Mapping marine habitat suitability and uncertainty of Bayesian networks: a case study using Pacific benthic macrofauna
Abstract Resource managers increasingly use habitat suitability map products to inform risk management and policy decisions. Modeling habitat suitability of data‐poor species over large areas requires careful attention to assumptions and limitations. Resulting habitat suitability maps can harbor unc...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-07-01
|
| Series: | Ecosphere |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/ecs2.1859 |
| _version_ | 1811167115303976960 |
|---|---|
| author | Andrea Havron Chris Goldfinger Sarah Henkel Bruce G. Marcot Chris Romsos Lisa Gilbane |
| author_facet | Andrea Havron Chris Goldfinger Sarah Henkel Bruce G. Marcot Chris Romsos Lisa Gilbane |
| author_sort | Andrea Havron |
| collection | DOAJ |
| description | Abstract Resource managers increasingly use habitat suitability map products to inform risk management and policy decisions. Modeling habitat suitability of data‐poor species over large areas requires careful attention to assumptions and limitations. Resulting habitat suitability maps can harbor uncertainties from data collection and modeling processes; yet these limitations are not always transparent to resource managers, who increasingly rely on maps for spatial planning and risk assessment purposes. Interpretation of habitat suitability maps can be improved by visually communicating model uncertainty and data foundations. We applied Bayesian networks (BNs) to a small, marine dataset to model the probability of occurrence (PO) of benthic macrofauna. We also used BNs to create maps displaying model parameter uncertainty and data limitations. We developed BN models for three macrofauna species: a marine gastropod, Aystris gausapata, a marine bivalve, Axinopsida serricata, and a marine worm, Sternaspis fossor. We produced three map products from the BN models of each species: (1) a habitat suitability map of the PO projected from regional predictor variables; (2) an uncertainty map, displaying statistical variance of model predictions of occurrence probability; and (3) an experience map, displaying the empirical basis for PO predictions (equivalent sample size). Map results showed occurrence probability to be high and widespread for Ax. serricata, low to moderate and more limited to deeper offshore areas for Ay. gausapata, and low to high in shallow sandy regions and deeper silty regions, respectively, for S. fossor. The uncertainty and experience maps for each species helped identify regions to prioritize for future sampling. Our results are the first to show that BNs can effectively model habitat suitability of benthic macrofauna, and our detailed methods can be applied to a variety of taxa and systems. Visually describing statistical model uncertainty and equivalent sample size in map format improves interpretation of habitat suitability map predictions and supports place‐based risk management of marine management. |
| first_indexed | 2024-04-10T16:03:38Z |
| format | Article |
| id | doaj.art-6344aebc6ad1477f81990d247fb7be52 |
| institution | Directory Open Access Journal |
| issn | 2150-8925 |
| language | English |
| last_indexed | 2024-04-10T16:03:38Z |
| publishDate | 2017-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | Ecosphere |
| spelling | doaj.art-6344aebc6ad1477f81990d247fb7be522023-02-10T07:33:12ZengWileyEcosphere2150-89252017-07-0187n/an/a10.1002/ecs2.1859Mapping marine habitat suitability and uncertainty of Bayesian networks: a case study using Pacific benthic macrofaunaAndrea Havron0Chris Goldfinger1Sarah Henkel2Bruce G. Marcot3Chris Romsos4Lisa Gilbane5Active Tectonics and Seafloor Mapping Laboratory College of Earth, Ocean, and Atmospheric Sciences Oregon State University 104 CEOAS Administration Building Corvallis Oregon 97331 USAActive Tectonics and Seafloor Mapping Laboratory College of Earth, Ocean, and Atmospheric Sciences Oregon State University 104 CEOAS Administration Building Corvallis Oregon 97331 USABenthic Ecology Laboratory Department of Integrative Biology Hatfield Marine Science Center Oregon State University 2030 SE Marine Science Dr Newport Oregon 97365 USADepartment of Agriculture Pacific Northwest Research Station United States Forest Service 1220 SW 3rd Ave Portland Oregon 97204 USAActive Tectonics and Seafloor Mapping Laboratory College of Earth, Ocean, and Atmospheric Sciences Oregon State University 104 CEOAS Administration Building