10 datasets found
  1. f

    Data_Sheet_1_Biogeography Meets Niche Modeling: Inferring the Role of Deep...

    • frontiersin.figshare.com
    zip
    Updated Jun 6, 2023
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    Victoria Culshaw; Mario Mairal; Isabel Sanmartín (2023). Data_Sheet_1_Biogeography Meets Niche Modeling: Inferring the Role of Deep Time Climate Change When Data Is Limited.zip [Dataset]. http://doi.org/10.3389/fevo.2021.662092.s001
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    zipAvailable download formats
    Dataset updated
    Jun 6, 2023
    Dataset provided by
    Frontiers
    Authors
    Victoria Culshaw; Mario Mairal; Isabel Sanmartín
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Geographic range shifts are one major organism response to climate change, especially if the rate of climate change is higher than that of species adaptation. Ecological niche models (ENM) and biogeographic inferences are often used in estimating the effects of climatic oscillations on species range dynamics. ENMs can be used to track climatic suitable areas over time, but have often been limited to shallow timescales; biogeographic inference can reach greater evolutionary depth, but often lacks spatial resolution. Here, we present a simple approach that treats them as independent and complementary sources of evidence, which, when used in partnership, can be employed to reconstruct geographic range shifts over deep evolutionary timescales. For testing this, we chose two extreme African disjunctions: Camptoloma (Scrophulariaceae) and Canarina (Campanulaceae), each comprising of three species disjunctly distributed in Macaronesia and eastern/southern Africa. Using inferred ancestral ranges in tandem with preindustrial and paleoclimate ENM hindcastings, we show that the disjunct pattern was the result of fragmentation and extinction events linked to Neogene aridification cycles. Our results highlight the importance of considering temporal resolution when building ENMs for rare endemics with small population sizes and restricted climatic tolerances such as Camptoloma, for which models built on averaged monthly variables were more informative than those based on annual bioclimatic variables. Additionally, we show that biogeographic information can be used as truncation threshold criteria for building ENMs in the distant past. Our approach is suitable when there is sparse sampling on species occurrences and associated patterns of genetic variation, such as in the case of ancient endemics with widely disjunct distributions as a result of climate change.

  2. B

    Future climatic suitability of native tree species in restoration sites in...

    • borealisdata.ca
    • search.dataone.org
    • +1more
    Updated May 9, 2025
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    Gabriela Barragán; Tongli Wang; Jeanine Rhemtulla (2025). Future climatic suitability of native tree species in restoration sites in Northwestern Ecuador [Dataset]. http://doi.org/10.5683/SP3/4EV7M8
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    May 9, 2025
    Dataset provided by
    Borealis
    Authors
    Gabriela Barragán; Tongli Wang; Jeanine Rhemtulla
    License

    Attribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
    License information was derived automatically

    Area covered
    Ecuador, Ecuador
    Description

    The dataset includes climatic suitability of 10 tree species in 1237 restoration sites in Northwest Ecuador under baseline climatic conditions (1960-1990) and two climate scenarios (RCP 4.5 and 8.5) in the 2030s, 2050s, and 2070s. We built bioclimatic niche models to obtain the climatic suitability values for the species at each restoration site. The suitability values ranged from 0 (unsuitable) to 1 (suitable). Suitability thresholds (i.e., MaxSS, MTP, 10% TP) per species are provided to categorize species as "suitable" or "unsuitable" at the restoration sites. We used this dataset on species climatic suitability in restoration sites to assess the species climatic viability (i.e., persistence over time). Geographical data on the restoration sites location is not publicly available due to privacy restrictions from the government of Ecuador under the permit (MAE-SG-2018-6447-E).

  3. f

    Reconstructing the Mexican Tropical Dry Forests via an Autoecological Niche...

    • figshare.com
    docx
    Updated May 31, 2023
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    David A. Prieto-Torres; Octavio R. Rojas-Soto (2023). Reconstructing the Mexican Tropical Dry Forests via an Autoecological Niche Approach: Reconsidering the Ecosystem Boundaries [Dataset]. http://doi.org/10.1371/journal.pone.0150932
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    docxAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    PLOS ONE
    Authors
    David A. Prieto-Torres; Octavio R. Rojas-Soto
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    We used Ecological Niche Modeling (ENM) of individual species of two taxonomic groups (plants and birds) in order to reconstruct the climatic distribution of Tropical Dry Forests (TDFs) in Mexico and to analyze their boundaries with other terrestrial ecosystems. The reconstruction for TDFs’ distribution was analyzed considering the prediction and omission errors based upon the combination of species, obtained from the overlap of individual models (only plants, only birds, and all species combined). Two verifications were used: a primary vegetation map and 100 independent TDFs localities. We performed a Principal Component (PCA) and Discriminant Analysis (DA) to evaluate the variation in the environmental variables and ecological overlap among ecosystems. The modeling strategies showed differences in the ecological patterns and prediction areas, where the “all species combined” model (with a threshold of ≥10 species) was the best strategy to use in the TDFs reconstruction. We observed a concordance of 78% with the primary vegetation map and a prediction of 98% of independent locality records. Although PCA and DA tests explained 75.78% and 97.9% of variance observed, respectively, we observed an important overlap among the TDFs with other adjacent ecosystems, confirming the existence of transition zones among them. We successfully modeled the distribution of Mexican TDFs using a number of bioclimatic variables and co-distributed species. This autoecological niche approach suggests the necessity of rethinking the delimitations of ecosystems based on the recognition of transition zones among them in order to understand the real nature of communities and association patterns of species.

