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03 Oct 2023
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Integrating biodiversity assessments into local conservation planning: the importance of assessing suitable data sources

Biodiversity databases are ever more numerous, but can they be used reliably for Species Distribution Modelling?

Recommended by ORCID_LOGO based on reviews by 2 anonymous reviewers

Proposing efficient guidelines for biodiversity conservation often requires the use of forecasting tools. Species Distribution Models (SDM) are more and more used to predict how the distribution of a species will react to environmental change, including any large-scale management actions that could be implemented. Their use is also boosted by the increase of publicly available biodiversity databases[1]. The now famous aphorism by George Box "All models are wrong but some are useful"[2] very well summarizes that the outcome of a model must be adjusted to, and will depend on, the data that are used to parameterize it. The question of the reliability of using biodiversity databases to parameterize biodiversity models such as SDM –but the question would also apply to other kinds of biodiversity models, e.g. Population Viability Analysis models[3]– is key to determine the confidence that can be placed in model predictions. This point is often overlooked by some categories of biodiversity conservation stakeholders, in particular the fact that some data were collected using controlled protocols while others are opportunistic. 

In this study[4], the authors use a collection of databases covering a range of species as well as of geographic scales in France and using different data collection and validation approaches as a case study to evaluate the impact of data quality when performing Strategic Environmental Assessment (SEA). Among their conclusions, the fact that a large-scale database (what they call the “country” level) is necessary to reliably parameterize SDM. Besides this and other conclusions of their study, which are likely to be in part specific to their case study –unfortunately for its conservation, biodiversity is complex and varies a lot–, the merit of this work lies in the approach used to test the impact of data on model predictions.


1.  Feng, X. et al. A review of the heterogeneous landscape of biodiversity databases: Opportunities and challenges for a synthesized biodiversity knowledge base. Global Ecology and Biogeography 31, 1242–1260 (2022).

2.  Box, G. E. P. Robustness in the Strategy of Scientific Model Building. in Robustness in Statistics (eds. Launer, R. L. & Wilkinson, G. N.) 201–236 (Academic Press, 1979).

3.  Beissinger, S. R. & McCullough, D. R. Population Viability Analysis. (The University of Chicago Press, 2002).

4.  Ferraille, T., Kerbiriou, C., Bigard, C., Claireau, F. & Thompson, J. D. (2023) Integrating biodiversity assessments into local conservation planning: the importance of assessing suitable data sources. bioRxiv, ver. 3 peer-reviewed and recommended by Peer Community in Ecology.

Integrating biodiversity assessments into local conservation planning: the importance of assessing suitable data sourcesThibaut Ferraille, Christian Kerbiriou, Charlotte Bigard, Fabien Claireau, John D. Thompson<p>Strategic Environmental Assessment (SEA) of land-use planning is a fundamental tool to minimize environmental impacts of artificialization. In this context, Systematic Conservation Planning (SCP) tools based on Species Distribution Models (SDM)...Biodiversity, Conservation biology, Species distributions, Terrestrial ecologyNicolas Schtickzelle2023-05-11 09:41:05 View
16 Jun 2023
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Colonisation debt: when invasion history impacts current range expansion

Combining stochastic models and experiments to understand dispersal in heterogeneous environments

Recommended by based on reviews by 2 anonymous reviewers

Dispersal is a key element of the natural dynamics of meta-communities, and plays a central role in the success of populations colonizing new landscapes. Understanding how demographic processes may affect the speed at which alien species spread through environmentally-heterogeneous habitat fragments is therefore of key importance to manage biological invasions. This requires studying together the complex interplay of dispersal and population processes, two inextricably related phenomena that can produce many possible outcomes. Stochastic models offer an opportunity to describe this kind of process in a meaningful way, but to ensure that they are realistic (sensu Levins 1966) it is also necessary to combine model simulations with empirical data (Snäll et al. 2007).

Morel-Journel et al. (2023) put together stochastic models and experimental data to study how population density may affect the speed at which alien species spread through a heterogeneous landscape. They do it by focusing on what they call ‘colonisation debt’, which is merely the impact that population density at the invasion front may have on the speed at which the species colonizes patches of different carrying capacities. They investigate this issue through two largely independent approaches. First, a stochastic model of dispersal throughout the patches of a linear, 1-dimensional landscape, which accounts for different degrees of density-dependent growth. And second, a microcosm experiment of a parasitoid wasp colonizing patches with different numbers of host eggs. In both cases, they compare the velocity of colonization of patches with lower or higher carrying capacity than the previous one (i.e. what they call upward or downward gradients).

