Geology topics
C. S. Jarnevich
Publications and source records attributed to C. S. Jarnevich.
Challenges in identifying sites climatically matched to the native ranges of animal invaders
Background: Species distribution models are often used to characterize a species' native range climate, so as to identify sites elsewhere in the world that may be climatically similar and therefore at risk of invasion by the species. This endeavor provoked intense public controversy over recent attempts to model areas at risk of invasion by the Indian Python (Python molurus). We evaluated a number of MaxEnt models on this species to assess MaxEnt's utility for vertebrate climate matching. Methodology/Principal Findings: Overall, we found MaxEnt models to be very sensitive to modeling choices and selection of input localities and background regions. As used, MaxEnt invoked minimal protections against data dredging, multi-collinearity of explanatory axes, and overfitting. As used, MaxEnt endeavored to identify a single ideal climate, whereas different climatic considerations may determine range boundaries in different parts of the native range. MaxEnt was extremely sensitive to both the choice of background locations for the python, and to selection of presence points: inclusion of just four erroneous localities was responsible for Pyron et al.'s conclusion that no additional portions of the U.S. mainland were at risk of python invasion. When used with default settings, MaxEnt overfit the realized climate space, identifying models with about 60 parameters, about five times the number of parameters justifiable when optimized on the basis of Akaike's Information Criterion. Conclusions/Significance: When used with default settings, MaxEnt may not be an appropriate vehicle for identifying all sites at risk of colonization. Model instability and dearth of protections against overfitting, multi-collinearity, and data dredging may combine with a failure to distinguish fundamental from realized climate envelopes to produce models of limited utility. A priori identification of biologically realistic model structure, combined with computational protections against these statistical problems, may produce more robust models of invasion risk.
Data for giant constrictors - Biological management profiles and an establishment risk assessment for nine large species of pythons, anacondas, and the boa constrictor
Giant Constrictors' Climate Space The giant constrictors' climate space data set represents the information needed to recreate the climate space and climate matching analyses in Reed and Rodda (2009). A detailed methodology and results are included in that report. The data include locations for nine species of large constrictors including Python molurus, Broghammerus reticulatus, P. sebae, P. natalensis, Boa constrictor, Eunectes notaeus, E. deschauenseei, E. beniensis, and E. murinus. The locations are from published sources. Climate data are included for monthly precipitation and average monthly temperature along with the species locations. The individual spreadsheets of location data match the figures in the Reed and Rodda (2009) report, illustrating areas of the mainland United States that match the climate envelope of the native range. The precipitation and temperature data at each location were used to determine the climate space for each species. Graphs of climate space formed the basis for the algorithms in the data set, and more details can be found in Reed and Rodda (2009). These algorithms were used in ArcGIS to generate maps of areas in the United States that matched the climate space of locations of the snakes in their native range. We discovered a rounding error in ArcGIS in the implementation of the algorithms, which has been corrected here. Therefore the shapefiles are slightly different than those appearing in the risk assessment figures illustrating areas of the United States that match the climate envelope of the species in their native ranges. However, the suitable localities are not different at the scale of intended use for these maps, although there are more noticeable differences between areas classified as 'too cold' and 'too hot'.
Ensemble habitat mapping of invasive plant species
Ensemble species distribution models combine the strengths of several species environmental matching models, while minimizing the weakness of any one model. Ensemble models may be particularly useful in risk analysis of recently arrived, harmful invasive species because species may not yet have spread to all suitable habitats, leaving species-environment relationships difficult to determine. We tested five individual models (logistic regression, boosted regression trees, random forest, multivariate adaptive regression splines (MARS), and maximum entropy model or Maxent) and ensemble modeling for selected nonnative plant species in Yellowstone and Grand Teton National Parks, Wyoming; Sequoia and Kings Canyon National Parks, California, and areas of interior Alaska. The models are based on field data provided by the park staffs, combined with topographic, climatic, and vegetation predictors derived from satellite data. For the four invasive plant species tested, ensemble models were the only models that ranked in the top three models for both field validation and test data. Ensemble models may be more robust than individual species-environment matching models for risk analysis. ?? 2010 Society for Risk Analysis.
