Philippa Kaur

1.
Li, S., Wang, P., Yuan, W., Su, Z. & Bullard, S. H. Endocidal regulation of secondary metabolites in the producing organisms. Sci. Rep. 6, 29315. https://doi.org/10.1038/srep29315 (2016).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 
2.
Dudareva, N., Negre, F., Nagegowda, D. A. & Orlova, I. Plant volatiles: recent advances and future perspectives. Crit. Rev. Plant Sci. 25, 417–440. https://doi.org/10.1080/07352680600899973 (2006).
CAS  Article  Google Scholar 

3.
Holopainen, J. K. Multiple functions of inducible plant volatiles. Trends Plant Sci. 9, 529–533. https://doi.org/10.1016/j.tplants.2004.09.006 (2004).
CAS  Article  PubMed  Google Scholar 

4.
Dudareva, N., Klempien, A., Muhlemann, J. K. & Kaplan, I. Biosynthesis, function and metabolic engineering of plant volatile organic compounds. New Phytol. 198, 16–32. https://doi.org/10.1111/nph.12145 (2013).
CAS  Article  PubMed  Google Scholar 

5.
Effah, E., Holopainen, J. K. & Clavijo McCormick, A. Potential roles of volatile organic compounds in plant competition. Perspect. Plant Ecol. Evol. Syst. 38, 58–63. https://doi.org/10.1016/j.ppees.2019.04.003 (2019).
Article  Google Scholar 

6.
Flamini, G., Tebano, M. & Cioni, P. L. Volatiles emission patterns of different plant organs and pollen of Citrus limon. Anal. Chim. Acta 589, 120–124 (2007).
CAS  Article  Google Scholar 

7.
Holopainen, J. K. & Gershenzon, J. Multiple stress factors and the emission of plant VOCs. Trends Plant Sci. 15, 176–184. https://doi.org/10.1016/j.tplants.2010.01.006 (2010).
CAS  Article  PubMed  Google Scholar 

8.
Bracho-Nunez, A., Welter, S., Staudt, M. & Kesselmeier, J. Plant-specific volatile organic compound emission rates from young and mature leaves of Mediterranean vegetation. J. Geophys. Res. Atmos. https://doi.org/10.1029/2010jd015521 (2011).
Article  Google Scholar 

9.
Vivaldo, G., Masi, E., Taiti, C., Caldarelli, G. & Mancuso, S. The network of plants volatile organic compounds. Sci. Rep. 7, 1–18 (2017).
CAS  Article  Google Scholar 

10.
Himanen, S. J. et al. Birch (Betula spp.) leaves adsorb and re-release volatiles specific to neighbouring plants—a mechanism for associational herbivore resistance? New Phytol. 186, 722–732. https://doi.org/10.1111/j.1469-8137.2010.03220.x (2010).
CAS  Article  PubMed  Google Scholar 

11.
Camacho-Coronel, X., Molina-Torres, J. & Heil, M. Sequestration of exogenous volatiles by plant cuticular waxes as a mechanism of passive associational resistance: a proof of concept. Front. Plant Sci. https://doi.org/10.3389/fpls.2020.00121 (2020).
Article  PubMed  PubMed Central  Google Scholar 

12.
Clavijo McCormick, A. Can plant–natural enemy communication withstand disruption by biotic and abiotic factors?. Ecol. Evol. 6, 8569–8582. https://doi.org/10.1002/ece3.2567 (2016).
Article  PubMed  PubMed Central  Google Scholar 

13.
Shiojiri, K. et al. Functions of plant infochemicals in tritrophic interactions between plants, herbivores and carnivorous natural enemies. Jpn. J. Appl. Entomol. Zool. 46, 117–133 (2002).
CAS  Article  Google Scholar 

14.
Pichersky, E. & Gershenzon, J. The formation and function of plant volatiles: perfumes for pollinator attraction and defense. Curr. Opin. Plant Biol. 5, 237–243 (2002).
CAS  Article  Google Scholar 

15.
Kigathi, R. N., Weisser, W. W., Reichelt, M., Gershenzon, J. & Unsicker, S. B. Plant volatile emission depends on the species composition of the neighboring plant community. BMC Plant Biol. 19, 58. https://doi.org/10.1186/s12870-018-1541-9 (2019).
Article  PubMed  PubMed Central  Google Scholar 

16.
Effah, E. et al. Natural variation in volatile emissions of the invasive weed Calluna vulgaris in New Zealand. Plants 9, 283 (2020).
Article  Google Scholar 

17.
Inderjit, S. et al. Volatile chemicals from leaf litter are associated with invasiveness of a Neotropical weed in Asia. Ecology 92, 316–324. https://doi.org/10.1890/10-0400.1 (2011).
CAS  Article  PubMed  Google Scholar 

18.
Broz, A. K. et al. Plant neighbor identity influences plant biochemistry and physiology related to defense. BMC Plant Biol. 10, 115. https://doi.org/10.1186/1471-2229-10-115 (2010).
CAS  Article  PubMed  PubMed Central  Google Scholar 

19.
Corbin, J. D. & D’Antonio, C. M. Competition between native perennial and exotic annual grasses: implications for an historical invasion. Ecology 85, 1273–1283. https://doi.org/10.1890/02-0744 (2004).
Article  Google Scholar 

20.
Leger, E. A. & Espeland, E. K. Perspective: coevolution between native and invasive plant competitors: implications for invasive species management. Evol. Appl. 3, 169–178. https://doi.org/10.1111/j.1752-4571.2009.00105.x (2010).
Article  PubMed  PubMed Central  Google Scholar 

21.
Alvarez-Suarez, J. M., Gasparrini, M., Forbes-Hernández, T. Y., Mazzoni, L. & Giampieri, F. The composition and biological activity of honey: a focus on Manuka honey. Foods 3, 420–432 (2014).
Article  Google Scholar 

22.
Almasaudi, S. B. et al. Antioxidant, anti-inflammatory, and antiulcer potential of manuka honey against gastric ulcer in rats. Oxid. Med. Cell. Longev. 2016, 3643824 (2016).
Article  Google Scholar 

23.
Ronghua, Y., Mark, A. F. & Wilson, J. B. Aspects of the ecology of the indigenous shrub Leptospermum scoparium (Myrtaceae) in New Zealand. N. Z. J. Bot. 22, 483–507. https://doi.org/10.1080/0028825X.1984.10425282 (1984).
Article  Google Scholar 

24.
Stephens, J. M. C., Molan, P. C. & Clarkson, B. D. A review of Leptospermum scoparium (Myrtaceae) in New Zealand. N. Z. J. Bot. 43, 431–449. https://doi.org/10.1080/0028825X.2005.9512966 (2005).
Article  Google Scholar 

25.
Smale, M. C. Ecology of Dracophyllum subulatum-dominant heathland on frost flats at Rangitaiki and north Pureora, central North Island New Zealand. N. Z. J. Bot. 28, 225–248. https://doi.org/10.1080/0028825X.1990.10412311 (1990).
Article  Google Scholar 

26.
Rogers, G. M. North Island seral tussock grasslands 1. Origins and land-use history. N. Z. J. Bot. 32, 271–286. https://doi.org/10.1080/0028825X.1994.10410471 (1994).
Article  Google Scholar 

27.
Bagnall, A. Heather at Tongariro. A study of a weed introduction. Tussock Grassl. Mountainlands Inst. Rev. 41, 17–21 (1982).
Google Scholar 

28.
Buddenhagen, C. E. Broom Control Monitoring at Tongariro National Park. (Department of Conservation, 2000).

29.
Perry, N. B. et al. Essential oils from New Zealand manuka and kanuka: chemotaxonomy of Leptospermum. Phytochemistry 44, 1485–1494. https://doi.org/10.1016/S0031-9422(96)00743-1 (1997).
CAS  Article  Google Scholar 

30.
Douglas, M. H. et al. Essential oils from New Zealand manuka: triketone and other chemotypes of Leptospermum scoparium. Phytochemistry 65, 1255–1264. https://doi.org/10.1016/j.phytochem.2004.03.019 (2004).
CAS  Article  PubMed  Google Scholar 

31.
Guenther, A. B., Zimmerman, P. R., Harley, P. C., Monson, R. K. & Fall, R. Isoprene and monoterpene emission rate variability: model evaluations and sensitivity analyses. J. Geophys. Res. Atmos. 98, 12609–12617. https://doi.org/10.1029/93jd00527 (1993).
ADS  Article  Google Scholar 

32.
Pratt, J. D., Keefover-Ring, K., Liu, L. Y. & Mooney, K. A. Genetically based latitudinal variation in Artemisia californica secondary chemistry. Oikos 123, 953–963. https://doi.org/10.1111/oik.01156 (2014).
Article  Google Scholar 

33.
Soler, C. C. L., Proffit, M., Bessière, J.-M., Hossaert-McKey, M. & Schatz, B. Evidence for intersexual chemical mimicry in a dioecious plant. Ecol. Lett. 15, 978–985. https://doi.org/10.1111/j.1461-0248.2012.01818.x (2012).
Article  PubMed  Google Scholar 

34.
Anderson, M. J. Permutational multivariate analysis of variance. Department of Statistics, University of Auckland, Auckland 26, 32–46 (2005).

35.
Anderson, M. J. Permutational multivariate analysis of variance (PERMANOVA). Wiley statsref: statistics reference online, 1–15 (2014).

36.
Copolovici, L. & Niinemets, Ü. In Deciphering Chemical Language of Plant Communication 35–59 (Springer, 2016).

37.
Valolahti, H., Kivimäenpää, M., Faubert, P., Michelsen, A. & Rinnan, R. Climate change-induced vegetation change as a driver of increased subarctic biogenic volatile organic compound emissions. Glob. Change Biol. 21, 3478–3488. https://doi.org/10.1111/gcb.12953 (2015).
ADS  Article  Google Scholar 

38.
Laothawornkitkul, J., Taylor, J. E., Paul, N. D. & Hewitt, C. N. Biogenic volatile organic compounds in the Earth system. New Phytol. 183, 27–51. https://doi.org/10.1111/j.1469-8137.2009.02859.x (2009).
CAS  Article  PubMed  Google Scholar 

39.
Loreto, F. & Schnitzler, J.-P. Abiotic stresses and induced BVOCs. Trends Plant Sci. 15, 154–166. https://doi.org/10.1016/j.tplants.2009.12.006 (2010).
CAS  Article  PubMed  Google Scholar 

40.
Possell, M. & Loreto, F. In Biology, Controls and Models of Tree Volatile Organic Compound Emissions 209–235 (Springer, Berlin, 2013).

41.
Peñuelas, J. & Staudt, M. BVOCs and global change. Trends Plant Sci. 15, 133–144. https://doi.org/10.1016/j.tplants.2009.12.005 (2010).
CAS  Article  PubMed  Google Scholar 

42.
Pare, P. W. & De Tumlinson, J. H. De novo biosynthesis of volatiles induced by insect herbivory in cotton plants. Plant Physiol. 114, 1161. https://doi.org/10.1104/pp.114.4.1161 (1997).
CAS  Article  PubMed  PubMed Central  Google Scholar 

43.
Holopainen, J. & Blande, J. Where do herbivore-induced plant volatiles go?. Front. Plant Sci. https://doi.org/10.3389/fpls.2013.00185 (2013).
Article  PubMed  PubMed Central  Google Scholar 

44.
Niinemets, Ü, Kännaste, A. & Copolovici, L. Quantitative patterns between plant volatile emissions induced by biotic stresses and the degree of damage. Front. Plant Sci. https://doi.org/10.3389/fpls.2013.00262 (2013).
Article  PubMed  PubMed Central  Google Scholar 

45.
Litt, A. R., Cord, E. E., Fulbright, T. E. & Schuster, G. L. Effects of invasive plants on arthropods. Conserv. Biol. 28, 1532–1549. https://doi.org/10.1111/cobi.12350 (2014).
Article  PubMed  Google Scholar 

46.
Dicke, M. & Baldwin, I. T. The evolutionary context for herbivore-induced plant volatiles: beyond the ‘cry for help’. Trends Plant Sci. 15, 167–175. https://doi.org/10.1016/j.tplants.2009.12.002 (2010).
CAS  Article  PubMed  Google Scholar 

47.
Clavijo McCormick, A., Unsicker, S. B. & Gershenzon, J. The specificity of herbivore-induced plant volatiles in attracting herbivore enemies. Trends Plant Sci. 17, 303–310. https://doi.org/10.1016/j.tplants.2012.03.012 (2012).
CAS  Article  PubMed  Google Scholar 

48.
Turlings, T. C. J. & Erb, M. Tritrophic interactions mediated by herbivore-induced plant volatiles: mechanisms, ecological relevance, and application potential. Annu. Rev. Entomol. 63, 433–452. https://doi.org/10.1146/annurev-ento-020117-043507 (2018).
CAS  Article  PubMed  Google Scholar 

49.
Bernasconi, M. L., Turlings, T. C. J., Ambrosetti, L., Bassetti, P. & Dorn, S. Herbivore-induced emissions of maise volatiles repel the corn leaf aphid, Rhopalosiphum maidis. Entomol. Exp. Appl. 87, 133–142. https://doi.org/10.1046/j.1570-7458.1998.00315.x (1998).
CAS  Article  Google Scholar 

50.
De Moraes, C. M., Mescher, M. C. & Tumlinson, J. H. Caterpillar-induced nocturnal plant volatiles repel conspecific females. Nature 410, 577–580. https://doi.org/10.1038/35069058 (2001).
ADS  CAS  Article  PubMed  Google Scholar 

51.
Clavijo McCormick, A. et al. Herbivore-induced volatile emission in black poplar: regulation and role in attracting herbivore enemies. Plant Cell Environ. 37, 1909–1923. https://doi.org/10.1111/pce.12287 (2014).
Article  PubMed  Google Scholar 

52.
Irmisch, S. et al. Herbivore-induced poplar cytochrome P450 enzymes of the CYP71 family convert aldoximes to nitriles which repel a generalist caterpillar. Plant J. 80, 1095–1107. https://doi.org/10.1111/tpj.12711 (2014).
CAS  Article  PubMed  Google Scholar 

53.
Ehrenfeld, J. G. Effects of exotic plant invasions on soil Nutrient cycling processes. Ecosystems 6, 503–523. https://doi.org/10.1007/s10021-002-0151-3 (2003).
CAS  Article  Google Scholar 

54.
Vallés, S. M., Fernández, J. B. G., Dellafiore, C. & Cambrollé, J. Effects on soil, microclimate and vegetation of the native-invasive Retama monosperma (L.) in coastal dunes. Plant Ecol. 212, 169–179. https://doi.org/10.1007/s11258-010-9812-z (2011).
Article  Google Scholar 

55.
Rogers, G. M. Demography, and post-control response of heather in the central north island. Sci. Conserv. 9, 20 (1995).
Google Scholar 

56.
Fogarty, G. & Facelli, J. M. Growth and competition of Cytisus scoparius, an invasive shrub, and Australian native shrubs. Plant Ecol. 144, 27–35. https://doi.org/10.1023/A:1009808116068 (1999).
Article  Google Scholar 

57.
Haubensak, K. A. & Parker, I. M. Soil changes accompanying invasion of the exotic shrub Cytisus scoparius in glacial outwash prairies of western Washington [USA]. Plant Ecol. 175, 71–79. https://doi.org/10.1023/B:VEGE.0000048088.32708.58 (2004).
Article  Google Scholar 

58.
Caldwell, B. A. Effects of invasive scotch broom on soil properties in a Pacific coastal prairie soil. Appl. Soil. Ecol. 32, 149–152. https://doi.org/10.1016/j.apsoil.2004.11.008 (2006).
Article  Google Scholar 

59.
Chen, Y., Schmelz, E. A., Wäckers, F. & Ruberson, J. R. Cotton plant, Gossypium hirsutum L., defense in response to nitrogen fertilization. J. Chem. Ecol. 34, 1553–1564. https://doi.org/10.1007/s10886-008-9560-x (2008).
CAS  Article  PubMed  Google Scholar 

60.
Peñuelas, J. & Llusià, J. Influence of intra- and inter-specific Interference on terpene emission by Pinus Halepensis and Quercus Ilex seedlings. Biol. Plant. 41, 139–143. https://doi.org/10.1023/A:1001789222741 (1998).
Article  Google Scholar 

61.
Ormeño, E., Fernandez, C. & Mévy, J. P. Plant coexistence alters terpene emission and content of Mediterranean species. Phytochemistry 68, 840–852. https://doi.org/10.1016/j.phytochem.2006.11.033 (2007).
CAS  Article  PubMed  Google Scholar 

62.
Kigathi, R. N., Weisser, W. W., Veit, D., Gershenzon, J. & Unsicker, S. B. Plants suppress their emission of volatiles when growing with conspecifics. J. Chem. Ecol. 39, 537–545. https://doi.org/10.1007/s10886-013-0275-2 (2013).
CAS  Article  PubMed  Google Scholar 

63.
Ishizaki, S., Shiojiri, K., Karban, R. & Ohara, M. Effect of genetic relatedness on volatile communication of sagebrush (Artemisia tridentata). J. Plant Interact. 6, 193–193 (2011).
CAS  Article  Google Scholar 

64.
Wason, E. L. & Hunter, M. D. Genetic variation in plant volatile emission does not result in differential attraction of natural enemies in the field. Oecologia 174, 479–491 (2014).
ADS  Article  Google Scholar 

65.
Karban, R. & Shiojiri, K. Self-recognition affects plant communication and defense. Ecol. Lett. 12, 502–506 (2009).
Article  Google Scholar  More

Minimal fatal shock
The first step in identifying the MiFaS for a given system is to define a desired state (mathbf {X_0}). We then assume that, prior to perturbations, the system resides on (mathbf {X_0}) and that a shock—applied at (t=0)—kicks the system’s state instantaneously to (mathbf {X}(0)). A shock—now defined as (mathbf {x}(0) = mathbf {X}(0)-mathbf {X_0})—is said to be fatal if (mathbf {X}(0)) is located outside the basin of (mathbf {X_0}) and non-fatal if (mathbf {X}(0)) is located within the basin of (mathbf {X_0}). Accordingly, the MiFaS is a vector which displays the shortest distance between the desired state and its basin boundary and the corresponding direction in state space (Fig. 1a).
Figure 1

Representation of the Minimal Fatal Shock and the related search algorithm. (a) The MiFaS (red arrow) is the smallest perturbation to the desired state (mathbf {X_0}) which puts the system outside the basin of (mathbf {X_0}) and into the basin of an alternative attractor (mathbf {X_a}). (b) The search algorithm starts with a relatively large perturbation magnitude. The related subspace of allowed initial conditions is given by the largest circle and the direction of maximum amplification is displayed by the green arrow. As the magnitude of allowed perturbations is reduced, the direction of maximum amplification converges towards the MiFaS. Color coding marks the objective function (distance to the desired state after a short integration time) with dark colors displaying large values and bright colors small values. This figure was generated using MATLAB version R2020a (https://www.mathworks.com/).

