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.

Identifying the factors which favour the establishment and spread of non-native species in novel environments is one of the keys to predict - and hence prevent or control - biological invasions. This includes biological factors (i.e. factors associated with the invasive species themselves), and one of the prevailing hypotheses is that some species traits may explain their impressive success to establish and spread in novel environments [1]. In animals, most research studies have focused on traits associated with fecundity, age at maturity, level of affiliation to humans or dispersal ability for instance. The “composite picture” of the perfect (i.e. successful) invader that has gradually emerged is a small-bodied animal strongly affiliated to human activities with high fecundity, high dispersal ability and a super high level of plasticity. Of course, the story is not that simple, and actually a perfect invader sometimes – if not often- takes another form… Carrying on to identify what makes a species a successful invader or not is hence still an important research axis with major implications.
In this manuscript, Charbonnel and collaborators [2] provide an interesting opportunity to gain novel insights into our understanding of (the) traits underlying invasion success. They nicely combine the power of Next-Generation Sequencing (NGS) with a clever comparative approach of two closely-related invasive rodents (the house mouse Mus musculus and the black rat Rattus rattus) in a common environment. They use this experimental design to test the appealing hypothesis that pathogens may be actors of the story, and may indirectly explain why some non-native species are so successful in invading novel habitats.
It is generally assumed that the community of pathogens encountered by non-native species in novel environments is different from that of their native area. On the one hand (the enemy-release hypothesis), it can be hypothesized that non-native species, when they arrive into a novel environment, will be relaxed from the pressure imposed by their native pathogens because local pathogens are not adapted (and hence do not infect) to this novel host. Because immune defence against pathogens is highly costly, non-native species establishing into a novel environment could hence reallocate these costs to other functions such as fecundity or dispersal apparatus. This scenario has been termed the “evolution of increased competitive ability” (EICA) hypothesis [3]. On the other hand (the EICA-refined hypothesis [4]), one can assume that invaders will encounter new pathogens in newly established areas, and will allocate energy toward cost-effective immune pathways to permit allocating a non-negligible amount of energy toward other functions. Finally, a last hypothesis (the “immune protection” hypothesis) assumes major changes in pathogen composition between native and invaded areas, which should lead to an overall increase in immune investment by the native species to successfully invade novel environments [4]. This last hypothesis suggests that only non-native species being able to take up the associated costs of immunity will be successful invaders.
The role of immunity in invasion success has yet been poorly investigated, mainly because of the difficulty to simultaneously analyse multiple immune pathways [4]. Charbonnel and collaborators [2] overpass this difficulty by screening all genes expressed (using a whole RNA sequencing approach) in an immune tissue: the spleen. They do so along the invasion routes of two sympatric invasive rodents in Africa and compare anciently and newly invaded areas (respectively). For one of the two species (the house mouse), they found a high number of immune-related genes to be up-regulated in newly invaded areas compared to anciently invaded areas. All categories of immune pathways (costly and cost-effective) were up-regulated, suggesting an overall increase in immune investment in the mouse, which corroborates the “immune protection” hypothesis. For the black rat, patterns of gene expression were somewhat different, with much less pronounced differentiation in gene expression between newly and anciently invaded areas. Among the few differentiated genes, a few were associated to immune responses and some of theses genes were even down-regulated in the newly invaded areas. This pattern may actually corroborate the EICA hypothesis, although it could alternatively suggest that stochastic processes (drift) associated to recent decrease in population size (which is expected during a colonisation event) are more important than selection imposed by pathogens in shaping patterns of immune gene expression.
Overall, this study [2] suggests (i) that immune-related traits are important in predicting invasion success and (ii) that two successful species with a similar invasion history and living in similar environments can use different life-history strategies to reach the same success. This later finding is particularly relevant and intriguing as it suggests that the traits and strategies deployed by species to colonise new habitats might actually be idiosyncratic, and that, if general trends actually emerge in regards of traits predicting the success of invaders, the devil might actually be into the details. Comparative studies are extremely important to identify the general rules and the specificities sustaining actual patterns, but these approaches are yet poorly used in biological invasions (at least empirically). The work presented by Charbonnel and colleagues [2] calls for future comparative studies performed at multiple spatial scales (native vs. non-native areas, anciently vs. recently invaded areas), multiple taxonomic resolutions and across multiple traits (to search for trade-offs), so that the success of invasive species can be properly understood and predicted.

