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Population viability analysis (PVA) began life as an attempt to
answer the question, ``How large must a population be for it to have a
reasonable chance of survival for a reasonably long period of time?''
A reasonable chance of survival was often taken as 95%.3 A reasonably long period was often taken to be 100
years.4 In its early life population
viability analyses were used in an attempt to identify the smallest
population that would have a reasonable chance of survival for a
reasonable period of time - the minimum viable population size
(MVP). Although, as we'll see, assessing the viability of any
population is far from easy, it's a useful idea because it
crystallizes several other ideas:
- It identifies the population as the critical unit for
conservation purposes.5 Until about
25 years ago conservationists tended to focus only on protecting land,
not on managing the populations of plants and animals that occur
there. Moreover, even with the recent shift in emphasis to
``ecosystem-level'' conservation, we mustn't forget that the species
that make up an ecosystem are found in populations. So management of
some populations may still be necessary.
- The term ``viability'' stresses that we're concerned with
persistence of the population over some reasonably long period of
time. Furthermore, it emphasizes that we're interested in the
prospects that the population can be self-sustaining.
- The idea of ``minimum'' emphasizes the idea that there are
certain critical aspects of the interaction of a species with its
environment. If the population gets too small, it may no longer be
able to cope. There may be a threshold below which a population is
doomed to extinction. That it has survived its current decline is not
evidence that it can suffer a further decline.
There are, however, several problems associated with the idea of minimum viable populations.
- The first is the practical problem that if we identify the
minimum size of a population that is sufficient for conservation
purposes, that's all were likely to get.
- The second, and more fundamental, is that because of the
enormous uncertainties associated with forecasting the fate of
populations,6 we're unlikely to be able to provide
a good estimate of an MVP. Unless MVPs are determined with
a large safety factor, specifying an MVP might actually promote
extinction, rather than retard it.
- Morris and Doak [5, p. 43] go so far as to
argue that ``no good PVA should attempt to evaluate the risk of utter
extinction.'' There are so many things that can go wrong and so many
uncertainties that we're better off focusing on quasi-extinction, the number of individuals below which the
population is likely to be immediately and critically imperiled.
- Perhaps most fundamentally of all, populations of a species may
cease performing the ecological functions they provide well before
they become extinct. Kent Redford [6] points out, for
example, that a botanically intact forest will cease to exist if the
animal communities responsible for pollination and seed dispersal go
extinct or become so rare that pollination and seed dispersal success
declines to a point where it is insufficient to allow their
persistence.7
Today we'll be talking about the principles of population viability
analysis. Over the following two lectures we'll examine two
applications of the principles: the northern spotted owl and the bay
checkerspot butterfly. What we've seen in the last several lectures
reminds us that we'll need to take account of stochastic processes in
these populations. Before we can start talking about including that
stochasticity, however, we have to take a detailed look at the
deterministic models to which we will add probabilistic components.
Today I'll give an overview of age- and stage-structured demographic
models, because they are the most widely used and most widely
applicable.8 There are several reasons for doing this:
- You have to have a good understanding of the basic model before
you can understand what it's like when randomness is added to it.
- Recall that many threats to population persistence are systematic.
Analysis of deterministic a model helps to identify those systematic
threats, especially if they're not immediately obvious.
- Analysis of a deterministic model also allow us to identify the
life-history stages that are most critical in determining individual
abundance so that we can focus management efforts where they are
likely to be most successful.
- Even if a complete PVA is not necessary or possible, knowing
what one would include helps to structure thinking about management
options and leads to the design of management programs that can be
expanded into a full PVA if needed.
Next: Leslie matrix models
Up: Introduction
Previous: Introduction
Kent Holsinger