Download Levin, S.A. (editor). Encyclopedia

Provided for non-commercial research and educational use only.
Not for reproduction, distribution or commercial use.
This article was originally published in the Encyclopedia of Biodiversity, second edition, the copy attached is provided
by Elsevier for the author’s benefit and for the benefit of the author’s institution, for non-commercial research
and educational use. This includes without limitation use in instruction at your institution, distribution to specific
colleagues, and providing a copy to your institution’s administrator.
All other uses, reproduction and distribution, including without limitation commercial reprints, selling or licensing
copies or access, or posting on open internet sites, your personal or institution’s website or repository, are prohibited.
For exceptions, permission may be sought for such use through Elsevier’s permissions site at:
http://www.elsevier.com/locate/permissionusematerial
Pearson Scott M. (2013) Landscape Ecology and Population Dynamics. In: Levin S.A. (ed.) Encyclopedia of
Biodiversity, second edition, Volume 4, pp. 488-502. Waltham, MA: Academic Press.
© 2013 Elsevier Inc. All rights reserved.
Author's personal copy
Landscape Ecology and Population Dynamics
Scott M Pearson, Mars Hill College, Mars Hill, NC, USA
Published by Elsevier Inc.
Glossary
Core and satellite model Network of habitat patches in
which centrally located core patches harbor
stable populations that provide emigrants (or colonists) to
ephemeral populations in peripheral satellite patches.
Deme Localized breeding group or subpopulation, often
confined to a single habitat patch.
Effective population size (Ne) Number of individuals in
population that participate in breeding.
Extinction vortex Complex of interacting mechanisms
that lead to the extinction of small populations.
These mechanisms include loss of genetic diversity
as well as environmental and demographic
stochasticity.
Habitat fragmentation Process in which habitat loss
converts a landscape dominated by large connected patches
Introduction
Basic Concepts and Terminology about Population Dynamics
The discipline of landscape ecology emphasizes the causes and
consequences of spatial pattern on the functioning of populations, communities, and ecosystems. Nature is generally not
homogenous with respect to ecological properties. Those
properties include gradients in abiotic factors, such as the
temperature, moisture, and the abundance of required resources; and biotic factors, such as the presence of predators,
pathogens, and competing species. This spatial heterogeneity
exists at multiple scales of space and time. The term ‘‘landscape’’ evokes patterns over a broad scale, such as the view
from an airplane or satellite extending over several square
kilometers and including the mosaic of forests, grasslands,
wetlands, agricultural fields, and urban areas seen below
(Figure 1). This mosaic and the diversity of habitats therein is
the setting where species make their living. The spatial pattern
of habitats determines how species and their populations are
distributed across this view from above. The influence of
spatial pattern is also important at finer scales. Studies in
landscape ecology have illustrated how the spatial pattern of
resources at the scale of 1 m2 altered the habitat use by beetles
and ants (Wiens and Milne, 1989; Wiens et al., 1993). ‘‘Scale’’
is an important concept in landscape ecology because the
influence of spatial patterns often depends on the scale in
which they are observed and measured. It is common to
measure spatial heterogeneity at multiple scales in order to
best discern which scales are important for a given ecological
phenomenon (e.g., Price et al., 2005). No matter what scale is
studied, the spatial pattern of resources alternatively provides
opportunities for population spread and increase, limits and
constrains population growth, or threatens the persistence of
some species.
488
of habitat to one in which habitat exists in small,
disconnected patches or a condition in which
habitat is spatially distributed as small, disconnected
patches.
Landscape matrix The complex of habitats occupying the
space between habitat patches.
Metapopulation Network of individual demes connected
by the processes of immigration and emigration.
Population dynamics Change in population size through
time and the processes that bring about these changes.
Sink population Deme in which death rates exceed
birthrates. Sink populations will go extinct without an input
of immigrants.
Source population Deme in which birthrates exceed
death rates. Sources produce an excess of individuals that
emigrate from that deme.
A population is a group of interbreeding organisms that produce viable, fertile offspring; under the biological species
concept, members of a population belong to a single species.
Localized breeding groups are also known as ‘‘demes.’’ In a
typical landscape, the suitable habitat for most species exists in
a patchy pattern (Figure 1), so patches of habitat usually define the location and boundaries of demes.
Ecologists are interested in the number of individuals in a
population (that is, population size classically denoted by the
letter N). The change in N through time (dN/dt) is population
dynamics. Population size changes by the demographic processes of birth, immigration, death, and emigration (BIDE).
This BIDE model of population growth is
dN=dt ¼ Births þ Immigrants2Deaths2Emigrants:
Populations in which immigration and/or emigration occurs are considered to be open populations and are connected
to other demes by the movement of individuals. The distances
between demes influence the frequency of these deme-todeme movements. Movements may happen regularly for the
population but occur infrequently during the life cycle of an
individual. Movements often take the form of dispersal from
the natal site to a different habitat patch. Dispersal lessens the
probability of competition and interbreeding with parents and
siblings, especially when the population in the natal patch is
small. Analyses of population viability and genetic diversity
emphasize effective population size (Ne), which is the number
of individuals actually participating in breeding. In most
populations, NeoN because some individuals are unsuccessful in attracting a mate, reproductively immature, or of
postreproductive age.
Encyclopedia of Biodiversity, Volume 4
http://dx.doi.org/10.1016/B978-0-12-384719-5.00417-2
Author's personal copy
Landscape Ecology and Population Dynamics
Aerial photograph
0
0.25
489
Classified land cover
0.5
1 Km
Streams
Roads
Agriculture
Wetland
Mixed forest
Hardwood
Miscellaneous
nonforest
Conifer
Developed
Figure 1 Landscapes are a mosaic of habitat types. Color infrared aerial photograph from 1998 (left) and classification of land cover in 2001
(right) for a region of southern Appalachian Mountains, Madison County, NC, USA.
Population Dynamics in Landscapes
The number of individuals in a population, N, is limited
by available resources. The limiting resource(s) may include
food or nest sites for animals, or moisture, sunlight, and/or
soil nutrients for plants. Suitable habitats are the locations
and conditions that provide these resources. Given that resources are finite, N has a maximum value that is sustainable.
Ecologists use the term carrying capacity (K) to refer to
the maximum N that a given environment can support
over the long term. The logistic growth equation is a simple
model of population dynamics learned by most students of
ecology:
dN
KN
¼ rN
dt
K
The value dN/dt represents change in N over a unit of time (t),
and r is the intrinsic rate of increase, which is a measure of per
capita population growth:
r¼
births deaths
N
Assumptions of this model include a closed population (i.e.,
there is no immigration or emigration) and that r and K are
constants. Other ecological forces that could influence population growth, such as predation or competition, are not included. The logistic growth model implies that a population
attains equilibrium with resources in its environment. The
continuous form of this model produces graphs of N against
time as an S-shaped curve in which the population grows until
N ¼ K and then N remains constant at that value. The assumptions of the logistic growth model are rather restrictive,
and very few if any populations in nature would fulfill all of
them. Nevertheless, it expresses a central premise that populations are limited by availability of resources and the amount
of habitat in the landscapes where they live. Furthermore,
landscape ecologists study how the spatial arrangement of
habitat patches can alter population dynamics.
Landscape-level changes in habitat abundance and spatial
pattern affect population dynamics. Data on populations of
grassland birds in North America provide example of how
changes in habitat availability alter population size. At the
continental scale, the abundance of bird species that require
grassland habitats has been declining since the mid-1900s in
Author's personal copy
490
Landscape Ecology and Population Dynamics
Regional response to habitat protection
Population trends for two grassland birds
(a)
Meadowlark
Field sparrow
30
25
20
15
10
5
Henslow’s sparrow
Regional abundance
(Birds/5000 hours)
50
45
40
35
30
25
20
15
10
5
0
1965
FISP abundance index
EAME abundance index
250
1985
1995
2005
Year
150
100
50
0
1975
0
1975
200
(b)
1980
1985
1990
1995
2000
2005
Year
Figure 2 Change in abundance for grassland birds at (a) continental scale for eastern North America and (b) regional scale for Illinois. (a) Data
for Eastern meadowlark (Sturnella magna) and field sparrow (Spizella pusilla) provided by United States Geological Survey (USGS) North
American Breeding Bird Survey, http://www.mbr-pwrc.usgs.gov/bbs/bbs.html. Solid line is mean estimate, with dashed lines showing 95%
credible interval. (a) and (b) Reprinted from Herkert JR (2007) Evidence for a recent Henslow’s Sparrow population increase in Illinois. Journal of
Wildlife Management 71: 1229–1233, with permission from Wiley.
North America (Peterjohn and Sauer, 1999; Figure 2) and
parts of Europe (Chamberlain and Fuller, 2001; Figure 2).
