Geology topics

Demetra A. Williams

Publications and source records attributed to Demetra A. Williams.

6 recordsLinked to original sources

USGS2026

The Global Observation System for Mercury dataset for mercury stable isotope signatures in environmental media

The natural variability in the abundance ratios of stable isotopes of mercury (Hg), which collectively forms its isotopic composition, provides information on Hg sources and biogeochemical pathways. Modern mass spectrometry has allowed for measurement of Hg isotope ratios across atmospheric, aquatic, and terrestrial compartments, often at trace levels. Since realizing the utility of Hg isotope ratios, hundreds of studies have generated a wealth of data that is increasingly challenging to summarize and interpret for individual scientists. Arising as a complimentary initiative under the Global Observation System for Mercury (GOS 4 M) that supports the Minamata Convention on Mercury, we describe the establishment of the iGOS 4 M Hg isotope dataset. In its present form, iGOS 4 M has > 11,000 data entries from 190 studies, which together with metadata on sample type, geographical location, Hg concentration, and other environmental and ecological parameters, provide a long-needed catalogue of knowledge gathered by and for the Hg isotope community. We introduce the structure of iGOS 4 M and a standardization rubric to promote global consistency in measurement and data reporting.

Scientific Data↗

USGS2026

Current and near-future conditions of aquatic spatial data for use in ecological models in the United States

To address increasing demand for ecological models of aquatic species that can inform the management of national freshwater resources, we leveraged manager input to develop suites of environmental data layers characterizing freshwater habitats for the contiguous United States. Using the National Hydrography Dataset, these new data cover lentic and lotic systems under current and near-future environmental conditions. The data include a variety of covariate categories including climate, soil chemistry, land use and land cover, and human modification of the surrounding landscape. The predictor resolution for atmospheric climate predictors was the lake (wetland) or stream reach, and, for the terrestrial proxies, the subwatershed (HUC12) surrounding the lake or stream reach was chosen to capture the relevant land features surrounding the habitat. Future land use, land cover and streamflow predictions were included from present to mid-century. These data are available for the development of freshwater ecological models in the contiguous United States for a variety of applications, including species distribution modeling and exploring change in spatially diverse aquatic systems in time.

contiguous United States↗

USGS2025

How to model a new invader? US-invaded range models outperform global or combined range models after 100 occurrences

Invasive species are an economic and ecological burden, and efforts to limit their impact are greatly improved with reliable maps based on species distribution models (SDMs). However, the potential distribution of new invaders is difficult to anticipate because they are still spreading with few observations in their invaded habitat. Therefore, an accepted practice in predicting the distribution of invasive species has been to incorporate habitat information from its entire geographic distribution (invaded and native ranges) into SDMs. Yet, this approach, due to niche shifts, niche expansions, and data deficiencies, commonly misrepresents where an invasive species is found in its new range. Here, we use time series records (invasion stages) from 13 invasive plant species in North America to explore the tension between modeling invasive species using global range and invaded range data and to determine if there is a “tipping point” at which one SDM strategy performs better than the other in predicting the ultimate distribution. At the earliest invasion stage, models developed using both invaded range and global occurrences on average performed better and had less variability across species than other model strategies at this stage. However, after as few as 100 observations of an invasive plant had been made, US-invaded range models, on average, outperformed global range models and models that combined occurrences. By building models with global and US-scale predictors, we show that higher performance of invaded range models was in part because of greater data quality at the invaded-range scale. Our work demonstrates that after relatively few observations of an invasive species in its invaded range, it is more accurate to model its potential distribution using only information from the invaded range while disregarding information from other regions. This work develops a robust and comprehensive approach to modeling novel distributions of newly observed invasive species.

