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 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 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 Comparison of Species Distribution Models for a Migratory Bird Based on Citizen Science and Satellite Tracking Data with Implication for Climate Change written by Christopher Coxen and published by . This book was released on 2017 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt: Species distribution models can provide critical baseline distribution information for the conservation of poorly understood species. Species occurrence data sources traditionally used in these models, such as museum specimen records, field survey observations, or animal tracking studies, may be outdated, have poor coverage, or be prohibitively expensive to collect. Crowd sourced citizen science observation data, such as the Cornell Lab of Ornithology's eBird project, can provide present-day, large coverage species occurrence data at no cost to the user. Here, I compared the performance of band-tailed pigeon (Patagioenas fasciata) species distribution models created using Maxent and derived from two separate presence-only occurrence data sources in New Mexico: 1) satellite tracked birds and 2) observations reported in eBird basic data set. Both models had good accuracy (test AUC> 0.8 and True Skill Statistic > 0.4), and high overlap between suitability scores (I statistic 0.786) and suitable habitat patches (relative rank 0.639). My results suggest that, at the state-wide level, eBird occurrence data can effectively model similar species distributions as satellite tracking data. Models based on eBird occurrence records can provide a low cost first step towards identifying sites for targeted field surveys, and assist with conservation planning for avian species with outdated or unknown distribution information. Our future climate models for the band-tailed pigeon predict a 35% loss in suitable habitat by 2070 if atmospheric CO2 emissions drop to 1990 levels by 2100, and a 45% loss by 2070 if we continue current CO2 emission levels through the end of the century. These numbers may be conservative given the predicted increase in drought, wildlife, and forest pest impacts to the coniferous forests the species inhabits in New Mexico. The northern portion of the species range in New Mexico is predicted to be the most viable through time. Our eBird comparison results suggest preliminary climate change models may be created based on eBird occurrence data to determine high priority areas for band-tailed pigeon conservation in other states.

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 Integrating Ensemble Species Distribution Modeling and Statistical Phylogeography to Inform Projections of Climate Change Impacts on Species Distributions written by Brenna R. Forester and published by . This book was released on 2012 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Data Analysis in Community and Landscape Ecology written by R. H. Jongman and published by Bernan Press(PA). This book was released on 1987 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Development of Integrated Species Distribution Models to Improve Occupancy Predictions of Avian Species of Concern written by Fiona Lunt and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Species distribution models have the potential to improve our understanding of the relationship among species, communities, and their environment. This modeling framework is further improved through the process of data integration, which incorporates multiple data sources into a single model and has the potential to improve predictive accuracy if certain data biases are accounted for. The first chapter of this project aims to improve the existing integrated species distribution model framework by addressing how to best treat non-standardized and older sources of data. Using the community science platform eBird, we build a set of hierarchical occupancy models that systematically test how to filter the data, how to incorporate effort to account for observation errors, and how to integrate the additional data sources, the 2nd Pennsylvania Breeding Bird Atlas (BBA) and the North American Breeding Bird Survey (BBS), into a single integrated model, as well as whether older data from these sources is beneficial for predicting contemporary distributions. These models are tested with four avian species on the Pennsylvania State Wildlife Action Plan's list of species with greatest conservation need-- Canada Warbler (Cardellina canadensis), Cerulean Warbler (Setophaga cerulea), Golden-winged Warbler (Vermivora chrysoptera), and Wood Thrush (Hylocichla mustelina). We find that filtering eBird data by limiting spatial variability and spatial balancing improves the dataset's predictive accuracy for usage in a formal joint-likelihood model. Additionally, using an alternative integration method by treating the data as a covariate offers a separate improvement on the joint-likelihood model. Treating older data as a model covariate, as opposed to weighing it the same as modern data, also improves model fit. These results suggest that non-standardized data like eBird and older data like BBA are useful for species distribution models if proper steps are taken to filter and integrate. In the second chapter, we apply some of these techniques to a distributional assessment of Golden-winged Warbler (Vermivora chrysoptera), a species facing steep population declines, and its competitor the Blue-winged Warbler (Vermivora cyanoptera). We asked whether incorporating a co-occurrence model improves occupancy predictions for the former species and if there are any environmental relationships that predict distributional overlap in their breeding range in Pennsylvania. We find that model predictions improve when using Blue-winged Warbler occupancy as a predictor of Golden-winged Warbler presence, and that the likelihood a Golden-winged Warbler is present greatly increases if Blue-winged Warbler is present. We did not find any significant environmental predictors of co-occurrence. Utilizing a fully integrated model for this chapter was useful in capturing more information about these uncommon species, which may have not been as easily achieved with a single dataset. Overall, our analyses highlight the benefits of properly structuring and integrating multiple data sources into species distribution models and offer methods of expansion for more specific ecological questions, such as through incorporating a co-occurrence structure.

