See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/245587818 Biplot presentation of diet composition data: An alternative for fish stomach contents analysis Article in Journal of Fish Biology · April 2000 DOI: 10.1111/j.1095-8649.2000.tb00885.x CITATIONS READS 58 477 3 authors , including: Véronique de Billy Sylvain Dolédec Office Français de la Biodiversité Claude Bernard University Lyon 1 28 PUBLICATIONS 220 CITATIONS 159 PUBLICATIONS 12,145 CITATIONS SEE PROFILE SEE PROFILE Some of the authors of this publication are also working on these related projects: Multivariate analyses View project Funtional diversity View project All content following this page was uploaded by Sylvain Dolédec on 22 September 2017. The user has requested enhancement of the downloaded file.
Journal of Fish Biology (2000) 56, 961–973 doi:10.1006/jfbi.1999.1222, available online at http://www.idealibrary.com on Biplot presentation of diet composition data: an alternative for fish stomach contents analysis V. C B *§, S. D † D. C ‡ *Cemagref Aix-en-Provence, Unite ´ de recherche ‘ Hydrobiologie ’, BP 31 le Tholonet, 13612 Aix-en-Provence Cedex 1, France; † CNRS 5023, Laboratoire des Hydrosyste `mes Fluviaux, Universite ´ Claude Bernard Lyon 1, 43 bd. du 11 Novembre 1918, 69622 Villeurbanne Cedex, France and ‡ CNRS 5558, Biome ´trie et Biologie Evolutive, Universite ´ Claude Bernard Lyon 1, 43 bd. du 11 Novembre 1918, 69622 Villeurbanne Cedex, France ( Received 2 October 1999, Accepted 23 December 1999 ) A multivariate analysis derived from principal components analysis (PCA), and which allows the investigation on diet composition data, is introduced. To illustrate the method, prey composition data of stomach contents of brown trout Salmo trutta L. collected in a regulated stream were used. The diet composition, foraging strategies and related patterns of fish diet variation were analysed at a macrohabitat scale (i.e. ri ffl es and glides) by way of biplots. These graphical presentations were consistent with PCA on proportions. � 2000 The Fisheries Society of the British Isles Key words: principal component analysis; stomach contents; feeding habits; diet variation; habitat variation; brown trout. INTRODUCTION Several methods have been proposed to study fish diet (Strauss, 1979; Hyslop, 1980; Mohan & Sankaran, 1988; Costello, 1990; Tokeshi, 1991; Cortes, 1997). For example, feeding habit variations were investigated in relation to biological (age, size), ethological (intra- and inter-specific relationships), spatial (habitat, location) and temporal (seasonal, diurnal) patterns. In such studies, diet patterns were generally analysed at the population level often neglecting the di ff erences among the feeding habits of individual fish (Bridcut & Giller, 1995). However, investigations at the individual level can display important informa- tion on diet composition and feeding strategy. According to Amundsen et al . (1996), a ‘ population with a narrow niche width must necessarily be composed of individuals with narrow and specialized niches ’ whereas ‘ a population with a broad niche may consist of individuals with either narrow or wide niches, or a combination of both ’. Thus, a broad niche width may be the result from either a true generalist behaviour of each individual of a population (high within- individual variation) or a specialization of the individuals of the population on di ff erent prey (high between-individual variation). Therefore, a diet study should necessarily include the contribution of the intra-individual diet variations, i.e. variation in the use of resources by each individual (the within-phenotype component, WPC). In addition, such a study should evaluate the inter- §Author to whom correspondence should be addressed. Tel.: (33) 4 42 66 99 30; fax: (33) 4 42 66 99 34; email: debilly@servaix1.aix.cemagref.fr 961 0022–1112/00/040961+13 $35.00/0 � 2000 The Fisheries Society of the British Isles
