631 research outputs found

    Combining local- and large-scale models to predict the distributions of invasive plant species

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    Habitat-distribution models are increasingly used to predict the potential distributions of invasive species and to inform monitoring. However, these models assume that species are in equilibrium with the environment, which is clearly not true for most invasive species. Although this assumption is frequently acknowledged, solutions have not been adequately addressed. There are several potential methods for improving habitat-distribution models. Models that require only presence data may be more effective for invasive species, but this assumption has rarely been tested. In addition, combining modeling types to form ‘ensemble’ models may improve the accuracy of predictions. However, even with these improvements, models developed for recently invaded areas are greatly influenced by the current distributions of species and thus reflect near- rather than long-term potential for invasion. Larger scale models from species’ native and invaded ranges may better reflect long-term invasion potential, but they lack finer scale resolution. We compared logistic regression (which uses presence/absence data) and two presence-only methods for modeling the potential distributions of three invasive plant species on the Olympic Peninsula in Washington State, USA. We then combined the three methods to create ensemble models. We also developed climate-envelope models for the same species based on larger scale distributions and combined models from multiple scales to create an index of near- and long-term invasion risk to inform monitoring in Olympic National Park (ONP). Neither presence-only nor ensemble models were more accurate than logistic regression for any of the species. Larger scale models predicted much greater areas at risk of invasion. Our index of near- and long-term invasion risk indicates that \u3c4% of ONP is at high near-term risk of invasion while 67-99% of the Park is at moderate or high long-term risk of invasion. We demonstrate how modeling results can be used to guide the design of monitoring protocols and monitoring results can in turn be used to refine models. We propose that by using models from multiple scales to predict invasion risk and by explicitly linking model development to monitoring, it may be possible to overcome some of the limitations of habitat-distribution models

    The ecological niche of storm-petrels in the North Pacific and a global model of dimethylsulfide concentration

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    Thesis (M.S.) University of Alaska Fairbanks, 2010Ecological niche modeling techniques were used to create global, monthly predictions of sea surface dimethylsulfide (DMS) concentrations, and breeding season distribution of Leach's Storm-Petrel (Oceanodroma luncorhoa) and Fork-Tailed Storm-Petrel (O. furcata) in the North Pacific. This work represents the first attempt to model DMS concentrations on a global scale using ecological niche modeling, and the first models of Storm-Petrel distribution for the North Pacific. Storm-Petrels have been shown to be attracted to DMS, and it is therefore likely that a model of sea surface DMS concentration would help explain and predict Storm-Petrel distribution. We have successfully created the most accurate models of sea surface DMS concentrations that we are currently aware of with global correlation (r) values greater than 0.45. We also created Storm-Petrel models with area under the receiver operating characteristic curve (AUC) values of greater than 0.90. Using just DMS as a predictor variable we were also able to create models with AUC values upwards of 0.84. Future conservation efforts on pelagic seabird species may be dependent on models like the ones created here, and it is therefore important that these methods are improved upon to help seabird management on all scales (global, national, regional and local).General introduction -- Storm-petrels of the North Pacific -- Dimethylsulfide -- Ecological niche modeling -- Data mining (TreeNet) -- Study goals -- References -- 1. Predicting monthly surface seawater dimethylsulfide (DMS) concentrations on a global scale using a machine learning algorithm (TreeNet) -- 2. Predicted distribution of storm-petrels (Oceanodroma) in the North Pacific using Geographic Information Systems (GIS), TreeNet and dimethylsulfide (DMS) concentrations -- Acknowledgements -- Literature cited -- General conclusions -- Dimethylsulfide -- Storm petrels -- Final conclusions -- References -- Appendices

    Integrated Systems Modeling to Improve Watershed Habitat Management and Decision Making

