Philippa Kaur

Psyllids or jumping plant-lice are a group of small, generally host-specific plant-sap sucking insects with around 4000 described species1. A few species are major pests on fruits or vegetables, mostly by transmitting plant pathogens. Others damage forest plantations or ornamental plants by removal of plant-sap, stunting new growth, inducing galls or secreting honeydew and wax, an ideal substrate for sooty mould which reduces photosynthesis2. Modern psyllids, defined by the enlarged and immobile metacoxae in adults allowing them to jump, display a wide range of morphological diversity regarding the head, antennae, legs, forewings, terminalia, etc. in adults and body shape, antennal structure and the type of setae or wax pores in immatures. Modern psyllids are documented in the fossil record since the Eocene (Lutetian)3 (Fig. 1). The stem-group of modern psyllids constitutes, according to Burckhardt & Poinar, 20194, the paraphyletic Liadopsyllidae Martynov, 19265 with 17 species and six genera (Liadopsylla Handlirsch, 19256, Gracilinervia Becker-Migdisova, 19857, Malmopsylla Becker-Migdisova, 19857, Mirala Burckhardt & Poinar, 20194, Neopsylloides Becker-Migdisova, 19857 and Pauropsylloides Becker-Migdisova, 19857) from early Jurassic to late Cretaceous4,8. Shcherbakov9 added three species from the Lower Cretaceous for one of which he erected the genus Stigmapsylla and for the other two the subgenus Liadopsylla (Basicella). He also transferred two previously described species from Liadopsylla to Cretapsylla Shcherbakov9. Further he resurrected the Malmopsyllidae Becker-Migdisova, 19857 splitting it into Malmopsyllinae (for Gracilinervia, Malmopsylla, Neopsylloides and Pauropsylloides) and Miralinae Shcherbakov9 (for Mirala). Apart from three species described from amber fossils, all Mesozoic psyllids are poorly preserved impression fossils of which usually only the forewing is preserved. The current classification of Mesozoic psyllids (Liadopsyllidae and Malmopsyllidae) is based almost exclusively upon forewing characters7,9, despite that several phylogenetically significant characters from other body parts have been described from amber inclusions4,8. Judging from the impression fossils, Liadopsyllidae and Malmopsyllidae appear morphologically quite homogeneous but this may be a result of the surprisingly scarce fossil record of psyllids compared to other insect groups. The discoveries of Cretaceous amber fossils radically alter this picture, e.g. the recently described Mirala burmanica Burckhardt & Poinar, 2019 from Myanmar amber4.
Figure 1

Relationships and stratigraphic distribution of Liadopsyllidae and its subunits within Sternorrhyncha according to Drohojowska & Szwedo10, Hakim et al.11 and Drohojowska et al.12, modified. Numbers denote described taxa of fossil Liadopsyllidae—1: Liadopsylla geinitzi Handlirsch, 1925—Lower Jurassic, Mecklenburg, Germany, 2: Liadopsylla obtusa Ansorge, 1996—Lower Jurassic, Mecklenburg-Vorpommern, Germany, 3: Liadopsylla asiatica Becker-Migdisova, 1985—Upper Jurassic, Karatau, Kazakhstan, 4: Liadopsylla brevifurcata Becker-Migdisova, 1985—Upper Jurassic, Karatau, Kazakhstan, 5: Liadopsylla grandis Becker-Migdisova, 1985—Upper Jurassic, Karatau, Kazakhstan, 6. Liadopsylla karatavica Becker-Migdisova, 1985—Upper Jurassic, Karatau, Kazakhstan, 7. Liadopsylla longiforceps Becker-Migdisova, 1985—Upper Jurassic, Karatau, Kazakhstan, 8. Liadopsylla tenuicornis Martynov, 1926—Upper Jurassic, Karatau, Kazakhstan, 9. Liadopsylla turkestanica Becker-Migdisova, 1949—Upper Jurassic, Karatau, Kazakhstan, 10. Gracilinervia mastimatoides Becker-Migdisova, 1985—Upper Jurassic, Karatau, Kazakhstan, 11. Malmopsylla karatavica Becker- Migdisova, 1985 – Upper Jurassic, Karatau, Kazakhstan, 12. Neopsylloides turutanovae Becker-Migdisova, 1985—Upper Jurassic, Karatau, Kazakhstan, 13. Pauropsylloides jurassica Becker-Migdisova, 1985—Upper Jurassic, Karatau, Kazakhstan, 14. Liadopsylla mongolica Shcherbakov, 1988—Lower Cretaceous, Bon Tsagaan, Mongolia 15. Liadopsylla apedetica Ouvrard, Burckhardt et Azar, 2010—Lower Cretaceous, Lebanon, 16. Liadopsylla lautereri (Shcherbakov, 2020)—Lower Cretaceous, Buryatia, Russia 17. Liadopsylla loginovae (Shcherbakov, 2020)—Lower Cretaceous, Buryatia, Russia 18. Stigmapsylla klimaszewskii Shcherbakov, 2020—Lower Cretaceous, Buryatia, Russia 19. Mirala burmanica Burckhardt et Poinar, 2019—mid-Cretaceous, Kachin amber, 20. Amecephala pusilla gen. et sp. nov.—mid-Cretaceous, Kachin amber, 21. Liadopsylla hesperia Ouvrard et Burckhardt, 2010—Upper Cretaceous, Raritan amber, U.S.A.

Full size image

Here we describe a second taxon of Mesozoic psyllids from Kachin amber, Amecephala pusilla gen. et sp. nov., possessing a series of characters unique within Mesozoic psyllids, discuss the phylogenetic relationships within the group, and provide an updated key to genera as well a checklist of recognised species (Table 1).
To satisfy a requirement by Article 8.5.3 of the International Code of Zoological Nomenclature this publication has been registered in ZooBank with the LSID: urn:lsid:zoobank.org:act:D3AF7597-47BF-4D6C-9020-982F4C20315E.
Table 1 Annotated checklist of known species of Liadopsyllidae Martynov, 19265.
Full size table

Systematic palaeontology
Order Hemiptera Linnaeus, 175817
Suborder Sternorrhyncha Amyot et Audinet-Serville, 184318
Superfamily Psylloidea Latreille, 180719
Family Liadopsyllidae Martynov, 19265
Genus †Amecephala gen. nov
urn:lsid:zoobank.org:act:9DABC236-FFB9-4305-82EC-4E293212849B
Type species
† Amecephala pusilla sp. nov., by present designation and monotypy.
Etymology From ancient Greek ἡ άμε [ē áme] = shovel and ἡ κεφαλή [ē kefalé] = head for its shovel-shaped head. Gender: feminine.
Diagnosis
Vertex rectangular; coronal suture developed in apical half; median ocellus on ventral side of head, situated at the apex of frons which is large, triangular; genae not produced into processes; toruli oval, medium sized, situated in front of eyes below vertex. Eyes hemispheric, relatively small (Fig. 2a,b,e,g). Antenna with pedicel about as long as flagellar segments 1 and 8, longer than remainder of segments. Pronotum ribbon-shaped, relatively long, laterally of equal length as medially. Forewing (Fig. 2a,b,f,g) elongate, widest in the middle, narrowly rounded at apex; pterostigma short and broad, triangular, not delimited at base by a vein thus vein R1 not developed; veins R and M + Cu subequal in length; vein Rs relatively short, slightly curved towards fore margin; vein M shorter than its branches which are of subequal length; cell cu1 low and very long. Female terminalia short, cuneate.
Figure 2

(a‒i) Amecephala pusilla gen. et sp. nov. imago. Drawing of body in dorsal view (a), Body in dorsal view (b), Metatarsus (c), Drawing of hind leg (d), Head in dorsal view (e), Forewing (f), Body in ventral view (g), Basal part of claval suture (h), Distal part of claval suture (i); Scale bars: 0.5 mm (a,b); 0.2 mm (f,g); 0.1 mm (c,d,e,h,i).

Full size image

Description
Head weakly inclined from longitudinal body axis; about as wide as pronotum and mesoscutum, dorso-ventrally compressed. Vertex rectangular; anterior margin weakly curved, indented in the middle; posterior margin slightly concavely curved; coronal suture developed in apical half, basal half not visible; lateral ocelli near posterior angles of vertex, hardly raised; median ocellus on ventral side of head, situated at the apex of frons which is large, triangular; genae not produced into processes; preocular sclerites lacking; toruli oval, medium sized, situated in front of eyes below vertex; clypeus partly covered by gas bubble, appearing flattened, pear-shaped. Eyes hemispheric, relatively small (Fig. 2a,b,e,g). Antenna 10-segmented, filiform, moderately long, flagellum 1.6 times as long as head width; pedicel very long, about as long as flagellar segments 1 and 8; rhinaria not visible (Fig. 2a,b). Thorax (ventrally not visible) with pronotum wider than mesopraescutum as wide as mesoscutum, laterally of the same length as medially. Mesothorax large; mesopraescutum triangular, with arcuate anterior margin, almost twice wider than long in the middle; mesopraescutum slightly longer than pronotum in the middle; mesoscutum subtrapezoid with slightly arched anterior margin, about 3.0 times wider than long in the middle; delimitation between mesoscutum and mesoscutellum clearly visible. Metascutellum trapezoid, narrower than mesoscutellum with a submedian longitudinal low ridge on either side. Parapterum and tegula forming small oval structures of about the same size; the former slightly in front of the latter. Forewing (Fig. 2a,b,f) membranous, elongate, narrow at base, widest in the middle, narrowly rounded at apex which lies in cell m1 near the apex of vein M3+4; vein C + Sc narrow; cell c + sc long, widening toward apex; costal break not visible, perhaps absent; pterostigma short and broad, triangular, not delimited at base by a vein thus vein R1 not developed; vein R + M + Cu relatively short; veins R and M + Cu subequal in length; vein R2 relatively short and straight; vein Rs relatively short, slightly curved towards fore margin; vein M shorter than its branches which are of subequal length; vein Cu short, splitting into very long Cu1a and short Cu1b, hence cell cu1 low and very long; claval suture visible (Fig. 2h,i); anal break near to apex of vein Cu1b (Fig. 2f,i). Hindwing (Fig. 2a) shorter than forewing, more than twice as long as wide, membranous; venation indistinct. Legs similar in shape and size, long, slender (Fig. 2c,d,g); femora slightly enlarged distally, tibiae long and slightly enlarged distally; metatibia lacking genual spine and apical sclerotized spurs, but bearing several apical bristles and, in distal quarter, a row of short bristles (Fig. 2d); tarsi two-segmented, tubular of similar length though basal segment slightly thicker than apical one, claws large, one-segmented, pulvilli absent (Fig. 2c–d). Abdomen appearing flattened, tergites and sternites not clearly visible. Female terminalia short, slightly shorter than head width, cuneate (Fig. 2a,b,g).
Revised key to Mesozoic psylloid genera (after Burckhardt & Poinar4 , modified)
1.
Forewing lacking pterostigma………………………………………………………………………………………………………………Liadopsylla Handlirsch, 1921 (= Cretapsylla Shcherbakov, 2020 syn. nov.; = Basicella Shcherbakov, 2020 syn. nov.)
-Forewing bearing pterostigma………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………….2

2.
Vein Rs in forewing straight, veins Rs and M subparallel; vein M not branched; vein R shorter than M + Cu; vein Cu1b almost straight, directed toward wing base………………………………………………….Mirala Burckhardt et Poinar, 2020
-Combination of characters different. Vein Rs in forewing concavely curved towards fore margin (not visible in Stigmapsylla), veins Rs and M from base to apex first converging then diverging; vein M branched; vein Cu1b straight or curved, directed toward hind margin or apex of wing………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………3

3.
Vein R of forewing distinctly shorter than M + Cu……………………………………………………………………………………………………………………………………………………………………………………………………………….Stigmapsylla Shcherbakov, 2020
-Vein R of forewing distinctly longer than M + Cu, or veins R and M + Cu subequal in length…………………………………………………………………………………………………………………………………………………………………………………………….4

4.
Vein R of forewing distinctly longer than M + Cu; vein Cu1a almost straight…………………………………………………………………………………………………………………………………………………………………..Malmopsylla Becker-Migdisova, 1985
-Veins R and M + Cu of forewing subequal in length; vein Cu1a distinctly curved……………………………………………………………………………………………………………………………………………………………………………………………………………..5

5.
Forewing with cell cu1 low and very long, around 6.0 times as long high……………………………………………………………………………………………………………………………………………………………………………………………..Amecephala gen. nov.
-Forewing with cell cu1 higher and shorter, less than 2.5 times as long high…………………………………………………………………………………………………………………………………………………………………………………………………………………….6

6.
Forewing with long pterostigma, vein R2 straight……………………………………………………………………………………………………………………………………………………………………………………………………..Neopsylloides Becker-Migdisova, 1985
-Forewing with short pterostigma, vein R2 curved……………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………….7

7.
Vein R + M + Cu of forewing ending at basal quarter of wing……………………………………………………………………………………………………………………………………………………………………………………..Gracilinervia Becker-Migdisova, 1985
-Vein R + M + Cu of forewing ending at basal third of wing…………………………………………………………………………………………………………………………………………………………………………………..Pauropsylloides Becker-Migdisova, 1985

†Amecephala pusilla sp. nov
urn:lsid:zoobank.org:act:6B20A4F4-57DB-4F06-A43C-5DE3653D76E3 (Fig. 2a–i)
Etymology
From Latin pusillus = tiny, very small—for its small body size.
Holotype
Female, specimen number MAIG 6686; deposited in the Museum of Amber Inclusion, University of Gdańsk, Gdańsk, Poland. Complete and well-preserved (Fig. 2b,g), probably slightly compressed dorso-ventrally; the wings appear slightly detached from thorax and have been probably forced away from the thorax by the compression. Several gas bubbles on the ventral body side obscure parts of the head, thorax, abdomen, legs and the right forewing (Fig. 2g). Syniclusions: Aleyrodidae (part; second part in broken piece).
Locality and stratum
Myanmar, Kachin State, Hukawng Valley, SW of Maingkhwan, former Noije Bum 2001 Summit Site amber mine (closed). Lowermost Cenomanian, Upper Cretaceous.
Species diagnosis
As for the genus.
Description
Female; male unknown. Body minute, 1.20 mm long including forewing when folded over body. Head (ventrally partly covered by gas bubble) 0.28 mm wide, 0.10 mm long; vertex width 0.20 mm wide, 0.09 mm long; microsculpture or setae not visible. Antenna (Fig. 2a,b) with globular scape and cylindrical pedicel, thinner and longer than scape; flagellum 0.40 mm long; 1.6 times as long as head width; flagellar segments slightly more slender than pedicel, relative lengths as 1.0:0.7:0.6:0.6:0.6:0.6:0.7:1.0; flagellar segment 8 bearing two subequal terminal setae shorter that the segment. Clypeus and rostrum not visible, covered by gas bubble. Forewing (Fig. 2a,b,f,g) 0.90 mm long, 0.30 mm wide, 3.0 times as long as wide; membrane transparent, colourless, veins pale; anterior margin curved basally, posterior margin almost straight; vein R + M + Cu ending in basal fifth of wing; vein R slightly shorter that M + Cu; bifurcation of vein R proximal to middle of wing; cell r1 relatively narrow; vein R2 distinctly shorter than Rs; vein Rs relatively short, strongly curved towards fore margin; vein M slightly longer than veins R and M + Cu; M branching proximal to Rs–Cu1a line; cell m1 value more than 2.6, cell cu1 value more than 6.0; surface spinules not visible. Hindwing (Fig. 2b,f) membranous, transparent and colourless. Female terminalia (Fig. 2a,b,g) with apically pointed proctiger; circumanal ring irregularly oval, about half as long as proctiger. More

