In 'Simulation to evaluate response of population models to annual trends in detectability', we provide data and R code necessary to create simulation scenarios and estimate trends with different population models (Monroe et al. 2019). Literature cited: Monroe, A. P., G. T. Wann, C. L. Aldridge, and P. S. Coates. 2019. The importance of simulation assumptions when evaluating detectability in population models. Ecosphere 10(7):e02791. 10.1002/ecs2.2791

These data were compiled to determine whether transient population dynamics substantially alter population growth rates of sagebrush after disturbance, impede resilience and restoration, and in turn drive ecosystem transformation. Data were collected from 2014-2016 on sagebrush population height distributions at 531 sites across the Great Basin that had burned and were subsequently reseeded by the BLM. These data include field data on sagebrush density in 6 size classes and site attributes (seeding year, sampling year, random site designation, elevation, seeding rate). Also included are modeled spring soil moisture data at each site from the year of seeding to sampling. This data release includes associated software code allows the inference of demographic rates (survival, reproduction, and individual growth) of sagebrush using Hamiltonian Monte Carlo approaches in Stan (https://mc-stan.org/).

These data were compiled to determine whether transient population dynamics substantially alter population growth rates of sagebrush after disturbance, impede resilience and restoration, and in turn drive ecosystem transformation. Data were collected from 2014-2016 on sagebrush population height distributions at 531 sites across the Great Basin that had burned and were subsequently reseeded by the BLM. These data include field data on sagebrush density in 6 size classes and site attributes (seeding year, sampling year, random site designation, elevation, seeding rate). Also included are modeled spring soil moisture data at each site from the year of seeding to sampling. This data release includes associated software code allows the inference of demographic rates (survival, reproduction, and individual growth) of sagebrush using Hamiltonian Monte Carlo approaches in Stan (https://mc-stan.org/).

We simulate over 28,000 datasets and saved their model outputs to answer the following three questions: (1) what is an adequate sampling design for the multi-scale occupancy model when there are a priori expectations of parameter estimates?, (2) what is an adequate sampling design when we have no expectations of parameter estimates?, and (3) what is the cost (in terms of bias, accuracy, precision and coverage) in occupancy estimates) if availability is not accounted for? Specifically, we simulated data under four scenarios: Scenario 1 (n = 10,000): Species availability is constant across sites (but less than one), Scenario 2 (n = 9,358): Species availability is heterogenous across sites, Scenario 3 (n = 2,815): Species availability is heterogenous across years, and Scenario 4 (n = 5,942): Species availability is correlated to their detection probability. Then, for each scenario except the first, we analyzed the data using four different estimators: (i) constant multi-scale occupancy model, (ii) multi-scale occupancy model with a random-effects term in the availability part of the model, (iii) constant single-scale occupancy model, and (iv) single-scale occupancy model with a random-effects term in the detection part of the model. Note the formulation of the random-effects terms included in the models mimicked the way that data were simulated (e.g., if species availability was heterogenous across sites, then a site random-effects term was included in the models). The first scenario was analyzed using models (i) and (iii) only. For simplicity, we refer to models (i) and (iii) as ‘constant’ models or 'fixed-effects' models. We refer to models (ii) and (iv) as ‘random-effects’ models. The summary of simulated data and model estimates are located in four folders, each corresponding to a different simulated scenario: Scenario 1 (n = 10,000): Folder ModelOutput_Scen1_TwolevelSim = csv files holding data are named Results_TwoLevelAvail_2lev_x.csv Scenario 2 (n = 9,358): Folder ModelOutput_Scen2_HeteroSite = csv files holding data are named Results_TwoLevelAvail_Hetero_x.csv Scenario 3 (n = 2,815): Folder ModelOutput_Scen3_HeteroYear = csv files holding data are named Results_TwoLevelAvail_HeteroSeason_x.csv Scenario 4 (n = 5,942): Folder ModelOutput_Scen4_Cor = csv files holding data are named Results_TwoLevelAvail_Cor_x.csv Each row in each of the csv files contains information related to a different simulated dataset and includes information related to: sampling design, true parameter values, and model estimates. Other files in the folder correspond to the entire model output (.rda files), time for model run to complete (time_..csv), and a file indicating whether or not the model run finished (nsim...csv). For more information related to those files, we point the user to the code that generated them: Scenario 1 (n = 10,000): Scen1_Constant.R Scenario 2 (n = 9,358): Scen2_HeteroSite.R Scenario 3 (n = 2,815): Scen3_HeteroYear.R Scenario 4 (n = 5,942): Scen4_Corr.R

