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06 Nov 2023
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Influence of mimicry on extinction risk in Aculeata: a theoretical approach

Mullerian and Batesian mimicry can influence population and community dynamics

Recommended by based on reviews by Jesus Bellver and 1 anonymous reviewer

Mimicry between species has long attracted the attention of scientists. Over a century ago, Bates first proposed that palatable species should gain a benefit by resembling unpalatable species (Bates 1862). Not long after, Müller suggested that there could also be a mutual advantage for two unpalatable species to mimic one another to reduce predator error (Müller 1879). These forms of mimicry, Batesian and Müllerian, are now widely studied, providing broad insights into behaviour, ecology and evolution.

Numerous taxa, including both invertebrates and vertebrates, show examples of Batesian or Müllerian mimicry. Bees and wasps provide a particularly interesting case due to the differences in defence between females and males of the same species. While both males and females may display warning colours, only females can sting and inject venom to cause pain and allow escape from predators. Therefore, males are palatable mimics and can resemble females of their own species or females of another species (dual sex-limited mimicry). This asymmetry in defence could have impacts on both population structure and community assembly, yet research into mimicry largely focuses on systems without sex differences.

Here, Boutin and colleagues (2023) use a differential equations model to explore the effect of mimicry on population structure and community assembly for sex-limited defended species. Specifically, they address three questions, 1) how do female noxiousness and sex-ratio influence the extinction risk of a single species?; 2) what is the effect of mimicry on species co-existence? and 3) how does dual sex-limited mimicry influence species co-existence? Their results reveal contexts in which populations with undefended males can persist, the benefit of Müllerian mimicry for species coexistence and that dual sex-limited mimicry can have a destabilising impact on species coexistence.

The results not only contribute to our understanding of how mimicry is maintained in natural systems but also demonstrate how changes in relative abundance or population structure of one species could impact another species. Further insight into the population and community dynamics of insects is particularly important given the current population declines (Goulson 2019; Seibold et al 2019).

References

Bates, H. W. 1862. Contributions to the insect fauna of the Amazon Valley, Lepidoptera: Heliconidae. Trans. Linn. Soc. Lond. 23:495- 566. https://doi.org/10.1111/j.1096-3642.1860.tb00146.x

Boutin, M., Costa, M., Fontaine, C., Perrard, A., Llaurens, V. 2022 Influence of sex-limited mimicry on extinction risk in Aculeata: a theoretical approach. bioRxiv, ver. 2 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2022.10.21.513153

Goulson, D. 2019. The insect apocalypse, and why it matters. Curr. Biol. 29: R967-R971. https://doi.org/10.1016/j.cub.2019.06.069

Müller, F. 1879. Ituna and Thyridia; a remarkable case of mimicry in butterflies. Trans. Roy. Entom. Roc. 1879:20-29.

Seibold, S., Gossner, M. M., Simons, N. K., Blüthgen, N., Müller, J., Ambarlı, D., ... & Weisser, W. W. 2019. Arthropod decline in grasslands and forests is associated with landscape-level drivers. Nature, 574: 671-674. https://doi.org/10.1038/s41586-019-1684-3

Influence of mimicry on extinction risk in Aculeata: a theoretical approachMaxime Boutin, Manon Costa, Colin Fontaine, Adrien Perrard, Violaine Llaurens<p style="text-align: justify;">Positive ecological interactions, such as mutualism, can play a role in community structure and species co-existence. A well-documented case of mutualistic interaction is Mullerian mimicry, the convergence of colour...Biodiversity, Coexistence, Eco-evolutionary dynamics, Evolutionary ecology, Facilitation & MutualismAmanda Franklin2022-10-25 19:11:55 View
23 Oct 2023
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The Moa the Merrier: Resolving When the Dinornithiformes Went Extinct

Are Moas ancient Lazarus species?

Recommended by ORCID_LOGO based on reviews by Tim Coulson and Richard Holdaway

Ancient human colonisation often had catastrophic consequences for native fauna. The North American Megafauna went extinct shortly after humans entered the scene and Madagascar suffered twice, before 1500 CE and around 1700 CE after the Malayan and European colonisation. Maoris colonised New Zealand by about 1300 and a century later the giant Moa birds (Dinornithiformes) sharply declined. But did they went extinct or are they an ancient example of Lazarus species, species thought to be extinct but still alive? Scattered anecdotes of late sightings of living Moas even up to the 20th century seem to suggest the latter. The quest for later survival has also a criminal aspect. Who did it, the Maoris or the white colonisers in the late 18th century?

