The current lack of social diversity - that is, gender, race and ethnicity - in academia reinforces a historical pattern of exclusion, wherein the knowledge, perspectives and advances of certain groups dominate the narrative, set rhythms and agenda, while the contributions of others are minimised or overlooked. The underrepresentation of and discrimination against women in academia is a well-documented and persistent issue. Despite policies designed to increase female representation and mitigate the structural processes that lead women to abandon their academic careers (Shaw & Stanton, 2012), women scientists continue to face inequalities in authorship, publications, funding, salaries, recognition and decision-making spaces (Astegiano et al., 2019, Woolston, 2019; Fox et a. 2023; Fontanarrosa et al. 2024; Zandonà, 2022; among others). Making these inequalities visible and fostering open discussion is a critical first step toward dismantling them. In this sense, the study by Rodrigues Barreto et al. (2025) makes an important contribution. The authors examine gender bias in seminar series within the field of Ecology, Evolution and Conservation Biology at the University of São Paulo (Brazil), using audience size as an indirect measure of speaker recognition.

The most interesting and novel finding of this work is that talks given by women -especially by female professors- attract smaller audiences than those given by men counterparts, despite the women having comparable levels of academic productivity, similar career trajectories, and presenting on equivalent topics to those of their male colleagues. These results suggest that seminar culture is not gender-blind (Dupas et al., 2021) and provide a new layer of evidence on how gender-based stereotypes continue to influence the visibility and recognition of women in science.

The authors also investigated whether the implementation of affirmative actions (i.e., open calls for volunteer speakers that prioritised women) improved both the representation of female speakers and the size of their audiences. As expected, these actions did succeed in increasing the representation of women among presenters, especially at senior academic levels; however, they did not lead to a proportional rise in audience size. While the time series and number of talks considered before the implementation of affirmative actions were considerably higher than those after this policy, the comparison remains relevant given the importance and timeliness of the topic.

The authors found that women give fewer talks than men. However, as they discuss, their results do not allow them to distinguish whether this inequality is due to gender bias or to a structural gender imbalance. Through a supplementary analysis of a subset of data from the University of São Paulo community, they find some evidence that the underrepresentation of women in the academic population itself may partly explain the gender gap in the seminar series. In any case, these findings, along with similar results from other studies (e.g. Greska, 2023) raise valuable questions: Will simply increasing the number of women in academia be enough to close the recognition gap between men and women scholars? What role might affirmative actions play in attracting a wider audience or enhancing the visibility and recognition of women's work? What kinds of initiatives could change the way we acknowledge women researchers’  contributions? What could reconfigure that “recognition landscape” from a feminist perspective, i.e., one less competitive, less hierarchical, more communitarian and less individual-centered?

As the authors acknowledge, the study has limitations (e.g., its focus on a single institution, a short timeframe to assess the impact of affirmative actions), but it provides valuable evidence to initiate broader approaches that include other disciplines, institutions, experimental approaches, and intersectional perspectives.

References

Astegiano J, Sebastián-González E, Castanho C de T (2019) Unravelling the gender 465 productivity gap in science: a meta-analytical review. Royal Society Open Science 6: 466 181566. https://doi.org/10.1098/rsos.181566​

Dupas P, Modestino AS, Niederle M, Wolfers J, Collective TSD (2021) Gender and the Dynamics of Economics Seminars. Cambridge, MA: National Bureau of Economic Research. http://www.nber.org/papers/w28494.pdf

Fontanarrosa, G., Zarbá, L., Aschero, V., Dos Santos, D. A., Montellano, M. G. N. et al. (2024). Over twenty years of publications in Ecology: Over-contribution of women reveals a new dimension of gender bias. PLoS ONE 19(9): e0307813.  https://doi.org/10.1371/journal.pone.0307813

Fox CW, Meyer J, Aimé E (2023) Double-blind peer review affects reviewer ratings and 509 editor decisions at an ecology journal. Functional Ecology 37: 1144–1157. https://doi.org/10.1111/1365-2435.14259​

