Landscape Ecology 12: 261–271, 1997.
 1997 Kluwer Academic Publishers. Printed in the Netherlands.
The effects of landscape composition and physiognomy on metapopulation
size: the role of corridors
Greg S. Anderson* and Brent J. Danielson
Department of Animal Ecology, 124 Science II, Iowa State University, Ames, IA 50011-3221; *Current
address: 116 Highland, New Castle, WY 82701, U.S.A. Please send all correspondence for this manuscript to B.J. Danielson, email: jessie@iastate.edu; telephone: 515-294-5248; fax: 515-294-7874
Keywords: connectivity, corridor, landscape, model, metapopulation, dispersal
Abstract
We develop and analyze a model that examines the effects of corridor quality, quantity, and arrangement
on metapopulation sizes. These ideas were formerly investigated by Lefkovitch and Fahrig (1985) and
Henein and Merriam (1990). Our simulations provide results similar to the Henein and Merriam model,
indicating that the quality of corridors in a landscape and their arrangement will influence the size of a
metapopulation. We then go one step further, describing how corridor arrangement alters the metapopulation, and provide a method for predicting which corridor arrangements should support larger metapopulations. In contrast to the Henein and Merriam model, we find that the number of corridor connections has
no influence on the size of a metapopulation in a landscape unless there is an accompanying change in
the uniformity of the distribution of corridor connections among patches.
Introduction
As habitat available to many organisms shifts from
large, contiguous areas to small, discrete patches,
it is critical to develop an understanding of how
effectively populations are able to exploit new
habitat arrangements. The interest in fragmentation has prompted the development of a large number of theoretical metapopulation models (e.g.,
Lefkovitch and Fahrig 1985; Pulliam et al. 1992;
Gotelli and Kelley 1993; Gyllenberg et al. 1993;
Goldwasser et al. 1994; Hanski 1994). Typically,
the term metapopulation refers to a group of discrete, localized subpopulations. The majority of
dynamics occur within each population, but dispersal events between populations are frequent
enough to have an impact on local dynamics by
either decreasing the chance of extirpation or
reducing fluctuations at local sites (Taylor 1990;
Kozakiewicz 1993). The amount of exchange
between subpopulations within a metapopulation
is dependent on a landscape’s physiognomy and
composition (Dunning et al. 1992). Taylor et al.
(1993) also point out that the degree of connectivity, which results from specific physiognomic
arrangements of the landscape’s components, can
play a significant role in regulating metapopulation dynamics. High connectivity implies that
organisms can move easily between suitable habitat patches, and low connectivity indicates that
movement between suitable patches is somehow
difficult. Connectivity can be increased via the
addition of dispersal corridors (i.e., altering composition) or by the spatial rearrangement of existing corridors (i.e., altering physiognomy) so that
organisms can exploit a landscape’s resources
more efficiently. The idea that connectivity influences an individual’s ability to move between
habitat patches in a fragmented landscape has generated a lot of interest. Empirical studies have
demonstrated that corridors can have an impact on
the movement of organisms between habitat patches (Fahrig and Merriam 1985; Lorenz and Barrett
1990; Zhang and Usher 1991; Bennett et al. 1994).
Despite evidence that corridors can benefit populations, there is very little known regarding the
262
effects of corridor quality, corridor arrangement,
and the number of corridors connecting isolated
patches.
Henein and Merriam (1990) developed a model (hereafter referred to as the HM model) that
described the effects of varying the quantity and
quality of corridors in metapopulations of whitefooted mice (Peromyscus leucopus). Since whitefooted mice have been shown to perceive habitat
quality within a landscape, it is reasonable to
assume that the quality of habitat in a corridor will
also affect individuals (Adler and Wilson 1987;
Wegner and Merriam 1990; Barnum et al. 1992).
