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    Ruminants reveal Eocene Asiatic palaeobiogeographical provinces as the origin of diachronous mammalian Oligocene dispersals into Europe

    Mammalia Linnaeus, 175829.Artiodactyla Owen, 184830.Ruminantia Scopoli, 177731.Infraorder Tragulina Flower, 188332.Family Lophiomerycidae Janis, 198733.Included generaLophiomeryx, Zhailimeryx, Krabimeryx, Chiyoumeryx nov. gen.Genus Krabimeryx Métais, Chaimanee, Jaeger, and Ducroq, 200117.EtymologyKrabi—from Krabi Basin, where the fossils were found, and—meryx is the Greek word for ruminant.Diagnosis [modified after Métais et al.17]Small primitive ruminant with lower molars morphologically close to those of Zhailimeryx. Krabimeryx differs from Zhailimeryx in: more laterally compressed lingual cuspids in the lower molars; an entoconid displaced to anterior with respect to the hypoconid; the lack of both a paraconid and a hypoconulid in m1 and m2; a p4 with a mesolingual conid that is located more posterior and less individualized; a p4 without a distinct posterolingual conid. Krabimeryx differs from Lophiomeryx by less selenodont labial cuspids in the lower molars, the presence of a developed external postmetacristid, and by a distinct groove on the anterior side of the entoconid, the entoconidian groove. Krabimeryx can be distinguished from Iberomeryx in having a well-marked entoconidian groove; the lack of a clear external postprotocristid; the third lobe of m3 not forming a complete buckle; and a more transversely compressed hypoconulid in the m3. Krabimeryx possesses a huge notch in lingual view between the entoconid and the third lobe in the m3.Type speciesKrabimeryx primitivus Métais, Chaimanne, Jaeger, and Ducroq, 200117.Included speciesKrabimeryx gracilis nov. comb. (Miao, 198220).Krabimeryx gracilis nov. comb. (Miao, 198220).Figure 1A and Figure S1.Figure 1Dentition of Krabimeryx gracilis nov. comb. (Miao, 1982)20 (A, B, G, H), Chiyoumeryx nov. gen. shinaoensis (Miao, 1982)20 (C, D), Chiyoumeryx nov. gen. flavimperatoris nov. sp. (E) and Iberomeryx miaoi nov. sp. (F–I). Krabimeryx gracilis nov. comb. (Miao, 1982)20: (A) IVPP V 6546-1 (holotype), partial skull with right and left M1–M3; (B) IVPP V 6546-2 (holotype), right fragmented mandible with m2–m3. Chiyoumeryx nov. gen. shinaoensis (Miao, 1982)20: (C) IVPP V 6531 (holotype), right mandible with p2–m3 and tooth socket of p1; (D) IVPP V 6532 (paratype), right fragmented maxillary with P4-M3. Chiyoumeryx nov. gen. flavimperatoris nov. sp.: (E) IVPP V 6547 (holotype), right mandible with p4–m3; Iberomeryx miaoi nov. sp.: (F) IVPP V 6551 (holotype), left mandible with m1–m3 (mirrored); (G) lower molar Lophiomerycidae dental nomenclature (based on the m3 of IVPP 6546-2): 1 internal postmetacristid, 2 metaconid, 3 external postmetacristid, 4 internal preentocristid, 5 entoconidian groove, 6 external preentocristid, 7 entoconid, 8 posthypoconulidcristid, 9 hypoconulid, 10 prehypoconuldicristid, 11 posthypocristid, 12 hypoconid, 13 prehypocristid, 14 ectostylid, 15 postprotocristid, 16 protoconid, 17 preprotocristid, 18 anterior cingulid; (H) upper molar Lophiomerycidae dental nomenclature (based on the M2 of IVPP 6546-1): 1 postmetacrista, 2 metacone, 3 premetacrista, 4 mesostyle, 5 postparacrista, 6 paracone, 7 paraconid labial groove, 8 preparacrista, 9 parastyle, 10 preprotocrista, 11 anterolingual cingulum, 12 protocone, 13 postprotocrista, 14 entostyle, 15 additional cone, 16 premetaconulecrista, 17 metaconule, 18 postmetaconulecrista; (I) lower molar Tragulidae dental nomenclature (based on the m2 of IVPP V 6551, reversed): 1 metaconid, 2 external postmetacristid, 3 Dorcatherium fold, 4 internal postmetacristid, 5 preentocristid, 6 entoconid, 7 postentocristid, 8 posterior cingulid, 9 posthypocristid, 10 hypoconid, 11 prehypocristid, 12 ectostylid, 13 external postprotocristid, 14 Tragulus fold, 15 internal postprotocristid, 16 protoconid, 17 preprotocristid, 18 paraconid, 19 preparacristid. (J) phylogenetic position and stratigraphie of the Shinao/Yangjiachong/Xiaerhete ruminants (topology2). a stem Ruminantia, b Archaeomeryx, c Chiyoumeryx nov. gen. and Krabimeryx gracilis, d crown Ruminantia, e Iberomeryx miaoi nov. sp.; 1 lingual view, 2 occlusal view. Scale bare is 1 cm.Full size image*v pars1982 Lophiomeryx gracilis—Miao: 532, Table 3, Figs. 6 and 720.v non1982 Lophiomeryx gracilis?—Miao: 536, Fig. 820.v pars1987 L. gracilis—Janis: 21133.v pars1997 L. gracilis—Vislobokova: Fig. 321.v pars2000 L. gracilis—Guo, Dawson, and Beard: 247, Table 214.v pars2001 L. gracilis—Métais, Chaimanee, Jaeger, and Ducroq: 239, 24117.v pars2012 L. gracilis—Mennecart: 6234.NeodiagnosisKrabimeryx gracilis has an m2 that is wider than the m3; this is the other way round in K. primitivus. Moreover, the entoconid is less anterior relative to the hypoconid in K. gracilis than it is in K primitivus. The ectostylid is large in K. gracilis, while it is absent in K. primitivus. The cingulum on the upper molars in K. gracilis is more developed than in K. primitivus.HolotypeIVPP V 6546, partial skull with right and left M1–M3 (IVPP V 6546-1) and an associated right fragmented mandible with m2–m3 (IVPP V 6546-2) found in occlusion with the skull.Additional materialIVPP V 6549, right m3 on fragmented mandible; IVPP V 6550 left fragmented mandible with m1–m2; IVPP V 26638, right m1. Measurements are given in Table S1.LocalitiesShinao Basin, Panxian County, Southwestern Guizhou, China; Xiaerhete locality, Jiminay County, Xingjiang, China. Late Eocene.Taxonomical attributionThe herein described specimens were first attributed to the genus Lophiomeryx20. However, the thorough reassessment of the specimens now leads to the conclusion that Lophiomeryx gracilis sensu Miao20 contains three different species and genera, but none of them can be assigned to Lophiomeryx.Based on the presence of a strong lingual cingulum in upper molars and a short anteroposteriorly oriented postprotocrista, as well as the absence of a premetacristid and an anterior fossa widely open in the lower molars, we can conclude that the specimens, IVPP V 6546-1, IVPP V 6546-2, IVPP V 6549, and IVPP V 6550, belong to Lophiomerycidae or Tragulidae35,36. However, the absence of a large paraconid and the absence of an elongated external postmetacristid distinguish the specimens from primitive Tragulidae17,36. In Zhailimeryx jingweni, the cuspids are more slender than in the herein described specimens14, a feature the taxon shares with K. primitivus. In Z. jingweni, m1 and m2 are of relative similar width14, while in K. primitivus and the herein described specimens from Shinao the m2 is clearly bigger than the m117. Similarly to K. primitivus, the herein described specimens differ from Z. jingweni in its lower molar lingual cusps being more laterally compressed, and in an entoconid that is slightly shifted to anterior with respect to the hypoconid, while it is more posterior in Z. jingweni14,17. Furthermore, K. primitivus and the herein described specimens from Shinao both lack the rudimentary paraconid present in Z. jingweni14,17.Like K. primitivus, the here-described specimens differ from Chiyoumeryx nov. gen. (described below) and the Lophiomeryx species L. mouchelini, L. chalaniati and L. angarae by having more massive and more bunomorph lower molars16,17,24,34,37. Furthermore, Zhailimeryx jingweni, K. primitivus, and the herein described specimens differ from Lophiomeryx by the presence of a developed external postmetacristid and by a distinct entoconidian groove on the anterior side of the compressed entoconid14,17. In Lophiomeryx, the back fossa of m3 is widely open due to the strong reduction of the posthypoconulidcristid34,37. In contrast to this, Krabimeryx primitivus possesses a clearly developed posthypoconulidcristid forming a buckle on the m3 back basin17, similarly to the specimens from Shinao described here.Summing up, the general morphology of the teeth in the herein described specimens is most similar to the one observed in K. primitivus. They both share a similar huge notch in lateral view between the third lobe of m3 and the entoconid and the entoconidian groove, features that clearly distinguishing them both from Lophiomeryx and Zhailimeryx. Thus, we attribute the specimens IVPP V 6546-1, IVPP V 6546-2, IVPP V 6549, and IVPP V 6550 to the genus Krabimeryx. However, significant differences occur with the type species, ruling out the synonymisation of K. gracilis nov. comb. and Krabimeryx primitivus. While both species are very similar in size, K. primitivus has an m3 wider than m2, while it is the converse for K. gracilis nov. comb. Moreover, the entoconid is less shifted to the anterior with respect to the hypoconid in K. gracilis nov. comb. than in K primitivus. There is no ectostylid in K. primitivus, while it is large in K. gracilis nov. comb., forming a transverse cristid between the protoconid and the