These declines can be explained by the loss of grassland
habitats because of changing agricultural practices, expansion
of suburban development, conversion of grassland habitats to
forests, and long-term disruption of ecological processes (e.g.,
fire and grazing) that create and maintain grassland habitats
(Askins et al., 2007). Landscape-level analyses have revealed
the specific habitat features required for foraging and nesting
by these birds (Ribic and Sample, 2001; Veech, 2006). These
species are sensitive to the fragmentation of their habitats. In
response to these population declines, management initiatives
at the regional scale have focused on habitats protection and
restoration. One such initiative is the US Department of
Agriculture’s Conservation Reserve Program (CRP). The CRP
encourages the protection of large patches of grassland habitat
and has the potential to increase habitat availability for these
birds. Herkert (2007) documented an increase in Henslow’s
sparrow (Ammodramus henslowii), an obligate grassland species, in response to the establishment of 400,000 ha of CRP
lands in Illinois (Figure 2) but cautioned that local management practices (such as farmers’ mowing schedules) can affect
habitat quality. The positive effects of similar habitat restoration efforts have been documented in other regions (e.g.,
Benson et al., 2006).
Monitoring population dynamics at the landscape scale is
challenging. Various survey techniques are available for estimating population size, but measurement of demographic
rates is more difficult. It is generally necessary to estimate
demographic rates (i.e., births, deaths, and immigration) by
sampling a limited number of sites or habitat patches in the
landscape because of the large effort required to produce these
estimates. Often it is the variation in demographic rates
among habitat patches that is of greatest interest. For example,
Herkert et al. (2003) sampled 39 sites to estimate the effects of
patch size on nest survival and parasitism in three grassland
bird species. In a study of song sparrow (Melospiza melodia)
populations, Jewell and Arcese (2008) linked the population
growth rates of the sparrows indirectly to landscape-level
patterns of land use (Figure 3). Rates of brood parasitism by
cowbirds (Molothrus ater) reduced sparrow reproduction, and
cowbird abundance was strongly correlated with land uses
such as urban and agricultural lands with livestock grazing.
These land uses created areas where parasitism rates were so
high that local reproduction in sparrow populations could not
replace individuals lost to mortality (i.e., ro0). Their results
suggest that these populations were maintained by immigration from areas with lower parasitism rates rather than by
local reproduction.
Simulation models are often employed to explore the effects of landscape patterns on population dynamics. Spatially
explicit models can simulate the occupancy of specific locations as well as the demographic rates characteristic of
populations at those locations. Such models allow the researcher to test hypotheses about population dynamics in the
complex settings of real and theoretical landscapes. These
models are especially useful for studying the impacts of various scenarios of landscape change on populations. Pearson
et al. (1999) examined how management options related to
forest clearing would affect habitats, and therefore the abundances, of a suite of native species. Complex population
processes such as survival, reproduction, and movement of
individuals can be represented in spatially explicit models. For
example, Holdo et al. (2011) used a model to study the impact
of road construction that would interfere with migratory
movements of the Serengeti wildebeest (Connochaetes spp.;
Figure 4). Their simulations suggest that this barrier to
movement could reduce wildebeest population by one-third
because these animals would be less able to migrate to the
most productive foraging sites. Locations of the most productive sites shift in location seasonally, and the road interfered with the animals’ ability to track and migrate to those
sites. Moreover, the road was predicted to divide a single
wildebeest population into two separate subpopulations.
Author's personal copy
Landscape Ecology and Population Dynamics
Brood parasitism rates
491
Population growth
Expected population growth rate
Parasitism rate
0⋅0−0.2 Low
0⋅2−0.4 Moderate
0⋅4−0.8 High
< 0⋅90 Strong sink
0⋅90−1⋅00 Weak sink
1⋅00−1⋅10 Weak source
No song sparrows
N
012 4 6 8
(a)
N
012 4 6 8
(km)
(b)
(km)
Figure 3 Maps of brood parasitism and population growth of song sparrows (Melospiza melodia) in the Southern Gulf Islands of British
Columbia, Canada. Rates of parasitism by brown-headed cowbirds (Molothrus ater) (a) are affected by land use and results in variation in
population growth rates of their sparrow hosts (b). Growth rates are expressed in terms of finite rates of increase. Rates Z1 indicate stable or
growing populations (sources), whereas rates o1 represent declining or sink populations. Reprinted from Jewell KJ and Arcese P (2008)
Consequences of parasite invasion and land use on the spatial dynamics of host populations. Journal of Applied Ecology 45: 1180–1188, with
permission from Wiley.
1600
Wildebeest (1000s)
1° S
Lake Victoria
1200
Wildebeest
dry season
Nairobi
Musoma
800
Kenya
400
Tanzania
0
0
20
40
60
Time (yr)
Total population (no road)
Total population (road)
80
Wildebeest
wet season
3° S
100
Northern subpopulation (road)
Southern subpopulation (road)
Arusha
33° E
Migration route
Proposed road
Figure 4 Potential impact of a proposed road on Serengeti wildebeest populations. (a) Results of spatially explicit simulation model on
abundance of wildebeest (Connochaetes spp.) comparing the landscapes with (red line) and without (black line) proposed road. The road would
separate wildebeest into northern and southern populations shown by blue and green lines. Graph reprinted from Holdo RM, Fryxell JM, Sinclair
ARE, Dobson A, and Holt RD (2011) Predicted impact of barriers to migration on the Serengeti wildebeest population. Public Library of Science
One 6: 1–7. Map provided by Stuart Pimm, with permission from PLOS.
Although the overall abundance of required resources is
of great importance for populations, the patchiness and spatial arrangement of habitat types have the potential to complicate population dynamics in landscapes. Demographic
rates, such as survival and reproduction, may be affected by
patch size and shape. Movements between patches can be
influenced by the distance between patches and other spatial
features. Therefore, an understanding of how population dynamics play out in space is important for research and
management.
Author's personal copy
492
Landscape Ecology and Population Dynamics
Landscape Structure and Population Dynamics
Patches, Matrix, and Corridors
Landscape ecologists are interested in spatial patterns.
For populations, the overall abundance of habitat and its
spatial distribution have a strong influence on the genetic
structure and population dynamics of species. The concepts
of patch, matrix, and corridor are useful for understanding
how populations are influenced by the spatial structure
of landscapes (Figure 5). For the time being, assume
that patches are recognizable as discrete areas of similar
habitat and that all habitat patches contain suitable habitat
with enough resources to sustain a population. (This assumption of discrete, homogeneous patches will be relaxed
later.) The unsuitable habitats found between habitat patches
comprise the landscape matrix. Corridors are linear strips of
habitat that connect patches and facilitate the movements
among patches (Figure 5). The number, sizes, shapes, and
spatial arrangement of habitat patches, as well as the presence
of corridors, impose a spatial structure on the species that
occupies them.
Several terms contain the word ‘‘structure.’’ The spatial
distribution of habitat may be referred to as ‘‘patch structure’’
when applied to a specific habitat type or as ‘‘landscape
structure’’ when applied to the spatial pattern of all habitat
types in the landscape. The term ‘‘population structure’’ refers
to the spatial distribution of breeding groups (i.e., demes) and
the rates of exchange of individuals, via emigration and immigration, among these groups. Much of the problem of
analyzing population dynamics at the landscape level relates
to understanding the influence of landscape (or patch) structure on population structure.
The patch structure of a landscape influences the dynamics of
populations living therein because patch structure affects the
spatial distribution of individuals and their movements
(Saunders et al., 1991). Patch size is one of most important
qualities of landscape structure because it is directly related to
local population size. Larger patches have a greater abundance
of resources and will support a greater N over the long term
(i.e., the patch’s carrying capacity), whereas smaller patches
support fewer individuals. Immigration and emigration are
affected by the distance between patches and the intervening
landscape matrix. When patches are far from one another, the
chance of successful movement between them is reduced.
Dispersing animals can suffer mortality from hazards of predation, starvation, or accident while crossing the
unsuitable matrix. The dispersal mechanisms of plant seeds
may fail to deliver them to distant patches, and dispersing
animals may not find a suitable patch.
Corridors connect patches and facilitate interpatch movements. Similarly, small habitat patches (i.e., habitat fragments)
scattered throughout the matrix can act as ‘‘stepping stones’’ to
promote movement between large patches. Interpatch distances between these fragments are small, and the habitat
fragments provide temporary refuge for the dispersing animal
before crossing the next gap. Likewise, these fragments may be
too small to support a plant population over the long term,
but temporary populations can produce seeds capable of
colonizing nearby patches. The effectiveness of corridors and
stepping stones for movement can be altered by characteristics
of the landscape matrix (Baum et al., 2004; Vergara, 2011).