  4. n

    Data from: Hindcast-validated species distribution models reveal future...

    • data.niaid.nih.gov
    • zenodo.org
    • +1more
    zip
    Updated Aug 30, 2022
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    Richard Hodel; Douglas Soltis; Pamela Soltis (2022). Hindcast-validated species distribution models reveal future vulnerabilities of mangroves and salt marsh species [Dataset]. http://doi.org/10.5061/dryad.08kprr55b
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    zipAvailable download formats
    Dataset updated
    Aug 30, 2022
    Dataset provided by
    Muséum national d'Histoire naturelle
    University of Florida
    Authors
    Richard Hodel; Douglas Soltis; Pamela Soltis
    License

    https://spdx.org/licenses/CC0-1.0.htmlhttps://spdx.org/licenses/CC0-1.0.html

    Description

    Rapid climate change threatens biodiversity via habitat loss, range shifts, increases in invasive species, novel species interactions, and other unforeseen changes. Coastal and estuarine species are especially vulnerable to the impacts of climate change due to sea level rise and may be severely impacted in the next several decades. Species distribution modeling can project the potential future distributions of species under scenarios of climate change using bioclimatic data and georeferenced occurrence data. However, models projecting suitable habitat into the future are impossible to ground truth. One solution is to develop species distribution models for the present and project them to periods in the recent past where distributions are known to test model performance before making projections into the future. Here, we develop models using abiotic environmental variables to quantify the current suitable habitat available to eight Neotropical coastal species: four mangrove species and four salt marsh species. Using a novel model validation approach that leverages newly available monthly climatic data from 1960-2018, we project these niche models into two time periods in the recent past (i.e., within the past half-century) when either mangrove or salt marsh dominance was documented via other data sources. Models were hindcast-validated and then used to project the suitable habitat of all species at four time periods in the future under a model of climate change. For all future time periods, the projected suitable habitat of mangrove species decreased, and suitable habitat declined more severely in salt marsh species. Methods Data acquisition We obtained specimen-based occurrence data for each species from iDigBio (Integrated Digitized Biocollections; idigbio.org) and GBIF (Global Biodiversity Information Facility; gbif.org) and supplemented these data with locality data from personal collections for three mangrove species (Avicennia germinans, Laguncularia racemosa, Rhizophora mangle). Four of the species included in the analysis are mangroves (Avicennia germinans, black mangrove; Laguncularia racemosa, white mangrove; and Rhizophora mangle, red mangrove) or mangrove-associated species (Conocarpus erectus, buttonwood). For simplicity, these four species will hereafter be collectively referred to as ‘mangroves,’ even though Conocarpus erectus is not considered a true mangrove (Tomlinson, 2016). We also selected four salt marsh species (Batis maritima, turtleweed; Sesuvium portulacastrum, sea purslane; Spartina alterniflora, smooth cordgrass; and Sporobolus virginicus, seashore dropseed) for analyses. These four species were selected because they occur in close proximity to one another—indicating the presence of salt marsh habitat—and because of their broad and exclusively coastal distributions in the Neotropics. We used SDM to investigate changes in suitable habitat for all eight species. The raw data were cleaned using standard approaches and R scripts (e.g., Marchant et al., 2017); duplicates and incorrect data (e.g., latitude and longitude of 0) were removed from the data set (all scripts used in this paper were deposited in GitHub (github.com/richiehodel/coastal_ENM), and all cleaned occurrence data, layers, and models were deposited in Dryad. We included species that had exclusively coastal or estuarine distributions, and only species with at least 50 occurrence points (after cleaning) were used in the analyses. Given the complexities of the modeling approach, we focused on the Neotropics as opposed to a global analysis; only mangrove and salt marsh species with native ranges in the Americas were used (i.e., cosmopolitan species were excluded). Certain species that inhabit salt marshes, but that have extensive inland distributions, including freshwater wetlands, were excluded (e.g., Distichlis spicata). We acquired bioclimatic environmental layers from Worldclim 2.1 (worldclim.org; Fick & Hijmans, 2017) for multiple time periods. The bioclimatic layers, which contain temperature and precipitation data for every continent except Antarctica, have been used extensively and successfully in SDM studies (Booth, 2018). In Worldclim 2.1, annual precipitation, maximum temperature, and minimum temperature data are available for every month from 1960-2018 at 2.5 arc minute resolution; these three variables can be used to calculate values of all 19 bioclimatic variables (Harris et al., 2014; Fick & Hijmans, 2017; Hijmans et al., 2017). We considered the present to be 2013-2018, the 1980s salt marsh dominance