Geology topics

Thomas J. Stohlgren

Publications and source records attributed to Thomas J. Stohlgren.

At least 19 recordsLinked to original sources

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↗

USGS2019

The plant diversity sampling design for The National Ecological Observatory Network

The National Ecological Observatory Network (NEON) is designed to facilitate an understanding of the impact of environmental change on ecological systems. Observations of plant diversity—responsive to changes in climate, disturbance, and land use, and ecologically linked to soil, biogeochemistry, and organisms—result in NEON data products that cross a range of organizational levels. Collections include samples of plant tissue to enable investigations of genetics, plot‐based observations of incidence and cover of native and non‐native species, observations of plant functional traits, archived vouchers of plants, and remote sensing airborne observations. Spatially integrating many ecological observations allows a description of the relationship of plant diversity to climate, land use, organisms, and substrates. Repeating the observations over decades and across the United States will iteratively improve our understanding of those relationships and allow for the testing of system‐level hypotheses as well as the development of predictions of future conditions.

Ecosphere↗

USGS2017

Forecasting weed distributions using climate data: a GIS early warning tool

The number of invasive exotic plant species establishing in the United States is continuing to rise. When prevention of exotic species from entering into a country fails at the national level and the species establishes, reproduces, spreads, and becomes invasive, the most successful action at a local level is early detection followed eradication. We have developed a simple geographic information system (GIS) analysis for developing watch lists for early detection of invasive exotic plants that relies upon currently available species distribution data coupled with environmental data to aid in describing coarse-scale potential distributions. This GIS analysis tool develops environmental envelopes for species based upon the known distribution of a species thought to be invasive and represents the first approximation of its potential habitat while the necessary data are collected to perform more in-depth analyses. To validate this method we looked at a time series of species distributions for 66 species in Pacific Northwest, and northern Rocky Mountain counties. The time series analysis presented here did select counties that the invasive exotic weeds invaded in subsequent years, showing that this technique could be useful in developing watch lists for the spread of particular exotic species. We applied this same habitat-matching model based upon bioclimaric envelopes to 100 invasive exotics with various levels of known distributions within continental U.S. counties. For species with climatically limited distributions, county watch lists describe county-specific vulnerability to invasion. Species with matching habitats in a county would be added to that county's list. These watch lists can influence management decisions for early warning, control prioritization, and targeted research to determine specific locations within vulnerable counties. This tool provides useful information for rapid assessment of the potential distribution based upon climate envelopes of current distributions for new invasive exotic species.

Invasive Plant Science and Management↗

USGS2017

Ecology and space: A case study in mapping harmful invasive species

The establishment and invasion of non-native plant species have the ability to alter the composition of native species and functioning of ecological systems with financial costs resulting from mitigation and loss of ecological services. Spatially documenting invasions has applications for management and theory, but the utility of maps is challenged by availability and uncertainty of data, and the reliability of extrapolating mapped data in time and space. The extent and resolution of projections also impact the ability to inform invasive species science and management. Early invasive species maps were coarse-grained representations that underscored the phenomena, but had limited capacity to direct management aside from development of watch lists for priorities for prevention and containment. Integrating mapped data sets with fine-resolution environmental variables in the context of species-distribution models allows a description of species-environment relationships and an understanding of how, why, and where invasions may occur. As with maps, the extent and resolution of models impact the resulting insight. Models of cheatgrass ( Bromus tectorum ) across a variety of spatial scales and grain result in divergent species-environment relationships. New data can improve models and efficiently direct further inventories. Mapping can target areas of greater model uncertainty or the bounds of modeled distribution to efficiently refine models and maps. This iterative process results in dynamic, living maps capable of describing the ongoing process of species invasions.