Their results show that density-dependent processes influence the speed at which new fragments are colonized is significantly reduced by positive density dependence. When either population growth or dispersal rate depend on density, colonisation debt limits the speed of invasion, which turns out to be dependent on the strength and direction of the gradient between the conditions of the invasion front, and the newly colonized patches. Although this result may be quite important to understand the meta-population dynamics of dispersing species, it is important to note that in their study the environmental differences between patches do not take into account eventual shifts in the scenopoetic conditions (i.e. the values of the environmental parameters to which species niches’ respond to; Hutchinson 1978, see also Soberón 2007). Rather, differences arise from variations in the carrying capacity of the patches that are consecutively invaded, both in the in silico and microcosm experiments. That is, they account for potential differences in the size or quality of the invaded fragments, but not on the costs of colonizing fragments with different environmental conditions, which may also determine invasion speed through niche-driven processes. This aspect can be of particular importance in biological invasions or under climate change-driven range shifts, when adaptation to new environments is often required (Sakai et al. 2001; Whitney & Gabler 2008; Hill et al. 2011).

The expansion of geographical distribution ranges is the result of complex eco-evolutionary processes where meta-community dynamics and niche shifts interact in a novel physical space and/or environment (see, e.g., Mestre et al. 2020). Here, the invasibility of native communities is determined by niche variations and how similar are the traits of alien and native species (Hui et al. 2023). Within this context, density-dependent processes will build upon and heterogeneous matrix of native communities and environments (Tischendorf et al. 2005), to eventually determine invasion success. What the results of Morel-Journel et al. (2023) show is that, when the invader shows density dependence, the invasion process can be slowed down by variations in the carrying capacity of patches along the dispersal front. This can be particularly useful to manage biological invasions; ongoing invasions can be at least partially controlled by manipulating the size or quality of the patches that are most adequate to the invader, controlling host populations to reduce carrying capacity. But further, landscape manipulation of such kind could be used in a preventive way, to account in advance for the effects of the introduction of alien species for agricultural exploitation or biological control, thereby providing an additional safeguard to practices such as the introduction of parasitoids to control plagues. These practical aspects are certainly worth exploring further, together with a more explicit account of the influence of the abiotic conditions and the characteristics of the invaded communities on the success and speed of biological invasions.


Hill, J.K., Griffiths, H.M. & Thomas, C.D. (2011) Climate change and evolutionary adaptations at species' range margins. Annual Review of Entomology, 56, 143-159.

Hui, C., Pyšek, P. & Richardson, D.M. (2023) Disentangling the relationships among abundance, invasiveness and invasibility in trait space. npj Biodiversity, 2, 13.

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

Levins, R. (1966) The strategy of model building in population biology. American Scientist, 54, 421-431. 

Mestre, A., Poulin, R. & Hortal, J. (2020) A niche perspective on the range expansion of symbionts. Biological Reviews, 95, 491-516.

Morel-Journel, T., Haond, M., Duan, L., Mailleret, L. & Vercken, E. (2023) Colonisation debt: when invasion history impacts current range expansion. bioRxiv, 2022.11.13.516255, ver. 3 peer-reviewed and recommended by Peer Community in Ecology.

Snäll, T., B. O'Hara, R. & Arjas, E. (2007) A mathematical and statistical framework for modelling dispersal. Oikos, 116, 1037-1050.

Sakai, A.K., Allendorf, F.W., Holt, J.S., Lodge, D.M., Molofsky, J., With, K.A., Baughman, S., Cabin, R.J., Cohen, J.E., Ellstrand, N.C., McCauley, D.E., O'Neil, P., Parker, I.M., Thompson, J.N. & Weller, S.G. (2001) The population biology of invasive species. Annual Review of Ecology and Systematics, 32, 305-332.

Soberón, J. (2007) Grinnellian and Eltonian niches and geographic distributions of species. Ecology Letters, 10, 1115-1123.

Tischendorf, L., Grez, A., Zaviezo, T. & Fahrig, L. (2005) Mechanisms affecting population density in fragmented habitat. Ecology and Society, 10, 7.

Whitney, K.D. & Gabler, C.A. (2008) Rapid evolution in introduced species, 'invasive traits' and recipient communities: challenges for predicting invasive potential. Diversity and Distributions, 14, 569-580.

Colonisation debt: when invasion history impacts current range expansionThibaut Morel-Journel, Marjorie Haond, Lana Duan, Ludovic Mailleret, Elodie Vercken<p>Demographic processes that occur at the local level, such as positive density dependence in growth or dispersal, are known to shape population range expansion, notably by linking carrying capacity to invasion speed. As a result of these process...Biological invasions, Colonization, Dispersal & Migration, Experimental ecology, Landscape ecology, Population ecology, Spatial ecology, Metacommunities & Metapopulations, Theoretical ecologyJoaquín HortalAnonymous, Anonymous2022-11-16 15:52:08 View
01 Oct 2023
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Seasonality of host-seeking Ixodes ricinus nymph abundance in relation to climate

Assessing seasonality of tick abundance in different climatic regions

Recommended by based on reviews by 2 anonymous reviewers

Tick-borne pathogens are considered as one of the major threats to public health – Lyme borreliosis being a well-known example of such disease. Global change – from climate change to changes in land use or invasive species – is playing a role in the increased risk associated with these pathogens. An important aspect of our knowledge of ticks and their associated pathogens is seasonality – one component being the phenology of within-year tick occurrences. This is important both in terms of health risk – e.g., when is the risk of encountering ticks high – and ecological understanding, as tick dynamics may depend on the weather as well as different hosts with their own dynamics and habitat use.

