Geology topics

Catherine S. Jarnevich

Publications and source records attributed to Catherine S. Jarnevich.

At least 37 records · Page 2Linked to original sources

USGS2023

Shifting hotspots: Climate change projected to drive contractions and expansions of invasive plant abundance habitats

Aim Preventing the spread of range-shifting invasive species is a top priority for mitigating the impacts of climate change. Invasive plants become abundant and cause negative impacts in only a fraction of their introduced ranges, yet projections of invasion risk are almost exclusively derived from models built using all non-native occurrences and neglect abundance information. Location Eastern USA. Methods We compiled abundance records for 144 invasive plant species from five major growth forms. We fit over 600 species distribution models based on occurrences of abundant plant populations, thus projecting which areas in the eastern United States (U.S.) will be most susceptible to invasion under current and +2°C climate change. Results We identified current invasive plant hotspots in the Great Lakes region, mid-Atlantic region, and along the northeast coast of Florida and Georgia, each climatically suitable for abundant populations of over 30 invasive plant species. Under a +2°C climate change scenario, hotspots will shift an average of 213 km, predominantly towards the northeast U.S., where some areas are projected to become suitable for up to 21 new invasive plant species. Range shifting species could exacerbate impacts of up to 40 invasive species projected to sustain populations within existing hotspots. On the other hand, within the eastern U.S., 62% of species will experience decreased suitability for abundant populations with climate change. This trend is consistent across five plant growth forms. Main Conclusions We produced species range maps and state-specific watch lists from these analyses, which can inform proactive regulation, monitoring, and management of invasive plants most likely to cause future ecological impacts. Additionally, areas we identify as becoming less suitable for abundant populations could be prioritized for restoration of climate-adapted native species. This research provides a first comprehensive assessment of risk from abundant plant invasions across the eastern U.S.

Diversity and Distributions↗

USGS2023

Modeling habitat suitability across different levels of invasive plant abundance

Predicting where invasive plants are likely to spread and become abundant is critical for informing invasive plant management. Species distribution models are a key tool for informing the geography of invasion risk, but most distribution models are limited by their use of presence data, including no information on invader population abundance. In this study, we ask how habitat suitability varies for different levels of abundance for three invasive plants: stiltgrass ( Microstegium vimineum ), sericea lespedeza ( Lespedeza cuneata ), and privet ( Ligustrum sinense ). For each species, we used an ensemble distribution modeling approach to compare suitability for invasion estimated from subsets of point location data: all presences vs. locations with percent cover ≥ 1%, ≥ 5%, ≥ 10%, ≥ 25%, and ≥ 50%. For all species, the total area predicted as suitable for abundant populations was 32%–68% less than the area predicted as suitable for presence. For stiltgrass and sericea lespedeza, the area suitable for invasion decreased when predicted from higher levels of abundance, whereas for privet, suitable area was similar across abundance levels. Stiltgrass and sericea lespedeza are therefore likely to become highly abundant in a smaller portion of their ranges, while privet could become highly abundant anywhere it can establish at low abundance. Different environmental predictors explained suitability for presence versus abundance, suggesting the environmental niche associated with presence differs from that associated with high population abundance. Analyses of more species and growth forms are still needed, but our results combined with previous studies consistently show that fitting distribution models to point locations with ≥ 5–10% cover refines range maps and can produce a more targeted assessment of invasion risk.

Biological Invasions↗

USGS2023

The invasive plant data landscape: A synthesis of spatial data and applications for research and management in the United States

Context An increase in the number and availability of datasets cataloging invasive plant distributions offers opportunities to expand our understanding, monitoring, and management of invasives across spatial scales. These datasets, created using on-the-ground observations and modeling techniques, are made both for and by researchers and managers. Objectives The large number and variety of data types and associated datasets can be difficult to navigate, require high levels of data literacy, and can overwhelm the intended end-users. By providing a synthesis of available data types and datasets, this work may facilitate data understanding and use among researchers and managers. Methods We synthesize types of invasive plant distribution data sources, highlighting publicly available datasets and their potential applications and limitations for research and management. Results Eight data types and their potential applications for research and management are described. We also describe gaps in current invasive species distribution data usability and outline a path forward for improving the use of invasive plant data in future research and management. Conclusions Accessible and usable invasive plant spatial data are needed for developing landscape scale analysis and management plans. By synthesizing the invasive plant data available, with examples and limitations for application, this work will serve as a guide to facilitate appropriate and efficient data choices in current and future research and management.

