Community ecology usually concerns itself with understanding the causes and consequences of diversity at a given taxonomic resolution, most classically at the species level. Yet there is no doubt that diversity exists at all scales, and phenotypic variability within a taxon can be comparable to differences between taxa, as observed from bacteria to fish and trees. The question that motivates an active and growing body of work (e.g. Raffard et al 2019) is not so much whether intraspecific variability matters, but what we get wrong by ignoring it and how to incorporate it into our understanding of communities. There is no established way to think about diversity at multiple nested taxonomic levels, and it is tempting to summarize intraspecific variability simply by measuring species mean and variance in any trait and metric.

In this study, Girard-Tercieux et al (2023a) propose that, to understand its impact on community-level outcomes and in particular on species coexistence, we should carefully distinguish between two ways of thinking about intraspecific variability:

-"unstructured" variation, where every individual's features are like an independent random draw from a species-specific distribution, for instance, due to genetic lottery and developmental accidents

-"structured" variation that is due to each individual encountering a different but enduring microenvironment.

The latter type of variability may still appear complex and random-like when the environment is high-dimensional (i.e. multifaceted, with many different factors contributing to each individual's performance and development). Thus, it is not necessarily "structured" in the sense of being easily understood -- we may need to measure more aspects of the environment than is practical if we want to fully predict these variations.

What distinguishes this "structured" variability is that it is, in a loose sense, inheritable: individuals from the same species that grow in the same microenvironment will have the same performance, in a repeatable fashion. Thus, if each species is best at exploiting at least a fraction of environmental conditions, it is likely to avoid extinction by competition, except in the unlucky case of no propagule reaching any of the favorable sites.
By contrast, drawing each individual's preferences and performance randomly at each generation (from its own species distribution, but independently from other and past individuals) leads to stochastic dynamics, so-called ecological drift, that easily induce a large number of species extinctions.

The core intuition, that the complex spatial structure and high-dimensional nature of the environment plays a key explanatory role in species coexistence, is a running thread through several of the authors' work (e.g. Clark et al 2010), clearly inspired by their focus on tropical forests. This study, by tackling the question of intraspecific determinants of interspecific outcomes, makes a compelling addition to this line of investigation, coming as a theoretical companion to a more data-oriented study (Girard-Tercieux et al 2023b). But I believe it raises a question that is even broader in scope.

This kind of intraspecific variability, due to different individuals growing in different microenvironments, is perhaps most relevant for trees and other sessile organisms, but the distinction made here between "unstructured" and "structured" variability can likely be extended to many other ecological settings.

In my understanding, what matters most in "structured" variability is not so much it stemming from a fixed environment, but rather it being maintained across generations, rather than possibly lost by drift. This difference between variability in the form of "frozen" randomness and in the form of stochastic drift over time is highly relevant in other theoretical fields (e.g. in physics, where it is the difference between a disordered solid and a liquid), and thus, I expect that it is a meaningful distinction to make throughout community ecology.

References

James S. Clark, David Bell, Chengjin Chu, Benoit Courbaud, Michael Dietze, Michelle Hersh, Janneke HilleRisLambers et al. (2010) "High‐dimensional coexistence based on individual variation: a synthesis of evidence." Ecological Monographs 80, no. 4 : 569-608. https://doi.org/10.1890/09-1541.1

Camille Girard-Tercieux, Ghislain Vieilledent, Adam Clark, James S. Clark, Benoît Courbaud, Claire Fortunel, Georges Kunstler, Raphaël Pélissier, Nadja Rüger, Isabelle Maréchaux (2023a) "Beyond variance: simple random distributions are not a good proxy for intraspecific variability in systems with environmental structure." bioRxiv, ver. 4 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2022.08.06.503032

Camille Girard‐Tercieux, Isabelle Maréchaux, Adam T. Clark, James S. Clark, Benoît Courbaud, Claire Fortunel, Joannès Guillemot et al. (2023b) "Rethinking the nature of intraspecific variability and its consequences on species coexistence." Ecology and Evolution 13, no. 3 : e9860. https://doi.org/10.1002/ece3.9860

Allan Raffard, Frédéric Santoul, Julien Cucherousset, and Simon Blanchet. (2019) "The community and ecosystem consequences of intraspecific diversity: A meta‐analysis." Biological Reviews 94, no. 2: 648-661. https://doi.org/10.1111/brv.12472

