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Principles of using River Habitat Survey to predict the distribution of aquatic species: an example applied to the native white‐clawed crayfish Austropotamobius pallipes

Principles of using River Habitat Survey to predict the distribution of aquatic species: an... 1. The Environment Agency has compiled a comprehensive database of the physical structure of British rivers using the River Habitat Survey (RHS) methodology. 2. A series of models was developed to ascertain whether RHS could be used to predict the environmental requirements of riverine species. Austropotamobius pallipes (Lereboullet) was chosen for the model, as it is an endangered species throughout Europe. In Britain, many populations have been eradicated or severely depleted by the crayfish plague (an introduced fungal disease), pollution, competition from non‐native species and loss of habitat. 3. A series of models was developed to predict crayfish occurrence according to habitat features recorded on the Environment Agency RHS database, and also from map‐derived information. Sites surveyed for crayfish were matched to existing RHS sites. Incidental sightings during Agency routine biological or fisheries monitoring were also included. 4. Logistic regressions on RHS variables yielded two models which showed suitable fit of the data, and high success rates when tested on an independent sample of sites. The first model mainly comprised variables on channel vegetation and on bank and channel structure. Further analyses without channel vegetation yielded a second model with variables more closely related to crayfish habitat. The variables identified as having a positive impact on crayfish presence were overhanging boughs, the presence of boulders, the amount of tree shading and the number of riffles. The variables with a negative impact were exposed tree roots, eroding cliffs, the amount of poached or reinforced banks, gravel/pebble/sand banks and cobble substrate. 5. A discriminant analysis on crayfish presence/absence according to altitude, slope and distance from source transformed for normality, showed that crayfish could be predicted from map‐derived information. Results were discussed and consideration was given to the wider applicability and potential uses of the models. © 1998 John Wiley & Sons, Ltd. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Aquatic Conservation: Marine and Freshwater Ecosystems Wiley

Principles of using River Habitat Survey to predict the distribution of aquatic species: an example applied to the native white‐clawed crayfish Austropotamobius pallipes

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References (17)

Publisher
Wiley
Copyright
Copyright © 1998 John Wiley & Sons, Ltd.
ISSN
1052-7613
eISSN
1099-0755
DOI
10.1002/(SICI)1099-0755(199807/08)8:4<515::AID-AQC261>3.0.CO;2-J
Publisher site
See Article on Publisher Site

Abstract

1. The Environment Agency has compiled a comprehensive database of the physical structure of British rivers using the River Habitat Survey (RHS) methodology. 2. A series of models was developed to ascertain whether RHS could be used to predict the environmental requirements of riverine species. Austropotamobius pallipes (Lereboullet) was chosen for the model, as it is an endangered species throughout Europe. In Britain, many populations have been eradicated or severely depleted by the crayfish plague (an introduced fungal disease), pollution, competition from non‐native species and loss of habitat. 3. A series of models was developed to predict crayfish occurrence according to habitat features recorded on the Environment Agency RHS database, and also from map‐derived information. Sites surveyed for crayfish were matched to existing RHS sites. Incidental sightings during Agency routine biological or fisheries monitoring were also included. 4. Logistic regressions on RHS variables yielded two models which showed suitable fit of the data, and high success rates when tested on an independent sample of sites. The first model mainly comprised variables on channel vegetation and on bank and channel structure. Further analyses without channel vegetation yielded a second model with variables more closely related to crayfish habitat. The variables identified as having a positive impact on crayfish presence were overhanging boughs, the presence of boulders, the amount of tree shading and the number of riffles. The variables with a negative impact were exposed tree roots, eroding cliffs, the amount of poached or reinforced banks, gravel/pebble/sand banks and cobble substrate. 5. A discriminant analysis on crayfish presence/absence according to altitude, slope and distance from source transformed for normality, showed that crayfish could be predicted from map‐derived information. Results were discussed and consideration was given to the wider applicability and potential uses of the models. © 1998 John Wiley & Sons, Ltd.

Journal

Aquatic Conservation: Marine and Freshwater Ecosystems – Wiley

Published: Jul 1, 1998

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