Population Dynamics and Habitat Conservation for the Golden-cheeked Warbler

Adam Duarte Department of Biology Texas State University

Larry Thompson

Co-authors Jeff S. Hatfield, USGS Patuxent Wildlife Research Center Floyd W. Weckerly, Texas State University Jennifer L. R. Jensen, Texas State University James E. Hines, USGS Patuxent Wildlife Research Center James D. Nichols, USGS Patuxent Wildlife Research Center Todd M. Swannack, US Army Engineer Research and Development Center Michael R. J. Forstner, Texas State University M. Clay Green, Texas State University

Acknowledgments

This project was sponsored in part by the Department of the Army, U.S. Army Garrison-Fort Hood, Directorate of Public Works, Environmental Division, Natural and Cultural Resources Management Branch (NRMB). The content of the information does not necessarily reflect the position or the policy of the NRMB, and no official endorsement should be inferred.

Sufficient breeding habitat protected for continued existence ≥1 viable, self-sustaining population in each of 8 regions

Potential for gene flow exists across regions between demographically self-sustaining populations for long-term viability

Sufficient and sustainable non-breeding habitat to support breeding populations

All existing populations public lands protected and managed for continued existence

All criteria met 10 consecutive years

Recovery plan

Previous PVA

Support 3,000 bp

Current status Approx. 263,339 M < Required protected habitat

See: Hatfield, J. S., F. W. Weckerly, and A. Duarte. 2012. Shifting foundations and metrics for golden-cheeked warbler recovery. Wildlife Society Bulletin 36(3):415-422.

Species viability

Habitat dynamics

Survival

Movement Population viability analysis

Outline

Assess range-wide breeding habitat change: 2000–2010 Hypothesis: Habitat loss and fragmentation occurred at higher rates around San Antonio and Austin

Duarte, A., J. L. R. Jensen, J. S. Hatfield, and F. W. Weckerly. 2013. Spatiotemporal variation in range-wide golden-cheeked warbler breeding habitat. Ecosphere 4:152.

Habitat dynamics

30 m Resolution

9 Images Required

Winter and Summer

Time Steps 1999–2001 2010–2011

Morrison et al. 2010

Landsat 5 imagery

Mean area

Total area

Number of patches

Aggregation index

Habitat patch metrics

Total habitat

0

100

200

300

400

500

1 2 3 4 5 6 7 8

Tota

l hab

itat (

ha x

103 )

Recovery unit

2000 2010

* *

*

0200400600800

1000120014001600

1 2 3 4 5 6 7 8

Num

ber o

f pat

ches

Recovery unit

2000 2010

Number of patches

*

* *

Mean patch size

0

100

200

300

400

500

600

700

1 2 3 4 5 6 7 8

Mea

n pa

tch

size

(ha)

Recovery unit

2000 2010

*

*

*

Aggregation index

80

82

84

86

88

90

92

1 2 3 4 5 6 7 8

Aggr

egat

ion

inde

x (%

)

Recovery unit

2000 2010

* *

Large reduction in range-wide breeding habitat

Habitat loss and fragmentation occurred at higher rates in recovery units 5, 6, and 8

Conclusions

Habitat dynamics ✔

Survival

Movement Population viability analysis

Outline

Update survival

Duarte, A., J. E. Hines, J. D. Nichols, J. S. Hatfield, and F. W. Weckerly. 2014. Age-specific survival of male golden-cheeked warblers on the Fort Hood Military Reservation, Texas. Avian Conservation and Ecology 9:4.

Survival

Time

Random-effects model

Overall mean: 0.47 (±0.02) Process variance: 0.0120 Sampling variance: 0.0113 (48.5%)

Adult estimates

Survival

Age and time

Random-effects model

Overall mean: 0.28 (±0.06) Process variance: 0.0076 Sampling variance: 0.0149 (66.2%)

Juvenile estimates

No strong evidence Spatial variability Linear temporal patterns Transient birds

Updated mean survival and variances

Juvenile survival no different Adult survival is 16% lower

Movement?

R. M. Buquoi

Conclusions

Habitat dynamics ✔

Survival ✔

Movement Population viability analysis

Outline

Objectives

Estimate immigration using survey data Model the effect of immigration Hypothesis: Immigration is driving local population dynamics

Adam Duarte

Duarte, A., F. W. Weckerly, M. Schaub, and J. S. Hatfield. 2015. Estimating golden-cheeked warbler immigration: implications for the spatial scale of conservation. Animal Conservation 19:65-74.

Integrated population model (IPM)

N

State-space model

F

φA

φJ

Capture-resight model

Immigration (ω)

Count

Fledglings

Territories

Fecundity model p

Marked birds

Study areas

Golden-cheeked warbler IPM

State-space model Countt ~ Poisson(Nt) Nt = Binomial(NF,t-1, φJ,t-1)+Binomial(Nt-1, φA,t-1)+Poisson(Nt-1*ωt) Fecundity model Jt ~ Poisson(Tt*Ft) NF,t ~ Poisson(0.5*Nt*Ft) Capture-resight model φJ,t ~ Beta(αJ,βJ) φA,t ~ Beta(αA,βA) x̄J ~ Normal(0.28,0.015) x̄A ~ Normal(0.47,0.011) σJ

2 = 0.008 σA2 = 0.012

IPM results

Greg Lasley

λ= 1.04 (CI: 1.02–1.07) Greg Lasley

Parameter Mean (±SD)

Temporal variance

Immigration rate (ω) 0.33 (±0.15)

0.031

Fledgling rate (F) 1.42 (±0.13)

0.242

No immigration

λ = 0.70 (CI: 0.42–0.98) Survival multiplier = 1.43 φAdult = 0.73 φJuvenile = 0.37 φAdult = true survival φJuvenile = 0.69

Rebekah Rylander

Current survival estimates φAdult = 0.47 φJuvenile = 0.28

Habitat dynamics ✔

Survival ✔

Movement ✔ Population viability analysis

Outline

Estimate habitat transition probabilities Examine if viability is possible given the current habitat dynamics and vital rate estimates Evaluate if protecting a greater amount of habitat would increase carrying capacity in the future

Population viability analysis

Duarte, A., J. S. Hatfield, T. M. Swannack, M. R. J. Forstner, M. C. Green, and F. W. Weckerly. 2016. Simulating range-wide population and breeding habitat dynamics for an endangered woodland warbler in the face of uncertainty. Ecological Modeling 320(7691):52-61.

