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31 Aug 2023
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Assessing species interactions using integrated predator-prey models

Addressing the daunting challenge of estimating species interactions from count data

Recommended by ORCID_LOGO and ORCID_LOGO based on reviews by 2 anonymous reviewers

Trophic interactions are at the heart of community ecology. Herbivores consume plants, predators consume herbivores, and pathogens and parasites infect, and sometimes kill, individuals of all species in a food web. Given the ubiquity of trophic interactions, it is no surprise that ecologists and evolutionary biologists strive to accurately characterize them. 

The outcome of an interaction between individuals of different species depends upon numerous factors such as the age, sex, and even phenotype of the individuals involved and the environment in which they are in. Despite this complexity, biologists often simplify an interaction down to a single number, an interaction coefficient that describes the average outcome of interactions between members of the populations of the species. Models of interacting species tend to be very simple, and interaction coefficients are often estimated from time series of population sizes of interacting species. Although biologists have long known that this approach is often approximate and sometimes unsatisfactory, work on estimating interaction strengths in more complex scenarios, and using ecological data beyond estimates of abundance, is still in its infancy. 

In their paper, Matthieu Paquet and Frederic Barraquand (2023)​ develop a demographic model of a predator and its prey. They then simulate demographic datasets that are typical of those collected by ecologists and use integrated population modelling to explore whether they can accurately retrieve the values interaction coefficients included in their model. They show that they can with good precision and accuracy. The work takes an important step in showing that accurate interaction coefficients can be estimated from the types of individual-based data that field biologists routinely collect, and it paves for future work in this area.

As if often the case with exciting papers such as this, the work opens up a number of other avenues for future research. What happens as we move from demographic models of two species interacting such as those used by Paquet and Barraquand​ to more realistic scenarios including multiple species? How robust is the approach to incorrectly specified process or observation models, core components of integrated population modelling that require detailed knowledge of the system under study? 

Integrated population models have become a powerful and widely used tool in single-species population ecology. It is high time the techniques are extended to community ecology, and this work takes an important step in showing that this should and can be done. I would hope the paper is widely read and cited.


Paquet, M., & Barraquand, F. (2023). Assessing species interactions using integrated predator-prey models. EcoEvoRxiv, ver. 2 peer-reviewed and recommended by Peer Community in Ecology.

Assessing species interactions using integrated predator-prey modelsMatthieu Paquet, Frederic Barraquand<p style="text-align: justify;">Inferring the strength of species interactions from demographic data is a challenging task. The Integrated Population Modelling (IPM) approach, bringing together population counts, capture-recapture, and individual-...Community ecology, Demography, Euring Conference, Food webs, Population ecology, Statistical ecologyTim Coulson Ilhan Özgen-Xian2023-01-05 17:02:22 View
04 Apr 2023
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Data stochasticity and model parametrisation impact the performance of species distribution models: insights from a simulation study

Species Distribution Models: the delicate balance between signal and noise

Recommended by ORCID_LOGO based on reviews by Alejandra Zarzo Arias and 1 anonymous reviewer

Species Distribution Models (SDMs) are one of the most commonly used tools to predict where species are, where they may be in the future, and, at times, what are the variables driving this prediction. As such, applying an SDM to a dataset is akin to making a bet: that the known occurrence data are informative, that the resolution of predictors is adequate vis-à-vis the scale at which their impact is expressed, and that the model will adequately capture the shape of the relationships between predictors and predicted occurrence.

In this contribution, Lambert & Virgili (2023) perform a comprehensive assessment of different sources of complications to this process, using replicated simulations of two synthetic species. Their experimental process is interesting, in that both the data generation and the data analysis stick very close to what would happen in "real life". The use of synthetic species is particularly relevant to the assessment of SDM robustness, as they enable the design of species for which the shape of the relationship is given: in short, we know what the model should capture, and can evaluate the model performance against a ground truth that lacks uncertainty.