Nevada↗

USGS2023

Invaders at the doorstep: Using species distribution modeling to enhance invasive plant watch lists

Watch lists of invasive species that threaten a particular land management unit are useful tools because they can draw attention to invasive species at the very early stages of invasion when early detection and rapid response efforts are often most successful. However, watch lists typically rely on the subjective selection of invasive species by experts or on the use of spotty occurrence records. Further, incomplete records of invasive plant occurrences bias these watch lists towards the inclusion of invasive plant species that may already be present in a land management unit, because the occurrences have not been formally integrated into publicly accessible biodiversity databases. However, these problems may be overcome by an iterative approach that guides more complete detection and compilation of invasive plant species records within land management units. To address issues from unobserved or unrecorded occurrences, we combined predicted suitable habitat from species distribution models and aggregated invasive plant occurrence records to develop ranked watch lists of 146 priority invasive plant species on >4000 land management units from five different administrative types within the United States. Based on this analysis, we determined that on average 84% of priority invasive plants with suitable habitat within a given land management unit were as yet unobserved, and that 41% of those were ‘doorstep species’ – found within 50 miles of the unit boundary yet not detected within the unit. Two case studies, developed in collaboration with staff at U.S. Fish and Wildlife Service Refuges, showed that by combining both habitat suitability models and invasive plant occurrence records, we could identify additional problematic invasive plants that had been previously overlooked. Model-based watch lists of ‘doorstep species’ are useful tools because they can objectively alert land managers to threats from invasive plants with high likelihood of establishment.

contiguous United States↗

USGS2023

Modeling, mapping, and measuring the risk of freshwater invasive species across Alaska

