Michael Raatz (2023) provides here a relevant exploration of higher-order interactions, i.e. interactions involving more than two related species (Terry et al. 2019), in the case of food web and competition interactions. More precisely, he shows by modeling that essential resources may significantly mediate focal species' persistence. Simultaneously, the provision of essential resources may strongly affect the resulting community structure, by driving to extinction first the predator and then, depending on the higher-order interaction, potentially also the associated competitor. 

Today, all ecologists should be aware of the potential effects of high-order interactions on species' (and likely on ecosystem's) fate (Golubski et al. 2016, Grilli et al. 2017). Yet, we should soon be prepared to include any high-order interaction into any interaction network (i.e. not only between species, but also between species and abiotic components, and between biotic, anthropogenic and abiotic components too). For this purpose, we will need innovative approaches such as hypergraphs (Golubski et al. 2016) and discrete-event models (Gaucherel and Pommereau 2019, Thomas et al. 2022) able to manage highly complex interactions, with numerous interacting components and variables. Such a rigorous study is a necessary and preliminary step in taking into account such a higher complexity. 

References

Gaucherel, C. and F. Pommereau. 2019. Using discrete systems to exhaustively characterize the dynamics of an integrated ecosystem. Methods in Ecology and Evolution 00:1–13. https://doi.org/10.1111/2041-210X.13242

Golubski, A. J., E. E. Westlund, J. Vandermeer, and M. Pascual. 2016. Ecological Networks over the Edge: Hypergraph Trait-Mediated Indirect Interaction (TMII) Structure trends in Ecology & Evolution 31:344-354. https://doi.org/10.1016/j.tree.2016.02.006

Grilli, J., G. Barabas, M. J. Michalska-Smith, and S. Allesina. 2017. Higher-order interactions stabilize dynamics in competitive network models. Nature 548:210-213. https://doi.org/10.1038/nature23273

Raatz, M. 2023. Provision of essential resources as a persistence strategy in food webs. bioRxiv, ver. 3 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2023.01.27.525839

Terry, J. C. D., R. J. Morris, and M. B. Bonsall. 2019. Interaction modifications lead to greater robustness than pairwise non-trophic effects in food webs. Journal of Animal Ecology 88:1732-1742. https://doi.org/10.1111/1365-2656.13057

Thomas, C., M. Cosme, C. Gaucherel, and F. Pommereau. 2022. Model-checking ecological state-transition graphs. PLoS Computational Biology 18:e1009657. https://doi.org/10.1371/journal.pcbi.1009657

