Ecology

Within-generation HGM
After matings of compatible hyphal tips grown from spores, haploid dikaryotic nuclei (n + n) of A. gallica fuse to produce diploid monokaryons (2n). As monokaryons are persistent in vegetative stages and often possess two distinct molecular-marker alleles, the model of vegetative heterozygous diploidy is widely accepted. But since other studies show vegetative stages can possess recombinant, haploid nuclei, an alternative hypothesis has been advanced. This hypothesis proposes a life cycle in which a vegetative-stage haploidization produces HGM6,7,17,18. Our analyses confirm that vegetative-stage hyphae can be haploid (Fig. 1, Supplementary Table S1), while still possessing two different molecular-marker alleles (Supplementary Table S2).
Although RFLP data are consistent with both heterozygous diploid and haploid genetic mosaic models, DNA content data and EF1α sequence data both argue against the heterozygous diploid model. Since EF1α is a single-copy gene, multiple cloned sequences isolated from a single hyphal filament should have only 1 haplotype if the filament is a diploid homozygote or 2 haplotypes if it is a diploid heterozygote; but it could have 1, 2, 3 or more haplotypes if it is a haploid genetic mosaic. The upper limit on the number of haplotypes detected in a hyphal filament is set by the number of hyphal compartments recovered during cell-line isolation. We estimate that, on average, six contiguous compartments were harvested each time we isolated a hyphal filament line; and there were 26 instances in which 3 or more clones were successfully sequenced from within a single hyphal filament line. In these 26 lines, we detected 1 or 2 haplotypes 11 times and 3 or 4 haplotypes 15 times (Table 1, Supplementary Table S3a–c). The 11 instances in which 1 or 2 haplotypes were detected are compatible with either model; but the 15 instances in which 3 or 4 haplotypes were detected are compatible with only the haploid genetic mosaic model. In conjunction with the finding of haploidy in vegetative stages, this finding argues against the heterozygous diploid model and supports the haploid genetic mosaic model. We define a haploid genetic mosaic as a mycelium with haplotypes that vary within and among hyphae. As an example, Fig. 6 depicts two haploid genetic mosaic rhizomorph hyphal filament lines that were isolated from the Raynham genet.
Figure 6

Haploid Genetic Mosaicism is exemplified in two rhizomorph hyphal filament lines (09r27 and 09r50) isolated from the Raynham genet. The mycelium containing hyphae with these haplotypes exhibits both within-line and among-line nuclear heterogeneity.

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Haplotype designations hap 1, hap 3…hap 13 refer to EF1α haplotypes listed in rows 1, 3, 5, 6, 8, 12, and 13 of Table 1. Note that (1) haplotype 13 is the only haplotype shared by both filament lines; (2) the order of the nuclei in the filaments is not known, so it is arbitrarily shown as numerical; (3) the spacers are hypothetical, as usually a maximum of 6 nuclei were included in an isolate.
We are not the first to propose HGM in Armillaria. Ullrich and Anderson19 considered stable diploidy as the most likely explanation for prototrophy in mated auxotrophs of Armillaria mellea. However, they also presented an alternative hypothesis that they considered a less likely but possible explanation for their results: “Alternatively, it is possible that an unusual (unprecedented) type of heterokaryon is present, i.e., one that is vegetatively stable in a filamentous fungus with uninucleate cells and intact septa.” Our results appear to be an example of Ullrich and Anderson’s alternative model.
Because hyphal extension requires mitosis, contiguous compartments within growing hyphal tips should contain a series of identical nuclei. How then, in rhizomorphs capable of undergoing mitosis for decades, can within-hyphal filament HGM persist? Korhonen20 was the first to document nuclear migration through cytoplasmic bridges in Armillaria. We found cytoplasmic bridges to be common in monokaryotic rhizomorph hyphae collected in nature (Fig. 3) and hyphae grown in culture (Fig. 4). Because nuclei were frequently found in or near bridges, we propose nuclear exchange through bridges as a mechanism that maintains within-line and among-line HGM (Fig. 7).
Figure 7

In this model, Haploid Genetic Mosaicism is maintained by nuclear exchange across cytoplasmic bridges connecting rhizomorph hyphal filament tips.

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Growth
Gallic acid growth experiments revealed significant line effects, treatment effects, and line × treatment effects for all 4 sets of Raynham and Bridgewater cell-lines (ANOVA P  More

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.

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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.
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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).

