Philippa Kaur

Comparative characteristics of rations for feeding cattle from different regions of the Republic of Kazakhstan and the impact of animal feeding types on the faecal microbiotaDue to the huge differences in the natural and climatic conditions of Kazakhstan, animals from different regions of Kazakhstan were enrolled for this study. The difference in soil and climatic conditions of different zones has a significant impact on the type of feeding (Table 1) and the composition of diets, which has a certain effect on the microbiota of intestinal contents and methanogenic archaea in particular.Table 1 Animal diets in different regions of Kazakhstan.Full size tableIn the course of the research work, regions and specific agricultural formations were identified in the context of these regions.In North Kazakhstan, the fodder base is represented by such fodders as alfalfa hay, herb hay, alfalfa haylage, wheat straw, fodder wheat and sunflower cake. The feed is mainly of 2 quality classes. The live weight of cattle ranged from 375 to 480 kg. Feeding type: hay-concentrate and haylage-hay-concentrate.In the Western region, the animals were on the pasture, represented by the green mass of feather grass, hair, sage and tansy. Beef cattle are represented by the following breeds: Kazakh white-headed, Aberdeen-Angus and Hereford. Average live weight is 350–550 kg.In the Southeast region, the fodder base consists of wheat hay, sainfoin + alfalfa hay, mountain hay, herb haylage, corn silage and crushed corn. The feed is mainly of 2 and 3 classes. Hay-concentrate type of feeding is used, as well as pastures. Livestock of Angus, Kazakh white-headed breeds and animals of the local population are kept. Live weight of young animals is in the range of 360–380 kg.The diets of the Southern Region include natural grass hay, alfalfa hay, wheat straw, alfalfa haylage and concentrates. Hay-concentrate type of livestock feeding is widespread in the region. The average live weight of bulls for fattening of the Kazakh white-headed and Angus breeds—360–420 kg with a daily increase in live weight of 870–920 g.The composition of the fecal microbiota depending on the type of feeding is presented in Table 2.Table 2 The content of methanogenic archaea in feces.Full size tableFrom the data of Table 2 it follows that the largest amount of Bacteria was found in the faeces of animals with silage-concentrated feeding (98.59 ± 13.0%), and the smallest—with pasture-concentrated (93.24 ± 3.73%) and haylage—concentrated (93.8 ± 12.41%) types of feeding. The differences amounted to 5.35 and 4.79 absolute percent, respectively. However, the differences were not significant at P  More

Systematic palaeontologyOrder Hemiptera Linnaeus, 1758Suborder Sternorrhyncha Amyot et Audinet-Serville, 1843Superfamily Protopsyllidioidea Carpenter, 1931Family Postopsyllidiidae Hakim, Azar et Huang, 2019Genus Megalophthallidion Drohojowska et Szwedo, gen. nov.LSID urn:lsid:zoobank.org:act:A6F71390-9B8E-4A19-8F30-C2A024B6EFB1Type speciesMegalophthallidion burmapateron Drohojowska et Szwedo, sp. nov.; by present designation and monotypy.EtymologyGeneric name is derived from Classic Greek megas (μέγας)—large, ophthalmos (ὀφθαλμός)—an eye and Greek form of generic name Psyllidium. Gender: masculine.Type localityNorthern Myanmar: state of Kachin, Noije bum 2001 Summit Site amber mine in the Hukawng Valley, SW of Maingkhwan.Type stratumLowermost Cenomanian, Upper Cretaceous (‘mid-Cretaceous’).DiagnosisHead capsule with 12 stiff setae on tubercles (18 setae in Postopsyllidium); fore wing without pterostigma (tiny pterostigma, widening of ScP + RA present in Postopsyllidium); vein CuP not thickened distally (distinctly thickened distally in Postopsyllidium); profemur with a row of ventral (ventrolateral) setae (two rows in Postopsyllidium).Megalophthallidion burmapateron Drohojowska et Szwedo, sp. nov.LSID urn:lsid:zoobank.org:act:F3F971F4-AE04-4F41-98B0-9A0A04470625.(Figs. 1A–F, 2A–I).Figure 1Megalophthallidion burmapteron gen. et sp. nov., holotype (MAIG 6687), imago. (A) Photo of body, ventral side; (B) photo of right antennae and (C) drawing of antenna; (D) drawing of body, dorsal side; (E) drawing of thorax structure with sclerites marked: red—pronotum; orange—mesopraescutum; yellow—mesoscutum; light green—mesoscutellum, dark green—mesopostnotum; light blue—metascutum; dark blue—metascutellum; violet—metapostnotum; (F) photo of thorax dorsal side. Scale bars: 0.5 mm (A), 0.2 mm (B–D), 0.1 mm (F).Full size imageFigure 2Megalophthallidion burmapteron gen. et sp. nov., holotype (MAIG 6687), imago. (A) Photo of right fore wing; (B) photo of right wings; (C) photo of antenna and proleg; (D) photo of proleg and mesoleg, and (E) photo of femur of proleg, and (F) photo of right metatarsus and left mesotarsus in the background, and (G) photo of right mesotarsus of mesoleg, and (H) Photo of tarsi; (I) photo of male genital block. Scale bars: 0.5 mm (A–D), 0.2 mm (B,E,F,H), 0.1 mm (G,I).Full size imageMaterialHolotype, number MAIG 6687 (BUB 96), deposited in Museum of Amber Inclusions (MAIG), University of Gdańsk, Poland. Imago, a complete and well-preserved male. Piece of amber 8 × 6 × 3 mm, cut from larger lump, polished flat on both sides.Type localityNorthern Myanmar: state of Kachin, Noije bum 2001 Summit Site amber mine in the Hukawng Valley, SW of Maingkhwan.Type stratumLowermost Cenomanian, Upper Cretaceous (‘mid-Cretaceous’).DiagnosisAs for the genus with the following additions: three ocelli distinct, antennomere IX the longest, about as long as pedicel, antennomeres III–VII and XI of similar length, antennomere XII the shortest, subconically tapered in apical portion. Paramere lobate, ventral margin with acute, small process, apical and dorsal margins rounded. Aedeagus geniculately bent at base, directed dorsally, tapered apicad.DescriptionMale (Figs. 1A–F, 2A–I). Head with compound eyes distinctly wider than pronotum (Fig. 1D–F). Compound eyes subglobular, protruding laterally. Vertex short in midline, about 2.5 times as wide as posterior margin and as long in middle; trapezoidal, anterior margin slightly arched, lateral margins diverging posteriad, posterior margin shallowly arched, disc of vertex with distinct setae on large tubercles: four setae at posterior margin, two at anterior angles of compound eyes, two medial, over the median ocellus. Three ocelli present, median ocellus distinct, visible from above, lateral ocelli near anterior angles of compound eyes. Frons about as wide as long in midline, two rows of setae on tubercles, upper row at level of median ocellus, lower one, below half of compound eye height. Clypeus, elongate, triangular, in lower portion roof-like; two setae on tubercles near upper margin. Genae very narrow. Rostrum reaching slightly beyond mesocoxae, apical segment slightly shorter than subapical one, darker. Antennae bases placed at lower margin of compound eyes; antennal fovea elevated; scapus shorter than pedicel, cylindrical; pedicel cylindrical; antennomeres IIIrd–VIIth and XIth of similar length, VIIIth slightly longer than VIIth, as long as Xth antennomere, IXth the longest, XIIth the shortest, tapered apically; rhinaria absent.Thorax (Fig. 1D–F): pronotum quadrangular, about as long as mesothorax; pronotum with anterior and posterior margins parallel, merely arcuate, disc with transverse groove in the median portion, lateral margins slightly arcuate, two distinct setae on tubercles in anterolateral angle, two setae on tubercles anterior margin at distance1/3 to median line, three distinct setae on tubercles in posterolateral angles. Mesopraescutum subtriangular, with apex widely rounded, about 0.4 times as wide as pronotum, about 0.4 times as long as wide, delicately separated from mesoscutum. Mesoscutum as wide as pronotum at widest point, distinctly narrowed medially, anterior angles rounded, anterolateral margin sigmoid, lateral angle acute, posterior angles wide, posterior margin V-shape incised, posterolateral areas of mesoscutum disc declivent posteriorly; disc with two setae on tubercles, at 1/3 of mesoscutum width. Mesoscutellum about as long as wide, diamond-shape, anterior and lateral angles acute, posterior angle rounded. Mesopostnotum in form of transverse band, slightly widened in median portion. Metascutum narrower than mesoscutum, anterior angles widely rounded, lateral angles acute, anterolateral margin concave, posterior margin arcuate, with deep median arcuate incision. The suture between metascutum and metascutellum weakly visible, metascutellum subtriangular, longer than wide at base.Parapteron with three distinct setae.Fore wing (Fig. 2A,B) membranous, narrow, elongate, about 3.5 times as long as wide, widest at 2/3 of length. Anterior margin merely arcuate, slightly bent at very base, anteroapical angle widely arcuate, apex rounded, posteroapical angle widely arcuate, tornus arcuate, claval margin straight, with incision between terminals of Pcu (claval apex) and A1. Stem ScP + R + MP + CuA slightly arcuate, very short stalk ScP + R + MP + CuA leaving basal cell, stem ScP + R oblique, straight, forked in basal half of fore wing length, branch ScP + RA, oblique, reaching anterior margin slightly distally of half of fore wing length, slightly distally of ending of CuA2 branch; branch RP slightly arcuate, a little more curved in basal section, reaching margin at anteroapical angle; stalk MP + CuA slightly shorter than basal cell; stem MP almost straight, forked in apical half of fore wing, at about 2/3 of fore wing length, with three terminals reaching margin between apex and posteroapical angle; stem CuA shorter than branches CuA1 and CuA2, about half as long as branch CuA1; claval vein CuP weak at base, not thickened distally; claval vein Pcu straight, claval vein A1 straight. Basal cell present, subtriangular, about twice as long as wide, basal veinlet cua-cup oblique, no other veinlets present; cell r (radial) very long, longer than half of fore wing length; cell m (medial) the shortest, shorter than cell cu (areola postica). Margins of fore wing with fringe of long setae, starting on costal margin near base of fore wing, ending at level of middle of cell cu; longitudinal veins with distinct, scarcely but evenly dispersed, movable setae; terminal section of CuP with two setae; costal margin with row of short, densely distributed setae, apical margin, tornus and claval margin with rows of scaly setae.Hind wing (Fig. 2B) membranous, shorter than fore wing, 3.23 times as long as wide. Costal margin bent at base, then almost straight up to the level of ScP + RA end and wing coupling lobe, then straight to anteroapical angle, anteroapical angle widely arcuate, apex arcuate, posteroapical angle arcuate, tornus straight, claval margin merely arcuate, posteroclaval angle angulate; stem ScP + R + MP bent at base, then straight, stem ScP + R short, branch ScP + RA short, about as long as stem ScP + R, branch RP arcuate basally than straight, reaching apex; stem MP arcuate, forked slightly distad CuA1 terminus level, branch MP1+2 slightly arcuate, reaching margin at posteroapical angle, branch MP3+4 straight, reaching tornus; stem CuA slightly bent at base, then straight, forked slightly distad ScP + R forking, branch CuA1 arcuate, branch CuA2 short, straight, slightly oblique, reaching tornus; claval vein CuP weak, visible only at base, claval vein Pcu slightly arcuate; wing coupling apparatus (fold) with a few short setae.Legs slender, relatively long, profemora armed (Fig. 2C–H). Procoxa as long as profemur, narrow, flattened. Protrochanter scaphoid, elongate, with long apical and subapical setae. Profemur flattened laterally, about as long as protibia, ventrally armed with four large setae on elevated plinths; dorsal margin with row of short, decumbent setae. Protibia narrow, rounded in cross section, covered with short setae, a few longer setae in distal portion. Protarsus—single, long tarsomere, plantar surface with row of semi-erect setae; tarsal claws long, straight, directed ventrally, no arolium nor empodium.Mesocoxa elongate, narrow, slightly flattened. Mesotrochanter scaphoid. Mesofemur slender, flattened laterally, dorsal margin with short setae. Mesotibia subequal to mesofemur, slender, covered with setae, two apical setae slightly thicker and longer. Mesotarsus with three tarsomeres, basimesotarsomere the longest, shorter than cumulative length of mid- and apical mesotarsomere, plantar margins with setae, two apical setae slightly longer and thicker; midmesotarsomere the shortest, 1/3 of basimesotarsomere length, a few setae on plantar surface; apical tarsomere shorter than basimesotarsomere, twice as long as midmesotarsomere, plantar surface with a few, scarcely dispersed setae, tarsal claws long, narrow, directed ventrally, no arolium nor empodium.Metacoxa conical, narrow. Metatrochanter scaphoid, elongate. Metafemur slender, laterally flattened, longer than mesofemur, dorsal margin with row of short setae. Metatibia, long, slender, 1.6 times as long as metafemur, with suberect setae of different size, two larger and longer and two shorter setae subapical setae. Metatarsus slightly less than half of metatibia length, with three tarsomeres, basimetatarsomere the longest, more than twice as long as apical metatarsomere, 1.5 times as long as combined length of mid- and apical metatarsomere, plantar surface with scarce decumbent setae; mid metatarsomere the shortest, 1/4 of basimetatarsomere length, plantar surface with a few setae, two apical ones slightly thicker; apical metatarsomere about 0.4 of basimetatarsomere length, with scarcely dispersed setae on along plantar surface; tarsal claws, long, slender, other pretarsal structures absent.Abdomen (Fig. 1F) narrowly attached to thorax, tergite segment shorter, 2nd tergite distinctly longer, 3rd to 8th tergites of similar length; pygofer narrowing apicad, ventral margin strongly elongated posteriorly; anal tube short, directed posterodorsad, anal style shorter than anal tube. Paramere lobate, ventral margin with acute, small process, apical and dorsal margins rounded. Aedeagus (Fig. 2I) geniculately bent at base, directed dorsad, tapered apicad.Female. Unknown.Megalophthallidion sp. (5th instar nymph)(Figs. 3A–D, 4A–F)Figure 3Megalophthallidion sp. (MAIG 6688), nymph. (A) Photo of body, dorsal side and (B) drawing of body dorsal side; (C) photo of body dorsal side and (D) drawing of body ventral side. Scale bars: 0.5 mm (A–D).Full size imageFigure 4Megalophthallidion sp. (MAIG 6688), nymph. Photo of clypeus and (B) drawing of clypeus; (C) photo of proleg, and (D) photo of mesoleg, and (E) photo of metaleg; (F) photo of posterior part of abdomen ventral side. Scale bars: 0.1 mm (A–F).Full size imageMaterialNymph, 5th instar, MAIG 6688 (BUB 1799), deposited in Museum of Amber Inclusions (MAIG), University of Gdańsk, Poland. Piece of amber 13 × 6 × 2 mm, cut from larger lump, polished flat on one side, more convex on the other.Diagnostic charactersThe nymph of Megalophthallidion gen. nov. is similar in general body shape to the only known fossil protopsyllidioidean nymph described from Lower Cretaceous Lebanese amber—Talaya batraba Drohojowska et Szwedo, 2013. The nymph of Talaya batraba is 2nd or 3rd instar, therefore some features are difficult to compare with this last instar nymph of Megalophthallidion gen. nov. The morphological states observed in those two specimens are: head covered with strongly expanded disc and expanded disc of pronotum, however shapes and ratios of these structures differ; compound eyes on ventral side of head, shifted laterad (ommatidia on cones in T. batraba, while ventroposterior expansions are present in Megalophthallidion gen. nov.); compound eyes visible from above as short, stout cones in fissure between posterior margin of disc (hypertrophied vertex) and anterior margin of pronotum (compound eyes (?) are visible on dorsal side of Permian Aleuronympha bibulla Riek, 1974); in Megalophthallidion gen. nov. rostrum reached mesocoxa, while in Talaya batraba distinctly exceeds length of the body; abdomen with 9 segments; tergites of abdominal segments 5th–9th expanded posterolaterad in form of fan-like expansion; 9th abdominal segment short, placed ventral; anal tube short, cylindrical, epiproct (?) globular.DescriptionNymph, 5th instar (Figs. 3A–D, 4A–F). Body oval shaped, dorso-ventrally flattened, 1.5 times longer than wide with segmentation visible; on the ventral side slightly concave. Length of body c. 1.56 mm long, outline, in dorsal view, maximum width of body 0.94 mm; length of head and pronotum (cephaloprothorax) c. 0.46 mm in midline, width c. 0.83 mm; cumulative length of mesonotum + metanotum c. 0.25 mm; abdomen c. 0.8 mm long. Dorsal side (Fig. 3A,B) with distinct median line (ecdysial line), not reaching anterior or posterior margin of the body, the line distinctly roof-like in abdominal portion. Anterior margin of head (cephaloprothorax) disc arcuate, lateral angles rounded; anterior margin of pronotum arcuate, lateral margins arcuately diverging posteriad, posterior margin distinctly arcuate, anterior angles widely rounded, posterior angles acutely rounded, disc elevated, convex, lateral portions declivitous; the fissure between posterior margin of head disc and anterior margin of pronotum narrow, widened medially, with stalked compound eyes popping out.Head partly separated from prothorax, wide in ventral view. Bases of antennae protruding anterolaterally, wide, anterior margin arcuate, with a small lump extending anteriorly connecting margin with vertex expansion. Suture separating anteclypeus and postclypeus visible in ventral aspect (Fig. 4A,B). Postclypeus about three times as long as wide, oval, slightly swollen, without any setae; weak traces of salivary pump muscle attachments visible. Anteclypeus about as long as postclypeus, widened in upper section below clypeal suture, convex, carinately elevated in lower section, with sides distinctly declivitous, clypellus long, carinately elevated. Lora (mandibulary plates) distinct, separated from anteclypeus by shallow suture, with upper angles at half of postclypeus length, lower angles at half of anteclypeus length, about as wide as half of postclypeus width. Maxillary plates narrow. Genal portion of head enlarged, medial portion arcuately convex; lateral sections narrowing laterally, terminally encircling bases of compound eyes. Antennae short (Fig. 3C,D), placed in front of genal portion. Antennal flagellum indistinctly subdivided into four segments. Rostrum (Fig. 4A,B) three-segmented, 0.2 mm long, with apex reaching apex of mesocoxae; apical segment about 2.5 times as long as subapical one.No lateral sclerites on meso- and metathorax, only one plus one large medial sclerite on both meso- and metathorax. Mesothoracic and metathoracic wing pads distinct, wide, subtriangular, with posterior apices directed posteriorly; lateral portions of mesothoracic wing pads arcuate. Fore wing pad 0.6 mm long, with small, straight humeral lobe, forming a right angle, not protruding anteriorly. Mesothoracic tergites slightly larger than metathoracic segments (respectively c. 0.14 mm and c. 0.12 mm long in midline, 0.26 mm and 0.27 mm in lateral lines); mesothoracic tergum with distinct median elevation (low double crest with ecdysial line in between), slightly wider than long in midline, anterior margin arcuate, lateral margins straight, subparallel, posterior margin concave. Metathoracic wing pad apex slightly exceeding mesothoracic wing pad. Metathoracic tergum wider than long, slightly shorter than mesothoracic tergum, with distinct elevation in the middle.Legs relatively long (Figs. 3C,D, 4C–E). Coxae of legs placed near the median axis of the body. Prolegs: procoxal pit with margins elevated, procoxa conical (c. 0.1 mm long), protrochanter scaphoid, about as long as procoxa, profemur c. 0.13 mm long, slightly flattened laterally, merely thickened, protibia longer than profemur, c. 0.23 mm long; tarsus shorter than protibia, basiprotarsomere about as long as apical protarsomere, the latter with distinct tarsal claws, and wide arolium. Mesoleg similar to proleg, mesocoxa conical (c. 0.1 mm long), mesotrochanter scaphoid, mesofemur (c. 0.13 mm) slightly flattened laterally, mesotibia slightly longer than mesofemur (c. 0.18 mm), mesotarsus slightly shorter than mesotibia, three-segmented, basimesotarsomere the longest (c. 0.07 mm), about as long as combined length of mid- and apical mesotarsomeres (c. 0.04 mm respectively), arolium wide, tarsal claws distinct. Metaleg: metacoxa conical (c. 0.1 mm), metatrochanter scaphoid, about as long as metacoxa (c. 0.12 mm). Metafemur (c. 0.17 mm) slightly more thickened than pro- and mesofemur, metatibia slightly longer (0.19 mm) than pro- and mesotibiae. Metatarsus three-segmented: basimetatarsomere about as long (0.08 mm) as combined length of mid- and apical metatarsomeres (0.04 mm respectively), arolium lobate, wide, tarsal claws distinct, widely spread.Abdomen (Fig. 3A–D) 9-segmented, narrow at base, widening fan-shape posteriorly, 1st segment visible from above, segmentation visible, abdominal terga 5th–9th expanded posterolaterally. Tergites carinately elevated in the middle, separated by ecdysial line. 1st sternite visible in ventral view, sternites 2nd–4th fused medially, sternites 5th–9th separated; 9th abdominal segment short (Fig. 4F), placed ventrally, under tergal expansion; anal tube short, cylindrical, epiproct (?) globular. More

