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Models for species-detection data collected along transects in the presence of abundance-induced heterogeneity and clustering in the detection process

Guillera-Arroita, Gurutzeta, Ridout, Martin S., Morgan, Byron J. T., Linkie, Matthew (2012) Models for species-detection data collected along transects in the presence of abundance-induced heterogeneity and clustering in the detection process. Methods in Ecology and Evolution, 3 (2). pp. 358-367. ISSN 2041-210X. (doi:10.1111/j.2041-210X.2011.00159.x) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:32974)

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Official URL
http://dx.doi.org/10.1111/j.2041-210X.2011.00159.x

Abstract

1. Models have been devised previously that allow the estimation of abundance from detection data

for discrete sampling protocols, i.e. replicated detection ? non-detection or count data. Furthermore,

are cases in which this assumption is likely to be violated. For example, in surveys along transects,

2. Here, we propose models to estimate abundance from species-detection data collected continu-

detections of each individual. We account for clustering by describing the detection process as a

assessing the impact of unmodelled detection clustering.

tions is not accounted for and how an estimator with better coverage properties is obtained if clus-

estimated simultaneously, given enough data.

veys in Kerinci Seblat National Park in Sumatra. The analysis suggested strong abundance-induced

when clustering was accounted for. This example illustrates how unmodelled clustering can affect

5. Estimates of abundance need to be reliable to ensure that conservation and management inter-

mated from detection data of unmarked individuals. This requires an adequate description of the

discussion provided here deal with the issue of clustering within the detections of individuals and

mal abundance.

Item Type: Article
DOI/Identification number: 10.1111/j.2041-210X.2011.00159.x
Uncontrolled keywords: clustered data, Markov-modulated Poisson process, Poisson mixture, replicated counts, species occupancy, Sumatran tiger, superposition of point processes
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Q Science > QH Natural history > QH541 Ecology
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: Byron Morgan
Date Deposited: 14 Jan 2013 16:48 UTC
Last Modified: 16 Feb 2021 12:44 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/32974 (The current URI for this page, for reference purposes)
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