Submit a preprint

Latest recommendationsrsstwitter

IdTitle▲AuthorsAbstractPictureThematic fieldsRecommenderReviewersSubmission date
07 Aug 2019
article picture

Is behavioral flexibility related to foraging and social behavior in a rapidly expanding species?

Understanding geographic range expansions in human-dominated landscapes: does behavioral flexibility modulate flexibility in foraging and social behavior?

Recommended by ORCID_LOGO and ORCID_LOGO based on reviews by Pizza Ka Yee Chow and Esther Sebastián González

Which biological traits modulate species distribution has historically been and still is one of the core questions of the macroecology and biogeography agenda [1, 2]. As most of the Earth surface has been modified by human activities [3] understanding the strategies that allow species to inhabit human-dominated landscapes will be key to explain species geographic distribution in the Anthropocene. In this vein, Logan et al. [4] are working on a long-term and integrative project aimed to investigate how great-tailed grackles rapidly expanded their geographic range into North America [4]. Particularly, they want to determine which is the role of behavioral flexibility, i.e. an individual’s ability to modify its behavior when circumstances change based on learning from previous experience [5], in rapid geographic range expansions. The authors are already working in a set of complementary questions described in pre-registrations that have already been recommended at PCI Ecology: (1) Do individuals with greater behavioral flexibility rely more on causal cognition [6]? (2) Which are the mechanisms that lead to behavioral flexibility [7]? (3) Does the manipulation of behavioral flexibility affect exploration, but not boldness, persistence, or motor diversity [8]? (4) Can context changes improve behavioral flexibility [9]?
In this new pre-registration, they aim to determine whether the more behaviorally flexible individuals have more flexible foraging behaviors (i.e. use a wider variety of foraging techniques in the wild and eat a larger number of different foods), habitat use (i.e. higher microhabitat richness) and social relationships (i.e., are more likely to have a greater number of bonds or stronger bonds with other individuals; [4]). The project is ambitious, combining both the experimental characterization of individuals’ behavioral flexibility and the field characterization of the foraging and social behavior of those individuals and of wild ones.
The current great-tailed grackles project will be highly relevant to understand rapid geographic range expansions in a changing world. In this vein, this pre-registration will particularly help to go one step further in our understanding of behavioral flexibility as a determinant of species geographic distribution. Logan et al. [4] pre-registration is very well designed, main and alternative hypotheses have been thought and written and methods are presented in a very detailed way, which includes the R codes that authors will use in their analyses. Authors have answered in a very detailed way each comment that reviewers have pointed out and modified the pre-registration accordingly, which we consider highly improved the quality of this work. That is why we strongly recommend this pre-registration and look forward to see the results.


[1] Gaston K. J. (2003) The structure and dynamics of geographic ranges. Oxford series in Ecology and Evolution. Oxford University Press, New York.
[2] Castro-Insua, A., Gómez‐Rodríguez, C., Svenning, J.C., and Baselga, A. (2018) A new macroecological pattern: The latitudinal gradient in species range shape. Global ecology and biogeography, 27(3), 357-367. doi: 10.1111/geb.12702
[3] Newbold, T., Hudson, L. N., Hill, S. L. L., Contu, S., Lysenko, I., Senior, R. A., et al. (2015). Global effects of land use on local terrestrial biodiversity. Nature, 520(7545), 45–50. doi: 10.1038/nature14324
[4] Logan CJ, McCune K, Bergeron L, Folsom M, Lukas D. (2019). Is behavioral flexibility related to foraging and social behavior in a rapidly expanding species? In principle recommendation by Peer Community In Ecology.
[5] Mikhalevich, I., Powell, R., and Logan, C. (2017). Is Behavioural Flexibility Evidence of Cognitive Complexity? How Evolution Can Inform Comparative Cognition. Interface Focus 7: 20160121. doi: 10.1098/rsfs.2016.0121.
[6] Fronhofer, E. (2019) From cognition to range dynamics: advancing our understanding of macroecological patterns. Peer Community in Ecology, 100014. doi: 10.24072/pci.ecology.100014
[7] Vogel, E. (2019) Adapting to a changing environment: advancing our understanding of the mechanisms that lead to behavioral flexibility. Peer Community in Ecology, 100016. doi: 10.24072/pci.ecology.100016
[8] Van Cleve, J. (2019) Probing behaviors correlated with behavioral flexibility. Peer Community in Ecology, 100020. doi: 10.24072/pci.ecology.100020
[9] Coulon, A. (2019) Can context changes improve behavioral flexibility? Towards a better understanding of species adaptability to environmental changes. Peer Community in Ecology, 100019. doi: 10.24072/pci.ecology.100019

Is behavioral flexibility related to foraging and social behavior in a rapidly expanding species?Corina Logan, Luisa Bergeron, Carolyn Rowney, Kelsey McCune, Dieter LukasThis is one of the first studies planned for our long-term research on the role of behavioral flexibility in rapid geographic range expansions. Project background: Behavioral flexibility, the ability to change behavior when circumstances change ba...Behaviour & Ethology, Preregistrations, ZoologyJulia Astegiano2018-10-23 00:47:03 View
27 Apr 2021
article picture

Joint species distributions reveal the combined effects of host plants, abiotic factors and species competition as drivers of species abundances in fruit flies

Understanding the interplay between host-specificity, environmental conditions and competition through the sound application of Joint Species Distribution Models

Recommended by based on reviews by Joaquín Calatayud and Carsten Dormann

Understanding why and how species coexist in local communities is one of the central questions in ecology. There is general agreement that species distribution and coexistence are determined by a number of key mechanisms, including the environmental requirements of species, dispersal, evolutionary constraints, resource availability and selection, metapopulation dynamics, and biotic interactions (e.g. Soberón & Nakamura 2009; Colwell & Rangel 2009; Ricklefs 2015). These factors are however intricately intertwined in a scale-structured fashion (Hortal et al. 2010; D’Amen et al. 2017), making it particularly difficult to tease apart the effects of each one of them. This could be addressed by the novel field of Joint Species Distribution Modelling (JSDM; Okasvainen & Abrego 2020), as it allows assessing the effects of several sets of factors and the co-occurrence and/or covariation in abundances of potentially interacting species at the same time (Pollock et al. 2014; Ovaskainen et al. 2016; Dormann et al. 2018). However, the development of JSDM has been hampered by the general lack of good-quality detailed data on species co-occurrences and abundances (see Hortal et al. 2015).

