Connecting Earth observation to high-throughput biodiversity data


Understandably, given the fast pace of biodiversity loss, there is much interest in using Earth observation technology to track biodiversity, ecosystem functions and ecosystem services. However, because most biodiversity is invisible to Earth observation, indicators based on Earth observation could be misleading and reduce the effectiveness of nature conservation and even unintentionally decrease conservation effort. We describe an approach that combines automated recording devices, high-throughput DNA sequencing and modern ecological modelling to extract much more of the information available in Earth observation data. This approach is achievable now, offering efficient and near-real-time monitoring of management impacts on biodiversity and its functions and services.

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Figure 1: Connecting Earth observation to biodiversity and ecosystems.
Figure 2: Metabarcoding and metagenomic processing pipelines for high-throughput biodiversity surveys.
Figure 3: Three statistical pathways to map community composition and summary metrics from the combination of biodiversity point samples and continuous Earth observation maps.


  1. 1

    Verrelst, J. et al. Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties – A review. ISPRS J. Photogramm. Remote Sens. 108, 273–290 (2015).

  2. 2

    Wulder, M. A. et al. Virtual constellations for global terrestrial monitoring. Remote Sens. Environ. 170, 62–76 (2015).

  3. 3

    Toth, C. & Jóźków, G. Remote sensing platforms and sensors: a survey. ISPRS J. Photogramm. Remote Sens. 115, 22–36 (2016).

  4. 4

    O’Connor, B. et al. Earth observation as a tool for tracking progress towards the Aichi Biodiversity Targets. Remote Sens. Ecol. Conserv. 1, 19–28 (2015).

  5. 5

    Skidmore, A. K. et al. Agree on biodiversity metrics to track from space. Nature 523, 403–405 (2015).

  6. 6

    Pettorelli, N. et al. Framing the concept of Satellite Remote Sensing Essential Biodiversity Variables: challenges and future directions. Remote Sens. Ecol. Conserv. 2, 122–131 (2016).

  7. 7

    Decision adopted by the Conference of the Parties to the Convention on Biological Diversity at its Tenth Meeting. Decision X/2. The Strategic Plan for Biodiversity 2011–2020 and the Aichi Biodiversity Targets (UNEP/CBD/COP/DEC/X/2, 2010).

  8. 8

    Transforming our World: the 2030 Agenda for Sustainable Development A/RES/70/1 (United Nations General Assembly, 2015).

  9. 9

    Adoption of the Paris Agreement FCCC/CP/2015/L.9/Rev.1 (UNFCCC, 2015).

  10. 10

    Tittensor, D. P. et al. A mid-term analysis of progress toward international biodiversity targets. Science 346, 241–244 (2014).

  11. 11

    Durance, I. et al. The challenges of linking ecosystem services to biodiversity. Adv. Ecol. Res. 54, 87–134 (2016).

  12. 12

    Pettorelli, N., Owen, H. & Duncan, C. How do we want satellite remote sensing to support biodiversity conservation globally? Methods Ecol. Evol. 7, 656–665 (2016).

  13. 13

    Decision and Scoping Report for the IPBES Global Assessment on Biodiversity and Ecosystem Services IPBES-4/1 (IPBES, 2016).

  14. 14

    Dawson, T. P., Cutler, M. E. J. & Brown, C. The role of remote sensing in the development of SMART indicators for ecosystem services assessment. Biodiversity 17, 136–148 (2016).

  15. 15

    Pereira, H. M. et al. Essential Biodiversity Variables. Science 339, 277–278 (2013).

  16. 16

    Proença, V. et al. Global biodiversity monitoring: from data sources to Essential Biodiversity Variables. Biol. Conserv. (2016).

  17. 17

    Ferrier, S. Extracting more value from biodiversity change observations through integrated modeling. BioScience 61, 96–97 (2011).

  18. 18

    Belward, A. S. & Skøien, J. O. Who launched what, when and why; trends in global land-cover observation capacity from civilian earth observation satellites. ISPRS J. Photogramm. Remote Sens. 103, 115–128 (2015).

  19. 19

    Roy, D. P. et al. Landsat-8: science and product vision for terrestrial global change research. Remote Sens. Environ. 145, 154–172 (2014).