Greska L (2023) Women in Academia: Why and where does the pipeline leak, and how can we fix it? MIT Science Policy Review 4: 102–109. https://doi.org/10.38105/spr.xmvdiojee1​

Rodrigues Barreto J, Romitelli I, Santana PC, Aprígio Assis A.N., Pardini R, de Souza Leite M. (2025) Is the audience gender-blind? Smaller attendance in female talks highlights imbalanced visibility in academia. EcoEvoRxiv, ver.4 peer-reviewed and recommended by PCI Ecology https://doi.org/10.32942/X25607

Shaw AK, Stanton DE (2012) Leaks in the pipeline: separating demographic inertia from ongoing gender differences in academia. Proceedings of the Royal Society B: Biological Sciences 279: 3736–3741. https://doi.org/10.1098/rspb.2012.0822​

Woolston C (2019) Scientists’ salary data highlight US$18,000 gender pay gap. Nature 565: 597 527–527. https://doi.org/10.1038/d41586-019-00220-y​

Zandonà E (2022) Female ecologists are falling from the academic ladder: A call for action. 602 Perspectives in Ecology and Conservation 20: 294–299. ​​​​https://doi.org/10.1016/j.pecon.2022.04.001​

The study of eco-evolutionary dynamics, i.e. of the inter-twinning between ecological and evolutionary processes when they occur at comparable time scales, is of growing interest in the current context of global change (Carroll, Hendry, Reznick, & Fox, 2007; Govaert et al., 2019). Demo-genetic agent-based models (DG-ABMs) have gained popularity to address this issue because of their abilities to consider feedback loops between ecological and evolutionary processes and to track populations of interacting individuals with adaptive traits variations (Berzaghi et al., 2019; Lamarins et al., 2022). This type of individual- and process-based simulation modelling where interindividual variation in fitness and hence opportunities for selection emerge from demography, which in turn affects the genetic composition of the population over successive generations (feedback loop), is only beginning to be applied to forest trees (Oddou-Muratorio, Hendrik, & Lefèvre, 2020). Examples include studies investigating the dispersal capacity of transgenes in forest landscapes using spatially explicit DG-ABMs with different demographic rates for transgenic and wild-type trees (DiFazio, Slavov, Burczyk, Leonardi, & Strauss, 2004; Kuparinen & Schurr, 2007), the effect of assortative mating and selection on genetic and plastic differentiation along environmental gradients (Soularue et al., 2023) or the interactions and feedback between tree thinning, genetic evolution, and forest stand dynamics, eventually in the context of drought-induced disturbances (Fririon, Davi, Oddou‐Muratorio, Ligot, & Lefèvre, 2024; Godineau et al., 2023).

In this study, Devresse et al. (2025) extend the current DG-ABM framework for forest trees by incorporating interspecific interactions within diverse, uneven-aged forests. To this end, they adapted an existing multi-species, process-based forest dynamics model—ForCEEPS (Morin et al., 2021)—enabling the evolution of selected tree functional traits across generations. Their work focuses on three quantitative traits: drought tolerance, shade tolerance, and maximal growth rate. Using this enhanced DG-ABM, the authors investigate the conditions under which evolutionary rescue might occur in a mixed beech-fir forest facing climate change. Their results demonstrate that greater trait variability and higher heritability can mitigate short-term (century-scale) forest cover loss under climate warming. The study also shows that assisted gene flow facilitates species adaptation to climate change, while the introduction of pre-adapted species (assisted migration) may enhance post-disturbance recovery but simultaneously constrain the evolutionary rescue of local species.

This work represents a major interdisciplinary advancement in forest ecology and nicely illustrates how integrating evolutionary processes into ecology-focused models can offer novel insights into forest dynamics. The implementation of genetic variability and inheritance via the infinitesimal model of quantitative genetics, along with its limitations, is described in detail, and the various research questions explored using the coupled DG‑ABM are presented as proof of concept for this successful integration. Beyond its methodological contribution, the study highlights the importance of more integrated approaches to understanding forest responses to climate change—approaches that account for both within- and between-species diversity and that promote nature-based solutions. It also underscores the urgent need for experimental studies exploring the genetic variation and architecture of adaptive traits in forest species to better anticipate and support their adaptive potential in a rapidly changing environment.

