We created a comprehensive estimate of potential distribution for a subset of 17 ecosystem modifying invasive plants (EMIPs) in Hawaiʻi. This work uses methods that integrate a wide set of data sources including agency and citizen science data, but perhaps more importantly, the integration of regional and global distribution information for these species. We developed transferable and comparable general species distribution models (SDMs) at global and regional scales based on a minimum set of biologically plausible predictors. We built three sets of ensemble species distribution models (SDMs) for each species. We first built global and regional ensemble distribution models for each species. Then, to create a comprehensive estimate of potential invasive species distribution for our study species in Hawaiʻi, we built nested regional models that integrate our global and regional ensemble models. This approach and the resulting mapped distributions are the most comprehensive to date for Hawaiian invasive plants and can possibly be applied more broadly to other species in the future. These models are available as both habitat suitability maps with pixel values ranging from 0 (low suitability) to 1 (high suitability); and as binary maps that separate areas of potential presence (1) from those where presence is not expected (0) based on the environmental predictors considered. This data set contains two nested model 17 band geospatial raster stacks for the suitability and binary maps with one band per each of the 17 EMIP species selected.

We created a comprehensive estimate of potential distribution for a subset of 17 ecosystem modifying invasive plants (EMIPs) in Hawaiʻi. This work uses methods that integrate a wide set of data sources including agency and citizen science data, but perhaps more importantly, the integration of regional and global distribution information for these species. We developed transferable and comparable general species distribution models (SDMs) at global and regional scales based on a minimum set of biologically plausible predictors. The global models were developed for each species using all global location data and pseudo-absences (PAs), excluding those found in Hawaiʻi, and using WorldClim2 bioclimatic variables (1 km) and were only fit and projected for Hawaiʻi. These models are available as both habitat suitability maps with pixel values ranging from 0 (low suitability) to 1 (high suitability); and as binary maps that separate areas of potential presence (1) from those where presence is not expected (0) based on the environmental predictors considered. This data set contains two global model 17 band geospatial raster stacks for the suitability and binary maps with one band per each of the 17 EMIP species selected.

We created a comprehensive estimate of potential distribution for a subset of 17 ecosystem modifying invasive plants (EMIPs) in Hawaiʻi. This work uses methods that integrate a wide set of data sources including agency and citizen science data, but perhaps more importantly, the integration of regional and global distribution information for these species. We developed transferable and comparable general species distribution models (SDMs) at global and regional scales based on a minimum set of biologically plausible predictors. The global models were developed for each species using all global location data and pseudo-absences (PAs), excluding those found in Hawaiʻi, and using WorldClim2 bioclimatic variables (1 km) and were only fit and projected for Hawaiʻi. These models are available as both habitat suitability maps with pixel values ranging from 0 (low suitability) to 1 (high suitability); and as binary maps that separate areas of potential presence (1) from those where presence is not expected (0) based on the environmental predictors considered. This data set contains two global model 17 band geospatial raster stacks for the suitability and binary maps with one band per each of the 17 EMIP species selected.

We created a comprehensive estimate of potential distribution for a subset of 17 ecosystem modifying invasive plants (EMIPs) in Hawaiʻi. This work uses methods that integrate a wide set of data sources including agency and citizen science data, but perhaps more importantly, the integration of regional and global distribution information for these species. We developed transferable and comparable general species distribution models (SDMs) at global and regional scales based on a minimum set of biologically plausible predictors. The regional models were developed for each species using only regional location data and pseudo-absences (PAs) wihtin the extent of the main Hawaiian Islands and regionally derived bioclimatic variables (250 m). These models are available as both habitat suitability maps with pixel values ranging from 0 (low suitability) to 1 (high suitability); and as binary maps that separate areas of potential presence (1) from those where presence is not expected (0) based on the environmental predictors considered. This data set contains two regional model 17 band geospatial raster stacks for the suitability and binary maps with one band per each of the 17 EMIP species selected.

We created a comprehensive estimate of potential distribution for a subset of 17 ecosystem modifying invasive plants (EMIPs) in Hawaiʻi. This work uses methods that integrate a wide set of data sources including agency and citizen science data, but perhaps more importantly, the integration of regional and global distribution information for these species. We developed transferable and comparable general species distribution models (SDMs) at global and regional scales based on a minimum set of biologically plausible predictors. The regional models were developed for each species using only regional location data and pseudo-absences (PAs) wihtin the extent of the main Hawaiian Islands and regionally derived bioclimatic variables (250 m). These models are available as both habitat suitability maps with pixel values ranging from 0 (low suitability) to 1 (high suitability); and as binary maps that separate areas of potential presence (1) from those where presence is not expected (0) based on the environmental predictors considered. This data set contains two regional model 17 band geospatial raster stacks for the suitability and binary maps with one band per each of the 17 EMIP species selected.