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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

Characteristics of oasis change in the Hexi Corridor
Oasis area variation at the river basin and county scales
The distribution of stable oasis in three river basins and seventeen administration regions is shown in Fig. 1a. The total oasis area in the Hexi Corridor has increased from 10,707.7 km2 in 1986 to 14,950.1 km2 in 2015 (Fig. 1b), with an increase factor of 1.4 from the start to the end years and an average annual increase of 140 km2. At the river basin scale, the HHRB has the largest oasis area with 47% of the total oasis area, followed by SYRB with 40%. The SLRB charactered by drier environments has the least oasis area of 13%. The oasis change types in the Hexi Corridor over the last 30 years are mainly “expansion”, which is supplemented by “retreating” (Fig. 1b). The oasis area variation of administration regions during the past thirty years is shown in Fig. 1c. It is observed that the variation tendency of the oasis area at administration regions scale was the same as that on the river basin scale. The oasis areas in Liangzhou District, Ganzhou District, Minqin County, Yongchang County, Suzhou District, and Shandan Country were more than 1000 km2 in most time. Conversely, the oasis area in Jiayuguan District and Sunan County was less than 200 km2.
Figure 1

Variation of oases area in three river basins of the Hexi Corridor during the past thirty years. (a) was generated using ArcGIS 10.3, www.esri.com.

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The stable oasis and maximum oasis distribution
The stable oasis was extracted from the area where the oasis exists in all seven periods, and the maximum oasis area was depicted from the area where the oasis existed once in the past thirty years. It can be seen that the stable oasis area is 9062 km2, while the maximum oasis area reaches 16,374 km2, which is almost two times larger than that of the stable oasis.
The stable oases distribute in alluvial and pluvial fans, the river plains in middle reaches, and the catchment area in the lower reaches (Fig. 2). The maximum oases extended from the stable oases, which mainly located at the edges of the alluvial–proluvial fans, low-lying areas next to rivers and ditches, and the oases-deserts ecotone.
Figure 2

The distribution of stable oases and maximum oases in the Hexi Corridor. The map was generated using ArcGIS 10.3, www.esri.com.

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The constraints of oasis development
Geomorphological characteristics of oasis distribution
The geomorphological conditions, formed in the geological history period, is critical for the process of oases development. To investigate the possible relationship between limiting factors and oasis distribution, the distribution frequency, which is the ratio of number in specific condition among all oasis raster number, was introduced and the scatter plots and normal distribution fitting curves were plotted. Figure 3a shows the altitude of the oasis is mainly between 1000 m that is near the lowest value in the study area to 2500 m. The elevation of stable (maximum) oasis peaks in 1500 (1450) m, and accounted for 3.5% (4.5%), which suggests that when oases expand, they tend to occupy the lower elevation. The oases are mainly located in the plains along rivers or irrigation canal systems where slopes flatter than 5° (Fig. 3b), most of them are located in the level ground with a slope flatter than 3°. The area of the stable and the maximum oases located in flat place (slope = 0) account 64% and 76%, respectively, which indicated that the oasis expansion mainly occurs on flat ground. The analysis of the oasis on eight slopes shows that the majority of the slope oasis is concentrated in the north slope and the northeast slope, accounting for about 60%, while the east slope and the northwest slope also have a part, accounting for 30% (Fig. 3c). The aspect of slope oasis expansion mainly takes place in sunny slope (Northwest, West, Southwest, south, southeast), which due to that almost all of the shady slope has been covered by oasis. On the contrary, there are many deserts in sunny slope, as long as the necessary moisture conditions will be occupied by the oasis. The different aspects result in varying amounts of solar radiation, which affects evapotranspiration and consequently water balance in the soil. More specifically shady aspects have more moisture for vegetation growth due to less evapotranspiration, on the other hand, sunny aspects experience potentially higher rates of evapotranspiration, supporting less moisture for vegetation growth26. The fitted normal distribution formula of DEM frequency for stable and maximum oases are given in Fig. 3a, the fits reached a significant level (p  total population  > AWD  > Primary industry  > GDP  > tertiary industry  > secondary industry (Table 1).
Table 1 The relative degree of incidence between urban expansion and driving factors based on panel data in the Hexi Corridor (1986–2015).
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The GRD of Population, especially for the rural laborer, is the highest. The increase of the nonagricultural population directly stimulated urban residential, commercial, industrial, transportation, and other related industry development. Consequently, urban land expanded in this area. The population growth was a major factor in oasis variation29. During the past 30 years, the population increased from 1.06 to 5.07 million (378% increase), while the oasis area increased from 10,707 to 14,950 km2 (39.6% increase) in the Hexi Corridor. The rise in population will unavoidably lead to an increase in arable land for survival.
Secondly, the GRD between oasis expansion and AWD is pervasively high with the value around 0.9. The water resource including the precipitation and runoff play an important role in the spatial expansion of oasis. The Hexi Corridor located in a typical arid region, where the most vital limiting factor for both vegetation growth and economic development is the limited water resource. Concerning the shortage of water resources, it is difficult to irrigate many newly reclaimed agricultural oases in the Hexi Corridor. That is to say, water resources cannot afford continuous growth. Thus, AWD of oases is significantly positively correlated with the area of oases.
Thirdly, the GRD between economic factors, including GDP, Primary industry, Secondary industry and Tertiary industry, is about 0.6, which is relatively low comparing to that of population, water resource. The GRD of primary industry is highest with a value of 0.7 among the economic factors. Separately, for the type of agriculture oases contains most of the administration regions in the study area, the GRD of the primary industry was considerably higher than that of secondary and tertiary industry, the agriculture was their first driving force. For the resource-based cities and towns, like Jinchuan District, Jiayuguan City, the GRD of secondary and tertiary industry is essentially equal to that of primary industry, the secondary industry and tertiary industry played a vital role in oasis development. 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.

