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Taxonomy, distribution and classification of ecosystem-types, integrating the recent IUCN function-based typology and local conceptualizations
<p>1. Introduction:</p> <p>This dataset is a work in progress. It compiles data gathered on ecosystem-types and their distribution based on a series of field studies led by the author, in Seychelles and West and Central Africa (Senterre 2014, Senterre & Wagner 2014, Senterre 2016, Senterre et al. 2017, 2019, 2020, 2021a, 2022). The aims of this dataset are:</p> <p>a. To share in an explicit and transparent way data on proposed taxonomies of ecosystems, i.e. conceptualizations of ecosystem-types, including explicit ecosystem names and management of synonymies.</p> <p>b. To develop ecosystem red listing based on transparent and falsifiable distribution raw data, combining distribution modeling (maps) and in situ observation of individual stand occurrences.</p> <p>c. To illustrate in detail how to deal with ecosystem data following the approach described in Senterre et al. (2021b) (i.e. "ecosystemology" approach).</p> <p>d. To integrate the above approach with the newly developed function-based typology of ecosystems (Keith et al. 2022), therefore contributing to bridging the persistent gap between the global and the local scales in ecosystem descriptions and classifications.</p> <p> </p> <p>2. Context and versions:</p> <p>This dataset was initially planned for publication on GBIF (Global Biodiversity Information Facility), as part of a project developed for the review of Key Biodiversity Areas in Seychelles: "Mainstreaming recent species and ecosystem distribution data into Key Biodiversity Areas assessments in Seychelles" (<a href="https://www.gbif.org/dataset/f513fe98-b1c3-45ee-8e14-7f2a5b7890bf">https://www.gbif.org/dataset/f513fe98-b1c3-45ee-8e14-7f2a5b7890bf</a>).</p> <p>In the first version of the GBIF dataset (<a href="https://www.gbif.org/dataset/f513fe98-b1c3-45ee-8e14-7f2a5b7890bf">https://www.gbif.org/dataset/f513fe98-b1c3-45ee-8e14-7f2a5b7890bf</a>), we proposed an analysis of the potential 'core' and 'extension' files available in GBIF for a publication of ecosystem-type names (and synonymies) and their corresponding occurrences recorded from field observations. This is an original analysis of taxonomic principles managed entirely at the scale of local observable objects, and their history of identifications or interpretations.</p> <p>Toward the end of the above-mentioned GBIF project, considering the limitations and gaps currently present in GBIF, it was decided to restrict the GBIF dataset to a simple 'metadata' entry and to publish the complete version of this dataset in Zenodo. This allows to include all tables needed, as well as all required fields without having to accommodate them within the limited GBIF structure (see metadata description on GBIF for more details). The fields of the tables published here are described in the GBIF metadata entry and in the ecosystemology paper (Senterre et al. 2021b).</p> <p> </p> <p>3. New development on typology aspects:</p> <p>In addition, considering that the new IUCN global typology of ecosystems is now published (Keith et al. 2022), we have reviewed in detail the possibility of integration of ecosystems conceptualized using our ecosystemology approach within the new IUCN typology. The result of this analysis is being considered for a publication, and this Zenodo dataset would then be published in full (i.e. including all typology aspects) as supplementary materials. In the meantime, I would be happy to discuss any of these aspects with whoever is interested.</p> <p> </p> <p>4. Access to ecosystem data for conservation actors:</p> <p>Finally, the actual data (published here) on ecosystem-types, their names, synonymies, classification, distribution, and red list status are compiled into a format that we designed to be useful to conservation actors in the form of interactive webpages (produced with R as shiny apps). This development is based on very limited resources, and the author is still quite new to R, so any help or feedback on ways to improve the scripts would be very much welcomed.