Linking Changes in Landscape Composition and Configuration with Sandfly Occurrence in Southwest France

Abstract

In recent years, several vector-borne, parasitic or Provided that landscape elements critical to the parasite, zoonotic diseases have (re)-emerged and spread in Europe and vector or host survival are known and can be detected by elsewhere with major health, ecological, socio-economical and remote sensing, satellite imagery, through a wide range of political consequences. One of these diseases is leishmaniasis. In spectral, spatial and temporal resolutions, and geographical southwestern France, it is transmitted by two sandfly vectors information systems (GIS) provide efficient tools for locating (Phlebotomus ariasi and Phlebotomus perniciosus). The objective environments capable of maintaining vector populations [1, 2, of this research was to assess the effects of land cover and land cover change and fragmentation on the spatial distribution of 3, 4, 5]. Spatial aspects, particularly landscape and land-use Phlebotomus ariasi in southwestern France. Using GIS and patterns, play an important role in the transmission of vectorremote sensing techniques we analysed the relationships between borne and zoonotic diseases since they control both the spatial vectors and landscape-level environmental variables based on distribution of vectors or hosts and the likelihood of contact time series of fine resolution satellite data over the last two between infectious vector and susceptible host [6, 7]. decades. Environmental variables were extracted from Landsat If the distribution of a disease can be mapped (often by TM images and included both landscape composition and mapping the distribution of its vector or intermediate host) configuration. Changes in landscape composition and . rr * a *n*r*eti* configuration were analysed and a logistic regression was used to ctroleffrs in e ndemic situations a nd interventio test the association between sandfly presence/absence and these strategies in epidemic situations may be more efficiently variables. The study suggested that, although relevant changes directed [3]. The combination of different methods and tools, in landscape composition and structure were not found between especially high resolution remote sensing, GIS, and1984 and 2003, there is a significant association with some multivariate statistical analysis is promising for an integrated environmental variables describing sandfly habitat.approach of spatial issues in epidemiology.

Integrated Mapping of Establishment Risk for Emerging Vector-Borne Infections: A Case Study of Canine Leishmaniasis in Southwest France

“…July was chosen because it was found to be the summer month when both vectors were abundant. The study area, situated between latitude 42°20′N and 43°40′N and between longitude 0°E and 3°E, encompassed the forested southwest foothills of the Massif Central (the Montagne Noire), the forested northeast foothills of the eastern Pyrenees mountains and the intervening lower ground that has a land cover containing more settlements and arable crops [23] . The altitude of collection sites ranged from 96.8 to 811 metres above sea level as measured by GPS.…”

“…The statistical model developed to predict sandfly abundances used two sorts of satellite data; high resolution (30 m pixels) LANDSAT TM data and low resolution (1 km pixels) Terra MODIS and other data. The LANDSAT data were those previously described and used by Martínez et al [23] and the MODIS data and their processing are described by Scharlemann et al [24] . The high resolution imagery, which had previously been classified into land-cover classes (see Table 1 in Martinez et al [23] ), was used to provide several predictor variables reflecting land-cover and composition that were thought to be important for sandflies.…”

“…The LANDSAT data were those previously described and used by Martínez et al [23] and the MODIS data and their processing are described by Scharlemann et al [24] . The high resolution imagery, which had previously been classified into land-cover classes (see Table 1 in Martinez et al [23] ), was used to provide several predictor variables reflecting land-cover and composition that were thought to be important for sandflies. These derived variables included shape (as documented by the Shape Index in FRAGSTATS [25] ) and size of forest patches and crop patches (aggregated crop classes), the distance between forest patches (‘proximity’), the proportion of the surrounding area occupied by urban areas, several crops, pasture, coniferous, deciduous and sclerophylous forests and other types of vegetation, and the Shannon's diversity index as defined by Forman [26] .…”

Leishmaniasis emergence in Europe

“…However, bioclimate zones and their vegetation indicators vary regionally, and ongoing climate change may alter the patterns of land cover and land use. The geographic information system (GIS)-based spatial modelling of the Emerging Diseases in a changing European Environment (EDEN) project is permitting an analysis of changes in climate and land cover [64] and their effects on sandflies.…”

“…urbanisation, deforestation) and socio-economic patterns Deforestation and urbanisation are known to affect leishmaniasis worldwide [6] because of the associations of many vectors and reservoirs with natural or rural areas. Based on the EDEN partners' findings, most Mediterranean regions have at least one vector associated more closely with rural or peri-urban zones [64]. From 1945, most of the socio-economic changes favoured a reduction in 'infantile visceral leishmaniasis' (caused by L. infantum) in southern Europe, including better nutrition, widespread insecticide spraying (against malaria-transmitting mosquitoes), better housing and a reduction in the rural population.…”