Ecology

1.
Tscharntke, T. et al. Landscape moderation of biodiversity patterns and processes: eight hypotheses. Biol. Rev. 87, 661–685 (2012).
PubMed  Article  Google Scholar 
2.
Fahrig, L. et al. Functional landscape heterogeneity and animal biodiversity in agricultural landscapes: Heterogeneity and biodiversity. Ecol. Lett. 14, 101–112 (2011).
PubMed  Article  Google Scholar 

3.
Rundlöf, M., Nilsson, H. & Smith, H. G. Interacting effects of farming practice and landscape context on bumble bees. Biol. Conserv. 141, 417–426 (2008).
Article  Google Scholar 

4.
Wamser, S., Diekötter, T., Boldt, L., Wolters, V. & Dauber, J. Trait-specific effects of habitat isolation on carabid species richness and community composition in managed grasslands: Effects of habitat isolation on carabid beetles. Insect Conser. Divers. 5, 9–18 (2012).
Article  Google Scholar 

5.
Sonnier, G., Jamoneau, A. & Decocq, G. Evidence for a direct negative effect of habitat fragmentation on forest herb functional diversity. Landsc. Ecol. 29, 857–866 (2014).
Article  Google Scholar 

6.
Wilcove, D. S. & McLellan, C. H. Habitat fragmentation in the temperate zone. Conserv. Biol. 1, 237–256 (1986).
Google Scholar 

7.
Wilcox, B. A. & Murphy, D. D. Conservation strategy: The effects of fragmentation on extinction. Am. Nat. 125, 879–887 (1985).
Article  Google Scholar 

8.
Leibold, M. A. et al. The metacommunity concept: A framework for multi-scale community ecology: The metacommunity concept. Ecol. Lett. 7, 601–613 (2004).
Article  Google Scholar 

9.
Fahrig, L. Ecological responses to habitat fragmentation per se. Annu. Rev. Ecol. Evol. Syst. 48, 1–23 (2017).
Article  Google Scholar 

10.
Fletcher, R. J. et al. Is habitat fragmentation good for biodiversity?. Biol. Conserv. 226, 9–15 (2018).
Article  Google Scholar 

11.
Fahrig, L. et al. Is habitat fragmentation bad for biodiversity?. Biol. Conserv. 230, 179–186 (2019).
Article  Google Scholar 

12.
Gámez-Virués, S. et al. Landscape simplification filters species traits and drives biotic homogenization. Nat. Commun. 6, 8568 (2015).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

13.
Perović, D. et al. Configurational landscape heterogeneity shapes functional community composition of grassland butterflies. J. Appl. Ecol. 52, 505–513 (2015).
Article  Google Scholar 

14.
Concepción, E. D. et al. Contrasting trait assembly patterns in plant and bird communities along environmental and human-induced land-use gradients. Ecography 40, 753–763 (2017).
Article  Google Scholar 

15.
Rocha-Santos, L. et al. The loss of functional diversity: A detrimental influence of landscape-scale deforestation on tree reproductive traits. J. Ecol. 108, 212–223 (2019).
Article  Google Scholar 

16.
Provost, G. L. et al. Land-use history impacts functional diversity across multiple trophic groups. PNAS 117, 1573–1579 (2020).
PubMed  Article  CAS  Google Scholar 

17.
Solé-Senan, X. O., Juárez-Escario, A., Robleño, I., Conesa, J. A. & Recasens, J. Using the response-effect trait framework to disentangle the effects of agricultural intensification on the provision of ecosystem services by Mediterranean arable plants. Agric. Ecosyst. Environ. 247, 255–264 (2017).
Article  Google Scholar 

18.
Grime, J. P. Trait convergence and trait divergence in herbaceous plant communities: mechanisms and consequences. J. Veg. Sci. 17, 255–260 (2006).
Article  Google Scholar 

19.
Macarthur, R. & Levins, R. The limiting similarity, convergence, and divergence of coexisting species. Am. Nat. 101, 377–385 (1967).
Article  Google Scholar 

20.
de Bello, F. et al. Evidence for scale- and disturbance-dependent trait assembly patterns in dry semi-natural grasslands. J. Ecol. 101, 1237–1244 (2013).
Article  Google Scholar 

21.
Muscarella, R. & Uriarte, M. Do community-weighted mean functional traits reflect optimal strategies?. Proc. R. Soc. B 283, 20152434 (2016).
PubMed  Article  Google Scholar 

22.
de Bello, F. et al. Partitioning of functional diversity reveals the scale and extent of trait convergence and divergence. J. Veg. Sci. 20, 475–486 (2009).
Article  Google Scholar 

23.
Mouchet, M. A., Villéger, S., Mason, N. W. H. & Mouillot, D. Functional diversity measures: an overview of their redundancy and their ability to discriminate community assembly rules: Functional diversity measures. Funct. Ecol. 24, 867–876 (2010).
Article  Google Scholar 

24.
Shmida, A. & Wilson, M. V. Biological determinants of species diversity. J. Biogeogr. 12, 1–20 (1985).
Article  Google Scholar 

25.
Baudry, J. & Papy, F. The role of landscape heterogeneity in the sustainability of cropping systems. In Crop Science: Progress and Prospects (eds Baudry, J. & Papy, F.) 243–249 (CABI Publishing, Oxfordshire, 2001).
Google Scholar 

26.
Duflot, R., Georges, R., Ernoult, A., Aviron, S. & Burel, F. Landscape heterogeneity as an ecological filter of species traits. Acta Oecol. 56, 19–26 (2014).
ADS  Article  Google Scholar 

27.
Cleland, E., Chuine, I., Menzel, A., Mooney, H. & Schwartz, M. Shifting plant phenology in response to global change. Trends Ecol. Evol. 22, 357–365 (2007).
PubMed  Article  PubMed Central  Google Scholar 

28.
Hendrickx, F. et al. Pervasive effects of dispersal limitation on within- and among-community species richness in agricultural landscapes. Glob. Ecol. Biogeogr. 18, 607–616 (2009).
Article  Google Scholar 

29.
Dunning, J. B., Danielson, B. J. & Pulliam, H. R. Ecological processes that affect populations in complex landscapes. Oikos 65, 169 (1992).
Article  Google Scholar 

30.
Jonason, D. et al. Weak functional response to agricultural landscape homogenisation among plants, butterflies and birds. Ecography 40, 1221–1230 (2017).
Article  Google Scholar 

31.
Kuussaari, M. et al. Extinction debt: a challenge for biodiversity conservation. Trends Ecol. Evol. 24, 564–571 (2009).
PubMed  Article  Google Scholar 

32.
Diamond, J. M. Biogeographic kinetics: estimation of relaxation times for avifaunas of Southwest Pacific Islands. Proc. Natl. Acad. Sci. 69, 3199–3203 (1972).
ADS  CAS  PubMed  Article  Google Scholar 

33.
Hanski, I. & Ovaskainen, O. Extinction debt at extinction threshold. Conserv. Biol. 16, 666–673 (2002).
Article  Google Scholar 

34.
Helm, A., Hanski, I. & Partel, M. Slow response of plant species richness to habitat loss and fragmentation. Ecol. Lett. 9, 72–77 (2005).
Google Scholar 

35.
Sang, A., Teder, T., Helm, A. & Pärtel, M. Indirect evidence for an extinction debt of grassland butterflies half century after habitat loss. Biol. Conserv. 143, 1405–1413 (2010).
Article  Google Scholar 

36.
Lindborg, R. Evaluating the distribution of plant life-history traits in relation to current and historical landscape configurations. J. Ecol. 95, 555–564 (2007).
Article  Google Scholar 

37.
Saar, L., de Bello, F., Pärtel, M. & Helm, A. Trait assembly in grasslands depends on habitat history and spatial scale. Oecologia 184, 1–12 (2017).
ADS  PubMed  Article  Google Scholar 

38.
Yamanaka, S., Akasaka, T., Yamaura, Y., Kaneko, M. & Nakamura, F. Time-lagged responses of indicator taxa to temporal landscape changes in agricultural landscapes. Ecol. Ind. 48, 593–598 (2015).
Article  Google Scholar 

39.
Piqueray, J. et al. Plant species extinction debt in a temperate biodiversity hotspot: Community, species and functional traits approaches. Biol. Conserv. 144, 1619–1629 (2011).
Article  Google Scholar 

40.
Barbaro, L. & van Halder, I. Linking bird, carabid beetle and butterfly life-history traits to habitat fragmentation in mosaic landscapes. Ecography 32, 321–333 (2009).
Article  Google Scholar 

41.
Grime, J. P. Benefits of plant diversity to ecosystems: Immediate, filter and founder effects. J. Ecol. 86, 902–910 (1998).
Article  Google Scholar 

42.
Lortie, C. J. et al. Rethinking plant community theory. Oikos 107, 433–438 (2004).
Article  Google Scholar 

43.
Turnbull, L. A., Rees, M. & Crawley, M. J. Seed mass and the competition/colonization trade-off: A sowing experiment. J. Ecol. 87, 899–912 (1999).
Article  Google Scholar 

44.
van Kleunen, M., Fischer, M. & Schmid, B. Effects of intraspecific competition on size variation and reproductive allocation in a clonal plant. Oikos 94, 515–524 (2001).
Article  Google Scholar 

45.
Zambrano, J. et al. The effects of habitat loss and fragmentation on plant functional traits and functional diversity: What do we know so far?. Oecologia 191, 505–518 (2019).
ADS  PubMed  Article  Google Scholar 

46.
Atauri, J. A. & de Lucio, J. V. The role of landscape structure in species richness distribution of birds, amphibians, reptiles and lepidopterans in Mediterranean landscapes. Landsc. Ecol. 16, 147–159 (2001).
Article  Google Scholar 

47.
Weibull, A.-C., Östman, Ö. & Granqvist, Å. Species richness in agroecosystems: the effect of landscape, habitat and farm management. Biodivers. Conserv. 12, 1335–1355 (2003).
Article  Google Scholar 

48.
Smith, H. G., Dänhardt, J., Lindström, Å. & Rundlöf, M. Consequences of organic farming and landscape heterogeneity for species richness and abundance of farmland birds. Oecologia 162, 1071–1079 (2010).
ADS  PubMed  Article  Google Scholar 

49.
Sirami, C. et al. Increasing crop heterogeneity enhances multitrophic diversity across agricultural regions. PNAS 116, 16442–16447 (2019).
CAS  PubMed  Article  Google Scholar 

50.
Redon, M., Bergès, L., Cordonnier, T. & Luque, S. Effects of increasing landscape heterogeneity on local plant species richness: How much is enough?. Landsc. Ecol. 29, 773–787 (2014).
Article  Google Scholar 

51.
Fahrig, L. Rethinking patch size and isolation effects: the habitat amount hypothesis. J. Biogeogr. 40, 1649–1663 (2013).
Article  Google Scholar 

52.
MacDonald, Z. G., Anderson, I. D., Acorn, J. H. & Nielsen, S. E. The theory of island biogeography, the sample-area effect, and the habitat diversity hypothesis: Complementarity in a naturally fragmented landscape of lake islands. J. Biogeogr. 45, 2730–2743 (2018).
Article  Google Scholar 

53.
Smart, S. M., Bunce, R. G. H., Firbank, L. G. & Coward, P. Do field boundaries act as refugia for grassland plant species diversity in intensively managed agricultural landscapes in Britain?. Agric. Ecosyst. Environ. 91, 73–87 (2002).
Article  Google Scholar 

54.
Klimesova, J., Latzel, V., de Bello, F. & van Groenendael, J. M. Plant functional traits in studies of vegetation changes in response to grazing and mowing: Towards a use of more specific traits. Preslia 80, 245–253 (2008).
Google Scholar 

55.
Fuller, R. J., Chamberlain, D. E., Burton, N. H. K. & Gough, S. J. Distributions of birds in lowland agricultural landscapes of England and Wales: How distinctive are bird communities of hedgerows and woodland?. Agric. Ecosyst. Environ. 84, 79–92 (2001).
Article  Google Scholar 

56.
Hinsley, S. A. & Bellamy, P. E. The influence of hedge structure, management and landscape context on the value of hedgerows to birds: A review. J. Environ. Manage. 60, 33–49 (2000).
Article  Google Scholar 

57.
Noh, J., Echeverría, C., Pauchard, A. & Cuenca, P. Extinction debt in a biodiversity hotspot: the case of the Chilean Winter Rainfall-Valdivian Forests. Landsc. Ecol. Eng. 15, 1–12 (2019).
Article  Google Scholar 

58.
Saar, L., Takkis, K., Pärtel, M. & Helm, A. Which plant traits predict species loss in calcareous grasslands with extinction debt? Traits predicting extinctions in grasslands. Divers. Distrib. 18, 808–817 (2012).
Article  Google Scholar 

59.
Figueiredo, L., Krauss, J., Steffan-Dewenter, I. & Cabral, J. S. Understanding extinction debts: Spatio–temporal scales, mechanisms and a roadmap for future research. Ecography 42, 1973–1990 (2019).
Article  Google Scholar 

60.
Krauss, J. et al. Habitat fragmentation causes immediate and time-delayed biodiversity loss at different trophic levels: Immediate and time-delayed biodiversity loss. Ecol. Lett. 13, 597–605 (2010).
PubMed  PubMed Central  Article  Google Scholar 

61.
With, K. A. How fast do migratory songbirds have to adapt to keep pace with rapidly changing landscapes?. Landsc. Ecol 30, 1351–1361 (2015).
Article  Google Scholar 

62.
Andrén, H. Effects of habitat fragmentation on birds and mammals in landscapes with different proportions of suitable habitat: A review. Oikos 71, 355–366 (1994).
Article  Google Scholar 

63.
Kavelaars, M. M. et al. Breeding habitat loss reveals limited foraging flexibility and increases foraging effort in a colonial breeding seabird. Mov. Ecol. 8, 45 (2020).
PubMed  PubMed Central  Article  Google Scholar 

64.
van Zanten, B. T. et al. European agricultural landscapes, common agricultural policy and ecosystem services: A review. Agron. Sustain. Dev. 34, 309–325 (2014).
Article  Google Scholar 

65.
Ramalho, C. E., Laliberté, E., Poot, P. & Hobbs, R. Effects of fragmentation on the plant functional composition and diversity of remnant woodlands in a young and rapidly expanding city. J. Veg. Sci. 29, 285–296 (2018).
Article  Google Scholar 

66.
Jackson, S. T. & Sax, D. F. Balancing biodiversity in a changing environment: Extinction debt, immigration credit and species turnover. Trends Ecol. Evol. 25, 153–160 (2010).
PubMed  Article  PubMed Central  Google Scholar 

67.
Renner, S. S. & Zohner, C. M. Climate change and phenological mismatch in trophic interactions among plants, insects, and vertebrates. Annu. Rev. Ecol. Evol. Syst. 49, 165–182 (2018).
Article  Google Scholar 

68.
Damien, M. & Tougeron, K. Prey–predator phenological mismatch under climate change. Curr. Opin. Insect Sci. 35, 60–68 (2019).
PubMed  Article  PubMed Central  Google Scholar 

69.
Lalechère, E., Archaux, F. & Jabot, F. Relative importance of landscape and species characteristics on extinction debt, immigration credit and relaxation time after habitat turnover. Popul. Ecol. 61, 383–395 (2019).
Article  Google Scholar 

70.
Ernoult, A. et al. Potential landscape drivers of biodiversity components in a flood plain: Past or present patterns?. Biol. Conserv. 127, 1–17 (2006).
Article  Google Scholar 

71.
Meeus, J. H. A., Wijermans, M. P. & Vroom, M. J. Agricultural landscapes in Europe and their transformation. Landsc. Urban Plan. 18, 289–352 (1990).
Article  Google Scholar 

72.
McGarigal, K., Cushman, S. & Ene, E. FRAGSTATS v4: Spatial Pattern Analysis Program for Categorical and Continuous Maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. http://www.umass.edu/landeco/research/fragstats/fragstats.html. (2012).

