Showing papers by "Víctor Sánchez-Cordero published in 2009"

Using biotic interaction networks for prediction in biodiversity and emerging diseases.

Abstract: Networks offer a powerful tool for understanding and visualizing inter-species ecological and evolutionary interactions. Previously considered examples, such as trophic networks, are just representations of experimentally observed direct interactions. However, species interactions are so rich and complex it is not feasible to directly observe more than a small fraction. In this paper, using data mining techniques, we show how potential interactions can be inferred from geographic data, rather than by direct observation. An important application area for this methodology is that of emerging diseases, where, often, little is known about inter-species interactions, such as between vectors and reservoirs. Here, we show how using geographic data, biotic interaction networks that model statistical dependencies between species distributions can be used to infer and understand inter-species interactions. Furthermore, we show how such networks can be used to build prediction models. For example, for predicting the most important reservoirs of a disease, or the degree of disease risk associated with a geographical area. We illustrate the general methodology by considering an important emerging disease - Leishmaniasis. This data mining methodology allows for the use of geographic data to construct inferential biotic interaction networks which can then be used to build prediction models with a wide range of applications in ecology, biodiversity and emerging diseases.

Systematic conservation assessment for the Mesoamerica, Chocó, and Tropical Andes biodiversity hotspots: a preliminary analysis

Abstract: Using IUCN Red List species as biodiversity surrogates, supplemented with additional analyses based on ecoregional diversity, priority areas for conservation in Mesoamerica, Choco, and the Tropical Andes were identified using the methods of systematic conservation planning. Species’ ecological niches were modeled from occurrence records using a maximum entropy algorithm. Niche models for 78 species were refined to produce geographical distributions. Areas were prioritized for conservation attention using a complementarity-based algorithm implemented in the ResNet software package. Targets of representation for Red List species were explored from 10 to 90% of the modeled distributions at 10% increments; for the 53 ecoregions, the target was 10% for each ecoregion. Selected areas were widely dispersed across the region, reflecting the widespread distribution of Red List species in Mesoamerica, Choco, and the Tropical Andes, which underscores the region’s importance for biodiversity. In general, existing protected areas were no more representative of biodiversity than areas outside them. Among the countries in the region, the protected areas of Belize performed best and those of Colombia and Ecuador worst. A high representation target led to the selection of a very large proportion of each country except Colombia and Ecuador (for a 90% target, 83–95% of each country was selected). Since such large proportions of land cannot realistically be set aside as parks or reserves, biodiversity conservation in Mesoamerica, Choco, and the Tropical Andes will require integrative landscape management which combines human use of the land with securing the persistence of biota.

Socioeconomic context of land use and land cover change in Mexican biosphere reserves

Abstract: Land use/land cover change (LULC) is a major threat to natural protected areas worldwide. This paper explores the relationships between four estimated LULC parameters for 17 Mexican biosphere reserves (BRs) for 1993–2002 on a GIS platform, and ten socioeconomic factors obtained from census data. These relationships were tested through linear correlations and multivariate analysis. BRs showed lower human demographic pressure, but higher population dispersion, social marginality, percentage of rain-fed agriculture area, and dependence upon agriculture and cattle compared to nationwide values. BRs also varied in their indigenous population, and showed cattle overpopulation, and low immigration and road density. Socioeconomic factors explained 87% of LULC variation. High population and road density, cattle overpopulation and low percentage indigenous population were related to percentage of transformed area (2002). Conversely, small population and road density, large proportion of indigenous population and high dependency on agriculture and cattle, were related to the rate of change in transformed area (1993–2002). High human population growth and urban concentration occurred when BRs suffered higher LULC than their corresponding ecoregions. Including socioeconomic conditions prevailing in BRs and their influence on LULC in reserve management and rural development planning will improve strategies for the confluence of conservation and development goals.

A Global Public Database of Disease Vector and Reservoir Distributions

Abstract: Alexander Moffett is with UT Austin, Stavana Strutz is with UT Austin, Nelson Guda is with UT Austin, Camila Gonzalez is with National Autonomous University of Mexico, Maria Cristina Ferro is with the National Institute of Health Bogota, Victor Sanchez-Cordero is with National Autonomous University of Mexico, Sahotra Sarkar is with UT Austin.