Download or read book Wrong But Useful Regional Species Distribution Models May Not be Improved by Range wide Data Under Biased Sampling written by and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: Species distribution modeling (SDM) is an essential method in ecology and conservation. SDMs are often calibrated within one country's borders, typically along a limited environmental gradient with biased and incomplete data, making the quality of these models questionable. In this study, we evaluated how adequate are national presence‐only data for calibrating regional SDMs. We trained SDMs for Egyptian bat species at two different scales: only within Egypt and at a species‐specific global extent. We used two modeling algorithms: Maxent and elastic net, both under the point‐process modeling framework. For each modeling algorithm, we measured the congruence of the predictions of global and regional models for Egypt, assuming that the lower the congruence, the lower the appropriateness of the Egyptian dataset to describe the species' niche. We inspected the effect of incorporating predictions from global models as additional predictor ("prior") to regional models, and quantified the improvement in terms of AUC and the congruence between regional models run with and without priors. Moreover, we analyzed predictive performance improvements after correction for sampling bias at both scales. On average, predictions from global and regional models in Egypt only weakly concur. Collectively, the use of priors did not lead to much improvement: similar AUC and high congruence between regional models calibrated with and without priors. Correction for sampling bias led to higher model performance, whatever prior used, making the use of priors less pronounced. Under biased and incomplete sampling, the use of global bats data did not improve regional model performance. Without enough bias‐free regional data, we cannot objectively identify the actual improvement of regional models after incorporating information from the global niche. However, we still believe in great potential for global model predictions to guide future surveys and improve regional sampling in data‐poor regions. Abstract : Species distribution models are often calibrated within one country's borders, typically along a limited environmental gradient with biased and incomplete data, making the quality of these models questionable. In this study, we evaluated how adequate are national presence‐only data for calibrating regional SDMs and how much improvement occurs after incorporating information from global models as additional predictor (priors). Correction for sampling bias led to higher model performance, whatever prior used, making the use of priors less pronounced. Under biased and incomplete sampling, the use of global data did not improve regional model performance. However, we still believe in great potential for global model predictions to guide future surveys and improve regional sampling in data‐poor regions.
Download or read book Open Citizen Science Data and Methods written by Anne Bowser and published by Frontiers Media SA. This book was released on 2022-11-25 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Improving Species Distribution Models with Bias Correction and Geographically Weighted Regression written by Richard Inman and published by . This book was released on 2018 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work investigates the effects of non-random sampling on our understanding of species distributions and their niches. In its most general form, bias is systematic error that can obscure interpretation of analytical results by skewing samples away from the average condition of the system they represent. Here I use species distribution modelling (SDM), virtual species, and multiscale geographically weighted regression (MGWR) to explore how sampling bias can alter our perception of broad patterns of biodiversity by distorting spatial predictions of habitat, a key characteristic in biogeographic studies. I use three separate case studies to explore: 1) How methods to account for sampling bias in species distribution modeling may alter estimates of species distributions and species-environment relationships, 2) How accounting for sampling bias in fossil data may change our understanding of paleo-distributions and interpretation of niche stability through time (i.e. niche conservation), and 3) How a novel use of MGWR can account for environmental sampling bias to reveal landscape patterns of local niche differences among proximal, but non-overlapping sister taxa. Broadly, my work shows that sampling bias present in commonly used federated global biodiversity observations is more than enough to degrade model performance of spatial predictions and niche characteristics. Measures commonly used to account for this bias can negate much loss, but only in certain conditions, and did not improve the ability to correctly identify explanatory variables or recreate species-environment relationships. Paleo-distributions calibrated on biased fossil records were improved with the use of a novel method to directly estimate the biased sampling distribution, which can be generalized to finer time slices for further paleontological studies. Finally, I show how a novel coupling of SDM and MGWR can illuminate local differences in niche separation that more closely match landscape genotypic variability in the two North American desert tortoise species than does their current taxonomic delineation.
Download or read book Habitat Suitability and Distribution Models written by Antoine Guisan and published by Cambridge University Press. This book was released on 2017-09-14 with total page 513 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces the key stages of niche-based habitat suitability model building, evaluation and prediction required for understanding and predicting future patterns of species and biodiversity. Beginning with the main theory behind ecological niches and species distributions, the book proceeds through all major steps of model building, from conceptualization and model training to model evaluation and spatio-temporal predictions. Extensive examples using R support graduate students and researchers in quantifying ecological niches and predicting species distributions with their own data, and help to address key environmental and conservation problems. Reflecting this highly active field of research, the book incorporates the latest developments from informatics and statistics, as well as using data from remote sources such as satellite imagery. A website at www.unil.ch/hsdm contains the codes and supporting material required to run the examples and teach courses.