Hoch et al. (2023) provide a detailed data set on the phenology of one species of tick, Ixodes ricinus, in 6 different locations of France. Whereas relatively cool sites showed a clear peak in spring-summer, warmer sites showed in addition relatively high occurrences in fall-winter, with a minimum in late summer-early fall. Such results add robust data to the existing evidence of the importance of local climatic patterns for explaining tick phenology.

Recent analyses have shown that the phenology of Lyme borreliosis has been changing in northern Europe in the last 25 years, with seasonal peaks in cases occurring now 6 weeks earlier (Goren et al. 2023). The study by Hoch et al. (2023) is of too short duration to establish temporal changes in phenology (“only” 8 years, 2014-2021, see also Wongnak et al 2021 for some additional analyses; given the high year-to-year variability in weather, establishing phenological changes often require longer time series), and further work is needed to get better estimates of these changes and relate them to climate, land-use, and host density changes. Moreover, the phenology of ticks may also be related to species-specific tick phenology, and different tick species do not respond to current changes in identical ways (see for example differences between the two Ixodes species in Finland; Uusitalo et al. 2022). An efficient surveillance system requires therefore an adaptive monitoring design with regard to the tick species as well as the evolving causes of changes.


Goren, A., Viljugrein, H., Rivrud, I. M., Jore, S., Bakka, H., Vindenes, Y., & Mysterud, A. (2023). The emergence and shift in seasonality of Lyme borreliosis in Northern Europe. Proceedings of the Royal Society B: Biological Sciences, 290(1993), 20222420.

Hoch, T., Madouasse, A., Jacquot, M., Wongnak, P., Beugnet, F., Bournez, L., . . . Agoulon, A. (2023). Seasonality of host-seeking Ixodes ricinus nymph abundance in relation to climate. bioRxiv, ver.4 peer-reviewed and recommended by Peer Community In Ecology.

Uusitalo, R., Siljander, M., Lindén, A., Sormunen, J. J., Aalto, J., Hendrickx, G., . . . Vapalahti, O. (2022). Predicting habitat suitability for Ixodes ricinus and Ixodes persulcatus ticks in Finland. Parasites & Vectors, 15(1), 310.

Wongnak, P., Bord, S., Jacquot, M., Agoulon, A., Beugnet, F., Bournez, L., . . . Chalvet-Monfray, K. (2022). Meteorological and climatic variables predict the phenology of Ixodes ricinus nymph activity in France, accounting for habitat heterogeneity. Scientific Reports, 12(1), 7833.

Seasonality of host-seeking *Ixodes ricinus* nymph abundance in relation to climateThierry Hoch, Aurélien Madouasse, Maude Jacquot, Phrutsamon Wongnak, Fréderic Beugnet, Laure Bournez, Jean-François Cosson, Frédéric Huard, Sara Moutailler, Olivier Plantard, Valérie Poux, Magalie René-Martellet, Muriel Vayssier-Taussat, Hélène Ve...<p style="text-align: justify;">There is growing concern about climate change and its impact on human health. Specifically, global warming could increase the probability of emerging infectious diseases, notably because of changes in the geographic...Climate change, Population ecology, Statistical ecologyNigel Yoccoz2022-10-14 18:43:56 View
20 Feb 2023
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Best organic farming deployment scenarios for pest control: a modeling approach

Towards model-guided organic farming expansion for crop pest management

Recommended by based on reviews by Julia Astegiano, Lionel Hertzog and Sylvain Bart