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

Google Scholar 
Grzybowski, M. & Glińska-Lewczuk, K. Principal threats to the conservation of freshwater habitats in the continental biogeographical region of Central Europe. Biodivers. Conserv. 28, 4065–4097 (2019).Article 

Google Scholar 
Søndergaard, M. & Jeppesen, E. Anthropogenic impacts on lake and stream ecosystems, and approaches to restoration. J. Appl. Ecol. 44, 1089–1094 (2007).Article 

Google Scholar 
Paerl, H. W., Fulton, R. S., Moisander, P. H. & Dyble, J. Harmful freshwater algal blooms, with an emphasis on cyanobacteria. Sci. World J. 1, 76–113 (2001).CAS 
Article 

Google Scholar 
Krztoń, W., Kosiba, J., Pociecha, A. & Wilk-Woźniak, E. The effect of cyanobacterial blooms on bio- and functional diversity of zooplankton communities. Biodivers. Conserv. 28, 1815–1835 (2019).Article 

Google Scholar 
Adrian, R. et al. Lakes as sentinels of climate change. Limnol. Oceanogr. 54, 2283–2297 (2009).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Dokulil, M. T. et al. Increasing maximum lake surface temperature under climate change. Clim. Change 165, 1–17 (2021).Article 

Google Scholar 
Yan, X. et al. Climate warming and cyanobacteria blooms: Looks at their relationships from a new perspective. Water Res. 125, 449–457 (2017).CAS 
PubMed 
Article 

Google Scholar 
Paerl, H. W., Hall, N. S. & Calandrino, E. S. Controlling harmful cyanobacterial blooms in a world experiencing anthropogenic and climatic-induced change. Sci. Total Environ. 409, 1739–1745 (2011).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Anderson, D., Glibert, P. & Burkholder, J. Harmful algal blooms and eutrophication: Nutrient sources, compositions, and consequences. Estuaries 25, 704–726 (2002).Article 

Google Scholar 
Li, D. et al. Factors associated with blooms of cyanobacteria in a large shallow lake, China. Environ. Sci. Eur. https://doi.org/10.1186/s12302-018-0152-2 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Rigosi, A., Carey, C. C., Ibelings, B. W. & Brookes, J. D. The interaction between climate warming and eutrophication to promote cyanobacteria is dependent on trophic state and varies among taxa. Limnol. Ocean. 59, 99–144 (2014).Article 

Google Scholar 
Paerl, H. W. & Huisman, J. Blooms like it hot. Science 320, 57–58 (2008).CAS 
PubMed 
Article 

Google Scholar 
Paerl, H. W. Nuisance phytoplankton blooms in coastal, estuarine, and inland waters 1. Limnol. Oceanogr. 33, 823–843 (1988).ADS 
CAS 

Google Scholar 
Schindler, D. W. et al. Eutrophication of lakes cannot be controlled by reducing nitrogen input: Results of a 37-year whole-ecosystem experiment. Proc. Natl. Acad. Sci. 105, 11254–11258 (2008).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Förster, W. et al. Phosphorous supply to a eutrophic artificial lake: Sedimentary versus groundwater sources. Water 13, 1–20 (2021).ADS 
Article 

Google Scholar 
Lang, P. et al. Phytoplankton community responses in a shallow lake following lanthanum-bentonite application. Water Res. 97, 55–68 (2016).CAS 
PubMed 
Article 

Google Scholar 
Lürling, M. & van Oosterhout, F. Case study on the efficacy of a lanthanum-enriched clay (Phoslock®) in controlling eutrophication in Lake Het Groene Eiland (The Netherlands). Hydrobiologia 710, 253–263 (2013).Article 

Google Scholar 
Bishop, W. M. & Richardson, R. J. Influence of Phoslock® on legacy phosphorus, nutrient ratios, and algal assemblage composition in hypereutrophic water resources. Environ. Sci. Pollut. Res. 25, 4544–4557 (2018).CAS 
Article 

Google Scholar 
Drugă, B. et al. The impact of cation concentration on Microcystis (cyanobacteria) scum formation. Sci. Rep. 9, 1–11 (2019).ADS 
Article 

Google Scholar 
Stockenreiter, M., Isanta Navarro, J., Buchberger, F. & Stibor, H. Community shifts from eukaryote to cyanobacteria dominated phytoplankton: The role of mixing depth and light quality. Freshw. Biol. 66, 2145–2157 (2021).Article 

Google Scholar 
Drugă, B. et al. term acclimation might enhance the growth and competitive ability of Microcystis aeruginosa in warm environments. Freshw. Biol. https://doi.org/10.1111/fwb.13865 (2022).Article 

Google Scholar 
Fordham, D. A. Mesocosms reveal ecological surprises from climate change. PLOS Biol. 13, 1–7 (2015).Article 

Google Scholar 
Reinl, K. L. et al. Cyanobacterial blooms in oligotrophic lakes: Shifting the high-nutrient paradigm. Freshw. Biol. 66, 1846–1859 (2021).Article 

Google Scholar 
Tillich, U. M., Wolter, N., Franke, P., Dühring, U. & Frohme, M. Screening and genetic characterization of thermo-tolerant Synechocystis sp. PCC6803 strains created by adaptive evolution. BMC Biotechnol. 14, 1–15 (2014).Article 

Google Scholar 
Burki, F., Roger, A. J., Brown, M. W. & Simpson, A. G. B. The new tree of eukaryotes. Trends Ecol. Evol. 35, 43–55 (2020).PubMed 
Article 

Google Scholar 
LaPanse, A. J., Krishnan, A. & Posewitz, M. C. Adaptive Laboratory Evolution for algal strain improvement: Methodologies and applications. Algal Res. 53, 102122 (2021).Article 

Google Scholar 
Deeg, C. M. et al. Chromulinavorax destructans, a pathogen of microzooplankton that provides a window into the enigmatic candidate phylum Dependentiae. PLOS Pathog. 15, e1007801 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Glöckner, F. O. et al. Complete genome sequence of the marine planctomycete Pirellula sp. strain 1. Proc. Natl. Acad. Sci. U. S. A. 100, 8298–8303 (2003).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sowell, S. M. et al. Transport functions dominate the SAR11 metaproteome at low-nutrient extremes in the Sargasso Sea. ISME J. 3, 93–105 (2009).CAS 
PubMed 
Article 

Google Scholar 
Chiriac, M.-C. et al. Ecogenomics sheds light on diverse lifestyle strategies in freshwater CPR. Microbiome https://doi.org/10.21203/rs.3.rs-776685/v2 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Von Der Heyden, S., Chao, E. E. & Cavalier-Smith, T. Genetic diversity of goniomonads: An ancient divergence between marine and freshwater species. Eur. J. Phycol. 39, 343–350 (2004).Article 

Google Scholar 
Kim, B. R., Nakano, S. I., Kim, B. H. & Han, M. S. Grazing and growth of the heterotrophic flagellate Diphylleia rotans on the cyanobacterium Microcystis aeruginosa. Aquat. Microb. Ecol. 45, 163–170 (2006).Article 

Google Scholar 
Varol, M., Bekleyen, A., Şen, B. & Gökot, B. First record of the order Choanoflagellida in Turkey. Turkish J. Fish. Aquat. Sci. 11, 1–2 (2011).
Google Scholar 
Cabrerizo, M. J. et al. Warming and CO2 effects under oligotrophication on temperate phytoplankton communities. Water Res. https://doi.org/10.1016/j.watres.2020.115579 (2020).Article 
PubMed 

Google Scholar 
Maberly, S. C., Pitt, J.-A., Davies, P. S. & Carvalho, L. Nitrogen and phosphorus limitation and the management of small productive lakes. Inl. Waters 10, 159–172 (2020).Article 

Google Scholar 
Li, J., Sellner, K., Place, A., Cornwell, J. & Gao, Y. Mitigation of cyanohabs using phoslock® to reduce water column phosphorus and nutrient release from sediment. Int. J. Environ. Res. Public Health https://doi.org/10.3390/ijerph182413360 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Nwosu, E. C. et al. Species-level spatio-temporal dynamics of cyanobacteria in a hard-water temperate lake in the southern Baltics. Front. Microbiol. 12, 1–17 (2021).ADS 
Article 

Google Scholar 
Vörös, L., Callieri, C., V-Balogh, K. & Bertoni, R. Freshwater picocyanobacteria along a trophic gradient and light quality range. Hydrobiologia 369–370, 117–125 (1998).Article 

