Using mark-recapture methods to estimate population size and survival of pyjama sharks Poroderma africanum in Mossel Bay, South Africa

The pyjama shark Poroderma africanum (family Scyliorhinidae) is endemic to coastal waters of South Africa but its population characteristics are poorly known. This study aims to estimate baseline demographic parameters for P. africanum in Mossel Bay. We applied mark-recapture methods following Pollock’s robust design (PRD) and Cormack–Jolly–Seber (CJS) models, using five-year tag-recapture data and one-year acoustic-telemetry data, respectively. Estimates of abundance, survival, capture probabilities and temporary emigration were obtained using these models. The PRD model indicated random temporary emigration (0.955 period–1 [95% CI 0.755–0.993]), constant survival, and season-varying capture and recapture probabilities with a negative behavioural response to first capture (β = −5.34 [−6.89 to −3.80]). Abundance estimates ranged from 11 (10.01–13.21) to 53 (52.05–56.82) sharks in Mossel Bay. The CJS model indicated constant survival (0.271 y–1 [95% CI 0.0–0.75]) and sex-independent probabilities of being captured at least once annually (1.0 y–1 for both sexes). This study provides the first demographic information for the management of this population. Further studies should utilise larger sample sizes and more complex models, and investigate specific management options.Keywords: acoustic telemetry, benthic sharks, capture-recapture, Cormack–Jolly Seber model, Pollock’s robust design, population dynamic

Supporting marine spatial planning with an ecosystem model of Algoa Bay, South Africa

The Ecopath with Ecosim (EwE) modelling framework was used to develop a model of Algoa Bay and test the ecosystem impacts of the  implementation of the Addo Elephant National Park Marine Protected Area (MPA). The Ecopath model included 37 functional groups ranging from  phytoplankton to top predators and was fitted to 12 and 14 time-series of biomass and landings, respectively, from 2010 to 2019 (calibration  period), using Ecosim. Two scenarios representing different degrees of fisheries closures in the MPA were explored through a 30% and a 100%  reduction in fishing effort. Temporal simulations were run until 2059. The fitting procedure identified the best-fit model as the one including the  effects of fishing, the six most-sensitive predator–prey interactions, and environmental forcing (primary production anomaly on small  phytoplankton). Overall, the predicted biomass and catch time-series reasonably reproduced the observed time-series for 2010–2019, with the  biomass of sardine Sardinops sagax, round herring Etrumeus whiteheadi, and African penguins Spheniscus demersus showing the best fits to data.  Both MPA scenarios resulted in higher total biomass compared with the baseline by the end of the simulation and decreased catches due to less  fishing effort. The most profound biomass increases under the MPA scenarios were observed in apex and pelagic elasmobranchs, yellowtail Seriola  lalandi and African penguins. Future research is needed to improve the more-uncertain model parameters and include other key sectors in Algoa  Bay, such as shipping. However, this model provides a good foundation for future work including the application of spatially explicit modelling of the  bay using Ecospace

The difficult medical emergency call: A register-based study of predictors and outcomes

BACKGROUND: Pre-hospital emergency care requires proper categorization of emergency calls and assessment of emergency priority levels by the medical dispatchers. We investigated predictors for emergency call categorization as “unclear problem” in contrast to “symptom-specific” categories and the effect of categorization on mortality. METHODS: Register-based study in a 2-year period based on emergency call data from the emergency medical dispatch center in Copenhagen combined with nationwide register data. Logistic regression analysis (N = 78,040 individuals) was used for identification of predictors of emergency call categorization as “unclear problem”. Poisson regression analysis (N = 97,293 calls) was used for examining the effect of categorization as “unclear problem” on mortality. RESULTS: “Unclear problem” was the registered category in 18% of calls. Significant predictors for “unclear problem” categorization were: age (odds ratio (OR) 1.34 for age group 76+ versus 18–30 years), ethnicity (OR 1.27 for non-Danish vs. Danish), day of week (OR 0.92 for weekend vs. weekday), and time of day (OR 0.79 for night vs. day). Emergency call categorization had no effect on mortality for emergency priority level A calls, incidence rate ratio (IRR) 0.99 (95% confidence interval (CI) 0.90–1.09). For emergency priority level B calls, an association was observed, IRR 1.26 (95% CI 1.18–1.36). DISCUSSIONS: The results shed light on the complexity of emergency call handling, but also implicate a need for further improvement. Educational interventions at the dispatch centers may improve the call handling, but also the underlying supportive tools are modifiable. The higher mortality rate for patients with emergency priority level B calls with “unclear problem categorization” could imply lowering the threshold for dispatching a high level ambulance response when the call is considered unclear. On the other hand a “benefit of the doubt” approach could hinder the adequate response to other patients in need for an ambulance as there is an increasing demand and limited resources for ambulance services. CONCLUSIONS: Age, ethnicity, day of week and time of day were significant predictors of emergency call categorization as “unclear problem”. “Unclear problem” categorization was not associated with mortality for emergency priority level A calls, but a higher mortality was observed for emergency priority level B calls