Comparison between old and new deciduousness metrics
At first, to check the reliability of the proposed metric, we estimated the deciduousness from the equation proposed by Cuba et al.14 (Eq. 1; referred as ‘old’) and the new metric proposed in this study (Eq. 2; referred as ‘new’) during the extreme and normal rainfall years. The results of dry and moist deciduous samples and 4 pheno-classes revealed an over-estimation and under-estimation of deciduousness with the old-metric, whereas the new metric revealed the accurate relative variability (Fig. 2b,c, Table S1). Table 1 provides the estimated deciduousness values from the old and new metrics for 22 homogeneous sample pixels representing four major vegetation types in the study area (refer Fig. 1 for their spatial locations and Fig. S1 for their annual growth profile). The litter fall information collected from literature revealed a higher litter fall quantity of 10–14.4 Mg Ha−1 year−1 for the moist deciduous forest39,40,41,42 and lower litter fall quantities of 1–8.65 Mg Ha−1 year−1 and 5.63–7.84 Mg Ha−1 year−1 for the dry deciduous forest42,43,44 and the semi-evergreen and evergreen forest44 , respectively. The new metric showed a relatively similar variability in deciduousness to ground observations especially for the moist and dry-deciduous forests than the old metric (Table 1).
Figure 2

Graphical illustration of deciduousness estimation: (a) Theoretical phenology curves from high and low deciduous vegetation and the parameters of deciduousness, (b) Actual RS derived annual growth profiles of moist and dry deciduous vegetation and their deciduousness estimation using the old and new metric, and (c) Annual growth profile of four theoretical pheno-classes for depicting the different magnitude of deciduousness.

Full size image

Table 1 Performance of old and new deciduous metric in a normal rainfall year (2011) using 22 samples from different vegetation types (spatial locations of these samples can be seen in Fig. 1).
Full size table

Further, the difference between the old and new metric was spatially checked and is shown at the center of Fig. 3, and the actual values are presented in the surrounding in eight different sub-set locations. The difference image denotes the under-estimated (70.76% of forest area) and the over-estimated (29.23% of forest area) deciduousness obtained by the old metric (Fig. 3). The under-estimated area observed was mainly in the moist forested regions of states- Chhattisgarh, Odisha, and Jharkhand states, whereas, the over-estimated area observed was mainly in the dry forested region of states—Madhya Pradesh, Maharashtra, Northern Chhattisgarh and some parts of Jharkhand (Fig. 3). The over- and under-estimations are with respect to the new metric, and not with the real in-situ measurements. However, the new metric is in good agreement with annual growth profiles of different vegetation types, and have positive relation with ground litter fall observations39,40,41,42,43,44.
Figure 3

Difference in the spatial distribution of deciduousness (central figure) and the actual deciduousness (subset boxes) derived from the new and old metric for the year 2011. (These maps were created using ESRI’s ArcMap 10.3—https://desktop.arcgis.com/en/arcmap/, and MS-Office PowerPoint 2007 software).

Full size image

The deciduousness derived from these two metrics were also tested for their statistical significance using ANOVA (Table 2). In this test 800 stratified random samples belonging to different deciduous forests of different density classes for dry (2002), normal (2011) and wet (2013) years were used. It was found that the mean deciduousness values from the old metric were similar in the majority of the cases and different rainfall conditions. Hence, it could not be used for understanding rainfall impact on the deciduousness. On the other hand, the new metric performed better than the old metric in terms of its variability under (a) different rainfall conditions (p  More

1.
Chambers, L. E. et al. Observed and predicted effects of climate on Australian seabirds. Emu Austr. Ornithol. 111, 235–251. https://doi.org/10.1071/MU10033 (2011).
Article  Google Scholar 
2.
Stephens, D. W. & Krebs, J. R. Foraging Theory Vol 1 (Princeton University Press, Princeton, 1986).
Google Scholar 

3.
Costa, D. P. The relationship between reproductive and foraging energetics and the evolution of the Pinnipedia. Symp. Zool. Soc. Lond. 66, 293–314 (1993).
Google Scholar 

4.
Weimerskirch, H., Le Corre, M., Jaquemet, S. & Marsac, F. Foraging strategy of a tropical seabird, the red-footed booby, in a dynamic marine environment. Mar. Ecol. Prog. Ser. 288, 251–261. https://doi.org/10.3354/meps288251 (2005).
ADS  Article  Google Scholar 

5.
Costa, D. P. A conceptual model of the variation in parental attendance in response to environmental fluctuation: Foraging energetics of lactating sea lions and fur seals. Aquat. Conserv. Mar. Freshw. Ecosyst. 17, S44–S52. https://doi.org/10.1002/aqc.917 (2007).
ADS  Article  Google Scholar 

6.
Villegas-Amtmann, S., McDonald, B. I., Páez-Rosas, D., Aurioles-Gamboa, D. & Costa, D. P. Adapted to change: Low energy requirements in a low and unpredictable productivity environment, the case of the Galapagos sea lion. Deep Sea Res. Part II Top. Stud. Oceanography 140, 94–104 (2017).
ADS  Article  Google Scholar 

7.
Trillmich, F. & Limberger, D. Drastic effects of El Niño on Galapagos pinnipeds. Oecologia 67, 19–22 (1985).
ADS  Article  Google Scholar 

8.
Dunstan, P. K. et al. Global patterns of change and variation in sea surface temperature and chlorophyll a. Sci. Rep. 8, 14624. https://doi.org/10.1038/s41598-018-33057-y (2018).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

9.
IPCC. Summary for Policymakers. (2019).

10.
Cai, W., Shi, G., Cowan, T., Bi, D. & Ribbe, J. The response of the Southern Annular Mode, the East Australian Current, and the southern mid-latitude ocean circulation to global warming. Geophys. Res. Lett. https://doi.org/10.1029/2005GL024701 (2005).
Article  Google Scholar 

11.
Cai, W. et al. Increasing frequency of extreme El Niño events due to greenhouse warming. Nat. Clim. Change 4, 111. https://doi.org/10.1038/nclimate2100 (2014).
ADS  CAS  Article  Google Scholar 

12.
Cai, W. et al. Increased frequency of extreme Indian Ocean Dipole events due to greenhouse warming. Nature 510, 254–258. https://doi.org/10.1038/nature13327 (2014).
ADS  CAS  Article  PubMed  Google Scholar 

13.
Poloczanska, E. S. et al. Climate change and Australian marine life. Oceanogr. Mar. Biol. 45, 407 (2007).
Google Scholar 

14.
Beentjes, M. P. & Renwick, J. A. The relationship between red cod, Pseudophycis bachus, recruitment and environmental variables in New Zealand. Environ. Biol. Fishes 61, 315–328. https://doi.org/10.1023/A:1010943906264 (2001).
Article  Google Scholar 

15.
Hewitt, R. P., Theilacker, G. H. & Lo, N. C. H. Causes of mortality in young jack mackerel. Mar. Ecol. Prog. Ser. 26, 1–10 (1985).
ADS  Article  Google Scholar 

16.
Rindorf, A., Wanless, S. & Harris, M. P. Effects of changes in sandeel availability on the reproductive output of seabirds. Mar. Ecol. Prog. Ser. 202, 241–252. https://doi.org/10.3354/meps202241 (2000).
ADS  Article  Google Scholar 

17.
Wanless, S., Harris, M. P., Redman, P. & Speakman, J. R. Low energy values of fish as a probable cause of a major seabird breeding failure in the North Sea. Mar. Ecol. Prog. Ser. 294, 1–8. https://doi.org/10.3354/meps294001 (2005).
ADS  Article  Google Scholar 

18.
Carroll, M. J. et al. Kittiwake breeding success in the southern North Sea correlates with prior sandeel fishing mortality. Aquat. Conserv. Mar. Freshw. Ecosys. 27, 1164–1175. https://doi.org/10.1002/aqc.2780 (2017).
Article  Google Scholar 

19.
Ridgway, K. R. Long-term trend and decadal variability of the southward penetration of the East Australian Current. Geophys. Res. Lett. 34, L13613. https://doi.org/10.1029/2007GL030393 (2007).
ADS  Article  Google Scholar 

20.
Hobday, A. J. & Pecl, G. T. Identification of global marine hotspots: Sentinels for change and vanguards for adaptation action. Rev. Fish Biol. Fish. 24, 415–425. https://doi.org/10.1007/s11160-013-9326-6 (2014).
Article  Google Scholar 

21.
Hobday, A. J. & Lough, J. M. Projected climate change in Australian marine and freshwater environments. Mar. Freshw. Res. 62, 1000–1014. https://doi.org/10.1071/MF10302 (2011).
Article  Google Scholar 

22.
Johnson, C. R. et al. Climate change cascades: Shifts in oceanography, species’ ranges and subtidal marine community dynamics in eastern Tasmania. J. Exp. Mar. Biol. Ecol. 400, 17–32. https://doi.org/10.1016/j.jembe.2011.02.032 (2011).
Article  Google Scholar 

23.
Thompson, P. A., Baird, M. E., Ingleton, T. & Doblin, M. A. Long-term changes in temperate Australian coastal waters: Implications for phytoplankton. Mar. Ecol. Prog. Ser. 394, 1–19. https://doi.org/10.3354/meps08297 (2009).
ADS  CAS  Article  Google Scholar 

24.
Last, P. R. et al. Long-term shifts in abundance and distribution of a temperate fish fauna: A response to climate change and fishing practices. Glob. Ecol. Biogeogr. 20, 58–72. https://doi.org/10.1111/j.1466-8238.2010.00575.x (2011).
Article  Google Scholar 

25.
Robinson, L. M. et al. Rapid assessment of short-term datasets in an ocean warming hotspot reveals “high” confidence in potential range extensions. Glob. Environ. Change 31, 28–37 (2015).
Article  Google Scholar 

26.
Frederiksen, M., Edwards, M., Richardson, A. J., Halliday, N. C. & Wanless, S. From plankton to top predators: Bottom-up control of a marine food web across four trophic levels. J. Anim. Ecol. 75, 1259–1268. https://doi.org/10.1111/j.1365-2656.2006.01148.x (2006).
Article  PubMed  Google Scholar 

27.
27Warneke, R. M. & Shaughnessy, P. D. in Studies of Sea Mammals in South Latitudes 53–77 (1985).

28.
McIntosh, R. R. et al. Understanding meta-population trends of the Australian fur seal, with insights for adaptive monitoring. PLoS One 13, e0200253. https://doi.org/10.1371/journal.pone.0200253 (2018).
CAS  Article  PubMed  PubMed Central  Google Scholar 

29.
Gibbens, J. & Arnould, J. P. Y. Interannual variation in pup production and the timing of breeding in benthic foraging Australian fur seals. Mar. Mammal Sci. 25, 573–587. https://doi.org/10.1111/j.1748-7692.2008.00270.x (2009).
Article  Google Scholar 

30.
Kirkwood, R. et al. Continued population recovery by Australian fur seals. Mar. Freshw. Res. 61, 695–701. https://doi.org/10.1071/MF09213 (2010).
CAS  Article  Google Scholar 

31.
Arnould, J. P. Y. & Warneke, R. M. Growth and condition in Australian fur seals (Arctocephalus pusillus doriferus) (Carnivora: Pinnipedia). Aust. J. Zool. https://doi.org/10.1071/zo01077 (2002).
Article  Google Scholar 

32.
Boness, D. J. & Bowen, W. D. The evolution of maternal care in pinnipeds. Bioscience 46, 645–654. https://doi.org/10.2307/1312894 (1996).
Article  Google Scholar 

33.
Arnould, J. P. Y. & Hindell, M. A. Dive behaviour, foraging locations, and maternal-attendance patterns of Australian fur seals (Arctocephalus pusillus doriferus). Can. J. Zool. 79, 35–48. https://doi.org/10.1139/cjz-79-1-35 (2001).
Article  Google Scholar 

34.
Arnould, J. P. Y. & Kirkwood, R. Habitat selection by female Australian fur seals (Arctocephalus pusillus doriferus). Aquat. Conserv. Mar. Freshw. Ecosyst. 17, S53–S67. https://doi.org/10.1002/aqc.908 (2008).
Article  Google Scholar 

35.
Kirkwood, R., Hume, F. & Hindell, M. Sea temperature variations mediate annual changes in the diet of Australian fur seals in Bass Strait. Mar. Ecol. Prog. Ser. 369, 297–309. https://doi.org/10.3354/meps07633 (2008).
ADS  Article  Google Scholar 

36.
Deagle, B. E., Kirkwood, R. & Jarman, S. N. Analysis of Australian fur seal diet by pyrosequencing prey DNA in faeces. Mol. Ecol. 18, 2022–2038. https://doi.org/10.1111/j.1365-294X.2009.04158.x (2009).
CAS  Article  PubMed  Google Scholar 

37.
Gales, R., Pemberton, D., Lu, C. C. & Clarke, M. R. Cephalopod diet of the Australian fur seal: Variation due to location, season and sample type. Mar. Freshw. Res. 44, 657–671. https://doi.org/10.1071/MF9930657 (1993).
Article  Google Scholar 

38.
Gibbs, C. F., Tomczak, M. Jr. & Longmore, A. R. Nutrient regime of Bass Strait. Aust. J. Mar. Freshw. Res. 37, 451–466 (1986).
CAS  Article  Google Scholar 