73.
Duflot, R., Aviron, S., Ernoult, A., Fahrig, L. & Burel, F. Reconsidering the role of ‘semi-natural habitat’ in agricultural landscape biodiversity: A case study. Ecol. Res. 30, 75–83 (2015).
Article  Google Scholar 

74.
Bibby, C. J., Burgess, N. D., Hill, D. A. & Mustoe, S. Bird Census Techniques (Elsevier, Amsterdam, 2000).
Google Scholar 

75.
Kühn, I., Durka, W. & Klotz, S. BiolFlor: A new plant-trait database as a tool for plant invasion ecology: BiolFlor: A plant-trait database. Divers. Distrib. 10, 363–365 (2004).
Article  Google Scholar 

76.
Kleyer, M. et al. The LEDA Traitbase: a database of life-history traits of the Northwest European flora. J. Ecol. 96, 1266–1274 (2008).
Article  Google Scholar 

77.
Duquet, M. Tout sur les Oiseaux d’Europe (Delachaux, Colombes, 2015).
Google Scholar 

78.
Dormann, C. F. et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46 (2013).
Article  Google Scholar 

79.
Garnier, E. et al. Plant functional markers capture ecosystem properties during secondary succession. Ecology 85, 2630–2637 (2004).
Article  Google Scholar 

80.
Sonnier, G., Shipley, B. & Navas, M.-L. Quantifying relationships between traits and explicitly measured gradients of stress and disturbance in early successional plant communities. J. Veg. Sci. 21, 1014–1024 (2010).
Article  Google Scholar 

81.
R Core Team. R: A Language and Environment for Statistical Computing (R Core Team, Vienna, 2020).
Google Scholar 

82.
Blomberg, S. P., Garland, T. & Ives, A. R. Testing for phylogenetic signal in comparative data: Behavioral traits are more labile. Evolution 57, 717–745 (2003).
PubMed  Article  Google Scholar 

83.
Blomberg, S. P. & Garland, T. Tempo and mode in evolution: Phylogenetic inertia, adaptation and comparative methods: Phylogenetic inertia. J. Evol. Biol. 15, 899–910 (2002).
Article  Google Scholar 

84.
Zanne, A. E. et al. Three keys to the radiation of angiosperms into freezing environments. Nature 506, 89–92 (2014).
ADS  CAS  PubMed  Article  Google Scholar 

85.
Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).
ADS  CAS  PubMed  Article  Google Scholar 

86.
Revell, L. J. phytools: An R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).
Article  Google Scholar 

87.
de Bello, F. et al. On the need for phylogenetic ‘corrections’ in functional trait-based approaches. Folia Geobot. 50, 349–357 (2015).
Article  Google Scholar 

88.
Bernard-Verdier, M. et al. Community assembly along a soil depth gradient: Contrasting patterns of plant trait convergence and divergence in a Mediterranean rangeland. J. Ecol. 100, 1422–1433 (2012).
Article  Google Scholar 

89.
Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (Springer, New York, 2002).
Google Scholar 

90.
Fox, J. & Weisberg, S. An R Companion to Applied Regression (Sage, Thousand Oaks, 2019).
Google Scholar 

91.
Fahrig, L. Effects of Habitat Fragmentation on Biodiversity. Annu. Rev. Ecol. Evol. Syst. 34, 487–515 (2003).
Article  Google Scholar  More

1.
Pugh, T. A. et al. Role of forest regrowth in global carbon sink dynamics. Proc. Natl Acad. Sci. USA 116, 4382–4387 (2019).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 
2.
Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

3.
Hubau, W. et al. Asynchronous carbon sink saturation in African and Amazonian tropical forests. Nature 579, 80–87 (2020).

4.
Domke, G. M., Oswalt, S. N., Walters, B. F. & Morin, R. S. Tree planting has the potential to increase carbon sequestration capacity of forests in the United States. Proc. Natl Acad. Sci. USA 117, 24649–24651 (2020).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

5.
Baccini, A. et al. Tropical forests are a net carbon source based on aboveground measurements of gain and loss. Science 358, 230–234 (2017).
ADS  MathSciNet  CAS  PubMed  MATH  Article  PubMed Central  Google Scholar 

6.
Luyssaert, S. et al. CO2 balance of boreal, temperate, and tropical forests derived from a global database. Glob. Change Biol. 13, 2509–2537 (2007).
ADS  Article  Google Scholar 

7.
Harmon, M. E. et al. Ecology of coarse woody debris in temperate ecosystems. Adv. Ecol. Res. 15, 133–302 (1986).
Article  Google Scholar 

8.
Weedon, J. T. et al. Global meta‐analysis of wood decomposition rates: a role for trait variation among tree species? Ecol. Lett. 12, 45–56 (2009).
PubMed  Article  PubMed Central  Google Scholar 

9.
McGee, G. G. The contribution of beech bark disease-induced mortality to coarse woody debris loads in northern hardwood stands of Adirondack Park, New York, USA. Can. J. Res. 30, 1453–1462 (2000).
Article  Google Scholar 

10.
Woodall, C. W. et al. Net carbon flux of dead wood in forests of the Eastern US. Oecologia 177, 861–874 (2015).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

11.
Campbell, J. L. et al. Estimating uncertainty in the volume and carbon storage of downed coarse woody debris. Ecol. Appl. 29, e01844 (2019).
PubMed  PubMed Central  Article  Google Scholar 

12.
Russell, M. B. et al. Quantifying carbon stores and decomposition in dead wood: a review. Ecol. Manag. 350, 107–128 (2015).
Article  Google Scholar 

13.
Campbell, J., Alberti, G., Martin, J. & Law, B. E. Carbon dynamics of a ponderosa pine plantation following a thinning treatment in the northern Sierra Nevada. Ecol. Manag. 257, 453–463 (2009).
Article  Google Scholar 

14.
Chambers, J. Q. et al. Response of tree biomass and wood litter to disturbance in a Central Amazon forest. Oecologia 141, 596–611 (2004).
ADS  PubMed  Article  PubMed Central  Google Scholar 

15.
Domke, G. M., Woodall, C. W. & Smith, J. E. Accounting for density reduction and structural loss in standing dead trees: implications for forest biomass and carbon stock estimates in the United States. Carbon Balance Manag. 6, 14 (2011).
PubMed  PubMed Central  Article  Google Scholar 

16.
Janisch, J. E. & Harmon, M. E. Successional changes in live and dead wood carbon stores: implications for net ecosystem productivity. Tree Physiol. 22, 77–89 (2002).
CAS  PubMed  Article  PubMed Central  Google Scholar 

17.
Keith, H., Mackey, B. G. & Lindenmayer, D. B. Re-evaluation of forest biomass carbon stocks and lessons from the world’s most carbon-dense forests. Proc. Natl Acad. Sci. USA 106, 11635–11640 (2009).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

18.
Martin, A. R., Doraisami, M. & Thomas, S. C. Global patterns in wood carbon concentration across the world’s trees and forests. Nat. Geosci. 11, 915–922 (2018).
ADS  CAS  Article  Google Scholar 

19.
Thomas, S. C. & Martin, A. R. Carbon content of tree tissues: a synthesis. Forests 3, 332–352 (2012).
Article  Google Scholar 

20.
Martin, A. R. & Thomas, S. C. A reassessment of carbon content in tropical trees. PLoS ONE 6, e23533 (2011).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

21.
Weggler, K., Dobbertin, M., Jüngling, E., Kaufmann, E. & Thürig, E. Dead wood volume to dead wood carbon: the issue of conversion factors. Eur. J. Res. 131, 1423–1438 (2012).
Article  Google Scholar 

22.
Gorgolewski, A., Rudz, P., Jones, T., Basiliko, N. & Caspersen, J. Assessing coarse woody debris nutrient dynamics in managed northern hardwood forests using a matrix transition model. Ecosystems 23, 541–554 (2019).
Article  CAS  Google Scholar 

23.
Moreira, A. B., Gregoire, T. G. & do Couto, H. T. Z. Wood density and carbon concentration of coarse woody debris in native forests. Braz. Ecosyst. 6, 18 (2019).
Article  Google Scholar 

24.
Sandström, F., Petersson, H., Kruys, N. & Ståhl, G. Biomass conversion factors (density and carbon concentration) by decay classes for dead wood of Pinus sylvestris, Picea abies and Betula spp. in boreal forests of Sweden. Ecol. Manag. 243, 19–27 (2007).
Article  Google Scholar 

25.
Cousins, S. J., Battles, J. J., Sanders, J. E. & York, R. A. Decay patterns and carbon density of standing dead trees in California mixed conifer forests. Ecol. Manag. 353, 136–147 (2015).
Article  Google Scholar 

26.
Harmon, M. E., Fasth, B., Woodall, C. W. & Sexton, J. Carbon concentration of standing and downed woody detritus: effects of tree taxa, decay class, position, and tissue type. For. Ecol. Manag. 291, 259–267 (2013).

27.
Köster, K., Metslaid, M., Engelhart, J. & Köster, E. Dead wood basic density, and the concentration of carbon and nitrogen for main tree species in managed hemiboreal forests. Ecol. Manag. 354, 35–42 (2015).
Article  Google Scholar 

28.
Clark, D. B., Clark, D. A., Brown, S., Oberbauer, S. F. & Veldkamp, E. Stocks and flows of coarse woody debris across a tropical rain forest nutrient and topography gradient. Ecol. Manag. 164, 237–248 (2002).
Article  Google Scholar 

29.
Yang, F. F. et al. Dynamics of coarse woody debris and decomposition rates in an old-growth forest in lower tropical China. Ecol. Manag. 259, 1666–1672 (2010).
Article  Google Scholar 

30.
Chao, K. J. et al. Carbon concentration declines with decay class in tropical forest woody debris. Ecol. Manag. 391, 75–85 (2017).
Article  Google Scholar 

31.
Guo, J., Chen, G., Xie, J., Yang, Z. & Yang, Y. Patterns of mass, carbon and nitrogen in coarse woody debris in five natural forests in southern China. Ann. Sci. 71, 585–594 (2014).
Article  Google Scholar 

32.
Martin, A. R., Gezahegn, S. & Thomas, S. C. Variation in carbon and nitrogen concentration among major woody tissue types in temperate trees. Can. J. Res. 45, 744–757 (2015).
CAS  Article  Google Scholar 

33.
Gao, B., Taylor, A. R., Chen, H. Y. & Wang, J. Variation in total and volatile carbon concentration among the major tree species of the boreal forest. Ecol. Manag. 375, 191–199 (2016).
Article  Google Scholar 

34.
Dossa, G. G. et al. The cover uncovered: bark control over wood decomposition. J. Ecol. 106, 2147–2160 (2018).
Article  Google Scholar 

35.
Jones, D. A. & O’Hara, K. L. Variation in carbon fraction, density, and carbon density in conifer tree tissues. Forests 9, 430 (2018).
Article  Google Scholar 

36.
Fukasawa, Y. The geographical gradient of pine log decomposition in Japan. For. Ecol. Manag. 349, 29–35 (2015).

37.
IPCC. in 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Vol. 4: Agriculture, Forestry and Other Land Use (eds Blain, D., Agus, F., Alfaro, M. A. & Vreuls, H.) 68 (IPCC, 2019).

38.
Jones, D. A. & O’Hara, K. L. The influence of preparation method on measured carbon fractions in tree tissues. Tree Physiol. 36, 1177–1189 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

39.
Beech, E., Rivers, M., Oldfield, S. & Smith, P. GlobalTreeSearch: the first complete global database of tree species and country distributions. J. Sustain. 36, 454–489 (2017).
Article  Google Scholar 

40.
Lamlom, S. H. & Savidge, R. A. A reassessment of carbon content in wood: variation within and between 41 North American species. Biomass Bioenergy 25, 381–388 (2003).
CAS  Article  Google Scholar 

41.
Thomas, S. C. & Malczewski, G. Wood carbon content of tree species in Eastern China: interspecific variability and the importance of the volatile fraction. J. Environ. Manag. 85, 659–662 (2007).
CAS  Article  Google Scholar 

42.
Hafner, S. D., Groffman, P. M. & Mitchell, M. J. Leaching of dissolved organic carbon, dissolved organic nitrogen, and other solutes from coarse woody debris and litter in a mixed forest in New York State. Biogeochemistry 74, 257–282 (2005).
CAS  Article  Google Scholar 

43.
Hillis, W. Chemical aspects of heartwood formation. Wood Sci. Technol. 2, 241–259 (1968).
CAS  Article  Google Scholar 

44.
Meerts, P. Mineral nutrient concentrations in sapwood and heartwood: a literature review. Ann. Sci. 59, 713–722 (2002).
Article  Google Scholar 

45.
Bert, D. & Danjon, F. Carbon concentration variations in the roots, stem and crown of mature Pinus pinaster (Ait.). Ecol. Manag. 222, 279–295 (2006).
Article  Google Scholar 

46.
Jones, D. A. & O’Hara, K. L. Carbon density in managed coast redwood stands: implications for forest carbon estimation. Forestry 85, 99–110 (2012).
Article  Google Scholar 

47.
Ma, S. et al. Variations and determinants of carbon content in plants: a global synthesis. Biogeosciences 15, 693 (2018).
ADS  CAS  Article  Google Scholar 

48.
Cornelissen, J. H. C. et al. Leaf digestibility and litter decomposability are related in a wide range of subarctic plant species and types. Funct. Ecol. 18, 779–786 (2004).
Article  Google Scholar 

49.
Ganjegunte, G. K., Condron, L. M., Clinton, P. W., Davis, M. R. & Mahieu, N. Decomposition and nutrient release from radiata pine (Pinus radiata) coarse woody debris. Ecol. Manag. 187, 197–211 (2004).
Article  Google Scholar 

50.
Pettersen, R. C. in The Chemistry of Solid Wood (ed. Rowell, R.) 57–126 (American Chemical Society, 1984).

51.
Berg, B., Ekbohm, G. & McClaugherty, C. Lignin and holocellulose relations during long-term decomposition of some forest litters. Long-term decomposition in a Scots pine forest. IV. Can. J. Bot. 62, 2540–2550 (1984).
CAS  Article  Google Scholar 

52.
Schowalter, T. D., Zhang, Y. L. & Sabin, T. E. Decomposition and nutrient dynamics of oak Quercus spp. logs after five years of decomposition. Ecography 21, 3–10 (1998).
Article  Google Scholar 

53.
Buxton, R. D. Termites and the turnover of dead wood in an arid tropical environment. Oecologia 51, 379–384 (1981).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

54.
Riley, R. et al. Extensive sampling of basidiomycete genomes demonstrates inadequacy of the white-rot/brown-rot paradigm for wood decay fungi. Proc. Natl Acad. Sci. USA 111, 9923–9928 (2014).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

55.
Moore, T. R., Trofymow, J. A., Prescott, C. E., Titus, B. D. & Group, C. W. Can short-term litter-bag measurements predict long-term decomposition in northern forests? Plant Soil 416, 419–426 (2017).
CAS  Article  Google Scholar 

56.
vandenEnden, L., Frey, S. D., Nadelhoffer, K. J., LeMoine, J. M., Lajtha, K. & Simpson, M. J. Molecular-level changes in soil organic matter composition after 10 years of litter, root and nitrogen manipulation in a temperate forest. Biogeochemistry 141, 183–197 (2018).
CAS  Article  Google Scholar 

57.
Warner, D. L., Villarreal, S., McWilliams, K., Inamdar, S. & Vargas, R. Carbon dioxide and methane fluxes from tree stems, coarse woody debris, and soils in an upland temperate forest. Ecosystems 20, 1205–1216 (2017).
CAS  Article  Google Scholar 

58.
Van Mantgem, P. J. et al. Widespread increase of tree mortality rates in the western United States. Science 323, 521–524 (2009).
ADS  PubMed  Article  CAS  PubMed Central  Google Scholar 

59.
Brando, P. M. et al. Abrupt increases in Amazonian tree mortality due to drought–fire interactions. Proc. Natl Acad. Sci. USA 111, 6347–6352 (2014).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

60.
Brad, B. et al. The taxonomic name resolution service: an online tool for automated standardization of plant names. BMC Bioinform. 14, 16 (2013).
Article  Google Scholar 

61.
Krankina, O. N. & Harmon, M. E. Dynamics of the dead wood carbon pool in northwestern Russian boreal forests. Water Air Soil Pollut. 82, 227–238 (1995).
ADS  CAS  Article  Google Scholar 

62.
Kattge, J. et al. TRY plant trait database—enhanced coverage and open access. Glob. Change Biol. 26, 119–188 (2020).
ADS  Article  Google Scholar 

63.
Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Article  Google Scholar 

64.
Lenth, R. V. Least-squares means: the R Package lsmeans. J. Stat. Softw. 69, 1–33 (2016).
Article  Google Scholar 

65.
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2009).