The present work by Floe Foxon (2023) tries to settle this question. It uses a survival modelling approach and an assessment of the reliability of nearly 100 alleged sightings. The model favours the so-called overkill hypothesis, that Moas probably went extinct in the 15th century shortly after Maori colonisation. A small but still remarkable probability remained for survival up to 1770. Later sightings turned out to be highly unreliable.

The paper is important as it does not rely on subjective discussions of late sightings but on a probabilistic modelling approach with sensitivity testing prior applied to marsupials. As common in probabilistic approaches, the study does not finally settle the case. A probability of as much as 20% remained for late survival after 1450 CE. This is not improbable as New Zealand was sufficiently unexplored in those days to harbour a few refuges for late survivors. However, in this respect, it is a bit unfortunate that at the end of the discussion, the paper cites Heuvelmans, the founder of cryptozoology, and it mentions the ivory-billed woodpecker, which has recently been redetected. No Moa remains were found after 1450.

References

Foxon F (2023) The Moa the Merrier: Resolving When the Dinornithiformes Went Extinct. bioRxiv, 2023.08.07.552261, ver. 2 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2023.08.07.552261

The Moa the Merrier: Resolving When the Dinornithiformes Went ExtinctFloe Foxon<p style="text-align: justify;">The Moa (Aves: Dinornithiformes) are an extinct group of the ratite clade from New Zealand. The overkill hypothesis asserts that the first New Zealand settlers hunted the Moa to extinction by 1450 CE, whereas the st...Conservation biology, Human impact, Statistical ecology, ZoologyWerner Ulrich Tim Coulson, Richard Holdaway2023-08-08 17:14:30 View
11 Oct 2023
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Identification of microbial exopolymer producers in sandy and muddy intertidal sediments by compound-specific isotope analysis

Disentangling microbial exopolymer dynamics in intertidal sediments

Recommended by and ORCID_LOGO based on reviews by 2 anonymous reviewers

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

References

  1. Flemming, H.-C. (2016) EPS-then and now. Microorganisms 4, 41 https://doi.org/10.3390/microorganisms4040041
  2. Wolfaardt, G.M. et al. (1999) Function of EPS. In Microbial Extracellular Polymeric Substances, pp. 171–200, Springer Berlin Heidelberg https://doi.org/10.1007/978-3-642-60147-7
  3. Flemming, H.-C. et al. (2007) The EPS matrix: the “house of biofilm cells.” J. Bacteriol. 189, 7945–7947 https://doi.org/10.1128/jb.00858-07
  4. Hubas, C. et al. (2022) Identification of microbial exopolymer producers in sandy and muddy intertidal sediments by compound-specific isotope analysis. bioRxiv, ver. 2 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2022.12.02.516908
Identification of microbial exopolymer producers in sandy and muddy intertidal sediments by compound-specific isotope analysisCédric Hubas, Julie Gaubert-Boussarie, An-Sofie D’Hondt, Bruno Jesus, Dominique Lamy, Vona Meleder, Antoine Prins, Philippe Rosa, Willem Stock, Koen Sabbe<p style="text-align: justify;">Extracellular polymeric substances (EPS) refer to a wide variety of high molecular weight molecules secreted outside the cell membrane by biofilm microorganisms. In the present study, EPS from marine microphytobenth...Biodiversity, Ecological stoichiometry, Ecosystem functioning, Food webs, Marine ecology, Microbial ecology & microbiology, Soil ecologyUte Risse-Buhl2022-12-06 14:13:11 View
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.

References

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). https://doi.org/10.1111/geb.13497

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). https://doi.org/10.1016/B978-0-12-438150-6.50018-2.

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.  https://doi.org/10.1101/2023.05.09.539999

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
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 ORCID_LOGO 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.

