Occupancy–detection models with museum specimen data: Promise and pitfalls
Abstract Historical museum records provide potentially useful data for identifying drivers of change in species occupancy. However, because museum records are typically obtained via many collection methods, methodological developments are needed to enable robust inferences. Occupancy–detection model...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-02-01
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| Series: | Methods in Ecology and Evolution |
| Subjects: | |
| Online Access: | https://doi.org/10.1111/2041-210X.13896 |
| _version_ | 1797766972694331392 |
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| author | Vaughn Shirey Rassim Khelifa Leithen K. M'Gonigle Laura Melissa Guzman |
| author_facet | Vaughn Shirey Rassim Khelifa Leithen K. M'Gonigle Laura Melissa Guzman |
| author_sort | Vaughn Shirey |
| collection | DOAJ |
| description | Abstract Historical museum records provide potentially useful data for identifying drivers of change in species occupancy. However, because museum records are typically obtained via many collection methods, methodological developments are needed to enable robust inferences. Occupancy–detection models, a relatively new and powerful suite of statistical methods, are a potentially promising avenue because they can account for changes in collection effort through space and time. We use simulated datasets to identify how and when patterns in data and/or modelling decisions can bias inference. We focus primarily on the consequences of contrasting methodological approaches for dealing with species' ranges and inferring species' non‐detections in both space and time. We find that not all datasets are suitable for occupancy–detection analysis but, under the right conditions (namely, datasets that are broken into more time periods for occupancy inference and that contain a high fraction of community‐wide collections, or collection events that focus on communities of organisms), models can accurately estimate trends. Finally, we present a case study on eastern North American odonates where we calculate long‐term trends of occupancy using our most robust workflow. These results indicate that occupancy–detection models are a suitable framework for some research cases and expand the suite of available tools for macroecological analysis available to researchers, especially where structured datasets are unavailable. |
| first_indexed | 2024-03-12T20:32:04Z |
| format | Article |
| id | doaj.art-2fc8a18089844a8e811fe7887c4bd6bb |
| institution | Directory Open Access Journal |
| issn | 2041-210X |
| language | English |
| last_indexed | 2024-03-12T20:32:04Z |
| publishDate | 2023-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | Methods in Ecology and Evolution |
| spelling | doaj.art-2fc8a18089844a8e811fe7887c4bd6bb2023-08-01T19:00:34ZengWileyMethods in Ecology and Evolution2041-210X2023-02-0114240241410.1111/2041-210X.13896Occupancy–detection models with museum specimen data: Promise and pitfallsVaughn Shirey0Rassim Khelifa1Leithen K. M'Gonigle2Laura Melissa Guzman3Department of Biology Georgetown University Washington DC USADepartment of Zoology University of British Columbia Vancouver BC CanadaDepartment of Biological Sciences Simon Fraser University Burnaby BC CanadaDepartment of Biological Sciences Simon Fraser University Burnaby BC CanadaAbstract Historical museum records provide potentially useful data for identifying drivers of change in species occupancy. However, because museum records are typically obtained via many collection methods, methodological developments are needed to enable robust inferences. Occupancy–detection models, a relatively new and powerful suite of statistical methods, are a potentially promising avenue because they can account for changes in collection effort through space and time. We use simulated datasets to identify how and when patterns in data and/or modelling decisions can bias inference. We focus primarily on the consequences of contrasting methodological approaches for dealing with species' ranges and inferring species' non‐detections in both space and time. We find that not all datasets are suitable for occupancy–detection analysis but, under the right conditions (namely, datasets that are broken into more time periods for occupancy inference and that contain a high fraction of community‐wide collections, or collection events that focus on communities of organisms), models can accurately estimate trends. Finally, we present a case study on eastern North American odonates where we calculate long‐term trends of occupancy using our most robust workflow. These results indicate that occupancy–detection models are a suitable framework for some research cases and expand the suite of available tools for macroecological analysis available to researchers, especially where structured datasets are unavailable.https://doi.org/10.1111/2041-210X.13896global changehierarchical modelmuseum specimensoccupancy‐detection models |
| spellingShingle | Vaughn Shirey Rassim Khelifa Leithen K. M'Gonigle Laura Melissa Guzman Occupancy–detection models with museum specimen data: Promise and pitfalls Methods in Ecology and Evolution global change hierarchical model museum specimens occupancy‐detection models |
| title | Occupancy–detection models with museum specimen data: Promise and pitfalls |
| title_full | Occupancy–detection models with museum specimen data: Promise and pitfalls |
| title_fullStr | Occupancy–detection models with museum specimen data: Promise and pitfalls |
| title_full_unstemmed | Occupancy–detection models with museum specimen data: Promise and pitfalls |
| title_short | Occupancy–detection models with museum specimen data: Promise and pitfalls |
| title_sort | occupancy detection models with museum specimen data promise and pitfalls |
| topic | global change hierarchical model museum specimens occupancy‐detection models |
| url | https://doi.org/10.1111/2041-210X.13896 |
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