Abstract
Accurate biodiversity and population monitoring is a requirement for effective conservation decision making. Survey method bias is therefore a concern, particularly when research programs face logistical and cost limitations.We employed point counts (PCs) and autonomous recording units (ARUs) to survey avian biodiversity within comparable, high elevation, temperate mountain habitats at opposite ends of the Americas: nine mountains in British Columbia (BC), Canada, and 10 in southern Chile. We compared detected species richness against multiyear species inventories and examined method-specific detection probability by family. By incorporating time costs, we assessed the performance and efficiency of single versus combined methods.Species accumulation curves indicate ARUs can capture ~93% of species present in BC but only ~58% in Chile, despite Chilean mountain communities being less diverse. The avian community, rather than landscape composition, appears to drive this dramatic difference. Chilean communities contain less-vocal species, which ARUs missed. Further, 6/13 families in BC were better detected by ARUs, while 11/11 families in Chile were better detected by PCs. Where survey conditions differentially impacted method performance, PCs mostly varied over the morning and with canopy cover in BC, while ARUs mostly varied seasonally in Chile. Within a single year of monitoring, neither method alone was predicted to capture the full avian community, with the exception of ARUs in the alpine and subalpine of BC. PCs contributed little to detected diversity in BC, but including this method resulted in negligible increases in total time costs. Combining PCs with ARUs in Chile significantly increased species detections, again, for little cost.Combined methods were among the most efficient and accurate approaches to capturing diversity. We recommend conducting point counts, while ARUs are being deployed and retrieved in order to capture additional diversity with minimal additional effort and to flag methodological biases using a comparative framework.