hypoconid. The cingulum on the upper molars is more developed in K. gracilis nov. comb. than in K. primitivus.Due to these differences we decided to create the new combination Krabimeryx gracilis nov. comb.Chiyoumeryx nov. gen.ZooBank LSIDurn:lsid:zoobank.org:act:464C46E0-5A69-4AC1-A9DD-8A7DF76D5CC0.EtymologyChiyou is a tribe leader of the ancient China, about 5–4 k years ago. Chiyou’s tribe was believed to be in relation with the peoples in southern China; -meryx means ruminant in Greek.DiagnosisChiyoumeryx nov. gen. differs from Zhailimeryx and Krabimeryx notably by the absence of the entoconidian groove. The lower teeth are more laterally compressed in Chiyoumeryx nov. gen. and the metaconid is linguo-labiallly more central than in the two other genera. The posthypoconulidcristid in the lower molars of Chiyoumeryx nov. gen. is longer than in Krabimeryx and its p4 is posteriorly extended, while this part is reduced in Krabimeryx. Chiyoumeryx nov. gen. differs from Lophiomeryx by the shape of the mandible. In Chiyoumeryx nov. gen. there is no diastema between p1 and p2 and the diastema between c and p1 is extremely reduced. The outline of the mandible in occlusal view is relatively straight in this species. Lophiomeryx possesses a long diastema between c and p1 and a small one between p1 and p2, as well as a regularly curved occlusal outline of the corpus. The lower premolars of Chiyoumeryx nov. gen. are laterally compressed giving a more elongated aspect to these teeth than in Lophiomeryx. The trigonid is smaller than the talonid in m1 and m2 in Chiyoumeryx nov. gen. and the preprotocristid terminates centrally and does not reach the lingual side. In Lophiomeryx the trigonid and talonid are of similar size and the preprotocristid is longer and reaches the lingual side. Moreover, in Chiyoumeryx nov. gen., the posthypoconulidcristid is longer than in Lophiomeryx. The shape of the P4 in Chiyoumeryx nov. gen. differs from the one in Lophiomeryx: the posterolingual crista does not meet the posterolabial crista.Type speciesChiyoumeryx nov. gen. shinaoensis (Miao, 198220).Included speciesChiyoumeryx nov. gen. flavimperatoris nov. sp.; ?Chiyoumeryx nov. gen. turgaicus (Flerow 193838).Chiyoumeryx nov. gen. shinaoensis (Miao, 198220).Figure 1B and Figure S2.*v1982 Lophiomeryx shinaoensis—Miao: 530, Table 3, Figs. 3–520.v1987 Lophiomeryx shinaoensis—Janis: 203, 204, 211, 212, Fig. 8B33.v1997 Lophiomeryx shinaoensis—Vislobokova: Fig. 321.v2000 L. shinaoensis—Guo, Dawson, and Beard: 247, Table 214.v2001 L. shinaoensis—Métais, Chaimanee, Jaeger, and Ducroq: 239–241, 24117.v2012 L. shinaoensis—Mennecart: 6234.NeodiagnosisChiyoumeryx nov. gen. shinaoensis is bigger than Chiyoumeryx nov. gen. flavimperatoris nov. sp. but smaller than ?Chiyoumeryx turagicus. The transversely oriented anterior conid in the p4 in Chiyoumeryx nov. gen. shinaoensis differs from the obliquely oriented one in Chiyoumeryx nov. gen. flavimperatoris nov. sp. In Chiyoumeryx nov. gen. shinaoensis, the posterolingual conid is vestigial on p4. Chiyoumeryx nov. gen. shinaoensis has no anterior cingulid, while in Chiyoumeryx nov. gen. flavimperatoris nov. sp. there is a tiny anterior cingulid. Chiyoumeryx nov. gen. shinaoensis possesses lower crowns than ?Chiyoumeryx nov. gen. turgaicus. Chiyoumeryx nov. gen. flavimperatoris nov. sp. possesses an ectostylid, which is absent in ?Chiyoumeryx nov. gen. turgaicus.HolotypeIVPP V 6531, right mandible with p2–m3 and tooth socket of p1.ParatypeIVPP V 6532, right fragmented maxillary with P4–M3.Additional materialIVPP V 6533, right mandible with p2–m3 and tooth socket of i1–p1; IVPP V 6534, left fragments mandible with m1–m3; IVPP V 6535, right fragmented mandible with m1–m3; IVPP V 6536, left fragmented mandible with p4–m3; IVPP V 6537, right fragmented mandible with p4–m2; IVPP V 6538, left p4; IVPP V 6539, right maxillary with P3–M3; IVPP V 6540, right maxillary with P4–M2; IVPP V 6541, right maxillary with M2–M3; IVPP V 6542, left maxillary with P3–M1; IVPP V 6543, right maxillary with M1–M3; IVPP V 6544, Left M3; IVPP V 6545, left maxillary with P4–M3. Measurements are given in Table S1.LocalityShinao Basin, Panxian County, Southwestern Guizhou, China. Late Eocene.Taxonomical attributionMiao20 attributed the here described specimens to the genus Lophiomeryx assuming that these fossils belong to a traguloid. “Lophiomeryx” shinaoensis clearly is a Lophiomerycidae: anterior and posterior fossae are open on the lower molars due to the absence of a premetacristid and the extreme reduction or absence of a postentocristid, there is no external postprotocristid, there is a mesolingual conid on the p4, the symphysis of the mandible extends backward up to the p12,36. It also shares with undisputable Lophiomerycidae a reduced posthypoconulidcristid that does not enclose the third lobe lingually.“Lophiomeryx” shinaoensis differs from Zhailimeryx and Krabimeryx in the absence of the entoconidian groove14,17. Moreover, the teeth are more laterally compressed in “Lophiomeryx” shinaoensis and the metaconid is linguo-labially more centeral14,17. The posthypoconulidcristid in “Lophiomeryx” shinaoensis is more elongated than in Krabimeryx and its p4 has an extended posterior part, while it is reduced in Krabimeryx17.Contrary to what was suggested by Métais and Vislobokova2, Miomeryx altaicus24 is currently known only by its holotype, which is an upper tooth row (AMNH 20383, see Matthew and Granger24). Comparable to M. altaicus, the postprotocrista reaches the premetaconulecrista on the M2 in “Lophiomeryx” shinaoensis. These two cristae fuse totally on the M3 in the here described specimens. However, even if both genera also bear a very strong cingulum, “Lophiomeryx” shinaoensis clearly differs from M. altaicus in having broader and squarer molars and straighter lingual cristae in the P4.Miao20 compared the here revised fossils with the seven Lophiomeryx species considered valid at that time. Unfortunately, very few specimens document most of these species and there is considerable doubt considering the genus attribution of most of them34,36,37,38,39. In any case, we agree with Miao20 (p. 535) that “L. [= Praetragulus] gobiae is readily distinguished from other known Lophiomeryx species as well as from L. shinaoensis by the absence of p1, the anterior flange of metaconid joining protoconid crescent.”. Miao20 (p. 535) already noticed that “Lophiomeryx chalaniati, Lophiomeryx gaudry [= Iberomeryx minor], and Lophiomeryx benarensis are radically different from the present specimens in the anterior branches of the protoconid crescent [= preprotocristid], of m1 and m2 not reaching the lingual border while the posterior branches of hypoconid crescent [= posthypocristid], doing so”. “Lophiomeryx” shinaoensis shares this condition with the Mongolian Lophiomeryx angarae24. However, the trigonid is smaller than the talonid on m1 and m2 in “Lophiomeryx” shinaoensis and the preprotocristid ends in the labio-lingual axis of the molars, while trigonid and talonid are of more similar width combined with a longer preprotocristid in the European Lophiomeryx species and L. angarae16,34,37. The shape of the P4 in “Lophiomeryx” shinaoensis is very different from Lophiomeryx (see Brunet and Sudre37, Figs. 4 and 6). In Lophiomeryx, the posterolingual crista fuses with the posterolabial crista. In “Lophiomeryx” shinaoensis, the curved posterolingual crista does not join the distal end of the posterolabial crista but reaches the labial side. Furthermore, “Lophiomeryx” shinaoensis clearly differs from L. angarae L. mouchelini, and L. chalaniati in the shape of the mandible. These three species of Lophiomeryx possess a very elongated diastema between c and p1 and a small one between p1 and p224,36,37. As part of the genus diagnosis, Mennecart34 (p. 62 and p. 67), adapted from Brunet and Sudre37 and Métais and Vislobokova2, noticed that “the corpus mandibulae presents [in Lophiomeryx: L. angarae, L. mouchelini, and L. chalaniati24,34,37] a concave ventral profile just behind the mandible symphysis, then it becomes regularly convex until the beginning of the ramus, where there is a rounded incisura vasorum. […] On the anterior part of the mandible there are two foramen mentale.” Moreover he wrote that the “p1 is always reduced and leaf-like, separated from c and p2 by diastemata.” (Mennecart34, p. 67). In “Lophiomeryx” shinaoensis there is no diastema between p1 and p2 and the diastema between c and p1 is extremely reduced. The p1 is relatively big considering the root size. The lower outline of the mandible in lateral view is relatively straight. “Lophiomeryx” shinaoensis shares these characteristics with “Lophiomeryx” turgaicus40. Miao20 (p. 535) already noticed strong similarities between “Lophiomeryx” turgaicus and “Lophiomeryx” shinaoensis. The lower premolars of “Lophiomeryx” turgaicus and “Lophiomeryx” shinaoensis are strongly laterally compressed and the p4 is rectangular, giving the lower premolar toothrow an more elongated aspect than in L. angarae, L. mouchelini, and L. chalaniati20,24,30,38,40. Moreover, in these two species, the posthypoconulidcristid is of similar length, longer than in L. angarae, L. mouchelini, and L. chalaniati.Based on these observations, we can assume that “Lophiomeryx” shinaoensis and “Lophiomeryx” turagicus cannot be assigned to the genus Lophiomeryx and may both belong to the same new Lophiomerycidae genus that we here name Chiyoumeryx nov. gen. Chiyoumeryx nov. gen. shinaoensis differs from ?Chiyoumeryx nov. gen. turgaicus nov. comb. in being lower crowned, smaller, possessing an ectostylid, having the symphysis starting under p1, and a shorter diastema.Chiyoumeryx nov. gen. flavimperatoris nov. sp.Figure 1C and Figure S3.v1961 cf. Miomeryx sp.