Worldwide, city expansion is happening at a fast rate and at the same time, urbanists are more and more required to make place for biodiversity. Choices have to be made regarding the area and spatial arrangement of suitable spaces for non-human living organisms, that will favor the long-term survival of their populations. To guide those choices, it is necessary to understand the mechanisms driving the effects of land management on biodiversity.

Research results on the effects of urbanization on genetic diversity have been very diverse, with studies showing higher genetic diversity in rural than in urban populations (e.g. Delaney et al. 2010), the contrary (e.g. Miles et al. 2018) or no difference (e.g. Schoville et al. 2013). The same is true for studies investigating genetic differentiation. The reasons for these differences probably lie in the relative intensities of gene flow and genetic drift in each case study, which are hard to disentangle and quantify in empirical datasets.

In their paper, Savary et al. (2024) used an elegant and powerful simulation approach to better understand the diversity of observed patterns and investigate the effects of dispersal limitation on genetic patterns (diversity and differentiation). Their simulations involved the landscapes of 325 real European cities, each under three different scenarios mimicking 3 virtual urban tolerant species with different abilities to move within cities while genetic drift intensity was held constant across scenarios. The cities were chosen so that the proportion of artificial areas was held constant (20%) but their location and shape varied. This design allowed the authors to investigate the effect of connectivity and spatial configuration of habitat on the genetic responses to spatial variations in dispersal in cities. 

The main results of this simulation study demonstrate that variations in dispersal spatial patterns, for a given level of genetic drift, trigger variations in genetic patterns. Genetic diversity was lower and genetic differentiation was larger when species had more difficulties to move through the more hostile components of the urban environment. The increase of the relative importance of drift over gene flow when dispersal was spatially more constrained was visible through the associated disappearance of the pattern of isolation by resistance. Forest patches (usually located at the periphery of the cities) usually exhibited larger genetic diversity and were less differentiated than urban green spaces. But interestingly, the presence of habitat patches at the interface between forest and urban green spaces lowered those differences through the promotion of gene flow. 

One other noticeable result, from a landscape genetic method point of view, is the fact that there might be a limit to the detection of barriers to genetic clusters through clustering analyses because of the increased relative effect of genetic drift. This result needs to be confirmed, though, as genetic structure has only been investigated with a recent approach based on spatial graphs. It would be interesting to also analyze those results with the usual Bayesian genetic clustering approaches. 

Overall, this study addresses an important scientific question about the mechanisms explaining the diversity of observed genetic patterns in cities. But it also provides timely cues for connectivity conservation and restoration applied to cities.  
 
References

Delaney, K. S., Riley, S. P., and Fisher, R. N. (2010). A rapid, strong, and convergent genetic response to urban habitat fragmentation in four divergent and widespread vertebrates. PLoS ONE, 5(9):e12767.
https://doi.org/10.1371/journal.pone.0012767
 
Miles, L. S., Dyer, R. J., and Verrelli, B. C. (2018). Urban hubs of connectivity: Contrasting patterns of gene flow within and among cities in the western black widow spider. Proceedings of the Royal Society B, 285(1884):20181224. https://doi.org/10.1098/rspb.2018.1224
 
Savary P., Tannier C., Foltête J.-C., Bourgeois M., Vuidel G., Khimoun A., Moal H., and Garnier S. (2024). How does dispersal shape the genetic patterns of animal populations in European cities? A simulation approach. EcoEvoRxiv, ver. 3 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.32942/X2JS41.
 
Schoville, S. D., Widmer, I., Deschamps-Cottin, M., and Manel, S. (2013). Morphological clines and weak drift along an urbanization gradient in the butterfly, Pieris rapae. PLoS ONE, 8(12):e83095.
https://doi.org/10.1371/journal.pone.0083095

As so often quoted, "all models are wrong; more specifically, we always neglect potentially important factors in our models of ecological systems. We may neglect these factors because no-one has built a computational framework to include them; because including them would be computationally infeasible; or because we don't have enough data.  When considering whether to include a particular process or form of heterogeneity, the gold standard is to fit models both with and without the component, and then see whether we needed the component in the first place ​-- that is, whether including that component leads to an important difference in our conclusions. However, this approach is both tedious and endless, because there are an infinite number of components that we could consider adding to any given model.

