| data science in wildlife conservation: Artificial Intelligence and Conservation Fei Fang, Milind Tambe, Bistra Dilkina, Andrew J. Plumptre, 2019-03-28 With the increasing public interest in artificial intelligence (AI), there is also increasing interest in learning about the benefits that AI can deliver to society. This book focuses on research advances in AI that benefit the conservation of wildlife, forests, coral reefs, rivers, and other natural resources. It presents how the joint efforts of researchers in computer science, ecology, economics, and psychology help address the goals of the United Nations' 2030 Agenda for Sustainable Development. Written at a level accessible to conservation professionals and AI researchers, the book offers both an overview of the field and an in-depth view of how AI is being used to understand patterns in wildlife poaching and enhance patrol efforts in response, covering research advances, field tests and real-world deployments. The book also features efforts in other major conservation directions, including protecting natural resources, ecosystem monitoring, and bio-invasion management through the use of game theory, machine learning, and optimization. |
| data science in wildlife conservation: Spatial Complexity, Informatics, and Wildlife Conservation Samuel A. Cushman, Falk Huettmann, 2009-12-21 As Earth faces the greatest mass extinction in 65 million years, the present is a moment of tremendous foment and emergence in ecological science. With leaps in advances in ecological research and the technical tools available, scientists face the critical task of challenging policymakers and the public to recognize the urgency of our global crisis. This book focuses directly on the interplay between theory, data, and analytical methodology in the rapidly evolving fields of animal ecology, conservation, and management. The mixture of topics of particular current relevance includes landscape ecology, remote sensing, spatial modeling, geostatistics, genomics, and ecological informatics. The greatest interest to the practicing scientist and graduate student will be the synthesis and integration of these topics to provide a composite view of the emerging field of spatial ecological informatics and its applications in research and management. |
| data science in wildlife conservation: Soft Computing in Data Science Bee Wah Yap, Azlinah Hj Mohamed, Michael W. Berry, 2018-12-10 This book constitutes the refereed proceedings of the 4th International Conference on Soft Computing in Data Science, SCDS 2018, held in Bangkok, Thailand, in August 2018. The 30 revised full papers presented were carefully reviewed and selected from 75 submissions. The papers are organized in topical sections on machine and deep learning, image processing, financial and fuzzy mathematics, optimization algorithms, data and text analytics, data visualization. |
| data science in wildlife conservation: Quantitative Analyses in Wildlife Science Leonard A. Brennan, Andrew N. Tri, Bruce G. Marcot, 2019-09-10 An authoritative guide to quantitative methods that will help wildlife scientists improve analysis and decision-making. Over the past fifty years, wildlife science has become increasingly quantitative. But to wildlife scientists, many of whom have not been formally trained as biometricians, computer modelers, or mathematicians, the wide array of available techniques for analyzing wildlife populations and habitats can be overwhelming. This practical book aims to help students and professionals alike understand how to use quantitative methods to inform their work in the field. Covering the most widely used contemporary approaches to the analysis of wildlife populations and habitats, Quantitative Analyses in Wildlife Science is divided into five broad areas: • general statistical methods • demographic estimation • dynamic process modeling • analysis of spatially based data on animals and resources • numerical methods Addressing a variety of topics, from population estimation and growth trend predictions to the study of migration patterns, this book presents fresh data on such pressing issues as sustainable take, control of invasives, and species reintroduction. Authored by leading researchers in wildlife science, each chapter considers the structure of data in relation to a particular analytical technique, as well as the structure of variation in those data. Providing conceptual and quantitative overviews of modern analytical methods, the techniques covered in this book also apply to conservation research and wildlife policy. Whether a quick refresher or a comprehensive introduction is called for, Quantitative Analyses in Wildlife Science is an indispensable addition to every wildlife professional's bookshelf. Contributors: William M. Block, Leonard A. Brennan, Stephen T. Buckland, Christopher C. Chizinski, Evan C. Cooch, Raymond J. Davis, Stephen J. DeMaso, Randy W. DeYoung, Jane Elith, Joseph J. Fontane, Julie A. Heinrichs, Mevin B. Hooten, Julianna M. A. Jenkins, Zachary S. Laden, Damon B. Lesmeister, Daniel Linden, Jeffrey J. Lusk, Bruce G. Marcot, David L. Miller, Michael L. Morrison, Eric Rexstad, Jamie S. Sanderlin, Joseph P. Sands, Erica F. Stuber, Chris Sutherland, Andrew N. Tri, David B. Wester, Gary C. White, Christopher K. Williams, Damon L. Williford |
| data science in wildlife conservation: Getting a Job in Wildlife Biology Stephanie Grace Schuttler, 2020-09-21 Are you thinking about a career in wildlife biology? Confused about the steps you need to take? This is the book for you! With nearly two decades of experience, Dr. Stephanie Schuttler shares her journey of becoming a wildlife biologist, what she has learned about this field, and provides advice for how you can become competitive for jobs. Wildlife biology careers have changed tremendously over the few decades. A lot of advice students receive or find on the Internet is outdated and no longer applies. With more and more students graduating, the field is more competitive than ever before. Dr. Schuttler has years of experience working in and applying for jobs in research, education, and science communication. In this book, she shares her personal journey of how she became a wildlife biologist, detailed accounts of working in museums, zoos, in academia, and for the government, what has made her competitive for jobs, and why she didn't get specific jobs despite having all of the qualifications and years of pertinent experience. In addition to her own story, she goes over the types of career opportunities available to wildlife biologists, where wildlife biologists work, what educational requirements are needed, and what else you need to become competitive for jobs in this field. Although tailored for jobs wildlife biology, this book will also help those interested in other natural history fields. Reverse engineer your career by learning from Dr. Schuttler's journey to find your dream job. |
| data science in wildlife conservation: Data Science Yang Wang, Guobin Zhu, Qilong Han, Hongzhi Wang, Xianhua Song, Zeguang Lu, 2022-08-10 This two volume set (CCIS 1628 and 1629) constitutes the refereed proceedings of the 8th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2022 held in Chengdu, China, in August, 2022. The 65 full papers and 26 short papers presented in these two volumes were carefully reviewed and selected from 261 submissions. The papers are organized in topical sections on: Big Data Mining and Knowledge Management; Machine Learning for Data Science; Multimedia Data Management and Analysis. |