</p> <p>The interactive page is available here (currently filtered to Seychelles' data only, although the dataset contains data beyond the Seychelles): https://shiny.bio.gov.sc/bioeco/</p> <p>The R scripts are available on Github: https://github.com/bsenterre/ecosystemology</p> <p> </p> <p>5. Tables contained in this dataset:</p> <p>a. Ecosystem taxonomy tables:</p> <p>ecoSpecies: Contains the list of all ecosystem-type names with their unique identifier.</p> <p>ecoOccurrences: Contains the list of individual stand occurrences, including ecosystem characters as standardized in Senterre et al. (2021b; i.e. virtual ecosystem specimen).</p> <p>ecoSpeciesProfiles: Contains basic metadata on ecosystem-types, such as their Red List evaluations.</p> <p>ecoIdentifications: Contains all the different interpretations/identifications (referring to the table ecoSpecies or to higher levels of classification, see below) made on the stands observed in the ecoOccurrences table.</p> <p> </p> <p>b. Ecosystem typology tables (TO BE ADDED LATER):</p> <p>IUCNL3: This is just a transcription, as is, of the IUCN global typology version 2.1.</p> <p>IUCNL3BIOCrossover: This table defines and comments correspondences between BIOL2 (the level 2 of the typology used by us) and the IUCN typology L3 (level 3).</p> <p>BIOL2: This is a variation based on the IUCN typology, here our level 2.</p> <p>BIOL3: This is a variation based on the IUCN typology, here our level 3.</p> <p>BIOL4: This is a variation based on the IUCN typology, here our level 4.</p> <p>ecoGenus: This is a general type of stand (thus excluding any regional ecosystem connotation), defined at a local scale and never combined with any geographic connotation (see ecosystemology paper: Senterre et al. 2021b).</p> <p>ecoFamily: This is a generalized version of the ecoGenus (i.e. still excluding any regional, sub-regional or geographic aspect).</p> <p>ecoOrder: This is a further generalized version of the ecoGenus (see also Senterre et al. 2020).</p> <p>lifeZone: This is a basic and incomplete list of life zones as defined following the Holdridge (1967) approach, with some additional elements proposed in Senterre et al. (2021b).</p> <p> </p> <p>6. Literature cited:</p> <p>Holdridge, L. R. 1967. Life zone ecology. Tropical Science Center, San Jose, Costa Rica.</p> <p>Keith, D. A., J. R. Ferrer-Paris, E. Nicholson, M. J. Bishop, B. A. Polidoro, E. Ramirez-Llodra, M. G. Tozer, J. L. Nel, R. Mac Nally, E. J. Gregr, K. E. Watermeyer, F. Essl, D. Faber-Langendoen, J. Franklin, C. E. R. Lehmann, A. Etter, D. J. Roux, J. S. Stark, J. A. Rowland, N. A. Brummitt, U. C. Fernandez-Arcaya, I. M. Suthers, S. K. Wiser, I. Donohue, L. J. Jackson, R. T. Pennington, T. M. Iliffe, V. Gerovasileiou, P. Giller, B. J. Robson, N. Pettorelli, A. Andrade, A. Lindgaard, T. Tahvanainen, A. Terauds, M. A. Chadwick, N. J. Murray, J. Moat, P. Pliscoff, I. Zager, and R. T. Kingsford. 2022. A function-based typology for Earth’s ecosystems. . Nature 610:513–518. doi:10.1038/s41586-022-05318-4.</p> <p>Senterre, B. 2014. Mapping habitat-types within the Hummingbird site at Dugbe (Liberia, West Africa). Consultancy Report, Missouri Botanical Garden. P. 56. https://doi.org/10.13140/RG.2.2.32628.48003.</p> <p>Senterre, B. 2016. Habitat-type ground-truthing and assessment of ecosystem conservation value in the Bel Air Alufer mining site (Guinea, West Africa), with recommendations for improving the draft map of land cover types. Consultancy Report, Missouri Botanical Garden, A study conducted for Alufer Mining Limited. P. 54.</p> <p>Senterre, B., E. Bidault, and T. Stévart. 2019. Identification et évaluation des écosystèmes menacés du Mont Nimba. Rapport de consultance, Missouri Botanical Garden (MBG), Africa and Madagascar Department. P. 106. https://doi.org/10.13140/RG.2.2.13242.93129.</p> <p>Senterre, B., E. Bidault, T. Stévart, and P. P. Lowry II. 2020. Assessment of Key Biodiversity Areas in the Lofa-Gola-Mano & Nimba complexes (West Africa) using ecosystem criteria. Final Report, Missouri Botanical Garden. P. 146. 10.13140/RG.2.2.17934.89924.</p> <p>Senterre, B., E. Bidault, T. Stévart, M. Wagner, and P. Lowry. 