Google Scholar 
Camacho, A. On the occurrence and ecological features of deep chlorophyll maxima (DCM) in Spanish stratified lakes. Limnetica 25, 453–478 (2006).Article 

Google Scholar 
Cabello-Yeves, P. J. et al. Novel synechococcus genomes reconstructed from freshwater reservoirs. Front. Microbiol. 8, 1151 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Prihantini, N. B., Addana, F., Sjamsuridzal, W. & Yokota, A. The effect of temperature on the growth of genus Synechococcus isolated from four Indonesian hot springs and Agathis small lake of Universitas Indonesia. AIP Conf. Proc. 1729 (2016).Callieri, C. Synechococcus plasticity under environmental changes. FEMS Microbiol. Lett. 364, 1–8 (2017).Article 

Google Scholar 
Acinas, S. G., Haverkamp, T. H. A., Huisman, J. & Stal, L. J. Phenotypic and genetic diversification of Pseudanabaena spp. (cyanobacteria). ISME J. 3, 31–46 (2009).CAS 
PubMed 
Article 

Google Scholar 
Kehoe, D. M. & Gutu, A. Responding to color: The regulation of complementary chromatic adaptation. Annu. Rev. Plant Biol. 57, 127–150 (2006).CAS 
PubMed 
Article 

Google Scholar 
Bell, T. & Kalff, J. The contribution of picophytoplankton in marine and freshwater systems of different trophic status and depth. Limnol. Oceanogr. 46, 1243–1248 (2001).ADS 
Article 

Google Scholar 
Jezberová, J. & Komárková, J. Morphological transformation in a freshwater Cyanobium sp. induced by grazers. Environ. Microbiol. 9, 1858–1862 (2007).PubMed 
Article 

Google Scholar 
Chu, Z., Jin, X., Iwami, N. & Inamori, Y. The effect of temperature on growth characteristics and competitions of Microcystis aeruginosa and Oscillatoria mougeotii in a shallow, eutrophic lake simulator system. In Eutrophication of Shallow Lakes with Special Reference to Lake Taihu, China (eds Qin, B. et al.) 217–223 (Springer, 2007).Chapter 

Google Scholar 
Ma, J. et al. The persistence of cyanobacterial (Microcystis spp,) blooms throughout winter in Lake Taihu, China. Limnol. Oceanogr. 61, 711–722 (2016).ADS 
Article 

Google Scholar 
Davis, T. W., Berry, D. L., Boyer, G. L. & Gobler, C. J. The effects of temperature and nutrients on the growth and dynamics of toxic and non-toxic strains of Microcystis during cyanobacteria blooms. Harmful Algae 8, 715–725 (2009).CAS 
Article 

Google Scholar 
Jankowiak, J., Hattenrath-Lehmann, T., Kramer, B. J., Ladds, M. & Gobler, C. J. Deciphering the effects of nitrogen, phosphorus, and temperature on cyanobacterial bloom intensification, diversity, and toxicity in western Lake Erie. Limnol. Oceanogr. 64, 1347–1370 (2019).ADS 
CAS 
Article 

Google Scholar 
Martin, R. M. et al. Episodic decrease in temperature increases mcy gene transcription and cellular microcystin in continuous cultures of Microcystis aeruginosa PCC 7806. Front. Microbiol. 11, 3081 (2020).Article 

Google Scholar 
You, J., Mallery, K., Hong, J. & Hondzo, M. Temperature effects on growth and buoyancy of Microcystis aeruginosa. J. Plankton Res. 40, 16–28 (2018).Article 

Google Scholar 
Aguilar, P., Acosta, E., Dorador, C. & Sommaruga, R. Large differences in bacterial community composition among three nearby extreme waterbodies of the high Andean plateau. Front. Microbiol. 7, 1–8 (2016).Article 

Google Scholar 
Echeverría-Vega, A. et al. Watershed-induced limnological and microbial status in two oligotrophic andean lakes exposed to the same climatic scenario. Front. Microbiol. 9, 1–17 (2018).Article 

Google Scholar 
Schmidt, M. L., White, J. D. & Denef, V. J. Phylogenetic conservation of freshwater lake habitat preference varies between abundant bacterioplankton phyla. Environ. Microbiol. 18, 1212–1226 (2016).PubMed 
Article 

Google Scholar 
Kaboré, O. D., Godreuil, S. & Drancourt, M. Planctomycetes as host-associated bacteria: A perspective that holds promise for their future isolations, by mimicking their native environmental niches in clinical microbiology laboratories. Front. Cell Infect. Microbiol. 10, 1–19 (2020).Article 

Google Scholar 
Song, H., Li, Z., Du, B., Wang, G. & Ding, Y. Bacterial communities in sediments of the shallow Lake Dongping in China. J. Appl. Microbiol. 112, 79–89 (2012).CAS 
PubMed 
Article 

Google Scholar 
Sutcliffe, I. C. A phylum level perspective on bacterial cell envelope architecture. Trends Microbiol. 18, 464–470 (2010).CAS 
PubMed 
Article 

Google Scholar 
Zhang, W. et al. Phenotype changes of cyanobacterial and microbial distribution characteristics of surface sediments in different periods of cyanobacterial blooms in Taihu Lake. Aquat. Ecol. 54, 591–607 (2020).CAS 
Article 

Google Scholar 
Waidner, L. A. & Kirchman, D. L. Diversity and distribution of ecotypes of the aerobic anoxygenic phototrophy gene pufM in the Delaware estuary. Appl. Environ. Microbiol. 74, 4012–4021 (2008).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sisson, C., Gulla-Devaney, B., Katz, L. A. & Grattepanche, J. D. Seed bank and seasonal patterns of the eukaryotic SAR (Stramenopila, Alveolata and Rhizaria) clade in a New England vernal pool. J. Plankton Res. 40, 376–390 (2018).Article 

Google Scholar 
Moser, M. & Weisse, T. The outcome of competition between the two chrysomonads Ochromonas sp. and Poterioochromonas malhamensis depends on pH. Eur. J. Protistol. 47, 79–85 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
Pröschold, T. et al. An integrative approach sheds new light onto the systematics and ecology of the widespread ciliate genus Coleps (Ciliophora, Prostomatea). Sci. Rep. 11, 1–19 (2021).Article 

Google Scholar 
Jones, H. A classification of mixotrophic protists based on their behaviour. Freshw. Biol. 37, 35–43 (1997).Article 

Google Scholar 
Fischer, R., Giebel, H. A. & Ptacnik, R. Identity of the limiting nutrient (N vs. P) affects the competitive success of mixotrophs. Mar. Ecol. Prog. Ser. 563, 51–63 (2017).ADS 
CAS 
Article 

Google Scholar 
Gillette, J. P., Stewart, D. J., Teece, M. A. & Schulz, K. L. Abundance of mixoplanktonic algae in relation to prey, light and nutrient limitation in a dystrophic lake: A mesocosm study. Mar. Freshw. Res. 72, 1760–1772 (2021).CAS 
Article 

Google Scholar 
Harder, C. B. et al. Local diversity of heathland Cercozoa explored by in-depth sequencing. ISME J. 10, 2488–2497 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ortiz-Álvarez, R., Triadó-Margarit, X., Camarero, L., Casamayor, E. O. & Catalan, J. High planktonic diversity in mountain lakes contains similar contributions of autotrophic, heterotrophic and parasitic eukaryotic life forms. Sci. Rep. 8, 1–12 (2018).Article 

Google Scholar 
Sakharova, E. G. & Korneva, L. G. Phytoplankton in the Littoral and Pelagial zones of the Rybinsk reservoir in years with different temperature and water-level regimes. Inl. Water Biol. 11, 6–12 (2018).Article 

Google Scholar 
Cruaud, P. et al. Annual Protist community dynamics in a freshwater ecosystem undergoing contrasted climatic conditions: The saint-Charles River (Canada). Front. Microbiol. https://doi.org/10.3389/fmicb.2019.02359 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Lürling, M., Eshetu, F., Faassen, E. J., Kosten, S. & Huszar, V. L. M. Comparison of cyanobacterial and green algal growth rates at different temperatures. Freshw. Biol. 58, 552–559 (2013).Article 

Google Scholar 
Jensen, J. P., Jeppesen, E., Olrik, K. & Kristensen, P. Impact of nutrients and physical factors on the shift from cyanobacterial to chlorophyte dominance in shallow Danish lakes. Can. J. Fish. Aquat. Sci. 51, 1692–1699 (1994).Article 

Google Scholar 
Dragoș, N. An Introduction to the Algae and the Culture Collection of Algae at the Institute of Biological Research, Cluj-Napoca (Cluj University Press, 1997).
Google Scholar 
Frank, J. A. et al. Critical evaluation of two primers commonly used for amplification of bacterial 16S rRNA genes. Appl. Environ. Microbiol. 74, 2461–2470 (2008).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Allen, M. M. & Stanier, R. Y. Growth and division of some unicellular blue-green algae. J. Gen. Microbiol. 51, 199–202 (1968).CAS 
PubMed 
Article 

Google Scholar 
IPCC. IPCC report Global warming of 1.5°C. Ipcc Sr15 2, 17–20 (2018).Kalendar, R., Khassenov, B., Ramankulov, Y., Samuilova, O. & Ivanov, K. I. FastPCR: An in silico tool for fast primer and probe design and advanced sequence analysis. Genomics 109, 312–319 (2017).CAS 
PubMed 
Article 

Google Scholar 
Ye, J. et al. Primer-BLAST: A tool to design target-specific primers for polymerase chain reaction. BMC Bioinform. 13, 1–11 (2012).Article 

Google Scholar 
Kimura, S. et al. Diurnal infection patterns and impact of Microcystis cyanophages in a Japanese pond. Appl. Environ. Microbiol. 78, 5805–5811 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pinto, F., Pacheco, C. C., Ferreira, D., Moradas-Ferreira, P. & Tamagnini, P. Selection of suitable reference genes for RT-qPCR analyses in cyanobacteria. PLoS ONE 7, e34983 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2− ΔΔCT method. Methods 25, 402–408 (2001).CAS 
PubMed 
Article 

Google Scholar 
Hadziavdic, K. et al. Characterization of the 18S rRNA gene for designing universal eukaryote specific primers. PLoS ONE 9, e87624 (2014).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Herlemann, D. P. R. et al. Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. ISME J. 5, 1571–1579 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
DeSantis, T. Z. et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72, 5069–5072 (2006).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gurevich, A., Saveliev, V., Vyahhi, N. & Tesler, G. QUAST: Quality assessment tool for genome assemblies. Bioinformatics 29, 1072–1075 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lozupone, C. & Knight, R. UniFrac: A new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71, 8228–8235 (2005).ADS 
CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar  More

Kejun, J. et al. Transition of the Chinese economy in the face of deep greenhouse gas emissions cuts in the future. Asian Econ. Policy Rev. 16(1), 142–162 (2021).
Google Scholar 
COP26, United nations climate change. https://unfccc.int/news/cop26-facts-and-figures, (2020).Dong, Y., Coleman, M. and Miller, S. A. Greenhouse gas emissions from air conditioning and refrigeration service expansion in developing countries. Annual Rev. Environ. Resour. 46 (2021).Azam, M. & Khan, A. Q. Testing the Environmental Kuznets Curve hypothesis: A comparative empirical study for low, lower middle, upper middle and high income countries. Renew. Sustain. Energy Rev. 63, 556–567 (2016).CAS 

Google Scholar 
Li, Z. et al. An economic analysis software for evaluating best management practices to mitigate greenhouse gas emissions from cropland. Agric. Syst. 186, 102950 (2021).
Google Scholar 
Dinda, S. Environmental Kuznets curve hypothesis: A survey. Ecol. Econ. 49(4), 431–455 (2004).
Google Scholar 
Xia, Q. et al. Drivers of global and national CO2 emissions changes 2000–2017. Climate Policy 21(5), 604–615 (2021).
Google Scholar 
Fatima, T., Shahzad, U. & Cui, L. Renewable and nonrenewable energy consumption, trade and CO2 emissions in high emitter countries: Does the income level matter?. J. Environ. Planning Manage. 64(7), 1227–1251 (2021).
Google Scholar 
Kılavuz, E. & Doğan, İ. Economic growth, openness, industry and CO2 modelling: Are regulatory policies important in Turkish economies?. Int. J. Low-Carbon Technol. 16(2), 476–487 (2021).
Google Scholar 
Setyari, N. P. W. & Kusuma, W. G. A. Economics and environmental development: Testing the environmental Kuznets Curve hypothesis. Int. J. Energy Econ. Policy 11(4), 51 (2021).
Google Scholar 
Gołasa, P. et al. Sources of greenhouse gas emissions in agriculture, with particular emphasis on emissions from energy used. Energies 14(13), 3784 (2021).
Google Scholar 
Liobikienė, G. & Butkus, M. The challenges and opportunities of climate change policy under different stages of economic development. Sci. Total Environ. 642, 999–1007 (2018).ADS 
PubMed 

Google Scholar 
Koondhar, M. A. et al. A visualization review analysis of the last two decades for environmental Kuznets curve “EKC” based on co-citation analysis theory and pathfinder network scaling algorithms. Environ. Sci. Pollut. Res. 28(13), 16690–16706 (2021).CAS 

Google Scholar 
Bilgili, F., Koçak, E. & Bulut, Ü. The dynamic impact of renewable energy consumption on CO2 emissions: A revisited Environmental Kuznets Curve approach. Renew. Sustain. Energy Rev. 54, 838–845 (2016).
Google Scholar 
Gorus, M. S. & Aydin, M. The relationship between energy consumption, economic growth, and CO2 emission in MENA countries: Causality analysis in the frequency domain. Energy 168, 815–822 (2019).
Google Scholar 
Kirikkaleli, D. & Adebayo, T. S. Do renewable energy consumption and financial development matter for environmental sustainability? New global evidence. Sustain. Develop. 29(4), 583–594 (2021).
Google Scholar 
Godil, D. I. et al. Investigate the role of technology innovation and renewable energy in reducing transport sector CO2 emission in China: A path toward sustainable development. Sustain. Develop. (2021).An, T., Xu, C. & Liao, X. The impact of FDI on environmental pollution in China: Evidence from spatial panel data. Environ. Sci. Pollut. Res. 1–13 (2021).Halliru, A. M., Loganathan, N. and Golam Hassan, A. A. Does FDI and economic growth harm environment? Evidence from selected West African countries. Trans. Corp. Rev., 13(2), 237–251 (2021.).Al-Mulali, U., Ozturk, I. & Solarin, S. A. Investigating the environmental Kuznets curve hypothesis in seven regions: The role of renewable energy. Ecol. Ind. 67, 267–282 (2016).
Google Scholar 
Zhang, D. et al. Public spending and green economic growth in BRI region: Mediating role of green finance. Energy Policy 153, 112256 (2021).
Google Scholar 
Usman, M. et al. How do financial development, energy consumption, natural resources, and globalization affect Arctic countries’ economic growth and environmental quality? An advanced panel data simulation. Energy, 122515 (2021).Rehman, A. et al. The impact of globalization, energy use, and trade on ecological footprint in Pakistan: does environmental sustainability exist?. Energies 14(17), 5234 (2021).CAS 