  20. 20

    Turner, W. et al. Free and open-access satellite data are key to biodiversity conservation. Biol. Conserv. 182, 173–176 (2015).

  21. 21

    Butler, D. Earth observation enters next phase. Nature 508, 160–161 (2014).

  22. 22

    Berger, M., Moreno, J., Johannessen, J. A., Levelt, P. F. & Hanssen, R. F. ESA's sentinel missions in support of Earth system science. Remote Sens. Environ. 120, 84–90 (2012).

  23. 23

    Malenovský, Z. et al. Sentinels for science: Potential of Sentinel-1, -2, and -3 missions for scientific observations of ocean, cryosphere, and land. Remote Sens. Environ. 120, 91–101 (2012).

  24. 24

    Asner, G. P. et al. Airborne laser-guided imaging spectroscopy to map forest trait diversity and guide conservation. Science 355, 385–389 (2017).

  25. 25

    Petrou, Z. I., Manakos, I. & Stathaki, T. Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets. Biodiv. Conserv. 24, 2333–2363 (2015).

  26. 26

    Wulder, M. A., Masek, J. G., Cohen, W. B., Loveland, T. R. & Woodcock, C. E. Opening the archive: How free data has enabled the science and monitoring promise of Landsat. Remote Sens. Environ. 122, 2–10 (2012).

  27. 27

    Lindenmayer, D. B. & Likens, G. E. Direct measurement versus surrogate indicator species for evaluating environmental change and biodiversity loss. Ecosystems 14, 47–59 (2011).

  28. 28

    Mueller, M. & Geist, J. Conceptual guidelines for the implementation of the ecosystem approach in biodiversity monitoring. Ecosphere 7, e01305 (2016).

  29. 29

    Snaddon, J., Petrokofsky, G., Jepson, P. & Willis, K. J. Biodiversity technologies: tools as change agents. Biol. Lett. 9, 20121029 (2013).

  30. 30

    Turner, W. Sensing biodiversity. Science 346, 301–302 (2014).

  31. 31

    Acevedo, M. A. & Villanueva-Rivera, L. J. Using automated digital recording systems as effective tools for the monitoring of birds and amphibians. Wildlife Soc. Bull. 34, 211–214 (2006).

  32. 32

    Lammers, M. O., Brainard, R. E., Au, W. W. L., Mooney, T. A. & Wong, K. B. An ecological acoustic recorder (EAR) for long-term monitoring of biological and anthropogenic sounds on coral reefs and other marine habitats. J. Acoust. Soc. Am. 123, 1720–1728 (2008).

  33. 33

    Jung, K. & Kalko, E. K. V. Adaptability and vulnerability of high flying Neotropical aerial insectivorous bats to urbanization. Div. Distrib. 17, 262–274 (2011).

  34. 34

    Aide, T. M. et al. Real-time bioacoustics monitoring and automated species identification. PeerJ 1, e103 (2013).

  35. 35

    Sollmann, R. et al. Quantifying mammal biodiversity co-benefits in certified tropical forests. Div. Distrib. 23, 317–328 (2017).

  36. 36

    Yu, D. W. et al. Biodiversity soup: metabarcoding of arthropods for rapid biodiversity assessment and biomonitoring. Methods Ecol. Evol. 3, 613–623 (2012).

  37. 37

    Taberlet, P., Coissac, E., Hajibabaei, M. & Rieseberg, L. H. Environmental DNA. Mol. Ecol. 21, 1789–1793 (2012).

  38. 38

    Bohmann, K. et al. Environmental DNA for wildlife biology and biodiversity monitoring. Trends Ecol. Evol. 29, 358–367 (2014).

  39. 39

    Ji, Y. et al. Reliable, verifiable and efficient monitoring of biodiversity via metabarcoding. Ecol. Lett. 16, 1245–1257 (2013).

  40. 40

    Lejzerowicz, F. et al. High-throughput sequencing and morphology perform equally well for benthic monitoring of marine ecosystems. Sci. Rep. 5, 13932 (2015).

  41. 41

    Edwards, D. P. et al. Selective-logging and oil palm: multitaxon impacts, biodiversity indicators, and trade-offs for conservation planning. Ecol. Appl. 24, 2029–2049 (2014).

  42. 42

    Chariton, A. A. et al. Emergent technologies and analytical approaches for understanding the effects of multiple stressors in aquatic environments. Mar. Freshw. Res. 67, 414–428 (2015).