39.
Sandery, P. A. & Kämpf, J. Transport timescales for identifying seasonal variation in Bass Strait, south-eastern Australia. Estuar. Coast. Shelf Sci. 74, 684–696. https://doi.org/10.1016/j.ecss.2007.05.011 (2007).
ADS  Article  Google Scholar 

40.
Sandery, P. A. & Kämpf, J. Winter-Spring flushing of Bass Strait, South-Eastern Australia: A numerical modelling study. Estuar. Coast. Shelf Sci. 63, 23–31. https://doi.org/10.1016/j.ecss.2004.10.009 (2005).
ADS  Article  Google Scholar 

41.
Costa, D. P. & Gales, N. J. Energetics of a benthic diver: Seasonal foraging ecology of the Australian sea lion, Neophoca cinerea. Ecol. Monogr. 73, 27–43 (2003).
Article  Google Scholar 

42.
Costa, D. P., Kuhn, C. E., Weise, M. J., Shaffer, S. A. & Arnould, J. P. Y. When does physiology limit the foraging behaviour of freely diving mammals?. Int. Congr. Ser. 1275, 359–366. https://doi.org/10.1016/j.ics.2004.08.058 (2004).
Article  Google Scholar 

43.
Arnould, J. P. Y. & Costa, D. Sea Lions of the World: Conservation and Research in the 21st Century 309–323 (Fairbanks, Alaska, 2006).
Google Scholar 

44.
Welsford, D. C. & Lyle, J. M. Redbait (Emmelichthys nitidus): A Synopsis of Fishery and Biological Data (Tasmanian Aquaculture and Fisheries Institute, Marine Research Laboratories, Hobart, 2003).
Google Scholar 

45.
Smith-Vaniz, W. F. et al. Trachurus declivis. Report No. e.T20437665A67871520 (2018).

46.
Gaughan, D., Di Dario, F. & Hata, H. Sardinops sagax. Report No. e.T183347A143831586 (2018).

47.
Hume, F., Hindell, M. A., Pemberton, D. & Gales, R. Spatial and temporal variation in the diet of a high trophic level predator, the Australian fur seal (Arctocephalus pusillus doriferus). Mar. Biol. 144, 407–415. https://doi.org/10.1007/s00227-003-1219-0 (2004).
Article  Google Scholar 

48.
Gibbens, J. & Arnould, J. P. Y. Age-specific growth, survival, and population dynamics of female Australian fur seals. Can. J. Zool. 87, 902–911 (2009).
Article  Google Scholar 

49.
Hoskins, A. J. & Arnould, J. P. Y. Relationship between long-term environmental fluctuations and diving effort of female Australian fur seals. Mar. Ecol. Prog. Ser. 511, 285–295. https://doi.org/10.3354/meps10935 (2014).
ADS  Article  Google Scholar 

50.
diveMove. R package version 1.4.5 (2019).

51.
R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, Austria, 2019).

52.
Hoskins, A. J., Costa, D. P., Wheatley, K. E., Gibbens, J. R. & Arnould, J. P. Y. Influence of intrinsic variation on foraging behaviour of adult female Australian fur seals. Mar. Ecol. Prog. Ser. 526, 227–239 (2015).
ADS  Article  Google Scholar 

53.
Hoskins, A. J. & Arnould, J. P. Y. Temporal allocation of foraging effort in female Australian fur seals (Arctocephalus pusillus doriferus). PLoS One 8, e79484. https://doi.org/10.1371/journal.pone.0079484 (2013).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

54.
Costa, D. P. & Gales, N. J. Foraging energetics and diving behavior of lactating New Zealand sea lions, Phocarctos hookeri. J. Exp. Biol. 203, 3655–3665 (2000).
CAS  PubMed  Google Scholar 

55.
Volpov, B. L. et al. Dive characteristics can predict foraging success in Australian fur seals (Arctocephalus pusillus doriferus) as validated by animal-borne video. Biol. Open 5, 262–271. https://doi.org/10.1242/bio.016659 (2016).
Article  PubMed  PubMed Central  Google Scholar 

56.
Nel, D. C. et al. Exploitation of mesoscale oceanographic features by grey-headed albatross Thalassarche chrysostoma in the southern Indian Ocean. Mar. Ecol. Prog. Ser. 217, 15–26. https://doi.org/10.3354/meps217015 (2001).
ADS  Article  Google Scholar 

57.
Gibbs, C. F. Oceanography of Bass Strait: Implications for the food supply of little penguins Eudyptula minor. Emu Aust. Ornithol. 91, 395–401. https://doi.org/10.1071/MU9910395 (1991).
Article  Google Scholar 

58.
Nieblas, A. E., Sloyan, B. M., Hobday, A. J., Coleman, R. & Richardsone, A. J. Variability of biological production in low wind-forced regional upwelling systems: A case study off southeastern Australia. Limnol. Oceanogr. 54, 1548–1558. https://doi.org/10.4319/lo.2009.54.5.1548 (2009).
ADS  Article  Google Scholar 

59.
Hoskins, A. J., Costa, D. P. & Arnould, J. P. Y. Utilisation of intensive foraging zones by female Australian fur seals. PLoS One 10, 1–19. https://doi.org/10.1371/journal.pone.0117997 (2015).
CAS  Article  Google Scholar 

60.
Beggs, H. et al. RAMSSA—an operational, high-resolution, Regional Australian Multi-Sensor Sea surface temperature analysis over the Australian region. Aust. Meteorol. Oceanogr. J. 61, 1–22. https://doi.org/10.22499/2.6101.001 (2011).
Article  Google Scholar 

61.
NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group. Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Chlorophyll Data (NASA OB.DAAC, Greenbelt, MD, USA, 2018 Reprocessing). https://doi.org/10.5067/ORBVIEW-2/SEAWIFS/L3M/CHL/2018.

62.
NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group. Moderate-resolution Imaging Spectroradiometer (MODIS) Aqua Chlorophyll Data (NASA OB.DAAC, Greenbelt, MD, USA, 2018 Reprocessing). https://doi.org/10.5067/AQUA/MODIS/L3M/CHL/2018.

63.
Hobday, A. J. et al. A hierarchical approach to defining marine heatwaves. Prog. Oceanogr. 141, 227–238. https://doi.org/10.1016/j.pocean.2015.12.014 (2016).
ADS  Article  Google Scholar 

64.
Saji, N. H., Goswami, B. N., Vinayachandran, P. N. & Yamagata, T. A dipole mode in the tropical Indian Ocean. Nature 401, 360–363. https://doi.org/10.1038/43854 (1999).
ADS  CAS  Article  PubMed  Google Scholar 

65.
Saji, N. H. & Yamagata, T. Possible impacts of Indian Ocean dipole mode events on global climate. Clim. Res. 25, 151–169. https://doi.org/10.3354/cr025151 (2003).
Article  Google Scholar 

66.
Neira, F. J., Lyle, J. M., Ewing, G. P., Keane, J. P. & Tracey, S. R. Evaluation of Egg Production as a Method of Estimating Spawning Biomass of Redbait off the East Coast of Tasmania (Institute for Marine, Tasmania, 2008).
Google Scholar 

67.
Kemp, J., Jenkins, G. P. & Swearer, S. E. The reproductive strategy of red cod, Pseudophycis bachus, a key prey species for high trophic-level predators. Fish. Res. 125, 161–172. https://doi.org/10.1016/j.fishres.2012.02.021 (2012).
Article  Google Scholar 

68.
Zuur, A., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14. https://doi.org/10.1111/j.2041-210X.2009.00001.x (2010).
Article  Google Scholar 

69.
Zuur, A., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer, Berlin, 2009).
Google Scholar 

70.
nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1–140 (2019).

71.
Wood, S. N. Generalized Additive Models: An Introduction with R (Chapman and Hall, London, 2017).
Google Scholar 

72.
Wood, S. N. Thin-plate regression splines. J. R. Stat. Soc. (B) 65, 95–114 (2003).
MathSciNet  Article  Google Scholar 

73.
Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. (B) 73, 3–36 (2011).
MathSciNet  Article  Google Scholar 

74.
MuMIn: Multi-Model Inference. R package version 1.43.6 (2019).

75.
Burnham, K. & Anderson, D. Model Selection and Multi-model Inference. 2nd (2002).

76.
Smale, D. A. et al. Marine heatwaves threaten global biodiversity and the provision of ecosystem services. Nat. Clim. Change 9, 306–312. https://doi.org/10.1038/s41558-019-0412-1 (2019).
ADS  Article  Google Scholar 

77.
Babcock, R. C. et al. Severe continental-scale impacts of climate change are happening now: Extreme climate events impact marine habitat forming communities along 45% of Australia’s coast. Front. Mar. Sci. 6, 411. https://doi.org/10.3389/fmars.2019.00411 (2019).
Article  Google Scholar 

78.
Jones, T. et al. Massive mortality of a planktivorous seabird in response to a marine heatwave. Geophys. Res. Lett. 45, 3193–3202. https://doi.org/10.1002/2017GL076164 (2018).
ADS  Article  Google Scholar 

79.
Willis-Norton, E. et al. Climate change impacts on leatherback turtle pelagic habitat in the Southeast Pacific. Deep Sea Res. Part II Top. Stud. Oceanography 113, 260–267. https://doi.org/10.1016/j.dsr2.2013.12.019 (2015).
ADS  Article  Google Scholar 

80.
Merrifield, M. A., Thompson, P. R. & Lander, M. Multidecadal sea level anomalies and trends in the western tropical Pacific. Geophys. Res. Lett. https://doi.org/10.1029/2012GL052032 (2012).
Article  Google Scholar 

81.
Kliska, K. Environmental Correlates of Temporal Variation in the Diet of Australian fur Seals. Master of Research thesis, Macquarie University (2015).

82.
Tosh, C. A. et al. The importance of seasonal sea surface height anomalies for foraging juvenile southern elephant seals. Mar. Biol. 162, 2131–2140. https://doi.org/10.1007/s00227-015-2743-4 (2015).
CAS  Article  Google Scholar 

83.
Foo, D., Hindell, M., McMahon, C. R. & Goldsworthy, S. D. Identifying foraging habitats of adult female long-nosed fur seal Arctocephalus forsteri based on vibrissa stable isotopes. Mar. Ecol. Prog. Ser. 628, 223–234. https://doi.org/10.3354/meps13113 (2019).
ADS  CAS  Article  Google Scholar 

84.
Lovenduski, N. S. Impact of the southern annular mode on Southern Ocean circulation and biology. Geophys. Res. Lett. https://doi.org/10.1029/2005gl022727 (2005).
Article  Google Scholar 

85.
Middleton, J. F. et al. El Niño effects and upwelling off South Australia. J. Phys. Oceanogr. 37, 2458–2477. https://doi.org/10.1175/jpo3119.1 (2007).
ADS  Article  Google Scholar 

86.
Armbrecht, L. H. et al. Phytoplankton composition under contrasting oceanographic conditions: Upwelling and downwelling (Eastern Australia). Cont. Shelf Res. 75, 54–67. https://doi.org/10.1016/j.csr.2013.11.024 (2014).
ADS  Article  Google Scholar 

87.
Falkowski, P. & Kiefer, D. A. Chlorophyll a fluorescence in phytoplankton: Relationship to photosynthesis and biomass. J. Plankton Res. 7, 715–731. https://doi.org/10.1093/plankt/7.5.715 (1985).
CAS  Article  Google Scholar 

88.
Lanz, E., Nevarez-Martinez, M., López-Martínez, J. & Dworak, J. A. Small pelagic fish catches in the Gulf of California associated with sea surface temperature and chlorophyll. CalCOFI Rep. 20, 134–146 (2009).
Google Scholar 

89.
Ronconi, R. A. & Burger, A. E. Limited foraging flexibility: Increased foraging effort by a marine predator does not buffer against scarce prey. Mar. Ecol. Prog. Ser. 366, 245–258. https://doi.org/10.3354/meps07529 (2008).
ADS  Article  Google Scholar 

90.
Kernaleguen, L. et al. From video recordings to whisker stable isotopes: A critical evaluation of timescale in assessing individual foraging specialisation in Australian fur seals. Oecologia 180, 657–670. https://doi.org/10.1007/s00442-015-3407-2 (2016).
ADS  Article  PubMed  Google Scholar 

91.
Meyers, N. The Cost of a Meal: Foraging Ecology of Female Australian fur Seals. Master of Science in Marine Biological Resources (IMBRSea) thesis, Deakin University (2019).

92.
Cai, W., Cowan, T. & Sullivan, A. Recent unprecedented skewness towards positive Indian Ocean Dipole occurrences and its impact on Australian rainfall. Geophys. Res. Lett. https://doi.org/10.1029/2009gl037604 (2009).
Article  Google Scholar 

93.
Sparling, C. E., Georges, J. Y., Gallon, S. L., Fedak, M. A. & Thompson, D. How long does a dive last? Foraging decisions by breath-hold divers in a patchy environment: A test of a simple model. Anim. Behav. 74, 207–218. https://doi.org/10.1016/j.anbehav.2006.06.022 (2007).
Article  Google Scholar 

94.
Gutiérrez, M., Castillo, R., Segura, M., Peraltilla, S. & Flores, M. Trends in spatio-temporal distribution of Peruvian anchovy and other small pelagic fish biomass from 1966–2009. Latin Am. J. Aquat. Res. 40, 633–648. https://doi.org/10.3856/vol40-issue3-fulltext-12 (2012).
Article  Google Scholar 

95.
Crocker, D., Costa, D. P., Le Boeuf, B. J., Webb, P. M. & Houser, D. S. Impact of El Niño on the foraging behavior of female northern elephant seals. Mar. Ecol. Prog. Ser. 309, 1–10. https://doi.org/10.3354/meps309001 (2006).
ADS  Article  Google Scholar 

96.
Gillett, N. P., Kell, T. D. & Jones, P. D. Regional climate impacts of the Southern Annular Mode. Geophys. Res. Lett. https://doi.org/10.1029/2006gl027721 (2006).
Article  Google Scholar 

97.
Costa, D. P. et al. Approaches to studying climatic change and its role on the habitat selection of antarctic pinnipeds. Integr. Comp. Biol. 50, 1018–1030. https://doi.org/10.1093/icb/icq054 (2010).
Article  PubMed  Google Scholar 

98.
Tommasi, D. et al. Managing living marine resources in a dynamic environment: The role of seasonal to decadal climate forecasts. Prog. Oceanogr. 152, 15–49. https://doi.org/10.1016/j.pocean.2016.12.011 (2017).
ADS  Article  Google Scholar 

