Justin M. Calabrese
Other affiliations: Helmholtz Centre for Environmental Research - UFZ, Smithsonian Institution, Helmholtz-Zentrum Dresden-Rossendorf ...read more
Bio: Justin M. Calabrese is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Population & Biology. The author has an hindex of 30, co-authored 91 publications receiving 4162 citations. Previous affiliations of Justin M. Calabrese include Helmholtz Centre for Environmental Research - UFZ & Smithsonian Institution.
Papers published on a yearly basis
Goethe University Frankfurt1, University of Maryland, College Park2, University of Guelph3, Duke University4, Radboud University Nijmegen5, Swedish University of Agricultural Sciences6, Federal University of Mato Grosso do Sul7, University of Alberta8, Royal Veterinary College9, Wildlife Conservation Society10, Mississippi State University11, Sao Paulo State University12, Michigan Department of Natural Resources13, University of California, Davis14, Aarhus University15, Max Planck Society16, University of Potsdam17, Middle Tennessee State University18, Mammal Research Institute19, Edmund Mach Foundation20, Harvard University21, Smithsonian Conservation Biology Institute22, University of Évora23, University of Montpellier24, Parks Victoria25, Monash University26, Ohio State University27, Fiji National University28, University of Massachusetts Amherst29, United States Geological Survey30, Save the Elephants31, University of Oxford32, German Primate Center33, Technische Universität München34, Institute of Ecosystem Studies35, University of British Columbia36, University of Zurich37, University of Wyoming38, University of Washington39, University of Montana40, Bavarian Forest National Park41, University of Freiburg42, University of Toulouse43, University of Veterinary Medicine Vienna44, University College Cork45, North Carolina State University46, North Carolina Museum of Natural Sciences47, Karatina University48, University of Lethbridge49, Lamont–Doherty Earth Observatory50, University of Valencia51, Stony Brook University52, International Union for Conservation of Nature and Natural Resources53, University of Alicante54, Empresa Brasileira de Pesquisa Agropecuária55, University of Glasgow56, New York University57, University of Oslo58, Hebrew University of Jerusalem59, Norwegian University of Science and Technology60, Field Museum of Natural History61, University of Bayreuth62, University of Grenoble63, University of New South Wales64, Pennsylvania Game Commission65, Princeton University66, University of Konstanz67, University of Haifa68, Polish Academy of Sciences69, Instituto Superior de Agronomia70, University of Porto71, University of Lisbon72, University of California, Santa Cruz73, University of Pretoria74, Colorado State University75
TL;DR: Using a unique GPS-tracking database of 803 individuals across 57 species, it is found that movements of mammals in areas with a comparatively high human footprint were on average one-half to one-third the extent of their movements in area with a low human footprint.
Abstract: Animal movement is fundamental for ecosystem functioning and species survival, yet the effects of the anthropogenic footprint on animal movements have not been estimated across species. Using a unique GPS-tracking database of 803 individuals across 57 species, we found that movements of mammals in areas with a comparatively high human footprint were on average one-half to one-third the extent of their movements in areas with a low human footprint. We attribute this reduction to behavioral changes of individual animals and to the exclusion of species with long-range movements from areas with higher human impact. Global loss of vagility alters a key ecological trait of animals that affects not only population persistence but also ecosystem processes such as predator-prey interactions, nutrient cycling, and disease transmission.
TL;DR: This framework illustrates the data requirements, spatial scales, and information yields of a range of different connectivity measures and allows practitioners to make more informed decisions concerning connectivity measurement.
Abstract: Connectivity is an important but inconsistently defined concept in spatial ecology and conservation biology. Theoreticians from various subdisciplines of ecology argue over its definition and measurement, but no consensus has yet emerged. Despite this disagreement, measuring connectivity is an integral part of many resource management plans. A more practical approach to understanding the many connectivity metrics is needed. Instead of focusing on theoretical issues surrounding the concept of connectivity, we describe a data-dependent framework for classifying these metrics. This framework illustrates the data requirements, spatial scales, and information yields of a range of different connectivity measures. By highlighting the costs and benefits associated with using alternative metrics, this framework allows practitioners to make more informed decisions concerning connectivity measurement.
