Spatial Autocorrelation in Ecological Studies: A Legacy of Solutions and Myths

By Fortin, Marie-Josee; Dale, Mark R. T. | Geographical Analysis, October 2009 | Go to article overview

Spatial Autocorrelation in Ecological Studies: A Legacy of Solutions and Myths


Fortin, Marie-Josee, Dale, Mark R. T., Geographical Analysis


A major aim of including the spatial component in ecological studies is to characterize the nature and intensity of spatial relationships between organisms and their environment. The growing awareness by ecologists of the importance of including spatial structure in ecological studies (for hypothesis development, experimental design, statistical analyses, and spatial modeling) is beneficial because it promotes more effective research. Unfortunately, as more researchers perform spatial analysis, some misconceptions about the virtues of spatial statistics have been carried through the process and years. Some of these statistical concepts and challenges were already presented by Cliff and Ord in 1969. Here, we classify the most common misconceptions about spatial autocorrelation into three categories of challenges: (1) those that have no solutions, (2) those where solutions exist but are not well known, and (3) those where solutions have been proposed but are incorrect. We conclude in stressing where new research is needed to address these challenges.

Introduction

A central goal in ecology since Watts's crucial article (1947) is to understand the relation between observed pattern (e.g., in the form of spatial structure) and the processes that both generate it and arise from it. However, the consensus is that we cannot safely deduce process from pattern, in part because different processes can give rise to indistinguishable spatial signatures (Fortin and Dale 2005). Nevertheless, knowledge of the characteristics of spatial structure is almost always the first step to understanding ecological complexity. Hence, the current norm for ecological studies is to acknowledge the importance of spatial aspects of the systems under study (Levin 1992; Legendre 1993; Dungan et al. 2002; Fortin and Dale 2005; Wagner and Fortin 2005), and to include them as much as possible in a study's design (Legendre et al. 2002), analysis (Legendre et al. 2004), or modeling (Keitt et al. 2002; Lichstein et al. 2002; Griffith and Peres-Neto 2006; Dormann et al. 2007).

One outcome of this practice is that ecologists now are so used to performing spatial analyses that they may forget that this was not always the case. Spatial "awareness" was evident in studies of ecological processes and their resulting pattern (Watts 1947), but quantification of pattern using spatial statistics came later, using spatial methods developed in other fields such as human geography, in which the seminal work of Cliff and Ord (1969, 1973, 1981) was most influential. This influence has worked its way from Cliff and Ord's original article (1969) through a variety of channels, such as Sokal and Oden (1978a, b), Cormack and Ord (1979), and Legendre and Fortin (1989), into the general ecological literature.

Today, the concepts originally introduced or explained by Cliff and Ord (1969) may seem more important than their technical contributions, although those did lead to further developments. Of the range of topics discussed by Cliff and Ord (1969), many of the original challenges still remain, whereas many of the technical concerns have become less relevant; for example, concerns about normality and limiting distributions are now superceded by reliance on randomization techniques.

The key message to ecologists stemming from Cliff and Ord's (1969) article, and their subsequent books (1973, 1981), is that the presence of spatial autocorrelation can have large impacts on statistical inference, impacts that cannot be ignored. Cliff and Ord (1969) also stress the importance of estimating spatial autocorrelation using weighting methods, and using neighbors beyond the first order: these are now common practice (Fortin and Dale 2005). This was not the case in the 1960s, motivating Cliff and Ord (1969) to address some of the characteristics they perceived as weaknesses in the approaches of Geary (1954) or Moran (1950).

As for the critical statistical issues related to the evaluation and implications of spatial autocorrelation, we suggest that there remain three types of challenges: (1) those that have no solutions, (2) those where solutions exist but are not well known, and (3) those where "solutions" have been proposed but are incorrect. …

The rest of this article is only available to active members of Questia

Sign up now for a free, 1-day trial and receive full access to:

  • Questia's entire collection
  • Automatic bibliography creation
  • More helpful research tools like notes, citations, and highlights
  • Ad-free environment

Already a member? Log in now.

Notes for this article

Add a new note
If you are trying to select text to create highlights or citations, remember that you must now click or tap on the first word, and then click or tap on the last word.
One moment ...
Default project is now your active project.
Project items

Items saved from this article

This article has been saved
Highlights (0)
Some of your highlights are legacy items.

Highlights saved before July 30, 2012 will not be displayed on their respective source pages.

You can easily re-create the highlights by opening the book page or article, selecting the text, and clicking “Highlight.”

Citations (0)
Some of your citations are legacy items.

Any citation created before July 30, 2012 will labeled as a “Cited page.” New citations will be saved as cited passages, pages or articles.

We also added the ability to view new citations from your projects or the book or article where you created them.

Notes (0)
Bookmarks (0)

You have no saved items from this article

Project items include:
  • Saved book/article
  • Highlights
  • Quotes/citations
  • Notes
  • Bookmarks
Notes
Cite this article

Cited article

Style
Citations are available only to our active members.
Sign up now to cite pages or passages in MLA, APA and Chicago citation styles.

(Einhorn, 1992, p. 25)

(Einhorn 25)

1

1. Lois J. Einhorn, Abraham Lincoln, the Orator: Penetrating the Lincoln Legend (Westport, CT: Greenwood Press, 1992), 25, http://www.questia.com/read/27419298.

Cited article

Spatial Autocorrelation in Ecological Studies: A Legacy of Solutions and Myths
Settings

Settings

Typeface
Text size Smaller Larger Reset View mode
Search within

Search within this article

Look up

Look up a word

  • Dictionary
  • Thesaurus
Please submit a word or phrase above.
Print this page

Print this page

Why can't I print more than one page at a time?

Full screen

matching results for page

Cited passage

Style
Citations are available only to our active members.
Sign up now to cite pages or passages in MLA, APA and Chicago citation styles.

"Portraying himself as an honest, ordinary person helped Lincoln identify with his audiences." (Einhorn, 1992, p. 25).

"Portraying himself as an honest, ordinary person helped Lincoln identify with his audiences." (Einhorn 25)

"Portraying himself as an honest, ordinary person helped Lincoln identify with his audiences."1

1. Lois J. Einhorn, Abraham Lincoln, the Orator: Penetrating the Lincoln Legend (Westport, CT: Greenwood Press, 1992), 25, http://www.questia.com/read/27419298.

Cited passage

Welcome to the new Questia Reader

The Questia Reader has been updated to provide you with an even better online reading experience.  It is now 100% Responsive, which means you can read our books and articles on any sized device you wish.  All of your favorite tools like notes, highlights, and citations are still here, but the way you select text has been updated to be easier to use, especially on touchscreen devices.  Here's how:

1. Click or tap the first word you want to select.
2. Click or tap the last word you want to select.

OK, got it!

Thanks for trying Questia!

Please continue trying out our research tools, but please note, full functionality is available only to our active members.

Your work will be lost once you leave this Web page.

For full access in an ad-free environment, sign up now for a FREE, 1-day trial.

Already a member? Log in now.