Genetic analysis of the breeding system of an invasive subterranean termite, Reticulitermes santonensis, in urban and natural habitats.

Reticulitermes santonensis is a subterranean termite that invades urban areas in France and elsewhere where it causes damage to human-built structures. We investigated the breeding system, colony and population genetic structure, and mode of dispersal of two French populations of R. santonensis. Termite workers were sampled from 43 and 31 collection points, respectively, from a natural population in west-central France (in and around the island of Oleron) and an urban population (Paris). Ten to 20 workers per collection point were genotyped at nine variable microsatellite loci to determine colony identity and to infer colony breeding structure. There was a total of 26 colonies, some of which were spatially expansive, extending up to 320 linear metres. Altogether, the analysis of genotype distribution, F-statistics and relatedness coefficients suggested that all colonies were extended families headed by numerous neotenics (nonwinged precocious reproductives) probably descended from pairs of primary (winged) reproductives. Isolation by distance among collection points within two large colonies from both populations suggested spatially separated reproductive centres with restricted movement of workers and neotenics. There was a moderate level of genetic differentiation (F(ST) = 0.10) between the Oleron and Paris populations, and the number of alleles was significantly higher in Oleron than in Paris, as expected if the Paris population went through bottlenecks when it was introduced from western France. We hypothesize that the diverse and flexible breeding systems found in subterranean termites pre-adapt them to invade new or marginal habitats. Considering that R. santonensis may be an introduced population of the North American species R. flavipes, a breeding system consisting primarily of extended family colonies containing many neotenic reproductives may facilitate human-mediated spread and establishment of R. santonensis in urban areas with harsh climates.

Classification of urban multi-angular image sequences by aligning their manifolds

When dealing with multi-angular image sequences, problems of reflectance changes due either to illumination and acquisition geometry, or to interactions with the atmosphere, naturally arise. These phenomena interplay with the scene and lead to a modification of the measured radiance: for example, according to the angle of acquisition, tall objects may be seen from top or from the side and different light scatterings may affect the surfaces. This results in shifts in the acquired radiance, that make the problem of multi-angular classification harder and might lead to catastrophic results, since surfaces with the same reflectance return significantly different signals. In this paper, rather than performing atmospheric or bi-directional reflection distribution function (BRDF) correction, a non-linear manifold learning approach is used to align data structures. This method maximizes the similarity between the different acquisitions by deforming their manifold, thus enhancing the transferability of classification models among the images of the sequence.

Geospatial approach for defining the Wildland-Urban Interface in the Alpine environment

Problems related to fire hazard and fire management have become in recent decades one of the most relevant issues in the Wildland-Urban Interface (WUI), that is the area where human infrastructures meet or intermingle with natural vegetation. In this paper we develop a robust geospatial method for defining and mapping the WUI in the Alpine environment, where most interactions between infrastructures and wildland vegetation concern the fire ignition through human activities, whereas no significant threats exist for infrastructures due to contact with burning vegetation. We used the three Alpine Swiss cantons of Ticino, Valais and Grisons as the study area. The features representing anthropogenic infrastructures (urban or infrastructural components of the WUI) as well as forest cover related features (wildland component of the WUI) were selected from the Swiss Topographic Landscape Model (TLM3D). Georeferenced forest fire occurrences derived from the WSL Swissfire database were used to define suitable WUI interface distances. The Random Forest algorithm was applied to estimate the importance of predictor variables to fire ignition occurrence. This revealed that buildings and drivable roads are the most relevant anthropogenic components with respect to fire ignition. We consequently defined the combination of drivable roads and easily accessible (i.e. 100 m from the next drivable road) buildings as the WUI-relevant infrastructural component. For the definition of the interface (buffer) distance between WUI infrastructural and wildland components, we computed the empirical cumulative distribution functions (ECDF) of the percentage of ignition points (observed and simulated) arising at increasing distances from the selected infrastructures. The ECDF facilitates the calculation of both the distance at which a given percentage of ignition points occurred and, in turn, the amount of forest area covered at a given distance. Finally, we developed a GIS ModelBuilder routine to map the WUI for the selected buffer distance. The approach was found to be reproducible, robust (based on statistical analyses for evaluating parameters) and flexible (buffer distances depending on the targeted final area covered) so that fire managers may use it to detect WUI according to their specific priorities.