Environmental Engineering Reference
In-Depth Information
4
There are two fundamental approaches to assessing sustainability. First, we can
assess past and current sustainability through statistical analysis of our datasets and
second, we can use this understanding to predict the effects of future changes.
Predictions require the use of mechanistic models, which capture our understand-
ing of the important processes that drive system dynamics. In this chapter, we dis-
cuss the way in which we can use data to derive sustainability indicators, and in
Chapter 5, we develop models for predicting future sustainability, including the
effects of conservation interventions.
There are four main areas for which sustainability indicators can be developed:
the biological processes (e.g. species abundance); the interaction between hunters
and prey (e.g. offtake rates); the hunter household (e.g. profitability); the social
setting (e.g. consumer preferences). These then map onto the facets of sustainability
discussed in Chapter 1—biological, social and financial sustainability. We can
translate the data we collect into a statistical model that tells us something about
sustainability in a number of ways:
●
Simple comparisons, for example, with a reference point or between sampling
units.
●
Regression analyses of trends in a variable linked to sustainability.
●
Multivariate analyses of relationships between a set of explanatory variables
(which may include time or spatial location) and a dependent variable linked
to sustainability.
●
Meta-analyses of factors associated with sustainability in a range of studies.
Table 4.1 gives an overview of the kinds of data that could be used to develop sus-
tainability indicators and their pros and cons. We then discuss how to translate
these data into indicators using the four types of modelling approach listed above.
We can make
comparisons between sampling units
, for example, between resource
abundance in different locations, or between consumption rates in different
villages, and ask whether there are statistically significant differences between
them. Suitable statistical tests include an analysis of variance, a
t
-test or a