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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).
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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).

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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.
Noss, R. F. Indicators for monitoring biodiversity: A hierarchical approach. Conserv. Biol. 4, 355–364 (1990).
Article  Google Scholar 
2.
Lambeck, R. J. Focal species: A multi-species umbrella for nature conservation. Conserv. Biol. 11, 849–856 (1997).
Article  Google Scholar 

3.
Knight, T. M., McCoy, M. W., Chase, J. M., McCoy, K. A. & Holt, R. D. Trophic cascades across ecosystems. Nature 437, 880–883 (2005).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

4.
Rudolf, V. H. W. & Rasmussen, N. L. Ontogenetic functional diversity: Size structure of a keystone predator drives functioning of a complex ecosystem. Ecology 94, 1046–1056 (2013).
PubMed  Article  PubMed Central  Google Scholar 

5.
Bulánková, E. Dragonflies (Odonata) as bioindicators. Biologia, Bratislava 52, 177–180 (1997).
Google Scholar 

6.
Catling, P. M. A potential for the use of dragonfly (Odonata) diversity as a bioindicator of the efficiency of sewage lagoons. Can. Field Nat. 119, 233 (2005).
Article  Google Scholar 

7.
Vorster, C. et al. Development of a new continental-scale index for freshwater assessment based on dragonfly assemblages. Ecol. Indic. 109, 105819 (2020).
Article  Google Scholar 

8.
Jeremiason, J. D., Reiser, T. K., Weitz, R. A., Berndt, M. E. & Aiken, G. R. Aeshnid dragonfly larvae as bioindicators of methylmercury contamination in aquatic systems impacted by elevated sulfate loading. Ecotoxicology 25, 456–468 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

9.
Chovanec, A. & Raab, R. Dragonflies (Insecta, Odonata) and the ecological status of newly created wetlands—examples for long-term bioindication programmes. Limnologica 27, 381–392 (1997).
Google Scholar 

10.
Chovanec, A. & Waringer, J. Ecological integrity of river–floodplain systems—assessment by dragonfly surveys (Insecta: Odonata). Regul. Rivers Res. Manag. 17, 493–507 (2001).
Article  Google Scholar 

11.
Rocha-Ortega, M., Rodríguez, P. & Córdoba-Aguilar, A. Can dragonfly and damselfly communities be used as bioindicators of land use intensification?. Ecol. Indic. 107, 105553 (2019).
Article  Google Scholar 

12.
Kalkman, V. J. et al. Diversity and conservation of European dragonflies and damselflies (Odonata). Hydrobiologia 811, 269–282 (2018).
Article  Google Scholar 

13.
Harvey, J. A. et al. International scientists formulate a roadmap for insect conservation and recovery. Nat. Ecol. Evol. 4, 174–176 (2020).
PubMed  Article  PubMed Central  Google Scholar 

14.
Falck, J. & Johansson, F. Patterns in size, sex ratio and time at emergence in a South Swedish population of Sympetrum sanguineum (Odonata). Aquat. Insects 22, 311–317 (2000).
Article  Google Scholar 

15.
Farkas, A. et al. Sex ratio in Gomphidae (Odonata) at emergence: Is there a relationship with water temperature?. Int. J. Odonatol. 16, 279–287 (2013).
Article  Google Scholar 

16.
Cardoso, P., Erwin, T. L., Borges, P. A. V. & New, T. R. The seven impediments in invertebrate conservation and how to overcome them. Biol. Conserv. 144, 2647–2655 (2011).
Article  Google Scholar 

17.
Daigle, R. J. Sea-level rise estimates for New Brunswick municipalities: Saint John, Sackville, Richibucto, Shippagan, Caraquet, Le Goulet. Report for the Atlantic Climate Adaptation Solutions Association (2011).

18.
Tockner, K., Pusch, M., Borchardt, D. & Lorang, M. S. Multiple stressors in coupled river–floodplain ecosystems. Freshw. Biol. 55, 135–151 (2010).
Article  Google Scholar 

19.
Nakano, S. & Murakami, M. Reciprocal subsidies: Dynamic interdependence between terrestrial and aquatic food webs. Proc. Natl. Acad. Sci. 98, 166–170 (2001).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

20.
Rantala, M. J., Ilmonen, J., Koskimäki, J., Suhonen, J. & Tynkkynen, K. The macrophyte, Stratiotes aloides, protects larvae of dragonfly Aeshna viridis against fish predation. Aquat. Ecol. 38, 77–82 (2004).
Google Scholar 

21.
Suhonen, J., Suutari, E., Kaunisto, K. M. & Krams, I. Patch area of macrophyte Stratioites aloides as a critical resource for declining dragonfly Aeshna viridis. J. Insect Conserv. 17, 393–398 (2013).
Google Scholar 

22.
Bell, H. L. Effect of low pH on the survival and emergence of aquatic insects. Water Res. 5, 313–319 (1971).
Google Scholar 

