NCCOS Assessment: Predicted habitat suitability for Leiopathes glaberrima in the US Gulf of Mexico, 2016-01-17 to 2017-09-30 (NCEI Accession 0276866)
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title: NCCOS Assessment: Predicted habitat suitability for Leiopathes glaberrima in the US Gulf of Mexico, 2016-01-17 to 2017-09-30 (NCEI Accession 0276866)
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abstract: This data collection contains outputs from spatial predictive models of habitat suitability for the black coral Leiopathes glaberrima in the U.S. Gulf of Mexico. The models were derived from records of Leiopathes glaberrima occurrence and environmental and oceanographic variables describing conditions that may influence the distribution of deep-sea corals, including measures of depth, steepness, and complexity of the seafloor, composition of sediments on the seafloor, and ocean productivity.
purpose: Deep-sea corals are of particular conservation concern due to their slow growth rates and vulnerability to disturbance. Predictive modeling of deep-sea coral habitat can aid conservation planning, inform management of offshore activities affecting the seafloor, and guide exploration. Modeling can also lend insights into the environmental factors driving the distribution of deep-sea corals, helping to build our understanding of how these unique ecosystems function. This model of habitat suitability for Leiopathes glaberrima was created in support of the NOAA Deep Sea Coral Research and Technology Program (DSCRTP) Southeast Deep Coral Initiative (SEDCI, Etnoyer et al. 2021) to 1) ascertain the spatial distribution of Leiopathes glaberrima, a deep-sea black coral [Antipatharia:Leiopathidae] in the U.S. Gulf of Mexico; 2) determine the environmental factors that contribute to its occurrence, and 3) to produce maps of the predicted habitat suitability for the purposes of management and exploration.
credit: Related Funding Agency: US DOC; NOAA; NMFS; Deep Sea Coral Research and Technology Program (DSCRTP)
credit: Related Funding Agency: US DOC; NOAA; NOS; National Centers for Coastal Ocean Science
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supplementalInformation: Submission Package ID: BR43JR Methods: Habitat suitability for Leiopathes glaberrima in the U.S. Gulf of Mexico was modeled using a Maximum Entropy (MaxEnt) modeling framework. Models used the values of spatially explicit environmental predictor variables describing conditions that may influence the distribution of Leiopathes glaberrima at locations where Leiopathes glaberrima was observed to estimate the relationships between these environmental conditions and the presence of Leiopathes glaberrima. These relationships were then used to predict and map habitat suitability for Leiopathes glaberrima across the U.S. Gulf of Mexico. Records of Leiopathes glaberrima occurrence in the U.S. Gulf of Mexico were obtained from the NOAA National Database for Deep-Sea Corals and Sponges (https://deepseacoraldata.noaa.gov/). Data records with identifiable positional errors were removed following quality control. Environmental predictor variables included measures of seafloor topography derived from depth data, seafloor substrate derived from surficial sediment survey data, and physical and biological oceanography derived from in situ data and remotely sensed data. A stepwise model selection procedure was used to select a best model that balanced predictive performance with model complexity. At each step a model was fit and predictive performance and complexity were calculated. The area under the receiver operating characteristic curve (AUC) was calculated using data withheld from model fitting in order to assess model performance. Model complexity was assessed using Akaike’s information criterion corrected for small sample size (AICc). The least important environmental predictor variable was identified and removed prior to the next step of model fitting using a jackknife test that determined which predictor variable resulted in the smallest reduction in AUC when omitted from the model. Following the stepwise procedure, the models were ranked in terms of AUC (highest AUC = best performing model) and AICc (lowest AICc = least complex model) and the best model was selected based on these rankings. To allow direct comparisons to predictions of habitat suitability for other deep-sea coral taxa, the relative habitat suitability (i.e., the logistic output from MaxEnt) was reclassified into a series of habitat suitability classes using breakpoints calculated using specific ratios of the cost of false positive errors versus the cost of false negative errors. Essentially, the higher the habitat suitability class the greater the penalty for overpredicting the area considered to be suitable habitat. An additional robust very high habitat suitability class was assigned to model grid cells predicted to be in the highest class of habitat suitability for each of the replicate model runs of the best model. For additional details, see Etnoyer et al. (2018). File Information:Total File Size: 34.2 MB total, 15 files in 1 folder (unzipped), 18.3 MB (zipped) Data File Format(s): Geotiff .TIF (and ancillary files .TFW, .CPG, .DBF) Data File Compression: zip Data File Resolution: 370.65 x 370.65 meters #GIS Projection: WGS 84 UTM Zone 15N Data Files: Leiopathes_Classified_Predicted_Habitat_Suitability Leiopathes_Classified_Predicted_Habitat_Suitability_Variability Leiopathes_Predicted_Habitat_Suitability Leiopathes_Robust_Very_High_Predicted_Habitat_Suitability Documentation Files: Leiopathes_model_output.JPG DataDocumentation.PDF
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description: Parameter or Variable: Habitat Suitability (calculated); Units: none; Observation Category: model output; Sampling Instrument: Models/Analyses > Data Analysis > Environmental Modeling; Sampling and Analyzing Method: Records of Leiopathes glaberrima occurrence in the U.S. Gulf of Mexico were obtained from the NOAA National Database for Deep-Sea Corals and Sponges (https://deepseacoraldata.noaa.gov/). Data records with identifiable positional errors were removed following quality control. Maximum Entropy (MaxEnt) models were generated to relate the occurrence of Leiopathes glaberrima to spatially explicit environmental predictor variables describing conditions that may influence the distribution of Leiopathes glaberrima. Models were then used to predict and map the relative habitat suitability (i.e., the logistic output from MaxEnt) of Leiopathes glaberrima across the U.S. Gulf of Mexico. The relative habitat suitability was reclassified into a series of habitat suitability classes using breakpoints calculated using specific ratios of the cost of false positive errors versus the cost of false negative errors. For more details, see Etnoyer et al. (2018).; Data Quality Method: Replicate models were fit to 10 subsets of the occurrence data, each containing 70% of the data to use for model fitting and 30% of the data to use for model testing. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), which indicates how well the models predicted Leiopathes glaberrima occurrence at test data locations compared to a random selection of background locations. The AUC was calculated for each of the ten replicate models and averaged (i.e., the mean test AUC). Variability (i.e., uncertainty) in model predictions was calculated as the difference in the predicted habitat suitability classes assigned to the upper and lower limits of the 95% confidence interval of the predicted habitat suitability from the 10 replicate models. An additional robust very high habitat suitability class was assigned to model grid cells predicted to be in the highest class of habitat suitability for each of the replicate model runs. For more details, see Etnoyer et al. (2018)..
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