Genetics & Metapopulations Dr Stacie J Robinson Dr. Stacie J. Robinson Dept. of Forest & Wildlife Ecology, UW Madison
Robinson ~ UW SJR OUTLINE Metapopulation impacts on evolutionary processes Metapopulation impacts on current genetic patterns Genetic tools and measures Applications of genetic data to studying metapops Extinction & colonization dynamics Migration rates and patterns
Metapopulation ti Processes Impact Evolutionary Processes
Evolutionary Processes Panmixia vs Metapopulation Fisher Large panmictic populations Selection occurs primarily through random mutation within a large gene pool Contains large amounts of variation available for adaptation as environmental changes arise The population evolves through gradual adaptive refinement Wright Many small subdivided populations The population evolves through a balance of random drift and gene flow Variations arise by chance, and different variations may be locally selected for within different demes Adaptive variations have an advantage when spread to new demes via migration
Evolutionary Processes Population subdivision allows microevolutionary processes (local adaptation) Adaptive processes (due to local selection) vs Stochastic processes (due to small fragments) vs Balancing processes (through mixing/migration)
Evolutionary Processes Flow vs Drift Genetic Drift populations differentiate through random mutation or consequences of reproductive success Gene Flow populations blend together as genetic material is shared through migration Gene Flow Migration Mixture via outbreeding Genetic Drift Isolation Stochastic events (mutation, chance loss)
Metapopulation ti Processes Impact Contemporary Genetic Patterns
Contemporary Patterns Effective Population Size = The effective number of individuals contributing genes to the next generation Metapopulation structure re increases effective e population size only if all demes contribute equally to mating success Subdivision decreases effective population size if there is variance in reproductive contribution of demes
Contemporary Patterns Effective Population Size Local extinction / recolonization reduces effective population size Local extinction lowers census s size Increases variance in reproductive success New colonizers may leave >> offspring Those in saturated demes may leave << Those in extinct demes can leave none
Contemporary Patterns Population Genetic Structure The partitioning of genetic variation across space
Contemporary Patterns What is the effect of variations of metapopulation structure? Classical Mainland-Island Patchy Non-Equilibrium Intermediate Harrison & Hastings 1996 TREE
Contemporary Patterns Classical frequent turn over and colonization yield low differentiation w/i and between demes Mainland-Island genetic drift in isolated islands counteracts homoginization of migration Patchy population is not fully subdivided, population is approximately panmictic Non-Equilibrium population divergence likely, but so is extinction
Contemporary Patterns Deme turnover impacts on population differentiation: Depend on severity of bottleneck with initial re-colonization Depend on rate and diversity of migrants to recover after initial bottleneck Migration model drives population structure
Contemporary Patterns Deme age impacts on population differentiation: Founder effect in recently colonized demes increases differentiation from others Older demes exposed to more mixing through migration amongst demes Metapopulation dynamics create an age-structured pattern with genetic differentiation decreasing with deme age McCauley et al 1995 in Heredity
Contemporary Patterns Migration models Propagule pool model Continent-Island Model migrants drawn from a large continental source Continent gene frequencies largely unaffected by local extinction dynamics Propagule-pool colonization = increase in FST Migrant pool model Infinite Infinite-Island Island Model migrants drawn from multiple islands themselves Gene frequencies evolve in response to local metapopulation processes Migrant-pool colonization = decrease in FST
Genetic Tools & Measures
Robinson ~ UW SJR Genetic Data Mitochondrial DNA sequences Broad spatial scales, long time periods Maternally inherited DNA sequences / SNPs Full genome coverage Neutral or selective Microsatellites Highly variable Fine spatial and temporal scale ATATATATATATATAT ATATATATAT
Robinson ~ UW SJR Genetic Data Locus a particular segment of the genome Allele each variation of the DNA code at a locus Allele Frequency the proportional incidence of a particular DNA variant within a population
Robinson ~ UW SJR Genetic Data Heterozygosity (individuals) having different alleles at each copy of a corresponding locus; (at a population level) the proportion of individuals in a population heterozygous at a particular locus Ho Observed Heterozygosity the observed proportion of heterozygotes in a population = # heterozygotes / # genotyped He Expected Heterozygosity the proportion p of heterozygotes predicted assuming Mendalian assortment of alleles with their given frequencies in a population
