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The geographic area covered by the WOODIV database is the Euro-Mediterranean region, as defined by Médail et al.1. The northern Mediterranean region was selected following the definition of terrestrial ecoregions of the world by Olson et al.13. The study area covers all or part of the following countries and islands: Albania, Croatia, Cyprus, France, Greece, Italy, Malta, Montenegro, Portugal, Slovenia, Southern Macedonia, and Spain, including the Balearic archipelago, Corsica, Sardinia, Sicily, and Crete.We focused on the 245 tree taxa (210 species and 35 subspecies) identified in the Euro-Mediterranean checklist from Médail et al.1. These taxa belong to 33 families and 64 genera and include 46 endemics (as defined by Médail et al.1, i.e. range-restricted taxa in and outside of the study area).Observed occurrence dataWe collected tree occurrence data (at the species or subspecies level) from 23 sources: national databases and floras, regional databases, and publications (Table 1). Some records still unpublished were specifically provided at the grid level for this project by experts for southern Macedonia, Malta, Montenegro, and Sicily (four sources, Table 1).Table 1 Sources of the occurrence records, giving the name of the dataset (Source name; ined. if unpublished), the Type of data (records with geographic coordinates (records), records at the grid level (gridded records), or atlas-type (atlas) data), and the Countries/Islands covered by the source.Full size tableWhen considering the subspecies level, the WOODIV database lacks the occurrences of 11 sub-species among the 35 listed by Médail et al.1. When aggregated at the species level (to match the taxonomic resolution of the functional and phylogenetic data which are available at the species level only), the WOODIV database lacks only the occurrences of 3 of the 210 species from the Médail et al.1 checklist (n = 207; Table 2; Supplementary Table 2): Pyrus elaeagrifolia Pall., which occurs in Albania and Macedonia (and in northeastern Greece but outside the Mediterranean biome), P. syriaca Boiss. and Tamarix passerinoides Desv., which occur in Cyprus and in Sardinia, respectively.Table 2 Summary of the availability of data in the WOODIV database: total number of species among the 210 species from the Médail et al.1 checklist with (1) observed occurrences; (2) functional traits data, including the detail of the number of species with available data for 4 traits: adult plant height (Height), seed mass (SeedMass), specific leaf area (SLA) and wood density (SSD) (see “Functional data” section); and, (3) genetic data including the detail of the number of species with available data for 3 DNA-regions: matK, rbcL and psbA-trnH (see “Genetic data” section).Full size tableAlso, due to the taxonomic heterogeneity of the different data sources, we recommend aggregating the occurrences of certain tree taxa at the species’ group level (see sections Data Records and Usage Notes): i.e. to aggregate Pinus uncinata DC. and P. mugo Turra into P. mugo aggr., Juniperus deltoides R.P.Adams and J. oxycedrus L. into J. oxycedrus aggr. and Alnus lusitanica Vít, Douda & Mandák., A. rohlenae Vít, Douda & Mandák, and A. glutinosa (L.) Gaertn. into A. glutinosa aggr. The WOODIV database thus contains reliable occurrences of 200 species and three aggregated species (n = 203; Table 2; Supplementary Table 2).The raw dataset obtained from gathering occurrences from all sources included a total of 1,248,701 occurrence records distributed across the participating countries.The raw occurrence data were aggregated at a resolution of 10 × 10 km in line with an INSPIRE14 compliant 10 × 10 km grid (SCR 4258). This gridding procedure provided a way to standardize data from different sources. We selected this spatial grain because it was the finest resolution available for some countries of the study area (e.g. Slovenia, Croatia, Greece). Sources of occurrence data with a resolution