cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7923/vj7d-ks92
title: Data from: Modeling tree canopy height using machine learning over mixed vegetation landscapes

description: Although the random forest algorithm has been widely applied to remotely sensed data to predict characteristics of forests, such as tree canopy height, the effect of spatial non-stationarity in the modeling process is oftentimes neglected. Previous studies have proposed methods to address the spatial variance at local scales, but few have explored the spatial autocorrelation pattern of residuals in modeling tree canopy height or investigated the relationship between canopy height and model performance. By combining Light Detection and Ranging (LiDAR) and Landsat datasets, we used spatially-weighted geographical random forest (GRF) and traditional random forest (TRF) methods to predict tree canopy height in a mixed dry forest woodland in complex mountainous terrain. Comparisons between TRF and GRF models show that the latter can lower predefined extreme residuals, and thus make the model performance relatively stronger. Moreover, the relationship between model performance and degree of variation of true canopy height can vary considerably within different height quantiles. Both models are likely to present underestimates and overestimates when the corresponding tree canopy heights are high (>95% quantile) and low (<median), respectively. This study provides a critical insight into the relationship between tree canopy height and predictive abilities of random forest models when taking account of spatial non-stationarity. Conclusions indicate that a trade-off approach based on the actual need of project should be taken when selecting an optimal model integrating both local and global effects in modeling attributes such as canopy height from remotely sensed data.
DOI: 10.7923/y123-yh55
title: Data From: Using an Ultraviolet Light Test to Improve Sagebrush Identification and Predict Forage Quality for Wildlife

description: Sagebrush identification can be improved by using a relatively easy ultraviolet (UV) light test on specimens. Sagebrush produces a variety of water-soluble polyphenols called coumarins, which fluoresce a blue color under UV light and can help differentiate species, subspecies, and hybrids. We tested 16 different sagebrush taxa (including species and subspecies) from herbarium specimens and found 3 taxa (low sagebrush, Artemisia arbuscula; Wyoming sagebrush, A. tridentata wyomingensis; and basin sagebrush, A. t. tridentata) that were often misidentified. We show that the UV light test can greatly improve identification of these species. Moreover, given that the UV+ chemicals that discriminate taxa are also considered an indirect biomarker of sagebrush palatability for some herbivores, the UV light test can be used to predict forage quality for threatened species like sage-grouse (Centrocercus spp.) and pygmy rabbits (Brachylagus idahoensis). Collecting voucher specimens of sagebrush at wildlife study sites and comparing their UV intensity to historical herbarium specimens could help identify both current and changing availability of palatable sagebrush for wildlife. We found that even herbarium specimens >80 years old still fluoresce under UV light.
DOI: 10.7923/30ca-6g41
title: Data from: Near-infrared spectroscopy aids ecological restoration by classifying variation of taxonomy and phenology of a native shrub

description: Plant communities are composed of complex phenotypes that not only differ among taxonomic groups and habitats but also change over time within a species. Restoration projects (e.g., translocations, reseeding) can introduce new functional variation in plants, which further diversifies phenotypes and complicates our ability to identify locally adaptive phenotypes for future restoration. Near-infrared spectroscopy (NIRS) offers one approach to detect the chemical phenotypes that differentiate plant species, populations, and phenological states of individual plants over time. We used sagebrush (Artemisia spp.) as a case study to test the accuracy by which NIRS can classify variation within taxonomy and phenology of a plant that is extensively managed and restored. Our results demonstrated that NIRS can accurately classify species of sagebrush within a study site (75-96%), populations of sagebrush within a subspecies (99%), annual phenology within a population (>99%), and seasonal phenology within individual plants (>97%). Low classification accuracy by NIRS in some sites may reflect heterogeneity associated with natural hybridization, translocation of non-local seed sources from past restoration, or complex gene-by-environment (GxE) interactions. Advances in our ability to detect and interpret spectral signals from plants may improve both the selection of seed sources for targeted conservation and the capacity to monitor long-term changes in vegetation.
DOI: 10.7923/e3v2-sk66
title: Data from: Development of an in vitro method of propagation for basin big sagebrush (Artemisia tridentata subsp. tridentata)

description: Data and code files included within release v1.0 from the Sagebrush_in_vitro_prop GitRepo. Please visit the associated project website for specific details about project protocols and workflow.

