Modelling behavioural and fitness ... - Wiley Online Library

Journal of Applied Ecology 2006 43, 92–100

Modelling behavioural and fitness consequences of disturbance for geese along their spring flyway Blackwell Publishing, Ltd.

MARCEL KLAASSEN,* SILKE BAUER,* JESPER MADSEN† and INGUNN TOMBRE‡ *Centre for Limnology, Netherlands Institute of Ecology, PO Box 1299, 3600 BG Maarssen, the Netherlands; †Department of Arctic Environment, National Environmental Research Institute, PO Box 358, 4000 Roskilde, Denmark; and ‡Norwegian Institute for Nature Research, The Polar Environmental Centre, 9296 Tromsø, Norway

Summary 1. For migratory birds the implications of environmental change may be difficult to predict because they use multiple sites during their annual cycle. Moreover, the migrants’ use of these sites may be interdependent. Along the flyway of the Svalbard pinkfooted goose Anser brachyrhynchus population, Norwegian farmers use organized scaring to minimize goose use of their grasslands in spring. We assessed the consequences of this practice for regional site use of pink-footed geese along their spring migration route. 2. We used dynamic programming to find the sequence of migratory decisions that maximizes the fitness of female geese during spring migration, assuming scaring impinges on both food-intake rates and predation risk. The parameterization of the model was based on data gathered from individually marked pink-footed geese between 1991 and 2003. 3. The effect of scaring in terms of fitness and site use was most noticeable regarding food-intake rate. Scaring resulted in a redistribution of geese along the flyway. Furthermore, the outcomes of the modelling exercises were highly dependent on whether or not the geese were omniscient or naive: at moderate scaring levels naive geese were predicted to succumb. 4. On a qualitative basis there was good correspondence between the predictions from the model and the empirical evidence gathered to date. 5. Synthesis and applications. Besides highlighting the importance of learning and changing behaviour in an adaptive fashion, our modelling exercise indicated the potential vulnerability of the geese to abrupt environmental change. In addition, the exercise emphasized the interdependence of site use along the migratory flyway. The model supports the necessity for an integrated flyway management approach. In Norway, discussion is ongoing about the future management of the spring conflict between farming interests and geese. Farmers in north and mid-Norway have announced that they will expand the scaring campaign if a long-term solution, including a compensation scheme, is not forthcoming. If scaring on such a large scale is implemented abruptly, it may have severe consequences for the population: management of both the scaring intensity and its geographical extent is urgently required. Key-words: Anser brachyrhynchus, dynamic programming, environmental change, flyway management, pink-footed goose, migration, scaring Journal of Applied Ecology (2006) 43, 92–100 doi: 10.1111/j.1365-2664.2005.01109.x

Introduction

© 2006 British Ecological Society

Correspondence: Marcel Klaassen, Centre for Limnology, Netherlands Institute of Ecology, PO Box 1299, 3600 BG Maarssen, the Netherlands (fax +31 294232224; e-mail [email protected]).

In a world where the environment changes rapidly as a result of human perturbations, there is great concern regarding how individual populations, communities and ecosystems will react. Adaptability to new situations

