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- Artículo con arbitraje
Local and landscape drivers of biodiversity of four groups of ants in coffee landscapes
De la Mora, Aldo ; Murnen, C. J. (coaut.) ; Philpott, Stacy M. (coaut.) ;
Contenido en: Biodiversity and Conservation Vol. 22, no. 4 (2013), p. 871–888 ISSN: 0960-3115
Resumen en: Inglés |
Resumen en inglés

Agriculture of varying management intensity dominates fragmented tropical areas and differentially impacts organisms across and within taxa. We examined impacts of local and landscape characteristics on four groups of ants in an agricultural landscape in Chiapas, Mexico comprised of forest fragments and coffee agroecosystems varying in habitat quality. We sampled ground ants found in leaf litter and rotten logs and arboreal ants found in hollow coffee twigs and on tree trunks. Then using vegetation and agro- chemical indices and conditional inference trees, we examined the relative importance of local (e.g. vegetation, elevation, agrochemical) and landscape variables (e.g. distance to and amount of nearby forest and rustic coffee) for predicting richness and abundance of ants. Leaf litter ant abundance increased with vegetation complexity; richness and abun- dance of ants from rotten logs, twig-nests, and tree trunks were not affected by vegetation complexity. Agrochemical use did not affect species richness or abundance of any ant group. Several local factors (including humus mass, degree of decay of logs, number of hollow twigs, tree circumference, and absence of fertilizers) were significant positive predictors of abundance and richness of some ant groups. Two landscape factors (forest within 200 m, and distance from forest) predicted richness and abundance of twig-nesting and leaf litter ants. Thus, different ant groups were influenced by different characteristics of agricultural landscapes, but all responded primarily to local characteristics. Given that ants provide ecosystem services (e.g. pest control) in coffee farms, understanding ant responses to local and landscape characteristics will likely inform farm management decisions.