. Studies that collected data from fewer than three lakes or that included lakes with a salinity greater than 5‰ were excluded from our data set. The upper 5‰ threshold for salinity was chosen so that our review focused on freshwater lakes. Above this threshold, the survival and reproduction of many freshwater zooplankton are negatively affected (e.g. Wissel et al. , 2011 ). For each study, we extracted information on which physicochemical variables were measured and whether the investigators found a significant relationship between zooplankton communities and these variables. We classified a variable as significantly related to zooplankton community structure when an inferential statistical test was performed in the study and the null hypothesis was rejected (correlation, regression, RDA). We did not assess variable significance based on exploratory ordinations (e.g. PCA). Where available, we also obtained the proportion of variation in zooplankton community structure explained by each variable in a study, as well as the mean, minimum and maximum values measured for each environmental variable. When necessary, units of measurement were converted to ensure that all data were analyzed on the same scale.

To determine the frequency with which environmental variables were measured in the literature, we calculated how many studies had measured each variable and we assigned variables into categories based on their frequency of measurement: infrequent = measured in <25% of studies, somewhat frequent = 25–50%, moderately frequent = 50–75% and frequent if measured in >75% of studies. The calculations for these frequency of measurement categories also included variables that might have been known but did not appear to be incorporated into analyses (e.g. fish presence/absence, latitude). For each variable, we summarized the mean percent variance explained across all studies, regardless of the statistical method used in individual studies. This summary included R 2 values from linear or multiple regressions, redundancy analyses, as well as r values from correlations (converted to R 2 to be on the same scale). Since the studies in our final database were based on field surveys, their analyses and conclusions were correlative in nature. However, these correlations are presumed to result from mechanistic links driving each association. To detail the possible mechanistic links, we examined the 10 environmental variables that most frequently showed a significant association with abundance and richness and provided a summary of the mechanisms that have been proposed to explain those associations.

To explore possible reasons for the choices of variables measured by investigators, we examined if variables that were more difficult to measure were less likely to be included. To do this, we classified the 24 most common variables into categories based on whether they were measured in the field, laboratory or as part of geographic information system (GIS) work. We further divided the field and GIS categories according to whether we considered individual variables to be difficult or expensive for zooplankton ecologists to measure ( Supplementary Table SI ). This resulted in five categories: lab-based, field-based simple, field-based difficult/expensive, GIS-based and GIS-based difficult. We considered watershed area to be difficult because it requires skill and a thorough understanding of GIS programs such as ArcGIS. Turbidity, chlorophyll- a and color were classified as difficult/expensive based on the requirement to have specialized sensors that are more costly than simple meters that measure variables such as temperature and conductivity. Mean depth was also considered to be a difficult variable to measure since it requires the collection of bathymetric data, which are both expensive and time consuming. To determine if the probability of a variable being measured across studies differed according to the categories we established, we ran a Fisher’s exact test in R using the Fisher’s test function ( R Development Core Team, 2019 ).

In addition to the difficulty of measuring variables, we considered other explanations for investigators’ choices of environmental variables. We hypothesized that investigators would be likely to select variables that had been found to be significantly related to zooplankton community structure in previous studies. To evaluate this hypothesis, we conducted two analyses. First, we examined the relationship between the proportion of studies that found a significant association between zooplankton community structure and an environmental variable versus the number of studies that measured that environmental variable. Our second analysis examined the correlation between the proportion of studies selecting a variable in a particular year and the proportion of studies that found that variable to be significant in studies conducted prior to that year. We then examined the correlation coefficient by year to determine if the relationship became stronger over time, suggesting that investigators were choosing variables based on their significance in past studies. We also hypothesized that researchers from different regions of the world might have different traditions of measuring certain suites of variables, so we used PCA to examine variation in variable selection by continent. The PCA was run on a matrix where each row represented a study and each column was an environmental variable (coded 0 or 1). We also considered that the suite of variables typically measured by investigators might have evolved through time; therefore, we colored our PCA results according to decade of publication. To examine if the country of study was a significant determinant of variable choice, we conducted a hierarchical cluster analysis using Euclidean distances and Ward’s minimum variance method as implemented in the hclust function in R. The results were plotted in a dendrogram using the dendextend package in R ( Galili, 2015 ). Our hypothesis was that studies conducted in the same country would be found in the same cluster, indicating that they had selected the same variables to measure. Finally, we examined the mean number of variables measured in each study by year to determine if the number of variables measured for each study had changed through time.

While our original desire was to conduct the study within a meta-analytic framework, the types of analyses and data available from the literature made this difficult. Unfortunately, results collected using the most common techniques in this field (RDA, multiple regression) are very difficult to incorporate into a meta-analytical framework due to the necessity of having standardized regression coefficients, which are rarely reported in the literature. In addition, each study uses a unique combination of variables in a multiple regression or RDA model, and these variables may exhibit varying degrees of collinearity, changing the regression coefficients or the estimates of R 2 for individual variables. For example, if one study used multiple regression and incorporated phosphorus levels, while another study excluded it from the model, it is not possible to easily compare the coefficients and R 2 values for the other variables between studies. However, we were able to conduct a meta-analysis of correlation coefficients for zooplankton abundance and richness. The analysis was run for variables for which we could obtain correlation coefficients from greater than three studies. The analysis was conducted using the metacor function in the meta package for R ( Viechtbauer, 2010 ), which fits fixed and random effects models using Fisher’s z -transformed correlation coefficients. The output of the metacor function provides estimated effect sizes with confidence intervals and a P -value showing the results of a significance test for each variable. Because the number of studies for each variable was low, we present the results for the fixed effect models ( Borenstein et al. , 2009 ).

Map of sites where studies were conducted. Black circles represent studies collected in one specific area, while shades of gray represent studies with multiple spatially distant sampling sites. Each unique study with multiple spatially distant sites is represented by one shade of gray.

Map of sites where studies were conducted. Black circles represent studies collected in one specific area, while shades of gray represent studies with multiple spatially distant sampling sites. Each unique study with multiple spatially distant sites is represented by one shade of gray.

Cumulative number of studies through time examining variation in physicochemical variables among lakes as drivers of zooplankton community structure.

Cumulative number of studies through time examining variation in physicochemical variables among lakes as drivers of zooplankton community structure.

Our review of the literature assumed that each study provided a test of the hypothesis that zooplankton community structure is related to the environmental variables measured. In other words, if a variable was not selected as significant in the statistical tests used in the study, then it was truly not related to community structure. This ignores issues with Type II statistical errors and the power to detect differences. We considered two issues that might contribute to the likelihood of committing a Type II error: small sample sizes (few lakes) and a lack of range in environmental variables in the study area. The latter issue is often described as having a lack of range in predictor variables, and power is sometimes increased by adding “extreme” treatments to extend this range ( Krzywinski and Altman, 2013 ). To determine if these two issues might have been problematic in our data set, we calculated the range of the six most frequently measured environmental variables in each study and compared the range for each variable when the variable was found to be significantly related to community structure versus the range when each variable was not. For example, we tested if the range of pH values was higher in studies that found pH to be a significant predictor of community structure than in studies that did not find a significant relationship. In addition, we compared the number of lakes or sites studied versus whether an environmental variable was found to be significant. In both cases, we used Welch’s t -tests to determine if the mean values (range or number of lakes) differed between studies that found a statistically significant association versus those that did not.

Number of studies measuring each physicochemical variable. Gray shading indicates studies that found a significant relationship between the measured variable and zooplankton community structure. The 25 most commonly measured variables are included in this figure.

Number of studies measuring each physicochemical variable. Gray shading indicates studies that found a significant relationship between the measured variable and zooplankton community structure. The 25 most commonly measured variables are included in this figure.

Proportion of studies where a variable was identified as being significantly associated with zooplankton community structure for variables included in ≥10 studies.

Proportion of studies where a variable was identified as being significantly associated with zooplankton community structure for variables included in ≥10 studies.

To explore why a particular variable might be important for zooplankton in some studies, but not others, we constructed classification trees with the rpart function in R using default parameters ( Therneau and Atkinson, 2019 ). The algorithm used to build the trees follows Breiman et al. ( Breiman et al., 1984 ). The aim of building classification trees is to divide data into groups that are homogeneous as possible while maximizing the heterogeneity between groups and then using those group assignments to examine potential drivers of group membership ( De’ath and Fabricius, 2000 ). We conducted this analysis for eight of the most common variables, including total phosphorus (TP), chlorophyll- a , conductivity, pH, surface area, dissolved oxygen, maximum depth and temperature. The response variable in each case was categorical (yes, no), meaning that the variable was either significantly associated with zooplankton structure in a study or it was not. The predictor variables in each model were the other common environmental variables measured in each study: dissolved oxygen, calcium, conductivity, dissolved organic carbon, elevation, maximum depth, total nitrogen, pH, TP, chlorophyll- a , Secchi depth, temperature and surface area. The models were plotted using the rpart plot library in R ( Milborrow, 2019 ).

Our review of the literature found 126 studies between 1970 and 2019 that examined associations between zooplankton community structure and environmental variables ( Supplementary Table SII ). Studies were concentrated in North America, Western Europe and South America ( Fig. 1 ). The rate of increase in the publication of studies remained relatively constant until ~2000 and accelerated thereafter ( Fig. 2 ). The studies in our data set measured 346 unique environmental variables that were used to explain variability in zooplankton communities ( Supplementary Table SIII ). Of these variables, 95.5% were measured infrequently (<25% of studies), while 1.6% were measured somewhat frequently (25–50% of studies), 2.1% were measured moderately frequently (50–75% of studies) and 0.8% were measured frequently (>75% of studies). The 25 most common variables are found in Fig. 3 . Some of the most commonly measured variables were also frequently found to have a statistically significant association with zooplankton community structure ( Fig. 3 ). Variables that were most often found to have a statistically significant association with zooplankton community structure when measured are displayed in Fig. 4 . Out of the top 20 variables most often found to be significantly associated with zooplankton community structure ( Fig. 4 ), 12 (color, alkalinity, mean depth, calcium, nitrate, dissolved organic carbon, volume, magnesium, silicate, aluminum, fish presence/absence and total hardness) were measured infrequently by zooplankton ecologists ( Fig. 3 ). Based on our search of the literature, Table I provides some proposed hypotheses to explain why significant associations between these 12 infrequently measured environmental variables and zooplankton structure might exist.

Fifty-two studies examined variables associated with richness, while 67 considered zooplankton abundance. A comparison of the variables found to be significantly related to richness and abundance, showed that temperature, phosphorous, pH and maximum depth were in the top 10 for both metrics ( Fig. 5 ). For richness, surface area, dissolved oxygen, elevation/altitude, mean depth and calcium were also often significant ( Fig. 5A ). For abundance, conductivity showed an association in the largest proportion of studies, while primary production/chlorophyll- a , latitude, Secchi depth, transparency/turbidity and nitrate levels were also often significant ( Fig. 5B ). Of the variables most often associated with richness and/or abundance, mean depth, calcium, transparency/turbidity and nitrate were measured infrequently (<25% of studies). Based on our search of the literature, Table II provides some proposed mechanistic links describing why significant associations between the environmental variables in Fig. 5 and abundance or richness exist.

Possible explanations for links between infrequently measured predictor variables and zooplankton communities

Variable measured infrequentlyPossible explanations for relationshipsSupporting references
Color• Changes in UV penetration , (1999)
• Changes in predation risk
Alkalinity• Buffer against variability in pH (1994),
• Linked to calcium levels
Mean depth• Related to the availability of differing habitat types in a lake (2000)
Calcium• Important for species with high calcium demand (e.g. daphnids) (2017), (2016)
Nitrate• Can be an indicator of agricultural pollution (2018), (2012)
• Can increase growth rates of some macrophytes
DOC• Can reduce toxicity of some metals (2009), (1999), (2003)
• Changes in predation risk due to association with water color
• Decreased UV predation in high DOC lakes
Volume• Related to the availability of differing habitat types in a lake (2011)
Magnesium• One of the major ions in freshwater (1996)
• Contributes to conductivity
Silicate• Related to primary production through control on diatom growth (1980)
Aluminum• Can be toxic to zooplankton (2017)
• Related to lake pH
Fish presence/absence• Important structuring factor through predation (2019)
Total hardness• Related to conductivity (2009),
• Related to alkalinity
• Related to pH
• Related to availability of Ca
Variable measured infrequentlyPossible explanations for relationshipsSupporting references
Color• Changes in UV penetration , (1999)
• Changes in predation risk
Alkalinity• Buffer against variability in pH (1994),
• Linked to calcium levels
Mean depth• Related to the availability of differing habitat types in a lake (2000)
Calcium• Important for species with high calcium demand (e.g. daphnids) (2017), (2016)
Nitrate• Can be an indicator of agricultural pollution (2018), (2012)
• Can increase growth rates of some macrophytes
DOC• Can reduce toxicity of some metals (2009), (1999), (2003)
• Changes in predation risk due to association with water color
• Decreased UV predation in high DOC lakes
Volume• Related to the availability of differing habitat types in a lake (2011)
Magnesium• One of the major ions in freshwater (1996)
• Contributes to conductivity
Silicate• Related to primary production through control on diatom growth (1980)
Aluminum• Can be toxic to zooplankton (2017)
• Related to lake pH
Fish presence/absence• Important structuring factor through predation (2019)
Total hardness• Related to conductivity (2009),
• Related to alkalinity
• Related to pH
• Related to availability of Ca

Listed here are the 12 infrequently measured variables that were found in the top 20 variables most often found to be significantly associated with zooplankton community structure (see Fig. 4 ). DOC, dissolved organic carbon.

Proportion of studies that found significant relationships between environmental variables and richness (Panel A) or abundance (Panel B). The variable depth (unspecified) refers to cases where the study did not indicate if it was maximum, mean or sample depth.

Proportion of studies that found significant relationships between environmental variables and richness (Panel A ) or abundance (Panel B ). The variable depth (unspecified) refers to cases where the study did not indicate if it was maximum, mean or sample depth.

