Geology topics

Peder Engelstad

Publications and source records attributed to Peder Engelstad.

10 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↗

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↗

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

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↗

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

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↗

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↗

USGS2020

A modeling workflow that balances automation and human intervention to inform invasive plant management decisions at multiple spatial scales

Predictions of habitat suitability for invasive plant species can guide risk assessments at regional and national scales and inform early detection and rapid-response strategies at local scales. We present a general approach to invasive species modeling and mapping that meets objectives at multiple scales. Our methodology is designed to balance trade-offs between developing highly customized models for few species versus fitting non-specific and generic models for numerous species. We developed a national library of environmental variables known to physiologically limit plant distributions and relied on human input based on natural history knowledge to further narrow the variable set for each species before developing habitat suitability models. To ensure efficiency, we used largely automated modeling approaches and human input only at key junctures. We explore and present uncertainty by using two alternative sources of background samples, including five statistical algorithms, and constructing model ensembles. We demonstrate the use and efficiency of the Software for Assisted Habitat Modeling [SAHM 2.1.2], a package in VisTrails, which performs the majority of the modeling analyses. Our workflow includes solicitation of expert feedback on model outputs such as spatial prediction results and variable response curves, and iterative improvement based on new data availability and directed field validation of initial model results. We highlight the utility of the models for decision-making at regional and local scales with case studies of two plant species that invade natural areas: fountain grass ( Pennisetum setaceum ) and goutweed ( Aegopodium podagraria ). By balancing model automation with human intervention, we can efficiently provide land managers with mapped predicted distributions for multiple invasive species to inform decisions across spatial scales.

PLoS ONE↗