Influence of an abscisic acid (ABA) seed coating on seed germination rate and timing of Bluebunch Wheatgrass.

Semi-arid rangeland degradation is a reoccurring issue throughout the world. In the Great Basin of North America, seeds sown in the fall to restore degraded sagebrush (Artemisia spp.) steppe plant communities may experience high mortality in winter due to exposure of seedlings to freezing temperatures and other stressors. Delaying germination until early spring when conditions are more suitable for growth may increase survival. We evaluated the use of BioNik™ (Valent BioSciences LLC) abscisic acid (ABA) to delay germination of bluebunch wheatgrass (Pseudoroegneria spicata). Seed was either left untreated or coated at five separate rates of ABA ranging from 0.25 to 6.0 g 100 g-1 of seed. Seeds were incubated at five separate constant temperatures from 5 to 25°C. From the resultant germination data, we developed quadratic thermal accumulation models for each treatment and applied them to 4 years of historic soil moisture and temperature data across six sagebrush steppe sites to predict germination timing. Total germination percentage remained similar across all temperatures except at 25°C, where high ABA rates had slightly lower values. All ABA doses delayed germination, with the greatest delays at 5-10°C. For example, the time required for 50% of the seeds to germinate at 5°C was increased by 16-46 d, depending on the amount of ABA applied. Seed germination models predicted that the majority of untreated seed would germinate 5-11 weeks after a 15 October simulated planting date. In contrast, seeds treated with ABA were predicted to delay germination to late winter or early spring. These results indicate that ABA coatings may delay germination of fall planted seed until conditions are more suitable for plant survival and growth.

Use of auto-germ to model germination timing in the sagebrush-steppe.

Germination timing has a strong influence on direct seeding efforts, and therefore is a closely tracked demographic stage in a wide variety of wildland and agricultural settings. Predictive seed germination models, based on soil moisture and temperature data in the seed zone are an efficient method of estimating germination timing. We utilized Visual Basic for Applications (VBA) to create Auto-Germ, which is an Excel workbook that allows a user to estimate field germination timing based on wet-thermal accumulation models and field temperature and soil moisture data. To demonstrate the capabilities of Auto-Germ, we calculated various germination indices and modeled germination timing for 11 different species, across 6 years, and 10 Artemisia-steppe sites in the Great Basin of North America to identify the planting date required for 50% or more of the simulated population to germinate in spring (1 March or later), which is when conditions are predicted to be more conducive for plant establishment. Both between and within the species, germination models indicated that there was high temporal and spatial variability in the planting date required for spring germination to occur. However, some general trends were identified, with species falling roughly into three categories, where seeds could be planted on average in either fall (Artemisia tridentata ssp. wyomingensis and Leymus cinereus), early winter (Festuca idahoensis, Poa secunda, Elymus lanceolatus, Elymus elymoides, and Linum lewisii), or mid-winter (Achillea millefolium, Elymus wawawaiensis, and Pseudoroegneria spicata) and still not run the risk of germination during winter. These predictions made through Auto-Germ demonstrate that fall may not be an optimal time period for sowing seeds for most non-dormant species if the desired goal is to have seeds germinate in spring.

Postfire soil water repellency in piñon-juniper woodlands: Extent, severity, and thickness relative to ecological site characteristics and climate.

Wildfires can create or intensify water repellency in soil, limiting the soil's capacity to wet and retain water. The objective of this research was to quantify soil water repellency characteristics within burned piñon-juniper woodlands and relate this information to ecological site characteristics. We sampled soil water repellency across forty-one 1,000 m2 study plots within three major wildfires that burned in piñon-juniper woodlands. Water repellency was found to be extensive-present at 37% of the total points sampled-and strongly related to piñon-juniper canopy cover. Models developed for predicting SWR extent and severity had R2adj values of 0.67 and 0.61, respectively; both models included piñon-juniper canopy cover and relative humidity the month before the fire as coefficient terms. These results are important as they suggest that postfire water repellency will increase in the coming years as infilling processes enhance piñon-juniper canopy cover. Furthermore, reductions in relative humidity brought about by a changing climate have the potential to link additively with infilling processes to increase the frequency and intensity of wildfires and produce stronger water repellency over a greater spatial extent. In working through these challenges, land managers can apply the predictive models developed in this study to prioritize fuel control and postfire restoration treatments.

An object-based image analysis of pinyon and juniper woodlands treated to reduce fuels.