These data were estimated for use in the bioecomomic model simulation of the rainbow trout population in the Colorado River in Marble Canyon. The initial rainbow trout abundance is a vector (RBT_intN) representing the population of rainbow trout within each river segment (151 mile long sergments) along the mainstem of the Colorado River from Lees Ferry to 151 river miles downstream. The movement matrix (MMat) is a distribution that estimates the probability that a rainbow trout wil move to any one of the 151 river segments downstream of Lees Ferry.

We developed habitat suitability models for invasive plant species selected by Department of Interior land management agencies. We applied the modeling workflow developed in Young et al. 2020 to species not included in the original case studies. Our methodology balanced trade-offs between developing highly customized models for a few species versus fitting non-specific and generic models for numerous species. We developed a national library of environmental variables known to physiologically limit plant distributions and relied on human input based on natural history knowledge to further narrow the variable set for each species before developing habitat suitability models. We developed models using five algorithms with VisTrails: Software for Assisted Habitat Modeling [SAHM 2.1.2]. We accounted for uncertainty related to sampling bias by using two alternative sources of background samples, and constructed model ensembles using the 10 models for each species (five algorithms by two background methods) for four different thresholds. Each species folder contains the potential distribution of the species and all raster layers were produced using VisTrails:SAHM [SAHM 2.1.2]. Each of the 8 rasters represent the following: 1) MPP - minimum predicted presence threshold 2) 0.01 - one percentile threshold 3) 0.1 - ten percentile threshold 4) MaxSS - maximum sensitivity plus specificity threshold 5) MPP - minimum predicted presence threshold with Restricted Environmental Conditions 6) 0.01 - one percentile threshold with Restricted Environmental Conditions 7) 0.1 - ten percentile threshold with Restricted Environmental Conditions 8) MaxSS - maximum sensitivity plus specificity threshold with Restricted Environmental Conditions These rasters will be integrated into the Invasive Species Habitat Tool (INHABIT), a web application displaying visual and statistical summaries of nationwide habitat suitability models for manager identified invasive plant species. These species include: African rue (Peganum harmala), Air potato (Dioscorea bulbifera), Amur honeysuckle (Lonicera maackii), Amur peppervine (Ampelopsis brevipedunculata), Annual bluegrass (Poa annua ), Annual rye (Lolium multiflorum), Asian mustard (Brassica tournefortii), Beefsteak mint (Perilla frutescens), Bigleaf periwinkle (Vinca major), Bird vetch (Vicia cracca), Bishop's goutweed (Aegopodium podagraria), Black henbane (Hyoscyamus niger), Bohemian knotweed (Fallopia bohemica), Bradford pear (Pyrus calleryana), Buffelgrass (Cenchrus ciliaris), Bulbous bluegrass (Poa bulbosa), Bull thistle (Cirsium vulgare), Bur buttercup (Ranunculus testiculatus), Burning bush (Euonymus alatus), Camelthorn (Alhagi maurorum), Canada thistle (Cirsium arvense), Cereal rye (Secale cereale), Cheatgrass (Bromus tectorum), Chinaberry (Melia azedarach), Chinese holly (Ilex cornuta), Chinese privet (Ligustrum sinense), Chinese tallowtree (Triadica sebifera), Chinese wisteria (Wisteria sinensis), Chocolate vine (Akebia quinata), Clasping pepperweed (Lepidium perfoliatum), Cogongrass (Imperata cylindrica), Common crupina (Crupina vulgaris), Common gorse (Ulex europaeus ), Common reed (Phragmites australis), Common tansy (Tanacetum vulgare), Coral ardisia (Ardisia crenata), Crape myrtle (Lagerstroemia indica), Creeping bentgrass (Agrostis stolonifera), Creeping buttercup (Ranunculus repens), Crested wheatgrass (Agropyron cristatum), Crown vetch (Securigera varia), Dalmatian toadflax (Linaria dalmatica), Diffuse knapweed (Centaurea diffusa), Dyer's woad (Isatis tinctoria), English holly (Ilex aquifolium), English ivy (Hedera helix), European beachgrass (Ammophila arenaria ), False brome (Brachypodium sylvaticum), Field brome (Bromus arvensis), Fountaingrass (Pennisetum setaceum), French broom (Genista monspessulana), Fuller's teasel (Dipsacus fullonum), Garlic mustard (Alliaria petiolata), Giant knotweed (Fallopia sachalinensis), Hairy cat's ear (Hypochaeris radicata), Halogeton (Halogeton glomeratus),

