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Fig. 51. Consensus tree for 13 in Polybia, Paraphyly, and Polistine Phylogeny
Fig. 51. Consensus tree for 13 cladograms resulting from simultaneous analysis of the combined character data in tables 1–3.
Fig. 50. Consensus tree for 79 in Polybia, Paraphyly, and Polistine Phylogeny
Fig. 50. Consensus tree for 79 strictly supported cladograms resulting from analysis of the larval character matrix in table 2, excluding the three terminals that have all missing values.
Text-fig. 9. Phylogenetic tree indicating the number of required character state changes (steps) under parsimony for various positions of Miranthus gen. nov. in a molecular based backbone tree (see material and methods for additional details). in Early Flowers Of Primuloid Ericales From The Late Cretaceous Of Portugal And Their Ecological And Phytogeographic Implications
Text-fig. 9. Phylogenetic tree indicating the number of required character state changes (steps) under parsimony for various positions of Miranthus gen. nov. in a molecular based backbone tree (see material and methods for additional details).
Fig. 5. Combined tree from Fig. 4 with mapped morphological characters. Numbers above branches represent characters from Table 3. Mapped using WinClada ver. 1.61 in A revision of Scipopus Enderlein including the subgenera Scipopus s. str., Phaeopterina Frey and Parascipopus subgen. nov. (Diptera, Micropezidae, Taeniapterinae)
Fig. 5. Combined tree from Fig. 4 with mapped morphological characters. Numbers above branches represent characters from Table 3. Mapped using WinClada ver. 1.61 (Nixon 1999–2002) with unambiguous characters only.
Eurasian tree sparrows are more food neophobic and habituate to novel objects more slowly than house sparrows
<p>Introductions of non-native species throughout the world have had severe ecological consequences. However, most research has focused on environmental and ecological factors that allow for introduced species to succeed and become invasive, with fewer studies assessing the roles of behavioural and cognitive traits. To help fill this knowledge gap, we studied neophobia, an aversion towards novelty, in the non-native Eurasian tree sparrow (<em>Passer montanus</em>), and compared results to previous work in a more successful invasive congener, the house sparrow (<em>Passer domesticus</em>). We assessed the neophobia of wild-caught Eurasian tree sparrows by measuring their responses to novel objects and novel foods and their ability to habituate to initially novel objects. We predicted that Eurasian tree sparrows, as less successful invaders, would overall be more neophobic than house sparrows. Although we did not observe differences in neophobia towards novel objects in the two species, Eurasian tree sparrows were significantly less willing to try novel foods than house sparrows. Eurasian tree sparrows were also slower to habituate to repeated presentations of the same initially novel object compared to house sparrows. Multiple factors certainly influence invasion success, but our results suggest that neophobia might limit the success of an introduced species in novel environments.</p>
I-MAESTRO data: 42 million trees from three large European landscapes in France, Poland and Slovenia
<p>Here we present three datasets describing three large European landscapes in France (Bauges Geopark - 89,000 ha), Poland (Milicz forest district - 21,000 ha) and Slovenia (Snežnik forest - 4,700 ha) down to the tree level. Individual trees were generated combining inventory plot data, vegetation maps and Airborne Laser Scanning (ALS) data. Together, these landscapes (hereafter virtual landscapes) cover more than 100,000 ha including about 64,000 ha of forest and consist of more than 42 million trees of 51 different species.</p><p>For each virtual landscape we provide a table (in .csv format) with the following columns:<br>- cellID25: the unique ID of each 25x25 m² cell<br>- sp: species latin names<br>- n: number of trees. n is an integer >= 1, meaning that a specific set of species "sp", diameter "dbh" and height "h" can be present multiple times in a cell.<br>- dbh: tree diameter at breast height (cm)<br>- h: tree height (m)</p><p>We also provide, for each virtual landscape, a raster (in .asc format) with the cell IDs (cellID25) which makes data spatialisation possible. The coordinate reference systems are EPSG: 2154 for the Bauges, EPSG: 2180 for Milicz, and EPSG: 3912 for Sneznik.</p><p>The v2.0.0 presents the algorithm in its final state.