Regional moose (Alces alces) responses to forestry cutblocks are driven by landscape-scale patterns of vegetation composition and regrowth
Published in: | Forest Ecology and Management |
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Main Authors: | , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
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Elsevier BV
2021
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Online Access: | http://dx.doi.org/10.1016/j.foreco.2020.118763 https://api.elsevier.com/content/article/PII:S0378112720315322?httpAccept=text/xml https://api.elsevier.com/content/article/PII:S0378112720315322?httpAccept=text/plain |
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crelsevierbv:10.1016/j.foreco.2020.118763 2024-05-19T07:27:51+00:00 Regional moose (Alces alces) responses to forestry cutblocks are driven by landscape-scale patterns of vegetation composition and regrowth Mumma, Matthew A. Gillingham, Michael P. Marshall, Shelley Procter, Christopher Bevington, Alexandre R. Scheideman, Matthew 2021 http://dx.doi.org/10.1016/j.foreco.2020.118763 https://api.elsevier.com/content/article/PII:S0378112720315322?httpAccept=text/xml https://api.elsevier.com/content/article/PII:S0378112720315322?httpAccept=text/plain en eng Elsevier BV https://www.elsevier.com/tdm/userlicense/1.0/ Forest Ecology and Management volume 481, page 118763 ISSN 0378-1127 Management, Monitoring, Policy and Law Nature and Landscape Conservation Forestry journal-article 2021 crelsevierbv https://doi.org/10.1016/j.foreco.2020.118763 2024-04-19T07:30:56Z Article in Journal/Newspaper Alces alces ScienceDirect (Elsevier) Forest Ecology and Management 481 118763 |
institution |
Open Polar |
collection |
ScienceDirect (Elsevier) |
op_collection_id |
crelsevierbv |
language |
English |
topic |
Management, Monitoring, Policy and Law Nature and Landscape Conservation Forestry |
spellingShingle |
Management, Monitoring, Policy and Law Nature and Landscape Conservation Forestry Mumma, Matthew A. Gillingham, Michael P. Marshall, Shelley Procter, Christopher Bevington, Alexandre R. Scheideman, Matthew Regional moose (Alces alces) responses to forestry cutblocks are driven by landscape-scale patterns of vegetation composition and regrowth |
topic_facet |
Management, Monitoring, Policy and Law Nature and Landscape Conservation Forestry |
format |
Article in Journal/Newspaper |
author |
Mumma, Matthew A. Gillingham, Michael P. Marshall, Shelley Procter, Christopher Bevington, Alexandre R. Scheideman, Matthew |
author_facet |
Mumma, Matthew A. Gillingham, Michael P. Marshall, Shelley Procter, Christopher Bevington, Alexandre R. Scheideman, Matthew |
author_sort |
Mumma, Matthew A. |
title |
Regional moose (Alces alces) responses to forestry cutblocks are driven by landscape-scale patterns of vegetation composition and regrowth |
title_short |
Regional moose (Alces alces) responses to forestry cutblocks are driven by landscape-scale patterns of vegetation composition and regrowth |
title_full |
Regional moose (Alces alces) responses to forestry cutblocks are driven by landscape-scale patterns of vegetation composition and regrowth |
title_fullStr |
Regional moose (Alces alces) responses to forestry cutblocks are driven by landscape-scale patterns of vegetation composition and regrowth |
title_full_unstemmed |
Regional moose (Alces alces) responses to forestry cutblocks are driven by landscape-scale patterns of vegetation composition and regrowth |
title_sort |
regional moose (alces alces) responses to forestry cutblocks are driven by landscape-scale patterns of vegetation composition and regrowth |
publisher |
Elsevier BV |
publishDate |
2021 |
url |
http://dx.doi.org/10.1016/j.foreco.2020.118763 https://api.elsevier.com/content/article/PII:S0378112720315322?httpAccept=text/xml https://api.elsevier.com/content/article/PII:S0378112720315322?httpAccept=text/plain |
genre |
Alces alces |
genre_facet |
Alces alces |
op_source |
Forest Ecology and Management volume 481, page 118763 ISSN 0378-1127 |
op_rights |
https://www.elsevier.com/tdm/userlicense/1.0/ |
op_doi |
https://doi.org/10.1016/j.foreco.2020.118763 |
container_title |
Forest Ecology and Management |
container_volume |
481 |
container_start_page |
118763 |
_version_ |
1799469357648052224 |