The preprint "Environmental perturbations and transitions between ecological and evolutionary equilibria: an eco-evolutionary feedback framework" by Coulson (2020) presents a general framework for evolutionary ecology, useful to interpret patterns of selection and evolutionary responses to environmental transitions. The paper is written in an accessible and intuitive manner. It reviews important concepts which are at the heart of evolutionary ecology. Together, they serve as a worldview which you can carry with you to interpret patterns in data or observations in nature. I very much appreciate it that Coulson (2020) presents his personal intuition laid bare, the framework he uses for his research and how several strong concepts from theoretical ecology fit in there. Overviews as presented in this paper are important to understand how we as researchers put the pieces together.
A main message of the paper is that resource detection and acquisition traits, broadly called "resource accrual traits" are at the core of evolutionary dynamics. These traits and the processes they are involved in often urge some degree of individual specialization. Not all traits are resource accrual traits all the time. Guppies are cited as an example, which have traits in high predation environments that make foraging easier for them, such as being less conspicuous to predators. In the absence of predators, these same traits might be neutral. Their colour pattern might then contribute much less to the odds of obtaining resources.
"Resource accrual" reminds me of discussions of resource holding potential (Parker 1974), which can be for example the capacity to remain on a bird feeder without being dislodged. However, the idea is much broader and aggression does not need to be important for the acquisition of resources. Evolutionary success is reserved for those steadily obtaining resources. This recalls the pessimization principle of Metz et al. (2008), which applies in a restricted set of situations and where the strategy which persists at the lowest resource levels systematically wins evolutionary contests. If this principle would apply universally, the world then inherently become the worst possible. Resources determine energy budgets and different life history strategies allocate these differently to maximize fitness. The fine grain of environments and the filtration by individual histories generate a lot of variation in outcomes. However, constraint-centered approaches (Kempes et al. 2019, Kooijman 2010) are mentioned but are not at the core of this preprint. Evolution is rather seen as dynamic programming optimization with interactions within and between species. Coulson thus extends life history studies such as for example Tonnabel et al. (2012) with eco-evolutionary feedbacks. Examples used are guppies, algae-rotifer interactions and others. Altogether, this makes for an optimistic paper pushing back the pessimization principle.
Populations are expected to spend most of the time in quasi-equilibrium states where the long run stochastic growth rate is close to zero for all genotypes, alleles or other chosen classes. In the preprint, attention is given to reproductive value calculus, another strong tool in evolutionary dynamics (Grafen 2006, Engen et al. 2009), which tells us how classes within a population contribute to population composition in the distant future. The expected asymptotic fitness of an individual is equated to its expected reproductive value, but this might require particular ways of calculating reproductive values (Coulson 2020). Life history strategies can also be described by per generation measures such as R0 (currently on everyone's radar due to the coronavirus pandemic), generation time etc. Here I might disagree because I believe that this focus in per generation measures can lead to an incomplete characterization of plastic and other strategies involved in strategies such as bet-hedging. A property at quasi-equilibrium states is precise enough to serve as a null hypothesis which can be falsified: all types must in the long run leave equal numbers of descendants. If there is any property in evolutionary ecology which is useful it is this one and it rightfully merits attention.
However, at quasi-equilibrium states, directional selection has been observed, often without the expected evolutionary response. The preprint aims to explain this and puts forward the presence of non-additive gene action as a mechanism. I don't believe that it is the absence of clonal inheritance which matters very much in itself (Van Dooren 2006) unless genes with major effect are present in protected polymorphisms. The preprint remains a bit unclear on how additive gene action is broken, and here I add from the sphere in which I operate. Non-additive gene action can be linked to non-linear genotype-phenotype maps (Van Dooren 2000, Gilchrist and Nijhout 2001) and if these maps are non-linear enough to create constraints on phenotype determination, by means of maximum or minimum phenotypes which cannot be surpassed for any combination of the underlying traits, then they create additional evolutionary quasi-equilibrium states, with directional selection on a phenotype such as body size. I believe Coulson hints at this option (Coulson et al. 2006), but also at a different one: if body size is mostly determined by variation in resource accrual traits, then the resource accrual traits can be under stabilizing selection while body size is not. This requires that all resource accrual traits affect other phenotypic or demographic properties next to body size. In both cases, microevolutionary outcomes cannot be inferred from inspecting body sizes alone, either resource accrual traits need to be included explicitly, or non-linearities, or both when the map between resource accrual and body size is non-linear (Van Dooren 2000).
The discussion of the phenotypic gambit (Grafen 1984) leads to another long-standing issue in evolutionary biology. Can predictions of adaptation be made by inspecting and modelling individual phenotypes alone? I agree that with strongly non-linear genotype-phenotype maps they cannot and for multivariate sets of traits, genetic and phenotypic correlations can be very different (Hadfield et al. 2007). However, has the phenotypic gambit ever claimed to be valid globally or should it rather be used locally for relatively small amounts of variation? Grafen (1984) already contained caveats which are repeated here. As a first approximation, additivity might produce quite correct predictions and thus make the gambit operational in many instances. When important individual traits are omitted, it may just be misspecified. I am interested to see cases where the framework Coulson (2020) proposes is used for very large numbers of phenotypic and genotypic attributes. In the end, these highly dimensional trait distributions might basically collapse to a few major axes of variation due to constraints on resource accrual.
I highly recommend reading this preprint and I am looking forward to the discussion it will generate.

