Bat Analysis Summary for NYSERDA Project Advisory Committee

Transcription

1 Bat Analysis Summary for NYSERDA Project Advisory Committee Kelly Perkins, Matt Schlesinger, Tim Howard New York Natural Heritage Program 5/16/2012 Introduction White-nose syndrome has recently decimated populations of cave bat species including the federally endangered Indiana bat (Myotis sodalis) (71% mortality) and the formerly abundant little brown bat (Myotis lucifugus) (91% mortality) with high fatality rates documented by New York State Department of Environmental Conservation (NYSDEC) in their winter hibernacula. Losses of additional individuals and subpopulations of rare or declining species further reduces their genetic pool, making recovery to healthy populations that much more difficult. For this reason, protecting summer habitat of cave bats and minimizing mortality during migration is a priority. It is also important to conserve and protect the migratory tree bat species occurring in New York. Although little is known about these difficult to study species, they may be the bats with the greatest potential to maintain viable populations in the face of white-nose because they do not typically overwinter in caves. They are long-distance migrants and like migratory birds, are vulnerable during this time of seasonal travel. Previous studies have shown that migratory tree bats comprise the majority (75%) of bat carcasses found at wind turbines (Arnett et al. 2008, Cryan and Barclay 2009, Kunz et al. 2007), whether or not or to what extent this source of mortality may impact rangewide population numbers has not yet been conclusively studied. As part of the species distribution modeling project, distributions (modeled using Heritage occurrence data) of two bat species tracked by NY Natural Heritage, Indiana bat and smallfooted bat, will be examined. In addition, we brought together existing datasets on the nine bat species occurring in New York to model summer distributions, migratory distances from hibernacula of two species, and the characteristics of turbine sites related to increased bat fatalities. We evaluated the potential for the available datasets to accurately predict summer distributions, areas of the state where encounters with a traveling Indiana or little brown bat during migration may occur, and bat fatalities at wind turbines with low (or at least measureable) bias. Our agreement with NYSERDA specified that we would conduct the analyses and data synthesis described below. Prior to moving on to the next step of mapping these bat data we would secure approval from NYSERDA. The following document contains additional detail on each analysis and our recommendations for proceeding with spatial modeling for species with sufficient data and model accuracies to move forward. Bat Summer Distributions Purpose: To determine the relative model strength relating bat presence or absence with a suite of environmental variables for each of the nine species of bats occurring in New York during the summer.

2 Data sources: 1. Statewide 2009 and 2010 acoustic surveys from 49 fifteen-mile road transects. 2. Mist-net data (318 points) from various targeted projects Species Modeled: Big brown bat (Eptesicus fuscus), little brown bat (Myotis lucifugus), eastern red bat (Lasiurus borealis), hoary bat (Lasiurus cinereus), tri-colored bat (Periomyotis subflavus), northern bat (Myotis septentrionalis), Indiana bat (Myotis sodalis), and small-footed bat (Myotis leibii) were each modeled separately. Response Variable: Presence or absence of each species. Explanatory variables: See table 1. Methods: We obtained occurrence data from two sources: a statewide acoustic survey (Figure 1) and independent mist-net surveys. We used mist-net locations and locations along acoustic routes where bats of each species were not detected as non-detection points in each model. To reduce spatial auto-correlation, we randomly placed non-detection points along sections of routes with no detections in a spatially balanced way and randomly removed presence points (from dense areas) so that they were at least 100 m apart. Most routes were only sampled once a season and bats are highly mobile with large home range sizes. To increase the likelihood that nondetections were true absences we examined model fit using different separation distances between presence locations and non-detections (100 m, 1 km, 2.5 km). We used random forest (RF) analysis (Breiman 2001) to model the presence or absence of each bat species related to 57 environmental variables including climate, geology, soil, topography, land cover, and land use (Table 1). We also included spatial coordinates as explanatory variables in the models to account for unexplained variation. In addition to the model accuracy statistics provided by RF we examined model accuracy using an approach, AUC-RF, that optimizes the number of environmental variables and model fit based on cross validation of the area-under-the ROC curve (AUC) (Calle et al. 2011). We applied this approach using the AUCRF package (Urrea and Calle 2011) in R as an additional test of model accuracy in our study. We used partial dependence plots, available in the randomforest package (Liaw and Wiener 2001), to determine the direction of the effect of individual explanatory variables on the response variable. Outcome: We had sufficient numbers of detections to model eight of the nine bat species, with the exception of silver-haired bat (Lasionycteris noctivagans). Overall, out-of-bag (OOB) error rates were good across the board for all species (Table 2). The ability of the model to predict presence was poor for Indiana, small-footed, northern, and tri-colored so variable importance was not examined for these species. Overall model fit determined by both OOB error rate and AUC-RF, as well as presence error rate, was good for eastern red, hoary, little brown and big brown bats (Table 2). Presence error rates were lower for models with larger separation distances between presences and non-detections. Increasing agricultural cover within 5 km was related to absence of all species while increasing shrub cover within 1 km was a predictor of presence for each species. Low, low-intermediate,

