STATISTICAL ECOLOGY: LITERATURE

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1 STATISTICAL ECOLOGY: LITERATURE Backhaus, K., Erichson, B., Plinke, W. & Weiber, R. (2000). - Multivariate Analysenmethoden. Eine Anwendungsorientierte Einführung. Springer, Berlin. 660 pp. Zur Verwendung dieses Buches: Zielsetzung; Daten und Skalen; Einteilung multivariater Analysemethoden; Struktur-prüfende und Struktur-entdeckende Verfahren; Verwendung von SPSS; Daten und Daten-Editor; Variable definieren; Einfache Statistiken und Graphiken; Kommandosprache; Aufbau einer Syntaxdatei; Syntax der Kommandos; Kommandos zur Datendefinition; Prozedur- und Hilfskommandos; Ausführen der Syntaxdatei; die Systeme von SPSS. 1. Regressionsanalyse: Problemstellung; Formulierung des Modells; Schätzung der Regressionsfunktion; Einfache und multiple Regression; Prüfung der Regressionsfunktion; Bestimmtheitsmass; F-Statistik; Standardfehler der Schätzung; Prüfung der Regressionskoeffizienten; t-test der Regressionskoeffizienten; Konfidenzintervalle; Prüfung auf Verletzung der Prämissen des Regressionsmodells; Nichtlinearität; Unvollständigkeit des Modells, Heteroskedastizität; Autokorrelation; Multikollinearität; Nicht-Normalverteilung der Störgrössen; Blockweise und schrittweise (Stepwise) Regressionsanalyse; SPSS- Kommandos; Mathematischer Anhang. 2. Varianzanalyse: Problemstellung; Einfaktorielle und zweifaktorielle Varianzanalyse; Erweiterungen; Anwendungsempfehlungen; SPSS-Kommandos. 3. Logistische Regression: Problemstellung; Einführung; Rechensatz der Logistischen Regression; Schätzung der Koeffizienten; Beurteilung der Güte des Gesamtmodells; Aussreisserdiagnostik; Beurteilung einzelner unabhängiger Variablen; Interpretation der Ergebnisse; Anwendungsempfehlungen; SPSS-Kommandos; Mathematischer Anhang. 4. Diskriminanzanalyse: Problemstellung; Defintion der Gruppen; Formulierung und Schätzung der Diskriminanzfunktion; Diskriminanzkriterium; Geometrische Ableitung; Normierung der Diskriminanzfunktion; Vergleich mit Regressionsanalyse; Mehrfache Diskriminanzfunktionen; Prüfung von Klassifikation, Diskriminanzkriterium, Merkmalsvariablen; Schrittweise (Stepwise) Diskriminanzanalyse; Klassifizierung von neuen Elementen; Klassifizierungsfunktionen; Distanzkonzept; Wahrscheinlichkeitskonzept; Berechnung von Klassifizierungswahrscheinlichkeiten; Überprüfung der Klassifizierung; Anwendungsempfehlungen; SPSS-Kommandos; Behandlung von Missing Values; Mathematischer Anhang. 5. Kreuztabellierung und Kontingenzanalyse: Problemstellung; Untersuchung von 2 und von mehr als 2 Variablen; Anwendungsempfehlungen; SPSS-Kommandos. 6. Faktorenanalyse: Problemstellung; Variablenauswahl und Errechnung der Korrelationsmatrix; Korrelationsanalyse zur Aufdeckung der Variablenzusammenhänge; Eignung der Korrelationsmatrix; Extraktion der Faktoren; Fundamentaltheorem; Graphische Interpretation von Faktoren; Problem der Faktorextraktion; Bestimmung der Kommunalitäten; Zahl der zu extrahierenden Faktoren; Faktoreninterpretation; Bestimmung der Faktorwerte; Anwendungsempfehlungen; Probleme; Missing Values; SPSS-Kommandos. 7. Clusternanalyse: Problemstellung; Quantifizierung der Ähnlichkeit zwischen den Objekten; Algorithmen zur Gruppenbildung; Partitionierende und hierarchische Verfahren; Anwendungsempfehlungen; SPSS- Kommandaos. 8. Der LISREL-Ansatz der Kausalanalyse: Problemstellung; Grundgedanke der Kausalanalyse; Begriff der Kausalität (Kovarianz u. Korrelation); Überprüfung kausaler Zusammenhänge im LISREL-Modell; Pfadanalyse; Hypothesen; Schätzung der Parameter; Interpretation; Test der Modellstruktur; Anwendungsempfehlungen; LISREL-Kommandos. 9. Multidimensionale Skalierung: Problemstellung; Aufbau und Ablauf einer MDS; Messung von Ähnlichkeiten; Wahl des Distanzmodells; Ermittlung der Konfiguration; Zahl und Interpretation der Dimensionen; Aggregation von Personen; Einbeziehung von Präferenzurteilen oder Eigenschaftsurteilen; Anwendungsempfehlungen; SPSS-Kommandos. 10. Conjoint measurement: Problemstellung; Eigenschaften und Eigenschaftsausprägungen; Erhebungsdesign; Datenauswertung; Anwendungsempfehlungen; SPSS- 1

2 Kommandos. 11. Anhang: verwendete Datensätze; Tabellen: t, F, c nach Cochran, Chi-Quadrat, Durbin-Watson, q-werte. Alle Verfahren mit Fallbeispielen illustriert! Collins, C. & Seeney, F. (1999). - Statistical experiment design and interpretation. An introduction with agricultural examples. J. Wiley & Sons, Ltd. Chichester. 280 pp. 1. Introduction. 1.1 Notation. 1.2 A little history. 1.3 Populationv versus samples. 2. Planning. 2.1 Formulating the edea. 2.1 Defining the objectives. 2.3 Defining the population. 2.4 Formulating hypotheses. 2.5 Hypothesis testing 2.6 Anticipating treatment differences. 3. Design. 3.1 Variables. 3.2 Choosing the treatments. 3.3 Constraints. 3.4 Replication. 3.5 Blocking 3.6 Randomization. 3.7 Covariates. 3.8 Confounding. 4. Trial structure. 4.1 Considerations. 4.2 Single-treatment factor designs. 4.3 Multi-treatment factor designs. 4.4 Some other designs. 5. Data entry and exploration. 5.1 Data entry. 5.2 Data. 5.3 Data checking. 5.4 Data exploration. 6. Analytical techniques. 6.1 Parametric techniques: Comparison of one or two means; comparison of two variances; analysis of variance: comparison of more than two means; complications associated with analysis of variance; basic experiment designs; analysis of means; regression analysis; multiple linear regression. 6.2 Nonparametric techniques: comparison of single samples; comparison of two paired samples; comparison of two independent samples; comparison of more than two matched samples; comparison of more than two independent samples; comparison of tests. 6.3 Comparison of parametric and non-parametric techniques. 7. Other analytical techniques. 7.1 Multivariate analysis. 7.2 Time series analysis. 8. Aspects of computing. Appendices. I. Glossary of statistical terms. II. Analysis of variance formulae. Index. Davis, J.C. (2002). - Statistics and Data Analysis in Geology. J. Wiley & Sons, Inc. New York. 638 pp. 1. Introduction. 2. Elementary statistics Probability; Continuous random variables; Statistics; Summary statistics; Joint variation of 2 variables; Induced correlation; Logratio transformation; Comparing normal populations; Central limits theorem; Testing the mean; P-values; t-distribution; F- distribution (equality of variances; ANOVA); Chi-square distribution; The logarithmic and other transformations; Nonparametric methods (Mann-Whitney; Kruskal-Wallis; Nonparametric correlation; Kolmogorov-Smirnov test. 3. Matrix algebra Elementary operations; Multiplication; Inversion; Transposition; Determinants; Eigenvalues and Eigenvectors. 4. Analysis of sequences of data Geologic measurements of sequences; Markov chains; Runs Test; Least squares methods and regression analysis; Splines; Segmenting sequences; Autocorrelation; Cross-correlation; Semivariograms; Spectral analysis. 5. Spatial analysis Distribution of points; Distribution of lines; Analysis of directional data; Spherical distributions; Fractal analysis; Shape; Spatial analysis by ANOVA; Computer contouring; Trend surfaces; Kriging. 6. Analysis of multivariate data Multiple Regression; Discriminant functions; Multivariate extensions of elementary statistics; Cluster analysis; Principal component analysis; R-mode factor analysis; Q-mode factor analysis; Principal coordinates analysis; Correspondence analysis; Multidimensial scaling; Simultaneous R- and Q-mode analysis; Multigroup discriminant functions; Canonical correlation. Appendix Tables Cumulative probabilities for the standardized normal distribution; Critical values of t; Critical values of F; Critical values of Chi-square; Probabilities of occurrence of specified values of the Mann-Whitney Wx test statistic; Critical values of Spearman ρ; Critical values of D in the Kolmogorov-Smirnov goodness-of-fit test; Critical values of the Lilliefors test statistic, T, for testing goodness of fit to a normal distribution; Maximum likelihood estimates of the concentration parameter k for calculated values of ; Critical values of for Raleygh's 2

