Wind Energy Bird and Bat Monitoring Database Summary of the Findings from Post-Construction Monitoring Reports

Wind Energy Bird and Bat Monitoring Database Summary of the Findings from Post-construction Monitoring Reports

Bird Studies Canada, Canadian Wind Energy Association, Environment Canada and Ontario Ministry of Natural Resources

July 2016

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

SUMMARY

The Wind Energy Bird and Bat Monitoring Database is a joint initiative established to enable the collection and analysis of bird and bat monitoring information from Canadian wind power projects.

This report presents summary results from Canadian post-construction bird and bat mortality monitoring data contained within the Wind Energy Bird and Bat Monitoring Database. This report describes:  the relative number of uncorrected individual fatalities recorded for each species compared using fractional ranking, and  corrected mortality estimates for bats, raptors and non-raptor bird species based on a subset of mortality monitoring studies for which correction factor data were available.

SECTION 1: Uncorrected Analysis

Fatality patterns were based on data collected from 65 wind power projects between 2006 and 2014. The numbers of observed bat fatalities were higher than bird fatalities, nationally and within Alberta and Ontario. In Atlantic Canada bird fatalities were higher than bat fatalities. Bird fatalities were dominated by passerines with relatively low numbers of raptor and waterbird fatalities. The most prevalent bird species found across Canada during mortality monitoring surveys were Horned Lark, Golden-crowned Kinglet and Red-eyed Vireo. Migratory bat fatalities were higher than resident bat fatalities and the most prevalent bat species found were hoary bat, silver-haired bat and eastern red bat.

SECTION 2: Corrected Mortality Estimates

Corrected mortality estimates for birds and bats were based on a subset of data collected from 46 wind power projects between 2007 and 2014 and were calculated based on correction methods prescribed by the Ontario Ministry of Natural Resources and Forestry (OMNRF 2011a, OMNRF 2011b). Estimates were not completed for the additional regions and provinces within Canada due to a lack of available data. It is hoped that increased submission to the Database will allow for analysis of these regions in future reports.

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

Corrected bird fatalities showed no prominent seasonal fatality patterns between May and October although mortality was generally higher during the fall migratory period. The highest numbers of corrected bat fatalities occurred between July and September (peaking between mid-August and early September), which corresponds with typical swarming and migratory period for bats (Davis and Hitchcock 1965, Thomas et al 1979, Schowalter 1980, Parsons et al 2003).

Non-Raptor Birds The average annual non-raptor bird mortality estimates (within 50 meters (m) of turbine bases from May 1st to October 31st) were:

 2.65 ± 0.75 birds/turbine in Alberta,  1.17 ± 1.01 birds/turbine in Atlantic Canada, and  6.14 ± 0.31 birds/turbine in Ontario.

Raptors The average annual raptor mortality estimates (within 50 meters (m) of turbine bases from May 1st to October 31st) were:

 0.06 ± 0.06 birds/turbine in Alberta,  0 ± 0 birds/turbine in Atlantic Canada, and  0.20 ± 0.01 birds/turbine in Ontario.

Bats The average annual bat mortality estimates (within 50 meters (m) of the turbine base from May 1st to October 31st) were:

 8.34 ± 2.46 bats/turbine in Alberta,  0.26 ± 0.11 bats/turbine in Atlantic Canada, and  18.52 ± 0.79 bats/ turbine in Ontario.

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

Table of Contents

SUMMARY ...... 2 SECTION 1: Uncorrected Analysis ...... 2 SECTION 2: Corrected Mortality Estimates ...... 2 Non-Raptor Birds ...... 3 Raptors ...... 3 Bats...... 3 INTRODUCTION ...... 6 SECTION 1 ...... 8 METHODS ...... 11 RESULTS ...... 12 Bird and bat fatalities ...... 12 Species Composition: Birds ...... 13 Canada ...... 13 Alberta ...... 14 Ontario ...... 14 Atlantic ...... 15 Species Composition: Bats ...... 16 Canada ...... 16 Alberta ...... 16 Ontario ...... 17 Atlantic Canada ...... 17 SECTION 2 ...... 18 METHODS ...... 21 Correction Factors ...... 21 Search Radius ...... 21 Percent Area Surveyed ...... 23 Searcher Efficiency ...... 23 Scavenger (Carcass) Removal...... 24 Correction Factor Resolution ...... 25

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

Length of Monitoring Period ...... 26 Corrected Mortality Estimates ...... 28 RESULTS ...... 29 Correction Factors ...... 29 Estimated Mortality ...... 32 Birds: Alberta ...... 32 Birds: Ontario ...... 32 Birds: Atlantic Canada ...... 33 Bats: Alberta ...... 33 Bats: Ontario ...... 33 Bats: Atlantic Canada ...... 34 DISCUSSION ...... 35 REFERENCES ...... 38 APPENDIX 1 ...... 40 APPENDIX 2 ...... 46

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

INTRODUCTION

Recent increased growth in the wind energy sector has, in part, been related to growing concerns surrounding climate change and the use of fossil fuels. The installed capacity of wind energy in Canada as of December 2015 was approximately 11,200 megawatts (MW), marking a five year average annual growth rate of 23 per cent per year. National installed wind capacity is expected to continue to grow. In 2006 the Wind Energy Bird and Bat Monitoring Database (“the Database”) was established to help improve understanding of the direct effects of wind turbines on birds and bats in Canada.

The Database is a joint initiative established by Environment and Climate Change Canada (ECCC), the Canadian Wind Energy Association (CanWEA), Bird Studies Canada (BSC) and the Ontario Ministry of Natural Resources and Forestry (OMNRF), to enable the collection and analysis of bird and bat monitoring information from Canadian wind power projects. The Database aims to facilitate an improved understanding of the impacts of wind turbines on birds and bats, improve consistency in the assessment of wind turbine impacts across the country, and inform guidelines and approvals processes.

The Database is intended to be a dynamic tool that summarizes Canadian wind power project bird and bat monitoring data. The data summaries presented here represent our current state of knowledge based on data submitted to the Database. This report is an updated version of the previous Database summaries posted in November 2011, August 2012, December 2013, and July 2014.

The number of carcasses recovered during post-construction mortality monitoring is normally a subset of the actual mortalities that occur at a particular site, due to several factors (Loss et al. 2013, Smallwood 2013, Zimmerling et al. 2013) including:  animals that fall outside of the area searched under each turbine (percent area searched);  carcasses that are removed from the search area by scavengers between site visits before they are detected (scavenger (carcass) removal);  the efficiency of searchers at detecting carcasses (searcher efficiency); and  the proportion of the year over which monitoring is completed.

These factors contribute to inaccuracies in determining the estimated number of bird and bat mortalities based on recovered carcasses alone. Therefore, correction factors have been developed to generate more accurate estimates of mortality.

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

Due to a lack of available and standardized data, the first three Database summary reports (November 2011, August 2012, December 2013) presented analyses of found or “uncorrected” mortalities which did not correct for these factors. To help standardize data collection methods among sites, templates for data collection and reporting were developed and made available by the Database project in 2011. With the submission of data collected using complementary data collection methods and submitted through these Database templates, the first corrected mortality analyses of the Database were published in the July 2014 summary report. This report expands on those analyses as the amount of standardized data available has grown by 85% (54 project-years to 100 project- years) since the previous report.

This report is divided into two sections.  Section 1 presents the relative proportion of recovered carcasses each species represents. The results presented in Section 1 are generated using data from all 65 available wind power projects.  Section 2 provides estimates of mortality generated from a subset of 46 wind power projects based on the application of correction factors.

The uncorrected analyses presented in Section 1 are included for comparison to previous reports and as a means to present data from all submitted studies, regardless of the availability of correction factors.

The previous database report (July 2014) included uncorrected analysis of monthly patterns in fatality numbers. In this report the uncorrected analyses have been replaced with weekly corrected mortality estimates of temporal fatality patterns. In order to generate these corrected distributions, datasets which monitor for the entire time period are required, therefore the monitoring season here has been defined as the months of May through October in order to allow for this analysis. For information on seasonal fatality distributions outside of this period please consult the July 2014 Database Summary Report (Bird Studies Canada et al. 2014).

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

SECTION 1

This section presents the uncorrected findings of post-construction monitoring activity at 1,889 turbines from 65 different sites in eight provinces (Table 1), collected between 2006 and 2014. Currently, the Database contains insufficient information to allow comparisons of species composition and seasonal fatality patterns between all provinces. A significant amount of monitoring effort has been focused in Ontario, where 104 of the 139 (75%) project-years (the sum of the number of years monitored by each project) of monitoring available within the Database have occurred. Sufficient information to allow for species composition analysis has also been submitted for Alberta (15 project-years) and Atlantic Canada (12 project-years). Therefore, this section presents data analysis on two spatial scales: the first includes information collected from all sites in Canada, while the second discusses data from three regions: Alberta, Atlantic Canada and Ontario. Table 2 reflects details on information that has been submitted to the Database. This report does not represent all wind power projects carrying out post-construction bird and bat mortality monitoring in Canada as not all projects submit data to the Database.

