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Modern Applied Statistics With S

Ecologists often face the task of studying the association between single species and one or several groups of sites representing habitat types, community types, or other categories. Besides characterizing the ecological preference of the species, the strength of the association usually presents a lot of interest for conservation biology, landscape mapping and management, and natural reserve design, among other applications. The indices most frequently employed to assess these relationships are the phi coefficient of association and the indicator value index (IndVal). We compare these two approaches by putting them into a broader framework of related measures, which includes several new indices. We present permutation tests to assess the statistical significance of species-site group associations and bootstrap methods for obtaining confidence intervals. Correlation measures, such as the phi coefficient, are more context-dependent than indicator values but allow focusing on the preference of the species. In contrast, the two components of an indicator value index directly assess the value of the species as a bioindicator because they can be interpreted as its positive predictive value and sensitivity. Ecologists should select the most appropriate index of association strength according to their objective and then compute confidence intervals to determine the precision of the estimate.

/pdf/associations-between-species-and-groups-of-sites-indices-and-8v4wcr1gz0.pdf

Associations between species and groups of sites: indices and statistical inference

A flexible method is needed for constrained ordination on the basis of any distance or dissimilarity measure, which will display a cloud of multivariate points by reference to a specific a priori hypothesis. We suggest the use of principal coordinate analysis (PCO, metric MDS), followed by either a canonical discriminant analysis (CDA, when the hypothesis concerns groups) or a canonical correlation analysis (CCorA, when the hypothesis concerns relationships with environmental or other variables), to provide a flexible and meaningful constrained ordination of ecological species abundance data. Called “CAP” for “Canonical Analysis of Principal coordinates,” this method will allow a constrained ordination to be done on the basis of any distance or dissimilarity measure. We describe CAP in detail, including how it can uncover patterns that are masked in an unconstrained MDS ordination. Canonical tests using permutations are also given, and we show how the method can be used (1) to place a new observation into...

Canonical analysis of principal coordinates: a useful method of constrained ordination for ecology

MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

Indicator species are species that are used as ecological indicators of community or habitat types, environmental conditions, or environmental changes. In order to determine indicator species, the characteristic to be predicted is represented in the form of a classification of the sites, which is compared to the patterns of distribution of the species found at the sites. Indicator species analysis should take into account the fact that species have different niche breadths: if a species is related to the conditions prevailing in two or more groups of sites, an indicator species analysis undertaken on individual groups of sites may fail to reveal this association. In this paper, we suggest improving indicator species analysis by considering all possible combinations of groups of sites and selecting the combination for which the species can be best used as indicator. When using a correlation index, such as the point-biserial correlation, the method yields the combination where the difference between the observed and expected abundance/frequency of the species is the largest. When an indicator value index (IndVal) is used, the method provides the set of site-groups that best matches the observed distribution pattern of the species. We illustrate the advantages of the method in three different examples. Consideration of combinations of groups of sites provides an extra flexibility to qualitatively model the habitat preferences of the species of interest. The method also allows users to cross multiple classifications of the same sites, increasing the amount of information resulting from the analysis. When applied to community types, it allows one to distinguish those species that characterize individual types from those that characterize the relationships between them. This distinction is useful to determine the number of types that maximizes the number of indicator species.

/pdf/improving-indicator-species-analysis-by-combining-groups-of-4w9x9t1o55.pdf

Improving indicator species analysis by combining groups of sites

Questions: Does fuzzy clustering provide an appropriate numerical framework to manage vegetation classifications? What is the best fuzzy clustering method to achieve this?



Material: We used 531 releves from Catalonia (Spain), belonging to two syntaxonomic alliances of mesophytic and xerophytic montane pastures, and originally classified by experts into nine and 13 associations, respectively.



Methods: We compared the performance of fuzzy C-means (FCM), noise clustering (NC) and possibilistic C-means (PCM) on four different management tasks: (1) assigning new releve data to existing types; (2) updating types incorporating new data; (3) defining new types with unclassified releves; and (4) reviewing traditional vegetation classifications.



Results: As fuzzy classifiers, FCM fails to indicate when a given releve does not belong to any of the existing types; NC might leave too many releves unclassified; and PCM membership values cannot be compared. As unsupervised clustering methods, FCM is more sensitive than NC to transitional releves and therefore produces fuzzier classifications. PCM looks for dense regions in the space of species composition, but these are scarce when vegetation data contain many transitional releves.



