Resource partitioning by Lake Tana barbs predicted ... - Springer Link

Reviews in Fish Biology and Fisheries 10: 393–437, 2001. © 2001 Kluwer Academic Publishers. Printed in the Netherlands.

393

Resource partitioning by Lake Tana barbs predicted from fish morphometrics and prey characteristics Ferdinand A. Sibbing1 & Leo A.J. Nagelkerke2 1 Experimental

Zoology Group, Wageningen Institute of Animal Sciences (WIAS), Wageningen University, Marijkeweg 40, 6709 PG Wageningen, The Netherlands (E-mail: [email protected]); 2 Fish Culture and Fisheries Group, Wageningen Institute of Animal Sciences (WIAS), Wageningen University, Marijkeweg 40, 6709 PG Wageningen, The Netherlands

Accepted 24 May 2001

Contents Abstract Introduction Framework and methodology The Food-Fish Model (FFM) Food properties Structural options of cyprinid fish to cope with food properties Materials and techniques Study area and fish samples Measuring trophic morphology in Barbus Analysis of gut contents and available food resources Processing of morphometric data for trophic predictions Processing of gut contents data Comparison of predicted versus actual diet spectra and food partitioning Results Trophic morphology in Lake Tana Barbus Actual diet of the Lake Tana Barbus species Matching between predicted and actual gut contents Discussion Strength and weakness of the FFM Food properties evoking resource partitioning: incompatible feeding modes and food types Which of the measured characters are redundant? Behavioral options to improve feeding efficiency Specialists versus generalists Perspectives Conclusions Acknowledgements Appendix References

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431 431 432 434

Keywords: Barbus, cyprinids, ecomorphology, feeding mechanisms, food properties, functional morphology Abstract We develop a food-fish model (FFM), which quantitatively relates properties of aquatic food types (size, shape, escape velocity, habitat, mechanical properties and chemical quality) to feeding structures of cyprinid fish. The model is based on functional morphology and experiments on search, capture, selection, and internal processing of food by fish. The FFM shows which food properties are most critical in feeding and how fish can optimise coping with them. Relative food size imposes the highest demands, followed by prey velocity, food

394 habitat and mechanical properties. These overrule taxonomic affinities of food types. Highly demanding food types (large, fast prey, suspensions of plankton, benthic prey and mechanically tough items) impose incompatible morphological requirements on fish. We apply the FFM to the endemic Barbus species flock of Lake Tana (Ethiopia), since the structural diversity of its 14 species reflects recent adaptations to trophic niches. We predict their potentials in utilising different food types by quantitative comparisons of 35 parameters, measured for each species, with the values for each food specialist derived from the FFM. These diet predictions are tested against gut contents from 4,711 fish, sampled over seasons and habitats. Gut contents and predictions show a good overall fit. The value of the model is shown by its resolution in predicting resource partitioning among the barbs. For the 14 barbs a trophic hierarchy with six major trophic groups is reconstructed which closely matches the predictions. Trophic specialists (> 65% by volume of a single food type) are also structurally specialised, whereas less extreme anatomical structures characterise trophic generalists, allowing them to switch between feeding modes. Trophic generalists are best defined by behavioral flexibility, since feeding modes integrate both fish and food characters. The FFM is of practical use in evaluating the role of morphological diversity in an ecosystem and enables the analysis of trophic interactions in fish communities and of the cascading effects by environmental change. Such an approach can be instrumental in the development of management strategies for fisheries and in conservation of biodiversity.

Introduction Feeding strategies are important for the survival and reproduction of organisms. Natural selection favours body constructions that effectively enhance these strategies. This concept is basic to all studies tracing correlations between morphological diversity of fish species and environmental factors (review in Keast and Webb, 1966; Gatz, 1979a,b; Felley, 1984; Barel et al., 1989; Liem, 1990; Witte and van Oijen, 1990; Winnemiller, 1991; Douglas and Matthews, 1992; Motta and Kotrschal, 1992; Wainwright and Reilly, 1994; Norton, 1995; Wainwright and Richard, 1995; Hugueny and Pouilly, 1999). In fish communities, resource partitioning mainly occurs along the trophic dimension (Ross, 1986). Yet, few studies that have correlated trophic morphology of fish and patterns of resource use were successful (Goulding, 1985; Grossman, 1986; Kotrschal, 1989; Motta et al., 1995; Norton, 1995; Piet, 1998). The major bottlenecks obscuring relations between morphology and ecology are: (1) The phylogenetic history of the compared species is highly different. Morphological differences that result from long separate evolutionary histories may constrain and even obscure the tuning between structural and ecological features (Felley, 1984; Strauss, 1987; Douglas and Matthews, 1992). (2) Of the large set of food properties that fish have to cope with, few food properties are taken into

