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opencc-by-4.0Nov 2024View details →

Baseline and Future (2050s and 2090s) Climate Suitability Scores for 137 Useful Tree Species and 273 locations from the United Republic of Tanzania

Climate suitability scores were calculated for 137 Useful Tree Species identified by filtering native tree species from the United Republic of Tanzania via the <a href="https://patspo.shinyapps.io/GlobalUsefulTrees/">GlobalUsefulNativeTrees</a> database, matching species with those described in the <a href="https://apps.worldagroforestry.org/usefultrees/">RELMA-ICRAF Useful Tree and Shrub Species for Tanzania manual</a> and checking for the availability of globally observed environmental ranges from the <a href="https://doi.org/10.5281/zenodo.13132613">TreeGOER</a> database. <ul> <li>Score = 3 means that in 'environmental space' the planting site occurs within the 25% - 75% species's range (as documented in the <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/gcb.16914" target="_blank" rel="noopener">TreeGOER</a> ) for all variables</li> <li>Score = 2 corresponds to the 5% - 95% species's range for all variables</li> <li>Score = 1 corresponds to the 0% - 100% species's range for all variables</li> <li>Score = 0 means that the planting site occurs outside the 0% - 100% species's range for some of the variable</li> <li>Score = -1 means that the species is not documented by TreeGOER</li> </ul>   Locations corresponded to cities within the target countries sourced from the <a href="https://doi.org/10.5281/zenodo.10004594">CitiesGOER</a> database. This database provides bioclimatic conditions for the historical (baseline) and three future climate change scenarios. Bioclimatic variables for future climates correspond to the median values from 24 Global Climate Models (GCMs) for Shared Socio-Economic Pathway (SSP) 1-2.6 for the 2050s (2041-2060), from 21 GCMs for SSP 3-7.0 for the 2050s and from 13 GCMs for SSP 5-8.5 for the 2090s. Investigations were made for two different sets of bioclimatic variables, allowing for sensitivity analysis: <ul> <li>One set of bioclimatic variables included BIO01, BIO12, climaticMoistureIndex, monthCountByTemp10, growingDegDays5, BIO05, BIO06, BIO16, BIO17 and MCWD. These are the same bioclimatic variables available internally in the <a href="https://worldagroforestry.org/output/globalusefulnativetrees">GlobalUsefulNativeTrees</a> for climate filtering.</li> <li>One set only included BIO01 (= Mean Annual Temperature), which is the same bioclimatic variables available from the BGCI <a href="https://cat.bgci.org/">Climate Assessment Tool</a>.</li> </ul> Calculations were made with similar scripting pipelines in the R statistical environment as documented here: <a href="https://rpubs.com/Roeland-KINDT/1168650">https://rpubs.com/Roeland-KINDT/1168650</a>. These scripts use similar calculations methods as those used for the global case studies of the TreeGOER manuscript (Kindt <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">2023</a>), and used internally in the <a href="https://worldagroforestry.org/output/globalusefulnativetrees">GlobalUsefulNativeTrees</a> online database. Interested readers should especially refer to the manuscript for further details on methods used and their justification. The maps show the frequency distribution of tree species with climate scores 3, 2, 1 and 0, excluding 6 species not documented by the TreeGOER.   The Excel database allows filtering useful tree species by some of the attributes available in the <a href="https://patspo.shinyapps.io/GlobalUsefulTrees/">GlobalUsefulNativeTrees</a> database. Species can be filtered for ten categories of documented human uses (see <a href="https://kew.iro.bl.uk/concern/datasets/7243d727-e28d-419d-a8f7-9ebef5b9e03e">Diazgranados et al. 2020</a> for details): <ul> <li>AF: Animal Food.</li> <li>EU: Environmental Uses.</li> <li>FU: Fuel.</li> <li>GS: Gene Sources.</li> <li>HF: Human Food.</li> <li>IF: Invertebrate Food.