Facon et al. (2021) use a particularly large compilation of field surveys to study the abundance and co-occurrence of Tephritidae fruit flies in c. 400 orchards, gardens and natural areas throughout the island of Réunion. Further, they combine such information with lab data on their host-selection fundamental niche (i.e. in the absence of competitors), codifying traits of female choice and larval performances in 21 host species. They use Poisson Log-Normal models, a type of mixed model that allows one to jointly model the random effects associated with all species, and retrieve the covariations in abundance that are not explained by environmental conditions or differences in sampling effort. Then, they use a series of models to evaluate the effects on these matrices of ecological covariates (date, elevation, habitat, climate and host plant), species interactions (by comparing with a constrained residual variance-covariance matrix) and the species’ host-selection fundamental niches (through separate models for each fly species).

The eight Tephritidae species inhabiting Réunion include both generalists and specialists in Solanaceae and Cucurbitaceae with a known history of interspecific competition. Facon et al. (2021) use a comprehensive JSDM approach to assess the effects of different factors separately and altogether. This allows them to identify large effects of plant hosts and the fundamental host-selection niche on species co-occurrence, but also to show that ecological covariates and weak –though not negligible– species interactions are necessary to account for all residual variance in the matrix of joint species abundances per site. Further, they also find evidence that the fitness per host measured in the lab has a strong influence on the abundances in each host plant in the field for specialist species, but not for generalists. Indeed, the stronger effects of competitive exclusion were found in pairs of Cucurbitaceae specialist species. However, these analyses fail to provide solid grounds to assess why generalists are rarely found in Cucurbitaceae and Solanaceae. Although they argue that this may be due to Connell’s (1980) ghost of competition past (past competition that led to current niche differentiation), further data on the evolutionary history of these fruit flies is needed to assess this hypothesis.

Finding evidence for the effects of competitive interactions on species’ occurrences and spatial distributions is often difficult, perhaps because these effects occur over longer time scales than the ones usually studied by ecologists (Yackulic 2017). The work by Facon and colleagues shows that weak effects of competition can be detected also at the short ecological timescales that determine coexistence in local communities, under the virtuous combination of good-quality data and sound analytical designs that account for several aspects of species’ niches, their biotopes and their joint population responses. This adds a new dimension to the application of Hutchinson’s (1978) niche framework to understand the spatial dynamics of species and communities (see also Colwell & Rangel 2009), although further advances to incorporate dispersal-driven metacommunity dynamics (see, e.g., Ovaskainen et al. 2016; Leibold et al. 2017) are certainly needed. Nonetheless, this work shows the potential value of in-depth analyses of species coexistence based on combining good-quality field data with well-thought out JSDM applications. If many studies like this are conducted, it is likely that the uprising field of Joint Species Distribution Modelling will improve our understanding of the hierarchical relationships between the different factors affecting species coexistence in ecological communities in the near future.



Colwell RK, Rangel TF (2009) Hutchinson’s duality: The once and future niche. Proceedings of the National Academy of Sciences, 106, 19651–19658.

Connell JH (1980) Diversity and the Coevolution of Competitors, or the Ghost of Competition Past. Oikos, 35, 131–138.

D’Amen M, Rahbek C, Zimmermann NE, Guisan A (2017) Spatial predictions at the community level: from current approaches to future frameworks. Biological Reviews, 92, 169–187.

Dormann CF, Bobrowski M, Dehling DM, Harris DJ, Hartig F, Lischke H, Moretti MD, Pagel J, Pinkert S, Schleuning M, Schmidt SI, Sheppard CS, Steinbauer MJ, Zeuss D, Kraan C (2018) Biotic interactions in species distribution modelling: 10 questions to guide interpretation and avoid false conclusions. Global Ecology and Biogeography, 27, 1004–1016.

Facon B, Hafsi A, Masselière MC de la, Robin S, Massol F, Dubart M, Chiquet J, Frago E, Chiroleu F, Duyck P-F, Ravigné V (2021) Joint species distributions reveal the combined effects of host plants, abiotic factors and species competition as drivers of community structure in fruit flies. bioRxiv, 2020.12.07.414326. ver. 4 peer-reviewed and recommended by Peer community in Ecology.

Hortal J, de Bello F, Diniz-Filho JAF, Lewinsohn TM, Lobo JM, Ladle RJ (2015) Seven Shortfalls that Beset Large-Scale Knowledge of Biodiversity. Annual Review of Ecology, Evolution, and Systematics, 46, 523–549.

Hortal J, Roura‐Pascual N, Sanders NJ, Rahbek C (2010) Understanding (insect) species distributions across spatial scales. Ecography, 33, 51–53.

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

Leibold MA, Chase JM, Ernest SKM (2017) Community assembly and the functioning of ecosystems: how metacommunity processes alter ecosystems attributes. Ecology, 98, 909–919.

Ovaskainen O, Abrego N (2020) Joint Species Distribution Modelling: With Applications in R. Cambridge University Press, Cambridge.

Ovaskainen O, Roy DB, Fox R, Anderson BJ (2016) Uncovering hidden spatial structure in species communities with spatially explicit joint species distribution models. Methods in Ecology and Evolution, 7, 428–436.

Pollock LJ, Tingley R, Morris WK, Golding N, O’Hara RB, Parris KM, Vesk PA, McCarthy MA (2014) Understanding co-occurrence by modelling species simultaneously with a Joint Species Distribution Model (JSDM). Methods in Ecology and Evolution, 5, 397–406.

Ricklefs RE (2015) Intrinsic dynamics of the regional community. Ecology Letters, 18, 497–503.

Soberón J, Nakamura M (2009) Niches and distributional areas: Concepts, methods, and assumptions. Proceedings of the National Academy of Sciences, 106, 19644–19650.

Yackulic CB (2017) Competitive exclusion over broad spatial extents is a slow process: evidence and implications for species distribution modeling. Ecography, 40, 305–313.