23.
Farkas, A., Jakab, T., Tóth, A., Kalmár, A. F. & Dévai, G. Emergence patterns of riverine dragonflies (Odonata: Gomphidae) in Hungary: Variations between habitats and years. Aquat. Insects 34, 77–89 (2012).
Google Scholar 

24.
Boda, R. et al. Emergence behaviour of the red listed Balkan Goldenring (Cordulegaster heros Theischinger, 1979) in Hungarian upstreams: Vegetation structure affects the last steps of the larvae. J. Insect Conserv. 19, 547–557 (2015).
Google Scholar 

25.
Remsburg, A. J. & Turner, M. G. Aquatic and terrestrial drivers of dragonfly (Odonata) assemblages within and among north-temperate lakes. J. N. Am. Benthol. Soc. 28, 44–56 (2009).
Google Scholar 

26.
Remsburg, A. Relative influence of prior life stages and habitat variables on dragonfly (Odonata: Gomphidae) densities among lake sites. Diversity 3, 200–216 (2011).
Google Scholar 

27.
Aoki, T. Larval development, emergence and seasonal regulation in Asiagomphus pryeri (Selys) (Odonata: Gomphidae). Hydrobiologia 394, 179–192 (1999).
Google Scholar 

28.
Paulson, D. Dragonflies and Damselflies of the East (Princeton University Press, Princeton, 2011).
Google Scholar 

29.
Foster, S. E. & Soluk, D. A. Evaluating exuvia collection as a management tool for the federally endangered Hine’s emerald dragonfly, Somatochlora hineana Williamson (Odonata: Cordulidae). Biol. Conserv. 118, 15–20 (2004).
Google Scholar 

30.
Raebel, E. M., Merckx, T., Riordan, P., Macdonald, D. W. & Thompson, D. J. The dragonfly delusion: Why it is essential to sample exuviae to avoid biased surveys. J. Insect Conserv. 14, 523–533 (2010).
Article  Google Scholar 

31.
Needham, J. G., Westfall, M. J. & May, M. L. Dragonflies of North America: The Odonata (Anisoptera) Fauna of Canada, the Continental United States, Northern Mexico and the Greater Antilles (Scientific Publishers, Jodhpur, 2014).
Google Scholar 

32.
Aliberti Lubertazzi, M. A. & Ginsberg, H. S. Persistence of dragonfly exuviae on vegetation and rock substrates. Northeast. Nat. 16, 141–147 (2009).
Article  Google Scholar 

33.
Brodin, T. & Johansson, F. Effects of predator-induced thinning and activity changes on life history in a damselfly. Oecologia 132, 316–322 (2002).
ADS  PubMed  Article  Google Scholar 

34.
Johansson, F., Crowley, P. H. & Brodin, T. Sexual size dimorphism and sex ratios in dragonflies (Odonata). Biol. J. Linn. Soc. 86, 507–513 (2005).
Article  Google Scholar 

35.
Lamit, L. J. et al. Genotype variation in bark texture drives lichen community assembly across multiple environments. Ecology 96, 960–971 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

36.
Wolman, M. G. A method of sampling coarse river-bed material. EOS Trans. Am. Geophys. Union 35, 951–956 (1954).
Article  Google Scholar 

37.
Richter, B. D., Baumgartner, J. V., Powell, J. & Braun, D. P. A method for assessing hydrologic alteration within ecosystems. Conserv. Biol. 10, 1163–1174 (1996).
Article  Google Scholar 

38.
Sabo, J. L., Bastow, J. L. & Power, M. E. Length–mass relationships for adult aquatic and terrestrial invertebrates in a California watershed. J. N. Am. Benthol. Soc. 21, 336–343 (2002).
Article  Google Scholar 

39.
Sample, B. E., Cooper, R. J., Greer, R. D. & Whitmore, R. C. Estimation of insect biomass by length and width. Am. Midl. Nat. 129, 234 (1993).
Article  Google Scholar 

40.
McCune, B. & Mefford, M. J. PC-ORD. Multivariate Analysis of Ecological Data, Version 5.0 for Windows. MjM Software, Gleneden Beach, Oregon, U.S.A. (2006).

41.
Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-2. https://CRAN.R-project.org/package=vegan (2018).

42.
R Core Team. R: A language and environment for statistical computing. R Found. Stat. Comput. Vienna Austria (2018).

43.
Anderson, M. J. Permutational Multivariate Analysis of Variance (Department of Statistics, University of Auckland, Auckland, 2005).
Google Scholar 

44.
Manly, B. F. J. Randomization, Bootstrap and Monte Carlo Methods in Biology 3rd edn. (CRC Press, Boca Raton, 2006).
Google Scholar 

45.
Goslee, S. C. & Urban, D. L. The ecodist Package for dissimilarity-based analysis of ecological data. J. Stat. Softw. 22, 1–19 (2007).
Article  Google Scholar 

