Presented as part of the session on the use of dynamic models for understanding range shifts.

1. Integrated population models for
predicting spatial and temporal dynamics
at range margins
Richard Chandler and Samuel Merker
Warnell School of Forestry and Natural Resources
University of Georgia

2. Trailing-edge populations
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3. Trailing-edge populations
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4. Trailing-edge populations
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5. Trailing-edge populations
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6. Trailing-edge populations
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7. Trailing-edge populations
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8. Trailing-edge populations
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9. Trailing-edge populations
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10. Motivating questions
What are the mechanisms governing range shifts?
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11. Motivating questions
What are the mechanisms governing range shifts?
• Phenological mismatch
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12. Motivating questions
What are the mechanisms governing range shifts?
• Phenological mismatch
• Competition/Predation
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13. Motivating questions
What are the mechanisms governing range shifts?
• Phenological mismatch
• Competition/Predation
• Direct physiological effects
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14. Motivating questions
What are the mechanisms governing range shifts?
• Phenological mismatch
• Competition/Predation
• Direct physiological effects
• Allee effects
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15. Motivating questions
What are the mechanisms governing range shifts?
• Phenological mismatch
• Competition/Predation
• Direct physiological effects
• Allee effects
What will be the consequences for trailing-edge
populations?
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16. Motivating questions
What are the mechanisms governing range shifts?
• Phenological mismatch
• Competition/Predation
• Direct physiological effects
• Allee effects
What will be the consequences for trailing-edge
populations?
How can we draw inferences, make reliable
forecasts, and inform conservation efforts?
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17. What would be the ideal model?
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18. What would be the ideal model?
Components
• Survival, reproduction, dispersal
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19. What would be the ideal model?
Components
• Survival, reproduction, dispersal
• Spatial and temporal variation
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20. What would be the ideal model?
Components
• Survival, reproduction, dispersal
• Spatial and temporal variation
• Density dependence
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21. What would be the ideal model?
Components
• Survival, reproduction, dispersal
• Spatial and temporal variation
• Density dependence
• Species interactions
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22. What would be the ideal model?
Components
• Survival, reproduction, dispersal
• Spatial and temporal variation
• Density dependence
• Species interactions
• Demographic and environmental stochasticity
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23. What would be the ideal model?
Components
• Survival, reproduction, dispersal
• Spatial and temporal variation
• Density dependence
• Species interactions
• Demographic and environmental stochasticity
• Individual heterogeneity
Age
Sex
Size
Other traits
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24. Ecological process model
Spatio-temporal point process model
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25. Ecological process model
Spatio-temporal point process model
Initial distribution and abundance
si,1 ∝ λ(s)
E(N1) = Λ =
S
λ(s) ds
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26. Ecological process model
Spatio-temporal point process model
Initial distribution and abundance
si,1 ∝ λ(s)
E(N1) = Λ =
S
λ(s) ds
zi,1 ∼ Bernoulli(Λ/M)
N1 =
M
i=1
zi,1
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27. Ecological process model
Spatio-temporal point process model
Initial distribution and abundance
si,1 ∝ λ(s)
E(N1) = Λ =
S
λ(s) ds
zi,1 ∼ Bernoulli(Λ/M)
N1 =
M
i=1
zi,1
Survival and recruitment
zi,t ∼ Bernoulli(zi,t−1φ(s) + ai,t−1γ(s) )
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28. Ecological process model
Spatio-temporal point process model
Initial distribution and abundance
si,1 ∝ λ(s)
E(N1) = Λ =
S
λ(s) ds
zi,1 ∼ Bernoulli(Λ/M)
N1 =
M
i=1
zi,1
Survival and recruitment
zi,t ∼ Bernoulli(zi,t−1φ(s) + ai,t−1γ(s) )
Dispersal
si,t ∼ k(si,t−1)
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29. Observation process models
Capture-recapture data
Distance sampling data
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30. Observation process models
Capture-recapture data
pi,j,k,t = p0 exp( si,t − xj /(2σ2
))
yi,j,k,t ∼ Bern(pi,j,k,t × zi,t)
Distance sampling data
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31. Observation process models
Capture-recapture data
pi,j,k,t = p0 exp( si,t − xj /(2σ2
))
yi,j,k,t ∼ Bern(pi,j,k,t × zi,t)
Distance sampling data
ui,t,k ∼ Normal(si,t, σ)
qi,j,k,t = exp( ui,t,k − xj /(2τ2
))
yi,j,k,t ∼ Bern(qi,j,k,t × zi,t)
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32. Study area
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33. Study area
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34. Study design
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35. Study design
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36. Study design
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37. Study design
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38. Study design
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39. Study design
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40. Data
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41. Data
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42. Data
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43. Data
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44. Model
Initial abundance
• Elevation
• Rhododendron index (NDVI)
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45. Model
Initial abundance
• Elevation
• Rhododendron index (NDVI)
Survival – constant
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46. Model
Initial abundance
• Elevation
• Rhododendron index (NDVI)
Survival – constant
Recruitment
• Elevation
• Rhododendron index
• Density dependence
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47. Model
Initial abundance
• Elevation
• Rhododendron index (NDVI)
Survival – constant
Recruitment
• Elevation
• Rhododendron index
• Density dependence
Detection
• Distance
• Observer
• Playback
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48. Results
Process Parameter Median Lower CI Upper CI
Initial abundance Intercept 0.171 -0.190 0.501
Elevation 0.983 0.711 1.239
NDVI 0.056 -0.239 0.339
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49. Results
Process Parameter Median Lower CI Upper CI
Initial abundance Intercept 0.171 -0.190 0.501
Elevation 0.983 0.711 1.239
NDVI 0.056 -0.239 0.339
Survival — 0.432 0.229 0.803
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50. Results
Process Parameter Median Lower CI Upper CI
Initial abundance Intercept 0.171 -0.190 0.501
Elevation 0.983 0.711 1.239
NDVI 0.056 -0.239 0.339
Survival — 0.432 0.229 0.803
Recruitment Intercept -1.614 -4.340 -0.910
Elevation 1.270 0.508 2.523
NDVI 0.336 -0.943 0.908
Density -0.178 -0.008 -0.639
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51. Results – Initial distribution
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52. Results – Initial distribution
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53. Results – Population growth
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54. Results – Forecasts
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55. Conclusions
(1) Spatial IPMs allow for inference on mechanisms
governing range shifts.
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56. Conclusions
(1) Spatial IPMs allow for inference on mechanisms
governing range shifts.
(2) Underlying point process model can be informed by
numerous data types.
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57. Conclusions
(1) Spatial IPMs allow for inference on mechanisms
governing range shifts.
(2) Underlying point process model can be informed by
numerous data types.
(3) Canada Warbler results are preliminary but suggest a
strong effect of climate on recruitment.
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58. Conclusions
(1) Spatial IPMs allow for inference on mechanisms
governing range shifts.
(2) Underlying point process model can be informed by
numerous data types.
(3) Canada Warbler results are preliminary but suggest a
strong effect of climate on recruitment.
(4) Future work will allow for causal inference by
combining observational and experimental data.
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59. Thanks
• Robert Cooper
• Jeff Hepinstall-Cymerman
• Len Reitsma
• Samuel Hach´e
• Heather Abernathy
• Brittney Ferrari
• Anna Joy Lehmicke
• Carly Chandler
• Jared Feura (photos)
• Alex Wilson
• Vincent Weber
• Angela Hsiung

60. Questions?