In the sections below, we first describe the structure and parameters
of the wolf population model and then describe its application to the
management questions.
2.3.1 A Gray Wolf Population Model
We developed a demographic, stage-structured, stochastic simulation model
of wolf dynamics. The model was designed to simulate a wolf population
living in a human-dominated landscape with abundant, well-distributed
prey. The landscape was bounded by the assumption that it could support
a maximum of 64 pack territories. Each territory was classified based on
the dominant land use (e.g., agriculture or wilderness). The number of
territories and the land-use classifications varied with the objectives of the
application.
To simulate wolf life history, we created a stage-class model for the
dynamics of each pack. The model used stochastic difference equations with
a 1-year time step to simulate the mortality, dispersal, and birth of wolves
and the fate of dispersing wolves. Detailed lists of model assumptions and
demographic parameter values are given in specific applications in Haight
and Mech (1997), Haight et al. (1998), and Cochrane (2000). For illustration, we describe the parameter values used to predict the performance of
alternative wolf removal strategies for population size control (see Section
2.3.6). These parameter values represent 5- to 10-year averages of observations in north central Minnesota (Fuller 1989) and Wisconsin (Wydeven
et al. 1995).
Each pack was characterized by the number of wolves of each sex in each
of four stages, which were defined based on age and breeding status. Three
age classes for nonbreeding wolves were pup (0 to 12 months), yearling
(12 to 24 months), and adult (>24 months). The fourth stage was defined
for the breeding pair, each of which must be at least 12 months old by
the first of May. Because breeding was assumed to take place in March, the
minimum breeding age was 22 months.
The annual cycle of events (Figure 2.2) began in autumn with the tally
of population attributes, including population size and the number of
packs. The first demographic event was mortality in autumn and winter,
which represented losses from natural and human (accidental and illegal)
causes. The number of wolves that died in each life-history stage was
a binomial random variable with a mean that depended on wolf age.
Pups were subject to a 65% mortality rate, while yearlings and adults had
a 32% mortality rate. In other applications, the age-dependent mortality rates varied from pack to pack, depending on the land-use class (e.g.,
adult mortality rates were lower in packs in wilderness areas compared
with packs in agricultural areas because there was less human-caused
mortality).
28
Jean Fitts Cochrane et al.
of the wolf population model and then describe its application to the
management questions.
2.3.1 A Gray Wolf Population Model
We developed a demographic, stage-structured, stochastic simulation model
of wolf dynamics. The model was designed to simulate a wolf population
living in a human-dominated landscape with abundant, well-distributed
prey. The landscape was bounded by the assumption that it could support
a maximum of 64 pack territories. Each territory was classified based on
the dominant land use (e.g., agriculture or wilderness). The number of
territories and the land-use classifications varied with the objectives of the
application.
To simulate wolf life history, we created a stage-class model for the
dynamics of each pack. The model used stochastic difference equations with
a 1-year time step to simulate the mortality, dispersal, and birth of wolves
and the fate of dispersing wolves. Detailed lists of model assumptions and
demographic parameter values are given in specific applications in Haight
and Mech (1997), Haight et al. (1998), and Cochrane (2000). For illustration, we describe the parameter values used to predict the performance of
alternative wolf removal strategies for population size control (see Section
2.3.6). These parameter values represent 5- to 10-year averages of observations in north central Minnesota (Fuller 1989) and Wisconsin (Wydeven
et al. 1995).
Each pack was characterized by the number of wolves of each sex in each
of four stages, which were defined based on age and breeding status. Three
age classes for nonbreeding wolves were pup (0 to 12 months), yearling
(12 to 24 months), and adult (>24 months). The fourth stage was defined
for the breeding pair, each of which must be at least 12 months old by
the first of May. Because breeding was assumed to take place in March, the
minimum breeding age was 22 months.
The annual cycle of events (Figure 2.2) began in autumn with the tally
of population attributes, including population size and the number of
packs. The first demographic event was mortality in autumn and winter,
which represented losses from natural and human (accidental and illegal)
causes. The number of wolves that died in each life-history stage was
a binomial random variable with a mean that depended on wolf age.
Pups were subject to a 65% mortality rate, while yearlings and adults had
a 32% mortality rate. In other applications, the age-dependent mortality rates varied from pack to pack, depending on the land-use class (e.g.,
adult mortality rates were lower in packs in wilderness areas compared
with packs in agricultural areas because there was less human-caused
mortality).
28
Jean Fitts Cochrane et al.