Next Article in Journal
The Use of Nadir and Oblique UAV Images for Building Knowledge
Previous Article in Journal
Land-Use Suitability in Northeast Iran: Application of AHP-GIS Hybrid Model
Article Menu
Issue 12 (December) cover image

Export Article

Open AccessFeature PaperArticle
ISPRS Int. J. Geo-Inf. 2017, 6(12), 391; https://doi.org/10.3390/ijgi6120391

Species Distribution Modeling: Comparison of Fixed and Mixed Effects Models Using INLA

1
CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, InBIO Laboratório Associado, Pólo Açores, Universidade dos Açores, 9501-801 Ponta Delgada, Portugal
2
CMMG, Grupo de Estudos do Clima, Meteorologia e Mudanças Globais, Instituto de Investigação em Tecnologias Agrárias e Ambiente, Faculdade de Ciências Agrárias e do Ambiente, Universidade dos Açores, 9700-042 Angra do Heroísmo, Portugal
3
CE3C—Centre for Ecology, Evolution and Environmental Changes/Azorean Biodiversity Group, Faculdade de Ciências Agrárias e do Ambiente, Universidade dos Açores, 9700-042 Angra do Heroísmo, Portugal
*
Authors to whom correspondence should be addressed.
Received: 3 October 2017 / Revised: 21 November 2017 / Accepted: 26 November 2017 / Published: 1 December 2017
Full-Text   |   PDF [11048 KB, uploaded 1 December 2017]   |  

Abstract

Invasive alien species are among the most important, least controlled, and least reversible of human impacts on the world’s ecosystems, with negative consequences affecting biodiversity and socioeconomic systems. Species distribution models have become a fundamental tool in assessing the potential spread of invasive species in face of their native counterparts. In this study we compared two different modeling techniques: (i) fixed effects models accounting for the effect of ecogeographical variables (EGVs); and (ii) mixed effects models including also a Gaussian random field (GRF) to model spatial correlation (Matérn covariance function). To estimate the potential distribution of Pittosporum undulatum and Morella faya (respectively, invasive and native trees), we used geo-referenced data of their distribution in Pico and São Miguel islands (Azores) and topographic, climatic and land use EGVs. Fixed effects models run with maximum likelihood or the INLA (Integrated Nested Laplace Approximation) approach provided very similar results, even when reducing the size of the presences data set. The addition of the GRF increased model adjustment (lower Deviance Information Criterion), particularly for the less abundant tree, M. faya. However, the random field parameters were clearly affected by sample size and species distribution pattern. A high degree of spatial autocorrelation was found and should be taken into account when modeling species distribution. View Full-Text
Keywords: Gaussian random field; INLA; fixed effects models; mixed models Gaussian random field; INLA; fixed effects models; mixed models
Figures

Figure 1a

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Supplementary material

SciFeed

Share & Cite This Article

MDPI and ACS Style

Dutra Silva, L.; Brito de Azevedo, E.; Bento Elias, R.; Silva, L. Species Distribution Modeling: Comparison of Fixed and Mixed Effects Models Using INLA. ISPRS Int. J. Geo-Inf. 2017, 6, 391.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top