In an era of rapid global change, conservation managers urgently need improved tools to track and counter declining ecosystem conditions. This need is particularly acute in the marine realm, where threats are out of sight, inadequately mapped, cumulative, and often poorly understood, thereby generating impacts that are inefficiently managed. Recent advances in macroecology, statistical analysis, and the compilation of global data will play a central role in improving conservation outcomes, provided that global, regional, and local data streams can be integrated to produce locally relevant and interpretable outputs. Progress will be assisted by () expanded rollout of systematic surveys that quantify species patterns, including some carried out with help from citizen scientists; () coordinated experimental research networks that utilize large-scale manipulations to identify mechanisms underlying these patterns; () improved understanding of consequences of threats through the application of recently developed statistical techniques to analyze global species' distributional data and associated environmental and socioeconomic factors; () development of reliable ecological indicators for accurate and comprehensible tracking of threats; and () improved data-handling and communication tools.


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Literature Cited

  1. Agardy T, Di Sciara GN, Christie P. 2011. Mind the gap: addressing the shortcomings of marine protected areas through large scale marine spatial planning. Mar. Policy 35:226–32 [Google Scholar]
  2. Anderson DP, Cobb J, Korpela E, Lebofsky M, Werthimer D. 2002. SETI@home: an experiment in public-resource computing. Commun. ACM 45:56–61 [Google Scholar]
  3. Andréfouët S, Hochberg E. 2005. Multi-scale remote sensing of coral reefs. Remote Sensing of Coastal Aquatic Environments RL Miller, CED Castillo, BA McKee 297–315 Dordrecht, Neth: Springer [Google Scholar]
  4. Appeltans W, Dujardin F, Flavell M, Miloslavich P, Webb TJ. 2015. Biodiversity baselines in the global ocean. In. Open Ocean Technical Assessment Report for the GEF Transboundary Water Assessment Programme Nairobi/Paris: UNEP/IOC-UNESCO. In press [Google Scholar]
  5. Banerjee S, Carlin BP, Gelfand AE. 2014. Hierarchical Modeling and Analysis for Spatial Data Boca Raton, FL: CRC [Google Scholar]
  6. Banerjee S, Gelfand AE, Finley AO, Sang H. 2008. Gaussian predictive process models for large spatial data sets. J. R. Stat. Soc. B 70:825–48 [Google Scholar]
  7. Barabási A-L. 2005. The origin of bursts and heavy tails in human dynamics. Nature 435:207–11 [Google Scholar]
  8. Barrett NS, Buxton CD, Edgar GJ. 2009. Changes in invertebrate and macroalgal populations in Tasmanian marine reserves in the decade following protection. J. Exp. Mar. Biol. Ecol. 370:104–19 [Google Scholar]
  9. Basher Z, Bowden DA, Costello MJ. 2014. Global Marine Environmental Datasets (GMED) http://gmed.auckland.ac.nz [Google Scholar]
  10. Bates AE, Barrett NS, Stuart-Smith RD, Holbrook NJ, Thompson PA, Edgar GJ. 2014. Resilience and signatures of tropicalization in protected reef fish communities. Nat. Clim. Change 4:62–67 [Google Scholar]
  11. Benedetti-Cecchi L, Osio GC. 2007. Replication and mitigation of effects of confounding variables in environmental impact assessment: effect of marinas on rocky-shore assemblages. Mar. Ecol. Prog. Ser. 334:21–35 [Google Scholar]
  12. Besbeas P, Freeman SN, Morgan BJ, Catchpole EA. 2002. Integrating mark–recapture–recovery and census data to estimate animal abundance and demographic parameters. Biometrics 58:540–47 [Google Scholar]
  13. Bessudo S, Soler GA, Klimley AP, Ketchum JT, Hearn A, Arauz R. 2011. Residency of the scalloped hammerhead shark (Sphyrna lewini) at Malpelo Island and evidence of migration to other islands in the Eastern Tropical Pacific. Environ. Biol. Fishes 91:165–76 [Google Scholar]
  14. Bestelmeyer BT, Ellison AM, Fraser WR, Gorman KB, Holbrook SJ. et al. 2011. Analysis of abrupt transitions in ecological systems. Ecosphere 2:art129 [Google Scholar]
  15. Bird TJ, Bates AE, Lefcheck JS, Hill NA, Thomson RJ. et al. 2014. Statistical solutions for error and bias in global citizen science datasets. Biol. Conserv. 173:144–54 [Google Scholar]
  16. BirdLife Int 2004a. State of the World's Birds Cambridge, UK: BirdLife Int. [Google Scholar]
