ORIGINAL PAPER

The importance of stopover habitat for developing effectiveconservation strategies for migratory animals

Justin Sheehy • Caz M. Taylor • D. Ryan Norris

Received: 18 January 2011 / Revised: 11 February 2011 / Accepted: 7 March 2011 / Published online: 29 March 2011

� Dt. Ornithologen-Gesellschaft e.V. 2011

Abstract Although stopover habitats are used by many

species as refuelling stations during migration and can be

critical for survival and successful reproduction, they are

rarely incorporated in year-round population models and

conservation strategies. We incorporate stopover habitat

into a density-dependent population model and then use

this model to examine how optimizing one-time land pur-

chase strategies for a migratory population is influenced by

variation in the quality and the strength of density-depen-

dence in a stopover habitat used for both fall and spring

migration. As the strength of the density-dependence in the

stopover habitat increases, the optimal amount of stopover

habitat purchased increases while the amount of habitat

during the stationary periods of the annual cycle (breeding

and wintering) decreases. Any change in the cost of pur-

chasing stopover habitat affects investment strategies in all

three periods of the annual cycle. When the quality of the

stopover habitat is high, the optimal strategy is to invest in

low-quality habitat during breeding and wintering and

when the stopover habitat quality is low, the optimal

strategy switches to investing in high-quality habitat during

the stationary periods. We apply this model to a threatened

warbler population and demonstrate how purchase deci-

sions to conserve stopover habitat that are not coordinated

with conservation actions on the breeding and wintering

grounds can potentially result in a lower population car-

rying-capacity compared to considering habitat in all three

periods of the annual cycle simultaneously. Our model

provides potential guidelines for developing conservation

strategies for animals that rely on refueling habitats

between the stationary breeding and non-breeding periods

of the migratory cycle.

Keywords Conservation models � Population dynamics �Migratory birds � Stopover sites

Introduction

Although evidence suggests that migratory animal popu-

lations are influenced by the interaction of events

throughout the annual cycle (Fretwell 1972; Bety et al.

2003; Bearhop et al. 2004; Norris et al. 2004; Norris and

Taylor 2006), conservation strategies for species typically

focus on a single period of the year (e.g., Robbins et al.

1992; Basili and Temple 1999; Dankel et al. 2008; but see

Klaassen et al. 2008). Stopover sites are used by many

species as key refueling stations during migration and have

a large influence on the rate of mass gain (Bairlein 1985;

Kelly et al. 2002; Delingat et al. 2006; Schaub et al. 2008),

which can, in turn, have consequences for the timing (e.g.,

Smith and Moore 2003) and success (e.g., Sandberg and

Moore 1996; Bety et al. 2003; Drent et al. 2003) of

reproduction, as well as survival during the stationary non-

breeding season (e.g., Haramis et al. 1986; Pfister et al.

1998). Possible mechanisms by which stopover habitat can

Communicated by F. Bairlein.

Electronic supplementary material The online version of thisarticle (doi:10.1007/s10336-011-0682-5) contains supplementarymaterial, which is available to authorized users.

J. Sheehy (&) � D. R. Norris

Department of Integrative Biology, University of Guelph,

Guelph, ON N1G 2W1, Canada

e-mail: jsheehy@uoguelph.ca

C. M. Taylor

Department of Ecology and Evolutionary Biology,

Tulane University, 400 Lindy Boggs Center,

New Orleans, LA 70118, USA

123

J Ornithol (2011) 152 (Suppl 1):S161–S168

DOI 10.1007/s10336-011-0682-5

influence migratory populations include density-dependent

intra-specific competition for limited resources (e.g., Rus-

sell et al. 1992; Kelly et al. 2002) or variation in habitat

quality via weather (e.g., Schaub and Jenni 2001). Thus,

stopover sites should be a critical component of conser-

vation plans.