Therefore, thoughtful exercises that evaluate the importance of particular complications under a realistic range of simulations and a representative set of case studies are extremely valuable for the field. While they cannot provide ironclad guarantees, they give researchers a general sense of when they can (probably) safely ignore some factors in their analyses. This paper by Sollmann (2024) shows that for a very wide range of scenarios, temporal and spatiotemporal variability in the probability of detection have little effect on the conclusions of spatial capture-recapture and occupancy models.  The author is thoughtful about when such variability may be important, e.g. when variation in detection and density is correlated and thus confounded, or when variation is driven by animals' behavioural responses to being captured.

Reference

Sollmann R (2024). Mt or not Mt: Temporal variation in detection probability in spatial capture-recapture and occupancy models. bioRxiv, 2023.08.08.552394, ver. 2 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2023.08.08.552394

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

Ecosystems shelter a huge number of species, continuously interacting. Each species interact in various ways, with trophic interactions, but also non-trophic interactions, not mentioning the abiotic and anthropogenic interactions. In particular, pollination, competition, facilitation, parasitism and many other interaction types are simultaneously present at the same place in terrestrial ecosystems [1-2]. For this reason, we need today to improve our understanding of such complex interaction networks to later anticipate their responses. This program is a huge challenge facing ecologists and they today join their forces among experimentalists, theoreticians and modelers. While some of us struggle in theoretical and modeling dimensions [3-4], some others perform brilliant works to observe and/or experiment on the same ecological objects [5-6]. In this nice study [6], Magrach et al. succeed in studying relatively large plant-pollinator interaction networks in the field, in Mediterranean ecosystems. For the first time to my knowledge, they study community-wide interactions instead of traditional and easier accessible pairwise interactions. On the basis of a statistically relevant survey, they focus on plant reproductive success and on the role of pollinator interactions in such a success. A more reductionist approach based on simpler pairwise interactions between plants and pollinators would not be able to highlight the interaction network structure (the topology) possibly impacting its responses [1,5], among which the reproductive success of some (plant) species. Yet, such a network analysis requires a fine control of probable biases, as those linked to size or autocorrelation between data of various sites. Here, Magrach et al. did a nice work in capturing rigorously the structures and trends behind this community-wide functioning. To grasp possible relationships between plant and pollinator species is a first mandatory step, but the next critical step requires understanding processes hidden behind such relationships. Here, the authors succeed to reach this step too, by starting interpreting the processes at stake in their studied plant-pollinator networks [7]. In particular, the niche complementarity has been demonstrated to play a determinant role in the plant reproductive success, and has a positive impact on it [6]. When will we be able to detect a community-wise process? This is one of my team’s objectives, and we developed new kind of models with this aim. Also, authors focus here on plant-pollinator network, but the next step might be to gather every kind of interactions into a huge ecosystem network which we call the socio-ecosystemic graph [4]. Indeed, why to limit our view to certain interactions only? It will take time to grasp the whole interaction network an ecosystem is sheltering, but this should be our next challenge. And this paper of Magrach et al. [6] is a first fascinating step in this direction. **References** [1] Campbell, C., Yang, S., Albert, R., and Shea, K. (2011). A network model for plant–pollinator community assembly. Proceedings of the National Academy of Sciences, 108(1), 197-202. doi: [10.1073/pnas.1008204108](https://dx.doi.org/10.1073/pnas.1008204108) [2] Kéfi, S., Miele, V., Wieters, E. A., Navarrete, S. A., and Berlow, E. L. (2016). How structured is the entangled bank? The surprisingly simple organization of multiplex ecological networks leads to increased persistence and resilience. PLoS biology, 14(8), e1002527. doi: [10.1371/journal.pbio.1002527](https://dx.doi.org/10.1371/journal.pbio.1002527) [3] Gaucherel, C. (2019). The Languages of Nature. When nature writes to itself. Lulu editions, Paris, France. [4] Gaucherel, C., and Pommereau, F. Using discrete systems to exhaustively characterize the dynamics of an integrated ecosystem. Methods in Ecology and Evolution, 10(9), 1615-1627. doi: [10.1111/2041-210X.13242](https://dx.doi.org/10.1111/2041-210X.13242) [5] Bennett, J. M. et al. (2018). A review of European studies on pollination networks and pollen limitation, and a case study designed to fill in a gap. AoB Plants, 10(6), ply068. doi: [10.1093/aobpla/ply068](https://dx.doi.org/10.1093/aobpla/ply068) [6] Magrach, A., Molina, F. P., and Bartomeus, I. (2020). Niche complementarity among pollinators increases community-level plant reproductive success. bioRxiv, 629931, ver. 7 peer-reviewed and recommended by PCI Ecology. doi: [10.1101/629931](https://dx.doi.org/10.1101/629931) [7] Bastolla, U., Fortuna, M. A., Pascual-García, A., Ferrera, A., Luque, B., and Bascompte, J. (2009). The architecture of mutualistic networks minimizes competition and increases biodiversity. Nature, 458(7241), 1018-1020. doi: [10.1038/nature07950](https://dx.doi.org/10.1038/nature07950)