In this meta-analysis, Shivani et al. [1] investigate 1) whether dominance and reproductive success are generally associated across social mammals and 2) whether this relationship varies according to a) life history traits (e.g., stronger for species with large litter size), b) ecological conditions (e.g., stronger when resources are limited) and c) the social environment (e.g., stronger for cooperative breeders than for plural breeders). Generally, the results are consistent with their predictions, except there was no clear support for this relationship to be conditional on the ecological conditions. considered

As I have previously recommended the preregistration of this study [2,3], I do not have much to add here, as such recommendation should not depend on the outcome of the study. What I would like to recommend is the whole scientific process performed by the authors, from preregistration sent for peer review, to preprint submission and post-study peer review. It is particularly recommendable to notice that this project was a Masters student project, which shows that it is possible and worthy to preregister studies, even for such rather short-term projects. I strongly congratulate the authors for choosing this process even for an early career short-term project. I think it should be made possible for short-term students to conduct a preregistration study as a research project, without having to present post-study results. I hope this study can encourage a shift in the way we sometimes evaluate students’ projects.

I also recommend the readers to look into the whole pre- and post- study reviewing history of this manuscript and the associated preregistration, as it provides a better understanding of the process and a good example of the associated challenges and benefits [4]. It was a really enriching experience and I encourage others to submit and review preregistrations and registered reports!

References

[1] Shivani, Huchard, E., Lukas, D. (2022). The effect of dominance rank on female reproductive success in social mammals. EcoEvoRxiv, rc8na, ver. 10 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.32942/osf.io/rc8na

[2] Shivani, Huchard, E., Lukas, D. (2020). Preregistration - The effect of dominance rank on female reproductive success in social mammals In principle acceptance by PCI Ecology of the version 1.2 on 07 July 2020. https://dieterlukas.github.io/Preregistration_MetaAnalysis_RankSuccess.html

[3] Paquet, M. (2020) Why are dominant females not always showing higher reproductive success? A preregistration of a meta-analysis on social mammals. Peer Community in Ecology, 100056. https://doi.org/10.24072/pci.ecology.100056

[4] Parker, T., Fraser, H., & Nakagawa, S. (2019). Making conservation science more reliable with preregistration and registered reports. Conservation Biology, 33(4), 747-750. https://doi.org/10.1111/cobi.13342

Changes in the spatial range of many species are one of the major consequences of the profound alteration of environmental conditions due to human activities. Some species expand, sometimes spectacularly during invasions; others decline; some shift. Because these changes result in local biodiversity loss (whether local species go extinct or are replaced by colonizing ones), understanding the factors driving spatial range dynamics appears crucial to predict biodiversity dynamics. Identifying the factors that shape individual movement is a main step towards such understanding. The study described in this preregistration (McCune et al. 2020) falls within this context by testing possible links between individual exploration behaviour and movements related to daily space use in an avian study model currently rapidly expanding, the great-tailed grackle (Quiscalus mexicanus).