3 and high forest cover values were predictors of presence of hoary bats, while intermediate and high-intermediate forest cover were not predictors of hoary bats. High forest cover was a predictor of absence of Eastern red bats. High wetland cover within 5 km predicted little brown bat presence. Recommendation: Create spatial models for little brown bat, eastern red bat, hoary bat, and big brown bat. Do not create spatial models for species with high error rates for predicting presence. Travel distances from hibernacula for two cave bats Purpose: The purpose was to determine areas of the state where you might expect to encounter a traveling little brown or Indiana bat during migration. We sought to examine likely travel distances of Indiana bats from hibernacula to summer roost sites based on radio-tracking data and to apply this knowledge to model travel distances of Indiana bats from other hibernacula where summer locations aren t known from radio-tracking data. Since radio-tracked locations aren t available for little brown bats the goal was to determine if potential travel distances from the literature could be used to in conjunction with the newly acquired summer distribution data and known hibernacula to model areas of the state where they might or might not be encountered during migration. Data sources: 1. Radio-tracked locations of Indiana bats from hibernacula to roost trees. 2. Acoustic and mist-net detections of little brown bat. 3. Known little brown bat hibernacula. Outcome: Surveys of winter hibernacula conducted by NYSDEC document that Indiana bat populations have declined 71% since white-nose disease was first reported. Very few or no Indiana bats remain in caves that did not receive radio-tracking studies. This eliminates value in modeling hibernacula-to-summer roost distances for caves lacking Indiana bats, but emphasizes the importance of existing data that link bats from hibernacula with populations still left to speak of with their respective roosts. An examination of known travel distances of little brown bats coupled with the acoustic and mist-net detections from the summer bat distribution project demonstrate a statewide distribution of this species during the summer with seasonal migration distances of individual bats capable of easily covering half the state. This leads us to the conclusion that based on 2009 and 2010 data there is potential to encounter a traveling little brown bat anywhere in the state during the seasonal migration period. Recommendation: Do not provide a spatial model of travel distances from hibernacula for little brown or Indiana bats. Provide a GIS layer from the DEC radio-tracking surveys (Figure 3) indicating the location