Table 1: The number of post-construction wind power projects and the number of years of monitoring data in the Wind Energy Bird and Bat Mortality Monitoring Database by Province/Territory Province Number of Projects Years of Monitoring Data in the Database submitted to the Database Alberta 7 15 British Columbia 3 6 Manitoba 0 0 New Brunswick 2 3 Newfoundland and Labrador 2 2 Nova Scotia 1 2 Northwest Territories 0 0 Nunavut 0 0 Ontario 46 104 Prince Edward Island 3 6 Québec 0 0 Saskatchewan 1 1 Yukon 0 0 Total 65 139

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

Table 2: Wind power projects with data submitted to the Wind Energy Bird and Bat Mortality Monitoring Database as of February 2016 Name of Project Province Number First Year Last Year of of Data of Data Turbines Submitted Submitted Taber AB 37 2007 2009 Soderglen EcoPower Centre AB 47 2007 2009 Magrath AB 20 2006 2006 Halkirk II Wind Project AB 83 2013 2014 Ghost Pine Wind Farm AB 51 2011 2011 Chin Chute Wind Power Project AB 20 2007 2009 Blue Trail Wind Farm AB 22 2010 2011 Quality Wind Project BC - 2013 2014 Dokie Wind Project 1 BC 48 2011 2011 Bear Mountain Wind Park BC 34 2009 2011 Kent Hills Wind Farm Expansion NB 32 2012 2012 Caribou Wind Park NB 33 2010 2011 St. Lawrence Wind Energy Project NL 9 2009 2009 Fermeuse Wind Energy Project NL 9 2011 2011 Lingan III NS 7 2008 2009 Wolfe Island Wind Farm ON 86 2009 2011 Talbot Wind Farm ON 43 2011 2013 Swanton Line Wind Farm ON 5 2010 2013 Summerhaven Wind ON 56 2014 2014 South Side Wind Farm Project ON 5 2011 2013 South Kent Wind Project ON 124 2014 2014 South Branch Windfarm ON 5 2014 2014 Ripley Wind Power Project ON 38 2008 2008 Richardson Wind Farm Project ON 5 2011 2012 Ravenswood Wind Power Project ON 4 2008 2008 Raleigh Wind Farm ON 52 2012 2013 Providence Bay / Spring Bay Wind Farm ON 2 2013 2013 Proof Line Wind Power Project ON 4 2010 2011 Prince Wind Power Project ON 126 2006 2009 Port Dover and Nanticoke Wind Project (PDNWP) ON 58 2014 2014 Port Alma Wind Power Project ON 44 2009 2010 Port Alma and Chatham Projects Combined KEPA/KEC ON 88 2011 2012 9

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

Pointes Aux Roches ON 27 2012 2014 Plateau. All 18 turbines from I, II & III ON 18 2012 2014 Pickering Turbine ON 1 2002 2002 Oxley Wind Farm ON 3 2014 2014 North Malden Wind Farm Project ON 5 2011 2013 Naylor Wind Farm ON 5 2012 2013 Mohawk Point Wind Farm ON 6 2009 2013 Melancthon II Wind Farm ON 88 2009 2010 Melancthon I Wind Farm ON 45 2006 2007 Marsh Line Wind Farm ON 5 2010 2010 Kingsbridge I Wind Project ON 22 2006 2007 Kent Breeze Wind Farms ON 8 2011 2014 Harrow I Wind Farm Project ON 24 2010 2013 Greenwich Wind Farm ON 50 2013 2013 Grand Valley Wind Farm I and II ON 9 2012 2014 Gracey Wind Farm ON 5 2011 2013 Gosfield Wind Project ON 22 2011 2013 Gesner Wind Farm ON 5 2013 2014 Front Line Wind Farm ON 5 2010 2013 Ferndale Wind Farm ON 3 2007 2007 Erieau Wind Farm ON 55 2014 2014 Erie Shores Wind Farm ON 66 2006 2007 Enbridge Ontario Wind Power Project ON 110 2009 2013 East Lake St. Clair ON 55 2014 2014 Cruickshank Wind Farm Ltd ON 5 2009 2013 Conestogo Highlands Wind Farm Project 2 ON 10 2013 2014 Comber Wind Farm ON 72 2013 2013 Bisnett Wind Farm ON 5 2010 2012 Arthur Wind Farm Project ON 5 2011 2013 West Cape Wind Park Project (Phase II) PE 11 2010 2011 Summerside Wind Farm PE 4 2010 2011 Norway Wind Park Project PE 3 2007 2008 Cypress Wind Power Facility I SK 9 2003 2003 Total 1963

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

METHODS

Species composition is represented as the proportion a species contributes to the total number of identified carcasses found at Canadian wind power projects. Because of variation in the location, size and data collection methods of wind power projects contained in the Database, direct comparisons of species composition using the proportion of carcasses of each species found can be biased. Therefore, in addition to calculating proportions, we also compared species composition using fractional ranking. Within wind power project sites, species ranks are largely unaffected by the amount of effort devoted to monitoring turbines for mortality. As such, we ranked species in order from most to least common within each wind power project site. This method allows a comparison between sites with varying levels of monitoring effort, as each site is given an equal weight when calculating average ranks for each species.

Species ranks within each site have the potential to be affected by other factors, such as the region where the project is located, the time of year when the monitoring occurred, and differences between carcass detectability (e.g., varying visibility as result of vegetation overgrowth or other visibility-obstructing factors within a carcass search area). It is hoped that the Database will provide the means to identify and understand patterns of mortality linked to some of these variables.

Ranks for birds and bats within each site were determined based on the total number of carcasses found for each species. With the fractional ranking method, the species with the highest number of mortalities is given a rank of 1, the species with the second highest number of mortalities is assigned a rank of 2, etc. Species with equal numbers of carcasses receive the same ranking number, which corresponds to the minimum value of what they would have received under ordinal rankings. In cases where the number of carcasses is zero for a given species within a project, we assigned the maximum rank possible across all sites. The total number of identified species reported across all sites was 9 for bats and 173 for birds. Therefore, all species that were not found at a given site were either given a fractional rank of 9 (for bats) or 173 (for birds). Without this correction, the rank assigned to species that had not been found at a specific site would be biased by the number of species found at that site.

Results are expressed both as ranks and as percent composition of all identified mortalities across all sites. Using fractional ranking, each site is given an equal weight when calculating average ranks for each species; therefore, it is possible that a species with a higher rank may have a higher percent composition then a species with a lower rank. 11

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

RESULTS

Bird and bat fatalities

Across Canada, bat fatalities were reported more often than birds, accounting for 75% of all carcasses found. This pattern was largely driven by data from Ontario, where 77% of the casualties found were bats. Alberta also reported bat fatalities more often, with bats making up 55% of all fatalities. In contrast, bird fatalities were reported more often than bats in Atlantic Canada, with birds accounting for 63% of all carcasses found (Table 3).

Table 3: Cumulative number of bird and bat fatalities reported across Canada and within each region for which sufficient data is contained in the database from 2007-2014. Region Birds Bats Canada 2,585 6,924 Alberta 355 432 Ontario 2,111 6,316 Atlantic Canada 26 15

Within bird guilds, passerines were most commonly found in all areas, representing approximately 69% of all bird fatalities nationally (Table 4). Migratory bats (eastern red bat, hoary bat and silver-haired bat) were reported more often the resident bats (i.e., little brown myotis, big brown bat, tri-coloured bat, northern long-eared myotis and eastern small- footed bat) representing approximately 69% of all bats found (Table 4).

Table 4: Proportion of fatalities reported by bird and bat guild across Canada and each region for which sufficient data is contained in the database from 2007-2014. Bird Guilds Bat Guilds Water- Water- Other/ Migratory Resident Region Passerine Raptor bird fowl Gull Unknown Bat Bat 1792 200 96 85 44 368 4743 2181 Canada (69.4%) (7.7%) (3.7%) (3.3%) (1.7%) (14.2%) (68.5%) (31.5%) 224 18 27 48 2 36 408 24 Alberta (63.1%) (5.1%) (7.6%) (13.5%) (0.6%) (10.1%) (94.4%) (5.6%) 1465 180 68 36 39 323 4244 2072 Ontario (69.4%) (8.5%) (3.2%) (1.7%) (1.9%) (15.3%) (67.2%) (32.8%) Atlantic 20 1 1 3 5 10 Canada (76.9%) (3.9%) 0 (3.9%) (11.5%) 1 (3.9%) (33.3%) (66.7%)

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

Species Composition: Birds

Across Canada, for all projects and years monitored, a total of 2,182 bird carcasses were found comprised of 173 identifiable species. The below tables provide the top 20 bird species found during post-construction mortality monitoring at wind power projects and the proportion of carcasses found of each species across Canada and within each of the three regions for which the database contains sufficient information, listed from lowest rank (most prevalent across sites) to highest rank (least commonly found). A full list of fractional rankings by species is available in Appendix 1.

Canada Table 5: The top 20 bird species found at wind power projects in Canada based on fractional ranking and percent species composition. A full list of fractional rankings by species is available in Appendix 1. Rank Species Percent Composition 1 Horned Lark 8.61% 2 Golden-crowned Kinglet 9.99% 3 Red-eyed Vireo 5.31% 4 Purple Martin 4.85% 5 Tree Swallow 6.64% 6 Red-tailed Hawk 3.80% 7 Ruby-crowned Kinglet 2.61% 8 Cliff Swallow 2.10% 9 Turkey Vulture 2.65% 10 Mourning Dove 2.24% 11 Magnolia Warbler 1.32% 12 European Starling 1.97% 13 Cedar Waxwing 1.60% 14 Killdeer 1.69% 15 American Robin 1.09% 16 Red-breasted Nuthatch 1.55% 17 Barn Swallow 0.96% 18 Brown-headed Cowbird 1.14% 19 Yellow-bellied Sapsucker 0.73% 20 Ring-billed Gull 1.09%

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

Alberta Table 6: The top 15 bird species found at wind power projects in Alberta based on fractional ranking and percent species composition. A full list of fractional rankings by species is available in Appendix 1. Rank Species Percent Composition 1 Horned Lark 28.2% 2 Vesper Sparrow 4.82% 3 Eared Grebe 4.13% 4 Gray Partridge 4.48% 5 Swainson's Hawk 2.75% 6 Dark-eyed Junco 1.72% 7 Mallard 11.7% 8 European Starling 3.44% 9 Savannah Sparrow 3.79% 10 Red-tailed Hawk 1.37% 11 Red-breasted Nuthatch 3.44% 12 Western Grebe 1.37% 13 Gray Catbird 1.03% 14 Warbling Vireo 0.68% 15 Wilson's Warbler 0.68%

Ontario Table 7: The top 15 bird species found at wind power projects in Ontario based on fractional ranking and percent species composition. A full list of fractional rankings by species is available in Appendix 1. Rank Species Percent Composition 1 Golden-crowned Kinglet 11.3% 2 Red-eyed Vireo 6.20% 3 Horned Lark 5.81% 4 Purple Martin 5.92% 5 Tree Swallow 8.04% 6 Red-tailed Hawk 4.41% 7 Turkey Vulture 3.24% 8 Cliff Swallow 2.51% 9 Magnolia Warbler 1.62% 10 Mourning Dove 2.57% 11 Ruby-crowned Kinglet 2.68% 12 Killdeer 2.01% 13 Barn Swallow 1.17% 14 Cedar Waxwing 1.73% 15 European Starling 1.78%

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

Atlantic Table 8: The top 15 bird species found at wind power projects in Atlantic Canada based on fractional ranking and percent species composition. Rank Species Percent Composition 1 Red-eyed Vireo 22.7% 2 Ruby-crowned Kinglet 9.09% 3 Cedar Waxwing 4.54% 4 Northern Parula 4.54% 5 American Black Duck 4.54% 6 American Robin 9.09% 7 Golden-crowned Kinglet 9.09% 8 Great Blue Heron 4.54% 9 Great Crested Flycatcher 4.54% 10 Herring Gull 4.54% 11 Leach's Storm-Petrel 4.54% 12 Black Guillemot 4.54% 13 American Crow 4.54% 14 European Starling 4.54% 15 Yellow-bellied Flycatcher 4.54%

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

Species Composition: Bats Across Canada, 6,643 bat carcasses were found, representing nine different species. Hoary bat was the species most often found (29.2% of all bat fatalities) followed by silver- haired bat (21.2% of all bat fatalities) and eastern red bat (20.9% of all bat fatalities). The below tables provide the top bat species found during post-construction mortality monitoring at wind power projects and the proportion of carcasses found of each species across Canada and within each of the three regions for which the database contains sufficient information, listed from lowest rank (most prevalent across sites) to highest rank (least commonly found).

