This dataset has been used for the paper A tale of three vines: current and future threats to wild Eurasian grapevine by vineyards and invasive rootstocks by Petitpierre et al.

It contains the species distribution of five Vitis species in North America and Europe (Vitis acerifolia, Vitis aestivalis, Vitis rupestris, Vitis riparia and Vitis berlandieri), so as the distribution of the Eurasian wild grapevine (Vitis vinifera ssp. sylvestris) and cultivated grapevine in Europe (Vitis vinifera ssp. vinifera). 40 bioclimatic variables are associated to these distributions. These variables were derived from the Climond dataset (Kriticos et al., 2012).

Species distribution data were gathered using several databases and sources. The list of the different sources is cited in methods section of the related manuscript. The distribution of each Vitis taxa was rasterized at a resolution of 0.167° (coordinate reference system WGS84; EPSG:4326). X and Y coordinates correspond to the center of each cell. Environmental variables were extracted from the CliMond database (Kriticos et al., 2012) for each site. It consists of a set of 40 bioclimatic variables at a resolution of 0.167°, grouped into 4 categories: temperature, precipitation, moisture, and solar radiation. The description of these 40 variables can be found in the original CliMond publication (Kriticos et al., 2012).

Each file contains the distribution of one Vitis taxa.

v_ace.csv contains the distribution of Vitis acerifolia in North America.

v_aes.csv contains the distribution of Vitis aestivalis in North America.

v_ber.csv contains the distribution of Vitis berlandieri in North America.

v_rip.csv contains the distribution of Vitis riparia in North America.

v_rup.csv contains the distribution of Vitis rupestris in North America.

v_root.csv contains the distribution of the American vitis taxa (Vitis acerifolia, Vitis aestivalis, Vitis berlandieri, Vitis riparia and Vitis rupestris) in Europe.

v_vin.csv contains the distribution of the vineyards (i.e. Vitis vinifera ssp. vinifera, the cultivated grapevine) in Europe.

v_syl.csv contains the distribution of the vineyards (i.e. Vitis vinifera ssp. sylvestris, the wild European grapevine) in Europe.

Reference Kriticos, D.J., Webber, B.L., Leriche, A., Ota, N., Macadam, I., Bathols, J., Scott, J.K., 2012. CliMond: Global high-resolution historical and future scenario climate surfaces for bioclimatic modelling. Methods Ecol. Evol. 3, 53–64. https://doi.org/10.1111/j.2041-210X.2011.00134.x

GBIF data for Vitis vinifera ssp. vinifera. GBIF.org (11 November 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.hacmjx GBIF data for Vitis ssp. sylvestris. GBIF.org (11 November 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.qu7txv GBIF data for Vitis berlandieri. GBIF.org (11 November 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.w6dt7r GBIF data for Vitis riparia. GBIF.org (11 November 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.aqh55u GBIF data for Vitis acerifolia. GBIF.org (11 November 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.csd7wc GBIF data for Vitis aestivalis. GBIF.org (11 November 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.f5jfzf GBIF data for Vitis rupestris. GBIF.org (02 September 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.gqjm7w

This dataset shows the projected current and future (2070) climatic suitability for the invasive plant species Clidemia hirta, Hiptage benghalensis, Miconia calvescen, Miconia nervosa, Miconia racemose, Stevia ovata,and Turbina corymbosa across North Queensland. Modelled using CLIMEX.

Method:

CLIMEX (Sutherst & Maywald 1985; Sutherst et al. 2007) is a modelling package that enables users to model the climatic potential distribution of organisms based primarily on their current distribution, through taking into consideration climate response information from other knowledge domains if this is available.

CLIMEX is a dynamic model that integrates the weekly responses of a population to climate using a series of annual indices. CLIMEX uses an annual growth index (GIA) to describe the potential for population growth as a function of soil moisture and temperature during favourable conditions, and up to eight stress indices (cold, wet, hot, dry, cold-wet, cold-dry, hot-wet and hot-dry) to determine the probability that the population can survive unfavourable conditions. The growth and stress indices are calculated weekly and are then combined into an overall annual index of climatic suitability, the Ecoclimatic Index (EI), which gives an overall measure of the potential of a given location to support a permanent population of the species. The Ecoclimatic Index (EI), ranges from 0 for locations at which the species is not able to persist to 100 for locations that are optimal for the species year round.

CLIMEX is a bioclimatic model, relying on a database of climatic variables of long-term monthly precipitation totals, averages of minimum and maximum temperatures, and averages of relative humidity at 09:00 and 15:00 hours. The historical climate dataset used for these analyses was the CliMond dataset (www.climond.org), with a spatial resolution of 10¿, using station records centred on 1975 (Kriticos et al. 2012).

The impacts of climate change on the potential for each species to grow or pose an invasion risk were explored using a climate scenario model for 2070 taken from the CliMond dataset (Kriticos et al. 2012). The selected climate datasets were developed using the A1B emission scenario applied to the CSIRO Mk 3.0 global climate model.

For each species, we used parameter sets that were either published or which we have developed.

The CLIMEX parameters used in the model for Clidemia hirta are published in: Breadon R. C., Brooks S. J. & Murphy H. T. (2012) Biology of Australian Weeds: Clidemia hirta L.D.Don. Plant Protection Quarterly 27, 3-18.

The CLIMEX parameters used in the model for Hiptage benghalensis, Miconia calvescens, Miconia nervosa, Miconia racemose, Stevia ovata and Turbina corymbosa can be obtained by contacting the author.

