Biology subjects

Philippe-Lesaffre, M.

Publications and source records attributed to Philippe-Lesaffre, M..

3 recordsLinked to original sources

bioRxiv2024

Extinction potential from invasive alien species

Biological invasions threaten biodiversity, ecosystem services, human health, and cultural heritage, yet their impacts are often underappreciated, leading to insufficient management efforts and suboptimal conservation results. We argue that the lack of quantitative, continuous metrics of impact of invasive alien species (IAS) contribute this lack of appreciation. To bridge this knowledge-action gap, we propose the Extinction Potential Metric (EPM), a suite of quantitative metrics designed to assess the ecological damage caused by IAS. The EPM score of an IAS is the number of current and future species extinctions attributable to this IAS over the next 50 years under a business-as-usual scenario. EPM includes three variants: EPM-A (absolute EPM), EPM-R (relative EPM, which accounts for other anthropogenic pressures), and EPM-U (EPM for Unique species, adjusted for phylogenetic uniqueness of impacted native species), to capture different dimensions of IAS impacts. We applied EPM to evaluate the impact of IAS on 2178 amphibians, 920 birds, 865 reptiles, and 473 mammals. Our analyses revealed that the impact of the worst IAS was between 90 and 380 times higher than any IAS with an impact of 1 extinct native species. Importantly, several of the most impactful IAS disproportionately affect evolutionarily unique native species. The EPM framework offers a standardised approach for measuring ecological impacts of IAS but also other anthropogenic pressures at different spatial, temporal, and taxonomic scales. EPM could also guide the development of standardised indicators for assessing the impacts of other anthropogenic stressors. Ultimately, EPM will pave the way to answer ecological questions important to design better conservation policies, to enhance the management of biological invasions and reach global biodiversity conservation goals.

ecology↗

bioRxiv2024

Can we predict the predation pressure of owned domestic cats on all birds in the United States?

Domestic cats (Felis catus) have shared a common history with humans since their domestication 10,000 years ago and today they are one of the worlds most widespread predatory mammals. Different populations of domestic cats around the world show a high degree of variability in terms of their autonomy from humans for feeding or moving about, with common descriptions ranging from owned domestic cats to feral domestic cats on the spectrum from the most dependent to the most autonomous about humans. Distinguishing between owned and other domestic cats, I proposed a framework based on machine learning and citizen science data to predict the annual predation pressure on bird species per domestic cat considering traits, phylogeny and geographical distribution. Leveraging the Random Forest model and data from Mori et al. (2019), I assessed the predation pressure for each native continental bird species of the United States. Findings revealed that geographical distribution, phylogeny and traits influenced the predictive value of predation pressure, while a specific trait combination was also associated with high predation pressure. Furthermore, 35% of species experienced high predation pressure from owned domestic cats regardless of the existing threats. The results are consistent with former empirical evidence of predation by domestic cats in the United States and highlight the urgency of understanding the ecological impact of domestic cats. By producing a quantitative value for predation pressure, the framework allows the development of more reliable models of species extinction risk, including for the effects of domestic cat predation, and thus the use of more specific management strategies to sensitive populations. Although the study requires further refinement, the framework offers promising insights. With expanded citizen science protocols, it could help improve the extinction risk models and guide precise management strategies, which are crucial for mitigating the impact of domestic cats on native wildlife.

ecology↗

bioRxiv2024

Advancing ecological networks: moving beyond binary classification to probabilistic interactions.

Leveraging trophic interactions to deduce macro-ecological patterns has become a prevalent method, taking advantage of the extensive databases on binary trophic interactions (i.e., prey-predator relationships). However, this binary approach oversimplifies complex ecological dynamics and fails to capture the nuanced structure of food webs. The challenge lies in the scarcity and limited availability of data on non-binary interactions, which are crucial for a more comprehensive understanding of ecological networks. This study explores the use of binary classifiers, particularly the XGBOOST algorithm to address the limitations of traditional binary approaches to prey-predator relationships. By predicting predation probabilities among nine mammalian predators using species traits, my findings demonstrate the classifiers robust predictive capabilities to binary predictions but also a good correlation between probabilistic predation derived from binary classifiers and observed prey preferences. It also highlighted the importance of selecting informative species traits for predicting interaction, with performance contrastingly superior to null models. Despite a small sample size, this work provides insightful results and sets a foundation for future research to expand these models to broader ecological networks, emphasizing the need for comprehensive prey preference data.

ecology↗