The degree of spatial complexity in the environment, or clutter, affects the quality of foraging habitats for bats and their detection with acoustic systems. Clutter has been assessed in a variety of ways but there are no standardized methods for measuring clutter. We compared four methods (Visual Clutter, Cluster, Single Variable, and Clutter Index) and related these to the probability of detecting bat calls. From June to July, 2005–2006, we used Anabat detectors to conduct acoustic surveys for 2–4 nights at each of 71 points representing three visual clutter classes. We used a cluster analysis to identify groups of plots with sim- ilar characteristics. We used backwards stepwise discriminant analyses to identify important plot struc- ture variables that differentiated among clutter classes and used discriminant analyses to test the effectiveness of the plot structure variables in classifying plots into visual clutter classes or clusters. Two clutter volume indices (Indexmax and Index15m) were computed for each plot by calculating the ratio of vegetation volume to available space in the plot. We assessed the effects of the clutter estimation methods on the probability of detecting bats in low and high frequency phonic groups. Occupancy rates ranged from 0.30 to 0.78 and probability of detecting any bat was P0.78 for each period; however, few identiﬁable calls were recorded. Live tree basal area, midstory live stem count, and canopy crown volume were the most effective measures of clutter for bats because each was a plausible predictor of bat detection and the former two were important for discriminating among plots with differing structure. The use of clutter indices has promise but such methods need to be tested prior to implementation. In future studies of bat foraging habitat, quantitative measures should be used to assess clutter so it is possible to make comparisons among habitats or studies.
Canopy crown volume
O Keefe, Joy M.; Loeb, Susan C.; Hill Jr., Hoke S.; Lanham, J. Drew. 2014. Quantifying clutter: A comparison of four methods and their relationship to bat detection. Forest Ecology and Management. 322: 1-9. 9 p. 10.1016/j.foreco.2014.02.036