Other references:

Kriticos, D. J., B. L. Webber, A. Leriche, N. Ota, J. Bathols, I. Macadam, and J. K. Scott. 2012. CliMond: global high resolution historical and future scenario climate surfaces for bioclimatic modelling. Methods in Ecology and Evolution 3:53-64. doi: 10.1111/j.2041-210X.2011.00134.x

Sutherst, R. W., G. F. Maywald, and D. J. Kriticos. 2007. CLIMEX Version 3: User's Guide. Hearne Scientific Software Pty Ltd, www.Hearne.com.au.

Sutherst, R. W., G. F. Maywald, T. Yonow, and P. M. Stevens. 1999. CLIMEX. Predicting the Effects of Climate on Plants and Animals. User Guide. CSIRO Publishing, Melbourne, Australia.

Format:

14 shapefiles in polygon format using the spatial reference of GCS_WGS_1984.

For each of the invasive species investigated there are 2 shapefiles, one for its projected climate suitability as at 1975 and another for its projected climate suitability in 2070. The shapefiles are: ¿ C_hirta_1975.* ¿ C_hirta_2070.* ¿ H_benghal_1975.* ¿ H_benghal_2070.* ¿ M_calvescens_1975.* ¿ M_calvescens_2070.* ¿ M_nervosa_1975.* ¿ M_nervosa_2070.* ¿ M_racemosa_1975.* ¿ M_racemosa_2070.* ¿ S_ovata_1975.* ¿ S_ovata_2070.* ¿ T_corymbosa_1975.* ¿ T_corymbosa_2070.*

Data Dictionary:

Each shapefile has the same attributes. - Longitude: - Latitude: - GI: unknown - EI: range (0-100), Ecoclimatic Index, gives an overall measure of the potential of a given location to support a permanent population of the species.

Data Location:

This dataset is filed in the eAtlas enduring data repository at: data\NERP-TE\7.2_Invasive-species

Ecologists have traditionally studied intraspecific variation by sampling species across their geographic ranges. However, whether this classic approach produces samples that accurately represent species’ climatic niches is largely unknown. Alternative, niche-based study designs using species’ climatic niches to inform sampling locations should more efficiently and completely capture the breadth of the niche, but the magnitude of this difference and how it may vary is unclear. Here we use conifers as a model system to explore these issues and reach specific recommendations for future sampling designs. Using an independent dataset of high-quality species’ occurrences, we first show that recent publications examining variation across geographic space do a poor job of capturing the full breadth of species’ niches, such that on average, only 22% of species’ niche space was sampled. This was also true of a large compiled database, the International Tree-Ring Data Bank (ITRDB), which yielded average niche coverage of only 45%. Finally, we simulated common sampling designs (i.e., random points, grids, and transects) in both geographic and niche-based sampling frameworks. Using two sampling metrics, niche coverage and niche undersampling, we measured how completely and evenly these simulated studies characterized the niches of 64 North American conifers. Niche-based sampling better represented species’ niches than geographic sampling, with the magnitude of this difference depending on study design and sample size. Niche-based gridded study designs achieved the most complete sampling at all but the smallest sample sizes, covering ~15-25% more of a species’ niche than similar designs implemented geographically. With fewer than 10 samples, however, all study designs performed poorly, and niche-based transects achieved slightly higher niche coverage. Consequently, when more than a handful of samples are collected, we recommend that studies seeking to characterize variation across a species’ niche consider using a gridded study design implemented in a niche-based sampling framework. Methods All the data used in this paper were compiled from freely and publicly available sources.

Conifer occurrence data was downloaded from the Conifer Database: https://herbaria.plants.ox.ac.uk/bol/conifers ; contact Aljos Farjon ( a.farjon@kew.org ) for use.

Climate data was downloaded from the CliMond database: https://www.climond.org/ , and is the same climate dataset as that used in Perret et al. 2019.

Metadata for the International Tree-Ring Data Bank (ITRDB) is available from the public data repository: https://www1.ncdc.noaa.gov/pub/data/paleo/treering/

banksia.climateOccurrence data from AVH and climate data from CLiMOND for the genus Banksia.hakea.climateOccurence data from AVH and climate data from CLiMond for the genus Hakeaprotea.climateOccurrence records from SANBi and climate data from CLiMond for the genus Proteamoraea.climateOccurrence records from SANBi and climate data from CLiMond for the genus MoraeaOUwie_model_comparison_results_all_generaThis compressed folder contains results from an evolutionary model comparison of 5 OU (OU1, OUM, OUMA, OUMV, OUMVA) and 2 BM (BMS, BMM) models in the R package OUwie. We investiagted 9 environmental niche axes in each of the four genera. for each niche axis we reran the model comparison 50 times across 50 different stochastic maps derived from a BioGeoBEARS analysis in R. So here we present, for each genus, nine .csv files containing the results of 50 different model comparisons.

naturalized pine occurrences and associated climatic dataA comma-delimited data file that contains naturalized occurrences for 25 pine species, globally. Each occurrence is a row, with species indicated, georeferenced coordinates, country, and associated climate data. Each occurrence corresponds with an herbarium specimen; for herbarium data, contact the authors. Climatic data column names correspond with BioClim variables downloaded from CliMond and other sources. See main article text for further details and variables used in main analyses. Native range occurrences are available from the Conifer Database (for use, contact Aljos Farjon; a.farjon@kew.org).pinus_occurrences_fordryad_exoticonly.csvglobal climate dataA comma-delimited file that contains climate data used to create the global climate space background. Data is in point format, where x and y locations correspond to the center of a 10 arc-minute grid cell. Column names correspond with climatic variables downloaded from CliMond...