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UPRLIMET: UPstream Regional LiDAR Model for Extent of Trout in stream networks

Predicting the edges of species distributions is fundamental for species conservation, ecosystem services, and management decisions. In North America, the location of the upstream limit of fish in forested streams receives special attention, because fish-bearing portions of streams have more protect...

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Autores principales: Penaluna, Brooke E., Burnett, Jonathan D., Christiansen, Kelly, Arismendi, Ivan, Johnson, Sherri L., Griswold, Kitty, Holycross, Brett, Kolstoe, Sonja H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9715699/
https://www.ncbi.nlm.nih.gov/pubmed/36456610
http://dx.doi.org/10.1038/s41598-022-23754-0
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author Penaluna, Brooke E.
Burnett, Jonathan D.
Christiansen, Kelly
Arismendi, Ivan
Johnson, Sherri L.
Griswold, Kitty
Holycross, Brett
Kolstoe, Sonja H.
author_facet Penaluna, Brooke E.
Burnett, Jonathan D.
Christiansen, Kelly
Arismendi, Ivan
Johnson, Sherri L.
Griswold, Kitty
Holycross, Brett
Kolstoe, Sonja H.
author_sort Penaluna, Brooke E.
collection PubMed
description Predicting the edges of species distributions is fundamental for species conservation, ecosystem services, and management decisions. In North America, the location of the upstream limit of fish in forested streams receives special attention, because fish-bearing portions of streams have more protections during forest management activities than fishless portions. We present a novel model development and evaluation framework, wherein we compare 26 models to predict upper distribution limits of trout in streams. The models used machine learning, logistic regression, and a sophisticated nested spatial cross-validation routine to evaluate predictive performance while accounting for spatial autocorrelation. The model resulting in the best predictive performance, termed UPstream Regional LiDAR Model for Extent of Trout (UPRLIMET), is a two-stage model that uses a logistic regression algorithm calibrated to observations of Coastal Cutthroat Trout (Oncorhynchus clarkii clarkii) occurrence and variables representing hydro-topographic characteristics of the landscape. We predict trout presence along reaches throughout a stream network, and include a stopping rule to identify a discrete upper limit point above which all stream reaches are classified as fishless. Although there is no simple explanation for the upper distribution limit identified in UPRLIMET, four factors, including upstream channel length above the point of uppermost fish, drainage area, slope, and elevation, had highest importance. Across our study region of western Oregon, we found that more of the fish-bearing network is on private lands than on state, US Bureau of Land Mangement (BLM), or USDA Forest Service (USFS) lands, highlighting the importance of using spatially consistent maps across a region and working across land ownerships. Our research underscores the value of using occurrence data to develop simple, but powerful, prediction tools to capture complex ecological processes that contribute to distribution limits of species.
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spelling pubmed-97156992022-12-03 UPRLIMET: UPstream Regional LiDAR Model for Extent of Trout in stream networks Penaluna, Brooke E. Burnett, Jonathan D. Christiansen, Kelly Arismendi, Ivan Johnson, Sherri L. Griswold, Kitty Holycross, Brett Kolstoe, Sonja H. Sci Rep Article Predicting the edges of species distributions is fundamental for species conservation, ecosystem services, and management decisions. In North America, the location of the upstream limit of fish in forested streams receives special attention, because fish-bearing portions of streams have more protections during forest management activities than fishless portions. We present a novel model development and evaluation framework, wherein we compare 26 models to predict upper distribution limits of trout in streams. The models used machine learning, logistic regression, and a sophisticated nested spatial cross-validation routine to evaluate predictive performance while accounting for spatial autocorrelation. The model resulting in the best predictive performance, termed UPstream Regional LiDAR Model for Extent of Trout (UPRLIMET), is a two-stage model that uses a logistic regression algorithm calibrated to observations of Coastal Cutthroat Trout (Oncorhynchus clarkii clarkii) occurrence and variables representing hydro-topographic characteristics of the landscape. We predict trout presence along reaches throughout a stream network, and include a stopping rule to identify a discrete upper limit point above which all stream reaches are classified as fishless. Although there is no simple explanation for the upper distribution limit identified in UPRLIMET, four factors, including upstream channel length above the point of uppermost fish, drainage area, slope, and elevation, had highest importance. Across our study region of western Oregon, we found that more of the fish-bearing network is on private lands than on state, US Bureau of Land Mangement (BLM), or USDA Forest Service (USFS) lands, highlighting the importance of using spatially consistent maps across a region and working across land ownerships. Our research underscores the value of using occurrence data to develop simple, but powerful, prediction tools to capture complex ecological processes that contribute to distribution limits of species. Nature Publishing Group UK 2022-12-01 /pmc/articles/PMC9715699/ /pubmed/36456610 http://dx.doi.org/10.1038/s41598-022-23754-0 Text en © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2022, corrected publication 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Penaluna, Brooke E.
Burnett, Jonathan D.
Christiansen, Kelly
Arismendi, Ivan
Johnson, Sherri L.
Griswold, Kitty
Holycross, Brett
Kolstoe, Sonja H.
UPRLIMET: UPstream Regional LiDAR Model for Extent of Trout in stream networks
title UPRLIMET: UPstream Regional LiDAR Model for Extent of Trout in stream networks
title_full UPRLIMET: UPstream Regional LiDAR Model for Extent of Trout in stream networks
title_fullStr UPRLIMET: UPstream Regional LiDAR Model for Extent of Trout in stream networks
title_full_unstemmed UPRLIMET: UPstream Regional LiDAR Model for Extent of Trout in stream networks
title_short UPRLIMET: UPstream Regional LiDAR Model for Extent of Trout in stream networks
title_sort uprlimet: upstream regional lidar model for extent of trout in stream networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9715699/
https://www.ncbi.nlm.nih.gov/pubmed/36456610
http://dx.doi.org/10.1038/s41598-022-23754-0
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