Geology topics
Matthew J. Germino
Publications and source records attributed to Matthew J. Germino.
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.
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.
A nontarget, disturbance-resilient native species influences post-fire recovery and multiphasic herbicide-seeding outcomes in drylands threatened by exotic annual grasses
Native species that are abundant and persistent across disturbance-succession cycles can affect recovery and restoration of plant communities, especially in drylands. In the sagebrush-steppe deserts of North America, restoring deep-rooted perennial bunchgrasses (DRPBGs) is key to the strategy for breaking an increasingly problematic cycle of wildfire promoted by exotic annual grasses (EAGs) and displacement of perennials by post-fire increases in EAGs. We asked how Sandberg bluegrass ( Poa secunda , POSE)—a common native grass that shares traits with EAGs such as resilience to disturbance and rapid, shallow-rooted, early season growth—(1) recovered after wildfire, (2) responded to different combinations of native-plant seedings of DRPBGs and EAG-targeting herbicides; and (3) in turn, related to DRPBG recovery. In repeated sampling of up to approximately 2,000 plots spanning 113,000 burned hectares, POSE was initially more abundant than DRPBGs until POSE began to decline 5 years post-fire, and POSE was marginally affected or unaffected by restoration treatments targeting DRPBGs or EAGs. In comparison, EAG cover decreased 8–16% where preemergent herbicide was sprayed and DRPBG cover increased 3–9% where they were drill-seeded, and the greatest EAG reductions and DRPBG increases were seen where seeding and herbicides were combined in a time-staggered fashion. Treatments had less target effects where POSE cover was high (>18%), which is also where EAG cover was scarcer and DRPBG cover greater, regardless of post-fire interventions. Consideration of the ecological role of disturbance-resilient species with ruderal characteristics that match traits of key invaders may improve the efficiency and effectiveness of restoration interventions.
High-resolution thermal imagery reveals how interactions between crown structure and genetics shape plant temperature
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. When considered across the whole season, 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 microclimate 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.
Experimental manipulation of soil-surface albedo alters phenology and growth of Bromus tectorum (cheatgrass)
Purpose The sensitivity of wildland plants to temperature can be directly measured using experimental manipulations of temperature in situ. We show that soil surface temperature and plant density (per square meter) have a significant impact on the germination, growth, and phenology of Bromus tectorum L., cheatgrass, a short-statured invasive winter-annual grass, and assess a new experimental temperature manipulation method: the application of black and white gravel to warm and cool the soil surface. Methods We monitored height, seed production, and phenological responses of cheatgrass, seeded into colored gravel at low and high densities at two sites in the western USA: Boise, ID and Cheyenne, WY. Soil surface temperature and volumetric water content were measured to assess treatment effects on soil surface microclimate. Results Black gravel increased mean temperatures of the surface soil by 1.6 and 2.6 °C compared to white gravel in Cheyenne and Boise, respectively, causing 21–24 more days with soil temperatures > 0 °C, earlier cheatgrass germination, and up to 2.8-fold increases in cheatgrass height. Higher seeding density of cheatgrass led to 1.4-fold taller plants on black gravel plots at both sites, but not white gravel at the Boise site, indicating a possible thermal benefit or reduction of water demand due to plant clustering in warmer treatments. Conclusions Manipulating soil-surface albedo altered the soil microclimate and thus growth and phenology of cheatgrass, whose life history and growth form confer a strong dependency on soil-surface conditions.
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.
Does post-fire recovery of native grasses across abiotic-stress and invasive-grass gradients match theoretical predictions, in sagebrush steppe?
Interactions among species can strongly affect how plant communities reassemble after disturbances, and variability among native and invasive species across environmental gradients must be known in order to manage plant-community recovery. The stress-gradient hypothesis (SGH) predicts species interactions will be more positive in abiotically stressful conditions and conversely, more negative in benign conditions, and the resistance-resilience concept (RRC) may predict where and when invasions will complicate ecosystem recovery. We evaluated how abiotic stress and biotic interactions determine native bunchgrass abundances across environmental gradients using additive models of cover data from over 500 plots re-measured annually for 5 years as they recovered naturally (untreated) after a megafire (>100,000 ha) in sagebrush steppe threated by the invasive-grass and fire cycle. The species included native bunchgrasses, bluebunch wheatgrass ( Pseudoroegneria spicata ) and Sandberg bluegrass ( Poa secunda ), and the exotic and invasive annual cheatgrass ( Bromus tectorum ). We asked whether associations between native bunchgrasses and cheatgrass were context dependent and if the SGH could help predict interspecific associations between species in a semiarid environment. The association of cover of each native bunchgrass to cheatgrass was not uniform, and instead varied from neutral to negative across environmental gradients in both space and time (i.e., weather), to which the species had nonlinear and sometimes threshold-like responses. Consistent with the SGH, bunchgrasses were generally more negatively related to cheatgrass (i.e., putative competition) in conditions which increased the cover of each bunchgrass – which were higher elevations and temperatures and lower solar heatload, and, for Sandberg bluegrass, drier conditions. There were few indications of positive interactions (i.e., putative facilitation) in stressful conditions, and instead associations were again negative, albeit weaker, in some of the conditions evaluated. Synthesis . These findings demonstrate that the negative association among native bunchgrasses and cheatgrass is context dependent and is determined by the abundances of both interacting species which is driven by environmental stress. This led to a hypothesis that together Sandberg bluegrass and bluebunch wheatgrass provide complementary resistance to cheatgrass at the landscape level, despite their different ecology and contrary to the management preference for bluebunch wheatgrass. Sandberg bluegrass might be critical for providing resistance against cheatgrass where invasion potential is greatest, i.e., at lower elevations, where bluebunch wheatgrass is scarce.