Reduce the impact the intensification of human activities has on the environmental is the challenge the humanity faces today, a major challenge that could be compared to climbing Everest without an oxygen supply. Indeed, over-population, pollution, burning fossil fuels, and deforestation are all evils which have had hugely detrimental effects on the environment such as climate change, soil erosion, poor air quality, and scarcity of drinking water to name but a few. In response to the ever-growing consumer demand, agriculture has intensified massively along with a drastic increase in the use of chemicals to ensure an adequate food supply while controlling crop pests. In this context, to address the disastrous effects of the intensive usage of pesticides on both human health and biodiversity, organic farming (OF) revealed as a miracle remedy with multiple benefits. Delattre et al. (2023) present a powerful modelling approach to decipher the crossed effects of the landscape structure and the OF expansion scenario on the pest abundance, both in organic and conventional (CF) crop fields. To this end, the authors ingeniously combined a grid-based landscape model with a spatially explicit predator-pest model. Based on an extensive in silico simulation process, they explore a diversity of landscape structures differing in their amount of semi-natural habitats (SHN) and in their fragmentation, to finally propose a ranking of various expansion scenarios according to the pest control methods in organic farming as well as to the pest and predators’ dissemination capacities. In total, 9 landscape structures (3 proportions of SHN x 3 fragmentation levels) were crossed with 3 expansion scenarios (RD = a random distribution of OF and CF in the grid; IP = isolated CF are converted; GP = CF within aggregates are converted), 4 pest management practices, 3 initial densities and 36 biological parameter combinations driving the predator’ and pest’s population dynamics. This exhaustive exploration of possible combinations of landscape and farming practices highlighted the main drivers of the various OF expansion scenarios, such as increased spillover of predators in isolated OF/CF fields, increased pest management efficiency in large patches of CF and the importance of the distance between OF and CF. In the end, this study brings to light the crucial role that landscape planning plays when OF practices have limited efficiency on pests. It also provides convincing arguments to the fact that converting to organic isolated CF as a priority seems to be the most promising scenario to limit pest densities in CF crops while improving predator to pest ratios (considered as a proxy of conservation biological control) in OF ones without increasing pest densities. Once further completed with model calibration validation based on observed life history traits data for both predators and pests, this work should be very helpful in sustaining policy makers to convince farmers of engaging in organic farming.


Delattre T, Memah M-M, Franck P, Valsesia P, Lavigne C (2023) Best organic farming deployment scenarios for pest control: a modeling approach. bioRxiv, 2022.05.31.494006, ver. 2 peer-reviewed and recommended by Peer Community in Ecology.

Best organic farming deployment scenarios for pest control: a modeling approachThomas Delattre, Mohamed-Mahmoud Memah, Pierre Franck, Pierre Valsesia, Claire Lavigne<p style="text-align: justify;">Organic Farming (OF) has been expanding recently around the world in response to growing consumer demand and as a response to environmental concerns. Its share of agricultural landscapes is expected to increase in t...Agroecology, Biological control, Landscape ecologySandrine Charles2022-06-03 11:41:14 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
01 Mar 2022
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Dissimilarity of species interaction networks: quantifying the effect of turnover and rewiring

How to evaluate and interpret the contribution of species turnover and interaction rewiring when comparing ecological networks?

Recommended by ORCID_LOGO based on reviews by Ignasi Bartomeus and 1 anonymous reviewer

A network includes a set of vertices or nodes (e.g., species in an interaction network), and a set of edges or links (e.g., interactions between species). Whether and how networks vary in space and/or time are questions often addressed in ecological research. 

Two ecological networks can differ in several extents: in that species are different in the two networks and establish new interactions (species turnover), or in that species that are present in both networks establish different interactions in the two networks (rewiring). The ecological meaning of changes in network structure is quite different according to whether species turnover or interaction rewiring plays a greater role. Therefore, much attention has been devoted in recent years on quantifying and interpreting the relative changes in network structure due to species turnover and/or rewiring.

Poisot et al. (2012) proposed to partition the global variation in structure between networks, \( \beta_{WN} \) (WN = Whole Network) into two terms: \( \beta_{OS} \) (OS = Only Shared species) and \( \beta_{ST} \) (ST = Species Turnover), such as \( \beta_{WN} = \beta_{OS} + \beta_{ST} \).

The calculation lays on enumerating the interactions between species that are common or not to two networks, as illustrated on Figure 1 for a simple case. Specifically, Poisot et al. (2012) proposed to use a Sorensen type measure of network dissimilarity, i.e., \( \beta_{WN} = \frac{a+b+c}{(2a+b+c)/2} -1=\frac{b+c}{2a+b+c} \) , where \( a \) is the number of interactions shared between the networks, while \( b \) and \( c \) are interaction numbers unique to one and the other network, respectively. \( \beta_{OS} \) is calculated based on the same formula, but only for the subnetworks including the species common to the two networks, in the form \( \beta_{OS} = \frac{b_{OS}+c_{OS}}{2a_{OS}+b_{OS}+c_{OS}} \) (e.g., Fig. 1). \( \beta_{ST} \) is deduced by subtracting \( \beta_{OS} \) from \( \beta_{WN} \) and represents in essence a "dissimilarity in interaction structure introduced by dissimilarity in species composition" (Poisot et al. 2012).

Figure 1. Ecological networks exemplified in Fründ (2021) and discussed in Poisot (2022). a is the number of shared links (continuous lines in right figures), while b+c is the number of edges unique to one or the other network (dashed lines in right figures).

