ECOGRAPHY
Research
Testing Bergmann’s rule in marine copepods
Max D. Campbell, David S. Schoeman, William Venables, Rana Abu-Alhaija, Sonia D. Batten,
Sanae Chiba, Frank Coman, Claire H. Davies, Martin Edwards, Ruth S. Eriksen, Jason D. Everett,Yutaka Fukai, Mitsuo Fukuchi, Octavio Esquivel Garrote, Graham Hosie, Jenny A. Huggett,David G. Johns, John A. Kitchener, Philippe Koubbi, Felicity R. McEnnulty, Erik Muxagata, Clare Ostle,
Karen V. Robinson, Anita Slotwinski, Kerrie M. Swadling, Kunio T. Takahashi, Mark Tonks,
Julian Uribe-Palomino, Hans M. Verheye, William H. Wilson, Marco M. Worship, Atsushi Yamaguchi,
Wuchang Zhang and Anthony J. Richardson
M. D. Campbell (https://orcid.org/0000-0003-2860-1580) ✉ (maxcampbe@gmail.com), School of Mathematics and Statistics, Univ. of New South Wales,
Kensington, NSW, Australia, and: Australian Rivers Inst. – Coasts and Estuaries, School of Environment and Science, Griffith Univ., Nathan, QLD,
Australia. – D. S. Schoeman (https://orcid.org/0000-0003-1258-0885), Global-Change Ecology Research Group, School of Science and Engineering,
Univ. of the Sunshine Coast, Maroochydore, QLD, Australia. – W. Venables (https://orcid.org/0000-0001-8432-3365), Commonwealth Scientific andIndustrial Research Organisation (CSIRO) Data61, Ecosciences Precinct, Dutton Park, QLD, Australia. – WV, J. D. Everett and A. J. Richardson (https://
orcid.org/0000-0002-9289-7366), School of Mathematics and Physics, Univ. of Queensland, St Lucia, QLD, Australia. – R. Abu-Alhaija, The Cyprus
Inst., Nicosia, Cyprus. – S. D. Batten, CPR Survey, Marine Biological Association (MBA), Nanaimo, BC, Canada, and: North Pacific Marine Science
Organization (PICES), Sidney, BC, Canada. – S. Chiba (https://orcid.org/0000-0002-2838-1840), Japan Agency for Marine-Earth Science and Technology(JAMSTEC), Yokohama, Japan, and: UNEP-WCMC, Cambridge, UK. – F. Coman (https://orcid.org/0000-0001-6743-5154), J. D. Everett (https://
orcid.org/0000-0002-6681-8054), A. Slotwinski, M. Tonks, J. Uribe-Palomino (https://orcid.org/0000-0002-6867-2108) and AJR, Commonwealth
Scientific and Industrial Research Organisation (CSIRO) Oceans and Atmosphere, BioSciences Precinct (QBP), St Lucia, QLD, Australia. – C. H. Davies,
R. S. Eriksen (https://orcid.org/0000-0002-5184-2465) and F. R. McEnnulty (https://orcid.org/0000-0002-3819-044X), Commonwealth Scientific andIndustrial Research Organisation (CSIRO) Oceans and Atmosphere, Hobart, TAS, Australia. – M. Edwards (https://orcid.org/0000-0002-5716-4714),
G. Hosie, D. G. Johns (https://orcid.org/0000-0003-3270-6764), C. Ostle (https://orcid.org/0000-0001-6923-5535) and W. H. Wilson, CPR Survey,
Marine Biological Association (MBA), Plymouth, UK. – Y. Fukai and A. Yamaguchi (https://orcid.org/0000-0002-5646-3608), Hokkaido Univ., Graduate
School of Fisheries Sciences, Hakodate, Hokkaido, Japan. – M. Fukuchi and K. T. Takahashi, National Inst. of Polar Research, Tokyo, Japan. – O. Esquivel
Garrote (https://orcid.org/0000-0003-2462-6609) and E. Muxagata (https://orcid.org/0000-0002-4210-5252), Laboratório de Zooplâncton, Univ. Federal
do Rio Grande – FURG, Rio Grande, Brazil. – J. A. Huggett (https://orcid.org/0000-0001-9315-8672)
and H. M. Verheye, Dept of Biological Sciences and Marine Research Inst., Univ. of Cape Town, Cape
Ecography
Town, South Africa. – JH and M. M. Worship (https://orcid.org/0000-0001-5775-7969), Oceans and
44: 1283–1295, 2021
Coasts Research, Dept of Forestry and Fisheries and the Environment, Cape Town, South Africa. – J. A.
doi: 10.1111/ecog.05545
Kitchener (https://orcid.org/0000-0003-4642-3044), Australian Antarctic Division, Dept of Agriculture,
Water and the Environment, Kingston, TAS, Australia. – P. Koubbi, IFREMER, Laboratoire Halieutique
Subject Editor: Malin Pinsky
Manche mer du Nord, Boulogne-sur-Mer, France, and: UFR 918 Terre Environnement Biodiversité, Paris,
Editor-in-Chief: Miguel Araújo
France. – K. V. Robinson, National Inst. of Water and Atmospheric Research (NIWA), Christchurch, New
Accepted 7 June 2021
Zealand. – K. M. Swadling, AustralianAntarctic Program Partnership and Inst. for Marine and Antarctic
Studies, Univ. of Tasmania, Sandy Bay, TAS, Australia. – W. Zhang, Inst. of Oceanology, Chinese Academyof Sciences, Qingdao, PR China.

Macroecological relationships provide insights into rules that govern ecological systems. Bergmann’s rule posits that members of the same clade are larger at colder temperatures. Whether temperature drives this relationship is debated because several
other potential drivers covary with temperature. We conducted a near-global comparative analysis on marine copepods (97 830 samples, 388 taxa) to test Bergmann’srule, considering other potential drivers. Supporting Bergmann’s rule, we found temperature better predicted size than did latitude or oxygen, with body size decreasing by––––––––––––––––––––––––––––––––––––––––

www.ecography.org© 2021 The Authors. Ecography published by John Wiley & Sons Ltd on behalf of Nordic Society Oikos
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43.9% across the temperature range (-1.7 to 30ºC). Body size also decreased by 26.9% across the range in food availability.
Our results provide strong support for Bergman’s rule in copepods, but emphasises the importance of other drivers in modifying this pattern. As the world warms, smaller copepod species are likely to emerge as ‘winners’, potentially reducing rates offisheries production and carbon sequestration.
Keywords: allometry, chlorophyll, continuous plankton recorder, ectotherms, environmental drivers, invertebrate,
macroecology, statistical modelling, temperature-size rule, zooplankton

Introduction
Although much of ecology has focused on seemingly large
differences among organisms and ecosystems, especially interms of their spatial and temporal variation, the discipline
has been criticised for lacking general unifying principles(Lawton 1999, Allen and Hoekstra 2015, O’Connor et al.
2019). Macroecology seeks to discover ecological principles
by averaging over finer-scale variation to reveal large-scale statistical relationships (Brown and Maurer 1989, Gaston andBlackburn 2008). One such unifying principle is the importance of body size. Body size plays a central role in the physiology and ecology of organisms, governing processes such as
respiration, metabolism, movement and trophic interactions(Peters 1986, Woodward et al. 2005, Yvon-Durocher et al.
2011). As a result, body size is increasingly used in ecosystem models to generalise traits across vast numbers of taxa,
particularly in the marine environment (Blanchard et al.
2017). Better understanding of global patterns of body size,
and their drivers, will provide stronger unifying principles inecology and help support development of ecosystem models.
One of the earliest relationships identified in ecology
was formulated by Bergmann (1848) in German, althoughtranslation into English (Mayr 1956, James 1970) subsequently contributed to confusion surrounding its definition.
Bergmann’s rule has been tested and verified for many taxa,
including mammals (Brown and Maurer 1989, Ashton et al.
2000), birds (James 1970, Ashton 2002), fish (Wilson
2009, Saunders and Tarling 2018), reptiles (Ashton andFeldman 2003, Angilletta et al. 2004), amphibians (OlallaTárraga and Rodríguez 2007), phytoplankton (Sommer et al.
2017), nematodes (Van Voorhies 1996), insects (Chown
and Gaston 2010, Osorio-Canadas et al. 2016, Tseng andSoleimani Pari 2019), crustaceans (Manyak-Davis et al.
2013, Garzke et al. 2015, Leinaas et al. 2016) and planktonic ciliates (Wang et al. 2020). Yet, the taxonomic levelat which Bergmann’s rule applies is commonly debated.
Some theorists consider it to be intraspecific (Ashton et al.
2000, Olalla-Tárraga 2011), whilst others consider it interspecific (Blackburn et al. 1999, Hessen et al. 2013), leading to some confusion in the literature. Further, since thepattern was first described in endotherms, some question its
applicability to ectotherms (Pincheira-Donoso et al. 2008,
Watt et al. 2010). In this study, we consider Bergmann’s rule
to be defined as Bergmann (1848) himself defined it: species
from the same taxonomic clade (here subclass) are generally
smaller in warmer regions and larger in cooler regions (i.e.
we consider only the interspecific version of Bergmann’s rule,1284

