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 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 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. 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. 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.

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.

Dataset includes publicly available geologic and rainfall data, and environmental and ecological data derived or collected for this project. Specifically, water infiltration measurements, interepreted field-saturated hydraulic conductivity values, ungulate activity, vegetation cover, general soil and weather conditions data are included. Soil samples were collected, lab analyzed, and are included in the dataset. Field-collected data are associated with plots that encompassed approximately a 3 x 3 m area; site data represent approximately 20 x 20 m. First posted: 4 March 2020 (available from author) Revised: 2 April 2020 (version 2.0) The revision is provided due to minor refinement of the dataset and updated analysis. Additional model variables are presented in this version (2.0).