  17. BirdLife Int 2004b. Tracking Ocean Wanderers: The Global Distribution of Albatrosses and Petrels. Results from the Global Procellariform Tracking Workshop, 1–5 September, 2003, Gordon's Bay, South Africa. Cambridge, UK: BirdLife Int. [Google Scholar]
  18. Bivand R. 2015. “The problem of spatial autocorrelation:” forty years on http://cran.r-project.org/web/packages/spdep/vignettes/CO69.pdf [Google Scholar]
  19. Block BA, Jonsen ID, Jorgensen SJ, Winship AJ, Shaffer SA. et al. 2011. Tracking apex marine predator movements in a dynamic ocean. Nature 475:86–90 [Google Scholar]
  20. Bodin Ö, Österblom H. 2013. International fisheries regime effectiveness—activities and resources of key actors in the Southern Ocean. Glob. Environ. Change 23:948–56 [Google Scholar]
  21. Bolker BM, Brooks ME, Clark CJ, Geange SW, Poulsen JR. et al. 2009. Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol. Evol. 24:127–35 [Google Scholar]
  22. Bondarenko O, Kingsford M, Kininmonth SJ. 2010. Deployment of wireless sensor network to study oceanography of coral reefs. Int. J. Adv. Netw. Serv. 3:85–95 [Google Scholar]
  23. Borcard D, Legendre P. 2002. All-scale spatial analysis of ecological data by means of principal coordinates of neighbour matrices. Ecol. Model. 153:51–68 [Google Scholar]
  24. Borchers DL, Efford M. 2008. Spatially explicit maximum likelihood methods for capture–recapture studies. Biometrics 64:377–85 [Google Scholar]
  25. Borer ET, Harpole WS, Adler PB, Lind EM, Orrock JL. et al. 2014. Finding generality in ecology: a model for globally distributed experiments. Methods Ecol. Evol. 5:65–73 [Google Scholar]
  26. Brunsdon C, Fotheringham S, Charlton M. 1998. Geographically weighted regression. J. R. Stat. Soc. D 47:431–43 [Google Scholar]
  27. Buckland ST, Anderson DR, Burnham KP, Laake JL. 1993. Distance Sampling: Estimating Abundance of Biological Populations New York: Chapman & Hall [Google Scholar]
  28. Burke L, Reytar K, Spalding MD, Perry A. 2011. Reefs at risk revisited Rep., World Resour. Inst., Washington, DC [Google Scholar]
  29. Burnham KP, Anderson DR, Laake JL. 1980. Estimation of Density from Line Transect Sampling of Biological Populations Wildl. Monogr. 72 Bethesda, MD: Wildl. Soc. [Google Scholar]
  30. Caldow C, Monaco ME, Pittman SJ, Kendall MS, Goedeke TL. et al. 2015. Biogeographic assessments: a framework for information synthesis in marine spatial planning. Mar. Policy 51:423–32 [Google Scholar]
  31. Callaway R, Engelhard G, Dann J, Cotter J, Rumohr H. 2007. A century of North Sea epibenthos and trawling: comparison between 1902–1912, 1982–1985 and 2000. Mar. Ecol. Prog. Ser. 346:27–43 [Google Scholar]
  32. Caruana R, Elhawary M, Munson A, Riedewald M, Sorokina D. et al. 2006. Mining citizen science data to predict prevalence of wild bird species. Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining909–15 New York: Assoc. Comput. Mach. [Google Scholar]
  33. Carwardine J, Rochester WA, Richardson KS, Williams KJ, Pressey RL, Possingham HP. 2007. Conservation planning with irreplaceability: Does the method matter?. Biodivers. Conserv. 16:245–58 [Google Scholar]
  34. Chassot E, Bonhommeau S, Dulvy NK, Mélin F, Watson R. et al. 2010. Global marine primary production constrains fisheries catches. Ecol. Lett. 13:495–505 [Google Scholar]
  35. Cheney B, Thompson PM, Ingram SN, Hammond PS, Stevick PT. et al. 2013. Integrating multiple data sources to assess the distribution and abundance of bottlenose dolphins Tursiops truncatus in Scottish waters. Mamm. Rev. 43:71–88 [Google Scholar]
  36. Cheung WW, Lam VW, Sarmiento JL, Kearney K, Watson R, Pauly D. 2009. Projecting global marine biodiversity impacts under climate change scenarios. Fish Fish. 10:235–51 [Google Scholar]
  37. CIESIN (Cent. Int. Earth Sci. Inf. Netw.) 2014. Gridded Population of the World, version 4 (GPWv4). http://www.ciesin.columbia.edu/data/gpw-v4 [Google Scholar]
  38. Cinner JE, Graham NAJ, Huchery C, Macneil MA. 2013. Global effects of local human population density and distance to markets on the condition of coral reef fisheries. Conserv. Biol. 27:453–58 [Google Scholar]
  39. Clarke C, Lea J, Ormond R. 2012. Comparative abundance of reef sharks in the Western Indian Ocean. See Yellowlees & Hughes 2012, chap. ICRS2012_13D_1
  40. Cliff AD, Ord JK. 1968. The Problem of Spatial Autocorrelation Bristol, UK: Univ. Bristol [Google Scholar]
  41. Collen B, Nicholson E. 2014. Taking the measure of change. Science 346:166–67 [Google Scholar]
