(Crowder et al., 1994), requires data on sex ratio, recruitment rates (proportion of nesters that are breeding for the first time), and annual survival; uncertainty in these parameters has been incorporated through resampling of known or presumed distributions to provide a range of possible population sizes (Turtle Expert Working Group, 2007). Extrapolation of nesting data to estimate population size is even more problematic because of uncertainty in survival and cohort variability. A lack of sufficient information on survival rates resulted in a range of a factor of five to ten in estimates of population sizes among best-fit models for Kemp’s ridley sea turtles even though cohort strength (annual hatchling production) was well known on the basis of extensive monitoring of nests for the entire species (Turtle Expert Working Group, 2000; Heppell et al., 2005).
Older sea-turtle assessments relied heavily on simple regression analysis of nesting-beach data to evaluate population trends, but recent assessments published by NMFS have included Bayesian state-space modeling and diffusion approximation methods to estimate trends and uncertainty in population trajectories (Turtle Expert Working Group, 2007, 2009; Conant et al., 2009). The most recent status assessment of Atlantic loggerhead turtles also includes a “matrix threat analysis” that is essentially a deterministic matrix sensitivity analysis to ascertain potential changes in population growth that result from additional mortality (Conant et al., 2009). The analysis is far more comprehensive than past sensitivity analyses (e.g., Crowder et al., 1994; Heppell et al., 2003) in that it accounts for uncertainty in estimates of parameter values. The potential cumulative effects of anthropogenic stressors affecting all life stages of each population unit are then modeled as additive mortality, and ranges of potential asymptotic growth rates are compared. The exercise is informative insomuch as it shows that even under the most optimistic scenarios, there is a high probability that current mortality is too high to be sustained by most loggerhead populations. However, it is a largely heuristic exercise with little or no real power for prediction because of the high level of uncertainty and of assumptions required for deterministic age-structured models. There is no attempt to fit models to data, in part because the time lags involved in sea-turtle life history make it very difficult to establish a likely past or current age structure of the population.
Population assessment for management requires an integration of abundance data and demography to account for species’ life history and