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

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 lin..., 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.
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[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...,