period to be 1984-1989, and the early 2000s mangrove dominance to be 2001-2006. These time periods were selected to capture the optimal amount of either mangrove or salt marsh dominance during each documented oscillation (Cavanaugh et al., 2019), and we selected these windows of time so that the present and past time periods were all six years. Although many of the study species may be longer-lived than each of the time periods (i.e., six years), we prioritized using time periods that captured either mangrove or salt marsh dominance. Due to the oscillations of mangrove versus salt marsh dominance, many individual plants were likely exterminated on short time scales. We used all occurrence data to construct an SDM for each species for our defined present time (2013-2018) regardless of when the specimens were collected. It would be ideal to use separate occurrence specimens from each time period to assess SDM performance, but this was not possible with the temporal distribution of georeferenced data points. For each six-year time period, we averaged the annual precipitation, maximum temperature, and minimum temperature for each month (e.g., average values of these three variables were calculated across the six January months, six February months, etc. in each time period) and used the resulting 12 monthly averages to calculate the standard 19 bioclimatic variable values using the ‘biovars’ function in the ‘dismo’ R package for each six-year time period (Hijmans et al., 2017). The standard 19 bioclimatic variables are not available on a monthly basis because some of them incorporate seasonality and require data for at least one year. By using monthly data for annual precipitation, maximum temperature, and minimum temperature variables, all of the 19 bioclimatic variables can be calculated (Hijmans et al., 2017). All layers were then trimmed so that the extent of the study area was between -120 and -32 degrees longitude, and -36 and 36 degrees latitude using custom scripts and the R package ‘raster’ (Hijmans et al., 2015) and exported in ASCII format (Fig. 1). This study area was selected because it included subtropical and tropical regions of both the Northern and Southern Hemispheres, captured the ecotone between mangrove and salt marsh species in both Hemispheres, and allowed for an expansion zone as some species may expand their ranges in the future as the climate changes. Regions such as Hawaii, where some Neotropical mangrove species have been introduced, were not included in the study. We used an R script and the R package ‘raster’ (Hijmans et al., 2015) to measure the pairwise correlation of the 19 bioclimatic variables. When variables were correlated with one another (r > 0.7), only one of the layers was retained for subsequent analyses (Dormann et al., 2013). After removing correlated layers, we had a data set of six bioclimatic variables (BIO2, mean diurnal temperature range; BIO5, maximum temperature of the warmest month; BIO6, minimum temperature of the coldest month; BIO12, annual precipitation; BIO15, precipitation seasonality; BIO18, precipitation of warmest quarter). BIO6 and BIO1 were highly correlated (r = 0.956), and BIO1 (mean annual temperature) was excluded even though it is frequently included in SDM analyses because BIO6 has been identified as an important variable shaping range limits of coastal species (Tomlinson, 2016). All layers were clipped using the ‘mask’ function in the ‘raster’ R package (Hijmans et al., 2015) such that all cells with an elevation greater than 10m were considered ‘no data’ cells. This was done to ensure that the SDM analyses were not trained on inland regions representing areas where these coastal species do not occur.

    Species Distribution Modeling The occurrence data obtained from digitized herbaria records and the six environmental layers were used as input for the SDM analyses. SDM uses the occurrence data for each species in the present to identify pixels that have suitable habitat for the species of interest based on environmental data. We used the maximum entropy algorithm implemented in MAXENT v3.4.1 (Phillips et al., 2006; Phillips et al., 2017) to conduct SDM analyses. The maximum entropy algorithm uses presence data and random background sampling to develop the model, and it has been shown to perform well with presence-only data (Elith et al., 2006; Wisz et al., 2008). Optimal settings for MAXENT model fit were determined using the ‘ENMevaluate’ function in the ENMeval R package (Muscarella et al., 2014). We investigated regularization multipliers from 0.5 to 4 at intervals of 0.5 and the following features/combinations of features: linear, linear/quadratic, linear/quadratic/hinge, linear/quadratic/hinge/product, linear/quadratic/product/threshold, and linear/quadratic/hinge/product/threshold. The ‘ENMevaluate’ function was run for each species, using the same 10,000 background points, occurrence data for the species of interest, and the ‘maxnet’ algorithm with the ‘checkerboard2’ method. The DAICc scores for all models tested for each species were compared to determine the optimal model to be inputted into MAXENT. Other non-default settings used include five-fold cross-validation, a minimum training presence threshold, and fading by clamping. Cloglog output was used because it

  5. d

    Data from: The past ecology of Abies alba provides new perspectives on...