| data science in wildlife conservation: Spatial Database for GPS Wildlife Tracking Data Ferdinando Urbano, Francesca Cagnacci, 2014-03-12 This book guides animal ecologists, biologists and wildlife and data managers through a step-by-step procedure to build their own advanced software platforms to manage and process wildlife tracking data. This unique, problem-solving-oriented guide focuses on how to extract the most from GPS animal tracking data, while preventing error propagation and optimizing analysis performance. Based on the open source PostgreSQL/PostGIS spatial database, the software platform will allow researchers and managers to integrate and harmonize GPS tracking data together with animal characteristics, environmental data sets, including remote sensing image time series, and other bio-logged data, such as acceleration data. Moreover, the book shows how the powerful R statistical environment can be integrated into the software platform, either connecting the database with R, or embedding the same tools in the database through the PostgreSQL extension Pl/R. The client/server architecture allows users to remotely connect a number of software applications that can be used as a database front end, including GIS software and WebGIS. Each chapter offers a real-world data management and processing problem that is discussed in its biological context; solutions are proposed and exemplified through ad hoc SQL code, progressively exploring the potential of spatial database functions applied to the respective wildlife tracking case. Finally, wildlife tracking management issues are discussed in the increasingly widespread framework of collaborative science and data sharing. GPS animal telemetry data from a real study, freely available online, are used to demonstrate the proposed examples. This book is also suitable for undergraduate and graduate students, if accompanied by the basics of databases. |
| data science in wildlife conservation: Analysis of Wildlife Radio-Tracking Data Gary C. White, Robert A. Garrott, 2012-12-02 With the substantial advances in the miniaturization of electronic components, wildlife biologists now routinely monitor the movements of free-ranging animals with radio-tracking devices. This book explicates the many analytical techniques and computer programs available to extract biological information from the radio tracking data. - Presentation of software programs for solving specific problems - Design of radio-tracking studies - Mechanics of data collection - Estimation of position by triangulation - Graphic presentation of animal migration, dispersal, fidelity, and association - Home range estimation, habitat utilization, and estimation of survival rates and population size |
| data science in wildlife conservation: Wildlife Conservation in a Changing Climate Jedediah F. Brodie, Eric S. Post, Daniel F. Doak, 2013 Bringing together leaders in the fields of climate change ecology, wildlife population dynamics, and environmental policy, this title examines the impacts of climate change on populations of terrestrial vertebrates. It also includes chapters that assess the details of climate change ecology. |
| data science in wildlife conservation: Sea Otter Conservation Shawn Larson, James L. Bodkin, Glenn R VanBlaricom, 2014-12-23 Sea otters are good indicators of ocean health. In addition, they are a keystone species, offering a stabilizing effect on ecosystem, controlling sea urchin populations that would otherwise inflict damage to kelp forest ecosystems. The kelp forest ecosystem is crucial for marine organisms and contains coastal erosion. With the concerns about the imperiled status of sea otter populations in California, Aleutian Archipelago and coastal areas of Russia and Japan, the last several years have shown growth of interest culturally and politically in the status and preservation of sea otter populations. Sea Otter Conservation brings together the vast knowledge of well-respected leaders in the field, offering insight into the more than 100 years of conservation and research that have resulted in recovery from near extinction. This publication assesses the issues influencing prospects for continued conservation and recovery of the sea otter populations and provides insight into how to handle future global changes. - Covers scientific, cultural, economic and political components of sea otter conservation - Provides guidance on how to manage threats to the sea otter populations in the face of future global changes - Highlights the effects that interactions of coastal animals have with the marine ecosystem |
| data science in wildlife conservation: Problem-Solving in Conservation Biology and Wildlife Management James P. Gibbs, Malcolm L. Hunter, Jr., Eleanor J. Sterling, 2011-08-31 This set of exercises has been created expressly for students and teachers of conservation biology and wildlife management who want to have an impact beyond the classroom. The book presents a set of 32 exercises that are primarily new and greatly revised versions from the book's successful first edition. These exercises span a wide range of conservation issues: genetic analysis, population biology and management, taxonomy, ecosystem management, land use planning, the public policy process and more. All exercises discuss how to take what has been learned and apply it to practical, real-world issues. Accompanied by a detailed instructor’s manual and a student website with software and support materials, the book is ideal for use in the field, lab, or classroom. Also available: Fundamentals of Conservation Biology, 3rd edition (2007) by Malcolm L Hunter Jr and James Gibbs, ISBN 9781405135450 Saving the Earth as a Career: Advice on Becoming a Conservation Professional (2007) by Malcolm L Hunter Jr, David B Lindenmayer and Aram JK Calhoun, ISBN 9781405167611 |
| data science in wildlife conservation: Wildlife in Airport Environments Travis L. DeVault, Bradley F. Blackwell, Jerrold L. Belant, 2013-11-15 S. Department of Agriculture--Cecilia Soldatini Journal of Field Ornithology |
| data science in wildlife conservation: Mastering Data Science Cybellium Ltd, Unleash the Power of Insights from Data Are you ready to embark on a transformative journey into the world of data science? Mastering Data Science is your comprehensive guide to unlocking the full potential of data for extracting valuable insights and driving informed decisions. Whether you're an aspiring data scientist looking to enhance your skills or a business leader seeking to leverage data-driven strategies, this book equips you with the knowledge and tools to master the art of data science. Key Features: 1. Dive into Data Science: Immerse yourself in the realm of data science, understanding its core principles, methodologies, and applications. Build a solid foundation that empowers you to extract meaningful insights from complex datasets. 2. Data Exploration and Visualization: Master the art of data exploration and visualization. Learn how to analyze datasets, uncover patterns, and create compelling visualizations that reveal hidden trends. 3. Statistical Analysis and Hypothesis Testing: Uncover the power of statistical analysis and hypothesis testing. Explore techniques for making data-driven inferences, validating assumptions, and drawing meaningful conclusions. 4. Machine Learning Fundamentals: Delve into machine learning concepts and techniques. Learn about supervised and unsupervised learning, feature engineering, model selection, and evaluation. 5. Predictive Analytics: Discover the realm of predictive analytics. Learn how to build predictive models that forecast future outcomes, enabling proactive decision-making. 6. Natural Language Processing (NLP) and Text Mining: Explore NLP and text mining techniques. Learn how to process and analyze textual data, extract sentiments, and uncover insights from unstructured content. 7. Time Series Analysis: Master time series analysis for modeling sequential data. Learn how to forecast trends, identify seasonality, and make predictions based on temporal patterns. 8. Big Data and Data Wrangling: Dive into big data analytics and data wrangling. Learn how to handle and preprocess large datasets, ensuring data quality and usability. 9. Deep Learning and Neural Networks: Uncover the world of deep learning and neural networks. Learn how to build and train deep learning models for tasks like image recognition and natural language understanding. 10. Real-World Applications: Gain insights into real-world applications of data science across industries. From healthcare to finance, explore how organizations harness data science for strategic decision-making. Who This Book Is For: Mastering Data Science is an indispensable resource for aspiring data scientists, analysts, and business professionals who want to excel in extracting insights from data. Whether you're new to data science or seeking advanced techniques, this book will guide you through the intricacies and empower you to harness the full potential of data for innovation. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com |