References

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. https://doi.org/10.1098/rspb.2022.2420

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. https://doi.org/10.1101/2022.07.25.501416

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. https://doi.org/10.1186/s13071-022-05410-8

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. https://doi.org/10.1038/s41598-022-11479-z

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
29 Sep 2023
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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.

References

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.
https://doi.org/10.1007/978-3-642-39010-4
 
2.           Fehlmann, G.; King, A.J. Bio-Logging. Curr. Biol. 2016, 26, R830-R831.
https://doi.org/10.1016/j.cub.2016.05.033
 
3.           Jacoby, D.M.; Freeman, R. Emerging Network-Based Tools in Movement Ecology. Trends Ecol. Evol. 2016, 31, 301-314.
https://doi.org/10.1016/j.tree.2016.01.011
 
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.
https://doi.org/10.1111/2041-210X.12578
 
5.           Wang, G. Machine Learning for Inferring Animal Behavior from Location and Movement Data. Ecol. Inform. 2019, 49, 69-76.
https://doi.org/10.1016/j.ecoinf.2018.12.002
 
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.
https://doi.org/10.1002/ajp.22181
 
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.
https://doi.org/10.1111/ele.12165
 
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.
https://doi.org/10.1101/2023.03.05.531080
 
9.           Ardoin, T.; Sueur, C. Automatic Identification of Stone-Handling Behaviour in Japanese Macaques Using LabGym Artificial Intelligence. 2023, https://doi.org/10.13140/RG.2.2.30465.02402
 
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.
https://doi.org/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
12 Sep 2023
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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 https://zenodo.org/record/5543191, 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. 

References

[1] Levin, S. A. 1992. The problem of pattern and scale in ecology. Ecology 73:1943–1967. https://doi.org/10.2307/1941447

[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.  https://doi.org/10.1101/2021.10.11.463913

[3] Baron, J. W. and Galla, T. 2020. Dispersal-induced instability in complex ecosystems. Nature Communications  11, 6032. https://doi.org/10.1038/s41467-020-19824-4

[4] Cushing, J. M. 1977. Integrodifferential equations and delay models in population dynamics 
 Springer-Verlag, Berlin. https://doi.org/10.1007/978-3-642-93073-7

[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. https://doi.org/10.1111/j.1461-0248.2004.00608.x

[6] M. Pardo-Araujo, D. García-García, D. Alonso, and F. Bartumeus. 2023. Epidemic thresholds and human mobility. Scientific reports 13 (1), 11409. https://doi.org/10.1038/s41598-023-38395-0

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

Addressing the daunting challenge of estimating species interactions from count data

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

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

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

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

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

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

References

Paquet, M., & Barraquand, F. (2023). Assessing species interactions using integrated predator-prey models. EcoEvoRxiv, ver. 2 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.32942/X2RC7W

Assessing species interactions using integrated predator-prey modelsMatthieu Paquet, Frederic Barraquand<p style="text-align: justify;">Inferring the strength of species interactions from demographic data is a challenging task. The Integrated Population Modelling (IPM) approach, bringing together population counts, capture-recapture, and individual-...Community ecology, Demography, Euring Conference, Food webs, Population ecology, Statistical ecologyTim Coulson Ilhan Özgen-Xian2023-01-05 17:02:22 View
29 Aug 2023
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Provision of essential resources as a persistence strategy in food webs

High-order interactions in food webs may strongly impact persistence of species

Recommended by ORCID_LOGO based on reviews by Jean-Christophe POGGIALE and 1 anonymous reviewer

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

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

References

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

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

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

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

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

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

Provision of essential resources as a persistence strategy in food websMichael Raatz<p style="text-align: justify;">Pairwise interactions in food webs, including those between predator and prey are often modulated by a third species. Such higher-order interactions are important structural components of natural food webs that can ...Biodiversity, Coexistence, Competition, Ecological stoichiometry, Food webs, Interaction networks, Theoretical ecologyCédric Gaucherel2023-02-23 17:48:26 View
28 Aug 2023
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Implementing a rapid geographic range expansion - the role of behavior changes

Behavioral changes in the rapid geographic expansion of the great-tailed grackle

Recommended by ORCID_LOGO based on reviews by Francois-Xavier Dechaume-Moncharmont, Pizza Ka Yee Chow and 1 anonymous reviewer