References

[1] Jeschke, J. M., & Strayer, D. L. (2006). Determinants of vertebrate invasion success in Europe and North America. Global Change Biology, 12(9), 1608-1619. doi: 10.1111/j.1365-2486.2006.01213.x
[2] Blossey, B., & Notzold, R. (1995). Evolution of increased competitive ability in invasive nonindigenous plants: a hypothesis. Journal of Ecology, 83(5), 887-889. doi: 10.2307/2261425
[3] Charbonnel, N., Galan, M., Tatard, C., Loiseau, A., Diagne, C. A., Dalecky, A., Parrinello, H., Rialle, S., Severac, D., & Brouat, C. (2019). Differential immune gene expression associated with contemporary range expansion of two invasive rodents in Senegal. bioRxiv, 442160, ver. 5 peer-reviewed and recommended by PCI Ecology. doi: 10.1101/442160
[4] Lee, K. A., & Klasing, K. C. (2004). A role for immunology in invasion biology. Trends in Ecology & Evolution, 19(10), 523-529. doi: 10.1016/j.tree.2004.07.012

Alternative stable states describe ecosystems that can persist in more than one configuration. An ecosystem can shift between stable states following some form of perturbation. There has been much work on predicting when ecosystems will shift between stable states, but less work on why some ecosystems are able to exist in alternative stable states in the first place. The paper by Ardichvili, Loeuille, and Dakos (2022) addresses this question using a simple model of a shallow lake. Their model is based on a trade-off between access to light and nutrient availability in the water column, two essential resources for the macrophytes they model. They then identify conditions when the ancestral macrophyte will diversify resulting in macrophyte species living at new depths within the lake. The authors find a range of conditions where alternative stable states can evolve, but the range is narrow. Nonetheless, their model suggests that for alternative stable states to exist, one requirement is for there to be asymmetric competition between competing species, with one species being a better competitor on one limiting resource, with the other being a better competitor on a second limiting resource. 

These results are interesting and add to growing literature on how asymmetric competition can aid species coexistence. Asymmetric competition may be widespread in nature, with closely related species often being superior competitors on different resources. Incorporating asymmetric competition, and its evolution, into models does complicate theoretical investigations, but Ardichvili, Loeuille, and Dakos’ paper elegantly shows how substantial progress can be made with a model that is still (relatively) simple.

References 

Ardichvili A, Loeuille N, Dakos V (2022) Evolutionary emergence of alternative stable states in shallow lakes. bioRxiv, 2022.02.23.481597, ver. 3 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2022.02.23.481597

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

As far back as Clements [1] and Gleason [2], the historical schism between deterministic and stochastic perspectives has divided ecologists. Deterministic theories tend to emphasize niche-based processes such as environmental filtering and species interactions as the main drivers of species distribution in nature, while stochastic theories mainly focus on chance colonization, random extinctions and ecological drift [3]. Although the old days when ecologists were fighting fiercely over null models and their adequacy to capture niche-based processes is over [4], the ghost of that debate between deterministic and stochastic perspectives came back to haunt ecologists in the form of the ‘environment versus space’ debate with the development of metacommunity theory [5]. While interest in that question led to meaningful syntheses of metacommunity dynamics in natural systems [6], it also illustrated how context-dependant the answer was [7]. One of the next frontiers in metacommunity ecology is to identify the underlying drivers of this observed context-dependency in the relative importance of ecological processus [7, 8].
Reflecting on seminal work by Robert MacArthur emphasizing different processes at different spatial scales [9, 10] (the so-called ‘MacArthur paradox’), Chase and Myers proposed in 2011 that a key in solving the deterministic versus stochastic debate was probably to turn our attention to how the relative importance of local processes changes across spatial scales [3]. Scale-dependance is a well-acknowledged challenge in ecology, hampering empirical syntheses and comparisons between studies [11-14]. Embracing the scale-dependance of ecological processes would not only lead to stronger syntheses and consolidation of current knowledge, it could also help resolve many current debates or apparent contradictions [11, 15, 16].
The timely study by Siqueira et al. [17] fits well within this historical context by exploring the relative importance of ecological drift and selection across a gradient of community size (number of individuals in a given community). More specifically, they tested the hypothesis that small communities are more dissimilar among each other because of ecological drift compared to large communities, which are mainly structured by niche selection [17]. That smaller populations or communities should be more affected by drift is a mathematical given [18], but the main questions are i) for a given community size how important is ecological drift relative to other processes, and ii) how small does a community have to be before random assembly dominates? The authors answer these questions using an extensive stream dataset with a community size gradient sampled from 200 streams in two climatic regions (Brazil and Finland). Combining linear models with recent null model approaches to measure deviations from random expectations [19], they show that, as expected based on theory and recent experimental work, smaller communities tend to have higher β-diversity, and that those β-diversity patterns could not be distinguished from random assembly processes [17]. Spatial turnover among larger communities is mainly driven by niche-based processes related to species sorting or dispersal dynamics [17]. Given the current environmental context, with many anthropogenic perturbations leading to reduced community size, it is legitimate to wonder, as the authors do, whether we are moving toward a more stochastic and thus less predictable world with obvious implications for the conservation of biodiversity [17].
The real strength of the study by Siqueira et al. [17], in my opinion, is in the inclusion of stream data from boreal and tropical regions. Interestingly and most importantly, the largest communities in the tropical streams are as large as the smallest communities in the boreal streams. This is where the study should really have us reflect on the notions of context-dependency in observed patterns because the negative relationship between community size and β-diversity was only observed in the tropical streams, but not in the boreal streams [17]. This interesting nonlinearity in the response means that a study that would have investigated the drift versus niche-based question only in Finland would have found very different results from the same study in Brazil. Only by integrating such a large scale gradient of community sizes together could the authors show the actual shape of the relationship, which is the first step toward building a comprehensive synthesis on a debate that has challenged ecologists for almost a century.