| data science in wildlife conservation: Applied Geospatial Data Science with Python David S. Jordan, 2023-02-28 Intelligently connect data points and gain a deeper understanding of environmental problems through hands-on Geospatial Data Science case studies written in Python The book includes colored images of important concepts Key Features Learn how to integrate spatial data and spatial thinking into traditional data science workflows Develop a spatial perspective and learn to avoid common pitfalls along the way Gain expertise through practical case studies applicable in a variety of industries with code samples that can be reproduced and expanded Book DescriptionData scientists, when presented with a myriad of data, can often lose sight of how to present geospatial analyses in a meaningful way so that it makes sense to everyone. Using Python to visualize data helps stakeholders in less technical roles to understand the problem and seek solutions. The goal of this book is to help data scientists and GIS professionals learn and implement geospatial data science workflows using Python. Throughout this book, you’ll uncover numerous geospatial Python libraries with which you can develop end-to-end spatial data science workflows. You’ll learn how to read, process, and manipulate spatial data effectively. With data in hand, you’ll move on to crafting spatial data visualizations to better understand and tell the story of your data through static and dynamic mapping applications. As you progress through the book, you’ll find yourself developing geospatial AI and ML models focused on clustering, regression, and optimization. The use cases can be leveraged as building blocks for more advanced work in a variety of industries. By the end of the book, you’ll be able to tackle random data, find meaningful correlations, and make geospatial data models.What you will learn Understand the fundamentals needed to work with geospatial data Transition from tabular to geo-enabled data in your workflows Develop an introductory portfolio of spatial data science work using Python Gain hands-on skills with case studies relevant to different industries Discover best practices focusing on geospatial data to bring a positive change in your environment Explore solving use cases, such as traveling salesperson and vehicle routing problems Who this book is for This book is for you if you are a data scientist seeking to incorporate geospatial thinking into your workflows or a GIS professional seeking to incorporate data science methods into yours. You’ll need to have a foundational knowledge of Python for data analysis and/or data science. |
| data science in wildlife conservation: Data Science and Analytics Usha Batra, Nihar Ranjan Roy, Brajendra Panda, 2020-05-27 This two-volume set (CCIS 1229 and CCIS 1230) constitutes the refereed proceedings of the 5th International Conference on Recent Developments in Science, Engineering and Technology, REDSET 2019, held in Gurugram, India, in November 2019. The 74 revised full papers presented were carefully reviewed and selected from total 353 submissions. The papers are organized in topical sections on data centric programming; next generation computing; social and web analytics; security in data science analytics; big data analytics. |
| data science in wildlife conservation: Who Cares About Wildlife? Michael J. Manfredo, 2009-06-29 Who Cares About Wildlife? integrates social science theory in order to provide a conceptual structure for understanding and studying human interaction with wildlife. A thorough review of the current literature in conceptual areas, including norms, values, attitudes, emotions, wildlife value orientations, cultural change, and evolutionary forces/inherited tendencies is provided, and the importance of these areas in studying human-wildlife relationships is highlighted. No other book both considers the human relationship with wildlife and provides a theoretical framework for understanding this relationship on the individual, as well as cultural level. Who Cares About Wildlife? will be valuable both to students and to practitioners in wildlife management and conservation, as well those interested in the human relationship with wildlife, natural resources, and the environment. |
| data science in wildlife conservation: Conservation of Wildlife Populations L. Scott Mills, 2012-12-17 Population ecology has matured to a sophisticated science with astonishing potential for contributing solutions to wildlife conservation and management challenges. And yet, much of the applied power of wildlife population ecology remains untapped because its broad sweep across disparate subfields has been isolated in specialized texts. In this book, L. Scott Mills covers the full spectrum of applied wildlife population ecology, including genomic tools for non-invasive genetic sampling, predation, population projections, climate change and invasive species, harvest modeling, viability analysis, focal species concepts, and analyses of connectivity in fragmented landscapes. With a readable style, analytical rigor, and hundreds of examples drawn from around the world, Conservation of Wildlife Populations (2nd ed) provides the conceptual basis for applying population ecology to wildlife conservation decision-making. Although targeting primarily undergraduates and beginning graduate students with some basic training in basic ecology and statistics (in majors that could include wildlife biology, conservation biology, ecology, environmental studies, and biology), the book will also be useful for practitioners in the field who want to find - in one place and with plenty of applied examples - the latest advances in the genetic and demographic aspects of population ecology. Additional resources for this book can be found at: www.wiley.com/go/mills/wildlifepopulations. |
| data science in wildlife conservation: Data Analytics and Artificial Intelligence for Earth Resource Management Deepak Kumar, Tavishi Tewary, Sulochana Shekhar, 2024-11-15 Data Analytics and Artificial Intelligence for Earth Resource Management offers a detailed look at the different ways data analytics and artificial intelligence can help organizations make better-informed decisions, improve operations, and minimize the negative impacts of resource extraction on the environment. The book explains several different ways data analytics and artificial intelligence can improve and support earth resource management. Predictive modeling can help organizations understand the impacts of different management decisions on earth resources, such as water availability, land use, and biodiversity. Resource monitoring tracks the state of earth resources in real-time, identifying issues and opportunities for improvement. Providing managers with real-time data and analytics allows them to make more informed choices. Optimizing resource management decisions help to identify the most efficient and effective ways to allocate resources. Predictive maintenance allows organizations to anticipate when equipment might fail and take action to prevent it, reducing downtime and maintenance costs. Remote sensing with image processing and analysis can be used to extract information from satellite images and other remote sensing data, providing valuable information on land use, water resources, and other earth resources. - Provides a comprehensive understanding of data analytics and artificial intelligence (AI) for earth resource management - Includes real-world case studies and examples to demonstrate the practical applications of data analytics and AI in earth resource management - Presents clear illustrations, diagrams, and pictures that make the content more understandable and engaging |
