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Integrating Multiple Distribution Models to Guide Conservation Efforts of an Endangered Toad.

Treglia ML, Fisher RN, Fitzgerald LA - PLoS ONE (2015)

Bottom Line: Declines of the species are largely attributed to habitat degradation associated with vegetation encroachment, invasive predators, and altered hydrologic regimes.Generally, inclusion of remotely-sensed variables reduced modeled suitability of sites, thus many areas modeled as potential habitat were not modeled as current habitat.Our general approach can be employed to guide conservation efforts of virtually any species with sufficient data necessary to develop appropriate distribution models.

View Article: PubMed Central - PubMed

Affiliation: Department of Wildlife and Fisheries Sciences, Biodiversity Research and Teaching Collections, Applied Biodiversity Science Program, Texas A&M University, College Station, Texas, United States of America.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus

Comparison of two models of the distribution of the arroyo toad in southwestern California.This map was derived from two models for the distribution of the arroyo toad in southwestern California. Both models focused on streams and stream-side areas, and used relatively stable, long-term predictor variables characterizing aspects of soil, topography, and climate. The first model (potential model) only used those predictor variables and was designed to identify areas that may be suitable for the species based on intrinsic characteristics of the landscape. The second model (current model) also integrated more dynamic variables associated with current land cover conditions, and was designed to identify sites that may be suitable for the species, given constraints of land cover characteristics. This map represents the differences in predictions among the two models: black areas represent sites for which prediction of habitat did not change from the potential to the current model; blue represents sites predicted as potential but not current habitat, and yellow represents sites predicted as current but not potential habitat.
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pone.0131628.g004: Comparison of two models of the distribution of the arroyo toad in southwestern California.This map was derived from two models for the distribution of the arroyo toad in southwestern California. Both models focused on streams and stream-side areas, and used relatively stable, long-term predictor variables characterizing aspects of soil, topography, and climate. The first model (potential model) only used those predictor variables and was designed to identify areas that may be suitable for the species based on intrinsic characteristics of the landscape. The second model (current model) also integrated more dynamic variables associated with current land cover conditions, and was designed to identify sites that may be suitable for the species, given constraints of land cover characteristics. This map represents the differences in predictions among the two models: black areas represent sites for which prediction of habitat did not change from the potential to the current model; blue represents sites predicted as potential but not current habitat, and yellow represents sites predicted as current but not potential habitat.

Mentions: Of the 46,305 sample units in our study area, 3,260 were modeled as potential habitat, but not current habitat (Fig 4). This represents 7.04% of our focal area, which has potential to be employed in habitat improvement and conservation efforts, but is not currently suitable for the species. An additional 1,467 sample units were modeled as current but not potential habitat. Individually, our models predict potential habitat in 14.37% and current habitat in 10.50% of the sample units in our study area. Thus, we estimate a net decrease of 26.93% in modeled habitat, resulting from constraints associated with dynamic variables in our current model, representing land cover characteristics.


Integrating Multiple Distribution Models to Guide Conservation Efforts of an Endangered Toad.

Treglia ML, Fisher RN, Fitzgerald LA - PLoS ONE (2015)

Comparison of two models of the distribution of the arroyo toad in southwestern California.This map was derived from two models for the distribution of the arroyo toad in southwestern California. Both models focused on streams and stream-side areas, and used relatively stable, long-term predictor variables characterizing aspects of soil, topography, and climate. The first model (potential model) only used those predictor variables and was designed to identify areas that may be suitable for the species based on intrinsic characteristics of the landscape. The second model (current model) also integrated more dynamic variables associated with current land cover conditions, and was designed to identify sites that may be suitable for the species, given constraints of land cover characteristics. This map represents the differences in predictions among the two models: black areas represent sites for which prediction of habitat did not change from the potential to the current model; blue represents sites predicted as potential but not current habitat, and yellow represents sites predicted as current but not potential habitat.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4488373&req=5

pone.0131628.g004: Comparison of two models of the distribution of the arroyo toad in southwestern California.This map was derived from two models for the distribution of the arroyo toad in southwestern California. Both models focused on streams and stream-side areas, and used relatively stable, long-term predictor variables characterizing aspects of soil, topography, and climate. The first model (potential model) only used those predictor variables and was designed to identify areas that may be suitable for the species based on intrinsic characteristics of the landscape. The second model (current model) also integrated more dynamic variables associated with current land cover conditions, and was designed to identify sites that may be suitable for the species, given constraints of land cover characteristics. This map represents the differences in predictions among the two models: black areas represent sites for which prediction of habitat did not change from the potential to the current model; blue represents sites predicted as potential but not current habitat, and yellow represents sites predicted as current but not potential habitat.
Mentions: Of the 46,305 sample units in our study area, 3,260 were modeled as potential habitat, but not current habitat (Fig 4). This represents 7.04% of our focal area, which has potential to be employed in habitat improvement and conservation efforts, but is not currently suitable for the species. An additional 1,467 sample units were modeled as current but not potential habitat. Individually, our models predict potential habitat in 14.37% and current habitat in 10.50% of the sample units in our study area. Thus, we estimate a net decrease of 26.93% in modeled habitat, resulting from constraints associated with dynamic variables in our current model, representing land cover characteristics.

Bottom Line: Declines of the species are largely attributed to habitat degradation associated with vegetation encroachment, invasive predators, and altered hydrologic regimes.Generally, inclusion of remotely-sensed variables reduced modeled suitability of sites, thus many areas modeled as potential habitat were not modeled as current habitat.Our general approach can be employed to guide conservation efforts of virtually any species with sufficient data necessary to develop appropriate distribution models.

View Article: PubMed Central - PubMed

Affiliation: Department of Wildlife and Fisheries Sciences, Biodiversity Research and Teaching Collections, Applied Biodiversity Science Program, Texas A&M University, College Station, Texas, United States of America.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus