1. Habitat Evaluation Procedures 1969-1976 – an enlightened Congress passes conservation legislation Affecting management of fish & wildlife resources NEPA (National Environmental Policy Act) ESA Forest & Rangelands Renewable Resources Planning Act Federal Land Policy & Management Act

2. Habitat Evaluation Procedures Stimulates federal & state agencies to change management, thus: 1)simple, rapid, reliable methods to determine & predict the species and habitats present on lands; 2) expand database for T/E, rare species; 3)Predict effects of various land use actions

3. Habitat Evaluation Procedures USFWS Habitat analysis models Goal = Assess impacts at a community level (i.e., species representative of all habitats being studied) e.g., use guild of species?

4. Habitat Evaluation Procedures USFWS Habitat analysis models What is a model? Important points to consider relative to models? What variables should be measured and/or included in the model?

5. Habitat Evaluation Procedures Three Categories of Techniques: 1) Single-species models a) simple correlation models e.g., vegetation type-species matrix Species habitat matrix

7. Habitat Evaluation Procedures Three Categories of Techniques: 1) Single-species models b) statistical models i.e., prediction of distribution and/or abundance What types?

8. Carnivore Habitat Research at CMU Spatial Ecology Overlay hexagon grid onto landcover map Compare bobcat habitat attributes to population of hexagon core areas

9. Carnivore Habitat Research at CMU Spatial Ecology Landscape metrics include: Composition (e.g., proportion cover type) Configuration (e.g., patch isolation, shape, adjacency) Connectivity (e.g., landscape permeability)

10. Carnivore Habitat Research at CMU Spatial Ecology Calculate and use Penrose distance to measure similarity between more bobcat & non-bobcat hexagons Where: population i represent core areas of radio-collared bobcats population j represents NLP hexagons p is the number of landscape variables evaluated μ is the landscape variable value k is each observation V is variance for each landscape variable after Manly (2005).

11. Penrose Model for Michigan Bobcats VariableMean Vector bobcat hexagons NLP hexagons % ag-openland15.832.4 % low forest51.410.4 % up forest17.643.7 % non-for wetland8.62.3 % stream3.40.9 % transportation3.05.2 Low for core27.63.6 Mean A per disjunct core 0.72.6 Dist ag50.044.9 Dist up for55.043.6 CV nonfor wet A208.3120.1

12. Carnivore Habitat Research at CMU Spatial Ecology Each hexagon in NLP then receives a Penrose Distance (PD) value Remap NLP using these hexagons Determine mean PD for bobcat-occupied hexagons Preuss 2005

13. Habitat Evaluation Procedures Three Categories of Techniques: 1) Single-species models b) statistical models * modern statistical modeling & model selection techniques e.g., logistic regression & Resource Selection Probability Functions (RSF) & RSPF for determining amount & dist. of favorable habitat

14. X Y 0 1 Habitat Evaluation Procedures Logistic regression: Y = β0 + β 1 X 1 + β 2 X 2 + β 3 X 3 = logit(p) Pr(Y = 1 | the explanatory variables x) = π π = e –logit(p) / [1+ e –logit(p) ]

15. Resource Selection Functions (RSF) Ciarniello et al. 2003 Resource Selection Function Model for grizzly bear habitat landcover types, landscape greenness, dist to roads

16. Resource Selection Probability Functions (RSPF) Mladenoff et al. 1995 Resource Selection Probability Function Model for gray wolf habitat road density

17. Predicted American Woodcock Abundance Map

18. Quantifying Habitat Use – Resource Selection Ratios Need: 1) Determine use (e.g., prop. Use) 2) Determine availability (e.g., prop avail.) Selection ratio – for a given resource category i w i = prop use / prop avail. If w i = 1, 1

19. Quantifying Habitat Use – Resource Selection Ratios Selection ratio w i = prop use / prop avail. w i = (U i /U + ) / (A i /A + ) U i = # observations in habitat type i U + = total # observations (n) A i = # random points in habitat type i A + = total # of random points

20. Quantifying Habitat Use – Resource Selection Ratios Look at Neu et al. (1974) moose data = 117 observations of moose tracks within 4 different vegetation [habitat] types

21. Quantifying Habitat Use – Resource Selection Ratios Veg. TypeUseAvailwi Interior burn250.340(25/117)/0.340 = 0.628 Edge burn220.101 Edge unburned300.104 Interior unburned 400.455 Totals1171.000

22. Quantifying Habitat Use – Resource Selection Ratios Veg. TypeUseAvailwi Interior burn250.340(25/117)/0.340 = 0.628 Edge burn220.101(22/117)/0.101 = 1.862 Edge unburned300.104 Interior unburned 400.455 Totals1171.000

23. Quantifying Habitat Use – Resource Selection Ratios Veg. TypeUseAvailwi Interior burn250.340(25/117)/0.340 = 0.628 Edge burn220.101(22/117)/0.101 = 1.862 Edge unburned300.1042.465 Interior unburned 400.455 Totals1171.000

24. Quantifying Habitat Use – Resource Selection Ratios Veg. TypeUseAvailwi Interior burn250.340(25/117)/0.340 = 0.628 Edge burn220.101(22/117)/0.101 = 1.862 Edge unburned300.1042.465 Interior unburned 400.4550.751 Totals1171.000

25. Quantifying Habitat Use – Resource Selection Ratios Selection ratio * Generally standardize w i to 0-1 scale for comparison among habitat types std w i = w i / Σ (w i )

