DOI: 10.5061/dryad.h18931zxb
title: Data from: Improving species distribution models for stream networks by incorporating spatial autocorrelation in multi-sourced datasets: An assessment of Idaho giant salamander status and future risk

description: Fundamental to species conservation efforts is the development of accurate
 distribution models, but doing so is challenging for many stream
 organisms, where limited funding often necessitates the compilation of
 incidental observations from multiple sources, which lack an overall
 sampling design and may be spatially clustered. We demonstrate the
 application of specialized spatial-statistical-network models (SSNMs),
 which incorporate autocorrelation among observations and significantly
 outperform non-spatial models when used to develop distribution models for
 the Idaho giant salamander (IGS; Dicamptodon aterrimus). The study was
 located in the Rocky Mountains in west-central North America. We compiled
 a comprehensive presence-absence dataset for IGS from previous studies,
 natural resource agencies, museum collections, and new surveys and linked
 these data to geospatial habitat covariates. The dataset was modeled using
 a suite of candidate SSNMs and results were compared to generalized linear
 models (GLM). The top-ranked models were used to predict range-wide IGS
 occurrence probabilities for scenarios that represent historical baselines
 and futures associated with two model covariates (water temperature and
 riparian tree canopy density) that were changing with environmental trends
 in the study area.** The classification accuracy of salamander
 observations was higher with SSNMs than GLMs (90.8% versus 63.2%) and the
 spatial models identified fewer significant habitat relationships, which
 simplified model interpretation. Baseline range estimates from the models
 were similar (13,090–14,114 stream km) and both predicted small range
 expansions (2.0% to 24.8%) with warming because many streams were
 sub-optimally cold for IGS. However, these expansions could be partially
 offset in future scenarios which included decreases in riparian canopy
 density. **SSNMs significantly improve distribution models for stream
 organisms by incorporating spatial autocorrelation and provide an
 inexpensive means of developing new information from existing datasets.
 This incentivizes aggregation of datasets, which may be further leveraged
 to create efficient monitoring and inventory programs using the
 spatially-explicit outputs from SSNMs.
description: A presence-absence dataset for Idaho giant salamander that
 consisted of 707 unique sampling locations was collected using
 electrofishing and eDNA surveys. Many of the surveys were aggregated from
 existing sources such as previous peer-reviewed studies, grey literature
 reports, state and federal agency databases, and natural resource museum
 records. The survey locations were attached to reaches within stream
 networks across the species range, linked to geospatial habitat
 covariates, and processed using the open-source SSNbler R package into a
 landscape network object suitable for spatial-stream-network model
 analysis using the SSN2 R package.
description: # Data from: Improving species distribution models for stream networks by
 incorporating spatial autocorrelation in multi-sourced datasets: An
 assessment of Idaho giant salamander status and future risk
 [https://doi.org/10.5061/dryad.h18931zxb](https://doi.org/10.5061/dryad.h18931zxb) ## Description of the data and file structure This dataset was used in a manuscript published in Diversity and Distributions and consists of several elements: 1) an annotated R script for running an SSNM species distribution model analysis of Idaho giant salamanders (AnnotatedRscript_IGSAnalysis_SDM_n707.R), 2) a zipfile which contains a .ssn directory of files with the observations, covariates, range-wide prediction points, and other helper files needed to conduct a spatial-stream-network model analysis (IGS_LSN3.ssn.zip), 3) the master database of presence-absence surveys as an Excel file (Isaak2025D_D_MasterDataset_IGS-Observations.xlsx), 4) a high-resolution .pdf map showing the survey locations used to develop the distribution model overlaid on occurrence probabilities predicted for a baseline scenario throughout the Idaho giant salamander range (Isaak2025D_D_IGSModel3_Scenario1_HighResDistributionMap.pdf), and 5) an ArcGIS shapefile of seven occurrence scenarios and prediction standard errors (Isaak2025D_D_IGS_ScenarioDistributions_Shapefiles.zip).  ### Files and variables Files are described above. Variables are described in the annotated R script for the analysis, a metadata tab in Excel file of the database, and in the manuscript published in Diversity and Distributions ## Code/software The analysis is run using the R statistical program and the SSN2 package, both of which can be downloaded from the CRAN website [https://www.r-project.org/](https://www.r-project.org/)  ## Access information Other publicly accessible locations of the data: * na Data was derived from the following sources: * Museum archives, natural resource agency surveys, peer-reviewed studies, publicly available eDNA databases as described in the published *Diversity and Distribution* manuscript.