GENERATING BIOLOGICALLY RELEVANT ENVIRONMENTAL DATA FROM REMOTE SENSING IMAGERIES AND OCEANOGRAPHIC MODELS TO SUPPORT SPATIAL PRIORITIZATION OF MARINE BIODIVERSITY CONSERVATION AND MANAGEMENT IN INDONESIA from Yayasan TERANGI

Long term Earth observation data stored in Google Earth Engine (GEE) can be ingested and derived to biologically relevant environmental variables that can used as the predictors of a species niche. The aim of this research was to create a script using GEE to generate biologically meaningful environmental variables from various Earth observation data and models in Indonesia. Elevation and bathymetry raster data from GEBCO were land masked and benthic terrain modelling were done in order to get the aspect, depth, curvature, and slope. HYCOM and MODIS AQUA dataset were filtered using spatial (Indonesia and surrounding region) and temporal filter (from 2002–2017), and reduced to biologically meaningful variables, the maximum, minimum, and mean. Water speed vector (northward and eastward) data were also converted in to scalar unit. In order to fill data gaps, kriging was done using Bayesian slope. Results shows the water depth in Indonesia ranges from 0 – 6827 m, with slope ranging from 0 – 34.33°, aspect from 0 – 359.99°, and curvature from 0 – 0.94. Variables representing water energy, mean sea surface elevation ranges from 0 – 0.85 m, and mean scalar water velocity 0 – 4 m/s. Mean surface salinity ranges from 20.09 – 35.32‰. Variables representing water quality includes mean of particulate organic carbon which ranges from 25.31 – 953.47‰ and mean of clorophyll-A concentration from 0.05 – 13.63‰. These data can be used as the input for species distribution models or spatially explicit decision support systems such as Marxan for spatial planning and zonation in Marine and Coastal Zone Management Plan.

Biologically relevant environmental data; marine; bathymetry; water quality; Google Earth Engine

Ames KM. 2016. Acropora habitat evaluation and restoration site selection using a species distribution modelling approach. Dissertation, University of South Florida: 179.

Ban. N.C., G.J.A. Hansen, M. Jones, A.C.J. Vincent. 2009. Systematic marine conservation planning in data-poor regions: Socioeconomic data is essential. Marine Policy 33: 794-800 pp.

Berkelmans, R. and B.L. Willis, 1999. Seasonal and local spatial patterns in the upper thermal limits of corals on the inshore Central Great Barrier Reef, Coral Reefs, 18, 219-228.

Elith, J., Graham, C.H., Anderson, R.P., Dudík, M., Ferrier, S., Guisan, A., Hijmans, R.J., Huettman, F., Leathwick, J.R., Lehmann, A., Li, J., Lohmann, L.G., Loiselle, B.A., Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y., Overton, J.M., Peterson, A.T., Phillips, S.J., Richardson, K., Scachetti-Pereira, R., Schapire, R.E., Soberón, J., Williams, S., Wisz, M.S. & Zimmermann, N.E. (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29, 129–151.

Estradivari, M Syahrir, N Susilo, S Yusri, & S Timotius. 2007. Terumbu karang Jakarta: Pengamatan jangka panjang terumbu karang Kepulauan Seribu (2004 -2005). Yayasan TERANGI, Jakarta: ix + 87.

Fick, S.E. and R.J. Hijmans, 2017. Worldclim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology.

Franklin, E. 2013. Predictive modelling of coral distribution and abundance in the Hawaiian Islands. Mar. Eco. Prog. Ser. 481: 121 – 132.

Gleeson, M. W. and A. E. Strong, 1995. Applying MCSST to coral reef bleaching. Adv. Space Res., 16(10), 10,151-10,154.

Glynn PW, D’Croz L (1990) Experimental evidence for high temperature stress as the cause of El Nino-coincident coral mortality. Coral Reefs 8:181–191

Guisan, A., R. TIngley, J.B. Baumgartner, I. Naujokaitis-Lewis, P.R. Sutclife, A.T. Tlloch, T.J. Regan, L. Brotons, E. McDonald-Madden, C. Mantyka-Pringle, T.G. Martin, J.R. Rhodes, R. Maggini, S. A. Setterfield, J. Elith, M.W. Schartz, B.A. Wintle, O. Broennimann, M. Austin, S. Ferrier, M.R. Kearney, H.P. Possingham, & Y.M. Buckley. 2013. Predicting Species distributions for conservation decisions. Ecology Letters, 16(12): 1424-1435 doi: 10.1111/ele.12189

J. A. Cummings and O. M. Smedstad. 2013: Variational Data Assimilation for the Global Ocean. Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications vol II, chapter 13, 303-343.

NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group. 2014. Moderate-resolution Imaging Spectroradiometer (MODIS) Aqua Ocean Color Data, NASA OB.DAAC, Greenbelt, MD, USA.

Phillips, S.J., Anderson, R.P. & Schapire, R.E. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190, 231–259.

Reaser, J. K., R. Pomerance, and P. O. Thomas, 2000. Coral bleaching and global climate change: Scientific findings and policy recommendations, Conservation Biology, 14, 1500-1511.