Conclusions: All three models have advantages and disadvantages, although the NC model may be a good compromise between the restricted FCM model and the robust but impractical PCM model. In our opinion, fuzzy clustering might provide a suitable framework to manage vegetation classifications using a consistent operational definition of vegetation type. Regardless of the framework chosen, national/regional vegetation classification panels should promote methodological standards for classification practices with numerical tools.

The management of vegetation classifications with fuzzy clustering

We develop a proximity function between an individual and a population from a distance between multivariate observations. We study some properties of this construction and apply it to a distance{based discrimination rule, which contains the classic linear discriminant function as a particular case. Additionally, this rule can be used advantageously for categorical or mixed variables, or in problems where a probabilistic model is not well determined. This approach is illustrated and compared with other classic procedures using four real data sets. The authors thank M.Abrahamowicz, J.C. Gower and M. Greenacre for their helpful comments, and W.J. Krzanowski for providing us with a data set and his quadratic location model program.

The proximity of an individual to a population with applications in discriminant analysis

Many multivariate programs are expensive commercial packages or require expensive third party software. Other applications are freely available to academic researchers but are limited to one operating system. This paper presents GINKGO, a free easy-to-use multi-platform Java application mainly oriented towards to non-standard classification analysis. This orientation is pursued (1) by providing users with several similarity and dissimilarity measures, (2) by allowing the execution of several crisp or fuzzy prototype-based clustering methods on arbitrary distance matrices, and (3) by including standard and non-standard facilities for evaluating the quality of clusters. Along with the software presentation, two methodological improvements are also given, which are available in the program. First, the implementation of a new

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GINKGO, a program for non-standard multivariate fuzzy analysis

Background

The effectiveness of drugs aimed at counteracting cancer cachexia is generally tested in pre-clinical rodent models, where only the tumour-induced alterations are taken into account, excluding the co-presence of anti-tumour molecules that could worsen the scenario and/or interfere with the treatment.



Methods

The aim of the present investigation has been to assess the efficacy of a multifactorial treatment, including formoterol and megestrol acetate, in cachectic tumour-bearing rats (Yoshida AH-130, a highly cachectic tumour) undergoing chemotherapy (sorafenib).



Results

Treatment of cachectic tumour-bearing rats with sorafenib (90 mg/kg) causes an important decrease in tumour cell content due to both reduced cell proliferation and increased apoptosis. As a consequence, animal survival significantly improves, while cachexia occurrence persists. Multi-factorial treatment using both formoterol and megestrol acetate is highly effective in preventing muscle wasting and has more powerful effects than the single formoterol administration. In addition, both physical activity and grip strength are significantly improved as compared with the untreated tumour-bearing animals. The effects of the multi-factorial treatment include increased food intake (likely due to megestrol acetate) and decreased protein degradation, as shown by the reduced expression of genes associated with both proteasome and calpain proteolytic systems.



Conclusions

The combination of the two drugs proved to be a promising strategy for treating cancer cachexia in a pre-clinical setting that better resembles the human condition, thus providing a strong rationale for the use of such combination in clinical trials involving cachectic cancer patients.

/pdf/a-multifactorial-anti-cachectic-approach-for-cancer-cachexia-16ir898p1b.pdf

A multifactorial anti-cachectic approach for cancer cachexia in a rat model undergoing chemotherapy

Question: Diagnostic species are useful tools for the identification and ecological interpretation of community types. Vegetation databases facilitate the computation of diagnostic values of regional validity, but it is essential to understand the behaviour of fidelity measures in large data sets. Methods: We focused our study on the phi-coefficient (Φ) of association and its limit value, the Ochiai index. The northeast Spanish releve database was stratified using an arbitrary distance threshold in species composition. Diagnostic species analysis was undertaken using three methods of context selection: I. within a syntaxon of higher rank; II. including releves with similar composition to that of the target unit; III. using the entire stratified database. Species diagnostic values were computed as well as bootstrap percentile confidence intervals. Results: Many species deemed as diagnostic by method I have their optima in vegetation types neighbouring the unit chosen as context. In contrast, metho...

Francesc Oliva

Papers

The management of vegetation classifications with fuzzy clustering

The proximity of an individual to a population with applications in discriminant analysis

GINKGO, a program for non-standard multivariate fuzzy analysis

A multifactorial anti-cachectic approach for cancer cachexia in a rat model undergoing chemotherapy

Assessing species diagnostic value in large data sets: A comparison between phi-coefficient and Ochiai index