Biology subjects

Choquet, R.

Publications and source records attributed to Choquet, R..

6 recordsLinked to original sources

bioRxiv2024

Declining juvenile survival of Adelie penguins in Antarctica

As summer sea ice around Antarctica reaches modern lows, quantifying the demographic response of polar species to such environmental changes becomes critical. To achieve this, synthesizing results across species ranges and elucidating the environmental factors driving population dynamics are key. Adelie penguins are considered reliable indicators of changes in Antarctica but the processes through which sea ice and other environmental factors shape their population dynamics are still unclear, especially for critical age groups such as juveniles. Using a 17-year dataset of Adelie penguins electronically tagged in Adelie Land (Antarctica), we found that juvenile survival probability was most impacted by sea ice concentration near their natal colony right after fledging, with lower ice concentrations detrimental to survival. Importantly, we found that juvenile survival declined by 32% from 2007 to 2020, mirroring trends at other distant colonies. The emergence of similar patterns at opposite ends of the continent may be an early signal for shifts in population trends expected from climate change.

ecology↗

bioRxiv2024

Estimating population size with repeated observations of non-invasive sampling in presence of misidentification

Although non-invasive sampling is increasingly used in capture-recapture (CR) monitoring, it carries a risk of misidentification that, if ignored, causes an overestimation of population size. Models that deal with misidentification have been proposed. However, these models assume that only one sample can be collected per individual at one occasion. This is not true for several monitoring programs based on DNA, for example for those that extract the DNA from faecal samples. The models do not take repeated observations into account, leading to biased estimates. In this paper, we develop an approach that extends the latent multinomial model (LMM) of Link et al., 2010 using a Poisson distribution to model the number of samplings of the same individual on a given occasion. We then conduct simulations to test how our new model performs. As an illustration, we applied the new Poisson model to a collection of Eurasian otter faeces (Lampa et al., 2015). Our model yields unbiased estimates of population size when the expected number of samples per individual ({lambda}) is sufficiently high: simulations with{lambda} [≥] 0.36 and five capture occasions or with{lambda} [≥] 0.23 and seven or more occasions. In contrast, when{lambda} = 0.11 (corresponding to about 42%, 53% and 62% of the individuals being detected with respectively 5, 7 and 9 occasions), the population size is consistently underestimated. Applying the model to the otter dataset confirms the presence of misidentifications, consistent with the authors expectations. Our findings indicate that repeated observations can be modelled without bias. The application on otters shows that our model is necessary to accurately estimate population size in presence of misidentification and repeated observations.

ecology↗

bioRxiv2024

Evolution of Virulence in Emerging Epidemics: From Theory to Experimental Evolution and Back

The experimental validation of theoretical predictions is a crucial step in demonstrating the predictive power of a model. While quantitative validations are common in infectious diseases epidemiology, experimental microbiology primarily focuses on the evaluation of a qualitative match between model predictions and experiments. In this study, we develop a method to deepen the quantitative validation process with a polymorphic viral population. We analyse the data from an experiment carried out to monitor the evolution of the temperate bacteriophage {lambda} spreading in continuous cultures of Escherichia coli. This experimental work confirmed the influence of the epidemiological dynamics on the evolution of transmission and virulence of the virus. A variant with larger propensity to lyse bacterial cells was favoured in emerging epidemics (when the density of susceptible cells was large), but counter-selected when most cells were infected. Although this approach qualitatively validated an important theoretical prediction, no attempt was made to fit the model to the data nor to further develop the model to improve the goodness of fit. Here, we show how theoretical analysis - including calculations of the selection gradients - and model fitting can be used to estimate key parameters of the phage life cycle and yield new insights on the evolutionary epidemiology of the phage {lambda}. First, we show that modelling explicitly the infected bacterial cells which will eventually be lysed improves the fit of the transient dynamics of the model to the data. Second, we carry out a theoretical analysis that yields useful approximations that capture at the onset and at the end of an epidemic the effects of epidemiological dynamics on selection and differentiation across distinct life stages of the virus. Finally, we estimate key phenotypic traits characterizing the two strains of the virus used in our experiment such as the rates of prophage reactivation or the probabilities of lysogenization. This study illustrates the synergy between experimental, theoretical and statistical approaches; and especially how interpreting the temporal variation in the selection gradient and the differentiation across distinct life stages of a novel variant is a powerful tool to elucidate the evolutionary epidemiology of emerging infectious diseases.

