Download or read book Data Fusion and Spatio temporal Approaches to Model Species Distribution written by Narmadha Meenabhashini Mohankumar and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Species distribution models (SDMs) are increasingly used in ecology, biogeography, and wildlife management to learn about the distribution of species across space and time. Determining the species-habitat relationships and the distributional pattern of a species is important to increase scientific knowledge, inform management decisions, and conserve biodiversity. I propose approaches to address some of the most pressing issues encountered in studies of species distributions and contribute towards improving predictions and inferences from SDMs. First, I present a modeling framework to model occupancy data that accounts for both traditional and nontraditional spatial dependence as well as false absences. Occupancy data are used to estimate and map the true presence of a species, which may depend on biotic and abiotic factors as well as spatial autocorrelation. Traditionally, spatial autocorrelation is accounted for by using a correlated normally distributed site-level random effect, which might be incapable of modeling nontraditional spatial dependence such as discontinuities and abrupt transitions. Machine learning approaches have the potential to model nontraditional spatial dependence, but these approaches do not account for observer errors such as false absences. I combine the flexibility of Bayesian hierarchal modeling and machine learning approaches and present a modeling framework to account for both traditional and nontraditional spatial dependence and false absences. I illustrate the framework using six synthetic data sets containing traditional and nontraditional spatial dependence and then apply the approach to understand the spatial distribution of Thomson's gazelle (Eudorcas thomsonii) in Tanzania and sugar gliders (Petaurus breviceps) in Tasmania. Second, I develop a model-based approach for data fusion of distance sampling (DS) and capture-recapture (CR) data. DS and CR are two widely collected data types to learn about species-habitat relationships and abundance; still, they are seldomly used in SDMs due to the lack of spatial coverage. However, data fusion of the sources of data can increase spatial coverage, which can reduce parameter uncertainty and make predictions more accurate, and therefore, can be used for species distribution modeling. My modeling approach accounts for two common missing data issues: 1) missing individuals that are missing not at random (MNAR) and 2) partially missing location information. Using a simulation experiment, I evaluated the performance of the modeling approach and compared it to existing approaches that use ad-hoc methods to account for missing data issues. I demonstrated my approach using data collected for Grasshopper Sparrows (Ammodramus savannarum) in north-eastern Kansas, USA. Third, I extend my data fusion approach to a spatio-temporal modeling framework to investigate the influence of the temporal support in spatio-temporal point process models to model species distribution. Temporal dynamics of ecological processes are complex, and their influence on species-habitat relationships and abundance operate in multiple spatio-temporal scales. Spatio-temporal point process models are widely used to model species-habitat relationships and estimate abundance across multiple spatio-temporal scales; however, the robustness of the models to changing temporal scales is rarely studied. Understanding the temporal dynamics of ecological processes across the entirety of spatio-temporal scales is key to learning about species' distribution. Therefore, investigating the influence of temporal support on the robustness of spatio-temporal point processes to model species distributions is needed. In my approach, I combine DS and CR data in a spatio-temporal point process modeling framework and investigate the robustness of the model to changing temporal scales. My fused data spatio-temporal model alleviates constraints in individual data sources such as lack of spatio-temporal coverage and enables the study of complex phenomena on multiple-scale species-habitat relationships and abundance. To investigate the impact of temporal support on models' robustness, I conducted a simulation experiment. Then, I illustrate the influence of temporal support to model species-habitat relationships and abundance using data on Grasshopper Sparrows (Ammodramus savannarum) in north-eastern Kansas, USA.