2017. Mapping habitat-types in south-east Kouilou (Republic of Congo). Consultancy Report, Missouri Botanical Garden (MBG), Africa and Madagascar Department, St. Louis, Missouri, USA. P. 163.</p> <p>Senterre, B., R. M. Bristol, G. Gendron, and E. Henriette. 2021a. Fine-tuning conservation priorities in Seychelles at the landscape scale, using global KBA guidelines with both species and ecosystem criteria. Consultancy Report, United Nations Development Programme, GOS/UNDP/GEF Programme Coordination Unit, Victoria, Seychelles.</p> <p>Senterre, B., P. P. Lowry II, E. Bidault, and T. Stévart. 2021b. Ecosystemology: a new approach toward a taxonomy of ecosystems. . Ecological Complexity 47:100945. doi:https://doi.org/10.1016/j.ecocom.2021.100945.</p> <p>Senterre, B., A.-H. Paradis, E. Bidault, T. Stévart, and P. P. Lowry II. 2022. Qualité et distribution des savanes montagnardes du Nimba. Rapport de consultance, Missouri Botanical Garden (MBG), Africa and Madagascar Department. P. 73. http://dx.doi.org/10.13140/RG.2.2.13433.34401.</p> <p>Senterre, B., and M. Wagner. 2014. Mapping Seychelles habitat-types on Mahé, Praslin, Silhouette, La Digue and Curieuse. Consultancy Report, Government of Seychelles, United Nations Development Programme, Victoria, Seychelles. P. 119. https://doi.org/10.13140/RG.2.1.4558.6009.</p>
FIG. 9 in Functional inferences on the long bones of Ischyrictis zibethoides (Blainville, 1841) (Carnivora, Mustelidae) from the middle Miocene locality of Sansan (Gers, France)
FIG. 9. — Distal view of the distal epiphysis of the left femur of several species of Mustelidae: Gulo gulo (Linnaeus, 1758) (A), Martes foina (Erxleben, 1777) (B), Meles meles (Schreber, 1778) (C), Taxidea taxus (Linnaeus, 1758) (D), and Ischyrictis zibethoides (Blainville, 1841) from Sansan (E), shown at the same size for a better comparison. Scale bar: 1 cm.
FIG. 4 in Functional inferences on the long bones of Ischyrictis zibethoides (Blainville, 1841) (Carnivora, Mustelidae) from the middle Miocene locality of Sansan (Gers, France)
FIG. 4. — Caudal view of the distal epiphysis of the right humerus of several species of Mustelidae: Gulo gulo (Linnaeus, 1758) (A), Martes foina (Erxleben, 1777) (B), Meles meles (Schreber, 1778) (C), Taxidea taxus (Linnaeus, 1758) (D), and Ischyrictis zibethoides (Blainville, 1841) from Sansan (E), shown at the same size for a better comparison. Scale bar: 1 cm.
Machine learning links T cell function and spatial localization to neoadjuvant immunotherapy and clinical outcome in pancreatic cancer
<p>Data supporting the findings of "<a href="https://doi.org/10.1158/2326-6066.CIR-23-0873" target="_blank" rel="noopener">Machine learning links T cell function and spatial localization to neoadjuvant immunotherapy and clinical outcome in pancreatic cancer</a>" publication. Files include patient and tissue region metadata (in metadata folder) and output of multiplex immunohistochemistry computational image processing workflow for each tissue region (in mIHC_files folder). The code used to produce the results of this study is available at: <a href="https://github.com/kblise/PDAC_mIHC_paper">https://github.com/kblise/PDAC_mIHC_paper</a>.</p>
FIGURE 3 in A cascade of dams affects fish spatial distributions and functional groups of local assemblages in a subtropical river
FIGURE 3 | Analysis of multivariate homogeneity of group dispersions (PERMIDISP) by betadisper boxplot, using the Bray-Curtis index, considering the abundance (CPUEN) of movement/reproductive/size (A) and trophic categories (B), and biomass (CPUEB) of movement/ reproductive/size categories (C) and trophic categories (D). The data was based on sampling sites, and presented by environment groups: [DU, DD] = Sampling points away from the dams; [R1, R2, R3] = sampling points located in the reservoirs; [DR1, DR2, DR3] = locations downstream from dams. Greater distance to spatial median indicates larger dispersion and therefore a more diverse/heterogeneous environment. Box lower and upper endpoints represent the 25th and 75th quartiles, respectively. The horizontal bar and plus symbol inside each box represent median, excluding outliers, which are presented by open circles. See Tabs. 4 and 5 for P values from environments comparisons.
