Invasive species distribution modeling (iSDM): Are absence data and dispersal constraints needed to predict actual distributions?

Species distribution models (SDMs) based on statistical relationships between occurrence data and underlying environmental conditions are increasingly used to predict spatial patterns of biological invasions and prioritize locations for early detection and control of invasion outbreaks. However, invasive species distribution models (iSDMs) face special challenges because (i) they typically violate SDM's assumption that the organism is in equilibrium with its environment, and (ii) species absence data are often unavailable or believed to be too difficult to interpret. This often leads researchers to generate pseudo-absences for model training or utilize presence-only methods, and to confuse the distinction between predictions of potential vs. actual distribution. We examined the hypothesis that true-absence data, when accompanied by dispersal constraints, improve prediction accuracy and ecological understanding of iSDMs that aim to predict the actual distribution of biological invasions. We evaluated the impact of presence-only, true-absence and pseudo-absence data on model accuracy using an extensive dataset on the distribution of the invasive forest pathogen Phytophthora ramorum in California. Two traditional presence/absence models (generalized linear model and classification trees) and two alternative presence-only models (ecological niche factor analysis and maximum entropy) were developed based on 890 field plots of pathogen occurrence and several climatic, topographic, host vegetation and dispersal variables. The effects of all three possible types of occurrence data on model performance were evaluated with receiver operating characteristic (ROC) and omission/commission error rates. Results show that prediction of actual distribution was less accurate when we ignored true-absences and dispersal constraints. Presence-only models and models without dispersal information tended to over-predict the actual range of invasions. Models based on pseudo-absence data exhibited similar accuracies as presence-only models but produced spatially less feasible predictions. We suggest that true-absence data are a critical ingredient not only for accurate calibration but also for ecologically meaningful assessment of iSDMs that focus on predictions of actual distributions.     

Comparing model averaging with other model selection strategies for benchmark dose estimation

Model averaging (MA) has been proposed as a method of accommodating model uncertainty when estimating risk. Although the use of MA is inherently appealing, little is known about its performance using general modeling conditions. We investigate the use of MA for estimating excess risk using a Monte Carlo simulation. Dichotomous response data are simulated under various assumed underlying dose–response curves, and nine dose–response models (from the USEPA Benchmark dose model suite) are fit to obtain both model specific and MA risk estimates. The benchmark dose estimates (BMDs) from the MA method, as well as estimates from other commonly selected models, e.g., best fitting model or the model resulting in the smallest BMD, are compared to the true benchmark dose value to better understand both bias and coverage behavior in the estimation procedure. The MA method has a small bias when estimating the BMD that is similar to the bias of BMD estimates derived from the assumed model. Further, when a broader range of models are included in the family of models considered in the MA process, the lower bound estimate provided coverage close to the nominal level, which is superior to the other strategies considered. This approach provides an alternative method for risk managers to estimate risk while incorporating model uncertainty.

成都市生活垃圾分类支付意愿及影响因素的问卷调查分析

为了为成都市垃圾分类政策制定提供科学依据,文章基于问卷调研,运用条件价值评估法结合二元Logistic回归模型、多元线性回归模型,分析了成都市居民对城市生活垃圾分类的支付意愿(WTP)及其影响因素。调研发现:问卷填报者中95.3%的人支持强制垃圾分类,但仅21.0%人深入掌握垃圾分类知识;造成成都市垃圾分类失效的原因,73.8%的人认为政策不到位,需要采取强制监管措施,67.6%的人认为垃圾分类桶等基础硬件设施建设不到位,69.2%的人认为居民自身环保意识差,需要加强环保宣传教育。成都市居民生活垃圾治理的WTP金额为12.65元/月/户。年龄、有无住房、户口类型和月收入4个因素对支付额度有显著影响。     

Development and case study of a new-generation model-VAT for analyzing the boundary conditions influence on atmospheric mercury simulation

