Biodiversity loss can have important consequences for ecosystem functions, as exemplified by a large body of literature spanning at least three decades [1–3]. While connections between species diversity and ecosystem functions are now well-defined and understood, the importance of diversity within species is more elusive. Despite a surge in theoretical work on how intraspecific diversity can affect coexistence in simple community types [4,5], not much is known about how intraspecific diversity drives ecosystem processes in more complex community types. One particular challenge is that intraspecific diversity can be expressed as observable variation of functional traits, or instead subsist as genetic variation of which the consequences for ecosystem processes are harder to grasp.
Raffard et al. [6] examined how intraspecific biodiversity loss in a consumer fish changes species diversity at lower trophic levels and ecosystem processes in pond mesocosms. An interesting feature of this experiment is that it crosses functional and genetic intraspecific diversity. To do so, Raffard and colleagues measured and genotyped European minnow (P. phoxinus) individuals sampled from streams across southern France. Combining these collected specimens into experimental ponds allowed them to control functional (population variance of body size) and genetic intraspecific richness (number of genotypes).
Effects on minnow biomass production were mostly small; biomass was significantly reduced only when lowering both functional and genetic richness. However, the consequences for lower trophic levels (zooplankton and macroinvertebrates) were more pronounced and – importantly – not intuitive. For instance, the macroinvertebrate community was less species-diverse at higher minnow functional richness. If minnows with different body sizes would be the main regulator factors [7] explaining macroinvertebrate interactions, one would expect a more diverse set of minnow body sizes (i.e. higher functional minnow richness) to permit higher instead of lower macroinvertebrate richness. At the same time, the macroinvertebrate community was more species-diverse at higher minnow genotype richness, which could indicate unobserved minnow traits determining macroinvertebrate diversity more than the usual suspects (functional consumer richness). Such unobserved traits could be behavioral traits, allowing for resource partitioning among fish.
The consequences of functional minnow diversity loss on zooplankton diversity were negative, as expected in case body size differences among fish would facilitate coexistence of their zooplankton prey, as explained above. However, this was only the case when genetic diversity was high, suggesting nonstraightforward interactive effects of observed and non-observed traits on prey diversity.
The effects of functional and genetic minnow diversity loss on invertebrate (macroinvertebrates and zooplankton) abundance were more consistent than for invertebrate diversity. This suggests again nonstraightforward relationships in this experimental ecosystem, but now between invertebrate diversity and abundance. When using abundance as a proxy for an ecosystem process (which the authors did not), this result illustrates that biodiversity loss in multitrophic communities can have consequences that are challenging to interpret, let alone predict [8,9]. Path analyses showed how the observed changes of invertebrate diversity and abundance co-determined decomposition, a key ecosystem function. These path analyses had highest explanatory power show when including both kinds of intraspecific diversity.
Taken together, the results by Raffard and colleagues suggest that genetic consumer richness can drive species diversity of connected trophic levels and ecosystem processes with similar magnitude as functional diversity. Indeed, the effects of genetic consumer richness were shown to be so strong as to compensate or exacerbate the loss of observed functional richness. The exact mechanisms explaining these effects remain to be identified, however. The possibility that fish grazing by fish with different (observed or not observed) traits regulates coexistence among invertebrate prey, for instance, would depend on how strong fish consumption feeds back on prey growth during a 30-week experiment. As the authors indicate, detailed studies on resource partitioning among consumers (e.g. using stable isotope labelling) can shed light on these matters. Doing so may address a more fundamental question, which is if the mechanisms linking intraspecific diversity to function are different from those linking interspecific diversity to function, and at what time scales.