Movement and exploration: which direction(s) for the link between exploration and dispersal?
Individuals are known to differ in their tendency to explore the environment (Réale et al. 2007; Wolf and Weissing 2012) and therefore in their motivation to move. Accordingly, exploration has been shown to relate to dispersal behaviour, i.e. movements between breeding sites (Dingemanse et al. 2003, Le Galliard et al. 2011, Rasmussen and Belk 2012; reviews in Cote et al. 2010, Ronce et al. 2012). Yet, the mechanisms underlying this link often remain unclear, due to the correlative nature of the data. A classical assumption is that dispersers may benefit from a high capacity to explore, allowing them to familiarize quicker with their new environment once reached, thus alleviating dispersal costs (Bonte et al. 2012). The association between dispersal and exploration would in this case result from selection for this combination of traits (Ronce et al. 2012), even though dispersal event itself may be independent from (and precede the effect of) exploration behaviour. Alternatively (but not exclusively), dispersal may simply be the final outcome of longer movements by individuals exploring larger ranges (Badyaev et al. 1996, Schliehe-Diecks et al. 2012). In the absence of easy ways to manipulate dispersal behaviour, on the one hand, and exploration tendency, on the other hand, investigating detailed, small-scale individual movements in relation to exploration should thus shed light on which processes may yield the observed relations between exploration as an individual personality trait and large-scale, long-term movements, such as dispersal, underlying species range dynamics.
In this project, the exploration behaviour of grackles will be measured in controlled conditions using standardized tests in captivity (McCune et al. 2019) before individuals are released and their daily space use behaviour will then be measured using remote tracking over long time periods (McCune et al. 2020). Importantly, these coupled measures will be obtained for individuals captured in three different populations: within the historical range of the species, in the middle of its expanding range and at the edge of the range (McCune et al. 2020). Therefore, the project will test (i) whether daily space use of individuals is linked to their intrinsic exploration tendency and (ii) whether space use differs between individuals from different populations along the expanding range. The preregistration echoes a complementary project by the same team that will focus on exploration and test (iii) whether exploration tendency differs between individuals from these different populations. Taken together, these three analyses will therefore provide solid background information to assess the role of exploration in the individuals’ decisions leading to movement and range dynamics in this species.
As underlined in the preregistration, previous studies addressing the links between individual exploration behaviour and movements have mostly focused on dispersal. A first type of studies have (as will be done here) measured exploration behaviour of individuals, often in captivity (Dingemanse et al. 2003, Korsten et al. 2013) but also in the wild (Rasmussen and Belk 2012, Debeffe et al. 2013), and related these measures to subsequent dispersal behaviour. The (often implicit) underlying assumption is that more exploratory individuals will be more likely to move further, explore different habitats and thus end up breeding farther than less explorative ones. In other words, exploration tendency precedes and drives dispersal. Sometimes, exploratory behaviour is measured on individuals of known dispersal status, i.e. after the dispersal event (Hoset et al. 2011), in which case selection for certain exploration phenotypes among dispersers may already have occurred. Besides this first approach, another type of studies have measured ‘exploration’ behaviour under the form of prospecting movements of individuals and linked these movements to subsequent dispersal (often in the context of habitat selection). While these studies were in the past based on direct thus potentially biased observations (Reed et al. 1999), they now rely more and more on technological advances using (miniaturized) remote tracking devices (Ponchon et al. 2013) that provide far more complete and unbiased movement data, and sometimes also complementary measures of individuals’ internal state. In this case, the implicit assumption is that individuals prospecting farther and/or in more habitat patches will be more likely to settle in a site located farther away from their departure site, because of a more exhaustive sampling of possible sites allowing individuals to identify higher-quality sites (Badyaev et al. 1996). In other words, exploration tendency would not directly lead to higher movements or longer distances, but would allow individuals to optimize their habitat choice among more numerous options, thus leading to an increased dispersal probability or distance; the relation between exploration and dispersal would thus be indirect. Prospecting studies address more closely the underlying mechanisms of movement; however, they cannot easily separate intrinsic individual exploratory tendency from the prospecting movements themselves, with potential feedback effects of the information already gathered on future exploration of other sites or patches, thus on subsequent movements.
By focusing on individual daily space use movements as a mechanistic approach to understand large-scale movements potentially involved in colonization and range expansion, the grackle study described in this preregistration (McCune et al. 2020) will thus contribute to bridge the knowledge gaps between exploration and dispersal. By linking exploration measures obtained from a battery of standardized tests conducted in controlled conditions to individual daily space use and movements recorded in the wild, the grackle project is set in between previous studies addressing the links between exploration and dispersal: it will document exploration in a separate and independent context with respect to the movements themselves, and it will use a mechanistic view of detailed movements by the same individuals in the wild to explore potential implications for dispersal and range expansion. Testing differences between the three study populations over the species range will indeed inform about potential large-scale, population implications of among-individual variation in the link between exploration and movements. Because this study will only measure already settled adult individuals whose previous history is unknown, there will nevertheless be no direct possible exploration of the link with either previous or subsequent dispersal behaviour. Thus, the potential links studied here relate more directly to post-dispersal benefits of exploration for an optimal exploitation of the new environment. Yet, if exploration is a life-long personality trait linked to daily movement patterns, it may also relate to natal dispersal movements in young individuals.