Any simulation study is limited by the assumptions established by the investigators; when it comes to spatial data, the "shape" of the landscape, both in terms of auto-correlation and in where the predictors are available. Lambert & Virgili (2023) nicely circumvent these issues by simulating synthetic species against the empirical distribution of predictors; in other words, the species are synthetic, but the environment for which the prediction is made is real. This is an important step forward when compared to the use of e.g. neutral landscapes (With 1997), which can have statistical properties that are not representative of natural landscapes (see e.g. Halley et al., 2004).

A striking point in the study by Lambert & Virgili (2023) is that they reveal a deep, indeed deeper than expected, stochasticity in SDMs; whether this is true in all models remains an open question, but does not invalidate their recommendation to the community: the interpretation of outcomes is a delicate exercise, especially because measures that inform on the goodness of the model fit do not capture the predictive quality of the model outputs. This preprint is both a call to more caution, and a call to more curiosity about the complex behavior of SDMs, while also providing a sensible template to perform future analyses of the potential issues with predictive models.


Halley, J. M., et al. (2004) “Uses and Abuses of Fractal Methodology in Ecology: Fractal Methodology in Ecology.” Ecology Letters, vol. 7, no. 3, pp. 254–71.

Lambert, Charlotte, and Auriane Virgili (2023). Data Stochasticity and Model Parametrisation Impact the Performance of Species Distribution Models: Insights from a Simulation Study. bioRxiv, ver. 2 peer-reviewed and recommended by Peer Community in Ecology.

With, Kimberly A. (1997) “The Application of Neutral Landscape Models in Conservation Biology. Aplicacion de Modelos de Paisaje Neutros En La Biologia de La Conservacion.” Conservation Biology, vol. 11, no. 5, pp. 1069–80.

Data stochasticity and model parametrisation impact the performance of species distribution models: insights from a simulation studyCharlotte Lambert, Auriane Virgili<p>Species distribution models (SDM) are widely used to describe and explain how species relate to their environment, and predict their spatial distributions. As such, they are the cornerstone of most of spatial planning efforts worldwide. SDM can...Biogeography, Habitat selection, Macroecology, Marine ecology, Spatial ecology, Metacommunities & Metapopulations, Species distributions, Statistical ecologyTimothée Poisot2023-01-20 09:43:51 View
27 Nov 2023
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Modeling Tick Populations: An Ecological Test Case for Gradient Boosted Trees

Gradient Boosted Trees can deliver more than accurate ecological predictions

Recommended by ORCID_LOGO based on reviews by 2 anonymous reviewers

Tick-borne diseases are an important burden on public health all over the globe, making accurate forecasts of tick population a key ingredient in a successful public health strategy. Over long time scales, tick populations can undergo complex dynamics, as they are sensitive to many non-linear effects due to the complex relationships between ticks and the relevant (numerical) features of their environment.

But luckily, capturing complex non-linear responses is a task that machine learning thrives on. In this contribution, Manley et al. (2023) explore the use of Gradient Boosted Trees to predict the distribution (presence/absence) and abundance of ticks across New York state.

This is an interesting modelling challenge in and of itself, as it looks at the same ecological question as an instance of a classification problem (presence/absence) or of a regression problem (abundance). In using the same family of algorithm for both, Manley et al. (2023) provide an interesting showcase of the versatility of these techniques. But their article goes one step further, by setting up a multi-class categorical model that estimates jointly the presence and abundance of a population. I found this part of the article particularly elegant, as it provides an intermediate modelling strategy, in between having two disconnected models for distribution and abundance, and having nested models where abundance is only predicted for the present class (see e.g. Boulangeat et al., 2012, for a great description of the later).

One thing that Manley et al. (2023) should be commended for is their focus on opening up the black box of machine learning techniques. I have never believed that ML models are more inherently opaque than other families of models, but the focus in this article on explainable machine learning shows how these models might, in fact, bring us closer to a phenomenological understanding of the mechanisms underpinning our observations.

There is also an interesting discussion in this article, on the rate of false negatives in the different models that are being benchmarked. Although model selection often comes down to optimizing the overall quality of the confusion matrix (for distribution models, anyway), depending on the type of information we seek to extract from the model, not all types of errors are created equal. If the purpose of the model is to guide actions to control vectors of human pathogens, a false negative (predicting that the vector is absent at a site where it is actually present) is a potentially more damaging outcome, as it can lead to the vector population (and therefore, potentially, transmission) increasing unchecked.