3 test for the presence of a preferred trend; Critical values of for the test of uniformity of a spherical distribution. Index. Duller, C. (2007). - Einführung in die Statistik mit EXCEL und SPSS. Physica-Verlag (Springer), Heidelberg. 285 pp. I EINFÜHRUNG. 1 Was ist Statistik? 2 Ablauf einer statistischen Analyse: Planung; Merkmale und Merkmalstypen; Methode der Datengewinnung; Datenerfassung und -aufbereitung; Abschlussbericht; Problemfelder in der Praxis. 3 Anmerkungen zum Umgang mit dem Computer. 4 Das Tabellenkalkulationsprogramm EXCEL. 5 Das Statistikpaket SPSS. II DESKRIPTIVE STATISTIK. 6 Eindimensionale Häufigkeitsverteilungen: Diskrete Merkmale; Stetige Merkmale; grafische Darstellung von Verteilungen; Die empirischen Verteilungsfunktion. 7 Masszahlen für eindimensionale Verteilungen: Lagemasse (Mittel, Median, Modus, Quantile); Streuungsmasse; Eigenschaften von Lage- und Streuungsmassen; Schiefe und Wölbung. 8 Multivariate deskriptive Statistik: Zweidimensionale Häufigkeitsverteilungen; Randverteilungen; Bedingte Verteilung; Masse für den Zusammenhang zweier Merkmale; Grafische Darstellung; Korrelation und Kausalität. 9 Die Regressionsanalyse: Die lineare Einfachregression. III WAHRSCHEINLICHKEITSRECHNUNG. 10 Wahrscheinlichkeitsrechnung: Exkurs: Mengenlehre; Grundbegriffe der Wahrscheinlichkeitsrechnung; Denkmodelle für den Wahrscheinlichkeitsbegriff; Rechnen mit Wahrscheinlichkeiten. 11 Diskrete Wahrscheinlichkeitsverteilungen: Dichte und Verteilungsfunktion; Lage- und Streuungsparameter; spezielle diskrete Verteilungen; Rechnen mit diskreten Verteilungen. 12 Stetige Wahrscheinlichkeitsverteilungen: Dichte und Verteilungsfunktion; Unabhängigkeit zweier stetiger Zufallsvariablen; Lage- und Streuungsparameter; Die stetige Gleichverteilung; Die Normalverteilung; Approximation durch die Normalverteilung. IV SCHLIESSENDE STATISTIK. 13 Die Gedankenwelt der schliessenden Statistik: Stichprobenverteilung; Parameterschätzung; Schätzen von Anteilen bzw. Mittelwerten; Konfidenzintervalle in EXCEL und SPSS. 14 Statistisches Testen: Grundbegriffe der Testtheorie; Testen von Hypothesen über Anteile. bzw. Mittelwerte; Testen von Hypothesen in EXCEL und SPSS; Der Chi-Quadrat-Test auf Unabhängigkeit. Tabellen. Lösungen zu den Übungsaufgaben. Symbolverzeichnis. Literaturverzeichnis. Sachverzeichnis. Dytham, C. (1999). - Choosing and Using Statistics. A Biologist's Guide. Blackwell Science Ltd. Oxford. 218 pp. 1. Preface. 1. Eight steps to successful data analysis. 2. The basics. 3. Choosing a test: a key. 4. Hypothesis testing, sampling and experimental design. 5. Statistics, variables and distributions. 6. Descriptive and presentional techniques. 7. The tests 1: tests to look at differences. 8. The tests 2: tests to look at relationships. 9. The tests 3: tests for data exploration. 10. Symbols and letters used in statistics. 11. Assumptions of the tests. 12. Hints and tips. Glossary. Bibliography and short reviews of selected texts. Index. Ehrenberg, A.S.C. (1986). - Statistik oder der Umgang mit Daten. Eine praktische Einführung mit Übungen. VCH Verlagsgesellschaft mbh, Weinheim (Deutschland). 344 pp. I. Statistische Daten: 1. Mittelwerte. 2. Streuung. 3. Strukturierte Tabellen. II. Häufigkeitsverteilungen: 4. Beobachtete Verteilungen. 5. Theoretische Verteilungen. 6. Wahrscheinlichkeitsmodelle. III. Stichproben: 7. Stichprobenauswahl. 8. Variation der Stichprobenmittelwerte. 9. Schätzuung. 10. Statistische Tests. IV. Beziehungen: 11. Korrelation. 12. Regression. 13. Viele Datenmengen. 14. Viele 3

4 Variablen. V. Mitteilung von Daten: 15. Rundung. 16. Tabellen. 17. Graphische Darstellungen. 18. Worte. VI. Empirische Verallgemeinerung: 19. Beschreibung. 20. Erklärung. 21. Beobachtet und Experiment. Anhang A: Statistische Tabellen. Anhang B: Lösungen für Übungsaufgaben. Register. Grundlegende Formeln. Grundlegende Symbole. Falissard, B. (1996). - Comprendre et utiliser les statistiques dans les sciences de la vie. Masson. 314 pp. Avant-propos. Introduction. Première partie: Méthodes univariées. 1. Les représentations graphiques. 2. L'estimation. 3. Les tests d'hypothèse. Deuxième partie: Méthodes multivariées. 1. Régression linéaire et analyse de variance. 2. T2 de Hotelling, MANOVA. 3. Mesures répétées. 4. Régression logistique. 5. Analyse discriminante. 6. Modèle log-linéaire. 7. Analyse en clusters. 8. Analyse en composantes principales. 9. Analyse des correspondances. 10. Multidimensional scaling. 11. Analyse des corrélations canoniques. 12. Analyse factorielle. 13. Données de survie. Bibliographie. Index. Fry, J.C. (Ed.)(1994). - Biological Data Analysis. A practical Approach. Oxford University Press. 418 pp. List of contributors. Symbols and abbreviations used in Part 1. Symbols and abbreviations used in Part 2. PART 1: STATISTICS. 1. One-way analysis of variance: introduction; basic ANOVA; assumptions; transformation; outliers; comparison of means; what if assumptions are not met (J.C. Fry). 2. Crossed and hierarchical analysis of variance: introduction; diagnostic checking; balanced experiments with two factors; balanced experiments with more than two factors; tests on means of main effects; unbalanced experiments; designing experiments (T.C. Iles). 3. Bivariate regression: introduction; least squares linear regression; comparison of regression lines; robust linear regression; curved regression lines (J.C. Fry). 4. Multiple regression: introduction; estimation and interpretation of the regression equation; tests and confidence intervals; diagnostic checking; multicollinearity; selection of variables; dummy variables (T.C. Iles). 5. Ordination: why do we need multivariate analysis; ordination methods (PCA; Q- or R-mode; reciprocal averaging; detrended correspondence analysis); relating species distribution with environmental factors (P.F. Randerson). 6. Classification: measures of resemblance; clustering methods; testing classifications; character coding and weighting; some applications of cluster analysis; computation (P.D. Bridge). 7. Time series Analysis: preliminary analysis; autocorrelation; analysis in the frequency domain; model fitting; prediction; regression models; several time series; missing values (F.D.J. Dunstan). PART 2: MODELLING. 8. Dynamic models of homogeneous systems: the modelling procedure (D.W. Bowker). 9. Compartment models: introduction; model structure and function; model behaviour; special topics (R.G. Wiegert). Appendix A: Software packages. Appendix B: Statistical tables. Index. Heiberger, R.M. & Holland, B. (2004). - Statistical analysis and data display. An intermediate course with examples in S-Plus, R, and SAS. Springer Verlag, New York. 729 pp Preface. Audience; structure; data and programs; software; exercises. 1. Introduction and motivation. 1.1 Statistics in context. 1.2 Examples of uses of statistics. 1.3 The rest of the book. 2. Data and statistics. 2.1 Types of data. 2.2 Data display and calculation. 2.3 Importing data. 2.4 Analysis with missing data. 2.5 Tables and graphs. 2.6 Files for Statistical analysis and data display. 3. Statistics concepts. 3.1 A brief introduction to probability. 3.2 Random variables and probability distributions. 3.3 Concepts that are used when discussing distributions. 3.4 Three probability 4

5 distributions. 3.5 Sampling distributions. 3.6 Estimation. 3.7 Hypothesis testing. 3.8 Examples of statistical tests. 3.9 Power and operating characteristics Sampling Exercises. 4. Graphs Definition; examples - ecological correlation, life expectancy; scatterplot; scatterplot matrices; data transformations; SAS graphics; exercises. 4.3 Scatterplots. 4.4 Scatterplot matrix 5. Introductory inference. 5.1 Normal (z) intervals and tests. 5.2 t-intervals and tests for the mean of a population with unknown standard deviation. 5.3 Confidence interval on the variance or standard deviation of a normal population. 5.4 Comparisons of two populations based on independent samples. 5.5 Paired data. 5.6 Sample size determination. 5.7 Goodness of fit. 5.8 Normal probability plots and quantile plots. 5.9 Kolmogorov-Smirnov goodness of fit tests Exercises. 6. One-way analysis of variance. 6.1 Example - catalyst data. 6.2 Fixed effects. 6.3 Multiple comparisons (Tukey). 6.4 Random effects Examples catalyst data, batch data, Turkey data. 6.9 Contrasts Tests of homogeneity of variance Exercises. 7. Multiple comparisons. 7.1 Multiple comparison procedures. 7.2 The mean-mean multiple comparison display. 7.3 Exercises. 8. Linear regression by least squares. 8.1 Introduction. 8.2 Example - body fat data. 8.3 Simple linear regression. 8.4 Diagnostics. 8.5 Graphics Exercises. 9. Multiple regression - more than one predictor. 9.1 Regression with two predictors - least squares geometry. 9.2 Multiple regression - algebra. 9.3 Multiple regression - Two-X analysis Geometry of multiple regression. 9.5 Programming. 9.6 Partial F-tests. 9.8 Polynomial models. 9.9 Models without a constant term Prediction Example - Longley data Variable selection Residual plots Example - U.S. air pollution data Exercises. 10. Multiple regression - dummy variables and contrasts Dummy (Indicator) variables Example - height and weight Equivalence of linear independent X-variable for regression Polynomial contrasts and orthogonal polynomials Analysis using a concomitant variable (analysis of covariance) Example - Hot dog data ancova function Exercises. Multiple regression - regression diagnostics Example - rent data Checks on model assumptions Case statistics Exercises. 12. Two-way analysis of variance. 13. Design of experiments - factorial designs Example - display panel data Statistical model Main effects and interactions Two-way interaction plot Sums of squares in the two-way ANOVA table Treatment and blocking factors Fixed and random effects Randomized complete block designs 12.9 Example - the blood plasma data Random effects models and mixed models Introduction to nesting Example - display panel data Example - the Rhizobium data Example - animal feed data Exercises. 13. Designs of experiments - factorial designs A three-way ANOVA - muscle data Latin square designs Simple effects for interaction analyses Nested factorial experiment Specification of model formulas Sequential and conditional tests Exercises.14. Design of experiments - complex designs Confounding Split plot designs Example - Yates oat data Introduction to fractional factorial designs Introduction to crossover designs Example - apple tree data Example - testscore.dat The Tukey one degree of freedom for nonadditivity Exercises. 15. Bivariate statistics - discrete data Two-dimensional contingency tables - Chi-square analysis Two-dimensional contingency tables - Fisher's Exact test Simpson's paradox Relative risk and odds ratios Retrospective and prospective studies Mantel-Haenszel test Example - Salk polio vaccine Exercises.16. Nonparametrics Introduction Sign test for the location of a single population Comparing the locations of paired populations Mann-Whitney test for two independent samples Kruskall-Wallis test for comparing the locations of at least three populations. 17. Logistic regression Example - the Space Shuttle Challenger disaster Estimation Examples budworm data, lymph nodes Numerical printout Graphics Model specification Fitting models when the response is a sample proportion LogXact Exercises. 18. Time series analysis. 18.1Introduction The ARIMA approach to time series modelling Autocorrelation Analysis steps Some algebraic development, including forecasting Graphical displays for time series analysis Models with seasonal components. 5