Here, we use a three-season, state-structured population

model to optimize the carrying-capacity of a migratory

population that occupies multiple breeding and wintering

habitats and a single stopover site. The model is designed

to predict how to optimally allocate funds to purchase

habitat across these three periods of the annual cycle given

the relative strength of density-dependence and habitat

cost, differences in habitat quality within a season, density-

dependence on the stopover site, general life history, and

total budget size. We use our model to explore how the

optimal amount of breeding, wintering, and stopover hab-

itat is affected by the density-dependence, habitat cost, and

quality of the stopover habitat, as well as by the general life

history of the species. We then apply the model to a

threatened migratory warbler population to examine how

stopover sites influence optimal conservation decisions.

Population model

In a two-season model, the annual cycle begins in the

wintering (or non-breeding) season, W, and ends after the

breeding season B. The population size, N, during the

breeding season, NB, can be represented as the population

size during the previous wintering season, NW, multiplied

by the per capita reproductive output during the breeding

season (bt):

NBt¼ NWt

ð1þ btÞ ð1Þ

The population size on the wintering grounds, NW, can

be represented as the population size at the end of the

breeding season in the previous year multiplied by the

probability of an individual surviving the wintering season

(dt):

NWt¼ NBt�1

ðdtÞ ð2Þ

The per capita reproductive output (bt) and the wintering

survival (dt) can be represented as linear functions:

bt ¼ b� b0NWt

ð3Þ

dt ¼ d � d0NBt�1

ð4Þ

where b and d are density-independent parameters (intrinsic

habitat quality as the population approaches zero) for the

breeding and non-breeding periods, respectively, and b0 and

d0 are density-dependent parameters for the same periods.

The values of b and d can be influenced by the overall habitat

quality, with higher values associated with higher quality

breeding and wintering habitat, respectively. The values of b0

and d0 are affected by changes in population size or amount of

habitat. Although the values of b0 and d0 are small (e.g.,

b0 = 0.00005; Sutherland 1996), they can have a significant

impact on population abundance. Since an individual’s mass

from the stopover habitat may affect reproductive success

(breeding grounds) or survival (wintering grounds), b and d

can then be multiplied by a fitness function for the stopover

site, (fss = x - x0N), where x and x0 represent the density-

independence (intrinsic quality) and density-dependence at

the stopover site, respectively. The equations for NB, and NW

can now be modified such that:

NBt¼ NWt

ð1þ ðbðx� x0NWtÞ � b

0NWtÞÞ ð5Þ

NWt¼ NBt�1

ðdðx� x0NBt�1Þ þ d

0NBt�1Þ ð6Þ

The value of x varies between 0 and 1, with 1 being the

quality of the stopover site in which there is no effect on b or

d (i.e., highest quality). For x0, a value of 0 implies that

density-dependence during stopover has no effect on b or

d. The stopover habitat may appear to act in a similar fashion

as a carry-over effect (i.e., a residual effect in one season that

can carry-over to influence individual success in the

following season) from one stationary period to another

(Norris 2005; Norris and Taylor 2006), but there are

important differences. Where the carry-over effect acts in a

single direction (e.g., from winter to summer), the stopover

habitat function influences both b and d over the course of an

annual cycle. Also, carry-over effects outlined by Norris

(2005) and Norris and Taylor (2006) only affect the habitat

quality parameter of the stationary period, while the stopover

parameters incorporated here influence both quality and

density-dependent parameters during stationary periods.

When the population is at equilibrium, the population

size at the end of both breeding and wintering seasons

are equal to the sizes during the previous year ðNWt¼

NWt�1;NBt

¼ NBt�1Þ. We define the carrying-capacity, K, as

the population size at the end of the wintering period, NW,

at equilibrium because it captures both birth and death

processes over the course of a single annual cycle and is,

therefore, always the lowest population estimate for any

given period of the year. Defining K as the population size

at the end of the breeding period risks developing ‘optimal’

conservation plans for populations that could potentially go

extinct by the end of the following winter season due to

high mortality rates.

