Filtering multi-collinear predictor variables from multi-resolution rasters of WorldClim 2.1 for Ecological Niche Modeling in Indonesian context

##plugins.themes.bootstrap3.article.main##

PRAKASH PRADHAN
https://orcid.org/0000-0003-0193-3382
AHMAD DWI SETYAWAN

Abstract

Abstract. Pradhan P, Setyawan AD. 2021. Filtering multi-collinear predictor variables from multi-resolution rasters of WorldClim 2.1 for Ecological Niche Modeling in Indonesian context. Asian J For 5: 111-122. WorldClim is one of the popular environmental datasets which hosts multi-resolution interpolated gridded climate raster surfaces and derived bioclimatic variables for both the immediate past, present and future scenarios. Bioclimatic variables along with other environmental factors like solar radiation, wind speed, water vapour pressure etc. have been used as primary set of explanatory variables for mapping and spatial modeling of many biological processes, including defining environmental niche of a species and identifying potential areas for its distribution through machine learning methods like Ecological Niche Modeling or Species Distribution Modeling or Habitat Suitability Modeling. However, the interpolated explanatory datasets are known to cause over-fitting of the models mainly due to multi-collinearity or redundancy within the variables. In the present study, 58 bioclimatic and environmental variables of Indonesian extent extracted from WorldClim 2.1 are screened to investigate the presence of multi-collinearity or redundancy. From the total 3364 variable pairs per raster resolution, 174 variable pairs were known to be affected by multicollinearity, from which temperature related bioclimatic variables, water vapour pressure and elevation associated variables were highly notable. For all the raster resolutions, bioclimatic variable 2, 3, 4, 15, 18 and 19, as well as slope, aspect, solar radiation for January, April, May, September, wind speed for August and November were found to be non-collinear. While, solar radiation for March and July were found to be non-collinear for 30s, 2.5m and 5m raster resolutions; Wind speed of July was non-collinear for 30s and 2.5m; Solar radiation for February and June were non-collinear for 10m; water vapour pressure for August for 2.5m and wind speed for January was non-collinear for 30s raster resolutions. The results of this study might serve as a convenient reference for investigators of the region for selection of bioclimatic and other environmental variables for conducting ecological niche modeling studies.

