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

Yudaputra, Angga; Astuti, Inggit Puji; Cropper, Wendell P.

doi:10.13057/biodiv/d200830

Cited by 15 publications

(10 citation statements)

References 26 publications

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“…They are mostly based on the postulation of equilibrium conditions and the concept of the ecological niche ( Gaudreau et al., 2015 ), and thus seek to reproduce environmental determinism of the species distribution at a given time ( Bertrand, 2012 ). Several modelling algorithms exist in the literature with varying accuracies, but finding the best model in terms of predicting the potential distribution of a species remains a challenge ( Yudaputra et al., 2019 ). As such, the use of a combination of algorithms seems to provide better accuracy in predictions than a single algorithm.…”

Section: Introductionmentioning

confidence: 99%

Predicting the potential impacts of climate change on the endangered Caesalpinia bonduc (L.) Roxb in Benin (West Africa)

Wouyou

Lokonon

Idohou

et al. 2022

Heliyon

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Section: Introductionmentioning

confidence: 99%

Predicting the potential impacts of climate change on the endangered Caesalpinia bonduc (L.) Roxb in Benin (West Africa)

Wouyou

Lokonon

Idohou

et al. 2022

Heliyon

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“…This model is also known as the habitat suitability model by using various environmental predictors to model the spatial distribution of species across landscapes or ecoregions. This model can understand species' ecological requirements and predict habitat suitability (Segurado and Araújo 2004;Moisen and Frescino 2002;Hirzel et al 2002;Araújo and Peterson 2012;Yudaputra et al 2019). The analytical framework used in this study is presented in Figure 3.…”

Section: Discussionmentioning

confidence: 99%

Spatial distribution and habitat characteristics of Macadamia hildebrandii in the Sintuwu Maroso Protection Forest, Central Sulawesi, Indonesia

2020

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Abstract. Akhbar, Nuryanti S, Naharuddin. 2020. Spatial distrubution and habitat characteristics of Macadamia hildebrandii in the Sintuwu Maroso Protection Forest, Central Sulawesi, Indonesia. Biodiversitas 21: 770-779. Research on the spatial distribution of Macadamia hildebrandii Steen (haleka) tree species is one of the important stages in the preservation of Sulawesi's endemic plants. The purpose of this research was to obtain information regarding the spatial distribution of M. hildebrandii in the area of Sintuwu Maroso Forest Management Unit, Central Sulawesi and the relationship between its occurrence with habitat characteristics in term of land and climate variables. Data was collected by establishing sampling plots using stratified systematic sampling based on land cover, namely primary and secondary dryland forests. Index of dispersion was used to calculate the distance between population while the chi-square test was used to investigate the relationship between M. hildebrandii population and variables of land and climate. The results showed that the M. hildebrandii trees mostly grew in the mountain rain zone ecosystem, clumped in groups according to the characteristics of the dominant natural habitat. The important characteristics of its habitat were it had red-yellow podzolic-lithosol soil type, altitude of ≥ 1,500 m asl, slope class of ≥ 25%, middle slope aspect, tineba volcano rock formation (which is volcanic sedimentary rocks), metamorphic parent material, secondary dryland forests, and rainfall of 1,600-2,000 mm/year. The results of study can be used as baseline information for the conservation and sustainable utilization, including cultivation, of M. hildebrandii especially in the montane zone of rain forest ecosystem in Sulawesi.

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“…The ensemble model was used by combining multiple models, namely Random Forest (RF) and Support Vector Machine (SVM). These models were chosen because some previous study stated these models produced good results in model evaluation [23]. Furthermore, RF produced high accuracy model in forecasting the spatial distribution of invasive plant across landscape of Bali [24].…”

Section: Data Miningmentioning

confidence: 99%

Geographic Distribution Shift of Invasive Plant Austroeupatorium inulifolium in the Future Climate Projection

Yudaputra

Fijridiyanto

Astuti

et al. 2021

ARRB

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Aims: This study aims to predict the future geographic distribution shift of invasive plant species Austroeupathorium inulifolium as the impact of global climate change. Study Design: The rising temperature and precipitation change lead to the geographic distribution shift of organisms. A. inulifolium belongs to invasive plant species that often causes a substantial economic loss and ecological degradation in the invaded areas. Modelling of species distribution using the climate-based model could be used to understand the geographic distribution shift of invasive species in the future scenario under global climate change. Place and Duration of Study: Center for Plant Conservation and Botanic Gardens – LIPI and 6 months. Methodology: The total 2228 of occurrence records were derived from the Global Biodiversity Information Facility (GBIF) database. The seven climatic variables were selected from 19 variables using a pairwise correlation test (vifcor) with a threshold >0.7. The ensemble model was used by combining Random Forest (RF) and Support Vector Machine (SVM). Results: Both two models are well-performed either using AUC or TSS evaluation methods. RF and SVM have AUC >0.95, and TSS >0.8. The predicted current distribution tends to have larger distribution areas compared to observed occurrence records. The predicted future distribution seems to be shifted in several parts of North America and Europe. Conclusion: The geographic distribution of invasive plant species A. inulifolium will be shifted to the Northern part of globe in 2090. Mean temperature of driest quarter and precipitation of warmest quarter are the two most important variables that determine the distribution pattern of the A. inulifolium. The predictive distribution pattern of invasive plant A. inulifolium would be important to provide information about the impact of climate change to the geographic distribution shift of this species.

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

Cited by 15 publications

References 26 publications

Predicting the potential impacts of climate change on the endangered Caesalpinia bonduc (L.) Roxb in Benin (West Africa)

Predicting the potential impacts of climate change on the endangered Caesalpinia bonduc (L.) Roxb in Benin (West Africa)

Spatial distribution and habitat characteristics of Macadamia hildebrandii in the Sintuwu Maroso Protection Forest, Central Sulawesi, Indonesia

Geographic Distribution Shift of Invasive Plant Austroeupatorium inulifolium in the Future Climate Projection

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