Boulangeat I, Gravel D, Thuiller W. Accounting for dispersal and biotic interactions to disentangle the drivers of species distributions and their abundances: The role of dispersal and biotic interactions in explaining species distributions and abundances. Ecol Lett. 2012;15: 584-593.

Manley W, Tran T, Prusinski M, Brisson D. (2023) Modeling tick populations: An ecological test case for gradient boosted trees. bioRxiv, 2023.03.13.532443, ver. 3 peer-reviewed and recommended by Peer Community in Ecology.

Modeling Tick Populations: An Ecological Test Case for Gradient Boosted TreesWilliam Manley, Tam Tran, Melissa Prusinski, Dustin Brisson<p style="text-align: justify;">General linear models have been the foundational statistical framework used to discover the ecological processes that explain the distribution and abundance of natural populations. Analyses of the rapidly expanding ...Parasitology, Species distributions, Statistical ecologyTimothée PoisotAnonymous, Anonymous2023-03-23 23:41:17 View
28 Feb 2023
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Acoustic cues and season affect mobbing responses in a bird community

Two common European songbirds elicit different community responses with their mobbing calls

Recommended by ORCID_LOGO based on reviews by 2 anonymous reviewers

Many bird species participate in mobbing in which individuals approach a predator while producing conspicuous vocalizations (Magrath et al. 2014). Mobbing is interesting to behavioral ecologists because of the complex array of costs of benefits. Costs range from the obvious risk of approaching a predator while drawing that predator’s attention to the more mundane opportunity costs of taking time away from other activities, such as foraging. Benefits may involve driving the predator to leave, teaching relatives to recognize predators, signaling quality to conspecifics, or others. An added layer of complexity in this system comes from the inter-specific interactions that often occur among different mobbing species (Magrath et al. 2014).

This study by Salis et al. (2023) explored the responses of a local bird community to mobbing calls produced by individuals of two common mobbing species in European forests, coal tits, and crested tits. Not only did they compare responses to these two different species, they assessed the impact of the number of mobbing individuals on the stimulus recordings, and they did so at two very different times of the year with different social contexts for the birds involved, winter (non-breeding) and spring (breeding). The experiment was well-designed and highly powered, and the authors tested and confirmed an important assumption of their design, and thus the results are convincing. It is clear that members of the local bird community responded differently to the two different species, and this result raises interesting questions about why these species differed in their tendency to attract additional mobbers. For instance, are species that recruit more co-mobbers more effective at recruiting because they are more reliable in their mobbing behavior (Magrath et al. 2014), more likely to reciprocate (Krams and Krama, 2002), or for some other reason? Hopefully this system, now of proven utility thanks to the current study, will be useful for following up on hypotheses such as these. Other convincing results, such as the higher rate of mobbing response in winter than in spring, also merit following up with further work.

Finally, their observation that playback of vocalizations of multiple individuals often elicited a more mobbing response that the playback of vocalizations of a single individual are interesting and consistent with other recent work indicating that groups of mobbers recruit more additional mobbers than do single mobbers (Dutour et al. 2021). However, as acknowledged in the manuscript, the design of the current study did not allow a distinction between the effect of multiple individuals signaling versus an effect of a stronger stimulus. Thus, this last result leaves the question of the effect of mobbing group size in these species open to further study.


Dutour M, Kalb N, Salis A, Randler C (2021) Number of callers may affect the response to conspecific mobbing calls in great tits (Parus major). Behavioral Ecology and Sociobiology, 75, 29.

Krams I, Krama T (2002) Interspecific reciprocity explains mobbing behaviour of the breeding chaffinches, Fringilla coelebs. Proceedings of the Royal Society of London. Series B: Biological Sciences, 269, 2345–2350.

Magrath RD, Haff TM, Fallow PM, Radford AN (2015) Eavesdropping on heterospecific alarm calls: from mechanisms to consequences. Biological Reviews, 90, 560–586.

Salis A, Lena JP, Lengagne T (2023) Acoustic cues and season affect mobbing responses in a bird community. bioRxiv, 2022.05.05.490715, ver. 5 peer-reviewed and recommended by Peer Community in Ecology.