coarser than 10 × 10 km (e.g. Atlas Florae Europaeae15) were not considered. The considered area includes 10,042 grid cells with at least one occurrence record (Fig. 1a). The occurrence dataset provided by the WOODIV database, i.e. aggregated records for species considered as native in the given grid cell using the 10 × 10 km grid (removal of duplicate species within a grid cell) includes 140,279 occurrences.Fig. 1Geographic scope of the WOODIV database, spatial distribution, and validation of trees occurrences. (a) Number of species within a 10 × 10 km grid cell based on modelled occurrence data for the 171 modelled species, with the addition of the occurrence data of the 21 small-range species; and, within grid cells of Atlas Flora Europaeae (AFE; 50x50km) (b) Number of species with presences recorded in AFE but not in the WOODIV dataset on the 104 species present both in the AFE and WOODIV data; and, (c) Number of species with presences recorded in the WOODIV dataset but not in AFE on the 104 species present both in the AFE and WOODIV data.Full size imageModelled occurrence dataThe WOODIV database provides modelled occurrences of the species from the Médail et al.1 checklist. From the 10 × 10 km gridded observed occurrence data, we modelled the distribution of each species across the Euro-Mediterranean area using Species Distribution Models (SDM). SDM statistically relate species occurrence records to environmental variables to predict the potential distribution of species16.Due to the extent of the study area, we only related species occurrence to climate gradients17. Bioclimatic variables were extracted from the CHELSA database V1.218 available at a resolution of 30 arc‐sec (http://chelsa‐climate.org/) and then averaged to a 10 × 10 km resolution. The selection of the environmental predictors for niche modeling is a source of uncertainty in model predictions that can be reduced with sound statistical methods and ecological knowledge of the target species19. We also focused on proximal predictors that directly influence species distribution and selected a low number of predictive variables to reduce the issues of model overfitting and multicollinearity20. We selected four bioclimatic variables that previous studies had reported to be relevant predictors of the distribution of plant species, especially in environments such as those that characterize the Mediterranean Basin21,22,23,24: “Minimum temperature of the coldest month” (Bio06, in °C) quantifies potentially lethal frost events and more generally, stress due to low temperatures; “Total annual precipitation” (Bio12, in mm) approximates average water availability; “Precipitation of the driest month” (Bio14, in mm) describes the extremes associated with drought events and stress due to low water availability, and “Temperature seasonality” (Bio04, no dimension) describes the variability of temperature during the year. All selected predictors showed VIF (variance inflation factor25) values below 5, indicating that a given predictor was not correlated with any linear combinations of the other predictors (VIF Bio04 = 1.68, VIF Bio06 = 2.06, VIF Bio12 = 1.53, and VIF Bio14 = 2.07).We related species occurrence to these four bioclimatic variables using the Random Forest algorithm26. As only presence data are archived in the WOODIV database, we randomly sampled a number of pseudo-absences equal to the number of observed occurrences27. This random selection of pseudo-absences was repeated 10 times for each species. When comparing the floras, occurrence data in the Italian Peninsula, Sardinia and/or Sicily were highly unrepresentative of the distribution of some species (n = 84; see Supplementary Table 3). To overcome this potential bias in the models, we did not include these regions in the model calibration step (Supplementary Table 3). The model was projected in these areas after having tested the similarity in the variables between the projection dataset (Italy, Sicily, and Sardinia) and the fitting