Abstract not found

Landscape ecology is a field that has grown from realizing that maintenance of ecological resources requires management at several spatial and temporal scales, including landscape-level ecosystems as whole units of study and management (Forman, 1995; Grumbine, 1994; Noss, 1983 and 1996). The subsequent need for characterizing landscape structure has led to a variety of measurements for assessing different aspects of spatial patterns; however, most of these measurements are known to depend on both the spatial extent of a specified landscape and the measurement grain. Therefore, measurements that incorporate a range of scales would be most informative. In response, this paper introduces a new method for obtaining a multi-resolution characterization of land cover fragmentation patterns within a fixed geographic extent. Our particular interest is in watersheddelineated extents. The method applies the concept of conditional entropy as one moves from larger “parent ” land cover pixels to smaller “child ” pixels that are heirarchically nested within the parent pixels. When applied over a range of resolutions, one obtains a “conditional entropy profile”. The conceptual and methodological development of conditional entropy profiles is presented, followed by current and future directions for evaluating and applying this methodology. 1

Abstract: Spatially explicit information on the species composition and structure of forest vegetation is needed at broad spatial scales for natural resource policy analysis and ecological research. We present a method for predictive vegetation mapping that applies direct gradient analysis and nearest-neighbor imputation to ascribe detailed ground attributes of vegetation to each pixel in a digital landscape map. The gradient nearest neighbor method integrates vegetation measurements from regional grids of field plots, mapped environmental data, and Landsat Thematic Mapper (TM) imagery. In the Oregon coastal province, species gradients were most strongly associated with regional climate and geographic location, whereas variation in forest structure was best explained by Landsat TM variables. At the regional level, mapped predictions represented the range of variability in the sample data, and predicted area by vegetation type closely matched sample-based estimates. At the site level, mapped predictions maintained the covariance structure among multiple response variables. Prediction accuracy for tree species occurrence and several measures of vegetation structure and composition was good to moderate. Vegetation maps produced with the gradient nearest neighbor method are appropriately used for regional-level planning, policy analysis, and research, not to guide local management decisions. Résumé: Afin d’effectuer l’analyse des politiques touchant les ressources naturelles et appuyer la recherche écologique, il est nécessaire d’obtenir une information spatiale précise sur la structure de la végétation forestière et sur la composition des espèces et ce, à une vaste échelle spatiale. Nous présentons une méthode de cartographie prévisionnelle

Canonical correspondence analysis (CCA) is introduced as a multivariate extension of weighted averaging ordination, which is a simple method for arranging species along environmental variables. CCA constructs those linear combinations of environmental variables, along which the distributions of the species are max-imally separated. The eigenvalues produced by CCA measure this separation. As its name suggests, CCA is also a correspondence analysis technique, but one in which the ordination axes are constrained to be linear combinations of environmental variables. The ordination diagram generated by CCA visualizes not only a pattern of community variation (as in standard ordination) but also the main features of the distributions of species along the environmental variables. Applications demonstrate that CCA can be used both for detecting species-environment relations, and for investigating specific questions about the response of species to environmental variables. Questions in community ecology that have typically been studied by 'indirect ' gradient analysis (i.e. ordination followed by external interpretation of the axes) can now be answered more directly by CCA.

this article we extend the reduced-rank idea to the VGLM/VGAM classes to obtain subclasses which we term RR-VGLMs and RR-VGAMs. The multinomial logit model (MLM; Nerlove and Press, 1973) for categorical data is used as the main example to bring out some of the characteristics of the RR-subclasses, and investigate its use to regression and classication problems. Recently, Srivastava (1997) considered the problem of reduced-rank regression for classication or discrimination, but only for the Gaussian model. Hastie and Tibshirani (1996) also discuss the ideas of reduced rank regression to discrimination problems, but in a larger framework involving mixture models. Gabriel (1998) and Aldrin (2000) are also recent works. One model where the reduced-rank regression idea has been applied to non-Gaussian errors is the MLM. This was proposed and referred to as the stereotype model by Anderson (1984). However, in that paper and in subsequent papers by others, the reduced-rank regression idea was not explicitly stated in the framework presented below. The aim of this paper is twofold. Firstly, we extend the reduced-rank concept to the VGLM and VGAM class. Secondly, we describe and motivate the reduced-rank idea applied to regression models for categorical data analysis, especially the MLM. We do this by elaborating on its connections to other statistical models such as neural networks, projection pursuit regression, linear discriminant analysis, canonical correspondence analysis and biplots. An outline of this paper is as follows. In the remainder of this section we briey review 2 VGLMs and VGAMs|further details can be found in Yee and Wild (1996). In Section 2 we propose reduced-rank regression for the VGLM class. In Section 3 we focus on the RR-MLM, and show how it relates ...

