Ecology

Systematic palaeontology
Clade Bilateria, Clade Protostomia
Vermilituus gregarius gen. et sp. nov.
Etymology: Genus name from vermis (Latin) meaning worm and lituus (Latin) meaning a curved trumpet, alluding to the shape of the fossils. Species name from gregarius (Latin), meaning flock or herd.
Holotype: YKLP 13079a, b (counterparts), U-shaped tube (Fig. 1a, b), 6.5-mm long, and reaching a maximum width of 0.6 mm: the holotype is associated with Vetulicola rectangulata YKLP 13075a, b. Paratypes (with preserved shell annulation), YKLP 13084 and 13085 (Fig. 1c, f) associated with V. rectangulata YKLP 13074, and YKLP 13082 and 13083 (Fig. 1e) associated with V. rectangulata YKLP 13073.
Fig. 1: Different styles of preservation and morphology of Vermilituus gregarius.

a, b Holotype, YKLP 13079a, flattened specimen showing U-shape morphology, under cross-polarised light (a) and fluorescence light (b). c Paratype YKLP 13084, partial 3D with well-preserved annulation, J-shape morphology. d, h YKLP 13086 under direct light (d) and fluorescence light (h), white arrow shows possible soft tissues. e Paratypes YKLP 13082 and 13083, preserved in 3D with annulation visible proximally: sinusoidal shape and J-shape morphology, respectively (the latter is broken distally and shows sediment fill). f Paratype YKLP 13085, partial 3D with well-preserved annulation, sinusoidal morphology. g, i–k Scanning electron microscopy images. g YKLP 13087 with J-shape morphology. i, j YKLP 13088, boxed area in “i” shows possible paired soft tissues at the termination, magnified in “j”. k YKLP 13089, with possible paired soft tissues at the terminal end. Scale bars: a–d, g–i, k, 500 μm; e, f, 1 mm; j, 200 μm.

Full size image

Referred material: About 192 specimens from Ercaicun, 75 from Mafang and 10 from Jianshan associated with Vetulicola rectangulata, all in the collections of the Yunnan Key Laboratory for Palaeobiology (YKLP). In total, 17 specimens from Xiaolantian and 55 specimens from Heimadi associated with Vetulicola cuneata, all in the collections of the Chengjiang Fossil Museum (CJHMD, Supplementary data file).
Locality: Ercaicun (type locality), Mafang and Jianshan localities in the Haikou area of Kunming, and Xiaolantian and Heimadi in Chengjiang County, Yunnan Province, China (for localities see ref. 5).
Horizon: Yu’anshan Member, Chiungchussu Formation, Eoredlichia-Wutingaspis trilobite Biozone, Nangaoan Stage of Chinese regional usage, Cambrian Series 2, Stage 3. All specimens are from rapidly sedimented ‘event beds’5.
Diagnosis for genus (monotypic) and species. Small (0.8–7.2-mm long) elongated, conical tubes having three general forms, as a U-shape, J-shape or complex sinusoid, the latter being the dominant type: occasionally the tube also begins with a 360° planispiral coil before straightening. Coiling can be both dextral and sinistral and is in a single plane. The proximal end of the tube blunts (no bulb-like origin). Tubes increase in diameter very slowly, the proximal diameter being about 0.2 mm and the distal diameter reaching 1 mm. No longitudinal ornament. The transverse ornament of the tube consists of distinct annulation, there being about 12–16 annulae per mm. Most tubes are discrete, but in some cases two or more tubes cross. The tube wall appears to be very thin, and there is no evidence of internal septae, pseudopunctae or punctae. Paired crescentic structures are preserved at the open end of the tube in some specimens.
Host–symbiont association
All specimens of Vermilituus gregarius are associated with vetulicolians, a group of extinct animals of disputed phylogenetic affinity that possessed a convex anterior part with frontal and lateral openings, articulating with a tail-like posterior extension (Figs. 2–6; Supplementary Figs. 1 and 2; for a summary of vetulicolians see ref. 6). The soft anatomy of these animals is largely unknown, but the anterior part of Vetulicola has been hypothesised to comprise a pharynx with gill-like structures that flexed by means of horizontal and longitudinal muscle fibres attached to four flexible plates covered by a thin outer membrane (see below).
Fig. 2: Vetulicola cuneata infested by Vermilituus gregarius.

a CJHMD 00031a, right view of the internal mould. Specimen infested with circa 20 V. gregarius. b CJHMD 00031b, left view of the internal mould. c Close-up of the area indicated in the box of image “a”, showing concentration of V. gregarius specimens in the anterior section. d, e CJHMD 00032b, interior surface of the right side (dorsal to top) of the anterior section, and close-up of a sinusoidal V. gregarius tube. f Enlargement of arrowed area in image “a”, showing concentration of three specimens along the central groove. An—anus, Ao—anterior opening, As—anterior section, Dp—posterodorsal projection, Lg—lateral groove, Lp—lateral pouch, Ls—lip-like structure, Ps—posterior section, S—segment, Vp—posteroventral projection (see ref. 2 for terminology). Scale bars: a, b, d 1 cm; c 5 mm; e, f 1 mm.

Full size image

Fig. 3: Vetulicola rectangulata infested by Vermilituus gregarius.

a, b YKLP 13073, left view of the internal mould of the anterior section (incomplete) and part of the posterior section. Specimen infested with circa 46 V. gregarius, with one aggregate toward the anterodorsal area (seen in “b”) comprising 29 specimens. c–f YKLP 13075, left view of the internal mould of the anterior section, and composite mould of the posterior section infested with 88 V. gregarius, including three aggregates of between 10 and 25 specimens (e.g., seen in “e”), and concentration of 24 specimens along the central groove (close-up in “f”): note that these are oriented with the narrow end associated with the groove. This specimen also shows four specimens in the tail (“d”). g, h YKLP 13074, right view of the internal mould of the anterior section infested with about 52 V. gregarius that form aggregates of between 5 and 14, including those that preserve annulation (“h”). Scale bars: a, c, g, 1 mm; b, d, e, f, h 2 mm.

Full size image

Fig. 4: Vetulicola cuneata, CJHMD 00033 showing taphonomic relationships with Vermilituus gregarius.

Stereo images have a tilt of 20°, to emphasise that both the worms and the Vetulicola are 3-dimensional. a, b Lateral view (stereo pair) of the whole specimen and c, d close-up of the anterior section (stereo pair), respectively. The specimen is a composite mould, with the external surface (ES) evident only in part of the posterior section, while most of the fossil shows an interior surface (see also Supplementary Fig. 3). Scale bars: a, b 1 cm; c, d 5 mm.

Full size image

Fig. 5: Host specificity of Vermilituus gregarius with Vetulicola cuneata.

a CJHMD 00033, Vetulicola cuneata preserved on rock slab with the fossil Eldonia. Vetulicola infested with circa 17 V. gregarius. Note that Eldonia was not infested. b CJHMD 00034, Vetulicola infested with circa 34 V. gregarius. c Close-up of the area indicated in the box of image “a”, showing one V. gregarius specimen near the anterior opening. d, e Close-up of the area indicated in the box of image “b”, showing concentration of three specimens along the central groove and one specimen at the position of the junction between the anterior and posterior section. Scale bars: a, b 1 cm; c–e 2 mm.

Full size image

Fig. 6: Reconstruction of Vetulicola cuneata (left) and V. rectangulata (right) in life.

Infestation by Vermilituus gregarius is below the surface of the anterior section, that is, within the exoskeleton. Reconstructions are based on specimens about 6-cm long.

Full size image

Vermilituus gregarius occurs in four specimens of Vetulicola cuneata from the Chengjiang region (Figs. 2, 4, and 5), plus six specimens of Vetulicola rectangulata from the Haikou region (Fig. 3; Supplementary Figs. 1 and 2; Supplementary data file). Overall, at least 400 specimens of V. rectangulata and 80 specimens of V. cuneata have been collected from the Chengjiang biota (YKLP and CJHMD collections), meaning that Vermilituus gregarius is a rare associate of vetulicolians. Vetulicolian fossils occur as composite moulds where the rock splits through the specimen, each part containing components of both the external and internal surfaces (Supplementary Fig. 3). For the anterior part of Vetulicola, we interpret Vermilituus gregarius as occupying the space between the interior of the exoskeleton, and the convex surface that appears to demarcate the position of the internal anatomy (Figs. 2a–c, 3a, b, g, 4a, b; Supplementary Figs. 1b–e, 2a, b).
The number of Vermilituus gregarius per associated vetulicolian is variable, ranging from a single tube to 88 individuals (Supplementary data file), and in some cases, V. gregarius occurs in local aggregates of up to 25 individuals, for example in YKLP 13075 (Fig. 3c, e, f). In most cases where V. gregarius aggregates, the individuals are discrete, but occasionally some overlap. The overall size of V. gregarius is from 0.8 to 7.2 mm in length, with maximum diameter ranging from 0.4 to 1 mm (proximal width is circa 0.2 mm). Average tube length varies within individual Vetulicola specimens (Supplementary data file), by a minimum of 1.6 mm (specimens associated with CJHMD 00031) to a maximum of 6.4 mm (specimens associated with YKLP 13073).
Rather than representing post-mortem assemblages, or the result of Vermilituus scavenging or colonising vetulicolian carcasses, all evidence suggests that Vermilituus attached to the body surfaces of living vetulicolians. All infested vetulicolians are preserved within ‘event beds’5. This means that they were rapidly buried by sediment, and therefore post-mortem colonisation at the seabed is highly unlikely. Tubes of Vermilituus gregarius occur almost exclusively inside the vetulicolians, rather than the external body surface, and preferentially within the anterior part (Figs. 2–5; Supplementary Figs. 1 and 2). They are absent from other fossils preserved adjacent on the same slabs (Fig. 5a), and indeed have never been observed in other Chengjiang fossils in our investigations over the last three decades. Most specimens of V. gregarius occur in the anterior section of the vetulicolian body (n  > 345) (Figs. 2–5, Supplementary Figs. 1 and 2), with just 4 specimens associated with the posterior section of the most-infested specimen in our collection (YKLP 13075, Fig. 3d). In this rare case, V. gregarius may have over-spilled onto the external surface of the animal or has been displaced post-mortem. Among those in the anterior part, most are located in the convex area between the central groove and the fin-like margins, with some concentrations often in the anterodorsal region (Fig. 3a, b). Only a few tubes of V. gregarius occur along the margins of Vetulicola. In one case, at least 10 U-shaped tubes grow with a posterior orientation in Vetulicola YKLP 10906 (Supplementary Fig. 1e). In Vetulicola YKLP 13075, there is a clear association of 24 V. gregarius with the central groove (Fig. 3f), each having a distinctive orientation with the narrow end of the tube pointing towards the groove.
The consistent occurrence of Vermilituus gregarius inside the anterior section of vetulicolians, combined with the observed patterns of localisation and occasional preferred orientation (Supplementary data file), argues against a chance post-mortem association, or generalist epibiontic habit. In the latter scenarios, the posterior section should also be infested. Furthermore, V. gregarius is absent from any other fossil organism in the Chengjiang biota, suggesting a highly specific relationship. The robust (possibly biomineralised) and curved tubes of V. gregarius are consistent with a sessile, attached ecology, but not with a motile scavenger that might have fed on vetulicolians after death. The size range of Vermilituus on each specimen (Supplementary data file) suggests animals growing in situ for some time, rather than colonising carrion. In addition, the lack of evidence for decay and disarticulation of infested vetulicolians combined with their preservation in event beds supports an in vivo association. In this light, the observed patterns in size, number and distribution of V. gregarius tubes also shed light on vetulicolian biology and the ecological relationship between the taxa. More

1.
Michener, C. D. The Bees of the World (The Johns Hopkins University Press, Baltimore, 2007).
Google Scholar 
2.
Wcislo, W. T. & Tierney, S. M. Behavioural environments and niche construction: The evolution of dim-light foraging in bees. Biol. Rev. 84, 19–37 (2009).
PubMed  Google Scholar 

3.
Warrant, E. J. Nocturnal bees. Curr. Biol. 17, 991–992 (2007).
Google Scholar 

4.
Warrant, E. J. Seeing in the dark: Vision and visual behaviour in nocturnal bees and wasps. J. Exp. Biol. 211, 1737–1746 (2008).
PubMed  Google Scholar 

5.
Somanathan, H., Borges, R. M., Warrant, E. J. & Kelber, A. Visual ecology of Indian carpenter bees I: Light intensities and flight activity. J. Comp. Physiol. A Neuroethol. Sensory Neural Behav. Physiol. 194, 97–107 (2008).
Google Scholar 

6.
Somanathan, H., Saryan, P. & Balamurali, G. S. Foraging strategies and physiological adaptations in large carpenter bees. J. Comp. Physiol. A Neuroethol. Sensory Neural Behav. Physiol. 205, 387–398 (2019).
Google Scholar 

7.
Engel, M. S. Classification of the bee tribe Augochlorini (Hymenoptera: Halictidae). Bull. Am. Museum Nat. Hist. 250, 1–90 (2000).
Google Scholar 

8.
Silveira, F. A., Melo, G. A. R. & Almeida, E. A. B. Abelhas Brasileiras: Sistemática e Identificação (Fundação Araucária, Belo Horizonte, 2002).
Google Scholar 

9.
Wcislo, W. T. et al. The evolution of nocturnal behaviour in sweat bees, Megalopta genalis and M. ecuadoria (Hymenoptera: Halictidae): An escape from competitors and enemies?. Biol. J. Linn. Soc. 83, 377–387 (2004).
Google Scholar 

10.
Carvalho, A. T., Maia, A. C. D., Ojima, P. Y., dos Santos, A. A. & Schlindwein, C. Nocturnal bees are attracted by widespread floral scents. J. Chem. Ecol. 38, 315–318 (2012).
CAS  PubMed  Google Scholar 

11.
Janzen, D. Notes on nesting and foraging behavior of Megalopta (Hymenoptera: Halictidae) in Costa Rica. J. Kansas Entomol. Soc. 41, 342–350 (1968).
Google Scholar 

12.
Roberts, R. B. Biology of the crepuscular bee Ptiloglossa guinnae N. sp. with notes on associated bees, mites, and yeasts. J. Kansas Entomol. Soc. 44, 283–294 (1971).
Google Scholar 

13.
Rozen, J. G. Nesting biology of Diphaglossine bees (Hymenoptera, Colletidae). Am. Museum Novit. 2786, 1–33 (1984).
Google Scholar 

14.
Santos, L. M., Tierney, S. M. & Wcislo, W. T. Nest descriptions of Megalopta aegis (Vachal) and M. guimaraesi Santos & Silveira (Hymenoptera, Halictidae) from the Brazilian Cerrado. Rev. Bras. Entomol. 54, 332–334 (2010).
Google Scholar 

15.
Sarzetti, L., Genise, J., Sanchez, M. V., Farina, J. & Molina, A. Nesting behavior and ecological preferences of five Diphaglossinae species (Hymenoptera, Apoidea, Colletidae) from Argentina and Chile. J. Hymenopt. Res. 33, 63–82 (2013).
Google Scholar 

16.
Wolda, H. & Roubik, D. W. Nocturnal bee abundance and seasonal bee activity in a Panamanian forest. Ecology 67, 426–433 (1986).
Google Scholar 

17.
Linsley, E. G. & Cazier, M. A. Some competitive relationships among matinal and late afternoon foraging activities of Caupolicanine bees in Southeastern Arizona (Hymenoptera, Colletidae). J. Kansas Entomol. Soc. 43, 251–261 (1970).
Google Scholar 

18.
Roulston, T. H. Hourly capture of two species of Megalopta (Hymenoptera: Apoidea; Halictidae) at black lights in Panama with notes on nocturnal foraging by bees. J. Kansas Entomol. Soc. 70, 189–196 (1997).
Google Scholar 

19.
Smith, A. R., López Quintero, I. J., Moreno Patiño, J. E., Roubik, D. W. & Wcislo, W. T. Pollen use by Megalopta sweat bees in relation to resource availability in a tropical forest. Ecol. Entomol. 37, 309–317 (2012).
Google Scholar 

20.
Dafni, A., Kevan, P. G. & Husband, B. C. Practical Pollination Biology (Enviroquest Ltd., Cambridge, 2005).
Google Scholar 

21.
Somanathan, H. & Borges, R. M. Nocturnal pollination by the carpenter bee Xylocopa tenuiscapa (Apidae) and the effect of floral display on fruit set of Heterophragma quadriloculare (Bignoniaceae) in India. Biotropica 33, 78–89 (2001).
Google Scholar 

22.
Contrera, F. A. L. & Nieh, J. C. The effect of ambient temperature on forager sound production and thoracic temperature in the stingless bee, Melipona panamica. Behav. Ecol. Sociobiol. 61, 887–897 (2007).
Google Scholar 

23.
Willmer, P. G. Thermal constraints on activity patterns in nectar-feeding insects. Ecol. Entomol. 8, 455–469 (1983).
Google Scholar 

24.
Linsley, E. G. The ecology of solitary bee. Hilgardia 27, 543–599 (1958).
Google Scholar 

25.
Figueiredo-Mecca, G., Bego, L. R. & Nascimento, F. S. Foraging behavior of Scaptotrigona depilis (Hymenoptera, Apidae, Meliponini) and its relationship with temporal and abiotic factors. Sociobiology 60, 277–282 (2013).
Google Scholar 

26.
Streinzer, M., Huber, W. & Spaethe, J. Body size limits dim-light foraging activity in stingless bees (Apidae: Meliponini). J. Comp. Physiol. A 202, 643–655 (2016).
Google Scholar 