References

[1] Clements, F. E. (1936). Nature and structure of the climax. Journal of ecology, 24(1), 252-284. doi: 10.2307/2256278
[2] Gleason, H. A. (1917). The structure and development of the plant association. Bulletin of the Torrey Botanical Club, 44(10), 463-481. doi: 10.2307/2479596
[3] Chase, J. M., and Myers, J. A. (2011). Disentangling the importance of ecological niches from stochastic processes across scales. Philosophical transactions of the Royal Society B: Biological sciences, 366(1576), 2351-2363. doi: 10.1098/rstb.2011.0063
[4] Diamond, J. M., and Gilpin, M. E. (1982). Examination of the “null” model of Connor and Simberloff for species co-occurrences on islands. Oecologia, 52(1), 64-74. doi: 10.1007/BF00349013
[5] Leibold M. A., et al. (2004). The metacommunity concept: a framework for multi‐scale community ecology. Ecology letters, 7(7), 601-613. doi: 10.1111/j.1461-0248.2004.00608.x
[6] Cottenie, K. (2005). Integrating environmental and spatial processes in ecological community dynamics. Ecology letters, 8(11), 1175-1182. doi: 10.1111/j.1461-0248.2005.00820.x
[7] Leibold, M. A. and Chase, J. M. (2018). Metacommunity Ecology. Monographs in Population Biology, vol. 59. Princeton University Press. [8] Vellend, M. (2010). Conceptual synthesis in community ecology. The Quarterly review of biology, 85(2), 183-206. doi: 10.1086/652373
[9] MacArthur, R. H., and Wilson, E. O. (1963). An equilibrium theory of insular zoogeography. Evolution, 17(4), 373-387. doi: 10.1111/j.1558-5646.1963.tb03295.x
[10] MacArthur, R. H., and Levins, R. (1967). The limiting similarity, convergence, and divergence of coexisting species. The American Naturalist, 101(921), 377-385. doi: 10.1086/282505
[11] Viana, D. S., and Chase, J. M. (2019). Spatial scale modulates the inference of metacommunity assembly processes. Ecology, 100(2), e02576. doi: 10.1002/ecy.2576
[12] Chave, J. (2013). The problem of pattern and scale in ecology: what have we learned in 20 years?. Ecology letters, 16, 4-16. doi: 10.1111/ele.12048
[13] Patrick, C. J., and Yuan, L. L. (2019). The challenges that spatial context present for synthesizing community ecology across scales. Oikos, 128(3), 297-308. doi: 10.1111/oik.05802
[14] Chase, J. M., and Knight, T. M. (2013). Scale‐dependent effect sizes of ecological drivers on biodiversity: why standardised sampling is not enough. Ecology letters, 16, 17-26. doi: 10.1111/ele.12112
[15] Horváth, Z., Ptacnik, R., Vad, C. F., and Chase, J. M. (2019). Habitat loss over six decades accelerates regional and local biodiversity loss via changing landscape connectance. Ecology letters, 22(6), 1019-1027. doi: 10.1111/ele.13260
[16] Chase, J. M, Gooriah, L., May, F., Ryberg, W. A, Schuler, M. S, Craven, D., and Knight, T. M. (2019). A framework for disentangling ecological mechanisms underlying the island species–area relationship. Frontiers of Biogeography, 11(1). doi: 10.21425/F5FBG40844.
[17] Siqueira T., Saito V. S., Bini L. M., Melo A. S., Petsch D. K. , Landeiro V. L., Tolonen K. T., Jyrkänkallio-Mikkola J., Soininen J. and Heino J. (2019). Community size affects the signals of ecological drift and niche selection on biodiversity. bioRxiv 515098, ver. 4 peer-reviewed and recommended by PCI Ecology. doi: 10.1101/515098
[18] Hastings A., Gross L. J. eds. (2012). Encyclopedia of theoretical ecology (University of California Press, Berkeley).
[19] Chase, J. M., Kraft, N. J., Smith, K. G., Vellend, M., and Inouye, B. D. (2011). Using null models to disentangle variation in community dissimilarity from variation in α‐diversity. Ecosphere, 2(2), 1-11. doi: 10.1890/ES10-00117.1