Rapid assessment in conservation research: a critique of avifaunal assessment techniques illustrated by Ecuadorian and Madagascan case study data

The urgency of conservation concerns in the tropics, linked with the limitations imposed on research efforts by the tropical environment has resulted in the development of methods for rapid assessment of biological communities. One such method, the MacKinnon list technique, has been increasingly applied in avifaunal surveys worldwide. Using paired tropical bird data sets from Ecuadorian cloud forest and Madagascan littoral forest, we compare the performance of the MacKinnon list with that of the more standard method of point counts in indicating when a site has been adequately surveyed, estimating the magnitude of species richness, quantifying relative species abundance, and providing an α‐index of diversity. In species‐rich Ecuadorian cloud forest, neither method produced data indicating adequate survey effort, despite extensive sampling, whereas in the relatively species‐poor Madagascan littoral forests, data collected by both methods indicated that the area had been sufficiently surveyed with comparable sampling effort. Species richness estimates generated from MacKinnon list data provided a more accurate estimate of the magnitude of the species richness for the Ecuadorian avifauna, whereas estimates for the Madagascan avifauna stabilised with relatively few samples using either method. Data collected by each method reflected different patterns of relative abundance among the five most abundant species, with MacKinnon list data showing a bias towards solitary and territorial species and against monospecific flocking species relative to the point count data. As a consequence of this bias, MacKinnon list data also fail to reflect accurately the structure of communities as quantified by an index of community evenness. Point counts, on the other hand, failed to capture the full species complement of the species‐rich Ecuadorian study area. As techniques for the rapid assessment of unsurveyed areas, both methods are subject to biases that limit their value, if used alone, in collecting data of scientific and management value. We propose a hybrid rapid assessment methodology that capitalises on the strengths of both techniques while compensating for their weaknesses.