26. Quantifying Habitat Use – Resource Selection Ratios Veg. TypewiStd wi Interior burn0.6280.628/5.706 = 0.110 Edge burn1.8621.862/5.706 = 0.326 Edge unburned2.4650.432 Interior unburned 0.7510.132 Totals5.7061.000

27. Habitat Evaluation Procedures Three Categories of Techniques: 1) Single-species models c) Habitat Suitability Index (HSI) models

28. Habitat Suitability Index (HSI)

29. Model (assess) habitat (physical & biological attributes) for a wildlife species, e.g., USFWS -Habitat Units (HU) = (HSI) x (Area of available habitat) -Ratio value of interest divided by std comparison HSI = study area habitat conditions optimum habitat conditions

30. Habitat Suitability Index (HSI) Model (assess) habitat (physical & biological attributes) for a wildlife species, e.g., USFWS -HSI = index value (units?) of how suitable habitat is -0 = unsuitable; 1= most suitable -value assumed proportional to K

31. Habitat Suitability Index (HSI) include top environmental variables related to a species’ presence, distribution & abundance

32. Habitat Suitability Index (HSI) List of Habitat Suitability Index (HSI) models http://el.erdc.usace.army.mil/emrrp/emris/emrishel p3/list_of_habitat_suitability_index_hsi_models_p ac.htmhttp://el.erdc.usace.army.mil/emrrp/emris/emrishel p3/list_of_habitat_suitability_index_hsi_models_p ac.htm e.g., HSI for red-tailed hawk

33. Habitat Suitability Index (HSI) Red-tailed Hawk

38. For Grassland: Food Value HSI = (V 1 2 x V 2 x V 3 ) 1/4 For Deciduous Forest: Food Value HSI = (V 4 x 0.6) Reproductive value HSI = V 5 Habitat Suitability Index (HSI) Red-tailed Hawk

39. Habitat Evaluation Procedures Three Categories of Techniques: 1) Single-species models c) Habitat Capability (HC) models - USFS - describe habitat conditions associated with or necessary to maintain different population levels of a species ( compositions)

40. Habitat Evaluation Procedures Three Categories of Techniques: 1) Single-species models c) Habitat Capability (HC) models - uses weighted values based on habitat capacity rates at each successional stage of veg. for reproduction, resting, and feeding

41. Habitat Evaluation Procedures Three Categories of Techniques: 1) Single-species models c) Habitat Capability (HC) models -

42. Habitat Evaluation Procedures Three Categories of Techniques: 1) Single-species models c) Pattern Recognition (PATREC) models - use conditional probabilities to assess whether habitat is suitable for a species - must know what is suitable & unsuitable habitat

43. Habitat Evaluation Procedures Three Categories of Techniques: 1) Single-species models c) Pattern Recognition (PATREC) models - use series of habitat attributes - must know relation of attributes to population density

44. PATREC Models Expected Habitat Suitability (EHS) = [P(H) x P (I/H)] / [P(H) x P (I/H)] + [P (L) x P (I/L)] P(H) = prop. high density habitat P (I/H)] = prop. area has high population potential P (L) = prop. low density habitat P (I/L) = prop. area has low population potential * Low & high population potential identified from surveys

45. Habitat Evaluation Procedures Three Categories of Techniques: 1) Multiple-species models a) Integrated Habitat Inventory and Classification System (IHICS) - BLM - system of data gathering, classification, storage - no capacity for predicting use or how change affects species

46. Habitat Evaluation Procedures Three Categories of Techniques: 1) Multiple-species models b) Life-form Model - USFS -

47. Habitat Evaluation Procedures Three Categories of Techniques: 1) Multiple-species models b) Community Guild Models - can be used to estimate responses of species to alteration of habitat - (like Life-form model) clusters species with similar habitat requirements for feeding & reproduction

48. A = B = alpha (  ) diversity – within habitat C = beta (  ) diversity – among habitat D = gamma (  ) diversity – geographic scale Three Scales of Diversity

49. Alpha & Gamma Species Diversity Indices Shannon-Wiener Index – most used -sensitive to change in status of rare species H’ = diversity of species (range 0-1+) s = # of species p i = proportion of total sample belonging to ith species

50. Alpha & Gamma Species Diversity Indices Shannon-Wiener Index

52. Alpha & Gamma Species Diversity Indices Simpson Index – sensitive to changes in most abundant species D = diversity of species (range 0-1) s = # of species p i = proportion of total sample belonging to ith species

53. Alpha & Gamma Species Diversity Indices Simpson Index

55. Alpha & Gamma Species Diversity Indices Species Evenness H’ max = maximum value of H’ = ln(s)

56. Beta Species Diversity Indices Sorensen’s Coefficient of Community Similarity – weights species in common S s = coefficient of similarity (range 0-1) a = # species common to both samples b = # species in sample 1 c = # species in sample 2

57. Beta Species Diversity Indices Sorensen’s Coefficient of Community Similarity Dissimilarity = D S = b + c / 2a + b + c Or 1.0 - S s

58. SpeciesSample 1Sample 2 111 210 311 400 511 600 700 810 911 1000 1111 1200

59. Sorensen’s Coefficient Sample 1 –Total occurrences = b = 7 -# joint occurrences = a = 5 Sample 2 –Total occurrences = c = 5 -# joint occurrences = a = 5 2*a/(2a+b+c) S s = 2 * 5 / 10 + 7 + 5 = 0.45 (45%) D s = 1 – 0.45 = 0.55 (55%)