99.
Schumann, N., Gales, N. J., Harcourt, R. G. & Arnould, J. P. Y. Impacts of climate change on Australian marine mammals. Aust. J. Zool. https://doi.org/10.1071/zo12131 (2013).
Article  Google Scholar 

100.
Evans, P. G. & Bjørge, A. Impacts of climate change on marine mammals. MCCIP Sci. Rev. https://doi.org/10.14465/2013.arc15.134-148 (2013).
Article  Google Scholar 

101.
Cansse, T., Fauchet, L., Wells, M. R. & Arnould, J. P. Y. Factors influencing prey capture success and profitability in Australasian gannets (Morus serrator). Biol. Open. https://doi.org/10.1242/bio.047514 (2020).
Article  PubMed  PubMed Central  Google Scholar 

102.
Kowalczyk, N. D., Reina, R. D., Preston, T. J. & Chiaradia, A. Environmental variability drives shifts in the foraging behaviour and reproductive success of an inshore seabird. Oecologia 178, 967–979. https://doi.org/10.1007/s00442-015-3294-6 (2015).
ADS  Article  PubMed  Google Scholar 

103.
Hindell, M. A. et al. Circumpolar habitat use in the southern elephant seal: Implications for foraging success and population trajectories. Ecosphere 7, e01213 (2016).
Article  Google Scholar 

104.
Gong, T., Feldstein, S. B. & Luo, D. The impact of ENSO on wave breaking and southern annular mode events. J. Atmos. Sci. 67, 2854–2870. https://doi.org/10.1175/2010jas3311.1 (2010).
ADS  Article  Google Scholar 

105.
Luo, J. et al. Interaction between El Niño and extreme Indian Ocean Dipole. J. Clim. 23, 726–742. https://doi.org/10.1175/2009JCLI3104.1 (2010).
ADS  Article  Google Scholar 

106.
Chambers, L. E. et al. Determining trends and environmental drivers from long-term marine mammal and seabird data: Examples from Southern Australia. Reg. Environ. Change 15, 197–209. https://doi.org/10.1007/s10113-014-0634-8 (2014).
Article  Google Scholar 

107.
Goldsworthy, S. D. et al. Trophodynamics of the eastern Great Australian Bight ecosystem: Ecological change associated with the growth of Australia’s largest fishery. Ecol. Model. 255, 38–57. https://doi.org/10.1016/j.ecolmodel.2013.01.006 (2013).
Article  Google Scholar 

108.
Watson, R. A. et al. Ecosystem model of Tasmanian waters explores impacts of climate-change induced changes in primary productivity. Ecol. Model. 264, 115–129. https://doi.org/10.1016/j.ecolmodel.2012.05.008 (2013).
Article  Google Scholar 

109.
Grose, M., Timbal, B., Wilson, L., Bathols, J. & Kent, D. The subtropical ridge in CMIP5 models, and implications for projections of rainfall in southeast Australia. Aust. Meteorol. Oceanogr. J. 65, 90–106 (2015).
Article  Google Scholar 

110.
Pante, E. & Simon-Bouhet, B. marmap: A Package for importing, plotting and analyzing bathymetric and topographic data in R. PLoS One 8(9), e73051. https://doi.org/10.1371/journal.pone.0073051 (2013).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

111.
Kelley, D. & Richards, C. oce: Analysis of Oceanographic Data. R package version 1.1-1. https://CRAN.R-project.org/package=oce (2019).

112.
Kelley, D. ocedata: Oceanographic Data Sets for ‘oce’ Package. R package version 0.1.5. https://CRAN.R-project.org/package=ocedata (2018).

113.
Adobe Inc. Adobe Illustrator. https://adobe.com/products/illustrator. (2019). More

1.
Kwon, E. Y., Primeau, F. & Sarmiento, J. L. The impact of remineralization depth on the air–sea carbon balance. Nat. Geosci. 2, 630–635 (2009).
CAS  Article  Google Scholar 
2.
Volk, T. & Hoffert, M. I. in The Carbon Cycle and Atmospheric CO2: Natural Variations Archean to Present Vol. 32 (eds Sundquist, E. T. & Broeker, W. S.) 99–110 (American Geophysical Union, 1985).

3.
Passow, U. & Carlson, C. A. The biological pump in a high CO2 world. Mar. Ecol. Prog. Ser. 470, 249–271 (2012).
CAS  Article  Google Scholar 

4.
Martiny, A. C. et al. Strong latitudinal patterns in the elemental ratios of marine plankton and organic matter. Nat. Geosci. 6, 279–283 (2013).
CAS  Article  Google Scholar 

5.
DeVries, T. New directions for ocean nutrients. Nat. Geosci. 11, 15–16 (2018).
CAS  Article  Google Scholar 

6.
Redfield, A. in James Johnstone Memorial Volume (ed. Daniel, R. J.) 177–192 (University Press of Liverpool, 1934).

7.
Kroeker, K. J., Kordas, R. L., Crim, R. N. & Singh, G. G. Meta-analysis reveals negative yet variable effects of ocean acidification on marine organisms. Ecol. Lett. 13, 1419–1434 (2010).
Article  Google Scholar 

8.
Riebesell, U. & Tortell, P. D. in Ocean Acidification (eds Gattuso, J. P. & Hansson, L.) 99–121 (Oxford Univ. Press, 2011).

9.
Boyd, P. W. & Newton, P. P. Does planktonic community structure determine downward particulate organic carbon flux in different oceanic provinces? Deep Sea Res. I 46, 63–91 (1999).
CAS  Article  Google Scholar 

10.
Stange, P. et al. Ocean acidification-induced restructuring of the plankton food web can influence the degradation of sinking particles. Front. Mar. Sci. https://doi.org/10.3389/fmars.2018.00140 (2018).

11.
Engel, A. et al. Impact of CO2 enrichment on organic matter dynamics during nutrient induced coastal phytoplankton blooms. J. Plankton Res. 36, 641–657 (2014).
CAS  Article  Google Scholar 

12.
Riebesell, U. et al. Technical note: a mobile sea-going mesocosm system—new opportunities for ocean change research. Biogeosciences 10, 1835–1847 (2013).
Article  Google Scholar 

13.
Sswat, M. et al. Food web changes under ocean acidification promote herring larvae survival. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-018-0514-6 (2018).

14.
Riebesell, U. et al. Enhanced biological carbon consumption in a high CO2 ocean. Nature 450, 545–548 (2007).
CAS  Article  Google Scholar 

15.
Finkel, Z. V. et al. Phytoplankton in a changing world: cell size and elemental stoichiometry. J. Plankton Res. 32, 119–137 (2010).
CAS  Article  Google Scholar 

16.
van de Waal, D. B., Verschoor, A. M., Verspagen, J. M. H., van Donk, E. & Huisman, J. Climate-driven changes in the ecological stoichiometry of aquatic ecosystems. Front. Ecol. Environ. 8, 145–152 (2010).
Article  Google Scholar 

17.
Tagliabue, A., Bopp, L. & Gehlen, M. The response of marine carbon and nutrient cycles to ocean acidification: large uncertainties related to phytoplankton physiological assumptions. Glob. Biogeochem. Cycle https://doi.org/10.1029/2010gb003929 (2011).

18.
Thomas, H., Ittekkot, V., Osterroht, C. & Schneider, B. Preferential recycling of nutrients—the ocean’s way to increase new production and to pass nutrient limitation? Limnol. Oceanogr. 44, 1999–2004 (1999).
CAS  Article  Google Scholar 

19.
Schneider, B., Schlitzer, R., Fischer, G. & Nothig, E. M. Depth-dependent elemental compositions of particulate organic matter (POM) in the ocean. Glob. Biogeochem. Cycle https://doi.org/10.1029/2002gb001871 (2003).

20.
Cripps, G., Flynn, K. J. & Lindeque, P. K. Ocean acidification affects the phyto-zoo plankton trophic transfer efficiency. PLoS ONE https://doi.org/10.1371/journal.pone.0151739 (2016).

21.
Thor, P. & Oliva, E. O. Ocean acidification elicits different energetic responses in an Arctic and a boreal population of the copepod Pseudocalanus acuspes. Mar. Biol. 162, 799–807 (2015).
CAS  Article  Google Scholar 

22.
Endres, S., Galgani, L., Riebesell, U., Schulz, K. G. & Engel, A. Stimulated bacterial growth under elevated (p_{{rm{CO}}_{2}}): results from an off-shore mesocosm study. PLoS ONE 9, e99228 (2014).
Article  Google Scholar 

23.
Piontek, J. et al. Response of bacterioplankton activity in an Arctic fjord system to elevated (p_{{rm{CO}}_{2}}): results from a mesocosm perturbation study. Biogeosciences 10, 297–314 (2013).
Article  Google Scholar 

24.
Bopp, L. et al. Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models. Biogeosciences 10, 6225–6245 (2013).
Article  Google Scholar 

25.
Taucher, J. & Oschlies, A. Can we predict the direction of marine primary production change under global warming? Geophys. Res. Lett. https://doi.org/10.1029/2010gl045934 (2011).

26.
Schneider, B., Engel, A. & Schlitzer, R. Effects of depth- and CO2-dependent C:N ratios of particulate organic matter (POM) on the marine carbon cycle. Glob. Biogeochem. Cycle https://doi.org/10.1029/2003gb002184 (2004).

27.
Oschlies, A., Schulz, K. G., Riebesell, U. & Schmittner, A. Simulated 21st century’s increase in oceanic suboxia by CO2-enhanced biotic carbon export. Glob. Biogeochem. Cycle https://doi.org/10.1029/2007gb003147 (2008).

28.
Broecker, W. S. & Henderson, G. M. The sequence of events surrounding Termination II and their implications for the cause of glacial–interglacial CO2 changes. Paleoceanography 13, 352–364 (1998).
Article  Google Scholar 

29.
Broecker, W. S. Ocean chemistry during glacial time. Geochim. Cosmochim. Acta 46, 1689–1705 (1982).
CAS  Article  Google Scholar 

30.
Boxhammer, T., Bach, L. T., Czerny, J. & Riebesell, U. Technical note: sampling and processing of mesocosm sediment trap material for quantitative biogeochemical analysis. Biogeosciences 13, 2849–2858 (2016).
Article  Google Scholar 

31.
Sharp, J. H. Improved analysis for “particulate” organic carbon and nitrogen from seawater. Limnol. Oceanogr. 19, 984–989 (1974).
CAS  Article  Google Scholar 

32.
IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).

33.
Hedges, L. V., Gurevitch, J. & Curtis, P. S. The meta-analysis of response ratios in experimental ecology. Ecology 80, 1150–1156 (1999).
Article  Google Scholar 

34.
Schartau, M., Landry, M. R. & Armstrong, R. A. Density estimation of plankton size spectra: a reanalysis of IronEx II data. J. Plankton Res. 32, 1167–1184 (2010).
Article  Google Scholar 

35.
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).
CAS  Article  Google Scholar 

36.
Utermöhl, V. H. Neue Wege in der quantitativen Erfassung des Planktons. (Mit besondere Beriicksichtigung des Ultraplanktons). Verh. Internat. Verein. Theor. Angew. Limnol. 5, 567–595 (1931).
Google Scholar 

37.
Bach, L. T., Riebesell, U. & Schulz, K. G. Distinguishing between the effects of ocean acidification and ocean carbonation in the coccolithophore Emiliania huxleyi. Limnol. Oceanogr. 56, 2040–2050 (2011).
CAS  Article  Google Scholar 

38.
Stange et al. Quantifying the time lag between organic matter production and export in the surface ocean: implications for estimates of export efficiency. Geophys. Res. Lett. 44, 268–276 (2017).
CAS  Article  Google Scholar  More

A time-resolved meta-omics dataset
To characterize the niche space of lipid-accumulating populations as well as resistance and resilience of the microbial community, we sampled a municipal BWWTP weekly over a 14-months period (from 2011-03-21 to 2012-05-03). Additionally, two preliminary time-points outside of the time-series were included13,26. Samples were split into intracellular and extracellular fractions, followed by concomitant biomolecular extractions27 and high-throughput measurements (Fig. 1). MG, MT, and MP data were obtained on the intracellular fractions and MM data was generated on both the intracellular and extracellular fractions.
Fig. 1: Overview of the study design.

Samples are derived from in situ sampling of an anoxic tank of a municipal biological wastewater treatment plant. Metagenomic (MG), metatranscriptomic (MT), metaproteomic (MP), and meta-metabolomic (MM) data is generated. Physicochemical data is also collected. Additionally, MG and MT data is generated for ex situ experiments using biomass from the same system and fed with oleic acid under different oxic conditions to evaluate short-term responses to pulse disturbance. The time-series meta-omics data is integrated to define metagenome-assembled genomes (MAGs) over all time points. Representative MAGs (rMAGs) across time are selected for further analysis. The rMAGs’ functional potential is used to infer the fundamental niches. Abundance and activity data are derived from the functional omics and substrate usage is inferred per population. The variability of gene expression is used to assess the phenotypic plasticity of the individual populations.

