A fish rots from the head down: how to use the leading digits of ecological data to detect their falsification

Abstract

AbstractManaging wildlife populations requires good data. Researchers and policy makers need reliable population estimates and, in case of commercial or recreational harvesting, also trustworthy information about the number of removed individuals. However, auditing schemes are often weak and political or economic pressure could lead to data fabrication or falsification. Time-series data and population models are crucial to detect anomalies, but they are not always available nor feasible. Therefore, researchers need other tools to identify suspicious patterns in ecological and environmental data, to prioritize their controls. We showed how the Benford’s law might be used to identify anomalies and potential manipulation in ecological data, by testing for the goodness-of-fit of the leading digits with the Benford’s distribution. For this task, we inspected two datasets that were found to be falsified, containing data about estimated large carnivore populations in Romania and Soviet commercial whale catches in the Pacific Ocean. In both the two datasets, the first and second digits numerical series deviated from the expected Benford’s distribution. In data about large carnivores, the first too digits, taken together, also deviated from the expected Benford’s distribution and were characterized by a high Mean Absolute Deviation. In Soviet whale catches, while the single digits deviated from the Benford’s distribution and the Mean Absolute Deviation was high, the first two digits were not anomalous. This controversy invites researchers to combine multiple measures of nonconformity and to be cautious in analyzing mixtures of data. Testing the distribution of the leading digits might be a very useful tool to inspect ecological datasets and to detect potential falsifications, with great implications for policymakers and researchers as well. For example, if policymakers revealed anomalies in harvesting data or population estimates, commercial or recreational harvesting could be suspended and controls strengthened. On the other hand, revealing falsification in ecological research would be crucial for evidence-based conservation, as well as for research evaluation.