TL;DR: Approximate Bayesian Computing and Pattern-Oriented Modelling are discussed, their potential for integrating stochastic simulation models into a unified framework for statistical modelling is demonstrated, and principles and advantages of these methods are discussed.
Abstract: Statistical models are the traditional choice to test scientific theories when observations, processes or boundary conditions are subject to stochasticity Many important systems in ecology and biology, however, are difficult to capture with statistical models Stochastic simulation models offer an alternative, but they were hitherto associated with a major disadvantage: their likelihood functions can usually not be calculated explicitly, and thus it is difficult to couple them to well-established statistical theory such as maximum likelihood and Bayesian statistics A number of new methods, among them Approximate Bayesian Computing and Pattern-Oriented Modelling, bypass this limitation These methods share three main principles: aggregation of simulated and observed data via summary statistics, likelihood approximation based on the summary statistics, and efficient sampling We discuss principles as well as advantages and caveats of these methods, and demonstrate their potential for integrating stochastic simulation models into a unified framework for statistical modelling
TL;DR: The ctmm package for the R statistical computing environment implements all of the CTSPs currently in use in the ecological literature and couples them with powerful statistical methods for autocorrelated data adapted from geostatistics and signal processing, including variograms, periodograms and non‐Markovian maximum likelihood estimation.
Abstract: Summary Movement ecology has developed rapidly over the past decade, driven by advances in tracking technology that have largely removed data limitations. Development of rigorous analytical tools has lagged behind empirical progress, and as a result, relocation data sets have been underutilized. Discrete-time correlated random walk models (CRW) have long served as the foundation for analyzing relocation data. Unfortunately, CRWs confound the sampling and movement processes. CRW parameter estimates thus depend sensitively on the sampling schedule, which makes it difficult to draw sampling-independent inferences about the underlying movement process. Furthermore, CRWs cannot accommodate the multiscale autocorrelations that typify modern, finely sampled relocation data sets. Recent developments in modelling movement as a continuous-time stochastic process (CTSP) solve these problems, but the mathematical difficulty of using CTSPs has limited their adoption in ecology. To remove this roadblock, we introduce the ctmm package for the R statistical computing environment. ctmm implements all of the CTSPs currently in use in the ecological literature and couples them with powerful statistical methods for autocorrelated data adapted from geostatistics and signal processing, including variograms, periodograms and non-Markovian maximum likelihood estimation. ctmm is built around a standard workflow that begins with visual diagnostics, proceeds to candidate model identification, and then to maximum likelihood fitting and AIC-based model selection. Once an accurate CTSP for the data has been fitted and selected, analyses that require such a model, such as quantifying home range areas via autocorrelated kernel density estimation or estimating occurrence distributions via time-series Kriging, can then be performed. We use a case study with African buffalo to demonstrate the capabilities of ctmm and highlight the steps of a typical CTSP movement analysis workflow.
TL;DR: This work derives an autocorrelated KDE (AKDE) from first principles to use autcorrelated data, making it perfectly suited for movement data sets, and illustrates the vastly improved performance of AKDE using analytical arguments, relocation data from Mongolian gazelles, and simulations based upon the gazelle's observed movement process.
Abstract: Quantifying animals' home ranges is a key problem in ecology and has important conservation and wildlife management applications. Kernel density estimation (KDE) is a workhorse technique for range delineation problems that is both statistically efficient and nonparametric. KDE assumes that the data are independent and identically distributed (IID). However, animal tracking data, which are routinely used as inputs to KDEs, are inherently autocorrelated and violate this key assumption. As we demonstrate, using realistically autocorrelated data in conventional KDEs results in grossly underestimated home ranges. We further show that the performance of conventional KDEs actually degrades as data quality improves, because autocorrelation strength increases as movement paths become more finely resolved. To remedy these flaws with the traditional KDE method, we derive an autocorrelated KDE (AKDE) from first principles to use autocorrelated data, making it perfectly suited for movement data sets. We illustrate the vastly improved performance of AKDE using analytical arguments, relocation data from Mongolian gazelles, and simulations based upon the gazelle's observed movement process. By yielding better minimum area estimates for threatened wildlife populations, we believe that future widespread use of AKDE will have significant impact on ecology and conservation biology.
