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BIODIVERSITY ASSESSMENT USING STRUCTURED INVENTORY: CAPTURING THE ANT FAUNA OF A TROPICAL RAIN FOREST
Author(s) -
Longino John T.,
Colwell Robert K.
Publication year - 1997
Publication title -
ecological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.864
H-Index - 213
eISSN - 1939-5582
pISSN - 1051-0761
DOI - 10.1890/1051-0761(1997)007[1263:bausic]2.0.co;2
Subject(s) - fauna , biodiversity , ecology , fogging , rainforest , malaise , biology , environmental science , physics , optics , immunology
The goal of “strict inventory” (as opposed to community characterization) is to obtain species lists for specific sites. Quantitatively structured inventory can improve inventory efficiency (defined as the steepness of species accumulation curves). As part of the Arthropods of La Selva project (ALAS), a structured inventory of the ants of a lowland tropical rain forest was carried out. A novel method of sample processing was developed, in which parataxonomists prepared specimens based on their own sorting of morphospecies within samples (repeating the process for each sample, and thus not attempting to cross‐reference morphospecies among samples), and a taxonomic specialist later sorted the resultant pool of prepared specimens. Efficacy of stratifying by sampling method (Berlese samples, Malaise traps, and canopy fogging), habitat, and time was investigated. Novel methods of analysis were used, including (1) curves depicting cost in prepared specimens of adding species to the inventory, as a function of number of species already captured, (2) within‐ vs. among‐treatment species accumulation curves, and (3) matched rank‐abundance plots. Over 400 species of ants are known from the site, of which the structured inventory captured 253. Projection of the species accumulation curve revealed that continuation of the same methods would not be an efficient method of capturing additional species, and that additional methods would be needed. Considered separately, Berlese, Malaise, and fogging samples were similarly efficient. Berlese samples combined with either Malaise or fogging samples were far more efficient than single methods because the faunas they sampled were highly complementary. Combining Malaise and fogging samples did not increase efficiency because the faunas they sampled had low complementarity. At the scale of our sampling, there was little evidence that spatial, temporal, or habitat stratification increased efficiency of inventory for Berlese and Malaise samples. For canopy fogging, processing a portion of the catch from multiple trees was more efficient than processing the entire catch from one tree. However, stratifying by tree species did not improve efficiency.