No abstract

Spatial heterogeneity may have differential effects on the distribution of native and nonnative plant species richness. We examined the effects of spatial heterogeneity on native and nonnative plant species richness distributions in the central part of Rocky Mountain National Park, Colorado, USA. Spatial heterogeneity around vegetation plots was characterized using landscape metrics, environmental/topographic variables (slope, aspect, elevation, and distance from stream or river), and soil variables (nitrogen, clay, and sand). The landscape metrics represented five components of landscape heterogeneity and were measured at four spatial extents (within varying radii of 120, 240, 480, and 960 m) using the FRAGSTATS landscape pattern analysis program. Akaike's Information Criterion adjusted for small sample size (AICc) was used to select the best models from a set of multiple linear regression models developed for native and nonnative plant species richness at four spatial extents and three levels of ecological hierarchy (i.e., landscape, land cover, and community). Both native and nonnative plant species richness were positively correlated with edge density, Simpson's diversity index and interspersion/juxtaposition index, and were negatively correlated with mean patch size. The amount of variation explained at four spatial extents and three hierarchical levels ranged from 30% to 70%. At the landscape level, the best models explained 43% of the variation in native plant species richness and 70% of the variation in nonnative plant species richness (240-m extent). In general, the amount of variation explained was always higher for nonnative plant species richness, and the inclusion of landscape metrics always significantly improved the models. The best models explained 66% of the variation in nonnative plant species richness for both the conifer land cover type and lodgepole pine community. The relative influence of the components of spatial heterogeneity differed for native and nonnative plant species richness and varied with the spatial extent of analysis and levels of ecological hierarchy. The study offers an approach to quantify spatial heterogeneity to improve models of plant biodiversity. The results demonstrate that ecologists must recognize the importance of spatial heterogeneity in managing native and nonnative 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.

No abstract

The diatom Didymosphenia geminata is a single‐celled alga found in lakes, streams, and rivers. Nuisance blooms of D geminata affect the diversity, abundance, and productivity of other aquatic organisms. Because D geminata can be transported by humans on waders and other gear, accurate spatial prediction of habitat suitability is urgently needed for early detection and rapid response, as well as for evaluation of monitoring and control programs. We compared four modeling methods to predict D geminata's habitat distribution; two methods use presence–absence data (logistic regression and classification and regression tree [CART]), and two involve presence data (maximum entropy model [Maxent] and genetic algorithm for rule‐set production [GARP]). Using these methods, we evaluated spatially explicit, bioclimatic and environmental variables as predictors of diatom distribution. The Maxent model provided the most accurate predictions, followed by logistic regression, CART, and GARP. The most suitable habitats were predicted to occur in the western US, in relatively cool sites, and at high elevations with a high base‐flow index. The results provide insights into the factors that affect the distribution of D geminata and a spatial basis for the prediction of nuisance blooms.

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.

No abstract

Accurate assessment of insect pest establishment risk is needed by national plant protection organizations to negotiate international trade of horticultural commodities that can potentially carry the pests and result in inadvertent introductions in the importing countries. We used mechanistic and correlative niche models to quantify and map the global patterns of the potential for establishment of codling moth (Cydia pomonella L.), a major pest of apples, peaches, pears, and other pome and stone fruits, and a quarantine pest in countries where it currently does not occur. The mechanistic model CLIMEX was calibrated using species-specific physiological tolerance thresholds, whereas the correlative model MaxEnt used species occurrences and climatic spatial data. Projected potential distribution from both models conformed well to the current known distribution of codling moth. None of the models predicted suitable environmental conditions in countries located between 20°N and 20°S potentially because of shorter photoperiod, and lack of chilling requirement (<60 d at ≤10°C) in these areas for codling moth to break diapause. Models predicted suitable conditions in South Korea and Japan where codling moth currently does not occur but where its preferred host species (i.e., apple) is present. Average annual temperature and latitude were the main environmental variables associated with codling moth distribution at global level. The predictive models developed in this study present the global risk of establishment of codling moth, and can be used for monitoring potential introductions of codling moth in different countries and by policy makers and trade negotiators in making science-based decisions.