Download or read book Species Distribution Modeling written by Lifei Wang and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
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: A comprehensive account of joint species distribution modelling, covering statistical analyses in light of modern community ecology theory.
Download or read book The New Natural History of Madagascar written by Steven M. Goodman and published by Princeton University Press. This book was released on 2022-11-15 with total page 2296 pages. Available in PDF, EPUB and Kindle. Book excerpt: A marvelously illustrated reference to the natural wonders of one of the most spectacular places on earth Separated from Africa’s mainland for tens of millions of years, Madagascar has evolved a breathtaking wealth of biodiversity, becoming home to thousands of species found nowhere else on the planet. The New Natural History of Madagascar provides the most comprehensive, up-to-date synthesis available of this island nation’s priceless biological treasures. Now fully revised and expanded, this beautifully illustrated compendium features contributions by more than 600 globally renowned experts who cover the history of scientific exploration in Madagascar, as well as the island’s geology and soils, climate, forest ecology, human ecology, marine and coastal ecosystems, plants, invertebrates, fishes, amphibians, reptiles, birds, and mammals. This invaluable two-volume reference also includes detailed discussions of conservation efforts in Madagascar that showcase several successful protected area programs that can serve as models for threatened ecosystems throughout the world. Provides the most comprehensive overview of Madagascar’s rich natural historyCoedited by 18 different specialistsFeatures hundreds of new contributions by world-class expertsIncludes hundreds of new illustrationsCovers a broad array of topics, from geology and climate to animals, plants, and marine lifeSheds light on newly discovered species and draws on the latest scienceAn essential resource for anyone interested in Madagascar or tropical ecosystems in general, from biologists and conservationists to ecotourists and armchair naturalists
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 Ensemble Methods in Data Mining written by Giovanni Seni and published by Morgan & Claypool Publishers. This book was released on 2010 with total page 127 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Ensemble methods have been called the most influential development in Data Mining and Machine Learning in the past decade. They combine multiple models into one usually more accurate than the best of its components. Ensembles can provide a critical boost to industrial challenges -- from investment timing to drug discovery, and fraud detection to recommendation systems -- where predictive accuracy is more vital than model interpretability. Ensembles are useful with all modeling algorithms, but this book focuses on decision trees to explain them most clearly. After describing trees and their strengths and weaknesses, the authors provide an overview of regularization -- today understood to be a key reason for the superior performance of modern ensembling algorithms. The book continues with a clear description of two recent developments: Importance Sampling (IS) and Rule Ensembles (RE). IS reveals classic ensemble methods -- bagging, random forests, and boosting -- to be special cases of a single algorithm, thereby showing how to improve their accuracy and speed. REs are linear rule models derived from decision tree ensembles. They are the most interpretable version of ensembles, which is essential to applications such as credit scoring and fault diagnosis. Lastly, the authors explain the paradox of how ensembles achieve greater accuracy on new data despite their (apparently much greater) complexity."--Publisher's website.
Download or read book Testing the Reliability of Canada wide and Regional Species Distribution Models with Independent Field Surveys and Evaluating Their Use for Conservation written by Julie L. Nadeau and published by . This book was released on 2010 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Illuminating Taxonomic and Ecoregional Variation in Bias Among Community Science Data to Inform Conservation based Species Distribution Modeling written by Lindsay Lacey and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Citizen science, also known as community science, has been growing rapidly in use and availability within ecology and conservation. Many community science efforts provide participants the opportunity to submit data on species occurrences anywhere and at any time, deeming the information collected "opportunistic data." The flexibility of this opportunistic approach has led to a larger participant base that has contributed massive amounts of data, however the lack of a standard methodology has also led to uncertainty and bias in those datasets that hold the potential to influence subsequent analyses. These large datasets are often freely available, making them an excellent resource for groups with limited funding for research and data acquisition, such as conservation non-profits. One particularly frequent application in which opportunistically collected species occurrence data are used is in species distribution modeling, which can inform conservation efforts by helping researchers understand where wildlife are distributed across the landscape. Opportunistically collected data can be influenced by multiple sources of bias including spatial, temporal, and taxonomic bias. The most prevalent of these three, spatial bias, can greatly influence the results of species distribution models (SDMs) and cause them to overemphasize certain areas within a modeling region. For example, observed occurrence data can often exhibit bias toward areas of greater human activity such as roads, trails, and infrastructure, which could lead to inferences about artificially high suitability for a species modeled in these areas. While there are many approaches to address such bias, there is little consensus on when to use certain methods and why, and a limited understanding exists on how different methods and subjective parametrizations will influence SDM results. While these various methods of addressing bias and their differential impacts on SDMs have not been assessed across the diverse taxonomic groups and ecoregions of the United States, studies in other regions of the world have indicated the accuracy of such methods may vary by geographic region or habitat type. Such variation could impact the accuracy of modeling efforts based on a given geographic area of interest, the extent of which remains unknown across the diversity of ecoregions in the United States. To address this, the following study aims to (1) synthesize existing approaches of identifying and controlling for spatial bias in SDMs using community science data and develop a flow chart outlining specific tools, methods, and use cases presented in the literature in a structure beneficial to conservation professionals and early career professionals looking to use these methods, (2) assess the spatial, temporal, and taxonomic bias in community science datasets accessed from the Global Biodiversity Information Facility (GBIF) for 40 species selected to represent each of the 10 EPA Level I ecoregions in the contiguous United States, and (3) generate SDMs with and without the methods identified for addressing bias and quantify the differences in model performance across taxonomic groups and ecoregions in the contiguous U.S.
