Trends in Abundance and Abundance Indexes

The most common evaluations of sea-turtle population status are those of nesting-beach trends, which may be based on counts of nests or nesting females (see Chapter 4). Linear regression is often used to identify an exponential growth rate for each nesting beach and used with data that is pooled by region (e.g., National Marine Fisheries Service Southeast Fisheries Science Center, 2001). Regression methods have also been used to evaluate trends in abundance indexes derived from juvenile and adult sampling at sea. Slopes and confidence intervals from simple regression analysis are easy to interpret but may fail to include important biological complexities that relate what is counted (such as nests) to a trend at the population level. The numbers of nests or nesting females may be highly variable because of environmental effects on the probability of breeding and other factors (Solow et al., 2002) so data are sometimes smoothed by using a running sum or averaging (e.g., Turtle Expert Working Group, 2007; Snover and Heppell, 2009).

Uncertainty in population trends has been evaluated with Bayesian state-space methods that are not restricted to parametric statistical evaluation and permit a more transparent evaluation of the probability of population decline (Turtle Expert Working Group, 2007, 2009). In the Bayesian approach, trends are expressed as probabilities of increase or decline rather than as slopes and confidence intervals but still require biological information for extrapolation of nest counts to population abundance. More complex trend-evaluation models that incorporate environmental drivers, such as nonparametric regression or Bayesian generalized additive models (Bjorndal et al., 1999; Chaloupka, 2001b; Balazs and Chaloupka, 2004b; Troëng and Rankin, 2005), have also been applied. The advantage of the Bayesian approach is that the confidence intervals do not require normal approximation assumptions but are based on the data themselves, and this provides a natural means of evaluating both sampling uncertainty and process error caused by environmental variance.

Without estimates of breeding probability (remigration interval) and of recruitment of new turtles to the breeding population, assessment of population trends on the basis of nesting-beach data is highly tenuous. A change in the number of nests may be due to a change in the frequency of nesting, a change in adult-female survival, or a change in the number of first-time breeders, none of which is monitored by the agencies. Estimates of trends in juvenile-turtle abundance through in-water surveys, aerial surveys, and frequency of strandings have generally been evaluated with regression analysis after an evaluation of data uncertainty (e.g., Turtle Expert Working Group, 2009).

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