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Species-Habitat Associations PowerPoint Presentation
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Species-Habitat Associations

Species-Habitat Associations

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Species-Habitat Associations

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  1. The challenge of statistically identifying species-resource relationships on an uncooperative landscape Or… Facts, true facts, and statistics: a lesson in numeracy Barry D. Smith & Kathy Martin Canadian Wildlife Service, Pacific Wildlife Research Centre Delta, B.C., Canada Clive Goodinson Free Agent,Vancouver, B.C., Canada

  2. Species-Habitat Associations Objective: To incorporate habitat suitability predictions into a stand-level forest ecosystem model + =

  3. Can we show statistically that the relative quantity of a resource on the landscape predicts the presence of a species such as Northern Flicker?

  4. Logistic regression model output Predicted Predicted 0 1 0 1 ü û 123 16 0 Observed û ü 9 74 1

  5. Logistic regression model • Observed Groups and Predicted Probabilities • 20 + 1 + • I 1 I • I 1 I • F I 1 1 I • R 15 + 1 1 + • E I 1 1 1 1 I • Q I 1 1 1 111 1 1 I • U I 11 11 11 111 1 11 I • E 10 + 1 11111 11 11111 11 1 + • N I 1 11011110111111111 1 I • C I 0111100110011101011111 1 I • Y I 011100001001110001111111 I • 5 + 00 00110000000011000000111111111 + • I 001000100000000000000001111101 1 11 I • I 0 00000000000000000000000010001000110 11 I • I 0 1 00000000000000000000000000100000000001101111 1 I • Predicted --------------+--------------+--------------+--------------- • Prob: 0 .25 .5 .75 1 • Group: 000000000000000000000000000000111111111111111111111111111111 0 = Absent 1 = Present

  6. Predicted Sampling intensity is too low; birds occur within good habitat but sampling does not capture all occurrences. 0 1 ü û 0 Observed Habitat is not 100% saturated; there are areas of good habitat which are unoccupied. û ü 1 Spatial variability is too low or spatial periodicity of key habitat attributes is too high, given sampling intensity. Habitat is over 100% saturated; birds occur in areas of poor habitat. The playback tape pulls in individuals from outside the point-count radius.

  7. So, can we expect be successful in detecting species-habitat associations when they exist? • We use simulations where: • we generated a landscape, then • populated that landscape with a (territorial) species, then • sampled the species and landscape repeatedly to assess our ability to detect a known association

  8. Sample Simulation > Sample Sim’on

  9. To be as realistic as possible we need to make decisions concerning… • The characteristics of the landscape (resources) • The species’ distribution on the landscape • The sampling method • The statistical model(s)

  10. Spatial contrast is essential for, but doesn’t guarantee, success

  11. High Landscape Spatial Periodicity (SP)

  12. Medium Landscape Spatial Periodicity (SP)

  13. Low Landscape Spatial Periodicity (SP)

  14. It might help to conceptualize required resources by consolidating them into four fundamental suites: • Shelter (e.g., sleeping, breeding) • Food (self, provisioning) • Comfort (e.g. weather, temperature) • Safety (predation risk)

  15. To be as realistic as possible we had to make decisions concerning: • The characteristics of the landscape • The species’ distribution on the landscape • The sampling method • The statistical model(s)

  16. Territory establishment can be… Species centred Resource centred …but in either case sufficient resources must be accumulated for an individual to establish a territory

  17. If territory establishment is… Species centred …then the ‘Position function” sets the parameters for territory establishment

  18. Territory establishment Saturation Half-saturation

  19. Territory densities may be… High Low …so realistic simulations must be calibrated to the real world

  20. To be as realistic as possible we had to make decisions concerning: • The characteristics of the landscape • The species’ distribution on the landscape • The sampling method • The statistical model(s)

  21. Detection Function Point-count radius Vegetation plot radius

  22. To be as realistic as possible we had to make decisions concerning: • The characteristics of the landscape • The species’ distribution on the landscape • The sampling method • The statistical model(s)

  23. The statistical model • Deterministic model structure • Multiple regression, Logistic • Model error • Normal, Poisson, Binomial • Model selection • Parsimony (AIC), Bonferroni’s alpha, Statistical significance

