USGS Science⌕ Search

Geology topics

Catherine S. Jarnevich

Publications and source records attributed to Catherine S. Jarnevich.

At least 19 recordsLinked to original sources
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↗
USGS2026

Future aquatic invaders of the Northeast U.S.: How climate change, human vectors, and natural history could bring southern and western species north

As environmental conditions change, land managers are increasingly concerned about the potential for new aquatic invasive species to move into their jurisdictions. Because managers may have limited resources, detecting invasive species early is important as prevention is more effective and less costly than ongoing mitigation of established populations. Tools built to assist early detection efforts often use information on pathways of spread (how species move through a landscape) and maps of suitability (where habitat allows a species to live and reproduce). While each is useful, information on pathways or suitability alone provides only a part of the story of invasion risk. To better anticipate the risk of invasive species expanding their ranges into the Northeast U.S., there is a need to improve the way we combine and use pathways and suitability information, especially across large areas (e.g., states, regions). To fill this need, we took a new approach that combines estimates of current and future suitability with a diverse variety of pathways that gives us invasion risk scores for more than 100 freshwater invaders (fishes, plants, and invertebrates) across the Northeast U.S. In this report, we provide an overview of our methodology, results, and a description of the ongoing work to make the data publicly available. This work can be used to aid early detection efforts and associated management activities at state and local levels, including the identification of invasion risk hotspots and ranking of individual species risk to help anticipate and prevent invader establishment.

northeast United States↗
USGS2026

A regional simulation modeling framework for evaluating invasive annual grass management across the sagebrush biome

Invasive annual grasses (IAG) continue to spread within the sagebrush biome of the western United States, degrading plant communities and wildlife habitat, decreasing forage for ranching livelihoods, and heightening wildfire risk. Effective management of IAGs requires action and long-term strategic planning across the sagebrush biome, but the cumulative effects of IAG treatments over time and space are not well understood, especially over broad extents defined for strategies like the Sagebrush Conservation Design. We developed a simulation model and sampling framework that allow local-scale actions to be ‘scaled up’ to evaluate large-scale regional and biome-wide management strategy outcomes. We worked with natural resource managers and experts to co-develop a spatially explicit state-and-transition simulation model of IAG dynamics in sagebrush landscapes that can be used to evaluate alternative management strategies. We evaluated our framework by contrasting two baseline scenarios in terms of their long-term effects on the sagebrush biome. We show that focusing management efforts on moderate to high IAG cover was effective at reducing full conversion to IAGs but failed to prevent widespread establishment of IAGs in core sagebrush areas, exposing them to increased risk of wildfire and wildlife habitat degradation. The results of our model help quantify the extent of the problem that IAGs pose to sagebrush ecosystems given current knowledge and management efforts. Our framework provides a platform to explore alternative management strategy outcomes and can help managers develop informed conservation plans with realistic expectations for return on investment of resources committed to sagebrush landscapes.

Arizona, California, Colorado, Idaho, Montana, Nev↗
USGS2026

A site prioritization tool for invasive species management: Integrating diverse spatial data to improve decision making

Resource managers are tasked with protecting natural areas from invasive species with limited resources. Further, invasive management goals can vary greatly based on different management priorities specific to management agencies or taxa of interest. The site prioritization tool for invasive species management addresses these challenges by creating a platform to view and combine diverse spatial data layers to estimate cumulative invasion risk based on user-specific needs. For this tool, we developed a human transport risk layer, estimating invasion risk based on proximity to human population centers and transportation corridors, and created maps of non-native species richness across the conterminous United States. The tool also includes spatial layers showing projected changes in key climate variables through the end of the century to identify areas where invasion risk may shift. Users can explore these layers to prioritize sites based on the invasive taxa of interest, likely invasion pathways, and disturbances that may elevate invasion risk. This interactive tool will allow managers to make the spatial comparisons needed to focus efforts on areas that are highly susceptible to invasion and efficiently target monitoring and suppression efforts.

