Win-Win for Wind and Wildlife: A Vision to Facilitate Sustainable Development

Wind energy offers the potential to reduce carbon emissions while increasing energy independence and bolstering economic development. However, wind energy has a larger land footprint per Gigawatt (GW) than most other forms of energy production, making appropriate siting and mitigation particularly important. Species that require large unfragmented habitats and those known to avoid vertical structures are particularly at risk from wind development. Developing energy on disturbed lands rather than placing new developments within large and intact habitats would reduce cumulative impacts to wildlife. The U.S. Department of Energy estimates that it will take 241 GW of terrestrial based wind development on approximately 5 million hectares to reach 20% electricity production for the U.S. by 2030. We estimate there are ∼7,700 GW of potential wind energy available across the U.S., with ∼3,500 GW on disturbed lands. In addition, a disturbance-focused development strategy would avert the development of ∼2.3 million hectares of undisturbed lands while generating the same amount of energy as development based solely on maximizing wind potential. Wind subsidies targeted at favoring low-impact developments and creating avoidance and mitigation requirements that raise the costs for projects impacting sensitive lands could improve public value for both wind energy and biodiversity conservation

Table_2_Dakota skipper distribution model for North Dakota, South Dakota, and Minnesota aids conservation planning under changing climate scenarios.docx

Species distribution models are useful conservation planning tools for at-risk species, especially if they are linked to planning efforts, conservation delivery, and a changing environment. The Dakota skipper (Hesperia dacotae) is an endemic butterfly of mixed and tallgrass prairie of the northern Great Plains that is listed as federally threatened in the United States and Canada. We modeled broad-scale habitat suitability for the Dakota skipper by relating occurrence observations collected via non-probabilistic population surveys and a stratified sample of pseudo-absences to environmental predictors using a machine learning approach (i.e. Random Forest classification model). Predictors were summarized at two local scales and one landscape scale to reflect a potential spatial hierarchy of settlement responses. We used recursive feature elimination to select the top 25 covariates from a suite of predictor variables related to climate, topography, vegetation cover, biomass, surface reflectance, disturbance history, and soil characteristics. The top model included six bioclimatic, one soil, and 18 local- and landscape-scale vegetation variables and indicated an association with undisturbed grasslands with higher perennial grass and forb cover and biomass. The model performed well, with kappa and AUC estimates of 0.92 and 0.99, respectively, for 20% of data withheld for validation. To understand how climate change might affect Dakota skipper distribution, we applied the model using future 30-year bioclimatic predictions. Predicted suitable habitat declined and the climate envelope associated with Dakota skipper occurrence shifted north into Canada. While it is unknown to what degree the bioclimatic relationships in the model are biologically meaningful or are simply correlative with our non-probabilistic sample of occurrences, our results present an urgency to improve data collection for Dakota skipper populations and better understand climatic relationships, as climate change could have profound effects on populations and conservation planning. Regardless of climate or model uncertainty, our results demonstrate the importance of maintaining sufficient quantities and quality of grass on the landscape to support populations of Dakota skipper.</p

Table_1_Dakota skipper distribution model for North Dakota, South Dakota, and Minnesota aids conservation planning under changing climate scenarios.docx

Species distribution models are useful conservation planning tools for at-risk species, especially if they are linked to planning efforts, conservation delivery, and a changing environment. The Dakota skipper (Hesperia dacotae) is an endemic butterfly of mixed and tallgrass prairie of the northern Great Plains that is listed as federally threatened in the United States and Canada. We modeled broad-scale habitat suitability for the Dakota skipper by relating occurrence observations collected via non-probabilistic population surveys and a stratified sample of pseudo-absences to environmental predictors using a machine learning approach (i.e. Random Forest classification model). Predictors were summarized at two local scales and one landscape scale to reflect a potential spatial hierarchy of settlement responses. We used recursive feature elimination to select the top 25 covariates from a suite of predictor variables related to climate, topography, vegetation cover, biomass, surface reflectance, disturbance history, and soil characteristics. The top model included six bioclimatic, one soil, and 18 local- and landscape-scale vegetation variables and indicated an association with undisturbed grasslands with higher perennial grass and forb cover and biomass. The model performed well, with kappa and AUC estimates of 0.92 and 0.99, respectively, for 20% of data withheld for validation. To understand how climate change might affect Dakota skipper distribution, we applied the model using future 30-year bioclimatic predictions. Predicted suitable habitat declined and the climate envelope associated with Dakota skipper occurrence shifted north into Canada. While it is unknown to what degree the bioclimatic relationships in the model are biologically meaningful or are simply correlative with our non-probabilistic sample of occurrences, our results present an urgency to improve data collection for Dakota skipper populations and better understand climatic relationships, as climate change could have profound effects on populations and conservation planning. Regardless of climate or model uncertainty, our results demonstrate the importance of maintaining sufficient quantities and quality of grass on the landscape to support populations of Dakota skipper.</p