</p><p>Finally, we provide a proof of how our algorithm makes it possible to reach the total BA and the BA proportion of broadleaf trees provided by the ALS mapping using the alpha correction coefficient and how it maintains the Dg ratios observed on the field plots between the different species (see algorithm presented in the associated Open Research Europe article).</p><p>Below is an example of R code that opens the datasets and creates a tree density map.</p><p>------------------------------------------------------------<br># load package</p><p>library(terra)</p><p>library(dplyr)</p><p> </p><p># set work directory</p><p>setwd() # define path to the I-MAESTRO_data folder</p><p> </p><p># load tree data</p><p>tree <- read.csv2('./sneznik/sneznik_trees.csv', sep = ',')</p><p> </p><p># load spatial data</p><p>cellID <- rast('./sneznik/sneznik_cellID25.asc')</p><p> </p><p># set coordinate reference system</p><p># Bauges:</p><p># crs(cellID) <- "epsg:2154"</p><p># Milicz:</p><p># crs(cellID) <- "epsg:2180"</p><p># Sneznik:</p><p># crs(cellID) <- "epsg:3912"</p><p> </p><p># convert raster into dataframe</p><p>cellIDdf <- as.data.frame(cellID)</p><p>colnames(cellIDdf) <- 'cellID25'</p><p> </p><p># calculate tree density from tree dataframe</p><p>dens <- tree %>% group_by(cellID25) %>% summarise(n = sum(n))</p><p> </p><p># merge the two dataframes</p><p>dens <- left_join(cellIDdf, dens, join_by(cellID25))</p><p> </p><p># add density to raster</p><p>cellID$dens <- dens$n</p><p> </p><p># plot density map</p><p>plot(cellID$dens)</p>
Fault tree reliability analysis via squarefree polynomials
<p>Artefact for the paper "Fault tree reliability analysis via squarefree polynomials" by Milan-Lopuhaä-Zwakenberg, MODELSWARD 2024.</p>
Fig. 6. RAxML tree for 18S in Two New Species of Centrohelid Heliozoans: Acanthocystis costata sp. nov. and Choanocystis symna sp. nov.
Fig. 6. RAxML tree for 18S rRNA genes of 8 pterocystid heliozoans (1320 nucleotide positions). Only bootstrap values more than 50% are shown. New sequence is in bold.
Fig. 5. RAxML tree for 18S in Two New Species of Centrohelid Heliozoans: Acanthocystis costata sp. nov. and Choanocystis symna sp. nov.
Fig. 5. RAxML tree for 18S rRNA genes of 16 heliozoans from the genus Acanthocystis and Polyplacocystis ambigua as an outgroup (1520 nucleotide positions). Only bootstrap values more than 50% are shown. New sequence is in bold. Crossed branches were shortened fourfold.
Milky Way Over Quiver Tree
<p>Winner in the 2023 IAU OAE Astrophotography Contest, category Still images with smartphones-mobile devices: Milky Way Over Quiver Tree, by Jianfeng Dai.</p> <p>This breathtaking photograph was captured on 17 June 2023, near Keetmanshoop, Namibia, with a smartphone. Dominating the night sky, the majestic arc of the Milky Way creates a celestial bridge across the heavens. The image captures a range of notable astronomical objects: the Large and Small Magellanic Clouds, seen towards the bottom of the image and appearing as fuzzy clouds; Antares, seen towards the top left of the image; and the coalsack nebula (referred to by various names by Indigenous cultures around the world), seen vertically above the Large Magellanic Cloud. Silhouetted against this astral backdrop, the trees — which are actually succulent aloe plants native to southern Africa — add a touch of Earth's unique beauty. Historically, these plants were known as ‘quiver trees’ because groups of local Indigenous people would use their hollowed branches to hold darts. The serene Namibian landscape, combined with the brilliance of the southern hemisphere's stars, offers a glimpse into the majesty of our Universe.</p> <p>Credit: Jianfeng Dai/IAU OAE (<a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC BY 4.0</a>)</p>
Figure 1 in A New Species of Metaprotella (Crustacea: Amphipoda: Caprellidae) from One Tree Island, Southern Great Barrier Reef, Queensland, Australia
Figure 1. Metaprotella lowryi sp. nov., holotype male, 7.08 mm, AM P.100147, and paratype female, 6.02 mm, AM P.100149, One Tree Island, Great Barrier Reef, Queensland, Australia, 23°29'05"S 152°04'07"E. Scale 1.0 mm.
Figure 2 in A New Species of Metaprotella (Crustacea: Amphipoda: Caprellidae) from One Tree Island, Southern Great Barrier Reef, Queensland, Australia
Figure 2. Metaprotella lowryi sp. nov., holotype male, 7.08 mm, AM P.100147, One Tree Island, southern Great Barrier Reef, Queensland, Australia, 23°29'05"S 152°04'07"E. L, left; LL, lower lip; MD, mandible; MX, maxilla, MXP, maxilliped; R, right, and UL, upper lip. Scale = 0.05 mm.