93 Modelling consequences of goose scaring

© 2006 British Ecological Society, Journal of Applied Ecology, 43, 92–100

through phenotypic plasticity or flexibility, learning or selection are important assets for any organism (Piersma & Lindstrom 1997; Agrawal 2001; Alerstam, Hedenström & Åkesson 2003). Tools are required to predict the fate of organisms responding to local and global changes; such tools would be particularly useful if they could take into account adaptive behaviour. In the case of migratory birds the implications of environmental change may be particularly difficult to predict because these animals use a succession of sites during their annual cycle. Use of some of these sites by migrants may be interdependent of external factors. A theoretical framework using dynamic programming (Mangel & Clark 1988) has been applied previously to the problem of migratory birds using distinct stop-over sites (Weber, Houston & Ens 1999). These authors also noted that the same modelling approach could be used to study reactions to environmental change of fully informed birds, showing fully adaptive behaviour, and reactions by birds that are non-responsive to environmental change, showing non-adaptive, naive behaviour. In this study we used their approach to model the spring migration of the Svalbard breeding pink-footed goose Anser brachyrhynchus Baillon. We generated temporally and spatially explicit predictions of goose behaviour as well as fitness consequences of environmental change along their flyway. Most populations of migratory geese in the northern hemisphere breed in the Arctic and winter in the temperate zone, with discrete staging areas in-between. While conditions on the breeding grounds have generally remained unaffected by human activities, the staging and wintering areas have undergone dramatic changes as a result of human development. As a consequence, most wintering populations have changed from feeding on natural or semi-natural habitats to a convergent use of agricultural land (van Roomen & Madsen 1992). Because of a combination of improved winter feeding opportunities and reduced hunting pressure throughout their ranges (caused by cessation of hunting, better regulation of hunting kill rates and more reserves), most populations wintering in Europe and North America have increased dramatically in numbers during the last three to five decades (Madsen, Reed & Andreev 1996). The convergence on farmland and increased population sizes have caused an escalating conflict between agriculture and geese in the wintering and staging areas. Local and national management initiatives have been taken to alleviate the conflict by means of scaring, economic compensation schemes and provision of alternative feeding areas (van Roomen & Madsen 1992). However, few studies have addressed the response of the geese to the management measures, i.e. in terms of site and habitat use (Meire & Kuijken 1991; Patterson 1999; Tombre et al. 2005) or energetic and fitness parameters (Madsen 1995). In this study we assessed the consequences of organized scaring of pink-footed geese, used as a tool to minimize goose use of grasslands in spring. Following Weber, Houston & Ens (1999), we used dynamic programming to

find the sequence of migratory decisions that maximizes the fitness of the female pink-footed geese during spring migration. These sequences were calculated for birds in any condition and at any of the staging sites along the spring flyway, providing a decision matrix with the dimensions time, place and body condition. In the modelling we assumed that the scaring of geese intrudes on their food intake rate and their perceived predation risk (sensu Beale & Monaghan 2004, considering people as predation-free predators). The parameterization of the model was based on data gathered from individually marked pink-footed geese over the years 1991–2003. Only data from geese that did not experience any organized scaring while staging during a particular year were used. In addition, we aimed to identify the optimal migratory itinerary for the geese when the environment changes. We focused on the impact of various scaring levels on the optimal behavioural decisions and used this information to predict the fate of fully informed (i.e. using an appropriate optimal decision matrix) and naive (i.e. using a dated decision matrix) pink-footed geese in an environment where scaring levels fluctuated. Predictions were compared with changes in migratory behaviour and breeding success observed in the Svalbard pink-footed goose population over recent years that have been related to the scaring practices in the staging areas (Madsen 2001; J. Madsen, M. Klaassen & I. Tombre, unpublished data).

Methods The Svalbard breeding population of pink-footed geese increased in numbers from approximately 15 000 individuals in the 1960s to 23 000–30 000 in the 1980s; during the 1990s the population increased again, reaching approximately 45 000 in the early 2000s (Ganter & Madsen 2001; Fox et al. 2005). The population winters in Denmark, the Netherlands and Belgium. During March and April, the population congregates in west Jutland, Denmark, before migrating via Norwegian stopover sites to the breeding grounds in Svalbard. In Norway, the geese have traditionally stopped in Vesterålen in Nordland, but since the late 1980s increasing numbers of geese have also stopped in Trøndelag, central Norway (Fig. 1). During winter and early spring, the geese predominantly feed on pastures and winter green cereals. During spring in west Jutland (Denmark), they switch to newly sown cereal fields as soon as sowing of spring cereals commences; to alleviate damage to the crops, geese are baited with grain at five sites (Madsen 1996). In Trøndelag, the geese feed on fertilized pastures and stubble grain fields (or unharvested fields from the previous season), gradually turning to newly sown cereal fields as sowing commences (Madsen et al. 1997). In Vesterålen, the geese primarily feed on fertilized pastures. During 1993–95, and most intensively since 1999, farmers in Vesterålen have organized campaigns

