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Near Eastern Neolithic farmers introduced several species of domestic plants and animals as they dispersed into Europe. Dogs were the only domestic species present in both Europe and the Near East prior to the Neolithic. Here, we assessed whether early Near Eastern dogs possessed a unique mitochondrial lineage that differentiated them from Mesolithic European populations. We then analysed mitochondrial DNA sequences from 99 ancient European and Near-Eastern dogs spanning the Upper Palaeolithic to the Bronze Age to assess if incoming farmers brought Near Eastern dogs with them, or instead primarily adopted indigenous European dogs after they arrived. Our results show that European pre-Neolithic dogs all possessed the mitochondrial haplogroup C, and that the Neolithic and Post-Neolithic dogs associated with farmers from Southeastern Europe mainly possessed haplogroup D. Thus, the appearance of haplogroup D most likely resulted from the dissemination of dogs from the Near East into Europe. In Western and Northern Europe, the turnover is incomplete and C haplogroup persists well into the Chalcolithic at least. These results suggest that dogs were an integral component of the Neolithic farming package and a mitochondrial lineage associated with the Near East was introduced into Europe alongside pigs, cows, sheep, and goats. It got diluted into the native dog population when reaching the Western and Northern margins of Europe. modern and ancient dog mt sequence (HVR1)all-sequence file.fst

About This dataset is an update of the dataset 'A “Dirty” Footprint: Soil macrofauna biodiversity and fertility in Amazonian Dark Earths and adjacent soils' previously published in Dryad repository (https://doi.org/10.5061/dryad.3tx95x6cc). This dataset now include information on soil humification index, soil carbon related to different soil minerals and soil fatty acids characterization. Description Soils were sampled in Brazilian Amazonia in the municipalities of Iranduba-AM, Belterra-PA and Porto Velho-RO. In each region, paired sites with anthropogenic dark earths (ADE) and nearby reference (REF) non-anthropogenic soils were sampled under three different land-use systems: native secondary vegetation (dense ombrophilous forest) classified as old secondary forest when >20 years old, or young regeneration forest when 2 mm body width) were manually hand-sorted and fixed in 92% ethanol. Earthworms, ants and termites were identified to species or genus level and other macroinvertebrates were sorted into morphospecies with higher taxonomic level assignations. Density (number of individuals) and biomass of the soil macrofauna surveyed using the TSBF method were extrapolated per square meter. For the earthworms, ants and termites (ecosystem engineers) additional samples were performed, especially in forest sites to better estimate species richness of these taxa. Earthworms were collected at four additional cardinal points of the grid at all sites, and hand-sorted from holes of similar dimensions as the TSBF monoliths. Termites were sampled in the forest sites only forests (except one of the REF young forests at Porto Velho), in five 20 m2 (2 x 10 m) plots (close to the five main soil monoliths) by manually digging the soil and looking for termitaria in the soil, as well as in the litter and on trees using a modification of the transect method (Jones and Eggleton, 2000). Ants were sampled in 10 pitfall traps (300 ml plastic cups) set up as two 5-trap transects on the sides of each 1 ha plot, as well as in two traps to the side of each TSBF monolith (distant ~5 m) only in the forest systems of Iranduba and Belterra (not Porto Velho). Each cup was filled to a third of its volume with water, salt and detergent solution. Termites and ants were preserved in 80% ethanol and earthworms in 96% ethanol and the alcohol changed after cleaning the samples within 24 h. All the animals (earthworms, ants, termites) were identified to species level or morphospecies level (with genus assignations) by co-authors SWJ/MLCB (earthworms), AA (termites) and ACF/RMF (ants). Soil samples for