Distinguishing the Impacts of Inadequate Prey and Vessel Traffic on an Endangered Killer Whale (Orcinus orca) Population

Managing endangered species often involves evaluating the relative impacts of multiple anthropogenic and ecological pressures. This challenge is particularly formidable for cetaceans, which spend the majority of their time underwater. Noninvasive physiological approaches can be especially informative in this regard. We used a combination of fecal thyroid (T3) and glucocorticoid (GC) hormone measures to assess two threats influencing the endangered southern resident killer whales (SRKW; Orcinus orca) that frequent the inland waters of British Columbia, Canada and Washington, U.S.A. Glucocorticoids increase in response to nutritional and psychological stress, whereas thyroid hormone declines in response to nutritional stress but is unaffected by psychological stress. The inadequate prey hypothesis argues that the killer whales have become prey limited due to reductions of their dominant prey, Chinook salmon (Oncorhynchus tshawytscha). The vessel impact hypothesis argues that high numbers of vessels in close proximity to the whales cause disturbance via psychological stress and/or impaired foraging ability. The GC and T3 measures supported the inadequate prey hypothesis. In particular, GC concentrations were negatively correlated with short-term changes in prey availability. Whereas, T3 concentrations varied by date and year in a manner that corresponded with more long-term prey availability. Physiological correlations with prey overshadowed any impacts of vessels since GCs were lowest during the peak in vessel abundance, which also coincided with the peak in salmon availability. Our results suggest that identification and recovery of strategic salmon populations in the SRKW diet are important to effectively promote SRKW recovery

Photographic mark-recapture analysis of clustered mammal-eating killer whales around the Aleutian Islands and Gulf of Alaska

We used photographic mark-recapture methods to estimate the number of mammal-eating ‘‘transient’’ killer whales using the coastal waters from the central Gulf of Alaska to the central Aleutian Islands, around breeding rookeries of endangered Steller sea lions. We identified 154 individual killer whales from 6,489 photographs collected between July 2001 and August 2003. A Bayesian mixture model estimated seven distinct clusters (95% probability interval = 7–10) of individuals that were differentially covered by 14 boat-based surveys exhibiting varying degrees of association in space and time. Markov Chain Monte Carlo methods were used to sample identification probabilities across the distribution of clusters to estimate a total of 345 identified and undetected whales (95% probability interval = 255–487). Estimates of covariance between surveys, in terms of their coverage of these clusters, indicated spatial population structure and seasonal movements from these near-shore waters, suggesting spatial and temporal variation in the predation pressure on coastal marine mammals

Patterns and consequences of age-linked change in local relatedness in animal societies

This is the author accepted manuscript.Data availability: Data to reproduce these analyses are available at: osf.io/pzfex. Anonymised data to derive kinship dynamics are included for: banded mongooses, chimpanzees, killer whales and spotted hyena. Data sharing agreements mean that for the remaining species, anonymised data to reproduce the analysis needs to be requested from the corresponding author, all other forms of data request should be addressed to the manager of the system in question.Code availability: Code to reproduce these analyses are available at: osf.io/pzfex. The repository includes: a Mathematica file to run and reproduce the mathematical model; R code to implement the kinship dynamics simulation model; and R code to analyse both the simulation and observed kinship dynamics data. A simplified version of the simulation model can be explored at samellisq.shinyapps.io/kinship_dynamics_shinyapp_basic/ or downloaded from github.com/samellisq/kinship_dynamics_shinyapp. In addition, an R package, comparekin, created as part of this study, can be accessed at github.com/samellisq/comparekin.The ultimate payoff of behaviours depends not only on their direct impact on an individual but also on the impact on their relatives. Local relatedness – the average relatedness of an individual to their social environment – therefore has profound impacts on social and life history evolution. Recent work has begun to show that local relatedness has the potential to change systematically over an individual’s lifetime, a process called kinship dynamics. However, it is unclear how general these kinship dynamics are, whether they are predictable in real systems and their impacts on behaviour and life history evolution. In this study, we combine modelling with data from real systems to explore the extent and impact of kinship dynamics. We use data from seven group-living mammals with diverse social and mating systems to demonstrate not only that kinship dynamics occur in animal systems, but also that the direction and magnitude of kinship dynamics can be accurately predicted using a simple model. We use a theoretical model to demonstrate that kinship dynamics can profoundly impact lifetime patterns of behaviour and can drive sex differences in helping and harming behaviour across the lifespan in social species. Taken together this work demonstrates that kinship dynamics are likely to be a fundamental dimension of social evolution, especially when considering age-linked changes and sex differences in behaviour and life history.Leverhulme TrustNatural Environment Research Council (NERC