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Birds are a frequently chosen group for biodiversity monitoring as they are comparatively straightforward and inexpensive to sample and often perform well as ecological indicators. Two commonly used techniques for monitoring tropical forest bird communities are point counts and mist nets. General strengths and weaknesses of these techniques have been well-defined; however little research has examined how their effectiveness is mediated by the ecology of bird communities and their habitats. We examine how the overall performance of these methodologies differs between two widely separated tropical forests-Cusuco National Park (CNP), a Honduran cloud forest, and the lowland forests of Buton Forest Reserves (BFR) located on Buton Island, Indonesia. Consistent survey protocols were employed at both sites, with 77 point count stations and 22 mist netting stations being surveyed in each location. We found the effectiveness of both methods varied considerably between ecosystems. Point counts performed better in BFR than in CNP, detecting a greater percentage of known community richness (60% versus 41%) and generating more accurate species richness estimates. Conversely, mist netting performed better in CNP than in BFR, detecting a much higher percentage of known community richness (31% versus 7%). Indeed, mist netting proved overall to be highly ineffective within BFR. Best Akaike's Information Criterion models indicate differences in the effectiveness of methodologies between study sites relate to bird community composition, which in turn relates to ecological and biogeographical influences unique to each forest ecosystem. Results therefore suggest that, while generalized strengths and weaknesses of both methodologies can be defined, their overall effectiveness is also influenced by local characteristics specific to individual study sites. While this study focusses on ornithological surveys, the concept of local factors influencing effectiveness of field methodologies may also hold true for techniques targeting a wide range of taxonomic groups; this requires further research.
PMID: 28072883 [PubMed - in process]