ArticleInsects2018
Towards the Development of a More Accurate Monitoring Procedure for Invertebrate Populations, in the Presence of an Unknown Spatial Pattern of Population Distribution in the Field.
Article in Insects, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Using mathematical modelling to highlight challenges in understanding trap counts obtained by a baited trap.Scientific reports · 2025Article
- Spatial Aggregations of the Grey Field SlugInsects · 2024Article
- A predictive model and a field study on heterogeneous slug distribution in arable fields arising from density dependent movement.Scientific reports · 2022Article
- Stability of Patches of Higher Population Density within the Heterogenous Distribution of the Gray Field SlugInsects · 2020Article
- Movement patterns of the grey field slug (Deroceras reticulatum) in an arable field.Scientific reports · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Studies addressing many ecological problems require accurate evaluation of the total population size. In this paper, we revisit a sampling procedure used for the evaluation of the abundance of an invertebrate population from assessment data collected on a spatial grid of sampling locations. We first discuss how insufficient information about the spatial population density obtained on a coarse sampling grid may affect the accuracy of an evaluation of total population size. Such information deficit in field data can arise because of inadequate spatial resolution of the population distribution (spatially variable population density) when coarse grids are used, which is especially true when a strongly heterogeneous spatial population density is sampled. We then argue that the average trap count (the quantity routinely used to quantify abundance), if obtained from a sampling grid that is too coarse, is a random variable because of the uncertainty in sampling spatial data. Finally, we show that a probabilistic approach similar to bootstrapping techniques can be an efficient tool to quantify the uncertainty in the evaluation procedure in the presence of a spatial pattern reflecting a patchy distribution of invertebrates within the sampling grid.
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Registered trials
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