Univariate methods to analyse abundance of decapod larvae

M. Pan, Alejandro Gallego, S. Hay, E. N. Ieno, Graham John Pierce, A. F. Zuur, G. M. Smith

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This chapter illustrates how to decide between the application of parametric models (linear regression models) and non-parametric methods (additive models). The techniques applied in this chapter will use as explanatory variables some abiotic (temperature, salinity) and biotic (algal food biomass, as indicated by chlorophyll a) factors that affect the meroplanktonic larvae. We aim to provide preliminary information about some of the pre-settlement processes and the relative influences of environmental factors and variability. Also, the taxonomic identification of some decapod larvae is often difficult, and processing the samples is time consuming. By using information from the samples already analysed and the other available data, such as how many samples per year we could analyse, we may optimise the number of samples examined to achieve the best outcomes and interpretations. In this chapter we therefore also discuss how some models can be used to optimise the number of samples for further sample analysis in other years.
Original languageEnglish
Title of host publicationAnalysing Ecological Data
EditorsAlain F. Zuur, Elena N. Ieno, Graham M. Smith
Place of PublicationNew York, NY, USA
PublisherSpringer Science+Business Media
Pages373-388
Number of pages16
ISBN (Electronic)9780387459721
ISBN (Print)0387459677, 9780387459677
DOIs
Publication statusPublished - 23 May 2007

Publication series

NameStatistics for Biology and Health
PublisherSpringer Science+Business Media
ISSN (Print)1431-8776

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Pan, M., Gallego, A., Hay, S., Ieno, E. N., Pierce, G. J., Zuur, A. F., & Smith, G. M. (2007). Univariate methods to analyse abundance of decapod larvae. In A. F. Zuur, E. N. Ieno, & G. M. Smith (Eds.), Analysing Ecological Data (pp. 373-388). (Statistics for Biology and Health). Springer Science+Business Media. https://doi.org/10.1007/978-0-387-45972-1_20