It is good practice to evaluate the returns and effectiveness of investments, whether the investment be on the stock exchange or in managing threats to native flora and fauna. While it’s easy to keep track of the performance of investments on the stock exchange using an indicator such as the Dow Jones index, monitoring the outcomes of management actions for conservation is not so straightforward.
Selecting an appropriate indicator for evaluating the success of conservation management for a suite of species or whole biological system can be a considerable challenge. Despite the widespread use of indicators in conservation and natural resource management, their selection is often based on methodology that fails to consider the relative costs, benefits and uncertainty associated with each candidate indicator.
Part of my PhD research has been focussed on the process of selecting indicator species, which we have recently published in Biological Conservation (co-authored by myself, Kerrie Wilson and Hugh Possingham). We focus on indicator selection for invasive fox control in south-west Western Australia, although our approaches are applicable to other threatening processes and taxonomic groups.
Fox baiting now occurs over 35,000km2 of the Western Australian biodiversity hotspot that supports 177 reptile, 280 bird and 59 mammal species. Clearly the cost of monitoring every one of these species to evaluate the impact of fox control would be prohibitive, which means that prioritisation is a crucial step in making a wise and cost-effective selection of an indicator species.
Apart from considering the varying costs of monitoring each species, the relative ease of detecting a response to fox management is also important. Since many species in south-western Australia have suffered drastic declines in numbers, they are often highly cryptic or rare. Although research has demonstrated that reducing fox numbers can have a beneficial effect on a wide range of native mammals, there are still cases where their responses to fox control can be inconsistent, and not all species respond positively (Dexter and Murray 2009; Orell 2004; Banks 1999). We compared two different methods to select an indicator species: a qualitative ‘scoring’ approach by experts and a quantitative ‘cost-effectiveness’ metric. Both approaches account for the relative value of each species as an indicator, albeit in very different ways.
The scoring approach was easy to use and explain. We had experts evaluate the relative utility of 12 candidate species using 17 criteria (e.g. abundance in the landscape, public awareness of species, cost of monitoring). When we ignored the costs of monitoring, we found that the top ranking indicator was the woylie (Bettongia penicillata), an endangered ground-dwelling bettong that is rare in the landscape but shows a highly positive responsive to fox baiting. When costs were accounted for, the indicator rankings changed so that the top-ranked species was the western brushtail possum (Trichosurus vulpecula) – a relatively common medium-sized mammal, which can be monitored for a much lower cost than the woylie.
Some downsides of the scoring approach are that value judgements and scorer bias can influence the results and there are problems associated with aggregating unlike units, which can obscure the relative importance of individual criteria (Wolman 2006). To overcome these limitations, we developed a more objective approach to indicator selection that explicitly incorporates quantitative data on the cost of monitoring each species, how representative each species is of the other species, and the likelihood that the indicator will respond to management. The most cost-effective indicator selected by this quantitative metric was the western brushtail possum, the same species selected by the scoring approach that accounted for the costs of monitoring.
If we had stopped here we might have concluded that the choice of approach to selecting an indicator species is more or less irrelevant as long as costs are sensibly incorporated…but we find that in some circumstances it can be important. For example, we also investigated different scenarios of information availability and uncertainty in underlying data. The scoring approach was not robust to minor data adjustments and the choice of indicator species using this approach was ambiguous once we accounted for certainty of the responses from each expert. The quantitative metric was comparatively robust to minor data adjustments, and enabled evaluation of data uncertainty in a transparent way, giving managers a more objective and repeatable method to selecting the most cost-effective and informative indicator.
More info: Ayesha Tulloch email@example.com