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The second essential step, is defining a norm for the perturbation size. It is important to note that the use of a certain norm is not only a technical but also an interpretative decision. Throughout this work, we use the Euclidean distance to the desired state (mathbf {X_0}) to quantify the magnitude d of a perturbation

$$begin{aligned} d ; = , ||mathbf {x}(0) || , = , ||mathbf {X}(0) – mathbf {X_0}||. end{aligned}$$
(1)

To determine the MiFaS, we develop a search algorithm which is based on the minimal seed approach41 and which can be divided into two stages, the global random initialization (stage I) and the local non-random optimization (stage II).
In stage I, we randomly draw initial conditions from a shrinking subspace in state space to find a fatal shock with a preferably small magnitude d (see “Methods” and Supplementary Fig. S1). Stage II starts with the smallest fatal shock received from stage I (Supplementary Fig. S1). From this point on, we take two seemingly opposing steps. First, we adapt the direction of (mathbf {x}(0)) in order to move (mathbf {X}(0)) away from the basin of (mathbf {X_0}) while keeping d fixed. Second, we move (mathbf {X}(0)) towards the basin by reducing d by a step size (Delta d). By repeating these two steps iteratively, we attain smaller and smaller fatal shocks which finally converge towards a local MiFaS (see Fig. 1b and Supplementary Fig. S1). It is important to note that the outcome of the search—and thus the achieved local MiFaS—is dependent on the initialization in stage I. Accordingly, to attain the global MiFaS, we need to run the search algorithm multiple times and select the minimum of the local MiFaS as the global one.
Figure 2

Minimal Fatal Shock for an exemplary plant–pollinator network. (a) Direction of the MiFaS. The perturbation vector is scaled to a length of 1. The relative contribution of each element of the vector (node in the network) to the overall perturbation is represented by the area and the color saturation of the respective squares and circles. A pink coloring denotes a loss and a green coloring a gain in species abundance at the respective node. Squares portray pollinators and circles plants. Species being lost after the perturbation are marked by the yellow shaded region. Placement of the vertices is based on the Kamada–Kawai algorithm66 obtained from python-igraph version 0.7.1 (https://igraph.org/). (b) Transient behavior following the MiFaS. Dark gray area shows the situation before the perturbation (desired state). Lighter gray area shows how the state variables are altered due to the perturbation. Light gray area depicts the transient behavior after the system has been perturbed. (c) Evolution over a longer time span. Vertical line displays the time interval shown in (b). The figure was generated using MATLAB version R2020a (https://www.mathworks.com/).

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The centerpiece of the outlined algorithm is the adaptation of the direction of (mathbf {x}(0)) during stage II, which aims at maximizing the distance between (mathbf {X}(0)) and the basin boundary of (mathbf {X_0}). However, since this distance is not easily accessible, it is approximated by an objective function which can be maximized within a constraint optimization. For the two applications we present here, the objective function can be thought of as the amplification of the shock over a preselected time T (see “Methods” for specific definition). The mechanism behind this is that trajectories close to the basin boundary stay close to it for long times as they move along the stable manifold of a saddle-type state while trajectories far off the boundary approach an alternative attractor faster and thus lead to earlier and stronger amplifications.
In summary, as a result of the optimization procedure we obtain the magnitude of the smallest distance to the basin boundary which can be utilized as a quantitative measure of global stability and the direction of the perturbation in the high-dimensional phase space.
Plant–pollinator networks
In our first example, we consider a simple model of mutualism which captures the crucial aspects of a system of plants and their corresponding pollinators43,45. The mutualistic system is described as a bipartite network, with one set of nodes representing a number of (N_P) plant species and one set representing a number of (N_A) animal species whose dynamics are given by

$$begin{aligned} frac{mathrm {d} P_i}{mathrm {d} t} ,&= , alpha P_i , – , sum _{k=1}^{N_P} beta _{ik} P_i P_k , + , frac{sum _{j=1}^{N_A} gamma _{ij} A_j P_i}{1 + h sum _{j=1}^{N_A} gamma _{ij} A_j},nonumber \ frac{mathrm {d} A_j}{mathrm {d} t} ,&= , alpha A_j , – , sum _{l=1}^{N_A} {tilde{beta }}_{jl} A_j A_l , + , frac{sum _{i=1}^{N_P} {tilde{gamma }}_{ji} P_i A_j}{1 + h sum _{i=1}^{N_P} {tilde{gamma }}_{ji} P_i}, end{aligned}$$
(2)

where (P_i) denotes the abundance of plant species i ((i=1, ldots , N_P)) and (A_j) the abundance of animal species j ((j=1, ldots , N_A)). In Eq. (2), the parameter (alpha) gives the intrinsic growth rate, (beta _{ik}) (({tilde{beta }}_{jl})) the competitive pressure of plant (animal) species k (l) on plant (animal) species i (j), (gamma _{ij}) (({tilde{gamma }}_{ji})) the benefit plant (animal) species i (j) obtains from animal (plant) species j (i) and h the handling time for pollination. As a general principle, we assume the benefit a species gains from pollination to be obligatory for its own growth, an assumption which is necessary to obtain multistability in this model57. Therefore, we choose the net growth rate (alpha le 0).
In order to keep the parametrization as simple as possible, we set (alpha), (beta _{ii}) (({tilde{beta }}_{jj})) and h to be equal for all species. To reduce the complexity of the overall interaction pattern, we assume all-to-all coupling for the interspecific competition between species within one set, whereby (beta _{ik}=beta _0/(N_{P}-1)) for (i ne k) (({tilde{beta }}_{jl}=beta _0/(N_{A}-1)) for (j ne l)). By contrast, a mutualistic interaction between an animal and a plant species can either be absent, in which case (gamma _{ij}=0) (({tilde{gamma }}_{ji}=0)), or present, in which case (gamma _{ij}=gamma _0/kappa _i) (({tilde{gamma }}_{ji}=gamma _0/{tilde{kappa }}_j)), where (kappa _i) (({tilde{kappa }}_j)) denotes the degree or the number of mutualistic partners of plant (animal) species i (j). This formulation corresponds to a full trade-off between the benefit a species attains from one partner and the number of partners this species has45. An important aspect of the chosen parametrization is that species solely differ on account of their position in the mutualistic network. In the following, we determine the MiFaS for realistic plant–pollinator networks from the Web of Life Database58 representing networks from different geographic locations across various climate zones (see Supplementary Fig. S5 and Supplementary Table S2). With (alpha = -0.3), (beta _{ii}=1.0), (beta _0 = 1.0), (gamma _0 = 4.5) and (h=0.1), we choose the model parameters in a way that ensures that each of the studied systems possesses a state in which all species coexist. This ’desired’ state (mathbf {X_0}) is opposed to multiple ’undesired’ states in which one or more species are gone extinct (the MiFaS is actually fatal).
To interpret the results, it is useful to state some general considerations first. Due to the mutualism, the growth of a species depends on the abundance of its mutualistic partners. As the growth of these partners can also depend on further other partners, these further partners indirectly support the growth of the first species. We could continue building this chain of dependencies but essential is that species being close to each other within the network and especially those sharing partners benefit from each other. On the other hand, due to competition high abundances of one species directly impede the growth of all species within the same group (animals or plants). Hence, the net effect which an increase or decrease of a species’ abundance has on another species depends on the interplay between the two processes. The indirect benefits can either balance or enhance the negative effects due to competition depending on whether species are close (balance) or far apart (enhance).
At first, we compute the minimal fatal shock (MiFaS) for an exemplary network from Morant Point in Jamaica (Fig. 2a). The topology of this system is characterized by an asymmetric division into a small tree-like part and a large core, i.e. a large mostly well connected component. This topological division is mirrored in the direction of the MiFaS which is visualized by the color-coding. A small negatively perturbed part consisting of the tree-like periphery (nodes within the yellow shaded region in Fig. 2a) plus its single non-peripheral neighbor is opposed to the rest of the network which is positively perturbed. This division exemplifies how the mutualistic and competitive interactions between species shape the system’s response to perturbations. In the tree-shaped part of the network, all species are close to each other but far away from most other species. Furthermore, due to the sole connection between the two characteristic structural parts of the network, the share of partners between the two is minimal. As a result, the interdependency of species within the tree-shaped part is extremely high. Accordingly, the loss of abundance of any species in the tree-like structure—as it is the case in the MiFaS (Fig. 2)—significantly affects all other species in this tree-like periphery. On the contrary, the competitive stress due to species within the large component is high as it is not balanced by the indirect benefits. It is actually even enhanced as the increase of abundance of one species boosts the growth of its partners which again enhances the competive stress on the peripheral tree-like structure.
Figure 3

Magnitudes of 59 and direction of six MiFaS in plant–pollinator networks. The 59 networks are ordered, from low to high, and labeled according to their respective magnitude of the MiFaS. In addition, the direction of the MiFaS is shown for six exemplary networks. Perturbation vectors are scaled to a length of 1. The relative contribution of each element of the vector (node in the network) to the overall perturbation is represented by the area and the color saturation of the respective squares and circles. A pink coloring denotes a loss and a green coloring a gain in species abundance at the respective node. Squares portray pollinators and circles plants. Species being lost after the perturbation are marked by the yellow shaded region. Placement of the vertices is based on the Kamada–Kawai algorithm66 obtained from python-igraph version 0.7.1 (https://igraph.org/). The figure was generated using MATLAB version R2020a (https://www.mathworks.com/).

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After the system has been hit by the MiFaS, all ten species within the tree-like periphery are lost in the long run (Fig. 2c and yellow shaded region in Fig. 2a). The remaining species—except for the single neighbor of the periphery—tend to higher abundances as the competitive pressure on them is relaxed. Accordingly, the new asymptotic state (Fig. 2c) again shows that the net impact of the peripheral species on most other species has been negative. Apart from the new asymptotic state, the transient leading there (Fig. 2b,c) is of interest as well. In fact, the transient behavior is typical for an initial state close to the basin boundary which is made up by the stable manifold of a saddle point. The transient at first moves towards the saddle fast (Fig. 2b), stays in its vicinity for some time as the repulsion is weak and finally settles on an attractor which, in this case, is the undesired state of partial extinction (Fig. 2c).
Figure 4

Minimal Fatal Shock in the Great Britain power grid. (a) Direction of the MiFaS. The perturbation vector is scaled to a length of 1. The relative contribution of each element of the vector (node in the network) to the overall perturbation is represented by the area and the color saturation of the respective squares and circles. A blue coloring denotes a deceleration and a pink coloring an acceleration at the respective node. Squares portray consumers and circles generators. Width of transmission line scales with respective initial transmission load. (b) Blow-up of tree-like structure in (a). (c, d) Transient behavior following the MiFaS. (c) Time series of the loads on the transmission lines included in (b). Colors of highlighted loads correspond to colors of transmission lines in (b), remaining loads are depicted in white. (d) Time series of the frequency deviations of all oscillators, color coding corresponds to perturbation magnitude and direction at each node. The figure was generated using MATLAB version R2020a (https://www.mathworks.com/).

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Overall, we examine the MiFaS for a total of 59 plant–pollinator systems, each being based on one of the real-world network topologies. For comparison, we order the networks from sensitive to robust according to the magnitude of their respective MiFaS and depict the direction of the MiFaS for five further exemplary systems (Fig. 3).
Figure 5

Local Minimal Fatal Shocks in the Great Britain power grid. Direction of the local MiFaS. The perturbation vectors are scaled to a length of 1. The relative contribution of each element of the vector (node in the network) to the overall perturbation is represented by the area and the color saturation of the respective squares and circles. A blue coloring denotes a deceleration and a pink coloring an acceleration at the respective node. Squares portray consumers and circles generators. (a–d) Blow-ups of the significantly perturbed area of four local MiFaS which correspond to different outcomes of the optimization process. Highlighted edges represent the trigger transmission line of the particular perturbation. The figure was generated using MATLAB version R2020a (https://www.mathworks.com/).

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Some characteristics found for the MiFaS of the exemplary network (Fig. 2) prove to be generally valid. For each system, the division of the MiFaS into a small negatively perturbed part and a larger but weaker positively perturbed part displays how mutualistic interdependency and competition shape the system’s response to perturbations. In this context, the negatively perturbed part marks the weakest point of the network at whose outer edge the extinction occurs. Speaking in ecological terms, we find these weak points always being associated with specialization and the distribution of negative perturbations depends on the nature of the caused interdependency: in the exemplary system (network 1 in Fig. 3), where the specialization among all species within the tree-like structure is rather mutual, all involved species are significantly perturbed (the same for network 13 and partly for network 4, Fig. 3). However, the more asymmetric the specialization gets—meaning that many specialists are connected to a single generalist—the stronger the negative perturbation focuses on this generalist (networks 4 (rightarrow) 26 (rightarrow) 27 (rightarrow) 49, Fig. 3). This perturbation structure proofs to be efficient as the dependency of the generalist on each single specialist is low but its cumulated dependency on all specialized partners is high. A perturbation at the generalist therefore induces a negative feedback whose strength also depends on the number of connections the generalist has to other-non-specialized species. Accordingly, network 49 is much more robust than network 26 as the decisive generalist is highly connected to the core.
The positive contribution to the overall MiFaS marks the impact of competitive forces which depends on the global interdependency among species. In the case of a single well-connected core and a periphery which only consists of specialists being directly connected to this core, indirect positive effects between species balance competive effects as all species are close and well connected. Accordingly, we do not find any significant contribution of positive perturbations to the overall MiFaS (networks 37, 49, Fig. 3). The contrary is the case if the core is not well build, meaning that only a few connections between important hub nodes exist (networks 4, 26) or if—due to strong reciprocal specialization—a larger peripheral structure exists (networks 1, 13). In such cases, positive perturbations at rather central core-species contribute significantly to the overall MiFaS and thus to the extinction of peripheral species. In summary, a strong global interdependency among all species favors a system’s robustness whereas a strong local interdependency paired with a weak global interdependency depicts the worst case scenario.
Great Britain power grid
As a second example we consider a coarse-grained model of a power grid which exhibits synchronization dynamics. In this framework, a power grid is described as a network of Kuramoto-like13 second order phase oscillators whose dynamics are given by

$$begin{aligned} frac{mathrm {d} phi _i}{mathrm {d} t}&= omega _i nonumber \ frac{mathrm {d} omega _i}{mathrm {d} t}&= P_i – alpha omega _i + sum ^N_{j=1} K_{ji} , sin (phi _j-phi _i), end{aligned}$$
(3)