Near term climate projections for invasive species distributions
Climate change and invasive species pose important conservation issues separately, and should be examined together. We used existing long term climate datasets for the US to project potential climate change into the future at a finer spatial and temporal resolution than the climate change scenarios generally available. These fine scale projections, along with new species distribution modeling techniques to forecast the potential extent of invasive species, can provide useful information to aide conservation and invasive species management efforts. We created habitat suitability maps for Pueraria montana (kudzu) under current climatic conditions and potential average conditions up to 30 years in the future. We examined how the potential distribution of this species will be affected by changing climate, and the management implications associated with these changes. Our models indicated that P. montana may increase its distribution particularly in the Northeast with climate change and may decrease in other areas. ?? 2008 Springer Science+Business Media B.V.
Modelling invasion for a habitat generalist and a specialist plant species
Predicting suitable habitat and the potential distribution of invasive species is a high priority for resource managers and systems ecologists. Most models are designed to identify habitat characteristics that define the ecological niche of a species with little consideration to individual species' traits. We tested five commonly used modelling methods on two invasive plant species, the habitat generalist Bromus tectorum and habitat specialist Tamarix chinensis, to compare model performances, evaluate predictability, and relate results to distribution traits associated with each species. Most of the tested models performed similarly for each species; however, the generalist species proved to be more difficult to predict than the specialist species. The highest area under the receiver-operating characteristic curve values with independent validation data sets of B. tectorum and T. chinensis was 0.503 and 0.885, respectively. Similarly, a confusion matrix for B. tectorum had the highest overall accuracy of 55%, while the overall accuracy for T. chinensis was 85%. Models for the generalist species had varying performances, poor evaluations, and inconsistent results. This may be a result of a generalist's capability to persist in a wide range of environmental conditions that are not easily defined by the data, independent variables or model design. Models for the specialist species had consistently strong performances, high evaluations, and similar results among different model applications. This is likely a consequence of the specialist's requirement for explicit environmental resources and ecological barriers that are easily defined by predictive models. Although defining new invaders as generalist or specialist species can be challenging, model performances and evaluations may provide valuable information on a species' potential invasiveness.
What parts of the US mainland are climatically suitable for invasive alien pythons spreading from Everglades National Park?
The Burmese Python (Python molurus bivittatus) is now well established in southern Florida and spreading northward. The factors likely to limit this spread are unknown, but presumably include climate or are correlated with climate. We compiled monthly rainfall and temperature statistics from 149 stations located near the edge of the python's native range in Asia (Pakistan east to China and south to Indonesia). The southern and eastern native range limits extend to saltwater, leaving unresolved the species' climatic tolerances in those areas. The northern and western limits are associated with cold and aridity respectively. We plotted mean monthly rainfall against mean monthly temperature for the 149 native range weather stations to identify the climate conditions inhabited by pythons in their native range, and mapped areas of the coterminous United States with the same climate today and projected for the year 2100. We accounted for both dry-season aestivation and winter hibernation (under two scenarios of hibernation duration). The potential distribution was relatively insensitive to choice of scenario for hibernation duration. US areas climatically matched at present ranged up the coasts and across the south from Delaware to Oregon, and included most of California, Texas, Oklahoma, Arkansas, Louisiana, Mississippi, Alabama, Florida, Georgia, and South and North Carolina. By the year 2100, projected areas of potential suitable climate extend northward beyond the current limit to include parts of the states of Washington, Colorado, Illinois, Indiana, Ohio, West Virginia, Pennsylvania, New Jersey, and New York. Thus a substantial portion of the mainland US is potentially vulnerable to this ostensibly tropical invader. ?? 2008 Springer Science+Business Media B.V.