Book chapter↗

USGS2016

Integrating remote sensing with species distribution models; Mapping tamarisk invasions using the Software for Assisted Habitat Modeling (SAHM)

Early detection of invasive plant species is vital for the management of natural resources and protection of ecosystem processes. The use of satellite remote sensing for mapping the distribution of invasive plants is becoming more common, however conventional imaging software and classification methods have been shown to be unreliable. In this study, we test and evaluate the use of five species distribution model techniques fit with satellite remote sensing data to map invasive tamarisk ( Tamarix spp.) along the Arkansas River in Southeastern Colorado. The models tested included boosted regression trees (BRT), Random Forest (RF), multivariate adaptive regression splines (MARS), generalized linear model (GLM), and Maxent. These analyses were conducted using a newly developed software package called the Software for Assisted Habitat Modeling (SAHM). All models were trained with 499 presence points, 10,000 pseudo-absence points, and predictor variables acquired from the Landsat 5 Thematic Mapper (TM) sensor over an eight-month period to distinguish tamarisk from native riparian vegetation using detection of phenological differences. From the Landsat scenes, we used individual bands and calculated Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and tasseled capped transformations. All five models identified current tamarisk distribution on the landscape successfully based on threshold independent and threshold dependent evaluation metrics with independent location data. To account for model specific differences, we produced an ensemble of all five models with map output highlighting areas of agreement and areas of uncertainty. Our results demonstrate the usefulness of species distribution models in analyzing remotely sensed data and the utility of ensemble mapping, and showcase the capability of SAHM in pre-processing and executing multiple complex models.

Journal of Visualized Experiments↗

USGS2015

Caveats for correlative species distribution modeling

Correlative species distribution models are becoming commonplace in the scientific literature and public outreach products, displaying locations, abundance, or suitable environmental conditions for harmful invasive species, threatened and endangered species, or species of special concern. Accurate species distribution models are useful for efficient and adaptive management and conservation, research, and ecological forecasting. Yet, these models are often presented without fully examining or explaining the caveats for their proper use and interpretation and are often implemented without understanding the limitations and assumptions of the model being used. We describe common pitfalls, assumptions, and caveats of correlative species distribution models to help novice users and end users better interpret these models. Four primary caveats corresponding to different phases of the modeling process, each with supporting documentation and examples, include: (1) all sampling data are incomplete and potentially biased; (2) predictor variables must capture distribution constraints; (3) no single model works best for all species, in all areas, at all spatial scales, and over time; and (4) the results of species distribution models should be treated like a hypothesis to be tested and validated with additional sampling and modeling in an iterative process.

Ecological Informatics↗

USGS2014

From hybrid swarms to swarms of hybrids

Science has shown that the introgression or hybridization of modern humans ( Homo sapiens ) with Neanderthals up to 40,000 YBP may have led to the swarm of modern humans on earth. However, there is little doubt that modern trade and transportation in support of the humans has continued to introduce additional species, genotypes, and hybrids to every country on the globe. We assessed the utility of species distributions modeling of genotypes to assess the risk of current and future invaders. We evaluated 93 locations of the genus Tamarix for which genetic data were available. Maxent models of habitat suitability showed that the hybrid, T. ramosissima x T. chinensis , was slightly greater than the parent taxa (AUCs > 0.83). General linear models of Africanized honey bees, a hybrid cross of Tanzanian Apis mellifera scutellata and a variety of European honey bee including A. m. ligustica , showed that the Africanized bees (AUC = 0.81) may be displacing European honey bees (AUC > 0.76) over large areas of the southwestern U.S. More important, Maxent modeling of sub-populations (A1 and A26 mitotypes based on mDNA) could be accurately modeled (AUC > 0.9), and they responded differently to environmental drivers. This suggests that rapid evolutionary change may be underway in the Africanized bees, allowing the bees to spread into new areas and extending their total range. Protecting native species and ecosystems may benefit from risk maps of harmful invasive species, hybrids, and genotypes.

