Geology topics

John Kartesz

Publications and source records attributed to John Kartesz.

4 recordsLinked to original sources

USGS2017

Comparison of four modeling tools for the prediction of potential distribution for non-indigenous weeds in the United States

This study compares four models for predicting the potential distribution of non-indigenous weed species in the conterminous U.S. The comparison focused on evaluating modeling tools and protocols as currently used for weed risk assessment or for predicting the potential distribution of invasive weeds. We used six weed species (three highly invasive and three less invasive non-indigenous species) that have been established in the U.S. for more than 75 years. The experiment involved providing non-U. S. location data to users familiar with one of the four evaluated techniques, who then developed predictive models that were applied to the United States without knowing the identity of the species or its U.S. distribution. We compared a simple GIS climate matching technique known as Proto3, a simple climate matching tool CLIMEX Match Climates, the correlative model MaxEnt, and a process model known as the Thornley Transport Resistance (TTR) model. Two experienced users ran each modeling tool except TTR, which had one user. Models were trained with global species distribution data excluding any U.S. data, and then were evaluated using the current known U.S. distribution. The influence of weed species identity and modeling tool on prevalence and sensitivity effects was compared using a generalized linear mixed model. Each modeling tool itself had a low statistical significance, while weed species alone accounted for 69.1 and 48.5% of the variance for prevalence and sensitivity, respectively. These results suggest that simple modeling tools might perform as well as complex ones in the case of predicting potential distribution for a weed not yet present in the United States. Considerations of model accuracy should also be balanced with those of reproducibility and ease of use. More important than the choice of modeling tool is the construction of robust protocols and testing both new and experienced users under blind test conditions that approximate operational conditions.

Biological Invasions↗

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↗

USGS2006

Species richness and patterns of invasion in plants, birds, and fishes in the United States

We quantified broad-scale patterns of species richness and species density (mean # species/km 2 ) for native and non-indigenous plants, birds, and fishes in the continental USA and Hawaii. We hypothesized that the species density of native and non-indigenous taxa would generally decrease in northern latitudes and higher elevations following declines in potential evapotranspiration, mean temperature, and precipitation. County data on plants ( n = 3004 counties) and birds ( n =3074 counties), and drainage (6 HUC) data on fishes ( n = 328 drainages) showed that the densities of native and non-indigenous species were strongly positively correlated for plant species ( r = 0.86, P < 0.0001), bird species ( r = 0.93, P <0.0001), and fish species ( r = 0.41, P <0.0001). Multiple regression models showed that the densities of native plant and bird species could be strongly predicted (adj. R 2 = 0.66 in both models) at county levels, but fish species densities were less predictable at drainage levels (adj. R 2 = 0.31, P <0.0001). Similarly, non-indigenous plant and bird species densities were strongly predictable (adj. R 2 = 0.84 and 0.91 respectively), but non-indigenous fish species density was less predictable (adj. R 2 = 0.38). County level hotspots of native and non-indigenous plants, birds, and fishes were located in low elevation areas close to the coast with high precipitation and productivity (vegetation carbon). We show that (1) native species richness can be moderately well predicted with abiotic factors; (2) human populations have tended to settle in areas rich in native species; and (3) the richness and density of non-indigenous plant, bird, and fish species can be accurately predicted from biotic and abiotic factors largely because they are positively correlated to native species densities. We conclude that while humans facilitate the initial establishment, invasions of non-indigenous species, the spread and subsequent distributions of non-indigenous species may be controlled largely by environmental factors.

Biological Invasions↗

USGS2005

Plant species invasions along the latitudinal gradient in the United States

It has been long established that the richness of vascular plant species and many animal taxa decreases with increasing latitude, a pattern that very generally follows declines in actual and potential evapotranspiration, solar radiation, temperature, and thus, total productivity. Using county-level data on vascular plants from the United States (3000 counties in the conterminous 48 states), we used the Akaike Information Criterion (AIC) to evaluate competing models predicting native and nonnative plant species density (number of species per square kilometer in a county) from various combinations of biotic variables (e.g., native bird species density, vegetation carbon, normalized difference vegetation index), environmental/topographic variables (elevation, variation in elevation, the number of land cover classes in the county, radiation, mean precipitation, actual evapotranspiration, and potential evapotranspiration), and human variables (human population density, cropland, and percentage of disturbed lands in a county). We found no evidence of a latitudinal gradient for the density of native plant species and a significant, slightly positive latitudinal gradient for the density of nonnative plant species. We found stronger evidence of a significant, positive productivity gradient (vegetation carbon) for the density of native plant species and nonnative plant species. We found much stronger significant relationships when biotic, environmental/topographic, and human variables were used to predict native plant species density and nonnative plant species density. Biotic variables generally had far greater influence in multivariate models than human or environmental/topographic variables. Later, we found that the best, single, positive predictor of the density of nonnative plant species in a county was the density of native plant species in a county. While further study is needed, it may be that, while humans facilitate the initial establishment invasions of nonnative plant species, the spread and subsequent distributions of nonnative species are controlled largely by biotic and environmental factors.

Ecology↗