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The influence of mitigation on sage-grouse habitat selection within an energy development field.

Fedy BC, Kirol CP, Sutphin AL, Maechtle TL - PLoS ONE (2015)

Bottom Line: However, birds still avoided areas of high well density and nests were not found in areas with greater than 4 wells per km2 and the majority of nests (63%) were located in areas with ≤ 1 well per km2.We found more improvement in habitat rank between the two time periods around mitigated wells compared to non-mitigated wells.We recommend that any mitigation effort include well-informed plans to monitor the effectiveness of the implemented mitigation actions that assess both habitat use and relevant fitness parameters.

View Article: PubMed Central - PubMed

Affiliation: Department of Environment and Resource Studies, University of Waterloo, Waterloo, Ontario, Canada.

ABSTRACT
Growing global energy demands ensure the continued growth of energy development. Energy development in wildlife areas can significantly impact wildlife populations. Efforts to mitigate development impacts to wildlife are on-going, but the effectiveness of such efforts is seldom monitored or assessed. Greater sage-grouse (Centrocercus urophasianus) are sensitive to energy development and likely serve as an effective umbrella species for other sagebrush-steppe obligate wildlife. We assessed the response of birds within an energy development area before and after the implementation of mitigation action. Additionally, we quantified changes in habitat distribution and abundance in pre- and post-mitigation landscapes. Sage-grouse avoidance of energy development at large spatial scales is well documented. We limited our research to directly within an energy development field in order to assess the influence of mitigation in close proximity to energy infrastructure. We used nest-location data (n = 488) within an energy development field to develop habitat selection models using logistic regression on data from 4 years of research prior to mitigation and for 4 years following the implementation of extensive mitigation efforts (e.g., decreased activity, buried powerlines). The post-mitigation habitat selection models indicated less avoidance of wells (well density β = 0.18 ± 0.08) than the pre-mitigation models (well density β = -0.09 ± 0.11). However, birds still avoided areas of high well density and nests were not found in areas with greater than 4 wells per km2 and the majority of nests (63%) were located in areas with ≤ 1 well per km2. Several other model coefficients differed between the two time periods and indicated stronger selection for sagebrush (pre-mitigation β = 0.30 ± 0.09; post-mitigation β = 0.82 ± 0.08) and less avoidance of rugged terrain (pre-mitigation β = -0.35 ± 0.12; post-mitigation β = -0.05 ± 0.09). Mitigation efforts implemented may be responsible for the measurable improvement in sage-grouse nesting habitats within the development area. However, we cannot reject alternative hypotheses concerning the influence of population density and intraspecific competition. Additionally, we were unable to assess the actual fitness consequences of mitigation or the source-sink dynamics of the habitats. We compared the pre-mitigation and post-mitigation models predicted as maps with habitats ranked from low to high relative probability of use (equal-area bins: 1 - 5). We found more improvement in habitat rank between the two time periods around mitigated wells compared to non-mitigated wells. Informed mitigation within energy development fields could help improve habitats within the field. We recommend that any mitigation effort include well-informed plans to monitor the effectiveness of the implemented mitigation actions that assess both habitat use and relevant fitness parameters.

No MeSH data available.


Related in: MedlinePlus

Margins Plots.Margins plot depicting the functional form of greater sage-grouse response to the three variables with different coefficient estimates between the pre- and post-mitigation time periods. Model predictions are based on the global Resource Selection Function models for nesting habitat in the Powder River Basin, Wyoming, U.S.A. Model predictions for the pre-mitigation model (2004–2007) are represented by the dashed lines with darker gray 95% confidence intervals. Model predictions for the post-mitigation model (2008–2011) are represented by the solid lines and lighter gray 95% confidence intervals. VRM: Mean Vector Roughness Measure.
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pone.0121603.g002: Margins Plots.Margins plot depicting the functional form of greater sage-grouse response to the three variables with different coefficient estimates between the pre- and post-mitigation time periods. Model predictions are based on the global Resource Selection Function models for nesting habitat in the Powder River Basin, Wyoming, U.S.A. Model predictions for the pre-mitigation model (2004–2007) are represented by the dashed lines with darker gray 95% confidence intervals. Model predictions for the post-mitigation model (2008–2011) are represented by the solid lines and lighter gray 95% confidence intervals. VRM: Mean Vector Roughness Measure.

Mentions: Global models for each timeframe contained 6 covariates representing sagebrush, topographic indices, roads, and well density. The coefficient estimates for the pre- and post-mitigation models were generally similar and, with the exception of well density, in the same direction. However, there were a few covariates for which the standard errors of the coefficient estimates did not overlap between the two time periods (Fig 1). Sagebrush, well density, and VRM coefficient estimates did not overlap. Inspection of the marginal effects plots revealed a generally positive association with sagebrush across the two time periods and a generally flat association with well density across the two time periods (Fig 2). The distribution of these two covariates did not differ at available sites between the two time periods. Spearman rank correlation values between the area-adjusted frequency of validation points and RSF bin across the five folds had an average of 0.82 for pre-mitigation models and 0.96 for post-mitigation models. These average values across the folds suggested that models performed well at predicting nest sites in both the pre- and post-mitigation landscapes and the slightly lower performance of the pre-mitigation models may be due to the smaller sample size.