Acoustic cues and season affect mobbing responses in a bird communityAmbre Salis, Jean Paul Lena, Thierry Lengagne<p>Heterospecific communication is common for birds when mobbing a predator. However, joining the mob should depend on the number of callers already enrolled, as larger mobs imply lower individual risks for the newcomer. In addition, some ‘communi...Behaviour & Ethology, Community ecology, Social structureTim Parker2022-05-06 09:29:30 View
16 Jun 2020
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Environmental perturbations and transitions between ecological and evolutionary equilibria: an eco-evolutionary feedback framework

Stasis and the phenotypic gambit

Recommended by based on reviews by Jacob Johansson, Katja Räsänen and 1 anonymous reviewer

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.


[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

Environmental perturbations and transitions between ecological and evolutionary equilibria: an eco-evolutionary feedback frameworkTim Coulson<p>I provide a general framework for linking ecology and evolution. I start from the fact that individuals require energy, trace molecules, water, and mates to survive and reproduce, and that phenotypic resource accrual traits determine an individ...Eco-evolutionary dynamics, Evolutionary ecologyTom Van Dooren2019-01-03 10:05:16 View
13 May 2023
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Symbiotic nutrient cycling enables the long-term survival of Aiptasia in the absence of heterotrophic food sources

Constraining the importance of heterotrophic vs autotrophic feeding in photosymbiotic cnidarians

Recommended by ORCID_LOGO based on reviews by 2 anonymous reviewers

The symbiosis with autotrophic dinoflagellate algae has enabled heterotrophic Cnidaria to thrive in nutrient-poor tropical waters (Muscatine and Porter 1977; Stanley 2006). In particular, mixotrophy, i.e. the ability to acquire nutrients through both autotrophy and heterotrophy, confers a competitive edge in oligotrophic waters, allowing photosymbiotic Cnidaria to outcompete benthic organisms limited to a single diet (e.g., McCook 2001). However, the relative importance of autotrophy vs heterotrophy in sustaining symbiotic cnidarian’s nutrition is still the subject of intense research. In fact, figuring out the cellular mechanisms by which symbiotic Cnidaria acquire a balanced diet for their metabolism and growth is relevant to our understanding of their physiology under varying environmental conditions and in response to anthropogenic perturbations.

In this study's long-term starvation experiment, Radecker & Meibom (2023) investigated the survival of the photosymbiotic sea anemone Aiptasia in the absence of heterotrophic feeding. After one year of heterotrophic starvation, Apitasia anemones remained fully viable but showed an 85 % reduction in biomass. Using 13C-bicarbonate and 15N-ammonium labeling, electron microscopy and NanoSIMS imaging, the authors could clearly show that the contribution of algal-derived nutrients to the host metabolism remained unaffected as a result of increased algal photosynthesis and more efficient carbon translocation. At the same time, the absence of heterotrophic feeding caused severe nitrogen limitation in the starved Apitasia anemones.

Overall, this study provides valuable insights into nutrient exchange within the symbiosis between Cnidaria and dinoflagellate algae at the cellular level and sheds new light on the importance of heterotrophic feeding as a nitrogen acquisition strategy for holobiont growth in oligotrophic waters.


McCook L (2001) Competition between corals and algal turfs along a gradient of terrestrial influence in the nearshore central Great Barrier Reef. Coral Reefs 19:419–425.

Muscatine L, Porter JW (1977) Reef corals: mutualistic symbioses adapted to nutrient-poor environments. Bioscience 27:454–460.

Radecker N, Meibom A (2023) Symbiotic nutrient cycling enables the long-term survival of Aiptasia in the absence of heterotrophic food sources. bioRxiv, ver. 3 peer-reviewed and recommended by Peer Community in Ecology.

Stanley GD Jr (2006) Photosymbiosis and the evolution of modern coral reefs. Science 312:857–858.