4 of hibernacula, summer roosts buffered ( 2.5-miles) to indicate the presumed extent of movements during the summer, and lines connecting hibernacula to known roost sites. Late Summer/Early Fall Fatality at Wind Turbines Purpose: To examine model fit relating environmental variables at the site of the turbine or in the surrounding landscape to increased bat fatalities at the turbine. Data sources: 1. Thirteen fatality datasets from nine wind farms. Species and Groups Modeled: Little brown bat, Eastern red bat, hoary bat, silver-haired bat, migratory tree bats, and total bats. Response Variable: Bat carcass abundance metric. Explanatory variables: See table 1. Methods: Thirteen fall bat fatality datasets from nine wind farms (Figure 2) across New York State ( ) were extracted from reports made available to us from the NYS Department of Environmental Conservation. We calculated an abundance metric for each bat species at each turbine during the migration season (defined as August 1 st -September 30 th ). We divided the number of bats found at a turbine during the sampling season by searcher and scavenging efficiency rates to estimate the number of bat carcasses of each species at a turbine that would be expected given flawless searcher efficiency and no scavenging. The result was divided by the number of days the turbine was sampled to yield bats/turbine/day, a metric which could be compared among turbines and wind farms with varying numbers of survey days, searcher efficiencies and scavenging rates. We treated turbines that were surveyed but where no individuals of a species were found as nondetection locations for that species. Bats are highly mobile especially during migration. Scavenging rates of carcasses were also high and searcher efficiencies rates were low. To increase the likelihood that non-detections used in the model were true absences we examined model fit using three separation distances (0 km, 1 km and 2 km) between detections and nondetection locations. Each turbine point was attributed with 57 explanatory variables in GIS including climate, geology, soil, topography, land cover, and land use (Table 1). Land cover and land use variables were examined at multiple spatial scales (Table 1). We also included spatial coordinates as explanatory variables in the models to account for unexplained variation. Site (wind farm) was incorporated into the model to determine its importance related to numbers of bat fatalities and to account for variation not explained by environmental variables. We used random forest analysis (Breiman 2001) to model the abundance of bat carcasses related to the environmental variables. We used partial dependence plots, available in the randomforest package (Liaw and Wiener 2001), to determine the direction of the effect of individual explanatory variables on the response variable.

5 Outcome: Models using non-detections that were at least 2 km from a known presence had the best model fit (Table 3). Models for migratory tree bats, Eastern red bats, hoary bats, and silver-haired bats was accounted for enough variation to warrant further examination of variable importance, while model fit was poor for little brown bats (Table 3). Site was the first or second variable in each model. Distance to nearest wetland was second in variable importance for Eastern red bats with decreasing distances to wetland related to increasing abundance of carcasses. Second and third in the model for migratory tree bats were forest and open cover at the 300 m scale. Increasing values of forest cover within 300 m of the turbine were related to increasing abundance of migratory tree bat kills, while increasing amounts of open cover within 300 m of the turbine was related to decreasing numbers of migratory tree bat kills. Several topography variables entered into the top ten in variable importance for each species. Increasing topographic index, a measure of topographic complexity, at two spatial scales for silver-haired bats, increasing slope for eastern red bats, and increasing elevation for hoary bats were related to increased abundances of carcasses of each species respectively. Southerly aspects were related to increasing abundance of hoary bat kills. These land cover and topographic characteristics may account for some of the variation in bat kills at wind turbines but should be tested in a larger study design across the northeast. Standardized sampling across wind facilities in New York and the northeast would facilitate comparative results and control for more variation in the model. Recommendation: Do not provide a statewide spatial model of abundance of bat carcasses at wind facilities. Although there were some interesting variables, such as topography and land cover, at the site of a turbine that were predictors of increased fatalities, the variation among wind farms was high and site was the top variable in almost every model. The dataset we examined (turbines within 9 wind farms) is likely not representative of fatalities that could occur across environmental conditions statewide and therefore is inadequate to predict fatalities at a statewide level. Removing site from the model demonstrates that other variables in the model still account for variation and are likely biologically meaningful. We propose to provide NYSERDA with the results of these analyses and tables documenting variable importance. Literature Cited Arnett, E.B., Brown, W.K., Erickson, W.P., Fiedler, J.K., Hamilton, B.L., Henry, T.H., Jain, A., Johnson, G.D., Kerns, J., Koford, R.R., Nicholson, C.P., O Connell, T.J., Piorkowski, M.D., Tankersley, R.D., Patterns of Bat Fatalities at Wind Energy Facilities in North America. Journal of Wildlife Management 72, Breiman, L., Random forests. Machine Learning 45, Calle, M.L., Urrea, V., Boulesteix, A.L., Malats, N., AUC-RF: A new strategy for Genomic profiling with random forest. Human Heredity 72,

