ABSTRACT
This paper will demonstrate how the parallelism and expressiveness of the Chapel programming language are used to achieve an enormous improvement in computational speed for a problem related to coral reef conservation. Chapel’s concise syntax and versatile data structures enable this problem to be solved in under 300 lines of code, while reducing the time to solution from days down to the order of seconds. This improvement is so substantial that it represents a paradigm shift in the way biodiversity can be measured at scale, providing a wealth of novel information for marine ecosystem managers and opening up brand new avenues for scientific inquiry. This paper will review the solution strategy and data structures in Chapel that allowed these improvements to be realized, and will preview future extensions of this work that have been made possible by this drastic speedup.
- Mitchell B. Lyons, Chris M. Roelfsema, Emma V. Kennedy, Eva M. Kovacs, Rodney Borrego-Acevedo, Kathryn Markey, Meredith Roe, Doddy M. Yuwono, Daniel L. Harris, Stuart R. Phinn, 2020. Mapping the world’s coral reefs using a global multiscale earth observation framework. Remote Sensing in Ecology and Conservation 6, 4 (2020), 557–568.Google ScholarCross Ref
- Zoltán Botta-Dukát. 2005. Rao’s quadratic entropy as a measure of functional diversity based on multiple traits. Journal of vegetation science 16, 5 (2005), 533–540.Google ScholarCross Ref
- Alessandro Chiarucci. 2007. To sample or not to sample? That is the question... for the vegetation scientist. Folia Geobotanica 42 (2007), 209–216.Google ScholarCross Ref
- Antoine Collin and Serge Planes. 2012. Enhancing coral health detection using spectral diversity indices from worldview-2 imagery and machine learners. Remote Sensing 4, 10 (2012), 3244–3264.Google ScholarCross Ref
- Mayeul Dalleau, Serge Andrefouet, Colette CC Wabnitz, Claude Payri, Laurent Wantiez, Michel Pichon, KIM Friedman, Laurent Vigliola, and Francesca Benzoni. 2010. Use of habitats as surrogates of biodiversity for efficient coral reef conservation planning in Pacific Ocean islands. Conservation Biology 24, 2 (2010), 541–552.Google ScholarCross Ref
- Shawna A Foo and Gregory P Asner. 2019. Scaling up coral reef restoration using remote sensing technology. Frontiers in Marine Science (2019), 79.Google Scholar
- Kevin J Gaston. 2000. Global patterns in biodiversity. Nature 405, 6783 (2000), 220–227.Google Scholar
- EP Green, PJ Mumby, AJ Edwards, and CD Clark. 1996. A review of remote sensing for the assessment and management of tropical coastal resources. Coastal management 24, 1 (1996), 1–40.Google Scholar
- Alastair R Harborne, Peter J Mumby, Kamila ZŻychaluk, John D Hedley, and Paul G Blackwell. 2006. Modeling the beta diversity of coral reefs. Ecology 87, 11 (2006), 2871–2881.Google ScholarCross Ref
- John D Hedley, Chris M Roelfsema, Iliana Chollett, Alastair R Harborne, Scott F Heron, Scarla J. Weeks, William J Skirving, Alan E Strong, C Mark Eakin, Tyler RL Christensen, 2016. Remote sensing of coral reefs for monitoring and management: a review. Remote Sensing 8, 2 (2016), 118.Google ScholarCross Ref
- Joaquín Hortal, Francesco de Bello, José Alexandre F Diniz-Filho, Thomas M Lewinsohn, Jorge M Lobo, and Richard J Ladle. 2015. Seven shortfalls that beset large-scale knowledge of biodiversity. Annual Review of Ecology, Evolution, and Systematics 46 (2015), 523–549.Google ScholarCross Ref
- Matthew S Kendall, Thomas J Miller, and Simon J Pittman. 2011. Patterns of scale-dependency and the influence of map resolution on the seascape ecology of reef fish. Marine Ecology Progress Series 427 (2011), 259–274.Google ScholarCross Ref
- Emma V Kennedy, Chris M Roelfsema, Mitchell B Lyons, Eva M Kovacs, Rodney Borrego-Acevedo, Meredith Roe, Stuart R Phinn, Kirk Larsen, Nicholas J Murray, Doddy Yuwono, 2021. Reef Cover, a coral reef classification for global habitat mapping from remote sensing. Scientific Data 8, 1 (2021), 196.Google ScholarCross Ref
- Anders Knudby, Ellsworth LeDrew, and Candace Newman. 2007. Progress in the use of remote sensing for coral reef biodiversity studies. Progress in Physical Geography 31, 4 (2007), 421–434.Google ScholarCross Ref
- Anders Knudby, Chris Roelfsema, Mitchell Lyons, Stuart Phinn, and Stacy Jupiter. 2011. Mapping fish community variables by integrating field and satellite data, object-based image analysis and modeling in a traditional Fijian fisheries management area. Remote Sensing 3, 3 (2011), 460–483.Google ScholarCross Ref
- Robert H MacArthur. 1984. Geographical ecology: patterns in the distribution of species. Princeton University Press.Google Scholar
