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Terrestrial laser scanning reveals below-canopy bat trait relationships with forest structure.
- Source :
-
Remote Sensing of Environment . Sep2017, Vol. 198, p40-51. 12p. - Publication Year :
- 2017
-
Abstract
- Three-dimensional structure of vegetation plays a key role in animal ecology and the arrival of LiDAR technologies has given ecologists the ability to understand species-habitat relationships in increasing detail. However, few studies have investigated the trait-environment relationships that underpin diverse animal relationships with vegetation structure. We used terrestrial laser scanning (TLS) and acoustic bat surveys to investigate relationships between forest structure and bat communities across a vegetation structural gradient at community, species and trait levels. We developed 20 measures of site scale vegetation structure and also quantified landscape scale vegetation cover and water availability. We predicted that overall bat activity would increase in open and decrease in cluttered vegetation, but this would vary with species, underpinned by ecomorphological traits. Overall bat activity was negatively associated with stem density, with total activity halving (from 380 to 190 calls night − 1 ) as stem densities increased from 60 to 1350 stems ha − 1 , while foraging activity declined from 8 to < 1 feeding buzzes night − 1 over the same range. Bat activity varied among species and structures and foraging strategy explained more of this variability than call, body size or wing traits. As predicted, above-canopy and edge-space foraging bats were negatively associated with local-scale clutter, while closed-space species were positively associated with cluttered stands. Stem density was the strongest predictor of bat-environment relationships, although there was evidence for differences in bat habitat use across different structural elements. Our study is the first to link detailed LiDAR-derived 3D forest structural metrics to multiple animal traits. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00344257
- Volume :
- 198
- Database :
- Academic Search Index
- Journal :
- Remote Sensing of Environment
- Publication Type :
- Academic Journal
- Accession number :
- 124247411
- Full Text :
- https://doi.org/10.1016/j.rse.2017.05.038