• Asking questions
• Some physical and chemical properties of seawater
• Taxonomic classification and identification
• Marine bacteria
• Phytoplankton
• Attached marine plants
• Photosynthetic pigments of marine plants
• Some lower marine invertebrates
• Marine mollusks
• Marine arthropods
• Echinoderms
• Marine zooplankton
• Cartilaginous fishes
• Bony fishes
• Adaptations in marine mammals.

Now in full color, Laboratory and Field Investigations in Marine Life is a unique marine biology laboratory and field manual that engages students in the excitement and challenges of understanding marine organisms and the environments in which they live. With activities designed to encourage students to develop their own powers of critical observation and analysis, students will benefit from a thorough examination of topics such as the physical and chemical properties of seawater, marine microbes, algae, and a wide variety of invertebrate and vertebrate animals. The new, more convenient spiral binding allows the manual to lay flat on lab tables while students work, and they can easily tear out pages to submit for a grade. Laboratory and Field Investigations in Marine Life, Eleventh Edition is an ideal resource to accompany Introduction to the Biology of Marine Life, Eleventh Edition and the clear choice for an engaging, hands-on exploration of marine biology.
(source: Nielsen Book Data)9781284090543 20170213

• Basics. Animal Biodiversity: Origins and Evolution. Swimming. Natural Flight. Terrestrial Locomotion.
• (source: Nielsen Book Data)9781138035768 20170703

Animal Locomotion: Physical Principles and Adaptations is a professional-level, state of the art review and reference summarizing the current understanding of macroscopic metazoan animal movement. The comparative biophysics, biomechanics and bioengineering of swimming, flying and terrestrial locomotion are placed in contemporary frameworks of biodiversity, evolutionary process, and modern research methods, including mathematical analysis. The intended primary audience is advanced-level students and researchers primarily interested in and trained in mathematics, physical sciences and engineering. Although not encyclopedic in its coverage, anyone interested in organismal biology, functional morphology, organ systems and ecological physiology, physiological ecology, molecular biology, molecular genetics and systems biology should find this book useful.
(source: Nielsen Book Data)9781138035768 20170703

• PART 1 OVERVIEW-- PART 2 MONITORING-- PART 3 MANAGING-- PART 4 CONSERVING.
• (source: Nielsen Book Data)9780198723288 20170605

We live in a complex and dynamic world. Understanding how to monitor, manage and conserve species and habitats - the goal of applied ecology - is of ever-increasing importance. Applied Ecology shows students how an understanding of ecological theory can be used to address the most important issues facing ecologists today. Its explicitly problem-solving approach reflects the reality of using ecological tools and approaches in applied contexts, while also highlighting the key ecological theories that underpin those applications to make the link between theory and practice clear. With an emphasis throughout on the realities of applying ecological theory, the book features interviews with a range of leading applied ecologists, and over 30 case studies to give students a clear sense of contemporary applied ecology in action. In addition, over 20 Hot Topic panels capture issues and approaches at the forefront of current practice. Online Resource Centre: The Online Resource Centre to accompany Applied Ecology features: For students: - Twelve bonus case studies to augment those featured in the book - Extended versions of the Interviews with Applied Ecologists that appear in the book For lecturers: - Problem-solving activities for use in a workshop, seminar, or tutorial setting - Figures from the book in digital format, for use in lecture presentations.
(source: Nielsen Book Data)9780198723288 20170605

• Table of Contents Acknowledgements Preface Guide to the beaked whales Arnoux's Beaked Whale Baird's Beaked Whale Northern Bottlenose Whale Southern Bottlenose Whale Longman's Beaked Whale Sowerby's Beaked Whale Andrews' Beaked Whale Hubbs' Beaked Whale Blainville's Beaked Whale Gervais' Beaked Whale Ginkgo-toothed Whale Gray's Beaked Whale Hector's Beaked Whale Deraniyagala's Beaked Whale Strap-toothed Whale True's Beaked Whale Perrin's Beaked Whale Peruvian Beaked Whale Stejneger's Beaked Whale Spade-toothed Beaked Whale Shepherd's Beaked Whale Cuvier's Beaked Whale About Beaked Whales Classification Distribution Teeth Scarring Feeding From Land to Water to Tooth LossBeaked whales vs. the Navy Bibliography.
• (source: Nielsen Book Data)9781421421827 20170612

