PCA – A Powerful Method for Analyze Ecological Niches

Janžekovič, Franc; Novak, Tone

doi:10.5772/38538

Cited by 53 publications

(34 citation statements)

References 63 publications

(51 reference statements)

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“…When applied to environmental measures collected at each site, our ellipsoid approach shows how species are distributed across multiple environmental dimensions. By identifying the region within this hyperspace occupied by each species, the ellipsoids can be interpreted as the realised range of environmental conditions within which a focal species occurs (Doledec et al , Janžeković and Novak ). More notably, they provide novel ways to derive metrics of species turnover, modularity or nestedness.…”

Section: Methodsmentioning

confidence: 99%

A comprehensive framework for the study of species co‐occurrences, nestedness and turnover

Ulrich

Kryszewski

Sewerniak

et al. 2017

Oikos

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Binary presence-absence matrices (rows  species, columns  sites) are often used to quantify patterns of species co-occurrence, and to infer possible biotic interactions from these patterns. Previous classifications of co-occurrence patterns as nested, segregated, or modular have led to contradictory results and conclusions. These analyses usually do not incorporate the functional traits of the species or the environmental characteristics of the sites, even though the outcomes of species interactions often depend on trait expression and site quality. Here we address this shortcoming by developing a method that incorporates realized functional and environmental niches, and relates them to species co-occurrence patterns. These niches are defined from n-dimensional ellipsoids, and calculated from the n eigenvectors and eigenvalues of the variancecovariance matrix of measured environmental or trait variables. Average niche overlap among species and the spatial distribution of niches define a triangle plot with vertices of species segregation (low niche overlap), nestedness (high niche overlap), and modular co-occurrence (clusters of overlapping niches). Applying this framework to temperate understorey plant communities in southwest Poland, we found a consistent modular structure of species occurrences, a pattern not detected by conventional presence-absence analysis. These results suggest that, in our case study, habitat filtering is the most important process structuring understorey plant communities. Furthermore, they demonstrate how incorporating trait and environmental data into co-occurrence analysis improves pattern detection and provides a stronger theoretical framework for understanding community structure.

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Section: Methodsmentioning

confidence: 99%

A comprehensive framework for the study of species co‐occurrences, nestedness and turnover

Ulrich

Kryszewski

Sewerniak

et al. 2017

Oikos

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show abstract

“…PCA has been widely used in various fields of investigation. These studies concern either environmental variation (Janzekovic & Novak, 2012), the investigated species, or communities characteristics. In aquatic habitat studies, it has been applied for evaluation of aquatic habitat suitability, their seasonal, and spatial variation (Ahmadi-Nedushan et al, 2006).…”

Section: Discussionmentioning

confidence: 99%

Past, current, and future trends of red spiny lobster based on PCA with MaxEnt model in Galapagos Islands, Ecuador

Moya

Jácome

2017

Ecology and Evolution

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In order to enhance in terms of accuracy and predict the modeling of the potential distribution of species, the integration of using principal components of environmental variables as input of maximum entropy (MaxEnt) has been proposed in this study. Principal components selected previously from the principal component analysis results performed in Arc GIS in the environmental variables was used as an input data of MaxEnt instead of raw data to model the potential distribution of red spiny lobster from the year 1997 to 2015 and for three different future scenarios 2020, 2050, and 2070. One set of six original environmental variables pertaining to the years 1997–2015 and one set of four variables for future scenarios were transformed independently into a single multiband raster in Arc GIS in order to select the variables whose eigenvalues explains more than 5% of the total variance with the purpose to use in the modeling prediction in MaxEnt. The years 1997 and 1998 were chosen to compare the accuracy of the model, showing better results using principal components instead of raw data in terms of area under the curve and partial receiver operating characteristic as well as better predictions of suitable areas. Using principal components as input of MaxEnt enhances the prediction of good habitat suitability for red spiny lobster; however, future scenarios suggest an adequate management by researches to elaborate appropriate guidelines for the conservation of the habitat for this valuable specie with face to the climate change.

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“…However, because modern industrial processes often present a large number of highly correlated process (Harrou et al (2015)), principal component analysis (PCA) (Harrou et al (2013)), canonical variate analysis, independent component analysis (Chiang et al (2001)), neural networks (Neumann and G. Deerberg (1999)), and support vector machine based methods (Dehestani et al (2011))). Data-based monitoring methods, especially those that utilize PCA or its extensions, have been applied across a wide range of industries, for example in the chemical industry (Simoglou et al (1997)), for water treatment (George et al (2009)), and in ecological studies (Janzekovic and Novak (2012)).…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Amalgamation of anomaly-detection indices for enhanced process monitoring

Harrou

Sun

Khadraoui

2016

Journal of Loss Prevention in the Process Industries

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Accurate and effective anomaly detection and diagnosis of modern industrial systems are crucial for ensuring reliability and safety and for maintaining desired product quality. Anomaly detection based on principal component analysis (PCA) has been studied intensively and largely applied to multivariate processes with highly cross-correlated process variables; howver conventional PCA-based methods often fail to detect small or moderate anomalies. In this paper, the proposed approach integrates two popular process-monitoring detection tools, the conventional PCA-based monitoring indices Hotelling's T 2 and Q and the exponentially weighted moving average (EWMA). We develop two EWMA tools based on the Q and T 2 statistics, T 2-EWMA and Q-EWMA, to detect anomalies in the process mean. The performances of the proposed methods were compared with that of conventional PCA-based anomaly-detection methods by applying each method to two examples: a synthetic data set and experimental data collected from a flow heating system. The results clearly show the benefits and effectiveness of the proposed methods over conventional PCA-based methods.

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PCA – A Powerful Method for Analyze Ecological Niches

Cited by 53 publications

References 63 publications

A comprehensive framework for the study of species co‐occurrences, nestedness and turnover

A comprehensive framework for the study of species co‐occurrences, nestedness and turnover

Past, current, and future trends of red spiny lobster based on PCA with MaxEnt model in Galapagos Islands, Ecuador

Amalgamation of anomaly-detection indices for enhanced process monitoring

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