FIGURE 2 in A cascade of dams affects fish spatial distributions and functional groups of local assemblages in a subtropical river
FIGURE 2 | Ordination plots produced by the non-metric multidimensional scaling analysis (NMDS) using the Bray-Curtis index, considering the abundance (CPUEN) of movement/reproductive/size (A) and trophic categories (B), and biomass (CPUEB) of movement/ reproductive/size categories (C) and trophic categories (D), among survey locations. [DU (red triangle), DD (yellow triangle)] = Sampling points away from the dams; [R1 (red circle), R2 (green circle), R3 (orange circle)] = sampling points located in the reservoirs; [DR1 (green square), DR2 (orange square), DR3 (red square)] = locations downstream from dams. Labels are for Detritivores, Invertivores, Carnivores, Omnivores, and Piscivores; S/SM = sedentary/short migration, NPC = no parental care, PC = parental care, IF = internal fertilization, and LM = long migration; and Small, Medium, and Large body sizes.
FIGURE 1 in A cascade of dams affects fish spatial distributions and functional groups of local assemblages in a subtropical river
FIGURE 1 | Sampling locations in the Upper Uruguai River, Brazil. The symbols indicate survey sites: circles represent sites within the reservoirs (R1 = site 3 to 5; R2 = site 7 to 9; R3 = site 11 to 14); squares indicate sites located just below dams [Downstream R1 (DR1) = 6, Downstream R2 (DR2) = 10, Downstream R3 (DR3) = 15], and triangles indicate sites that are most distant from dams [Distant Upstream (DU) = 1 and 2; Distant Downstream (DD) = 16 and 17].
FIGURE 4 in Fish functional responses to local habitat variation in streams within multiple land uses areas in the Amazon
FIGURE 4 | Relationships between the ecomorphological traits of fish species and environmental variables (land use and local habitat) in the streams evaluated in this study. Traits are represented by labels: relative head length (RHL), relative mouth width (RMW), relative height (RH), relative area of pectoral fin (RAPF) and relative caudal peduncle length (RCPL).
FIGURE 3 in Fish functional responses to local habitat variation in streams within multiple land uses areas in the Amazon
FIGURE 3 | Relationships between fish functional trophic groups and environmental variables (land use and local habitat) in the streams evaluated in this study. The groups are represented by the labels: Diurnal channel drift feeders (FTG 3), Diurnal backwater drift feeders (FTG 4), Diurnal surface pickers (FTG 7), Diggers (FTG 9) and Ambush and stalking predators (FTG 11). The FTG's with a correlation between 0.2 and -0.2 have been omitted for better visualization of the results.
FIGURE 2 in Fish functional responses to local habitat variation in streams within multiple land uses areas in the Amazon
FIGURE 2 | Ordination of the types of land use in the catchment areas of the study streams at the Capim River basin, eastern Amazon.
FIGURE 1 in Fish functional responses to local habitat variation in streams within multiple land uses areas in the Amazon
FIGURE 1 | Location of sampling streams (Middle Capim River basin, Pará State, Brazil). The characteristics of each land use class are described in the Material and Methods section.