Symbiotic nutrient cycling enables the long-term survival of Aiptasia in the absence of heterotrophic food sourcesNils Radecker, Anders Meibom<p style="text-align: justify;">Phototrophic Cnidaria are mixotrophic organisms that can complement their heterotrophic diet with nutrients assimilated by their algal endosymbionts. Metabolic models suggest that the translocation of photosynthates...Eco-evolutionary dynamics, Microbial ecology & microbiology, SymbiosisUlisse Cardini2022-12-12 10:50:55 View
11 Oct 2023
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Identification of microbial exopolymer producers in sandy and muddy intertidal sediments by compound-specific isotope analysis

Disentangling microbial exopolymer dynamics in intertidal sediments

Recommended by and ORCID_LOGO based on reviews by 2 anonymous reviewers

The secretion of extracellular polymeric substances (EPS) enables microorganisms to shape and interact with their environment [1]. EPS support cell adhesion and motility, offer protection from unfavorable conditions, and facilitate nutrient acquisition and transfer between microorganisms [2]. EPS production and consumption thus control the formation and structural organization of biofilms [3]. However, in marine environments, our understanding of the sources and composition of EPS is limited.
In this study, Hubas et al. [4] compare the carbon and nitrogen isotope ratios in EPS with the carbon isotope ratios of fatty acid biomarkers to identify the main EPS producers in intertidal sediments. The authors find pronounced differences in the diversity, composition, isotope signatures, and production/consumption dynamics of EPS between muddy and sandy environments. While the contribution of diatoms was highest in the bound fraction of EPS in muddy environments, diatom contribution was highest in the colloidal fraction of EPS in sandy environments. These differences between sites likely reflect the functional differences in EPS dynamics of epipelic and episammic sediment communities.
Taken together, the innovative approach of the authors provides insights into the diversity and origin of EPS in microphytobenthic communities and highlights the importance of different microbial groups in EPS production. These findings are vital for understanding EPS dynamics in microbial interactions and their role in the functioning of coastal ecosystems.


  1. Flemming, H.-C. (2016) EPS-then and now. Microorganisms 4, 41
  2. Wolfaardt, G.M. et al. (1999) Function of EPS. In Microbial Extracellular Polymeric Substances, pp. 171–200, Springer Berlin Heidelberg
  3. Flemming, H.-C. et al. (2007) The EPS matrix: the “house of biofilm cells.” J. Bacteriol. 189, 7945–7947
  4. Hubas, C. et al. (2022) Identification of microbial exopolymer producers in sandy and muddy intertidal sediments by compound-specific isotope analysis. bioRxiv, ver. 2 peer-reviewed and recommended by Peer Community in Ecology.
Identification of microbial exopolymer producers in sandy and muddy intertidal sediments by compound-specific isotope analysisCédric Hubas, Julie Gaubert-Boussarie, An-Sofie D’Hondt, Bruno Jesus, Dominique Lamy, Vona Meleder, Antoine Prins, Philippe Rosa, Willem Stock, Koen Sabbe<p style="text-align: justify;">Extracellular polymeric substances (EPS) refer to a wide variety of high molecular weight molecules secreted outside the cell membrane by biofilm microorganisms. In the present study, EPS from marine microphytobenth...Biodiversity, Ecological stoichiometry, Ecosystem functioning, Food webs, Marine ecology, Microbial ecology & microbiology, Soil ecologyUte Risse-Buhl2022-12-06 14:13:11 View
19 Dec 2020
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Hough transform implementation to evaluate the morphological variability of the moon jellyfish (Aurelia spp.)

A new member of the morphometrics jungle to better monitor vulnerable lagoons

Recommended by based on reviews by Julien Claude and 1 anonymous reviewer

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.


[1] Kendall, D. G. (1989). A survey of the statistical theory of shape. Statistical Science, 87-99. doi:
[2] Rohlf, F. J., and Marcus, L. F. (1993). A revolution morphometrics. Trends in ecology & evolution, 8(4), 129-132. doi:
[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:
[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:
[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:
[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.

