Fine-Scale Movements of the Broadnose Sevengill Shark and Its Main Prey, the Gummy Shark

Information on the fine-scale movement of predators and their prey is important to interpret foraging behaviours and activity patterns. An understanding of these behaviours will help determine predator-prey relationships and their effects on community dynamics. For instance understanding a predator's movement behaviour may alter pre determined expectations of prey behaviour, as almost any aspect of the prey's decisions from foraging to mating can be influenced by the risk of predation. Acoustic telemetry was used to study the fine-scale movement patterns of the Broadnose Sevengill shark Notorynchus cepedianus and its main prey, the Gummy shark Mustelus antarcticus, in a coastal bay of southeast Tasmania. Notorynchus cepedianus displayed distinct diel differences in activity patterns. During the day they stayed close to the substrate (sea floor) and were frequently inactive. At night, however, their swimming behaviour continually oscillated through the water column from the substrate to near surface. In contrast, M. antarcticus remained close to the substrate for the entire diel cycle, and showed similar movement patterns for day and night. For both species, the possibility that movement is related to foraging behaviour is discussed. For M. antarcticus, movement may possibly be linked to a diet of predominantly slow benthic prey. On several occasions, N. cepedianus carried out a sequence of burst speed events (increased rates of movement) that could be related to chasing prey. All burst speed events during the day were across the substrate, while at night these occurred in the water column. Overall, diel differences in water column use, along with the presence of oscillatory behaviour and burst speed events suggest that N. cepedianus are nocturnal foragers, but may opportunistically attack prey they happen to encounter during the day

Spatial memory in the grey mouse lemur (Microcebus murinus)

Wild animals face the challenge of locating feeding sites distributed across broad spatial and temporal scales. Spatial memory allows animals to find a goal, such as a productive feeding patch, even when there are no goal-specific sensory cues available. Because there is little experimental information on learning and memory capabilities in free-ranging primates, the aim of this study was to test whether grey mouse lemurs (Microcebus murinus), as short-term dietary specialists, rely on spatial memory in relocating productive feeding sites. In addition, we asked what kind of spatial representation might underlie their orientation in their natural environment. Using an experimental approach, we set eight radio-collared grey mouse lemurs a memory task by confronting them with two different spatial patterns of baited and non-baited artificial feeding stations under exclusion of sensory cues. Positional data were recorded by focal animal observations within a grid system of small foot trails. A change in the baiting pattern revealed that grey mouse lemurs primarily used spatial cues to relocate baited feeding stations and that they were able to rapidly learn a new spatial arrangement. Spatially concentrated, non-random movements revealed preliminary evidence for a route-based restriction in mouse lemur space; during a subsequent release experiment, however, we found high travel efficiency in directed movements. We therefore propose that mouse lemur spatial memory is based on some kind of mental representation that is more detailed than a route-based network map

Bayesian Inference for Multistate ‘Step and Turn’ Animal Movement in Continuous Time

Mechanistic modelling of animal movement is often formulated in discrete time despite problems with scale invariance, such as handling irregularly timed observations. A natural solution is to formulate in continuous time, yet uptake of this has been slow. This lack of implementation is often excused by a difficulty in interpretation. Here we aim to bolster usage by developing a continuous-time model with interpretable parameters, similar to those of popular discrete-time models that use turning angles and step lengths. Movement is defined by a joint bearing and speed process, with parameters dependent on a continuous-time behavioural switching process, creating a flexible class of movement models. Methodology is presented for Markov chain Monte Carlo inference given irregular observations, involving augmenting observed locations with a reconstruction of the underlying movement process. This is applied to well-known GPS data from elk (Cervus elaphus), which have previously been modelled in discrete time. We demonstrate the interpretable nature of the continuous-time model, finding clear differences in behaviour over time and insights into short-term behaviour that could not have been obtained in discrete time

Movement patterns of an arboreal marsupial at the edge of its range: a case study of the koala

Background: Conservation strategies derived from research carried out in one part of the range of a widely distributed species and then uniformly applied over multiple regions risk being ineffective due to regional variations in species-habitat relationships. This is particularly true at the edge of the range where information on animal movements and resource selection is often limited. Here, we investigate home range size, movement patterns and resource selection of koalas Phascolarctos cinereus in the semi-arid and arid landscapes of southwest Queensland, Australia. We placed collars with GPS units on 21 koalas in three biogeographic regions. Home range sizes, resource selection and movement patterns were examined across the three regions