Geology topics

Robert Arkle

Publications and source records attributed to Robert Arkle.

13 recordsLinked to original sources

USGS2024

Leveraging extensive soil, vegetation, fire, and land treatment data to inform restoration across the sagebrush biome

Context Widespread ecological degradation has prompted calls for massive global investments in ecological restoration, yet limited resources necessitate efficient application of restoration efforts. In western North America, altered fire regimes are increasing the scale of restoration needed to preserve the sagebrush ( Artemisia species) biome but prioritizing and implementing effective restoration is complicated by the vast and heterogeneous sagebrush landscape, which includes gradients in climate, disturbance, and species composition. Objectives To develop spatially explicit and context-dependent estimates of treatment efficacy and sagebrush recovery rates. Methods We leveraged a suite of spatio-temporally extensive datasets to evaluate the influence of restoration treatments and environmental conditions on trends in post-disturbance sagebrush cover, with an emphasis on understanding differences between sites recovering naturally and sites receiving restoration treatments. We used estimates from these models to develop spatially explicit projections for sagebrush recovery, conditional on disturbance, restoration practice, and environmental conditions. Results We found seeding Artemisia spp. increased sagebrush cover over time relative to natural recovery, but this relationship depended on spring soil moisture availability and treatment methods. Natural recovery was positively influenced by soil moisture and sagebrush cover and negatively influenced by cumulative burns and annual herbaceous cover, while the influence of perennial herbaceous cover varied with soil moisture. Conclusions Our results provide biome-wide insights and spatially explicit tools that can inform economic cost-effectiveness analyses, restoration prioritization tools, and other scientific endeavors to ensure managers have the tools and information needed to effectively steward the sagebrush biome in a rapidly changing world.

Landscape Ecology↗

USGS2024

Climate futures for lizards and snakes in western North America may result in new species management issues

We assessed changes in fundamental climate-niche space for lizard and snake species in western North America under modeled climate scenarios to inform natural resource managers of possible shifts in species distributions. We generated eight distribution models for each of 130 snake and lizard species in western North America under six time-by-climate scenarios. We combined the highest-performing models per species into a single ensemble model for each scenario. Maps were generated from the ensemble models to depict climate-niche space for each species and scenario. Patterns of species richness based on climate suitability and niche shifts were calculated from the projections at the scale of the entire study area and individual states and provinces, from Canada to Mexico. Squamate species' climate-niche space for the recent-time climate scenario and published known ranges were highly correlated ( r = 0.81). Overall, reptile climate-niche space was projected to move northward in the future. Sixty-eight percent of species were projected to expand their current climate-niche space rather than to shift, contract, or remain stable. Only 8.5% of species were projected to lose climate-niche space in the future, and these species primarily occurred in Mexico and the southwestern U.S. We found few species were projected to lose all suitable climate-niche space at the state or province level, although species were often predicted to occupy novel areas, such as at higher elevations. Most squamate species were projected to increase their climate-niche space in future climate scenarios. As climate niches move northward, species are predicted to cross administrative borders, resulting in novel conservation issues for local landowners and natural resource agencies. However, information on species dispersal abilities, landscape connectivity, biophysical tolerances, and habitat suitability is needed to contextualize predictions relative to realized future niche expansions.

Ecology and Evolution↗

USGS2022

Reestablishing a foundational species: limitations on post-wildfire sagebrush seedling establishment

Improving post-wildfire restoration of foundational plant species is crucial for conserving imperiled ecosystems. We sought to better understand the initial establishment of sagebrush ( Artemisia sp.), a foundational shrubland species over a vast area of western North America, in the first 1–2 years post-wildfire, a critical time period for population recovery. Field data from 460 sagebrush populations sampled across the Great Basin revealed several patterns. Sagebrush seedlings were uncommon in the first 1–2 years after fire, with none detected in 69% of plots, largely because most fires occurred in areas of low resistance to invasive species and resilience to disturbance (hereafter, R&R). Post-fire aerial seeding of sagebrush dramatically increased seedling occupancy, especially in low R&R areas, which exhibited a 3.4-fold increase in occupancy over similar unseeded locations. However, occupancy models and repeat surveys suggested exceptionally high mortality, as occupancy rates declined by as much as 50% between the first and second years after fire. We found the prevalence of “fertile island” microsites (patches beneath fire-consumed sagebrush) to be the best predictor of seedling occupancy, followed by aerial seeding status, native perennial grass cover, and years since fire. In populations where no sagebrush seeding occurred, seedlings were most likely to occur in locations with a combination of high fertile island microsite cover and close proximity to a remnant sagebrush plant. These important attributes were only present in 13% of post-fire locations, making them rare across the Great Basin. Finally, in the absence of fertile islands and remnant plants, seedling establishment was not observed in any unseeded areas, and rarely in seeded locations. Thus, local extirpation of sagebrush could have important, long-term implications for sagebrush reestablishment following future fires if there are no mature individuals to leave behind fertile islands or serve as remnant individuals. These findings highlight the importance of landscape legacy effects and could help guide where and how big sagebrush restoration is conducted in the future.

