Spatial occupancy models for large data sets
Since its development, occupancy modeling has become a popular and useful tool for ecologists wishing to learn about the dynamics of species occurrence over time and space. Such models require presence–absence data to be collected at spatially indexed survey units. However, only recently have resear...
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ftdatacite:10.6084/m9.figshare.c.3305427 2023-05-15T18:04:23+02:00 Spatial occupancy models for large data sets Johnson, Devin S. Conn, Paul B. Mevin B. Hooten Ray, Justina C. Pond, Bruce A. 2016 https://dx.doi.org/10.6084/m9.figshare.c.3305427 https://figshare.com/collections/Spatial_occupancy_models_for_large_data_sets/3305427 unknown Figshare https://dx.doi.org/10.1890/12-0564.1 CC-BY http://creativecommons.org/licenses/by/3.0/us CC-BY Environmental Science Ecology FOS Biological sciences Collection article 2016 ftdatacite https://doi.org/10.6084/m9.figshare.c.3305427 https://doi.org/10.1890/12-0564.1 2021-11-05T12:55:41Z Since its development, occupancy modeling has become a popular and useful tool for ecologists wishing to learn about the dynamics of species occurrence over time and space. Such models require presence–absence data to be collected at spatially indexed survey units. However, only recently have researchers recognized the need to correct for spatially induced overdisperison by explicitly accounting for spatial autocorrelation in occupancy probability. Previous efforts to incorporate such autocorrelation have largely focused on logit-normal formulations for occupancy, with spatial autocorrelation induced by a random effect within a hierarchical modeling framework. Although useful, computational time generally limits such an approach to relatively small data sets, and there are often problems with algorithm instability, yielding unsatisfactory results. Further, recent research has revealed a hidden form of multicollinearity in such applications, which may lead to parameter bias if not explicitly addressed. Combining several techniques, we present a unifying hierarchical spatial occupancy model specification that is particularly effective over large spatial extents. This approach employs a probit mixture framework for occupancy and can easily accommodate a reduced-dimensional spatial process to resolve issues with multicollinearity and spatial confounding while improving algorithm convergence. Using open-source software, we demonstrate this new model specification using a case study involving occupancy of caribou ( Rangifer tarandus ) over a set of 1080 survey units spanning a large contiguous region (108 000 km 2 ) in northern Ontario, Canada. Overall, the combination of a more efficient specification and open-source software allows for a facile and stable implementation of spatial occupancy models for large data sets. Article in Journal/Newspaper Rangifer tarandus DataCite Metadata Store (German National Library of Science and Technology) Canada |
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Open Polar |
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DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
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language |
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topic |
Environmental Science Ecology FOS Biological sciences |
spellingShingle |
Environmental Science Ecology FOS Biological sciences Johnson, Devin S. Conn, Paul B. Mevin B. Hooten Ray, Justina C. Pond, Bruce A. Spatial occupancy models for large data sets |
topic_facet |
Environmental Science Ecology FOS Biological sciences |
description |
Since its development, occupancy modeling has become a popular and useful tool for ecologists wishing to learn about the dynamics of species occurrence over time and space. Such models require presence–absence data to be collected at spatially indexed survey units. However, only recently have researchers recognized the need to correct for spatially induced overdisperison by explicitly accounting for spatial autocorrelation in occupancy probability. Previous efforts to incorporate such autocorrelation have largely focused on logit-normal formulations for occupancy, with spatial autocorrelation induced by a random effect within a hierarchical modeling framework. Although useful, computational time generally limits such an approach to relatively small data sets, and there are often problems with algorithm instability, yielding unsatisfactory results. Further, recent research has revealed a hidden form of multicollinearity in such applications, which may lead to parameter bias if not explicitly addressed. Combining several techniques, we present a unifying hierarchical spatial occupancy model specification that is particularly effective over large spatial extents. This approach employs a probit mixture framework for occupancy and can easily accommodate a reduced-dimensional spatial process to resolve issues with multicollinearity and spatial confounding while improving algorithm convergence. Using open-source software, we demonstrate this new model specification using a case study involving occupancy of caribou ( Rangifer tarandus ) over a set of 1080 survey units spanning a large contiguous region (108 000 km 2 ) in northern Ontario, Canada. Overall, the combination of a more efficient specification and open-source software allows for a facile and stable implementation of spatial occupancy models for large data sets. |
format |
Article in Journal/Newspaper |
author |
Johnson, Devin S. Conn, Paul B. Mevin B. Hooten Ray, Justina C. Pond, Bruce A. |
author_facet |
Johnson, Devin S. Conn, Paul B. Mevin B. Hooten Ray, Justina C. Pond, Bruce A. |
author_sort |
Johnson, Devin S. |
title |
Spatial occupancy models for large data sets |
title_short |
Spatial occupancy models for large data sets |
title_full |
Spatial occupancy models for large data sets |
title_fullStr |
Spatial occupancy models for large data sets |
title_full_unstemmed |
Spatial occupancy models for large data sets |
title_sort |
spatial occupancy models for large data sets |
publisher |
Figshare |
publishDate |
2016 |
url |
https://dx.doi.org/10.6084/m9.figshare.c.3305427 https://figshare.com/collections/Spatial_occupancy_models_for_large_data_sets/3305427 |
geographic |
Canada |
geographic_facet |
Canada |
genre |
Rangifer tarandus |
genre_facet |
Rangifer tarandus |
op_relation |
https://dx.doi.org/10.1890/12-0564.1 |
op_rights |
CC-BY http://creativecommons.org/licenses/by/3.0/us |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.6084/m9.figshare.c.3305427 https://doi.org/10.1890/12-0564.1 |
_version_ |
1766175750501695488 |