Integration of weed-suppressive bacteria with herbicides to reduce exotic annual grasses and wildfire problems on ITD right-of-ways
Invasion by exotic-annual grasses such as cheatgrass is impacting semiarid rangelands and especially transportation corridors, where it causes increased wildfire and many other environmental issues. Methods of reducing exotic annual grasses and restoring native perennials are needed, particularly testing of their intended target or unintended, non-target effects. In a series of experiments arrayed across different site and plant-community conditions on Idaho Transportation Department right-of-ways, the effects of chemical or biological herbicides, site preparation and co-treatments such as raking, and/or seeding were evaluated over 3 years. Strains of the soil bacterium Pseudomonas fluorescens that are supposedly weed-suppressive were generally ineffective, and resulted in relatively weak effects at a small proportion of plots and only at one site, but also resulted in highly undesirable non-target effects at another site. The chemical herbicides imazapic and especially indaziflam (Rejuvra) tended to have more consistent and stronger effects, and indaziflam furthermore provided a longer period of control, although additional years of observation would be required to assess its endurance. Seeding effects were weak, and preparation of seed beds through raking was not effective. In conclusion, indaziflam appeared to be the most effective tool for reducing cheatgrass, but techniques for increasing perennials after its application are needed.
Spatial models can improve the experimental design of field-based transplant gardens by preventing bias due to neighborhood crowding
Field-based transplant gardens, including common and reciprocal garden experiments, are a powerful tool for studying genetic variation and gene-by-environment interactions. These experiments assume that individuals within the garden represent independent replicates growing in a homogenous environment. Plant neighborhood interactions are pervasive across plant populations and could violate assumptions of transplant garden experiments. We demonstrate how spatially explicit models for plant–plant interactions can provide novel insights on genotypes' performance in field-transplant garden designs. We used individual-based models, based on data from a sagebrush ( Artemisia spp.) common garden, to simulate the impact of spatial plant–plant interactions on between-group differences in plant growth. We found that planting densities within the range of those used in many common gardens can bias experimental outcomes. Our results demonstrate that higher planting densities can lead to inflated group differences and may confound genotypes' competitive ability and genetically underpinned variation. Synthesis. We propose that spatially explicit models can help avoid biased results by informing the design and analysis of field-based transplant garden experiments. Alternately, including neighborhood effects in post hoc analyses of transplant garden experiments is likely to provide novel insights into the roles of biotic factors and density dependence in genetic differentiation.
Plant community trajectories following livestock exclusion for conservation vary and hinge on initial invasion and soil-biocrust conditions in shrub steppe
Adjustments or complete withdrawal of livestock grazing are among the most common conservation actions in semiarid uplands, but outcomes can vary considerably with ecological context. Invasion by exotic annual grasses and the excessive wildfire they promote are increasing threats to semiarid shrub-steppe, and plant-community response to livestock exclusion in these areas may be complicated by the rapid colonization ability of invaders. We evaluated vegetation-community changes over 14-year interval (2007–2021) in a shrub-steppe landscape where a >100-year history of livestock grazing had been terminated in 1996. Field surveys revealed that bare-soil exposure decreased >20% over the 14 years owing to biomass accumulation, but this was primarily due to large increases in exotic annual “cheatgrass” ( Bromus tectorum , +1.8-fold) and the litter it produces (+1.5-fold). Soil biocrusts increased 11.9% and perennial bunchgrasses increased 3% over the 14 years. These community changes varied at the patch scale and entailed inverse relationships of (1) both cheatgrass and biocrusts to plant-community basal cover, (2) cheatgrass to both biocrusts and perennial grasses, and (3) biocrusts to cheatgrass and litter. The spatiotemporal variability in vegetation constituted changes in plant-community states, according to cluster analysis. The modeled probability of a community transitioning to a cheatgrass state was (1) strongly and positively related to the initial (2007) cover of cheatgrass in hotspots where initial cheatgrass cover was >20%, and (2) negatively related to biocrust cover where initial biocrust cover was >4% of ground area. The decision space for this landscape can be framed as a shifting from acceptance towards resisting further degradation by removing livestock and their trampling of soil surfaces and utilization of perennial herbs. However, cheatgrass appears to be the most impactful agent of change and continued invasion appears imminent. Active restoration may help resist further degradation and direct change towards tolerable conditions.
