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Enhanced migratory waterfowl distribution modeling by inclusion of depth to water table data.

Kreakie BJ, Fan Y, Keitt TH - PLoS ONE (2012)

Bottom Line: Also, MaxEnt models created with DWT often had higher accuracy when compared with models created with an alternative measure of wetland habitat.The results of this analysis also illustrate that, as expected, all waterfowl species are tightly affiliated with shallow water table habitat.However, this study illustrates that the intensity of affiliation is not constant between seasons for a species, nor is it consistent between species.

View Article: PubMed Central - PubMed

Affiliation: Section of Integrative Biology, University of Texas at Austin, Austin, Texas, United States of America. kreakie.betty@epa.gov

ABSTRACT
In addition to being used as a tool for ecological understanding, management and conservation of migratory waterfowl rely heavily on distribution models; yet these models have poor accuracy when compared to models of other bird groups. The goal of this study is to offer methods to enhance our ability to accurately model the spatial distributions of six migratory waterfowl species. This goal is accomplished by creating models based on species-specific annual cycles and introducing a depth to water table (DWT) data set. The DWT data set, a wetland proxy, is a simulated long-term measure of the point either at or below the surface where climate and geological/topographic water fluxes balance. For species occurrences, the USGS' banding bird data for six relatively common species was used. Distribution models are constructed using Random Forest and MaxEnt. Random Forest classification of habitat and non-habitat provided a measure of DWT variable importance, which indicated that DWT is as important, and often more important, to model accuracy as temperature, precipitation, elevation, and an alternative wetland measure. MaxEnt models that included DWT in addition to traditional predictor variables had a considerable increase in classification accuracy. Also, MaxEnt models created with DWT often had higher accuracy when compared with models created with an alternative measure of wetland habitat. By comparing maps of predicted probability of occurrence and response curves, it is possible to explore how different species respond to water table depth and how a species responds in different seasons. The results of this analysis also illustrate that, as expected, all waterfowl species are tightly affiliated with shallow water table habitat. However, this study illustrates that the intensity of affiliation is not constant between seasons for a species, nor is it consistent between species.

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Map of the simulated equilibrium water table depth for the contiguous US [18].The values illustrate the depth in meters below the surface where the simulated water table is located.
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pone-0030142-g002: Map of the simulated equilibrium water table depth for the contiguous US [18].The values illustrate the depth in meters below the surface where the simulated water table is located.

Mentions: The DWT data layer is a simulated data set that reliably predicts the location of natural wetlands (Figure 2) [18], [35]. The depth to water table is determined by finding the long-term stable solution of the balance between the climate-driven fluxes (precipitation and evapotranspiration) and geologic/topographic water fluxes (riverine and groundwater movement) balance. Initially, the water table was set at the surface and at each time step the modeled DWT was recalculated based on water inputs or outputs. The model was allowed to run until the water table for each cell (9-second resolution) was stable (less than 1 mm change). The DWT model was validated using 500,000+ USGS field observations of water table depth from 1927–2005; the mean of the residuals (simulated DWT – observed DWT) is 0.443 m. Fan and Miguez-Macho [18] further tested the ability of the data to locate wetlands on the landscape. They found a strong correlation (0.8469) between field-mapped wetlands and the simulated data thresholded to 1.0 m water table depth. There is a -0.36 correlation between the DWT data and NLCD percent wetland data, which was used as an alternative measure of wetland habitat for this study. The DWT data were obtained directly from Fan and Miguez-Macho, and the referenced manuscripts provide in-depth details on model development and validation [18], [35].


Enhanced migratory waterfowl distribution modeling by inclusion of depth to water table data.

Kreakie BJ, Fan Y, Keitt TH - PLoS ONE (2012)

Map of the simulated equilibrium water table depth for the contiguous US [18].The values illustrate the depth in meters below the surface where the simulated water table is located.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3260213&req=5

pone-0030142-g002: Map of the simulated equilibrium water table depth for the contiguous US [18].The values illustrate the depth in meters below the surface where the simulated water table is located.
Mentions: The DWT data layer is a simulated data set that reliably predicts the location of natural wetlands (Figure 2) [18], [35]. The depth to water table is determined by finding the long-term stable solution of the balance between the climate-driven fluxes (precipitation and evapotranspiration) and geologic/topographic water fluxes (riverine and groundwater movement) balance. Initially, the water table was set at the surface and at each time step the modeled DWT was recalculated based on water inputs or outputs. The model was allowed to run until the water table for each cell (9-second resolution) was stable (less than 1 mm change). The DWT model was validated using 500,000+ USGS field observations of water table depth from 1927–2005; the mean of the residuals (simulated DWT – observed DWT) is 0.443 m. Fan and Miguez-Macho [18] further tested the ability of the data to locate wetlands on the landscape. They found a strong correlation (0.8469) between field-mapped wetlands and the simulated data thresholded to 1.0 m water table depth. There is a -0.36 correlation between the DWT data and NLCD percent wetland data, which was used as an alternative measure of wetland habitat for this study. The DWT data were obtained directly from Fan and Miguez-Macho, and the referenced manuscripts provide in-depth details on model development and validation [18], [35].

Bottom Line: Also, MaxEnt models created with DWT often had higher accuracy when compared with models created with an alternative measure of wetland habitat.The results of this analysis also illustrate that, as expected, all waterfowl species are tightly affiliated with shallow water table habitat.However, this study illustrates that the intensity of affiliation is not constant between seasons for a species, nor is it consistent between species.

View Article: PubMed Central - PubMed

Affiliation: Section of Integrative Biology, University of Texas at Austin, Austin, Texas, United States of America. kreakie.betty@epa.gov

ABSTRACT
In addition to being used as a tool for ecological understanding, management and conservation of migratory waterfowl rely heavily on distribution models; yet these models have poor accuracy when compared to models of other bird groups. The goal of this study is to offer methods to enhance our ability to accurately model the spatial distributions of six migratory waterfowl species. This goal is accomplished by creating models based on species-specific annual cycles and introducing a depth to water table (DWT) data set. The DWT data set, a wetland proxy, is a simulated long-term measure of the point either at or below the surface where climate and geological/topographic water fluxes balance. For species occurrences, the USGS' banding bird data for six relatively common species was used. Distribution models are constructed using Random Forest and MaxEnt. Random Forest classification of habitat and non-habitat provided a measure of DWT variable importance, which indicated that DWT is as important, and often more important, to model accuracy as temperature, precipitation, elevation, and an alternative wetland measure. MaxEnt models that included DWT in addition to traditional predictor variables had a considerable increase in classification accuracy. Also, MaxEnt models created with DWT often had higher accuracy when compared with models created with an alternative measure of wetland habitat. By comparing maps of predicted probability of occurrence and response curves, it is possible to explore how different species respond to water table depth and how a species responds in different seasons. The results of this analysis also illustrate that, as expected, all waterfowl species are tightly affiliated with shallow water table habitat. However, this study illustrates that the intensity of affiliation is not constant between seasons for a species, nor is it consistent between species.

Show MeSH