References

[1] Tilman D, Downing JA (1994) Biodiversity and stability in grasslands. Nature, 367, 363–365. https://doi.org/10.1038/367363a0
[2] Cardinale BJ, Duffy JE, Gonzalez A, Hooper DU, Perrings C, Venail P, Narwani A, Mace GM, Tilman D, Wardle DA, Kinzig AP, Daily GC, Loreau M, Grace JB, Larigauderie A, Srivastava DS, Naeem S (2012) Biodiversity loss and its impact on humanity. Nature, 486, 59–67. https://doi.org/10.1038/nature11148
[3] De Laender F, Rohr JR, Ashauer R, Baird DJ, Berger U, Eisenhauer N, Grimm V, Hommen U, Maltby L, Meliàn CJ, Pomati F, Roessink I, Radchuk V, Brink PJV den (2016) Reintroducing Environmental Change Drivers in Biodiversity–Ecosystem Functioning Research. Trends in Ecology & Evolution, 31, 905–915. https://doi.org/10.1016/j.tree.2016.09.007
[4] Hart SP, Schreiber SJ, Levine JM (2016) How variation between individuals affects species coexistence. Ecology Letters, 19, 825–838. https://doi.org/10.1111/ele.12618
[5] Barabás G, D’Andrea R (2016) The effect of intraspecific variation and heritability on community pattern and robustness. Ecology Letters, 19, 977–986. https://doi.org/10.1111/ele.12636
[6] Raffard A, Cucherousset J, Montoya JM, Richard M, Acoca-Pidolle S, Poésy C, Garreau A, Santoul F, Blanchet S (2020) Intraspecific diversity loss in a predator species alters prey community structure and ecosystem functions. bioRxiv, 2020.06.10.144337, ver. 3 peer-reviewed and recommended by PCI Ecology. https://doi.org/10.1101/2020.06.10.144337
[7] Pásztor L, Botta-Dukát Z, Magyar G, Czárán T, Meszéna G. Theory-Based Ecology: A Darwinian approach. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199577859.001.0001
[8] Binzer A, Guill C, Rall BC, Brose U (2016) Interactive effects of warming, eutrophication and size structure: impacts on biodiversity and food-web structure. Global Change Biology, 22, 220–227. https://doi.org/10.1111/gcb.13086
[9] Schwarz B, Barnes AD, Thakur MP, Brose U, Ciobanu M, Reich PB, Rich RL, Rosenbaum B, Stefanski A, Eisenhauer N (2017) Warming alters energetic structure and function but not resilience of soil food webs. Nature Climate Change, 7, 895–900. https://doi.org/10.1038/s41558-017-0002-z

Traditionally, dispersal has often been seen as “random” or “neutral” as Lowe & McPeek (2014) have put it. This simplistic view is likely due to dispersal being intrinsically difficult to measure empirically as well as “random” dispersal being a convenient simplifying assumption in theoretical work. Clobert et al. (2009), and many others, have highlighted how misleading this assumption is. Rather, dispersal seems to be usually a complex reaction norm, depending both on internal as well as external factors. One such internal factor is the sex of the dispersing individual. A recent review of the theoretical literature (Li & Kokko 2019) shows that while ideas explaining sex-biased dispersal go back over 40 years this state-dependency of dispersal is far from comprehensively understood.

Sevchik et al. (2021) tackle this challenge empirically in a bird species, the great-tailed grackle. In contrast to most bird species, where females disperse more than males, the authors report genetic evidence indicating male-biased dispersal. The authors argue that this difference can be explained by the great-tailed grackle’s social and mating-system.

Dispersal is a central life-history trait (Bonte & Dahirel 2017) with major consequences for ecological and evolutionary processes and patterns. Therefore, studies like Sevchik et al. (2021) are valuable contributions for advancing our understanding of spatial ecology and evolution. Importantly, Sevchik et al. also lead to way to a more open and reproducible science of ecology and evolution. The authors are among the pioneers of preregistering research in their field and their way of doing research should serve as a model for others.

References

Bonte, D. & Dahirel, M. (2017) Dispersal: a central and independent trait in life history. Oikos 126: 472-479. doi: https://doi.org/10.1111/oik.03801

Clobert, J., Le Galliard, J. F., Cote, J., Meylan, S. & Massot, M. (2009) Informed dispersal, heterogeneity in animal dispersal syndromes and the dynamics of spatially structured populations. Ecol. Lett.: 12, 197-209. doi: https://doi.org/10.1111/j.1461-0248.2008.01267.x

Li, X.-Y. & Kokko, H. (2019) Sex-biased dispersal: a review of the theory. Biol. Rev. 94: 721-736. doi: https://doi.org/10.1111/brv.12475

Lowe, W. H. & McPeek, M. A. (2014) Is dispersal neutral? Trends Ecol. Evol. 29: 444-450. doi: https://doi.org/10.1016/j.tree.2014.05.009

Sevchik, A., Logan, C. J., McCune, K. B., Blackwell, A., Rowney, C. & Lukas, D. (2021) Investigating sex differences in genetic relatedness in great-tailed grackles in Tempe, Arizona to infer potential sex biases in dispersal. EcoEvoRxiv, osf.io/t6beh, ver. 5 peer-reviewed and recommended by Peer community in Ecology. doi: https://doi.org/10.32942/osf.io/t6beh

In their pre-registered paper [1], McCune and colleagues propose a field-based study of social versus individual learning mechanisms in an avian species (great-tailed grackles) that has been expanding its geographic range. The study forms part of a longer-term project that addresses various aspects of this species’ behaviour and biology, and the experience of the team is clear from the preprint. Assessing variation in learning mechanisms in different sections of the grackles’ distribution range, the researchers will investigate how individual learning and social transmission may impact learning about novel challenges in the environment. Considering that this is a social species, the authors expect both individual learning and social transmission to occur, when groups of grackles encounter new challenges/ opportunities in the wild. This in itself is not a very unusual idea to test [2, 3], but the authors are rigorously distinguishing between imitation, emulation, local enhancement, and social enhancement. Such rigour is certainly valuable in studies of cognition in the wild.
Further, the authors predict that the contribution of individual versus social learning could vary between populations, as the core may contain fewer unfamiliar/novel stimuli than the edge, where artificial sources of water (for example) may be more common. They make an argument that the core, middle, and edge populations would experience differing levels of environmental predictability. If true, their field experiments could yield very novel results on how changes in environmental predictability affect social/individual learning in a single study species. Their data would then give unusual insights into the ecological value of individual learning and distinct forms of social learning – something that is not easy to test in wild animals. The authors consider a variety of alternative hypotheses that may ultimately explain their findings, and clarify their methods and analyses in fine detail. The authors also set out limitations clearly, and give a thorough account of their approaches and thinking.
The reviewers and I have a still-unanswered question, which is central to the study: what is the predictability or unpredictability of the core versus edge environments? Although the authors have explained similarities and distinctions between the different sections of the grackles’ range, their description feels a bit vague -- it's not as rigorous or well-defined as the rest of the paper. Such a lack of definition may be inevitable in the limitations of a preprint, but ultimately it does suggest that there may be real uncertainty about the qualitative differences between the core, edge, and middle environments. The authors do explain that a lack of variation in individual responses to the field experiments would preclude the testing of further hypothesis, but do not mention how a salient lack of variation in novelty/ predictability between the environments could impact their hypotheses.
An assessment/quantification of the rate at which the different populations of grackles encounter novel stimuli would be a cornerstone of the success of this proposed study. Certainly, the authors cannot address this in much more detail during the preprint stage, but they need to consider how to best assess/describe differences before starting the full study. Such an assessment could take the form of either a GIS desktop study (comparing, for example, rates of dam/canal construction in core versus edge sections of the distribution range), or observational/ movement data contrasting how frequently members of core versus edge populations encounter artificial sources of water/food in a given month/year. Considering the long-term nature of the larger project, it is possible that these data are already available, but I am speculating. I would highly recommend that such an assessment be undertaken, beyond the mere mention of expected differences. This would solidify the central idea that there are concrete differences between the environments.
Despite this concern, the authors attended well to the comments and recommendations of the two reviewers – both experts in cognitive ecology. It is a preprint showing clear thinking and a consideration of most of the challenges that may be encountered during the course of the study. My own opinion and the estimations of the two reviewers all underscore the originality and value of this project – this should be a very valuable and potentially novel study. I look forward to seeing the outcomes of the research.

References

[1] McCune, K. B., McElreath, R., and Logan, C. J. (2019). Investigating the use of learning mechanisms in a species that is rapidly expanding its geographic range. In principle recommendation by Peer Community In Ecology. corinalogan.com/Preregistrations/g_sociallearning.html
[2] Benson-Amram, S. and Holekamp, K. E. (2012). Innovative problem solving by wild spotted hyenas. Proceedings of the Royal Society B: Biological Sciences, 279(1744), 4087–4095. doi: 10.1098/rspb.2012.1450
[3] Federspiel, I. G., Boeckle, M., von Bayern, A. M. P. and Emery, N. J. (2019). Exploring individual and social learning in jackdaws (Corvus monedula). Learning & Behavior, 47(3), 258–270. doi: 10.3758/s13420-019-00383-8