66.
Fox, J. & Weisberg, S. An R Companion to Applied Regression 2nd edn (Sage, 2011).

67.
Messier, J., McGill, B. J. & Lechowicz, M. J. How do traits vary across ecological scales? A case for trait-based ecology. Ecol. Lett. 13, 838–848 (2010).
PubMed  Article  PubMed Central  Google Scholar 

68.
Martin, A. R. et al. Intraspecific trait variation across multiple scales: the leaf economics spectrum in coffee. Funct. Ecol. 31, 604–612 (2017).
Article  Google Scholar 

69.
Pinheiro, J. et al. nlme: linear and nonlinear mixed effects models. R package version 3.1-131. https://CRAN.R-project.org/package=nlme (2017).

70.
Paradis, E., Claude, J. & Strimmer, K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004). More

Physiological responses
Small fragments from five source colonies from the two experimental sites (N- and O-sites) were used to conduct a reciprocal transplant experiment in Maunalua Bay, Hawaii (Fig. 1). The results revealed clear physiological response differences between the two populations. The transplantation resulted in a significant reduction in the average tissue layer thickness (TLT) in only one treatment: O-corals transplanted to N-site (O → N) (Tukey-HSD, P-adj  2 at FDR = 0.01. Proteins associated with key GO terms were colored in different colors, and the top 10 abundant proteins in each population are annotated. The bottom bars indicate the total numbers of significantly abundant proteins for each population.

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Response difference in transplant to the offshore site (N → O vs. O → O)
A total of 3236 distinct coral proteins were identified at O-site: 2217 (68.5%) were shared between the two populations, 656 unique to N → O corals, and 363 to O → O corals (Fig. S1C). GO analysis identified 35 enriched terms specific to N → O, which involved amino acid biosynthetic process, ATP metabolic process, TCA cycles, fatty acid oxidation, and monosaccharide metabolic process. There were 15 specific GO terms in O → O corals, including nucleotide monophosphate biosynthetic process, intracellular protein transport, vesicle organization, and GTP binding (SI.2B).
Quantitative analysis on protein abundances indicated a total of 665 proteins to be significantly differentially abundant at O-site: N → O corals had 155 abundant-proteins, and O → O corals had 510 abundant-proteins (Fig. 3B). GO analysis resulted in identifying 39 enriched terms from abundant proteins in O → O corals, while only one met the cutoff in N → O corals (SI.2B). Although the number of abundant-proteins and enriched terms identified in O → O corals were relatively high, the enriched terms predominantly consisted of cellular functions related to protein translation; organonitrogen biosynthetic process and organic acid metabolic process, both leading to single child terms for BP, CC, and MF (tRNA aminoacylation for protein translation, cytosolic large ribosomal subunit, and tRNA aminoacyl ligase activity). The enriched term in N → O corals was a non-specific term of ‘extracellular region’, indicating that despite the higher number of abundant-proteins, the main functional difference between N → O and O → O corals was an enhanced protein translation activity in O → O corals.
Response comparisons to cross transplantation
Effects of cross transplantation yielded a more diverse proteomic stress-response in O-corals as they moved nearshore than N-corals as they were moved offshore (Fig. S2). The total number of abundant-proteins between the sites was much higher for O-corals (440, O → N vs. O → O) than N-corals (135, N → N vs. N → O) (Table S1), and the number of unique GO terms identified between the sites was also higher in O-corals (69, SI.2C) than in N-corals (46, SI.2D). The number of overlapping proteins between the sites was lower in O-corals than in N-corals (70% vs. 79%), and log-fold changes of all identified proteins between the sites were significantly larger for O-corals than N-corals (Wilcoxon Rank-Sum test, P = 6.02 × 10–9), all emphasizing the larger metabolic reshuffling needed to respond to cross transplantation in O-corals. GO enrichment analysis indicated that N-corals responded to transplantation to O-site with increased abundance of proteins involved in amino acid biosynthesis, fatty acid beta oxidation, TCA cycle, chitin catabolism, coenzyme biosynthesis and translational initiation. O-corals responded to transplantation to N-site by increasing the abundance of proteins associated with detoxification, antioxidant activity, protein complex subunit organization, and multiple metabolic processes (amino acid, fatty acid, ATP, monosaccharide, and carbohydrate derivative) (SI.2E). The shared responses between the cross-transplanted corals (N → O and O → N corals) included increased proteins involved in fatty-acid beta oxidation, TCA cycle, carbohydrate derivative catabolic process, pyridoxal phosphate binding, and ‘oxidoreductase activity acting on the CH-CH group of donors with flavin as acceptor’, likely representing the effects of transplantation to a non-native environment.
Proteome patterns across the four treatments
Comparing enriched GO terms across all treatments (SI.2E) highlighted the unique state of O → N corals; O → N corals had a much higher number of uniquely enriched GO terms (n = 27) compared to those in the rests (4 in O → O, 5 in N → N, and 15 in N → O corals). The most notable difference among the treatments was enrichment of detoxification and antioxidant activity exclusively in O → N corals (Fig. 4). Also, lipid oxidation was highly enriched in O → N corals with four terms associated to this category identified (Fig. 4, SI.2E).
Figure 4

Enriched GO terms uniquely identified to specific treatment groups. Treatment groups are shown in the right column (e.g. N-coral = N-corals at both sites, N-site = N- and O-corals at N-site, CrossT = cross transplantation). The heat-map represents P-values for the associated GO terms. The GO terms are grouped by the parent–child terms with the most parent term in bold (for values, see SI-2E).

Full size image

Examining the relative abundance of individual proteins associated with detoxification (‘detox-proteins’) revealed the following interesting patterns. (1) Distinct sets of proteins were abundant in different treatments, rather than all detox-proteins to be elevated in one treatment, and the direction and magnitude of responses to transplantation were protein specific and varied between populations (Fig. S4A). (2) Two peroxiredoxin (Prx) proteins, Prx-1 (m.6147) and Prx-6 (m.9595), dominated the relative abundance of detox-proteins by having over an order of magnitude higher abundance values, and they were consistently more abundant in N-corals than O-corals (ave. 44%, Kruskal Test, P = 0.004–0.01) (Fig. S4B, SI.1B). (3) Some proteins with the same or similar annotations had contrasting responses between the populations. For example, Prx-4 (m.17739), which belongs to the same subfamily as Prx-1, was significantly more abundant in O-corals at both sites (Fig. S4B, SI.2F,G), while Prx-1 was more abundant in N-corals. Similarly, seven peroxidasin (PXDN) homologs were identified, of which m.17686 was significantly more abundant in O → N corals, while m.9432 was significantly more abundant in N → N corals (Fig. S4B, SI.2F), suggesting that the two populations potentially utilize different class/kind of enzymes as primary proteins in detoxification/antioxidant pathways. Of the seven PXDN homologs, two (m.1440, m.9432) were consistently higher in N-corals, two (m.10928, m.15200) were consistently higher in O-corals, and three (m.12572, m.17686, m.9657) increased abundance at N-site in both corals, but m.12572 and m.17686 being higher in O-corals, while m.9657 higher in N-corals (Fig. S3B).
To ascertain that the proteins with the same annotations are indeed different proteins, sequences of matched peptides were assessed for those that showed contrasting responses. The pairwise comparison of Prx-1 and Prx-4 showed only seven of the total 65 peptides (11%) were identical between the two, revealing that these protein sequences are significantly different and they each have unique peptides that be detected and quantified accurately (SI.1C1). Similarly the majority of PXDN-like proteins identified had no overlapping peptides between the contrasting pairs (0–19%, median = 0, SI.1C2), indicating that corals possess multiple types of PXDN, and N- and O-corals respond to stressors with different sets of PXDN.
In addition to lipid oxidation being significantly enriched in O → N corals, a single term (fatty acid beta-oxidation,) was also enriched in N → O corals, which suggests that cross-transplantation had an effect on lipid oxidation processes. However, the abundances of most proteins associated with lipid oxidation were higher in O-corals than N-corals at both sites (Fig. S4A). Statistically, three proteins (medium-chain sp acyl-CoA:m.22274, very-long-chain sp. acyl-CoA:m.17984, and trifunctional enzyme subunit alpha:m.6724) showed a difference in abundance between the two populations at N-site (Fig. S4C) and one (isovaleryl-CoA dehydrogenase:m.27714) at O-site, all of which were higher in O-corals than N-corals. More

1.
Connell JH. Diversity in Tropical Rain Forests and Coral Reefs. Science. 1978;199:1302–10.
CAS  PubMed  Article  PubMed Central  Google Scholar 
2.
Moreno-Mateos D, Barbier EB, Jones PC, Jones HP, Aronson J, López-López JA, et al. Anthropogenic ecosystem disturbance and the recovery debt. Nat Commun. 2017;8:14163.
CAS  PubMed  PubMed Central  Article  Google Scholar 

3.
Rodil IF, Lohrer AM, Chiaroni LD, Hewitt JE, Thrush SF. Disturbance of sandflats by thin terrigenous sediment deposits: consequences for primary production and nutrient cycling. Ecol Appl. 2011;21:416–26.
PubMed  Article  PubMed Central  Google Scholar 

4.
Carnell PE, Keough MJ. More severe disturbance regimes drive the shift of a kelp forest to a sea urchin barren in south-eastern Australia. Sci Rep. 2020;10:11272.
PubMed  PubMed Central  Article  CAS  Google Scholar 

5.
McDowell NG, Michaletz ST, Bennett KE, Solander KC, Xu C, Maxwell RM, et al. Predicting Chronic Climate-Driven Disturbances and Their Mitigation. Trends Ecol Evol. 2018;33:15–27.
PubMed  Article  PubMed Central  Google Scholar 

6.
Shade A, Peter H, Allison SD, Baho D, Berga M, Buergmann H, et al. Fundamentals of Microbial Community Resistance and Resilience. Front Microbiol. 2012;3:417.
PubMed  PubMed Central  Article  Google Scholar 

7.
Allison SD, Martiny JBH. Resistance, resilience, and redundancy in microbial communities. Proc Natl Acad Sci. 2008;105:11512–9.
CAS  PubMed  Article  PubMed Central  Google Scholar 

8.
Shade A, Read JS, Welkie DG, Kratz TK, Wu CH, McMahon KD. Resistance, resilience and recovery: aquatic bacterial dynamics after water column disturbance: Bacterial community recovery after lake mixing. Environ Microbiol. 2011;13:2752–67.
CAS  PubMed  Article  PubMed Central  Google Scholar 

9.
Shade A, Read JS, Youngblut ND, Fierer N, Knight R, Kratz TK, et al. Lake microbial communities are resilient after a whole-ecosystem disturbance. ISME J. 2012;6:2153–67.
CAS  PubMed  PubMed Central  Article  Google Scholar 

10.
Dethlefsen L, Relman DA. Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation. Proc Natl Acad Sci. 2011;108:4554–61.
CAS  PubMed  Article  PubMed Central  Google Scholar 

11.
Heinsen F-A, Knecht H, Neulinger SC, Schmitz RA, Knecht C, Kühbacher T, et al. Dynamic changes of the luminal and mucosa-associated gut microbiota during and after antibiotic therapy with paromomycin. Gut Microbes. 2015;6:243–54.
CAS  PubMed  PubMed Central  Article  Google Scholar 

12.
Fukuyama J, Rumker L, Sankaran K, Jeganathan P, Dethlefsen L, Relman DA, et al. Multidomain analyses of a longitudinal human microbiome intestinal cleanout perturbation experiment. PLOS Comput Biol. 2017;13:e1005706.
PubMed  PubMed Central  Article  CAS  Google Scholar 

13.
Subramanian S, Huq S, Yatsunenko T, Haque R, Mahfuz M, Alam MA, et al. Persistent gut microbiota immaturity in malnourished Bangladeshi children. Nature. 2014;510:417–21.
CAS  PubMed  PubMed Central  Article  Google Scholar 

14.
Antwis RE, Garcia G, Fidgett AL, Preziosi RF. Tagging Frogs with Passive Integrated Transponders Causes Disruption of the Cutaneous Bacterial Community and Proliferation of Opportunistic Fungi. Appl Environ Microbiol. 2014;80:4779–84.
PubMed  PubMed Central  Article  CAS  Google Scholar 

15.
Bates KA, Shelton JMG, Mercier VL, Hopkins KP, Harrison XA, Petrovan SO, et al. Captivity and Infection by the Fungal Pathogen Batrachochytrium salamandrivorans Perturb the Amphibian Skin Microbiome. Front Microbiol. 2019;10:1834.
PubMed  PubMed Central  Article  Google Scholar 

16.
Gimblet C, Meisel JS, Loesche MA, Cole SD, Horwinski J, Novais FO, et al. Cutaneous Leishmaniasis Induces a Transmissible Dysbiotic Skin Microbiota that Promotes Skin Inflammation. Cell Host Microbe. 2017;22:13–24.e4.
CAS  PubMed  PubMed Central  Article  Google Scholar 

17.
Jani AJ, Briggs CJ. The pathogen Batrachochytrium dendrobatidis disturbs the frog skin microbiome during a natural epidemic and experimental infection. Proc Natl Acad Sci. 2014;111:E5049–E5058.
CAS  PubMed  Article  PubMed Central  Google Scholar 

18.
Kong HH, Oh J, Deming C, Conlan S, Grice EA, Beatson MA, et al. Temporal shifts in the skin microbiome associated with disease flares and treatment in children with atopic dermatitis. Genome Res. 2012;22:850–9.
CAS  PubMed  PubMed Central  Article  Google Scholar 

19.
Longcore JE, Pessier AP, Nichols DK. Batrachochytrium Dendrobatidis gen. et sp. nov., a Chytrid Pathogenic to Amphibians. Mycologia. 1999;91:219–27.
Article  Google Scholar 

20.
Berger L, Speare R, Daszak P, Green DE, Cunningham AA, Goggin CL, et al. Chytridiomycosis causes amphibian mortality associated with population declines in the rain forests of Australia and Central America. Proc Natl Acad Sci. 1998;95:9031–6.
CAS  PubMed  Article  PubMed Central  Google Scholar 

21.
Crawford AJ, Lips KR, Bermingham E. Epidemic disease decimates amphibian abundance, species diversity, and evolutionary history in the highlands of central Panama. Proc Natl Acad Sci. 2010;107:13777–82.
CAS  PubMed  Article  PubMed Central  Google Scholar 

22.
Lips KR, Brem F, Brenes R, Reeve JD, Alford RA, Voyles J, et al. Emerging infectious disease and the loss of biodiversity in a Neotropical amphibian community. Proc Natl Acad Sci USA. 2006;103:3165–70.
CAS  PubMed  Article  PubMed Central  Google Scholar 

23.
Vredenburg VT, Knapp RA, Tunstall TS, Briggs CJ. Dynamics of an emerging disease drive large-scale amphibian population extinctions. Proc Natl Acad Sci. 2010;107:9689–94.
CAS  PubMed  Article  PubMed Central  Google Scholar 

24.
Bletz MC, Loudon AH, Becker MH, Bell SC, Woodhams DC, Minbiole KPC, et al. Mitigating amphibian chytridiomycosis with bioaugmentation: characteristics of effective probiotics and strategies for their selection and use. Ecol Lett. 2013;16:807–20.
PubMed  Article  PubMed Central  Google Scholar 

25.
Hardy BM, Pope KL, Piovia-Scott J, Brown RN, Foley JE. Itraconazole treatment reduces Batrachochytrium dendrobatidis prevalence and increases overwinter field survival in juvenile Cascades frogs. Dis Aquat Organ. 2015;112:243–50.
PubMed  Article  PubMed Central  Google Scholar 

26.
McMahon TA, Sears BF, Venesky MD, Bessler SM, Brown JM, Deutsch K, et al. Amphibians acquire resistance to live and dead fungus overcoming fungal immunosuppression. Nature. 2014;511:224–7.
CAS  PubMed  PubMed Central  Article  Google Scholar 

27.
Harris RN, Brucker RM, Walke JB, Becker MH, Schwantes CR, Flaherty DC, et al. Skin microbes on frogs prevent morbidity and mortality caused by a lethal skin fungus. ISME J. 2009;3:818–24.
CAS  PubMed  Article  PubMed Central  Google Scholar 

28.
Muletz CR, Myers JM, Domangue RJ, Herrick JB, Harris RN. Soil bioaugmentation with amphibian cutaneous bacteria protects amphibian hosts from infection by Batrachochytrium dendrobatidis. Biol Conserv. 2012;152:119–26.
Article  Google Scholar 

29.
Becker MH, Harris RN, Minbiole KPC, Schwantes CR, Rollins-Smith LA, Reinert LK, et al. Towards a Better Understanding of the Use of Probiotics for Preventing Chytridiomycosis in Panamanian Golden Frogs. Ecohealth. 2011;8:501–6.
PubMed  Article  PubMed Central  Google Scholar 

30.
Woodhams DC, Geiger CC, Reinert LK, Rollins-Smith LA, Lam B, Harris RN, et al. Treatment of amphibians infected with chytrid fungus: learning from failed trials with itraconazole, antimicrobial peptides, bacteria, and heat therapy. Dis Aquat Organ. 2012;98:11–25.
CAS  PubMed  Article  PubMed Central  Google Scholar 

31.
Belden LK, Hughey MC, Rebollar EA, Umile TP, Loftus SC, Burzynski EA, et al. Panamanian frog species host unique skin bacterial communities. Front Microbiol. 2015; 6:1171.

