Biology subjects

Courtiol, A.

Publications and source records attributed to Courtiol, A..

5 recordsLinked to original sources

bioRxiv2026

Megaherbivore mortality risk from temperature extremes is buffered by vegetation productivity but amplified by rainfall

Demographic risks from climate change remain poorly understood for megaherbivore species which face unique challenges because their large food requirements impose strong bottom-up limitation while their small surface-area-to-volume ratio limits heat dissipation. Here, we test how megaherbivore mortality is shaped by the concurrent effects of temperature and rainfall along with vegetation productivity, a potentially critical modifier of climate effects. By analysing four decades of monthly mortality records from 4,457 semi-captive Asian elephants across Myanmar, we identify multiple environmental pathways regulating megaherbivore mortality. Elephant mortality increased at extreme hot and cold temperatures in regions with low annual vegetation productivity, whereas high vegetation productivity buffered against such U-shaped effects of temperature. Furthermore, high rainfall amplified the negative impacts of extreme heat, underlining risks arising from the joint effects of heat and humidity. Seasonal declines in vegetation productivity did not explain the elevated mortality at temperature extremes. Together, our findings show that megaherbivores face elevated risks from global warming, but such risks strongly depend on vegetation productivity and humidity, highlighting multiple pathways through which climate change can shape the dynamics of megaherbivore populations.

ecology↗

bioRxiv2025

How monitoring behavior can inform conservation? The case of Spix's macaws under human care

The Spixs macaw (Cyanopsitta spixii), extinct in the wild since 2019, is currently maintained in exsitu breeding facilities. Captive breeding expanded the global population from 53 individuals in 2000 to ca. 400 by December 2025, enabling two soft releases. To support reintroduction, we introduce an ethogram of 85 behaviours and 1357 quantitative records based on observations of 123 birds at the ACTP Germany facility. Based on the analysis of the time-activity patterns of 10 pairs for 17 hours each, we also explore whether behavioural compatibility during the non-breeding season--measured here as time-activity similarity--relates to the breeding output of these pairs. Individual activity patterns were consistent, but females were markedly more synchronised with their mates than with other males. However, only pairs showing a high time-activity similarity bred successfully. Adjusting pair matching based on such similarity and improving general husbandry conditions (diet & enrichment) led to higher breeding output: pairs raising chicks increased from 0 in 2019 to 16 in 2024, fertility rates increased from 27% in 2019 to 64% in 2024, and annual chick output increased from 11 to 44 between 2019 and 2024. These results, based on 86 forced-mating attempts, support the idea that systematic behavioural monitoring can substantially contribute to enhancing conservation breeding in monogamous parrot species.

animal behavior and cognition↗

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

Effects of environmental change on population growth: monitoring time-varying carrying capacity in free-ranging spotted hyenas

In conservation, a growing population is often taken as a sign of success. But trends in population size can be misleading. When individuals are long-lived, populations may keep growing--for a time--even as the environment begins to stabilize or deteriorate. Trends in carrying capacity (K) would better reflect the situation that a population finds itself in, yet K is commonly assumed to be static. We developed an individual-based modelling approach to estimate time-varying carrying capacity (Kt) in a population of free-ranging spotted hyenas. Kt noticeably varied due to events such as disease outbreaks, but not in response to recent conservation interventions. Although the population tripled in size during the 26-year study period, we found no corresponding trend in Kt. Rather, population recovery resulted from earlier environmental improvements. Our approach, which can be applied to populations of other species, allows for a faster and more accurate assessment of conservation needs.

ecology↗

bioRxiv2017

Modelling isoscapes using mixed models

AbstractIsoscapes are maps depicting the continuous spatial (and sometimes temporal) variation in isotope composition. They have various applications ranging from the study of isotope circulation in the main earth systems to the determination of the provenance of migratory animals. Isoscapes can be produced from the fit of statistical models to observations originating from a set of discrete locations. Mixed models are powerful tools for drawing inferences from correlated data. While they are widely used to study non-spatial variation, they are often overlooked in spatial analyses. In particular, they have not been used to study the spatial variation of isotope composition. Here, we introduce this statistical framework and illustrate the methodology by building isoscapes of the isotope composition of hydrogen (measured in{delta} 2H) for precipitation water in Europe. For this example, the approach based on mixed models presents a higher predictive power than a widespread alternative approach. We discuss other advantages offered by mixed models including: the ability to model the residual variance in isotope composition, the quantification of prediction uncertainty, and the simplicity of model comparison and selection using an adequate information criterion: the conditional AIC (cAIC). We provide all source code required for the replication of the results of this paper as a small R package to foster a transparent comparison between alternative frameworks used to model isoscapes.\n\nAbbreviations used in this paper O_LIAIC: Akaike Information Criterion\nC_LIO_LIBLUP: Best Linear Unbiased Predictor\nC_LIO_LIBWR: a method for building isoscape introduced by Bowen and Wilkinson (2002) and Bowen and Revenaugh (2003)\nC_LIO_LIcAIC: conditional Akaike Information Criterion\nC_LIO_LIDHGLM: Double Hierarchical Generalised Linear Model\nC_LIO_LIGLM: Generalised Linear Model\nC_LIO_LIGLMM: Generalised Linear Mixed-effects Model\nC_LIO_LIGNIP: Global Network for Isotopes in Precipitation\nC_LIO_LILM: Linear Model\nC_LIO_LILMM: Linear Mixed-effects Model\nC_LIO_LIMAE: Mean Absolute Error\nC_LIO_LIML: Maximum Likelihood\nC_LIO_LIREML: Restricted Maximum Likelihood\nC_LIO_LIRMSE: Root Mean Squared Error\nC_LI

ecology↗