Peter Zeilhofer

Habitat suitability mapping of Anopheles darlingi in the surroundings of the Manso hydropower plant reservoir, Mato Grosso, Central Brazil

BackgroundHydropower plants provide more than 78 % of Brazils electricity generation, but the countrys reservoirs are potential new habitats for main vectors of malaria. In a case study in the surroundings of the Manso hydropower plant in Mato Grosso state, Central Brazil, habitat suitability of Anopheles darlingi was studied. Habitat profile was characterized by collecting environmental data. Remote sensing and GIS techniques were applied to extract additional spatial layers of land use, distance maps, and relief characteristics for spatial model building.ResultsLogistic regression analysis and ROC curves indicate significant relationships between the environment and presence of An. darlingi. Probabilities of presence strongly vary as a function of land cover and distance from the lake shoreline. Vector presence was associated with spatial proximity to reservoir and semi-deciduous forests followed by Cerrado woodland. Vector absence was associated with open vegetation formations such as grasslands and agricultural areas. We suppose that non-significant differences of vector incidences between rainy and dry seasons are associated with the availability of anthropogenic breeding habitat of the reservoir throughout the year.ConclusionSatellite image classification and multitemporal shoreline simulations through DEM-based GIS-analyses consist in a valuable tool for spatial modeling of A. darlingi habitats in the studied hydropower reservoir area. Vector presence is significantly increased in forested areas near reservoirs in bays protected from wind and wave action. Construction of new reservoirs under the tropical, sub-humid climatic conditions should therefore be accompanied by entomologic studies to predict the risk of malaria epidemics.

Distribution of phlebotomine sandflies (Diptera: Psychodidae) of medical importance in Mato Grosso State, Brazil

Intense environmental impacts, causing alterations of the natural habitats of fauna, including those of sandfly disease vectors are observed in Mato Grosso State, Central Brazil. Entomologic survey of phlebotomines was based on light trap and was carried out by entomological nucleus of the FUNASA and SES in the period between 1996 and 2001. Eighty eight species were identified, including the following sandflies with medical importance to leishmaniasis: Lutzomyia amazonensis, L. anduzei, L. antunesi, L. ayrozai, L. carrerai carrerai, L. complexa, L. cruzi, L. flaviscutellata, L. intermedia, L. longipalpis, L. migonei, L. paraensis, L. ubiquitalis, L. whitmani and L. yuilli yuilli. Most sandflies of medical importance occurred in the Amazon forest and savannah. L. longipalpis and L. cruzi had high densities in the savannah region. L. flaviscutellata is predominating in both the Amazon forest and the savannah region. L. whitmani and L. antunesi were sampled in the Amazon forest, savannah and marsh land.

Spatial modelling of Lutzomyia (Nyssomyia) whitmani s.l. (Antunes & Coutinho, 1939) (Diptera: Psychodidae: Phlebotominae) habitat suitability in the state of Mato Grosso, Brazil.

Lutzomyia (Nyssomyia) whitmani s.l.is the main vector of cutaneous leishmaniasis in state of Mato Grosso, but little is known about environmental determinants of its spatial distribution on a regional scale. Entomologic surveys of this sand fly species, conducted between 1996 and 2001 in 41 state municipalities, were used to investigate the relationships between environmental factors and the presence of the species, and to develop a spatial model of habitat suitability. The relationship between averaged CDC light trap indexes and 15 environmental and socio-economic factors were tested by logistic regression (LR) analysis. Spatial layers of deforestation tax and the Brazilian index of gross net production (IGNP) were identified as significant explanatory variables for vector presence in the LR model, and these were then overlaid with habitat maps. The highest habitat suitability in 2001 was obtained for the heavily deforested areas in the Central-North, South, East, and Southwest of Mato Grosso, particularly in municipalities with lower IGNP values.

