A comparison between time of exposure, number of pitfall traps and the sampling cost to capture ground-dwelling poneromorph ants (Hymenoptera: Formicidae)
DOI:
https://doi.org/10.13102/sociobiology.v65i2.1207Keywords:
Amazon, ant survey, biomonitoring, cost-effectiveness, sampling protocolAbstract
Using effective survey protocols to address the effects of environmental change are key to saving time, resources and costs. Although exhaustive sampling in any location has been shown as impractical, biodiversity sampling projects must capture sufficient information to show how species assemblages change with the environmental variables. This study investigated time of exposure in the field and the number of pitfall traps that efficiently sampled poneromorph ants in 30 250 m long plots across an area of 25 km2 of tropical rain forest in Brazil. The treatments used for the surveys included two days and 300 traps, 14 days and 300 traps, 14 days and 750 traps, and were considered the minimum, intermediate and maximum sampling efforts, respectively. We characterized each assemblage of ants in relation to a gradient of soil texture, terrain slope and leaf and branch litter volume, and then tested whether the ecological relationships observed with the maximum effort were comparable to data on intermediate and minimum sampling efforts. We also estimated the cost-effectiveness of using the protocols in survey programs. The assemblage of species sampled during 14 days was similar to the assemblage captured during two days, indicating that the number of days influenced the assemblage similarity more than the number of sampling traps. All ecological patterns detected with the maximum effort were also captured with lesser sampling efforts. Overall, both the intermediate and minimum sampling efforts represented savings around 26˗40% of total project costs and 43˗45% of time to process the samples. We recommend that two days of trapping time combined with 300 pitfall traps is a highly effective shortcut for monitoring assessment, which can be applied to large-scale biodiversity surveys in tropical forests.
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