Hough transform implementation to evaluate the morphological variability of the moon jellyfish (Aurelia spp.)Céline Lacaux, Agnès Desolneux, Justine Gadreaud, Bertrand Martin-Garin and Alain Thiéry<p>Variations of the animal body plan morphology and morphometry can be used as prognostic tools of their habitat quality. The potential of the moon jellyfish (Aurelia spp.) as a new model organism has been poorly tested. However, as a tetramerous...MorphometricsVincent Bonhomme2020-03-18 17:40:51 View
28 Mar 2019
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Direct and transgenerational effects of an experimental heat wave on early life stages in a freshwater snail

Escargots cooked just right: telling apart the direct and indirect effects of heat waves in freashwater snails

Recommended by based on reviews by Amanda Lynn Caskenette, Kévin Tougeron and arnaud sentis

Amongst the many challenges and forms of environmental change that organisms face in our era of global change, climate change is perhaps one of the most straightforward and amenable to investigation. First, measurements of day-to-day temperatures are relatively feasible and accessible, and predictions regarding the expected trends in Earth surface temperature are probably some of the most reliable we have. It appears quite clear, in particular, that beyond the overall increase in average temperature, the heat waves locally experienced by organisms in their natural habitats are bound to become more frequent, more intense, and more long-lasting [1]. Second, it is well appreciated that temperature is a major environmental factor with strong impacts on different facets of organismal development and life-history [2-4]. These impacts have reasonably clear mechanistic underpinnings, with definite connections to biochemistry, physiology, and considerations on energetics. Third, since variation in temperature is a challenge already experienced by natural populations across their current and historical ranges, it is not a completely alien form of environmental change. Therefore, we already learnt quite a lot about it in several species, and so did the species, as they may be expected to have evolved dedicated adaptive mechanisms to respond to elevated temperatures. Last, but not least, temperature is quite amenable to being manipulated as an experimental factor.
For all these reasons, experimental studies of the consequences of increased temperature hit some of a sweetspot and are a source of very nice research, in many different organisms. The work by Leicht and Seppala [5] complements a sequence of earlier studies by this group, using the freshwater snail Lymnaea stagnalis as their model system [6-7].
In the present study, the authors investigate how a heat wave (a period of abnormally elevated temperature, here 25°C versus a normal 15°C) may have indirect effects on the next generation, through maternal effects. They question whether such indirect effects exist, and if they exist, how they compare, in terms of effect size, with the (more straightforward) direct effects observed in individuals that directly experience a heat wave. Transgenerational effects are well-known to occur following periods of physiological stress, and might thus have non negligible contributions to the overall effect of warming.
In this freshwater snail, heat has very strong direct effects: mortality increases at high temperature, but survivors grow much bigger, with a greater propensity to lay eggs and a (spectacular) three-fold increase in the number of eggs laid [6]. Considering that, it is easy to consider that transgenerational effects should be small game. And indeed, the present study also observes the big and obvious direct effects of elevated temperature: higher mortality, but greater propensity to oviposit. However, it was also found that the eggs were smaller if from mothers exposed to high temperature, with a correspondingly smaller size of hatchlings. This suggests that a heat wave causes the snails to lay more eggs, but smaller ones, reminiscent of a size-number trade-off. Unfortunately, clutch size could not be measured in this experiment, so this cannot be investigated any further. For this trait, the indirect effect may indeed be regarded as small game : eggs and hatchlings were about 15 % smaller, an effect size pretty small compared to the mammoth direct positive effect of temperature on shell length (see Figure 4 ; and also [6]). The same is true for developmental time (Figure 3).
However, for some traits the story was different. In particular, it was found that the (smaller) eggs produced from heated mothers were more likely to hatch by almost 10% (Figure 2). Here the indirect effect not only goes against the direct effect (hatching rate is lower at high temperature), but it also has similar effect size. As a consequence, taking into account both the indirect and direct effects, hatching success is essentially the same at 15°C and 25°C (Figure 2). Survival also had comparable effect sizes for direct and indirect effects. Indeed, survival was reduced by about 20% regardless of whom endured the heat stress (the focal individual or her mother; Figure 4). Interestingly, the direct and indirect effects were not quite cumulative: if a mother experienced a heat wave, heating up the offspring did not do much more damage, as though the offspring were ‘adapted’ to the warmer conditions (but keep in mind that, surprisingly, the authors’ stats did not find a significant interaction; Table 2).
At the end of the day, even though at first heat seems a relatively simple and understandable component of environmental change, this study shows how varied its effects can be effects on different components of individual fitness. The overall impact most likely is a mix of direct and indirect effects, of shifts along allocation trade-offs, and of maladaptive and adaptive responses, whose overall ecological significance is not so easy to grasp. That said, this study shows that direct and indirect (maternal) effects can sometimes go against one another and have similar intensities. Indirect effects should therefore not be overlooked in this kind of studies. It also gives a hint of what an interesting challenge it is to understand the adaptive or maladaptive nature of organism responses to elevated temperatures, and to evaluate their ultimate fitness consequences.