Google Scholar 
Bremond, U. et al. A vision of European biogas sector development towards 2030: Trends and challenges. J. Clean. Prod. 287, 125065 (2021).
Google Scholar 
Abdul Latif, S. N. et al. The trend and status of energy resources and greenhouse gas emissions in the malaysia power generation mix. Energies 14(8), 2200 (2021).CAS 

Google Scholar 
Chen, P.-Y. et al. Modeling the global relationships among economic growth, energy consumption and CO2 emissions. Renew. Sustain. Energy Rev. 65, 420–431 (2016).CAS 

Google Scholar 
Kais, S. & Sami, H. An econometric study of the impact of economic growth and energy use on carbon emissions: Panel data evidence from fifty eight countries. Renew. Sustain. Energy Rev. 59, 1101–1110 (2016).
Google Scholar 
Rüstemoğlu, H. & Andrés, A. R. Determinants of CO2 emissions in Brazil and Russia between 1992 and 2011: A decomposition analysis. Environ. Sci. Policy 58, 95–106 (2016).
Google Scholar 
Yao, C., Feng, K. & Hubacek, K. Driving forces of CO2 emissions in the G20 countries: An index decomposition analysis from 1971 to 2010. Eco. Inform. 26, 93–100 (2015).
Google Scholar 
González, P. F., Landajo, M. & Presno, M. The driving forces behind changes in CO2 emission levels in EU-27. Differences between member states. Environ. Sci. Policy 38, 11–16 (2014).
Google Scholar 
Nathaniel, S. P. Environmental degradation in ASEAN: assessing the criticality of natural resources abundance, economic growth and human capital. Environ. Sci. Pollut. Res. 28(17), 21766–21778 (2021).
Google Scholar 
Baloch, M. A., Mahmood, N. & Zhang, J. W. Effect of natural resources, renewable energy and economic development on CO2 emissions in BRICS countries. Sci. Total Environ. 678, 632–638 (2019).ADS 
PubMed 

Google Scholar 
Balsalobre-Lorente, D. et al. How economic growth, renewable electricity and natural resources contribute to CO2 emissions?. Energy Policy 113, 356–367 (2018).
Google Scholar 
Bekun, F. V., Alola, A. A. & Sarkodie, S. A. Toward a sustainable environment: Nexus between CO2 emissions, resource rent, renewable and nonrenewable energy in 16-EU countries. Sci. Total Environ. 657, 1023–1029 (2019).ADS 
CAS 
PubMed 

Google Scholar 
Baloch, M. A. & Meng, F. Modeling the non-linear relationship between financial development and energy consumption: Statistical experience from OECD countries. Environ. Sci. Pollut. Res. 26(9), 8838–8846 (2019).
Google Scholar 
Dong, K., Sun, R. & Hochman, G. Do natural gas and renewable energy consumption lead to less CO2 emission? Empirical evidence from a panel of BRICS countries. Energy 141, 1466–1478 (2017).
Google Scholar 
Omri, A. et al. Determinants of environmental sustainability: Evidence from Saudi Arabia. Sci. Total Environ. 657, 1592–1601 (2019).ADS 
CAS 
PubMed 

Google Scholar 
Zhu, H. et al. The effects of FDI, economic growth and energy consumption on carbon emissions in ASEAN-5: Evidence from panel quantile regression. Econ. Model. 58, 237–248 (2016).
Google Scholar 
Cheng, C. et al. Heterogeneous impacts of renewable energy and environmental patents on CO2 emission-evidence from the BRIICS. Sci. Total Environ. 668, 1328–1338 (2019).ADS 
CAS 
PubMed 

Google Scholar 
Zhang, C. & Zhou, X. Does foreign direct investment lead to lower CO2 emissions? Evidence from a regional analysis in China. Renew. Sustain. Energy Rev. 58, 943–951 (2016).
Google Scholar 
Phung, T. Q., Rasoulinezhad, E. and Luong Thi Thu, H. How are FDI and green recovery related in Southeast Asian economies? Econ. Change Restruct. 1–21 (2022).Quang, P.T. and Thao, D. P. Analyzing the green financing and energy efficiency relationship in ASEAN. J. Risk Financ. (2022)(ahead-of-print).Ahmad, M. et al. Modelling the dynamic linkages between eco-innovation, urbanization, economic growth and ecological footprints for G7 countries: Does financial globalization matter?. Sustain. Cities Soc. 70, 102881 (2021).
Google Scholar 
Murshed, M. An empirical analysis of the non-linear impacts of ICT-trade openness on renewable energy transition, energy efficiency, clean cooking fuel access and environmental sustainability in South Asia. Environ. Sci. Pollut. Res. 27(29), 36254–36281 (2020).CAS 

Google Scholar 
Díaz-García, C., González-Moreno, Á. & Sáez-Martínez, F. J. Eco-innovation: Insights from a literature review. Innovation 17(1), 6–23 (2015).
Google Scholar 
Wang, L. et al. Are eco-innovation and export diversification mutually exclusive to control carbon emissions in G-7 countries?. J. Environ. Manage. 270, 110829 (2020).PubMed 

Google Scholar 
Su, H.-N. & Moaniba, I. M. Does innovation respond to climate change? Empirical evidence from patents and greenhouse gas emissions. Technol. Forecast. Soc. Chang. 122, 49–62 (2017).
Google Scholar 
Ding, Q., Khattak, S. I. & Ahmad, M. Towards sustainable production and consumption: assessing the impact of energy productivity and eco-innovation on consumption-based carbon dioxide emissions (CCO2) in G-7 nations. Sustain. Prod. Consum. 27, 254–268 (2021).
Google Scholar 
Zhang, Y.-J. et al. Can environmental innovation facilitate carbon emissions reduction? Evidence from China. Energy Policy 100, 18–28 (2017).
Google Scholar 
Solarin, S. A. & Bello, M. O. Energy innovations and environmental sustainability in the US: the roles of immigration and economic expansion using a maximum likelihood method. Sci. Total Environ. 712, 135594 (2020).ADS 
CAS 
PubMed 

Google Scholar 
Hashmi, R. & Alam, K. Dynamic relationship among environmental regulation, innovation, CO2 emissions, population, and economic growth in OECD countries: A panel investigation. J. Clean. Prod. 231, 1100–1109 (2019).
Google Scholar 
Sinha, A., Sengupta, T. & Alvarado, R. Interplay between technological innovation and environmental quality: Formulating the SDG policies for next 11 economies. J. Clean. Prod. 242, 118549 (2020).
Google Scholar 
Gormus, S. & Aydin, M. Revisiting the environmental Kuznets curve hypothesis using innovation: New evidence from the top 10 innovative economies. Environ. Sci. Pollut. Res. 27(22), 27904–27913 (2020).
Google Scholar 
Usman, M. & Hammar, N. Dynamic relationship between technological innovations, financial development, renewable energy, and ecological footprint: Fresh insights based on the STIRPAT model for Asia Pacific Economic Cooperation countries. Environ. Sci. Pollut. Res. 28(12), 15519–15536 (2021).
Google Scholar 
Shahbaz, M., Mutascu, M. & Azim, P. Environmental Kuznets curve in Romania and the role of energy consumption. Renew. Sustain. Energy Rev. 18, 165–173 (2013).
Google Scholar 
Kong, Q. et al. Trade openness and economic growth quality of China: Empirical analysis using ARDL model. Financ. Res. Lett. 38, 101488 (2021).
Google Scholar 
Kasman, A. & Duman, Y. S. CO2 emissions, economic growth, energy consumption, trade and urbanization in new EU member and candidate countries: A panel data analysis. Econ. Model. 44, 97–103 (2015).
Google Scholar 
Ali, S. et al. Impact of trade openness, human capital, public expenditure and institutional performance on unemployment: Evidence from OIC countries. Int. J. Manpower, (2021).Chen, F., Jiang, G. & Kitila, G. M. Trade openness and CO2 emissions: The heterogeneous and mediating effects for the belt and road countries. Sustainability 13(4), 1958 (2021).
Google Scholar 
Sun, H. et al. Nexus between environmental infrastructure and transnational cluster in one belt one road countries: Role of governance. Bus. Strategy Develop. 1(1), 17–30 (2018).
Google Scholar 
Jebli, M. B. & Youssef, S. B. The environmental Kuznets curve, economic growth, renewable and non-renewable energy, and trade in Tunisia. Renew. Sustain. Energy Rev. 47, 173–185 (2015).
Google Scholar 
Jebli, M. B., Youssef, S. B. & Ozturk, I. Testing environmental Kuznets curve hypothesis: The role of renewable and non-renewable energy consumption and trade in OECD countries. Ecol. Ind. 60, 824–831 (2016).
Google Scholar 
Shahbaz, M. et al. Economic growth, electricity consumption, urbanization and environmental degradation relationship in United Arab Emirates. Ecol. Ind. 45, 622–631 (2014).CAS 

Google Scholar 
Xu, B. & Lin, B. How industrialization and urbanization process impacts on CO2 emissions in China: Evidence from nonparametric additive regression models. Energy Econ. 48, 188–202 (2015).
Google Scholar 
Ertugrul, H. M. et al. The impact of trade openness on global carbon dioxide emissions: Evidence from the top ten emitters among developing countries. Ecol. Ind. 67, 543–555 (2016).
Google Scholar 
Najarzadeh, R. et al. Kyoto Protocol and global value chains: Trade effects of an international environmental policy. Environ. Develop. 40, 100659 (2021).
Google Scholar 
Liobikienė, G. & Butkus, M. Environmental Kuznets Curve of greenhouse gas emissions including technological progress and substitution effects. Energy 135, 237–248 (2017).
Google Scholar 
Liobikienė, G. The revised approaches to income inequality impact on production-based and consumption-based carbon dioxide emissions: Literature review. Environ. Sci. Pollut. Res. 27(9), 8980–8990 (2020).
Google Scholar 
Li, G., Zakari, A. & Tawiah, V. Energy resource melioration and CO2 emissions in China and Nigeria: Efficiency and trade perspectives. Resour. Policy 68, 101769 (2020).
Google Scholar 
Ali, M. U. et al. Fossil energy consumption, economic development, inward FDI impact on CO2 emissions in Pakistan: Testing EKC hypothesis through ARDL model. Int. J. Financ. Econ. 26(3), 3210–3221 (2021).
Google Scholar 
Özbuğday, F. C. & Erbas, B. C. How effective are energy efficiency and renewable energy in curbing CO2 emissions in the long run? A heterogeneous panel data analysis. Energy 82, 734–745 (2015).
Google Scholar 
Wang, Q., Chiu, Y.-H. & Chiu, C.-R. Driving factors behind carbon dioxide emissions in China: A modified production-theoretical decomposition analysis. Energy Econ. 51, 252–260 (2015).
Google Scholar 
Dong, K. et al. Energy intensity and energy conservation potential in China: A regional comparison perspective. Energy 155, 782–795 (2018).
Google Scholar 
Tan, R. & Lin, B. What factors lead to the decline of energy intensity in China’s energy intensive industries?. Energy Econ. 71, 213–221 (2018).
Google Scholar 
Tariq, G. et al. Energy consumption and economic growth: Evidence from four developing countries. Am. J. Multidiscip. Res. 7(1), (2018).Sharif, A. et al. Revisiting the role of renewable and non-renewable energy consumption on Turkey’s ecological footprint: Evidence from quantile ARDL approach. Sustain. Cities Soc. 57, 102138 (2020).
Google Scholar 
Khan, I., Hou, F. & Le, H. P. The impact of natural resources, energy consumption, and population growth on environmental quality: Fresh evidence from the United States of America. Sci. Total Environ. 754, 142222 (2021).ADS 
CAS 
PubMed 

Google Scholar 
Bölük, G. & Mert, M. Fossil & renewable energy consumption, GHGs (greenhouse gases) and economic growth: Evidence from a panel of EU (European Union) countries. Energy 74, 439–446 (2014).
Google Scholar 
Sugiawan, Y. & Managi, S. The environmental Kuznets curve in Indonesia: Exploring the potential of renewable energy. Energy Policy 98, 187–198 (2016).
Google Scholar 
Bölük, G. & Mert, M. The renewable energy, growth and environmental Kuznets curve in Turkey: An ARDL approach. Renew. Sustain. Energy Rev. 52, 587–595 (2015).
Google Scholar 
Sebri, M. & Ben-Salha, O. On the causal dynamics between economic growth, renewable energy consumption, CO2 emissions and trade openness: Fresh evidence from BRICS countries. Renew. Sustain. Energy Rev. 39, 14–23 (2014).
Google Scholar 
Tiwari, A. K. A structural VAR analysis of renewable energy consumption, real GDP and CO2 emissions: Evidence from India. Econ. Bull. 31(2), 1793–1806 (2011).
Google Scholar 
Apergis, N. & Payne, J. E. Renewable energy consumption and growth in Eurasia. Energy Econ. 32(6), 1392–1397 (2010).
Google Scholar 
Menyah, K. & Wolde-Rufael, Y. CO2 emissions, nuclear energy, renewable energy and economic growth in the US. Energy Policy 38(6), 2911–2915 (2010).CAS 

Google Scholar 
Fareed, Z. et al. Financial inclusion and the environmental deterioration in Eurozone: The moderating role of innovation activity. Technol. Soc. 69, 101961 (2022).
Google Scholar 
Adebayo, T. S. Renewable energy consumption and environmental sustainability in Canada: does political stability make a difference? Environ. Sci. Pollut. Res., 1–16 (2022).Shahbaz, M. et al. Does foreign direct investment impede environmental quality in high-, middle-, and low-income countries?. Energy Econ. 51, 275–287 (2015).
Google Scholar 
Tariq, G. et al. Trade liberalization, FDI inflows economic growth and environmental sustanaibility in Pakistan and India. J. Agric. Environ. Int. Develop. (JAEID) 112(2), 253–269 (2018).
Google Scholar 
Lee, J. W. The contribution of foreign direct investment to clean energy use, carbon emissions and economic growth. Energy Policy 55, 483–489 (2013).
Google Scholar 
Sun, H.-P. et al. Evaluating the environmental effects of economic openness: Evidence from SAARC countries. Environ. Sci. Pollut. Res. 26(24), 24542–24551 (2019).CAS 