Proposed mechanistic associations between predictor variables and the richness and abundance of zooplankton

Response variablePredictorProposed mechanistic associationSupporting studies
RichnessTemperatureLow temperatures limit the number of species that can persist in a lake due to physiological limitations. Therefore, richness is often reduced in colder lakes at high latitudes. ,
Surface areaLarger lakes are likely to have a variety of different habitat types (habitat heterogeneity), allowing for a larger number of species. , , (2012), (2009)
PhosphorusResponse may be unimodal. At low levels, food limitation may occur. At intermediate levels, food limitation no longer limits species persistence. High phosphorus levels may result in poor water quality as a result of eutrophication, potentially lowering the number of species that can persist in a lake. (2000)
Dissolved oxygenDissolved oxygen can be an indicator of water quality, and many sensitive species cannot survive at low oxygen concentrations.
pHMost zooplankton species require pH levels in the 6–8 range. Lower pH levels due to anthropogenic acidification can reduce the number of species that can persist in a lake. (1986), , (1994)
Maximum depthDeeper lakes will have a larger variety of habitats and will be able to support species that tend to live in deeper waters. , (2009)
Elevation/altitudeLakes at higher elevations are often colder and have shorter growing seasons, limiting the number of species that can persist. , ,
Mean depthDeeper lakes are likely to have larger number of different habitat types, increasing richness. (2018)
CaSome zooplankton species are limited by low calcium levels; therefore, higher Ca level may support a larger number of species in a lake. (2012)
Depth (unspecified)Deeper lakes are likely to have larger number of different habitat types in comparison with shallow lakes, increasing richness. , (2002)
AbundanceConductivityMost freshwater zooplankton faces difficulty with osmoregulation when conductivity levels increase, depressing reproductive rates. (2015)
TemperatureHigher temperatures can allow for higher rates of metabolism and higher reproductive rates. (2008)
PhosphorusIntermediate phosphorus levels may allow for high algae concentrations, supporting larger zooplankton communities. (2008)
pHLow pH levels due to acidification can cause stress, lowering rates of reproduction.
Primary production/chlorophyll- Primary production represents the available food supply for zooplankton.
LatitudeLatitude is associated with temperature in the water and the length of the growing season. Colder lakes with shorter growing season are likely to have lower zooplankton abundances.
Secchi depthSecchi depth is affected by water color, chlorophyll- concentrations, dissolved organic carbon levels, suspended sediments, etc. , (2019), ,
High Secchi depths might indicate low food availability and/or low turbidity.
Low Secchi depths might indicate high food availability and/or high turbidity.
Transparency/turbiditySimilar to Secchi depth, turbidity is affected by water color, chlorophyll- concentrations, dissolved organic carbon levels, suspended sediments, etc. (2000), (2004), (2003)
High turbidity might indicate low food availability and/or low suspended solids.
Low turbidity might indicate high food availability and/or high suspended solids.
Maximum depthDeeper lakes provide dark hypolimnetic refuges from some predators. (2009)
NitrateHigh nitrate levels may indicate that lake is close to agricultural activity and is likely receiving nutrients from fertilizers, sewage or other sources. , (2013)
Response variablePredictorProposed mechanistic associationSupporting studies
RichnessTemperatureLow temperatures limit the number of species that can persist in a lake due to physiological limitations. Therefore, richness is often reduced in colder lakes at high latitudes. ,
Surface areaLarger lakes are likely to have a variety of different habitat types (habitat heterogeneity), allowing for a larger number of species. , , (2012), (2009)
PhosphorusResponse may be unimodal. At low levels, food limitation may occur. At intermediate levels, food limitation no longer limits species persistence. High phosphorus levels may result in poor water quality as a result of eutrophication, potentially lowering the number of species that can persist in a lake. (2000)
Dissolved oxygenDissolved oxygen can be an indicator of water quality, and many sensitive species cannot survive at low oxygen concentrations.
pHMost zooplankton species require pH levels in the 6–8 range. Lower pH levels due to anthropogenic acidification can reduce the number of species that can persist in a lake. (1986), , (1994)
Maximum depthDeeper lakes will have a larger variety of habitats and will be able to support species that tend to live in deeper waters. , (2009)
Elevation/altitudeLakes at higher elevations are often colder and have shorter growing seasons, limiting the number of species that can persist. , ,
Mean depthDeeper lakes are likely to have larger number of different habitat types, increasing richness. (2018)
CaSome zooplankton species are limited by low calcium levels; therefore, higher Ca level may support a larger number of species in a lake. (2012)
Depth (unspecified)Deeper lakes are likely to have larger number of different habitat types in comparison with shallow lakes, increasing richness. , (2002)
AbundanceConductivityMost freshwater zooplankton faces difficulty with osmoregulation when conductivity levels increase, depressing reproductive rates. (2015)
TemperatureHigher temperatures can allow for higher rates of metabolism and higher reproductive rates. (2008)
PhosphorusIntermediate phosphorus levels may allow for high algae concentrations, supporting larger zooplankton communities. (2008)
pHLow pH levels due to acidification can cause stress, lowering rates of reproduction.
Primary production/chlorophyll- Primary production represents the available food supply for zooplankton.
LatitudeLatitude is associated with temperature in the water and the length of the growing season. Colder lakes with shorter growing season are likely to have lower zooplankton abundances.
Secchi depthSecchi depth is affected by water color, chlorophyll- concentrations, dissolved organic carbon levels, suspended sediments, etc. , (2019), ,
High Secchi depths might indicate low food availability and/or low turbidity.
Low Secchi depths might indicate high food availability and/or high turbidity.
Transparency/turbiditySimilar to Secchi depth, turbidity is affected by water color, chlorophyll- concentrations, dissolved organic carbon levels, suspended sediments, etc. (2000), (2004), (2003)
High turbidity might indicate low food availability and/or low suspended solids.
Low turbidity might indicate high food availability and/or high suspended solids.
Maximum depthDeeper lakes provide dark hypolimnetic refuges from some predators. (2009)
NitrateHigh nitrate levels may indicate that lake is close to agricultural activity and is likely receiving nutrients from fertilizers, sewage or other sources. , (2013)

Included in this table are the 10 most frequently measured predictor variables for richness and abundance.

The results of our meta-analysis of correlation coefficients showed that for total abundance, a significant positive association with chlorophyll- a , dissolved oxygen, pH and TP was supported ( P -values < 0.05; Fig. 6A ). The effect size for dissolved oxygen was not significant ( P  > 0.05). The 95% confidence intervals for the effect sizes overlapped among the variables examined for abundance, suggesting that there were no significant differences in effect sizes among variables. For richness, there was support for a negative association with latitude and positive associations for pH, surface area and temperature ( P -values < 0.05; Fig. 6B ). The effect size for TP was not significantly different from zero ( P  > 0.05). The effect sizes for latitude and temperature were larger than the other variables examined for richness, and the 95% confidence intervals for these variables did not overlap with the others ( Fig. 6B ).

Effect sizes for correlation coefficients from a meta-analysis using variables for which we could obtain data from greater than three studies. Panel A shows effect sizes for zooplankton abundance, while Panel B shows the same for zooplankton richness. The error bars represent the 95% confidence interval of the effect size.

Effect sizes for correlation coefficients from a meta-analysis using variables for which we could obtain data from greater than three studies. Panel A shows effect sizes for zooplankton abundance, while Panel B shows the same for zooplankton richness. The error bars represent the 95% confidence interval of the effect size.

How do investigators choose environmental variables to measure for their study?

The number of studies that measured a particular variable was not related to the proportion of studies that found that variable to be significantly associated with zooplankton community structure ( Fig. 7 ). There were nine core variables that were measured by a majority of studies including lake surface area, pH, phosphorus, nitrogen, dissolved oxygen, conductivity, primary productivity/chlorophyll- a , maximum depth and temperature ( Fig. 7 ). The nine core variables were often found to have significant associations with zooplankton community structure, but several other variables, including hardness, dissolved organic carbon, fish presence/absence, among others, appeared to be under sampled based on their track record in the literature ( Fig. 7 ). Our analysis comparing the proportion of studies selecting a variable in a particular year and the proportion of studies that found that variable to be significant in studies conducted prior to that year showed that there was no trend in correlation coefficients through time ( Supplementary Fig. S1 ). The probability of measurement of a variable was not independent of our categorizations for those variables (lab-based, field-based simple, field-based difficult/expensive, GIS-based, GIS-based difficult) (Fisher’s exact test, P  < 0.001). Our mosaic plot showed that variables in the field-based simple category were measured more often than all other categories and that watershed area (the only variable in the GIS difficult category) was measured less often than variables in all other categories ( Supplementary Fig. S2 ). The results of our PCA did not reveal any patterns in variable selection according to the continent or country where the study was conducted or by decade ( Supplementary Figs S3 and S4 ). Our cluster analysis did not show clear patterns in the choice of variables by country of study, with three of the four clusters having a mixture of studies from different countries ( Supplementary Fig. S5 ). One of the four clusters (colored red in the figure) was made up primarily of studies conducted in Canada, but these appeared to be grouped by topic since many of them were focused on the effects of acidification on zooplankton communities ( Supplementary Fig. S5 ). In addition, the mean number of variables measured per study did not show a pattern through time ( Supplementary Fig. S6 ).

Relationship between the proportion of studies that found a significant association between zooplankton community structure and an environmental variable versus the number of studies that measured that variable. Points were replaced by text for variables that were measured infrequently in comparison to the proportion of studies that found them significant and for variables that were measured in more than 50% of studies in our database. Only variables included in at least 10 studies were included in the plot, and text labels were shifted slightly when necessary to prevent overlap. Fish P/A = fish presence/absence.

Relationship between the proportion of studies that found a significant association between zooplankton community structure and an environmental variable versus the number of studies that measured that variable. Points were replaced by text for variables that were measured infrequently in comparison to the proportion of studies that found them significant and for variables that were measured in more than 50% of studies in our database. Only variables included in at least 10 studies were included in the plot, and text labels were shifted slightly when necessary to prevent overlap. Fish P/A = fish presence/absence.

The classification trees constructed to determine when TP, chlorophyll- a , conductivity, pH, surface area, dissolved oxygen, maximum depth and temperature were significant predictors of zooplankton community structure are shown in Supplementary Figs S6 and S7 . The analysis suggested that TP was a significant predictor in studies where lakes had warmer mean temperatures and conductivities >139 μS/cm. For studies with lower conductivity lakes, TP was also important if lakes had a mean surface area >20.2 ha ( Supplementary Fig. S7A ). For chlorophyll- a , the only division was based on pH, with chlorophyll being significant for studies with low pH lakes ( Supplementary Fig. S7B ). Conductivity was significant in studies that had productive lakes with chlorophyll- a levels >12.9 μg/L and for studies of less productive lakes where phosphorus levels were intermediate (9.5 > TP < 20 μg/L; Supplementary Fig. S7C ). pH was significant in studies that had lakes with small surface areas (<1.6 ha) and for studies with larger lakes with depths >21.7 m ( Supplementary Fig. S7D ). Surface area was significant in studies with a mean conductivity >22.3 μS/cm with elevations >514 m ( Supplementary Fig. S8A ). Dissolved oxygen was significant in studies where mean TP was <6.85 μg/L and for lakes with lower TP but surface areas smaller than 11.9 ha ( Supplementary Fig. S8B ). Maximum depth was significant in studies that had lakes with large surface areas (>135 ha) ( Supplementary Fig. S8C ), while temperature was significant in studies where pH levels were >7.5 or for studies with lower pH lakes that had surface areas <9 ha ( Supplementary Fig. S8D ).

Variables commonly measured explained between 20 and 55% of variation in zooplankton community structure ( Fig. 8 ). The percent variation explained of key variables related to zooplankton community structure differed greatly among studies ( Fig. 8 ). For example, conductivity explained <20% of variation while temperature explained ~60%. The most popular statistical analyses were RDA, CCA, multiple regression, correlation and univariate linear regression ( Fig. 9 ). The complexity of statistical analysis has changed through time, with early studies relying on univariate analyses such as correlation, or no inferential statistics at all, while later studies have moved into a combination of both univariate and multivariate methods ( Supplementary Fig. S9 ).

When variables are used in analyses, how much variation in zooplankton community structure do they explain? The figure shows R2 values for the individual variables for which we could obtain values from five or more studies. Note that the results shown in this figure should be interpreted with caution, as the values were extracted directly from the literature and were not corrected for differences in the number of variables incorporated in the models for each study.

When variables are used in analyses, how much variation in zooplankton community structure do they explain? The figure shows R 2 values for the individual variables for which we could obtain values from five or more studies. Note that the results shown in this figure should be interpreted with caution, as the values were extracted directly from the literature and were not corrected for differences in the number of variables incorporated in the models for each study.

Types of statistical analyses used in studies to examine relationships between zooplankton communities and local variables. Note that some studies used more than one analysis type; therefore, totals for bars do not add to the number of studies in the data set.

Types of statistical analyses used in studies to examine relationships between zooplankton communities and local variables. Note that some studies used more than one analysis type; therefore, totals for bars do not add to the number of studies in the data set.

The mean range of commonly measured environmental variables did not differ significantly between studies that found a variable to be significantly related to zooplankton community structure and those that did not ( t -tests; P  > 0.05 in all cases; Supplementary Fig. S10 ). There were also no significant differences in the number of lakes sampled versus statistical significance for commonly measured environmental variables ( t -tests; P  > 0.05 in all cases; Supplementary Fig. S11 ).

Our review of the literature showed that the types of variables measured by zooplankton ecologists varied widely among studies, with 383 different variables measured in just 126 studies. Despite this variation, we identified nine core variables that were routinely measured: lake surface area, pH, phosphorus, nitrogen, dissolved oxygen, conductivity, primary productivity/chlorophyll- a , maximum depth and temperature. More importantly, we identified several variables that were frequently found to be associated with zooplankton community structure but that are infrequently measured (hardness, fish presence/absence, Al, Si, Mg, dissolved organic carbon, volume, nitrate). In addition, some variables, such as watershed area and chloride concentration, seem to be included in studies more often than would be expected given how infrequently they were found to be associated with zooplankton communities. While failing to collect data for a few overlooked environmental variables might seem trivial, the exclusion of appropriate variables could have significant implications for both basic and applied studies of zooplankton, including (i) a failure to recognize environmental variables that have important effects on zooplankton biodiversity and structure in a particular habitat; (ii) errors in predicting or understanding how biodiversity might change in the face of stressors such as climate change, permafrost thaw, development and agricultural pollution and (iii) misdiagnosing the importance of other factors structuring zooplankton communities (e.g. dispersal) when important spatially structured environmental variables are left out of analyses. Given these implications, we believe that it is important for zooplankton ecologists to carefully consider the environmental variables that will be measured when designing a field sampling program. Based on our review of the literature, we have compiled a list of recommended environmental variables based on the frequency with which they were significant in past studies and on the mean proportion of variance they explained in those studies (see Table III ). We believe that this list could be useful when planning a new field survey or when evaluating an ongoing monitoring program. In particular, we believe that it could reduce inefficiencies by allowing investigators to limit effort and expenditure to the most important environmental variables rather than trying to measure a large number of variables that have not been shown to be consistently related to zooplankton community structure in past studies.

Variables that we suggest every zooplankton ecologist should consider measuring based on the frequency with which they were significant in past studies and on the mean proportion of variance they explained in those studies

Physical variablesProperties of the waterBiological variables
Elevation/altitude, maximum depth, mean depth, surface areaAlkalinity, calcium, color, conductivity, dissolved oxygen, nitrate, pH, Secchi depth, temperature, total nitrogen, TP, magnesium, turbidityChlorophyll- macrophyte abundance, fish presence/absence
Physical variablesProperties of the waterBiological variables
Elevation/altitude, maximum depth, mean depth, surface areaAlkalinity, calcium, color, conductivity, dissolved oxygen, nitrate, pH, Secchi depth, temperature, total nitrogen, TP, magnesium, turbidityChlorophyll- macrophyte abundance, fish presence/absence

Given the significant amount of variability in the selection of physicochemical variables measured among studies, we examined if choices made by investigators were largely idiosyncratic or if there might be patterns within our database that could provide some insights into this variability. While we acknowledge that individual studies may have focused on a specific problem, leading to the selection of a particular variable of interest (e.g. shoreline development), we expected that overall choices regarding the suite of physicochemical variables measured would have a rational basis. First, we hypothesized that investigators might have made their choices based on the region where they conducted the study, as there might be regional scientific traditions in training and approaches to research ( Livingstone, 1995 ). Second, we hypothesized that the types of variables measured might evolve over time, such that the composition of variables measured for a study would differ according to the year of publication. This hypothesis was based on the assumption that there would be trends in topics and methods of research over time ( Carneiro et al. , 2008 ; Cao et al. , 2012 ). However, the absence of clear patterns in our PCAs indicated that there were no clear groupings in terms of the types of variables measured according to the region of study or by year. Our cluster analysis also showed that studies did not clearly group by the country where each study conducted. In addition, the number of variables measured did not change through time, indicating that investigators did not add or subtract a significant number of variables from studies as we learned more about the factors structuring zooplankton communities. Our third hypothesis was that investigators were choosing environmental variables based on information from past studies that showed which variables were most often related to community structure. However, we failed to find a relationship between the proportion of studies that found a significant association between zooplankton community structure and a particular variable versus the number of studies that measured that variable. Our final hypothesis was that the selection of variables for each study was related to convenience of measurement. Our contingency analysis showed that there was a significant relationship between our categorization of variables based on their type and ease of measurement and the frequency with which they were measured. Variables that were difficult to measure, such as watershed area and turbidity, were under sampled compared to those that were easy to measure, such as pH and conductivity. This suggests that investigators may be making some decisions involving variable selection based on considerations of practicality and convenience, rather than on an evolving understanding of the fundamental factors that structure zooplankton communities.