and don’t consider size differences within species,
Blackburn et al. 1999). It is clear that regardless of the precise definition used, much can be gained by investigating
Bergmann’s rule (Olalla-Tárraga 2011) because spatial patterns in body size at all taxonomic levels strongly influence
the ecology of a system (Peters 1986).
Commonly, temperature is considered the primary driverof Bergmann’s rule, although some have argued that otherdrivers might modify the anticipated patterns across taxonomic groups (James 1970, Millien et al. 2006, Yom-Tov
and Geffen 2011). This is because latitude (and therefore
temperature) is confounded with light availability, oxygen
concentration (in aquatic systems), predation rate and foodavailability (Ho et al. 2010). Of these, light is unlikely to
be a direct driver of Bergmann’s rule because many species
that follow the pattern do not depend directly on light.
However, primary productivity depends on light, which
could in turn influence the food available for many groups.
It is also likely that drivers of Bergmann’s rule differ between
endotherms and ectotherms. For endotherms, it is generally
accepted that temperature is the selective mechanism driving
Bergmann’s rule, with species from cooler regions conserving
heat by being larger and consequently having lower body surface-area-to-volume ratios (Mayr 1956, Hessen et al. 2013).
But this is not true for ectotherms (Olalla-Tárraga et al.
2006, Watt et al. 2010). Ectotherms might benefit at cooler
temperatures from increased cell sizes (Van Voorhies 1996,
Hessen et al. 2013, Leinaas et al. 2016), selective protection
from mortality or increased fecundity, all of which scale withbody size (Yampolsky and Scheiner 1996, Vinarski 2014).
Alternatively, the pattern might emerge as a result of a confounded driver such as oxygen concentration. For example,
the ‘oxygen (limitation) hypothesis’ suggests that the size ofmarine ectotherms is limited by concentrations of dissolved
O2 (Chapelle and Peck 1999, Spicer and Morley 2019).
Because gas solubility and water temperature are inversely
correlated, this would predict larger sizes at cooler temperatures (Forster et al. 2012, Rollinson and Rowe 2018).
Food availability is another potential driver of Bergmann’srule, where more food can result in faster growth rates(Lin et al. 2013) and larger body sizes (Vidal 1980, Huston
and Wolverton 2011, Andriuzzi and Wall 2018). Conversely,
larger body sizes might be favoured in areas where food is
scarce, because animals must forage further to find food
(Belovsky 1997, Brown et al. 2017), or size might provide
some other selective advantage. Additionally, latitudinal variation in diet quality could further influence size (Berrigan
and Charnov 1994, Ho et al. 2010).

Latitudinal variation in predation rate is also a plausible driver of Bergmann’s rule within a taxonomic group(Wallerstein and Brusca 1982, Angilletta et al. 2004,
Manyak-Davis et al. 2013) because predation rate tends todecline from the equator to the poles (Freestone et al. 2011).
Predation rate could affect the size and growth of communities in several ways: through evolution towards species thatmature at varied sizes (Kiørboe 2011, Manyak-Davis et al.
2013); through selective predator behaviour (Kiørboe 2011);
through predation-related mortality prior to maximum size
(Angilletta et al. 2004); and through selective advantages ofallocating energy to predator defences (Kiørboe 2011).
We focus on Bergmann’s rule in marine pelagic copepods,
arguably the most abundant multicellular organism on Earth(Schminke 2007). Copepods are the primary link between
phytoplankton and fish in aquatic systems, and they playa central role in fisheries production (Verity and Smetacek
1996). They are crustaceans that swim weakly and thus driftin currents. Marine copepods are an ideal group for testing
Bergmann’s rule in ectotherms because they are widespreadover diverse environments, from the poles to the equator.
There are a large number of copepod species, facilitating amore robust test of Bergmann’s rule. Moreover, copepods
are sensitive to many plausible drivers of Bergmann’s rule,
including temperature (Vidal 1980, Miller and Wheeler2012), food availability (Rutherford et al. 1999, Miller
and Wheeler 2012), predation (Kiørboe 2011, Miller and
Wheeler 2012), oxygen concentration (Rollinson and Rowe2018) and latitude (Tseng and Soleimani Pari 2019).
A search for Bergmann’s rule in the literature on copepods
returned no previous studies; however, Brun et al. (2016)
investigated what has been called the temperature–size rule
(Atkinson 1994) in marine copepods. This rule describes theplastic phenotypic response to temperature within a species,
with warmer temperatures leading to smaller individuals(Diamond and Kingsolver 2009, Ghosh et al. 2013). As
the study actually compared the mean sizes of species andnot individual sizes within a species, it effectively tested
Bergmann’s rule. Based on a varied dataset collected using
many different nets, Brun et al. (2016) found that body size
declined weakly at warmer temperatures, but there was little
or no effect of temperature between 10 and 30°C. However,
the temperature effect was dwarfed by the effect of food,
which surprisingly led to a decline in body size with increasing
food availability. This contrasts with many other studies thathave shown increasing body size with food concentration in
copepods (Vidal 1980) and other ectotherms (Pafilis et al.
2009, Huston and Wolverton 2011, Andriuzzi and Wall2018). In a recent study, Evans et al. (2020) found marine
copepods in the North Atlantic conform to Bergmann’sRule. Although they found that temperature (2–27°C) had
a more profound relationship with body size than describedin Brun et al. (2016), they did not consider the effect offood availability. Other studies have found the intraspecificversion of Bergmann’s rule (often called temperature-size ruleor James’s rule) holds true for several marine copepods species(Garzke et al. 2015, Leinaas et al. 2016).

Here, we test whether food is a more important driverof body size than temperature in marine copepods, whilst
considering other drivers of the potential relationships
with body size, such as oxygen levels, which have notbeen investigated previously for copepods. We test these
relationships simultaneously because they are likely partiallyconfounded with one another, which could modify
their perceived relationships with body size when tested
independently. Further, we account for natural differencesin size based on diet. These tests are facilitated at a nearglobal scale by virtue of the continuous plankton recorder(CPR) survey dataset, the largest (~100 000 samples), mostconsistent (collected using the same device), global dataset
on marine copepods (Richardson et al. 2006, Batten et al.
2019). We used a spatial comparative analysis to identify
statistical relationships over environmental gradients acrossspace (Brown 1995, Gaston and Blackburn 2008).

Material and methods
Sample collection

We chose the CPR dataset, assembled by the Global Allianceof CPR Surveys (GACS 2011, Batten et al. 2019), because
it provides the largest, consistent, most spatially-extensive,
species-resolved plankton dataset (Richardson et al. 2006).
Data were sourced from four surveys: the North Atlantic
CPR Survey, the Scientific Committee on Antarctic Research(SCAR) Southern Ocean CPR Survey (Hosie 2020),
the North Pacific CPR Survey and the Australian CPR
Survey (Fig. 1).
Although CPR surveys have better coverage in polar
and temperate regions than in subtropical and tropicalregions, samples have been collected in waters as warm
as 30°C. All surveys use similar methods to collect andcount copepods (Reid et al. 2003, Richardson et al. 2006
for more details). Specifically, all CPRs use the same meshsize (270 µm), the same mesh material (silk), the same
size mouth opening (1.61 cm2), towed at the same depths(5–10 m) and have similar designs (Hosie et al. 2003,
Reid et al. 2003). The CPR is mainly towed behind shipsof opportunity on their normal trading routes, but alsobehind research vessels. Each CPR tow is usually up to
450 nautical miles. The internal silk roll that captures theplankton is cut up into samples representing either 5 or
10 nautical miles, and microscopic counts of copepods areconverted to number per m3.
Copepod data

Copepods are identified to species whenever possible, andshared training and staff exchanges amongst surveys ensurecomparable data. Names of all copepod taxa were updated
using the World Register of Marine Species (WoRMS)(<www.marinespecies.org/>). Data were available for 388
taxa and from 97 830 samples.