Ecological Informatics↗
USGS2025

Modeling current and future distribution of invasive tegu lizards along geopolitical boundaries in the contiguous United States: Implications for invasion threat

Historically, constrained temperature ranges limited the spread of invasive herpetofauna into temperate climates, but climate change is predicted to facilitate broader distributions. There are three species of tegu lizards native to South America and available in the pet trade that have a high risk of invasion and deleterious impacts to native ecosystems in the United States (US). There are four populations of the black and white tegu ( Salvator merianae ) in Florida and sightings as far north as North Carolina and west as California. Red tegus ( S. rufescens ) have been observed in Florida, and there is an established population of gold tegus ( Tupinambis teguixin ) in Florida. We updated previous distribution models for the contiguous United States (CONUS) that used occurrence points from their native range in South America to evaluate potential changes given current and future climate scenarios (+2 °C and +4 °C warming). Under current climate conditions, one or more tegu species have the potential to occupy most ecoregions in the CONUS. Under a + 4 °C warming scenario, suitable habitat increases by 11 % for S. merianae , 31 % for S. rufescens . The proportion of suitable habitat for T. teguixin was small under all scenarios, but increased from 0.0003 to 0.0017. For S. merianae , parts of Florida become less suitable, while suitability increases in this region for the other two species. Additionally, much of the western US is projected to be suitable for S. rufescens . Our case study underscores the potential for climate change to compound invasion threats that could outpace effective managerial responses.

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↗
USGS2025

Vertebrates in trade that pose high invasion risk to the United States

The United States imports thousands of live vertebrate species annually as part of legal trade. Escapes and releases from captivity are major pathways of invasion, however, the risk posed by the thousands of imported vertebrate species has not been systematically assessed. We conducted a horizon scan that used a data-driven climate match to filter a list of nearly 15,000 taxa drawn from across the globe of imported fish, amphibians, reptiles, birds, and mammals for rapid assessment by taxonomic experts. Experts evaluated 840 species and identified 32 (22 reptiles and 10 fishes) as having the highest risk for establishment, spread, and negative impacts. Of those high-risk species, the majority have the capacity to disrupt ecosystem processes via their role as top predators or the unique ecological niches that they occupy, while several of the snake species pose a threat to human health. High-risk species were often scored with high confidence while in contrast, low scores were attributed to a combination of ecological redundancy, low propagule pressure, or low climate match while low confidence arose from a lack of information in the literature (i.e., data deficiency). Our study therefore highlights legally imported species likely to cause the greatest harm with the recognition that many other species could also become invasive in the United States. The ranked list of vertebrate threats can be used to prioritize watchlists and inform the development of targeted regulations for importation can be applied to regions to provide a rapid, preliminary screening for large pools of potential invaders.

Biological Conservation↗
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

Phenology forecasting models for detection and management of invasive annual grasses

Non-native annual grasses can dramatically alter fire frequency and reduce forage quality and biodiversity in the ecosystems they invade. Effective management techniques are needed to reduce these undesirable invasive species and maintain ecosystem services. Well-timed management strategies, such as grazing, that are applied when invasive grasses are active prior to native plants can control invasive species spread and reduce their impact; however, anticipating the timing of key phenological stages that are susceptible to management over vast landscapes is difficult, as the phenology of these species can vary greatly over time and space. To address this challenge, we created range-wide phenology forecasts for two problematic invasive annual grasses: cheatgrass ( Bromus tectorum ), and red brome ( Bromus rubens ). We tested a suite of 18 mechanistic phenology models using observations from monitoring experiments, volunteer science, herbarium records, timelapse camera imagery, and downscaled gridded climate data to identify the models that best predicted the dates of flowering and senescence of the two invasive grass species. We found that the timing of flowering and senescence of cheatgrass and red brome were best predicted by photothermal time models that had been adjusted for topography using gridded continuous heat-insolation load index values. Phenology forecasts based on these models can help managers make decisions about when to schedule management actions such as grazing to reduce undesirable invasive grasses and promote forage production, quality, and biodiversity in grasslands; to predict the timing of greatest fire risk after annual grasses dry out; and to select remote sensing imagery to accurately map invasive grasses across topographic and latitudinal gradients. These phenology models also have the potential to be operationalized for within-season or within-year decision support.

