Environmental Engineering Reference
In-Depth Information
age or size classes
, as younger classes tend to be more susceptible to competition,
and may be the key point of population regulation (Eberhardt 2002).
There are important pitfalls that you should be aware of in attempting to quan-
tify density dependence. Although density dependence is a universal property of
natural populations, it is surprisingly difficult to measure, and great effort is usu-
ally required to do so. Precisely because of density-dependent regulation, natural
populations may fluctuate rather little, giving us very little information about the
true form of density dependence. Even given data from a good range of abun-
dances, any density responses may be obscured by the population's response to envi-
ronmental variation and by observation error. The effect of population density and
the environment on an individual's vital rates (their probability of survival, growth
and reproduction) can depend on many factors, such as their body size, genetics
and age. There is a growing body of research using high quality, long term individ-
ual-based datasets to tease these influences apart (e.g. Pelletier
et al
. 2007). These
studies show that time-series of population size alone are likely to provide very
esti-
mation
of the form and strength of density dependence. Finally, observation error
is also an important source of bias, tending to result in an over-estimation of the
strength of density dependence, and the correction of this tendency may require
further sampling effort (Box 2.15). These caveats are not meant to put you off
attempting to quantify density dependence entirely; however, it is important to be
aware of them in order to judge what can reasonably be achieved with a given level
of effort. An alternative approach for species which are likely to show non-linear
density dependence is to use a range of more realistic functional forms, chosen
based on literature about similar species (Box 2.15), and then carry out sensitivity
analyses to see what effect your assumptions have on your results (Sections 4.4.1
and 5.3.6).
The physical size of an organism is often an important determinant of its contribu-
tion to population growth. Equally, from a harvester's point of view, size can be an
important determinant of profitability. It may therefore be useful to quantify indi-
vidual
growth patterns
in exploited populations in order to understand both har-
vester behaviour and population responses to harvest. For example, given an
age-structured population model of the kind described in Chapter 5, if we know how
individuals grow with age, we can predict total harvest biomass, which may be a more
sensitive determinant of harvester behaviour than numbers of individuals. Box 2.16
illustrates a commonly used model for quantifying
age-size relationships
.
In some species, particularly those that are long-lived and have continuous
growth potential (for example, many plants, fish and reptiles), it will usually be
much easier to measure size than age. In this case, when it comes to modelling the
harvest, it will probably be more convenient to use
size-structured
, rather than
age-structured, models, and this approach should definitely be used when growth
is not strictly linked to age (for example, plants may shrink as well as grow, Van der
Voort and McGraw 2006). In these models, transition from one class to another is