Marked birds were resighted in Denmark, Trøndelag and Vesterålen during the spring in 1991–2003. Resighting efforts were generally high in Denmark and Vesterålen, while in Trøndelag effort varied between years, with peak activity in 1996 and 2003. As an evaluation of body condition in marked individuals, abdominal profile indexes (API) were visually scored by trained and intercalibrated observers (for standards and indexes see Madsen & Klaassen 2006).

94 M. Klaassen et al.

               

Fig. 1. Assumed metabolizable energy intake rates as a function of date for pink-footed geese staging in Denmark, Trøndelag, Vesterålen and Svalbard. The plateau intake rate levels in Denmark, Trøndelag and Vesterålen are based on observed changes in abdominal profile scores. The data for Svalbard are based on unpublished observations by A. D. Fox and C. Hübner.


to scare the goose flocks away from sheep-grazed fields, where geese compete with sheep for the early grass. In areas with a scaring regime, goose numbers have been reduced and, among the remaining geese, daily energy intake rates have been reduced whilst energy expenditure has increased (I. M. Tombre, J. Madsen, H. Tømmervik & E. Eythorsson, unpublished data). As a consequence, these geese are unable to accumulate sufficient body stores (assessed by abdominal profiles) to breed as successfully as geese staging in areas without scaring (Madsen 1995). During the springs of 1990 – 2003, a total of 1810 pink-footed geese, including 807 females, were captured by cannon-netting in Denmark. All geese were aged and marked with a blue plastic neckband with a three-digit individual code.

Conversion of API to energy stores was made using data from carcass analyses of geese collected along the spring migratory flyway (Drent et al. 2003; Madsen & Klaassen 2006), and yielded an equivalent of 6214 kJ API−1 and 26735 kJ kg−1. Average flight costs, f, were estimated at 8·9 kJ km−1 by comparing the individual API scored prior to and after a migratory flight in both adult males and adult females (Madsen & Klaassen 2006). Bruinzeel et al. (1997) compiled data on daily energy expenditure of herbivorous birds. The majority of these birds were caged, and we regressed cage metabolism (CM; kJ day−1) against body mass (M; kg), yielding CM = 690M0·58 (r2 = 0·91, n = 11) and a predicted value of 1305 kJ day−1 for an average female pink-footed goose with a body mass of 3·000 kg. We took this figure to resemble daily energy expenditure, e, at all sites, not taking foraging costs into account. Metabolizable energy intake rates (g; kJ day−1) were estimated from changes in API scores in females taken from 21 March onwards (Madsen & Klaassen 2006) and adding daily energy expenditure, e. As it is our impression that during this annual phase the geese are primarily foraging in preparation for migration, we took these metabolizable energy intake rates to reflect the metabolizable energy intake rates at maximum foraging intensity, amounting to 1970, 2868 and 4038 kJ day−1 in Denmark, Trøndelag and Vesterålen, respectively. In comparison with Denmark, the onset of spring is later in Trøndelag and later still in Vesterålen. We therefore assumed the initial metabolizable energy intake rates at these sites to be zero, starting to increase from day 99 and day 115 for Trøndelag and Vesterålen, respectively (Fig. 1). For Svalbard only a limited set of data was available on intake rates and abdominal profile scores in pink-footed geese. We tentatively based the onset of spring and maximum pre-laying metabolizable intake rates on departure dates of geese from Vesterålen and unpublished observations of geese arriving at Svalbard (A. D. Fox & C. Hübner, unpublished data) (Fig. 1).

