Abstract:The bulk density of raw corn stover is a major limitation to its large-scale viability as a biomass feedstock. Raw corn stover has a bulk density of 50 kg/m 3 , which creates significant transportation costs and limits the optimization of transport logistics. Producing a densified corn stover product during harvest would reduce harvest and transportation costs, resulting in viable pathways for the use of corn stover as a biomass feedstock. This research investigated the effect of different process variables (compression pressure, moisture content, particle size, and material composition) on a densification method that produces briquettes from raw corn stover. A customized bench-scale densification system was designed to evaluate different corn stover inputs. Quality briquette production was possible using non-reduced particle sizes and low compression pressures achievable in a continuous in-field production system. At optimized bench settings, corn stover was densified to a dry bulk density of 190 kg/m 3 . Corn stover with a moisture content above 25% wb was not suitable for this method of bulk densification, and greater cob content had a positive effect on product quality.
Conservation and land management decisions often are based primarily on natural science, but could be more successful if human influences were effectively integrated into decision making. This is especially true for efforts to manage invasive plants, whose arrival is usually the product of deliberate human introduction. Risk-assessment models that predict the probability that a nonnative plant will naturalize or invade are useful tools for managing invasive plants. However, decisions based on such models could affect stakeholders differently. Careful assessment of risk-analysis methodologies should consider the importance of stakeholder participation. We surveyed the perceptions of four stakeholder groups (conservation professionals, master gardeners, professional horticulturists, and woodland landowners) in Iowa about invasive plants, general management approaches, and risk-assessment models. We also examined whether or not a stakeholder's nature relatedness plays a role in shaping his or her responses. Stakeholder perceptions varied less than expected across all four groups. Eighty-seven percent of respondents agreed invasive plants are a problem, and 88.4% agreed that we have a responsibility to manage them to protect natural areas. Support for the use of risk-assessment models also was high, with 78.7% of respondents agreeing that their use has potential to prevent plant invasions. Nature relatedness scores for all groups were correlated with respondent perspectives on invasive plants. Respondents believed biologically significant error rates (errors that might introduce a new invasive plant) should not exceed 5 to 10%. Respondents were more tolerant of horticulturally limiting errors (errors that restrict sale/use of a plant that would not have become invasive), reporting rates of 10 to 20% as acceptable. Researchers developing risk-assessment models might wish to aim for error rates within these bounds. General agreement among these stakeholder groups suggests potential support for future risk-management efforts related to invasive plants.
Accurate methods to predict the naturalization of non-native woody plants are key components of risk-management programs being considered by nursery and landscape professionals. The objective of this study was to evaluate four decision-tree models to predict naturalization (first tested in Iowa) on two new sets of data for non-native woody plants cultivated in the Chicago region. We identified life-history traits and native ranges for 193 species (52 known to naturalize and 141 not known to naturalize) in two study areas within the Chicago region. We used these datasets to test four models (one continental-scale and three regional-scale) as a form of external validation. Application of the continental-scale model resulted in classification rates of 72–76%, horticulturally limiting (false positive) error rates of 20–24%, and biologically significant (false negative) error rates of 5–6%. Two regional modifications to the continental model gave increased classification rates (85–93%) and generally lower horticulturally limiting error rates (16–22%), but similar biologically significant error rates (5–8%). A simpler method, the CART model developed from the Iowa data, resulted in lower classification rates (70–72%) and higher biologically significant error rates (8–10%), but, to its credit, it also had much lower horticulturally limiting error rates (5–10%). A combination of models to capture both high classification rates and low error rates will likely be the most effective until improved protocols based on multiple regional datasets can be developed and validated.