Geographic information systems and logistic regression for high-resolution malaria risk mapping in a rural settlement of the southern Brazilian Amazon

BackgroundIn Brazil, 99% of the cases of malaria are concentrated in the Amazon region, with high level of transmission. The objectives of the study were to use geographic information systems (GIS) analysis and logistic regression as a tool to identify and analyse the relative likelihood and its socio-environmental determinants of malaria infection in the Vale do Amanhecer rural settlement, Brazil.MethodsA GIS database of georeferenced malaria cases, recorded in 2005, and multiple explanatory data layers was built, based on a multispectral Landsat 5 TM image, digital map of the settlement blocks and a SRTM digital elevation model. Satellite imagery was used to map the spatial patterns of land use and cover (LUC) and to derive spectral indices of vegetation density (NDVI) and soil/vegetation humidity (VSHI). An Euclidian distance operator was applied to measure proximity of domiciles to potential mosquito breeding habitats and gold mining areas. The malaria risk model was generated by multiple logistic regression, in which environmental factors were considered as independent variables and the number of cases, binarized by a threshold value was the dependent variable.ResultsOut of a total of 336 cases of malaria, 133 positive slides were from inhabitants at Road 08, which corresponds to 37.60% of the notifications. The southern region of the settlement presented 276 cases and a greater number of domiciles in which more than ten cases/home were notified. From these, 102 (30.36%) cases were caused by Plasmodium falciparum and 174 (51.79%) cases by Plasmodium vivax. Malaria risk is the highest in the south of the settlement, associated with proximity to gold mining sites, intense land use, high levels of soil/vegetation humidity and low vegetation density.ConclusionsMid-resolution, remote sensing data and GIS-derived distance measures can be successfully combined with digital maps of the housing location of (non-) infected inhabitants to predict relative likelihood of disease infection through the analysis by logistic regression. Obtained findings on the relation between malaria cases and environmental factors should be applied in the future for land use planning in rural settlements in the Southern Amazon to minimize risks of disease transmission.

Land use effects on water quality in the urban agglomeration of Cuiabá and Várzea Grande, Mato Grosso State, central Brazil

This study investigates the relationship between spatial patterns of water quality and land occupation in the cities of Cuiabá and Várzea Grande, Mato Grosso state, Brazil, applying Geographic Information System (GIS) techniques and Polynomial Redundancy Analysis. The results show a strong relationship between water quality and population density, urbanization rate and regionalized low water runoff. Higher treatment rates improve oxygenation levels, but do not reduce concentrations of total nitrogen, phosphorus and coliforms, indicating inefficiency of the sanitation infrastructure. The presence of more sophisticated treatment systems positively influenced catchment pollution levels. Sub-catchments which impact water quality of the Cuiabá River throughout the year typically deliver loads of mixed origin (impervious surfaces and untreated domestic wastewater). Monitoring stations downstream of the urban reach of the river show a trend of chemical and biological degradation.

Unidades fitofisionômicas em mesoescala no Pantanal Norte e suas relações com a geomorfologia

The purpose of this work was to characterize and quantify phytophysiognomic units at mesoscale and evaluate their relationships with flood intensity, flood duration and topography. The study was conducted at a 25 km2 Long-Term Sampling Site (LTSS), located within the floodplain of the Cuiaba River in the Pantanal of Mato Grosso. The phytophysiognomic units were determined from the supervised classification of CBERS 2B sensor CCD satellite images. The maps of flood intensity, flood duration and of topography were generated from the spatial interpolation of planialtimetric field surveys. Four phytophysiognomic units were identified; Campo inundavel (flooded grassland) was the unit with the largest area of occurrence, followed by Cordilheiras (dense arboreal savanna), Landizal (seasonally flooded evergreen forest), and Cambarazal (monodominant Vochysia divergens Pohl. forest), respectively. Campo inundavel is distributed mainly in areas of high flood intensity and duration and low topographic positions. Cambarazal predominates in areas of medium flood intensity and duration and intermediate elevation, Landizal in areas of high flood intensity and duration and low topographic position, whereas Cordilheiras are characterized by low flood intensity and duration and location in higher elevations. Statistical tests indicated that flood intensity is the main factor responsible for the mesoscale distribution of phytophysiognomic units in the northern Pantanal.

