Aggregating the conceptualisation of movement data better captures real world and simulated animal-environment relationships

Habitat selection analysis is a widely applied statistical framework used in spatial ecology. Many of the methods used to generate movement and couple it with the environment are strongly integrated within GIScience. The choice of movement conceptualisation and environmental space can potentially have long-lasting implications on the spatial statistics used to infer movement–environment relationships. The aim of this study was to explore how systematically altering the conceptualisation of movement, environmental space and temporal resolution affects the results of habitat selection analyses using both real-world case studies and a virtual ecologist approach. Model performance and coefficient estimates did not differ between the finest conceptualisations of movement (e.g. vector and move), while substantial differences were found for the more aggregated representations (e.g. segment and area). Only segments modelled the expected movement–environment relationship with increasing linear feature resistance in the virtual ecologist approach and altering the temporal resolution identified inversions in the movement–environment relationship for vectors and moves. The results suggest that spatial statistics employed to investigate movement–environment relationships should advance beyond conceptualising movement as the (relatively) static conceptualisation of vectors and moves and replace these with (more) dynamic aggregations of longer-lasting movement processes such as segments and areal representations

The Local Food Environment of Children in London Ontario: A Methodological Comparison

The present study examined current methodological approaches to characterize the local food environment around children in London, Ontario, assessing variations in BMI and dietary preferences in relation to the choice of food environment measure. Taking advantage of a unique dataset that collected GPS trajectories of children’s schools and homes for a large sample of children between 11 and 14 years of age, two commonly-used approaches (i.e., network buffers and Euclidean buffers), and two novel measures of activity spaces (i.e., standard deviational ellipses and α-hulls) are used as ‘geographic containers’ (i.e., areal units) to derive food outlet measures. Results showed slight to low agreement in the percent of shared area between the various containers and the α-hulls. Kappa statistics further confirmed the slight to low agreement between the food outlet measures derived from activity space containers and Network and Euclidean buffer containers. There is considerable variation in the maximum number of outlets between the various group comparisons across gender, weight status and reported food outlet visit. In addition, results from logistic models point to consistent evidence of gender differences in dietary and weight outcomes across containers, but did not support an overall clear effect of food environment measures across choice of geographic container. When assessing the role of local food environment on children’s outcomes, studies should select the appropriate geographic container definition depending on whether the focus is on opportunities (accessibility) or affordances (exposure)

A Hybrid CUSUM approach to identify residency and transition periods for animal movement with an application to housed dairy cows

Over recent years there has been a significant advance in tracking technology which has allowed animal movement data to be collected with greater accuracy and in larger quantities. As a result, there has been a need for developing techniques to analyse this data and gather information such as bouts of foraging behaviour in wild animals, understanding how an animal’s movements relate to the resources within an environment or to provide potential indicators of animal welfare. The aim of this report is to develop an automated behavioural classification for residency and transition periods based on two-dimensional position data via a Hybrid CUSUM method. It is hoped that by detecting the amount of movement of an individual over a selected period of time and comparing it with expected results of healthy individuals will lead to an indication of welfare although this CUSUM method can also be used for other purposes such as gathering information about foraging patterns. The versatility of this method is that it can be applied to any animal that exhibits mostly residency and transitory behaviour. In order to automatically identify the residency and transition periods, a Hybrid CUSUM method has been developed and has been applied to a data set involving housed dairy cows in the hope to identify differences in the typical movements of lame cows against the typical movements of non-lame cows. The Hybrid CUSUM has the novelty that the standard deviation is predefined to correspond to the expected deviation of an animal whilst in a state of residency, whilst the mean is calculated directly from the data. It also has the novelty that a second algorithm is initiated once the system is in a state of out-of-control to detect when the system is back in-control and then the CUSUM will restart around a new mean value. The outcome of this report is the ability to automatically identify when cows are resident within three distinct areas of the barn (resting area, feeding area and milking area) and to imply the types of transitions between areas of the barn. In earlier work, attempts to classify the behavioural state of cows using the same data set have been achieved using accelerometer data, whereas this report uses position data to identify residency periods within a particular zone of the barn and infer types of transitions between zones such as resting to feeding or transitions within the same zone such as feeding to feeding. It is found that non-lame cows tend to move around more and spend longer total time within the feeding area than lame cows

Cooperation, conflict and warfare in wild banded mongooses

Intergroup conflict can be a strong force in the lives of social species. Conflict can be dramatic, leading to serious injury or death, loss of territory or dominance status, and can impact behaviour, reproductive success and fitness. The impact of intergroup conflict on within-group behaviour is a growing area of research, and evidence for increased affiliation between group members after exposure to intergroup conflict has been found in several species. However, these studies focus on short timescales, the minutes and hours post-conflict, and it is unclear what effect intergroup conflict has on within-group behaviour in the longer term. In this thesis I use the banded mongoose (Mungos mungo) as a model system to investigate the effects of intergroup conflict on within-group behaviour in the longer term. I discovered that group level within-group affiliation was only affected in the hour after exposure to conflict, but individual social relationships were affected into the longer term, up to two days after exposure. Unlike other studied populations, banded mongooses reduced within-group affiliation and aggression, and these changes differed between males and females, and between younger and older mongooses. I found only tentative evidence that intergroup conflict affected group movement or home range use, however, the risk of intergroup conflict affected leadership, with evidence that females lead more successfully in areas of high risk at the edge of the territory, which may indirectly affect movements in the longer term. This thesis gives evidence that intergroup conflict affects behaviour in the longer term, beginning to bridge the gap between evolutionary theory and empirical observations, and highlights that groups do not respond in a heterogeneous way, as different sex and age classes react differently, potentially due to differential costs and benefits

Profiling and Grouping Space-time Activity Patterns of Urban Individuals

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