where (phi _i) and (omega _i) denote the phase and frequency deviation of oscillator i from a grid’s rated frequency (which will hereinafter be referred to as phase and frequency). The parameters (alpha) and (P_i) are the grid’s damping constant and the net power input/output of oscillator i, respectively. The capacities of the transmission lines and therefore also the topology of the grid are contained in the matrix K, with (K_{ji}=K_{ij} >0) if oscillators i and j are connected and (K_{ij}=0) otherwise.
As an example, we consider the Great Britain power grid which consists of 120 nodes and 165 transmission lines59. For reasons of simplification, we assume one half of the oscillators to be generators ((P_i=+P_0)) and one half to be consumers ((P_i=-P_0)) whose distribution within the grid we draw randomly (see Fig. 5). Furthermore, we choose the same maximum capacity for all transmission lines, either (K_{ij}=K_0) or (K_{ij}=0). In a realistic parameter setting of this model, one ’desired’ synchronized state ((phi _i=const) and (omega _i=0) for all i) representing stable operation competes with several ’undesired’ non-synchronized states. With (alpha =0.1), (P_0=1.0) and (K_0=5.0), we choose the model parameters accordingly. In this setting, the MiFaS represents the smallest perturbation to the synchronous state which induces a shift to one of the non-synchronous states interpreted as a power outage.
The combination of frequencies and phases is actually problematic when determining the MiFaS since they differ in units. We therefore only take into account perturbations in the frequencies (omega). In this context, choosing the frequencies (omega) instead of the phases (phi) seems reasonable as disturbances usually occur due to fluctuations in the power generation or consumption60. Such parametric disturbances would first affect the frequencies via (mathrm {d}omega /mathrm {d}t) (Eq. 3). Furthermore, considering only frequencies allows a clearer depiction of the MiFaS, since the corresponding vector contains exactly one entry per node of the power grid.
Examining one random realization of the power grid (Fig. 4a), we find that, like in the exemplary plant–pollinator network, the MiFaS is associated with a tree-like structure including the most peripheral nodes of the network (according to the resistance centrality proposed by61, see Supplementary Fig. S7). In fact, the same structure is highlighted by some of the eigenmodes of the graph Laplacian (see Supplementary Fig. S8). However, apart from the observation that the MiFaS is orthogonal to a neutral perturbation affecting all oscillators in the same way which is equivalent to its first eigenmode, we find no simple connection to the graph Laplacian (see Supplementary Information).
In order to understand the effectiveness of the MiFaS, it is instructive to have a closer look at how the desynchronization occurs after the system has been hit by the MiFaS (Fig. 4c,d). The desynchronization is triggered by an overload on the transmission line which connects the seven northermost oscillators to the rest of the grid (Fig. 4b). Due to the accumulation of consumers within this tree-like structure (5 consumers towards 2 generators), already in the unperturbed state, the load—(K sin (phi _j – phi _i)) for the line connecting nodes j and i—on the ’trigger transmission line’ is comparatively close to its maximum capacity K (see Fig. 4c). Intuitively, a strong deceleration of oscillators inside plus an acceleration of oscillators outside the tree-like structure seems to be an efficient way to induce an overload. Indeed, we find the strongest negative perturbations at the seven oscillators within (Fig. 4b) as well as positive perturbations at several oscillators outside the tree-like structure. However, in the northern part of the grid, the overall MiFaS roughly follows a broad gradient distribution with negative perturbations on both sides of the trigger transmission line and the strongest positive perturbations at rather distant nodes in the northwest of Great Britain. This distribution is efficient as the perturbations in frequencies first have to be transferred into phase deviations to induce an overload. A relatively smooth gradient ensures that the arising phase deviations are balanced slowly and thus a large transmission load can build up.
This transfer can be observed in the first stage of the transient following the MiFaS (Fig. 4c,d). In this stage, the system evolves rather smoothly towards a point where the frequency deviations of all oscillators are close to zero but where, at the same time, the transmission load on the trigger line (red line in Fig. 4) has passed its maximum capacity. The system subsequently enters a stage in which both transmission loads as well as frequencies oscillate erratically until the oscillations suddenly collapse and the system settles on an undesired attractor. It is remarkable that the final overload (green line in Fig. 4) is not located on the line which triggered the desynchronization but on a line deeper in the tree-like structure (Fig. 4c). The final overload is similar to a cutoff of two consumers from the rest of the grid, as the frequencies in the two departed components evolve more or less independently. It is however important to note that this particular undesired state represents only one of several possible outcomes. Indeed, already the slightest variation (smaller than the finite precision of the search algorithm) of the initial perturbation can lead to a different non-synchronous asymptotic state, although the trigger transmission line is always the same. Such high sensitivity is often an indicator for complexly intervowen basins of attraction, characteristic to many highly multistable systems62.
In order to gain more insights into how certain topological features harm a power grid’s stability against shocks, we examine some of the local MiFaS inducing power outages (Fig. 5). These local minima correspond to different outcomes of the applied optimization scheme for the same network topology and parametrization and thus represent further close but less crucial distances between the desired state and its basin boundary. As we are interested in distinct topological weak points of the grid, we take into account only those local minima which differ in the involved trigger transmission line (highlighted edges in Fig. 5).
The local MiFaS, and in particular the examination of the associated trigger transmission lines, reveal two mutually reinforcing sources for the emergence of weak points. Firstly, desynchronization events are triggered on transmission bottlenecks which result from the loose connection between a peripheral subgraph and the rest of the grid. Four out of five of the shown local MiFaS (Fig. 4 and Fig. 5a–c) are actually related to the most pronounced case of such a bottleneck which is a bridge, i.e. a single edge connecting two subnetworks. Secondly, the accumulation of oscillators of the same type within a subgraph induces a local mismatch between power generation and consumption (Fig. 4 and Fig. 5a–d). We find each of the shown local MiFaS to be related to such a local mismatch. Already in the unperturbed state, this mismatch has to be balanced by a high initial load on the connecting transmission line(s) which in turn results in a low threshold for an overload (Fig. 5d). This overload is then triggered by the MiFaS by reinforcing the generation/consumption imbalance between the two subgraphs. Accordingly, all fatal shocks involve strong frequency perturbations with a sign according to the already established power mismatch in the peripheral subgraph and frequency perturbations in the opposite direction in adjacent areas of the grid. However, as in the global MiFaS, the boundary between positive and negative perturbations is not sharp but more (Fig. 5a,c,d) or less (Fig. 5b) follows a kind of gradient.
Of particular interest is the local MiFaS shown in Fig. 5c as its underlying topological motif is quite common in the network: a node with degree 1, also termed ’dead end’32. Apart from the two dead ends within trees (Fig. 5a,b), the portrayed dead end is the one being most sensitive to perturbations despite or seemingly because it is connected to a rather central node of degree 6 (see also Supplementary Fig. S7). For none of the surroundings of the other dead ends, which are all adjacent to lower degree nodes, we find a local MiFaS of similar low magnitude. Accordingly, we conclude that a rather central position of the node from which the peripheral subgraph branches off might actually harm its robustness against particular perturbations. More

Distribution characteristics of REEs in ground water
In this study, ground water samples were collected from 18 ground water monitoring wells around tailings ponds and their chemical characteristics were also having been determined, as showed in Figure S1. Fe, Mn2+, Cl−, SO42−, ammonia nitrogen and total hardness showed the same trend and decreased with distance. The ground water environmental quality standard (III Grade, National Standard Bureau of PR China, GB3838-2002, the water quality above III Grade can be used for living and drinking after treatment, but the water quality below III Grade was bad and cannot be used as drinking water source) was used as the evaluation standard. The ratio of the number of wells with Fe, Mn2+, Cl−, SO42−, ammonia nitrogen and total hardness exceeding the standard in the total number of wells was 33.33%, 61.11%, 66.67%, 77.78%, 100% and 81.25%, respectively.
In order to study the accumulation of REEs in ground water, the concentration of REEs in 18 ground water samples around the tailings pond were measured. The total REEs concentrations in ground water ranged from 0.0820 to 12.3 μg/L, and rare earth in the ground water accumulated in the southeast of the tailings pond (Fig. 2). In addition, the concentrations of REEs in ground water around the tailings pond decreased in the order of Ce  > La  > Nd  > Pr  > Gd  > Sm  > Dy  > Er  > Eu  > Yb  > Tb  > Ho  > Tm  > Lu. Chondrite-normalized REEs patterns for ground waters around the tailings were shown in Fig. 4b and Table 1. The well points have the same normalization pattern with a predominance of LREEs over HREEs.
Figure 2

Distribution of rare earth elements in the ground water surrounding the rare earth tailings pond (μg/L).

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Table 1 Distribution characteristics of REEs in ground water surrounding tailings pond.
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The distribution patterns of REEs in ground water were characterized by obvious fractionation of LREEs and HREEs with the LREEs/HREEs ratios of 2.77 ~ 25.9, and (La/Yb)N of 1.445 ~ 50.67. The degree of LREEs fractionation with (La/Sm)N of 0.5806 ~ 5.216. Most sampling points presented the positive anomaly of Ce and Eu, however, GW1, GW5, GW6, GW9, GW10, GW13 and GW6 were negative anomalies of Ce, while GW1, GW5, GW7 and GW8 were negative anomalies of Eu. Individual anomalies showed differentiation between selected elements (Ce and Eu) and the other REEs (Table 1).
Baotou environmental monitoring station, Inner Mongolia, China detected ground water leakage around the pond, and various degrees of ground water pollution were found with relatively lower metals concentration and higher anionic concentration21,22,23. Therefore, in addition to REEs, for our ground water correlation analysis we chose to also look at Fe, Mn2+, Cl−, SO42−, ammonia nitrogen and some other ions (HCO3−, total hardness). Correlation analysis showed that total hardness (r = 0.541, p  More

1.
Woo, M. K. Permafrost Hydrology (Springer, New York, 2012).
Google Scholar 
2.
Braun, C., Hardy, D. R., Bradley, R. S. & Retelle, M. J. Streamflow and suspended sediment transfer to Lake Sophia, Cornwallis Island, Nunavut, Canada. Arct. Antarct. Alp. Res. 32(4), 456–465. https://doi.org/10.1080/15230430.2000.12003390 (2000).
Article  Google Scholar 

3.
Woo, M. K. & McCann, B. S. Climatic variability, climatic change, runoff, and suspended sediment regimes in northern Canada. Phys. Geogr. 15(3), 201–226. https://doi.org/10.1080/02723646.1994.10642513 (1994).
Article  Google Scholar 

4.
Frey, K. E. & McClelland, J. W. Impacts of permafrost degradation on arctic river biogeochemistry. Hydrol. Process. 23, 169–182. https://doi.org/10.1002/hyp.7196 (2009).
ADS  CAS  Article  Google Scholar 

5.
Lafrenière, M. J. et al. Chapter 6: Drivers, trends and uncertainties of changing freshwater systems. In From Science to Policy in the Eastern Canadian Arctic: An Integrated Regional Impact Study (IRIS) of Climate Change and Modernization (eds Bell, T. & Brown, T. M.) (ArcticNet, Halifax, 2018).
Google Scholar 

6.
Post, E. et al. The polar regions in a 2°C warmer world. Sci. Adv. 5(12), eaaw9883. https://doi.org/10.1126/sciadv.aaq9883 (2019).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

7.
Kokelj, S. V., Lantz, T. C., Tunnicliffe, J., Segal, R. & Lacelle, D. Climate-drive thaw of permafrost preserved glacial landscapes, northwestern Canada. Geology 45(4), 371–374. https://doi.org/10.1130/G38626.1 (2017).
ADS  Article  Google Scholar 

8.
Kokelj, S. V. et al. Thawing of massive ground ice in mega slumps drives increases in stream sediment and solute flux across a range of watershed scales. J. Geophys. Res. Earth Surf. 118, 681–692. https://doi.org/10.1002/jgrf.20063 (2013).
ADS  Article  Google Scholar 

9.
Rudy, A. C. A., Lamoureux, S. F., Kokelj, S. V., Smith, I. R. & England, J. H. Accelerating thermokarst transforms ice-cored terrain triggering a downstream cascade to the ocean. Geophys. Res. Lett. 44(21), 11080–11087. https://doi.org/10.1002/2017GL074912 (2017).
ADS  Article  Google Scholar 

10.
Malone, L., Lacelle, D., Kokelj, S. & Clark, I. D. Impacts of hillslope thaw slumps on the geochemistry of permafrost catchments (Stony Creek watershed, NWT, Canada). Chem. Geol. 356, 38–49. https://doi.org/10.1016/j.chemgeo.2013.07.010 (2013).
ADS  CAS  Article  Google Scholar 

11.
Kokelj, S. V. & Jorgenson, M. T. Advances in thermokarst research. Permafr. Periglac. Process. 24, 108–119. https://doi.org/10.1002/ppp.1779 (2013).
Article  Google Scholar 

12.
Lantz, T. C. & Kokelj, S. V. Increasing rates of retrogressive thaw slump activity in the Mackenzie Delta region, N.W.T., Canada. Geophys. Res. Lett. 35, L06502. https://doi.org/10.1029/2007/GL032433 (2008).
ADS  Article  Google Scholar 

13.
Lewkowicz, A. G. & Way, R. G. Extremes of summer climate trigger thousands of thermokarst landslides in a High Arctic environment. Nat. Commun. 10, 1329. https://doi.org/10.1038/s41467-019-09314-7(2019) (2019).
ADS  Article  PubMed  PubMed Central  Google Scholar 

14.
Bowden, W. B. et al. Sediment and nutrient delivery from thermokarst features in the foothills of the North Slope, Alaska: potential impacts on headwater stream ecosystems. J. Geophys. Res. 113, G02026. https://doi.org/10.1029/2007JG000470 (2008).
ADS  CAS  Article  Google Scholar 

15.
Lafrenière, M. J. & Lamoureux, S. F. Effects of changing permafrost conditions on hydrological processes and fluvial fluxes. Earth Sci. Rev. 191, 212–223. https://doi.org/10.1016/j.earscirev.2019.02.018 (2019).
ADS  CAS  Article  Google Scholar 

16.
Kokelj, S. V. & Burn, C. R. Geochemistry of the active layer and near-surface permafrost, Mackenzie Delta region, Northwest Territories, Canada. Can. J. Earth Sci. 42(1), 37–48. https://doi.org/10.1139/E04-089 (2005).
ADS  CAS  Article  Google Scholar 

17.
Keller, K., Blum, J. D. & Kling, G. W. Stream geochemistry as an indicator of increasing permafrost thaw depth in an arctic watershed. Chem. Geol. 273, 76–81. https://doi.org/10.1016/j.chemgeo.2010.02.013 (2010).
ADS  CAS  Article  Google Scholar 

18.
Vonk, J. E. et al. A centennial record of fluvial organic matter input from the discontinuous permafrost catchment of Lake Torneträsk. J. Geophys. Res. 117, G03018. https://doi.org/10.1029/2011JG001887 (2012).
Article  Google Scholar 

19.
Tank, S. E., Fellman, J. B., Hood, E. & Kritzberg, E. S. Beyond respiration: controls on lateral carbon fluxes across the terrestrial-aquatic interface. Limnol. Oceanogr. Lett. 3, 76–88. https://doi.org/10.1002/lol2.10065 (2018).
Article  Google Scholar 

20.
Abbott, B. W., Jones, J. B., Godsey, S. E., Larouche, J. R. & Bowden, W. B. Patterns and persistence of hydrologic carbon and nutrient export from collapsing upland permafrost. Biogeosciences 12, 3725–3740. https://doi.org/10.5194/bg-12-3725-2015 (2015).
ADS  CAS  Article  Google Scholar 

21.
Tarnocai, C. et al. Soil organic carbon pools in the northern circumpolar permafrost region. Glob. Biogeochem. Cycles 23, GB2023. https://doi.org/10.1029/2008GB003327 (2009).
ADS  CAS  Article  Google Scholar 

22.
Hugelius, G. et al. Estimated stocks of circumpolar permafrost carbon with quantified uncertainty ranges and identified data gaps. Biogeosciences 11, 6573–6593. https://doi.org/10.5194/bg-11-6573-2014 (2014).
ADS  Article  Google Scholar 

23.
Schuur, E. A. G. et al. Climate change and the permafrost carbon feedback. Nature 520, 171–179. https://doi.org/10.1038/nature14338 (2015).
ADS  CAS  Article  PubMed  Google Scholar 

24.
Semiletov, I. P. et al. Carbon transport by the Lena River from its headwaters to the Arctic Ocean, with emphasis on fluvial input of terrestrial organic carbon vs. carbon transport by coastal erosion. Biogeosciences 8, 2407–2426. https://doi.org/10.5194/bg-8-2407-2011 (2011).
ADS  CAS  Article  Google Scholar 

25.
Schädel, C. et al. Divergent patterns of experimental and model-derived permafrost ecosystem carbon dynamics in response to Arctic warming. Environ. Res. Lett. 13, 105002. https://doi.org/10.1088/1748-9326/aae0ff (2018).
ADS  CAS  Article  Google Scholar 

26.
Dean, J. F. et al. East Siberian Arctic inland waters emit mostly contemporary carbon. Nat. Commun. 11, 1627. https://doi.org/10.1038/s41467-020-15511-6 (2020).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

27.
O’Donnell, J. A. et al. DOM composition and transformation in boreal forest soils: The effects of temperature and organic-horizon decomposition state. J. Geophys. Res. 121(10), 2727–2744. https://doi.org/10.1002/2016JG003431 (2016).
CAS  Article  Google Scholar 

28.
Tank, S. E. et al. Landscape-level controls on dissolved carbon flux from diverse catchments of the circumboreal. Glob. Biogeochem. Cycles 26, GB0E02. https://doi.org/10.1029/2012GB004299 (2012).
CAS  Article  Google Scholar 

29.
Thienpont, J. R. et al. Biological responses to permafrost thaw slumping in Canadian Arctic lakes. Freshw. Biol. 58, 337–353. https://doi.org/10.1111/fwb.12061 (2012).
Article  Google Scholar 

30.
Vonk, J. et al. High biolability of ancient permafrost carbon upon thaw. Geophys. Res. Lett. 40(11), 2689–2693. https://doi.org/10.1002/grl.50348 (2013).
ADS  CAS  Article  Google Scholar 

31.
Littlefair, C. A., Tank, S. E. & Kokelj, S. V. Retrogressive thaw slumps temper dissolved organic carbon delivery to streams of the Peel Plateau, NWT, Canada. Biogeosciences 14, 5487–5505. https://doi.org/10.5194/bg-14-5487-2017 (2017).
ADS  CAS  Article  Google Scholar 

