cortex consultants inc. habitat supply for multiple wildlife in mpb attacked landscapes modeling...

Cortex Consultants Inc.

Habitat Supply for Multiple Habitat Supply for Multiple Wildlife in MPB Attacked Wildlife in MPB Attacked

LandscapesLandscapes

Modeling approach and Modeling approach and selected speciesselected species

22

Goals/OutcomesGoals/Outcomes

Effects of:Effects of: Mountain pine beetleMountain pine beetle Climate changeClimate change

UncertaintyUncertainty Management paradigmsManagement paradigms Conservation of speciesConservation of species

33

ChallengesChallenges

Project was both broad and deepProject was both broad and deep ExtensiveExtensive

15 million ha15 million ha Multiple wildlife species / variable ecosystemsMultiple wildlife species / variable ecosystems

IntensiveIntensive 70% Pl mortality70% Pl mortality Habitat quality at 1-ha resolutionHabitat quality at 1-ha resolution Multi-trophicMulti-trophic

Range of user expectationsRange of user expectations

44

Merits/DemeritsMerits/Demerits

Clear goalsClear goals Available toolsAvailable tools ExperienceExperience

Love a good challenge!Love a good challenge!

55

BackgroundBackground

Selection of modeling approachSelection of modeling approach Selection of speciesSelection of species General modelGeneral model Effect of MPBEffect of MPB Effect of BiogeoclimaticEffect of Biogeoclimatic

ApplicationApplication ResultsResults

66

Selection of Modeling Selection of Modeling ApproachApproach

Purpose – prediction / explanationPurpose – prediction / explanation Algorithm structure – mechanistic / correlativeAlgorithm structure – mechanistic / correlative Ecological complexity – multi-trophic / singularEcological complexity – multi-trophic / singular Treatment of time – forecast / staticTreatment of time – forecast / static Resolution (spatial/temporal/functional) – coarse / Resolution (spatial/temporal/functional) – coarse /

finefine Type of reasoning – inductive / deductiveType of reasoning – inductive / deductive Statistical foundation – frequency / probabilityStatistical foundation – frequency / probability Outputs – capability / suitabilityOutputs – capability / suitability Type of result – deterministic / stochasticType of result – deterministic / stochastic

77

Common ApproachesCommon Approaches

Element DistributionElement Distribution Habitat SupplyHabitat Supply Resource Selection FunctionResource Selection Function Habitat Suitability IndexHabitat Suitability Index Wildlife Habitat RatingWildlife Habitat Rating

88

Chosen ApproachChosen Approach

Bayesian-based habitat supplyBayesian-based habitat supply Spatially referenced probability of occurrenceSpatially referenced probability of occurrence Sensitive to resource requirementsSensitive to resource requirements Not temporally/spatially limitedNot temporally/spatially limited Explicit uncertaintyExplicit uncertainty Relatively transparent and flexibleRelatively transparent and flexible

Mechanistic, multi-trophic, deductive, and Mechanistic, multi-trophic, deductive, and deterministic way to forecast probabilistic deterministic way to forecast probabilistic explanations about habitat suitability at a explanations about habitat suitability at a relatively fine spatial, temporal, and functional relatively fine spatial, temporal, and functional resolution (whew! Never to be quoted please.)resolution (whew! Never to be quoted please.)

99

Selection of SpeciesSelection of Species

Most adversely affected by MPB Most adversely affected by MPB and/or management response to MPBand/or management response to MPB

Examples of hunted or trapped Examples of hunted or trapped speciesspecies

Closely related species that vary in Closely related species that vary in habitat requirementshabitat requirements

1010

Criteria for Negatively Criteria for Negatively AffectedAffected

CDC, COSEWIC statusCDC, COSEWIC status Stakeholder interestStakeholder interest Extent of distribution in BCExtent of distribution in BC Key ecological functionKey ecological function Relative dependence on pineRelative dependence on pine MPB threat on habitat structureMPB threat on habitat structure MPB related management threatsMPB related management threats

