High-resolution prediction of American red squirrel in Interior Alaska: a role model for conservation using open access data, machine learning, GIS and LIDAR

American red squirrels (Tamiasciurus hudsonicus) are small mammals that are abundantly distributed throughout North America. Urbanization in the Anthropocene is now a global process, and squirrels live in affected landscapes. This leads to squirrels adjusting to human developments. Not much is known...

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Published in:PeerJ
Main Authors: Robold, Richard B., Huettmann, Falk
Format: Text
Language:English
Published: PeerJ Inc. 2021
Subjects:
Online Access:http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8447940/
http://www.ncbi.nlm.nih.gov/pubmed/34611502
https://doi.org/10.7717/peerj.11830
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spelling ftpubmed:oai:pubmedcentral.nih.gov:8447940 2023-05-15T18:28:39+02:00 High-resolution prediction of American red squirrel in Interior Alaska: a role model for conservation using open access data, machine learning, GIS and LIDAR Robold, Richard B. Huettmann, Falk 2021-09-14 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8447940/ http://www.ncbi.nlm.nih.gov/pubmed/34611502 https://doi.org/10.7717/peerj.11830 en eng PeerJ Inc. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8447940/ http://www.ncbi.nlm.nih.gov/pubmed/34611502 http://dx.doi.org/10.7717/peerj.11830 © 2021 Robold and Huettmann https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. CC-BY PeerJ Animal Behavior Text 2021 ftpubmed https://doi.org/10.7717/peerj.11830 2021-10-10T00:23:39Z American red squirrels (Tamiasciurus hudsonicus) are small mammals that are abundantly distributed throughout North America. Urbanization in the Anthropocene is now a global process, and squirrels live in affected landscapes. This leads to squirrels adjusting to human developments. Not much is known about the distribution of squirrels and squirrel middens near humans, especially not in the subarctic and sub-urbanized regions. Although this species is hunted, there are no real publicly available distribution and abundance estimates nor management plans and bag limits for squirrels in Alaska or in the United States known by us, except the endangered Mt. Graham squirrel. In general, insufficient squirrel conservation research is carried out; they are underrepresented in research and its literature. To further the science-based management for such species, this study aims to generate the first digital open access workflow as a generic research template for small mammal work including the latest machine learning of open source and high-resolution LIDAR data in an Open Source Geographic Information System (QGIS) and ArcGIS. Machine learning has proven to be less modeler biased and improve accuracy of the analysis outcome, therefore it is the preferred approach. This template is designed to be rapid, simple, robust, generic and effective for being used by a global audience. As a unique showcase, here a squirrel midden survey was carried out for two years (2016 and 2017). These squirrel middens were detected in a research area of 45,5 hectares (0,455 km(2)) in downtown Fairbanks, interior boreal forest of Alaska, U.S. Transect distances were geo-referenced with a GPS and adjusted to the visual conditions to count all squirrel middens within the survey area. Different layers of proximity to humans and habitat characteristics were assembled using aerial imagery and LIDAR data (3D data needed for an arboreal species like the red squirrels) consisting of a 3 × 3 m resolution. The layer data was used to train a predictive ... Text Subarctic Alaska PubMed Central (PMC) Fairbanks PeerJ 9 e11830
institution Open Polar
collection PubMed Central (PMC)
op_collection_id ftpubmed
language English
topic Animal Behavior
spellingShingle Animal Behavior
Robold, Richard B.
Huettmann, Falk
High-resolution prediction of American red squirrel in Interior Alaska: a role model for conservation using open access data, machine learning, GIS and LIDAR
topic_facet Animal Behavior
description American red squirrels (Tamiasciurus hudsonicus) are small mammals that are abundantly distributed throughout North America. Urbanization in the Anthropocene is now a global process, and squirrels live in affected landscapes. This leads to squirrels adjusting to human developments. Not much is known about the distribution of squirrels and squirrel middens near humans, especially not in the subarctic and sub-urbanized regions. Although this species is hunted, there are no real publicly available distribution and abundance estimates nor management plans and bag limits for squirrels in Alaska or in the United States known by us, except the endangered Mt. Graham squirrel. In general, insufficient squirrel conservation research is carried out; they are underrepresented in research and its literature. To further the science-based management for such species, this study aims to generate the first digital open access workflow as a generic research template for small mammal work including the latest machine learning of open source and high-resolution LIDAR data in an Open Source Geographic Information System (QGIS) and ArcGIS. Machine learning has proven to be less modeler biased and improve accuracy of the analysis outcome, therefore it is the preferred approach. This template is designed to be rapid, simple, robust, generic and effective for being used by a global audience. As a unique showcase, here a squirrel midden survey was carried out for two years (2016 and 2017). These squirrel middens were detected in a research area of 45,5 hectares (0,455 km(2)) in downtown Fairbanks, interior boreal forest of Alaska, U.S. Transect distances were geo-referenced with a GPS and adjusted to the visual conditions to count all squirrel middens within the survey area. Different layers of proximity to humans and habitat characteristics were assembled using aerial imagery and LIDAR data (3D data needed for an arboreal species like the red squirrels) consisting of a 3 × 3 m resolution. The layer data was used to train a predictive ...
format Text
author Robold, Richard B.
Huettmann, Falk
author_facet Robold, Richard B.
Huettmann, Falk
author_sort Robold, Richard B.
title High-resolution prediction of American red squirrel in Interior Alaska: a role model for conservation using open access data, machine learning, GIS and LIDAR
title_short High-resolution prediction of American red squirrel in Interior Alaska: a role model for conservation using open access data, machine learning, GIS and LIDAR
title_full High-resolution prediction of American red squirrel in Interior Alaska: a role model for conservation using open access data, machine learning, GIS and LIDAR
title_fullStr High-resolution prediction of American red squirrel in Interior Alaska: a role model for conservation using open access data, machine learning, GIS and LIDAR
title_full_unstemmed High-resolution prediction of American red squirrel in Interior Alaska: a role model for conservation using open access data, machine learning, GIS and LIDAR
title_sort high-resolution prediction of american red squirrel in interior alaska: a role model for conservation using open access data, machine learning, gis and lidar
publisher PeerJ Inc.
publishDate 2021
url http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8447940/
http://www.ncbi.nlm.nih.gov/pubmed/34611502
https://doi.org/10.7717/peerj.11830
geographic Fairbanks
geographic_facet Fairbanks
genre Subarctic
Alaska
genre_facet Subarctic
Alaska
op_source PeerJ
op_relation http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8447940/
http://www.ncbi.nlm.nih.gov/pubmed/34611502
http://dx.doi.org/10.7717/peerj.11830
op_rights © 2021 Robold and Huettmann
https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
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