Biology subjects

Louvrier, J.

Publications and source records attributed to Louvrier, J..

5 recordsLinked to original sources

bioRxiv2024

demres: An R package to study time-varying demographic resilience

1- Quantifying the resilience of populations to disturbances is essential to assess how threatened populations are. Demographic resilience has been defined as the ability of populations to resist and recover from alterations in their demographic structures. Resilience metrics are typically obtained by applying transient analyses to matrix population models. Until now, the questions of how demographic resilience can change over time, how to quantify such temporal variation and when it is important to account for it have not yet been studied. However, since demographic rates fluctuate over time, it is essential to evaluate whether and under what conditions the assumption of time-constant demographic resilience remains appropriate. 2- In this study we introduce demres - an R package that offers functions to quantify time-varying and time-constant demographic resilience metrics, including time to convergence, damping ratio, inertia, maximum amplification, maximum attenuation and reactivity. The package also allows the comparison of these two approaches, both visually and by means of distance metrics. 3- We use a case study to illustrate the versatility of demres and demonstrate how this tool can be readily used by conservation biologists, managers and others to assess the time-varying resilience of wildlife populations using long-term demographic data. Our framework facilitates standardised comparisons of demographic resilience metrics, for example by means of comparative studies across populations and species.

ecology↗

bioRxiv2024

How reliably can species co-occurrence be inferred from occupancy models?

Interspecific interactions, such as predation, competition, and mutualism, are fundamental to the structure and function of any biotic community. Understanding these interactions provides valuable insights into community dynamics and informs conservation efforts. One common approach to studying these interactions is through co-occurrence patterns, which remain essential for understanding species relationships. Occupancy models have emerged as powerful tools to investigate these patterns, offering a flexible framework for ecological questions. Among these models, the co-occurrence framework developed by Rota et al. (2016) has seen widespread applications across various species and habitats. In this study, we used simulations to evaluate the performance of the Rota et al. (2016) co-occurrence model under different study designs and species interaction scenarios. Specifically, we examined how the bias and precision of species occupancy estimates are influenced by the presence or absence of another species, varying the number of sampling units surveyed and the number of temporal replicates per unit. Additionally, we compared the co-occurrence models performance with a simpler model that does not account for species interactions to determine when statistically significant co-occurrence patterns emerge. We observed that occupancy parameters become more precise as the intensity of species avoidance increased, and while the number of sampling sites significantly influenced precision, the number of temporal replicates did not. Notably, the co-occurrence model outperformed the non-interaction model only when species avoidance was strong and at least 150 sites were sampled. Based on these results, we recommend a minimum of 150 sampling sites when applying the Rota et al. (2016) model to evaluate co-occurrence between two potentially interacting species. Our study emphasizes the need for further development of methods capable of detecting interaction signals, even at low to moderate interaction levels, and in studies with fewer sampling sites.

evolutionary biology↗

bioRxiv2019

Identifying conservation priorities in a defaunated tropical biodiversity hotspot

AimUnsustainable hunting is leading to widespread defaunation across the tropics. To mitigate against this threat with limited conservation resources, stakeholders must make decisions on where to focus anti-poaching activities. Identifying priority areas in a robust way allows decision-makers to target areas of conservation importance, therefore maximizing the impact of conservation interventions.\n\nLocationAnnamite mountains, Vietnam and Laos.\n\nMethodsWe conducted systematic landscape-scale surveys across five study sites (four protected areas, one unprotected area) using camera-trapping and leech-derived environmental DNA. We analyzed detections within a Bayesian multi-species occupancy framework to evaluate species responses to environmental and anthropogenic influences. Species responses were then used to predict occurrence to unsampled regions. We used predicted species richness maps and occurrence of endemic species to identify areas of conservation importance for targeted conservation interventions.\n\nResultsAnalyses showed that habitat-based covariates were uninformative. Our final model therefore incorporated three anthropogenic covariates as well as elevation, which reflects both ecological and anthropogenic factors. Conservation-priority species tended to found in areas that are more remote now or have been less accessible in the past, and at higher elevations. Predicted species richness was low and broadly similar across the sites, but slightly higher in the more remote site. Occupancy of the three endemic species showed a similar trend.\n\nMain conclusionIdentifying spatial patterns of biodiversity in heavily-defaunated landscapes may require novel methodological and analytical approaches. Our results indicate to build robust prediction maps it is beneficial to sample over large spatial scales, use multiple detection methods to increase detections for rare species, include anthropogenic covariates that capture different aspects of hunting pressure, and analyze data within a Bayesian multi-species framework. Our models further suggest that more remote areas should be prioritized for anti-poaching efforts to prevent the loss of rare and endemic species.

ecology↗

bioRxiv2019

Combining multiple data sources in species distribution models while accounting for spatial dependence and overfitting with combined penalised likelihood maximisation

O_LIThe increase in availability of species data sets means that approaches to species distribution modelling that incorporate multiple data sets are in greater demand. Recent methodological developments in this area have led to combined likelihood approaches, in which a log-likelihood comprised of the sum of the log-likelihood components of each data source is maximised. Often, these approaches make use of at least one presence-only data set and use the log-likelihood of an inhomogeneous Poisson point process model in the combined likelihood construction. While these advancements have been shown to improve predictive performance, they do not currently address challenges in presence-only modelling such as checking and correcting for violations of the independence assumption of a Poisson point process model or more general challenges in species distribution modelling such as overfitting.\nC_LIO_LIIn this paper, we present an extension of the combined likelihood frame-work which accommodates alternative presence-only likelihoods in the presence of spatial dependence as well as lasso-type penalties to account for potential overfitting. We compare the proposed combined penalised likelihood approach to the standard combined likelihood approach via simulation and apply the method to modelling the distribution of the Eurasian lynx in the Jura Mountains in eastern France.\nC_LIO_LIThe simulations show that the proposed combined penalised likelihood approach has better predictive performance than the standard approach when spatial dependence is present in the data. The lynx analysis shows that the predicted maps vary significantly between the model fitted with the proposed combined penalised approach accounting for spatial dependence and the model fitted with the standard combined likelihood.\nC_LIO_LIThis work highlights the benefits of careful consideration of the presence-only components of the combined likelihood formulation, and allows greater flexibility and ability to accommodate real datasets.\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↗