32.
Fouché, J., Lafrenière, M. J., Rutherford, K. & Lamoureux, S. F. Seasonal hydrology and permafrost disturbance impacts on dissolved organic matter composition in High Arctic headwater catchments. Arct. Sci. 3, 378–405. https://doi.org/10.1139/as-2016-0031 (2017).
Article  Google Scholar 

33.
Lamoureux, S. F. & Lafrenière, M. J. Seasonal fluxes and age of particulate organic carbon exported from Arctic catchments impacted by localized permafrost slope disturbances. Environ. Res. Lett. 9, 045002. https://doi.org/10.1088/1748-9326/9/4/045002 (2014).
ADS  CAS  Article  Google Scholar 

34.
Guo, L., Ping, C.-L. & Macdonald, R. W. Mobilization pathways of organic carbon from permafrost to arctic rivers in a changing climate. Geophys. Res. Lett. 34, L13603. https://doi.org/10.1029/2007GL030689 (2007).
ADS  CAS  Article  Google Scholar 

35.
Schreiner, K. M., Bianchi, T. S. & Rosenheim, B. E. Evidence for permafrost thaw and transport from an Alaskan North Slope watershed. Geophys. Res. Lett. 41, 3117–3126. https://doi.org/10.1002/2014GL059514 (2014).
ADS  Article  Google Scholar 

36.
Wang, J.-J. et al. Differences in riverine and pond water dissolved organic matter composition and sources in Canadian High Arctic watersheds affected by active layer detachments. Environ. Sci. Technol. 52, 1062–1071. https://doi.org/10.1021/acs.est.7b05506 (2018).
ADS  CAS  Article  PubMed  Google Scholar 

37.
Guo, L. & Macdonald, R. W. Source and transport of terrigenous organic matter in the upper Yukon River: evidence from isotope (δ13C, Δ14C, and δ15N) composition of dissolved, colloidal, and particulate phases. Glob. Biogeochem. Cycles 20, GB2011. https://doi.org/10.1029/2005GB002593 (2006).
ADS  CAS  Article  Google Scholar 

38.
Bintanja, R. & Andry, O. Towards a rain-dominated Arctic. Nat. Clim. Change 7, 263–267. https://doi.org/10.1038/nclimate3240 (2017).
ADS  Article  Google Scholar 

39.
Bintanja, A. The impact of Arctic warming on increased rainfall. Sci. Rep. 8, 16001. https://doi.org/10.1038/s41598-018-34450-3 (2018).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

40.
Lewis, T., Lafrenière, M. J. & Lamoureux, S. F. Hydrochemical and sedimentary responses of paired High Arctic watersheds to unusual climate and permafrost change, Cape Bounty, Melville Island, Canada. Hydrol. Process. 26, 2003–2018. https://doi.org/10.1002/hyp.8335 (2012).
ADS  Article  Google Scholar 

41.
Beel, C. R., Lamoureux, S. F. & Orwin, J. F. Fluvial response to a period of hydrometeorological change and landscape disturbance in the Canadian High Arctic. Geophys. Res. Lett. 45(19), 10446–10455. https://doi.org/10.1029/2018GL079660 (2018).
ADS  Article  Google Scholar 

42.
Roberts, K. E. et al. Climate and permafrost effects on the chemistry and ecosystems of High Arctic lakes. Sci. Rep. 7, 13292. https://doi.org/10.1038/s41598-017-13658-9 (2017).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

43.
Lamoureux, S. F., Lafrenière, M. J. & Favaro, E. A. Erosion dynamics following localized permafrost slope disturbances. Geophys. Res. Lett. 41(15), 5499–5505. https://doi.org/10.1002/2014GL060677 (2014).
ADS  Article  Google Scholar 

44.
Lamhonwah, D., Lafrenière, M. J., Lamoureux, S. F. & Wolfe, B. B. Multi-year impacts of permafrost disturbance and thermal perturbation on High Arctic stream chemistry. Arct. Sci. 3, 254–276. https://doi.org/10.1139/as-2016-0024 (2017).
Article  Google Scholar 

45.
Lamoureux, S. F. & Lafrenière, M. J. More than just snowmelt: integrated watershed science for changing climate and permafrost at the Cape Bounty Arctic Watershed Observatory. WIREs Water 5(1), e1255. https://doi.org/10.1002/wat2.1255 (2017).
Article  Google Scholar 

46.
Hodgson, D. A., Vincent, J.-S. & Fyles, J. G. Quaternary Geology of Central Melville Island, Northwest Territories. Geological Survey of Canada, Paper 83-16. https://doi.org/10.4095/119784 (1984).

47.
Soil Classification Working Group. The Canadian System of Soil Classification 3rd edn, Vol. 1646 (Agriculture and Agri-Food Canada Publication, Revised, 1998). https://sis.agr.gc.ca/cansis/publications/manuals/1998-cssc-ed3/index.html

48.
Grewer, D. M., Lafrenière, M. J., Lamoureux, S. F. & Simpson, M. J. Redistribution of soil organic matter by permafrost disturbance in the Canadian High Arctic. Biogeochemistry 128(3), 397–415. https://doi.org/10.1007/s10533-016-0215-7 (2016).
CAS  Article  Google Scholar 

49.
Walker, D. A. et al. The Circumpolar Arctic vegetation map. J. Veg. Sci. 16, 267–282. https://doi.org/10.1111/j.1654-1103.2005.tb02365.x (2005).
Article  Google Scholar 

50.
Favaro, E. A. & Lamoureux, S. F. Antecedent controls on rainfall runoff response and sediment transport in a High Arctic catchment. Geogr. Ann. Phys. Geogr. 96(4), 433–446. https://doi.org/10.1111/geoa.12063 (2014).
Article  Google Scholar 

51.
Taylor, J. R. An Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements (University Science Books, Mill Valley, 1982).
Google Scholar 

52.
Watt, W. E., Lathem, K. W., Neill, C. R., Richards, T. L. & Rousselle, J. Hydrology of Floods in Canada: A Guide to Planning and Design (National Research Council of Canada, Ottawa, 1989).
Google Scholar 

53.
Government of Canada – Environment and Natural Resources. Historical Climate Data. www.climat.meteo.gc.ca (2017).

54.
Singh, V. Elementary Hydrology (Prentice Hall, Upper Saddle River, 1992).
Google Scholar 

55.
Emmerton, C. A., Lesack, L. F. W. & Vincent, W. F. Mackenzie River nutrient delivery to the Arctic Ocean and effects of the Mackenzie Delta during open water conditions. Glob. Biogeochem. Cycles 22, GB1024. https://doi.org/10.1029/2006GB002856 (2008).
ADS  CAS  Article  Google Scholar 

56.
Gareis, J. A. L. & Lesack, L. F. W. Fluxes of particulates and nutrients during hydrologically defined seasonal periods in an ice-affect great Arctic river, the Mackenzie. Water Resour. Res. 53, 6109–6132. https://doi.org/10.1002/2017WR020623 (2017).
ADS  CAS  Article  Google Scholar 

57.
Kennedy, P., Kennedy, H. & Papadimitriou, S. The effect of acidification on the determination of organic carbon, total nitrogen and their stable isotopic composition in algae and marine sediment. Rapid Commun. Mass Spectrom. 19, 1063–1068. https://doi.org/10.1002/rcm.1889 (2005).
ADS  CAS  Article  PubMed  Google Scholar 

58.
Komada, T., Anderson, M. R. & Dorfmeier, C. L. Carbonate removal from coastal sediments for the determination of organic carbon and its isotopic signatures, δ13C and Δ14C: comparison of fumigation and direct acidification by hydrochloric acid. Limnol. Oceanogr. Methods 6, 254–262. https://doi.org/10.4319/lom.2008.6.254 (2008).
CAS  Article  Google Scholar 

59.
Searcy, J. K. & Hardison, C. H. Double-mass curves. In Manual of Hydrology: Part 1. General Surface-Water Techniques. Water-Supply Paper 1541-B (US Geological Survey, 1960).

60.
Spencer, R. G. M. et al. Detecting the signature of permafrost thaw in Arctic rivers. Geophys. Res. Lett. 42, 2830–2835. https://doi.org/10.1002/2015GL063498 (2015).
ADS  Article  Google Scholar 

61.
Benner, R., Benitez-Nelson, B., Kaiser, K. & Amon, R. M. W. Export of young terrigenous dissolved organic carbon from rivers to the Arctic Ocean. Geophys. Res. Lett. 31, L05305. https://doi.org/10.1029/2003GL019251 (2004).
ADS  CAS  Article  Google Scholar 

62.
Raymond, P. et al. Flux and age of dissolved organic carbon exported to the Arctic Ocean: a carbon isotopic study of the five largest Arctic rivers. Glob. Biogeochem. Cycles 21, GB4011. https://doi.org/10.1029/2007GB002934 (2007).
ADS  CAS  Article  Google Scholar 

63.
Striegl, R. G., Dornblaser, M. M., Aiken, G. R., Wickland, K. P. & Raymond, P. A. Carbon export and cycling by the Yukon, Tanana, and Porcupine rivers, Alaska 2001–2005. Water Resour. Res. 43, W02411. https://doi.org/10.1029/2006WR005201 (2007).
ADS  CAS  Article  Google Scholar 

64.
Drake, T. W. et al. The ephemeral signature of permafrost carbon in an Arctic fluvial network. JGR Biogeosci. 123(5), 1475–1485. https://doi.org/10.1029/2017JG004311 (2018).
CAS  Article  Google Scholar 

65.
Pautler, B. G., Simpson, A. J., McNally, D. J., Lamoureux, S. F. & Simpson, M. J. Arctic permafrost active layer detachments stimulate microbial activity and degradation of soil organic matter. Environ. Sci. Technol. 44, 4076–4082. https://doi.org/10.1021/es903685j (2010).
ADS  CAS  Article  PubMed  Google Scholar 

66.
Grewer, D. M., Lafrenière, M. J., Lamoureux, S. F. & Simspon, M. J. Potential shifts in Canadian High Arctic sedimentary organic matter composition with permafrost active layer detachments. Org. Geochem. 79, 1–13. https://doi.org/10.1016/j.orggeochem.2014.11.007 (2015).
CAS  Article  Google Scholar 

67.
Kalbitz, K., Schwesig, D., Rethemeyer, J. & Matzner, E. Stabilization of dissolved organic matter by sorption to the mineral soil. Soil Biol. Biochem. 37(7), 1319–1331. https://doi.org/10.1016/j.soilbio.2004.11.028 (2005).
CAS  Article  Google Scholar 

68.
Owens, P. N., Petticrew, E. L. & van der Perk, M. Sediment response to catchment disturbances. J. Soils Sediments 10, 591–596. https://doi.org/10.1007/s11368-010-0235-1 (2010).
Article  Google Scholar 

69.
Grosse, G. et al. Vulnerability of high-latitude soil organic carbon in North America to disturbance. J. Geophys. Res. 116, G00K06. https://doi.org/10.1029/2010JG001507 (2011).
CAS  Article  Google Scholar 

70.
Vonk, J. E. et al. Reviews and syntheses: effects of permafrost thaw on Arctic aquatic ecosystems. Biogeosciences 12, 7129–7167. https://doi.org/10.5194/bg-12-7129-2015 (2015).
ADS  CAS  Article  Google Scholar 

71.
Schuur, E. A. G. et al. Expert assessment of vulnerability of permafrost carbon to climate change. Clim. Change 119, 359–374. https://doi.org/10.1007/s10584-013-0730-7 (2013).
ADS  CAS  Article  Google Scholar 

72.
Vonk, J. E., van Dongen, B. E. & Gustafsson, Ö. Selective preservation of old organic carbon fluvially released from sub-Arctic soils. Geophys. Res. Lett. 37, L11605. https://doi.org/10.1029/2010GL042909 (2010).
ADS  CAS  Article  Google Scholar 

73.
Gordeev, V. V. & Kravchishina, M. D. River flux of dissolved organic carbon (DOC) and particulate organic carbon (POC) to the Arctic Ocean: what are the consequences of the global changes. In Influence of Climate Change on the Changing Arctic and sub-Arctic Conditions (eds Nihoul, J. C. J. & Kostianoy, A. G.) 145–161 (Springer, Berlin, 2009).
Google Scholar 

74.
Rudy, A. C. A., Lamoureux, S. F., Treitz, P. & Collingwood, A. Identifying permafrost slope disturbance using multi-temporal optical satellite images and change detection techniques. Cold Reg. Sci. Technol. 88, 37–49. https://doi.org/10.1016/j.coldregions.2012.12.008 (2013).
Article  Google Scholar  More

1.
Deguines, N., Brashares, J. S. & Prugh, L. R. Precipitation alters interactions in a grassland ecological community. J. Anim. Ecol. 86, 262–272 (2017).
PubMed  Google Scholar 
2.
Bernabé, T. N. et al. Warming weakens facilitative interactions between decomposers and detritivores, and modifies freshwater ecosystem functioning. Glob. Change Biol. 24, 3170–3186 (2018).
ADS  Google Scholar 

3.
Boulangeat, I., Svenning, J. C., Daufresne, T., Leblond, M. & Gravel, D. The transient response of ecosystems to climate change is amplified by trophic interactions. Oikos 127, 1822–1833 (2018).
Google Scholar 

4.
Salt, J. L., Bulit, C., Zhang, W., Qi, H. & Montagnes, D. J. S. Spatial extinction or persistence: Landscape–temperature interactions perturb predator–prey dynamics. Ecography (Cop.) 40, 1177–1186 (2017).
Google Scholar 

5.
Zhang, L., Takahashi, D., Hartvig, M. & Andersen, K. H. Food-web dynamics under climate change. Proc. R. Soc. B 284, 20171772 (2017).
PubMed  Google Scholar 

6.
Campanati, C., Dupont, S., Williams, G. A. & Thiyagarajan, V. Differential sensitivity of larvae to ocean acidification in two interacting mollusc species. Mar. Environ. Res. 141, 66–74 (2018).
CAS  PubMed  Google Scholar 

7.
Woehler, E., Patterson, T. A., Bravington, M. V., Hobday, A. J. & Chambers, L. E. Climate and competition in abundance trends in native and invasive Tasmanian gulls. Mar. Ecol. Prog. Ser. 511, 249–263 (2014).
ADS  Google Scholar 

8.
Frizzi, F., Bartalesi, V. & Santini, G. Combined effects of temperature and interspecific competition on the mortality of the invasive garden ant, Lasius neglectus: A laboratory study. J. Therm. Biol. 65, 76–81 (2017).
PubMed  Google Scholar 

9.
Grainger, T. N., Rego, A. I. & Gilbert, B. Temperature-dependent species interactions shape priority effects and the persistence of unequal competitors. Am. Nat. 191, 197–209 (2018).
PubMed  Google Scholar 

10.
Friesen, O. C., Poulin, R. & Lagrue, C. Parasite-mediated microhabitat segregation between congeneric hosts. Biol. Lett. 14, 20170671 (2018).
PubMed  PubMed Central  Google Scholar 

11.
Srinivasan, U., Elsen, P. R., Tingley, M. W. & Wilcove, D. S. Temperature and competition interact to structure Himalayan bird communities. Proc. R. Soc. B Biol. Sci. 285, 20172593 (2018).
Google Scholar 

12.
Franke, F., Armitage, S. A. O., Kutzer, M. A. M., Kurtz, J. & Scharsack, J. P. Environmental temperature variation influences fitness trade-offs and tolerance in a fish–tapeworm association. Parasit. Vectors 10, 252 (2017).
PubMed  PubMed Central  Google Scholar 

13.
Castano-Vazquez, F., Martinez, J., Merino, S. & Lozano, M. Experimental manipulation of temperature reduce ectoparasites in nests of blue tits Cyanistes caeruleus. J. Avian Biol. 49, UNSP e01695 (2018).
Google Scholar 

14.
Paull, S. H. & Johnson, P. T. J. How temperature, pond-drying, and nutrients influence parasite infection and pathology. EcoHealth 15, 396–408 (2018).
PubMed  PubMed Central  Google Scholar 

15.
Larsen, M. H. & Mouritsen, K. N. Temperature–parasitism synergy alters intertidal soft-bottom community structure. J. Exp. Mar. Bio. Ecol. 460, 109–119 (2014).
Google Scholar 

16.
Marcogliese, D. J. The distribution and abundance of parasites in aquatic ecosystems in a changing climate: More than just temperature. Integr. Comp. Biol. 56, 611–619 (2016).
PubMed  Google Scholar 

17.
Mouritsen, K. N., Sørensen, M. M., Poulin, R. & Fredensborg, B. L. Coastal ecosystems on a tipping point: Global warming and parasitism combine to alter community structure and function. Glob. Change Biol. 24, 4340–4356 (2018).
ADS  Google Scholar 

18.
Hatcher, M. J., Dick, J. T. A. & Dunn, A. M. Diverse effects of parasites in ecosystems: Linking interdependent processes. Front. Ecol. Environ. 10, 186–194 (2012).
Google Scholar 

19.
Repetto, M. & Griffen, B. D. Physiological consequences of parasite infection in the burrowing mud shrimp, Upogebia pugettensis, a widespread ecosystem engineer. Mar. Freshw. Res. 63, 60–67 (2012).
Google Scholar 

20.
Boze, B. G. V. & Moore, J. The effect of a nematode parasite on feeding and dung-burying behavior of an ecosystem engineer. Integr. Comp. Biol. 54, 177–183 (2014).
CAS  PubMed  Google Scholar 

21.
Laverty, C. et al. Temperature rise and parasitic infection interact to increase the impact of an invasive species. Int. J. Parasitol. 47, 291–296 (2017).
PubMed  Google Scholar 

22.
Labaude, S., Cézilly, F. & Rigaud, T. Temperature-related intraspecific variability in the behavioral manipulation of acanthocephalan parasites on their gammarid hosts. Biol. Bull. 232, 82–90 (2017).