Alternatively, Fründ (2021) proposed to define \( \beta_{OS} = \frac{b_{OS}+c_{OS}}{2a+b+c} \) and \( \beta_{ST} = \frac{b_{ST}+c_{ST}}{2a+b+c} \), where \( b_{ST}=b-b_{OS} \)  and \( c_{ST}=c-c_{OS} \) , so that the components \( \beta_{OS} \) and \( \beta_{ST} \) have the same denominator. In this way, Fründ (2021) partitioned the count of unique \( b+c=b_{OS}+b_{ST}+c_{ST} \) interactions, so that \( \beta_{OS} \) and \( \beta_{ST} \) sums to \( \frac{b_{OS}+c_{OS}+b_{ST}+c_{ST}}{2a+b+c} = \frac{b+c}{2a+b+c} = \beta_{WN} \). Fründ (2021) advocated that this partition allows a more sensible comparison of \( \beta_{OS} \) and \( \beta_{ST} \), in terms of the number of links that contribute to each component.

For instance, let us consider the networks 1 and 2 in Figure 1 (left panel) such as \( a_{OS}=2 \) (continuous lines in right panel), \( b_{ST} + c_{ST} = 1 \) and \( b_{OS} + c_{OS} = 1 \) (dashed lines in right panel), and thereby \( a = 2 \), \( b+c=2 \), \( \beta_{WN} = 1/3 \). Fründ (2021) measured \( \beta_{OS}=\beta_{ST}=1/6 \) and argued that it is appropriate insofar as it reflects that the number of unique links in the OS and ST components contributing to network dissimilarity (dashed lines) are actually equal. Conversely, the formula of Poisot et al. (2012) yields \( \beta_{OS}=1/5 \), hence \( \beta_{ST} = \frac{1}{3}-\frac{1}{5}=\frac{2}{15}<\beta_{OS} \). Fründ (2021) thus argued that the method of Poisot tends to underestimate the contribution of species turnover.

To clarify and avoid misinterpretation of the calculation of \( \beta_{OS} \) and \( \beta_{ST} \) in Poisot et al. (2012), Poisot (2022) provides a new, in-depth mathematical analysis of the decomposition of \( \beta_{WN} \). Poisot et al. (2012) quantify in \( \beta_{OS} \) the actual contribution of rewiring in network structure for the subweb of common species. Poisot (2022) thus argues that \( \beta_{OS} \) relates only to the probability of rewiring in the subweb, while the definition of \( \beta_{OS} \) by Fründ (2021) is relative to the count of interactions in the global network (considered in denominator), and is thereby dependent on both rewiring probability and species turnover. Poisot (2022) further clarifies the interpretation of \( \beta_{ST} \). \( \beta_{ST} \) is obtained by subtracting \( \beta_{OS} \) from \( \beta_{WN} \) and thus represents the influence of species turnover in terms of the relative architectures of the global networks and of the subwebs of shared species. Coming back to the example of Fig.1., the Poisot et al. (2012) formula posits that \( \frac{\beta_{ST}}{\beta_{WN}}=\frac{2/15}{1/3}=2/5 \), meaning that species turnover contributes two-fifths of change in network structure, while rewiring in the subweb of common species contributed three fifths.  Conversely, the approach of Fründ (2021) does not compare the architectures of global networks and of the subwebs of shared species, but considers the relative contribution of unique links to network dissimilarity in terms of species turnover and rewiring. 

Poisot (2022) concludes that the partition proposed in Fründ (2021) does not allow unambiguous ecological interpretation of rewiring. He provides guidelines for proper interpretation of the decomposition proposed in Poisot et al. (2012).


Fründ J (2021) Dissimilarity of species interaction networks: how to partition rewiring and species turnover components. Ecosphere, 12, e03653.

Poisot T, Canard E, Mouillot D, Mouquet N, Gravel D (2012) The dissimilarity of species interaction networks. Ecology Letters, 15, 1353–1361.

Poisot T (2022) Dissimilarity of species interaction networks: quantifying the effect of turnover and rewiring. EcoEvoRxiv Preprints, ver. 4 peer-reviewed and recommended by Peer Community in Ecology.