46.
Crabot, J., Clappe, S., Dray, S. & Datry, T. Testing the Mantel statistic with a spatially-constrained permutation procedure. Methods Ecol. Evol. 10, 532–540 (2019).
Article  Google Scholar 

47.
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Methodol. 57, 289–300 (1995).
MathSciNet  MATH  Google Scholar 

48.
Clarke, K. R. & Gorley, R. N. PRIMER v7: User Manual/Tutorial 3rd edn. (Plymouth U. K. Primer-E Ltd, Wellington, 2015).
Google Scholar 

49.
Baird, I. R. C. & Burgin, S. An emergence study of Petalura gigantea (Odonata: Petaluridae). Int. J. Odonatol. 16, 193–211 (2013).
Article  Google Scholar 

50.
Richter, O., Suhling, F., Müller, O. & Kern, D. A model for predicting the emergence of dragonflies in a changing climate. Freshw. Biol. 53, 1868–1880 (2008).
Google Scholar 

51.
Kennedy, T. A. et al. Flow management for hydropower extirpates aquatic insects, undermining river food webs. Bioscience 66, 561–575 (2016).
Google Scholar 

52.
Worthen, W. B. & Horacek, H. J. The distribution of dragonfly larvae in a South Carolina stream: Relationships with sediment type, body size, and the presence of other larvae. J. Insect Sci. 15, 31–31 (2015).
PubMed  PubMed Central  Google Scholar 

53.
Corbet, P. A Biology of Dragonflies. HF & G. Witherby LTD (Northumberland Press Limited, Newcastle-upon-Tyne, 1962).
Google Scholar 

54.
Corbet, P. S. Dragonflies: Behavior and Ecology of Odonata (Cornell University Press, New York, 1999).
Google Scholar 

55.
Baxter, C. V., Fausch, K. D. & Saunders, W. C. Tangled webs: Reciprocal flows of invertebrate prey link streams and riparian zones. Freshw. Biol. 50, 201–220 (2005).
Article  Google Scholar 

56.
Burdon, F. J. & Harding, J. S. The linkage between riparian predators and aquatic insects across a stream-resource spectrum. Freshw. Biol. 53, 330–346 (2008).
Google Scholar 

57.
Grof-Tisza, P., LoPresti, E., Heath, S. K. & Karban, R. Plant structural complexity and mechanical defenses mediate predator–prey interactions in an odonate–bird system. Ecol. Evol. 7, 1650–1659 (2017).
PubMed  PubMed Central  Article  Google Scholar 

58.
Iwata, T. Linking stream habitats and spider distribution: Spatial variations in trophic transfer across a forest–stream boundary. Ecol. Res. 22, 619–628 (2007).
Article  Google Scholar 

59.
Jakob, C. & Suhling, F. Risky times? Mortality during emergence in two species of dragonflies (Odonata: Gomphidae, Libellulidae). Aquat. Insects 21, 1–10 (1999).
Article  Google Scholar 

60.
Kurata, M. Life history of Gomphys melaenops (Gomphidae). Tombo 14, 6–11 (1971).
Google Scholar 

61.
Corbet, P. S. The life-history of the emperor dragonfly anax imperator leach (Odonata: Aeshnidae). J. Anim. Ecol. 26, 1–69 (1957).
Google Scholar 

62.
Suhling, F. Temporal patterns of emergence of the riverine dragonfly Onychogomphus uncatus (Odonata: Gomphidae). Hydrobiologia 302, 113–118 (1995).
Google Scholar 

63.
Wissmar, R. C. & Beschta, R. L. Restoration and management of riparian ecosystems: A catchment perspective. Freshw. Biol. 40, 571–585 (1998).
Google Scholar 

64.
Evans, B. F., Townsend, C. R. & Crowl, T. A. Distribution and abundance of coarse woody debris in some southern New Zealand streams from contrasting forest catchments. N. Z. J. Mar. Freshw. Res. 27, 227–239 (1993).
Google Scholar 

65.
Studinski, J. M., Hartman, K. J., Niles, J. M. & Keyser, P. The effects of riparian forest disturbance on stream temperature, sedimentation, and morphology. Hydrobiologia 686, 107–117 (2012).
Google Scholar 

66.
Wang, L., Lyons, J., Kanehl, P. & Gatti, R. Influences of watershed land use on habitat quality and biotic integrity in wisconsin streams. Fisheries 22, 6–12 (1997).
Google Scholar 

67.
Martin, T. G. & Mcintyre, S. Impacts of livestock grazing and tree clearing on birds of woodland and riparian habitats. Conserv. Biol. 21, 504–514 (2007).
PubMed  PubMed Central  Google Scholar 

68.
Ormerod, S. J., Rundle, S. D., Lloyd, E. C. & Douglas, A. A. The influence of riparian management on the habitat structure and macroinvertebrate communities of upland streams draining plantation forests. J. Appl. Ecol. 30, 13–24 (1993).
Google Scholar 

69.
Cordero, A. Vertical stratification during emergence in odonates. Not. Odonatol. 4, 103–105 (1995).
Google Scholar 

70.
Coppa, G. Notes sur l’émergence d’Epitheca bimaculata (Charpentier)(Odonata: Corduliidae). Martinia 7, 7–16 (1991).
Google Scholar 