dataset (the rest of the study area). Indeed, when model predictions are projected into regions not analyzed in the fitting data, it is necessary to measure the similarity between the new environments and those in the training sample28, as models are not so reliable when predicting outside their domain29. Similarity analyses computed using ExDet30 indicated that all covariables in the projected area are within the univariate range of the fitting area and that there is no change in correlation between covariables (NT1 and NT2 = 0).Each of these 10 datasets (per species) was then randomly split into two datasets to evaluate model performance on pseudo-independent data31: 70% of the data was used to calibrate models and the 30% remaining data was used to evaluate model performance using the True Skill Statistic (TSS32) and the Area Under the Curve (AUC) of the receiver-operating characteristic (ROC) plot33 metrics. This split-sample step was repeated 10 times resulting in 100 models per species.For each of the 171 modelled species, a mean model (from the 100 replicates) was then used to predict potential species distribution. Predicted probabilities of occurrence were finally converted into presence/absence using the threshold maximizing the TSS. We fitted all models under the R environment R Core team34 and the package biomod235,36.The WOODIV database provides modelled occurrences of each of the 171 species for each 10 × 10 km grid cell (Fig. 1a). Thirty-two species with less than 10 occurrence records were not modelled (Supplementary Table 3). Among these 32 species, 21 are small-ranged species whose distribution is limited to a few grid cells (Supplementary Table 3). The observed occurrence records for these 21 species can be considered as representative of their distribution and we therefore recommend using the non-modelled records for these species for analyses. The occurrences of the remaining 11 species should be considered unrepresentative of their distribution.Functional dataFour functional traits were considered in this project: adult plant height (Height), seed mass (SeedMass), specific leaf area (SLA), and wood density (StemSpecDens). These traits have been proposed to reflect a global spectrum of plant strategies37,38: height is a commonly measured proxy for individual size and reflects several aspects including resource acquisition, competitive ability, or dispersal capacity. SeedMass represents the trade-off between fecundity, seed survival, and dispersal. SLA (the ratio between leaf area and dry mass) is correlated to photosynthetic capacity and leaf life span and is an indirect measure of the return on investments in carbon gain compared to water loss. StemSpecDens is a key component of woody plant growth linked to the mechanical support of the stem and its growth rate.We compiled the values for these traits at the species level for the trees from the Médail et al.1 checklist, referring mostly to 2 databases: TRY9 and BROT 2.039. Supplementary values were obtained from more specific databases (Global Wood Density Database40, Kew Seed Information Database41) or from the scientific literature and atlas42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61. In total, 92% of the entries were extracted from TRY, 7% from BROT 2.0 and the remaining were retrieved from the other sources. The original ID of records from the TRY and BROT databases is provided in order to make it possible to refer to the complete observation if a user needs to have some contextual information.The WOODIV database lacks all traits data for only 6 of the 210 species from the checklist (Table 2, Supplementary Table 2): Alnus lusitanica Vít, Douda & Mandák, Alnus rohlenae Vít, Douda & Mandák, Malus dasyphylla Borkh., Quercus infectoria Olivier, Tamarix arborea Ehrenb. ex Bunge and, Tamarix passerinoides Del. ex Desf.Adult plant height and seed mass data were available for more than 75% of the 210 species (Table 2; Fig. 2a), whereas wood density and specific leaf area were