Evolutionary and conservation perspectives
If the results of the project reveal that exploration tendency and daily space use movements are indeed linked, and that individuals from populations across the species range differ in these traits, new questions will emerge. A first question would be whether such among-individual differences are at the origin of range expansion or rather one of its consequences since, again, we deal with correlative data here. In other words, individuals may differ in exploration tendency, and this may confer them different ability to move around, find and colonize new habitats; or individuals may show differences in exploration following arrival in a new habitat, either because more explorative individuals gain fitness benefits and are thus selected, or because of behavioural plasticity and post-colonization adjustment of exploration behaviour when facing new ecological and social conditions in the new environment. Another open question relates to the link between daily space use and dispersal: is dispersal a by-product of higher daily movements that allow individuals to discover new favorable places where to settle? Exploring this link could involve measuring just fledged individuals before natal dispersal occurs and/or individuals chosen according to their own dispersal history, and this would then imply long-term population monitoring as an efficient (but constraining) tool to address such questions. Finally, assessing the fitness consequences of the link between exploration and space use behaviour, and whether these consequences differ between populations along the range expansion, would also be needed to understand the contribution of this link to the invasion success of this species.
The study model chosen for this project is a rapidly expanding species. Importantly, however, and as emphasized in the preregistration, documenting links between exploration and daily space use patterns as well as differences between populations with different trajectories can provide crucial information in general to understand population persistence in response to global climate and landscape changes, both regarding invasion ability or extinction risk. The information should be key to assess the probability that a species may decline, persist or expand in studies addressing biodiversity and community dynamics in a changing world.

References

Badayev, A. V., Martin, T. E and Etges, W. J. 1996. Habitat sampling and habitat selection by female wild turkeys: ecological correlates and reproductive consequences. Auk 113: 636-646. doi: https://doi.org/10.2307/4088984
Bonte, D. et al. 2012. Costs of dispersal. Biological Reviews 87: 290-312. doi: https://doi.org/10.1111/j.1469-185X.2011.00201.x
Cote, J., Clobert, J., Brodin, T., Fogarty, S. and Sih, A. 2010. Personality-dependent dispersal: characterization, ontogeny and consequences for spatially structured populations. Philosophical Transactions of the Royal Society B 365: 4065-4576. doi: https://doi.org/10.1098/rstb.2010.0176
Debeffe, L., Morellet, N., Cargnelutti, B., Lourtet, B., Coulon, A., Gaillard, J.-M., Bon, R. and Hewison A. J. M. 2013. Exploration as a key component of natal dispersal: dispersers explore more than philopatric individuals in roe deer. Animal Behaviour 86: 143-151. doi: https://doi.org/10.1016/j.anbehav.2013.05.005
Dingemanse, N. J., Both, C., van Noordwijk, A. J., Rutten, A. L. and Drent, P. J. 2003. Natal dispersal and personalities in great tits (Parus major). Proceedings of the Royal Society B 270: 741-747. doi: https://doi.org/10.1098/rspb.2002.2300
Hoset, K. S., Ferchaud, A.-L., Dufour, F., Mersch, D., Cote, J. and Le Galliard, J.-F. 2011. Natal dispersal correlates with behavioral traits that are not consistent across early life stages. Behavioral Ecology 22: 176–183. doi: https://doi.org/10.1093/beheco/arq188
Korsten, P., van Overveld, T., Adriaensen, F. and Matthysen, E. 2013. Genetic integration of local dispersal and exploratory behaviour in a wild bird. Nature Communications 4: 2362. doi: https://doi.org/10.1038/ncomms3362
Le Galliard, J.-F., Rémy, A., Ims, R. A. and Lambin, X. 2011. Patterns and processes of dispersal behaviour in arvicoline rodents. Molecular Ecology 21: 505-523. doi: https://doi.org/10.1111/j.1365-294X.2011.05410.x
McCune K, Ross C, Folsom M, Bergeron L, Logan CJ. 2020. Does space use behavior relate to exploration in a species that is rapidly expanding its geographic range? http://corinalogan.com/Preregistrations/gspaceuse.html In principle acceptance by PCI Ecology of the version on 23 Sep 2020 https://github.com/corinalogan/grackles/blob/master/Files/Preregistrations/gspaceuse.Rmd.
McCune K, MacPherson M, Rowney C, Bergeron L, Folsom M, Logan CJ. 2019. Is behavioral flexibility linked with exploration, but not boldness, persistence, or motor diversity? (http://corinalogan.com/Preregistrations/gexploration.html) In principle acceptance by PCI Ecology of the version on 27 Mar 2019 https://github.com/corinalogan/grackles/blob/master/Files/Preregistrations/gexploration.Rmd
Ponchon, A., Grémillet, D., Doligez, B., Chambert, T., Tveraa, T., González-Solís, J. and Boulinier, T. 2013. Tracking prospecting movements involved in breeding habitat selection: insights, pitfalls and perspectives. Methods in Ecology and Evolution 4: 143-150. doi: https://doi.org/10.1111/j.2041-210x.2012.00259.x
Rasmussen, J. E. and Belk, M. C. 2012. Dispersal behavior correlates with personality of a North American fish. Current Zoology 58: 260–270. doi: https://doi.org/10.1093/CZOOLO%2F58.2.260
Réale, D., Reader, S. M., Sol, D., McDougall, P. T. and Dingemanse, N. J. 2007. Integrating animal temperament within ecology and evolution. Biological Reviews 82: 291-318. doi: https://doi.org/10.1111/j.1469-185x.2007.00010.x
Reed, J. M., Boulinier, T., Danchin, E. and Oring, L. W. 1999. Informed dispersal: prospecting by birds for breeding sites. Current Ornithology 15: 189-259. doi: https://doi.org/10.1007/978-1-4757-4901-4_5
Ronce, O. and Clobert, J. 2012. Dispersal syndromes. pp. 119-138 In Dispersal Ecology and Evolution (eds. Clobert, J., Baguette, M., Benton, T. G. and Bullock, J. M.), pp. 119-138. Oxford University Press.
Schliehe-Diecks, S., Eberle, M. and Kappeler, P. M. 2012. Walk the line - dispersal movements of gray mouse lemurs (Microcebus murinus). Behavioral Ecology and Sociobiology 66: 1175-1185. doi: https://dx.doi.org/10.1007%2Fs00265-012-1371-y
Wolf, M. and Weissing, F. J. 2012. Animal personalities: consequences for ecology and evolution. Trends in Ecology and Evolution 27: 452-461. doi: https://doi.org/10.1016/j.tree.2012.05.001