Satellite patches
Corridors
Core patch
Satellite patches
Stepping stones
Habitat patches
Landscape matrix
Figure 5 Schematic diagram of patch–matrix–corridor model. In this example, a centrally located core patch is surrounded by smaller satellite
patches. Individuals from the population of the core patch disperse to the satellite patches, which support smaller populations that frequently go
extinct. Linear corridors or closely spaced habitat fragments (stepping stones) facilitate movement among patches.
Author's personal copy
Landscape Ecology and Population Dynamics
Characteristics of the landscape matrix may facilitate or
hinder movement. Habitats in the matrix may be harsh or
benign relative to the resource needs of individual species. The
matrix may contain barriers to movement. The barriers include natural features such as rivers or mountains and human
constructed features such as roadways, agricultural fields, and
urban areas. The relative ease or difficulty of movement
through the landscape matrix is called its permeability. The
spaces between habitat patches, filled by the matrix, are called
gaps. A species’ ability to move across this space is called its
gap-crossing ability. Gap-crossing ability and matrix permeability for a given species in a particular landscape depend on
the life-history characteristics, habitat needs, and vagility of
the species as well as the matrix itself (Chardon et al., 2003;
Baguette and Van Dyck, 2007). For example, a bird can readily
fly across 100-m wide plowed agricultural field, but that same
field is a barrier to a salamander, which risks predation or
death by desiccation during an attempt to cross it.
Landscape Context
The suitability and uses of particular habitat patches may be
affected by landscape context. The matrix and landscape-level
abundance of similar habitats alter the abundance of a species
at the patch scale (Radford and Bennett, 2007). When a given
habitat type is abundant in the broader landscape, then it is
more likely to see occupancy or visits to small patches (Pearson, 1993; Lewis et al., 2011). This effect has been documented
in mobile animals, such as mammals, flying insects, and birds,
which readily move between patches. Landscape context affects plant population dynamics by altering pollination services and rates of seed predation (Steffan-Dewenter et al.,
2001). The abundance and diversity of habitats in the broader
matrix affect the probability that pollinators or seed predators
will visit a given patch. Some animal species depend on a
variety of habitat types during their lives and the ability to
move among them. These species may be especially sensitive
to landscape context. For example, some amphibians require
wetlands for breeding but then disperse to upland habitats
after the breeding season (Pillsbury and Miller, 2008). Such
species require different habitat types in different seasons or
different parts of their life cycles. Therefore, a mix of patch
types that provide supplementary or complementary resources
(sensu Dunning et al., 1992) must be considered when assessing habitat quality and quantity at patch and landscape
scales. By altering rates of immigration and emigration,
landscape context can affect the genetic diversity and divergence among populations (Blevins et al., 2011).
Quantifying Landscape Structure and Connectivity
Spatial metrics provide a means of quantifying the spatial arrangement of habitat. The most straightforward metrics focus
on the statistical distribution of patch sizes. First, the landscape is classified into suitable versus unsuitable habitat (i.e., a
binary landscape), and the areas of suitable patches are tallied.
Then the ‘‘number of patches,’’ ‘‘mean patch size,’’ and ‘‘size of
the largest patch’’ are useful for comparing landscapes with
similar amounts of habitat but having a different spatial arrangement of habitat. Other metrics can measure more complex aspects of spatial pattern such as the shape of patches.
These metrics incorporate a ratio of perimeter-to-area for
493
patches. Compact shapes, such as circular or square patches,
have a lower perimeter-to-area ratio, whereas elongated patches or those with complex shapes have a greater perimeter-toarea ratio. The metric of ‘‘contagion’’ (e.g., Riitters et al., 1996)
measures the intermixing of habitat types, which is correlated
with number of patches as well as patch size and shape. There
are many spatial metrics available, and many are correlated
with each other (Hargis et al., 1998). No single metric will
measure all of the important aspects of spatial pattern. The
best choices will be those metrics that measure the qualities of
landscape structure predicted to influence the population
processes under study. One of the most important qualities is
habitat connectivity, which measures the ease of moving between habitat patches.
Landscape ecologists have developed a variety of metrics
for measuring the connectivity of landscapes (Kindlmann and
Burel, 2008). These metrics incorporate the interpatch distances, area of habitat at various distances from the focal
habitat, and the presence of stepping stones and corridors
(Leidner and Haddad, 2011). A proximity index rates the
relative amount and distance to suitable habitat for single
patches (Gustafson and Parker, 1994). Deciding which among
the available metrics is the best choice for any given species
remains challenging. Furthermore, connectivity has been
treated as both independent and dependent variables in
landscape ecology research (Goodwin, 2003). When considered an independent variable, connectivity was considered
a structural aspect of the landscape under study. These studies
often compared the performance of populations living in
landscapes having different patch structures. When treated as a
dependent variable, connectivity was a function of various
species responses to pattern in the same landscape. A highly
mobile species, such as a bird or large mammal, can easily
move between patches, but a sedentary organism or nonflying
invertebrate will find the same landscape disconnected (or
fragmented) if it cannot readily move among patches. Thus,
sensitivity to habitat loss and fragmentation depends on species’ habitat needs and life-history traits (Driscoll and Weir,
2005; Sutton and Morgan, 2009).
Landscape Structure, Population Viability, and
Conservation
Conservation biology naturally considers the effects of
landscape structure on population structure and population
viability. Therefore, there is a close and complementary connection between the disciplines of Conservation Biology and
Landscape Ecology. The conservation of any species depends
on the maintenance of viable populations, and the patch
structure of landscapes affects population viability at both the
patch and landscape level.
Population Viability in Small Patches
Population viability is strongly related to effective population
size (Ne) and genetic diversity, two characteristics that are
linked. Small populations suffer from demographic stochasticity and reduced genetic diversity. Populations in landscapes
Author's personal copy
494
Landscape Ecology and Population Dynamics
in which small isolated patches predominate show less genetic
diversity overall than populations in unfragmented landscapes
(e.g., butterfly (Collier et al., 2010) and toad (Allentoft et al.,
2009)). Moreover, population sizes in small patches fluctuate
to a greater degree because of demographic stochasticity,
which is the fluctuation in Ne, birthrates, and death rates
through time. They also fluctuate as a result of environmental
stochasticity, which is variation in demographic rates from
unpredictable events related to factors such as weather. Such
fluctuations create a greater chance of extinction and expose
the population to loss of diversity because of genetic drift and
population bottlenecks.
The viability of populations in small patches is reduced
because of limited genetic diversity. Small populations contain
less genetic diversity because there are fewer individuals. In
addition, genetic drift and inbreeding further reduce genetic
diversity over time. Genetic drift is the random change in allele
frequencies because of sampling error in the process of mating. Over the long term, drift ultimately leads to the loss of
alleles when their frequencies randomly reach zero. Levels of
heterozygosity, a common way of measuring genetic diversity,
are positively associated with population growth and persistence (Hanski and Saccheri, 2006). Individuals with higher
levels of heterozygosity typically have higher survival and reproductive rates than individuals with less heterozygosity
(Coltman et al., 1998). This phenomenon is called heterozygote advantage or heterosis. Heterozygosity declines as alleles are lost and the population becomes fixed for one allele.
Similarly, inbreeding reduces heterozygosity among offspring
when related individuals breed, and inbred populations have
a greater chance of extinction (Saccheri et al., 1998). Inbred
offspring have lower levels of heterozygosity and have a
greater chance of being homozygous for deleterious recessive
alleles. This reduced fitness is termed ‘‘inbreeding depression.’’
In small populations, the choice of mates is directly related to
Ne, and matings among related individuals will inevitably
occur. The primary conclusion is that small populations have
lower genetic diversity and stochastic processes of genetic drift,
inbreeding, and population bottlenecks will further reduce
genetic diversity over time.
The predominance of small patches in fragmented landscapes can affect demography and behaviors. Small populations may suffer reduced population growth rates because of
social factors called Allee effects. Allee (1931) suggested that
some species have a minimum viable population (MVP) size.
Birth and death rates are density-dependent, and recruitment
drops to unsustainable levels below a certain N. For example,
fertilization rates in flowering plant may depend on visitation
rates by pollinating insects, but a minimum density of individual plants is needed to attract the necessary numbers of
pollinators (Hackney and McGraw, 2001). When Ne or densities are too low, recruitment rates decline because of low
fertilization rates. Likewise, vulnerability to predators can increase in some animal groups. Flock size and herd size are
correlated with the ability to detect predators and thereby reduce predation risk for the individual (Lima and Dill, 1990).
Allee effects are apparent when survival and reproductive rates
are positively correlated with population density and density
is below the threshold for density-dependent population
regulation (i.e., the carrying capacity). However, care should
be taken not to confuse these effects with the negative effects
of small population size such as inbreeding (Stephens et al.,
1999). In fragmented landscapes, the small patches may
support too few individuals needed to accrue the positive
benefits of population size and/or density. For animals, patterns of behaviors such as movements can be altered by
habitat fragmentation. For example, Mitrovich et al. (2009)
found differences in the home range overlap and movements
and crowding for the coachwhip (Masticophis flagellum), a
large snake, in small patches relative to large patches. Movements of large highly mobile mammals may be affected. too.
