NWCC Webinar:Offsetting Unavoidable Take of Eagles

Taber D. Allison, Jean Fitts Cochrane, Eric Lonsdorf, and Carol Sanders-Reed

January 27, 2016

1

Welcome

• Introductions• Purpose of Webinar

o Context o Offset Mitigationo General Methodologyo Voluntary Lead Abatement o Reducing Eagle Vehicle Collisionso Next Stepso Questions

2

Regulatory Framework

Predict Take(Avoidance)

Implement ACPs (Minimization)

Offset Unavoidable Take (Compensation)

3

Context: AWWI’s Eagle Research Framework

Eagle Initiative

Technological Innovation

Science for Policy & Practice

Information Exchange

• Updated Eagle Take Model

Predicting and Avoiding Take

• Technology Verification Program

Minimizing Take (ACPs)

• Mitigation Toolbox

Mitigating Unavoidable Take

4

Quantifying Mitigation Offsets

Power Pole Retrofitting Model # Eagles Saved

Quantifiable & Verifiable

5

Possible Offsets in the ECPG

“…any compensatory mitigation that directly leads to…the avoided loss of these eagles (e.g., reducing vehicle/eagle collisions, making livestock water tanks ‘eagle-safe’, lead ammunition abatement, etc.) could be considered for compensation…”

Eagle Conservation Plan Guidance, Module 1 – Land-based Wind Energy, Version 2 U.S. Fish and Wildlife Service Division of Migratory Bird Management

6

Quantifying Mitigation Offsets

Mitigation Option ?? Model ?? # Eagles Saved

Quantifiable & Verifiable

7

AWWI Compensatory Mitigation Project

• Goal: develop predictive models for compensatory mitigation that will numerically compensate for eagle mortalityo Utilize expert elicitation to parameterize models

o Work with stakeholders to evaluate and implement models

o Long-term vision to expand the toolbox of reliable compensatory mitigation options

8

AWWI Mitigation Project - Status

9

EaglesCompensatory Mitigation Models

Lead Model: Published Vehicle Model: In Peer Review

Habitat Model: Model Development In Progress

Mitigation Model Development

• Assembled team of experts

• Specified geographic area – Wyoming

• Utilized structured approach to elicit expert judgmentso Model design

o Parameter values

• Individual expert uncertainty and diversity in responses captured as probability distributions

10

Elicitation Example: Mortality and Blood Lead

11

Estimating mortality based on blood lead levelsAssumptions:1) mortality is a direct result of lead consumption that produced this blood lead level (peak level post-scavenge) at any time during the month2) DO NOT include mortality due to any sources other than lead exposure (e.g., the "background" rate)

Lowest reasonable estimate for the

probability of death

Highest reasonable estimate for the

probability of death

Your best estimate for the probability of death

50 ug/dL

75 ug/dL

100 ug/dL

125 ug/dL

150 ug/dL

200 ug/dL

300 ug/dL

400 ug/dL

500 ug/dL

600 ug/dL

700 ug/dL

Given this maximum blood lead level at ANY TIME

during a month:

Any comments or sources for what are you thinking about as you answer?

How likely do you believe it is that a wild-living eagle will die as a direct result of having blood lead reach this level at some point during a month?

(answer between 0 and 100 probability in each box) How confident are you that the probability of death will be

within the range of yourlowest-to-highest estimates?

(answer between 50-100%)

These columns do NOT need to sum to 100; any probability may be appropriate for any box

3) blood lead levels here are MAXIMUM following a scavenge event with lead exposure (e.g., when eagles are sampled in the field or in rehab, many or most will have blood lead below their maximum exposure due to time lapsed since the scavenge event)

Example Elicitation Output

12

0102030405060708090

100

50 75 100 125 150 200 300 400 500 600 700

Prob

abili

ty (%

)

Expert 1

most likely0

102030405060708090

100

50 75 100 125 150 200 300 400 500 600 700

Expert 2

0102030405060708090

100

50 75 100 125 150 200 300 400 500 600 700Maximum blood lead level (ug/dL)

Expert 30

102030405060708090

100

50 75 100 125 150 200 300 400 500 600 700Maximum blood lead level (ug/dL)

Expert 4

Mitigation Model Development (cont’d)

• Created a custom computer model estimating eagle deaths from mortality source

• Ran 5,000 simulations of the model with stochastic sampling to estimate variance in expected outcomes

• Conducted sensitivity analyses of key parameters

• Iterative Process – reviewed output and revised

13

Model 1: Voluntary Lead Abatement

14

Voluntary Lead Abatement: Participating Experts

• Pete Bloom – Bloom Consulting, Inc.