    • search.dataone.org
    • data.niaid.nih.gov
    • +2more
    Updated Apr 1, 2025
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    Willy Tinner; Daniele Colombaroli; Oliver Heiri; Paul Henne; Marco Steinacher; Johanna Untenecker; Elisa Vescovi; Judy Allen; Gabriele Carraro; Marco Conedera; Fortunat Joos; André Lotter; Jürg Luterbacher; Stephanie Samartin; Verushka Valsecchi (2025). The past ecology of Abies alba provides new perspectives on future responses of silver fir forests to global warming [Dataset]. http://doi.org/10.5061/dryad.df3sn
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    Dataset updated
    Apr 1, 2025
    Dataset provided by
    Dryad Digital Repository
    Authors
    Willy Tinner; Daniele Colombaroli; Oliver Heiri; Paul Henne; Marco Steinacher; Johanna Untenecker; Elisa Vescovi; Judy Allen; Gabriele Carraro; Marco Conedera; Fortunat Joos; André Lotter; Jürg Luterbacher; Stephanie Samartin; Verushka Valsecchi
    Time period covered
    Jan 1, 2014
    Description

    Paleoecology can provide valuable insights into the ecology of species that complement observation and experiment-based assessments of climate-impact dynamics. New paleoecological records (e.g. pollen, macrofossils) from the Italian Peninsula suggest a much wider climatic niche of the important European tree species Abies alba (silver fir) than observed in its present spatial range. To explore this discrepancy between current and past distribution we analyse climatic data (e.g. temperature, precipitation, frost, humidity, sunshine) and vegetation-independent paleoclimatic reconstructions (e.g. lake levels, chironomids) and use global coupled carbon-cycle climate (NCAR CSM1.4) and dynamic vegetation (LANDCLIM) modelling. The combined evidence suggests that during the mid-Holocene (ca. 6000 years ago), prior to humanization of vegetation, A. alba formed forests under conditions that exceeded modern (1961-1990) upper temperature limit of the species by ca. 5-7 {degree sign}C (July means). ...

  6. d

    Collared Pika (Ochotona collaris) Habitat Suitability Change Models

    • datadiscoverystudio.org
    Updated Jan 17, 2014
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    (2014). Collared Pika (Ochotona collaris) Habitat Suitability Change Models [Dataset]. http://datadiscoverystudio.org/geoportal/rest/metadata/item/90c0a57647e24c4e8b6abd2546c8bf1c/html
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    Dataset updated
    Jan 17, 2014
    Area covered
    Description

    Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

  7. d

    Osprey (Pandion haliaetus) Habitat Suitability Change Models

    • datadiscoverystudio.org
    Updated Jan 17, 2014
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    (2014). Osprey (Pandion haliaetus) Habitat Suitability Change Models [Dataset]. http://datadiscoverystudio.org/geoportal/rest/metadata/item/964731d60532457d945822a0bb76af76/html
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    Dataset updated
    Jan 17, 2014
    Area covered
    Description

    Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

  8. Data from: Expert Vetted Distribution Models and Biodiversity Hotspot Maps...

    • researchdata.edu.au
    Updated Mar 29, 2018
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    Graham Erin; Pintor Anna; Jeremy James VanDerWal; Erin Marie Graham; Anna Francisca Pintor (2018). Expert Vetted Distribution Models and Biodiversity Hotspot Maps of Terrestrial and Freshwater Taxa of Conservation Concern in Northern Australia [Dataset]. http://doi.org/10.4225/28/5A9F31E23E80B
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    Dataset updated
    Mar 29, 2018
    Dataset provided by
    Australian Governmenthttp://www.australia.gov.au/
    James Cook University
    Authors
    Graham Erin; Pintor Anna; Jeremy James VanDerWal; Erin Marie Graham; Anna Francisca Pintor
    License

    Attribution-ShareAlike 3.0 (CC BY-SA 3.0)https://creativecommons.org/licenses/by-sa/3.0/
    License information was derived automatically

