Mimicry - Sheep in wolves' clothing

No abstract available

Does practice shape the brain?

No abstract available

Absolute abundance estimates from shallow water baited underwater camera surveys; a stochastic modelling approach tested against field data

This research was supported by a University of Glasgow Faculty Scholarship to KMD, a Collaborative Gearing Scheme grant from the Natural Environmental Research Council and the British Antarctic Survey (CGS-77) and an ASSEMBLE infrastructure access grant to DMB.Baited underwater cameras are becoming a popular tool to monitor fish and invertebrate populations within protected and inshore environments where trawl surveys are unsuitable. Modelling the arrival times of deep-sea grenadiers using an inverse square relationship has enabled abundance estimates, comparable to those from bottom trawl surveys, to be gathered from deep-sea baited camera surveys. Baited underwater camera systems in the shallow water environments are however, currently limited to relative comparisons of assemblages based on simple metrics such as MaxN (maximum number of fish seen at any one time). This study describes a stochastic simulation approach used to model the behaviour of fish and invertebrates around a BUC system to enable absolute abundance estimates to be generated from arrival patterns. Species-specific models were developed for the tropical reef fishes the black tip grouper (Epinephelus fasciatus) and moray eel (Gymnothorax spp.) and the Antarctic scavengers; the asteroid (Odontaster validus) and the nemertean worm (Parbolasia corrugatus). A sensitivity analysis explored the impact of input parameters on the arrival patterns (MaxN, time to the arrival of the first individual and the time to reach MaxN) for each species generated by the model. Sensitivity analysis showed a particularly strong link between MaxN and abundance indicating that this model could be used to generate absolute abundances from existing or future MaxN data. It in effect allows the slope of the MaxN vs. abundance relationship to be estimated. Arrival patterns generated by each model were used to estimate population abundance for the focal species and these estimates were compared to data from underwater visual census transects. Using a Bland-Altman analysis, baited underwater camera data processed using this model were shown to generate absolute abundance estimates that were comparable to underwater visual census data.PostprintPeer reviewe

Evolutionarily stable defence and signalling of that defence

We examine the evolution and maintenance of defence and conspicuousness in prey species using a game theoretic model. In contrast to previous works, predators can raise as well as lower their attack probabilities as a consequence of encountering moderately defended prey. Our model predicts four distinct possibilities for evolutionarily stable strategies (ESSs) featuring maximum crypsis. Namely that such a solution can exist with (1) zero toxicity, (2) a non-zero but non-aversive level of toxicity, (3) a high, aversive level of toxicity or (4) that no such maximally cryptic solution exists. Maximally cryptic prey may still invest in toxins, because of the increased chance of surviving an attack (should they be discovered) that comes from having toxins. The toxin load of maximally cryptic prey may be sufficiently strong that the predators will find them aversive, and seek to avoid similar looking prey in future. However, this aversiveness does not always necessarily trigger aposematic signalling, and highly toxic prey can still be maximally cryptic, because the increased initial rate of attack from becoming more conspicuous is not necessarily always compensated for by increased avoidance of aversive prey by predators. In other circumstances, the optimal toxin load may be insufficient to generate aversion but still be non-zero (because it increases survival), and in yet other circumstances, it is optimal to make no investment in toxins at all. The model also predicts ESSs where the prey are highly defended and aversive and where this defence is advertised at a cost of increased conspicuousness to predators. In many circumstances there is an infinite array of these aposematic ESSs, where the precise appearance is unimportant as long as it is highly visible and shared by all members of the population. Yet another class of solutions is possible where there is strong between-individual variation in appearance between conspicuous, poorly defended prey

On the evolutionary stability of zero-cost pooled-equilibrium signals

A key question in the development of understanding of animal communication has been what maintains the honesty of signals, stopping dishonesty (cheating) from spreading. The dominant theory used to address this question is a refinement of Zahavi's handicap principle. The vital thing about handicap signals is that their honesty requires that those signals are costly to the sender over and above the minimum costs associated with transmission; these costs are generally called strategic costs. An alternative "pooled equilibria" has been proposed. If signalling is constrained to two levels, then it can be demonstrated that even if there is no cost associated with giving a signal, there can be a signalling evolutionarily stable strategy (ESS) where signallers are arranged into pools according to their state: those below a threshold give one signal, those above this threshold always give the other. Further, this can be generalized to any finite number of discrete signals. Here we explore the consequence of generalizing to a continuously varying signal form. We show that unless there is some physical impediment to the diversity of signals possible, then pooled-equilibrium signalling strategies are not stable. Such a strategy would be invaded by a more complex signal, where some individuals within a "pool" benefit from signalling their difference from other individuals within the pool. We suggest that such impediments to variation in signal form will be uncommon in nature, and thus so will pooled equilibria

Consequences of variation in predator attack for the evolution of the selfish herd

There is a strong body of evidence that patterns of collective behaviour in grouping animals are governed by interactions between small numbers of individuals within the group. These findings contrast with study of the ‘selfish herd’, where increasingly complex individual-level movement rules have been proposed to explain the rapid increase in aggregation observed when prey groups are startled by or detect a predator. While individuals using simple rules take into account the position of only a few neighbours, those using complex rules incorporate multiple neighbours, and their relative distance, to determine their movement direction. Here, we simulate the evolution of selfish herd behaviour to assess the conditions under which simple and complex movement rules might evolve, explicitly testing predictions arising from previous work. We find that complex rules outperform simple ones under a range of predator attack strategies, but that simple rules can fix in populations particularly when they are already in the majority, suggesting strong positive frequency dependence in rule success. In addition, we explore whether a movement rule derived from studies of collective behaviour (where individuals use the position of seven neighbours to determine movement direction) performs as successfully as more complex rules, finding again positive frequency dependence in rule success, and a particular role for predator attack strategy (from within or outside the group).PostprintPeer reviewe