Can we manage marine mammal bycatch effectively in low‐data environments?

A stochastic model for estimating sustainable limits to wildlife mortality in a changing world

Human-caused mortality of wildlife is a pervasive threat to biodiversity. Assessing the population-level impact of fisheries bycatch and other human-caused mortality of wildlife has typically relied upon deterministic methods. However, population declines are often accelerated by stochastic factors that are not accounted for in such conventional methods. Building on the widely applied potential biological removal (PBR) equation, we devised a new population modeling approach for estimating sustainable limits to human-caused mortality and applied it in a case study of bottlenose dolphins affected by capture in an Australian demersal otter trawl fishery. Our approach, termed sustainable anthropogenic mortality in stochastic environments (SAMSE), incorporates environmental and demographic stochasticity, including the dependency of offspring on their mothers. The SAMSE limit is the maximum number of individuals that can be removed without causing negative stochastic population growth. We calculated a PBR of 16.2 dolphins per year based on the best abundance estimate available. In contrast, the SAMSE model indicated that only 2.3-8.0 dolphins could be removed annually without causing a population decline in a stochastic environment. These results suggest that reported bycatch rates are unsustainable in the long term, unless reproductive rates are consistently higher than average. The difference between the deterministic PBR calculation and the SAMSE limits showed that deterministic approaches may underestimate the true impact of human-caused mortality of wildlife. This highlights the importance of integrating stochasticity when evaluating the impact of bycatch or other human-caused mortality on wildlife, such as hunting, lethal control measures, and wind turbine collisions. Although population viability analysis (PVA) has been used to evaluate the impact of human-caused mortality, SAMSE represents a novel PVA framework that incorporates stochasticity for estimating acceptable levels of human-caused mortality. It offers a broadly applicable, stochastic addition to the demographic toolbox to evaluate the impact of human-caused mortality on wildlife.

Effect of matching uncertainty on population parameter estimation in mark-recapture analysis of photo-identification data

Quantifying and dealing with uncertainty are key aspects of ecological studies. Population parameter estimation from mark-recapture analyses of photo-identification data hinges on correctly matching individuals from photographs and assumes that identifications are detected with certainty, marks are not lost over time, and that individuals are recognised when they are resighted. Matching photographs is an inherently subjective process. Traditionally, two photographs are not considered a “match” unless the photo reviewer is 100% certain. This decision may carry implications with respect to sample size and the bias and precision of the resultant parameter estimates. Here, we present results from a photo-identification experiment on Pacific white-sided dolphins to assign one of three levels of certainty that a pair of photographs represented a match. We then illustrate how estimates of abundance and survival varied as a function of the matching certainty threshold used. As expected, requiring 100% certainty of a match resulted in fewer matches, which in turn led to higher estimates of abundance and lower estimates of survival than if a lower threshold were used to determine a match. The tradition to score two photographs as a match only when the photo reviewer is 100% certain stems from a desire to be conservative, but potential over-estimation of abundance means that there may be applications (e.g., assessing sustainability of bycatch) in which it is not precautionary. We recommend exploring the consequences of matching uncertainty and incorporating that uncertainty into the resulting estimates of abundance and survival.