    Area covered
    Description

    Abstract: This collection is comprised of raster layers of present-day distributions of terrestrial and freshwater taxa that are of conservation concern in Northern Australia. The spatial extent of the region includes the 'North Australian Tropical Savanna' and 'North Eastern Australia Tropical Rainforests' Australian Conservation Management Zones as well as drainage basins intersecting these regions boundaries. Taxon distributions were derived either from species distribution models (also referred to as 'ecological niche models', 'habitat suitability models', 'habitat models' or 'bioclimatic envelope models' created with the program Maxent or from buffered occurrence records cut to suitable areas (for data deficient taxa) and were vetted by experts and modified accordingly. The additional biodiversity hotspot maps (i.e. areas with relatively high concentrations of taxa of conservation concern) included in this collection show the number of taxa within each relevant taxonomic, functional or threatened species status group (e.g. all endangered species or all vulnerable species) present across the study region. The collection also includes a supplementary spreadsheet ('NESP vetting and taxa Information') with further information on each taxon, such as relevant ecological traits, conservation listings and model details (e.g. thresholds used for Maxent raw outputs, model quality as assessed by experts, AUC/ model fit statistics, cautionary notes on any remaining issues or uncertainties). The collection was created to inform conservation decision-making in Northern Australia and was funded by the Australian Government’s National Environmental Science Program (NESP) as part of the Northern Australia Environmental Resources Hub.

  9. Baseline and Future (Shared Socio-economic Pathways 1-2.6 and 3-7.0 for the...

    • zenodo.org
    zip
    Updated Jan 8, 2025
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    Roeland Kindt; Roeland Kindt; Fabio Pedercini; Fabio Pedercini (2025). Baseline and Future (Shared Socio-economic Pathways 1-2.6 and 3-7.0 for the 2050s) Climate Suitability Maps for 499 Useful Tree Species for Tanzania [Dataset]. http://doi.org/10.5281/zenodo.14587104
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    zipAvailable download formats
    Dataset updated
    Jan 8, 2025
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Roeland Kindt; Roeland Kindt; Fabio Pedercini; Fabio Pedercini
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Tanzania
    Description

    Climate suitability scores were calculated for 499 useful tree species included in the Useful trees and shrubs for Tanzania or in the list of native useful tree species obtained from the GlobalUsefulNativeTrees database (Kindt et al. 2023a). After compiling the list of species, we checked afterwards for the availability of globally observed environmental ranges for these species from the TreeGOER database (Kindt 2023). Species with fewer than 10 observations in TreeGOER were excluded.

    The climate scoring system is the same that is used in the GlobalUsefulNativeTrees database:

    • Score = 3 means that in 'environmental space' the planting site occurs within the 25% - 75% species's range (as documented in the TreeGOER) for all variables
    • Score = 2 corresponds to the 5% - 95% species's range for all variables
    • Score = 1 corresponds to the 0% - 100% species's range for all variables
    • Score = 0.5 means that the planting site occurs outside the 0% - 100% species's range for some of the variables, but for heat-related bioclimatic variables (used to produce the maps shown here: BIO01, monthCountByTemp10, growingDegDays5, BIO05 and BIO06) to be below the minimum (‘too cold but not too hot’) and for water-related bioclimatic variables (used here: BIO12, climaticMoistureIndex, BIO16, BIO17 and MCWD) to be above the maximum (‘too wet but not too dry’)
    • Score = 0 means that the planting site occurs outside the 0% - 100% species's range for some of the variables

    Climate scores were obtained for future climates (2050s: 2041-2060) from the median values from 24 Global Climate Models (GCMs) for Shared Socio-Economic Pathway (SSP) 1-2.6 and from 22 GCMs for SSP 3-7.0. Future and baseline bioclimatic layers were processed from raster layers obtained from WorldClim 2.1 at resolutions of 2.5 arc-minutes. Similar methods were used to obtain median values for the ClimateForecasts and CitiesGOER databases.

    Calculations of climate scores were made with similar scripting pipelines in the R statistical environment as documented here: https://rpubs.com/Roeland-KINDT/1168650. These scripts use similar calculation methods as those used for the global case studies of the TreeGOER manuscript (Kindt 2023), and used internally in the GlobalUsefulNativeTrees online database. Interested readers should especially refer to the manuscript for further details on methods used and their justification.

    Alternative future climate maps were obtained by calculating climate scores for each GCM and SSP separately, then counting the number of GCMs that projected that the species would be suitable under the future climatic conditions. A species was estimated to be suitable for a particular combination of GCM and SSP if its score was 1 or above. The maps distinguished areas where 33% or fewer of the GCMs predicted that the species would be suitable, and areas where 66% or more of the GCMs predicted that the species would be suitable. Those thresholds correspond to the Mastrandea et al. (2011) likelihood scale, which was adopted earlier in another climate change atlas (Kindt et al. 2023b; https://atlas.worldagroforestry.org/). A separate mapping category shows where 66% or more GCMs had a climate score of 2 or 3.