References

[1] Coulson, T. (2020) Environmental perturbations and transitions between ecological and evolutionary equilibria: an eco-evolutionary feedback framework. bioRxiv, 509067, ver. 4 peer-reviewed and recommended by PCI Ecology. doi: 10.1101/509067
[2] Coulson, T., Benton, T. G., Lundberg, P., Dall, S. R. X., and Kendall, B. E. (2006). Putting evolutionary biology back in the ecological theatre: a demographic framework mapping genes to communities. Evolutionary Ecology Research, 8(7), 1155-1171.
[3] Engen, S., Lande, R., Sæther, B. E. and Dobson, F. S. (2009) Reproductive value and the stochastic demography of age-structured populations. The American Naturalist 174: 795-804. doi: 10.1086/647930
[4] Gilchrist, M. A. and Nijhout, H. F. (2001). Nonlinear developmental processes as sources of dominance. Genetics, 159(1), 423-432.
[5] Grafen, A. (1984) Natural selection, kin selection and group selection. In: Behavioural Ecology: An Evolutionary Approach,2nd edn (JR Krebs & NB Davies eds), pp. 62–84. Blackwell Scientific, Oxford.
[6] Grafen, A. (2006). A theory of Fisher's reproductive value. Journal of mathematical biology, 53(1), 15-60. doi: 10.1007/s00285-006-0376-4
[7] Hadfield, J. D., Nutall, A., Osorio, D. and Owens, I. P. F. (2007). Testing the phenotypic gambit: phenotypic, genetic and environmental correlations of colour. Journal of evolutionary biology, 20(2), 549-557. doi: 10.1111/j.1420-9101.2006.01262.x
[8] Kempes, C. P., West, G. B., and Koehl, M. (2019). The scales that limit: the physical boundaries of evolution. Frontiers in Ecology and Evolution, 7, 242. doi: 10.3389/fevo.2019.00242
[9] Kooijman, S. A. L. M. (2010) Dynamic Energy Budget theory for metabolic organisation. University Press, third edition.
[10] Metz, J. A. J., Mylius, S.D. and Diekman, O. (2008) When does evolution optimize?. Evolutionary Ecology Research 10: 629-654.
[11] Parker, G. A. (1974). Assessment strategy and the evolution of fighting behaviour. Journal of theoretical Biology, 47(1), 223-243. doi: 10.1016/0022-5193(74)90111-8
[12] Tonnabel, J., Van Dooren, T. J. M., Midgley, J., Haccou, P., Mignot, A., Ronce, O., and Olivieri, I. (2012). Optimal resource allocation in a serotinous non‐resprouting plant species under different fire regimes. Journal of Ecology, 100(6), 1464-1474. doi: 10.1111/j.1365-2745.2012.02023.x
[13] Van Dooren, T. J. M. (2000). The evolutionary dynamics of direct phenotypic overdominance: emergence possible, loss probable. Evolution, 54(6), 1899-1914. doi: 10.1111/j.0014-3820.2000.tb01236.x
[14] Van Dooren, T. J. M. (2006). Protected polymorphism and evolutionary stability in pleiotropic models with trait‐specific dominance. Evolution, 60(10), 1991-2003. doi: 10.1111/j.0014-3820.2006.tb01837.x