• Michael Collopy – University of Nevada - Reno

• Chris Franson – U. S. Geological Survey

• Grainger Hunt – The Peregrine Fund

• Todd Katzner – University of West Virginia

• Terra Kelly – UC Davis

• Mike Kochert – U. S. Geological Survey (ret.)

• Brian Millsap – U. S. Fish and Wildlife Service

• Robert Murphy – U. S. Fish and Wildlife Service

• Leslie New – U. S. Geological Survey

• Patrick Redig – University of Minnesota

• Bruce Rideout – San Diego Zoo15

Voluntary Lead Abatement

16

Voluntary Lead Abatement Model Assumptions

• Eagles are adept at finding gut piles

• Expected scavenging rate can be calculate directly from eagle density

• Maximum blood lead is a useful index of lead exposure and potential mortality

• Predicting probability of “acute” poisoning mortality within one month is reasonable

• Population-based model accurately represents natural variation in individual eagle deaths

17

Model Simulation and Results

• ~3% of Wyoming eagle population dies from lead poisoning related to big game hunting in Wyoming

• Lead shot replacement more effective than gut pile removalo 50% ammunition replacement -> 50% reduction in mortalityo 50% gut pile removal ≈ 1/3 reduction in mortality

Cochrane, J.F., Lonsdorf, E., Allison, T.D., Sanders-Reed, C.A. 2015. Modeling with uncertain science: estimating mitigation credits from abating lead poisoning in Golden Eagles. Ecol. Appl. 25, 1518–1533.

18

Sensitivity Analysis

Key Variables1. Mortality rate by maximum blood lead level – rescue birds2. Lead exposure per gut pile ingested – broad uncertainty on

absorption3. Number gut piles eaten in month4. Minimum lag time between gut pile ingestion

19

• What percentage of hunter participation in switching from lead to non-lead bullets is needed to offset unavoidable take?

• Given an expected level of hunter participation, how many eagles do we estimate will be saved?

• How do estimates change if we apply different levels of risk tolerance?

Mitigation Scenario: Potential Questions

20

• Project Location: region encompassing Casper, Wyoming that includes big game hunting units 22, 34, 66, 67, 88, 89, an area of approximately 16,303 km2

• Unavoidable Take: five eagles per year

• Key Model Inputs: o Eagle abundance: 679 eagles (4.17/100 km2)o Lead “availability”: 6.46 gut piles/golden eagle

21

Sample Mitigation Scenario

Mitigation Example: Casper, WY Area

22

Num

ber o

f eag

le d

eath

s avo

ided

Mitigation rate (% ammunition non-lead)

5.0

8.2

0

5

10

15

20

0 10 20 30 40 50 60 70 80 90 100

Expected (median) mitigation estimates

2.2

5.0

0

5

10

15

20

0 10 20 30 40 50 60 70 80 90 100

Cautionary (20th percentile) mitigation Cautionary (20th percentile) mitigation estimates

Model 2: Vehicle Collision Reduction

23

Vehicle Collision Reduction Model

• Pete Bloom – Bloom Consulting, Inc.• Clint Boal – USGS/Texas Tech

University (co-author)• Michael Collopy – University of

Nevada - Reno• Todd Katzner – U. S. Geological

Survey• Mike Kochert – U. S. Geological

Survey (ret.)• Brian Millsap – U. S. Fish and

Wildlife Service• Robert Murphy – U. S. Fish and

Wildlife Service• Bob Oakleaf – Wyoming Game and

Fish Department• Leslie New – U. S. Geological Survey• Ben Skipper – Texas Tech University

Probability that a golden eagle present around a road-killed carcass will be hit by a (any) vehicle during a "use-hour,"

considering the eagle's age and the road traffic volume, and whether the roadside is 'forested' or 'open'?