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Figure 1. The Global Alliance of Continuous Plankton Recorder Surveys (GACS) sampling effort since September 1997. Different colouredlines represent sampling transects of individual surveys: the North Atlantic CPR Survey (n = 54 176), the SCAR Southern Ocean CPR
Survey (n = 34 005), the North Pacific CPR Survey (n = 4674) and the Australian CPR Survey (n = 4975).

To test Bergmann’s rule, we calculated the mean lengthof copepods in each sample and related this to environmental drivers. Maximum and minimum lengths of different
copepod taxa were obtained from the Marine Planktonic
Copepod Database from the Observatoire Océanologiquede Banyuls-sur-Mer (<https://copepodes.obs-banyuls.fr/en/
index.php>; Razouls et al. 2020) and from Richardson et al.
(2006). Because juveniles are more difficult to identify
to species and reliably assign a size than adults, they were
not included in the estimate of mean copepod size for eachsample, although adults tend to be more common in CPR
samples (Richardson et al. 2006). Each taxon was assigned
a single size (the midpoint between their minimum andmaximum lengths). We calculated the mean length of copepods in a CPR sample by multiplying abundance of adultsof each taxon in the sample by their assigned length, andthen dividing their sum by the total abundance of all adults
within the sample.
Bergmann’s rule could also potentially be influenced byspatial differences in the trophic structure of communities
because body size is linked to the ecological role of a species(Woodward et al. 2005, Yvon-Durocher et al. 2011). Because
carnivorous copepods are generally larger than omnivorous
ones (Mauchline 1998, Supporting information), which
could affect observed relationships, we distinguished obvious
differences in diets among taxa. We used a combination of
dietary studies from the literature (Richardson and Schoeman
2004) and morphological differences in copepod mouthparts(Huys and Boxshall 1991) to assign each taxon to one of twodiet categories: carnivore; or herbivore/omnivore (hereafter
called omnivore). To calculate the proportion of omnivores

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we divided the summed abundance of omnivore within eachsample by the total abundance of copepods within samples.
Environmental data

We used sea surface temperature (SST) as an estimate ofocean temperature for the near-surface CPR samples, andchlorophyll-a concentration (Chl-a) as a proxy for foodavailability to copepods. Chl-a is correlated with copepod
growth and fecundity in herbivorous and omnivorous species (Richardson and Verheye 1998, 1999, Hirst and Bunker
2003, Bunker and Hirst 2004), and more Chl-a leads tomore of these grazers and thus more carnivorous zooplankton (Richardson and Schoeman 2004). As an index of foodavailability to copepods on each sample, we considered using
the phytoplankton colour index, which is a visual assessmentof the greenness of the silk in four levels (no colour, very
pale green, pale green and green) (Richardson et al. 2006).
This index is useful for analysis when averaged in time andspace (Raitsos et al. 2005, 2014); however Chl-a from satellite is more accurate for individual samples (Richardson et al.
2006). Further, temperature is rarely measured on CPR tows,
so we used remotely-sensed Chl-a and SST data to ensure
measurements were consistent among samples. We matched
the location and time of collection of CPR samples withestimates of SST and Chl-a averaged over eight days prior
to sampling to limit loss of data due to cloud cover and torepresent feeding conditions over the recent past. For SST,
we used daily Group for High Resolution SST data, a cloudfree global product based on satellite, buoy and ship data,and interpolated at 0.2° × 0.2° resolution (<https://doi.

org/10.5067/GHCMC-4FM02>). For surface Chl-a from
September 1997 until the end of 2016, we used daily datafrom the European Space Agency Ocean Colour Climate
Change Initiative data (<https://doi.org/10.5285/9c334fbe6
d424a708cf3c4cf0c6a53f5>). Because this is a merged product from MERIS, Aqua-MODIS, SeaWiFS and VIIRS satellites, these data provide better coverage than products froma single satellite, and are more accurate globally (Mélin et al.
2017). For 2017 and 2018, we combined data from AquaMODIS (<https://doi.org/10.5067/AQUA/MODIS/L3M/
CHL/2018>) and VIIRS (<https://doi.org/10.5067/NPP/VIIRS/L3M/CHL/2018>), the only available satellites.
Chl-a data were retrieved at 4-km resolution and aggregated
to 0.2° × 0.2° resolution to limit loss of data due to cloud
cover and match the resolution of SST data. As there are no
robust satellite Chl-a data before 1997, all analyses used CPR
samples collected after this time.
To investigate whether oxygen is a driver of Bergmann’srule (Forster et al. 2012, Rollinson and Rowe 2018), we useddata from the World Ocean Atlas 2018 (WOA-18) (<https://doi.org/10.17616/R3JZ3J>). We used these relatively
coarser-resolution data (monthly climatology averaged at
1° × 1° resolution) because we could not find an observed
global oxygen product at a finer resolution. We matched theresolution of the predictors for this investigation, by using the
WOA-18 1° × 1° resolution products for SST and oxygen,and by aggregating latitude to 1° × 1° resolution.
Although it is plausible that predation pressure may alter
patterns in Bergmann’s rule, we were unable to accountrobustly for this effect in our study. This is because there are
no reliable estimates of predation pressure on copepods byfish or invertebrate predators, which are poorly estimated in
CPR samples (Richardson et al. 2006).
Statistical analyses

To test Bergmann’s rule, we used a model-building approach.
We fitted a weighted generalised linear mixed-effect model(GLMM, Bates et al. 2011), with fixed effects for all predictors (SST, latitude, oxygen, Chl-a and the proportion ofomnivores) (Supporting information). We included the proportion of omnivores in the model to account for omnivorous copepods generally being smaller than carnivorous ones
(Supporting information). For these models, we transformed
the proportion of omnivores using an arcsine-square-root
transformation, and Chl-a using a square-root transformation, because these predictors were extremely skewed andthereby not adequately distributed across their range. Upon
visual inspection of model residuals, their magnitude generally increased at larger fitted values. We thus used a gamma
error structure with a log-link function (there were no zero
values for size). Because estimates of mean length of copepods
in a sample are more precise when there are more specimens
in a sample, we tried several weighting schemes to accountfor this (including weighting points by their square-root andfourth-root). However, we found that this choice did notaffect our results in any meaningful way, so we opted for an
unweighted approach.
To account for temporal and spatial correlation in thedata, we used four random effects associated with thetime and location of sampling. The first was a random
intercept for Longhurst Province, which represents a global
bioregionalisation of the ocean based primarily on differencesin physical oceanography (Longhurst et al. 1995; <www.
marineregions.org>). This accounts for natural, historical andsampling differences among marine regions, which minimises
bias associated with seasonal differences in sampling regions
as well as any tendency to oversample different times ofthe day within regions. The second was a random intercept
associated with differences amongst CPR surveys, which
accounts for the use of different vessels, large-scale regional
effects and any minor methodological differences. The third
was a random intercept of Tow within Survey, which adjustsfor both temporal and spatial differences amongst tows. These
intercepts further reduce biases due to time of sampling, anddue to seasonal effects (especially because tows are of relatively
short duration when compared with the seasons). The last
was a random slope for days elapsed on Tow within Survey,
which accounts for spatiotemporal autocorrelation amongst
samples on tows, which arises due to differing weather andcollection conditions throughout the period of the tow. This
helped remove any general linear trends over the period of
the tow.
Due to the large number of samples (~100 000) and thus
high statistical power, we did not assess the significance ofpredictors. Rather, we selected the best model using theBayesian information criterion (BIC), which is based on
the goodness of fit (log-likelihood ratio) relative to model
complexity (number of parameters). BIC is suitable to fitting heuristic models with large numbers of observations; itmore harshly penalises overfitting than the more commonlyused Akaike information criterion (Schwarz 1978, Aho
et al. 2014).
From a preliminary analysis we found that SST, latitude
and oxygen were strongly correlated (all r > 0.59, Supporting
information) and could not be included together in themodel. We thus first assessed their relative importance inseparate models with the other predictors (Chl-a, and proportion of omnivores), and then retained the most importantvariable amongst SST, latitude and oxygen for inclusion insubsequent analyses.
We used the pseudo R-squared described in Nakagawa
and Schielzeth (2013) to estimate the proportion of variation
explained by the gamma GLMMs (Nakagawa et al. 2017). To
interpret the ecological relevance of the drivers, we evaluatedeffect sizes using expected relationships of the mean lengthwith predictors (Nakagawa and Cuthill 2007, Sullivan andFeinn 2012). We also evaluated ecological relevance by
converting copepod length estimates to body mass (using wetweight (mg) = 0.03493 × length (mm)2.9878, Pearre Jr. 1980)
because the food that is available to higher-level predatorssuch as fish is related to body mass rather than length.

