AbstractSecuring the long-term acceptance of large carnivores such as the wolf (Canis lupus) in Europe and North America raises a difficult challenge to conservation biologists: planning removals to reduce depredations on livestock while ensuring population viability. We use stochastic-stage-structured population models to investigate wolf population dynamics and to assess alternative management strategies. Among the various management strategies advocated by agencies, zoning that involves eliminating wolves outside a restricted area should be designed with caution, because probabilities of extinction are extremely sensitive to the maximum number of packs that a zone can support and to slight changes in stage specific survival probabilities. In a zoned population, viability is enhanced more by decreasing mortality rates in all classes than by increasing wolf zone size. An alternative to zoning is adaptive management, where there is no limit on pack number but population control can be operated whenever some predefined demographic conditions are met. It turns out that an adaptive management strategy that removes a moderate percentage (10%) of the population following each year of more than 5% of total population growth would provide visible actions addressing public concerns while keeping extinction probability low.
Introduction
Conflicts with human populations remain the major threat to carnivore persistence. Carnivore may kill domestic and game animals as well as threaten humans and, as a consequence, many carnivore species have been facing widespread persecution. Protected
areas often offer insufficient protection, as they may be too small to encompass full home ranges, and substantial mortality is caused by contact with people at reserve borders. Large carnivore conservation may be successful in the long term only if people can accept free ranging predators in their area. This can be achieved if adequate conservation strategies maintaining viable populations while allowing removal of individuals are implemented.
Focusing on the recent expansion of the wolf Canis lupus in Western Europe, this paper addresses thedual nature of the conservation problem raised by large carnivores: regulating the population to address public concern, while maximizing population viability. [...]
Conflicts with human populations remain the major threat to carnivore persistence. Carnivore may kill domestic and game animals as well as threaten humans and, as a consequence, many carnivore species have been facing widespread persecution. Protected
areas often offer insufficient protection, as they may be too small to encompass full home ranges, and substantial mortality is caused by contact with people at reserve borders. Large carnivore conservation may be successful in the long term only if people can accept free ranging predators in their area. This can be achieved if adequate conservation strategies maintaining viable populations while allowing removal of individuals are implemented.
Focusing on the recent expansion of the wolf Canis lupus in Western Europe, this paper addresses thedual nature of the conservation problem raised by large carnivores: regulating the population to address public concern, while maximizing population viability. [...]
[...] In this paper, we develop stochastic models to study the dynamics of a recolonizing wolf population and compare several management strategies, with an emphasis on the wolf population in the Western Alps. We model a zoning strategy where wolves are tolerated in a restricted area only, and an adaptive management strategy where some wolves are removed whenever the annual population growth rate reaches a given threshold. We examine whether such strategies would make it possible to maintain a viable population while allowing for population control to minimize depredation on livestock. [...]
Methods
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| Pack of wolves with Alpha chief (first) |
The unit of a wolf population is the pack, consisting of a breeding pair and their offspring (from one or more generations). The dominant adult female in each pack breeds every year, usually producing a single litter. Subordinates rarely become dominant in their natal pack. Pups reach their adult size by winter, and most of them disperse as yearlings. A dispersing wolf may colonize a vacant territory, or it may join another pack and replace a missing breeding member. When both breeding adults die, the pack usually disintegrates, leaving the territory vacant and creating an opportunity for recolonization.Wolves are not habitat-specific and can live wherever they have sufficient food resources and are tolerated by humans. There is no simple relationship between human density and wolf persistence in a given area. [...]
Life cycle modelling
All our population projections involve the same sequence of events. (i) Winter mortality affects the whole population and accounts for annual mortality. (ii) Dispersal of subordinates is conditional to the survival of the breeding pair: if the breeding pair disappears (both partners die), remaining pack members disperse, but if at least one breeder survives, subordinates disperse with some probability specific to their class. (iii) Dispersing wolves search for a vacant territory and a partner. We neglect the probability that a dispersing wolf joins an extant pack where no breeder is missing. (iv) Reproduction takes place in spring if
a breeding pair is present. Age at first reproduction is always 22 months (dispersing juveniles must wait one year before looking for a mate). Only one litter is produced per year. (v) Pup mortality takes place in summer and accounts for infectious diseases that are often deadly for pups. In autumn, the distributon of wolves in the population is censused and then updated according to the following scheme (see also Fig. 1).