</li> <li>MA: Materials.</li> <li>ME: Medicines.</li> <li>PO: Poisons.</li> <li>SU: Social Uses</li> </ul> Species can also be filtered for the Climatic Moisture Index (CMI). See this Zenodo archive (<a href="https://zenodo.org/records/8252756">https://zenodo.org/records/8252756</a>) to see the distribution of CMI zones across the United Republic of Tanzania. Codings refer to the species reaching the upper part of the range in the zone (code: 2), the zone being included in teh middle part of the ranage (code: 9) or the species reaching the lower part of the range in this zone (code:3). <ul> <li>CMI.A (CMI ≥ 0.5 ; P >= 2 * PET; ‘extremely humid’ lands)</li> <li>CMI.B (0 ≤ CMI < 0.5 ; PET <= P < 2 * PET ; ‘very humid’ lands)</li> <li>CMI.C (−0.35 ≤ CMI < 0 ; 0.65 <= P/PET < 1 ; ‘humid’ lands)</li> <li>CMI.D ( −0.5 ≤ CMI < −0.35 ; 0.50 <= P/PET < 0.65 ; dry sub-humid drylands)</li> <li>CMI.E (−0.8 ≤ CMI < −0.5 ; 0.20 <= P/PET < 0.50 ; semi-arid drylands)</li> <li>CMI.F (−0.95 ≤ CMI < −0.8 ; 0.05 <= P/PET < 0.20 ; arid drylands)</li> <li>CMI.G (CMI < −0.95 ; P/PET < 0.05 ; hyper-arid drylands)</li> </ul>   References <ul> <li>Kindt, R. (2023). TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. Global Change Biology, 00, 1–16. <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914</a>.</li> <li>Kindt, R. (2024). TreeGOER: Tree Globally Observed Environmental Ranges (2024.07) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.13132613" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13132613</a></li> <li>Kindt, R., Graudal, L., Lillesø, JP.B. et al. (2023). GlobalUsefulNativeTrees, a database documenting 14,014 tree species, supports synergies between biodiversity recovery and local livelihoods in landscape restoration. Sci Rep 13, 12640. <a href="https://doi.org/10.1038/s41598-023-39552-1">https://doi.org/10.1038/s41598-023-39552-1</a></li> <li>Kindt, R. (2023). CitiesGOER: Globally Observed Environmental Data for 52,602 Cities with a Population ≥ 5000 (2023.10) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.10004594" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10004594</a></li> <li>Kindt, R. (2024). ClimateForecasts: Globally Observed Environmental Data for 15,504 Weather Station Locations (2024.07) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.12679832" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12679832</a></li> <li>Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1‐km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302–4315. <a href="https://doi.org/10.1002/joc.5086">https://doi.org/10.1002/joc.5086</a></li> <li>Title, P. O., & Bemmels, J. B. (2018). ENVIREM: An expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. Ecography, 41(2), 291–307. <a href="https://doi.org/10.1111/ecog.02880">https://doi.org/10.1111/ecog.02880</a></li> <li>Opendatasoft (2023) Geonames - All Cities with a population > 1000. <a href="https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/information/?disjunctive.cou_name_en&sort=name">https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/information/?disjunctive.cou_name_en&sort=name</a> (accessed 22-JULY-2023)</li> <li>Meteostat (2024) Weather stations: Lite dump with active weather stations. <a href="https://github.com/meteostat/weather-stations">https://github.com/meteostat/weather-stations</a> (accessed 17-FEB-2024)</li> <li>Diazgranados, M., Allkin, B., Black, N., Cámara-Leret, R., Canteiro, C., Carretero, J., Eastwood, R., Hargreaves, S., Hudson, A., Milliken, W. and Nesbitt, M., 2020. World checklist of useful plant species. Royal Botanic Gardens, Kew. <a href="https://knb.ecoinformatics.org/view/doi:10.5063/F1CV4G34">https://knb.ecoinformatics.org/view/doi:10.5063/F1CV4G34</a></li> </ul>   Funding The data sets and maps available in this archive were created through funding by the U. S. Agency for International Development (USAID) to CIFOR-ICRAF, here specifically in the context of the On-farm Land Restoration for Livelihoods and Environmental Benefits project.