available for only around 50%. The WOODIV database includes all four trait values for 41% of the 210 species (Fig. 2b; Supplementary Table 2), three trait values for 56% more species.Fig. 2Prevalence of traits and genetic data among the 210 species from Médail et al.1 checkist: (a) For each of the four considered functional traits (adult plant height (Height), seed mass (SeedMass), wood density (SSD) and specific leaf area (SLA)), percentage of the 210 species with existing data; (b) Percentage of the 210 species for which none to four functional traits data are available; (c) For each of the three considered DNA regions (matK, rbcL and psbA-trnH), percentage of the 210 species with existing data (in grey species with only one available sequence for the considered region, in black species with consensus sequence for that region); and, (d) Percentage of the 210 species for which none to three DNA regions data are available.Full size imageThe database provides an R script that can be used to estimate missing trait values using the taxonomic classification if needed.Genetic dataThree different DNA regions from the plastid genome corresponding to the most commonly used DNA barcode regions62,63,64 were considered in this project: the ribulose-bisphosphate/carboxylase Large-subunit gene (rbcL), the maturase-K gene (matK), and the psbA-trnH intergenic spacer (trnH).In a first step, we collected all sequences from GenBank (https://www.ncbi.nlm.nih.gov/genbank/) for the three DNA regions available for the species from the Médail et al.1 checklist at the species level: rbcL: n = 650 sequences for 146 species, matK: n = 644 sequences for 127 species, trnH: n = 493 sequences for 129 species). To fill the gaps, we obtained DNA from fresh samples collected in the field or gathered from herbarium specimens (Supplementary Table 4). DNA extraction and sequencing were performed at INRA-URFM, Avignon (France) and the National Research Council (IBBR-CNR), Florence (Italy) (rbcL: n = 233 for 125 species, matK: n = 162 for 91 species, trnH: n = 200 for 120 species). Methods used for DNA isolation and Sanger sequencing are described by Albassatneh et al.65. When more than one sequence was available for a given DNA region/species, a sequence alignment was performed to check data quality and a taxon-consensus sequence was generated. Consensus sequences were built using the IUPAC-IUB ambiguity66 code for a total of 119 (rbcL), 109 (matK), and 110 species (trnH), respectively (Fig. 2c). All newly created sequences were uploaded to GenBank.The WOODIV database lacks the DNA-region sequences data of only 6 of the 210 species from the Médail et al.1 checklist (Table 2, Fig. 2d): Alnus lusitanica Vít, Douda & Mandák, Cytisus aeolicus Guss., Celtis planchoniana K.I. Chr., Salix appendiculata Vill., Tamarix hampeana Boiss. & Heldr. and, Tamarix minoa J.L. Villar, Turland, Juan, Gaskin, M.A. Alonso & M.B. Crespo.PhylogenyThe WOODIV database provides a phylogram including the 204 species for which at least one piece of DNA-region sequence data was available (Supplementary Table 2) and phylograms including the 210 species from the Medail et al.1 list (Supplementary Fig. 1).Uneven taxon sampling focused on a single biogeographic area such as ours, can bias phylogenetic inferences67. Our goal here is to provide DNA sequence data that can be readily re-used to estimate, e.g. comparable phylogenetic diversity indices, not phylogenetic inferences per se. To illustrate our DNA-sequences data and to facilitate their use for future analyses (to calculate phylogenetic diversity for example), we constructed a molecular phylogeny encompassing the 204 Euro-Mediterranean tree species. Each gene was independently aligned using the MAFFT program68 and parsed using the program Gblocks69 to exclude the segments characterized by several variable positions or gaps from final alignments. An appropriate substitution model of sequence evolution was selected for each of the three plastid DNA regions using the