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Indicative distribution map for Ecosystem Functional Group T3.1 Seasonally dry tropical shrublands

This archive contains indicative distribution maps and profiles for T3.1 Seasonally dry tropical shrublands, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith et al. (2020) for details. The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).

Indicative distribution map for Ecosystem Functional Group T3.2 Seasonally dry temperate heath and shrublands

This archive contains indicative distribution maps and profiles for T3.2 Seasonally dry temperate heath and shrublands, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith et al. (2020) for details. The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).

Indicative distribution map for Ecosystem Functional Group T7.2 Sown pastures and fields

This archive contains indicative distribution maps and profiles for T7.2 Sown pastures and fields, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith et al. (2020) for details. The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).

Indicative distribution map for Ecosystem Functional Group T2.4 Warm temperate laurophyll forests

This archive contains indicative distribution maps and profiles for T2.4 Warm temperate laurophyll forests, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith et al. (2020) for details. The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).

Indicative distribution map for Ecosystem Functional Group S2.1 Anthropogenic subterranean voids

This archive contains indicative distribution maps and profiles for S2.1 Anthropogenic subterranean voids, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith et al. (2020) for details. The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).

Indicative distribution map for Ecosystem Functional Group T1.4 Tropical heath forests

This archive contains indicative distribution maps and profiles for T1.4 Tropical heath forests, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith et al. (2020) for details. The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).

openCC0Jul 2021View details →

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Stem maps of eight 1 ha forest plots distributed around Ann Arbor, MI and around the University of Michigan Biological Station (UMBS)

In this project we established a network of forest inventory plots to gather the data needed to forecast future forest performance under global change. Data collected from forest inventory plots, i.e., size and location of individual trees from all ages and species, have been shown to be particularly useful to link tree species demographic rates (survival, growth, age at maturity, fecundity) with community characteristics (assemblages and species turnovers), and are also widely used to estimate biomass removal (logging) and biomass production (carbon sequestration).

opencc-by-4.0Jan 2024View details →

Mapping the global distribution of C4 vegetation using observations and optimality theory

This dataset includes annual C4 vegetation distribution and its uncertainty from 2001 to 2019. We also provide the distribution of C4 natural grasses and C4 crops during the same period, as well as the code and interim dataset to generate the main figures. Please refer to manuscript for more details: Luo, X., Zhou, H., Satriawan, T.W., Tian, J., Zhao, R., Keenan, T.F., Griffith, D. M., Sitch, S. Smith, N.G. & Still, C.J. (2024). Mapping the global distribution of C4 vegetation using observations and optimality theory. Nature Communications. https://doi.org/10.1038/s41467-024-45606-3. Update (Nov 2023): we have updated the observational constraint from a linear model to a non-linear model - logistic curve, to better depict how C4 photosynthetic advantage translates into C4 grass coverage changes (C4_distribution_NUS_v2.2.nc). Update (August 2023): we corrected the issue caused by a bias in the remote sensing grassland base map, and released the version 2 of the C4 vmap (C4_distribution_NUS_v2.nc). Update (June 2023): we noticed there is a critical issue in the version 1 of our C4 map, due to the quality of remote sensing grassland base map used. We are now working on providing a new version (V2) in the next few months (Jun 2023).

opencc-by-4.0Jan 2024View details →

Adult baobab trees's distribution map across Sahel

The baobab tree (Adansonia digitata L.) is an integral part of rural livelihoods throughout the African continent. However, the combined effects of climate change and increasing global demand for baobab products are currently exerting pressure on the sustainable utilization of these resources. Here we employ sub-meter resolution satellite imagery to identify nearly 3 million baobab trees in the Sahel, a dryland region of 1.5 million km2. This achievement is considered an essential step towards improving valuable woody species' management and monitoring system. To prevent mismanagement of this specific tree species, we aggregated every single adult baobab tree map to 5 × 5 km grids. We also classified the baobab trees using the tree crown diameters( small: 3-9m; medium 9m-13m; large: >13m).  The baobab tree count map is also available for this three different size classes.

