Geology topics

Michael L. Treglia

Publications and source records attributed to Michael L. Treglia.

2 recordsLinked to original sources

USGS2018

Multi-scale effects of land cover and urbanization on the habitat suitability of an endangered toad

Habitat degradation, entwined with land cover change, is a major driver of biodiversity loss. Effects of land cover change on species can be direct (when habitat is converted to alternative land cover types) or indirect (when land outside of the species habitat is altered). Hydrologic and ecological connections between terrestrial and aquatic systems are well understood, exemplifying how spatially disparate land cover conditions may influence aquatic habitats, but are rarely examined. We sought to quantify relative effects of land cover at two different but interacting scales on habitat suitability for the endangered arroyo toad ( Anaxyrus californicus ). Based on an existing distribution model for the arroyo toad and available land cover data, we estimated effects of land cover along streams and within entire watersheds on habitat suitability using structural equation modeling. Relationships between land cover and habitat suitability differed between scales, and broader, watershed-scale conditions influenced land cover along the embedded stream networks. We found anthropogenic development and forest cover at the watershed-scale negatively impacted habitat suitability, but development along stream networks was positively associated with suitability. The positive association between development along streams and habitat suitability may be attributable to increased spatial heterogeneity along urbanized streams, or related factors including policies designed to conserve riparian habitats amidst development. These findings show arroyo toad habitat is influenced by land cover across multiple scales, and can inform conservation of the species. Furthermore, our methodology can help elucidate similar dynamics with other taxa, particularly those reliant on both terrestrial and aquatic environments.

California↗

USGS2015

Integrating multiple distribution models to guide conservation efforts of an endangered toad

Species distribution models are used for numerous purposes such as predicting changes in species’ ranges and identifying biodiversity hotspots. Although implications of distribution models for conservation are often implicit, few studies use these tools explicitly to inform conservation efforts. Herein, we illustrate how multiple distribution models developed using distinct sets of environmental variables can be integrated to aid in identification sites for use in conservation. We focus on the endangered arroyo toad ( Anaxyrus californicus ), which relies on open, sandy streams and surrounding floodplains in southern California, USA, and northern Baja California, Mexico. Declines of the species are largely attributed to habitat degradation associated with vegetation encroachment, invasive predators, and altered hydrologic regimes. We had three main goals: 1) develop a model of potential habitat for arroyo toads, based on long-term environmental variables and all available locality data; 2) develop a model of the species’ current habitat by incorporating recent remotely-sensed variables and only using recent locality data; and 3) integrate results of both models to identify sites that may be employed in conservation efforts. We used a machine learning technique, Random Forests, to develop the models, focused on riparian zones in southern California. We identified 14.37% and 10.50% of our study area as potential and current habitat for the arroyo toad, respectively. Generally, inclusion of remotely-sensed variables reduced modeled suitability of sites, thus many areas modeled as potential habitat were not modeled as current habitat. We propose such sites could be made suitable for arroyo toads through active management, increasing current habitat by up to 67.02%. Our general approach can be employed to guide conservation efforts of virtually any species with sufficient data necessary to develop appropriate distribution models.

California↗