Predicting species distributions and community composition using satellite remote sensing predictors
Abstract Biodiversity is rapidly changing due to changes in the climate and human related activities; thus, the accurate predictions of species composition and diversity are critical to developing conservation actions and management strategies. In this paper, using satellite remote sensing products...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2021-08-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-021-96047-7 |
| _version_ | 1818430587255390208 |
|---|---|
| author | Jesús N. Pinto-Ledezma Jeannine Cavender-Bares |
| author_facet | Jesús N. Pinto-Ledezma Jeannine Cavender-Bares |
| author_sort | Jesús N. Pinto-Ledezma |
| collection | DOAJ |
| description | Abstract Biodiversity is rapidly changing due to changes in the climate and human related activities; thus, the accurate predictions of species composition and diversity are critical to developing conservation actions and management strategies. In this paper, using satellite remote sensing products as covariates, we constructed stacked species distribution models (S-SDMs) under a Bayesian framework to build next-generation biodiversity models. Model performance of these models was assessed using oak assemblages distributed across the continental United States obtained from the National Ecological Observatory Network (NEON). This study represents an attempt to evaluate the integrated predictions of biodiversity models—including assemblage diversity and composition—obtained by stacking next-generation SDMs. We found that applying constraints to assemblage predictions, such as using the probability ranking rule, does not improve biodiversity prediction models. Furthermore, we found that independent of the stacking procedure (bS-SDM versus pS-SDM versus cS-SDM), these kinds of next-generation biodiversity models do not accurately recover the observed species composition at the plot level or ecological-community scales (NEON plots are 400 m2). However, these models do return reasonable predictions at macroecological scales, i.e., moderately to highly correct assignments of species identities at the scale of NEON sites (mean area ~ 27 km2). Our results provide insights for advancing the accuracy of prediction of assemblage diversity and composition at different spatial scales globally. An important task for future studies is to evaluate the reliability of combining S-SDMs with direct detection of species using image spectroscopy to build a new generation of biodiversity models that accurately predict and monitor ecological assemblages through time and space. |
| first_indexed | 2024-12-14T15:35:47Z |
| format | Article |
| id | doaj.art-c0030fb815054b09b89bad869a406d0e |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-14T15:35:47Z |
| publishDate | 2021-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-c0030fb815054b09b89bad869a406d0e2022-12-21T22:55:44ZengNature PortfolioScientific Reports2045-23222021-08-0111111210.1038/s41598-021-96047-7Predicting species distributions and community composition using satellite remote sensing predictorsJesús N. Pinto-Ledezma0Jeannine Cavender-Bares1Department of Ecology, Evolution and Behavior, University of MinnesotaDepartment of Ecology, Evolution and Behavior, University of MinnesotaAbstract Biodiversity is rapidly changing due to changes in the climate and human related activities; thus, the accurate predictions of species composition and diversity are critical to developing conservation actions and management strategies. In this paper, using satellite remote sensing products as covariates, we constructed stacked species distribution models (S-SDMs) under a Bayesian framework to build next-generation biodiversity models. Model performance of these models was assessed using oak assemblages distributed across the continental United States obtained from the National Ecological Observatory Network (NEON). This study represents an attempt to evaluate the integrated predictions of biodiversity models—including assemblage diversity and composition—obtained by stacking next-generation SDMs. We found that applying constraints to assemblage predictions, such as using the probability ranking rule, does not improve biodiversity prediction models. Furthermore, we found that independent of the stacking procedure (bS-SDM versus pS-SDM versus cS-SDM), these kinds of next-generation biodiversity models do not accurately recover the observed species composition at the plot level or ecological-community scales (NEON plots are 400 m2). However, these models do return reasonable predictions at macroecological scales, i.e., moderately to highly correct assignments of species identities at the scale of NEON sites (mean area ~ 27 km2). Our results provide insights for advancing the accuracy of prediction of assemblage diversity and composition at different spatial scales globally. An important task for future studies is to evaluate the reliability of combining S-SDMs with direct detection of species using image spectroscopy to build a new generation of biodiversity models that accurately predict and monitor ecological assemblages through time and space.https://doi.org/10.1038/s41598-021-96047-7 |
| spellingShingle | Jesús N. Pinto-Ledezma Jeannine Cavender-Bares Predicting species distributions and community composition using satellite remote sensing predictors Scientific Reports |
| title | Predicting species distributions and community composition using satellite remote sensing predictors |
| title_full | Predicting species distributions and community composition using satellite remote sensing predictors |
| title_fullStr | Predicting species distributions and community composition using satellite remote sensing predictors |
| title_full_unstemmed | Predicting species distributions and community composition using satellite remote sensing predictors |
| title_short | Predicting species distributions and community composition using satellite remote sensing predictors |
| title_sort | predicting species distributions and community composition using satellite remote sensing predictors |
| url | https://doi.org/10.1038/s41598-021-96047-7 |
| work_keys_str_mv | AT jesusnpintoledezma predictingspeciesdistributionsandcommunitycompositionusingsatelliteremotesensingpredictors AT jeanninecavenderbares predictingspeciesdistributionsandcommunitycompositionusingsatelliteremotesensingpredictors |