  42. Collin A, Hench J, Planes S. 2012. A novel spaceborne proxy for mapping coral cover. See Yellowlees & Hughes 2012, chap. ICRS2012_5A_1
  43. Costello MJ, Vanden Berghe E. 2006. “Ocean biodiversity informatics”: a new era in marine biology research and management. Mar. Ecol. Prog. Ser. 316:203–14 [Google Scholar]
  44. Daw TM, Cinner JE, McClanahan TR, Graham NAJ, Wilson SK. 2011. Design factors and socioeconomic variables associated with ecological responses to fishery closures in the western Indian Ocean. Coast. Manag. 39:412–24 [Google Scholar]
  45. Deville P, Linard C, Martin S, Gilbert M, Stevens FR. et al. 2014. Dynamic population mapping using mobile phone data. PNAS 111:15888–93 [Google Scholar]
  46. Devlin M, Brodie J, Wenger A, Silva E, Alvarez Romero JG. 2012. Extreme weather conditions in the Great Barrier Reef: drivers of change?. See Yellowlees & Hughes 2012, chap. ICRS2012_21A_1
  47. Diniz-Filho JAF, Bini LM, Hawkins BA. 2003. Spatial autocorrelation and red herrings in geographical ecology. Glob. Ecol. Biogeogr. 12:53–64 [Google Scholar]
  48. Dormann C, McPherson J, Araújo M, Bivand R, Bolliger J. et al. 2007. Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography 30:609–28 [Google Scholar]
  49. Dornelas M, Gotelli NJ, McGill B, Shimadzu H, Moyes F. et al. 2014. Assemblage time series reveal biodiversity change but not systematic loss. Science 344:296–99 [Google Scholar]
  50. Duffy JE, Reynolds PL, Boström C, Coyer JA, Cusson M. et al. 2015. Biodiversity mediates top-down control in eelgrass ecosystems: a global comparative-experimental approach. Ecol. Lett. 18:696–705 [Google Scholar]
  51. Durack PJ, Wijffels SE. 2010. Fifty-year trends in global ocean salinities and their relationship to broad-scale warming. J. Clim. 23:4342–62 [Google Scholar]
  52. Edgar GJ, Banks SA, Bensted-Smith R, Calvopiña M, Chiriboga A. et al. 2008. Conservation of threatened species in the Galapagos Marine Reserve through identification and protection of marine key biodiversity areas. Aquat. Conserv. Mar. Freshw. Ecosyst. 18:955–68 [Google Scholar]
  53. Edgar GJ, Banks SA, Bessudo S, Cortés J, Guzmán HM. et al. 2011. Variation in reef fish and invertebrate communities with level of protection from fishing across the eastern tropical Pacific seascape. Glob. Ecol. Biogeogr. 20:730–43 [Google Scholar]
  54. Edgar GJ, Barrett NS. 1999. Effects of the declaration of marine reserves on Tasmanian reef fishes, invertebrates and plants. J. Exp. Mar. Biol. Ecol. 242:107–44 [Google Scholar]
  55. Edgar GJ, Barrett NS. 2000. Impact of the Iron Baron oil spill on subtidal reef assemblages in Tasmania. Mar. Pollut. Bull. 40:36–49 [Google Scholar]
  56. Edgar GJ, Stuart-Smith RD. 2009. Ecological effects of marine protected areas on rocky reef communities: a continental-scale analysis. Mar. Ecol. Prog. Ser. 388:51–62 [Google Scholar]
  57. Edgar GJ, Stuart-Smith RD. 2014. Systematic global assessment of reef fish communities by the Reef Life Survey program. Sci. Data 1:140007 [Google Scholar]
  58. Edgar GJ, Stuart-Smith RD, Willis TJ, Kininmonth S, Banks S. et al. 2014. Global conservation outcomes depend on marine protected areas with five key features. Nature 506:216–20 [Google Scholar]
  59. Eken G, Bennun L, Brooks TM, Darwall W, Fishpool LDC. et al. 2004. Key biodiversity areas as site conservation targets. BioScience 54:1110–18 [Google Scholar]
  60. Elith J, Leathwick JR, Hastie T. 2008. A working guide to boosted regression trees. J. Anim. Ecol. 77:802–13 [Google Scholar]
  61. Engelhard GH, Righton DA, Pinnegar JK. 2014. Climate change and fishing: a century of shifting distribution in North Sea cod. Glob. Change Biol. 20:2473–83 [Google Scholar]
  62. Essington TE, Beaudreau AH, Wiedenmann J. 2006. Fishing through marine food webs. PNAS 103:3171–75 [Google Scholar]
  63. Fewster RM, Buckland ST, Siriwardena GM, Baillie SR, Wilson JD. 2000. Analysis of population trends for farmland birds using generalized additive models. Ecology 81:1970–84 [Google Scholar]
  64. Folke C. 2015. Globalization, marine regime shifts and the Soviet Union. Philos. Trans. R. Soc. B 370:20130278 [Google Scholar]
  65. Fort J, Beaugrand G, Grémillet D, Phillips RA. 2012. Biologging, remotely-sensed oceanography and the continuous plankton recorder reveal the environmental determinants of a seabird wintering hotspot. PLOS ONE 7:e41194 [Google Scholar]