Conservation model

Sheehy et al. (2010) showed that the relative density-

dependence between the breeding (b0) and wintering (d0)

S162 J Ornithol (2011) 152 (Suppl 1):S161–S168

123

habitats, along with the relative habitat costs (CB and CW)

and density-independence (b and d), can be used to predict

the optimal proportion of each habitat to purchase for a

migratory population that occupies a single breeding and

wintering habitat. Assuming that when habitat is lost the

population will occupy the remainder of the habitat, such

that the new density will equal the previous density mul-

tiplied by the inverse of the proportion of habitat remain-

ing, NB and NW can be written as:

NBt¼ NWt

1þ b xspring �x0

spring

xNWt

!� b

0NWt

! !

ð7Þ

NWt¼ NBt�1

d xfall �x0

fall

xNBt�1

!� b

0NBt�1

!ð8Þ

where p, q, and x are the proportion of breeding, wintering,

and stopover habitat purchased, respectively (all vary

between 0 and 1). To incorporate two different quality

habitats in the breeding and wintering season, we assume

an equal area within each habitat and that there is a higher

cost associated with the high-quality habitat. A difference

in habitat quality is represented by variation in the density-

independent parameter (dhigh, dlow for wintering, bhigh, blow

for breeding). Thus, d and b are the weighted averages of

these parameters. For example, d is:

d ¼ qlowðdlowÞ þ qhighðdhighÞqlow þ qhigh

ð9Þ

A similar equation applies in a two-quality breeding

habitat model, and in both cases higher values are

associated with higher qualities. For both breeding and

wintering habitats, the proportion purchased is the average

of the two different quality habitats purchased. For

example, p is:

p ¼ phigh þ plow

2ð10Þ

Cost constraints

CI is the cost of purchasing habitat during the given season

and is equal to the amount of habitat available, LI (in hect-

ares), multiplied by the cost per unit of habitat, PI (in dollars/

hectare), where I = B (breeding), W (wintering), or S

(stopover site). We assume that the total budget, Ct, is fixed

and is always less that the cost of purchasing all of the winter,

breeding, or stopover habitat. Thus, the optimal strategy will

always entail spending the entire budget, such that:

Ct ¼ ðplowÞðCBlowÞ þ ðphighÞðCBhigh

Þ þ ðqlowÞðCWlowÞ

þ ðqhighÞðCWhighÞ þ ðxÞðCSÞ

Dividing CB, CW and CS by Ct gives:

l ¼ ðplowÞðC�BlowÞ þ ðphighÞðC�Bhigh

Þ þ ðqlowÞðC�WlowÞ

þ ðqhighÞðC�WhighÞ þ ðxÞðC�SÞ ð11Þ

where CB* , CW

* and CS* are the ratios of the costs needed to

purchase all LB, LW and LS in relation to the total budget,

respectively.

Simulations

We ran simulations to maximize K (population size at the

end of the wintering period) in the following way. For a

given set of parameter values, we varied the amount

of each habitat and/or habitat quality purchased between

0 and 1 in increments of 0.001 to find the strategy that

resulted in the highest K and also met the constraint of

being less than or equal to Ct. All simulations were run

using PELLES C (ver. 5.0). We used population parameter

estimates from Eurasian Oystercatchers (Haematopus

ostralegus; Sutherland 1996) and obtained approximate

land cost values from the MBCC 2008 Report (Migratory

Bird Conservation Commission 2008). These parameter

estimates were used to establish realistic values for the

simulations and not to investigate conservation strategies

specifically for the Oystercatcher.

Model analysis and results

Effect of including stopover habitat

on population model

Because the abundance parameters (NW or NB) in the

reproductive output and survival equations are multiplied

by the density-dependent and density-independent stopover

parameters [Eqs. (5, 6)], an increase in the intrinsic quality

of the breeding or wintering habitat (b or d) increases the

density-dependence on the stopover habitat during the

following migration. This, in turn, buffers the positive

effect of an increase in the breeding or wintering habitat

quality on population size. In other words, there is a rela-

tively smaller response of K to changes in b or d compared

to models that do not incorporate stopover habitat because

of the two-way compensatory response at the stopover site

(Fig. 1).

