Ecology

1.
Dullo, W. C., Flögel, S. & Rüggeberg, A. Cold-water coral growth in relation to the hydrography of the Celtic and Nordic European continental margin. Mar. Ecol. Prog. Ser. 371, 165–176 (2008).
ADS  Article  Google Scholar 
2.
Puerta, P. et al. Influence of water masses on the biodiversity and biogeography of deep-sea benthic ecosystems in the North Atlantic. Front. Mar. Sci. 7, 239. https://doi.org/10.3389/fmars.2020.00239 (2020).
Article  Google Scholar 

3.
Davies, A. J. & Guinotte, J. M. Global habitat suitability for framework-forming cold-water corals. PLoS ONE 6(4), e18483. https://doi.org/10.1371/journal.pone.0018483 (2011).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

4.
Davies, A. J. et al. Downwelling and deep-water bottom currents as food supply mechanisms to the cold-water coral Lophelia pertusa (Scleractinia) at the Mingulay Reef Complex. Limnol. Oceanogr. 54, 620–629 (2009).
ADS  Article  Google Scholar 

5.
Xu, G., McGillicuddy, D. J. Jr., Mills, S. W. & Mullineaux, L. S. Dispersal of hydrothermal vent larvae at East Pacific rise 9–10° N segment. J. Geophys. Res. Oceans 123, 7877–7895 (2018).
ADS  Article  Google Scholar 

6.
Bracco, A., Liu, G., Galaska, M., Quattrini, A. M. & Herrera, S. Integrating physical circulation models and genetic approaches to investigate population connectivity in deep-sea corals. J. Mar. Syst. 198, 103189. https://doi.org/10.1016/j.jmarsys.2019.103189 (2019).
Article  Google Scholar 

7.
Kenchington, E. et al. Connectivity modelling of areas closed to protect vulnerable marine ecosystems in the northwest Atlantic. Deep Sea Res. I Oceanogr. Res. Pap. 143, 85–103 (2019).
ADS  Article  Google Scholar 

8.
Zeng, X., Adams, A., Roffer, M. & He, R. Potential connectivity among spatially distinct management zones for bonefish (Albula vulpes) via larval dispersal. Environ. Biol. Fishes 102, 233–252 (2019).
Article  Google Scholar 

9.
Lange, M. & van Sebille, E. Parcels v0.9: Prototyping a lagrangian ocean analysis framework for the petascale age. Geosci. Model Dev. 10, 4175–4186 (2017).
ADS  Article  Google Scholar 

10.
Knudby, A., Kenchington, E. & Murillo, F. J. Modeling the distribution of Geodia sponges and sponge grounds in the northwest Atlantic. PLoS ONE 8(12), e82306. https://doi.org/10.1371/journal.pone.0082306 (2013).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

11.
Knudby, A., Lirette, C., Kenchington, E. & Murillo, F. J. Species distribution models of black corals, large gorgonian corals and sea pens in the NAFO Regulatory Area. Ser. No. N6276. NAFO SCR Doc. 13/78 (2013). (Accessed 5 November 2020); https://www.nafo.int/Portals/0/PDFs/sc/2013/scr13-078.pdf.

12.
Beazley, L., Kenchington, E., Yashayaev, I. & Murillo, F. J. Drivers of epibenthic megafaunal composition in the sponge grounds of the Sackville Spur, northwest Atlantic. Deep Sea Res. I Oceanogr. Res. Pap. 98, 102–114 (2015).
ADS  Article  Google Scholar 

13.
Murillo, F. J., Kenchington, E., Lawson, J. M., Li, G. & Piper, D. Ancient deep-sea sponge grounds on the Flemish Cap and Grand Bank, northwest Atlantic. Mar. Biol. 163, 63. https://doi.org/10.1007/s00227-016-2839-5 (2016).
CAS  Article  PubMed  PubMed Central  Google Scholar 

14.
Kenchington, E., Yashayaev, I., Tendal, O. S. & Jørgensbye, H. Water mass characteristics and associated fauna of a recently discovered Lophelia pertusa (Scleractinia: Anthozoa) reef in Greenlandic waters. Polar Biol. 40, 321–337 (2017).
Article  Google Scholar 

15.
FAO. International Guidelines for the Management of Deep-Sea Fisheries in the High Seas p73 (FAO, Quebec, 2009).
Google Scholar 

16.
NAFO. Conservation and Enforcement Measures. Ser. No. N6638. NAFO/FC Doc. 17/01 (2017). (Accessed 5 November 2020); https://www.nafo.int/Portals/0/PDFs/fc/2017/CEM-2017-web.pdf.

17.
Williams, J. C., Revelle, C. S. & Levin, S. A. Spatial attributes and reserve design models: A review. Environ. Model. Assess. 10, 163–181 (2005).
Article  Google Scholar 

18.
Yashayaev, I. Hydrographic changes in the Labrador Sea, 1960–2005. Prog. Oceanogr. 73, 242–276 (2007).
ADS  Article  Google Scholar 

19.
Yashayaev, I. & Loder, J. W. Recurrent replenishment of Labrador Sea Water and associated decadal-scale variability. J. Geophys. Res. Oceans 121, 8095–8114 (2016).
ADS  Article  Google Scholar 

20.
Wang, S., Wang, Z., Lirette, C., Davies, A. & Kenchington, E. Comparison of physical connectivity particle tracking models in the Flemish Cap region. Can. Tech. Rep. Fish. Aquat. Sci. 3353, 39 (2019).
Article  Google Scholar 

21.
Morato, T. et al. Climate-induced changes in the habitat suitability of cold-water corals and commercially important deep-sea fish in the North Atlantic. Glob. Change Biol. 26, 2181–2202 (2020).
ADS  Article  Google Scholar 

22.
Han, G. & Wang, Z. Monthly-mean circulation in the Flemish Cap region: A modeling study. In Estuarine and Coastal Modeling: Proceedings of the Ninth International Conference on Estuarine and Coastal Modeling (ed. Spaulding, M. L.) 138–154 (American Society of Civil Engineers, Reston, 2006).
Google Scholar 

23.
Han, G. et al. Seasonal variability of the Labrador current and shelf circulation off Newfoundland. J. Geophys. Res. Oceans 113, C10013. https://doi.org/10.1029/2007JC004376 (2008).
ADS  Article  Google Scholar 

24.
Maldonado, M. The ecology of the sponge larva. Can. J. Zool. 84, 175–194 (2006).
Article  Google Scholar 

25.
Wang, Z., Hamilton, J. & Su, J. Variations in freshwater pathways from the Arctic Ocean into the North Atlantic Ocean. Progr. Oceanogr. 155, 54–73 (2017).
ADS  Article  Google Scholar 

26.
Ross, R. E., Nimmo-Smith, W. A. M. & Howell, K. L. Increasing the depth of current understanding: Sensitivity testing of deep-sea larval dispersal models for ecologists. PLoS ONE 11(8), e0161220. https://doi.org/10.1371/journal.pone.0161220 (2016).
CAS  Article  PubMed  PubMed Central  Google Scholar 

27.
Wang, Z., Brickman, D. & Greenan, B. J. W. Characteristic evolution of the Atlantic Meridional Overturning Circulation from 1990 to 2015: An eddy-resolving ocean model study. Deep Sea Res. I Oceanogr. Res. Pap. 149, 103056. https://doi.org/10.1016/j.dsr.2019.06.002 (2019).
Article  Google Scholar 

28.
Lazier, J. R. N. & Wright, D. G. Annual velocity variations in the Labrador current. J. Phys. Oceanogr. 23, 659–678 (1993).
ADS  Article  Google Scholar 

29.
Hall, M. M., Torres, D. J. & Yashayaev, I. Absolute velocity along the AR7W section in the Labrador sea. Deep Sea Res. I Oceanogr. Res. Pap. 72, 72–87 (2013).
ADS  Article  Google Scholar 

30.
Schneider, L. et al. Variability of Labrador Sea water transported through Flemish Pass during 1993–2013. J. Geophys. Res. Oceans 120, 5514–5533 (2015).
ADS  Article  Google Scholar 

31.
Varotsou, E., Jochumsen, K., Serra, N., Kieke, D. & Schneider, L. Interannual transport variability of Upper Labrador Sea Water at Flemish Cap. J. Geophys. Res. Oceans 120, 5074–5089 (2015).
ADS  Article  Google Scholar 

32.
Layton, C., Greenan, B. J. W., Hebert, D. E. & Kelley, D. Low-frequency oceanographic variability near Flemish Cap and Sackville Spur. J. Geophys. Res. Oceans 123, 1814–1826 (2018).
ADS  Article  Google Scholar 

33.
Wang, Z., Brickman, D., Greenan, B. J. W. & Yashayaev, I. An abrupt shift in the Labrador current system in relation to winter NAO events. J. Geophys. Res. Oceans 121, 5338–5349 (2016).
ADS  Article  Google Scholar 

34.
Yashayaev, I. & Loder, J. Further intensification of deep convection in the Labrador Sea in 2016. Geophys. Res. Lett. 44, 1429–1438 (2016).
ADS  Article  Google Scholar 

35.
Delandmeter, P. & van Sebille, E. The parcels v2.0 Lagrangian framework: New field interpolation schemes. Geosci. Model Dev. 12, 3571–3584 (2019).
ADS  Article  Google Scholar 

36.
Brickman, D., Wang, Z. & DeTracey, B. Variability of current streams in Atlantic Canadian Waters: A model study. Atmos. Ocean 54, 1–12 (2015).
Google Scholar 

37.
Brickman, D., Hebert, D. & Wang, Z. Mechanism for the recent ocean warming events on the Scotian Shelf of eastern Canada. Cont. Shelf Res. 156, 11–22 (2018).
ADS  Article  Google Scholar 

38.
Pepin, P., Han, G. & Head, E. J. Modelling the dispersal of Calanus finmarchicus on the Newfoundland Shelf: Implications for the analysis of population dynamics from a high frequency monitoring site. Fish. Oceanogr. 22, 371–387 (2013).
Article  Google Scholar 

39.
Le Corre, N., Pepin, P., Han, G., Ma, Z. & Snelgrove, P. V. R. Assessing connectivity patterns among management units of the Newfoundland and Labrador shrimp population. Fish. Oceanogr. 28, 183–202 (2019).
Article  Google Scholar 

40.
Han, G. & Kulka, D. Dispersion of eggs, larvae and pelagic juveniles of White Hake (Urophycis tenuis) in relation to ocean currents of the Grand Bank: A modelling approach. J. Northw. Atl. Fish. Sci. 41, 183–196 (2009).
Article  Google Scholar 

41.
Lynch, D. G. D. et al. Particles in the Coastal Ocean. Theory and Applications 389–452 (Cambridge University Press, Cambridge, 2014).
Google Scholar 

42.
Murillo, F. J., Serrano, A., Kenchington, E. & Mora, J. Epibenthic assemblages of the tail of the Grand Bank and Flemish Cap (northwest Atlantic) in relation to environmental parameters and trawling intensity. Deep Sea Res. I Oceanogr. Res. Pap. 109, 99–122 (2016).
ADS  Article  Google Scholar 

43.
Mariani, S., Uriz, M.-J. & Turon, X. The dynamics of sponge larvae assemblages from northwestern Mediterranean nearshore bottoms. J. Plankton Res. 27, 249–262 (2005).
Article  Google Scholar 

44.
Mariani, S., Uriz, M.-J. & Alcoverro, T. Dispersal strategies in sponge larvae: Integrating the life history of larvae and the hydrologic component. Oecologia 149, 174–184 (2006).
ADS  Article  PubMed  Google Scholar 

45.
NAFO. Northwest Atlantic Fisheries Organization. Conservation and Enforcement Measures 2020. Ser. No. N7028. NAFO/COM Doc. 20-01 (2020). (Accessed 5 November 2020); https://www.nafo.int/Portals/0/PDFs/com/2020/CEM-2020-web.pdf.

46.
Goldsmit, J. et al. Where else? Assessing zones of alternate ballast water exchange in the Canadian eastern Arctic. Mar. Pollut. Bull. 139, 74–90 (2019).
CAS  Article  PubMed  Google Scholar 

47.
Kim, M. et al. Transit time distributions and storage selection functions in a sloping soil lysimeter with time-varying flow paths: Direct observation of internal and external transport variability. Water Resour. Res. 52, 7105–7129 (2016).
ADS  Article  Google Scholar 

48.
Gary, S.F. The Interior Pathway of the Atlantic Meridional Overturning Circulation. Doctor of Philosophy Thesis (Duke University, 2011). (Accessed 5 November 2020); https://dukespace.lib.duke.edu/dspace/handle/10161/4980.