California, Idaho, Nevada, Oregon, Utah↗

USGS2019

Transient population dynamics impede restoration and may promote ecosystem transformation after disturbance

The apparent failure of ecosystems to recover from increasingly widespread disturbance is a global concern. Despite growing focus on factors inhibiting resilience and restoration, we still know very little about how demographic and population processes influence recovery. Using inverse and forward demographic modelling of 531 post‐fire sagebrush populations across the western US, we show that demographic processes during recovery from seeds do not initially lead to population growth but rather to years of population decline, low density, and risk of extirpation after disturbance and restoration, even at sites with potential to support long‐term, stable populations. Changes in population structure, and resulting transient population dynamics, lead to a > 50% decline in population growth rate after disturbance and significant reductions in population density. Our results indicate that demographic processes influence the recovery of ecosystems from disturbance and that demographic analyses can be used by resource managers to anticipate ecological transformation risk.

Ecology Letters↗

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↗

USGS2018

Adapting management to a changing world: Warm temperatures, dry soil, and interannual variability limit restoration success of a dominant woody shrub in temperate drylands

Restoration and rehabilitation of native vegetation in dryland ecosystems, which encompass over 40% of terrestrial ecosystems, is a common challenge that continues to grow as wildfire and biological invasions transform dryland plant communities. The difficulty in part stems from low and variable precipitation, combined with limited understanding about how weather conditions influence restoration outcomes, and increasing recognition that one‐time seeding approaches can fail if they do not occur during appropriate plant establishment conditions. The sagebrush biome, which once covered over 620,000 km 2 of western North America, is a prime example of a pressing dryland restoration challenge for which restoration success has been variable. We analyzed field data on Artemisia tridentata (big sagebrush) restoration collected at 771 plots in 177 wildfire sites across its western range, and used process‐based ecohydrological modeling to identify factors leading to its establishment. Our results indicate big sagebrush occurrence is most strongly associated with relatively cool temperatures and wet soils in the first spring after seeding. In particular, the amount of winter snowpack, but not total precipitation, helped explain the availability of spring soil moisture and restoration success. We also find considerable interannual variability in the probability of sagebrush establishment. Adaptive management strategies that target seeding during cool, wet years or mitigate effects of variability through repeated seeding may improve the likelihood of successful restoration in dryland ecosystems. Given consistent projections of increasing temperatures, declining snowpack, and increasing weather variability throughout midlatitude drylands, weather‐centric adaptive management approaches to restoration will be increasingly important for dryland restoration success.

Great Basin↗

USGS2018

Estimating vegetation biomass and cover across large plots in shrub and grass dominated drylands using terrestrial lidar and machine learning