—Xu: 316, 323, 32426.v pars1982 Lophiomeryx gracilis—Miao: 532, Table 3, Fig. 9a,b20.v non1982 Lophiomeryx gracilis?—Miao: 536, Fig. 820.1983 Lophiomeryx sp.—Wang & Zhang: 122, 12741.v1983 cf. Miomeryx sp.—Wang & Zhang: 12341.v1997 Miomeryx sp.—Vislobokova: Fig. 321.v pars1997 L. gracilis—Vislobokova: Fig. 321.v1999 cf. Miomeryx sp.—Zhang, Long, Ji, & Ding: 7, Table 527.v pars2000 L. gracilis—Guo, Dawson, and Beard: 247, Table 214.v pars2001 L. gracilis—Métais, Chaimanee, Jaeger, and Ducrocq: 239, 24117.v2007 Miomeryx sp.—Métais and Vislobokova: 1942.v pars2012 L. gracilis—Mennecart: 6234.ZooBank LSIDurn:lsid:zoobank.org:act:1DF6F58C-F08B-4657-BD4A-7C597653926F.Etymologymeaning yellow (flavor-) emperor (imperatoris) in latin. Chiyou fought with the Yellow Emperor, the ancestor of Chinese, but was defeated.DiagnosisChiyoumeryx nov. gen. flavimperatoris nov. sp. shows the above-mentioned characteristics of the genus. Chiyoumeryx nov. gen. flavimperatoris nov. sp. is smaller than Chiyoumeryx nov. gen. shinaoensis and ?Chiyoumeryx nov. gen. turgaicus. The p4 of Chiyoumeryx nov. gen. flavimperatoris nov. sp. differs from Chiyoumeryx nov. gen. shinaoensis by an oblique anterior conid, which is labio-lingually oriented in the larger species. A very short posterolingual conid is located between the posterolabial cristid and the transverse cristid in the p4 of Chiyoumeryx nov. gen. flavimperatoris nov. sp., while it is absent on Chiyoumeryx nov. gen. shinaoensis. In Chiyoumeryx nov. gen. flavimperatoris nov. sp., there is a tiny anterior cingulid, while it is absent in Chiyoumeryx nov. gen. shinaoensis.HolotypeIVPP V 6547, right mandible with p4–m3 (previously attributed to Lophiomeryx gracilis20).ParatypeIVPP V 6548, left mandible with p4–m3 (previously attributed to Lophiomeryx gracilis20).Additional materialIVPP V 2600, left p4–m2 (previously attributed to cf. Miomeryx sp.26). Measurements are given in Table S1.LocalitiesYangjiachong locality lying in the Caijiachong marls, Qujing, Yunnan, China; Shinao Basin, Panxian County, Southwestern Guizhou, China. Late Eocene.Taxonomical attributionIVPP V 6547 and IVPP V 6548 from Shinao were previously attributed to Lophiomeryx gracilis20, while IVPP V 2600 from Caijiachong marls was first described as cf. Miomeryx sp.26. All these specimens share the same size and dental morphology, and originate from a similar stratigraphic position. That is why we attribute them to the same species.None of these specimens can be attributed to Krabimeryx or Zhailymeryx, as the entoconidian groove is absent14,17. Furthermore, the external postmetacristid is more marked in the considered specimens than in Krabimeryx and Zhailymeryx, forming a deep groove. The third basin is also very different in the here-described specimens from Krabimeryx and Zhailymeryx: the third lobe is a little tilted parallel with the prehypoconulidcristid and posthypoconulidcristid. The back fossa of m3 is very narrow.Furthermore, the here-described specimens can be distinguished from K. gracilis (previously attributed to the same species), by a smaller size and a slenderer shape. The ectostylid is smaller than in K. gracilis. The anterior cingulid in the lower molars is stronger in K. gracilis than in the here-considered specimens. The small postentocristid (especially on m3) of the here-described specimens is absent in K. gracilis.The here-described specimens possess all characteristics in the lower molars that are typical for Chiyoumeryx nov. gen. and distinguish this genus from Lophiomeryx24,34,37. Furthermore, as in Chiyoumeryx nov. gen. shinaoensis, the p4 is laterally compressed giving it a more elongated aspect than in Lophiomeryx24,34,37. Therefore, we consider it justified assigning the here-described specimens to Chiyoumeryx nov. gen. However, they differ from Chiyoumeryx nov. gen. shinaoensis in as smaller size and the morphology of the p4: (1) the anterior conid is oblique while it is labio-lingually oriented in Chiyoumeryx nov. gen. shinaoensis. (2) There is a tiny anterior cingulid that is absent in Chiyoumeryx nov. gen. shinaoensis. (3) There is no additional cristid on the mesolingual conid, which is a well-rounded conid, while in Chiyoumeryx nov. gen. shinaoensis, there is a short posterolingual cristid. (4) The posterolingual conid stands between the posterolabial cristid and the transverse cristid, while in Chiyoumeryx nov. gen. shinaoensis, the posterolingual conid is very small and oblique between the transverse cristid and the posterior stylid and does not join the posterolabial cristid. Due to these distinct differences we erect a new species: Chiyoumeryx nov. gen. flavimperatoris nov. sp.Family Tragulidae Milne-Edwards, 186442.Genus Iberomeryx Gabunia, 196443.Diagnosis (modified from Mennecart et al.36)Small-sized ruminant with upper molars possessing the following combination of characters: well-marked parastyle and mesostyle in small-column shape; strong paracone rib; metacone rib absent; metastyle absent; unaligned external walls of metacone and paracone; strong postprotocrista stopping against the anterior side of the premetaconulecrista; continuous lingual cingulum, stronger under the protocone. Lower dental formula is primitive (3–1–4–3) with non-molarized premolars. Tooth c is adjacent to i3. Tooth p1 is single-rooted, reduced and separated from c and p2 by a short diastema. The premolars have a well-developed anterior conid. Teeth p2–p3 display a distally bifurcated mesolabial conid. Tooth p3 is the largest premolar. Tooth p4 displays no mesolingual conid and a large posterior valley. Regarding the lower molars, the trigonid and talonid are lingually open with a trigonid more tapered than the talonid. The anterior fossa is open, due to a forward orientation of the preprotocristid and the presence of a paraconid. The internal postprotocristid is oblique and the external postprotocristid reaches the prehypocristid. The internal postprotocristid, postmetacristid and preentocristid are fused and Y-shaped. Protoconid and metaconid display a weak Tragulus fold and a well-developed Dorcatherium fold, respectively. The mandible displays a regularly concave ventral profile in lateral view, a marked incisura vasorum, a strong mandibular angular process, a vertical ramus, and a stout condylar process.Type speciesIberomeryx parvus Gabunia, 196443 from Benara (Georgia), late Oligocene44.Included speciesI. minor45, Iberomeryx miaoi nov. sp.Iberomeryx miaoi nov. sp.Figure 1D and Figure S4.v 1982 Lophiomeryx gracilis?—Miao: 536, Fig. 820.ZooBank LSIDurn:lsid:zoobank.org:act:EE3F88E9-0EAF-4EC6-A46F-8623241E614B.DiagnosisIberomeryx with a very large paraconid, which is smaller in Iberomeryx minor and Iberomeryx parvus. The metastylid is not strong but is more developed than in the other species. The ectostylid is big on m1, smaller on m2 and absent on m3, while I. minor displays an ectostylid on all molars and I. parvus none at all. Iberomeryx miaoi nov. sp. is of similar size to I. minor and its m2 is smaller than the one of I. parvus. It differs from I. minor by a thin anterior cingulid. Moreover, its protoconid is positioned slightly more anterior than in I. parvus. The molars appear to be more massive and bulkier in this species than in I. minor and I. parvus.HolotypeIVPP V 6551, left mandible with m1–m3 (only specimen known). m1 5.1 × 3.5, m2 5.2 × 4.1, m3 8.0 × 4.0.EtymologyWe dedicate this species to Prof. Miao Desui who was the first to describe the Shinao fauna.Locality and horizonShinao Basin, Panxian County, Southwestern Guizhou, China. Late Eocene.Taxonomical attributionThis minute ruminant was referred to Lophiomeryx gracilis? by Miao20. However, he already noticed that the size of this individual was smaller than in the other specimens attributed to Lophiomeryx gracilis. Miao20 excluded an attribution of IVPP V 6551 to “Lophiomeryx” gaudryi due to a closed posterior section of the posterior fossa on the m3. However, in both teeth, the posterior fossa is still open by the reduction of the postentocristid.The here-described specimen clearly differs from Lophiomeryx