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A new cost‐effective approach to survey ecological communities
Author(s) -
Guillaume Blanchet F.,
Legendre Pierre,
He Fangliang
Publication year - 2016
Publication title -
oikos
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.672
H-Index - 179
eISSN - 1600-0706
pISSN - 0030-1299
DOI - 10.1111/oik.02838
Subject(s) - quadrat , sampling (signal processing) , transect , ecology , abundance (ecology) , statistics , relative species abundance , global biodiversity , unit (ring theory) , environmental science , mathematics , biology , computer science , biodiversity , mathematics education , filter (signal processing) , computer vision
Surveying ecological communities often means the tedious work of collecting detailed information on each species within each sampling unit (e.g. trap, transect, quadrat). In this paper, we first argue that presence–absence and abundance data are the two extremes of a spectrum of data formats. By counting individuals of each species within a sampling unit until either a predefined (user‐defined) number of individuals is reached or all individuals of the species are counted, all intermediate cases can be generated. By independently correlating each intermediate case with the complete abundance data, we show that it is not necessary to count all individuals to recover the patterns of variation characterizing a community data table. When the same procedure is applied in combination with different distance coefficients such as the Hellinger, chord, χ 2 , percentage difference or modified Gower, or the distance between species profiles, an even lower number of individuals per species need to be counted within a sampling unit for the patterns of variation defining a community to be recovered. By applying the same counting procedure to data collected during a pilot study, we show that the maximum number of individuals that need to be counted within a sampling unit for a species can be estimated from a pilot study containing as little as 3% of randomly selected sampling units throughout the complete survey area. An example of how to apply this new counting method is presented, using data from a boreal forest Carabidae community sampled in northwestern Alberta, Canada.