Risk analysis is a decision-making framework used to evaluate risk, or the probability of harm given an exposure. Invasive plants pose risks to natural ecosystems because they can significantly alter ecosystem function and decrease native species diversity. Managing these risks comes with many challenges, and may take many forms. This thesis examines two primary aspects of risk analysis: (1) the validation and development of risk-assessment models that can predict the naturalization of non-native woody plants; and (2) the perspectives of stakeholders on invasive plants, risk-assessment models, and nature relatedness.Good power and accuracy are primary goals of risk-assessment models to predict the naturalization of non-native plants. Testing previously developed models with a new set of species, or external validation, is one way to ensure these goals are met. Validation of four risk-assessment models -previously designed to evaluate the risk of naturalization for woody plants in Iowa -had mixed results when applied to a new selection of species. Classification rates ranged from 62.1 to 93.1%, biologically significant error rates from 11.5 to 18.5%, and horticulturally limiting error rates from 11.1 to 38.5%. Another way to reach the goal of good power and accuracy is to develop new risk-assessment models based on different statistical techniques. Creation of a new risk-assessment model for Iowa using a random forest approach yielded a high initial classification rate (92.0%), no biologically significant errors and 8.7% horticulturally limiting errors. When validated, the random forest model maintained a relatively high classification rate (82.8%), but produced one biologically significant error (4.2%) and more horticulturally limiting errors (29.2%). Differences in performance among the various models were not always significant due to the small sample size of the validating data set (n = 29), but the random forest model shows promise as a new technique to sort benign non-native woody plants from naturalizing or invasive ones.Implementation of risk-assessment models will depend on the cooperation of diverse stakeholder groups. Addressing their perspectives on invasive plants is therefore an important component of the risk analysis process. Stakeholders in Iowa who will be affected by or involved in implementation of risk-assessment models agreed that invasive plants are a
Use of risk-assessment models that can predict the naturalization and invasion of non-native woody plants is a potentially beneficial approach for protecting human and natural environments. This study validates the power and accuracy of four risk-assessment models previously tested in Iowa, and examines the performance of a new random forest modeling approach. The random forest model was fitted with the same data used to develop the four earlier risk-assessment models. The validation of all five models was based on a new set of 11 naturalizing and 18 non-naturalizing species in Iowa. The fitted random forest model had a high classification rate (92.0%), no biologically significant errors (accepting a plant that has a high risk of naturalizing), and few horticulturally limiting errors (rejecting a plant that has a low risk of naturalizing) (8.7%). Classification rates for validation of all five models ranged from 62.1 to 93.1%. Horticulturally limiting errors for the four models previously developed for Iowa ranged from 11.1 to 38.5%, and biologically significant errors from 4.2 to 18.5%. Because of the small sample size, few classification and error rate results were significantly different from the original tests of the models. Overall, the random forest model shows promise for powerful and accurate risk-assessment, but mixed results for the other models suggest a need for further refinement.
Numerous predictive models have been developed to determine the likelihood that non-native plants will escape from cultivation and potentially become invasive. Given the substantial biological and economic costs that can result from the introduction of a new invasive plant and the unending pressures of world trade and transport, the creation and implementation of effective predictive models are becoming increasingly important. One key question in the development of such models focuses on the geographic scope at which models can best be developed and applied. We have developed models to predict woody-plant naturalization in five local areas within the Upper Midwest (United States). Herein, we consider whether naturalization can be reasonably predicted from a single model for the entire region or whether local models are required for each specific area. We develop a random forest model to predict the probability of naturalization in the region and compare out-of-sample prediction errors between the regional and local models. The regional model makes better predictions of the probability of naturalization for those species observed to naturalize but worse predictions for those not currently observed to naturalize. This model development process has given us an opportunity (not previously addressed in the literature) to examine the strengths and weaknesses of local and regional approaches, with the ultimate intent of optimizing geographic scope. Keywords invasion, geographic scope, model-based decision making, non-native plants, risk analysis, woody plants Disciplines Ecology and Evolutionary Biology | Horticulture | Natural Resource Economics | Natural Resources Management and Policy | Statistical Models Comments This is a post-peer-review, pre-copyedit version of an article published in Biological Invasions. The final authenticated version is available online at. 2015. The effectiveness of a single regional model in predicting non-native woody plant naturalization in five areas within the Upper Midwest (United States). Biological Invasions 17: 3531-3545. AbstractNumerous predictive models have been developed to determine the likelihood that non-native plants will escape from cultivation and potentially become invasive. Given the substantial biological and economic costs that can result from the introduction of a new invasive plant and the unending pressures of world trade and transport, the creation and implementation of effective predictive models are becoming increasingly important. One key question in the development of such models focuses on the geographic scope at which models can best be developed and applied. We have developed models to predict woody-plant naturalization in five local areas within the Upper Midwest (United States). Herein, we consider whether naturalization can be reasonably predicted from a single model for the entire region or whether local models are required for each specific area. We develop a random forest model to predict the probability of naturalization in the region and compare out...
The selection, introduction, and cultivation of non-native woody plants beyond their native ranges can have great benefits, but also unintended consequences. Among these consequences is the tendency for some species to naturalize and become invasive pests in new environments to which they were introduced. In lieu of lengthy and costly field trials, risk-assessment models can be used to predict the likelihood of naturalization. We compared the relative performance of five established risk-assessment models on species datasets from two previously untested areas: southern Minnesota and northern Missouri. Model classification rates ranged from 64.2 to 90.5%, biologically significant errors ranged from 4.4 to 9.3%, and horticulturally limiting errors ranged from 6.6 to 30.4%. For the random forest model, we investigated the importance of variables used to predict naturalization by examining datasets for five distinct study areas across the Upper Midwest. Geographic-risk ratios were the most important predictors of species' tendency to naturalize. Other factors, such as quick maturity, record of invading elsewhere, and production of fleshy, bird-dispersed fruit were also important in the random forest models. Although some models tested need additional refinement, the random forest models maintain robustness and provide additional information on plant-specific characteristics that contribute to naturalization.
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