1. Metapopulations Definitions Quantitative Details Empirical Examples Conservation Implications

2. Another Paradigm Shift (Hanski and Simberloff 1997) Another manifestation of the shift to include larger temporal and spatial scales as well as explicit focus on patches in our thinking?

3. Getting a Grip on Metapopulations Progression of thought: Levins 1970, Gilpin and Hanski 1991, Hanski and Gilpin 1997 “any assemblage of discrete local populations with migration among them” (Hanski and Gilpin 1997, p2) Populations that are spatially structured into assemblages of local breeding populations with migration between them that affects local population dynamics, including the possibility of reestablishment following extinction (Hanski and Simberloff 1997, p 6) Contrast with panmictic population where every individual has equal liklihood of interacting with every other one

4. Formal Definitions (Hanski and Simberloff 1997) Local Population: “Population, subpopulation, deme” –Set of individuals that live in the same habitat patch and therefore interact with each other; most practically applied to “populations” living in such small patches that all individuals practically share a common environment Metapopulation: –Set of local populations within some larger area, where typically migration from one local population to at least some other patches is possible (but see non-equilibrium metapopulation where this is not needed)

5. Types of Metapopulations Levins metapopulation: “classical metapopulation” –A large network of similar small patches, with local dynamics occurring at a much faster time scale than metapopulation dynamics; sometimes used to describe a system in which all local populations have a high risk of extinction Mainland-island metapopulation: “Boorman-Levitt metapopulation” –System of habitat patches located within dispersal distance from a very large habitat patch where the local population never goes extinct (hence, M-I metapopulations never go extinct)

6. More Types of Metapopulations Source-sink metapopulation –System where at low density there are subpopulations with negative growth rates (in absence of dispersal) and positive growth rates Nonequilibrium metapopulation –System in which long-term extinction rates exceed colonization or vice-versa; an extreme case is where isolation among subpopulations is so great that dispersal (and hence recolonization) is precluded

7. (Harrison and Taylor 1997)

8. Scale Matters Dispersal abilities of animals determine metapopulation boundaries and point out key connections in the landscape Chetkiewicz et al. 2006

9. (Harrison and Taylor 1997)

10. Populations and Species vs. Ecosystems? Focus on metapopulations and focus on genetics makes the population and the species the dominant levels of concern in conservation biology But, many managers yearn to manage at the Ecosystem scale? –Single species management too difficult or too expensive? –Ecosystem management too imprecise—if you build it, will they come? –Need to manage for landscape and ecosystem-level processes, while carefully managing for individual species (Coarse- and fine-filter approaches)

11. Key Processes Extinction –usually a constant risk multiplied times number of occupied patches Colonization –dependent on number of occupied (sources of colonists) and empty (targets) patches Turnover –Extinction of local populations and establishement of new local populations in empty habitat patches by migrants from existing local populations Note focus on populations not species (in contrast to island biogeography)

12. Key Processes in Real Populations Common Eiders breeding colonies on island have high turnover as expected, but population size rather than isolation or island size best predicted extinction and colonization (Chaulk et al. 2006). –Migratory species with good dispersal ability and large island have more predators than small ones Insects in European sand dune systems also had extinction and colonization dynamics consistent with metapopulations and in these species with limited dispersal patch size and isolation were important predictors of turnover (Maes and Bonte 2006). –Greatest diversity in large, connected dune systems (left)

13. Math for Levins Model

14. Key Predictions Metapopulation persists if e/c<1 P increases with increasing patch area –Due to decreasing extinction P increases with decreasing distance among patches –Due to increasing colonization

15. Adding Stochasticity T m = expected time to metapopulation extinction T L = expected time to local extinction P = fraction of occupied patches at a stochastic steady state H = # suitable habitat patches (Nisbet and Gurney 1982, Hanski 1997) Assuming T m >100T L as a criteria for long-term persistence

16. Population Persistence in Butterfly Metapopulations

17. Some Conservation Messages (Hanski 1997) MVMP are on order of 10-20 small and well- connected habitat patches –Even larger if regional autocorrelation is strong and stochasticity is great The state of the “living dead” may be common –Nonequilibrium metapopulations fading to extinction –10/94 butterflies studied by Hanski and Kuussaari 1995 Arrangement of reserve patches is a compromise between getting them close enough for colonization and dispersal and far enough apart so that their dynamics are not autocorrelated –Autocorrelation may also be reduced by increasing habitat quality differences among patches—just getting all optimal habitat may not be adequate

18. Need to Also Consider Evolution Reduction in habitat may be an important selective force driving local adaptation and rapid evolution (Handcock and Britton 2006) Population size is important to allow enough time for adaptation before stochastic extinction (Glomulkiewicz and Holt (1995) Fragmentation may also affect evolution—the degree likely depends on population sizes, gene and culture flow between populations—that is basic metapopulation dynamics

