Geology topics

Anna C. Knight

Publications and source records attributed to Anna C. Knight.

21 records · Page 2Linked to original sources

USGS2022

Soil depth and precipitation moderate soil textural effects on seedling survival of a foundation shrub species

In drylands, there is a need for controlled experiments over multiple planting years to examine how woody seedlings respond to soil texture and the potentially interactive effects of soil depth and precipitation. Understanding how multiple environmental factors interactively influence plant establishment is critical to restoration ecology and in this case to broad-scale restoration efforts in western US drylands dominated by big sagebrush ( Artemisia tridentata ). We planted sagebrush seedlings across a range of soil textures and depths in the southern portion of the species' range, on the Colorado Plateau. We evaluated survival of repeated plantings of caged and uncaged seedlings over two years across 20 plots in wet vs. average precipitation years at one site, and examined broader patterns of sagebrush seedling survival during an average precipitation year in 56 plots across four sites. First-year survival was >9x higher under wet than average precipitation. Under favorable (wet) conditions, early sagebrush seedling survival was highest on coarser soils, especially those that also had a shallower restrictive layer (e.g., 50-100 cm). Under average precipitation, soil texture and depth effects on survival of newly-planted seedlings were much weaker, but older (>1 yr) seedlings benefitted from growing on coarser textured soils. It may be possible to increase survival by sheltering seedlings with small mesh cages, which likely improve moisture availability. Our results provide new insights into environmental factors that limit woody seedling survival in drylands and illustrate that planting in wet years and incorporating detailed soil setting information could increase survival of sagebrush seedlings in restoration projects.

Restoration Ecology↗

USGS2022

A quantitative soil-geomorphic framework for developing and mapping ecological site groups

Land management decisions need context about how landscapes will respond to different circumstances or actions. As ecologists’ understanding of nonlinear ecological dynamics has evolved into state-and-transition models (STMs), they have put more emphasis on defining and mapping the soil, geomorphological, and climate parameters that mediate these dynamics. The US Department of Agriculture Natural Resources Conservation Service ecological site descriptions (ESDs) have become the foremost system in classifying lands into ecological units based on STMs. However, an exhaustive inventory of ESDs has proved challenging to complete in the United States, and there have been questions about the consistency of detail in areas completed and the ability to objectively support some assertions made in existing ESDs. To address these issues, this study examines ESDs in the diverse Upper Colorado River region, where ESDs are only partially complete, to look at quantitative approaches to generalizing ecological site concepts based on unifying underlying soil, geomorphology, and climate patterns. Using existing ESDs and vegetation monitoring plot data, results show that a simple hierarchical soil geomorphic unit (SGU) framework based on topographic mediation of moisture, soil salinity, soil depth, slope, rock content, and soil texture can represent much of the ecological dynamics cataloged in ESDs. Analyses of reference plant production data, ecological state attribution, and regional monitoring data show that the new SGUs represent more variation than common climate parameters. This study also included predictively mapping SGUs at 30-m resolution (Kappa of 0.53, 74% agreement with top two predictions in validation). An optimized combination of SGUs with climate zones derived from an aridity index and maximum temperature of the hottest month resulted in an ecological site group framework that condensed over 826 unique ecological site records at various stages of completeness in the regional soil survey down to 35 intuitive and mappable ecological site groups.

Rangeland Ecology and Management↗

USGS2021

Improving Landsat predictions of rangeland fractional cover with multitask learning and uncertainty

Operational satellite remote sensing products are transforming rangeland management and science. Advancements in computation, data storage and processing have removed barriers that previously blocked or hindered the development and use of remote sensing products. When combined with local data and knowledge, remote sensing products can inform decision‐making at multiple scales. We used temporal convolutional networks to produce a fractional cover product that spans western United States rangelands. We trained the model with 52,012 on‐the‐ground vegetation plots to simultaneously predict fractional cover for annual forbs and grasses, perennial forbs and grasses, shrubs, trees, litter and bare ground. To assist interpretation and to provide a measure of prediction confidence, we also produced spatiotemporal‐explicit, pixel‐level estimates of uncertainty. We evaluated the model with 5,780 on‐the‐ground vegetation plots removed from the training data. Model evaluation averaged 6.3% mean absolute error and 9.6% root mean squared error. Evaluation with additional datasets that were not part of the training dataset, and that varied in geographic range, method of collection, scope and size, revealed similar metrics. Model performance increased across all functional groups compared to the previously produced fractional product. The advancements achieved with the new rangeland fractional cover product expand the management toolbox with improved predictions of fractional cover and pixel‐level uncertainty. The new product is available on the Rangeland Analysis Platform (https://rangelands.app/), an interactive web application that tracks rangeland vegetation through time. This product is intended to be used alongside local on‐the‐ground data, expert knowledge, land use history, scientific literature and other sources of information when making interpretations. When being used to inform decision‐making, remotely sensed products should be evaluated and utilized according to the context of the decision and not be used in isolation.

Methods in Ecology and Evolution↗