What predisposes two individuals to form and maintain a relationship is a fundamental question. Using facial recognition to see whether couples' faces change over time to become more and more similar, psychology researchers have concluded that couples tend to be formed from the start between people whose faces are more similar than average [1]. As the saying goes, birds of a feather flock together.

And what about in nature? Are these rules of assembly valid for communities of different species?

In his seminal contribution, Robert MacArthur (1984) wrote ‘To do science is to search for repeated patterns’ [2]. Identifying the mechanisms that govern the arrangement of life is a hot research topic in the field of ecology for decades, and an absolutely essential prerequisite to answer the outstanding question of what shape ecological patterns in multi-species communities such as species-area relationships, relative species abundances, or spatial and temporal turnover of community composition; amid others [3]. To explain ecological patterns in nature, some rely on the concept that every species - through evolutionary processes and the acquisition of a unique set of traits that allow a species to be adapted to its abiotic and biotic environment - occupies a unique niche: Species coexistence comes as the result of niche differentiation [4,5]. Such a view has been challenged by the recognition of the key role of neutral processes [6], however, in which demographic stochasticity contributes to shape multi-species communities and to explain why congener species coexist much more frequently than expected by chance [7,8]. While the niche-based and neutral theories appear seemingly opposed at first sight [9], the dichotomy may be more philosophical than empirical [4,5]. Many examples have come to support that both concepts are not incompatible as they together influence the structure, diversity and functioning of communities [10], and are simply extreme cases of a continuum [11]. From this perspective, extrinsic factors, i.e., environmental heterogeneity, may influence the location of a given community along the niche-neutrality continuum. 

The walk of species in nature is therefore neither random nor ecologically predestined. In microbial assemblages, the co-existence of these two antagonistic mechanisms has been shown both theoretically and empirically. It has been shown that a combination of stabilising (niche) and equalising (neutral) mechanisms was responsible for the existence of groups of coexistent species (clumps) in a phytoplankton rich community [12]. Analysing interannual changes (2003-2009) in the weekly abundance of diatoms and dinoflagellates located in a temperate coastal ecosystem of the Western English Channel, Mutshinda et al. [13] found a mixture of biomass dynamics consistent with the neutrality-niche continuum hypothesis. While niche processes explained the dynamic of phytoplankton functional groups (i.e., diatoms vs. dinoflagellates) in terms of biomass, neutral processes mainly dominated - 50 to 75% of the time - the dynamics at the species level within functional groups [13]. From one endpoint to another, defining the location of a community along the continuum is all matter of scale [4,11].

