Metapopulations, Patchiness, and Connectivity

Biol 112: 10 October 2005

Population Ecology – Simple• Independent populations

• Identical individuals

• Dynamics and persistence = f(B,D)

• Models predict dynamics and change as f(environment and population)

• Add: Age structure, Spatial Clumping

Acorn woodpecker – long-term persistence of local populations dependent on immigration(Stacey &Taper, 1992)

Fennoscandian voles – large-scale spatial synchrony of population dynamics due to nomadic avian predators (Heikkila et al. 1994)

Small Extinction-Prone Populations• Edith’s checkerspot butterfly

• 3 populations over 35 years

• One extinction/reestablishment (1964/1967)

• Two extinctions(Paul Ehrlich lab, McGarrahan 1997)

Focal populations – not isolated

Interact with other conspecific local populations across some larger region through process of migration - dispersal

“Metapopulation” A population consisting of many local populations – Levins 1970

Individuals biological

reproduction

Local populations

spatial reproduction

MetapopulationLocal populations ephemeral

Persistence of metapopulation = f(B,D,M)

(at least one local population spatially replicates itself at least once in its lifetime)

Equilibrium in the Levins model

Poriginal

P = fraction of patches occupied

All local populations and habitats the same

Poriginal

Rate

: lo

cal p

op

ula

tion

s/u

nit

ti

me

Occupied patches/Total patches

0 Pnew Pnew

Remove a patch Reduce patch areas

1 0 1

ST

Rate

: sp

ecie

s/u

nit

ti

me

Number of species

Extinction

Colonization

S0 SE

Levins’ model is a single species version of M&W

Goes down with increasing isolation

Goes up with decreasing size

Metapopulation –

a set of local populations that interact over space and time

Metapopulation Concept:

• Applies best to physically patchy habitat

• Patches big enough to support breeding populations

The spatial distribution of most species at most spatial scales is patchy.

For some the world is patchier by the day

Metapopulation, Fragmentation, Conservation

Metapopulation: View of the world

Binary Landscapes

• Habitat

• Size

•Matrix

•Distance

Spatially Implicit• All local populations equally connected• All local populations equivalent• Asynchronous extinction and colonization

Spatially Explicit• Migration f(interpatch distance)

Contributions• Local populations can go extinct due to emigration• Occupancy does not mean habitat suitability• Threshold condition for metapopulation survival (E,I)• Extinction is expected before last habitat is gone• Complex patterns emerge from simple dynamics

Some (needed?) Evidence

• population size is affected by migration• population density is affected by area and isolation• asynchronous local dynamics• local extinctions and colonizations• empty habitat exists• metapopulations persist despite local extinctions• extinction risk depends on area• colonization rate depends on isolation

Different dispersal modes, tendencies, behaviors, and risks

Landscape Ecology: View of the world

Complex landscape structure

Influences, and results from, ecological processes

low high

Dispersal Resistance

known occupancyLeast cost pathShortest euclidean path

“An important general challenge for the future is to advance a more comprehensive synthesis of spatial ecology, incorporating key elements from landscape ecology, metapopulation ecology, . . . . “

Hanski 1999

metapopulation

behavior

population

evolution community

disease conservation

dispersal landscape

Swords to Species

• 729 DoD installations • 224 (30%) contain species at risk • 523 different species, two-thirds of which are plants.

• 47 of these 523 species are candidates for federal listing • remainder are considered critically imperiled or imperiled • twenty-four of these species are endemic to individual installations

Florida Scrub Jay

Saint Francis’ satyr

eastern tiger salamander

Carolina gopher frog

Red cockaded woodpecker and its range

Mapping Habitat Connectivity for Multiple Rare, Threatened, and Endangered Species on and

Around Military Installations (SI-1471)

Aaron MoodyDepartment of Geography

University of North Carolina at Chapel Hill

BRIEF TO THE SCIENTIFIC ADVISORY BOARD

20 October 2005

Performers

Dr. Aaron MoodyUniversity of North Carolina, Chapel HillSpecialist in Landscape Ecology, Remote Sensing, GIS

Dr. Nick HaddadNorth Carolina State University Specialist in Landscape Ecology and Conservation Biology

Dr. Bill MorrisDuke UniversitySpecialist in Population Ecology, Dispersal Modeling

Dr. Jeffrey WaltersVirginia TechSpecialist in Avian and Population Ecology

Dr. Jeffrey PriddyDuke UniversitySpecialist in Demographic Modeling

Problem Statement

• Two strategies drive land acquisition:Conservation of high quality habitat

Conservation of connecting habitats

• Which land parcels to conserve to balance needs of different species and military and non-military land uses?

Technical Objective

Develop approaches to quantify, map and manage habitat connectivity for multiple species with different life-histories and dispersal habitats.