Environment and Ecology Research↗

USGS2013

Regional distribution models with lack of proximate predictors: Africanized honeybees expanding north

Species distribution models have often been hampered by poor local species data, reliance on coarse-scale climate predictors and the assumption that species–environment relationships, even with non-proximate predictors, are consistent across geographical space. Yet locally accurate maps of invasive species, such as the Africanized honeybee (AHB) in North America, are needed to support conservation efforts. Current AHB range maps are relatively coarse and are inconsistent with observed data. Our aim was to improve distribution maps using more proximate predictors (phenology) and using regional models rather than one across the entire range of interest to explore potential differences in drivers.

Diversity and Distributions↗

USGS2013

The Hyper-Envelope Modeling Interface (HEMI): A Novel Approach Illustrated Through Predicting Tamarisk ( Tamarix spp.) Habitat in the Western USA

Habitat suitability maps are commonly created by modeling a species’ environmental niche from occurrences and environmental characteristics. Here, we introduce the hyper-envelope modeling interface (HEMI), providing a new method for creating habitat suitability models using Bezier surfaces to model a species niche in environmental space. HEMI allows modeled surfaces to be visualized and edited in environmental space based on expert knowledge and does not require absence points for model development. The modeled surfaces require relatively few parameters compared to similar modeling approaches and may produce models that better match ecological niche theory. As a case study, we modeled the invasive species tamarisk ( Tamarix spp.) in the western USA. We compare results from HEMI with those from existing similar modeling approaches (including BioClim, BioMapper, and Maxent). We used synthetic surfaces to create visualizations of the various models in environmental space and used modified area under the curve (AUC) statistic and akaike information criterion (AIC) as measures of model performance. We show that HEMI produced slightly better AUC values, except for Maxent and better AIC values overall. HEMI created a model with only ten parameters while Maxent produced a model with over 100 and BioClim used only eight. Additionally, HEMI allowed visualization and editing of the model in environmental space to develop alternative potential habitat scenarios. The use of Bezier surfaces can provide simple models that match our expectations of biological niche models and, at least in some cases, out-perform more complex approaches.

Environmental Management↗

USGS2012

Environmental conditions associated with bat white-nose syndrome in the north-eastern United States

1. White-nose syndrome (WNS) is an emerging disease of hibernating North American bats that is caused by the cold-growing fungus Geomyces destructans . Since first observed in the winter of 2007, WNS has led to unprecedented mortality in several species of bats and may threaten more than 15 additional hibernating bat species if it continues across the continent. Although the exact means by which fungal infection causes mortality are undetermined, available evidence suggests a strong role of winter environmental conditions in disease mortality. 2. By 2010, the fungus G. destructans was detected in new areas of North America far from the area it was first observed, as well as in eight European bat species in different countries, yet mortality was not observed in many of these new areas of North America or in any part of Europe. This could be because of the differences in the fungus, rates of disease progression and/or in life-history or physiological traits of the affected bat species between different regions. Infection of bats by G. destructans without associated mortality might also suggest that certain environmental conditions might have to co-occur with fungal infection to cause mortality. 3. We tested the environmental conditions hypothesis using Maxent to map and model landscape surface conditions associated with WNS mortality. This approach was unique in that we modelled possible requisite environmental conditions for disease mortality and not simply the presence of the causative agent. 4. The top predictors of WNS mortality were land use/land cover types, mean air temperature of wettest quarter, elevation, frequency of precipitation and annual temperature range. Model results suggest that WNS mortality is most likely to occur in landscapes that are higher in elevation and topographically heterogeneous, drier and colder during winter, and more seasonally variable than surrounding landscapes. 5. Synthesis and applications . This study mapped the most likely environmental surface conditions associated with bat mortality owing to WNS in the north-eastern United Sates; maps can be used for selection of priority monitoring sites. Our results provide a starting point from which to investigate and predict the potential spread and population impacts of this catastrophic emerging disease.

