Geology topics

Christopher A Anthony

Publications and source records attributed to Christopher A Anthony.

3 recordsLinked to original sources

USGS2025

A systematic review and meta-analysis of post-fire seeding and herbicide treatment effectiveness for controlling exotic annual grasses in the sagebrush biome

Introduction Outcomes of ecological restoration treatments can be highly variable and challenging to generalize, even for the same treatment type applied in similar ecological communities at different times and places. Notable examples are the herbicide and seeding treatments that have been extensively applied across the perennial sagebrush steppe of the United States to reduce impacts of fire-promoting exotic annual grasses (EAGs) such as Cheatgrass ( Bromus tectorum ). Objectives We asked if statistically based generalizations about the effects of pre-emergent herbicide and drill seeding of perennials, implemented following wildfires when threats of annual-grass invasion are greatest, could be made from the available literature. Methods We conducted a meta-analysis of these treatment effects for 1228 treatment-control comparisons from 35 studies published from 1990 to 2023 that met basic criteria for topical relevance and repeatability. Results EAGs and forbs were each reduced by herbicides and by seeding perennial grasses. The combination of herbicide and seeding reduced annuals and led to the largest increases in perennials. Although these outcomes support the intended effects of the treatments, there was high variability in outcomes among studies. Conclusions Combined use of pre-emergent herbicides and seeding can increase the success of restoration interventions that are aimed at reducing the invasion of exotic annual grasses and increasing perennials after fire in sagebrush steppe. Our analysis revealed, however, that the available literature was not suited to answering more specific questions, in spite of the massive amount of post-fire herbicide and seedings that have been applied in burned sagebrush steppe. Specifically, there were too few topically relevant studies with adequate scientific reporting to properly evaluate differences among specific treatment methodologies, including specific herbicides, that affect restoration success.

Restoration Ecology↗

USGS2024

Propensity score matching mitigates risk of faulty inferences in observational studies of effectiveness of restoration trials

Determining effectiveness of restoration treatments is an important requirement of adaptive management, but it can be non-trivial where only portions of large and heterogeneous landscapes of concern can be treated and sampled. Bias and non-randomness in the spatial deployment of treatment and thus sampling is nearly unavoidable in the data available for large-scale management trials, and the biophysical landscape characteristics underlying the bias are key but rare considerations in analyses of treatment effects. Treatment effects from large-scale management trials are typically estimated with multivariable regression (MVR) models. However, this method is unsuited to reliable estimations of treatment effects when treated and untreated areas differ in their underlying biophysical variability. An alternative to conventional regression is to use propensity score (PS) matching, which can limit the differences in confounding variables among treatment groups and assure the data collected or selected for analysis are more consistent with a randomized and unconfounded experiment. Thus, PS is expected to identify treatment effects more accurately. We used data from a large-scale monitoring effort of a megafire to evaluate the efficacy of PS matching in making inferences on treatment effects when treatments are applied non-randomly over a large heterogeneous area. We compared the resulting inference to both traditional MVR methods and to “naïve” methods that do not consider treatment allocation bias. Treatment effects varied between the different statistical methods for controlling selection bias and confounding biophysical factors. The PS-matched model revealed a weaker treatment effect of drill seeding and a greater effect of herbicide spraying on the cover of perennial bunchgrasses when compared to MVR or naïve modelled estimates. The inferences from the PS-matched model are considered more reliable because the treated and untreated plots are more similar in their underlying biophysical characteristics. Synthesis and applications . Failure to consider the non-random and selective deployment of restoration treatments by managers leads to faulty inference on their effectiveness. However, tools such as propensity-score matching can be used to remove the bias from analyses of the outcomes of management trials or to devise sampling plans that efficiently protect against the bias.

Journal of Applied Ecology↗

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↗