Geology topics

Cara Applestein

Publications and source records attributed to Cara Applestein.

At least 19 recordsLinked to original sources

USGS2025

Post-fire recovery of sagebrush-steppe communities is better explained by elevation than climate-derived indicators of resistance and resilience

More landscapes require restoration than can feasibly be treated, and so decision-support tools to prioritize areas for treatment are needed. Moreover, restoration is complicated by the threat of biological invasion in disturbed areas, and so indicators of ecosystem resistance to invasion and resilience to disturbance (hereafter R&R) are important candidate criteria for prioritizing sites for restoration. We asked how climate-based R&R indicators that differed in being either categorical or continuous compared in their ability to explain plant-community recovery after six wildfires that collectively encompassed >750,000 ha and 7803 plot-year observations in sagebrush steppe of the western USA. Unique associations of species that most frequently co-occurred were identified using structural topic modelling. Mixed effect random forests were used to identify the relative importance of various R&R indicators in explaining post-fire plant associations compared with weather, landscape characteristics and treatment history. Simple metrics (elevation, latitude, longitude and year of monitoring) were more informative predictors of post-fire recovery than climate-based R&R indicators. However, small differences in the abundances of perennial grass and especially annual grass associations were predicted by the spring modified Thornthwaite Moisture Index (difference between precipitation and potential evapotranspiration). Synthesis and applications : The convenience of categorical resistance and resilience indicators has led to their widespread adoption for large-scale planning of restoration. Our results reveal that none of the resistance and resilience indicators assessed effectively explained post-fire restoration better than elevation, although a simple continuous resistance and resilience indicator describing water balance performed better than categorical indicators for explaining small but critical differences in cheatgrass association abundances.

California, Idaho, Nevada, Oregon↗

USGS2025

An accuracy assessment of satellite-derived rangeland fractional cover

Satellite-derived maps of vegetation cover provide detailed information about vegetation spatiotemporal patterns and are increasingly used to better understand and manage rangelands. Despite their utility, questions remain regarding the regional and site level accuracy for these maps, especially compared to field-collected data. We conducted an accuracy assessment of the Rangeland Analysis Platform (RAP), using over 17,000 field plots sampled through nationwide rangeland vegetation monitoring programs in the continental U.S. We observed higher overall nationwide map error compared to previous validations of RAP, and absolute error (Mean Absolute Error [MAE] and Root Mean Square Error [RMSE]) was highest for perennial herbaceous and bare ground and lowest for trees (MAE range = 2.98 –10.22 %). There were also differences in map agreement with field data across ecoregions. Generally, map agreement was highest in the Great Basin and lowest in the Great Plains and Desert Southwest. Additionally, we assessed the suitability of using RAP in riparian and wetland areas, which are absent in the current version’s training. Errors for bare ground in riparian areas were lower than errors of upland accuracy assessments (upland MAE = 10.22 %, riparian MAE = 7.22 %), but for all other functional groups, riparian error was higher (ΔMAE range: 0.21 – 20.49 %). We examine how our results could inform regional applications of fractional cover data while considering error and uncertainty and identify areas for potential model improvement. Our findings inform the use of RAP regionally and provide a technique for evaluating other vegetation mapping products for use in rangeland management.

Ecological Indicators↗

USGS2024

Analysis adapted from text mining quantitively reveals abrupt and gradual plant-community transitions after fire in sagebrush steppe

Context Plant communities vary both abruptly and gradually over time but differentiating between types of change can be difficult with existing classification and ordination methods. Structural topic modeling (STRUTMO), a text mining analysis, offers a flexible methodology for analyzing both types of temporal trends. Objectives Our objectives were to (1) identify post-fire dominant sagebrush steppe plant association types and ask how they vary with time at a landscape (multi-fire) scale and (2) ask how often major association changes are apparent at the plot-level scale. Methods We used STRUTMO and plant species cover collected between 2002–2022 across six large burn areas (1941 plots) in the Great Basin, USA to characterize landscape change in dominant plant association up to 14 years post-fire. In a case study, we assessed frequency of large annual changes (≥ 10% increase in one association and decrease in another) between associations at the plot-level scale. Results STRUTMO revealed 10 association types dominated by either perennial bunchgrasses, mixed perennial or annual grasses and forbs, or exotic annual grasses. Across all study fires, associations dominated by large-statured perennial bunchgrasses increased then stabilized, replacing the Sandberg bluegrass ( Poa secunda )-dominated association. The cheatgrass ( Bromus tectorum )-dominant association decreased and then increased. At the plot-level, bidirectional changes among associations occurred in ~ 75% of observations, and transitions from annual invaded to perennial associations were more common than the reverse. Conclusions The analysis revealed that associations dominated by some species (i.e. crested wheatgrass, Agropyron cristatum , Siberian wheatgrass, Agropyron fridgida , or medusahead, Taeniatherum caput-medusae ) were more stable than associations dominated by others (i.e. Sandberg bluegrass or cheatgrass). Strong threshold-like transitions were not observed at the multi-fire scale, despite frequent ephemeral plot-level changes.