[1] Meehl, G. A., & Tebaldi, C. (2004). More intense, more frequent, and longer lasting heat waves in the 21st century. Science (New York, N.Y.), 305(5686), 994–997. doi: 10.1126/science.1098704
[2] Adamo, S. A., & Lovett, M. M. E. (2011). Some like it hot: the effects of climate change on reproduction, immune function and disease resistance in the cricket Gryllus texensis. The Journal of Experimental Biology, 214(Pt 12), 1997–2004. doi: 10.1242/jeb.056531
[3] Deutsch, C. A., Tewksbury, J. J., Tigchelaar, M., Battisti, D. S., Merrill, S. C., Huey, R. B., & Naylor, R. L. (2018). Increase in crop losses to insect pests in a warming climate. Science (New York, N.Y.), 361(6405), 916–919. doi: 10.1126/science.aat3466
[4] Sentis, A., Hemptinne, J.-L., & Brodeur, J. (2013). Effects of simulated heat waves on an experimental plant–herbivore–predator food chain. Global Change Biology, 19(3), 833–842. doi: 10.1111/gcb.12094
[5] Leicht, K., & Seppälä, O. (2019). Direct and transgenerational effects of an experimental heat wave on early life stages in a freshwater snail. BioRxiv, 449777, ver. 4 peer-reviewed and recommended by PCI Ecology. doi: 10.1101/449777
[6] Leicht, K., Seppälä, K., & Seppälä, O. (2017). Potential for adaptation to climate change: family-level variation in fitness-related traits and their responses to heat waves in a snail population. BMC Evolutionary Biology, 17(1), 140. doi: 10.1186/s12862-017-0988-x
[7] Leicht, K., Jokela, J., & Seppälä, O. (2013). An experimental heat wave changes immune defense and life history traits in a freshwater snail. Ecology and Evolution, 3(15), 4861–4871. doi: 10.1002/ece3.874

Direct and transgenerational effects of an experimental heat wave on early life stages in a freshwater snailKatja Leicht, Otto Seppälä<p>Global climate change imposes a serious threat to natural populations of many species. Estimates of the effects of climate change‐mediated environmental stresses are, however, often based only on their direct effects on organisms, and neglect t...Climate changevincent calcagno2018-10-22 22:19:22 View
06 Sep 2019
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Assessing metacommunity processes through signatures in spatiotemporal turnover of community composition

On the importance of temporal meta-community dynamics for our understanding of assembly processes

Recommended by ORCID_LOGO based on reviews by Joaquín Hortal and 2 anonymous reviewers

The processes that trigger community assembly are still in the centre of ecological interest. While prior work mostly focused on spatial patterns of co-occurrence within a meta-community framework [reviewed in 1, 2] recent studies also include temporal patterns of community composition [e.g. 3, 4, 5, 6]. In this preprint [7], Franck Jabot and co-workers extend they prior approaches to quasi neutral community assembly [8, 9, 10] and develop an analytical framework of spatial and temporal diversity turnover. A simple and heuristic path model for beta diversity and an extended ecological drift model serve as starting points. The model can be seen as a counterpart to Ulrich et al. [5]. These authors implemented competitive hierarchies into their neutral meta-community model while the present paper focuses on environmental filtering. Most important, the model and parameterization of four empirical data sets on aquatic plant and animal meta-communities used by Jabot et al. returned a consistent high influence of environmental stochasticity on species turnover. Of course, this major result does not come to a surprise. As typical for this kind of models it depends also to a good deal on the initial model settings. It nevertheless makes a strong conceptual point for the importance of environmental variability over dispersal and richness effects. One interesting side effect regards the impact of richness differences (ΔS). Jabot et al. interpret this as a ‘nuisance variable’ as they do not have a stringent explanation. Of course, it might be a pure statistical bias introduced by the Soerensen metric of turnover that is normalized by richness. However, I suspect that there is more behind the ΔS effect. Richness differences are generally associated with respective differences in total abundances and introduce source – sink dynamics that inevitably shape subsequent colonization – extinction processes. It would be interesting to see whether ΔS alone is able to trigger observed patterns of community assembly and community composition. Such an analysis would require partitioning of species turnover into richness and nestedness effects [11]. I encourage Jabot et al. to undertake such an effort.
The present paper is also another call to include temporal population variability into metapopulation models for a better understanding of the dynamics and triggering of community assembly. In a next step, competitive interactions should be included into the model to infer the relative importance of both factors.