27.
Linsley, E. G. Temporal patterns of flower visitation by solitary bees, with particular reference to the southwestern United States. J. Kansas Entomol. Soc. 51, 531–546 (1978).
Google Scholar 

28.
Borges, R. M., Somanathan, H. & Kelber, A. Patterns and processes in nocturnal and crepuscular pollination services. Q. Rev. Biol. 91, 389–418 (2016).
PubMed  Google Scholar 

29.
Warrant, E. J. Seeing better at night: Life style, eye design and the optimum strategy of spatial and temporal summation. Vis. Res. 39, 1611–1630 (1999).
CAS  PubMed  Google Scholar 

30.
Warrant, E. J. et al. Nocturnal vision and landmark orientation in a tropical halictid bee. Curr. Biol. 14, 1309–1318 (2004).
CAS  PubMed  Google Scholar 

31.
Warrant, E. Vision in the dimmest habitats on Earth. J. Comp. Physiol. A Neuroethol. Sensory Neural Behav. Physiol. 190, 765–789 (2004).
Google Scholar 

32.
Warrant, E. & Dacke, M. Vision and visual navigation in nocturnal insects. Annu. Rev. Entomol. 56, 239–254 (2011).
CAS  PubMed  Google Scholar 

33.
Rozenberg, G. V. Twilight (Springer, New York, 1966).
Google Scholar 

34.
O’Carroll, D. C. & Warrant, E. J. Vision in dim light: Highlights and challenges. Philos. Trans. R. Soc. B Biol. Sci. 372, 20160062 (2017).
Google Scholar 

35.
Smith, A. R., Kitchen, S. M., Toney, R. M. & Ziegler, C. Is nocturnal foraging in a tropical bee an escape from interference competition?. J. Insect Sci. 17, 1–7 (2017).
CAS  Google Scholar 

36.
Kapustjanskij, A., Streinzer, M., Paulus, H. F. & Spaethe, J. Bigger is better: implications of body size for flight ability under different light conditions and the evolution of alloethism in bumblebees. Funct. Ecol. 21, 1130–1136 (2007).
Google Scholar 

37.
Lorenzi, H. Brazilian Trees: A Guide to the Identification and Cultivation of Brazilian Native Trees (Instituto Plantarum de Estudos da Flora, Nova Odessa, 2002).
Google Scholar 

38.
Cordeiro, G. D., Pinheiro, M., Dötterl, S. & Alves-dos-Santos, I. Pollination of Campomanesia phaea (Myrtaceae) by night-active bees: A new nocturnal pollination system mediated by floral scent. Plant Biol. 19, 132–139 (2017).
CAS  PubMed  Google Scholar 

39.
Kelber, A. et al. Light intensity limits foraging activity in nocturnal and crepuscular bees. Behav. Ecol. 17, 63–72 (2006).
Google Scholar 

40.
Polatto, L. P., Chaud-Netto, J. & Alves-Junior, V. V. Influence of abiotic factors and floral resource availability on daily foraging activity of bees. J. Insect Behav. 27, 593–612 (2014).
Google Scholar 

41.
Willis, D. S. & Kevan, P. G. Foraging dynamics of Peponapis pruinosa (Hymenoptera: Anthophoridae) on pumpkin (Cucurbita pepo) in Southern Ontario. Can. Entomol. 127, 167–175 (1995).
Google Scholar 

42.
Wcislo, W. T. & Cane, J. H. Floral resource utilization by solitary bees (Hymenoptera: Apoidea) and exploitation of their stored foods by natural enemies. Annu. Rev. Entomol. 41, 257–286 (1996).
CAS  PubMed  Google Scholar 

43.
Bellusci, S. & Marques, M. D. Circadian activity rhythm of the foragers of a eusocial bee (Scaptotrigona aff depilis, Hymenoptera, Apidae, Meliponinae) outside the nest. Biol. Rhythm Res. 32, 117–124 (2001).
Google Scholar 

44.
Bloch, G., Bar-Shai, N., Cytter, Y. & Green, R. Time is honey: Circadian clocks of bees and flowers and how their interactions may influence ecological communities. Philos. Trans. R. Soc. B Biol. Sci. 372, 20160256 (2017).
Google Scholar 

45.
Enright, J. T. Ecological aspects of endogenous rhythmicity. Annu. Rev. Ecol. Evol. Syst. 1, 221–238 (1970).
Google Scholar 

46.
Shelly, T. E., Villalobos, E. M., Buchmann, S. L. & Cane, J. H. Temporal patterns of floral visitation for two bee species foraging on Solanum. J. Kansas Entomol. Soc. 66, 319–327 (1993).
Google Scholar 

47.
Gottlieb, D., Keasar, T., Shmida, A. & Motro, U. Possible foraging benefits of bimodal daily activity in Proxylocopa olivieri (Lepeletier) (Hymenoptera: Anthophoridae). Environ. Entomol. 34, 417–424 (2005).
Google Scholar 

48.
Franco, E. L. & Gimenes, M. Pollination of Cambessedesia wurdackii in Brazilian campo rupestre vegetation, with special reference to crepuscular bees. J. Insect Sci. 11, 1–13 (2011).
Google Scholar 

49.
Oliveira, F. S., Ribeiro, M. H. M., Nunez, C. V. & de Albuquerque, M. C. Flowering phenology of Mouriri guianensis (Melastomataceae) and its interaction with the crepuscular bee Megalopta amoena (Halictidae) in the restinga of Lençóis Maranhenses National Park, Brazil. Acta Amaz. 46, 281–290 (2016).
Google Scholar 

50.
Willmer, P. & Stone, G. Temperature and water relations in desert bees. J. Therm. Biol. 22, 453–465 (1997).
Google Scholar 

51.
Krug, C. et al. Nocturnal bee pollinators are attracted to guarana flowers by their scents. Front. Plant Sci. 9, 1072 (2018).
PubMed  PubMed Central  Google Scholar 

52.
Siqueira, E. et al. Pollination of Machaerium opacum (Fabaceae) by nocturnal and diurnal bees. Arthropod. Plant. Interact. 12, 633–645 (2018).
Google Scholar 

53.
Orbán, L. L. & Plowright, C. M. S. Getting to the start line: How bumblebees and honeybees are visually guided towards their first floral contact. Insectes Soc. 61, 325–336 (2014).
PubMed  PubMed Central  Google Scholar 

54.
Burger, H., Dotterl, S. & Ayasse, M. Host-plant finding and recognition by visual and olfactory floral cues in an oligolectic bee. Funct. Ecol. 24, 1234–1240 (2010).
Google Scholar 

55.
Milet-Pinheiro, P., Ayasse, M., Schlindwein, C., Dobson, H. E. M. & Dötterl, S. Host location by visual and olfactory floral cues in an oligolectic bee: Innate and learned behavior. Behav. Ecol. 23, 531–538 (2012).
Google Scholar 

56.
Kantsa, A. et al. Community-wide integration of floral colour and scent in a Mediterranean scrubland. Nat. Ecol. Evol. 1, 1502–1510 (2017).
PubMed  Google Scholar 

57.
Peel, M. C., Finlayson, B. L. & McMahon, T. A. Updated world map of the Koppen-Geiger climate classification. Hydrol. Earth Syst. Sci. 11, 1633–1644 (2007).
ADS  Google Scholar 

58.
Michener, C. D. & Lange, R. B. Observations on the behavior of Brasilian halictid bees, III. Univ. Kansas Sci. Bull. 39, 473–505 (1958).
Google Scholar 

59.
Meinel, A. B. & Meinel, M. P. Sunsets, Twilights, and Evening Skies (Cambridge University, Cambridge, 1991).
Google Scholar 

60.
R Core Team, R. R: A language and environment for statistical computing. R Found. Stat. Comput. Vienna, Austria. www.R-project.org (2017). Accessed 15 Dec 2017.

61.
Bolker, B. & R Core Team, R. bbmle: Tools for general maximum likelihood estimation. R Packag. version 1.0.20. https://CRAN.R-project.org/package=bbmle (2017). Accessed 15 Dec 2017.

62.
Hartig, F. DHARMa: Residual diagnostics for hierarchical (multi-level/mixed) regression models. R Packag. version 0.1.5. https://CRAN.R-project.org/package=DHARMa (2017). Accessed 15 Dec 2017.

63.
Wickham, H., Francois, R., Henry, L. & Müller, K. dplyr: A grammar of data manipulation. R Packag. version 0.7.4. https://CRAN.R-project.org/package=dplyr (2017). Accessed 15 Dec 2017.

64.
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, New York, 2009).
Google Scholar 

65.
Sarkar, D. Lattice: Multivariate data visualization with R. R Packag. version 0.20–38. https://CRAN.R-project.org/package=lattice (2008). Accessed 15 Dec 2017.

66.
Sarkar, D. & Andrews, F. latticeExtra: Extra graphical utilities based on lattice. R Packag. version 0.6-28. https://CRAN.R-project.org/package=latticeExtra (2016). Accessed 15 Dec 2017.

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

68.
Kelley, D. & Richards, C. oce: Analysis of oceanographic data. R Packag. version 0.9-22. https://CRAN.R-project.org/package=oce (2017). Accessed 15 Dec 2017.

69.
Wickham, H. & Henry, L. tidyr: Easily tidy data with ‘spread()’ and ‘gather()’ functions. R Packag. version 0.8.0. https://CRAN.R-project.org/package=tidyr (2018). Accessed 15 Dec 2017.

70.
Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer, New York, 2009).
Google Scholar  More

1.
Koštál, V. Eco-physiological phases of insect diapause. J. Insect Physiol. 52, 113–127 (2006).
PubMed  Google Scholar 
2.
Saunders, D. S. Insect Clocks 3rd edn. (Elsevier Science, Amsterdam, 2002).
Google Scholar 

3.
Tougeron, K. Diapause research in insects: historical review and recent work perspectives. Entomol. Exp. Appl. 167, 27–36 (2019).
Google Scholar 

4.
Saulich, A. H. & Musolin, D. L. Four seasons: diversity of seasonal adaptations and ecological mechanisms controlling seasonal development in true bugs (Heteroptera) in the temperate climate. Proc. Biol. Inst. St. Petersburg State Univ. 53, 25–106 (2007).
Google Scholar 

5.
Niva, C. C. & Takeda, M. Effects of photoperiod, temperature and melatonin on nymphal development, polyphenism and reproduction in Halyomorpha halys (Heteroptera: Pentatomidae). Zool. Sci. 20, 963–970 (2003).
CAS  PubMed  Google Scholar 

6.
Nielsen, A. L., Chen, S. & Fleischer, S. J. Coupling developmental physiology, photoperiod, and temperature to model phenology and dynamics of an invasive heteropteran, Halyomorpha halys. Front. Physiol. 7, 165. https://doi.org/10.3389/fphys.2016.00165 (2016).
Article  PubMed  PubMed Central  Google Scholar 

7.
Inkley, D. B. Characteristics of home invasion by the brown marmorated stink bug (Hemiptera: Pentatomidae). J. Entomol. Sci. 47, 125–130 (2012).
Google Scholar 

8.
Lee, D.-H. et al. Characterization of overwintering sites of the invasive brown marmorated stinkbug in natural landscapes using human surveyors and detector canines. PLoS ONE 9(4), e91575. https://doi.org/10.1371/journal.pone.0091575 (2014).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

9.
Cambridge, J., Payenski, A. & Hamilton, G. C. The distribution of overwintering brown marmorated stink bugs (Hemiptera: Pentatomidae) in college dormitories. Fla. Entomol. 98, 1257–1259 (2015).
Google Scholar 

10.
Leskey, T. C. & Nielsen, A. L. Impact of the invasive brown marmorated stink bug in North America and Europe: history, biology, ecology, and management. Annu. Rev. Entomol. 63, 599–618 (2018).
CAS  PubMed  Google Scholar 

11.
Duthie, C. Risk analysis of Halyomorpha halys (brown marmorated stinkbug) on all pathways. Ministry for primary industries. (Accessed 31 August 2019); https://mpi.govt.nz/dmsdocument/2909-halyomorpha-halys-brown-marmorated-stink-bug-riskanalysis-november-2012 (2012).

12.
Vandervoet, T. F., Bellamy, D. E., Anderson, D. & MacLellan, R. Trapping for early detection of the brown marmorated stink bug, Halyomorpha halys, New Zealand. N. Z. Plant. Protect. S.E. 72, 36–43 (2019).
Google Scholar 

13.
Leskey, T. C. et al. Pest status of the brown marmorated stink bug, Halyomorpha halys in the USA. Outlooks Pest Manag. 23, 218–226 (2012).
Google Scholar 

14.
Kamminga, K., Herbert, D. A., Toews, M. D., Malone, S. & Kuhar, T. Halyomorpha halys (Hemiptera: Pentatomidae) feeding injury on cotton bolls. J. Cotton Sci. 18, 68–74 (2014).
Google Scholar 

15.
Rice, K. B. et al. Biology, ecology, and management of brown marmorated stink bug (Hemiptera: Pentatomidae). J. Integr. Pest Manag. 5(3), 1–13 (2014).
Google Scholar 

16.
Dobson, R. C., Rogers, M., Moore, J. L. C. & Bessin, R. T. Exclusion of the brown marmorated stink bug from organically grown peppers using barrier screens. HortTechnology 26, 191–198 (2016).
Google Scholar 

17.
Acebes-Doria, A. L., Leskey, T. C. & Bergh, J. C. Injury to apples and peaches at harvest from feeding by Halyomorpha halys (Stål) (Hemiptera: Pentatomidae) nymphs early and late in the season. Crop Prot. 89, 58–65 (2018).
Google Scholar 

18.
Hamilton, G. C. et al. Halyomorpha halys (Stål). In Invasive Stink Bugs and Related Species (Pentatomoidea)—Biology, Higher Systematics, Semiochemistry, and Management (ed. McPherson, J. E.) 243–292 (CRC Press, Boca Raton, 2018).
Google Scholar 

19.
Moore, L. et al. Characterizing damage potential of the brown marmorated stink bug (Hemiptera: Pentatomidae) in cherry orchards. Entomol. Gen. 39, 271–283 (2019).
Google Scholar 

20.
Addo-Bediako, A., Chown, S. L. & Gaston, K. J. Thermal tolerance, climatic variability and latitude. P. Roy. Soc. B-Biol. Sci. 267, 739–745 (2000).
CAS  Google Scholar 

21.
Sinclair, B. J., Coello-Alvarado, L. E. & Ferguson, L. V. An invitation to measure insect cold tolerance: methods, approaches, and workflow. J. Therm. Biol. 53, 180–197 (2015).
PubMed  Google Scholar 

22.
Halbritter, D. A., Teets, N. M., Williams, C. M. & Daniels, J. C. Differences in winter cold hardiness reflect the geographic range disjunction of Neophasia menapia and Neophasia terlooii (Lepidoptera: Pieridae). J. Insect Physiol. 107, 204–211 (2018).
CAS  PubMed  Google Scholar 

23.
Kistner, E. J. Climate change impacts on the potential distribution and abundance of the brown marmorated stink bug (Hemiptera: Pentatomidae) with special reference to North America and Europe. Environ. Entomol. 46, 1212–1224 (2017).
PubMed  Google Scholar 

24.
Kriticos, D. J. et al. The potential global distribution of the brown marmorated stink bug, Halyomorpha halys, a critical threat to plant biosecurity. J. Pest Sci. 90, 1033–1043 (2017).
Google Scholar 

25.
Lee, R. E. A primer on insect cold-tolerance. In Low Temperature Biology of Insects (eds Denlinger, D. L. et al.) 3–34 (Cambridge University Press, New York, 2010).
Google Scholar 

26.
Cira, T. M. et al. Cold tolerance of Halyomorpha halys (Hemiptera: Pentatomidae) across geographic and temporal scales. Environ. Entomol. 45, 484–491 (2016).
PubMed  Google Scholar 

27.
Asahina, E. Frost resistance in insects. Adv. Insect Physiol. 6, 1–49 (1970).
Google Scholar 

28.
Storey, K. B. & Storey, J. M. Freeze tolerance: constraining forces, adaptive mechanisms. Can. J. Zool. 66, 1122–1127 (1987).
Google Scholar 

29.
Danks, H. V. Dehydration in dormant insects. J. Insect Physiol. 46, 837–852 (2000).
ADS  CAS  PubMed  Google Scholar 

30.
Musolin, D. L. Insects in a warmer world: ecological, physiological and life-history responses of true bugs (Heteroptera) to climate change. Glob. Change Biol. 13, 1565–1585 (2007).
ADS  Google Scholar 

31.
Saulich, A. Kh. & Musolin, D. L. Diapause in the seasonal cycle of stink bugs from the temperate zone. Entomol. Rev. 92, 1–26 (2012).
Google Scholar 

32.
Takeda, K., Musolin, D. L. & Fujisaki, K. Dissecting insect responses to climate warming: overwintering and post-diapause performance in the southern green stink bug, Nezara viridula, under simulated climate-change conditions. Physiol. Entomol. 35, 343–353 (2010).
Google Scholar 

33.
Lowenstein, D. M. & Walton, V. M. Halyomorpha halys (Hemiptera: Pentatomidae) winter survival, feeding activity, and reproduction rates based on episodic cold shock and winter temperature regimes. J. Econ. Entomol. 111, 1210–1218 (2018).
PubMed  Google Scholar 

34.
Cira, T. M., Koch, R. L., Burkness, E. C., Hutchison, W. D. & Venette, R. C. Effects of diapause on Halyomorpha halys (Hemiptera: Pentatomidae) cold tolerance. Environ. Entomol. 47, 997–1004 (2018).
PubMed  Google Scholar 

35.
Skillman, V. P. Nutrient profile and nursery feeding damage of Halyomorpha halys. MSc Thesis, Oregon State University (2017).