Effect of density-dependence and cost of stopover

habitat on purchase decisions

The between-season purchase decisions are determined by

the relative density-dependence (Fig. A1A in supplemen-

tary material) and cost [Electronic Supplementary Material

(ESM) Fig. A1B] between the wintering, breeding, and

J Ornithol (2011) 152 (Suppl 1):S161–S168 S163

123

stopover habitats (Fig. 2a). As the strength of the density-

dependence on the stopover site (x0) increases, the optimal

amount of stopover site purchased increases and the opti-

mal amount of the breeding and wintering habitat decreases

(Fig. 2a). More generally, a change in the cost of habitat in

any of the three periods influences the optimal purchase

strategy for all three periods. For example, as the cost of

purchasing stopover habitat (CS) increases, the amount of

habitat to purchase in all three periods of the annual cycle

should be reduced. However, the greatest reduction in

purchase should occur during the period when the cost of

habitat has decreased (results not shown). Lastly, because

the density-dependence for the stopover habitat is multi-

plied by the intrinsic growth rates (b and d), any increase in

these values (through purchasing high-quality habitat) may

also increase the amount of stopover habitat purchased.

This has the effect of actually decreasing the amount of

breeding and wintering habitat purchased (results not

shown).

Effect of stopover habitat quality on purchase decisions

Which quality of breeding or wintering habitat to invest in

depends on the quality of the stopover site (x). If x = 1

(high-quality stopover site) and the optimal purchase

strategy is to invest in low-quality habitat (in either

breeding or wintering seasons), then there is a threshold

value of x in which the strategy changes to investing in

high-quality habitat. Otherwise, x will not have an effect

on the quality of habitat invested in during either stationary

period of the annual cycle. Based on the parameter values

we used (refer to Fig. 2 legend), x typically has an effect

on the quality of wintering habitat purchased and does not

affect the quality of the breeding habitat (Fig. 2b). How-

ever, it is possible for x to only affect the investment in the

type of breeding habitat, both breeding and wintering

habitat, simultaneously (ESM Fig. A2A), or neither (ESM

Fig. A2B). This depends on the relative qualities and costs

within a season.

Effect of life history on purchase decisions

When the ratio of b:(1 - d) is small (low breeding output

relative to mortality, akin to K-selected species), a higher

overall quality of stopover habitat is required to maintain a

positive population growth rate compared to when the

b:(1 - d) ratio is large (akin to r-selected species; ESM

Fig. A2A). Furthermore, consistent with Sheehy et al.

(2010), the optimal purchase strategy typically involves

purchasing high-quality wintering habitat for K-selected

species and low-quality wintering habitat for r-selected

species. Thus, variation in the quality of the stopover

habitat will typically not affect purchase strategies for

Fig. 1 The percentage decrease in K (population size at the end of

the wintering period) in relation to the percentage decrease of

b (intrinsic growth rate for breeding period) for a population model

which does not include stopover habitat [Eqs. (3) and (4)] and a model

that includes stopover habitat [Eqs. (5) and (6)]. For both models,

d = 0.95, b0 = 0.00005, and d0 = 0.00011. For the model that

incorporates stopover habitat, x = 1 and x0 = 0.0000275

Fig. 2 The optimal proportion of habitat purchased on the breeding,

wintering, and stopover habitat in relation to the mean strength of

density-dependence on the stopover site (x0) (a) and the strength of

density-independence (intrinsic quality) on the stopover site (x) (b).

For a, low-quality breeding and wintering habitats are not shown as they

closely follow the general trends displayed in the high-quality habitats.

The optimal proportion of habitat is equal to the amount purchased

divided by the amount available. For a, x0spring varies and x = 1. For b,

x0spring = 0.0000125 and x varies. For b, the dotted line represents the

value of x at which there is no effect on the intrinsic growth rates (b and

d). For a and b, b0 = 0.00005, d0 = 0.00011, bhigh = 0.4, blow = 0.35,

dhigh = 0.95, dlow = 0.94, C�Bhigh= 1.85, C�Blow

= 1.4, C�Whigh= 2.6,

C�Wlow= 2.15, C�S = 2, and x

0

fall = 0.0000275

S164 J Ornithol (2011) 152 (Suppl 1):S161–S168

123

K-selected species (ESM Fig. A2B) but will affect strate-

gies for r-selected species (ESM Fig. A2A).

