Testing global biodiversity indicators

The Convention on Biological Diversity (CBD) was signed in the 1990s by 168 countries, who committed themselves to a significant reduction in the rate of loss of biodiversity by 2010. A range of indicators showed that they failed to meet this rather ambitious aim. At the tenth meeting of the Conference of the Parties, held in October 2010 in Nagoya, Japan, a new set of more concrete targets was adopted, the Aichi Targets, accompanied by a suite of indicators to measure progress towards the targets. Two key questions arise from the CBD targets and indicators:

  1. Can the targets be achieved, and with which policies?
  2. Are the CBD indicators capable of signalling changes in biodiversity as a result of these targets and policies?

These were two questions we examined in a paper that has just come out in PLoS One, in collaboration with, amongst others, Ben Collen at the ZSL and EJ Milner-Gulland at Imperial College London. We argue that policies and indicators should be evaluated with models before being implemented, to assess whether they perform as expected. We then tested our ideas with two case studies, looking at protected areas in Africa and the impacts of bottom trawling on marine ecosystems respectively.

The our case study on African protected areas (PAs) focussed on Aichi Target 11, which calls for at least 17% of terrestrial areas to be placed in effective and well managed protected areas by 2020. Unfortunately, protected area coverage alone gives little information on how well they are performing in protecting biodiversity. Recent research by Ian Craigie and colleagues (2010) found that the population abundance of large mammals in 78 African PAs declined on average by 59% between 1970 and 2005, with a lot of variation between species and regions. There is very little information about how these species are faring outside reserves, other than it is probably worse. We compared different policy options, modelling the effects of policy on the fate of 53 mammals species, including lions, zebras, impala and African wild dogs. We compared: 1) doing nothing; 2) increasing the reserve system to 17% (but with the current population declines); 3) halting declines in the current reserve system; and 4) increasing the reserve system to 17% AND stopping declines. We fed the population trends into the Red List Index, an index of extinction risk for species of plants and animals based on the IUCN Red List status of species.

Lions eat zebras, if they can catch them – photo shamelessly stolen from http://desktopia.net/

We found that the Red list Index could differentiate between the policies – this is handy for two reasons. It suggests we could pick up positive changes due to such policies when we are monitoring biodiversity using the index. It is also shows that the Red list Index can provide a useful way to draw together the outputs of scenario modelling for some policies to compare options, particularly when presenting them to decision-makers. Our second main result was that management effectiveness was a much better option for improving the fate of the 53 mammal species than expanding protected areas without changing the way we manage them. In fact, expanding to 17% provided little benefit over doing nothing, while expanding and improving management was only a bit better than just improving management of the current protected area network. Keep in mind that our modelling was pretty simple (just projecting current trends), and we assumed that all the species responded similarly – their population trends all improved with effective management, and there were no interactions (e.g. lions and zebras both went up in better managed reserves, whereas if the lions increased, the number of zebras would probably decrease!).

In our second case study, we looked at stopping bottom trawling – one of the most destructive fishing methods currently used. This fits with the very general CBD target on fisheries management, Aichi Target 6: “By 2020 all fish and invertebrate stocks and aquatic plants are managed and harvested sustainably, legally and applying ecosystem based approaches… . We modelled the effects of halting or halving the amount of bottom trawling in six ocean systems using 10 ecosystem models, using a modelling framework called Ecopath with Ecosim, which models the interactions of food webs. This means that the effects of the policy trickle down through the food web: when trawling is halted, the fish species that are main target of the fishery will increase. Their main food species will decrease in number, as will species they compete with for food or other resources. By contrast, anything that eats targeted fish will have more food available and will increase in numbers too. We fed the changes in biomass from the scenarios into the Living Planet Index, another key CBD indicator, which measures changes in abundance in vertebrate species.

Who doesn’t like puffins? from Wikipedia

What did we find? Mainly that it is hard to find consistent and simple results from using such complex models with a single indicator. The main problem was that not all species in the ecosystem models are used by the Living Planet Index, which relies on available time series data collected for other purposes. People like birds, and therefore there is a lot of bird data in the Living Planet Index – the majority in the marine systems we looked at. This means that our results in many regions were driven by what happened to the birds, and for some regions birds actually decreased in number. This was particularly the case in the Mediterranean and the North Sea, where birds eat the discards of trawling – stop the trawling and the birds lose a big chunk (up to 30%!) of their food source. Other than birds, the rest of the species that make up the Living Planet Index are a rather patchy lot. In some regions the key beneficiaries of stopping bottom trawling, such as skates and rays, were simply not included in the Living Planet Index, so there was no bump to the index from the policy, while species who were recipients of knock-on effects, whether positive or negative, were represented to varying degrees. As a result, the index was all over the place depending on which species were included, and not consistent with the overall changes in biodiversity, which were generally positive as a result of reducing bottom trawling. Our main conclusion is that the complexity of the responses to a policy such as stopping bottom trawling can’t be readily reflected in one indicator, especially if it doesn’t contain a very representative set of species – more indicators are needed, and indicators that target particular aspects of biodiversity.

A spotted eagle ray on Wikipedia

Surprisingly, although biodiversity indicators are used to show how biodiversity is trending, they are rarely tested to see if those trends are telling the story we think they are telling. Within fisheries science, the performance of indicators is tested more thoroughly (e.g. the IndiSEAS project), something we in conservation should learn from.

The Aichi targets are a good start to trying to change the decline in biodiversity, but without testing whether the policy options that stem from the targets work, they run the risk of being as ineffectual as the CBD goal to slow the loss of biodiversity by 2010 – see our example showing that expanding ‘paper parks’ doesn’t do much to stop declines in key species. To be useful and relevant, conservation scientists must make testable predictions about the impact of global policy on biodiversity to ensure that targets such as those set at Nagoya catalyse effective and measurable change.

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]

This research now features in the Biodiversity Indicators Partnership’s Aichi Passport and an app for your smartphone (so you can check the progress to meeting Aichi targets?) – see this link.


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