Full size image

After quality filtering, the per-sample averages of MG and MT reads were 5.3 × 107 (±7.7 × 106 s.d.) and 3.3 × 107 reads (±1.2 × 107 s.d.), respectively (Supplementary Data 1). We performed sample-specific genome assemblies (average of 4.1 × 105 contigs per sample) followed by binning28 yielding a total of 1364 MAGs passing our quality filtering criteria (see “Methods” section). To track the abundance, gene expression, and activity of individual microbial populations over time, we dereplicated29 the MAGs across samples to generate 220 representative MAGs (rMAGs). From these, we further selected those with the highest completeness resulting in 78 rMAGs (76.2% mean completeness, 2.2% mean contamination) (Supplementary Data 2). These genomes represent the major populations across the time-series, with an average mapping percentage of 26% ± 3% (s.d.) and 27% ± 3% (s.d.) of total MG reads and total MT reads per time-point, respectively, and are corroborated by a previous study based on 16S rRNA amplicon sequencing13. For the MP measurements, we obtained a per-sample average of 1.5 × 105 ± 8.2 × 103 (s.d.) MS2 spectra and a total of 7.6 × 106 MS2 spectra. Of 7.8 × 105 identified peptides, 3.3 × 105 (43%) could be matched to 2.1 × 105 predicted coding sequences of the 78 rMAGS. Per time-point, on average 1.5 × 104 ± 4.5 × 103 (s.d.) spectral matches, i.e., on average 94% of all rMAG-associated matches could be assigned to genes with predicted functions, i.e., assigned KEGG ortholog groups (KOs). To study the community-wide resource space and metabolite turnover, we measured metabolite levels by an untargeted approach using gas chromatography (GC) coupled with mass spectrometry (MS) (Supplementary Data 3). In total, 89% (58 of 65) of the identified metabolites could be linked to enzymes encoded by the rMAGs. We estimated resource uptake by calculating intracellular vs. extracellular metabolite ratios for 42 metabolites detected in both fractions (Supplementary Data 3). Additionally, six abiotic parameters were measured during sampling, as well as 34 parameters recorded continuously as part of the BWWTP online monitoring (Supplementary Data 4).
We also generated MG and MT data for the ex situ experiments. These simulated the fluctuating conditions within the BWWTP, namely the short-term response to pulse disturbances of oleic acid influx under shifting dissolved oxygen conditions. We sequenced DNA and RNA fractions obtained at 0, 5, and 8 h after addition of oleic acid, yielding on average 1.02 × 108 MG and 9.33 × 107 MT reads per sample. The increased sequencing depth compared to the in situ time-series was important to obtain a fine-grained view on short-term responses to oleic acid. Mapping of the sequencing reads to the selected set of rMAGs revealed mapping percentages comparable to the in situ time-series (mean: 21% ± s.d.: 1% for both MG reads and MT reads).
Overall, our meta-omics dataset comprehensively describes mixed microbial communities underlying lipid-accumulation processes in BWWTPs, and in particular their functional potential, composition, activity, as well as substrate availability and assimilation.
Distinct niche types
To resolve the fundamental niches of the pertinent bacterial populations through their functional genomic potential, we assigned KOs to the rMAGs’ predicted coding sequences. We hypothesized that individual populations would form clusters based on the similarity/dissimilarity of their functional potential. We found four distinct clusters of rMAGs by projecting pairwise Jaccard distances of KO presence (Fig. 2a and Supplementary Fig. 1). These functional clusters (FunCs) represent differences of known, overall metabolic capabilities of the rMAGs and reflect their fundamental niches. FunC-1 consisted of Actinobacteria, and FunC-2 was primarily comprised of members of the Bacteroidetes phylum, mainly of the Sphingobacteriia class (Fig. 2a). FunC-3 contained Betaproteobacteria and Gammaproteobacteria whereas FunC-4 appeared more diverse, containing Spirochaetia as a subcluster, Deltaproteobacteria, and taxonomically unclassified rMAGs. We found mash-based genomic distance30 to be strongly linked to FunC assignment (PROCRUSTES sum of squares: 0.399, correlation 0.775, PROTEST p-value 0.001, Supplementary Fig. 2a), highlighting that phylogeny is a strong determinant for FunC assignment. However, some distantly linked subgroups were defined by their shared functional complement, i.e., assigned to a different FunC than their neighbors in a corresponding phylogenetic tree (Supplementary Fig. 2b). This shows that KO profile similarity-based analyses provide important information in addition to phylogeny-based approaches31.
Fig. 2: Fundamental niche types.

a Multidimensional scaling (MDS) of Jaccard distances for the functional repertoire (presence of KEGG ortholog groups [KOs]) for each rMAG. Ellipses containing 95% (inner) or 99% (outer) of cluster-assigned data points are shown resulting in four distinct functional clusters (FunCs). Colors indicate the class-level taxonomy of the rMAGs. b Numbers of shared and unique KO assignments between the FunCs. Colored bars show the total number of nonredundant KO assignments within the individual FunCs. Overlaps between different sets of FunCs and their unique KOs are represented by the central black bars with the points below defining the members of the respective sets. c Presence of key functions within the four FunCs. Bars next to metabolic conversions show the proportion of rMAGs encoding characteristic enzymes for the respective reaction or pathway adjusted for mean rMAG completeness. Pathways ubiquitously present across rMAGs are shown in gray color. Source data are provided as a Source Data file. red. reductase, GLN syn. glutamine synthetase, GLU dhg. glutmatate dehydrogenase, glyox. cyc. glyoxylate cycle, ethylm.-CoA ethylmalonyl-CoA pathway, PHA depolym. PHA depolymerase.

Full size image

A total of 1857 KOs was shared between all FunCs and we found that FunCs 1, 3, and 4 contained comparable numbers of nonredundant KOs with 4276, 4177, and 4129 KOs, respectively (Fig. 2b). FunC-2 exhibited a reduced number of KOs (3550), however it also represented the least taxonomically diverse FunC as it almost exclusively consisted of Haliscomenobacter spp. and Chitinophaga spp. (Supplementary Data 2). We tested for the molecular functions that were significantly enriched in individual FunCs and found, among others, functions related to lipid metabolism for FunC-1, amino sugar, and nucleotide sugar metabolism for FunC-2, and biofilm and secretion systems for FunC-3 to be enriched (Fig. 2c and Supplementary Data 5; one-sided Fisher′s exact test, adjusted p-values < 0.05). While lipid-accumulating organisms hold great potential for the recovery of high-value molecules5, interactions between these organisms as well as the community at large are understudied in situ. We found that diacylglycerol O-acyltransferase (DGAT/WS), which is involved in lipid storage32, was encoded in 23 out of 24 rMAGs of FunC-1, pointing to the importance of TAG accumulation in this cluster. Most FunC-3 members also encoded DGAT/WS (14 of 19). Moreover, PHA synthase was enriched in this cluster (15 of 19). All rMAGs encoded lipases, functions involved in fatty acid synthesis, or beta-oxidation. However, several acyl-CoA and acyl-ACP dehydrogenases were overrepresented in FunC-1 and FunC-3. Additionally, acetyl-CoA acetyltransferases involved in the degradation and biosynthesis of fatty acids were prevalent throughout all FunCs. The enrichment in FunC-1 and FunC-3 for genes involved in lipid accumulation are consistent with previous metabolic characterizations, with FunC-1 consisting mainly of Actinobacteria for which TAG accumulation has been described33. FunC-3 contains Betaproteobacteria and Gammaproteobacteria that have been characterized as TAG, WE, and/or PHA accumulators, e.g., Thauera spp., Albidiferax spp., or Acinetobacter spp.33,34. Importantly, we observed a difference between these FunCs in the utilization of acetyl-CoA. Specifically, FunC-1 members showed an enrichment in functions related to the ethylmalonyl-CoA pathway (crotonyl-CoA reductase and enoyl ACP reductase), while FunC-3 members encoded key enzymes involved in the glyoxylate cycle (malate synthase and isocitrate lyase). We further determined specific functional enrichment for the four FunCs in relation to the breakdown of other macromolecules (including CAZymes and proteases), nitrogen cycling, stress response, and motility (Supplementary Data 5). The discriminating functions point towards interdependencies between the different FunCs, e.g., in terms of denitrification (Fig. 2c). We found that the separation into taxonomically consistent groups is accompanied by specific conserved functions, e.g., strong enrichment in FunC-1 for WhiB transcriptional regulators characteristic of the Actinobacteria35. Overall, we observed a widely distributed set of core functions in foaming sludge microbiomes and identified groups of populations characterized by distinct functional potential in lipid metabolism, amino sugar, and nucleotide sugar metabolism as well as biofilm and secretion systems. Community dynamics and stability To understand whether population dynamics can be related to substrate availability and other abiotic factors36, we used MG depth-of-coverage to infer rMAG population abundance across the time-series. We computed distances between the rMAGs’ abundance profiles (based on their pairwise correlations) and found that the dynamics of rMAGs can be partially explained by the FunC assignment (PERMANOVA R2 = 0.12, Pr > F = 0.002; no significant difference in dispersion; Supplementary Fig. 3), thereby linking FunC membership to temporal abundance shifts. The most abundant taxa (Supplementary Data 2) included Candidatus Microthrix (26.0% relative abundance across the time-series; referred to as Microthrix in the remaining text), Acinetobacter (8.1%), Haliscomenobacter (8.0%), Intrasporangium (7.2%), Leptospira (6.3%), Albidiferax (5.7%), and Dechloromonas (2.4%) (Fig. 3a). Several of the recovered rMAGs belonged to filamentous taxa according to the MiDAS field guide database for organisms in activated sludge37, such as the highly abundant Microthrix, and Haliscomenobacter, as well as the less abundant Anaerolinea (1.1 %) and Gordonia (0.2 %).
Fig. 3: Community structure and function dynamics.

a, b Relative abundance and expression levels of recovered populations represented by rMAGs over time based on MG depth (a) and MT depth (b) of coverage, respectively, representing mapping percentages of MG [26% ± 3% (s.d.)] and MT [27% ± 3% (s.d.)]. The relative abundance of individual rMAGs is grouped based on genus-level taxonomic assignment with rMAGs of unresolved taxonomy grouped in “Other”. Recovered genera with mean abundance below 2% are summarized as a single group (light gray). c, d Ordination of Bray–Curtis dissimilarity of relative abundances, MG (c) and MT (d), of individual rMAGs constrained by selected abiotic factors (metabolite levels, metabolite-ratios, and physico-chemical parameters shown as black arrows with arrow lengths indicating environmental scores as predictors for each factor). Points are colored by month of sampling and point-shape reflects the year of sampling. Thin black lines connecting the points visualize the time course of sampling. Source data are provided as a Source Data file.

Full size image

Variations during the operation of BWWTPs occur largely due to changes in the influent wastewater composition and climatic conditions38. We observed gradual changes in the community structure with the seasons (Fig. 3a). In October 2011 (month seven of the timeseries), the community composition began to shift, with a markedly altered composition in late November 2011. This shift was characterized by spikes in the relative abundance of Leptospira (peak at 2011-11-23) and Acinetobacter (peak at 2011-11-29) (Fig. 3a), and co-occurred with a pronounced shift in substrates (Fig. 4 and Supplementary Fig. 4). The substrates included mainly nonpolar metabolites, including long-chain fatty acids (LCFAs) and glycerides, as well as polar metabolites mannose, glucose, disaccharides, ethanolamine, and putrescine. We found that the intersample distances of MG-based abundances could partially be explained by a subset of the abiotic factors (Fig. 3c). Summer samples were characterized by higher temperatures, phosphate levels and higher intracellular vs. extracellular oleic acid ratios. Higher extracellular mannose levels and a slight increase in conductivity marked the beginning of the autumn shift. During November, intracellular and extracellular levels for LCFAs increased, indicating a higher availability or turnover of LCFAs, but not necessarily an equivalent conversion to neutral storage lipids. In the subsequent winter time-points, substrate levels normalized and the community transitioned back to the predisturbance state.
Fig. 4: Levels of metabolites and physico-chemical parameters.

Z-score transformed metabolite intensities, metabolite ratios, and physico-chemical parameter levels over time are shown. Row annotations highlight classes of metabolites and parameters, measurement types (bnp: intracellular nonpolar metabolites, bp: intracellular polar m., pcparams: physico-chemical parameters, ratio: metabolite intrac./extrac. ratio, snp: extracellular nonpolar m., sp: extracellular polar m.), and the subtype or fraction of the measurement (manual: measured during sampling, online: measured during WWTP operation). Selected rows are shown (comprehensive heatmap shown in Supplementary Fig. 6). Source data are provided as a Source Data file.

Full size image

The dominance of Microthrix was re-established within approximately ten generations, given estimates for in situ growth rates of 0.12–0.3 growth cycles per day7,8. The stability39 of the individual rMAGs was heavily affected by the November shift (mean population stability: 1.43 ± 0.69 s.d.), compared to the stability when excluding the respective time-points (mean population stability: 2.39 ± 1.28 s.d.; 2011-11-02 to 2011-11-29; Supplementary Data 2). The observed population dynamics indicate that the community composition is resilient, i.e., recovers after pronounced changes in available substrates, and resistant to small-scale environment fluctuations over time.
While MG depth was used as a proxy for population abundance, MT depth enabled the analysis of transcriptional activity within the community and of individual populations (Fig. 3b). The comparison of intersample distances based on mean, relative MT depth showed similar patterns to MG-based results (Fig. 3c), albeit with a higher degree of variability indicated by increased inter-sample distances (Fig. 3d). A comparison of relative MP counts showed a more even distribution between populations with comparable overall trends (Supplementary Fig. 5). Samples collected in April 2011 and 2012 appeared to represent transition states between seasons. Additionally, a set of late winter and early spring samples in 2011 and 2012 showed higher similarities at the expression level than at the abundance level. Interestingly, the high abundance of individual genera, such as Microthrix or Chitinophaga was not necessarily reflected in their mean expression levels (Fig. 3b and Supplementary Fig. 5): populations assigned to Leptospira, Haliscomenobacter, Anaerolinea, and Acinetobacter showed higher mean expression overall. Spikes in relative MT depth as for Acinetobacter rMAGs (Fig. 3b; 2011-04-14, 2011-05-08, and 2012-04-25) point towards increased activity around these time-points, which however did not lead to major shifts in community structure. Notably, higher expression levels of Acinetobacter were succeeded by increased expression levels of Haliscomenobacter (2011-04-14 to 2011-05-20) or Anaerolinea (2011-05-08 to 2011-09-19). On average, MT-based stability values were less affected by the community shift than MG-based stability values (Supplementary Table 2). We also observed adaptation of metabolic pathway activity to environmental conditions (Fig. 5). Pentose to EMP pathway intermediates exhibited the highest correlation between MT and MP abundances, followed by Hydrogen metabolism and Fatty acid oxidation. Several pathways exhibited a characteristic drop during the November shift, e.g., hydrogen metabolism, hydrocarbon degradation, and TCA cycle, while fatty acid oxidation showed a marked peak. This highlights the transition from dominance by generalist, lipid-accumulating populations towards a lipolytic community.
Fig. 5: Gene levels over time grouped by functional categories.

Metatranscriptomic and metaproteomic levels (normalized relative expression for MT data and normalized relative spectral counts for MP data) of rMAG-derived genes assigned to FOAM ontology-based functional categories. Pearson correlation coefficients (r) of MT and MP values are shown in the title of each panel. Panels are ordered from highest to lowest mean MP relative count in row-major order. Source data are provided as a Source Data file.