1111

The 13 SpeciesThe 13 Species

MapeMape UrarUrar RataRata GuguGugu SpgrSpgr MaamMaam LewoLewo TahuTahu

OdheOdhe LycaLyca CeelCeel AlalAlal StgrStgr

1212

General Model StructureGeneral Model Structure

Species Occurrence

Composite effect: forage usefulness

Life requisite: dens/nests

Life requisite: thermal cover

Life requisite: locomotion cost

Life requisite: forage

Life requisite: security cover

Composite effect: mortality potential

Management lever

Subnet: Spatial factors

Subnet: Physical/ahabitat barriers

Modifying factor: displacement

Modifying factor: competition

Modifying factor: mortality sources

Key ecological correlate







1313

Effect of MPBEffect of MPBAMC: NI: Additive MPB CWD

>200 m^3/ha50 to 200 m^3/ha<50 m^3/ha0 m^3/ha

25.025.025.025.0

SSS: NI: Subzone Structural StageAttain NeverAttain YoungAttain Old

33.333.333.3

PCLS: RI: % Compositon Leading Spp. (...>85%75 to 85 %65 to 75%56 to 65%46 to 55 %36 to 45 %26 to 35 % <25 %

12.512.512.512.512.512.512.512.5

LTS: RI: Leading Tree Species (vri)Black CottonwoodBlack SpruceDouglas FirEngleman SpruceLodgepole PinePonderosa PineSubalpine FirTrembling AspenWhite SpruceOther

10.010.010.010.010.010.010.010.010.010.0

AMS: NI: Additive MPB Snags>35 stems >30cm dbh /ha15-35 stems >30cm dbh /ha<15 stems >30cm dbh /ha0 stems >30cm dbh /ha

25.025.025.025.0

CCC: RI: Canopy Crown Closure (vri)>60%30 to 60%<30%

33.333.333.3

MSI: S: MPB Stand InfluenceBeetle Killed StandBeetle Modified StandNot Impacted

33.333.333.3

ESS: NI: Effective Structural Stagess 1ss 2-4ss 5-7ss 8ss 9

20.020.020.020.020.0

TSD: RI: Time Since Death (mpb)<25 yrs25 to 80 yrs80 to 140 yrs141 to 250 yrs>250 yrsNot dead or < 70% recent att...

20.020.020.020.020.0 0

ECV: NI: Effective Crown VolumeNo EffectReduced/Loss of Crown

50.050.0

EFA: NI: Effective Forest Age< 25 yrs26 to 80 yrs81 to 140 yrs141 to 250 yrs> 250 yrs

20.020.020.020.020.0

FA: RI: Forest Age (vri)<20 yrs (1) and all veg NP21 to 80 yrs (2,3,4)81 to 140 yrs (5,6,7)141 to 250 yrs (8)>250 yrs (9)

20.020.020.020.020.0

ECC: NI: Effective Crown Closure>60%30 to 60%<30%

33.333.333.3

1414

Biogeoclimatic EffectsBiogeoclimatic Effects

R S : S : R e l a t i v e S n o w f a l l

> 5 0 0 c m2 0 0 t o 5 0 0< 2 0 0 c m

3 3 . 33 3 . 33 3 . 3

S S P 1 : S : S u b z o n e S n o w f a l l P o t e n t i a l 1

V e r y d e e pD e e pM o d e r a t eS h a l l o wV e r y s h a l l o w

2 0 . 02 0 . 02 0 . 02 0 . 02 0 . 0

S S P : N I : S u b z o n e S n o w f a l l P o t e n t i a l

V e r y d e e pD e e pM o d e r a t eS h a l l o wV e r y s h a l l o w

2 0 . 02 0 . 02 0 . 02 0 . 02 0 . 0

R M T W M : S : R e l a t i v e M e a n T e m p W a r m e s . . .