evolutionary biology↗

bioRxiv2019

Changes in social cohesion in a long-lived species under a perturbation regime

O_LIUnderstanding the behaviour of a population under perturbations is crucial and can help to mitigate the effects of global change. Sociality can influence the dynamics of behavioural processes and plays an important role on populations resilience. However little is known about the effects of perturbations on the social cohesion of group-living animals.\nC_LIO_LITo explore the strength of social cohesion and its dynamics under perturbations, we studied an ecological system involving a colonial, long-lived species living in a site experiencing a shift to a perturbed regime. This regime, caused by the invasion of predators, led this colony to hold from 70% to only 3% of the total world population in only one decade. Because birds breed aggregated in discrete and annually changing patches within large colonies, we could disentangle whether annual aggregation was random or resulted from social bonding among individuals. Our goals were 1) to uncover if there was any long-term social bonding between individuals and 2) to examine whether the perturbation regime affected social cohesion.\nC_LIO_LIWe explored social cohesion by means of contingency tables and, within the Social Network Analysis framework, by modeling interdependencies among observations using additive and multiplicative effects (AME) and accounted for missing data. We analysed 25 years of monitoring with an individual capture-recapture database of more than 3,500 individuals.\nC_LIO_LIWe showed that social bonding occurs over years in this species. We additionally show that social bonding strongly decreased after the perturbation regime. We propose that sociality and individual behavioural heterogeneity have been playing a major role driving dispersal and thus population dynamics over the study period.\nC_LIO_LIPerturbations may lead not only to changes in individuals behaviours and fitness but also to a change in populations social cohesion. The demographic consequences of the breaking down of social bonds are still not well understood, but they can be critical for population dynamics of social species. Further studies considering individual heterogeneity, sociality and different types of perturbations should be carried out to improve our understanding on the resilience of social species.\nC_LI

ecology↗

bioRxiv2017

R2ucare: An R package to perform goodness-of-fit tests for capture-recapture models

O_LIAssessing the quality of fit of a statistical model to data is a necessary step for conducting safe inference.\nC_LIO_LIWe introduce R2ucare, an R package to perform goodness-of-fit tests for open single-and multi-state capture-recapture models. R2ucare also has various functions to manipulate capture-recapture data.\nC_LIO_LIWe remind the basics and provide guidelines to navigate towards testing the fit of capture-recapture models. We demonstrate the functionality of R2ucare through its application to real data.\nC_LI

ecology↗

bioRxiv2017

Mapping and explaining wolf recolonization in France using dynamic occupancy models and opportunistic data

While large carnivores are recovering in Europe, assessing their distributions can help to predict and mitigate conflicts with human activities. Because they are highly mobile, elusive and live at very low density, modeling their distributions presents several challenges due to i) their imperfect detectability, ii) their dynamic ranges over time and iii) their monitoring at large scales consisting mainly of opportunistic data without a formal measure of the sampling effort. Not accounting for these issues can lead to flawed inference about the distribution.\n\nHere, we focused on the wolf (Canis lupus) that has been recolonizing France since the early 90s. We evaluated the sampling effort a posteriori as the number of observers present per year in a cell based on their location and professional activities. We then assessed wolf range dynamics from 1993 to 2014, while accounting for species imperfect detection and time- and space-varying sampling effort using dynamic site-occupancy models.\n\nIgnoring the effect of sampling effort on species detectability led to underestimating the number of occupied sites by 50% on average. Colonization increased with increasing number of occupied sites at short and long-distances, as well as with increasing forest cover, farmland cover and mean altitude. Colonization decreased when high-altitude increased. The growth rate, defined as the number of sites newly occupied in a given year divided by the number of occupied sites in the previous year, decreased over time, from over 100% in 1994 to 5% in 2014. This suggests that wolves are expanding in France but at a rate that is slowing down. Our work shows that opportunistic data can be analyzed with species distribution models that control for imperfect detection, pending a quantification of sampling effort. Our approach has the potential for being used by decision-makers to target sites where large carnivores are likely to occur and mitigate conflicts.

ecology↗