32.
Bletz MC, Goedbloed DJ, Sanchez E, Reinhardt T, Tebbe CC, Bhuju S, et al. Amphibian gut microbiota shifts differentially in community structure but converges on habitat-specific predicted functions. Nat Commun. 2016;7:13699.
CAS  PubMed  PubMed Central  Article  Google Scholar 

33.
Jani AJ, Briggs CJ. Host and Aquatic Environment Shape the Amphibian Skin Microbiome but Effects on Downstream Resistance to the Pathogen Batrachochytrium dendrobatidis Are Variable. Front Microbiol. 2018;9:487.
PubMed  PubMed Central  Article  Google Scholar 

34.
Kueneman JG, Parfrey LW, Woodhams DC, Archer HM, Knight R, McKenzie VJ. The amphibian skin-associated microbiome across species, space and life history stages. Mol Ecol. 2014;23:1238–50.
PubMed  PubMed Central  Article  Google Scholar 

35.
Kueneman JG, Bletz MC, McKenzie VJ, Becker CG, Joseph MB, Abarca JG, et al. Community richness of amphibian skin bacteria correlates with bioclimate at the global scale. Nat Ecol Evol. 2019;3:381–9.
PubMed  Article  PubMed Central  Google Scholar 

36.
Küng D, Bigler L, Davis LR, Gratwicke B, Griffith E, Woodhams DC. Stability of Microbiota Facilitated by Host Immune Regulation: Informing Probiotic Strategies to Manage Amphibian Disease. PLoS ONE. 2014;9:e87101.
PubMed  PubMed Central  Article  CAS  Google Scholar 

37.
McKenzie VJ, Bowers RM, Fierer N, Knight R, Lauber CL. Co-habiting amphibian species harbor unique skin bacterial communities in wild populations. ISME J. 2012;6:588–96.
CAS  Article  Google Scholar 

38.
Prest TL, Kimball AK, Kueneman JG, McKenzie VJ. Host-associated bacterial community succession during amphibian development. Mol Ecol. 2018;27:1992–2006.
CAS  PubMed  Article  PubMed Central  Google Scholar 

39.
Rebollar EA, Hughey MC, Medina D, Harris RN, Ibáñez R, Belden LK. Skin bacterial diversity of Panamanian frogs is associated with host susceptibility and presence of Batrachochytrium dendrobatidis. ISME J. 2016;10:1682–95.
CAS  PubMed  PubMed Central  Article  Google Scholar 

40.
Harrison XA, Price SJ, Hopkins K, Leung WTM, Sergeant C, Garner TWJ. Diversity-Stability Dynamics of the Amphibian Skin Microbiome and Susceptibility to a Lethal Viral Pathogen. Front Microbiol. 2019;10:2883.
PubMed  PubMed Central  Article  Google Scholar 

41.
Jani AJ, Knapp RA, Briggs CJ. Epidemic and endemic pathogen dynamics correspond to distinct host population microbiomes at a landscape scale. Proc R Soc B-Biol Sci. 2017;284:20170944.
Article  Google Scholar 

42.
Walke JB, Becker MH, Loftus SC, House LL, Teotonio TL, Minbiole KPC, et al. Community Structure and Function of Amphibian Skin Microbes: an Experiment with Bullfrogs Exposed to a Chytrid Fungus. PLOS ONE. 2015;10:e0139848.
PubMed  PubMed Central  Article  CAS  Google Scholar 

43.
Knutie SA, Wilkinson CL, Kohl KD, Rohr JR. Early-life disruption of amphibian microbiota decreases later-life resistance to parasites. Nat Commun. 2017;8:86.
PubMed  PubMed Central  Article  CAS  Google Scholar 

44.
Rachowicz LJ, Knapp RA, Morgan JA, Stice MJ, Vredenburg VT, Parker JM, et al. Emerging infectious disease as a proximate cause of amphibian mass mortality. Ecology. 2006;87:1671–83.
PubMed  Article  PubMed Central  Google Scholar 

45.
Jones MEB, Paddock D, Bender L, Allen JL, Schrenzel MD, Pessier AP. Treatment of chytridiomycosis with reduced-dose itraconazole. Dis Aquat Organ. 2012;99:243–9.
CAS  PubMed  Article  PubMed Central  Google Scholar 

46.
Brannelly LA. Reduced Itraconazole Concentration and Durations Are Successful in Treating Batrachochytrium dendrobatidis Infection in Amphibians. JOVE-J Vis Exp. 2014;85:e51166.
Google Scholar 

47.
Hyatt AD, Boyle DG, Olsen V, Boyle DB, Berger L, Obendorf D, et al. Diagnostic assays and sampling protocols for the detection of Batrachochytrium dendrobatidis. Dis Aquat Organ. 2007;73:175–92.
CAS  PubMed  Article  PubMed Central  Google Scholar 

48.
Boyle DG, Boyle DB, Olsen V, Morgan JAT, Hyatt AD. Rapid quantitative detection of chytridiomycosis (Batrachochytrium dendrobatidis) in amphibian samples using real-time Taqman PCR assay. Dis Aquat Organ. 2004;60:141–8.
CAS  PubMed  Article  PubMed Central  Google Scholar 

49.
Kozich JJ, Westcott SL, Baxter NT, Highlander SK, Schloss PD. Development of a Dual-Index Sequencing Strategy and Curation Pipeline for Analyzing Amplicon Sequence Data on the MiSeq Illumina Sequencing Platform. Appl Environ Microbiol. 2013;79:5112–20.
CAS  PubMed  PubMed Central  Article  Google Scholar 

50.
Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 2013;41:e1–e1.
CAS  PubMed  Article  PubMed Central  Google Scholar 

51.
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.
CAS  PubMed  PubMed Central  Article  Google Scholar 

52.
Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, et al. Introducing mothur: Open-Source, Platform-Independent, Community-Supported Software for Describing and Comparing Microbial Communities. Appl Environ Microbiol. 2009;75:7537–41.
CAS  PubMed  PubMed Central  Article  Google Scholar 

53.
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–596.
CAS  PubMed  Article  PubMed Central  Google Scholar 

54.
Frøslev TG, Kjøller R, Bruun HH, Ejrnæs R, Brunbjerg AK, Pietroni C, et al. Algorithm for post-clustering curation of DNA amplicon data yields reliable biodiversity estimates. Nat Commun. 2017;8:1188.
PubMed  PubMed Central  Article  CAS  Google Scholar 

55.
Arisdakessian C, Cleveland SB, Belcaid M. MetaFlow|mics: Scalable and Reproducible Nextflow Pipelines for the Analysis of Microbiome Marker Data. Pract Exp Adv Res Comput. 2020. Association for Computing Machinery, New York, NY, USA, pp 120–4.

56.
Lozupone C, Knight R. UniFrac: a New Phylogenetic Method for Comparing Microbial Communities. Appl Environ Microbiol. 2005;71:8228–35.
CAS  PubMed  PubMed Central  Article  Google Scholar 

57.
Anderson MJ. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 2001;26:32–46.
Google Scholar 

58.
Anderson MJ. Permutational Multivariate Analysis of Variance (PERMANOVA). Wiley statsref: statistics reference online. American Cancer Society;2017. p. 1–15.

59.
Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12:R60.
PubMed  PubMed Central  Article  Google Scholar 

60.
Joseph MB, Knapp RA. Disease and climate effects on individuals jointly drive post-reintroduction population dynamics of an endangered amphibian. bioRxiv. 2018; 332114.

61.
SanMiguel AJ, Meisel JS, Horwinski J, Zheng Q, Bradley CW, Grice EA. Antiseptic Agents Elicit Short-Term, Personalized, and Body Site–Specific Shifts in Resident Skin Bacterial Communities. J Investig Dermatol. 2018;138:2234–43.
CAS  PubMed  Article  PubMed Central  Google Scholar 

62.
Volkman J. Sterols in microorganisms. Appl Microbiol Biotechnol. 2003;60:495–506.
CAS  PubMed  Article  PubMed Central  Google Scholar 

63.
Niño DF, Cauvi DM, De Maio A. Itraconazole, a Commonly Used Antifungal, Inhibits Fcγ Receptor–Mediated Phagocytosis: Alteration of Fcγ Receptor Glycosylation and Gene Expression. Shock. 2014;42:52.
PubMed  PubMed Central  Article  CAS  Google Scholar 

64.
Tang C, Kamiya T, Liu Y, Kadoki M, Kakuta S, Oshima K, et al. Inhibition of Dectin-1 Signaling Ameliorates Colitis by Inducing Lactobacillus-Mediated Regulatory T Cell Expansion in the Intestine. Cell Host Microbe. 2015;18:183–97.
CAS  Article  Google Scholar 

65.
Zuo T, Wong SH, Cheung CP, Lam K, Lui R, Cheung K, et al. Gut fungal dysbiosis correlates with reduced efficacy of fecal microbiota transplantation in Clostridium difficile infection. Nat Commun. 2018;9:3663.
PubMed  PubMed Central  Article  CAS  Google Scholar 

66.
Zaneveld JR, McMinds R, Vega Thurber R. Stress and stability: applying the Anna Karenina principle to animal microbiomes. Nat Microbiol. 2017;2:17121.
CAS  PubMed  Article  PubMed Central  Google Scholar 

67.
Wilber MQ, Jani AJ, Mihaljevic JR, Briggs CJ. Fungal infection alters the selection, dispersal and drift processes structuring the amphibian skin microbiome. Ecol Lett. 2019;23:88–98.
PubMed  Article  PubMed Central  Google Scholar 

68.
Loudon AH, Woodhams DC, Parfrey LW, Archer H, Knight R, McKenzie V, et al. Microbial community dynamics and effect of environmental microbial reservoirs on red-backed salamanders (Plethodon cinereus). ISME J. 2013;8:830–40.
PubMed  PubMed Central  Article  CAS  Google Scholar 

69.
Santillan E, Constancias F, Wuertz S. Press Disturbance Alters Community Structure and Assembly Mechanisms of Bacterial Taxa and Functional Genes in Mesocosm-Scale Bioreactors. mSystems. 2020;5:e00471–20.
CAS  PubMed  PubMed Central  Article  Google Scholar 

70.
Rebollar EA, Gutiérrez-Preciado A, Noecker C, Eng A, Hughey MC, Medina D, et al. The Skin Microbiome of the Neotropical Frog Craugastor fitzingeri: inferring Potential Bacterial-Host-Pathogen Interactions From Metagenomic Data. Front Microbiol. 2018;9:466.
PubMed  Article  PubMed Central  Google Scholar 

71.
Mountain Yellow-legged Frog Interagency Technical Team. Interagency Conservation Strategy for Mountain Yellow-legged Frogs in the Sierra Nevada (Rana sierrae and Rana muscosa). Version 1. California Department of Fish and Wildlife, National Park Service, U.S. Fish and Wildlife Service, U.S. Forest Service; 2018. More

1.
Warren J, Topping CJ, James P. A unifying evolutionary theory for the biomass–diversity–fertility relationship. Theor Ecol. 2009;2:119–26.
Article  Google Scholar 
2.
Al-Mufti MM, Sydes CL, Furness SB, Grime JP, Band SR. A quantitative analysis of shoot phenology and dominance in herbaceous vegetation. J Ecol. 1977;65:759–91.
Article  Google Scholar 

3.
Grace JB, Anderson TM, Seabloom EW, Borer ET, Adler PB, Harpole WS, et al. Integrative modelling reveals mechanisms linking productivity and plant species richness. Nature. 2016;529:390–3.
CAS  PubMed  Article  PubMed Central  Google Scholar 

4.
Hooper DU, Chapin FS III, Ewel JJ, Hector A, Inchausti P, Lavorel S, et al. Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecol Monogr. 2005;75:3–35.
Article  Google Scholar 

5.
Tilman D, Wedin D, Knops J. Productivity and sustainability influenced by biodiversity in grassland ecosystems. Nature. 1996;379:718–20.
CAS  Article  Google Scholar 

6.
Grace JB. The factors controlling species density in herbaceous plant communities: an assessment. Perspect Plant Ecol. 1999;2:1–28.
Article  Google Scholar 

7.
Grime JP. Plant strategies and vegetation processes. Chichester-New York-Brisbane-Toronto: John Wiley & Sons, Ltd.; 1979.