Although there was not a clear relationship between the frequency of measurement of a variable and its track record of significance in the literature, most studies included a selection of variables that were often found to be related to zooplankton community structure. We ranked physicochemical variables (measured in ≥10 studies) according to the proportion of studies that found them to be significant ( Fig. 4 ). Out of the top 25 variables found on this list, 22 of them were also on a list of the top 25 most frequently measured variables ( Fig. 4 vs. Fig. 7 ). Unsurprisingly, this confirms that the selection of physicochemical variables to measure was not random, or solely based on convenience of measurement, but was likely based on investigators’ intuition and understanding of the literature. However, we argue that the review conducted for the current study provides investigators with information that could prevent them from overlooking important variables in future work. We should note that the appearance of variables in either of the aforementioned lists does not necessarily mean that a causal relationship exists between those variables and zooplankton community structure (i.e. correlation does not equal causation). Some of the variables that show strong relationships with community structure in our review have been tested in controlled field or laboratory experiments (pH, temperature; Morgan, 1986 ; Uye, 1988 ; Belanger and Cherry, 1990 ; Moore and Folt, 1993 ; Leech and Williamson, 2001 ; Chen et al. , 2008 ; Dupuis and Hann, 2009 ; Medeiros et al. , 2013 ). However, many of the variables important for zooplankton community structure are not amenable to controlled experiments (surface area, maximum depth, elevation, latitude), making it important to critically evaluate how these variables are linked to variation in zooplankton communities. An exploration of the potential mechanistic relationships between each physicochemical variable and zooplankton communities could fill several volumes, but we provided a summary of some of these potential links in Tables I and II . Our summary suggests that there are clear reasons to expect relationships between some of these environmental variables and zooplankton communities, but we also believe that the field would benefit from a thorough review of these associations. Such a review might consider which associations have been supported through controlled studies, which are strongly supported through correlational studies and/or our understanding of zooplankton physiology and ecology and which require more investigation and skepticism.

In addition to examining the selection of physicochemical variables, we were able to gather information on the types of statistical analyses used for each study in our database. We found that multivariate statistical techniques (CCA, RDA) were most frequently used, followed by univariate linear analyses (multiple linear regression, correlation, linear regression). The types of data sets collected by zooplankton ecologists are multivariate by nature, containing a list of study sites (e.g. lakes) and a list of species in those lakes. Therefore, we often aim to explain differences in the relative abundance of those species among study sites using environmental variables, necessitating a multivariate approach. CCA and RDA have very similar applications, with the exception that CCA is often used when the response of species to environmental gradients is assumed to be unimodal rather than linear ( ter Braak and Verdonschot, 1995 ). Both CCA and RDA work with a matrix of explanatory environmental variables and a matrix of zooplankton species (response variables) with the goal of separating systematic variation from noise ( ter Braak and Verdonschot, 1995 ). Univariate analyses were also frequently used in studies and often focused on exploring relationships between univariate measures of community structure (diversity, richness, total abundance) and environmental variables. Interestingly, nonlinear approaches were used infrequently, suggesting that either zooplankton–environment relationships are well-described by linear relationships or transformations of variables allow them to be analyzed with these tools. Early studies throughout the 1970s and 1980s relied on univariate analyses, or non-inferential statistics, while multivariate analysis became increasingly important throughout the 2000s–present. Although CCA and RDA were developed by the mid-1980s ( ter Braak, 2014 ), the popularity of multivariate methods likely increased in the early 2000s due to the rise of the desktop computers with simple operating systems and programs such as DECORANA and CANOCO that made the analyses accessible to ecologists. For example, the first Windows version of CANOCO launched in 1998 and included both RDA and CCA ( ter Braak, 2014 ). While the choice of statistical analysis for a particular project will be closely related to the question under study, we believe that our analysis showing the common statistical techniques may be helpful to someone new to the field that is exploring options for the design and analysis of the types of data encountered by zooplankton ecologists.

Throughout our review of the literature, we have assumed that each study represented a well-designed test of the hypothesis that the environmental variables measured were related in some way to zooplankton community structure. However, it is possible that conclusions in individual studies were affected by issues with statistical power, such that studies had committed Type II statistical errors. Our analyses looking at sample size (number of lakes or sites) and range in environmental variables did not show evidence that statistical power was an issue overall. For six of the most commonly measured environmental variables (pH, conductivity, TP, temperature, surface area and dissolved oxygen), the mean number of lakes sampled did not differ between studies that found the variable to be significantly related to zooplankton communities and those that did not. Similarly, mean values for the range of those variables did not differ between studies that found the variable to be significantly related to zooplankton communities and those that did not. While these analyses certainly do not mean that every study in our database had an adequate level of statistical power, it does suggest that there was not a systematic discrepancy between studies that found particular variables to be significant versus those that did not. This provided us with confidence that our review was able to uncover the most important environmental variables for zooplankton communities and suggests that our conclusions are not simply reflecting bias due to issues with statistical rigor.

If differences among studies in the variables that were significant predictors of zooplankton community structure were not related to issues with sample size or range of the variables, then what is causing this variation? Our classification analysis revealed some interesting relationships that might provide insight into when certain variables are likely to be significantly associated with zooplankton community structure ( Supplementary Figs S7 and S8 ). The model for TP suggested that when temperature and conductivity were not limiting (i.e. low), TP was a significant predictor of zooplankton community structure. This model might reflect the effects of low temperatures and conductivity levels in high-latitude lakes that often appear more important than nutrients (e.g. Vucic et al. , 2020 ). For chlorophyll- a , pH of lakes in a study were an important determinant, and we speculate that this might relate to decreases in productivity due to interactions between acidification and phosphorus availability in acidified lakes ( Jansson et al. , 1986 ; Kopáček et al. , 2001 ). The significance of conductivity depended on lake productivity, with conductivity being important in studies that had lakes with TP and chlorophyll- a levels consistent with mesotrophic conditions (chlorophyll- a  > 12.9 μg/L and 9.5 > TP < 20 μg/L; Carlson, 1977 ). This might indicate that zooplankton structure is controlled by factors other than conductivity in oligotrophic and eutrophic systems. Maximum depth was significant in studies with lakes that had larger surface areas, perhaps due to the larger range of depths in these lakes. Explanations for the models for pH, dissolved oxygen and temperature are less clear, and we will leave those for the reader to evaluate. While we believe that our classification analysis might provide some insight, we also believe that these results should be interpreted with caution, as these patterns are strictly correlative and it can be tempting to develop “Just So” stories ( Kipling, 1902 ) to explain each model.

Our review of the literature showed that zooplankton ecologists measure a diverse collection of environmental variables in an effort to explain differences in zooplankton communities among sites. Despite variation in the types of environmental variables measured among studies, we found that there was a set of nine core variables measured by a majority of studies: lake surface area, pH, phosphorus, nitrogen, dissolved oxygen, conductivity, chlorophyll- a , maximum depth and temperature. The nine core variables were frequently found to be associated with zooplankton community structure, but there were several other promising variables such as dissolved organic carbon, nitrate and magnesium that were often associated with community structure when measured but were rarely included in published studies. Surprisingly, the selection of variables measured in past studies did not correlate with how often those variables were found to be significant in the literature. In addition, our analyses suggested that variable selection was not associated with the geographic region or country where a study was conducted, or the decade that it was published, suggesting that there are no geographic biases or clear responses to fads or trends in research topics. The choice of variables to measure could have been related to the training and experiences of investigators that occurs at geographic scales below the country of study, but we were not able to test this hypothesis. We did, however, find evidence that the selection of variables to measure for a study may have been related to the ease of measurement. We encourage investigators to use the information in our review to evaluate the types of variables to include in future studies involving zooplankton communities. By making an effort to ensure that the most important environmental variables are measured, we can avoid errors that may complicate our ability to understand the basic factors structuring zooplankton communities and can improve models used to predict how zooplankton will respond to changes in their environment.

E. Chang assisted with collating papers for an early version of the database used in this study. This work was supported by a Discovery Grant from the Natural Sciences and Engineering Research Council of Canada to D.K.G.

Data collected as part of the literature review were uploaded to the Dryad repository. The data can be accessed at https://doi.org/10.5061/dryad.z08kprr90 .

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Monitoring and modelling marine zooplankton in a changing climate

Lavenia ratnarajah.

1 Integrated Marine Observing System, Hobart, Tasmania Australia

2 Global Ocean Observing System, International Oceanographic Commission, UNESCO, Paris, France

Rana Abu-Alhaija

3 Cyprus Subsea Consulting and Services C.S.C.S. ltd, Lefkosia, Cyprus

Angus Atkinson

4 Plymouth Marine Laboratory, Prospect Place, The Hoe, PL1 3DH Plymouth, UK

Sonia Batten

5 North Pacific Marine Science Organization (PICES), 9860 West Saanich Road, V8L 4B2 Sidney, BC Canada

Nicholas J. Bax

6 CSIRO Oceans & Atmosphere, Hobart, Tasmania Australia

Kim S. Bernard

7 College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, 104 CEOAS Admin Bldg., Corvallis, OR 97330 USA

Gabrielle Canonico

8 US Integrated Ocean Observing System (US IOOS), NOAA, Silver Spring, MD USA

Astrid Cornils

9 Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Section Polar Biological Oceanography, Am Handelshafen 12, Bremerhaven, Germany

Jason D. Everett

10 School of Mathematics and Physics, University of Queensland, St. Lucia, QLD Australia

11 CSIRO Oceans and Atmosphere, Queensland Biosciences Precinct, St Lucia, 4067 Australia

12 Evolution and Ecology Research Centre, University of New South Wales, Sydney, NSW Australia

Maria Grigoratou

13 Gulf of Maine Research Institute, 350 Commercial St, Portland, ME 04101 USA

14 Mercator Ocean International, 2 Av. de l’Aérodrome de Montaudran, 31400 Toulouse, France

Nurul Huda Ahmad Ishak

15 Faculty of Science and Marine Environment, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu Malaysia

16 Institute of Oceanography and Environment, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu Malaysia

David Johns

17 The Marine Biological Association (MBA), The Laboratory, Citadel Hill, Plymouth PL1 2PB UK

Fabien Lombard

18 Sorbonne Université, Centre National de la Recherche Scientifique, Laboratoire d’Océanographie de Villefranche (LOV), Villefranche-sur-Mer, France

19 Research Federation for the Study of Global Ocean Systems Ecology and Evolution, FR2022/Tara Oceans GOSEE, 75016 Paris, France

20 Institut Universitaire de France, 75231 Paris, France

Erik Muxagata

21 Universidade Federal de Rio Grande - FURG - Laboratório de Zooplâncton - Instituto de Oceanografia, Av. Itália, Km 8 - Campus Carreiros, 96203-900 Rio Grande, RS Brazil

Clare Ostle

Sophie pitois.

22 Centre for Environment, Fisheries and Aquaculture Centre (Cefas), Pakefield Road, Lowestoft, NR330HT UK

Anthony J. Richardson

Katrin schmidt.

23 School of Geography, Earth and Environmental Sciences, University of Plymouth, Plymouth, PL4 8AA UK

Lars Stemmann

Kerrie m. swadling.

24 Institute for Marine and Antarctic Studies & Australian Antarctic Program Partnership, University of Tasmania, Hobart, Tasmania Australia

25 Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, 7 Nanhai Road, Qingdao, 266071 PR China

Lidia Yebra

26 Centro Oceanográfico de Málaga (IEO, CSIC), Puerto Pesquero s/n, 29640 Fuengirola, Spain

Associated Data

Zooplankton are major consumers of phytoplankton primary production in marine ecosystems. As such, they represent a critical link for energy and matter transfer between phytoplankton and bacterioplankton to higher trophic levels and play an important role in global biogeochemical cycles. In this Review, we discuss key responses of zooplankton to ocean warming, including shifts in phenology, range, and body size, and assess the implications to the biological carbon pump and interactions with higher trophic levels. Our synthesis highlights key knowledge gaps and geographic gaps in monitoring coverage that need to be urgently addressed. We also discuss an integrated sampling approach that combines traditional and novel techniques to improve zooplankton observation for the benefit of monitoring zooplankton populations and modelling future scenarios under global changes.

Zooplankton are a critical link to higher trophic levels and play an important role in global biogeochemical cycles. This Review examines key responses of zooplankton to ocean warming, highlights key knowledge and geographic gaps that need to be addressed, and discusses how better use of observations and long-term zooplankton monitoring programmes can help fill these gaps.

Introduction

Zooplankton are a critical component of marine ecosystems. They are an important pathway for energy transfer between primary producers and higher trophic levels such as fish, seabirds and marine mammals 1 – 4 , and they influence oceanic biogeochemical cycles through direct and indirect feedback loops 5 – 12 . Decades of laboratory and field investigations into zooplankton physiology, community composition, and distribution, have shown the sensitivity of zooplankton to the changing ocean – climate-induced poleward shifts in the distribution of some zooplankton had already been observed by the 1960s 13 – 15 . However, advances in observational data coverage have largely been focussed on the Northern Hemisphere with inconsistent patterns. Changes in zooplankton are altering biogeochemical cycling, energy transfer pathways and the ecosystem services humankind receives from the ocean, but how and where zooplankton will moderate or amplify these processes, particularly under future ocean conditions, has received little attention.

In this Review, we (1) outline the recent and projected changes in global and regional climatic conditions that can impact zooplankton, (2) review climate-driven changes in zooplankton ecological dynamics and their role in ecosystem functioning at local and regional scales, highlighting similarities and discrepancies in trends, (3) highlight existing limitations of zooplankton modelling and discuss how better use of observations can fill some of these gaps, (4) identify current long-term zooplankton monitoring programmes globally, highlighting data availability and gaps in coverage, and lastly (5) look towards the future of global zooplankton research, where we draw on our existing knowledge to advocate for an integrated approach to zooplankton research and monitoring that will link to global needs.

Here, we consider all zooplankton groups, but we focus on net-caught zooplankton, which include copepods, ichthyoplankton (fish eggs and larvae), euphausiids and salps. These groups were selected because they represent the dominant metazoan groups with key ecological and biogeochemical significance and provide ecosystem services such as nutrient recycling and carbon sequestration, as well as supporting fish stocks and fisheries. Additionally, these groups are the most broadly studied geographically, providing the opportunity for a comprehensive global review. Although not the focus, we acknowledge the importance of other zooplankton groups, especially the understudied protists, amphipods, chaetognaths, pteropods and other gelatinous groups (for example, jellyfish, ctenophores and siphonophores), and when possible, we include them in the text.

Recent and projected climate-driven environmental changes

Climate varies on local and regional scales, and patterns are not necessarily equal across all oceanic basins. For example, sea surface temperature (SST) is increasing across all basins, but its rate of increase varies regionally, with projections showing the North Atlantic warming at a much faster rate than the Southern Ocean (Fig.  1 ). Net primary production (NPP) on the other hand is projected to generally increase towards the poles and decrease toward the equator, but with considerable regional variation (Fig.  1 ). Future warming leads to enhanced ocean stratification, and this process impacts regional phytoplankton productivity 16 , 17 . Uncertainties in projected NPP have increased in the latest Coupled Model Intercomparison Project (CMIP6) compared with those of earlier models from CMIP5 (and despite better agreement with the historical records), as more realism has been included in the models, which is likely to increase in the future 17 . Whilst changes in SST and NPP, as well as interannual fluctuations in large-scale climate oscillations (see Box  1 ) are occurring simultaneously, and influencing zooplankton ecology and ecosystem function, we focus our Review on the impacts of ocean warming and not the regionally variable impacts of changing phytoplankton quality and quantity. Below we untangle some of these climate-driven environmental impacts on zooplankton at local or regional scales.

