DOI: 10.5061/dryad.sc818d5
title: Data from: Predicting species occurrences with habitat network models

description: 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.
description: 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 package