Joint species distributions reveal the combined effects of host plants, abiotic factors and species competition as drivers of species abundances in fruit fliesBenoit Facon, Abir Hafsi, Maud Charlery de la Masselière, Stéphane Robin, François Massol, Maxime Dubart, Julien Chiquet, Enric Frago, Frédéric Chiroleu, Pierre-François Duyck & Virginie Ravigné<p style="text-align: justify;">The relative importance of ecological factors and species interactions for phytophagous insect species distributions has long been a controversial issue. Using field abundances of eight sympatric Tephritid fruit fli...Biodiversity, Coexistence, Community ecology, Competition, Herbivory, Interaction networks, Species distributionsJoaquín Hortal Carsten Dormann, Joaquín Calatayud2020-12-08 06:44:25 View
05 Apr 2022
article picture

Late-acting self-incompatible system, preferential allogamy and delayed selfing in the heterostylous invasive populations of Ludwigia grandiflora subsp. hexapetala

Water primerose (Ludwigia grandiflora subsp. hexapetala) auto- and allogamy: an ecological perspective

Recommended by ORCID_LOGO based on reviews by Juan Arroyo, Emiliano Mora-Carrera and 1 anonymous reviewer

Invasive plant species are widely studied by the ecologist community, especially in wetlands. Indeed, alien plants are considered one of the major threats to wetland biodiversity (Reid et al., 2019). Ludwigia grandiflora subsp. hexapetala (Hook. & Arn.) G.L.Nesom & Kartesz, 2000 (Lgh) is one of them and has received particular attention for a long time (Hieda et al., 2020; Thouvenot, Haury, & Thiebaut, 2013). The ecology of this invasive species and its effect on its biotic and abiotic environment has been studied in previous works. Different processes were demonstrated to explain their invasibility such as allelopathic interference (Dandelot et al., 2008), resource competition (Gérard et al., 2014), and high phenotypic plasticity (Thouvenot, Haury, & Thiébaut, 2013), to cite a few of them. However, although vegetative reproduction is a well-known invasive process for alien plants like Lgh (Glover et al., 2015), the sexual reproduction of this species is still unclear and may help to understand the Lgh population dynamics.

Portillo Lemus et al. (2021) showed that two floral morphs of Lgh co-exist in natura, involving self-compatibility for short-styled phenotype and self-incompatibility for long-styled phenotype processes. This new article (Portillo Lemus et al., 2022) goes further and details the underlying mechanisms of the sexual reproduction of the two floral morphs.

Complementing their previous study, the authors have described a late self-incompatible process associated with the long-styled morph, which authorized a small proportion of autogamy. Although this represents a small fraction of the L-morph reproduction, it may have a considerable impact on the L-morph population dynamics. Indeed, authors report that “floral morphs are mostly found in allopatric monomorphic populations (i.e., exclusively S-morph or exclusively L-morph populations)” with a large proportion of L-morph populations compared to S-morph populations in the field. It may seem counterintuitive as L-morph mainly relies on cross-fecundation. 

Results show that L-morph autogamy mainly occurs in the fall, late in the reproduction season. Therefore, the reproduction may be ensured if no exogenous pollen reaches the stigma of L-morph individuals. It partly explains the large proportion of L-morph populations in the field. 

Beyond the description of late-acting self-incompatibility, which makes the Onagraceae a third family of Myrtales with this reproductive adaptation, the study raises several ecological questions linked to the results presented in the article. First, it seems that even if autogamy is possible, Lgh would favour allogamy, even in S-morph, through the faster development of pollen tubes from other individuals. This may confer an adaptative and evolutive advantage for the Lgh, increasing its invasive potential. The article shows this faster pollen tube development in S-morph but does not test the evolutive consequences. It is an interesting perspective for future research. It would also be interesting to describe cellular processes which recognize and then influence the speed of the pollen tube. Second, the importance of sexual reproduction vs vegetative reproduction would also provide information on the benefits of sexual dimorphism within populations. For instance, how fruit production increases the dispersal potential of Lgh would help to understand Lgh population dynamics and to propose adapted management practices (Delbart et al., 2013; Meisler, 2009).

To conclude, the study proposes a morphological, reproductive and physiological description of the Lgh sexual reproduction process. However, underlying ecological questions are well included in the article and the ecophysiological results enlighten some questions about the role of sexual reproduction in the invasiveness of Lgh. I advise the reader to pay attention to the reviewers’ comments; the debates were very constructive and, thanks to the great collaboration with the authorship, lead to an interesting paper about Lgh reproduction and with promising perspectives in ecology and invasion ecology.


Dandelot S, Robles C, Pech N, Cazaubon A, Verlaque R (2008) Allelopathic potential of two invasive alien Ludwigia spp. Aquatic Botany, 88, 311–316.

Delbart E, Mahy G, Monty A (2013) Efficacité des méthodes de lutte contre le développement de cinq espèces de plantes invasives amphibies : Crassula helmsii, Hydrocotyle ranunculoides, Ludwigia grandiflora, Ludwigia peploides et Myriophyllum aquaticum (synthèse bibliographique). BASE, 17, 87–102.

Gérard J, Brion N, Triest L (2014) Effect of water column phosphorus reduction on competitive outcome and traits of Ludwigia grandiflora and L. peploides, invasive species in Europe. Aquatic Invasions, 9, 157–166.

Glover R, Drenovsky RE, Futrell CJ, Grewell BJ (2015) Clonal integration in Ludwigia hexapetala under different light regimes. Aquatic Botany, 122, 40–46.

Hieda S, Kaneko Y, Nakagawa M, Noma N (2020) Ludwigia grandiflora (Michx.) Greuter & Burdet subsp. hexapetala (Hook. & Arn.) G. L. Nesom & Kartesz, an Invasive Aquatic Plant in Lake Biwa, the Largest Lake in Japan. Acta Phytotaxonomica et Geobotanica, 71, 65–71.

Meisler J (2009) Controlling Ludwigia hexaplata in Northern California. Wetland Science and Practice, 26, 15–19.

Portillo Lemus LO, Harang M, Bozec M, Haury J, Stoeckel S, Barloy D (2022) Late-acting self-incompatible system, preferential allogamy and delayed selfing in the heteromorphic invasive populations of Ludwigia grandiflora subsp. hexapetala. bioRxiv, 2021.07.15.452457, ver. 4 peer-reviewed and recommended by Peer Community in Ecology.

Portillo Lemus LO, Bozec M, Harang M, Coudreuse J, Haury J, Stoeckel S, Barloy D (2021) Self-incompatibility limits sexual reproduction rather than environmental conditions in an invasive water primrose. Plant-Environment Interactions, 2, 74–86.

Reid AJ, Carlson AK, Creed IF, Eliason EJ, Gell PA, Johnson PTJ, Kidd KA, MacCormack TJ, Olden JD, Ormerod SJ, Smol JP, Taylor WW, Tockner K, Vermaire JC, Dudgeon D, Cooke SJ (2019) Emerging threats and persistent conservation challenges for freshwater biodiversity. Biological Reviews, 94, 849–873.

Thouvenot L, Haury J, Thiebaut G (2013) A success story: water primroses, aquatic plant pests. Aquatic Conservation: Marine and Freshwater Ecosystems, 23, 790–803.

