A country bug in the city: urban infestation by the Chagas disease vector Triatoma infestans in Arequipa, Peru.

BACKGROUND: Interruption of vector-borne transmission of Trypanosoma cruzi remains an unrealized objective in many Latin American countries. The task of vector control is complicated by the emergence of vector insects in urban areas. METHODS: Utilizing data from a large-scale vector control program in Arequipa, Peru, we explored the spatial patterns of infestation by Triatoma infestans in an urban and peri-urban landscape. Multilevel logistic regression was utilized to assess the associations between household infestation and household- and locality-level socio-environmental measures. RESULTS: Of 37,229 households inspected for infestation, 6,982 (18.8%; 95% CI: 18.4 - 19.2%) were infested by T. infestans. Eighty clusters of infestation were identified, ranging in area from 0.1 to 68.7 hectares and containing as few as one and as many as 1,139 infested households. Spatial dependence between infested households was significant at distances up to 2,000 meters. Household T. infestans infestation was associated with household- and locality-level factors, including housing density, elevation, land surface temperature, and locality type. CONCLUSIONS: High levels of T. infestans infestation, characterized by spatial heterogeneity, were found across extensive urban and peri-urban areas prior to vector control. Several environmental and social factors, which may directly or indirectly influence the biology and behavior of T. infestans, were associated with infestation. Spatial clustering of infestation in the urban context may both challenge and inform surveillance and control of vector reemergence after insecticide intervention.

Spatial and temporal corroboration of a fire-scar-based fire history in a frequently burned ponderosa pine forest.

Fire scars are used widely to reconstruct historical fire regime parameters in forests around the world. Because fire scars provide incomplete records of past fire occurrence at discrete points in space, inferences must be made to reconstruct fire frequency and extent across landscapes using spatial networks of fire-scar samples. Assessing the relative accuracy of fire-scar fire history reconstructions has been hampered due to a lack of empirical comparisons with independent fire history data sources. We carried out such a comparison in a 2780-ha ponderosa pine forest on Mica Mountain in southern Arizona (USA) for the time period 1937-2000. Using documentary records of fire perimeter maps and ignition locations, we compared reconstructions of key spatial and temporal fire regime parameters developed from documentary fire maps and independently collected fire-scar data (n = 60 plots). We found that fire-scar data provided spatially representative and complete inventories of all major fire years (> 100 ha) in the study area but failed to detect most small fires. There was a strong linear relationship between the percentage of samples recording fire scars in a given year (i.e., fire-scar synchrony) and total area burned for that year (y = 0.0003x + 0.0087, r2 = 0.96). There was also strong spatial coherence between cumulative fire frequency maps interpolated from fire-scar data and ground-mapped fire perimeters. Widely reported fire frequency summary statistics varied little between fire history data sets: fire-scar natural fire rotations (NFR) differed by < 3 yr from documentary records (29.6 yr); mean fire return intervals (MFI) for large-fire years (i.e., > or = 25% of study area burned) were identical between data sets (25.5 yr); fire-scar MFIs for all fire years differed by 1.2 yr from documentary records. The known seasonal timing of past fires based on documentary records was furthermore reconstructed accurately by observing intra-annual ring position of fire scars and using knowledge of tree-ring growth phenology in the Southwest. Our results demonstrate clearly that representative landscape-scale fire histories can be reconstructed accurately from spatially distributed fire-scar samples.

Cluster analysis of structural stage classes to map wildland fuels in a Madrean ecosystem.

Geospatial information technology is changing the nature of fire mapping science and management. Geographic information systems (GIS) and global positioning system technology coupled with remotely sensed data provide powerful tools for mapping, assessing, and understanding the complex spatial phenomena of wildland fuels and fire hazard. The effectiveness of these technologies for fire management still depends on good baseline fuels data since techniques have yet to be developed to directly interrogate understory fuels with remotely sensed data. We couple field data collections with GIS, remote sensing, and hierarchical clustering to characterize and map the variability of wildland fuels within and across vegetation types. One hundred fifty six fuel plots were sampled in eight vegetation types ranging in elevation from 1150 to 2600 m surrounding a Madrean 'sky island' mountain range in the southwestern US. Fuel plots within individual vegetation types were divided into classes representing various stages of structural development with unique fuel load characteristics using a hierarchical clustering method. Two Landsat satellite images were then classified into vegetation/fuel classes using a hybrid unsupervised/supervised approach. A back-classification accuracy assessment, which uses the same pixels to test as used to train the classifier, produced an overall Kappa of 50% for the vegetation/fuels map. The map with fuel classes within vegetation type collapsed into single classes was verified with an independent dataset, yielding an overall Kappa of 80%.