Seasonal movement behaviors of white-tail deer (Odocoileus
virginianus) varied with the mean size and number of patches
in a landscape in the midwestern USA (Grovenburg et al.,
2011). Deer in landscapes with many large patches tended to
be sedentary residents, whereas those in landscapes with small
patches were more likely to be migratory.
The genetic and demographic problems of small populations interact synergistically in a phenomenon referred to as
an extinction vortex (Gilpin and Soule, 1986). When Ne falls,
genetic diversity is lost because of the phenomena of bottlenecks, drift, and inbreeding. Reduced genetic diversity causes
drops in population growth, reducing mean fitness because
genetic diversity is correlated with the levels heterozygosity in
individuals and the population as a whole. In small populations that have lost a significant amount of genetic diversity,
low or negative growth rates further reduce N, which results in
greater loss of genetic diversity. Moreover, reduced genetic
diversity limits the ability of the population to adapt, via
natural selection, to changes in its environment. Therefore,
small populations are more sensitive to environmental change
than large populations having more diversity. This combination of interacting processes eventually threatens the small
population with extinction.
What population size qualifies a small population?
Although the most accurate answer will depend on the life
history of specific species, the ‘‘50/500’’ rule of thumb from
conservation biology (Franklin, 1980) provides a useful starting
point when detailed life-history information is lacking. This
rule states that isolated demes with an effective population size
(Ne) greater than 500 will be relatively safe from the dangers of
the loss of genetic diversity. These risks progressively increase as
Ne declines from 500 toward 50. At Ne o 50, all combined
forces of the extinction vortex act on the population, and extinction may be inevitable without management intervention.
Thus, a Ne450 is needed for short-term population viability
and Ne4500 needed for long-term viability. The ‘‘50/500’’ rule
was based on goal of maintaining genetic diversity (Franklin,
1980). Shaffer (1981) outlined the concept known as the
minimum viable population. Estimates of a MVP include three
parameters: a Ne, a probability of extinction (or persistence),
and a specific time period. For example, ‘‘An MVP for any given
species in any given habitat is the smallest isolated population
having a 99% chance of remaining extant for 1000 years
despite the foreseeable effects of demographic, environmental,
and genetic stochasticity, and natural catastrophes’’ (Shaffer
1981, p. 132). When incorporating the dangers of demographic
and environmental stochasticity, the minimum Ne should be
in the range of thousands rather than hundreds (Traill et al.,
2010).
Author's personal copy
Landscape Ecology and Population Dynamics
Some scientists criticize the notion of general guidelines for
MVPs, arguing that the minimum Ne for long-term persistence
must be determined on a species-by-species basis (Flather
et al., 2011). Population viability and MVP will be affected by
the unique combination of species’ traits, including its phylogeny, life history, resource needs, habitat availability, population structure and demography, and environmental threats
(both natural and human caused) within the context of the
landscape where it lives. For landscape-level conservation efforts, it should be more profitable to identify and address the
specific causes of population declines (Caughley, 1994) rather
than focus on maintaining a minimum number of individuals. Nevertheless, an estimate of MVP can be useful for
labeling species as being at risk of extinction, motivating
policy makers and managers to take appropriate actions, and
setting a target population size for restoration efforts.
Immigration and Rescue Effects
Despite the problems described in the previous section, small
populations are frequently observed in nature and seem to
persist over the long term. Their persistence can be explained
by the fact that these populations are open and connected to
other demes in the landscape via the movement of individuals. Population biologists commonly refer to these
movements as ‘‘immigration’’ (into) and ‘‘emigration’’ (from)
when in reference to a specific deme.
Immigration has several benefits to a small population and
some risks. Immigrant individuals can increase genetic diversity if they bring alleles that are absent or rare in the deme.
Even infrequent or low levels of immigration are sufficient to
restore genetic diversity lost to genetic drift (Slatkin, 1985).
Immigrants provide a form of gene flow. The interpatch
transport of gametes, such as pollen for flowering plants, is
another example of gene flow. Moreover, immigrants provide
a form of population growth when local recruitment rates are
low (see BIDE model in the section Basic Concepts and Terminology about Population Dynamics). Thus, immigration
can periodically rescue a population from the negative effects
of demographic stochasticity and loss of genetic diversity, but
there are significant risks associated with the movement of
individuals. ‘‘Outbreeding depression’’ occurs when immigrants introduce genetic traits that are poorly suited for the
local environmental conditions. Offspring expressing these
traits will have reduced fitness, and the mean fitness of the
population will be lower until natural selection eliminates
these traits. If immigration of these traits persists, then the
mean fitness and population growth rate will be determined
by a balance between immigration and selection. A second
risk is the introduction of pathogens and parasites carried by
immigrants.
Preserving Habitat at Landscape Level: The SLOSS Debate
Conservation biologists have debated the best strategy for
preserving habitat for threatened and endangered species. If
the total area of habitat that can be protected is limited, then
what is the best scheme for distributing that habitat in space?
The essence of this question is whether to protect the same
495
acreage of habitat in ‘‘single large or several small’’ (i.e., the
acronym SLOSS) patches. There are advantages and disadvantages for both choices (Diamond et al., 1976). A single
large preserve provides habitat to support the largest possible
population. If Ne is sufficiently large, then this choice will
avoid the problems of small populations discussed previously.
However, the danger of a single habitat patch is that some
environmental catastrophe such as a hurricane or fire or an
introduced virulent pathogen could extirpate the entire species. If the species is spread across several smaller patches
sufficiently separated in space, then the chance of a catastrophic event affecting all of them is less than if they are
confined to a single patch. For the ‘‘several small’’ strategy to
be viable, the demes of respective patches must be large
enough to avoid the extinction vortex, or the populations
must be open to movement between patches in order to accrue the benefits of immigration. The movement of immigrants may be natural or facilitated by human management in
the form of translocations of individuals.
Land management around preserved patches may include
the preservation of corridors to connect patches and facilitate
movements among them. Corridors likewise have their relative advantages and disadvantages (Haddad et al., 2011), and
their effectiveness depends on the natural history of the target
species (Gilbert-Norton et al., 2010). Species that are dispersallimited or have difficulty crossing the landscape matrix receive
the greatest benefits corridors. For animals, corridors of lowquality habitat can enhance movement among habitat patches
even if populations cannot persist within the corridor itself
(Haddad and Tewksbury, 2005).
Landscape Ecology and Metapopulation Dynamics
Most populations in nature have a patchy distribution because
of patchiness of their habitat, and population structure is
determined by the exchange of individuals (e.g., immigration
and emigration) among patches. If the rates of exchange are
high and frequent, such as by a highly mobile species, then
there is simply one population occupying multiple patches. If
exchanges among patches are very infrequent or nonexistent,
then each patch has a distinct and separate population. An
intermediate level of exchange would include regular movements among patches by dispersing individuals moving from
their natal site to another patch. This amount of exchange
characterizes metapopulations.
The dynamics of open populations persisting in a patchy
landscape often take the form of a metapopulation. A metapopulation is a network of individual demes connected by the
processes of immigration and emigration. Metapopulation
dynamics exist when species have demes in discrete habitat
patches. Each deme has a finite probability of extinction,
but extinction events are not synchronized in time across
space. Empty patches are common, but none of them are
too isolated to be recolonized from an extant deme. Thus,
a metapopulation exhibits turnover in the occupancy of
patches that is a function of local extinctions and recolonizations (Hanski, 1999). McCullough (1996) provides
examples of metapopulation ecology applied to problems of
wildlife conservation.
Author's personal copy
496
Landscape Ecology and Population Dynamics
Metapopulation theory has been an important tool for
helping ecologists think about population dynamics in fragmented landscapes, but detecting metapopulations and parameterizing metapopulation models from field data is still
challenging (Driscoll, 2007). The incidence function model of
(Hanski et al., 1996b) has been one of the most useful because
it requires only patch occupancy data. By monitoring how
many and which patches are occupied over time, ecologists
can relate the probability of occupancy to patch size, shape,
and isolation. Alternative techniques include analyses of
genetic structure to infer the relative isolation of habitat patches and the frequency of immigration among them (Wang
et al., 2008).