  66. Francisco-Ramos V, Arias-González JE. 2013. Additive partitioning of coral reef fish diversity across hierarchical spatial scales throughout the Caribbean. PLOS ONE 8:e78761 [Google Scholar]
  67. Fraser HM, Greenstreet S, Piet GJ. 2007. Taking account of catchability in groundfish survey trawls: implications for estimating demersal fish biomass. ICES J. Mar. Sci. 64:1800–19 [Google Scholar]
  68. Fulton E, Smith ADM, Punt AE. 2005. Which ecological indicators can robustly detect effects of fishing?. J. Mar. Sci. 62:540–51 [Google Scholar]
  69. GEO BON (Group Earth Obs. Biodivers. Obs. Netw.) 2011. Adequacy of biodiversity observation systems to support the CBD 2020 targets: a report prepared by the Group on Earth Observations Biodiversity Observation Network (GEO BON), for the Convention on Biological Diversity. Rep., GEO BON, Pretoria, S. Afr. http://www.earthobservations.org/documents/cop/bi_geobon/2011_cbd_adequacy_report.pdf [Google Scholar]
  70. Gerritsen H, Lordan C. 2011. Integrating vessel monitoring systems (VMS) data with daily catch data from logbooks to explore the spatial distribution of catch and effort at high resolution. ICES J. Mar. Sci. 68:245–52 [Google Scholar]
  71. Graham NAJ, Dulvy NK, Jennings S, Polunin NVC. 2005. Size-spectra as indicators of the effects of fishing on coral reef fish assemblages. Coral Reefs 24:118–24 [Google Scholar]
  72. Graham NAJ, Jennings S, MacNeil A, Mouillot D, Wilson SK. 2015. Predicting climate-driven regime shifts versus rebound potential in coral reefs. Nature 518:94–97 [Google Scholar]
  73. Grassle JF. 2000. The Ocean Biogeographic Information Database (OBIS): an on-line, worldwide atlas for accessing, modeling and mapping marine biological data in a multidimensional geographic context. Oceanography 13:35–7 [Google Scholar]
  74. Halpern BS, Longo C, Hardy D, McLeod KL, Samhouri JF. et al. 2012. An index to assess the health and benefits of the global ocean. Nature 488:615–20 [Google Scholar]
  75. Halpern BS, Selkoe KA, Micheli F, Kappel CV. 2007. Evaluating and ranking the vulnerability of global marine ecosystems to anthropogenic threats. Conserv. Biol. 21:1301–15 [Google Scholar]
  76. Halpern BS, Walbridge S, Selkoe K, Kappel CV, Micheli F. et al. 2008. A global map of human impact on marine ecosystems. Science 319:948–52 [Google Scholar]
  77. Hardisty A, Roberts D. Biodivers. Inform. Community 2013. A decadal view of biodiversity informatics: challenges and priorities. BMC Ecol. 13:16 [Google Scholar]
  78. Hendee J, Gramer LJ, Heron SF, Jankulak M, Shoemaker M. et al. 2012. Wireless architectures for coral reef environmental monitoring. See Yellowlees & Hughes 2012, chap. ICRS2012_5B_1
  79. Hochachka WM, Caruana R, Fink D, Munson ART, Riedewald M. et al. 2007. Data-mining discovery of pattern and process in ecological systems. J. Wildl. Manag. 71:2427–37 [Google Scholar]
  80. Hodgson G. 1999. A global assessment of human effects on coral reefs. Mar. Pollut. Bull. 38:345–55 [Google Scholar]
  81. Holt BG, Rioja-Nieto R, Aaron Macneil M, Lupton J, Rahbek C. 2013. Comparing diversity data collected using a protocol designed for volunteers with results from a professional alternative. Methods Ecol. Evol. 4:383–92 [Google Scholar]
  82. Hosie GW, Fukuchi M, Kawaguchi S. 2003. Development of the Southern Ocean Continuous Plankton Recorder survey. Prog. Oceanogr. 57:263–83 [Google Scholar]
  83. Hosoda S, Ohira T, Nakamura T. 2008. A monthly mean dataset of global oceanic temperature and salinity derived from Argo float observations. JAMSTEC Rep. Res. Dev. 8:47–59 [Google Scholar]
  84. Illian JB, Sørbye SH, Rue H. 2012. A toolbox for fitting complex spatial point process models using integrated nested Laplace approximation (INLA). Ann. Appl. Stat. 6:1499–530 [Google Scholar]
  85. Jackson JBC. 2008. Ecological extinction and evolution in the brave new ocean. PNAS 105:11458–65 [Google Scholar]
  86. Jennings S, Dulvy NK. 2005. Reference points and reference directions for size-based indicators of community structure. ICES J. Mar. Sci. 62:397–404 [Google Scholar]
  87. Jiguet F, Devictor V, Julliard R, Couvet D. 2012. French citizens monitoring ordinary birds provide tools for conservation and ecological sciences. Acta Oecol. 44:58–66 [Google Scholar]
  88. Joly S, Davies TJ, Archambault A, Bruneau A, Derry A. et al. 2014. Ecology in the age of DNA barcoding: the resource, the promise and the challenges ahead. Mol. Ecol. Resour. 14:221–32 [Google Scholar]
