Landscape Ecology
Metapopulation biology
Terms/people:
Metapopulation (sensu stricto and sensu lato definitions, and types)
Ron Pulliam
Richard Levins
source-sink
winking dynamics
patchy population
Subpopulation
mainland-island
Ilkka Hanski
Susan Harrison
Keitt et al. 1997
Landscapes experience both natural and (increasingly) anthropogenic fragmentation,
which has important ramifications on the structure and persistence of populations of
organisms. Metapopulations are literally populations of populations, such as spatially
subdivided populations existing in discrete habitat remnant patches as a result of habitat
fragmentation. The metapopulation concept has changed how ecologists view spatial
heterogeneity and judge the impacts of fragmentation, effecting a paradigm shift from a
static patch-matrix island biogeography perspective to a more dynamic view. From a
highly theoretical beginning, the metapopulation concept has become increasingly used
in conservation, epidemiological, and genetic applications. Knowledge about the
properties of metapopulations may assist in predicting and preventing extinction events,
designing reserves, measuring the spread of disease, and characterizing other topics of
interest.
Metapopulation –
Sensu stricto –
Sensu lato Why should we care about metapopulations?
Ecology provides many examples of how including spatial patchiness generates effects
that would not be present in the absence of a spatially heterogeneous system:
-competition (where one species is clearly a loser in competitive interactions, patchiness
allows coexistence);
-predator-prey systems (in cases where a predator is locally efficient, patchiness may
provide refugia for prey and thus enhance potential for future pest outbreaks).
Population genetics is increasingly concerned about the role of patchiness in mediating
gene flow.
Conservation biology as targeted on rare species is especially concerned with habitat
fragmentation and isolated populations.
Landscape ecologists claim spatial pattern as their own domain, so metapopulation
biology is a key topic in landscape ecology. In this class, our interest in metapopulations
is from the perspective of identifying those cases where the pattern of available habitat
patches in a landscape has implications for population dynamics or persistence. These
cases are of increasing concern in taking a landscape approach to conservation biology,
especially with respect to the effects of the fragmentation of intact habitats into small and
perhaps disconnected habitat remnants.
Terminology/definitions
subpopulation
a habitat patch
local extinction
recolonization
population turnover
winking dynamics
Click here for a list of terms and types of metapopulations.
History of the metapopulation concept
Richard Levins (1969) is the originator of the metapopulation concept and coined the
term "metapopulation." He developed a model that keeps track of p, the proportion of
patches occupied by a species of interest:
dp/dt = cp(1-p) - ep
where e and c are extinction and colonization rates, dp/dt is rate of change in ppn of
patches occupied, cp(1-p) represents colonization of unoccupied patches ("birth" of new
occupied patches), and ep represents local extinction ("death" of occupied patches)
predicted equilibrium: in the long run, the ppn of occupied patches will be:
P@equil = 1 - (e/c)
metapop. will only persist if c > e
This was the first model of population dynamics in a heterogeneous environment.
Previously, all other models considered populations to occur panmictically in a
homogeneous system (a clearly unrealistic scenario).
Then there was a 20+ year gap in metapopulation thinking. Why?
Hanski and Gilpin (1991) further delineate some additional key characteristics of
metapopulations:
At the local scale, individuals interact with each other during the normal course of
feeding and breeding (living); an individual belongs to one local population for most of
its life.
A metapopulation is a constellation of local populations that are linked by dispersal.
Not all spatially subdivided populations are metapopulations.
Early metapopulation studies were not spatially explicit nor particularly complex in their
treatment of environmental heterogeneity; the incorporation of spatial dynamics (such as
patch area and isolation) into metapopulation models is reviewed by Hanski and
Ovaskainen (2003).
Types of metapopulations:
Susan Harrison has described several variations on the metapopulation theme (Harrison
1991, 1994; Harrison and Taylor 1997):
The "Classic" (Levins) Metapopulation (metapopulation sensu stricto)
Mainland/Island Metapopulations
Source/Sink Metapopulations
sources, sinks, and pseudosinks
density as a misleading indicator of habitat quality (see the classic paper by Van
Horne 1983)
Sinks aren’t useless - may be important as stepping stones, providing greater
connectivity in the metapop. as a whole (Foppen et al. 2000).
