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 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 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 Development and Application of Hierarchical Models for Monitoring Avian Soundscapes Populations and Communities written by Jeffrey W. Doser and published by . This book was released on 2022 with total page 89 pages. Available in PDF, EPUB and Kindle. Book excerpt: Climate change, land use change, and other anthropogenic pressures are increasing species extinctions, phenology shifts, and drastic population declines. Avian populations and communities are particularly vulnerable to global change given their mobile and migratory life history strategies. Avian abundance has drastically declined throughout North America over several decades, which is compounded by phenological shifts in breeding periods and migratory patterns. Informed management and conservation of avian populations and communities requires large-scale monitoring programs, as well as associated inferential tools to provide statistically robust inference using multiple data sources. In this dissertation, I develop a suite of hierarchical modeling approaches to understand avian soundscapes, populations, and communities. I leverage a hierarchical Bayesian modeling framework, which is ideally suited for complex wildlife data with numerous types of observation error and dependencies among data points. In Chapter 1, I provide a brief overview of avian monitoring approaches and their associated statistical analysis frameworks. In Chapters 2 and 3, I develop hierarchical models for the analysis of complex avian soundscape data, and apply these approaches to two case studies. In Chapter 2, I apply a two-stage hierarchical beta regression model to quantify the relationship between anthropogenic and biological sounds in avian soundscapes in western New York. In Chapter 3, I use a multivariate linear mixed model to assess disturbance impacts of a shelterwood logging on avian soundscapes in northern Michigan. In Chapter 4, I develop a multi-region, multi-species abundance model to quantify trends of avian species and communities using point count data across a network of National Parks in the northeastern US. In Chapters 5 and 6, I use a model-based data integration approach to yield improved inference on avian population and communities. In Chapter 5, I integrate automated acoustic recording data with point count data to estimate avian abundance, which I apply to a case study on the Eastern Wood Pewee (Contopus virens) in a National Historical Park in Vermont. In Chapter 6, I develop an integrated community occupancy model that combines multiple types of detection-nondetection data for inference on species-specific and community level occurrence dynamics, which I use to assess occurrence dynamics of a foliage-gleaning bird community in New Hampshire. These results exhibit the value of hierarchical models to partition ecological data into distinct observation and ecological components for improved inference on avian population and community dynamics. Future work should continue to leverage complex data sources within hierarchical modeling frameworks to address pressing conservation and management questions on avian populations, communities, and the ecosystem services they provide.
Download or read book Invasion Dynamics written by Cang Hui and published by Oxford University Press. This book was released on 2017-01-26 with total page 607 pages. Available in PDF, EPUB and Kindle. Book excerpt: Humans have moved organisms around the world for centuries but it is only relatively recently that invasion ecology has grown into a mainstream research field. This book examines both the spread and impact dynamics of invasive species, placing the science of invasion biology on a new, more rigorous, theoretical footing, and proposing a concept of adaptive networks as the foundation for future research. Biological invasions are considered not as simple actions of invaders and reactions of invaded ecosystems, but as co-evolving complex adaptive systems with emergent features of network complexity and invasibility. Invasion Dynamics focuses on the ecology of invasive species and their impacts in recipient social-ecological systems. It discusses not only key advances and challenges within the traditional domain of invasion ecology, but introduces approaches, concepts, and insights from many other disciplines such as complexity science, systems science, and ecology more broadly. It will be of great value to invasion biologists analyzing spread and/or impact dynamics as well as other ecologists interested in spread processes or habitat management.