Tritrophic interactions have a central role in ecological theory and applications [1-3]. Particularly, systems comprised of plants, herbivores and predators have historically received wide attention given their ubiquity and economic importance [4]. Although ecologists have long aimed to understand the forces that govern alternating ecological effects at successive trophic levels [5], several key open questions remain (at least partially) unanswered [6]. In particular, the analysis of complex food webs has questioned whether ecosystems can be viewed as a series of trophic chains [7,8]. Moreover, whether systems are mostly controlled by top-down (trophic cascades) or bottom-up processes remains an open question [6].
Traditionally, studies have addressed how species diversity at different food chain compartments affect the strength and direction of trophic cascades [9]. For example, many studies have tested whether biological control was more efficient with more than one species of natural enemies [10-12]. Much less attention has been given to the role of within-species variation in shaping trophic cascades [13]. In particular, whereas the impact of trait variation within species of plants or predators on successive trophic levels has been recently addressed [14,15], the impact of intraspecific herbivore variation is in its infancy (but see [16]). This is at odds with the resurgent acknowledgment of the importance of individual variation for several ecological processes operating at higher levels of biological organization [17].
Sources of variation within species can come in many flavours. In herbivores, striking ecological variation can be found among populations occurring on different host plants, which become genetically differentiated, thus forming host races [18,19]. Curiously, the impact of variation across host races on the strength of trophic cascades has, to date, not been explored. This is the gap that the manuscript by Sentis and colleagues [20] fills. They experimentally studied a curious tri-trophic system where the primary consumer, pea aphids, specializes in different plant hosts, creating intraspecific variation across biotypes. Interestingly, there is also ecological variation across lineages from the same biotype. The authors set up experimental food chains, where pea aphids from different lineages and biotypes were placed in their universal legume host (broad bean plants) and then exposed to a voracious but charming predator, ladybugs. The full factorial design of this experiment allowed the authors to measure vertical effects of intraspecific variation in herbivores on both plant productivity (top-down) and predator individual growth (bottom-up).
The results nicely uncover the mechanisms by which intraspecific differences in herbivores precipitates vertical modulation in food chains. Herbivore lineage and host-plant specialization shaped the strength of trophic cascades, but curiously these effects were not modulated by density-dependence. Further, ladybugs consuming pea aphids from different lineages and biotypes grew at distinct rates, revealing bottom-up effects of intraspecific variation in herbivores.
These findings are novel and exciting for several reasons. First, they show how intraspecific variation in intermediate food chain compartments can simultaneously reverberate both top-down and bottom-up effects. Second, they bring an evolutionary facet to the understanding of trophic cascades, providing valuable insights on how genetically differentiated populations play particular ecological roles in food webs. Finally, Sentis and colleagues’ findings [20] have critical implications well beyond their study systems. From an applied perspective, they provide an evident instance on how consumers’ evolutionary specialization matters for their role in ecosystems processes (e.g. plant biomass production, predator conversion rate), which has key consequences for biological control initiatives and invasive species management. From a conceptual standpoint, their results ignite the still neglected value of intraspecific variation (driven by evolution) in modulating the functioning of food webs, which is a promising avenue for future theoretical and empirical studies.