Spatial heterogeneity of ecological structures originates either from the physical forcing of environmental variables or from community processes. In both cases, spatial structuring plays a functional role in ecosystems. Ecological models should explicitly take into account the spatial structure of ecosystems. In previous work, we used a polynomial function of the geographic coordinates of the sampling sites to model broad-scale spatial variation in a canonical (regression-type) modelling context. In this paper, we propose a method for detecting and quantifying spatial patterns over a wide range of scales. This is obtained by eigenvalue decomposition of a truncated matrix of geographic distances among the sampling sites. The eigenvectors corresponding to positive eigenvalues are used as spatial descriptors in regression or canonical analysis. This method can be applied to any set of sites providing a good coverage of the geographic sampling area. This paper investigates the behaviour of the method using numerical simulations and an artificial pseudo-ecological data set of known properties. 2002 Elsevier Science B.V. All rights reserved.

1 Canonical community ordination comprises a collection of methods that relate species assemblages to their environment, in both observational studies and designed experiments. Canonical ordination differs from ordination sensu stricto in that species and environment data are analyzed simultaneously. Part I reviews the theory in a non-mathematical way with emphasis on new insights for the interpretation of ordination diagrams. The interpretation depends on the ordination method used to create the diagram. After the basic theory, Part I is focused on the ordination diagrams in linear methods of canonical community ordination, in particular principal components analysis, redundancy analysis and canonical correlation analysis. Special attention is devoted to the display of qualitative environmental variables. Key words: principal components analysis, redundancy analysis, canonical correlation analysis, biplot, ordination diagram, species-environment relations. 2

Two new methods for inferring pH from diatoms are presented. Both are based on the observation that the relationships between diatom taxa and pH are often unimodal. The first method is maximum likelihood calibration based on Gaussian logit response curves of taxa against pH. The second is weighted averaging. In a lake with a particular pH, taxa with an optimum close to the lake pH will be most abundant, so an intuitively reasonable estimate of the lake pH is to take a weighted average of the pH optima of the species present. Optima and tolerances of diatom taxa were estimated from contemporary pH and proportional diatom counts in littoral zone samples from 97 pristine soft water lakes and pools in Western Europe. The optima showed a strong relation with Hustedt’s pH preference groups. The two new methods were then compared with existing calibration methods on the basis of differences between inferred and observed pH in a test set of 62 additional samples taken between 1918 and 1983. The methods were ranked in order of performance as follows (between brackets the standard error of inferred pH in pH units); maximum likelihood (0.63)> weighted averaging (0.71) = multiple regression using pH groups (0.71) = the Gasse & Tekaia method (0.71)> Renberg & Hellberg’s Index B (0.83) % multiple regression

Methods for improving estimation of cetacean abundance from line transect and markrecapture surveys are proposed. Using either generalized linear or generalized additive models (GLMs or GAMs), two approaches are suggested whichallowheterogeneity in the spatial distribution of cetaceans to be modelled from standard line transect data. In the rst approach, the transect lines are divided into smaller discrete units, and the expected number of detections in each unit is modelled using explanatory spatial covariates. In the second approach, the response is derived from the observed waiting times (or distances) between detections. Fitting this model within the usual GLM or GAM framework would require restrictive assumptions, therefore an iterative procedure is formulated which enables a realistic model to be tted. Alternatively, it is shown how this approach can be framed as a point process model, and it is suggested how the likelihood for the observed along-trackline distances could be maximized. The methods are illustrated using line transect data from a survey of Antarctic minke whales. A surface representing the variation in density throughout the survey region is obtained, from which abundance may be estimated by numerical integration. It is also

We propose a new approach to the multivariate analysis of data sets with known sampling site spatial positions. A between-sites neighbouring relationship must be derived from site positions and this relationship is introduced into the multivariate analyses through neighbouring weights (number of neighbours at each site) and through the matrix of the neighbouring graph. Eigenvector analysis methods (e.g., principal component analysis, correspondence analysis) can then be used to detect total, local and global structures. The introduction of the D-centring (centring with respect to the neighbouring weights) allows us to write a total variance decomposition into local and global components, and to propose a unified view of several methods. After a brief review of the matrix approach to this problem, we present the results obtained on both simulated and real data sets, showing how spatial structure can be detected and analysed. Freely available computer programs to perform computations and graphical displays are proposed.