Full size image

With each of the four FunCs comprising multiple organisms encoding similar KOs and, hence, metabolic capabilities, we studied how individual populations adapt to their environment. To this end, we linked changes in community structure and in the expression levels of individual populations to the influence of environmental parameters. While rMAG abundance patterns could be linked to FunC assignment (Supplementary Fig. 3), we could not identify an analogous categorization when correlating rMAG abundances to abiotic factors. Instead, correlation patterns indicating similar preferences to environmental conditions emerged for subgroups of rMAGs across different FunCs (Supplementary Fig. 7). This shows that populations with a similar fundamental niche type responded differently to the environmental conditions pointing towards functional plasticity and, thus, adaptations of their realized niches
Niche characteristics of in situ and ex situ time-series
While we identified four fundamental niche types, it may be assumed that cohabiting species cannot occupy the same realized niches, leading to realized niche segregation within and between types. We hypothesized that different degrees of niche overlap, leading to variable levels of competition, must exist40,41. To better understand the complementarity of realized niches, we used the functional omics data to study how rMAGs overlapped in relation to their encoded genes and how rMAGs varied in their expression profiles. While the former represents competition between populations with overlapping profiles, the latter is an important factor for the adaptability and overall survival strategy of individual populations. We distinguished between expressed KOs and nonexpressed KOs based on MT/MG ratios as well as MP data and computed distances between the resulting time-point-specific expression profiles. While the separation based on the functional potential was preserved in a clustering of expression profiles (in particular for FunC-2), the expression profiles of FunCs-1, FunCs-3, and FunCs-4 overlapped to a greater extent than those of FunC-2 (Fig. 6a). Two Anaerolinea populations assigned to FunC-1 appeared to express similar functions compared to the rMAGs of FunC-3 and FunC-4 and were found in a subgroup of rMAGs that showed a higher overall activity in terms of MT/MG ratios also when clustering expression profiles per time-point (Supplementary Fig. 8). Overall, the clusters based on KO expression status per time-point did not exhibit a separation according to the grouping into FunCs (Supplementary Fig. 8). This indicates a propensity of the respective rMAGs to more frequently express shared KOs than discriminatory KOs and, consequently, increased the competition for specific substrates.
Fig. 6: Realized niches.

a MDS of time-point specific expression profiles based on MT/MG ratios or evidence at the MP level. Colors indicate FunC assignment of the individual rMAGs. Point shape represents cluster assignment based on automated clustering of the embedded points. Ellipses containing 95% of cluster-assigned data points are shown. Points size represents the average MT/MG depth ratios of the individual rMAGs. The amounts of variance explained by the first two dimensions are shown on the respective axes. b Mean MT/MG depth ratios over all time-points are shown per condition for 78 rMAGs (boxplots show: center line, median; box limits, upper and lower quartiles; whiskers, 1.5× interquartile range; Each group of boxplots corresponds to a group of rMAGs (FunC-1 n = 24, FunC-2 n = 23, FunC-3 n = 19, FunC-4 n = 12), each boxplot represents an independent experiment.). c Mean MT/MG depth ratios grouped according to class-level taxonomic assignment of the rMAGs with the number of rMAGs for each group are shown in the top of the plot (n). Source data are provided as a Source Data file.

Full size image

To investigate the importance of individual, discriminatory functions, we selected rMAG clusters, based on gene expression and MP counts, to which the two most abundant rMAGs (D51_G1.1.2, A01_O1.2.4) had been assigned. We observed that clusters into which rMAG D51_G1.1.2 (Microthrix) was consistently categorized showed expression of few KOs with the majority being ribosomal proteins, TCA cycle-related enzymes such as pyruvate, malate, and glyceraldehyde 3-phosphate dehydrogenases, chaperones, and most frequently the WhiB family transcriptional regulator (19 time-points; Supplementary Data 6).
Clusters containing rMAG A01_O1.2.4 (Acinetobacter) frequently exhibited expression of genes related to motility and chemotaxis as well as stress response, but also functions related to phosphate accumulation, such as K08311 and K00937 (Supplementary Data 6). KOs related to lipid metabolism were also frequently expressed in these clusters e.g. acylglycerol lipase (in 35 time-points) or diacylglycerol O-acyltransferase (25 time-points). This indicates that high expression of key functionalities is an integral part of the strategies of the populations within these clusters even though they differed with respect to their encoded functions.
We next studied how the observed distinction between populations with high activity is linked to phenotypic plasticity. As alternating oxygen levels in BWWTPs play an important role in selecting for lipid accumulating populations7,42, we added oleic acid, the preferred carbon source for Microthrix43, in lab-scale experiments under different oxygen fluctuation conditions8 (see “Methods” section; Fig. 1). These ex situ conditions involved aerobic, anoxic, aerobically preconditioned biomass followed by hourly anoxic alternations, and anoxically preconditioned followed by hourly aerobic alternations. The MT/MG ratios for FunC-1 and FunC-3 were higher ex situ when compared to the in situ samples, and vice versa for FunC-2 and FunC-4 rMAGs (Fig. 6b). Furthermore, especially for FunC-3, average MT/MG ratios were highest in the aerobic conditions and lowest in the anaerobic conditions. This is in line with FunC-3 being comprised mainly of Betaproteobacteria and Gammaproteobacteria, which include mostly aerobic genera44. A more fine-grained view on differences in specific activity was obtained, when grouping rMAGs based on taxonomic assignment (Fig. 6c). While rMAGs of the classes Acidimicrobia and Actinobacteria (FunC-1) showed the lowest mean MT/MG ratios across the in situ time-series (0.5), the ratio was twice as high in the ex situ experiments across all conditions which can be attributed to the oleic acid pulse. Betaproteobacteria (FunC-3) behaved similarly, while Gammaproteobacteria (FunC-3) showed a tendency towards higher activity with increased oxygen levels. We observed high activity for rMAGs assigned to Anaerolineae and Spirochaetia in the in situ time-series. Interestingly, this was not the case for Spirochaetia in the ex situ experiments, which points towards the necessity for additional substrates. The Anaerolineae rMAGs, with taxonomically related species being mainly anaerobic45, showed the lowest MT/MG ratio under the alternating conditions, while Deltaproteobacteria rMAGs showed high MT/MG ratios. Overall, the differentiated responses under alternating conditions point to distinct short-term and long-term adaptation strategies.
To study how fast the adaptations in response to the influx of oleic acid occur, we compared the baseline (0 h time-points, before oleic acid addition) against the 5 and 8 h time-points (after oleic acid addition). At 5 h, lipases, involved in TAG hydrolysis, for which high expression in the in situ samples was observed, were downregulated in the ex situ response to the addition of oleic acid (Supplementary Fig. 9a). An increased number of genes related to beta-oxidation were upregulated at 5 h, particularly in rMAGs assigned to FunC-3 (Supplementary Fig. 9b). Similar effects were observed when comparing the 0 h and 8 h time-points (Supplementary Fig. 10a, b). This suggests that responses in gene expression happen within the 5 h timeframe but on distinct time scales for different populations. In-depth analyses of the populations exhibiting the highest expression levels for TAG lipases, DGAT/WS, and PHB synthases (Supplementary Note 1 and Supplementary Figs. 11–14) underline the previously determined role of Microthrix as a key lipid accumulator in BWWTPs13,46. The results also indicate that populations such as Anaerolinea, Leptospira and Acinetobacter overlap with Microthrix in terms of their capacity to assimilate LCFAs and available neutral lipids. Niche complementarity and plasticity, i.e., overlapping fundamental and realized niches, as well as gene expression variability, impart population-independent processing of lipids in situ. From an ecosystem perspective, this community-wide trait confers functional resistance and resilience. More

Mercator Research Institute on Climate Change and Global Commons, Berlin, Germany
Neal R. Haddaway

Stockholm Environment Institute, Stockholm, Sweden
Neal R. Haddaway & Biljana Macura

Africa Centre for Evidence, University of Johannesburg, Johannesburg, South Africa
Neal R. Haddaway

College of Medicine and Health, Exeter University, Exeter, UK
Alison Bethel

Department of Zoology, University of Cambridge, Cambridge, UK
Lynn V. Dicks

School of Biological Sciences, University of East Anglia, Norwich, UK
Lynn V. Dicks

Department of Biological Sciences, Royal Holloway University of London, Egham, UK
Julia Koricheva

Department of Zoology, University of Oxford, Oxford, UK
Gillian Petrokofsky

Collaboration for Environmental Evidence, UK Centre, School of Natural Sciences, Bangor University, Bangor, UK
Andrew S. Pullin

Liljus ltd, London, UK
Sini Savilaakso

Department of Forest Sciences, University of Helsinki, Helsinki, Finland
Sini Savilaakso

Evidence Synthesis Lab, School of Natural and Environmental Sciences, University of Newcastle, Newcastle-upon-Tyne, UK
Gavin B. Stewart More

1.
van Mantgem PJ, Stephenson NL, Byrne JC, Daniels LD, Franklin JF, Fule PZ, et al. Widespread increase of tree mortality rates in the western United States. Science. 2009;323:521–4.
PubMed  Article  CAS  PubMed Central  Google Scholar 
2.
Allen CD, Macalady AK, Chenchouni H, Bachelet D, McDowell N, Vennetier M, et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For Ecol Manag. 2010;259:660–84.
Article  Google Scholar 

3.
Carnicer J, Coll M, Ninyerola M, Pons X, Sanchez G, Penuelas J. Widespread crown condition decline, food web disruption, and amplified tree mortality with increased climate change-type drought. Proc Natl Acad Sci USA. 2011;108:1474–8.
CAS  PubMed  Article  PubMed Central  Google Scholar 

4.
Brown N, Vanguelova E, Parnell S, Broadmeadow S, Denman S. Predisposition of forests to biotic disturbance: predicting the distribution of Acute Oak Decline using environmental factors. For Ecol Manag. 2018;407:145–54.
Article  Google Scholar 

5.
Denman S, Doonan J, Ransom-Jones E, Broberg M, Plummer S, Kirk S, et al. Microbiome and infectivity studies reveal complex polyspecies tree disease in Acute Oak Decline. ISME J. 2017. https://doi.org/10.1038/ismej.2017.170.

6.
Denman S, Barrett G, Kirk SA, McDonald JE, Coetzee MPA. Identification of Armillaria species on oak in Britain: implications for Oak Health. Forestry. 2017;90:148–61.
Article  Google Scholar 

7.
Martınez-Vilalta J, Lloret F, Breshears DD. Drought-induced forest decline: causes, scope and implications. Biol Lett. 2012;8:689–91.
PubMed  Article  PubMed Central  Google Scholar 

8.
McDowell NG, Ryan MG, Zeppel MJB, Tissue DT. Improving our knowledge of drought-induced forest mortality through experiments, observations, and modeling. N. Phytologist. 2013;200:289–93.
Article  Google Scholar 

9.
Thomas FM, Blank R, Hartmann G. Abiotic and biotic factors and their interactions as causes of oak decline in central Europe. Pathol. 2002;32:277–307.
Article  Google Scholar 

10.
Niinemets Ü. Responses of forest trees to single and multiple environmental stresses from seedlings to mature plants: past stress history, stress interactions, tolerance and acclimation. Ecol Manag. 2010;260:1623–39.
Article  Google Scholar 

11.
Amoroso MM, Daniels LD, Larson BC. Temporal patterns of radial growth in declining Austrocedrus chilensis forests in Northern Patagonia: the use of treerings as an indicator of forest decline. Ecol Manag. 2012;265:62–70.
Article  Google Scholar 

12.
Bansal S, Hallsby G, Löfvenius MO, Nilsson MC. Synergistic, additive and antagonistic impacts of drought on herbivory on Pinus sylvestris: leaf, tissue and whole-plant responses and recovery. Tree Physiol. 2013;33:451–63.
CAS  PubMed  Article  Google Scholar 

13.
Whyte G, Howard K, Hardy GEStJ, Burgess T. The Tree Decline Recovery Seesaw; a conceptual model of the decline and recovery of drought stressed plantation trees. For Ecol Manag. 2016;370:102–13.
Article  Google Scholar 

14.
Calder JA, Kirkpatrick JB. Climate change and other factors influencing the decline of the Tasmanian cider gum (Eucalyptus gunnii). Australian J Botany. 2008;56. https://doi.org/10.1071/BT08105.

15.
Avila JM, Gallardo A, Ibáñez B, Gómez‐Aparicio L. Quercus suber dieback alters soil respiration and nutrient availability in Mediterranean forests. J Ecol. 2016;104:1441–52.
Article  Google Scholar 

16.
Crawford N. Nitrate: nutrient and signal for plant growth. Plant Cell. 1995;7:859–68.
CAS  PubMed  PubMed Central  Google Scholar 

17.
Lovett GM, Arthur MA, Weathers KC, Griffin JM. Long-term changes in forest carbon and nitrogen cycling caused by an introduced pest/pathogen complex. Ecosystems. 2010;13:1188–1200.
CAS  Article  Google Scholar 

18.
Throop H, Lerdau MT. Effects of nitrogen deposition on insect herbivory: Implications for community and ecosystem processes. Ecosystems. 2004;7:109–33.
CAS  Article  Google Scholar 

19.
Thomas FM, Ahlers U. Effects of excess nitrogen on frost hardiness and freezing injury of above-ground tissue in young oaks (Quercus petraea and Q. robur). N. Phytologist. 1999;144:73–83.
Article  Google Scholar 

20.
Hardham AR. The cell biology behind Phytophthora pathogenicity. Australas Plant Pathol. 2001;30:91–98.
Article  Google Scholar 

21.
Brown N, Jeger M, Kirk S, Xu X, Denman S. Spatial and temporal patterns in symptom expression within eight woodlands affected by acute Oak Decline. For Ecol Manag. 2016;360:97–109.
Article  Google Scholar 

22.
Scarlett K, Guest DI, Daniel R. Elevated soil nitrogen increases the severity of dieback due to Phytophthora cinnamomi. Australas Plant Pathol. 2013;42:155–62.
Article  Google Scholar 

23.
Yao H, Bowman D, Shi W. Seasonal variations in soil microbial biomass and activity in warm and cool season turfgrass systems. Soil Biol Biochem. 2011;43:1536–43.
CAS  Article  Google Scholar 

24.
Prosser JI, Nicol GW. Relative contributions of archaea and bacteria to aerobic ammonia oxidation in the environment. Environ Microbiol. 2008;10:2931–41.
CAS  PubMed  Article  Google Scholar 

25.
Prosser JI, Nicol GW. Archaeal and bacterial ammonia oxidisers in soil: the quest for niche specialisation and differentiation. Trends Microbiol. 2012;20:523–31.
CAS  PubMed  Article  Google Scholar 

26.
Erguder TH, Boon N, Wittebolle L, Marzorati M, Verstraete W. Environmental factors shaping the ecological niches of ammonia‐oxidizing archaea. FEMS Microbiol Rev. 2009;33:855–69.
CAS  PubMed  Article  Google Scholar 