Basin big sagebrush (Artemisia tridentata subsp. tridentata) is a keystone species of the sagebrush steppe, a widespread ecosystem of western North America threatened by climate change. The study’s goal was to develop an in vitro method of propagation for this taxon to support genome sequencing and genotype-by-environment research on drought tolerance. Such research may ultimately facilitate the reintroduction of big sagebrush in degraded habitats. Seedlings were generated from two diploid mother plants (2n = 2x = 18) collected in environments with contrasting precipitation regimes. The effects of IBA and NAA on rooting of shoot tips were tested on 45 individuals and 15 shoot tips per individual. Growth regulator and individual-seedling effects on percent rooting and roots per shoot tip were evaluated using statistical and clustering analyses. Furthermore, rooted shoot tips were transferred into new media to ascertain their continued growth in vitro. The results suggest that A. tridentata is an outbred species, as shown by individuals’ effect on rooting and growth. IBA addition was the most effective method for promoting adventitious rooting, especially in top-performing individuals. These individuals also have high survival and growth rates upon transferring to new media, making them suitable candidates for generating biomass for genome sequencing and producing clones for genotype-by-environment research.
DOI: 10.7923/6z9m-wz55
title: Data from: Mapping foodscapes and sagebrush morphotypes with unmanned aerial systems for multiple herbivores

description: Context
The amount and composition of phytochemicals in forage plants influences habitat quality for wild herbivores. However, evaluating forage quality at fine resolutions across broad spatial extents (i.e., foodscapes) is challenging. Unmanned aerial systems (UAS) provide an avenue for bridging this gap in spatial scale.

Objectives
We evaluated the potential for UAS technology to accurately predict nutritional quality of sagebrush (Artemisia spp.) across landscapes. We mapped seasonal forage quality across two sites in Idaho, USA, with different mixtures of species but similar structural morphotypes of sagebrush.

Methods
We classified the sagebrush at both study sites using structural features of shrubs with object-based image analysis and machine learning and linked this classification to field measurements of phytochemicals to interpolate a foodscape for each phytochemical with regression kriging. We compared fine-scale landscape patterns of phytochemicals between sites and seasons.

Results
Classification accuracy for morphotypes was high at both study sites (81–87%). Forage quality was highly variable both within and among sagebrush morphotypes. Coumarins were the most accurately mapped (r2 = 0.57–0.81), whereas monoterpenes were the most variable and least explained. Patches with higher crude protein were larger and more connected in summer than in winter.

Conclusions
UAS allowed for a rapid collection of imagery for mapping foodscapes based on the phytochemical composition of sagebrush at fine scales but relatively broad extents. However, results suggest that a more advanced sensor (e.g., hyperspectral camera) is needed to map mixed species of sagebrush or to directly measure forage quality.

Data Usage Notes
Spatial Reference
NAD83 UTM Zone 11N [Camas]/12N [Cedar Gulch]

Patch Type Classifications
25-cm resolution, classes are 3=on mound, 4=off mound, 5=dwarf. On-mound refers to mima mounds with deeper soils that pygmy rabbits use to dig their burrows and are dominated by big sagebrush (Artemisia tridentata), while off-mound refers to patches dominated by big sagebrush but not on mima mounds, while dwarf patches are dominated by short-statured sagebrush species (e.g., black sagebrush [A. nova], low sagebrush [A. arbuscula]).