6 Cryan, P.M., Barclay, R.M.R., Causes of Bat Fatalities at Wind Turbines: Hypotheses and Predictions. Journal of Mammalogy 90, Kunz, T.H., Arnett, E.B., Erickson, W.P., Hoar, A.R., Johnson, G.D., Larkin, R.P., Strickland, M.D., Thresher, R.W., Tuttle, M.D., Ecological Impacts of Wind Energy Development on Bats: Questions, Research Needs, and Hypotheses. Frontiers in Ecology and the Environment 5, Liaw, A., Wiener, M., n.d. Classification and Regression by randomforest. R News 2(3), Urrea, V., Calle, M.L., AUCRF: Variable Selection with Random Forest and the Area Under the Curve.

7 Table 1. Environmental variables used in random forest analysis. Description (units) Data Source awc_mm Available water holding capacity, area weighted median (mm) USDA NRCS caco31t Calcium carbonate in surface layer, area weighted median (% * 10) USDA NRCS canopy01 NLCD neighborhood analysis, percent canopy cover in a 1-cell (30 m) radius NOAA canopy10 NLCD neighborhood analysis, percent canopy cover in a 10-cell (300 m) radius NOAA canopy33 NLCD neighborhood analysis, percent canopy cover in a 33-cell (300m) radius (Homer et al. NOAA ccap_nhp land use, land cover from NOAA coastal change analysis program CCAP (Dobson et al. NOAA 1995), 22 types ccap_nhp6 Grouping of CCAP data into 6 groups: forest, wetland, water, open, developed, NOAA ccapfor10 CCAP neighborhood analysis, percent forest cover within 10-cell (300 m) radius NOAA ccapfor33 CCAP neighborhood analysis, percent forest cover within 33-cell (990 m) radius NOAA ccapopn10 CCAP neighborhood analysis, percent open cover within 10-cell (300 m) radius NOAA ccapopn33 CCAP neighborhood analysis, percent open cover within 33-cell (990 m) radius NOAA ccapscs10 CCAP neighborhood analysis, percent shrub cover within 10-cell (300 m) radius NOAA ccapscs33 CCAP neighborhood analysis, percent shrub cover within 33-cell (990 m) radius NOAA ccapwet10 CCAP neighborhood analysis, percent wetland within 10-cell (300 m) radius NOAA ccapwet33 CCAP neighborhood analysis, percent wetland within 33-cell (990 m) radius NOAA ccapwtr10 CCAP neighborhood analysis, percent water within 10-cell (300 m) radius NOAA ccapwtr33 CCAP neighborhood analysis, percent water within 33-cell (990 m) radius NOAA cec1t CEC in surface layer, area weighted median (value * 10) USDA NRCS clay1t % clay in surface layer, area weighted (% * 10) USDA NRCS Distcalc Distance to nearest soil polygon containing calcium carbonate (m) Derived from maxcaco31 Disttowet Calculated distance in meters from turbine to nearest wetland Derived from CCAP Dvrclss5 Count of the number of cells classified as developed within 5km of turbine Derived from CCAP freefm Mean number of frost free days per year USDA PRISM data Frstrclss Count of the number of cells classified as forest within 5km of the turbine. Derived from CCAP Geoclass Bedrock Geology Class, as category NYS Geological Survey max_phh Absolute maximum of regional percent ph ranges * 10 USDA NRCS Maxc31 Calcium carbonate in surface layer, maximum (% * 10) USDA NRCS Min_phl Absolute minimum of regional ph ranges * 10 USDA NRCS minav05 Average minimum temperature ( C/10) in May USDA PRISM data minav06 Average minimum temperature ( C/10) in June USDA PRISM data minav07 Average minimum temperature ( C/10) in July USDA PRISM data minav13 Average annual minimum temperature ( C/10) USDA PRISM data