Relationship between land use/cover and surface temperatures in the urban agglomeration of Cuiabá-Várzea Grande, Central Brazil

We focus on the surface urban heat island (SUHI) and the spatiotemporal relationship between land use and surface temperatures (Ts) in Cuiabá-Várzea Grande, Mato Grosso, one of the major urban agglomerations of central-western Brazil, which has suffered intense urbanization processes since the 1960s. Supervised maximum likelihood classifications of optical bands of Landsat Thematic Mapper (Landsat TM) imagery from 1986 and 2007 are applied to generate land use/cover maps. Surface emissivity is determined using the logarithmic transformation of the normalized difference vegetation index. The Ts is retrieved from the thermal bands utilizing a radiative transfer equation. In both cities, urban expansion followed two main development axes, which are reflected in the spatial patterns of Ts. The highest values of Ts were found in bare soil and urbanized areas. Between 1986 and 2007, Ts increased 0.96°C on average and a maximum of 5.49°C in the urban agglomeration. The SUHI in Várzea Grande suffered intensification with an increase of 1.34°C in the downtown area. This tendency was stronger in the center of Cuiabá, where Ts increased 3.12°C. Slowing this rapid rate of temperature increase would demand decisive intervention by municipal authorities, such as restricting annual occupation taxes, reducing the occupation coefficient in new districts, preserving native vegetation, and designating new green areas.

Soil Mapping in the Pantanal of Mato Grosso, Brazil, using Multitemporal Landsat TM data

KeywordsMultitemporal data sets from the Landsat Thematic Mapper (TM) were used to evaluate their applicability for exploratory soil mapping in the floodplain of the Northern Pantanal of Mato Grosso, Brazil. Fifty-four soil profiles were classified into 21 soil units according to the FAO–UNESCO system. Information layers of vegetation types and dynamics of flooding were elaborated by applying supervised hierarchical classification rules. Geomorphologic units were mapped by visual image interpretation. Multinomial logistic regression was applied to test relations between thematic layers and soil units as well as aggregated soil clusters, developing a statistical mapping model. Northern Pantanal floodplain soils show a high variability as a function of age and granulemetry of underlying sediments, as well as soil moisture and flooding regimes. GIS layers of nine vegetation formations, three geomorphologic units and three multi-temporal moisture types were elaborated. Cross-tabulations and multinomial logistic regression models indicate significant relations between FAO–UNESCO soil units and GIS layers. As soil sampling density had been low, a final predictive model was developed for the mapping of six aggregated soil clusters, obtaining a high significance level (p<0.05) for prediction. Applied methodology was found to be appropriate to develop models on soil–landscape relationships and improve information on spatial distribution of soil groupings in the Northern Pantanal.

Comparison of capture methods for the diagnosis of adult anopheline populations from State of Mato Grosso, Brazil

INTRODUCTION The present study compares human landing catches of primary malaria vectors with two alternative methods of capture: the Shannon trap and the Mosquito magnet. METHODS This study used regression models to adjust capture data to a negative binominal distribution. RESULTS Capture numbers and relative percentages obtained from the three methods vary strongly between species. The highest overall captures were obtained for Anopheles triannulatus with captures for the Shannon trap and the Mosquito magnet measuring more than 330% higher than captures obtained by human landings. For Anopheles darlingi, captures by the Shannon trap and the Mosquito magnet were about 14% and 26% of human landing catches, respectively. Another species with malaria transmission potential that was not sampled by human landing captures weascaptured by the Shannon trap and the Mosquito magnet (Anopheles oswaldoi). Both alternative sampling techniques can predict the human landing of Anopheles triannulatus, but without proportionality. Models for Anopheles darlingi counts, after totaling daily captures, are significant and proportional, but prediction models are more reliable when using the Shannon trap compared with the Mosquito magnet captures. CONCLUSIONS These alternative capture methods can be partially recommended for the substitution of human landing captures or, at least, as complementary forms of monitoring for malarial mosquitoes.

Seasonal variations in litter production and its relation with MODIS vegetation indices in a semi-deciduous forest of Mato Grosso

Litter production is related to canopy processes including the timing and amount of leaf development, reproduction and net primary production. However, quantifying spatial and temporal patterns in litter production is complicated in Amazonian semi-deciduous forests because of high spatial heterogeneity and seasonal variation in rainfall. Here, we use monthly measurements of litter production and Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) composites over a 6-year period to assess whether MODIS can be used to quantify litter production of tropical semi-deciduous forests. Original MODIS NDVI and EVI values were poorly related to the seasonal periodicity in litter production, but after using singular spectrum analysis (SSA) signal extraction techniques, clear relationships between litter production and the NDVI and EVI emerged. These results indicate that MODIS NDVI and EVI data are useful for detecting temporal patterns in litter production for Amazonian semi-deciduous forests if signal extraction analyses such as SSA are conducted.