Freshwater ecosystems of the Alaskan Arctic and Subarctic provide resources that are culturally, ecologically, and economically invaluable. Presently, these regions are relatively free of the impacts from invasive species compared to southern latitudes. To date, there have been relatively few verified introductions of aquatic invasive species (AIS) to freshwater ecosystems in Alaska. The expanding list and distribution of AIS has led to significant negative ecological and economic impacts (e.g., waterweed Elodea nuttalli ; E. canadensis and northern pike Esox Lucius introduced outside its native range in Alaska). Escalating human activity across Alaskan lands and waters, coupled with rapidly shifting environmental conditions, increases the potential for new species introductions and subsequent establishment. Creating a proactive framework for well-informed decision-making and action can improve the effectiveness of prevention efforts and bolster decision support tools that help resource managers direct limited resources. Prioritizing AIS that may be introduced and become established, as well as the locations at highest risk of invasion, is foundational to building a proactive invasive species management framework in Alaska. This project sought to identify and prioritize AIS known to be invasive in the contiguous United States, evaluate current and future habitat suitability for AIS in Alaska, and assess potential for AIS to be transported to habitats across Alaska, utilizing similar assessment methods as implemented for Bering Sea marine invasive species and non-native plants in Alaska. To accomplish this goal, the objectives of the project were to: 1) develop a formal ranked list of potential AIS to freshwater systems of Alaska; 2) assess the level of establishment risk for potential AIS by developing habitat suitability models for waterbodies across Alaska; and 3), identify potential pathways and specific vectors for high-risk AIS to invade Alaska and develop a framework for how vector analysis will be completed to understand transport risk. Overall, our goal is horizon scanning which is defined by Roy et al. (2019) as “a systematic examination of potential threats and opportunities, within a given context, and likely future developments, which are at the margin of current thinking and planning.” The scans include pathway analyses and risk screening of species present at pathway origin points, with a focus on identifying species at high risk of being introduced, becoming established, spreading, and causing harm. We refined a list of 28 AIS from a list of hundreds based on characterizations of species’ invasiveness and species’ proximity to Alaska (USGS 2020; GBIF 2022). Next, we evaluated the relative invasiveness of individual species to create an initial AIS ranking. We sought to characterize habitat suitability of AIS by selecting variables that were continental in scale, covering North America to include Alaska as well as the lower 48 states comparing natural discharge, sub-basin average terrain slope (degrees), average silt fraction, average organic carbon, lithological class, and human footprint in sub-basin in 2009. We estimated AIS habitat suitability across the entire state of Alaska using the physiological tolerances of the AIS (Appendix 2). We also evaluated pathways and vectors for the introduction of AIS (Appendix 2). Many pathways and vectors considered did not meet the criteria for Alaska or freshwater systems. Of the 28 ranked species that we categorized as very high, high, and moderate levels of invasiveness; all three risk groups included fish and mollusks (Appendix 2). One commonality of the very high-invasiveness-ranked species was the availability of Ecological Risk Screening Summary documents (USFWS, 2022) produced by U.S. Fish and Wildlife Service (USFWS), except for the goldfish ( Carassius auratus ) and the New Zealand mudsnail ( Potamopyrgus antipodarum ). The Ecological Risk Screening Summary is now available for New Zealand mudsnails. In general, fish species often ranked very high or high in invasiveness and included sportfish and aquarium fish, suggesting the importance of pathways such as aquarium trade, fishing industry, intentional (but illegal) introductions of sportfishes and aquarium fishes for establishment. The technique we used for habitat suitability models necessitated aquatic environmental datasets that were continental in scale, which was often interpolated from very coarse resolution source data layers, particularly in Alaska. Better spatial data representing aquatic environments would likely improve this approach. While the lack of introductions in Alaska and nearby provinces and states is encouraging, the lack of occurrence data for the focal species also created complications for habitat suitability modeling. Despite the challenges, the habitat suitability models indicated limited suitability for warmwater species while some species, such as Brook trout (S alvelinus fontinalis ), have high habitat suitability across Alaska no matter what threshold approach is taken. Some environmental predictors were more important than others. Specifically, the most important predictor variable, ‘frost free days,’ was critical for 15 out of 28 species as expected due to harsh winter conditions in Arctic and Subarctic regions. The second most important predictor was ‘subbasin land surface runoff’, a variable that indicates the amount of discharge and runoff, while the third most important predictor was ‘snow cover’ another indication of winter conditions. Overall, the ability to understand the effect of future climate scenarios on the establishment of AIS was challenging. A detailed dataset of freshwater temperatures and water chemistry (e.g., pH, calcium) would greatly improve the ability to predict invasiveness of freshwater species to Alaska’s ecosystems on a regional basis. Future studies may benefit from a more focused geographic scope examining a group of subbasins or a regional basin rather than the entire state. These drainages could be selected based upon the mostly likely locations of introduction pathways. The two most prevalent pathway risks for AIS are in-state transfer and stowaways/contaminants. Although there are examples of introductions from other pathways, the risk is somewhat mitigated by Alaska’s climate and regulations. However, variable application of protocols for inspection and cleaning of fishing gear, watercraft, and other similar items while traveling into Alaska as well as transferring from waterbody to waterbody within the state creates a substantial risk in introducing invasive species. We plot cumulative invasive vulnerability for all subbasins and for the top 10% of subbasins (Appendix 3).

Alaska↗

USGS2022

Models combining multiple scales of inference capture hydrologic and climatic drivers of riparian tree distributions

Predicting species geographic distributions is key to managing invasive species, conserving biodiversity, and understanding species' environmental requirements. Species distribution models (SDMs) commonly focus on climatic predictors, but other environmental factors can also be essential, particularly for species with specialized habitats defined by hydrologic, topographic, or edaphic conditions (e.g., riparian, wetland, alpine, coastal, serpentine). Here, we demonstrate a novel approach for capturing strong effects of both hydrologic and climatic predictors in SDMs for riparian plants, by merging analyses targeted at environmental drivers within riparian ecosystems and across the western USA (3.8 × 10 6 km 2 ). We developed presence-background SDMs from five algorithms for three invasive riparian trees ( Tamarix ramossisima / chinensis [saltcedar], Elaeagnus angustifolia [Russian olive], and Ulmus pumila [Siberian elm]) and three native Populus spp. (cottonwoods). We used separate background datasets to develop models with different spatial scales of inference: (1) spatially filtered random points to represent available habitat across the study area and (2) target-group points from Salix (willow) occurrences to represent available riparian habitat. Random-background models captured hydrologic drivers of riparian tree distributions relative to the largely upland western USA, whereas Salix -background models captured climatic drivers within the context of riparian ecosystems. Combining predictions from the two backgrounds identified hydrologically suitable habitats within climatically suitable regions, resulting in fewer false “absences” than either background alone, improving predictions over previous SDMs, and providing more complete information to guide management decisions. Surprisingly, the predicted habitat for U. pumila , a newly recognized riparian invader, was as or more extensive than Populus deltoides / fremontii , T. ramossisima / chinensis , and E. angustifolia , the most common riparian tree complexes in the western USA. Watersheds constituting 20% of U. pumila predicted habitat contained no occurrence records, indicating high risk of future and unrecognized invasions. Combining models from random and ecosystem-specific target-group backgrounds may improve SDMs for species from many specialized habitats, providing a method to link predicted distributions to localized geographic features while capturing broad-scale climatic requirements.