Behavioral flexibility is a key for species adaptation to new environments. Predicting species responses to new contexts hence requires knowledge on the amount to and conditions in which behavior can be flexible. This is what Logan and collaborators propose to assess in a series of experiments on the great-tailed grackles, in a context of rapid range expansion. This pre-registration is integrated into this large research project and concerns more specifically the manipulability of the cognitive aspects of behavioral flexibility. Logan and collaborators will use reversal learning tests to test whether (i) behavioral flexibility is manipulatable, (ii) manipulating flexibility improves flexibility and problem solving in a new context, (iii) flexibility is repeatable within individuals, (iv) individuals are faster at problem solving as they progress through serial reversals. The pre-registration carefully details the hypotheses, their associated predictions and alternatives, and the plan of statistical analyses, including power tests. The ambitious program presented in this pre-registration has the potential to provide important pieces to better understand the mechanisms of species adaptability to new environments.

Understanding why and how species coexist in local communities is one of the central questions in ecology. There is general agreement that species distribution and coexistence are determined by a number of key mechanisms, including the environmental requirements of species, dispersal, evolutionary constraints, resource availability and selection, metapopulation dynamics, and biotic interactions (e.g. Soberón & Nakamura 2009; Colwell & Rangel 2009; Ricklefs 2015). These factors are however intricately intertwined in a scale-structured fashion (Hortal et al. 2010; D’Amen et al. 2017), making it particularly difficult to tease apart the effects of each one of them. This could be addressed by the novel field of Joint Species Distribution Modelling (JSDM; Okasvainen & Abrego 2020), as it allows assessing the effects of several sets of factors and the co-occurrence and/or covariation in abundances of potentially interacting species at the same time (Pollock et al. 2014; Ovaskainen et al. 2016; Dormann et al. 2018). However, the development of JSDM has been hampered by the general lack of good-quality detailed data on species co-occurrences and abundances (see Hortal et al. 2015).

Facon et al. (2021) use a particularly large compilation of field surveys to study the abundance and co-occurrence of Tephritidae fruit flies in c. 400 orchards, gardens and natural areas throughout the island of Réunion. Further, they combine such information with lab data on their host-selection fundamental niche (i.e. in the absence of competitors), codifying traits of female choice and larval performances in 21 host species. They use Poisson Log-Normal models, a type of mixed model that allows one to jointly model the random effects associated with all species, and retrieve the covariations in abundance that are not explained by environmental conditions or differences in sampling effort. Then, they use a series of models to evaluate the effects on these matrices of ecological covariates (date, elevation, habitat, climate and host plant), species interactions (by comparing with a constrained residual variance-covariance matrix) and the species’ host-selection fundamental niches (through separate models for each fly species).

The eight Tephritidae species inhabiting Réunion include both generalists and specialists in Solanaceae and Cucurbitaceae with a known history of interspecific competition. Facon et al. (2021) use a comprehensive JSDM approach to assess the effects of different factors separately and altogether. This allows them to identify large effects of plant hosts and the fundamental host-selection niche on species co-occurrence, but also to show that ecological covariates and weak –though not negligible– species interactions are necessary to account for all residual variance in the matrix of joint species abundances per site. Further, they also find evidence that the fitness per host measured in the lab has a strong influence on the abundances in each host plant in the field for specialist species, but not for generalists. Indeed, the stronger effects of competitive exclusion were found in pairs of Cucurbitaceae specialist species. However, these analyses fail to provide solid grounds to assess why generalists are rarely found in Cucurbitaceae and Solanaceae. Although they argue that this may be due to Connell’s (1980) ghost of competition past (past competition that led to current niche differentiation), further data on the evolutionary history of these fruit flies is needed to assess this hypothesis.

Finding evidence for the effects of competitive interactions on species’ occurrences and spatial distributions is often difficult, perhaps because these effects occur over longer time scales than the ones usually studied by ecologists (Yackulic 2017). The work by Facon and colleagues shows that weak effects of competition can be detected also at the short ecological timescales that determine coexistence in local communities, under the virtuous combination of good-quality data and sound analytical designs that account for several aspects of species’ niches, their biotopes and their joint population responses. This adds a new dimension to the application of Hutchinson’s (1978) niche framework to understand the spatial dynamics of species and communities (see also Colwell & Rangel 2009), although further advances to incorporate dispersal-driven metacommunity dynamics (see, e.g., Ovaskainen et al. 2016; Leibold et al. 2017) are certainly needed. Nonetheless, this work shows the potential value of in-depth analyses of species coexistence based on combining good-quality field data with well-thought out JSDM applications. If many studies like this are conducted, it is likely that the uprising field of Joint Species Distribution Modelling will improve our understanding of the hierarchical relationships between the different factors affecting species coexistence in ecological communities in the near future.