Ecosphere↗
USGS2024

Modeling rare plant habitat together with public land managers using an iterative, coproduced process to inform decision-making on multiple-use public lands

Public lands across the United States are managed for multiple uses, resources, and values ranging from energy development to rare plant conservation. Intensified energy development and other land use changes across the Southwestern United States have increased the need for proactive management to mitigate impacts to rare plants. Habitat suitability models can inform decision-making and lead to more effective conservation of rare plants and their habitats, but high-quality models that are suited for use at local scales are lacking for many species. Our team of scientists and managers developed ensembles of habitat suitability models for five rare plant species in New Mexico using a coproduced, iterative framework complemented by comprehensive ground truthing and tailoring of products for use in public land decisions. Our process resulted in substantial differences from initial models through changes to environmental predictors, species occurrence and background data, and development of new species-specific predictors. Involving species experts and end users in model development can strengthen the process and resulting model and build understanding and trust in final products. Both factors can promote use of models to inform public land permitting and planning decisions that may affect rare plants, including by guiding development away from highly suitable habitats.

Colorado, New Mexico↗
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↗
USGS2024

Accuracy, accessibility, and institutional capacity shape the utility of habitat models for managing and conserving rare plants on western public lands

Public lands are often managed for multiple uses ranging from energy development to rare plant conservation. Habitat models can help land managers assess and mitigate potential effects of projects on rare plants, but it is unclear how models are currently being used. Our goal was to better understand how staff in the Bureau of Land Management currently use habitat models to inform their decisions, and perceived challenges and benefits associated with that use. We first examined litigation documents to determine whether the agency has been challenged on its use of data for rare plants and found no relevant legal challenges. Second, we analyzed model use in National Environmental Policy Act (NEPA) documents and found no clear citations of habitat models. Finally, we conducted interviews with agency staff who analyze potential effects of proposed actions on rare plants in NEPA documents. The primary challenges interviewees faced in using models related to data organization and access, model quality and accuracy, and institutional capacity. Interviewees believed models could be used more to inform decisions and actions to conserve rare plants and rare plant habitat on public lands and recommended improving staff access to models, creating models for additional species, and addressing staffing limitations.

Arizona, California, Colorado, New Mexico, Utah, W↗
USGS2024

Modeling the potential spread of the non-native regal demoiselle, Neopomacentrus cyanomos, in the western Atlantic

Predicting the potential distribution of a non-native species can assist management efforts to mitigate impacts on recipient ecosystems. However, such predictions are lacking for marine species, such as the non-native regal demoiselle, Neopomacentrus cyanomos , that is currently expanding its distribution in the western Atlantic. We used correlative species distribution models with three common algorithms to predict suitable habitat for N. cyanomos in the region. We compared models developed using native, non-native, and global occurrences to differentiate drivers across separate ranges using a suite of 12 environmental characteristics. While final models included an ensemble of variables, the majority ranked the combined effect of temperature variables as a key predictor correlated with the distribution of N. cyanomos. Habitat suitability increased as water temperatures increased beyond 16 °C and where annual thermal ranges were greater than 10 °C at the shallowest depth with substrate within a study cell (~ 9.2 km 2 resolution). Habitat suitability also increased where maximum surface temperatures were greater than 27 °C. In the non-native range, the proportion of reef available in each cell was another important variable increasing the suitable habitat for N. cyanomos . Our models predicted high habitat suitability for N. cyanomos throughout the Greater Caribbean, in higher latitudes along North and South American Atlantic coasts, in the eastern Pacific Ocean, and highlights key areas where managers can monitor and target potential removal efforts. The distribution of this non-native species is likely to continue expanding throughout the region with little known about potential implications on native communities.