Thouvenot L, Haury J, Thiébaut G (2013) Seasonal plasticity of Ludwigia grandiflora under light and water depth gradients: An outdoor mesocosm experiment. Flora - Morphology, Distribution, Functional Ecology of Plants, 208, 430–437.

Late-acting self-incompatible system, preferential allogamy and delayed selfing in the heterostylous invasive populations of Ludwigia grandiflora subsp. hexapetalaLuis O. Portillo Lemus, Maryline Harang, Michel Bozec, Jacques Haury, Solenn Stoeckel, Dominique Barloy<p style="text-align: justify;">Breeding system influences local population genetic structure, effective size, offspring fitness and functional variation. Determining the respective importance of self- and cross-fertilization in hermaphroditic flo...Biological invasions, Botany, Freshwater ecology, PollinationAntoine Vernay2021-07-16 09:53:50 View
12 Sep 2023
article picture

Linking intrinsic scales of ecological processes to characteristic scales of biodiversity and functioning patterns

The impact of process at different scales on diversity and ecosystem functioning: a huge challenge

Recommended by ORCID_LOGO based on reviews by Shai Pilosof, Gian Marco Palamara and 1 anonymous reviewer

Scale is a big topic in ecology [1]. Environmental variation happens at particular scales. The typical scale at which organisms disperse is species-specific, but, as a first approximation, an ensemble of similar species, for instance, trees, could be considered to share a typical dispersal scale. Finally, characteristic spatial scales of species interactions are, in general, different from the typical scales of dispersal and environmental variation. Therefore, conceptually, we can distinguish these three characteristic spatial scales associated with three different processes: species selection for a given environment (E), dispersal (D), and species interactions (I), respectively.  

From the famous species-area relation to the spatial distribution of biomass and species richness, the different macro-ecological patterns we usually study emerge from an interplay between dispersal and local interactions in a physical environment that constrains species establishment and persistence in every location. To make things even more complicated, local environments are often modified by the species that thrive in them, which establishes feedback loops.  It is usually assumed that local interactions are short-range in comparison with species dispersal, and dispersal scales are typically smaller than the scales at which the environment varies (I < D < E, see [2]), but this should not always be the case. 

The authors of this paper [2] relax this typical assumption and develop a theoretical framework to study how diversity and ecosystem functioning are affected by different relations between the typical scales governing interactions, dispersal, and environmental variation. This is a huge challenge. First, diversity and ecosystem functioning across space and time have been empirically characterized through a wide variety of macro-ecological patterns. Second, accommodating local interactions, dispersal and environmental variation and species environmental preferences to model spatiotemporal dynamics of full ecological communities can be done also in a lot of different ways. One can ask if the particular approach suggested by the authors is the best choice in the sense of producing robust results, this is, results that would be predicted by alternative modeling approaches and mathematical analyses [3]. The recommendation here is to read through and judge by yourself.  

The main unusual assumption underlying the model suggested by the authors is non-local species interactions. They introduce interaction kernels to weigh the strength of the ecological interaction with distance, which gives rise to a system of coupled integro-differential equations. This kernel is the key component that allows for control and varies the scale of ecological interactions. Although this is not new in ecology [4], and certainly has a long tradition in physics ---think about the electric or the gravity field, this approach has been widely overlooked in the development of the set of theoretical frameworks we have been using over and over again in community ecology, such as the Lotka-Volterra equations or, more recently, the metacommunity concept [5].

In Physics, classic fields have been revised to account for the fact that information cannot travel faster than light. In an analogous way, a focal individual cannot feel the presence of distant neighbors instantaneously. Therefore, non-local interactions do not exist in ecological communities. As the authors of this paper point out, they emerge in an effective way as a result of non-random movements, for instance, when individuals go regularly back and forth between environments (see [6], for an application to infectious diseases), or even migrate between regions. And, on top of this type of movement, species also tend to disperse and colonize close (or far) environments. Individual mobility and dispersal are then two types of movements, characterized by different spatial-temporal scales in general. Species dispersal, on the one hand, and individual directed movements underlying species interactions, on the other, are themselves diverse across species, but it is clear that they exist and belong to two distinct categories. 

In spite of the long and rich exchange between the authors' team and the reviewers, it was not finally clear (at least, to me and to one of the reviewers) whether the model for the spatio-temporal dynamics of the ecological community (see Eq (1) in [2]) is only presented as a coupled system of integro-differential equations on a continuous landscape for pedagogical reasons, but then modeled on a discrete regular grid for computational convenience. In the latter case, the system represents a regular network of local communities,  becomes a system of coupled ODEs, and can be numerically integrated through the use of standard algorithms. By contrast,  in the former case, the system is meant to truly represent a community that develops on continuous time and space, as in reaction-diffusion systems. In that case, one should keep in mind that numerical instabilities can arise as an artifact when integrating both local and non-local spatio-temporal systems. Spatial patterns could be then transient or simply result from these instabilities. Therefore, when analyzing spatiotemporal integro-differential equations, special attention should be paid to the use of the right numerical algorithms. The authors share all their code at, and all this can be checked out. In any case, the whole discussion between the authors and the reviewers has inherent value in itself, because it touches on several limitations and/or strengths of the author's approach,  and I highly recommend checking it out and reading it through.

Beyond these methodological issues, extensive model explorations for the different parameter combinations are presented. Several results are reported, but, in practice, what is then the main conclusion we could highlight here among all of them?  The authors suggest that "it will be difficult to manage landscapes to preserve biodiversity and ecosystem functioning simultaneously, despite their causative relationship", because, first, "increasing dispersal and interaction scales had opposing
effects" on these two patterns, and, second, unexpectedly, "ecosystems attained the highest biomass in scenarios which also led to the lowest levels of biodiversity". If these results come to be fully robust, this is, they pass all checks by other research teams trying to reproduce them using alternative approaches, we will have to accept that we should preserve biodiversity on its own rights and not because it enhances ecosystem functioning or provides particular beneficial services to humans. 


[1] Levin, S. A. 1992. The problem of pattern and scale in ecology. Ecology 73:1943–1967.

[2] Yuval R. Zelnik, Matthieu Barbier, David W. Shanafelt, Michel Loreau, Rachel M. Germain. 2023. Linking intrinsic scales of ecological processes to characteristic scales of biodiversity and functioning patterns. bioRxiv, ver. 2 peer-reviewed and recommended by Peer Community in Ecology.

[3] Baron, J. W. and Galla, T. 2020. Dispersal-induced instability in complex ecosystems. Nature Communications  11, 6032.

