DOI: 10.5061/dryad.3j9kd51wk
title: Data and code from: Thresholding species distribution models: Simple approaches for land-use planning in multifunctional landscapes

description: Species distribution models (SDMs) are often used to understand changes to
 species’ distributions and their habitats under different land-use
 scenarios, enabling decision-makers to prioritize areas for management
 efforts and balance environmental conservation with socio-economic demands
 on the landscape. However, application of SDMs in land-use planning and
 Environmental Impact Assessments (EIAs) remains limited due to challenges
 in interpreting and communicating continuous predictions resulting from
 these SDMs. Although different binarization methods have been used to
 overcome such challenges, the choice of threshold can profoundly alter the
 resulting binary habitat map, and most methods lack simplicity and require
 access to underlying species occurrence and environmental data used to
 develop the SDMs. Hence, there is a demand for testing simple alternative
 binarization methods to enable in-house application of SDMs by
 practitioners and to facilitate interpretation and communication. Using
 SDMs of 103 boreal bird species in Alberta, Canada, we transform species
 relative abundance predictions of SDMs into direct estimates of habitat
 area, a proxy for habitat suitability, using four simple and three complex
 thresholding methods. We compare the performance of the binarized models
 for each bird species and between forest specialists
 vs. generalists under land-use change scenarios. We found that
 thresholded models reflect losses in suitable habitat under industrial
 disturbance scenarios more realistically compared to continuous relative
 abundance models. Notably, simple thresholding methods, particularly the
 mean predicted relative abundance, performed similarly to complex
 thresholding methods in predicting suitable habitat areas, and as
 indicated by model evaluations using the area under the curve. These
 findings suggest that using the mean as a binarization threshold can
 effectively bridge the gap between complex SDMs and their application in
 policy and planning, without sacrificing predictive accuracy. We conclude
 that simple threshold binarization methods, such as the mean, can leverage
 the strong predictive power of SDMs to provide insights into future
 changes in species’ habitat during land-use planning scenarios, account
 for their uncertainties, and expand their utility to facilitate
 interpretation for science-informed decision-making in multifunctional
 landscapes.
description: # Data and code from: Thresholding species distribution models: Simple
 approaches for land-use planning in multifunctional landscapes
 [https://doi.org/10.5061/dryad.3j9kd51wk](https://doi.org/10.5061/dryad.3j9kd51wk) ## Description of the data and file structure ### Files and variables #### File: Supplement_S1.pdf **Description:**  Details on ABMI bird species distribution models. #### File: Supplement_S2.pdf **Description:**  Threshold values and percent of habitat suitability selected from the seven binarization thresholding methods to transform estimate of relative abundance into area of suitable habitat for all 103 bird species. #### File: Supplement_S3.csv **Description:**  Data file (Supplement_S2.csv) containing the individual species responses (AUC scores) under habitat loss scenarios. ##### Variables * ser: a unique identifier (serial number) of models * Disturbance: Ranging from 0-100 % and present the percent of urban-industry per cell. * Model: All 103 birds. Models are identified by bird's common name. * Method: Full, full model; Modified, modified model (urban-industry coefficient =0), mean (𝜇), median (md), 75th percentile (p75), 90th percentile (p90), maximum sum of sensitivity and specificity (mss), sensitivity equals specificity (ses), and minimum ROC distance (mrd). * values: AUC scores for all models  #### File: Supplement_S4.pdf **Description:**  AUC scores ± SD, and 95 % CI for the contentious and modified models, and seven thresholding methods for 103 bird species. #### File: Supplement_S5.pdf **Description:**  Pairwise multiple comparison showing differences in AUC scores with 95 % confidence intervals for each model pair across all 103 birds, and per functional group (77 forest generalists and 26 specialists). #### File: Supplement_S6.pdf **Description:**  Percent difference in delineated suitable habitat resulting from bootstrapping of bird models. 
 
 ### Software files hosted by Zenodo #### Folder:
 Abdel_Moniem_et_al_2025_SDMs_thresholds_Code.zip 5.75 MB **Description:** 
 * R code: SDMs-LUP, contains code needed for map figures * Folder:
 Analysis, contains 3 subfolders: * data: contains species lookups and
 sahpefiles folder 'nsr' * R: contains several R functions needed
 for the analysis * src: R script for analysis and model validation at
 different scales.  ## Access information Other publicly accessible
 locations of the data: *
 [https://abmi.ca/biobrowser/species-group/species-table.html?groupId=1](https://abmi.ca/biobrowser/species-group/species-table.html?groupId=1) Data was derived from the following sources: * Alberta Biodiversity Monitoring Institute (ABMI)