    We used dynamic programming to find the sequence of migratory decisions that would maximize the fitness of


a female pink-footed goose under various environmental conditions during spring migration. The dynamic program largely followed the concepts presented by Weber, Ens & Houston (1998), Weber, Houston & Ens (1999) and Beekman, Nolet & Klaassen (2002). We distinguished five potential staging sites, i: the wintering site (Denmark), three stop-over sites (Trøndelag in central Norway, Vesterålen in northern Norway and the coastal areas of Svalbard) and the breeding site (Svalbard). The distances, Di, between these sites are 780, 630, 1130 and 10 km. The migration period was divided into whole days, t. Preparations for spring migration were assumed to start in Denmark on 21 March (day 80). We assumed that, at time t, the expected future fitness, F, of a female pink-footed goose is a function of its fuel stores, x, and its location, i: F(x,t,i). Body store x varies between 0, where the goose used in our simulations has reached a body mass of 2·4 kg and dies of starvation, and xmax, where it has reached its maximum fuel load of 34 336 kJ at a body mass of 3·6 kg. At the breeding or destination site, N, the expected future fitness is F(x,t). For each time step when the goose has not yet arrived at its destination, it has two behavioural options: either foraging at intensity u (0 ≤ u ≤ 1) or, if fuel stores permit, flying to one of the next sites. Using dynamic programming equations, a matrix was compiled containing the optimal behavioural decisions for all combinations of fuel stores, time and site. This decision matrix allowed us to follow the fate of individual birds during their migratory journey, i.e. the timing and intensity of use of the various sites along the migration route. Weber, Ens & Houston (1998), Weber, Houston & Ens (1999) and Beekman, Nolet & Klaassen (2002) provide detailed account of the dynamic programming equations used in this study. Here only a brief description is provided, emphasizing the differences with the earlier models. Expected fitness in terms of young produced at the destination (i = N ) and in future years is a function of state upon arrival, K(x), date of arrival, K(t), and the expected fitness from future breeding attempts, B(x): F ( x, t, N ) = K (t ) × K ( x ) + B( x )


K (x ) =

w ( x− xc ) − w ( x− xc )  1 e −e + 1  w ( x− xc ) − w ( x− xc ) 2 e +e 

eqn 3

where w and xc are set to 0·028 and 73, respectively, to describe the relationship between state and number of produced young (J. Madsen & M. Klaassen, unpublished data). If the female and its mate are unable to complete their migratory journey successfully, the female’s expected fitness equals B. The survival and future reproductive fitness of pink-footed geese is positively related with the state of the geese upon arrival at the breeding grounds. We thus approximated the lifetime future reproductive success of the geese as: 2

B( x ) = BT × ( a0 + a1x + a2 x )

eqn 4

where BT is set to 2, to resemble the approximate average lifetime reproductive success of females in a stable population, and a0, a1 and a2 are set to 0·773, 8·3 × 10−4 and 3·6 × 10−6, respectively, to mimic the effect of state upon arrival on survival. following the findings of J. Madsen & M. Klaassen (unpublished data). The maximum intake rate that a foraging pinkfooted goose may attain is site and time dependent [g(i, t), kJ day−1] and is corrected for costs associated with foraging and digestive efficiency. The actual intake rate is determined by the foraging intensity, u. How much of this intake rate ultimately is stored as body stores depends on the energy expenditure, e (kJ day−1; not including costs associated with foraging because these are included in g). Maintaining fuel stores incurs a fitness cost in terms of increased risks of predation and injury (Witter & Cuthill 1993). We assume that this is only a small fitness cost β to the pink-footed geese: a+1

β( x, u ) = mβ

( x + ug(i , t ) − e ) − x ( a + 1)(ug(i , t ) − e )

a+1

eqn 5

eqn 1

Over the years 1991–2002 the available data on departure dates of individual geese from Vesterålen and the number of young with which they return in the subsequent winter indicate that successful breeding is only possible if geese arrive at the breeding grounds within a narrow time window. The optimal period of arrival at the breeding grounds was set between 20 and 26 May (days 140 –146). We assumed that arriving at the breeding grounds outside this period led to breeding failure: 0 if t < 140 or t > 146 K (t ) =  1 if 140 ≤ t ≤ 146

