Transcript Metapopulations

Metapopulations
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Definitions
Quantitative Details
Empirical Examples
Conservation Implications
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?
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
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)
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)
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
(Harrison and Taylor 1997)
Scale Matters
Dispersal abilities of
animals determine
metapopulation
boundaries
• and point out key
connections in the
landscape
•Chetkiewicz et al.
2006
(Harrison and Taylor 1997)
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)
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)
Key Processes in
Real Populations
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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
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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)
Math for Levins Model
dP / dt  cP (1  P )  eP
e
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P  1
c
P  fraction of currently occupied patches
Pˆ  equilibriu m fraction of occupied patches
e  probabilit y of extant local population going extinct
c  colonizati on rate per empty patch and extant local population
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
Adding Stochasticity
Tm  TLe
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
 HPˆ 2

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ˆ 
2
1

P


 
Tm = expected time to metapopulation extinction
TL = expected time to local extinction
P = fraction of occupied patches at a stochastic
steady state
H = # suitable habitat patches
Minimum Viable Metapopul ation  Pˆ H  3
Assuming Tm>100TL as a criteria for long-term persistence
(Nisbet and Gurney 1982, Hanski 1997)
Population Persistence in Butterfly
Metapopulations
Some Conservation Messages
(Hanski 1997)
• MVMP are on order of 10-20 small and wellconnected 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
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
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
Selection
Population
Size
Mutation
Genetic
Variation
Genetic
Assimilation
Drift
Behavioral
Innovation
Phenotypic
Learning
Plasticity
Heritability
Stochasticity
Population
Isolation
Gene-Cullture
Coevolution
Gene
Flow
Social Learning
Genetic and
Cultural Change
Ne
λ
Extinction
Microevolution
Local
Adaptation
Micro- to
Macroevolution
Genetic
Variation
Consistent
with
Designation
of Urban
Races,
Subspecies,
Species, and
Higher Taxa
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 Robin
289
375
Bewick’s Wren
160
210
Dark-eyed Junco
141
339
Song Sparrow
1177
867
Spotted Towhee
533
848
Swainson’s Thrush
647
433
Winter Wren
195
552
A Diversity of Nest Predators
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
Reserves
Changing
Developed
% Successful
61.2
70.6
64.4
% 2nd Brood
7.5
16.4
0.16
Fledglings/nest attempt
1.56
2.00
2.14
Fledgling/female
0.78
1.00
1.07
Song Sparrow
Average number of fledglings per female
Landscape specific productivity estimates :
1.2
1.0
Reserves
Developing Sites
Developed Sites
0.8
0.6
0.4
0.2
0.0
American
Robin
Bewick's Dark-eyed
Wren
Junco
Song
Sparrow
Spotted
Towhee
Swainson's Winter
Thrush
Wren
American Robin = 25-50%,
not responsive to forest
Improved estimation
using Telemetry
(Whittaker and Marzluff in press)
Song Sparrow = 48%,
declining with loss of forest
Apparent Annual Survival
Spotted Towhee = 33%,
declining with loss of forest
1.0
0.8
Reserves
Developing Sites
Developed Sites
JUVENILES
0.6
0.4
0.2
0.0
American Bewick's Dark-eyed Song Spotted Swainson's Winter
Robin
Wren
Junco Sparrow Towhee Thrush
Wren
Swainson’s Thrush = 42%,
not responsive to forest
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.
1.8
Winter Wren
1.6
Source/ growing populations
1.4
Lambda
1.2
Stable population
1.0
0.8
0.6
0.4
Sink / declining
0.2
0.0
Reserve
Changing
Developed
For each species/landscape we estimated lambda using the
mean parameter estimates and the upper and lower 95% CI
bound value for each parameter.
1.8
1.8
Winter Wren
1.6
1.4
1.4
1.2
1.2
1.0
Lambda
Lambda
Swainson's Thrush
1.6
0.8
0.6
1.0
0.8
0.6
0.4
0.4
0.2
0.2
0.0
Reserve
Changing
Developed
0.0
Reserve
Changing
Developed
Possible Source – Sink Dynamics
1.8
American Robin
1.6
Need to know movement patterns
to confirm
1.4
Lambda
1.2
1.0
0.8
0.6
0.4
0.2
0.0
Reserve
Changing
Developed
1.8
Song Sparrow
1.6
No obvious response in
growth rate by landscape.
1.4
1.0
0.8
0.6
0.4
0.2
0.0
Reserve
Changing
Developed
1.8
Spotted Towhee
1.6
1.4
1.2
Lambda
Lambda
1.2
1.0
0.8
0.6
0.4
0.2
0.0
Reserve
Changing
Developed
1.8
Dark-eyed Junco
1.6
Possible sink during
development for some
species followed by
recovery as subdivision
ages?
1.4
1.0
0.8
0.6
0.4
0.2
0.0
Reserve
Changing
Developed
1.8
Bewick's Wren
1.6
1.4
1.2
Lambda
Lambda
1.2
1.0
0.8
0.6
0.4
0.2
0.0
Reserve
Changing
Developed
Number detected within
50m during 10mins
Number detected within
50m during 10mins
2.0
Number detected within
50m during 10mins
Junco’s occur at low numbers
but appear ‘stable’ in
reserves, and are most
abundant and possibly
increasing in developed
areas.
2.0
2.0
Reserves
Bewicks' Wren
Dark-eyed Junco
1.5
1.0
0.5
0.0
Changing
1.5
1.0
0.5
0.0
Developed
1.5
1.0
0.5
0.0
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Year
2005
2006
2007
2008
Literature Cited
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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.