23.
Labaude, S., Rigaud, T. & Cézilly, F. Additive effects of temperature and infection with an acanthocephalan parasite on the shredding activity of Gammarus fossarum (Crustacea: Amphipoda): The importance of aggregative behavior. Glob. Chang. Biol. 23, 1415–1424 (2017).

24.
MacNeil, C., Dick, J. T. A. & Elwood, R. W. The trophic ecology of freshwater Gammarus spp. (crustacea:amphipoda): Problems and perspectives concerning the functional feeding group concept. Biol. Rev. 72, 349–364 (1997).
Google Scholar 

25.
Piscart, C., Genoel, R., Doledec, S., Chauvet, E. & Marmonier, P. Effects of intense agricultural practices on heterotrophic processes in streams. Environ. Pollut. 157, 1011–1018 (2009).
CAS  PubMed  Google Scholar 

26.
Degani, G., Bromley, H. J., Ortal, R., Netzer, Y. & Harari, N. Diets of rainbow trout (Salmo gairdneri) in a thermally constant stream. Vie Milieu 37, 99–103 (1987).
Google Scholar 

27.
Friberg, N. et al. The effect of brown trout (Salmo trutta L.) on stream invertebrate drift, with special reference to Gammarus pulex L. Hydrobiologia 294, 105–110 (1994).
Google Scholar 

28.
Kelly, D. W., Dick, J. T. A. & Montgomery, W. I. The functional role of Gammarus (Crustacea, Amphipoda): Shredders, predators, or both?. Hydrobiologia 485, 199–203 (2002).
Google Scholar 

29.
Piscart, C., Bergerot, B., Laffaille, P. & Marmonier, P. Are amphipod invaders a threat to regional biodiversity?. Biol. Invasions 12, 853–863 (2010).
Google Scholar 

30.
Constable, D. & Birkby, N. J. The impact of the invasive amphipod Dikerogammarus haemobaphes on leaf litter processing in UK rivers. Aquat. Ecol. 50, 273–281 (2016).
Google Scholar 

31.
Foucreau, N., Puijalon, S., Hervant, F. & Piscart, C. Effect of leaf litter characteristics on leaf conditioning and on consumption by Gammarus pulex. Freshw. Biol. 58, 1672–1681 (2013).
Google Scholar 

32.
Maltby, L., Clayton, S. A., Wood, R. M. & McLoughlin, N. Evaluation of the Gammarus pulex in situ feeding assay as a biomonitor of water quality: Robustness, responsiveness, and relevance. Environ. Toxicol. Chem. 21, 361–368 (2002).
CAS  PubMed  Google Scholar 

33.
Benesh, D. P., Lafferty, K. D. & Kuris, A. A life cycle database for parasitic acanthocephalans, cestodes, and nematodes. Ecology 98, 882–882 (2017).
PubMed  Google Scholar 

34.
Crompton, D. W. T. & Nickol, B. B. Biology of the Acanthocephala (Cambridge University Press, Cambridge, 1985).
Google Scholar 

35.
Bakker, T. C. M., Frommen, J. G. & Thünken, T. Adaptive parasitic manipulation as exemplified by acanthocephalans. Ethology https://doi.org/10.1111/eth.12660 (2017).
Article  Google Scholar 

36.
Bethel, W. M. & Holmes, J. C. Altered evasive behavior and responses to light in amphipods harboring acanthocephalan cystacanths. J. Parasitol. 59, 945–956 (1973).
Google Scholar 

37.
Bauer, A., Trouvé, S., Grégoire, A., Bollache, L. & Cézilly, F. Differential influence of Pomphorhynchus laevis (Acanthocephala) on the behaviour of native and invader gammarid species. Int. J. Parasitol. 30, 1453–1457 (2000).
CAS  PubMed  Google Scholar 

38.
Kaldonski, N., Perrot-Minnot, M.-J. & Cézilly, F. Differential influence of two acanthocephalan parasites on the antipredator behaviour of their common intermediate host. Anim. Behav. 74, 1311–1317 (2007).
Google Scholar 

39.
McCahon, C. P., Brown, A. F. & Pascoe, D. The effect of the acanthocephalan Pomphorhynchus laevis (Müller 1776) on the acute toxicity of cadmium to its intermediate host, the amphipod Gammarus pulex (L.). Arch. Environ. Contam. Toxicol. 17, 239–243 (1988).
CAS  Google Scholar 

40.
Médoc, V., Piscart, C., Maazouzi, C., Simon, L. & Beisel, J.-N. Parasite-induced changes in the diet of a freshwater amphipod: Field and laboratory evidence. Parasitology 138, 537–546 (2011).
PubMed  Google Scholar 

41.
Cornet, S., Franceschi, N., Bauer, A., Rigaud, T. & Moret, Y. Immune depression induced by acanthocephalan parasites in their intermediate crustacean host: Consequences for the risk of super-infection and links with host behavioural manipulation. Int. J. Parasitol. 39, 221–229 (2009).
CAS  PubMed  Google Scholar 

42.
Plaistow, S. J., Troussard, J.-P. & Cézilly, F. The effect of the acanthocephalan parasite Pomphorhynchus laevis on the lipid and glycogen content of its intermediate host Gammarus pulex. Int. J. Parasitol. 31, 346–351 (2001).
CAS  PubMed  Google Scholar 

43.
Bollache, L., Rigaud, T. & Cézilly, F. Effects of two acanthocephalan parasites on the fecundity and pairing status of female Gammarus pulex (Crustacea: Amphipoda). J. Invertebr. Pathol. 79, 102–110 (2002).
CAS  PubMed  Google Scholar 

44.
Dezfuli, B. S., Lui, A., Giovinazzo, G. & Giari, L. Effect of Acanthocephala infection on the reproductive potential of crustacean intermediate hosts. J. Invertebr. Pathol. 98, 116–119 (2008).
CAS  PubMed  Google Scholar 

45.
Labaude, S., Cézilly, F., Tercier, X. & Rigaud, T. Influence of host nutritional condition on post-infection traits in the association between the manipulative acanthocephalan Pomphorhynchus laevis and the amphipod Gammarus pulex. Parasit. Vectors 8, 403 (2015).
PubMed  PubMed Central  Google Scholar 

46.
Rumpus, A. E. & Kennedy, C. R. The effect of the acanthocephalan Pomphorhynchus laevis upon the respiration of its intermediate host, Gammarus pulex. Parasitology 68, 271–284 (1974).
CAS  PubMed  Google Scholar 

47.
Perrot-Minnot, M.-J., Kaldonski, N. & Cézilly, F. Increased susceptibility to predation and altered anti-predator behaviour in an acanthocephalan-infected amphipod. Int. J. Parasitol. 37, 645–651 (2007).
PubMed  Google Scholar 

48.
Hindsbo, O. Effects of Polymorphus (Acanthocephala) on colour and behaviour of Gammarus lacustris. Nature 238, 333 (1972).
ADS  Google Scholar 

49.
Dianne, L. et al. Protection first then facilitation: A manipulative parasite modulates the vulnerability to predation of its intermediate host according to its own developmental stage. Evolution 65, 2692–2698 (2011).
PubMed  Google Scholar 

50.
Lagrue, C., Kaldonski, N., Perrot-Minnot, M.-J., Motreuil, S. & Bollache, L. Modification of hosts’ behavior by a parasite: Field evidence for adaptive manipulation. Ecology 88, 2839–2847 (2007).
PubMed  Google Scholar 

51.
Kaldonski, N., Perrot-Minnot, M.-J., Motreuil, S. & Cézilly, F. Infection with acanthocephalans increases the vulnerability of Gammarus pulex (Crustacea Amphipoda) to non-host invertebrate predators. Parasitology 135, 627–632 (2008).
CAS  PubMed  Google Scholar 

52.
Gillooly, J. F., Brown, J. H., West, G. B., Savage, V. B. & Charnov, E. L. Effects of size and temperature on metabolic rate. Science 293, 2248–2251 (2001).
ADS  CAS  PubMed  Google Scholar 

53.
Roux, C. & Roux, A. L. Température et métabolisme respiratoire d’espèces sympatriques de gammares du groupe pulex (Crustacés, Amphipodes). Ann. Limnol. 3, 3–16 (1967).
Google Scholar 

54.
Issartel, J., Hervant, F., Voituron, Y., Renault, D. & Vernon, P. Behavioural, ventilatory and respiratory responses of epigean and hypogean crustaceans to different temperatures. Comp. Biochem. Physiol. A. Mol. Integr. Physiol. 141, 1–7 (2005).
PubMed  Google Scholar 

55.
Foucreau, N., Cottin, D., Piscart, C. & Hervant, F. Physiological and metabolic responses to rising temperature in Gammarus pulex (Crustacea) populations living under continental or Mediterranean climates. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 168, 69–75 (2014).
CAS  PubMed  Google Scholar 

56.
Moenickes, S. et al. From population-level effects to individual response: Modelling temperature dependence in Gammarus pulex. J. Exp. Biol. 214, 3678–3687 (2011).
PubMed  Google Scholar 

57.
Barber, I., Berkhout, B. W. & Ismail, Z. Thermal change and the dynamics of multi-host parasite life cycles in aquatic ecosystems. Integr. Comp. Biol. 56, 561–572 (2016).
PubMed  PubMed Central  Google Scholar 

58.
Olson, R. E. & Pratt, I. The life cycle and larval development of Echinorhynchus lageniformis Ekbaum, 1938 (Acanthocephala: Echinorhynchidae). J. Parasitol. 57, 143–149 (1971).
Google Scholar 

59.
Tokeson, J. P. E. & Holmes, J. C. The effects of temperature and oxygen on the development of Polymorphus marilis (Acanthocephala) in Gammarus lacustris (Amphipoda). J. Parasitol. 68, 112–119 (1982).
Google Scholar 

60.
Sheath, D. J., Andreou, D. & Britton, J. R. Interactions of warming and exposure affect susceptibility to parasite infection in a temperate fish species. Parasitology 143, 1340–1346 (2016).
PubMed  Google Scholar 

61.
VanCleave, H. J. Seasonal distribution of some acanthocephala from fresh-water hosts. J. Parasitol. 2, 106–110 (1916).
Google Scholar 

62.
Muzzall, P. M. & Rabalais, F. C. Studies on Acanthocephalus jacksoni Bullock, 1962 (Acanthocephala: Echinorhynchidae). I. Seasonal periodicity and new host records. Proc. Helminthol. Soc. Wash. 42, 31–34 (1975).
Google Scholar 

63.
Brown, A. F. Seasonal dynamics of the acanthocephalan Pomphorhynchus laevis (Muller, 1776) in its intermediate and preferred definitive hosts. J. Fish Biol. 34, 183–194 (1989).
Google Scholar 

64.
Rauque, C. A. & Semenas, L. Infection pattern of two sympatric acanthocephalan species in the amphipod Hyalella patagonica (Amphipoda: Hyalellidae) from Lake Mascardi (Patagonia, Argentina). Parasitol. Res. 100, 1271–1276 (2007).
PubMed  Google Scholar 

65.
Wali, A. et al. Distribution of helminth parasites in intestines and their seasonal rate of infestation in three freshwater fishes of Kashmir. J. Parasitol. Res. https://doi.org/10.1155/2016/8901518 (2016).
Article  PubMed  PubMed Central  Google Scholar 

66.
Guinnee, M. A. & Moore, J. The effect of parasitism on host fecundity is dependent on temperature in a cockroach-acanthocephalan system. J. Parasitol. 90, 673–677 (2004).
PubMed  Google Scholar 

67.
Benesh, D. P., Hasu, T., Seppälä, O. & Valtonen, E. T. Seasonal changes in host phenotype manipulation by an acanthocephalan: Time to be transmitted?. Parasitology 136, 219–230 (2009).
CAS  PubMed  Google Scholar 

68.
Perrot-Minnot, M.-J., Maddaleno, M., Balourdet, A. & Cézilly, F. Host manipulation revisited: No evidence for a causal link between altered photophobia and increased trophic transmission of amphipods infected with acanthocephalans. Funct. Ecol. 26, 1007–1014 (2012).
Google Scholar 

69.
Benesh, D. P., Duclos, L. M. & Nickol, B. B. The behavioral response of amphipods harboring Corynosoma constrictum (Acanthocephala) to various components of light. J. Parasitol. 91, 731–736 (2005).
PubMed  Google Scholar 

70.
Dianne, L., Bollache, L., Lagrue, C., Franceschi, N. & Rigaud, T. Larval size in acanthocephalan parasites: Influence of intraspecific competition and effects on intermediate host behavioural changes. Parasit. Vectors 5, 166 (2012).
PubMed  PubMed Central  Google Scholar 

71.
Franceschi, N. et al. Co-variation between the intensity of behavioural manipulation and parasite development time in an acanthocephalan–amphipod system. J. Evol. Biol. 23, 2143–2150 (2010).
CAS  PubMed  Google Scholar 

72.
Perrot-Minnot, M.-J., Sanchez-Thirion, K. & Cézilly, F. Multidimensionality in host manipulation mimicked by serotonin injection. Proc. R. Soc. B Biol. Sci. 281, 20141915 (2014).
Google Scholar 

73.
Franceschi, N., Bauer, A., Bollache, L. & Rigaud, T. The effects of parasite age and intensity on variability in acanthocephalan-induced behavioural manipulation. Int. J. Parasitol. 38, 1161–1170 (2008).
CAS  PubMed  Google Scholar 

74.
Franceschi, N. et al. Variation between populations and local adaptation in acanthocephalan-induced parasite manipulation. Evolution 64, 2417–2430 (2010).
PubMed  Google Scholar 

75.
Cézilly, F., Grégoire, A. & Bertin, A. Conflict between co-occuring manipulative parasites; an experimental study of the joint influence of two acanthocephalan parasites on the behaviour of Gammarus pulex. Parasitology 120, 625–630 (2000).
PubMed  Google Scholar 

76.
Bauer, A., Haine, E. R., Perrot-Minnot, M.-J. & Rigaud, T. The acanthocephalan parasite Polymorphus minutus alters the geotactic and clinging behaviours of two sympatric amphipod hosts: the native Gammarus pulex and the invasive Gammarus roeseli. J. Zool. 267, 39–43 (2005).
Google Scholar 

77.
Xu, Y., Castel, T., Richard, Y., Cuccia, C. & Bois, B. Burgundy regional climate change and its potential impact on grapevines. Clim. Dyn. 39, 1613–1626 (2012).
Google Scholar 

78.
Gunn, J. & Crumley, C. L. Global energy balance and regional hydrology: A Burgundian case study. Earth Surf. Process. Landforms 16, 579–592 (1991).
ADS  Google Scholar 

79.
Rowell, D. P. A scenario of European climate change for the late twenty-first century: Seasonal means and interannual variability. Clim. Dyn. 25, 837–849 (2005).
Google Scholar 

80.
Bollache, L., Gambade, G. & Cézilly, F. The influence of micro-habitat segregation on size assortative pairing in Gammarus pulex (L.) (Crustacea, Amphipoda). Arch. für Hydrobiol. 147, 547–558 (2000).
Google Scholar 

81.
Dezfuli, B. S., Zanini, N., Reggiani, G. & Rossi, R. Echinogammarus stammen (Amphipoda) as an intermediate host for Pomphorhynchus laevis (Acanthocephala) parasite of fishes from the river Brenta. Bolletino di Zool. 58, 267–271 (1991).
Google Scholar 

82.
Dianne, L., Perrot-Minnot, M.-J., Bauer, A., Guvenatam, A. & Rigaud, T. Parasite-induced alteration of plastic response to predation threat: increased refuge use but lower food intake in Gammarus pulex infected with the acanothocephalan Pomphorhynchus laevis. Int. J. Parasitol. 44, 211–216 (2014).
PubMed  Google Scholar 

83.
Hammond, B. A. the proboscis mechanism of Acanthocephalus ranae. J. Exp. Biol. 45, 203–213 (1966).
Google Scholar 

84.
Taraschewski, H. Host–parasite interactions in Acanthocephala: A morphological approach. Adv. Parasitol. 46, 1–179 (2000).
CAS  PubMed  Google Scholar 

85.
Perrot-Minnot, M.-J., Gaillard, M., Dodet, R. & Cézilly, F. Interspecific differences in carotenoid content and sensitivity to UVB radiation in three acanthocephalan parasites exploiting a common intermediate host. Int. J. Parasitol. 41, 173–181 (2011).
CAS  PubMed  Google Scholar 

86.
Kennedy, C. R., Broughton, P. F. & Hine, P. M. The status of brown and rainbow trout, Salmo trutta and S. gairdneri as hosts of the acanthocephalan, Pomphorhynchus laevis. J. Fish Biol. 13, 265–275 (1978).
Google Scholar 

