Datasets generated and analyzed within the study area located in the Côte-Nord region of Québec, Canada. To identify species-specific movement rules that were implemented in the IBM, we used empirical data collected for caribou, moose, and wolves over the study area. To evaluate the umbrella value of management strategies for caribou habitat conservation, we used predictive models of species occupancy to characterize concurrent impacts on bird and beetle diversity.

Description of the data and file structure

"Beetles_ProbaOcc.xlsx"; "Birds_ProbaOcc_DecMixte.xlsx" and "Birds_ProbaOcc_Mature.xlsx" were used to determine the Jaccard dissimilarity index for each group: beetles, birds associated with boreal mixed forests and birds associated with boreal coniferous forests. The probability of occurrence presented in these files was based on predictive models of occupancy of birds and beetles previously developed from field observations (Bouderbala et al. 2023) as mentioned in the manuscript. The first columns corresponded to 76 beetle species, 20 and 7 bird species associated with boreal mixed or boreal coniferous forests, respectively. Scenario, CC (climate change) and LUC (land use change) represented the details of the scenarios presented in the study.
Bouderbala, I., Labadie, G., Béland, J., Boulanger, Y., Hébert, C., Desrosiers, P., Allard, A., Fortin, D., 2023. Effects of global change on bird and beetle populations in boreal forest landscape: an assemblage dissimilarity analysis. Divers. Distrib. 29, 757–773.

Bird codes - All birds were identified at the species level:

Beetle codes - The beetles were identified at the species level when possible; otherwise (if "species"=NA), the identification to the genus level was standardized
(92% identifications were at the species level):

"DataFinal_BirdsBeetlesOccChanges.csv" corresponded to the percentage change ("PercChange") of beetles and birds ("taxa") for each species ("species" determined with the codes explained above) in function of the different scenarios ("CC": climate change, "LUC": land use change). The column "IncDec" indicated if the value of "PercChange" was for the group that increased (inc) or decreased (dec). The column "n" represented the total number of species that increased or decreased for each scenario and taxa.

"DataFinal_IBM_Caribou_Winter.csv" corresponded to the IBM outputs with the proportion of caribou agents killed ("Prop.Caribou_killed": number of caribou killed/total number of caribou), in function of the different scenarios ("CC", "LUC", "Year", "Season", "Scenario"). Each simulation was replicated ten times. The columns "Prop.CutsRoads", "Prop.Fire", "Prop.Broadleaf", "Homogenization", "Isolation" correspond to the different variables we tested to predict the cumulative impact of anthropogenic disturbance and climate change. To explore how changes in forest structure and composition impacted the proportion of caribou killed, we used the proportion of areas disturbed by cuts and roads ("Prop.CutsRoads"), burned areas ("Prop.Fire"), and landscape characteristics, such as the proportion of deciduous vegetation ("Prop.Broadleaf"), landscape homogenization ("Homogenization") and isolation ("Isolation") of mature conifer stands.

To assess changes in landscape configuration, we used the ‘landscapemetrics’ package in R (Hesselbarth et al. 2019). At the patch level (i.e., neighboring cells belonging to the same land cover class), we calculated the mean “isolation index” (calculated as 1 − “cohesion index”), characterized as the connectedness of patches belonging to a land cover class (Hesselbarth et al., 2019). If the value of the “isolation index” was close to 0, patches of the same class were aggregated, while an increase in the value indicated that patches became isolated. At the landscape level, we calculated the “homogenization index” (calculated as 1/“conditional entropy,” characterized as the complexity of a landscape pattern configuration) (Hesselbarth et al.,
2019;Nowosad &Stepinski, 2019). If the value of the “homogenization index” is small, cells of one category are adjacent to cells of many different categories. All the details of these variables are presented in the study.
Hesselbarth, M. H. K., M. Sciaini, K. A. With, K. Wiegand, and J. Nowosad. 2019. “Landscapemetrics: An Open-Source R Tool to Calculate Landscape Metrics.” Ecography 42: 1648–57.
Nowosad, J., and T. F. Stepinski. 2019. “Information Theory as a Consistent Framework for Quantification and Classification of Landscape Patterns.” Landscape Ecology 34: 2091–101.