Extracting robust trends in species’ distributions from unstructured opportunistic data: a comparison of methods

Abstract

1. Policy-makers increasingly demand robust measures of biodiversity change over short time periods. Long-term monitoring schemes provide high-quality data, often on an annual basis, but are taxonomically and geographically restricted. By contrast, opportunistic biological records are relatively unstructured but vast in quantity. Recently, these data have been applied to increasingly elaborate science and policy questions, using a range of methods. At present we lack a firm understanding of which methods, if any, are capable of delivering unbiased trend estimates on policy-relevant timescales. 2. We identified a set of candidate methods that employ either selection criteria or correction factors to deal with variation in recorder activity. We designed a computer simulation to compare the statistical properties of these methods under a suite of realistic data collection scenarios. We measured the Type I error rates of each method-scenario combination, as well as the power to detect genuine trends. 3. We found that simple methods produce biased trend estimates, and/or had low power. Most methods are robust to variation in sampling effort, but biases in spatial coverage, sampling effort per visit, and detectability, as well as turnover in community composition all induced some methods to fail. No method was robust to all forms of variation in recorder activity. 4. We warn against the use of simple methods. We identify three methods with complementary strengths and weaknesses that are useful for estimating timely trends. Sophisticated correction factor methods, including Occupancy and Frescalo, offer the greatest potential in the long-term. Methods based solely on selection criteria are inherently limited, but a combination or ensemble of approaches may be required to generate trends that are both robust and powerful. Small amounts of information about sampling intensity, captured at the point of data collection, would greatly enhance the utility of opportunistic data and make future trend estimates more reliable