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Multi-model mean projections for SST and NPP from 10 CMIP6 Earth system models for the historical period (1995–2014), future (2081–2100) and the change in SST and NPP by 2081–2100 relative to 1995–2014 based on SSP5–8.5. Publicly available datasets were analysed in this review. The 10 CMIP6 Earth system models used were ACCESS-ESM1.5, CESM2, CESM2-WACCM, CNRM-ESM2-1, GFDL-ESM4, IPSL-CM6A-LR, MIROS-ES2L, MPI-ESM1.2HR, NorESM2-LM and UKESM1-0-LL. This data can be found at: https://esgf.llnl.gov/ .

Box 1 Definition of climate events

Marine heatwavePersistent anomalously warm waters for at least 5 consecutive days.
El Niño Southern Oscillation is the strongest year-to-year climate variability on the planet, originating in the equatorial Pacific Ocean through coupled ocean-atmosphere interactions. ENSO manifests itself in anomalous surface warming or cooling that tends to peak in boreal winter.
El Niño is the warm phase of ENSO, characterised by anomalous surface warming and weaker trade winds in the equatorial Pacific Ocean.
North Atlantic Oscillation (NAO) is the primary model of internal atmospheric variability in the North Atlantic characterised by a north-south dipole of alternating sea level pressure anomalies between the subtropics and high latitudes.
Southern Annular Mode (SAM) is the leading mode of large-scale atmospheric variability in the Southern Hemisphere, characterised by an anomalous pressure centre over Antarctica and zonally symmetric pressure anomaly of opposite sign at midlatitudes. The positive and negative phases of the SAM are respectively associated with a poleward and equatorward displacement of the midlatitude westerly winds.

Sources for definition: Marine heatwave 51 , ENSO 181 , El Niño 181 , NAO 181 , SAM 181

Untangling climate effects on zooplankton ecology and ecosystem function

Multiple climate-induced stressors such as ocean warming and ocean acidification favour some taxa over others and thereby modulate zooplankton community structure. Long-term time series have proved invaluable for elucidating these patterns, particularly in highlighting three ‘universal’ responses to warming despite multiple interacting stressors. These include shifts in phenological timing, typically towards earlier seasonal occurrence of spring or summer species and later occurrence of autumn species 18 , 19 , poleward shifts in geographical range 20 , and shifts towards smaller size in warmer conditions 21 – 23 . These changes can have cascading impacts on the efficiency of the biological carbon pump (the biologically driven sequestration of atmospheric carbon dioxide into the ocean’s interior) and transfer efficiency throughout food webs, including desynchronising ecological interactions (for example, between predator and prey), ultimately threatening ecosystem function and services, including fisheries 24 . In the following subsections we discuss similarities and discrepancies in observed patterns and trends by oceanic regions, explore the connectivity between zooplankton ecology and ecosystem function, and highlight key knowledge gaps that drive these uncertainties.

Zooplankton phenology

Phenology is highly responsive to a species’ temperature sensitivity 18 , 25 , 26 , thermal optima 27 and adaptation rates 28 . In the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report, a systematic review of marine phenology studies suggested that zooplankton timing is moving earlier and responding faster compared to that observed for other marine animals 29 . For example, in the Subarctic Pacific Ocean, ocean warming has caused the biomass of the dominant copepod, Neocalanus plumchrus , to peak 14 days earlier per decade or 73 days earlier per increase in °C 30 . In the Central North Sea region of the Atlantic Ocean, the timing of peak biomass for copepods, decapods, echinoderm larvae and other meroplankton and holozooplankton all occurred earlier, ranging from 2.2 to 10 days earlier per decade, or 11.1 to 52 days earlier per increase in °C 18 , 31 , 32 . In Narragansett Bay, an inlet of the North Atlantic Ocean, the biomass of the comb jelly, Mnemiopsis leidyi , peaked 11.1 days earlier per decade or 49.2 days earlier per increase in °C, although there was no statistically significant change in the phenology of one of their dominant copepod prey, Acartia tonsa , over the same time period 33 . In the Gironde estuary in southwest France, zooplankton phenology moved earlier over the past three decades, and this was also apparent in the arrival of fish species into the estuary 34 . Most of the time-series studies on zooplankton phenology have occurred in the Northern Hemisphere, and less is known of the tropics and the Southern Hemisphere 32 , 35 , highlighting a critical knowledge gap that needs urgent address. However, long-term monitoring of the pteropod Limacina helicina antarctica at the Palmer Station Antarctica Long Term Ecological Research (LTER) project shows that the phenology of this pteropod along the Western Antarctic Peninsula has remained relatively stable despite considerable environmental variability 36 .

Zooplankton range shifts

Under a warming environment, species have generally shifted polewards and/or to deeper layers to maintain their core within their optimum water temperature ranges 32 , 37 , 38 . These range shifts, however, are not consistently observed and vary greatly in strength and direction and are often species-specific 39 . For example, in the North Atlantic, some copepods including Centropages chierchiae and Temora stylifera were shown to move northward at a rate of 157–260 km per decade 37 , 40 . However, Chust et al. 14 determined that movements of the copepod Calanus finmarchicus were an order of magnitude lower at 8–16 km per decade, whilst Edwards et al. 38 showed that warming led to a decrease in krill abundance but no northward range shifts. In the Southern Ocean, poleward range shifts of Antarctic krill ( Euphausia superba ), the dominant zooplankton species, and salps have been recorded 41 – 43 , whereas the distribution of copepod species seems to have been resilient to warming of their habitat and remained fairly fixed 44 . Furthermore, range shifts of Antarctic krill were out of step with the pace of warming, with an abrupt shift occurring during a warming hiatus 45 . This nonlinearity was attributed to population processes, including an abrupt occupation of a new spawning ground in the south.

Species with low mobility (for example, planktonic foraminifera) can be more sensitive to environmental changes and water properties compared to mobile species (for example, euphausiids) that can modulate their spatial distribution to some extent. For example, global changes in temperature since the pre-industrial era have caused a range shift of ~40 km per decade of total foraminifera communities across the globe 46 . Furthermore, organisms inhabiting semi-closed basins (like the Mediterranean Sea) would not be able to shift poleward and may move to deeper layers, reflected as decreasing abundance trends in the surface warming waters 47 . These examples show that range shifts are often decoupled from the general poleward progression of isotherms 39 , 44 and underscore the need for further studies to understand the mechanisms of climate change responses.

Zooplankton size

Alongside shifts in phenology and range, declines in body size 21 have been described as the third universal response to climate warming. Global studies on marine copepods, the most abundant multicellular aquatic animal on Earth, revealed that temperature was a better predictor of body size than either latitude or oxygen, with body size decreasing by 43.9% across the temperature range of −1.7–30 °C 22 , 23 . This suggests that with continued ocean warming, smaller copepod species are likely to dominate, with cascading effects on fisheries production and carbon sequestration 22 , 23 . This trend has not been observed in the Southern Ocean where average copepod community size shows a shift towards larger copepod species, but reasons for this difference are still unknown 48 . Within individual copepod species, adult body size was found to be generally reduced under warming (the temperature-size rule), but this temperature-dependence was recorded only during the spring to autumn growth period and was modulated by density-dependent effects from predation or competition 49 . Likewise, Antarctic krill showed a more complex size response under warming, where the changing balance of recruitment and mortality has led to a long-term increase in mean krill length 41 , opposite to predictions of reduced size with warming. Body size responses can be governed by factors beyond temperature or food, such as species behavioural and life history traits 50 , population dynamics 41 or competition and predation 49 . Clearly, adjustments in body size for species or assemblages occur in parallel to shifts in range and phenology and they need to be studied within an integrated framework.

Complex effects of climate change

Alongside the gradual and long-term change in global climate, marine heatwaves, defined as anomalously warm waters persistent for at least 5 consecutive days 51 , together with regional climate events (for example, El-Niño Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Southern Annular Mode (SAM); see Box  1 ), intensify observed trends in zooplankton phenology, range and size. For example, between 2014 and 2016, the northeast Pacific experienced two successive warming events — a marine heatwave and El Niño. The anomalously warm conditions in the upper ocean led to the California Current Ecosystem being dominated by gelatinous zooplankton, copepods and euphausiid species that were observed further north than their typical ranges 52 – 54 , which impacted predator-prey dynamics. The pelagic tunicate, Pyrosoma atlanticum , was a new arrival to the Northern California Current during the marine heatwaves of 2014–2016, and exerted considerable grazing pressure on local phytoplankton stocks 55 . The marine heatwave also resulted in changes to zooplankton size structure where a sharp decline in the body sizes of juveniles and adults of the euphausiid, E. pacifica , was observed in coastal waters of northern California 56 .

In the North Atlantic, NAO-dependent northerly winds increase on-shelf transport and recruitment of copepodites as they vertically migrate from deeper waters 57 . In the Northwest Atlantic, zooplankton responses to NAO are not uniform, with different copepod species exhibiting different responses to the NAO driven by their varying temperature preferences and traits, such as diapause ability (see Ref. 58 and references therein). For instance, C. helgolandicus (warmer water species, does not exhibit diapause) increases in abundance during positive phases and decreases during negative phases, whereas C. finmarchicus (cold-water species that does exhibit diapause) increases during negative NAO phases and decreases during positive phase 58 . In the Southern Ocean, negative phase SAM has been associated with increased phytoplankton biomass 59 , greater abundances of the ice krill E. crystallorophias 60 , and higher condition factor in mature female Antarctic krill 61 . In the Bering Sea, North Sea and northern Humboldt upwelling system, jellyfish biomass is positively correlated to SST, NAO and El Niño, respectively, as well as the availability of their prey 62 – 64 . Jellyfish ingest zooplankton, juvenile fish and fish eggs, and in the Bering Sea, jellyfish biomass peaked with moderate SST and low zooplankton biomass and moderate juvenile pollock biomass, but jellyfish biomass decreased with very warm temperatures, low zooplankton biomass and very high juvenile pollock biomass 62 .

Studies are increasingly highlighting the complexity and interrelatedness of responses to climate change. Aside from long-term ocean warming and regional climate events impacting phenology, range and size, to complicate matters further, climate change can have both direct effects on zooplankton through their biology, and indirect effects via their food. A combination of these effects was invoked to explain a pronounced and long-term, but summer-specific, decline in copepods across the Northeast Atlantic and fringing seas 65 . Warmer summers were suggested to have led to increased energetic demands for copepod metabolism, at the same time as leading to earlier spring blooms and a longer stratified and more nutrient-starved period favouring picocyanobacteria, which are too small to be ingested by most zooplankton 65 . Similarly, in the South-West Mediterranean, warmer and longer summers are suggested to drive the increased abundances of cladocerans in autumn, delaying the dominance of copepods towards the winter season 47 . Further studies are needed, particularly from the tropics and southern regions, to examine how multiple interacting factors influence the phenology, range and size of zooplankton, as this will undoubtedly have strong implications on ecosystem function.

The biological carbon pump

Zooplankton play a critical role in the biological carbon pump (Fig.  2 ). Through a series of transformations, zooplankton both recycle essential resources (for example, iron, dissolved organic carbon, ammonium, nitrogen and phosphorus) required for phytoplankton and bacterial growth, and export carbon to deeper waters 5 , 7 – 12 , 66 – 69 .

An external file that holds a picture, illustration, etc.
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a Zooplankton graze on phytoplankton, transferring carbon and nutrients. Excess nutrients in zooplankton are recycled via excretion and egestion either within the upper ocean or throughout the entire water column as some zooplankton undertake diel vertical migration. Unconsumed phytoplankton form aggregates, and together with zooplankton faecal pellets, these particles rapidly sink and are exported to deeper waters. However, bacteria remineralise much of these sinking particles along its descent. b The smaller figure showcases the potential direction of change on three zooplankton processes – respiration, grazing, and excretion and egestion, under ocean warming. Studies to date show that zooplankton respiration will increase under a future warmer ocean, however the magnitude of grazing and excretion and egestion are unclear. Consequently, the magnitude of carbon exported through zooplankton-related activities under ocean warming remains unclear. This figure was designed by Dr Stacey McCormack (Visual Knowledge).

In the iron-limited Southern Ocean, iron is rapidly recycled by the copepod C. simillimus , Antarctic krill and the salp, Salpa thompsoni 7 , 8 , 12 , 66 , 70 – 72 , via grazing, excretion, egestion, vertical migration and bacterial remineralisation of sinking faecal pellets, which supports phytoplankton 12 and bacterial production 6 . In contrast, at least one study has found that S. thompsoni faecal pellets deplete iron from Southern Ocean surface waters 73 , providing motivation for future studies on this topic 74 . Despite fewer studies in other oceanic regions, it is clear that zooplankton recycle a range of essential resources that support phytoplankton production (for example, iron and ammonium in the Atlantic Ocean 75 , 76 , nitrogen and phosphorus in the South Pacific 68 ), but the few and conflicting studies highlight our uncertainties surrounding the magnitude and relative importance of various zooplankton groups 74 .

Zooplankton also contribute substantially to the cycling of carbon via respiration, sinking faecal pellets, sinking carcasses and diel vertical migration (DVM) across all oceanic basins (for example, Arctic Ocean 77 , 78 , Atlantic Ocean 79 – 83 , Pacific Ocean 84 , 85 , Southern Ocean 11 , 86 – 89 ). However, there is large spatiotemporal variability with complex, and at times unclear, drivers. For example, the magnitude of the DVM-induced carbon flux depends on the species composition and biomass of zooplankton communities and can account for 4–70% of the total particulate organic carbon flux 90 . This active flux can exceed the passive sinking flux in the presence of mesoscale features (like eddies and fronts) 91 , whose weakening due to global warming would affect diel migrating and diapausing species biomass and downward fluxes 92 . Additionally, based on long-term Continuous Plankton Recorder (CPR) data, carbon fluxes over the last 55 years have increased along the northern and north-western boundaries from Iceland to the Gulf of Maine and decreased across much of the open northern North Atlantic and the European Shelf Seas 13 . Changes (and variability) in flux estimates could be due to changes in the distribution of copepod populations, related to the NAO which controls ocean currents and the advection of copepods, as well as the species-specific response in the distribution of plankton species to climate change 13 .

While ocean warming increases the energetic demands of zooplankton, it also increases stratification-driven nutrient limitation and leads to smaller zooplankton body size, which have negative effects on community production and carbon export 13 , 65 , 93 . Furthermore, large-scale climate oscillations drive shifts in zooplankton biomass and community structure 46 , 52 – 54 , 58 , 60 , 94 – 98 . So how will the role of zooplankton within the biological carbon pump change with ocean warming? Whilst there are many uncertainties, below we consider the direction of change for three key activities — respiration, grazing and excretion or egestion — that recycle and export nutrients and carbon, and drive a component of the biological carbon pump.

Respiration is a major loss term for organic carbon. Approximately 50% of the carbon ingested by zooplankton is respired, but this is strongly influenced by temperature and body mass 69 . The proportional increase in respiration per 10 °C rise in temperature (Q 10 ) for zooplankton is on average ~1.9 69 but this is variable. For example, salp ( S. fusiformis ) metabolic rates more than doubled with a temperature increase from 10 °C to 17 °C (1.66 to 3.95 μmol O 2 g −1 h −1 wet weight, Q 10 of 3.45 99 ). In addition, while respiration increases with total body mass, weight-specific respiration decreases with body mass 69 . Thus, smaller sized zooplankton, as projected under a warmer climate, will have higher weight-specific respiration rates, leading to a greater loss of carbon (Fig.  2 ). Other factors that influence respiration rates include pressure, turbulence, oxygen, pH and feeding conditions (reviewed elsewhere 100 ).