by the presence of an external postmetacristid forming a slight Dorcatherium fold, a developed external postprotocristid (clearly visible at least on m2), and a large paraconid36. Furthermore the external postprotocristid and prehypocristid are connected on their distal ends and the third basin of m3 forms a well-formed buckle, unlike the condition in Lophiomerycidae14,16,33,36,37. The combination of these characters is typical for Tragulidae36.Very few taxa are so far known in the early evolution of the Tragulidae. Only Archaeotragulus, Iberomeryx, and Nalameryx are recognized as potential Paleogene Tragulidae17,36,46, of which Archaeotragulus is currently the oldest representative described17,47. Archaeotragulus possesses lower molars with a broadened talonid in comparison to the trigonid and displays an entoconidian groove36. In the case of IVPP V 6551, the trigonid and talonid are of similar size and no specific entoconidian groove can be observed. Mennecart et al.36 considered Nalameryx a Tragulidae notably based on the presence of the M structure (the external postmetacristid, the internal postmetacristid, the internal postprotocristid, and the external postprotocristid are interconnected forming a M in occlusal view), including the Tragulus fold and Dorcatherium fold, and the absence of a rounded mesolingual conid in the p435. IVPP V 6551 differs from Nalameryx in having an m3 wider than m1 and similar m1 and m2 widths17. In size proportions and molar morphology, IVPP V 6551 resembles the genus Iberomeryx. In IVPP V 6551, the relative size of the m2 is more similar to I. minor. In Iberomeryx minor, the anterior cingulid is big36,46, while in Iberomeryx parvus the cingulid is thin48 like in IVPP V 6551. The teeth of IVPP V 6551 appear to be more massive and bulkier than in I. minor and I. parvus36,48. Similarly to I. minor, the protoconid of IVPP V 6551 is a little more anterior than in I. parvus36,48. IVPP V 6551 clearly differs from I. parvus and I. minor by the presence of a very large paraconid, which is smaller in the two other species36,48. Moreover, the metastylid in IVPP V 6551 is slightly more developed than in I. minor and not present in I. parvus43,48. Iberomeryx minor displays an ectostylid on all molars36, while this structure is absent from I. parvus48. The ectostylid in IVPP V 6551 is large on m1 to absent on m3. Based on these differences we decided to erect the new species Iberomeryx miaoi nov. sp.Origin of crown Ruminantia and dispersal pattern of Paleogene Eurasian ruminantsSo far five families and 13 genera of Ruminantia are known during the middle and late Eocene in Eurasia2,18,19. Based on molecular data, the origin of crown ruminants should be searched for between the latest late Paleocene (56.5 Ma) and the latest early Oligocene (29 Ma)49,50. With the description of stem Tragulidae from the early Oligocene of Western Europe (Iberomeryx) and the late Eocene from Southern Thailand (Archaeotragulus)17, Mennecart et al.26 and Mennecart and Métais51 verified that the oldest crown ruminants date back at least to the latest Eocene (34 Mya). The presence of the tragulid genus Iberomeryx in Shinao, Southern China, further confirms this and may actually represent the oldest fossil of a Tragulidae known and thus of a crown Ruminantia (37–35 Mya, Fig. 1), since no Pecora is known during the Eocene so far51.The here presented reassesment of the Shinao ruminants in combination with literature data reveals a clear pattern in the distribution of Eocene ruminants. Among Archaeomerycidae, Archaeomeryx and Miomeryx are found in Northern and Central Asia [Kazhakstan, Mongolia, and northern part of China2,21,53 (see Fig. 2)]. The lophiomerycid Lophiomeryx (as Lophiomeryx angarae) as well as the Asiatic Praetragulidae (Praetragulus) occupy the same area2. The Mongolian Lophiomeryx angarae is most likely closely related to the European species Lophiomeryx mouchelini. Due to the strong morphological similarities, some specimens of L. mouchelini were actually first described as Lophiomeryx cf. angarae54. Lophiomeryx mouchelini or its ancestors arrived in Europe with the Grande-Coupure dispersal event at the Eocene–Oligocene transition ca. 34 Mya ago (oldest European records: Calaf, Spain, MP22; Möhren 9, Germany, MP21-22; age comprised between the German localities Haag2 MP21 and Möhren 13 MP2234,37,53). The close relationship of these European and the Mongolian species confirms that the origin of the Grande-Coupure cohort may be deeply anchored in the Eocene of Central-Northern Asia (Fig. 2).Figure 2Paleobiogeography of the Eurasiatic ruminants during the Eocene at the genus level. The localities are from the synthesis of data2,17,18,22,49. The palinspastic map is modified from Scotese52.Full size imageThe Southern part of Asia presents a totally different ruminant community at the genus level and includes the Archaeomerycidae Indomeryx and Notomeryx, the Lophiomerycidae Krabimeryx and Chiyoumeryx nov. gen., the Bachitheriidae Bachitherium and the Tragulidae Archaetrogulus and Iberomeryx2,17,18,19,21,53 (see Fig. 2). The oldest Bachitherium is currently known from the Balkan area during the Eocene18,19. The Tethys Ocean separated this area from Western Europe until its progressive disappearance during the Oligocene, ca. 31 Mya55,56. Bachitherium and a cohort of rodents (Pseudocricetodon, Paracricetodon, and the Melissodontinae)19 did not reach Western Europe prior to the opening of this passage. Similarly to the genus Bachitherium, Iberomeryx arrived in Western Europe after the drying out of the Tethys Ocean ca. 31 Mya, during the Bachitherium dispersal event18,19,36. Iberomeryx is mainly known from the middle early Oligocene of Western Europe34,36,57 and the late Oligocene of Anatolia and Georgia43,48,58. Discovering Iberomeryx in the Eocene of Eastern Asia confirms an Asiatic origin of this genus. The close relationship between South-eastern Europe and South-eastern Asia is furthermore supported by anthracotheriids (extinct artiodactyls related to hippopotamids) and rhinocerotoids59,60.Mennecart et al.18,19 proposed that mammals originating from Asia arrived in Western Europe during the early Oligocene in two faunal events: the Grande-Coupure, ca. 33.9 Mya and the Bachitherium dispersal event, ca. 31 Mya. These two faunal events imply two different and diachronous ways of dispersal. The fact that Eocene taxa from South-eastern Asia did not arrive in Western Europe prior to 31 Mya indicates that the Bachitherium dispersal Event cohort might be deeply anchored in the Eocene of Southern Asia (Fig. 2), while genera recorded from the Eocene of Central Asia are known to have arrived already during the Grande-Coupure and thus originated from a different palaeobiogeographic province. The Grande-Coupure was a dispersal event using a Northern way over the closed Turgai Strait and probably originating from Central Asia (Fig. 2). The Bachitherium dispersal event is a stepwise story with a first dispersion from Southern Asia to South-eastern Europe along the Southern path (Fig. 2) and then the dispersal throughout Europe thanks to the closure of the Tethyian Ocean18,19.The south-eastern part of Asia has shown very few changes from a warm and humid climate and environment since the Eocene4, while Northern Asia underwent a transition from warm and humid subtropical environments during the Eocene to steppe environments in the Pliocene, e.g.3,4,5. In this light it is not surprising that an increasing number of paleontological and geological studies indicate that Asia had already experienced a strong latitudinal environmental zonation during the middle and the late Eocene, e.g.6,13.These different climatic and environmental conditions in Central and South Asia led to two distinct palaeobiogeographical provinces clearly traceable in assemblages of herbivores like ruminants that was already apparent during the Eocene. The Central Asian ruminants were living in a more arid environment than the ones from South-eastern Asia (see Fig. 2). The tropical and wet environments from the South-eastern Asia led to the emergence of the Tragulidae (Iberomeryx and Archaeotragulus) and of the anthracotheriids. More

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    Who wants to be a polar bear?