western United States↗

USGS2022

Regional models do not outperform continental models for invasive species

Aim : Species distribution models can guide invasive species prevention and management by characterizing invasion risk across space. However, extrapolation and transferability issues pose challenges for developing useful models for invasive species. Previous work has emphasized the importance of including all available occurrences in model estimation, but managers attuned to local processes may be skeptical of models based on a broad spatial extent if they suspect the captured responses reflect those of other regions where data are more numerous. We asked whether species distribution models for invasive plants performed better when developed at national versus regional extents. Location : Continental United States. Methods : We developed ensembles of species distribution models trained nationally, on sagebrush habitat, or on sagebrush habitat within three ecoregions (Great Basin, eastern sagebrush, and Great Plains) for nine invasive plants of interest for early detection and rapid response at local or regional scales. We compared the performance of national versus regional models using spatially independent withheld test data from each of the three ecoregions. Results : We found that models trained using a national spatial extent tended to perform better than regionally trained models. Regional models did not outperform national ones even when considerable occurrence data were available for model estimation within the focal region. Information was often unavailable to fit informative regional models precisely in those areas of greatest interest for early detection and rapid response. Main conclusions : Habitat suitability models for invasive plant species trained at a continental extent can reduce extrapolation while maximizing information on species’ responses to environmental variation. Standard modeling methods can capture spatially varying limiting factors, while regional or hierarchical models may only be advantageous when populations differ in their responses to environmental conditions, a condition expected to be relatively rare at the expanding boundaries of invasive species’ distributions.

NeoBiota↗

USGS2022

Potential cheatgrass abundance within lightly invaded areas of the Great Basin

Context Anticipating where an invasive species could become abundant can help guide prevention and control efforts aimed at reducing invasion impacts. Information on potential abundance can be combined with information on the current status of an invasion to guide management towards currently uninvaded locations where the threat of invasion is high. Objectives We aimed to support management by developing predictive maps of potential cover for cheatgrass ( Bromus tectorum ), a problematic invader that can transform plant communities. We integrated our predictions of potential abundance with mapped estimates of current cover to quantify invasion potential within lightly invaded areas. Methods We used quantile regression to model cheatgrass abundance as a function of climate, weather, and disturbance, treating outputs as low to high invasion scenarios. We developed a species-specific set of covariates and validated model performance using spatially and temporally independent data. Results Potential cheatgrass abundance was higher in areas that had burned, at low elevations, and when fall germination conditions were more favorable. Our results highlight the extensive areas across the Great Basin where cheatgrass abundance could increase to levels that can alter fire behavior and cause other ecological impacts. Conclusions We predict potential cheatgrass abundance to quantify relative invasion risk. Our model results provide high and low scenarios of cheatgrass abundance to guide resource allocation and planning efforts across shrubland ecosystems of the Great Basin that remain relatively uninvaded. Combining information on an invasive species’ current and potential abundance can yield spatial predictions to guide resource allocation and management action.

California, Idaho, Nevada, Oregon, Utah↗

USGS2022

A framework to integrate innovations in invasion science for proactive management

Invasive alien species (IAS) are a rising threat to biodiversity, national security, and regional economies, with impacts in the hundreds of billions of U.S. dollars annually. Proactive or predictive approaches guided by scientific knowledge are essential to keeping pace with growing impacts of invasions under climate change. Although the rapid development of diverse technologies and approaches has produced tools with the potential to greatly accelerate invasion research and management, innovation has far outpaced implementation and coordination. Technological and methodological syntheses are urgently needed to close the growing implementation gap and facilitate interdisciplinary collaboration and synergy among evolving disciplines. A broad review is necessary to demonstrate the utility and relevance of work in diverse fields to generate actionable science for the ongoing invasion crisis. Here, we review such advances in relevant fields including remote sensing, epidemiology, big data analytics, environmental DNA (eDNA) sampling, genomics, and others, and present a generalized framework for distilling existing and emerging data into products for proactive IAS research and management. This integrated workflow provides a pathway for scientists and practitioners in diverse disciplines to contribute to applied invasion biology in a coordinated, synergistic, and scalable manner.

