A guide to the study of the impacts of wind power on wildlife

Transcription

1 A guide to the study of the impacts of wind power on wildlife D R. D A L E S T R I C K L A N D W E S T E R N E C O S Y S T E M S T E C H N O L O G Y, I N C. U N I T E D S T A T E S O F A M E R I C A Conference on Wind Power and Environmental Impacts Stockholm, Sweden 5 7 February, 2013

2 Strickland, M.D., E.B. Arnett, W.P. Erickson, D.H. Johnson, G.D. Johnson, M.L., Morrison, J.A. Shaffer, and W. WarrenHicks Comprehensive Guide to Studying Wind Energy/Wildlife Interactions. Prepared for the National Wind Coordinating Collaborative, Washington, D.C., USA.

3 Purpose of The Guide Reference document used to: Maximize the knowledge gained from the emerging study of wind energy/wildlife interactions to reduce potential risk to wildlife in existing and future facilities Comparable data among projects Need for common basis for: Prediction of potential risk/impact Evaluate actual impacts Identify potential mitigation of significant adverse impacts Evaluate effectiveness of mitigation Support management decisions

4 Framework Tiered Risk Assessment Tier 1 Landscape scale assessment Tier 2 Site assessment Tier 3 Preconstruction studies for risk and impact prediction Tier 4 Postconstruction monitoring for estimation of impacts: Fatalities Habitat Tier 5 Project specific research, mitigation evaluation, other research Decision Decision Decision Decision Decision Common/Inexpensive/Easy/Quick Uncommon/Expensive/Time Consuming

5 Study Design and Analysis Considerations Unique sites not a random selection of sites available Statistical inference within sites dependent on study protocol Inference beyond the studied facility limited to professional judgment Multiple sites increase level of confidence Metaanalysis for statistical inference regarding global effects Field studies are generally observational Models are useful, particularly when empirical data are sparse Manipulative studies possible within facilities Studies should address areas of significance and uncertainty Significance is based on weight of evidence

6 Level of Certainty Based on Available Weight of Evidence HIGH LOW

7 CONFIDENCE CONFIDENCE HIGH LOW

8 1 Raptor Fatality CONFIDENCE CONFIDENCE HIGH LOW 1 Raptor Fatality

9 CONFIDENCE CONFIDENCE HIGH LOW Low Use 1 Raptor Low 1 Raptor Fatality Use Fatality

10 Behavior CONFIDENCE CONFIDENCE HIGH LOW Low 1 Raptor Use Low Fatality 1 Raptor Use Behavior Fatality

11 CONFIDENCE CONFIDENCE HIGH LOW Raptor Nest Density Low Raptor 1 Raptor Use Behavio Fatality Low Nest 1 Raptor Density Fatality r Use Behavior

12 Metrics for fatalities 1.5 MW turbine Fatalities per turbine 100 kw turbine Fatalities per MW (Nameplate) Fatalities per MW (Site capacity factor) Fatalities per rotorswept area Fatalities per rotorswept hour Fatalities per MW produced

13 Risk and biological significance Risk may be to individuals or populations Risk to individuals seldom biologically significant, but may have social or legal significance Risk to populations is difficult to measure and to verify, but has biological significance Risk to individuals influenced by species characteristics and behavior Risk to populations is primarily influenced by demographics of the population Abundance Lambda

14 Reproduction Immigration Population Management Unit Boundary Emigration Mortality What do we need to know to manage/understand a population?

15 Reproduction Immigration Population Management Unit Boundary Emigration Mortality Control for confounding variables through study design?

16 McIntyre, et al., 2008 The Auk 125(1): Movements of juvenile golden eagles from Denali National Park and Preserve, AK, USA, during their first year of independence, using satellite telemetry.

17 Case Study Biological significance of wind enery fatalities in the Pacific Northwest, USA 5,086 MW capacity operational Dec 11 98% of the MW in the PNW come from the Columbia Plateau Ecoregion (CPE). CPE

18 Case Study Swainson s Hawks, Columbia Plateau, USA Estimated fatality rate 75 percentile : per MW per year At the current and possible buildout, fatalities equal 12% of the estimated population Half of all SWHA fatalities were found in August

19 Case Study Eagle Fatality Prediction Approaches USFWS Bayesian Approach F = Exposure * Collision Risk Prior distribution for exposure Prior distribution for collision risk Linear Model Approach (no prior) f = β*exposure (eagle use or eagle exposure) Band Model F=Exposure *(1Avoidance) * Collision probability

20 Case Study USFWS Model for Predicting Eagle Fatalities Bayesian approach to predicting annual fatality rates Predictions of annual eagle mortality (F) E int : the preconstruction measure of eagle activity within areas of potential eaglewind turbine interactions, C: the collision risk factor (fatality rate/eagle activity) Upper 80% credible interval used for the prediction