87.
Foucreau, N., Piscart, C., Puijalon, S. & Hervant, F. Effects of rising temperature on a functional process: Consumption and digestion of leaf litter by a freshwater shredder. Fundam. Appl. Limnol./Arch. für Hydrobiol. 187, 295–306 (2016).
Google Scholar 

88.
Noguchi, K., Gel, Y. R., Brunner, E. & Konietschke, F. nparLD: An R software package for the nonparametric analysis of longitudinal data in factorial experiments. J. Stat. Softw. 50, 1–23 (2012).
Google Scholar 

89.
Pellan, L., Médoc, V., Renault, D., Spataro, T. & Piscart, C. Feeding choice and predation pressure of two invasive gammarids, Gammarus tigrinus and Dikerogammarus villosus, under increasing temperature. Hydrobiologia 781, 43–54 (2015).
Google Scholar 

90.
Maure, F. et al. The cost of a bodyguard. Biol. Lett. 7, 843–846 (2011).
PubMed  PubMed Central  Google Scholar 

91.
Maazouzi, C., Piscart, C., Legier, F. & Hervant, F. Ecophysiological responses to temperature of the ‘killer shrimp’ Dikerogammarus villosus: Is the invader really stronger than the native Gammarus pulex?. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 159, 268–274 (2011).
CAS  PubMed  Google Scholar 

92.
Maynard, B. J., Wellnitz, T. A., Zanini, N., Wright, W. G. & Dezfuli, B. S. Parasite-altered behavior in a crustacean intermediate host: Field and laboratory studies. J. Parasitol. 84, 1102–1106 (1998).
CAS  PubMed  Google Scholar 

93.
Dezfuli, B. S., Maynard, B. J. & Wellnitz, T. A. Activity levels and predator detection by amphipods infected with an acanthocephalan parasite, Pomphorhynchus laevis. Folia Parasitol. (Praha) 50, 129–134 (2003).
Google Scholar 

94.
Stone, C. F. & Moore, J. Parasite-induced alteration of odour responses in an amphipod-acanthocephalan system. Int. J. Parasitol. 44, 969–975 (2014).
CAS  PubMed  Google Scholar 

95.
Jacquin, L., Mori, Q., Pause, M., Steffen, M. & Medoc, V. Non-specific manipulation of gammarid behaviour by P. minutus parasite enhances their predation by definitive bird hosts. PLoS ONE 9, e101684 (2014).
ADS  PubMed  PubMed Central  Google Scholar 

96.
Thünken, T. et al. Impact of olfactory non-host predator cues on aggregation behaviour and activity in Polymorphus minutus infected Gammarus pulex. Hydrobiologia 654, 137–145 (2010).
Google Scholar 

97.
Dianne, L. et al. Intraspecific conflict over host manipulation between different larval stages of an acanthocephalan parasite. J. Evol. Biol. 23, 2648–2655 (2010).
CAS  PubMed  Google Scholar 

98.
Thomas, F., Brown, S. P., Sukhdeo, M. V. K. & Renaud, F. Understanding parasite strategies: A state-dependent approach?. Trends Parasitol. 18, 387–390 (2002).
PubMed  Google Scholar 

99.
Baldauf, S. A. et al. Infection with an acanthocephalan manipulates an amphipod’s reaction to a fish predator’s odours. Int. J. Parasitol. 37, 61–65 (2007).
PubMed  Google Scholar 

100.
Durieux, R., Rigaud, T. & Médoc, V. Parasite-induced suppression of aggregation under predation risk in a freshwater amphipod. Sociality of infected amphipods. Behav. Process. 91, 207–213 (2012).
Google Scholar 

101.
Lewis, S. E., Hodel, A., Sturdy, T., Todd, R. & Weigl, C. Impact of acanthocephalan parasites on aggregation behavior of amphipods (Gammarus pseudolimnaeus). Behav. Process. 91, 159–163 (2012).
Google Scholar 

102.
Labaude, S., Rigaud, T. & Cézilly, F. Host manipulation in the face of environmental changes: Ecological consequences. Int. J. Parasitol. Parasit. Wildl. 4, 442–451 (2015).
Google Scholar 

103.
Rahmstorf, S. & Coumou, D. Increase of extreme events in a warming world. PNAS 108, 17905–17910 (2011).
ADS  CAS  PubMed  Google Scholar  More

1.
Mittermeier, R. A. et al. Wilderness and biodiversity conservation. Proc. Natl. Acad. Sci. USA 100, 10309–10313 (2003).
ADS  CAS  PubMed  Google Scholar 
2.
Watson, J. E. M. et al. Protect the last of the wild. Nature 563, 27–30 (2018).
ADS  CAS  PubMed  Google Scholar 

3.
Chown, S. L. et al. The changing form of Antarctic biodiversity. Nature 522, 431–438 (2015).
ADS  CAS  PubMed  Google Scholar 

4.
Rintoul, S. R. et al. Choosing the future of Antarctica. Nature 558, 233–241 (2018).
ADS  CAS  PubMed  Google Scholar 

5.
Pertierra, L. R., Hughes, K. A., Vega, G. C. & Olalla-Tárraga, M. Á. High resolution spatial mapping of human footprint across Antarctica and its implications for the strategic conservation of avifauna. PLoS One 12, e0168280 (2017).
PubMed  PubMed Central  Google Scholar 

6.
Hughes, K. A., Fretwell, P., Rae, J., Holmes, K. & Fleming, A. Untouched Antarctica: mapping a finite and diminishing environmental resource. Antarct. Sci. 23, 537–548 (2011).
ADS  Google Scholar 

7.
Secretariat of the Antarctic Treaty. Protocol on Environmental Protection to the Antarctic Treaty https://www.ats.aq/e/protocol.html (Antarctic Treaty Secretariat, 1991).

8.
Coetzee, B. W. T., Convey, P. & Chown, S. L. Expanding the protected area network in Antarctica is urgent and readily achievable. Conserv. Lett. 10, 670–680 (2017).
Google Scholar 

9.
Keys, H. Towards Additional Protection of Antarctic Wilderness Areas https://documents.ats.aq/ATCM23/ip/ATCM23_ip080_e.doc (submitted by the Government of New Zealand, Doc. IP80, ATCM XXIII, 1999).

10.
Summerson, R. & Tin, T. Twenty years of protection of wilderness values in Antarctica. Polar J. 8, 265–288 (2018).
Google Scholar 

11.
Di Marco, M., Ferrier, S., Harwood, T. D., Hoskins, A. J. & Watson, J. E. M. Wilderness areas halve the extinction risk of terrestrial biodiversity. Nature 573, 582–585 (2019).
ADS  PubMed  Google Scholar 

12.
Cole, D. N. & Landres, P. B. Threats to wilderness ecosystems: impacts and research needs. Ecol. Appl. 6, 168–184 (1996).
Google Scholar 

13.
Watson, J. E. M. et al. Catastrophic declines in wilderness areas undermine global environment targets. Curr. Biol. 26, 2929–2934 (2016).
CAS  PubMed  Google Scholar 

14.
Lim, E. et al. Australian hot and dry extremes induced by weakenings of the stratospheric polar vortex. Nat. Geosci. 12, 896–901 (2019).
ADS  CAS  Google Scholar 

15.
Summerson, R. & Riddle, M. J. in Antarctic Ecosystems: Models for Wider Ecological Understanding (eds Davison, W. et al.) 303–307 (New Zealand Natural Sciences, Christchurch, 2000).

16.
Bastmeijer, K. & van Hengel, S. The role of the protected area concept in protecting the world’s largest natural reserve: Antarctica. Utrecht Law Rev. 5, 61–79 (2009).
Google Scholar 

17.
Chown, S. L. et al. Antarctica and the strategic plan for biodiversity. PLoS Biol. 15, e2001656 (2017).
PubMed  PubMed Central  Google Scholar 

18.
Brooks, S. T., Jabour, J., van den Hoff, J. & Bergstrom, D. M. Our footprint on Antarctica competes with nature for rare ice-free land. Nat. Sustain. 2, 185–190 (2019).
Google Scholar 

19.
Hughes, K. A. et al. Human-mediated dispersal of terrestrial species between Antarctic biogeographic regions: a preliminary risk assessment. J. Environ. Manage. 232, 73–89 (2019).
PubMed  Google Scholar 

20.
Lee, J. R. et al. Climate change drives expansion of Antarctic ice-free habitat. Nature 547, 49–54 (2017).
ADS  CAS  PubMed  Google Scholar 

21.
Hughes, K. A., Cowan, D. A. & Wilmotte, A. Protection of Antarctic microbial communities—‘out of sight, out of mind’. Front. Microbiol. 6, 151 (2015).
PubMed  PubMed Central  Google Scholar 

22.
Hughes, K. A. et al. Pristine Antarctica: threats and protection. Antarct. Sci. 25, 1 (2013).
ADS  Google Scholar 

23.
Shaw, J. D., Terauds, A., Riddle, M. J., Possingham, H. P. & Chown, S. L. Antarctica’s protected areas are inadequate, unrepresentative, and at risk. PLoS Biol. 12, e1001888 (2014).
PubMed  PubMed Central  Google Scholar 

24.
Secretariat of the Antarctic Treaty. Antarctic Protected Areas Database https://www.ats.aq/devph/en/apa-database (2019).

25.
Committee for Environmental Protection (CEP). Understanding Concepts of Footprint and Wilderness Related to Protection of the Antarctic Environment https://documents.ats.aq/ATCM34/wp/ATCM34_wp035_e.doc (submitted by the Government of New Zealand, Doc. WP35, ATCM XXXIV, 2011).

26.
Committee for Environmental Protection (CEP). Annex V Inviolate and Reference Areas: Current Management Practices https://documents.ats.aq/ATCM35/ip/ATCM35_ip049_e.doc (submitted by ASOC, IP 49, ATCM XXXV, 2012).

27.
Committee for Environmental Protection (CEP). Report of the Twenty-second Meeting of the Committee for Environmental Protection https://documents.ats.aq/ATCM42/fr/ATCM42_fr001_e.pdf (CEP, 2019).

28.
Terauds, A. & Lee, J. R. Antarctic biogeography revisited: updating the Antarctic Conservation Biogeographic Regions. Divers. Distrib. 22, 836–840 (2016).
Google Scholar 

29.
Council of Managers of National Antarctic Programs. Antarctic Facilities Operated by National Antarctic Programs in the Antarctic Treaty Area (South of 60° Latitude South) version 3.0.1 https://www.comnap.aq (COMNAP, accessed 8 August 2018).

30.
Tin, T., Liggett, D., Maher, P. T. & Lamers, M. (eds) Antarctic Futures: Human Engagement with the Antarctic Environment (Springer, Dordrecht, 2014).

31.
Dingwall, P. R. (ed.) Antarctica in the Environmental Era (Department of Conservation, Wellington, 1998).

32.
Summerson, R. in Protection of the Three Poles (ed. Huettmann, F.) 77–109 (Springer, Tokyo, 2012).

33.
Brooks, S. T., Tejedo, P. & O’Neill, T. A. Insights on the environmental impacts associated with visible disturbance of ice-free ground in Antarctica. Antarct. Sci. 31, 304–314 (2019).
ADS  Google Scholar 

34.
O’Neill, T. A., Balks, M. R. & López-Martínez, J. Visual recovery of desert pavement surfaces following impacts from vehicle and foot traffic in the Ross Sea region of Antarctica. Antarct. Sci. 25, 514–530 (2013).
ADS  Google Scholar 

35.
Convey, P. The influence of environmental characteristics on life history attributes of Antarctic terrestrial biota. Biol. Rev. Camb. Philos. Soc. 71, 191–225 (1996).
Google Scholar 

36.
Ayres, E. et al. Effects of human trampling on populations of soil fauna in the McMurdo Dry Valleys, Antarctica. Conserv. Biol. 22, 1544–1551 (2008).
PubMed  Google Scholar 

37.
Convey, P., Hughes, K. A. & Tin, T. Continental governance and environmental management mechanisms under the Antarctic Treaty System: sufficient for the biodiversity challenges of this century? Biodiversity (Nepean) 13, 234–248 (2012).
Google Scholar 

38.
Chown, S. L. & Brooks, C. M. The state and future of Antarctic environments in a global context. Annu. Rev. Environ. Res. 44, 1–30 (2019).
Google Scholar 

39.
Brooks, C. M. et al. Science-based management in decline in the Southern Ocean. Science 354, 185–187 (2016).
CAS  PubMed  Google Scholar 

40.
Secretariat of the Antarctic Treaty. Revised Guidelines for Environmental Impact Assessment in Antarctica https://documents.ats.aq/recatt/Att605_e.pdf (Antarctic Treaty Secretariat, Buenos Aires, 2016).

41.
Agence Nationale Recherche. East Antarctic International Ice Sheet Traverse (DS0101) https://anr.fr/Project-ANR-16-CE01-0011 (ANR, France, 2016).

42.
Harris, C. M. et al. Important Bird Areas in Antarctica 2015 (BirdLife International and Environmental Research & Assessment Ltd., Cambridge, 2015).

43.
Cowan, D. A. et al. Non-indigenous microorganisms in the Antarctic: assessing the risks. Trends Microbiol. 19, 540–548 (2011).
CAS  PubMed  Google Scholar 

44.
Montross, S. et al. Debris-rich basal ice as a microbial habitat, Taylor Glacier, Antarctica. Geomicrobiol. J. 31, 76–81 (2014).
Google Scholar 

45.
Archer, S. D. J. et al. Airborne microbial transport limitation to isolated Antarctic soil habitats. Nat. Microbiol. 4, 925–932 (2019).
CAS  PubMed  Google Scholar 

46.
Fretwell, P. T., Convey, P., Fleming, A. H., Peat, H. J. & Hughes, K. A. Detecting and mapping vegetation distribution on the Antarctic Peninsula from remote sensing data. Polar Biol. 34, 273–281 (2011).
Google Scholar 

47.
Schwaller, M. R., Lynch, H. J., Tarroux, A. & Prehn, B. A continent-wide search for Antarctic petrel breeding sites with satellite remote sensing. Remote Sens. Environ. 210, 444–451 (2018).
ADS  Google Scholar 

48.
Duffy, G. A. et al. Barriers to globally invasive species are weakening across the Antarctic. Divers. Distrib. 23, 982–996 (2017).
Google Scholar 

49.
Consultative Parties to the Antarctic Treaty. Santiago Declaration https://www.ats.aq/documents/ATCM39/ad/atcm39_ad003_e.pdf (Antarctic Treaty Secretariat, Buenos Aires, 2016).
Google Scholar 

50.
Pebesma, E. J. & Bivand, R. S. Classes and methods for spatial data in R. R News 5, 9–13 (2005).
Google Scholar 

51.
R Core Team. R: a language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, 2017).

52.
Environmental Systems Research Institute (ESRI). ArcGIS Desktop, release 10.6 (Environmental Systems Research Institute, Redlands, CA, 2011).

53.
Scientific Committee on Antarctic Research (SCAR). Antarctic Digital Database version 7 https://www.add.scar.org/ (2018).

54.
Headland, R. K. Chronological List of Antarctic Expeditions and Related Historical Events (Cambridge Univ. Press, Cambridge, 1989).
Google Scholar 

55.
Scientific Committee on Antarctic Research. Composite Gazetteer of Antarctica https://data.aad.gov.au/aadc/gaz/scar/ (GCMD Metadata, 1992, updated 2014).

56.
Evans, J. S. spatialEco. R package version 0.0.1-7 https://CRAN.R-project.org/package=spatialEco (2017).

57.
Hijmans, R. J. raster: geographic data analysis and modeling. R package version 2.6-7 https://CRAN.R-project.org/package=raster (2017).

58.
Hughes, K. A. How committed are we to monitoring human impacts in Antarctica? Environ. Res. Lett. 5, 041001 (2010).
ADS  Google Scholar 

59.
Bivand, R., Keitt, T. & Rowlingson, B. rgdal: bindings for the ‘geospatial’ data abstraction library. R package version 1.3-4 https://CRAN.R-project.org/package=rgdal (2018).

60.
International Association of Antarctica Tour Operators (IAATO). 2017–2018 Tourism Statistics http://iaato.org/tourism-statistics (IAATO, accessed 29 October 2018).

61.
United States Antarctic Program (USAP). USAP Science Planning Summaries 2003–2016 https://www.usap.gov/sciencesupport/2179/ (USAP, 2019).

62.
Bargagli, R. Antarctic Ecosystems: Environmental Contamination, Climate Change, and Human Impact (Springer, Berlin, 2005).

