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

Even though all living organisms are, at the same time, involved in host-parasite interactions and embedded in complex food webs, the indirect effects of parasitism are only beginning to be unveiled.

Prosnier et al. investigated the direct and indirect effects of parasitism making use of a very interesting biological system comprising the freshwater zooplankton Daphnia magna and its highly specific parasite, the iridovirus DIV-1 (Daphnia-iridescent virus 1). Daphnia are typically semitransparent, but once infected develop a white phenotype with a characteristic iridescent shine due to the enlargement of white fat cells.

In a combination of infection trials and comparison of white and non-white phenotypes collected in natural ponds, the authors demonstrated increased mortality and reduced lifetime fitness in infected Daphnia. Furthermore, white phenotypes had lower mobility, increased reflectance, larger body sizes and higher protein content than non-white phenotypes. As a consequence, total energy content was effectively doubled in white Daphnia when compared to non-white broodless Daphnia. 

Next the authors conducted foraging trials with Daphnia predators Notonecta (the backswimmer) and Phoxinus (the European minnow). Focusing on Notonecta, unchanged search time and increased handling time were more than compensated by the increased energy content of white Daphnia. White Daphnia were 24% more profitable and consistently preferred by Notonecta, as the optimal foraging theory would predict. The authors argue that menu decisions of optimal foragers in the field might be different, however, as the prevalence – and therefore availability - of white phenotypes in natural populations is very low.

The study therefore contributes to our understanding of the trophic context of parasitism. One shortcoming of the study is that the authors rely exclusively on phenotypic signs for determining infection. On their side, DIV-1 is currently known to be highly specific to Daphnia, their study site is well within DIV-1 distributional range, and the symptoms of infection are very conspicuous. Furthermore, the infection trial – in which non-white Daphnia were exposed to white Daphnia homogenates - effectively caused several lethal and sublethal effects associated with DIV-1 infection, including iridescence. However, the infection trial also demonstrated that part of the exposed individuals developed intermediate traits while still keeping the non-white, non-iridescent phenotype. Thus, there may be more subtleties to the association of DIV-1 infection of Daphnia with ecological and evolutionary consequences, such as costs to resistance or covert infection, that the authors acknowledge, and that would be benefitted by coupling experimental and observational studies with the determination of actual infection and viral loads.​​​

References

Prosnier L., N. Loeuille, F.D. Hulot, D. Renault, C. Piscart, B. Bicocchi, M, Deparis, M. Lam, & V. Médoc. (2023). Parasites make hosts more profitable but less available to predators. BioRxiv, ver. 4 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2022.02.08.479552

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

Urban areas are expanding worldwide, and have become a dominant part of the landscape for many species. Urbanization can fragment pre-existing populations of vulnerable species leading to population declines and the loss of connectivity. On the other hand, expansion of urban areas can also facilitate the spread of human commensals including pests. Knowledge of the features of cityscapes that facilitate gene flow and maintain diversity of pests is thus key to their management and eradication.
Cities are complex mosaics of natural and manmade surfaces, and habitat quality is not only influenced by physical aspects of the cityscape but also by socioeconomic factors and human behaviour. Constant development means that cities also change rapidly in time; contemporary urban life reflects only a snapshot of the environmental conditions faced by populations. It thus remains a challenge to identify the features that actually drive ecology and evolution of populations in cities [1]. While several studies have highlighted strong urban clines in genetic structure and adaption [2], few have considered the influence of factors beyond physical aspects of the cityscape or historical processes.
In this paper, Stragier et al. [3] sought to identify the current and past features of the cityscape and socioeconomic factors that shape genetic structure and diversity of the house mouse (Mus musculus domesticus) in Dakar, Senegal. The authors painstakingly digitized historical maps of Dakar from the time of European settlement in 1862 to present. The authors found that the main spatial genetic cline was best explained by historical cityscape features, with higher apparent gene flow and genetic diversity in areas that were connected earlier to initial European settlements. Beyond the main trend of spatial genetic structure, they found further evidence that current features of the cityscape were important. Specifically, areas with low vegetation and poor housing conditions were found to support large, genetically diverse populations. The authors demonstrate that their results are reproducible using several statistical approaches, including modeling that explicitly accounts for spatial autocorrelation.
The work of Stragier et al. [3] thus highlights that populations of city-dwelling species are the product of both past and present cityscapes. Going forward, urban evolutionary ecologists should consider that despite the potential for rapid evolution in urban landscapes, the signal of a species’ colonization can remain for generations.

References

[1] Rivkin, L. R., Santangelo, J. S., Alberti, M. et al. (2019). A roadmap for urban evolutionary ecology. Evolutionary Applications, 12(3), 384-398. doi: 10.1111/eva.12734
[2] Miles, L. S., Rivkin, L. R., Johnson, M. T., Munshi‐South, J. and Verrelli, B. C. (2019). Gene flow and genetic drift in urban environments. Molecular ecology, 28(18), 4138-4151. doi: 10.1111/mec.15221
[3] Stragier, C., Piry, S., Loiseau, A., Kane, M., Sow, A., Niang, Y., Diallo, M., Ndiaye, A., Gauthier, P., Borderon, M., Granjon, L., Brouat, C. and Berthier, K. (2020). Interplay between historical and current features of the cityscape in shaping the genetic structure of the house mouse (Mus musculus domesticus) in Dakar (Senegal, West Africa). bioRxiv, 557066, ver. 4 peer-reviewed and recommended by PCI Ecology. doi: 10.1101/557066

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