[1] Götzenberger, L. et al. (2012). Ecological assembly rules in plant communities—approaches, patterns and prospects. Biological reviews, 87(1), 111-127. doi: 10.1111/j.1469-185X.2011.00187.x
[2] Ulrich, W., & Gotelli, N. J. (2013). Pattern detection in null model analysis. Oikos, 122(1), 2-18. doi: 10.1111/j.1600-0706.2012.20325.x
[3] Grilli, J., Barabás, G., Michalska-Smith, M. J., & Allesina, S. (2017). Higher-order interactions stabilize dynamics in competitive network models. Nature, 548(7666), 210. doi: 10.1038/nature23273
[4] Nuvoloni, F. M., Feres, R. J. F., & Gilbert, B. (2016). Species turnover through time: colonization and extinction dynamics across metacommunities. The American Naturalist, 187(6), 786-796. doi: 10.1086/686150
[5] Ulrich, W., Jabot, F., & Gotelli, N. J. (2017). Competitive interactions change the pattern of species co‐occurrences under neutral dispersal. Oikos, 126(1), 91-100. doi: 10.1111/oik.03392
[6] Dobramysl, U., Mobilia, M., Pleimling, M., & Täuber, U. C. (2018). Stochastic population dynamics in spatially extended predator–prey systems. Journal of Physics A: Mathematical and Theoretical, 51(6), 063001. doi: 10.1088/1751-8121/aa95c7
[7] Jabot, F., Laroche, F., Massol, F., Arthaud, F., Crabot, J., Dubart, M., Blanchet, S., Munoz, F., David, P., and Datry, T. (2019). Assessing metacommunity processes through signatures in spatiotemporal turnover of community composition. bioRxiv, 480335, ver. 3 peer-reviewed and recommended by PCI Ecology. doi: 10.1101/480335
[8] Jabot, F., & Chave, J. (2011). Analyzing tropical forest tree species abundance distributions using a nonneutral model and through approximate Bayesian inference. The American Naturalist, 178(2), E37-E47. doi: 10.1086/660829
[9] Jabot, F., & Lohier, T. (2016). Non‐random correlation of species dynamics in tropical tree communities. Oikos, 125(12), 1733-1742. doi: 10.1111/oik.03103
[10] Datry, T., Bonada, N., & Heino, J. (2016). Towards understanding the organisation of metacommunities in highly dynamic ecological systems. Oikos, 125(2), 149-159. doi: 10.1111/oik.02922
[11] Baselga, A. (2010). Partitioning the turnover and nestedness components of beta diversity. Global ecology and biogeography, 19(1), 134-143. doi: 10.1111/j.1466-8238.2009.00490.x

Assessing metacommunity processes through signatures in spatiotemporal turnover of community compositionFranck Jabot, Fabien Laroche, Francois Massol, Florent Arthaud, Julie Crabot, Maxime Dubart, Simon Blanchet, Francois Munoz, Patrice David, Thibault Datry<p>Although metacommunity ecology has been a major field of research in the last decades, with both conceptual and empirical outputs, the analysis of the temporal dynamics of metacommunities has only emerged recently and still consists mostly of r...Biodiversity, Coexistence, Community ecology, Spatial ecology, Metacommunities & MetapopulationsWerner Ulrich2018-11-29 14:58:54 View