49.
Good, S. A., Martin, M. J. & Rayner, N. A. EN4: Quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates. J. Geophys. Res. Oceans 118, 6704–6716 (2013).
ADS  Article  Google Scholar 

50.
Boyer, T. P. et al. World Ocean Database 09. In NOAA Atlas NESDIS 66 (ed. Levitus, S.) (U.S. Government Printing Office, New York, 2009).
Google Scholar 

51.
Wang, S., Wang, Z., Kenchington, E., Yashayaev, I. & Davies, A. 3-D ocean particle tracking modeling reveals extensive vertical movement and downstream interdependence of closed areas in the northwest Atlantic. Mendeley Data https://doi.org/10.17632/chfcjmnvcv.1 (2020).
Article  Google Scholar  More

1.
Umanzor, S., Ladah, L. & Calderon-aguilera, L. E. Testing the relative importance of intertidal seaweeds as ecosystem engineers across tidal heights. J. Exp. Mar. Bio. Ecol. 511, 100–107 (2018).
Article  Google Scholar 
2.
Smale, D. A., Burrows, M. T., Moore, P., O’Connor, N. & Hawkins, S. J. Threats and knowledge gaps for ecosystem services provided by kelp forests: a northeast Atlantic perspective. Ecol. Evol. 3, 4016–4038 (2013).
Article  PubMed  PubMed Central  Google Scholar 

3.
Krause-Jensen, D. & Duarte, C. M. Substantial role of macroalgae in marine carbon sequestration. Nat. Geosci. 9, 737–742 (2016).
ADS  CAS  Article  Google Scholar 

4.
King, N. G., McKeown, N. J., Smale, D. A. & Moore, P. J. The importance of phenotypic plasticity and local adaptation in driving intraspecific variability in thermal niches of marine macrophytes. Ecography (Cop.) 41, 1469–1484 (2018).
Article  Google Scholar 

5.
Helmuth, B., Mieszkowska, N., Moore, P. & Hawkins, S. J. Living on the edge of two changing worlds: forecasting the responses of rocky intertidal ecosystems to climate change. Annu. Rev. Ecol. Evol. Syst. 37, 373–404 (2006).
Article  Google Scholar 

6.
Fernández, Á. et al. Additive effects of emersion stressors on the ecophysiological performance of two intertidal seaweeds. Mar. Ecol. Prog. Ser. 536, 135–147 (2015).
ADS  Article  Google Scholar 

7.
IPCC. Summary for Policymakers. In: Climate Change 2014, Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds. Edenhofer, O. et al.). (Cambridge University Press, Cambridge, 2014).

8.
Meehl, G. A. & Tebaldi, C. More intense, more frequent, and longer lasting heat waves in the 21st century. Science 305, 994–997 (2004).
ADS  CAS  Article  PubMed  Google Scholar 

9.
Koffi, B. & Koffi, E. Heat waves across Europe by the end of the 21st century: multiregional climate simulations. Clim. Res. 36, 153–168 (2008).
Article  Google Scholar 

10.
Lüning, K. Temperature, salinity and other abiotic factors in Seaweeds. Their Environment, Biogeography and Ecophysiology (Wiley, New York, 1990).
Google Scholar 

11.
Pang, S. J., Jin, Z. H., Sun, J. Z. & Gao, S. Q. Temperature tolerance of young sporophytes from two populations of Laminaria japonica revealed by chlorophyll fluorescence measurements and short-term growth and survival performances in tank culture. Aquaculture 262, 493–503 (2007).
Article  Google Scholar 

12.
Nielsen, S. L., Nielsen, H. D. & Pedersen, M. F. Juvenile life stages of the brown alga Fucus serratus L. Are more sensitive to combined stress from high copper concentration and temperature than adults. Mar. Biol. 161, 1895–1904 (2014).
CAS  Article  Google Scholar 

13.
Schonbeck, M. W. & Norton, T. A. The effects on intertidal fucoid algae of exposure to air under various conditions. Bot. Mar. 23, 141–148 (1980).
Article  Google Scholar 

14.
Pearson, G. A., Lago-Leston, A. & Mota, C. Frayed at the edges: Selective pressure and adaptive response to abiotic stressors are mismatched in low diversity edge populations. J. Ecol. 97, 450–462 (2009).
Article  Google Scholar 

15.
Ferreira, J. G., Arenas, F., Martínez, B., Hawkins, S. J. & Jenkins, S. R. Physiological response of fucoid algae to environmental stress: comparing range centre and southern populations. New Phytol. 202, 1157–1172 (2014).
Article  PubMed  Google Scholar 

16.
Smale, D. A. & Wernberg, T. Extreme climatic event drives range contraction of a habitat-forming species. Proc. R. Soc. B. 280, 20122829 (2013).
Article  PubMed  Google Scholar 

17.
Jueterbock, A. et al. Thermal stress resistance of the brown alga Fucus serratus along the North-Atlantic coast: Acclimatization potential to climate change. Mar. Genomics 13, 27–36 (2014).
Article  PubMed  Google Scholar 

18.
Hurd, K., Harrison, P.J., Bischof, K. & Lobban, C.S. Light and photosynthesis in Seaweed Ecology and Physiology (eds. Hurd, K., Harrison, P.J., Bischof, K. & Lobban, C.S.) 176–237. (Cambridge University Press, Cambridge, 2014).

19.
Mota, C. F. et al. Differentiation in fitness-related traits in response to elevated temperatures between leading and trailing edge populations of marine macrophytes. PLoS ONE 13, 1–17 (2018).
Article  CAS  Google Scholar 

20.
Martínez, B. et al. Physical factors driving intertidal macroalgae distribution: physiological stress of a dominant fucoid at its southern limit. Oecologia 170, 341–353 (2012).
ADS  Article  PubMed  Google Scholar 

21.
Pereira, T. R., Engelen, A. H., Pearson, G. A., Valero, M. & Serrão, E. A. Response of kelps from different latitudes to consecutive heat shock. J. Exp. Mar. Bio. Ecol. 463, 57–62 (2015).
Article  Google Scholar 

22.
Madsen, T. V. & Maberly, S. C. A comparison of air and water as environments for photosynthesis by the intertidal alga Fucus spiralis (Phaeophyta). J. phycol. 26(1), 24–30 (1990).
Article  Google Scholar 

23.
Contreras-Porcia, L., López-Cristoffanini, Meynard, A., & Kumar, M. Tolerance pathways to desiccation stress in seaweeds. In Systems Biology of Marine Ecosystems (eds. Kumar, M. & Ralhp, P.) 13–29. (Springer, Berlin, 2017).

24.
Helmuth, B. et al. Climate change and latitudinal patterns of intertidal thermal stress. Science 298, 1015–1017 (2002).
ADS  CAS  Article  PubMed  Google Scholar 

25.
King, N. G. et al. Cumulative stress restricts niche filling potential of habitat-forming kelps in a future climate. Funct. Ecol. 32, 288–299 (2017).
Article  PubMed  PubMed Central  Google Scholar 

26.
Hereward, H. F. R., King, N. G. & Smale, D. A. Intra-annual variability in responses of a canopy forming kelp to cumulative low tide heat stress: implications for populations at the trailing range edge. J. Phycol. 56, 146–158 (2019).
Article  PubMed  Google Scholar 

27.
Fernández, C. The retreat of large brown seaweeds on the north coast of Spain: the case of Saccorhiza polyschides. Eur. J. Phycol. 46, 352–360 (2011).
Article  Google Scholar 

28.
Méndez-Sandín, M. & Fernández, C. Changes in the structure and dynamics of marine assemblages dominated by Bifurcaria bifurcata and Cystoseira species over three decades (1977–2007). Estuar. Coast. Shelf Sci. 175, 46–56 (2016).
ADS  Article  Google Scholar 

29.
Wilson, K. L., Skinner, M. A. & Lotze, H. K. Projected 21st-century distribution of canopy-forming seaweeds in the Northwest Atlantic with climate change. Divers. Distrib. 25, 582–602 (2019).
Article  Google Scholar 

30.
Martínez, B., Viejo, R. M., Carreño, F. & Aranda, S. C. Habitat distribution models for intertidal seaweeds: Responses to climatic and non-climatic drivers. J. Biogeogr. 39, 1877–1890 (2012).
Article  Google Scholar 

31.
Nyström, M. et al. Confronting feedbacks of degraded marine ecosystems. Ecosystems 15, 695–710 (2012).
Article  Google Scholar 

32.
O’Brien, B. S., Mello, K., Litterer, A. & Dijkstra, J. A. Seaweed structure shapes trophic interactions: a case study using a mid-trophic level fish species. J. Exp. Mar. Biol. Ecol. 506, 1–8 (2018).
Article  Google Scholar 

33.
Voerman, S. E., Llera, E. & Rico, J. M. Climate driven changes in subtidal kelp forest communities in NW Spain. Mar. Environ. Res. 90, 119–127 (2013).
CAS  Article  PubMed  Google Scholar 

34.
Duarte, L. et al. Recent and historical range shifts of two canopy-forming seaweeds in North Spain and the link with trends in sea surface temperature. Acta Oecol. 51, 1–10 (2013).
ADS  Article  Google Scholar 

35.
Viejo, R. M., Martínez, B., Arrontes, J., Astudillo, C. & Herna, L. Reproductive patterns in central and marginal populations of a large brown seaweed: drastic changes at the southern range limit. Ecography 34, 75–84 (2011).
Article  Google Scholar 

36.
Duarte, L. & Viejo, R. M. Environmental and phenotypic heterogeneity of populations at the trailing range-edge of the habitat-forming macroalga Fucus serratus. Mar. Environ. Res. 136, 16–26 (2018).
CAS  Article  PubMed  Google Scholar 

37.
Thomsen, M. S. et al. Local extinction of bull kelp (Durvillaea spp.) due to a marine heatwave. Front. Mar. Sci. 6, 1–10 (2019).
Article  Google Scholar 

38.
Darling, E. S. & Côté, I. M. Quantifying the evidence for ecological synergies. Ecol. Lett. 11, 1278–1286 (2008).
Article  Google Scholar 

39.
Gómez-Gesteira, M. et al. The state of climate in NW Iberia. Clim. Res. 48, 109–144 (2011).
Article  Google Scholar 

40.
Kersting, D.K. Cambio Climático en El Medio Marino Español: Impactos, Vulnerabilidad y Adaptación. Oficina Española de Cambio Climático, Ministerio de Agricultura, Alimentación y Medio Ambiente. Madrid. http://cort.as/-HXq9 (2016).

41.
Philippart, C. J. M. et al. Impacts of climate change on European marine ecosystems: observations, expectations and indicators. J. Exp. Mar. Biol. Ecol. 400, 52–69 (2011).
Article  Google Scholar 

42.
Lima, F. P., Ribeiro, P. A., Queiroz, N., Hawkins, S. J. & Santos, A. M. Do distributional shifts of northern and southern species of algae match the warming pattern?. Glob. Chang. Biol. 13, 2592–2604 (2007).
ADS  Article  Google Scholar 

43.
Díez, I., Muguerza, N., Santolaria, A., Ganzedo, U. & Gorostiaga, J. M. Seaweed assemblage changes in the eastern Cantabrian Sea and their potential relationship to climate change. Estuar. Coast. Shelf Sci. 99, 108–120 (2012).
ADS  Article  Google Scholar 

44.
Piñeiro-Corbeira, C., Barreiro, R. & Cremades, J. Decadal changes in the distribution of common intertidal seaweeds in Galicia (NW Iberia). Mar. Environ. Res. 113, 106–115 (2016).
Article  CAS  PubMed  Google Scholar 

45.
García-Fernández, A. & Bárbara, I. Studies of Cystoseira assemblages in Northern Atlantic Iberia. Ann. del Jard. Bot. Madrid 73, 1–21 (2016).
Google Scholar 

46.
García, A. G., Olabarria, C., Arrontes, J., Álvarez, Ó. & Viejo, R. M. Spatio-temporal dynamics of Codium populations along the rocky shores of N and NW Spain. Mar. Environ. Res. 140, 394–402 (2018).
Article  CAS  PubMed  Google Scholar 

47.
Fernández, C. Current status and multidecadal biogeographical changes in rocky intertidal algal assemblages: the northern Spanish coast. Estuar. Coast. Shelf Sci. 171, 35–40 (2016).
ADS  Article  Google Scholar 

48.
Eggert, A. Seaweed responses to temperature. In Seaweed Biology. Novel Insights into Ecophysiology, Ecology and Utilization (eds. Wiencke, C. & Bischof, K) 47–66 (Springer, Berlin, 2012).

49.
Karsten, U. Seaweed acclimation to salinity and desiccation stress. In Seaweed biology. Novel Insights into Ecophysiology, Ecology and Utilization (eds. Wiencke, C. & Bischof, K) 87–108 (Springer, Berlin, 2012).

50.
Phelps, C. M., Boyce, M. C. & Huggett, M. J. Future climate change scenarios differentially affect three abundant algal species in southwestern Australia. Mar. Environ. Res. 126, 69–80 (2017).
CAS  Article  PubMed  Google Scholar 

51.
Olabarria, C., Arenas, F., Fernández, Á., Troncoso, J. S. & Martínez, B. Physiological responses to variations in grazing and light conditions in native and invasive fucoids. Mar. Environ. Res. 139, 151–161 (2018).
CAS  Article  PubMed  Google Scholar 

52.
Schagerl, M. & Möstl, M. Drought stress, rain and recovery of the intertidal seaweed. Fucus spiralis. Mar. Biol. 158, 2471–2479 (2011).
Article  Google Scholar 

53.
Lamela-Silvarrey, C., Fernández, C., Anadón, R. & Arrontes, J. Fucoid assemblages on the north coast of Spain: Past and present (1977–2007). Bot. Mar. 55, 199–207 (2012).
Article  Google Scholar 

54.
Martínez, B., Arenas, F., Trilla, A., Viejo, R. M. & Carreño, F. Combining physiological threshold knowledge to species distribution models is key to improving forecasts of the future niche for macroalgae. Glob. Change Biol. 21, 1422–1433 (2014).
ADS  Article  Google Scholar 

55.
Piñeiro-Corbeira, C., Barreiro, R., Cremades, J. & Arenas, F. Seaweed assemblages under a climate change scenario: Functional responses to temperature of eight intertidal seaweeds match recent abundance shifts. Sci. Rep. 8, 1–9 (2018).
Article  CAS  Google Scholar 

56.
Figueroa, F. L. et al. Yield losses and electron transport rate as indicators of thermal stress in Fucus serratus (Ochrophyta). Algal Res. 41, 101560 (2019).
Article  Google Scholar 

57.
Davison, I. R. & Pearson, G. A. Stress tolerance in intertidal seaweeds. J. Phycol. 32, 197–211 (1996).
Article  Google Scholar 

58.
Schreiber, U., Bilger, W. & Neubauer, C. Chlorophyll Fluorescence as a Nonintrusive Indicator for Rapid Assessment of In Vivo Photosynthesis. In Ecophysiology of photosynthesis (eds. Schulze, E.D. & Caldwell, M.M.) 49–70 (Springer, Berlin, 2003).