In their study, testing predictions made by an emergent neutrality model, Graco-Roza et al. [14] provide empirical evidence that neutral and niche processes joined together to shape and drive planktonic communities in a riverine ecosystem. Body size - the 'master trait' - is used here as a discriminant ecological dimension along the niche axis. From their analysis, they not only show that the specific abundance is organised in clumps and gaps along the niche axis, but also reveal that different clumps exist along the river course. They identify two main clumps in body size - with species belonging to three different morphologically-based functional groups - and characterise that among-species differences in biovolume are driven by functional redundancy at the clump level; species functional distinctiveness being related to the relative biovolume of species. By grouping their variables according to seasons (cold-dry vs. warm-wet) or river elevation profile (upper, medium and lower course), they hereby highlight how environmental heterogeneity contributes to shape species assemblages and their dynamics and conclude that emergent neutrality models are a powerful approach to explain species coexistence; and therefore ecological patterns.

References

[1] Tea-makorn PP, Kosinski M (2020) Spouses’ faces are similar but do not become more similar with time. Scientific Reports, 10, 17001. https://doi.org/10.1038/s41598-020-73971-8.

[2] MacArthur RH (1984) Geographical Ecology: Patterns in the Distribution of Species. Princeton University Press.

[3] Vellend M (2020) The Theory of Ecological Communities (MPB-57). Princeton University Press.

[4] Wennekes PL, Rosindell J, Etienne RS (2012) The Neutral—Niche Debate: A Philosophical Perspective. Acta Biotheoretica, 60, 257–271. https://doi.org/10.1007/s10441-012-9144-6.

[5] Gravel D, Guichard F, Hochberg ME (2011) Species coexistence in a variable world. Ecology Letters, 14, 828–839. https://doi.org/10.1111/j.1461-0248.2011.01643.x.

[6] Hubbell SP (2001) The Unified Neutral Theory of Biodiversity and Biogeography (MPB-32). Princeton University Press.

[7] Leibold MA, McPeek MA (2006) Coexistence of the Niche and Neutral Perspectives in Community Ecology. Ecology, 87, 1399–1410. https://doi.org/10.1890/0012-9658(2006)87[1399:COTNAN]2.0.CO;2.

[8] Pielou EC (1977) The Latitudinal Spans of Seaweed Species and Their Patterns of Overlap. Journal of Biogeography, 4, 299–311. https://doi.org/10.2307/3038189.

[9] Holt RD (2006) Emergent neutrality. Trends in Ecology & Evolution, 21, 531–533. https://doi.org/10.1016/j.tree.2006.08.003. 

[10] Scheffer M, Nes EH van (2006) Self-organized similarity, the evolutionary emergence of groups of similar species. Proceedings of the National Academy of Sciences, 103, 6230–6235. https://doi.org/10.1073/pnas.0508024103.

[11] Gravel D, Canham CD, Beaudet M, Messier C (2006) Reconciling niche and neutrality: the continuum hypothesis. Ecology Letters, 9, 399–409. https://doi.org/10.1111/j.1461-0248.2006.00884.x.

[12] Vergnon R, Dulvy NK, Freckleton RP (2009) Niches versus neutrality: uncovering the drivers of diversity in a species-rich community. Ecology Letters, 12, 1079–1090. https://doi.org/10.1111/j.1461-0248.2009.01364.x.

[13] Mutshinda CM, Finkel ZV, Widdicombe CE, Irwin AJ (2016) Ecological equivalence of species within phytoplankton functional groups. Functional Ecology, 30, 1714–1722. https://doi.org/10.1111/1365-2435.12641.

[14] Graco-Roza C, Segura AM, Kruk C, Domingos P, Soininen J, Marinho MM (2021) Clumpy coexistence in phytoplankton: The role of functional similarity in community assembly. bioRxiv, 869966, ver. 6 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/869966