Dissimilarity of species interaction networks: quantifying the effect of turnover and rewiringTimothée Poisot<p style="text-align: justify;">Despite having established its usefulness in the last ten years, the decomposition of ecological networks in components allowing to measure their β-diversity retains some methodological ambiguities. Notably, how to ...Biodiversity, Interaction networks, Theoretical ecologyFrançois Munoz2021-07-31 00:18:41 View
01 Jun 2018
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Data-based, synthesis-driven: setting the agenda for computational ecology

Some thoughts on computational ecology from people who I’m sure use different passwords for each of their accounts

Recommended by based on reviews by Matthieu Barbier and 1 anonymous reviewer

Are you an ecologist who uses a computer or know someone that does? Even if your research doesn’t rely heavily on advanced computational techniques, it likely hasn’t escaped your attention that computers are increasingly being used to analyse field data and make predictions about the consequences of environmental change. So before artificial intelligence and robots take over from scientists, now is great time to read about how experts think computers could make your life easier and lead to innovations in ecological research. In “Data-based, synthesis-driven: setting the agenda for computational ecology”, Poisot and colleagues [1] provide a brief history of computational ecology and offer their thoughts on how computational thinking can help to bridge different types of ecological knowledge. In this wide-ranging article, the authors share practical strategies for realising three main goals: (i) tighter integration of data and models to make predictions that motivate action by practitioners and policy-makers; (ii) closer interaction between data-collectors and data-users; and (iii) enthusiasm and aptitude for computational techniques in future generations of ecologists. The key, Poisot and colleagues argue, is for ecologists to “engage in meaningful dialogue across disciplines, and recognize the currencies of their collaborations.” Yes, this is easier said than done. However, the journey is much easier with a guide and when everyone involved serves to benefit not only from the eventual outcome, but also the process.


[1] Poisot, T., Labrie, R., Larson, E., & Rahlin, A. (2018). Data-based, synthesis-driven: setting the agenda for computational ecology. BioRxiv, 150128, ver. 4 recommended and peer-reviewed by PCI Ecology. doi: 10.1101/150128

Data-based, synthesis-driven: setting the agenda for computational ecologyTimothée Poisot, Richard Labrie, Erin Larson, Anastasia RahlinComputational ecology, defined as the application of computational thinking to ecological problems, has the potential to transform the way ecologists think about the integration of data and models. As the practice is gaining prominence as a way to...Meta-analyses, Statistical ecology, Theoretical ecologyPhillip P.A. Staniczenko2018-02-05 20:51:41 View
22 May 2019
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Sex makes them sleepy: host reproductive status induces diapause in a parasitoid population experiencing harsh winters

The response of interacting species to biotic seasonal cues

Recommended by and based on reviews by Anne Duplouy and 1 anonymous reviewer

In temperate regions, food abundance and quality vary greatly throughout the year, and the ability of organisms to synchronise their phenology to these changes is a key determinant of their reproductive success. Successful synchronisation requires that cues are perceived prior to change, leaving time for physiological adjustments.
But what are the cues used to anticipate seasonal changes? Abiotic factors like temperature and photoperiod are known for their driving role in the phenology of a wide range of plant an animal species [1,2] . Arguably though, biotic cues directly linked to upcoming changes in food abundance could be as important as abiotic factors, but the response of organisms to these cues remains relatively unexplored.
Biotic cues may be particularly important for higher trophic levels because of their tight interaction with the hosts or preys they depend on. In this study Tougeron and colleagues [3] address this topic using interacting insects, namely herbivorous aphids and the parasitic wasps (or parasitoids) that feed on them. The key finding of the study by Tougeron et al. [3] is that the host morph in which parasitic wasp larvae develop is a major driver of diapause induction. More importantly, the aphid morph that triggers diapause in the wasp is the one that will lay overwintering eggs in autumn at the onset of harsh winter conditions. Its neatly designed experimental setup also provides evidence that this response may vary across populations as host-dependent diapause induction was only observed in a wasp population that originated from a cold area. As the authors suggests, this may be caused by local adaptation to environmental conditions because, relative to warmer regions, missing the time window to enter diapause in colder regions may have more dramatic consequences. The study also shows that different aphid morphs differ greatly in their chemical composition, and points to particular types of metabolites like sugars and polyols as specific cues for diapause induction.
This study provides a nice example of the complexity of biological interactions, and of the importance of phenological synchrony between parasites and their hosts. The authors provide evidence that phenological synchrony is likely to be achieved via chemical cues derived from the host. A similar approach was used to demonstrate that the herbivorous beetle Leptinotarsa decemlineata uses plant chemical cues to enter diapause [4]. Beetles fed on plants exposed to pre-wintering conditions entered diapause in higher proportions than those fed on control plants grown at normal conditions. As done by Tougeron et al. [3], in [4] the authors associated diapause induction to changes in the composition of metabolites in the plant. In both studies, however, the missing piece is to unveil the particular chemical involved, an answer that may be provided by future experiments.
Latitudinal clines in diapause induction have been described in a number of insect species [5]. Correlative studies, in which the phenology of different trophic levels has been monitored, suggest that these clines may in part be governed by lower trophic levels. For example, Phillimore et al. [6] explored the relative contribution of temperature and of host plant phenology on adult flight periods of the butterfly Anthocharis cardamines. Tougeron et al. [3], by using aphids and their associated parasitoids, take the field further by moving from observational studies to experiments. Besides, aphids are not only a tractable host-parasite system in the laboratory, they are important agricultural pests. Improving our basic knowledge of their ecological interactions may ultimately contribute to improving pest control techniques. The study by Tougeron et al. [3] exemplifies the multiple benefits that can be gained from addressing fundamental questions in species that are also directly relevant to society.