References

Colwell RK, Rangel TF (2009) Hutchinson’s duality: The once and future niche. Proceedings of the National Academy of Sciences, 106, 19651–19658. https://doi.org/10.1073/pnas.0901650106

Connell JH (1980) Diversity and the Coevolution of Competitors, or the Ghost of Competition Past. Oikos, 35, 131–138. https://doi.org/10.2307/3544421

D’Amen M, Rahbek C, Zimmermann NE, Guisan A (2017) Spatial predictions at the community level: from current approaches to future frameworks. Biological Reviews, 92, 169–187. https://doi.org/10.1111/brv.12222

Dormann CF, Bobrowski M, Dehling DM, Harris DJ, Hartig F, Lischke H, Moretti MD, Pagel J, Pinkert S, Schleuning M, Schmidt SI, Sheppard CS, Steinbauer MJ, Zeuss D, Kraan C (2018) Biotic interactions in species distribution modelling: 10 questions to guide interpretation and avoid false conclusions. Global Ecology and Biogeography, 27, 1004–1016. https://doi.org/10.1111/geb.12759

Facon B, Hafsi A, Masselière MC de la, Robin S, Massol F, Dubart M, Chiquet J, Frago E, Chiroleu F, Duyck P-F, Ravigné V (2021) Joint species distributions reveal the combined effects of host plants, abiotic factors and species competition as drivers of community structure in fruit flies. bioRxiv, 2020.12.07.414326. ver. 4 peer-reviewed and recommended by Peer community in Ecology. https://doi.org/10.1101/2020.12.07.414326

Hortal J, de Bello F, Diniz-Filho JAF, Lewinsohn TM, Lobo JM, Ladle RJ (2015) Seven Shortfalls that Beset Large-Scale Knowledge of Biodiversity. Annual Review of Ecology, Evolution, and Systematics, 46, 523–549. https://doi.org/10.1146/annurev-ecolsys-112414-054400

Hortal J, Roura‐Pascual N, Sanders NJ, Rahbek C (2010) Understanding (insect) species distributions across spatial scales. Ecography, 33, 51–53. https://doi.org/10.1111/j.1600-0587.2009.06428.x

Hutchinson, G.E. (1978) An introduction to population biology. Yale University Press, New Haven, CT.

Leibold MA, Chase JM, Ernest SKM (2017) Community assembly and the functioning of ecosystems: how metacommunity processes alter ecosystems attributes. Ecology, 98, 909–919. https://doi.org/10.1002/ecy.1697

Ovaskainen O, Abrego N (2020) Joint Species Distribution Modelling: With Applications in R. Cambridge University Press, Cambridge. https://doi.org/10.1017/9781108591720

Ovaskainen O, Roy DB, Fox R, Anderson BJ (2016) Uncovering hidden spatial structure in species communities with spatially explicit joint species distribution models. Methods in Ecology and Evolution, 7, 428–436. https://doi.org/10.1111/2041-210X.12502

Pollock LJ, Tingley R, Morris WK, Golding N, O’Hara RB, Parris KM, Vesk PA, McCarthy MA (2014) Understanding co-occurrence by modelling species simultaneously with a Joint Species Distribution Model (JSDM). Methods in Ecology and Evolution, 5, 397–406. https://doi.org/10.1111/2041-210X.12180

Ricklefs RE (2015) Intrinsic dynamics of the regional community. Ecology Letters, 18, 497–503. https://doi.org/10.1111/ele.12431

Soberón J, Nakamura M (2009) Niches and distributional areas: Concepts, methods, and assumptions. Proceedings of the National Academy of Sciences, 106, 19644–19650. https://doi.org/10.1073/pnas.0901637106

Yackulic CB (2017) Competitive exclusion over broad spatial extents is a slow process: evidence and implications for species distribution modeling. Ecography, 40, 305–313. https://doi.org/10.1111/ecog.02836