Terrestrial laser scanning (TLS) has been shown to enable an efficient, precise, and non-destructive inventory of vegetation structure at ranges up to hundreds of meters. We developed a method that leverages TLS collections with machine learning techniques to model and map canopy cover and biomass of several classes of short-stature vegetation across large plots. We collected high-definition TLS scans of 26 1-ha plots in desert grasslands and big sagebrush shrublands in southwest Idaho, USA. We used the Random Forests machine learning algorithm to develop decision tree models predicting the biomass and canopy cover of several vegetation classes from statistical descriptors of the aboveground heights of TLS points. Manual measurements of vegetation characteristics collected within each plot served as training and validation data. Models based on five or fewer TLS descriptors of vegetation heights were developed to predict the canopy cover fraction of shrubs (R 2 = 0.77, RMSE = 7%), annual grasses (R 2 = 0.70, RMSE = 21%), perennial grasses (R 2 = 0.36, RMSE = 12%), forbs (R 2 = 0.52, RMSE = 6%), bare earth or litter (R 2 = 0.49, RMSE = 19%), and the biomass of shrubs (R 2 = 0.71, RMSE = 175 g) and herbaceous vegetation (R 2 = 0.61, RMSE = 99 g) (all values reported are out-of-bag). Our models explained much of the variability between predictions and manual measurements, and yet we expect that future applications could produce even better results by reducing some of the methodological sources of error that we encountered. Our work demonstrates how TLS can be used efficiently to extend manual measurement of vegetation characteristics from small to large plots in grasslands and shrublands, with potential application to other similarly structured ecosystems. Our method shows that vegetation structural characteristics can be modeled without classifying and delineating individual plants, a challenging and time-consuming step common in previous methods applying TLS to vegetation inventory. Improving application of TLS to studies of shrub-steppe ecosystems will serve immediate management needs by enhancing vegetation inventories, environmental modeling studies, and the ability to train broader datasets collected from air and space.

Idaho↗

USGS2017

Methodological considerations of terrestrial laser scanning for vegetation monitoring in the sagebrush steppe

Terrestrial laser scanning (TLS) provides fast collection of high-definition structural information, making it a valuable field instrument to many monitoring applications. A weakness of TLS collections, especially in vegetation, is the occurrence of unsampled regions in point clouds where the sensor’s line-of-sight is blocked by intervening material. This problem, referred to as occlusion, may be mitigated by scanning target areas from several positions, increasing the chance that any given area will fall within the scanner’s line-of-sight from at least one position. Because TLS collections are often employed in remote regions where the scope of sampling is limited by logistical factors such as time and battery power, it is important to design field protocols which maximize efficiency and support increased quantity and quality of the data collected. This study informs researchers and practitioners seeking to optimize TLS sampling methods for vegetation monitoring in dryland ecosystems through three analyses. First, we quantify the 2D extent of occluded regions based on the range from single scan positions. Second, we measure the efficacy of additional scan positions on the reduction of 2D occluded regions (area) using progressive configurations of scan positions in 1 ha plots. Third, we test the reproducibility of 3D sampling yielded by a 5-scan/ha sampling methodology using redundant sets of scans. Analyses were performed using measurements at analysis scales of 5 to 50 cm across the 1-ha plots, and we considered plots in grass and shrub-dominated communities separately. In grass-dominated plots, a center-scan configuration and 5 cm pixel size sampled at least 90% of the area up to 18 m away from the scanner. In shrub-dominated plots, sampling at least 90% of the area was only achieved within a distance of 12 m. We found that 3 and 5 scans/ha are needed to sample at least ~ 70% of the total area (1 ha) in the grass and shrub-dominated plots, respectively, using 5 cm pixels to measure sampling presence-absence. The reproducibility of 3D sampling provided by a 5 position scan layout across 1-ha plots was 50% (shrub) and 70% (grass) using a 5-cm voxel size, whereas at the 50-cm voxel scale, reproducibility of sampling was nearly 100% for all plot types. Future studies applying TLS in similar dryland environments for vegetation monitoring may use our results as a guide to efficiently achieve sampling coverage and reproducibility in datasets.

Idaho↗

USGS2017

Refining the cheatgrass–fire cycle in the Great Basin: Precipitation timing and fine fuel composition predict wildfire trends

Larger, more frequent wildfires in arid and semi-arid ecosystems have been associated with invasion by non-native annual grasses, yet a complete understanding of fine fuel development and subsequent wildfire trends is lacking. We investigated the complex relationships among weather, fine fuels, and fire in the Great Basin, USA. We first modeled the annual and time-lagged effects of precipitation and temperature on herbaceous vegetation cover and litter accumulation over a 26-year period in the northern Great Basin. We then modeled how these fine fuels and weather patterns influence subsequent wildfires. We found that cheatgrass cover increased in years with higher precipitation and especially when one of the previous 3 years also was particularly wet. Cover of non-native forbs and native herbs also increased in wet years, but only after several dry years. The area burned by wildfire in a given year was mostly associated with native herb and non-native forb cover, whereas cheatgrass mainly influenced area burned in the form of litter derived from previous years’ growth. Consequently, multiyear weather patterns, including precipitation in the previous 1–3 years, was a strong predictor of wildfire in a given year because of the time needed to develop these fine fuel loads. The strong relationship between precipitation and wildfire allowed us to expand our inference to 10,162 wildfires across the entire Great Basin over a 35-year period from 1980 to 2014. Our results suggest that the region's precipitation pattern of consecutive wet years followed by consecutive dry years results in a cycle of fuel accumulation followed by weather conditions that increase the probability of wildfire events in the year when the cycle transitions from wet to dry. These patterns varied regionally but were strong enough to allow us to model annual wildfire risk across the Great Basin based on precipitation alone.