    Percentage of GCMs projecting that the species is suitableCount of GCMs for SSP 1-2.6Count of GCMs for SSP 3-7.0
    0 %00
    <= 33 % ('Unlikely')1 - 81 - 7
    33 % < percentage < 66 %9 - 158 - 14
    >= 66 % ('Likely')16 - 2415 - 22
    >= 66 % with a Climate Score > 1 ('Likely')16 - 2415 - 22

    The maps include a red polygon showing the country outline of Tanzania obtained from the GADM database. The first map for the baseline climate includes presence observations in the country obtained from the RAINBIO database (Dauby et al. 2016) and from the Global Biodiversity Information Facility (filtered from the occurrences that informed the TreeGOER database; GBIF.org 2021 GBIF Occurrence Download https://doi.org/10.15468/dl.77gcvq).

    The MS Excel file contains columns that include information from World Flora Online, including hyperlinks to this online flora. Other columns indicate whether the species was included among assemblages of native useful tree species for the country in the GlobalUsefulNativeTrees database.

    References

    • Mbuya L., Msanga H., Ruffo C., Birnie A. & Tengnas B. 1994. Useful trees and shrubs for Tanzania. Identification, propagatation and management for agricultural and pastoral communities. Regional Soil Conservation Unit, Nairobi. Accessed online X-2016 via http://www.worldagroforestry.org/usefultrees
    • Kindt, R. (2023). TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. Global Change Biology, 00, 1–16. https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914.
    • Kindt, R. (2024). TreeGOER: Tree Globally Observed Environmental Ranges (2024.07) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.13132613
    • Kindt, R., Graudal, L., Lillesø, JP.B. et al. (2023a). GlobalUsefulNativeTrees, a database documenting 14,014 tree species, supports synergies between biodiversity recovery and local livelihoods in landscape restoration. Sci Rep 13, 12640. https://doi.org/10.1038/s41598-023-39552-1
    • Kindt R, Abiyu A, Borchardt P, Dawson IK, Demissew S, Graudal L, Jamnadass R, Lillesø J-PB, Moestrup S, Pedercini F, Wieringa JJ, Wubalem T. 2023. The Climate change atlas for Africa of tree species prioritized for forest landscape restoration in Ethiopia: A description of methods used to develop the atlas. Working Paper No. 17. Bogor, Indonesia; and Nairobi, Kenya: Center for International Forestry Research and World Agroforestry (CIFOR-ICRAF). https://doi.org/10.17528/cifor-icraf/008977
    • Kindt, R. (2023). CitiesGOER: Globally Observed Environmental Data for 52,602 Cities with a Population ≥ 5000 (2023.10) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10004594
    • Kindt, R. (2024). ClimateForecasts: Globally Observed Environmental Data for 15,504 Weather Station Locations (2024.07) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.12679832
    • 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. https://doi.org/10.1002/joc.5086
    • 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. https://doi.org/10.1111/ecog.02880
    • Mastrandrea, M.D., Mach, K.J., Plattner, GK. et al. The IPCC AR5 guidance note on consistent treatment of uncertainties: a common approach across the working groups. Climatic Change 108, 675 (2011). https://doi.org/10.1007/s10584-011-0178-6
    • Dauby G, Zaiss R, Blach-Overgaard A, Catarino L, Damen T, Deblauwe V, Dessein S, Dransfield J, Droissart V, Duarte MC, Engledow H, Fadeur G, Figueira R, Gereau RE, Hardy OJ, Harris DJ, de Heij J, Janssens S, Klomberg Y, Ley AC, Mackinder BA, Meerts P, van de Poel JL, Sonké B, Sosef MSM, Stévart T, Stoffelen P, Svenning J-C, Sepulchre P, van der Burgt X, Wieringa JJ, Couvreur TLP (2016) RAINBIO: a mega-database of tropical African vascular plants distributions. PhytoKeys 74: 1-18. https://doi.org/10.3897/phytokeys.74.9723

  10. Baseline and Future (Shared Socio-economic Pathways 1-2.6 and 3-7.0 for the...

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    Updated Jan 8, 2025
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    Roeland Kindt; Roeland Kindt; Fabio Pedercini; Fabio Pedercini; Thacien Hagenimana; Thacien Hagenimana; Lars Graudal; Lars Graudal (2025). Baseline and Future (Shared Socio-economic Pathways 1-2.6 and 3-7.0 for the 2050s) Climate Suitability Atlas for 291 Useful Tree Species for Rwanda [Dataset]. http://doi.org/10.5281/zenodo.14586390
    Explore at:
    zipAvailable download formats
    Dataset updated
    Jan 8, 2025
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Roeland Kindt; Roeland Kindt; Fabio Pedercini; Fabio Pedercini; Thacien Hagenimana; Thacien Hagenimana; Lars Graudal; Lars Graudal
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Rwanda
    Description