Akaike Information Criterion (AIC) as implemented in the JModeltest 2 program70. The optimal substitution model identified was the same for all three sequences: GTR + I + G. We obtained a concatenated matrix with 1615 aligned bases. We used the Maximum Likelihood analysis71 as implemented in the RAxML V8 program72. The DNA sequence matrix of 1615 sites was analyzed using three partitions with the GTRGAMMAI model (GTR + Gamma substitution model + proportion of invariant sites). We searched for the optimal tree, running at least 20 independent maximum likelihood analyses; full analyses also consisted of 100 bootstrap replicates72.For users who would like to work on the complete pool of 210 tree species, we also built a 210 species phylogram including all Euro-Mediterranean trees. The six missing species for which no DNA-region sequence was available were added to the phylogenetic tree using the Simulation with Uncertainty for Phylogenetic Investigating (SUNPLIN) method73, with 100 replicates. The geometric median tree was computed from the set of 100 replicates with the medTree function from the R package treespace74. Both the median tree and the set of 100 replicates are provided in the WOODIV database, together with the molecular tree with 204 species. More

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Altogether nine polished pieces of the lower Cenomanian Kachin amber from northern Myanmar (Figs. 1A–D, 2A–E) were examined in this study (depository: Russian Museum of Biodiversity Hotspots, N. Laverov Federal Center for Integrated Arctic Research of the Ural Branch of the Russian Academy of Sciences, Arkhangelsk, Russia). A brief description of each amber piece is given below.Figure 1Lower Cenomanian Kachin amber samples with specimens and borings of †Palaeolignopholas kachinensis gen. & sp. nov. from northern Myanmar used in this study. (A) RMBH biv1115 (frontal view with the holotype). (B) RMBH biv1101 (lateral view with two paratypes and a shell fragment). (C) RMBH biv1116 (frontal view with the fossilized paratype). (D) RMBH biv1100 (frontal view with borings). The red frames indicate position of the type specimens (holotype and some paratypes). The red arrows indicate bivalve borings. Scale bars = 5 mm. (Photos: Ilya V. Vikhrev).Full size imageFigure 2Lower Cenomanian Kachin amber samples with borings of †Palaeolignopholas kachinensis gen. & sp. nov. from northern Myanmar used in this study. (A) RMBH biv1102 (frontal view). (B) RMBH biv1103 (frontal view). (C) RMBH biv1114 (frontal view). (D) RMBH biv1118 (frontal view). (E) RMBH biv1117 (frontal view). The red arrows indicate bivalve borings. Scale bars = 5 mm. (Photos: Ilya V. Vikhrev).Full size imageRMBH biv1115: Size 8.5 × 5.8 × 8.1 mm (Fig. 1A). Inclusions: articulated shell of †Palaeolignopholas kachinensis gen. & sp. nov., “floating” in the resin (the holotype).RMBH biv1101: Size 15.6 × 6.4 × 11.5 mm (Fig. 1B). Inclusions: two complete articulated shells (paratypes) and a shell fragment of †Palaeolignopholas kachinensis gen. & sp. nov., “floating” in the resin.RMBH biv1116: Size 22.5 × 8.3 × 16.5 mm (Fig. 1C). Inclusions: fossilized shell of †Palaeolignopholas kachinensis gen. & sp. nov. (paratype), borings of this species (filled with fine gray sand), unidentified fly specimens (Insecta: Diptera), and unidentified organic fragments (probably, plant debris).RMBH biv1100: Size 17.5 × 4.9 × 12.0 mm (Fig. 1D). Inclusions: borings of †Palaeolignopholas kachinensis gen. & sp. nov. (filled with fine gray sand), and an unidentified caddisfly specimen (Insecta: Trichoptera).RMBH biv1102: Size 15.6 × 5.1 × 12.7 mm (Fig. 2A). Inclusions: borings of †Palaeolignopholas kachinensis gen. & sp. nov. (filled with fine gray sand), and unidentified organic fragments (probably, plant debris).RMBH biv1103: Size 19.6 × 4.7 × 14.3 mm (Fig. 2B). Inclusions: borings of †Palaeolignopholas kachinensis gen. & sp. nov. (filled with fine gray sand), an unidentified beetle specimen (Insecta: Coleoptera), and