Throughout the world, the invasion of nonnative plants is an increasing threat to native biodiversity and ecosystem sustainability. Invasion is especially prevalent in areas affected by land transformation and disturbance. Surface mines are a major land transformation, and thus may promote the establishment and persistence of invasive plant communities. Using the Shale Hills region of Alabama as a case study, we assessed the use of landscape characteristics in predicting the probability of occurrence of six invasive plant species: sericea lespedeza, Japanese honeysuckle, Chinese privet, autumn-olive, royal paulownia, and sawtooth oak. Models were generated for invasive species occurrence using logistic regression and maximum entropy methods. The predicted probabilities of species occurrence were applied to the mined landscape to assess the probable prevalence of each species across the landscape. Japanese honeysuckle had the highest probable prevalence on the landscape (48% of the area), with royal paulownia having the lowest (less than 1%). Overall, 67% of the landscape was predicted to have at least one invasive plant species, with 20% of the landscape predicted to have two or more species, and 3% of the landscape predicted to have three or more species. Japanese honeysuckle, sericea lespedeza, privet, and autumn-olive showed higher occurrence on the reclaimed sites than across the broader region. We found that geospatial modeling of these invasive plants at this scale offered potential for management, both for identifying habitat types at risk and areas that need management attention. However, the most immediate action for reducing the prevalence of invasive plants on reclaimed mines is to remove invasive plants from the reclamation planting list. Three (sericea lespedeza, autumn-olive, and sawtooth oak) out of the six most common invasive plants in this study were planted as part of reclamation activities.
Distribution models of invasive plants are very useful tools for conservation management. There are challenges in modeling expanding populations, especially in a dynamic environment, and when data are limited. In this paper, predictive habitat models were assessed for three invasive plant species, at differing levels of occurrence, using two different habitat modeling techniques: logistic regression and maximum entropy. The influence of disturbance, spatial and temporal heterogeneity, and other landscape characteristics is assessed by creating regional level models based on occurrence records from the USDA Forest Service's Forest Inventory and Analysis database. Logistic regression and maximum entropy models were assessed independently. Ensemble models were developed to combine the predictions of the two analysis approaches to obtain a more robust prediction estimate. All species had strong models with Area Under the receiver operator Curve (AUC) of >0.75. The species with the highest occurrence, Ligustrum spp., had the greatest agreement between the models (93%). Lolium arundinaceum had the most disagreement between models at 33% and the lowest AUC values. Overall, the strength of integrative modeling in assessing and understanding habitat modeling was demonstrated.
OPEN ACCESSForests 2012, 3 800
Emergent biological processes result from complex interactions within and across levels of biological organization, ranging from molecular to environmental dynamics. Powerful theories, database tools, and modeling methods have been designed to characterize network connections within levels, such as those among genes, proteins, biochemicals, cells, organisms and species. Here, we propose that developing integrative models of organismal function in complex environments can be facilitated by taking advantage of these methods to identify key nodes of communication across levels of organization. Mapping key drivers or connections among levels of organization will provide data and leverage to model potential rule-sets by which organisms respond and adjust to perturbations at any level of biological organization.
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