59.
Hurd, K., Harrison, P.J., Bischof, K. & Lobban, C.S. Physico-chemical factors as environmental stressors in seaweed biology. In Seaweed Ecology and Physiology (eds. Hurd, K., Harrison, P.J., Bischof, K. & Lobban, C.S.) 294–348 (Cambridge University Press, Cambridge, 2014).

60.
Kumar, M. et al. Desiccation induced oxidative stress and its biochemical responses in intertidal red alga Gracilaria corticata (Gracilariales, Rhodophyta). Environ. Exp. Bot. 72, 194–201 (2011).
CAS  Article  Google Scholar 

61.
Bischof, K. & Rautenberger, R. Seaweed responses to environmental stress: Reactive oxygen and antioxidative strategies. In Seaweed biology. Novel insights into Ecophysiology, Ecology and Utilization (eds. Wiencke, C., Bischof, K.) 109–134 (Springer, Berlin, 2012).

62.
Allakhverdiev, S. I. et al. Heat stress: An overview of molecular responses in photosynthesis. Photosynth. Res. 98, 541–550 (2008).
CAS  Article  PubMed  Google Scholar 

63.
Mota, C. F., Engelen, A. H., Serrão, E. A. & Pearson, G. A. Some don’t like it hot: Microhabitat-dependent thermal and water stresses in a trailing edge population. Funct. Ecol. 29, 640–649 (2015).
Article  Google Scholar 

64.
Hunt, L. J. H. & Denny, M. W. Desiccation protection and disruption: A trade-off for an intertidal marine alga. J. Phycol. 44, 1164–1170 (2008).
Article  PubMed  Google Scholar 

65.
Staehr, P. A. & Wernberg, T. Physiological responses of Ecklonia radiata (Laminariales) to a latitudinal gradient in ocean temperature. J. Phycol. 45, 91–99 (2009).
CAS  Article  PubMed  Google Scholar 

66.
Davison, I. R. & Davison, J. O. The effect of growth temperature on enzyme activities in the brown alga Laminaria saccharina. Br. Phycol. J. 22, 77–87 (1987).
Article  Google Scholar 

67.
Young, E. B., Dring, M. J., Savidge, G., Birkett, D. A. & Berges, J. A. Seasonal variations in nitrate reductase activity and internal N pools in intertidal brown algae are correlated with ambient nitrate concentrations. Plant, Cell Environ. 30, 764–774 (2007).
CAS  Article  Google Scholar 

68.
Monteiro, C. et al. Canopy microclimate modification in central and marginal populations of a marine macroalga. Mar. Biodivers. 49, 415–424 (2019).
Article  Google Scholar 

69.
Dawes, C. J. Physiological ecology. In Marine Botany (ed. Dawes, C.J.) (Wiley, New York, 1998).

70.
Rohde, S., Hiebenthal, C., Wahl, M., Karez, R. & Bischof, K. Decreased depth distribution of Fucus vesiculosus (Phaeophyceae) in the Western Baltic: Effects of light deficiency and epibionts on growth and photosynthesis. Eur. J. Phycol. 43, 143–150 (2008).
Article  Google Scholar 

71.
Lüning, K. Seaweed vegetation of the cold and warm temperate regions of the northern hemisphere. In Seaweeds. Their Environment, Biogeography and Ecophysiology. (ed. Lüning, K.) (Wiley, New York, 1990).

72.
Schiel, D. R., Lilley, S. A., South, P. M. & Coggins, J. H. J. Decadal changes in sea surface temperature, wave forces and intertidal structure in New Zealand. Mar. Ecol. Prog. Ser. 548, 77–95 (2016).
ADS  Article  Google Scholar 

73.
Fernández de la Hoz, C. F. et al. OCLE: a European open access database on climate change effects on littoral and oceanic ecosystems. Prog. Oceanogr. 168, 222–231 (2018).
ADS  Article  Google Scholar 

74.
Fernández de la Hoz, C., Ramos, E., Puente, A. & Juanes, J. A. Climate change induced range shifts in seaweeds distributions in Europe. Mar. Environ. Res. 148, 1–11 (2019).
Article  CAS  Google Scholar 

75.
Crain, C. M., Kroeker, K. & Halpern, B. S. Interactive and cumulative effects of multiple human stressors in marine systems. Ecol. Lett. 11, 1304–1315 (2008).
Article  PubMed  PubMed Central  Google Scholar 

76.
Halekoh, U., Højsgaard, S. & Yan, J. The R package geepack for generalized estimating equations. J. Stat. Softw. 15, 1–11 (2006).
Article  Google Scholar 

77.
Fox, J. Applied Regression Analysis and Generalized Linear Models 3rd edn. (Sage, Thousand Oaks, 2016).
Google Scholar  More

To produce our global Forest Landscape Integrity Index (FLII), we combined four sets of spatially explicit datasets representing: (i) forest extent23; (ii) observed pressure from high impact, localized human activities for which spatial datasets exist, specifically: infrastructure, agriculture, and recent deforestation27; (iii) inferred pressure associated with edge effects27, and other diffuse processes, (e.g., activities such as hunting and selective logging)27 modeled using proximity to observed pressures; and iv) anthropogenic changes in forest connectivity due to forest loss27 (see Supplementary Table 1 for data sources). These datasets were combined to produce an index score for each forest pixel (300 m), with the highest scores reflecting the highest forest integrity (Fig. 1), and applied to forest extent for the start of 2019. We use globally consistent parameters for all elements (i.e., parameters do not vary geographically). All calculations were conducted in Google Earth Engine (GEE)60.
Forest extent
We derived a global forest extent map for 2019 by subtracting from the Global Tree Cover product for 200023 annual Tree Cover Loss 2001–2018, except for losses categorized by Curtis and colleagues24 as those likely to be temporary in nature (i.e., those due to fire, shifting cultivation and rotational forestry). We applied a canopy threshold of 20% based on related studies e.g.31,61, and resampled to 300 m resolution and used this resolution as the basis for the rest of the analysis (see Supplementary Note 1 for further methods).
Observed human pressures
We quantify observed human pressures (P) within a pixel as the weighted sum of impact of infrastructure (I; representing the combined effect of 41 types of infrastructure weighted by their estimated general relative impact on forests (Supplementary Table 3), agriculture (A) weighted by crop intensity (indicated by irrigation levels), and recent deforestation over the past 18 years (H; excluding deforestation from fire, see Discussion). Specifically, for pixel i:

$${mathrm{P}}_{mathrm{i}} = {mathrm{exp}}left( { – {upbeta}_1{mathrm{I}}_{mathrm{i}}} right) + {mathrm{exp}}left( { – {upbeta}_2{mathrm{A}}_{mathrm{i}}} right) + {mathrm{exp}}left( { – {upbeta}_3{mathrm{H}}_{mathrm{i}}} right)$$
(1)

whereby the values of β were selected so that the median of the non-zero values for each component was 0.75. This use of exponents is a way of scaling variables with non-commensurate units so that they can be combined numerically, while also ensuring that the measure of observed pressure is sensitive to change (increase or decrease) in the magnitude of any of the three components, even at large values of I, A, or H. This is an adaptation of the Human Footprint methodology62. See Supplementary Note 3 for further details.
Inferred human pressures
Inferred pressures are the diffuse effects of a set of processes for which directly observed datasets do not exist, that include microclimate and species interactions relating to the creation of forest edges63 and a variety of intermittent or transient anthropogenic pressures such as selective logging, fuelwood collection, hunting; spread of fires and invasive species, pollution, and livestock grazing64,65,66. We modeled the collective, cumulative impacts of these inferred effects through their spatial association with observed human pressure in nearby pixels, including a decline in effect intensity according to distance, and partitioning into stronger short-range and weaker long-range effects. The inferred pressure (P′) on pixel i from source pixel j is:

$$Pprime _{i,j} = P_jleft( {w_{i,j} + v_{i,j}} right)$$
(2)

where wi,j is the weighting given to the modification arising from short-range pressure, as a function of distance from the source pixel, and vi,j is the weighting given to the modification arising from long-range pressures.
Short-range effects include most of the processes listed above, which together potentially affect most biophysical features of a forest, and predominate over shorter distances. In our model, they decline exponentially, approach zero at 3 km, and are truncated to zero at 5 km (see Supplementary Note 4).

$$begin{array}{l}{mathrm{w}}_{i,j} = alpha ,{mathrm{exp}}( – lambda {mathrm{d}}_{i,j}),,,,,,[{mathrm{for}},{mathrm{d}}_{{mathrm{i,j}}} le {mathrm{5km}}]\ {mathrm{w}}_{i,j} = {mathrm{0}},,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,[{mathrm{for}},{mathrm{d}}_{i,j} > {mathrm{5km}}]end{array}$$
(3)

where α is a constant set to ensure that the sum of the weights across all pixels in the range is 1.85 (see below), λ is a decay constant set to a value of 1 (see67 and other references in Supplementary Note 4) and di,j is the Euclidean distance between the centers of pixels i and j expressed in units of km.
Long-range effects include over-exploitation of high socio-economic value animals and plants, changes to migration and ranging patterns, and scattered fire and pollution events. We modeled long-range effects at a uniform level at all distances below 6 km and they then decline linearly with distance, conservatively reaching zero at a radius of 12 km65,68 (and other references in Supplementary Note 4):

$$begin{array}{l}{mathrm{v}}_{i,j} = gamma ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,[for,d_{i,j} le 6km]\ {mathrm{v}}_{i,j} = gamma left( {12 – d_{i,j}} right)/6,,,,[{mathrm{for}},6{mathrm{km}}, < ,{mathrm{d}}_{i,j} le 12{mathrm{km}}]\ {mathrm{v}}_{i,j} = 0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,[for,{mathrm{d}}_{i,j} > 12{mathrm{km}}]end{array}$$
(4)

where γ is a constant set to ensure that the sum of the weights across all pixels in the range is 0.15 and di,j is the Euclidean distance between the centers of pixels i and j, expressed in kilometers.
The form of the weighting functions for short- and long-range effects and the sum of the weights (α + γ) were specified based on a hypothetical reference scenario where a straight forest edge is adjacent to a large area with uniform human pressure, and ensuring that in this case total inferred pressure immediately inside the forest edge is equal to the pressure immediately outside, before declining with distance. γ is set to 0.15 to ensure that the long-range effects conservatively contribute no more than 5% to the final index in the same scenario, based on expert opinion and supported e.g., Berzaghi et al.69 regarding the approximate level of impact on values that would be affected by severe defaunation and other long-range effects.
The aggregate effect from inferred pressures (Q) on pixel i from all n pixels within range (j = 1 to j = n) is then the sum of these individual, normalized, distance-weighted pressures, i.e.,

$$Q_i = mathop {sum}_{j=1}^{n} {P{prime}_{i,j}}$$
(5)

Loss of forest connectivity
Average connectivity of forest around a pixel was quantified using a method adapted from Beyer et al.70. The connectivity Ci around pixel i surrounded by n other pixels within the maximum radius (numbered j = 1, 2…n) is given by:

$${mathrm{C}}_i = mathop {sum}_{j=1}^{n} {left( {{mathrm{F}}_j{mathrm{G}}_{i,j}} right)}$$
(6)

where Fj is the forest extent is a binary variable indicating if forested (1) or not (0) and Gi,j is the weight assigned to the distance between pixels i and j. Gi,j uses a normalized Gaussian curve, with σ = 20 km and distribution truncated to zero at 4σ for computational convenience (see Supplementary Note 2). The large value of σ captures landscape connectivity patterns operating at a broader scale than processes captured by other data layers. Ci ranges from 0 to 1 (Ci∈[0,1]).
Current Configuration (CCi) of forest extent in pixel i was calculated using the final forest extent map and compared to the Potential Configuration (PC) of forest extent without extensive human modification, so that areas with naturally low connectivity, e.g., coasts and natural vegetation mosaics, are not penalized. PC was calculated from a modified version of the map of Laestadius et al38. and resampled to 300 m resolution (see Supplementary Note 2 for details). Using these two measures, we calculated Lost Forest Configuration (LFC) for every pixel as:

$${mathrm{LFC}}_i = 1 – left( {{mathrm{CC}}_i/{mathrm{PC}}_i} right)$$
(7)