Atmospheric models are essential tools to study the behavior of air pollutants. To interpret the complicated atmospheric model simulations, a new-generation Model Visualization and Analysis Tool (Model-VAT) has been developed for scientists to analyze the model data and visualize the simulation results. The Model-VAT incorporates analytic functions of conventional tools and enhanced capabilities in flexibly accessing, analyzing, and comparing simulated results from multi-scale models with different map projections and grid resolutions. The performance of the Model-VAT is demonstrated by a case study of investigating the influence of boundary conditions (BCs) on the ambient Hg formation and transport simulated by the CMAQ model over the Pearl River Delta (PRD) region. The alternative BC options are taken from (1) default time-independent profiles, (2) outputs from a CMAQ simulation of a larger nesting domain, and (3) concentration files from GEOS-Chem (re-gridded and re-projected using the Model-VAT). The three BC inputs and simulated ambient concentrations and deposition were compared using the Model-VAT. The results show that the model simulations based on the static BCs (default profile) underestimates the Hg concentrations by ~6.5%, dry depositions by ~9.4%, and wet depositions by ~43.2% compared to those of the model-derived (e. g. GEOS-Chem or nesting CMAQ) BCs. This study highlights the importance of model nesting approach and demonstrates that the innovative functions of Model-VAT enhances the efficiency of analyzing and comparing the model results from various atmospheric model simulations.

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Does the choice of climate baseline matter in ecological niche modelling?

Ecological niche models (ENMs) have multiple applications in ecology, evolution and conservation planning. They relate the known locations of a species to characteristics of its environment (usually climate) over its geographical range. Most ENMs are trained using standard 30-year (1961-1990) or 50-year (1951-2000) baselines to represent current climate conditions. Species occurrence records used as input to the models, however, are frequently collected from time periods that differ from those from which the climate is derived. Since climate variability can be significant within and outside baselines, and the distributions of some plants and animals (e.g., annual plants, insects) can adjust to environmental conditions on much shorter time scales, this mismatch between collection records and climatic baselines may affect the utility and accuracy of model outputs. We investigated how the choice of baseline periods influenced modelling efforts, anticipating that climate baselines derived from the same temporal period as the species records would yield improved ENMs. Ten simulated species’ distributions were modelled using an ENM (Maxent) for (a) occurrences and climates within the same temporal period, based on eighteen 10-year baselines within the 20th century and (b) all available samples and climate baselines from 1951-2000 and 1961-1990. Each model was projected onto all the available 10-year climate scenarios and compared to the models trained on the corresponding scenario. We show that temporal mismatches of species occurrences and climate baselines can result in significantly poorer distribution models. Such temporal mismatch may be unavoidable for many studies, but we emphasize here the need to match the time range of samples and climate data whenever possible.     

Integrated assessment of agricultural policies with dynamic land use change modelling

With about half of its territory being farmed, agriculture is the main land use in the European Union (EU). As over 10% of the total EU manufacturing output comes from the agri-food sector, it also is an economic factor of great importance. Moreover, EU policy in this sector has far-reaching consequences ranging from the EU's status as a global trade partner to landscape preservation and development. The LUMOCAP Policy Support System is targeted towards policy makers in the European Commission (EC) and its Member States (MS) and aims to provide support in the field of sustainable agricultural and rural development. To this end it incorporates an integrated model with socio-economic and bio-physical processes, operating at different spatial scales. For supporting integrated assessment, a large number of policy levers is included as inputs for these models and outputs are transformed into policy-relevant social, economic and environmental indicators. The whole system is framed in a flexible, modular and easy to use software package that is useable for process experts and policy-analysts alike.This paper describes the integrated model, the individual models and a first calibration of the system. It demonstrates the system's behaviour for typical scenario runs and concludes with a reflection on the current status of the system and some recommendations for further development.     

