Abstract
Ecological indicators of habitat and biodiversity in a Neotropical landscape: multitaxonomic perspective. The use of indicator species to characterize specific ecological areas is of high importance in conservation/restoration biology. The objective of this study was to identify indicator species of diverse taxa that characterize different landscape units, and to better understand how management alters species composition. We identified two ecomosaics, tropical rain forest and the agricultural matrix, each one comprised of four landscape units. The taxonomic groups studied included birds (highly mobile), butterflies (moderately mobile), terrestrial gastropods (less mobile) and trees (sessile). Sampling efficiency for both ecomosaics was > or = 86%. We found 50 mollusks, 74 butterflies, 218 birds and 172 tree species, for a total of 514 species. Using ordination and cluster analysis, we distinguished three habitat types in the landscape: tropical rainforest, secondary vegetation and pastures with scattered trees and live fences. The InVal (> or = 50%) method identified 107 indicator species, including 45 tree species, 38 birds, 14 butterflies and 10 gastropods. Of these, 35 trees, 10 birds, four butterflies and eight gastropods were forest indicators. Additionally, 10, 28, 10 and two species, respectively per group, were characteristic of the agricultural matrix. Our results revealed a pattern of diversity decrease of indicator species along the rainforest-secondary forest-pasture gradient. In the forest, the gastropods Carychium exiguum, Coelocentrum turris, Glyphyalinia aff. indentata y Helicina oweniana were significantly correlated (p < 0.05) with 90% of the other groups of flora and fauna indicator species. These findings suggest that gastropods may be good indicators of forest habitat quality and biodiversity. The secondary vegetation is an intermediate disturbance phase that fosters high diversity in the agricultural matrix. We exemplify a multitaxa approach, including mesofauna, for ecological monitoring of agricultural landscapes.
Highlights
De manera complementaria, se ha considerado que la conservación biológica debe contar con una cantidad apropiada de áreas eficientemente protegidas (Turner 1996) alternadas con matrices de uso de suelo de alta calidad ecológica, que faciliten el refugio de la flora y fauna silvestre (Perfecto & Vandermeer 2002, Cook et al 2004, Kupfer et al 2006)
AJ=vegetación secundaria, acahual o barbecho joven, AL=árboles en línea formando cercos vivos, AM=vegetación secundaria, acahual o barbecho maduro, MA=Matriz agropecuaria, PAD=potrero con árboles dispersos, RE=Bosque tropical lluvioso, REAJ=bosque referente de vegetación secundaria joven en ladera, REAM=bosque referente de vegetación secundaria madura en cima de montaña, REL=perturbaciones lineales o caminos en el bosque, REPAD=bosque referente de potreros en partes planas
AJ=young secondary vegetation or young fallow, AL=trees in a linear arrangement forming live fences, AM=mature secondary vegetation or mature fallow, MA=agricultural matrix, PAD=pasture with scattered trees, RE=tropical rainforest, REAJ=young secondary, hillside reference vegetation, REAM=mature secondary, hill crest reference vegetation, REL=linear disturbance or paths in forests, REPAD=forest reference vegetation for pastures with scattered trees in flat land
Summary
Zona de estudio: El estudio se llevó a cabo en el ejido Niños Héroes de Chapultepec (en adelante “ejido”), municipio de Tenosique, Estado de Tabasco, Sureste de México, que se localiza entre las coordenadas 17°15’00” - 17°40’48” N y 90°59’09” - 91°38’16” O (Ochoa-Gaona et al 2008). La herramienta InVal permite identificar a aquellos táxones asociados con mayor peso a las diferentes unidades que configuran el paisaje del ejido, siendo de mayor relevancia aquellas que identifican al bosque, como ecosistema de referencia para la zona. Siguiendo a Schulze et al (2004) y a Heino et al (2009), para evaluar la posible existencia de relaciones entre las diferentes especies indicadoras del bosque, se aplicó una correlación de Pearson, mediante el uso del número de registros de los individuos con un InVal ≥50%, se usó esta porque permite relacionar dos variables de manera adimensional (Ruiz-Díaz et al 1998).
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