- Norman WH Mason, David Mouillot, William G Lee, and J Bastow Wilson. 2005. Functional richness, functional evenness and functional divergence: the primary components of functional diversity. Oikos 111, 1 (2005), 112–118.Google ScholarCross Ref
- Peter J Mumby. 2001. Beta and habitat diversity in marine systems: a new approach to measurement, scaling and interpretation. Oecologia 128 (2001), 274–280.Google ScholarCross Ref
- Harini Nagendra. 2001. Using remote sensing to assess biodiversity. International journal of remote sensing 22, 12 (2001), 2377–2400.Google ScholarCross Ref
- Michael W Palmer, Peter G Earls, Bruce W Hoagland, Peter S White, and Thomas Wohlgemuth. 2002. Quantitative tools for perfecting species lists. Environmetrics: The official journal of the International Environmetrics Society 13, 2 (2002), 121–137.Google Scholar
- Nathalie Pettorelli, Jon Olav Vik, Atle Mysterud, Jean-Michel Gaillard, Compton J Tucker, and Nils Chr Stenseth. 2005. Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in ecology & evolution 20, 9 (2005), 503–510.Google Scholar
- Nathalie Pettorelli, Martin Wegmann, Leigh Gurney, and Gregoire Dubois. 2016. Monitoring protected areas from space. Protected Areas: Are They Safeguarding Biodiversity? (2016), 242–259.Google Scholar
- Sam J Purkis, Arthur CR Gleason, Charlotte R Purkis, Alexandra C Dempsey, Philip G Renaud, Mohamed Faisal, Steven Saul, and Jeremy M Kerr. 2019. High-resolution habitat and bathymetry maps for 65,000 sq. km of Earth’s remotest coral reefs. Coral Reefs 38 (2019), 467–488.Google ScholarCross Ref
- Andy Purvis and Andy Hector. 2000. Getting the measure of biodiversity. Nature 405, 6783 (2000), 212–219.Google Scholar
- C Radhakrishna Rao. 1982. Diversity and dissimilarity coefficients: a unified approach. Theoretical population biology 21, 1 (1982), 24–43.Google Scholar
- Russ Rew and Glenn Davis. 1990. NetCDF: an interface for scientific data access. IEEE computer graphics and applications 10, 4 (1990), 76–82.Google Scholar
- Carlo Ricotta and Michela Marignani. 2007. Computing β -diversity with Rao’s quadratic entropy: A change of perspective. Diversity and Distributions 13, 2 (2007), 237–241.Google ScholarCross Ref
- Duccio Rocchini, Doreen S Boyd, Jean-Baptiste Féret, Giles M Foody, Kate S He, Angela Lausch, Harini Nagendra, Martin Wegmann, and Nathalie Pettorelli. 2016. Satellite remote sensing to monitor species diversity: Potential and pitfalls. Remote Sensing in Ecology and Conservation 2, 1 (2016), 25–36.Google ScholarCross Ref
- Duccio Rocchini, Matteo Marcantonio, and Carlo Ricotta. 2017. Measuring Rao’s Q diversity index from remote sensing: An open source solution. Ecological indicators 72 (2017), 234–238.Google Scholar
- Chris Roelfsema, Eva Kovacs, Juan Carlos Ortiz, Nicholas H Wolff, David Callaghan, Magnus Wettle, Mike Ronan, Sarah M Hamylton, Peter J Mumby, and Stuart Phinn. 2018. Coral reef habitat mapping: A combination of object-based image analysis and ecological modelling. Remote Sensing of Environment 208 (2018), 27–41.Google ScholarCross Ref
- Christian Rossi, Mathias Kneubühler, Martin Schütz, Michael E Schaepman, Rudolf M Haller, and Anita C Risch. 2021. Remote sensing of spectral diversity: A new methodological approach to account for spatio-temporal dissimilarities between plant communities. Ecological Indicators 130 (2021), 108106.Google ScholarCross Ref
- Kenichiro Shimatani. 2001. On the measurement of species diversity incorporating species differences. Oikos 93, 1 (2001), 135–147.Google ScholarCross Ref
- Thomas B Smith, Salit Kark, Christopher J Schneider, Robert K Wayne, and Craig Moritz. 2001. Biodiversity hotspots and beyond: the need for preserving environmental transitions. Trends in Ecology & Evolution 16, 8 (2001), 431.Google ScholarCross Ref
- Woody Turner, Carlo Rondinini, Nathalie Pettorelli, Brice Mora, Allison K Leidner, Zoltan Szantoi, Graeme Buchanan, Stefan Dech, John Dwyer, Martin Herold, 2015. Free and open-access satellite data are key to biodiversity conservation. Biological Conservation 182 (2015), 173–176.Google ScholarCross Ref
- Simon Van Wynsberge, Serge Andrefouet, Mélanie A Hamel, and Michel Kulbicki. 2012. Habitats as surrogates of taxonomic and functional fish assemblages in coral reef ecosystems: a critical analysis of factors driving effectiveness. PloS one 7, 7 (2012), e40997.Google ScholarCross Ref
- Steven D Warren, Martin Alt, Keith D Olson, Severin DH Irl, Manuel J Steinbauer, and Anke Jentsch. 2014. The relationship between the spectral diversity of satellite imagery, habitat heterogeneity, and plant species richness. Ecological Informatics 24 (2014), 160–168.Google ScholarCross Ref
Index Terms
- High-Performance Programming and Execution of a Coral Biodiversity Mapping Algorithm Using Chapel
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