Predicting species diversity of benthic communities within turbid nearshore using full-waveform bathymetric LiDAR and machine learners

PLoS ONE

Volumn 6, Issue 6, 2011, Pages

  Collin, Antoine ^a   Archambault, Phillippe ^b   Long, Bernard ^a  

^a CENTRE EAU TERRE ENVIRONNEMENT   (Canada)

^b INSTITUT DES SCIENCES DE LA MER DE RIMOUSKI   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ARTICLE; CANADA; CLASSIFICATION; COASTAL WATERS; HYDRODYNAMICS; MACHINE LEARNING; MACROBENTHOS; MARINE BIOLOGY; NONHUMAN; POPULATION ABUNDANCE; SPECIES DIVERSITY; SPECIES RICHNESS; THEORY; ANIMAL; BIODIVERSITY; GEOGRAPHY; INVERTEBRATE; METHODOLOGY;

ANIMALS; BIODIVERSITY; CANADA; GEOGRAPHY; INVERTEBRATES; MARINE BIOLOGY;

EID: 79959287954     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0021265     Document Type: Article

References (55)

1. Myers N, Mittermeier R, Mittermeier CG, daFonseca GAB, Kent J, (2000) Biodiversity hotspots for conservation priorities. Nature 403: 853-858.
2. Austin MP, (2007) Species distribution models and ecological theory: A critical assessment and some possible new approaches. Ecological Modelling 200: 1-19.
3. Guisan A, Zimmermann NE, (2000) Predictive habitat distribution models in ecology. Ecological Modelling 135: 147-186.
4. Austin MP, (2002) Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecological Modelling 157: 101-118.
5. Harborne R, Mumby PJ, Zychaluk K, Hedley JD, Blackwell PG, (2006) Modeling the beta diversity of coral reefs. Ecology 87 (11): 2871-2881.
6. Pittman SJ, Christensen JD, Caldow C, Menza C, Monaco ME, (2007) Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean. Ecological Modelling 204: 9-21.
7. Purkis SJ, Graham NAJ, Riegl BM, (2008) Predictability of reef fish diversity and abundance using remote sensing data in Diego Garcia (Chagos Archipelago). Coral reefs 27: 167-178.
8. Lévesque M, Archambault P, McKindsey CW, Vaz S, Archambault D, (2010) Predictive benthic habitat suitability model for the Estuary and the northern Gulf of St. Lawrence. Can. Tech. Rep. Fish. Aquat. Sci 2893 vii +20 pp.
9. Maxwell DL, Stelzenmüller V, Eastwood PD, Rogers SI, (2009) Modelling the spatial distribution of plaice (Pleuronectes platessa), sole (Solea solea), and thornbach ray (Raja clavata) in UK waters for marine management and planning. Journal of Sea Research 6: 258-267.
10. Elith J, Graham CH, (2009) Do they? How do they? Why do they differ? On finding reasons for differing performances of species distribution models Forum piece of Ecograph.
11. Ward TJ, Vanderklift MA, Nicholls AO, Kenchington RA, (1999) Selecting marine reserves using habitats and species assemblages as surrogates for biological diversity. Ecological Applications 9: 691-698.
12. Galparsoro I, Borja A, Bald J, Liria P, Chust G, (2009) Predicting suitable habitat for the European lobster (Homarus gammarus), on the Basque continental shelf (Bay of Biscay), using Ecological-Niche Factor Analysis. Ecological Modelling 220: 556-567.
13. Kobler A, Dzeroski S, Keramitsoglou I, (2006) Habitat mapping using machine learning-extended kernel-based reclassification of an Ikonos satellite image. Ecological modelling 191: 83-95.
14. Adjeroud M, (1997) Factors influencing spatial patterns on coral reefs around Moorea, French Polynesia. Marine Ecology Progress Series 159: 105-119.
15. Wedding LM, Friedlander AM, McGranaghan M, Yost RS, Monaco ME, (2008) Using bathymetric LiDAR to define nearshore benthic habitat complexity: Implications for management of reef fish assemblages in Hawaii. Remote Sensing of Environment 112: 4159-4165.
16. Gray JS, (1974) Animal-sediment relationships. Oceanography and Marine Biology: An Annual Review 12: 223-261.
17. Mc Arthur R, Wilson EO, (1967) The Theory of Island Biogeography. Princeton Univ. Press.
18. Guenther GC, Cunningham AG, LaRocque PE, Reid DJ, (2000) Meeting the Accuracy Challenge in Airborne LiDAR Bathymetry. Presented at EARSel, Dresden.
19. Pittman SJ, Costa BM, Battista TA, (2009) Using Lidar bathymetry and boosted regression trees to predict the diversity and abundance of fish and corals. Journal of Coastal Research 53: 27-38.
20. Collin A, Archambault P, Long B, (2008) Mapping the shallow water seabed habitat with the SHOALS. IEEE Transactions on Geoscience and Remote Sensing 46 (10): 2947-2955.
21. Brock JC, Wrigth CW, Kuffner IB, Hernandez R, Thompson P, (2006) Airborne lidar sensing of massive stone coral colonies on patch reefs in the northern Florida reef tract. Remote Sensing of Environment 104: 31-42.
22. Collin A, Long B, Archambault P, (2010) Salt-marsh mapping and zonation heterogeneity through a dual-wavelength LiDAR. Remote Sensing of Environment 114 (3): 520-530.