Hypotheses:

Units 5, 6, and 8 have the highest rates of habitat loss

Transitions will be directional

Habitat transitions

National land cover database

200 ha hexagons

Habitat State % Habitat High (H) ≥70 Medium (M) ≥40 Low (L) ≥10 None (N) <10

Estimating transitions

Multistate CMR models (ψrs)

Projection model

Programmed in Python Spatially explicit Male based Pre-breeding census Wintering range not included Base unit = 200 ha hexagon

Michael A. Murphy

Projection model – 2nd loop

Environmental stochasticity Juvenile survival (φJ,t) Beta Adult survival (φA,t) Beta Productivity (F) Lognormal Demographic stochasticity Juvenile survivors ~ Binomial(NF,t-1, φJ,t-1) Adult survivors ~ Binomial(Nt-1, φA,t-1) Fledglings ~ Poisson(0.5*Nt*Ft) Emigrants ~ Binomial([NF,t-1 - juvenile survivors], Disp) Natal site dispersal

Projection model – 2nd loop

Density dependence Ceiling model Overshoot K – breeding site dispersal Dispersal Randomly select available hexagons (with replacement) Overshoot K (3×) Habitat change Every 5 years Only non-protected lands

Scenarios

Productivity Juvenile survival Adult survival

Model Mean Variance Mean Variance Mean Variance

I 1.42 0.2415 0.28 0.0076 0.47 0.0120

II 2.52 0.2415 0.28 0.0076 0.52 0.0120

III 3.60 0.2415 0.28 0.0076 0.57 0.0120

Dispersal Started with 0.25; range = 0.05–0.55

Protected habitat Increased K to 1500, 3000, 4500 per recovery unit

All simulations 500 iterations; 50 years

Habitat transitions

Directional Rarely skipped states

Population dynamics

Dispersal Model I Model II Model III

0.05 NA 0.06 0.00

0.15 NA 0.00 0.00

0.25 1.00 0.00 0.00

0.35 1.00 NA NA

0.45 1.00 NA NA

0.55 0.00 NA NA

Dispersal Model I Model II Model III

0.05

NA

5379 (4923–5870)

289712* (287935–291624)

0.15

NA

288515* (286487–290380)

289708* (287761–291510)

0.25

4 (0–14)

289719* (287901–291588)

289669* (287780–291625)

0.35

100 (58–152)

NA

NA

0.45

1847 (1619–2096)

NA

NA

Ben Wilson

Population dynamics

Dispersal Model I Model II Model III

0.05 NA 0.06 0.00

0.15 NA 0.00 0.00

0.25 1.00 0.00 0.00

0.35 1.00 NA NA

0.45 1.00 NA NA

0.55 0.00 NA NA

Dispersal Model I Model II Model III

0.05

NA

5379 (4923–5870)

289712* (287935–291624)

0.15

NA

288515* (286487–290380)

289708* (287761–291510)

0.25

4 (0–14)

289719* (287901–291588)

289669* (287780–291625)

0.35

100 (58–152)

NA

NA

0.45

1847 (1619–2096)

NA

NA

Ben Wilson

Population dynamics

Dispersal Model I Model II Model III

0.05 NA 0.06 0.00

0.15 NA 0.00 0.00

0.25 1.00 0.00 0.00

0.35 1.00 NA NA

0.45 1.00 NA NA

0.55 0.00 NA NA

Dispersal Model I Model II Model III

0.05

NA

5379 (4923–5870)

289712* (287935–291624)

0.15

NA

288515* (286487–290380)

289708* (287761–291510)

0.25

4 (0–14)

289719* (287901–291588)

289669* (287780–291625)

0.35

100 (58–152)

NA

NA

0.45

1847 (1619–2096)

NA

NA

Ben Wilson

Population dynamics

Dispersal Model I Model II Model III

0.05 NA 0.06 0.00

0.15 NA 0.00 0.00

0.25 1.00 0.00 0.00

0.35 1.00 NA NA

0.45 1.00 NA NA

0.55 0.00 NA NA

Dispersal Model I Model II Model III

0.05

NA

5379 (4923–5870)

289712* (287935–291624)

0.15

NA

288515* (286487–290380)

289708* (287761–291510)

0.25

4 (0–14)

289719* (287901–291588)

289669* (287780–291625)

0.35

100 (58–152)

NA

NA

0.45

1847 (1619–2096)

NA

NA

Ben Wilson

Low terminal extinction risk Considerable uncertainty

Habitat loss is occurring at high rates

Conserving large tracts of habitat is warranted

Conservation implications

Donald Brown

Quantified range-wide breeding habitat change Large-scale habitat loss over last decade Identified recovery units for prioritization Updated survival estimates Adult survival is 16% lower than previously reported Provided 1st movement estimate Immigration is driving local population dynamics – spatial scale Projected habitat dynamics into the future Conserving large tracts of habitat is warranted Projected population dynamics into the future Low terminal extinction risk

Summary of Duarte dissertation:

Future directions

Adam Duarte’s email: adam.duarte@oregonstate.edu

Questions