Comparing six different species distribution models with several subsets of environmental variables: Predicting the potential current distribution of Guettarda speciosa in Indonesia

Abstract. Yudaputra A, Pujiastuti I, Cropper Jr. WP. 2019. Comparing six different species distribution models with several subsets of environmental variables: predicting the potential current distribution of zebra Guettarda speciosa in Indonesia. Biodiversitas 20: 2321-2328. There are many algorith...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	angga yudaputra, Inggit Puji Astuti, Wendell P. Cropper
Formato:	article
Lenguaje:	EN
Publicado:	MBI & UNS Solo 2019
Materias:	algorithms, guettarda speciosa, species distribution modeling Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/0993c3fcab08465da449e86b6bba6997
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:0993c3fcab08465da449e86b6bba6997
record_format	dspace
spelling	oai:doaj.org-article:0993c3fcab08465da449e86b6bba69972021-11-21T21:43:54ZComparing six different species distribution models with several subsets of environmental variables: Predicting the potential current distribution of Guettarda speciosa in Indonesia1412-033X2085-472210.13057/biodiv/d200830https://doaj.org/article/0993c3fcab08465da449e86b6bba69972019-07-01T00:00:00Zhttps://smujo.id/biodiv/article/view/4026https://doaj.org/toc/1412-033Xhttps://doaj.org/toc/2085-4722Abstract. Yudaputra A, Pujiastuti I, Cropper Jr. WP. 2019. Comparing six different species distribution models with several subsets of environmental variables: predicting the potential current distribution of zebra Guettarda speciosa in Indonesia. Biodiversitas 20: 2321-2328. There are many algorithms of species distribution modeling that widely used to predict the potential distribution pattern of diverse organisms. Finding the best model in terms of predicting the potential distribution of many species remains a challenge. The objective of this study is to compare six different algorithms for predicting the potential current distribution pattern of Guettarda speciosa (zebra wood). The occurrence records of G. speciosa are derived from herbarium database, Bogor Botanic Gardens’s plant inventory database and direct field surveys through NKRI expedition. Seven climatic variables and elevation data are extracted from global data. R open-source software is used to run those algorithms and QGIS is used to prepare the spatial data. The result shows that MAXENT outperforms other predictive models with the highest AUC score 0.89, followed by SVM (0.87), RF (0.86), and GLM (0.82), DOMAIN (0.73), and BIOCLIM (0.62). Based on the AUC score, the four predictive models (MAXENT, SVM, RF, GLM) are categorized into good predictive models, indicating those are quite better to predict the potential current distribution pattern of G. speciosa. Whereas, DOMAIN is fair predictive model and BIOCLIM is poor predictive model. The predictive map derived from four models (MAXENT, SVM, RF, and GLM) shows almost similar appearance in predicting of potential current distribution of G. speciosa. The predictive map of current distribution would be useful to provide information regarding the potential habitat of G. speciosa across the landscape of Indonesia.angga yudaputraInggit Puji AstutiWendell P. CropperMBI & UNS Soloarticlealgorithms, guettarda speciosa, species distribution modelingBiology (General)QH301-705.5ENBiodiversitas, Vol 20, Iss 8 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	algorithms, guettarda speciosa, species distribution modeling Biology (General) QH301-705.5
spellingShingle	algorithms, guettarda speciosa, species distribution modeling Biology (General) QH301-705.5 angga yudaputra Inggit Puji Astuti Wendell P. Cropper Comparing six different species distribution models with several subsets of environmental variables: Predicting the potential current distribution of Guettarda speciosa in Indonesia
description	Abstract. Yudaputra A, Pujiastuti I, Cropper Jr. WP. 2019. Comparing six different species distribution models with several subsets of environmental variables: predicting the potential current distribution of zebra Guettarda speciosa in Indonesia. Biodiversitas 20: 2321-2328. There are many algorithms of species distribution modeling that widely used to predict the potential distribution pattern of diverse organisms. Finding the best model in terms of predicting the potential distribution of many species remains a challenge. The objective of this study is to compare six different algorithms for predicting the potential current distribution pattern of Guettarda speciosa (zebra wood). The occurrence records of G. speciosa are derived from herbarium database, Bogor Botanic Gardens’s plant inventory database and direct field surveys through NKRI expedition. Seven climatic variables and elevation data are extracted from global data. R open-source software is used to run those algorithms and QGIS is used to prepare the spatial data. The result shows that MAXENT outperforms other predictive models with the highest AUC score 0.89, followed by SVM (0.87), RF (0.86), and GLM (0.82), DOMAIN (0.73), and BIOCLIM (0.62). Based on the AUC score, the four predictive models (MAXENT, SVM, RF, GLM) are categorized into good predictive models, indicating those are quite better to predict the potential current distribution pattern of G. speciosa. Whereas, DOMAIN is fair predictive model and BIOCLIM is poor predictive model. The predictive map derived from four models (MAXENT, SVM, RF, and GLM) shows almost similar appearance in predicting of potential current distribution of G. speciosa. The predictive map of current distribution would be useful to provide information regarding the potential habitat of G. speciosa across the landscape of Indonesia.
format	article
author	angga yudaputra Inggit Puji Astuti Wendell P. Cropper
author_facet	angga yudaputra Inggit Puji Astuti Wendell P. Cropper
author_sort	angga yudaputra
title	Comparing six different species distribution models with several subsets of environmental variables: Predicting the potential current distribution of Guettarda speciosa in Indonesia
title_short	Comparing six different species distribution models with several subsets of environmental variables: Predicting the potential current distribution of Guettarda speciosa in Indonesia
title_full	Comparing six different species distribution models with several subsets of environmental variables: Predicting the potential current distribution of Guettarda speciosa in Indonesia
title_fullStr	Comparing six different species distribution models with several subsets of environmental variables: Predicting the potential current distribution of Guettarda speciosa in Indonesia
title_full_unstemmed	Comparing six different species distribution models with several subsets of environmental variables: Predicting the potential current distribution of Guettarda speciosa in Indonesia
title_sort	comparing six different species distribution models with several subsets of environmental variables: predicting the potential current distribution of guettarda speciosa in indonesia
publisher	MBI & UNS Solo
publishDate	2019
url	https://doaj.org/article/0993c3fcab08465da449e86b6bba6997
work_keys_str_mv	AT anggayudaputra comparingsixdifferentspeciesdistributionmodelswithseveralsubsetsofenvironmentalvariablespredictingthepotentialcurrentdistributionofguettardaspeciosainindonesia AT inggitpujiastuti comparingsixdifferentspeciesdistributionmodelswithseveralsubsetsofenvironmentalvariablespredictingthepotentialcurrentdistributionofguettardaspeciosainindonesia AT wendellpcropper comparingsixdifferentspeciesdistributionmodelswithseveralsubsetsofenvironmentalvariablespredictingthepotentialcurrentdistributionofguettardaspeciosainindonesia
_version_	1718418623808143360

Comparing six different species distribution models with several subsets of environmental variables: Predicting the potential current distribution of Guettarda speciosa in Indonesia

Ejemplares similares