8.
Loreau M, Hector A. Partitioning selection and complementarity in biodiversity experiments. Nature. 2001;412:72–6.
CAS  PubMed  Article  PubMed Central  Google Scholar 

9.
Michalet R, Brooker RW, Cavieres LA, Kikvidze Z, Lortie CJ, Pugnaire FI, et al. Do biotic interactions shape both sides of the humped-back model of species richness in plant communities? Ecol Lett. 2006;9:767–73.
PubMed  Article  PubMed Central  Google Scholar 

10.
Rajaniemi TK. Explaining productivity-diversity relationships in plants. Oikos. 2003;101:449–57.
Article  Google Scholar 

11.
Wardle DA, Bonner KI, Barker GM, Yeates GW, Nicholson KS, Bardgett RD, et al. Plant remobals in perennial grassland: vegetation dynamics, decomposers, soil biodiversity, and ecosystem properties. Ecol Monogr. 1999;69:535–68.
Article  Google Scholar 

12.
Fraser LH, Pither J, Jentsch A, Sternberg M, Zobel M, Askarizadeh D, et al. Worldwide evidence of a unimodal relationship between productivity and plant species richness. Science. 2015;349:302–5.
CAS  PubMed  Article  PubMed Central  Google Scholar 

13.
Adler PB, Seabloom EW, Borer ET, Hillebrand H, Hautier Y, Hector A, et al. Productivity is a poor predictor of plant species richness. Science. 2011;333:1750–3.
CAS  PubMed  Article  PubMed Central  Google Scholar 

14.
Bastida F, García C, Fierer N, Eldridge DJ, Bowker MA, Abades S, et al. Global ecological predictors of the soil priming effect. Nat Commun. 2019;10:3481.
PubMed  PubMed Central  Article  CAS  Google Scholar 

15.
Crowther TW, van den Hoogen J, Wan J, Mayes MA, Keiser AD, Mo L, et al. The global soil community and its influence on biogeochemistry. Science. 2019;365:eaav0550.
CAS  PubMed  Article  PubMed Central  Google Scholar 

16.
Delgado-Baquerizo M, Reich PB, Trivedi C, Eldridge DJ, Abades S, Alfaro FD, et al. Multiple elements of soil biodiversity drive ecosystem functions across biomes. Nat Ecol Evol. 2020;4:210–20.
PubMed  Article  PubMed Central  Google Scholar 

17.
Delgado-Baquerizo M, Oliverio AM, Brewer TE, Benavent-González A, Eldridge DJ, Bardgett RD, et al. A global atlas of the dominant bacteria found in soil. Science. 2018;359:320–5.
CAS  PubMed  Article  PubMed Central  Google Scholar 

18.
Fierer N. Embracing the unknown: disentangling the complexities of the soil microbiome. Nat Rev Microbiol 2017;15:579–90.
CAS  PubMed  Article  PubMed Central  Google Scholar 

19.
Tedersoo L, Bahram M, Põlme S, Kõljalg U, Yorou NS, Wijesundera R, et al. Global diversity and geography of soil fungi. Science. 2014;346:1256688.
PubMed  Article  CAS  PubMed Central  Google Scholar 

20.
Bardgett RD, Wardle DA. Herbivore-mediated linkages between aboveground and belowground communities. Ecology. 2003;84:2258–68.
Article  Google Scholar 

21.
Wardle DA. Communities and ecosystems linking the aboveground and belowground components (MPB-34). Princeton (New Jersey): Princeton University Press; 2002.

22.
Geyer KM, Barrett JE. Unimodal productivity–diversity relationships among bacterial communities in a simple polar soil ecosystem. Environ Microbiol. 2019;21:2523–32.
CAS  PubMed  Article  PubMed Central  Google Scholar 

23.
Bahram M, Hildebrand F, Forslund SK, Anderson JL, Soudzilovskaia NA, Bodegom PM, et al. Structure and function of the global topsoil microbiome. Nature. 2018;560:233–7.
CAS  PubMed  Article  PubMed Central  Google Scholar 

24.
Wardle DA. A comparative assessment of factors which influence microbial biomass carbon and nitrogen levels in soil. Biol Rev. 1992;67:321–58.
Article  Google Scholar 

25.
Geyer KM, Altrichter AE, Van Horn DJ, Takacs-Vesbach CD, Gooseff MN, Barrett JE. Environmental controls over bacterial communities in polar desert soils. Ecosphere. 2013;4:art127.
Article  Google Scholar 

26.
Langenheder S, Prosser JI. Resource availability influences the diversity of a functional group of heterotrophic soil bacteria. Environ Microbiol. 2008;10:2245–56.
CAS  PubMed  Article  PubMed Central  Google Scholar 

27.
Hopkins FM, Torn MS, Trumbore SE. Warming accelerates decomposition of decades-old carbon in forest soils. Proc Natl Acad Sci USA. 2012;109:1753–61.
Article  Google Scholar 

28.
Lal R. Soil carbon sequestration impacts on global climate change and food security. Science. 2004;304:1623–7.
CAS  PubMed  Article  PubMed Central  Google Scholar 

29.
Bertness MD, Callaway R. Positive interactions in communities. Trends Ecol Evol. 1994;9:191–3.
CAS  PubMed  Article  PubMed Central  Google Scholar 

30.
Hammarlund SP, Harcombe WR. Refining the stress gradient hypothesis in a microbial community. Proc Natl Acad Sci USA. 2019;116:15760.
CAS  PubMed  Article  PubMed Central  Google Scholar 

31.
Bastida F, Torres IF, Moreno JL, Baldrian P, Ondoño S, Ruiz-Navarro A, et al. The active microbial diversity drives ecosystem multifunctionality and is physiologically related to carbon availability in Mediterranean semi-arid soils. Mol Ecol. 2016;25:4660–73.
CAS  PubMed  Article  PubMed Central  Google Scholar 

32.
Delgado-Baquerizo M, Maestre FT, Reich PB, Jeffries TC, Gaitan JJ, Encinar D, et al. Microbial diversity drives multifunctionality in terrestrial ecosystems. Nat Commun. 2016;7:10541.
CAS  PubMed  PubMed Central  Article  Google Scholar 

33.
Wagg C, Bender SF, Widmer F, van der Heijden MGA. Soil biodiversity and soil community composition determine ecosystem multifunctionality. Proc Natl Acad Sci USA. 2014;111:5266–70.
CAS  PubMed  Article  PubMed Central  Google Scholar 

34.
Wieder WR, Allison SD, Davidson EA, Georgiou K, Hararuk O, He Y, et al. Explicitly representing soil microbial processes in Earth system models. Glob Biogeochem Cycles. 2015;29:1782–1800.
CAS  Article  Google Scholar 

35.
Glassman SI, Weihe C, Li J, Albright MBN, Looby CI, Martiny AC, et al. Decomposition responses to climate depend on microbial community composition. Proc Natl Acad Sci USA. 2018;115:11994–9.
CAS  PubMed  Article  PubMed Central  Google Scholar 

36.
Maestre FT, Quero J, Gotelli NJ, Escudero A, Ochoa V, Delgado-baquerizo M, et al. Plant species richness and ecosystem multifunctionality in global drylands. Science. 2012;335:214–8.
CAS  PubMed  PubMed Central  Article  Google Scholar 

37.
Delgado-Baquerizo M, Bardgett RD, Vitousek PM, Maestre FT, Williams MA, Eldridge DJ, et al. Changes in belowground biodiversity during ecosystem development. Proc Natl Acad Sci USA. 2019;116:6891–6.
CAS  PubMed  Article  PubMed Central  Google Scholar 

38.
Kettler TA, Doran JW, Gilbert TL. Simplified method for soil particle-size determination to accompany soil-quality analyses. Soil Science Society of America journal. vol. 65. Lincoln, Nebraska: 2001. p. 849–52. Journal Series no. 13277 of the Agric Res Div, Univ Neb, Linc, Ne.

39.
Bligh EG, Dyer WJ. A rapid method of total lipid extraction and purification. Can J Biochem Physiol. 1959;37:911–7.
CAS  PubMed  Article  PubMed Central  Google Scholar 

40.
Buyer JS, Sasser M. High throughput phospholipid fatty acid analysis of soils. Appl Soil Ecol. 2012;61:127–30.
Article  Google Scholar 

41.
Frostegård A, Bååth E. The use of phospholipid fatty acid analysis to estimate bacterial and fungal biomass in soil. Biol Fertil Soils. 1996;22:59–65.
Article  Google Scholar 

42.
Rinnan R, Bååth E. Differential utilization of carbon substrates by bacteria and fungi in tundra soil. Appl Environ Microbiol. 2009;75:3611–20.
CAS  PubMed  PubMed Central  Article  Google Scholar 

43.
Kaiser C, Frank A, Wild B, Koranda M, Richter A. Negligible contribution from roots to soil-borne phospholipid fatty acid fungal biomarkers 18:2ω6,9 and 18:1ω9. Soil Biol Biochem. 2010;42:1650–2.
CAS  PubMed  PubMed Central  Article  Google Scholar 

44.
Frostegård A, Tunlid A, Bååth E. Use and misuse of PLFA measurements in soils. Soil Biol Biochem. 2011;43:1621–5.
Article  CAS  Google Scholar 

45.
Lauber CL, Hamady M, Knight R, Fierer N. Pyrosequencing-based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale. Appl Environ Microbiol. 2009;75:5111–20.
CAS  PubMed  PubMed Central  Article  Google Scholar 

46.
Ramirez KS, Leff JW, Barberán A, Bates ST, Betley J, Crowther TW, et al. Biogeographic patterns in below-ground diversity in New York City’s Central Park are similar to those observed globally. Proc R Soc B. 2014;281:20141988.
PubMed  Article  PubMed Central  Google Scholar 

47.
Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–6.
CAS  PubMed  PubMed Central  Article  Google Scholar 

48.
Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013;10:996–8.
CAS  PubMed  Article  PubMed Central  Google Scholar 

49.
Breiman L. Random forests. Mach Learn. 2001;45:5–32.
Article  Google Scholar 

50.
Delgado-Baquerizo M, Giaramida L, Reich PB, Khachane AN, Hamonts K, Edwards C, et al. Lack of functional redundancy in the relationship between microbial diversity and ecosystem functioning. J Ecol. 2016;104:936–46.
Article  Google Scholar 

51.
Burnham KP, Anderson DR. Model selection and multimodel inference: a practical information-theoretic approach. New York: Springer; 2003.

52.
Grace JB. Structural equation modeling and natural systems. Cambridge: Cambridge University Press; 2006.

53.
Quinlan JR. Combining instance-based and model-based learning. In: Proceedings of the Tenth International Conference on International Conference on Machine Learning. Amherst, MA, USA: Morgan Kaufmann Publishers Inc.; 1993.

54.
Delgado-Baquerizo M. Obscure soil microbes and where to find them. ISME J. 2019;13:2120–4.
PubMed  PubMed Central  Article  Google Scholar 

55.
Kuhn SW, Keefer C, Coulter N. Cubist: rule- and instance-based regression modeling. R package version 0.0.19; 2016.

56.
Bailey VL, Peacock AD, Smith JL, Bolton H. Relationships between soil microbial biomass determined by chloroform fumigation-extraction, substrate-induced respiration, and phospholipid fatty acid analysis. Soil Biol Biochem. 2002;34:1385–9.
CAS  Article  Google Scholar 

57.
Fierer N, Strickland MS, Liptzin D, Bradford MA, Cleveland CC. Global patterns in belowground communities. Ecol Lett. 2009;12:1238–49.
PubMed  Article  PubMed Central  Google Scholar 

58.
Xu X, Thornton PE, Post WM. A global analysis of soil microbial biomass carbon, nitrogen and phosphorus in terrestrial ecosystems. Glob Ecol Biogeogr. 2013;22:737–49.
Article  Google Scholar 

59.
Six J, Frey SD, Thiet RK, Batten KM. Bacterial and fungal contributions to carbon sequestration in agroecosystems. Soil Sci Soc Am J. 2006;70:555–69.
CAS  Article  Google Scholar 

60.
Schimel JP, Schaeffer SM. Microbial control over carbon cycling in soil. Front Microbiol. 2012;348:1–11.
Google Scholar 

61.
Liang C, Schimel JP, Jastrow JD. The importance of anabolism in microbial control over soil carbon storage. Nat Microbiol. 2017;2:17105.
CAS  PubMed  Article  PubMed Central  Google Scholar 

62.
Fierer N, Jackson RB. The diversity and biogeography of soil bacterial communities. Proc Natl Acad Sci USA. 2006;103:626–31.
CAS  PubMed  Article  PubMed Central  Google Scholar 

63.
Maestre FT, Delgado-Baquerizo M, Jeffries TC, Eldridge DJ, Ochoa V, Gozalo B, et al. Increasing aridity reduces soil microbial diversity and abundance in global drylands. Proc Natl Acad Sci USA. 2015;112:15684–9.
CAS  PubMed  Article  PubMed Central  Google Scholar 

64.
Delgado-Baquerizo M, Eldridge DJ. Cross-biome drivers of soil bacterial alpha diversity on a worldwide scale. Ecosystems. 2019;22:1220–31.
Article  Google Scholar 

65.
Větrovský T, Kohout P, Kopecký M, Machac A, Man M, Bahnmann BD, et al. A meta-analysis of global fungal distribution reveals climate-driven patterns. Nat Commun. 2019;10:5142.
PubMed  PubMed Central  Article  CAS  Google Scholar 

66.
Gaston KJ. Global patterns in biodiversity. Nature. 2000;405:220–7.
CAS  PubMed  Article  PubMed Central  Google Scholar 

67.
Srivastava DS, Lawton JH. Why more productive sites have more species: an experimental test of theory using tree-hole communities. Am Naturalist. 1998;152:510–29.
CAS  Article  Google Scholar 

68.
Storch D, Bohdalková E, Okie J. The more-individuals hypothesis revisited: the role of community abundance in species richness regulation and the productivity–diversity relationship. Ecol Lett. 2018;21:920–37.
PubMed  Article  PubMed Central  Google Scholar 

69.
Paquette A, Messier C. The effect of biodiversity on tree productivity: from temperate to boreal forests. Glob Ecol Biogeogr. 2011;20:170–80.
Article  Google Scholar 

70.
Dorrepaal E, Toet S, van Logtestijn RSP, Swart E, van de Weg MJ, Callaghan TV, et al. Carbon respiration from subsurface peat accelerated by climate warming in the subarctic. Nature. 2009;460:616–9.
CAS  Article  Google Scholar 

71.
Melillo JM, Butler S, Johnson J, Mohan J, Steudler P, Lux H, et al. Soil warming, carbon–nitrogen interactions, and forest carbon budgets. Proc Natl Acad Sci USA. 2011;108:9508–12.
CAS  PubMed  Article  PubMed Central  Google Scholar 

72.
Crowther TW, Todd-Brown KEO, Rowe CW, Wieder WR, Carey JC, Machmuller MB, et al. Quantifying global soil carbon losses in response to warming. Nature. 2016;540:104–8.
CAS  PubMed  Article  PubMed Central  Google Scholar 

73.
Tilman D, Cassman KG, Matson PA, Naylor R, Polasky S. Agricultural sustainability and intensive production practices. Nature. 2002;418:671–7.
CAS  PubMed  Article  PubMed Central  Google Scholar 

74.
Navarrete AA, Tsai SM, Mendes LW, Faust K, de Hollander M, Cassman NA, et al. Soil microbiome responses to the short-term effects of Amazonian deforestation. Mol Ecol. 2015;24:2433–48.
CAS  PubMed  Article  PubMed Central  Google Scholar 

75.
Rodrigues JLM, Pellizari VH, Mueller R, Baek K, Jesus EdC, Paula FS, et al. Conversion of the Amazon rainforest to agriculture results in biotic homogenization of soil bacterial communities. Proc Natl Acad Sci USA. 2013;110:988–93.
CAS  PubMed  Article  PubMed Central  Google Scholar 

76.
Bastida F, García C, von Bergen M, Moreno JL, Richnow HH, Jehmlich N. Deforestation fosters bacterial diversity and the cyanobacterial community responsible for carbon fixation processes under semiarid climate: a metaproteomics study. Appl Soil Ecol. 2015;93:65–7.
Article  Google Scholar 

77.
Huang J, Yu H, Guan X, Wang G, Guo R. Accelerated dryland expansion under climate change. Nat Clim Change. 2016;6:166–71.
Article  Google Scholar 

78.
Maron PA, Sarr A, Kaisermann A, Léveque J, Mathieu O, Guigue J, et al. High microbial diversity promotes soil ecosystem functioning. Appl Environ Microbiol. 2018;84:e02738–17.
CAS  PubMed  PubMed Central  Article  Google Scholar 

79.
Chen C, Chen HYH, Chen X, Huang Z. Meta-analysis shows positive effects of plant diversity on microbial biomass and respiration. Nat Commun. 2019;10:1332.
PubMed  PubMed Central  Article  CAS  Google Scholar 

80.
Delgado-Baquerizo M, Grinyer J, Reich PB, Singh BK. Relative importance of soil properties and microbial community for soil functionality: insights from a microbial swap experiment. Funct Ecol. 2016;30:1862–73.
Article  Google Scholar 

81.
Kottek M, Grieser J, Beck C, Rudolf B, Rubel F. World Map of the Köppen-Geiger climate classification updated. Meteorol. Z. 2006;15:259–63.
Article  Google Scholar  More

1.
Sala, O. E. et al. Global biodiversity scenarios for the year 2100. Science 287, 1770–1774 (2000).
CAS  PubMed  Article  PubMed Central  Google Scholar 
2.
Cardinale, B. J., Gonzalez, A., Allington, G. R. & Loreau, M. Is local biodiversity declining or not? A summary of the debate over analysis of species richness time trends. Biol. Conserv. 219, 175–183 (2018).
Article  Google Scholar 

3.
Hautier, Y. et al. Local loss and spatial homogenization of plant diversity reduce ecosystem multifunctionality. Nat. Ecol. Evol. 2, 50 (2018).
PubMed  Article  PubMed Central  Google Scholar 

4.
Tovar-Sánchez, E., Hernández-Plata, I., Martínez, M. S., Valencia-Cuevas, L. & Galante, P. M. Heavy metal pollution as a biodiversity threat. Heavy Met. 383 (2018).

