Biology subjects

Chapuis, M.-P.

Publications and source records attributed to Chapuis, M.-P..

4 recordsLinked to original sources

bioRxiv2026

Unraveling Environmental Drivers of Insect Pest Dynamics Across Multiple Landscapes

The persistence of pest populations during crop off-seasons is a major challenge for integrated pest management, yet the ecological processes underlying survival and re-infestation often remain unclear. We investigated the oriental fruit fly, Bactrocera dorsalis, a major mango pest, through a two-year demographic monitoring across 56 orchards in the two main mango production basins in Senegal: the Sahelian Niayes and the tropical Casamance. Using a framework combining mechanistic and machine learning models, we identified the main environmental drivers of two key demographic parameters: the local abundance during the mango off-season and the onset of population growth in orchards. In the Niayes, harsh dry-season conditions confined residual populations to micro-habitats providing humidity and shade, while alternative hosts such as citrus trees could act as catalysts for early outbreaks. In contrast, under tropical conditions in the Casamance basin, B.dorsalis populations were more abundant year-round, and higher off-season densities appear to delay outbreak onset, likely due to density-dependent processes such as competition and predation. These findings show that distinct ecological processes shape B. dorsalis off-season persistence across environments, thereby determining population carry-over and growth onset, and ultimately leading to contrasting population dynamics across environments. They support region-specific management strategies to effectively reduce pest pressure: targeting refuges and early host resources in the Niayes, and enhancing natural regulation in Casamance.

ecology↗

bioRxiv2023

Hierarchizing multi-scale environmental effects on agricultural pest population dynamics: a case study on the annual onset of Bactrocera dorsalis population growth in Senegalese orchards

Implementing integrated pest management programs to limit agricultural pest damage requires an understanding of the interactions between the environmental variability and population demographic processes. However, identifying key environmental drivers of spatio-temporal pest population dynamics remains challenging as numerous candidate factors can operate at a range of scales, from the field (e.g. agricultural practices) to the regional scale (e.g. weather variability). In such a context, data-driven approaches applied to pre-existing data may allow identifying patterns, correlations, and trends that may not be apparent through more restricted hypothesis-driven studies. The resulting insights can lead to the generation of novel hypotheses and inform future experimental work focusing on a limited and relevant set of environmental predictors. In this study, we developed an ecoinformatics approach to unravel the multi-scale environmental conditions that lead to the early re-infestation of mango orchards by a major pest in Senegal, the oriental fruit fly Bactrocera dorsalis (BD). We gathered abundance data from a three-year monitoring conducted in 69 mango orchards as well as environmental data (i.e. orchard management, landscape structure and weather variability) across a range of spatial scales. We then developed a flexible analysis pipeline centred on a recent machine learning algorithm, which allows the combination of gradient boosting and grouped random effects models or Gaussian processes, to hierarchize the effects of multi-scale environmental variables on the onset of annual BD population growth in orchards. We found that physical factors (humidity, temperature), and to some extent landscape variables, were the main drivers of the spatio-temporal variability of the onset of population growth in orchards. These results suggest that favourable microclimate conditions could provide refuges for small BD populations that could survive, with little or no reproduction, during the mango off-season and, then, recolonize neighbouring orchards at the beginning of the next mango season. Confirmation of such a hypothesis could help to prioritize surveillance and preventive control actions in refuge areas.

ecology↗

bioRxiv2019

A young age of subspecific divergence in the desert locust Schistocerca gregaria

AO_SCPLOWBSTRACTC_SCPLOWDating population divergence within species from molecular data and relating such dating to climatic and biogeographic changes is not trivial. Yet it can help formulating evolutionary hypotheses regarding local adaptation and future responses to changing environments. Key issues include statistical selection of a demographic and historical scenario among a set of possible scenarios, and estimation of the parameter(s) of interest under the chosen scenario. Such inferences greatly benefit from new statistical approaches including approximate Bayesian computation - Random Forest (ABC-RF), the latter providing reliable inference at a low computational cost, with the possibility to take into account prior knowledge on both biogeographical history and genetic markers. Here, we used ABC-RF, including independent information on evolutionary rate and pattern at microsatellite markers, to decipher the evolutionary history of the African arid-adapted pest locust, Schistocerca gregaria. We found that the evolutionary processes that have shaped the present geographical distribution of the species in two disjoint northern and southern regions of Africa were recent, dating back 2.6 Ky (90% CI: 0.9 - 6.6 Ky). ABC-RF inferences also supported a southern colonization of Africa from a low number of founders of northern origin. The inferred divergence history is better explained by the peculiar biology of S. gregaria, which involves a density-dependent swarming phase with some exceptional spectacular migrations, rather than a continuous colonization resulting from the continental expansion of open vegetation habitats during more ancient Quaternary glacial climatic episodes.

evolutionary biology↗

bioRxiv2017

Challenges in estimating heritability of phase polyphenism: Insights from measured and simulated data in the desert locust

Quantitative genetics experiments aim at understanding and predicting the evolution of phenotypic traits. Running such experiments often bring the same questions: Should I bother with maternal effects? Could I estimate those effects? What is the best crossing scheme to obtain reliable estimates? Can I use molecular markers to spare time in the complex task of keeping track of the experimental pedigree?\n\nWe explored those practical issues in the desert locust, Schistocerca gregaria using morphologic and coloration traits, known to be influenced by maternal effects. We ran quantitative genetic analyses with an experimental dataset and used simulations to explore i) the efficiency of animal models to accurately estimate both heritability and maternal effects, ii) the influence of crossing schemes on the precision of estimates and iii) the performance of a marker-based method compared to the pedigree-based method.\n\nThe simulations indicated that maternal effects deeply affect heritability estimates and very large datasets are required to properly distinguish and estimate maternal effects and heritabilities. In particular, ignoring maternal effects in the animal model resulted in overestimation of heritabilities and a high rate of false positives whereas models specifying maternal variance suffer from lack of power. Maternal effects can be estimated more precisely than heritabilities but with low power. To obtain better estimates, bigger datasets are required and, in the presence of maternal effects, increasing the number of families over the number of offspring per families is recommended. Our simulations also showed that, in the desert locust, using relatedness based on available microsatellite markers may allow reasonably reliable estimates while rearing locusts in group.\n\nIn the light of the simulation results, our experimental dataset suggested that maternal effects affected various phase traits. However the statistical limitations, revealed by the simulation approach, didnt allow precise variance estimates. We stressed out that doing simulations is a useful step to design an experiment in quantitative genetics and interpret the outputs of the statistical models.

evolutionary biology↗