Corvallis Oregon 97331 USABureau of Ocean Energy Management U.S. Department of the Interior 760 Paseo Camarillo, Suite 102 (CM 102) Camarillo California 93010 USAAbstract Resource managers increasingly use habitat suitability map products to inform risk management and policy decisions. Modeling habitat suitability of data‐poor species over large areas requires careful attention to assumptions and limitations. Resulting habitat suitability maps can harbor uncertainties from data collection and modeling processes; yet these limitations are not always transparent to resource managers, who increasingly rely on maps for spatial planning and risk assessment purposes. Interpretation of habitat suitability maps can be improved by visually communicating model uncertainty and data foundations. We applied Bayesian networks (BNs) to a small, marine dataset to model the probability of occurrence (PO) of benthic macrofauna. We also used BNs to create maps displaying model parameter uncertainty and data limitations. We developed BN models for three macrofauna species: a marine gastropod, Aystris gausapata, a marine bivalve, Axinopsida serricata, and a marine worm, Sternaspis fossor. We produced three map products from the BN models of each species: (1) a habitat suitability map of the PO projected from regional predictor variables; (2) an uncertainty map, displaying statistical variance of model predictions of occurrence probability; and (3) an experience map, displaying the empirical basis for PO predictions (equivalent sample size). Map results showed occurrence probability to be high and widespread for Ax. serricata, low to moderate and more limited to deeper offshore areas for Ay. gausapata, and low to high in shallow sandy regions and deeper silty regions, respectively, for S. fossor. The uncertainty and experience maps for each species helped identify regions to prioritize for future sampling. Our results are the first to show that BNs can effectively model habitat suitability of benthic macrofauna, and our detailed methods can be applied to a variety of taxa and systems. Visually describing statistical model uncertainty and equivalent sample size in map format improves interpretation of habitat suitability map predictions and supports place‐based risk management of marine management.https://doi.org/10.1002/ecs2.1859Axinopsida serricataAystris gausapataBayesian networkbenthic macrofaunacontinental shelfequivalent sample size |
| spellingShingle | Andrea Havron Chris Goldfinger Sarah Henkel Bruce G. Marcot Chris Romsos Lisa Gilbane Mapping marine habitat suitability and uncertainty of Bayesian networks: a case study using Pacific benthic macrofauna Ecosphere Axinopsida serricata Aystris gausapata Bayesian network benthic macrofauna continental shelf equivalent sample size |
| title | Mapping marine habitat suitability and uncertainty of Bayesian networks: a case study using Pacific benthic macrofauna |
| title_full | Mapping marine habitat suitability and uncertainty of Bayesian networks: a case study using Pacific benthic macrofauna |
| title_fullStr | Mapping marine habitat suitability and uncertainty of Bayesian networks: a case study using Pacific benthic macrofauna |
| title_full_unstemmed | Mapping marine habitat suitability and uncertainty of Bayesian networks: a case study using Pacific benthic macrofauna |
| title_short | Mapping marine habitat suitability and uncertainty of Bayesian networks: a case study using Pacific benthic macrofauna |
| title_sort | mapping marine habitat suitability and uncertainty of bayesian networks a case study using pacific benthic macrofauna |
| topic | Axinopsida serricata Aystris gausapata Bayesian network benthic macrofauna continental shelf equivalent sample size |
| url | https://doi.org/10.1002/ecs2.1859 |
| work_keys_str_mv | AT andreahavron mappingmarinehabitatsuitabilityanduncertaintyofbayesiannetworksacasestudyusingpacificbenthicmacrofauna AT chrisgoldfinger mappingmarinehabitatsuitabilityanduncertaintyofbayesiannetworksacasestudyusingpacificbenthicmacrofauna AT sarahhenkel mappingmarinehabitatsuitabilityanduncertaintyofbayesiannetworksacasestudyusingpacificbenthicmacrofauna AT brucegmarcot mappingmarinehabitatsuitabilityanduncertaintyofbayesiannetworksacasestudyusingpacificbenthicmacrofauna AT chrisromsos mappingmarinehabitatsuitabilityanduncertaintyofbayesiannetworksacasestudyusingpacificbenthicmacrofauna AT lisagilbane mappingmarinehabitatsuitabilityanduncertaintyofbayesiannetworksacasestudyusingpacificbenthicmacrofauna |