François Leroy

François Leroy

Researcher in Macroecology and Biogeography

Posts by Collection

portfolio

projects

Project 1

Published:

Master thesis

publications

Optimising occurrence data in species distribution models: sample size, positional uncertainty, and sampling bias matter

Published in Ecography, 2024

Moudrý, V., Bazzichetto, M., Remelgado, R., Devillers, R., Lenoir, J., Mateo, R. G., Lembrechts, J. J., Sillero, N., Lecours, V., Cord, A. F., Barták, V., Balej, P., Rocchini, D., Torresani, M., Arenas-Castro, S., Man, M., Prajzlerová, D., Gdulová, K., Prošek, J., Marchetto, E., Zarzo-Arias, A., Gábor, L., Leroy, F., Martini, M., Malavasi, M., Cazzolla Gatti, R., Wild, J., & Šímová, P.

Accessible here

talks

Untangling biodiversity changes across a continuum of spatial scales

Published:

Magnitude assessment of biodiversity changes is challenging, even in well surveyed groups such as birds. Especially, trends of biodiversity can be driven by the spatial and temporal scales considered, specifically by spatial grains (i.e. area of a sampling unit), geographic extent (i.e. size of the area of interest), temporal grain (i.e. duration of a sampling event) and temporal extent (i.e. length of the time series). However, the influence of spatio-temporal scales on biodiversity trends is seldom documented. Here, we empirically address this issue by using high-quality spatially and temporally heterogenous time-series on bird biodiversity of Czech Republic.

Decomposing abundance change to recruitment and loss: analysis of the North-American avifauna

Published:

Species richness is the most commonly used metric to assess biodiversity crisis, but fluctuations in species number start with fluctuations in the number of individuals (i.e. abundance). Population abundances are known to be globally plummeting with, e.g., three billion fewer birds in the US compared to the 70’s. However, assessing population decline doesn’t give insight on the dynamic of the ecological processes driving abundance change, namely losses and recruitments of individuals.

Neural Dynamic N-mixture model: a deep learning framework to infer demographic rates from abundance data

Published:

Temporal changes of abundance arise from the difference between recruitment rate (i.e. proportion of new individuals entering the population through birth or immigration) and loss rate (i.e. proportion of individuals removed from the population by death or emigration). The interplay between these vital rates and how they change through time at large spatio-temporal scales remains largely unexplored, mainly given the cost of gathering such mark-recapture data. Bridging this knowledge gap would provide deeper insights into the mechanisms of the ongoing biodiversity crisis and help shape effective conservation strategies.

teaching

GIS

Undergraduate course, Czech University of Life Sciences, Dept. of Spatial Sciences, 2021

Introduction to GIS using ArcGIS