Microsoft PowerPoint Portada taller Jaguares

processes constitutes a model, even if it is a mental model within the mind of the 

expert and perhaps only vaguely specified to others (or even to the expert himself 

or herself). 

 

A number of properties of the problem of assessing vulnerability of a population to 

extinction make it difficult to rely on mental or intuitive models. Numerous 

processes impact population dynamics, and many of the factors interact in complex 

ways. For example, increased fragmentation of habitat can make it more difficult to 

locate mates, can lead to greater mortality as individuals disperse greater distances 

across unsuitable habitat, and can lead to increased inbreeding which in turn can 

further reduce ability to attract mates and to survive. In addition, many of the 

processes impacting population dynamics are intrinsically probabilistic, with a 

random component. Sex determination, disease, predation, mate acquisition — 

indeed, almost all events in the life of an individual — are stochastic events, 

occurring with certain probabilities rather than with absolute certainty at any given 

time. The consequences of factors influencing population dynamics are often 

delayed for years or even generations. With a long-lived species, a population 

might persist for 20 to 40 years beyond the emergence of factors that ultimately 

cause extinction. Humans can synthesize mentally only a few factors at a time, 

most people have difficulty assessing probabilities intuitively, and it is difficult to 

consider delayed effects. Moreover, the data needed for models of population 

dynamics are often very uncertain. Optimal decision-making when data are 

uncertain is difficult, as it involves correct assessment of probabilities that the true 

values fall within certain ranges, adding yet another probabilistic or chance 

component to the evaluation of the situation. 

The difficulty of incorporating multiple, interacting, probabilistic processes into a 

model that can utilize uncertain data has prevented (to date) development of 

analytical models (mathematical equations developed from theory) which 

encompass more than a small subset of the processes known to affect wildlife 

population dynamics. It is possible that the mental models of some biologists are 

sufficiently complex to predict accurately population vulnerabilities to extinction 

under a range of conditions, but it is not possible to assess objectively the precision 

of such intuitive assessments, and it is difficult to transfer that knowledge to others 

who need also to evaluate the situation. Computer simulation models have 

increasingly been used to assist in PVA. Although rarely as elegant as models 

framed in analytical equations, computer simulation models can be well suited for 

the complex task of evaluating risks of extinction. Simulation models can include 

as many factors that influence population dynamics as the modeler and the user of 

the model want to assess. Interactions between processes can be modeled, if the 

nature of those interactions can be specified. Probabilistic events can be easily 

simulated by computer programs, providing output that gives both the mean 

expected result and the range or distribution of possible outcomes. In theory, 

simulation programs can be used to build models of population dynamics that 

include all the knowledge of the system which is available to experts. In practice, 

the models will be simpler, because some factors are judged unlikely to be 

important, and because the persons who developed the model did not have access 

to the full array of expert knowledge. 

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Although computer simulation models can be complex and confusing, they are 

precisely defined and all the assumptions and algorithms can be examined. 

Therefore, the models are objective, testable, and open to challenge and 

improvement. PVA models allow use offal available data on the biology of the 

taxon, facilitate testing of the effects of unknown or uncertain data, and expedite 

the comparison of the likely results of various possible management options. 

PVA models also have weaknesses and limitations. A model of the population 

dynamics does not define the goals for conservation planning. Goals, in terms of 

population growth, probability of persistence, number of extant populations, 

genetic diversity, or other measures of population performance must be defined by 

the management authorities before the results of population modeling can be used. 

Because the models incorporate many factors, the number of possibilities to test 

can seem endless, and it can be difficult to determine which of the factors that were 

analyzed are most important to the population dynamics. PVA models are 

necessarily incomplete. We can model only those factors which we understand and 

for which we can specify the parameters. Therefore, it is important to realize that 

the models probably underestimate the threats facing the population. Finally, the 

models are used to predict the long-term effects of the processes presently acting 

on the population. Many aspects of the situation could change radically within the 

time span that is modeled. Therefore, it is important to reassess the data and model 

results periodically, with changes made to the conservation programs as needed. 

The 

VORTEX 

Population Viability Analysis Model 

For the analyses presented here, the 

VORTEX 

computer software (Lacy 1993a) for 

population viability analysis was used. 

VORTEX 

models demographic stochastic (the 

randomness of reproduction and deaths among individuals in a population), 

environmental variation in the annual birth and death rates, the impacts of sporadic 

catastrophes, and the effects of inbreeding in small populations. 

VORTEX 

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allows analysis of the effects of losses or gains in habitat, harvest or 

supplementation of populations, and movement of individuals among local 

populations. 

Density dependence in mortality is modeled by specifying a carrying capacity of 

the habitat. When the population size exceeds the carrying capacity, additional 

morality is imposed across all age classes to bring the population back down to the 

carrying capacity. The carrying capacity can be specified to change linearly over 

time, to model losses or gains in the amount or quality of habitat. Density 

dependence in reproduction is modeled by specifying the proportion of adult 

females breeding each year as a function of the population size. 

VORTEX 

models loss of genetic variation in populations, by simulating the 

transmission of alleles from parents to offspring at a hypothetical genetic locus. 

Each animal at the start of the simulation is assigned two unique alleles at the 

locus. During the simulation, 

VORTEX 

monitors how many of the original alleles 

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