36.
Ciancio, J. Overwintering biology of the brown marmorated stink bug, Halyomorpha halys (Hemiptera: Pentatomidae). MSc Thesis, The University of Western Ontario, Electronic Thesis and Dissertation Repository, 5813. (Accessed 31 August 2019); https://ir.lib.uwo.ca/etd/5813 (2018).

37.
Sinclair, B. J. & Marshall, K. E. The many roles of fats in overwintering insects. J. Exp. Biol. 221, 161836. https://doi.org/10.1242/jeb.161836 (2018).
Article  Google Scholar 

38.
Skillman, V. P., Wiman, N. G. & Lee, J. C. Nutrient declines in overwintering Halyomorpha halys populations. Entomol. Exp. Appl. 166, 778–789 (2018).
CAS  Google Scholar 

39.
Leather, S. R., Walters, K. F. A. & Bale, J. S. The Ecology of Insect Overwintering (Cambridge University Press, Cambridge, 1993).
Google Scholar 

40.
Bale, J. S. & Hayward, S. A. L. Insect overwintering in a changing climate. J. Exp. Biol. 213, 980–994 (2010).
CAS  PubMed  Google Scholar 

41.
Lee, R. E., Chen, C. P. J. & Denlinger, D. L. A rapid cold-hardening process in insects. Science 238, 1415–1417 (1987).
ADS  PubMed  Google Scholar 

42.
Ju, R. T., Xiao, Y. Y. & Li, B. Rapid cold hardening increases cold and chilling tolerances more than acclimation in the adults of the sycamore lace bug, Corythucha ciliata (Say) (Hemiptera: Tingidae). J. Insect Physiol. 57, 1577–1582 (2011).
CAS  PubMed  Google Scholar 

43.
Andreadis, S. S. & Athanassiou, C. G. A review of insect cold hardiness and its potential in stored product insect control. Crop Prot. 91, 93–99 (2017).
Google Scholar 

44.
Badeck, F. W. et al. Responses of spring phenology to climate change. New Phytol. 162, 295–309 (2004).
Google Scholar 

45.
Wilby, R. L. & Perry, G. L. W. Climate change, biodiversity and the urban environment: a critical review based on London, UK. Prog. Phys. Geog. 30, 73–98 (2006).
Google Scholar 

46.
Kiritani, K. The impact of global warming and land-use change on the pest status of rice and fruit bugs (Heteroptera) in Japan. Glob. Change Biol. 13, 1586–1595 (2007).
ADS  Google Scholar 

47.
Musolin, D. L., Tougou, D. & Fujisaki, K. Too hot to handle? Phenological and life-history responses to simulated climate change of the southern green stink bug Nezara viridula (Heteroptera: Pentatomidae). Glob. Change Biol. 16, 73–87 (2010).
ADS  Google Scholar 

48.
Forrest, J. R. Complex responses of insect phenology to climate change. Curr. Opin. Insect Sci. 17, 49–54 (2016).
PubMed  Google Scholar 

49.
Rigby, J. R. & Porporato, A. Spring frost risk in a changing climate. Geophys. Res. Lett. 35, L12703. https://doi.org/10.1029/2008GL033955 (2008).
ADS  Article  Google Scholar 

50.
Crozier, L. & Dwyer, G. Combining population-dynamic and ecophysiological models to predict climate-induced insect range shifts. Am. Nat. 167, 853–866 (2006).
PubMed  Google Scholar 

51.
Inouye, D. W. The ecological and evolutionary significance of frost in the context of climate change. Ecol. Lett. 3, 457–463 (2000).
Google Scholar 

52.
Lee, D.-H., Short, B. D., Joseph, S. V., Bergh, J. C. & Leskey, T. C. Review of the biology, ecology, and management of Halyomorpha halys (Hemiptera: Pentatomidae) in China, Japan, and the Republic of Korea. Environ. Entomol. 42, 627–641 (2013).
PubMed  Google Scholar 

53.
Lee, D.-H. & Leskey, T. C. Flight behavior of foraging and overwintering brown marmorated stink bug, Halyomorpha halys (Hemiptera: Pentatomidae). Bull. Entomol. Res. 105, 566–573 (2015).
PubMed  PubMed Central  Google Scholar 

54.
Bergh, J. C., Morrison, W. R. III., Joseph, S. V. & Leskey, T. C. Characterizing spring emergence of adult Halyomorpha halys using experimental overwintering shelters and commercial pheromone traps. Entomol. Exp. Appl. 162, 336–345 (2017).
Google Scholar 

55.
Skillman, V. P., Wiman, N. G. & Lee, J. C. Monitoring nutrient status of brown marmorated stink bug adults and nymphs on summer holly. Insects 9, 120. https://doi.org/10.3390/insects9030120 (2018).
Article  PubMed Central  Google Scholar 

56.
Lee, R. E. Principles of insect low temperature tolerance. In Insects at Low Temperature (eds Lee, R. E. et al.) 17–46 (Chapman and Hall, New York, 1991).
Google Scholar 

57.
Chown, S. L., Jumbam, K. R., Sørensen, J. G. & Terblanche, J. S. Phenotypic variance, plasticity and heritability estimates of critical thermal limits depend on methodological context. Funct. Ecol. 23, 133–140 (2009).
Google Scholar 

58.
Terblanche, J. S. et al. Ecologically relevant measures of tolerance to potentially lethal temperatures. J. Exp. Biol. 214, 3713–3725 (2011).
PubMed  Google Scholar 

59.
Overgaard, J., Kristensen, T. N. & Sørensen, J. G. Validity of thermal ramping assays used to assess thermal tolerance in arthropods. PLoS ONE 7, e32758. https://doi.org/10.1371/journal.pone.0032758 (2012).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

60.
Kasule, F. K. Repetitive mating and female fitness in Dysdercus cardinalis (Hemiptera: Pyrrhocoridae). Zool. J. Linnean Soc. 88, 191–199 (1986).
Google Scholar 

61.
De Loof, A. Longevity and aging in insects: is reproduction costly; cheap; beneficial or irrelevant? A critical evaluation of the ‘“trade-off”’ concept. J. Insect Physiol. 57, 1–11 (2011).
PubMed  Google Scholar 

62.
Nielsen, A. L. et al. Phenology of brown marmorated stink bug described using female reproductive development. Ecol. Evol. 7, 6680–6690. https://doi.org/10.1002/ece3.3125 (2017).
Article  PubMed  PubMed Central  Google Scholar 

63.
SAS Institute. PROC user’s manual, 6th edn. SAS Institute, Cary, NC. (Accessed 31 August 2019); https://support.sas.com/documentation/cdl/en/indbug/68442/PDF/default/indbug.pdf (2016).

64.
Robertson, J. L., Savin, N. E., Preisler, H. K. & Russell, R. M. Bioassays with Arthropods 2nd edn. (CRC Press, Boca Raton, 2007).
Google Scholar 

65.
Funayama, K. Importance of apple fruits as food for the brown-marmorated stink bug, Halyomorpha halys (Stål) (Heteroptera: Pentatomidae). Appl. Entomol. Zool. 39, 617–623 (2004).
Google Scholar 

66.
Scaccini, D., Duso, C. & Pozzebon, A. Lethal effects of high temperatures on brown marmorated stink bug adults before and after overwintering. Insects 10, 355. https://doi.org/10.3390/insects10100355 (2019).
Article  PubMed Central  Google Scholar  More

1.
Tomitani A, Knoll AH, Cavanaugh CM, Ohno T. The evolutionary diversification of Cyanobacteria: molecular-phylogenetic and paleontological perspectives. Proc Natl Acad Sci USA. 2006;103:5442–7.
CAS  PubMed  Google Scholar 
2.
Schirrmeister BE, Gugger M, Donoghue PCJ. Cyanobacteria and the Great Oxidation Event: evidence from genes and fossils. Palaeontology. 2015;58:769–85.
PubMed  PubMed Central  Google Scholar 

3.
Fischer WW, Hemp J, Johnson JE. Evolution of oxygenic photosynthesis. Annu Rev Earth Planet Sci. 2016;44:647–83.
CAS  Google Scholar 

4.
Soo RM, Hemp J, Parks DH, Fischer WW, Hugenholtz P. On the origins of oxygenic photosynthesis and aerobic respiration in Cyanobacteria. Science. 2017;355:1436–40.
CAS  PubMed  Google Scholar 

5.
Biller SJ, Berube PM, Lindell D, Chisholm SW. Prochlorococcus: the structure and function of collective diversity. Nat Rev Microbiol. 2015;13:13–27.
CAS  PubMed  Google Scholar 

6.
Sánchez-Baracaldo P. Origin of marine planktonic Cyanobacteria. Sci Rep. 2015;5:14–17.
Google Scholar 

7.
Shang JL, Chen M, Hou S, Li T, Yang YW, Li Q, et al. Genomic and transcriptomic insights into the survival of the subaerial cyanobacterium Nostoc flagelliforme in arid and exposed habitats. Environ Microbiol. 2019;21:845–63.
CAS  PubMed  Google Scholar 

8.
Chrismas NAM, Anesio AM, Śanchez-Baracaldo P. The future of genomics in polar and alpine Cyanobacteria. FEMS Microbiol Ecol. 2018;94:fiy032.
PubMed Central  Google Scholar 

9.
Kashtan N, Roggensack SE, Rodrigue S, Thompson JW, Biller SJ, Coe A, et al. Single-cell genomics reveals hundreds of coexisting subpopulations in wild Prochlorococcus. Science. 2014;344:416–20.
CAS  PubMed  Google Scholar 

10.
Larsson J, Celepli N, Ininbergs K, Dupont CL, Yooseph S, Bergman B, et al. Picocyanobacteria containing a novel pigment gene cluster dominate the brackish water Baltic Sea. ISME J. 2014;8:1892–903.
CAS  PubMed  PubMed Central  Google Scholar 

11.
Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19:455–77.
CAS  PubMed  PubMed Central  Google Scholar 

12.
Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.
CAS  PubMed  PubMed Central  Google Scholar 

13.
Gurevich A, Saveliev V, Vyahhi N, Tesler G. QUAST: quality assessment tool for genome assemblies. Bioinformatics. 2013;29:1072–5.
CAS  PubMed  PubMed Central  Google Scholar 

14.
Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30:2068–9.
CAS  Google Scholar 

15.
Mistry J, Finn RD, Eddy SR, Bateman A, Punta M. Challenges in homology search: HMMER3 and convergent evolution of coiled-coil regions. Nucleic Acids Res. 2013;41:e121.
CAS  PubMed  PubMed Central  Google Scholar 

16.
Wu M, Scott AJ. Phylogenomic analysis of bacterial and archaeal sequences with AMPHORA2. Bioinformatics. 2012;28:1033–4.
CAS  PubMed  Google Scholar 

17.
Capella-Gutierrez S, Silla-Martinez JM, Gabaldon T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics. 2009;25:1972–3.
CAS  PubMed  PubMed Central  Google Scholar 

18.
Waterhouse RM, Seppey M, Simão FA, Manni M, Ioannidis P, Klioutchnikov G, et al. BUSCO applications from quality assessments to gene prediction and phylogenomics. Mol Biol Evol. 2018;35:543–8.
CAS  PubMed  Google Scholar 

19.
Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30:772–80.
CAS  PubMed  PubMed Central  Google Scholar 

20.
Miller MA, Pfeiffer W, Schwartz T. Creating the CIPRES Science Gateway for inference of large phylogenetic trees. In: Proceedings of the Gateway Computing Environments Workshop (GCE). New Orleans (LA): IEEE; 2010. pp 1–8.

21.
Nguyen L-T, Schmidt HA, von Haeseler A, Minh BQ. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol. 2015;32:268–74.
CAS  Google Scholar 

22.
Di Rienzi SC, Sharon I, Wrighton KC, Koren O, Hug LA, Thomas BC, et al. The human gut and groundwater harbor non-photosynthetic bacteria belonging to a new candidate phylum sibling to Cyanobacteria. Elife. 2013;2:e01102.
PubMed  PubMed Central  Google Scholar 

23.
Matheus Carnevali PB, Schulz F, Castelle CJ, Kantor RS, Shih PM, Sharon I, et al. Hydrogen-based metabolism as an ancestral trait in lineages sibling to the Cyanobacteria. Nat Commun. 2019;10:1–16.
CAS  Google Scholar 

24.
Letunic I, Bork P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 2019;47:W256–9.
CAS  PubMed  PubMed Central  Google Scholar 

25.
Tung HoLS, Ané C. A linear-time algorithm for Gaussian and non-Gaussian trait evolution models. Syst Biol. 2014;63:397–408.
Google Scholar 

26.
Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B. 1995;57:289–300.
Google Scholar 

27.
Gan F, Bryant DA. Adaptive and acclimative responses of Cyanobacteria to far-red light. Environ Microbiol. 2015;17:3450–65.
CAS  PubMed  Google Scholar 

28.
Revell LJ. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol Evol. 2012;3:217–23.
Google Scholar 

29.
Levy A, Salas Gonzalez I, Mittelviefhaus M, Clingenpeel S, Herrera Paredes S, Miao J, et al. Genomic features of bacterial adaptation to plants. Nat Genet. 2018;50:138–50.
CAS  Google Scholar 

30.
Zhu Q, Kosoy M, Dittmar K. HGTector: an automated method facilitating genome-wide discovery of putative horizontal gene transfers. BMC Genom. 2014;15:717.
Google Scholar 

31.
Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2014;12:59–60.
PubMed  Google Scholar 

32.
Csurös M. Count: evolutionary analysis of phylogenetic profiles with parsimony and likelihood. Bioinformatics. 2010;26:1910–2.
PubMed  Google Scholar 

33.
Enright AJ. An efficient algorithm for large-scale detection of protein families. Nucleic Acids Res. 2002;30:1575–84.
CAS  PubMed  PubMed Central  Google Scholar 

34.
Komárek J. A polyphasic approach for the taxonomy of Cyanobacteria: principles and applications. Eur J Phycol. 2016;51:346–53.
Google Scholar 

35.
Komárek J, Kaštovský J, Mareš J, Johansen JR. Taxonomic classification of cyanoprokaryotes (Cyanobacterial genera) 2014, using a polyphasic approach. Preslia. 2014;86:295–335.
Google Scholar 

36.
Ponce-Toledo RI, Deschamps P, López-García P, Zivanovic Y, Benzerara K, Moreira D. An early-branching freshwater Cyanobacterium at the origin of plastids. Curr Biol. 2017;27:386–91.
CAS  PubMed  PubMed Central  Google Scholar 

37.
de Vries J, Archibald JM. Endosymbiosis: did plastids evolve from a freshwater Cyanobacterium? Curr Biol. 2017;27:R103–5.
PubMed  Google Scholar 

38.
Dagan T, Roettger M, Stucken K, Landan G, Koch R, Major P, et al. Genomes of stigonematalean Cyanobacteria (subsection V) and the evolution of oxygenic photosynthesis from prokaryotes to plastids. Genome Biol Evol. 2013;5:31–44.
PubMed  Google Scholar 

39.
Shih PM, Wu D, Latifi A, Axen SD, Fewer DP, Talla E, et al. Improving the coverage of the cyanobacterial phylum using diversity-driven genome sequencing. Proc Natl Acad Sci USA. 2013;110:1053–8.
CAS  PubMed  Google Scholar 

40.
Sánchez-Baracaldo P, Raven JA, Pisani D, Knoll AH. Early photosynthetic eukaryotes inhabited low-salinity habitats. Proc Natl Acad Sci USA. 2017;114:E7737–45.
PubMed  Google Scholar 

41.
FitzJohn RG, Maddison WP, Otto SP. Estimating trait-dependent speciation and extinction rates from incompletely resolved phylogenies. Syst Biol. 2009;58:595–611.
PubMed  Google Scholar 

42.
Monk JM, Charusanti P, Aziz RK, Lerman JA, Premyodhin N, Orth JD, et al. Genome-scale metabolic reconstructions of multiple Escherichia coli strains highlight strain-specific adaptations to nutritional environments. Proc Natl Acad Sci USA. 2013;110:20338–43.
CAS  PubMed  Google Scholar 

43.
Tripp HJ, Bench SR, Turk KA, Foster RA, Desany BA, Niazi F, et al. Metabolic streamlining in an open-ocean nitrogen-fixing cyanobacterium. Nature. 2010;464:90–4.
CAS  PubMed  Google Scholar 

44.
Scanlan DJ, Ostrowski M, Mazard S, Dufresne A, Garczarek L, Hess WR, et al. Ecological genomics of marine Picocyanobacteria. Microbiol Mol Biol Rev. 2009;73:249–99.
CAS  PubMed  PubMed Central  Google Scholar 

45.
Poulton NJ, Acinas SG, Lauro FM, Cavicchioli R, Swan BK, Hanson NW, et al. Prevalent genome streamlining and latitudinal divergence of planktonic bacteria in the surface ocean. Proc Natl Acad Sci USA. 2013;110:11463–8.
PubMed  Google Scholar 

46.
Bentkowski P, Van Oosterhout C, Ashby B, Mock T. The effect of extrinsic mortality on genome size evolution in prokaryotes. ISME J. 2017;11:1011–8.
CAS  PubMed  Google Scholar 

47.
Steele JH, Brink KH, Scott BE. Comparison of marine and terrestrial ecosystems: suggestions of an evolutionary perspective influenced by environmental variation. ICES J Mar Sci. 2019;76:50–9.
Google Scholar 

48.
Philippot L, Andersson SGE, Battin TJ, Prosser JI, Schimel JP, Whitman WB, et al. The ecological coherence of high bacterial taxonomic ranks. Nat Rev Microbiol. 2010;8:523–9.
CAS  PubMed  Google Scholar 