All our population projections involve the same sequence of events. (i) Winter mortality affects the whole population and accounts for annual mortality. (ii) Dispersal of subordinates is conditional to the survival of the breeding pair: if the breeding pair disappears (both partners die), remaining pack members disperse, but if at least one breeder survives, subordinates disperse with some probability specific to their class. (iii) Dispersing wolves search for a vacant territory and a partner. We neglect the probability that a dispersing wolf joins an extant pack where no breeder is missing. (iv) Reproduction takes place in spring if
a breeding pair is present. Age at first reproduction is always 22 months (dispersing juveniles must wait one year before looking for a mate). Only one litter is produced per year. (v) Pup mortality takes place in summer and accounts for infectious diseases that are often deadly for pups. In autumn, the distributon of wolves in the population is censused and then updated according to the following scheme (see also Fig. 1).
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| Fig. 1. Life cycle graph for a wolf stage-structured population. P: Pups, J: Juveniles, S: Subadults, Di: Dispersers, A: Adults, Do: Dominants. All stages are in packs except dispersers. See text for arrow details. |
2. Surviving juveniles that have not dispersed become subadults.
3. Surviving juveniles that have dispersed become dispersing wolves.
4. Surviving subadults that have not dispersed become adults.
5. Surviving subadults that have dispersed and found a territory and a mate become pack breeders.
6. Surviving adults that have not dispersed remain in the adult class.
7. Surviving adults that have dispersed and found a territory and a mate become pack breeders.
8. Dispersing wolves that found a territory and a mate become pack breeders.
9. Surviving pack breeders keep the same status.
10. Surviving pack breeders give birth to pups. [...]
Spatial structure
Subordinate dispersal probabilities are not equal between packs as they depend upon the actual survival of pack leaders. Therefore, our model describes explicitly the spatial arrangement of territories and individual movements between them. We assume that any territory may be either empty, or occupied by one pack. We fix the environment carrying capacity to K = 20 territories. One pack can occupy only one territory and this induces a ceiling-type density dependence on pack numbers. [...]
Stochastic simulations
Reproduction occurs in territories containing a male and a female breeder. Survival and fecundity are treated as binomial and Poisson variates, respectively. Our Monte Carlo simulations involve 250 runs each. We tested on a restricted number of simulations that a higher number of runs did not change result precision. A population qualifies as extinct once all classes are empty. [...]
Discussion
Our analysis shows that a wolf population has a high potential growth rate under favourable ecological conditions, but can decline dramatically in response to reduced survival. The wolf is a species sensitive to high killing rates, as exemplified by its eradication from many areas, in contrast with smaller, more versatile species such as the red fox (Vulpes vulpes). However, the wolf shows a strong ability for recolonization once persecutions are stopped. Maximum annual growth rates obtained from field studies can reach 43%. [...]
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| Fig. 3a. Extinction probabilities as a function of changes of parameters under median scenario S2. |
[...] Our study is aimed at identifying management strategies to help maintaining a viable population while allowing for population control to reduce depredations on livestock. One important conclusion is that viability thresholds under a zoning strategy are extremely sensitive to the number of packs and to slight changes in demographic parameters. In particular, population viability critically relies on securing a sufficient number of packs in the wolf zone while keeping their mortality rates as low as possible. As a consequence, for a population under or at its zoned viability threshold, the removal of wolves should be firmly discouraged. Although zoning strategies are likely to be implemented in the future for wolves in western Europe, such strategies should be considered carefully and implemented only when enough demographic data are available, so that reliable estimates of population size and parameters are available. [...]
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| Fig. 4. Extinction probabilities as a function of initial population size (in packs). Carrying capacity is 20 packs. |
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| Fig. 5. Extinction probabilities calculated when population (in packs) is prevented for exceeding its initial size. This corresponds to a zoning management strategy. |
[...] Our study has broader implications for the management of social carnivores. An important conclusion of our model is that the difference between a viable and unviable population takes place over a short parameter space (Figs. 4 and 5). [...]
[...] One more general conclusion of our modelling exercise is that the control of population or the design of reserves, particularly for social species where the parameter space between viability and decline is reduced, requires the development of species-specific demographic models. Indeed, the social structure of the species needs to be fully explicit in the life cycle graph. This can be achieved by incorporating classes of social status rather than age classes, with probabilistic transitions between them.
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