| data science in wildlife conservation: Conservation Technology Serge A. Wich, Alex K. Piel, 2021 The global loss of biodiversity is occurring at an unprecedented pace. Despite the considerable effort devoted to conservation science and management, we still lack the basic data on the distribution and density of most animal and plant species, which in turn hampers our efforts to study changes over time. In addition, we often lack behavioural data from the very animals most influenced by environmental changes; this is largely due to the financial and logistical limitations associated with gathering scientific data on animals that are either widely distributed, cryptic, or negatively influenced by human presence. To overcome these limitations, conservationists are increasingly integrating/employing/incorporating technology to facilitate such data collection. The use of camera traps, acoustic sensors, satellite data, drones, and sophisticated computer algorithms to analyse the large datasets collected are becoming increasingly common. Although there are several specialist books on some of these technologies, there is currently no overarching volume that describes the available technology for conservation and evaluates its varied applications. This edited volume will fill this void, bringing together a team of international experts using a diverse range of approaches-- |
| data science in wildlife conservation: Statistics for Terrified Biologists Helmut F. van Emden, 2019-07-09 Makes mathematical and statistical analysis understandable to even the least math-minded biology student This unique textbook aims to demystify statistical formulae for the average biology student. Written in a lively and engaging style, Statistics for Terrified Biologists, 2nd Edition draws on the author’s 30 years of lecturing experience to teach statistical methods to even the most guarded of biology students. It presents basic methods using straightforward, jargon-free language. Students are taught to use simple formulae and how to interpret what is being measured with each test and statistic, while at the same time learning to recognize overall patterns and guiding principles. Complemented by simple examples and useful case studies, this is an ideal statistics resource tool for undergraduate biology and environmental science students who lack confidence in their mathematical abilities. Statistics for Terrified Biologists presents readers with the basic foundations of parametric statistics, the t-test, analysis of variance, linear regression and chi-square, and guides them to important extensions of these techniques. It introduces them to non-parametric tests, and includes a checklist of non-parametric methods linked to their parametric counterparts. The book also provides many end-of-chapter summaries and additional exercises to help readers understand and practice what they’ve learned. Presented in a clear and easy-to-understand style Makes statistics tangible and enjoyable for even the most hesitant student Features multiple formulas to facilitate comprehension Written by of the foremost entomologists of his generation This second edition of Statistics for Terrified Biologists is an invaluable guide that will be of great benefit to pre-health and biology undergraduate students. |
| data science in wildlife conservation: Camera Trapping for Wildlife Research Francesco Rovero, Fridolin Zimmermann, 2016-06-18 Camera trapping is a powerful and now widely used tool in scientific research on wildlife ecology and management. It provides a unique opportunity for collecting knowledge, investigating the presence of animals, or recording and studying behaviour. Its visual nature makes it easy to successfully convey findings to a wide audience. This book provides a much-needed guide to the sound use of camera trapping for the most common ecological applications to wildlife research. Each phase involved in the use of camera trapping is covered: - Selecting the right camera type - Set-up and field deployment of your camera trap - Defining the sampling design: presence/absence, species inventory, abundance; occupancy at species level; capture-mark-recapture for density estimation; behavioural studies; community-level analysis - Data storage, management and analysis for your research topic, with illustrative examples for using R and Excel - Using camera trapping for monitoring, conservation and public engagement. Each chapter in this edited volume is essential reading for students, scientists, ecologists, educators and professionals involved in wildlife research or management. |
| data science in wildlife conservation: The North American Model of Wildlife Conservation Shane P. Mahoney, Valerius Geist, 2019-09-10 The foremost experts on the North American Model of Wildlife Conservation come together to discuss its role in the rescue, recovery, and future of our wildlife resources. At the end of the nineteenth century, North America suffered a catastrophic loss of wildlife driven by unbridled resource extraction, market hunting, and unrelenting subsistence killing. This crisis led powerful political forces in the United States and Canada to collaborate in the hopes of reversing the process, not merely halting the extinctions but returning wildlife to abundance. While there was great understanding of how to manage wildlife in Europe, where wildlife management was an old, mature profession, Continental methods depended on social values often unacceptable to North Americans. Even Canada, a loyal colony of England, abandoned wildlife management as practiced in the mother country and joined forces with like-minded Americans to develop a revolutionary system of wildlife conservation. In time, and surviving the close scrutiny and hard ongoing debate of open, democratic societies, this series of conservation practices became known as the North American Model of Wildlife Conservation. In this book, editors Shane P. Mahoney and Valerius Geist, both leading authorities on the North American Model, bring together their expert colleagues to provide a comprehensive overview of the origins, achievements, and shortcomings of this highly successful conservation approach. This volume • reviews the emergence of conservation in late nineteenth–early twentieth century North America • provides detailed explorations of the Model's institutions, principles, laws, and policies • places the Model within ecological, cultural, and socioeconomic contexts • describes the many economic, social, and cultural benefits of wildlife restoration and management • addresses the Model's challenges and limitations while pointing to emerging opportunities for increasing inclusivity and optimizing implementation Studying the North American experience offers insight into how institutionalizing policies and laws while incentivizing citizen engagement can result in a resilient framework for conservation. Written for wildlife professionals, researchers, and students, this book explores the factors that helped fashion an enduring conservation system, one that has not only rescued, recovered, and sustainably utilized wildlife for over a century, but that has also advanced a significant economic driver and a greater scientific understanding of wildlife ecology. Contributors: Leonard A. Brennan, Rosie Cooney, James L. Cummins, Kathryn Frens, Valerius Geist, James R. Heffelfinger, David G. Hewitt, Paul R. Krausman, Shane P. Mahoney, John F. Organ, James Peek, William Porter, John Sandlos, James A. Schaefer |
| data science in wildlife conservation: Case Studies in Applied Bayesian Data Science Kerrie L. Mengersen, Pierre Pudlo, Christian P. Robert, 2020-05-28 Presenting a range of substantive applied problems within Bayesian Statistics along with their Bayesian solutions, this book arises from a research program at CIRM in France in the second semester of 2018, which supported Kerrie Mengersen as a visiting Jean-Morlet Chair and Pierre Pudlo as the local Research Professor. The field of Bayesian statistics has exploded over the past thirty years and is now an established field of research in mathematical statistics and computer science, a key component of data science, and an underpinning methodology in many domains of science, business and social science. Moreover, while remaining naturally entwined, the three arms of Bayesian statistics, namely modelling, computation and inference, have grown into independent research fields. While the research arms of Bayesian statistics continue to grow in many directions, they are harnessed when attention turns to solving substantive applied problems. Each such problem set has its own challenges and hence draws from the suite of research a bespoke solution. The book will be useful for both theoretical and applied statisticians, as well as practitioners, to inspect these solutions in the context of the problems, in order to draw further understanding, awareness and inspiration. |