Additional studies by Lorenz and Barrett (1990),
Szacki et al. (1993), and Bennett et al. (1994) provide empirical evidence that habitat in a corridor
does indeed affect corridor use. The results of the
HM model demonstrate that the overall size of a
metapopulation is affected by corridor quality, the
arrangement of corridors, and the number of corridor connections among patches in a landscape.
The questions posed by Henein and Merriam
(1990) are valuable, but there appear to be inconsistencies in their model that we feel warrant further investigation. The HM model tracks groups
of nestlings, juveniles, subadults, and adult mice
for a 33-week breeding period followed by an
overwintering period. Their model is structured so
that nestlings graduate to the juvenile class after
a given period. Juvenile dispersal via corridors is
then calculated. A portion of surviving juveniles
graduate to subadulthood, after which subadults
emigrate. Finally, a portion of subadults graduate
to adulthood and the adults emigrate via selected
corridors. The unifying feature of all the equations
in the HM model that detail the dynamics mentioned above is that none of them contain a density-dependent term. Some parameters in the model equations change according to the time of year,
but there are no terms that change in relation to
density. Without a density-dependent feature, all
populations described by deterministic equations
should behave in one of three ways, depending on
initial conditions. The populations should either 1)
increase indefinitely, 2) decrease to zero, or 3)
remain at an unstable equilibrium from the start.
The populations tracked in the HM model increase
or decrease after initial seeding and then stabilize
at various levels, dependent on the landscape they
occupy.
We feel that the HM model erroneously stabilizes at non-zero equilibria values. We have thus
developed a similar model to re-examine some of
the conclusions regarding corridor effects on
metapopulations and to extend the analyses of corridor effects beyond that of Henein and Merriam
(1990). Our model includes a density-dependent
feature to allow populations to realistically stabilize at non-zero equilibria. Given the few differences between our model and the HM model, we
are interested in determining if metapopulations in
our simulations behave in a way that is qualitatively similar to HM metapopulations. In particular, we want to determine the effect of corridor
quality, corridor quantity, and corridor arrangement on the size of a metapopulation.
Methods
Model description
The framework developed by Henein and Merriam (1990) was innovative and allows us to effectively test each of our objectives. Therefore, we
use four-patch metapopulations similar to the HM
model to test for corridor effects. However, the
intra-year and age-specific dynamics found in the
HM model are not relevant to detailing the qualitative effects of corridors on metapopulations.
Instead of adjusting model parameters for groups
of juveniles, subadults, and adults and the time of
year, we hold parameters constant for all animals
and avoid intra-year dynamics.
The populations in our model are subjected to
a set of simple rules. Stated explicitly, for each
patch (i):
Nt+1,i = Nt,i + Bt,i + It,i – Dt,i – Et,i
where B represents births, I is immigration, D is
deaths, and E is emigration. The population in
patch i at time t shown as Nt,i. In our model, the
values of B, I, E, and D take the following forms:
Bt,i = 4bNt,i
263
Fig. 1. Flow diagram of our simulation model.
3
It,i =
Σ θijEj
j=1
Dt,i = [1–(Nt,i + Bt,i + It,i – Et,i)
+ It,i – Et,i)
–1/4] *
(Nt,i + Bt,i
Et,i = e (4bNt,i + Nt,i)
The variables b and e are the constants representing the probabilities of attempting reproduction
and dispersal, and θij is corridor survival from
patch j to patch i.
Populations in our model go through a yearly
cycle where individuals are censused, breed, disperse, and are subject to overwinter mortality (Fig.
1). Following the initial seeding of animals in
patches, the model consists of 3 steps. To begin
with, a density-independent proportion of each
patch population reproduces during each season.
If an individual is selected to reproduce, it has 4
offspring which are immediately added to the population of that patch.
The dispersal phase of the model follows breeding. As with reproduction, dispersal is density
independent. Dispersal in our model consists of
three processes. Initially, each individual in a
patch is declared a disperser (with probability e)
or nondisperser (with probability 1–e). This probability is independent of both density and the presence or absence of corridors of any given quality.
