The effects of scale and sample size on the accuracy of spatial predictions of tiger beetle (Cicindelidae) species richness

Steven S. Carroll, David Pearson

Research output: Contribution to journalArticle

29 Citations (Scopus)

Abstract

We apply geostatistical modeling techniques to investigate spatial patterns of species richness. Unlike most other statistical modeling techniques that are valid only when observations are independent, geostatistical methods are designed for applications involving spatially dependent observations. When spatial dependencies, which are sometimes called autocorrelations, exist, geostatistical techniques can be applied to produce optimal predictions in areas (typically proximate to observed data) where no observed data exist. Using tiger beetle species (Cicindelidae) data collected in western North America, we investigate the characteristics of spatial relationships in species numbers data. First, we compare the accuracy of spatial predictions of species richness when data from grid squares of two different sizes (scales) are used to form the predictions. Next we examine how prediction accuracy varies as a function of areal extent of the region under investigation. Then we explore the relationship between the number of observations used to build spatial prediction models and prediction accuracy. Our results indicate that, within the taxon of tiger beetles and for the two scales we investigate, the accuracy of spatial predictions is unrelated to scale and that prediction accuracy is not obviously related to the areal extent of the region under investigation. We also provide information about the relationship between sample size and prediction accuracy, and, finally, we show that prediction accuracy may be substantially diminished if spatial correlations in the data are ignored.

Original languageEnglish (US)
Pages (from-to)401-414
Number of pages14
JournalEcography
Volume21
Issue number4
DOIs
StatePublished - Jan 1 1998

Fingerprint

Cicindelinae
beetle
species richness
species diversity
prediction
sampling
effect
autocorrelation
methodology
modeling

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics

Cite this

The effects of scale and sample size on the accuracy of spatial predictions of tiger beetle (Cicindelidae) species richness. / Carroll, Steven S.; Pearson, David.

In: Ecography, Vol. 21, No. 4, 01.01.1998, p. 401-414.

Research output: Contribution to journalArticle

@article{ff3c0d90f91d4d168799d60d81c8c95b,
title = "The effects of scale and sample size on the accuracy of spatial predictions of tiger beetle (Cicindelidae) species richness",
abstract = "We apply geostatistical modeling techniques to investigate spatial patterns of species richness. Unlike most other statistical modeling techniques that are valid only when observations are independent, geostatistical methods are designed for applications involving spatially dependent observations. When spatial dependencies, which are sometimes called autocorrelations, exist, geostatistical techniques can be applied to produce optimal predictions in areas (typically proximate to observed data) where no observed data exist. Using tiger beetle species (Cicindelidae) data collected in western North America, we investigate the characteristics of spatial relationships in species numbers data. First, we compare the accuracy of spatial predictions of species richness when data from grid squares of two different sizes (scales) are used to form the predictions. Next we examine how prediction accuracy varies as a function of areal extent of the region under investigation. Then we explore the relationship between the number of observations used to build spatial prediction models and prediction accuracy. Our results indicate that, within the taxon of tiger beetles and for the two scales we investigate, the accuracy of spatial predictions is unrelated to scale and that prediction accuracy is not obviously related to the areal extent of the region under investigation. We also provide information about the relationship between sample size and prediction accuracy, and, finally, we show that prediction accuracy may be substantially diminished if spatial correlations in the data are ignored.",
author = "Carroll, {Steven S.} and David Pearson",
year = "1998",
month = "1",
day = "1",
doi = "10.1111/j.1600-0587.1998.tb00405.x",
language = "English (US)",
volume = "21",
pages = "401--414",
journal = "Ecography",
issn = "0906-7590",
publisher = "Wiley-Blackwell",
number = "4",

}

TY - JOUR

T1 - The effects of scale and sample size on the accuracy of spatial predictions of tiger beetle (Cicindelidae) species richness

AU - Carroll, Steven S.

AU - Pearson, David

PY - 1998/1/1

Y1 - 1998/1/1

N2 - We apply geostatistical modeling techniques to investigate spatial patterns of species richness. Unlike most other statistical modeling techniques that are valid only when observations are independent, geostatistical methods are designed for applications involving spatially dependent observations. When spatial dependencies, which are sometimes called autocorrelations, exist, geostatistical techniques can be applied to produce optimal predictions in areas (typically proximate to observed data) where no observed data exist. Using tiger beetle species (Cicindelidae) data collected in western North America, we investigate the characteristics of spatial relationships in species numbers data. First, we compare the accuracy of spatial predictions of species richness when data from grid squares of two different sizes (scales) are used to form the predictions. Next we examine how prediction accuracy varies as a function of areal extent of the region under investigation. Then we explore the relationship between the number of observations used to build spatial prediction models and prediction accuracy. Our results indicate that, within the taxon of tiger beetles and for the two scales we investigate, the accuracy of spatial predictions is unrelated to scale and that prediction accuracy is not obviously related to the areal extent of the region under investigation. We also provide information about the relationship between sample size and prediction accuracy, and, finally, we show that prediction accuracy may be substantially diminished if spatial correlations in the data are ignored.

AB - We apply geostatistical modeling techniques to investigate spatial patterns of species richness. Unlike most other statistical modeling techniques that are valid only when observations are independent, geostatistical methods are designed for applications involving spatially dependent observations. When spatial dependencies, which are sometimes called autocorrelations, exist, geostatistical techniques can be applied to produce optimal predictions in areas (typically proximate to observed data) where no observed data exist. Using tiger beetle species (Cicindelidae) data collected in western North America, we investigate the characteristics of spatial relationships in species numbers data. First, we compare the accuracy of spatial predictions of species richness when data from grid squares of two different sizes (scales) are used to form the predictions. Next we examine how prediction accuracy varies as a function of areal extent of the region under investigation. Then we explore the relationship between the number of observations used to build spatial prediction models and prediction accuracy. Our results indicate that, within the taxon of tiger beetles and for the two scales we investigate, the accuracy of spatial predictions is unrelated to scale and that prediction accuracy is not obviously related to the areal extent of the region under investigation. We also provide information about the relationship between sample size and prediction accuracy, and, finally, we show that prediction accuracy may be substantially diminished if spatial correlations in the data are ignored.

UR - http://www.scopus.com/inward/record.url?scp=0031818885&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0031818885&partnerID=8YFLogxK

U2 - 10.1111/j.1600-0587.1998.tb00405.x

DO - 10.1111/j.1600-0587.1998.tb00405.x

M3 - Article

AN - SCOPUS:0031818885

VL - 21

SP - 401

EP - 414

JO - Ecography

JF - Ecography

SN - 0906-7590

IS - 4

ER -