49.
Luo H, Csűros M, Hughes AL, Moran MA. Evolution of divergent life history strategies in marine Alphaproteobacteria. MBio. 2013;4:1–8.
Google Scholar 

50.
Whitton BA (editor). Ecology of Cyanobacteria II. Dordrecht, Netherlands: Springer; 2012.

51.
Yoshihara S, Katayama M, Geng X, Ikeuchi M. Cyanobacterial phytochrome-like PixJ1 holoprotein shows novel reversible photoconversion between blue- and green-absorbing forms. Plant Cell Physiol. 2004;45:1729–37.
CAS  PubMed  Google Scholar 

52.
Bhaya D, Takahashi A, Grossman AR. Light regulation of type IV pilus-dependent motility by chemosensor-like elements in Synechocystis PCC6803. Proc Natl Acad Sci USA. 2001;98:7540–5.
CAS  PubMed  Google Scholar 

53.
Yang Y, Lam V, Adomako M, Simkovsky R, Jakob A, Rockwell NC, et al. Phototaxis in a wild isolate of the cyanobacterium Synechococcus elongatus. Proc Natl Acad Sci USA. 2018;115:E12378–87.
CAS  PubMed  Google Scholar 

54.
Kehoe DM, Gutu A. Responding to color: the regulation of complementary chromatic adaptation. Annu Rev Plant Biol. 2006;57:127–50.
CAS  PubMed  Google Scholar 

55.
Sánchez-Baracaldo P, Bianchini G, Di Cesare A, Callieri C, Chrismas NAM. Insights Into the evolution of Picocyanobacteria and Phycoerythrin Genes (mpeBA and cpeBA). Front Microbiol. 2019;10:45.
PubMed  PubMed Central  Google Scholar 

56.
Ting CS, Rocap G, King J, Chisholm SW. Cyanobacterial photosynthesis in the oceans: the origins and significance of divergent light-harvesting strategies. Trends Microbiol. 2002;10:134–42.
CAS  PubMed  Google Scholar 

57.
Gan F, Zhang S, Rockwell NC, Martin SS, Lagarias JC, Bryant DA. Extensive remodeling of a cyanobacterial photosynthetic apparatus in far-red light. Science. 2014;345:1312–7.
CAS  PubMed  Google Scholar 

58.
Thiel V, Tank M, Bryant DA. Diversity of chlorophototrophic bacteria revealed in the Omics Era. Annu Rev Plant Biol. 2018;69:21–49.
CAS  PubMed  Google Scholar 

59.
Kühl M, Trampe E, Mosshammer M, Johnson M, Larkum AWD, Frigaard N-U, et al. Substantial near-infrared radiation-driven photosynthesis of chlorophyll f-containing Cyanobacteria in a natural habitat. Elife. 2020;9:e50871.
PubMed  PubMed Central  Google Scholar 

60.
Oren A. Microbial life at high salt concentrations: phylogenetic and metabolic diversity. Saline Syst. 2008;4:1–13.
Google Scholar 

61.
Sääf A, Baars L, von Heijne G. The internal repeats in the Na+/Ca 2+ exchanger-related Escherichia coli protein YrbG have opposite membrane topologies. J Biol Chem. 2001;276:18905–7.
PubMed  Google Scholar 

62.
Price GD, Woodger FJ, Badger MR, Howitt SM, Tucker L. Identification of a SulP-type bicarbonate transporter in marine Cyanobacteria. Proc Natl Acad Sci USA. 2004;101:18228–33.
CAS  PubMed  Google Scholar 

63.
Sakamoto T, Inoue-Sakamoto K, Bryant DA. A novel nitrate/nitrite permease in the marine cyanobacterium Synechococcus sp. strain PCC 7002. J Bacteriol. 1999;181:7363–72.
CAS  PubMed  PubMed Central  Google Scholar 

64.
Carrieri D, Wawrousek K, Eckert C, Yu J, Maness PC. The role of the bidirectional hydrogenase in Cyanobacteria. Bioresour Technol. 2011;102:8368–77.
CAS  PubMed  Google Scholar 

65.
Tamagnini P, Axelsson R, Lindberg P, Oxelfelt F, Wunschiers R, Lindblad P. Hydrogenases and hydrogen metabolism of Cyanobacteria. Microbiol Mol Biol Rev. 2002;66:1–20.
CAS  PubMed  PubMed Central  Google Scholar 

66.
Huisman J, Codd GA, Paerl HW, Ibelings BW, Verspagen JMH, Visser PM. Cyanobacterial blooms. Nat Rev Microbiol. 2018;16:471–83.
CAS  PubMed  Google Scholar 

67.
Ben Fekih I, Zhang C, Li YP, Zhao Y, Alwathnani HA, Saquib Q, et al. Distribution of arsenic resistance genes in prokaryotes. Front Microbiol. 2018;9:2473.
PubMed  PubMed Central  Google Scholar 

68.
Fürst-Jansen JMR, de Vries S, de Vries J. Evo-physio: on stress responses and the earliest land plants. J Exp Bot. 2020;71:3254–69.
PubMed  PubMed Central  Google Scholar 

69.
Murik O, Oren N, Shotland Y, Raanan H, Treves H, Kedem I, et al. What distinguishes Cyanobacteria able to revive after desiccation from those that cannot: the genome aspect. Environ Microbiol. 2017;19:535–50.
CAS  PubMed  Google Scholar 

70.
Gul N, Poolman B. Functional reconstitution and osmoregulatory properties of the ProU ABC transporter from Escherichia coli. Mol Membr Biol. 2013;30:138–48.
PubMed  Google Scholar 

71.
Pathak J, Ahmed H, Singh PR, Singh SP, Häder D-P, Sinha RP. Mechanisms of photoprotection in Cyanobacteria. In: Mishra AK, Tiwari DN, Rai AN. editors. Cyanobacteria. Cambridge: Academic Press; 2019. pp. 145–171.

72.
Meulenbroek EM, Peron Cane C, Jala I, Iwai S, Moolenaar GF, Goosen N, et al. UV damage endonuclease employs a novel dual-dinucleotide flipping mechanism to recognize different DNA lesions. Nucleic Acids Res. 2013;41:1363–71.
CAS  PubMed  Google Scholar 

73.
Richardson EJ, Bacigalupe R, Harrison EM, Weinert LA, Lycett S, Vrieling M, et al. Gene exchange drives the ecological success of a multi-host bacterial pathogen. Nat Ecol Evol. 2018;2:1468–78.
PubMed  Google Scholar 

74.
Wiedenbeck J, Cohan FM. Origins of bacterial diversity through horizontal genetic transfer and adaptation to new ecological niches. FEMS Microbiol Rev. 2011;35:957–76.
CAS  PubMed  Google Scholar 

75.
Smillie CS, Smith MB, Friedman J, Cordero OX, David LA, Alm EJ. Ecology drives a global network of gene exchange connecting the human microbiome. Nature. 2011;480:241–4.
CAS  PubMed  Google Scholar 

76.
Sheppard SK, Guttman DS, Fitzgerald JR. Population genomics of bacterial host adaptation. Nat Rev Genet. 2018;19:1–17.
Google Scholar 

77.
Oliveira PH, Touchon M, Rocha EPC. Regulation of genetic flux between bacteria by restriction-modification systems. Proc Natl Acad Sci USA. 2016;113:5658–63.
CAS  PubMed  Google Scholar 

78.
Jain R, Rivera MC, Lake JA. Horizontal gene transfer among genomes: the complexity hypothesis. Proc Natl Acad Sci USA. 1999;96:3801–6.
CAS  PubMed  Google Scholar 

79.
Pál C, Papp B, Lercher MJ. Adaptive evolution of bacterial metabolic networks by horizontal gene transfer. Nat Genet. 2005;37:1372–5.
PubMed  Google Scholar  More

Compared to the pre-war baseline, our results show an overall numerical population depletion of 77% across all mammal species during the war period, with some species experiencing a decline of up to 80% of their pre-war baseline abundance. Moreover, this degree of wildlife decline was not reversed by the end of the post-war period. This overall pattern of marked large mammal declines has not been previously documented at sites exposed to intense armed conflicts, which in Angola and other combatant countries profoundly dismantle the socio-political structure, natural resource management activities and enforcement practices such as bushmeat market inspection22,23. We emphasize that even during post-war peace times, wild mammal populations in Angola will fail to recover as long as rural people living in war-torn countries remain armed and wildlife management regulations cannot be enforced.
In Angola, there has been a process of slow disarmament of citizens by the government, which has disrupted hunting practices and reduced hunting pressure on local wildlife populations. However, meaningful recovery of institutional policy on protected areas and wildlife populations have not yet been implemented in all the Quiçama region, which is now largely occupied by a mix of native peoples, war refugees, and former combatants. As a consequence, post-war mammal population rebounds have been entirely restricted to some small-bodied species, likely due to their higher fecundity, in contrast with the low reproductive rate of medium- to large-bodied species, which continue to be slaughtered by fire weapons and other hunting techniques. Automatic rifle confiscation from citizens is an important factor in reducing hunting pressure, thereby favouring the recovery of local game biomass13,24. However, without the critical intervention of well-designed government policies, the baseline structure of large terrestrial vertebrate assemblages is unlikely to recover. For example, in the post war-zone Gorongosa National Park, Mozambique, the total biomass density of nine focal large mammal species had recovered in 2018 by ~ 80% of the pre-war baseline density, but the community composition had shifted dramatically compared to the pre-war baseline due to asymmetric recovery rates across species, with smaller antelope species exceeding the abundance of formerly dominant megaherbivores25. In particular, waterbuck abundance had increased by an order of magnitude, with more than 55,000 individuals accounting for over 74% of large-herbivore biomass by 2018. By contrast, elephant, hippo, and buffalo, which accounted for 89% of the pre-war biomass, now comprised only 23%25.
Considering carnivores, only lion populations in Mozambique’s Gorongosa National Park persisted throughout the war26, whereas leopards also persisted at intermediate abundance in forest environments in our study area. Both of these studies also recorded hyenas and jackals. At Quiçama, however, only two local informants had seen or killed hyenas or jackals over the last 5 years. The collapse of these carnivores has important ecological implications on their roles in key ecosystem linkages, such as necromass scavengers and energy and nutrient transfer27.
Defaunation can have important impacts not only in terms of severe depletion of vulnerable species but also on general ecosystem functions, including predation, herbivory, carrion removal and disease control28,29. For example, the Mozambican Civil War (1977–1992) induced to a catastrophic large‐herbivore die-off in Gorongosa National Park, which was followed by 35 years of woodland expansion, most severely in areas where pre‐war herbivore biomass was greatest7. This expansion included the invasive Mimosa pigra shrub—considered one of the world’s 100 worst invasive plant species30. Tree cover increased in four of the park’s five major habitat zones by 51% to 134%. Local informants in our study explained that in many areas of Quiçama the landscape have become more wooded since the collapsed of large herbivores, although this remains anecdotal. The most parsimonious explanation in both Mozambique and Quiçama is that a severe reduction in browsing pressure enhanced tree growth, survival and/or recruitment7.
Before the Angolan civil war, the protected areas of the Quiçama region once safeguarded one of the largest world populations of Red Buffalos (around 8,000 individuals) across both savannah and forest landscapes31. However, we found that poaching had severely reduced Red Buffalos to small populations restricted to some forest fragments in the southern Quiçama area. Landscape structure and vegetation cover clearly interfere with the degree of hunting efficiency because they affect hunter velocity, understorey visibility, size-selective prey detectability, and hunting techniques. In open savannah areas, larger animals can be easily detected, resulting in far more efficient use of long-range projectiles fired by automatic rifles and other weapons carried by distant hunters17. Also, compared to forest environments, motor vehicles gain much more feasible access into savannah landscapes when both pursuing prey and transporting carcasses to markets, which further explains the higher depletion rates of the savannah megafauna32. Mammals inhabiting more accessible open areas are therefore more vulnerable. For example, a study on Europe’s largest terrestrial mammal (Bison bonasus) showed that stronger pre-historic hunting pressure in open landscapes forced these animals into closed-canopy forest as a refuge habitat since the Pleistocene, leaving the legacy of the last native bison populations being restricted to forest areas15. However, habitat quality in forest refugia is not necessarily suitable. For instance, eland and roan antelope at Quiçama were unable to seek refugia in forest remnants, unlike other large-bodied species such as elephant and red buffalo. This likely explains why over 90% of our interviewees reported the conspicuous absence of those two ungulate species in the entire area.
Our model shows that commercially valuable target species in both savannah and forest habitats were not necessarily the most abundant during the early stages of the war. This is likely because the abundance of large-bodied species was then not low enough to discourage hunters from pursuing them. However, during the late and post-war periods, depletion rates of large-bodied prey in savannahs habitats were so high that pursuing them had become less worthwhile than pursuing midsized species. Because of the elevated time/energy costs of capturing large-bodied prey species in savannah areas, hunters become more selective in this habitat compared to the forest. On the other hand, given that levels of depletion of large-bodied species in forest areas were lower, most of these species continued to be killed in this habitat type, but resulted in smaller offtakes. Hunters also selected midsized species to compensate for any losses in the overall biomass of prey profiles. In the aftermath of the war, the gradual shift in prey size structure towards smaller-bodied species progressed and midsized species were most frequently selected by hunters in both savannah and forest habitats. In a study in Ghana, commercial trophy hunting for ivory, as opposed to subsistence hunting, was more sensitive to the density of elephants and enforcement efforts to inhibit poaching, supporting the notion that commercial hunting often depends mainly on overall prey abundance33.
Hunter preference for large- and medium-bodied species is higher because they yield higher catch-per-unit-effort in terms of meat biomass and other products (e.g. ivory and skin). As such, most species smaller than 12 kg were not a target game species and their relative abundance remained unchanged over the assessed periods. The fact of whether or not any given species had been reported as a hunting target during the war did not affect its pre- to post-war change in perceived abundance (see Fig. 4A) was influenced by the depletion of some small-bodied species which were not commercially harvested during the war, but were still hunted—because they were crop-raiders or depredated livestock—at a time when plenty of ammunition was readily available. That subsistence and/or commercial game hunting can have a profound detrimental effect on the biomass of large-bodied species has been widely documented34,35. However, we note that the abundance of medium-sized species at Quiçama continues to decline. In contemporary Africa, mammal populations have shown a ‘U-shaped’ abundance trend. Perhaps because small-bodied species are higher-fecundity and/or bypassed by hunters, large-bodied species have been targeted by wildlife management and conservation programs, whereas intermediate-sized species have experienced the steepest declines as they are usually hunted, but lack active management and can exhibit slow reproductive rates36. Therefore, there is a need to also directly manage midsized species, rather than assume that management actions targeting the most iconic ‘umbrella’ taxa will lead to effective conservation of all species. In our study area, for example, the greatest conservation focus should be allocated to bushbuck (Tragelaphus scriptus), currently the most hunted species at Quiçama (mainly for trade). This ungulate species has received no attention from regional to national scale conservation programs37.
We found little or no change in the relative abundance of small mammals, perhaps because these small-bodied species were neither commercially valuable nor harvested for local subsistence. However, comparing our results with other studies using combined sampling techniques such as camera traps, net, and microphones16, we recognize that some small mammals could have been undersampled, despite the enormous usefulness of LEK approaches in meeting the aims of this study. Regarding the primates, cultural influences such as food taboos may have important roles in mediating population declines of overexploited species. However, primates elsewhere in Africa and the Neotropics comprise the largest number of species threatened by hunting across the world’s mammals38. We therefore caution that the future bushmeat trade in Angola could, in fact, begin to target primates as other more desirable large-bodied species become gradually depleted and economically extinct. In addition, we highlight the increased risk of zoonotic diseases, given that our close phylogenetic relationship with nonhuman primates increases the likelihood of animal-to-human pathogen spillover39 and because the risk of disease emergence among mammalian orders is highest in bats (risk rate = 2.64), followed by primates (2.23), ungulates (2.09), rodents (1.81) and carnivores (1.39)40.
Modern armed conflicts affect terrestrial wildlife through a range of interactions, including tactical military operations. However, the consequences of socio-economic upheaval and livelihood disruption associated with a civil war can outweigh the direct effects of military activity9. Among the 24 mechanisms through which armed conflicts are known to affect wildlife, eight (86% of all existing case studies by 2016) were “non-tactical” pathways involving institutional decay, displacement of people and economic upheaval13. Accordingly, our results show that the main consequences of the war in the Quiçama region were non-tactical, such as much greater access to powerful fire-weapons, which were widely used by hunters and the military, even though their initial distribution purpose was to arm the population to fight against rival militias. The widespread use of automatic weapons intensified the overkill of large mammals, increasing hunting efficiency and the number of hunted species. In addition, wildlife culls were intensified during all brief periods of cease-fire because once the probability of encountering guerrilla groups was reduced, armed hunters felt safer and increased the amount of time allocated to hunting activities as well as the size of their catchment areas.
Ivory tusks from elephants killed at Quiçama were removed by the natural resource sector of each political party responsible for the catch, probably in exchange for automatic weapons1,41. Consequently, Angola’s elephants during the 1980s drew international alarm with reports of up to 100,000 elephants exterminated within rebel-controlled territories42. Park rangers were also victims of the threat from rebel groups, which was exacerbated by hundreds of outside hunters gaining access to the Quiçama area. Similarly, in the Okapi Reserve in the Democratic Republic of Congo, park guards were forced to abandon their posts following guerrilla attacks and were unable to prevent elephant poaching and bushmeat extraction13,43.
Strategic installation of both fixed and mobile military bases throughout protected areas is a tactical manoeuvre that greatly facilitates access to rifles and ammunition by all residents. However, in some situations this can potentially benefit wildlife populations elsewhere by effectively creating a “no human’s land”. This was the case in the Demilitarized Zone separating North and South Korea, which has been uninhabited by humans, thereby becoming a unique nature reserve containing the last refugia of Korean natural heritage23. Therefore, some pathways can show both positive and negative consequences for wildlife, depending on the spatial extent and timescale considered. In fact, if on one hand, exclusion zones often create protected areas for wild nature, on the other hand, sites overrun by war refugees will succumb to much greater hunting pressure. Where the civil war was most intensive in Eastern Angola, many populations of endangered wild species have been identified44, whereas in Western Angola, where the armed conflict was patchy or episodic, we found that wild populations of a similar set of species spiralled down into steep declines or were driven to local extinction. Despite intensive post-war efforts in clearing and deactivating landmines, millions of hectares of these explosive weapons zones remain under interdiction in Europe, Africa, and Asia45. This unpredictable distribution of landmines is also a double-edged sword because many refugees did not return to their original households after the war terminated because of risks associated with landmines. Some of the most intact ecosystems of Central America, for example, have not been threatened by habitat conversion by agrarian peasants because they were seeded with landmines during the civil wars46. Nevertheless, landmines also pose threats to wildlife, killing for example at least 30 elephants in Angola’s southern provinces42. Also, when landmines explode, they shatter soil systems, rip up plant life and disrupt water flows, all of which accelerate widespread ecosystem disruption46.
The main impacts of the Angolan civil war on terrestrial mammals of Quiçama occurred indirectly from military tactics or from “non-tactical” pathways and resulted from wholesale institutional and socioeconomic changes, rather than directly from military tactics. In view of all our findings and related literature, we present a summary flow diagram showing how modern armed conflicts can impact wildlife in modern war zones (Fig. 6). We divide the impact of wars into (A) tactical pathways, which are directly or indirectly derived from military unrest, associated military tactics or supporting military activities; and (B) “non-tactical” pathways, which stem from broad socio-political and economic changes associated with armed conflicts, including major institutional or policy failure, movement of refugees, and severely altered economies, local livelihoods and ecosystems.
Figure 6

Pathways through which modern armed conflicts can affect wildlife populations within war zones. Distinct pathways linking armed conflict to wildlife outcomes organized thematically in “tactical” pathways (which arise directly from the conflict itself and are associated with military tactics or supporting military activities) and “non- tactical” pathways (which stem from broad socio-political and economic changes associated with the armed conflict, including changing institutional dynamics, movement of people, and altered economies and livelihoods). Blue and red boxes represent either positive or negative effects, respectively.