Genetic Measures Population p Differentiation Fst genetic variation between subgroups relative to entire population O = admixture 1 = fixation Fst = (H T H S )/ H T
Genetic Measures Population p Differentiation Fst genetic variation between subgroups relative to entire population O = admixture 1 = fixation Fst = 1 + Ne / k 1+ 4Nm + 2Ne (1 φ (1 ½ k)) k = number of colonizing individuals φ = probability that 2 genes come from the same source deme φ = 1 ~ propagule p pool model φ = 0 ~ migrant pool model
Robinson ~ UW SJR Genetic Measures Migration Rate Nm genetic migration = moving genes among populations: number of migrants per generation calculated from measures of differentiation i.e. how many migrants required to maintain observed levels of differentiation Population Ancestry assignment the probability that an individual originates from any given population based how likely its genotype is to arise from the allele frequencies observed in each population
Genetic Insights to Metapopulation Dynamics
Robinson ~ UW SJR CASE STUDY OUTLINE Bottleneck Tests Population Assignment - detecting declines - sources of colonizers Source Sink Dynamics - asymmetrical migration Migration Rates Migration Distances Landscape Patterns - mixing and colonization - mixing and colonization - complex models
Robinson ~ UW SJR Extinction & Colonization Extinction and colonization dynamics are central to metapopulation dynamics These processes leave genetic signatures Based on genetic diversity within demes Based on sources of migrants / colonists Based on connectivity between demes
Extinction & Colonization Inferring demographic processes from the genetic structure of a metapopulation of Boltonia decurrens DeWoody et al. 2004 in Conservation Genetics Flood plain species, frequent extinctions / colonizations expected due to flood dynamics Test for patterns of frequent bottlenecks to demonstrate t metapopulation dynamics Determine sources of colonizers to determine role of pollen vs seed dispersal
Population Bottlenecks Local extinction followed by recolonization by a few founders results in a genetic bottleneck. Bottleneck = severe reduction in population size Increase demographic stochasticity Increase rate of inbreeding Loss of genetic variation Loss of adaptive potential
Population Bottlenecks Heterozygosity Excess Test Rare alleles are lost 1 st Allelic diversity decreases faster than heterozygosity Observed He will exceed that expected for the given # of alleles at equilibrium Luikart & Cornuet 1998 in Cons Bio
Population Bottlenecks M Ratio Test Rare alleles are lost 1 st Number of alleles will decrease faster than the overall size range in alleles Ratio of # alleles : size range will decrease w/ bottlenecks Garza & Williamson 2001 in Mol Ecol
Population Bottlenecks Used Het. excess test 12 of 14 demes showed recent bottlenecks Demonstrated extinction / colonization Possible senescence Possible P new founding
Origins of Colonizers Genetic ancestry may be drawn back to source population through assignment tests Probability (assignment to pop. x) P(descent from Genotype = & genepool x) of the individual Distribution of alleles in genepool x
Origins of Colonizers Genetic ancestry may be drawn back to source population through assignment tests Probability (assignment to pop. x) P(descent from Genotype = & genepool x) of the individual Distribution of alleles in genepool x
Origins of Colonizers Colonizing g migrants could be assigned to a few primary source demes Up and downstream gene flow are important Relation of source to colonized demes demonstrates importance of seed dispersal by flood overflow and/or other vectors
Origins of Colonizers Patterns of colonization in a metapopulation of grey seals Gaggiotti et al. 2002 in Nature Used an adapted assignment to determine the contribution of established colonies to new ones Incorporated parameter to account for decay of migration rate with distance Incorporated parameter to account for increased rates of migration w/ higher population productivity
Origins of Colonizers Contribution of potential sources to new demes decreased with distance Contribution of potential sources to new demes increased with productivity
Source Sink Dynamics Some demes may be large, stable, productive, while others are small, unproductive, and experience population losses The population may persist thanks to contribution tion of productive source populations Genetic signatures of asymmetrical migration can be used to indicate source-sink dynamics