The state in which a female goose arrives at the breeding grounds also importantly determines its breeding success (J. Madsen & M. Klaassen, unpublished data):

eqn 2

where the predation risk coefficient, mβ, is set to 10−8 and the mass-dependent predation risk exponent, a, is set to 2 (Madsen, Frederiksen & Ganter 2002). If a pink-footed goose decides to forage, it should forage with an intensity, u, that yields the maximum expected fitness at the destination: Hf ( x, t, i ) = max[(1 − β( x )) u

F ( x + ug(i , t ) − e, t + 1, i )]

eqn 6

Alternatively, an individual can depart to the next or a preceding site depending on its fuel stores, x, and the distance, D (km), to the destination site. Its fuel stores upon arrival at the destination xa are calculated using:


2   c xa =  − 1 × x max −0⋅5 2  (c − c(1 − (1 + x /x max ) − D ))  eqn 7

where c is a flight range parameter that is calculated using: c=

Dmax 1 − ( x f /x max )

eqn 8

−0 ⋅5

and Dmax is the maximum flight range when dedicating fraction xf of the maximum fuel load xmax to flight. For pink-footed geese in this study, we used xf = xmax. Dmax was calculated by dividing the maximum fuel load by the flight costs in terms of energy over time: Dmax =

x max f

eqn 9

Assuming females vary in body mass between 2·4 kg and 3·6 kg, we derived a maximum fuel load, xmax, of 32 082 kJ. The maximum flight range, Dmax, was thus calculated at 3588 km and the flight range parameter, c, at 12249. If an individual decides to depart, it should fly to site j, yielding the maximum expected fitness at the destination:   Hd ( x, t, i ) = max F  x a , t j   

 j−1   + ∑ Dz /v , j    z=i   

eqn 10

where v is flight speed, which is estimated at 979 km day−1 following an allometric equation provided by Clausen et al. (2002). The optimal decision is the behavioural alternative, foraging or departing, yielding the highest future expected reproductive success: F ( x, t, i ) = max[H f ( x, t, i ), H d ( x, t, i )]

eqn 11

For computational reasons, x, t and i must be whole numbers. In the dynamic program we therefore adopted a whole number unit of energy that was equivalent to 321 kJ.




With the dynamic programming equations presented above, a matrix can be compiled containing the optimal behavioural decisions for all combinations of fuel stores, time and site. We constructed such matrices for a range of environmental conditions that differed in the level of scaring at the two Norwegian staging sites in Trøndelag and Vesterålen. We assumed that scaring might impinge on the metabolizable energy intake rates at the two sites and the perceived predation risk. Depending on the level of scaring (0 –0·9), we assumed the realized metabolizable energy intake rate at the two sites to vary between 100% and 10% of the nominal metabolizable energy intake rate. The predation risk

coefficient, mβ, was set to vary between the nominal 10−8 under situations without scaring and the high 10−6 under heavy scaring. In a forward simulation the decision matrix was used to follow the fate of individual birds during their migratory journey. Starting in Denmark at t = 80 (21 March), female pink-footed geese with a state x of 15 315 kJ (i.e. the average calculated body condition of female pink-footed geese in Denmark between days 83 and 87; SD = 4 704, n = 284) were modelled through time using a particular decision matrix, until the bird either died, arrived at the breeding grounds on Svalbard or passed the end of the decision matrix at time point t = 150 at any other site. Using different initial states drawn from the observed body condition distribution of female pink-footed geese in Denmark at the start of the forward simulations had little impact on the modelling outcome. All individuals rapidly converged to a common trajectory before leaving Denmark. The forward simulation could be conducted using decision matrices that were optimal for the environmental conditions that the bird could meet. Thus the bird was assumed to have full knowledge of its environment. Alternatively, in the forward simulation, suboptimal decision matrices could be used that were constructed using dated environmental information. Both approaches were used to study the effect of scaring on migratory behaviour, first, to calculate the expected fitness consequences and, secondly, to assess the predicted timing of arrival at, and the duration of stay on, the various sites along the migration route of Svalbard pink-footed geese. In modelling the effect of scaring via the metabolizable energy intake rate function, we used decision matrices that were constructed both with and without this information, thus mimicking the behaviour of omniscient and naive individuals during the forward simulation, respectively. In modelling the effect of scaring via perceived predation risk we used decision matrices constructed at levels of predation risk varying between the high and nominal values but maintaining the default nominal predation risk in the forward simulation. Thus three different scenarios were tested, with two scenarios for omniscient geese, where metabolizable energy intake rate decreased as a result of scaring but perceived predation risk remained unchanged or increased with level of scaring. For naive geese only the effect of a sudden confrontation with reduced metabolizable energy intake rate as a result of scaring was tested.