[1] Tauber, M. J., Tauber, C. A., and Masaki, S. (1986). Seasonal Adaptations of Insects. Oxford, New York: Oxford University Press.
[2] Bradshaw, W. E., and Holzapfel, C. M. (2007). Evolution of Animal Photoperiodism. Annual Review of Ecology, Evolution, and Systematics, 38(1), 1–25. doi: 10.1146/annurev.ecolsys.37.091305.110115
[3] Tougeron, K., Brodeur, J., Baaren, J. van, Renault, D., and Lann, C. L. (2019b). Sex makes them sleepy: host reproductive status induces diapause in a parasitoid population experiencing harsh winters. bioRxiv, 371385, ver. 6 peer-reviewed and recommended by PCI Ecology. doi: 10.1101/371385
[4] Izzo, V. M., Armstrong, J., Hawthorne, D., and Chen, Y. (2014). Time of the season: the effect of host photoperiodism on diapause induction in an insect herbivore, Leptinotarsa decemlineata. Ecological Entomology, 39(1), 75–82. doi: 10.1111/een.12066
[5] Hut Roelof A., Paolucci Silvia, Dor Roi, Kyriacou Charalambos P., and Daan Serge. (2013). Latitudinal clines: an evolutionary view on biological rhythms. Proceedings of the Royal Society B: Biological Sciences, 280(1765), 20130433. doi: 10.1098/rspb.2013.0433
[6] Phillimore, A. B., Stålhandske, S., Smithers, R. J., and Bernard, R. (2012). Dissecting the Contributions of Plasticity and Local Adaptation to the Phenology of a Butterfly and Its Host Plants. The American Naturalist, 180(5), 655–670. doi: 10.1086/667893

Sex makes them sleepy: host reproductive status induces diapause in a parasitoid population experiencing harsh wintersTougeron K., Brodeur J., van Baaren J., Renault D. and Le Lann C.<p>When organisms coevolve, any change in one species can induce phenotypic changes in traits and ecology of the other species. The role such interactions play in ecosystems is central, but their mechanistic bases remain underexplored. Upper troph...Coexistence, Evolutionary ecology, Experimental ecology, Host-parasite interactions, PhysiologyAdele Mennerat2018-07-18 18:51:03 View
29 May 2023
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Using integrated multispecies occupancy models to map co-occurrence between bottlenose dolphins and fisheries in the Gulf of Lion, French Mediterranean Sea

Mapping co-occurence of human activities and wildlife from multiple data sources

Recommended by based on reviews by Mason Fidino and 1 anonymous reviewer

Two fields of research have grown considerably over the past twenty years: the investigation of human-wildlife conflicts (e.g. see Treves & Santiago-Ávila 2020), and multispecies occupancy modelling (Devarajan et al. 2020). In their recent study, Lauret et al. (2023) combined both in an elegant methodological framework, applied to the study of the co-occurrence of fishing activities and bottlenose dolphins in the French Mediterranean.

A common issue with human-wildlife conflicts (and, in particular, fishery by-catch) is that data is often only available from those conflicts or interactions, limiting the validity of the predictions (Kuiper et al. 2022). Lauret et al. use independent data sources informing the occurrence of fishing vessels and dolphins, combined in a Bayesian multispecies occupancy model where vessels are "the other species". I particularly enjoyed that approach, as integration of human activities in ecological models can be extremely complex, but can also translate in phenomena that can be captured as one would of individuals of a species, as long as the assumptions are made clearly. Here, the model is made more interesting by accounting for environmental factors (seabed depth) borrowing an approach from Generalized Additive Models in the Bayesian framework. While not pretending to provide (yet) practical recommendations to help conserve bottlenose dolphins (and other wildlife conflicts), this study and the associated code are a promising step in that direction.


Devarajan, K., Morelli, T.L. & Tenan, S. (2020), Multi-species occupancy models: review, roadmap, and recommendations. Ecography, 43: 1612-1624.

Kuiper, T., Loveridge, A.J. and Macdonald, D.W. (2022), Robust mapping of human–wildlife conflict: controlling for livestock distribution in carnivore depredation models. Anim. Conserv., 25: 195-207.

Lauret V, Labach H, David L, Authier M, & Gimenez O (2023) Using integrated multispecies occupancy models to map co-occurrence between bottlenose dolphins and fisheries in the Gulf of Lion, French Mediterranean Sea. Ecoevoarxiv, ver. 2 peer-reviewed and recommended by PCI Ecology.