Once the number of dispersers from each patch
has been determined, each disperser independently selects an emigration route. If a patch has no
corridor connections, all dispersing animals disperse into the matrix and are lost from the
metapopulation. Henein and Merriam (1990) used
a slightly different verbal definition for a disperser, but ultimately, dispersal in the HM model is
identical to our model with respect to the effect
of corridor presence or absence. When a patch is
connected by more than one corridor, dispersers
select randomly (without regard to corridor quality) which corridor they will use to emigrate. Following the selection of an emigration route, individuals suffer mortality with probability 1–θc
while enroute to new patches. Survivorship, θc,
during dispersal varies depending on the quality
of the corridor selected by an animal. Differential
mortality is the only feature which determines corridor quality. A higher percentage of animals die
while dispersing in poor-quality corridors than
those using high-quality corridors (i.e., θp < θh).
Finally, surviving dispersers are added to the populations of the patches at the terminus of their
respective corridor routes.
During the final phase of the model, individuals are subject to overwinter mortality. As we
mentioned previously, in order for populations to
stabilize at non-zero equilibria they must be influenced by some sort of density-dependent factor.
We have incorporated a density-dependent feature
in our model in the form of overwinter mortality.
Krohne et al. (1984) determined that dispersal in
white-footed mice was density independent, and
264
Henein and Merriam (1990) use this observation
to support their arguments for density-independent
dispersal. This seems to be a common conclusion
regarding small mammal dispersal (e.g., Gaines
and McClenaghan 1980; but see Krebs 1992 for
an exception). Accordingly, we use density-independent dispersal in our model.
Terman (1993) found that reproduction in
white-footed mice varied over a wide range of
densities and that there was no consistent relationship between density and reproduction. Therefore, by process of elimination, we use densitydependent mortality to generate equilibrial populations. The winter survival probability in our
model was 1/(Nt,i)1/4, where Nt,i is the population
of patch i at time t. This value is used because it
allows the patch populations to stabilize at values
large enough to minimize stochastic patch-population extinctions. Note that the exact form and
value of the density-dependent feature can be
altered within a wide range of values without
changing the qualitative results of the model
(Table 1).
Animals not surviving the winter are subtracted from their respective patch populations. The
remaining animals are carried over to the next year
when the processes of birth, dispersal, and mortality are repeated.
In addition to the incorporation of a densitydependent feature, the other key difference
between the HM model and ours is the stochastic
nature of our model. In order to avoid the problems of dealing with fractions of animals, as is
usually the case when a population (integer) is
simply multiplied by proportional constants, we
allow each animal to make a decision regarding
reproduction, dispersal, and survival. At each decision-making event in our model, every live animal is assigned a random number. Based on a
comparison between the assigned random number
and the parameter in question (reproduction, dispersal, or survival), an individual either experiences an event or it does not. As an example, suppose that the expectation of surviving dispersal
through a high-quality corridor is 0.8. In a deterministic model, 0.8Ni may result in a noninteger
number of animals in each patch. In our stochastic model, each of the Ni individuals experience
random mortality with probability 1–θc, thus
Table 1. List of parameters used in model simulations. The
parameters under the analysis column were the values used
for the simulations presented in this paper. The columns listed alternate show two other sets of parameters that were used
to check that the qualitative results of our model were parameter independent.
Parameter
Probability of
reproducing
Probability of
dispersing
Survival in poor
corridor
Survival in good
corridor
Overwinter survival
in patch i
Analysis
Alternate
Alternate
0.60
0.30
0.90
0.23
0.15
0.30
0.4
0.4
0.4
0.8
0.8
0.8
1/Ni1/4
1/Ni1/2
1/Ni1/4
avoiding fractions of individuals. Henein and Merriam (1990) carried fractional animals from dispersal through reproduction and then rounded the
number of animals in each patch to an integer value after calculating overwinter survival at the end
of each simulated year (Henein, personal communication).