19. Selection Drift Gene Flow Urban Environment Exotic and Subsidized Predators and Competitors, Human Persecution, Novel Plant Communities, Anthropogenic Subsidies, Altered Disturbance Regimes, Changed Biogeochemical Cycles, Movement Barriers, New Land Cover and Land Use Dynamics, Altered Climate, Pollutants, Toxins Population Isolation Population Size Social Learning Mutation Local Adaptation Extinction Genetic Variation Consistent with Designation of Urban Races, Subspecies, Species, and Higher Taxa Stochasticity Genetic Variation Behavioral Innovation Phenotypic Plasticity Genetic Assimilation Learning Gene-Cullture Coevolution Heritability Genetic and Cultural Change NeλNeλ Environment Microevolution Micro- to Macroevolution

20. Monitoring Productivity and Survivorship Productivity---Territory success and fledgling estimates via spot mapping and nest monitoring. Color-banded individuals of 7 species: # Colorbanded Individuals # Territories/Nests Monitored American Robin289375 Bewick’s Wren160210 Dark-eyed Junco141339 Song Sparrow1177867 Spotted Towhee533848 Swainson’s Thrush647433 Winter Wren195552

21. A Diversity of Nest Predators

22. Landscape specific productivity estimates : From spot-mapping data and nest monitoring Territory success rates Number of fledglings/ successful nest We used these numbers to get estimate of fecundity ReservesChangingDeveloped Song Sparrow % Successful61.270.664.4 % 2 nd Brood7.516.40.16 Fledglings/nest attempt1.562.002.14 Fledgling/female0.781.001.07

23. Landscape specific productivity estimates :

24. Improved estimation using Telemetry (Whittaker and Marzluff in press) American Robin = 25-50%, not responsive to forest Song Sparrow = 48%, declining with loss of forest Spotted Towhee = 33%, declining with loss of forest Swainson’s Thrush = 42%, not responsive to forest

25. These are the parameters (adult survival, juvenile survival, and fecundity) we need to estimate λ, the intrinsic population growth rate for each species in these three landscapes, we did so using Ramas GIS. For each species/landscape we estimated lambda using the mean parameter estimates and the upper and lower 95% CI bound value for each parameter. Sink / declining Stable population Source/ growing populations

26. Possible Source – Sink Dynamics Need to know movement patterns to confirm

27. No obvious response in growth rate by landscape.

28. Possible sink during development for some species followed by recovery as subdivision ages?

29. Junco’s occur at low numbers but appear ‘stable’ in reserves, and are most abundant and possibly increasing in developed areas.

30. Literature Cited Hanski, IA and ME Gilpin 1997. Metapopulation biology: ecology, genetics, and evolution. Academic Press. San Diego Gilpin, ME and IA Hanski 1991. Metapopulation dynamics: empeirical and theoretical investigations. Academic Press. San Diego Levins, R. 1970 Extinction. Pp75-107. In: (M. Gerstenhaber,ed.) Some mathematical problems in biology. American Mathematical Society, Providence. Hanski, IA and D. Simberloff. 1997. The metapopulation approach, its history, conceptual domain, and application to conservation. pp5-26. In: (Hanski, IA and ME Gilpin, eds.) Metapopulation biology: ecology, genetics, and evolution. Academic Press. San Diego. Harrison, S. and AD Taylor. 1997. Emperical evidence for metapopulation dynamics. pp27-42. In: (Hanski, IA and ME Gilpin, eds.) Metapopulation biology: ecology, genetics, and evolution. Academic Press. San Diego. Hanski, IA. 1997. Metapopulation dynamics, from concepts and observations to predictive models. Pp69-91. In: (Hanski, IA and ME Gilpin, eds.) Metapopulation biology: ecology, genetics, and evolution. Academic Press. San Diego. Nisbet, RM. And WSC Gurney. 1982. Modelling fluctuating populations. J Wiley & Sons. New York. Maes, D. and D. Bonte. 2006. Using distributin patterns of five threatened invertebrates in a hightly fragmented dune landscape to develop a multispecies conservation approach. Biological Conservation 133:490-499. Chault, K. G., G. J. Robertson, and W. A. Montevecchi. 2006. Extinction, colonization, and distribution pattersns of common eider populations nesting in a naturally fragmented landscape. Canadian Journal of Zoology 84:1402-1408. Gomulkiewicz, R. and R. D. Holt 1995. When does evolution by natural selection prevent extinction? Evolution 49:201-207. Hancock, P.J.F. and N.F. Britton. 2006. Adaptive responses to spatial aggregation and habitat descruvction in heterogeneous landscpaes. Evolutionary Ecology Research 8:1349-1376. Chetkiewica, C-L. B. C. Cassady St. Clair, and M. S. Boyce. 2006. Corridors for conservation: integrating pattern and process. Annual Review of Ecology, Evolution, and Systematics 37:317-342.