Grazing transfers nutrients and carbon from phytoplankton to zooplankton. Under a warming ocean, grazing pressure could increase or decrease (Fig.  2 ). Lewandowska et al. 101 proposes a conceptual model of grazing pressure based on the prevailing nutrient regime. In oligotrophic conditions, phytoplankton growth is regulated by low nutrient supply, and zooplankton grazing is constrained 101 , 102 . In contrast, in eutrophic conditions, ocean warming influences plankton through metabolic changes, thus warming leads to an increase in grazing pressure and a decrease in phytoplankton standing stock 101 . Multiple mesocosm and modelling studies have pointed towards an increase in zooplankton grazing rates under a warming ocean causing a decrease in phytoplankton standing stocks because heterotrophic metabolism is more sensitive to temperature than autotrophic metabolism 102 – 105 . However, warming has also led to a decrease in zooplankton biomass which then reduces grazing and leads to greater carbon export 103 , 106 . Thus, direction of change for grazing rates could depend on the combined effects of temperature driven changes on metabolism and biomass, as well as the prevailing nutrient conditions.

Nutrients and carbon grazed in excess of demand are excreted and egested from zooplankton. We can examine if excretion and egestion will increase or decrease under a warmer ocean based on the stoichiometric imbalance of C:N:P ratio between predator (zooplankton) and prey (phytoplankton). If predator and prey stoichiometries are similar, then assimilation is optimal and recycling is low but departure from this optimal stoichiometry leads to a decrease in assimilation efficiency and an increase in nutrient recycling 107 . Healthy natural assemblages of marine plankton often tend to have molar C:N:P ratios of around 106:16:1 (Redfield ratio), but this ratio can vary depending on environmental conditions. The ratio is higher in oligotrophic subtropical gyres (~195:28:1) and lower in eutrophic polar waters (~78:13:1) 108 . There is increasing evidence that phytoplankton elemental stoichiometry varies with environmental conditions, physiological demand and evolutionary factors (for example, luxury iron uptake to counter iron deplete oceanic waters) 109 . It is unclear how zooplankton stoichiometry will change under warming and, consequently, the magnitude and direction of nutrient cycling based on predator-prey stoichiometry is uncertain.

In summary, aside from zooplankton respiration, which is expected to accelerate under ocean warming, the direction of change for grazing and excretion and egestion are unclear. As we layer these uncertainties with those of zooplankton phenology, range shifts and size, particularly noting the paucity of studies with at times contradictory findings from the Southern Hemisphere, we are unable to predict how zooplankton will modulate the biological carbon pump under future conditions. Complex multi-driver experiments in a fully factorial matrix can quickly become logistically impractical. To combat this challenge, designing multi-driver experiments with variables that reflect local or regional settings (for example, current and projected changes in SST, or phytoplankton community structure, quality and quantity, amongst others) can be an important and informative step to discern emerging patterns and guide model parameterisation and validation.

Interactions with higher trophic levels

Zooplankton act as a conduit for the transfer of energy from phytoplankton to higher trophic levels, including commercially important fisheries — an industry estimated to be valued at US$401 billion in 2018 110 . Evidence is mounting that the phenology of lower trophic levels (phytoplankton, zooplankton) is moving 5–10 days earlier per decade, faster and more consistently than higher trophic levels (adult fish, seabirds, marine reptiles and mammals) that are moving earlier by 0–2.5 days per decade 29 . This contrasting response could lead to trophic mismatch, whereby the timing of predators and their prey responds asynchronously to climate change 18 , with potential ecosystem consequences including poorer fish recruitment 111 , 112 , altered fish migration 113 – 115 and changes to the spawning of fish 116 , 117 , crabs and squid 118 .

Whilst overfishing can set pressures on fish stocks, below we showcase how the survival of larval fish also depends on the mean size, seasonal timing and abundance of prey 119 . In the Sea of Japan/East Sea region, increased squid catches were attributed to increases in zooplankton biomass, particularly euphausiids and amphipods 120 . In the North Sea, reductions in euphausiid and copepod size and abundance has led to the decrease in cod recruitment since the 1980s 119 . In the Straits of Georgia and more broadly within the Northern California Current, lower zooplankton biomass resulted in the impoverished growth and survival of juvenile salmon and herring 121 , 122 . In the Western Mediterranean Sea, it is suspected that changes in the zooplankton communities might be behind the decline in European sardine and European anchovy stocks 123 . Given that these species and their larvae prey on the most abundant copepods in the region 124 , changes in the composition and distribution of their prey (notably lipid content 125 ) could affect the condition and success of these small pelagic fishes with important socioeconomic consequences, and therefore concomitant monitoring of plankton and fishes is recommended to elucidate the relationship between zooplankton variability and fisheries success 126 . There have been increasing efforts to develop ecosystem based management of fisheries; however, fisheries evaluations or models typically include oceanographic and chlorophyll (derived from satellite sensors) data as variables and not zooplankton biomass or abundance, despite the importance of zooplankton in understanding the transfer of energy to fish and fisheries 127 . Further cooperation between fisheries and plankton experts is needed to understand the type of zooplankton data and traits needed by modellers to include this crucial component of the trophic web in fisheries management models.

Changes in zooplankton distribution can also influence top predators; however, elucidating direct relation to zooplankton is difficult due to the complex structure of oceanic food webs involving multiple trophic levels. Around the California coast, fewer, high nutritional quality euphausiids due to the 2014–2016 marine heatwave resulted in a shift in the foraging behaviour of whales, which contributed to increased rates of their entanglement 128 , whilst seabird populations also experienced unprecedented die-offs in the same location 129 . Similarly, off the Northeast coast of the US and the Gulf of Maine, there have been declines in Northern Right Whale preferred prey species ( C. finmarchicus ), causing range shifts in the whales, leading to increased entanglements and ship strikes, and negatively impacting calf mortality rates 130 .

In the Southern Ocean, seal diving patterns reflect the cascading vertical distribution of prey 131 . Specifically, sea ice break-out stimulates a strong resource pulse of phytoplankton in the Ross Sea which then triggers cascading changes in the vertical distribution of zooplankton, fishes and seals 131 . Although interpretations of the response of higher trophic levels to climate driven changes in zooplankton are difficult, coupling long-term physical, biogeochemical and biological observations can shed light on the synchrony of timing between primary, secondary and tertiary production, and ultimately ecosystem function. Incorporating these observations into models could provide strategic insight that enables predictions of future climate-driven changes across spatial and temporal scales, which is not possible from observations alone. However, models require a range of information to improve parameterisation and validation, as described below.

The challenges of modelling zooplankton in a changing climate

Models provide a powerful method that extends inherent limitations of field and laboratory experiments to improve our understanding of marine ecosystems under a changing climate 132 – 134 . Many models describe plankton organisms based on their multiple functionalities (for example, coccolithophores are represented as autotrophs, calcifiers and prey) and come in various designs and levels of complexity, such as a simple food chain of Nutrient–Phytoplankton–Zooplankton to complex microbial food web with bacteria, autotrophs, mixotrophs and heterotrophs 132 . Despite advances, zooplankton are misrepresented in most numerical models 132 , 135 due to the complex life cycles of mixotrophs and heterotrophs (especially metazoans), the complex species-specific behaviour of mesozooplankton and the costly field and laboratory observations that limit our knowledge to a few dominant species mostly from ciliates, copepods and krill.

Ultimately, the gap between what is observed versus modelled coupled with over simplistic representation in models due to the lack of mechanistic constraints, reduces our confidence in model projections. Even small changes in zooplankton parameterisation can have significant effects on projected population dynamics, community structure and energy transfer 4 , 136 , 137 . Empirical data needed for model parameterisation, comparison and validation can be categorised in three main components: (1) rates, such as ingestion, respiration, growth, reproduction, excretion and egestion; (2) traits, such as size, foraging, diet breadth, reproduction and stoichiometry; and (3) stocks, such as spatial and temporal coverage of biomass and abundance of various types of plankton. The interactions between these three components directly control how zooplankton influence biogeochemical processes, thereby driving the need of integrating these parameters within biogeochemical models.

For models that parameterise species ecophysiology, qualitative information exists for most zooplankton traits such as body size, foraging, diet, reproduction and predation 138 . However, the lack of quantitative information on individual species and community trade-offs limits our understanding of their dynamic response to environmental stimuli. For example, many copepods can rapidly switch their foraging strategy in response to prey availability and predator presence 139 , and recent studies suggest opportunistic foraging by zooplankton that indicate their diet breadth is greater than previously considered 140 – 143 . New observational insights obtained regarding zooplankton predator–prey interactions and DNA metabarcoding (individual organisms and water) revealing diet composition can provide greater detail on diet breadth 144 , which will improve grazing parameterisations in models.

When it comes to stocks, most models describe individuals or populations in terms of biomass. Unfortunately, most of the global zooplankton biomass estimations are of sample biomass (that is, the biomass of all zooplankton and non-zooplankton included in the sample). The lack of empirical data expressed in useful formats for models leads to unequal comparisons between model and observations and delays on model improvements. For example, complex life historical models for metazoans 145 , 146 or ecosystem models with various zooplankton size groups 147 lack empirical data in forms that will enable a direct model–observation comparison for validation and further advancement. Key to overcoming this barrier is the use of image analysis methods that deliver trustworthy biomass calculations based on individual body properties, such as size-dependent and taxa-dependant allometric biomass to volume conversion factors 148 . Recent development of in situ imaging technologies 149 , 150 and recognition algorithms 151 deliver zooplankton biomass distribution for different taxa and sizes in a currency consistent with models much faster compared to sampling and taxonomic determination under microscope 151 . Additionally, a thorough examination of the many existing conversion equations for estimating biomass from abundances or biovolume 152 are needed alongside the development of a universal conversion table agreed by the international community.

Additionally, zooplankton biomass and rates in most biogeochemical models are restricted to the epipelagic layer, due to the lack of knowledge and data from the mesopelagic and bathypelagic regions (both spatial and temporal). Consequently, active fluxes driven by zooplankton vertical migration are disregarded in the models. Insights on mesopelagic and bathypelagic zooplankton communities and DVM will improve quantitative knowledge on zooplankton contributions to the biological pump and nutrient recycling. Together, these advances will enable us to quantitatively explore key scientific questions at regional and global scales. For example, what are the processes that drive species movement or displacement as observed in the North Pacific (poleward migration 37 , 40 ) and Southern Ocean (poleward migration 41 and possible displacement of Antarctic krill by salps 42 )? What are the implications of such movement or displacement on biogeochemical processes and/or trophic interactions? In a broader sense, to project the impacts of ocean change on regional fisheries production, higher trophic levels and the biological carbon pump with confidence, zooplankton needs to be fully integrated in various modelling frameworks. Whilst enormous progress has been made through various monitoring programmes and networks, our fragmentary picture based on observations largely from the Northern Hemisphere, is not sufficient.

Sustained observations to quantify impacts of climate change

Untangling natural from anthropogenic change across temporal scales (for example, long-term due to global climate change versus multi-annual fluctuations due to regional climate forcings such as El Niño or the NAO) requires sustained observations over many decades 106 , 153 , 154 . Yet most funded projects run for 3–4 years and funding for long-term research is in decline 155 . LTER, defined as studies in which ecological data have been collected regularly and systematically from a site, or set of sites, over a period of more than 10 years 156 , 157 , enables us to: quantify ecological responses to environmental change, including climate change; understand complex multi-year ecosystem processes; develop theoretical ecological models and parameterise and validate simulation and management models; serve as collaborative platforms promoting multidisciplinary research; and support evidence-based policy, decision-making, and management 158 .

For example, in the Eastern Bering Sea, long-term oceanographic research has identified ecosystem regime shifts that oscillate in response to multi-year variability in the size of the Eastern Bering Sea cold pool 94 – 96 , 98 . The Palmer LTER project in Antarctica has demonstrated connectivity between ecosystem productivity at the Western Antarctic Peninsula and the climatological indices of the SAM and ENSO 59 – 61 . Similarly, in Brazil, the Brazilian LTER at the estuary of the Patos Lagoon and adjacent coast revealed that composition of phytoplankton, zooplankton, benthic flora and macrofauna were affected by different scales of variability related to ENSO 97 . In the Mediterranean Sea, a meta-analysis of several time series over the last 50 years highlighted substantial changes in plankton community composition that resulted from direct local anthropogenic nutrient input or basin-scale decadal evolution related to the NAO 159 . When Northern Hemisphere time series data were aggregated, a regime shift (large, persistent change in the state of the community or ecosystem) was identified in the 1980s due to an increase in Northern Hemisphere air and SST and a strongly positive phase of the Arctic Oscillation; however, there was considerable regional variability 160 .

Complex physical, biogeochemical and biological processes interact to shape a given region; thus, scaling local and regional findings into a global context is likely to be non-linear. Additionally, relatively few multi-decadal long-term studies and datasets exist for ocean ecosystems 161 . We identified 168 long-term zooplankton monitoring programmes and CPR surveys undertaken in 6 oceanic regions (through the Marine Ecological Time Series Database 162 , EU Horizon 2020 EuroSea survey 163 and surveys undertaken as part of this Review). In Fig.  3 (see also Supplementary Data  1 ), we separate the 168 long-term zooplankton monitoring programmes and 6 CPR survey regions because, while they both sample zooplankton, CPR surveys have a much wider spatial coverage and the CPR also surveys for large phytoplankton. Of all these monitoring programmes, ~19% had their data freely available, 9% had data that partially available (that is, part of the data was available and part was restricted or unavailable for various reasons), data for 13% were available on request, data for 7% were not available and ~52% were undefined (unable to determine data availability) (Fig.  3 ). Of the programmes that had their data publicly available, zooplankton were sampled using different techniques and the data were stored in various repositories, thus identifying comparable descriptors is challenging.

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Blue lines indicate Continuous Plankton Recorder (CPR) surveys and symbols indicate sites of specific long-term monitoring programmes (see Supplementary Data  1 for details of numbered sites). Stars indicate data is freely available to download, squares indicate data available on request, triangle indicates partially available, and circles indicate data either not available or unclear on data availability. Only programmes where coordinates were available were plotted. Data sourced from the Marine Ecological Time Series Database, EuroSea survey and surveys undertaken as part of this review effort. More information and coordinates are provided in the Supplementary information. This figure was designed by Dr Stacey McCormack (Visual Knowledge).

An overwhelming 81% of the data collected from long-term monitoring programmes is either partially available or not publicly available, which prevents the scientific community from answering large-scale questions about the response of zooplankton to climate variability and long-term climate change. Renewed effort is needed from the research community, funders and journals alike to ensure that crucial long-term monitoring data, particularly on zooplankton abundance, biomass and diversity required to understand phenology and range shifts, is made publicly available for global analysis to be undertaken. We provide two examples that highlight the success of open-source data. The first is the recent introduction of jellyfish into the PlankTOM11 model 164 using observational data extracted from the MAREDAT database 165 . Modelling jellyfish abundance against observed data provided confidence in the model results, including the important role jellyfish have in regulating marine ecosystems, particularly in controlling macrozooplankton biomass and its cascading impact through the ecosystem 164 . A second example is the inclusion of zooplankton data from the COPEPOD database 166 to produce robust global maps of zooplankton biomass and abundance of different functional groups used to test a global zooplankton size-spectrum model and the possible effects of changes in zooplankton on fish stock 146 .

Aside from national monitoring programmes, regional and global networks that foster international collaboration and undertake sustained observations across national boundaries do exist within the zooplankton scientific community. However, there are still large regions, especially in the open ocean or areas beyond national jurisdiction, which remain uncovered by this collaborative structure (Fig.  3 ). Regional and global zooplankton networks include the Global Alliance of Continuous Plankton Recorder Surveys (GACS), the International Council for the Exploration of the Sea (ICES) Working Group on Zooplankton Ecology (WGZE), the North Pacific Marine Science Organization (PICES) working groups focussed in the Pacific region, the Working Group on Mediterranean Zooplankton Ecology (MedZoo.bio), and the Integrated Marine Observing System (IMOS) National Reference Stations focussed within the Australian region. Although CPR surveys (blue lines, Fig.  3 ) play a key part in surveying much of the North Atlantic, the northern reaches of the North Pacific and parts of the Southern Ocean, large gaps exist across the rest of the global oceans, particularly the South and Equatorial Pacific, Indian Ocean, South Atlantic and the Arctic Ocean. Long-term monitoring programmes (points, Fig.  3 ) are well represented in coastal Australia, Europe, South Africa, and North America, but large gaps exist in coastal Asia, South America and much of Africa. Long-term monitoring programmes are also severely lacking in offshore, open ocean locations. Moving forward, we must fill these gaps, either through establishing long-term monitoring programmes and/or developing new technologies that facilitate data collection in these remote locations.