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To assess the effect of influential points within themodel, we performed sensitivity tests by iteratively identifying and removing groups of outliers and high-leverageobservations; these procedures had no impact on overall
results. Results presented thus include all available data. By
mapping residuals globally both with and without random
effects (Supporting information), we confirmed that the useof random effects reduced spatial autocorrelation among
samples; plotting residuals through time within Surveys
and on Tows confirmed that our models reduced temporalautocorrelation. The code for the statistical analyses is publicly available on GitHub (<www.github.com/maxcampb/
Bergmann-Rule-Copepods>).

the entire temperature range. Using a linear approximation,
we found for each 1°C increase in temperature, the mean
length of copepods was ~0.026 mm shorter.
The mean length of copepods also declined with increased
Chl-a (Fig. 2e), where for each square increase in Chl-a(based on square-root transformation), the mean length of
copepods decreases by a factor of 0.899. This is equivalent toa 0.45 mm decrease across the entire Chl-a range (0.02–9.51
mg m−3) or a 26.9% decrease in mean length. This equates toan 60.7% decrease in copepod mass across the Chl-a range.
Using a linear approximation, we estimate for each square
increase in Chl-a that copepods are ~0.153 mm shorter.
The proportion of omnivores was included in the modelto account for the effect of trophic role on copepod length.
The mean length of copepods decreased in samples that had
a higher proportion of omnivorous copepods (Fig. 2f ). The
mean length was 1.65 mm or 51.8% smaller in samples
comprising only omnivores when compared with samples
comprising only carnivores. This equates to a 88.7% decreasein copepod mass.
Investigating the relative importance of fixed effects
showed that both SST and Chl-a were of similar importancein determining the mean length of copepods, as indicated bytheir BIC. Removing either of these substantially degraded
BIC (Table 1), while removing the proportion of omnivores
within samples degraded the BIC the most (Table 1).

Results
We found that the model containing SST instead of latitude
or oxygen concentration had much lower BIC, so SST wasused in all subsequent models (Table 1).
The final model for mean copepod length included SST,
Chl-a (square-root transformed) and proportion of omnivores
as predictors, and random effects for Longhurst Province,
Survey and Tow within Survey. All predictors reduced BIC
and increased the pseudo R2 (Table 1). Together, fixed effects
explained 9.2% of the variance in mean length of copepods(Table 1). Random effects explained a further 40.5%, where
0.1% was attributed to Longhurst Province (Fig. 2a), 2.3%
was attributed to Survey (Fig. 2c) and 38.3% to the Tow
within Survey effects (Fig. 2b).
The mean length of copepods declined with warmer SST(Fig. 2d), where the mean length of copepods decreased
by a factor of 0.982 for each 1°C increase in SST, which isequivalent to a 0.85 mm decrease across the temperature range(−1.7 to 30°C) or a 43.9% decrease in mean length. Thisequates to an approximate linear decrease in copepod mass of~2.7% per °C, equivalent to an 82.2% decrease in mass acrossDiscussion
We believe that our study provides a robust test of Bergmann’srule because it is the largest study to date (based on 97 830
samples and 388 taxa within an order), is near-global in its
distribution, and it applies a statistical approach that allows
us to disentangle multiple correlated predictors (which is a
substantial challenge in comparative analyses). We foundevidence that marine copepods follow Bergmann’s rule, where
we see a strong decline in copepod body size with temperature,

Table 1. Step 1 compares three models with the WOA-18 data, to determine whether latitude, oxygen concentration or SST is the best
predictor of size (n = 90 665). Step 2 compares five competing models to explain Bergmann’s rule in marine copepods including only besttemperature-dependent predictor (SST, n = 97 830). BIC was the basis for model selection, where lower values indicate a more parsimonious
model. Additionally, log-likelihood represents the goodness of fit (higher is better), and degrees of freedom (df) used represents the complexity.
The R2 value reported is the marginal estimate of the psuedo-R2. All models have the same representation of random effects (Longhurst
Province, Survey and Tow within Survey intercept and slope).
Model

df

Log-likelihood

BIC

Pseudo-R2

10−51 934

103 9820.059

10−51 933

103 9790.059

10−51 796

103 707*0.095

9−60 340120 7840.051

9−56 760

113 6230.050

9−56 708

113 5200.069

10−56 555

113 225*

0.092

Step 1 models – WOA-18 data
Length ~ Latitude + Chl-a + Omnivore-proportion
Length ~ Oxygen + Chl-a + Omnivore-proportion
Length ~ SST(WOA-18 data) + Chl-a + Omnivore-proportion
Step 2 models – higher resolution data
Length ~ SST + Chl-a
Length ~ Chl-a + Omnivore-proportion
Length ~ SST + Omnivore-proportion
Length ~ SST + Chl-a + Omnivore-proportion

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Figure 2. A visual summary of the GLMM for Bergmann’s rule showing relationships between mean length of copepods and (d) SST, (e)Chl-a and (f ) proportion of omnivorous copepods. Blue solid lines represent expected values of mean length for each predictor, the grey
band represents the 95% confidence interval for the expected values, and points are the observations. The (a) Longhurst Province and (c)Survey random effects are shown with the intercepts and 95% confidence intervals (partially hidden by the mean estimate), and (b) the tow
random effects, visualised with a hexplot of the random intercept and random slope for days elapsed (n = 97 830).

1289

as reported by Evans et al. (2020). We also found evidence
that is consistent with the hypothesis that temperature is more
important than latitude or concentration of dissolved oxygen.
Furthermore, food availability was of similar importance totemperature, contrary to the findings by Brun et al. (2016),
who found that body size was much more strongly related tofood availability than to temperature. Nevertheless, our results
corroborate findings by Brun et al. (2016) that copepod size
decreases where more food is available. This contrasts withmost previous work on copepods (Vidal 1980), nematodes(Andriuzzi and Wall 2018), lizards (Pafilis et al. 2009) andmammals (Huston and Wolverton 2011), which found thatbody size increases with food availability.
Explanations for Bergmann’s rule

Although the mechanism underlying Bergmann’s rule incopepods is unclear, it is likely that the relationship withtemperature is more than a spurious correlation driven
by differences in food availability or differences in trophic
structure (McNab 1971, Belovsky 1997, Brown et al.
2017). Even after these drivers are accounted for, a strong
negative relationship between copepod size and temperatureremains. It is plausible that reduced predation rates in cooler
environments may alter this relationship (Wallerstein and
Brusca 1982, Angilletta et al. 2004, Manyak-Davis et al.
2013), and more work is needed to uncover the relationshipbetween size and predation pressure. We also found that
dissolved oxygen concentration (which decreases withincreasing temperature) does not seem to adequately
account for changes in size when compared with effects oftemperature, which is consistent with the hypothesis thatoxygen limitation is not responsible for Bergmann’s rule.
Instead, it is more likely that copepod size is regulated bytemperature or some other unmeasured variable confoundedwith temperature. A potential mechanism is the negative
correlation between growth efficiency and temperature, sothat colder waters could produce larger copepods (Ikeda et al.
2001, Isla et al. 2008).
Our results suggest that when the negative relationshipbetween taxon body size and temperature is adjusted for, therelationship between taxon body size and food availability
is also negative. The direction of this relationship seems
counterintuitive because typically more food leads to faster
growth (Lin et al. 2013) and larger size (Vidal 1980, Berrigan
and Charnov 1994). A potential explanation for the negative
relationship between copepod length and increasing Chl-a isthat it might result from diel vertical migration and satiation.
Larger copepod species vertically migrate extensively,
moving between near-surface and deeper layers, whereas
smaller species are unable to migrate far and spend mostof their time near the surface (Hays et al. 1994). For larger
species that can balance the tradeoff between greater foodavailability at the surface and the reduced predation pressure
at depth through diel vertical migration, individuals thatare satiated spend less time in surface waters (Huntley and
Brooks 1982, Atkinson et al. 1992, Huggett and Richardson

