Data from: Predicting species occurrences with habitat network models

1. Biodiversity conservation requires modelling tools capable of predicting the presence or absence (i.e. occurrence-state) of species in habitat patches. Local habitat characteristics of a patch (lh), the cost of traversing the landscape matrix between patches (weighted connectivity; (wc), and the position of the patch in the habitat network topology (nt) all influence occurrence-state. Existing models are data demanding or consider only local habitat characteristics. We address these shortcomings and present a network-based modelling approach, which aims to predict species occurrence-state in habitat patches using readily available presence-only records. 2. For the tree frog Hyla arborea on the Swiss Plateau, we delineated habitat network nodes from an ensemble habitat suitability model, and used different cost surfaces to generate the edges of three networks: one limited only by dispersal distance (Uniform), another incorporating traffic, and a third based on inverse habitat suitability. For each network, we calculated explanatory variables representing the three categories (lh, wc and nt). The response variable, occurrence-state, was parametrized by a sampling-intensity procedure assessing observations of comparable species over a threshold of patch visits. The explanatory variables from the three networks and an additional non-topological model were related to the response variable with boosted regression trees. 3. The habitat network models had a similar fit; they all outperformed the non-topological model. Habitat suitability index ((lh) was the most important predictor in all networks, followed by third-order neighborhood (nt). Patch size (lh) was unimportant in all three networks. 4. We found that topological variables of habitat networks are relevant for the prediction of species occurrence-state, a step-forward from models considering only local habitat characteristics. For any habitat patch, occurrence-state is most prominently influenced by its habitat suitability, and then by the number of patches in a wide neighborhood. Our approach is generic and can be applied to multiple species in different habitats.Predictor processing for HSM, presences processing, study area & mask definitionDefinition of study area, preparation of species records and predictors for Habitat Suitability Modelling (HSM), mask definition [Python script]1_Def_study_area-presences_prep_Predictor_processing_for_HSM-mask_definition.pyEnsemble HSMEnsemble Habitat Suitability Modelling (HSM), with predictor selection [R Script]2_EnsembleHSM.RMask, Patch delineation, HSI calc to csvMask application, delineation of patches, and calculation of mean Habitat Suitability Index (HSI) per patch [Python script]3_Mask-Patch_delineation-HSI_to_csv.pyCost surface definitionCost surface definition [Python script]4_Cost_surface_definition.pyCreate networks + HabAv calculationEdge definition (Generation of networks) and Habitat availability (HabAv) calculation [Python script]5_Create_networks_plus_HabAv_calculation.pyAbsences defiinition, network vars, BRTsDefinition of absences, calculation of network variables and Boosted Regression Trees (BRTs) modelling [R script]6_Absences_def_networkvars_BRTs.RReadme_scripts-Ortiz-Rodriguez_etal-Habitat_networks_and_species_occurrence_2019Readme of the full packag