  89. Jones JPG, Collen B, Atkinson G, Baxter PWJ, Bubb P. et al. 2011. The why, what, and how of global biodiversity indicators beyond the 2010 target. Conserv. Biol. 25:450–57 [Google Scholar]
  90. Jones MC, Cheung WWL. 2015. Multi-model ensemble projections of climate change effects on global marine biodiversity. ICES J. Mar. Sci. 72:741–52 [Google Scholar]
  91. Keith SA, Webb TJ, Böhning-Gaese K, Connolly SR, Dulvy NK. et al. 2012. What is macroecology?. Biol. Lett. 8:904–6 [Google Scholar]
  92. Kerr JT, Kharouba HM, Currie DJ. 2007. The macroecological contribution to global change solutions. Science 316:1581–84 [Google Scholar]
  93. Kéry M, Gardner B, Monnerat C. 2010. Predicting species distributions from checklist data using site-occupancy models. J. Biogeogr. 37:1851–62 [Google Scholar]
  94. King R. 2012. A review of Bayesian state-space modelling of capture–recapture–recovery data. Interface Focus 2:190–204 [Google Scholar]
  95. Kininmonth S. 2007. Considerations in establishing environmental sensor networks. Proceedings of the 2007 International Conference on Intelligent Sensors, Sensor Networks and Information Processing M Palaniswami, S Marusic, YW Law 687–91 New York: IEEE [Google Scholar]
  96. Kohler KE, Gill SM. 2006. Coral Point Count with Excel extensions (CPCe): a Visual Basic program for the determination of coral and substrate coverage using random point count methodology. Comput. Geosci. 32:1259–69 [Google Scholar]
  97. Korneliussen RJ, Ona E. 2002. An operational system for processing and visualizing multi-frequency acoustic data. ICES J. Mar. Sci. 59:293–313 [Google Scholar]
  98. Kühn I. 2007. Incorporating spatial autocorrelation may invert observed patterns. Divers. Distrib. 13:66–69 [Google Scholar]
  99. Langhammer PF, Bakarr MI, Bennun LA, Brooks TM, Clay RP. et al. 2007. Identification and Gap Analysis of Key Biodiversity Areas: Targets for Comprehensive Protected Area Systems Best Pract. Prot. Areas Guidel. Ser. 15 Gland, Switz: Int. Union Conserv. Nat. [Google Scholar]
  100. Leaper R, Dunstan P, Foster S, Barrett NS, Edgar GJ. 2012. Comparing large-scale bioregions and fine-scale community-level biodiversity predictions from subtidal rocky reefs across south-eastern Australia. J. Appl. Ecol. 49:851–60 [Google Scholar]
  101. Lee J, South AB, Jennings S. 2010. Developing reliable, repeatable, and accessible methods to provide high-resolution estimates of fishing-effort distributions from vessel monitoring system (VMS) data. ICES J. Mar. Sci. 67:1260–71 [Google Scholar]
  102. Levy O, Ball BA, Bond-Lamberty B, Cheruvelil KS, Finley AO. et al. 2014. Approaches to advance scientific understanding of macrosystems ecology. Front. Ecol. Environ. 12:15–23 [Google Scholar]
  103. Leyk S, Buttenfield BP, Nagle NN. 2013. Modeling ambiguity in census microdata allocations to improve demographic small area estimates. Trans. GIS 17:406–25 [Google Scholar]
  104. Link JS, Yemane D, Shannon LJ, Coll M, Shin Y-J. et al. 2010. Relating marine ecosystem indicators to fishing and environmental drivers: an elucidation of contrasting responses. ICES J. Mar. Sci. 67:787–95 [Google Scholar]
  105. Liu G, Eakin CM, Rauenzahn JL, Christensen TRL, Scott F. et al. 2012. NOAA Coral Reef Watch's decision support system for coral reef management. See Yellowlees & Hughes 2012, chap. ICRS2012_5A_6
  106. MacKenzie DI, Nichols JD, Lachman GB, Droege S, Andrew Royle J, Langtimm CA. 2002. Estimating site occupancy rates when detection probabilities are less than one. Ecology 83:2248–55 [Google Scholar]
  107. Mallet D, Pelletier D. 2014. Underwater video techniques for observing coastal marine biodiversity: a review of sixty years of publications (1952–2012). Fish. Res. 154:44–62 [Google Scholar]
  108. Mallet D, Wantiez L, Lemouellic S, Vigliola L, Pelletier D. 2014. Complementarity of rotating video and underwater visual census for assessing species richness, frequency and density of reef fish on coral reef slopes. PLOS ONE 9:e84344 [Google Scholar]
  109. Marin-Perianu M, Chatterjea S, Marin-Perianu R, Bosch S, Dulman S. et al. 2008. Wave monitoring with wireless sensor networks. Proceedings of the 2008 International Conference on Intelligent Sensors, Sensor Networks and Information Processing A Bouzerdoum, M Palaniswami, G Dissanayake, A Sowmya 611–16 New York: IEEE [Google Scholar]