Source Sink Dynamics Landscape attributes and life history variability shape genetic structure of trout populations in a stream network Neville et al. 2006 in Landscape Ecology Variation in habitat quality and barriers to up-stream movement are suspected to lead to source-sink Use genetic estimates of migration to test identify sources of migrants and demes dependent on input
Source Sink Dynamics Tests for Asymmetrical Migration Measure differentiation as: = 4 * Ne * migration Estimate Ne of each based on genetic data Estimate m1 and m2 using likelihood model based on coalescent theory Beerli & Felsenstein 1999 in Genetics
Source Sink Dynamics Many populations differed in levels of gene flow Headwater populations tended to be source populations Sources Sink
Migration Rates Propagule pool model migration is asymmetrical, few sources contribute most genetic differentiation high b/c recolonization by few founders youngest populations (most recently colonized) most distinct Migrant pool model closer to island model migration & recolonization are random between populations genetic differentiation low b/c colonizers are from many sources
Migration Rates Meta-population structure in a coral reef fish demonstrated by genetic data on patterns of migration, extinction and recolonization Bay et al. 2008 in BMC Evolutionary Biology Reef fish Limited movement between reefs = low migration rates between demes Use genetic data to test migration models
Migration Rates Measure differentiation Measure reciprocal using Fst migration rates Fst varies among pops Find asymmetrical rates consistently w/ sea currents Support propagule pool
Migration Distance Migration with spatial distance Genetic distance with migration Genetic distance relative to spatial distance between populations can reveal the extent of effective migration
Migration Distance Metapopulation genetic structure and migration pathways in the land snail Helix aspersa: influence of landscape heterogeneity Arnaud et al. 2003 in Landscape Ecology Habitat is patchy and ephemeral in human / agriculture dominated landscape Maintenance of population dependant on frequent recolonization after local extinction Recolonization by migrants suspected to depend d on distance between habitat t patches
Migration Distance Isolation By Distance
Migration Distance Isolation By Distance
Migration Distance Isolation By Distance 1 Genetic Distance 2 1 3 2 1 4 3 2 1 5 4 3 2 1 6 5 4 3 2 1 7 6 5 4 3 2 1 X 5 1 Spatial Distance 2 1 3 2 1 4 3 2 1 4 3 2 1 6 5 4 3 2 1 7 6 5 4 3 2 1 8 7 6 5 4 3 2 1 8 7 6 5 4 3 2 1
Migration Distance Spatial Genetic Autocorrelation Autocorrelation Distance
Migration Distance Spatial Genetic Autocorrelation Autocorrelation Distance
Migration Distance Short-distance autocorrelation suggests dispersal limited to nearby populations Directionality indicated step-wise expansion into new habitat patches
Landscape Genetics Migration is not shaped by distance alone We know that island models and even distance-based models are too simple Landscape genetics offers tools to b ild onto Landscape genetics offers tools to build onto the model, incorporating landscape characteristics such as barriers, corridors, or differential movement through varied habitats
Landscape Genetics A few novel methods Assessment of gene flow across barriers (Epps et al. 2007) Matrix regression w/ spatial distance and ecological distance (Cushman et al. 2006) Ordination based on genetic distance (Legendre et al. 2010) Network based models (Murphey et al. 2010) Resistance surface models (McRae et al. 2008)
Landscape Genetics Optimizing dispersal and corridor models using landscape genetics Epps et al. 2007 in J of Applied Ecology Big horn sheep exist in remnant populations with low migration Migration rates affected by distance and topography Built least cost dispersal corridor models by assessing fit of landscape classifications to gene flow patterns
Landscape Genetics Correlation of landscape features to gene flow revealed migration patterns and help identify corridors Require slope > 10% Escape cover required Human features disrupt dispersal ability
Useful References General Metapopulation Genetics Harrison S, Hastings A (1996) Genetic and evolutionary consequences of metapopulation structure. Trends in Ecology & Evolution 11, 180 183. Hastings A, Harrison S (1994) Metapopulation dynamics and genetics. Annual Review of Ecology and Systematics, 167 188. McCauley D, Raveill J, Antonovics J (1995) Local founding events as determinants of genetic structure in a plant metapopulation. Heredity 75, 630 636. Pannell J, Charlesworth B (2000) Effects of metapopulation processes on measures of genetic diversity. Proceedings of the Royal Society of London B 355, 1851 1864. Wade M, Goodnight C (1998) Perspective: the theories of Fisher and Wright in the context of metapopulations: when nature does many small experiments. Evolution 52, 1537 1553.
