Competing resource selection modeling predicts risk for mitigating impacts to flying birds

Competing resource selection modeling predicts risk for preventing and mitigating impacts to flying birds from industrial wind energy development Tricia Miller, Robert Brooks, Michael Lanzone, David Brandes, Jeff Cooper, Kieran O’Malley, Adam Duerr, Todd Katzner

There are direct effects on wildlife from wind energy development, but the costs are not equal among facilities, seasons or species.

•Site to site variation •Within site variation •Seasonal and other temporal variation Photo: Alameda County

Indirect effects of development are difficult to see and measure, but may be increasingly important as more facilities come online.

• Habitat loss • Habitat fragmentation • Displacement

Photo: John Terry

Small population of Golden Eagles migrates in a narrow band through the central Appalachian Mountains United States

Katzner et al. 2012

Canada

Wind power development is located in the migratory path of Golden Eagles

2012 - 41 existing or planned facilities with over 1191 turbines

Objective: Develop a tool to reduce direct and indirect effects of wind power development on Golden Eagles

To reach our objective: 1. Created a spatial model of habitat selection by low flying eagles

M. Lanzone

2. Created spatial model predicting “habitat� selection for wind turbines

Photo: John Terry

3. Combined these to create a risk map

Photo: John Terry, http://vawind.org/

Study area consists of three topographically distinct regions Northern Plateau

Allegheny Mountains

Ridge & Valley

Generalized estimating equation to model resource selection for eagles and wind turbines Elevation

“Good�

Winds

Northness Eastness Updraft Potential

Data sources: USGS, Nat. Renewable Energy Labs, Ecological Land Units (TNC) Updraft: Brandes & Ombalski 2004

Side Slopes Steep Slopes Summit

Habitat quality for wind power

Overlap = Risk

Habitat quality for eagles

Miller et al. 2014 Conservation Biology

Ranked risk by comparing habitat quality

Risk

Low Value

Eagle Habitat Quality

Poor

Low Risk Poor Mod – Fair – Ext Risk Excellent Moderate Risk Fair High Risk Good Extreme Risk Excellent

Wind Power Habitat Quality Poor Fair Excellent Poor Fair Excellent Fair Excellent Fair Excellent

Eagles used more high risk habitat in the Ridge & Valley NP

AM

RV

Every eagle in the Ridge & Valley encountered highest risk areas at least once during migration NP

AM

RV

Most turbines in the Allegheny Mountains are low risk NP

AM RV

Can apply the model to wind facilities and individual turbines

Identify turbine risk

But there are suitable areas for wind power where there is NOT overlap.

Risk Low Value Low Risk Mod - Extreme Moderate Risk High Risk Extreme Risk

We can use our models for conservation by making recommendations for siting turbines in low risk areas

We identified regional variation in risk Guide regional level management decisions •Restrict development •Increase pre-/postconstruction monitoring •Additional requirements to reduce risk

We identified project level risk Use to guide management decisions •Restrict development •Require mitigation for existing projects •Use with turbine level information prioritize monitoring •Make site and/or turbine level adjustments

Applied framework to understand seasonal variation in risk

We will apply this framework to other regions and species to mitigate risk from wind turbines

Funding: Penn State Ecology Penn State Earth and Environmental Institute Fellowship State Wildlife Grant Lindbergh Foundation Hydro Quebec Quebec MNRF Grant PA Wild Resources Conservation Fund Virginia Department of Game & Inland Fisheries Grant

Field assistants: Jeanette Parker, Dave Kramer, Eric Frank, Frank Nicoletti, B. Baillargeon, & Philipe BeauprĂŠ

Thank you!

Regional variation in risk – Ridge & Valley has NP

AM

RV

Eagles are present in the eastern US during fall, spring & winter

There may be greater risk during fall and winter when eagles fly at lower altitudes n = 48 (79,315)

n = 19 (25,778)

n = 43 (28,883)

Calculated logistic GEEs for eagles and wind developments in each region using 75% of data Left out 25% of data for model validation Repeated measures : Individual eagles Wind turbine facilities Backward step-wise selection, p<0.05 Calculated spatial models: resource selection functions for eagles – used vs. available w * = exp( β 1 x 1 + ... + β n x n )

resource selection probability functions for turbines – used vs. unused ∧

w =

exp( β 0 + β 1 x 1 + ... + β n x n ) 1 + exp( β 0 + β 1 x 1 + ... + β n x n )

Manly 2001

Reclassified continuous spatial models into four habitat quality bins Eagle Models Turbine Models

Validated eagle models postreclassification by comparing observed to expected per bin Assessed: R2>

•slope different from 1 •slope different from 0 •intercept different from 0 •goodness-of-fit

0.98

Random model slope = 0 Ideal model, slope = 1, intercept = 0 Johnson et al. 2006

Validated wind models pre-reclassification using area-under-the-curve & Kappa

AUC = 0.908±0.016 K = 0.572±0.051

AUC = 0.960±0.008 K = 0.761±0.035

AUC = 0.977±0.008 K = 0.794±0.041

Most projects have at least one high risk turbine NP

AM

RV