Idaho↗

USGS2016

Transition of vegetation states positively affects harvester ants in the Great Basin, United States

Invasions by non-native plants can alter ecosystems such that new ecological states are reached, but less is known about how these transitions influence animal populations. Sagebrush (Artemisia tridentata) ecosystems are experiencing state changes because of fire and invasion by exotic annual grasses. Our goal was to study the effects of these state changes on the Owyhee and western harvester ants ( Pogonomyrmex salinus Olsen and P. occidentalis Cresson, respectively). We sampled 358 1-ha plots across the northern Great Basin, which captured unburned and burned conditions across 1 −≥31 years postfire. Our results indicated an immediate and consistent change in vegetation states from shrubland to grassland between 1 and 31 years postfire. Harvester ant occupancy was unrelated to time since fire, whereas we observed a positive effect of fire on nest density. Similarly, we discovered that fire and invasion by exotic annuals were weak predictors of harvester ant occupancy but strong predictors of nest density. Occupancy of harvester ants was more likely in areas with finer-textured soils, low precipitation, abundant native forbs, and low shrub cover. Nest density was higher in arid locations that recently burned and exhibited abundant exotic annual and perennial (exotic and native) grasses. Finally, we discovered that burned areas that received postfire restoration had minimal influence on harvester ant occupancy or nest density compared with burned and untreated areas. These results suggest that fire-induced state changes from native shrublands to grasslands dominated by non-native grasses have a positive effect on density of harvester ants (but not occupancy), and that postfire restoration does not appear to positively or negatively affect harvester ants. Although wildfire and invasion by exotic annual grasses may negatively affect other species, harvester ants may indeed be one of the few winners among a myriad of losers linked to vegetation state changes within sagebrush ecosystems.

Great Basin↗

USGS2016

Landsat 8 and ICESat-2: Performance and potential synergies for quantifying dryland ecosystem vegetation cover and biomass

The Landsat 8 mission provides new opportunities for quantifying the distribution of above-ground carbon at moderate spatial resolution across the globe, and in particular drylands. Furthermore, coupled with structural information from space-based and airborne laser altimetry, Landsat 8 provides powerful capabilities for large-area, long-term studies that quantify temporal and spatial changes in above-ground biomass and cover. With the planned launch of ICESat-2 in 2017 and thus the potential to couple Landsat 8 and ICESat-2 data, we have unprecedented opportunities to address key challenges in drylands, including quantifying fuel loads, habitat quality, biodiversity, carbon cycling, and desertification. In this study, we explore the strengths of Landsat 8's Operational Land Imager (OLI) in estimating vegetation structure in a dryland ecosystem, and compare these results to Landsat 5's Thematic Mapper (TM). We also demonstrate the potential of OLI when coupled with light detection and ranging (lidar) in estimating vegetation cover and biomass in a dryland ecosystem. The OLI and TM predictions were similarly positive, indicating data from these sensors may be used in tandem for long-term time-series analysis. Results indicate shrub and herbaceous cover are well predicted with multi-temporal OLI data, and a combination of OLI and lidar derivatives improves most of these estimates and reduces uncertainty. For example, significant improvements were made for shrub cover (R 2 = 0.64 and 0.78 using OLI only and both OLI and lidar data, respectively). Importantly, a time series of OLI, with some improvement from lidar, provides strong estimates of herbaceous cover (68% of the variance is explained with OLI alone). In contrast, OLI data explain roughly 59% of the variance in total shrub biomass, however approximately 71% of the variance is explained when combined with lidar derivatives. To estimate the potential synergies of OLI and ICESat-2 we used simulated ICESat-2 photon data to predict vegetation structure. In a shrubland environment with a vegetation mean height of 1 m and mean vegetation cover of 33%, vegetation photons are able to explain nearly 50% of the variance in vegetation height. These results, and those from a comparison site, suggest that a lower detection threshold of ICESat-2 may be in the range of 30% canopy cover and roughly 1 m height in comparable dryland environments and these detection thresholds could be used to combine future ICESat-2 photon data with OLI spectral data for improved vegetation structure. Overall, the synergistic use of Landsat 8 and ICESat-2 may improve estimates of above-ground biomass and carbon storage in drylands that meet these minimum thresholds, increasing our ability to monitor drylands for fuel loading and the potential to sequester carbon.