Download or read book Mapping Species Distributions written by Janet Franklin and published by Cambridge University Press. This book was released on 2010-01-07 with total page 538 pages. Available in PDF, EPUB and Kindle. Book excerpt: Maps of species' distributions or habitat suitability are required for many aspects of environmental research, resource management and conservation planning. These include biodiversity assessment, reserve design, habitat management and restoration, species and habitat conservation plans and predicting the effects of environmental change on species and ecosystems. The proliferation of methods and uncertainty regarding their effectiveness can be daunting to researchers, resource managers and conservation planners alike. Franklin summarises the methods used in species distribution modeling (also called niche modeling) and presents a framework for spatial prediction of species distributions based on the attributes (space, time, scale) of the data and questions being asked. The framework links theoretical ecological models of species distributions to spatial data on species and environment, and statistical models used for spatial prediction. Providing practical guidelines to students, researchers and practitioners in a broad range of environmental sciences including ecology, geography, conservation biology, and natural resources management.

Download or read book Predicting Species Occurrences written by J. Michael Scott and published by Island Press. This book was released on 2002-02 with total page 940 pages. Available in PDF, EPUB and Kindle. Book excerpt: Predictions about where different species are, where they are not, and how they move across a landscape or respond to human activities -- if timber is harvested, for instance, or stream flow altered -- are important aspects of the work of wildlife biologists, land managers, and the agencies and policymakers that govern natural resources. Despite the increased use and importance of model predictions, these predictions are seldom tested and have unknown levels of accuracy.Predicting Species Occurrences addresses those concerns, highlighting for managers and researchers the strengths and weaknesses of current approaches, as well as the magnitude of the research required to improve or test predictions of currently used models. The book is an outgrowth of an international symposium held in October 1999 that brought together scientists and researchers at the forefront of efforts to process information about species at different spatial and temporal scales. It is a comprehensive reference that offers an exhaustive treatment of the subject, with 65 chapters by leading experts from around the world that: review the history of the theory and practice of modeling and present a standard terminology examine temporal and spatial scales in terms of their influence on patterns and processes of species distribution offer detailed discussions of state-of-the-art modeling tools and descriptions of methods for assessing model accuracy discuss how to predict species presence and abundance present examples of how spatially explicit data on demographics can provide important information for managers An introductory chapter by Michael A. Huston examines the ecological context in which predictions of species occurrences are made, and a concluding chapter by John A. Wiens offers an insightful review and synthesis of the topics examined along with guidance for future directions and cautions regarding misuse of models. Other contributors include Michael P. Austin, Barry R. Noon, Alan H. Fielding, Michael Goodchild, Brian A. Maurer, John T. Rotenberry, Paul Angermeier, Pierre R. Vernier, and more than a hundred others.Predicting Species Occurrences offers important new information about many of the topics raised in the seminal volume Wildlife 2000 (University of Wisconsin Press, 1986) and will be the standard reference on this subject for years to come. Its state-of-the-art assessment will play a key role in guiding the continued development and application of tools for making accurate predictions and is an indispensable volume for anyone engaged in species management or conservation.

Download or read book Joint Species Distribution Modelling written by Otso Ovaskainen and published by Cambridge University Press. This book was released on 2020-06-11 with total page 389 pages. Available in PDF, EPUB and Kindle. Book excerpt: Joint species distribution modelling (JSDM) is a fast-developing field and promises to revolutionise how data on ecological communities are analysed and interpreted. Written for both readers with a limited statistical background, and those with statistical expertise, this book provides a comprehensive account of JSDM. It enables readers to integrate data on species abundances, environmental covariates, species traits, phylogenetic relationships, and the spatio-temporal context in which the data have been acquired. Step-by-step coverage of the full technical detail of statistical methods is provided, as well as advice on interpreting results of statistical analyses in the broader context of modern community ecology theory. With the advantage of numerous example R-scripts, this is an ideal guide to help graduate students and researchers learn how to conduct and interpret statistical analyses in practice with the R-package Hmsc, providing a fast starting point for applying joint species distribution modelling to their own data.