California, Idaho, Nevada, Oregon↗

USGS2024

Multiple plant-community traits improve predictions of later-stage outcomes of restoration drill seedings: Implications for metrics of success

Success of ecological restoration is often only knowable if treatments meet criteria defined by biotic thresholds, but analytical frameworks to determine metrics of success and their underlying thresholds are needed. Early indicators of longer-term recovery trajectories are particularly critical where re-treatments may be required, such as in harsh climates or where repeated disturbances or invasive pressures prevail. We developed a framework for identifying which biotic traits would provide the best initial indication of longer-term target restoration goals and applied the framework to restoration drill-seedings of deep-rooted perennial bunchgrasses (DRPBGs) used to rehabilitate and restore semiarid rangelands threatened by exotic annual grasses (EAGs, e.g. cheatgrass) and the recurrent wildfire that EAGs cause. Initial traits measured included cover, basal diameter, height, and density (#plants/area) of DRPBGs and cover of EAGs and Sandberg bluegrass ( Poa secunda , POSE, a disturbance-adapted perennial). The longer-term target objective was ≥25 % DRPBG cover and ≤13 % EAG cover by the 5th year following drill-seedings. Measurements were made on 112 plots spanning 113,000 ha in sagebrush steppe on the Soda wildfire scar, in the Northern Great Basin, USA. Traits of DRPBGs tended to be uncorrelated with one another, thus each was informative in describing vegetation condition. Where DRPBG cover was initially >17 %, it tended to become >25 % by the 5th-year post-seeding. In plots that overcame an initial risk of not meeting the target objective (i.e. <17 % initial DRPBG cover), DRPBG tended be large DRPBGs (>22.8 cm height) and plots also had >7 % cover of POSE. Additional “sets” of initial vegetation traits were also predictive of longer-term restoration success or failure. Restoration drill-seeding of DRPBGs is a key but varied-outcome tool for breaking the exotic grass-fire cycle, and, contrary to a conventional tendency to rely on a limited number of mean traits such as % cover, a suite of biotic traits appears necessary to monitor to reliably know if trials are likely to yield success.

Ecological Indicators↗

USGS2024

Systematic process for determining field-sampling effort required to know vegetation changes in large, disturbed rangelands where management treatments have been applied

Adequate numbers of replicated, dispersed, and random samples are the basis for reliable sampling inference on resources of concern, particularly vegetation cover across large and heterogenous areas such as rangelands. Tools are needed to predict and assess data precision, specifically the sampling effort required to attain acceptable levels of precision, before and after sampling. We describe and evaluate a flexible and scalable process for assessing the sampling effort requirement for a common monitoring context (responses of rangeland vegetation cover to post-fire restoration treatments), using a custom R script called “SampleRange.” In SampleRange, vegetation cover is estimated from available digital-gridded or field data (e.g., using the satellite-derived cover from the Rangeland Assessment Platform). Next, the sampling effort required to estimate cover with 20% relative standard error (RSE) or to saturate sampling effort is determined using simulations across the environmental gradients in areas of interest to estimate the number of needed plots (“SampleRange quota”). Finally, the SampleRange quota are randomly identified for actual sampling. A 2022 full-cycle trial of SampleRange using the best available digital and prior field data for areas treated after a 2017 wildfire in sagebrush-steppe rangelands revealed that differences in the predicted compared with realized RSEs are inevitable. Future efforts to account for uncertainty in remotely sensed−based vegetative products will enhance tool utility.