opencc-by-4.0Dec 2021View details →

Distribution of large carnivores in Europe 2012 - 2016: Distribution map for Golden Jackal (Canis aureus)

Abstract Regular assessments of species’ status are an essential component of conservation planning and adaptive management. They allow the progress of past or ongoing conservation actions to be evaluated and can be used to redirect and prioritize future conservation actions. Most countries perform periodic assessments for their own national adaptive management procedures or national red lists. Furthermore, the countries of the European Union have to report on the status of all species listed on the directives of the Habitats Directive every 6 years as part of their obligations under Article 17. However, these national level assessments are often made using non-standardized procedures and do not always adequately reflect the biological units (i.e., the populations) which are needed for ecologically meaningful assessments. Since the early 2000’s the Large Carnivore Initiative for Europe (a Specialist Group of the IUCN’s Species Survival Commission) has been coordinating periodic surveys of the status of large carnivores across Europe (e.g., von Arx et al. 2004; Salvatori & Linnell 2005, Kaczensky et al. 2013). These have covered the Eurasian lynx (Lynx lynx), the wolf (Canis lupus), the brown bear (Ursus arctos) and the wolverine (Gulo gulo). The golden jackal (Canis aureus) has been added to the LCIE prerogatives in 2014. The species is rapidly expanding in Europe (Trouwborst et al. 2015; Männil & Ranc 2022), a large-scale phenomenon that resembles that of the other large carnivores. Golden jackals are thriving in human-dominated landscapes (Ćirović et al. 2016; Lanszki et al. 2018; Fenton et al. 2021), where they are often functioning as the top predators, despite having smaller body size that is typical for large carnivores. The expansion of the species triggers many questions among scientists, stakeholders, and policy makers (Trouwborst et al. 2015; Hatlauf et al. 2021), that are closely connected to those raised by the other large carnivores (e.g., potential conflicts with livestock or hunting). In this context, monitoring the species’ expansion, delineating populations, assessing the species' legal and protection status, and addressing the concerns raised by this rapidly expanding carnivore requires a high level of coordination among regional experts. These surveys involve the contributions of the best available experts and sources of information. While the underlying data quality and field methodology varies widely across Europe, these coordinated assessments do their best to integrate the diverse data in a comparable manner and make the differences transparent. They also endeavor to conduct the assessments on the most important scales. This includes the continental scale (all countries except for Russia, Belarus, Moldova and the parts of Ukraine outside the Carpathian Mountain range), the scale of the EU 28 (where the Habitats Directive operates) and of the biological populations which reflect the scale at which ecological processes occur (Linnell et al. 2008). In this way, the independent LCIE assessments provide a valuable complement to the ongoing national processes. Our last assessments covered the period 2006-2011 (Kaczensky et al. 2013; Chapron et al. 2014) but, at the time, did not include golden jackals. The current assessment is based on the period 2012-2016 and broadly follows the same methodology. Explicit distinctions are made between classification based on empirical data and expert opinion. The population definitions used in this report follow those proposed in (Ranc et al. 2018); areas whose presence category was defined by expert opinion were not assigned to a specific population, though.    