We created a comprehensive estimate of potential distribution for a subset of 17 ecosystem modifying invasive plants (EMIPs) in Hawaiʻi. This work uses methods that integrate a wide set of data sources including agency and citizen science data, but perhaps more importantly, the integration of regional and global distribution information for these species. We built three sets of ensemble species distribution models (SDMs) for each species. We first built global and regional ensemble distribution models for each species. Then, to create a comprehensive estimate of potential invasive species distribution for our study species in Hawaiʻi, we built nested regional models that integrate our global and regional ensemble models. This approach and the resulting mapped distributions are the most comprehensive to date for Hawaiian invasive plants and can possibly be applied more broadly to other species in the future. These models are available as both habitat suitability maps with pixel values ranging from 0 (low suitability) to 1 (high suitability); and as binary maps that separate areas of potential presence (1) from those where presence is not expected (0) based on the environmental predictors considered.

We created a comprehensive estimate of potential distribution for a subset of 17 ecosystem modifying invasive plants (EMIPs) in Hawaiʻi. This work uses methods that integrate a wide set of data sources including agency and citizen science data, but perhaps more importantly, the integration of regional and global distribution information for these species. We built three sets of ensemble species distribution models (SDMs) for each species. We first built global and regional ensemble distribution models for each species. Then, to create a comprehensive estimate of potential invasive species distribution for our study species in Hawaiʻi, we built nested regional models that integrate our global and regional ensemble models. This approach and the resulting mapped distributions are the most comprehensive to date for Hawaiian invasive plants and can possibly be applied more broadly to other species in the future. These models are available as both habitat suitability maps with pixel values ranging from 0 (low suitability) to 1 (high suitability); and as binary maps that separate areas of potential presence (1) from those where presence is not expected (0) based on the environmental predictors considered.

This dataset comprises high-resolution geotif files representing various aspects of the ʻākohekohe (Palmeria dolei) potential habitat on the Island of Hawaiʻi. It includes a habitat suitability map showing average suitability scores, a map of homogenous forested areas (HFAs) depicting clusters with consistent suitability scores, and a map of pixel-wise standard deviation across habitat suitability models. These maps were generated through a comprehensive analysis using lidar-based metrics, offering detailed insights into the habitat preferences of ʻākohekohe.

This dataset comprises high-resolution geotif files representing various aspects of the ʻākohekohe (Palmeria dolei) potential habitat on the Island of Hawaiʻi. It includes a habitat suitability map showing average suitability scores, a map of homogenous forested areas (HFAs) depicting clusters with consistent suitability scores, and a map of pixel-wise standard deviation across habitat suitability models. These maps were generated through a comprehensive analysis using lidar-based metrics, offering detailed insights into the habitat preferences of ʻākohekohe.

This is the primary output dataset from the project to access the potential impacts of climate change on vegetation management strategies within Hawaii Volcanoes National Park (HAVO). The key objective of this project was to combine climate projections from the International Pacific Research Center (IPRC) and plant distribution models from Price et al. to produce a series of projected species range maps over the next century. Although the project focused on HAVO, the projected species range maps were created for seven of the main Hawaiian Islands. We stored the model output as rasters (.TIF files); additionally we created multi-panel maps of these rasters that are available separately. In summary, this dataset consists of 4,095 rasters that delineate plant species range, both present and future, for various climate change scenarios and years. The series covers 39 species, 7 islands, and 15 different combinations of climate trajectory and year. The contents of each raster varies slightly, but the contents can be determined from the specific filename. Filenames have a consistent naming convenion, as follows: Species name + island + file type + climate trajectory + year.TIF, where the following definitions apply: Species name = abbreviated code representing genus and species; Island = 1 of the main 7 Hawaiian Islands (Hawaii, Maui, Kahoolwe, Lanai, Molokai, Oahu, and Kauai); File type = one of 3 file types: (1) RANGE = present species range as of year 2000, (2) 80 PCT = binary raster of habitat suitability, (3) CHANGE TO 80 = raster showing the change in suitability between the year 2000 and the year indicated in the file name; Climate trajectory = lower (concave upward trajectory of change in rainfall and temperature over the century), middle (linear change in rainfall and temperature), upper (concave downward trajectory of change in rainfall and temperature), or future (where all three trajectories converge in 2090); Year = one of the following years: 2000, 2040, 2070, or 2090. For example, consider this filename: Acakoa Hawaii 80 pct future2090.tif. This filename defines the following: Species name = Acakoa (Acacia koa), Island = Hawaii island, File type = 80 pct, indicating that it is a binary raster of habitat suitability where a value of 1 means 80% of model iterations forecast suitable habitat, and a value of 0 means less than 80% of model runs project suitability, Climate trajectory = future, which represents the point in the future (2090) where the lower, middle and upper trajectories converge, Year = 2090 (end of century since that's when our climate data set series ends).