[4] Cushing, J. M. 1977. Integrodifferential equations and delay models in population dynamics 
 Springer-Verlag, Berlin.

[5] M. A. Leibold, M. Holyoak, N. Mouquet, P. Amarasekare, J. M. Chase, M. F. Hoopes, R. D. Holt, J. B. Shurin, R. Law, D. Tilman, M. Loreau, A. Gonzalez. 2004. The metacommunity concept: a framework for multi-scale community ecology. Ecology Letters, 7(7): 601-613.

[6] M. Pardo-Araujo, D. García-García, D. Alonso, and F. Bartumeus. 2023. Epidemic thresholds and human mobility. Scientific reports 13 (1), 11409.

Linking intrinsic scales of ecological processes to characteristic scales of biodiversity and functioning patternsYuval R. Zelnik, Matthieu Barbier, David W. Shanafelt, Michel Loreau, Rachel M. Germain<p style="text-align: justify;">Ecology is a science of scale, which guides our description of both ecological processes and patterns, but we lack a systematic understanding of how process scale and pattern scale are connected. Recent calls for a ...Biodiversity, Community ecology, Dispersal & Migration, Ecosystem functioning, Landscape ecology, Theoretical ecologyDavid Alonso2021-10-13 23:24:45 View
09 Nov 2023
article picture

Mark loss can strongly bias estimates of demographic rates in multi-state models: a case study with simulated and empirical datasets

Marks lost in action, biased estimations

Recommended by ORCID_LOGO based on reviews by Olivier Gimenez, Devin Johnson and 1 anonymous reviewer

Capture-Mark-Recapture (CMR) data are commonly used to estimate ecological variables such as abundance, survival probability, or transition rates from one state to another (e.g. from juvenile to adult, or migration from one site to another). Many studies have shown how estimations can be affected by neglecting one aspect of the population under study (e.g. the heterogeneity in survival between individuals) or one limit of the methodology itself (e.g. the fact that observers might not detect an individual although it is still alive). Strikingly, very few studies have yet assessed the robustness of one fundamental assumption of all CMR-based inferences: marks are supposed definitive and immutable. If they are not, how are estimations affected? Addressing this issue is the main goal of the paper by Touzalin et al. (2023), and they did a very nice work. But, because the answer is not that simple, it also calls for further investigations.

When and why would mark loss bias estimation? In at least two situations. First, when estimating survival rates: if an individual loses its mark, it will be considered as dead, hence death rates will be overestimated. Second, more subtly, when estimating transition rates: if one individual loses its mark at the specific moment where its state changes, then a transition will be missed in data. The history of the marked individual would then be split into two independent CMR sequences as if there were two different individuals, including one which died.

Touzalin et al. (2023) thoroughly studied these two situations by estimating ecological parameters on 1) well-thought simulated datasets, that cover a large range of possible situations inspired from a nice compilation of hundreds of estimations from fish and bats studies, and 2) on their own bats dataset, for which they had various sources of information about mark losses, i.e. different mark types on the same individuals, including mark based on genotypes, and marks found on the soil in the place where bats lived. Their main findings from the simulated datasets are that there is a general trend for underestimation of survival and transition rates if mark loss is not accounting for in the model, as it would be intuitively expected. However, they also showed from the bats dataset that biases do not show any obvious general trend, suggesting complex interactions between different ecological processes and/or with the estimation procedure itself.

The results by Touzalin et al. (2023) strongly suggest that mark loss should systematically be included in models estimating parameters from CMR data. In addition to adapt the inferential models, the authors also recommend considering either a double marking, or even a single but ‘permanent’ mark such as one based on the genotypes. However, the potential gain of a double marking or of the use of genotypes is still to be evaluated both in theory and practice, and it seems to be not that obvious at first sight. First because double marking can be costly for experimenters but also for the marked animals, especially as several studies showed that marks can significantly affect survival or recapture rates. Second because multiple sources of errors can affect genotyping, which would result in wrong individual assignations especially in populations with low genetic diversity or high inbreeding, or no individual assignation at all, which would increase the occurrence of missing data in CMR datasets. Touzalin et al. (2023) supposed in their paper that there were no genotyping errors, but one can doubt it to be true in most situations. They have now important and interesting other issues to address.


Frédéric Touzalin, Eric J. Petit, Emmanuelle Cam, Claire Stagier, Emma C. Teeling, Sébastien J. Puechmaille (2023) Mark loss can strongly bias demographic rates in multi-state models: a case study with simulated and empirical datasets. BioRxiv, ver. 3 peer-reviewed and recommended by Peer Community in Ecology.

Mark loss can strongly bias estimates of demographic rates in multi-state models: a case study with simulated and empirical datasetsFrédéric Touzalin, Eric J. Petit, Emmanuelle Cam, Claire Stagier, Emma C. Teeling, Sébastien J. Puechmaille<p style="text-align: justify;">1. The development of methods for individual identification in wild species and the refinement of Capture-Mark-Recapture (CMR) models over the past few decades have greatly improved the assessment of population demo...Conservation biology, DemographySylvain Billiard2022-04-12 18:49:34 View
02 Dec 2021
article picture

Metabarcoding faecal samples to investigate spatiotemporal variation in the diet of the endangered Westland petrel (Procellaria westlandica)

The promise and limits of DNA based approach to infer diet flexibility in endangered top predators

Recommended by based on reviews by Francis John Burdon and Babett Günther

There is growing evidence of worldwide decline of populations of top predators, including marine ones (Heithaus et al, 2008, Mc Cauley et al., 2015), with cascading effects expected at the ecosystem level, due to global change and human activities, including habitat loss or fragmentation, the collapse or the range shifts of their preys. On a global scale, seabirds are among the most threatened group of birds, about one-third of them being considered as threatened or endangered (Votier& Sherley, 2017). The large consequences of the decrease of the populations of preys they feed on (Cury et al, 2011) points diet flexibility as one important element to understand for effective management (McInnes et al, 2017).  Nevertheless, morphological inventory of preys requires intrusive protocols, and the differential digestion rate of distinct taxa may lead to a large bias in morphological-based diet assessments. The use of DNA metabarcoding on feces (or diet DNA, dDNA) now allows non-invasive approaches facilitating the recollection of samples and the detection of multiple preys independently of their digestion rates (Deagle et al., 2019). Although no gold standard exists yet to avoid bias associated with metabarcoding (primer bias, gaps in reference databases, inability to differentiate primary from secondary predation…), the use of these recent techniques has already improved the knowledge of the foraging behaviour and diet of many animals (Ando et al., 2020).