Methods The mapping approach follows the methods described in Chapron et al. (2014) and Kaczensky et al. (2013). It updates the published Species Online Layers (SPOIS) to the period 2012-2016. In short, large carnivore presence was mapped at a 10x10 km ETRS89-LAEA Europe grid scale. This grid is widely used for the Flora-Fauna-Habitat reporting by the European Union (EU) and can be downloaded at: http://www.eea.europa.eu/data-and-maps/data/eea-reference-grids-2 The map encompasses the EU countries plus the non-EU Balkan states, Switzerland, Norway, and the Carpathian region of Ukraine. Presence in a grid cell was ideally mapped based on carnivore presence and frequency in a cell resulting in: 1 = Permanent (presence confirmed in >= 3 years in the last 5 years OR in >50% of the time OR reproduction confirmed within the last 3 years) 3 = Sporadic (highly fluctuating presence) (presence confirmed in <3 years in the last 5 years OR in <50% of the time) 5 = Expert-based presence (high confidence) (expert-based opinion; very suitable habitat near permanent presence areas) 6 = Expert-based presence (low confidence or unconfirmed records) (expert-based opinion; suitable habitat near presence areas or unconfirmed C3 records of jackal presence) 7 = Expert-based absence (high confidence) (jackal presence according to coarse-resolution hunting bag data but experts think, with high confidence, the species is not present) 8 = Expert-based absence (low confidence) (jackal presence according to coarse-resolution hunting bag data but experts think the species is not present) Where grid cells were assigned different values between neighboring countries; the “disputed” cells were given the “higher” presence values e.g., a cell categorized as “sporadic” by one country and “permanent” by another was categorized as “permanent”. Data-based categories (1,3) were given priority over expert-based categories (5 through 8). To assess the quality of carnivore signs we used the SCALP criteria developed for the standardized monitoring of Eurasian lynx (Lynx lynx) in the Alps (Molinari-Jobin et al. 2012): Category 1 (C1): “Hard facts”, verified and unchallenged large carnivore presence signs (e.g., dead animals, DNA, verified camera trap images); Category 2 (C2): Large carnivore presence signs controlled and confirmed by a large carnivore expert (e.g., trained member of the network), which requires documentation of large carnivore signs; and Category 3 (C3): Unconfirmed category 2 large carnivore presence signs and all presence signs such as sightings and calls which, if not additionally documented, cannot be verified. See Hatlauf and Böcker (2022) for best practices regarding golden jackal records.   Usage Notes The data available consists of a shapefile at a 10 x 10 km resolution compiled for the period 2012-2016 for the Large Carnivore Initiative of Europe IUCN Specialist Group and for the IUCN Red List Assessment.   References Boitani, L., F. Alvarez, O. Anders, H. Andren, E. Avanzinelli, V. Balys, J. C. Blanco, U. Breitenmoser, G. Chapron, P. Ciucci, A. Dutsov, C. Groff, D. Huber, O. Ionescu, F. Knauer, I. Kojola, J. Kubala, M. Kutal, J. Linnell, A. Majic, P. Mannil, R. Manz, F. Marucco, D. Melovski, A. Molinari, H. Norberg, S. Nowak, J. Ozolins, S. Palazon, H. Potocnik, P.-Y. Quenette, I. Reinhardt, R. Rigg, N. Selva, A. Sergiel, M. Shkvyria, J. Swenson, A. Trajce, M. Von Arx, M. Wolfl, U. Wotschikowsky and D. Zlatanova. 2015. Key actions for Large Carnivore populations in Europe. Institute of Applied Ecology (Rome, Italy). Report to DG Environment, European Commission, Bruxelles. Contract no. 07.0307/2013/654446/SER/B3 Ćirović, D., A. Penezić and M. Krofel. 