References

Berzaghi, F., Wright, I. J., Kramer, K., Oddou-Muratorio, S., Bohn, F. J., Reyer, C. P. O., … Hartig, F. (2019). Towards a new generation of trait-flexible vegetation models. Trends in Ecology & Evolution, 35(3), 191–205. https://doi.org/10.1016/j.tree.2019.11.006

Carroll, S. P., Hendry, A. P., Reznick, D. N., & Fox, C. W. (2007). Evolution on ecological time-scales. Functional Ecology, 21(3), 387–393. https://doi.org/10.1111/j.1365-2435.2007.01289.x

Devresse, L., Way, F., Postic, T., de Coligny, F. & Morin, X. (2025) Evolutionary rescue in a mixed beech-fir forest: insights from a quantitative-genetics approach in a process-based model. HAL, ver.4 peer-reviewed and recommended by PCI Ecology. https://hal.science/hal-04575070

DiFazio, S. P., Slavov, G. T., Burczyk, J., Leonardi, S., & Strauss, S. H. (2004). Gene flow from tree plantations and implications for transgenic risk assessment. In Plantation Forest Biotechnology for the 21st Century (pp. 405–422). https://doi.org/10.1016/j.diagmicrobio.2009.10.017

Fririon, V., Davi, H., Oddou‐Muratorio, S., Ligot, G., & Lefèvre, F. (2024). Can Thinning Foster Forest Genetic Adaptation to Drought? A Demo‐Genetic Modelling Approach With Disturbance Regimes. Evolutionary Applications, 17(12). https://doi.org/10.1111/eva.70051

Godineau, C., Fririon, V., Beudez, N., de Coligny, F., Courbet, F., Ligot, G., … Lefèvre, F. (2023). A demo-genetic model shows how silviculture reduces natural density-dependent selection in tree populations. Evolutionary Applications, (March), 1–15. https://doi.org/10.1111/eva.13606

Govaert, L., Fronhofer, E. A., Lion, S., Eizaguirre, C., Bonte, D., Egas, M., … Matthews, B. (2019). Eco-evolutionary feedbacks—Theoretical models and perspectives. Functional Ecology, 33(1), 13–30. https://doi.org/10.1111/1365-2435.13241

Kuparinen, A., & Schurr, F. M. (2007). A flexible modelling framework linking the spatio-temporal dynamics of plant genotypes and populations: Application to gene flow from transgenic forests. Ecological Modelling, 202(3–4), 476–486. https://doi.org/10.1016/j.ecolmodel.2006.11.015

Lamarins, A., Fririon, V., Folio, D., Vernier, C., Daupagne, L., Labonne, J., … Oddou-Muratorio, S. (2022). Importance of interindividual interactions in eco-evolutionary population dynamics: The rise of demo-genetic agent-based models. Evolutionary Applications, 15(12), 1988–2001. https://doi.org/10.1111/eva.13508

Morin, X., Bugmann, H., de Coligny, F., Martin-StPaul, N., Cailleret, M., Limousin, J. M., … Guillemot, J. (2021). Beyond forest succession: A gap model to study ecosystem functioning and tree community composition under climate change. Functional Ecology, 35(4), 955–975. https://doi.org/10.1111/1365-2435.13760

Oddou-Muratorio, S., Hendrik, D., & Lefèvre, F. (2020). Integrating evolutionary, demographic and ecophysiological processes to predict the adaptive dynamics of forest tree populations under global change. Tree Genetics & Genomes, 16(5), 1–22. https://doi.org/10.1007/s11295-020-01451-1

Soularue, J. P., Firmat, C., Caignard, T., Thöni, A., Arnoux, L., Delzon, S., … Kremer, A. (2023). Antagonistic Effects of Assortative Mating on the Evolution of Phenotypic Plasticity along Environmental Gradients. American Naturalist, 202(1), 18–39. https://doi.org/10.1086/724579