Values of CCi/PCi  > 1 are assigned a value of 1 to ensure that LFC is not sensitive to apparent increases in forest connectivity due to inaccuracy in estimated potential forest extent – low values represent least loss, high values greatest loss (LFCi∈[0,1]).
Calculating the Forest Landscape Integrity Index
The three constituent metrics, LFC, P, and Q, all represent increasingly modified conditions the larger their values become. To calculate a forest integrity index in which larger values represent less degraded conditions we, therefore, subtract the sum of those components from a fixed large value (here, 3). Three was selected as our assessment indicates that values of LFC + P + Q of 3 or more correspond to the most severely degraded areas. The metric is also rescaled to a convenient scale (0-10) by multiplying by an arbitrary constant (10/3). The FLII for forest pixel i is thus calculated as:

$${mathrm{FLII}}_i = left[ {10/3} right] (3 – {mathrm{min}}(3,,[P_i + Q_i + {mathrm{LFC}}_i]))$$
(8)

where FLIIi ranges from 0 to 10, forest areas with no modification detectable using our methods scoring 10 and those with the most scoring 0.
Illustrative forest integrity classes
Whilst a key strength of the index is its continuous nature, the results can also be categorized for a range of purposes. In this paper three illustrative classes were defined, mapped, and summarized to give an overview of broad patterns of integrity in the world’s forests. The three categories were defined as follows.
High Forest Integrity (scores ≥ 9.6) Interiors and natural edges of more or less unmodified naturally regenerated (i.e., non-planted) forest ecosystems, comprised entirely or almost entirely of native species, occurring over large areas either as continuous blocks or natural mosaics with non-forest vegetation; typically little human use other than low-intensity recreation or spiritual uses and/or low-intensity extraction of plant and animal products and/or very sparse presence of infrastructure; key ecosystem functions such as carbon storage, biodiversity, and watershed protection and resilience expected to be very close to natural levels (excluding any effects from climate change) although some declines possible in the most sensitive elements (e.g., some high value hunted species).
Medium Forest Integrity (scores  > 6.0 but More

1.
Hoegh-Guldberg, O. et al. Coral reefs under rapid climate change and ocean acidification. Science 318, 1737–1742 (2007).
ADS  CAS  Article  Google Scholar 
2.
Heron, S. F., Maynard, J. A., van Hooidonk, R. & Eakin, C. M. Warming trends and bleaching stress of the world’s coral reefs 1985-2012. Sci. Rep. 6, 38402 (2016).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

3.
Hughes, T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science 359, 80–83 (2018).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

4.
Ainsworth, T. D. et al. Climate change disables coral bleaching protection on the Great Barrier Reef. Science 352, 338–342 (2016).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

5.
Smale, D. A. et al. Marine heatwaves threaten global biodiversity and the provision of ecosystem services. Nat. Clim. Change 9, 306–312 (2019).
ADS  Article  Google Scholar 

6.
LaJeunesse, T. C. et al. Systematic revision of Symbiodiniaceae highlights the antiquity and diversity of coral endosymbionts. Curr. Biol. 28, 2570–2580.e6 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

7.
Douglas, A. E. Coral bleaching—how and why? Mar. Pollut. Bull. 46, 385–392 (2003).
CAS  PubMed  Article  PubMed Central  Google Scholar 

8.
Eakin, C. M., Sweatman, H. P. A. & Brainard, R. E. The 2014–2017 global-scale coral bleaching event: insights and impacts. Coral Reefs 38, 539–545 (2019).
ADS  Article  Google Scholar 

9.
Lough, J. M., Anderson, K. D. & Hughes, T. P. Increasing thermal stress for tropical coral reefs: 1871-2017. Sci. Rep. 8, 6079 (2018).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

10.
Oliver, E. C. J. et al. Projected marine heatwaves in the 21st century and the potential for ecological impact. Front. Mar. Sci. 6, 734 (2019).

11.
van Hooidonk, R. et al. Local-scale projections of coral reef futures and implications of the Paris Agreement. Sci. Rep. 6, 1–8 (2016).
Article  CAS  Google Scholar 

12.
Bruno, J. F., Côté, I. M. & Toth, L. T. Climate change, coral loss, and the curious case of the parrotfish paradigm: Why don’t marine protected areas improve reef resilience? Annu. Rev. Mar. Sci. 11, 307–334 (2019).
ADS  Article  Google Scholar 

13.
Bates, A. E. et al. Climate resilience in marine protected areas and the ‘Protection Paradox’. Biol. Conserv. 236, 305–314 (2019).
Article  Google Scholar 

14.
Stat, M., Gates, R. D. & Clade, D. Symbiodinium in scleractinian corals: a “nugget” of hope, a selfish opportunist, an ominous Sign, or all of the above? J. Mar. Biol. 2011, e730715 (2011).
Article  Google Scholar 

15.
Silverstein, R. N., Cunning, R. & Baker, A. C. Change in algal symbiont communities after bleaching, not prior heat exposure, increases heat tolerance of reef corals. Glob. Change Biol. 21, 236–249 (2015).
ADS  Article  Google Scholar 

16.
van Oppen, M. J. H., Oliver, J. K., Putnam, H. M. & Gates, R. D. Building coral reef resilience through assisted evolution. Proc. Natl Acad. Sci. USA 112, 2307–2313 (2015).
ADS  PubMed  Article  CAS  PubMed Central  Google Scholar 

17.
Chakravarti, L. J., Beltran, V. H. & Oppen, M. J. Hvan Rapid thermal adaptation in photosymbionts of reef-building corals. Glob. Change Biol. 23, 4675–4688 (2017).
ADS  Article  Google Scholar 

18.
Oppen, M. J. Hvan et al. Shifting paradigms in restoration of the world’s coral reefs. Glob. Change Biol. 23, 3437–3448 (2017).
ADS  Article  Google Scholar 

19.
Baker, A. C., Starger, C. J., McClanahan, T. R. & Glynn, P. W. Coral reefs: corals’ adaptive response to climate change. Nature 430, 741 (2004).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

20.
Berkelmans, R. & Van Oppen, M. J. The role of zooxanthellae in the thermal tolerance of corals: a ‘nugget of hope’for coral reefs in an era of climate change. Proc. R. Soc. B Biol. Sci. 273, 2305–2312 (2006).
Article  Google Scholar 

21.
Magel, J. M. T., Dimoff, S. A. & Baum, J. K. Direct and indirect effects of climate change-amplified pulse heat stress events on coral reef fish communities. Ecol. Appl. 30, e-2124 (2020).
Article  Google Scholar 

22.
Magel, J. M. T., Burns, J. H. R., Gates, R. D. & Baum, J. K. Effects of bleaching-associated mass coral mortality on reef structural complexity across a gradient of local disturbance. Sci. Rep. 9, 2512 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

23.
Claar, D. C., Cobb, K. M. & Baum, J. K. In situ and remotely sensed temperature comparisons on a Central Pacific atoll. Coral Reefs 38, 1343–1349 (2019).
ADS  Article  Google Scholar 

24.
Hume, B. C. et al. SymPortal: A novel analytical framework and platform for coral algal symbiont next‐generation sequencing ITS2 profiling. Mol. Ecol. Resour. 19, 1063–1080 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

25.
Hume, B. C. et al. Ancestral genetic diversity associated with the rapid spread of stress-tolerant coral symbionts in response to Holocene climate change. Proc. Natl Acad. Sci. USA 113, 4416–4421 (2016).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

26.
Cunning, R., Silverstein, R. N. & Baker, A. C. Investigating the causes and consequences of symbiont shuffling in a multi-partner reef coral symbiosis under environmental change. Proc. R. Soc. B Biol. Sci. 282, 20141725 (2015).
CAS  Article  Google Scholar 

27.
Glynn, P. W., Maté, J. L., Baker, A. C. & Calderón, M. O. Coral bleaching and mortality in Panama and Ecuador during the 1997-1998 El Niño-Southern Oscillation Event: spatial/temporal patterns and comparisons with the 1982-1983 event. Bull. Mar. Sci. 69, 79–109 (2001).
Google Scholar 

28.
Jones, A. M., Berkelmans, R., van Oppen, M. J. H., Mieog, J. C. & Sinclair, W. A community change in the algal endosymbionts of a scleractinian coral following a natural bleaching event: field evidence of acclimatization. Proc. R. Soc. B Biol. Sci. 275, 1359–1365 (2008).
CAS  Article  Google Scholar 

29.
Hoegh-Guldberg, O. Coral reef ecosystems and anthropogenic climate change. Reg. Environ. Change 11, 215–227 (2011).
Article  Google Scholar 

30.
Putnam, H. M., Barott, K. L., Ainsworth, T. D. & Gates, R. D. The vulnerability and resilience of reef-building corals. Curr. Biol. 27, R528–R540 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

31.
Hoegh-Guldberg, O. Climate change and world’s coral reefs: implications for the Great Barrier Reef. Mar. Freshw. Res. 50, 839–866 (1999).
Google Scholar 

32.
Glynn, P. W. Coral reef bleaching: facts, hypotheses and implications. Glob. Change Biol. 2, 495–509 (1996).
ADS  Article  Google Scholar 

33.
Cunning, R., Ritson-Williams, R. & Gates, R. Patterns of bleaching and recovery of Montipora capitata in Kāne’ohe Bay, Hawai’i, USA. Mar. Ecol. Prog. Ser. 551, 131–139 (2016).
ADS  CAS  Article  Google Scholar 

34.
Coffroth, M. A., Poland, D. M., Petrou, E. L., Brazeau, D. A. & Holmberg, J. C. Environmental symbiont acquisition may not be the solution to warming seas for reef-building corals. PLoS ONE 5, e13258 (2010).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

35.
Lee, M. J., Jeong, H. J., Jang, S. H., Lee, S. Y. & Kang, N. S. S. Most low-abundance ‘background’ Symbiodinium spp. are transitory and have minimal functional significance for symbiotic corals. Microb. Ecol. 71, 771–783 (2016).
PubMed  Article  PubMed Central  Google Scholar 

36.
Bay, L. K., Doyle, J., Logan, M. & Berkelmans, R. Recovery from bleaching is mediated by threshold densities of background thermo-tolerant symbiont types in a reef-building coral. R. Soc. Open Sci. 3, 160322 (2016).
ADS  PubMed  PubMed Central  Article  Google Scholar 

37.
Rowan, R. Thermal adaptation in reef coral symbionts. Nature 430, 742–742 (2004).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

38.
Palumbi, S. R., Barshis, D. J., Traylor-Knowles, N. & Bay, R. A. Mechanisms of reef coral resistance to future climate change. Science 344, 895–898 (2014).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

39.
van Oppen, M. J. H., Baker, A. C., Coffroth, M. A. & Willis, B. L. Bleaching resistance and the role of algal endosymbionts. in Coral Bleaching: Patterns, Processes, Causes and Consequences (eds. van Oppen, M. J. H. & Lough, J. M.) 83–102 (Springer, 2009). https://doi.org/10.1007/978-3-540-69775-6_6.

40.
Buddemeier, R. W. & Fautin, D. G. Coral bleaching as an adaptive mechanism. Bioscience 43, 320–326 (1993).
Article  Google Scholar 

41.
Hoegh-Guldberg, O., Jones, R. J., Ward, S. & Loh, W. K. Communication arising. Is coral bleaching really adaptive? Nature 415, 601–602 (2002).
CAS  PubMed  Article  PubMed Central  Google Scholar 

42.
Cantin, N. E., van Oppen, M. J. H., Willis, B. L. & Mieog, J. C. Juvenile corals can acquire more carbon from high-performance algal symbionts. Coral Reefs 28, 405 (2009).
ADS  Article  Google Scholar 

43.
Shore-Maggio, A., Callahan, S. M. & Aeby, G. S. Trade-offs in disease and bleaching susceptibility among two color morphs of the Hawaiian reef coral, Montipora capitata. Coral Reefs 37, 507–517 (2018).
ADS  Article  Google Scholar 

44.
Littman, R. A., Bourne, D. G. & Willis, B. L. Responses of coral-associated bacterial communities to heat stress differ with Symbiodinium type on the same coral host. Mol. Ecol. 19, 1978–1990 (2010).
CAS  PubMed  Article  PubMed Central  Google Scholar 

45.
Morris, L. A., Voolstra, C. R., Quigley, K. M., Bourne, D. G. & Bay, L. K. Nutrient Availability and Metabolism Affect the Stability of Coral–Symbiodiniaceae Symbioses. Trends Microbiol. 27, 678–689 (2019).
CAS  PubMed  Article  PubMed Central  Google Scholar 

46.
Hoegh-Guldberg, O. et al. The human imperative of stabilizing global climate change at 1.5 °C. Science 365, eaaw6974 (2019).
CAS  PubMed  Article  PubMed Central  Google Scholar 

47.
Bamston, A. G., Chelliah, M. & Goldenberg, S. B. Documentation of a highly ENSO‐related SST region in the equatorial pacific: research note. Atmos-Ocean 35, 367–383 (1997).
Article  Google Scholar 

48.
Walsh, S. M. Ecosystem-scale effects of nutrients and fishing on coral reefs. J. Mar. Biol. 2011, 1–13 (2011).
Article  Google Scholar 

49.
Watson, M. S., Claar, D. C. & Baum, J. K. Subsistence in isolation: Fishing dependence and perceptions of change on Kiritimati, the world’s largest atoll. Ocean Coast. Manag. 123, 1–8 (2016).
Article  Google Scholar 

50.
Morate, O. 2015 Population and Housing Census. Volume 1: Management Report and Basic Tables. (National Statistics Office, Ministry of Finance, Bairiki, Tarawa, Kiribati, 2016).

