			Home About us Contact

Ecological Data Sets (ecological + data_set)

Distribution by Scientific Domains

Mathematics and Statistics	60%
Life Sciences	40%

Selected Abstracts

On Distance-Based Permutation Tests for Between-Group Comparisons

BIOMETRICS, Issue 2 2010
Philip T. Reiss
Summary Permutation tests based on distances among multivariate observations have found many applications in the biological sciences. Two major testing frameworks of this kind are multiresponse permutation procedures and pseudo- F,tests arising from a distance-based extension of multivariate analysis of variance. In this article, we derive conditions under which these two frameworks are equivalent. The methods and equivalence results are illustrated by reanalyzing an ecological data set and by a novel application to functional magnetic resonance imaging data. [source]

Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment

DIVERSITY AND DISTRIBUTIONS, Issue 3 2007
Simon Ferrier
ABSTRACT Generalized dissimilarity modelling (GDM) is a statistical technique for analysing and predicting spatial patterns of turnover in community composition (beta diversity) across large regions. The approach is an extension of matrix regression, designed specifically to accommodate two types of nonlinearity commonly encountered in large-scaled ecological data sets: (1) the curvilinear relationship between increasing ecological distance, and observed compositional dissimilarity, between sites; and (2) the variation in the rate of compositional turnover at different positions along environmental gradients. GDM can be further adapted to accommodate special types of biological and environmental data including, for example, information on phylogenetic relationships between species and information on barriers to dispersal between geographical locations. The approach can be applied to a wide range of assessment activities including visualization of spatial patterns in community composition, constrained environmental classification, distributional modelling of species or community types, survey gap analysis, conservation assessment, and climate-change impact assessment. [source]

Zero tolerance ecology: improving ecological inference by modelling the source of zero observations

ECOLOGY LETTERS, Issue 11 2005
Tara G. Martin
Abstract A common feature of ecological data sets is their tendency to contain many zero values. Statistical inference based on such data are likely to be inefficient or wrong unless careful thought is given to how these zeros arose and how best to model them. In this paper, we propose a framework for understanding how zero-inflated data sets originate and deciding how best to model them. We define and classify the different kinds of zeros that occur in ecological data and describe how they arise: either from ,true zero' or ,false zero' observations. After reviewing recent developments in modelling zero-inflated data sets, we use practical examples to demonstrate how failing to account for the source of zero inflation can reduce our ability to detect relationships in ecological data and at worst lead to incorrect inference. The adoption of methods that explicitly model the sources of zero observations will sharpen insights and improve the robustness of ecological analyses. [source]

Comparison of missing value imputation methods for crop yield data

ENVIRONMETRICS, Issue 4 2006
Ravindra S. Lokupitiya
Abstract Most ecological data sets contain missing values, a fact which can cause problems in the analysis and limit the utility of resulting inference. However, ecological data also tend to be spatially correlated, which can aid in estimating and imputing missing values. We compared four existing methods of estimating missing values: regression, kernel smoothing, universal kriging, and multiple imputation. Data on crop yields from the National Agricultural Statistical Survey (NASS) and the Census of Agriculture (Ag Census) were the basis for our analysis. Our goal was to find the best method to impute missing values in the NASS datasets. For this comparison, we selected the NASS data for barley crop yield in 1997 as our reference dataset. We found in this case that multiple imputation and regression were superior to methods based on spatial correlation. Universal kriging was found to be the third best method. Kernel smoothing seemed to perform very poorly. Copyright © 2005 John Wiley & Sons, Ltd. [source]

Estimating the Species Accumulation Curve Using Mixtures

BIOMETRICS, Issue 2 2005
Chang Xuan Mao
Summary As a significant tool in ecological studies, the species accumulation curve or the collector's curve is the graph of the expected number of detected species as a function of sampling effort. The problem of estimating the species accumulation curve based on an empirical data set arising from quadrat sampling is studied in a nonparametric binomial mixture model. It will be shown that estimating the species accumulation curve not only is independent of the unknown number of species but also includes estimating the number of species as a limiting case. For the purpose of interpolation, moment-based estimators, associated with asymptotic confidence intervals, are developed from several points of view. A likelihood-based procedure is developed for the purpose of extrapolation, associated with bootstrap confidence intervals. The proposed methods are illustrated by ecological data sets. [source]