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

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 | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 13 February 2025 V1 Latest version Share on 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 Authors : Dan Isaak 0000-0003-2963-1546 [email protected] , Mike Dumelle , Dona Horan , Dan Mason , Thomas Franklin , Dave Nagel , Jay Ver Hoef , and Mike Young Authors Info & Affiliations https://doi.org/10.22541/au.173943936.67630866/v1 301 views 147 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Fundamental to species conservation efforts are an understanding of habitat relationships, development of accurate distribution models, and risk factor assessments. Achieving these tasks is challenging for non-marque stream organisms where limited funding often necessitates compilation of incidental observations from multiple sources which lack an overall sampling design. Compounding matters, appropriate statistical techniques for flow directed networks like streams and the unique forms of spatial dependence that may arise among such observations are necessary. We aggregated a comprehensive presence-absence dataset for Idaho giant salamander (Dicamptodon aterrimus), a species of conservation concern that inhabits mountain streams across a restricted range in western North American and linked these data to geospatial habitat covariates. The dataset was modeled using spatial-stream-network models (SSNM) which account for autocorrelation and results were compared to non-spatial generalized linear models (GLM). 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 (four versus seven), which simplified model interpretation. The top-ranked SSNM and GLM were used to predict range-wide occurrence probabilities for scenarios representing historical baselines and futures associated with two significant model covariates (water temperature and riparian tree canopy density) that are changing with environmental trends in the study area. 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 Idaho giant salamander. However, these expansions were partially or entirely offset in future scenarios which included decreases in riparian canopy density. Although the Idaho giant salamander does not appear to be at acute risk, a monitoring program for tracking future changes would be beneficial and could leverage the large dataset compiled for this study as well as spatially-explicit predictions from SSNMs. Supplementary Material File (igsrevision_2-11-25_ecographysubmission_maintext.docx) Download 97.29 KB Information & Authors Information Version history V1 Version 1 13 February 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords idaho giant salamander spatial autocorrelation spatial stream network model species distribution model Authors Affiliations Dan Isaak 0000-0003-2963-1546 [email protected] US Forest Service View all articles by this author Mike Dumelle US Environmental Protection Agency View all articles by this author Dona Horan US Forest Service View all articles by this author Dan Mason US Forest Service View all articles by this author Thomas Franklin US Forest Service View all articles by this author Dave Nagel US Forest Service View all articles by this author Jay Ver Hoef National Oceanic and Atmospheric Administration View all articles by this author Mike Young US Forest Service View all articles by this author Metrics & Citations Metrics Article Usage 301 views 147 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Dan Isaak, Mike Dumelle, Dona Horan, et al. 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. Authorea . 13 February 2025. DOI: https://doi.org/10.22541/au.173943936.67630866/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.173943936.67630866/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9ff6ede66af658f4',t:'MTc3OTQwMTYxNg=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();