Canada Table 9: Bat species found at wind power projects in Canada based on fractional ranking and percent species composition. Rank Species Percent Composition 1 hoary bat 29.2% 2 silver-haired bat 21.2% 3 eastern red bat 20.9% 4 big brown bat 16.3% 5 little brown myotis 11.6% 6 northern long-eared myotis 0.31% 7 tri-coloured bat 0.18% 8 long-legged bat 0.03% 9 eastern small-footed bat 0.04%

Alberta Table 10: Bat species found at wind power projects in Alberta based on fractional ranking and percent species composition. Rank Species Percent Composition 1 hoary bat 43.5% 2 silver-haired bat 50.5% 3 big brown bat 4.42% 4 eastern red bat 0.93% 5 little brown myotis 0.46%

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

Ontario Table 11: Bat species found at wind power projects in Ontario based on fractional ranking and percent species composition. Rank Species Percent Composition 1 hoary bat 28.8% 2 eastern red bat 22.8% 3 big brown bat 17.5% 4 silver-haired bat 18.5% 5 little brown myotis 11.7% 6 northern long-eared myotis 0.24% 7 tri-coloured bat 0.19% 8 eastern small-footed bat 0.04%

Atlantic Canada Table 12: Bat species found at wind power projects in Atlantic Canada based on fractional ranking and percent species composition. Rank Species Percent Composition 1 hoary bat 20% 2 little brown myotis 66.6% 3 eastern red bat 13.3%

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

SECTION 2

The number of carcasses found during post-construction mortality monitoring is considered a subset of the actual mortalities that occur at a particular site, due to several factors (Loss et al. 2013, Smallwood 2013, Zimmerling et al. 2013) including:  animals that fall outside of the area searched under each turbine (percent area searched);  carcasses that are removed from the search area by scavengers between site visits before they are detected (scavenger (carcass) removal);  the efficiency of searchers at detecting carcasses (searcher efficiency); and  the proportion of the year over which monitoring is completed.

These factors contribute to inaccuracies in determining the actual impact of wind turbines on bird and bat mortality based on recovered carcasses alone. Therefore correction factors have been developed to generate estimates of the true mortality levels. This section presents estimates of the mortality of birds and bats using data from wind power projects which conformed to the following standards:

 correction factors were calculated, compatible and available,  the study included a minimum of 4 weeks of monitoring conducted between May 1 and October 31, and  a search radius of at least 50 m from the turbine base was used

A total of 46 wind power projects completed at least one season of monitoring that met the criteria for inclusion in the corrected mortality analysis and were used to produce the corrected mortality estimates. In total, these 46 projects collected 100 project-years (sum of all the number of years of monitoring completed at each project) of mortality monitoring data (Table 13).

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

Table 13: Wind power projects for which data was submitted to the Wind Energy Bird and Bat Mortality Monitoring Database and for which correction factor information was available.

Number Number of First Year Most Recent Name of Project Province of Years of of Data Year of Data Turbines Monitoring

West 168 2007 2011 7 Soderglen EcoPower Centre AB 47 2007 2009 3 Blue Trail Wind Farm AB 22 2010 2011 2 Ghost Pine Wind Farm AB 51 2011 2011 1 Dokie Wind Project 1 BC 48 2011 2011 1 Atlantic 92 2008 2012 7 Caribou Wind Park NB 33 2010 2011 2 Fermeuse Wind Energy Project NL 9 2011 2011 1 Kent Hills Wind Farm Expansion NB 32 2012 2012 1 Lingan III NS 7 2008 2009 2 West Cape Wind Park Project II PE 11 2011 2011 1 Ontario 1202 2007 2014 86 Cruickshank Wind Farm Ltd ON 5 2009 2013 4 Enbridge Ontario Wind Power Project ON 110 2009 2013 4 Mohawk Point Wind Farm ON 6 2010 2013 4 Wolfe Island Wind Farm ON 86 2009 2011 3 Bisnett Wind Farm ON 5 2010 2012 3 Front Line Wind Farm ON 5 2010 2013 4 Harrow I Wind Farm Project ON 24 2010 2013 4 Marsh Line Wind Farm ON 5 2010 2010 1 Melancthon II Wind Farm ON 88 2010 2010 1 Proof Line Wind Power Project ON 4 2010 2011 2 Swanton Line Wind Farm ON 5 2010 2013 3 Arthur Wind Farm Project ON 5 2011 2013 3 Gosfield Wind Project ON 22 2011 2013 3 Gracey Wind Farm ON 5 2011 2013 3 Kent Breeze Wind Farms ON 8 2011 2014 4 Port Alma and Chatham Projects KEPA ON 88 2011 2012 2

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

North Malden Wind Farm Project ON 5 2011 2013 3 Richardson Wind Farm Project ON 5 2011 2012 2 South Side Wind Farm Project ON 5 2011 2013 3 Talbot Wind Farm ON 43 2011 2013 3 Grand Valley Wind Farm I and II ON 9 2012 2014 3 Naylor Wind Farm ON 5 2012 2013 2 Plateau I, II & III ON 18 2012 2014 3 Pointes Aux Roches ON 27 2012 2014 3 Raleigh Wind Farm ON 52 2012 2013 2 Comber Wind Farm ON 72 2013 2013 1 Conestogo Highlands Wind Farm Project 2 ON 10 2013 2014 2 Gesner Wind Farm ON 5 2013 2014 2 Greenwich Wind Farm ON 50 2013 2013 1 Providence Bay ON 2 2013 2013 1 East Lake St. Clair ON 55 2014 2014 1 Erieau Wind Farm ON 55 2014 2014 1 Oxley Wind Farm ON 3 2014 2014 1 Port Dover and Nanticoke Wind Project ON 58 2014 2014 1 South Branch Windfarm ON 5 2014 2014 1 South Kent Wind Project ON 124 2014 2014 1 Summerhaven Wind ON 56 2014 2014 1 Total 1462 2007 2014 100 * Note that year range is not inclusive; some projects did not have adequate data available for all years

During 18 project-years, operational mitigation for bat mortalities was implemented at a subset of turbines in Ontario. A total of 216 turbines (421 turbine-years) implemented operational mitigation, the majority of which increased cut in speeds to 5.5 m/s during the period from dusk to dawn in the late summer/early fall. Including estimates from periods when operational mitigation is in place may lead to negative bias in mortality estimates for non-mitigated turbines.

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

METHODS

In order to produce the corrected mortality rates presented in this report: 1. correction factors for proportion of area searched, searcher efficiency, and scavenger removal for each project were either extracted or calculated from site- specific data; 2. A project specific correction factor was developed to account for differences in the length of monitoring period. This correction factor was calculated using a subset of projects for which monitoring was conducted for the entire study duration; and 3. Total mortality per monitoring period was calculated using project specific correction factors.

Detailed methods and an identification of the limitations and assumptions for each of the correction factors are provided below.

Correction Factors Uncorrected carcass recovery data and calculated correction factors from monitoring conducted prior to the availability of the Database templates were extracted from post- construction mortality monitoring reports for this analysis. All reported mortalities were imported into the Database for all projects. The correction factor data used in this analysis were obtained for each of the 100 project-years from site specific data.

Search Radius In order to compare between projects and regions 50 m was selected as a standard search radius and only carcasses that fell within 50 m of the base of a turbine were included within this analysis. This was based on the availability of data in the database as the majority of project-years included in this analysis used a 50 m search radius (86%) surrounding the base of each turbine. Other search area dimensions included a 120 m by 120 m square (4%), 100 m by 100 m square (7%), 60 m radius (1%), and a 57 m radius (2%). The majority of carcass records included an estimated distance from the turbine base (89%) and only those that fell within 50 m of the turbine base we included. Of the 766 (10%) carcasses where distances from turbine base were not reported, 91% were from projects with a search radius of 50 m and were therefore assumed to fall within the 50 m. The remaining 2 carcasses (representing 0.03% of the total carcasses found and reported to the Database), for which distance was not reported and the search radius was greater than 50 m, were included in the corrected mortality calculations to ensure that no carcasses that may have been recovered within 50 m of the turbine were excluded from the analysis.

The number of birds found with increasing distance from the base of the turbine remained fairly consistent, although numbers began to decline between 40 and 50 m (Figure 1). 21

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

However, the number of bats found declined steadily with increasing distance from the turbine base after 20 m (Figure 2). Given the fairly even distribution of bird carcasses and that at wind power projects with search radii greater than 50 m, carcasses were recovered beyond 50 m from the turbine base, calculating mortality estimates based on a 50 m radius search area has the potential to underestimate true mortality; particularly for bird species. Sufficient data for a robust analysis of the proportion of mortalities recovered outside of 50 m is not currently available from the database. Approaches to quantifying this effect and its potential impact are outlined in the discussion.

250

200 10.1% 10.0% 10.1% 10.2% 8.8% 8.4% 7.7% 150 7.6% 7.3% 5.8% 100

50 Number of Bird of NumberCarcasses

0 0-5 5-10 10-15 15-20 20-25 25-30 30-35 35-40 40-45 45-50 Distance from the Turbine (m)

Figure 1: Uncorrected number of bird carcasses found at 46 wind power projects over 100 project-years of monitoring within each 5 m distance band from the base of the turbine.