Biological Reviews↗

USGS2022

INHABIT: A web-based decision support tool for invasive plant species habitat visualization and assessment across the contiguous United States

Narrowing the communication and knowledge gap between producers and users of scientific data is a longstanding problem in ecological conservation and land management. Decision support tools (DSTs), including websites or interactive web applications, provide platforms that can help bridge this gap. DSTs can most effectively disseminate and translate research results when producers and users collaboratively and iteratively design content and features. One data resource seldom incorporated into DSTs are species distribution models (SDMs), which can produce spatial predictions of habitat suitability. Outputs from SDMs can inform management decisions, but their complexity and inaccessibility can limit their use by resource managers or policy makers. To overcome these limitations, we present the Invasive Species Habitat Tool (INHABIT), a novel, web-based DST built with R Shiny to display spatial predictions and tabular summaries of habitat suitability from SDMs for invasive plants across the contiguous United States. INHABIT provides actionable science to support the prevention and management of invasive species. Two case studies demonstrate the important role of end user feedback in confirming INHABIT’s credibility, utility, and relevance.

PLoSOne↗

USGS2022

Coupling process-based and empirical models to assess management options to meet conservation goals

Conservation lands face a mounting threat of ecosystem transformation and the loss of biodiversity from the invasion of fire-prone perennial and annual grasses. Managers must make difficult decisions to find efficient ways to expend limited resources to manage large and complex landscapes amidst substantial uncertainty regarding effective treatment strategies, climates, and invader-induced novel processes. We developed a state-and-transition simulation model coupled with a fire behavior model to study impacts to native biodiversity and fire regimes in a national park invaded by a perennial grass. We evaluated resources required to meet management objectives, and how to spatially allocate available resources efficiently and effectively. Management strategies and ecological scenarios strongly influenced the ability to minimize potential impacts; the inclusion of a new management strategy, aerial precision spot spraying targeting low cover levels in remote regions, was needed to supplement current treatments that can only target large, dense patches. Adding this new treatment method may be enough to conserve the desert ecosystem from small scale transformation through invasive competition and from broad functional transformations through invasive-induced fire regime changes. Spot spraying may also be highly beneficial if wetter monsoonal conditions create faster growth rates, decreasing the ability to contain the invasion even with unlimited management resources. Given current annual budgets for control, and the new treatment option, invasion control and preservation of native biodiversity may be possible regardless of spatial prioritization. Coupled empirical and process-based models efficiently simulated the effects of management activities, quantifying potential management costs and ecological impacts, while considering a wide range of possible future uncertainties associated with climate, spread rates, and wildfires lacking historical precedent. These techniques could be applied to other situations to evaluate the feasibility of conservation goals and to determine actions that would be most efficient and effective in meeting those goals.

Arizona↗

USGS2022

Climate matching with the climatchR R package

Climate matching allows comparisons of climatic conditions between different locations to understand location and species range climatic suitability. The approach may be used as part of horizon scanning exercises such as those conducted for invasive species. We implemented the CLIMATCH algorithm into an R package, climatchR . The package allows automated and scripted climate matching exercises across all steps from downloading data to summarizing species climate matches. We also show how climatchR may be used with high-throughput computing to process many species. For example, we were able to calculate climate scores for over 8,000 species in less than 3 days using this package. This automation allows high-throughput processing of species data, a new development for improving the efficiency and speed of climate matching and horizon scanning.

Environmental Software & Modeling↗

USGS2022

Climate change and ‘alien species in National Parks’: Revisited

The US National Park Service mission includes conserving native species and historical landscapes ‘unimpaired for the enjoyment of future generations’. However, humans have increased the introduction of non-native species that can become invasive and which have harmful impacts on native species and landscapes. We revisit two previous papers, ‘Alien Species in National Parks: Drawing Lines in Space and Time’, published in 1995 by D.B. Houston and E.G. Schreiner, and ‘Climate Change and “Alien Species in National Parks”: Revisited’, published in 2014 by T.J. Stohlgren, J.R. Resnik and G.E. Plumb, to demonstrate the organizational progress that has been made in reducing impacts of invasive species despite the increasing pressure of increasing numbers of non-native species. The National Park Service has continued efforts on invasive plant management, established an Invasive Animal Program in 2018 and developed a Pest & Invasive Species Project Kit to compile information to inform management regardless of taxonomic group. Additionally, the Park Service has expanded their toolset to make decisions related to invasive species and climate change to focus on achievable goals. Since the 1995 publication, the scale of invasion has increased, and impacts of climate change are more noticeable since the 2014 publication, increasing the complexity in trying to achieve the National Park Service mission.