71.
Miller, P. L. Notes on Ictinogomphus ferox Rambur (Odonata: Gomphidae). Entomologist 97, 2–66 (1964).
Google Scholar 

72.
Worthen, W. B. Emergence-site selection by the dragonfly Epitheca spinosa (Hagen). Southeast. Nat. 9, 251–258 (2010).
Article  Google Scholar 

73.
Magoba, R. N. & Samways, M. J. Recovery of benthic macroinvertebrate and adult dragonfly assemblages in response to large scale removal of riparian invasive alien trees. J. Insect Conserv. 14, 627–636 (2010).
Article  Google Scholar 

74.
Cothran, M. L. & Thorp, J. H. Emergence patterns and size variation of Odonata in a thermal reservoir. Freshw. Invertebr. Biol. 1, 30–39 (1982).
Article  Google Scholar 

75.
Mccauley, S. J., Hammond, J. I., Frances, D. N. & Mabry, K. E. Effects of experimental warming on survival, phenology, and morphology of an aquatic insect (Odonata). Ecol. Entomol. 40, 211–220 (2015).
PubMed  Article  PubMed Central  Google Scholar 

76.
McCauley, S. J., Hammond, J. I. & Mabry, K. E. Simulated climate change increases larval mortality, alters phenology, and affects flight morphology of a dragonfly. Ecosphere 9, e02151 (2018).
PubMed  PubMed Central  Article  Google Scholar 

77.
de Nadaï-Monoury, E., Gilbert, F. & Lecerf, A. Forest canopy cover determines invertebrate diversity and ecosystem process rates in depositional zones of headwater streams. Freshw. Biol. 59, 1532–1545 (2014).
Article  Google Scholar 

78.
Flenner, I. & Sahlén, G. Dragonfly community re-organisation in boreal forest lakes: Rapid species turnover driven by climate change?. Insect Conserv. Divers. 1, 169–179 (2008).
Article  Google Scholar 

79.
Harper, M. P. & Peckarsky, B. L. Emergence cues of a mayfly in a high-altitude stream ecosystem: Potential response to climate change. Ecol. Appl. 16, 612–621 (2006).
PubMed  Article  PubMed Central  Google Scholar 

80.
Jonsson, M. et al. Climate change modifies the size structure of assemblages of emerging aquatic insects. Freshw. Biol. 60, 78–88 (2015).
Article  Google Scholar 

81.
Dudgeon, D. et al. Freshwater biodiversity: Importance, threats, status and conservation challenges. Biol. Rev. 81, 163–182 (2006).
PubMed  Article  PubMed Central  Google Scholar 

82.
Sala, O. E. Global biodiversity scenarios for the year 2100. Science 287, 1770–1774 (2000).
CAS  Article  Google Scholar 

83.
Likens, G. E. & Bormann, F. H. Linkages between terrestrial and aquatic ecosystems. Bioscience 24, 447–456 (1974).
Article  Google Scholar 

84.
England, L. E. & Rosemond, A. D. Small reductions in forest cover weaken terrestrial-aquatic linkages in headwater streams. Freshw. Biol. 49, 721–734 (2004).
Article  Google Scholar 

85.
Lafage, D. et al. Local and landscape drivers of aquatic-to-terrestrial subsidies in riparian ecosystems: A worldwide meta-analysis. Ecosphere 10, e02697 (2019).
Article  Google Scholar  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.
Simon, N., Cras, A. L., Foulon, E. & Lemée, R. Diversity and evolution of marine phytoplankton. C. R. Biol 332, 159–170 (2009).
PubMed  Article  Google Scholar 
2.
Field, C. B., Behrenfeld, M. J., Randerson, J. T. & Falkowski, P. Primary production of the biosphere: integrating terrestrial and oceanic components. Science 281, 237–240 (1998).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

3.
Diaz, S. et al. IPBES: Summary for Policymakers of the Global Assessment Report on Biodiversity and Ecosystem Services. IPBES secretariat, Bonn, Germany, 56 pp. (2019).

4.
IPCC. The Ocean and Cryosphere in a changing climate (2019).

5.
Blowes, S. A. et al. The geography of biodiversity change in marine and terrestrial assemblages. Science 366, 339–345 (2019).
ADS  CAS  PubMed  Article  Google Scholar 

6.
Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

7.
Gamfeldt, L. et al. Marine biodiversity and ecosystem functioning: what´s known and what´s next? Oikos 124, 252–265 (2015).
Article  Google Scholar 

8.
Kardol, P., Fanin, N. & Wardle, D. A. Long-term effects of species loss on community properties across contrasting ecosystems. Nature 557, 710–713 (2018).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

9.
Klawonn, I. et al. Cell-specific nitrogen- and carbon-fixation of cyanobacteria in a temperate marine system (Baltic Sea). Environ. Microb. 18(12), 4596–4609 (2016).
CAS  Article  Google Scholar 

10.
Cloern, J. E., Jassby, A. D., Schraga, T. S., Nejad, E. & Martin, C. Ecosystem variability along the estuarine salinity gradient: examples from long-term study of San Francisco Bay. Limnol. Oceanogr. 62, S272–S291 (2017).
ADS  CAS  Article  Google Scholar 

11.
Karlusich, J. J. P., Ibarbalz, F. M., & Bowler. C. Phytoplankton in the Tara Ocean. Annu. Rev. Mar. Sci. 12, 233–265 (2020).

