Data from: Integrated SDM database: Enhancing the relevance and utility of species distribution models in conservation management
Creators: name: Frans, Veronica F.
name: Augé, Amélie A.
name: Fyfe, Jim
name: Zhang, Yuqian
name: McNally, Nathan
name: Edelhoff, Hendrik A.
name: Balkenhol, Niko
name: Engler, Jan O.
Publisher: name: Zenodo
Year: 2021
Subjects: Phocarctos hookeri
New Zealand
multi-state species distribution model
SDM
multi-criteria decision analysis
MCDA
anthropogenic impact
qualitative decision-making
range shift
recolonisation

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 <i>integrated SDM database (iSDMdb)</i>. 2. The<i> iSDMdb</i> 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<i>,</i> we used the endangered New Zealand sea lion (<i>Phocarctos hookeri</i>). 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 <i>iSDMdb</i>'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 <i>iSDMdb</i> addresses these issues and enhances the practical relevance and utility of SDMs for stakeholders, rangers, and decision-makers. We exemplify how to build an <i>iSDMdb</i> using open-source tools, and how to make diverse, complex assessments more accessible for end-users.
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. Funding provided by: Department of Conservation, New Zealand<br>Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100009933<br>Award Number: 2017-22Funding provided by: National Science Foundation<br>Crossref Funder Registry ID: http://dx.doi.org/10.13039/100000001<br>Award Number: 2018253044
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.