Toward an Identification of Resources Influencing Habitat Use in a Multi-Specific Context

Interactions between animal behaviour and the environment are both shaping observed habitat use. Despite the importance of inter-specific interactions on the habitat use performed by individuals, most previous analyses have focused on case studies of single species. By focusing on two sympatric populations of large herbivores with contrasting body size, we went one step beyond by studying variation in home range size and identifying the factors involved in such variation, to define how habitat features such as resource heterogeneity, resource quality, and openness created by hurricane or forest managers, and constraints may influence habitat use at the individual level. We found a large variability among individual's home range size in both species, particularly in summer. Season appeared as the most important factor accounting for observed variation in home range size. Regarding habitat features, we found that (i) the proportion of area damaged by the hurricane was the only habitat component that inversely influenced roe deer home range size, (ii) this habitat type also influenced both diurnal and nocturnal red deer home range sizes, (iii) home range size of red deer during the day was inversely influenced by the biomass of their preferred plants, as were both diurnal and nocturnal core areas of the red deer home range, and (iv) we do not find any effect of resource heterogeneity on home range size in any case. Our results suggest that a particular habitat type (i.e. areas damaged by hurricane) can be used by individuals of sympatric species because it brings both protected and dietary resources. Thus, it is necessary to maintain the openness of these areas and to keep animal density quite low as observed in these hunted populations to limit competition between these sympatric populations of herbivores

Assessment of spring habitat selection of red deer (Cervus elapbus L.) based on census data

Data from 11 years of spring census over a 6,325 ha area were used to estimate the habitat preferences of red deer in the West Hertogenwald forest (Ardenne, Belgium). Each year, 1 to 4 censuses were organised to assess deer population density. The census method was based on a systematic coverage of the forest by experienced spotters. Each observation was described (number, sex, behaviour) and located in a GIS. Within the study area, 915 groups of non-antlered deer (hind, yearling and calf), corresponding to 2,775 individuals, were recorded during the 24 censuses of the period from 1990 to 2000. Different environmental variables describing the study area, such as forest cover (tree species and structure), canopy closure, altitude, distance to roads and pastures, were identified and integrated into the GIS. Each observation of a group of non-antlered deer was spatially related to these environmental variables to preform a habitat selection analysis. The results of this analysis showed significant preferences for certain forest types (eg spruce vs beech), and for open canopy areas. Positive correlations to high altitude areas, pastures and spruce thicket stages and a negative correlation to roads were highlighted. A comparison procedure involving GPS-collared animals in the same area was performed and showed a good correlation between census and GPS data. The methodology of this approach and the management implications of the results are discussed

Modeling recent bark stripping by red deer (Cervus elaphus) in South Belgium coniferous stands

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Using Call Data and Stigmergic Similarity to Assess the Integration of Syrian Refugees in Turkey

By absorbing more than 3.4 millions Syrians, Turkey has shown a remarkable resilience. But the host community hostility toward these newcomers is rising. Thus, the formulation of effective integration policies is needed. However, assessing the effectiveness of such policies demands tools able to measure the integration of refugees despite the complexity of such phenomena. In this work, we propose a set of metrics aimed at providing insights and assessing the integration of Syrians refugees, by analyzing the CDR dataset of the challenge. Specifically, we aim at assessing the integration of refugees, by exploiting the similarity between refugees and locals in terms of calling behavior and mobility, considering different spatial and temporal features. Together with the already known methods for data analysis, in this work we use a novel computational approach to analyze users' mobility: computational stigmergy, a bio-inspired scalar and temporal aggregation of samples. Computational stigmergy associates each sample to a virtual pheromone deposit (mark) defined in a multidimensional space and characterized by evaporation over time. Marks in spatiotemporal proximity are aggregated into functional structures called trail. The stigmergic trail summarizes the spatiotemporal dynamics in data and allows to compute the stigmergic similarity between them