DataSheet_1_Dakota skipper distribution model for North Dakota, South Dakota, and Minnesota aids conservation planning under changing climate scenarios.pdf

Species distribution models are useful conservation planning tools for at-risk species, especially if they are linked to planning efforts, conservation delivery, and a changing environment. The Dakota skipper (Hesperia dacotae) is an endemic butterfly of mixed and tallgrass prairie of the northern Great Plains that is listed as federally threatened in the United States and Canada. We modeled broad-scale habitat suitability for the Dakota skipper by relating occurrence observations collected via non-probabilistic population surveys and a stratified sample of pseudo-absences to environmental predictors using a machine learning approach (i.e. Random Forest classification model). Predictors were summarized at two local scales and one landscape scale to reflect a potential spatial hierarchy of settlement responses. We used recursive feature elimination to select the top 25 covariates from a suite of predictor variables related to climate, topography, vegetation cover, biomass, surface reflectance, disturbance history, and soil characteristics. The top model included six bioclimatic, one soil, and 18 local- and landscape-scale vegetation variables and indicated an association with undisturbed grasslands with higher perennial grass and forb cover and biomass. The model performed well, with kappa and AUC estimates of 0.92 and 0.99, respectively, for 20% of data withheld for validation. To understand how climate change might affect Dakota skipper distribution, we applied the model using future 30-year bioclimatic predictions. Predicted suitable habitat declined and the climate envelope associated with Dakota skipper occurrence shifted north into Canada. While it is unknown to what degree the bioclimatic relationships in the model are biologically meaningful or are simply correlative with our non-probabilistic sample of occurrences, our results present an urgency to improve data collection for Dakota skipper populations and better understand climatic relationships, as climate change could have profound effects on populations and conservation planning. Regardless of climate or model uncertainty, our results demonstrate the importance of maintaining sufficient quantities and quality of grass on the landscape to support populations of Dakota skipper.</p

Trends in May pond numbers for seven waterfowl survey strata showing significant increases, 1974–2013.

<p>Number of May ponds (in thousands per year) increased significantly (p<0.05) in waterfowl survey strata 28, 29, 41, 46, 47, 48, and 49 of the Waterfowl Breeding Population and Habitat Survey, 1974–2013.</p

Waterfowl Conservation in the US Prairie Pothole Region: Confronting the Complexities of Climate Change

<div><p>The Prairie Pothole Region (PPR) is the most important waterfowl production area in North America. However, waterfowl populations there are predicted to decline because of climate-related drying of wetlands. Consequently, changes in the geographic focus of PPR waterfowl conservation have been recommended, which could have long-lasting and costly impacts. We used a 40-year dataset of pond counts collected in the PPR to test hypotheses about climate-related drying. We assessed May (1974–2013) and July (1974–2003) pond numbers in 20 waterfowl survey strata to determine if trends in pond numbers were consistent with predictions of drying. We also assessed trends in precipitation and temperature for the 20 strata and developed models describing May pond numbers from 1974 through 2010 as a function of precipitation, temperature, the previous year’s pond numbers, and location. None of the 20 strata showed significant declines in May pond numbers, although seven strata showed increases over time. July pond numbers declined significantly in one stratum, and increased in seven strata. An index to hydroperiod showed significant increasing trends in three strata, and no strata had decreasing trends. Precipitation increased significantly in two strata and decreased in two from 1974 to 2010; no strata showed significant changes in temperature. The best linear model described pond numbers within all strata as a function of precipitation, temperature, the previous year’s pond numbers, and the latitude and longitude of the stratum, and explained 62% of annual variation in pond numbers. We hypothesize that direct effects of climate change on prairie pothole wetlands and waterfowl may be overshadowed by indirect effects such as intensified land use and increased pressure to drain wetlands. We recommend that an adaptive, data-driven approach be used to resolve uncertainties regarding direct and indirect effects of climate change on prairie wetlands and waterfowl, and guide future conservation efforts.</p></div

Trends in precipitation for 20 waterfowl survey strata, 1974–2010.

<p>Slope of trends in mean monthly total precipitation (mm per year) from 1974 to 2010 in 20 strata of the Waterfowl Breeding Population and Habitat Survey in the Prairie Pothole Region of the US and Canada. Data acquired from <a href="http://climate.geog.udel.edu/~climate/html_pages/download.htm" target="_blank">http://climate.geog.udel.edu/~climate/html_pages/download.htm</a><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100034#pone.0100034-Matsuura1" target="_blank">[32]</a>. White stars indicate statistically significant trends (p<0.05).</p