Leveraging Machine Learning and Geo-Tagged Citizen Science Data to Disentangle the Factors of Avian Mortality Events at the Species Level
Abrupt environmental changes can affect the population structures of living species and cause habitat loss and fragmentations in the ecosystem. During August–October 2020, remarkably high mortality events of avian species were reported across the western and central United States, likely resulting f...
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ftmdpi:oai:mdpi.com:/2072-4292/14/10/2369/ 2023-08-20T04:05:58+02:00 Leveraging Machine Learning and Geo-Tagged Citizen Science Data to Disentangle the Factors of Avian Mortality Events at the Species Level Anni Yang Matthew Rodriguez Di Yang Jue Yang Wenwen Cheng Changjie Cai Han Qiu 2022-05-14 application/pdf https://doi.org/10.3390/rs14102369 EN eng Multidisciplinary Digital Publishing Institute Environmental Remote Sensing https://dx.doi.org/10.3390/rs14102369 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 14; Issue 10; Pages: 2369 avian conservation citizen science random forest earth observations mortality natural hazards Text 2022 ftmdpi https://doi.org/10.3390/rs14102369 2023-08-01T05:03:05Z Abrupt environmental changes can affect the population structures of living species and cause habitat loss and fragmentations in the ecosystem. During August–October 2020, remarkably high mortality events of avian species were reported across the western and central United States, likely resulting from winter storms and wildfires. However, the differences of mortality events among various species responding to the abrupt environmental changes remain poorly understood. In this study, we focused on three species, Wilson’s Warbler, Barn Owl, and Common Murre, with the highest mortality events that had been recorded by citizen scientists. We leveraged the citizen science data and multiple remotely sensed earth observations and employed the ensemble random forest models to disentangle the species responses to winter storm and wildfire. We found that the mortality events of Wilson’s Warbler were primarily impacted by early winter storms, with more deaths identified in areas with a higher average daily snow cover. The Barn Owl’s mortalities were more identified in places with severe wildfire-induced air pollution. Both winter storms and wildfire had relatively mild effects on the mortality of Common Murre, which might be more related to anomalously warm water. Our findings highlight the species-specific responses to environmental changes, which can provide significant insights into the resilience of ecosystems to environmental change and avian conservations. Additionally, the study emphasized the efficiency and effectiveness of monitoring large-scale abrupt environmental changes and conservation using remotely sensed and citizen science data. Text Common Murre MDPI Open Access Publishing Remote Sensing 14 10 2369 |
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MDPI Open Access Publishing |
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English |
topic |
avian conservation citizen science random forest earth observations mortality natural hazards |
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avian conservation citizen science random forest earth observations mortality natural hazards Anni Yang Matthew Rodriguez Di Yang Jue Yang Wenwen Cheng Changjie Cai Han Qiu Leveraging Machine Learning and Geo-Tagged Citizen Science Data to Disentangle the Factors of Avian Mortality Events at the Species Level |
topic_facet |
avian conservation citizen science random forest earth observations mortality natural hazards |
description |
Abrupt environmental changes can affect the population structures of living species and cause habitat loss and fragmentations in the ecosystem. During August–October 2020, remarkably high mortality events of avian species were reported across the western and central United States, likely resulting from winter storms and wildfires. However, the differences of mortality events among various species responding to the abrupt environmental changes remain poorly understood. In this study, we focused on three species, Wilson’s Warbler, Barn Owl, and Common Murre, with the highest mortality events that had been recorded by citizen scientists. We leveraged the citizen science data and multiple remotely sensed earth observations and employed the ensemble random forest models to disentangle the species responses to winter storm and wildfire. We found that the mortality events of Wilson’s Warbler were primarily impacted by early winter storms, with more deaths identified in areas with a higher average daily snow cover. The Barn Owl’s mortalities were more identified in places with severe wildfire-induced air pollution. Both winter storms and wildfire had relatively mild effects on the mortality of Common Murre, which might be more related to anomalously warm water. Our findings highlight the species-specific responses to environmental changes, which can provide significant insights into the resilience of ecosystems to environmental change and avian conservations. Additionally, the study emphasized the efficiency and effectiveness of monitoring large-scale abrupt environmental changes and conservation using remotely sensed and citizen science data. |
format |
Text |
author |
Anni Yang Matthew Rodriguez Di Yang Jue Yang Wenwen Cheng Changjie Cai Han Qiu |
author_facet |
Anni Yang Matthew Rodriguez Di Yang Jue Yang Wenwen Cheng Changjie Cai Han Qiu |
author_sort |
Anni Yang |
title |
Leveraging Machine Learning and Geo-Tagged Citizen Science Data to Disentangle the Factors of Avian Mortality Events at the Species Level |
title_short |
Leveraging Machine Learning and Geo-Tagged Citizen Science Data to Disentangle the Factors of Avian Mortality Events at the Species Level |
title_full |
Leveraging Machine Learning and Geo-Tagged Citizen Science Data to Disentangle the Factors of Avian Mortality Events at the Species Level |
title_fullStr |
Leveraging Machine Learning and Geo-Tagged Citizen Science Data to Disentangle the Factors of Avian Mortality Events at the Species Level |
title_full_unstemmed |
Leveraging Machine Learning and Geo-Tagged Citizen Science Data to Disentangle the Factors of Avian Mortality Events at the Species Level |
title_sort |
leveraging machine learning and geo-tagged citizen science data to disentangle the factors of avian mortality events at the species level |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2022 |
url |
https://doi.org/10.3390/rs14102369 |
genre |
Common Murre |
genre_facet |
Common Murre |
op_source |
Remote Sensing; Volume 14; Issue 10; Pages: 2369 |
op_relation |
Environmental Remote Sensing https://dx.doi.org/10.3390/rs14102369 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs14102369 |
container_title |
Remote Sensing |
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14 |
container_issue |
10 |
container_start_page |
2369 |
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1774716790649126912 |