| data science in wildlife conservation: R for Conservation and Development Projects Nathan Whitmore, 2020-12-21 This book is aimed at conservation and development practitioners who need to learn and use R in a part-time professional context. It gives people with a non-technical background a set of skills to graph, map, and model in R. It also provides background on data integration in project management and covers fundamental statistical concepts. The book aims to demystify R and give practitioners the confidence to use it. Key Features: • Viewing data science as part of a greater knowledge and decision making system • Foundation sections on inference, evidence, and data integration • Plain English explanations of R functions • Relatable examples which are typical of activities undertaken by conservation and development organisations in the developing world • Worked examples showing how data analysis can be incorporated into project reports |
| data science in wildlife conservation: Citizen Science Alan Irwin, 2002-01-10 We are all concerned by the environmental threats facing us today. Environmental issues are a major area of concern for policy makers, industrialists and public groups of many different kinds. While science seems central to our understanding of such threats, the statements of scientists are increasingly open to challenge in this area. Meanwhile, citizens may find themselves labelled as `ignorant' in environmental matters. In Citizen Science Alan Irwin provides a much needed route through the fraught relationship between science, the public and the environmental threat. |
| data science in wildlife conservation: Shark Biology and Conservation Daniel C. Abel, R. Dean Grubbs, 2020-09-01 Feed your fascination with sharks! This complete resource enlightens readers on the biology, ecology, and behavior of sharks with approachable explanations and more than 250 stunning color illustrations. Studies of shark biology have flourished over the last several decades. An explosion of new research methods is leading to a fascinating era of oceanic discovery. Shark Biology and Conservation is an up-to-date, comprehensive overview of the diversity, evolution, ecology, behavior, physiology, anatomy, and conservation of sharks. Written in a style that is detailed but not intimidating by world-renowned shark specialists Dan Abel and Dean Grubbs, it relays numerous stories and insights from their exciting experiences in the field. While explaining scientific concepts in terms that non-specialists and students can understand, Abel and Grubbs reveal secrets that will illuminate even the experts. The text provides readers with a robust and wide range of essential knowledge as it • introduces emerging as well as traditional techniques for classifying sharks, understanding their behavior, and unraveling the mysteries of their evolution; • draws on both established shark science and the latest breakthroughs in the field, from molecular approaches to tracking technologies; • highlights the often-neglected yet fascinating subject of shark physiology, including heart function, sensory biology, digestion, metabolic performance, and reproduction; • addresses big picture ecological questions like Which habitats do sharks prefer? and Where do sharks migrate and for what purpose?; • describes the astonishing diversity of sharks' adaptations to their environment; • discusses which shark conservation techniques do and don't work; and • comments on the use and misuse of science in the study of sharks. Enhanced by hundreds of original color photographs and beautifully detailed line drawings, Shark Biology and Conservation will appeal to anyone who is spellbound by this wondrous, ecologically important, and threatened group, including marine biologists, wildlife educators, students, and shark enthusiasts. |
| data science in wildlife conservation: Data Science Applied to Sustainability Analysis Jennifer Dunn, Prasanna Balaprakash, 2021-05-11 Data Science Applied to Sustainability Analysis focuses on the methodological considerations associated with applying this tool in analysis techniques such as lifecycle assessment and materials flow analysis. As sustainability analysts need examples of applications of big data techniques that are defensible and practical in sustainability analyses and that yield actionable results that can inform policy development, corporate supply chain management strategy, or non-governmental organization positions, this book helps answer underlying questions. In addition, it addresses the need of data science experts looking for routes to apply their skills and knowledge to domain areas. - Presents data sources that are available for application in sustainability analyses, such as market information, environmental monitoring data, social media data and satellite imagery - Includes considerations sustainability analysts must evaluate when applying big data - Features case studies illustrating the application of data science in sustainability analyses |
| data science in wildlife conservation: A Textbook of Data Science for Class 9 Shweta Mittal , Prof. (Dr.) Shalini Verma , Goyal Brothers Prakashan, 2021-09-01 Data sCIenCe is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Data science or data-driven science enables better decision-making, predictive analysis, and pattern discovery. It lets you find the leading cause of a problem by asking the right questions and performing an exploratory study on the data. It models the data using various algorithms and communicates and visualizes the results via graphs, dashboards, etc. This book is based on the latest CBSE syllabus. The book is divided into two sections: Part A and Part B. Part A includes the “Employability Skills” and Part B covers the “Subject-specific Skills”. This book presents the concepts in a very simple language with easy-to-understand examples adapted from day-to-day utilization of Data science technology. The chapters are supplemented with figures and additional information in the form of “DID yoU knoW”. In between the chapters, the students are given a chance to revise and challenge their understanding with the help of “CheCk yoUr knoWleDGe” and fun activities. At the end of every chapter, Multiple Choice Questions, Short and Long answer questions are given. It includes HOTS (Higher Order Thinking Skills) questions and Applied Projects for advanced and practical kinds of questions. |
| data science in wildlife conservation: Urban Wildlife Conservation Robert A. McCleery, Christopher E. Moorman, M. Nils Peterson, 2014-11-11 In the past, wildlife living in urban areas were ignored by wildlife professionals and urban planners because cities were perceived as places for people and not for wild animals. Paradoxically, though, many species of wildlife thrive in these built environments. Interactions between humans and wildlife are more frequent in urban areas than any other place on earth and these interactions impact human health, safety and welfare in both positive and negative ways. Although urban wildlife control pest species, pollinate plants and are fun to watch, they also damage property, spread disease and even attack people and pets. In urban areas, the combination of dense human populations, buildings, impermeable surfaces, introduced vegetation, and high concentrations of food, water and pollution alter wildlife populations and communities in ways unseen in more natural environments. For these ecological and practical reasons, researchers and mangers have shown a growing interest in urban wildlife ecology and management. This growing interest in urban wildlife has inspired many studies on the subject that have yet to be synthesized in a cohesive narrative. Urban Wildlife: Theory and Practice fills this void by synthesizing the latest ecological and social knowledge in the subject area into an interdisciplinary and practical text. This volume provides a foundation for the future growth and understanding of urban wildlife ecology and management by: • Clearly defining th e concepts used to study and describe urban wildlife, • Offering a cohesive understanding of the coupled natural and social drivers that shape urban wildlife ecology, • Presenting the patterns and processes of wildlife response to an urbanizing world and explaining the mechanisms behind them and • Proposing means to create physical and social environments that are mutually beneficial for both humans and wildlife. |