Both promise and shortcomings of this approach are illustrated in the article “Metabarcoding faecal samples to investigate spatiotemporal variation in the diet of the endangered Westland petrel (Procellaria westlandica)” by Quereteja et al. (2021). In this work, the authors assessed the nature and spatio-temporal flexibility of the foraging behaviour and consequent diet of the endangered petrel Procellaria westlandica from New-Zealand through metabarcoding of faeces samples.

The results of this dDNA, non-invasive approach, identify some expected and also unexpected prey items, some of which require further investigation likely due to large gaps in the reference databases. They also reveal the temporal (before and after hatching) and spatial (across colonies only 1.5km apart) flexibility of the foraging behaviour, additionally suggesting a possible influence of fisheries activities in the surroundings of the colonies. This study thus both underlines the power of the non-invasive metabarcoding approach on faeces, and the important results such analysis can deliver for conservation, pointing a potential for diet flexibility that may be essential for the resilience of this iconic yet endangered species.


Ando H, Mukai H, Komura T, Dewi T, Ando M, Isagi Y (2020) Methodological trends and perspectives of animal dietary studies by noninvasive fecal DNA metabarcoding. Environmental DNA, 2, 391–406.

Cury PM, Boyd IL, Bonhommeau S, Anker-Nilssen T, Crawford RJM, Furness RW, Mills JA, Murphy EJ, Österblom H, Paleczny M, Piatt JF, Roux J-P, Shannon L, Sydeman WJ (2011) Global Seabird Response to Forage Fish Depletion—One-Third for the Birds. Science, 334, 1703–1706.

Deagle BE, Thomas AC, McInnes JC, Clarke LJ, Vesterinen EJ, Clare EL, Kartzinel TR, Eveson JP (2019) Counting with DNA in metabarcoding studies: How should we convert sequence reads to dietary data? Molecular Ecology, 28, 391–406.

Heithaus MR, Frid A, Wirsing AJ, Worm B (2008) Predicting ecological consequences of marine top predator declines. Trends in Ecology & Evolution, 23, 202–210.

McCauley DJ, Pinsky ML, Palumbi SR, Estes JA, Joyce FH, Warner RR (2015) Marine defaunation: Animal loss in the global ocean. Science, 347, 1255641.

McInnes JC, Jarman SN, Lea M-A, Raymond B, Deagle BE, Phillips RA, Catry P, Stanworth A, Weimerskirch H, Kusch A, Gras M, Cherel Y, Maschette D, Alderman R (2017) DNA Metabarcoding as a Marine Conservation and Management Tool: A Circumpolar Examination of Fishery Discards in the Diet of Threatened Albatrosses. Frontiers in Marine Science, 4, 277.

Querejeta M, Lefort M-C, Bretagnolle V, Boyer S (2021) Metabarcoding faecal samples to investigate spatiotemporal variation in the diet of the endangered Westland petrel (Procellaria westlandica). bioRxiv, 2020.10.30.360289, ver. 4 peer-reviewed and recommended by Peer Community in Ecology.

Votier SC, Sherley RB (2017) Seabirds. Current Biology, 27, R448–R450.

Metabarcoding faecal samples to investigate spatiotemporal variation in the diet of the endangered Westland petrel (Procellaria westlandica)Marina Querejeta, Marie-Caroline Lefort, Vincent Bretagnolle, Stéphane Boyer<p style="text-align: justify;">As top predators, seabirds can be indirectly impacted by climate variability and commercial fishing activities through changes in marine communities. However, high mobility and foraging behaviour enables seabirds to...Conservation biology, Food webs, Marine ecology, Molecular ecologySophie Arnaud-Haond2020-10-30 20:14:50 View
12 Jan 2024
article picture

Methods for tagging an ectoparasite, the salmon louse Lepeophtheirus salmonis

Marking invertebrates using RFID tags

Recommended by ORCID_LOGO based on reviews by Simon Blanchet and 1 anonymous reviewer

Guiding and monitoring the efficiency of conservation efforts needs robust scientific background information, of which one key element is estimating wildlife abundance and its spatial and temporal variation. As raw counts are by nature incomplete counts of a population, correcting for detectability is required (Clobert, 1995; Turlure et al., 2018). This can be done with Capture-Mark-Recapture protocols (Iijima, 2020). Techniques for marking individuals are diverse, e.g. writing on butterfly wings, banding birds, or using natural specific patterns in the individual’s body such as leopard fur or whale tail. Advancement in technology opens new opportunities for developing marking techniques, including strategies to limit mark identification errors (Burchill & Pavlic, 2019), and for using active marks that can transmit data remotely or be read automatically.

The details of such methodological developments frequently remain unpublished, the method being briefly described in studies that use it. For a few years, there has been however a renewed interest in proper publishing of methods for ecology and evolution. This study by Folk & Mennerat (2023) fits in this context, offering a nice example of detailed description and testing of a method to mark salmon ectoparasites using RFID tags. Such tags are extremely small, yet easy to use, even with automatic recording procedure. The study provides a very good basis protocol that should help researchers working for small species, in particular invertebrates. The study is complemented by a video illustrating the placement of the tag so the reader who would like to replicate the procedure can get a very precise idea of it.


Burchill, A. T., & Pavlic, T. P. (2019). Dude, where’s my mark? Creating robust animal identification schemes informed by communication theory. Animal Behaviour, 154, 203–208.

Clobert, J. (1995). Capture-recapture and evolutionary ecology: A difficult wedding ? Journal of Applied Statistics, 22(5–6), 989–1008.

Folk, A., & Mennerat, A. (2023). Methods for tagging an ectoparasite, the salmon louse Lepeophtheirus salmonis (p. 2023.08.31.555695). bioRxiv, ver. 2 peer-reviewed and recommended by Peer Community in Ecology.

Iijima, H. (2020). A Review of Wildlife Abundance Estimation Models: Comparison of Models for Correct Application. Mammal Study, 45(3), 177–188.

Turlure, C., Pe’er, G., Baguette, M., & Schtickzelle, N. (2018). A simplified mark–release–recapture protocol to improve the cost effectiveness of repeated population size quantification. Methods in Ecology and Evolution, 9(3), 645–656.