Sources and Sinks
In the simplest case of population structure, the size of patches
and the quality of habitat contained within them is similar,
but this case is likely uncommon in nature. In a metapopulation spread over several habitat patches, variation in population growth rates can exist because of patch-specific
differences in habitat quality, interspecific interactions, or
other ecological factors. Pulliam (1988) described the situation in which some patches have intrinsic rates of increase
(r ¼birthrate death rate) greater than 0 – that is, local
birthrates exceed local death rates. These patches were termed
‘‘sources’’ because local population growth will produce an
excess of individuals. The local population growth exceeds the
patch’s carrying capacity, and these excess individuals must
emigrate; if not, then feedbacks from local resource limitation
will reduce growth rates. In the same metapopulation, other
patches called sinks have ro0 because the local birthrate is
less than the death rate. If isolated from other populations,
extinction is inevitable for sink populations. However, sinks
can persist in the context of a metapopulation because excess
individuals from source patches can immigrate into sinks.
These sinks receive a demographic subsidy and are able to
persist because the sum of (births þ immigrants deaths)Z0.
The knowledge of source and sink patches is critical for
understanding how a particular metapopulation functions.
This understanding is important for landscape-level conservation efforts in which choices are made about the protection
(or allowing the destruction) of some fraction of the existing
patches. If a critical number of the sources patches are lost,
then the entire metapopulation will collapse (Hanski and
Ovaskainen, 2002). Unfortunately, patch occupancy alone is
not a reliable indicator of the source/sink status of individual
patches. The hard work of estimating demographic rates must
be undertaken to fully understand this aspect of population
dynamics.
Core and Satellite Model
In conservation planning, the ‘‘core and satellite model’’ of
habitat protection has been used at the landscape level (Shen
et al., 2008; Snep and Ottburg, 2008). This model envisions
critical habitat distributed in patches varying in area and
quality. Core patches support the largest Ne and populations
that persist through time. Core populations may act as
demographic sources, and their greatest value to the broader
metapopulation is to produce emigrants destined for satellite
populations (Runge et al., 2006). Small patches located near
the periphery of the core patches support small satellite
populations that periodically go extinct. Satellite populations
depend on immigrants from the core patches to avoid extinction or for recolonization after local extinction. Satellite
populations may be demographic sinks or simply have a small
Ne that is vulnerable to the problems of small populations.
Landscape Change, Habitat Fragmentation, Edge
Effects, and Extinction Debt
Landscape changes, particularly those caused by human land
uses, can alter the abundance and connectivity of suitable habitat.
Habitat fragmentation results when habitat loss converts a
landscape dominated by large connected patches of habitat to
one in which habitat exists in small, disconnected fragments.
Fragmentation of native habitats is happening on a global scale
(Riiters et al., 2000). This phenomenon affects habitat quality as
well as its quantity and spatial distribution. Consequentially,
fragmentation affects the spatial structure of populations. The
effects of fragmentation can be conceptually separated into
the effects of (1) reduced patch size and (2) increased patch
isolation. The processes of habitat loss and fragmentation often
occur concurrently, but Fahrig (2003) argued that their effects
need to be considered independently to best understand
the consequences of landscape change on populations and
communities.
In fragmented landscapes, the effects of small patch size are
confounded with edge effects. Small fragments have more
edge, which can alter habitat quality. Small habitat patches
have a greater edge-to-interior ratio because of simple geometry. As the area of a circle (p radius2) decreases, the perimeter of a circle (2 p radius) decreases more slowly. Circles
and squares are simple, compact shapes in which length and
width are approximately equal. Habitat fragments of elongated and complex shapes have a greater amount of perimeter
compared to the area. The patch perimeter or ‘‘edge’’ is the
boundary between the habitat of the patch and the landscape
matrix, and the ecological conditions at the patch edge can be
modified by the conditions in the matrix (Matlack, 1993). For
example, consider a forest patch in a matrix of agricultural
fields. The edges of forest are drier, warmer, windier, and receive more sunlight than locations deep in the interior of the
forest patch. These altered conditions are termed ‘‘edge effects’’
and may extend some distance into the forest patch. Edge
effects also include differences in the biotic interactions. For
example, predation rates may be greater near edges. The ‘‘interior habitat’’ – sites within the patch but some minimum
distance from the patch boundary – is considered free of edge
effects. Some species are sensitive to the differences between
edges and interiors. So-called ‘‘edge-sensitive’’ or ‘‘interiordependent’’ species avoid edges or experience lower survival
and/or reproductive rates in edges. As fragment size decreases,
the edge-to-interior ratio increases; edge-sensitive species have
less and less interior habitat for use. Below a particular patch
size, interior habitat is absent because edge effects penetrate to
all regions of the small patch. Therefore, interior-dependent
Author's personal copy
Landscape Ecology and Population Dynamics
species are absent from the smallest patches that are dominated by edge-tolerant species (Banks-Leite et al., 2010). Although the example of forest habitat was discussed here, edge
effects also exist for grasslands and other nonforest habitats
(Renfrew et al., 2005).
The response of populations to the process of habitat loss
and fragmentation may lag behind the changes in habitat
quantity and quality. For example, some edge-sensitive species
may persist in habitat fragments in spite of the loss of interior
habitat. This lag occurs because individuals survive in small
patches but long-term recruitment rates drop below mortality
rates. Thus, small patches are occupied and some reproduction
may be occurring, but not enough for long-term population
persistence. In addition, the metapopulation dynamics may be
disrupted by the overall loss of participating patches or the
loss of a critical patch, and the metapopulation collapses
(Hanski et al., 1996a). Eventually, these species will go extinct
in this landscape, but extinction is delayed by the presence
of long-lived individuals and some low levels of reproduction. This delay is termed ‘‘extinction debt’’ (Hanski and
Ovaskainen, 2002; Honnay et al., 2005), and it depends on
the life history of species affected by habitat fragmentation
(Piqueray et al., 2011). Krauss et al. (2010) reviewed data for a
variety of species living in fragmented grasslands of Europe.
They found that long-lived perennial plant species were more
likely to exhibit extinction debt than short-lived, mobile species such as butterflies. For species exhibiting extinction debt,
their distribution on the landscape is better explained by past
habitat patterns than by the distribution and quality of habitat
in the contemporary landscape.
Environmental Gradients and Habitat Heterogeneity at
Multiple Scales
The concept of source and sink populations (Pulliam, 1988)
raised awareness of how variation in demographic performance among patches affects the occupancy of individual patches as well as the landscape-wide distribution of a
population. The spatial arrangement of patches is important
because sink patches may persist only if they are within the
dispersal window of at least one demographic source (Foppen
et al., 2000). In the original models of source and sink
metapopulations, within-patch conditions were considered
homogeneous, but this situation is seldom typical in real
landscapes. Spatial heterogeneity within patches may affect
demographic rates and population dynamics at fine scales.
For large patches, spatial heterogeneity in habitat quality
may create source–sink dynamics among sites within the
patch. Environmental heterogeneity can create a mosaic of
sites having higher and lower rates of survival, reproduction,
and recruitment (Meekins and McCarthy, 2001). Within-patch
heterogeneity can be especially high when patches are defined
by a coarse-level criterion, such as land-cover type, and finescale heterogeneity is ignored. Fine-scale heterogeneity in
temperature, moisture, soil nutrients, etc. may be correlated
with occurrence of individuals (Palmer, 1990; Fraterrigo et al.,
2006) and demographic rates (Meekins and McCarthy, 2001).
If habitat quality is strongly correlated with fine-scale edaphic
conditions (or some other measure of resource abundance for
497
such food availability), then these conditions may be used to
estimate the demographic potential of a given site (Seydack
et al., 2000; Ecke et al., 2002; Gonzalez-Megias et al., 2005;
Flinn, 2007). Although not separated into discrete patches,
source-sink dynamics can occur among the sites within patches. The demographic rates associated with an entire patch
will be a function of the average habitat quality of its embedded sites. Landscape ecologists, with their appreciation for
issues of scale, are well-equipped to study the relative effects of
within-patch, among-patch, and landscape heterogeneity on
population dynamics.
Changing patterns of biodiversity in changing landscapes
illustrate how spatial patterns at different scales interact with
population processes. In portions of Europe and North
America, forests are expanding following agricultural abandonment. New forest patches are developing from abandoned
farmland via the process of secondary succession; this new
habitat is also called postagricultural forest. The spatial arrangement of old forest patches is of great importance for
native herbaceous plant species that depend on forest habitats.
At the landscape scale, the abundance and proximity of ‘‘ancient forests’’ (not cleared for agriculture) is correlated with
the patterns of plant diversity among recovering forest patches
(Matlack, 1994a, b). The ancient forests preserved a pool of
forest plant species during the time period of heavy agricultural use in the landscape (Pearson et al., 1998). During the
period of recovery after the peak of agriculture, these species
are able to disperse from the ancient forests to the new forest
patches. Moreover, life-history traits influence the establishment and persistence of populations (Vellend, 2003; Flinn and
Vellend, 2005) in particular patches. Verheyen et al. (2003)
studied the distribution of forest herbaceous plant species in
these postagricultural forests and their landscapes. They found
that recovery after disturbance is a two-stage process in which
the dispersal and colonizing ability of a species is initially
most important in establishing populations in new forest
patches. Next, after a species had colonized a new forest patch,
habitat quality and niche specialization of the species influenced abundance and persistence within patches (also Pearson and Fraterrigo, 2011). The distribution of dispersal limited
plant species was not correlated with habitat quality at the
landscape scale because these species simply could not reach
isolated forest patches. In other words, habitat fragmentation
negated any effects of varying habitat quality for these postagricultural forests in the initial phase of recovery. Habitat
quality became important only if and when a species reached
a patch. This process takes many years to play out and will
affect forest biodiversity for a century or more of recovery
(Vellend et al., 2007).