Our simple model only has a few parameters
(Table 1). For the sake of consistency, we use
Henein and Merriam’s “moderate” weekly birth
rate and “moderate” weekly emigration rate for
our reproductive and dispersal parameters. The
survival value of θh = 0.8 for high-quality corridors was also chosen to correspond with the value found in the HM model. However, instead of
using the θp = 0.73, for survival in low-quality
corridors, value from the HM model, we opt to
halve the survival value for high-quality corridors
to θp = 0.4. Due to the stochastic nature of our
simulations and the subsequent variance associated with numerous runs, a more pronounced difference between high-quality and low-quality corridors made the efforts of corridor quality easier
to detect. In addition to the simulations using the
parameters listed in the first column of Table 1,
we ran the model for the same landscapes using
the “high” and “low” birth and dispersal rates
found in Henein and Merriam (1990). These simulations validate the fact that parameter changes
have no effect on the qualitative results of our
265
Fig. 2. Four patch landscape arrangements used in simulations. Solid lines represent high quality corridors (θh = 0.8) and dashed
lines indicate the presence of poor quality corridors (θp = 0.4). Mean metapopulations from 500 simulations are listed below each
landscape. Standard deviations are given in parenthesis.
model. Naturally, parameter changes affect the
absolute populations in our landscapes, but all of
the generalizations presented below hold true for
all combinations of parameters.
The parameter values in our model are roughly
representative of white-footed mice and are selected to maintain similarity between our model and
the HM model. Our model should not, however,
be viewed as a quantitative descriptor of any particular small-mammal population. Our goal is not
to simulate the dynamics of a particular real population, but to mechanistically determine the ways
in which corridors influence metapopulations. In
light of this, the absolute number of animals in the
landscapes of our simulations is not important.
Instead, the reader should focus on the number of
animals in each landscape relative to the numbers
in comparable landscapes.
Simulations
All of the landscape patterns used for our analysis are shown in Fig. 2. The simulations used for
our analysis consisted of seeding each patch in a
landscape with 25 animals. The model then
tracked the population of each patch in a metapopulation for 100 years. We chose 100-year simula-
266
tions because a non-stochastic version of our model (one that followed real number populations
instead of whole number populations) revealed
that the populations in all landscapes stabilized by
50 years. We ran the stochastic model simulations
for an additional 50 years to ensure that the final
population size would be close to, if not at, an
equilibrial level; visual inspection of a large number of runs confirmed that this was achieved. We
simulated each landscape 500 times. This large
number of simulations was necessary because the
large variance associated with multiple runs of
each landscape tended to obscure subtle differences in the mean metapopulation sizes among
various landscape arrangements.
Results
First, we conduct a test to determine whether or
not metapopulation size is affected by the quality
of corridor connections. To do this, we group landscapes that have similar numbers of corridors and
similar arrangements. Since we suspect that the
substitution of poor-quality connections for highquality connections would decrease overall
metapopulation sizes (Henein and Merriam 1990),
we regress the number of poor-quality connections
against the average metapopulation size for a particular arrangement (Table 2). The lines for all
seven landscape arrangements are negative and
highly significant, providing clear evidence that
metapopulations do decrease as the number of
poor-quality corridor connections increases (given
that the total number of connections remained constant). The HM model demonstrated a similar
trend.
Second, we test for an effect of the arrangement
of corridors in a landscape on metapopulation size.
For this analysis, we group landscapes that have
the same number of connections and similar numbers of low-quality and high-quality connections
(Fig. 3). Since we have no a priori predictions
regarding the effects of different arrangements, we
use one-way ANOVAs to detect differences in the
mean metapopulations within each of the groups
in Fig. 3. The analysis reveals that the arrangement of corridors among patches does have an
effect on the overall metapopulation size in a land-
Table 2. Linear regressions of landscape groups with a constant number of corridors and similar arrangements. Only the
number of poor quality corridors is varied within each group.