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    As a wildlife-conservation biologist studying climate change, I want to understand the evolving environment through the eyes of large animals. My work — usually in cold, remote places — involves finding animals, and ways to eat, sleep and be warm. I might be miserable, but I get insights that others cannot into what animals are doing.For about 15 years I’ve been interested in musk oxen (Ovibos moschatus), social herd animals that roamed with woolly mammoths. This picture was taken on Wrangel Island, off the northeast coast of Russia, when I was studying how musk oxen react to polar bears. Because polar ice is melting, more polar bears are hunting on land, and they’re known to have killed musk oxen. These herd animals typically don’t flee from predators such as grizzly bears. They tend to form huddles instead, and male musk oxen have killed grizzlies. Would they try to kill polar bears, too?To find out, I dressed as a polar bear, pulling a bear head on and placing a cape over a range finder, camera and data books. I was cold and nervous. I didn’t want to be killed by a charging musk ox — or by anything else. If some oxen charged, I’d throw off my costume and stand up straight, as I’m doing here; so far, that had stopped them. I’d also encountered a female polar bear with newborn cubs, but she’d left me alone. This picture is from the end of a session, and I’d lived another day. Whew!I learnt that musk oxen are more likely to flee from polar bears than from grizzlies. But during this trip to Russia, I was arrested — over a date error on my permits. In court, the only word I understood was ‘CIA’. I was let go, but banned from returning for three years, so I’m now studying the huemul (Hippocamelus bisulcus), an endangered species of deer that lives in the shadows of glaciers at the tip of South America. As glaciers recede, how will huemul populations respond?

    Nature 597, 296 (2021)
    doi: https://doi.org/10.1038/d41586-021-02429-2

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    Socio-demographic correlates of wildlife consumption during early stages of the COVID-19 pandemic

    We focused our research on countries/territories in Asia (specifically, Hong Kong SAR, Japan, Myanmar, Thailand and Vietnam) because COVID-19 had not spread much outside Asia at the time of data collection and the global effects were predominantly concentrated in East and Southeast Asia. Our five survey countries/territories were chosen because they all have relatively high levels of wildlife trade but also represent very different forms of trade (for example, the pet trade in Japan versus the wild-meat trade in Vietnam). Surveying respondents from markets with these different forms of trade thus allowed an examination of how the full variety of wildlife consumption types may be impacted by perceived disease risk. Budgetary constraints precluded the inclusion of further countries, although we believe those that were surveyed provide a valid snapshot of the main regional issues and patterns. The exception to this may be the exclusion of China, a key global player in the wildlife trade and the possible origin of the COVID-19 virus. Conducting research in China requires an extensive process to obtain permission that was not consistent with the opportunistic nature of our survey, which was mobilized quickly to target opinions from a snapshot view of an (at that time) emerging disease. Given the time-sensitive nature of the research, we were therefore unable to wait for the necessary permissions to include China in this survey.Our online survey was conducted between March 3–11, 2020 and surveyed 1,000 respondents in each of the five target countries/territories. We designed and translated our questionnaires with local experts to ensure questions were culturally appropriate, understandable and relevant. The survey was a quantitative data collection instrument that comprised 32 questions, lasted on average 8 minutes, and respondents were offered an incentive for participating. Respondents aged 18+ were invited via email from an online panel of over 2.5 million people in the target countries/territories, and could answer on any internet-capable device (for example smartphone, tablet, laptop) at their convenience. Only respondents aged 18 and over were eligible to take the survey, which was entirely voluntary. Any respondents working in advertising, public relations, marketing, market research or media industries were screened out to prevent possible bias. The email invite that was sent to participants did not specify the exact nature of the survey to avoid skewing the participants towards those that believed they know about the topic. Instead, the invite indicated that the questions would be about ‘consumption and shopping habits’. The panel is maintained by Toluna (https://tolunacorporate.com/), an online data collection group focused on providing high-quality market research data to clients in various business and non-business sectors. Toluna builds and maintains large online consumer panels to collect these data while adhering to stringent global and local guidelines for panel management and data quality, and is a member of the European Society for Opinion and Market Research (https://www.esomar.org).Toluna respects privacy and is committed to protecting personal data. Their privacy policy (https://tolunacorporate.com/legal/privacy-policy/) provides information on how Toluna collects and processes personal data, explains privacy rights and gives an overview of applicable legislation protecting the handling of personal information. Toluna only uses personal data when the law allows the data to be used.Respondents were asked demographic questions, and quotas based on the most recent census data for each country/territory were used to ensure the final sample profile was nationally representative of age and gender, except in Myanmar where internet access skewed online panel members to a younger male demographic. Specifically, participants were excluded once quotas on age and gender were filled, and again, participants working in advertising/public relations, marketing research or media were excluded from the survey as we believed these jobs could influence responses. Respondents were asked about societal, economic and environmental concerns, their perception of COVID-19 and their attitudes towards wildlife and wildlife consumption (Supplementary Methods). We also excluded respondents who stated that they were unsure whether they or anyone in their social circle had recently purchased wildlife products (n = 421), as well as an additional n = 39 respondents who were unable to answer survey questions that were later included as covariates in our models.Because of the potentially sensitive nature of wildlife consumption, we asked about past wildlife purchases indirectly, questioning respondents on whether anyone within their social circle, including themselves, had recently purchased wildlife products. Indirect questions can improve answer rates for questions that people may feel uncomfortable about answering honestly27. During the pandemic, respondents may have felt uncomfortable about revealing wildlife purchases, given links between wildlife consumption and COVID-19. Additionally, although most wildlife consumption is legal (with restrictions) in the markets surveyed, some is not, and researchers can be perceived as having interests contrary to that of the respondent. For less-sensitive questions on future wildlife consumption and changes in consumption resulting from COVID-19, we asked respondents for their own response rather than that of their social group.Previous studies have found a high correlation between an individual’s admission of using a wildlife product and their likelihood of being within a network of individuals who buy such products28, and suggested that this is linked to homophily in social networks, especially in Southeast Asia. The homophily principle states that people’s personal networks are homogeneous with regard to many socio-demographic, behavioural and intrapersonal characteristics29. Research on wildlife consumption in other Southeast Asian contexts suggests that social groups can be a motivator to begin or maintain consumption of wildlife products28,30. Our own previous research supports this, indicating a strong correlation between one’s own tiger and ivory purchases and knowing someone within one’s social circle who has purchased such products. Additionally and recognizing the homophily principle, behaviour change campaigns targeted at social networks rather than individuals per se are likely to achieve better results than non-targeted campaigns. Changing perceptions of acceptability is a key aspect of social marketing and is used in the social mobilization domain of social and behaviour change communications, which has become a popular framework for reducing demand for illegally traded wildlife products31. Influencing people within a wildlife consumer’s social network may therefore have a higher rate of efficacy than attempting to influence the perceptions of individuals who do not know any consumers of wildlife.We used hierarchical Bayesian regression models to assess relationships between socio-demographic explanators and our three response variables: (1) self-reported recent wildlife consumption, (2) change in wildlife consumption as a result of COVID-19 and (3) anticipated future wildlife consumption. Explanatory variables included 22 non-collinear variables in six categories: basic demographics, awareness and level of worry of COVID-19, COVID-19 personal impacts, support for and effectiveness of wildlife market closures, international travel habits and general attitudes towards global issues (Supplementary Table 1). Aside from household income (measured in US dollars per year), age (midpoint of year categories from the survey question) and education (ordinal, reflecting increasing level of schooling), all other variables were categorical; those with more than two categories were collapsed into dummy variables. Income, age and education were standardized and included to investigate whether a person’s general socio-economic status affects wildlife consumption. General attitudes towards global issues were expected to reflect aspects of respondents’ political tendencies, while travel habits were included to test the hypothesis that those who travel internationally more habitually are, and will be, more frequent consumers of wildlife. Questions regarding awareness and impacts of COVID-19, and concern about future disease epidemics, were asked to determine how the pandemic may be shaping wildlife consumption. Finally, support and perceived effectiveness of wildlife market closures were included as predictor variables since this measure has been suggested as a strong policy lever to reduce wildlife consumption.The general structure of all three models was as follows:$$y_{ij}sim {{{mathrm{Bernoulli}}}}left( {theta _{ij}} right)$$
    (1)
    $${mathrm{logit}}left( theta right) = alpha + {{u}_1} + {beta} {mathbf{X}} + {{u}_2}{mathbf{Z}}$$
    (2)
    This model allowed both coefficients and intercepts to vary across countries (that is, a ‘random-slope random-intercept’ model). In equation (1), yij is whether or not individual i in country j reported wildlife consumption, modelled as a Bernoulli trial with probability θij. The logit transformation of θ (equation 2) is a linear function of parameters α and u1 (the fixed intercept term and a vector of the country-specific intercept terms, respectively), as well as a vector of fixed regression coefficients β and a vector of country-specific regression coefficients u2, with X and Z being the corresponding design matrices32. For α and β, we used an improper flat prior over the real numbers, while the group level parameters u1 and u2 were assumed to arise from a multivariate normal distribution with mean 0 and unknown covariance matrix. The covariance matrix was parameterized by a correlation matrix having a Lewandowski–Kurowicka–Joe prior, and a standard deviation with half-Student t prior with three degrees of freedom32.For the three dependent variables, we evaluated the predictive power of a model containing all 22 variables, as well as six subset models, using Watanabe–Akaike Information Criterion and leave-one-out cross-validation33. Each of these six subset models contained all explanatory variables except for those within one of the six categories described above (for example, all explanatory variables except those relating to international travel habits, all explanatory variables except those relating to support for wildlife market closures). We used this model-comparison approach to test whether any of these categories of explanatory variable were more or less important in explaining wildlife consumption; if particular categories of variable are stronger predictors of wildlife consumption, this could help inform where future conservation interventions should focus on. Watanabe–Akaike Information Criterion and leave-one-out cross-validation are both measures of model predictive accuracy (both use log predictive density as the utility function or comparison metric) and have been suggested as useful metrics for Bayesian model selection33. We interpreted variable coefficients whose 95% Bayesian credible intervals did not contain 0 as providing strong evidence for the impact of that variable on the outcome in each of the three models for self-reported wildlife consumption (that is, recent, future and changes due to COVID-19). Models were estimated using the R statistical computing software34, in particular the package brms32, with four chains of 1,000 iterations each, a 500-iteration warm-up period, and with successful convergence verified by confirming that R-hat statistical values were less than or equal to 1.01 (ref. 22).We used the Bayesian hierarchical model of anticipated future wildlife consumption and generated predicted probabilities of future consumption for our sample population (Fig. 2, grey bars). We then predicted future consumption probabilities for a hypothetical behaviour-change intervention (Fig. 2, coloured bars). This intervention was simulated by setting the ‘medical impact’ variable to zero for all individuals, and by assigning all individuals into the ‘aware lots’ and ‘support very likely’ categories for questions related to level of awareness of COVID-19 and level of support for government closure of domestic wildlife markets, respectively. All other variables for individuals were held at the levels recorded in the surveys. We considered the difference between these two predicted probabilities as the impact of the hypothetical behaviour-change intervention, which we examined at the level of the country/territory and within education, age, income and gender demographic classes. Strong evidence for the effectiveness of this hypothetical intervention among countries and demographic classes was suggested where Bayesian credible intervals around the mean predicted difference were less than zero (Supplementary Table 3).Reporting SummaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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    Proteomic traits vary across taxa in a coastal Antarctic phytoplankton bloom

    Field samplingWe collected samples once per week over four weeks at the Antarctic sea ice edge, in McMurdo Sound, Antarctica (December 28, 2014 “GOS-927”; January 6 “GOS-930”, 15 “GOS-933”, and 22 “GOS-935”, 2015; as previously described in [27]). Sea water (150–250 l) was pumped sequentially through three filters of decreasing size (3.0, 0.8, and 0.1 μm, 293 mm Supor filters). Separate filter sets were acquired for metagenomic, metatranscriptomic, and metaproteomic analyses, over the course of ∼3 h, each week (36 filters in total). Filters for nucleic acid analyses were preserved with a sucrose-based buffer (20 mM EDTA, 400 mM NaCl, 0.75 M sucrose, 50 mM Tris-HCl, pH 8.0) with RNAlater (Life Technologies, Inc.). Filters for protein analysis were preserved in the same sucrose-based buffer but without RNAlater. Filters were flash frozen in liquid nitrogen in the field and subsequently stored at −80 °C until processed in the laboratory.Metagenomic and metatranscriptomic sequencingWe used metagenomics and metatranscriptomics to obtain reference databases of potential proteins for metaproteomics. We additionally used a database assembled from a similarly processed metatranscriptomic incubation experiment [28], conducted with source water from the January 15, 2015 time point (these samples were collected on a 0.2 μm Sterivex filter and processed as previously described).For samples from the GOS-927, GOS-930, GOS-933, and GOS-935 filters, RNA was purified from a DNA and RNA mixture [29]. In total, 2 µg of the DNA and RNA mixture was treated with 1 µl of DNase (2 U/µl; Turbo DNase, TURBO DNase, Thermo Fisher Scientific), followed by processing with an RNA Clean and Concentrator kit (Zymo Research). An Agilent TapeStation 2200 was used to observe and verify the quality of RNA. In total, 200 ng of total RNA was used as input for rRNA removal using Ribo-Zero (Illumina) with a mixture of plant, bacterial, and human/mouse/rat Removal Solution in a ratio of 2:1:1. An Agilent TapeStation 2200 was used to subsequently observe and verify the quality of rRNA removal from total RNA. rRNA-deplete total RNA was used for cDNA synthesis with the Ovation RNA-Seq System V2 (TECAN, Redwood City, USA). DNA was extracted for metagenomics from the field samples (GOS-927, GOS-930, GOS-933, and GOS-935) according to [29]. RNase digestion was performed with 10 µl of RNase A (20 mg/ml) and 6.8 μl of RNase T1 (1000 U/µl), which were added to 2 µg of genomic DNA and RNA mixture in a total volume of 100 µl, followed by 1 h incubation at 37 °C and subsequent ethanol precipitation in −20 °C overnight.Samples of double stranded cDNA and DNA were fragmented using a Covaries E210 system with the target size of 400 bp. In total, 100 ng of fragmented cDNA or DNA was used as input into the Ovation Ultralow System V2 (TECAN, Redwood City, USA), following the manufacturer’s protocol. Ampure XP beads (Beckman Coulter) were used for final library purification. Library quality was analyzed on a 2200 TapeStation System with Agilent High Sensitivity DNA 1000 ScreenTape System (Agilent Technologies, Santa Clara, CA, USA). Twelve DNA and 18 cDNA libraries were