Google Scholar 
Adebayo, T. S. Environmental consequences of fossil fuel in Spain amidst renewable energy consumption: a new insights from the wavelet-based Granger causality approach. Int. J. Sustain. Develop. World Ecol. 1–14 (2022).Adebayo, T. S. et al. Impact of tourist arrivals on environmental quality: A way towards environmental sustainability targets. Current Issues Tourism, 1–19 (2022).Akadiri, S.S. et al. Testing the role of economic complexity on the ecological footprint in China: A nonparametric causality-in-quantiles approach. Energy Environ. 0958305X221094573 (2022).Xie, Q. et al. Race to environmental sustainability: Can renewable energy consumption and technological innovation sustain the strides for China? Renew. Energy (2022).Du, L. et al. Asymmetric effects of high-tech industry and renewable energy on consumption-based carbon emissions in MINT countries. Renew. Energy 196, 1269–1280 (2022).CAS 

Google Scholar 
Al-Mulali, U. & Tang, C. F. Investigating the validity of pollution haven hypothesis in the gulf cooperation council (GCC) countries. Energy Policy 60, 813–819 (2013).
Google Scholar 
Jiang, Y. Foreign direct investment, pollution, and the environmental quality: A model with empirical evidence from the Chinese regions. Int. Trade J. 29(3), 212–227 (2015).
Google Scholar 
Ren, S. et al. International trade, FDI (foreign direct investment) and embodied CO2 emissions: A case study of Chinas industrial sectors. China Econ. Rev. 28, 123–134 (2014).
Google Scholar 
Tang, C. F. & Tan, B. W. The impact of energy consumption, income and foreign direct investment on carbon dioxide emissions in Vietnam. Energy 79, 447–454 (2015).
Google Scholar 
Omri, A. & Kahouli, B. Causal relationships between energy consumption, foreign direct investment and economic growth: Fresh evidence from dynamic simultaneous-equations models. Energy Policy 67, 913–922 (2014).
Google Scholar 
Dong, K.-Y. et al. A review of China’s energy consumption structure and outlook based on a long-range energy alternatives modeling tool. Pet. Sci. 14(1), 214–227 (2017).
Google Scholar 
WDI, World Development Indicator. https://data.worldbank.org/, (2022).OECD, Organisation for Economic Co-operation and Development. https://data.oecd.org/, (2021).Levin, A., Lin, C.-F. & Chu, C.-S.J. Unit root tests in panel data: Asymptotic and finite-sample properties. J. Econom. 108(1), 1–24 (2002).MathSciNet 
MATH 

Google Scholar 
Breitung, J. The local power of some unit root tests for panel data. (Emerald Group Publishing Limited, 2001).Im, K. S., Pesaran, M. H. & Shin, Y. Testing for unit roots in heterogeneous panels. J. Econom. 115(1), 53–74 (2003).MathSciNet 
MATH 

Google Scholar 
Hlouskova, J. & Wagner, M. The performance of panel unit root and stationarity tests: Results from a large scale simulation study. Economet. Rev. 25(1), 85–116 (2006).MathSciNet 
MATH 

Google Scholar 
Narayan, P. K. & Narayan, S. Carbon dioxide emissions and economic growth: Panel data evidence from developing countries. Energy Policy 38(1), 661–666 (2010).
Google Scholar 
Pedroni, P. Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bull. Econ. Stat. 61(S1), 653–670 (1999).
Google Scholar 
Pedroni, P. Panel cointegration: Asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis. Economet. Theor. 20(3), 597–625 (2004).MathSciNet 
MATH 

Google Scholar 
Kao, C. Spurious regression and residual-based tests for cointegration in panel data. J. Econom. 90(1), 1–44 (1999).MathSciNet 
MATH 

Google Scholar 
Breusch, T. S. & Pagan, A. R. The Lagrange multiplier test and its applications to model specification in econometrics. Rev. Econ. Stud. 47(1), 239–253 (1980).MathSciNet 
MATH 

Google Scholar 
Baltagi, B. H. and Hashem Pesaran, M. Heterogeneity and cross section dependence in panel data models: Theory and applications introduction. 229–232 (Wiley Online Library, 2007).Levine, S. & Kendall, K. Energy efficiency and conservation: Opportunities, obstacles, and experiences. Vt. J. Envtl. L. 8, 101 (2006).
Google Scholar 
Stock, J. H. and Watson, M. W. A simple estimator of cointegrating vectors in higher order integrated systems. Econometrica: J. Econom. Soc. 783–820 (1993).Phillips, P.C. and Hansen, B.E. Estimation and inference in models of cointegration: A simulation study. (Cowles Foundation for Research in Economics, Yale University, 1988).Pedroni, P. Fully modified OLS for heterogeneous cointegrated panels, in Nonstationary panels, panel cointegration, and dynamic panels. (Emerald Group Publishing Limited, 2001).Kao, C. and Chiang, M.-H. On the estimation and inference of a cointegrated regression in panel data, in Nonstationary panels, panel cointegration, and dynamic panels. (Emerald Group Publishing Limited, 2001).Liobikienė, G. & Butkus, M. Scale, composition, and technique effects through which the economic growth, foreign direct investment, urbanization, and trade affect greenhouse gas emissions. Renew. Energy 132, 1310–1322 (2019).
Google Scholar 
Balsalobre-Lorente, D. et al. The environmental Kuznets curve, based on the economic complexity, and the pollution haven hypothesis in PIIGS countries. Renew. Energy 185, 1441–1455 (2022).CAS 

Google Scholar 
Sarkodie, S. A. & Adams, S. Renewable energy, nuclear energy, and environmental pollution: Accounting for political institutional quality in South Africa. Sci. Total Environ. 643, 1590–1601 (2018).ADS 
CAS 
PubMed 

Google Scholar 
Mohamued, E. A. et al. Global oil price and innovation for sustainability: The impact of R&D spending, oil price and oil price volatility on GHG emissions. Energies 14(6), 1757 (2021).
Google Scholar 
Iqbal, N. et al. Does exports diversification and environmental innovation achieve carbon neutrality target of OECD economies?. J. Environ. Manage. 291, 112648 (2021).PubMed 

Google Scholar 
Edenhofer, O. et al. Renewable energy sources and climate change mitigation: Special report of the intergovernmental panel on climate change. (Cambridge University Press, 2011).Owusu, P. A. & Asumadu-Sarkodie, S. A review of renewable energy sources, sustainability issues and climate change mitigation. Cogent Eng. 3(1), 1167990 (2016).
Google Scholar  More

Filgueira, R. R., Fournier, L. L., Cerisola, C. I., Gelati, P. & Garcia, M. G. Particle-size distribution in soils: A critical study of the fractal model validation. Geoderma 134, 327–334 (2006).ADS 
Article 

Google Scholar 
Deng, J. F., Li, J. H., Deng, G., Zhu, H. Y. & Zhang, R. H. Fractal scaling of particle-size distribution and associations with soil properties of Mongolian pine plantations in the Mu Us Desert, China. Sci. Rep. 7, 6742 (2018).ADS 
Article 

Google Scholar 
Gao, Y. J. et al. “Fertile islands” beneath three desert vegetation on soil phosphorus fractions, enzymatic activities, and microbial biomass in the desert-oasis transition zone. CATENA 212, 106090 (2022).CAS 
Article 

Google Scholar 
Zeraatpisheh, M., Ayoubi, S., Mirbagheri, Z., Mosaddeghi, M. R. & Xu, M. Spatial prediction of soil aggregate stability and soil organic carbon in aggregate fractions using machine learning algorithms and environmental variables. Geoderma Regioanl. 27, e00440 (2021).Article 

Google Scholar 
Zha, C., Shao, M., Jia, X. & Zhang, C. Particle size distribution of soils (0–500 cm) in the Loess Plateau, China. Geoderma 7, 251–258 (2016).Article 

Google Scholar 
Callesen, I., Keck, H. & Andersen, T. J. Particle size distribution in soils and marine sediments by laser diffraction using Malvern Mastersizer 2000-method uncertainty including the effect of hydrogen peroxide pretreatment. J. Soils Sediments 18, 2500–2510 (2018).CAS 
Article 

Google Scholar 
He, Y. J. & Lv, D. Y. Fractal expression of soil particle-size distribution at the basin scale. Open Geosci. 14, 70–78 (2022).Article 

Google Scholar 
Besalatpour, A. A., Ayoubi, S., Hajabbasi, M. A., Mosaddeghi, M. R. & Schulin, R. Estimating wet soil aggregate stability from easily available properties in a highly mountainous watershed. CATENA 111, 72–79 (2013).Article 

Google Scholar 
Besalatpour, A. A., Ayoubi, S., Hajabbasi, M. A., Yousefian, J. A. & Gharipour, A. Feature selection using parallel genetic algorithm for the prediction of geometric mean diameter of soil aggregates by machine learning methods. Arid Land Res. Manag. 28, 383–394 (2014).Article 

Google Scholar 
Xu, G. C., Li, Z. B. & Li, P. Fractal features of soil particle-size distribution and total soil nitrogen distribution in a typical watershed in the source area of the middle Dan River, China. CATENA 101, 17–23 (2013).CAS 
Article 

Google Scholar 
Jia, W. R. et al. Grain size distribution at four developmental stages of crescent dunes in the hinterland of the Taklimakan Desert, China. J. Arid Land 8, 722–733 (2016).Article 

Google Scholar 
Rabot, E., Wiesmeier, M., Schlüter, S. & Vogel, H. J. Soil structure as an indicator of soil functions: A review. Geoderma 314, 122–137 (2018).ADS 
Article 

Google Scholar 
Ghanbarian, B. & Daigle, H. Fractal dimension of soil fragment mass-size distribution: A critical analysis. Geoderma 245–246, 98–103 (2015).ADS 
Article 

Google Scholar 
Deng, Y., Cai, C., Xia, D., Ding, S. & Chen, J. Fractal features of soil particle size distribution under different land-use patterns in the alluvial fans of collapsing gullies in the hilly granitic region of southern China. PLoS ONE 12, e0173555 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Zhai, J. Y. et al. Change in soil particle size distribution and erodibility with latitude and vegetation restoration chronosequence on the Loess Plateau, China. Int. J Environ. Res. Public Health 17, 822 (2020).CAS 
PubMed Central 
Article 

Google Scholar 
Gao, Z. Y., Niu, F. J., Lin, Z. J. & Luo, J. Fractal and multifractal analysis of soil particle-size distribution and correlation with soil hydrological properties in active layer of Qinghai-Tibet Plateau, China. CATENA 203, 105373 (2021).Article 

Google Scholar 
Chen, T. L. et al. Multifractal characteristics and spatial variability of soil particle-size distribution in different land use patterns in a small catchment of the Three Gorges Reservoir Region, China. J. Mt. Sci. 18, 111–125 (2021).Article 

Google Scholar 
Gui, D. W. et al. Characterizing variations in soil particle size distribution in oasis farmlands-a case study of the Cele Oasis. Math. Comput. Model. 51, 1306–1311 (2010).Article 

Google Scholar 
Millán, H., Gonzalez-Posada, M., Aguilar, M., Domınguez, J. & Céspedes, L. On the fractal scaling of soil data. Particle-size distributions. Geoderma 117, 117–128 (2003).ADS 
Article 

Google Scholar 
Qi, F. et al. Soil particle size distribution characteristics of different land-use types in the Funiu mountainous region. Soil Till. Res. 184, 45–51 (2018).Article 

Google Scholar 
Muhtar, Q., Hiroki, T. & Mijit, H. Formation and internal structure of Tamarix cones in the Taklimakan Desert. J. Arid Environ. 50, 81–97 (2002).Article 

Google Scholar 
Zhao, Y. J. & Xia, X. C. Research on the Relationship Between Tamarix Cone and Environmental Change in Lop Nur Region of Xinjiang 38–142 (Sci. Press, 2011) (in Chinese).
Google Scholar 
Yin, C. H., Shi, Q. M., Liang, F. & Tian, C. Y. Distribution pattern of soil salinity in Tamarix Nebkhas in Tarim Basin. Bull Soil Water Conserv. 33, 287–293 (2013) (in Chinese).
Google Scholar 
Zheng, T., Li, J. G., Li, W. H. & Wan, J. H. Soil heterogeneity and its effects on plant community in oasis desert transition zone in the lower peaches of Tarim River. J. Desert Res. 30, 128–134 (2010) (in Chinese).
Google Scholar 
Liu, J. H., Wang, X. Q., Ma, Y. & Tan, F. Z. Spatial variation of soil salinity on Tamarix ramosissima nebkhas and interdune in oasis-desert ecotone. J. Desert Res. 36, 181–189 (2016) (in Chinese).CAS 

Google Scholar 
Dong, Z. W. et al. Stoichiometric features of C, N, and P in soil and litter of Tamarix cones and their relationship with environmental factors in the Taklimakan Desert, China. J. Soils Sediments 20, 690–704 (2020).CAS 
Article 

Google Scholar 
Dong, Z. W., Li, S. Y., Mao, D. L. & Lei, J. Q. Distribution pattern of soil grain size in Tamarix sand dune in the southwest of Gurbantunggut Desert. J. Soil Water Conserv. 35, 64-72/79 (2021) (in Chinese).
Google Scholar 
Dong, Z. W., Zhao, Y., Lei, J. Q. & Xi, Y. Q. Distribution pattern and influencing factors of soil salinity at Tamarix cones in the Taklimakan Desert. Chin. J. Plant Eco. 42, 873–884 (2018) (in Chinese).Article 

Google Scholar 
Xu, L. S. et al. Oasis microclimate effect on the dust deposition in Cele Oasis at southern Tarim Basin, China. Arab J. Geosci. 9, 294 (2016).Article 

Google Scholar 
Liu, J. H., Wang, X. Q., Ma, Y. & Tan, F. Z. Spatial heterogeneity of soil grain size on Tamarix ramosissima nebkhas and interdune in desert-oasis ecotone. J. Beijing For. Univ. 37, 89–99 (2015) (in Chinese).CAS 

Google Scholar 
Mao, D. L. et al. Fractal characteristics of grain size of sand and dust in aeolian sand movement in Cele oasis-desert ecotone in Xinjiang, China. Acta Pedol. Sinica 55, 88–99 (2018) (in Chinese).
Google Scholar 
Li, J. R. & Ravib, S. Interactions among hydrological-aeolian processes and vegetation determine grain-size distribution of sediments in a semi-arid coppice dune (nebkha) system. J. Arid Environ. 154, 24–33 (2018).ADS 
Article 

Google Scholar 
Ayoubi, S., Karchegani, P. M., Mosaddeghi, M. R. & Honarjoo, N. Soil aggregation and organic carbon as affected by topography and land use change in western Iran. Soil Till. Res. 121, 18–26 (2012).Article 

Google Scholar 
Wang, X. M., Dong, Z. B., Zhang, J. W. & Chen, G. T. Geomorphology of sand dunes in the Northeast Taklimakan Desert. Geomorphology 42, 183–195 (2002).ADS 
Article 