Landscape groups
Total # of corridor
(#’s are from Fig. 2) Connections Slope
r2
p
1,2,3,4,5,6,7
8,9,10,11,12,13
14,15,16,17,18,19
20,21,22,23,24
25,26,27,28,29
30,31,32,33
34,35,36,37
0.42
0.43
0.42
0.46
0.40
0.46
0.45
0.000
0.000
0.000
0.000
0.000
0.000
0.000
6
5
5
4
4
3
3
–6.7
–8.2
–8.0
–10.4
–9.0
–13.4
–13.0
scape. These results also support the conclusions
of the HM model.
Third, we look for evidence that peripheral
patches are less beneficial to the metapopulation
than interior patches. We define a peripheral patch
as one that has fewer corridor connections than the
patch or patches to which it is connected. An interior patch is one that has an equal or greater number of connections than the patch or patches it is
connected to (Fig. 4). Inspection of mean population values suggests that landscapes with more
peripheral patches (landscape B, Fig. 4) support
smaller metapopulations than landscapes with fewer peripheral patches (landscape A, Fig. 4). To test
this hypothesis, we group the landscapes in Fig. 5
which contain only interior patches by our definition. For each landscape, we then compute the
average metapopulation size from 500 simulations.
One-way ANOVAs support our prediction that
there are no differences in the metapopulation
sizes between the landscapes in each group (group
1: F = 0.21, p = 0.811; group 2: F = 1.68, p =
0.187). As further evidence, we determine the
mean patch populations for each patch in the landscape shown in Fig. 6. A t-test reveals that the
means of the 3 peripheral patches are indeed lower than the interior patch (t = 65.658, p = 0.000).
These results indicate that, where the quality of
connections is constant, a factor influencing the
size of a population in a particular patch is not the
connections to that patch (provided it has at least
one corridor connection), but the ratio of the number of connections for the patch to the number of
connections of its neighbor patches.
Our final question concerns the influence of the
267
Fig. 3. Landscapes used to compare the influence of corridor arrangement on metapopulation size. Landscapes are grouped so that
the number of corridor connections and the number of low and high quality corridors are constant. Differences within groups were
tested for using one-way ANOVA’s. F values and the probability of a greater F value are listed next to each group.
number of corridor connections on metapopulation
size. In light of the fact that different corridor
arrangements can impact metapopulation size,
caution must be exercised when testing for effects
due to the number of corridors because the addition or subtraction of a corridor from a landscape
inherently changes the landscape’s physiognomy.
The two groups of landscapes shown in Fig. 5 contain only interior patches and have equivalent
metapopulations. The fact that these groups contain landscapes with 6, 4, and 2 corridors demon-
strates that metapopulation size is not influenced
simply by the number of connections in a landscape, even if all the connections are of equivalent quality. This result contrasts with Henein and
Merriam’s (1990) general conclusion that increasing the number of high-quality corridors increases
the metapopulation and increasing the number of
poor-quality corridors decreases the metapopulation.
268
Fig. 4. Two landscapes that contain peripheral and interior
patches. Darkened patches represent peripheral patches. They
have fewer connections than the patch(es) to which they are
connected. Landscape A has 2 peripheral patches and 2 interior patches. Landscape B has 3 peripheral patches and 1 interior patch. Note that the metapopulation size of A is significantly greater than B (t-test).
Fig. 5. Groups of landscapes containing all interior patches.
One-way ANOVA’s demonstrate that the mean metapopulation size is the same within each group despite differences in
the number of connections.
Discussion
Our model clearly demonstrates that the quality of
corridors connecting patches in a landscape does
influence the size of a metapopulation. The results
of the HM model are similar. Given the structure
and assumptions of our model, this result is not
surprising. Two of the assumptions of our model
are that dispersal and reproduction are densityindependent. This means that a constant proportion of the population leaves a patch each year.