unidentified organic fragments (probably, plant debris).RMBH biv1114: Size 33.1 × 7.8 × 21.7 mm (Fig. 2C). Inclusions: multiple borings of †Palaeolignopholas kachinensis gen. & sp. nov. (filled with fine gray sand), and unidentified plant remains.RMBH biv1118: Size 25.1 × 8.4 × 14.3 mm (Fig. 2D). Inclusions: separate borings of †Palaeolignopholas kachinensis gen. & sp. nov. (filled with fine gray sand), a plant fragment with a cluster of borings around, and an unidentified insect specimen.RMBH biv1117: Size 15.5 × 3.9 × 10.7 mm (Fig. 2E). Inclusions: borings of †Palaeolignopholas kachinensis gen. & sp. nov. (filled with fine gray sand), and an unidentified insect specimen.Additionally, six amber samples containing adult and sub-adult specimens of †Palaeolignopholas kachinensis gen. & sp. nov. were examined using photographs in published works as follows: BMNH 20205 (Department of Palaeontology, Natural History Museum, London, UK)15, NIGP 169623 and NIGP 169624 (Nanjing Institute of Geology and Palaeontology, Chinese Academy of Sciences, Nanjing, China)20, RS.P1450 (Ru D. A. Smith collection, Kuala Lumpur, Malaysia)19, and AMNH (Division of Invertebrates, American Museum of Natural History, New York, NY, United States of America)16.Based on morphological analyses of the fossil piddock shells, it was found to be a genus and species new to science, which is described here.Systematic paleontologyPhylum Mollusca Linnaeus, 1758Class Bivalvia Linnaeus, 1758Family Pholadidae Lamarck, 1809Subfamily Martesiinae Grant & Gale, 1931†Palaeolignopholas gen. novLSID: http://zoobank.org/urn:lsid:zoobank.org:act:1D686DCE-A5E9-41DA-9504-2EC58C93D988Type species: †Palaeolignopholas kachinensis gen. & sp. nov.Etymology. This name is derived from the prefix ‘Palaeo-’ (ancient), and ‘-lignopholas’, the name of a recent genus of estuarine and freshwater piddocks boring into wood, mudstone rocks, brickwork, laterites, etc.11,13. Masculine in gender.Diagnosis. The new monotypic genus is conchologically similar to several other piddock genera such as Lignopholas, Martesia, and Diplothyra Tryon 1862 but can be distinguished from these taxa by the following combination of characters: mesoplax relatively small, triangular, divided longitudinally, posterior slope without concentric sculpture, sculptured valve with concave parallel ridges (Martesia-like “rasping teeth”) curved anteriorly, periostracal lamellae dense, fine, hair-like. The fossil genus †Opertochasma Stephenson, 1952 shares a divided mesoplax but it clearly differs from both †Palaeolignopholas gen. nov. and Lignopholas by having two radial grooves on the shell surface21.Distribution. Kachin State, northern Myanmar; Upper Cretaceous (lower Cenomanian)15,19,22.Comments. Both †Palaeolignopholas gen. nov. and Lignopholas appear to be closely related to each other because they share a longitudinally divided mesoplax and periostracal lamellae, which are considered diagnostic features distinguishing this clade from Martesia + Diplothyra. Based on available conchological characters, we assume that †Palaeolignopholas gen. nov. might be placed on the ancestral stem lineage of the Lignopholas clade, although a possibility of homeomorphy could not entirely be excluded.†Palaeolignopholas kachinensis gen. & sp. nov = Plant Antheridia or Fungal Sporangia indet. sensu Grimaldi et al. (2002): 9, fig. 2a,b (bivalve specimens), fig. 3 (borings), fig. 5 (shell reconstruction of an immature specimen), figs. 6 and 7 (SEMs of borings surface showing rasped ornament at different magnifications)16. = Palaeoclavaria burmitis Poinar & Brown (2003): 765, figs. 1–4 (borings) [this fungal taxon was introduced using a trace fossil (boring) as the holotype]17; Poinar (2016): 2, figs. 10, 15, 16 (borings)18. = Martesiinae indet. sensu Smith & Ross (2018): 4, figs. 1a–c, 2a,b, 3a–d (borings), 4a,b, 5a–e (bivalve specimens)19. = Pholadidae indet. sensu Mao et al. (2018): 99, figs. 8a–f (borings), 8g,h (bivalve specimens)20. = Martesia sp. 2 sensu Mayoral