51.
Bosserelle, C., Reddy, S. & Lai, D. WACOP wave climate reports. (WACOP Kiribati, Kirtimati, 2015).

52.
Yeager, L. A., Marchand, P., Gill, D. A., Baum, J. K. & McPherson, J. M. Marine socio-environmental covariates: queryable global layers of environmental and anthropogenic variables for marine ecosystem studies. Ecology 98, 1976 (2017).
PubMed  Article  PubMed Central  Google Scholar 

53.
Glynn, P. W. & D’Croz, L. Experimental evidence for high temperature stress as the cause of El Niño-coincident coral mortality. Coral Reefs 8, 181–191 (1990).
ADS  Article  Google Scholar 

54.
Liu, G. et al. NOAA Coral Reef Watch’s 5km satellite coral bleaching heat stress monitoring product suite version 3 and four-month outlook version 4. Reef. Encount. 32, 39–45 (2017).
Google Scholar 

55.
Beijbom, O. et al. Towards automated annotation of benthic survey images: Variability of human experts and operational modes of automation. PLoS ONE 10, e0130312 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

56.
Stat, M., Loh, W. K. W., LaJeunesse, T. C., Hoegh-Guldberg, O. & Carter, D. A. Stability of coral-endosymbiont associations during and after a thermal stress event in the southern Great Barrier Reef. Coral Reefs 28, 709–713 (2009).
ADS  Article  Google Scholar 

57.
Baker, A. C. & Cunning, R. Bulk gDNA extraction from coral samples. https://doi.org/10.17504/protocols.io.dyq7vv (2016)

58.
LaJeunesse, T. C. Investigating the biodiversity, ecology, and phylogeny of endosymbiotic dinoflagellates in the genus Symbiodinium using the ITS region: in search of a ‘species’ level marker. J. Phycol. 37, 866–880 (2001).
CAS  Article  Google Scholar 

59.
Cunning, R., Gates, R. D. & Edmunds, P. J. Using high-throughput sequencing of ITS2 to describe Symbiodinium metacommunities in St. John, US Virgin Islands. PeerJ 5, e3472 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

60.
Pochon, X., Pawlowski, J., Zaninetti, L. & Rowan, R. High genetic diversity and relative specificity among Symbiodinium-like endosymbiotic dinoflagellates in soritid foraminiferans. Mar. Biol. 139, 1069–1078 (2001).
Article  Google Scholar 

61.
Stat, M., Pochon, X., Cowie, R. O. M. & Gates, R. D. Specificity in communities of Symbiodinium in corals from Johnston Atoll. Mar. Ecol. Prog. Ser. 386, 83–96 (2009).
ADS  CAS  Article  Google Scholar 

62.
Hume, B. C. C. et al. SymPortal: a novel analytical framework and platform for coral algal symbiont next-generation sequencing ITS2 profiling. Mol. Ecol. Resour. 19, 1063–1080 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

63.
McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

64.
Sampayo, E. M., Dove, S. G. & LaJeunesse, T. C. Cohesive molecular genetic data delineate species diversity in the dinoflagellate genus. Symbiodinium. Mol. Ecol. 18, 500–519 (2009).
CAS  PubMed  Article  PubMed Central  Google Scholar 

65.
Schloss, P. D. et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).
CAS  PubMed  PubMed Central  Article  Google Scholar 

66.
Camacho, C. et al. BLAST+: architecture and applications. BMC Bioinform. https://doi.org/10.1186/1471-2105-10-421 (2009)

67.
Eren, A. M. et al. Minimum entropy decomposition: unsupervised oligotyping for sensitive partitioning of high-throughput marker gene sequences. ISME J. 9, 968–979 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

68.
Mieog, J. C., van Oppen, M. J. H., Berkelmans, R., Stam, W. T. & Olsen, J. L. Quantification of algal endosymbionts (Symbiodinium) in coral tissue using real-time PCR. Mol. Ecol. Resour. 9, 74–82 (2009).
CAS  PubMed  Article  PubMed Central  Google Scholar 

69.
Cunning, R. & Baker, A. C. Excess algal symbionts increase the susceptibility of reef corals to bleaching. Nat. Clim. Change 3, 259–262 (2013).
ADS  Article  Google Scholar 

70.
van Oppen, M. J., Willis, B. L., Vugt, H. W. & Miller, D. J. Examination of species boundaries in the Acropora cervicornis group (Scleractinia, cnidaria) using nuclear DNA sequence analyses. Mol. Ecol. 9, 1363–1373 (2000).
CAS  PubMed  Article  PubMed Central  Google Scholar 

71.
Smith, E. G., Hume, B. C. C., Delaney, P., Wiedenmann, J. & Burt, J. A. Genetic structure of coral-Symbiodinium symbioses on the world’s warmest reefs. PLoS ONE 12, e0180169 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

72.
Santos, S. R. & Coffroth, M. A. Molecular genetic evidence that dinoflagellates belonging to the genus Symbiodinium Freudenthal are haploid. Biol. Bull. 204, 10–20 (2003).
CAS  PubMed  Article  PubMed Central  Google Scholar 

73.
Wright, E. S. Using DECIPHER v2.0 to analyze big biological sequence data in R. R. J. 8, 352–359 (2016).
Article  Google Scholar 

74.
Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analysis in R. Bioinformatics 35, 525–528 (2019).
Article  CAS  Google Scholar 

75.
Pochon, X. & Gates, R. D. A new Symbiodinium clade (Dinophyceae) from soritid foraminifera in Hawaii. Mol. Phylogenet. Evol. 56, 6 (2010).
Article  CAS  Google Scholar 

76.
Putnam, H. M., Stat, M., Pochon, X. & Gates, R. D. Endosymbiotic flexibility associates with environmental sensitivity in scleractinian corals. Proc. R. Soc. B Biol. Sci. 279, 4352–4361 (2012).
Article  Google Scholar 

77.
Schliep, K. P. phangorn: phylogenetic analysis in R. Bioinformatics 27, 592–593 (2011).
CAS  Article  Google Scholar 

78.
Oksanen, J. Vegan: an introduction to ordination. http://cran.r-project.org/web/packages/vegan/vignettes/introvegan. (2017)

79.
Bates, D. et al. Package ‘lme4’. Convergence 12, 2 (2015).
Google Scholar 

80.
Lenth, R., Singmann, H. & Love, J. Emmeans: Estimated marginal means, aka least-squares means. R Package Version 1, (2018). More

Target species and basic assumptions
We developed and analyzed spatially explicit models that describe range expansion of an invader species’ population that consists of many sub-populations. Invasive species often expand their range by stratified dispersal using human-mediated long-distance dispersal in addition to local expansion of sub-populations20. Extending a model rigorously analyzed by Takahashi et al.31, which explicitly involves (1) short-distance dispersal that expands the area of current sub-populations, and (2) long-distance dispersal that establishes new sub-populations beyond existing sub-populations (Fig. 4), we consider spatially inhomogeneous factors that influence on the long-distance dispersal (e.g., vectors’ distribution). The parameters and functions used in these models are listed in Table 1.
Figure 4

A schematic of short- and long-distance dispersal modes. Human activities may influence the long-distance dispersal by: (1) changing the number of propagules starting the long-distance dispersal ((R cdot varphi (x,y))), and (2) introducing biases in their spatial locations ((psi (x,y))). By compositing these short- and long-distance dispersals, we predict population establishment at the next time step.

Full size image

Table 1 Symbols and their default values.
Full size table

We estimated the population size of the invader species as the area covered by any of these sub-populations in the study area. This simple measure of the population size was based on our assumption that the inside of a sub-population is homogeneous and these sub-populations vary only in their positions, sizes, and shapes. This assumption may oversimplify spatial architectures of the sub-populations, because even clonal colonies of perennial plants often have concentric structures that can influence reproductive output40; but this simplification is applicable when the reproductive rate of the species is high enough to reach a constant carrying capacity quickly.
Short- and long-distance dispersals
We considered stratified dispersals of the invader species. Note that we explicitly considered invader species’ dispersal but their vectors’ movement (e.g., human traffics) was included only implicitly. The short-distance dispersal expands the species’ range only by a constant velocity7. Therefore, we modeled the short-distance dispersal as radial expansion of these sub-populations with a constant speed g. Meanwhile, long-distance dispersal introduces new sub-populations into the population out of its parent sub-population. Empirical observations showed that long-distance dispersal mediated by human activities introduces a new sub-population to an area at a long distance from its source population, e.g., vehicles can move plant seeds for more than hundreds of kilometers41. Long-distance dispersal diminishes the influence of the source location on the destination of a dispersal event, so as a simplifying approximation we assumed that the destination of the long-distance dispersal is independent of the source location of a sub-population.
The assumption of source location independence of the dispersal destination allows us to describe a process of long-distance dispersal by two functions on (S): (1) a function (varphi (x,y)) describing spatial variation in disperser production rates, and (2) a function (psi (x,y)) describing the probability that a coordinate ((x,y)) is selected as a destination of the disperser. Following the notation of Jongejans et al.18, we call (varphi (x,y)) and (psi (x,y)) the source and destination functions, respectively. Note that we define the spatial average of the source function to be one (i.e., ({{int_{S} {varphi (x,y),dxdy} } mathord{left/ {vphantom {{int_{S} {varphi (x,y),dxdy} } {|S|}}} right. kern-nulldelimiterspace} {|S|}} = 1)). We define R as the regional average of the disperser production rate per unit area (a spatially homogeneous component of the disperser production rate). Using this formulation, we calculate an expected disperser production rate of a given area by integrating (Rvarphi (x,y)) over the area. The spatial integration of the destination function over the study area is one because we assume the population of the invader species will reach full carrying capacity.
We have defined a population of the invader species as a spatial union of all sub-populations in the study area because we assume sub-populations are homogeneous. Within the study area, (rho_{t} (x,y)) is 1 if the coordinates ((x,y)) are within at least one of the sub-populations of the invader species at time (t) (and otherwise 0). The integration of (Rvarphi (x,y)) is the expected disperser production rate for a given (rho_{t} (x,y)), and the integration (Rint_{S} {rho_{t} (x,y)varphi (x,y),dxdy}) is the expected total production of dispersers from a population in the t-th time step. Thus, assuming that the total number of dispersers that start long-distance dispersal at time (t) (denoted by (n_{{{text{d,}}t}})) follow a Poisson distribution, we can write a probability that the population produces k dispersers in the t-th time, Eq. (1).

$$ Pr [n_{{{text{d}},t}} = k] = frac{{lambda^{k} e^{ – lambda } }}{k!}{, }lambda = Rint_{S} {rho_{t} (x,y)varphi (x,y),dxdy} . $$
(1)

The destination of the disperser is determined by the destination function (psi (x,y)), which determines probabilities of ending a dispersal event at ((x,y)), which results in a new sub-population being established at that location. In total, the spatial distribution of new sub-populations introduced by long-distance dispersal follows a Poisson point process of which intensities are given as (psi (x,y)Rint_{S} {rho_{t} (x,y)varphi (x,y),dxdy}).
Three model types
We compared three different types of models by varying interactions between the species’ long-distance dispersal and human activities. These included: (1) a source-mediated-dispersal model assuming that the source function (varphi (x,y)) varies spatially by human activity while the destination function (psi (x,y)) is uniform, (2) a destination-mediated-dispersal model assuming that the destination function varies spatially while the source function is uniform, and (3) a full model assuming that both source and destination functions vary spatially.
Let (h(x,y)) be a function representing intensity of human activities at coordinates ((x,y)). Without loss of generality, we can assume that the function (h(x,y)) satisfies (int_{S} {h(x,y),dxdy} = 1), the total intensity over the study area is scaled to one. In the source-mediated-dispersal model, the source function is proportional to the human-activity intensity, i.e., (varphi (x,y) = |S| cdot h(x,y)), and the destination function (psi (x,y)) is uniformly equal to ({1 mathord{left/ {vphantom {1 {|S|}}} right. kern-nulldelimiterspace} {|S|}}). On the contrary, the source function of the destination-mediated-dispersal model is uniform and the destination function is (psi (x,y) = h(x,y)). The full model is a combination of the source- and destination-mediated-dispersal models in which both source and destination functions vary with area. In this study, we assume that a single factor determines both the source and destination functions, i.e., (varphi (x,y) = left| S right| cdot h(x,y)) and (psi (x,y) = h(x,y)) (Table 2).
Table 2 Definitions of model types.
Full size table