Rangeland Ecology and Management↗

USGS2023

Satellite-derived prefire vegetation predicts variation in field-based invasive annual grass cover after fire

Aims Invasion by annual grasses (IAGs) and concomitant increases in wildfire are impacting many drylands globally, and an understanding of factors that contribute to or detract from community resistance to IAGs is needed to inform postfire restoration interventions. Prefire vegetation condition is often unknown in rangelands but it likely affects variation in postfire invasion resistance across large burned scars. Whether satellite-derived products like the Rangeland Analysis Platform (RAP) can fulfill prefire information needs and be used to parametrize models of fire recovery to inform postfire management of IAGs is a key question. Methods We used random forests to ask how IAG abundances in 669 field plots measured in the 2-3 years following megafires in sagebrush steppe rangelands of western USA responded to RAP estimates of annual:perennial prefire vegetation cover, the effects of elevation, heat load, postfire treatments, soil moisture–temperature regimes, and land-agency ratings of ecosystem resistance to invasion and resilience to disturbance. Results Postfire IAG cover measured in the field was 22¯% and RAP-estimated prefire annual herbaceous cover was 15.7¯%. The random forest model had an R 2 of 0.36 and a root-mean-squared error (RMSE) of 4.41. Elevation, postfire herbicide treatment, and prefire estimates from RAP for the ratio of annual:perennial and shrub cover were the most important predictors of postfire IAG cover. Threshold-like relationships between postfire IAG cover and the predictors indicate that maintaining annual:perennial cover below 0.4 and shrub cover below <10% prior to wildfire would decrease invasion, at low elevations below 1400 m above sea level. Conclusion Despite known differences between RAP and field-based estimates of vegetation cover, RAP was still a useful predictor of variation in IAG abundances after fire. IAG management is oftentimes reactive, but our findings indicate impactful roles for more inclusively addressing the exotic annual community, and focusing on prefire maintenance of annual:perennial herbaceous and shrub cover at low elevations.

Applied Vegetation Science↗

USGS2023

Satellite-derived plant cover maps vary in performance depending on version and product

Understanding the accuracy and appropriate application scale of satellite-derived maps of vegetation cover is essential for effective management of the vast, remote rangelands of the world. However, the underlying models are updated frequently and may combine with rapidly changing vegetation conditions to cause variations in accuracy and precision over time. We sought to assess how model performance changed between different versions of satellite-derived cover products (Rangeland Analysis Platform, RAP, and Rangeland Condition Monitoring and Assessment Protocol, RCMAP) and how the performance of LandCart compared to RAP and RCMAP. Additionally, we asked how variability in agreement between LandCart and field-based models varied with scale. We utilized an intensive dataset of grid-point intercept functional group cover data collected between 2016 and 2020 across the ∼113 kHA 2015 Soda Wildfire to 1) evaluate r 2 agreement between versions of each satellite-derived product and plot-level field data and 2) assess relative standard error of agreement in cover between LandCart and continuous field-based Empirical Bayesian Kriging (EBK) regression models. Agreement between satellite- compared to field-plot values of cover (r 2 ) increased for RCMAP Version 5.0 compared to Version 2.0, but there were negligible changes between versions of RAP. Despite this, r 2 values of RCMAP and LandCart were nearly always less than RAP. Variability in agreement between EBK regression model cover and LandCart-derived cover decreased with the scale of consideration. Variability in agreement between satellite-derived cover products and field-based metrics is lowest at larger scale (mega-fire or regional) and varies from year to year and across versions, which could complicate detection of temporal changes in plant cover.

Idaho, Oregon↗

USGS2022

Post-fire seed dispersal of a wind-dispersed shrub declined with distance to seed source, yet had high levels of unexplained variation