Continuous Habitat Variability in Landscapes
The patch–matrix–corridor approach to characterizing spatial
pattern is useful for landscapes in which patches exist as discrete readily identifiable units, and there are greater differences
among patches than within them. However, in many landscapes, habitat variation occurs along environmental gradients
that change relatively slowly across space (Figure 6). In such a
landscape, drawing a definite boundary to denote habitat
Author's personal copy
498
Landscape Ecology and Population Dynamics
0
Contours
125 250
500 m
Drier
Relative soil moisture
Streams
Wetter
Figure 6 Map of predicted soil moisture as an example of continuous variation in habitat quality. Soil moisture is influenced by topography
shown by 10-m contour lines as well as slope, aspect, and surrounding landforms. Drier sites are found on ridges and peaks, whereas wetter
sites are located in ravines and valleys. This map was developed using a digital elevation model to calculate a topographic relative moisture index
(Parker, 1982). (Reproduced with permission from Simon SA, Collins TK, Kauffman GL, McNab WH, and Ulrey CJ (2005) Ecological zones in the
Southern Appalachians: First Approximation. Asheville, NC: US Department of Agriculture, Southern Research Station. Forest Service Research
Paper SRS-41). Map shows 50-m grid cells.
patches is nearly impossible because suitability varies in a
complex and continuous manner across space. In mountainous landscapes, topography creates gradients in temperature,
moisture, and light related to elevation, aspect, slope, and
terrain shape (Figure 6). Instead of maps of discrete patches of
suitable and unsuitable habitat, these landscapes are best
represented by maps showing continuous variation in habitat
quality (Thompson et al., 2006; Franklin, 2009).
Quantifying the spatial pattern of habitats in these landscapes is challenging (Dorner et al., 2002). The statistical
characteristics of this continuous variation can be quantified
using a range of statistical techniques including blocking,
spectral analysis, measures of spatial autocorrelation (Turner
et al., 1991), or surface metrics (McGarigal et al., 2009). Patterns of habitat heterogeneity can change between fine and
coarse scales, and these analyses can identify the particular
spatial scale(s) in which spatial pattern and population processes are linked (Fortin and Dale, 2005). To be useful, the
analysis should be approached from an organism-based perspective; that is, landscape structure can be understood by
relating environmental gradients to the tolerances and resource needs of individual species (Cushman et al., 2010) and
to population processes such as dispersal ability and sizes of
home ranges. The influence of environmental gradients in
determining species distributions and community composition has long been recognized by the ecologists. However,
the theory and analytical techniques for studying populations
in these types of landscapes is still under development.
Landscape Structure and Interspecific Interactions
Interspecific interactions are important determinants of
population dynamics, and landscape structure can influence
these interactions. All species interact with predators, parasites,
competitors, and so on as the biotic portion of their environment. In breeding birds of forests, nest predation rates
are affected by both landscape-level and within-patch habitat
patterns (Lloyd et al., 2005). Predation rates increase as patch
sizes decline and the landscape-wide abundance of nonforest
Author's personal copy
Landscape Ecology and Population Dynamics
Schematic
499
Forest stands
Time since disturbance (years)
0
10
20
40
≥60
(a)
0
1
2
4 km
Stand origin
Prior 1880
1900−1910
1936−1950
1986−2005
1880−1899
1911−1935
1951−1985
No data
(b)
Figure 7 Illustration of a landscape as a shifting mosaic of sites in different stages of recovery from disturbance. In the schematic (a), sites
fully recover from disturbance after 60 years of recovery. A landscape in the Pisgah National Forest in North Carolina (b) is a mosaic of forest
stands of various ages as measured by time since the year of stand origin. This pattern is a consequence of natural disturbances and human
land uses such as timber management.
habitats increases. Moreover, landscape structure influences
the movement patterns of different predator species so that the
risk of encountering a specific predator species depends on the
precise location in the landscape (Bergin et al., 2000). Conversely, spatial complexity can promote coexistence between
predators and prey. ‘‘Refugia’’ are habitats (or microhabitats)
where preys are relatively safe from predators. When predators
are abundant, refugia prevent them from driving their prey to
extinction.
Spatial heterogeneity is one of the principal explanations
for the coexistence of competing species (Amarasekare, 2003)
in community ecology. Coexistence can be achieved by niche
partitioning when species specialize on different habitats or
resources. Habitat heterogeneity within and among patches
promotes coexistence. Spatial structure and heterogeneity
through time are also necessary for the coexistence by the
competition–colonization trade-off (Tilman, 1994). In this
mechanism, competing species differ in their competitive
abilities and ability to colonize new habitat patches. The superior competitors have low colonizing abilities, and the
better colonizers are poor competitors. Disturbance periodically kills off local populations of the superior competitor, and
for a brief time those sites or patches are empty. The superior
colonizer (but poor competitor) quickly occupies the empty
sites and holds them until the superior competitor arrives.
Regular, localized disturbances permit coexistence by continually providing habitat for the poor competitor (e.g.,
Debout et al., 2009). Coexistence is possible when the landscape is a shifting mosaic of disturbed patches and sites at
different stages of recovery from prior disturbances (Figure 7).
Disturbance and its effects on landscape structure through
time are a recurring research theme in landscape ecology.
Summary
Landscape ecology is concerned with the causes and consequences of spatial pattern in nature at multiple spatial
scales. Spatial pattern in habitats and resources influences the
growth, persistence, and decline of populations. The
patch–matrix–corridor model of describing spatial heterogeneity is a useful conceptual model that is directly applicable
to populations. Species frequently exhibit a patchy distribution in landscapes as a function of the spatial arrangement
of their habitats. This landscape-level pattern (i.e., the landscape structure) determines how populations are organized in
Author's personal copy
500
Landscape Ecology and Population Dynamics
local breeding groups and how frequently individuals move
between these groups. This population structure in turn affects
population dynamics. Landscape structure, in combination
with life-history traits of a species, determines whether that
species exists as a single large population, as a metapopulation
of demes connected by movement of individuals, or as a
collection of separate isolated populations. An understanding
of population structure reveals the conservation challenges
facing many species. In landscapes in which habitat is highly
fragmented, isolated populations in small patches face a
number of problems ranging from loss of genetic diversity to
edge effects. Thus, the landscape ecology of populations is
related to conservation. Some landscapes exhibit continuous
variation in habitat quality, and the patch–matrix–corridor
model is not directly applicable. Techniques for describing
habitat heterogeneity at multiple scales are available, but
understanding how this type of variation affects population
dynamics is more challenging.
See also: Habitat Loss and Fragmentation. Metapopulations
References
Allee WC (1931) Animal Aggregations: A Study in General Sociology. Chicago, IL:
University of Chicago Press.
Allentoft M, Siegismund H, Briggs L, and Andersen L (2009) Microsatellite analysis
of the natterjack toad (Bufo calamita) in Denmark: Populations are islands in a
fragmented landscape. Conservation Genetics 10: 15–28.
Amarasekare P (2003) Competitive coexistence in spatially structured environments:
A synthesis. Ecology Letters 6: 1109–1122.
Askins RA, Chavez-Ramirez F, Dale BC, et al. (2007) Conservation of grassland
birds in North America: Understanding ecological processes in different regions.
Auk/Ornithological Monographs 124: 1–46.
Baguette M and Van Dyck H (2007) Landscape connectivity and animal behavior:
Functional grain as a key determinant for dispersal. Landscape Ecology 22:
1117–1129.
Banks-Leite C, Ewers RM, and Metzger J-P (2010) Edge effects as the principal
cause of area effects on birds in fragmented secondary forest. Oikos 119:
918–926.
Baum KA, Haynes KJ, Dillemuth FP, and Cronin JT (2004) The matrix enhances the
effectiveness of corridors and stepping stones. Ecology 85: 2671–2676.
Benson TJ, Dinsmore JJ, and Hohman WI (2006) Changes in land cover and
breeding bird populations with restoration of riparian habitats in East-central
Iowa. Journal of the Iowa Academy of Science 113: 10–16.
Bergin TM, Best LB, Freemark KE, and Koehler KJ (2000) Effects of landscape
structure on nest predation in roadsides of a midwestern agroecosystem: A
multiscale analysis. Landscape Ecology 15: 131–143.