References

[1] Price, P. W., Bouton, C. E., Gross, P., McPheron, B. A., Thompson, J. N., & Weis, A. E. (1980). Interactions among three trophic levels: influence of plants on interactions between insect herbivores and natural enemies. Annual review of Ecology and Systematics, 11(1), 41-65. doi: 10.1146/annurev.es.11.110180.000353
[2] Olff, H., Brown, V.K. & Drent, R.H. (1999). Herbivores: between plants and predators. Blackwell Science, Oxford.
[3] Tscharntke, T. & Hawkins, B.A. (2002). Multitrophic level interactions. Cambridge University Press. doi: 10.1017/CBO9780511542190
[4] Agrawal, A. A. (2000). Mechanisms, ecological consequences and agricultural implications of tri-trophic interactions. Current opinion in plant biology, 3(4), 329-335. doi: 10.1016/S1369-5266(00)00089-3
[5] Pace, M. L., Cole, J. J., Carpenter, S. R., & Kitchell, J. F. (1999). Trophic cascades revealed in diverse ecosystems. Trends in ecology & evolution, 14(12), 483-488. doi: 10.1016/S0169-5347(99)01723-1
[6] Abdala‐Roberts, L., Puentes, A., Finke, D. L., Marquis, R. J., Montserrat, M., Poelman, E. H., ... & Mooney, K. (2019). Tri‐trophic interactions: bridging species, communities and ecosystems. Ecology letters, 22(12), 2151-2167. doi: 10.1111/ele.13392
[7] Polis, G.A. & Winemiller, K.O. (1996). Food webs. Integration of patterns and dynamics. Chapmann & Hall, New York. doi: 10.1007/978-1-4615-7007-3
[8] Torres‐Campos, I., Magalhães, S., Moya‐Laraño, J., & Montserrat, M. (2020). The return of the trophic chain: Fundamental vs. realized interactions in a simple arthropod food web. Functional Ecology, 34(2), 521-533. doi: 10.1111/1365-2435.13470
[9] Polis, G. A., Sears, A. L., Huxel, G. R., Strong, D. R., & Maron, J. (2000). When is a trophic cascade a trophic cascade?. Trends in Ecology & Evolution, 15(11), 473-475. doi: 10.1016/S0169-5347(00)01971-6
[10] Sih, A., Englund, G., & Wooster, D. (1998). Emergent impacts of multiple predators on prey. Trends in ecology & evolution, 13(9), 350-355. doi: 10.1016/S0169-5347(98)01437-2
[11] Diehl, E., Sereda, E., Wolters, V., & Birkhofer, K. (2013). Effects of predator specialization, host plant and climate on biological control of aphids by natural enemies: a meta‐analysis. Journal of Applied Ecology, 50(1), 262-270. doi: 10.1111/1365-2664.12032
[12] Snyder, W. E. (2019). Give predators a complement: conserving natural enemy biodiversity to improve biocontrol. Biological control, 135, 73-82. doi: 10.1016/j.biocontrol.2019.04.017
[13] Des Roches, S., Post, D. M., Turley, N. E., Bailey, J. K., Hendry, A. P., Kinnison, M. T., ... & Palkovacs, E. P. (2018). The ecological importance of intraspecific variation. Nature Ecology & Evolution, 2(1), 57-64. doi: 10.1038/s41559-017-0402-5
[14] Bustos‐Segura, C., Poelman, E. H., Reichelt, M., Gershenzon, J., & Gols, R. (2017). Intraspecific chemical diversity among neighbouring plants correlates positively with plant size and herbivore load but negatively with herbivore damage. Ecology Letters, 20(1), 87-97. doi: 10.1111/ele.12713
[15] Start, D., & Gilbert, B. (2017). Predator personality structures prey communities and trophic cascades. Ecology letters, 20(3), 366-374. doi: 10.1111/ele.12735
[16] Turcotte, M. M., Reznick, D. N., & Daniel Hare, J. (2013). Experimental test of an eco-evolutionary dynamic feedback loop between evolution and population density in the green peach aphid. The American Naturalist, 181(S1), S46-S57. doi: 10.1086/668078
[17] Bolnick, D. I., Amarasekare, P., Araújo, M. S., Bürger, R., Levine, J. M., Novak, M., ... & Vasseur, D. A. (2011). Why intraspecific trait variation matters in community ecology. Trends in ecology & evolution, 26(4), 183-192. doi: 10.1016/j.tree.2011.01.009
[18] Drès, M., & Mallet, J. (2002). Host races in plant–feeding insects and their importance in sympatric speciation. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 357(1420), 471-492. doi: 10.1098/rstb.2002.1059
[19] Magalhães, S., Forbes, M. R., Skoracka, A., Osakabe, M., Chevillon, C., & McCoy, K. D. (2007). Host race formation in the Acari. Experimental and Applied Acarology, 42(4), 225-238. doi: 10.1007/s10493-007-9091-0
[20] Sentis, A., Bertram, R., Dardenne, N., Simon, J.-C., Magro, A., Pujol, B., Danchin, E. and J.-L. Hemptinne (2020) Intraspecific difference among herbivore lineages and their host-plant specialization drive the strength of trophic cascades. bioRxiv, 722140, ver. 4 peer-reviewed and recommended by PCI Ecology. doi: 10.1101/722140