Uncertainty analyses for calibrating a soil carbon balance model to agricultural field trial data in Sweden and Kenya

How do additional data of the same and/or different type contribute to reducing model parameter and predictive uncertainties? Most modeling applications of soil organic carbon (SOC) time series in agricultural field trial datasets have been conducted without accounting for model parameter uncertainty. There have been recent advances with Monte Carlo-based uncertainty analyses in the field of hydrological modeling that are applicable, relevant and potentially valuable in modeling the dynamics of SOC. Here we employed a Monte Carlo method with threshold screening known as Generalized Likelihood Uncertainty Estimation (GLUE) to calibrate the Introductory Carbon Balance Model (ICBM) to long-term field trail data from Ultuna, Sweden and Machang’a, Kenya. Calibration results are presented in terms of parameter distributions and credibility bands on time series simulations for a number of case studies. Using these methods, we demonstrate that widely uncertain model parameters, as well as strong covariance between inert pool size and rate constant parameters, exist when root mean square simulation errors were within uncertainties in input estimations and data observations. We show that even rough estimates of the inert pool (perhaps from chemical analysis) can be quite valuable to reduce uncertainties in model parameters. In fact, such estimates were more effective at reducing parameter and predictive uncertainty than an additional 16 years time series data at Ultuna. We also demonstrate an effective method to jointly, simultaneously and in principle more robustly calibrate model parameters to multiple datasets across different climatic regions within an uncertainty framework. These methods and approaches should have benefits for use with other SOC models and datasets as well.     

Using a model selection criterion to identify appropriate complexity in aquatic biogeochemical models

Aquatic biogeochemical models are widely used as tools for understanding aquatic ecosystems and predicting their response to various stimuli (e.g., nutrient loading, toxic substances, climate change). Due to the complexity of these systems, such models are often elaborate and include a large number of estimated parameters. However, correspondingly large data sets are rarely available for calibration purposes, leading to models that may be overfit and possess reduced predictive capabilities. We apply, for the first time, information-theoretic model-selection techniques to a set of spatially explicit (1D) algal dynamics models of varying parameter dimension. We demonstrate that increases in complexity tend to produce a better model fit to calibration data, but beyond a certain degree of complexity the benefits of adding parameters are diminished (the risk of overfitting becomes greater). The particular approach taken here is computationally expensive, but several suggestions are made as to how multimodel methods may practically be extended to more sophisticated models.     

Modeling ecosystem responses to prescribed fires in a phosphorus-enriched Everglades wetland: I. Phosphorus dynamics and cattail recovery

We have developed and applied a process-based model, the Wetland Ecosystem Model (WEM), to evaluate the effects of a prescribed fire on the phosphorus (P) dynamics and cattail (Typha domingensis) growth in a P-enriched area in the Florida Everglades. The WEM couples major ecosystem processes including carbon (C), nitrogen (N) and P biogeochemical cycles, plant growth, hydrology, and fire disturbance. The model is used to assess the effects of a prescribed fire on P dynamics and cattail growth through dynamic interaction among four modules: fire, water chemistry, soil, and vegetation. The simulation results are in agreement with observed data including cattail above- and belowground biomass and dead mass, P concentration in surface-water, pore-water, and soil, and soil and water temperature. Cattail aboveground biomass reached the unburned level one year after burn; belowground biomass recovered to unburned level one and half years after the fire, however, dead mass did not completely reach unburned level two years after fires. The fire increased water and soil temperatures in the short term, while indirectly increasing the sensitivity of water and soil temperature post-fire response to air temperature by altering the energy exchange between air and water through a canopy gap created by fire. The fire also altered the P dynamics in surface-water and pore-water. A post-fire P pulse that lasted for less than one month was observed in surface-water. A similar P pulse, but in a small magnitude and a longer duration, was also observed in the pore-water total phosphorus (TP), and then came back to normal level after approximately three months. No significant changes in soil TP was observed during the study period. Meanwhile, no significant changes in water nutrients were observed downstream of the study plot. This finding indicated that the P-enriched wetlands in Everglades act as a buffer in regulating the P concentration in surface-water. Our study showed that the distance of fire effects on a 300 m × 300 m plot was less than 300 m downstream. Sensitivity analysis identified that the air temperature and hydrological conditions are two important driving factors which may alter the cattail community dynamics in response to prescribed fires. Similar to the filed studies, this study provided evidences that fire played an important role in managing plant growth and P dynamics in the Florida Everglades.