< 1 5 d c e l i u s1 5 t o 1 8 d c e l i u s> 1 8 d c e l i u s

3 3 . 33 3 . 33 3 . 3

M N F F D : S : M a x N u m b e r o f F r o s t F r e e D a y s

< 1 5 01 5 0 t o 2 0 0> 2 0 0

3 3 . 33 3 . 33 3 . 3

I V C : S : I n t e r i o r v r s C o a s t a l

I n t e r i o rC o a s t a l

5 0 . 05 0 . 0

S S S : N I : S u b z o n e S t r u c t u r a l S t a g e

A t t a i n n e v e rA t t a i n y o u n gA t t a i n o l d

3 3 . 33 3 . 33 3 . 3

L O E S : S : L i k l i h o o d o f E a r l y S p r i n g

H i g hM o d e r a t eL o w

3 3 . 33 3 . 33 3 . 3

Z T R C : S : Z o n e T e m p e r a t u r e R e g i m e C o . . .

hwmkcvc hc mc s

1 1 . 11 1 . 11 1 . 11 1 . 11 1 . 11 1 . 11 1 . 11 1 . 11 1 . 1

S S : N I : S u b z o n e S n o w m e l t

V e r y e a r l yE a r l yL a t eV e r y l a t e

2 5 . 02 5 . 02 5 . 02 5 . 0

T S : S : T e m p e r a t u r e S u m m a r y

h , wm , kc , v

3 3 . 33 3 . 33 3 . 3

Z T R : R I : Z o n e T e m p e r a t u r e R e g i m e

hwmkcvs

1 4 . 31 4 . 31 4 . 31 4 . 31 4 . 31 4 . 31 4 . 3

S R M : N I : S u b z o n e R e l a t i v e M o i s t u r e

V e r y d r yD r yM o i s tW e tV e r y w e tV e r y w e t c o l d

1 6 . 71 6 . 71 6 . 71 6 . 71 6 . 71 6 . 7

S R M 1 : S : S u b z o n e R e l a t i v e M o i s t u r e 1

V e r y d r yD r yM o i s tW e tV e r y w e tV e r y w e t c o l d

1 6 . 71 6 . 71 6 . 71 6 . 71 6 . 71 6 . 7

Z : R I : Z o n e

B A F AS W BB W B SE S S FS B SIM AM SS B P SIC HID FB GP PC M AM HC W H

6 . 6 76 . 6 76 . 6 76 . 6 76 . 6 76 . 6 76 . 6 76 . 6 76 . 6 76 . 6 76 . 6 76 . 6 76 . 6 76 . 6 76 . 6 7

S B F R : N I : S u b z o n e B e a r F o o d R e t e n t i o n

H i g hL o w

5 0 . 05 0 . 0

A : S : A l p i n e

A l p i n eO t h e r

5 0 . 05 0 . 0

R T P : S : R e l a t i v e T o p o P o s i t i o n

A l p i n eM i d - t o h i g h - e l e v a t i o nL o w t o m i d - e l e v a t i o n

3 3 . 33 3 . 33 3 . 3

S A C S : N I : S u b z o n e A b u n d a n c e o f C W D a n . . .

H i g hM o d e r a t eL o wV e r y l o w

2 5 . 02 5 . 02 5 . 02 5 . 0

Z M : R I : Z o n e M o d i f i e r

wpn o n e

3 3 . 33 3 . 33 3 . 3

S O : N I : S u b z o n e o p e n e s s

O p e nS o m e w h a t o p e nN o t o p e n

3 3 . 33 3 . 33 3 . 3

Z P R : R I : Z o n e P r e c i p i t a t i o n R e g i e m e

xdmwv

2 0 . 02 0 . 02 0 . 02 0 . 02 0 . 0

M M P D M : S : M a x o f M e a n P r e c i p i n D r i e s t . . .