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 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 Structured Decision Making written by Robin Gregory and published by John Wiley & Sons. This book was released on 2012-03-19 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book outlines the creative process of making environmental management decisions using the approach called Structured Decision Making. It is a short introductory guide to this popular form of decision making and is aimed at environmental managers and scientists. This is a distinctly pragmatic label given to ways for helping individuals and groups think through tough multidimensional choices characterized by uncertain science, diverse stakeholders, and difficult tradeoffs. This is the everyday reality of environmental management, yet many important decisions currently are made on an ad hoc basis that lacks a solid value-based foundation, ignores key information, and results in selection of an inferior alternative. Making progress – in a way that is rigorous, inclusive, defensible and transparent – requires combining analytical methods drawn from the decision sciences and applied ecology with deliberative insights from cognitive psychology, facilitation and negotiation. The authors review key methods and discuss case-study examples based in their experiences in communities, boardrooms, and stakeholder meetings. The goal of this book is to lay out a compelling guide that will change how you think about making environmental decisions. Visit www.wiley.com/go/gregory/ to access the figures and tables from the book.

Download or read book Spatial Point Patterns written by Adrian Baddeley and published by CRC Press. This book was released on 2015-11-11 with total page 830 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern Statistical Methodology and Software for Analyzing Spatial Point PatternsSpatial Point Patterns: Methodology and Applications with R shows scientific researchers and applied statisticians from a wide range of fields how to analyze their spatial point pattern data. Making the techniques accessible to non-mathematicians, the authors draw on th

Download or read book Ecological Niches and Geographic Distributions MPB 49 written by A. Townsend Peterson and published by Princeton University Press. This book was released on 2011-11-20 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: Terminology, conceptual overview, biogeography, modeling.

Download or read book Ecosystems and Human Well being written by Joseph Alcamo and published by . This book was released on 2003 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ecosystems and Human Well-Being is the first product of the Millennium Ecosystem Assessment, a four-year international work program designed to meet the needs of decisionmakers for scientific information on the links between ecosystem change and human well-being. The book offers an overview of the project, describing the conceptual framework that is being used, defining its scope, and providing a baseline of understanding that all participants need to move forward. The Millennium Assessment focuses on how humans have altered ecosystems, and how changes in ecosystem services have affected human well-being, how ecosystem changes may affect people in future decades, and what types of responses can be adopted at local, national, or global scales to improve ecosystem management and thereby contribute to human well-being and poverty alleviation. The program was launched by United National Secretary-General Kofi Annan in June 2001, and the primary assessment reports will be released by Island Press in 2005. Leading scientists from more than 100 nations are conducting the assessment, which can aid countries, regions, or companies by: providing a clear, scientific picture of the current sta

Download or read book Multivariate Analysis of Ecological Data written by Michael Greenacre and published by Fundacion BBVA. This book was released on 2014-01-09 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: La diversidad biológica es fruto de la interacción entre numerosas especies, ya sean marinas, vegetales o animales, a la par que de los muchos factores limitantes que caracterizan el medio que habitan. El análisis multivariante utiliza las relaciones entre diferentes variables para ordenar los objetos de estudio según sus propiedades colectivas y luego clasificarlos; es decir, agrupar especies o ecosistemas en distintas clases compuestas cada una por entidades con propiedades parecidas. El fin último es relacionar la variabilidad biológica observada con las correspondientes características medioambientales. Multivariate Analysis of Ecological Data explica de manera completa y estructurada cómo analizar e interpretar los datos ecológicos observados sobre múltiples variables, tanto biológicos como medioambientales. Tras una introducción general a los datos ecológicos multivariantes y la metodología estadística, se abordan en capítulos específicos, métodos como aglomeración (clustering), regresión, biplots, escalado multidimensional, análisis de correspondencias (simple y canónico) y análisis log-ratio, con atención también a sus problemas de modelado y aspectos inferenciales. El libro plantea una serie de aplicaciones a datos reales derivados de investigaciones ecológicas, además de dos casos detallados que llevan al lector a apreciar los retos de análisis, interpretación y comunicación inherentes a los estudios a gran escala y los diseños complejos.

Download or read book The Geographic Mosaic of Coevolution written by John N. Thompson and published by University of Chicago Press. This book was released on 2005-06-15 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: Coevolution—reciprocal evolutionary change in interacting species driven by natural selection—is one of the most important ecological and genetic processes organizing the earth's biodiversity: most plants and animals require coevolved interactions with other species to survive and reproduce. The Geographic Mosaic of Coevolution analyzes how the biology of species provides the raw material for long-term coevolution, evaluates how local coadaptation forms the basic module of coevolutionary change, and explores how the coevolutionary process reshapes locally coevolving interactions across the earth's constantly changing landscapes. Picking up where his influential The Coevolutionary Process left off, John N. Thompsonsynthesizes the state of a rapidly developing science that integrates approaches from evolutionary ecology, population genetics, phylogeography, systematics, evolutionary biochemistry and physiology, and molecular biology. Using models, data, and hypotheses to develop a complete conceptual framework, Thompson also draws on examples from a wide range of taxa and environments, illustrating the expanding breadth and depth of research in coevolutionary biology.