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Remote sensing captures varying temporal patterns of vegetation between human-altered and natural landscapes
Global change has led to shifts in phenology, potentially disrupting species interactions such as plant–pollinator relationships. Advances in remote sensing techniques allow one to detect vegetation phenological diversity between different land use types, but it is not clear how this translates to o...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4540021/ https://www.ncbi.nlm.nih.gov/pubmed/26290795 http://dx.doi.org/10.7717/peerj.1141 |
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author | Leong, Misha Roderick, George K. |
author_facet | Leong, Misha Roderick, George K. |
author_sort | Leong, Misha |
collection | PubMed |
description | Global change has led to shifts in phenology, potentially disrupting species interactions such as plant–pollinator relationships. Advances in remote sensing techniques allow one to detect vegetation phenological diversity between different land use types, but it is not clear how this translates to other communities in the ecosystem. Here, we investigated the phenological diversity of the vegetation across a human-altered landscape including urban, agricultural, and natural land use types. We found that the patterns of change in the vegetation indices (EVI and NDVI) of human-altered landscapes are out of synchronization with the phenology in neighboring natural California grassland habitat. Comparing these findings to a spatio-temporal pollinator distribution dataset, EVI and NDVI were significant predictors of total bee abundance, a relationship that improved with time lags. This evidence supports the importance of differences in temporal dynamics between land use types. These findings also highlight the potential to utilize remote sensing data to make predictions for components of biodiversity that have tight vegetation associations, such as pollinators. |
format | Online Article Text |
id | pubmed-4540021 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-45400212015-08-19 Remote sensing captures varying temporal patterns of vegetation between human-altered and natural landscapes Leong, Misha Roderick, George K. PeerJ Biodiversity Global change has led to shifts in phenology, potentially disrupting species interactions such as plant–pollinator relationships. Advances in remote sensing techniques allow one to detect vegetation phenological diversity between different land use types, but it is not clear how this translates to other communities in the ecosystem. Here, we investigated the phenological diversity of the vegetation across a human-altered landscape including urban, agricultural, and natural land use types. We found that the patterns of change in the vegetation indices (EVI and NDVI) of human-altered landscapes are out of synchronization with the phenology in neighboring natural California grassland habitat. Comparing these findings to a spatio-temporal pollinator distribution dataset, EVI and NDVI were significant predictors of total bee abundance, a relationship that improved with time lags. This evidence supports the importance of differences in temporal dynamics between land use types. These findings also highlight the potential to utilize remote sensing data to make predictions for components of biodiversity that have tight vegetation associations, such as pollinators. PeerJ Inc. 2015-08-04 /pmc/articles/PMC4540021/ /pubmed/26290795 http://dx.doi.org/10.7717/peerj.1141 Text en © 2015 Leong and Roderick http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://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. |
spellingShingle | Biodiversity Leong, Misha Roderick, George K. Remote sensing captures varying temporal patterns of vegetation between human-altered and natural landscapes |
title | Remote sensing captures varying temporal patterns of vegetation between human-altered and natural landscapes |
title_full | Remote sensing captures varying temporal patterns of vegetation between human-altered and natural landscapes |
title_fullStr | Remote sensing captures varying temporal patterns of vegetation between human-altered and natural landscapes |
title_full_unstemmed | Remote sensing captures varying temporal patterns of vegetation between human-altered and natural landscapes |
title_short | Remote sensing captures varying temporal patterns of vegetation between human-altered and natural landscapes |
title_sort | remote sensing captures varying temporal patterns of vegetation between human-altered and natural landscapes |
topic | Biodiversity |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4540021/ https://www.ncbi.nlm.nih.gov/pubmed/26290795 http://dx.doi.org/10.7717/peerj.1141 |
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