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#           Integrating Bayesian networks to forecast sea-level rise impacts on barrier island characteristics and habitat availability      

      November 3, 2022    

Evaluation of sea-level rise (SLR) impacts on coastal landforms and habitats is a persistent need for informing coastal planning and management, including policy decisions, particularly those that balance human interests and habitat protection throughout the coastal zone. Bayesian networks (BNs) are used to model barrier island change under different SLR scenarios that are relevant to management and policy decisions. BNs utilized here include a shoreline change model and two models of barrier island biogeomorphological evolution at different scales (50 and 5 m). These BNs were then linked to another BN to predict habitat availability for piping plovers (Charadrius melodus), a threatened shorebird reliant on beach habitats. We evaluated the performance of the two linked geomorphology BNs and further examined error rates by generating hindcasts of barrier island geomorphology and habitat availability for 2014 conditions. Geomorphology hindcasts revealed that model error declined with a greater number of known inputs, with error rates reaching 55% when multiple outputs were hindcast simultaneously. We also found that, although error in predictions of piping plover nest presence/absence increased when outputs from the geomorphology BNs were used as inputs in the piping plover habitat BN, the maximum error rate for piping plover habitat suitability in the fully-linked BNs was only 30%. Our findings suggest this approach may be useful for guiding scenario-based evaluations where known inputs can be used to constrain variables that produce higher uncertainty for morphological predictions. Overall, the approach demonstrates a way to assimilate data and model structures with uncertainty to produce forecasts to inform coastal planning and management.

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October 3, 2022

####                 LinkedBNs_4Habitat - Matlab files to link Bayesian networks to generate habitat predictions              

Woods Hole Coastal and Marine Science Center

#### Ben Gutierrez, PhD

##### Geologist

Woods Hole Coastal and Marine Science Center

bgutierrez@usgs.gov

508-457-2289

#### Sara L Zeigler, Ph.D

##### Research Geographer

St. Petersburg Coastal and Marine Science Center

szeigler@usgs.gov

508-457-2290

#### Erika Lentz, PhD

##### Research Geologist

Woods Hole Coastal and Marine Science Center

elentz@usgs.gov

508-457-2238

#### Nathaniel Plant, Ph.D.

##### Center Director

St. Petersburg Coastal and Marine Science Center

nplant@usgs.gov

727-502-8072

## Related Content

October 3, 2022

####                 LinkedBNs_4Habitat - Matlab files to link Bayesian networks to generate habitat predictions              

Woods Hole Coastal and Marine Science Center

#### Ben Gutierrez, PhD

##### Geologist

Woods Hole Coastal and Marine Science Center

bgutierrez@usgs.gov

508-457-2289

#### Sara L Zeigler, Ph.D

##### Research Geographer

St. Petersburg Coastal and Marine Science Center

szeigler@usgs.gov

508-457-2290

#### Erika Lentz, PhD

##### Research Geologist

Woods Hole Coastal and Marine Science Center

elentz@usgs.gov

508-457-2238

#### Nathaniel Plant, Ph.D.

##### Center Director

St. Petersburg Coastal and Marine Science Center

nplant@usgs.gov

727-502-8072

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####               St. Petersburg Coastal and Marine Science Center          

600 4th Street SouthSt. Petersburg, FL33701United States

727-502-8000

727-502-8181

#### Woods Hole Coastal and Marine Science Center

384 Woods Hole Rd.Woods Hole, MA02543United States

508-548-8700

508-457-2310

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