  110. Matabos M, Bui AOV, Mihály S, Aguzzi J, Juniper SK, Ajayamohan RS. 2014. High-frequency study of epibenthic megafaunal community dynamics in Barkley Canyon: a multi-disciplinary approach using the NEPTUNE Canada network. J. Mar. Syst. 130:56–68 [Google Scholar]
  111. McCauley DJ, Pinsky ML, Palumbi SR, Estes JA, Joyce FH, Warner RR. 2015. Marine defaunation: animal loss in the global ocean. Science 347:1255641 [Google Scholar]
  112. McIntyre AE. 2010. Life in the World's Oceans: Diversity, Distribution, and Abundance Hoboken, NJ: Wiley-Blackwell [Google Scholar]
  113. McKenzie L, Collier C, Waycott M, Unsworth R, Yoshida R, Smith N. 2012. Monitoring inshore seagrasses of the GBR and responses to water quality. See Yellowlees & Hughes 2012, chap. ICRS2012_15B_4
  114. Meinshausen N. 2006. Quantile regression forests. J. Mach. Learn. Res. 7:983–99 [Google Scholar]
  115. Mellin C, Delean S, Caley MJ, Edgar GJ, Meekan MG. et al. 2011. Effectiveness of biological surrogates for predicting patterns of marine biodiversity. PLOS ONE 6:e20141 [Google Scholar]
  116. Michener WK, Jones MB. 2012. Ecoinformatics: supporting ecology as a data-intensive science. Trends Ecol. Evol. 27:85–93 [Google Scholar]
  117. Monk J. 2014. How long should we ignore imperfect detection of species in the marine environment when modelling their distribution?. Fish Fish. 15:352–58 [Google Scholar]
  118. Mora C, Aburto-Oropeza O, Ayotte PM, Banks S, Bauman AG. et al. 2011. Global human footprint on the linkage between biodiversity and ecosystem functioning in reef fishes. PLOS Biol. 9:e1000606 [Google Scholar]
  119. Nicholson E, Collen B, Barausse A, Blanchard JL, Costelloe BT. et al. 2012. Making robust policy decisions using global biodiversity indicators. PLOS ONE 7:e41128 [Google Scholar]
  120. Nicholson MD, Jennings S. 2004. Testing candidate indicators to support ecosystem-based management: the power of monitoring surveys to detect temporal trends in fish community metrics. ICES J. Mar. Sci. 61:35–42 [Google Scholar]
  121. OBIS (Ocean Biogeogr. Inf. Syst.) 2011. Quality control of OBIS data. http://www.iobis.org/node/47 [Google Scholar]
  122. O'Connell AF, Talancy NW, Bailey LL, Sauer JR, Cook R, Gilbert AT. 2006. Estimating site occupancy and detection probability parameters for meso- and large mammals in a coastal ecosystem. J. Wildl. Manag. 70:1625–33 [Google Scholar]
  123. Patil AP, Okiro EA, Gething PW, Guerra CA, Sharma SK. et al. 2009. Defining the relationship between Plasmodium falciparum parasite rate and clinical disease: statistical models for disease burden estimation. Malar. J. 8:186 [Google Scholar]
  124. Pauly D, Christensen V, Walters C. 2000. Ecopath, Ecosim, and Ecospace as tools for evaluating ecosystem impact of fisheries. ICES J. Mar. Sci. 57:697–706 [Google Scholar]
  125. Pauly D, Watson R, Alder J. 2005. Global trends in world fisheries: impacts on marine ecosystems and food security. Philos. Trans. R. Soc. B 360:5–12 [Google Scholar]
  126. Pereira HM, Ferrier S, Walters M, Geller GN, Jongman RHG. et al. 2013. Essential biodiversity variables. Science 339:277–78 [Google Scholar]
  127. Pesaresi M, Huadong G, Blaes X, Ehrlich D, Ferri S. et al. 2013. A global human settlement layer from optical HR/VHR RS data: concept and first results. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 6:2102–31 [Google Scholar]
  128. Porter JH, Hanson PC, Lin C-C. 2012. Staying afloat in the sensor data deluge. Trends Ecol. Evol. 27:121–29 [Google Scholar]
  129. Pressey RL, Cabeza M, Watts ME, Cowling RM, Wilson KA. 2007. Conservation planning in a changing world. Trends Ecol. Evol. 22:583–92 [Google Scholar]
  130. R Dev. Core Team 2013. R: a language and environment for statistical computing. Vienna, Austria: R Found. Stat. Comput http://www.r-project.org [Google Scholar]
  131. Raiter KG, Possingham HP, Prober SM, Hobbs RJ. 2014. Under the radar: mitigating enigmatic ecological impacts. Trends Ecol. Evol. 29:635–44 [Google Scholar]
  132. Rasmussen CE, Williams CKI. 2006. Gaussian Processes for Machine Learning Cambridge, MA: MIT Press [Google Scholar]
  133. Raudenbush SW, Yang M-L, Yosef M. 2000. Maximum likelihood for generalized linear models with nested random effects via high-order, multivariate Laplace approximation. J. Comput. Graph. Stat. 9:141–57 [Google Scholar]
  134. Ready J, Kaschner K, South AB, Eastwood PD, Rees T. et al. 2010. Predicting the distributions of marine organisms at the global scale. Ecol. Model. 221:467–78 [Google Scholar]
