Effects of global change on animal biodiversity in boreal forest landscape: an assemblage dissimilarity analysis

Abstract

Despite an increasing number of studies highlighting the impacts of climate change on boreal species, the main factors that will drive changes in species assemblages remain ambiguous. We quantify two climate-induced pathways based on direct and indirect effects on species occupancy and assemblage dis-similarity under different harvest management scenarios. The direct climate effects illustrate the impact of climate variables while the indirect effects are reflected through the changes in land cover composition. To understand the main causes in assemblage dissimilarity, we analyze the regional and the latitudinal species assemblage dissimilarity by decomposing it into balanced variation in species occupancy and occurrence and occupancy and occurrence gradient. We develop empirical models to predict the distribution of more than 100 bird and beetle species in the Cote-Nord region of Quebec over the next century. Our results show that the two pathways are complementary and alter biodiversity, mainly caused by balanced variation in species occupancy and occurrence. At the regional scale, both effects have an impact on decreasing the number of winning species. Yet, responses are much larger in magnitude under mixed climate effects (a mixture of direct and indirect effects). Regional assemblage dissimilarity reached 0.77 and 0.69 under mixed effects versus 0.09 and 0.10 under indirect effects for beetles and birds, respectively, between RCP8.5 and baseline climate scenarios when considering harvesting. Therefore, inclusion of climatic variables considers aspects other than just those related to forest landscapes, such as life cycles of animal species. Latitudinally, assemblage dissimilarity increased following the climate conditions pattern. Our analysis contributes to the understanding of how climate change alters biodiversity by reshaping community composition and highlights the importance of climate variables in biodiversity prediction.