account. Food size is often the only parameter measured. (3) Few structural parameters of fish, critical to foraging and food processing, are considered (Webb, 1984; Hoyle and Keast, 1987, 1988; Wahl and Stein, 1988; Kotrschal, 1989; Hambright, 1991; Wainwright and Richard, 1995). Mouth size is often the only parameter measured (Norton, 1995). (4) Most studies focus on correlations between morphological and ecological parameters rather than searching for explanations (Keast and Webb, 1966; Felley, 1984; Grossman, 1986; Wikramanayake, 1990; Douglas and Matthews, 1992; Motta et al., 1995; Piet, 1998). Lack of knowledge of the relation between form, function, behavior (feeding performance) and ecology limits the understanding of functional links between patterns of structural and ecological diversity (Barel et al., 1989; Norton, 1995; Bouton et al., 1998). The first part of this paper reviews functional relationships between the structural characteristics of fish and their ability to cope with specific food properties, synthesising these relations into a food-fish model (FFM). In the second part, predictions from the FFM on feeding potential and resource partitioning are tested using the highly diversified Lake Tana Barbus species flock. Studies predicting the feeding potentials of species are rare (Wootton, 1990; Gerking, 1994), and predictions are seldom tested in the field (Ricklefs

395 and Miles, 1994). How are predictions made in the present study and how are the preceding bottlenecks solved? (1) The 14 endemic Barbus species of Lake Tana (Ethiopia) most probably descended from the ancestral Barbus intermedius species living in the Blue Nile river (Nagelkerke et al., 1994, 1995; Nagelkerke and Sibbing, 1997; Sibbing et al., 1998). After the recent formation of Lake Tana, through volcanic blocking of the Blue Nile (Mohr, 1962), this species radiated into new ecological niches presented by the lake, forming an incipient species flock. Since trophic specialisation was instrumental in this radiation, the morphological differences among the barb species are taken as adaptations to the use of new food resources. Lake Tana is an evolutionary laboratory. (2) By taking a large set of food properties (size, shape, habitat, velocity, mechanical properties and chemical aspects) and identifying their specific demands on fish to facilitate their use, we are able to make detailed functional links between food and fish. The wide spectrum of aquatic food types implies a large spectrum of mechanical designs in fish. (3) In addition to foraging, the internal processing of food by fish is investigated: search, detection, approach, capture, size-selection, taste-selection, transport, mastication, swallowing and digestion (Sibbing, 1991a). Depending on the food type, some actions are more critical than others. For example, pursuit and capture are critical in piscivores, mechanical breakdown in molluscivores and herbivorous fish, and retention of small particles in planktivores. In this study, fish structures that are crucial in all feeding actions are measured to avoid one-dimensional comparisons. (4) The selection of fish parameters for comparing feeding abilities among species is largely based on proven form-function relationships of fish structures, tested in experimental studies, not limited to previously found correlations between morphological and ecological features. For example, experimental studies on branchial sieving (van den Berg et al., 1992, 1994) and pharyngeal mastication (Sibbing, 1982, 1991b) explain the critical role of structural variety for feeding performance and utilisation of different food types (Nagelkerke and Sibbing, 1996). Physical models of such systems enable the recognition of important design parameters, they define the limits imposed on struc-

tures and lead to hypotheses about possible optimisation (Muller et al., 1982; Otten, 1981; Hoogenboezem et al., 1991). Framework and methodology In developing the FFM, we define and review food types according to their properties. For example, we define prey fish as a large and elongate, fast moving food item, occurring in varied habitats, having a tough, compliant-fibrous skin and a low carbon/nitrogen ratio. We review the options that cyprinid fish have to cope with different food properties. Food properties and fish parameters are functionally linked in the FFM, explaining the optimum value of 35 fish parameters for utilizing each of 14 food types as an outcome. This ‘structural profile’ for each trophic specialist, given by its 35 optimum parameter values, accounts for every part of the feeding process. By applying the model to 14 Lake Tana Barbus species, we then predict the potential food niche for individual species from their morphometrics, and the partitioning of aquatic food sources among them. This requires a comparison of two independent value sets for the same 35 parameters (Figure 1). The first set gives the values measured for each of the 14 Barbus species, the barb value set (BVS). The second set gives the optimal values for each of the 14 food specialists according to the FFM, the specialist value set (SVS). Each BVS is matched with each SVS. Correlation coefficients quantify these matches and thereby show the potential of a particular species to use each of the food types, and its predicted diet spectrum or potential food niche. The potential food niche of each barb is tested with its realised food niche, the gut contents, by calculating their correlation. A comparison per food type of its use by the barb species shows the potential and realised food partitioning. In this way we constructed a formal trajectory from fish morphology to predictions of food utilisation and resource partitioning, to be used for analysing fish-food interactions in aquatic ecosystems, and for evaluating the significance of morphological diversity among fishes.