Download or read book Using Occupancy Models to Predict Grassland Bird Distributions in Southeastern Alberta written by Nathan David Clements and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Occupancy Estimation and Modeling written by Darryl I. MacKenzie and published by Elsevier. This book was released on 2017-11-17 with total page 668 pages. Available in PDF, EPUB and Kindle. Book excerpt: Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence, Second Edition, provides a synthesis of model-based approaches for analyzing presence-absence data, allowing for imperfect detection. Beginning from the relatively simple case of estimating the proportion of area or sampling units occupied at the time of surveying, the authors describe a wide variety of extensions that have been developed since the early 2000s. This provides an improved insight about species and community ecology, including, detection heterogeneity; correlated detections; spatial autocorrelation; multiple states or classes of occupancy; changes in occupancy over time; species co-occurrence; community-level modeling, and more. Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence, Second Edition has been greatly expanded and detail is provided regarding the estimation methods and examples of their application are given. Important study design recommendations are also covered to give a well rounded view of modeling. - Provides authoritative insights into the latest in occupancy modeling - Examines the latest methods in analyzing detection/no detection data surveys - Addresses critical issues of imperfect detectability and its effects on species occurrence estimation - Discusses important study design considerations such as defining sample units, sample size determination and optimal effort allocation
Download or read book Integrating Dynamic and Statistical Modelling Approaches in Order to Improve Predictions for Scenarios of Environmental Change written by Damaris Zurell and published by . This book was released on 2011 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: Species respond to environmental change by dynamically adjusting their geographical ranges. Robust predictions of these changes are prerequisites to inform dynamic and sustainable conservation strategies. Correlative species distribution models (SDMs) relate species’ occurrence records to prevailing environmental factors to describe the environmental niche. They have been widely applied in global change context as they have comparably low data requirements and allow for rapid assessments of potential future species’ distributions. However, due to their static nature, transient responses to environmental change are essentially ignored in SDMs. Furthermore, neither dispersal nor demographic processes and biotic interactions are explicitly incorporated. Therefore, it has often been suggested to link statistical and mechanistic modelling approaches in order to make more realistic predictions of species’ distributions for scenarios of environmental change. In this thesis, I present two different ways of such linkage. (i) Mechanistic modelling can act as virtual playground for testing statistical models and allows extensive exploration of specific questions. I promote this ‘virtual ecologist’ approach as a powerful evaluation framework for testing sampling protocols, analyses and modelling tools. Also, I employ such an approach to systematically assess the effects of transient dynamics and ecological properties and processes on the prediction accuracy of SDMs for climate change projections. That way, relevant mechanisms are identified that shape the species’ response to altered environmental conditions and which should hence be considered when trying to project species’ distribution through time. (ii) I supplement SDM projections of potential future habitat for black grouse in Switzerland with an individual-based population model. By explicitly considering complex interactions between habitat availability and demographic processes, this allows for a more direct assessment of expected population response to environmental change and associated extinction risks. However, predictions were highly variable across simulations emphasising the need for principal evaluation tools like sensitivity analysis to assess uncertainty and robustness in dynamic range predictions. Furthermore, I identify data coverage of the environmental niche as a likely cause for contrasted range predictions between SDM algorithms. SDMs may fail to make reliable predictions for truncated and edge niches, meaning that portions of the niche are not represented in the data or niche edges coincide with data limits. Overall, my thesis contributes to an improved understanding of uncertainty factors in predictions of range dynamics and presents ways how to deal with these. Finally I provide preliminary guidelines for predictive modelling of dynamic species’ response to environmental change, identify key challenges for future research and discuss emerging developments.
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 Mapping Species Distributions written by Janet Franklin and published by . This book was released on 2014-05-14 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensive summary of species distribution modeling methods integrating ecological and statistical models with spatial data, and a framework for implementation.
Download or read book Invasive Species written by Andrew P. Robinson and published by Cambridge University Press. This book was released on 2017-06-08 with total page 427 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reviews the latest risk-based techniques to protect national interests from invasive pests and pathogens before, at and within national borders.
Download or read book New Approaches to Ecological Surveys written by Patricia Catherine Cramer and published by Transportation Research Board. This book was released on 2009 with total page 84 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 Hierarchical Modeling and Inference in Ecology written by J. Andrew Royle and published by Elsevier. This book was released on 2008-10-15 with total page 463 pages. Available in PDF, EPUB and Kindle. Book excerpt: A guide to data collection, modeling and inference strategies for biological survey data using Bayesian and classical statistical methods.This book describes a general and flexible framework for modeling and inference in ecological systems based on hierarchical models, with a strict focus on the use of probability models and parametric inference. Hierarchical models represent a paradigm shift in the application of statistics to ecological inference problems because they combine explicit models of ecological system structure or dynamics with models of how ecological systems are observed. The principles of hierarchical modeling are developed and applied to problems in population, metapopulation, community, and metacommunity systems. The book provides the first synthetic treatment of many recent methodological advances in ecological modeling and unifies disparate methods and procedures.The authors apply principles of hierarchical modeling to ecological problems, including * occurrence or occupancy models for estimating species distribution* abundance models based on many sampling protocols, including distance sampling* capture-recapture models with individual effects* spatial capture-recapture models based on camera trapping and related methods* population and metapopulation dynamic models* models of biodiversity, community structure and dynamics - Wide variety of examples involving many taxa (birds, amphibians, mammals, insects, plants) - Development of classical, likelihood-based procedures for inference, as well as Bayesian methods of analysis - Detailed explanations describing the implementation of hierarchical models using freely available software such as R and WinBUGS - Computing support in technical appendices in an online companion web site
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 The Species Area Relationship written by Thomas J. Matthews and published by Cambridge University Press. This book was released on 2021-03-18 with total page 503 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides a comprehensive synthesis of a fundamental phenomenon, the species-area relationship, addressing theory, evidence and application.
Download or read book Southern Forest Resource Assessment written by David N. Wear and published by . This book was released on 2002 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forces of change; Social and economics systems; Forest area conditions; Terrestrial ecosystems; Water quality, wetlands, and aquatic ecosystems.