Results The expected fitness, in terms of young produced at Svalbard, now and in future years, for female pinkfooted geese was 2·6 in the non-scaring situation. Naive geese appeared to be much more susceptible to scaring than omniscient geese. At scaring intensities higher than 0·2 (and thus a decrease in the potential metabolizable energy intake rate of 20%) in Trøndelag and 0·5


Fig. 2. Contour plots of the expected reproductive success for Svalbard breeding pink-footed geese as a function of varying scaring levels at goose staging sites in Trøndelag and Vesterålen. Data are provided for two different scenarios: omniscient geese not perceiving an increased predation pressure but only a decreased metabolizable energy intake rate as a result of scaring, and naive geese being confronted unexpectedly with decreased metabolizable energy intake rates as a result of scaring. To avoid population decline an average fitness value of at least two is required.

Fig. 3. Contour plots of the expected duration of stay in pink-footed geese in Denmark, Trøndelag and Vesterålen as a function of varying scaring levels at goose staging sites in Trøndelag and Vesterålen. Data are provided for three different scenarios: omniscient geese not perceiving an increased predation pressure but only a decreased metabolizable energy intake rate as a result of scaring (top panel), naive geese suddenly being confronted with a decreased metabolizable energy intake rate as a result of scaring (middle panel) and omniscient geese suffering from both an increased perceived predation pressure and a decreased metabolizable energy intake rate as a result of scaring (lower panel).


in Vesterålen, the geese were expected to succumb and die. In contrast, although unable to breed, omniscient geese were able to survive combined scaring levels as high as 0·9 in Trøndelag and Vesterålen. Irrespective of the scaring conditions in Trøndelag, the omniscient geese were able to maintain a high expected breeding success up to a scaring level of 0·4 in Vesterålen (Fig. 2). Simulations with increased levels of perceived predation had only marginal effects on these results.

After 20 March, the predicted durations of stay in the unscared situation were 28, 18 and 15 days for Denmark, Trøndelag and Vesterålen, respectively. As expected for omniscient geese, which anticipated the changed situation along the migratory route, staging in Denmark increased up to 50 days with a deterioration in the foraging conditions in Norway (Fig. 3). The duration of stay in Denmark increased most markedly with an increase in scaring in Trøndelag, with only


scaring levels higher than 0·4 in Vesterålen leading to noticeable effects on staging duration in Denmark. The staging duration in Trøndelag was predicted to increase up to 30 days at maximum scaring levels in Vesterålen. In contrast, scaring in Trøndelag itself resulted in a decrease in staging duration, the site being avoided altogether at scaring levels above 0·6. Staging duration in Vesterålen slightly increased with deteriorating conditions in Trøndelag, and decreased at scaring levels higher than 0·5 in Vesterålen itself. Vesterålen was predicted to be avoided altogether at local scaring levels higher than 0·7. For naive geese confronted with deteriorated conditions while on migration, the predicted behavioural reactions were more marked than for the omniscient geese. As one would expect for naive birds unaware of the conditions further down the migratory flyway, duration of stay in Denmark was unaffected by scaring conditions in Norway. Similarly for Trøndelag, staging duration was not affected by changing conditions in Vesterålen. The geese initially increased their staging duration with local scaring levels in order to try and obtain the required fuel stores to make it to Vesterålen, until they could not longer cope with the situation at scaring levels over 0·2 and died. Similarly in Vesterålen, geese initially increased their duration of stay up to scaring levels of 0·4, but at higher scaring levels the birds died. Combined scaring in Vesterålen and Trøndelag worsened the prospects for the geese. For omniscient geese that not only saw their intake rates reduced as a consequence of scaring but also perceived this scaring as an increased predation risk, the predictions were very similar to those for geese that only experienced a decreased intake rate.