Ecology is the study of the distribution of organisms in space and time and their interactions. As such, there is a tradition of studies relating abiotic environmental conditions to species distribution, while another one is concerned by the effects of consumers on the abundance of their resources.  Interestingly, joining the dots appears more difficult than it would suggest: eluding the effect of species interactions on distribution remains one of the greatest challenges to elucidate nowadays (Kissling et al. 2012). Theory suggests that yes, species interactions such as predation and competition should influence range limits (Godsoe et al. 2017), but the common intuition among many biogeographers remains that over large areas such as regions and continents, environmental drivers like temperature and precipitation overwhelm their local effects. Answering this question is of primary importance in the context where species are moving around with climate warming.  Inconsistencies in food web structure may arise with asynchronized movements of consumers and their resources, leading to a major disruption in regulation and potentially ecosystem functioning. Solving this problem, however, remains very challenging because we have to rely on observational data since experiments are hard to perform at the biogeographical scale. 

The study of St-Gelais is an interesting step forward to solve this problem. Their main objective was to assess the strength of the association between phytoplankton and zooplankton communities at a large spatial scale, looking at the spatial covariation of both taxonomic and functional composition. To do so, they undertook a massive survey of more than 100 lakes across three regions of the boreal region of Québec. Species and functional composition were recorded, along with a set of abiotic variables. Classic community ecology at this point. The difficulty they faced was to disentangle the multiple causal relationships involved in the distribution of both trophic levels. Teasing apart bottom-up and top-down forces driving the assembly of plankton communities using observational data is not an easy task. On the one hand, both trophic levels could respond to variations in temperature, nutrient availability and dissolved organic carbon. The interpretation is fairly straightforward if the two levels respond to different factors, but the situation is much more complicated when they do respond similarly. There are potentially three possible underlying scenarios. First, the phyto and zooplankton communities may share the same environmental requirements, thereby generating a joint distribution over gradients such as temperature and nutrient availability. Second, the abiotic environment could drive the distribution of the phytoplankton community, which would then propagate up and influence the distribution of the zooplankton community. Alternatively, the abiotic environment could constrain the distribution of the zooplankton, which could then affect the one of phytoplankton. In addition to all of these factors, St-Gelais et al also consider that dispersal may limit the distribution, well aware of previous studies documenting stronger dispersal limitations for zooplankton communities. 

