Habitat Evaluation Procedures

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


• 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?


• 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 ProceduresThree Categories of Techniques:

1) Single-species modelsa) simple correlation models

e.g., vegetation type-species matrix

Species habitat matrix


1) Single-species modelsb) 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


• Landscape metrics include:

• Composition (e.g., proportion cover

type)

• Configuration(e.g., patch isolation,

shape, adjacency)• Connectivity

(e.g., landscape permeability)


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


• 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


1) Single-species modelsb) 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


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 iwi = prop use / prop avail.

If wi = 1 , < 1, > 1


Selection ratiowi = 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


Look at Neu et al. (1974) moose data= 117 observations of moose tracks within 4

different vegetation [habitat] types


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



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



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



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


Selection ratio* Generally standardize wi to 0-1 scale

for comparison among habitat types

std wi = wi / Σ (wi)


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


1) Single-species modelsc) 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

http://el.erdc.usace.army.mil/emrrp/emris/emrishelp3/list_of_habitat_suitability_index_hsi_models_pac.htm



Habitat Suitability Index (HSI)Red-tailed Hawk

For Grassland: Food Value HSI = (V1

2 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


1) Single-species modelsc) Habitat Capability (HC)

models - USFS- describe habitat conditions associated with or necessary to maintain different population levels of a species ( compositions)



models - uses weighted values based on habitat capacity rates at each

successional stage of veg. for reproduction, resting, and

feeding



models -


1) Single-species modelsc) Pattern Recognition (PATREC)

models - use conditional probabilities to

assess whether habitat is suitable for a species

- must know what is suitable & unsuitable habitat


1) Single-species modelsc) Pattern Recognition (PATREC)

models - use series of habitat attributes- must know relation of attributes

to population density

PATREC ModelsExpected 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 potentialP (L) = prop. low density habitatP (I/L) = prop. area has low population potential

* Low & high population potential identified from surveys


1) Multiple-species modelsa) Integrated Habitat Inventory

and Classification System (IHICS) - BLM- system of data gathering, classification, storage

- no capacity for predicting use or how change affects species


1) Multiple-species modelsb) Life-form Model - USFS-


1) Multiple-species modelsb) 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 speciespi = proportion of total sample

belonging to ith species


• Shannon-Wiener Index

s

iii ppH

1

))(ln('


• Simpson Index – sensitive to changes in most abundant species

s

iipD

1

2)(1

D = diversity of species (range 0-1)s = # of speciespi = proportion of total sample

belonging to ith species


• Simpson Index

s

iipD

1

2)(1


• Species Evenness

max''

HHJ

H’max = maximum value of H’ = ln(s)

Beta Species Diversity Indices• Sorensen’s Coefficient of Community

Similarity – weights species in common

cbaaSS

2

2

Ss = coefficient of similarity

(range 0-1)a = # species common to both samplesb = # species in sample 1c = # 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 21 1 12 1 03 1 14 0 05 1 16 0 07 0 08 1 09 1 110 0 011 1 112 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%)

Habitat Evaluation Procedures

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