Discussion


The outcomes of the modelling exercises were highly dependent on whether or not the geese could adapt to a changed world; in other words, whether they were omniscient or naive (Weber, Houston & Ens 1999). Whereas naive pink-footed geese were dramatically affected by moderate scaring levels of 0·2 in Trøndelag or 0·5 in Vesterålen, omniscient individuals were expected to adapt to the situation and even attain unaffected high fitness levels. Geese are able to acquire new behavioural patterns during their lifetime, learn from previous experience and alter their behaviour in an adaptive fashion. This is exemplified by the discovery by pink-footed geese of the coast of Flanders in Belgium and their use of the site as a major staging area during winter since the early 1960s (Meire & Kuijken 1991), their discovery and rapidly expanding use of Trøndelag since the late 1980s (Madsen et al. 1999) and their rapid northerly winter range expansion as a result of increased use of winter cereal crops in Denmark (Therkildsen & Madsen 2000; Fox et al. 2005). Although capable of acquiring new behavioural patterns, geese are also known for their traditional ways.

Our modelling approach revealed that changing patterns of resource use and exploration of new grounds of which a bird has little knowledge bears great risks; the model shows naive geese to have much lower fitness than experienced geese that are able to anticipate and adapt to changes in their environment. Perceived predation pressure seemed to have only marginal effects on the model outcomes. This raises the question of what the sensitivity of the model is to changes in other parameters, such as predation risk and intake rate as a function of scaring and the robustness of the model’s predictions. To address that question we conducted a sensitivity or elasticity analysis in which we changed other model parameter values by 10%. We compared the simulations that used altered parameters to those of the original parameter set by tracking the changes in staging times on the stop-over sites. The most profound influence on staging resulted from varying the intake functions gi(t), i.e. timing and food quantity, and energy expenditure on the sites. Nevertheless, in the majority of cases the sensitivity was small (< 5% changes in staging times with a 10% change in the parameter value), with the notable exception of the timing of food availability (changes of up to 25% in staging times). Besides highlighting the potential importance of learning, our modelling exercise also highlighted the potential vulnerability of the geese to abrupt environmental change. For example, a sudden major deterioration of the staging conditions in Trøndelag or Vesterålen would result in a catastrophe for pinkfooted geese. In contrast, changes over a longer time period would possibly allow the geese to learn and to adapt to the new situation. There are some suitable staging sites currently not used by the geese that may buffer some of the adverse impacts of scaring. North of Trøndelag, such areas appear to be few and only scattered flocks of geese occur outside the known staging sites (I. Tombre, unpublished data). In southern Norway, pink-footed geese have started to occur in a few new sites since the late 1990s, which may be a response to the deterioration of staging opportunities further north or it may have been caused by a combination of the slight population increase (from 33 000 in 1991 to 45 000 in 2003; Ganter & Madsen 2001; Fox et al. 2005) and global warming, which has advanced spring by 2–3 weeks within the last two decades (I. Tombre, K. A. Høgda, J. Madsen, L. Griffin, E. Kuijken, P. Shimmings, & C. Verscheure, unpublished data). However, it is questionable whether alternative sites will become established, as farmers are expected to start a scaring campaign to avoid agricultural damage. Our modelling also underscores the interdependence of site use along the migratory flyway, as anticipated by Weber, Houston & Ens (1999). Notably in omniscient, experienced birds, site use was highly correlated because environmental changes at a specific site could result in changes in staging durations at both the