Beaked whales have been shrouded in mystery for most of the twentieth century. Denizens of deep, remote ocean waters and highly resistant to life in captivity, they have proven notoriously difficult for humans to observe. Over the past few decades, however, scientists have gained a better understanding of this distinct group of cetaceans, deciphering the natural history of the twenty-two beaked whale species. Here, famed artist and naturalist Richard Ellis and leading beaked whale researcher James G. Mead bring these elusive marine mammals into the limelight. Beaked whales' generous life spans can extend well past 70 years. They spend their decades diving to extreme depths in search of prey, which they capture by expanding their oral cavity suddenly to suck in the squid or fish they are hunting. It appears that these sleek predators may engage in fierce, clandestine aquatic battles, as the bodies of many males are covered in scars. Because many species are only somewhat larger than dolphins, they are often confused with porpoises; however, some larger beaked whale species may grow to 40 feet. These enigmatic and compelling creatures need our help; their numbers are declining, perhaps due to the damaging effects of naval sonar on their sophisticated auditory systems. In Ellis and Mead's book, the beaked whales finally get their due. The duo provides a combination of captivating stories about the species, original Richard Ellis art, and photos from leading natural history photographers. The result is an accessible, beautiful book-the first of its kind on this unusual group of cetaceans. Meet the beaked whales, and enjoy the fascinating and mysterious world in which they live.
(source: Nielsen Book Data)9781421421827 20170612