Full size image

Finally, we highlight that 36 countries worldwide are currently experiencing civil wars and most of these conflicts are fuelled or funded by international interests or started after an external intervention. These internationalized conflicts are more prolonged and less likely to find a political solution47. Mirroring our study area, protected areas confronting military conflicts elsewhere become surrounded by armed citizens and can rely on little, if any, national and international support to combat poaching by armed people48. Therefore, considering measures can reduce the impact of warfare on wildlife, we emphasize the intentional or inadvertent complicity of foreign powers, which should also promote policies to mitigate the detrimental environmental impacts of armed conflicts.
We conclude that armed conflicts remain a poorly understood driver of wildlife population collapses and our results indicate that although individual conflicts can have either positive or negative impacts, the overarching trend is clearly negative and the mere propagation of warzones, regardless of their intensity, is sufficient to heavily deplete wildlife populations. In the interest of preventing wildlife collapses in other parts of the world, we highlight that civil wars can vastly increase the availability of automatic weapons/ammunition which are typically used to deplete wildlife; this consequently leads to intense slaughter and major wildlife declines, especially in more accessible open habitats. This may be easier stated than done, but we conclude that policy strategies that can prevent the consequences of warfare, as shown here, remains a key conservation priority. We realize, however, that this rests on recalcitrant political will to promote robust public policies, which are rare priorities in rebuilding nation-states. It is critical to restore vertebrate community structure, but this may take many decades and require active intervention efforts. A multifaceted strategy to prevent previous war-zones from becoming “empty forests” or “empty savannas’’—severely degrading patterns of diversity, ecosystems functioning and ultimately human welfare—is therefore quintessential. More

Model components
The results of this paper are derived from a model that is built around the economic sectors outlined as the most important drivers of planetary pressures in Supplementary Table 1. This includes production sectors that have an important direct effect on the ESPs or that have important links to such sectors. They may be linked by using output from such sectors as inputs, providing inputs to such sectors, competing for inputs with such sectors or providing outputs that serve as substitutes for the output from those sectors. The resulting set of included production sectors are: agriculture (producing food and biofuel), energy services, fossil-fuel extraction, renewable energy (other than biofuel), fertilizer production, phosphate extraction, water supply, fisheries, and industrial manufacturing. The demand for final consumption goods is derived from the maximization of households’ utility. Since we have economic policies in the model, we are implicitly assuming some government entity that imposes these policies, but since we consider the policies exogenous (not, e.g., determined to optimize some objective) we do not explicitly model the government.
We solve the model as a competitive equilibrium where we assume that all agents maximize their respective objectives while taking prices as given (prices are given from the perspective of the individual agent, but are endogenously determined by aggregate supply and demand). We then analyze changes in the endogenously determined model variables in response to an assumed exogenous change in economic policy.
In the model, competition for resources thus leads to a number of important trade-offs. These arise from three main sources including, alternative uses of the output of a sector (e.g., output from the agricultural sector can be used as food or biofuels), sectors competing for the use of inputs (e.g., land can be used for agriculture, forestry or maintained as undisturbed natural land) or from inputs being substitutes or complements in production or consumption (e.g., nitrogen and phosphorus preferably being used in fixed proportions).
The production sectors are modeled either by using an explicit production function or by a production cost function. A production function is specified for agriculture, energy services, fertilizer production, fisheries, timber production and industrial manufacturing sectors since their factor inputs are directly connected to one or more ESPs (see the previous section on “Economic drivers of planetary pressures”), thus making their input substitutability important. For all sectors except agriculture, we use one level constant elasticity of substitution (CES) functions. For agriculture, we use a nested CES function (see below). Sectors whose production processes are of less importance, are represented by a production cost function. These sectors include phosphate, water, fossil fuel, and renewable energy. Also, in many sectors, certain inputs e.g., labor and capital, are economically important but their explicit modeling is not directly relevant for our analysis (i.e., of negligible importance to the ESPs). To account for these inputs, we include an aggregate input, which we refer to as other inputs, in all production sectors except energy services and assume that these are supplied with a given sector-specific price elasticity of supply. The possibility of adjusting these other inputs leads to decreased use in sectors where their marginal value decreases and increased use in sectors where their marginal value increases, and thus to some extent captures the possibility to move inputs between sectors in response to changing economic conditions.
We will now present the model sectors in more detail. A list of model quantities, their prices and uses can be found in Table 1 (different uses of a quantity are denoted by subscripts).
Table 1 Model quantities, prices and uses.
Full size table

The agricultural sector uses inputs land (LA), fertilizers (P), water (W), energy services (({{mathcal{E}}}_{A})) and other inputs (MA) as inputs to produce output that can be used for food or biofuels. Producers maximize their profit, taking prices as given. Their profit maximization problem is

$$mathop{max }limits_{{L}_{A},P,W,{{mathcal{E}}}_{A},{M}_{A}}{p}_{A}Aleft({L}_{A},P,W,{{mathcal{E}}}_{A},{M}_{A}right)-{p}_{L}{c}_{A}({L}_{A}){L}_{A}\ -, {p}_{P}P-{p}_{W}W-{p}_{{mathcal{E}}}{{mathcal{E}}}_{A}-{p}_{{M}_{A}}{M}_{A},$$
(1)

where cA(LA) captures the cost of converting land to agricultural land. The agricultural production function is a CES function between land and non-land inputs, where non-land inputs are aggregated using a CES function.
The energy-services sector combines energy from different sources into a bundle of energy services (({mathcal{E}})). The different sources are biofuels (AB), fossil fuels (({E}_{{mathcal{E}}})) and renewables (R). The producers in this sector solve the profit maximization problem

$$mathop{max }limits_{{A}_{B},{E}_{{mathcal{E}}},R}{p}_{{mathcal{E}}}{mathcal{E}}({A}_{B},{E}_{{mathcal{E}}},R)-{p}_{A}{A}_{B}-{p}_{E}{E}_{{mathcal{E}}}-{p}_{R}R.$$
(2)

We model production of fertilizers (P) as using fossil fuel (EP), phosphate (({mathcal{P}})) and other inputs (MP). The use of fossil fuel is intended to capture the fossil-fuel (more specifically natural-gas) intensive production of the nitrogen component of fertilizers. We thus treat fossil fuel use in fertilizer production as a proxy for nitrogen. The profit maximization problem of fertilizer producers is

$$mathop{max }limits_{{E}_{P},{mathcal{P}},{M}_{P}}{p}_{P}Pleft({E}_{P},{mathcal{P}},{M}_{P}right)-{p}_{E}{E}_{P}-{p}_{{mathcal{P}}}{mathcal{P}}-{p}_{{M}_{P}}{M}_{P}.$$
(3)

For timber production (T) we only consider the input land (LT) and other inputs (MT). The producers then solve the maximization problem

$$mathop{max }limits_{{L}_{T},{M}_{T}}{p}_{T}T({L}_{T},{M}_{T})-{p}_{L}{c}_{T}({L}_{T}){L}_{T}-{p}_{{M}_{T}}{M}_{T},$$
(4)

where cT is a cost of converting (e.g., clearing) land for forestry.
Industrial manufacturing (Y) requires energy (({{mathcal{E}}}_{Y})) and other inputs (MY). While we refer to this sector as manufacturing, the substitutability between energy and other inputs is chosen to match that of the economy as a whole. The substitutability thus reflects not only the manufacturing sector but also the service sector that has a significantly lower energy intensity but is economically important. The maximization problem of the representative producer is

$$max {p}_{Y}Yleft({{mathcal{E}}}_{Y},{M}_{Y}right)-{p}_{{mathcal{E}}}{{mathcal{E}}}_{Y}-{p}_{{M}_{Y}}{M}_{Y}.$$
(5)

The fisheries sector uses inputs fossil fuel (EF) and other inputs (MF). The producers solve the maximization problem

$$mathop{max }limits_{{E}_{F},{M}_{F}}{p}_{F}F({E}_{F},{M}_{F})-{p}_{E}{E}_{F}-{p}_{{M}_{F}}{M}_{F}.$$
(6)

Extraction of fossil fuel (E) is modeled by assuming a gross extraction cost (gE) that increases with increased extraction (gE(E) thus gives the total cost of extracting quantity E). We assume that the tax on fossil fuels (a percentage tax τE) is paid by the firms that extract and sell it. Extraction firms solve the profit maximization problem

$$mathop{max }limits_{E}frac{{p}_{E}}{1+{tau }_{E}}E-{g}_{E}(E).$$
(7)

The sectors phosphate (({mathcal{P}})), water (W), renewable energy (other than biofuels) (R) and the other inputs (MA, MF, MP, MT, and MY) are similarly represented by a production or extraction cost and the profit-maximization problem of the producers are given by

$$mathop{max }limits_{X}{p}_{X}X-{g}_{X}(X) ,, {rm{for}} ,, Xin {{mathcal{P}},W,R,{M}_{A},{M}_{F},{M}_{P},{M}_{T},{M}_{Y}}.$$
(8)

We have now described the maximization problems underlying decisions made by all producers. The representative household also solves a maximization problem, maximizing the utility derived from consumption. The households’ preferences are represented by utility function U and the utility-maximization problem, subject to the income being I, is given by

$$mathop{max }limits_{{A}_{{mathcal{F}}},F,Y,{L}_{U},T} Uleft({mathcal{F}}left({A}_{{mathcal{F}}},Fright),tilde{{mathcal{F}}}left(Y,{L}_{U},Tright)right)\ {rm{s}}.{rm{t}}. ,, {p}_{A}{A}_{{mathcal{F}}}+{p}_{F}F+{p}_{Y}Y+{p}_{L}{L}_{U}+{p}_{T}Tle I.$$
(9)

This specification has divided consumption into two levels. While this division is not necessary at this level of generality, it clarifies the assumed substitutabilities between goods. We assume greater substitutability within than between categories. The upper level consists of food (({mathcal{F}})) and non-food ((tilde{{mathcal{F}}})) goods, with the former category consisting of food from agriculture and from fisheries, and the latter of manufactured goods, natural land and timber. The inclusion of natural land is intended to capture various ways in which households’ demand for natural lands lead to land being kept from other uses, e.g., preservation of land as national parks. We assume that timber is consumed directly by the households.
This completes the description of the modeling of all decision-making agents in the model. In addition to conditions derived from these maximization problems, we must also specify market-clearing conditions that make sure that supplied and demanded quantities add up.
For land (L), the total supply is assumed to be fixed:

$$L={L}_{A}+{L}_{T}+{L}_{U}.$$
(10)

The remaining market-clearing conditions are for agricultural production

$$A={A}_{{mathcal{F}}}+{A}_{B},$$
(11)

fossil fuel

$$E={E}_{{mathcal{E}}}+{E}_{F}+{E}_{P}$$
(12)

and energy services

$${mathcal{E}}={{mathcal{E}}}_{A}+{{mathcal{E}}}_{Y}.$$
(13)

In summary, production functions, market-clearing conditions, budget constraints and first-order conditions from the maximization problems of representative agents provide us with 41 equilibrium conditions pinning down the 41 endogenous prices and quantities. The full set of equilibrium conditions are available in the Supplementary Methods.
Solution Approach
We note a few features of our model, some of which have already been mentioned: there are no explicit externalities; policies are applied exogenously; all sectors are assumed to be competitive; market clearing determines the equilibrium. In this context, we can work with the decentralized equilibrium, which may be analyzed by considering the first order conditions. In our model, there are 41 unknown prices and quantities in the model, determined by 41 equilibrium conditions. Being exogenous, policies represent parameters that are known in advance; denote a generic “policy” pertaining to any one ESP by τ. Let Xi denote the generic ith variable, an endogenous price or quantity. The jth equilibrium condition can then generally be written as:

$${G}_{j}left({X}_{1},ldots ,{X}_{41};tau right)=0.$$
(14)

This system of equations implicitly define all resulting equilibrium quantities and prices as functions of the policy i.e. ({X}_{i}={X}_{i}left(tau right)).
There are now two solution approaches: the first is to solve the set of resulting non-linear equations (and thereby obtain all the equilibrium values); the second is to trace out marginal changes in the equilibrium values in response to a change in the policy, τ. The latter approach can be illustrated by considering the total derivative of the equilibrium conditions with respect to the policy. This leads to a system of equations, with the jth equation being

$$mathop{sum }limits_{i}^{41}left[frac{{X}_{i}}{{G}_{j}}frac{partial {G}_{j}}{partial {X}_{i}}hat{{X}_{i}}right]=-frac{1}{{G}_{j}}frac{partial {G}_{j}}{partial tau },$$
(15)

where

$$hat{{X}_{i}}equiv frac{1}{{X}_{i}}frac{d{X}_{i}}{dtau }$$
(16)

is the relative change in variable Xi. These can be interpreted as a linear approximation of the percentage change in the variable induced by a one percentage point increase in the fossil fuel tax. Assume, for instance, that we get ({hat{X}}_{i}=2) and consider a one percentage point increase in the tax rate, ΔτE = 0.01. We would then get (frac{1}{{X}_{i}}Delta {X}_{i}approx {hat{X}}_{i}Delta {tau }_{E}=0.02). Hence, a one percentage point increase in the tax induces a two percent increase in the quantity. The result is a system of 41 equations in 41 unknowns, the (hat{{X}_{i}}), and is most useful because of linearity in the unknowns. Indeed this approach can be viewed as linear approximation of the equilibrium response to a change in the policy parameter. The required empirical parameter values needed for numerical computations are fewer, easier to find, and easier to interpret. Furthermore, if considering changes in other parameters of the model (e.g., changes in other policies) only the right-hand side of (15) needs to be changed.
Data and parametrization
We parameterize the model based partly on data extracted directly from the widely-used GTAP database, described below, and partly on empirical estimates from various sources in the literature. As described above, we mainly need three types of values: quantity shares, expenditure shares and elasticities of various kinds. In total, we need 39 empirical estimates to run the model. In our computations, we set the initial carbon price equal to zero. In reality there are various forms of carbon prices. It is difficult to get a precise measure of all these, but the global average is likely a relatively small negative price. For our analysis, this makes little difference. Assuming a different initial price would scale all results somewhat since the effect of a one percentage point increase in the price would, relatively speaking, be smaller or larger depending on the initial price. All other parameter values that we use are empirically derived based on the current effective carbon price. In the following section, we provide tables with parameter values and their sources.
The first type of parameter that occurs are quantity shares. By quantity share ({Q}_{X,{X}_{Z}}) we mean the share of total quantity X used in a specific sector Z. The full set of values, including their sources are given in Table 2. The exceptions are the quantity shares of fossil fuel going to different sectors and the share of agricultural production going to food or biofuel. These were derived as follows.
Table 2 Parameters—quantity shares.
Full size table