Probability (0-100) that a golden eagle will be hit by a vehicle, per "use-hour" around a road-killed carcass

Most likely estimates

Young eagle (

Vehicle Collision Reduction Model

25

Eagle Vehicle Collision Model Assumptions

• Total scavenging or use-hours increased gradually (modeled as a power function) as eagle density increased

• Juvenile eagles (< 1 yr) spent proportionally more time scavenging than did older, experienced foragers

• Average population age ratio of 0.17 juvenile eagles (

0

1

2

3

4

5

6

7

5 15 25 35 45 55 65 75 85 95 105

Tota

l eag

le d

eath

s

Vehicles per hour (vph)

Percentile10090805020100

Eagle Collision Deaths – Natrona County, WY

Simulation Results

27

Results are preliminary and should not be quoted or cited.

5

15

25

35

Road Class (vph)

0

0.2

0.4

0.6

0.8

1

1.2

5 4 3 2 1

Med

ian

eagl

es sa

ved

Carcass Removal Interval (days)

Mitigation credits

0

1

2

3

4

5 4 3 2 1

Tota

l driv

ing

(100

00 k

m)

Carcass Removal Interval (days) (days)

Mitigation effort

0.0

0.2

0.4

0.6

0.8

1.0

1.2

5 4 3 2 1

Eagl

es sa

ved

/ 100

00 k

mCarcass Removal Interval (days)

Mitigation efficiency

Estimating Mitigation Efficiency

28

Results are preliminary and should not be quoted or cited.

Fig 4Designing Mitigation

29

Credits/10,000 km driven

Results are preliminary and should not be quoted or cited.

Collision Model Sensitivity Analysis

• Key Variableso Total carcasses availableo Collision rate per scavenging use-houro Scavenging use-hours per available carcasso Avoidance rate by traffic volume

• Less Importanto Eagle density o Carcass-days per carcasso Age ratio of eagle scavenging use-hours

30

Results are preliminary and should not be quoted or cited.

Vehicle Collisions Highlights

• Carcass removal efficiency (eagles saved/km traveled) was highest on low traffic volume roads: fewer eagles, but less frequent road maintenance

• Update model predictions with site-specific parameters, e.g., eagle density, roads, traffic, road maintenance, and road kill (carcass) abundance and distributions

• Eagle carcass use and avoidance behaviors are tractable, high priority targets for study

• Increase offset? Go to another county!

31

Results are preliminary and should not be quoted or cited.

Summary and Next Steps

• Alternative mitigation options are predicted to provide sufficient offset credit

• Models are hypotheses that need to be evaluated and improved

o Lead Abatement: looking for partners to test and improve the model

o Vehicle Collisions: Currently conducting feasibility study for more complex field trials (planned for next year)

• Complete prey habitat enhancement models (increase eagle productivity)

32

Questions?

Eagle Research Framework Available at www.awwi.org 33

NWCC Webinar:�Offsetting Unavoidable Take of EaglesWelcomeRegulatory FrameworkContext: AWWI’s Eagle Research FrameworkQuantifying Mitigation OffsetsPossible Offsets in the ECPGQuantifying Mitigation OffsetsAWWI Compensatory Mitigation ProjectAWWI Mitigation Project - StatusMitigation Model DevelopmentElicitation Example: Mortality and Blood LeadExample Elicitation OutputMitigation Model Development (cont’d)Model 1: Voluntary Lead AbatementVoluntary Lead Abatement: Participating ExpertsVoluntary Lead AbatementVoluntary Lead Abatement Model AssumptionsModel Simulation and ResultsSensitivity AnalysisMitigation Scenario: Potential QuestionsSample Mitigation ScenarioMitigation Example: Casper, WY AreaModel 2: Vehicle Collision ReductionVehicle Collision Reduction ModelVehicle Collision Reduction ModelEagle Vehicle Collision Model AssumptionsSlide Number 27Slide Number 28Slide Number 29Collision Model Sensitivity AnalysisVehicle Collisions HighlightsSummary and Next StepsQuestions?