27.
Hink L, Lycus P, Gubry-Rangin C, Frostgard A, Nicol GW, Prosser JI, et al. Kinetics of NH3‐oxidation, NO‐turnover, N2O‐production and electron flow during oxygen depletion in model bacterial and archaeal ammonia oxidisers. Environ Microbiol. 2017;19:4882–96.
CAS  PubMed  Article  Google Scholar 

28.
Gubry-Rangin C, Hai B, Quince C, Engel M, Thomson BC, James P, et al. Niche specialization of terrestrial archaeal ammonia oxidisers. Proc Natl Acad Sci. 2011;108:21206–11.
CAS  PubMed  Article  Google Scholar 

29.
Leininger S, Schloter UT, Schwark I, Qi J, Nicol GW, Prosser JI, et al. Archaea predominate among ammonia-oxidizing prokaryotes in soils. Nature. 2006;442:806–9.
CAS  PubMed  Article  Google Scholar 

30.
Gubry-Rangin C, Nicol GW, Prosser JI. Archaea rather than bacterial control nitrification in two agricultural acidic soils. FEMS Microbiol Ecol. 2010;74:566–74.
CAS  PubMed  Article  PubMed Central  Google Scholar 

31.
Verhamme DT, Prosser JI, Nicol GW. Ammonia concentration determines differential growth of ammonia oxidizing archaeal and bacteria in soil microcosms. ISME J. 2011;5:1067–71.
CAS  PubMed  PubMed Central  Article  Google Scholar 

32.
Di HJ, Cameron KC, Shen J-P, Winefield CS, O’Callaghan M, Bowatte S, et al. Ammonia-oxidizing bacteria and archaea grow under contrasting soil nitrogen conditions. FEMS Microbiol Ecol. 2010;72:386–94.
CAS  PubMed  Article  PubMed Central  Google Scholar 

33.
Hink L, Gubry-Rangin C, Nicol GW, Prosser J. The consequences of niche and physiological differentiation of archaeal and bacterial ammonia oxidisers for nitrous oxide emissions. ISME J. 2018;12:1084–93.
CAS  PubMed  PubMed Central  Article  Google Scholar 

34.
Clark D, McKew B, Dong L, Leung G, Dumbrell AJ, Stott A, et al. Mineralization and nitrification: archaea dominate ammonia-oxidising communities in grassland soils. Soil Biol Biochem. 2020;143:107725.
CAS  Article  Google Scholar 

35.
Delgado-Baquerizo M, Maestre FT, Eldridge DJ, Singh BK. Microsite differentiation drives the abundance of soil ammonia oxidizing bacteria along aridity gradients. Front Microbiol. 2016;7:505.
PubMed  PubMed Central  Article  Google Scholar 

36.
Martens-Habbena W, Berube PM, Urakawa H, de la Torre JR, Stahl DA. Ammonia oxidation kinetics determine niche separation of nitrifying Archaea and Bacteria. Nature. 2009;461:976–9.
CAS  PubMed  Article  PubMed Central  Google Scholar 

37.
Barta J, Tahovska K, Santruckova H, Oulehle F. Microbial communities with distinct denitrification potential in spruce and beech soils differing in nitrate leaching. Sci Rep Nat. 2017;7:9738.
Article  CAS  Google Scholar 

38.
Fierer N, Lauber CL, Ramirez KS, Zaneveld J, Bradford MA, Knight R. Comparative metagenomic, phylogenetic and physiological analyses of soil microbial communities across nitrogen gradients. ISME J. 2012;6:1007–17. https://doi.org/10.1038/ismej.2011.159.
CAS  Article  PubMed  Google Scholar 

39.
Ramirez KS, Lauber CL, Knight R, Bradford MA, Fierer N. Consistent effects of nitrogen fertilization on soil bacterial communities in contrasting systems. Ecology. 2010;91:3463–70. https://doi.org/10.1890/10-0426.1.
Article  PubMed  PubMed Central  Google Scholar 

40.
Cranfield University 2020. The Soils Guide. www.landis.org.uk. UK; Cranfield University.

41.
G Kerr, J Haufe. Thinning practice. A Silvicultural Guide. Bristol: Forestry Commission; 2011;1:54.

42.
Cools N, De Vos B Sampling and Analysis of Soil. In: Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. Hamburg: UNECE, ICP Forests; 2010, pp. 208.

43.
MAFF. Code of good agricultural practice for the protection of soil. London, UK: Ministry of Agriculture, Fisheries and Food; 1993.
Google Scholar 

44.
Li J, Nedwell DB, Beddow J, Dumbrell AJ, McKew BA, Thorpe EL, et al. amoA gene abundances and nitrification potential rates suggest that benthic ammonia-oxidizing bacteria (AOB) not archaea (AOA) dominate N cycling in the Colne estuary, UK. Appl Environ Microbiol. 2015;81:159–65.
PubMed  Article  CAS  Google Scholar 

45.
Beddow J, Stolpe B, Cole PA, Lead JR, Sapp M, Lyons BP, et al. Nanosilver inhibits nitrification and reduces ammonia-oxidizing bacterial but not archaeal amoA gene abundance in estuarine sediments. Environ Microbiol. 2017;19:500–10.
CAS  PubMed  Article  Google Scholar 

46.
Tourna M, Freitag TE, Nicol GW, Prosser JI. Growth, activity and temperature responses of ammonia-oxidizing archaea and bacteria in soil microcosms. Environ Microbiol. 2008;10:1357–64.
CAS  PubMed  Article  Google Scholar 

47.
Rotthauwe JH, Witzel KP, Liesack W. The ammonia monooxygenase structural gene amoA as a functional marker: molecular fine-scale analysis of natural ammonia-oxidizing populations. Appl Environ Microbiol. 1997;63:4704–12.
CAS  PubMed  PubMed Central  Article  Google Scholar 

48.
Throbäck IN, Enwall K, Jarvis A, Hallin S. Reassessing PCR primers targeting nirS, nirK and nosZ genes for community surveys of denitrifying bacteria with DGGE. FEMS Microbiol Ecol. 2004;49:401–17.
PubMed  Article  CAS  Google Scholar 

49.
Braker G, Fesefeldt A, Witzel KP. Development of PCR primer systems for amplification of nitrite reductase genes (nirK and nirS) to detect denitrifying bacteria in environmental samples. Appl Environ Microbiol. 1998;64:3769–75.
CAS  PubMed  PubMed Central  Article  Google Scholar 

50.
Henry S, Bru D, Stres B, Hallet S, Philippot L. Quantitative detection of the nosZ gene, encoding nitrous oxide reductase, and comparison of the abundances of 16S rRNA, narG, nirK, and nosZ Genes in Soils. Appl Environ Microbiol. 2006;72:5181–9.
CAS  PubMed  PubMed Central  Article  Google Scholar 

51.
Herlemann D, Labrenz M, Jürgens K, Bertilsson S, Waniek J, Andersson A. Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. ISME J. 2011;5:1571–9.
CAS  PubMed  PubMed Central  Article  Google Scholar 

52.
Raskin L, Stromley JM, Rittmann BE, Stahl DA. Group-specific 16S rRNA hybridization probes to describe natural communities of methanogens. Appl Environ Microbiol. 1994;60:1232–40.
CAS  PubMed  PubMed Central  Article  Google Scholar 

53.
Stahl DA, Amann R Development and application of nucleic acid probes. In: Nucleic acid techniques in bacterial systematics. Stackebrandt, E, Goodfellow M, editors. Chichester, UK: John Wiley & Sons Ltd; 1991. pp. 205–48.

54.
Dumbrell AJ, Ferguson RMW, Clark DR. Microbial community analysis by single-amplicon high-throughput next generation sequencing: Data analysis—from raw output to ecology. In: McGenity T, Timmis K, Nogales B, editors. Hydrocarbon and Lipid Microbiology Protocols. Berlin, Heidelberg: Springer Protocols Handbooks. Springer; 2016. 155–206.

55.
Joshi NA, Fass JN Sickle: A Sliding-Window, Adaptive, Quality-Based Trimming Tool for FastQ Files 2011; (Version 1.33).

56.
Nurk S, Bankevich A, Antipov D, Gurevich AA, Korobeynikov A, Lapidus A, et al. Assembling single-cell genomes and mini-metagenomes from chimeric MDA products. J Comput Biol. 2013;20:714–37.
CAS  PubMed  PubMed Central  Article  Google Scholar 

57.
Nikolenko SI, Korobeynikov AI, Alekseyev MA. BayesHammer: Bayesian clustering for error correction in single-cell sequencing. BMC Genomics SP. 2013;14:S7.
Article  Google Scholar 

58.
Rognes T, Flouri T, Nichols B, Quince C, Mahé F VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016;4. https://doi.org/10.7717/peerj.2584.

59.
Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics. 2011;16:2194–200.
Article  CAS  Google Scholar 

60.
Wang Q, Quensen JF, Fish JA, Lee TK, Sun Y, Tiedje JM, et al. Ecological patterns of nifH genes in four terrestrial climatic zones explored with targeted metagenomics using FrameBot, a new informatics tool. MBio. 2013;4:e00592–13.
PubMed  PubMed Central  Google Scholar 

61.
Wang Q, Garrity GM, Tiedje JM, Cole JR. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol. 2007;16:5261–7.
Article  CAS  Google Scholar 

62.
Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;5:1792–7.
Article  CAS  Google Scholar 

63.
Tamura K, Stecher G, Peterson D, Filipski A, Kumar S. MEGA6: molecular evolutionary genetics analysis version 6.0. Mol Biol Evol. 2013;12:2725–9.
Article  CAS  Google Scholar 

64.
Fish J, Chai B, Wang Q, Sun Y, Brown CT, Tiedje JM, et al. FunGene: the functional gene pipeline and repository. Front Microbiol. 2013;4:291.
PubMed  PubMed Central  Article  Google Scholar 

65.
Altschul S, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997;25:3389–402.
CAS  PubMed  PubMed Central  Article  Google Scholar 

66.
Lefcheck J. PIECEWISESEM: Piecewise structural equation modelling in R for ecology, evolution, and systematics. Methods Ecol Evol. 2016;7:573–9.
Article  Google Scholar 

67.
Shipley B. A new inferential test for path models based on directed acyclic graphs. Struct Equ Modeling. 2000;7:206–18.
Article  Google Scholar 

68.
Grace JB. Structural Equation Modelling and Natural Systems. New York, NY: Cambridge University Press; 2006.
Google Scholar 

69.
Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. Vegan: Community Ecology Package. R package Version. 2017;2:4–3.
Google Scholar 

70.
Leininger Wang Y, Naumann U, Wright S, Warton D. Mvabund—an R package for model‐based analysis of multivariate abundance data. Methods Ecol Evolution. 2012;3:471–4.
Article  Google Scholar 

71.
Lehtovirta-Morley LE, Stoecker K, Vilcinskas A, Prosser JI, Nicol GW. Cultivation of an obligate acidophilic ammonia oxidizer from a nitrifying acid soil. Proc Natl Acad Sci USA. 2011;108:15892–7.
CAS  PubMed  Article  PubMed Central  Google Scholar 

72.
Hu H, Zhang L, Dai Y, et al. pH-dependent distribution of soil ammonia oxidizers across a large geographical scale as revealed by high-throughput pyrosequencing. J Soils Sediment. 2013;13:1439–49.
Article  CAS  Google Scholar 

73.
Hu BL, Liu S, Wang W, Shen LD, Lou LP, Liu WP, et al. pH-dominated niche segregation of ammonia-oxidising microorganisms in Chinese agricultural soils. FEMS Microbiol Ecol. 2014;90:290–9. https://doi.org/10.1111/1574-6941.12391.
CAS  Article  Google Scholar 

74.
Hu H, Zhang L, Yuan C, Zheng Y, Wang J, Chen D, et al. The large-scale distribution of ammonia oxidizers in paddy soils is driven by soil pH, geographic distance, and climatic factors. Front Microbiol. 2015;6:938.
PubMed  PubMed Central  Google Scholar 

75.
Delgado-Baquerizo M, Gallardo A, Wallenstein MD, Maestre FT. Vascular plants mediate the effects of aridity and soil properties on ammonia-oxidizing bacteria and archaea. FEMS Microbiol Ecol. 2013;13:273–82.
Article  CAS  Google Scholar 

76.
Eldridge DJ, Beecham G, Grace J. Do shrubs reduce the adverse effects of grazing on soil properties? Ecohydrology. 2015;8:1503–13.
Article  Google Scholar 

77.
Berdugo M, Soliveres S, Maestre FT. Vascular plants and biocrusts modulate how abiotic factors affect wetting and drying events in drylands. Ecosystems. 2014;17:1242–56.
CAS  Article  Google Scholar 

78.
Köhler S, Levia DF, Jungkunst HF, Gerold G. An In Situ Method to Measure and Map Bark pH. J Wood Chem Technol. 2015;35:438–49.
Article  CAS  Google Scholar 

79.
Matschonat G, Falkengren-Grerup U. Recovery of soil pH, Cation-exchange Capacity and the Saturation of Exchange Sites from Stemflow-induced Soil Acidification in Three Swedish Beech (Fagus sylvatica L.) Forests. Scand J For Res. 2000;15:39–48.
Article  Google Scholar 

80.
Wang Y, Uchida Y, Shimomura U, Akiyama H, Hayatsu M. Responses of denitrifying bacterial communities to short-term waterlogging of soils. Sci Rep. 2017;7:803.
PubMed  PubMed Central  Article  CAS  Google Scholar 

81.
Liu J, Yu Z, Yao Q, Sui Y, Shi Y, Chu H, et al. Ammonia-oxidizing Archaea show more distinct biogeographic distribution patterns than ammonia-oxidizing bacteria across the black soil zone of Northeast China. Front Microbiol. 2018;9:171.
PubMed  PubMed Central  Article  Google Scholar 

82.
Shen C, Xiong J, Zhang H, Feng Y, Lin X, Li X, et al. Soil pH drives the spatial distribution of bacterial communities along elevation on Changbai Mountain. Soil Biol Biochem. 2013;57:204–11.
CAS  Article  Google Scholar 

83.
Nicol GW, Leininger S, Schleper C, Prosser JI. The influence of soil pH on the diversity, abundance and transcriptional activity of ammonia oxidizing archaea and bacteria. Environ Microbiol. 2008;10:2966–78.
CAS  PubMed  Article  Google Scholar 

84.
Rousk J, Baath E, Brookes PC, Lauber CL, Lozupone C, Caporaso JG, et al. Soil bacterial and fungal communities across a pH gradient in an arable soil. ISME J. 2010;4:1340–51.
PubMed  Article  Google Scholar 

85.
Yuan YL, Si GC, Wang J, Luo TX, Zhang GX. Bacterial community in alpine grasslands along an altitudinal gradient on the Tibetan Plateau. FEMS Microbiol Ecol. 2014;87:121–32.
CAS  PubMed  Article  Google Scholar 

86.
Kaiser k, Wemheuer B, Korolkow V, Wemheuer F, Nacke H, Schöning I, et al. Driving forces of soil bacterial community structure, diversity, and function in temperate grasslands and forests. Sci Rep. 2017;7:9738.
Article  CAS  Google Scholar 

87.
Meaden S, Metcalf CJE, Koskella B. The effects of host age and spatial location on bacterial community composition in the English Oak tree (Quercus robur). Environ Microbiol Rep. 2016;8:649–58.
CAS  PubMed  Article  Google Scholar 

88.
Patra AK, Abbadie L, Clays-Josserand A, Degrange V, Grayston SJ, Guillaumaud N, et al. Effects of management regime and plant species on the enzyme activity and genetic structure of N-fixing, denitrifying and nitrifying bacterial communities in grassland soils. Environ Microbiol. 2006;8:1005–16.
CAS  PubMed  Article  Google Scholar 

89.
Bremer C, Braker G, Matthies D, Beierkuhnlein C, Conrad R. Plant presence and species combination, but not diversity, influence denitrifier activity and the composition of nirK-type denitrifier communities in grassland soil. FEMS Microbiol Ecol. 2009;70:377–87.
CAS  PubMed  Article  Google Scholar  More

1.
Hamilton, W. D. The genetic theory of social behaviour I and II. J. Theor. Biol. 7, 1–52 (1964).
CAS  PubMed  Article  Google Scholar 
2.
Ward, A., & Webster, M. Sociality: the behaviour of group-living animals (Springer,2016).

