Geology topics

Cara Applestein

Publications and source records attributed to Cara Applestein.

24 records · Page 2Linked to original sources

USGS2020

Impact of unburned remnant sagebrush versus outplants on post-fire landscape rehabilitation

Nearly half of the vast sagebrush steppe in the western United states has lost many or nearly all native plant species, largely due to the interaction of invasive species and increased wildfire. Re-establishing sagebrush, a keystone component of these ecosystems, has become a management focus in recent decades using aerial broadcast seeding or limited plantings. One promising avenue for improving the planning and assessment of post-fire seedings involves the spatial patchiness of burn patterns and in the recovery of sagebrush after fire. Unburned remnant or post-fire planted islands (or patches) of sagebrush could be valuable seed sources for species recovery in surrounding burned areas. Information on how much spatial expansion of unburned remnant patches is expected over time could help in the planning of post-fire treatments. However, previous research has indicated that sagebrush seeds do not disperse far, which would imply that unburned or created patches do not contribute much to sagebrush reestablishment effects. Our objective was to determine whether remnant/unburned sagebrush patches contribute to sagebrush recovery in the surrounding burned areas. We quantified seed rain and seedling establishment in relation to patches of sagebrush that were either unburned remnant or had been planted in the first year or so after wildfire. We conducted a seed trapping experiment across 6 different wildfires during two winters to determine seed transport distances. We paired this with a seedling recruitment study on the Soda wildfire where we mapped distances between remnants and seedlings. We found that although a few seeds did travel much farther than previously recorded (maximum of 26 m), seed dispersal was highly variable across sites and patches, and only a small portion of seeds dispersed farther than a few meters from sagebrush patches. Our seedling recruitment assessment confirmed a limited contribution of remnants to seedling recruitment. Specifically, a microsite was only marginally more likely to have a sagebrush seedling even if there was >50 neighbors within a 40 m radius. There were no differences in the quantity of seeds dispersed from remnant versus actively managed patches. Overall, we found that isolated sagebrush patches are unlikely to significantly contribute to landscape regeneration of sagebrush on large fires and that aerial seeding is likely needed to overcome seed limitations. We did detect substantial variation in site-level sagebrush seed production among years, including one site that did not produce any seed in one year. Variability in seed production in space and time appeared to be a potentially more important variable potentially affecting sagebrush seed availability than dispersal distances and is a topic that merits more investigation.

Idaho, Oregon↗

USGS2019

Can’t see the random forest for the decision trees: Selecting predictive models for restoration ecology

Improving predictions of restoration outcomes is increasingly important to resource managers for accountability and adaptive management, yet there is limited guidance for selecting a predictive model from the multitude available. The goal of this paper was to identify an optimal predictive framework for restoration ecology using eleven modeling frameworks (including, machine learning, inferential, and ensemble approaches), and three data groups (field data, geographic data [GIS], and a combination thereof). We test this approach with a dataset from a large post-fire sagebrush reestablishment project in the Great Basin, USA. Predictive power varied among models and data groups, ranging from 58-79% accuracy. Finer scale field data generally had the greatest predictive power, although GIS data were present in the best models overall. An ensemble prediction computed from the ten models parameterized to field data was well above average for accuracy but was outperformed by others that prioritized model parsimony by selecting predictor variables based on rankings of their importance among all candidate models. The variation in predictive power among a suite of modeling frameworks underscores the importance of a model comparison and refinement approach that evaluates multiple models and data groups, and selects variables based on their contribution to predictive power. The enhanced understanding of factors influencing restoration outcomes accomplished by this framework has the potential to aid the adaptive management process for improving future restoration outcomes.

Restoration Ecology↗

USGS2019

Appropriate sample sizes for monitoring burned pastures in sagebrush steppe: How many plots are enough, and can one size fit all?

Statistically defensible information on vegetation conditions is needed to guide rangeland management decisions following disturbances such as wildfire, often for heterogeneous pastures. Here we evaluate the number of plots needed to make informed adaptive management decisions using >2000 plots sampled on the 2015 Soda Fire that burned across 75 pastures and 113,000 ha in Idaho and Oregon, USA. We predicted that the number of plots required to generate a threshold of standard error/mean ≤0.2 (TSR, threshold sampling requirement) for plant cover within pasture units would vary between sampling methods (rapid ocular versus grid-point intercept) and among plot sizes (1, 6, or 531 m2), as well as relative to topography, elevation, pasture size, complexity of soils and vegetation treatments applied, and dominance by exotic annual or perennial grasses. Sampling was adequate for determining exotic annual and perennial grass cover in about half of the pastures. A tradeoff in number versus size of plots sampled was apparent, whereby TSR was attainable with less area searched using smaller plot sizes (1 compared to 531 m2) in spite of less variability between larger plots. TSR for both grass types decreased as their dominance increased (0.5-1.5 plots per % cover increment). TSR decreased for perennial grass but increased for exotic annual grass with higher elevations. TSR increased with standard deviation of elevation for perennial grass sampled with grid-point intercept. Sampling effort could be more reliably predicted from landscape variables for the grid-point compared to ocular sampling method. These findings suggest that adjusting the number and size of sample plots within a pasture or burn area using easily determined landscape variables could increase monitoring efficiency and effectiveness.