Landscape Connectivity

Our goal is to optimize connectivity for conservation of species that have different habitat requirements

Landscape Connectivity

Connectivity Near Installations

NE Area

Multiple species of concern

low highDispersal Resistance

known occupancypredicted dispersal pathobserved dispersal path

Science Ready for Exploitation

•Spatial framework •Environmental data•Dispersal models•Habitat-specific movement data•Computational methods

flexible decision-support environment for quantifying and managing habitat connectivity

Technical Approach

Our work modernizes approaches to habitat conservation near

installations• Currently relies on expert opinion• Connectivity virtually ignored• Dispersal considered only for focal species, and

ignores habitat quality• Behavioral approaches for modeling dispersal

can be combined with the spatially explicit approach to map habitat connectivity

Technical Approach

Spatial Data Acquisition

Movement Data Collection

Dispersal Modeling

Spatial Modeling

Evaluations &Model Updates

Field Data Collection

Inte

gra

ted

Sp

ati

al D

ata

base

Implement Decision Support System for Habitat Management

Transition

Spatial Data Development

Collection of Movement Data St. Francis’ Satyr

OHMHx

x x

OH = optimal habitat

MH = matrix habitat

x = sample site

r

r = release point

• Visually track movement behavior of naturally occurring SFS and surrogate species in relation to landscape features

• Visually track behaviors of experimentally released surrogate species in dominant habitats and at their boundaries

• Monitor dispersal events in natural habitats using capture-recapture

Spatial Data: Acquisition and Development

Coordinate with Ft. Bragg NRD

Acquisition & Integration (Y1) ● Maps & Infrastructure ● LiDAR, ASTER, DOQQs

● Field Data

Development (Y1 – Y2)● Known Population Clusters● Land-Use● Canopy Structure● Terrain & Hydroperiod

Final Validations (Y3)

Elevation

Hydroperiod

Infrastructure

Soils

Land Use

Zoning

Site Data

Canopy Structure

Spatial Data Layers

Field Data Collection and Environmental Data Development : Land Use

Pasture, Row Crop

Forest Plantation

Upland Forest

Longleaf Pine Woodland

LLP/herbaceous

LLP/woody

Wetlandssedge-

meadowwoody shortforested

Field data support training, validation, and update of supervised spectral classifiers used to map Land Use with ASTER data – Extended using DOQQs and other ancillary data

UNC’s Mason farm biological reserve

Spectral distribution functions for each type

Random samples stratified by land-use class ~ focus on 8 types

30 Gauges

Data used to calibrate statistical inundation model with inputs of • depressions (LiDAR DEM) • flowpaths (LiDAR DEM) • antecedent rainfall • soil type • stream flow data

ASTER data used to expand verification and support mapping over study area

Crest gauges in known ( ) and potential amphibian habitats

Heights monitored through breeding seasons

Field Data Collection and Environmental Data Development : Hydroperiod

Spatial Modeling: Habitat

Habitat Maps

Habitat Models

Elevation

Hydroperiod

Infrastructure

Soils

Land Use

Zoning

Site Data

Canopy Structure

Spatial Data Layers

+

Occurrence and dispersal data

Land use

Canopy Structure

Hydroperiod

Validate by visiting predicted habitat

L1

L2

L3

A1

A2

Habitat AHigh Resistance

L1 L2

A1

Habitat BHigh Conductivity

Boundary

Movement Data

Turn angle Move lengthTurn angle

Move length

Habitat AHabitat B

Computer Simulation

Boundary Behavior

Analytical Models

Kareiva and Shigesada 1983

Number of moves

Mea

n s

qu

ared

dis

tan

ce

CosACosA

LLVarSlope

1

2)( 2

If turns are symmetric:

Habitat A

Habitat B

Dispersal Modeling

Spatial Modeling: Landscape Resistance

Translate environmental data to resistance surfaces

for habitat k and species i:

rki = low high Water Bldg

Number of moves

Mea

n s

qu

ared

dis

tan

ce

Ob

serv

ed d

isp

erse

rs

HB HA HC HD

Habitat B

Habitat A

rki = 1/slope rki = nki/nk

Landscape: network of habitats and connecting paths

Connectivity: resistance weighted distance along path

Least cost paths, Least cost networks, Sensitivity Analysis

Spatial Modeling: Connectivity Analysis

Connectivity between two patches

Connectivity of a patch to others

Total connectivity of a landscape

Composition of landscapes and status of patches can be modified for scenario testing

Single or multiple species

Cost of altering a pathway?

Value of patch to overall connectivity?

Does optimizing connectivity for one species benefit or impair others?

Can connectivity be improved for multiple species with minimal loss of optimality for one?

rki = low high Water Bldg

Landscape: network of habitats and connecting paths

Least cost connecting paths solved on resistance surface

Connectivity: resistance weighted distance along path

Least cost paths, Least cost networks, Sensitivity Analysis

Spatial Modeling: Connectivity Analysis

Connectivity between two patches

Connectivity of a patch to others

Total connectivity of a landscape configuration

rki = low high Water Bldg

patches i,jspecies khabitat hlength ldistance dij

distance weight wk

subject to constraints

1

( )H

ij hk h ij kh

C r l d w

Testing Models

• test against observed dispersals and habitat occupancy

• cross-validation between models

• assess trade-off between information value and data requirements of methods

• test sensitivity of models to data quality

low highDispersal Resistance

known occupancypredicted dispersal pathobserved dispersal path

Implementation

Use the data and tools to:

Map value of land parcels for conservation of habitat connectivity in areas of high priority for Ft. Bragg

Test connectivity impacts of management alternatives in consultation with Ft. Bragg

Can connectivity be improved for multiple species with minimal loss of optimality for one?

Does optimizing connectivity for one species benefit or impair others?