Patchy Populations
Non-equilibrium Metapopulations
Important note: a population may exist as different types of metapopulations at different
times.
Population processes affecting metapopulations
Metapopulation dynamics reflect the occurrence and rates of local extinction and
recolonization, and factors effecting these processes. Metapopulations subject to high
extinction rates, but with correspondingly high rates of recolonization, also have high
population turnover.
Factors affecting extinction
There are three proximate agents of local extinction that act in a stochastic manner
(Shaffer 1981, Lande 1988):
Demographic stochasticity, which is important for populations that fall below a certain
threshold size.
Genetic stochasticity, for example the loss of heterozygosity or loss of fitness due to
inbreeding ("inbreeding depression") in very small populations.
Environmental stochasticity, such as vagaries of the weather that result in fluctuations
in food supply or habitat quality.
A fourth cause of local extinctions is directional habitat loss (= fragmentation). In
contrast to the stochastic agents above, these threats are more deterministic in their
action. Many argue that these are the major controls on real metapopulations.
Factors affecting recolonization
Because recolonization depends on the successful establishment of new recruits in a
habitat patch, the factors influencing recolonization must reflect the life-history traits that
effect this.
For plants:
seed size and viability (often related to seed size);
dispersal vector (wind, water, animal);
habitat (seedbed) requirements.
For animals:
distance;
"resistance" of intervening habitats;
dispersal behavior (preference or avoidance of some habitats, search and orientation
during dispersal, use of stepping stones or corridors);
mortality rates during dispersal itself, and agents of this mortality.
For both:
competition with individuals (or species) already out there.
Difficulties in studying metapopulations
Metapopulations are logistically difficult to study. A metapopulation is defined by
dispersal events that need occur only once every generation or so, and so it stands to
reason that these events are unlikely to be witnessed in the field. This places an emphasis
on modeling endeavors, yet empirical documentation is critical for applied endeavors.
Empirical studies of metapopulations have proceeded in a variety of ways (Kareiva
1990):
Most have dealt with short-lived species, often insects, for which short generations times
make it a bit easier to observe metapopulation dynamics directly.
For larger and longer-lived organisms, metapop. dynamics are inferred indirectly from
data like:
-Genetic similarity: in a metapopulation, we might expect to see spatial patterns in
genetic similarity of individuals collected in different patches. This requires that the rates
of genetic differentiation be appreciable relative to isolation time and also requires
species with "well-behaved" genes. Giles and Goudet (1997) provide an in-depth case
study and review other examples of applications of metapopulation theory to population
genetics.
-Percent occupancy, or turnover in occupancy, in habitat patches as a function of
between-patch distance or some other index of isolation. For example, one might use
logistic regression to model patch occupancy (presence/absence) as a function of distance
to the nearest other occupied patch.
-Similarity in demographic rates, or autocorrelation in population levels. For example,
Smith and Gilpin (1997) found spatial autocorrelation in census-based population growth
rates of the American pika, suggesting an important role of dispersal in influencing
metapopulation dynamics.
Because of these difficulties, Harrison (1991, 1994; Harrison and Taylor 1997) has
reviewed the empirical evidence in support of metapopulations and suggests that
although there are a few cases that are sufficiently well-documented to support the
existence of metapopulation dynamics, in more cases the evidence is simply not strong
enough to demonstrate that metapopulation dynamics are occurring.
Examining metapopulations: one example
The logistical difficulties of testing metapopulations empirically led Keitt et al. (1997) to
pursue an alternative approach that did not require detailed information about the
dispersal behavior of the species of interest (in this case, the Mexican Spotted Owl). They
instead focused on the pattern of habitat patches, and asked the question, "What can we
infer about this (meta?)population, given the pattern of available habitat and limited
knowledge of the biology of the species?" They began with a postulated map of potential
habitat for the owl (based on known ecological requirements of the species), and
developed an analysis to quantify habitat pattern relative to the owl's estimated dispersal
capabilities. The analysis consisted of a few simple steps:
1. Define a raster map of clusters of potential owl habitat (in this case, derived from
remote-sensing images).
2. Aggregate the cells of the map into discrete clusters based on functional adjacency at a
specified joining distance (dispersal distance). In this, two cells were in the same cluster
(habitat patch) if they were within the specified joining distance. The presumption was, if
owls could disperse that distance, the cells would function for an owl as part of the same
habitat patch.