• 1 OVERVIEW OF THIS BOOK 1 1.1 VOLUMES I AND II 1 1.1.1 Volume I 1 1.1.2 Volume II 1 1.2 WHAT TYPE OF SPATIAL DATA DO WE ANALYSE IN THIS BOOK? 1 1.2.1 Areal and lattice data 1 1.2.2 Geostatistical data 2 1.2.3 Spatial point pattern data 3 1.3 OUTLINE OF THIS BOOK 3 1.4 PREREQUISITES 4 1.5 AVAILABILITY OF R CODE AND DATA 4 2 RECOGNISING STATISTICAL DEPENDENCY 5 2.1 PSEUDOREPLICATION 5 2.2 LINEAR REGRESSION APPLIED TO SPATIAL DATA 7 2.2.1 Irish pH data 7 2.2.2 Protocol from Zuur et al. (2016) 8 2.2.3 Visualisation of the experimental design 9 2.2.4 Data exploration 9 2.2.5 Dependency 12 2.2.6 Statistical model 15 2.2.7 Fit the model 16 2.2.8 Model validation 17 2.3 GAM APPLIED TO TEMPORAL DATA 21 2.3.1 Subnivium temperature data 21 2.3.2 Sources of dependency 22 2.3.3 The model 23 2.3.4 Model validation 24 2.4 GLMM APPLIED ON HIERARCHICAL AND SPATIAL DATA 26 2.5 TECHNICALITIES 28 2.5.1 Matrix notation 28 2.5.2 How is dependency causing problems? 31 2.6 DISCUSSION 32 3 TIME SERIES AND GLS 33 3.1 OSPREYS 33 3.2 COVARIANCE AND CORRELATION COEFFICIENTS 33 3.3 LINEAR REGRESSION MODEL 35 3.4 FOCUSSING ON THE RESIDUAL COVARIANCE MATRIX 35 3.5 DEPENDENCY AND THE COVARIANCE MATRIX 36 3.6 GLS: DEALING WITH TEMPORAL DEPENDENCY 39 3.6.1 Adelie penguins 39 3.6.2 Do we have dependency? 40 3.6.3 Formulation of the linear regression model 40 3.6.4 Application of the linear regression model 41 3.6.5 R code for acf and variogram 45 3.6.6 Formulation of the GLS model 46 3.6.7 Implementation using the gls function 50 3.7 MULTIPLE TIME SERIES 51 3.8 DISCUSSION 53 4 SPATIAL DATA AND GLS 55 4.1 VARIOGRAM MODELS FOR SPATIAL DEPENDENCY 55 4.2 APPLICATION ON THE IRISH PH DATA 57 4.3 MATERN CORRELATION FUNCTION 59 5 LINEAR MIXED EFFECTS MODELS AND DEPENDENCY 61 5.1 WHITE STORKS 61 5.2 CONSIDERING THE DATA (WRONGLY) AS ONE-WAY NESTED 62 5.3 FITTING THE ONE-WAY NESTED MODEL USING LMER 65 5.4 MODEL VALIDATION 67 5.5 SKETCHING THE FITTED VALUES 68 5.6 CONSIDERING THE DATA (CORRECTLY) AS TWO-WAY NESTED 69 5.7 APPLICATIONS TO SPATIAL AND TEMPORAL DATA 72 5.8 DIFFERENCE WITH THE AR1 PROCESS APPROACH 72 6 MODELLING SPACE EXPLICITLY 73 6.1 MODEL FORMULATION 73 6.2 COVARIANCE MATRIX OF THE SPATIAL RANDOM EFFECT 75 6.3 SPATIAL-TEMPORAL CORRELATION* 79 7 INTRODUCTION TO BAYESIAN STATISTICS 83 7.1 WHY GO BAYESIAN? 83 7.2 GENERAL PROBABILITY RULES 84 7.3 THE MEAN OF A DISTRIBUTION* 85 7.4 BAYES' THEOREM AGAIN 87 7.5 CONJUGATE PRIORS 88 7.6 MARKOV CHAIN MONTE CARLO SIMULATION 93 7.6.1 Underlying idea 93 7.6.2 Installing JAGS and R2jags 94 7.6.3 Flowchart for running a model in JAGS 94 7.6.4 Preparing the data for JAGS 95 7.6.5 JAGS code 96 7.6.6 Initial values and parameters to save 98 7.6.7 Running JAGS 99 7.6.8 Accessing numerical output from JAGS 100 7.6.9 Assess mixing 100 7.6.10 Posterior information 101 7.7 INTEGRATED NESTED LAPLACE APPROXIMATION* 103 7.7.1 Joint posterior distribution 103 7.7.2 Marginal distributions 105 7.7.3 Back to high school 107 7.7.4 INLA 109 7.8 EXAMPLE USING R-INLA 110 7.9 DISCUSSION 114 8 MULTIPLE LINEAR REGRESSION IN R-INLA 115 8.1 INTRODUCTION 115 8.2 DATA EXPLORATION 116 8.3 MODEL FORMULATION 117 8.4 LINEAR REGRESSION RESULTS 117 8.4.1 Executing the model in R-INLA 117 8.4.2 Output for the betas 117 8.4.3 Output for the hyper-parameters 119 8.4.4 Fitted model 123 8.5 MODEL VALIDATION 123 8.6 MODEL SELECTION 126 8.6.1 Should we do it? 126 8.6.2 Using the DIC 126 8.6.3 Out of sample prediction 131 8.6.4 Posterior predictive check 133 8.7 VISUALISING THE MODEL 135 9 MIXED EFFECTS MODELLING IN R-INLA TO ANALYSE OTOLITH DATA 139 9.1 OTOLITHS IN PLAICE 139 9.2 MODEL FORMULATION 140 9.3 DEPENDENCY 140 9.4 DATA EXPLORATION 141 9.5 RUNNING THE MODEL IN R-INLA 143 9.6 MODEL VALIDATION 146 9.7 MODEL SELECTION 149 9.8 MODEL INTERPRETATION 149 9.8.1 Option 1 for prediction: Adding extra data 150 9.8.2 Option 2 for prediction: Using the inla.make.lincombs 153 9.8.3 Adding extra data or inla.make.lincombs? 155 9.9 MULTIPLE RANDOM EFFECTS 155 9.10 CHANGING PRIORS OF FIXED PARAMETERS 156 9.11 CHANGING PRIORS OF HYPERPARAMETERS 158 9.12 SHOULD WE CHANGE PRIORS? 164 10 POISSON, NEGATIVE BINOMIAL, BINOMIAL AND GAMMA GLMS IN R-INLA 165 10.1 POISSON AND NEGATIVE BINOMIAL GLMS IN R-INLA 165 10.1.1 Introduction 165 10.1.2 Poisson GLM in R-INLA 166 10.1.3 Negative binomial GLM in R-INLA 172 10.1.4 Model selection for the NB GLM 175 10.1.5 Visualisation of the NB GLM 177 10.2 BERNOULLI AND BINOMIAL GLM 180 10.2.1 Bernoulli GLM 181 10.2.2 Model selection with the marginal likelihood 184 10.2.3 Binomial GLM 185 10.3 GAMMA GLM 187 11 MATERN CORRELATION AND SPDE 191 11.1 CONTINUOUS GAUSSIAN FIELD 191 11.2 MODELS THAT WE HAVE IN MIND 191 11.3 MATERN CORRELATION 192 11.4 SPDE APPROACH 197 12 LINEAR REGRESSION MODEL WITH SPATIAL DEPENDENCY FOR THE IRISH PH DATA 205 12.1 INTRODUCTION 205 12.2 MODEL FORMULATION 205 12.3 LINEAR REGRESSION RESULTS 206 12.4 MODEL VALIDATION 207 12.5 ADDING SPATIAL CORRELATION TO THE MODEL 208 12.6 DEFINING THE MESH FOR THE IRISH PH DATA 212 12.7 DEFINE THE WEIGHT FACTORS AIK 216 12.8 DEFINE THE SPDE 218 12.9 DEFINE THE SPATIAL FIELD 218 12.10 DEFINE THE STACK 218 12.11 DEFINE THE FORMULA FOR THE SPATIAL MODEL 221 12.12 EXECUTE THE SPATIAL MODEL IN R 221 12.13 RESULTS 222 12.14 MODEL SELECTION 227 12.15 MODEL VALIDATION 228 12.16 MODEL INTERPRETATION 228 12.17 DETAILED INFORMATION ABOUT THE STACK* 232 12.17.1 Stack for the fitted model again 232 12.17.2 Stack for the new covariate values 234 12.17.3 Combine the two stacks 236 12.17.4 Run the model 236 13 SPATIAL POISSON MODELS APPLIED TO PLANT DIVERSITY 239 13.1 INTRODUCTION 239 13.2 DATA EXPLORATION 239 13.2.1 Sampling locations 239 13.2.2 Outliers 241 13.2.3 Collinearity 242 13.2.4 Relationships 243 13.2.5 Numbers of zeros 244 13.2.6 Conclusions data exploration 244 13.3 MODEL FORMULATION 244 13.4 GLM RESULTS 245 13.5 ADDING SPATIAL CORRELATION TO THE MODEL 248 13.5.1 Model formulation 248 13.5.2 Mesh 248 13.5.3 Projector matrix 253 13.5.4 SPDE 254 13.5.5 Spatial field 254 13.5.6 Stack 254 13.5.7 Formula 255 13.5.8 Run R-INLA 255 13.5.9 Inspect results 256 13.6 SIMULATING FROM THE MODEL 262 13.7 WHAT TO WRITE IN A PAPER 265 14 TIME-SERIES ANALYSIS IN R-INLA 267 14.1 SIMULATION STUDY 267 14.2 TRENDS IN MIGRATION DATES OF SOCKEYE SALMON 269 14.2.1 Applying a random walk trend model 269 14.2.2 Posterior distribution of the sigmas 272 14.2.3 Covariates and trends 273 14.2.4 Making the trend smoother 274 14.3 TRENDS IN POLAR BEAR MOVEMENTS 280 14.4 TRENDS IN WHALE STRANDINGS 283 14.5 MULTIVARIATE TIME SERIES FOR HAWAIIAN BIRDS 285 14.5.1 Importing and preparing the data 285 14.5.2 Data exploration 286 14.5.3 Model formulation 287 14.5.4 Executing the models 288 14.5.5 Mixing Poisson and negative binomial distributions 295 14.6 AR1 TRENDS 297 14.6.1 AR1 trend for regularly spaced time-series data 297 14.6.2 AR1 trend for irregularly spaced time-series data 299 15 SPATIAL-TEMPORAL MODELS FOR ORANGE-CROWNED WARBLERS COUNT DATA 307 15.1 INTRODUCTION 307 15.2 POISSON GLM 308 15.3 MODEL WITH SPATIAL CORRELATION 312 15.4 SPATIAL-TEMPORAL CORRELATION: AR1 318 15.4.1 Why do it? 318 15.4.2 Explanation of the model 318 15.4.4 Simulating a spatial-temporal AR random field 320 15.4.5 Implementation of AR1 model in R-INLA 323 15.4.6 More detailed information on the code 326 15.5 SPATIAL-TEMPORAL CORRELATION: EXCHANGEABLE 328 15.6 SPATIAL-TEMPORAL CORRELATION: REPLICATED 329 15.7 SIMULATION STUDY 330 15.8 DISCUSSION 333 16 SPATIAL-TEMPORAL BERNOULLI MODELS FOR CORAL DISEASE DATA 335 16.1 INTRODUCTION 335 16.2 BERNOULLI MODEL IN R-INLA 336 16.3 SPATIAL CORRELATED BERNOULLI MODEL 338 16.4 SPATIAL-TEMPORAL CORRELATED BERNOULLI MODEL 342 REFERENCES 347 INDEX 353 OTHER BOOKS 357.
• (source: Nielsen Book Data)9780957174191 20170814

