Professional Registration of Probation Practitioners in a Devolved Welsh Probation Service

In this paper, we consider the newly implemented Probation Professional Register Policy Framework in the context of the intent of the Welsh Government (WG) to work towards the devolution of justice, including probation. Thus, we reflect on devolution and its potential implications, the specific forms of partnership working and development in Wales, probation organisational culture, and questions of probation's legitimacy. We suggest that to make the most of the professional register's potential for professionalisation of probation practice, it needs to be embedded in an organisational structure and culture that fully owns and promotes the ethics and values of partnership working, taking a rights-based approach in the support for those who cause harm to victims and communities, and evidence-based practice

The Impact of Climate Change on a Tropical Carnivore: From Individual to Species

Climate change is impacting species globally. Predicting which species will be impacted, where, when, and by how much, is vital to conserve biodiversity in a warming world. In this thesis, I evaluate the likely impacts of climate change on an endangered species, the African wild dog, Lycaon pictus, for which direct impacts of high ambient temperature on behaviour and recruitment have previously been identified. // Wild dogs hunt mainly in daylight, and I show they are unlikely to be able to adapt to a warming climate by hunting at night. I found nocturnal hunting was constrained by the availability of moonlight, and by the need to guard pups in the den, restricting the use of cooler night-time hours. I also show high ambient temperatures are associated with increased adult mortality, appearing to increase mortality due to human causes and disease, which is linked to human pressures through transmission from domestic dogs. // Having quantified the impacts of ambient temperature on key vital rates, I develop an Individual-Based Model to project the likely effects of climate change on population growth. I show that population projections for this species are sensitive to the emissions scenario and population size, with population collapse predicted for smaller populations under the worst-case scenario. // Finally, I use my Individual-Based Model to make spatially explicit predictions of population changes throughout the species’ remaining range. My model predicts that populations in cooler coastal regions will suffer the smallest population declines, along with populations located in East Africa. Predicted threat status of the species was dependant on the emissions scenario. // My study shows how behavioural and demographic data can be used to inform conservation planning in a changing climate. My findings also inform efforts to incorporate climate change impacts into assessments of species’ threat status by the IUCN Red List

Towards a devolved Probation Service in Wales: A collection of papers by the Probation Development Group

A publication on the future of the probation service in Wales by the Probation Development Group, Welsh Centre for Crime and Social Justic

Climate change is predicted to cause population collapse in a cooperative breeder

It has been suggested that animals may have evolved cooperative breeding strategies in response to extreme climatic conditions. Climate change, however, may push species beyond their ability to cope with extreme climates, and reduce the group sizes in cooperatively breeding species to a point where populations are no longer viable. Predicting the impact of future climates on these species is challenging as modelling the impact of climate change on their population dynamics requires information on both group- and individual-level responses to climatic conditions. Using a single-sex individual-based model incorporating demographic responses to ambient temperature in an endangered species, the African wild dog Lycaon pictus, we show that there is a threshold temperature above which populations of the species are predicted to collapse. For simulated populations with carrying capacities equivalent to the median size of real-world populations (nine packs), extinction risk increases once temperatures exceed those predicted in the best-case climate warming scenario (Representative Concentration Pathway [RCP] 2.6). The threshold is higher (between RCP 4.5 and RCP 6.0) for larger simulated populations (30 packs), but 84% of real-world populations number <30 packs. Simulated populations collapsed because, at high ambient temperatures, juvenile survival was so low that packs were no longer recruiting enough individuals to persist, leading them to die out. This work highlights the importance of social dynamics in determining impacts of climatic variables on social species, and the critical role that recruitment can play in driving population-level impacts of climate change. Population models parameterised on long-term data are essential for predicting future population viability under climate change

Socio-Technological Aspects of Knowledge Disclosure: The Relevance of Storytelling in Knowledge Ecology Based Organizations