Mechanical and prescribed fire treatments are commonly used to reduce fuel loads and maintain or restore sagebrush steppe rangelands across the Great Basin where pinyon (Pinus) and juniper (Juniperus) trees are encroaching and infilling. Geospatial technologies, particularly remote sensing, could potentially be used in these ecosystems to (1) evaluate the longevity of fuel reduction treatments, (2) provide data for planning and designing future fuel-reduction treatments, and (3) assess the spatial distribution of horizontal fuel structure following fuel-reduction treatments. High-spatial resolution color-infrared imagery (0.06-m pixels) was acquired for pinyon and juniper woodland plots where fuels were reduced by either prescribed fire, tree cutting, or mastication at five sites in Oregon, California, Nevada, and Utah. Imagery was taken with a Vexcel UltraCam X digital camera in June 2009. Within each treatment plot, ground cover was measured as part of the Sagebrush Steppe Treatment Evaluation Project. Trimble eCognition Developer was used to classify land cover classes using object-based image analysis (OBIA) techniques. Differences between cover estimates using OBIA and ground-measurements were not consistently higher or lower for any land cover class and when evaluated for individual sites, were within ±5 % of each other. The overall accuracy and the K hat statistic for classified thematic maps for each treatment were: prescribed burn 85 % and 0.81; cut and fell 82 % and 0.77, and mastication 84 % and 0.80. Although cover assessments from OBIA differed somewhat from ground measurements, they are sufficiently accurate to evaluate treatment success and for supporting a broad range of management concerns.

Western land managers will need all available tools for adapting to climate change, including grazing: a critique of Beschta et al.

In a previous article, Beschta et al. (Environ Manag 51(2):474-491, 2013) argue that grazing by large ungulates (both native and domestic) should be eliminated or greatly reduced on western public lands to reduce potential climate change impacts. The authors did not present a balanced synthesis of the scientific literature, and their publication is more of an opinion article. Their conclusions do not reflect the complexities associated with herbivore grazing. Because grazing is a complex ecological process, synthesis of the scientific literature can be a challenge. Legacy effects of uncontrolled grazing during the homestead era further complicate analysis of current grazing impacts. Interactions of climate change and grazing will depend on the specific situation. For example, increasing atmospheric CO2 and temperatures may increase accumulation of fine fuels (primarily grasses) and thus increase wildfire risk. Prescribed grazing by livestock is one of the few management tools available for reducing fine fuel accumulation. While there are certainly points on the landscape where herbivore impacts can be identified, there are also vast grazed areas where impacts are minimal. Broad scale reduction of domestic and wild herbivores to help native plant communities cope with climate change will be unnecessary because over the past 20-50 years land managers have actively sought to bring populations of native and domestic herbivores in balance with the potential of vegetation and soils. To cope with a changing climate, land managers will need access to all available vegetation management tools, including grazing.

Feature extraction techniques for measuring piñon and juniper tree cover and density, and comparison with field-based management surveys.

Western North America is experiencing a dramatic expansion of piñon (Pinus spp.) and juniper (Juniperus spp.) (P-J) trees into shrub-steppe communities. Feature extracted data acquired from remotely sensed imagery can help managers rapidly and accurately assess this land cover change in order to manage rangeland ecosystems at a landscape-scale. The objectives of this study were to: (1) develop an effective and efficient method for accurately quantifying P-J tree canopy cover and density directly from high resolution photographs and (2) compare feature-extracted data to typical in-situ datasets used by land managers. Tree cover was extracted from aerial-photography using Feature Analyst®. Tree density was calculated as the sum of the total number of individual polygons (trees) within the tree cover output file after isolation using a negative buffer post-processing technique. Feature-extracted data were compared to ground reference measurements from Utah's Division of Wildlife Resources Range Trend Project (DWR-RTP). We found that the proposed feature-extraction techniques used for measuring cover and density were highly correlated to ground reference and DWR-RTP datasets. Feature-extracted measurements of cover generally showed a near 1:1 relationship to these data, while tree density was underestimated; however, after calibration for juvenile trees, a near 1:1 relationship was realized. Feature-extraction techniques used in this study provide an efficient method for assessing important rangeland indicators, including: density, cover, and extent of P-J tree encroachment. Correlations found between field and feature-extracted data provide evidence to support extrapolation between the two approaches when assessing woodland encroachment.