Obtaining accurate estimates of population trends is crucial to assess populations’ status and make more informed decisions, notably for conservation measures. However, analyzing data we have at hand, including data from systematic monitoring programs, typically induces some bias one way or another (Buckland and Johnston 2017). For example, sampling can be biased towards some types of environments (sometimes historically, before being realized and corrected), and observer identity and experience can vary through time (e.g., an increase in observed experience, if ignored, would cause bias towards positive trends). One way to deal with such biases can be to discard some data, for example, from some overrepresented habitats or from first years surveys to minimize observer bias. However, this may lead to sample sizes becoming too small to detect any trends of interest, especially for surveys with already small temporal resolution (e.g., if time series are too short or with too many missing years).

In this study, Rieger et al. (2025) analyzed data from bird surveys from the Ecological Area Sampling in the German federal state North Rhine-Westphalia in order to assess population trends. This survey uses a ‘rolling’ design, meaning that each site is only visited one year within a multi-year rotation (here six), but this allows to cover a high number of sites. To deal with spatial bias, they analyzed trends per natural region. To control for observer effects, they used a correction factor as an explanatory variable (based on the ratio between the total abundance of all species per site per survey year and the mean total abundance on the same site across all survey years). To deal with the fact that count data for some species but not others may be zero inflated and/or over dispersed, they performed species-specific optimization regarding data distribution (and also regarding inclusion of continuous and categorical covariates). Finally, they deal with the many missing values per year per site (due to the rolling design) by using generalized additive mixed models with site identity as a random intercept.

Importantly, the authors assess how accounting for these biases affects estimates (quite strongly so for some species) and study the consistency of the results with trends estimated from the German Common Bird Monitoring scheme using the software TRIM (Pannekoek and van Strien 2001).

I appreciated their cautious interpretation of their results and of the generalizability of their approach to other datasets. I also recommend that the readers read the review history of the preprint (and I take the opportunity to thank the reviewers and the authors again for the very constructive exchange).

References

Buckland, S., and A. Johnston. 2017. Monitoring the biodiversity of regions: Key principles and possible pitfalls. Biological Conservation 214: 23-34. https://doi.org/10.1016/j.biocon.2017.07.034

Pannekoek, J., van Strienand, A. J. 2001. TRIM 3 manual (Trends & Indices for Monitoring Data). CBS Statistics Netherlands, Voorburg, The Netherlands.

Rieger, M. R., Grüneberg, C., Oberhaus, M., Trautmann, S., Parepa, M., Anthes, N., 2025. Bird population trend analyses for a monitoring scheme with a highly structured sampling design. BioRxiv, ver.3 peer-reviewed and recommended by PCI Ecology https://doi.org/10.1101/2024.06.30.601382

Many ecological, evolutionary, biogeographic studies on animals and plants have focused on endemism (e.g. (Crisp et al., 2001; Kier et al., 2009; Matthews et al., 2024, 2022; Qian et al., 2024). Ecological hotspots were first defined on endemic species (Myers et al., 2000). Nevertheless, despite the fact that the concept of endemism is crucial in biogeography and also in palaeontology there is still no stringent definition of endemism and very different concepts of endemism are used. It is another example of a concept that tries to define the undefinable (Darwin, 1859). ‘Definitions’ are either based on geographic and genetic isolation (Myers et al., 2000; Qian et al., 2024) or founded in geometric approaches that define restricted range sizes (Kinzig and Harte, 2000). Often, an ad hoc concept is used to cover taxon specificity and the habitats studied. 