    Climate suitability scores were calculated for 291 useful tree species included in the Know Some Useful Trees and Shrubs for Agricultural and Pastoral Communities of Rwanda or in the list of native useful tree species obtained from the GlobalUsefulNativeTrees database (Kindt et al. 2023a). After compiling the list of species, we checked afterwards for the availability of globally observed environmental ranges for these species from the TreeGOER database (Kindt 2023). Species with fewer than 10 observations in TreeGOER were excluded.

    The climate scoring system is the same that is used in the GlobalUsefulNativeTrees database:

    • Score = 3 means that in 'environmental space' the planting site occurs within the 25% - 75% species's range (as documented in the TreeGOER) for all variables
    • Score = 2 corresponds to the 5% - 95% species's range for all variables
    • Score = 1 corresponds to the 0% - 100% species's range for all variables
    • Score = 0.5 means that the planting site occurs outside the 0% - 100% species's range for some of the variables, but for heat-related bioclimatic variables (used to produce the maps shown here: BIO01, monthCountByTemp10, growingDegDays5, BIO05 and BIO06) to be below the minimum (‘too cold but not too hot’) and for water-related bioclimatic variables (used here: BIO12, climaticMoistureIndex, BIO16, BIO17 and MCWD) to be above the maximum (‘too wet but not too dry’)
    • Score = 0 means that the planting site occurs outside the 0% - 100% species's range for some of the variables

    Climate scores were obtained for future climates (2050s: 2041-2060) from the median values from 24 Global Climate Models (GCMs) for Shared Socio-Economic Pathway (SSP) 1-2.6 and from 22 GCMs for SSP 3-7.0. Future and baseline bioclimatic layers were processed from raster layers obtained from WorldClim 2.1 at resolutions of 2.5 arc-minutes. Similar methods were used to obtain median values for the ClimateForecasts and CitiesGOER databases.

    Calculations of climate scores were made with similar scripting pipelines in the R statistical environment as documented here: https://rpubs.com/Roeland-KINDT/1168650. These scripts use similar calculations methods as those used for the global case studies of the TreeGOER manuscript (Kindt 2023), and used internally in the GlobalUsefulNativeTrees online database. Interested readers should especially refer to the manuscript for further details on methods used and their justification.

    Alternative future climate maps were obtained by calculating climate scores for each GCM and SSP separately, then counting the number of GCMs that projected that the species would be suitable under the future climatic conditions. A species was estimated to be suitable for a particular combination of GCM and SSP if its score was 1 or above. The maps distinguished areas where 33% or fewer of the GCMs predicted that the species would be suitable, and areas where 66% or more of the GCMs predicted that the species would be suitable. Those thresholds correspond to the Mastrandea et al. (2011) likelihood scale, which was adopted earlier in another climate change atlas (Kindt et al. 2023b; https://atlas.worldagroforestry.org/). A separate mapping category shows where 66% or more GCMs had a climate score of 2 or 3.

    Percentage of GCMs projecting that the species is suitableCount of GCMs for SSP 1-2.6Count of GCMs for SSP 3-7.0
    0 %00
    <= 33 % ('Unlikely')1 - 81 - 7
    33 % < percentage < 66 %9 - 158 - 14
    >= 66 % ('Likely')16 - 2415 - 22
    >= 66 % with a Climate Score > 1 ('Likely')16 - 2415 - 22

    The maps include a red polygon showing the country outline of Rwanda obtained from the GADM database. The first map for the baseline climate includes presence observations in the country obtained from the RAINBIO database (Dauby et al. 2016) and from the Global Biodiversity Information Facility (filtered from the occurrences that informed the TreeGOER database; GBIF.org 2021 GBIF Occurrence Download https://doi.org/10.15468/dl.77gcvq).

    The MS Excel file contains columns that include information from World Flora Online, including hyperlinks to this online flora. Other columns indicate whether the species was included among assemblages of native useful tree species for the country in the GlobalUsefulNativeTrees database.

