Lessons from integrating behaviour and resource selection: activity-specific responses of African wild dogs to roads

Understanding how anthropogenic features affect species' abilities to move within landscapes is essential to conservation planning and requires accurate assessment of resource selection for movement by focal species. Yet, the extent to which an individual's behavioural state (e.g. foraging, resting, commuting) influences resource selection has largely been ignored. Recent advances in Global Positioning System (GPS) tracking technology can fill this gap by associating distinct behavioural states with location data. We investigated the role of behaviour in determining the responses of an endangered species of carnivore, the African wild dog Lycaon pictus, to one of the most widespread forms of landscape alteration globally: road systems. We collected high‐resolution GPS and activity data from 13 wild dogs in northern Botswana over a 2‐year period. We employed a step selection framework to measure resource selection across three behavioural states identified from activity data (high‐speed running, resting and travelling) and across a gradient of habitats and seasons, and compared these outputs to a full model that did not parse for behaviour. The response of wild dogs to roads varied markedly with both the behavioural and the landscape contexts in which roads were encountered. Specifically, wild dogs selected roads when travelling, ignored roads when high‐speed running and avoided roads when resting. This distinction was not evident when all movement data were considered together in the full model. When travelling, selection for roads increased in denser vegetative environments, suggesting that roads may enhance movement for this species. Our findings indicate that including behavioural information in resource selection models is critical to understanding wildlife responses to landscape features and suggest that successful application of resource selection analyses to conservation planning requires explicit examination of the behavioural contexts in which movement occurs. Thus, behaviour‐specific step selection functions offer a powerful tool for identifying resource selection patterns for animal behaviours of conservation significance

Deforestation and wildlife management: are elephants attracted by recently deforested areas?

Deforestation is a major cause or wildlife decline in tropical ecosystems. The conversion of mature forest to fields by shifting cultivation leaves behind follow lands with secondary vegetation. Paradoxically, secondary forest regrowth that provides abundant forage in comparison with mature forests can benefit some species as the African elephant (Loxodonta africana) but they are also attracted towards human communities and cultivations raising conservation issues. The study was conducted in Gile National Reserve, Mozambique, an unfenced protected area composed of Miombo woodland. Among 60 elephants remaining in the Reserve, 5 individuals were equipped with GPS collars in 2014 in 2016. Deforestation was monitored by remote sensing from 1990 to 2016 and a map of forest productivity was built To test our hypothesis, we modelled resource selection functions using the GPS data. Elephants spend about half of their time in the core area and half in the buffer zone where most of the deforestation occurs. Elephants neither prefer nor avoid pristine forest habitats and cleared between 1990 and 2005. They prefer areas cleared since 2005 where forest regrowth occurred since 2009. The areas the most selected were cleared between 2010 and 2013 and were in cultivation during the study. Shifting agriculture leads to the displacement elephants toward cultivated fields and regenerating forest vegetation thus increasing Human/Elephant conflicts. This resource selection strategy also raises conservation questions related to the Reserve management aiming at reducing deforestation. Diversity of habitats should be maintained and resource selection linked to vegetation dynamics should be further understood

Optimizing cooperative cognitive radio networks with opportunistic access

Optimal resource allocation for cooperative cognitive radio networks with opportunistic access to the licensed spectrum is studied. Resource allocation is based on minimizing the symbol error rate at the receiver. Both the cases of all-participate relaying and selective relaying are considered. The objective function is derived and the constraints are detailed for both scenarios. It is then shown that the objective functions and the constraints are nonlinear and nonconvex functions of the parameters of interest, that is, source and relay powers, symbol time, and sensing time. Therefore, it is difficult to obtain closed-form solutions for the optimal resource allocation. The optimization problem is then solved using numerical techniques. Numerical results show that the all-participate system provides better performance than its selection counterpart, at the cost of greater resources

An Online Outlier Detection Technique for Wireless Sensor Networks

We propose an online and local outlier detection technique with low resource consumption based on an unsupervised centered quarter-sphere support vector machine for wireless sensor networks. Using synthetic data, we demonstrate that our technique achieves better mining performance in terms of parameter selection using difference kernel functions compared to an earlier o²ine outlier detection technique