Pinheiro et al. (2025) focus on species restricted to oceanic islands and rightly remark that these work as cradles for species origination and also as museums that contribute to lineages persistence. However, they also notice that in the case of islands any definition of endemism from species occurring only on single islands would be too narrow. Rather, endemism shows a spatial scaling with an increasing number of species occurring of multiple islands. In this respect they introduce the concept of provincial-island endemism and study the importance of single and multiple-island endemic species to island biodiversity

Pinheiro et al. (2025) use data from 7,289 fish species associated with reef environments of 87 oceanic islands and 189 coastal reefs around the world. A strong negative correlation appeared between the number of endemic species and the number of islands they occur. This relationship directly translates into our assessment of whether an archipelago is rich or poor in endemics. Pinheiro et al. (2025) explicitly demonstrate this with the examples of the Hawaiian Islands and Rapa Nui. They conclude that biogeographers need to clarify whether they deal with single-island or multiple island endemics. We can adapt this distinction to terrestrial and freshwater habitats and differentiate between single and multiple restricted areas and water bodies, for instance rivers, lakes, alpine valleys, mountains, or deserts. 

Of course, the idea that endemism patterns are scale dependent is not new. Daru et al. (2020), Graham et al. (2018), or  Keil et al. (2015) already noticed the importance of spatial scale and Townsend Peterson and Watson (1998) introduced the partly equivalent concepts of weighted spatial and phylogenetic endemism that also contain the scaling component. Pinheiro et al. (2025) add to this by providing a sound analysis of the strength of the scaling component. They argue that fish endangerment categories and fishery limits might change when considering multiple island endemics. 

References

Crisp, M.D., Laffan, S., Linder, H.P., Monro, A., 2001. Endemism in the Australian flora. J. Biogeogr. 28, 183–198. https://doi.org/10.1046/j.1365-2699.2001.00524.x

Daru, B.H., Farooq, H., Antonelli, A., Faurby, S., 2020. Endemism patterns are scale dependent. Nat. Commun. 11, 2115. https://doi.org/10.1038/s41467-020-15921-6

Darwin, C., 1859. On the origin of species by means of natural selection, or the preservation of favoured races in the struggle for life. John Murray, London.

Graham, C.H., Storch, D., Machac, A., 2018. Phylogenetic scale in ecology and evolution. Glob. Ecol. Biogeogr. 27, 175–187. https://doi.org/10.1111/geb.12686

Keil, P., Storch, D., Jetz, W., 2015. On the decline of biodiversity due to area loss. Nat. Commun. 6, 8837. https://doi.org/10.1038/ncomms9837

Kier, G., Kreft, H., Lee, T.M., Jetz, W., Ibisch, P.L., Nowicki, C., Mutke, J., Barthlott, W., 2009. A global assessment of endemism and species richness across island and mainland regions. Proc. Natl. Acad. Sci. 106, 9322–9327. https://doi.org/10.1073/pnas.0810306106

Kinzig, A.P., Harte, J., 2000. Implications of Endemics–Area Relationships for Estimates of Species Extinctions. Ecology 81, 3305–3311. https://doi.org/10.1890/0012-9658(2000)081[3305:IOEARF]2.0.CO;2

Matthews, T.J., Triantis, K.A., Wayman, J.P., Martin, T.E., Hume, J.P., Cardoso, P., Faurby, S., Mendenhall, C.D., Dufour, P., Rigal, F., Cooke, R., Whittaker, R.J., Pigot, A.L., Thébaud, C., Jørgensen, M.W., Benavides, E., Soares, F.C., Ulrich, W., Kubota, Y., Sadler, J.P., Tobias, J.A., Sayol, F., 2024. The global loss of avian functional and phylogenetic diversity from anthropogenic extinctions. Science 386, 55–60. https://doi.org/10.1126/science.adk7898

Matthews, T.J., Wayman, J.P., Cardoso, P., Sayol, F., Hume, J.P., Ulrich, W., Tobias, J.A., Soares, F.C., Thébaud, C., Martin, T.E., Triantis, K.A., 2022. Threatened and extinct island endemic birds of the world: Distribution, threats and functional diversity. J. Biogeogr. 49, 1920–1940. https://doi.org/10.1111/jbi.14474

Myers, N., Mittermeier, R.A., Mittermeier, C.G., da Fonseca, G.A.B., Kent, J., 2000. Biodiversity hotspots for conservation priorities. Nature 403, 853–858. https://doi.org/10.1038/35002501