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    Regulated rivers provide opportunities to improve habitat quality by managing the times, locations, and magnitudes of reservoir releases and diversions across the watershed. To identify these opportunities, managers select priority species and determine when, where, and how to allocate water between competing human and environmental users in the basin. Systems models have been used to recommend allocation of water between species. However, many models consider species’ water needs as constraints on instream flow that is managed to maximize human beneficial uses. Many models also incorporate uncertainty in the system and report an overwhelmingly large number of management alternatives. This dissertation presents three new novel models to recommend the allocation of water and money to improve habitat quality. The new models also facilitate communicating model results to managers and to the public. First, a new measurable and observable habitat metric quantifies habitat area and quality for priority aquatic, floodplain, and wetland habitat species. The metric is embedded in a systems model as an ecological objective to maximize. The systems model helps managers to identify times and locations at which to apply scarce water to most improve habitat area and quality for multiple competing species. Second, a cluster analysis approach is introduced to reduce large dimensional uncertainty problems in habitat models and focus management efforts on the important parameters to measure and monitor more carefully. The approach includes manager preferences in the search for clusters. It identifies a few, easy-to-interpret management options from a large multivariate space of possible alternatives. Third, an open-access web tool helps water resources modelers display model outputs on an interactive web map. The tool allows modelers to construct node-link networks on a web map and facilitates sharing and visualizing spatial and temporal model outputs. The dissertation applies all three studies to the Lower Bear River, Utah, to guide ongoing habitat conservation efforts, recommend water allocation strategies, and provide important insights on ways to improve overall habitat quality and area

    Developing A Geospatial Protocol For Coral Epizootiology

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    This dissertation explores how geographic information systems (GIS) and spatial statistics, specifically the techniques used to map, detect, and spatially analyze disease epidemics, could be used to advance our understanding of coral reef health. Given that different types of spatial analysis, as well as different parameter settings within each analysis, can produce noticeably different results, poor selection or improper use of a given technique would likely lead to inaccurate representations of the spatial distribution and false interpretations of the disease. For this reason, I performed a comprehensive review of the following types of exploratory spatial data analysis (ESDA): mapping and visualization methods; centrographic and distance-based point pattern analyses; spatial kernel density estimates (KDE) using single and dual versions of adaptive and fixed-distance KDEs in which the fixed-distance KDEs were performed using bandwidths calculated using 12 different estimation methods; SaTScan’s spatial scan statistic using both the Bernoulli and Poisson probability models; and last, local and global versions of the Moran’s I and Getis-ord G spatial autocorrelation statistics. Each technique was applied to an artificial dataset with known cluster locations in order to determine which methods provided the most accurate results. These results were then used to develop different geospatial analytical protocols based on the types of coral data available, noting that the most meaningful results would be produced using local spatial statistics to analyze data of diseased colonies and colonies from the underlying coral population at risk. Last, I applied the techniques from one of the protocols to data from a 2004 White-Band Disease (WBD) outbreak on a population of Acropora palmata corals in the US Virgin Islands. The results of this work represent the first application of geospatial analytical techniques in visualizing the spatial nature of a coral disease and provides important information about the epizootiology of this particular outbreak. Specifically, the results indicated that WBD prevalence was low with numerous significant disease clusters occurring throughout the study area, suggesting WBD may be caused by a ubiquitous stressor. The material presented in this dissertation will provide researchers with the necessary tools and information needed to perform the most accurate geospatial analysis possible based on the coral data available

    Deep Learning Methods for Remote Sensing

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    Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing

    Quantitative Techniques in Participatory Forest Management

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    Forest management has evolved from a mercantilist view to a multi-functional one that integrates economic, social, and ecological aspects. However, the issue of sustainability is not yet resolved. Quantitative Techniques in Participatory Forest Management brings together global research in three areas of application: inventory of the forest variables that determine the main environmental indices, description and design of new environmental indices, and the application of sustainability indices for regional implementations. All these quantitative techniques create the basis for the development of scientific methodologies of participatory sustainable forest management
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