IT has become an essential enabler of community members finding, disseminating, and applying knowledge. While most KM champions agree that the key lies in focusing on building an integrated Information Management System (IMS) that will allow a community to thrive under any circumstance is key, we feel that it is fundamental to focus on the social aspects of sharing knowledge. The purpose of our research is to highlight the role of the triple network - knowledge, people, technology - and more specifically, with the adoption of the Actor Network Theory, to understand how storytelling can help organizational competence to emerge from a knowledge ecosystem (which can be understood as people networks creating knowledge networks, supported by technology networks). In this sense, social computing can be seen as the way to link digital systems with social information and context to enhance the activity and performance of people, organizations, and systems

Counter intrusion software : Malware detection using structural and behavioural features and machine learning

Over the past twenty-five years malicious software has evolved from a minor annoyance to a major security threat. Authors of malicious software are now more likely to be organised criminals than bored teenagers, and modern malicious software is more likely to be aimed at stealing data (and hence money) than trashing data. The arms race between malware authors and manufacturers of anti-malware software continues apace, but despite this, the majority of anti-malware solutions still rely on relatively old technology such as signature scanning, which works well enough in the majority of cases but which has long been known to be ineffective if signatures are not updated regularly. The need for regular updating means there is often a critical window---between the publication of a flaw exploitable by malware and the distribution of the appropriate counter measures or signature. At this point a user system is open to attack by hitherto unseen malware. The object of this thesis is to determine if it is practical to use machine learning techniques to abstract generic structural or behavioural features of malware which can then be used to recognise hitherto unseen examples. Although a sizeable amount of research has been done on various ways in which malware detection might be automated, most of the proposed methods are burdened by excessive complexity. This thesis looks specifically at the possibility of using learning systems to classify software as malicious or nonmalicious based on easily-collectable structural or behavioural data. On the basis of the experimental results presented herein it may be concluded that classification based on such structural data is certainly possible, and on behavioural data is at least feasible

Coping with climate change: limited behavioral responses to hot weather in a tropical carnivore

Climate change is widely accepted to be one of the greatest threats to species globally. Identifying the species most at risk is, therefore, a conservation priority. Some species have the capacity to adapt to rising temperatures through changing their phenology, behavior, distribution, or physiology, and, therefore, may be more likely to persist under rising temperatures. Recent findings suggest that the African wild dog Lycaon pictus may be impacted by climate change, since reproductive success is consistently lower when pup-rearing coincides with periods of high ambient temperature. We used GPS collars, combined with generalized linear mixed-effects models, to assess wild dogs’ potential to adapt to high ambient temperatures through flexible timing of hunting behavior. On days with higher maximum temperatures, wild dogs showed lower daytime activity and greater nocturnal activity, although nocturnal activity did not fully balance the decrease in daytime activity, particularly during the denning period. Increases in nocturnal activity were confined mainly to moonlit nights, and were seldom observed when packs were raising pups. Our findings suggest that nocturnal activity helps this cursorial hunter to cope with high daytime temperatures. However, wild dogs appear not to use this coping strategy when they are raising pups, suggesting that their resource needs may not be fulfilled during the pup-rearing period. Given that moonlight availability—which will not change as the climate changes—constrains wild dogs’ nocturnal activity, the species may have insufficient behavioral plasticity to mitigate increasing diurnal temperatures. These findings raise concerns about climate change impacts on this endangered species, and highlight the need for behavior to be considered when assessing species’ vulnerability to climate change

A review of factors to consider when using camera traps to study animal behavior to inform wildlife ecology and conservation

Camera traps (CTs) are an increasingly popular method of studying animal behavior. However, the impact of cameras on detected individuals—such as from mechanical noise, odor, and emitted light—has received relatively little attention. These impacts are particularly important in behavioral studies in conservation that seek to ascribe changes in behavior to relevant environmental factors. In this article, we discuss three sources of bias that are relevant to conservation behavior studies using CTs: (a) disturbance caused by cameras; (b) variation in animal-detection parameters across camera models; and (c) biased detection across individuals and age, sex, and behavioral classes. We propose several recommendations aimed at mitigating responses to CTs by wildlife. Our recommendations offer a platform for the development of more rigorous and robust behavioral studies using CT technology and, if adopted, would result in greater applied benefits for conservation and management