The future of global zooplankton research

As highlighted above, ocean warming is impacting zooplankton phenology, range, and size, with  flow on impacts on the biogeochemical cycles and transfer of energy and matter to higher trophic levels. There are key observation and modelling knowledge gaps as well as observational data coverage that need urgent address. In the following subsections we discuss how different technologies can be used together to improve knowledge and geographic coverage, and how the data obtained can be used to meet global needs.

Filling the gap with an integrated approach to zooplankton research and monitoring

There have been several recent reviews on modern plankton sampling techniques and observing systems 149 , 167 – 169 . Here, we highlight how an integrated approach using established and new technologies can elucidate the response of zooplankton to climate change. Nets (for example, bongo, WP2, ring, neuston, rectangular midwater trawl) with various mesh sizes capture different zooplankton groups and enable analysis of species abundance, composition, community size structure and spatial distribution. However, limitations of using nets include their depth integrative nature, potentially destructive collection mode toward delicate organisms, and poor sampling of some taxa such as gelatinous zooplankton 132 . The increasing need for zooplankton data, limited budgets for monitoring, and the potential for gaining new insights into different aspects of zooplankton dynamics has driven the development of new technologies for collecting zooplankton information. Newer technologies such as the Continuous Automatic Litter and Plankton Sampler (CALPS 167 ), automatic sensors using acoustic (Acoustic Doppler Current Profilers (ADCP) or echosounders), and in-situ imaging (for example, PlanktoScope 170 , Imaging FlowCytobot 148 , Plankton Imager 171 , Zooglider 172 or Underwater Vision Profiler (UVP) 173 , 174 ) can sample large ocean areas, have high vertical and horizontal resolution sampling capacities, but low taxonomic resolution compared to manual analysis of samples via microscope. Although novel molecular methods (such as eDNA, eRNA, (meta)genomics, (meta)barcoding, (meta)transcriptomics) allow for unprecedented taxonomic identification capabilities and qualitative description, they suffer from a lack of established reference samples, and as yet unproven quantitative rigour, but they are steadily improving. There are trade-offs associated with the choice of sampling method, and selecting a method depends on the scientific question and available budget. It is important to note that time series length is a key determinant of its statistical power to detect change 175 . Thus, the many existing time series based on traditional methods should not be replaced by new methods unless they provide nearly identical information. Instead, combining traditional tools with new technologies and omics can open new horizons on the type of data collected, and this can provide a mechanistic understanding of species behaviour (for example, adaptation or trade-offs) and rates. The data can also be used for model parameterisation and validation to advance our forecasting tools and policy decisions.

The increasing variety of sampling devices and strategies used prevents homogeneous and inter-comparable data and provides a challenge for co-designing an integrated approach. However, to address the cross-disciplinary questions posed in this review, we need an integrated approach allowing cross comparison and merging between the various traditional and modern techniques. For example, qualitative eDNA is largely used for biodiversity estimates, but ground-truthing eDNA data with quantitative data from nets can improve our monitoring of spatial and temporal variation in zooplankton structure. Similarly, imaging platforms (such as Zooglider) provide quantitative data on zooplankton distribution at higher spatial resolution than nets 172 . Coupling nets with imaging and eDNA enables us to gain innovative insights into zooplankton community structure at finer scales over larger distances. To progress this further, combining zooplankton data with physical and biogeochemical characteristics (for example, from biogeochemical ARGO floats, CTDs or satellites) can provide observational insight on how zooplankton are impacted by the environment. This information constitutes the basis of mathematical and numerical analysis. In addition to mathematical modelling, recent machine learning methods can be used to model zooplankton distribution from environmental data 176 . Incorporating mathematical and numerical modellers at the onset of such studies can both improve model parameterisations and expand spatial and temporal extrapolations of observational data. Such efforts have been performed in several studies to investigate the Arctic zooplankton communities with in situ observed and net–collected plankton 177 , in mesopelagic layers by combining imaging systems, nets and BGC ARGO floats 178 , in the Celtic sea to compare an imaging system with automated sample collection and traditional vertical ring net vertical deployment 171 , and in the USA through the Bio-Global Ocean Ship-based Hydrographic Investigations Program (GO-SHIP) 179 . These studies merging various traditional and modern techniques are prototypes for a global integrated approach and demonstrate that the oceanographic research community can work together to increase the broader application of their individual information (Fig.  4 ).

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Traditional techniques such as Continuous Plankton Recorder (CPR), nets and Niskin bottles have been used to monitor zooplankton for decades with great success. However, coupling traditional techniques with newer methods such as molecular data (for example, DNA, RNA and proteins), advanced sensors, in situ imaging approaches and satellites can improve geographic coverage, particularly in under sampled regions and improve our understanding of the impact of climate change on zooplankton communities. Whilst the CPR, nets and Niskin bottles are shown together, they are not generally conducted simultaneously. This figure was designed by Dr Stacey McCormack (Visual Knowledge).

Two key pathways forward are to increase monitoring efforts in prioritised under sampled regions and undertake collaborative sampling and experiments. For the former, the deployment of a network of ARGO floats with UVPs, or other novel technologies, in remote locations as well as poorly monitored coastal areas can help fill this geographic and knowledge gap but this will need to be supported with more detailed local sampling to build an understanding of local taxonomy and phenology (Fig.  4 ). Ships of opportunity could also be more widely used to expand CPR routes and include automated tools (Fig.  4 ). For the latter, stronger collaborations are required between zooplankton ecologists, and other biological and biogeochemical oceanographers, as well as modellers, to identify the key parameters and processes that need quantitative information. These collaborations, if formed from the onset, will lead to novel multi-driver experimental-modelling approaches, and ensure that all components of the system being examined are thoroughly thought through from data collection to the delivery of interpreted information.

Linking zooplankton observations to global needs

Tracking how marine life responds to increased human use and climate change will empower the global community to understand, predict, protect and interact sustainably with our ocean. The global observing community recognises the need to expand the continuous, long-term observation of marine life, and to fill gaps where these observations are lacking. The biology and ecosystem Essential Ocean Variables (EOVs) — of which zooplankton biomass and diversity is one — represents one approach to coordinating the global ocean observing community 180 . The EOVs and requirements for global marine life observations are being advanced by the Global Ocean Observing System (GOOS) in partnership with the Marine Biodiversity Observation Network (MBON) Essential Biodiversity Variables (EBVs) and the Ocean Biodiversity Information System (OBIS). The aim is to ensure open data sharing that aligns with and facilitates global marine biodiversity assessments. The biology and ecosystem EOVs are also a means for reporting on biological Essential Climate Variables to the Global Climate Observing System (GCOS), where zooplankton is also included.

Information on trends in diversity, distribution, and abundance of zooplankton will also contribute to Sustainable Development Goal (SDG) 14 (life below water), the Convention on Biological Diversity post-2020 Agenda, and World Ocean Assessments, among other mechanisms. Progress on coordinated zooplankton observations as a contribution to this global imperative will require expert communities coming together to address gaps in observing by supporting increased long-term monitoring, including in under sampled regions. To that end, we propose four key steps forward to meet global needs: (1) Protect existing, and build new, time series programmes; (2) Better integrate time series data; (3) Broaden our understanding of climate change responses; and (4) Improve cross-disciplinary approaches (Box  2 ). The results of improved integration of sampling, modelling, and reporting activities will lead to increasingly rapid understanding of the dynamics of zooplankton communities at regional and global scales, their likely response to ongoing climate change, and the implications that this response and its regional variability will have on local resource production and global ecosystem services. Such improved understanding will benefit the research community and could address societal needs.

Box 2 Towards an improved implementation of zooplankton monitoring to address global needs

Protect existing and build new time series programmesTime series can be difficult to establish due to lack of long-term funding, lack of widespread understanding of the importance of long time series to the study of climate change-scale processes, and pressures for monitoring programmes to adopt new technology. Even subtle changes (for example, a slight change in net design) require a lengthy parallel intercalibration period to ensure comparability. Additionally, as highlighted in Fig.  , there are large gaps in coastal Asia, South America and much of Africa, and offshore, open-ocean regions where monitoring is crucially needed.
Better integrate time series dataEfforts are needed to actively engage with monitoring programmes as well as regional and global networks to better integrate time series data. This includes making existing data more easily available, encouraging group efforts to synthesise data across multiple time series, and ‘rescuing’ and combining old data which will allow for large spatio-temporal studies to understand climate change responses.
Broaden our understanding of climate change responsesExisting understanding is relatively unbalanced, often dominated by productive, mid latitude shelf ecosystems, and mostly of adult stages of dominant taxa. Copepoda is the most studied zooplankton group with the literature being focused on a few dominant species (for example, many of the species and the coastal species ). Modern technology (for example, moorings, acoustics, molecular approaches, particle imaging) can be used to increase observations in poorly sampled systems, and address issues such as extreme events (responses to heatwaves or storms). A key focus should also extend observing and data collection efforts for holo- and meroplanktonic taxa (including small or gelatinous forms).
Improve cross-disciplinary approachesTime series data, experimentation, and modelling in combination can provide a powerful approach to understand the mechanisms that zooplankton use to adjust to recent climate change. Particularly experiments that examine multiple climatic stressors, as this will enable better understanding of how the biological carbon pump and food web structure may be impacted. Likewise, engagement of zooplankton ecologists with working groups on resource management (for example, fisheries or conservation zones) and policy can ensure the data products are well utilised and will support the continuation of monitoring.

Supplementary information

Acknowledgements.

This work was organised in the framework of the Global Ocean Observing System (GOOS) Biology and Ecosystems panel. The European component in Fig.  3 was produced based on a survey of marine monitoring programmes within Europe through the EU Horizon 2020 EuroSea action (grant no. 862626). GOOS Biology and Ecosystems panel is funded through the Commonwealth Scientific and Industrial Research Organisation (CSIRO) and the Australian Institute of Marine Science (AIMS). E.M. was funded from Instituto Nacional de Ciência e Tecnologia Antártico de Pesquisas Ambientais (INCT-APA) and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and Horizon 2020 AtlantECO (grant no. 862923). AA was funded from the Natural Environment Research Council’s (NERC) Climate Linked Atlantic Sector Science (grant no. NE/R015953/1). KS was funded from NERC MOSAiC-Thematic project SYM-PEL: “Quantifying the contribution of sympagic versus pelagic diatoms to Arctic food webs and biogeochemical fluxes: application of source-specific highly branched isoprenoid biomarkers” (NE/S002502/1). CO was funded from the North Pacific Marine Science Organization (PICES) and comprising the North Pacific Research Board (NPRB), Exxon Valdez Oil Spill Trustee Council through Gulf Watch Alaska, Canadian Department of Fisheries and Oceans (DFO). Horizon 2020: 862428 Atlantic Mission, 862923 AtlantECO. NHAI was funded by Yayasan Penyelidikan Antartika Sultan Mizan (YPASM) Research Grant under Smart Partnership Initiative 2020: “The Fate of Salps in a Changing Ocean: Emerging Environmental Consequences for the Indian Ocean Sector of the Southern Ocean” (Vot. No. 53479) and the Long-Term Research Grant Scheme (LRGS) funded by the Department of Higher Education, Ministry of Higher Education Malaysia (LRGS/1/2020/UMT/01/1; LRGS UMT Vot No. 56040). KSB was funded from US National Science Foundation. JDE was funded by Australian Research Council Discovery Project No. DP19010229. FL was funded through the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 871153 (JERICO-S3) and grant agreement no. 862923 (AtlantECO) and by the Institut Universitaire de France. MG was supported by the National Science Foundation (OCE-1851866) and the Partnership Instrument managed by the European Commission’s Service for Foreign Policy Instruments (EU4OceanObs, PI/2020/417-631). LY was supported by the Andalusian Government (Consejería de Economía, Innovación y Ciencia) and EU (European Regional Development Fund) through project MICROZOO-ID (P20_00743).

Author contributions

L.R. conceived the paper and led the writing. L.R., R.A.A, A.A., S.B., N.J.B., K.S.B., G.C., A.C., J.D.E., M.G., N.H.A.I., D.J., F.L., E.M., C.O., S.P., A.J.R., K.S., L.S., K.M.S., G.Y., L.Y. contributed equally to the writing of the paper and figure design and are listed in alphabetical order of last name.

Peer review

Peer review information.

Nature Communications thanks Kelly Robinson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Competing interests

The authors declare no competing interests.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The online version contains supplementary material available at 10.1038/s41467-023-36241-5.

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eDNA zooplankton

The biodiversity of zooplankton has been underexposed in Dutch monitoring programs for years. However, animal plankton (zooplankton) plays a key role in marine and larger fresh waters in the transmission of primary production (phytoplankton) to fish and higher trophic levels (fish-eating birds, seals). Knowledge about zooplankton is therefore important in the development and assessment of effective measures in relation to nature-inclusive activities, such as aquaculture, fishing (fish and shellfish) and nature development. Recently, new measurement methods have been and are being developed that can be used in new measurement and analysis programs to be developed. These methodologies need to be improved and simultaneous development offers the opportunity to compare their opportunities, limitations, advantages and disadvantages.

The biodiversity of zooplankton in Dutch large water bodies such as the Wadden Sea and lakes in the IJsselmeer region is not monitored, while the zooplankton is a very important component in the healthy functioning of aquatic ecosystems. Within WUR, various measurement methods are being tailored to be made applicable for cost-effective monitoring of zooplankton. Examples include the application of hydroacoustics, image analysis and eDNA techniques. These new techniques are further developed, tested and assessed for applicability here.

The aim of the project is to develop knowledge for biodiversity monitoring of zooplankton in the Wadden Sea and large fresh water bodies. In addition, the knowledge can be applied to other waters (Deltas, seas, lakes) and other groups of organisms (eg phytotoxin-producing algae, fish).

The measurement and analysis program to be developed can be applied to assess effective measures in relation to nature-inclusive activities, such as aquaculture, fishing (fish and shellfish) and nature development.

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Nature positive transitions: 4-years of societal impact, short review on zooplankton in the dutch wadden sea : considerations for zooplankton monitoring.

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  • Introduction
  • Limitations of the Current Evidence
  • Observations: Neuroimmunological Complications of SARS-CoV-2 Vaccination
  • Conclusions
  • Article Information

The first phase of screening excluded 1343 articles. Articles that did not include millions of vaccine doses of millions of vaccinated patients were removed if there existed articles including this number of patients or vaccinations, for example, for Guillain-Barré syndrome. If, as was the case of myasthenia gravis, there was insufficient evidence including millions of patients or vaccine doses, the articles evaluating the largest cohort of patients were included in the analysis. Articles were screened for a focus on neurological disease occurrence or worsening following SARS-CoV-2 vaccination. Neurological disease included central and peripheral nervous system complications, including but not limited to autoimmune diseases, cerebrovascular disease, and psychiatric disease.