Application of the model

The Hooded Warbler (Wilsonia citrina) is a small (approx

10 g) long-distance migratory passerine that is listed as

threatened under the Canadian Species at Risk Act

(COSEWIC 2000), with the only Canadian breeding pop-

ulation located in southwestern Ontario. Based on stable

isotope studies, evidence suggests that the Ontario popu-

lation uses stopover sites in the gulf coast of the USA

during the spring and fall migration (Langin et al. 2009).

Populations over-winter from east-central Mexico to Belize

(Ogden and Stutchbury 1994). We used habitat quality and

density-dependent estimates from this species and closely

related wood-warblers (Black-throated Blue Warbler

Dendroica caerulescens, American Redstart Setophaga

ruticilla, and Wilson’s Warbler Wilsonia pusilla; see

Appendix and Table A1 in the ESM) and assumed that the

size of the stopover habitat was equal to half of the

available breeding/wintering habitat.

Sheehy et al. (2010) used land cost estimates from the

breeding grounds in southern Ontario and the wintering

grounds in Belize to estimate the optimal amount of

habitat to be purchased between these two periods of the

annual cycle. With a total budget equal to the combined

costs of these two parcels of habitat, the optimal strategy

was to purchase 164 ha of high-quality breeding habitat

and 95 ha of high-quality wintering habitat, despite the

fact that breeding habitat was over tenfold more expen-

sive. Here, we ask what the optimal purchase decision

would be if we added a single stopover site and used a

state-structured model. In 2008, the Cat Island National

Wildlife Refuge in Louisiana, which Hooded Warblers

commonly use as stopover habitat during both the spring

and fall migration, was expanded by 345 ha at a cost of

USD $1.755 million (MBCC 2009). Thus, we added

$1.755 million to the budget (total = $4.055 million),

assumed that the stopover habitat was not yet purchased,

and then calculated the optimal combination of habitat

purchases when considering all three periods of the annual

cycle simultaneously. If these extra funds were included in

the model from Sheehy et al. (2010) with no stopover

habitat incorporated, the optimal strategy would be to

purchase 288 ha of high-quality breeding habitat and

166 ha of high-quality wintering. Interestingly, when we

included stopover habitat into our stage-structured model,

we found that the optimal strategy was to not spend most

of the additional budget on stopover habitat, but to pur-

chase less (266 ha) high-quality breeding habitat in

southern Ontario, more (420 ha) high-quality wintering

habitat in Belize, and only 12 ha of stopover habitat in

Louisiana (see Appendix in ESM for calculations). Thus,

for this particular strategy, funds that would have been

allocated towards the breeding habitat are now directed

towards the wintering and stopover habitat. This purchase

strategy resulted in a K of 69 individuals, which was

higher than that when all of the extra funds was invested

solely on the stopover site (345 ha; K = 57). This strategy

also yielded a higher K than that obtained when the

combination of land purchase decisions was based solely

on what would be for sale in the current market (29 ha of

breeding habitat, the 2,500 ha of the wintering habitat, and

the 345 ha of stopover habitat; K = 0; see Appendix in

the ESM for calculations), which is not a formal coordi-

nated effort to protect this species across these three

periods of the annual cycle. Of course, these results apply

when there is limited funding and do not address how to

maximize long-term population persistence.

We also performed a sensitivity analysis and found that

the optimal amounts of each habitat purchased were most

sensitive to changes in the breeding habitat parameters and

least sensitive to changes in the stopover habitat parameters

(for both density-dependence and habitat cost; ESM Table

A2). For example, a 25% decrease in the strength of

breeding density-dependence resulted in purchasing 4 ha

less high-quality breeding habitat (-1.4%), 49 ha more

high-quality wintering habitat (?11.7%), and 1 ha more

stopover habitat (?7.7%). In comparison, a 25% decrease

in the strength of density-dependence in the stopover

habitat (during the fall migration) resulted in purchasing

0.94 ha more high-quality breeding habitat (?0.4%),

6.6 ha more high-quality wintering habitat (-1.6%), and

0.95 ha less stopover habitat (-7.7%). However, the

strategy remained to invest primarily in high-quality win-

tering habitat and purchase high-quality breeding habitat

and small amounts of stopover habitat (ESM Table A2).