2016. Jackals as cleaners: Ecosystem services provided by a mesocarnivore in human-dominated landscapes. Biological Conservation, 199: 51–55. Chapron, G., Kaczensky, P., Linnell, J.D.C., von Arx, M., Huber, D., Andrén, H., López-Bao, J.V., Adamec, M., Álvares, F., Anders, O., Balčiauskas, L., Balys, V., Bedő, P., Bego, F., Blanco, J.C., Breitenmoser, U., Brøseth, H., Bufka, L., Bunikyte, R., Ciucci, P., Dutsov, A., Engleder, T., Fuxjäger, C., Groff, C., Holmala, K., Hoxha, B., Iliopoulos, Y., Ionescu, O., Jeremić, J., Jerina, K., Kluth, G., Knauer, F., Kojola, I., Kos, I., Krofel, M., Kubala, J., Kunovac, S., Kusak, J., Kutal, M., Liberg, O., Majić, A., Männil, P., Manz, R., Marboutin, E., Marucco, F., Melovski, D., Mersini, K., Mertzanis, Y., Mysłajek, R.W., Nowak, S., Odden, J., Ozolins, J., Palomero, G., Paunović, M., Persson, J., Potočnik, H., Quenette, P.-Y., Rauer, G., Reinhardt, I., Rigg, R., Ryser, A., Salvatori, V., Skrbin&scaron;ek, T., Stojanov, A., Swenson, J.E., Szemethy, L., Trajçe, A., Tsingarska[1]Sedefcheva, E., Váňa, M., Veeroja, R., Wabakken, P., Wölfl, M., Wölfl, S., Zimmermann, F., Zlatanova, D. and Boitani, L. 2014. Recovery of large carnivores in Europe’s modern human-dominated landscapes. Science 346: 1517-1519. Fenton, S., Moorcroft, P.R., Ćirović, D., Lanszki, J., Heltai, M., Cagnacci, F., Breck, S., Bogdanović, N., Pantelić, I., Ács, K. and Ranc, N. 2021. Movement, space-use and resource preferences of European golden jackals in human-dominated landscapes: insights from a telemetry study. Mammalian Biology, 101: 619–630. Hatlauf, J. and Böcker, F. 2022. Recommendations for the documentation and assessment of golden jackal (Canis aureus) records in Europe. BOKU reports on wildlife research and willdife management 27. Ed: Institute of Wildlife Biology and Game Management (IWJ), University of Natural Resources and Life Sciences, Vienna. ISBN: 978-3-900932-94-7 Hatlauf, J., Bayer, K., Trouwborst, A. and Hackländer, K. 2021. New rules or old concepts? The golden jackal (Canis aureus) and its legal status in Central Europe. European Journal of Wildlife Research, 67, 25. Kaczensky, P., Chapron, G., Von Arx, M., Huber, D., Andrén, H. and Linnell, J. 2013. Status, management and distribution of large carnivores - bear, lynx, wolf and wolverine - in Europe. Istituto di Ecologia Applicata, Rome, Italy. Lanszki, J., Schally, G., Heltai, M. and Ranc, N. 2018. Golden jackal expansion in Europe: first telemetry evidence of a natal dispersal. Mammalian Biology, 88: 81–84. Linnell, J.D.C., Salvatori, V. and Boitani, L. 2008. Guidelines for population level management plans for large carnivores in Europe. A Large Carnivore Initiative for Europe report prepared for the European Commission (contract 070501/2005/424162/MAR/B2). Männil, P. and Ranc, N. 2022. Golden jackal (Canis aureus) in Estonia: development of a thriving population in the boreal ecoregion. Mammalian Research, 67: 245-250. Molinari-Jobin, A., Kéry, M., Marboutin, E., Molinari, P., Koren, I., Fuxjäger, C., Breitenmoser-Würsten, C., Wölfl, S., Fasel, M., Kos, I., Wölfl, M. and Breitenmoser, U. 2012. Monitoring in the presence of species misidentification: the case of the Eurasian lynx in the Alps. Animal Conservation 15: 266-273. Ranc, N., Krofel, M. and Cirovic, D. 2018. IUCN Red List Mapping for the regional assessment of the Golden Jackal (Canis aureus) in Europe. IUCN Red List Threatened Species, 13. Salvatori, V. and Linnell, J.D.C. 2005. Report on the conservation status and threats for wolf (Canis lupus) in Europe. Council of Europe Report T-PVS/Inf (2005) 16. Trouwborst, A., Krofel, M. and Linnell, J.D.C. 2015. Legal implications of range expansions in a terrestrial carnivore: the case of the golden jackal (Canis aureus) in Europe. Biodiversity Conservation, 24: 2593–2610. von Arx, M., Breitenmoser-Würsten, C., Zimmermann, F. and Breitenmoser, U. 2004. Status and conservation of the Eurasian lynx (Lynx lynx) in Europe in 2001. KORA Report 19e: 1-330.   Contact information Nathan Ranc, nathan.ranc@inrae.fr

