IMPROVING PRECISION AND REDUCING BIAS IN BIOLOGICAL SURVEYS: ESTIMATING FALSE‐NEGATIVE ERROR RATES

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…

• AR
  Australian Research Council