Asymptotic growth rate of a population
Spatial dimension introduces complexity, though rigorous mathematical analysis is still viable for a small population with few small sub-populations. This situation may arise with an accidentally transferred population. Here, we consider infinitesimally small populations to be rare for invasive species and derive an asymptotic value of the growth rate to the size of the area inhabited by the population.
Each sub-population includes age, so we incorporated age-structured population dynamics29,31 into the model, described by the differential equation,

$$ frac{partial n(a,t)}{{partial t}} + frac{partial n(a,t)}{{partial a}} = 0, $$
(2)

where (n(a,t)) represents the frequency of sub-populations with age a at time t. Note that Eq. (2) assumes no extinction of sub-populations. The equation has two boundary conditions: (1) (n(a,0)) represents an age distribution of the initial population, and (2) (n(0,t)) represents the number of new sub-populations (i.e., age 0 sub-populations) introduced by the long-distance dispersal at time t.
To determine the number of new sub-populations, we need to determine how many long-distance dispersers will emerge from a given population by including spatial heterogeneities. Recall that a sub-population expands outward by a constant speed g. Therefore, if we ignore overlaps among sub-populations each sub-population keeps a circular shape of radius proportional to age. In addition, if a sub-population is young, i.e., its size is small, we can regard the value of the source function (varphi (x,y)) inside the sub-population as uniform. Let ((x_{i} ,y_{i} )) and (a_{i}) be the position of the center and age of the (i)-th sub-population, respectively. With the above approximations, we can simply derive the expected number of long-distance dispersers that start dispersal from the (i)-th sub-population as (pi R cdot (ga_{i} )^{2} varphi (x_{i} ,y_{i} )).
On the other hand, existing sub-populations also originate from long-distance dispersal. Therefore, a position of the sub-population also follows the destination function (psi (x,y)). Building on the expected number of new sub-populations we described at the last paragraph, we calculate an average over the study area to calculate a mean-field approximation of the number of long-distance dispersers from an age a sub-population as (pi Rint_{S} {psi (x,y)(ga)^{2} varphi (x,y),dxdy}).
We assume that a population consists of a few small sub-populations in this formulation and a disperser will always establish outside existing sub-populations. Therefore, the total number of a new sub-population (i.e., (n(0,t))) is a summation of new sub-populations produced by each of the existing sub-populations,

$$ begin{gathered} n(0,t) = pi Rint_{0}^{t} {n(a,t)left[ {int_{S} {psi (x,y)(ga)^{2} varphi (x,y),dxdy} } right],da} \ = pi Rleft( {int_{S} {psi (x,y)varphi (x,y),dxdy} } right)int_{0}^{t} {(ga)^{2} n(a,t),da} . \ end{gathered} $$
(3)

With Eq. (3), the asymptotic growth rate of the Eq. (2) can be calculated as follows29,31,

$$ left( {2pi Rg^{2} int_{S} {psi (x,y)varphi (x,y),dxdy} } right)^{1/3} . $$
(4)

The asymptotic growth rate indicates that the integration (int_{S} {psi (x,y)varphi (x,y),dxdy}) describes the influence of the source and the destination functions in the early phases of population growth. Therefore, hereafter we call (int_{S} {psi (x,y)varphi (x,y),dxdy}) a spatial factor (F_{{text{h}}}) of the long-distance dispersal. Note that the spatial factor reduces to 1 for both source- and destination-mediated dispersal models. For the full models of which source and destination functions are (varphi (x,y) = left| S right|h(x,y)) and (psi (x,y) = h(x,y)), respectively, we can reduce the spatial factor (F_{{text{h}}}) to (int_{S} {left( {sqrt {left| S right|} h(x,y)} right)^{2} dxdy}), equivalent to Simpsons’ diversity index.
Numerical analysis
We evaluated the effects of the dispersal vector on distribution with an individual-based approach that describes colonies in a population as groups of individuals within a circular shape of various sizes (Fig. 1a,c,e for typical model outputs, see supplemental information SI 1 for detailed settings). To evaluate the effect of spatial heterogeneity on population dynamics, for each model type we generated 100 of (h(x,y)) randomly (see shaded area of Fig. 1a,c,e, and SI 2 for the algorithm used) and ran 100 independent realizations for each (h(x,y)). For each realization, we split the time course into three phases: establishment, expansion, and naturalization. The phases were based on the proportion of area inhabited by the population (Fig. 2a; less than 5%, 5% to 95%, and 95% to complete occupation of the total area, respectively), and measured the length of each phase. We linearly interpolated the time course based on area covered for each phase.
Using the same set of realized time courses, we estimated the asymptotic growth rate as the peak of a distribution of the logarithmic value of the instantaneous growth rate, defined as a difference of logarithmic values of covered area that are adjacent in a time course. We excluded periods that a population covers less than 1% or more than 50% of the total area to avoid strong demographic stochasticity of initial dynamics and a deceleration phase of S-shaped growth, respectively. We gathered these logarithmic values of instantaneous growth rates from 100 realizations with the same (h(x,y)) and dispersal type, then estimated the density distribution using Gaussian kernel estimation. Finally, we determined the maximum point of the estimated density distribution as the estimated asymptotic growth rate. More

1.
Haakenstad, A. et al. Tracking spending on malaria by source in 106 countries, 2000–16: an economic modelling study. Lancet Infect. Dis. 19, 703–716 (2019).
PubMed  PubMed Central  Article  Google Scholar 
2.
Dhiman, S. Are malaria elimination efforts on right track? An analysis of gains achieved and challenges ahead. Infect. Dis. Poverty. 8, 14 (2019).
PubMed  PubMed Central  Article  Google Scholar 

3.
Alimi, T. O. et al. Prospects and recommendations for risk mapping to improve strategies for effective malaria vector control interventions in Latin America. Malar. J. 14, 519 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

4.
Peterson, I., Borrell, L. N., El-Sadr, W. & Teklehaimanot, A. A temporal-spatial analysis of malaria transmission in Adama, Ethiopia. Am. J. Trop. Med. Hyg. 81, 944–949 (2009).
PubMed  Article  PubMed Central  Google Scholar 

5.
Bannister-Tyrrell, M. et al. Micro-epidemiology of malaria in an elimination setting in Central Vietnam. Malar. J. 17, 119 (2018).
PubMed  PubMed Central  Article  Google Scholar 

6.
Durnez, L. et al. Identification and characterization of areas of high and low risk for asymptomatic malaria infections at sub-village level in Ratanakiri Cambodia. Malar. J. 17, 27 (2018).
PubMed  PubMed Central  Article  Google Scholar 

7.
Ye, Y., Kyobutungi, C., Louis, V. R. & Sauerborn, R. Micro-epidemiology of Plasmodium falciparum malaria: is there any difference in transmission risk between neighbouring villages?. Malar. J. 6, 46 (2007).
PubMed  PubMed Central  Article  Google Scholar 

8.
Bousema, T. et al. Identification of hot spots of malaria transmission for targeted malaria control. J. Infect. Dis. 201, 1764–1774 (2010).
PubMed  Article  PubMed Central  Google Scholar 

9.
Sissoko, M. S. et al. Spatial patterns of Plasmodium falciparum clinical incidence, asymptomatic parasite carriage and Anopheles density in two villages in Mali. Am. J. Trop. Med. Hyg. 93, 790 (2015).
PubMed  PubMed Central  Article  Google Scholar 

10.
Rulisa, S. et al. Malaria prevalence, spatial clustering and risk factors in a low endemic area of eastern Rwanda: a cross sectional study. PLoS ONE 8, e69443 (2013).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

11.
Seyoum, D. et al. Household level spatio-temporal analysis of Plasmodium falciparum and Plasmodium vivax malaria in Ethiopia. Parasit. Vectors. 10, 196 (2017).
PubMed  PubMed Central  Article  Google Scholar 

12.
Kabaghe, A. N. et al. Fine-scale spatial and temporal variation of clinical malaria incidence and associated factors in children in rural Malawi: a longitudinal study. Parasit. Vectors. 11, 129 (2018).
PubMed  PubMed Central  Article  Google Scholar 

13.
Staedke, S. G. et al. Short report: proximity to mosquito breeding sites as a risk factor for clinical malaria episodes in an urban cohort of Ugandan children. Am. J. Trop. Med. Hyg. 69, 244–246 (2003).
PubMed  Article  PubMed Central  Google Scholar 

14.
Zhou, S. S. et al. Spatial correlation between malaria cases and water-bodies in Anopheles sinensis dominated areas of Huang-Huai plain China. Parasit. Vectors. 5, 106 (2012).
PubMed  PubMed Central  Article  Google Scholar 

15.
Debebe, Y., Hill, S. R., Tekie, H., Ignell, R. & Hopkins, R. J. Shady business: understanding the spatial ecology of exophilic Anopheles mosquitoes. Malar. J. 17, 351 (2018).
PubMed  PubMed Central  Article  Google Scholar 

16.
Hawkes, F. M. et al. Vector compositions change across forested to deforested ecotones in emerging areas of zoonotic malaria transmission in Malaysia. Sci. Rep. 9, 13312 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

17.
Ye, Y. et al. Housing conditions and Plasmodium falciparum infection: protective effect of iron-sheet roofed houses. Malar. J. 5, 8 (2006).
PubMed  PubMed Central  Article  Google Scholar 

18.
Wanzirah, H. et al. Mind the gap: house structure and the risk of malaria in Uganda. PLoS ONE 10, e0117396 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

19.
Ondiba, I. M. et al. Malaria vector abundance is associated with house structures in Baringo County Kenya. PLoS ONE 13, e0198970 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

20.
Plucinski, M. M. et al. Evaluation of a universal coverage bed net distribution campaign in four districts in Sofala Province Mozambique. Malar. J. 13, 427 (2014).
PubMed  PubMed Central  Article  Google Scholar 

21.
Levitz, L. et al. Effect of individual and community-level bed net usage on malaria prevalence among under-fives in the Democratic Republic of Congo. Malar. J. 17, 39 (2018).
PubMed  PubMed Central  Article  Google Scholar 

22.
Kaindoa, E. W., Mkandawile, G., Ligamba, G., Kelly-Hope, L. A. & Okumu, F. O. Correlations between household occupancy and malaria vector biting risk in rural Tanzanian villages: implications for high-resolution spatial targeting of control interventions. Malar. J. 15, 199 (2016).
PubMed  PubMed Central  Article  Google Scholar 

23.
Bousema, T. et al. Hitting hotspots: spatial targeting of malaria for control and elimination. PLoS Med. 9, e1001165 (2012).
PubMed  PubMed Central  Article  Google Scholar 

24.
World Health Organization. Global technical strategy for malaria 2016–2030. (World Health Organization, 2015).

25.
Ma, B. O. & Roitberg, B. D. The role of resource availability and state-dependence in the foraging strategy of blood-feeding mosquitoes. Evol. Ecol. Res. 10(8), 1111–1130 (2008).
Google Scholar 

26.
Omondi, A. B., Ghaninia, M., Dawit, M., Svensson, T. & Ignell, R. Age-dependent regulation of host seeking in Anopheles coluzzii. Sci. Rep. 9, 9699 (2019).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

27.
Agrawal, A. A., Lau, J. A. & Hamback, P. A. Plant community heterogeneity and the evolution of plant–herbivore interactions. Q. Rev. Biol. 81, 349–376 (2006).
PubMed  Article  PubMed Central  Google Scholar 

28.
Jones, R.E. Search Behaviour: strategies and outcomes. In: Proceedings of 8th International Symposium on Insect-Plant Relationships (eds. Menken, S. B. J., Visser, J. H., Harrewijn, P.) 93–102 (Dordrecht, 1992).

29.
Root, R. B. Organization of a plant-arthropod association in simple and diverse habitats: the fauna of collards (Brassica oleracea). Ecol. Monogr. 43, 95–124 (1973).
Article  Google Scholar 

30.
Cromartie, W. J. The effect of stand size and vegetational background on the colonization of cruciferous plants by herbivorous insects. J. Appl. Ecol. 12, 517–533 (1975).
Article  Google Scholar 

31.
Lidicker, W.Z. & Peterson, J.A. Responses of small mammals to habitat edges. In Landscape Ecology of Small Mammals (eds. Barrett, G.W. & Peles, J.D.) 221–227 (Springer, 1999).

32.
Forman, R.T.T. Land mosaics. The ecology of landscapes and regions (Cambridge University Press, 1995).

33.
Bousema, T. et al. The impact of hotspot-targeted interventions on malaria transmission in Rachuonyo South District in the Western Kenyan highlands: a cluster-randomized controlled trial. PLoS Med. 13, e1001993 (2016).
PubMed  PubMed Central  Article  Google Scholar 

34.
Platt, A., Obala, A. A., MacIntyre, C., Otsyula, B. & O’Meara, W. P. Dynamic malaria hotspots in an open cohort in western Kenya. Sci. Rep. 8, 647 (2018).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

35.
Charlwood, J.D & Alecrim, W.A. Capture-recapture studies with the South American malaria vector Anopheles darlingi, Root. Ann. Trop. Med. Parasit. 83, 569–576 (1989).

36.
Takken, W., Charlwood, J.D., Billingsley, P.F. & Gort G. Dispersal and survival of Anopheles funestus and A. gambiae s.l. (Diptera: Culicidae) during the rainy season on southeast Tanzania. B. Entomol. Res. 88, 561–566 (1998).

37.
Thiemann, T. C., Wheeler, S. S., Barker, C. M. & Reisen, W. K. Mosquito host selection varies seasonally with host availability and mosquito density. PLoS Negl. Trop. Dis. 5, e1452 (2011).
PubMed  PubMed Central  Article  Google Scholar 

38.
Cummins, B., Cortez, R., Foppa, I. M., Walbeck, J. & Hyman, J. M. A spatial model of mosquito host-seeking behaviour. PLoS Comput. Biol. 8, e1002500 (2012).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

39.
Gillies, M.T. Studies of house leaving and outside resting of Anopheles gambiae Giles and Anopheles funestus Giles in East Africa. I.-The outside resting population. Bull. Ent. Res. 45, 361–374 (1954).