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2000, Hays et al. 2001). Thus, under poor food conditions(here low Chl-a), larger species will spend longer in surface
waters when Chl-a is low, increasing the mean size of thecommunity observed in the CPR, which samples in the top
10 m. By contrast, in a rich food environment (high Chl-a),
copepods are more rapidly satiated and larger copepods canreturn to deeper waters sooner, where they are not capturedby the CPR. Thus, the negative relationship between copepod
length and Chl-a could be the result of a combinationof copepod behaviour and how samples were collected.
This would be an artefact of the study, and this hypothesis
requires further testing. In our dataset this explanation isprobably more likely than copepods being larger in response
to having to forage further (and deeper) to find food in areas
where food is scarce, as suggested in other studies (Belovsky
1997, Brown et al. 2017). An alternative explanation is that
copepods might grow larger in response to seasonality of theirfood supply (Brun et al. 2016). For example, copepods growlarger in systems with short seasonal pulses of food (e.g. Chla) by accumulating lipids for survival when food is limited(Kattner et al. 2007). Thus, being larger and having greater
reserves could allow better survival during periods withoutfood.
It is always difficult disentangling highly correlated
environmental variables with considerable spatiotemporalautocorrelation present. Although we were able substantiallyreduce the amount of spatiotemporal autocorrelation using
random effects, we acknowledge that there is still a significant
amount of spatial autocorrelation remaining in the residuals(Supporting information). Strictly speaking, this could
confound the effects in our analyses, which could in term
lead to selecting sub-optimal candidate models using BIC andlead to us making poor inferences about effects. However, we
believe that our model is an adequate representation of reality,
because our results remained relatively consistent regardlessof the choices we made within the modelling and because wehave a large number of samples across a vast spatial extent, sostrongly confounded effects are less likely to be present.
Nevertheless, we believe we were able to estimate therelative contributions of the drivers of Bergmann’s rulebecause of our large number of samples, and through the useof random effects to adjust for spatial and temporal variations.
Using BIC allowed us to select robust models that explain
the data well without overfitting (Schwarz 1978, Aho et al.
2014), which is important when analysing very large datasets.
We are more confident in the relationship at temperatures
typical of temperate regions (5–20°C), and at lower Chl-a,where we have most data. Unfortunately, we have a paucity
of data in tropical regions, although these are the largest areasin the ocean.
Implications for climate change

Although comparative studies like ours does not necessarily mean the analogous patterns will emerge with warming through time, space-for-time substitution in ecologicalmodelling remains a robust approach for providing insights

into future changes (Blois et al. 2013). The effect of temperature seems profound when considered across its range,
with a 43.9% decrease in copepod length from −1.7 to
30°C. Bergmann’s rule thus suggests that as oceans warm
under climate change, the size (and mass) of copepods islikely to decline (Walther et al. 2002). Under a high-emissions scenario (RCP8.5), SST is likely to warm by ~2.7°C
in 2090–2099 (compared to 1990–1999) based on themean of the Coupled Model Intercomparison Project 5(Bopp et al. 2013), or warm by ~0.6°C under a low-emissions scenario (RCP2.6) (Bopp et al. 2013). Based on our
statistical model, the effect of warming of ~2.7°C under
RCP8.5 could equate to a decrease in body mass of copepods globally of ~7.3%. For RCP2.6, the ~0.6°C warming
could equate to a decrease in body mass of copepods globally of ~1.6%. These estimates would translate to a similar
decline in copepod biomass globally assuming abundance
remains unchanged.
However, most Earth System models also project a
decline in primary production and Chl-a (Bopp et al. 2013,
Stock et al. 2014, Lefort et al. 2015, Galbraith et al. 2017,
Woodworth-Jefcoats et al. 2017). The relationship between
copepod length and Chl-a is more counter-intuitive, lesswell understood and less substantiated by other studies than
the relationship between copepod length and SST. Moreempirical evidence is required to confirm how copepod
size varies with Chl-a. Regardless, decreases in primary
production of between 2% and 16% by 2100 are predictedunder RCP8.5 (Lefort et al. 2015). Using net primary
production estimates from Bopp et al. (2013) and theconversion to Chl-a from Marañón et al. (2014), we find that
under the RCP8.5 Chl-a is projected to decrease globally by~0.086 mg m−3, and could lead to an increase in body massof copepods globally by ~1.2%. Under the RCP2.6, Chl-a isprojected to decrease globally by ~0.020 mg m−3, and could
lead to an increase in body mass of copepods globally by~0.3%. Thus given that the relationship between copepod
length and Chl-a is more than an artefact of our study, thecombined effects of increased temperature and decreased
Chl-a are likely to decrease global copepod biomass by ~6.2%
under the RCP8.5, or decrease by ~1.3% under a RCP2.6.
Current Earth System Models also project a future decline
in zooplankton biomass (Woodworth-Jefcoats et al. 2017)– and copepods dominate zooplankton biomass (Verity andSmetacek 1996, Sommer et al. 2001) – by ~7.9% globally(Stock et al. 2014). This decline in copepod size and mass
could negatively impact global fisheries (Sheridan andBickford 2011). No Earth System Models consider the effectof Bergmann’s rule on copepod size.
There could be several other important ecosystemconsequences of copepod size following Bergmann’s rule as
the climate warms. Because swimming ability and thus theamplitude of their vertical migration is related to their size
(Hays et al. 1994, Ohman and Romagnan 2016), a decline
in body size with warming implies less extensive verticalmigration. Thus, reductions in body size could potentiallyweaken the biological pump that transfers carbon fromsurface layers to the deep ocean (Cavan et al. 2019). Further,
copepods significantly contribute to carbon exports via theirsinking faeces and moults following ecdysis – at rates mostly
determined by their body size (Stamieszkin et al. 2015).
Therefore, reduction in copepod body size with warming
could have significant ramifications for deep-ocean systems(Levin and Le Bris 2015, Sweetman et al. 2017) and for
feedbacks to the climate system (Portner et al. 2019).
Final thoughts

There are now many studies that support Bergmann’s rule
– across marine (Saunders and Tarling 2018, Wang et al.
2020) and terrestrial systems (Arnett and Gotelli 2003,
Ho et al. 2010) – and across ectothermic (Olalla-Tárragaand Rodríguez 2007, Wilson 2009) and endothermic taxa
(Ashton et al. 2000, Brown et al. 2017). Despite this scientific support, there remains limited uptake of Bergmann’s rule
– and other macroecological ideas – in modelling studies. To
project changes in biodiversity, ecosystems and fisheries underclimate change, a host of modelling approaches are increasingly being coupled with Earth System Models (Everett et al.
2017), including nutrient–phytoplankton–zooplankton
models (Stock et al. 2014), population models (Feng et al.
2018), size-spectrum models (Carozza et al. 2019), end-toend ecosystem models (Griffith et al. 2011, 2012) and statistical models (Grieve et al. 2017). There is considerable
opportunity to include well-tested macroecological principles
such as Bergmann’s rule in future modelling efforts focusedon climate change. Our analysis shows that these principles
could substantially influence future projections.
This study also highlights the utility of using large global
datasets for testing macroecological theory. Datasets such as
the CPR, which have been collected consistently for decades
have predominantly been used to understand ecosystem
dynamics or describe global change (Edwards et al. 2010).
There is great potential for comparative analyses with similar
consistent, global datasets. Further, with the advent andincreasing accessibility of powerful statistical techniques suchas GLMMs – that make it possible to test multiple predictors
whilst adjusting for spatial and temporal autocorrelation– there is increasing opportunity for providing robust andnuanced tests of macroecological relationships through spatialcomparative analyses (Bolker et al. 2009). We recommendthat future studies appropriately account for spatial andtemporal autocorrelation, and consider simultaneously
testing multiple potential predictors to avoid spurious andconfounded relationships, as both were common in the past.
There is still much to learn about Bergmann’s rule. Future
research could be directed towards testing the rule acrossvaried taxonomic levels, detailed investigations of regional
differences, and testing nonlinear relationships between sizeand drivers of Bergmann’s rule. These studies could consider
the trophic roles of species when investigating these relationships, particularly for groups with diverse roles such as copepods, because they can explain a large amount of variationin body size and thereby may confound the relationships of1291

interest. With directed research in this area, we could get
closer to understanding and resolving the many complexities
of Bergmann’s rule that have been debated for decades.
Acknowledgements – Pacific CPR data collection is supported by aconsortium for the North Pacific CPR survey coordinated by theNorth Pacific Marine Science Organisation (PICES) and comprising
the North Pacific Research Board (NPRB), Exxon Valdez Oil Spill
Trustee Council (EVOS TC), Canadian Department of Fisheries
and Oceans (DFO) and the Marine Biological Association, UK.
AusCPR data were sourced from the Integrated Marine Observing
System (IMOS). IMOS is a national collaborative research
infrastructure, supported by the Australian Government. SO-CPR
data were sourced from the Scientific Committee on Antarctic
Research (SCAR) sponsored Southern Ocean CPR (SO-CPR)
Survey Database, hosted by the Australian Antarctic Data Centre(AADC). The AADC is part of the Australian Antarctic Division(AAD, a division of the Department of Agriculture, Water andthe Environment (DAWE) of the Australian Government). The
SO-CPR Survey and database are also funded, supported andpopulated by the Australian Government through the DAWEAAD approved AAS project 4107 and the Integrated Marine
Observing System (IMOS) funded by the Australian Government
National Collaborative Research Infrastructure Strategy and theSuper Science Initiative, the Japanese National Institute of Polar
Research (NIPR), the New Zealand National Institute of Water
and Atmospheric Research (NIWA), the German Alfred Wegener
Institute (AWI), the United States of America – Antarctic Marine
Living Resources programme (NOAA US-AMLR), the Russian
Arctic and Antarctic Research Institute (AARI), the Brazilian
Programa Antártico Brasileiro (PROANTAR), the Chilean Instituto
Antártico Chileno (INACH), the South African Department ofEnvironment, Forestry and Fisheries (DEFF), the French Institut
polaire francais Paul-Émile Victor (IPEV) and Université Pierre etMarie Curie (UPMC).
Funding – Funding that supports the running of the North Atlantic
CPR network used in this project includes DEFRA UK ME-5308,
NSF USA OCE-1657887, DFO CA F5955-150026/001/HAL,
NERC UK NC-R8/H12/100 and IMR Norway.
Conflicts of interest – Authors declare no conflicts of interest.

