Geology topics

A.W. Crall

Publications and source records attributed to A.W. Crall.

4 recordsLinked to original sources

USGS2008

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.

Diversity and Distributions↗

USGS2007

Modeling aboveground biomass of Tamarix ramosissima in the Arkansas River Basin of Southeastern Colorado, USA

Predictive models of aboveground biomass of nonnative Tamarix ramosissima of various sizes were developed using destructive sampling techniques on 50 individuals and four 100-m2 plots. Each sample was measured for average height (m) of stems and canopy area (m2) prior to cutting, drying, and weighing. Five competing regression models (P < 0.05) were developed to estimate aboveground biomass of T. ramosissima using average height and/or canopy area measurements and were evaluated using Akaike's Information Criterion corrected for small sample size (AICc). Our best model (AICc = -148.69, ??AICc = 0) successfully predicted T. ramosissima aboveground biomass (R2 = 0.97) and used average height and canopy area as predictors. Our 2nd-best model, using the same predictors, was also successful in predicting aboveground biomass (R2 = 0.97, AICc = -131.71, ??AICc = 16.98). A 3rd model demonstrated high correlation between only aboveground biomass and canopy area (R2 = 0.95), while 2 additional models found high correlations between aboveground biomass and average height measurements only (R2 = 0.90 and 0.70, respectively). These models illustrate how simple field measurements, such as height and canopy area, can be used in allometric relationships to accurately predict aboveground biomass of T. ramosissima. Although a correction factor may be necessary for predictions at larger scales, the models presented will prove useful for many research and management initiatives.

Western North American Naturalist↗

USGS2006

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.

Biological Invasions↗

USGS2006

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.

Diversity and Distributions↗