------------------- GENERAL INFORMATION ------------------ Date: 2019-08-29 Authors: Thomas Lake Ryan Briscoe Runquist (rbriscoe@umn.edu) David Moeller Contents: Data for Species Distribution Models and Joint Species Distribution Models of Nine Invasive Species in North America Data: Data Repository for U of M Date of data collection: 2017-01-01 to 2019-08-01 Funding Sources: Minnesota Invasive Terrestrial Plants and Pests Center (MITPPC) Recommended citation for the data: Lake, Thomas, A.; Briscoe Runquist, Ryan, D.; Moeller, David, A.. (2019). Species Distribution Models and Joint Species Distribution Models of Nine Invasive Species in North America. Retrieved from the Data Repository for the University of Minnesota, http://hdl.handle.net/11299/206482. --------------------- DATA & FILE OVERVIEW --------------------- see "SDM DRUM Data.xlsx" for a file inventory -------------------------- METHODOLOGICAL INFORMATION -------------------------- #MAXENT Model Data Name: MaxEnt software for modeling species niches and distributions Source: https://biodiversityinformatics.amnh.org/open_source/maxent/ Version: 3.4.1 The ENM Eval Model Results.csv file represents the results of each individual model run. These are the results of how changing model parameters influences the output model statistics. The original code from the R ENMEval package (https://github.com/bobmuscarella/ENMeval) has been modified to output additional columns to the autogenerated output file (see the Data-Specific Information section below for more detail) The species Tif Projection Stack.csv files are essentially subsets of the larger ENMEval Model Results.csv file. From ENM Eval Model Results.csv, top-performing models were selected. The number of TIFs per stack differ as each species had a different number of top-performing models. These top performing model current and future projections were stacked together into MaxEnt Predictions TIF Projection files, and the TIF stacks are documented in the species TIF Projection Stacks.csv files. ---------------------------- DATA-SPECIFIC INFORMATION ---------------------------- ##### Current and Future Bioclimatic Variables Dataset: Current Climatic Data Name: NA_wc2_BioClim19.grd Number of Files: 2 Source: http://worldclim.org/version2 Resolution: 30s Dataset: 2050 and 2070 Future Climatic Data Name: NA_cc45bi50_BioClim19.grd Number of Files: 20 File Name Explanation: NA (Extent) CC (CCSM4) 45 (RCP) bi (BioClimatic) 50 (2070) BioClim19 (nineteen variables included) Source: http://worldclim.org/cmip5_30s GCMs: CCSM4, GFDL-CM3, IPSL-CM5A-LR, MIROC-ESM, MRI-CGCM3 RCP: RCP45 Resolution: 30s Variables: BIO1 = Annual Mean Temperature BIO2 = Mean Diurnal Range (Mean of monthly (max temp - min temp)) BIO3 = Isothermality (BIO2/BIO7) (* 100) BIO4 = Temperature Seasonality (standard deviation *100) BIO5 = Max Temperature of Warmest Month BIO6 = Min Temperature of Coldest Month BIO7 = Temperature Annual Range (BIO5-BIO6) BIO8 = Mean Temperature of Wettest Quarter BIO9 = Mean Temperature of Driest Quarter BIO10 = Mean Temperature of Warmest Quarter BIO11 = Mean Temperature of Coldest Quarter BIO12 = Annual Precipitation BIO13 = Precipitation of Wettest Month BIO14 = Precipitation of Driest Month BIO15 = Precipitation Seasonality (Coefficient of Variation) BIO16 = Precipitation of Wettest Quarter BIO17 = Precipitation of Driest Quarter BIO18 = Precipitation of Warmest Quarter BIO19 = Precipitation of Coldest Quarter For more information about these variables: http://worldclim.org/bioclim ##### Species Coordinate Data Dataset: Species Longitude, Latitude Coordinate Pairs Name: xxx_occurrences.csv or [species]_dataset_[date].csv Number of Files: 9 Sources: EDDMapS. 2019. Early Detection & Distribution Mapping System. The University of Georgia - Center for Invasive Species and Ecosystem Health. Available online at http://www.eddmaps.org/; last accessed August 1, 2019. Global Biodiversity Information Facility (GBIF) Online Database **The spreadsheets downloaded from GBIF were processed to remove observations with high levels of uncertainty prior to modeling Japanese Hops - humulus_japonicus_clean_22June2018.csv GBIF.org (25 May 2016) GBIF Occurrence Download. https://doi.org/10.15468/dl.wnav0k Dalmatian Toadflax - dalmatian_toadflax_occurrences.csv GBIF.org (21 June 2016) GBIF Occurrence Download. https://doi.org/10.15468/dl.aksyj6 Brown Knapweed - brown_knapweed_occurrences.csv GBIF.org (13 July 2016) GBIF Occurrence Download. https://doi.org/10.15468/dl.mfyuk1 Common Teasel - common_teasel_occurrences.csv GBIF.org (23 January 2019) GBIF Occurrence Download. https://doi.org/10.15468/dl.cavtwt Black Swallowwort - black_swallowwort_dataset.csv GBIF.org (23 January 2019) GBIF Occurrence Download. https://doi.org/10.15468/dl.fu9wnd Wild Parsnip - wild_parsnip_occurrences.csv GBIF.org (23 January 2019) GBIF Occurrence Download. https://doi.org/10.15468/dl.cckfvg Common Tansy - common_tansy_occurrences.csv GBIF.org (24 January 2019) GBIF Occurrence Download. https://doi.org/10.15468/dl.e5owy4 Narrowleaf Bittercress - narrowleaf_bittercress_dataset_28June2018.csv GBIF.org (25 May 2016) GBIF Occurrence Download. https://doi.org/10.15468/dl.nhu5rb Oriental Bittersweet - oriental_bittersweet_dataset_16May2018.csv GBIF.org (11 September 2019) GBIF Occurrence Download. https://doi.org/10.15468/dl.po0unh ##### ENM Eval Model Results.csv Variables: model number = Index used to enumerate models. settings = MaxEnt Feature class settings (LQHPT) with Beta Multiplier value (1-4). features = Maxent Feature class (Linear, Quadratic, Hinge, Product, Threshold). rm = Regularization (Beta) Multiplier value used to construct models. train.AUC = Model AUC value generated from training cross-validated dataset. Training dataset is 75% of data. Testing dataset is 25% of data. avg.test.AUC = Average AUC value of the testing data, calculated from the training data. var.test.AUC = Variation between four AUC testing datasets. avg.diff.AUC = Average difference between four AUC testing datasets. var.diff.AUC = Variation in difference between four AUC testing datasets. avg.test.orMTP = Average minimum training presence (MTP) with 0 percent omission. var.test.orMTP = Variation in minimum training presence (MTP) with 0 percent omission. avg.test.or10pct = Average minimum training presence (MTP) with 10 percent omission. var.test.or10pct = Variation in minimum training presence (MTP) with 10 percent omission. AICc = Akaike information criterion value. delta.AICc = difference between AICc of a given model and the AICc of the model with the lowest AICc. w.AIC = Akaike weight divided by the sum of the likelihood values of all models included in a run. Useful for model averageing. parameters= Number of MaxEnt model parameters used to fit the MaxEnt model. AUC_bin.1 = AUC of testing bin 1, from a 25% training and 75% testing cross-validated dataset. AUC_bin.2 = See AUC_bin.1 AUC_bin.3 = See AUC_bin.1 AUC_bin.4 = See AUC_bin.1 diff.AUC_bin.1 = Difference between AUC of testing bin 1 relative to bins 2-4, from a 25% training and 75% testing cross-validated dataset. diff.AUC_bin.2 = See diff.AUC_bin.1 diff.AUC_bin.3 = See diff.AUC_bin.1 diff.AUC_bin.4 = See diff.AUC_bin.1 test.or10pct_bin.1 = MTP with 10% omission of testing bin 1, from a 25% training and 75% testing cross-validated dataset. test.or10pct_bin.2 = See test.or10pct_bin.1 test.or10pct_bin.3 = See test.or10pct_bin.1 test.or10pct_bin.4 = See test.or10pct_bin.1 test.orMTP_bin.1 = MTP of testing bin 1, from a 25% training and 75% testing cross-validated dataset. test.orMTP_bin.2 = See test.orMTP_bin.1 test.orMTP_bin.3 = See test.orMTP_bin.1 test.orMTP_bin.4 = See test.orMTP_bin.1 TSS_bin.1 = TSS of testing bin 1, from a 25% training and 75% testing cross-validated dataset. TSS_bin.2 = See TSS_bin.1 TSS_bin.3 = See TSS_bin.1 TSS_bin.4 = See TSS_bin.1 Boyce_bin.1 = Continuous Boyce Index of testing bin 1, from a 25% training and 75% testing cross-validated dataset. Boyce_bin.2 = See Boyce_bin.1 Boyce_bin.3 = See Boyce_bin.1 Boyce_bin.4 = See Boyce_bin.1 Average Boyce = Average of four Boyce_bin.[1-4] tp_bin.1 = True positive rate of testing bin 1, from a 25% training and 75% testing cross-validated dataset. tp_bin.2 = See tp_bin.1 tp_bin.3 = See tp_bin.1 tp_bin.4 = See tp_bin.1 fp_bin.1 = False positive rate of testing bin 1, from a 25% training and 75% testing cross-validated dataset. fp_bin.2 = See fp_bin.1 fp_bin.3 = See fp_bin.1 fp_bin.4 = See fp_bin.1 fn_bin.1 = False negative rate of testing bin 1, from a 25% training and 75% testing cross-validated dataset. fn_bin.2 = See fn_bin.1 fn_bin.3 = See fn_bin.1 fn_bin.4 = See fn_bin.1 tn_bin.1 = True negative rate of testing bin 1, from a 25% training and 75% testing cross-validated dataset. tn_bin.2 = See tn_bin.1 tn_bin.3 = See tn_bin.1 tn_bin.4 = See tn_bin.1 Sens Bin 1 = Sensitivity of testing bin 1, from a 25% training and 75% testing cross-validated dataset. Sens Bin 2 = See Sens Bin 1 Sens Bin 3 = See Sens Bin 1 Sens Bin 4 = See Sens Bin 1 Average Sens = Average Sensitivity calculated from Sens Bin [1-4] V57= Model filename [file stem may not be relevant] #####JSDM Raster Projection Files See "JSDM file upload metadata.xlsx" in JSDM Raster Projection Files.zip for more information about these files. ##### [species]_[AreaOfAnalysis]_gjamPredictObj.rda ***For more information see: https://cran.r-project.org/web/packages/gjam/gjam.pdf*** Variables: x = design matrix sdList = list of predictive means and standard errors includes yMu, yPe (predictive mean, SE), wMu, wSe = (mean latent states and SEs) piList = predictive intervals, only generated if length(y) < 10000, includes yLo, yHi (0.025, 0.975) prediction interval, wLo, wHi (0.025, 0.975) for latent states prPresent = n x S matrix of probabilities of presence ematrix = effort ychains = full prediction chains if FULL = T