    References

    • Nduwayezu, J.B., Ruffo, C.K., Minani, V., Munyaneza, E. and Nshutiyayesu, S. 2009. Know Some Useful Trees and Shrubs for Agricultural and Pastoral Communities of Rwanda. Institute of Scientific and Technological Research, Butare, Rwanda.
    • Kindt, R. (2023). TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. Global Change Biology, 00, 1–16. https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914.
    • Kindt, R. (2024). TreeGOER: Tree Globally Observed Environmental Ranges (2024.07) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.13132613
    • Kindt, R., Graudal, L., Lillesø, JP.B. et al. (2023a). GlobalUsefulNativeTrees, a database documenting 14,014 tree species, supports synergies between biodiversity recovery and local livelihoods in landscape restoration. Sci Rep 13, 12640. https://doi.org/10.1038/s41598-023-39552-1
    • Kindt R, Abiyu A, Borchardt P, Dawson IK, Demissew S, Graudal L, Jamnadass R, Lillesø J-PB, Moestrup S, Pedercini F, Wieringa JJ, Wubalem T. 2023. The Climate change atlas for Africa of tree species prioritized for forest landscape restoration in Ethiopia: A description of methods used to develop the atlas. Working Paper No. 17. Bogor, Indonesia; and Nairobi, Kenya: Center for International Forestry Research and World Agroforestry (CIFOR-ICRAF). https://doi.org/10.17528/cifor-icraf/008977
    • Kindt, R. (2023). CitiesGOER: Globally Observed Environmental Data for 52,602 Cities with a Population ≥ 5000 (2023.10) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10004594
    • Kindt, R. (2024). ClimateForecasts: Globally Observed Environmental Data for 15,504 Weather Station Locations (2024.07) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.12679832
    • 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. https://doi.org/10.1002/joc.5086
    • 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. https://doi.org/10.1111/ecog.02880
    • Mastrandrea, M.D., Mach, K.J., Plattner, GK. et al. The IPCC AR5 guidance note on consistent treatment of uncertainties: a common approach across the working groups. Climatic Change 108, 675 (2011). https://doi.org/10.1007/s10584-011-0178-6
    • Dauby G, Zaiss R, Blach-Overgaard A, Catarino L, Damen T, Deblauwe V, Dessein S, Dransfield J, Droissart V, Duarte MC, Engledow H, Fadeur G, Figueira R, Gereau RE, Hardy OJ, Harris DJ, de Heij J, Janssens S, Klomberg Y, Ley AC, Mackinder BA, Meerts P, van de Poel JL, Sonké B, Sosef MSM, Stévart T, Stoffelen P, Svenning J-C, Sepulchre P, van der Burgt X, Wieringa JJ, Couvreur TLP (2016) RAINBIO: a mega-database of tropical African vascular plants distributions. PhytoKeys 74: 1-18. https://doi.org/10.3897/phytokeys.74.9723

  11. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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Victoria Culshaw; Mario Mairal; Isabel Sanmartín (2023). Data_Sheet_1_Biogeography Meets Niche Modeling: Inferring the Role of Deep Time Climate Change When Data Is Limited.zip [Dataset]. http://doi.org/10.3389/fevo.2021.662092.s001

Data_Sheet_1_Biogeography Meets Niche Modeling: Inferring the Role of Deep Time Climate Change When Data Is Limited.zip

Related Article
Explore at:
zipAvailable download formats
Dataset updated
Jun 6, 2023
Dataset provided by
Frontiers
Authors
Victoria Culshaw; Mario Mairal; Isabel Sanmartín
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

Geographic range shifts are one major organism response to climate change, especially if the rate of climate change is higher than that of species adaptation. Ecological niche models (ENM) and biogeographic inferences are often used in estimating the effects of climatic oscillations on species range dynamics. ENMs can be used to track climatic suitable areas over time, but have often been limited to shallow timescales; biogeographic inference can reach greater evolutionary depth, but often lacks spatial resolution. Here, we present a simple approach that treats them as independent and complementary sources of evidence, which, when used in partnership, can be employed to reconstruct geographic range shifts over deep evolutionary timescales. For testing this, we chose two extreme African disjunctions: Camptoloma (Scrophulariaceae) and Canarina (Campanulaceae), each comprising of three species disjunctly distributed in Macaronesia and eastern/southern Africa. Using inferred ancestral ranges in tandem with preindustrial and paleoclimate ENM hindcastings, we show that the disjunct pattern was the result of fragmentation and extinction events linked to Neogene aridification cycles. Our results highlight the importance of considering temporal resolution when building ENMs for rare endemics with small population sizes and restricted climatic tolerances such as Camptoloma, for which models built on averaged monthly variables were more informative than those based on annual bioclimatic variables. Additionally, we show that biogeographic information can be used as truncation threshold criteria for building ENMs in the distant past. Our approach is suitable when there is sparse sampling on species occurrences and associated patterns of genetic variation, such as in the case of ancient endemics with widely disjunct distributions as a result of climate change.

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