Total energy consumption in 2011 was 12,225 Mtoe30. Out of this, 10624 Mtoe came from fossil fuel related sources. Fertilizer production uses about 1.2% of total energy supply and almost all of this comes from fossil fuels31. Hence we assume that the share of fossil fuels going to fertilizer production is ({Q}_{E,{E}_{P}}=frac{12,225}{10,624}times 1.2 % approx 1.4 %). For fisheries production, we assume a global fuel consumption of 40 billion litre’s of fuel32. Assuming that this is mostly diesel, this corresponds to 40 Mtoe of fossil fuel or ({Q}_{E,{E}_{F}}=frac{40}{10,624}approx 0.4 %) of total fossil fuel use. Finally we assume the remaining fossil fuels are used in energy production i.e., 98.2%.
In order to compute the share of agricultural production going to bioufuels we used data underlying the FAO Agricultural Outlook report 2016–202533. For each major agricultural commodity (e.g., wheat, maize, rice, etc.) we computed the share of agricultural production used for biofuels and then computed a weighted sum using the fraction of land used to harvest a specific commodity as weight. This resulted in a quantity share ({Q}_{A,{A}_{B}}approx 3.8 %).
Agriculture accounts for only a relatively small proportion of total final energy demand in both industrialized and developing countries. In OECD countries, for example, around 3–5% of total final energy consumption is used directly in the agriculture sector, while for developing countries, the equivalent figure is likely slightly higher in the a range of 4–8% of total final commercial energy use34. Based on these estimates, we concluded that ({Q}_{{mathcal{E}},{{mathcal{E}}}_{A}}) = 5% constitutes a reasonable baseline.
The second type of that occurs in our equilibrium conditions are expenditure shares. The expenditure share ({Gamma }_{X}^{Z}) of input X in sector Z is the share of total spending on inputs in sector Z that goes to X. To pin down these at the global level, we employed the GTAP database15. More specifically, we used the GTAP data set corresponding to the year 2014, for 141 countries and 57 sectors. The GTAP database is a unique global economic data set constructed by collating and reconciling data on national input-output tables, international trade, production, consumption, and macro-economic data sets from various international data sources. This has further been extended by ref. 35 to include renewable energy commodities, based on several energy data sources, including the International Energy Agency (IEA) data set and the World Bank data set. Furthermore, ref. 36 has extended this even further to include water as an endowment, used in both agricultural and other sectors. Finally, we have a data set in which we can derive the shares of labor, capital, land, water, and several other inputs in producing all commodities. Some inputs, such as fertilizers are not separately identified in this data set, but they are subsumed in broader GTAP sectors such as chemicals, rubber, and plastics. Therefore, we make broad reasonable assumptions to derive the shares of such granular-level inputs; for example, we assume that most of agricultural consumption of output from the GTAP sectors chemicals, rubber, and plastics are fertilizers and pesticides. For all production sectors except energy services, we assign the residual expenditure share, remaining when all inputs of direct interest have been accounted for, to other inputs M. The details are given below and summarized in Table 3.
Table 3 Parameters: expenditure shares (source: GTAP).
Full size table

Agriculture. Our agricultural production function distinguishes between land and non-land inputs (with “other inputs” in the non-land category). The expenditure share of land is 19.2%. The expenditure shares of fertilizers, water, energy, and other inputs are 6.43%, 1.93%, 3.33%, and 71.1%, respectively. Their respective shares out of non-land inputs are their total shares divided by the total non-land share. This means that ({Gamma }_{{L}_{A}}^{A}=0.192), ({Gamma }_{{tilde{L}}_{A}}^{A}=0.808), ({Gamma }_{P}^{{tilde{L}}_{A}}=frac{0.0643}{0.808}=0.0796), ({Gamma }_{W}^{{tilde{L}}_{A}}=frac{0.0193}{0.808}=0.0239), ({Gamma }_{{{mathcal{E}}}_{A}}^{{tilde{L}}_{A}}=frac{0.0643}{0.808}=0.0412), and ({Gamma }_{{M}_{A}}^{{tilde{L}}_{A}}=frac{0.711}{0.808}=0.880).
Energy services. The expenditure shares of biofuels, fossil fuels and renewables are 0.37%, 94.33%, and 5.30% respectively. That is ({Gamma }_{{A}_{B}}^{{mathcal{E}}}=0.0037), ({Gamma }_{{E}_{{mathcal{E}}}}^{{mathcal{E}}}=0.9433), and ({Gamma }_{R}^{{mathcal{E}}}=0.0530).
Utility. The expenditure shares of food from agriculture, fish, manufactured goods, recreational land use, and timber are 11.93%, 0.42%, 86.86%, 0.15%, and 0.65%. This gives expenditure share of food ({Gamma }_{{mathcal{F}}}^{U}=0.1235) and expenditure share of non-food goods ({Gamma }_{tilde{{mathcal{F}}}}^{U}=0.8765). The within-category expenditure shares are ({Gamma }_{{A}_{{mathcal{F}}}}^{{mathcal{F}}}=frac{11.93}{12.35}=0.9660), ({Gamma }_{F}^{{mathcal{F}}}=frac{0.42}{12.35}=0.0340), ({Gamma }_{Y}^{tilde{{mathcal{F}}}}=frac{86.86}{87.65}=0.9910), ({Gamma }_{{L}_{U}}^{tilde{{mathcal{F}}}}=frac{0.15}{87.65}=0.001711), and ({Gamma }_{T}^{tilde{{mathcal{F}}}}=frac{0.65}{87.65}=0.007416).
Timber. The expenditure shares of land and other inputs are 37.48% and 62.52%, respectively. That is ({Gamma }_{{L}_{T}}^{T}=0.3748) and ({Gamma }_{{M}_{T}}^{T}=0.6252).
Composite goods. The expenditure shares of energy services and other inputs are 6.38% and 93.62%, respectively. That is, ({Gamma }_{{{mathcal{E}}}_{Y}}^{Y}=0.0638) and ({Gamma }_{{M}_{Y}}^{Y}=0.9362).
Fertilizers. The expenditure share of energy is 10.95%. The factor share of phosphate is assumed to be a share ({xi }_{{mathcal{P}}}=0.5) out of the factor share of non energy intermediates 62.53%. That is ({Gamma }_{{E}_{P}}^{P}=0.1095) and ({Gamma }_{{mathcal{P}}}^{P}=0.5* 0.6253=0.3127). this leaves the expenditure share or other inputs as ({Gamma }_{{M}_{P}}^{P}=0.5778).
Finally, we need several estimates of elasticities, including the elasticity of substitution, price elasticity of supply and elasticities of conversion costs. For the majority of parameters, we were able to track down estimates from the literature which are presented together with their corresponding reference in Table 4. Where the uncertainty in the estimates were high we employed a wide band for the sensitivity analysis. The parameters that are varied in the sensitivity analysis are indicated as [min, max, and mean] with mean being the baseline values.
Table 4 Parameters—elasticities and quantities.
Full size table

Numerical results
The full sets of changes in our model quantities and prices resulting from the two policies are presented in Table 5.
Table 5 Baseline results.
Full size table

We now describe the mapping from changes in model variables to effects on ESPs. For the model variables freshwater (W), natural land-use (LU), phosphate (({mathcal{P}})), and nitrogen (assumed to be proportional to fossil fuel use in fertilizer production EP), there is a simple one-to-one mapping with model variables. For climate change, ocean acidification, biodiversity loss and aerosol loading, however, the mapping is more complicated. For climate change and ocean acidification, we measure the change in pressure on both ESPs as the net change in CO2 emissions. For biosphere integrity, we measure changes in pressure as a change in threats to endangered species (more details on this are given below). We measure aerosol loading as changes in aerosol optical depth. For chemical pollution and ozone depletion, we map pressures to contributing sectors, but do not make any quantitative analysis of the net effects.
Climate change and ocean acidification—are both driven by carbon emissions and we use these emissions as our proxy for the pressures inflicted on these boundaries. To translate changes in model variables into changes in emissions, we use data from refs. 37,38. From the figure on page 2 of ref. 37 we get the percentage contribution of carbon dioxide emissions per sector outlined in the report. Using these percentages we can thus recover the amount of actual carbon emissions in gigaton carbondioxide (GtCO2) per year connected to a specific variable in our model.
Using this approach, we start by looking at the energy-related emissions that, according to ref. 37, account for a total of 66.5%. Multiplying by the aggregate total emissions in 2005 (44.15 GtCO2) we get 29,36 GtCO2. Next, we allocate these energy-related emissions to the energy service production sector, fossil fuel extraction, and emissions from fertilizer production. From ref. 37 we have that 6.4% (2.826 GtCO2 eq) of the total energy-related emissions is due to extraction processes. Based on ref. 38, fertilizer production is estimated to cause emissions of 0.575 GtCO2 eq. Hence we can split the total energy-related emission of 29.36 GtCO2 based on these percentages. This implies that 25,960 GtCO2 will be connected to the energy services output in our model, 2.826 GtCO2 is attributed to the fossil fuel extraction process and 0.575 GtCO2 is connected to fertilizer production.
The other emission-related variables in our model are more straightforward. Emissions from industrial processes in ref. 37 are assigned to manufacturing in our model (In total 4.6% = 2.031 GtCO2). Emission from land-use change are assigned to the change in natural land in our model (12.2% = 5.387 GtCO2). Emissions from agriculture are assigned to the total agricultural production variable (13.8% = 6.093 GtCO2). For the fisheries sector,39 estimate carbon dioxide emissions to be ~0.14 GtCO2.
Using these assignments as a status quo, we can calculate the total policy impact by simply multiplying the percentage change in our model variables resulting from the policy by the status quo emission levels. In total, our model variables cover ~97.4% of the emissions outlined in ref. 37. The results of this exercise, in terms of percentage changes to each planetary pressure, is outlined in Supplementary Table 2 for the carbon tax policy and Supplementary Table 3 for the combined carbon tax and biofuel tax policy.
To summarize, we find that a 1% increase in the carbon tax leads to a reduction in carbon dioxide emissions by −0.25% or −0.11 GtCO2 yr−1, which is what we use as an indicator of the change in pressure accrued to the climate change and ocean acidification boundary. For the combination of carbon and biofuel tax, the change is −0.26% or −0.12 GtCO2 yr−1.
Biodiversity loss—is a notoriously difficult task to assess at a global scale. Studies that quantify terrestrial biodiversity losses resulting from the environmental pressures of human activities typically focus on land-related impacts40,41. There are, however, multiple other environmental pressures causing loss of biodiversity that are not related to land-use42. In ref. 3 the global extinction rate is used as one way of quantifying this boundary (defined as extinctions per million species-years). Here, we will make use of the IUCN Red List of Threatened Species to derive a measure of biodiversity loss. The Red List identifies not only the species that have been confirmed to have gone extinct but also the species that are currently threatened and, if pressures remain, may become extinct in the future.43 identify the drivers behind the prevalent threats to the species on the Red List in a comprehensive assessment of more than 8000 species. These drivers can be directly identified as variables in our model. In ref. 43 there is overlap between threats in the sense that multiple activities can pose threats to a given species. We refer to a decrease in an activity posing a threat to a certain number of species as a decrease in threats. Without knowing the overlap between threats, we can not translate this into changes in number of threatened species. Therefore, we use the change in threats as our measure. Agricultural activity poses threats to 5295 species, which is the largest number of threats. The second-largest threat comes from logging, which threatens 4049 species, and we assign this to timber production in our model. Apart from those, we make the following assignments. Pollution from agriculture threatens 1523 of the species and this is assigned to fertilizer production. Over exploitation (fishing), threatening 1118 species, is assigned to fisheries production. Energy production (oil and gas) and renewable energy production account for threats to 56 species, which we assign to fossil fuel extraction and renewables. Finally, threats from urban development (industrial), pollution (except agriculture), human disturbance (work), transport, energy production (mining) summed to 3573 which we assign to manufacturing. There are also significant biodiversity effects of climate change, which threatens 1688 of the species. In this analysis, we abstract from the effects of changes in one ESP on other ESPs (unless the ESP is directly captured by a model variable). We can note, however, that including the effects of climate change would lead to larger decreases of biodiversity loss.
Hence, having connected the categories of threats to species by driver in ref. 43 to our model variables, we can measure the biodiversity impact of a policy by assessing whether the number of threats increases or declines as a result of the policy. For example, if the agricultural production increases by 1% as a result of a policy in our model then this would increase the number of threats from agricultural activity by 52.95 (0.01 × 5296).
The results, in terms of percentage change to the number of threatened species, are outlined in the column labeled Biodiv. in Supplementary Table 2 for the carbon tax policy and Supplementary Table 3 for the combined carbon tax and biofuel subsidy removal. To summarize, this implies that the total number of threats decline by 0.018% for the carbon tax and by 0.011% for the combined carbon and biofuel tax.
Finally, it should be noted that there are indeed several caveats to our approach for assessing biodiversity loss. First, it should be noted that this measure of biodiversity loss is just a proxy for true biodiversity loss. Future work would benefit from assessing the drivers of the actual rate of species loss as defined in e.g., ref. 1. Furthermore, we have taken the description of threats in43 and mapped them to our model variables. For instance, all threats assigned to agriculture in ref. 43 are assigned to agricultural production in our model. Perhaps some part of these threats come from land use change associated with agriculture rather than agriculture as such. In that case they should be mapped to our land use variables. We do not have a proper basis for such reassignment and, therefore, stick close to their assignment. Qualitatively, this distinction could matter for the carbon tax in isolation, but will not be important for the carbon tax combined with biofuel policy.
Aerosol loading—is proxied following3 which use aerosol optical depth (AOD) as an indicative measure of planetary pressure. To determine how AOD changes as a result of policy, we use data from three sources44,45,46. The impact is calculated as follows. First, we calculate a global average estimate of AOD from the main regional anthropogenic sources (sulfur (0.0392), black carbon (0.0003) and organic carbon (0.0011)) provided by ref. 44. Second, we use data from ref. 45 to calculate the share of global aerosol contributing emissions for each of these respective sources (sulfur (3.6%), black carbon (32%) and organic carbon (63%)) that stem specifically from biomass burning (assuming that approximately 90% of biomass burning emissions result from land-use change46). Third, using these estimates, we can calculate the amount of global AOD which ought to be attributed to emissions from fossil fuel and biofuels (0.038) and biomass burning (0.0022). These estimates are then connected to the model variables fossil fuel consumption (in energy services, fertilizer production and fisheries), biofuel production and change in natural land. In total, a 1% carbon tax leads to a 0.0136% (−5.5 × 10−6) decline in AOD and the combined carbon tax and biofuel policy leads to a decline of 0.014% (−5.7 × 10−6) (further details can be found in Supplementary Tables 2 and Table 3).
Stratospheric ozone depletion and chemical pollution—are not directly quantified in terms of their effects on the boundaries. Stratospheric ozone depletion increases with N2O emissions from agricultural production, fossil fuel use, manufacturing, and biofuels. For an increase in the carbon tax all these activities except for biofuel use decreases. The net effect is thus potentially ambiguous. If the carbon tax increase is complemented with a decrease in biofuel subsidies, all relevant variables decrease and we conclude that the net effect is a decrease in the pressure.
Chemical pollution. Chemical pollution increases in manufacturing, extracted fossil fuels, total agricultural production, agricultural production for food, fossil fuel use in fertilizer production, and fossil fuel use in energy services production. All these activities decrease with a carbon tax, with or without a biofuel policy. Hence, we conclude that chemical pollution will decrease in both cases.
For the remaining boundaries—the impacts are easier to assess since they are directly tied to specific model variables. First, the impact on the biogeochemical flows is assigned to the model variables phosphate and fossil-fuel use in fertilizer production. While the former is self-explanatory, the latter is used as a proxy for nitrogen, which relies almost entirely on fossil fuels in its production. For phosphorus, we translate the change into Gg P yr−1 using the value for current flows (mined and applied to erodible soils) from ref. 3:  −0.000068 × 14,000 ≈ −0l9 Gg P yr−1 for the carbon tax and  −0.0005 × 14,000 ≈ −7 Gg P yr−1 for the combined carbon and biofuel policy. For nitrogen, we translate the change into TgN yr−1 using ref. 3: −0.0013 × 150 ≈ −0.2 TgN yr−1 for the carbon tax and   −0.0018 × 150 ≈ −0.28 TgN yr−1 for the combined carbon and biofuel policy. Second, for the land-system boundary, we rely on the model variable natural land use as an indicator of the direction this boundary is moving in. This is translated into MHa using the average (between high and low value) for “natural forests” in ref. 47:  −0.00014 × 3507 ≈ −0.5 MHa for the carbon tax and 0.00043 × 3507 ≈ 1.5 MHa for the combined carbon and biofuel policy. Third, the freshwater boundary is directly tied to the water variable in our model. We translate this into km3 yr−1 using the value for current use in ref. 3, we the reduction is given by 2600 × 0.00009 ≈ 0.24 km3 yr−1 for the carbon tax policy and  −2600 × 0.00036 ≈ −0.93 km3 yr−1 combined carbon tax and biofuel policy.
Reporting summary
Further information on research design is available in the Nature Research Reporting Summary linked to this article. More

Cranial skeleton
The right premaxilla, the left maxilla, some teeth, the palatine and the palpebral are the best-preserved cranial remains, although a fragmentary right prefrontal could be also present. Complete descriptions for these bones were possible after a micro CT-scanning (Fig. 2).
Figure 2

3D reconstruction of the skull of Ogresuchus furatus (MCD-7149) in (a) lateral, (b) medial, (c) dorsal, (d) palatal, and (e) cranial view. (f) Volume rendering of the segmented neurovascular network of the trigeminal nerve overlaid on the articulated premaxilla and maxilla. app anterior palpebral, ch choana, dn dentary notch, en external naris, f neuro-vascular foramen, if inferior foramen, l-mx lacrimal-maxilla contact, m1-5 maxillary tooth, mes medial shelf, mx maxilla, paf palatal foramen, pd paramedian depressions, pfr prefrontal, plt palatine, pltf palatine foramen, pm1-4 premaxillary tooth, pmx premaxilla, pmx-mx premaxilla-maxilla contact, poas posantral strut, s apicobasal sulcus, snv-tgn V supranarial vessels and the trigeminal nerve V (ophthalmic branch), mv-tgn V maxillary vessels and the trigeminal nerve V (maxillary branch). Scale bar = 2 cm.