Maps of Phytochemistry
25-cm resolution and were generated with regression kriging using the patch type layer and point values from leaf chemistry. Phytochemicals: Crude Protein, Coumarins, Total Monoterpenes, Chemical Diversity of Monoterpenes and two individual monoterpenes (1,8-cineole and camphor). If there was no spatial autocorrelation present in the semivariogram, then maps were not generated for that phytochemical.
DOI: 10.7923/rk1c-jz69
title: Data from: Food quality, security, and thermal refuge influence use of microsites and patches by pygmy rabbits (Brachylagus idahoensis) across landscapes and seasons

description: How intensely animals use habitat features depends on their functional properties (i.e., how the feature influences fitness) and the spatial and temporal scale considered. For herbivores, habitat use is expected to reflect the competing risks of starvation, predation, and thermal stress, but the relative influence of each functional property is expected to vary in space and time. We examined how a dietary and habitat specialist, the pygmy rabbit (Brachylagus idahoensis), used these functional properties of its sagebrush habitat—food quality, security, and thermal refuge—at two hierarchical spatial scales (microsite and patch) across two seasons (winter and summer). At the microsite and patch scales, we determined which plant functional traits predicted the number of bites (i.e., foraging) by pygmy rabbits and the number of their fecal pellets (i.e., general habitat use). Pygmy rabbits used microsites and patches more intensely that had higher crude protein and aerial concealment cover and were closer to burrows. Food quality was more influential when rabbits used microsites within patches. Security was more influential in winter than summer, and more at Cedar Gulch than Camas. However, the influence of functional properties depended on phytochemical and structural properties of sagebrush and was not spatiotemporally consistent. These results show function-dependent habitat use that varied according to specific activities by a central-place browsing herbivore. Making spatially explicit predictions of the relative value of habitat features that influence different types of habitat use (i.e., foraging, hiding, thermoregulating) will improve how we predict patterns of habitat use by herbivores and how we monitor and manage functional traits within habitats for wildlife.
DOI: 10.7923/1jnh-z422
title: Compiled Idaho sagebrush chemistry and plant herbivore interaction data

description: Compilation of sagebrush chemistry and herbivore interaction data from Idaho USA. Chemical data includes percent crude protein, percent nitrogen, coumarins, phenolics, total monoterpenes, monoterpene chemical diversity, and quantifiable amounts of the following monoterpenes: aPinene, Camphene, bPinene, p_cymene, 1,8_cineole, monoterpene "unknown 20.1", Camphor, and Borneol. Herbivore interaction data includes bite mark counts, fecal pellet counts, and/or evidence of foraging presence/absence data for pygmy rabbits and sage grouse at select study sites.

At the time of publication (2022-02-09) subsets of these data are utilized in multiple publications (See Related Content).
DOI: 10.7923/9cft-9g84
title: Data from: Assessing accuracy of GAP and LANDFIRE land cover datasets in winter habitats used by greater sage-grouse in Idaho and Wyoming, USA

description: Remotely sensed land cover datasets have been increasingly employed in studies of wildlife habitat use. However, meaningful interpretation of these datasets is dependent on how accurately they estimate habitat features that are important to wildlife. We evaluated the accuracy of the GAP dataset, which is commonly used to classify broad cover categories (e.g., vegetation communities) and LANDFIRE datasets, which are classify narrower cover categories (e.g., plant species) and structural features of vegetation. To evaluate accuracy, we compared classification of cover types and estimates of percent cover and height of sagebrush (Artemisia spp.) derived from GAP and LANDFIRE datasets to field-collected data in winter habitats used by greater sage-grouse (Centrocercus urophasianus). Accuracy was dependent on the type of dataset used as well as the spatial scale (point, 100-m, 1-km and 5-km) and biological level (community versus dominant species) investigated. GAP datasets had the highest overall classification accuracy of broad sagebrush cover types (46.1%) compared to LANDFIRE datasets for narrower cover types (43.8% community-level; 42.6% species-level). Percent cover and height were not accurately estimated in the LANDFIRE dataset. Our results suggest that researchers must be cautious when applying GAP or LANDFIRE datasets to classify narrow categories of land cover types or to predict percent cover or height of sagebrush within sagebrush-dominated landscapes. We conclude that ground-truthing is critical for successful application of land cover datasets in landscape-scale evaluations and management planning, particularly when wildlife use relatively rare habitat types compared to what is available.
DOI: 10.7923/earc-0518
title: Data from: Human population growth and accessibility from cities shape rangeland condition in the American West

