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

Mapping habitat indices across river networks using spatial statistical modelling of River Habitat Survey data
Mapping habitat indices across river networks using spatial statistical modelling of River Habitat Survey data
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.
habitat mapping, habitat indices, channel substrate, regression kriging, river habitat survey, geostatistics
1470-160X
20-29
Naura, M.
d8f3cf43-ccf7-497e-955e-e77281a58316
Clark, M.J.
1d51194b-87b8-4c3d-bb47-41c0237ea41e
Sear, D.A.
ccd892ab-a93d-4073-a11c-b8bca42ecfd3
Atkinson, P.M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Hornby, D.D.
75cfaf57-72c1-4392-a78c-89b4b1033dca
Kemp, P.S.
9e33fba6-cccf-4eb5-965b-b70e72b11cd7
England, J.
7fb08139-f99c-408e-bfd5-910a1684b12e
Peirson, G.
5a0bd1eb-7cd5-47c2-99ed-6e41d67b676f
Bromley, C.
f4e6dc94-72ce-4480-88d3-b6018f7db8b4
Carter, M.G.
11481b7f-ebe7-44c2-b35f-e7ca6958b9dc
Naura, M.
d8f3cf43-ccf7-497e-955e-e77281a58316
Clark, M.J.
1d51194b-87b8-4c3d-bb47-41c0237ea41e
Sear, D.A.
ccd892ab-a93d-4073-a11c-b8bca42ecfd3
Atkinson, P.M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Hornby, D.D.
75cfaf57-72c1-4392-a78c-89b4b1033dca
Kemp, P.S.
9e33fba6-cccf-4eb5-965b-b70e72b11cd7
England, J.
7fb08139-f99c-408e-bfd5-910a1684b12e
Peirson, G.
5a0bd1eb-7cd5-47c2-99ed-6e41d67b676f
Bromley, C.
f4e6dc94-72ce-4480-88d3-b6018f7db8b4
Carter, M.G.
11481b7f-ebe7-44c2-b35f-e7ca6958b9dc

Naura, M., Clark, M.J., Sear, D.A., Atkinson, P.M., Hornby, D.D., Kemp, P.S., England, J., Peirson, G., Bromley, C. and Carter, M.G. (2016) Mapping habitat indices across river networks using spatial statistical modelling of River Habitat Survey data. Ecological Indicators, 66, 20-29. (doi:10.1016/j.ecolind.2016.01.019).

Record type: Article

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.

Full text not available from this repository.

More information

Accepted/In Press date: 12 January 2016
e-pub ahead of print date: 1 February 2016
Published date: July 2016
Keywords: habitat mapping, habitat indices, channel substrate, regression kriging, river habitat survey, geostatistics
Organisations: Water & Environmental Engineering Group

Identifiers

Local EPrints ID: 388592
URI: https://eprints.soton.ac.uk/id/eprint/388592
ISSN: 1470-160X
PURE UUID: f85739b9-fb6e-44c7-b2f7-6574705a72c9
ORCID for D.A. Sear: ORCID iD orcid.org/0000-0003-0191-6179
ORCID for P.M. Atkinson: ORCID iD orcid.org/0000-0002-5489-6880
ORCID for D.D. Hornby: ORCID iD orcid.org/0000-0001-6295-1360
ORCID for P.S. Kemp: ORCID iD orcid.org/0000-0003-4470-0589

Catalogue record

Date deposited: 01 Mar 2016 09:13
Last modified: 03 Dec 2019 02:02

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