This proportion leaves regardless of quantity or
quality of corridors connecting a given patch to
other patches in a system. Thus, if a patch has no
corridor connections, the B, D, and E terms in
equation 1 remain unchanged, but I = 0 because
our model assumes that all animals in the matrix
are lost to mortality. This results in a low
metapopulation for a landscape with no corridor
connections (landscape 50, Fig. 2). A patch connected by high-quality corridors will have few animals lost enroute, and I will approach the loss due
to E. Poor-quality connections result in an I that
is close to 1/2 of E. It is clear that the addition of
each poor-quality corridor in a system formerly
containing only high-quality connections results in
a decrease in I for the two patches linked by the
poor corridor. It is this reduction in immigration
that results in the overall decrease in metapopulation size.
It may be unrealistic to assume that all animals
entering the matrix are lost to the population.
Fig. 6. A landscape composed of 1 interior and 3 peripheral
patches. The population of the interior patch (2) is significantly larger than the average populations of the 3 peripheral
patches (1, 3, and 4) (t-test).
However, even if the rules of our model allowed
animals to reach new patches by dispersing
through the matrix, the model results would be the
same. A corridor, by definition, allows a better
chance of survival during dispersal than movement
through an unfavorable matrix (Forman and
Godron 1981). If we allowed animals to disperse
through the matrix, it would be analogous to
adding a third type of corridor (i.e., very-poor
quality corridors) to our model, and we would still
see the same trend of landscapes with higher-quality connections supporting larger metapopulations.
Where we differ with Henein and Merriam
(1990) is with regard to their conclusion that
increasing the number of high-quality connections
increases metapopulation size but increasing the
number of low-quality connections decreases the
269
size of a metapopulation. Certainly, where isolated patches can be connected by high-quality corridors to other clusters of connected patches, the
addition of the corridor will result in an increase
in the metapopulation. The Lefkovitch and Fahrig
(1985) model, the HM model, and our model quite
clearly show this. However, given that all patches are connected, adding, rearranging, or even
deleting (compare landscape 30 with 48 and 33
with 49, Fig. 2) corridors can result in increasing
the equilibrial metapopulation only if the alterations result in more equitable distributions of connections among patches within the landscape.
If the number of corridors is increased in such
a fashion that the ratio of interior to peripheral
patches remains the same, the additional corridors
will have no effect on the equilibrial metapopulation. While this phenomena may, in fact, occur in
the other models, it was not shown or discussed.
Given the assumption that the number of emigrants and survival during emigration represent a
constant proportion of each patch population, we
feel our results are more easily explained. Here
again, it is important to keep in mind that the only
element of the models altered by changing corridor arrangement, quality, and quantity is I for each
patch. If the quality of corridors remains constant,
then I should not change for any patch regardless
of the number of connections. To illustrate the
point, consider landscapes 20 and 48 in Fig. 2.
Landscape 20 has 4 high-quality connections
whereas landscape 48 has only 2. For reference,
the top most patch in each landscape we refer to
as patch 1, the middle patch as patch 2, the lower left patch as patch 3, and the lower right patch
as patch 4. If we think about the dynamics in patch
1 in each landscape, it becomes clear why the
number of connections has no effect on the size
of a metapopulation. During dispersal, patch 1 in
landscape 20 loses a proportion (eN) of its population. Half of eN goes down each corridor, arriving in patches 2 and 4 respectively. In return, patch
1 receives eN/2 from both patches 2 and 4. The
net result is that patch 1 loses eN animals and gets
back eN minus a certain proportion [1–θc)eN] that
died during dispersal. Patch 1 in landscape 48 has
only 1 corridor, but the dispersal loss is still eN.
All eN animals emigrate to patch 2. Since patch 2
also has only 1 connection, patch 1 receives
[(1–θc)eV] back. In both these landscapes, all of
the patches lose eN animals to dispersal and get
back (1–θc)eN animals. This situation will hold
true as long as all the patches in the landscape
have the same number of corridor connections.