800 13.0% 700 10.9% 600 10.0% 10.0% 9.5% 9.7% 9.3% 500 400 6.7% 5.4% 300 3.9% 200

NumberofBat Carcasses 100 0 0-5 5-10 10-15 15-20 20-25 25-30 30-35 35-40 40-45 45-50 Distance from the Turbine (m)

Figure 2: Uncorrected number of bat carcasses found at 46 wind power projects over 100 project- years of monitoring within each 5 m distance band from the base of the turbine. 22

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

Percent Area Surveyed The percent area searched (Ps) was calculated as the proportion of the area searched within a 50 m radius of the turbine base and was extracted from post-construction mortality monitoring reports for each project, often in the form of averages applied across seasons or monitoring periods. Two projects with a search radius of 50 m did not report any information on the percent area searched. In these cases, Ps was assumed to be equal to one. The percent area searched has the potential to bias corrected mortality estimates based on the configuration of searchable areas in relation to the turbine as the density of some species changes with distance for the turbine base (Arnett et al. 2009a, Huso 2010, Huso and Dalthorp 2014) This bias is not quantifiable without knowledge of the spatial distribution of the search area (Figure 1 and 2).

Searcher Efficiency Searcher efficiency (Se) values were extracted from post-construction mortality monitoring reports, often in the form of averages applied across seasons or monitoring periods. The majority of reports calculated searcher efficiency in the same manner, where the proportion of carcasses recovered by each individual during trial searchers was multiplied by the proportion of turbines searched by that individual and the resulting values are summed.

The formula used to calculate Se for most Projects contained in the Database was: 푛 푝푖 푆푒 = ∑ ∗ 푠푖 푃푖 푖=0

Pi = Number of carcasses placed in trials completed by searcher i pi = Number of carcasses found by searcher i during trials si = Proportion of turbines that were searched by searcher i n = Total number of searchers

Data from reports that used different estimators was not included unless raw data was available and the searcher efficiency could be re-calculated using that raw data.

Individual wind power projects used slightly different methodologies when completing searcher efficiency trials. Differences in methods between projects can produce biased searcher efficiency correction factors. Sources of bias could include the following.  Variability in the number of trials conducted per observer, which can affect the robustness of a searcher efficiency estimate.

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

 Variability in species used to complete trials. Species can vary in the degree to which they contrast with the substrate affecting their detectability to a searcher.  Variability in the marking of carcasses. If carcasses were obviously marked as trial carcasses, searchers could become aware of the trial and alter their search behaviour in response.  Variability in the means of distributing the carcasses within the search area (random, systematic, substrate dependent) which can also affect the visibility of carcasses by searchers.  Variability in the frequency and distribution of trials across the monitoring period which can affect searcher efficiency estimates as increased vegetation height and thickness decreases the ability of searchers to detect carcasses.

It is known that variations in methodologies have the potential to bias searcher efficiency estimates between projects and make comparisons less robust. However, correction factor data is most accurate when it is searcher and project specific; therefore, project specific correction factors, extracted from mortality monitoring reports, were used for this analysis.

Scavenger (Carcass) Removal Scavenger removal (Sc) values were extracted from post-construction mortality monitoring reports, often in the form of averages per season or monitoring period. In the majority of cases, scavenger removal estimates were calculated by placing carcasses around turbines or in nearby plots and returning after a set time period to determine whether or not carcasses had been removed by scavengers. Scavenger removal correction factors were calculated using the following equation, where the time between visits is equal to the time between scheduled carcass searches.

푛 + 푛 + 푛 + ⋯ + 푛 푆푐 = 1 2 3 푖 푛0 + 푛1 + 푛2 + ⋯ + 푛푖−1

Sc = Scavenger Removal correction factor n0 = Number of carcasses placed ni = Number of carcasses remaining after visit i

Where different estimators were used or where scavenger removal values were calculated for visit frequencies that differed from the visit frequencies of mortality monitoring, scavenger removal correction values were re-calculated using raw data when available.

Although the overall approach to calculating scavenger removal values was largely 24

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

consistent between projects, individual projects often used slightly different methodologies when completing scavenger removal trials. The following variations in scavenger removal trials occurred among projects.

 Number of trials, which affects the robustness of the scavenger removal estimate.  Length of time between monitoring events, which may bias the scavenger removal estimate based on carcass persistence and as attractiveness of carcasses to scavengers changes over time.  Number of carcasses that were placed per trial period.  Frequency of trials throughout the monitoring period, which may bias results if scavenger removal fluctuates with the time of year.  Methods used to mark trial carcasses. Some markings may have had the potential to attract scavengers.  Distribution of carcasses over a site (random, systematic, substrate dependent), which may affect how easily scavengers are able to detect and remove carcasses.  Condition of carcasses used for scavenger removal trials, which may affect their attractiveness to scavengers.  Species composition and size distribution, which may affect the probability that carcasses will be scavenged (e.g., some projects used domestic poultry chicks in their scavenger removal trials which are more conspicuous and may be more attractive to scavengers (Labross, 2008)).  Habitat type and number of turbines at which trials were conducted, which could bias results if the surveyed habitat types were not well represented in trial habitats.

These variations in methodologies have the potential to bias scavenger removal estimates between projects which can lead to over or under estimation of mortality. However, because scavenger removal rates are project-specific (based on local habitats, scavenger species composition and density, Labrosse 2008), project-specific scavenger removal rates were used to generate site specific mortality estimates.

Correction Factor Resolution The values for the three correction factors (i.e., percent area surveyed (Ps), scavenger removal (Sc), searcher efficiency (Se),) were extracted from reports based on trials conducted at each project during the survey period(s). Wherever correction factors were not calculated for a given time period, the correction factor from the next closest time period within the calendar year was used. However, if correction factor values were reported for both the time period before and after, these values were averaged to produce an estimate for the median time period. Within each of the 47 projects, correction factors were averaged within three seasons; spring (May to June), summer (Jul to Aug) and fall 25

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

(Sep to Oct). Correction factors used for this analysis were distinct to each project and calculated based on trials conducted during the monitoring period.

Bat mortality estimates were calculated separately from bird mortality estimates for all projects and non-raptor bird mortality estimates were calculated separately from raptor mortality estimates. Since raptors are generally much larger than non-raptors, the carcass persistence and searcher efficiency values for this guild are likely larger than those used for smaller individuals as they may be more easily detected by searchers and less often removed by scavengers. Raptor specific carcasses persistence (Sc) and searcher efficiency (Se) correction factors were calculated for Alberta and Ontario using data from projects that had Sc and Se trials that specifically used raptors (Appendix 2). There were no raptor mortalities observed in Atlantic Canada, therefore, raptor specific correction factors were not assigned. The correction factor summaries presented in this report are based on non-raptor birds and bats, excluding raptor specific correction factors.

Length of Monitoring Period The analysis in this report focused on mortality estimates between May 1 and October 31 as this was the period during which the majority of monitoring took place and is the search period required by the OMNRF bird and bat guidelines (OMNRF 2011a, OMNRF 2011b). A very limited number of projects performed carcass searches outside of this breeding and migration period (Bird Studies Canada et al. 2014) and the number of uncorrected carcasses located per turbine-month was low from November to April (Bird Studies Canada et al. 2014).

Of the 46 projects where correction factors were available, 20 completed carcass search monitoring a minimum of once every seven days for the entire period from May through October and did not undergo operational mitigation during this period. Projects with operational mitigation were excluded from this analysis as mitigation measures may skew any general trends in weekly strike distributions. Five of the 20 suitable projects completed this intensity of monitoring for 2 years and four projects completed it for 3 years resulting in a total of 33 project-years for which monitoring data from the entire period was available. No birds were observed in 3 of the 33 project-years, so 30 project-years were available for non-raptor birds. These projects were used to produce an estimate of the proportion of carcasses expected to be recovered during different time periods (Pt). This data was used to estimate Pt for the remaining 67 project-years to correct for mortalities expected to occur outside their respective monitoring periods for bats and non-raptors. Only 14 projects with a combined 17 project-years reported raptor mortalities. The weekly recoveries from these projects showed no clear seasonal patterns and the sample size was too small for aptly calculating Pt for raptors.

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

The weekly expected proportion of bat and non-raptor strikes was calculated by dividing the period between May 1 and October 31 into 27 weeks and calculating the corrected weekly mortality estimate using seasonal, project-specific correction factors. The estimated proportion of total carcasses found each week was calculated by dividing the corrected number of carcasses found that week by the corrected number of carcasses found over the entire monitoring period. These weekly proportions were then averaged over all 33 and 30 project-years to generate expected values of the proportion of carcasses found during each week for bats and non-raptor birds, and raptors respectively (Figure 3 and 4). Due to the erratic distribution of raptor mortalities (Figure 5) and the small sample size to calculate Pt for raptors the average proportion found across the 27 week period (0.037) was used as the expected proportion of carcasses found each week for raptors.

Pt was calculated by summing the expected proportion of carcasses found for each week where no monitoring was completed within the period from May 1 to October 31 for any given project and subtracting the resulting value from one. Due to differences in the expected seasonal distribution of bats, non-raptor birds, and raptors (Bird Studies Canada et al. 2014) Pt was estimated separately for each bats, non-raptor birds, and raptors.

Of the 33 project-years that completed surveys over the entire monitoring period, only one was located outside of Ontario; therefore provincially or regionally specific values for Pt could not be calculated. The Pt values presented in this report are therefore based primarily on Ontario data and on the subset of projects that completed May 1 to October 31 monitoring without any mitigation periods. Temporal distributions in carcasses recoveries could vary across Canada based on different climate factors and phenology. Increased data contributions to the database from wind power projects outside of Ontario will allow for regionally specific calculations to be performed in the future.

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

Corrected Mortality Estimates The estimated number of mortalities for each project within the actual monitoring period was calculated using the OMNRF estimator equation.

Mortality Per Monitoring Period:

푛 푐 퐶 = ∑ 푖 푃푠푖 ∗ 푆푒푖 ∗ 푆푐푖 푖=0

To determine the estimated number of bird and bat mortalities per project-year for the period of May 1 to October 31 for projects that did not monitor for the full period, the mortality estimates were corrected using the following equation.