5.
Das, K. K., Das, S. N. & Dhundasi, S. A. Nickel, its adverse health effects & oxidative stress. Indian J. Med. Res. 128, 412 (2008).
CAS  PubMed  PubMed Central  Google Scholar 

6.
Fabiano, C., Tezotto, T., Favarin, J. L., Polacco, J. C. & Mazzafera, P. Essentiality of nickel in plants: A role in plant stresses. Front. Plant Sci. 6, 754 (2015).
PubMed  PubMed Central  Article  Google Scholar 

7.
Sreekanth, T.V.M., Nagajyothi, P. C., Lee, K. D. & Prasad, T.N.V.K.V. Occurrence, physiological responses and toxicity of nickel in plants. Int.J.Environ.Sci.Technol.10(5), 1129–1140 (2013).

8.
Pietrini, F. et al. Evaluation of nickel tolerance in Amaranthus paniculatus L. plants by measuring photosynthesis, oxidative status, antioxidative response and metal-binding molecule content. Environ. Sci. Pollut.22, 482–494 (2015).

9.
Georgiadou, E. C. et al. Influence of heavy metals (Ni, Cu and Zn) on nitro-oxidative stress responses, proteome regulation and allergen production in basil (Ocimum basilicum L.) plants. Front. Plant Sci.9, 862 (2018).

10.
Shahid, M. et al. Foliar heavy metal uptake, toxicity and detoxification in plants: A comparison of foliar and root metal uptake. J. Hazard. Mater. 325, 36–58 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

11.
Shen, Z. J., Chen, Y. S. & Zhang, Z. Heavy metals translocation and accumulation from the rhizosphere soils to the edible parts of the medicinal plant Fengdan (Paeonia ostii) grown on a metal mining area. China. Ecotoxicol. Environ. Saf. 143, 19–27 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

12.
Xun, E., Zhang, Y., Zhao, J. & Guo, J. Translocation of heavy metals from soils into floral organs and rewards of Cucurbita pepo: Implications for plant reproductive fitness. Ecotox. Environ. Safe. 145, 235–243 (2017).
CAS  Article  Google Scholar 

13.
Meindl, G. A. & Ashman, T. L. Effects of soil metals on pollen germination, fruit production, and seeds per fruit differ between a Ni hyperaccumulator and a congeneric nonaccumulator. Plant Soil. 420, 493–503 (2017).
CAS  Article  Google Scholar 

14.
Temizer, İK., Güder, A., Temel, F. A. & Esin, A. V. C. I. A comparison of the antioxidant activities and biomonitoring of heavy metals by pollen in the urban environments. Environ. Monit. Assess. 190, 462 (2018).
PubMed  Article  CAS  PubMed Central  Google Scholar 

15.
Baumann, H. A., Morrison, L. & Stengel, D. B. Metal accumulation and toxicity measured by PAM—Chlorophyll fluorescence in seven species of marine macroalgae. Ecotoxicol. Environ. Saf. 72, 1063–1075 (2009).
CAS  PubMed  Article  PubMed Central  Google Scholar 

16.
Liang, S. et al. How Chlorella sorokiniana and its high tolerance to Pb might be a potential Pb biosorbent. Pol. J. Environ. Stud. 26, 1139–1146 (2017).
CAS  Article  Google Scholar 

17.
Ares, A., Itouga, M., Kato, Y. & Sakakibara, H. Differential Metal Tolerance and Accumulation Patterns of Cd, Cu, Pb and Zn in the Liverwort Marchantia polymorpha L. B. Environ. Contam. Tox. 100, 444–450 (2018).
CAS  Article  Google Scholar 

18.
Stanković, J. D., Sabovljević, A. D. & Sabovljević, M. S. Bryophytes and heavy metals: A review. Acta Bot. Croat. 77, 109–118 (2018).
Article  Google Scholar 

19.
Wang, S., Zhang, Z. & Wang, Z. Bryophyte communities as biomonitors of environmental factors in the Goujiang karst bauxite, southwestern China. Sci. Total Environ. 538, 270–278 (2015).
ADS  CAS  PubMed  Article  Google Scholar 

20.
Vanderpoorten, A. et al. To what extent are bryophytes efficient dispersers?. J. Ecol. 107, 2149–2154 (2019).
Article  Google Scholar 

21.
Carginale, V. et al. Accumulation, localisation, and toxic effects of cadmium in the liverwort Lunularia cruciata. Protoplasma. 223, 53–61 (2004).
CAS  PubMed  Article  PubMed Central  Google Scholar 

22.
Yan, Y., Zhang, Q., Wang, G. G. & Fang, Y. M. Atmospheric deposition of heavy metals in Wuxi, China: Estimation based on native moss analysis. Ecotox. Environ. Safe. 188, 360 (2016).
Google Scholar 

23.
Gupta, R. & Asthana, A. K. Diversity and distribution of liverworts across habitats and altitudinal gradient at Pachmarhi Biosphere Reserve (India). Plant Sci. Today 3, 354–359 (2016).
Article  Google Scholar 

24.
Gao, S., Yu, H. N., Xu, R. X., Cheng, A. X. & Lou, H. X. Cloning and functional characterization of a 4-coumarate CoA ligase from liverwort Plagiochasma appendiculatum. Phytochemistry 111, 48–58 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

25.
Wu, Y. F. et al. A bHLH Transcription factor regulates bisbibenzyl biosynthesis in the liverwort Plagiochasma appendiculatum. Plant Cell Physiol. 59, 1187–1199 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

26.
Venugopal, M. & Nair, M. C. Bryophyte diversity of Thamarassery pass (Wayanad pass) in the Western Ghats of Kerala. Plant Sci. Today 4, 41–48 (2017).
Article  Google Scholar 

27.
Pant, G. & Tewari, S. D. Bryophytes as Biogeoindicators: Bryophytic Associations of Mineral-Enriched Substrates in Kumaon Himalaya. Topics in Bryology 165–184 (Allied Publishers Ltd., New Delhi, 1998).
Google Scholar 

28.
Ghate, S. & Chaphekar, S. B. Plagiochasma appendiculatum as a biotest for water quality assessment. Environ. Pollut. 108, 173–181 (2000).
CAS  PubMed  Article  PubMed Central  Google Scholar 

29.
Choudhary, S.P., Kanwar, M., Bhardwaj, R., Yu, J.Q. & Tran, L.S.P. Chromium stress mitigation by polyamine-brassinosteroid application involves phytohormonal and physiological strategies in Raphanus sativus L. PLoS One. 7(3) (2012).

30.
Bai, C., Liu, L. & Wood, B. W. Nickel affects xylem Sap RNase a and converts RNase A to a urease. BMC Plant Biol. 13, 207 (2013).
PubMed  PubMed Central  Article  CAS  Google Scholar 

31.
Bai, C., Reilly, C. C. & Wood, B. W. Nickel deficiency disrupts metabolism of ureides, amino acids, and organic acids of young pecan foliage. Plant Physiol. 140, 433–443 (2006).
CAS  PubMed  PubMed Central  Article  Google Scholar 

32.
Kandeler, E. & Gerber, H. Short-term assay of soil urease activity using colorimetric determination of ammonium. Biol. Fertil. Soils. 6, 68–72 (1988).
CAS  Article  Google Scholar 

33.
Poonkothai, M. V. B. S. & Vijayavathi, B. S. Nickel as an essential element and a toxicant. Int. J. Environ. Sci. 1, 285–288 (2012).
Google Scholar 

34.
Freitas, D. S. et al.Hidden nickel deficiency? Nickel fertilization via soil improves nitrogen metabolism and grain yield in soybean genotypes. Front. Plant Sci.9(2018).

35.
Rout, G. R. & Das, P. Effect of metal toxicity on plant growth and metabolism: I. Zinc. in Sustainable Agriculture (pp. 873–884). (Springer, Dordrecht, 2009).

36.
Myking, T. et al.Effects of Air Pollution from a Nickel-Copper Industrial Complex on Boreal Forest Vegetation in the Joint Russian-Norwegian-Finnish Border Area (2009).

37.
Kowalska, J. B., Mazurek, R., Gąsiorek, M. & Zaleski, T. Pollution indices as useful tools for the comprehensive evaluation of the degree of soil contamination—A review. Environ. Geochem. Health. 1–26 (2018).

38.
Awadh, S. M., Al-Kilabi, J. A. & Khaleefah, N. H. Comparison the geochemical background, threshold and anomaly with pollution indices in the assessment of soil pollution: Al-Hawija, north of Iraq case study. Int. J. Sci. Res. 4, 2357–2363 (2015).
Google Scholar 

39.
Dung, T. T. T., Cappuyns, V., Swennen, R. & Phung, N. K. From geochemical background determination to pollution assessment of heavy metals in sediments and soils. Rev. Environ. Sci. Biotechnol. 12, 335–353 (2013).
CAS  Article  Google Scholar 

40.
Mazurek, R. et al. Assessment of heavy metals contamination in surface layers of Roztocze National Park forest soils (SE Poland) by indices of pollution. Chemosphere 168, 839–850 (2017).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

41.
Čecháková, K., Motyka, O., Válová, E., Macečková, B. & Stalmachová, B. Investigation of the influence of nickel in precipitation through the surface properties of moss Pleurozium schreberi Carpath. J. Earth Environ. 9, 153–158 (2014).
Google Scholar 

42.
Marchiol, L., Assolari, S., Sacco, P. & Zerbi, G. Phytoextraction of heavy metals by canola (Brassica napus) and radish (Raphanus sativus) grown on multiexcess soil. Environ. Pollut. 132, 21–27 (2004).
CAS  PubMed  Article  PubMed Central  Google Scholar 

43.
Tuna, A. L., Burun, B., Yokas, I. & Coban, E. The effects of heavy metals on pollen germination and pollen tube length in the tobacco plant. Turk. J. Biol. 26, 109–113 (2002).
CAS  Google Scholar 

44.
Mostofa, M. G., Hossain, M. A., Fujita, M. & Tran, L. S. P. Physiological and biochemical mechanisms associated with trehalose-induced copper-stress tolerance in rice. Sci. Rep. 5, 11433 (2015).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

45.
Choudhury, S. & Panda, S. K. Induction of oxidative stress and ultrastructural changes in moss Taxithelium nepalense (Schwaegr.) Broth. under lead and arsenic phytotoxicity. Curr. Sci. 342–348 (2004).

46.
Choudhury, S. & Panda, S. K. Toxic effects, oxidative stress and ultrastructural changes in moss Taxithelium nepalense (Schwaegr.) Broth. under chromium and lead phytotoxicity. Water Air Soil Pollut.167, 73–90 (2005).

47.
Penny, C., Dickinson, N. M. & Lepp, N. W. The effect of heavy metal contamination on the pigment profiles of Torreya sp. in Remediation and Management of Degraded Lands. (2018).

48.
Rau, S., Miersch, J., Neumann, D., Weber, E. & Krauss, G. J. Biochemical responses of the aquatic moss Fontinalis antipyretica to Cd, Cu, Pb and Zn determined by chlorophyll fluorescence and protein levels. Environ. Exp. Bot. 59, 299–306 (2007).
CAS  Article  Google Scholar 

49.
Foyer, C. H. & Noctor, G. Ascorbate and glutathione: The heart of the redox hub. Plant Physiol. 155, 2–18 (2011).
CAS  PubMed  PubMed Central  Article  Google Scholar 

50.
Hasanuzzaman, M., Nahar, K., Anee, T. I. & Fujita, M. Glutathione in plants: biosynthesis and physiological role in environmental stress tolerance. Physiol. Mol. Biol. Plants. 23, 249–268 (2017).
CAS  PubMed  PubMed Central  Article  Google Scholar 

51.
Yadav, N. S., Shukla, P. S., Jha, A., Agarwal, P. K. & Jha, B. The SbSOS1 gene from the extreme halophyte Salicornia brachiata enhances Na+ loading in xylem and confers salt tolerance in transgenic tobacco. BMC Plant Biol. 12, 188 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

52.
Subbiah, B. V. & Asija, G. L. A rapid procedure for estimation of available nitrogen in soils. Curr Sci. 25, 259–260 (1956).
CAS  Google Scholar 

53.
Olsen, S.R., Cole, C,V., Watanabe, F.S. & Dean, L. Estimation of Available Phosphorus in Soils by Extraction with Sodium Bicarbonate. U.S.D.A. Circ. 939. (U.S. Govt. Printing Office: Washington, DC) (1954).

54.
Qingjie, G., Jun, D., Yunchuan, X., Qingfei, W. & Liqiang, Y. Calculating pollution indices by heavy metals in ecological geochemistry assessment and a case study in parks of Beijing. J. China Univ. Geosci. 19, 230–241 (2008).
Article  Google Scholar 

55.
Choudhary, S. P., Kanwar, M., Bhardwaj, R., Gupta, B. D. & Gupta, R. K. Epibrassinolide ameliorates Cr (VI) stress via influencing the levels of indole-3-acetic acid, abscisic acid, polyamines and antioxidant system of radish seedlings. Chemosphere 84, 592–600 (2011).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

56.
Lacy, R.C., P.S. Miller & Traylor-Holzer, K. Vortex 10 User’s Manual. 1 June 2018 update. IUCN SSC Conservation Breeding Specialist Group, and Chicago Zoological Society, Apple Valley, Minnesota, USA (2018).

