Habitat of the endangered salt marsh harvest mouse (Reithrodontomys raviventris) in San Francisco Bay

Abstract

Understanding habitat associations is vital for conservation of at‐risk marsh‐endemic wildlife species, particularly those under threat from sea level rise. We modeled environmental and habitat associations of the marsh‐endemic, Federally endangered salt marsh harvest mouse ( Reithrodontomys raviventris , RERA) and co‐occurrence with eight associated small mammal species from annual trap data, 1998–2014, in six estuarine marshes in North San Francisco Bay, California. Covariates included microhabitat metrics of elevation and vegetation species and cover; and landscape metrics of latitude–longitude, distance to anthropogenic features, and habitat patch size. The dominant cover was pickleweed ( Salicornia pacifica ) with 86% mean cover and 37 cm mean height, and bare ground with about 10% mean cover. We tested 38 variants of Bayesian network (BN) models to determine covariates that best account for presence of RERA and of all nine small mammal species. Best models had lowest complexity and highest classification accuracy. Among RERA presence models, three best BN models used covariates of latitude–longitude, distance to paved roads, and habitat patch size, with 0% error of false presence, 20% error of false nonpresence, and 20% overall error. The all‐species presence models suggested that within the pickleweed marsh environment, RERA are mostly habitat generalists. Accounting for presence of other species did not improve prediction of RERA. Habitat attributes compared between RERA and the next most frequently captured species, California vole ( Microtus californicus ), suggested substantial habitat overlap, with RERA habitat being somewhat higher in marsh elevation, greater in percent cover of the dominant plant species, closer to urban areas, further from agricultural areas, and, perhaps most significant, larger in continuous size of marsh patch. Findings will inform conservation management of the marsh environment for RERA by identifying best microhabitat elements, landscape attributes, and adverse interspecific interactions.