chemical and particle size analysis were collected from each TSBF monolith after the soil fauna hand-sorting. Around 2 to 3 kg soil from each depth (0-10, 10-20, 20-30 cm) and the following soil properties were evaluated according to standard methodologies (Teixeira et al., 2017): pH (CaCl2); Ca2+, Mg2+, Al3+ (KCl 1 mol L-1); K+, P, Fe, Zn, Mn, Cu and Ni (Mehlich-1); Pseudo-total contents of trace elements (Ba, Cd, Co, Cu, Ni, Pb, Se and ZN) were determined by acid digestion (HNO3 + HCl); Fe (sulfuric extract); total nitrogen (TN) and carbon (TC) using an element analyzer (CNHS). Base saturation and cation exchange capacity (CEC) were calculated using standard formulae (Teixeira et al., 2017) and particle size fractions (% sand, silt, clay) were obtained following standard methodologies (Teixeira et al., 2017). Soil magnetic susceptibility (MS) and apparent electrical conductivity (ECa) (Siemens per meter – S m-1) were obtained using a KT-10 S/C magnetic susceptibility/conductivity meter (Terraplus) with 10 Hz of operating frequency. Soil macromorphology samples were taken close to the TSBF monolith (~2 m) using a 10 x 10 x 10 cm metal frame. The collected material was separated into different fractions including: living invertebrates, litter, roots, pebbles, pottery sherds, charcoal (biochar), non-aggregated/loose soil (NA), physical aggregates (PA), root-associated aggregates (RA), and fauna-produced aggregates (FA) using the methodology proposed by Velasquez et al. (2007). Laser-induced fluorescence spectroscopy analysis (LIFS) was performed on soil macroaggregate fraction (FA, PA, RA and NAS) from both YF and the pasture from Porto Velho to obtain the humification index of soil organic matter according to Milori et al. (2006). Were analysed fatty acids in soil macroaggregates (PA, RA and FA) from one site in Teotônio. The process involved extracting 2 g of each sample with a chloroform: methanol solution and a surrogate compound, 5α-cholestane. The extract was centrifuged, combined, and the solvent removed using a rotary evaporator and nitrogen. Extracts were stored at -20°C until analyzed by GC-MS. Samples were silylated, with excess silylating agent removed, followed by the addition of hexane and vortexing for GC-Q-MS analysis. The equipment used included an Agilent Technologies GC (7890B) and MS (5977A), with an autosampler and HP-5ms column. MassHunter and MSD ChemStation software facilitated analysis and quantification, respectively. Deconvolution and retention index calculations were performed using AMDIS software. Compounds were identified using NIST MS software, requiring at least three specific mass fragments per compound and a retention index deviation of less than 1.5%. Analyte intensities were normalized by dried soil sample weights and the internal standard. Soil samples from TSBF monoliths were fractionated by dry sieving into small (500 µm) aggregate size classes. These were further fractionated into sand-particulate organic matter (sand-MOP) (>53 µm), silt-organic matter associated with minerals (MOM) (53-2 µm), and clay-MOM (<2 µm). Total organic carbon and nitrogen in these fractions were measured using a Vario EL III elemental analyzer. Clay-MOM samples underwent a four-step sequential extraction with hydroxylamine, sodium dithionite, sodium pyrophosphate, and sodium hydroxide to determine carbon, silicon, iron, and aluminum contents associated with different soil components. For further details see Ramalho (2020). Soil bulk density and total porosity were determined using undisturbed core samples (0.05 m diameter, 0.05 m depth) collected at ~2 m from the TSBF samples following the method proposed by Teixeira et al. (2017). All data is provided in excel format, and includes 14 tabs in the data file: Metadata and legend, Site description, Soil chem, BD+POR, Macromorph, Seq_ext, Biomark, HLIF, Macro_den, Macro_bio, Morpho_TSBF, Add_worm, Add_ants, Add_termites. The Metadata and legend tab provides a detailed explanation for each variable included in each table, including the units used for each. The Site description tab include a brief description of the sites sampled. Soil