Plant-population recovery across large disturbance areas is often seed-limited. An understanding of seed dispersal patterns is fundamental for determining natural-regeneration potential. However, forecasting seed dispersal rates across heterogeneous landscapes remains a challenge. Our objectives were to determine (i) the landscape patterning of post-disturbance seed dispersal, and underlying sources of variation and the scale at which they operate, and (ii) how the natural seed dispersal patterns relate to a seed augmentation strategy. Vertical seed trapping experiments were replicated across 2 years and five burned and/or managed landscapes in sagebrush steppe. Multi-scale sampling and hierarchical Bayesian models were used to determine the scale of spatial variation in seed dispersal. We then integrated an empirical and mechanistic dispersal kernel for wind-dispersed species to project rates of seed dispersal and compared natural seed arrival to typical post-fire aerial seeding rates. Seeds were captured across the range of tested dispersal distances, up to a maximum distance of 26 m from seed-source plants, although dispersal to the furthest traps was variable. Seed dispersal was better explained by transect heterogeneity than by patch or site heterogeneity (transects were nested within patch within site). The number of seeds captured varied from a modelled mean of ~13 m −2 adjacent to patches of seed-producing plants, to nearly none at 10 m from patches, standardized over a 49-day period. Maximum seed dispersal distances on average were estimated to be 16 m according to a novel modelling approach using a ‘latent’ variable for dispersal distance based on seed trapping heights. Surprisingly, statistical representation of wind did not improve model fit and seed rain was not related to the large variation in total available seed of adjacent patches. The models predicted severe seed limitations were likely on typical burned areas, especially compared to the mean 95–250 seeds per m 2 that previous literature suggested were required to generate sagebrush recovery. More broadly, our Bayesian data fusion approach could be applied to other cases that require quantitative estimates of long-distance seed dispersal across heterogeneous landscapes.

Idaho↗

USGS2022

Relationship of greater sage-grouse to natural and assisted recovery of key vegetation types following wildfire: Insights from scat

Megafires are creating severe conservation problems worldwide for wildlife that have obligate dependencies on plant species that are foundational but fire-intolerant. Wildfire-induced loss of native perennials and increases in exotic annual grasses threaten greater sage-grouse (GRSG, Centrocercus urophasianus ) in its sagebrush steppe habitat in western North America. Post-fire restoration using herbicides, seeding, and planting of native perennials such as sagebrush are common, but there are few assessments of GRSG response to the treatments. We measured the presence of GRSG scat and modeled the probability of GRSG presence (PrGRSG-scat ) in relation to variation in plot-level and landscape-level predictors, and land treatments, in an intensive, repeat sampling from 2017-2020 of 113,000-ha area burned in 2015 in the Soda Megafire (Oregon and Idaho, USA). GRSG scat was present in <200 of >8000 observations, as would be expected for a philopatric species (i.e., high fidelity to home site) returning to degraded habitat. PrGRSG-scat was positively associated with sagebrush presence at the plot-level and was positively related to elevation, lower-angle slopes, and proximity to sagebrush seedling outplant islands. The statistical significance of relationships of PrGRSG-scat to restoration treatments was marginal at best, with the largest effect being a positive response of PrGRSG-scat to pre-emergent herbicide sprayed to reduce exotic annual grasses. More time may be required for restored sagebrush steppe to meet GRSG needs or for GRSG to “adopt” the restored vegetation. Moreover, whereas scat is a convenient and non-invasive method to monitor GRSG, its post-fire scarcity weakens the strength of statistical inference on GRSG recovery patterns and response to restoration.

Restoration Ecology↗

USGS2022

Interannual variation in climate contributes to contingency in post-fire restoration outcomes in seeded sagebrush steppe

Interannual variation, especially weather, is an often-cited reason for restoration “failures”; yet its importance is difficult to experimentally isolate across broad spatiotemporal extents, due to correlations between weather and site characteristics. We examined post-fire treatments within sagebrush-steppe ecosystems to ask: (1) Is weather following seeding efforts a primary reason why restoration outcomes depart from predictions? and (2) Does the management-relevance of weather differ across space and with time since treatment? Our analysis quantified range-wide patterns of sagebrush ( Artemisia spp.) recovery, by integrating long-term records of restoration and annual vegetation cover estimates from satellite imagery following thousands of post-fire seeding treatments from 1984 to 2005. Across the Great Basin, sagebrush growth increased in wetter, cooler springs; however, the importance of spring weather varied with sites' long-term climates, suggesting differing ecophysiological limitations across sagebrush's range. Incorporation of spring weather, including from the “planting year,” improved predictions of sagebrush recovery, but these advances were small compared to contributions of time-invariant site characteristics. Given extreme weather conditions threatening this ecosystem, explicit consideration of weather could improve the allocation of management resources, such as by identifying areas requiring repeated treatments; but improved forecasts of shifting mean conditions with climate change may more significantly aid the prediction of sagebrush recovery.