< 3 0 m m3 0 t o 5 0 m m> 5 0 m m

3 3 . 33 3 . 33 3 . 3


Model ApplicationModel Application

Input layers, data Input layers, data management, run sequencemanagement, run sequence


ResultsResults

Spatial results and meta-dataSpatial results and meta-data

1717

Spgr

Odhe Ceel

StgrLewo

Alal

Maam Gugu

Rata

Mape Lyca

1818

Modeling ResultsModeling Results

Mind mapMind map Netica input variable paletteNetica input variable palette Netica managerNetica manager Spatial layersSpatial layers

InputInput OutputOutput

Meta dataMeta data

1919

Issues: Data ManagementIssues: Data Management

MS Access 2 GB limitMS Access 2 GB limit Corrupted databasesCorrupted databases Adds additional processing steps to compact database Adds additional processing steps to compact database

or import data to new databaseor import data to new database Mid-model spatial processingMid-model spatial processing

Unscripted and done manuallyUnscripted and done manually Time intensiveTime intensive Can introduce errorCan introduce error

CPU spaceCPU space With 3-4 processing areas per machine, space becomes With 3-4 processing areas per machine, space becomes

an issuean issue Data management can introduce errorData management can introduce error

2020

Issues: Missing DataIssues: Missing Data

2121

Issues: Caribou EcotypeIssues: Caribou Ecotype

2222

Issues: VRIIssues: VRI

InterpretatiInterpretationon

Data Data managememanagementnt

2323

Issues: Background NoiseIssues: Background Noise

2424

Issues: Other DataIssues: Other Data

InterpretatiInterpretationon

Data Data managememanagementnt

2525

Issues: ResponsivenessIssues: Responsiveness

2626

Issues: ResourcesIssues: ResourcesS

cen

ari

o 3

Scen

ari

o 2

Scen

ari

o 1

PredatorPrey

Yr 2

0

Yr 1

0Yr 0

Interpretation

Habitat Supply Models

Habitat Supply

Species HabitatRelationships

InferredPop’n

Response

ManagementAlternatives

DisturbanceScheduler

&Forest Estate

Models

Timber Supply & Landscape Conditions

ResourceInventory

Disturbance&

Succession

2727

SolutionsSolutions

Research input data / data Research input data / data managementmanagement

Dump accessDump access Simplify models (but no loss of Simplify models (but no loss of

precision)precision) Contemplate implications of model Contemplate implications of model

structurestructure


Alpha- to Beta-level Alpha- to Beta-level ModelsModels

……and beyondand beyond

2929

Why Alpha to BetaWhy Alpha to Beta

Functional, multi-trophic models by Functional, multi-trophic models by their nature are complex and their nature are complex and intricateintricate

Application needs to be simple and Application needs to be simple and uncomplicateduncomplicated

3030

The Example of Mountain The Example of Mountain CaribouCaribou

Government wanted models that were Government wanted models that were transparent and mapped the thoughts of transparent and mapped the thoughts of science advisorsscience advisors

Once built, they then wanted models that Once built, they then wanted models that were easy to implementwere easy to implement

Simplification based on sensitivity Simplification based on sensitivity analyses and node reduction provided a analyses and node reduction provided a pragmatic result that could be transferred pragmatic result that could be transferred to other modeling platformsto other modeling platforms

3131

Early Winter Range: The Early Winter Range: The StoryStory

SP: S: Shrub Potential

ShrubsNo Shrubs

25.174.9

MC: S: Movement Cost

High costModerate costLow cost

62.515.821.7

ELE: I: Elevation (DEM)

> 2000 m< 2000 m

50.050.0

HFE: S: Heli Flight Effects

NilLowModerateHigh

33.917.917.930.4

HSUG: ML: Heli Ski Use Guidelines

ClosureOperating GuidelinesNo Guidelines

33.333.333.3

TUD: I: Tenure User Days (LWBC db)

Up to 500 daysBtw 500 to 3000 daysGt 3000 days

33.333.333.3

UI: S: User Intensity

LowModerateHigh

69.419.411.1

EDHS: S: Expected Displacement Heli-Ski

No influence10% Rata avoid site25% Rata avoid site50% Rata avoid site100% Rata avoid site

54.827.89.596.131.69

EDCS: S: Expected Displacement Cat Skiers


20.020.020.020.020.0

EDS: S: Expected Displacement Snowmo...