opencc-by-4.0Apr 2024View details →

Interactive map of distribution of gene fragments indicative of cyanotoxin biosynthesis and cyanotoxins in the European Alps

Distribution of cyanotoxins and cyanotoxin biosynthesis genes in Alpine region determined by LC-MS/MS and (q)PCR. Cyanotoxins and cyanotoxin genes are mapped on separate layers, and two basemaps are available (simple and relief). Results can be filtered by location, sample type, water body type, cyanotoxins and cyanotoxin genes. Note that cyanotoxin analyses were not performed on all sampling points.

opencc-by-4.0May 2024View details →

Distribution Map of Festuca dolichophylla (suplemental material-TS1)

The distribution map of Festuca dolichophylla relies on diverse data sources. Geographical coordinates (latitude and longitude) and country initials (countryCode) were extracted from Tropicos, the Gbif repository (up to May 2019), and the iDigBio database (up to July 2021). Additionally, data from other sources, including BMAP Peru (2023), Eduardo-Palomino (2022), Ccora et al. (2019), Arana et al. (2013), Castro (2019), Flores (2017), Gonzales (2017), and Martínez y Pérez (1999), were integrated. The Gbif data points are associated with gbifID numbers for reference. Please note that this compilation provides essential information for understanding the distribution of F. dolichophylla across various regions. <h3>Software</h3> Organized data by geographic coordinates was uploaded to ArcGIS Pro v. 3.2.0 for map production. Geospatial visualization and mapping were carried out using ArcGIS Pro, allowing us to create the distribution map of F. dolichophylla. <h2>Methods</h2> <div> The dataset for the distribution map of Festuca dolichophylla was meticulously collected from various sources. <ol> <li> Data Collection: <ul> <li>Tropicos: Data were extracted from Tropicos until December 2023.</li> <li>Gbif Repository: Data was sourced from the Gbif repository until May 2019.</li> <li>iDigBio Database: Additional data points were retrieved from the iDigBio database up to July 2021.</li> <li>Other Sources: We also incorporated data from various other sources, including BMAP Peru (2023), Eduardo-Palomino (2022), Ccora et al. (2019), Arana et al. (2013), Castro (2019), Flores (2017), Gonzales (2017), and Martínez y Pérez (1999).</li> </ul> </li> <li> Data Organization and Processing: <ul> <li>All collected data points were meticulously organized by coordinates.</li> <li>We ensured consistency by cross-referencing and validating the data.</li> <li>The dataset was then uploaded to ArcGIS Pro v. 3.2.0 for map production.</li> <li>Geospatial visualization and mapping were carried out using ArcGIS Pro, allowing us to create the distribution map of F. dolichophylla.</li> </ul> </li> </ol> </div> <h2>Funding</h2> <div> Neotropical Grassland Conservancy, Award: Memorial grant 2020 </div>

opencc-by-4.0Mar 2019View details →

Malwa site survey : archaeological site distribution maps

Malwa site survey : archaeological site distribution maps. (1) mosaic based on Survey of India maps; (2) (3) study area; (4) Vidisha Raisen area.

opencc-by-4.0Aug 2021View details →

Data for "Mapping the ghost: Estimating probabilistic snow leopard distribution across Mongolia"