57.
Brown, P. H., Welch, R. M. & Cary, E. E. Nickel: A micronutrient essential for higher plants. Plant Physiol. 85, 801–803 (1987).
CAS  PubMed  PubMed Central  Article  Google Scholar 

58.
Seregin, I. V., Kozhevnikova, A. D., Kazyumina, E. M. & Ivanov, V. B. Nickel toxicity and distribution in maize roots. Russ. J. Plant Physiol. 50, 711–717 (2003).
CAS  Article  Google Scholar 

59.
Shin, R., Berg, R. H. & Schachtman, D. P. Reactive oxygen species and root hairs in Arabidopsis root response to nitrogen, phosphorus and potassium deficiency. Plant Cell Physiol. 46, 1350–1357 (2005).
CAS  PubMed  Article  PubMed Central  Google Scholar 

60.
Saeed, A. I. et al. TM4a free, open-source system for microarray data management and analysis. Biotechniques 34, 374–378 (2003).
CAS  PubMed  Article  PubMed Central  Google Scholar  More

1.
Da Silva, J. F. & Williams, R. J. P. The biological chemistry of the elements: the inorganic chemistry of life. Oxford University Press (2001).
2.
Moore, C. M. et al. Processes and patterns of oceanic nutrient limitation. Nat. Geosci. 6, 701–710 (2013).
ADS  CAS  Article  Google Scholar 

3.
Bruland, K. W., Orians, K. J. & Cowen, J. P. Reactive trace metals in the stratified central North Pacific. Geochim. Cosmochim. 58, 3171–3182 (1994).
ADS  CAS  Article  Google Scholar 

4.
van Hulten, M. et al. Manganese in the west Atlantic Ocean in the context of the first global ocean circulation model of manganese. Biogeosciences 14, 1123–1152 (2017).
ADS  Article  CAS  Google Scholar 

5.
Baker, A. R. et al. Trace element and isotope deposition across the air–sea inter- face: progress and research needs. Philos. Trans. R. Soc. A: Math. Phys. Eng. Sci. 374, 2081 (2016).
Article  CAS  Google Scholar 

6.
Sunda, W. G., Huntsman, S. A. & Harvey, G. R. Photoreduction of manganese oxides in seawater and its geochemical and biological implications. Nature 301, 234–236 (1983).
ADS  CAS  Article  Google Scholar 

7.
Sunda, W. G. & Huntsman, S. A. Photoreduction of manganese oxides in seawater. Mar. Chem. 46, 133–152 (1994).
CAS  Article  Google Scholar 

8.
Sunda, W. G. & Huntsman, S. Effect of sunlight on redox cycles of manganese in the southwestern Sargasso Sea. Deep-Sea Res. Pt. A 35, 1297–1317 (1988).
ADS  CAS  Article  Google Scholar 

9.
Wagener, T., Guieu, C., Losno, R., Bonnet, S. & Mahowald, N. Revisiting atmospheric dust export to the Southern Hemisphere ocean: Biogeochemical implications. Glob. Biogeochem. Cycles 22, GB2006 (2008).
ADS  Article  CAS  Google Scholar 

10.
Tamsitt, V. et al. Spiraling pathways of global deep waters to the surface of the Southern Ocean. Nat. Commun. 8, 172 (2017).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

11.
Boyd, P. W. Environmental factors controlling phytoplankton processes in the Southern Ocean. J. Phycol. 38, 844–861 (2002).
Article  Google Scholar 

12.
Hansell, D. A., Carlson, C. A., Repeta, D. J. & Schlitzer, R. Dissolved organic matter in the ocean: a controversy stimulates new insights. Oceanography 22, 202–211 (2009).
Article  Google Scholar 

13.
Martin, J. H., Gordon, R. M. & Fitzwater, S. E. Iron in Antarctic waters. Nature 345, 156–158 (1990).
ADS  CAS  Article  Google Scholar 

14.
Sedwick, P. N., Edwards, P. R., Mackey, D. J., Griffiths, F. B. & Parslow, J. S. Iron and manganese in surface waters of the Australian subantarctic region. Deep Sea Res. Pt. I 44, 1239–1253 (1997).
CAS  Article  Google Scholar 

15.
Hatta, M., Measures, C. I., Selph, K. E., Zhou, M. & Hiscock, W. T. Iron fluxes from the shelf regions near the South Shetland Islands in the Drake Passage during the austral-winter 2006. Deep Sea Res. Pt. II 90, 89–101 (2013).
ADS  CAS  Article  Google Scholar 

16.
Middag, R., De Baar, H. J. W., Laan, P. & Huhn, O. The effects of continental margins and water mass circulation on the distribution of dissolved aluminum and manganese in Drake Passage. J. Geophys. Res. 117, C01019 (2012).
ADS  Google Scholar 

17.
Middag, R., de Baar, H. J., Klunder, M. B. & Laan, P. Fluxes of dissolved aluminum and manganese to the Weddell Sea and indications for manganese co‐limitation. Limnol. Oceanogr. 58, 287–300 (2013).
ADS  CAS  Article  Google Scholar 

18.
Browning, T. J. et al. Strong responses of Southern Ocean phytoplankton communities to volcanic ash. Geophys. Res. Lett. 41, 2851–2857 (2014).
ADS  CAS  Article  Google Scholar 

19.
Ito, T. & Follows, M. J. Preformed phosphate, soft tissue pump and atmospheric CO2. J. Mar. Res. 63, 813–839 (2005).
CAS  Article  Google Scholar 

20.
Kohfeld, K. E. and Ridgwell, A., 2009. Glacial-interglacial variability in atmospheric CO2. Surface Ocean-Lower Atmosphere Processes (Am. Geophys. Union, Washington DC), pp 251– 286 (2009).

21.
Petit, J. et al. Climate and atmospheric history of the past 420,000 years from the Vostok ice core, Antarctica. Nature 399, 429–436 (1999).
ADS  CAS  Article  Google Scholar 

22.
Lamy, F. et al. Increased dust deposition in the Pacific Southern Ocean during glacial periods. Science 343, 403–407 (2014).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

23.
Martin, J. H. Glacial‐interglacial CO2 change: the iron hypothesis. Paleoceanography 5, 1–13 (1990).
ADS  Article  Google Scholar 

24.
Watson, A. J., Bakker, D. C. E., Ridgwell, A. J., Boyd, P. W. & Law, C. S. Effect of iron supply on Southern Ocean CO2 uptake and implications for glacial atmospheric CO2. Nature 407, 730–CO733 (2000).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

25.
Sigman, D. M., Hain, M. P. & Haug, G. H. The polar ocean and glacial cycles in atmospheric CO2 concentration. Nature 466, 47–55 (2010).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

26.
Khatiwala, S., Schmittner, A. & Muglia, J. Air-sea disequilibrium enhances ocean carbon storage during glacial periods. Sci. Adv. 5, eaaw4981 (2019).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

27.
Wedepohl, K. H. The composition of the continental crust. Geochim. Cosmochim. Acta 59, 1217–1232 (1995).
ADS  CAS  Article  Google Scholar 

28.
Chance, R., Jickells, T. D. & Baker, A. R. Atmospheric trace metal concentrations, solubility and deposition fluxes in remote marine air over the south-east Atlantic. Mar. Chem. 177, 45–56 (2015).
CAS  Article  Google Scholar 

29.
Gaiero, D. M., Probst, J. L., Depetris, P. J., Bidart, S. M. & Leleyter, L. Iron and other transition metals in Patagonian riverborne and windborne materials: geochemical control and transport to the southern South Atlantic Ocean. Geochim. Cosmochim. Acta 67, 3603–3623 (2003).
ADS  CAS  Article  Google Scholar 

30.
Tagliabue, A. et al. The integral role of iron in ocean biogeochemistry. Nature 543, 51–59 (2017).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

31.
Peers, G. & Price, N. M. A role for manganese in superoxide dismutases and growth of iron‐deficient diatoms. Limnol. Oceanogr. 49, 1774–1783 (2004).
ADS  CAS  Article  Google Scholar 

32.
Wu, M. et al. Manganese and iron deficiency in Southern Ocean Phaeocystis antarctica populations revealed through taxon-specific protein indicators. Nat. Commun. 10, 1–10 (2019).
ADS  Article  CAS  Google Scholar 

33.
Bindoff, N. L. et al. Changing ocean, marine ecosystems, and dependent communities. In IPCC special report on the ocean and cryosphere in a changing climate (2019).

34.
Buma, A. G., De Baar, H. J., Nolting, R. F. & Van Bennekom, A. J. Metal enrichment experiments in the Weddell‐Scotia Seas: effects of iron and manganese on various plankton communities. Limnol. Oceanogr. 36, 1865–1878 (1991).
ADS  CAS  Article  Google Scholar 

35.
Scharek, R., Van Leeuwe, M. A. & De Baar, H. J. Responses of Southern Ocean phytoplankton to the addition of trace metals. Deep Sea Res. Pt. II 44, 209–227 (1997).
ADS  CAS  Article  Google Scholar 

36.
Sedwick, P. N., DiTullio, G. R. & Mackey, D. J. Iron and manganese in the Ross Sea, Antarctica: seasonal iron limitation in Antarctic shelf waters. J. Geophys. Res. 105, 11321–11336 (2000).
ADS  CAS  Article  Google Scholar 

37.
Saito, M. A., Goepfert, T. J. & Ritt, J. T. Some thoughts on the concept of colimitation: three definitions and the importance of bioavailability. Limnol. Oceanogr. 53, 276–290 (2008).
ADS  CAS  Article  Google Scholar 

38.
Hutchins, D. A. & Bruland, K. W. Iron-limited diatom growth and Si: N uptake ratios in a coastal upwelling regime. Nature 393, 561–564 (1998).
ADS  CAS  Article  Google Scholar 

39.
Takeda, S. Influence of iron availability on nutrient consumption ratio of diatoms in oceanic waters. Nature 393, 774–777 (1998).
ADS  CAS  Article  Google Scholar 

40.
Klunder, M. B. et al. Dissolved Fe across the Weddell Sea and Drake passage: impact of DFe on nutrient uptake. Biogeosciences 11, 651–669 (2014).
ADS  Article  Google Scholar 

41.
Thomalla, S. J., Fauchereau, N., Swart, S. & Monteiro, P. M. S. Regional scale characteristics of the seasonal cycle of chlorophyll in the Southern Ocean. Biogeosciences 8, 2849–2866 (2011).
ADS  CAS  Article  Google Scholar 

42.
Moore, C. M. Diagnosing oceanic nutrient deficiency. Philos. Tran. R. Soc. A 374, 20150290 (2016).
ADS  Article  CAS  Google Scholar 

43.
Middag, R. D., De Baar, H. J. W., Laan, P., Cai, P. V. & Van Ooijen, J. C. Dissolved manganese in the Atlantic sector of the Southern Ocean. Deep Sea Res. Pt. II 58, 2661–2677 (2011).
ADS  CAS  Article  Google Scholar 

44.
Klunder, M. B., Laan, P., Middag, R., De Baar, H. J. W. & Van Ooijen, J. C. Dissolved iron in the Southern Ocean (Atlantic sector). Deep Sea Res. Pt. II 58, 2678–2694 (2011).
ADS  CAS  Article  Google Scholar 

45.
Boyd, P. W. et al. Mesoscale iron enrichment experiments 1993-2005: synthesis and future directions. Science 315, 612–617 (2007).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

46.
Parekh, P., Follows, M. J. & Boyle, E. A. Decoupling of iron and phosphate in the global ocean. Glob. Biogeochem. Cycles 19, GB2020 (2005).
ADS  Article  CAS  Google Scholar 

47.
Measures, C. I. et al. The influence of shelf processes in delivering dissolved iron to the HNLC waters of the Drake Passage, Antarctica. Deep Sea Res. Pt. I 90, 77–88 (2013).
ADS  CAS  Article  Google Scholar 

48.
Dulaiova, H., Ardelan, M. V., Henderson, P. B. & Charette, M. A. Shelf‐derived iron inputs drive biological productivity in the southern Drake Passage. Glob. Biogeochem. Cycles 23, GB4014 (2009).
ADS  Article  CAS  Google Scholar 

49.
Jiang, M. et al. Fe sources and transport from the Antarctic Peninsula shelf to the southern Scotia Sea. Deep Sea Res. Pt. I 150, 103060 (2019).
CAS  Article  Google Scholar 

50.
Anderson, T. R., Gentleman, W. C. & Yool, A. EMPOWER-1.0: an efficient model of planktonic ecosystems written in R. Geosci. Mod. Dev. 8, 2231–2262 (2015).
Article  Google Scholar 

51.
Resing, J. A. et al. Basin-scale transport of hydrothermal dissolved metals across the South Pacific Ocean. Nature 523, 200–203 (2015).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

52.
Bertrand, E. M. et al. Vitamin B12 and iron colimitation of phytoplankton growth in the Ross Sea. Limnol. Oceanogr. 52, 1079–1093 (2007).
ADS  CAS  Article  Google Scholar 

53.
Le Quéré, C. et al. Role of zooplankton dynamics for Southern Ocean phytoplankton biomass and global biogeochemical cycles. Biogeosciences 13, 4111–4133 (2016).
ADS  Article  CAS  Google Scholar 

54.
Calvo, E., Pelejero, C., Logan, G. A. & De Deckker, P. Dust‐induced changes in phytoplankton composition in the Tasman Sea during the last four glacial cycles. Paleoceanography 19, PA2020 (2004).
ADS  Article  Google Scholar 

55.
Sigman, D. M., Altabet, M. A., Francois, R., McCorkle, D. C. & Gaillard, J. F. The isotopic composition of diatom‐bound nitrogen in Southern Ocean sediments. Paleoceanography 14, 118–134 (1999).
ADS  Article  Google Scholar 

56.
De La Rocha, C. L., Brzezinski, M. A., DeNiro, M. J. & Shemesh, A. Silicon-isotope composition of diatoms as an indicator of past oceanic change. Nature 395, 680–683 (1998).
ADS  Article  CAS  Google Scholar 

57.
Browning, T. J. et al. Nutrient co-limitation at the boundary of an oceanic gyre. Nature 551, 242–246 (2017).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

58.
Venables, H. & Moore, C. M. Phytoplankton and light limitation in the Southern Ocean: learning from high‐nutrient, high‐chlorophyll areas. J. Geophys. Res. Oceans 115, C02015 (2010).
ADS  Article  CAS  Google Scholar 

59.
Van Heukelem, L. & Thomas, C. S. Computer-assisted high-performance liquid chromatography method development with applications to the isolation and analysis of phytoplankton pigments. J. Chromatogr. A 910, 31–49 (2001).
PubMed  Article  PubMed Central  Google Scholar 

60.
Mackey, M. D., Mackey, D. J., Higgins, H. W. & Wright, S. W. CHEMTAX-a program for estimating class abundances from chemical markers: application to HPLC measurements of phytoplankton. Mar. Ecol. Prog. Ser. 144, 265–283 (1996).
ADS  CAS  Article  Google Scholar 

61.
Gibberd, M. J., Kean, E., Barlow, R., Thomalla, S. & Lucas, M. Phytoplankton chemotaxonomy in the Atlantic sector of the Southern Ocean during late summer 2009. Deep Sea Res. Pt. I 78, 70–78 (2013).
CAS  Article  Google Scholar 

62.
Rapp, I., Schlosser, C., Rusiecka, D., Gledhill, M. & Achterberg, E. P. Automated preconcentration of Fe, Zn, Cu, Ni, Cd, Pb, Co, and Mn in seawater with analysis using high-resolution sector field inductively-coupled plasma mass spectrometry. Anal. Chim. Acta 976, 1–13 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

63.
Wilson, S. T. et al. Kīlauea lava fuels phytoplankton bloom in the North Pacific Ocean. Science 365, 1040–1044 (2019).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

64.
Wuttig, K. et al. Critical evaluation of a seaFAST system for the analysis of trace metals in marine samples. Talanta 197, 653–668 (2019).
CAS  PubMed  Article  PubMed Central  Google Scholar 

65.
Martin, J. H., Knauer, G. A., Karl, D. M. & Broenkow, W. W. VERTEX: carbon cycling in the northeast Pacific. Deep Sea Res. Pt. A 34, 267–285 (1987).
ADS  CAS  Article  Google Scholar 

66.
Buck, K. N., Sohst, B. & Sedwick, P. N. The organic complexation of dissolved iron along the US GEOTRACES (GA03) North Atlantic Section. Deep Sea Res. Pt. II 116, 152–165 (2015).
CAS  Article  Google Scholar 

67.
Parekh, P., Follows, M. J. & Boyle, E. Modeling the global ocean iron cycle. Glob. Biogeochem. Cycles 18, GB1002 (2004).
ADS  Article  CAS  Google Scholar 

68.
Dutkiewicz, S., Follows, M. J. & Parekh, P. Interactions of the iron and phosphorus cycles: a three‐dimensional model study. Glob. Biogeochem. Cycles 19, GB1012 (2005).
ADS  Article  CAS  Google Scholar 

69.
Glockzin, M., Pollehne, F. & Dellwig, O. Stationary sinking velocity of authigenic manganese oxides at pelagic redoxclines. Mar. Chem. 160, 67–74 (2014).
CAS  Article  Google Scholar 

70.
Buesseler, K. O., McDonnell, A. M., Schofield, O. M., Steinberg, D. K. & Ducklow, H. W. High particle export over the continental shelf of the west Antarctic Peninsula. Geophys. Res. Lett. 37, L22606 (2010).
ADS  Article  CAS  Google Scholar 

71.
de Boyer Montégut, C., Madec, G., Fischer, A. S., Lazar, A. & Iudicone, D. Mixed layer depth over the global ocean: an examination of profile data and a profile‐based climatology. J. Geophys. Res. 109, C12003 (2004).
ADS  Article  Google Scholar 

72.
Mahowald, N. et al. Dust sources and deposition during the last glacial maximum and current climate: a comparison of model results with paleodata from ice cores and marine sediments. J. Geophys. Res. Atmospheres 104, 15895–15916 (1999).
ADS  Article  Google Scholar  More

1.
Fuentes, M. M. et al. Adaptive management of marine mega-fauna in a changing climate. Mitig. Adapt. Strat. Glob. Change 21, 209–224 (2016).
Article  Google Scholar 
2.
Grose, S. O., Pendleton, L., Leathers, A., Cornish, A. & Waitai, S. Climate change will re-draw the map for marine megafauna and the people who depend on them. Front. Mar. Sci. 7, 547 (2020).
Article  Google Scholar 

3.
Halley, J. M., Van Houtan, K. S. & Mantua, N. How survival curves affect populations’ vulnerability to climate change. PLoS ONE 13, e0203124 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

4.
Zacharias, M. A. & Roff, J. C. Use of focal species in marine conservation and management: a review and critique. Aquat. Conserv. Mar. Freshw. Ecosyst. 11, 59–76 (2001).
Article  Google Scholar 

5.
Hazen, E. L. et al. Ontogeny in marine tagging and tracking science: technologies and data gaps. Mar. Ecol. Prog. Ser. 457, 221–240 (2012).
ADS  Article  Google Scholar 

6.
Hazen, E. L. et al. Predicted habitat shifts of Pacific top predators in a changing climate. Nat. Clim. Change 3, 234–238 (2013).
ADS  Article  Google Scholar 

7.
Jorgensen, S. J. et al. Killer whales redistribute white shark foraging pressure on seals. Sci. Rep. 9, 1–9. https://doi.org/10.1038/s41598-019-39356-2 (2019).
CAS  Article  Google Scholar 

8.
Domeier, M. L. Global perspectives on the biology and life history of the white shark (CRC Press, Boca Raton, 2012).
Google Scholar 

9.
Bruce, B. D. & Bradford, R. W. Habitat use and spatial dynamics of juvenile white sharks, Carcharodon carcharias, in eastern Australia. Global perspectives on the biology and life history of the white shark, 225–254 (2012).