| data science in wildlife conservation: Conducting Research in Conservation Helen Newing, 2010-10-18 Conducting Research in Conservation is the first textbook on social science research methods written specifically for use in the expanding and increasingly multidisciplinary field of environmental conservation. The first section on planning a research project includes chapters on the need for social science research in conservation, defining a research topic, methodology, and sampling. Section two focuses on practical issues in carrying out fieldwork with local communities, from fieldwork preparation and data collection to the relationships between the researcher and the study community. Section three provides an in-depth focus on a range of social science methods including standard qualitative and quantitative methods such as participant observation, interviewing and questionnaires, and more advanced methods, such as ethnobiological methods for documenting local environmental knowledge and change, and participatory methods such as the ‘PRA’ toolbox. Section four then demonstrates how to analyze social science data qualitatively and quantitatively; and the final section outlines the writing-up process and what should happen after the end of the formal research project. This book is a comprehensive and accessible guide to social science research methods for students of conservation related subjects and practitioners trained in the natural sciences. It features practical worldwide examples of conservation-related research in different ecosystems such as forests; grasslands; marine and riverine systems; and farmland. Boxes provide definitions of key terms, practical tips, and brief narratives from students and practitioners describe the practical issues that they have faced in the field. |
| data science in wildlife conservation: Information and Communication Technologies in Tourism 2022 Jason L. Stienmetz, Berta Ferrer-Rosell, David Massimo, 2022 This open access book presents the proceedings of the International Federation for IT and Travel & Tourism (IFITT)’s 29th Annual International eTourism Conference, which assembles the latest research presented at the ENTER2022 conference, which will be held on January 11–14, 2022. The book provides an extensive overview of how information and communication technologies can be used to develop tourism and hospitality. It covers the latest research on various topics within the field, including augmented and virtual reality, website development, social media use, e-learning, big data, analytics, and recommendation systems. The readers will gain insights and ideas on how information and communication technologies can be used in tourism and hospitality. Academics working in the eTourism field, as well as students and practitioners, will find up-to-date information on the status of research. |
| data science in wildlife conservation: Communicating with Data Deborah Nolan, Sara Stoudt, 2021-03-25 Communication is a critical yet often overlooked part of data science. Communicating with Data aims to help students and researchers write about their insights in a way that is both compelling and faithful to the data. General advice on science writing is also provided, including how to distill findings into a story and organize and revise the story, and how to write clearly, concisely, and precisely. This is an excellent resource for students who want to learn how to write about scientific findings, and for instructors who are teaching a science course in communication or a course with a writing component. Communicating with Data consists of five parts. Part I helps the novice learn to write by reading the work of others. Part II delves into the specifics of how to describe data at a level appropriate for publication, create informative and effective visualizations, and communicate an analysis pipeline through well-written, reproducible code. Part III demonstrates how to reduce a data analysis to a compelling story and organize and write the first draft of a technical paper. Part IV addresses revision; this includes advice on writing about statistical findings in a clear and accurate way, general writing advice, and strategies for proof reading and revising. Part V offers advice about communication strategies beyond the page, which include giving talks, building a professional network, and participating in online communities. This book also provides 22 portfolio prompts that extend the guidance and examples in the earlier parts of the book and help writers build their portfolio of data communication. |
| data science in wildlife conservation: Resolving Human-Wildlife Conflicts Michael R. Conover, 2001-08-29 As more and more people crowd onto less and less land, incidences of human-wildlife conflicts will only increase. A comprehensive overview of this emerging field, Resolving Human-Wildlife Conflicts: The Science of Wildlife Damage Management discusses the issues facing wildlife managers and anyone else dealing with interactions between wildlife and |
| data science in wildlife conservation: Multivariate Statistics for Wildlife and Ecology Research Kevin McGarigal, Samuel A. Cushman, Susan Stafford, 2013-12-01 With its focus on the practical application of the techniques of multivariate statistics, this book shapes the powerful tools of statistics for the specific needs of ecologists and makes statistics more applicable to their course of study. It gives readers a solid conceptual understanding of the role of multivariate statistics in ecological applications and the relationships among various techniques, while avoiding detailed mathematics and the underlying theory. More importantly, the reader will gain insight into the type of research questions best handled by each technique and the important considerations in applying them. Whether used as a textbook for specialised courses or as a supplement to general statistics texts, the book emphasises those techniques that students of ecology and natural resources most need to understand and employ in their research. While targeted for upper-division and graduate students in wildlife biology, forestry, and ecology, and for professional wildlife scientists and natural resource managers, this book will also be valuable to researchers in any of the biological sciences. |
| data science in wildlife conservation: Monitoring Animal Populations and Their Habitats Brenda McComb, Benjamin Zuckerberg, David Vesely, Christopher Jordan, 2010-03-11 In the face of so many unprecedented changes in our environment, the pressure is on scientists to lead the way toward a more sustainable future. Written by a team of ecologists, Monitoring Animal Populations and Their Habitats: A Practitioner’s Guide provides a framework that natural resource managers and researchers can use to design monitoring programs that will benefit future generations by distilling the information needed to make informed decisions. In addition, this text is valuable for undergraduate- and graduate-level courses that are focused on monitoring animal populations. With the aid of more than 90 illustrations and a four-page color insert, this book offers practical guidance for the entire monitoring process, from incorporating stakeholder input and data collection, to data management, analysis, and reporting. It establishes the basis for why, what, how, where, and when monitoring should be conducted; describes how to analyze and interpret the data; explains how to budget for monitoring efforts; and discusses how to assemble reports of use in decision-making. The book takes a multi-scaled and multi-taxa approach, focusing on monitoring vertebrate populations and upland habitats, but the recommendations and suggestions presented are applicable to a variety of monitoring programs. Lastly, the book explores the future of monitoring techniques, enabling researchers to better plan for the future of wildlife populations and their habitats. Monitoring Animal Populations and Their Habitats: A Practitioner’s Guide furthers the goal of achieving a world in which biodiversity is allowed to evolve and flourish in the face of such uncertainties as climate change, invasive species proliferation, land use expansion, and population growth. |