In Chapter 2 we discuss an important topic: dependency. Ignoring this means that we have pseudoreplication. We present a series of examples and discuss how dependency can manifest itself. We briefly discuss frequentist tools that are available for the analysis of temporal and spatial data in Chapters 3 and 4, and we will conclude that their application is rather limited, especially if non-Gaussian distributions are required. We will therefore consider alternative models, but these require Bayesian techniques. In Chapter 5 we discuss linear mixed-effects models to analyse hierarchical (i.e. clustered or nested) data, and in Chapter 6 we outline how we add spatial and spatial-temporal dependency to regression models via spatial (and/or temporal) correlated random effects. In Chapter 7 we introduce Bayesian analysis, Markov chain Monte Carlo techniques (MCMC), and Integrated Nested Laplace Approximation (INLA). INLA allows us to apply models to spatial, temporal, or spatial-temporal data. In Chapters 8 through 16 we present a series of INLA examples. We start by applying linear regression and mixed-effects models in INLA (Chapters 8 and 9), followed by GLM examples in Chapter 10. In Chapters 11 through 13 we show how to apply GLM models on spatial data. In Chapter 14 we discuss time-series techniques and how to implement them in INLA. Finally, in Chapters 15 and 16 we analyse spatial-temporal models in INLA.
(source: Nielsen Book Data)9780957174191 20170814