Discussion

We show how a model with stopover parameters will

generally lead to a more stable carrying-capacity, meaning

that changes in the intrinsic quality of the habitat during the

stationary periods of the annual cycle will have less of an

impact on K when a stopover site is included. This is a

general result that should be widely applicable to most

systems regardless of the structure of the model. Since

density-dependent and -independent effects from stopover

habitats have been shown to influence individual success in

migratory species (e.g., Russell et al. 1992; Schaub and

Jenni 2001; Kelly et al. 2002), a model incorporating

stopover sites should also have a better ability to predict

future changes in the population size of migratory species.

J Ornithol (2011) 152 (Suppl 1):S161–S168 S165

123

Our results also demonstrate the importance of including

stopover habitat into optimal conservation strategies. In our

model, the density-dependence, cost, and quality of the

stopover habitat had a significant impact, not only on the

amount of habitat purchased at the stopover site but also on

habitat purchased on the breeding and wintering grounds.

The strength of density-dependence and the cost of the

stopover habitat had similar effects on the optimal deci-

sions as analogous parameters on the stationary breeding

and non-breeding periods of the year (Sheehy et al. 2010).

As the cost of the stopover habitat increases, it is optimal to

invest less in all three periods of the migratory cycle. As

the strength of density-dependence at the stopover site

increases, the relative importance of the stopover site for

maximizing K increases, and it therefore becomes optimal

to invest more in stopover habitat. Again, these results

should be applicable to a wide variety of models and

parameter values.

We also found that the mean quality of the stopover

habitat influences whether to purchase high- or low-quality

habitat during the wintering or breeding period. When the

quality of the stopover habitat (x) is low, it is optimal to

invest in high-quality habitat during the stationary period to

compensate for the poor condition of the individuals

arriving from the stopover site. If only low-quality habitat

is purchased, then the survival rate is too low to maintain

the population. Conversely, when the quality of the stop-

over habitat is high, it becomes optimal to invest in low-

quality wintering and/or breeding habitat, which capitalizes

on the low cost of the low-quality habitat and results in a

larger amount of habitat that is conserved and a higher K.

Surprisingly, we found that the quality of the stopover

habitat has the opposite effect on the purchase strategies

compared to the breeding or wintering habitat quality.

When habitat quality increases on either the breeding or

wintering grounds, the optimal strategy is to purchase

smaller amounts of high-quality habitat during all other

periods of the annual cycle (Sheehy et al. 2010). However,

because we have shown here that it is optimal to purchase

low-quality winter habitat when the quality of the stopover

is high, the amount of funding for purchasing additional

habitat is also relatively high. As a consequence, the

optimal strategy involves purchasing additional habitat in

all three periods of the migratory cycle. Thus, somewhat

counter intuitively, the two general purchase strategies

involve either a large amount of high-quality stopover

habitat or a small amount of low-quality habitat. This result

reinforces the importance of estimating the quality of the

stopover habitat prior to the formation of any conservation

strategy.

Our model can be applied to developing conservation

strategies for migratory songbirds. Using the Hooded

Warbler as an example, we have shown that current efforts

to purchase these habitats (that are not coordinated) would

lead to a lower carrying-capacity compared to the results

predicted from our optimization model. The model also

predicted that an optimal K would be achieved by pur-

chasing only a small fraction of the stopover habitat that

was available (12 of 345 ha) and investing more in the

wintering habitat. This is likely due to the fact that stopover

habitat in this example had a much smaller density-

dependence to cost ratio than habitats in the other periods

of the annual cycle.

For Hooded Warblers, we also found that the optimal

habitat purchase strategy is most sensitive to changes in

breeding habitat parameters, likely due to higher density-

dependence on the breeding grounds relative to other

seasons of the annual cycle. When the density-depen-

dence on the breeding grounds is decreased, the relative

importance of the wintering and stopover habitat increa-

ses, and the amounts of both of these habitats increase by

a higher percentage due to their lower habitat costs.