| data science in wildlife conservation: Digital Innovations for Renewable Energy and Conservation Mukthar K. P., Jaheer, Jain, Vishal, Tsai, Sang Bing, Wu, Chia Huei, 2024-09-17 With the growing urgency of climate change, digital innovations emerge as pivotal tools in advancing renewable energy and conservation efforts. From sophisticated data analytics that optimize energy efficiency to smart grids that enhance the reliability of renewable sources, technology is transforming how we produce, manage, and conserve energy. To harness the full potential of these renewable innovations, effective research into cutting-edge digital technologies and practices is necessary. Digital Innovations for Renewable Energy and Conservation delves into the intersection of digitalization and sustainable growth, exploring how advancements in technology can be leveraged to foster environmental sustainability and economic prosperity. It examines the evolving landscape of the global economy in the digital age, identifying opportunities and challenges for achieving sustainable development goals. This book covers topics such as climatology, smart cities, and digital technology, and is a useful resource for policymakers, business owners, computer engineers, environmentalists, conservationists, academicians, scientists, and researchers. |
| data science in wildlife conservation: Wildlife Ecology, Conservation and Management Anthony R. E. Sinclair, John M. Fryxell, Graeme Caughley, 2009-03-12 The second edition of Wildlife Ecology, Conservation, and Management provides a thorough introduction to general ecological principles and examines how they can be applied to wildlife management and conservation. Expanded and updated, this second edition includes new chapters on understanding ecosystems and the use of computer models in wildlife management Gives a comprehensive, up-to-date overview of ecology including the latest theories on population dynamics and conservation Reviews practical applications and techniques and how these can be used to formulate realistic objectives with in an ecological framework Examples of real-life management situations from around the world provide a broad perspective on the international problems of conservation Worked examples on CD enable students to practice calculations explained in the text Artwork from the book is available to instructors online at www.blackwellpublishing.com/sinclair. An Instructor manual CD-ROM for this title is available. Please contact our Higher Education team at HigherEducation@wiley.com for more information. Note: CD-ROM/DVD and other supplementary materials are not included as part of eBook file. |
| data science in wildlife conservation: Global Re-introduction Perspectives Pritpal S. Soorae, 2010 This is the second issue in the Global Re-introduction Perspectives series and has been produced in the same standardized format as the previous one. The case-studies are arranged in the following order: Introduction, Goals, Success Indicators, Project Summary, Major Difficulties Faced, Major Lessons Learned, Success of Project with reasons for success or failure. For this second issue we received a total of 72 case-studies compared to 62 in the last issue. These case studies cover the following taxa as follows: invertebrates (9), fish (6), amphibians (5), reptiles (7), birds (13), mammals (20) and plants (12) ... We hope the information presented in this book will provide a broad global perspective on challenges facing re-introduction projects trying to restore biodiversity.--Pritpal S. Soorae. |
| data science in wildlife conservation: Wildlife Management and Conservation Paul R. Krausman, James W. Cain, 2022-09-20 The book contains the essential information that wildlife biologists and managers use to manage wildlife populations today, and it gives students the information they need to pursue a profession in wildlife management and conservation-- |
| data science in wildlife conservation: Handbook of Citizen Science in Ecology and Conservation Christopher A. Lepczyk, 2020-04-28 Handbook of Citizen Science in Ecology and Conservation is the first practical and comprehensive manual for creating, implementing, or improving natural science research and monitoring projects that involve collaboration between scientists and the general public. As citizen science projects become increasingly common, project leaders are seeking information on concrete best practices for planning and implementing projects—practices that allow them to guide and gauge success while also ensuring the collection of high-quality data and rewarding experiences for volunteers. In this handbook, citizen science practitioners from around the world and with decades of experience provide step-by-step instructions, insights, and advice, and they explore real-world applications through case studies from a variety of citizen science projects. This is the definitive reference guide for anyone interested in starting or improving a citizen science project with ecological or conservation applications, from professors and graduate students to agency staff and nongovernmental organizations. |
Leveraging Data Analytics for Sustainable Resource …
Abstract: Utilizing data analytics, Internet of Things technology, and machine learning algorithms, this study offers a novel framework for wildlife conservation that tackles the increasing …
Wildbook: Crowdsourcing, computer vision, and data science …
Wildbook: Crowdsourcing, computer vision, and data science for conservation. 1. INTRODUCTION. How many African elephants are left in the world and how fast are they …
Chapter 8. Data Analysis - US Forest Service
We begin with key concepts related to data analysis and then provide Internet links and references to statistical textbooks and methods that are designed specifically for natu-ral …
AI-Driven Wildlife Behavior Monitoring Using Computer Vision
forecasting wildlife behavior, edge AI for remote monitoring, and acoustic recognition to monitor elusive species. By offering real-time monitoring and data-driven insights, this AI-powered …
AI Tools In Wildlife Conservation - rjpn.org
Our research explores the application of AI tools in wildlife conservation, focusing on how machine learning, computer vision, and data analytics are set to change the traditional method of …
The role of big data and AI in enhancing biodiversity …
The integration of Big Data and Artificial Intelligence (AI) technologies has revolutionized biodiversity conservation and resource management in the USA, enabling more precise and …
Student Placement 2025/2026 Data Science in Wildlife Research
Its 60+ scientists carry out research into game, associated species and habitats, from its HQ at Fordingbridge, Hampshire, and at outstations throughout the country. The Trust’s primary …
Wildlife Identification using Object Detection using Computer …
Wildlife monitoring plays a vital role in conservation efforts by providing insights into population dynamics, habitat utilization and species behavior. This research paper aims to explore the …
Exploring Digital Innovation in Wildlife Conservation - Acta …
vanced data analysis techniques, digital innovation is revolution-izing the way we understand, monitor, and protect endangered species and their habitats. One of the most significant …
Spatial Data Analysis and Study of Wildlife Conservation …
Wildlife is a vital part of an ecological community as wildlife plays an important role in the environment. Wildlife Conservation is not just important for animals, but to all living things...