Figure 3 in A New Species of Metaprotella (Crustacea: Amphipoda: Caprellidae) from One Tree Island, Southern Great Barrier Reef, Queensland, Australia
Figure 3. Metaprotella lowryi sp. nov.: One Tree Island, southern Great Barrier Reef, Queensland, Australia, 23°29'05"S 152°04'07"E: A2, G1, G2 (M), P3–P7, holotype male, 7.08 mm, AM P.100147; G2 (M*), AB, paratype male, 8.59 mm, AM P.100148; G2 (F), paratype female, 6.02 mm, AM P.100149. A2, antenna 2; AB, abdomen; F, female; G1, gnathopod 1; G2, gnathopod 2; M, male; P3–P7, pereopod 1 to pereopod 7, respectively. Scale: G1, P3, P4, and AB = 0.1 mm; 0.2 mm for all others.
Supplementary Data for: A time-calibrated 'Tree of Life' of aquatic insects for knitting historical patterns of evolution and measuring extant phylogenetic biodiversity across the world
<p>This compendium of files includes the dated phylogenetic tree in Newick format (<strong>Data S1</strong>), the list of statistical routines used for the three empirical case studies (<strong>Data S2</strong>), and the high-resolution version of the figures in the supplementary materials and main text (<strong>Data S3</strong>) for the <em>Earth-Science Reviews</em> paper "A time-calibrated ‘Tree of Life’ of aquatic insects for knitting historical patterns of evolution and measuring extant phylogenetic biodiversity across the world", which is under consideration. The best-scoring molecular tree (<strong>Data S1</strong>) can be opened using freely available programs like R (R Development Core Team, 2021), Dendroscope (Huson and Scornavacca, 2012), and FigTree (Rambaut, 2018).</p> <p>Please, feel free to send an email to the maintainer Dr. Jorge García Girón (jogarg@unileon.es OR Jorge.Garcia-Giron@oulu.fi) if you face any trouble downloading, opening, or using these files.</p> <ul> <li>Huson, D. H., & Scornavacca, C. (2012). Dendroscope 3: An interactive tool for rooted phylogenetic trees and networks. <em>Systematic Biology</em>, <em>61(6)</em>, 1061–1067.</li> <li>R Development Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/</li> <li>Rambaut, A. (2018). FigTree. Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK. http://tree.bio.ed.ac.uk/software/figtree/</li> </ul>
Data set for "Drought response of the boreal forest carbon sink is driven by understory-tree composition"
<p>This data set is a compilation of 1) environmental conditions, 2) biometric- and chamber-based annual CO<sub>2</sub> fluxes, 3) vegetation phenological greenness, and 4) forest-floor environmental conditions, all measured over the Krycklan Catchment Study (KCS, <a href="https://www.slu.se/Krycklan">https://www.slu.se/Krycklan</a>), a multi-scale long-term monitored boreal catchment spanning 68 km<sup>2</sup> in northern Sweden.</p> <p>The environmental measurements cover the period 1991–2020. Specifically, meteorological conditions measured close to the central part of the KCS at the Svartberget reference climate station (64°14′N, 19°46′E, 225 m.a.s.l.) included air temperature at 1.7 m above ground (Ta, °C), global radiation at 1.7 m above ground (Rg, MJ m<sup>-2</sup>), and precipitation (P, mm). Drought conditions were characterized by the Standardized Precipitation Evapotranspiration Index (SPEI) computed at 3-month time scale. SPEI was retrieved from the 0.5° gridded dataset supplied in the Global SPEI Database (SPEIbase v2.8, <a href="https://spei.csic.es/database.html">https://spei.csic.es/database.html</a>). The data set comprises monthly values obtained during the long-term reference period 1991–2020 (LT<sub>91–20</sub>), the baseline period 2016–2017 (BL<sub>16–17</sub>), and the drought year 2018 (D<sub>18</sub>). The standardized anomaly (ɀ-score) was used to identify extreme environmental measurements during both the BL<sub>16–17 </sub>and D<sub>18</sub> periods relative to the LT<sub>91–20 </sub>period.