California, Colorado, Idaho, Nevada, Oregon, Utah,↗

USGS2022

Greater sage-grouse respond positively to intensive post-fire restoration treatments

Habitat loss is the most prevalent threat to biodiversity in North America. One of the most threatened landscapes in the United States is the sagebrush ( Artemisia spp.) ecosystem, much of which has been fragmented or converted to non-native grasslands via the cheatgrass-fire cycle. Like many sagebrush obligates, greater sage-grouse ( Centrocercus urophasianus ) depend upon sagebrush for food and cover and are affected by changes to this ecosystem. We investigated habitat selection by 28 male greater sage-grouse during each of 3 years after a 113,000-ha wildfire in a sagebrush steppe ecosystem in Idaho and Oregon. During the study period, seeding and herbicide treatments were applied for habitat restoration. We evaluated sage-grouse responses to vegetation and post-fire restoration treatments. Throughout the 3 years post-fire, sage-grouse avoided areas with high exotic annual grass cover but selected strongly for recovering sagebrush and moderately strongly for perennial grasses. By the third year post-fire, they preferred high-density sagebrush, especially in winter when sagebrush is the primary component of the sage-grouse diet. Sage-grouse preferred forb habitat immediately post-fire, especially in summer, but this selection preference was less strong in later years. They also selected areas that were intensively treated with herbicide and seeded with sagebrush, grasses, and forbs, although these responses varied with time since treatment. Wildfire can have severe consequences for sagebrush-obligate species due to loss of large sagebrush plants used for food and for protection from predators and thermal extremes. Our results show that management efforts, including herbicide application and seeding of plants, directed at controlling exotic annual grasses after a wildfire can positively affect habitat selection by sage-grouse.

Ecology and Evolution↗

USGS2022

How do accuracy and model agreement vary with versioning, scale, and landscape heterogeneity for satellite-derived vegetation maps in sagebrush steppe?

Maps of the distribution and abundance of dominant plants derived from satellite data are essential for ecological research and management, particularly in the vast semiarid shrub-steppe. Appropriate application of these maps requires an understanding of model accuracy and precision, and how it might vary across space, time, and different vegetation types. For a 113 k Ha burn area, we compared modeled maps of different vegetation cover types created from satellite data to ‘benchmark” models based on intensive field sampling (~1500-2000 plots resampled annually for 5 years) for three new satellite-derived models: USDA Rangeland Analysis Platform (RAP), the USGS Rangeland Condition Monitoring Assessment and Projection (RCMAP), and USGS fractional estimate of exotic annual grass cover (USGS-fractional-EAG). We assessed out-of-sample point accuracy and asked if and how accuracy changed each year due to vegetation shifts, new images, and model improvements (i.e. model versions). We also assessed how map agreement between satellite-based and field-based models changed with scale of application, topography, and time since fire. Accuracy and map agreement varied considerably among the vegetation types and across time and space (r 2 ranging from 0 to 0.53), and some of the variability was predictable. All models tended to over or underestimate cover when field-measured cover was relatively low or high, respectively, i.e. a “false moderating effect”. Accuracy was greater and improved with newer versions of RAP (+0.05 to 0.29 r 2 ) compared to RCMAP and USGS fractional model estimates, and in some cases was greater than field-based models. Variability in map agreement tended to decrease with larger areas sampled (particularly in areas >12 km), and this scale dependency was more evident in RAP and USGS-fractional-EAG models. Creating a “fair” basis for comparison of spatial models of low-statured semiarid vegetation derived from satellite compared to field data is not trivial because scaling the field data to the scale of large satellite pixels (or downscaling satellite-based models to field scale) requires modeling and associated model uncertainty. Accuracy can vary considerably and understanding the variation can help guide application of the models to the appropriate time, place, and variables.

