R. Scott McNay, Wildlife InfometricsR. Scott McNay, Wildlife InfometricsRandy Sulyma, BC Min. of ForestsRandy Sulyma, BC Min. of Forests

Building and Validating Building and Validating Bayesian ModelsBayesian Models

Identification of Mountain Identification of Mountain Goat Winter Range in North-Goat Winter Range in North-

central BCcentral BC

AcknowledgementsAcknowledgements

Funding from the BC Min. of EnvironmentFunding from the BC Min. of Environment Other participants included:Other participants included:

R. EllisR. Ellis D. Fillier, S. Gordon, L. Vanderstar, D. Heard, G. D. Fillier, S. Gordon, L. Vanderstar, D. Heard, G.

Watts, D. Wilson, J. Vinnedge, B. Brade, R. Watts, D. Wilson, J. Vinnedge, B. Brade, R. MacDonaldMacDonald

Line Giguere, Robin McKinleyLine Giguere, Robin McKinley Concepts and ideas:Concepts and ideas:

the last workshop in Chasethe last workshop in Chase subsequent discussions, most notably, B. subsequent discussions, most notably, B.

Marcot & S. Wilson, C. Apps Marcot & S. Wilson, C. Apps

Modeling ContextModeling Context

from Bunnell 1989from Bunnell 1989

Uses:Uses: PredictionPrediction

ManagementManagement Implications Implications

of of Predictions?Predictions?

ExplanationExplanation ResearchResearch Why?Why?

EmpiricalMechanistic

TheoryMechanistic

EmpiricalCorrelative

TheoryCorrelative

FrequencyProbability

Conceptual

RationaleRationale

General, portable modelGeneral, portable model Management & researchManagement & research Prediction & explanationPrediction & explanation Minimal resources to developMinimal resources to develop

Little information from FSJLittle information from FSJ Insufficient resources to develop Insufficient resources to develop

empirical or other more traditional empirical or other more traditional approachesapproaches

Limited Time FrameLimited Time Frame

Study AreasStudy Areas

Adjacent MUsAdjacent MUs Similar but not Similar but not

the samethe same Preliminary Preliminary

model already model already builtbuilt

Simple UWR ModelSimple UWR Model

= Spatial relationships of cells processed/defined in a GIS.

Typically a distance function from escape terrain.

S1: Escape Terrain

S2: UWR

I1: Slope I2: Aspect/Solar I3: Elevation

AR: I: Alpine and Rocks (FC)

RGH: Roughness from DEM LCC: S: Landcover Condition

BA: I: Bare Areas (BTM)

ET: S: Escape Terrain

SC: S: Spatial Configuration

PET: I: % of ET within Effective Forage

ETE: S: Escape Terrain Effectiveness

SL: AI: Solar Loading (Whr/m2)

EA: I: Amount (ha) of ET - 2.0 ha Neighbourhood

AEFT: I: Amount of Effective Forage Terrain (ha)

FWDB: AI: Forage-weighted Distance Buffer

A: S: Smooth / Good slope

EP: S: Winter Range Preference

SLP: I: Slope (Degrees) from DEM

FTE: S: Forage Terrain Effectiveness

= Spatial relationships of cells processed/defined in a GIS

GoatsGoats

AreaArea GoatsGoats RelocationsRelocations

OspikaOspika 2323 232232

OsilinkaOsilinka 1818 184184

Akie Akie (Pesika)(Pesika)

1414 3939

Model Construction ResultsModel Construction Results

Primary indicators:Primary indicators: AccuracyAccuracy

100% relocations100% relocations Maximize coverageMaximize coverage

PrecisionPrecision 100% alpine100% alpine Maximize area decreaseMaximize area decrease

OspikaOspika OsilinkOsilinkaa

PesikaPesika

LocLoc AtAt LocLoc AtAt LocLoc AtAt

11 2929 8282

5858 7777

22 2222 9393

6666 6767

33 6969 5656 3333 4848

Overall Results:Overall Results:

Basic model of EPBasic model of EP 65% relocations 65% relocations

coveredcovered 57% reduction in alpine 57% reduction in alpine

Spatially generalized Spatially generalized model (nearest model (nearest neighbor algorithm)neighbor algorithm)

92% relocations 92% relocations coveredcovered

70% reduction in alpine70% reduction in alpine

Model TestingModel Testing Random sample approach applied.Random sample approach applied. Aerial reconnaissance completed.Aerial reconnaissance completed. Data collected to evaluate/verify Data collected to evaluate/verify

both input parameters, and both input parameters, and summary results.summary results.

Given funding and timing Given funding and timing constraints, not possible to constraints, not possible to evaluate some of the spatial evaluate some of the spatial relationships.relationships.

AR: I: Alpine and Rocks (FC)

RGH: Roughness from DEM LCC: S: Landcover Condition

BA: I: Bare Areas (BTM)

ET: S: Escape Terrain

SC: S: Spatial Configuration

PET: I: % of ET within Effective Forage

ETE: S: Escape Terrain Effectiveness

SL: AI: Solar Loading (Whr/m2)

EA: I: Amount (ha) of ET - 2.0 ha Neighbourhood

AEFT: I: Amount of Effective Forage Terrain (ha)

FWDB: AI: Forage-weighted Distance Buffer

A: S: Smooth / Good slope

EP: S: Winter Range Preference

SLP: I: Slope (Degrees) from DEM

FTE: S: Forage Terrain Effectiveness

Model Testing ResultsModel Testing ResultsObserved

Preferred Equivocal Avoided Total

Preferred 31 13 6 50

Equivocal 0 0 0 0

Avoided 0 0 7 7

Total 31 13 13 57

Correct Classification Rate = 67%False Positive Error Rate = 73%False Negative Error Rate = 0%Κ = 0.50τ = 0.32

Modele

d

Model Testing ResultsModel Testing Results

Correct Classification Rate = 89%False Positive Error Rate = 46%False Negative Error Rate = 0%Κ = 0.64τ = 0.79

UWRUWR OtherOther TotalTotal

UWRUWR 4444 66 5050

OtherOther 00 77 77

TotalTotal 4444 1313 5757

Observed

Modele

d

DiscussionDiscussion

Were we able to restrict our search Were we able to restrict our search for UWR sufficiently yet remain for UWR sufficiently yet remain accurate?accurate?

Was it important that we were not Was it important that we were not strictly analytical in our Bayesian strictly analytical in our Bayesian learning?learning?

Was our test protocol appropriate Was our test protocol appropriate given the project goal?given the project goal?