DOI: 10.5061/dryad.6m905qg03
title: Data associated with ecological niche models and post-ENM statistical analyses for Trillium species distributions

description: This dataset consists of 1) occurrence data for 21 species
 of Trillium native to eastern North America, collected
 between 1900 and 2018 ("Trillium_Occurrences"); and 2)
 ecological niche model values and species reproductive life history
 traits used in post-ENM analyses
 ("Trillium_LifeHistoryTraits"). Occurrence datasets were
 collected by searching several publicly-available online
 databases, including the Global Biodiversity Information
 Facility, the SouthEast Regional Network of Expertise and
 Collections, Tropicos, and online regional herbaria
 databases, such as the University of Tennessee Herbarium. Half of
 all records were assigned latitude/longitude coordinates
 using GEOLocate software (Rios et al., 2010). A centroid of
 uncertainty of 3 km2 was automatically assigned to each locality by
 GEOLocate, and minimum uncertainties were adjusted
 manually based on specificity of record descriptions. Descriptive
 localities were georeferenced by one of three researchers, and all final
 coordinates and uncertainties were checked and confirmed by the first
 author. Occurrences are provided as latitude and longitude decimal
 degrees, and were used as training and testing data for ecological niche
 models, implemented using Maxent 3.4.1 (Phillips et al., 2006).
 For the second dataset, all final ecological niche model values were
 obtained or calculated based on the final ENMs produced for our study (see
 manuscript Methods for more details). NatureServe conservation status for
 each species were obtained from NatureServe Explorer (2020). Reproductive
 life history traits were obtained from Ohara (1989). These data were used
 to assess whether reproductive life history traits were significant
 predictors of the proportional occupancy of the predicted distribution,
 PO, using beta regression models. 
description: Occurrence datasets were generated using the following protocol
 (excerpt from manuscript): we obtained every publicly-available presence
 record for each <i>Trillium</i> species in ENA, with records
 dating back to 1900. The databases searched included the Global
 Biodiversity Information Facility (GBIF; https://www.gbif.org/,
 accessed July 28 - Aug. 1, 2018), the SouthEast Regional Network of
 Expertise and Collections (SERNEC; http://sernecportal.org/portal/, accessed Aug 1, 26, 30, and Sept. 1 - 4,  2018; July 12, 2019), Tropicos (https://www.tropicos.org/, March 28, 2019), and online regional herbaria databases, such as the University of Tennessee Herbarium (TENN; https://herbarium.utk.edu/, accessed Aug. 10 – 15, 2018) and the Arnold Arboretum of Harvard University (https://www.arboretum.harvard.edu/, accessed Aug. 17, 2018). Approximately half of all records we obtained consisted of descriptive localities without latitude/longitude coordinates. To assign geographic coordinates to these localities, we used the GEOLocate software (Rios et al., 2010; https://www.geo-locate.org/, accessed from August, 2018 – September, 2019). A centroid of uncertainty with an area of 3 km<sup>2</sup> was automatically assigned to each locality by GEOLocate. Minimum uncertainty was adjusted manually based on specificity of record descriptions. Descriptive localities were georeferenced by one of three researchers, and all final coordinates and uncertainties were checked and confirmed by the first author. Occurrences are provided as latitude and longitude decimal degrees. Additional information about the collection and processing of occurrence records, as appears in the manuscript Appendix 3: Occurrence data originated from a variety of sources, but the majority of occurrences were opportunistic. Approximately 2.5% of the occurrences originated from iNaturalist (https://www.inaturalist.org/; both GBIF and SERNEC provided occurrences derived originally from iNaturalist), representing citizen scientist collections. Approximately 25% of the occurrences originated from herbaria/museum records, and are therefore more likely to be reliable scientific specimens with accurate locality data, which may or may not have originated from standardized surveys. The remaining data have unknown sources. The majority of occurrences provided no information about sampling method or date; as such, it was not possible to accurately estimate sampling bias or the temporal range represented by the data set. However, because the data were derived from a variety of sources, we expect that the likelihood that they exhibit a consistent form of sampling bias is low, and the risk that sampling bias would drive distribution models is probably negligible. We estimate that ~ 75% of the occurrences originated within the past 100 years. For the second dataset, all final ecological niche model values (e.g., area of the EOO (km2), area of the PSA (km2), area of the intersection between the EOO and PSA (km2), and area of proportional occupancy (km2; calculated by dividing the area of the intersection between the EOO and PSA by the area of the PSA) were obtained or calculated based on the final ENMs produced for our study (see manuscript Methods for more details). The total number of occurrences is the final total number of occurrence records used in ENMs for each species. NatureServe conservation status for each species were obtained from NatureServe Explorer, 2020 (http://explorer.natureserve.org/). Reproductive life history traits (e.g., biomass (g), number of ovules, number of seeds produced per individual, seed setting rate, and flower type) were obtained from Ohara (1989)*. These data were used to assess whether reproductive life history traits were significant predictors of the proportional occupancy of the predicted distribution, PO, using beta regression models.  *Ohara, M. (1989). Life history evolution in the genus <i>Trillium</i>. <i>Plant Species Biology</i>, <i>4</i>(1), 1-28. 
description: The occurrence datasets are comma-separated values (.csv) files,
 and are organized in the manner required by the Maxent program (v. 3.4.1;
 Phillips et al., 2006; e.g., specific epithet, longitude, latitude).
 Lat/long coordinates are in decimal degrees.  A note on
 training occurrences: if the file is named “[species name]_training.csv,”
 that is the training dataset that was uploaded to Maxent. The data were
 split prior to uploading to Maxent, based on minimum uncertainty of
 georeferences. If the file is named “[species name]_both.csv,” that is the
 full set of occurrences (i.e., both training and testing) that was
 uploaded to Maxent. Maxent was allowed to split the data either 50/50 or
 70/30, depending on the species. Method of data splitting per species is
 included in Table 4 in the manuscript.  The life
 history dataset has some missing values for <em>T.
 cernuum </em>and <em>T. sulcatum. </em>Values of all reproductive life
 history predictor variables were obtained from Ohara (1989; Table 1 in the
 manuscript). Because the Ohara (1989) dataset only contained information
 for 19 of our 21 study species, excluding <i>T. cernuum
 </i>and <i>T. sulcatum, </i>we did not include either of
 these species<i> </i>in beta regression
 models.