  43. 43

    Aylagas, E., Borja, Á., Irigoien, X. & Rodríguez-Ezpeleta, N. Benchmarking DNA metabarcoding for biodiversity-based monitoring and assessment. Front. Mar. Sci. 3, 96 (2016).

  44. 44

    Visco, J. A. et al. Environmental monitoring: inferring the diatom index from next-generation sequencing data. Env. Sci. Technol. 49, 7597–7605 (2015).

  45. 45

    Xue, K. et al. Tundra soil carbon is vulnerable to rapid microbial decomposition under climate warming. Nat. Clim. Change 6, 595–600 (2016).

  46. 46

    Asner, G. P., Knapp, D. E., Anderson, C. B., Martin, R. E. & Vaughn, N. Large-scale climatic and geophysical controls on the leaf economics spectrum. Proc. Natl Acad. Sci. USA 113, E4043–E4051 (2016).

  47. 47

    Fisher, J. B., Sweeney, S. & Brzostek, E. R. Tree–mycorrhizal associations detected remotely from canopy spectral properties. Glob. Change Biol. 22, 2596–2607 (2016).

  48. 48

    Bohan, D. A. et al. Next-generation global biomonitoring: large-scale, automated reconstruction of ecological networks. Trends Ecol. Evol. (2017).

  49. 49

    Barnes, A. D. et al. Species richness and biomass explain spatial turnover in ecosystem functioning across tropical and temperate ecosystems. Philos. Trans. R. Soc. B 371, 20150279 (2016).

  50. 50

    Brose, U. & Hillebrand, H. Biodiversity and ecosystem functioning in dynamic landscapes. Philos. Trans. R. Soc. B 371, 20150267 (2016).

  51. 51

    Burley, H. M., Mokany, K., Ferrier, S. & Laffan, S. W. Macroecological scale effects of biodiversity on ecosystem functions under environmental change. Ecol. Evol. 6, 2579–2593 (2016).

  52. 52

    Tang, M. et al. High-throughput monitoring of wild bee diversity and abundance via mitogenomics. Methods Ecol. Evol. 6, 1034–1043 (2015).

  53. 53

    Wood, T. J., Holland, J. M. & Goulson, D. Providing foraging resources for solitary bees on farmland: current schemes for pollinators benefit a limited suite of species. J. Appl. Ecol. 54, 323–333 (2017).

  54. 54

    McIntyre, P. B., Jones, L. E., Flecker, A. S. & Vanni, M. J. Fish extinctions alter nutrient recycling in tropical freshwaters. Proc. Natl Acad. Sci. USA 104, 4461–4466 (2007).

  55. 55

    Solan, M. et al. Extinction and ecosystem function in the marine benthos. Science 306, 1177–1180 (2004).

  56. 56

    Sunarto, Sollmann, R., Mohamed, A. & Kelly, M. J. Camera trapping for the study and conservation of tropical carnivores. Raffles Bull. Zool. 28, 21–42 (2013).

  57. 57

    Sigsgaard, E. E. et al. Population characteristics of a large whale shark aggregation inferred from seawater environmental DNA. Nat. Ecol. Evol. 1, 0004 (2016).

  58. 58

    Ferrier, S. Mapping spatial pattern in biodiversity for regional conservation planning: where to from here? System. Biol. 51, 331–363 (2002).

  59. 59

    Ferrier, S. & Guisan, A. Spatial modelling of biodiversity at the community level. J. Appl. Ecol. 43, 393–404 (2006).

  60. 60

    Honrado, J. P., Pereira, H. M. & Guisan, A. Fostering integration between biodiversity monitoring and modelling. J. Appl. Ecol. 53, 1299–1304 (2016).

  61. 61

    D’Amen, M., Rahbek, C., Zimmermann, N. E. & Guisan, A. Spatial predictions at the community level: from current approaches to future frameworks. Biol. Rev. 92, 169–187 (2017).

  62. 62

    Warton, D. I. et al. So many variables: joint modeling in community ecology. Trends Ecol. Evol. 30, 766–779 (2015).

  63. 63

    Ovaskainen, O., Abrego, N., Halme, P. & Dunson, D. Using latent variable models to identify large networks of species-to-species associations at different spatial scales. Methods Ecol. Evol. 7, 549–555 (2016).