Coral Reefs↗
USGS2024

Non-native plant invasion after fire in western USA varies by functional type and with climate

Invasions by non-native plant species after fire can negatively affect important ecosystem services and lead to invasion-fire cycles that further degrade ecosystems. The relationship between fire and plant invasion is complex, and the risk of invasion varies greatly between functional types and across geographic scales. Here, we examined patterns and predictors of non-native plant invasion following fire across the western United States. We specifically analyzed how the abundance of non-native plants after fire was related to fire characteristics and environmental conditions, such as climate, soil, and topography, in 26,729 vegetation plots from government networks and individual studies. Non-native plant cover was higher in plots measured after wildfires compared to prescribed burns or unburned plots. The post-fire cover of non-native species varied by plant functional type, and only the cover of short-lived (i.e., annual and biennial) forbs and short-lived C3 grasses was significantly higher in burned plots compared to unburned plots. Cool-season short-lived grasses composed most of the non-native post-fire vegetation, with cheatgrass ( Bromus tectorum ) being the most recorded species in the dataset. Climate variables were the most influential predictors of the cover of non-native short-lived grasses and forbs after fires, with invasion being more common in areas with drier summers and a higher proportion of yearly precipitation falling in October through March. Models using future projected climate for mid (2041–2070) and end (2071–2100) of century showed a potential for increasing post-fire invasion risk at higher elevations and latitudes. These findings highlight priorities for mitigation, monitoring, and restoration efforts to reduce post-fire plant invasion risk across the western United States.

Western United States↗
USGS2024

Observed and potential range shifts of native and non-native species with climate change

There is broad concern that the range shifts of global flora and fauna will not keep up with climate change, increasing the likelihood of population declines and extinctions. Many populations of nonnative species already have advantages over native species, including widespread human-aided dispersal and release from natural enemies. But do nonnative species also have an advantage with climate change? Here, we review observed and potential range shifts for native and nonnative species globally. We show that nonnative species are expanding their ranges orders of magnitude faster than native species, reflecting both traits that enable rapid spread and ongoing human-mediated introduction. We further show that nonnative species have large potential ranges and range expansions with climate change, likely due to a combination of widespread introduction and broader climatic tolerances. With faster spread rates and larger potential to persist or expand, nonnative populations have a decided advantage in a changing climate.

Annual Review of Ecology, Evolution, and Systemati↗
USGS2024

A habitat suitability model for testing and refining the range of Zuni fleabane, a threatened plant species

Land managers and conservation practitioners need practical tools to protect rare species in light of rapidly changing climate and land use patterns. Habitat suitability models are tools that can inform multiple-use land management decisions and target conservation actions. The narrow endemic Zuni fleabane, Erigeron rhizomatus, occurs on lands managed for multiple uses and was listed as threatened under the Endangered Species Act in 1985 due to the main threat of surface mining. Despite intermittent surveys in recent decades, managers still do not have a comprehensive understanding of suitable habitat characteristics or the geographic extent of suitable habitat across its range. We developed and field-validated a habitat suitability model for Zuni fleabane using an iterative, ensemble approach. We tested the null hypothesis that the model would not identify major new populations outside the known range but rather assist in refining the boundaries of known suitable habitat. We also set out to improve our understanding of biotic and abiotic characteristics that define suitable habitat across geographically distant metapopulations. Our model identified areas with low, medium, high, and very high probability of containing suitable habitat. We identified a new metapopulation beyond the three known (disproving our null hypothesis) as well as additional suitable habitat within the previously known regions. This model predicts where Zuni fleabane habitat likely occurs and may help land managers and conservation practitioners identify new populations, survey habitat at fine scales, avoid impacts from multiple-use management activities, and recover this threatened species.

EcoEvoRxiv↗