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

In the face of worldwide declines in biodiversity, evaluating the effectiveness of conservation practices is an absolute necessity. Protected Areas (PA) are a key tool for conservation, and the question “Are PA effective” has been on many a research agenda, as the introduction to this preprint will no doubt convince you. A challenge we face is that, until now, few studies have been explicitly designed to evaluate PA, and despite the rise of meta-analyses on the topic, our capacity to quantify their effect on biodiversity remains limited.
This study by Cazalis et al. [1] uses the rich dataset of the North-American Breeding Bird Survey and a sound paired design to investigate how PA change bird assemblages. The methodological care brought to the study in itself is worth the read, and the results are insightful. I will not spoil too much by revealing here that things are “complicated”, and that effects – or lack thereof – depend on the type of ecosystem, and the type of species considered.
If you are interested in conservation, bird communities, species life-history, or like beautiful plots: go and read it.

References

[1] Cazalis, V., Belghali, S., & Rodrigues, A. S. (2019). Using a large-scale biodiversity monitoring dataset to test the effectiveness of protected areas at conserving North-American breeding birds. bioRxiv, 433037, ver. 4 peer-reviewed and recommended by PCI Ecology. doi: 10.1101/433037

Population ecologists are in the business of decrypting the drivers of variation in the abundance of organisms across space and time (Begon et al. 1986). Comprehensive studies of wild vertebrate populations which provide the necessary information on variations in vital rates in relation to environmental conditions to construct informative models of large-scale population dynamics are rare, ostensibly because of the huge effort required to monitor individuals across ecological contexts and over generations. In this current aim, Nater et al. (2024) are leading the way forward by combining distance sampling data collected through a large-scale citizen science (Fraisl et al. 2022) programme in Norway with state-of-the-art modelling approaches to build a comprehensive overview of the population dynamics of willow ptarmigan. Their work enhances our fundamental understanding of this system and provides evidence-based tools to improve its management (Williams et al. 2002). Even better, they are working for the common good, by providing an open-source workflow that should enable ecologists and managers together to predict what will happen to their favourite model organism when the planet throws its next curve ball. In the case of the ptarmigan, for example, it seems that the impact of climate change on their population dynamics will differ across the species’ distributional range, with a slower pace of life (sensu Stearns 1983) at higher latitudes and altitudes. 

On a personal note, I have often mused whether citizen science, with its inherent limits and biases, was just another sticking plaster over the ever-deeper cuts in the research budgets to finance long-term ecological research. Here, Nater et al. are doing well to convince me that we would be foolish to ignore such opportunities, particularly when citizens are engaged, motivated, with an inherent capacity for the necessary discipline to employ common protocols in a standardised fashion. A key challenge for us professional ecologists is to inculcate the next generation of citizens with a sense of their opportunity to contribute to a better understanding of the natural world.

References

Begon, Michael, John L Harper, and Colin R Townsend. 1986. Ecology: individuals, populations and communities. Blackwell Science.

Fraisl, Dilek, Gerid Hager, Baptiste Bedessem, Margaret Gold, Pen-Yuan Hsing, Finn Danielsen, Colleen B Hitchcock, et al. 2022. Citizen Science in Environmental and Ecological Sciences. Nature Reviews Methods Primers 2 (1): 64. https://doi.org/10.1038/s43586-022-00144-4

Chloé R. Nater, Francesco Frassinelli, James A. Martin, Erlend B. Nilsen (2024) Large-scale spatio-temporal variation in vital rates and population dynamics of an alpine bird. EcoEvoRxiv, ver.4 peer-reviewed and recommended by PCI Ecology https://doi.org/10.32942/X2VP6J

Stearns, S.C. 1983. The influence of size and phylogeny of covariation among life-history traits in the mammals. Oikos, 41, 173–187. https://doi.org/10.2307/3544261

Williams, Byron K, James D Nichols, and Michael J Conroy. 2002. Analysis and Management of Animal Populations. Academic Press.