description: Compiled data utilized to run model parameters for Requena-Mullor et al. 2023. These data lead to the following conclusions:
	• Human population growth contributes to the decline of sagebrush-steppe rangelands.
	• More accessible rangelands from population centers have higher quality.
	• Open space preservation provides opportunities for rangeland conservation in cities.
	• Coordinated conservation strategies are necessary to protect rangeland ecosystems.
DOI: 10.7923/h898-2606
title: Annotations from: Artemisia tridentata subsp. tridentata drought GxE experiment transcriptomics data-BioProject PRJNA934360

description: The data set contains the transcript annotations for the assembled IDT3 transcriptome. The trinotate annotation file provides an assembled transcript ID, gene name, how the gene was identified (the trinotate pipeline contains several methods of gene identification), and gene ontology information. The accompanying BAM alignment files contain the read mapping data for transcriptome samples used in the differential gene expression experiment. Raw read, BAM alignment and assembled IDT3 transcriptome data files are available from the NCBI SRA database as part of BioProject PRJNA934360.
DOI: 10.7923/s3mm-v098
title: Data from: Socio-ecological interactions promote outbreaks of a harmful invasive plant in an urban landscape

description: These data include the abundance, emergence, and persistence of puncturevine (*Tribulus terrestris*), a harmful invasive species in Western North America. We mapped the demography and distribution of this plant in Boise, ID, United States in summer 2020. These data include both the mapped plots and puncturevine points as well as .csv files with spatial covariates related to puncturevine outbreaks.
DOI: 10.7923/rcbw-kx90
title: Data from: Linking agricultural diversification practices, soil arthropod communities, and soil health

description: These data are associated with the manuscript "Linking agricultural diversification practices, soil arthropod communities, and soil health". Data were collected from the strip plots associated with the Landscapes in Transition Project (www.pnwlit.org). We used these data to evaluate the effects of agricultural diversification through rotation on soil arthropods and soil properties using replicated large-plot field studies representing two climatically distinct agroecological classes in the dryland cereal-growing region of the inland Pacific Northwest, USA. We investigated how different three-year annual crop rotations affected soil arthropod biodiversity and community structure. Treatments reflected “business-as-usual” rotations in dryland systems and diversified rotations achieved by incorporating novel winter pea or forage crops. We also assessed relationships between the Soil Biological Quality index (QBS-ar), which uses soil arthropods as bioindicators of soil health, and other biological and physiochemical soil health indicators. We collected 710 community samples with 82,509 arthropod specimens across 66 taxa. Novel crops in diversified rotations promoted soil arthropod abundance and biodiversity relative to crops they could replace in rotation. Crop type determined community composition. Most taxa driving differences in community structure were predators and detritivores associated with winter pea and forage crops. In addition to effects on soil arthropods associated with specific crops, effects were also detected across rotations. Incorporating winter pea into rotations improved QBS-ar across entire rotations, not just in the winter pea phases, in both AECs. Links between QBS-ar and other soil health indicators were complex and varied by AEC.
DOI: 10.7923/b68t-2s83
title: Data from: High-resolution thermal imagery reveals how interactions between crown structure and genetics shape plant temperature