• Introduction (Definitions)Heinz Mehlhorn, NNNN 1. Antarctic biodiversity of aquatic parasitesDavid J Marcogliese (david.marcogliese@ec.gc.ca) 2. Antarctic DigeneaRod Bray (r.bray@nhm.ac.uk) 3. Antarctic MonogeneaD C Kritsky (kritdela@isu.edu) W A Boeger (wboeger@gmail.de) 4. Antactic Cestoda and NematodaAnna Rocka (arocka@twarda.pan.pl)Zdzislaw Laskowski (laskowz@twarda.pan.pl) 5. Antarctic anisakid NematodaSven Klimpel, Thomas Kuhn, Julian Munster, NNNN 6. Antarctic AcanthocephalaKrzysztof Zdzitowiecki (kzdzit@twarda.pan.pl)Zdzislaw Laskowski (laskowz@twarda.pan.pl) 7. Antarctic parasitic CrustaceaJu-Shey Ho (jsho@csulb.edu)Oder: Jason D. Williams (biojdw@hofstra.edu) 8. Antarctic lice of sealsHeinz Mehlhorn (mehlhorn@uni-duesseldorf.de) 9. Antarctic leechesAleksander Bielecki (alekb@uwm.edu.pl) Magdalena Rokicka (magrok@biotech.ug.gda.pl) 10. Parasite communities of PenguinsJulia I Diaz (jidiaz@cepave.edu.ar) Andres Barbosa (barbosa@mncn.csic.es) 11. Parasite communities of mammalsRosemary A McFarlane (romcfarlane@bushlink.net.au) Oder:Simonetta Mattiucci (simonetta.mattiucci@uniroma1.it) Giuseppe Nascetti (nascetti@unitus.it) 12. Parasite communities of fish Simonetta Mattiucci (simonetta.mattiucci@uniroma1.it)Giuseppe Nascetti (nascetti@unitus.it)Oder: Mario George-Nascimento (mgeorgen@ucsc.cl) 13. History of Antarctic parasitological researchKen MacKenzie (k.mackenzie@abdn.ac.uk) William Hemmingsen (willy.hemmingsen@uit.no).
• (source: Nielsen Book Data)9783319463421 20170621

The Southern Oceans including Antarctic regions are peculiar and very sensitive water biotopes, where animal life and species interrelations are only poorly investigated. Especially the influence of parasites on their host species needs intensive consideration in times of global warming and worldwide pollution. Both factors may influence the finely balanced interrelationships between parasites and endangered hosts especially in specialized regions such as Antarctica. Before this background the present book offers a broad spectrum of important parasite-host interrelations in times of ecosystem changes written by experienced and renown international specialists.
(source: Nielsen Book Data)9783319463421 20170621

• Preface. Introduction. Biology and Ecology of Venomous Marine Snails. Profile of Venomous Marine Snails. Conotoxins. Envenomation of Cone Snails. Therapeutic Uses of Cone Snail Venoms. Index.
• (source: Nielsen Book Data)9781771883313 20170123

The first comprehensive volume exclusively on marine cone snails, this book provides descriptions of over 100 species of hazardous marine snails along with their biological and ecological characteristics; the characteristics of conotoxins; information on cone snail injuries and their treatment along with prevention measures; and the therapeutic and medicinal value of conotoxins, including as a powerful nonaddictive painkiller, an epilepsy drug, and more. Over 275 color illustrations accompany the text for easy identification. .
(source: Nielsen Book Data)9781771883313 20170123