6 18.8 Example of a seasonal model - the monthly co2 data Exercises. A. Software. A.1 Statistical software. A.2 Text editing software. A.3 Word processing software. A.4 Graphics display software. A.5 Operating systems. A.6 Mathematical fonts. A.7 Directory structure. B. S-Plus and R. B.1 Create your working directory and make the HH library available. B.2 Using S-Plus and R with HH. B.3 S- Plus for Windows. B.4 HH library functions. B.5 Learning the S language. B.6 S language style. B.7 S- Plus inexplicable error messages. B.8 Using S-Plus with Emacs and ESS. B.9 Constructing the HH library with S-Plus and R. C. SAS. C.1 Make the HH library available. C.2 Using SAS with HH. C.3 Macros. C.4 Learning the SAS language. C.5 SAS coding conventions. D. Probability distributions. D.1 Common probability distributions with S-Plus and SAS command. D.2 Noncentral probability distributions. E. Editors. E.1 Working style. E.2 Typography. E.3 Emacs and ESS. E.4 Microsoft Word. E.5 Microsoft Excel. E.6. Exhortations, some of which are writing style. F. Mathematics preliminaries. F.1 Algebra review. F.2 Elementary differential calculus. F.3 An application of differential calculus. F.4 Topics in matrix algebra. F.5 Combinations and permutations. F.6 Exercises. G. Graphs based on cartesian products. G.1 Structured sets of graphs. G.2 Scatterplot matrices splom and xysplom. G.3 Cartesian products of sets of functions. G.4 Graphs requiring multiple calls to xysplom. G.5 Asymmetric roles for the row and column sets. G.6 Rotated plots. G.7 Squared residual plots. G.8 Alternate presentations. References. List of datasets. Index. Köhler, W., Schachtel, G. & O. Voleske (1996). - Biostatistik. Springer Verlag, Berlin. 2. Auflage. 285 pp. Einleitung. 1. Merkmalauswahl. 2. Beschreibende Statistik. 3. Einführung in die schliessende Statistik. 4. Varianzanalyse bei normalverteilten Gesamtheiten. 5. Varianzanalyse bei ordinalskalierten Daten. 6. Regressionsanalyse. 7. Zur Versuchsplanung. Anhang: Einige Grundlagen der Wahscheinlichkeitsrechnung. Literaturhinweise. Tabellen-Anhang. Sachverzeichnis. Auswahl englischer Fachausdrücke. Krebs, C.J. (1999). - Ecological methodology. Addison-Wesley Publ. Co, New York. 576 pp. 1. Ecological data. Part I. Estimating abundance in animal and plant populations. 2. Estimating abundance: mark-recapture techniques. 2.1 Petersen method. 2.2 Schnabel method. 2.3 Jolly-Seber Method. 2.4 Tests of equal catchability. 2.5 Planning mark-recapture study. 2.6 What to do if nothing works. 3. Estimating abundance: removal methods and resight methods. 3.1 Exploited population techniques. 3.2 Resight methods. 3.3 Computer programs for population estimators. 3.4 Enumeration methods. 3.5 Estimating density. 4. Estimating methods: quadrat counts. 4.1 Quadrat size and shape. 4.2 Statistical distributions. 4.3 Line intercept method. 4.4 Aerial survey s of wildlife populations. 5. Estimating abundance: line transects and distance methods. 5.1 Line intersects. 5.2 Distance methods. Part II. Spatial pattern in animal and plant populations. 6. Spatial pattern and indices of dispersion. 6.1 Methods for spatial maps. 6.2 Continuous quadrats. 6.3 Spatial pattern from distance methods. 6.4 Indices of dispersion for quadrat counts. Part III. Sampling and experimental design. 7. Sample size determination and statistical power. 7.1 Sample size for continuous variables. 7.2 Sample size for discrete variables. 7.3 Sample size for specialized ecological variables. 7.4 Statistical power anaylsis. 7.5 What to do if nothing works. 8. Sampling designs: random, adaptive, and systematic sympling. 8.1 Simple random sampling. 8.2 Stratified random sampling. 8.3 Adaptive sampling. 8.4 Systematic sampling. 8.5 Multistage sampling. 9. Sequential sampling. 9.1 Two alternative hypotheses. 9.2 Three alternative hypotheses. 9.3 Stopping rules. 9.4 Ecological measurements. 9.5 Validating sequential sampling plans. 10. Experimental designs General principles of experimental design Types of experimental designs Environmental impact studies Where should I 6

7 go next? Part IV. Estimating community parameters. 11. Similarity coefficients and cluster analysis Measurement of similarity Data standardization Cluster analysis Recommendations for classifications Other multivariate techniques. 12. Species diversity measures Background problems Concepts of species diversity Species richness measures Heterogeneity measures Evenness measures Recommendations. 13. Niche measures and resource preferences What is a resource? 13.2 Niche breadth Niche overlap Measurement of habitat and dietary preferences. Part V. Ecological miscellanea. 14. Estimation of survival rates Finite and instantaneous rates Estimation from life tables Estimation of survival from age composition Radiotelemetry estimates of survival Estimation of bird survival rates Testing for differences in survival rates. 15. The garbage can Tranformations Repeatability Central trend lines in regression Measuring temporal variability of populations Jackknife and bootstrap techniques. Appendices. References. Index. Legendre, L. & Legendre, P. (1998). - Numerical Ecology. Developments in Environmental Modelling 20. 2nd Edition. 853 pp. Elsevier Scientific Publ. Co, Amsterdam. Preface. 1. Complex ecological data sets: autocorrelation and spatial structure; statistical testing by permutation; computers; ecological descriptors; coding; missing data. 2. Matrix algebra: a summary. 3. Dimensional analysis in ecology. 4. Multidimensional quantitative data: multidimensional variables and dispersion matrix; correlation matrix; multinormal distribution; principal axes; multiple and partial correlations; multinormal conditional distribution; tests of normality and multinormality. 5. Multidimensional semiquantitative data: nonparametric statistics (one-dimensional and multidimensional). 6. Multidimensional qualitative data: principles; information and entropy; two-way contingency tables; correspondence; species diversity. 7. Ecological resemblance: Q and R analysis; Q-mode, similarity and distance coefficients; R-mode, coefficients of dependance; choice of a coefficient; computer programs and packages. 8. Cluster analysis: search for discontinuities; basic model, single linkage; cophenetic matrix and ultrametric property; panoply of methods; hierarchical agglomerative clustering; reversals; hierarchical divisive clustering; partitioning by K-means; species clustering, biological associations; seriation; clustering statistics; cluster validation; representation and choice of method. 9. Ordination in reduced space: projecting data sets in a few dimensions; principal component analysis (PCA); principal coordinate analysis (PCoA); nonmetric multidimensional scaling (MDS); correspondence analysis (CA); factor analysis. 10. Interpretation of ecological structures: ecological structures; clustering and ordination; mathematics of ecological interpretation; regression; path analysis; matrix comparisons; the 4th-corner problem. 11. Canonical analysis: principles of canonical analysis; redundancy analysis; canonical correspondence analysis (CCA); partial RDA and CCA; canonical correlation analysis (CCorA); discriminant analysis; canonical analysis of species data. 12. Ecological data series: ecological series; characteristics of data series and research objectives; trend extraction and numerical filters; periodic variability (correlogram, periodogram, spectral analysis); detection of discontinuities in multivariate series; Box-Jenkins models; computer programs. 13. Spatial analysis: spatial patterns; structure functions; maps; patches and boundaries; unconstrained and constrained ordination maps; causal modelling, partial canonical analysis and partial Mantel analysis; computer programs. Bibliography. Tables. Subject index. Leyer, I. and Wesche, K. (2007). - Multivariate Statistik in der Ökologie. Eine Einführung. Springer, Berlin. 221 pp. 1. Einleitung: Uni- und multivariate Daten. 2. Statistische Grundlagen: Terminologie; Datentypen - Skalenniveaus; Korrelation; Regression; Lineare Regression; Multiple lineare Regression; Unimodale 7

8 Modelle (Gauss'sche Regression); logistische und Gauss'sche logistische Regression. 3. Datenmanipulationen: Normalverteilung und Transformationen; Standardisierungen; Transponieren, Umkodieren und Maskieren. 4. Ähnlichkeits- und Distanzmasse: Qualitative und quantitative Masse; Distanzmasse. 5. Ordinationen - das Prinzip: Dimensionsreduktion als Analysestrategie; Polare Ordination. 6. Korrespondenzanalyse (CA): Prinzip; Mathematische Artefakte - Probleme; Detrended CA (DCA). 7. Interpretation von CA und DCA: Umweltvariablen - Interaktionen von Effekten; Ordination und Umweltdaten. 8. Kanonische Ordination (constrained ordination): Prinzip; Interpretation; Forward selection. 9. Hauptkomponentenanalyse (PCA): Prinzip (geometrische Herleitung und mathematischer Ansatz); Optionen; Stärken u. Schwächen; Faktorenanalyse. 10. Lineare Methoden und Umweltdaten: PCA und RDA: Indirekte Ordination; Prinzip der Redundanzanalyse(RDA) und Interpretation. 11. Partielle Ordination und variance partitioning: Kovariablen; Partielle PCA, CA, DCA; Partielle kanonische Ordination. 12. Multidimensionale Skalierung: Metrische und nichtmetrische Skalierung. 13. Klassifikation - das Prinzip: Klassifikationsstrategien. 14. Agglomerative Klassifikationsverfahren: Clusteranalyse - Grundlagen; Auswertung von Dendrogrammen. 15. Divisive Klassifikationsverfahren: Ordination Space Partitioning; TWINSPAN. 16. Sonstige Verfahren zur Beschreibung von Gruppenstrukturen: nichthierarchische agglomerative oder divisive Verfahren; Diskriminanzanalyse. 17. Permutationsbasierte Tests: Prinzip; Test auf Signifikanz von Ordinationsachsen; Mantel-Test; Gruppenvergleiche - Mantel-Tests und MRPP (Multiple Response Permutation Procedure); Procrustes- Analysen; Indicator Species Analysis; Randomisierungsverfahren. Literatur. Sachverzeichnis. Lorenz, R.J. (1984). - Biometrie. Grundbegriffe der Biometrie. Gustav Fischer Verlag Stuttgart. 241 pp. 1. Biometrie, eine lästige Routine? 2. Beschreibende Statistik (Häufigkeitsverteilungen, Mittelwert, Median, Streuung, Varianz, Assoziations-, Kontingenz- u. Korrelationsmass, Regressionsanalyse). 3. Grundbegriffe der Wahscheinlichkeitsrechnung. 4. Wahrscheinlichkeitsverteilungen als Modelle realer Zufallsprozesse. 5. Schliessende Statistik: Das Schätzen unbekannter Grössen (Konfidenzintervalle, usw). 6. Schliessende Statistik: Signifikanztests (Vorzeichentest, Chiquadrattest, t-test, Mann- Whithney-Test). Anhänge: Formeln für die Binomial-, hypergeometrische-, Poisson- und Normalverteilung. Erzeugung von Zufallszahlen. Tafeln. Literatur. Verzeichnis der Beispiele. Sachverzeichnis. Lozan, J.L. & Kausch, H. (2007). - Angewandte Statistik für Naturwissenschaftler. 4. Auflage. Wissenschaftliche Auswertungen, Hamburg. 304 pp. Vorwort. Liste der verwendeten Symbole. Liste der wichtigsten Tests. Liste der Tabellen. Fachwörter auf deutsch, spanisch und english. 1. EINFÜHRUNG. 2. ERLÄUTERUNG EINIGER FACHBE- GRIFFE. Die in der Statistik am häufigsten verwendeten Tabellen. 3. CHARACTERISIERUNG VON STICHPROBEN. Mittelwerte. Variabilität. Vertrauensbereiche. Schiefe und Exzess. Randomisierung. Mindestanzahl der Messungen zur Schätzung des Mittelwertes. Mindestanzahl der Messungen zur Schätzung der Standardabweichung. 4. DARSTELLUNG VON STICHPROBEN. Graphische Darstellung. Kurvenglättung. 5. THEORETISCHE VERTEILUNGEN: Normalverteilung. Standardisierung: z-transformation. Logarithmische Normalverteilung. Der zentrale Grenzwertsatz. Prüfung auf Nicht-Normalverteilung. Ausreisser-Test nach Nalimov. Transformation zu Normalverteilung. Andere theoretische Verteilungen (Binomial, Poisson). 6. VERGLEICH ZWEIER STICHPROBEN. Vergleich zweier nicht-verbundener Stichproben (Parametrische und parameterfreie Verfahren). Vergleich zweier verbundener Stichproben (Parametrische und parameterfreie Verfahren). 7. VARIANZANALYSE. Einfache Varianzanalyse (Parametrische und parameterfreie Verfahren). Zweifache Varianzanalyse. Dreifache Varianzanalyse. 8. PRÜFUNG VON ABHÄNGIGKEITEN. Prüfung auf Abhängikeit 8