Download or read book Handbook of Environmental and Ecological Statistics written by Alan E. Gelfand and published by CRC Press. This book was released on 2019-01-15 with total page 798 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook focuses on the enormous literature applying statistical methodology and modelling to environmental and ecological processes. The 21st century statistics community has become increasingly interdisciplinary, bringing a large collection of modern tools to all areas of application in environmental processes. In addition, the environmental community has substantially increased its scope of data collection including observational data, satellite-derived data, and computer model output. The resultant impact in this latter community has been substantial; no longer are simple regression and analysis of variance methods adequate. The contribution of this handbook is to assemble a state-of-the-art view of this interface. Features: An internationally regarded editorial team. A distinguished collection of contributors. A thoroughly contemporary treatment of a substantial interdisciplinary interface. Written to engage both statisticians as well as quantitative environmental researchers. 34 chapters covering methodology, ecological processes, environmental exposure, and statistical methods in climate science.

Download or read book Geospatial Data in a Changing World written by Tapani Sarjakoski and published by Springer. This book was released on 2016-05-14 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book collects innovative research presented at the 19th Conference of the Association of Geographic Information Laboratories in Europe (AGILE) on Geographic Information Science, held in Helsinki, Finland in 2016.

Download or read book The Review and Application of New Methods for Species Distribution Modeling written by Samuel Dylan Veloz and published by . This book was released on 2008 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Spatiotemporal Modeling and Analysis in Marine Science written by Junyu He and published by Frontiers Media SA. This book was released on 2023-11-29 with total page 175 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the development of earth observation technologies (such as satellite remote sensing, unmanned aerial vehicle, autonomous underwater vehicle, etc.), an era of big data with important and non-negligible spatial/temporal attributes comes. Novel and rigorous spatiotemporal methodologies and models are needed to process and analyze marine big data. Since many marine environmental processes, such as pollutants diffusion, algae distributions etc., vary or evolve across spatiotemporal domains, detecting the distributions and patterns of marine fauna and, particularly in the coastal regions, will improve our understanding of marine systems and can be beneficial in marine environmental management. The goals of this Research Topic, therefore, are two-fold: (a) to develop methodologies and models in theory and applications, including spatiotemporal geostatistics, geographic information system, deep learning, etc.; (b) to quantitatively gain the knowledge of the marine environment. This Research Topic will provide a platform for researchers to share and exchange their new knowledge gained in a spatiotemporal domain of marine or coastal regions. This Research Topic will cover, but is not limited to, the following areas: • Spatiotemporal variations of physical/chemical/biological indicators (such as chlorophyll, temperature, salinity, colorful dissolved organic matter, suspended solids, nutrients, microplastic, etc.) in marine. • Spatiotemporal variations of potential fishing grounds in marine. • Spatiotemporal variations of the ecosystems in coastal regions, such as salt marshes, mangroves, seagrass, macroalgae, etc. • Spatiotemporal distributions of the pollutants (such as heavy metals, polycyclic aromatic hydrocarbon, etc.) in marine and sediments. • Spatiotemporal evolution pattern modeling and prediction of the marine disasters and abnormal phenomena (such as algal bloom, typhoons, SST anomalies, etc).

Download or read book Spatial Regresssion Methods Capture Prediction Uncertainty in Species Distribution Model Projections Through Time written by Alan Karl Swanson and published by . This book was released on 2012 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: Species distribution models (SDMs) relate observed locations of a species to climate, and are used for projecting the fate of a species under climate change scenarios. To be useful in a decision-making context, the uncertainty associated with these projections must be known. However, the uncertainty associated with SDM projections is largely ignored, perhaps because many current methods have been shown to produce biased estimates. Failure to account for spatial autocorrelation (SAC) of residual error explains much of this bias. Generalized linear mixed models (GLMM) have the ability to account for SAC through the inclusion of a spatially structured random intercept, interpreted to account for the effect of missing predictors. This framework promises a more realistic representation of parameter and prediction uncertainty. My work assesses the ability of GLMMs and a conventional SDM approach, based on generalized linear models (GLM), to produce accurate projections and estimates of prediction uncertainty. Bayesian methods were used to fit models to historical (1928-1940) observations for 99 woody plant species in California, USA, and assessed using modern "temporally independent" validation data (2000-2005). A set of climatic water balance metrics were calculated to inform the models. GLMMs provided a closer fit to historic data, had fewer significant covariates, were better able to nearly eliminate spatial autocorrelation of residual error, and had larger credible intervals for projections than GLMs. The accuracy of projections was similar between methods but the GLMMs better quantified projection uncertainty. Additionally, the GLMMs produced more conservative estimates of species range size and range size change than the GLMs. I conclude that the GLMM error structure allows for a more realistic characterization of SDM uncertainty. This is critical for conservation applications that rely on robust assessments of projection uncertainty.