Book chapter↗

USGS2021

Grassification and fast-evolving fire connectivity and risk in the Sonoran Desert, United States

In the southwestern United States, non-native grass invasions have increased wildfire occurrence in deserts and the likelihood of fire spread to and from other biomes with disparate fire regimes. The elevational transition between desertscrub and montane grasslands, woodlands, and forests generally occurs at ∼1,200 masl and has experienced fast suburbanization and an expanding wildland-urban interface (WUI). In summer 2020, the Bighorn Fire in the Santa Catalina Mountains burned 486 km 2 and prompted alerts and evacuations along a 40-km stretch of WUI below 1,200 masl on the outskirts of Tucson, Arizona, a metropolitan area of >1M people. To better understand the changing nature of the WUI here and elsewhere in the region, we took a multidimensional and timely approach to assess fire dynamics along the Desertscrub-Semi-desert Grassland ecotone in the Catalina foothills, which is in various stages of non-native grass invasion. The Bighorn Fire was principally a forest fire driven by a long-history of fire suppression, accumulation of fine fuels following a wet winter and spring, and two decades of hotter droughts, culminating in the hottest and second driest summer in the 125-yr Tucson weather record. Saguaro ( Carnegia gigantea ), a giant columnar cactus, experienced high mortality. Resprouting by several desert shrub species may confer some post-fire resiliency in desertscrub. Buffelgrass and other non-native species played a minor role in carrying the fire due to the patchiness of infestation at the upper edge of the Desertscrub biome. Coupled state-and-transition fire-spread simulation models suggest a marked increase in both burned area and fire frequency if buffelgrass patches continue to expand and coalesce at the Desertscrub/Semi-desert Grassland interface. A survey of area residents six months after the fire showed awareness of buffelgrass was significantly higher among residents that were evacuated or lost recreation access, with higher awareness of fire risk, saguaro loss and declining property values, in that order. Sustained and timely efforts to document and assess fast-evolving fire connectivity due to grass invasions, and social awareness and perceptions, are needed to understand and motivate mitigation of an increasingly fire-prone future in the region.

Arizona↗

USGS2021

Modelling presence versus abundance for invasive species risk assessment

Aim Invasive species prevention and management can be guided by comparisons of invasion risk across space and among species. Species distribution models are widely used to assess invasion risk and typically estimate suitability for species presence. However, suitability for presence may not capture patterns of abundance and impact. We asked how models estimating suitability for presence versus suitability for abundance aligned in their implications for risk assessment. Location Western United States. Methods We developed ensembles of species distribution models for presence and for abundance for four invasive plants. We visualized the distribution of presence and abundance in environmental and geographic space and compared model outputs using criteria relevant for decision-making: a comparison of risk across management units for each species, and a ranking of risk among species for each management unit. Results We found good overall agreement between models of presence versus abundance in the relative risk across management units and among species. However, the area predicted to be suitable for invasive species presence was often substantially higher than the area predicted to be suitable for abundance, especially within uninvaded management units. Main conclusions Models of suitability for invasive species presence and abundance yielded similar assessments of relative risk in comparisons across space and species. In addition, we found patterns of presence and abundance in environmental space can guide modelling decisions and model interpretation. Suitability for abundance can improve relative risk assessment when abundance locations occupy a well-defined subset of the environmental space corresponding to presence. Where abundance locations occur throughout this environmental space, as was particularly striking for Taeniatherum caput-medusae, suitability for presence may better reflect risk of ongoing population increases and spread. This species is at risk of becoming abundant across a substantial portion of the western United States.