  24. The deterministic model • Multiple regression (with 2 resources) • Yi= B0 + B1X1i + B2X2i + B12X1iX2i + εi • or Yi= f(X) + εi • Yi = detection (0,1,2,…) • X•i = resource value

  25. The deterministic model • Logarithmic: • Yi= e f(X) + εi • Yi = detection (0,1,2,...) • X•i = resource value

  26. The deterministic model • Logistic: • Yi= Ae f(X) /(1+ e f(X)) + εi • Yi = detection (0,1,2,…) • X•i = resource value

  27. Choosing the correct model form

  28. Linear model: 1 to 4 resources • 1 Resource: • Yi = B0 + B1X1i + εi • 4 Resources: • Yi = B0 + B1X1i + B2X2i + B3X3i + B4X4i • + B12X1iX2i + B13X1iX3i + B14X1iX4i • + B23X2iX3i + B24X2iX4i + B34X3iX4i • + B123X1iX2i X3i + B124X1iX2i X4i • + B134X1iX3i X4i + B234X2iX3i X4i • + B1234X1iX2i X3i X4i + εi Number of parameters required for… 1 Resource = 2 2 Resource = 4 3 Resource = 8 4 Resource = 16

  29. The statistical model • Deterministic model structure • Multiple regression, Logistic • Model error • Normal, Poisson, Binomial • Model selection • Parsimony (AIC), Bonferroni’s alpha, Statistical significance

  30. Poisson error Repeated samples of individuals randomly dispersed are Poisson-distributed

  31. Poisson error

  32. Negative-binomial error

  33. Normal error

  34. Binomial error

  35. The statistical model • Deterministic model structure • Multiple regression, Logistic • Model error • Normal, Poisson, Binomial • Model selection • Parsimony (AIC), Bonferroni’s alpha, Statistical significance

  36. Model Selection • Use AIC to judge the best of several trial models • The ‘best’ model must be statistically significant from the ‘null’ model to be accepted If =0.05, then Bonferroni’s adjusted  is: 1 Resource = 0.0500 2 Resource = .0169 3 Resource = 0.0073 4 Resource = 0.0034

  37. True, Valid and Misleading Models • If the ‘True’ model is: Yi = B0 + B123X1iX2i X3i • Then: • Yi = B0 + B3X3i is a ‘Valid’ model • Yi = B0 + B12X1i X2i is a ‘Valid’ model • Yi = B0 + B4X4i is a ‘Misleading’ model • Yi = B0 + B14X1i X4i is a ‘Misleading’ model

  38. 1 Resource Required - 1 Resource Queried Success identifying ‘True’ Model Logistic-Poisson Multiple Regression - Normal

  39. 1 Resource Required - 1 Resource Queried Success identifying ‘True’ Model Logistic-Poisson Logistic-Binomial

  40. 4 Resources Required - 4 Resources Queried Medium SP - Resources uncorrelated – 100% detection - Full True Valid Misleading

  41. 4 Resources Required - 4 Resources Queried High SP - Resources uncorrelated – 100% detection - Full True Valid Misleading

  42. 4 Resources Required - 4 Resources Queried Low SP - Resources uncorrelated – 100% detection - Full True Valid Misleading

  43. 1 Resources Required - 4 Resources Queried Medium SP - Resources uncorrelated – 100% detection - Full True / Valid Misleading

  44. 1 Resources Required - 4 Resources Queried High SP - Resources uncorrelated – 100% detection - Full True / Valid Misleading

  45. 1 Resources Required - 4 Resources Queried Low SP - Resources uncorrelated – 100% detection - Full True / Valid Misleading

  46. 1 Resources Required - 4 Resources Queried Medium SP - Resources 50% correlated – 100% detection - Full True / Valid Misleading

  47. 1 Resources Required - 4 Resources Queried Medium SP - Resources 50% correlated – 25% detection - Full True / Valid Misleading

  48. 1 Resources Required - 4 Resources Queried Medium SP - Resources 50% correlated - 25% detection - 50% Full True / Valid Misleading

  49. 1 Resources Required - 4 Resources Queried High SP - Resources 50% correlated – 25% detection – 50% Full True / Valid Misleading