et al. (2020): 10, figs. 4a (borings), 7b, 8a–l (bivalve specimens)15. = Pholadidae indet. sensu Balashov (2020): 623.Figures 1, 2, 3, 4, 5, 6 and 7.Figure 3Holotype and a paratype of †Palaeolignopholas kachinensis gen. & sp. nov. from lower Cenomanian Kachin amber, northern Myanmar. (A) Holotype: ventro-lateral view of articulated shell. (B) Paratype: anterio-lateral view of fossilized shell. VN ventral margin; DR dorsal margin; AN anterior margin; PS posterior margin; d disc; rs rasping surface of the valve; uvs umbonal ventral sulcus; pg pedal gape; pl periostracal lamellae. Scale bars = 500 µm. (Photos: Ilya V. Vikhrev).Full size imageFigure 4Paratypes of †Palaeolignopholas kachinensis gen. & sp. nov. from lower Cenomanian Kachin amber, northern Myanmar. (A) Paratype: dorsal view of articulated shell. Scale bar = 500 µm. (B) Paratype: dorsal view of articulated shell. The detached and deflected umbonal paired fragment of the valves is framed by red square. The blue contour indicates the lifetime position of this fragment. The blue arrows show the shell breakages. Scale bar = 200 µm. (C) Umbonal paired fragment of the holotype valves (inner view). The blue arrows show the shell breakage. Scale bar = 200 µm.  VN ventral margin; DR dorsal margin; AN anterior margin; PS posterior margin; ms longitudinally divided mesoplax (inner view); pr prora; d disc; rs rasping surface of the valve; uvs umbonal ventral sulcus; pg pedal gape; pl periostracal lamellae; sb shell breakage. (Photos: Ilya V. Vikhrev).Full size imageFigure 5Rasping surface of †Palaeolignopholas kachinensis gen. & sp. nov. shell. (A) Holotype shell. The red frame marks position of the enlarged area. (B) Undulated micro-sculpture of the rasping surface. Scale bar = 100 µm. (Photos: Ilya V. Vikhrev).Full size imageFigure 6Schematic reconstruction of †Palaeolignopholas kachinensis gen. & sp. nov. from lower Cenomanian Kachin amber, northern Myanmar based on the type series and other fossil material15,16,19,20. (A) Lateral view of adult specimen. (B) Dorsal view of adult specimen. (C) Ventral view of adult specimen (based on a paratype BMNH 2020515). (D) Anterio-ventral view of immature specimen. (E) Dorsal view of immature specimen. (F) Mesoplax of adult specimen. (G) Mesoplax of immature specimen. d disc; mt metaplax; ms mesoplax; hp hypoplax; ca callum; uvs umbonal ventral sulcus; pg pedal gape; pl periostracal lamellae. Scale bars = 1 mm (A–C). (Line graphics: Yulia E. Chapurina).Full size imageFigure 7Clavate borings of †Palaeolignopholas kachinensis gen. & sp. nov. from lower Cenomanian Kachin amber, northern Myanmar. (A) Cluster of borings. It marks drilling of immature piddocks into soft resin from the unidentified plant (wood?) fragment. (B–D) Clavate borings of adult piddocks. Scale bars = 1 mm. Abbreviation: bg a characteristic bioglyph indicating the shell rotation inside hardening resin. (Photos: Ilya V. Vikhrev).Full size imageLSID: http://zoobank.org/urn:lsid:zoobank.org:act:F6659EBF-B0A4-4B21-A99B-2C56BDB7EC9B.Common name. Kachin Amber Piddock.Holotype. RMBH biv1115, the adult shell with length 3.07 mm and width 1.13 mm “floating” in the resin (Figs. 1A, 3A, 5A,B), local collector leg., Russian Museum of Biodiversity Hotspots, N. Laverov Federal Center for Integrated Arctic Research of the Ural Branch of the Russian Academy of Sciences, Arkhangelsk, Russia.Paratypes. RMBH biv1116, the fossilized adult shell with length 4.05 mm and width 1.83 mm (Figs. 1C, 3B); RMBH biv1101, the immature specimen with articulated shell (width 1.86 mm) sharing a detached and deflected umbonal paired fragment of the valves due to the shell breakage (Figs. 1B, 4B,C); RMBH biv1101, the other immature specimen with shell length 2.68 mm and shell width 2.52 mm in this amber piece (Figs. 1B, 4A); BMNH 20205, adult specimen [illustrated in Mayoral et al. (2020): fig. 7B15], Department of Palaeontology, Natural History Museum, London, UK; NIGP 169623, adult specimen [illustrated in Mao et al. (2018): 