Download or read book Entropy Measures for Environmental Data written by Linda Altieri and published by Springer Nature. This book was released on with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book High Spatial Resolution Remote Sensing written by Yuhong He and published by CRC Press. This book was released on 2018-06-27 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: High spatial resolution remote sensing is an area of considerable current interest and builds on developments in object-based image analysis, commercial high-resolution satellite sensors, and UAVs. It captures more details through high and very high resolution images (10 to 100 cm/pixel). This unprecedented level of detail offers the potential extraction of a range of multi-resource management information, such as precision farming, invasive and endangered vegetative species delineation, forest gap sizes and distribution, locations of highly valued habitats, or sub-canopy topographic information. Information extracted in high spatial remote sensing data right after a devastating earthquake can help assess the damage to roads and buildings and aid in emergency planning for contact and evacuation. To effectively utilize information contained in high spatial resolution imagery, High Spatial Resolution Remote Sensing: Data, Analysis, and Applications addresses some key questions: What are the challenges of using new sensors and new platforms? What are the cutting-edge methods for fine-level information extraction from high spatial resolution images? How can high spatial resolution data improve the quantification and characterization of physical-environmental or human patterns and processes? The answers are built in three separate parts: (1) data acquisition and preprocessing, (2) algorithms and techniques, and (3) case studies and applications. They discuss the opportunities and challenges of using new sensors and platforms and high spatial resolution remote sensing data and recent developments with a focus on UAVs. This work addresses the issues related to high spatial image processing and introduces cutting-edge methods, summarizes state-of-the-art high spatial resolution applications, and demonstrates how high spatial resolution remote sensing can support the extraction of detailed information needed in different systems. Using various high spatial resolution data, the third part of this book covers a range of unique applications, from grasslands to wetlands, karst areas, and cherry orchard trees.

Download or read book Integrating Scale in Remote Sensing and GIS written by Dale A. Quattrochi and published by CRC Press. This book was released on 2017-01-06 with total page 463 pages. Available in PDF, EPUB and Kindle. Book excerpt: Integrating Scale in Remote Sensing and GIS serves as the most comprehensive documentation of the scientific and methodological advances that have taken place in integrating scale and remote sensing data. This work addresses the invariants of scale, the ability to change scale, measures of the impact of scale, scale as a parameter in process models, and the implementation of multiscale approaches as methods and techniques for integrating multiple kinds of remote sensing data collected at varying spatial, temporal, and radiometric scales. Researchers, instructors, and students alike will benefit from a guide that has been pragmatically divided into four thematic groups: scale issues and multiple scaling; physical scale as applied to natural resources; urban scale; and human health/social scale. Teeming with insights that elucidate the significance of scale as a foundation for geographic analysis, this book is a vital resource to those seriously involved in the field of GIScience.