VPS13B is localized at the interface between Golgi cisternae and is a functional partner of FAM177A1
<p>Source Data For Main Figures 1-4 and Supplemental Figures 1-3</p> <p>Tabular Data For Main Figure 1-3 and Supplemental Figure 3</p> <p>Training Dataset for Main Figure 4 embedded in <a href="../api/records/11243617/draft/files/Ilastik%20trained%20Data%20(Golgi%20reformation).ilp/content" target="_blank" rel="noopener noreferrer">Ilastik trained Data (Golgi reformation).ilp</a> file that requires you to download ilastik software.</p>
Data from: Genetic and functional variation across regional and local scales is associated with climate in a foundational prairie grass
<ul> <li>Global change forecasts in ecosystems require knowledge of within species diversity, particularly of dominant species within communities. We assessed site-level diversity and capacity for adaptation of the dominant species of the shortgrass steppe biome of the Central US, Bouteloua gracilis.</li> <li>We quantified genetic diversity from 17 sites across regional scales, north-south from New Mexico to South Dakota, and local scales in Northern Colorado. We also quantified phenotype and plasticity within and among sites and determined the extent to which phenotypic diversity in B. gracilis was related to climate.</li> <li>Genome sequencing indicated pronounced population structure at the regional scale, and local differences indicated gene flow and/or dispersal may also be limited. Within a common environment, we found evidence for genetic divergence in biomass-related phenotypes, plasticity, and phenotypic variance, indicating functional divergence and different adaptive potential. Phenotypes differentiated according to climate, chiefly median Palmer Hydrological Drought Index and other aridity metrics.</li> <li>Our results indicate conclusive differences in genetic variation, phenotype, and plasticity in this species and suggest a mechanism explaining variation in shortgrass steppe community responses to global change. This analysis of B. gracilis intraspecific diversity across spatial scales will improve conservation and management of the shortgrass steppe ecosystem moving forward.</li> </ul>
Species functional traits affect regional and local dominance across western Amazonian forests
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Genetic and functional variation across regional and local scales is associated with climate in a foundational prairie grass
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Data from: Parallel changes in gut microbiome composition and function during colonization, local adaptation and ecological speciation
<p>The processes of local adaptation and ecological speciation are often strongly shaped by biotic interactions such as competition and predation. One of the strongest lines of evidence that biotic interactions drive evolution comes from repeated divergence of lineages in association with repeated changes in the community of interacting species. Yet, relatively little is known about the repeatability of changes in gut microbial communities and their role in adaptation and divergence of host populations in nature. Here we utilize three cases of rapid, parallel adaptation and speciation in freshwater threespine stickleback to test for parallel changes in associated gut microbiomes. We find that features of the gut microbial communities have shifted repeatedly in the same direction in association with parallel divergence and speciation of stickleback hosts. These results suggest that changes to gut microbiomes can occur rapidly and predictably in conjunction with host evolution, and that host-microbe interactions might play an important role in host adaptation and diversification.</p>
Sequence-structure-function relationships in class I MHC: a local frustration perspective
