The Winner Takes it All: Risk Factors and Bayesian Modelling of the Probability of Success in Escaping from Big Cat Predation

Simple Summary

Predation is a complex behavioural interaction that is conditioned by biotic and abiotic factors. In their struggle for survival, the agents participating in the hunt interaction adapt their strategies seeking an opposite interest which leads to the same outcome, success in surviving. Predator/prey interaction data was obtained from on-line posted videos. The examination of records suggested that the species and age range of the predator, its status at the end of the hunt, the time elapsed between the sighting of its prey and the physical contact with it, the species of the prey and the relief of the land were determined success of escape of the prey in case of attack. The present study sheds light on the multietiological nature of predatory abilities and the strategies to fend off anti-predation strategies of the prey in big cats. The theoretical and empirical contents derived from this work will allow the design of environmental enrichment programs in captivity to be substantially improved by providing preys and enough space for them to express big cats predating strategies. The extrapolation of these results to domestic contexts may enable approaching selection strategies from two perspectives, with the aim to boost predating ability of domestic felids for pest control or to enhance defence in domestic ruminant prey from big cats.

Abstract

The individuals engaged in predation interactions modify their adaptation strategies to improve their efficiency to reach success in the fight for survival. This success is linked to either capturing prey (predator) or escaping (prey). Based on the graphic material available on digital platforms both of public and private access, this research aimed to evaluate the influence of those animal- and environment-dependent factors affecting the probability of successful escape of prey species in case of attack by big cats. Bayesian predictive analysis was performed to evaluate the outcomes derived from such factor combinations on the probability of successful escape. Predator species, age, status at the end of the hunting act, time lapse between first attention towards potential prey and first physical contact, prey species and the relief of the terrain, significantly conditioned (p < 0.05) escape success. Social cooperation in hunting may be more important in certain settings and for certain prey species than others. The most parsimonious model explained 36.5% of the variability in escaping success. These results can be useful to design translatable selective strategies not only seeking to boost predation abilities of domestic felids for pest control, but also, biological antipredator defence in potential domestic prey of big cats.

On the implementation of reliable early warning systems at European bathing waters using multivariate Bayesian regression modelling

For ensuring microbial safety, the current European bathing water directive (BWD) (76/160/EEC 2006) demands the implementation of reliable early warning systems for bathing waters, which are known to be subject to short-term pollution. However, the BWD does not provide clearly defined threshold levels above which an early warning system should start warning or informing the population. Statistical regression modelling is a commonly used method for predicting concentrations of fecal indicator bacteria. The present study proposes a methodology for implementing early warning systems based on multivariate regression modelling, which takes into account the probabilistic character of European bathing water legislation for both alert levels and model validation criteria. Our study derives the methodology, demonstrates its implementation based on information and data collected at a river bathing site in Berlin, Germany, and evaluates health impacts as well as methodological aspects in comparison to the current way of long-term classification as outlined in the BWD.