Google Scholar 
Liu, W. G. et al. Onset of permanent Taklimakan Desert linked to the mid-Pleistocene transition. Geology 48, 782–786 (2020).ADS 
CAS 
Article 

Google Scholar 
Yang, X. H. et al. Characteristics of soil particle size distribution and its effect on dust emission in Taklimakan Desert. Trans. CSAE 36, 167–174 (2020) (in Chinese).
Google Scholar 
Bao, S. D. Soil agricultural chemistry analysis 152–200 (China Agr. Press, 2000) (in Chinese).
Google Scholar 
Folk, R. L. & Ward, W. C. Brazos Riverbar: A study in the significance of grain size parameters. J. Sediment Petrol. 27, 3–26 (1957).ADS 
Article 

Google Scholar 
Weil, R. R. & Brady, N. C. The Nature and Properties of Soils 15th edn. (PrenticeHall Press, 2017).
Google Scholar 
Churchman, G. J. Game changer in soil science. Functional role of clay minerals in soil. J. Plant Nutr. Soil Sci. 181, 99–103 (2018).CAS 
Article 

Google Scholar 
Tyler, S. W. & Wheatcraft, S. W. Fractal scaling of soil particle size distributions: Analysis and limitations. Soil Sci. Soc. Am. J. 56, 362–369 (1992).ADS 
Article 

Google Scholar 
Lin, Y. C., Mu, G. J., Xu, L. S. & Zhao, X. The origin of bimodal grain-size distribution for aeolian deposits. Aeolian Res. 20, 80–88 (2016).ADS 
Article 

Google Scholar 
Sha, G. L., Wei, T. X., Chen, Y. X., Fu, Y. C. & Ren, K. Characteristics of soil particle size distribution of typical plantcommunities on the hilly areas of Loess Plateau. Arid Land Geogr. https://doi.org/10.12118/j.issn.1000-6060.2021.487 (2022) (in Chinese).Article 

Google Scholar 
Yang, J. D., Li, G. J., Dai, Y., Rao, W. B. & Ji, J. F. Isotopic evidences for provenances of loess of the Chinese Loess Plateau. Earth Sci. Front. 16, 195–206 (2009) (in Chinese).
Google Scholar 
Wu, L. & Zhang, Y. M. Precipitation and soil particle size co-determine spatial distribution of biological soil crusts in the Gurbantunggut Desert, China. J. Arid Land 10, 701–711 (2018).Article 

Google Scholar 
Li, X. B. et al. Relationship between soil particle size distribution and soil nutrient distribution characteristics in typical communities of desert grassland. Actabot. Boreal-Occident Sin. 37, 1635–1644 (2017) (in Chinese).
Google Scholar 
Gao, G. L. et al. Fractal scaling of particle size distribution and relationships with topsoil properties affected by biological soil crusts. PLoS ONE 9, e88559 (2014).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zhao, Y., Feng, Q. & Yang, H. Soil salinity distribution and its relationship with soil particle size in the lower reaches of Heihe River, Northwestern China. Environ. Earth Sci. 75, 1–18 (2016).ADS 
Article 

Google Scholar 
Zhang, X. Y. et al. Sources of Asian dust and role of climate change versus desertification in Asian dust emission. Geophys. Res. Lett. 30, 2272 (2003).ADS 
Article 

Google Scholar  More

Berghauser Pont, M. Y., Perg, P. G., Haupt, P. A. & Heyman, A. A systematic review of the scientifically demonstrated effects of densification. IOP Conf. Ser. Earth Environ. Sci. 588, 052031 (2020).
Google Scholar 
Cimburova, Z. & Berghauser Pont, M. Location matters: A systematic review of spatial contextual factors mediating ecosystem services of urban trees. Ecosyst. Serv. 50, 101296 (2021).
Google Scholar 
De Valck, J. et al. Valuing urban ecosystem services in sustainable brownfield redevelopment. Ecosyst. Serv. 35, 139–149 (2019).
Google Scholar 
Zuzolo, D. et al. Divide et disperda: Thirty years of fragmentation and impacts on the eco-mosaic in the case study of the metropolitan city of Naples. Land 10, 485 (2021).
Google Scholar 
Nelson, E. The Economics of Ecosystems and Biodiversity: Ecological and Economic Foundations , edited by Pushpam Kumar, London, Earthscan Publications, United Nations Environment Programme, 2010, xxxix + 410 pp., US$76.95 (hardback), ISBN 978-1-84971-212-5. J. Nat. Resour. Policy Res. 5, 68–70 (2013).
Google Scholar 
Duraiappah, A. K. et al. Millennium Ecosystem Assessment, 2005. Ecosystems and human well-being: Synthesis. World Resources Institute vol. 5 http://www.who.int/entity/globalchange/ecosystems/ecosys.pdf (2005).Cariñanos, P., Casares-Porcel, M. & Quesada-Rubio, J. M. Estimating the allergenic potential of urban green spaces: A case-study in Granada, Spain. Landsc. Urban Plan. 123, 134–144 (2014).
Google Scholar 
Haase, D. et al. A quantitative review of urban ecosystem service assessments: Concepts, models, and implementation. Ambio 43, 413–433 (2014).PubMed 
PubMed Central 

Google Scholar 
Mexia, T. et al. Ecosystem services: Urban parks under a magnifying glass. Environ. Res. 160, 469–478 (2018).CAS 
PubMed 

Google Scholar 
Brzoska, P., Grunewald, K. & Bastian, O. A multi-criteria analytical method to assess ecosystem services at urban site level, exemplified by two German city districts. Ecosyst. Serv. 49, 101268 (2021).
Google Scholar 
Zulian, G. et al. Practical application of spatial ecosystem service models to aid decision support. Ecosyst. Serv. 29, 465–480 (2018).PubMed 
PubMed Central 

Google Scholar 
Balmford, A. et al. Ecology: Economic reasons for conserving wild nature. Science (80-). 297, 950–953 (2002).ADS 
CAS 

Google Scholar 
Koulov, B., Ivanova, E., Borisova, B., Assenov, A. & Ravnachka, A. GIS-based valuation of ecosystem services in mountain regions: A case study of the Karlovo municipality in Bulgaria. One Ecosyst. 2, e14062 (2017).
Google Scholar 
Robertson, G. P. & Swinton, S. M. Reconciling agricultural productivity and environmental integrity: A grand challenge for agriculture. Front. Ecol. Environ. 3, 38–46 (2005).
Google Scholar 
Sandhu, H. S., Wratten, S. D., Cullen, R. & Case, B. The future of farming: The value of ecosystem services in conventional and organic arable land. An experimental approach. Ecol. Econ. 64, 835–848 (2008).
Google Scholar 
Berglihn, E. C. & Gómez-Baggethun, E. Ecosystem services from urban forests: The case of Oslomarka, Norway. Ecosyst. Serv. 51, 101358 (2021).
Google Scholar 
Nowak, D. J. Understanding i-Tree. (2020). https://doi.org/10.2737/NRS-GTR-200.Selvakumaran, S., Plank, S., Geiß, C., Rossi, C. & Middleton, C. Remote monitoring to predict bridge scour failure using Interferometric Synthetic Aperture Radar (InSAR) stacking techniques. Int. J. Appl. Earth Obs. Geoinf. 73, 463–470 (2018).ADS 

Google Scholar 
Gómez-Baggethun, E. & Barton, D. N. Classifying and valuing ecosystem services for urban planning. Ecol. Econ. 86, 235–245 (2013).
Google Scholar 
Gren, Å. & Andersson, E. Being efficient and green by rethinking the urban-rural divide—Combining urban expansion and food production by integrating an ecosystem service perspective into urban planning. Sustain. Cities Soc. 40, 75–82 (2018).
Google Scholar 
Grêt-Regamey, A., Celio, E., Klein, T. M. & Wissen Hayek, U. Understanding ecosystem services trade-offs with interactive procedural modeling for sustainable urban planning. Landsc. Urban Plan. 109, 107–116 (2013).
Google Scholar 
Bennett, E. M., Peterson, G. D. & Gordon, L. J. Understanding relationships among multiple ecosystem services. Ecol. Lett. 12, 1394–1404 (2009).PubMed 

Google Scholar 
Bradford, J. B. & D’Amato, A. W. Recognizing trade-offs in multi-objective land management. Front. Ecol. Environ. 10, 210–216 (2012).
Google Scholar 
Cueva, J. et al. Synergies and trade-offs in ecosystem services from urban and peri-urban forests and their implication to sustainable city design and planning. Sustain. Cities Soc. 82, 103903 (2022).
Google Scholar 
Allocca, V., Coda, S., Calcaterra, D. & De Vita, P. Groundwater rebound and flooding in the Naples’ periurban area (Italy). J. Flood Risk Manag. 15, e12775 (2022).
Google Scholar 
Padulano, R. et al. Using the present to estimate the future: A simplified approach for the quantification of climate change effects on urban flooding by scenario analysis. Hydrol. Process. 35, e14436 (2021).
Google Scholar 
D’Amato, G. et al. Allergenic pollen and pollen allergy in Europe. Allergy 62, 976–990 (2007).PubMed 

Google Scholar 
Prigioniero, A., Zuzolo, D., Sciarrillo, R. & Guarino, C. Assessing pollinosis risk in the Vesuvius National Park: A novel approach for Index of Urban Green Zones Allergenicity. Environ. Res. 197, 111063 (2021).CAS 
PubMed 

Google Scholar 
AgCult 2020 Classifica visitatori 2019: Capodimonte rientra nella classifica dei primi 30 musei d’Italia.La Valva, V., Guarino, C., De Natale, A., Cuozzo, V., Menale, B. La flora del Parco di Capodimonte di Napoli. in 33–34: 143–177. (Delpinoa, 1992).Stevens, P. F. Angiosperm Phylogeny Website. 2001. http://www.mobot.org/MOBOT/research/APweb/. (2017).James Barth, B., Ian FitzGibbon, S. & Stuart Wilson, R. New urban developments that retain more remnant trees have greater bird diversity. Landsc. Urban Plan. 136, 122–129 (2015).
Google Scholar 
Heckmann, K. E., Manley, P. N. & Schlesinger, M. D. Ecological integrity of remnant montane forests along an urban gradient in the Sierra Nevada. For. Ecol. Manage. 255, 2453–2466 (2008).
Google Scholar 
Prigioniero, A. et al. Role of historic gardens in biodiversity-conservation strategy: the example of the Giardino Inglese of Reggia di Caserta (UNESCO) (Italy). Plant Biosyst. 155, 983–993 (2021).
Google Scholar 
Song, Q., Wang, B., Wang, J. & Niu, X. Endangered and endemic species increase forest conservation values of species diversity based on the Shannon-Wiener index. IForest 9, 469–474 (2016).
Google Scholar 
Hess, M. C. M., Mesléard, F. & Buisson, E. Priority effects: Emerging principles for invasive plant species management. Ecol. Eng. 127, 48–57 (2019).
Google Scholar 
Carli, E. et al. Using vegetation dynamics to face the challenge of the conservation status assessment in semi-natural habitats. Rend. Lincei. Sci. Fis. e Nat. 29, 363–374 (2018).
Google Scholar 
Canedoli, C. et al. Evaluation of ecosystem services in a protected mountain area: Soil organic carbon stock and biodiversity in alpine forests and grasslands. Ecosyst. Serv. 44, 101135 (2020).
Google Scholar 
FAO. Global Forest Resources Assessment 2010. Main report. (2010).Lindén, L., Riikonen, A., Setälä, H. & Yli-Pelkonen, V. Quantifying carbon stocks in urban parks under cold climate conditions. Urban For. Urban Green. 49, 126633 (2020).
Google Scholar 
Nowak, D. J., Hirabayashi, S., Bodine, A. & Greenfield, E. Tree and forest effects on air quality and human health in the United States. Environ. Pollut. 193, 119–129 (2014).CAS 
PubMed 

Google Scholar 
Nowak, D. J., Crane, D. E. & Stevens, J. C. Air pollution removal by urban trees and shrubs in the United States. Urban For. Urban Green. 4, 115–123 (2006).
Google Scholar 
Nowak, D. J. & Crane, D. E. Carbon storage and sequestration by urban trees in the USA. Environ. Pollut. 116, 381–389 (2002).CAS 
PubMed 

Google Scholar 
Kocić, K., Spasić, T., Urošević, M. A. & Tomašević, M. Trees as natural barriers against heavy metal pollution and their role in the protection of cultural heritage. J. Cult. Herit. 15, 227–233 (2014).
Google Scholar 
Yang, J., McBride, J., Zhou, J. & Sun, Z. The urban forest in Beijing and its role in air pollution reduction. Urban For. Urban Green. 3, 65–78 (2005).
Google Scholar 
Zupancic, T., Westmacott, C., Bulthuis, M. The impact of green space on heat and air pollution in urban communities: A meta-narrative systematic review (2015).Cariñanos, P., Adinolfi, C., Díaz de la Guardia, C., De Linares, C. & Casares-Porcel, M. Characterization of Allergen Emission Sources in Urban Areas. J. Environ. Qual. 45, 244–252 (2016).PubMed 

Google Scholar 
D’Auria, A., De Toro, P., Fierro, N. & Montone, E. Integration between GIS and multi-criteria analysis for ecosystem services assessment: A methodological proposal for the National Park of Cilento, Vallo di Diano and Alburni (Italy). Sustain 10, 3329 (2018).
Google Scholar 
Prigioniero, A., Zuzolo, D., Niinemets, Ü. & Guarino, C. Nature-based solutions as tools for air phytoremediation: A review of the current knowledge and gaps. Environ. Pollut. 277, 116817 (2021).CAS 
PubMed 

Google Scholar 
Szkop, Z. Evaluating the sensitivity of the i-Tree Eco pollution model to different pollution data inputs: A case study from Warsaw, Poland. Urban For. Urban Green. 55, 126859 (2020).
Google Scholar 
Tao, J. et al. Elevation-dependent effects of growing season length on carbon sequestration in Xizang Plateau grassland. Ecol. Indic. 110, 105880 (2020).CAS 

Google Scholar 
Chen, Y. et al. Grassland carbon sequestration ability in China: A new perspective from Terrestrial Aridity Zones. Rangel. Ecol. Manag. 69, 84–94 (2016).
Google Scholar 
Gopalakrishnan, V., Hirabayashi, S., Ziv, G. & Bakshi, B. R. Air quality and human health impacts of grasslands and shrublands in the United States. Atmos. Environ. 182, 193–199 (2018).ADS 
CAS 