Urban areas are expanding worldwide, and have become a dominant part of the landscape for many species. Urbanization can fragment pre-existing populations of vulnerable species leading to population declines and the loss of connectivity. On the other hand, expansion of urban areas can also facilitate the spread of human commensals including pests. Knowledge of the features of cityscapes that facilitate gene flow and maintain diversity of pests is thus key to their management and eradication.
Cities are complex mosaics of natural and manmade surfaces, and habitat quality is not only influenced by physical aspects of the cityscape but also by socioeconomic factors and human behaviour. Constant development means that cities also change rapidly in time; contemporary urban life reflects only a snapshot of the environmental conditions faced by populations. It thus remains a challenge to identify the features that actually drive ecology and evolution of populations in cities [1]. While several studies have highlighted strong urban clines in genetic structure and adaption [2], few have considered the influence of factors beyond physical aspects of the cityscape or historical processes.
In this paper, Stragier et al. [3] sought to identify the current and past features of the cityscape and socioeconomic factors that shape genetic structure and diversity of the house mouse (Mus musculus domesticus) in Dakar, Senegal. The authors painstakingly digitized historical maps of Dakar from the time of European settlement in 1862 to present. The authors found that the main spatial genetic cline was best explained by historical cityscape features, with higher apparent gene flow and genetic diversity in areas that were connected earlier to initial European settlements. Beyond the main trend of spatial genetic structure, they found further evidence that current features of the cityscape were important. Specifically, areas with low vegetation and poor housing conditions were found to support large, genetically diverse populations. The authors demonstrate that their results are reproducible using several statistical approaches, including modeling that explicitly accounts for spatial autocorrelation.
The work of Stragier et al. [3] thus highlights that populations of city-dwelling species are the product of both past and present cityscapes. Going forward, urban evolutionary ecologists should consider that despite the potential for rapid evolution in urban landscapes, the signal of a species’ colonization can remain for generations.

References

[1] Rivkin, L. R., Santangelo, J. S., Alberti, M. et al. (2019). A roadmap for urban evolutionary ecology. Evolutionary Applications, 12(3), 384-398. doi: 10.1111/eva.12734
[2] Miles, L. S., Rivkin, L. R., Johnson, M. T., Munshi‐South, J. and Verrelli, B. C. (2019). Gene flow and genetic drift in urban environments. Molecular ecology, 28(18), 4138-4151. doi: 10.1111/mec.15221
[3] Stragier, C., Piry, S., Loiseau, A., Kane, M., Sow, A., Niang, Y., Diallo, M., Ndiaye, A., Gauthier, P., Borderon, M., Granjon, L., Brouat, C. and Berthier, K. (2020). Interplay between historical and current features of the cityscape in shaping the genetic structure of the house mouse (Mus musculus domesticus) in Dakar (Senegal, West Africa). bioRxiv, 557066, ver. 4 peer-reviewed and recommended by PCI Ecology. doi: 10.1101/557066

Understanding the relative importance of species-specific traits and environmental factors in modulating species distributions is an intriguing question in ecology [1]. Both behavioral flexibility (i.e., the ability to change the behavior in changing circumstances) and habitat availability are known to influence the ability of a species to expand its geographic range [2,3]. However, the role of each factor is context and species dependent and more information is needed to understand how these two factors interact. In this pre-registration, Logan et al. [4] explain how they will use Great-tailed grackles (Quiscalus mexicanus), a species with a flexible behavior and a rapid geographic range expansion, to evaluate the relative role of habitat and behavior as drivers of the species’ expansion [4]. The authors present very clear hypotheses, predicted results and also include alternative predictions. The rationales for all the hypotheses are clearly stated, and the methodology (data and analyses plans) are described with detail. The large amount of information already collected by the authors for the studied species during previous projects warrants the success of this study. It is also remarkable that the authors will make all their data available in a public repository, and that the pre-registration in already stored in GitHub, supporting open access and reproducible science. I agree with the three reviewers of this pre-registration about its value and I think its quality has largely improved during the review process. Thus, I am happy to recommend it and I am looking forward to seeing the results.

References

[1] Gaston KJ. 2003. The structure and dynamics of geographic ranges. Oxford series in Ecology and Evolution. Oxford University Press, New York.

