Open AccessEstimating mean plant cover from different types of cover data: a coherent statistical framework
Author(s) -
Damgaard C.
Publication year - 2014
Publication title -
ecosphere
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.255
H-Index - 57
ISSN - 2150-8925
DOI - 10.1890/es13-00300.1
Subject(s) - cover (algebra) , plant cover , sampling (signal processing) , statistics , vegetation cover , vegetation (pathology) , physical geography , ecology , mathematics , geography , computer science , biology , canopy , mechanical engineering , medicine , engineering , computer vision , filter (signal processing) , pathology , grazing
Plant cover is measured by different methods and it is important to be able to estimate mean cover and to compare estimates of plant cover across different sampling methods in a coherent statistical framework. Here, a framework that incorporates (1) pin‐point cover data, (2) visually determined cover data, and (3) ordinal cover classification systems (e.g., Braun‐Blanquet cover data) is presented and tested on simulated plant cover data. The effect of measurement error when applying a visual determination of plant cover is considered. Generally, the estimation of the mean plant cover was well‐behaved and unbiased for all the three methods, whereas the estimate of the intra‐plot correlation tended to be upward biased and especially so if the plant cover data was collected using the Braun‐Blanquet method. It was surprising that the Braun‐Blanquet sampling procedure provided mean plant cover estimates that were comparable to the other sampling schemes. This method shows promise in the attempt to use the large amount of historic Braun‐Blanquet plant cover data in the investigation of the underlying causes for observed vegetation changes.