Google Scholar 
Pace, R. et al. Comparing i-Tree eco estimates of particulate matter deposition with leaf and canopy measurements in an urban mediterranean Holm Oak Forest. Environ. Sci. Technol. 55, 6613–6622 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Losos, J. B., Walton, B. M. & Bennett, A. F. Trade-offs between sprinting and clinging ability in Kenyan Chameleons. Funct. Ecol. 7, 281 (1993).
Google Scholar 
Pretzsch, H., Moser-Reischl, A., Rahman, M. A., Pauleit, S. & Rötzer, T. Towards sustainable management of the stock and ecosystem services of urban trees. From theory to model and application. Trees – Struct. Funct. (2021). https://doi.org/10.1007/s00468-021-02100-3.Grunewald, K. et al. Lessons learned from implementing the ecosystem services concept in urban planning. Ecosyst. Serv. 49, 101273 (2021).
Google Scholar 
Baldacchini, C., Sgrigna, G., Clarke, W., Tallis, M. & Calfapietra, C. An ultra-spatially resolved method to quali-quantitative monitor particulate matter in urban environment. Environ. Sci. Pollut. Res. 26, 18719–18729 (2019).CAS 

Google Scholar 
De Luca, P., Guarino, C., Gullo, G., La Valva V., 1992. Il Parco di Capodimonte di Napoli: storia ed attualità. in 33–34: 143–177. (Delpinoa, 1992).Pignatti, S. Flora d’Italia vol.2. (2017).Braun-Blanquet, J. Plant Sociology (McGraw-Hill Book Company, 1932).
Google Scholar 
Catorci, A. et al. Reproductive traits variation in the herb layer of a submediterranean deciduous forest landscape. Plant Ecol. 214, 737–749 (2013).
Google Scholar 
Šumrada, T. et al. Are result-based schemes a superior approach to the conservation of High Nature Value grasslands? Evidence from Slovenia. Land Use Policy 111, 105749 (2021).
Google Scholar 
POWO. Plants of the World Online. Facilitated by the Royal Botanic Gardens, Kew. Board of Trustees of the Royal Botanic Gardens, Kew http://www.plantsoftheworldonline.org/ (2022).Bímová, K., Mandák, B. & Kašparová, I. How does Reynoutria invasion fit the various theories of invasibility?. J. Veg. Sci. 15, 495–504 (2004).
Google Scholar 
Wild, J., Neuhäuslová, Z. & Sofron, J. Changes of plant species composition in the Šumava spruce forests, SW Bohemia, since the 1970s. For. Ecol. Manag. 187, 117–132 (2004).
Google Scholar 
Damato, G. & Lobefalo, G. Allergenic pollens in the southern Mediterranean area. J. Allergy Clin. Immunol. 83, 116–122 (1989).CAS 

Google Scholar 
Cariñanos, P. et al. Assessing allergenicity in urban parks: A nature-based solution to reduce the impact on public health. Environ. Res. 155, 219–227 (2017).PubMed 

Google Scholar 
Cariñanos, P. et al. Estimation of the allergenic potential of urban trees and urban parks: Towards the healthy design of urban green spaces of the future. Int. J. Environ. Res. Public Health 16, 1357 (2019).PubMed Central 

Google Scholar  More

Díez B, Ininbergs K. Ecological importance of cyanobacteria. In Cyanobacteria (pp. 41–63). John Wiley & Sons, Ltd. (2013) https://doi.org/10.1002/9781118402238.ch3Fristachi A, Sinclair JL, Hall S, Berkman JAH, Boyer G, Burkholder J, et al. Occurrence of cyanobacterial harmful algal blooms workgroup report. Adv Experimental Med Biol. 2008;619:45–103. https://doi.org/10.1007/978-0-387-75865-7_3CAS 
Article 

Google Scholar 
Huisman J, Codd GA, Paerl HW, Ibelings BW, Verspagen JMH, Visser PM. Cyanobacterial blooms. Nat Rev Microbiol. 2018;16:471–83. https://doi.org/10.1038/s41579-018-0040-1CAS 
Article 
PubMed 

Google Scholar 
Plaas HE, Paerl HW. Toxic Cyanobacteria: A Growing Threat to Water and Air Quality. In Environmental Science and Technology (Vol. 55, Issue 1, pp. 44–64). American Chem Soc. 2021. https://doi.org/10.1021/acs.est.0c06653Kurmayer R, Deng L, Entfellner E. Role of toxic and bioactive secondary metabolites in colonization and bloom formation by filamentous cyanobacteria Planktothrix. Harmful Algae. 2016;54:69–86. https://doi.org/10.1016/j.hal.2016.01.004CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Rohrlack T, Christiansen G, Kurmayer R. Putative antiparasite defensive system involving ribosomal and nonribosomal oligopeptides in cyanobacteria of the genus planktothrix. Appl Environ Microbiol. 2013;79:2642–7. https://doi.org/10.1128/AEM.03499-12CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Legnani E, Copetti D, Oggioni A, Tartari G, Palumbo MT, Morabito G. Planktothrix rubescens’ seasonal dynamics and vertical distribution. J Limnol. 2005;64:61–73.Article 

Google Scholar 
Walsby A, Ng G, Dunn C, Davis PA. Comparison of the depth where Planktothrix rubescens stratifies and the depth where the daily insolation supports its neutral buoyancy. New Phytologist. 2004;162:133–45. https://doi.org/10.1111/j.1469-8137.2004.01020.xArticle 

Google Scholar 
Bruning K. Effects of temperature and light on the population dynamics of the Asterionella-Rhizophydium association. J Plankton Res. 1991a;13:707–19. https://doi.org/10.1093/plankt/13.4.707Article 

Google Scholar 
Rohrlack T, Haande S, Molversmyr Å, Kyle M. Environmental Conditions Determine the Course and Outcome of Phytoplankton Chytridiomycosis. 2015;1–17. https://doi.org/10.1371/journal.pone.0145559Tao Y, Wolinska J, Hölker F, Agha R. Light intensity and spectral distribution affect chytrid infection of cyanobacteria via modulation of host fitness. Parasitology. 2020;147:1206–15. https://doi.org/10.1017/S0031182020000931CAS 
Article 
PubMed 

Google Scholar 
Davis PA, Walsby A. Comparison of measured growth rates with those calculated from rates of photosynthesis in Planktothrix spp. isolated from Blelham Tarn, English Lake District. New Phytologist. 2002;156:225–39. https://doi.org/10.1046/j.1469-8137.2002.00495.xCAS 
Article 
PubMed 

Google Scholar 
Oberhaus L, Briand JF, Leboulanger C, Jacquet S, Humbert JF. Comparative effects of the quality and quantity of light and temperature on the growth of Planktothrix agardhii and P. rubescens 1. J Phycol. 2007;43:1191–9. https://doi.org/10.1111/j.1529-8817.2007.00414.xCAS 
Article 

Google Scholar 
Reynolds CS Growth and replication of phytoplankton. In The Ecology of Phytoplankton (pp. 145–77). Cambridge University Press (2009). https://doi.org/10.1017/CBO9780511542145.005Litchman E, Klausmeier CA . Trait-based community ecology of phytoplankton. Ann Rev Ecol, Evol, Syst. 2008;39:615–39.Edwards KF, Thomas MK, Klausmeier CA, Litchman E. Phytoplankton growth and the interaction of light and temperature: A synthesis at the species and community level. Limnol Oceanography. 2016;61:1232–44.Article 

Google Scholar 
Thomas MK, Kremer CT, Litchman E. Environment and evolutionary history determine the global biogeography of phytoplankton temperature traits. Global Ecol Biogeog. 2016;25:75–86. https://doi.org/10.1111/geb.12387Article 

Google Scholar 
Bright DI, Walsby A. The daily integral of growth by Planktothrix rubescens calculated from growth rate in culture and irradiance in Lake Zürich. New Phytologist. 2000;146:301–16. https://doi.org/10.1046/j.1469-8137.2000.00640.xArticle 
PubMed 

Google Scholar 
Jann-Para G, Schwob I, Feuillade M. Occurrence of toxic Planktothrix rubescens blooms in lake Nantua, France. Toxicon. 2004;43:279–85.CAS 
Article 

Google Scholar 
Jacquet S, Briand JF, Leboulanger C, Avois-Jacquet C, Oberhaus L, Tassin B, et al. The proliferation of the toxic cyanobacterium Planktothrix rubescens following restoration of the largest natural French lake (Lac du Bourget). Harmful Algae. 2005;4:651–72.Article 

Google Scholar 
Lenard T. Metalimnetic bloom of Planktothrix rubescens in relation to environmental conditions. Oceanological Hydrobiological Studies. 2009;38:45–53.
Google Scholar 
Van den Wyngaert S, Salcher MM, Pernthaler J, Zeder M, Posch T. Quantitative dominance of seasonally persistent filamentous cyanobacteria (Planktothrix rubescens) in the microbial assemblages of a temperate lake. Limnol Oceanogr. 2011;56:97–109.Article 

Google Scholar 
Walsby A. Stratification by cyanobacteria in lakes: A dynamic buoyancy model indicates size limitations met by Planktothrix rubescens filaments. New Phytologist. 2005;168:365–76. https://doi.org/10.1111/j.1469-8137.2005.01508.xArticle 
PubMed 

Google Scholar 
Conroy JD, Kane DD, Quinlan EL, Edwards WJ, Culver DA. Abiotic and biotic controls of phytoplankton biomass dynamics in a freshwater tributary, estuary, and large lake ecosystem: Sandusky bay (lake erie) chemostat. Inland Waters. 2017;7:473–92. https://doi.org/10.1080/20442041.2017.1395142CAS 
Article 

Google Scholar 
Sommer U, Maciej Gliwics Z, Lampert W, Duncan A. The PEG-model of seasonal succession of planktonic events in fresh waters. Archiv Für Hydrobiologie. 1986;106:433–71.
Google Scholar 
Sommer U, Adrian R, De Senerpont Domis L, Elser JJ, Gaedke U, Ibelings B, et al. Beyond the plankton ecology group (PEG) model: Mechanisms driving plankton succession. Ann Rev Ecol, Evol, Syst. 2012;43:429–48. https://doi.org/10.1146/annurev-ecolsys-110411-160251Article 

Google Scholar 
Hatcher MJ, Dunn AM Parasites in ecological communities: from interactions to ecosystems. Cambridge University Press (2011).Marcogliese DJ. Parasites: Small Players with Crucial Roles in the Ecological Theater. EcoHealth. 2004;1:151–64. https://doi.org/10.1007/s10393-004-0028-3Article 

Google Scholar 
Sime-Ngando T, Lafferty KD, Biron DG. Roles and Mechanisms of Parasitism in Aquatic Microbial Communities. 2007. https://doi.org/10.3389/978-2-88919-588-6Frenken T, Alacid E, Berger SA, Bourne EC, Gerphagnon M, Grossart HP, et al. Integrating chytrid fungal parasites into plankton ecology: research gaps and needs. Environmental Microbiology. 2017;19:3802–22. https://doi.org/10.1111/1462-2920.13827Article 
PubMed 

Google Scholar 
Brussaard CPD, Kuipers B, Veldhuis MJW. A mesocosm study of Phaeocystis globosa population dynamics: I. Regulatory role of viruses in bloom control. Harmful Algae. 2005;4:859–74. https://doi.org/10.1016/j.hal.2004.12.015Article 

Google Scholar 
Gerphagnon M, Macarthur DJ, Gachon C, Van Ogtrop F, Latour D, et al. The biological factors affecting the dynamics of cyanobacterial blooms. 2009.Gleason FH, Jephcott TG, Küpper FC, Gerphagnon M, Sime-Ngando T, Karpov SA, et al. Potential roles for recently discovered chytrid parasites in the dynamics of harmful algal blooms. Fungal Biol Rev. 2015;29:20–33. https://doi.org/10.1016/j.fbr.2015.03.002Article 

Google Scholar 
Ibelings BW, Gsell AS, Mooij WM, Van Donk E, Van Den Wyngaert S, De Senerpont Domis LN. Chytrid infections and diatom spring blooms: Paradoxical effects of climate warming on fungal epidemics in lakes. Freshwater Biol. 2011;56:754–66. https://doi.org/10.1111/j.1365-2427.2010.02565.xArticle 

Google Scholar 
Kagami M, De Bruin A, Ibelings BW, Van Donk E. Parasitic chytrids: Their effects on phytoplankton communities and food-web dynamics. Hydrobiologia. 2007;578:113–29. https://doi.org/10.1007/s10750-006-0438-zArticle 

Google Scholar 
Lips KR, Brem F, Brenes R, Reeve JD, Alford RA, Voyles J, et al. Emerging infectious disease and the loss of biodiversity in a Neotropical amphibian community. PNAS. 2005;103:3165–70.Article 

Google Scholar 
McKenzie VJ, Peterson AC. Pathogen pollution and the emergence of a deadly amphibian pathogen. Molecular Ecol. 2012;21:5151–4. https://doi.org/10.1111/mec.12013Article 

Google Scholar 
Skerratt LF, Berger L, Speare R, Cashins S, McDonald KR, Phillott AD, et al. Spread of chytridiomycosis has caused the rapid global decline and extinction of frogs. EcoHealth. 2007;4:125–34. https://doi.org/10.1007/s10393-007-0093-5Article 

Google Scholar 
Ibelings BW, De Bruin A, Kagami M, Rijkeboer M, Brehm M, Van Donk E. Host parasite interactions between freshwater phytoplankton and chytrid fungi (Chytridiomycota). J Phycol. 2004;40:437–53.Article 

Google Scholar 
Bosch J, Martínez-Solano I, García-París. Evidence of a chytrid fungus infection involved in the decline of the common midwife toad (Alytes obstetricans) in protected areas of central Spain. Biological Conserv. 2001;97:331–7. https://doi.org/10.1016/S0006-3207(00)00132-4Article 

Google Scholar 
Bruning K, Lingeman R, Ringelberg J. Estimating the impact of fungal parasites on phytoplankton populations. Limnol Oceanogr. 1992;37:252–60. https://doi.org/10.4319/lo.1992.37.2.0252Article 

Google Scholar 
Paterson RA. Infestation of Chytridiaceous Fungi on Phytoplankton in Relation to Certain Environmental Factors. Ecology. 1960;41:416–24. https://doi.org/10.2307/1933316Article 

Google Scholar 
Ṣen B. Fungal parasitism of planktonic algae in Shearwater. IV: Parasitic occurrence of a new chytrid species on the blue-green alga Microcystis aeruginosa Kuetz. emend. Elenkin. 1998.van Donk E, Ringelberg J. The effect of fungal parasitism on the succession of diatoms in Lake Maarsseveen I. Netherlands Freshwater Biol. 1983;13:241–51. https://doi.org/10.1111/j.1365-2427.1983.tb00674.xArticle 