May1 -Oct 31 Mortality:

퐶 푀 = 푃푡 C = total estimated mortality per monitoring period n = the total number of seasons/months for which separate correction factors were calculated ci = number of recovered carcasses per season/month Psi = percent area searched within 50m of the turbine base per season/month Sei = the weighted searcher efficiency per season/month (proportion of known carcasses recovered by observers during trial searches, weighted by the number of searches completed by each observer) Sci = carcass persistence per season/month (proportion of carcasses not removed by scavengers during the period between searches) M = mortality between May1 and Oct 31 Pt = the estimated proportion of May 1st to Oct 31st mortality that is expected to occur during the time period surveyed

All mortality estimators are based on a variety of underlying assumptions that have the potential to bias estimated mortality results. This estimator was selected based on its appropriateness for use with the limitations in data submitted to the Database.

Time dependent factors associated with the estimation of searcher efficiency and scavenger removal are not taken into account by this estimator (Huso 2010, Warren- Hicks et al. 2013). For example, scavenger removal estimates are based on trials that place all carcasses at the beginning of the search interval, maximizing the amount of time carcasses are available to be scavenged. In reality, mortality events would fall 28

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

periodically throughout the search period. This estimator also assumes that any carcass missed during a search will not be detected on subsequent visits. Both these assumptions have the potential to overestimate mortality.

The length of time between monitoring events varied between projects included in the databases, with search intervals ranging from daily to once every 7 days. This may affect the amount of temporal bias in scavenger removal estimation and search efficiency estimation (Warren-Hicks et al. 2013).

Due to the limited number of projects that have submitted data to the Database from outside of Ontario, provincially specific estimates of mortality could not be calculated for all provinces or regions. Mortality estimates were only calculated for areas for which data from a minimum of 3 projects were available. As such, mortality estimates were generated for Alberta, Ontario and Atlantic Canada (Nova Scotia, New Brunswick, Prince Edward Island and insular Newfoundland combined). Regional bird/bat mortalities per turbine estimates were calculated by weighting the average mortality per turbine for each wind power project within a region by the number of turbines operating in that project. The regional per turbine mortality values were then multiplied by the number of turbines in that region to produce an estimate of total regional mortality (observed within 50 m of the turbine base).

RESULTS

Correction Factors Table 8 shows the average correction factors for percent area searched, scavenger removal, and searcher efficiency. Averages are presented for information purposes only. Project specific correction factors were applied when calculating estimated mortality.

Correction factor values for percent area searched ranged between 0.33 and 1 in the spring, 0.08 and 1 in the summer and 0.06 and 1 in the fall across all regions. Mean national percent area searched was highest in spring as would be expected when minimal vegetation interfered with search area.

Correction values for scavenger removal ranged between 0.11 and 0.95 in the spring, 0.045 and 0.92 in the summer and 0.17 to 1 in the fall across all regions. Confidence intervals overlapped for all mean scavenger removal values suggesting scavenger removal is not strongly influenced by season.

Correction values for searcher efficiency ranged between 0.22 and 1 in the spring, 0.25

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

and 0.95 in the summer and 0.20 and 1 in the fall across all regions. Confidence intervals overlapped for all mean searcher efficiency values suggesting searcher efficiency is not strongly influenced by season

This variability in correction factor estimates underscores the need to use project specific correction factors when calculating estimated mortality.

Table 14: Average values of percent area searched, scavenger removal and searcher efficiency seasonal correction factors based on small birds/bats across 100 project-years of monitoring effort. Scavenger Searcher Percent Area Removal* Efficiency* Surveyed* (Sc) ± 95% (Se) ± 95% Season n (Ps) ± 95% CI CI CI Spring 66 0.91 ± 0.04 0.70 ± 0.04 0.64 ± 0.04 Summer 103 0.82 ± 0.05 0.63 ± 0.04 0.66 ± 0.03 Fall 118 0.82 ± 0.05 0.64 ± 0.04 0.70 ± 0.03 * Note that averages are presented for information purposes only. Project specific correction factors were applied when calculating estimated mortality

The proportion of total corrected non-raptor mortality found during each week between May 1st and Oct 31st ranged between 0.01 and 0.09. During the spring and summer (May 1st to Mid-July) the average proportion per week was 0.034, the proportion of birds found per week during the fall (Mid July to Oct 31st) averaged 0.042 suggesting bird mortality may be higher during the fall than the spring and summer (Figure 3).

The proportion of carcasses found during each week of the monitoring period ranged between 0.001 and 0.125 for bats. The highest proportions of bat mortalities occurred between July 1st and October 1st averaging 0.060 per week (Figure 4).

The proportion of carcasses found during each week of the monitoring period ranged between 0 and 0.126 for raptors. Raptor mortalities were sparser and were not concentrated over any given season (Figure 5) therefore the average proportion found across the 27 week period (0.037) was used as the expected proportion of carcasses found each week for raptors. .

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

0.09

0.08

0.07

0.06

0.05

0.04 Raptor CarcassesRaptor perTurbine

- 0.03

0.02

0.01

25 41 18 19 20 22 23 24 27 28 29 31 32 33 34 36 37 38 40 42

ProportionofNon

Oct Oct 1st39 -

Sep1st35 -

May-17 1st

July 1st -26

Aug -1st 30

June 1st 21 - Oct 31st43 - Week (May 1-Oct 31)

Figure 3: The average proportion of total corrected non-raptor bird mortality found during each week between May 1 and Oct 31 for 20 wind energy projects totaling 30 project-years of post- construction monitoring. 0.14

0.12

0.1

0.08

0.06

0.04

0.02

ProportionCarcassesofBat per Turbine

24 18 19 20 22 23 25 27 28 29 31 32 33 34 36 37 38 40 41 42

Oct Oct 1st39 -

Sep 1st - 35 1st Sep-

May-17 1st

July 1st -26

Aug -1st 30

June 1st 21 - Oct 31st43 - Week (May 1-Oct 31)

Figure 4: The average proportion of total corrected bat mortality found during each week between May 1 and Oct 31 for 20 wind energy projects totaling 33 project-years of post-construction monitoring. 31

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

0.14

0.12

0.1

0.08

0.06

0.04

0.02

18 19 20 22 23 24 25 27 28 29 31 32 33 34 36 37 38 40 41 42

ProportionCarcassesofRaptor perTurbine

Oct Oct 1st39 -

Sep1st35 -

May-17 1st

July 1st -26

Aug -1st 30

June 1st 21 - Oct 31st43 - Week (May 1 - Oct 31)

Figure 5: The average proportion of total corrected raptor bird mortality found during each week between May 1 and Oct 31 for 14 wind energy projects totaling 17 project-years of post- construction monitoring.

Estimated Mortality

Birds: Alberta Based on data collected at 3 wind power projects for 6 project-years the estimated Alberta turbine mortality for non-raptors was 2.65 ± 0.75 birds/turbine and 0.06 ± 0.06 birds/turbine for raptors for the period of May 1 to October 31 (Table 15). Mortalities for non-raptors ranged between 0.94 and 5.17 birds/turbine and 0 to 0.38 birds/turbine for raptors.

The total number of installed turbines in Alberta as of December 2015 was 958 (CanWEA, personal communication) resulting in an estimate of 2,538 non-raptor bird fatalities (95% confidence interval of 1,818 to 3,258 fatalities) and 60 raptor fatalities (95% confidence interval of 4 to 117 fatalities) in this region between May 1st and Oct 31st based on December 2015 installed capacity.

Birds: Ontario

Based on data collected at 37 wind power projects for 86 project-years between 2007 and 2014, the estimated Ontario turbine mortality for non-raptors was 6.14 ± 0.31 32

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

birds/turbine and 0.20 ± 0.01 birds/turbine for raptors (Table 15) for the period of May 1st to October 31st. Mortalities for non-raptors ranged from 0 and 44.31 birds/turbine and 0 to 1.20 birds/turbine for raptors

The total number of installed turbines in Ontario as of December 2015 was 2,303 (CanWEA, personal communication) resulting in an estimate of 14,144 non-raptor bird fatalities (95% confidence interval of 13,430 to 14,858 fatalities) and 462 raptor fatalities (95% confidence interval of 439 to 485 fatalities) in this region between May 1 and Oct 31 based on December 2015 installed capacity.

Birds: Atlantic Canada

Based on data collected at 5 wind power projects for 7 project-years between 2008 and 2012, the estimated Atlantic Canada turbine mortality for non-raptors was 1.17 ± 1.01 birds/turbine and 0 birds/turbine for raptors as there were no recorded mortalities in Atlantic Canada (Table 15) for the period of May 1st to October 31st.Mortality for non- raptors ranged between 0 and 7.09 birds/turbine

The total number of installed turbines in Atlantic Canada as of December 2015 was 521 (CanWEA, personal communication) resulting in an estimate of 612 non-raptor bird fatalities (95% confidence interval of 83 to 1,140 fatalities) in this region between May 1st and Oct 31st based on December 2015 installed capacity. As there were no raptor fatalities reported from Atlantic Canada no raptor specific estimates have been done.

Bats: Alberta

Based on data collected at 3 wind power projects for 6 project-years between 2007 and 2011, the estimated Alberta turbine mortality of bats was 8.34 ± 2.46 bats/turbine (Table 15) for the period of May 1st to October 31st and ranged between 1.87 and 18.37 bats/turbine.

The total number of installed turbines in Alberta as of December 2015 was 958 (CanWEA, personal communication) resulting in an estimate of 7,991 bat fatalities (95% confidence interval of 5,638 to 10,344 fatalities) in this region between May 1st and October 31st based on December 2015 installed capacity.

Bats: Ontario

Based on data collected at 37 wind power projects for 86 project-years between 2007 and 2014, the estimated Ontario turbine mortality of bats was 18.52 ± 0.79 bats/turbine 33

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

(Table 15) for the period of May 1st to October 31st and ranged between 1.06 and 119.86 bats/turbine.

The total number of installed turbines in Ontario as of December 2015 was 2,303 (CanWEA, personal communication) resulting in an estimate of 42,656 bat fatalities (95% confidence interval of 40,833 to 44,480 fatalities) in Ontario between May 1st and October 31st based on December 2015 installed capacity.

