Scaling laws of ambush predator 'waiting' behaviour are tuned to a common ecology

The decisions animals make about how long to wait between activities can determine the success of diverse behaviours such as foraging, group formation or risk avoidance. Remarkably, for diverse animal species, including humans, spontaneous patterns of waiting times show random ‘burstiness’ that appears scale-invariant across a broad set of scales. However, a general theory linking this phenomenon across the animal kingdom currently lacks an ecological basis. Here, we demonstrate from tracking the activities of 15 sympatric predator species (cephalopods, sharks, skates and teleosts) under natural and controlled conditions that bursty waiting times are an intrinsic spontaneous behaviour well approximated by heavy-tailed (power-law) models over data ranges up to four orders of magnitude. Scaling exponents quantifying ratios of frequent short to rare very long waits are species-specific, being determined by traits such as foraging mode (active versus ambush predation), body size and prey preference. A stochastic–deterministic decision model reproduced the empirical waiting time scaling and species-specific exponents, indicating that apparently complex scaling can emerge from simple decisions. Results indicate temporal power-law scaling is a behavioural ‘rule of thumb’ that is tuned to species’ ecological traits, implying a common pattern may have naturally evolved that optimizes move–wait decisions in less predictable natural environments.

List of species and vegetal communities of Isla Grosa (Murcia, España)

The vegetal population of Isla Grosa, island located in the neighbourhood of Mar Menos (Mediterranean Sea), is studies. It is registered the presence of the association Mayteno-Periplocetum. Likewise a registrer of other species is given, being this the first vegetal contribution to this volcanic island.

List 229: Irish and English Dendrochronology

These results cover dating undertaken since the last published list of dated building from Ireland (Brown (2002)); one English church building is also included in the list. Thanks are due to the owners of the buildings and especially to everyone who assisted in taking of the samples: Phil Barrett, Sapphire Mussen, Charles Lyons, Jon Pilcher and Mike Baillie, Amanda Pedlow, Caimin O’Brien and Martin Timoney. Most of the descriptions of the buildings are taken from the National Inventory of Architectural Heritage http://www.buildingofi reland.ie/. The correlation values were generated by CROSS84 (Munro, 1984), which provides a signifi cance level for the date to be correct; *** (extremely signifi cant), ** (very signifi cant), * (signifi cant), nsm (not signifi cant). Estimated felling date ranges are based on the Belfast sapwood estimate of 32 ± 9 years. Date ranges have been calculated by adding and subtracting 9 years from the calculated estimated felling dates. Timbers from the following buildings could not be dated. Cork: St Finbarre’s Cathedral (W 675 715); Dublin: Christchurch Cathedral (O 152 341); Galway: Cloghan Castle (M 972 119); Kilkenny: Rothe House (S 506 563); Offaly: Boveen House (S 075 956); Waterford: Christchurch Cathedral (S 616 121). Generally only single oak samples were recovered from these structures. References: D.Brown, ‘Dendrochronological dating building from Ireland’, VA 33 (2002), 71–3; M. Munro, ‘An improved algorithm for crossdating tree-ring series’, Tree-Ring Bulletin 44 (1984), 17–27.

A Bayesian Network Hybrid Model for Representing Accident and Emergency Waiting Times

The paper introduces a new modeling approach that represents the waiting times in an Accident and Emergency (A&E) Department in a UK based National Health Service (NHS) hospital. The technique uses Bayesian networks to capture the heterogeneity of arriving patients by representing how patient covariates interact to influence their waiting times in the department. Such waiting times have been reviewed by the NHS as a means of investigating the efficiency of A&E departments (Emergency Rooms) and how they operate. As a result activity targets are now established based on the patient total waiting times with much emphasis on trolley waits.