3. This clustering aggregation was repeated for a wide range of joining
distances/potential dispersal distances (0, 5, 10, ..., 100 km).
4. Habitat connectedness was summarized as length of habitat, indexing how "connected"
the habitat patches were at a given joining (dispersal) distance.
The question is, given that the habitat patches coalesce at some joining distance to form
essentially a single large patch, at what distance does this occur, relative to the dispersal
capabilities of the species? A habitat mosaic that coalesces at joining distances that are
well within the dispersal capabilities of the species would not be experienced as discrete
habitat patches (the species might function as a single patchy population). A landscape
that coalesced well beyond the dispersal range of the species would be experienced as a
collection of discrete patches (the species would operate as some sort of metapopulation).
A habitat mosaic that coalesces at a joining distance similar to the dispersal range of the
species would be expected to behave as a metapopulation.
The elegance of this approach is that it does not require exhaustive information about the
dispersal range of the species (nor mortality, nor fitness)--it requires merely that we know
its approximate dispersal range relative to the critical joining distance at which the
landscape coalesces!
Keitt et al.’s paper is available free online (URL is given in the reference section below).
Epilogue
The metapopulation concept has changed how biologists think about the world: it has
gotten them to think about patches and dispersal. In other words, it has gotten them to
think like landscape ecologists.
References:
Foppen, R.P.B., J.P. Chardon, and W. Liefveld. 2000. Understanding the role of sink
patches in source-sink metapopulations: Reed Warbler in an agricultural landscape.
Conservation Biology 14:1881-1892.
Giles, B.E., and J. Goudet. 1997. A case study of genetic structure in a plant
metapopulation. Pp. 429-454 in: Metapopulation Biology: Ecology, Genetics, and
Evolution (I. Hanski and M. Gilpin, eds.). Academic Press, San Diego, CA.
Hanski, I., and M. Gilpin, eds. 1991. Metapopulation Dynamics: Empirical and
Theoretical Investigations. Academic Press, London, UK.
Hanski, I., and M. Gilpin, eds. 1997. Metapopulation Biology: Ecology, Genetics, and
Evolution. Academic Press, San Diego, CA.
Hanski, I., and O. Ovaskainen. 2003. Metapopulation theory for fragmented landscapes.
Theoret. Pop. Biol. 64:119-127.
Harrison, S. 1991. Local extinction in a metapopulation context: an empirical evaluation.
Biol. J. Linnean Soc. 42:73-88.
Harrison, S. 1994. Metapopulations and conservation. Pp. 111-128 in: Large-scale
Ecology and Conservation Biology (P.J. Edwards, N.R. Webb, and R.M. May, eds.).
Blackwell, Oxford, UK.
Harrison, S., and A.D. Taylor. 1997. Empirical evidence for metapopulation dynamics.
Pp. 27- 42 in: Metapopulation Biology: Ecology, Genetics, and Evolution (I. Hanski and
M. Gilpin, eds.). Academic Press, San Diego, CA.
Kareiva, P. 1990. Population dynamics in spatially complex environments: theory and
data. Phil. Trans. R. Soc. Lond. B 330:175-190.
Keitt, T.H., D.L. Urban, and B.T. Milne. 1997. Detecting critical scales in fragmented
landscapes. Conservation Ecol. 1(1):4.
Lande, R. 1988. Genetics and demography in biological conservation. Science 241:14551460.
Levins, R. 1969. Some demographic and genetic consequences of environmental
heterogeneity for biological control. Bull. Entomol. Soc. Am. 15:237-240.
Pulliam, H.R. 1988. Sources, sinks, and population regulation. Am. Nat. 132:652-661.
Shaffer, M.L. 1981. Minimum populations sizes for species conservation. BioScience
31:131- 134.
Smith, A.T., and M.E. Gilpin. 1997. Spatially correlated dynamics in a pika
metapopulation. Pp. 407-428 in: Metapopulation Biology: Ecology, Genetics, and
Evolution (I. Hanski and M. Gilpin, eds.). Academic Press, San Diego, CA.
Van Horne, B. 1983. Density as a misleading indicator of habitat quality. Journal of
Wildlife Management 47:893-901.