23. Long B, Desrosiers G, (2006) Étude hydrodynamique, sédimentologique et biologique des sites de Maria, Saint-Siméon, Bonaventure, Newport et Cap-d'espoir dans la baie des Chaleurs, Québec, Canada. Ministère des Transports du Québec: 121 pages + 4 appendices.
24. Archambault P, Banwell K, Underwood AJ, (2001) Temporal variation in the structure of emerged assemblages following the removal of sewage. Marine Ecology Progress Series 222: 51-62.
25. Irish JL, McClung JK, Lillycrop WJ, (2000) Airborne lidar bathymetry: the SHOALS system. PIANC Bulletin 103: 43-53.
26. Collin A, Long B, Archambault P, (2011) Structural complexity assessment and accurate mapping of seamless coastal habitats using a multispectral LiDAR. Remote Sensing of Environment, submitted.
27. Rosenzweig ML, (1995) Species Diversity in Space and Time. Cambridge University Press.
28. Research Systems (2005) ENVI User's Guide, ENVI Version 4.2 Boulder, CO.
29. Wood J, (1996) The Geomorphological Characterization of Digital Elevation Models. Leicester, UK. Ph.D. Thesis, University of Leicester, Department of Geography.
30. Peterson CGJ, (1913) Valuation of the sea. II. The animal communities of the sea bottom and their importance for marine zoogeography. Report of the Danish Biological Station 16: 229-311.
31. Thorson G, (1957) Bottom communities (sublittoral or shallow shelf). Memorandum of the Geographical Society of America 67: 461-534.
32. Wei CL, Rowe GT, Escobar-Briones E, Boetius A, Soltwedel T, et al. (2010) Global Patterns and Predictions of Seafloor Biomass Using Random Forests. PLoS ONE 5 (12): e15323.
33. Mumby PJ, (2001) Beta and habitat diversity in marine systems: a new approach to measurement, scaling and interpretation. Oecologia 128: 274-280.
34. Austin MP, Heyligers PC, (1989) Vegetation survey design for conservation: gradsect sampling of forests in North-eastern New South Wales. Biology Conservation 50: 13-32.
35. Malanson GP, Westman WE, Van YL, (1992) Realized versus fundamental niche functions in a model of chaparral response to climatic change. Ecological Modelling 64: 261-277.
36. Mitchell T, (1997) Machine Learning. McGraw Hill.
37. Breiman L, (2001) Random forests. Machine Learning 45 (1): 5-32.
38. Quinlan JR, (1993) C4.5 Programs for machine learning. Morgan Kaufmann Publishers Inc.
39. Hsu C-W, Chang C-C, Lin C-J, (2010) A Practical Guide to Support Vector Classification.
40. Clark P, Niblett T, (1989) The CN2 induction algorithm. Machine Learning 3 (4): 261-283.
41. Demšar J, Zupan B, Leban G, Curk T, (3202/2004) Orange: From Experimental Machine Learning to Interactive Data Mining. Lecture Notes in Computer Science pp. 537-539.
42. Anselin L, (2003) GeoDa 0.9 User's Guide. Spatial Analysis Laboratory, University of Illinois, Urbana-Champaign, IL.
43. Kononenko I, Bratko I, (1991) Information-based evaluation criterion for classifier's performance. Machine Learning 6 (1): 67-80.
44. Brier GW, (1950) Verification of forecasts expressed in terms of probability. Monthly Weather Review 78: 1-3.
45. SAS Institute Inc. (2009) JMP® 8 User's Guide, Second Edition. NC, USA. Cary.
46. Long BF, Robitaille V, (2009) Use of a multi-beam autonomous portable laser equipment (MAPLE) to measure the reflectance of shallow water facies and habitats- a new tool to calibrate airborne laser bathymetry instruments. Proceedings of the 2nd FUDOTERAM Workshop (Québec, Canada).
47. Hewitt JE, Thrush SF, Halliday J, Duffy C, (2005) The importance of small-scale habitat structure for maintaining beta diversity. Ecology 86 (6): 1619-1626.
48. Friedman JH, (2002) Stochastic gradient boosting. Computational Statistics and Data Analysis 38 (4): 367-378.
49. Ridgeway G, (2006) Generalized boosted regression models. Documentation on the R Package 'gbm', version 1.5-7. Available: http://www.i-pensieri.com/gregr/gbm.shtml. Accessed 2011 May 31.
50. Minchin PR, (1989) Montane vegetation of the Mt. Field Massif, Tasmania: a test of some hypotheses about properties of community patterns. Vegetatio 83: 97-110.
51. Austin MP, Pausas JG, Nicholls AO, (1996) Patterns of tree species richness in relation to environment in south-eastern New South Wales, Australia. Aust. J. Ecol 21: 154-164.
52. Leathwick JR, Burns BR, Clarkson BD, (1998) Environmental correlates of tree alpha-diversity in New Zealand primary forests. Ecography 21: 235-246.
53. Rydgren K, Okland RH, Okland T, (2003) Species response curves along environmental gradients. A case study from SE Norwegian swamp forests. J. Veg. Sci 14: 869-880.
54. Turner MG, Gardner RH, O'Neill RV, (2001) Landscape ecology in theory and practice: pattern and process. Springer Science New York.
55. Rigby RA, Stasinopoulos DM, (2001) The GAMLSS project: a flexible approach to statistical modelling. In: Klein B, Korsholm L, editors. New Trends in Statistical Modelling: Proceedings of the 16th International Workshop on Statistical Modelling. Odense, Denmark pp. 249-256.