The forest plot was created using data hand-extracted from 9 articles that recorded cases of Guillain-Barré syndrome following an AV vaccine, the background incidence in their cohort—or where the background incidence could be taken from another cohort, which was possible for the British data—and the number of vaccinated individuals for that specific vaccine. Articles containing Guillain-Barré syndrome data not included in the plot did not include the parameters needed to calculate the excess cases per 100 000 vaccines, for example, only the total number of Guillain-Barré syndrome cases for a given vaccine with no expected or background rate available or without the total number of individuals vaccinated using a specific vaccine. The excess cases per 100 000 vaccines and the 95% CIs were calculated from the number of vaccinated individuals for a given vaccine as well as the number of excess Guillain-Barré syndrome cases in that population. All the articles identified an excess of GBS cases following AV vaccination.

eMethods 1. Detailed Search Strategy

eMethods 2. Detailed Analysis of Identified Data for Bell Palsy, Myasthenia Gravis, Multiple Sclerosis and Central Demyelination, and Neuromyelitis Optica Spectrum Disorders and MOG Antibody–Associated Disease

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Willison AG , Pawlitzki M , Lunn MP , Willison HJ , Hartung H , Meuth SG. SARS-CoV-2 Vaccination and Neuroimmunological Disease : A Review . JAMA Neurol. 2024;81(2):179–186. doi:10.1001/jamaneurol.2023.5208

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SARS-CoV-2 Vaccination and Neuroimmunological Disease : A Review

  • 1 Department of Neurology, University Hospital Düsseldorf, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
  • 2 Centre for Neuromuscular Disease, National Hospital for Neurology and Neurosurgery, Queen Square, London, United Kingdom
  • 3 Department of Neuromuscular Disease, Institute of Neurology, University College London, London, United Kingdom
  • 4 College of Medicine, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom
  • 5 Department of Neurology, Medical University of Vienna, Vienna, Austria
  • 6 Brain and Mind Center, University of Sydney, Sydney, Australia
  • 7 Department of Neurology, Palacky University, Olomouc, Czech Republic

Importance   The temporal association between the occurrence of neurological diseases, many autoimmune diseases, and vaccination against SARS-CoV-2 has been topically interesting and remains hotly debated both in the medical literature and the clinic. Given the very low incidences of these events both naturally occurring and in relation to vaccination, it is challenging to determine with certainty whether there is any causative association and most certainly what the pathophysiology of that causation could be.

Observations   Data from international cohorts including millions of vaccinated individuals suggest that there is a probable association between the adenovirus-vectored vaccines and Guillain-Barré syndrome (GBS). Further associations between other SARS-CoV-2 vaccines and GBS or Bell palsy have not been clearly demonstrated in large cohort studies, but the possible rare occurrence of Bell palsy following messenger RNA vaccination is a topic of interest. It is also yet to be clearly demonstrated that any other neurological diseases, such as central nervous system demyelinating disease or myasthenia gravis, have any causative association with vaccination against SARS-CoV-2 using any vaccine type, although it is possible that vaccination may rarely trigger a relapse or worsen symptoms or first presentation in already-diagnosed or susceptible individuals.

Conclusions and Relevance   The associated risk between SARS-CoV-2 vaccination and GBS, and possibly Bell palsy, is slight, and this should not change the recommendation for individuals to be vaccinated. The same advice should be given to those with preexisting neurological autoimmune disease.

Infection with SARS-CoV-2 results in COVID-19 in many, which can be severe in some. After a year of variable worldwide government-mandated social restrictions, the arrival in 2021 of vaccines against SARS-CoV-2 was very welcome. These used new medical technologies, and there was public and medical concern about any potential serious neurological adverse effects that might occur. As a result, passive and active academic and international clinical surveillance systems focused on public reassurance and ensuring pharmacological safety. The neurological diseases of concern, including specific adverse events of special interest, are all individually rare. Most occur with a background incidence of naturally occurring disease recorded with variable accuracy in nonpandemic historical cohorts. Furthermore, a heterogeneity of ascertainment, recording, and coding strategies have hampered efforts to identify or refute causality. The reporting systems in place are by necessity usually passive, with variable ability to corroborate reports and clean data. Rarely, reliable active ascertainment methods can generate more accurate data. 1 In general, evidence that vaccination is causally significant in the pathogenesis of autoimmune neurological syndromes is rarely validated even by large, well-conducted epidemiological studies. 2 An almost unique exception to this is the cerebral venous sinus thrombosis, now termed vaccine-associated immune thrombosis and thrombocytopenia (VITT), identified as a rare and specific complication of the adenoviral vector (AV) ChAdOx1 (Oxford/AstraZeneca) and Ad26.COV2.S (Janssen) vaccines. 3 , 4 An increased risk of developing other neurological autoimmune disorders has been extensively sought, but only the very low incidence of Guillain-Barré syndrome (GBS) following AV vaccine administration has been supported by significant evidence.

We performed an extensive literature search for this narrative review in the PubMed and Scopus databases, with no limitation on the time period searched, using the MeSH search terms “COVID-19” OR “SARS-CoV-2” AND “vaccination” AND “autoimmune,” returning 1005 articles in PubMed and 1364 articles in Scopus ( Figure 1 ; eMethods 1 in the Supplement ). Relevant studies for inclusion were identified by A. G. W. and M. P., who independently screened all titles and abstracts. A hierarchical selection was used where only high-quality epidemiological studies exploring millions of participants or vaccine doses were included to review the highest certainty evidence. Case studies have only been included where no or very few large studies were identified.

The forest plot ( Figure 1 ) was created using data hand-extracted from 10 articles that recorded cases of GBS following an AV vaccine, the background incidence in their cohort—or, where the background incidence could be taken from another cohort, which was possible for the British data—and the number of vaccinated individuals for that specific vaccine. Articles containing GBS data not included in the plot did not include the parameters needed to calculate the excess cases per 100 000 vaccines—for example, only the total number of GBS cases for a given vaccine with no expected or background rate available or without the total number of individuals vaccinated using a specific vaccine. The excess cases per 100 000 vaccines and the 95% CI were calculated from the number of vaccinated individuals for a given vaccine as well as the number of excess GBS cases in that population. The parameters were calculated in Excel version 16.69 (Microsoft), and the plot was created in RStudio version 2022.12.0 (Posit) using the ggplot2 package.

These data are all particularly vulnerable to small study effects, publication bias, outcome reporting bias, and clinical heterogeneity, as neurological disease following SARS-CoV-2 vaccination is rare, event numbers are small, and public interest due to the COVID-19 pandemic is huge. We therefore preferentially selected large cohort studies for this review. Even in these studies, acquisition bias is important, as the interest in GBS, facial palsy, and other neurological diseases likely led to significant overreporting or duplicate reporting. As illustrated in data from the UK, cohorts often interrogated the same data sets, and so cohorts reporting the same participant were reported in more than 1 article. Multiple reporting of patients also occurred in the passively reported Yellow Card system in the UK and possibly Vaccine Adverse Events Reporting System (VAERS) in the US; these systems have limited cross-checking and data cleaning methods, unlike the National Health Service England (NHSE) Intravenous Immunoglobulin (IVIG) used by Keh et al. 1 The risk of overrepresenting the incidence of neurological disease following vaccination is further increased by the background rates of relatively mild diseases being underestimated in some epidemiological cohorts. People with mild GBS or mild Bell palsy may not present to hospital, and so the true background rate may well be higher than the background rate estimated by the literature. This imbalance is problematic for then understanding true incidences following vaccination. Additionally, the diagnostic certainty of GBS, Bell palsy, myasthenia gravis, and other diseases is often very limited in passive systems, and to our knowledge, only the NHSE IVIG database had some prospective diagnostic check. In the Yellow Card UK data set, fewer than 20% of patients were in a diagnostic certainty category or had a Brighton score of 1 to 3.

The most frequently investigated temporal association between an autoimmune neurological disease and SARS-CoV-2 vaccination was GBS, an acute-onset immune-mediated postinfectious polyradiculoneuropathy. 5 The background incidence of GBS in North America and Europe is approximately between 0.8 and 1.9 (median, 1.11) cases per 100 000 person-years. 6 GBS usually occurs within 4 weeks of a triggering infection, but an interval of up to 6 weeks was established following swine flu vaccination in 1976/1977, and the consensus is to consider this period of vaccination-attributable risk the same as the infection’s at-risk period. 1 , 7 Numerous studies based on national or insurance-based surveillance systems have been published, many including millions of vaccines or vaccinated individuals ( Table 1 ). 1 , 8 - 17 We present these separated by country. The major differences in certainty of the conclusions are diagnostic categorization (from patient-reported passive reporting to dedicated clinician-identified and criterion-supported diagnoses) and study size; the latter is crucial in rare associations where huge populations are required to identify low event numbers reliably.

A UK self-controlled case series including unverified coding data of hospital admissions assessed GBS incidence 1 to 28 days postvaccination. A total of 20 417 752 individuals vaccinated with a first dose of ChAdOx1 were included. An increased incidence rate ratio (IRR) for hospital admission or death due to GBS was reported from 15 to 21 days (IRR, 2.90; 95% CI, 2.15-3.92) and 22 to 28 days (IRR, 2.21; 95% CI, 1.59-3.09) after vaccination. An increased risk of GBS in the 1 to 28 days postvaccination (IRR, 2.04; 95% CI, 1.60-2.60) equated to an excess of 38 GBS cases per 10 million exposed to ChAdOx1. 9 No association was demonstrable with BNT162b2. A Scottish validation cohort supported these findings. 9 A subsequent self-controlled English case series looking for GBS, transverse myelitis, and Bell palsy using a very similar (possibly the same) data set coded in primary care from emergency department and secondary institutions but using slightly different methodology included 7 783 441 individuals vaccinated with ChAdOx1. 8 New coded but diagnostically unverified episodes of GBS logged from 4 to 42 days after vaccination were used in the analysis. A total of 517 cases of GBS resulted in an increased post–ChAdOx1 vaccination IRR of 2.85 (95% CI, 2.33-3.47), corresponding to 11 excess cases of GBS per 1 million vaccines. The excess was again after the first dose only, and the relative increase in GBS cases was highest among individuals aged 40 to 64 years. 8 There was no association with the BNT162b2 (BioNTech/Pfizer) vaccine. 8

Significantly more reliable GBS diagnosis and case ascertainment using the UK National Immunoglobulin Database/NHSE IVIG database paired with known immunization type and date allowed for the evaluation of criteria-supported GBS diagnosis in relation to 20 300 000 ChAdOx1 doses, 11 500 000 of BNT162b2 doses, and 300 000 mRNA-1273 (Moderna) doses. 1 Only 90 to 140 excess UK GBS cases above the background rate could be identified, distributing in a peak about 24 days after a first dose of the ChAdOx1 vaccine. 1 The excess risk of GBS in the first 42 days following vaccination using ChAdOx1 was 0.576 cases (95% CI, 0.481-0.691) per 100 000 doses, and this risk is established with higher certainty from this study. Using a prospective case collection in a multicenter UK surveillance database, no specific vaccine-associated GBS phenotype was identified, illustrating how hard it is to identify cases with causal association from background occurrences. 1 Tamborska et al 18 also identified that ChAdOx1 may be associated with GBS using an independent online open-access passive reporting national surveillance system, but it should be noted that the the cases in their study are a subset of those in the study by Keh et al. 1

Cases of GBS occurring between 3 and 42 days postvaccination reported to the German national surveillance system were analyzed. 19 Following vaccination with ChAdOx1 and Ad.26.COV2.S, the expected number of GBS cases was exceeded by a factor of 3.1 and 4.2, respectively. This was not observed for messenger RNA (mRNA) vaccines or for the additionally included influenza vaccine. Lehmann et al 19 also suggested a higher frequency of bilateral facial paresis in GBS cases occurring after vaccination; however, acquisition may have been overestimated by the inclusion of Bell phenomenon (normal upward elevation of the ocular globe on voluntary eye closure) as well as Bell palsy and its synonyms as a search criterion for identifying cases.

Using data from the French national health data system (Système National des Données de Santé), with 139 million doses of 4 vaccinees (BNT162b2, mRNA-1273, ChAdOx1, and Ad26.COV2.S), an excess of approximately 6 cases of GBS per million persons occurred within 42 days of the first dose of each of the AV vaccines. 13 There was no evidence of an increased risk after the second or third doses. An increased risk following Ad26.COV2.S was only observed in individuals 50 years or older. This cohort only included cases of GBS for patients requiring hospitalization, possibly underestimating the true incidence.

US reporting systems are smaller in size compared with the UK, as they frequently rely on insurance-based monitoring, as no true nationwide systems exist. Hanson et al 10 performed a cohort study of surveillance data (Vaccine Safety Datalink) from 7 894 989 individuals. An increased incidence of GBS following the Ad.26.COV2.S AV vaccine (only 483 053 doses) was observed in this cohort calculated from the 11 reported and confirmed GBS cases following this vaccine. The unadjusted incidence rate of GBS cases per 100 000 person-years (32.4; 95% CI, 14.8-61.5) in the 1 to 21 days following vaccination was highest in the first 14 days. This figure is 15-fold to 30-fold the background GBS rate 10 ; subsequent cases of GBS that have clearly not occurred at this frequency in larger vaccinated cohorts and overestimated risk illustrates the problems of small studies with low event numbers. As a further illustration of this, 91% of patients had facial weakness or paralysis in addition to limb weakness, and Hanson et al 10 suggested that AV vaccines may precipitate a form of GBS that has distinct facial involvement. This was not borne out in practice or other larger more reliable series. The slightly larger VAERS reported a small but statistically significant safety concern for GBS following Ad26.COV2.S vaccination. 15 The estimated crude reporting rate was 1 case of GBS per 100 000 doses of Ad26.COV2.S. Woo et al 15 calculated in the worst-case-scenario analysis that the estimated absolute rate increase of GBS was 6.36 per 100 000 person-years following Ad26.COV2.S vaccination. From these small studies, neither the high incidence of facial paralysis nor GBS has occurred. A more recent publication from Abara et al 11 also used VAERS to identify GBS cases among 487 651 785 SARS-CoV-2 vaccine doses (17 944 515 Ad26.COV2.S doses, 266 859 784 BnT162b2 doses, and 202 847 486 mRNA-1273 doses) within 21 days and 42 days of vaccination. The data indicated an association between Ad26.COV2.S and an increased risk of GBS (observed-to-expected [OE] ratio at 21 days, 3.79; 95% CI, 2.88-4.88; OE ratio at 42 days, 2.34; 95% CI, 1.83-2.94), which was not observed for the mRNA vaccines (OE ratio less than 1 for both mRNA vaccines). 11

Mexico administered 81 842 426 doses of ChAdOx1, rAd26-rAd5 (Sputnik V, AV vaccine), Ad5-nCoV (Convidecia, AV vaccine), Ad26.COV2.S, mRNA-1273, BNT162b2, and CoronaVac (Sinovac, inactivated whole virus). Using CoronaVac as a comparator, higher incidences of GBS per 1 000 000 administered doses were observed among those vaccinated with Ad26.COV2.S (3.86; 95% CI, 1.50-9.93) and BNT162b2 (1.92; 95% CI, 1.36-2.71). 12 GBS incidence per 1 000 000 administered doses was higher among mRNA-based vaccine recipients in this cohort (1.85; 95% CI, 1.33-2.57). This is the only study to suggest a risk from BNT162b2, but it is one of the largest population studies reported.

In Victoria, Australia, an enhanced passive (spontaneous) and active surveillance system was used to identify GBS cases following vaccination against SARS-CoV-2. Within 42 days of vaccination, the observed GBS incidence rate was 1.85 per 100 000 doses of ChAdOx1 following the first dose, with the expected rate given as 0.39 presentations per 100 000 adult population. 14 The rate was not increased for BNT162b2 or mRNA-1273.

Maramattom et al 16 reported a case series in which they observed a 1.4-fold to 10-fold increase in the incidence of GBS during a 4-week period between mid-March to mid-April 2021 in 3 districts of Kerela, India. The number of individuals in this cohort was estimated at only 1.2 million. In the UK, Singapore, and other countries with comprehensive pre–COVID-19 pandemic reporting of GBS cases, the case numbers remained largely the same as pre–COVID-19 pandemic, and reported significant increases in cases in small series is probably artifactual.