We developed habitat suitability models for invasive plant species selected by Department of Interior land management agencies. We applied the modeling workflow developed in Young et al. 2020 to species not included in the original case studies. Our methodology balanced trade-offs between developing highly customized models for a few species versus fitting non-specific and generic models for numerous species. We developed a national library of environmental variables known to physiologically limit plant distributions and relied on human input based on natural history knowledge to further narrow the variable set for each species before developing habitat suitability models. We developed models using five algorithms with VisTrails: Software for Assisted Habitat Modeling [SAHM 2.1.2]. We accounted for uncertainty related to sampling bias by using two alternative sources of background samples, and constructed model ensembles using the 10 models for each species (five algorithms by two background methods) for four different thresholds. Each species folder contains the potential distribution of the species and all raster layers were produced using VisTrails:SAHM [SAHM 2.1.2]. Each of the 8 rasters represent the following: 1) MPP - minimum predicted presence threshold 2) 0.01 - one percentile threshold 3) 0.1 - ten percentile threshold 4) MaxSS - maximum sensitivity plus specificity threshold 5) MPP - minimum predicted presence threshold with Restricted Environmental Conditions 6) 0.01 - one percentile threshold with Restricted Environmental Conditions 7) 0.1 - ten percentile threshold with Restricted Environmental Conditions 8) MaxSS - maximum sensitivity plus specificity threshold with Restricted Environmental Conditions These rasters will be integrated into the Invasive Species Habitat Tool (INHABIT), a web application displaying visual and statistical summaries of nationwide habitat suitability models for manager identified invasive plant species. These species include: African rue (Peganum harmala), Air potato (Dioscorea bulbifera), Amur honeysuckle (Lonicera maackii), Amur peppervine (Ampelopsis brevipedunculata), Annual bluegrass (Poa annua ), Annual rye (Lolium multiflorum), Asian mustard (Brassica tournefortii), Beefsteak mint (Perilla frutescens), Bigleaf periwinkle (Vinca major), Bird vetch (Vicia cracca), Bishop's goutweed (Aegopodium podagraria), Black henbane (Hyoscyamus niger), Bohemian knotweed (Fallopia bohemica), Bradford pear (Pyrus calleryana), Buffelgrass (Cenchrus ciliaris), Bulbous bluegrass (Poa bulbosa), Bull thistle (Cirsium vulgare), Bur buttercup (Ranunculus testiculatus), Burning bush (Euonymus alatus), Camelthorn (Alhagi maurorum), Canada thistle (Cirsium arvense), Cereal rye (Secale cereale), Cheatgrass (Bromus tectorum), Chinaberry (Melia azedarach), Chinese holly (Ilex cornuta), Chinese privet (Ligustrum sinense), Chinese tallowtree (Triadica sebifera), Chinese wisteria (Wisteria sinensis), Chocolate vine (Akebia quinata), Clasping pepperweed (Lepidium perfoliatum), Cogongrass (Imperata cylindrica), Common crupina (Crupina vulgaris), Common gorse (Ulex europaeus ), Common reed (Phragmites australis), Common tansy (Tanacetum vulgare), Coral ardisia (Ardisia crenata), Crape myrtle (Lagerstroemia indica), Creeping bentgrass (Agrostis stolonifera), Creeping buttercup (Ranunculus repens), Crested wheatgrass (Agropyron cristatum), Crown vetch (Securigera varia), Dalmatian toadflax (Linaria dalmatica), Diffuse knapweed (Centaurea diffusa), Dyer's woad (Isatis tinctoria), English holly (Ilex aquifolium), English ivy (Hedera helix), European beachgrass (Ammophila arenaria ), False brome (Brachypodium sylvaticum), Field brome (Bromus arvensis), Fountaingrass (Pennisetum setaceum), French broom (Genista monspessulana), Fuller's teasel (Dipsacus fullonum), Garlic mustard (Alliaria petiolata), Giant knotweed (Fallopia sachalinensis), Hairy cat's ear (Hypochaeris radicata), Halogeton (Halogeton glomeratus),

