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Landscape Ecology

, Volume 33, Issue 7, pp 1023–1028 | Cite as

multifit: an R function for multi-scale analysis in landscape ecology

Short Communication

Abstract

Context

Multi-scale analyses are a common approach in landscape ecology. Their aim is to find the appropriate spatial scale for a particular landscape attribute in order to perform a correct interpretation of results and conclusions.

Objectives

I present an R function that performs statistical analysis relating a biological response with a landscape attribute at a set of specified spatial scales and extracts the statistical strength of the models through a specified criterion index. Also, it draws a plot with the value of these indexes, allowing the user to choose the most appropriate spatial scale. This paper introduces the usage of multifit and demonstrates its functionality through a case study.

Conclusions

The spatial scale at which ecologists conduct studies may change study outcomes and conclusions. Because of this, landscape ecologists commonly conduct multi-scale studies in order to establish an appropriate spatial scale for particular biological or ecological responses. The tool presented here allows ecologists to simultaneously run several statistical models for a response variable and a specified set of spatial scales, automating the process of multi-scale analysis.

Keywords

Landscape size Spatial scale Spatial extent Buffer Focal site design Scale of effect Scale of response 

Notes

Acknowledgements

I am grateful to two anonymous reviewers for suggestions and comments that improved previous versions of the manuscript and R code. I specially thank to María del Carmen Romero and Gustavo Giménez for introducing me to R programming. I thank to Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) and Universidad Nacional de Córdoba (UNC) for financial support, only possible with sustainable public policies for science.

Author contributions

PYH conceived the idea, programmed the R code and wrote the manuscript.

Supplementary material

10980_2018_657_MOESM1_ESM.pdf (226 kb)
Electronic supplementary material 1 (PDF 226 kb)
10980_2018_657_MOESM2_ESM.r (17 kb)
Electronic supplementary material 2 (R 17 kb)

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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Instituto Multidisciplinario de Biología VegetalFCEFyN (CONICET, Universidad Nacional de Córdoba)CórdobaArgentina

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