Bats: Atlantic Canada

Based on data collected at 5 wind power projects for 7 project-years between 2008 and 2012, the estimated Atlantic Canada turbine mortality of bats was 0.26 ± 0.11 bats/turbine (Table 15) between May 1 and Oct 31 and ranged between 0 and 0.71 bats/turbine.

The total number of installed turbines in Atlantic Canada as of December 2015 was 521 (CanWEA, personal communication) resulting in an estimate of 134 bat fatalities (95% confidence interval of 75 to 193 fatalities) between May 1 and October 31 based on December 2015 installed capacity.

Table 15: The average estimated mortality per turbine for 3 regions within Canada Alberta, Ontario, and Atlantic Canada (Nova Scotia, New Brunswick, Prince Edward Island and Newfoundland) based on data collected at 34 projects between 2007 and 2014. Regional mortality estimates and number of turbines are based on installed capacity of wind energy in Canada as of December 2015

Total Turbines Bat Bat Non-raptor Non-raptor Raptor Raptor Region Installed In Mortality Regional Mortality Regional Mortality Regional Turbines Analysis /Turbine Mortality /Turbine Mortality /Turbine Mortality Alberta 958 120 8.34 ± 2.46 7,991 ± 2,353 2.65 ± 0.75 2538 ± 720 0.06 ± 0.06 60 ± 57 Ontario 2303 1202 18.52 ± 0.79 40,833 ± 1,823 6.14 ± 0.31 14,144 ± 714 0.20 ± 0.01 462 ± 23 Atlantic 521 92 0.26 ± 0.12 108 ± 47 1.17 ± 1.01 917 ± 425 0 0 Canada British Data 217 Data Deficient Data Deficient Data Deficient Data Deficient Data Deficient Columbia Deficient Data Data Deficient Data Deficient Data Deficient Data Deficient Data Deficient Manitoba 133 Deficient Data Data Deficient Data Deficient Data Deficient Data Deficient Data Deficient Quebec 1783 Deficient Data Data Deficient Data Deficient Data Deficient Data Deficient Data Deficient Saskatchewan 143 Deficient Data Data Deficient Data Deficient Data Deficient Data Deficient Data Deficient Territories 6 Deficient

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

DISCUSSION The estimated number of bat fatalities was higher than the estimated number of bird fatalities in Alberta and Ontario; however, this pattern was not consistent in Atlantic Canada where bird fatalities were higher than bat fatalities. Bat fatalities were highest in Ontario, followed by Alberta and Atlantic Canada based on both per turbine and total estimated mortality.

Average mortality per turbine was highest in Ontario for both birds and bats when compared to Atlantic Canada and Alberta. Bird and/or bat concentrations are potentially higher in the Great Lakes Region of Ontario than in other regions because migratory species congregate along these shorelines (Ewert, 2011) and the majority of turbines in Ontario are located in the Great Lakes Region; this could drive the differences in mortality estimates. However, these differences could also be the result of sampling bias as sample size in Ontario (n=37) was much higher than that of Western (n=3) or Eastern (n=5) Canada, or it could be driven by differences between regions. This underscores the need to expand contributions to the Database beyond Ontario and to standardize data collection methods nationally in order to make it possible to determine the broad scale distribution of bird and bat mortalities in relation to landscape level factors in the absence of sampling bias.

The corrected proportion of bird mortalities was slightly higher between July and October than between May and June, suggesting that bird mortality may increase during the fall. This is potentially linked to an increase in the total number of birds on the landscape following the breeding season and to an influx of inexperienced hatch year birds undergoing migration (Ralph, 1971).

The highest numbers of bat fatalities occur between July and September corresponding with the swarming and migratory period for bats (Davis and Hitchcock 1965, Thomas et al 1979, Schowalter 1980, Parsons et al 2003). Migratory bat fatalities were higher than resident bat fatalities for Ontario and Alberta but were lower than resident bat fatalities in Atlantic Canada.

There were a limited number (n=20) of projects for which complete data from May 1 to October 31 was submitted, and only one from outside Ontario, therefore the sample size for calculating Pt was limited and heavily based on data from Ontario wind power projects. Seasonal fatality patterns may be linked to the latitude, phenology and geography of different regions in Canada, which may also influence the estimate of Pt for different regions. Regionally specific Pt values may be calculated in the future if more data is submitted to the Database.

Based on uncorrected counts, bird fatalities were dominated by passerines with relatively low numbers of raptors and waterbirds. Migratory bat mortalities were more common than resident bat mortalities. The most prevalent bird species found across Canada during mortality monitoring surveys were: Horned Lark, Golden-crowned Kinglet and Red-eyed

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

Vireo. The most prevalent bat species found were: hoary bat, silver-haired bat and eastern red bat.

Mortality estimates presented in this report are based on a subset of data from mortality monitoring completed between 2007 and 2014. Bird and bat species populations of many species fluctuate over time. The species compositions and estimated mortality presented here therefore may also fluctuate as the number of individuals of each species on the landscape changes. This may be especially true of resident bat populations that have been strongly affected by White Nose Syndrome such as big brown bat, little brown myotis, northern long-eared myotis and tri-coloured bat. Population numbers have dropped dramatically in Eastern Canada and Ontario in recent years for some of these species (Frick et al. 2010, Turner et al. 2011).

The confidence intervals surrounding the estimates of bird and bat mortality presented in this report are based only on the error associated with generating a weighted mean and applying it across all turbines. These confidence intervals do not account for the error associated with correction factor estimates, nor for the potential biases associated with the mortality estimator itself. Correction factor error may be substantial and would likely expand the confidence intervals around the values presented here.

The mortality estimates presented here potentially underestimate true mortality as they are based solely on carcasses that fell within 50 m of the turbine base. It is expected that a certain proportion of birds and bats will fall outside of this radius, and there are several different approaches to quantifying this correction factor as can be inferred based on extrapolation of Figures 1 and 2. Zimmerling et al. (2013) reported that turbine heights were very similar (~80 m) for most turbines installed in Canada as of 2011 and estimated the proportion of carcasses expected to fall outside of 50 m to be up to 51.8% of birds, based on 4 studies that searched a radius up to 85 m. These values were further validated based on a field trial that searched up to 85 m from the turbine base (Zimmerling et al. 2013). Smallwood (2013) found that the proportion of both birds and bats that fell within 50 m of the turbine base varied with turbine height and estimated higher correction factor values for carcasses falling outside of 50 m than Zimmerling et al. 2013. Smallwood (2013) fit a logistic function to carcass distributions, and the proportions of carcasses falling within the search radius were calculated based on a variety of search radius and turbine height combinations. For 80 m turbines, carcasses were expected to fall to a maximum distance of 156 m. These findings indicate that the mortality estimates presented here may underestimate true mortality, but still allow for comparisons amongst sites and regions as long as turbine heights are similar; this is an important consideration for future investigation of landscape level factors and mitigation measures.

The analysis presented in this report calculates bird and bat mortality from carcasses found during mortality monitoring surveys using the equations outlined in Section 2. This 36

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

method is partially based on Environment Canada’s Recommended Protocols for Monitoring Impacts of Wind Turbines on Birds (EC-CWS 2007) and the Ontario Ministry of Natural Resources and Forestry Guidelines for Wind Power Projects (OMNRF 2011a, OMNRF 2011b) as these methods are most commonly used for the data submitted to the database and therefore the most applicable to the largest amount of Database data. All mortality estimators (equations used to estimate mortality) available in the scientific literature and used by various jurisdictions have multiple sources of bias, both within the estimator equations themselves and within the methods used to collect correction factor data. Time-dependent factors associated with searcher efficiency and carcass persistence wherein the attractiveness of carcasses to predators may decrease with time, searcher efficiency that changes with varying vegetation heights throughout the season, and correction factor differences between birds and bats have all been identified as possible sources of bias in estimating mortality (Huso 2010, Warren-Hicks et al. 2013). The mortality estimator used within this summary report was most appropriate given the constraints in the format of data submitted to the database to-date, and is not intended to represent a preferred methodology. As more complete information in the format of the Database templates is submitted, it may be possible to use various other estimators to produce bird and bat mortality estimates at Canadian wind power projects.

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

REFERENCES

Bird Studies Canada, Canadian Wind Energy Association, Environment Canada and Ontario Ministry of Natural Resources 2014. Wind Energy Bird and Bat Monitoring DatabaseSummary of the Findings from Post-construction Monitoring Reports. http://www.birdscanada.org/resources/wind/Wind_database_summary_July2014.pdf

Davis, W.H., and H.B. Hitchcock. 1965. Biology and Migration of the Bat, Myotis lucifugus, in New England. Journal of Mammalogy 46(2): 296-313

Environment Canada-Canadian Wildlife Service (EC-CWS). 2007. Recommended Protocols for Monitoring Impacts of Wind Turbines on Birds. April 2007. 33p.

Ewert, D.N., M.J. Hamas, R.J. Smith, M.E. Dallman, and S.W. Jorgensen. 2011. Distribution of Migratory Landbirds Along the Northern Lake Huron Shoreline. The Wilson Journal of Ornithology 123(3): 536-547.

Frick, W. F., J. F. Pollock, A. C. Hicks, K. E. Langwig, D. S. Reynolds, G. G. Turner, C.M. Butchkoski, T. H. Kunz. 2010. An emerging disease causes regional population collapse of a common North American bat species. Science, 329(5992): 679-682.

Huso, M.M.P. 2010. An estimator of wildlife fatality from observed carcasses Environmetrics 22: 318–329

Huso, M.M.P. and D. Dalthorp. 2014. The Journal of Wildlife Management 78(2):347–358

Labrosse, A. A. 2008. Determining factors affecting carcass removal and searching efficiency during the post-construction monitoring of wind farms. Thesis. University of Northern British Columbia, Prince George, British Columbia, Canada.

Loss, R.S., Tom Will, Peter P. Marra, 2013. Estimates of bird collision mortality at wind facilities in the contiguous United States. Biological Conservation 168: 201–209

Ontario Ministry of Natural Resources and Forestry (OMNRF) 2011a. Birds and Bird Habitats: Guidelines for Wind Power Projects. First Edition Queen‘s Printer for Ontario, MNR Number 52695.

Ontario Ministry of Natural Resources and Forestry (OMNRF) 2011b. Birds and Bird Habitats: Guidelines for Wind Power Projects. Second Edition Queen‘s Printer for Ontario, MNR Number 52696.