10.
Lowe, C. G. et al. Historic fishery interactions with white sharks in the Southern California Bight. Global Perspectives on the Biology and Life History of the White Shark’.(Ed. ML Domeier.) pp, 169–186 (2012).

11.
Villafaña, J. A. et al. First evidence of a palaeo-nursery area of the great white shark. Sci. Rep. 10, 1–8 (2020).
Article  CAS  Google Scholar 

12.
Oñate-González, E. C. et al. Importance of Bahia Sebastian Vizcaino as a nursery area for white sharks (Carcharodon carcharias) in the Northeastern Pacific: a fishery dependent analysis. Fish. Res. 188, 125–137 (2017).
Article  Google Scholar 

13.
Klimley, A. P. The areal distribution and autoecology of the white shark, Carcharodon carcharias, off the west coast of North America. Mem. Southern Calif. Acad Sci 9, 15–40 (1985).
Google Scholar 

14.
Weng, K. C. et al. Movements, behavior and habitat preferences of juvenile white sharks Carcharodon carcharias in the eastern Pacific. Mar. Ecol. Prog. Ser. 338, 211–224 (2007).
ADS  Article  Google Scholar 

15.
White, C. F. et al. Quantifying habitat selection and variability in habitat suitability for juvenile white sharks. PLoS ONE 14, e0214642. https://doi.org/10.1371/journal.pone.0214642 (2019).
Article  PubMed  PubMed Central  Google Scholar 

16.
Di Lorenzo, E. & Mantua, N. Multi-year persistence of the 2014/15 North Pacific marine heatwave. Nat. Clim. Change 6, 1042–1047 (2016).
ADS  Article  Google Scholar 

17.
Peterson, W. T. et al. The pelagic ecosystem in the Northern California Current off Oregon during the 2014–2016 warm anomalies within the context of the past 20 years. J. Geophys. Res.: Oceans 122, 7267–7290 (2017).
ADS  Article  Google Scholar 

18.
Thompson, A. et al. State of the California current: a new anchovy regime and Marine Heatwave? California Cooperative Oceanic Fisheries Investigations Reports. Calif. Cooper. Ocean. Fish. Investig. 60, 1–61 (2019).
Google Scholar 

19.
Gentemann, C. L., Fewings, M. R. & García-Reyes, M. Satellite sea surface temperatures along the West Coast of the United States during the 2014–2016 northeast Pacific marine heat wave. Geophys. Res. Lett. 44, 312–319 (2017).
ADS  Article  Google Scholar 

20.
Sanford, E., Sones, J. L., García-Reyes, M., Goddard, J. H. & Largier, J. L. Widespread shifts in the coastal biota of northern California during the 2014–2016 marine heatwaves. Sci. Rep. 9, 1–14 (2019).
ADS  Article  CAS  Google Scholar 

21.
Choy, C. A. et al. The vertical distribution and biological transport of marine microplastics across the epipelagic and mesopelagic water column. Sci. Rep. 9, 1–9 (2019).
Article  CAS  Google Scholar 

22.
Kohl, W. T., McClure, T. I. & Miner, B. G. Decreased temperature facilitates short-term sea star wasting disease survival in the keystone intertidal sea star Pisaster ochraceus. PLoS ONE 11, e0153670 (2016).
PubMed  PubMed Central  Article  Google Scholar 

23.
Laake, J. L., Lowry, M. S., DeLong, R. L., Melin, S. R. & Carretta, J. V. Population growth and status of California sea lions. J. Wildl. Manag. 82, 583–595 (2018).
Article  Google Scholar 

24.
Jones, T. et al. Unusual mortality of Tufted puffins (Fratercula cirrhata) in the eastern Bering Sea. PLoS ONE 14, e0216532 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

25.
Savage, K. Alaska and British Columbia large whale unusual mortality event summary report. (2017).

26.
Gravem, S. A. & Morgan, S. G. Shifts in intertidal zonation and refuge use by prey after mass mortalities of two predators. Ecology 98, 1006–1015 (2017).
PubMed  Article  PubMed Central  Google Scholar 

27.
Cheung, W. W. & Frölicher, T. L. Marine heatwaves exacerbate climate change impacts for fisheries in the northeast Pacific. Sci. Rep. 10, 1–10 (2020).
Article  CAS  Google Scholar 

28.
Kanive, P. E. et al. Size-specific apparent survival rate estimates of white sharks using mark–recapture models. Can. J. Fish. Aquat. Sci. 76, 2027–2034 (2019).
Article  Google Scholar 

29.
California_State_Senate. Budget Act of 2018. Senate Bill 840 2017–2018 (2018).

30.
Miller-Rushing, A., Primack, R. & Bonney, R. The history of public participation in ecological research. Front. Ecol. Environ. 10, 285–290 (2012).
Article  Google Scholar 

31.
Vianna, G. M., Meekan, M. G., Bornovski, T. H. & Meeuwig, J. J. Acoustic telemetry validates a citizen science approach for monitoring sharks on coral reefs. PLoS ONE 9, e95565 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

32.
Klimley, A. P., Anderson, S. D., Pyle, P. & Henderson, R. Spatiotemporal patterns of white shark (Carcharodon carcharias) predation at the South Farallon Islands, California. Copeia, 680–690 (1992).

33.
Fredston-Hermann, A., Selden, R., Pinsky, M., Gaines, S. D. & Halpern, B. S. Cold range edges of marine fishes track climate change better than warm edges. Glob. Change Biol. 26, 2908–2922 (2020).
ADS  Article  Google Scholar 

34.
Cheung, W. W., Watson, R. & Pauly, D. Signature of ocean warming in global fisheries catch. Nature 497, 365–368 (2013).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

35.
Mcclure, M. M. et al. Incorporating climate science in applications of the US Endangered Species Act for aquatic species. Conserv. Biol. 27, 1222–1233 (2013).
PubMed  Article  PubMed Central  Google Scholar 

36.
Silber, G. K. et al. Projecting marine mammal distribution in a changing climate. Front. Mar. Sci. 4, 413 (2017).
Article  Google Scholar 

37.
Cao, J., Thorson, J. T., Punt, A. E. & Szuwalski, C. A novel spatiotemporal stock assessment framework to better address fine-scale species distributions: Development and simulation testing. Fish Fish. 21, 350–367. https://doi.org/10.1111/faf.12433 (2020).
Article  Google Scholar 

38.
Dewar, H., Domeier, M. & Nasby-Lucas, N. Insights into young of the year white shark, Carcharodon carcharias, behavior in the Southern California Bight. Environ. Biol. Fishes 70, 133–143 (2004).
Article  Google Scholar 

39.
Moxley, J. H., Nicholson, T. E., Van Houtan, K. S. & Jorgensen, S. J. Non-trophic impacts from white sharks complicate population recovery for sea otters. Ecol. Evol. 9, 6378–6388. https://doi.org/10.1002/ece3.5209 (2019).
Article  PubMed  PubMed Central  Google Scholar 

40.
Cury, P. et al. Small pelagics in upwelling systems: patterns of interaction and structural changes in “wasp-waist” ecosystems. ICES J. Mar. Sci. 57, 603–618 (2000).
Article  Google Scholar 

41.
Nicholson, T. E. et al. Gaps in kelp cover may threaten the recovery of California sea otters. Ecography 41, 1751–1762 (2018).
Article  Google Scholar 

42.
Kenyon, K. W. The sea otter in the eastern Pacific Ocean. (US Bureau of Sport Fisheries and Wildlife, 1969).

43.
Tinker, M. T., Hatfield, B. B., Harris, M. D. & Ames, J. A. Dramatic increase in sea otter mortality from white sharks in California. Mar. Mammal Sci. 32, 309–326 (2016).
Article  Google Scholar 

44.
Miller, M. A. et al. Predators, disease, and environmental change in the nearshore ecosystem: mortality in Southern Sea Otters (Enhydra lutris nereis) From 1998–2012. Front. Mar. Sci. 7, 582 (2020).
Article  Google Scholar 

45.
Estes, J. A. & Palmisano, J. F. Sea otters: their role in structuring nearshore communities. Science 185, 1058–1060 (1974).
ADS  CAS  PubMed  Article  Google Scholar 

46.
Hughes, B. B. et al. Recovery of a top predator mediates negative eutrophic effects on seagrass. Proc. Natl. Acad. Sci. USA 110, 15313–15318. https://doi.org/10.1073/pnas.1302805110 (2013).
ADS  Article  PubMed  Google Scholar 

47.
Becker, S. L., Nicholson, T. E., Mayer, K. A., Murray, M. J. & Van Houtan, K. S. Environmental Factors May Drive the Post-release Movements of Surrogate-Reared Sea Otters. Frontiers in Marine Science 7, doi:https://doi.org/10.3389/fmars.2020.539904 (2020).

48.
Mayer, K. A. et al. Surrogate rearing a keystone species to enhance population and ecosystem restoration. Oryx, 1–11 (2019).

49.
Jorgensen, S. J. et al. Philopatry and migration of Pacific white sharks. Proc. R. Soc. B: Biol. Sci. 277, 679–688 (2010).
Article  Google Scholar 

50.
Breaker, L. & Broenkow, W. W. The circulation of Monterey Bay and related processes. Moss Land. Mar. Lab. Tech. Publ. 89, 114 (1989).
Google Scholar 

51.
Santora, J. A. et al. Habitat compression and ecosystem shifts as potential links between marine heatwave and record whale entanglements. Nat. Commun. 11, 536. https://doi.org/10.1038/s41467-019-14215-w (2020).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

52.
Zanna, L., Khatiwala, S., Gregory, J. M., Ison, J. & Heimbach, P. Global reconstruction of historical ocean heat storage and transport. Proc. Natl. Acad. Sci. 116, 1126–1131 (2019).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

53.
Gaines, S. D. & Denny, M. W. The largest, smallest, highest, lowest, longest, and shortest: extremes in ecology. Ecology 74, 1677–1692 (1993).
Article  Google Scholar 

54.
Hobday, A. J. et al. A hierarchical approach to defining marine heatwaves. Prog. Oceanogr. 141, 227–238 (2016).
ADS  Article  Google Scholar 

55.
Oliver, E. C. et al. Longer and more frequent marine heatwaves over the past century. Nat. Commun. 9, 1–12 (2018).
CAS  Article  Google Scholar 

56.
Worm, B. et al. Global catches, exploitation rates, and rebuilding options for sharks. Mar. Policy 40, 194–204 (2013).
Article  Google Scholar 

57.
McCauley, D. J., DeSalles, P. A., Young, H. S., Gardner, J. P. & Micheli, F. Use of high-resolution acoustic cameras to study reef shark behavioral ecology. J. Exp. Mar. Biol. Ecol. 482, 128–133 (2016).
Article  Google Scholar 

58.
Ward-Paige, C. A. & Worm, B. Global evaluation of shark sanctuaries. Global Environ. Change 47, 174–189 (2017).
Article  Google Scholar 

59.
Van Houtan, K. S. et al. Coastal sharks supply the global shark fin trade. Biol. Let. 16, 20200609 (2020).
Article  CAS  Google Scholar 

60.
Benson, J. F. et al. Juvenile survival, competing risks, and spatial variation in mortality risk of a marine apex predator. J. Appl. Ecol. 55, 2888–2897 (2018).
Article  Google Scholar 

61.
Cleveland, W. S. & Devlin, S. J. Locally weighted regression: an approach to regression analysis by local fitting. J. Am. Stat. Assoc. 83, 596–610 (1988).
MATH  Article  Google Scholar 

62.
Beck, J., Ballesteros-Mejia, L., Nagel, P. & Kitching, I. J. Online solutions and the ‘W allacean shortfall’: what does GBIF contribute to our knowledge of species’ ranges?. Divers. Distrib. 19, 1043–1050 (2013).
Article  Google Scholar 

63.
Dickinson, J. L., Zuckerberg, B. & Bonter, D. N. Citizen science as an ecological research tool: challenges and benefits. Annu. Rev. Ecol. Evol. Syst. 41, 149–172 (2010).
Article  Google Scholar 

64.
Van Horn, G. et al. in Proceedings of the IEEE conference on computer vision and pattern recognition. 8769–8778.

65.
Teo, S. L. et al. Validation of geolocation estimates based on light level and sea surface temperature from electronic tags. Mar. Ecol. Prog. Ser. 283, 81–98 (2004).
ADS  Article  Google Scholar 

66.
Handcock, M. S. Package ‘reldist’. (2016).

67.
Reynolds, R. & Banzon, V. NOAA Optimum Interpolation 1/4 Degree Daily Sea Surface Temperature (OISST) Analysis, Version 2. NOAA National Centers for Environmental Information. 10, V5SQ8XB5 (2008).

68.
Banzon, V., Smith, T. M., Chin, T. M., Liu, C. & Hankins, W. A long-term record of blended satellite and in situ sea-surface temperature for climate monitoring, modeling and environmental studies. (2016).

69.
Lyons, K. et al. The degree and result of gillnet fishery interactions with juvenile white sharks in southern California assessed by fishery-independent and-dependent methods. Fish. Res. 147, 370–380 (2013).
ADS  Article  Google Scholar 

70.
Mayer, L. et al. The Nippon Foundation—GEBCO seabed 2030 project: The quest to see the world’s oceans completely mapped by 2030. Geosciences 8, 63 (2018).
ADS  Article  Google Scholar 

71.
Tanaka, K. R. et al. Mesoscale climatic impacts on the distribution of Homarus americanus in the US inshore Gulf of Maine. Can. J. Fish. Aquat. Sci. 76, 608–625 (2019).
Article  Google Scholar 

72.
R_Core_Team. (Vienna, Austria, 2019). More