While many species' populations are declining, primarily due to human-related impacts (McKnee et al., 2014), certain species have thrived by utilizing human-influenced environments, leading to their population expansion (Muñoz & Real, 2006). In this context, the capacity to adapt and modify behaviors in response to new surroundings is believed to play a crucial role in facilitating species' spread to novel areas (Duckworth & Badyaev, 2007). For example, an increase in innovative behaviors within recently established communities could aid in discovering previously untapped food resources, while a decrease in exploration might reduce the likelihood of encountering dangers in unfamiliar territories (e.g., Griffin et al., 2016). To investigate the contribution of these behaviors to rapid range expansions, it is essential to directly measure and compare behaviors in various populations of the species.

The study conducted by Logan et al. (2023) aims to comprehend the role of behavioral changes in the range expansion of great-tailed grackles (Quiscalus mexicanus). To achieve this, the researchers compared the prevalence of specific behaviors at both the expansion's edge and its middle. Great-tailed grackles were chosen as an excellent model due to their behavioral adaptability, rapid geographic expansion, and their association with human-modified environments. The authors carried out a series of experiments in captivity using wild-caught individuals, following a detailed protocol. The study successfully identified differences in two of the studied behavioral traits: persistence (individuals participated in a larger proportion of trials) and flexibility variance (a component of the species' behavioral flexibility, indicating a higher chance that at least some individuals in the population could be more flexible). Notably, individuals at the edge of the population exhibited higher values of persistence and flexibility, suggesting that these behavioral traits might be contributing factors to the species' expansion. Overall, the study by Logan et al. (2023) is an excellent example of the importance of behavioral flexibility and other related behaviors in the process of species' range expansion and the significance of studying these behaviors across different populations to gain a better understanding of their role in the expansion process.

Finally, it is important to underline that this study is part of a pre-registration that received an In Principle Recommendation in PCI Ecology (Sebastián-González 2020) where objectives, methodology, and expected results were described in detail. The authors have identified any deviation from the original pre-registration and thoroughly explained the reasons for their deviations, which were very clear. 

References

Duckworth, R. A., & Badyaev, A. V. (2007). Coupling of dispersal and aggression facilitates the rapid range expansion of a passerine bird. Proceedings of the National Academy of Sciences, 104(38), 15017-15022. https://doi.org/10.1073/pnas.0706174104

Griffin, A.S., Guez, D., Federspiel, I., Diquelou, M., Lermite, F. (2016). Invading new environments: A mechanistic framework linking motor diversity and cognition to establishment success. Biological Invasions and Animal Behaviour, 26e46. https://doi.org/10.1017/CBO9781139939492.004

Logan, C. J., McCune, K., LeGrande-Rolls, C., Marfori, Z., Hubbard, J., Lukas, D. 2023. Implementing a rapid geographic range expansion - the role of behavior changes. EcoEvoRxiv, ver. 3 peer-reviewed and recommended by PCI Ecology. https://doi.org/10.32942/X2N30J

McKee, J. K., Sciulli, P. W., Fooce, C. D., & Waite, T. A. (2004). Forecasting global biodiversity threats associated with human population growth. Biological Conservation, 115(1), 161-164. https://doi.org/10.1016/S0006-3207(03)00099-5

Muñoz, A. R., & Real, R. (2006). Assessing the potential range expansion of the exotic monk parakeet in Spain. Diversity and Distributions, 12(6), 656-665. https://doi.org/10.1111/j.1472-4642.2006.00272.x

Sebastián González, E. (2020) The role of behavior and habitat availability on species geographic expansion. Peer Community in Ecology, 100062. https://doi.org/10.24072/pci.ecology.100062. Reviewers: Caroline Nieberding, Tim Parker, and Pizza Ka Yee Chow.

Implementing a rapid geographic range expansion - the role of behavior changesLogan CJ, McCune KB, LeGrande-Rolls C, Marfori Z, Hubbard J, Lukas D<p>It is generally thought that behavioral flexibility, the ability to change behavior when circumstances change, plays an important role in the ability of species to rapidly expand their geographic range. Great-tailed grackles (<em>Quiscalus mexi...Behaviour & Ethology, Preregistrations, ZoologyEsther Sebastián González2023-04-12 11:00:42 View