40.
Gillies, M.T. Studies of house leaving and outside resting of Anopheles gambiae Giles and Anopheles funestus Giles in East Africa. II. The Exodus from houses and the house resting population. Bull. Ent. Res. 45(2), 375–387 (1954b).

41.
Jansson, S., et al. Real-time dispersal of malaria vectors in rural Africa monitored with lidar. PLoS ONE (accepted).

42.
Port, G. R., Boreham, P. F. L. & Bryan, J. H. The relationship of host size to feeding by mosquitoes of the Anopheles gambiae Giles complex (Diptera: Culicidae). B. Entomol. Res. 70, 133–144 (1980).
Article  Google Scholar 

43.
Kirby, M. J. et al. Risk factors for house-entry by malaria vectors in a rural town and satellite villages in the Gambia. Malar. J. 7, 2 (2008).
PubMed  PubMed Central  Article  Google Scholar 

44.
Thomas, C. J., Cross, D. E. & Bøgh, C. Landscape movements of Anopheles gambiae malaria vector mosquitoes in rural Gambia. PLoS ONE 8, e68679 (2013).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

45.
Bannister-Tyrrell, M. et al. Defining micro-epidemiology for malaria elimination: systematic review and meta-analysis. Malar. J. 16, 164 (2017).
PubMed  PubMed Central  Article  Google Scholar 

46.
Jansson, S., et al. Real-time dispersal of malaria vectors in rural Africa monitored with lidar. PloS One. Accepted.

47.
World Health Organization. Manual on practical entomology in malaria. Part 2: methods and techniques. (World Health Organization, 1975).

48.
Verrone, G. A. Outline for the determination of malarial mosquitoes in Ethiopia. Mosq. News. 22, 37–49 (1962).
Google Scholar 

49.
Gillies, M. & Coetzee, M. A supplement to the anopheline of Africa South of Sahara. S. Afr. Inst. Med. Res. 55, 143 (1987).
Google Scholar 

50.
Massebo, F., Balkew, M., Gebre-Michael, T. & Lindtjørn, B. Blood meal origins and insecticide susceptibility of Anopheles arabiensis from Chano in South-West Ethiopia. Parasit. Vectors. 6, 44 (2013).
PubMed  PubMed Central  Article  Google Scholar 

51.
Wrtiz, R., Avery, M. & Benedict, M. Methods in Anopheles research: Plasmodium sporozoite ELISA. (Center for Disease Control, 2007).

52.
Loha, E. & Lindtjorn, B. Predictors of Plasmodium falciparum malaria incidence in Chano Mille, South Ethiopia: a longitudinal study. Am. J. Trop. Med. Hyg. 87, 450–459 (2012).
PubMed  PubMed Central  Article  Google Scholar 

53.
Naing, L., Winn, T. & Rusli, B. N. Practical issues in calculating the sample size for prevalence studies. Arch. Orofac. Sci. 1, 9–14 (2006).
Google Scholar 

54.
World Health Organization. Basic malaria microscopy. Part I: Learner’s guide. (World Health Organizations, 1991).

55.
Ministry of Health of Ethiopia. Malaria diagnosis and treatment guidelines for health workers in Ethiopia. (Federal Democratic Republic of Ethiopia Ministry of Health, 2004).

56.
Getis, R. & Ord, J. K. The analysis of spatial association by use of distance statistics. Geogr. Anal. 24, 189–206 (1992).
Article  Google Scholar 

57.
Drakeley, C. et al. An estimation of the entomological inoculation rate for Ifakara: a semi-urban area in a region of intense malaria transmission. Trop. Med. Int. Health. 8, 767–774 (2003).
CAS  PubMed  Article  PubMed Central  Google Scholar  More

How rapidly can the major functional components of a soil ecosystem appear in volcanic ash?
The overall conclusion from comparing the functional composition of the developing ash-soil with natural forest soils from the same climates, is that the major biological aspects of the system have developed surprisingly quickly. Already by the 24-month and 36-month stage, the ash-soil had a similar diversity of bacterial functional genes to a forest soil (Fig. 8).
This contrasts with the findings of Fujimura et al.3, who found that ash deposited by the Miyakejima volcano still showed a very distinct and low diversity community compared to that expected of normal forest soils. This difference could be attributable to differences in the initial chemical composition of the ash: the pH value of the Miyakejima ash studied by Fujimura et al.3 was much lower than the ash we sampled, due to the persistence of acids from the initial eruption, or acidic rainfall from later outgassing. Our Sakurajima ash samples had only a slightly acidic pH (5.2) at the time of harvesting, which was similar to the average value (5.14) of 251 ash samples from Sakarajima collected from 1955 to 200125, and they remained in the 5–6 pH range at both 24 months and 36 months. Even the ash soil mesocosm samples that were situated on the lower slopes of the Sakurajima volcano, and the nearby natural forest soils at Sakurajima, remained in the pH 5–6 range and had a similar gene composition to other sites, suggesting that the area around the Sakurajima volcano is not subject to frequent highly acidic rain events. The difference between the Sakurajima ash and the Miyakejima ash hints at the importance of details of the chemical environment of the ash for determining its development into a functioning soil ecosystem.
Another factor which may explain the difference between the paths of biota development of the ash from the two volcanoes is the relative availability of propagules. Our ash soils were surrounded by developed ecosystems which could provide a rain of propagules of microorganisms and bryophytes in windblown dust or in rain splash. By contrast the Miyakejima ash field was an area of extensive ecosystem destruction. If bryophytes and lichens had not been able to establish due to unavailability of propagules, an important source of photosynthetically-fixed energy would have been unavailable to the Miyakejima system. This would have impeded the development of a decomposer food chain, of mineral weathering by organic acids and chelates, and of buffering of soil pH and water content and nutrient storage on organic matter and clay surfaces.
Was there a lower taxonomic diversity than is normally found in a developed soil?
At the broadest level, from the metagenomes, the taxonomic composition of the biota of the developing ash-soil resembled that of the developed forest soils—with archaea, fungi, protists and metazoans occurring at similar relative abundances in the ash-soils and forest soils. In this sense, the ash-soil had developed remarkably quickly in terms of the basic taxonomic framework of a functioning soil ecosystem. Bryophytes and lichens were able to establish10,26 with their propagules small enough to enter through the gauze covering, and presumably played an important role in providing carbon and extra niches to the developing ecosystem—even though they are unlikely to be as supportive of a diverse soil biota as the extensive root systems of vascular plants.
There was however—continuing a pattern seen in the 24-month results reported by Kerfahi et al.10—a lower OTU and Shannon diversity of bacteria based on the 16S rRNA amplicon sequencing. The continuing lower OTU-level 16S taxonomic diversity of the ash soils might have had multiple causes.
Firstly, limitations on dispersal and colonization of the ash soil systems might have kept OTU diversity lower. It is assumed that the biota present were mostly derived from the surrounding forest ecosystems—as windblown or rain splashed material. Dispersal limitation of soil biota is likely to play some role in all volcanic ash fields, although the existence of nearby areas of surviving ecosystem is also common20,27. Most volcanic explosions or ash fields result in no more than a few square kilometres of landscape being completely devastated, and isolated pockets of vegetation that survive are common27,28—in this respect the proximity of our mesocosms to natural vegetation may be fairly realistic. In natural ash deposits, upwards colonization by soil biota from buried ‘legacy soils’ is also possible20. In our experiment, upwards colonization from soil below the pots was not possible, and the plastic gauze covering may have prevented access by insects, birds or seeds that could have brought incidental soil biota. It is thus difficult to know for sure whether the overall role of dispersal limitation in our mesocosm systems is stronger or weaker than would generally be the case in natural ash fields. Since natural ash fields themselves are also very heterogenous in terms of area, depth and degree of devastation of natural vegetation, it is especially hard to generalise.
Secondly, the relatively extreme chemical and physical conditions of the volcanic ash itself are also likely play a major role in limiting the bacterial taxonomic diversity of the ash-soil biota in the mesocosms. Although the pH of the ash in our mesocosm systems was not extreme, it may be expected to be droughty due to low organic matter content, and low in available nutrients and organic matter that could sustain soil food chains. Even by 36 months, the organic carbon of the ash-soil was still orders of magnitude lower than in the surrounding forest soils (Supplementary Fig. S1)10,26. Thus, fewer niche types may be viable in the environment of the developing ash.
Nevertheless, although lower level taxonomic diversity of bacteria is clearly lower than in the established forest soils, the metagenome results from the ash soil mesocosms show that most higher level taxonomic groups of bacteria, archaea and eukaryotes are already present by the 24-month and 36-month stage, emphasizing that in some respects the establishment of soil biota has been rapid. While DNA of dead microorganisms blown as dust could have provided the impression of populations being present, it seems unlikely that dead material would be able to disperse in the same relative proportions as in a forest soil, to give a metagenome that resembled a developed soil in all its major groups, in roughly the same relative abundances (Please refer to Fig. 1 in Kerfahi et al.10).
Was there a lower diversity of categories of functional genes of soil biota than are normally found in a developed soil, indicating less functional complexity in the early ash-soil system?
Surprisingly, the richness and diversity of functional gene categories at Level 4 of the SEED Subsystem was not significantly different between the ash mesocosms and the forest soils. Around 97% of the metagenome reads in the ash mesocosms and the forest soil were bacterial, so the much lower OTU diversity of bacteria in the mesocosms would be expected to give a lower functional diversity of genes. The contrast between patterns of taxonomic and functional diversity in the ash mesocosms emphasises the redundancy of gene functions in soil organisms—such that losing a high proportion of taxonomic diversity apparently has no effect on functional diversity. If the level of functional gene diversity is high, this implies that in a general sense the ecosystem can potentially perform a wide variety of functions. Greater functional diversity is also considered to result in greater resilience of the ecosystem29.
We had also hypothesized that the distinct chemical environment of the developing ash would result in increased relative abundance of a number of specific gene categories:
Is there an increased relative abundance of genes (or of prokaryotic genera) associated with autotrophy (e.g. Rubisco gene, coxL gene) in the ash-soils?
We searched for potential taxa and genes which might indicate autotrophic carbon fixation through chemosynthetic oxidation processes. We found a higher relative abundance of Ktedonobacteraceae (Chloroflexi) in the ash soils compared to forest soils, which is a pattern that was also found in previous ash studies21,30. Ktedonobacteraceae is a novel taxonomic group that has only been recently added to the phylogenetic tree of Chloroflexi30,31. Their genome has been reported to be large and they are known to have a wide potential for different metabolic mechanisms32. Ktedonobacteraceae are mostly found in extreme environments, including volcanic ash and hydrothermal vents30.
We had hypothesized that the coxL (carbon monoxide dehydrogenase large chain) gene—which is implicated in autotrophy—would be more abundant in the ash soil samples. Likewise, the rbcL (ribulose bisphosphate carboxylase large chain) gene is involved in carbon fixation and we hypothesized its greater abundance in the ash-soil mesocosms. However, in fact both rbcL and coxL had higher relative abundances in the forest soils.
Although coxL has been mostly linked with chemotrophic organisms living in an extreme environment, due to its potential role for assisting heterotrophic growth in the environments lacking organic matter21,33, it is also one of the more abundant genes in natural forest soils34. Noting that natural forest is one of the largest global sinks of atmospheric CO34,35, the high relative abundance of coxL gene might be due to high abundance of CO in natural forests. Also, the coxL gene might have the potential to participate in other metabolic pathways. The presence of coxL genes in many different types of organisms (e.g. plant symbionts, animal pathogens, etc.) suggests other roles of CO-oxidation by the coxL gene33.
The higher abundance of rbcL in the forest soils in our study might be related to photosynthetic C fixation by cyanobacteria in both the forest soil and ash soils. rbcL gene also have been found in many natural forest sites. By contrast, Fujimura et al.3 found rbcL to be more abundant in developing volcanic ash soils, which might be attributable to differences in the chemical composition of ash or due to details of the mesoocosm system used in our study.
Is there increased relative abundance of genes that are associated with acquisition of nutrients from abiotic sources rather than decomposition of organic matter (e.g. nitrogen fixation genes)?
Contrary to expectations we found no differences in the relative abundance of nitrogen fixation gene (nifH) between the ash soils and forest soils. It is possible that this reflects nitrogen being relatively more limiting in the forest soils, where there is an abundance of organic carbon. It is also possible that in the forest soils, the abundance of labile carbon in the rhizosphere of N-limited plants encourages N fixing bacteria through root secretions36.
Is there increased relative abundance of stress response genes and dormancy related genes?
As hypothesized, we found that dormancy related genes were more abundant in the ash mesocosms than in the forest soils. This would seem to be adaptive in terms of survival of a well-drained ash-soil, low in organic matter, which is apt to dry out. This would depend, however, on the detailed microclimate. Field measurements suggested that in sunny conditions the microclimate in the static air in the trays in which our pots were held was generally around 0.5–1 °C higher than in the open air a meter away, although this difference disappeared in cloudy conditions and at night10. It is possible that our mesocosms (exposed in open sunlight, but covered by a gauze) would have either retained or lost moisture more easily than the soils, and that this might have affected the frequency of drying. We had also anticipated that stress response genes would be more common in the ash mesocosms. However, in this case the stress response category was less common than in the forest soil, surprisingly suggesting that at a cellular level stresses are in fact less common in the ash-soil.
Is there decreased relative abundance of cell–cell interaction related genes?
As anticipated, genes related to cell–cell interactions such as regulation and cell signalling genes and virulence, disease, and defense genes (Fig. 7a,b) were relatively less abundant in the ash mesocosms. This seems to emphasize that complex interactions are less common in the developing ash system than in a fully developed soil with its more abundant and diverse carbon sources. However, bacterial genes found as part of CRISPRs were relatively less abundant in the forests, implying lower intensity of defense mechanisms of bacteria against viruses in the forests compared to ash soils. The relatively low OTU diversity of the ash soils may perhaps allow greater spread and mortality of viruses through populations, necessitating greater investment in defences.
Since Odum37, it has been noted that developing ecosystems tend to be based on tighter and more effective interactions as succession continues. For example, Morriën et al.38 found that during secondary succession of old field ecosystems, co-occurrences of taxa become more predictable and carbon cycling in the decomposer chain more efficient. Empirically our findings seem to agree with the same paradigm, as we found cell–cell interaction genes found to be more abundant in the developed forest soils. It would be very interesting to directly measure decomposer carbon cycling efficiency in ash-soil mesocosm systems as they develop.
Although we focused on 16S rRNA amplicon sequence data for comparing OTU level diversity between samples as taxonomic assignment based on MG-RAST pipeline is very limited39,40, we had briefly compared the family level bacterial community composition assigned based on metagenome sequence data and based on 16S rRNA amplicon sequence data. It is still debatable if whole genome sequencing, which is free from primer bias, is superior in this respect to 16S rRNA amplicon sequencing40. Our data supports the idea that at the bacterial family level, metagenome sequencing covered a larger variety of taxonomic groups than 16S rRNA sequencing. More