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …
28 Jul 2005
TL;DR: The author guides the reader in about 350 pages from descriptive and basic statistical methods over classification and clustering to (generalised) linear and mixed models to enable researchers and students alike to reproduce the analyses and learn by doing.
Abstract: The complete title of this book runs ‘Analyzing Linguistic Data: A Practical Introduction to Statistics using R’ and as such it very well reflects the purpose and spirit of the book. The author guides the reader in about 350 pages from descriptive and basic statistical methods over classification and clustering to (generalised) linear and mixed models. Each of the methods is introduced in the context of concrete linguistic problems and demonstrated on exciting datasets from current research in the language sciences. In line with its practical orientation, the book focuses primarily on using the methods and interpreting the results. This implies that the mathematical treatment of the techniques is held at a minimum if not absent from the book. In return, the reader is provided with very detailed explanations on how to conduct the analyses using R . The first chapter sets the tone being a 20-page introduction to R. For this and all subsequent chapters, the R code is intertwined with the chapter text and the datasets and functions used are conveniently packaged in the languageR package that is available on the Comprehensive R Archive Network (CRAN). With this approach, the author has done an excellent job in enabling researchers and students alike to reproduce the analyses and learn by doing. Another quality as a textbook is the fact that every chapter ends with Workbook sections where the user is invited to exercise his or her analysis skills on supplemental datasets. Full solutions including code, results and comments are given in Appendix A (30 pages). Instructors are therefore very well served by this text, although they might want to balance the book with some more mathematical treatment depending on the target audience. After the introductory chapter on R, the book opens on graphical data exploration. Chapter 3 treats probability distributions and common sampling distributions. Under basic statistical methods (Chapter 4), distribution tests and tests on means and variances are covered. Chapter 5 deals with clustering and classification. Strangely enough, the clustering section has material on PCA, factor analysis, correspondence analysis and includes only one subsection on clustering, devoted notably to hierarchical partitioning methods. The classification part deals with decision trees, discriminant analysis and support vector machines. The regression chapter (Chapter 6) treats linear models, generalised linear models, piecewise linear models and a substantial section on models for lexical richness. The final chapter on mixed models is particularly interesting as it is one of the few text book accounts that introduce the reader to using the (innovative) lme4 package of Douglas Bates which implements linear mixed-effects models. Moreover, the case studies included in this
TL;DR: A new class of ecological connectivity models based in electrical circuit theory, which offer distinct advantages over common analytic connectivity models, including a theoretical basis in random walk theory and an ability to evaluate contributions of multiple dispersal pathways are introduced.
Abstract: Connectivity among populations and habitats is important for a wide range of ecological processes. Understanding, preserving, and restoring connectivity in complex landscapes requires connectivity models and metrics that are reliable, efficient, and process based. We introduce a new class of ecological connectivity models based in electrical circuit theory. Although they have been applied in other disciplines, circuit-theoretic connectivity models are new to ecology. They offer distinct advantages over common analytic connectivity models, including a theoretical basis in random walk theory and an ability to evaluate contributions of multiple dispersal pathways. Resistance, current, and voltage calculated across graphs or raster grids can be related to ecological processes (such as individual movement and gene flow) that occur across large population networks or landscapes. Efficient algorithms can quickly solve networks with millions of nodes, or landscapes with millions of raster cells. Here we review basic circuit theory, discuss relationships between circuit and random walk theories, and describe applications in ecology, evolution, and conservation. We provide examples of how circuit models can be used to predict movement patterns and fates of random walkers in complex landscapes and to identify important habitat patches and movement corridors for conservation planning.