Unfortunately, there is not a single statistical approach that could be taken from the shelf and used to elucidate drivers of co-distribution. Joint species distribution was once envisioned as a major step forward in this direction (Warton et al. 2015), but there are several limits preventing the direct interpretation that co-occurrence is linked to interactions (Blanchet et al. 2020). Rather, St-Gelais used a variety of multivariate statistics to reveal the structure in their observational data. First, using a Procrustes analysis (a method testing if the spatial variation of one community is correlated to the structure of another community), they found a significant correlation between phytoplankton and zooplankton communities, indicating a taxonomic coupling between the groups. Interestingly, this observation was maintained for functional composition only when interaction-related traits were considered. At this point, these results strongly suggest that interactions are involved in the correlation, but it's hard to decipher between bottom-up and top-down perspectives. A complementary analysis performed with a constrained ordination, per trophic level, provided complementary pieces of information. First observation was that only functional variation was found to be related to the different environmental variables, not taxonomic variation. Despite that trophic levels responded to water quality variables, spatial autocorrelation was more important for zooplankton communities and the two layers appear to respond to different variables. 

It is impossible with those results to formulate a strong conclusion about whether grazing influence the co-distribution of phytoplankton and zooplankton communities. That's the mere nature of observational data. While there is a strong spatial association between them, there are also diverging responses to the different environmental variables considered. But the contrast between taxonomic and functional composition is nonetheless informative and it seems that beyond the idiosyncrasies of species composition, trait distribution may be more informative and general. Perhaps the most original contribution of this study is the hierarchical approach to analyze the data, combined with the simultaneous analysis of taxonomic and functional distributions. Having access to a vast catalog of multivariate statistical techniques, a careful selection of analyses helps revealing key features in the data, rejecting some hypotheses and accepting others. Hopefully, we will see more and more of such multi-trophic approaches to distribution because it is now clear that the factors driving distribution are much more complicated than anticipated in more traditional analyses of community data. Biodiversity is more than a species list, it is also all of the interactions between them, influencing their distribution and abundance (Jordano 2016).

References

Blanchet FG, Cazelles K, Gravel D (2020) Co-occurrence is not evidence of ecological interactions. Ecology Letters, 23, 1050–1063. https://doi.org/10.1111/ele.13525

Godsoe W, Jankowski J, Holt RD, Gravel D (2017) Integrating Biogeography with Contemporary Niche Theory. Trends in Ecology & Evolution, 32, 488–499. https://doi.org/10.1016/j.tree.2017.03.008

Jordano P (2016) Chasing Ecological Interactions. PLOS Biology, 14, e1002559. https://doi.org/10.1371/journal.pbio.1002559

Kissling WD, Dormann CF, Groeneveld J, Hickler T, Kühn I, McInerny GJ, Montoya JM, Römermann C, Schiffers K, Schurr FM, Singer A, Svenning J-C, Zimmermann NE, O’Hara RB (2012) Towards novel approaches to modelling biotic interactions in multispecies assemblages at large spatial extents. Journal of Biogeography, 39, 2163–2178. https://doi.org/10.1111/j.1365-2699.2011.02663.x

St-Gelais NF, Vogt RJ, Giorgio PA del, Beisner BE (2021) The taxonomic and functional biogeographies of phytoplankton and zooplankton communities across boreal lakes. bioRxiv, 373332, ver. 4 peer-reviewed and recommended by Peer community in Ecology. https://doi.org/10.1101/373332

Warton DI, Blanchet FG, O’Hara RB, Ovaskainen O, Taskinen S, Walker SC, Hui FKC (2015) So Many Variables: Joint Modeling in Community Ecology. Trends in Ecology & Evolution, 30, 766–779. https://doi.org/10.1016/j.tree.2015.09.007

Wisz MS, Pottier J, Kissling WD, Pellissier L, Lenoir J, Damgaard CF, Dormann CF, Forchhammer MC, Grytnes J-A, Guisan A, Heikkinen RK, Høye TT, Kühn I, Luoto M, Maiorano L, Nilsson M-C, Normand S, Öckinger E, Schmidt NM, Termansen M, Timmermann A, Wardle DA, Aastrup P, Svenning J-C (2013) The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling. Biological Reviews, 88, 15–30. https://doi.org/10.1111/j.1469-185X.2012.00235.x