Remote Sensing of Environment↗

USGS2016

Ecosystem engineering of harvester ants: Effects on vegetation in a sagebrush-steppe ecosystem

Harvester ants are influential in many ecosystems because they distribute and consume seeds, remove vegetation, and redistribute soil particles and nutrients. Understanding the interaction between harvester ants and plant communities is important for management and restoration efforts, particularly in systems altered by fire and invasive species such as the sagebrush-steppe. Our objective was to evaluate how vegetation cover changed as a function of distance from Owyhee harvester ant ( Pogonomyrmex salinus ) nests within a sagebrush-steppe ecosystem. We sampled 105 harvester ant nests within southern Idaho, USA, that occurred in different habitats: annual grassland, perennial grassland, and native shrubland. The influence of Owyhee harvester ants on vegetation was larger at the edge of ant nests, but the relationship was inconsistent among plant species. Percent cover was positively associated with distance from harvester ant nests for plant species that were considered undesirable food sources and were densely distributed. However, percent cover was negatively associated with distance-from-nests for patchily distributed and desirable plant species. For some plant species, there was no change in cover associated with distance-from-nests. Total vegetation cover was associated with distance-from-nests in the shrubland habitat but not in the 2 grasslands. The dominant plant species in the shrubland habitat was a densely distributed shrub (winterfat, Krascheninnikovia lanata ) that was defoliated by harvester ants. Our results suggest that Owyhee harvester ants increase spatial heterogeneity in plant communities through plant clearing, but the direction and magnitude of effect will likely be contingent on the dominant vegetation groups. This information may inform future management and plant restoration efforts in sagebrush-steppe by directly considering the islands of influence associated with harvester ant engineering.

Idaho↗

USGS2015

Quantifying and predicting fuels and the effects of reduction treatments along successional and invasion gradients in sagebrush habitats

Sagebrush shrubland ecosystems in the Great Basin are prime examples of how altered successional trajectories can create dynamic fuel conditions and, thus, increase uncertainty about fire risk and behavior. Although fire is a natural disturbance in sagebrush, post-fire environments are highly susceptible to conversion to an invasive grass-fire regime (often referred to as a “grass-fire cycle”). After fire, native shrub-steppe plants are often slow to regenerate, whereas nonnative annuals, especially cheatgrass (Bromus tectorum) and medusahead (Taeniatherum caput-medusae), can establish quickly and outcompete native species. Once fire-prone annuals become established, fire occurrences increase, further promoting dominance of nonnative species. The invasive grass-fire regime also alters nutrient and hydrologic cycles, pushing ecosystems beyond ecological thresholds toward steady-state, fire-prone, nonnative communities. These changes affect millions of hectares in the Great Basin and increase fire risk, decrease habitat quality and biodiversity, accelerate soil erosion, and degrade rangeland resources for livestock production. In many sagebrush landscapes, constantly changing plant communities and fuel conditions hinder attempts by land managers to predict and control fire behavior, restore native communities, and provide ecosystem services (e.g., forage production for livestock). We investigated successional and nonnative plant invasion states and associated fuel loads in degraded sagebrush habitat in a focal study area, the Morley Nelson Snake River Birds of Prey National Conservation Area (hereafter the NCA), in the Snake River Plain Ecoregion of southern Idaho. We expanded our inference by comparing our findings to similar data collected throughout seven major land resource areas (MLRAs) across the Great Basin (JFSP Project “Fire Rehabilitation Effectiveness: A Chronosequence Approach for the Great Basin” [09-S-02-1]). 4 We used a combination of field-sampling, experimental treatments, and remotely sensed data to address the following questions: (1) How do fuel loads change along gradients of succession and invasion in sagebrush ecological sites? (2) How do fuel reduction treatments influence fuels in invaded areas formerly dominated by sagebrush? (3) How do fuel loads vary across landscapes and which remote sensing techniques are effective for characterizing them?

Idaho↗