Rangeland Ecology and Management↗

USGS2019

Bunchgrass root abundances and their relationship to resistance and resilience of a burned shrub-steppe landscape

Invasion of exotic annual grasses (EAG) and increased wildfire have motivated an emphasis on managing rangeland plant communities for resistance to invasion and resilience to disturbances. These traits are provided primarily by perennial bunchgrasses in rangelands such as shrub steppe, and specifically but also hypothetically, the abundances and functioning of bunchgrass roots. We asked how bunchgrass root abundances relate to annual grass invasion and to more-readily measured, aboveground indicators of bunchgrass vigor. We used a standardized USDA protocol for root measurement in 445 excavations made in 2016-2018 across a topographically and ecologically varied region of sagebrush steppe burned in 2015 Soda megafire in the Northern Great Basin USA. Nearly all (99%) bunchgrasses, including seedlings, had deeper roots than the surrounding annual grasses (mean depth of annuals = 6.8 ±3.3 cm), and 88% of seedlings remained rooted in response to the “tug test” (uprooting resistance to ~1 kg of upward pull on shoot), with smaller plants (mean height and basal diameters < 20 cm and <2 cm, respectively) more likely to fail the test regardless of their root abundances. Lateral roots of bunchgrasses were scarcer in larger basal gaps (interspace between perennials) but were surprisingly not directly related to cover of surrounding EAG. However, EAG increased with basal gap size and decreased with bunchgrass basal diameter size (in addition to pre-fire EAG abundance), although there was considerable unexplained variability in the relationships. These results provide some support for 1) the importance of basal gaps and bunchgrass diameters as indicators of both vulnerability to annual grass invasion and bunchgrass root abundances, and 2) the need for more detailed methods for root measurement than used here in order to substantiate their usefulness in understanding rangeland resistance and resilience.

Rangeland Ecology and Management↗

USGS2018

Vegetative community response to landscape-scale post-fire herbicide (imazapic) application

Disturbances such as wildfire create time-sensitive windows of opportunity for invasive plant treatment, and the timing of herbicide application relative to the time course of plant community development following fire can strongly influence herbicide effectiveness. We evaluated the effect of herbicide (imazapic) applied in the first winter or second fall after the 113,000 ha Soda wildfire on the target exotic annual grasses and also key non-target components of the plant community. We measured responses of exotic and native species cover, species diversity, and occurrence frequency of shrubs and forbs seeded before (1 to 2 or 9 to 10 mo) herbicide application. Additionally, we asked whether landscape factors, including topography, species richness, and/or soil characteristics, influenced the effectiveness of imazapic. Cover of exotic annual grass cover, but not of deep-rooted perennial bunchgrass, was less where imazapic had been applied, whereas more variability was evident in the response of Sandberg bluegrass ( Poa secunda J. Presl) and seeded shrubs and forbs. Regression-tree analysis of the subset of plots measured both before and after the second fall application revealed greater reductions of exotic annual grass cover in places where their cover was <42% before spraying. Otherwise, imazapic effects did not vary with the landscape factors we analyzed.

Invasive Plant Science and Management↗

USGS2018

Thresholds and hotspots for shrub restoration following a heterogeneous megafire

Context Reestablishing foundational plant species through aerial seeding is an essential yet challenging step for restoring the vast semiarid landscapes impacted by plant invasions and wildfire-regime shifts. A key component of the challenge stems from landscape variability and its effects on plant recovery. Objectives We assessed landscape correlates, thresholds, and tipping points for sagebrush presence from fine-scale sampling across a large, heterogeneous area burned the previous year, where we were able to quantify soil surface features that are typically occluded yet can strongly affect recovery patterns. Methods Hypothesis testing and binary-decision trees were used to evaluate factors affecting initial sagebrush establishment, using 2171 field plots (totaling ~ 2,000,000 m 2 sampled) over a 113,000-ha region. Results Sagebrush established in 50% of plots where it was seeded, a > 12-fold greater establishment frequency than in unseeded areas. Sagebrush establishment was enhanced in threshold-like ways by elevation (> 1200 m ASL), topographic features that alter heatload and soil water, and by soil-surface features such as “fertile islands” that bore the imprint of pre-fire sagebrush. Sagebrush occupancy had a negative, linear relationship with exotic-annual grass cover and parabolic relationship with perennial bunchgrasses (optimal at 40% cover). Conclusions Our approach revealed interactive, ecological relationships such as novel soil-surface effects on first year establishment of sagebrush across the burned landscape, and identified “hot spots” for recovery. The approach could be expanded across sites and years to provide the information needed to explain past seeding successes or failures, and in designing treatments at the landscape scale.

Idaho↗