A grid-based method for sampling and analysing spatially ambiguous plants

Jeffrey Fehmi, J. W. Bartolome

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

Spatial data can provide much information about the interrelations of plants and the relationship between individuals and the environment. Spatially ambiguous plants, i.e. plants without readily identifiable loci, and plants that are profusely abundant, present non-trivial impediments to the collection and analysis of vegetation data derived from standard spatial sampling techniques. Sampling with grids of presence/absence quadrats can ameliorate much of this difficulty. Our analysis of 10 fully-mapped grassland plots demonstrates the applicability of the grid-based approach which revealed spatial dependence at a much lower sampling effort than mapping each plant. Ripley's K-function, a test commonly used for point patterns, was effective for pattern analysis on the grids and the gridded quadrat technique was an effective tool for quantifying spatial patterns. The addition of spatial pattern measures should allow for better comparisons of vegetation structure between sites, instead of sole reliance on species composition data.

Original languageEnglish (US)
Pages (from-to)467-472
Number of pages6
JournalJournal of Vegetation Science
Volume12
Issue number4
StatePublished - 2001
Externally publishedYes

Fingerprint

sampling
methodology
spatial data
vegetation structure
grasslands
grassland
method
species diversity
loci
vegetation
analysis
testing
comparison
test
sampling technique

Keywords

  • Grassland
  • Nassella pulchra
  • Pattern analysis
  • Ripley's K-function
  • Sampling scale

ASJC Scopus subject areas

  • Forestry
  • Plant Science
  • Ecology

Cite this

A grid-based method for sampling and analysing spatially ambiguous plants. / Fehmi, Jeffrey; Bartolome, J. W.

In: Journal of Vegetation Science, Vol. 12, No. 4, 2001, p. 467-472.

Research output: Contribution to journalArticle

@article{961290b0b6bd4a4381112a6935bebc8c,
title = "A grid-based method for sampling and analysing spatially ambiguous plants",
abstract = "Spatial data can provide much information about the interrelations of plants and the relationship between individuals and the environment. Spatially ambiguous plants, i.e. plants without readily identifiable loci, and plants that are profusely abundant, present non-trivial impediments to the collection and analysis of vegetation data derived from standard spatial sampling techniques. Sampling with grids of presence/absence quadrats can ameliorate much of this difficulty. Our analysis of 10 fully-mapped grassland plots demonstrates the applicability of the grid-based approach which revealed spatial dependence at a much lower sampling effort than mapping each plant. Ripley's K-function, a test commonly used for point patterns, was effective for pattern analysis on the grids and the gridded quadrat technique was an effective tool for quantifying spatial patterns. The addition of spatial pattern measures should allow for better comparisons of vegetation structure between sites, instead of sole reliance on species composition data.",
keywords = "Grassland, Nassella pulchra, Pattern analysis, Ripley's K-function, Sampling scale",
author = "Jeffrey Fehmi and Bartolome, {J. W.}",
year = "2001",
language = "English (US)",
volume = "12",
pages = "467--472",
journal = "Journal of Vegetation Science",
issn = "1100-9233",
publisher = "Wiley-Blackwell",
number = "4",

}

TY - JOUR

T1 - A grid-based method for sampling and analysing spatially ambiguous plants

AU - Fehmi, Jeffrey

AU - Bartolome, J. W.

PY - 2001

Y1 - 2001

N2 - Spatial data can provide much information about the interrelations of plants and the relationship between individuals and the environment. Spatially ambiguous plants, i.e. plants without readily identifiable loci, and plants that are profusely abundant, present non-trivial impediments to the collection and analysis of vegetation data derived from standard spatial sampling techniques. Sampling with grids of presence/absence quadrats can ameliorate much of this difficulty. Our analysis of 10 fully-mapped grassland plots demonstrates the applicability of the grid-based approach which revealed spatial dependence at a much lower sampling effort than mapping each plant. Ripley's K-function, a test commonly used for point patterns, was effective for pattern analysis on the grids and the gridded quadrat technique was an effective tool for quantifying spatial patterns. The addition of spatial pattern measures should allow for better comparisons of vegetation structure between sites, instead of sole reliance on species composition data.

AB - Spatial data can provide much information about the interrelations of plants and the relationship between individuals and the environment. Spatially ambiguous plants, i.e. plants without readily identifiable loci, and plants that are profusely abundant, present non-trivial impediments to the collection and analysis of vegetation data derived from standard spatial sampling techniques. Sampling with grids of presence/absence quadrats can ameliorate much of this difficulty. Our analysis of 10 fully-mapped grassland plots demonstrates the applicability of the grid-based approach which revealed spatial dependence at a much lower sampling effort than mapping each plant. Ripley's K-function, a test commonly used for point patterns, was effective for pattern analysis on the grids and the gridded quadrat technique was an effective tool for quantifying spatial patterns. The addition of spatial pattern measures should allow for better comparisons of vegetation structure between sites, instead of sole reliance on species composition data.

KW - Grassland

KW - Nassella pulchra

KW - Pattern analysis

KW - Ripley's K-function

KW - Sampling scale

UR - http://www.scopus.com/inward/record.url?scp=0034765510&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0034765510&partnerID=8YFLogxK

M3 - Article

VL - 12

SP - 467

EP - 472

JO - Journal of Vegetation Science

JF - Journal of Vegetation Science

SN - 1100-9233

IS - 4

ER -