Pinheiro, H.T., Rocha, L.A., Quimbayo, J.P 2025. Scales of marine endemism in oceanic islands and the Provincial-Island endemism. bioRxiv, ver.2 peer-reviewed and recommended by PCI Ecology https://doi.org/10.1101/2024.07.12.603346

Qian, H., Mishler, B.D., Zhang, J., Qian, S., 2024. Global patterns and ecological drivers of taxonomic and phylogenetic endemism in angiosperm genera. Plant Divers. 46, 149–157. https://doi.org/10.1016/j.pld.2023.11.004

Townsend Peterson, A., Watson, D.M., 1998. Problems with areal definitions of endemism: the effects of spatial scaling. Divers. Distrib. 4, 189–194. https://doi.org/10.1046/j.1472-4642.1998.00021.x

The conservation of large carnivores remains a challenge for biodiversity conservation (Ingeman et al. 2022), as they combine strict ecological requirements (large territories, sensitivity to human disturbance) with coexistence conflicts with human activities (livestock farming, hunting, risk perception). Although the Eurasian lynx is currently considered as “least concerned” by the IUCN Red List, this favorable status conceals major disparities between the remaining historical population nuclei in Northern and Eastern Europe and small, isolated populations in Western Europe resulting from reintroduction programs for which long-term persistence remains in jeopardy (Chapron et al. 2014).
 
Several ambitious conservation programs have been launched to try and improve the long-term demographic status of these still fragile populations (e.g., Swiss Lynx Project, French National Action Plan for the Eurasian Lynx), and conservation actors have a dire need for modelling of population dynamics to project demographic trajectories and compare scenarios of alternative conservation actions (Gatti 2022). A major challenge for making accurate demographic predictions is that lynx are characterized by extensive territories, and their demographic processes are expected to be strongly dependent on landscape characteristics. To address this challenge and capture the complexity of interactions between landscape structure and lynx dispersal, survival and reproduction, Bauduin et al. (2025) develop here a spatially explicit individual-based model for the four Western European populations of Eurasian lynx: Alps, Jura, Vosges and Black Forest. They use fine-scale data on movement and habitat use as well as road collisions to build a detailed spatial layer of habitat suitability and collision risk to predict the demographic trajectory and spatial repartition of the four Western European core populations over the next 50 years. Their simulations reveal an optimistic outlook offer for the future of the lynx : the sizes of the four population cores are predicted to increase steadily until stabilization at saturation within 20-40 years. Furthermore, the four populations are expected to act as a functional metapopulation, with regular exchanges of individuals between adjacent populations.
These results open up a wide range of perspectives. First, different conservation scenarios (e.g., reintroduction strategies, landscape evolution, changes in fragmentation) can be run using the framework of the model and compared to identify priority actions. Second, the predictions of lynx expansion into new areas (like Italian and French Alps) can be used to anticipate potential usage conflicts and develop coexistence strategies to improve social acceptance of the species in these target areas. 
 
Although genetic information and the effects of inbreeding depression were not included in the model and could significantly lower the predicted growth rates in the long term, the conclusions are robust to a wide range of parameter values, and can be used both to inform lynx conservation strategies and to provide a priceless basis for the development of other SE-IBM for large mammals in human-inhabited landscapes.

References

Bauduin S, Germain E, Zimmermann F, Idelberger S, Herdtfelder M, Heurich M, Kramer-Schadt S, Duchamp C, Drouet-Hoguet N, Morand A, Blanc L, Charbonnel A, Gimenez O. 2025. Modelling Eurasian lynx populations in Western Europe: What prospects for the next 50 years?https://doi.org/10.1101/2021.10.22.465393

Chapron G, et al. 2014. Recovery of large carnivore in Europe’s modern human-dominated landscapes. Science 345: 1517-1519 https://doi.org/10.1126/science.1257553

Gatti S. 2022. National Action Plan for the Eurasian Lynx: restoring the Lynx to a favorable conservation status un France (2022-2026), 176 p.

Ingeman, K.E., Zhao, L.Z., Wolf, C. et al. 2022. Glimmers of hope in large carnivore recoveries.Sci Rep 12, 10005 https://doi.org/10.1038/s41598-022-13671-7