Efficient spatial models for predicting the occurrence of subarctic estuarine‐associated fishes: implications for management
Abstract In many of the nearshore areas where development is most likely to occur, essential fish habitat data are incomplete and there is little information on species occurrence that can be used to inform management decisions. This research investigated the use of multivariate remotely sensed geom...
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crwiley:10.1111/fme.12148 2024-06-02T08:15:00+00:00 Efficient spatial models for predicting the occurrence of subarctic estuarine‐associated fishes: implications for management Miller, K. B. Huettmann, F. Norcross, B. L. 2015 http://dx.doi.org/10.1111/fme.12148 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Ffme.12148 https://onlinelibrary.wiley.com/doi/pdf/10.1111/fme.12148 en eng Wiley http://onlinelibrary.wiley.com/termsAndConditions#vor Fisheries Management and Ecology volume 22, issue 6, page 501-517 ISSN 0969-997X 1365-2400 journal-article 2015 crwiley https://doi.org/10.1111/fme.12148 2024-05-03T11:10:43Z Abstract In many of the nearshore areas where development is most likely to occur, essential fish habitat data are incomplete and there is little information on species occurrence that can be used to inform management decisions. This research investigated the use of multivariate remotely sensed geomorphic and landscape data to develop accurate predictive models of subarctic, estuarine‐associated fishes. The random forest algorithm was used to predict the occurrence of 26 fish species captured in 49 estuaries in Southeast Alaska. Model prediction accuracy ranged from 100 to 42% for species presence and 87 to 15% for species absence. Model goodness of fit and accuracy were assessed by comparing the number of species occurrences predicted by the model against the observed presences and absences of species in an independent data set. Sixty percent of the models were able to predict species presence with an accuracy of 70% or better. The models were used to predict species occurrence for 521 unsampled Southeast Alaskan estuaries to provide a regional map of predicted species distributions. Article in Journal/Newspaper Subarctic Alaska Wiley Online Library Fisheries Management and Ecology 22 6 501 517 |
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Wiley Online Library |
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crwiley |
language |
English |
description |
Abstract In many of the nearshore areas where development is most likely to occur, essential fish habitat data are incomplete and there is little information on species occurrence that can be used to inform management decisions. This research investigated the use of multivariate remotely sensed geomorphic and landscape data to develop accurate predictive models of subarctic, estuarine‐associated fishes. The random forest algorithm was used to predict the occurrence of 26 fish species captured in 49 estuaries in Southeast Alaska. Model prediction accuracy ranged from 100 to 42% for species presence and 87 to 15% for species absence. Model goodness of fit and accuracy were assessed by comparing the number of species occurrences predicted by the model against the observed presences and absences of species in an independent data set. Sixty percent of the models were able to predict species presence with an accuracy of 70% or better. The models were used to predict species occurrence for 521 unsampled Southeast Alaskan estuaries to provide a regional map of predicted species distributions. |
format |
Article in Journal/Newspaper |
author |
Miller, K. B. Huettmann, F. Norcross, B. L. |
spellingShingle |
Miller, K. B. Huettmann, F. Norcross, B. L. Efficient spatial models for predicting the occurrence of subarctic estuarine‐associated fishes: implications for management |
author_facet |
Miller, K. B. Huettmann, F. Norcross, B. L. |
author_sort |
Miller, K. B. |
title |
Efficient spatial models for predicting the occurrence of subarctic estuarine‐associated fishes: implications for management |
title_short |
Efficient spatial models for predicting the occurrence of subarctic estuarine‐associated fishes: implications for management |
title_full |
Efficient spatial models for predicting the occurrence of subarctic estuarine‐associated fishes: implications for management |
title_fullStr |
Efficient spatial models for predicting the occurrence of subarctic estuarine‐associated fishes: implications for management |
title_full_unstemmed |
Efficient spatial models for predicting the occurrence of subarctic estuarine‐associated fishes: implications for management |
title_sort |
efficient spatial models for predicting the occurrence of subarctic estuarine‐associated fishes: implications for management |
publisher |
Wiley |
publishDate |
2015 |
url |
http://dx.doi.org/10.1111/fme.12148 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Ffme.12148 https://onlinelibrary.wiley.com/doi/pdf/10.1111/fme.12148 |
genre |
Subarctic Alaska |
genre_facet |
Subarctic Alaska |
op_source |
Fisheries Management and Ecology volume 22, issue 6, page 501-517 ISSN 0969-997X 1365-2400 |
op_rights |
http://onlinelibrary.wiley.com/termsAndConditions#vor |
op_doi |
https://doi.org/10.1111/fme.12148 |
container_title |
Fisheries Management and Ecology |
container_volume |
22 |
container_issue |
6 |
container_start_page |
501 |
op_container_end_page |
517 |
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1800739051386437632 |