Improving Precision and Reducing Bias in Biological Surveys: Estimating False‐negative Error Rates

Abstract

Abstract. The use of presence/absence data in wildlife management and biological surveys is widespread. There is a growing interest in quantifying the sources of error associated with these data. We show that false-negative errors (failure to record a species when in fact it is present) can have a significant impact on statistical estimation of habitat models using simulated data. Then we introduce an extension of logistic modeling, the zero-inflated binomial (ZIB) model that permits the estimation of the rate of false-negative errors and the correction of estimates of the probability of occurrence for false-negative errors by using repeated visits to the same site. Our simulations show that even relatively low rates of false negatives bias statistical estimates of habitat effects. The method with three repeated visits eliminates the bias, but estimates are relatively imprecise. Six repeated visits improve precision of estimates to levels comparable to that achieved with conventional statistics in the absence of false-negative errors. In general, when error rates are Յ50% greater efficiency is gained by adding more sites, whereas when error rates are Ͼ50% it is better to increase the number of repeated visits. We highlight the flexibility of the method with three case studies, clearly demonstrating the effect of false-negative errors for a range of commonly used survey methods.

Developing robust field survey protocols in landscape ecology: a case study on birds, plants and butterflies

“…Field research projects face logistical, time and monetary constraints (Tyre et al 2003), which inherently limit the affordable survey intensity. Dense sampling schemes-such as those that use survey protocols which aim to cover at least three percent of the area of a landscape with at least five repeats (Bried et al 2011)-are rarely feasible.…”

Space-Time Models for a Panzootic in Bats, With a Focus on the Endangered Indiana Bat

“…It is possible, however, that some absences could be false negatives (i.e., the disease was present but not detected). Tyre et al (2003) demonstrated the potential for bias in models of species-habitat relations even when rates of false negatives are low. Thus, if our putative absences were contaminated by false negatives, then our model may underestimate the rate of disease spread and potentially fail to describe spread into new areas, which, in turn, would underestimate subsequent risk to unaffected wintering populations in those areas.…”

“…Diagnosing the leading edge in the spatial spread of a cryptic disease is notoriously difficult (Mertens and LowBeer, 1996;Filipe et al, 2012). Most methods for accommodating the potential for false absences require repeated surveys to control for detection when modeling species occurrence (MacKenzie et al, 2002;Tyre et al, 2003;Royle et al 2005). Unfortunately, multiple visits to hibernacula during winter are recommended against, according to the guidelines for ''Census Taking'' in the 1983 Indiana bat recovery plan (Appendix VI in USFWS, 1983;Brack et al, 1983).…”

A hierarchical model combining distance sampling and time removal to estimate detection probability during avian point counts

“…Detection probability is a factor of many variables (e.g., observer experience, time of day, habitat, weather), and simple presence / absence counts do not take into account false-negative error rates (Tyre et al 2003). We likely underestimated the number of points at which a particular species occurred, thus hindering our ability to completely describe species occurrence, distribution, or species-specific habitat affinities (Tyre et al 2003). The objective of this summary is to provide a general foundation upon which to base more detailed, species-specific habitat analyses.…”

“…The objective of this summary is to provide a general foundation upon which to base more detailed, species-specific habitat analyses. Subsequent analyses should model and incorporate detection probabilities (e.g., Tyre et al 2003, MacKenzie et al 2006) in order to more accurately describe occurrence and habitat affinities of birds within ANIA.…”