Lee et al 20 performed a nationwide time series correlation study using data collected from the National Health Insurance Service and Korea Disease Control and Prevention Agency databases to assess incidence of GBS prior to and during the COVID-19 pandemic. The cumulative incidence rate of GBS was significantly lower during the COVID-19 pandemic (2.1 per 100 000 population in 2020 to 2021 vs 2.4 per 100 000 population in 2017 to 2019), but time series correlation analysis demonstrated a strongly positive temporal association between SARS-CoV-2 vaccination and GBS in 2021. Lee et al 20 did not specifically assess whether this could be attributed to a single vaccine. In a prospective surveillance study including 38 828 691 total vaccine doses, of which 6 465 097 were AV vaccines (ChAdOx1 and Ad26.CoV2.S), Ha et al 21 concluded AV vaccines were associated with a 3-fold to 4-fold increased risk of developing GBS than mRNA vaccines in the same cohort, with GBS following the AV vaccine associated with the first dose. 21

Li et al 17 evaluated 4 376 535 ChAdOx1, 3 588 318 BNT162b2, 244 913 mRNA-1273, and 120 731 Ad26.CoV2 vaccinated individuals compared with a historical cohort of 14 330 080 individuals in the general populations of the UK and Spain. The authors did not identify an increased risk of GBS following any vaccine 0 to 21 days after the first dose. Using the World Health Organization global pharmacovigilance database (VigiBase), Kim et al 22 also found no association between GBS and SARS-CoV-2 vaccination compared with the influenza vaccines but warned regarding the heterogeneity of sources of information in the database. However, a further case report from VigiBase suggested that there is a risk of GBS following AV vaccines. 23

The 4 UK studies highlight the major difficulties of the rush to study and, subsequently, of any systematic synthesis. Estimates of risk were generated with differing reliability, and for any future synthesis, significant numbers of the cases in these 4 studies are likely the same case reported multiple times but with variable diagnostic certainty. We calculated and demonstrated the excess number of cases of GBS per 100 000 vaccines for 10 studies that evaluated AV vaccines ( Figure 2 ). 1 , 8 - 11 , 13 - 17 The data demonstrate a relatively equivalent number of cases of excess GBS per 100 000 vaccinations using AV vaccines. One limitation of this graph is that the observed postvaccination time period varies as follows: Keh et al, 1 42 days; Hanson et al, 10 21 days; Osowicki et al, 14 28 days; Maramattom et al, 16 28 days; Li et al, 17 21 days; Patone et al, 9 28 days; Abara et al, 11 42 days; Walker et al, 8 4 to 42 days; Woo et al, 15 42 days; and Le Vu et al, 13 42 days.

Studies including millions of vaccines or vaccinated individuals have been included ( Table 2 ). 8 , 9 , 17 , 24 The unilateral lower motor neuron facial nerve palsy, often referred to as Bell palsy , describes paralysis of the facial nerve that occurs in the absence of an identifiable cause, with an annual incidence of 15 to 30 per 100 000 persons. 25 The phase 3 clinical trials of the mRNA vaccines identified a numerical imbalance between Bell palsy occurrence in the vaccinated group compared with placebo, which instigated investigation into whether there was an association of Bell palsy with SARS-CoV-2 vaccination. 26 , 27 This safety signal concern raised from the mRNA vaccine clinical trials was investigated in a disproportionality analysis using the World Health Organization VigiBase, and the reporting rate of facial paralysis was not found to be higher than that observed with other vaccines. 28 This finding of no association was supported by an interim analysis of surveillance data from 6.2 million individuals in the US vaccinated with 11.8 million doses of mRNA vaccine. 29 However, following separate, independent analyses of the clinical trial data, Cirillo and Doan 30 and Ozonoff et al 31 both suggested a higher risk of developing facial palsy associated with the mRNA vaccines compared with the background population. A safety assessment by Sato et al 32 using the VAERS database then demonstrated that the incidence of Bell palsy following SARS-CoV-2 vaccination was lower than or equivalent to the rates associated with influenza vaccines; however, there was a statistically significant relationship between SARS-CoV-2 vaccination and BNT162b2 or mRNA-1273 vaccination above the background prevalence. 32 An important major flaw in the data is the inclusion of Bell phenomenon in the search criteria. 33 A full analysis of the articles evaluating the association between Bell palsy and vaccination is available in eMethods 2 in the Supplement . In summary, an association of vaccination with Bell palsy is unclear.

An association between vaccination and myasthenia gravis, multiple sclerosis and central demyelination, neuromyelitis optica spectrum disorders, and myelin oligodendrocyte glycoprotein antibody–associated disease occurrence is not clearly supported by the literature. A detailed analysis is included in eMethods 2 in the Supplement .

GBS remains the neurological condition with the clearest evidence of a causal link with SARS-CoV-2 vaccination. However, neither SARS-CoV-2 nor adenoviruses have been convincingly associated with GBS pathogenesis. Whether the vaccine data indicate that adenovirus may be of undetermined pathogenic importance in GBS is unclear, 34 but it would not be impossible. Discussion of the underlying pathogenesis of AV vaccine–driven GBS remains purely hypothetical owing to rarity, unstructured case ascertainment, and absence of widespread clinical biomarker sampling.

Identifying the molecular agent driving autoimmunity aids in any discussion of pathogenesis. This concerns some or all of the spike protein, the AV components, and the immune response to vaccination or infection. The autoimmune diseases that are reported to occur following COVID-19 only very rarely involve the peripheral nervous system. 35 The studies and data suggesting an association between SARS-CoV-2 infection and self-reactivity are dependent on temporal associations and have little else to support a proven causality. 35 Unlike vaccination, it is difficult to pinpoint an infection date or time of immune response in infection adding additional uncertainty to coassociation. Given the unprecedented conditions of the COVID-19 pandemic and the inherent complexity of autoimmunity, finding an autoimmunity and SARS-CoV-2 link will be challenging. It does not appear that the spike protein is a causal trigger of autoimmunity; if it were, then autoimmune diseases and GBS would occur with equal frequency in all vaccines. Repeated stimulation of C57B1/6 mice with recombinant SARS-CoV-2 spike protein does not induce any measurable autoimmunity. 36 It follows that the vaccine component common to the AV vaccines is the stimulus associated with the development of GBS. Despite the likelihood that AV components are the causative stimulus, it is then unclear if it is the inflammatory response to the vaccination, host genetic factors such as HLA haplotypes, autoreactivity of the adenovirus particles, or any or all of these could therefore be involved in the pathogenesis of GBS.

Identified adenoviral infection does not classically precede GBS onset in a frequency higher than background rates of infection and has not often been linked to GBS pathogenesis. 37 One study stands out as finding very high seroconversion rates, 38 but this finding has never been replicated. The adenoviral serotypes used in vaccinations are deliberately selected for their low virulence in humans. AV vaccines, in part due to the broad tissue tropism of adenovirus, are nonetheless potently immunogenic. The ChAdOx1 vaccine is based on the chimpanzee adenovirus (ChAd) Y25, and Ad26.COV2.S is based on species D human adenovirus serotype 26 (Ad26). Ad26 has a low seroprevalence in humans, 39 and a notable benefit to using ChAds is also the circumventing of any more generic preexisting human adenoviral immunity in the general population; the presence of antibodies against a given adenovirus serotype would greatly impede the immunogenicity of the vaccine antigen. 40 Whether this potent and distinctive immunogenicity of AV vaccination could be linked to driving autoimmunity through, for example, activating anergic B cells or activation of bystander T cells in susceptible individuals is pure speculation. 41 , 42 To our knowledge, both processes have yet to be associated with GBS. In addition, while there have been no known genetic associations when studying GBS as a group, there is little known about individual precipitating infections for HLA linkage, for example. Further investigation of this topic could aim to identify distinctive antibody or T-cell receptor signatures to AV fragments in individuals with post–AV vaccine GBS compared with unaffected AV vaccine–immunized controls, a unique HLA haplotype in those with post–AV vaccine GBS compared with controls, or specific neural/myelin-centric molecular targets for AV antibodies in those with post–AV vaccine GBS.

The occurrence of VITT following vaccination with ChAdOx and Ad26.COV2.S has a clearer pathomechanism. 43 VITT is estimated to occur in 3 to 15 persons per million first doses of AV vaccine, with some rare cases occurring after a second vaccination. Biomarkers such as thrombocytopenia, D-dimer elevation, and reduced plasma fibrinogen were identified in many cases. VITT was also associated with immunoglobulin G antibodies directed against platelet factor 4, which leads to greatly enhanced platelet activation. 44 Whether cross-reactivity of AV components and peripheral nerve (glyco-)proteins could similarly lead to GBS has been suggested in the literature. However, the electrostatic interaction of platelet factor 4 that initiates VITT is not easily replicated by peripheral nerve components. Further research is needed to investigate the cross-reactivity of antibodies against different vectors after immunization as well as the possible interaction between components of adenoviruses and surface molecules of peripheral nerve structures.

In this review, we found there was a small increased risk of GBS following AV-based SARS-CoV-2 vaccines. High-quality UK studies of large cohorts convincingly reproduced consistent similar numerical associations for the AV-based ChAdOx1 vaccine, and these have been replicated in other international studies. The risk of Bell palsy following SARS-CoV-2 vaccination was unclear. No quantifiable excess risk was identified for myasthenia gravis, multiple sclerosis, or neuromyelitis optica spectrum disorders.

There are substantial confounding factors in all of the studies, limiting the certainty of their conclusions. Vaccination of a substantial proportion of the world’s population happened after a year of severe pandemic illness and restricted interperson mixing, with background health and environmental risk substantially modifying health and immune exposures. The global search for a vaccine solution was met in many quarters by suspicion and criticism of new technology. There were many motivations for physicians, the public, and politicians to report any perceived complication. The lack of many organized, effective, and highly accurate national surveillance systems was quickly realized, and the data generated by multiple, heterogeneous acquisition and diagnosis-based systems are of questionable certainty for these rare and difficult-to-diagnose events. It is very unlikely that the risks of vaccination for any associated condition have been underestimated. But it is very clear that the reductions in illness episodes, hospitalizations, and deaths were the result of the huge, conferred benefits of SARS-CoV-2 vaccination at the individual and societal levels. 45

Accepted for Publication: November 10, 2023.

Published Online: January 16, 2024. doi:10.1001/jamaneurol.2023.5208

Corresponding Author: Hans-Peter Hartung, MD ( [email protected] ), and Sven Günther Meuth, MD, PhD ( [email protected] ), Department of Neurology, University Hospital Düsseldorf, Medical Faculty, Heinrich Heine University, Universitätsstraße 1, 40225 Düsseldorf, Germany.

Author Contributions: Drs Willison and Pawlitzki were co–first authors. Drs Hartung and Meuth were co–senior authors.

Conflict of Interest Disclosures: Dr A. Willison reported personal fees from Merck, Sanofi Aventis, and Novartis outside the submitted work. Dr Pawlitzki reported personal fees from Argenx, Alexion, Hexal, Janssen, Merck, Novartis, Bayer, and Biogen outside the submitted work. Dr H. Willison reported grants from Argenx and Annexon Biosciences as well as personal fees from Argenx, AOA Dx, AstraZeneca, Boehringer Ingelheim, Coronex, CSL, Gene Tx Therapeutics, GSK, Hoffman La Roche, Immunic Therapeutics, Longboard Pharma, Novartis, UCB Biopharma SRL, and Annexon Biosciences outside the submitted work. Dr Hartung reported personal fees from Horizon Therapeutics, Merck, Novartis, and Roche outside the submitted work. Dr Meuth reported personal fees from Academy 2, Argenx, Alexion, Almirall, Amicus Therapeutics Germany, Bayer Health Care, Biogen, BioNTech, Bristol Myers Squibb, Celgene, Datamed, Demecan, Desitin, Diamed, Diaplan, DIU Dresden, DPmed, Genzyme, Hexal AG, Impulze GmbH, Janssen Cilag, KW Medipoint, MedDay Pharmaceuticals, Merck Serono, MICE, Mylan, Neuraxpharm, Neuropoint, Novartis, Novo Nordisk, ONO Pharma, Oxford PharmaGenesis, Roche, Sanofi-Aventis, Springer Medizin Verlag, STADA, Chugai Pharma, QuintilesIMS, Teva, Wings for Life International, and Xcenda as well as grants from the German Ministry for Education and Research (BMBF), Bundesinstitut für Risikobewertung (BfR), Deutsche Forschungsgemeinschaft (DFG), Else Kröner Fresenius Foundation, Gemeinsamer Bundesausschuss (G-BA), German Academic Exchange Service, Hertie Foundation, Interdisciplinary Center for Clinical Studies (IZKF) Munster, German Foundation Neurology, Alexion, Almirall, Amicus Therapeutics Germany, Biogen, Diamed, Deutsche Gesellschaft für Materialkunde (DGM), Fresenius Medical Care, Genzyme, Gesellschaft von Freunden und Förderern der Heinrich-Heine-Universität Düsseldorf, HERZ Burgdorf, Merck Serono, Novartis, ONO Pharma, Roche, and Teva outside the submitted work. No other disclosures were reported.

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Bibliometrics & citations, view options, recommendations, live streaming commerce and consumers’ purchase intention: an uncertainty reduction perspective.

The fusion of live streaming and e-commerce is booming. However, it remains unclear how live streaming affects consumers’ purchase intention (PI) in online markets of clothes and cosmetics. On the basis of signaling theory and ...

Avatars in live streaming commerce: The influence of anthropomorphism on consumers' willingness to accept virtual live streamers

Although companies are investing more in avatars to improve interactivity and engage their customers better, the effectiveness of avatars in online markets needs more evidences. This study explores the influence mechanism of anthropomorphism on ...

  • Anthropomorphism positively influences consumers' willingness to accept virtual live streamers.
  • Psychological distance and trust form a chain mediation effect.
  • Product type plays a crucial moderator, the mediation is significant ...

Why do consumers hesitate to purchase in live streaming? A perspective of interaction between participants

  • Anchors and consumers are the main participants in e-commerce live streaming.

Live streaming commerce has rapidly developed in recent years; however serious purchase hesitation do exist at the final payment stage. Contrary to the previous studies focusing more on purchase intention, this research constructed a ...

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Our Critic’s Take on the 100 List: Books That ‘Cast a Sustained Spell’

Dwight Garner writes that voters, who “seemed to want a break from contemporary social reportage,” looked for immersive reads.

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A color photograph of a spread of paperback books.

By Dwight Garner

A long friendship between two girls in a poor neighborhood in Naples, Italy. The exodus of nearly six million Black Americans from South to North. The rise of Thomas Cromwell in cutthroat Tudor England. A series of unsolved murders in a Mexican border town. The Underground Railroad reimagined as a literal one, rails and all.

These are stories from some of the 100 books that — in the opinion of more than 500 novelists, nonfiction writers, librarians, poets, booksellers, editors, critics, journalists and other readers polled by the Book Review — are the best of this still-young century.

What do we mean by “best?” We left that to the respondents. Most appeared to agree with E.M. Forster, who wrote that “the final test for a novel will be our affection for it, as it is the test of our friends, and of anything else which we cannot define.” The only criterion for eligibility was publication in English on or after Jan. 1, 2000. (Somebody — one of you pedants who celebrated the new millennium a year after everyone else — is going to point out that the year 2000 is technically part of the 20th century. Don’t let it be you.)

The best of the best, Nos. 1 through 10, are linked for sure by sensitive intelligence and achieved ambition. But other connections can be made. Most are historical novels or narrative histories, as if readers, weary of the vacuity and smash-and-grab belligerence that dominate much of American political and social discourse, desired either to escape or to gaze backward, to better understand how we arrived here.

Memory and identity are especially strong concerns in the top 10. Readers seemed to want a break from contemporary social reportage; they wanted immersive and unfractured narratives that cast a sustained spell.

The highest tier also underlines a generational cohort. Each of the 10 writers, save the comparatively young Colson Whitehead, was born close to the middle of the last century. Besides Isabel Wilkerson, all of them are represented by novels. Three — Elena Ferrante, W.G. Sebald and Roberto Bolaño — made the list with books in translation.

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