63.
Hughes, K. A. & Convey, P. The protection of Antarctic terrestrial ecosystems from inter- and intra-continental transfer of non-indigenous species by human activities: a review of current systems and practices. Glob. Environ. Change 20, 96–112 (2010).
Google Scholar 

64.
Campbell, I. B., Claridge, G. G. C. & Balks, M. R. Short-and long-term impacts of human disturbances on snow-free surfaces in Antarctica. Polar Rec. (Gr. Brit.) 34, 15–24 (1998).
Google Scholar 

65.
Tejedo, P. et al. Soil trampling in an Antarctic Specially Protected Area: tools to assess levels of human impact. Antarct. Sci. 21, 229–236 (2009).
ADS  Google Scholar 

66.
Chown, S. L. et al. Continent-wide risk assessment for the establishment of nonindigenous species in Antarctica. Proc. Natl. Acad. Sci. USA 109, 4938–4943 (2012).
ADS  CAS  PubMed  Google Scholar 

67.
Duffy, G. A. & Lee, J. R. Ice-free area expansion compounds the non-native species threat to Antarctic terrestrial biodiversity. Biol. Conserv. 232, 253–257 (2019).
Google Scholar 

68.
Antarctica New Zealand. McMurdo Dry Valleys ASMA Manual 4th edn (Christchurch, New Zealand, 2015).

69.
BirdLife International. Antarctic Important Bird Areas http://datazone.birdlife.org/home (BirdLife International, Cambridge, 2018).

70.
Terauds, A. Antarctic Terrestrial Biodiversity Database (Australian Antarctic Data Centre, 2019).

71.
Casanovas, P., Black, M., Fretwell, P. & Convey, P. Mapping lichen distribution on the Antarctic Peninsula using remote sensing, lichen spectra and photographic documentation by citizen scientists. Polar Res. 34, 25633 (2015).
Google Scholar 

72.
Fretwell, P. T. et al. An emperor penguin population estimate: the first global, synoptic survey of a species from space. PLoS One 7, e33751 (2012).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

73.
Wauchope, H. S., Shaw, J. D. & Terauds, A. A snapshot of biodiversity protection in Antarctica. Nat. Commun. 10, 946 (2019).
ADS  PubMed  PubMed Central  Google Scholar 

74.
Lynch, H. J., Naveen, R. & Fagan, W. F. Censuses of penguin, blue-eyed shag Phalacrocorax atriceps and southern giant petrel Macronectes giganteus populations on the Antarctic Peninsula, 2001-2007. Mar. Ornithol. 36, 83–97 (2008).
Google Scholar 

75.
Burton-Johnson, A., Black, M., Fretwell, P. & Kaluza-Gilbert, J. An automated methodology for differentiating rock from snow, clouds and sea in Antarctica from Landsat 8 imagery: a new rock outcrop map and area estimation for the entire Antarctic continent. Cryosphere 10, 1665–1677 (2016).
ADS  Google Scholar  More

1.
Henley, W. J. Measurement and interpretation of photosynthetic light response curves in algae in the context of photoinhibition and diel changes. J. Phycol. 29, 729–739 (1993).
Article  Google Scholar 
2.
Kosugi, Y., Shibata, S. & Kobashi, S. Parameterization of the CO2 and H2O gas exchange of several temperate deciduous broad-leaved trees at the leaf scale considering seasonal changes. Plant Cell Environ. 26, 285–301 (2003).
Article  Google Scholar 

3.
Mission, L., Tu, K. P., Boniello, R. A. & Goldstein, A. H. Seasonality of photosynthetic parameters in a multi-specific and vertically complex forest ecosystem in the Sierra Nevada of California. Tree Physiol. 26, 729–741 (2006).
Article  Google Scholar 

4.
Coble, A. P., Vanderwall, B., Mau, A. & Cavaleri, M. How vertical patterns in leaf traits shift seasonally and the implications for modeling canopy photosynthesis in a temperate deciduous forest. Tree Physiol. 36, 1077–1091 (2016).
CAS  Article  Google Scholar 

5.
Wilson, K. B., Baldocchi, D. D. & Hanson, P. J. Leaf age affects the seasonal pattern of photosynthetic capacity and net ecosystem exchange of carbon in a deciduous forest. Plant Cell Environ. 24, 571–583 (2011).
Article  Google Scholar 

6.
Jin, S., Zhou, X. & Fan, J. Modeling daily photosynthesis of nine major tree species in northeast China. Forest Ecol. Manag. 184, 125–140 (2003).
Article  Google Scholar 

7.
Zhang, X. Q. & Xu, D. Y. Eco-physiological modelling of canopy photosynthesis and growth of a Chinese fir plantation. Forest Ecol. Manag. 173, 201–211 (2003).
Article  Google Scholar 

8.
Marino, G., Aqil, M. & Shipley, B. The leaf economics spectrum and the prediction of photosynthetic light-response curves. Funct. Ecol. 24, 263–272 (2010).
Article  Google Scholar 

9.
Lachapelle, P. P. & Shipley, B. Interspecific prediction of photosynthetic light response curves using specific leaf mass and leaf nitrogen content: Effects of differences in soil fertility and growth irradiance. Ann. Bot. 109, 1149–1157 (2012).
CAS  Article  Google Scholar 

10.
Xu, J. Z., Yu, Y. M., Peng, S. Z., Yang, S. H. & Liao, L. X. A modified nonrectangular hyperbola equation for photosynthetic light-response curves of leaves with different nitrogen status. Photosynthetica 52, 117–123 (2014).
CAS  Article  Google Scholar 

11.
Calama, R., Puértolas, J., Madrigal, G. & Pardos, M. Modeling the environmental response of leaf net photosynthesis in Pinus pinea L. natural regeneration. Ecol. Model. 251, 9–21 (2013).
Article  Google Scholar 

12.
Mayoral, C., Calama, R., Sánchez-González, M. & Pardos, M. Modelling the influence of light, water and temperature on photosynthesis in young trees of mixed Mediterranean forests. New For. 46, 485–506 (2015).
Article  Google Scholar 

13.
Liu, Q., Dong, L. H. & Li, F. R. Modeling net CO2, assimilation (AN) within the crown of young planted Larix olgensis trees. Can. J. For. Res. 48, 1085–1098 (2018).
CAS  Article  Google Scholar 

14.
Cavaleri, M. A., Oberbauer, S. F., Clark, D. B., Clark, D. A. & Ryan, M. G. Height is more important than light in determining leaf morphology in a tropical forest. Ecology 91, 1730–1739 (2010).
Article  Google Scholar 

15.
Han, Q. Height-related decreases in mesophyll conductance, leaf photosynthesis and compensating adjustments associated with leaf nitrogen concentrations in Pinus densiflora. Tree Physiol. 31, 976–984 (2011).
CAS  Article  Google Scholar 

16.
Kosugi, Y., Takanashi, S., Yokoyama, N. & Kamakura, M. Vertical variation in leaf gas exchange parameters for a Southeast Asian tropical rainforest in Peninsular Malaysia. J. Plant Res. 125, 735–748 (2012).
Article  Google Scholar 

17.
Liu, Q., Dong, L. H., Li, F. R. & Xie, L. F. Spatial heterogeneity of canopy photosynthesis for Larix olgensis. Chin. J. Appl. Ecol. 27, 2789–2796 (2016) (in Chinese).
Google Scholar 

18.
Liu, Q. & Li, F. R. Spatial and seasonal variations of standardized photosynthetic parameters under different environmental conditions for young planted Larix olgensis Henry Trees. Forests 9, 522 (2018).
Article  Google Scholar 

19.
Ye, Z. P. A new model for relationship between irradiance and the rate of photosynthesis in Oryza sativa. Photosynthetica 45, 637–640 (2007).
CAS  Article  Google Scholar 

20.
Mengistu, T., Sterck, F. J., Fetene, M., Tadesse, W. & Bongers, F. Leaf gas exchange in the frankincense tree (Boswellia papyrifera) of African dry woodlands. Tree Physiol. 31, 740–750 (2011).
Article  Google Scholar 

21.
Chen, Z. Y., Peng, Z. S., Yang, J., Chen, W. Y. & Ou-Yang, Z. M. A mathematical model for describing light-response curves in Nicotiana tabacum L. Photosynthetica 49, 467–471 (2011).
Google Scholar 

22.
Benomar, L., Desrochers, A. & Larocque, G. R. Changes in specific leaf area and photosynthetic nitrogen-use efficiency associated with physiological acclimation of two hybrid poplar clones to intraclonal competition. Can. J. For. Res. 41, 1465–1476 (2011).
CAS  Article  Google Scholar 

23.
Ye, Z. P., Suggett, D. J., Robakowski, P. & Kang, H. J. A mechanistic model for the photosynthesis–light response based on the photosynthetic electron transport of photosystem II in C3 and C4 species. New Phytol. 199, 110–120 (2013).
CAS  Article  Google Scholar 

24.
Xu, C. L., Sun, X. M., Zhang, S. G. & Dong, J. Maternal and paternal effects on photosynthetic characteristics of several Larix kaempferi × L. olgensis Hybrids . For. Res. 24, 8–12 (2011) (in Chinese).
Google Scholar 

25.
Casella, E. & Ceulemans, R. Spatial distribution of leaf morphological and physiological characteristics in relation to local radiation regime within the canopies of 3-year-old Populus clones in coppice culture. Tree Physiol. 22, 1277–1288 (2002).
CAS  Article  Google Scholar 

26.
Wieser, G., Oberhuber, W., Walder, L., Spieler, D. & Gruber, A. Photosynthetic temperature adaptation of Pinus cembra within the timberline ecotone of the Central Austrian Alps. Ann. For. Sci. 67, 201 (2010).
Article  Google Scholar 

27.
Wang, Z., Kang, S., Jensen, C. R. & Liu, F. L. Alternate partial root-zone irrigation reduces bundle-sheath cell leakage to CO2 and enhances photosynthetic capacity in maize leaves. J. Exp. Bot. 63, 1145–1153 (2012).
CAS  Article  Google Scholar 

28.
Quan, X. K. & Wang, C. K. Responses and influencing factors of foliar photosynthetic characteristics of Larix gmelinii to changing environments. Chin. Sci. Bull. 61, 2273–2286 (2016) (in Chinese).
Article  Google Scholar 

29.
Posada, J. M., Lechowicz, M. J. & Kitajima, K. Optimal photosynthetic use of light by tropical tree crowns achieved by adjustment of individual leaf angles and nitrogen content. Ann. Bot. 103, 795–805 (2009).
CAS  Article  Google Scholar 

30.
Rosati, A., Metcalf, S. G. & Lampinen, B. D. A simple method to estimate photosynthetic radiation use efficiency of canopies. Ann. Bot. 93, 567–574 (2004).
CAS  Article  Google Scholar 

31.
Kern, S. O., Hovenden, M. J. & Jordan, G. J. The impacts of leaf shape and arrangement on light interception and potential photosynthesis in southern beech (Nothofagus cunninghamii). Funct. Plant Bio. 31, 471–480 (2004).
Article  Google Scholar 

32.
Montalbán, I. A., De-Diego, N. & Moncaleán, P. Testing novel cytokinins for improved in vitro adventitious shoots formation and subsequent ex vitro performance in Pinus radiata. Forestry 84, 363–373 (2011).
Article  Google Scholar 

33.
Lewis, J. D., Mckane, R. B., Tingey, D. T. & Beedlow, P. Vertical gradients in photosynthetic light response within an old-growth Douglas-fir and western hemlock canopy. Tree Physiol. 20, 447–456 (2000).
Article  Google Scholar 

34.
Calder, W. J., Horn, K. J. & Clair, S. B. S. Conifer expansion reduces the competitive ability and herbivore defense of aspen by modifying light environment and soil chemistry. Tree Physiol. 31, 582–591 (2011).
CAS  Article  Google Scholar 

35.
Joesting, H. M., Mccarthy, B. C. & Brown, K. J. The photosynthetic response of American chestnut seedlings to differing light conditions. Can. J. For. Res. 37, 1714–1722 (2007).
CAS  Article  Google Scholar 

36.
Xu, L. & Baldocchi, D. D. Seasonal trends in photosynthetic parameters and stomatal conductance of blue oak (Quercus douglasii) under prolonged summer drought and high temperature. Tree Physiol. 23, 865–877 (2003).
Article  Google Scholar 

37.
Wang, Q., Iio, A., Tenhunen, J. & Kakubari, Y. Annual and seasonal variations in photosynthetic capacity of Fagus crenata along an elevation gradient in the Naeba Mountains, Japan. Tree Physiol. 28, 277–285 (2008).
Article  Google Scholar 

38.
Luo, Y. et al. Canopy quantum yield in a mesocosm study. Agric. For. Meteorol. 100, 35–48 (2000).
ADS  Article  Google Scholar 

39.
Gardiner, E. S. & Krauss, K. W. Photosynthetic light response of flooded cherrybark oak (Quercus pagoda) seedlings grown in two light regimes. Tree Physiol. 21, 1103–1111 (2001).
CAS  Article  Google Scholar 

40.
Wickham, H., Francois, R., Henry, L., Müller, K. RStudio. dplyr: A grammar of data manipulation. R Package Version 0.8.3. (2019). More

1.
Beijerinck, M. Oxydation des mangancarbonates durch Bakterien und Schimmelpilze. Folia Microbiol. (Delft) 2, 123–134 (1913).
Google Scholar 
2.
Nealson, K. H., Tebo, B. M. & Rosson, R. A. Occurrence and mechanisms of microbial oxidation of manganese. Adv. Appl. Microbiol. 33, 279–318 (1988).
CAS  Google Scholar 

3.
Tebo, B. M., Johnson, H. A., McCarthy, J. K. & Templeton, A. S. Geomicrobiology of manganese(II) oxidation. Trends Microbiol. 13, 421–428 (2005).
CAS  Google Scholar 

4.
Hansel, C. & Learman, D. R. in Ehrlich’s Geomicrobiology (eds Ehrlich, H. L. et al.) 401–452 (CRC, 2015).

5.
Myers, C. R. & Nealson, K. H. Bacterial manganese reduction and growth with manganese oxide as the sole electron acceptor. Science 240, 1319–1321 (1988).
ADS  CAS  Google Scholar 

6.
Lovley, D. R. & Phillips, E. J. Novel mode of microbial energy metabolism: organic carbon oxidation coupled to dissimilatory reduction of iron or manganese. Appl. Environ. Microbiol. 54, 1472–1480 (1988).
CAS  PubMed  PubMed Central  Google Scholar 

7.
Winogradsky, S. Über schwefelbakterien. Bot. Ztg 45, 489ff (1887).
Google Scholar 

8.
Kelly, D. P. & Wood, A. P. in The Prokaryotes: Prokaryotic Communities and Ecophysiology (eds Rosenberg, E. et al.) 275–287 (Springer, 2013).

9.
Daims, H. et al. Complete nitrification by Nitrospira bacteria. Nature 528, 504–509 (2015).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

10.
Könneke, M. et al. Isolation of an autotrophic ammonia-oxidizing marine archaeon. Nature 437, 543–546 (2005).
ADS  Google Scholar 

11.
Strous, M. et al. Missing lithotroph identified as new planctomycete. Nature 400, 446–449 (1999).
ADS  CAS  Google Scholar 

12.
van Kessel, M. A. H. J. et al. Complete nitrification by a single microorganism. Nature 528, 555–559 (2015).
ADS  PubMed  PubMed Central  Google Scholar 

13.
Watson, S. W. & Waterbury, J. B. Characteristics of two marine nitrite oxidizing bacteria, Nitrospina gracilis nov. gen. nov. sp. and Nitrococcus mobilis nov. gen. nov. sp. Arch. Mikrobiol. 77, 203–230 (1971).
Google Scholar 

14.
Lovley, D. R., Holmes, D. E. & Nevin, K. P. in Advances in Microbial Physiology (ed Poole, R. K.) 219–286 (Elsevier, 2004).

