Latest Articles

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  Section: Mathematical & Computational Biology ; Topics: Evolution, Genetics/genomics, Population biology

  SelNeTime: a python package inferring effective population size and selection intensity from genomic time series data

  Uhl, Mathieu;   Bunel, Paul; de Navascués, Miguel;   Boitard, Simon;   Servin, Bertrand  

  10.24072/pcjournal.708 - Peer Community Journal, Volume 6 (2026), article no. e46

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  Genomic samples collected from a single population over several generations provide direct access to the genetic diversity changes occurring within a specific time period. This provides information about both demographic and adaptive processes acting on the population during that period. A common approach to analyze such data is to model observed allele counts in finite samples using a Hidden Markov Model (HMM) where hidden states are true allele frequencies over time (i.e. a trajectory). The HMM framework allows one to compute the full likelihood of the data, while accounting both for the stochastic evolution of population allele frequencies along time and for the noise arising from sampling a limited number of individuals at possibly spread out generations. Several such HMM methods have been proposed so far, differing mainly in the way they model the transition probabilities of the Markov chain. Following Paris et al. (2019a), we consider here the Beta with Spikes approximation, which avoids the computational issues associated to the Wright-Fisher model while still including fixation probabilities, in contrast to other standard approximations of this model like the Gaussian or Beta distributions. To facilitate the analysis and exploitation of genomic time series data, we present an improved version of Paris et al. (2019a) ‘s approach, denoted SelNeTime, whose computation time is drastically reduced and which accurately estimates effective population size (assuming no selection) or the selection intensity at each locus (given a previously estimated value of N). We also evaluate the performance of this method in realistic situations where selection is present and both demography and selection need to be inferred. SelNeTime is implemented in a user friendly python package, which can also easily simulate genomic time series data under a user-defined evolutionary model and sampling design.

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  Section: Ecology ; Topics: Ecology

  Camera trap monitoring of unmarked animals: a map of the relationships between population size estimators

  Calenge, Clément  

  10.24072/pcjournal.725 - Peer Community Journal, Volume 6 (2026), article no. e45

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  The use of camera traps to monitor unmarked animal populations has expanded during the last decade, leading to the development of several density estimation methods. This plethora of methods may be confusing for the newcomer to the field. Some methods, such as the random encounter model, require the knowledge of the mean travel speed of the animals, while others, such as camera trap distance sampling, do not rely on such assumptions. Different methods, like instantaneous sampling, camera trap distance sampling, and the association model, rely on similar types of data, but do not seem identical. In this article, I explore the relationships between different density estimators, including the random encounter model, the random encounter and staying time model, the time in front of camera approach, the time-to-event model, camera-trap distance sampling, the association model, and the space-to-event model. I show how these different estimators are related under two simplifying assumptions (perfect detectability, and animals moving as molecules in an ideal gas). I develop a map of mathematical relationships between these estimators. This framework helps readers understand how these methods are interconnected, providing a clearer conceptual foundation for selecting and implementing density estimation studies.

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  Section: Zoology ; Topics: Ecology, Environmental sciences

  Revisiting the habitat selection of the Eurasian Woodcock in winter: insights from the Mediterranean region

  Beaumelle, Camille;   Barbet, Jessica; Cuby, Aurélie; Chautan, Marc; Etienne, Fabrice; Martel, Michel; Du Roure, Alix; Chabanne, Rémy; Lauer, Estelle; Le Rest, Kévin  

  10.24072/pcjournal.720 - Peer Community Journal, Volume 6 (2026), article no. e44

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  Habitat selection is a key mechanism that enables animals to optimize their fitness in response to varying environmental conditions. Differences in habitat selection between populations in different geographical areas may indicate behavioral adaptations to local environmental conditions. Understanding the adaptive potential of species across broad geographical ranges is of primary interest to anticipate possible changes in species behavior or distribution in the context of climate change. In this study, we investigated the habitat selection and the daily movement patterns of the Eurasian Woodcock, Scolopax rusticola, a bird species that winters across widely varying climatic zones. We tracked 84 individuals wintering in the Mediterranean regions with GPS-VHF transmitters, where climate and habitat conditions differ significantly from the regions with Atlantic climate influence, which have formed the main background for the ecology and behavior of this species in winter. To assess how woodcocks responded to varying habitat and environmental conditions, we collected data across three geographical regions spanning a gradient of Mediterranean climatic influence — ranging from northern subareas with denser forest and deeper soil to southern subareas characterized by less productive forests, garrigues, and rocky soil. In the northern Mediterranean region, woodcocks visited open habitats at night less than 53% of the time and less than 40% of the time in the two other regions with a stronger Mediterranean climate influence. This behavior was much less frequent than reported in studies conducted in areas with Atlantic climate influence (>80%). Woodcocks also changed their day/night activity patterns, as illustrated by their daily movements. They increased their daytime movements (11 to 29% higher) and reduced their nocturnal movements (12 to 18% lower) in the two regions with the strongest Mediterranean climate influence. During the day, when birds used only forested areas, denser forests were preferred in all studied Mediterranean subareas. Birds used different forested habitats between subareas, especially at night. For example, denser but shorter vegetation and higher rock cover were more strongly used at night in southern subareas. These forested habitats contrasted sharply with those in areas with Atlantic climate influence, the latter being plots rich in humus and deep soils. Our findings highlight that basic ecological knowledge of species can be biased towards those known in certain types of habitats. They also underscore the remarkable behavioral flexibility of woodcocks, highlighting their potential to adapt to global change. However, the occurrence of escape movements under the driest conditions suggest that this change in behavior and habitat selection may be an early warning sign of the effects of climate change on the wintering areas. Overall, our study emphasizes the need to study the ecology of species across diverse environmental conditions to better understand their habitat requirements and adaptive capacity.

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  Section: Mathematical & Computational Biology ; Topics: Statistics, Biophysics and computational biology, Genetics/genomics

  A new iterative framework for simulation-based population genetic inference with improved coverage properties of confidence intervals

  Rousset, François;   Leblois, Raphaël;   Estoup, Arnaud;   Marin, Jean-Michel  

  10.24072/pcjournal.721 - Peer Community Journal, Volume 6 (2026), article no. e43

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  Simulation-based methods such as approximate Bayesian computation (ABC) are widely used to infer the evolutionary history of populations from molecular genetic data. We describe and evaluate a new iterative method of statistical inference about model parameters, which revisits the idea of inferring a likelihood surface using simulation when the likelihood function cannot be evaluated. It is based on combining the random forest machine learning method, and multivariate Gaussian mixture (MGM) models, in an effective inference workflow, here used to fit models with up to 15 variable parameters. In addition to the traditional assessment of precision in terms of bias and mean square error, we also evaluate the coverage of confidence intervals. The method is compared with approximate Bayesian computation using random forests (ABC-RF), a non-iterative method sharing some technical features with the proposed approach, across scenarios of historical demographic inference from population genetic data. It is also compared to another iterative method, sequential neural likelihood estimation (SNLE). These comparisons highlight the importance of an iterative workflow for exploring the parameter space efficiently. For equivalent simulation effort of the data-generating process, the new summary-likelihood method provides intervals whose coverage is better controlled than the marginal coverage of intervals provided by ABC with random forests, and than generally reported for ABC methods. The iterative workflow can also yield greater improvements in estimator precision when larger datasets are used.