Idaho, Oregon↗

USGS2022

Monitoring for adaptive management of burned sagebrush-steppe rangelands: addressing variability and uncertainty on the 2015 Soda Megafire

• Use of adaptive management supported by robust monitoring is vital to solving severe rangeland problems, such as the exotic annual grass invasion and fire cycle in sagebrush-steppe rangelands. • Uncertainty in post-fire plant-community composition and plant response to treatments poses a challenge to land management and research but can be addressed with a high density of observations over short time frames. • The monitoring for adaptive management of the 2015 Soda Megafire area (113,000 Ha) sampled up to 2000 observation plots in each of five post-fire years, and provided important insights on challenges, solutions, and insights that can be applied to monitoring future burned areas.

Idaho, Oregon↗

USGS2021

Bayesian modeling can facilitate adaptive management in restoration

There is an urgent need for near-term predictions of ecological restoration outcomes despite imperfect knowledge of ecosystems. Restoration outcomes are always uncertain but integrating Bayesian modeling into the process of adaptive management allows researchers and practitioners to explicitly incorporate prior knowledge of ecosystems into future predictions. Although barriers exist, employing qualitative expert knowledge and previous case studies can help narrow the range of uncertainty in forecasts. Software and processes that allow for repeatable methodologies can help bridge the existing gap between theory and application of Bayesian methods in adaptive management.

Restoration Ecology↗

USGS2021

Patterns of post-fire invasion of semiarid shrub-steppe reveals a diversity of invasion niches within an exotic annual grass community

Disturbances such as fire provide an opportunity for invasive plant species to exploit newly created niche space. Whether initial invaders facilitate, compete with, or do not affect later invaders is important to determine in communities affected by multiple invaders. This analysis focuses on the newer invaders Taeniatherum caput-medusae (medusahead) and Ventenata dubia (ventenata) in sagebrush-steppe communities previously invaded by Bromus tectorum (cheatgrass), during the first 5 years of recovery after wildfire . We combined probabilistic co-occurrence analysis and Getis-Ord spatial clustering analysis to assess relationships between different exotic annual grass species and native and introduced perennial bunchgrasses, then used Bayesian generalized linear models to determine if and how medusahead and ventenata differed in their environmental relationships and thus invasion niches. Medusahead presence was positively associated with both other exotic annual grasses, but ventenata presence was negatively associated with cheatgrass presence. Medusahead hotspots were more spatially similar to cheatgrass hotspots while ventenata hotspots were unique. Both invaders were negatively related to total perennial bunchgrass cover but disassociations between invaders and different perennial bunchgrasses were species-specific. Medusahead and ventenata occupied different niches; medusahead in low elevation, low precipitation areas and ventenata in higher elevation, higher precipitation areas. Despite seemingly similar ecology and growth requirements among these annual grasses and a tendency to be considered uniformly in both research and management, the species appeared to have different invasion niches.

Idaho, Oregon↗

USGS2021

Weather affects post‐fire recovery of sagebrush‐steppe communities and model transferability among sites

Altered climate, including weather extremes, can cause major shifts in vegetative recovery after disturbances. Predictive models that can identify the separate and combined temporal effects of disturbance and weather on plant communities and that are transferable among sites are needed to guide vulnerability assessments and management interventions. We asked how functional group abundance responded to time since fire and antecedent weather, if long‐term vegetation trajectories were better explained by initial post‐fire weather conditions or by general five‐year antecedent weather, and if weather effects helped predict post‐fire vegetation abundances at a new site. We parameterized models using a 30‐yr vegetation monitoring dataset from burned and unburned areas of the Orchard Training Area (OCTC) of southern Idaho, USA, and monthly PRISM data, and assessed model transferability on an independent dataset from the well‐sampled Soda wildfire area along the Idaho/Oregon border. Sagebrush density increased with lower mean air temperature of the coldest month and slightly increased with higher mean air temperature of the hottest month, and with higher maximum January–June precipitation. Perennial grass cover increased in relation to higher precipitation, measured annually in the first four years after fire and/or in September–November the year of fire. Annual grass increased in relation to higher March–May precipitation in the year after fire, but not with September–November precipitation in the year of fire. Initial post‐fire weather conditions explained 1% more variation in sagebrush density than recent antecedent 5‐yr weather did but did not explain additional variation in perennial or annual grass cover. Inclusion of weather variables increased transferability of models for predicting perennial and annual grass cover from the OCTC to the Soda wildfire regardless of the time period in which weather was considered. In contrast, inclusion of weather variables did not affect transferability of the forecasts of post‐fire sagebrush density from the OCTC to the Soda site. Although model transferability may be improved by including weather covariates when predicting post‐fire vegetation recovery, predictions may be surprisingly unaffected by the temporal windows in which coarse‐scale gridded weather data are considered.