</p> <p>Annual CO<sub>2</sub> flux estimates were collected in 50 forest stands located across the KCS during the period 2016–2018 using biometric- and chamber-based methods. However, to prevent confounding effects, one forest stand that was subjected to thinning operations in spring 2018 was excluded from the analysis. The selected forest stands encompassed different landscape attributes such as 1) soil type (i.e., sediment and till), 2) dominant tree species (i.e., pine and spruce), and 3) stand age classes (i.e., initiation, young, middle-aged, mature, and old-growth stands). The annual CO<sub>2</sub> fluxes included the net ecosystem production (NEP) and its component fluxes, i.e., net primary production (NPP), total heterotrophic respiration (RH), net primary production of trees (NPP<sub>t</sub>) and its above- and belowground components (ANPP<sub>t</sub> and BNPP<sub>t</sub>, respectively), and net primary production of understory (NPP<sub>u</sub>) and its above- and belowground components (ANPP<sub>u</sub> and BNPP<sub>u</sub>, respectively). The impact of drought on annual CO<sub>2</sub> fluxes was evaluated by calculating both the absolute and relative anomalies (∆X and δX, respectively) of D<sub>18</sub> relative to BL<sub>16–17</sub>. To identify the temporal shift of the dominant contributor to ∆NEP, a moving-window correlation was conducted between the absolute anomaly of NEP (∆NEP) and the absolute anomalies of understory and tree NPP (∆NPP<sub>u</sub> and ∆NPP<sub>t</sub>, respectively), using a 7-forest-stand window with 1-forest-stand step.</p> <p>The study assessed the phenological greenness of the understory and trees in a ⁓110 years-old mixed-species forest stand in the central part of the KCS from 2016 to 2018. The greenness index (gcc) was derived from hourly images collected through digital repeat photography at the Integrated Carbon Observation System (ICOS) Svartberget ecosystem station (SE-Svb, 64°15′N, 19°46′E, 270 m.a.s.l., <a href="https://www.icos-sweden.se/svartberget">https://www.icos-sweden.se/svartberget</a>). Web cameras were used to capture images below- and above-tree canopy to define the gcc index for understory (gcc<sub>u</sub>) and trees (gcc<sub>t</sub>), respectively. The gcc<sub>u</sub> and gcc<sub>t</sub> values were then normalized (0–1) to describe the seasonal minimum and maximum of vegetation biomass development. A locally estimated scatterplot smoothing (loess) curve fit was then used through the normalized data points to improve visualization. The impact of drought on mean estimates of gcc<sub>u</sub> and gcc<sub>t</sub> during the growing season was evaluated by calculating the absolute and relative anomalies (∆X and δX, respectively) of D<sub>18</sub> relative to BL<sub>16–17</sub>.</p> <p>Environmental conditions at the forest-floor interface were measured in each of the 50 forest stands located across the KCS during the period 2016–2018. As before, one forest stand that was subjected to thinning operations in spring 2018 was excluded from the analysis to prevent confounding effects. The measured conditions included the below-canopy air temperature (Ta<sub>bc</sub>, °C), soil temperature at 10 cm depth (Ts, °C), and soil volumetric water content at 5 cm depth (SWC, %). The data set includes mean monthly and mean May-August values estimated during the BL<sub>16–17</sub> and D<sub>18</sub> periods, for which the absolute and relative anomalies (∆X and δX, respectively) were calculated.</p> <p>This data set consists of four Microsoft Excel workbooks:</p> <p>1_dataset_environmental_conditions.xlxs</p> <p>2_dataset_biometric_&_chamber-based_CO2_fluxes.xlxs</p> <p>3_dataset_vegetation_phenological_greenness.xlxs</p> <p>4_dataset_forest-floor_environmental_conditions.xlxs</p> <p>Further details can be found in Martínez-García et al. “Drought response of the boreal forest carbon sink is driven by understory-tree composition” (Nature Geoscience, <a href="https://doi.org/10.1038/s41561-024-01374-9" target="_blank" rel="noopener noreferrer">https://doi.org/10.1038/s41561-024-01374-9</a>).</p> <p>Contact information:</p> <p>Ph.D. Eduardo Martínez García<sup>1,2</sup> (<a href="mailto:eduardo.martinez@slu.se">eduardo.martinez@slu.se</a>, <a href="eduardo.martinezgarcia@luke.fi">eduardo.martinezgarcia@luke.fi</a>, <a href="mailto:edu.martinez.garcia@gmail.com">edu.martinez.garcia@gmail.com</a>)</p> <p>Professor Matthias Peichl<sup>1</sup> (<a href="mailto:matthias.peichl@slu.se">matthias.peichl@slu.se</a>)</p> <p><sup>1</sup> Department of Forest Ecology and Management, Swedish University of Agricultural Sciences (SLU), Skogsmarksgränd 17, SE-901 83, Umeå, Sweden</p> <p><sup>2</sup> Natural Resources Institute Finland (Luke), Latokartanonkaari 9, FI-00790, Helsinki, Finland</p>
Tree crowns of the north-west corner of the permanent sample area in the Kaluzhskiye Zaseki Nature Reserve