opencc-by-nc-4.0Aug 2021View details →

RibSeg Dataset and Strong Point Cloud Baselines for Rib Segmentation from CT Scans

Manual rib inspections in computed tomography (CT) scans are clinically critical but labor-intensive, as 24 ribs are typically elongated and oblique in 3D volumes. Automatic rib segmentation methods can speed up the process through rib measurement and visualization. However, prior arts mostly use in-house labeled datasets that are publicly unavailable and work on dense 3D volumes that are computationally inefficient. To address these issues, we develop a labeled rib segmentation benchmark, named RibSeg, including 490 CT scans (11,719 individual ribs) from a public dataset. For ground truth generation, we used existing morphology-based algorithms and manually refined its results. Then, considering the sparsity of ribs in 3D volumes, we thresholded and sampled sparse voxels from the input and designed a point cloud-based baseline method for rib segmentation. The proposed method achieves state-of-the-art segmentation performance (Dice\(\approx95\%\)) with significant efficiency (\(10\sim40\times\) faster than prior arts). The RibSeg dataset, code, and model in PyTorch are available at <a href="https://github.com/M3DV/RibSeg">https://github.com/M3DV/RibSeg</a>.   Note: This repository provides rib segmentation ("RibFrac31-rib-seg.nii.gz") and centerline ("RibFrac31-rib-cl.nii.gz") annotations for 490 cases in RibFrac dataset. Please download the corresponding CT images ("RibFrac31-image.nii.gz") at <a href="https://ribfrac.grand-challenge.org/">https://ribfrac.grand-challenge.org/</a> (1-click registration is needed via "Join").

Figure 6 from: Touroult J, Stéphane B (2014) Insects of French Guiana: a baseline for diversity and taxonomic effort. ZooKeys 434: 111-130. https://doi.org/10.3897/zookeys.434.7582

Figure 6 - Ratio between the number of known and expected species richness in French Guiana. Ratios are based on the number of known species in French Guiana and the number described at the world level (Zhang 2013). Other orders on the right of the histogram with no known species in French Guiana comprise: Mecoptera, Archaeognatha, Zygentoma, Embioptera, Grylloblattodea, Mantophasmatodea, Zoraptera, Phthiraptera, Rhaphidioptera.

Figure 4 from: Touroult J, Stéphane B (2014) Insects of French Guiana: a baseline for diversity and taxonomic effort. ZooKeys 434: 111-130. https://doi.org/10.3897/zookeys.434.7582

Figure 4 - Proportion of taxonomic contributions according to the status of the authors. a) descriptions of new species (n= 393) and b) new country records of species described from another country (n=344).

Figure 2 from: Touroult J, Stéphane B (2014) Insects of French Guiana: a baseline for diversity and taxonomic effort. ZooKeys 434: 111-130. https://doi.org/10.3897/zookeys.434.7582

Figure 2 - Cumulative rate of description of species belonging to the French Guiana fauna, for the four most diverse orders and for other excluded orders, from Linné to 2013.

Figure 1 from: Touroult J, Stéphane B (2014) Insects of French Guiana: a baseline for diversity and taxonomic effort. ZooKeys 434: 111-130. https://doi.org/10.3897/zookeys.434.7582

Figure 1 - Number of described species from the French Guiana fauna by five year period from Linné to 2013.

Figure 5 from: Touroult J, Stéphane B (2014) Insects of French Guiana: a baseline for diversity and taxonomic effort. ZooKeys 434: 111-130. https://doi.org/10.3897/zookeys.434.7582

Figure 5 - Origins of the holotypes of a sample of 393 species described from French Guiana between 2008 and 2013.

Figures 1-4 from: Webster RP, Davies AE, Klimaszewski J, Bourdon C (2016) Further contributions to the staphylinid fauna of New Brunswick, Canada, and the USA, with descriptions of two new Proteinus species (Coleoptera, Staphylinidae). In: Webster RP, Bouchard P, Klimaszewski J (Eds) The Coleoptera of New Brunswick and Canada: providing baseline biodiversity and natural history data. ZooKeys 573: 31–83. https://doi.org/10.3897/zookeys.573.7830

Figures 1-4 - Proteinus hughesi Webster & Klimaszewski, sp. n.: 1 male habitus in dorsal view 2 median lobe of aedeagus in lateral view 3 male tergite VII 4 male sternite VII.

Figures 18-21 from: Webster RP, Davies AE, Klimaszewski J, Bourdon C (2016) Further contributions to the staphylinid fauna of New Brunswick, Canada, and the USA, with descriptions of two new Proteinus species (Coleoptera, Staphylinidae). In: Webster RP, Bouchard P, Klimaszewski J (Eds) The Coleoptera of New Brunswick and Canada: providing baseline biodiversity and natural history data. ZooKeys 573: 31–83. https://doi.org/10.3897/zookeys.573.7830

Figures 18-21 - Carpelimus erichsoni (Sharp): 18 habitus in dorsal view 19 aedeagus in ventral view. Carpelimus gracilis (Mannerheim): 20 habitus in dorsal view 21 aedeagus in ventral view.