9 stetiger Zufallsvariablen (Parametrische und parameterfreie Verfahren). Prüfung auf Abhängikeiten nicht-stetiger Zufallsvariablen. 9. MULTIVARIATE VERFAHREN. Multiple regression. Der partielle Korrelationskoeffizient. Kovarianzanalyse. Hauptkomponentenanalyse. Faktorenanalyse. Kanonische Korrelationsanalyse und Redundanzanalyse. Diskriminanzanalyse. Ähnlichkeits-Distanz-Methoden. Cluster-Analyse. 10. PROBITANALYSE. Probit-Transformation. Probit-Regression nach der graphischen Methode. Probit-Regression nach der Maximum-Likelihood-Methode. Vergleich von L50- Werten. 11. LOGITANALYSE. Logit-Transformation. Logit-Regression nach der Maximum-Likelihood-Methode. 12. ZITIERTE LITERATUR. 13. SACHVERZEICHNIS. Ludwig, J.A. & J. F. Reynolds. (1988). - Statistical ecology. A primer on methods and computing. John Wiley & Sons, New York. 337 pp. I. ECOLOGICAL COMMUNITY DATA: 1. Background. II. SPATIAL PATTERN ANALYSIS. 2. Background. 3. Distribution methods. 4. Quadrat-variance methods. 5. Distance methods. III. SPECIES-ABUNDANCE RELATIONS. 6. Background. 7. Distribution models. 8. Diversity indices. IV. SPECIES AFFINITY. 9. Background. 10. Niche overlap indices. 11. Interspecific association. 12. Interspecific covariation. V. COMMUNITY CLASSIFICATION. 13. Background. 14. Resemblance functions. 15. Association analysis. 16. Cluster analysis. VI. COMMUNITY ORDINATION. 17. Background. 18. Polar ordination. 19. Principal component analysis. 20. Correspondence analysis. 21. Nonlinear ordinations. VI. COMMUNITY INTERPRETATION. 22. Background. 23. Classification interpretation. 24. Ordination interpretation. References. Index. Basic programs (included disk). McKillup, S. (2005). - Statistics explained. An introductory guide for life scientists. Cambridge University Press, Cambridge UK, 267 pp. 1. Introduction. 2. 'Doing science' - hypotheses, experiments, and disproof. 3. Collecting and displaying data. 4. Introductory concepts of experimental design. 5. Probability helps you make a decision about your results. 6. Working from samples - data, populations, and statistics. 7. Normal distribution - tests for comparing the means of one and two samples. 8. Type 1 and type 2 errors, power, and sample size. 9. Single factor analysis of variance. 10. Multiple comparisons after ANOVA. 11. Two factor analysis of variance. 12. Important assumptions of analysis of variance transformations and a test for equality of variances. 13. Two factor analysis of variance without replication, and nested analysis of variance. 14. Relationships between variables linear correlation and linear regression. 15. Simple linear regression. 16. Non-parametric statistics. 17. Non-parametric tests for nominal scale data. 18. Non-parametric tests for ratio, interval, or ordinal scale data. 19. Choosing a test. 20. Doing science responsibly and ethically. McPherson, G. (2001). Applying and interpreting statistics. A comprehensive guide. Springer Verlag, New York, 640 pp. Preface to the second edition. Preface to the first edition. Supplementary material on a web site. Plan of the book. Using the book. Commonly used symbols. 1. The importance of statistics in an information-based world. 1.1 The expanding role of statistics. 1.2 The place of statistics in investigation and operational activities. 1.3 Descriptive statistics. 1.4 Analytical statistics. Problems. 2. Data The factual information. 2.1 Data collection. 2.2 Types of data. Problems. 3. Statistical models the experimenters's view. 3.1 Components of a model. 3.2 The investigator's aims and statistical hypotheses

10 Distributional assumptions. 3.4 Design structure. Problems. 4. Comparing model and data. 4.1 Intuitive ideas. 4.2 The role of "statistics". 4.3 Measuring agreement between model and data. Problems. 5. Probability A fundamental tool of statistics. 5.1 Probability and statistics. 5.2 Sampling distributions. 5.3 Probability definitions and rules. 5.4 Random variables. Appendix. Combinatorial formulas. Problems. 6. Some widely used statistical models. 6.1 The binomial model. 6.2 The twostate population model. 6.3 A model for occurrences of events. 6.4 The multinomial model. 6.5 The normal distribution model. 6.6 The logistic model. Problems. 7. Some important statistics and their sampling distributions. 7.1 The binomial distribution. 7.2 The Poisson distribution. 7.3 The normal distribution. 7.4 The t-distribution. 7.5 The Chi-squared distribution. 7.6 The F-distribution. 7.7 Statistics based on signs and ranks Statistics based on permutations and simulation. Problems. 8. Statistical analysis the statistician's view. 8.1 The range in models and approaches. 8.2 Hypothesis testing. 8.3 Estimation. 8.4 Likelihood. 8.5 The Bayesian approach. 8.6 Choosing the best method. Problems. 9. Examining proportions and success rates. 9.1 Experimental aims. 9.2 Statistical analysis. Problems. 10. Model and data checking Sources of errors in models and data Detecting errors in statistical models Analyzing residuals Checking data Data modification or method modification? Problems. 11. Questions about the "average" value Experimental considerations Choosing and applying statistical methods. Problems. 12. Comparing two groups, treatments, or processes Forms of comparison Comparisons based on success rates and proportions Comparisons of means Comparisons of medians A consideration of rare events. Problems. 13. Comparative studies, surveys, and designed experiments Types of investigations Experimental and treatment designs Paired comparisons Surveys Dermining sample sizes. Appendix Steps in randomly selecting a sample from a population. Appendix Steps in randomly allocation of units between treatments. Problems. 14. Comparing more than two treatments or groups Approaches to analysis Statistical models Statistical methods Selecting a method Comparisons based on medians. Problems. 15. Comparing mean response when there are three or more treatments Experimental and statistical aims Defining and choosing designs Model and data checking Analysis of variance and one-stratum designs Designs with experimental design structure Factorial arrangement of treatments More detailed comparison of treatment differences Analyzing counts of events using a generalized lineal model approach. Problems. 16. Comparing patterns of response Frequency tables The scope of applications Statistical models Statistical methods. Problems. 17. Studying relations between variables Univariate versus multivariate applications The scope of multivariate applications The building blocks of multivariate methods Seeking evidence of a relationship between two variables Studying relations among three or more scaled variables Relations between three or more categorical variables Comparing patterns in groups or treatments Studying relations between individual units or subjects Classification Assigning objects to predefined groups. Problems. 18. Prediction and estimation The role of explanatory variables Regression analysis Statistical models Statistical methods Practical considerations Statistical analysis Applications of regression analysis Experimental design and regression analysis Generalized linear models and their analysis. Problems. 19. Questions about variability Variability its measurement and application Variance components. Problems. 20. Cause and effect Statistical perspectives The allocation of causality scientific aims and statistical approaches Statistical methods in use. 21. Studying changes in response over time Applications Time series Time series statistical models and methods Statistical quality control. A. Tables for some common probability distributions. A.1 The normal distribution. A.2 The t-distribution. A.3 The Chi-squared distribution. References. Index. 10