Idaho↗

USGS2021

Weather and distance to fire refugia limit landscape‐level occurrence of fungal disease in an exotic annual grass

The enemy release hypothesis proposes that invasion by exotic plant species is driven by their release from natural enemies (i.e. herbivores and pathogens) in their introduced ranges. However, in many cases, natural enemies, which may be introduced or managed to regulate invasive species, may fail to impact target host populations. Landscape heterogeneity, which can affect both the population dynamics of the pathogen and the susceptibility and the density of hosts, may contribute to why pathogens fail to control hosts despite established negative disease impacts. We explored patterns of post‐fire infection of the fungal head‐smut pathogen Ustilago bullata on the invasive annual cheatgrass Bromus tectorum , which has caused the notorious grass‐fire cycle and ecosystem degradation across Western North America. We asked whether infection level was a driver of host density or vice‐versa, and how weather affected infection and how spatial patterns of infection varied with time since fire, using a combination of structural equation modelling (SEM), proportional odds modelling and entropy‐based local indicator of spatial association (ELSA) on data from >700 plots spanning >100,000 ha remeasured annually for 4 years. Observed infection levels increased with greater prior‐year cheatgrass cover, and disease severity did not suppress cheatgrass populations. Warm, humid fall/winters and proximity to fire refugia (unburned patches) were associated with more infections. Infection clustering was most evident 2–3 years following fire with warm‐wet fall–winter conditions and decreased after two drier, colder winters. Synthesis . Severity of fungal disease did not result in measurable reductions of populations of a non‐native, invasive host species, cheatgrass, which suggests that natural enemies may not strongly regulate cheatgrass in its introduced range. Landscape heterogeneity associated with disturbance and weather limited population‐level infection of hosts by the fungal pathogen. Disturbance (specifically wildfire) and variable weather are key components of this and similar invasion systems, and likely need to be considered when evaluating disease dynamics and potential for natural enemies to influence invasion potential.

Journal of Ecology↗

USGS2021

Detecting shrub recovery in sagebrush steppe: Comparing Landsat-derived maps with field data on historical wildfires

Background The need for basic information on spatial distribution and abundance of plant species for research and management in semiarid ecosystems is frequently unmet. This need is particularly acute in the large areas impacted by megafires in sagebrush steppe ecosystems, which require frequently updated information about increases in exotic annual invaders or recovery of desirable perennials. Remote sensing provides one avenue for obtaining this information. We considered how a vegetation model based on Landsat satellite imagery (30 m pixel resolution; annual images from 1985 to 2018) known as the National Land Cover Database (NLCD) “Back-in-Time” fractional component time-series, compared with field-based vegetation measurements. The comparisons focused on detection thresholds of post-fire emergence of fire-intolerant Artemisia L . species, primarily A. tridentata Nutt. (big sagebrush). Sagebrushes are scarce after fire and their paucity over vast burn areas creates challenges for detection by remote sensing. Measurements were made extensively across the Great Basin, USA, on eight burn scars encompassing ~500 000 ha with 80 plots sampled, and intensively on a single 113 000 ha burned area where we sampled 1454 plots. Results Estimates of sagebrush cover from the NLCD were, as a mean, 6.5% greater than field-based estimates, and variance around this mean was high. The contrast between sagebrush cover measurements in field data and NLCD data in burned landscapes was considerable given that maximum cover values of sagebrush were ~35% in the field. It took approximately four to six years after the fire for NLCD to detect consistent, reliable signs of sagebrush recovery, and sagebrush cover estimated by NLCD ranged from 3 to 13% (equating to 0 to 7% in field estimates) at these times. The stabilization of cover and presence four to six years after fire contrasted with previous field-based studies that observed fluctuations over longer time periods. Conclusions While results of this study indicated that further improvement of remote sensing applications would be necessary to assess initial sagebrush recovery patterns, they also showed that Landsat satellite imagery detects the influence of burns and that the NLCD data tend to show faster rates of recovery relative to field observations.

Oregon, Idaho, Nevada, Utah↗