Parsons, K. N., G. Jones, F. Greenaway. 2003. Swarming activity of temperate zone microchiropteran bats: effects of season, time of night and weather conditions. Journal of Zoology 261(3): 257–264 38

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

Ralph, C.J. 1981. Age ratios and their possible use in determining autumn routes of passerine migrants. Wilson Bulletin 93(2):164-188.

Schowalter, D.B. 1980. Swarming, reproduction, and early hibernation of Myotis lucifugus and M. volans in Alberta, Canada. Journal of Mammalogy: 350-354.

Smallwood, K.S., 2013. Comparing bird and bat fatality-rate estimates among North American wind-energy projects. Wildlife Society Bulletin 37: 19–33.

Thomas, D.W., M. B. Fenton, R.M.R. Barclay. 1979. Social behavior of the little brown bat, Myotis lucifugus. Behavioral Ecology and Sociobiology 6(2): 129-136

Turner, G. G., D. Reeder, and J.T.H. Coleman. 2011 A Five-year Assessment of Mortality and Geographic Spread of White-Nose Syndrome in North American Bats, with a Look at the Future. Update of White-Nose Syndrome. Bats.Bat Research News: 13

Warren-Hicks, W., J. Newman, R. Wolpert, B. Karas, and Loan Tran on behalf of California Wind Energy Association. 2013. Improving Methods for Estimating Fatality Of Birds And Bats At Wind Energy Facilities. California Energy Commission. Publication Number: CEC-500-2012-086. Contract Number: PIR-08-028.

Zimmerling, J.R. Andrea C. Pomeroy, Marc V. d'Entremont, and Charles M. Francis, 2013. Canadian Estimate of Bird Mortality Due to Collisions and Direct Habitat Loss Associated with Wind Turbine Developments. Avian Conservation and Ecology 8(2): 10

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

APPENDIX 1

Bird Species Rank and Percent Species Composition (Canada). Expansion of Table 5. 1.Horned Lark(8.61%) 88.Lapland Longspur(0.13%) 2.Golden-crowned Kinglet(9.99%) 89.Rock Pigeon(0.13%) 3.Red-eyed Vireo(5.31%) 90.Bay-breasted Warbler(0.13%) 4.Purple Martin(4.85%) 91.Double-crested Cormorant(0.13%) 5.Tree Swallow(6.64%) 92.Osprey(0.18%) 6.Red-tailed Hawk(3.80%) 93.American Kestrel(0.18%) 7.Ruby-crowned Kinglet(2.61%) 94.Downy Woodpecker(0.13%) 8.Cliff Swallow(2.10%) 95.Merlin(0.13%) 9.Turkey Vulture(2.65%) 96.Canada Goose(0.13%) 10.Mourning Dove(2.24%) 97.Northern Harrier(0.13%) 11.Magnolia Warbler(1.32%) 98.Purple Finch(0.13%) 12.European Starling(1.97%) 99.Pine Siskin(0.13%) 13.Cedar Waxwing(1.60%) 100.Green-winged Teal(0.09%) 14.Killdeer(1.69%) 101.Gray-cheeked Thrush(0.09%) 15.American Robin(1.09%) 102.Common Nighthawk(0.18%) 16.Red-breasted Nuthatch(1.55%) 103.Wilson's Snipe(0.64%) 17.Barn Swallow(0.96%) 104.Northern Shoveler(0.09%) 18.Brown-headed Cowbird(1.14%) 105.Trail's Flycatcher (Willow or Alder)(0.09%) 19.Yellow-bellied Sapsucker(0.73%) 106.Western Meadowlark(0.09%) 20.Ring-billed Gull(1.09%) 107.Blue-winged Teal(0.09%) 21.American Redstart(0.68%) 108.Short-eared Owl(0.09%) 22.Mallard(2.74%) 109.Black-capped Chickadee(0.13%) 23.Red-winged Blackbird(0.77%) 110.Hermit Thrush(0.09%) 24.Ruby-throated Hummingbird(0.68%) 111.Black/Yellow-billed Cuckoo(0.09%) 25.Brown Creeper(0.73%) 112.Bald Eagle(0.09%) 26.Savannah Sparrow(1.09%) 113.Eastern Wood-Pewee(0.09%) 27.Northern Rough-winged Swallow(0.64%) 114.Broad-winged Hawk(0.13%) 28.Bank Swallow(0.87%) 115.American Pipit(0.09%) 29.Yellow-rumped Warbler(0.73%) 116.Winter Wren(0.09%) 30.Chestnut-sided Warbler(0.59%) 117.Willow Flycatcher(0.09%) 31.Black-billed Cuckoo(0.54%) 118.Scarlet Tanager(0.13%) 32.Bobolink(1.51%) 119.Ruddy Duck(0.09%) 33.Dark-eyed Junco(0.59%) 120.Blue-gray Gnatcatcher(0.09%) 34.Gray Catbird(0.45%) 121.Rough-legged Hawk(0.22%) 35.Common Grackle(0.50%) 122.Snow Bunting(0.09%) 36.Yellow-bellied Flycatcher(0.50%) 123.American Woodcock(0.13%)

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

37.Black-throated Green Warbler(0.59%) 124.House Finch(0.09%) 38.Black-and-white Warbler(0.54%) 125.Black-crowned Night-Heron(0.04%) 39.American Goldfinch(0.45%) 126.Great Blue Heron(0.04%) 40.Warbling Vireo(0.54%) 127.American Black Duck(0.04%) 41.Vesper Sparrow(0.77%) 128.Gadwall(0.09%) 42.Least Flycatcher(0.50%) 129.Brown Thrasher(0.04%) 43.Wilson's Warbler(0.32%) 130.Leach's Storm-Petrel(0.04%) 44.Blackburnian Warbler(0.41%) 131.Great Crested Flycatcher(0.04%) 45.Cooper's Hawk(0.32%) 132.Black Guillemot(0.04%) 46.Philadelphia Vireo(0.64%) 133.Northern Pintail(0.04%) 47.House Sparrow(0.41%) 134.Greater Scaup(0.04%) 48.Baltimore Oriole(0.41%) 135.Bohemian Waxwing(0.04%) 49.Black-throated Blue Warbler(0.41%) 136.Black-billed Magpie(0.04%) 50.Blue Jay(0.41%) 137.Black-throated Sparrow(0.04%) 51.Northern Parula(0.32%) 138.White-crowned Sparrow(0.09%) 52.Swainson's Thrush(0.41%) 139.Clay-colored Sparrow(0.09%) 53.Song Sparrow(0.27%) 140.Marsh Wren(0.04%) 54.Yellow Warbler(0.45%) 141.Hoary Redpoll(0.04%) 55.Common Yellowthroat(0.32%) 142.Boreal Chickadee(0.04%) 56.Eastern Kingbird(0.41%) 143.Ferruginous Hawk(0.04%) 57.Northern Flicker(0.32%) 144.American White Pelican(0.09%) 58.Chimney Swift(0.36%) 145.Whip-poor-will(0.04%) 59.Ovenbird(0.27%) 146.Horned Grebe(0.04%) 60.Eared Grebe(0.54%) 147.Eastern Phoebe(0.04%) 61.Lincoln's Sparrow(0.22%) 148.Semipalmated Sandpiper(0.04%) 62.Indigo Bunting(0.27%) 149.American Coot(0.09%) 63.American Crow(0.32%) 150.Peregrine Falcon(0.04%) 64.Nashville Warbler(0.27%) 151.Yellow-headed Blackbird(0.04%) 65.Gray Partridge(0.59%) 152.Northern Saw-whet Owl(0.04%) 66.Swainson's Hawk(0.36%) 153.Marbled Godwit(0.04%) 67.Sora(0.18%) 154.Baird's Sparrow(0.04%) 68.Blackpoll Warbler(0.18%) 155.Sandhill Crane(0.04%) 69.White-throated Sparrow(0.27%) 156.Red Crossbill(0.04%) 70.Swamp Sparrow(0.18%) 157.White-winged Crossbill(0.04%) 71.Yellow-billed Cuckoo(0.18%) 158.Northern Waterthrush(0.04%) 72.Chipping Sparrow(0.18%) 159.Sharp-tailed Grouse(0.04%) 73.Rose-breasted Grosbeak(0.18%) 160.Virginia Rail(0.04%) 74.Wood Thrush(0.18%) 161.Eastern Bluebird(0.04%) 75.Wild Turkey(0.27%) 162.Pectoral Sandpiper(0.04%) 76.Tennessee Warbler(0.27%) 163.Palm Warbler(0.04%) 41

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

77.Upland Sandpiper(0.27%) 164.Spotted Sandpiper(0.04%) 78.Canada Warbler(0.22%) 165.Blue-winged Warbler(0.04%) 79.Blue-headed Vireo(0.18%) 166.Sedge Wren(0.04%) 80.Ruffed Grouse(0.18%) 167.Eastern Meadowlark(0.04%) 81.Western Grebe(0.18%) 168.Evening Grosbeak(0.04%) 82.House Wren(0.13%) 169.Veery(0.04%) 83.Pine Warbler(0.13%) 170.Common Merganser(0.04%) 84.Rock Pigeon(0.27%) 171.Ring-necked Pheasant(0.04%) 85.Herring Gull(0.18%) 172.Lesser Yellowlegs(0.04%) 86.Brewer's Blackbird(0.13%) 173.American Tree Sparrow(0.04%) 87.Sharp-shinned Hawk(0.18%) *In 2010 Winter Wren was split into two species, Pacific Wren west of the Rocky Mountains (BC) and Winter Wren to the east. This report includes pre-2010 data for which all mortalities are “Winter Wren”.