preceding and following sites. In contrast, in naive birds only the use of following sites was affected by environmental change at a specific site. The model supports the necessity for an integrated, flyway management approach, rather than independent regional efforts, when targeting the conservation of migratory birds. In addition, our modelling exercise emphasized the effect of regional farming or hunting policies that depend on policies implemented elsewhere along the migratory route. Models are excellent tools for developing new hypotheses and guiding research. However, it is crucial that model predictions are subjected to testing in the natural environment. Although not intended as a scientific experiment, some of the predictions of our modelling exercise are currently being tested. Farmers in Vesterålen have started scaring campaigns targeted at pink-footed geese that compete with the interests of local sheep farming. Preliminary data show that the new scaring regime in Vesterålen has led to geese staging in Vesterålen departing to the breeding ground in poorer condition than previously and with a subsequent reduced breeding outcome (J. Madsen & M. Klaassen, unpublished data). Furthermore, an increasing proportion of the population now stays in Trøndelag and avoids Vesterålen. However, due to poorer foraging conditions in Trøndelag these birds probably end up on the breeding grounds in poorer conditions. Hence on a qualitative basis there is good correspondence between the predictions from the model and the empirical evidence. In terms of fitness consequences and population dynamics, we are now seeing the signs of decreasing annual breeding success and decreased summer survival as a result of poor body condition when the geese depart from the spring staging areas (J. Madsen & M. Klaassen, unpublished data). The modelling exercise presented here and the empirical data gathered since the implementation of scaring regimes on Norwegian staging sites thus suggest that management measures taken in the wintering and staging areas can have wide implications for this goose population. The recent introduction of a spring conservation hunt in Quebec, Canada, to stop the population growth in greater snow geese Anser caerulescens atlanticus provides a parallel case of a management scheme (although only indirectly targeted to protect agricultural crops) that also causes adverse disturbance to the geese at the ultimate spring staging grounds. The hunting disturbance has been shown to affect the habitat and site use by geese, their daily energetics and nutrient storage (Féret et al. 2003; Béchet et al. 2003, 2004) and, possibly, reproductive success (Mainguy et al. 2002).

  In Norway, the future management of the spring conflict between farming interests and geese is currently being discussed. Farmers in Vesterålen have announced that

they will expand the scaring campaign if a long-term solution, including a compensation scheme, cannot be reached. Farmers in Trøndelag started a scaring campaign in the spring of 2005 and will continue this if they fail to come to a permanent settlement for compensation with local and national authorities. The dynamic model predicts that if scaring on a large scale is implemented abruptly, it will have severe consequences for the population because the geese will not have had time to adjust their behaviour to their dramatically changed environment; geese subject to scaring in Trøndelag will leave earlier to travel northwards only to encounter even worse than expected conditions in Vesterålen. For the geese Vesterålen is a crucial stop-over site. If a future scaring campaign in Trøndelag cannot be avoided, it is of great importance that the scaring intensity and its geographical extent in Vesterålen is managed to avoid serious consequences for the population. It is recommended that a national or, preferably, international management plan is designed and implemented that acknowledges the interdependence of the sites along the flyway and their importance to pink-footed geese. If scaring continues to be used as a tool to minimize goose use in certain areas, geese should be allowed to gradually develop knowledge of where they are welcome and where they are not.

Acknowledgements The long-term marking–resighting project was partly funded by the Danish Research Councils, the Danish Ministry of Environment, the Norwegian Directorate for Nature Management and the Norwegian Research Councils (LANDRING programme). The analysis was carried out under the EU 5th Framework project FRAGILE (EVK2-2001–00235). We are deeply indebted to the many colleagues who took part in the data collection. Åke Lindström is thanked for his stimulating comments on an earlier version of the manuscript. This is publication 3638 of the Netherlands Institute of Ecology (NIOO-KNAW) and 406 of the Centre for Wetland Ecology.

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