population dynamics and habitat conservation for the
TRANSCRIPT
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
