DOI: 10.5061/dryad.t1g1jwt33
title: Data from: Integrated SDM database: Enhancing the relevance and utility of species distribution models in conservation management

description: 1. Species’ ranges are changing at accelerating rates. Species
 distribution models (SDMs) are powerful tools that help rangers and
 decision-makers prepare for reintroductions, range shifts, reductions,
 and/or expansions by predicting habitat suitability across landscapes.
 Yet, range-expanding or -shifting species in particular face other
 challenges that traditional SDM procedures cannot quantify, due to large
 differences between a species’ currently-occupied range and potential
 future range. The realism of SDMs is thus lost and not as useful for
 conservation management in practice. Here, we address these challenges
 with an extended assessment of habitat suitability through an integrated
 SDM database (iSDMdb). 2. The iSDMdb is a spatial database of predicted
 sites in a species’ prediction range, derived from SDM results, and is a
 single spatial feature that contains additional, user-friendly data fields
 that synthesise and summarise SDM predictions and uncertainty, human
 impacts, restoration features, novel preferences in novel spaces, and
 management priorities. To illustrate its utility, we used the endangered
 New Zealand sea lion (Phocarctos hookeri). We consulted with wildlife
 rangers, decision-makers, and sea lion experts to supplement SDM
 predictions with additional, more realistic, and applicable information
 for management. 3. Almost half the data fields included in this database
 resulted from engaging with these end-users during our study. The SDM
 found 395 predicted sites. However, the iSDMdb’s additional assessments
 showed that the actual suitability of most sites (90%) was questionable
 due to human impacts. &gt;50% of sites contained unnatural barriers
 (fences, grazing grasslands), and 75% of sites had roads located within
 the species’ range of inland movement. Just 5% of the predicted sites were
 mostly (&gt;80%) protected. 4. Integrating SDM results with
 supplemental assessments provides a way to address SDM limitations,
 especially for range-expanding or -shifting species. SDM products for
 conservation applications have been critiqued for lacking transparency and
 interpretation support, and ineffectively communicating uncertainty. The
 iSDMdb addresses these issues and enhances the practical relevance and
 utility of SDMs for stakeholders, rangers, and decision-makers. We
 exemplify how to build an iSDMdb using open-source tools, and how to make
 diverse, complex assessments more accessible for end-users.
description: We created an
 <em>iSDMdb</em> using the New Zealand sea lion
 (<em>Phocarctos hookeri</em>) as an example. Enclosed are the
 R scripts and table summaries that describe the methods. The original data used for this
 study were obtained from Land Information New Zealand (http://data.linz.govt.nz), Stats New Zealand (https://data.mfe.govt.nz), New Zealand Department of Conservation (DOC), https://koordinates.com, Ministry for the Environment, and Landcare New Zealand (https://lris.scinfo.org.nz), under New Zealand Creative Commons Attribution 3.0 and Landcare Data Use licensing. New Zealand sea lion location data were obtained from DOC via Dragonfly Data Science (https://sealions.dragonfly.co.nz/demographics/) and Frans et al. 2018 (https://doi.org/10.5061/dryad.14mt7). See the main text of the corresponding <em>Methods in Ecology and Evolution</em> publication for more details.
description: Contents: Read Me:
 Integrated SDM database framework components and data field
 descriptions Appendix S1: R-script*: Multi-state
 species distribution model training<br> Appendix S2: R-script*:
 Multi-state species distribution model prediction and suitable
 sites<br> Appendix S3: R-script*: Analysis of prediction
 uncertainties and limitations (CV, MESS, MOD, and limiting
 factors)<br> Appendix S4: Table: Pairwise comparisons and criteria
 weights for the multi-criteria analysis (MCDA) of human impacts<br>
 Appendix S5: R-script*: Integrated SDM database
 (raw data extractions)<br> Appendix S6: R-script*:
 Integrated SDM database (data simplification, final database
 and interactive map)<br> Appendix S7: Table: Integrated
 SDM database for the New Zealand sea lion (<em>Phocarctos
 hookeri</em>)<br> Appendix S8: R-script*:
 Querying and summarising sites near current pupping
 locations See Zenodo link for access to the R scripts
 or https://github.com/vffrans/iSDMdb.