Enhancing sampling design in mist-net bat surveys by accounting for sample size optimization

The advantages of mist-netting, the main technique used in Neotropical bat community studies to date, include logistical implementation, standardization and sampling representativeness. Nonetheless, study designs still have to deal with issues of detectability related to how different species behave and use the environment. Yet there is considerable sampling heterogeneity across available studies in the literature. Here, we approach the problem of sample size optimization. We evaluated the common sense hypothesis that the first six hours comprise the period of peak night activity for several species, thereby resulting in a representative sample for the whole night. To this end, we combined re-sampling techniques, species accumulation curves, threshold analysis, and community concordance of species compositional data, and applied them to datasets of three different Neotropical biomes (Amazonia, Atlantic Forest and Cerrado). We show that the strategy of restricting sampling to only six hours of the night frequently results in incomplete sampling representation of the entire bat community investigated. From a quantitative standpoint, results corroborated the existence of a major Sample Area effect in all datasets, although for the Amazonia dataset the six-hour strategy was significantly less species-rich after extrapolation, and for the Cerrado dataset it was more efficient. From the qualitative standpoint, however, results demonstrated that, for all three datasets, the identity of species that are effectively sampled will be inherently impacted by choices of sub-sampling schedule. We also propose an alternative six-hour sampling strategy (at the beginning and the end of a sample night) which performed better when resampling Amazonian and Atlantic Forest datasets on bat assemblages. Given the observed magnitude of our results, we propose that sample representativeness has to be carefully weighed against study objectives, and recommend that the trade-off between logistical constraints and additional sampling performance should be carefully evaluated

Location map.

<p>Location of the study region of all three datasets. Dark green, light green and orange indicate the Amazon forest biome, the Atlantic forest biome, and the Brazilian Cerrado biome, respectively. 1: Amazonia dataset; 2: Atlantic Forest dataset; 3: Cerrado dataset. Source data used for this map was downloaded from MapBiomas [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0174067#pone.0174067.ref030" target="_blank">30</a>].</p

Species responses in TITAN sorted by rarity and functional attributes.

<p>(A-B) Proportions of species with negative, positive and non-significant thresholds, and classed in terms of both rarity and functional groups. (D-F) TITAN results for individual species (or groups), presenting significant change points and 90% confidence limits; points are scaled in proportion to the magnitude of the response. Species codes on vertical axes: species number_ functional group_ rarity group, see text for codes.</p

Species compositional patterns.

<p>Congruence between the six-hour sampling strategies and randomly generated subsamples (null model) of the entire community for each dataset. (A, B, C) Calculations were performed on the basis of the Jaccard dissimilarity using presence/absence matrices. (D, E, F) Calculations were performed on the basis of the Bray-Curtis dissimilarity using abundance matrices. Solid circle: six-hour sampling strategy; Black cross: six-hour-B sampling strategy.</p