Summary1. Often less expensive and less invasive than conventional mark-recapture, so-called 'mark-resight' methods are popular in the estimation of population abundance. These methods are most often applied when a subset of the population of interest is marked (naturally or artificially), and non-invasive sighting data can be simultaneously collected for both marked and unmarked individuals. However, it can often be difficult to identify marked individuals with certainty during resighting surveys, and incomplete identification of marked individuals is potentially a major source of bias in mark-resight abundance estimators. Previously proposed solutions are ad hoc and will tend to underperform unless marked individual identification rates are relatively high (>90%) or individual sighting heterogeneity is negligible. 2. Based on a complete data likelihood, we present an approach that properly accounts for uncertainty in marked individual detection histories when incomplete identifications occur. The models allow for individual heterogeneity in detection, sampling with (e.g. Poisson) or without (e.g. Bernoulli) replacement, and an unknown number of marked individuals. Using a custom Markov chain Monte Carlo algorithm to facilitate Bayesian inference, we demonstrate these models using two example data sets and investigate their properties via simulation experiments. 3. We estimate abundance for grassland sparrow populations in Pennsylvania, USA when sampling was conducted with replacement and the number of marked individuals was either known or unknown. To increase marked individual identification probabilities, extensive territory mapping was used to assign incomplete identifications to individuals based on location. Despite marked individual identification probabilities as low as 67% in the absence of this territorial mapping procedure, we generally found little return (or need) for this timeconsuming investment when using our proposed approach. We also estimate rookery abundance from Alaskan Steller sea lion counts when sampling was conducted without replacement, the number of marked individuals was unknown, and individual heterogeneity was suspected as non-negligible. 4. In terms of estimator performance, our simulation experiments and examples demonstrated advantages of our proposed approach over previous methods, particularly when marked individual identification probabilities are low and individual heterogeneity levels are high. Our methodology can also reduce field effort requirements for marked individual identification, thus, allowing potential investment into additional marking events or resighting surveys.

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We examined the effects of research disturbance on the behavior and abundance of Steller sea lions (Eumetopias jubatus) at rookeries on Marmot and Ugamak Islands in Alaska. During 3 of 6 yr, researchers intentionally drove all adult and juvenile sea lions off at least part of the beach in order to permanently mark and measure sea lion pups. The research disturbance occurred after the majority of females had bred and when most pups were 1 mo old. We used generalized linear models to determine the relationship between research disturbance and sea lion behavior or abundance. Research disturbance was related to changes in the proportion of sea lions exhibiting two to three of nine behavior metrics: agonistic and resting females and active males at Marmot, and active and resting males and females at Ugamak. Model results indicated that changes lasted between 3 and 20 d depending on the sex, behavior, and rookery. Inclusion of research disturbance into Marmot abundance models did not improve the fit to the data, if variability between years was permitted. Optimally timed, low-frequency research disturbance did not appear to have long-term effects on sea lion behavior or abundance and was largely associated with changes that were similar to natural variation.

To monitor population trends of Steller sea lions (Eumetopias jubatus) in Alaska, newborn pups are counted during aerial surveys. These surveys are scheduled to occur after the majority of pups are born, but before pups begin to spend significant time in the water. Some studies have reported dispersal of mother-pup pairs away from breeding beaches during the pupping season (July), which may influence survey results. Using a multistate mark-recapture model with state uncertainty, we estimated the amount of dispersal during the pupping season based on observations of permanently marked sea lions. Research was conducted at land-based observation sites on Marmot Island, Alaska, between 2000 and 2013. Both marked adult females with dependent pups and marked pups were observed at two rookery beaches from May to July. Cumulative dispersal rates were minimal (< 1%) prior to the planned start of the aerial survey (23 June) and increased to 11.2% by the planned survey completion date (10 July). The increased cumulative dispersal rate during the remainder of the observation period (end of July) suggests potential bias in surveys that occur beyond 10 July, however surveys past this date are rare (< 10% between 1973 and 2016). As a result, movements of mother-pup pairs during the pupping season are not likely to influence aerial survey estimates.