Modelling the performance of bird surveys in non-standard weather conditions: General applications with special reference to mountain ecosystems

Abstract

Abstract Although considerable effort has been made to identify the appropriate climatic conditions for bird surveys, considered as standard conditions, in many occasions these conditions are not fulfilled. These are for instance the case of environmental impact assessments (EIA), where the field work is, recurrently, carried out in variable and non-standard weather conditions or in the scope of general ecological monitoring (GEM) programs, where different taxa (birds and other animal groups) are sampled simultaneously with distinct methodological requirements. The present work examined the applicability of a stochastic dynamic methodology (StDM) for predicting the richness and diversity of passerine surveys in mountain habitats characterized by variable and, predominantly, non-standard weather conditions. The relative variations of these metrics are the underlying database of our StDM model, providing some basis to analyse the accuracy of bird surveys. This model focuses on the interactions between conceptually isolated key-components, such as the passerine richness and diversity, and the influence of the prevailing climatic conditions. The proposed model was preceded by a conventional multivariate statistical procedure performed to discriminate the significant relationships between the selected metrics versus climatic variables. Since this statistical analysis is static, the dataset recorded from the field included true gradients of weather conditions (ranging from standard to extreme conditions). The results of the StDM simulations revealed significant variations in the performance of passerine surveys in response to several combinations of non-standard weather conditions, which enable us to calculate the appropriated correction factors for discrete climatic scenarios. This could be used, in the future, to improve the quality of passerine diversity and richness estimates, namely in the scope of EIA studies when the climatic conditions are inevitably adverse for rigorous passerine surveys.