Blevins E, Wisely S, and With K (2011) Historical processes and landscape context
influence genetic structure in peripheral populations of the collared lizard
(Crotaphytus collaris). Landscape Ecology 26: 1125–1136.
Caughley G (1994) Directions in conservation biology. Journal of Animal Ecology
63: 215–244.
Chamberlain DE and Fuller RJ (2001) Contrasting patterns of change in the
distribution and abundance of farmland birds in relation to farming systems in
lowland Britain. Global Ecology & Biogeography 10: 399–409.
Chardon JP, Adriaensen F, and Matthysen E (2003) Incorporating landscape
elements into a connectivity measure: A case study for the Speckled wood
butterfly (Pararge aegeria L.). Landscape Ecology 18: 561–573.
Collier N, Gardner M, Adams M, McMahon CR, Benkendorff K, and Mackay DA
(2010) Contemporary habitat loss reduces genetic diversity in an ecologically
specialized butterfly. Journal of Biogeography 37: 1277–1287.
Coltman DW, Bowen WD, and Wright JM (1998) Birth weight and neonatal survival
of harbour seal pups are positively correlated with genetic variation measured by
microsatellites. Proceedings of the Royal Society of London B 265: 803–809.
Cushman SA, Evans JS, McGarigal K, and Kiesecker JM (2010) Toward Gleasonian
Landscape Ecology: From Communities to Species, From Patches to Pixels. Fort
Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain
Research Station. Research Paper RMRS-RP-84.
Debout GDG, Dalecky A, Ngomi AN, and McKey DB (2009) Dynamics of species
coexistence: Maintenance of a plant-ant competitive metacommunity. Oikos 118:
873–884.
Diamond JM, Terborgh J, Whitcomb RF, et al. (1976) Island biogeography and
conservation: Strategy and limitations. Science 193: 1027–1032.
Dorner B, Lertzman K, and Fall J (2002) Landscape pattern in topographically
complex landscapes: Issues and techniques for analysis. Landscape Ecology 17:
729–743.
Driscoll DA (2007) How to find a metapopulation. Canadian Journal of Zoology 85:
1031–1048.
Driscoll DA and Weir T (2005) Beetle responses to habitat fragmentation depend on
ecological traits, habitat condition, and remnant size. Conservation Biology 19:
182–194.
Dunning JB, Danielson BJ, and Pulliam HR (1992) Ecological processes that affect
populations in complex landscapes. Oikos 65: 169–175.
Ecke F, Lofgren O, and Sorlin D (2002) Population dynamics of small mammals in
relation to forest age and structural habitat factors in northern Sweden. Journal
of Applied Ecology 39: 781.
Fahrig L (2003) Effects of habitat fragmentation on biodiversity. Annual Review of
Ecology, Evolution, and Systematics 34: 487–515.
Flather CH, Hayward GD, Beissinger SR, and Stephens PA (2011) Minimum viable
populations: Is there a ‘‘magic number’’ for conservation practitioners? Trends in
Ecology and Evolution 26: 307–316.
Flinn KM (2007) Microsite-limited recruitment controls fern colonization of postagricultural forests. Ecology 88: 3103–3114.
Flinn KM and Vellend M (2005) Recovery of forest plant communities in postagricultural landscapes. Frontiers in Ecology and the Environment 3: 243–250.
Foppen RPB, Chardon JP, and Liefveld W (2000) Understanding the role of sink
patches in source-sink metapopulations: Reed warbler in an agricultural
landscape. Conservation Biology 14: 1881–1892.
Fortin M-J and Dale M (2005) Spatial Analysis: A Guide for Ecologists. New York,
NY: Cambridge University Press.
Franklin IR (1980) Evolutionary change in small populations. In: Soule ME and
Wilcox BA (eds.) An Evolutionary-Ecological Perspective, pp. 135–150.
Sunderland, MA: Sinauer.
Franklin J (2009) Mapping Species Distributions: Spatial Inference and Prediction.
New York: Cambridge University Press.
Fraterrigo JM, Turner MG, and Pearson SM (2006) Interactions between past land
use, life-history traits and understory spatial heterogeneity. Landscape Ecology
21: 777–790.
Gilbert-Norton L, Wilson R, Stevens JR, and Beard KH (2010) A meta-analytic
review of corridor effectiveness. Conservation Biology 24: 660–668.
Gilpin ME and Soule ME (1986) Minimum viable populations: Processes of species
extinction. In: Soule ME (ed.) The Science of Scarcity and Diversity, pp. 19–34.
Sunderland, MA: Sinauer Associates.
Gonzalez-Megias A, Gomez JM, and Sanchez-Pinero F (2005) Regional dynamics of
a patchily distributed herbivore along an altitudinal gradient. Ecological
Entomology 30: 706–713.
Goodwin BJ (2003) Is landscape connectivity a dependent or independent variable?
Landscape Ecology 18: 687–699.
Grovenburg TW, Jacques CN, Klaver RW, et al. (2011) Influence of landscape
characteristics on migration strategies of white-tailed deer. Journal of
Mammalogy 92: 534–543.
Gustafson EJ and Parker GR (1994) Using an index of habitat patch proximity for
landscape design. Landscape and Urban Planning 29: 117–130.
Hackney EE and McGraw JB (2001) Experimental demonstration of an Allee effect
in American ginseng. Conservation Biology 15: 129–136.
Haddad NM, Hudgens B, Damschen EI, et al. (2011) Assessing positive and
negative ecological effects of corridors. In: Liu J, Hull V, Morzillo AT, and Wiens
JA (eds.) Sources, Sinks, and Sustainability, pp. 475–503. Cambridge, UK:
Cambridge University Press.
Haddad NM and Tewksbury JJ (2005) Low-quality habitat corridors as movement
conduits for two butterfly species. Ecological Applications 15: 250–257.
Hanski I (1999) Metapopulation Ecology. Oxford, UK: Oxford University Press.
Hanski I, Moilanen A, and Gyllenberg M (1996a) Minimum viable metapopulation
size. The American Naturalist 147: 527–541.
Hanski I, Moilanen A, Pakkala T, and Kuussaari M (1996b) The quantitative
incidence function model and persistence of an endangered butterfly
metapopulation. Conservation Biology 10: 578–590.
Author's personal copy
Landscape Ecology and Population Dynamics
Hanski I and Ovaskainen O (2002) Extinction debt at extinction threshold.
Conservation Biology 16: 666–673.
Hanski I and Saccheri I (2006) Molecular-level variation affects population growth
in a butterfly metapopulation. Public Library of Science Biology 4: e129.
Hargis CD, Bissonette JA, and David JL (1998) The behavior of landscape metrics
commonly used in the study of habitat fragmentation. Landscape Ecology 13:
167–186.
Herkert JR (2007) Evidence for a recent Henslow’s Sparrow population increase in
Illinois. Journal of Wildlife Management 71: 1229–1233.
Herkert JR, Reinking DL, Wiedenfeld DA, et al. (2003) Effects of prairie
fragmentation on the nest success of breeding birds in the Midcontinental
United States. Conservation Biology 17: 587–594.
Holdo RM, Fryxell JM, Sinclair ARE, Dobson A, and Holt RD (2011) Predicted
impact of barriers to migration on the Serengeti wildebeest population. Public
Library of Science One 6: 1–7.
Honnay O, Jacquemyn H, Bossuyt B, and Hermy M (2005) Forest fragmentation
effects on patch occupancy and population viability of herbaceous plant species.
New Phytologist 166: 723–736.
Jackson HB (2013) Habitat loss and fragmentation. Encyclopedia of Biodiversity,
2nd edn. Burlington, MA: Academic Press.
Jewell KJ and Arcese P (2008) Consequences of parasite invasion and land use on
the spatial dynamics of host populations. Journal of Applied Ecology 45:
1180–1188.
Kindlmann P and Burel F (2008) Connectivity measures: A review. Landscape
Ecology 23: 879–890.
Krauss J, Bommarco R, Guardiola M, et al. (2010) Habitat fragmentation causes
immediate and time-delayed biodiversity loss at different trophic levels. Ecology
Letters 13: 597–605.
Leidner AK and Haddad NM (2011) Combining measures of dispersal to identify
conservation strategies in fragmented landscapes. Conservation Biology 25:
1022–1031.
Lewis CW, Hodges KE, Koehler GM, and Mills LS (2011) Influence of stand and
landscape features on snowshoe hare abundance in fragmented forests. Journal
of Mammalogy 92: 561–567.
Lima SL and Dill LM (1990) Behavioral decisions made under the risk of predation:
A review and prospectus. Canadian Journal of Zoology 68: 619–640.