<p>Class I Major Histocompatibility Complex (MHC) binds short antigenic peptides with the help of Peptide Loading Complex (PLC), and presents them to T-cell Receptors (TCRs) of cytotoxic T-cells and Killer-cell Immunglobulin-like Receptors (KIRs) of Natural Killer (NK) cells. With more than 10000 alleles, the Human Leukocyte Antigen (HLA) chain of MHC is the most polymorphic protein in humans. This allelic diversity provides a wide coverage of peptide sequence space, yet does not affect the three-dimensional structure of the complex. Moreover, TCRs mostly interact with pMHC in a common diagonal binding mode, and KIR-pMHC interaction is allele-dependent. With the aim of establishing a framework for understanding the relationships between polymorphism (sequence), structure (conserved fold) and function (protein interactions) of the MHC, we performed here a local frustration analysis on pMHC homology models covering 1436 HLA I alleles. An analysis of local frustration profiles indicated that (1) variations in MHC fold are unlikely due to minimally-frustrated and relatively conserved residues within the HLA peptide-binding groove, (2) high frustration patches on HLA helices are either involved in or near interaction sites of MHC with the TCR, KIR, or Tapasin of the PLC, and (3) peptide ligands mainly stabilize the F-pocket of HLA binding groove.</p>
Quantum Chemical Topology of the Electron Localization Function in the Field of Attosecond Electron Dynamics
<p>Movies accompaning publication of the Article "Quantum Chemical Topology of the Electron Localization Function in the Field of Attosecond Electron Dynamics". Journal of Physical Chemistry Letters, ACS, DOI:10.1021/acs.jpclett.7b03379</p> <p><strong>Collision of water molecule by alpha particle. Time-Dependent Density Functional Theory simulations.</strong></p> <p><strong>Water molecule</strong></p> <p>- Collision of gas phase H<sub>2</sub>O with 100 MeV alpha particle. ELF isosurface of 0.88.</p> <p>Full duration of movie: 5fs. 1E2MeV_H2O_view1.mpeg</p> <p>- Collision of gas phase H<sub>2</sub>O with 1 MeV alpha particle. ELF isosurface of 0.88.</p> <p>Full duration of movie: 5fs. 1E0MeV_H2O_view1.mpeg</p> <p> </p> <p>- Collision of gas phase H<sub>2</sub>O with 0.1 MeV alpha particle. ELF isosurface of 0.70.</p> <p>Full duration of movie: 5fs. 1E-1MeV_H2O_view1.mpeg</p> <p>- Collision of gas phase H<sub>2</sub>O with 0.05 MeV alpha particle. ELF isosurface of 0.8.</p> <p>Full duration of movie: 5fs. 5E-2MeV_H2O_view1.mpeg</p> <p>- Collision of gas phase H<sub>2</sub>O with 0.01 MeV alpha particle. ELF isosurface of 0.8.</p> <p>Full duration of movie: 5fs. 1E-2MeV_H2O_view1.mpeg</p> <p><strong>Movies - guanine </strong></p> <p>Color code: Carbon in green, oxygen in red, nitrogen in blue, hydrogen and alpha particle in white. The ELF attractor are shown in yellow.</p> <p> </p> <p>- Collision of gas phase guanine with 1 MeV alpha particle. ELF isosurface of 0.8. Case i) the alpha particle impacts the central C=C bond. Full duration of movie: 430as. </p> <p>gua_1MeV_C=C_view1.mpeg, gua_1MeV_C=C_view2.mpeg</p> <p>- Collision of gas phase guanine with 1 MeV alpha particle. ELF isosurface of 0.8. Case ii) the alpha particle impact the 6-member ring N atom. Full duration of movie: 430as. </p> <p>gua_1MeV_6RN_view1.mpeg, gua_1MeV_6RN_view2.mpeg</p> <p>- Collision of gas phase guanine with 0.1 MeV alpha particle. ELF isosurface of 0.8. Case i) the alpha particle impacts the central C=C bond. Full duration of movie: 1.5fs. </p> <p>gua_01MeV_C=C_view1.mpeg, gua_01MeV_C=C_view2.mpeg</p> <p>- Collision of gas phase guanine with 0.1 MeV alpha particle. ELF isosurface of 0.8. Case ii) the alpha particle impact the 6-member ring N atom. Full duration of movie: 1.5fs. </p> <p>gua_01MeV_6RN_view1.mpeg, gua_01MeV_6RN_view2.mpeg</p>
Synthetic dataset for the testing of local conditioning of regularization function using geological uncertainty.
<p>This companion datasets relates to the model shown supplementary information to the manuscript "<strong>Integration of geological uncertainty into geophysical inversion by means of local gradient regularization</strong>", by J. Giraud, M. Lindsay, V. Ogarko, M. Jessell, R. Martin and E. Pakyuz-Charrier, submitted to Solid Earth. The archive contains the input and output geophysical data, starting and inverted models, probabilistic geological model and conditioning volume derived from the calculation of Shannon's entropy. </p> <p>This description will be updated with the accurate reference to the journal publication and acknowledgement prior to publication. </p>
Fig. 1 in The importance of isolated patches for maintaining local bird biodiversity and ecosystem function: a case study from the Pernambuco Center of Endemism, Northeast Brazil
Fig. 1. Rarefaction curve based at Mata do Cedro abundance and richness, state of Alagoas, Brazil.
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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)
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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.