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 The Structure and Dynamics of Geographic Ranges written by Kevin J. Gaston and published by Oxford University Press, USA. This book was released on 2003 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: A synthesis of present understanding of the structure of the geographic ranges of species, which is a core issue in ecology and biogeography with implications for many of the environmental issues presently facing humankind.
Download or read book Frontiers of Biogeography written by Mark V. Lomolino and published by Sinauer Associates Incorporated. This book was released on 2004-01-01 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: Developed & published in association with the International Biogeography Society, this book concentrates on advances in historical biogeography, island biogeography & marine biogeography during the past quarter of a century.
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 Incorporating Movement in Species Distribution Models written by Paul Holloway and published by . This book was released on 2016 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt: Climate change and concomitant urbanization have led to many species shifting their geographical distribution, while other species have simply gone extinct. Understanding the current and future distributions of species is therefore a critical component of biodiversity conservation, with species distribution models (SDMs) a powerful GIScience approach increasingly used to achieve this. Movement is an ecological process that influences the distribution of all species. Broad-scale (spatially and temporally) movement includes processes like dispersal and migration that determine whether newly suitable habitats are accessible, while fine-scale movement effects resource availability, and subsequently habitat suitability. In spite of this ecological significance, movement is rarely incorporated in SDMs. An increasingly important application of SDM is to study the effects of climate change on species distributions, and while several models that incorporate species dispersal abilities have been proposed, none have been tested or compared. Past data (British birds and North American flora) were used to calibrate and extrapolate species-environment relationships to the current time-period in order to assess the accuracy of these dispersal models. Significant differences in the accuracy and area projected as present by the dispersal models were identified, and moreover, results were substantially influenced by the scale at which SDMs were calibrated. Fine-scale regular movement behaviors are another important determinant of mobile species distributions that are not currently incorporated within SDM. Spatial simulation was used to model the dynamic relationship between movement and biotic resources for oilbirds in Venezuela, in order to generate a new environmental variable for use in model calibration. The use of this layer greatly improved the accuracy and ecological realism of the SDM projection compared to other commonly applied SDM scenarios. Finally, the incorporation of movement across multiple scales has not been addressed in SDM research. Broad-scale dispersal was combined with fine-scale regular movements to predict continental changes in oilbird distribution over a decade, which improved the ecological understanding of distribution shifts and identified a number of new conceptual and methodological limitations. The incorporation of movement should now be a compulsory aspect of any study projecting the current or future distributions of species.
Download or read book Dispersal and the Distributions of Mammals written by Sarah Louise Whitmee and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Climate change is predicted to become a major cause of species loss in the coming century. Shifts in distribution as a response to changing conditions have already been observed for many terrestrial organisms. A species' capacity to respond to climate change will depend greatly on its ability to track suitable conditions; those unable to track optimum conditions will be under increased threat of local extinction. There is, therefore, a need to include dispersal parameters in models that forecast the impact of climate change on species distributions, but this is limited by a paucity of dispersal data for many species. In this thesis I develop predictive models of dispersal ability to improve estimates of both distance and rate of dispersal in mammals. Chapter 2 presents a database of empirically derived dispersal distances for mammals and an analysis of the probability distribution of those distances, aimed at describing the 'tail' of the kernel, important in understanding long distance dispersal. Chapter 3 assesses the explanatory power of species' life history and ecology, within a phylogenetic framework, to predict dispersal distances. Chapter 4 examines the roles of dispersal and colonisation ability in mediating the extent to which a species can fill its potential environmental niche and quantifies the effects of model accuracy and projection extent on this approach. Chapter 5 utilises a new technique for identifying patterns of geographic and phylogenetic constraint to examine the dual roles of evolutionary history and environment in determining a species' ability to fill its potential environmental niche. This thesis helps to clarify controls on range limits and to incorporate such controls into species distribution models. By providing more accurate predictions of the impacts of climate change on species range size and location, this work helps us to better understand the threat to species diversity from global change.
