Uncertainty of conservation science

The conservation of endangered species is a venture marked by ambiguity. Whether the right populations are being protected from the appropriate threats to the precise degree, is a question that governments and conservationists are constantly evaluating. In such times of uncertainty, decision makers often turn to science for the answers. Whether it was the pug-mark census of yore or the modern camera-trapping method, tiger conservation, for instance, is purported to be based on science. Yet while using science to make decisions, there is a crucial assumption made both by the government and the general public. Their belief that science will provide the answers they want, may not always be true.

While it is true that science can provide results, these may not always be in a form that people desire.

Science may not have all answers

While governments and the public seem to expect that science can build a solid result based on the foundation of incomplete knowledge, this is unrealistic, particularly in the field of conservation science. The species that need protection are typically rare, shy and difficult to spot or follow. Very few people are familiar enough with an endangered vulture or snow leopard to recognise an individual and make distinctions. To many of us one great Indian bustard looks like another, as do the endangered Mahseer fish. Given that so little is known, how does science enable us to know more? Since the exact feeding, sleeping and breeding grounds of endangered species like the tiger or leopard are not well known, trying to find out exactly how many there are at any given point of time is next to impossible.

How do scientists know how many new animals have been born each year or how many have died far away from human eyes? To be able to “guesstimate” with some degree of accuracy, scientists use the little information that they have and make calculations for what they do not know. Using standardised techniques to gather information and combining the results with statistics, scientists predict what they have not seen. Since the predictions are based on incomplete knowledge, it comes as no surprise that the final result is by no means absolute. Such results are usually a range within which the absolute number publicised is just the average.

The public rarely gets to see ranges in scientific results. For example, Aircel perpetrated the myth of 1,411 tigers in its ‘Save Our Tigers’ campaign. While it is possible that there are exactly that number of tigers in Indian forests, such an unconditional result must have made many wonder about its accuracy. What made Aircel sure that there were not 1,415 or 1,401 tigers in the country’s forests? The answer is, nothing. What population science can tell us is that the estimated number of tigers lies within a range of, say, 1,390 and 1,450. Managers and advertisers find such ranges unwieldy and force a single answer to emerge out of a study that was not designed for accounts or advertising. Thus, the answer that science provides is not amenable to the “grabbing eyeballs” model or the corporate accounting model that runs Indian forests.

Of what use is an uncertain science?

When scientists talk about uncertainty, they are immediately vilified for having wasted everyone’s time and money. Ullas Karanth, an eminent tiger biologist, has argued for years about the inclusion of scientific uncertainty into the results of tiger population estimates. He suggests that uncertainty can help design better conservation measures for species. His cautionary approach involves the use of science to better understand the world of the wild, not its abuse to make claims about knowing everything.

Just like traditional knowledge, scientific thought is based on the gradual build-up of knowledge, while on the path to seeking absolute understanding. Even the most clear thinking visionaries will find their predictive powers clouded by lack of information about the past and present. Wildlife population science, therefore, has to come with some inaccuracy, just like the Indian population census. Any reports to the contrary must be viewed with skepticism. This is not to say that the results are wrong, or that scientists have no idea what is going on. Uncertainty in science helps narrow down on what exactly we do not know, giving us an approximation of what the eventual results will be.

By including uncertainty in their results, scientists are truthful about knowledge. Given how little is known about wildlife in the country, finding out that there are exactly 1,411 tigers in unrealistic. Extending Gandhian principles to the way we manage the wildlife in the country might not be a bad idea. A truthful science suggests that the results could be wrong within a limit, but since everyone knows what that limit is they can keep it in mind while planning how to protect species.

How does uncertainty help?

Decision making necessarily involves the consideration of a lot of grey areas that weigh in on assessments. Conservation decisions are always more contentious, and species biology is often given the lowest priority. This often means species populations are thought of in black and white, while economic and social conditions are painted in grey. While this eases the decision-makers’ troubles, it also explains the resistance to changing the way a species is accounted for by the Indian Forest Service. Certainly, uncertainty in population science will lengthen the already elaborate decision making processes. But other governments have found ways to use scientific uncertainty to their advantage.

The National Marine Fisheries Service (NMFS) of the US is heavily dependent on scientific assessments of fish populations in order to set sustainable fishing goals. Their logic is that they should know how many fish are in the water before they can decide how many can be harvested for seafood or other marine products each year. For a number of years they followed similar account keeping techniques as the Indian Forest Service does when it comes to the Indian wildlife. But this has recently changed. NMFS has now embraced uncertainty in science, believing that a fuzzy truth is better than an absolute half-truth. As a result, scientists provide them with a population range for each species of fish, upon which they base fishing quotas. NMFS uses this range to decide whether they want to play safe by estimating that the population is in the lower part of the range, or whether they want to risk its existence by estimating that the population is in the higher part of the range.

They employ a panel of scientific advisers who take stock of each species population, history and threatened status and accordingly decide the risk associated with a species in the fishery during each evaluation period. The recommendation sets the maximum limit on how many fish can be harvested in those years. Fishing communities can then choose to use this limit or set even lower limits on their fish catch to make the fishery sustainable. Thus uncertainty can be used to aid conservation practice.

There are a lot of myths perpetuated about and by science, and unconditional, absolute results are chief among them. If science and policy are meant to work together, they have to understand each other and not oversimplify the lessons that they learn. While policy has recognised that science provides answers, it has not yet learned how to use the answers it receives.

Creating new strategies that celebrate the truth, even if it makes for messy accounts, will give conservation policy in India a new lease of life.

Divya Karnad is a conservation scientist

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