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The Jolly-Seber method has been the traditional approach to the estimation of demographic parameters in long-term capture-recapture studies of wildlife and fish species. This method involves restrictive assumptions about capture probabilities that can lead to biased estimates, especially of population size and recruitment. Pollock (1982, Journal of Wildlife Management 46, 752-757) proposed a sampling scheme in which a series of closely spaced samples were separated by longer intervals such as a year. For this "robust design," Pollock suggested a flexible ad hoc approach that combines the Jolly-Seber estimators with closed population estimators, to reduce bias caused by unequal catchability, and to provide estimates for parameters that are unidentifiable by the Jolly-Seber method alone. In this paper we provide a formal modelling framework for analysis of data obtained using the robust design. We develop likelihood functions for the complete data structure under a variety of models and examine the relationship among the models. We compute maximum likelihood estimates for the parameters by applying a conditional argument, and compare their performance against those of ad hoc and Jolly-Seber approaches using simulation.

In large yield trials, variation in soil fertility (or, more generally, yield potential) can result in substantial heterogeneity within blocks and, thus, poor precision in treatment estimates. Precision may be improved using statistical analyses in which this spatial variation is accounted for in estimation of treatment or entry means. Three such types of spatial analysis are trend analysis, the Papadakis method, and analyses based on correlated errors models (which account for spatial variation through correlations between yields of neighboring plots). We reviewed the theory and empirical performance of these spatial analyses and compared them with the classical analyses. The classical analyses can be justified solely on the basis of randomization; spatial analyses depend on the model specified for the variation in yield potential. Performance depends on the polynomial used to describe yield potential in trend analysis, on the neighboring plots used to estimate fertility in the Papadakis analysis, and on the correlation structure in the correlated errors models. Empirical comparisons were based on data from 11 corn (Zea mays L.) yield trials and 1 soybean (Glycine max L.) trial, each showing evidence of heterogeneity within blocks. In comparison with the classical randomized blocks analysis, precision tended to be best for the trend and the trend plus correlated errors analyses, with the Papadakis method intermediate. Ranking of entries differed across analyses, because each analysis adjusts for spatial variation in a different way. Using a spatial analysis technique can improve precision, but selecting the most appropriate analysis for a given data set can be hard.

In epidemiologic studies, individuals may be misclassified with respect to exposure to a risk factor for disease. Such misclassification causes the relative risk of disease associated with the exposure in the population to be biased toward the null value. Here, a formula is derived for the apparent relative risk under misclassification (R) as a function of the sensitivity (U) and specificity (V) of the test for exposure and of the true relative risk (R) and true prevalence of exposure (P(E] in the population. The relative influence of U and V on the bias in R depends both on R and on P(E), with U tending to be more influential at higher values of P(E). When there is misclassification of exposure, variation in P(E) may bias comparisons of relative risk between groups or exposures, either by producing spurious differences or by masking true differences, and may generate spurious trends associated with a third variable such as age. Because the possible effects of misclassification of exposure on relative risk are complex and not easily generalized, the potential degree of bias should be evaluated individually in each situation.

Abstract. Misidentification of animals is potentially important when naturally existing features (natural tags) are used to identify individual animals in a capture-recapture study. Photographic identification (photoID) typically uses photographic images of animals' naturally existing features as tags (photographic tags) and is subject to two main causes of identification errors: those related to quality of photographs (non-evolving natural tags) and those related to changes in natural marks (evolving natural tags). The conventional methods for analysis of capture-recapture data do not account for identification errors, and to do so requires a detailed understanding of the misidentification mechanism. Focusing on the situation where errors are due to evolving natural tags, we propose a misidentification mechanism and outline a framework for modeling the effect of misidentification in closed population studies. We introduce methods for estimating population size based on this model. Using a simulation study, we show that conventional estimators can seriously overestimate population size when errors due to misidentification are ignored, and that, in comparison, our new estimators have better properties except in cases with low capture probabilities (,0.2) or low misidentification rates (,2.5%).

Males of the noctuid moths, Heliothis virescens and H. subflexa locate mates based on species‐specific responses to female‐emitted pheromones that are composed of distinct blends of volatile compounds. We conducted genetic crosses between these two species and used AFLP marker‐based mapping of backcross families (H. subflexa direction) to determine which of the 30 autosomes in these moths contained quantitative trait loci (QTL) controlling the proportion of specific chemical components in the pheromone blends. Presence/absence of single H. virescens chromosomes accounted for 7–34% of the phenotypic variation among backcross females in seven pheromone components. For a set of three similar 16‐carbon acetates, two H. virescens chromosomes interacted in determining their relative amounts within the pheromone gland and together accounted for 53% of the phenotypic variance. Our results are discussed relative to theories about population genetic processes and biochemical mechanisms involved in the evolution of new sexual communication systems.

Ecosystem-level impacts of two hurricane seasons were compared several years after the storms in the largest lagoonal estuary in the U.S., the Albemarle-Pamlico Estuarine System. A segmented linear regression flow model was developed to compare mass-water transport and nutrient loadings to a major artery, the Neuse River Estuary (NRE), and to estimate mean annual versus storm-related volume delivery to the NRE and Pamlico Sound. Significantly less water volume was delivered by Hurricane Fran (1996), but massive fish kills occurred in association with severe dissolved oxygen deficits and high contaminant loadings (total nitrogen, total phosphorus, suspended solids, and fecal bacteria). The high water volume of the second hurricane season (Hurricanes Dennis, Floyd, and Irene in 1999) delivered generally comparable but more dilute contaminant loads, and no major fish kills were reported. There were no discernable long-term adverse impacts on water quality. Populations of undesirable organisms, such as toxic dinoflagellates, were displaced down-estuary to habitats less conducive for growth. The response of fisheries was species-dependent: there was no apparent impact of the hurricanes on commercial landings of bivalve molluscs or shrimp. In contrast, interacting effects of hurricane floodwaters in 1999 and intensive fishing pressure led to striking reductions in blue crabs. Overall, the data support the premise that, in shallow estuaries frequently disturbed by hurricanes, there can be relatively rapid recovery in water quality and biota, and benefit from the scouring activity of these storms.estuaries ͉ fisheries ͉ resilience ͉ volume delivery ͉ water quality

SUMMARY·The resighting rather than recapturing of individuals that were initially captured, tagged and returned to a population offers a means of circumventing the often traumatic and sometimes fatal effect of repeated capture and handling of wild animals in a tag-recapture study. The behavioral effect of initial capture and tagging, however, must often be accounted for in the model. If sighting records are restricted to tagged individuals, with no attempt at estimating a tagged/untagged ratio in the population, the model requirements are simplified to include only the modeling of mortality among tagged individuals and to exclude recruitment parameters in the unobserved, untagged portion of the population. Short-term capture/ tagging effects of specified duration (i.e., lasting for only one time period) are incorporated into the proposed model, and their biasing effects thereby eliminated from estimates of time-specific survival rates. Standard error formulas and tests of the model are provided in this generalization of the Jolly-Seber method of tag-recapture analysis.