### Abstract

Original language | English |
---|---|

Pages (from-to) | 224-228 |

Number of pages | 5 |

Journal | Austral Ecology |

Volume | 21 |

Issue number | 3 |

Publication status | Published - 1996 |

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*Austral Ecology*,

*21*(3), 224-228.

}

*Austral Ecology*, vol. 21, no. 3, pp. 224-228.

**Hierarchical partitioning as an interpretative tool in multivariate inference.** / Mac Nally, R.

Research output: Contribution to journal › Article

TY - JOUR

T1 - Hierarchical partitioning as an interpretative tool in multivariate inference

AU - Mac Nally, R.

N1 - Cited By :137 Export Date: 6 June 2017

PY - 1996

Y1 - 1996

N2 - Much of biogeography, conservation and evolutionary biology, and ecology involves very large spatial and temporal extents. Direct manipulation to test hypotheses is usually almost impossible at appropriate scales so that multivariate modelling and especially regression are used to draw causal inferences about which 'independent' variables influence the distribution and abundances of species. Such inferences clearly are crucial for the successful management of biological resources and for conserving threatened species. A succession of regression approaches has arisen, many of which yield inconsistent implications. The main problem has been the quest for one (the 'best' or the 'optimal') regression model from which the impacts of independent variables are inferred. This note is to draw the attention of ecologists to a relatively recent method, hierarchical partitioning, that does not aim to identify a best regression model as such but rather uses all models in a regression hierarchy to distinguish those variables that have high independent correlations with the dependent variable. Such variables are likely to be most influential in controlling variation in the dependent variable. Hierarchical partitioning is not to be regarded as a substitute for experimental manipulation when that is appropriate, but it is likely to produce better deductions than common regression approaches in the many ecological situations in which manipulation is impossible or of doubtful value.

AB - Much of biogeography, conservation and evolutionary biology, and ecology involves very large spatial and temporal extents. Direct manipulation to test hypotheses is usually almost impossible at appropriate scales so that multivariate modelling and especially regression are used to draw causal inferences about which 'independent' variables influence the distribution and abundances of species. Such inferences clearly are crucial for the successful management of biological resources and for conserving threatened species. A succession of regression approaches has arisen, many of which yield inconsistent implications. The main problem has been the quest for one (the 'best' or the 'optimal') regression model from which the impacts of independent variables are inferred. This note is to draw the attention of ecologists to a relatively recent method, hierarchical partitioning, that does not aim to identify a best regression model as such but rather uses all models in a regression hierarchy to distinguish those variables that have high independent correlations with the dependent variable. Such variables are likely to be most influential in controlling variation in the dependent variable. Hierarchical partitioning is not to be regarded as a substitute for experimental manipulation when that is appropriate, but it is likely to produce better deductions than common regression approaches in the many ecological situations in which manipulation is impossible or of doubtful value.

M3 - Article

VL - 21

SP - 224

EP - 228

JO - Austral Ecology

JF - Austral Ecology

SN - 1442-9985

IS - 3

ER -