Author contributions

Max D. Campbell: Conceptualization (equal); Formal analysis (lead); Methodology (lead); Validation (lead); Visualization(lead); Writing – original draft (lead); Writing – review andediting (lead). David S. Schoeman: Formal analysis (supporting); Methodology (supporting); Supervision (equal);
Validation (supporting); Visualization (supporting); Writing– original draft (supporting); Writing – review and editing(supporting). William Venables: Formal analysis (supporting); Methodology (supporting); Validation (supporting);
Visualization (supporting); Writing – review and editing(supporting). Rana Abu-Alhaija: Data curation (supporting); Validation (supporting); Writing – review and editing(supporting). Sonia D. Batten: Data curation (supporting);
Validation (supporting); Writing – review and editing (supporting). Sanae Chiba: Data curation (supporting); Validation(supporting); Writing – review and editing (supporting).

1292

Frank Coman: Data curation (supporting); Validation (supporting); Writing – review and editing (supporting). Claire
H. Davies: Data curation (supporting); Validation (supporting); Writing – review and editing (supporting). Martin
Edwards: Data curation (supporting); Validation (supporting); Writing – review and editing (supporting). Ruth S.
Eriksen: Data curation (supporting); Validation (supporting); Writing – review and editing (supporting). Jason D.
Everett: Data curation (supporting); Validation (supporting);
Writing – review and editing (supporting). Yutaka Fukai:
Data curation (supporting); Validation (supporting); Writing– review and editing (supporting). Mitsuo Fukuchi: Data
curation (supporting); Validation (supporting); Writing –review and editing (supporting). Octavio Esquivel Garrote:
Data curation (supporting); Validation (supporting); Writing– review and editing (supporting). Graham Hosie: Data
curation (supporting); Validation (supporting); Writing –review and editing (supporting). Jenny A. Huggett: Data
curation (supporting); Validation (supporting); Writing– review and editing (supporting). David G. Johns: Data
curation (supporting); Validation (supporting); Writing –original draft (supporting). John A. Kitchener: Data curation (supporting); Validation (supporting); Writing – review
and editing (supporting). Philippe Koubbi: Data curation(supporting); Validation (supporting); Writing – review andediting (supporting). Felicity R. McEnnulty: Data curation(supporting); Validation (supporting); Writing – review andediting (supporting). Erik Muxagata: Data curation (supporting); Validation (supporting); Writing – review andediting (supporting). Clare Ostle: Data curation (supporting); Validation (supporting); Writing – review and editing(supporting). Karen V. Robinson: Data curation (supporting); Validation (supporting); Writing – review and editing(supporting). Anita Slotwinski: Data curation (supporting);
Validation (supporting); Writing – review and editing (supporting). Kerrie M. Swadling: Data curation (supporting);
Validation (supporting); Writing – review and editing (supporting). Kunio T. Takahashi: Data curation (supporting); Validation (supporting); Writing – review and editing(supporting). Mark Tonks: Data curation (supporting);
Validation (supporting); Writing – review and editing (supporting). Julian Uribe-Palomino: Data curation (supporting); Validation (supporting); Writing – review and editing(supporting). Hans M. Verheye: Data curation (supporting); Validation (supporting); Writing – review and editing(supporting). William H. Wilson: Data curation (supporting); Validation (supporting); Writing – review and editing(supporting). Marco M. Worship: Data curation (supporting); Validation (supporting); Writing – review and editing(supporting). Atsushi Yamaguchi: Data curation (supporting); Validation (supporting); Writing – review and editing(supporting). Wuchang Zhang: Data curation (supporting); Validation (supporting); Writing – review and editing(supporting). Anthony J. Richardson: Conceptualization(equal); Data curation (supporting); Formal analysis (supporting); Funding acquisition (supporting); Methodology(supporting); Supervision (equal); Validation (supporting);

Visualization (supporting); Writing – original draft (supporting); Writing – review and editing (supporting).
Data accessibility statement

Data are available from the Dryad Digital Repository : <http://dx.doi.org/10.5061/dryad.p8cz8w9qs> (Campbell et al. 2021).

References
Aho, K. et al. 2014. Model selection for ecologists: the worldviewsof AIC and BIC. – Ecology 95: 631–636.
Allen, T. F. and Hoekstra, T. W. 2015. Toward a unified ecology.
– Columbia Univ. Press.
Andriuzzi, W. S. and Wall, D. H. 2018. Grazing and resource availability control soil nematode body size and abundance–mass relationship in semi-arid grassland. – J. Anim. Ecol. 87: 1407–1417.
Angilletta, J., Michael J et al. 2004. Bergmann’s clines in ectotherms: illustrating a life-history perspective with sceloporine
lizards. – Am. Nat. 164: E168–E183.
Arnett, A. E. and Gotelli, N. J. 2003. Bergmann’s rule in larval ant
lions: testing the starvation resistance hypothesis. – Ecol.
Entomol. 28: 645–650.
Ashton, K. G. 2002. Patterns of within-species body size variationof birds: strong evidence for Bergmann’s rule. – Global Ecol.
Biogeogr. 11: 505–523.
Ashton, K. G. and Feldman, C. R. 2003. Bergmann’s rule innonavian reptiles: turtles follow it, lizards and snakes reverse it.
– Evolution 57: 1151–1163.
Ashton, K. G. et al. 2000. Is Bergmann’s rule valid for mammals?
– Am. Nat. 156: 390–415.
Atkinson, A. et al. 1992. Diel vertical migration and feeding ofcopepods at an oceanic site near South Georgia. – Mar. Biol.
113: 583–593.
Atkinson, D. 1994. Temperature and organism size: a biologicallaw for ectotherms? – Adv. Ecol. Res. 25: 1–58.
Bates, D. et al. 2011. Package ‘lme4’. Linear mixed-effects models
using S4 classes. – R package ver. 1.1-5. <https://cran.r-project.
org/web/packages/lme4/lme4.pdf>.
Batten, S. D. et al. 2019. A global plankton diversity monitoring
program. – Front. Mar. Sci. 6: 523–536.
Belovsky, G. E. 1997. Optimal foraging and community structure:
the allometry of herbivore food selection and competition. –Evol. Ecol. 11: 641–672.
Bergmann, C. 1848. Über die Verhältnisse der Wärmeökonomie
der Thiere zu ihrer Grösse. – Gottinger Studien 1: 595–708.
Berrigan, D. and Charnov, E. L. 1994. Reaction norms for age andsize at maturity in response to temperature: a puzzle for life
historians. – Oikos 70: 474–478.
Blackburn, T. M. et al. 1999. Geographic gradients in body size: aclarification of Bergmann’s rule. – Divers. Distrib. 5: 165–174.
Blanchard, J. L. et al. 2017. From bacteria to whales: using functional size spectra to model marine ecosystems. – Trends Ecol.
Evol. 32: 174–186.
Blois, J. L. et al. 2013. Space can substitute for time in predicting
climate-change effects on biodiversity. – Proc. Natl Acad. Sci.
USA 110: 9374–9379.
Bolker, B. M. et al. 2009. Generalized linear mixed models: a practical guide for ecology and evolution. – Trends Ecol. Evol. 24:
127–135.