When a landscape is composed of patches with
a variable number of connections (i.e., interior and
peripheral patches), the situation becomes somewhat more complicated. The results of our model, as well as the HM model, demonstrate that corridor arrangement will affect metapopulation size.
Our model shows a consistent trend that can be
explained by the structure of I in equation 1 and
demonstrates that landscapes with the greatest
number of interior patches will support the largest
metapopulations. Recall that the only portion of
equation 1 modified by altering the characteristics
of the corridor connections in a landscape is I.
This quantity is dependent on two things. First, it
is affected by θc which is corridor survival. Naturally, higher values of θc result in a higher number of emigrants reaching their destinations and,
thus, larger metapopulations. Second, the value of
I is influenced by eN which is the total number of
emigrants leaving each patch. Recall that all patches have the same, constant proportion of animals
emigrating regardless of corridor connections.
When a given patch has no connection, eN animals leave and are lost in the matrix and none
immigrate from other patches. This results in substantially lower populations in unconnected patches. If a patch has one connection, eN animals disperse through that corridor. If the corridor leads
to another patch that has no other additional connections, then the patch receives θceN animals
back through immigration. This situation changes
if the first patch is connected to a patch that has
additional corridors. If the second patch has one
additional corridor, patch 1 is returned θceN2/2 and
if the second patch has two additional connections,
patch 1 receives θceN2/3 animals. The best illustration of this point is landscape 34 in Fig. 2. In
this case, patch 2 has 3 corridor connections, while
patches 1, 3, and 4 have one connection apiece.
During the simulations, patches 1, 3, and 4 lose
eNi animals to dispersal. Each of these patches
only receives θceN2/3 animals from immigration.
All three of these patches are losing substantially
more animals from emigration than are being
270
replaced by immigration. Meanwhile, patch 2 loses eN2 animals to dispersal and gains θceN1 +
θceN3 + θceN4 through immigration. If patches 1,
3, and 4 support less than 1/3 the animals that
patch 2 supports, then the return to patch 2 is less
than it loses to dispersal. Our simulations show
that the populations of the peripheral patches are
more than 1/3 of the interior patch (Fig. 6), so
patch 2 actually has more animals immigrating
than emigrating. The overall result of the dynamics described above is a reduction in the metapopulation size for any landscape containing peripheral patches. The greater the ratio of peripheral
patches to internal patches in a landscape, the lower the metapopulation will be.
Our model provides clear evidence that corridor
quality and arrangement will influence metapopulation dynamics in a landscape of interconnected
patches. These results support the conclusions
found by Henein and Merriam (1990) using a similar model. The HM model did not, however, provide an explanation of why the corridor arrangement in a landscape should alter the size of a
metapopulation. We believe that landscapes with
a greater ratio of peripheral to interior patches will
support smaller metapopulations. In contrast to the
HM model, our model shows that the number of
corridor connections in a landscape does not influence metapopulation size unless it also alters the
peripheral:interior patch ratio. We feel that what
Henein and Merriam (1990) labeled as an effect
of the number of corridor connections was actually a change due to a subtle shift in the relative
numbers of peripheral and internal patches. This
emphasizes the need to carefully consider how
changes in a landscape’s composition also alters
its physiognomic properties. Overall, both models
provide evidence that the size of a metapopulation
will be affected by qualitative characteristics of
corridors within a landscape.
Acknowledgements
Versions of this manuscripts were critically read
by Kirk Moloney, Bill Clark, and Kringen Henein.
We are extremely grateful for their efforts. This
work was supported by grants to BJD from the
U.S. Environmental Protection Agency (CR
820668-02), the U.S. Forest Service Southeast
Experiment Station, and the Iowa State University Agriculture and Home Economics Experiment
Station and grants to GSA from the Theodore Roosevelt Fund of the American Museum of Natural
History and The Sigma Xi Society’s Grants-inAid. Journal Paper No. J-16508 of the Iowa Agriculture and Home Economics Experiment Station,
Ames, IA, Project Number 3399.
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