11 Mühlenberg, M. (1993). - Freilandökologie. Quelle & Meyer. UTB 595. Heidelberg, 512 pp. 1. Einführung. 2. Das Habitat "Wiese. Ökologische Untersuchungen im Grünland. 2.1 Methoden zur Erfassung der Vegetation. 2.2 Methoden zur Erfassung abiotischer Faktoren. 2.3 Welche Tiere gehören zum Lebensraum "Wiese"? Bestandesaufnahme für ein Arteninventar. 2.4 Wie häufig ist eine Art? 2.5 Artenreichtum und Einnischung. 2.6 Welche Faktoren bestimmen den Aufenthaltsort der Tiere? 2.7 Bedeutung von Graslandstreifen in der Kulturlandschaft für den Naturschutz. 3. Das Habitat "Wald". 3.1 Untersuchungen zum Wasserhaushalt von Gehölzpflanzen. 3.2 Lichtmessung. 3.3 Vegetationsstruktur und Tierartendiversität. 3.4 Ökologische Sonderung der Singvögel bei der Nahrungssuche. 3.5 Der Waldrand, ein Saumbiotop mit eigener Lebensgemeinschaft? 4. Was hat ein Tier an Nahrung aufgenommen? Zur Nahrungsökologie einheimischer Wildtiere. 4.1 Gewölle und Rupfungen. 4.2 Haaratlas zum Bestimmen der Beutereste carnivorer Säugetiere. 4.3 Gräser- Cuticularatlas. 4.4 Pollenanalyse. 4.5 Bestimmung des Beutespektrums carnivorer Arthropoden. 4.6 Einschätzung der Verbissbelastung in Wäldern. 5. Studien an ökologischen Kleinsystemen. 5.1 Baumstümpfe. 5.2 Aas. 5.3 Pflanzengallen. 6. Probleme der Inselökologie. 6.1 Vergleich von Waldinseln und Trockenstandorten. 6.2 Vergleich von Einzelpflanzen als Habitatinseln. 7. Zur Bewertung von Habitaten für Naturschutzfragen. 8. Quantitative Auswertungsmethoden. 8.1 Empfehlungen und Einschränkungen. 8.2 Stichproben 8.3 Datenmanagement. 8.4 Statistischen Auswertung. 8.5 Ausgewählte Berechnungsmethoden für verschiedene Indices und Fangergebnisse (Faunenähnlichkeit, Diversität, Faunenveränderung, Nischenberechnungen, Dispersionsindices, Flächen-Arten-Beziehungen. Populationsdichteschätzungen). 9. Geräte und Geländekartierung. 10. Literatur. 11. Register. Pielou, E.C. (1984). - The interpretation of ecological data. A primer on classification and ordination. John Wiley & Sons, New York. 263 pp. 1. INTRODUCTION. 1.1 Data matrices and scatter diagrams. 1.2 Some definitions and other preliminaries. 1.3 Aim and scope of this book. 2. CLASSIFICATION BY CLUSTERING. 2.1 Introduction. 2.2 Near-neighbor clustering. 2.3 Farthest-neighbor clustering. 2.4 Centroid clustering. 2.5 Minimum variance clustering. 2.6 Dissimilarity measures and distances. 2.7 Average linkage clustering. 2.8 Choosing among clustering methods. 2.9 Rapid nonhierarchical clustering. Appendix: Apollonius's theorem. Exercises. 3. TRANSFORMING DATA MATRICES. 3.1 Introduction. 3.2 Vector and matrix multiplication. 3.3 The product of a data matrix and its transpose. 3.4 The eigenvalues and eigenvectors of a square symmetric matrix. 3.5 The eigenanalysis of XX' and X'X. Exercices. 4. ORDINATION. 4.1 Introduction. 4.2 Principal component analysis (PCA). 4.3 Four different versions of PCA. 4.4 Principal coordinate analysis. 4.5 Reciprocal averaging (RA) or Correspondence analysis (CA). 4.6 Linear and nonlinear data structures. 4.7 Comparisons and conclusions. 5. DIVISIVE CLASSIFICATION. 5.1 Introduction. 5.2 Constructing and partitioning a minimum spanning tree. 5.3 Partitioning a PCA ordination. 5.4 Partitioning RA and DCA ordinations. Exercices. 6. DISCRIMINANT ORDINATION. 6.1 Introduction. 6.2 Unsymmetric square matrices. 6.3 Discriminant ordination of several sets of data. Exercises. ANSWERS TO EXERCISES. GLOSSARY. BIBLIOGRAPHY. INDEX. Pruscha, H. (2006). - Statistisches Methodenbuch. - Verfahren, Fallstudien, Programmcodes. Springer-Verlag, Berlin. 412 pp. 11

12 1. Grundbegriffe und Elementare Methoden. 1.1 Einführung. 1.2 Ein-Stichproben Situation. 1.3 Zwei-Stichproben Situation. 1.4 Bivariate Stichprobe. 1.5 Weiterführende Verfahren. 1.6 Bestimmungsschlüssel. 2. Variananalyse. 2.1 Einfache Klassifikation. 2.2 Zweifache Klassifikation. 2.3 Klassifikation mit 3 Faktoren. 2.4 Ein Faktor mit korrelierten Beobachtungen. 2.5 Rang-Varianzanalysen. 3. Lineare Regressionsanalyse. 3.1 Multiple lineare Regression. 3.2 Standarfehler, Konfidenzintervalle. 3.3 Variablenselektion 3.4 Prüfen der Vorraussetzungen. 3.5 Korrelationsanalyse. 3.6 Kovarianzanalyse. 4. Kategoriale Datenanalyse. 4.1 Binäre logistische Regression. 4.2 Multikategoriale logistische Regression. 4.3 Zweidimensionale Tafel Unabhängikeitsproblem. 4.4 Zweidimensionale tafel Homogenitätsproblem. 4.5 Mehrdimensionale Kontingenztafeln. 4.6 Logit- Modelle. 5. Nichtlineare, nichtparametrische Regression. 5.1 Nichtlineare Regression. 5.2 Nichtparametrische Regression Kernschätzer. 5.3 Nichtparametrische Regression Splineschätzer. 5.4 Additive Modelle. 6. MANOVA und Diskriminanzanalyse. 6.1 Einfache MANOVA. 6.2 Zweifache MANOVA. 6.3 Diskriminanzanalyse. 7. Hauptkomponenten- und Faktorenanalyse. 7.1 Hauptkomponentenanalyse. 7.2 Faktorenanalyse. 8. Clusteranalyse. 8.1 Probleme, Begriffe, Methodik. 8.2 Hierarchische Verfahren. 8.3 Nicht-hierarchische verfahren. 8.4 Clustern bei kategorialen Daten. 9. Zeitreihenanalyse. 9.1 Einführung. 9.2 Kenngrössen stationärer Prozesse. 9.3 Schätzen und Testen der Kenngrössen. 9.4 Zeitreihenmodelle. 9.5 Modelldiagnostik und Prognose. 9.6 Bivariate Zeitreihen. A Fallstudien zur Statistik. B Quantil-Tabellen. Literaturverzeichnis. Index. Quinn, G.P. & Keough, M.J. (2002). - Experimental design and data analysis for biologists. Cambridge University Press. 537 pp. Preface. 1. Introduction. 2. Estimation. 3. Hypothesis testing. 4. Graphical exploration of data. 5. Correlation and regression. 6. Multiple and complex regression. 7. Design and power analysis. 8. Comparing groups or treatments. 9. Multifactor analysis of variance. 10. Randomized blocks and simple repeated measures: unreplicated two factor designs. 11. Split-plot and repeated measures designs: partly nested analyses of variance. 12. Analyses of covariance. 13. Generalized linear models and logistic regression. 14. Analysing frequencies. 15. Introduction to multivariate analyses. 16. Multivariate analysis of variance and discriminant analysis. 17. Principal components and correspondence analysis. 18. Multidimensional scaling and cluster analysis. 19. Presentation of results. References. Index. Scheiner, S. M. & J. Gurevitch (Eds) (1993). - Design and analysis of ecological experiments. Chapman & Hall, New York, 445 pp. Preface. Acknowledgments. Contributors. 1. Introduction: Theories, hypotheses, and statistics. 2. Exploratory data analysis and graphic display. 3. ANOVA: Experiments in controlled environments. 4. ANOVA and ANCOVA: Field competition experiments. 5. MANOVA: Multiple response variables and multispecies interactions. 6. Repeated-measures analysis: Growth and other time-dependent measures. 7. Time-series intervention analysis: Unreplicated large-scale experiments. 8. Nonlinear curve fitting: Predation and functional response curves. 9. Multiple regression: Herbivory. 10. Path analysis: Pollination. 11. Population sampling and bootstrapping in complex designs: Demographic analysis. 12. Failure-time analysis: Emergence, flowering, survivorship, and other waiting times. 13. The bootstrap and the jackknife: Describing the precision of ecological indices. 14. Spatial statistics: Analysis of field experiments. 15. Mantel tests: Spatial structure in field experiments. 16. Model validation: Optimal foraging theory. 17. Meta-analysis: Combining the results of independent experiments. References. Author index. Subject index. 12

13 Sokal, R.R. & F.J. Rohlf (1995). - Biometry. The principles and practice of statistics in biological research. W.H. Freeman, New York. 887 pp. Preface. Notes on the third edition. 1. Introduction. 2. Data in biology. 3. The handling of data. 4. Descriptive statistics. 5. Introduction to probability distribution: binomial and Poisson. 6. The normal probability distribution. 7. Estimation and hypothesis testing. 8. Introduction to the analysis of variance. 9. Single classification analysis of variance. 10. Nested analysis of variance. 11. Two-way analysis of variance. 12. Multiway analysis of variance. 13. Assumption of analysis of variance. 14. Linear regression. 15. Correlation. 16. Multiple and curvilinear regression. 17. Analysis of frequencies. 18. Miscellaneous methods. Appendix: Mathematical proofs. Bibliography. Author index. Subject index. Southwood, T. R. E.; Henderson, P.A. (2000). -Ecological Methods. 3rd Edition. Blackwell Science Ltd, Oxford. 575 pp. 1. Introduction to the study of animal populations. 1.1 Population estimates. 1.2 Errors and confidence. 2. The sampling programme and the measurement and description of dispersion. 2.1 Preliminary sampling. 2.2 The sampling programme. 2.3 Data processing. 2.4 Jackknife and bootstrap techniques. 2.5 Dispersion. 2.6 Sequential sampling. 3. Absolute population estimates using capturerecapture experiments. 3.1 Capture-recapture methods. 3.2 Methods of marking animals. 4. Absolute population estimates by sampling a unit of habitat: air, plants, plant products, and vertebrate hosts. 4.1 Sampling from the air. 4.2 Sampling from plants. 4.3 Sampling from vertebrate hosts. 5. Absolute population estimates by sampling a unit of aquatic habitat. 5.1 Open water. 5.2 Vegetation. 5.3 Bottom fauna. 6. Absolute population estimates by sampling a unit of soil or litter habitat: extraction techniques. 6.1 Sampling. 6.2 Bulk staining. 6.3 Mechanical methods of extraction. 6.4 Behavioural or dynamic methods. 6.5 Summary of the applicability of the methods. 7. Relative methods of population measurement and the derivation of absolute estimates. 7.1 Factors affecting the size of relative estimates. 7.2 The uses of relative methods. 7.3 Relative methods: catch per unit effort. 7.4 Relative methods: trappping. 7.5 Traps that attract the animals by some natural stimulus or a substitute. 8. Indices: the use of signs, products, and effects. 8.1 Arthodpods. 8.2 Vertebrate products. 8.3 Effects. 9. Wildlife population estimates by census and distance-measuring techniques. 9.1 Census methods. 9.2 Point- and line-survey methods. 9.3 Spatial distribution and plotless density estimators. 10. Observational and experimental methods for the estimation of natality, mortality, and dispersal Natality Mortality 10.3 Dispersal. 11. The construction, description, and analysis of age-specific life-tables Types of life-table and the budget The construction of a budget The description of budgets and life-tables The analysis of life-table data. 12. Age-grouping, time-specific life-tables, and predictive population models Age grouping Time-specific life-tables and survival rates. 13. Species richness, diversity, and packing Diversity Similarity and the comparison and classification of samples Species packing. 14. The estimation of productivity and the construction of energy budgets Estimation of standing crop Estimation of energy flow The energy budget, efficiencies, and transfer coefficients Identification of ecological pathways using stable isotopes Assessment of energy and time costs of strategies. 15. Large scale spatial and temporal studies and the classification of habitats Remote sensing Long-term studies Geographical information systems Habitat classification. References. Index. Steland, A. (2004) Mathematische Grundlagen der empirischen Forschung. Springer- Verlag. Berlin. 375 pp. 13