Balancing data sharing requirements for analyses with data sensitivity
Data sensitivity can pose a formidable barrier to data sharing. Knowledge of species current distributions from data sharing is critical for the creation of watch lists and an early warning/rapid response system and for model generation for the spread of invasive species. We have created an on-line system to synthesize disparate datasets of non-native species locations that includes a mechanism to account for data sensitivity. Data contributors are able to mark their data as sensitive. This data is then 'fuzzed' in mapping applications and downloaded files to quarter-quadrangle grid cells, but the actual locations are available for analyses. We propose that this system overcomes the hurdles to data sharing posed by sensitive data. ?? 2006 Springer Science+Business Media B.V.
Filling in the gaps: Modelling native species richness and invasions using spatially incomplete data
Detailed knowledge of patterns of native species richness, an important component of biodiversity, and non-native species invasions is often lacking even though this knowledge is essential to conservation efforts. However, we cannot afford to wait for complete information on the distribution and abundance of native and harmful invasive species. Using information from counties well surveyed for plants across the USA, we developed models to fill data gaps in poorly surveyed areas by estimating the density (number of species km -2) of native and non-native plant species. Here, we show that native plant species density is non-random, predictable, and is the best predictor of non-native plant species density. We found that eastern agricultural sites and coastal areas are among the most invaded in terms of non-native plant species densities, and that the central USA appears to have the greatest ratio of non-native to native species. These large-scale models could also be applied to smaller spatial scales or other taxa to set priorities for conservation and invasion mitigation, prevention, and control efforts. ?? 2006 The Authors.
Evaluating dominance as a component of non-native species invasions
Many studies have quantified plant invasions by determining patterns of non-native species establishment (i.e. richness and absolute cover). Until recently, dominance has been largely overlooked as a significant component of invasion. Therefore, we re-examined a 6-year data set of 323 0.1 ha plots within 18 vegetation types collected in the Grand Staircase-Escalante National Monument from 1998 to 2003, including dominance (i.e. relative cover) in our analyses. We specifically focused on the non-native species Bromus tectorum , a notable dominant annual grass in this system. We found that non-native species establishment and dominance are both occurring in species-rich, mesic vegetation types. Therefore, non-native species dominance may result despite many equally abundant native species rather than a dominant few, and competitive exclusion does not seem to be a primary control on either non-native species establishment or dominance in this study. Unlike patterns observed for non-native species establishment, relative non-native species cover could not be predicted by native species richness across vegetation types ( R 2 < 0.001; P = 0.45). However, non-native species richness was found to be positively correlated with relative non-native species cover and relative B. tectorum cover ( R 2 = 0.46, P < 0.01; R 2 = 0.17, P < 0.01). Analyses within vegetation types revealed predominantly positive relationships among these variables for the correlations that were significant. Regression tree analyses across vegetation types that included additional biotic and abiotic variables were a little better at predicting non-native species dominance (PRE = 0.49) and B. tectorum dominance (PRE = 0.39) than at predicting establishment. Land managers will need to set priorities for control efforts on the more productive, species-rich vegetation types that appear to be susceptible to both components of invasion.
A tamarisk habitat suitability map for the continental US
This paper presents a national-scale map of habitat suitability for tamarisk ( Tamarix spp, salt cedar), a high-priority invasive species. We successfully integrate satellite data and tens of thousands of field sampling points through logistic regression modeling to create a habitat suitability map that is 90% accurate. This interagency effort uses field data collected and coordinated through the US Geological Survey and nationwide environmental data layers derived from NASA's MODerate Resolution Imaging Spectroradiometer (MODIS). We demonstrate the use of the map by ranking the 48 continental US states (and the District of Columbia) based on their absolute, as well as proportional, areas of “highly likely” and “moderately likely” habitat for Tamarix . The interagency effort and modeling approach presented here could be used to map other harmful species, in the US and globally.