Why conservation should embrace prediction

Henry Travers

As modern-day conservation science is said to have been born from quantitative biology, it is no surprise that it has a strong background in the use of predictive models. However, conservation today also has a prediction problem. Put simply, prediction isn’t used enough, be that in identifying future problems before they arise, working out which of a variety of intervention options might work best in a given context or helping to guide conservation practice in the field. How do we reconcile these seemingly contradictory statements? The answer is that much of the prediction in conservation is focused on the fauna and flora we aim to protect: predicting population dynamics, animal behaviour, responses to particular threats, the spread of invasive species, etc.

The problem with this is that it largely ignores the reason why conservation is necessary in the first place. Us. As humans, it is our behaviour that is responsible for the vast majority of the threats that are driving the rapid loss of biodiversity we see across the planet. Therefore, if our efforts to conserve global biodiversity are to be successful we need to address human behaviour. This is evident from the type of activities that conservation organisation typically involve themselves with (e.g. law enforcement, education, livelihoods, etc.). However, human behaviour is dependent on all kinds of factors ranging from individual preferences to local institutions to wider cultural and social norms. Context matters. As a result, what works well in one place may not be so effective elsewhere. Given the range of options available to practitioners, prior information about how different interventions might perform can be invaluable in guiding programme design and making better use of scant resources.

 

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To understand this more clearly, we can think about how conservation programmes have typically been designed. While evidence-based design is increasing, a lot of programmes are still based on the experiences and judgement of the practitioners developing them. This partly explains the lasting over-reliance on enforcement-based approaches that often do little to address the underlying drivers of illegal behaviour. This is where predictive approaches can come to the fore. Rather than relying on practitioner judgement about which intervention is most likely to succeed in a given context, predictive approaches can be used to evaluate the relative performance of a range of suitable options, as well as compare performance between different groups of people. For instance, if the objective is to reduce forest clearance among small-holder farmers, there could be a range of possible activities that might vary in efficacy between farmers that grow different crops. In this instance, we can use predictive methods to investigate which intervention is likely to prove most effective for the farmers of each crop. Coupled with understanding about the drivers of forest conversion, this will help to maximise behaviour change and reduce forest loss.

This is just one area in which prediction can benefit the sector. Our new paper, A manifesto for predictive conservation, takes a look at opportunities for conservation to incorporate prediction at all stages of programme development and implementation. From initial problem framing to evaluating programme performance, predictive methods can help us to improve decision-making, often at a fraction of the cost of alternative approaches.

                                                                   

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Predictive conservation can help improve decision-making at all stages of programme design and implementation.

 

In promoting this approach, I’ve often been challenged about how useful prediction can be given the complexity of many of the contexts in which conservation operates. Maybe because I’m British, I often like to use the example of the weather. In the UK, we famously spend a lot of time talking about the weather and a good part of that stems from its changeable nature. Looking out the window and seeing what the weather is like now isn’t necessarily the best way to gauge what it’s going to be doing in half an hour. Which is of course why we have weather forecasts. Only, in highly changeable systems, weather forecasts may also not always be entirely accurate. If you’re wondering whether you can nip to the shops without getting drenched, a forecast can help but you’re likely to make the best decision when you combine what’s out the window with what the weatherman says is on the way.

The same applies in conservation. Just as weather forecasters now apply probabilities to different possible scenarios, it’s important we understand that the predictions we make carry uncertainty. They won’t provide the definitive answer but they can help us to make more informed and better decisions. I’m certainly not advocating that we throw out the hard-earned experience on which much of conservation practice is based. But by becoming less reactive and more forward thinking by incorporating prediction into mainstream practice, we can increase the likelihood that our interventions succeed.

 

See Henry at ICCB on Tuesday 23rd: https://www.iccs.org.uk/content/iccs-iccb-2019