We developed habitat suitability models for invasive plant species selected by Department of Interior land management agencies. We applied the modeling workflow developed in Young et al. 2020 to species not included in the original case studies. Our methodology balanced trade-offs between developing highly customized models for a few species versus fitting non-specific and generic models for numerous species. We developed a national library of environmental variables known to physiologically limit plant distributions (Engelstad et al. 2022 Table S1: https://doi.org/10.1371/journal.pone.0263056) and relied on human input based on natural history knowledge to further narrow the variable set for each species before developing habitat suitability models. We developed models using five algorithms with VisTrails: Software for Assisted Habitat Modeling [SAHM 2.1.2]. We accounted for uncertainty related to sampling bias by using two alternative sources of background samples, and constructed model ensembles using the 10 models for each species (five algorithms by two background methods) for three different thresholds (conservative to targeted). This data bundle contains a single file of tabular summaries by management unit (including each species/ ensemble type combination) and a subfolder for each species that contains the merged data sets used to create models, the six raster files associated with the species, and tabular outputs including response curve data, variable importance information, and model assessment metrics. Each of the six rasters represent the following: 1) 0.01 - one percentile threshold 2) 0.1 - ten percentile threshold 3) MaxSS - maximum sensitivity plus specificity threshold 4) 0.01 - one percentile threshold with Restricted Environmental Conditions 5) 0.1 - ten percentile threshold with Restricted Environmental Conditions 6) MaxSS - maximum sensitivity plus specificity threshold with Restricted Environmental Conditions The bundle documentation files are: 1) 'INHABIT_V3_metdata.xml' (this file) which contains the project-level metadata 2) managementSummaries.csv is the tabular summaries by management unit. 3) 'mergedDataset.csv' contains the merged data set used to create the models, including location and associated environmental data, for each species. 4) XX.tif where XX is the raster type explained above (threshold; masked or not). 5) responseCurves.csv is the tabular information need to produce response curves for each predictor retained in each of the 10 models produced for each species. 6) variableImportance.csv is the tabular summaries indicating predictor importance for each of the 10 models produced for each species. 7) assessmentMetrics.csv is the tabular summaries of assessment metrics for each model or ensemble for each species. These data will be integrated into the third version of the Invasive Species Habitat Tool (INHABIT), a web application displaying visual and statistical summaries of nationwide habitat suitability models for manager identified invasive plant species. These species include: African rue (Peganum harmala), Air potato (Dioscorea bulbifera), Alkali swainsonpea (Sphaerophysa salsula), Amur honeysuckle (Lonicera maackii), Amur maple (Acer ginnala), Amur peppervine (Ampelopsis brevipedunculata), Annual bluegrass (Poa annua), Annual rye (Lolium multiflorum), Asian mustard (Brassica tournefortii), Autumn olive (Elaeagnus umbellata), Balloon vine (Cardiospermum halicacabum), Beefsteak mint (Perilla frutescens), Bermudagrass (Cynodon dactylon), Bigleaf periwinkle (Vinca major), Bird vetch (Vicia cracca), Bishop's goutweed (Aegopodium podagraria), Black henbane (Hyoscyamus niger), Bohemian knotweed (Fallopia bohemica), Bradford pear (Pyrus calleryana), Brazilian peppertree (Schinus terebinthifolius), Briton's wild petunia (Ruellia simplex), Broad leaved helleborine (Epipactis helleborine), Buffelgrass (Cenchrus ciliaris), Bulbous bluegrass (Poa bulbosa), Bull thistle (Cirsium vulgare), Bur