[2] Sol D, Lefebvre L. 2000. Behavioural flexibility predicts invasion success in birds introduced to new zealand. Oikos. 90(3): 599–605. https://doi.org/10.1034/j.1600-0706.2000.900317.x

[3] Hanski I, Gilpin M. 1991. Metapopulation dynamics: Brief history and conceptual domain. Biological journal of the Linnean Society. 42(1-2): 3–16. https://doi.org/10.1111/j.1095-8312.1991.tb00548.x

[4] Logan CJ, McCune KB, Chen N, Lukas D. 2020. Implementing a rapid geographic range expansion - the role of behavior and habitat changes (http://corinalogan.com/Preregistrations/gxpopbehaviorhabitat.html) In principle acceptance by PCI Ecology of the version on 16 Dec 2021 https://github.com/corinalogan/grackles/blob/0fb956040a34986902a384a1d8355de65010effd/Files/Preregistrations/gxpopbehaviorhabitat.Rmd.

In the recent years, morphometrics, the quantitative description of shape and its covariation [1] gained considerable momentum in evolutionary ecology. Using the form of organisms to describe, classify and try to understand their diversity can be traced back at least to Aristotle. More recently, two successive revolutions rejuvenated this idea [1–3]: first, a proper mathematical refoundation of the theory of shape, then a technical revolution in the apparatus able to acquire raw data. By using a feature extraction method and planning its massive use on data acquired by aerial drones, the study by Lacaux and colleagues [4] retraces this curse of events.
The radial symmetry of Aurelia spp. jelly fish, a common species complex, is affected by stress and more largely by environmental variations, such as pollution exposition. Aurelia spp. normally present four gonads so that the proportion of non-tetramerous individuals in a population has been proposed as a biomarker [5,6].
In this study, the authors implemented the Hough transform to largely automate the detection of the gonads in Aurelia spp. Such use of the Hough transform, a long-used approach to identify shapes through edge detection, is new to morphometrics. Here, the Aurelia spp. gonads are identified as ellipses from which aspect descriptors can be derived, and primarily counted and thus can be used to quantify the proportion of individuals presenting body plans disorders.

The sample sizes studied here were too low to allow finer-grained ecophysiological investigations. That being said, the proof-of-concept is convincing and this paper paths the way for an operational and innovative approach to the ecological monitoring of sensible aquatic ecosystems.

References

[1] Kendall, D. G. (1989). A survey of the statistical theory of shape. Statistical Science, 87-99. doi: https://doi.org/10.1214/ss/1177012589
[2] Rohlf, F. J., and Marcus, L. F. (1993). A revolution morphometrics. Trends in ecology & evolution, 8(4), 129-132. doi: https://doi.org/10.1016/0169-5347(93)90024-J
[3] Adams, D. C., Rohlf, F. J., and Slice, D. E. (2004). Geometric morphometrics: ten years of progress following the ‘revolution’. Italian Journal of Zoology, 71(1), 5-16. doi: https://doi.org/10.1080/11250000409356545
[4] Lacaux, C., Desolneux, A., Gadreaud, J., Martin-Garin, B. and Thiéry, A. (2020) Hough transform implementation to evaluate the morphological variability of the moon jellyfish (Aurelia spp.). bioRxiv, 2020.03.11.986984, ver. 3 peer-reviewed and recommended by Peer Community in Ecology. doi: https://doi.org/10.1101/2020.03.11.986984
[5] Gershwin, L. A. (1999). Clonal and population variation in jellyfish symmetry. Journal of the Marine Biological Association of the United Kingdom, 79(6), 993-1000. doi: https://doi.org/10.1017/S0025315499001228
[6] Gadreaud, J., Martin-Garin, B., Artells, E., Levard, C., Auffan, M., Barkate, A.-L. and Thiéry, A. (2017) The moon jellyfish as a new bioindicator: impact of silver nanoparticles on the morphogenesis. In: Mariottini GL, editor. Jellyfish: ecology, distribution patterns and human interactions. Nova Science Publishers; 2017. pp. 277–292.