chem, BD+POR, Macromorph, Seq_ext, Biomark and HLIF tables contain the data on soil chemical, physical, macromorphological, organic matter related to soil minerals, fatty acids and humidification index variables, respectively. The Macro_den and Macro_bio contain the data about density and biomass on all the soil invertebrate taxa found, respectively. The Morphosp_TSBF, Add_worm, Add_ants and Add_termites tables contain the invertebrate species/morphospecies occurrence in TBSF and extra samples for earthworms, ants and termites, respectively. References used in methods section Anderson, J.M., Ingram, J.S.I., 1993. Tropical Soil Biology and Fertility: A handbook of methods, 2 edition. ed. Oxford University Press, Oxford. https://doi.org/10.2307/2261129 Jones, D.T., Eggleton, P., 2000. Sampling termite assemblages in tropical forests: testing a rapid biodiversity assessment protocol. Journal of Animal Ecology 37, 191–203. https://doi.org/10.1046/j.1365-2664.2000.00464.x Milori, D.M.B.P., Galeti, H.V.A., Martin-Neto, L., Dieckow, J., González-Pérez, M., Bayer, C., Salton, J., 2006. Organic Matter Study of Whole Soil Samples Using Laser-Induced Fluorescence Spectroscopy. Soil Science Society of America Journal. 70, 57. https://doi.org/10.2136/sssaj2004.0270 Teixeira, P.C., Donagemma, G.K., Fontana, A., Teixeira, W.G., 2017. Manual de métodos de análise de solo, 3o. ed. Embrapa, Brasília. Ramalho. B., 2020. Caracterização das interações organo-mineral em Terra Preta de Índio. Thesis. Universidade Federal do Paraná. 113p. Velasquez, E., Pelosi, C., Brunet, D., Grimaldi, M., Martins, M., Rendeiro, A.C., Barrios, E., Lavelle, P., 2007. This ped is my ped: Visual separation and near infrared spectra allow determination of the origins of soil macroaggregates. Pedobiologia 51, 75–87. https://doi.org/10.1016/j.pedobi.2007.01.002 Funding The study was supported by the Newton Fund and Fundação Araucária (grant Nos. 45166.460.32093.02022015, NE/N000323/1), Natural Environment Research Council (NERC) UK (grant No. NE/M017656/1), a European Union Horizon 2020 Marie-Curie fellowship to LC (MSCA-IF-2014-GF-660378) and another to DWGS (No. 796877), by CAPES scholarships to WCD, ACC, TF, RFS, AF, LM, HSN, TS, AM and RSM (PVE A115/2013), Araucaria Foundation scholarships to LB, AS, ACC and ES, Post-doctoral fellowships to DWGS (NERC grant NE/M017656/1) and ES (CNPq No. 150748/2014-0), PEER (Partnerships for Enhanced Engagement in Research Science Program) NAS/USAID award number AID-OAA-A-11-0001 - project 3-188 to RMF, and by CNPq grants, scholarships and fellowships to ACF, GGB, RF, SWJ, EGN and PL (Nos. 140260/2016-1, 307486/2013-3, 302462/2016-3, 401824/2013-6, 307179/2013-3, 400533/2014-6). We thank INPA, UFOPA, Embrapa Rondônia, Embrapa Amazônia Ocidental and Embrapa Amazônia Oriental and their staff for logistical support, and the farmers for access to and permission to sample on their properties. Sampling permit for Tapajós National Forest was granted by ICMBio.

Tools and services evaluation speadsheet

Supporting material for the study: "Combining statistical and mechanistic models to unravel the drivers of mortality within a rear-edge beech population." Authors: Cathleen Petit-Cailleux1, Hendrik Davi1, François Lefèvre1, Joseph Garrigue2, Jean-André Magdalou2, Christophe Hurson2,3, Elodie Magnanou2,4, and Sylvie Oddou-Muratorio1. Adresses 1INRA, UR 629 Ecologie des Forêts Méditerranéennes, URFM, Avignon, France 2Réserve Naturelle Nationale de la Forêt de la Massane, France 3Fédération des Réserves Naturelles Catalanes, Prades, France 4Sorbonne Université, CNRS, Biologie Intégrative des Organismes Marins, BIOM, F-66650 Banyuls-sur-Mer, France ORCID: Cathleen Petit-Cailleux: https://orcid.org/0000-0001-7714-6583 François Lefèvre : https://orcid.org/0000-0003-2242-7251 Sylvie Oddou-Muratorio https://orcid.org/0000-0003-2374-8313 ------------- Raw data of the Table_Massane_moratlity_trees.csv and climate can be obtained from Joseph Garrigue, Jean-André Magdalou and Christophe Hurson. The inventories files and daily climate are the input dataset to run CASTANEA models. All details are provided in the article.