Lloyd P, Martin TE, Roland LR, Langner U, and Hart MM (2005) Linking
demographic effects of habitat fragmentation across landscapes to continental
source-sink dynamics. Ecological Applications 15: 1504–1514.
Matlack GR (1993) Microenvironment variation within and among deciduous forest
edge sites in the eastern United States. Biological Conservation 66: 185–194.
Matlack GR (1994a) Plant species migration in a mixed-history forest landscape in
eastern North America. Ecology 75: 1491–1502.
Matlack GR (1994b) Vegetation dynamics of the forest edge – Trends in space and
successional time. The Journal of Ecology 82: 113–123.
McCullough DR (ed.) (1996) Metapopulations and Wildlife Conservation.
Washington, DC: Island Press.
McGarigal K, Tagil S, and Cushman S (2009) Surface metrics: An alternative to
patch metrics for the quantification of landscape structure. Landscape Ecology
24: 433–450.
Meekins JF and McCarthy BC (2001) Effect of environmental variation on the
invasive success of a nonindigenous forest herb. Ecological Applications 11:
1336–1348.
Mitrovich MJ, Diffendorfer JE, and Fisher RN (2009) Behavioral response of the
coachwhip (Masticophis flagellum) to habitat fragment size and isolation in an
urban landscape. Journal of Herpetology 43: 646–656.
Palmer MW (1990) Spatial scale and patterns of vegetation, flora and species
richness in hardwood forests of the North Carolina Piedmont. Coenoses 5:
89–96.
Parker AJ (1982) The topographic relative moisture index: An approach to soilmoisture assessment in mountain terrain. Physical Geography 3: 160–168.
Pearson SM (1993) The spatial extent and relative influence of landscape-level
factors on wintering bird populations. Landscape Ecology 8: 3–18.
Pearson SM and Fraterrigo JM (2011) Habitat quality, niche breath, temporal
stochasticity, and the persistence of populations in heterogeneous landscapes. In: Liu J, Hull V, Morzillo AT, and Wiens JA (eds.) Sources, Sinks,
and Sustainability, pp. 115–138. Cambridge, UK: Cambridge University
Press.
Pearson SM, Smith AB, and Turner MG (1998) Forest patch size, land use and
mesic forest herbs in the French Broad River Basin, North Carolina. Castanea
63: 382–395.
501
Pearson SM, Turner MG, and Drake JB (1999) Landscape change and habitat
availability in the Southern Appalachian Highlands and Olympic Peninsula.
Ecological Applications 9: 1288–1304.
Peterjohn BG and Sauer JR (1999) Population status of North American grassland
birds from the North American Breeding Bird Survey. In: Vickery PD and Herkert
JR (eds.) Ecology and Conservation of Grassland Birds Of The Western
Hemisphere, pp. 27–44. Studies in Avian Biology No. 19.
Pillsbury FC and Miller JR (2008) Habitat and landscape characteristics underlying
anuran community structure along an urban-rural gradient. Ecological
Applications 18: 1107–1118.
Piqueray J, Bisteau E, Cristofoli S, Palm R, Poschlod P, and Mahy G (2011) Plant
species extinction debt in a temperate biodiversity hotspot: Community, species
and functional traits approaches. Biological Conservation 144: 1619–1629.
Price SJ, Marks DR, Howe RW, Hanowski JM, and Niemi GJ (2005) The
importance of spatial scale for conservation and assessment of anuran
populations in coastal wetlands of the Western Great Lakes, USA. Landscape
Ecology 20: 441–454.
Pulliam HR (1988) Sources, sinks, and population regulation. American Naturalist
132: 652–661.
Radford JQ and Bennett AF (2007) The relative importance of landscape properties
for woodland birds in agricultural environments. Journal of Applied Ecology 44:
737–747.
Renfrew RB, Ribic CA, Nack JL, and Bollinger EK (2005) Edge avoidance by
nesting grassland birds: A futile strategy in a fragmented landscape. Auk 122:
618–636.
Ribic CA and Sample DW (2001) Associations of grassland birds with landscape
factors in southern Wisconsin. American Midland Naturalist 146: 105.
Riiters K, Wickham J, O’Neill R, Jones B, and Smith E (2000) Global-scale patterns
of forest fragmentation. Conservation Ecology 4: 3.
Riitters KH, O’Neill RV, Wickham JD, and Jones KB (1996) A note on contagion
indices for landscape analysis. Landscape Ecology 11: 197–202.
Runge Jonathan P, Runge Michael C, and Nichols James D (2006) The role of
local populations within a landscape context: Defining and classifying sources
and sinks. The American Naturalist 167: 925–938.
Saccheri I, Kuussaari M, Kankare M, Vikman P, Fortelius W, and Hanski I (1998)
Inbreeding and extinction in a butterfly metapopulation. Nature 392: 491.
Saunders DA, Hobbs RJ, and Margules CR (1991) Biological consequences of
ecosystem fragmentation: A review. Conservation Biology 5: 18–32.
Seydack AHW, Vermeulen C, and Huisamen J (2000) Habitat quality and the decline
of an African elephant population: Implications for conservation. South African
Journal of Wildlife Research 30: 34.
Shaffer ML (1981) Minimum population sizes for species conservation. BioScience
31: 131–134.
Shen G, Feng C, Xie Z, Ouyang Z, Li J, and Pascal M (2008) Proposed
conservation landscape for giant pandas in the Minshan Mountains, China.
Conservation Biology 22: 1144–1153.
Simon SA, Collins TK, Kauffman GL, McNab WH, and Ulrey CJ (2005) Ecological
zones in the Southern Appalachians: First Approximation. Asheville, NC: US
Department of Agriculture, Southern Research Station. Forest Service Research
Paper SRS-41.
Slatkin M (1985) Gene flow in natural populations. Annual Review of Ecology &
Systematics 16: 393–430.
Snep R and Ottburg F (2008) The ’habitat backbone’ as strategy to conserve
pioneer species in dynamic port habitats: Lessons from the natterjack toad (Bufo
calamita) in the Port of Antwerp (Belgium). Landscape Ecology 23: 1277–1289.
Steffan-Dewenter I, Munzenberg U, and Tscharntke T (2001) Pollination, seed set
and seed predation on a landscape scale. Proceedings of the Royal Society of
London: Biological Sciences 268: 1685–1690.
Stephens PA, Sutherland WJ, and Freckleton RP (1999) What Is the Allee Effect?
Oikos 87: 185–190.
Sutton FM and Morgan JW (2009) Functional traits and prior abundance explain
native plant extirpation in a fragmented woodland landscape. Journal of Ecology
97: 718–727.
Thompson LM, van Manen FT, Schlarbaum SE, and DePoy M (2006) A spatial
modeling approach to identify potential butternut restoration sites in Mammoth
Cave National Park. Restoration Ecology 14: 289–296.
Tilman D (1994) Competition and biodiversity in spatially structured habitats.
Ecology 75: 2–16.
Traill LW, Brook BW, Frankham RR, and Bradshaw CJA (2010) Pragmatic population
viability targets in a rapidly changing world. Biological Conservation 143:
28–34.
Turner SJ, O’Neill. RV, Conley W, Conley MR, and Humphries HC (1991) Pattern
and scale: Statistics for landscape ecology. In: Turner MG and Gardner RH
Author's personal copy
502
Landscape Ecology and Population Dynamics
(eds.) Quantitative Methods in Landscape Ecology: The Analysis and
Interpretation of Landscape Heterogeneity, pp. 17–49. New York: SpringerVerlag.
Veech JA (2006) A comparison of landscapes occupied by increasing and
decreasing populations of grassland birds. Conservation Biology 20:
1422–1432.
Vellend M (2003) Habitat loss inhibits recovery of plant diversity as forests regrow.
Ecology 84: 1158–1164.
Vellend M, Verheyen K, Flinn KM, et al. (2007) Homogenization of forest plant
communities and weakening of species-environment relationships via agricultural
land use. Journal of Ecology 95: 565–573.
Vergara P (2011) Matrix-dependent corridor effectiveness and the abundance of
forest birds in fragmented landscapes. Landscape Ecology 26: 1085–1096.
Verheyen K, Guntenspergen GR, Biesbrouck B, and Hermy M (2003) An integrated
analysis of the effects of past land use on forest herb colonization at the
landscape scale. Journal of Ecology 91: 731–742.
Wang Y-H, Yang K-C, Bridgman C, and Lin L-K (2008) Habitat suitability modelling
to correlate gene flow with landscape connectivity. Landscape Ecology 23:
989–1000.
Wiens JA and Milne BT (1989) Scaling of ‘‘landscapes’’ in landscape ecology, or,
landscape ecology from a beetle’s perspective. Landscape Ecology 3: 87–96.
Wiens JA, Stenseth NC, Horne BV, and Ims RA (1993) Ecological Mechanisms and
Landscape Ecology. Oikos 66: 369–380.