Full size image

The right premaxilla is exposed on the rock in lateral view. It is a medio-laterally thin bone, and dorso-ventrally higher than rostro-caudally wide. The caudal margin is sinuous, making a dorso-caudal projection of the premaxilla for the contact with the nasals, and articulating with the lost right maxilla in a sigmoid suture (see specular image of Fig. 2). This margin is larger than the rostral premaxillary margin, making a sharp snout. The premaxilla makes the ventral, lateral and part of the dorsal margins of the external naris (Fig. 2a,b,e), which opens directly rostrally in the lower part of the snout. Except for the sloping wall of the naris and the lateral side of the tooth row, the lateral surface of the premaxilla is ornamented by a shallow pit-and-bulge pattern. Four premaxillary tooth positions are present (Fig. 2a,b,d). All the alveoli are of similar length and elliptical shape, although the third is slightly larger than the others. There is also a large foramen between the first alveolus and the naris. The third premaxillary tooth is preserved. It is conical with a very sharp crown and very labiolingually compressed. The crown is curved lingually and mesially. The mesial and distal margins of the crown are rounded and do not bear carinae. The enamel is ornamented with few apico-basal ridges that cross the crown continuously (Fig. 2a). In palatal view, the premaxilla makes a large incisive foramen separated from the tooth row. The premaxilla-maxilla suture is oriented anteromedially. At least two paramedial depressions are visible mesially and distally to the second tooth position of the premaxilla.
The left maxilla is also exposed in its lateral side, but on the opposite side of the rock respect to the premaxilla (Fig. 1C). It is latero-medially compressed, dorso-ventrally large and rostro-caudally short. The lateral surface is gently rugose, ornamented with the same pit-and-bulge pattern as the premaxilla. The maxilla is subpentagonal in outline. The anterior margin of the maxilla is oblique, because the premaxilla-maxilla suture is located into a notch for the reception of the dentary caniniform (Fig. 2a). The dorsal surface for the contact with the nasals is straight and reduced, and then, the dorsal border of the maxilla slopes ventro-caudally for contacting the lacrimal and, ventrally, the jugal. There is no evidence for an anteorbital fenestra. The maxilla projects medially from its ventral margin, making the secondary palate. In medial view, a septum appears on the lateral wall of the maxilla and turns caudally over the palatal portion of the bone, covering the internal breathing chamber. Caudally to the origin of this septum in the lateral wall, a big foramen opens to trigeminal passage. In palatal view, the maxillae branches meet completely anteriorly to the palatines (Fig. 2d). The maxilla makes the anterior border of the suborbital fenestra, precluding ectopterygoid-palatine contact in this margin.
Only five maxillary tooth positions are present (Fig. 2a,b,d). The maxilla preserves the second, third and fourth erupted teeth. The fist is partially preserved unerupted within the alveolus. The third maxillary tooth is the largest, whereas the fourth is the smallest of the three, although the fifth might be even smaller. The alveolar margin is ventrally arched, reaching the greatest depth at the third maxillary position. After the third maxillary tooth, the alveolar margin turns dorsally making a small notch, where the fourth alveolus is located. A row of eight foramina is present in the lateral side over the alveoli. The crowns are curved lingually and distally. The cross section is labio-lingually compressed. The mesial margins of the crowns are rounded, but the distal margins bear unserrated carinae. The enamel is ornamented with several conspicuous ridges that cross the crowns continuously from the base to the apex (Fig. 2a).
The palatine is an elongated bone rostro-caudally oriented, forming part of the narial passage. It is almost straight, though the caudal end is slightly wider than its rostral one. The anteriormost edge is not preserved, but the maxillary outline reveals a sharp anterior margin of the palatine, exceeding the anterior end of the suborbital fenestra and extending between the maxillae (Fig. 2d). The palatine forms the medial margin of the suborbital fenestra. The posterior ends of the palatines define the anterior and lateral margins of a large choanal opening. The anterior margin of the choana is situated between the suborbital fenestrae. Another D-shaped fenestra opens in the middle of the palatal shaft, anteriorly to the choana.
The anterior palpebral is large, and it is not sutured to the adjacent bones. The bone is subtriangular with a wider anterior end, and its major axis oriented antero-posteriorly (Fig. 2c). The anteromedial border is projected medially, forming a sharp crest for the articulation with the prefrontal. The bone is elongate posteriorly and forms the lateral margin of the supraorbital fenestra. The contact of palpebrals is not preserved, but the preserved portion suggests an oval supraorbital fenestra with an antero-posterior major axis.
Axial skeleton
Most of the dorsal series and few caudal vertebrae are identified. Preserved dorsal series includes seven complete and three fragmentary vertebrae, almost in articulation. These vertebrae are tentatively identified as 5th to 14th dorsal vertebrae. They are exposed in dorsal view, except 6th and 14th vertebrae that show their caudal view. Vertebral centra are amphicoelous. Prezygapophyses and postzygapophyses are well developed, with rounded margins, and laterally oriented. However, no variation in their orientation is observed along the dorsal series. The matrix partially hides the vertebrae, therefore some additional characters (i.e., orientation of articular facets; presence and morphology of a suprapostzygapophyseal lamina) cannot be assessed. The prezygapophyses seem to fuse with the transversal processes from the 7th dorsal vertebra on, as described in other related taxa as Notosuchus terrestris18, Baurusuchus albertoi19, Pissarrachampsa sera10 and Campinasuchus dinizi20. However this condition must be taken with caution, because it is only based on the 7th and 11th vertebrae. Transversal processes are hidden by the matrix in the rest of the series. Neural spines are broken in all the vertebrae except in the 14th dorsal. This spine is well developed and high, corresponding to half of the total height of the vertebra. However, based on the broken basis of neural spines along the series, the spine is medio-posteriorly located on the neural arch, as in B. albertoi19 and Campinasuchus20. A few distal caudal vertebral centra are also preserved, without association with neural spines and transverse processes.
In addition, some dorsal ribs are also identified. These elements are flattened. The proximal end shows the capitulum and the tuberculum for articulating with the associated vertebrae. Capitulum and tuberculum are separated by a well-marked U-shaped depression. The shaft is ventrally curved and shows a median longitudinal depression, unlike Campinasuchus20. At middle length the shaft makes torsion, being antero-posteriorly flattened at proximal half and medio-laterally fattened at distal half.
Forelimb
Only the right ulna, and the metacarpals I, II, III and IV are well identified. The proximal epiphysis of the right radius is also probably preserved (Fig. S9).
The ulna is an elongated and latero-medially flattened bone, as in other sebecids, baurusuchids, and notosuchians10,19,20. It is exposed in lateral side. In lateral view, the bone is arquated, displaying a concave anterior margin and a concave posterior one. The bone becomes shaper on its distal portion. The distal condyles are lost. The proximal end is cranio-caudally expanded. The proximal articular surface is concave, with the caudal olecranon process more developed than the cranio-lateral one. The lateral face bears a shallow longitudinal groove for the insertion of M. extensor carpi radialis brevis pars ulnaris, delimited caudally by a ridge for the insertion of M. flexor ulnaris10,21.
The proximal epiphysis of the radius is not well preserved. In proximal view it is a sub-squared bone with wide condyles, but it is strongly damaged hampering the assessment of detailed morphology.
Metacarpals were identified based on it general outline. The metecarpals I and II are almost complete, but the II, IV and the probable V are distally broken. Metacarpals decrease in width and robustness from the I to the V, being the first the largest. Each of them has an expanded proximal portion for articulating with the next metacarpal. The width of this expansion also decreases in size accordingly. In MI and III, the distal condyles bear a circular central depression for the attachment of M. interossei is observed21. These bones are similar to those referred to other baurusuchids10,19.
Hindlimb
A partial left femur, both tibiae and an indeterminate metatarsal were identified.
The femur is broken in two parts. The shaft seems almost cylindrical, but both proximal and distal ends are lost hindering any accurate morphological description.
Both tibiae are exposed in posterior view. They are long and medially curved bones, as in B. albertoi16, Sebecus22, Stratiotosuchus23 and Mariliasuchus8, differing from the straight condition in Crocodylia. Left tibia is preserved only in its distal portion, but the right tibia is almost complete. The tibial shaft is bowed posteriorly and medially, as in Sebecus22. This tibia is expanded at both ends, although the proximal articular surface is not well preserved. The distal end of the tibia is divided into lateral and medial portions. The medial portion is mesio-distally projected, forming an oblique distal margin. The lateral portion is well developed. This condition is present in other notosuchians as Stratiotosuchus, Notosuchus, Araripesuchus, Yacarerani, Pissarrachampsa and Sebecus8,10,22,24,25.
A left metatarsal is well preserved. It is a long and slender bone, compressed cranio-caudally. The shaft is almost straight with expanded proximal and distal ends. The proximal end shows well-marked lateral and medial condyles separated by a shallow concavity. The distal condyles are rounded, making a squared epiphysis. A lateral circular concavity is observed in both sides for the attachment of the M. interossei, as in the metacarpals. Based on the moderate expansion of the proximal end, this bone is tentatively considered as the metatarsal I.
Remarks
Based on the reconstructed 3D model, the general outline of the skull (Fig. 2), especially the lateromedially compressed and dorsoventrally high premaxillae and maxillae and the reduced dental formula (four or five maxillary teeth), resemble the typical doggy-shaped baurusuchid skulls7,26. The maxilla of Ogresuchus specially resembles that of Gondwanasuchus27, but although both taxa show apicobasal sulci on their teeth, Gondwanasuchus bears serrated dentition. The ornamentation pattern of Ogresuchus is also similar to Caipirasuchus teeth, although Caipirasuchus shows a highly specialized dentition composed by three serrated morphotypes in a continuous tooth row, not separated by a premaxillary-maxillary notch12. The anterior palpebral of Ogresuchus is unusually elongated. This bone differs from the morphology observed in most basal notosuchians, baurusuchids and sebecids; although comparisons are hindered because of the palpebral is not preserved in several species. On the other hand, the shape of the anterior palpebral is reminiscent to Gondwanasuchus27 and Araripesuchus tsangtsangana24. Finally, The absence of antorbital fenestra in Ogresuchus differs from many basal notosuchians and some baurusuchids12,26,27,28,29,30,31,32. This condition is similar to those observed in some basal notosuchians, a few baurusichids, and sebecids7,31,33. More

1.
Pabi, S., Van Dijken, G. L. & Arrigo, K. R. Primary production in the Arctic Ocean, 1998–2006. J. Geophys. Res. 113, 1998–2006 (2008).
Google Scholar 
2.
Uitz, J., Claustre, H., Gentili, B. & Stramski, D. Phytoplankton class-specific primary production in the world’s oceans: Seasonal and interannual variability from satellite observations. Global Biogeochemical Cycles 24, GB3016 (2010).
ADS  Google Scholar 

3.
Arrigo, K. R. & van Dijken, G. L. Continued increases in Arctic Ocean primary production. Prog. Oceanogr. 136, 60–70 (2015).
ADS  Google Scholar 

4.
Moore, C. M. et al. Processes and patterns of oceanic nutrient limitation. Nat. Geosci. 6, 701–710 (2013).
ADS  CAS  Google Scholar 

5.
de Baar, H. J. W. et al. Importance of iron for plankton blooms and carbon dioxide drawdown in the Southern Ocea. Nature 373, 412–415 (1995).
ADS  Google Scholar 

6.
Tremblay, J. -É & Gagnon, J. The effects of irradiance and nutrient supply on the productivity of Arctic waters: a perspective on climate change. In Influence of Climate Change on the Changing Arctic and Sub-Arctic Conditions (eds Nihoul, J. C. J. & Kostianoy, A. G.) 73–89 (Springer, Berlin, 2009).
Google Scholar 

7.
De Jong, J. T. M. et al. Sources and fluxes of dissolved iron in the Bellingshausen Sea (West Antarctica): the importance of sea ice, icebergs and the continental margin. Mar. Chem. 177, 518–535 (2015).
Google Scholar 

8.
Popova, E. E. et al. What controls primary production in the Arctic Ocean? Results from an intercomparison of five general circulation models with biogeochemistry. J. Geophys. Res. Ocean. 117, (2012).

9.
Nielsdóttir, M. C., Moore, C. M., Sanders, R., Hinz, D. J. & Achterberg, E. P. Iron limitation of the postbloom phytoplankton communities in the Iceland Basin. Glob. Biogeochem. Cycles 23, 1–13 (2009).
Google Scholar 

10.
Ryan-Keogh, T. J. et al. Spatial and temporal development of phytoplankton iron stress in relation to bloom dynamics in the high-latitude North Atlantic Ocean. Limnol. Oceanogr. 58, 533–545 (2013).
ADS  Google Scholar 

11.
Taylor, R. L. et al. Colimitation by light, nitrate, and iron in the Beaufort Sea in late summer. J. Geophys. Res. Ocean. 118, 3260–3277 (2013).
ADS  Google Scholar 

12.
Findlay, H. S. et al. Late winter biogeochemical conditions under sea ice in the Canadian High Arctic. Polar Res. 34, 24170 (2015).
Google Scholar 

13.
Mills, M. M. et al. Nitrogen limitation of the summer phytoplankton and heterotrophic prokaryote communities in the Chukchi Sea. Front. Mar. Sci. 5, 1–22 (2018).
ADS  Google Scholar 

14.
Hopwood, M. J. et al. Non-linear response of summertime marine productivity to increased meltwater discharge around Greenland. Nat. Commun. 9, 1–9 (2018).
CAS  Google Scholar 

15.
Codispoti, L. A. et al. Synthesis of primary production in the Arctic Ocean: III. Nitrate and phosphate based estimates of net community production. Prog. Oceanogr. 110, 126–150 (2013).
ADS  Google Scholar 

16.
Schauer, U. et al. Variation of measured heat flow through the Fram Strait between 1997 and 2006. In Arctic-Subarctic Ocean Fluxes: Defining the Role of the Northern Seas in Climate (eds Dickson, R. R. et al.) 65–85 (Springer, Berlin, 2008).
Google Scholar 

17.
Mouginot, J. et al. Fast retreat of Zachariæ Isstrøm, northeast Greenland. Science (80-, ) 350, 1357–1361 (2015).
ADS  CAS  Google Scholar 

18.
Smedsrud, L. H., Halvorsen, M. H., Stroeve, J. C., Zhang, R. & Kloster, K. Fram Strait sea ice export variability and September Arctic sea ice extent over the last 80 years. Cryosphere 11, 65–79 (2017).
ADS  Google Scholar 

19.
Rijkenberg, M. J. A., Slagter, H. A., Rutgers Van Der Loeff, M., Ooijen, J. Van. & Gerringa, L. J. A. Dissolved Fe in the deep and upper Arctic Ocean with a focus on Fe limitation in the Nansen Basin. Front. Mar. Sci. 5, 88 (2018).
Google Scholar 

20.
de Steur, L. et al. Freshwater fluxes in the East Greenland Current: a decade of observations. Geophys. Res. Lett. 36, 1–5 (2009).
Google Scholar 

21.
Beszczynska-Möller, A., Woodgate, R. A., Lee, C., Melling, H. & Karcher, M. A synthesis of exchanges through the main oceanic gateways to the Arctic Ocean. Oceanography 24, 82–99 (2011).
Google Scholar 

22.
Rudels, B. Arctic ocean circulation. in Encyclopedia of Ocean Sciences 262–277 (Elsevier Inc., 2019). https://doi.org/10.1016/B978-0-12-409548-9.11209-6

23.
Laukert, G. et al. Ocean circulation and freshwater pathways in the Arctic Mediterranean based on a combined Nd isotope, REE and oxygen isotope section across Fram Strait. Geochim. Cosmochim. Acta 202, 285–309 (2017).
ADS  CAS  Google Scholar 

24.
Michel, C. et al. Arctic Ocean outflow shelves in the changing Arctic: a review and perspectives. Prog. Oceanogr. 139, 66–88 (2015).
ADS  Google Scholar 

25.
Rudels, B. et al. The interaction between waters from the Arctic Ocean and the Nordic Seas north of Fram Strait and along the East Greenland Current: Results from the Arctic Ocean-02 Oden expedition. J. Mar. Syst. 55, 1–30 (2005).
Google Scholar 

26.
Lalande, C. et al. Lateral supply and downward export of particulate matter from upper waters to the seafloor in the deep eastern Fram Strait. Deep. Res. Part I Oceanogr. Res. Pap. 114, 78–89 (2016).
ADS  Google Scholar 

27.
Norwegian Polar Institute. Sea ice extent in the Fram Strait in September, 1979–2018. Environmental Monitoring of Svalbard and Jan Mayen (MOSJ) (2020). https://www.mosj.no/en/climate/ocean/sea-ice-extent-barents-sea-fram-strait.html. Accessed 10th January 2020.

