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  • Naura et al Mapping habitat indices 2016-revised

    Rights statement: This is the author’s version of a work that was accepted for publication in Ecological Indicators. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Ecological Indicators, 66, 2016 DOI: 10.1016/j.ecolind.2016.01.019

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    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

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Mapping habitat indices across river networks using spatial statistical modelling of River Habitat Survey data

Research output: Contribution to journalJournal articlepeer-review

Published
  • Marc Naura
  • Mike J. Clark
  • David A. Sear
  • Peter Michael Atkinson
  • Duncan D. Hornby
  • Paul Kemp
  • Judy England
  • Graeme Peirson
  • Chris Bromley
  • Matthew G. Carter
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<mark>Journal publication date</mark>07/2016
<mark>Journal</mark>Ecological Indicators
Volume66
Number of pages10
Pages (from-to)20-29
Publication StatusPublished
Early online date1/02/16
<mark>Original language</mark>English

Abstract

Freshwater ecosystems are declining faster than their terrestrial and marine counterparts because of physical pressures on habitats. European legislation requires member states to achieve ecological targets through the effective management of freshwater habitats. Maps of habitats across river networks would help diagnose environmental problems and plan for the delivery of improvement work. Existing habitat mapping methods are generally time consuming, require experts and are expensive to implement. Surveys based on sampling are cheaper but provide patchy representations of habitat distribution. In this study, we present a method for mapping habitat indices across networks using semi-quantitative data and a geostatistical technique called regression kriging. The method consists of the derivation of habitat indices using multivariate statistical techniques that are regressed on map-based covariates such as altitude, slope and geology. Regression kriging combines the Generalised Least Squares (GLS) regression technique with a spatial analysis of model residuals. Predictions from the GLS model are ‘corrected’ using weighted averages of model residuals following an analysis of spatial correlation. The method was applied to channel substrate data from the River Habitat Survey in Great Britain. A Channel Substrate Index (CSI) was derived using Correspondence Analysis and predicted using regression kriging. The model explained 74% of the main sample variability and 64% in a test sample. The model was applied to the English and Welsh river network and a map of CSI was produced. The proposed approach demonstrates how existing national monitoring data and geostatistical techniques can be used to produce continuous maps of habitat indices at the national scale.

Bibliographic note

This is the author’s version of a work that was accepted for publication in Ecological Indicators. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Ecological Indicators, 66, 2016 DOI: 10.1016/j.ecolind.2016.01.019