Methods for tagging an ectoparasite, the salmon louse *Lepeophtheirus salmonis*Alexius Folk, Adele Mennerat<p style="text-align: justify;">Monitoring individuals within populations is a cornerstone in evolutionary ecology, yet individual tracking of invertebrates and particularly parasitic organisms remains rare. To address this gap, we describe here a...Dispersal & Migration, Evolutionary ecology, Host-parasite interactions, Marine ecology, Parasitology, Terrestrial ecology, ZoologyNicolas Schtickzelle2023-09-04 15:25:08 View
27 Nov 2023
article picture

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 Apr 2023
article picture

Most diverse, most neglected: weevils (Coleoptera: Curculionoidea) are ubiquitous specialized brood-site pollinators of tropical flora

Pollination-herbivory by weevils claiming for recognition: the Cinderella among pollinators

Recommended by based on reviews by Susan Kirmse, Carlos Eduardo Nunes and 2 anonymous reviewers

Since Charles Darwin times, and probably earlier, naturalists have been eager to report the rarest pollinators being discovered, and this still happens even in recent times; e.g., increased evidence of lizards, cockroaches, crickets or earwigs as pollinators (Suetsugu 2018, Komamura et al. 2021, de Oliveira-Nogueira et al. 2023), shifts to invasive animals as pollinators, including passerine birds and rats (Pattemore & Wilcove 2012), new amazing cases of mimicry in pollination, such as “bleeding” flowers that mimic wounded insects (Heiduk et al., 2023) or even the possibility that a tree frog is reported for the first time as a pollinator (de Oliveira-Nogueira et al. 2023). This is in part due to a natural curiosity of humans about rarity, which pervades into scientific insight (Gaston 1994). Among pollinators, the apparent rarity of some interaction types is sometimes a symptom of a lack of enough inquiry. This seems to be the case of weevil pollination, given that these insects are widely recognized as herbivores, particularly those that use plant parts to nurse their breed and never were thought they could act also as mutualists, pollinating the species they infest. This is known as a case of brood site pollination mutualism (BSPM), which also involves an antagonistic counterpart (herbivory) to which plants should face. This is the focus of the manuscript (Haran et al. 2023) we are recommending here. There is wide treatment of this kind of pollination in textbooks, albeit focused on yucca-yucca moth and fig-fig wasp interactions due to their extreme specialization (Pellmyr 2003, Kjellberg et al. 2005), and more recently accompanied by Caryophyllaceae-moth relationship (Kephart et al. 2006). 

Here we find a detailed review that shows that the most diverse BSPM, in terms of number of plant and pollinator species involved, is that of weevils in the tropics. The mechanism of BSPM does not involve a unique morphological syndrome, as it is mostly functional and thus highly dependent on insect biology (Fenster & al. 2004), whereas the flower phenotypes are highly divergent among species. Probably, the inconspicuous nature of the interaction, and the overwhelming role of weevils as seed predators, even as pests, are among the causes of the neglection of weevils as pollinators, as it could be in part the case of ants as pollinators (de Vega et al. 2014). The paper by Haran et al (2023) comes to break this point.

Thus, the rarity of weevil pollination in former reports is not a consequence of an anecdotical nature of this interaction, even for the BSPM, according to the number of cases the authors are reporting, both in terms of plant and pollinator species involved. This review has a classical narrative format which involves a long text describing the natural history behind the cases. It is timely and fills the gap for this important pollination interaction for biodiversity and also for economic implications for fruit production of some crops. Former reviews have addressed related topics on BSPM but focused on other pollinators, such as those mentioned above. Besides, the review put much effort into the animal side of the interaction, which is not common in the pollination literature. Admittedly, the authors focus on the detailed description of some paradigmatic cases, and thereafter suggest that these can be more frequently reported in the future, based on varied evidence from morphology, natural history, ecology, and distribution of alleged partners. This procedure was common during the development of anthecology, an almost missing term for floral ecology (Baker 1983), relying on accumulative evidence based on detailed observations and experiments on flowers and pollinators. Currently, a quantitative approach based on the tools of macroecological/macroevolutionary analyses is more frequent in reviews. However, this approach requires a high amount of information on the natural history of the partnership, which allows for sound hypothesis testing. By accumulating this information, this approach allows the authors to pose specific questions and hypotheses which can be tested, particularly on the efficiency of the systems and their specialization degree for both the plants and the weevils, apparently higher for the latter. This will guarantee that this paper will be frequently cited by floral ecologists and evolutionary biologists and be included among the plethora of floral syndromes already described, currently based on more explicit functional grounds (Fenster et al. 2004). In part, this is one of the reasons why the sections focused on future prospects is so large in the review. 

I foresee that this mutualistic/antagonistic relationship will provide excellent study cases for the relative weight of these contrary interactions among the same partners and its relationship with pollination specialization-generalization and patterns of diversification in the plants and/or the weevils. As new studies are coming, it is possible that BSPM by weevils appears more common in non-tropical biogeographical regions. In fact, other BSPM are not so uncommon in other regions (Prieto-Benítez et al. 2017). In the future, it would be desirable an appropriate testing of the actual effect of phylogenetic niche conservatism, using well known and appropriately selected BSPM cases and robust phylogenies of both partners in the mutualism. Phylogenetic niche conservatism is a central assumption by the authors to report as many cases as possible in their review, and for that they used taxonomic relatedness. As sequence data and derived phylogenies for large numbers of vascular plant species are becoming more frequent (Jin & Quian 2022), I would recommend the authors to perform a comparative analysis using this phylogenetic information. At least, they have included information on phylogenetic relatedness of weevils involved in BSPM which allow some inferences on the multiple origins of this interaction. This is a good start to explore the drivers of these multiple origins through the lens of comparative biology.


Baker HG (1983) An Outline of the History of Anthecology, or Pollination Biology. In: L Real (ed). Pollination Biology. Academic Press.

de-Oliveira-Nogueira CH, Souza UF, Machado TM, Figueiredo-de-Andrade CA, Mónico AT, Sazima I, Sazima M, Toledo LF (2023). Between fruits, flowers and nectar: The extraordinary diet of the frog Xenohyla truncate. Food Webs 35: e00281.

Fenster CB W, Armbruster S, Wilson P, Dudash MR, Thomson JD (2004). Pollination syndromes and floral specialization. Annu. Rev. Ecol. Evol. Syst. 35: 375–403.

Gaston KJ (1994). What is rarity? In KJ Gaston (ed): Rarity. Population and Community Biology Series, vol 13. Springer, Dordrecht.

Haran J, Kergoat GJ, Bruno, de Medeiros AS (2023) Most diverse, most neglected: weevils (Coleoptera: Curculionoidea) are ubiquitous specialized brood-site pollinators of tropical flora. hal. 03780127, version 2 peer-reviewed and recommended by Peer Community in Ecology.