28.
de Steur, L., Peralta-Ferriz, C. & Pavlova, O. Freshwater export in the East Greenland current freshens the North Atlantic. Geophys. Res. Lett. 45, 13359–13366 (2018).
ADS  Google Scholar 

29.
Marnela, M., Rudels, B., Houssais, M.-N., Beszczynska-Möller, A. & Eriksson, P. B. Recirculation in the Fram Strait and transports of water in and north of the Fram Strait derived from CTD data. Ocean Sci. 9, 499–519 (2013).
ADS  Google Scholar 

30.
Beszczynska-Möller, A., Fahrbach, E., Schauer, U. & Hansen, E. Variability in Atlantic water temperature and transport at the entrance to the Arctic Ocean, 1997–2010. ICES J. Mar. Sci. 69, 852–863 (2012).
Google Scholar 

31.
de Steur, L., Hansen, E., Mauritzen, C., Beszczynska-Möller, A. & Fahrbach, E. Impact of recirculation on the East Greenland Current in Fram Strait: Results from moored current meter measurements between 1997 and 2009. Deep. Res. Part I Oceanogr. Res. Pap. 92, 26–40 (2014).
ADS  Google Scholar 

32.
Grasshoff, K., Kremlingl, K. & Ehrhardt, M. Methods of Seawater Analysis (Wiley, Hoboken, 1999). https://doi.org/10.1002/9783527613984.
Google Scholar 

33.
Cutter, G. et al. Sampling and Sample-handling Protocols for GEOTRACES Cruises. (2014).

34.
Rapp, I., Schlosser, C., Rusiecka, D., Gledhill, M. & Achterberg, E. P. Automated preconcentration of Fe, Zn, Cu, Ni, Cd, Pb Co, and Mn in seawater with analysis using high-resolution sector field inductively-coupled plasma mass spectrometry. Anal. Chim. Acta 976, 1–13 (2017).
CAS  PubMed  Google Scholar 

35.
Moore, C. M. Diagnosing oceanic nutrient deficiency. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 374, 20150290 (2016).
ADS  Google Scholar 

36.
Browning, T. J. et al. Nutrient co-limitation at the boundary of an oceanic gyre. Nature 551, 242–246 (2017).
ADS  CAS  PubMed  Google Scholar 

37.
Achterberg, E. P. et al. Natural iron fertilization by the Eyjafjallajökull volcanic eruption. Geophys. Res. Lett. 40, 921–926 (2013).
ADS  CAS  Google Scholar 

38.
Welschmeyer, N. A. Fluorometric analysis of chlorophyll a in the presence of chlorophyll b and pheopigments. Limnol. Oceanogr. 39, 1985–1992 (1994).
ADS  CAS  Google Scholar 

39.
Van Heukelem, L. & Thomas, C. S. Computer-assisted high-performance liquid chromatography method development with applications to the isolation and analysis of phytoplankton pigments. J. Chromatogr. A 910, 31–49 (2001).
PubMed  Google Scholar 

40.
Mackey, M. D., Mackey, D. J., Higgins, H. W. & Wright, S. W. CHEMTAX – a program for estimating class abundances from chemical markers: application to HPLC measurements of phytoplankton. Mar. Ecol. Prog. Ser. 144, 265–283 (1996).
ADS  CAS  Google Scholar 

41.
Coupel, P. et al. Pigment signatures of phytoplankton communities in the Beaufort Sea. Biogeosciences 12, 991–1006 (2015).
ADS  Google Scholar 

42.
Hong, C. N. et al. Sediment efflux of silicon on the Greenland margin and implications for the marine silicon cycle. Earth Planet. Sci. Lett. 529, 115877 (2020).
Google Scholar 

43.
Tonnard, M. et al. Dissolved iron in the North Atlantic Ocean and Labrador Sea along the GEOVIDE section (GEOTRACES section GA01). Biogeosciences 17, 917–943 (2020).
ADS  Google Scholar 

44.
Colombo, M., Jackson, S. L., Cullen, J. T. & Orians, K. J. Dissolved iron and manganese in the Canadian Arctic Ocean: On the biogeochemical processes controlling their distributions. Geochim. Cosmochim. Acta (2020). (in press).

45.
Ardiningsih, I. et al. Natural Fe-binding organic ligands in Fram Strait and over the Northeast Greenland shelf. Mar. Chem. 224, (2020).

46.
Le Moigne, F. A. C. et al. Sequestration efficiency in the iron-limited North Atlantic: implications for iron supply mode to fertilized blooms. Geophys. Res. Lett. 41, 4619–4627 (2014).
ADS  Google Scholar 

47.
Beszczynska-Möller, A. & Wisotzki, A. Physical oceanography during POLARSTERN cruise ARK-XXIII/2. Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, PANGAEA (2010). https://doi.org/10.1594/PANGAEA.733424.

48.
Kattner, G. & Ludwichowski, K.-U. Inorganic nutrients measured on water bottle samples during POLARSTERN cruise ARK-XXIII/2. Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, PANGAEA (2014). https://doi.org/10.1594/PANGAEA.832402.

49.
Randelhoff, A., Fer, I., Sundfjord, A., Tremblay, J. -É & Reigstad, M. Vertical fluxes of nitrate in the seasonal nitracline of the Atlantic sector of the Arctic Ocean. J. Geophys. Res. Ocean. 121, 3010–3028 (2016).
Google Scholar 

50.
Achterberg, E. P. et al. Iron biogeochemistry in the high latitude North Atlantic ocean. Sci. Rep. 8, 1–15 (2018).
ADS  CAS  Google Scholar 

51.
Torres-Valdés, S. et al. Export of nutrients from the Arctic Ocean. J. Geophys. Res. 118, 1625–1644 (2013).
ADS  Google Scholar 

52.
Ooijen, J. Van, Rijkenberg, M. J. A., Gerringa, L. J. A., Rabe, B. & van der Loeff, M. R. Inorganic nutrients measured on water bottle samples during POLARSTERN cruise PS94 (ARK-XXIX/3). (2016). https://doi.org/10.1594/PANGAEA.868396

53.
Slagter, H. A. et al. Organic Fe speciation in the Eurasian Basins of the Arctic Ocean and its relation to terrestrial DOM. Mar. Chem. 197, 11–25 (2017).
CAS  Google Scholar 

54.
Gerringa, L. J. A., Rijkenberg, M. J. A. & Slagter, H. A. Dissolved iron measured on board with Flow injection analysis and iron-binding dissolved organic ligands from Ultra Clean CTD collected depth profiles during GEOTRACES PS94 Arctic cruise on Polarstern. (2018). https://doi.org/10.1594/PANGAEA.890975

55.
Krawczyk, D. W. et al. Seasonal succession, distribution, and diversity of planktonic protists in relation to hydrography of the Godthåbsfjord system (SW Greenland). Polar Biol. 41, 2033–2052 (2018).
Google Scholar 

56.
Meire, L. et al. High export of dissolved silica from the Greenland Ice Sheet. Geophys. Res. Lett. 43, 9173–9182 (2016).
ADS  CAS  Google Scholar 

57.
Nöthig, E.-M. et al. Summertime plankton ecology in Fram Strait: a compilation of long- and short-term observations. Polar Res. 34, 1–18 (2015).
Google Scholar 

58.
Moore, C. M. et al. Relative influence of nitrogen and phosphorus availability on phytoplankton physiology and productivity in the oligotrophic sub-tropical North Atlantic Ocean. Limnol. Oceanogr. 53, 291–305 (2008).
ADS  CAS  Google Scholar 

59.
Browning, T. J. et al. Nutrient co-limitation at the boundary of an oceanic gyre_Supplementary Material. Nature 551, 242–246 (2017).
ADS  CAS  PubMed  Google Scholar 

60.
Kattner, G. & Budéus, G. Nutrient status of the Northeast Water Polynya. J. Mar. Syst. 10, 185–197 (1997).
Google Scholar 

61.
Smith, R. E. H., Gosselin, M. & Taguchi, S. The influence of major inorganic nutrients on the growth and physiology of high arctic ice algae. J. Mar. Syst. 11, 63–70 (1997).
Google Scholar 

62.
Maestrini, S. Y., Rochet, M., Legendre, L. & Demers, S. Nutrient limitation of the bottom-ice microalgal biomass (southeastern Hudson Bay, Canadian Arctic). Lim 31, 969–982 (1986).
ADS  CAS  Google Scholar 

63.
Ortega-Retuerta, E., Jeffrey, W. H., Ghiglione, J. F. & Joux, F. Evidence of heterotrophic prokaryotic activity limitation by nitrogen in the Western Arctic Ocean during summer. Polar Biol. 35, 785–794 (2012).
Google Scholar 

64.
Mann, E. L. & Chisholm, S. W. Iron limits the cell division rate of Prochlorococcus in the eastern equatorial Pacific. Limnol. Oceanogr. 45, 1067–1076 (2000).
ADS  CAS  Google Scholar 

65.
Vernet, M. et al. Influence of phytoplankton advection on the productivity along the Atlantic water inflow to the Arctic Ocean. Front. Mar. Sci. 6(583), 1–18 (2019).
Google Scholar 

66.
Moore, C. M. et al. Iron limits primary productivity during spring bloom development in the central North Atlantic. Glob. Chang. Biol. 12, 626–634 (2006).
ADS  Google Scholar 

67.
Browning, T. J. et al. Nutrient regulation of late spring phytoplankton blooms in the midlatitude North Atlantic. Limnol. Oceanogr. 9999, 1–13 (2019).
Google Scholar 

68.
Blain, S. et al. Availability of iron and major nutrients for phytoplankton in the northeast Atlantic Ocean. Limnol. Oceanogr. 49, 2095–2104 (2004).
ADS  CAS  Google Scholar 

69.
Boyd, P. W. & Ellwood, M. J. The biogeochemical cycle of iron in the ocean. Nat. Geosci. 3, 675–682 (2010).
ADS  CAS  Google Scholar 

70.
Twining, B. S. & Baines, S. B. The trace metal composition of marine phytoplankton. Ann. Rev. Mar. Sci. 5, 191–215 (2013).
PubMed  Google Scholar 

71.
Saito, M. A. et al. Multiple nutrient stresses at intersecting Pacific Ocean biomes detected by protein biomarkers. Science (80-. ) 345, 1173–1177 (2014).
ADS  CAS  Google Scholar 

72.
Ward, B. A., Dutkiewicz, S., Moore, C. M. & Follows, M. J. Iron, phosphorus, and nitrogen supply ratios define the biogeography of nitrogen fixation. Limnol. Oceanogr. 58, 2059–2075 (2013).
ADS  CAS  Google Scholar 

73.
Hattermann, T., Isachsen, P. E., Von Appen, W. J., Albretsen, J. & Sundfjord, A. Eddy-driven recirculation of Atlantic Water in Fram Strait. Geophys. Res. Lett. 43, 3406–3414 (2016).
ADS  Google Scholar 

74.
Klunder, M. B. et al. Dissolved iron in the Arctic shelf seas and surface waters of the central Arctic Ocean: impact of Arctic river water and ice-melt. J. Geophys. Res. Ocean. 117, 1–18 (2012).
Google Scholar 

75.
Charette, M. A. et al. The Transpolar Drift as a Source of Riverine and Shelf-Derived Trace Elements to the Central Arctic Ocean. J. Geophys. Res. Ocean. 125, e2019JC015920 (2020).
ADS  Google Scholar 

76.
Yamamoto-Kawai, M., Carmack, E. & McLaughlin, F. Nitrogen balance and Arctic throughflow. Nature 443, 43 (2006).
ADS  CAS  PubMed  Google Scholar 

77.
Rijkenberg, M. J. A. et al. The distribution of dissolved iron in the West Atlantic Ocean. PLoS ONE 9, 1–14 (2014).
Google Scholar 

78.
de Boyer Montégut, C., Madec, G., Fischer, A. S., Lazar, A. & Iudicone, D. Mixed layer depth over the global ocean: an examination of profile data and a profile-based climatology. J. Geophys. Res. Ocean. 109, 1–20 (2004).
Google Scholar 

79.
Randelhoff, A., Sundfjord, A. & Reigstad, M. Seasonal variability and fluxes of nitrate in the surface waters over the Arctic shelf slope. Geophys. Res. Lett. 42, 3442–3449 (2015).
ADS  CAS  Google Scholar 

80.
Randelhoff, A. et al. Pan-Arctic ocean primary production constrained by turbulent nitrate fluxes. Front. Mar. Sci. 7, 1–15 (2020).
Google Scholar 

81.
Rafter, P. A., Sigman, D. M. & Mackey, K. R. M. Recycled iron fuels new production in the eastern equatorial Pacific Ocean. Nat. Commun. 8, 1100 (2017).
ADS  PubMed  PubMed Central  Google Scholar 

82.
Stohl, A. et al. Arctic smoke: record high air pollution levels in the European Arctic due to agricultural fires in Eastern Europe in spring 2006. Atmos. Chem. Phys. 7, 511–534 (2007).
ADS  CAS  Google Scholar 

83.
Marsay, C. M. et al. Concentrations, provenance and flux of aerosol trace elements during US GEOTRACES Western Arctic cruise GN01. Chem. Geol. 502, 1–14 (2018).
ADS  CAS  Google Scholar 

84.
Conca, E. et al. Source identification and temporal evolution of trace elements in PM10 collected near to Ny-Ålesund (Norwegian Arctic). Atmos. Environ. 203, 153–165 (2019).
ADS  CAS  Google Scholar 

85.
Kadko, D. et al. The residence times of trace elements determined in the surface Arctic Ocean during the 2015 US Arctic GEOTRACES expedition. Mar. Chem. 208, 56–69 (2019).
CAS  Google Scholar 

86.
Wehrmann, L. M. et al. Iron and manganese speciation and cycling in glacially influenced high-latitude fjord sediments (West Spitsbergen, Svalbard): evidence for a benthic recycling-transport mechanism. Geochim. Cosmochim. Acta 141, 628–655 (2014).
ADS  CAS  Google Scholar 

87.
Bown, J. et al. Evidences of strong sources of DFe and DMn in Ryder Bay, Western Antarctic Peninsula. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 376, 20170172 (2018).
ADS  Google Scholar 

88.
Bucciarelli, E., Blain, S. & Tréguer, P. Iron and manganese in the wake of the Kerguelen Islands (Southern Ocean). Mar. Chem. 73, 21–36 (2001).
CAS  Google Scholar 

89.
Nishino, S. et al. Enhancement/Reduction of biological pump depends on ocean circulation in the sea-ice reduction regions of the Arctic Ocean. J. Oceanogr. 67, 305–314 (2011).
CAS  Google Scholar 

90.
Kipp, L. E., Charette, M. A., Moore, W. S., Henderson, P. B. & Rigor, I. G. Increased fluxes of shelf-derived materials to the central arctic ocean. Sci. Adv. 4, 1–10 (2018).
Google Scholar 

91.
Mayot, N. et al. Springtime export of Arctic Sea ice influences phytoplankton production in the Greenland Sea. J. Geophys. Res. Ocean. 125, 1–16 (2020).
Google Scholar 

92.
Randelhoff, A. et al. The evolution of light and vertical mixing across a phytoplankton ice-edge bloom. Elem. Sci. Anthr. 7, 1–19 (2019).
Google Scholar 

93.
Kahru, M., Brotas, V., Manzano-Sarabia, M. & Mitchell, B. G. Are phytoplankton blooms occurring earlier in the Arctic?. Glob. Chang. Biol. 17, 1733–1739 (2011).
ADS  Google Scholar 

94.
Ardyna, M. et al. Environmental drivers of under-ice phytoplankton bloom dynamics in the Arctic Ocean. Elem. Sci. Anthr. 8, 30 (2020).
Google Scholar 

95.
Tremblay, J. É et al. Vertical stability and the annual dynamics of nutrients and chlorophyll fluorescence in the coastal, southeast Beaufort Sea. J. Geophys. Res. Ocean. 113, 1–14 (2008).
Google Scholar 

96.
Castro de la Guardia, L. et al. Assessing the role of high-frequency winds and sea ice loss on arctic phytoplankton blooms in an ice-ocean-biogeochemical model. J. Geophys. Res. Biogeosci. 124, 2728–2750 (2019).
ADS  Google Scholar 

97.
Ardyna, M. et al. Recent Arctic Ocean sea ice loss triggers novel fall phytoplankton blooms. Geophys. Res. Lett. 41, 6207–6212 (2014).
ADS  Google Scholar 

98.
Wang, Q. et al. Intensification of the Atlantic Water supply to the Arctic Ocean through Fram Strait induced by Arctic sea ice decline. Geophys. Res. Lett. 47, e2019GL086682 (2020).
ADS  Google Scholar 

99.
Arrigo, K. R., van Dijken, G. & Pabi, S. Impact of a shrinking Arctic ice cover on marine primary production. Geophys. Res. Lett. 35, 1–6 (2008).
Google Scholar 

100.
Carmack, E. C., Macdonald, R. W. & Steve, J. Phytoplankton productivity on the Canadian Shelf of the Beaufort Sea. Mar. Ecol. Prog. Ser. 277, 37–50 (2004).
ADS  Google Scholar 

101.
Lasternas, S. & Agustí, S. Phytoplankton community structure during the record Arctic ice-melting of summer 2007. Polar Biol. 33, 1709–1717 (2010).
Google Scholar 

102.
Harding, K. et al. Symbiotic unicellular cyanobacteria fix nitrogen in the Arctic Ocean. Proc. Natl. Acad. Sci. 115, 13371–13375 (2018).
CAS  PubMed  Google Scholar 

103.
Sipler, R. E. et al. Preliminary estimates of the contribution of Arctic nitrogen fixation to the global nitrogen budget. Limnol. Oceanogr. 2, 159–166 (2017).
Google Scholar 

104.
Zehr, J. P. & Kudela, R. M. Nitrogen cycle of the open ocean: from genes to ecosystems. Annu. Rev. Mar. Sci. 3, 197–225 (2011).
ADS  Google Scholar 

105.
Acker, J. G. & Leptoukh, G. Online analysis enhances use of NASA Earth Science Data. Eos (Washington, DC) 88, 14–17 (2007).
ADS  Google Scholar 

106.
Schaffer, J. et al. A global, high-resolution data set of ice sheet topography, cavity geometry, and ocean bathymetry. Earth Syst. Sci. Data 8, 543–557 (2016).
ADS  Google Scholar  More