One big theme in our research is how to improve understanding of biodiversity change through better indicators and tools (e.g. the IUCN Red List of Ecosystems). Today Ben Collen (University College London) and Emily Nicholson had a new paper published, a perspective piece in Science as part of their series on Challenges in Conservation Science (coming after the brilliant and insightful Georgina Mace – no pressure at all).
In it, we argue that to understand how biodiversity is changing, we need reliable indicators that reflect the changes we are interested in. We also need to be able to interpret the changes effectively – what does an increase or decrease mean, and what caused those changes? For example, there are a range of biodiversity targets under the Convention on Biological Diversity (CBD), with recommended indicators to track progress towards each target. Surprisingly, very few of those indicators have been tested for their reliability.
We argue that there are 3 ways in which biodiversity indicators can better serve conservation needs:
- Objectives of conservation targets (such as the CBD targets) and biodiversity indicators need to be explicitly stated and closely aligned, so that the indicators can meaningfully report on progress towards targets;
- Impacts of alternative conservation policies need to be projected forwards to estimate which policies can achieve targets and which indicators can best detect the changes of interest;
- Rigorous testing is needed to evaluate whether indicators are fit for purpose.
In our research we are testing the performance of some of the key biodiversity indicators. One of these is the Living Planet Index (LPI), which was discussed in the media a lot this week (e.g. here and here) with the release of the WWF Living Planet Report . Our work, and that of other researchers, has shown that the Living Planet Index is structurally robust – ecological theory tells us that is has strong links with models of population viability so it is a good indicator of change in extinction risk – but that its performance is affected by the data used to calculate the index. For example, people like birds – both scientific researchers and amateur birdwatchers – so there is a lot of data on bird populations. This is especially pronounced within marine ecosystems where bird data can make up more than half of the data used to calculate the LPI in some marine regions (see our paper and post on it from 2012). This means that the trends shown in the index in those systems will be heavily influenced by what is happening to seabirds rather than across all vertebrate groups.
One of the best ways to test indicators is by using models, where we simulate change in biodiversity and the process of monitoring that change with indicators. We then have a ‘truth’ (our model) as a point of comparison, and can examine how well the indicator reflects the simulated changes. This has been used very effectively within fisheries management to test fisheries indicators (e.g. here, here and here), and we are learning from those processes to improve our understanding of biodiversity indicators. Models can also be used to test which policies can achieve targets, as well as which indicators are best suited to measuring that change.
We need to improve our understanding of how indicators behave with underlying changes in biodiversity, and when data used to measure biodiversity is biased. Biodiversity indicators need to be systematically evaluated in their capacity to report meaningfully on conservation targets and the means of achieving them – this remains a high priority for conservation science if we want to influence conservation progress.
Nicholson, E., Collen, B., Barausse, A., Blanchard, J. L., Costelloe, B. T., Sullivan, K. M. E., Underwood, F. M., Burn, R. W., Fritz, S., Jones, J. P. G., McRae, L., Possingham, H. P. & Milner-Gulland, E. J. (2012) Robust policy decisions with global biodiversity indicators. PLoS One, 7(7): e41128 [link]