20.020.020.020.020.0

BZOI: I: Basic Zone of Influence

Gt 75kmBtw 50 to 75kmBtw 30 to 50kmBtw 10 to 30kmBtw 2 to 10kmLt 2kmSite

14.314.314.314.314.314.314.3

CED: S: Combined Expected Displacement


1.563.777.3616.371.1

ITG: I: Inventory Type Group (FC)

Potentially impermeableAlw ays permeable

50.050.0

CC: S: Carrying Capacity (#/1000kms)

>200100 to 20050 to 10025 to 5012.5 to 25< 12.50

.0330.370.771.022.524.7090.6

2.37 ± 13

SFU: S: Seasonal Forage Usefulness

>50% of max availBetw een 25-50% max avail<25% of max avail0% of max avail

4.807.0925.762.4

0.0948 ± 0.18

TLRA: S: Terrestrial Lichen Rel. Abundance

Class 2-4Class 0-1

3.3396.7

MCS: I: Macro-climate - shrubs (BGC)

ICHxICHdICHmICHw ,v, ESSFdmESSFdkESSFw mMSdkAT or ATpSBS, Other ESSF, Other ICHOther

10.010.010.010.010.010.010.010.010.010.0

MR: AI: Moisture Regime (25m DEM)

Very xeric to sub-xeric(0-2)Sub-mesic (3)Mesic (4)Sub-hygric (5)Hygric to sub-hydric (6-7)

20.020.020.020.020.0

IBS: I: Ice and Bare Sites (BTM)

VegetatedAnything lacking vegetation

50.050.0

ISG: I: Interception Spp Group (Spp FC)

Full Crow n SppModerate Crow n SppOpen Crow n Spp and NP

33.333.333.3

SSI: I: Shade/Snow Interception (CC FC)

<30% CC and all veg NP30 to 60% CC>60% CC

33.333.333.3

45 ± 26

PSA: S: Palatable Shrub Abuncance

HighModLow

16.15.7378.2

BA: S: Bryoria Abundance

HighModLowNil

12.526.240.021.2

TSG: I: Tree Spp Group - Forage (Spp FC)

Very GoodGoodModeratePoor

25.025.025.025.0

FAE: I: Forest Age Effects (SA FC)

<30 yrs and all veg NP30 to 80 yrs80 to 140 yrs140 to 250 yrs>250 yrs

20.020.020.020.020.0

134 ± 110

SIP: S: Snow Interception Potential

60% reduced30% reducedNo reduction

18.914.666.6

MCSF: I: Macro-climate - snow fall (m ear...

Very DeepDeepModerateShallowVery Shallow

20.020.020.020.020.0

P: S: Permeability

Highly permeableMod permeableLow permeableImpremeable

33.330.0 0

36.7

TS: I: Terrain Steepness (% DEM)

< 40 % is best40 to 80% is OK> 80% is w orst

33.333.333.3

60 ± 35

LCP: I: Landcover Permeability (BTM)

Permeable nonforForestsImpermeable nonfor

33.333.333.3

WS: S: Windblown Sites

Snow Depth ReducedNo Reduction Of Snow

7.8392.2

WP: I: Wind Potential (m/s Map)

Very good > 8 m/sGood 6 to 8 m/sFair 4 to 6 m/sPoor < 4 m/s

25.025.025.025.0

MCLO: I: Landscape Openess (BGC)

At or ParklandESSFOther

33.333.333.3

SA: S: Snow Accumulation

< 1 m Betw een 1 m and 2.5 m > 2.5 m

44.127.028.8

WD_MC: AI: Weighted-distance MC node

Very permeableHighly permeableMod permeableImpermeable

25.025.025.025.0

CIHA: I: Caribou Within Herd Area (Wittme...

truefalse

50.050.0

AAF: S: Abund. Avail Forage (kg/ha)