8 minex13 Annual record minimum temperature ( C/10) USDA PRISM data Nyaspect Aspect (eight categories: N, NE, E, SE, S, SW, W, NW) Derived from elevation Nyelev30 Elevation (m) USGS Digital elevation nygdd13 Total annual growing degree days USDA PRISM data Nynlcd83 National Landcover Data (NLCD) class USGS Nyslope Slope (degrees) Derived from elevation Om1t % organic matter in surface layer, area weighted (% * 10) USDA NRCS Opnag5k Count of the number of cells classified as open agricultural lands within 5km of turbine Derived from CCAP Perm1t permeability of top layer (inches of water per hour * 10) USDA NRCS Pet Potential evapotranspiration independent of vegetation, AET + D, (mm) Derived from solar ph1t ph of top layer (ph * 10), area weighted average of median values USDA NRCS prec05 Precipitation (mm) in May USDA PRISM data prec06 Precipitation (mm) in June USDA PRISM data prec07 Precipitation (mm) in July USDA PRISM data prec13 Total annual precipitation (mm) USDA PRISM data Solrad Cumulative annual solar radiation (kj/m 2 ) Derived from monthly surfg22 Surficial geology material (depositional method), as category NYS Geological Survey surfg35 Surficial geology material (depositional method), as category NYS Geological Survey Swb1i Site water balance (swb) - cumulative annual water surplus or deficit (mm) Derived from solar Topo18 Topographic index in a 540 m radius (index) Derived from elevation Topo3 Topographic index in a 90 m radius (index) Derived from elevation Topo33 Topographic index in a 990 m radius (index) Derived from elevation Topoall Topographic index at radii of 90 m, 540 m and 990 m (index) Derived from elevation Twi_t Terrain wetness indicator (TWI) based on modeled flow accumulation (index) Derived from slope Wtlnd5k Count of the number of cells classified as wetlands or open water within 5km of turbine Derived from CCAP

9 Table 2. Outcomes of the randomforest and AUC-RF analyses. Model 1 OOB error rate (%) 1 presence class error # presence classified incorrectly # presence classified correctly RF AUCRF absence class error # absence classified correctly # absence classified incorrectly n OOBpresence Kopt 3 AUCopt 4 Big brown bat Big brown bat 1* Big brown bat Little brown bat 2.5* Little brown bat Little brown bat Eastern red bat 2.5* Eastern red bat Eastern red bat Hoary bat 2.5* < Hoary bat < Hoary bat Tri-colored bat < Tri-colored bat < Tri-colored bat < Northern bat Northern bat Northern bat Indiana bat Small-footed bat * This is the model for each species for which we discuss variable importance in the text. 1. The number after each species modeled refers to the minimum separation distance used between presences and non-detections either 100m, 1 km, or 2.5 km. 2. Out-of-bag model error rate. 3. Optimal number of variables entering the model. 4. AUC estimate for the model. 5. Corrected AUC estimate after cross-validation. AUC from CV 5

10 Table 3. Results of random forest analysis. Model 1,2 PseudoR 2 n Eastern red bat Eastern red bat Eastern red bat Hoary bat Hoary bat Hoary bat Silver-haired bat Silver-haired bat Silver-haired bat Little brown bat Little brown bat Little brown bat Migratory tree bats Migratory tree bats All bats All bats ntree=1000 and mtry=19 for all models. 2 The number after each species modeled refers to the minimum separation distance used between presences and non-detections either none, 1 km, or 2 km.

11 Figure 1. Locations of acoustic survey routes across New York State.

12 Figure 2. Locations of hibernacula and roost sites for Indiana bats with a 2.5 mile buffer demonstrating potential travel distances from roosts during summer movement. The lines demonstrate which bats came from which caves but travel routes are unknown. Figure 3. Locations of the nine wind farms used in analyses.