Google Scholar 
Agha R, Saebelfeld M, Manthey C, Rohrlack T, Wolinska J. Chytrid parasitism facilitates trophic transfer between bloom-forming cyanobacteria and zooplankton (Daphnia). Scientific Rep. 2016;6. https://doi.org/10.1038/srep35039Frenken T, Wierenga J, van Donk E, Declerck SAJ, de Senerpont Domis LN, Rohrlack T, et al. Fungal parasites of a toxic inedible cyanobacterium provide food to zooplankton. Limnol Oceanogr. 2018;63:2384–93. https://doi.org/10.1002/lno.10945Article 

Google Scholar 
Kagami M, von Elert E, Ibelings BW, de Bruin A, van Donk E. The parasitic chytrid, Zygorhizidium, facilitates the growth of the cladoceran zooplankter, Daphnia, in cultures of the inedible alga, Asterionella. Proc Biological Sci/ Royal Soc. 2007;274:1561–6. https://doi.org/10.1098/rspb.2007.0425Article 

Google Scholar 
Gsell AS, de Senerpont Domis LN, van Donk E, Ibelings BW. Temperature alters host genotype-specific susceptibility to chytrid infection. PLoS One. 2013;8:e71737. https://doi.org/10.1371/journal.pone.0071737CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
McKindles KM, Manes MA, McKay RM, Davis TW, Bullerjahn GS. Environmental factors affecting chytrid (Chytridiomycota) infection rates on Planktothrix agardhii. J Plankton Res. 2021a;43:658–72.Article 

Google Scholar 
Fallowfield HJ, Daft MJ. The extracellular release of dissolved organic carbon by freshwater cyanobacteria and algae and the interaction with Lysobacter CP-1. Br Phycol J. 1988;1617:317–26. https://doi.org/10.1080/00071618800650351Article 

Google Scholar 
Mueller B, den Haan J, Visser PM, Vermeij MJA, van Duyl FC. Effect of light and nutrient availability on the release of dissolved organic carbon (DOC) by Caribbean turf algae. Scientific Rep. 2016;6:1–9. https://doi.org/10.1038/srep23248CAS 
Article 

Google Scholar 
Bruning K. Infection of the diatom Asterionella by a chytrid. II. Effects of light on survival and epidemic development of the parasite. J Plankton Res. 1991c;13:119–29. https://doi.org/10.1093/plankt/13.1.119Article 

Google Scholar 
Van den Wyngaert S, Gsell AS, Spaak P, Ibelings BW. Herbicides in the environment alter infection dynamics in a microbial host-parasite system. Environ Microbiol. 2013;15:837–47. https://doi.org/10.1111/j.1462-2920.2012.02874.xCAS 
Article 
PubMed 

Google Scholar 
Almocera AES, Hsu SB, Sy PW. Extinction and uniform persistence in a microbial food web with mycoloop: Limiting behavior of a population model with parasitic fungi. Mathematical Biosci Eng. 2019;16:516–37.Article 

Google Scholar 
Frenken T, Miki T, Kagami M, Van de Waal DB, Van Donk E, Rohrlack T, et al. The potential of zooplankton in constraining chytrid epidemics in phytoplankton hosts. Ecology. 2020;101. https://doi.org/10.1002/ecy.2900Gerla DJ, Gsell AS, Kooi BW, Ibelings BW, Van Donk E, Mooij WM. Alternative states and population crashes in a resource-susceptible-infected model for planktonic parasites and hosts. FMeier, M. H. et al. (2015) Neuropsychological Decline in Schizophrenia from the Premorbid to Post-Onset Period: Evidence from a Population-Representative Longitudinal Study. American J Psychiatry. 2013;58:538–51. https://doi.org/10.1111/fwb.12010Article 

Google Scholar 
Miki T, Takimoto G, Kagami M. Roles of parasitic fungi in aquatic food webs: A theoretical approach. Freshwater Biol. 2011;56:1173–83. https://doi.org/10.1111/j.1365-2427.2010.02562.xArticle 

Google Scholar 
Guillard RRL, Lorenzen CJ. Yellow-green algae with chlorophyllid C. In Phycology. 1972;8:10–14.CAS 

Google Scholar 
McKindles KM, Jorge AN, McKay RM, Davis TW, Bullerjahn GS. Isolation and characterization of Rhizophydiales (Chytridiomycota), obligate parasites of Planktothrix agardhii in a Laurentian Great Lakes embayment. Appl Environ Microbiol. 2021b;87:e02308–20.CAS 
Article 

Google Scholar 
R Core Team. (2021). R: A Language and Environment for Statistical Computing.RStudio Team. (2021). RStudio: Integrated Development Environment for R (1.4.1106).Wickham, H (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York.Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, et al. Welcome to the {tidyverse}. J Open Source Software. 2019;4:1686. https://doi.org/10.21105/joss.01686Article 

Google Scholar 
Champely, S (2018). PairedData (1.1.1).Soetaert K, Petzoldt T, Setzer RW. Solving Differential Equations in {R}: Package deSolve. J Statistical Software. 2010;33:1–25. https://doi.org/10.18637/jss.v033.i09Article 

Google Scholar 
Frenken T, Velthuis M, de Senerpont Domis LN, Stephan S, Aben R, Kosten S, et al. Warming accelerates termination of a phytoplankton spring bloom by fungal parasites. Global Change Biol. 2016;22:299–309. https://doi.org/10.1111/gcb.13095Article 

Google Scholar 
Scholz B, Vyverman W, Küpper FC, Ólafsson HG, Karsten U. Effects of environmental parameters on chytrid infection prevalence of four marine diatoms: A laboratory case study. Botanica Marina. 2017;60:419–31. https://doi.org/10.1515/bot-2016-0105CAS 
Article 

Google Scholar 
Sønstebø JH, Rohrlack T. Possible implications of Chytrid parasitism for population subdivision in freshwater cyanobacteria of the genus Planktothrix. Appl Environ Microbiol. 2011;77:1344–51. https://doi.org/10.1128/AEM.02153-10CAS 
Article 
PubMed 

Google Scholar 
Bruning K. Infection of the diatom Asterionella by a chytrid. I. Effects of light on reproduction and infectivity of the parasite. J Plankton Res. 1991b;13:103–17. https://doi.org/10.1093/plankt/13.1.103Article 

Google Scholar 
Muehlstein LK, Amon JP, Leffler DL. Chemotaxis in the Marine Fungus Rhizophydium littoreum. Appl Environ Microbiol. 1988;54:1668–72. https://doi.org/10.1128/aem.54.7.1668-1672.1988CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Esch GW, Fernández JC. Introduction. In A Functional Biology of Parasitism (pp. 1–25). Springer Netherlands (1993). https://doi.org/10.1007/978-94-011-2352-5_1Gerphagnon M, Colombet J, Latour D, Sime-Ngando T. Spatial and temporal changes of parasitic chytrids of cyanobacteria. Scientific Rep. 2017;7:6056. https://doi.org/10.1038/s41598-017-06273-1CAS 
Article 

Google Scholar 
Maier MA, Peterson TD. Prevalence of chytrid parasitism among diatom populations in the lower Columbia River (2009–2013). Freshwater Biol. 2017;62:414–28. https://doi.org/10.1111/fwb.12876CAS 
Article 

Google Scholar 
Sime-Ngando T. Phytoplankton chytridiomycosis: Fungal parasites of phytoplankton and their imprints on the food web dynamics. Front Microbiol. 2012;3:361. https://doi.org/10.3389/fmicb.2012.00361Article 
PubMed 
PubMed Central 

Google Scholar 
Kagami M, Urabe J. Mortality of the planktonic desmid, Staurastrum dorsidentiferum, due to interplay of fungal parasitism and low light conditions. SIL Proceed. 2002;28:1001–5. https://doi.org/10.1080/03680770.2001.11901868Article 

Google Scholar  More

MaterialsWe collected from the literature and available databases a dataset of radiocarbon dates from Europe (West of 60°E and North of 37°N) either obtained from remains of the four analyzed species or from archaeological layers where they have been observed. However, we only considered observations dated between 7500 and 47,000 cal BP: their scarcity before this period may bias the GAMs, and after it, domesticated cattle, pigs and (later) horses arrived in Europe, making it difficult to differentiate them from their wild forms.We excluded any record fitting one or more of the following conditions: unreliable; not in accord with the expected chronology of their archaeological layer; without a reported standard error; available only as terminus ante/post quem.All dates were calibrated with OxCal5 version 4.4 using the IntCal20 curve51, and we further excluded any record for which calibration resulted in an error, resulting in the number of points presented in Table 1 as “Original dataset” (available at the link https://doi.org/10.6084/m9.figshare.20510364).Table 1 Number of observations for each species.Full size tableSDMs based on GAMs need presence/background data, not frequencies; moreover, multiple observations (i.e., presence in different archaeological layers) from the same site and time slice are likely to introduce stronger sample biases linked to chrono-geographically differential sampling efforts. For this reason, we collapsed our observations by keeping only one point per grid cell per time slice for each species, leaving the number of observations reported in Table 1 as “Collapsed datasets”, used for all the analyses presented in this work.To perform all analyses, we used the R package pastclim v. 1.042 to couple each observation from the collapsed datasets to paleoclimatic reconstructions published in8 by setting dataset = “Beyer2020”. These are based on the Hadley CM3 model, include 14 different bioclimatic variables at a spatial resolution of 0.5°, and are available for the whole world every 1000 years until 22 kya and every 2000 years before that date (referred to in the manuscript as “time slices”). Specifically, each observation was associated with the relevant bioclimatic reconstruction based on its average age and spatial coordinates.As already mentioned, the four species analyzed show different preferences regarding temperature, habitat, and altitude. Therefore, for the Species Distribution Modelling, we choose five environmental variables that should be able to capture such differences: two measures of temperature (BIO5, maximum temperature of the warmest month, and BIO6, minimum temperature of the coldest month); two variables to help capture habitat differentiation (BIO12, total annual precipitation, and Net Primary Productivity, NPP), and one measure of topography (rugosity42).High collinearity can be problematic in SDMs; we confirmed that all our variables had a correlation below 0.7, a threshold commonly adopted for this kind of analysis52,53.Whilst the GAMs predicted all time points; we visualized our results by creating an average estimate for the following periods: pre-LGM (from the beginning of the time range analyzed, i.e., 47 kya to 27 kya), LGM (from 27 to 18 kya), Late Glacial (from 18 to 11.7 kya), Holocene (from 11.7 kya to the end of the time range analyzed, i.e., 7.5 kya).MethodsWe generated 25 sets of background points for each species to adequately represent the existing climatic space in our SDMs. Each set was generated by sampling, for each observation, 50 random locations matched by time. This resulted in n = 25 datasets (“repetitions”) of background points and presences (observations) for each species, which we used to repeat our analyses to account for the stochastic sampling of the background. For each dataset, we used GAMs to fit two possible models: a “constant niche” model, which included only the environmental variables as covariates, and a “changing niche” model, that also included interactions of each environmental variable with time (fitted as tensor products).In GAMs, the effect of a given continuous predictor on the response variable (in our case, the logit transformed probability of a presence) is represented by a smooth function; this smooth function can be linear or non-linear and can become highly complex in shape depending on the number of knots selected by the GAM fitting algorithm. The interaction between two covariates is modelled by tensor products54; this approach is equivalent to an interaction term in a linear model but with the added complexity of the smooth function. In our models, we confine tensor products to the interaction between an environmental variable and time; a simple way to think about such a tensor product is that it allows the smooth representation of the relationship between the variable and the probability of a presence to change progressively over time.GAMs were fitted using the mgcv package in R54 using thin plate regression splines (TPNR; bs = “tp”, default in mgcv) for environmental variables and their tensor products with time in the “niche changing” models. The GAM algorithm automatically selects the complexity of the smooth most appropriate to the data that are being fitted; as GAM can have issues with overfitting, we added an additional penalty against overly complex smooths (gamma = 1.4) and used Restricted Maximum Likelihood (REML = TRUE), as recommended by54. It is possible that even with these settings, the complexity of the smooth is not sufficient; we used mgcv::gam.check() to check this, and increased the basis dimension of the smooth, k, to make sure that k-1 was larger than the estimated degrees of freedom (edf). We found the best maximum thresholds for k to be 16 for bio06 and 10 for all other variables.We checked for non-linear correlation among variables using the mgcv::collinearity function and checked the values of estimated concurvity. All estimates were below the threshold of 0.8 in all models, runs and variables except for a few instances for time (Supplementary Figs. 5–8). We consider this not to be worrying: this is most likely a result of sample bias, and GAM is known to be robust to correlation/concurvity55,56.We verified the model assumptions by inspecting the residuals using the R package DHARMa57. Standard tests for deviations from the expected distribution and dispersion were non-significant for all repetitions for all species, as were the tests for outliers. Furthermore, we tested for spatial autocorrelation among residuals by computing Moran’s I; all tests were either non-significant or, when significance was detected, the estimate of Moran’s I was very close to zero, revealing a trivial deviation from the assumptions which should not impact the results (Supplementary Tables 1–4).We performed model choice (Supplementary Tables 5–8) by comparing the constant- and changing-niche models for each combination of species and repetition using the Akaike Information Criterion (AIC). AIC strongly supported the changing-niche model in all species and repetitions, an inference supported by the higher Nagelkerke R2 and expected deviance for those models than for the constant-niche ones (Supplementary Tables 5–8).The model fit for each of the changing niche GAMs was evaluated with the Boyce Continuous Index25,26, designed to be used with presence-only data58,59. We set a threshold of Pearson’s correlation coefficient  >  0.8 to define acceptable models25 (Supplementary Table 9).The relative importance of each environmental variable was quantified for all the models above the BCI threshold of 0.8 in two different ways. Firstly, we computed the total deviance explained by each variable by simply fitting a GAM with only that variable. We then estimated the unique deviance explained by each variable by comparing the full model with one for which that variable was excluded (i.e., we computed the explained deviance lost by dropping that predictor). The difference between the two values represents the deviance explained by a variable which can also be accounted for by other variables (i.e., the deviance in common with other variables).To achieve more robust predictions60, we averaged in two different ensembles the repetitions for the changing niche GAMs with BCI  > 0.8: by mean and median. This step is intended to reduce the weight of models that are highly sensitive to the random sampling of the background60. Then, for each species, we selected the ensemble (either based on mean or median) with the higher BCI as the most supported and used it to perform all further analyses.The effect of different variables through time was visualized by plotting the interactions of the GAMs. For each model with a BCI  > 0.8, we used the R package gratia27 to generate a surface with time as the x-axis, the environmental variable as the y-axis, and the effect size as the z-axis (visualized as colour shades). We then plotted the mean surface for each species, which captures the signal consistent across all randomized background sets.To visualize the prediction for each species, we then transformed the predicted probabilities of occurrence from the ensemble into binary presence/absences by using the threshold needed to get a minimum predicted area encompassing 99% of our presences (function ecospat.mpa() from the ecospat R package61). The binary predictions were then visualized using the mean over the time steps within each major climatic period.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More