North America↗

USGS2024

First records distribution models to guide biosurveillance for non-native species

Quickly locating new populations of non-native species can reduce the ecological and economic costs of species invasions. However, the difficulty of predicting which new non-native species will establish, and where, has limited active post-border biosurveillance efforts. Because pathways of introduction underlie spatial patterns of establishment risk, an intuitive approach is to search for new non-native species in areas where many non-native species have first been detected in the past. We formalize this intuition via first records distribution models (FRDMs), which apply species distribution modeling methods to the collection of first occurrence records across species (i.e. one record per species). We define FRDMs as statistical models that quantify environmental conditions associated with species' first naturalized records to predict spatial patterns of establishment risk. We model the first records of non-native plants in the conterminous USA as a proof-of-concept. The novelty of FRDMs is that their inferences apply not just to the species that contributed data; they provide a rigorous framework for predicting hotspots of invasion for new non-native taxa that share a pathway of introduction with the modeled species. FRDMs can guide survey efforts for new non-native taxa at multiple scales and across ecosystems.

conterminous United States↗

USGS2024

Predicted occurrence and abundance habitat suitability of invasive plants in the contiguous United States: Updates for the INHABIT web tool.

Invasive plant species have substantial negative ecological and economic impacts. Geographic information on the potential and actual distributions of invasive plants is critical for their effective management. For many regions, numerous sources of predictive geographic information exist for invasive plants, often in the form of outputs from species distribution models ( SDMs ). The creation of a repository of consistently produced SDMs of regional- or national-scale information predicting the potential distribution of invasive plant species could provide information to managers in the prioritisation of invasive species management. Here, we present a novel set of not only habitat suitability models for occurrence for 259 manager requested invasive plant species in the contiguous United States (USA), but also habitat suitability models for abundance (≥ 5% cover) and high abundance (≥ 25% cover). These data provide an update to the Invasive Species Habitat Tool ( INHABIT ; gis.usgs.gov/inhabit). This tool contains information on the majority of invasive plant species in the contiguous USA with sufficient location data for model building. INHABIT provides a canonical set of predicted geographic distributions for invasive plants in the contiguous USA that can aid in the search for new populations of invasive plant species and help create watch lists for emerging invaders. As this tool contains information on nearly all of the most problematic invasive plants in the contiguous USA, it helps in prioritising management strategies by showing which plants are already present or abundant in a land management area and which may become present or abundant in the future.

contiguous United States↗

USGS2024

Predictor importance in habitat suitability models for invasive terrestrial plants

Aim Due to the socioeconomic and environmental damages caused by invasive species, predicting the distribution of invasive plants is fundamental for effectively targeting management efforts. A habitat suitability model (HSM) is a powerful tool to predict potential habitat of invasive species to help guide the early detection of invasive plants. Despite numerous studies of the predictors used in HSMs, there is little consensus about the most appropriate predictors to use in creating ecologically realistic predictions from HSMs. Location The contiguous United States. Methods We explore 220 invasive terrestrial plant species' existing HSMs constructed with consistent modelling algorithms, background generation methods, predictor resolution, and geographic extent, and calculate the relative importance of predictors for each species. We sort predictors into eight groups (topography, temperature, disturbance, atmospheric water, landscape water, substrate, biotic interaction, and radiation) and compare the importance of predictor groups by plant lifeforms and phylogenetic relatedness. Results Human modification and minimum winter temperature were generally the two highest performing individual predictors across the species studied. The highest-performing predictor groups were disturbance, temperature, and atmospheric water. Across lifeforms, there were minimal differences in the influences of predictor groups, although woody plant models exhibited the largest differences in predictor importance when compared with non-woody plant models. Additionally, we found no significant relationship between the importance of predictor groups and phylogenetic relatedness. Main Conclusions This study has implications for informing predictor selection in invasive plant HSMs, leading to more reliable and accurate models of invasive terrestrial plants. Our results emphasize the need to critically select predictors included in HSMs, with special consideration to temperature and disturbance predictors, to accurately predict habitat of invasive plant for detection and response of invasive plant species. With more accurate predictions, managers will be better prepared to address invasive species and reduce their threats to landscapes.

Diversity and Distributions↗