Conservation of endangered species - Ecological Applications: Toward a Sustainable World - page 126

This result provides a very useful reminder that understanding the details of indi-
vidual behavior can make a difference to conservation management.

5.4.4 How good is
your population
viability analysis?

In an ideal world a population viability analysis would enable us to produce a spe-
cifi c and reliable recommendation for an endangered species of the population size
that would permit persistence for a given period with a given level of probability.
Conservation biologists know this is not possible in practice, but it is important to
know how good our predictions are likely to be. Brook et al. (2000) addressed this
by conducting retrospective tests using 21 long-term ecological studies (involving
birds, mammals, a reptile and a fi sh). The demographic parameters for simulation
modeling were estimated on the basis of the fi rst half of each data set, while the
second half was used to test the accuracy of predictions. Brook's team scrutinized
fi ve commonly used population simulation packages, each with slightly different
model structures and underlying assumptions, some of which you have come across
in this chapter. The results provide comfort to managers because they show a close
relationship between model predictions (whichever package was used) and the his-
torical behavior of the 21 populations (Figure 5.8).
On the other hand, we shouldn't be lured into a false sense of security - no model
is better than the data upon which it is based. Within the inevitable constraints of
lack of knowledge and lack of time and opportunity to gather data, the model-build-
ing exercise is no more nor less than a rationalization of the problem and a careful
quantifi cation of ideas. Common sense tells us to trust the results only in a qualita-
tive fashion. Nevertheless, the examples I have discussed show how models can be
constructed to make the very best use of available data and provide the confi dence
to choose between various possible management options and to identify the relative
importance of factors that put a population at risk (Reed et al., 2003). The sorts of
management interventions that may then be recommended include translocating
individuals to augment target populations, restricting unwanted dispersal by fencing,
restoring habitat, creating larger reserves, raising carrying capacity by augmenting
resources, providing foster care for young, reducing mortality by controlling preda-
tors or poachers, and vaccinating against infl uential pathogens.
The most widely recognized system for ranking species at risk is the IUCN 'Red
List' of the World Conservation Union (Box 1.1). (The puzzling acronym IUCN arises
because the World Conservation Union was once called the International Union for

Fig. 5.8 Plots of the
proportion of popula-
tions predicted to
decline below a critical
threshold (an index of
extinction risk) versus
the actual number of
populations declining
below that threshold.
For each of the fi ve
population viability
analysis (PVA) software
packages, a perfect fi t
with reality lies on the
45 degree line shown.
You have come across
two of these packages
already in this chapter:
VORTEX in Section 5.4.1
and RAMAS - STAGE in
Section 5.4.2. (After
Brook et al., 2000, who
give details of the other
software packages.)

1.0

GAPPS INMAT RAMAS-

METAPOP

0.5

0

0 0.5 1.0 0 0.5 1.0 0 0.5 1.0

RAMAS-
STAGE

VORTEX

1.0

0.5

0

0 0.5 1.0 0 0.5 1.0
PVA-predicted probability of decline