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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 Local populations ephemeral Metapopulation Local populations spatial reproduction Individuals biological reproduction 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 P = fraction of patches occupied Rate: local populations/unit time All local populations and habitats the same Remove a patch 0 Pnew Poriginal 1 Reduce patch areas 0 Occupied patches/Total patches Pnew Poriginal 1 Levins’ model is a single species version of M&W Extinction Rate: species/unit time Colonization Goes up with decreasing size Goes down with increasing isolation S0 SE Number of species ST 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 • Habitat •Matrix • Size •Distance Binary Landscapes 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 known occupancy Least cost path Shortest euclidean path low high Dispersal Resistance “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 population behavior metapopulation evolution disease conservation community 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 Carolina gopher frog eastern tiger salamander Red cockaded woodpecker and its range Mapping Habitat Connectivity for Multiple Rare, Threatened, and Endangered Species on and Around Military Installations (SI-1471) Aaron Moody Department of Geography University of North Carolina at Chapel Hill BRIEF TO THE SCIENTIFIC ADVISORY BOARD 20 October 2005 Performers Dr. Aaron Moody University of North Carolina, Chapel Hill Specialist in Landscape Ecology, Remote Sensing, GIS Dr. Nick Haddad North Carolina State University Specialist in Landscape Ecology and Conservation Biology Dr. Bill Morris Duke University Specialist in Population Ecology, Dispersal Modeling Dr. Jeffrey Walters Virginia Tech Specialist in Avian and Population Ecology Dr. Jeffrey Priddy Duke University Specialist 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 Landscape Connectivity Our goal is to optimize connectivity for conservation of species that have different habitat requirements Connectivity Near Installations NE Area Multiple species of concern 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 known occupancy predicted dispersal path observed dispersal path low Dispersal Resistance high 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 Integrated Spatial Database Movement Data Collection Spatial Data Acquisition Field Data Collection Spatial Data Development Dispersal Modeling Spatial Modeling Evaluations & Model Updates Transition Implement Decision Support System for Habitat Management Collection of Movement Data St. Francis’ Satyr • 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 capturerecapture MH x x OH x OH = optimal habitat MH = matrix habitat x = sample site r r = release point 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) Spatial Data Layers Elevation Hydroperiod Infrastructure Soils Land Use Zoning Site Data Canopy Structure Field Data Collection and Environmental Data Development : Land Use Random samples stratified by land-use class ~ focus on 8 types Pasture, Row Crop Forest Plantation Spectral distribution functions for each type Upland Forest Longleaf Pine Woodland LLP/herbaceous LLP/woody Wetlands UNC’s Mason farm biological reserve sedge-meadow woody short forested 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 Field Data Collection and Environmental Data Development : Hydroperiod Crest gauges in known ( ) and potential amphibian habitats 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 Heights monitored through breeding seasons Spatial Modeling: Habitat Spatial Data Layers Elevation Land use Hydroperiod Infrastructure Soils Habitat Models + Land Use Zoning Site Data Canopy Structure Habitat Maps Canopy Structure Validate by visiting predicted habitat Hydroperiod Occurrence and dispersal data Movement Data Habitat A Boundary High Resistance L1 A1 L2 Habitat B High Conductivity L1 L2 L3 A1 A2 Dispersal Modeling Analytical Models If turns are symmetric: Mean squared distance Slope Var ( L) 2 L2 CosA 1 CosA Computer Simulation Habitat A Habitat B Turn angle Turn angle Move length Move length Habitat B Habitat A Number of moves Kareiva and Shigesada 1983 Boundary Behavior Spatial Modeling: Landscape Resistance Translate environmental data to resistance surfaces for habitat k and species i: Habitat B Habitat A Number of moves rki = nki/nk Observed dispersers Mean squared distance rki = 1/slope HB HA HC HD rki = low high Water Bldg Spatial Modeling: Connectivity Analysis Landscape: network of habitats and connecting paths Connectivity: resistance weighted distance along path Least cost paths, Least cost networks, Sensitivity Analysis Connectivity between two patches Cost of altering a pathway? Connectivity of a patch to others Total connectivity of a landscape Composition of landscapes and status of patches can be modified for scenario testing Value of patch to overall connectivity? Single or multiple species 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 Spatial Modeling: Connectivity Analysis 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 H Cij rhk lh (d ij wk ) h 1 subject to constraints patches species habitat length distance distance weight rki = low high Water Bldg i,j k h l dij wk Connectivity between two patches Connectivity of a patch to others Total connectivity of a landscape configuration 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 known occupancy predicted dispersal path observed dispersal path low Dispersal Resistance high 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?