>.72Betw een .18 - .72<.18

28.217.654.3

FP: S: Forest Permeability

PermeableImpermeable

90.010.0

ZOI: I: Zone of Influence

Gt 75kmBtw 50 to 75kmBtw 30 to 50kmBtw 10 to 30kmBtw 2 to 10kmLt 2kmSite

14.314.314.314.314.314.314.3

3232

Early Winter Range: Early Winter Range: ApplicationApplication

MCS: I: Macro-climate - shrubs (BGC)

ICHxICHdICHmICHw ,v, ESSFdmESSFdkESSFw mMSdkAT or ATpSBS, Other ESSF, Other ICHOther

0 0 0

100 0 0 0 0 0 0

MCSF: I: Macro-climate - snow fall (m ear...

Very DeepDeepModerateShallowVery Shallow

0 0

100 0 0

AAF: S: Abund. Avail Forage (kg/ha)

>.72Betw een .18 - .72<.18

75.523.31.18

MR: AI: Moisture Regime (25m DEM)

Very xeric to sub-xeric(0-2)Sub-mesic (3)Mesic (4)Sub-hygric (5)Hygric to sub-hydric (6-7)

0 0

100 0 0

TSG: I: Tree Spp Group - Forage (Spp FC)

Very GoodGoodModeratePoor

100 0 0 0

FAE: I: Forest Age Effects (SA FC)

<30 yrs and all veg NP30 to 80 yrs80 to 140 yrs140 to 250 yrs>250 yrs

0 0 0 0

100

MC: S: Movement Cost

High costModerate costLow cost

025.075.0

TS: I: Terrain Steepness (% DEM)

< 40 % is best40 to 80% is OK> 80% is w orst

100 0 0

LCP: I: Landcover Permeability (BTM)

Permeable nonforForestsImpermeable nonfor

0 100 0

SFU: S: Seasonal Forage Usefulness

>50% of max availBetw een 25-50% max avail<25% of max avail0% of max avail

56.636.46.720.30

0.569 ± 0.22

3333

Sensitivity Analysis in Sensitivity Analysis in NeticaNetica

Input FactorVariance

Reduction

Cumulative % of Total Variance

ReductionMutual Information

Variance in Beliefs

LCP 0.00405 34.43% 0.16407 0.028883

TS 0.003778 66.55% 0.15299 0.027238

FAE 0.002125 84.62% 0.063 0.007327

MCSF 0.0008541 91.88% 0.02831 0.003643

TSG 0.0006952 97.79% 0.01876 0.002627

MCS 0.0001262 98.86% 0.00324 0.000442

MR 0.0001017 99.73% 0.00266 0.000373

ISG 1.33E-05 99.84% 0.00038 5.57E-05

SSI 6.02E-06 99.89% 0.00031 0.000036

ITG 5.38E-06 99.94% 0.0003 6.95E-05

MCLO 4.61E-06 99.97% 0.00016 2.25E-05

WP 2.28E-06 99.99% 0.00008 1.11E-05

IBS 6.66E-07 100.00% 0.00006 1.9E-06

3434

Other Possible ActivitiesOther Possible Activities

Correction of errors (input data, scripting)Correction of errors (input data, scripting) Adjustment of conditional probabilitiesAdjustment of conditional probabilities Addition/elimination of KECsAddition/elimination of KECs Realignment of relationshipsRealignment of relationships Adjustment of input/output states (number Adjustment of input/output states (number

and/or cutpoints)and/or cutpoints) Trials with “other” less restrictive softwareTrials with “other” less restrictive software Expert review of resultsExpert review of results Verification of results with empirical Verification of results with empirical

informationinformation

3535

BenefitsBenefits

More reliable/applicable modelsMore reliable/applicable models Easier and more efficient applicationEasier and more efficient application More readily transferred to different More readily transferred to different

platformsplatforms

cortex consultants inc. habitat supply for multiple wildlife in mpb attacked landscapes modeling...

Documents

ha resolution habitat

pl mortality habitat

selected species

functional resolution

landscapes modeling

deterministic way

mpb andor management

static treatment of