15.
Henkel, J. V. et al. A bacterial isolate from the Black Sea oxidizes sulfide with manganese(IV) oxide. Proc. Natl Acad. Sci. USA 116, 12153–12155 (2019).
CAS  Google Scholar 

16.
Ghiorse, W. C. & Ehrlich, H. L. Microbial biomineralization of iron and manganese. Catena Suppl. 21, 75–99 (1992).
Google Scholar 

17.
Ehrlich, H. L. & Salerno, J. C. Energy coupling in Mn2+ oxidation by a marine bacterium. Arch. Microbiol. 154, 12–17 (1990).
CAS  Google Scholar 

18.
Ehrlich, H. L. Manganese as an energy source for bacteria. Environ. Biogeochem. 2, 633–644 (1976).
CAS  Google Scholar 

19.
Dick, G. J. et al. Genomic insights into Mn(II) oxidation by the marine alphaproteobacterium Aurantimonas sp. strain SI85-9A1. Appl. Environ. Microbiol. 74, 2646–2658 (2008).
CAS  PubMed  PubMed Central  Google Scholar 

20.
Nealson, K. H. in The Prokaryotes (eds Dworkin, M. et al.) 222–231 (Springer, 2006).

21.
van Veen, W. L. Biological oxidation of manganese in soils. Antonie van Leeuwenhoek 39, 657–662 (1973).
Google Scholar 

22.
Morgan, J. J. Kinetics of reaction between O2 and Mn(II) species in aqueous solutions. Geochim. Cosmochim. Acta 69, 35–48 (2005).
ADS  CAS  Google Scholar 

23.
Kits, K. D. et al. Kinetic analysis of a complete nitrifier reveals an oligotrophic lifestyle. Nature 549, 269–272 (2017).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

24.
Flagan, S. F. & Leadbetter, J. R. Utilization of capsaicin and vanillylamine as growth substrates by Capsicum (hot pepper)-associated bacteria. Environ. Microbiol. 8, 560–565 (2006).
CAS  PubMed  PubMed Central  Google Scholar 

25.
Kanzler, B. E. M., Pfannes, K. R., Vogl, K. & Overmann, J. Molecular characterization of the nonphotosynthetic partner bacterium in the consortium “Chlorochromatium aggregatum”. Appl. Environ. Microbiol. 71, 7434–7441 (2005).
CAS  PubMed  PubMed Central  Google Scholar 

26.
Emerson, D. & Moyer, C. Isolation and characterization of novel iron-oxidizing bacteria that grow at circumneutral pH. Appl. Environ. Microbiol. 63, 4784–4792 (1997).
CAS  PubMed  PubMed Central  Google Scholar 

27.
Neidhardt, F. C. Escherichia coli and Salmonella: Cellular and Molecular Biology, vol. 1 (ASM, 1996).

28.
Kostanjšek, R., Pašić, L., Daims, H. & Sket, B. Structure and community composition of sprout-like bacterial aggregates in a dinaric karst subterranean stream. Microb. Ecol. 66, 5–18 (2013).
Google Scholar 

29.
Wrighton, K. C. et al. Fermentation, hydrogen, and sulfur metabolism in multiple uncultivated bacterial phyla. Science 337, 1661–1665 (2012).
ADS  CAS  Google Scholar 

30.
Parks, D. H. et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat. Microbiol. 2, 1533–1542 (2017).
CAS  Google Scholar 

31.
Castelle, C. et al. A new iron-oxidizing/O2-reducing supercomplex spanning both inner and outer membranes, isolated from the extreme acidophile Acidithiobacillus ferrooxidans. J. Biol. Chem. 283, 25803–25811 (2008).
CAS  PubMed  PubMed Central  Google Scholar 

32.
Jeans, C. et al. Cytochrome 572 is a conspicuous membrane protein with iron oxidation activity purified directly from a natural acidophilic microbial community. ISME J. 2, 542–550 (2008).
CAS  Google Scholar 

33.
Croal, L. R., Jiao, Y. & Newman, D. K. The fox operon from Rhodobacter strain SW2 promotes phototrophic Fe(II) oxidation in Rhodobacter capsulatus SB1003. J. Bacteriol. 189, 1774–1782 (2007).
CAS  Google Scholar 

34.
Jiao, Y. & Newman, D. K. The pio operon is essential for phototrophic Fe(II) oxidation in Rhodopseudomonas palustris TIE-1. J. Bacteriol. 189, 1765–1773 (2007).
CAS  Google Scholar 

35.
He, S., Barco, R. A., Emerson, D. & Roden, E. E. Comparative genomic analysis of neutrophilic iron(II) oxidizer genomes for candidate genes in extracellular electron transfer. Front. Microbiol. 8, 1584 (2017).
PubMed  PubMed Central  Google Scholar 

36.
Richardson, D. J. et al. The ‘porin-cytochrome’ model for microbe-to-mineral electron transfer. Mol. Microbiol. 85, 201–212 (2012).
CAS  Google Scholar 

37.
Luther, G. W., III. Manganese(II) oxidation and Mn(IV) reduction in the environment—two one-electron transfer steps versus a single two-electron Step. Geomicrobiol. J. 22, 195–203 (2005).
CAS  Google Scholar 

38.
Lücker, S. et al. A Nitrospira metagenome illuminates the physiology and evolution of globally important nitrite-oxidizing bacteria. Proc. Natl Acad. Sci. USA 107, 13479–13484 (2010).
ADS  Google Scholar 

39.
Mundinger, A. B., Lawson, C. E., Jetten, M. S. M., Koch, H. & Lücker, S. Cultivation and transcriptional analysis of a canonical Nitrospira under stable growth conditions. Front. Microbiol. 10, 1325 (2019).
PubMed  PubMed Central  Google Scholar 

40.
Koch, H. et al. Growth of nitrite-oxidizing bacteria by aerobic hydrogen oxidation. Science 345, 1052–1054 (2014).
ADS  CAS  Google Scholar 

41.
Levicán, G., Ugalde, J. A., Ehrenfeld, N., Maass, A. & Parada, P. Comparative genomic analysis of carbon and nitrogen assimilation mechanisms in three indigenous bioleaching bacteria: predictions and validations. BMC Genomics 9, 581 (2008).
PubMed  PubMed Central  Google Scholar 

42.
Berg, I. A. Ecological aspects of the distribution of different autotrophic CO2 fixation pathways. Appl. Environ. Microbiol. 77, 1925–1936 (2011).
CAS  PubMed  PubMed Central  Google Scholar 

43.
Thauer, R. K., Jungermann, K. & Decker, K. Energy conservation in chemotrophic anaerobic bacteria. Bacteriol. Rev. 41, 100–180 (1977).
CAS  PubMed  PubMed Central  Google Scholar 

44.
Baradaran, R., Berrisford, J. M., Minhas, G. S. & Sazanov, L. A. Crystal structure of the entire respiratory complex I. Nature 494, 443–448 (2013).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

45.
Chadwick, G. L., Hemp, J., Fischer, W. W. & Orphan, V. J. Convergent evolution of unusual complex I homologs with increased proton pumping capacity: energetic and ecological implications. ISME J. 12, 2668–2680 (2018).
CAS  PubMed  PubMed Central  Google Scholar 

46.
Lücker, S., Nowka, B., Rattei, T., Spieck, E. & Daims, H. The genome of Nitrospina gracilis illuminates the metabolism and evolution of the major marine nitrite oxidizer. Front. Microbiol. 4, 27 (2013).
PubMed  PubMed Central  Google Scholar 

47.
Watson, S. W., Bock, E., Valois, F. W., Waterbury, J. B. & Schlosser, U. Nitrospira marina gen. nov. sp. nov.: a chemolithotrophic nitrite-oxidizing bacterium. Arch. Microbiol. 144, 1–7 (1986).
Google Scholar 

48.
Hippe, H. Leptospirillum gen. nov. (ex Markosyan 1972), nom. rev., including Leptospirillum ferrooxidans sp. nov. (ex Markosyan 1972), nom. rev. and Leptospirillum thermoferrooxidans sp. nov. (Golovacheva et al. 1992). Int. J. Syst. Evol. Microbiol. 50, 501–503 (2000).
Google Scholar 

49.
Henry, E. A. et al. Characterization of a new thermophilic sulfate-reducing bacterium Thermodesulfovibrio yellowstonii, gen. nov. and sp. nov.: its phylogenetic relationship to Thermodesulfobacterium commune and their origins deep within the bacterial domain. Arch. Microbiol. 161, 62–69 (1994).
CAS  Google Scholar 

50.
Lin, X., Kennedy, D., Fredrickson, J., Bjornstad, B. & Konopka, A. Vertical stratification of subsurface microbial community composition across geological formations at the Hanford site. Environ. Microbiol. 14, 414–425 (2012).
CAS  Google Scholar 

51.
Flagan, S., Ching, W.-K. & Leadbetter, J. R. Arthrobacter strain VAI-A utilizes acyl-homoserine lactone inactivation products and stimulates quorum signal biodegradation by Variovorax paradoxus. Appl. Environ. Microbiol. 69, 909–916 (2003).
CAS  PubMed  PubMed Central  Google Scholar 

52.
Leadbetter, J. R. & Greenberg, E. P. Metabolism of acyl-homoserine lactone quorum-sensing signals by Variovorax paradoxus. J. Bacteriol. 182, 6921–6926 (2000).
CAS  PubMed  PubMed Central  Google Scholar 

53.
Krumbein, W. E. & Altmann, H. J. A new method for the detection and enumeration of manganese oxidizing and reducing microorganisms. Helgol. Wiss. Meeresunters. 25, 347–356 (1973).
CAS  Google Scholar 

54.
Emerson, D. & Revsbech, N. P. Investigation of an iron-oxidizing microbial mat community located near Aarhus, Denmark: laboratory studies. Appl. Environ. Microbiol. 60, 4032–4038 (1994).
CAS  PubMed  PubMed Central  Google Scholar 

55.
Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ. Microbiol. 18, 1403–1414 (2016).
CAS  Google Scholar 

56.
Illumina. 16S Metagenomic sequencing library preparation, https://support.illumina.com/downloads/16s_metagenomic_sequencing_library_preparation.html (2013).

57.
Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).
CAS  PubMed  PubMed Central  Google Scholar 

58.
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).
CAS  Google Scholar 

59.
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).
CAS  Google Scholar 

60.
Lane, D. J. in Nucleic Acid Techniques in Bacterial Systematics (eds Stackebrandt, E. & Goodfellow, M.) 115–175 (John Wiley & Sons, 1991).

61.
Ludwig, W. et al. ARB: a software environment for sequence data. Nucleic Acids Res. 32, 1363–1371 (2004).
CAS  PubMed  PubMed Central  Google Scholar 

62.
Parks, D. H. et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat. Biotechnol. 36, 996–1004 (2018).
CAS  Google Scholar 

63.
Schönmann, S. et al. 16S rRNA gene-based phylogenetic microarray for simultaneous identification of members of the genus Burkholderia. Environ. Microbiol. 11, 779–800 (2009).
Google Scholar 

64.
Greuter, D., Loy, A., Horn, M. & Rattei, T. probeBase—an online resource for rRNA-targeted oligonucleotide probes and primers: new features 2016. Nucleic Acids Res. 44, D586–D589 (2016).
CAS  Google Scholar 

65.
Amann, R. I. et al. Combination of 16S rRNA-targeted oligonucleotide probes with flow cytometry for analyzing mixed microbial populations. Appl. Environ. Microbiol. 56, 1919–1925 (1990).
CAS  PubMed  PubMed Central  Google Scholar 

66.
Stoecker, K., Dorninger, C., Daims, H. & Wagner, M. Double labeling of oligonucleotide probes for fluorescence in situ hybridization (DOPE-FISH) improves signal intensity and increases rRNA accessibility. Appl. Environ. Microbiol. 76, 922–926 (2010).
CAS  PubMed  PubMed Central  Google Scholar 

67.
Schramm, A., Fuchs, B. M., Nielsen, J. L., Tonolla, M. & Stahl, D. A. Fluorescence in situ hybridization of 16S rRNA gene clones (Clone-FISH) for probe validation and screening of clone libraries. Environ. Microbiol. 4, 713–720 (2002).
CAS  PubMed  PubMed Central  Google Scholar 

68.
Daims, H., Stoecker, K. & Wagner, M. in Molecular Microbial Ecology (eds Osborn, M. A. and Smith, C. J.) 208–228 (Taylor & Francis, 2004).

69.
Daims, H., Lücker, S. & Wagner, M. daime, a novel image analysis program for microbial ecology and biofilm research. Environ. Microbiol. 8, 200–213 (2006).
CAS  Google Scholar 

70.
Taylor, G. J. & Crowder, A. A. Use of the DCB technique for extraction of hydrous iron oxides from roots of wetland plants. Am. J. Bot. 70, 1254 (1983).
CAS  Google Scholar 

71.
Polerecky, L. et al. Look@NanoSIMS—a tool for the analysis of nanoSIMS data in environmental microbiology. Environ. Microbiol. 14, 1009–1023 (2012).
CAS  Google Scholar 

72.
Brewer, P. G. & Spencer, D. W. Colorimetric determination of manganse in anoxic waters. Limnol. Oceanogr. 16, 107–110 (1971).
ADS  CAS  Google Scholar 

73.
Oldham, V. E., Miller, M. T., Jensen, L. T. & Luther, G. W. Revisiting Mn and Fe removal in humic rich estuaries. Geochim. Cosmochim. Acta 209, 267–283 (2017).
ADS  CAS  Google Scholar 

74.
Suzuki, M. T., Taylor, L. T. & DeLong, E. F. Quantitative analysis of small-subunit rRNA genes in mixed microbial populations via 5′-nuclease assays. Appl. Environ. Microbiol. 66, 4605–4614 (2000).
CAS  PubMed  PubMed Central  Google Scholar 

75.
William, S., Feil, H. & Copeland, A. Bacterial genomic DNA isolation using CTAB, Department of Energy Joint Genome Institute, https://jgi.doe.gov/user-programs/pmo-overview/protocols-sample-preparation-information/ (2012).

76.
Arkin, A. P. et al. KBase: the United States Department of Energy systems biology knowledgebase. Nat. Biotechnol. 36, 566–569 (2018).
CAS  PubMed  PubMed Central  Google Scholar 

77.
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
CAS  PubMed  PubMed Central  Google Scholar 

78.
Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).
MathSciNet  CAS  PubMed  PubMed Central  Google Scholar 

79.
Karst, S. M., Kirkegaard, R. H. & Albertsen, M. mmgenome: a toolbox for reproducible genome extraction from metagenomes. Preprint at https://www.biorxiv.org/content/ 10.1101/059121v1.full (2016).

80.
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

81.
Chen, I. A. et al. IMG/M v.5.0: an integrated data management and comparative analysis system for microbial genomes and microbiomes. Nucleic Acids Res. 47, D666–D677 (2019).
CAS  Google Scholar 

82.
NCBI Resource Coordinators. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 46, D8–D13 (2018).
Google Scholar 

83.
Bagos, P. G., Liakopoulos, T. D., Spyropoulos, I. C. & Hamodrakas, S. J. PRED-TMBB: a web server for predicting the topology of β-barrel outer membrane proteins. Nucleic Acids Res. 32, W400–W404 (2004).
CAS  PubMed  PubMed Central  Google Scholar 

84.
Federhen, S. The NCBI taxonomy database. Nucleic Acids Res. 40, D136–D143 (2012).
CAS  Google Scholar 

85.
Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).
CAS  PubMed  PubMed Central  Google Scholar 

86.
Pruesse, E., Peplies, J. & Glöckner, F. O. SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics 28, 1823–1829 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

87.
Ronquist, F. et al. MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space. Syst. Biol. 61, 539–542 (2012).
PubMed  PubMed Central  Google Scholar 

88.
Letunic, I. & Bork, P. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res. 44, W242–W245 (2016).
CAS  PubMed  PubMed Central  Google Scholar 

89.
Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).
CAS  PubMed  PubMed Central  Google Scholar 

90.
Sievers, F. et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol. Syst. Biol. 7, 539 (2011).
PubMed  PubMed Central  Google Scholar 

91.
Lever, M. A. et al. A modular method for the extraction of DNA and RNA, and the separation of DNA pools from diverse environmental sample types. Front. Microbiol. 6, 476 (2015).
PubMed  PubMed Central  Google Scholar 

92.
Kopylova, E., Noé, L. & Touzet, H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 28, 3211–3217 (2012).
CAS  Google Scholar 

93.
Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).
CAS  PubMed  Google Scholar 

94.
Pimentel, H., Bray, N. L., Puente, S., Melsted, P. & Pachter, L. Differential analysis of RNA-seq incorporating quantification uncertainty. Nat. Methods 14, 687–690 (2017).
CAS  Google Scholar 

95.
van Waasbergen, L. G., Hildebrand, M. & Tebo, B. M. Identification and characterization of a gene cluster involved in manganese oxidation by spores of the marine Bacillus sp. strain SG-1. J. Bacteriol. 178, 3517–3530 (1996).
PubMed  PubMed Central  Google Scholar 

96.
Jung, W. K. & Schweisfurth, R. Manganese oxidation by an intracellular protein of a Pseudomonas species. Z. Allg. Mikrobiol. 19, 107–115 (1979).
CAS  Google Scholar 

97.
Esteve-Núñez, A., Rothermich, M., Sharma, M. & Lovley, D. Growth of Geobacter sulfurreducens under nutrient-limiting conditions in continuous culture. Environ. Microbiol. 7, 641–648 (2005).
Google Scholar 

98.
Neubauer, S. C., Emerson, D. & Megonigal, J. P. Life at the energetic edge: kinetics of circumneutral iron oxidation by lithotrophic iron-oxidizing bacteria isolated from the wetland-plant rhizosphere. Appl. Environ. Microbiol. 68, 3988–3995 (2002).
CAS  PubMed  PubMed Central  Google Scholar 

99.
Nowka, B., Daims, H. & Spieck, E. Comparison of oxidation kinetics of nitrite-oxidizing bacteria: nitrite availability as a key factor in niche differentiation. Appl. Environ. Microbiol. 81, 745–753 (2015).
PubMed  PubMed Central  Google Scholar 

100.
Ehrich, S., Behrens, D., Lebedeva, E., Ludwig, W. & Bock, E. A new obligately chemolithoautotrophic, nitrite-oxidizing bacterium, Nitrospira moscoviensis sp. nov. and its phylogenetic relationship. Arch. Microbiol. 164, 16–23 (1995).
CAS  Google Scholar 

101.
Kim, S. & Lee, S. B. Catalytic promiscuity in dihydroxy-acid dehydratase from the thermoacidophilic archaeon Sulfolobus solfataricus. J. Biochem. 139, 591–596 (2006).
CAS  Google Scholar 

102.
Safarian, S. et al. Structure of a bd oxidase indicates similar mechanisms for membrane-integrated oxygen reductases. Science 352, 583–586 (2016).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

103.
Lovley, D. R. & Phillips, E. J. P. Manganese inhibition of microbial iron reduction in anaerobic sediments. Geomicrobiol. J. 6, 145–155 (1988).
CAS  Google Scholar 

104.
Perez-Benito, J. F., Arias, C. & Amat, E. A kinetic study of the reduction of colloidal manganese dioxide by oxalic acid. J. Colloid Interface Sci. 177, 288–297 (1996).
ADS  CAS  Google Scholar  More