<p>The studies were conducted in the Kaluga Zaseki Nature Reserve on a permanent sample plot (PSP) established in an old-growth broadleaved forest. The stand on the PSP has a complex structure, consisting of several tiers. There are 6 species of broad-leaved trees in the stand: oak (Quercus robur), ash (Fraxinus excelsior), elm (Ulmus glabra), sharp-leaved maple (Acer platanoides), field maple (A. campestre), linden (Tilia cordata) and 2 small-leaved trees - birch (Betula spp.) and aspen (Populus tremula). The oldest oak trees are about 300 years old. The size of the PPP is 440 × 200 m, this work was done on a 40 × 40 m plot located in the northwest corner of the PSP. For tree detection, orthophotomaps were used based on aerial photography materials taken with a DJI Phantom IV Pro quadcopter. Photogrammetric processing was carried out in Agisoft Metashape software (version 1.6.1.10009).</p> <p>The data set contains two fragments of multi-season orthophotos in tif format and corresponding files in shp., dbh. and shx. formats, which contain information on crown boundaries and species of marked trees.</p>
Data from: Tree functional traits across Caribbean island dry forests are remarkably similar
<p>Delineation of potential dry forest and estimated actual dry forest on Caribbean islands. Potential dry forest is delineated based on CHELSA climate data (<a href="http://www.chelsa-climate.org/">www.chelsa-climate.org</a>) and the FAO definition of dry forest. Estimated actual dry forest is corrected for land cover using data from Hansen et al. (2022) <a href="https://doi.org/10.1088/1748-9326/ac46ec">https://doi.org/10.1088/1748-9326/ac46ec</a>. Areas of potential dry forest, estimated actual dry forest, and area of built-up land covers are summarized by islands and joined to CHELSA bioclimatic variables for selected islands where data on functional traits are available. Trait values by sites are also included. The package consists of data outputs and R scripts to reproduce the data outputs from identified publicly available data sources.</p>
Functional genomics and co-occurrence in a diverse tropical tree genus: The roles of drought and defense related genes
<p>Tropical tree communities are among the most diverse in the world. A small number of genera often disproportionately contribute to this diversity. How so many species from a single genus can co-occur represents a major outstanding question in biology. Niche differences are likely to play a major role in promoting congeneric diversity, but the mechanisms of interest are often not well-characterized by the set of functional traits generally measured by ecologists. To address this knowledge gap, we used a functional genomic approach to investigate the mechanisms of co-occurrence in the hyper-diverse genus <em>Ficus</em>. Our study focused on over 800 genes related to drought and defense, providing detailed information on how these genes may contribute to the diversity of <em>Ficus</em> species. We find widespread and consistent evidence of the importance of defense gene dissimilarity in co-occurring species, providing genetic support for what would be expected under the Janzen-Connell mechanism. We also find that drought-related gene sequence similarity is related to <em>Ficus</em> co-occurrence, indicating that similar responses to drought promote co-occurrence. We provide the first detailed functional genomic evidence of how drought- and defense-related genes simultaneously contribute to the local co-occurrence in a hyper-diverse genus. Our results demonstrate the potential of community transcriptomics to identify the drivers of species co-occurrence in hyper-diverse tropical tree genera.</p>
Fig. 14 in First record of Metapolystoma (Monogenea: Polystomatidae) from Boophis tree frogs in Madagascar, with the description of five new species
Fig. 14. Bayesian tree inferred from the analysis of concatenated 18S, 28S and COI gene sequences. Node values indicate Bayesian posterior probabilities.
Fig. 10 in First record of Metapolystoma (Monogenea: Polystomatidae) from Boophis tree frogs in Madagascar, with the description of five new species
Fig. 10. Ventral view of Metapolystoma theroni n. sp. holotype. Abbreviations: eg, egg; gb, genital bulb; gc, genito–intestinal canal; ha, hamuli; hp, haptor; ic, intestinal caecum; mg, Mehlis gland; mo, mouth; oc, oncomiracidium; od, oviduct; oi, oo ¨–vitelline canal; o¨o, o¨otype; os, false oral sucker; ov, ovarium; ph, pharynx; su, sucker; sv, semen vesicle; te, testis; ut, uterus; va, vagina; vc, vaginal canal; vd, vas deferens; vi, vitelline; vl, vitelline duct; vv, vitello–vaginal canal.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.