100, fig. 8G20], and NIGP 169624, two adult specimens [illustrated in Mao et al. (2018): 100, fig. 8H20], Nanjing Institute of Geology and Palaeontology, Chinese Academy of Sciences, Nanjing, China; RS.P1450, two sub-adult specimens [illustrated in Smith & Ross (2018): 5, fig. 4A,B19], Ru D. A. Smith collection, Kuala Lumpur, Malaysia.Type locality and strata. The Noije Bum Hill mines, Hukawng Valley, near Tanai (26.3593°N, 96.7200°E), Kachin State, northern Myanmar; Upper Cretaceous (lower Cenomanian; absolute age of youngest zircons in enclosing marine sediment: 98.79 ± 0.62 Ma)19,22.Etymology. The name of this species reflects its type locality, which is situated in the Kachin State of Myanmar.Diagnosis. As for the genus.Description. Shell small (up to 9.3 mm in length15,19,20), conical, with a rounded anterior margin, tapering posteriorly (Figs. 3A,B, 4A–C, 6A–E); its shape is similar to those in the recent Lignopholas, Martesia, and Diplothyra. Valve sculptured, with concave parallel ridges (Martesia-like “rasping teeth”) curved anteriorly (Fig. 5A,B). The ridges share a characteristic wave-like micro-sculpture (Fig. 5B). Sulcus deep (Figs. 3A, 4C, 6A–C). Mesoplax longitudinally divided, relatively small, triangular, tapering or lobed anteriorly (Fig. 3A, 6B,F), in immature specimens sometimes with lateral lobes (Figs. 4C, 6E,G). Metaplax and hypoplax long, narrow, not longitudinally divided but sometimes slightly bifurcated posteriorly (Fig. 6A–C). Periostracum densely covered by fine, hair-like lamellae (Figs. 4B,C and 6D). Umbonal reflection with large flattened ridge. Pedal gape presents in immature (Figs. 4A,B, 6D) and some adult specimens (Fig. 3A) but it is covered by callum in older specimens (Figs. 3B, 6C). Morphological details of the new species were also presented in a series of micro-CT images published Mayoral et al. (see Fig. 8 in that paper15) and in the reconstruction of Grimaldy et al. (see Fig. 5 in that work16).Figure 8Recent freshwater piddock Lignopholas fluminalis (Blanford, 1867) in the middle reaches of the Kaladan River, Rakhine State, Myanmar13. (A) Habitat of the freshwater piddock: river pool with siltstone rocks at the bottom, a possible modern analogue of the Mesozoic riverine ecosystem with †Palaeolignopholas. (B) Siltstone rock fragment with living freshwater piddocks inside their clavate borings. (C) Ethanol-preserved piddock (dorsal view). (D) Living piddock with fully developed callum (ventral view). (E) Living piddock with pedal gape (ventral view). Abbreviations: d disc; mt metaplax; ms mesoplax; ca callum; uvs umbonal ventral sulcus; pg pedal gape; pl periostracal lamellae. Scale bar = 2 mm. (Photos: Olga V. Aksenova).Full size imageBorings and corresponding ichnotaxon. The borings produced by †Palaeolignopholas kachinensis gen. & sp. nov. represent club-shaped (clavate) structures (Figs. 1C,D, 2A–E, 7A–D), sometimes with a characteristic bioglyph revealing the shell rotation in hardening resin (Fig. 7C). These borings were illustrated in detail15,16,17,19,20, and were considered belonging to Teredolites clavatus Leymerie, 184215. Initially, the trace fossils produced by the Kachin amber piddock were described as sporocarps of Palaeoclavaria burmitis Poinar & Brown, 2003, a non-gilled hymenomycete taxon17. The holotype of this taxon represents a club-shaped piddock crypt labelled as follows: “Amber from the Hukawng Valley in Burma; specimen (in piece B with accession number B-P-1) deposited in the Poinar amber collection maintained at Oregon State University (holotype)17”. Hence, Palaeoclavaria Poinar & Brown, 2003 and P. burmitis Poinar & Brown, 2003 must be considered ichnogenus and ichnospecies, respectively. New ichnotaxonomic synonymies are formally proposed here as follows: Teredolites Leymerie, 1842 (= Palaeoclavaria Poinar & Brown, 2003 syn. nov.), and Teredolites clavatus Leymerie, 1842 (= Palaeoclavaria burmitis Poinar & Brown, 2003 syn. nov.). More

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