14 1. Grundlagen. 1.1 Mengenbegriff. 1.2 Elemente der Logik. 1.3 Zahlsysteme und elementares Rechnen. 1.4 Potenzen, Wurzeln. 1.5 Kombinatorik. 1.6 Reelle Zahlenfolgen. 1.7 Reihen. 1.8 Funktionen und Abbildungen. 1.9 Stetigkeit Exponentialfunktion Kontinuierliches Wachstum Der Logarithmus. 2. Deskriptive Statistik. 2.1 Grundbegriffe. 2.2 Klassifikation von Variablen. 2.3 Population und Stichproben. 2.4 Studiendesigns. 2.5 Datenmatrix (Datenbasis). 2.6 Visualisierung empirischer Daten (I). 2.7 Quantifizierung der Gestalt empirischer Verteilungen. 2.8 Streuung. 2.9 Quantile Schiefe versus Symmetrie Der Boxplot QQ-Plot (Quantildiagramm). 3. Differential- und Intergralrechnung. 3.1 Motivation. 3.2 Differenzierbarkeit. 3.3 Höhere Ableitungen. 3.4 Taylor-Entwicklung. 3.5 Optimierung von Funktionen. 3.6 Krümmungsverhalten. 3.7 Statistische Anwendungen der Optimierung. 3.8 Partielle Ableitung. 3.9 Motivation und Definition des Integrals Hauptsatz der Integralrechnung Integrationsregeln Integration empirischer Verlaufskurven. 4. Wahrscheinlichkeitsrechnung. 4.1 Grundbegriffe. 4.2 Verteilungsmodelle. 4.3 Grenzwertsätze und ihre Anwendung. 5. Schliessende Statistik. 5.1 Das Likelihood- Prinzip. 5.2 Güte statistischer Schätzer. 5.3 Konfidenzintervalle. 5.4 Experimente, Wahrscheinlichkeit und Entscheidungsverfahren. 5.5 Ein-Stichproben-Tests. 5.6 Zwei-Stichproben-Tests. 5.7 Korrelation und Regression. 5.8 Analyse von Kontingenztafeln. 5.9 Anpassungstests Multiples Testen Varianzanalyse Nichparametrische Varianzanalyse Multiple lineare Regression Logistische Regression. 6. Populationsdynamik. 6.1 Biologischer Hintergrund. 6.2 Diskrete Populationsdynamik. 6.3 Stetige Populationsdynamik. 7. Elemente der linearen Algebra. 7.1 Motivation. 7.2 Vektoren. 7.3 Geraden und Ebenen. 7.4 Längenmessung: Die Norm. 7.5 Winkelmessung: Das Skalarprodukt. 7.6 Matrizen und Gleichungssysteme. 7.7 Entwicklungsmodelle in diskreter Zeit. 7.8 Entwicklungsmodelle in stetiger Zeit. Anhang. A.1 Normalverteilung. A.2 t- Verteilung. A.3 χ 2 Verteilung. A.4 F-Verteilung. A.5 Studentisierte Spannweite. Literaturverzeichnis. Index. Tabachnick, B.G. & Fidell, L.S. (2001). - Using multivariate analysis. 4th Edition. Allyn and Bacon, Boston. 966 pp. 1. Introduction. Multivariate Statistics: Why? Domain of Multivariate Statistics: Numbers of IVs and DVs. Experimental and Nonexperimental Research. Computers and Multivariate Statistics. Why Not. Some Useful Definitions. Continuous, Discrete, and Dichotomous Data. Samples and Populations. Descriptive and Inferential Statistics. Orthogonality. Standard and Sequential Analyses. Combining Variables. Number and Nature of Variables to Include. Statistical Power. Data Appropriate for Multivariate Statistics. Data Matrix. Correlation Matrix. Variance-Covariance Matrix. Sum-of-Squares and Cross-Products Matrix. Residuals. 2. A Guide to Statistical Techniques: Using the Book. Research Questions and Associated Techniques. Degree of Relationship among Variables. Significance of Group. Prediction of Group Membership. Structure. Time Course of Events. A Decision Tree. Technique Chapters. Preliminary Check of the Data. 3. Review of Univariate and Bivariate Statistics. Hypothesis Testing. One-Sample z Test as Prototype. Power. Extensions of the Model. Analysis of Variance. One-Way Between-Subjects ANOVA. Factorial Between-Subjects ANOVA. Within-Subjects ANOVA. Mixed Between-Within-Subjects ANOVA. Design Complexity. Specific Comparisons. Parameter Estimation. Strength of Association. Bivariate Statistics: Correlation and Regression. Correlation. Regression. Chi-Square Analysis. 4. Cleaning Up Your Act: Screening Data Prior to Analysis. Important Issues in Data Screening. Accuracy of Data File. Honest Correlations. Missing Data. Outliers. Normality, Linearity, and Homoscedasticity. Common Data Transformations. Multicollinearity and Singularity. Checklist and Some Practical Recommendations. Complete Examples of Data Screening. Screening Ungrouped Data. Screening Grouped Data. 5. Multiple Regression. General Purpose and Description. Kinds of Research Questions. Degree of Relationship. Importance of IVs. Adding IVs. Changing IVs. Contingencies among IVs. Comparing Sets of IVs. 14

15 Predicting DV Scores for Members of a New Sample. Parameter Estimates. Limitations to Regression Analyses. Theoretical and practical issues. Fundamental Equations for Multiple Regression. General Linear Equations. Matrix Equations. Computer Analyses of Small Sample. Major Types of Multiple Regression. Standard Multiple Regression. Sequential Multiple Regression. Statistical (Stepwise) Regression. Choosing among Regression Strategies. Some Important Issues. Importance of IVs. Statistical Inference. Adjustment of R2. Suppressor Variables. Regression Approach to ANOVA. Centering when Interactions and Powers of IVs Are Included. Complete Examples of Regression Analysis. Evaluation of Assumptions. Standard Multiple Regression. Sequential Regression. Comparison of Programs. SPSS Package. SAS System. SYSTAT System (also for Chapter 6-16). 6. Canonical Correlation.General Purpose and Description. Kinds of Research Questions. Number of Canonical Variate Pairs. Interpretation of Canonical Variates. Importance of Canonical Variates. Canonical Variate Scores. Limitations. Theoretical Limitations. Practical Issues. Fundamental Equations for Canonical Correlation. Eigenvalues and Eigenvectors. Matrix Equations. Proportions of Variance Extracted. Computer Analyses of Small Sample Example. Some Important Issues. Importance of Canonical Variates. Interpretation of Canonical Variates. Complete Example of Canonical Correlation. Evaluation of Assumptions. Canonical Correlation. 7. Multiway Frequency Analysis. General Purpose and Description. Kinds of Research Questions. Associations among Variables. Effect on a Dependent Variable. Parameter Estimates. Importance of Effects. Strength of Association. Specific Comparisons and Trend Analysis. Limitations to Multiway Frequency Analysis. Theoretical Issues. Practical Issues. Fundamental Equations for Multiway Frequency Analysis. Screening for Effects. Modeling. Evaluation and Interpretation. Computer Analyses of Small Sample Example. Some Important Issues. Hierarchical and Nonhierarchical Models. Statistical Criteria. Strategies for Choosing a Model. Complete Example of Multiway Frequency Analysis. Evaluation of Assumptions: adequacy of Expected Frequencies. Hierarchical Loglinear Analysis. 8. Analysis of Covariance. General Purpose and Description. Kinds of Research Questions. Main Effects of IVs. Interactions among IVs. Specific Comparisons and Trend Analysis. Effects of Covariates. Strength of Association. Parameter Estimates. Limitations to Analysis of Covariance. Theoretical Issues. Practical Issues. Fundamental Equations for Analysis of Covariance. Sums of Squares and Cross Products. Significance Test and Strength of Association. Computer Analyses of Small Sample Example. Some Important Issues. Test for Homogeneity of Regression. Design Complexity. Evaluation of Covariates. Choosing Covariates. Alternatives to ANCOVA. Complete Example of Analysis of Covariance. Evaluation of Assumptions. Analysis of Covariance. 9. Multivariate Analysis of Variance and Covariance. General Purpose and Description. Kinds of Research Questions. Main Effects of IVs. Interactions among IVs. Importance of DVs. Parameter Estimates. Specific Comparisons and Trend Analysis. Strength of Association. Effects of Covariates. Limitations to Multivariate Analysis of Variance and Covariance. Theoretical Issues. Practical Issues. Fundamental Equations for Multivariate Analysis of Variance and Covariance. Multivariate Analysis of Variance. Computer Analyses of Small Sample Example. Multivariate Analysis of Covariance. Some Important Issues. Criteria for Statistical Inference. Assessing DVs. Specific Comparisons and Trend Analysis. Design Complexity. MANOVA vs. ANOVAs. Complete Examples of Multivariate Analysis of Variance and Covariance. Evaluation of Assumptions. Multivariate Analysis of Variance and Covariance. 10. Profile Analysis: The Multivariate Approach to Repeated Measures. General Purpose and Description. Kinds of Research Questions. Parallelism of Profiles. Overall Difference among Groups. Flatness of Profiles. Contrasts Following Profile Analysis. Parameter Estimates. Strength of Association. Limitations to Profile Analysis. Theoretical and Practical Issues. Fundamental Equations for Profile Analysis. Differences in Levels. Parallelism. Flatness. Computer Analyses of Small Sample Example. Some Important Issues. Contrasts in Profile Analysis. Univariate vs. Multivariate Approach to Repeated Measures. Doubly Multivariate Designs. Classifying Profiles. Imputation of Missing Values. Complete Examples of Profile Analysis. Profile Analysis of Subscales of the WISC. Doubly Multivariate Analysis of Reaction Time. 11. Discriminant Function 15