##plugins.themes.bootstrap3.article.details##

References
Anderson RP, Gonzales I. 2011. Species-specific tuning increases robustness to sampling bias in models of species distributions: An implementation with Maxent. Ecological Modelling 222: 2796–2811. DOI: 10.1016/j.ecolmodel.2011.04.011
Bivand R, Keitt T, Rowlingson B. 2020. rgdal: Bindings for the 'Geospatial' Data Abstraction Library. R package version 1.5-16. https://CRAN.R-project.org/package=rgdal
Bobrowski M, Weidinger J, Schickhoff U. 2021. Is New Always Better? Frontiers in Global Climate Datasets for Modeling Treeline Species in the Himalayas. Atmosphere 12: 543. DOI: 10.3390/atmos12050543
Chang W. 2020. webshot2: Takes screenshots of web pages, including Shiny applications and R Markdown documents. https://github.com/rstudio/webshot2.git
Faraway J. 2016. faraway: Functions and Datasets for Books by Julian Faraway. R package version 1.0.7. https://CRAN.R-project.org/package=faraway
Fick SE, Hijmans RJ. 2017. Worldclim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology. DOI: 10.1002/joc.5086. Data available at https://www.worldclim.org/data/worldclim21.html, accessed on 08.07.2021
Fox J, Weisberg S. 2019. An {R} Companion to Applied Regression, Third Edition. Thousand Oaks CA: Sage. URL: https://socialsciences.mcmaster.ca/jfox/Books/Companion/
Galili T, O'Callaghan A, Sidi J, Sievert C. 2017. heatmaply: an R package for creating interactive cluster heatmaps for online publishing, Bioinformatics, btx657, DOI: 10.1093/bioinformatics/btx657
Gunawan, Rizki MI, Anafarida O, Mahmudah N. 2021. Modeling potential distribution of Baccaurea macrocarpa in South Kalimantan, Indonesia. Biodiversitas 22: 3230-3236. DOI: 10.13057/biodiv/d220816
Hijmans RJ. 2020. raster: Geographic Data Analysis and Modeling. R package version 3.4-5. https://CRAN.R-project.org/package=raster
Jueterbock A, Smolina I, Coyer JA, Hoarau G. 2016. The fate of the Arctic seaweed Fucus distichus under climate change: an ecological niche modeling approach. Ecology and Evolution 6(6): 1712–1724. DOI: 10.1002/ece3.2001 R package available at https://cran.fhcrc.org/web/packages/MaxentVariableSelection/index.html.
Kass JM, Muscarella R, Galante PJ, Bohl CL, Pinilla-Buitrago GE, Boria RA, Soley-Guardia M, Anderson RP. 2021. ENMeval 2.0: Redesigned for customizable and reproducible modeling of species’ niches and distributions. Methods in Ecology and Evolution 00: 1-7. DOI: 10.1111/2041-210X.13628
Muscarella R, Galante PJ, Soley-Guardia M, Boria RA, Kass J, Uriarte M, Anderson RP. 2014. ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for ecological niche models. Methods in Ecology and Evolution 5(11): 1198-1205. DOI: 10.1111/2041-210X.12261
Naimi B, Hamm Na, Groen TA, Skidmore AK, Toxopeus AG. 2014. Where is positional uncertainty a problem for species distribution modelling. Ecography, 37: 191-203. DOI: 10.1111/j.1600-0587.2013.00205.x
Nursamsi I, Partasasmita R, Cundaningsih N, Ramadhani HS. 2018. Modeling the predicted suitable habitat distribution of Javan hawk-eagle Nisaetus bartelsi in the Java Island, Indonesia. Biodiversitas 19: 1539-1551. DOI: 10.13057/biodiv/d190447
O’Donnell MS, Ignizio DA. 2012, Bioclimatic predictors for supporting ecological applications in the conterminous United States: U.S. Geological Survey Data Series 691.
Pradhan P. 2016. Strengthening MaxEnt modelling through screening of redundant explanatory bioclimatic variables with variance inflation factor analysis. Researcher 8(5): 29-34. DOI: 10.7537/marsrsj080516.05
Pradhan P. 2019. Testing equivalency of interpolation derived bioclimatic variables with actual precipitation: A step towards selecting more realistic explanatory variables for species distribution modelling. Res J Chem Environ 23: 38-41.
Setyawan AD, Supriatna J, Darnaedi D, Rokhmatuloh, Sutarno, Sugiyarto, et al. 2017. Impact of climate change on potential distribution of xero-epiphytic Selaginellas (Selaginella involvens and S. repanda) in Southeast Asia. Biodiversitas 18(4): 1680-1695. DOI: 10.13057/biodiv/d180449
Setyawan AD, Supriatna J, Nisyawati, Nursamsi I, Sutarno, Sugiyarto et al. 2020a. Predicting potential impacts of climate change on the geographical distribution of mountainous selaginellas in Java, Indonesia. Biodiversitas 21(10): 4866-4877. DOI: 10.13057/biodiv/d211053
Setyawan AD, Supriatna J, Nisyawati, Nursamsi I, Sutarno, Sugiyarto, et al. 2020b. Anticipated climate changes reveal shifting in habitat suitability of high-altitude selaginellas in Java, Indonesia. Biodiversitas 21 (11): 5482-5497. DOI: 10.13057/biodiv/d211157
Setyawan AD, Supriatna J, Nisyawati, Nursamsi I, Sutarno, Sugiyarto, et al. 2021. Projecting expansion range of Selaginella zollingeriana in the Indonesian archipelago under future climate conditions. Biodiversitas 22 (4): 2088-2103. DOI: 10.13057/biodiv/d220458
Suwarto, Prasetyo LB, Kartono AP. 2016. Habitat suitability for Proboscis Monkey (Nasalis larvatus Wurmb, 1781) in the mangrove forest of Kutai National Park, East Kalimantan. Bonorowo Wetlands 6: 12-25. DOI: 10.13057/bonorowo/w060102
Vaidyanathan R, Xie Y, Allaire JJ, Cheng J, Russell K. 2019. htmlwidgets: HTML Widgets for R. R package version 1.5.1. https://CRAN.R-project.org/package=htmlwidgets
Warren DL, Glor RE, Turelli M. 2010. ENM Tools: a toolbox for comparative studies of environmental niche models. Ecography 33: 607–611. DOI: 10.1111/j.1600-0587.2009.06142.x.
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 Guettarda speciosa in Indonesia. Biodiversitas 20: 2321-2328. DOI: 10.13057/biodiv/d200830
Zurell D, Franklin J, König C, Bouchet PJ, Dormann CF, Elith J, et al. 2020. A standard protocol for reporting species distribution models. Ecography 43: 1261-1277. DOI: 10.1111/ecog.04960