3.
Lehmann, L., & Keller, L. The evolution of cooperation and altruism. A general framework and classification of models. J. Evol. Biol.19, 1365–1378 (2006).

4.
Sueur, C. et al. Collective decision-making and fission–fusion dynamics: a conceptual framework. Oikos 120, 1608–1617 (2011).
Article  Google Scholar 

5.
Jones, T. B. et al. Consistent sociality but flexible social associations across temporal and spatial foraging contexts in a colonial breeder. Ecol. Lett. https://doi.org/10.1111/ele.13507 (2020).
Article  PubMed  Google Scholar 

6.
Carter, G. G. & Wilkinson, G. S. Food sharing in vampire bats: reciprocal help predicts donations more than relatedness or harassment. Proc. R. Soc. B. 280, 20122573. https://doi.org/10.1098/rspb.2012.2573 (2013).
Article  PubMed  Google Scholar 

7.
Baglione, V., Canestrari, D., Marcos, J. M. & Ekman, J. Kin selection in cooperative alliances of carrion crows. Science 300, 1947–1949 (2003).
ADS  CAS  PubMed  Article  Google Scholar 

8.
Wahaj, S. A. et al. Kin discrimination in the spotted hyena (Crocuta crocuta): nepotism among siblings. Behav. Ecol. Sociobiol. 56, 237–247 (2004).
Article  Google Scholar 

9.
East, M. L. et al. Maternal effects on offspring social status in spotted hyenas. Behav. Ecol. 20, 478–483 (2009).
Article  Google Scholar 

10.
Komdeur, J., Burke, T., Dugdale, H.L., & Richardson, D.S. Seychelles warblers: Complexities of the helping paradox in Cooperative breeding in vertebrates: studies of ecology, evolution and behavior (ed. Koenig, W. D. & Dickinson, J. L.) 197–216 (Cambridge University Press, 2016).

11.
Krakauer, A. H. Kin selection and cooperative courtship in wild turkeys. Nature 434, 69–72 (2005).
ADS  CAS  PubMed  Article  Google Scholar 

12.
De Moor, D., Roos, C., Ostner, J. & Schülke, O. Bonds of bros and brothers: kinship and social bonding in post-dispersal male macaques. Mol. Ecol. https://doi.org/10.1111/mec.15560 (2020).
Article  PubMed  Google Scholar 

13.
Koykka, C. & Wild, W. Concessions, lifetime fitness consequences, and the evolution of coalitionary behaviour. Behav. Ecol. 28, 20–30 (2016).
Article  Google Scholar 

14.
Clutton-Brock, T. Cooperation between non-kin in animal societies. Nature 46, 51–57 (2009).
ADS  Article  CAS  Google Scholar 

15.
Schaller, G.B. The Serengeti Lion: a study of predator-prey relations. (University of Chicago Press, 1972).

16.
Bertram, B. C. Social factors influencing reproduction in wild lions. J. Zool. 177, 463–482 (1975).
Article  Google Scholar 

17.
Bygott, J. D., Bertram, B. C. & Hanby, J. P. Male lions in large coalitions gain reproductive advantages. Nature 282, 839 (1979).
ADS  Article  Google Scholar 

18.
Packer, C. & Pusey, A. E. Cooperation and competition within coalitions of male lions: Kin selection or game theory?. Nature 296, 740 (1982).
ADS  Article  Google Scholar 

19.
Grinnell, J., Packer, C. & Pusey, A. E. Cooperation in male lions: kinship, reciprocity or mutualism?. Anim. Behav. 49, 95–105 (1995).
Article  Google Scholar 

20.
Chakrabarti, S. & Jhala, Y. V. Selfish partners: resource partitioning in male coalitions of Asiatic lions. Behav. Ecol. 28, 1532–1539 (2017).
PubMed  PubMed Central  Article  Google Scholar 

21.
Packer, C. et al. Reproductive success of lions in Reproductive success (ed. Clutton-Brock, T.H.) 363–383 (University of Chicago Press, 1988).

22.
Packer, C., Gilbert, D. A., Pusey, A. E. & O’Brien, S. J. A molecular genetic analysis of kinship and cooperation in African lions. Nature 351, 562–565 (1991).
ADS  CAS  Article  Google Scholar 

23.
Connor, R. C., Smolker, R. A., & Richards, A. F. Two levels of alliance formation among male bottlenose dolphins (Tursiops sp.). PNAS. 89, 987–990 (1992).

24.
Parsons, K. M. et al. Kinship as a basis for alliance formation between male bottlenose dolphins, Tursiops truncatus, in the Bahamas. Anim. Behav. 66, 185–194 (2003).
Article  Google Scholar 

25.
Widdig, A., Streich, W. J. & Tembrock, G. Coalition formation among male Barbary macaques (Macaca sylvanus). Am. J. Primatol. 50, 37–51 (2000).
CAS  PubMed  Article  Google Scholar 

26.
Gottelli, D., Wang, J., Bashir, S. & Durant, S. M. Genetic analysis reveals promiscuity among female cheetahs. Proc. R. Soc. B. 274, 1993–2001 (2007).
PubMed  Article  Google Scholar 

27.
Bertram, B.C. Pride of lions. (JM Dent and Sons Ltd, 1978).

28.
O’Brien, S.J. Prides and Prejudice in Tears of the cheetah and other tales from the genetic frontier: the genetic secrets of our animal ancestors (ed. O’Brien, S.J.) 35–55 (Thomas Dunne Books, 2003).

29.
de Manuel, M. et al. The evolutionary history of extinct and living lions. PNAS 117, 10927–10934 (2020).
PubMed  Article  CAS  Google Scholar 

30.
Clutton-Brock, T. H. Reproductive skew, concessions and limited control. Trends Ecol. Evol. 13, 288–292 (1998).
CAS  PubMed  Article  Google Scholar 

31.
Queller, D. C. & Keith, F. G. Estimating relatedness using genetic markers. Evolution 43, 258–275 (1989).
PubMed  Article  Google Scholar 

32.
Wang, J. Estimating pairwise relatedness in a small sample of individuals. Heredity 119, 302–313 (2017).
CAS  PubMed  PubMed Central  Article  Google Scholar 

33.
Sandel, A. A., Langergraber, K. E. & Mitani, J. C. Adolescent male chimpanzees (Pan troglodytes) form social bonds with their brothers and others during the transition to adulthood. Am. J. Primatol. 82, 23091. https://doi.org/10.1002/ajp.23091 (2020).
Article  Google Scholar 

34.
Dal Pesco, F. Dynamics and fitness benefits of male-male sociality in wild Guinea baboons (Papio papio). (PhD thesis), Georg-August University, Göttingen, Germany.

35.
Christakis, N. A. & Fowler, J. H. Friendship and natural selection. PNAS 111, 10796–10801 (2014).
ADS  CAS  PubMed  Article  Google Scholar 

36.
Engh, A. L. et al. Behavioural and hormonal responses to predation in female chacma baboons (Papio hamadryas ursinus). Proc. R. Soc. B. 273, 707–712 (2006).
CAS  PubMed  Article  Google Scholar 

37.
Hill, K. R. et al. Co-residence patterns in hunter-gatherer societies show unique human social structure. Science 331, 1286–1289 (2011).
ADS  CAS  PubMed  Article  Google Scholar 

38.
Silk, J.B. Practicing Hamilton’s Rule: kin selection in primate groups in Cooperation in primates and humans (ed. Kappeler, P.M., & van Schaik, C.P.) 25–46 (Springer, 2006).

39.
Chakrabarti, S. et al. Adding constraints to predation through allometric relation of scats to consumption. J. Anim. Ecol. 85, 660–670 (2016).
PubMed  Article  Google Scholar 

40.
Møller, A. P. & Birkhead, T. R. Copulation behaviour in mammals: evidence that sperm competition is widespread. Biol. J. Linn. Soc. 38, 119–131 (1989).
Article  Google Scholar 

41.
Chakrabarti, S. & Jhala, Y. V. Battle of the sexes: a multi-male mating strategy helps lionesses win the gender war of fitness. Behav. Ecol. 30, 1050–1061 (2019).
Article  Google Scholar 

42.
Jhala, Y. V. et al. Asiatic lion: ecology, economics and politics of conservation. Front. Ecol. Evol. 7, 312 (2019).
ADS  Article  Google Scholar 

43.
Boom, R. C. J. A. et al. Rapid and simple method for purification of nucleic acids. J. Clin. Microbiol. 28, 495–503 (1990).
CAS  PubMed  PubMed Central  Article  Google Scholar 

44.
Antunes, A. et al. The evolutionary dynamics of the lion Panthera leo revealed by host and viral population genomics. PLoS Genet. 4, 1000251. https://doi.org/10.1371/journal.pgen.1000251 (2008).
CAS  Article  Google Scholar 

45.
Singh, A., Shailaja, K., Gaur, A., & Singh., L. Development and characterization of novel microsatellite markers in the Asiatic lion (Panthera leo persica). Mol. Ecol. Notes. 2, 542–543 (2002).

46.
Gaur, A. et al. Twenty polymorphic microsatellite markers in the Asiatic lion (Panthera leo persica). Conserv. Genet. 7, 1005–1008 (2006).
CAS  Article  Google Scholar 

47.
Menotti-Raymond, M. et al. A genetic linkage map of microsatellites in the domestic cat (Felis catus). Genomics 57, 9–23 (1999).
CAS  PubMed  Article  Google Scholar 

48.
Menotti-Raymond, M. et al. An STR forensic typing system for genetic individualization of domestic cat (Felis catus) samples. J. Forensic Sci. 50, 1061–1070 (2005).
CAS  PubMed  Article  Google Scholar 

49.
Williamson, J. E., Huebinger, R. M., Sommer, J. A., Louis, E. E. Jr. & Barber, R. C. Development and cross-species amplification of 18 microsatellite markers in the Sumatran tiger (Panthera tigris sumatrae). Mol. Ecol. Notes. 2, 110–112 (2002).
CAS  Article  Google Scholar 

50.
Drummond, A.J.A.B. et. al. v5. 4. Auckland (2011).

51.
Matschiner, M. & Salzburger, W. TANDEM: integrating automated allele binning into genetics and genomics workflows. Bioinformatics 25, 1982–1983 (2009).
CAS  PubMed  Article  Google Scholar 

52.
Taberlet, P. et al. Reliable genotyping of samples with very low DNA quantities using PCR. Nucleic Acids Res. 24, 3189–3194 (1996).
CAS  PubMed  PubMed Central  Article  Google Scholar 

53.
Peakall, R.O.D., & Smouse, P.E. GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Mol. Ecol. Notes. 6, 288–295 (2006).

54.
Bergner, L. M., Jamieson, I. G. & Robertson, B. C. Combining genetic data to identify relatedness among founders in a genetically depauperate parrot, the Kakapo (Strigops habroptilus). Conserv. Genet. 15, 1013–1020 (2014).
Article  Google Scholar 

55.
Gilbert, D. A., Packer, C., Pusey, A. E., Stephens, J. C. & O’Brien, S. J. Analytical DNA fingerprinting in lions: parentage, genetic diversity, and kinship. J. Hered. 82, 378–386 (1991).
CAS  PubMed  Article  Google Scholar 

56.
Pemberton, J. M., Albon, S. D., Guinness, F. E., Clutton-Brock, T. H. & Dover, G. A. Behavioral estimates of male mating success tested by DNA fingerprinting in a polygynous mammal. Behav. Ecol. 3, 66–75 (1992).
Article  Google Scholar 

57.
Dixson, A. F., Bossi, T. & Wickings, E. J. Male dominance and genetically determined reproductive success in the mandrill (Mandrillus sphinx). Primates 34, 525–532 (1993).
Article  Google Scholar 

58.
Krebs, J.R., & Davies, N.B. An introduction to behavioural ecology. (Blackwell Scientific Publications, 1987).

59.
Smith, J. M. Group selection and kin selection. Nature 201, 1145–1147 (1964).
ADS  Article  Google Scholar 

60.
Banerjee, K. & Jhala, Y. V. Demographic parameters of endangered Asiatic lions (Panthera leo persica) in Gir forests India. J. Mammal. 93, 1420–1430 (2012).
Article  Google Scholar 

61.
Meena, V. Reproductive strategy and behaviour of male Asiatic lions. [Dissertation/Ph.D. thesis]. (Forest Research Institute University, 2008).

62.
Banerjee, K. Ranging patterns, habitat use and food habits of the satellite lion populations (Panthera leo persica) in Gujarat, India. [Dissertation/Ph.D. thesis]. (Forest Research Institute Deemed University, 2012).

63.
Gogoi, K., Kumar, U., Banerjee, K. & Jhala, Y. V. Spatially explicit density and its determinants for Asiatic lions in the Gir forests. PLoS ONE 15, 0228374. https://doi.org/10.1371/journal.pone.0228374 (2020).
CAS  Article  Google Scholar 

64.
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. (2019). More