Bird Species Rank and Percent Species Composition (Alberta). Expansion of Table 6. 1.Horned Lark(28.2%) 33.Northern Pintail(0.34%) 2.Vesper Sparrow(4.82%) 34.Black-billed Magpie(0.34%) 3.Eared Grebe(4.13%) 35.Bohemian Waxwing(0.34%) 4.Gray Partridge(4.48%) 36.Yellow-bellied Sapsucker(0.34%) 5.Swainson's Hawk(2.75%) 37.White-crowned Sparrow(0.68%) 6.Dark-eyed Junco(1.72%) 38.Rock Pigeon(0.34%) 7.Mallard(11.7%) 39.Bank Swallow(0.34%) 8.European Starling(3.44%) 40.American White Pelican(0.68%) 9.Savannah Sparrow(3.79%) 41.Clay-colored Sparrow(0.68%) 10.Red-tailed Hawk(1.37%) 42.Ferruginous Hawk(0.34%) 11.Red-breasted Nuthatch(3.44%) 43.Killdeer(0.34%) 12.Western Grebe(1.37%) 44.Horned Grebe(0.34%) 13.Gray Catbird(1.03%) 45.Cliff Swallow(0.34%) 14.Warbling Vireo(0.68%) 46.American Coot(0.68%) 15.Wilson's Warbler(0.68%) 47.Ring-billed Gull(0.34%) 16.Golden-crowned Kinglet(1.37%) 48.Ovenbird(0.34%) 17.House Sparrow(1.37%) 49.Snow Bunting(0.34%) 18.Northern Shoveler(0.68%) 50.Red-winged Blackbird(0.34%) 19.Mourning Dove(1.03%) 51.Yellow-headed Blackbird(0.34%) 20.American Redstart(1.03%) 52.Winter Wren(0.34%) 21.Yellow-rumped Warbler(1.37%) 53.Baird's Sparrow(0.34%) 22.Western Meadowlark(0.68%) 54.Brown Creeper(0.34%) 23.Ruby-crowned Kinglet(0.68%) 55.Canada Goose(0.34%) 24.Short-eared Owl(0.68%) 56.Chipping Sparrow(0.34%)

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

25.American Robin(0.68%) 57.Northern Saw-whet Owl(0.34%) 26.Blue-winged Teal(0.68%) 58.Marbled Godwit(0.34%) 27.Brewer's Blackbird(0.68%) 59.Brown-headed Cowbird(0.34%) 28.House Wren(0.68%) 60.Common Yellowthroat(0.34%) 29.Lapland Longspur(0.68%) 61.Yellow Warbler(0.34%) 30.Gadwall(0.68%) 62.Upland Sandpiper(0.34%) 31.Lincoln's Sparrow(0.34%) 63.Sandhill Crane(0.34%) 32.Green-winged Teal(0.34%) 64.Sharp-tailed Grouse(0.34%)

Bird Species Rank and Percent Species Composition (Ontario) expansion of Table 7 1.Golden-crowned Kinglet(11.3%) 71.Rock Pigeon(0.16%) 2.Red-eyed Vireo(6.20%) 72.Chipping Sparrow(0.16%) 3.Horned Lark(5.81%) 73.Bay-breasted Warbler(0.16%) 4.Purple Martin(5.92%) 74.Double-crested Cormorant(0.16%) 5.Tree Swallow(8.04%) 75.Osprey(0.22%) 6.Red-tailed Hawk(4.41%) 76.American Kestrel(0.22%) 7.Turkey Vulture(3.24%) 77.Downy Woodpecker(0.16%) 8.Cliff Swallow(2.51%) 78.Merlin(0.16%) 9.Magnolia Warbler(1.62%) 79.Upland Sandpiper(0.27%) 10.Mourning Dove(2.57%) 80.Canada Warbler(0.22%) 11.Ruby-crowned Kinglet(2.68%) 81.Tennessee Warbler(0.16%) 12.Killdeer(2.01%) 82.Blue-headed Vireo(0.16%) 13.Barn Swallow(1.17%) 83.Common Nighthawk(0.22%) 14.Cedar Waxwing(1.73%) 84.Gray-cheeked Thrush(0.11%) 15.European Starling(1.78%) 85.Wilson's Snipe(0.78%) 16.Brown-headed Cowbird(1.22%) 86.Trail's Flycatcher (Willow or Alder)(0.11%) 17.Ring-billed Gull(1.28%) 87.Ruffed Grouse(0.16%) 18.Yellow-bellied Sapsucker(0.78%) 88.Black-capped Chickadee(0.16%) 19.Red-winged Blackbird(0.89%) 89.Hermit Thrush(0.11%) 20.Ruby-throated Hummingbird(0.83%) 90.Eastern Wood-Pewee(0.11%) 21.American Robin(0.95%) 91.Black/Yellow-billed Cuckoo(0.11%) 22.Northern Rough-winged Swallow(0.78%) 92.Bald Eagle(0.11%) 23.Red-breasted Nuthatch(0.89%) 93.Rock Pigeon(0.27%) 24.Brown Creeper(0.83%) 94.American Pipit(0.11%) 25.Chestnut-sided Warbler(0.72%) 95.Vesper Sparrow(0.16%) 26.Black-billed Cuckoo(0.67%) 96.White-throated Sparrow(0.11%) 27.Bobolink(1.84%) 97.Herring Gull(0.16%) 28.Mallard(1.45%) 98.Willow Flycatcher(0.11%)

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

29.Bank Swallow(1.00%) 99.Scarlet Tanager(0.16%) 30.American Redstart(0.50%) 100.Ruddy Duck(0.11%) 31.Common Grackle(0.61%) 101.Blue-gray Gnatcatcher(0.11%) 32.Yellow-rumped Warbler(0.67%) 102.Rough-legged Hawk(0.27%) 33.Black-and-white Warbler(0.67%) 103.American Woodcock(0.16%) 34.American Goldfinch(0.55%) 104.Canada Goose(0.11%) 35.Savannah Sparrow(0.72%) 105.House Finch(0.11%) 36.Yellow-bellied Flycatcher(0.55%) 106.Northern Harrier(0.11%) 37.Black-throated Green Warbler(0.67%) 107.Purple Finch(0.11%) 38.Blackburnian Warbler(0.50%) 108.Black-crowned Night-Heron(0.05%) 39.Cooper's Hawk(0.39%) 109.Brown Thrasher(0.05%) 40.Philadelphia Vireo(0.78%) 110.House Wren(0.05%) 41.Baltimore Oriole(0.50%) 111.Pine Siskin(0.05%) 42.Black-throated Blue Warbler(0.50%) 112.Greater Scaup(0.05%) 43.Blue Jay(0.50%) 113.Black-throated Sparrow(0.05%) 44.Least Flycatcher(0.44%) 114.Green-winged Teal(0.05%) 45.Gray Catbird(0.39%) 115.Marsh Wren(0.05%) 46.Eastern Kingbird(0.50%) 116.Whip-poor-will(0.05%) 47.Northern Flicker(0.39%) 117.Semipalmated Sandpiper(0.05%) 48.Chimney Swift(0.44%) 118.Peregrine Falcon(0.05%) 49.Northern Parula(0.33%) 119.Eastern Phoebe(0.05%) 50.Swainson's Thrush(0.39%) 120.Broad-winged Hawk(0.11%) 51.Indigo Bunting(0.33%) 121.Eastern Bluebird(0.05%) 52.Song Sparrow(0.27%) 122.Virginia Rail(0.05%) 53.Common Yellowthroat(0.33%) 123.Palm Warbler(0.05%) 54.House Sparrow(0.27%) 124.Pectoral Sandpiper(0.05%) 55.Nashville Warbler(0.33%) 125.Spotted Sandpiper(0.05%) 56.Ovenbird(0.27%) 126.Brewer's Blackbird(0.05%) 57.Sora(0.22%) 127.Blue-winged Warbler(0.05%) 58.Blackpoll Warbler(0.22%) 128.Lapland Longspur(0.05%) 59.Swamp Sparrow(0.22%) 129.Sedge Wren(0.05%) 60.Yellow-billed Cuckoo(0.22%) 130.Lincoln's Sparrow(0.05%) 61.Yellow Warbler(0.39%) 131.Eastern Meadowlark(0.05%) 62.Rose-breasted Grosbeak(0.22%) 132.Evening Grosbeak(0.05%) 63.Wood Thrush(0.22%) 133.Veery(0.05%) 64.Wild Turkey(0.33%) 134.Winter Wren(0.05%) 65.American Crow(0.33%) 135.Snow Bunting(0.05%) 66.Pine Warbler(0.16%) 136.Lesser Yellowlegs(0.05%) 67.Warbling Vireo(0.22%) 137.Ring-necked Pheasant(0.05%)

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

68.Wilson's Warbler(0.16%) 138.Common Merganser(0.05%) 69.Dark-eyed Junco(0.22%) 139.American Tree Sparrow(0.05%) 70.Sharp-shinned Hawk(0.22%)

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

APPENDIX 2

Raw Searcher Efficiency Trial Data for Raptors Province Year Species Scavenged Found AB 2013 Merlin N N AB 2013 Sharp-Shinned Hawk N Y AB 2013 Merlin N N AB 2014 Swainson's Hawk N Y AB 2010 Swainson's Hawk N Y AB 2010 Swainson's Hawk N Y AB 2014 Red-tailed Hawk N Y AB 2014 Red-tailed Hawk N Y AB 2014 Red-tailed Hawk N Y AB 2013 Merlin Y N AB 2013 Sharp-Shinned Hawk N N ON 2012 Eastern Screech-Owl N Y ON 2013 Eastern Screech-Owl N N ON 2012 Northern Saw-whet owl N Y ON 2012 Eastern Screech-Owl N Y ON 2012 Owl sp. N N ON 2012 Merlin N Y ON 2012 Eastern Screech-Owl N Y ON 2011 American Kestrel Y N ON 2014 Red- tailed Hawk N Y ON 2014 Turkey Vulture N Y

Raw Carcass Removal Trial Data for Raptors Scavenged Scavenged Scavenged Province Year Species Visit 1 Visit 2 Visit 3 AB 2014 Red-tailed hawk N N N AB 2014 Red-tailed hawk N N N AB 2014 Red-tailed hawk N N N AB 2013 Sharp-Shinned Hawk N Y Y AB 2013 Merlin N N Y AB 2013 Sharp-Shinned Hawk Y Y Y AB 2013 Merlin Y Y Y AB 2014 Swainson's Hawk N N Y ON 2014 Turkey Vulture N N N ON 2014 Turkey Vulture N N N 46

Wind Energy Bird and Bat Monitoring Database Summary: July 2016

ON 2012 Eastern Screech-Owl N N N ON 2013 Merlin Y Y Y Northern Saw-Whet N N N ON 2012 Owl ON 2014 Red Tailed Hawk N Y Y ON 2014 Red-tailed Hawk N N N