Metapopulations, Patchiness, and Connectivity Biol 112: 10 October 2005.
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Transcript of Metapopulations, Patchiness, and Connectivity Biol 112: 10 October 2005.
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?