Cerri, J. | Search bioRxiv

bioRxiv2018

Characterizing noncompliance in conservation: a multidimensional Randomized Response Technique for multinomial responses.

Rule violation is critical for biological conservation worldwide. Conventional questionnaires are not suitable to survey these violations and specialized questioning techniques that preserve respondents privacy, like the forced-response RRT, have been increasingly adopted by conservationists. However, most of these approaches do not measure multinomial answers and conservationists need a specialized questioning technique for real-world settings where non-compliance could occur in different forms. We developed a multidimensional, statistically-efficient, RRT which is suitable for multinomial answers (mRRT) and which allows researchers to test for respondents noncompliance during completion. Then, we applied it to measure the frequency of the various forms of illegal restocking of European catfish from specialized anglers in Italy, developing an operational code for the statistical software R. A total of 75 questionnaires were administered at a large fishing fair in Northern Italy, in winter 2018. Our questionnaires were easily compiled and the multinomial model revealed that around 6% of respondents had moved catfish across public freshwater bodies and private ponds. Future studies should better address their characteristics, and the mRRT could allow for modeling the effect of co-variates over restocking behavior. The multinomial mRRT could be adopted to measure many forms of rule violation in conservation that could take different forms, like various forms of fish restocking or different modes of wildlife persecution.

ecology↗

bioRxiv2018

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

Managing 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 Benfords 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 Benfords 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 Benfords distribution. In data about large carnivores, the first too digits, taken together, also deviated from the expected Benfords distribution and were characterized by a high Mean Absolute Deviation. In Soviet whale catches, while the single digits deviated from the Benfords 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.

ecology↗