Figures 5-9 from: Webster RP, Davies AE, Klimaszewski J, Bourdon C (2016) Further contributions to the staphylinid fauna of New Brunswick, Canada, and the USA, with descriptions of two new Proteinus species (Coleoptera, Staphylinidae). In: Webster RP, Bouchard P, Klimaszewski J (Eds) The Coleoptera of New Brunswick and Canada: providing baseline biodiversity and natural history data. ZooKeys 573: 31–83. https://doi.org/10.3897/zookeys.573.7830

Figures 5-9 - Proteinus parvulus LeConte: 5 male habitus in dorsal view 6–7 median lobe of aedeagus in lateral view 8 male tergite VII 9 male sternite VII.

Figures 14-17 from: Webster RP, Davies AE, Klimaszewski J, Bourdon C (2016) Further contributions to the staphylinid fauna of New Brunswick, Canada, and the USA, with descriptions of two new Proteinus species (Coleoptera, Staphylinidae). In: Webster RP, Bouchard P, Klimaszewski J (Eds) The Coleoptera of New Brunswick and Canada: providing baseline biodiversity and natural history data. ZooKeys 573: 31–83. https://doi.org/10.3897/zookeys.573.7830

Figures 14-17 - Sepedophilus immaculatus (Stephens): 14 habitus in dorsal view 15 aedeagus in ventral view. Carpelimus difficilis (Casey): 16 habitus in dorsal view 17 aedeagus in ventral view.

Figures 38-41 from: Webster RP, Davies AE, Klimaszewski J, Bourdon C (2016) Further contributions to the staphylinid fauna of New Brunswick, Canada, and the USA, with descriptions of two new Proteinus species (Coleoptera, Staphylinidae). In: Webster RP, Bouchard P, Klimaszewski J (Eds) The Coleoptera of New Brunswick and Canada: providing baseline biodiversity and natural history data. ZooKeys 573: 31–83. https://doi.org/10.3897/zookeys.573.7830

Figures 38-41 - Carpelimus weissi (Notman): 38 habitus in dorsal view 39 aedeagus in ventral view. Stenus (Hypostenus) destitutus Puthz: 32 habitus in dorsal view 33 aedeagus in ventral view.

Figures 10-13 from: Webster RP, Davies AE, Klimaszewski J, Bourdon C (2016) Further contributions to the staphylinid fauna of New Brunswick, Canada, and the USA, with descriptions of two new Proteinus species (Coleoptera, Staphylinidae). In: Webster RP, Bouchard P, Klimaszewski J (Eds) The Coleoptera of New Brunswick and Canada: providing baseline biodiversity and natural history data. ZooKeys 573: 31–83. https://doi.org/10.3897/zookeys.573.7830

Figures 10-13 - Proteinus sweeneyi Webster & Klimaszewski, sp. n.: 10 male habitus in dorsal view 11 median lobe of aedeagus in lateral view 12 male tergite VII 13 male sternite VII.

Figures 6-9 from: Lopes-Andrade C, Webster RP, Webster VL, Alderson CA, Hughes CC, Sweeney JD (2016) The Ciidae (Coleoptera) of New Brunswick, Canada: New records and new synonyms. In: Webster RP, Bouchard P, Klimaszewski J (Eds) The Coleoptera of New Brunswick and Canada: providing baseline biodiversity and natural history data. ZooKeys 573: 339–366. https://doi.org/10.3897/zookeys.573.7445

Figures 6-9 - Dorsal view of species from New Brunswick, Canada. 6 Cis horridulus Casey 7 Cis levettei (Casey) 8 Cis striatulus Mellié 9 Cis submicans Abeille de Perrin. Scale bar: 1 mm.

Figures 10-16 from: Lopes-Andrade C, Webster RP, Webster VL, Alderson CA, Hughes CC, Sweeney JD (2016) The Ciidae (Coleoptera) of New Brunswick, Canada: New records and new synonyms. In: Webster RP, Bouchard P, Klimaszewski J (Eds) The Coleoptera of New Brunswick and Canada: providing baseline biodiversity and natural history data. ZooKeys 573: 339–366. https://doi.org/10.3897/zookeys.573.7445

Figures 10-16 - Dorsal view of species from New Brunswick, Canada. 10 Dolichocis laricinus (Mellié) 11 Dolichocis manitoba Dury 12 Hadreule elongatula (Gyllenhal) 13 Malacocis brevicollis (Casey) 14 Orthocis punctatus (Mellié) 15 Plesiocis cribrum Casey 16 Octotemnus glabriculus (Gyllenhal). Scale bar: 1 mm.