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.
The effects of cheatgrass invasion on US Great Basin carbon storage depend on interactions between plant community composition, precipitation seasonality, and soil climate regime
Annual-grass invasions are transforming desert ecosystems in ways that affect ecosystem carbon (C) balance, but previous studies do not agree on the pattern, magnitude and direction of changes. A recent meta-analysis of 41 articles and 386 sites concludes that invasion by annual grasses such as cheatgrass ( Bromus tectorum L) reduces C in biomass across the Great Basin (Nagy et al., 2021). Reanalysis reveals that whether cheatgrass affects biomass C stocks is not generalizable, but rather depends on the considerable variation in climate across the subject sites. Our analysis suggests that accurate Great Basin-scale estimates of cheatgrass effects on C balance are not yet possible. Addition of climate variables to the meta-analysis reveals that cheatgrass invasion (a) reduced C in above-ground biomass in relatively summer-wet sites but not in summer-dry sites, (b) increased surface soil C in sites with intermediate resistance and resilience classifications (R&R) but not in low R&R sites—that is, mesic/aridic soil climates and (c) did not affect deep soil C. Considering that cheatgrass has expanded most in relatively summer-dry sites and mesic/aridic sites, omission of climate factors leads to model overestimates of cheatgrass effects on C when extrapolating to larger areas. Estimates of cheatgrass effects on C would also be improved if the analysis considered that (a) perennial grasslands are a common community state in the Great Basin that have intermediary C relative to annual grasslands and sagebrush stands, that is the omission of perennial grasslands from analysis inflates the baseline C storage of uninvaded Great Basin ecosystems, and( b) cheatgrass does not often exist in stable monocultures and soil carbon can reflect current or recent presence of other species. Synthesis and applications . Invasions often reveal heterogeneity in ecosystem structure and function that is not otherwise evident, and the heterogeneity can influence estimation of the net impacts of the invaders. For cheatgrass and other invaders, we propose that formally accounting for the spatial variability of invasion on ecosystem functions will improve the estimation of their net effect on ecosystem C, and thus improve prospects for adjusting management practices to optimize C sequestration.
Modeling of fire spread in sagebrush steppe using FARSITE: An approach to improving input data and simulation accuracy
Background: Model simulations of wildfire spread and assessments of their accuracy are needed for understanding and managing altered fire regimes in semiarid regions. The accuracy of wildfire spread simulations can be evaluated from post hoc comparisons of simulated and actual wildfire perimeters, but this requires information on pre-fire vegetation fuels that is typically not available. We assessed the accuracy of the Fire-Area Simulator (FARSITE) model parameterized with maps of fire behavior fuel models (FBFMs) obtained from the widely used LANDFIRE, as well as alternative means which utilized the classification of Rangeland Analysis Platform (RAP) satellite-derived vegetation cover maps to create FBFM maps. We focused on the 2015 Soda wildfire, which burned 113,000 ha of sagebrush steppe in the western USA, and then assessed the transferability of our RAP-to-FBFM selection process, which produced the most accurate reconstruction of the Soda wildfire, on the nearby 2016 Cherry Road wildfire. Results: Parameterizing FARSITE with maps of FBFMs from LANDFIRE resulted in low levels of agreement between simulated and observed area burned, with maximum Sorensen’s coefficient (SC) and Cohen’s kappa ( K ) values of 0.38 and 0.36, respectively. In contrast, maps of FBFMs derived from unsupervised classification of RAP vegetation cover maps led to much greater simulated-to-observed burned area agreement (SC = 0.70, K = 0.68). The FBFM map that generated the greatest simulated-to-observed burned area agreement for the Soda wildfire was then used to crosswalk FBFMs to another nearby wildfire (2016 Cherry Road), and this FBFM selection led to high FARSITE simulated-to-observed burned area agreement (SC = 0.80, K = 0.79). Conclusions: Using RAP to inform pre-fire FBFM selection increased the accuracy of FARSITE simulations compared to parameterization with the standard LANDFIRE FBFM maps, in sagebrush steppe. Additionally, the crosswalk method appeared to have regional generalizability. Flanking and backfires were the primary source of disagreements between simulated and observed fire spread in FARSITE, which are sources of error that may require modeling of lateral heterogeneity in fuels and fire processes at finer scales than used here.
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.