Bopp, L. et al. 2013. Multiple stressors of ocean ecosystems in the
21st century: projections with CMIP5 models. – Biogeosciences
10: 6225–6245.
Brown, J. H. 1995. Macroecology. – Univ. of Chicago Press.
Brown, J. H. and Maurer, B. A. 1989. Macroecology: the divisionof food and space among species on continents. – Science 243:
1145–1150.
Brown, J. S. et al. 2017. How foraging allometries and resource
dynamics could explain Bergmann’s rule and the body–size dietrelationship in mammals. – Oikos 126: 224–230.
Brun, P. et al. 2016. Trait biogeography of marine copepods – ananalysis across scales. – Ecol. Lett. 19: 1403–1413.
Bunker, A. and Hirst, A. 2004. Fecundity of marine planktonic
copepods: global rates and patterns in relation to chlorophyll a,temperature and body weight. – Mar. Ecol. Prog. Ser. 279:
161–181.
Campbell, M. D. et al. 2021. Data from: Testing Bergmann’s rulein marine copepods. – Dryad Public Repository, <http://dx.doi.org/10.5061/dryad.p8cz8w9qs>.
Carozza, D. A. et al. 2019. Metabolic impacts of climate change
on marine ecosystems: implications for fish communities andfisheries. – Global Ecol. Biogeogr. 28: 158–169.
Cavan, E. et al. 2019. Exploring the ecology of the mesopelagic
biological pump. – Progr. Oceanogr. 176: 102125.
Chapelle, G. and Peck, L. S. 1999. Polar gigantism dictated byoxygen availability. – Nature 399: 114.
Chown, S. L. and Gaston, K. J. 2010. Body size variation in insects:
a macroecological perspective. – Biol. Rev. 85: 139–169.
Diamond, S. E. and Kingsolver, J. G. 2009. Environmental
dependence of thermal reaction norms: host plant quality canreverse the temperature–size rule. – Am. Nat. 175: 1–10.
Edwards, M. et al. 2010. Multi-decadal oceanic ecological datasetsand their application in marine policy and management. –Trends Ecol. Evol. 25: 602–610.
Evans, L. E. et al. 2020. Temperature-mediated changes inzooplankton body size: large scale temporal and spatial analysis.
– Ecography 43: 581–590.
Everett, J. D. et al. 2017. Modeling what we sample and samplingwhat we model: challenges for zooplankton model assessment.
– Front. Mar. Sci. 4: 77.
Feng, Z. et al. 2018. Biogeographic responses of the copepod
Calanus glacialis to a changing Arctic marine environment. –Global Change Biol. 24: e159–e170.
Forster, J. et al. 2012. Warming-induced reductions in body sizeare greater in aquatic than terrestrial species. – Proc. Natl Acad.
Sci. USA 109: 19310–19314.
Freestone, A. L. et al. 2011. Stronger predation in the tropics shapes
species richness patterns in marine communities. – Ecology 92:
983–993.
GACS 2011. GACS – Global Alliance of Continuous Plankton
Recorder Surveys. – <www.globalcpr.org/>.
Galbraith, E. D. et al. 2017. A coupled human–Earth model perspective on long-term trends in the global marine fishery. – Nat.
Commun. 8: 14884.
Garzke, J. et al. 2015. Climate change affects low trophic level marine
consumers: warming decreases copepod size and abundance. –Oecologia 177: 849–860.
Gaston, K. and Blackburn, T. 2008. Pattern and process in macroecology. – Wiley.
Ghosh, S. M. et al. 2013. Temperature–size rule is mediated bythermal plasticity of critical size in Drosophila melanogaster. –Proc. R. Soc. B 280: 20130174.

1293

Grieve, B. D. et al. 2017. Projecting the effects of climate change
on Calanus finmarchicus distribution within the US Northeast
Continental Shelf. – Sci. Rep. 7: 1–12.
Griffith, G. P. et al. 2011. Effects of fishing and acidification-related
benthic mortality on the southeast Australian marine ecosystem.
– Global Change Biol. 17: 3058–3074.
Griffith, G. P. et al. 2012. Predicting interactions among fishing,ocean warming and ocean acidification in a marine system withwhole-ecosystem models. – Conserv. Biol. 26: 1145–1152.
Hays, G. C. et al. 1994. Interspecific differences in the dielvertical migration of marine copepods: the implications ofsize, color and morphology. – Limnol. Oceanogr. 39:
1621–1629.
Hays, G. C. et al. 2001. Individual variability in diel verticalmigration of a marine copepod: why some individuals remain
at depth when others migrate. – Limnol. Oceanogr. 46:
2050–2054
Hessen, D. O. et al. 2013. Temperature–size relations from thecellular-genomic perspective. – Biol. Rev. 88: 476–489.
Hirst, A. and Bunker, A. 2003. Growth of marine planktonic
copepods: global rates and patterns in relation to chlorophyll a,temperature and body weight. – Limnol. Oceanogr. 48:
1988–2010.
Ho, C.-K. et al. 2010. Is diet quality an overlooked mechanism for
Bergmann’s rule? – Am. Nat. 175: 269–276.
Hosie, G. 2020. Southern ocean continuous plankton recorder zooplankton records, ver. 8. – Australian Antarctic Data Centre,<http://dx.doi.org/doi:10.26179/5ee84f77cc4ec>.
Hosie, G. et al. 2003. Development of the Southern Ocean
continuous plankton recorder survey. – Progr. Oceanogr. 58:
263–283.
Huggett, J. A. and Richardson, A. J. 2000. A review of the biology
and ecology of Calanus agulhensis off South Africa. – ICES J.
Mar. Sci. 57: 1834–1849.
Huntley, M. and Brooks, E. R. 1982. Effects of age and foodavailability on diel vertical migration of Calanus pacificus. –Mar. Biol. 71: 23–31.
Huston, M. A. and Wolverton, S. 2011. Regulation of animal size
by eNPP, Bergmann’s rule and related phenomena. – Ecol.
Monogr. 81: 349–405.
Huys, R. and Boxshall, G. 1991. Copepod evolution, Vol. 159. –The Ray Society, London, pp. 1–468.
Ikeda, T. et al. 2001. Metabolic rates of epipelagic marine copepods
as a function of body mass and temperature. – Mar. Biol. 139:
587–596.
Isla, J. A. et al. 2008. Physiological response of the copepod Pseudocalanus sp. in the Baltic Sea at different thermal scenarios. –Global Change Biol. 14: 895–906.
James, F. C. 1970. Geographic size variation in birds and its
relationship to climate. – Ecology 51: 365–390.
Kattner, G. et al. 2007. Perspectives on marine zooplankton lipids.
– Can. J. Fish. Aquat. Sci. 64: 1628–1639.
Kiørboe, T. 2011. How zooplankton feed: mechanisms, traits andtrade-offs. – Biol. Rev. 86: 311–339.
Lawton, J. H. 1999. Are there general laws in ecology? – Oikos 84:
177–192.
Lefort, S. et al. 2015. Spatial and body-size dependent response ofmarine pelagic communities to projected global climate change.
– Global Change Biol. 21: 154–164.
Leinaas, H. P. et al. 2016. Inter- and intraspecific variation in bodyand genome size in calanoid copepods from temperate andarctic waters. – Ecol. Evol. 6: 5585–5595.