Data and code used for a country-wide occupancy survey of snow leopards in Mongolia, accompanying the paper "Mapping the ghost: Estimating probabilistic snow leopard distribution across Mongolia". This data contains the results of a survey of 1017 20x20km sampling units, out of a total of 1200 sampling units identified as potential snow leopard habitat (183 could not be sampled for various reasons), a near complete survey of potential snow leopard habitat in Mongolia, nearly 500,000 square kilometers, and an enormous effort by many researchers. If you make use of the data, please cite the following sources: <ul> <li>Data for "Mapping the ghost: Estimating probabilistic snow leopard distribution across Mongolia". (2021). Gantulga Bayandonoi, Koustubh Sharma, Justine Shanti Alexander, Purevjav Lkhagvajav, Ian Durbach, Darryl MacKenzie, Chimeddorj Buyanaa, Bariushaa Munkhtsog, Munkhtogtokh Ochirjav, Sergelen Erdenebaatar, Bilguun Batkhuyag, Nyamzav Battulga, Choidogjamts Byambasuren, Bayartsaikhan Uudus, Shar Setev, Lkhagvasuren Davaa, Khurel-Erdene Agchbayar, Naranbaatar Galsandorj, David Borchers. doi: https://doi.org/10.5281/zenodo.5257572</li> <li>Mapping the ghost: Estimating probabilistic snow leopard distribution across Mongolia. (2021). Gantulga Bayandonoi, Koustubh Sharma, Justine Shanti Alexander, Purevjav Lkhagvajav, Ian Durbach, Darryl MacKenzie, Chimeddorj Buyanaa, Bariushaa Munkhtsog, Munkhtogtokh Ochirjav, Sergelen Erdenebaatar, Bilguun Batkhuyag, Nyamzav Battulga, Choidogjamts Byambasuren, Bayartsaikhan Uudus, Shar Setev, Lkhagvasuren Davaa, Khurel-Erdene Agchbayar, Naranbaatar Galsandorj, David Borchers. To appear in Diversity and Distributions</li> </ul> Contents of zip file Data The main dataset is contained in `data\Mongolia_occupancy_inputs.Rdata` . Please see the paper for more detail on data collection. The following objects are contained in the file: - Pres: presence/absence occupancy survey results, used for model fitting - Site_Cov: unit-specific covariates, used for model fitting - SurvCov: survey-specific covariates, used for model fitting - Mongolia_studyarea: covariates for whole survey area, used for prediction - Mongolia_fullrange: covariates across whole expected snow leopard range, used for prediction Code Code is cloned from the GitHub repository <a href="https://github.com/iandurbach/mongolia-occupancy">https://github.com/iandurbach/mongolia-occupancy</a>, which may contain updates. The version here reproduces the analyses in the paper above. The run these analyses: - run *occupancy-analysis.R* to fit the main occupancy models (these are also saved in the `\output` folder), do model selection, and plot covariate effects - run *occupancy-goodness-of-fit.R* to calculate the c-hat statistic giving an indication of model fit for the best model - run *comparing-maps.R* to compare the occupancy results with similar metrics generated using a presence-only analysis (using MaxEnt) or an expert map generated through qualitative discussion (reproduces Figure 3 in the paper). Code in *occupancy-data-preproc.R* is not needed but included for completeness. It converts the csv files in `data\csv`, which contain various input datasets used by the occupancy model, into a single .Rdata file (`data\Mongolia_occupancy_inputs.Rdata`), which is then used by the scripts above. Some minimal pre-processing (excluding ununsed variables, renaming for consistency, etc) is performed.