  135. Resil. Alliance, Santa Fe Inst 2004. Thresholds and regime shifts in ecological and social-ecological systems http://www.resalliance.org/index.php/thresholds_database [Google Scholar]
  136. Ricard D, Minto C, Jensen OP, Baum JK. 2012. Examining the knowledge base and status of commercially exploited marine species with the RAM Legacy Stock Assessment Database. Fish Fish. 13:380–98 [Google Scholar]
  137. Rice JC. 2000. Evaluating fishery impacts using metrics of community structure. ICES J. Mar. Sci. 57:682–88 [Google Scholar]
  138. Richards Z. 2014. The status of hard coral communities at Kosrae, Micronesia. Mar. Biodivers. In press. doi: 10.1007/s12526-014-0266-8 [Google Scholar]
  139. Richardson AJ, Poloczanska ES. 2008. Ocean science: under-resourced, under threat. Science 320:1294–95 [Google Scholar]
  140. Richardson AJ, Walne AW, John AWG, Jonas TD, Lindley JA. et al. 2006. Using continuous plankton recorder data. Prog. Oceanogr. 68:27–74 [Google Scholar]
  141. Robertson DR. 2008. Global biogeographical data bases on marine fishes: caveat emptor. Divers. Distrib. 14:891–92 [Google Scholar]
  142. Rodrigues ASL, Brooks TM. 2007. Shortcuts for biodiversity conservation planning: the effectiveness of surrogates. Annu. Rev. Ecol. Evol. Syst. 38:713–37 [Google Scholar]
  143. Roemmich D, Gould WJ, Gilson J. 2012. 135 years of global ocean warming between the Challenger expedition and the Argo Programme. Nat. Clim. Change 2:425–28 [Google Scholar]
  144. Roemmich D, Johnson GC, Riser S, Davis R, Gilson J. et al. 2009. The Argo Program: observing the global ocean with profiling floats. Oceanography 22:234–43 [Google Scholar]
  145. Rogers-Bennett L, Haaker PL, Karpov KA, Kushner DJ. 2002. Using spatially explicit data to evaluate marine protected areas for abalone in Southern California. Conserv. Biol. 16:1308–17 [Google Scholar]
  146. Royle JA, Kéry M, Gautier R, Schmid H. 2007. Hierarchical spatial models of abundance and occurrence from imperfect survey data. Ecol. Monogr. 77:465–81 [Google Scholar]
  147. Rue H, Martino S, Chopin N. 2009. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J. R. Stat. Soc. B 71:319–92 [Google Scholar]
  148. Ruttenberg BI, Schofield PJ, Akins JL, Acosta A, Feeley MW. et al. 2012. Rapid invasion of Indo-Pacific lionfishes (Pterois volitans and Pterois miles) in the Florida Keys, USA: evidence from multiple pre- and post-invasion data sets. Bull. Mar. Sci. 88:1051–59 [Google Scholar]
  149. Shamir L, Yerby C, Simpson R, von Benda-Beckmann AM, Tyack P. et al. 2014. Classification of large acoustic datasets using machine learning and crowdsourcing: application to whale calls. J. Acoust. Soc. Am. 135:953–62 [Google Scholar]
  150. Shin Y-J, Rochet M-J, Jennings S, Field JG, Gislason H. 2005. Using size-based indicators to evaluate the ecosystem effects of fishing. ICES J. Mar. Sci. 62:384–96 [Google Scholar]
  151. Shotton J, Sharp T, Kohli P, Nowozin S, Winn J, Criminisi A. 2013. Decision jungles: compact and rich models for classification. Advances in Neural Information Processing Systems 26 CJC Burges, L Bottou, M Welling, Z Ghahramani, KQ Weinberger 234–42 Red Hook, NY: Curran Assoc. [Google Scholar]
  152. Simpson SD, Jennings S, Johnson MP, Blanchard JL, Schön P-J. et al. 2011. Continental shelf-wide response of a fish assemblage to rapid warming of the sea. Curr. Biol. 21:1565–70 [Google Scholar]
  153. Smale DA, Langlois TJ, Kendrick GA, Meeuwig JJ, Harvey ES. 2011. From fronds to fish: the use of indicators for ecological monitoring in marine benthic ecosystems, with case studies from temperate Western Australia. Rev. Fish Biol. Fish. 21:311–37 [Google Scholar]
  154. Song C, Qu Z, Blumm N, Barabási A-L. 2010. Limits of predictability in human mobility. Science 327:1018–21 [Google Scholar]
  155. Spear LB, Ainley DG, Hardesty BD, Howell SN, Webb SW. 2004. Reducing biases affecting at-sea surveys of seabirds: use of multiple observer teams. Mar. Ornithol. 32:147–57 [Google Scholar]
  156. Stattersfield AJ, Crosby MJ, Long AJ, Wege DC. 1998. Endemic Bird Areas of the World: Priorities for Biodiversity Conservation Cambridge, UK: BirdLife Int. [Google Scholar]
  157. Stevens FR, Gaughan AE, Linard C, Tatem AJ. 2015. Disaggregating census data for population mapping using random forests with remotely-sensed and ancillary data. PLOS ONE 10:e0107042 [Google Scholar]