1294

Levin, L. A. and Le Bris, N. 2015. The deep ocean under climatechange. – Science 350: 766–768.
Lin, K. et al. 2013. Copepod community growth rates in relation tobody size, temperature and food availability in the East China Sea:a test of metabolic theory of ecology. – Biogeosciences 10: 1877–92.
Longhurst, A. et al. 1995. An estimate of global primary productionin the ocean from satellite radiometer data. – J. Plankton Res.
17: 1245–1271.
Manyak-Davis, A. et al. 2013. The relative importance of predation
risk and water temperature in maintaining Bergmann’s rule ina marine ectotherm. – Am. Nat. 182: 347–358.
Marañón, E. et al. 2014. Resource supply overrides temperature asa controlling factor of marine phytoplankton growth. – PLoS
One 9: e99312.
Mauchline, J. 1998. Biology of calanoid copepods. The advances
in marine biology, Vol. 33. – Elsevier Science & Technology.
Mayr, E. 1956. Geographical character gradients and climatic
adaptation. – Evolution 10: 105–108.
McNab, B. K. 1971. On the ecological significance of Bergmann’srule. – Ecology 52: 845–854.
Mélin, F. et al. 2017. Assessing the fitness-for-purpose of satellite
multi-mission ocean color climate data records: a protocol
applied to OC-CCI chlorophyll-a data. – Remote Sens.
Environ. 203: 139–151.
Miller, C. B. and Wheeler, P. A. 2012. Biological oceanography. –Wiley.
Millien, V. et al. 2006. Ecotypic variation in the context of global
climate change: revisiting the rules. – Ecol. Lett. 9: 853–869.
Nakagawa, S. and Cuthill, I. C. 2007. Effect size, confidence
interval and statistical significance: a practical guide forbiologists. – Biol. Rev. 82: 591–605.
Nakagawa, S. and Schielzeth, H. 2013. A general and simple
method for obtaining R2 from generalized linear mixed-effects
models. – Methods Ecol. Evol. 4: 133–142.
Nakagawa, S. et al. 2017. The coefficient of determination R2 andintra-class correlation coefficient from generalized linear mixedeffects models revisited and expanded. – J. R. Soc. Interface 14:
20170213.
O’Connor, M. I. et al. 2019. Principles of ecology revisited: integrating information and ecological theories for a more unified
science. – Front. Ecol. Evol. 7: 219.
Ohman, M. D. and Romagnan, J. B. 2016. Nonlinear effects ofbody size and optical attenuation on diel vertical migration byzooplankton. – Limnol. Oceanogr. 61: 765–770.
Olalla-Tárraga, M. Á. 2011. ‘Nullius in Bergmann’ or the pluralistic
approach to ecogeographical rules: a reply to Watt et al. (2010).
– Oikos 120: 1441–1444.
Olalla-Tárraga, M. Á. and Rodríguez, M. Á. 2007. Energy andinterspecific body size patterns of amphibian faunas in Europe
and North America: anurans follow Bergmann’s rule, urodeles
its converse. – Global Ecol. Biogeogr. 16: 606–617.
Olalla-Tárraga, M. Á. et al. 2006. Broad-scale patterns of body sizein squamate reptiles of Europe and North America. – J. Biogeogr. 33: 781–793.
Osorio-Canadas, S. et al. 2016. Body size phenology in a regionalbee fauna: a temporal extension of Bergmann’s rule. – Ecol.
Lett. 19: 1395–1402.
Pafilis, P. et al. 2009. Intraspecific competition and high food availability are associated with insular gigantism in a lizard. – Naturwissenschaften 96: 1107–1113.
Pearre Jr., S. 1980. The copepod width–weight relation and its
utility in food chain research. – Can. J. Zool. 58: 1884–1891.

Peters, R. H. 1986. The ecological implications of body size. –Cambridge Univ. Press.
Pincheira-Donoso, D. et al. 2008. The evolution of body size underenvironmental gradients in ectotherms: why should Bergmann’srule apply to lizards? – BMC Evol. Biol. 8: 68.
Portner, H. et al. 2019. IPCC, 2019: IPCC special report on the oceanand cryosphere in a changing climate. – IPCC Intergovernmental
Panel on Climate Change: Geneva, Switzerland 1, no. 3.
Raitsos, D. E. et al. 2005. Extending the SeaWiFS chlorophyll data
set back 50 years in the northeast Atlantic. – Geophys. Res.
Lett. 32: L06603.
Raitsos, D. E. et al. 2014. From silk to satellite: half a century ofocean colour anomalies in the northeast Atlantic. – Global
Change Biol. 20: 2117–2123.
Razouls, C. et al. 2020. Diversity and geographic distribution of marine
planktonic copepods. – <http://copepodes.obs-banyuls.fr/en>.
Reid, P. et al. 2003. The continuous plankton recorder: concepts
and history, from plankton indicator to undulating recorders.
– Progr. Oceanogr. 58: 117–173.
Richardson, A. J. and Schoeman, D. S. 2004. Climate impact onplankton ecosystems in the mortheast Atlantic. – Science 305:
1609–1612.
Richardson, A. J. and Verheye, H. M. 1998. The relative importance of food and temperature to copepod egg production andsomatic growth in the southern Benguela upwelling system. – J.
Plankton Res. 20: 2379–2399.
Richardson, A. J. and Verheye, H. M. 1999. Growth rates of copepods in the southern Benguela upwelling system: the interplay
between body size and food. – Limnol. Oceanogr. 44: 382–392.
Richardson, A. J. et al. 2006. Using continuous plankton recorderdata. – Progr. Oceanogr. 68: 27–74.
Rollinson, N. and Rowe, L. 2018. Temperature-dependent oxygen
limitation and the rise of Bergmann’s rule in species withaquatic respiration. – Evolution 72: 977–988.
Rutherford, S. et al. 1999. Environmental controls on the geographic
distribution of zooplankton diversity. – Nature 400: 749.
Saunders, R. A. and Tarling, G. A. 2018. Southern Ocean mesopelagic
fish comply with Bergmann’s rule. – Am. Nat. 191: 343–351.
Schminke, H. K. 2007. Entomology for the copepodologist. – J.
Plankton Res. 29: 149–162.
Schwarz, G. 1978. Estimating the dimension of a model. – Ann.
Stat. 6: 461–464.
Sheridan, J. A. and Bickford, D. 2011. Shrinking body size as an
ecological response to climate change. – Nat. Clim. Change 1: 401.
Sommer, U. et al. 2001. Complementary impact of copepods andcladocerans on phytoplankton. – Ecol. Lett. 4: 545–550.
Sommer, U. et al. 2017. Do marine phytoplankton follow Bergmann’s rule sensu lato? – Biol. Rev. 92: 1011–1026.
Spicer, J. I. and Morley, S. A. 2019. Will giant polar amphipods be
first to fare badly in an oxygen-poor ocean? Testing hypotheses
linking oxygen to body size. – Phil. Trans. R. Soc. B 374:
20190034.

Stamieszkin, K. et al. 2015. Size as the master trait in modeled copepod fecal pellet carbon flux. – Limnol. Oceanogr. 60: 2090–2107.
Stock, C. et al. 2014. Drivers of trophic amplification of ocean
productivity trends in a changing climate. – Biogeosci. Discuss.
11: 7125–7135.
Sullivan, G. M. and Feinn, R. 2012. Using effect size – or why thep value is not enough. – J. Grad. Med. Educ. 4: 279–282.
Sweetman, A. K. et al. 2017. Major impacts of climate change on
deep-sea benthic ecosystems. – Elem. Sci. Anth. 5: 4.
Tseng, M. and Soleimani Pari, S. 2019. Body size explains interspecific variation in size–latitude relationships in geographicallywidespread beetle species. – Ecol. Entomol. 44: 151–156.
Van Voorhies, W. A. 1996. Bergmann size clines: a simple explanation for their occurrence in ectotherms. – Evolution 50:
1259–1264.
Verity, P. G. and Smetacek, V. 1996. Organism life cycles, predationand the structure of marine pelagic ecosystems. – Mar. Ecol.
Progr. Ser. 130: 277–293.
Vidal, J. 1980. Physioecology of zooplankton. I. Effects of phytoplankton concentration, temperature and body size on thegrowth rate of Calanus pacificus and Pseudocalanus sp. – Mar.
Biol. 56: 111–134.
Vinarski, M. 2014. On the applicability of Bergmann’s rule to ectotherms: the state of the art. – Biol. Bull. Rev. 4: 232–242.
Wallerstein, B. R. and Brusca, R. C. 1982. Fish predation: a preliminary study of its role in the zoogeography and evolution ofshallow water idoteid isopods (Crustacea: Isopoda: Idoteidae).
– J. Biogeogr.: 135–150.
Walther, G.-R. et al. 2002. Ecological responses to recent climatechange. – Nature 416: 389.
Wang, C. et al. 2020. Difference of planktonic ciliate communitiesof the tropical West Pacific, the Bering Sea and the Arctic
Ocean. – Acta Oceanol. Sin. 39: 9–17.
Watt, C. et al. 2010. Bergmann’s rule; a concept cluster? – Oikos
119: 89–100.
Wilson, A. B. 2009. Fecundity selection predicts Bergmann’s rulein syngnathid fishes. – Mol. Ecol. 18: 1263–1272.
Woodward, G. et al. 2005. Body size in ecological networks. –Trends Ecol. Evol. 20: 402–409.
Woodworth-Jefcoats, P. A. et al. 2017. Climate change is projected
to reduce carrying capacity and redistribute species richness inNorth Pacific pelagic marine ecosystems. – Global Change Biol.
23: 1000–1008.
Yampolsky, L. Y. and Scheiner, S. M. 1996. Why larger offspringat lower temperatures? A demographic approach. – Am. Nat.
147: 86–100.
Yom-Tov, Y. and Geffen, E. 2011. Recent spatial and temporalchanges in body size of terrestrial vertebrates: probable causes
and pitfalls. – Biol. Rev. 86: 531–541.
Yvon-Durocher, G. et al. 2011. Warming alters the size spectrum
and shifts the distribution of biomass in freshwater ecosystems.
– Global Change Biol. 17: 1681–1694.

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