Conversely, stopover habitat parameters have a very small

effect on the results due to the small density-dependence

to cost ratio.

Our model framework can be applied to any species that

relies on stopover sites during migration. For example,

Sockeye salmon (Oncorhynchus nerka) typically gather at

the mouths of major river outlets to wait for optimal con-

ditions prior to migrating upstream to spawn (Levy and

Cadenhead 1995). During southward migration, Monarch

butterflies (Danaus plexippus) often congregate in the

thousands at discrete coastal locations before crossing large

water bodies (Mackenzie and Friis 2006). Our model can

also be used to design conservation strategies for trans-

Saharan migratory birds, who breed in Europe and Asia

and use stopover sites in the Mediterranean and northern

Africa. One interesting case is the Aquatic Warbler

(Acrocephalus paludicola), which is one of the most

threatened long-distance migratory passerines in the world.

A significant portion of the breeding and stopover habitat

used by this species has been identified and protected

(Heredia et al. 1996; Atienza et al. 2001). However, large

portions of wintering habitat are not protected and pre-

sumably under threat (Schaffer et al. 2006). Applying our

models to this species would provide an estimate of how

much wintering habitat would be necessary to maximize

population size and how much, if any, additional breeding

and stopover habitats need to be conserved. It is important

to note, however, that applying our model to these exam-

ples requires detailed information on the habitat quality,

the cost of conserving habitats, and how these habitats

influence subsequent reproduction or survival. Given this

information, it is plausible that, in some situations, the

optimal strategy would be to divert funding towards the

protection of stopover sites in order to maximize the global

S166 J Ornithol (2011) 152 (Suppl 1):S161–S168

123

carrying-capacity. How much habitat to protect would

depend on the strength of the density-dependence and the

cost.

In the model presented here, we assumed that any

habitat lost on the stopover habitat was of average

quality, which best applies to small decreases in habitat

size (Sutherland 1996). However, it is relatively easy to

incorporate multiple stopover habitats into an optimiza-

tion model like the one presented here. The challenge

will be to track the degree of habitat loss and identify

different quality stopover sites in migratory species. For

simplicity, we only included a single stopover habitat,

whereas most migratory species typically use a series of

stopover locations between the breeding and wintering

habitats (e.g., Shimazaki et al. 2004; Lehnen and Kre-

mentz 2005), and some species will even use different

sets of stopover locations depending on the season (e.g.,

Spear and Ainley 1999). Useful extensions of this model

would be to incorporate multiple stopover sites in a

spatially and temporally explicit framework and to

incorporate multiple stopover habitats over the course of

the annual cycle.

We also assumed that, when habitat was lost, the

remainder of the individuals would crowd into the

remaining habitat [Eqs. 7, 8)]. This implies that individuals

cannot exclude others from the remaining habitat, a situa-

tion that may or may not hold for some species. However,

two observations suggest that this assumption may be quite

robust. First, most habitat that is lost in a given year is

relatively small compared to the total habitat available.

Thus, even in territorial species, individuals that formerly

occupied lost habitat are likely to find space in the

remaining habitat. Second, when large amounts of habitat

are lost, then the strength of the density-dependence

increases significantly in the crowded population. Com-

pensatory effects of increased mortality and decreased birth

rates in the remaining habitat likely produce similar pat-

terns compared to cases when mortality increases simply

due to exclusion.

Our model demonstrates that designing effective con-

servation strategies for migratory animals requires accurate

estimates of density-dependence, habitat quality, and the

costs of performing conservation actions at stopover hab-

itats. Although obtaining such parameter estimates will be

challenging for many species, such information is essential

for conserving migratory populations that rely on stopover

habitats during migration.

Acknowledgments DRN was supported by grants from the Natural

Sciences and Engineering Research Council of Canada, and an Early

Researcher Award from the Ontario Ministry of Innovation and

Training. Kevin McCann provided valuable comments on earlier

drafts of the manuscript.

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