Heiduk A, Brake I, Shuttleworth A, Johnson SD (2023) ‘Bleeding’ flowers of Ceropegia gerrardii (Apocynaceae-Asclepiadoideae) mimic wounded insects to attract kleptoparasitic fly pollinators. New Phytologist.

Jin, Y., & Qian, H. (2022). V. PhyloMaker2: An updated and enlarged R package that can generate very large phylogenies for vascular plants. Plant Diversity, 44(4), 335-339.

Kjellberg F, Jousselin E, Hossaert-Mckey M, Rasplus JY (2005). Biology, ecology, and evolution of fig-pollinating wasps (Chalcidoidea, Agaonidae). In: A. Raman et al (eds) Biology, ecology and evolution of gall-inducing arthropods 2, 539-572. Science Publishers, Enfield.

Komamura R, Koyama K, Yamauchi T, Konno Y, Gu L (2021). Pollination contribution differs among insects visiting Cardiocrinum cordatum flowers. Forests 12: 452.

Pattemore DE, Wilcove DS (2012) Invasive rats and recent colonist birds partially compensate for the loss of endemic New Zealand pollinators. Proc. R. Soc. B 279: 1597–1605.

Pellmyr O (2003) Yuccas, yucca moths, and coevolution: a review. Ann. Missouri Bot. Gard. 90: 35-55.

Prieto-Benítez S, Yela JL, Giménez-Benavides L (2017) Ten years of progress in the study of Hadena-Caryophyllaceae nursery pollination. A review in light of new Mediterranean data. Flora, 232, 63-72.

Suetsugu K (2019) Social wasps, crickets and cockroaches contribute to pollination of the holoparasitic plant Mitrastemon yamamotoi (Mitrastemonaceae) in southern Japan. Plant Biology 21 176–182.

Most diverse, most neglected: weevils (Coleoptera: Curculionoidea) are ubiquitous specialized brood-site pollinators of tropical floraJulien Haran, Gael J. Kergoat, Bruno A. S. de Medeiros<p style="text-align: justify;">In tropical environments, and especially tropical rainforests, a major part of pollination services is provided by diverse insect lineages. Unbeknownst to most, beetles, and more specifically hyperdiverse weevils (C...Biodiversity, Evolutionary ecology, Pollination, Tropical ecologyJuan Arroyo2022-09-28 11:54:37 View
29 Sep 2023
article picture

MoveFormer: a Transformer-based model for step-selection animal movement modelling

A deep learning model to unlock secrets of animal movement and behaviour

Recommended by ORCID_LOGO based on reviews by Jacob Davidson and 1 anonymous reviewer

The study of animal movement is essential for understanding their behaviour and how ecological or global changes impact their routines [1]. Recent technological advancements have improved the collection of movement data [2], but limited statistical tools have hindered the analysis of such data [3–5]. Animal movement is influenced not only by environmental factors but also by internal knowledge and memory, which are challenging to observe directly [6,7]. Routine movement behaviours and the incorporation of memory into models remain understudied.

Researchers have developed ‘MoveFormer’ [8], a deep learning-based model that predicts future movements based on past context, addressing these challenges and offering insights into the importance of different context lengths and information types. The model has been applied to a dataset of over 1,550 trajectories from various species, and the authors have made the MoveFormer source code available for further research.

Inspired by the step-selection framework and efforts to quantify uncertainty in movement predictions, MoveFormer leverages deep learning, specifically the Transformer architecture, to encode trajectories and understand how past movements influence current and future ones – a critical question in movement ecology. The results indicate that integrating information from a few days to two or three weeks before the movement enhances predictions. The model also accounts for environmental predictors and offers insights into the factors influencing animal movements.

Its potential impact extends to conservation, comparative analyses, and the generalisation of uncertainty-handling methods beyond ecology, with open-source code fostering collaboration and innovation in various scientific domains. Indeed, this method could be applied to analyse other kinds of movements, such as arm movements during tool use [9], pen movements, or eye movements during drawing [10], to better understand anticipation in actions and their intentionality.


1.           Méndez, V.; Campos, D.; Bartumeus, F. Stochastic Foundations in Movement Ecology: Anomalous Diffusion, Front Propagation and Random Searches; Springer Series in Synergetics; Springer: Berlin, Heidelberg, 2014; ISBN 978-3-642-39009-8.
2.           Fehlmann, G.; King, A.J. Bio-Logging. Curr. Biol. 2016, 26, R830-R831.
3.           Jacoby, D.M.; Freeman, R. Emerging Network-Based Tools in Movement Ecology. Trends Ecol. Evol. 2016, 31, 301-314.
4.           Michelot, T.; Langrock, R.; Patterson, T.A. moveHMM: An R Package for the Statistical Modelling of Animal Movement Data Using Hidden Markov Models. Methods Ecol. Evol. 2016, 7, 1308-1315.
5.           Wang, G. Machine Learning for Inferring Animal Behavior from Location and Movement Data. Ecol. Inform. 2019, 49, 69-76.
6.           Noser, R.; Byrne, R.W. Change Point Analysis of Travel Routes Reveals Novel Insights into Foraging Strategies and Cognitive Maps of Wild Baboons. Am. J. Primatol. 2014, 76, 399-409.
7.           Fagan, W.F.; Lewis, M.A.; Auger‐Méthé, M.; Avgar, T.; Benhamou, S.; Breed, G.; LaDage, L.; Schlägel, U.E.; Tang, W.; Papastamatiou, Y.P. Spatial Memory and Animal Movement. Ecol. Lett. 2013, 16, 1316-1329.
8.           Cífka, O.; Chamaillé-Jammes, S.; Liutkus, A. MoveFormer: A Transformer-Based Model for Step-Selection Animal Movement Modelling. bioRxiv 2023, ver. 4 peer-reviewed and recommended by Peer Community in Ecology.
9.           Ardoin, T.; Sueur, C. Automatic Identification of Stone-Handling Behaviour in Japanese Macaques Using LabGym Artificial Intelligence. 2023,
10.         Martinet, L.; Pelé, M. Drawing in Nonhuman Primates: What We Know and What Remains to Be Investigated. J. Comp. Psychol. Wash. DC 1983 2021, 135, 176-184, doi:10.1037/com0000251.

MoveFormer: a Transformer-based model for step-selection animal movement modellingOndřej Cífka, Simon Chamaillé-Jammes, Antoine Liutkus<p style="text-align: justify;">The movement of animals is a central component of their behavioural strategies. Statistical tools for movement data analysis, however, have long been limited, and in particular, unable to account for past movement i...Behaviour & Ethology, Habitat selectionCédric Sueur2023-03-22 16:32:14 View