21 USFWS Model Data Needs Information needed to apply model includes: measure of eagle flight minutes or eagle use effort (includes measure of survey time and area surveyed) the rotor radius or diameter the number of turbines Seasonal wind speed data or estimate of seasonal operating time along with seasonal eagle use can help to refine the model Source of prior data is the critical piece in estimating fatalities

22 Physical Collision Risk Modeling Approach necessary in situations with little empirical data on collision risk Sophisticated computer simulation to address collision risk potential Assumptions regarding avoidance/attraction critical Literature is expanding on empirical estimates of avoidance/attraction 120 Degrees L w deg/sec Stick Bird Velocity = v

23 Model Summary Individualbased Incorporates details for Birds Wind turbines Wind park Onsite wind conditions Potential collisions with all structures Temporal variability Output: collision probability estimates

24 Fatality Index Regression Approach with Golden Eagle Example 14+ studies Most are Preconstruction use estimates and post construction mortality larger turbines (>1.5 MW) 0,250 0,200 0,150 0,100 0,050 y = 0,2789x + 0,0005 R² = 0,4819 0,000 0,000 0,100 0,200 0,300 0,400 0,500 0,600 Exposure Index

25 Essentially, all models are wrong, some are useful. Box and Draper (1987)

26 Predicting Avian Fatalities Best to present multiple approaches to fatality prediction. Bayesian Model, or some alternative Linear regression Physical model Estimates based on existing facilities with similar species abundance

27 Case Study Use data for reducing risk Micrositing Macrositing

28 Resource Selection

29 Foote Creek Rim, Wyoming USA Use at the site level suggests high mortality (>3 eagles per year) Actual mortality <1 eagle per year Micrositing based on use

30 Case Study Fatality estimation Determine bird and bat fatality rates for the project Comparison of estimated and predicted fatality rates Detect withinsite variability in fatality rates Comparison to other similar project sites Species composition Is further action necessary, such as mitigation?

31 Good Study Design Characteristics Bats: Small plots Tighter transects Carcass removal/searcher efficiency trials Lower searcher efficiencies require study design modification Higher carcass removal rates/shorter search intervals Seasonally (focus on fall) Raptors/Large Birds: Larger plots Wider transects Carcass removal/searcher trials High searcher efficiency Low carcass removal rates/longer search intervals Year round/period of occupancy

32 Strategy for reducing cost: Double Sampling Approach Subset of plots fully cleared of vegetation Search 100% of area on fully cleared plots, noting which fatalities are found on roads or pads and which are found elsewhere Search a large number of turbines on roads and pads only The proportion of estimated fatalities from full plots found on roads and pads versus the estimated total is used to adjust the fatalities on road and pad only searches.

33 Cost/Effort Comparison Double Sampling Daily Searches Weekly Searches Number of Searches 5,016 8,702 1,254 Number of Hours 1,800 26,100 3,762 Simulated Estimate and Variability / / / 4.84

34 Case Study Displacement/behavior impacts on ground nesting birds (Shaffer and Johnson, Northern Prairie Wildlife Research Center, Jamestown, North Dakota, unpublished data).

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37 Fatalities/turbine/night Fatalities/turbine/night Tier 5 Case Study Operational Mitigation of Wind Turbines To Avoid Bat Fatalities (Erickson et al High estimates of bat mortality have seasonal patterns: bat mortality tends to increase after July 15 and tapers off after October 15. Studies have shown an inverse relationship between mortality and wind speed; more bat fatalities are observed on nights with low wind speeds. Sources: Arnett et al. 2005, Mountaineer PA, USA Meyersdale PA, USA windspeed (m/s) windspeed (m/s)

38 Fowler Ridge Wind Farm, Indiana, USA 2011 Control Treatment Treatment Treatment Results 9 turbines, normal operation, 3.5 m/s cut in speed 42 turbines, blades feathered below 3.5 m/s cut in speed 42 turbines, blades feathered below 4.5 m/s cut in speed 42 turbines, blades feathered below 5.5 m/s cut in speed 3.5 m/s cut in w/ feathering = 66 bats found 4.5 m/s cut in w/ feathering = 42 bats found 5.5 m/s cut in w/ feathering = 25 bats found Control = 105 bats found Experimental treatments showed 36%, 57%, and 73% reductions in bat fatalities, respectively

39 Summary Design study protocols to address the question, species, time period, area of interest, desired precision and resources available (e.g., budget) Adapt protocols to address uncertainties Replicate within study sites and with multiple study sites Implement measures to control and reduce errors Standardize related variables and identify confounding variables Identify and minimize obvious bias

40 Summary continued Impact indicators should follow generally accepted scientific principles and as defined by the standards agreed to by stakeholders. BACI increases confidence in scientific conclusions based on observational studies, or impact gradient, or other suitable designs, given the situation. Probability sampling plan, stratify on relatively permanent features and only for shortterm studies; Systematic sampling plan for longterm studies. Make use of peer review whenever possible