962 . . individual diet variation, i.e. variation in the use of resources among individuals (the between-phenotype component, BPC) (Wootton, 1990; Bridcut & Giller, 1995; Amundsen et al ., 1996). Qualitative (diet composition), semi-quantitative (prey proportions) and quan- titative (consumption rates) investigations are available from stomach content data sets. However, the total abundance of prey items depends on various factors that are not easy to control. These factors include, for instance, the food availability, the rapidity of prey digestion and the hierarchical interactions among predators (Hyslop, 1980). As a result, quantitative variations of prey items among stomach contents can be significant and can generate di ff erences among individuals. Because the use of proportions removes the unequal weight among individuals, semi-quantitative investigations are more appropriate for analyses at the individual level. The multivariate nature of data on diet composition (large number of prey-columns and individuals-rows) implies that a preliminary examination of the similarities among individual diets may be required before testing hypoth- eses. Crow (1979) was the first to apply principal component analysis (PCA) as a clustering procedure for fish diet analysis. Later, classical multivariate analyses have often been used for synthesizing large data arrays on fish food (Bridcut & Giller, 1993; Sempeski et al ., 1995). Nevertheless, although specific multivariate techniques potentially helpful for studying stomach contents have been intro- duced into general ecology (Ter Braak, 1983), rarely have they been used for fish. In this paper, a multivariate analysis is proposed specifically designed for diet composition data. This method, introduced by Aitchison (1983), is robust in case of a large number of prey. It is based on a principal component analysis performed on a proportion table having each row total equal to 1. In the following, this analysis is abbreviated to %PCA. The use of this method implies the application of a geometric concept (Gower, 1967), which is consistent with the numerical concept of the biplot presentation (Gabriel, 1971, 1978, 1981): the fish individuals and their prey items are analysed simultaneously and plotted on the same graph. Thus, the analysis is clearly established at the level of the individuals. The method is described in full and applied to trout diets to depict the feeding habits of individuals and the relationship to their habitat use. %PCA is shown as an alternative to traditional techniques for detecting patterns in individual diet and foraging behaviour. Furthermore, as for other multivariate techniques, variance partitioning (Dole ´dec & Chessel, 1987, 1989; Yoccoz & Chessel, 1988; Borcard et al ., 1992) is available for testing the diet variation at the population or higher level. MATERIAL AND METHODS PRINCIPLE OF %PCA Let A =[ a ij ] contain the number of prey items (columns, 1 � j � p ) found in the stomachs of n individuals (rows, 1 � i � n ). Let P =[ p j/i ] contain the proportion of the j th prey in the i th stomach so that � p j =1 p j/i =1 (i.e., 100%). Thus data are expressed as percentage of the row total. The usual graphical display of such data consists of a triangular diagram available in the simplest case where p =3 [Fig. 1(a)]. In the case of more than three categories, the triangular diagram is no longer feasible. So a reduction of the dimensions of matrix P is necessary to enable graphical display. The triangular diagram can be
963 0 – 100% (a) 21 15 24 1st category 3rd category 25 26 19 14 13 18 10 11 16 23 7 22 12 1 5 20 17 6 2 3 4 9 8 100% 0 2nd category 0 100% 2nd axis (b) 3 21 24 25 12 15 4 1 26 1st axis 9 17 20 14 19 11 6 2 3 8 13 18 22 16 5 10 23 7 1 1 –1 1 2 –1 (c) 44% 24% 25 32% F . 1. (a) Triangular presentation of theoretical composition data using three categories. (b) Vector presentation of the same data after a %PCA. The categories are plotted on the first factorial plane using the components of the first two eigenvectors (noted c j in equation 1). The length of arrows 1, 2 and 3 are proportional to the weight of categories. Sample 25 is positioned (position noted m i in equation 2) proportionally to its percentage of each category by averaging (noted m i in equation 2). (c) Mechanical presentation for sample 25 using the weight of each category presented by this sample at the arrow end of each category. Sample 25 is thus at the centre of gravity of the category distribution. obtained through a column centred PCA on proportions (%PCA) (Ter Braak, 1983). Let p j =1/ n � n i =1 p j/i be the mean for prey j . Let P 0 =[ p j/i � p ¯ j ] be the centred table (i.e., the centroid of the overall population is situated at the origin of axes). %PCA incorporates the following steps:
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