16 Analysis. General Purpose and Description. Kinds of Research Questions. Significance of Prediction. Number of Significant Discriminant Functions. Dimensions of Discrimination. Classification Functions. Adequacy of Classification. Strength of Association. Importance of Predictor Variables. Significance of Prediction with Covariates. Estimation of Group Means. Limits to Discriminant Function Analysis. Theoretical Issues. Practical Issues. Fundamental Equations for Discriminant Function Analysis. Derivation and Test of Discriminant Functions. Classification. Computer Analyses of Small Sample Example. Types of Discriminant Function Analysis. Direct Discriminant Function Analysis. Sequential Discriminant Function Analysis. Stepwise (Statistical) Discriminant Function Analysis. Some Important Issues. Statistical Inference. Number of Discriminant Functions. Interpreting Discriminant Functions. Evaluating Predictor Variables. Design Complexity: Factorial Designs. Use of Classification Procedures. Complete Example of Discriminant Function Analysis. Evaluation of Assumptions. Direct Discriminant Function Analysis. 12. Logistic Regression. General Purpose and Description. Kinds of Research Questions. Prediction of Group Membership or Outcome. Importance of Predictors. Interactions among Predictors. Parameter Estimates.Classification of Cases. Significance of Prediction with Covariates. Strength of Association. Limitations to Logistic Regression Analysis. Theoretical Issues. Practical Issues. Fundamental Equations for Logistic Regression. Testing and Interpreting Coefficients. Goodness-of-Fit. Comparing Models. Interpretation and Analysis of Residuals. Computer Analyses of Small Sample Example. Types of Logistic Regression. Direct Logistic Regression. Sequential Logistic Regression. Stepwise (Statistical) Logistic Regression. Probit and Other Analyses. Some Important Issues. Statistical Inference. Number and Type of Outcome Categories. Strength of Association for a Model. Coding Outcome and Predictor Categories. Classification of Cases. Hierarchical and Nonhierarchical Analysis. Interpretation of Coefficients Using Odds. Logistic Regression for Matched Groups. Complete Examples of Logistic Regression. Evaluation of Limitations. Direct Logistic Regression with Two-Category Outcome. Sequential Logistic Regression with Three Categories of Outcome. 13. Principal Components and Factor Analysis. General Purpose and Description. Kinds of Research Questions. Number of Factors. Nature of Factors. Importance of Solutions and Factors. Testing Theory in FA. Estimating Scores on Factors. Limitations. Theoretical Issues. Practical Issues. Fundamental Equations for Factor Analysis. Extraction. Orthogonal Rotation. Communalities, Variance, and Covariance. Factor Scores. Oblique Rotation. Computer Analyses of Small Sample Example. Major Types of Factor Analysis. Factor Extraction Techniques. Rotation. Some Practical Recommendations. Some Important Issues. Estimates of Communalities. Adequacy of Extraction and Number of Factors. Adequacy of Rotation and Simple Structure. Importance and Internal Consistency of Factors. Interpretation of Factors. Factor Scores. Comparisons among Solutions and Groups. Complete Example of FA. Evaluation of Limitations. Principal Factors Extraction with Varimax Rotation. 14. Structural Equation Modeling by Jodie B. Ullman. General Purpose and Description. Kinds of Research Questions. Adequacy of the Model. Testing Theory. Amount of Variance in the Variables Accounted for by the Factors. Reliability of the Indicators. Parameter Estimates. Mediation. Group Differences. Longitudinal Differences. Multilevel Modeling. Limitations to Structural Equation Modeling. Theoretical Issues. Practical Issues. Fundamental Equations for Structural Equations Modeling. Covariance Algebra. Model Hypotheses. Model Specification. Model Estimation. Model Evaluation. Computer Analysis of Small Sample Example. Some Important Issues. Model Identification. Estimation Techniques. Assessing the Fit of the Model. Model Modification. Reliability and Proportion of Variance. Discrete and Ordinal Data. Multiple Group Models. Mean and Covariance Structure Models. Complete Examples of Structural Equation Modeling Analysis. Model Specification for CFA. Evaluation of Assumptions for CFA. Model Modification. SEM Model Specification. SEM Model Estimation and Preliminary Evaluation. Model Modification. Comparison of Programs. EQS. LISREL. SAS System. AMOS. 15. Survival/Failure Analysis. General Purpose and description. Kinds of Research Questions. Proportions Surviving at Various Times. Group Differences in Survival. Survival Time with Covariates. Limitations to Survival Analysis. Theoretical 16

17 Issues. Practical Issues. Fundamental Equations for Survival Analysis. Life Tables. Standard Error of Cumulative Proportion Surviving. Hazard and Density Functions. Plot of Life Tables. Test for Group Differences. Computer Analyses of Small Sample Example. Types of Survival Analysis. Actuarial and Product-Limit Life Tables and Survivor Functions. Prediction of Group Survival Times from Covariates. Some Important Issues. Proportionality of Hazards. Censored Data. Effect Size and Power. Statistical Criteria. Odds Ratios. Complete Example of Survival Analysis. Evaluation of Assumptions. Cox Regression Survival Analysis. 16. Time Series Analysis. General Purpose and Description. Kinds of Experimental Questions. Pattern of Autocorrelation. Seasonal cycles and Trends. Forecasting. Effect of an Intervention. Comparing Time Series. Time Series with Covariates. Effect Size and Power. Assumptions of Time Series Analysis. Theoretical Issues. Practical Issues. Fundamental Equations for Time Series Arima Models. Identification of ARIMA (p, d, q) Models. Estimating Model Parameters. Diagnosing a Model. Computer Analysis of Small Sample Time Series Example. Types of Time Series Analysis. Models with Seasonal Components. Models with Interventions. Adding Continuous Variables. Some Important Issues. Patterns of ACFs and PACFs. Effect Size. Forecasting. Statistical Methods for Comparing Two Models. Complete Example of a Time Series Analysis. Evaluation of Assumptions. Baseline Model Identification. Baseline Model Diagnosis. Intervention Analysis. 17. An Overview of the General Linear Model. Linearity and the General Linear Model. Bivariate to Multivariate Statistics and Overview of Techniques Bivariate Form. Simple Multivariate Form. Full Multivariate Form. Alternative Research Strategies. Appendix A. A Skimpy Introduction to Matrix Algebra. The Trace of a Matrix. Addition or Subtraction of a Constant to a Matrix. Multiplication or Division of a Matrix by a Constant. Addition and Subtraction of Two Matrices. Multiplication, Transposes, and Square Roots of Matrices. Matrix "Division" (Inverses and Determinants). Eigenvalues and Eigenvectors: Procedures for Consolidating Variance from a Matrix. Appendix B. Research Designs for Complete Examples. Women's Health and Drug Study. Sexual Attraction Study. Learning Disabilities Data Bank. Reaction Time to Identify Figures. Clinical Trial for Primary Biliary cirrhosis. Impact of Seat Belt Law. Appendix C. Statistical Tables. Normal Curve Areas. Critical Values of the t Distribution for a =.05 and.01, Two-Tailed Test. Critical Values of the f Distribution. Critical Values of Chi Square (c^2). Critical Values for Squares Multiple Correlation (R^2) in Forward Stepwise Selection. Critical Values for Fmax (S^2max/S^2min) Distribution for a =.05 and.01. Townend, J. (2002). - Practical statistics for environmental and biological scientists. John Wiley & Sons, Ltd, New York. 276 pp. I. STATISTICS BASICS. 1. Introduction. 2. A brief tutorial on statistics. 3. Before you start. 4. Designing an experiment or survey. 5. Exploratory data analysis and data presentation. 6. Common assumptions or requirements of data for statistical tests. II. STATISTICAL METHODS. 7. t-tests and F-tests. 8. Analysis of variance. 9. Correlation and regression. 10. Multivariate ANOVA. 11. Repeated measures. 12. Chi-square tests. 13. Non-parametric tests. 14. Principal component analysis. 15. Cluster analysis. Appendices. Bibliography. Index. Untersteiner, H. (2005). - Biostatistik. Datenauswertung mit Excel und SPSS für Naturwissenschafter/innen und Mediziner/innen. Facultas Verlags- und Buchhandels AG, Wien. 212 pp. 1. Einleitung. 2. Statistische Grundlagen. 2.1 Deskriptive (beschreibende) Statistik Merkmalstypen Skalentypen Statistische Kenngrössen Masse der zentralen Tendenz (Modalwert; Zentralwert; Arithmetischer Mittelwert) Dispersionmasse (Spannweite; Quartilabstand; Standardabweichung) Formengrössen (Schiefe und Exzess) Häufigkeitsverteilungen. 17