  158. Stuart-Smith RD, Bates AE, Lefcheck JS, Duffy JE, Baker SC. et al. 2013. Integrating abundance and functional traits reveals new global hotspots of fish diversity. Nature 501:539–42 [Google Scholar]
  159. Sutherland WJ, Adams WM, Aronson RB, Aveling R, Blackburn TM. et al. 2009. One hundred questions of importance to the conservation of global biological diversity. Conserv. Biol. 23:557–67 [Google Scholar]
  160. Sweatman H, Delean S, Syms C. 2011. Assessing loss of coral cover on Australia's Great Barrier Reef over two decades, with implications for longer-term trends. Coral Reefs 30:521–31 [Google Scholar]
  161. Thomson CW. 1880. Report on the Scientific Results of the Voyage of H.M.S. Challenger During the Years 1872–76 London: Her Majesty's Station. Off. [Google Scholar]
  162. Tittensor DP, Mora C, Jetz W, Lotze HK, Ricard D. et al. 2010. Global patterns and predictors of marine biodiversity across taxa. Nature 466:1098–101 [Google Scholar]
  163. Treml E, Fidelman P, Kininmonth S, Ekstrom J, Bodin Ö. 2015. Analysing the (mis)fit between institutional and ecological networks of the Coral Triangle. Glob. Environ. Change 31:263–71 [Google Scholar]
  164. Tyberghein L, Verbruggen H, Pauly K, Troupin C, Mineur F, De Clerck O. 2012. Bio-ORACLE: a global environmental dataset for marine species distribution modeling. Glob. Ecol. Biogeogr. 21:272–81 [Google Scholar]
  165. Tyler EH, Somerfield PJ, Berghe EV, Bremner J, Jackson E. et al. 2012. Extensive gaps and biases in our knowledge of a well-known fauna: implications for integrating biological traits into macroecology. Glob. Ecol. Biogeogr. 21:922–34 [Google Scholar]
  166. Tyre AJ, Tenhumberg B, Field SA, Niejalke D, Parris K, Possingham HP. 2003. Improving precision and reducing bias in biological surveys: estimating false-negative error rates. Ecol. Appl. 13:1790–801 [Google Scholar]
  167. Vandepitte L, Bosch S, Tyberghein L, Waumans F, Vanhoorne B. et al. 2015. Fishing for data and sorting the catch: assessing the data quality, completeness and fitness for use of data in marine biogeographic databases. Database 2015:bau125 [Google Scholar]
  168. Varjopuro R, Andrulewicz E, Blenckner T, Dolch T, Heiskanen A-S, Pihlajamäki M. 2014. Coping with persistent environmental problems: systemic delays in reducing eutrophication of the Baltic Sea. Ecol. Soc. 19:48 [Google Scholar]
  169. Walters CJ, Holling CS. 1990. Large-scale management experiments and learning by doing. Ecology 71:2060–68 [Google Scholar]
  170. Webb TJ. 2012. Marine and terrestrial ecology: unifying concepts, revealing differences. Trends Ecol. Evol. 27:535–41 [Google Scholar]
  171. Webb TJ, Tyler EHM, Somerfield PJ. 2009. Life history mediates large-scale population ecology in marine benthic taxa. Mar. Ecol. Prog. Ser. 396:293–306 [Google Scholar]
  172. Webb TJ, Vanden Berghe E, O'Dor R. 2010. Biodiversity's big wet secret: the global distribution of marine biological records reveals chronic under-exploration of the deep pelagic ocean. PLOS ONE 5:e10223 [Google Scholar]
  173. Whittaker RJ, Willis KJ, Field R. 2001. Scale and species richness: towards a general, hierarchical theory of species diversity. J. Biogeogr. 28:453–70 [Google Scholar]
  174. Wikle CK. 2003. Hierarchical models in environmental science. Int. Stat. Rev. 71:181–99 [Google Scholar]
  175. Wilkinson B, Allen M. 1999. Parallel Programming Upper Saddle River, NJ: Prentice Hall [Google Scholar]
  176. Wilkinson C. 2008. Status of Coral Reefs of the World: 2008 Townsville, Aust: Glob. Coral Reef Monit. Netw. and Reef Rainfor. Res. Cent. [Google Scholar]
  177. Williams R, Hedley SL, Hammond PS. 2006. Modeling distribution and abundance of Antarctic baleen whales using ships of opportunity. Ecol. Soc. 11:1 [Google Scholar]
  178. Yellowlees D, Hughes TP. 2012. Proceedings of the 12th International Coral Reef Symposium Townsville, Aust: James Cook Univ. [Google Scholar]
  179. Young IR, Zieger S, Babanin AV. 2011. Global trends in wind speed and wave height. Science 332:451–55 [Google Scholar]
  180. Zhao H, Ai S, Lv Z, Li B. 2010. Parallel accessing massive NetCDF data based on MapReduce. Web Information Systems and Mining FL Wang, Z Gong, J Lei 425–31 Berlin: Springer-Verlag [Google Scholar]
  181. Zieger S, Stieglitz T, Kininmonth S. 2009. Mapping reef features from multibeam sonar data using multiscale morphometric analysis. Mar. Geol. 264:209–17 [Google Scholar]

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