Jennifer L. McKee, Julien Fattebert, Ellen O. Aikens, Jodi Berg, Scott Bergen, Eric K. Cole, Holly E. Copeland, Alyson B. Courtemanch, Sarah Dewey, Mark Hurley, Blake Lowrey, Jerod A. Merkle, Arthur D. Middleton, Tristan A. Nuñez, Hall Sawyer, Matthew J. Kauffman
{"title":"根据稀疏的运动数据估算麋鹿迁徙走廊","authors":"Jennifer L. McKee, Julien Fattebert, Ellen O. Aikens, Jodi Berg, Scott Bergen, Eric K. Cole, Holly E. Copeland, Alyson B. Courtemanch, Sarah Dewey, Mark Hurley, Blake Lowrey, Jerod A. Merkle, Arthur D. Middleton, Tristan A. Nuñez, Hall Sawyer, Matthew J. Kauffman","doi":"10.1002/ecs2.4983","DOIUrl":null,"url":null,"abstract":"<p>Many ungulates migrate between distinct summer and winter ranges, and identifying, mapping, and conserving these migration corridors have become a focus of local, regional, and global conservation efforts. Brownian bridge movement models (BBMMs) are commonly used to empirically identify these seasonal migration corridors; however, they require location data sampled at relatively frequent intervals to obtain a robust estimate of an animal's movement path. Fitting BBMMs to sparse location data violates the assumption of conditional random movement between successive locations, overestimating the area (and width) of a migration corridor when creating individual and population-level occurrence distributions and precluding the use of low-frequency, or sparse, data in mapping migration corridors. In an effort to expand the utility of BBMMs to include sparse GPS data, we propose an alternative approach to model migration corridors from sparse GPS data. We demonstrate this method using GPS data collected every 2 h from four mule deer (<i>Odocoileus hemionus</i>) and four elk (<i>Cervus canadensis</i>) herds within Wyoming and Idaho. First, we used BBMMs to estimate a baseline corridor for the 2-h data. We then subsampled the 2-h data to one location every 12 h (a proxy for sparse data) and fitted BBMMs to the 12-h data using a fixed motion variance (FMV) value, instead of estimating the Brownian motion variance empirically. A range of FMV values was tested to identify the value that best approximated the baseline migration corridor. FMV values within a species-specific range (mule deer: 400–1200 m<sup>2</sup>; elk: 600–1600 m<sup>2</sup>) successfully delineated migration corridors similar to the 2-h baseline corridors; overall, lower values delineated narrower corridors and higher values delineated wider corridors. Optimal FMV values of 800 m<sup>2</sup> (mule deer) and 1000 m<sup>2</sup> (elk) decreased the inflation of the 12-h corridors relative to the 2-h corridors from traditional BBMMs. This FMV approach thus enables using sparse movement data to approximate realistic migration corridor dimensions, providing an important alternative when movement data are collected infrequently. This approach greatly expands the number of datasets that can be used for migration corridor mapping—a useful tool for management and conservation across the globe.</p>","PeriodicalId":48930,"journal":{"name":"Ecosphere","volume":null,"pages":null},"PeriodicalIF":2.7000,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ecs2.4983","citationCount":"0","resultStr":"{\"title\":\"Estimating ungulate migration corridors from sparse movement data\",\"authors\":\"Jennifer L. McKee, Julien Fattebert, Ellen O. Aikens, Jodi Berg, Scott Bergen, Eric K. Cole, Holly E. Copeland, Alyson B. Courtemanch, Sarah Dewey, Mark Hurley, Blake Lowrey, Jerod A. Merkle, Arthur D. Middleton, Tristan A. Nuñez, Hall Sawyer, Matthew J. Kauffman\",\"doi\":\"10.1002/ecs2.4983\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Many ungulates migrate between distinct summer and winter ranges, and identifying, mapping, and conserving these migration corridors have become a focus of local, regional, and global conservation efforts. Brownian bridge movement models (BBMMs) are commonly used to empirically identify these seasonal migration corridors; however, they require location data sampled at relatively frequent intervals to obtain a robust estimate of an animal's movement path. Fitting BBMMs to sparse location data violates the assumption of conditional random movement between successive locations, overestimating the area (and width) of a migration corridor when creating individual and population-level occurrence distributions and precluding the use of low-frequency, or sparse, data in mapping migration corridors. In an effort to expand the utility of BBMMs to include sparse GPS data, we propose an alternative approach to model migration corridors from sparse GPS data. We demonstrate this method using GPS data collected every 2 h from four mule deer (<i>Odocoileus hemionus</i>) and four elk (<i>Cervus canadensis</i>) herds within Wyoming and Idaho. First, we used BBMMs to estimate a baseline corridor for the 2-h data. We then subsampled the 2-h data to one location every 12 h (a proxy for sparse data) and fitted BBMMs to the 12-h data using a fixed motion variance (FMV) value, instead of estimating the Brownian motion variance empirically. A range of FMV values was tested to identify the value that best approximated the baseline migration corridor. FMV values within a species-specific range (mule deer: 400–1200 m<sup>2</sup>; elk: 600–1600 m<sup>2</sup>) successfully delineated migration corridors similar to the 2-h baseline corridors; overall, lower values delineated narrower corridors and higher values delineated wider corridors. Optimal FMV values of 800 m<sup>2</sup> (mule deer) and 1000 m<sup>2</sup> (elk) decreased the inflation of the 12-h corridors relative to the 2-h corridors from traditional BBMMs. This FMV approach thus enables using sparse movement data to approximate realistic migration corridor dimensions, providing an important alternative when movement data are collected infrequently. This approach greatly expands the number of datasets that can be used for migration corridor mapping—a useful tool for management and conservation across the globe.</p>\",\"PeriodicalId\":48930,\"journal\":{\"name\":\"Ecosphere\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2024-09-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ecs2.4983\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ecosphere\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/ecs2.4983\",\"RegionNum\":3,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ECOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ecosphere","FirstCategoryId":"93","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/ecs2.4983","RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ECOLOGY","Score":null,"Total":0}
Estimating ungulate migration corridors from sparse movement data
Many ungulates migrate between distinct summer and winter ranges, and identifying, mapping, and conserving these migration corridors have become a focus of local, regional, and global conservation efforts. Brownian bridge movement models (BBMMs) are commonly used to empirically identify these seasonal migration corridors; however, they require location data sampled at relatively frequent intervals to obtain a robust estimate of an animal's movement path. Fitting BBMMs to sparse location data violates the assumption of conditional random movement between successive locations, overestimating the area (and width) of a migration corridor when creating individual and population-level occurrence distributions and precluding the use of low-frequency, or sparse, data in mapping migration corridors. In an effort to expand the utility of BBMMs to include sparse GPS data, we propose an alternative approach to model migration corridors from sparse GPS data. We demonstrate this method using GPS data collected every 2 h from four mule deer (Odocoileus hemionus) and four elk (Cervus canadensis) herds within Wyoming and Idaho. First, we used BBMMs to estimate a baseline corridor for the 2-h data. We then subsampled the 2-h data to one location every 12 h (a proxy for sparse data) and fitted BBMMs to the 12-h data using a fixed motion variance (FMV) value, instead of estimating the Brownian motion variance empirically. A range of FMV values was tested to identify the value that best approximated the baseline migration corridor. FMV values within a species-specific range (mule deer: 400–1200 m2; elk: 600–1600 m2) successfully delineated migration corridors similar to the 2-h baseline corridors; overall, lower values delineated narrower corridors and higher values delineated wider corridors. Optimal FMV values of 800 m2 (mule deer) and 1000 m2 (elk) decreased the inflation of the 12-h corridors relative to the 2-h corridors from traditional BBMMs. This FMV approach thus enables using sparse movement data to approximate realistic migration corridor dimensions, providing an important alternative when movement data are collected infrequently. This approach greatly expands the number of datasets that can be used for migration corridor mapping—a useful tool for management and conservation across the globe.
期刊介绍:
The scope of Ecosphere is as broad as the science of ecology itself. The journal welcomes submissions from all sub-disciplines of ecological science, as well as interdisciplinary studies relating to ecology. The journal''s goal is to provide a rapid-publication, online-only, open-access alternative to ESA''s other journals, while maintaining the rigorous standards of peer review for which ESA publications are renowned.