18 2.1.5 Multivariate Statistik Statistische Kenngrössen für den Zusammenhang von Merkmalen Korrelation auf verschiedenen Skalenniveaus (Interspezifische Assoziationskoeffizient; Rangkorrelation; Pearson'sche Korrelationskoeffizient) Regressionsanalyse (Lineare Abhängigkeit; Residuen; Multiple lineare Regression; Nichtlineare Regression) Survivalanalysen. 2.2 Schliessende (induktive) Statistik (Inferenzstatistik) Schätzen unbekannter Grössen Konfidenzgrenzen bei Normalverteilungen Konfidenzgrenzen bei Binomialverteilungen Konfidenzgrenzen bei Poisson-Verteilungen Signifikanztests - Testtheorie Einund zweiseitige Fragestellung Vorraussetzungen für die Anwendung statistischer Tests Normalverteilung Anwendungsbeispiel für wichtige Signifikanztests Tests bei normalverteilten Grundgesamtheiten; Tests zu ordinalskalierten Daten (Rangtests). T-Test; U-Test; H-Test; Varianzanalyse Faktorenanalyse Clusteranalyse (Klumpenanalyse) Ökologische Indizes Ökologische Indizes zur Beschreibung der Faunenähnlichkeit. Weiterführende Literatur. Anhang. Statistisches Fachwörterbuch Deutsch - English. Index. Weiss, C. (1999). - Basiswissen medizinische Statistik. Springer-Verlag. Berlin. 299 pp. Vorwort. 1. Einleitung. Teil I: Deskriptive Statistik. 2. Theoretische Grundlagen. 3. Univariate Datenbeschreibung (Häufigkeit; Mittel; Median; Varianz; Standardabweichung; Schiefe; Wölbung). 4. Bivariate Datenbeschreibung (Kontingenztafel; Korrelation; Kovarianz; Regressionsanalyse; Bestimmtheitsmass; Korrelation nach Spearman; Assoziationskoeffizient). Teil II: Wahrscheinlichkeitsrechnung. 5. Grundlage der Wahrscheinlichkeitsrechnung (Zufallsexperimente; Bayes-Theorem; Zufallsvariablen). 6. Spezielle Wahrscheinlichkeiten in der Medizin. 7. Einige theoretische Verteilungen (Binomial-, Polynomial-, Poisson-, negative Binomial-, hypergeometrische, Normal-, Exponential-, Weibull-Verteilungen; t-, Chi2-, F-Verteilung). Teil III: Induktive Statistik. 8. Schätzverfahren (Konfidenzintervalle). 9. Statistische Tests (t-test; Welch-Test; Wilcoxon-Test; U- Test; Binomial-Test; Vorzeichen-Test; Chi2-Test; Fisher's exakter-test; McNemar-Test; Logrank- Test). Teil IV: Versuchsplanung. 10. Grundlagen der Versuchsplanung. 11. Studientypen. Anhang. Tabellen. Glossar. Weiterführende Literatur. Sach- und Personenregister. Wratten, S.D. & Fry, G.L.A. (1980). - Field and Laboratory Exercises in Ecology. Edward Arnold, 227 pp. Section one - Sampling. Exercises 1 and 2: The number of sampling units. Exercises 3 and 4: The effects of quadrat size. Exercises 5 and 6: Quantitative measures of vegetation. Exercises 7 and 8: Mark-recapture techniques for estimating the density of an animal population. Section 2 - Spatial pattern. Exercises 9 and 10: Taylor's power law. Exercises 11 and 12: Nearest neighbour techniques. Exercises 13 and 14: The detection of pattern: comparison with the Poisson distribution. Exercises 15 and 16: Determination of the scale of pattern. Exercises 17 and 18: The detection of directional pattern. Section three - Populations. Exercises 19 and 20: Time specific life tables. Exercises 21 and 22: Morphological variation. Exercises 23 and 24: Self crowding effects in plants. Exercises 25 and 26: Intraspecific competition in aphids. Exercises 27 and 28: Insect flight. Exercises 29 and 30: Host plant finding and recognitioin in insects. Section four - Population interactions. Exercises 31 and 32: Responses of predators to changes in the numbers of their prey. Exercises 33 and 34: Interspecific competition in Crustacea. Exercises 35 and 36: Herbivore grazing as a factor in plant competition. Exercises 37 and 38: Allelopathy. Exercises 39 and 40: Batesian mimicry. Exercises 41 and 42: Camouflage and apostatic selection. Section five - Community analysis. Exercises 43 and 44: Diversity. Exercises 47 and 48: Distribution and abundance of organisms in time and space. Exercises 49 and 50: Detection of mortality in population of a leaf-mining moth and a snail. Exercises 51 and 52: 18

19 Ordination. Exercises 53 and 54: Classification of vegetation. Exercises 55 and 56: The ecology of plant-herbivore relationships. References. Tables. Index. Young L.J. & Young, J. H. (1998). - Statistical Ecology. Kluwer Academic Publishers, Boston 565 pp. Preface. Acknowledgments. 1. Probability distributions. Discrete distributions: negative binomial, geometric, binomial, Poisson; confidence intervals. Continuous distributions: normal, lognormal, exponential, gamma, Weibull. 2. Goodness-of-fit tests. Pearson's Chi-squared test; Likelihood Ratio test; Freeman-Tukey Chi-squared Test; power divergence statistic; Nass test; Kolmogorov-Smirnov test. 3. Models and sampling. Binomial, Poisson, negative binomial models; Bose-Einstein vs Maxwell-Boltzmann statistics; stochastic immigration model; model within field movement; restrictions on carrying capacity; sampling concepts; simple random, stratified random, systematic samplings; ratio estimation. 4. Sequential estimation. Sample size required to control CV (coefficient of variation) and to set confidence intervals; sequential estimations for negative binomial, geometric, Poisson, binomial; sequential estimation based on Iwao's patchiness regression and Taylor's power law. 5. Sequential hypothesis testing. Wald's Sequential Probability Ratio Test (SPRT) for negative binomial, Poisson and binomial distributions; operating characteristic and average sample number functions; the 2-SPRT. 6. Sequentially testing 3 hypotheses. Ecologist's sequential test; Sobel and Wald, Armitage's methods; testing composite hypotheses. 7. Aggregation and spatial correlation. Measures of aggregation; spatial correlation; Moran's I and Geary's c; geostatistics. 8. Spatial point patterns. Complete spatial randomness; K(h) and L(h) functions; Monte Carlo tests; nearest neighbor techniques. 9. Capture-recapture: closed populations. Lincoln-Petersen model; multiple recapture models; removal and catch effort models; change-in-ratio or selective removal models; density estimation. 10. Capture-recapture: Open population. Jolly-Seber model; adult band and tag recovery model. 11. Transect sampling. Strip transects and circular plots; line and point transects; design. 12. Degree-Day models. Assumptions; calculating degree-days. 13. Life-stage analysis. Life tables; key factor analysis; multi-cohort and single cohort stage frequency data; matrix models for reproducing populations. 14. Probit and survival analysis. Probit analysis; nest survival analysis; analysis of radiotelemetry data. 15. Chaos. Population models. References. Index. Zar, J.H. (1999). - Biostatistical Analysis. Prentice-Hall, Inc. London. 4th Ed. 931 pp. Preface. 1. Introduction. 2. Populations and samples. 3. Measures of central tendency. 4. Measures of dispersion and variability. 5. Probabilities. 6. The normal distribution. 7. One-sample hypotheses. 8. Two-sample hypotheses. 9. Paired-sample hypotheses. 10. Multisample hypotheses: The analysis of variance. 11. Multiple comparisons. 12. Two-factor analysis of variance. 13. Data transformation. 14. Multiway factorial analysis of variance. 15. Nested (hierarchical) analysis of variance. 16. Multivariate analysis of variance. 17. Simple linear regression. 18. Comparing simple linear regression equations. 19. Simple linear correlation. 20. Multiple regression and correlation. 21. Polynomial regression. 22. Testing for goodness of fit. 23. Contingency tables. 24. More on dichotomous variables. 25. Testing for randomness. 26. Circular distributions: descriptive statistics. 27. Circular distributions: hypotheses testing. Appendix A. Analysis of variance hypothesis testing. Appendix B. Statistical tables and graphs. Answers to exercises. Literature cited. Index. Zuur, A.F., Ieno, E.N., Smith, G.M. (2007). - Analysing ecological data. Springer Verlag, Berlin. 672 pp. 19

20 1 Introduction. 1.1 Part 1: Applied statistical theory. 1.2 Part 2: The case studies. 1.3 Data, software and flowcharts. 2 Data management and software. 2.1 Introduction. 2.2 Data management. 2.3 Data preparation. 2.4 Statistical software. 3 Advice for teachers. 3.1 Introduction. 4 Exploration. 4.1 The first steps. 4.2 Outliers, transformations and standardisations. 4.3 A final thought on data exploration. 5 Linear regression. 5.1 Bivariate linear regression. 5.2 Multiple linear regression. 5.3 Partial linear regression. 6. Generalised linear modelling. 6.1 Poisson regression. 6.2 Logistic regression. 7 Additive and generalised additive modelling. 7.1 Introduction. 7.2 The additive model. 7.3 Example of an additive model. 7.4 Estimate the smoother and amount of smoothing. 7.5 Additive models with multiple explanatory variables. 7.6 Choosing the amount of smoothing. 7.7 Model selection and validation. 7.8 Generalised additive modelling. 7.9 Where to go from here. 8 Introduction to mixed modelling. 8.1 Introduction. 8.2 The random intercept and slope model. 8.3 Model selection and validation. 8.4 A bit of theory. 8.5 Another mixed modelling example. 8.6 Additive mixed modelling. 9 Univariate tree models. 9.1 Introduction. 9.2 Pruning the tree. 9.3 Classification trees. 9.4 A detailed example: Ditch data. 10 Measures of association Introduction Association between sites: Q analysis Association among species: R analysis Q and R analysis: concluding remarks Hypothesis testing with measures of association. 11 Ordination First encounter Bray Curtis ordination. 12 Principal component analysis and redundancy analysis The underlying principle of PCA PCA: Two easy explanations PCA: Two technical explanations Example of PCA The biplot General remarks Chord and Hellinger transformations Explanatory variables Redundancy analysis Partial RDA and variance partitioning PCA regression to deal with collinearity. 13 Correspondence analysis and canonical correspondence analysis Gaussian regression and extensions Three rationales for correspondence analysis From RGR to CCA Understanding the CCA triplot When to use PCA, CA, RDA or CCA Problems with CA and CCA. 14 Introduction to discriminant analysis Introduction Assumptions Example The mathematics The numerical output for the sparrow data. 15 Principal coordinate analysis and non-metric multidimensional scaling Principal coordinate analysis Non-metric multidimensional scaling. 16 Time series analysis Introduction Using what we have already seen before Autoregressive integrated moving average models with exogenous variables. 17 Common trends and sudden changes Repeated LOESS smoothing Identifying the seasonal component Common trends: MAFA Common trends: Dynamic factor analysis Sudden changes: Chronological clustering. 18 Analysis and modelling of lattice data Lattice data Numerical representation of the lattice structure Spatial correlation Modelling lattice data More exotic models Summary. 19 Spatially continuous data analysis and modelling Spatially continuous data Geostatistical functions and assumptions Exploratory variography analysis Geostatistical modelling: Kriging A full spatial analysis of the bird radar data. 20 Univariate methods to analyse abundance of decapod larvae Introduction The data Data exploration Linear regression results Additive modelling results How many samples to take? 20.7 Discussion. 21 Analysing presence and absence data for flatfish distribution in the Tagus estuary, Portugal Introduction Data and materials Data exploration Classification trees Generalised additive modelling Generalised linear modelling Discussion. 22 Crop pollination by honeybees in Argentina using additive mixed modelling Introduction Experimental setup Abstracting the information First steps of the analyses: Data exploration Additive mixed modelling Discussion and conclusions. 23 Investigating the effects of rice farming on aquatic birds with mixed modelling Introduction The data Getting familiar with the data: Exploration Building a mixed model The optimal model in terms of random components Validating the optimal linear mixed model More numerical output for the optimal model Discussion. 24 Classification trees and radar detection of birds for North Sea wind farms Introduction From radars to data

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