Useful References Case Studies & Methods Papers Arnaud J-F (2003) Metapopulation genetic structure and migration pathways in the land snail Helix aspersa: influence of landscape heterogeneity. Landscape Ecology 18, 333-346. Bay L, Caley M, Crozier R (2008) Meta-population structure in a coral reef fish demonstrated by genetic data on patterns of migration, extinction and re-colonisation. BMC Evolutionary Biology 8, 248. Beerli P, Felsenstein J (1999) Maximum-likelihood estimation of migration rates and effective population numbers in two populations using a coalescent approach. Genetics 152, 763. CoranderC d J, Marttinen P (2006) Bayesian identification of admixture events using multi-locus l molecular l markers. Molecular l Ecology 15, 2833-2843. Cushman SA, McKelvey KS, Hayden J, Schwartz MK (2006) Gene flow in complex landscapes: testing multiple hypotheses with causal modeling. The American Naturalist 168, 486-499. Dewoody J, Nason J, Smith M (2004) Inferring demographic processes from the genetic structure of a metapopulation of Boltonia decurrens (Asteraceae). Conservation Genetics 5, 603-617. Epps CW, Wehausen JD, Bleich VC, Torres SG, Brashares JS (2007) Optimizing dispersal and corridor models using landscape genetics. Journal of Applied Ecology, pp. 11. Gaggiotti O, Jones F, Lee W, et al. (2002) Patterns of colonization in a metapopulation of grey seals. Nature 416, 424-427. Garza J, Williamson E (2001) Detection of reduction in population size using data from microsatellite loci. Molecular Ecology 10, 305-318. Legendre P, Fortin M (2010) Methodological advances - inference of population structure: Comparison of the Mantel test and alternative approaches for detecting complex multivariate relationships in the spatial analysis of genetic data. Molecular Ecology Resources 10, 831-844. Luikart G, Cornuet J (1998) Empirical evaluation of a test for identifying recently bottlenecked populations from allele frequency data. Conservation Biology 12, 228-237. McRae B, Dickson B, Keitt T, Shah V (2008) Using circuit theory to model connectivity in ecology, evolution, and conservation. Ecology 89, 2712-2724. Murphy M, Evans J, Storfer A (2010) Quantifying Bufo boreas connectivity in Yellowstone National Park with landscape genetics. Ecology 91, 252-261. 261. Neville H, Dunham J, Peacock M (2006) Landscape attributes and life history variability shape genetic structure of trout populations in a stream network. Landscape Ecology 21, 901-916. Pritchard JK, Stephens M, Peter D (2000) Inference of population structure using multilocus genotype data. Genetics 155, 945-959. Wagner HH, Holderegger R, Werth S, et al. (2005) Variogram analysis of the spatial genetic structure of continuous populations using multilocus microsatellite data. Genetics 169, 1739-1752.