Simon J. Bonner

Batch marking is common and useful for many capture–recapture studies where individual marks cannot be applied due to various constraints such as timing, cost, or marking difficulty. When batch marks are used, observed data are not individual capture histories but a set of counts including the numbers of individuals first marked, marked individuals that are recaptured, and individuals captured but released without being marked (applicable to some studies) on each capture occasion. Fitting traditional capture–recapture models to such data requires one to identify all possible sets of capture–recapture histories that may lead to the observed data, which is computationally infeasible even for a small number of capture occasions. In this paper, we propose a latent multinomial model to deal with such data, where the observed vector of counts is a non-invertible linear transformation of a latent vector that follows a multinomial distribution depending on model parameters. The latent multinomial model can be fitted efficiently through a saddlepoint approximation based maximum likelihood approach. The model framework is very flexible and can be applied to data collected with different study designs. Simulation studies indicate that reliable estimation results are obtained for all parameters of the proposed model. We apply the model to analysis of golden mantella data collected using batch marks in Central Madagascar.

Small populations of cetaceans with restricted ranges are particularly vulnerable to anthropogenic disturbances. The assessment of demographic parameters for these populations can provide meaningful insight into the ways in which they are being impacted by different threats. The Taiwanese white dolphin (TWD), Sousa chinensis taiwanensis , is a subspecies endemic to Taiwan and is classified as Critically Endangered. Multistate robust design mark–recapture models were fitted under a Bayesian framework to estimate demographic parameters and trends for the population from 2010 to 2018. Differences in these parameters by age category and the dolphins' use of their habitat were also explored. Abundance estimates varied from a maximum of 72 in 2010 (95% credible interval (CI) [65, 78]) to a minimum of 61 (95% CI [58, 64]) in 2017. In the oldest age category, abundance estimates varied from 10 (95% CI [10, 11]) in 2013 to 13 (95% CI [10, 13]) in 2016. Modelling showed that, over time, the abundance of dolphins in the oldest age group remained stable, while the remainder of the population, which includes reproductive individuals, experienced a steady decline. This decline is bound to affect the demographic recovery of TWD. Spatial heterogeneity indicated that some areas (i.e. with consistent high occurrences) likely represent important areas for biological functions of the TWD. This suggests that the recovery of the population may be enhanced with immediate and focused actions (such as the ban of gillnet fisheries) in these areas. However, with a small and declining population, reducing threats throughout their entire range is needed, including the new offshore windfarm development being constructed in and adjacent to the dolphin's range. Concerted actions to address known threats, continued monitoring of the population, and education of stakeholders are needed to prevent the extinction of the TWDs.