combined into two pools with concentration 4.93 and 4.85 ng/µl, respectively. Resulting library pools were subjected to one lane of 150 bp paired-end HiSeq 4000 sequencing (Illumina). Prior to sequencing, each library was spiked with 1% PhiX (Illumina) control library. Each lane of sequencing resulted in between 106,000 and 111,000 Mbp total and 6900–12,000 Mbp and 4800–6900 Mbp for individual DNA or cDNA libraries, respectively.Metagenomic and metatranscriptomic bioinformaticsMetagenomic and metatranscriptomic data were annotated with the same pipelines. Briefly, adapter and primer sequences were filtered out from the paired reads, and then reads were quality trimmed to Phred33. rRNA reads were identified and removed with riboPicker [30]. We then assembled reads into transcript contigs using CLC Assembly Cell, and then we used FragGeneScan to predict open reading frames (ORFs) [31]. ORFs were functionally annotated using Hidden Markov models and blastp against PhyloDB [32]. Annotations which had low mapping coverage were filtered out (less than 50 reads total over all samples), as were proteins with no blastp hits and no known domains. For each ORF, we assigned a taxonomic affiliation based on Lineage Probability Index taxonomy [32, 33]. Taxa were assigned using two different reference databases: NCBI nt and PhyloDB [32]. Unless otherwise specified, we used taxonomic assignments from PhyloDB, because of the good representation of diverse marine microbial taxa.ORFs were clustered by sequence similarity using Markov clustering (MCL) [34]. Sequences were assigned MCL clusters by first running blastp for all sequences against each other, where the query was the same as the database. The MCL algorithm was subsequently used with the input as the matrix of E-values from the blastp output, with default parameters for the MCL clustering. MCL clusters were then assigned consensus annotations based on KEGG, KO, KOG, KOG class, Pfam, TIGRFAM, EC, GO, annotation enrichment [28, 32, 35,36,37,38,39]. Proteins were assigned to coarse-grained protein pools (ribosomal and photosynthetic proteins) based on these annotations. For assignment, we used a greedy approach, such that a protein was assigned a coarse-grained pool if at least one of these annotation descriptions matched our search strings (we also manually examined the coarse grains to ensure there were no peptides that mapped to multiple coarse-grained pools). For photosynthetic proteins, we included light harvesting proteins, chlorophyll a-b binding proteins, photosystems, plastocyanin, and flavodoxin. For ribosomal proteins, we just included the term “ribosom*” (where the * represents a wildcard character), and excluded proteins responsible for ribosomal synthesis.Sample preparation and LC-MS/MSWe extracted proteins from the samples by first performing a buffer exchange from the sucrose-buffer to an SDS-based extraction buffer, after which proteins were extracted from each filter individually (as previously described) [27]. After extraction and acetone-based precipitation, we prepared samples for liquid chromatography tandem mass spectrometry (LC-MS/MS). Precipitated protein was first resuspended in urea (100 µl, 8 M), after which we measured the protein concentration in each sample (Pierce BCA Protein Assay Kit). We then reduced, alkylated, and enzymatically digested the proteins: first with 10 µl of 0.5 M dithiothreitol for reduction (incubated at 60 °C for 30 min), then with 20 µl of 0.7 M iodoacetamide (in the dark for 30 min), diluted with ammonium bicarbonate (50 mM), and finally digested with trypsin (1:50 trypsin:sample protein). Samples were then acidified and desalted using C-18 columns (described in detail in ref. [40]).To characterize each metaproteomic sample, we employed one-dimensional liquid chromatography coupled to the mass spectrometer (VelosPRO Orbitrap, Thermo Fisher Scientific, San Jose, California, USA; detailed in [40]). For each injection, protein concentrations were equivalent across sample weeks, but different across filter sizes. We had higher amounts of protein on the largest filter size (3.0 μm) and less on the smaller filters, so we performed three replicate injections per 3.0 µm filter sample, and two replicate filter injections for 0.8 and 0.1 µm filters. We used a non-linear LC gradient totaling 125 min. For separation, peptides eluted through a 75 µm by 30 cm column (New Objective, Woburn, MA), which was self-packed with 4 µm, 90 A, Proteo C18 material (Phenomenex, Torrance, CA), and the LC separation was conducted with a Dionex Ultimate 3000 UHPLC (Thermo Scientific, San Jose, CA).LC-MS/MS bioinformatics—database searching, configuration, and quantificationMetaproteomics requires a database of potential protein sequences to match observed mass spectra with known peptides. Because we had sample-specific metagenome and metatranscriptome sequencing for each metaproteomic sample, we assessed various database configurations, including those that we predict would be suboptimal, to examine potential options for future metaproteomics researchers. We used five different configurations, described below. In each case, we appended a database of common contaminants (Global Proteome Machine Organization common Repository of Adventitious Proteins). We evaluated the performance of different database configurations based on the number of peptides identified (using a peptide false discovery rate of 1%).In order to make these databases (Table 1), we performed three separate assemblies on (1) the metagenomic reads (from samples GOS-927, GOS-930, GOS-933, and GOS-935), (2) metatranscriptomic reads (from samples GOS-927, GOS-930, GOS-933, and GOS-935), and (3) metatranscriptomic reads from a concurrent metatranscriptomic experiment, started at the location where GOS-933 was taken [28]. Database configurations were created by subsetting from these assemblies. The first configuration was “one-sample database”, constructed to represent the scenario where only one sample was used for metagenomic and metatranscriptomic sequencing (we chose the first sampling week). Specifically, this was done by subsetting and including ORFs from the metagenomic and metatranscriptomic assemblies if reads from this time point were present in that sample (reads mapped as in [28]), and then removing redundant protein sequences (P. Wilmarth, fasta utilities). The second configuration was the “sample-specific database”, where each metaproteomic sample had one corresponding database (prepared from both metagenome and metatranscriptome sequencing completed at the same sampling site), also done by subsetting ORFs from the metagenomic and metatranscriptomic assemblies as described above. The third configuration was pooling databases across size fractions—such that all metagenomic and metatranscriptomic sequences across the same filter sizes (e.g., 3.0 µm) were combined. ORFs were subsetted from the metagenomic and metatranscriptomic assemblies as above. The fourth and fifth configurations are from the concurrent metatranscriptomic experiment [28]. The fourth configuration (“metatranscriptome experiment (T0)”) was the metatranscriptome of the in situ microbial community (i.e., at the beginning of the experiment). This database was created by subsetting from the “metatranscriptome experiment (all)” assembly. Finally, the fifth configuration was the metatranscriptome of all experimental treatments pooled together (two iron levels, three temperatures; “metatranscriptome experiment (all)”). The overlap between databases (potential tryptic peptides) in different samples is presented graphically in Supplementary Figs. S1–S3.Table 1 Characteristics of the five different database configurations we used for metaproteomic database searches.Full size tableAfter matching mass spectra with peptide sequences for each database configuration (MSGF + with OpenMS, with a 1% false discovery rate at the peptide level; [41, 42]), we used MS1 ion intensities to quantify peptides. Specifically, we used the FeatureFinderIdentification approach, which cross-maps identified peptides from one mass spectrometry experiment to unidentified features in another experiment—increasing the number of peptide quantifications [43]. This approach requires a set of experiments to be grouped together (i.e., which samples should use this cross-mapping?). We grouped samples based on their filter sizes (including those samples that are replicate injections). First, mass spectrometry runs within each group were aligned using MapAlignerIdentification [44], and then FeatureFinderIdentification was used for obtaining peptide quantities.After peptides have been identified and quantified, we mapped them to proteins or MCL clusters of proteins, which have corresponding functional annotations (KEGG, KO, KOG, Pfams, TIGRFAM; [28, 32, 35,36,37,38,39]). Functional annotations were used in three separate analyses. (1) Exploring the overall