Diversity and Distributions↗

USGS2021

Challenges in updating habitat suitability models: An example with the lesser prairie-chicken

Habitat loss from land-use change is one of the top causes of declines in wildlife species of concern. As such, it is critical to assess and reassess habitat suitability as land cover and anthropogenic features change for both monitoring and developing current information to inform management decisions. However, there are obstacles that must be overcome to develop consistent assessments through time. A range-wide lek habitat suitability model for the lesser prairie-chicken ( Tympanuchus pallidicinctus ), currently under review by the U. S. Fish and Wildlife Service for potential listing under the Endangered Species Act) was published in 2016. This model was based on lek data from 2002 to 2012, land cover data ranging from 2001 to 2013, and anthropogenic features from circa 2011, and has been used to help guide lesser prairie-chicken management and anthropogenic development actions. We created a second iteration model based on new lek surveys (2015 to 2019) and updated predictor layers (2016 land cover and cleaned/ updated anthropogenic data) to evaluate changes in lek suitability and to quantify current range-wide habitat suitability. Only three of 11 predictor variables were directly comparable between the iterations, making it difficult to directly assess what predicted changes resulted from changes in model inputs versus actual landscape change. The second iteration model showed a similar positive relationship with land cover and negative with anthropogenic features to the first iteration, but exhibited more variation among candidate models. Range-wide, more suitable habitat was predicted in the second iteration. The Shinnery Oak Ecoregion, however, exhibited a loss in predicted suitable habitat which could be due to predictor source changes. Iterated models such as this are important to ensure current information is being used in conservation and development decisions.

Colorado, Kansas, New Mexico, Oklahoma, Texas↗

USGS2021

Tools and technologies for quantifying spread and impacts of invasive species

The need for tools and technologies for understanding and quantifying invasive species has never been greater. Rates of infestation vary on the species or organism being examined across the United States, and notable examples can be found. For example, from 2001 to 2003 alone, ash ( Fraxinus spp.) mortality progressed at a rate of 12.97 km year −1 (Siegert et al. 2014), and cheatgrass ( Bromus tectorum ) is expected to increase dominance on 14% of Great Basin rangelands (Boyte et al. 2016). The magnitude and scope of problems that invasive species present suggest novel approaches for detection and management are needed, especially those that enable more cost-effective solutions. The advantages of using technologically advanced approaches and tools are numerous, and the quality and quantity of available information can be significantly enhanced by their use. They can also play a key role in development of decision-support systems; they are meant to be integrated with other systems, such as inventory and monitoring, because often the tools are applied after a species of interest has been detected and a threat has been identified. In addition, the inventory systems mentioned in Chap. 10 are regularly used in calibrating and validating models and decision-support systems. For forested areas, Forest Inventory and Analysis (FIA) data are most commonly used (e.g., Václavík et al. 2015) given the long history of the program. In non-forested systems, national inventory datasets have not been around as long (see Chap. 10), but use of these data to calibrate and validate spatial models is growing. These inventory datasets include the National Resources Inventory (NRI) (e.g., Duniway et al. 2012) and the Assessment Inventory and Monitoring program (AIM) (e.g., McCord et al. 2017). Similarly, use of the Nonindigenous Aquatic Species (NAS) database is growing as well (e.g., Evangelista et al. 2017). The consistent protocols employed by these programs prove valuable for developing better tools, but the data they afford are generally limited for some tools because the sampling intensity is too low.

Book chapter↗

USGS2020

Assessing ecological uncertainty and simulation model sensitivity to evaluate an invasive plant species’ potential impacts to the landscape

Ecological forecasts of the extent and impacts of invasive species can inform conservation management decisions. Such forecasts are hampered by ecological uncertainties associated with non-analog conditions resulting from the introduction of an invader to an ecosystem. We developed a state-and-transition simulation model tied to a fire behavior model to simulate the spread of buffelgrass ( Cenchrus ciliaris ) in Saguaro National Park, AZ, USA over a 30-year period. The simulation models forecast the potential extent and impact of a buffelgrass invasion including size and frequency of fire events and displacement of saguaro cacti and other native species. Using simulation models allowed us to evaluate how model uncertainties affected forecasted landscape outcomes. We compared scenarios covering a range of parameter uncertainties including model initialization (landscape susceptibility to invasion) and expert-identified ecological uncertainties (buffelgrass patch infill rates and precipitation). Our simulations showed substantial differences in the amount of buffelgrass on the landscape and the size and frequency of fires for dry years with slow patch infill scenarios compared to wet years with fast patch infill scenarios. We identified uncertainty in buffelgrass patch infill rates as a key area for research to improve forecasts. Our approach could be used to investigate novel processes in other invaded systems.

Scientific Reports↗