12.
Lehtimäki, J., Moisander, P., Sivonen, K. & Kononen, K. Growth, nitrogen fixation, and nodularin production by two Baltic Sea cyanobacteria. Appl. Environ. Microb. 63(5), 1647–1656 (1997).
Article  Google Scholar 

13.
Laamanen, M. J., Forsström, L. & Sivonen, K. Diversity of Aphanizomenon flos aquae (cyanobacterium) populations along a Baltic Sea salinity gradient. Appl. Environ. Microb. 68, 5296–5303 (2002).
CAS  Article  Google Scholar 

14.
Mazur-Marzec, H., Żeglińska, L. & Pliński, M. The effect of salinity on the growth, toxin production, and morphology of Nodularia spumigena isolated from Gulf of Gdańsk, southern Baltic Sea. J. Appl. Phycol. 17, 171–179 (2005).
CAS  Article  Google Scholar 

15.
Teikari, J. E. et al. Insight into the genome and brackish water adaptation strategies of toxic and bloom-forming Baltic Sea Dolichospermum sp. UHCC 0315. Sci. Rep. 9, 4888 (2019).

16.
Logares, R. et al. Infrequent marine–freshwater transitions in the microbial world. Trends Microbiol. 17, 414–422 (2009).
CAS  PubMed  Article  PubMed Central  Google Scholar 

17.
Snoeijs-Leijonmalm, P. Patterns of biodiversity. In Biological Oceanography of the Baltic Sea (eds Snoeijs-Leijonmalm, P. et al.) 123–191 (Springer, Dordrecht, 2017).
Google Scholar 

18.
Olli, K., Ptacnik, R., Klais, R. & Tamminen, T. Phytoplankton species richness along coastal and estuarine salinity continua. Am. Nat. 194, 2 (2019).
Article  Google Scholar 

19.
Frenken, T. et al. Warming accelerates termination of a phytoplankton spring bloom by fungal parasites. Glob. Change Biol. 22, 299–309 (2016).
ADS  Article  Google Scholar 

20.
Romo, S. & Villena, M-J. Phytoplankton strategies and diversity under different nutrient levels and planktivorous fish densities in a shallow Mediterranean lake. J. Plankton Res. 27, 1273–1286 (2005).

21.
Gasiunaite, Z. R. et al. Seasonality of coastal phytoplankton in the Baltic Sea: Influence of salinity and eutrophication. Est. Coast Shelf S. 65, 239–252 (2005).
Article  Google Scholar 

22.
R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. R version 3.5.1. https://www.R-project.org/ (2018).

23.
Rodhe, J. On the dynamics of the large-scale circulation of Skagerrak. J. Sea Res. 35, 9–21 (1996).
ADS  Google Scholar 

24.
Dutkiewicz, S. et al. Dimensions of marine phytoplankton diversity. Biogeosci. 17, 609–634 (2020).
ADS  Article  Google Scholar 

25.
Olofsson, M., Suikkanen, S., Kobos, J., Wasmund, N. & Karlson, B. Basin-specific changes in filamentous cyanobacteria community composition across four decades in the Baltic Sea. Harm. Alg. 91, 101685 (2020).
CAS  Article  Google Scholar 

26.
Andersson, A., Höglander, H., Karlsson, C. & Huseby, S. Key role of phosphorus and nitrogen in regulating cyanoacterial community composition in the northern Baltic Sea. Estuar. Coast Shelf S. 164, 161–171 (2015).
CAS  Article  Google Scholar 

27.
Olofsson, M. et al. Nitrate and ammonium fluxes to diatoms and dinoflagellates at a single cell level in mixed field communities in the sea. Sci. Rep. 9, 1424 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

28.
Adler, PB.. et al. Productivity is a poor predictor of plant species richness. Science 333: 1750–1753 (2011).

29.
Irigoien, J., Huisman, R. & Harris, P. Global biodiversity patterns of marine phytoplankton and zooplankton. Nature 429, 863–867 (2004).
ADS  CAS  PubMed  Article  Google Scholar 

30.
Vallina, S. M. et al. Global relationship between phytoplankton diversity and productivity in the ocean. Nat. Comms. 5, 4299 (2011).
ADS  Article  CAS  Google Scholar 

31.
Righetti, D., Vogt, M., Gruber, N., Psomas, A. & Zimmermann, N. E. Global pattern of phytoplankton diversity driven by temperature and environmental variability. Sci. Adv. 5, eaau6253 (2019).