Treves, A. & Santiago-Ávila, F.J. (2020). Myths and assumptions about human-wildlife conflict and coexistence. Conserv. Biol. 34, 811–818.

Using integrated multispecies occupancy models to map co-occurrence between bottlenose dolphins and fisheries in the Gulf of Lion, French Mediterranean SeaValentin Lauret, Hélène Labach, Léa David, Matthieu Authier, Olivier Gimenez<p style="text-align: justify;">In the Mediterranean Sea, interactions between marine species and human activities are prevalent. The coastal distribution of bottlenose dolphins (<em>Tursiops truncatus</em>) and the predation pressure they put on ...Marine ecology, Population ecology, Species distributionsPaul Caplat2022-10-21 11:13:36 View
22 Apr 2021
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The hidden side of the Allee effect: correlated demographic traits and extinction risk in experimental populations

Allee effects under the magnifying glass

Recommended by based on reviews by Tom Van Dooren, Dani Oro and 1 anonymous reviewer

For decades, the effect of population density on individual performance has been studied by ecologists using both theoretical, observational, and experimental approaches. The generally accepted definition of the Allee effect is a positive correlation between population density and average individual fitness that occurs at low population densities, while individual fitness is typically decreased through intraspecific competition for resources at high population densities.  Allee effects are very relevant in conservation biology because species at low population densities would then be subjected to much higher extinction risks. 

However, due to all kinds of stochasticity, low population numbers are always more vulnerable to extinction than larger population sizes. This effect by itself cannot be necessarily ascribed to lower individual performance at low densities, i.e, Allee effects. Vercken and colleagues (2021) address this challenging question and measure the extent to which average individual fitness is affected by population density analyzing 30 experimental populations. As a model system, they use populations of parasitoid wasps of the genus Trichogramma. They report Allee effect in 8 out 30 experimental populations. Vercken and colleagues's work has several strengths. 

First of all, it is nice to see that they put theory at work. This is a very productive way of using theory in ecology. As a starting point, they look at what simple theoretical population models say about Allee effects (Lewis and Kareiva 1993; Amarasekare 1998; Boukal and Berec 2002). These models invariably predict a one-humped relation between population-density and per-capita growth rate. It is important to remark that pure logistic growth, the paradigm of density-dependence, would never predict such qualitative behavior. It is only when there is a depression of per-capita growth rates at low densities that true Allee effects arise. Second, these authors manage to not only experimentally test this main prediction but also report additional demographic traits that are consistently affected by population density. 

In these wasps, individual performance can be measured in terms of the average number of individuals every adult is able to put into the next generation ---the lambda parameter in their analysis. The first panel in figure 3 shows that the per-capita growth rates are lower in populations presenting Allee effects, the ones showing a one-humped behavior in the relation between per-capita growth rates and population densities (see figure 2). Also other population traits, such maximum population size and exitinction probability, change in a correlated and consistent manner. 

In sum, Vercken and colleagues's results are experimentally solid and based on theory expectations. However, they are very intriguing. They find the signature of Allee effects in only 8 out 30 populations, all from the same genus Trichogramma, and some populations belonging to the same species (from different sampling sites) do not show consistently Allee effects. Where does this population variability comes from? What are the reasons underlying this within- and between-species variability? What are the individual mechanisms driving Allee effects in these populations? Good enough, this piece of work generates more intriguing questions than the question is able to clearly answer. Science is not a collection of final answers but instead good questions are the ones that make science progress. 


Amarasekare P (1998) Allee Effects in Metapopulation Dynamics. The American Naturalist, 152, 298–302.

Boukal DS, Berec L (2002) Single-species Models of the Allee Effect: Extinction Boundaries, Sex Ratios and Mate Encounters. Journal of Theoretical Biology, 218, 375–394.

Lewis MA, Kareiva P (1993) Allee Dynamics and the Spread of Invading Organisms. Theoretical Population Biology, 43, 141–158.

Vercken E, Groussier G, Lamy L, Mailleret L (2021) The hidden side of the Allee effect: correlated demographic traits and extinction risk in experimental populations. HAL, hal-02570868, ver. 4 peer-reviewed and recommended by Peer community in Ecology.

The hidden side of the Allee effect: correlated demographic traits and extinction risk in experimental populationsVercken Elodie, Groussier Géraldine, Lamy Laurent, Mailleret Ludovic<p style="text-align: justify;">Because Allee effects (i.e., the presence of positive density-dependence at low population size or density) have major impacts on the dynamics of small populations, they are routinely included in demographic models ...Demography, Experimental ecology, Population ecologyDavid Alonso2020-09-30 16:38:29 View