The influence of mitigation on sage-grouse habitat selection within an energy development field.

Fedy BC, Kirol CP, Sutphin AL, Maechtle TL - PLoS ONE (2015)

Margins Plots.Margins plot depicting the functional form of greater sage-grouse response to the three variables with different coefficient estimates between the pre- and post-mitigation time periods. Model predictions are based on the global Resource Selection Function models for nesting habitat in the Powder River Basin, Wyoming, U.S.A. Model predictions for the pre-mitigation model (2004–2007) are represented by the dashed lines with darker gray 95% confidence intervals. Model predictions for the post-mitigation model (2008–2011) are represented by the solid lines and lighter gray 95% confidence intervals. VRM: Mean Vector Roughness Measure.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4383447&req=5

pone.0121603.g002: Margins Plots.Margins plot depicting the functional form of greater sage-grouse response to the three variables with different coefficient estimates between the pre- and post-mitigation time periods. Model predictions are based on the global Resource Selection Function models for nesting habitat in the Powder River Basin, Wyoming, U.S.A. Model predictions for the pre-mitigation model (2004–2007) are represented by the dashed lines with darker gray 95% confidence intervals. Model predictions for the post-mitigation model (2008–2011) are represented by the solid lines and lighter gray 95% confidence intervals. VRM: Mean Vector Roughness Measure.
Mentions: Global models for each timeframe contained 6 covariates representing sagebrush, topographic indices, roads, and well density. The coefficient estimates for the pre- and post-mitigation models were generally similar and, with the exception of well density, in the same direction. However, there were a few covariates for which the standard errors of the coefficient estimates did not overlap between the two time periods (Fig 1). Sagebrush, well density, and VRM coefficient estimates did not overlap. Inspection of the marginal effects plots revealed a generally positive association with sagebrush across the two time periods and a generally flat association with well density across the two time periods (Fig 2). The distribution of these two covariates did not differ at available sites between the two time periods. Spearman rank correlation values between the area-adjusted frequency of validation points and RSF bin across the five folds had an average of 0.82 for pre-mitigation models and 0.96 for post-mitigation models. These average values across the folds suggested that models performed well at predicting nest sites in both the pre- and post-mitigation landscapes and the slightly lower performance of the pre-mitigation models may be due to the smaller sample size.

Bottom Line: However, birds still avoided areas of high well density and nests were not found in areas with greater than 4 wells per km2 and the majority of nests (63%) were located in areas with ≤ 1 well per km2.We found more improvement in habitat rank between the two time periods around mitigated wells compared to non-mitigated wells.We recommend that any mitigation effort include well-informed plans to monitor the effectiveness of the implemented mitigation actions that assess both habitat use and relevant fitness parameters.

View Article: PubMed Central - PubMed

Affiliation: Department of Environment and Resource Studies, University of Waterloo, Waterloo, Ontario, Canada.

ABSTRACT
Growing global energy demands ensure the continued growth of energy development. Energy development in wildlife areas can significantly impact wildlife populations. Efforts to mitigate development impacts to wildlife are on-going, but the effectiveness of such efforts is seldom monitored or assessed. Greater sage-grouse (Centrocercus urophasianus) are sensitive to energy development and likely serve as an effective umbrella species for other sagebrush-steppe obligate wildlife. We assessed the response of birds within an energy development area before and after the implementation of mitigation action. Additionally, we quantified changes in habitat distribution and abundance in pre- and post-mitigation landscapes. Sage-grouse avoidance of energy development at large spatial scales is well documented. We limited our research to directly within an energy development field in order to assess the influence of mitigation in close proximity to energy infrastructure. We used nest-location data (n = 488) within an energy development field to develop habitat selection models using logistic regression on data from 4 years of research prior to mitigation and for 4 years following the implementation of extensive mitigation efforts (e.g., decreased activity, buried powerlines). The post-mitigation habitat selection models indicated less avoidance of wells (well density β = 0.18 ± 0.08) than the pre-mitigation models (well density β = -0.09 ± 0.11). However, birds still avoided areas of high well density and nests were not found in areas with greater than 4 wells per km2 and the majority of nests (63%) were located in areas with ≤ 1 well per km2. Several other model coefficients differed between the two time periods and indicated stronger selection for sagebrush (pre-mitigation β = 0.30 ± 0.09; post-mitigation β = 0.82 ± 0.08) and less avoidance of rugged terrain (pre-mitigation β = -0.35 ± 0.12; post-mitigation β = -0.05 ± 0.09). Mitigation efforts implemented may be responsible for the measurable improvement in sage-grouse nesting habitats within the development area. However, we cannot reject alternative hypotheses concerning the influence of population density and intraspecific competition. Additionally, we were unable to assess the actual fitness consequences of mitigation or the source-sink dynamics of the habitats. We compared the pre-mitigation and post-mitigation models predicted as maps with habitats ranked from low to high relative probability of use (equal-area bins: 1 - 5). We found more improvement in habitat rank between the two time periods around mitigated wells compared to non-mitigated wells. Informed mitigation within energy development fields could help improve habitats within the field. We recommend that any mitigation effort include well-informed plans to monitor the effectiveness of the implemented mitigation actions that assess both habitat use and relevant fitness parameters.

No MeSH data available.


Related in: MedlinePlus