ANNA: A new prediction method for bioassessment programs

Linke, Simon, Norris, Richard H., Faith, Daniel P. and Stockwell, David (2005) ANNA: A new prediction method for bioassessment programs. Freshwater Biology, 50 1: 147-158. doi:10.1111/j.1365-2427.2004.01286.x

Author Linke, Simon
Norris, Richard H.
Faith, Daniel P.
Stockwell, David
Title ANNA: A new prediction method for bioassessment programs
Journal name Freshwater Biology   Check publisher's open access policy
ISSN 0046-5070
Publication date 2005-01
Sub-type Article (original research)
DOI 10.1111/j.1365-2427.2004.01286.x
Volume 50
Issue 1
Start page 147
End page 158
Total pages 12
Publisher Blackwell Publishing
Language eng
Subject 0608 Zoology
Abstract 1. Cluster analysis of reference sites with similar biota is the initial step in creating River Invertebrate Prediction and Classification System (RIVPACS) and similar river bioassessment models such as Australian River Assessment System (AUSRIVAS). This paper describes and tests an alternative prediction method, Assessment by Nearest Neighbour Analysis (ANNA), based on the same philosophy as RIVPACS and AUSRIVAS but without the grouping step that some people view as artificial. 2. The steps in creating ANNA models are: (i) weighting the predictor variables using a multivariate approach analogous to principal axis correlations, (ii) calculating the weighted Euclidian distance from a test site to the reference sites based on the environmental predictors, (iii) predicting the faunal composition based on the nearest reference sites and (iv) calculating an observed/expected (O/E) analogous to RIVPACS/AUSRIVAS. 3. The paper compares AUSRIVAS and ANNA models on 17 datasets representing a variety of habitats and seasons. First, it examines each model's regressions for Observed versus Expected number of taxa, including the r(2), intercept and slope. Second, the two models' assessments of 79 test sites in New Zealand are compared. Third, the models are compared on test and presumed reference sites along a known trace metal gradient. Fourth, ANNA models are evaluated for western Australia, a geographically distinct region of Australia. The comparisons demonstrate that ANNA and AUSRIVAS are generally equivalent in performance, although ANNA turns out to be potentially more robust for the O versus E regressions and is potentially more accurate on the trace metal gradient sites. 4. The ANNA method is recommended for use in bioassessment of rivers, at least for corroborating the results of the well established AUSRIVAS- and RIVPACS-type models, if not to replace them.
Keyword Marine & Freshwater Biology
Multivariate Statistics
Nearest Neighbour
Predictive Modelling
Running-water Sites
Benthic Macroinvertebrates
Environmental Data
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Unknown

Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 67 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 64 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Mon, 13 Aug 2007, 14:49:59 EST