opencc-by-4.0Sep 2022View details →

Geographical Distribution Maps of Western Palearctic Weevil Taxa

The electronic supplement belongs to the research article Schütte A, Stüben PE, Astrin JJ (2023) Molecular Weevil Identification Project: A Thoroughly Curated Barcode Release of 1300 Western Palearctic Weevil Species (Coleoptera: Curculionoidea) - Biodiversity Data Journal 11. The ZIP file contains 613 distribution maps from Western Palearctic weevil taxa. The distribution maps showing Europe originate from the Curculio Institute's website (www.curci.de). Additional information on distribution range and known synonyms were based on the information from the Löbl catalogs (Löbl & Smetana 2011, Löbl & Smetana 2013). The maximum distribution range of each species was measured in km with Google Earth's ruler function. An unzip software is needed to access the *.JPG files within the *.ZIP file. Microsoft operating systems support *.zip files natively since Windows XP.  MAC operating systems offer the "archive utility" to access *.zip files. Android users must install an app like Winzip, WinRAR, or 7ZIP. The iOS 13 operating system and onwards allow unzipping *.zip archives natively (iPhone and iPad). The *.zip filetype support can be installed on Linux operating systems via the terminal command: "sudo apt-get install unzip". Command to unzip: "unzip \*.zip". The *.JPG files can be opened with any picture viewer or internet browser. References Löbl L, Smetana A (2011) Catalogue of the Coleoptera. Vol. 7, Curculionoiodea I, Stenstrup, Apollo Books, 373 pp. Löbl L, Smetana A (2013) Catalogue of the Coleoptera. Vol. 8, Curculionoiodea II, Leiden & Boston, Brill, 700 pp.

opencc-by-4.0Feb 2023View details →

Global distribution map of Rhenish stoneware during the 16th to 18th century

The dataset provides a distribution map of Rhenish stonewares between the 16th and 18th century. The data was collected from published archaeological data (print and online) available to the author. According the published information the pottery was classified to different wares (Cologne, Frechen, Siegburg, Raeren, Westerwald). Values are given for individual sherd numbers. If no information was given in the publication, the value is set to "1". Bibligraphic reference is given by author - date. Full bibliographic reference can be found in the pdf-file. The csv-file contains next to location name, bibliographic reference and pottery counts values for longitude and latitude. The coordinate reference is WGS 84 - EPSG:4326.

opencc-by-4.0Feb 2020View details →

Fig. 14. Distribution map. 1 in New species and new records of terrestrial isopods (Crustacea, Isopoda, Oniscidea) of the families Philosciidae and Scleropactidae from Brazilian caves

Fig. 14. Distribution map. 1. Albosciajotajota Campos-Filho, Bichuette & Taiti sp. nov. 2. Androdeloscia akuanduba Campos-Filho, Cardoso & Taiti sp nov. 3. Atlantoscia inflata Campos-Filho & Araujo, 2015. 4. Benthana iporangensis Lima & Serejo, 1993. 5. B. longicornis Verhoeff, 1941. 6. B. olfersii (Brandt, 1833). 7. B. picta (Brandt, 1833). 8. B. taeniata Araujo & Buckup, 1994. 9. Metaprosekia igatuensis Campos-Filho, Fernandes & Bichuette sp. nov. 10. Paratlantoscia rubromarginata (Araujo & Leistikow, 1999). 11. Amazoniscus spica Campos-Filho, Aguiar & Taiti sp. nov. 12. Circoniscus bezzii Arcangeli, 1931. Light gray areas denote Brazilian conservation units. AL = Alagoas; BA = Bahia; CE = Ceará; DF = Distrito Federal; ES = Espírito Santo; GO = Goiás; MA = Maranhão; MG = Minas Gerais; MT = Mato Grosso; PA = Pará; PB = Paraíba; PE = Pernambuco; PI = Piauí; PR = Paraná; RJ = Rio de Janeiro; RN = Rio Grande do Norte; SE = Sergipe; SP = São Paulo; TO = Tocantins.

opencc-by-4.0Dec 2009View details →

Figure 1. All 4.683 in Mapping the terrestrial reptile distributions in Oman and the United Arab Emirates

Figure 1. All 4.683 records of terrestrial reptiles in Oman and the UAE. Although the coverage of records remains patchy, there are sufficient records to provide useful distribution information.

opencc-by-4.0Dec 2009View details →

Figure 6 in Mapping the terrestrial reptile distributions in Oman and the United Arab Emirates

Figure 6. Non-overlapping and contiguous distributions of two species of sand geckos Stenodactylus slevini and S. leptocosymbotes.