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

The loss of biodiversity is an issue of great concern, especially if the extinction of species or the loss of a large number of individuals within populations results in a loss of critical ecosystem services. We know that the most important threat to most species is habitat loss and degradation (Keil et al., 2015; Pimm et al., 2014); the latter can be caused by multiple anthropogenic activities, including pollution, introduction of invasive species and fragmentation (Brook et al., 2008; Scanes, 2018). Roads are a major cause of habitat fragmentation, isolating previously connected populations and being a direct source of mortality for animals that attempt to cross them (Spellberg, 1998).
While most studies have focused on the effect of roads on larger mammals (Bartonička et al., 2018; Litvaitis and Tash, 2008), in recent years many researchers have grown increasingly concerned about the risk of collision between bats and vehicles (Fensome and Mathews, 2016). For example, a recent publication by Medinas et al. (2021) found 509 bat casualties along a 51-km-long transect during a period of 3 years. Their study provides extremely valuable information to asses which factors primarily drive bat mortality on roads, yet it required a substantial investment of time coupled with the difficulty of detecting bat carcasses. Other studies have used acoustic monitoring as a proxy to gauge risk of collision based on estimates of bat density along roads (reviewed in Fensome and Mathews 2016); while the results of such studies are valuable, the number of passes recorded does not necessarily equal collision risk, as many species may simply avoid crossing the roads. Understanding the risk of collisions is of vital importance for adequate planning of road construction, particularly for key sites that harbor threatened bat species or unusually large populations, especially if these are already greatly impacted by other anthropogenic activities (e.g. wind turbines; Kunz et al. 2007) or unusually deadly pathogens (e.g. white-nose syndrome; Blehert et al. 2009).
The study by Roemer et al. (2020) titled “Influence of local landscape and time of year on bat-road collision risks”, is a welcome addition to our understanding of bat collision risk as it employs a more accurate assessment of bat collision risk based on acoustic monitoring and tracking of flight paths. The goal of the study of Roemer and collaborators, which was conducted at 66 study sites in the Mediterranean region, is to provide an assessment of collision risk based on bat activity near roads. They collected a substantial amount of information for several species: more than 30,000 estimated flight trajectories for 21+ species, including Barbastella barbastellus, Myotis spp., Plecotus sp., Rhinolophus ferrumequinum, Miniopterus schreibersii, Pipistrellus spp., Nyctalus leisleri, and others. They assess risk based on estimates of 1) species abundance from acoustic monitoring, 2) direction of flight paths along roads, and 3) bat-vehicle co-occurrence.
Their findings suggest that risk is habitat, species, guild, and season-specific. Roads within forested habitats posed the largest threats for most species, particularly since most flights within these habitats occurred at the zone of collision risk. They also found that bats typically fly parallel to the road axis regardless of habitat type, which they argue supports the idea that bats may use roads as corridors. The results of their study, as expected, also show that the majority of bat passes were detected during summer or autumn, depending on species, yet they provide novel findings of an increase in risky behaviors during autumn, when the number of passes at the zone of collision risk increased significantly. Their results also suggest that mid-range echolocators, a classification that is based on call design and parameters (Frey-Ehrenbold et al., 2013), had a larger portion of flights in the zone at risk, thus potentially making them more susceptible than short and long-range echolocators to collisions with vehicles.
The methods employed by Roemer et al. (2020) could further help us determine how roads pose species and site-specific threats in a diversity of places without the need to invest a significant amount of time locating bat carcasses. Their findings are also important as they could provide valuable information for deciding where new roads should be constructed, particularly if the most vulnerable species are abundant, perhaps due to the presence of important roost sites. They also show how habitats near larger roads could increase threats, providing an important first step for recommendations regarding road construction and maintenance. As pointed out by one reviewer, one possible limitation of the study is that the results are not supported by the identification of carcasses. For example, does an increase in the number of identified flights at the zone of risk really translate into an increase in the number of collisions? Regardless of the latter, the paper’s methods and results are very valuable and provide an important step towards developing additional tools to assess bat-vehicle collision risks.

References

[1] Bartonička T, Andrášik R, Duľa M, Sedoník J, Bíl M (2018) Identification of local factors causing clustering of animal-vehicle collisions. The Journal of Wildlife Management, 82, 940–947. https://doi.org/10.1002/jwmg.21467
[2] Blehert DS, Hicks AC, Behr M, Meteyer CU, Berlowski-Zier BM, Buckles EL, Coleman JTH, Darling SR, Gargas A, Niver R, Okoniewski JC, Rudd RJ, Stone WB (2009) Bat White-Nose Syndrome: An Emerging Fungal Pathogen? Science, 323, 227–227. https://doi.org/10.1126/science.1163874
[3] Brook BW, Sodhi NS, Bradshaw CJA (2008) Synergies among extinction drivers under global change. Trends in Ecology & Evolution, 23, 453–460. https://doi.org/10.1016/j.tree.2008.03.011
[4] Fensome AG, Mathews F (2016) Roads and bats: a meta-analysis and review of the evidence on vehicle collisions and barrier effects. Mammal Review, 46, 311–323. https://doi.org/10.1111/mam.12072
[5] Frey‐Ehrenbold A, Bontadina F, Arlettaz R, Obrist MK (2013) Landscape connectivity, habitat structure and activity of bat guilds in farmland-dominated matrices. Journal of Applied Ecology, 50, 252–261. https://doi.org/10.1111/1365-2664.12034
[6] Keil P, Storch D, Jetz W (2015) On the decline of biodiversity due to area loss. Nature Communications, 6, 8837. https://doi.org/10.1038/ncomms9837
[7] Kunz TH, Arnett EB, Erickson WP, Hoar AR, Johnson GD, Larkin RP, Strickland MD, Thresher RW, Tuttle MD (2007) Ecological impacts of wind energy development on bats: questions, research needs, and hypotheses. Frontiers in Ecology and the Environment, 5, 315–324. https://doi.org/10.1890/1540-9295(2007)5[315:EIOWED]2.0.CO;2
[8] Litvaitis JA, Tash JP (2008) An Approach Toward Understanding Wildlife-Vehicle Collisions. Environmental Management, 42, 688–697. https://doi.org/10.1007/s00267-008-9108-4
[9] Medinas D, Marques JT, Costa P, Santos S, Rebelo H, Barbosa AM, Mira A (2021) Spatiotemporal persistence of bat roadkill hotspots in response to dynamics of habitat suitability and activity patterns. Journal of Environmental Management, 277, 111412. https://doi.org/10.1016/j.jenvman.2020.111412
[10] Pimm SL, Jenkins CN, Abell R, Brooks TM, Gittleman JL, Joppa LN, Raven PH, Roberts CM, Sexton JO (2014) The biodiversity of species and their rates of extinction, distribution, and protection. Science, 344. https://doi.org/10.1126/science.1246752
[11] Roemer C, Coulon A, Disca T, Bas Y (2020) Influence of local landscape and time of year on bat-road collision risks. bioRxiv, 2020.07.15.204115, ver. 3 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2020.07.15.204115
[12] Scanes CG (2018) Chapter 19 - Human Activity and Habitat Loss: Destruction, Fragmentation, and Degradation. In: Animals and Human Society (eds Scanes CG, Toukhsati SR), pp. 451–482. Academic Press. https://doi.org/10.1016/B978-0-12-805247-1.00026-5
[13] Spellerberg I (1998) Ecological effects of roads and traffic: a literature review. Global Ecology & Biogeography Letters, 7, 317–333. https://doi.org/10.1046/j.1466-822x.1998.00308.x