The University of Southampton
University of Southampton Institutional Repository

An indirect method for assessing the abundance of introduced pest Myocastor coypus (Rodentia) in agricultural landscapes

An indirect method for assessing the abundance of introduced pest Myocastor coypus (Rodentia) in agricultural landscapes
An indirect method for assessing the abundance of introduced pest Myocastor coypus (Rodentia) in agricultural landscapes
Pest management requires the development of robust monitoring tools. In Italy, coypu Myocastor coypus (nutria) have been controlled since the early 1990s, but the effectiveness of these measures has never been tested. With the aim of developing a reliable and volunteer-based method for the long-term monitoring of coypu abundance in agricultural landscapes, we calibrated an index based on surveys for coypu paths against density estimates obtained through a standardized mark–recapture technique. Two trapping sessions were performed in winter for each of 12 1-km long stretches of irrigation canals and watercourses using 15 baited cage traps. Trapping sessions lasted 7 days each, with a 10-day break between sessions. Population size was assessed using three methods: Peterson–Lincoln's formula, capwire estimators and accumulation curves. Active coypu paths and five habitat variables were recorded by walking on the edge of both banks. The variables were then related to population size (y) by means of multi-regressive models, testing for the predictive power of the selected models by leave-one-out cross-validation. Multi-regressive models included only the number of coypu paths with the best performances achieved by the model based on Peterson–Lincoln formula, supporting path count as an effective method to assess the abundance of the coypu in agricultural landscapes. Concurrently, to assess the field suitability of the indirect method, surveys for coypu paths were carried out on 122 randomly chosen 3-km long stretches of irrigation canals and watercourses in the central part of the River Po valley (c. 15?000?km2; N Italy). The highest (>8/100?m) mean number of paths was recorded in the central part of the study area. According to the regression models, the overall number of coypu is predicted to range between 350 000 and 1 100 000, raising doubts about the effectiveness of current control measures.
0952-8369
37-45
Balestrieri, A.
5ccd7bf2-9853-4998-87ee-fb4080a24f69
Zenato, M.
ae4e7269-6f57-4bed-89eb-2588b14c08a3
Fontana, E.
12d51549-98dd-4bf0-85f9-5d78e744390d
Vezza, P.
b8f933f4-bd6e-468b-835c-ada608d08ecc
Remonti, L.
a3a3ade1-fc75-47b4-806d-88ed669db7ec
Caronni, F.E.
950dd34c-87ec-407b-83a9-b52f552b590f
Saino, N.
e6367389-32ec-4946-b449-e335fb530bd7
Prigioni, C.
60bcc721-7ad5-41e0-bb6f-25d502577398
Balestrieri, A.
5ccd7bf2-9853-4998-87ee-fb4080a24f69
Zenato, M.
ae4e7269-6f57-4bed-89eb-2588b14c08a3
Fontana, E.
12d51549-98dd-4bf0-85f9-5d78e744390d
Vezza, P.
b8f933f4-bd6e-468b-835c-ada608d08ecc
Remonti, L.
a3a3ade1-fc75-47b4-806d-88ed669db7ec
Caronni, F.E.
950dd34c-87ec-407b-83a9-b52f552b590f
Saino, N.
e6367389-32ec-4946-b449-e335fb530bd7
Prigioni, C.
60bcc721-7ad5-41e0-bb6f-25d502577398

Balestrieri, A., Zenato, M., Fontana, E., Vezza, P., Remonti, L., Caronni, F.E., Saino, N. and Prigioni, C. (2016) An indirect method for assessing the abundance of introduced pest Myocastor coypus (Rodentia) in agricultural landscapes. Journal of Zoology, 298 (1), 37-45. (doi:10.1111/jzo.12284).

Record type: Article

Abstract

Pest management requires the development of robust monitoring tools. In Italy, coypu Myocastor coypus (nutria) have been controlled since the early 1990s, but the effectiveness of these measures has never been tested. With the aim of developing a reliable and volunteer-based method for the long-term monitoring of coypu abundance in agricultural landscapes, we calibrated an index based on surveys for coypu paths against density estimates obtained through a standardized mark–recapture technique. Two trapping sessions were performed in winter for each of 12 1-km long stretches of irrigation canals and watercourses using 15 baited cage traps. Trapping sessions lasted 7 days each, with a 10-day break between sessions. Population size was assessed using three methods: Peterson–Lincoln's formula, capwire estimators and accumulation curves. Active coypu paths and five habitat variables were recorded by walking on the edge of both banks. The variables were then related to population size (y) by means of multi-regressive models, testing for the predictive power of the selected models by leave-one-out cross-validation. Multi-regressive models included only the number of coypu paths with the best performances achieved by the model based on Peterson–Lincoln formula, supporting path count as an effective method to assess the abundance of the coypu in agricultural landscapes. Concurrently, to assess the field suitability of the indirect method, surveys for coypu paths were carried out on 122 randomly chosen 3-km long stretches of irrigation canals and watercourses in the central part of the River Po valley (c. 15?000?km2; N Italy). The highest (>8/100?m) mean number of paths was recorded in the central part of the study area. According to the regression models, the overall number of coypu is predicted to range between 350 000 and 1 100 000, raising doubts about the effectiveness of current control measures.

Full text not available from this repository.

More information

Accepted/In Press date: 1 July 2015
e-pub ahead of print date: 11 August 2015
Published date: January 2016
Organisations: Water & Environmental Engineering Group

Identifiers

Local EPrints ID: 403144
URI: https://eprints.soton.ac.uk/id/eprint/403144
ISSN: 0952-8369
PURE UUID: cdfa5995-aeca-44a9-8fce-7a7097c27bc2

Catalogue record

Date deposited: 24 Nov 2016 16:27
Last modified: 15 Jul 2019 19:52

Export record

Altmetrics

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×