Analyzing Ecological DataSpringer, 29 août 2007 - 672 pages 'Which test should I apply?' During the many years of working with ecologists, biologists and other environmental scientists, this is probably the question that the authors of this book hear the most often. The answer is always the same and along the lines of 'What are your underlying questions?', 'What do you want to show?'. The answers to these questions provide the starting point for a detailed discussion on the ecological background and purpose of the study. This then gives the basis for deciding on the most appropriate analytical approach. Therefore, a better start ing point for an ecologist is to avoid the phrase 'test' and think in terms of 'analy sis'. A test refers to something simple and unified that gives a clear answer in the form of a p-value: something rarely appropriate for ecological data. In practice, one has to apply a data exploration, check assumptions, validate the models, per haps apply a series of methods, and most importantly, interpret the results in terms of the underlying ecology and the ecological questions being investigated. Ecology is a quantitative science trying to answer difficult questions about the complex world we live in. Most ecologists are aware of these complexities, but few are fully equipped with the statistical sophistication and understanding to deal with them. |
Table des matières
1 | |
7 | |
Advice for teachers | 17 |
Exploration | 23 |
Linear regression | 49 |
Generalised linear modelling 79 | 78 |
Additive and generalised additive modelling | 97 |
Introduction to mixed modelling | 125 |
Crop pollination by honeybees in Argentina using additive mixed | 403 |
Investigating the effects of rice farming on aquatic birds with mixed | 416 |
Classification trees and radar detection of birds for North Sea wind | 435 |
Fish stock identification through neural network analysis of parasite | 449 |
Using generalised least squares nonmetric | 463 |
Univariate and multivariate analysis applied on a Dutch sandy beach | 485 |
Multivariate analyses of SouthAmerican zoobenthic species spoilt | 503 |
Multivariate analyses of morphometric turtle data size and shape | 529 |
Univariate tree models | 143 |
Measures of association | 163 |
Ordination First encounter 189 | 188 |
Correspondence analysis and canonical correspondence analysis | 225 |
Introduction to discriminant analysis | 245 |
Principal coordinate analysis and nonmetric multidimensional scaling | 259 |
Time series analysis Introduction | 265 |
Common trends and sudden changes | 289 |
Analysis and modelling of lattice data | 321 |
Spatially continuous data analysis and modelling | 341 |
Univariate methods to analyse abundance of decapod larvae | 373 |
Analysing presence and absence data for flatfish distribution in the Tagus | 389 |
Redundancy analysis and additive modelling applied on savanna tree | 547 |
Canonical correspondence analysis of lowland pasture vegetation in | 561 |
Estimating common trends in Portuguese fisheries landings | 575 |
Common trends in demersal communities on the NewfoundlandLabrador | 589 |
A time series | 600 |
Time series analysis of Hawaiian waterbirds | 615 |
Spatial modelling of forest community features in the VolzhskoKamsky | 633 |
References | 649 |
651 | |
667 | |
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Expressions et termes fréquents
abundance additive modelling applied assumption auto-correlation axes axis beach biplot bird boxplots calculated Chapter Chi-square coefficient collinearity common trends component correlation correspondence analysis covariance cross-validation data exploration dataset degrees of freedom deviance discussed distribution effect eigenvalues environmental variables error estimated Euclidean distance example explanatory variables exposure F-statistic Figure fitted values function Gaussian generalised graph indicate intercept Jaccard index Legendre and Legendre linear regression linear regression model LOESS MAFA Mantel test matrix methods mixed modelling multivariate nominal variable obtained optimal option outliers p-value panel pattern plot points Poisson Poisson distribution principal component analysis random regression parameters relationship residuals response variable RIKZ data salinity sample scatterplot scores shows similar smoother smoothing curve spatial species richness squares statistical Table techniques temperature tion transect transformation tree triplot variance variation variogram versus Y₁ zero zoobenthic
Fréquemment cités
Page 650 - Beukema, JJ 1979. Biomass and species richness of the macrobenthic animals living on a tidal flat area in the Dutch Wadden Sea: Effects of a severe winter. Neth. J. Sea Res.
Page 651 - Bowering, WR, MJ Morgan, and WB Brodie. 1997. Changes in the population of American plaice (Hippoglossoides platessoides) off Labrador and northeastern Newfoundland: A collapsing stock with low exploitation. Fish. Res.