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Modelling variation in calling rates to develop a reliable monitoring method for the Australasian Bittern Botaurus poiciloptilus
Author(s) -
Williams Emma M.,
Armstrong Doug P.,
O'Donnell Colin F. J.
Publication year - 2019
Publication title -
ibis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.933
H-Index - 80
eISSN - 1474-919X
pISSN - 0019-1019
DOI - 10.1111/ibi.12611
Subject(s) - abundance (ecology) , range (aeronautics) , statistics , index (typography) , ecology , cloud cover , critically endangered , environmental science , geography , endangered species , demography , biology , computer science , habitat , mathematics , cloud computing , materials science , sociology , world wide web , composite material , operating system
Monitoring the abundance of cryptic species inevitably relies on the use of index methods. Unfortunately, detectability is often confounded by unidentified covariates. One such species is the critically endangered Australasian Bittern Botaurus poiciloptilus . Current monitoring relies upon the ability to count males based on the conspicuous breeding calls of males. However, as in many vocal species, calling rates vary spatially and temporally, making it necessary to account for this when using call counts to index abundance. We undertook 461 15‐min call counts of Australasian Bitterns, in a range of conditions, during two breeding seasons at Whangamarino wetland, New Zealand. We fitted a range of generalized linear mixed models to these data to determine which factors were the best predictors of calling rate per individual Bittern ( CRPI ), allowing us to make recommendations regarding the optimum time and conditions for monitoring. Bittern CRPI was predictable in terms of time of day, month, cloud cover, rainfall and certain moon parameters, but some spatial and temporal variation remained unexplained. Results showed that the best time to detect Australasian Bitterns was 1 h before sunrise, in September (austral spring), on a moonlit night with no cloud or rain. Such models are useful for identifying times and conditions when counts are the highest and least variable, and could be applied to any species or cue count monitoring method where detection depends on counting calling individuals. Results can be used to standardize index counts, or sensibly to adjust and compare counts from different times. Standardizing monitoring in this way can lead to the development of monitoring methods that have a greater power to show population changes across shorter time periods. Moreover, the use of modelling processes to estimate effect sizes creates potential for such methods to be applied in circumstances where monitoring conditions are rarely optimum and standardization creates logistical trade‐offs, something that is particularly common in studies of cryptic species.