Database of Biodiversity Data Sources for Conservation …
Generates model-based, large-scale predictions of natural occurrences of marine and aquatic species. Derived from GBIF, OBIS, FishBase, SeaLifeBase & AlgaeBase. BOLD is a cloud …
Harnessing Artificial Intelligence for Wildlife Conservation
Leveraging machine learning and computer vision, Conservation AI detects and classifies animals, humans, and poaching-related objects using visual spectrum and thermal infrared …
Data are ‘Trust Resources’: a Strategy for managing the U
The U.S. Fish and Wildlife Service has a data management problem. There is a large disparity between the resources devoted to collecting data and those devoted to documenting, …
Guardians of Wild: Artificial Intelligence for Wildlife …
We review important works that use deep learning to identify poaching and classify animal species. We analyse deep learning systems designed for wildlife conservation, including their …
A generalized approach for producing, quantifying, and …
Feb 27, 2015 · Abstract: Citizen science has the potential to expand the scope and scale of research in ecology and con-servation, but many professional researchers remain skeptical of …
AI in Wildlife Conservation: Species Monitoring, Poaching …
AI-powered camera traps and drones monitor wildlife populations, track animal movements, and detect endangered species in real time. Predictive models analyze environmental data to …
Modern Strategies for Wildlife Data Collection, Dissemination …
Wildlife survey data are generalized and incorporated into WDFW’s Priority Habitats and Species (PHS) map and digital data products, including an interactive data viewing and reporting tool
Uniting Experiments and Big Data to advance ecology and …
The Big Data Framework can often provide a foundation for documenting and monitoring pat-terns of biodiversity across spatial scales (from local to global [1]). The Experimental Framework can …
Internet of Things : Wildlife Conservation and its Challenges
animal monitoring and challenges of wildlife conservation. This paper also shows natural challenges and technical challenges while implementing animal tracking and health monitoring …
How artificial intelligence and big data can help preserve wildlife
Ecologists can use AI, and more specifically computer vision, to extract key features from images, videos and other visual forms of data in order to quickly classify wildlife species, count...
Leveraging Data Analytics for Sustainable Resource …
Abstract: Utilizing data analytics, Internet of Things technology, and machine learning algorithms, this study offers a novel framework for wildlife conservation that tackles the increasing …
Wildbook: Crowdsourcing, computer vision, and data science …
Wildbook: Crowdsourcing, computer vision, and data science for conservation. 1. INTRODUCTION. How many African elephants are left in the world and how fast are they …
Chapter 8. Data Analysis - US Forest Service
We begin with key concepts related to data analysis and then provide Internet links and references to statistical textbooks and methods that are designed specifically for natu-ral …
AI-Driven Wildlife Behavior Monitoring Using Computer Vision
forecasting wildlife behavior, edge AI for remote monitoring, and acoustic recognition to monitor elusive species. By offering real-time monitoring and data-driven insights, this AI-powered …
AI Tools In Wildlife Conservation - rjpn.org
Our research explores the application of AI tools in wildlife conservation, focusing on how machine learning, computer vision, and data analytics are set to change the traditional method …
The role of big data and AI in enhancing biodiversity …
The integration of Big Data and Artificial Intelligence (AI) technologies has revolutionized biodiversity conservation and resource management in the USA, enabling more precise and …
Student Placement 2025/2026 Data Science in Wildlife …
Its 60+ scientists carry out research into game, associated species and habitats, from its HQ at Fordingbridge, Hampshire, and at outstations throughout the country. The Trust’s primary …
Wildlife Identification using Object Detection using …
Wildlife monitoring plays a vital role in conservation efforts by providing insights into population dynamics, habitat utilization and species behavior. This research paper aims to explore the …
Exploring Digital Innovation in Wildlife Conservation - Acta …
vanced data analysis techniques, digital innovation is revolution-izing the way we understand, monitor, and protect endangered species and their habitats. One of the most significant …
Spatial Data Analysis and Study of Wildlife Conservation …
Wildlife is a vital part of an ecological community as wildlife plays an important role in the environment. Wildlife Conservation is not just important for animals, but to all living things...
Database of Biodiversity Data Sources for Conservation …
Generates model-based, large-scale predictions of natural occurrences of marine and aquatic species. Derived from GBIF, OBIS, FishBase, SeaLifeBase & AlgaeBase. BOLD is a cloud …
Harnessing Artificial Intelligence for Wildlife Conservation
Leveraging machine learning and computer vision, Conservation AI detects and classifies animals, humans, and poaching-related objects using visual spectrum and thermal infrared …
Data are ‘Trust Resources’: a Strategy for managing the U
The U.S. Fish and Wildlife Service has a data management problem. There is a large disparity between the resources devoted to collecting data and those devoted to documenting, …
Guardians of Wild: Artificial Intelligence for Wildlife …
We review important works that use deep learning to identify poaching and classify animal species. We analyse deep learning systems designed for wildlife conservation, including their …
A generalized approach for producing, quantifying, and …
Feb 27, 2015 · Abstract: Citizen science has the potential to expand the scope and scale of research in ecology and con-servation, but many professional researchers remain skeptical of …
AI in Wildlife Conservation: Species Monitoring, Poaching …
AI-powered camera traps and drones monitor wildlife populations, track animal movements, and detect endangered species in real time. Predictive models analyze environmental data to …
Modern Strategies for Wildlife Data Collection, …
Wildlife survey data are generalized and incorporated into WDFW’s Priority Habitats and Species (PHS) map and digital data products, including an interactive data viewing and reporting tool
Uniting Experiments and Big Data to advance ecology and …
The Big Data Framework can often provide a foundation for documenting and monitoring pat-terns of biodiversity across spatial scales (from local to global [1]). The Experimental Framework …
Internet of Things : Wildlife Conservation and its Challenges
animal monitoring and challenges of wildlife conservation. This paper also shows natural challenges and technical challenges while implementing animal tracking and health monitoring …
How artificial intelligence and big data can help preserve …
Ecologists can use AI, and more specifically computer vision, to extract key features from images, videos and other visual forms of data in order to quickly classify wildlife species, count...