32.
Vidal, T., Calado, A. J., Moita, M. T. & Cunha, M. R. Phytoplankton dynamics in relation to seasonal variability and upwelling and relaxation patterns at the mouth of Ria de Aveiro (West Iberian Margin) over a four-year period. PLoS ONE 12, e0177237 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

33.
Shimadzu, H., Dornelas, M., Henderson, P. A. & Magurran, A. E. Diversity is maintained by seasonal variation in species abundance. BMC Biol. 11, 98 (2013).
PubMed  PubMed Central  Article  Google Scholar 

34.
Ryther, J. & Sanders, J. Experimental evidence of zooplankton control of the species composition and size distribution of marine phytoplankton. Mar. Ecol. Prog. Ser. 3, 279–283 (1980).
ADS  Article  Google Scholar 

35.
Thomas, K. & Nielsen, T. G. Regulation of zooplankton biomass and production in a temperate, coastal ecosystem. 1. Copepods. Limnol. Oceanogr. 39, 493–507 (1994).
ADS  Article  Google Scholar 

36.
Amin, S. A., Parker, M. S. & Armbrust, E. V. Interactions between diatoms and bacteria. Microbiol. Mol. Biol. R. 76, 667–684 (2012).
CAS  Article  Google Scholar 

37.
Durham, B. P. et al. Cryptic carbon and sulfur cycling between surface ocean plankton. Proc. Nat. Acad. Sci. USA 112, 453–457 (2015).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

38.
Cirri, E. & Pohnert, G. Algae bacteria interactions that balance the planktonic microbiome. New Phytol. 223, 1–7 (2019).
Article  Google Scholar 

39.
Bunse, C. et al. High frequency multi-year variability in Baltic Sea Microbial plankton stocks and activities. Front. Mar. Sci. 9, 3296 (2019).
Google Scholar 

40.
Gallego, I., Venail, P. & Ibelings, B. W. Size differences predict niche and relative fitness differences between phytoplankton species but not their coexistence. ISME J. 13, 1133–1143 (2019).
PubMed  PubMed Central  Article  Google Scholar 

41.
Søndergaard, M., Jensen, L. M. & Ærtebjerg, G. Picoalgae in danish coastal waters during summer stratification. Mar. Ecol. Prog. Ser. 79, 139–149 (1992).
ADS  Article  Google Scholar 

42.
Jochem, F. Distribution and importance of autotrophic ultraplankton in a boreal inshore area (Kiel Bight, Western Baltic). Mar. Ecol. Prog. Ser. 53, 153–168 (1989).
ADS  Article  Google Scholar 

43.
Hu, Y. O. O., Karlson, B., Charvet, S. & Andersson, A. F. Diversity of pico- to mesoplankton along the 2000 km salinity gradient of the Baltic Sea. Front. Microbiol. 7, 17 (2016).
Google Scholar 

44.
Gran-Stadniczeñko, S. et al. Protist diversity and seasonal dynamics in skagerrak plankton communities as revealed by metabarcoding and microscopy. J. Eukaryot. Microbiol. 66, 494–513 (2019).
PubMed  Article  CAS  Google Scholar 

45.
Utermöhl, H. Zur Vervollkomnung der quantitativen Phytoplankton-Methodik. Mitt. Int. Ver. Theor. Angew. Limnol. 9, 1–38 (1958).
Google Scholar 

46.
Olenina, I. et al. Biovolumes and size-classes of phytoplankton in the Baltic Sea HELCOM Balt. Sea Environ. Proc. 106, 144 (2006).
Google Scholar 

47.
Menden-Deuer, S. & Lessard, E. J. Carbon to volume relationship for dinoflagellates, diatoms and other protest plankton. Limnol. Oceanogr. 45, 569–579 (2000).
ADS  CAS  Article  Google Scholar 

48.
HELCOM 2017. Manual for marine monitoring in the COMBINE Programme of HELCOM. Part B General guidelines on quality assurance for monitoring in the Baltic Sea. Annex B-9 Technical note on the determination of nutrients.

49.
Karlson, B. et al. Plankton toolbox—open source software making it easier to work with plankton data. In: MacKenzie AL (ed). Proceedings of 16th International Conference on Harmful Algae. Cawthron Institute, Nelson, New Zealand and the International Society for the Study of Harmful Algae (ISSHA), pp. 194–197 (2016).

50.
Hsieh, T. C., Ma, K. H. & Chao, A. iNEXT: an R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods Ecol. Evol. 7, 1451–1456 (2016).
Article  Google Scholar 

51.
Chao, A., Chiu, C. H. & Jost, L. Unifying species diversity, phylogenetic diversity, functional diversity, and related similarity and differentiation measures through Hill numbers. Annu. Rev. Ecol. Evol. S. 45, 297–324 (2014).
Article  Google Scholar 

52.
Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).
Article  Google Scholar 

53.
Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4(2), 133–142 (2012).
Article  Google Scholar 

54.
Harrison, X. A. et al. A brief introduction to mixed effects modelling and multi-model inference in ecology. PeerJ. 6, e4794 (2018).
PubMed  PubMed Central  Article  Google Scholar  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