  64. 64

    Ovaskainen, O., Roy, D. B., Fox, R. & Anderson, B. J. Uncovering hidden spatial structure in species communities with spatially explicit joint species distribution models. Methods Ecol. Evol. 7, 428–436 (2016).

  65. 65

    Ovaskainen, O. et al. How to make more out of community data? A conceptual framework and its implementation as models and software. Ecol. Lett. 20, 561–576 (2017).

  66. 66

    Dorazio, R. M. & Royle, J. A. Estimating size and composition of biological communities by modeling the occurrence of species. J. Am. Stat. Assoc. 100, 389–398 (2005).

  67. 67

    Ferrier, S., Manion, G., Elith, J. & Richardson, K. Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment. Div. Distrib. 13, 252–264 (2007).

  68. 68

    Hottola, J., Ovaskainen, O. & Hanski, I. A unified measure of the number, volume and diversity of dead trees and the response of fungal communities. J. Ecol. 97, 1320–1328 (2009).

  69. 69

    Mücke, W., Deák, B., Schroiff, A., Hollaus, M. & Pfeifer, N. Detection of fallen trees in forested areas using small footprint airborne laser scanning data. Can. J. Remote Sens. 39, S32–S40 (2013).

  70. 70

    Yang, C. Y. et al. Higher fungal diversity is correlated with lower CO2 emissions from dead wood in a natural forest. Sci. Rep. 6, 31066 (2016).

  71. 71

    Pasari, J. R., Levi, T., Zavaleta, E. S. & Tilman, D. Several scales of biodiversity affect ecosystem multifunctionality. Proc. Natl Acad. Sci. USA 110, 10219–10222 (2013).

  72. 72

    Wang, S. & Loreau, M. Ecosystem stability in space: α, β and γ variability. Ecol. Lett. 17, 891–901 (2014).

  73. 73

    Cardinale, B. J., Duffy, J. E., Gonzalez, A. & Hooper, D. U. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).

  74. 74

    Olson, J. R., Hawkins, C. P., Mock, K., Huntington, J. & Susfalk, R. System for Mapping And Predicting Species Of Concern (SMAP-SOC), Phase I Final Report and Phase II Plan., (NASA Earth Science Division/Applied Sciences Program, Washington D. C., 2014).

  75. 75

    Calabrese, J. M., Certain, G., Kraan, C. & Dormann, C. F. Stacking species distribution models and adjusting bias by linking them to macroecological models. Glob. Ecol. Biogeogr. 23, 99–112 (2014).

  76. 76

    Kéry, M. & Royle, A. J. in Modeling Demographic Processes in Marked Populations. Environmental and Ecological Statistics Vol. 3 (eds Thomson, D. L., Cooch, E. G. & Conroy, M. J. ) 639–656 (Springer, 2009).

  77. 77

    Ovaskainen, O. & Soininen, J. Making more out of sparse data: hierarchical modeling of species communities. Ecology 92, 289–295 (2011).

  78. 78

    Mokany, K., Harwood, T., Overton, J., Barker, G. & Ferrier, S. Combining α- and β-diversity models to fill gaps in our knowledge of biodiversity. Ecol. Lett. 14, 1043–1051 (2011).

  79. 79

    Ferretti, V. & Pomarico, S. Ecological land suitability analysis through spatial indicators: an application of the analytic network process technique and ordered weighted average approach. Ecol. Indic. 34, 507–519 (2013).

  80. 80

    Marcot, B. G. et al. Recent advances in applying decision science to managing national forests. Forest Ecol. Manag. 285, 123–132 (2012).

  81. 81

    Gregory, R., Long, G., Colligan, M., Geiger, J. G. & Laser, M. When experts disagree (and better science won’t help much): using structured deliberations to support endangered species recovery planning. J. Environ. Manag. 105, 30–43 (2012).

  82. 82

    Steidl, R. J., Hayes, J. P. & Schauber, E. Statistical power analysis in wildlife research. J. Wildlife Manag. 61, 270–279 (1997).

  83. 83

    Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).

  84. 84

    Newbold, T. et al. Has land use pushed terrestrial biodiversity beyond the planetary boundary? A global assessment. Science 353, 288–291 (2016).