1.
Guzik, M. T. et al. Is the Australian subterranean fauna uniquely diverse?. Invertebr. Syst. 24, 407–418 (2011).
Article  Google Scholar 
2.
Gonzalez, B. C., Iliffe, T. M., Macalady, J. L., Schaperdoth, I. & Kakuk, B. Microbial hotspots in anchialine blue holes: initial discoveries from the Bahamas. Hydrobiologia 677, 149–156 (2011).
CAS  Article  Google Scholar 

3.
Gibert, J. & Deharveng, L. Subterranean ecosystems: a truncated functional biodiversity: This article emphasizes the truncated nature of subterranean biodiversity at both the bottom (no primary producers) and the top (very few strict predators) of food webs and discusses the implic. Bioscience 52, 473–481 (2002).
Article  Google Scholar 

4.
Moldovan, O. T. An overview on the aquatic cave fauna. in Cave Ecology 173–194 (Springer, 2018).

5.
Adams, M. & Humphreys, W. F. Patterns of genetic diversity within selected subterranean fauna of the Cape Range peninsula, Western Australia: systematic and biogeographic implications. Rec. West. Aust. Museum Suppl. 45, 145–164 (1993).
Google Scholar 

6.
Page, T. J., Humphreys, W. F. & Hughes, J. M. Shrimps down under: evolutionary relationships of subterranean crustaceans from Western Australia (Decapoda: Atyidae: Stygiocaris). PLoS ONE 3, e1618 (2008).
ADS  Article  Google Scholar 

7.
Juan, C., Guzik, M. T., Jaume, D. & Cooper, S. J. B. Evolution in caves: Darwin’s ‘wrecks of ancient life’in the molecular era. Mol. Ecol. 19, 3865–3880 (2010).
Article  Google Scholar 

8.
Bradford, T., Adams, M., Humphreys, W. F., Austin, A. D. & Cooper, S. J. B. DNA barcoding of stygofauna uncovers cryptic amphipod diversity in a calcrete aquifer in Western Australia’s arid zone. Mol. Ecol. Resour. 10, 41–50 (2010).
CAS  Article  Google Scholar 

9.
Asmyhr, M. G., Linke, S., Hose, G. & Nipperess, D. A. Systematic conservation planning for groundwater ecosystems using phylogenetic diversity. PLoS ONE 9, e115132 (2014).
ADS  Article  Google Scholar 

10.
Matthews, E. F. et al. Scratching the surface of subterranean biodiversity: Molecular analysis reveals a diverse and previously unknown fauna of Parabathynellidae (Crustacea: Bathynellacea) from the Pilbara Western Australia. Mol. Phylogenet. Evol. 142, 106643 (2020).
Article  Google Scholar 

11.
Thomsen, P. F. et al. Detection of a diverse marine fish fauna using environmental DNA from seawater samples. PLoS ONE 7, 1 (2012).
Google Scholar 

12.
Evans, N. T. et al. Quantification of mesocosm fish and amphibian species diversity via environmental DNA metabarcoding. Mol. Ecol. Resour. 16, 29–41 (2016).
CAS  Article  Google Scholar 

13.
Olds, B. P. et al. Estimating species richness using environmental DNA. Ecol. Evol. 6, 4214–4226 (2016).
Article  Google Scholar 

14.
Stat, M. et al. Ecosystem biomonitoring with eDNA: metabarcoding across the tree of life in a tropical marine environment. Sci. Rep. 7, 12240 (2017).
ADS  Article  Google Scholar 

15.
Humphreys, W. et al. Geochemical and microbial diversity of Bundera sinkhole, an anchialine system in the eastern Indian ocean. Nat. Croat. Period. Musei Hist. Nat. Croat. 21, 59–63 (2012).

16.
Korbel, K., Chariton, A., Stephenson, S., Greenfield, P. & Hose, G. C. Wells provide a distorted view of life in the aquifer: implications for sampling, monitoring and assessment of groundwater ecosystems. Sci. Rep. 7, 40702 (2017).
ADS  CAS  Article  Google Scholar 

17.
Van Bekkum, M., Sainsbury, J. P., Daughney, C. J. & Chambers, G. K. Molecular analysis of bacterial communities in groundwaters from selected wells in the Hutt Valley and the Wairarapa, New Zealand. New Zeal. J. Mar. Freshw. Res. 40, 91–106 (2006).
Article  Google Scholar 

18.
Sohlberg, E. et al. Revealing the unexplored fungal communities in deep groundwater of crystalline bedrock fracture zones in Olkiluoto Finland. Front. Microbiol. 6, 573 (2015).
Article  Google Scholar 

19.
Purkamo, L. et al. Diversity and functionality of archaeal, bacterial and fungal communities in deep Archaean bedrock groundwater. FEMS Microbiol. Ecol. 94, fiy116 (2018).

20.
Miettinen, H. et al. Microbiome composition and geochemical characteristics of deep subsurface high-pressure environment Pyhäsalmi mine Finland. Front. Microbiol. 6, 1203 (2015).
Article  Google Scholar 

21.
Ali, J. R. & Aitchison, J. C. Time of re-emergence of Christmas Island and its biogeographical significance. Palaeogeogr. Palaeoclimatol. Palaeoecol. 109396 (2019).

22.
Grimes, K. G. Karst features of Christmas Island (Indian Ocean). Helictite 37, 41–58 (2001).
Google Scholar 

23.
Beeton, B. et al. Final Report Christmas Island Expert Working Group to Minister for the Environment. Herit. Arts (2010).

24.
James, D. J., Green, P. T., Humphreys, W. F. & Woinarski, J. C. Z. Endemic species of Christmas Island, Indian Ocean. Rec. West. Aust. Museum 34, (2019).

25.
Humphreys, W. F. Subterranean fauna of Christmas Island: habitats and salient features (Raffles Bull, Zool, 2014).
Google Scholar 

26.
Davie, P. J. F. & Ng, P. K. L. Two new species of Orcovita (Crustacea: Decapoda: Brachyura: Varunidae) from anchialine caves on Christmas Island, eastern Indian Ocean. Raffles Bull. Zool. 60, 1 (2012).

27.
Nester, G. M. et al. Development and evaluation of fish eDNA metabarcoding assays facilitates the detection of cryptic seahorse taxa (family: Syngnathidae) (Environ. DNA, 2020).

28.
Berry, T. E. et al. DNA metabarcoding for diet analysis and biodiversity: a case study using the endangered Australian sea lion (Neophoca cinerea). Ecol. Evol. 7, 5435–5453 (2017).
Article  Google Scholar 

29.
Pochon, X., Bott, N. J., Smith, K. F. & Wood, S. A. Evaluating detection limits of next-generation sequencing for the surveillance and monitoring of international marine pests. PLoS ONE 8, e73935 (2013).
ADS  CAS  Article  Google Scholar 

30.
Boyer, F., Mercier, C., Bonin, A., Taberlet, P. & Coissac, E. OBITools: a Unix-inspired software package for DNA metabarcoding. Mol. Ecol. Resour. 16, 176–182 (2014).
Article  Google Scholar 

31.
Wilkinson, S. P., Davy, S. K., Bunce, M. & Stat, M. Taxonomic identification of environmental DNA with informatic sequence classification trees. PeerJ Prepr. (2018).

32.
R Core Team. RStudio: integrated development for R. RStudio, Inc., Boston, MA 42, (2015).

33.
Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581 (2016).
CAS  Article  Google Scholar 

34.
Benson, D. A., Karsch-Mizrachi, I., Lipman, D. J., Ostell, J. & Wheeler, D. L. GenBank. Nucleic Acids Res. 33, D34–D38 (2005).
CAS  Article  Google Scholar 

35.
Mousavi-Derazmahalleh, M. et al. eDNAFlow, an automated, reproducible and scalable workflow for analysis of environmental DNA (eDNA) sequences exploiting Nextflow and Singularity (Manuscript submitted for publication, 2020).

36.
Hui, T. H., Naruse, T., Fujita, Y. & Kiat, T. S. Observations on the fauna from submarine and associated anchialine caves in Christmas Island, Indian Ocean Territory. (Australia, Raffles. Bull. Zool, 2014).

37.
Horton, T. et al. World Register of Marine Species (WoRMS). (2018).

38.
McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).
ADS  CAS  Article  Google Scholar 

39.
Clarke, K. R. & Gorley, R. N. PRIMER v7: User Manual/Tutorial. (2015).

40.
Anderson, M., Gorley, R. N. & Clarke, R. K. Permanova+ for Primer: Guide to Software and Statistical Methods. (Primer-E Limited, 2008).

41.
Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5–5. (2019).

42.
Wickham, H. ggplot2: elegant graphics for data analysis. (Springer, 2016).

43.
Mitsch, W. J. & Gosselink, J. G. Wetlands, 539 pp. (Van Nostrand Reinhold, NY, 1986).

44.
Humphreys, W. F. & Eberhard, S. Subterranean fauna of Christmas Island Indian Ocean. Helictite 37, 59–74 (2001).
Google Scholar 

45.
Greenslade, P. Systematic composition and distribution of Australian cave collembolan faunas with notes on exotic taxa. Helictite 38, 11–15 (2002).
Google Scholar 

46.
Morgan. Decapod crustacea of Christmas Island. History 10, 629–637 (2000).

47.
Campos, P. F. & Gilbert, T. M. P. DNA extraction from formalin-fixed material. in Ancient DNA, 81–85 (Springer, 2012).

48.
Humphreys, W., & Eberhard, S. Assessment of the ecological values and management options for cave use on Christmas Island: Project 97/002. (1998).

49.
Franklin, P. A. Dissolved oxygen criteria for freshwater fish in New Zealand: a revised approach. New Zeal. J. Mar. Freshw. Res. 48, 112–126 (2014).
CAS  Article  Google Scholar 

50.
Barrett, P. J. Searching for water on Christmas Island. Helictite 37, 37–39 (2001).
Google Scholar 

51.
Anon. Water Search – Christmas Island. in Unpublished report held in records of Phosphate Mining Company of Christmas Island 9 (1971).

52.
Gorički, Š. Environmental DNA as a conservation tool. in Encyclopedia of Caves 387–393 (Elsevier, 2019).

53.
Porritt, K. & Walker, K. Christmas Island GIS 2013 release. (2015).

54.
ESRI. ArcGIS Desktop: Release 10. Environmental Systems Research Institute, Redlands, CA (2011).

55.
Bruce, A. J. & Davie, P. J. F. A new anchialine shrimp of the genus Procaris from Christmas Island: the first occurrence of the Procarididae in the Indian Ocean (Crustacea: Decapoda: Caridea). Zootaxa 1238, 23–33 (2006).
Article  Google Scholar 

56.
Namiotko, T., Wouters, K., Danielopol, D. L. & Humphreys, W. F. On the origin and evolution of a new anchialine stygobitic Microceratina species (Crustacea, Ostracoda) from Christmas Island (Indian Ocean). J. Micropalaeontol. 23, 49–59 (2004).
Article  Google Scholar 

57.
Duy-Jacquemin, M. N. Two new species of Lophoturus (Diplopoda, Penicillata, Lophoproctidae) from caves in Christmas Island, Australia, including the second troglomorph in Penicillata. Zoosystema 36, 29–40 (2014).
Article  Google Scholar 

58.
Huynh, C. & Veenstra, A. A. Two new Lophoturus species (Diplopoda, Polyxenida, Lophoproctidae) from Queensland, Australia. Zookeys 133 (2018).

59.
Karanovic, T. & Pesce, G. Copepods from ground waters of Western Australia, VII. Nitokra humphreysi sp. nov. (Crustacea: Copepoda: Harpacticoida). Hydrobiologia 470, 5–12 (2002).

60.
Karanovic, T. Subterranean Copepoda from arid Western Australia. Crustaceana Monographs 3, i–vi (2004).
Google Scholar  More