functional changes in microbial community metabolism, we mapped peptides to MCL clusters—groups of proteins with similar sequences. These clusters have consensus annotations based on the annotations of proteins found within the clusters (described in detail in [28]). For this section, we only used peptides that uniquely map to MCL clusters. (2) We restricted the second analysis to two protein groups: ribosomal and photosynthetic proteins. For this analysis, we mapped peptides to one of these protein groups if at least one annotation mapped to the protein group (via string matching with keywords). This approach is “greedy” because does not exclude peptides if they also correspond with other functional groupings, but this is necessary because of the difficulties in comparing various annotation formats. (3) The last analysis for functional annotations was for targeted proteins, and we only mapped functions to peptides where the peptides uniquely identify a specific protein (e.g., plastocyanin).Code for the database setup and configuration, database searching, and peptide quantification is open source (https://github.com/bertrand-lab/ross-sea-meta-omics).LC-MS/MS bioinformatics—normalizationNormalization is an important aspect of metaproteomics: it influences all inferred peptide abundances. Typically, the abundance of a peptide is normalized by the sum of all identified peptide abundances. We use the term normalization factor for the inferred sum of peptide abundances. Note that the apparent abundance of observed peptides is dependent on the database chosen. In theory, if fewer peptides are observed because of a poorly matching database, this will decrease the normalization factor, and those peptides that are observed will appear to increase in abundance. It is not known how much this influences peptide quantification in metaproteomics.For each database configuration, we separately calculated normalization factors. We then correlated the sum of observed peptide abundances with each other. To get a database-independent normalization factor, we used the sum of total ion current (TIC) for each mass spectrometry experiment (using pyopenms; [45]), and also examined the correlation with database-dependent normalization factors. If normalization factors are highly correlated with each other, that would indicate database choice does not impact peptide quantification. Using TIC for normalization may have drawbacks, particularly if there are differences in contamination, or amounts of non-peptide ions across samples.Defining proteomic mass fractionProtein abundance can be calculated in two ways: (1) the number of copies of a protein (independent of a proteins’ mass), or (2) the total mass of the protein copies (the sum of peptides). We refer to the latter as a proteomic mass fraction. For example, to calculate a diatom-specific, ribosomal mass fraction, we sum all peptide abundances that are diatom- and ribosome-specific, and divide by the sum of peptide abundances that are diatom-specific. Note that this is slightly different to other methods, like the normalized spectral abundance factor, which normalizes for total protein mass (via protein length; [46]).Combining estimates across filter sizesOrganisms should separate according to their sizes when using sequential filtration with decreasing filter pore sizes. In practise, however, organisms can break because of pressure during filtration, and protein is typically present for large phytoplankton on the smallest filter size and vice versa. We used a simple method for combining observations across filter sizes, weighted by the number of observations per filter. We begin with the abundance of a given peptide, which was only considered present if it was observed across all injections of the same sample. We calculated the sum of observed peptide intensities (i.e., the normalization factor), and divided all peptide abundances by this normalization factor. Normalized peptide abundances are then averaged across replicate injections. If we are estimating the ribosomal mass fraction of the diatom proteome, we first normalize the diatom-specific peptide intensities as a proportion of diatom biomass (i.e., divide all diatom-specific peptides by the sum of all diatom-specific peptides). We then summed all diatom-normalized peptides intensities that are unique to both diatoms and ribosomal proteins, which would give us the ribosomal proportion of the diatom proteome. Yet, we typically would obtain multiple estimates of, for example, ribosomal mass fraction of diatoms, on different filters. We combined the three values by multiplying each by a coefficient that represents a weight for each observation (specific to a filter size). These coefficients sum to one, and are calculated by summing the total number of peptides observed at a time point for a filter, and dividing by the total number of peptides observed across filters (but within each time point). For example, if we observed 100 peptides that are diatom- and ribosome-specific, and 90 of these peptides were on the 3.0 µm filter and only ten were on the 0.8 μm filter, we would multiply the 3.0 µm filter estimate by 0.9 and the 0.8 µm filter by 0.1. This method uses all available information about proteome composition across different filter sizes (similar to [47]).When we estimate the proteomic mass fraction of a given protein pool, we do not need to adjust for the total protein on each filter. This is because this measurement is independent of total protein. However, for merging estimates of total relative abundance of different organisms across filters, we needed to additionally weight the abundance estimate by the amount of protein on each filter. Therefore, in addition to the weighting scheme described above, we multiplied taxon abundance estimates by the total protein on each filter divided by the total protein across filters on a given day.LC-MS/MS simulationWe used simulations of metaproteomes and LC-MS/MS to (1) quantify biases associated with inferring coarse-grained proteomes from metaproteomes, and (2) to mitigate these biases in our inferences. Specifically, we asked the question: how does sequence diversity impact quantification of coarse-grained proteomes from metaproteomes? Consider a three organism microbial community. If two organisms are extremely similar, there will be very few peptides that can uniquely map to those organisms, resulting in underestimated abundance. The third organism would also be underestimated, but to a lesser degree, unless it had a completely unique set of peptides. A similar outcome is anticipated with differences in sequence diversity across protein groups, such that highly conserved protein groups will be underestimated.Our mass spectrometry simulations offer a unique perspective on this issue: we know the “true” metaproteome, and we can compare this with an “inferred” metaproteome. We simulated variable numbers of taxonomic groups, each with different protein pools of variable sequence diversity. From this simulated metaproteome, we then simulated LC-MS/MS-like sampling of peptides. Complete details of the mass spectrometry simulation are available in [48] and the Supplementary materials. The only difference between this model and that presented in [48] is here we include dynamic exclusion. The ultimate outcomes from these simulations were (1) identifying which circumstances lead to biased inferences about proteomic composition, and (2) determining the underpinnings of these biases.Cofragmentation bias scores for peptidesWe recently developed a computational model (“cobia”) that predicts a peptides’ risk for interference by sample complexity (more specifically, by cofragmentation of multiple peptides; [48]). This study showed that coarse-grained taxonomic and functional groupings are more robust to bias, and that this model can also be used to estimate bias. We ran cobia with the sample-specific databases, which produces a “cofragmentation score”—a measure of risk of being subject to cofragmentation bias. Specifically, the retention time prediction method used was RTPredict [49] with an “OLIGO” kernel for the support vector machine. The parameters for the model were: 0.008333 (maximum injection time); 3 (precursor selection window); 1.44 (ion peak width); and 5 (degree of sparse sampling). Code for running this analysis, as well as the corresponding input parameter file, is found at https://github.com/bertrand-lab/ross-sea-meta-omics.Description of previously published datasets analyzedWe leveraged several previously published datasets to compare our metaproteomic results. Specifically, we used proteomic data of phytoplankton cultures of Phaeocystis antarctica and Thalassiosira pseudonana [27, 50], and of cultures of Escherichia coli under 22 different culture conditions [51]. Coarse-grained proteomic estimates were also compared with previously published targeted metaproteomic data [27]. More