Coastal California Gnatcatcher Habitat Suitability Model for Southern California
February 2, 2021 - February 2, 2021
This habitat model was developed to delineate a sampling frame for regional monitoring of coastal California gnatcatchers (Polioptila californica californica) to determine: 1) percent area occupied (PAO) in high and very high suitability habitat across conserved lands and participating military lands in the U.S. range in southern California; 2) changes in PAO over time; and 3) extinction and colonization rates. One purpose of the model is to identify areas recovering from disturbance, such as wildfire, that may not currently support coastal sage scrub vegetation used by coastal California gnatcatchers, but are otherwise highly suitable. In this way, we can monitor gnatcatcher occupancy associated with habitat changes over time.
We used the Partitioned Mahalanobis D2 modeling technique to construct alternative models with different combinations of environmental variables. Variables were calculated at each point in the center of a 150 m x 150 m cell in a grid of points across the southern California landscape. Variables reflect various aspects of topography, climate, land use (percent vegetation and urbanization at 150 m and 1 km scales), Normalized Difference Vegetation Index, and modeled California sagebrush (Artemisia californica) habitat suitability. Due to spatial unevenness in gnatcatcher location data, we divided southern California into five sampling regions and randomly subsampled 50 locations from each region. We repeated this process 1,000 times using a total of 1,063 spatially precise and non-redundant gnatcatcher locations as a calibration dataset. We model-averaged the results from sampling iterations to create a calibration model and partitions for each set of variables. We compared among calibration model-partitions using a validation dataset of 3,205 presence records independently collected from the calibration dataset and an equivalent number of pseudo-absence points randomly selected from the study area grid. For every model-partition, we calculated Habitat Similarity Index (HSI) predictions for presence and pseudo absence points ranging from Very High (0.75 - 1.00); High (0.50 - 0.74); Low (0.25 - 0.49); and Very Low (0 - 0.24). Suitable habitat is identified as grid cells with HSI greater than or equal to 0.5. We calculated Area Under the Curve (AUC) values from a Receiver Operating Curve (ROC) to determine how well models distinguish between presence and pseudo-absence points. We selected a best performing calibration model and partition based upon median HSI calibration and validation values and AUC results.
The top performing model-partition Run 18 Partition 1 of 19 alternative models has an AUC of 0.96 and a median calibration and validation HSI of 0.73 and 0.69, respec