description: Understanding interactions between environmental stress and genetic variation is crucial to predict the adaptive capacity of species to climate change. Leaf temperature is both a driver and a responsive indicator of plant physiological response to thermal stress, and methods to monitor it are needed. Foliar temperatures vary across leaf to canopy scales and are influenced by genetic factors, challenging efforts to map and model this critical variable. Thermal imagery collected using unoccupied aerial systems (UAS) offers an innovative way to measure thermal variation in plants across landscapes at leaf-level resolutions. We used a UAS equipped with a thermal camera to assess temperature variation among genetically distinct populations of big sagebrush (Artemisia tridentata), a keystone plant species that is the focus of intensive restoration efforts throughout much of western North America. We completed flights across a growing season in a sagebrush common garden to map leaf temperature relative to subspecies and cytotype, physiological phenotypes of plants, and summer heat stress. Our objectives were to: (1) determine whether leaf-level stomatal conductance corresponds with changes in crown temperature; (2) quantify genetic (i.e., subspecies and cytotype) contributions to variation in leaf and crown temperatures; and (3) identify how crown structure, solar radiation, and subspecies-cytotype relate to leaf-level temperature. Stomatal conductance was negatively, non-linearly correlated with crown-level temperature derived from UAS. Subspecies identity best explained crown-level temperature with no difference observed between cytotypes. However, structural phenotypes and microclimate best explained leaf-level temperature. These results show how fine-scale thermal mapping can decouple the contribution of genetic, phenotypic, and environmental factors on leaf temperature dynamics. As climate-change-induced heat stress becomes prevalent, thermal UAS represents a promising way to track plant phenotypes that emerge from gene-by-environment interactions.
DOI: 10.15468/dl.stajzf
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Arachis hypogea",
 "Country is Belize",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016975-250920141307145/datasets/export for details.
DOI: 10.15468/dl.cyrbj4
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Arachis hypogea",
 "Country is Lao People’s Democratic Republic",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016976-250920141307145/datasets/export for details.
DOI: 10.7923/ebbf-ec66
title: Camassia quamash demographic and allometric data

description: This data set includes three-years (2020-2022) of demographic (survival, growth, reproduction) data, one-year of allometric data, and seed germination data over a three-year period for Camassia quamash at the Camas Prairie Centennial Marsh near Hill City, Idaho, USA. The three-year data set also includes individual-level annual measurements of soil moisture, C. quamash percent cover, percent cover of all live vegetation, and percent cover of surrounding non-native Alopecurus arundinaceus biomass and necromass. The data was collected in approximately 1x1 m plots with each C. quamash plant permanently marked. The dataset includes plots that were part of a prescribed fire experiment, some that were part of a bulb harvest experiment, and some that were across a hydroperiod gradient.
DOI: 10.15468/dl.cdezg8
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Arachis hypogea",
 "Country is Lithuania",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016977-250920141307145/datasets/export for details.
DOI: 10.7923/5jce-ye15
title: Digital aerial imagery (RGB and multispectral) from the 2010-SOUTH TRAIL wildfire boundary near Hammett Idaho USA-2022

description: This data set includes raw RGB (red, green, blue) and multispectral (5-band) digital imagery and processed data products collected 2022-05-25 at the 2010-SOUTH TRAIL wildfire boundary (FireCode: ID4301711528820100724, Welty and Jeffries 2020) near Hammett Idaho, approximately one hour southeast of Boise Idaho off Interstate-84. We used a DJI Mavic 2 Pro with Hasselblad 20MP sensor (RGB) with Map Pilot Pro software and DJI Phantom 4 Multispectral sensor (5 band) with DJI GS Pro software to capture imagery over the area of interest. The RGB (Red, Green, Blue) imagery was collected in a crossgrid pattern at 41m above ground level; the resulting imagery have a ground resolution of 1cm/pixel. The multispectral imagery was collected at two altitudes: 37m and 66m above ground level (no crossgrid pattern); the resulting imagery have ground resolution of 2cm/pixel and 3.5cm/pixel, respectively. The images were processed and the products were created in OpenDroneMap version 2.8.8. All products are georectified and in WGS84 UTM Zone 11 N.