  85. 85

    Global Biodiversity Change Indicators Version 1.2 (GEO BON, 2015).

  86. 86

    Allnutt, T. F. et al. A method for quantifying biodiversity loss and its application to a 50-year record of deforestation across Madagascar. Conserv. Lett. 1, 173–181 (2008).

  87. 87

    Ferrier, S. et al. Mapping more of terrestrial biodiversity for global conservation assessment. BioScience 54, 1101–1109 (2004).

  88. 88

    Cardoso, P., Erwin, T. L., Borges, P. A. V. & New, T. R. The seven impediments in invertebrate conservation and how to overcome them. Biol. Conserv. 144, 2647–2655 (2011).

  89. 89

    Eigenbrod, F. et al. The impact of proxy-based methods on mapping the distribution of ecosystem services. J. Appl. Ecol. 47, 377–385 (2010).

  90. 90

    Fitzpatrick, M. C. & Keller, S. R. Ecological genomics meets community-level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation. Ecol. Lett. 18, 1–16 (2015).

  91. 91

    Crampton-Platt, A., Yu, D. W., Zhou, X. & Vogler, A. P. Mitochondrial metagenomics: letting the genes out of the bottle. GigaScience 5, 0120 (2016).

  92. 92

    Maron, M., Gordon, A., Mackey, B., Posssingham, H. P. & Watson, J. E. M. Stop misuse of biodiversity offsets. Nature 523, 401–403 (2015).

  93. 93

    Palumbo, I. et al. Building capacity in remote sensing for conservation: present and future challenges. Remote Sens. Ecol. Conserv. 3, 21–29 (2016).

  94. 94

    Dafforn, K. A., Johnston, E. L. & Ferguson, A. Big data opportunities and challenges for assessing multiple stressors across scales in aquatic ecosystems. Mar. Freshw. Res. 67, 393–413 (2015).

  95. 95

    Schmeller, D. S. et al. Towards a global terrestrial species monitoring program. J. Nat. Conserv. 25, 51–57 (2015).

  96. 96

    Peres, C. A., Emilio, T., Schietti, J., Desmoulière, S. J. M. & Levi, T. Dispersal limitation induces long-term biomass collapse in overhunted Amazonian forests. Proc. Natl Acad. Sci. USA 113, 892–897 (2016).

  97. 97

    Levi, T., Shepard, G. H. Jr, Ohl-Schacherer, J., Peres, C. A. & Yu, D. W. Modelling the long-term sustainability of indigenous hunting in Manu National Park, Peru: landscape-scale management implications for Amazonia. J. Appl. Ecol. 46, 804–814 (2009).

  98. 98

    Newton, A. C. Implications of Goodhart's Law for monitoring global biodiversity loss. Conserv. Lett. 4, 264–268 (2011).

  99. 99

    Smaldino, P. E. & McElreath, R. The natural selection of bad science. R. Soc. Open Sci. 3, 160384 (2016).

  100. 100

    Crowther, T. W. et al. Biotic interactions mediate soil microbial feedbacks to climate change. Proc. Natl Acad. Sci. USA 112, 7033–7038 (2015).

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This Perspective is a product of the EO-BESS Working Group, organized by H.B., D.R. and B.C. and funded by the UK Natural Environment Research Council. Individual author acknowledgements are in Supplementary Information.

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B.C. and H.B. led the sections on Earth observation technology. K.B. and D.W.Y. led the sections on biodiversity technology. A.B. led the sections on statistical modelling. A.B., R.S., A.W., O.O., and D.W.Y. led the sections on case studies (Box 3 and ‘From CEOBE to Aichi’). C.M. led the conclusions section. Figures were created by K.B., A.B., C.C. and A.Z. All authors contributed to multiple rewrites, with a large contribution by D.R; A.B. and D.W.Y. wrote the first draft and supervised the work.

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Correspondence to Douglas W. Yu.

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Competing interests

D.W.Y. and A.V. are co-founders of a private company that provides commercial metabarcoding services.

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Supplementary Notes 1–4, Supplementary Figure 1, Supplementary Acknowledgements, Supplementary References (PDF 778 kb)

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Bush, A., Sollmann, R., Wilting, A. et al. Connecting Earth observation to high-throughput biodiversity data. Nat Ecol Evol 1, 0176 (2017).

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