Journal of Applied Ecology↗

USGS2011

Bounding species distribution models

Species distribution models are increasing in popularity for mapping suitable habitat for species of management concern. Many investigators now recognize that extrapolations of these models with geographic information systems (GIS) might be sensitive to the environmental bounds of the data used in their development, yet there is no recommended best practice for “clamping” model extrapolations. We relied on two commonly used modeling approaches: classification and regression tree (CART) and maximum entropy (Maxent) models, and we tested a simple alteration of the model extrapolations, bounding extrapolations to the maximum and minimum values of primary environmental predictors, to provide a more realistic map of suitable habitat of hybridized Africanized honey bees in the southwestern United States. Findings suggest that multiple models of bounding, and the most conservative bounding of species distribution models, like those presented here, should probably replace the unbounded or loosely bounded techniques currently used.

Current Zoology↗

USGS2011

How will climate change affect the potential distribution of Eurasian tree sparrows Passer montanus in North America?

Habitat suitability models have been used to predict the present and future potential distribution of a variety of species. Eurasian tree sparrows Passer montanus , native to Eurasia, have established populations in other parts of the world. In North America, their current distribution is limited to a relatively small region around its original introduction to St. Louis, Missouri. We combined data from the Global Biodiversity Information Facility with current and future climate data to create habitat suitability models using Maxent for this species. Under projected climate change scenarios, our models show that the distribution and range of the Eurasian tree sparrow could increase as far as the Pacific Northwest and Newfoundland. This is potentially important information for prioritizing the management and control of this non-native species.

Current Zoology↗

USGS2011

Using maximum entropy modeling for optimal selection of sampling sites for monitoring networks

Environmental monitoring programs must efficiently describe state shifts. We propose using maximum entropy modeling to select dissimilar sampling sites to capture environmental variability at low cost, and demonstrate a specific application: sample site selection for the Central Plains domain (453,490 km 2 ) of the National Ecological Observatory Network (NEON). We relied on four environmental factors: mean annual temperature and precipitation, elevation, and vegetation type. A “sample site” was defined as a 20 km × 20 km area (equal to NEON’s airborne observation platform [AOP] footprint), within which each 1 km 2 cell was evaluated for each environmental factor. After each model run, the most environmentally dissimilar site was selected from all potential sample sites. The iterative selection of eight sites captured approximately 80% of the environmental envelope of the domain, an improvement over stratified random sampling and simple random designs for sample site selection. This approach can be widely used for cost-efficient selection of survey and monitoring sites.

Diversity↗

USGS2011

Widespread plant species: natives vs. aliens in our changing world

Estimates of the level of invasion for a region are traditionally based on relative numbers of native and alien species. However, alien species differ dramatically in the size of their invasive ranges. Here we present the first study to quantify the level of invasion for several regions of the world in terms of the most widely distributed plant species (natives vs. aliens). Aliens accounted for 51.3% of the 120 most widely distributed plant species in North America, 43.3% in New South Wales (Australia), 34.2% in Chile, 29.7% in Argentina, and 22.5% in the Republic of South Africa. However, Europe had only 1% of alien species among the most widespread species of the flora. Across regions, alien species relative to native species were either as well-distributed (10 comparisons) or more widely distributed (5 comparisons). These striking patterns highlight the profound contribution that widespread invasive alien plants make to floristic dominance patterns across different regions. Many of the most widespread species are alien plants, and, in particular, Europe and Asia appear as major contributors to the homogenization of the floras in the Americas. We recommend that spatial extent of invasion should be explicitly incorporated in assessments of invasibility, globalization, and risk assessments.

