habitat evaluation procedures 1969-1976 – an enlightened congress passes conservation legislation...
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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
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
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?
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?
Habitat Evaluation Procedures
Three Categories of Techniques:
1) Single-species models
a) simple correlation models
e.g., vegetation type-species matrix
Species habitat matrix
Habitat Evaluation Procedures
Three Categories of Techniques:
1) Single-species models
b) statistical models
i.e., prediction of distribution and/or abundance
What types?
Carnivore Habitat Research at CMU Spatial Ecology
• Overlay hexagon grid onto landcover map• Compare bobcat habitat attributes to population of hexagon
core areas
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)
Carnivore Habitat Research at CMU Spatial Ecology
p
kkkjkiij pVP
1
2 /
• 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).
Penrose Model for Michigan BobcatsVariable Mean Vector bobcat
hexagonsNLP hexagons
% ag-openland 15.8 32.4
% low forest 51.4 10.4
% up forest 17.6 43.7
% non-for wetland 8.6 2.3
% stream 3.4 0.9
% transportation 3.0 5.2
Low for core 27.6 3.6
Mean A per disjunct core
0.7 2.6
Dist ag 50.0 44.9
Dist up for 55.0 43.6
CV nonfor wet A 208.3 120.1
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
Habitat Evaluation ProceduresThree 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
X
Y
0
1
Habitat Evaluation Procedures
Logistic regression:Y = β0 + β1X1 + β2X2 + β3X3 = logit(p)
Pr(Y = 1 | the explanatory variables x) = π
π = e –logit(p) / [1+ e –logit(p)]
Resource Selection
Functions (RSF)
• Ciarniello et al. 2003• Resource Selection Function Model for grizzly bear habitat• landcover types, landscape greenness, dist to roads
Resource Selection
Probability Functions (RSPF)
• Mladenoff et al. 1995• Resource Selection Probability Function Model for gray wolf habitat• road density
Predicted American Woodcock Abundance Map
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
wi = prop use / prop avail.
If wi = 1 , < 1, > 1
Quantifying Habitat Use – Resource Selection Ratios
Selection ratio
wi = prop use / prop avail.
wi = (Ui /U+) / (Ai /A+)
Ui = # observations in habitat type i
U+ = total # observations (n)
Ai = # random points in habitat type i
A+ = total # of random points
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
Quantifying Habitat Use – Resource Selection Ratios
Veg. Type Use Avail wi
Interior burn 25 0.340 (25/117)/0.340 = 0.628
Edge burn 22 0.101
Edge unburned 30 0.104
Interior unburned
40 0.455
Totals 117 1.000
Quantifying Habitat Use – Resource Selection Ratios
Veg. Type Use Avail wi
Interior burn 25 0.340 (25/117)/0.340 = 0.628
Edge burn 22 0.101 (22/117)/0.101 = 1.862
Edge unburned 30 0.104
Interior unburned
40 0.455
Totals 117 1.000
Quantifying Habitat Use – Resource Selection Ratios
Veg. Type Use Avail wi
Interior burn 25 0.340 (25/117)/0.340 = 0.628
Edge burn 22 0.101 (22/117)/0.101 = 1.862
Edge unburned 30 0.104 2.465
Interior unburned
40 0.455
Totals 117 1.000
Quantifying Habitat Use – Resource Selection Ratios
Veg. Type Use Avail wi
Interior burn 25 0.340 (25/117)/0.340 = 0.628
Edge burn 22 0.101 (22/117)/0.101 = 1.862
Edge unburned 30 0.104 2.465
Interior unburned
40 0.455 0.751
Totals 117 1.000
Quantifying Habitat Use – Resource Selection Ratios
Selection ratio
* Generally standardize wi to 0-1 scale for comparison among habitat types
std wi = wi / Σ (wi)
Quantifying Habitat Use – Resource Selection Ratios
Veg. Type wi Std wi
Interior burn 0.628 0.628/5.706 = 0.110
Edge burn 1.862 1.862/5.706 = 0.326
Edge unburned 2.465 0.432
Interior unburned
0.751 0.132
Totals 5.706 1.000
Habitat Evaluation Procedures
Three Categories of Techniques:
1) Single-species models
c) Habitat Suitability Index (HSI) models
Habitat Suitability
Index (HSI)
Habitat Suitability Index (HSI)• 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
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
Habitat Suitability Index (HSI)
• include top environmental variables related to a species’ presence, distribution & abundance
Habitat Suitability Index (HSI)
• List of Habitat Suitability Index (HSI) models
• http://el.erdc.usace.army.mil/emrrp/emris/emrishelp3/list_of_habitat_suitability_index_hsi_models_pac.htm
e.g., HSI for red-tailed hawk
Habitat Suitability Index (HSI)Red-tailed Hawk
Habitat Suitability Index (HSI)Red-tailed Hawk
Habitat Suitability Index (HSI)Red-tailed Hawk
Habitat Suitability Index (HSI)Red-tailed Hawk
Habitat Suitability Index (HSI)Red-tailed Hawk
For Grassland:
Food Value HSI = (V12 x V2 x V3)1/4
For Deciduous Forest: Food Value HSI = (V4 x 0.6)
Reproductive value HSI = V5
Habitat Suitability Index (HSI)Red-tailed Hawk
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)
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
Habitat Evaluation Procedures
Three Categories of Techniques:
1) Single-species models
c) Habitat Capability (HC) models
-
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
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
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
Habitat Evaluation ProceduresThree 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
Habitat Evaluation ProceduresThree Categories of Techniques:
1) Multiple-species models
b) Life-form Model
- USFS
-
Habitat Evaluation ProceduresThree 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
A = B = alpha () diversity – within habitatC = beta () diversity – among habitatD = gamma () diversity – geographic scale
Three Scales of Diversity
Alpha & Gamma Species Diversity Indices
• Shannon-Wiener Index – most used
- sensitive to change in status of rare species
s
iii ppH
1
))(ln('
H’ = diversity of species (range 0-1+)
s = # of species
pi = proportion of total sample belonging to ith species
Alpha & Gamma Species Diversity Indices
• Shannon-Wiener Index
s
iii ppH
1
))(ln('
Alpha & Gamma Species Diversity Indices
• Simpson Index – sensitive to changes in most abundant species
s
iipD
1
2)(1
D = diversity of species (range 0-1)
s = # of species
pi = proportion of total sample belonging to ith species
Alpha & Gamma Species Diversity Indices
• Simpson Index
s
iipD
1
2)(1
Alpha & Gamma Species Diversity Indices
• Species Evenness
max'
'
H
HJ
H’max = maximum value of H’ = ln(s)
Beta Species Diversity Indices• Sorensen’s Coefficient of Community
Similarity – weights species in common
cba
aSS
2
2
Ss = coefficient of similarity
(range 0-1)
a = # species common to both samples
b = # species in sample 1
c = # species in sample 2
Beta Species Diversity Indices• Sorensen’s Coefficient of Community
Similarity
Dissimilarity = DS = b + c / 2a + b + c
Or 1.0 - Ss
Species Sample 1 Sample 2
1 1 1
2 1 0
3 1 1
4 0 0
5 1 1
6 0 0
7 0 0
8 1 0
9 1 1
10 0 0
11 1 1
12 0 0
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)
• Ss = 2 * 5 / 10 + 7 + 5 = 0.45 (45%)
• Ds = 1 – 0.45 = 0.55 (55%)
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