Download or read book Deep Learning for Marine Science written by Haiyong Zheng and published by Frontiers Media SA. This book was released on 2024-05-15 with total page 555 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning (DL), mainly composed of deep and complex neural networks such as recurrent network and convolutional network, is an emerging research branch in the field of artificial intelligence and machine learning. DL revolution has a far-reaching impact on all scientific disciplines and every corner of our lives. With continuing technological advances, marine science is entering into the big data era with the exponential growth of information. DL is an effective means of harnessing the power of big data. Combined with unprecedented data from cameras, acoustic recorders, satellite remote sensing, and large model outputs, DL enables scientists to solve complex problems in biology, ecosystems, climate, energy, as well as physical and chemical interactions. Although DL has made great strides, it is still only beginning to emerge in many fields of marine science, especially towards representative applications and best practices for the automatic analysis of marine organisms and marine environments. DL in nowadays' marine science mainly leverages cutting-edge techniques of deep neural networks and massive data which collected by in-situ optical or acoustic imaging sensors for underwater applications, such as plankton classification and coral reef detection. This research topic aims to expand the applications of marine science to cover all aspects of detection, classification, segmentation, localization, and density estimation of marine objects, organisms, and phenomena.

Download or read book Spatio Temporal Models for Ecologists written by James Thorson and published by CRC Press. This book was released on 2024-02-27 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ecological dynamics are tremendously complicated and are studied at a variety of spatial and temporal scales. Ecologists often simplify analysis by describing changes in density of individuals across a landscape, and statistical methods are advancing rapidly for studying spatio-temporal dynamics. However, spatio-temporal statistics is often presented using a set of principles that may seem very distant from ecological theory or practice. This book seeks to introduce a minimal set of principles and numerical techniques for spatio-temporal statistics that can be used to implement a wide range of real-world ecological analyses regarding animal movement, population dynamics, community composition, causal attribution, and spatial dynamics. We provide a step-by-step illustration of techniques that combine core spatial-analysis packages in R with low-level computation using Template Model Builder. Techniques are showcased using real-world data from varied ecological systems, providing a toolset for hierarchical modelling of spatio-temporal processes. Spatio-Temporal Models for Ecologists is meant for graduate level students, alongside applied and academic ecologists. Key Features: Foundational ecological principles and analyses Thoughtful and thorough ecological examples Analyses conducted using a minimal toolbox and fast computation Code using R and TMB included in the book and available online

Download or read book Spatially Explicit Distribution Models for Predicting Species Occurrences microform written by Pilar Hernandez and published by Library and Archives Canada = Bibliothèque et Archives Canada. This book was released on 2004 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt: Species distribution modeling is an essential tool for conservation planning. These models utilize the species-environment relationship to formulate a spatial depiction of its distribution pattern. Often these models are developed aspatially. That is they do not consider the spatial context of the species occurrence. Thereby, ignoring spatial components that contribute to the species distribution pattern such as species endogenous processes and the species dependence on its spatially structured physical environment. Species distribution modeling methods have been developed that explicitly account for these spatial processes. Spatially explicit modeling methods are reviewed and the importance of carefully considering interactions between the ecological, data and statistical components of the model is highlighted. A comparative evaluation of five spatially explicit methods and an aspatial method was performed to investigate their relative abilities to accurately predict three songbird occurrences. Results were mixed and dependent on characteristics of the species ecology and model data.

Download or read book Mapping Species Distributions written by Janet Franklin and published by Cambridge University Press. This book was released on 2009 with total page 339 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive summary of species distribution modeling methods integrating ecological and statistical models with spatial data, and a framework for implementation.

Download or read book Image Analysis and Processing ICIAP 2022 written by Stan Sclaroff and published by Springer Nature. This book was released on 2022-05-14 with total page 507 pages. Available in PDF, EPUB and Kindle. Book excerpt: The proceedings set LNCS 13231, 13232, and 13233 constitutes the refereed proceedings of the 21st International Conference on Image Analysis and Processing, ICIAP 2022, which was held during May 23-27, 2022, in Lecce, Italy, The 168 papers included in the proceedings were carefully reviewed and selected from 307 submissions. They deal with video analysis and understanding; pattern recognition and machine learning; deep learning; multi-view geometry and 3D computer vision; image analysis, detection and recognition; multimedia; biomedical and assistive technology; digital forensics and biometrics; image processing for cultural heritage; robot vision; etc.