Biological Invasions↗

USGS2011

Habitat suitability of patch types: a case study of the Yosemite toad

Understanding patch variability is crucial in understanding the spatial population structure of wildlife species, especially for rare or threatened species. We used a well-tested maximum entropy species distribution model (Maxent) to map the Yosemite toad (Anaxyrus (= Bufo) canorus) in the Sierra Nevada mountains of California. Twenty-six environmental variables were included in the model representing climate, topography, land cover type, and disturbance factors (e.g., distances to agricultural lands, fire perimeters, and timber harvest areas) throughout the historic range of the toad. We then took a novel approach to the study of spatially structured populations by applying the species-environmental matching model separately for 49 consistently occupied sites of the Yosemite toad compared to 27 intermittently occupied sites. We found that the distribution of the entire population was highly predictable (AUC = 0.95±0.03 SD), and associated with low slopes, specific vegetation types (wet meadow, alpine-dwarf shrub, montane chaparral, red fir, and subalpine conifer), and warm temperatures. The consistently occupied sites were also associated with these same factors, and they were also highly predictable (AUC = 0.95±0.05 SD). However, the intermittently occupied sites were associated with distance to fire perimeter, a slightly different response to vegetation types, distance to timber harvests, and a much broader set of aspect classes (AUC = 0.90±0.11 SD). We conclude that many studies of species distributions may benefit by modeling spatially structured populations separately. Modeling and monitoring consistently-occupied sites may provide a realistic snapshot of current species-environment relationships, important climatic and topographic patterns associated with species persistence patterns, and an understanding of the plasticity of the species to respond to varying climate regimes across its range. Meanwhile, modeling and monitoring of widely dispersing individuals and intermittently occupied sites may uncover environmental thresholds and human-related threats to population persistence.

Frontiers of Earth Science↗

USGS2011

Federated or cached searches: providing expected performance from multiple invasive species databases

Invasive species are a universal global problem, but the information to identify them, manage them, and prevent invasions is stored around the globe in a variety of formats. The Global Invasive Species Information Network is a consortium of organizations working toward providing seamless access to these disparate databases via the Internet. A distributed network of databases can be created using the Internet and a standard web service protocol. There are two options to provide this integration. First, federated searches are being proposed to allow users to search “deep” web documents such as databases for invasive species. A second method is to create a cache of data from the databases for searching. We compare these two methods, and show that federated searches will not provide the performance and flexibility required from users and a central cache of the datum are required to improve performance.

Frontiers of Earth Science↗

USGS2011

Improving national-scale invasion maps: Tamarisk in the western United States

New invasions, better field data, and novel spatial-modeling techniques often drive the need to revisit previous maps and models of invasive species. Such is the case with the at least 10 species of Tamarix , which are invading riparian systems in the western United States and expanding their range throughout North America. In 2006, we developed a National Tamarisk Map by using a compilation of presence and absence locations with remotely sensed data and statistical modeling techniques. Since the publication of that work, our database of Tamarix distributions has grown significantly. Using the updated database of species occurrence, new predictor variables, and the maximum entropy (Maxent) model, we have revised our potential Tamarix distribution map for the western United States. Distance-to-water was the strongest predictor in the model (58.1%), while mean temperature of the warmest quarter was the second best predictor (18.4%). Model validation, averaged from 25 model iterations, indicated that our analysis had strong predictive performance (AUC = 0.93) and that the extent of Tamarix distributions is much greater than previously thought. The southwestern United States had the greatest suitable habitat, and this result differed from the 2006 model. Our work highlights the utility of iterative modeling for invasive species habitat modeling as new information becomes available.

Western United States↗

USGS2010

Bringing modeling to the masses: A web based system to predict potential species distributions

Predicting current and potential species distributions and abundance is critical for managing invasive species, preserving threatened and endangered species, and conserving native species and habitats. Accurate predictive models are needed at local, regional, and national scales to guide field surveys, improve monitoring, and set priorities for conservation and restoration. Modeling capabilities, however, are often limited by access to software and environmental data required for predictions. To address these needs, we built a comprehensive web-based system that: (1) maintains a large database of field data; (2) provides access to field data and a wealth of environmental data; (3) accesses values in rasters representing environmental characteristics; (4) runs statistical spatial models; and (5) creates maps that predict the potential species distribution. The system is available online at www.niiss.org, and provides web-based tools for stakeholders to create potential species distribution models and maps under current and future climate scenarios.

Future Internet↗