396

Figure 1. Methodology of the present study. The Food – Fish Model (FFM) for cyprinid fish formulates the structural optimisations of 14 food specialists by the values of 35 fish parameters, the specialist value set (SVS). The same set of parameters was measured for the Lake Tana Barbus species (BVS). By comparing each BVS with each SVS, we predict which food types can be utilised effectively by each barb species (their potential diet). The predictions are tested against actual data from the barb gut contents.

The Food-Fish Model (FFM) Food properties The highly varied properties of freshwater food types (Table 1) impose a wide array of demands on fish. Physical properties of food organisms do not simply match taxonomic categories (Barel et al., 1989). Within each taxonomic unit, material properties may differ considerably (e.g., fibrous and non-fibrous plants), whereas items from different taxa may have similar physical properties (e.g., molluscs and seeds; both immobile, strong and stiff, with a similar range of size and habitat). In the FFM, food items with properties that deviate conspicuously from the average in their taxon are shifted into a better fitting category (e.g., chironomid and chaoborid larvae, physically different from adult insects, are grouped with the worms), or they are treated separately (e.g., seeds). Habitat Four habitats are distinguished in the water column: within the substratum (endobenthos), on the substratum (epibenthos), the complex pelagic area among rocks and vegetation, and the open pelagic area. Each habitat offers different conditions to fish for detection, approach and capture of food. The effectiveness of food stimuli to alert and guide the fish depends on the physical conditions in such habitats (light, turbidity, conductivity, and flow velocity of water). Endobenthic prey, hidden from predators in muddy or sandy

substrates, escape visual detection. Substratum feeders require chemosensory or electroreceptive organs and need to probe the substratum. Epibenthos imposes less strict demands on the forager, although light conditions become poor with depth, and epibenthic prey are often visually inconspicuous, sedentary animals. Complex and confined habitats among rocks and vegetation offer shelter for prey and feeding strategies vary accordingly (e.g., ambush hunting instead of pursuit hunting). In the open pelagic area, in light and clear conditions, large and contrasting prey is vulnerable to prolonged, vision-guided pursuit (Kotrschal et al., 1991). Particle size Prey size is widely accepted as a major factor in trophic segregation and resource partitioning (Werner, 1974; Wainwright and Richard, 1995). A large range of absolute size is apparent among aquatic food types (Table 1). Food size relative to the oral gape is most important for fish, and the maximum diameter of a food item, rather than its length, limits intake. Therefore, food size is expressed as its maximum diameter and, for feeding, is best expressed as a percentage of the fish’s oral gape diameter (OG). Particle size is important not only for intake, but also for retention. Micro-particles easily pass into the mouth of fish but need to be retained from expulsion through the gill slits. Prey size is categorised as: pico (maximal diameter < 1% OG), micro (1–10% OG), meso (10– 50% OG), macro (50–100% OG) and mega (> 100%

397 OG). If we take the oral gape as 10% of fork length (FL), then for fish larvae < 30 mm FL (and therefore with OG < 3 mm) large zooplankters (1.6 mm diameter) are macro-particles, whereas for adult fish (> 200 mm FL with OG > 20 mm) they are microfood. Prey velocity Aquatic organisms are categorised according to their velocity in avoiding predation (Table 1). To evaluate the importance of prey velocity in escaping predators it should be compared with the pursuit and suction speed of the forager (Christensen, 1996). Swimming abilities of both prey and predator are size-dependent, and usually expressed as body lengths per second. Particle shape Particle length may present special problems for intra-oral transport, for example, long stems of aquatic weeds in grasscarp (Ctenopharyngodon idella) (Vincent and Sibbing, 1992), or earthworms in common carp (Cyprinus carpio) (Sibbing et al., 1986). The shape of zooplankton determines the probability of retention by filterfeeders (van den Berg et al., 1994). We define particle shape by its length / diameter ratio (L/D) as spherical (L/D < 1.5), wide (1.5 < L/D < 4) or elongate (L/D > 4). Material properties Strength, compliance, and the fibrous texture of materials determine their resistance to fracture (i.e., toughness). Strength refers to the amount of stress (Newton per square meter, Nm−2 ) required to break materials; this is high for strong materials and low for weak materials. Compliant materials first absorb stresses by changing their shape. Compliance, which is measured as the percentage of deformation (strain) per unit of stress, is low for stiff materials. Fibrous materials, such as most plant structures and fish skin, absorb stresses that would otherwise propagate cracks (they are notch-resistant) (Vincent, 1990, 1991). Depending on the material properties, each food item requires specific types of loading (producing compression, tension, and/or shear stresses) for fracture (Gordon, 1976). These are applied by different modes of mastication (Sibbing, 1991b), to increase the efficiency of digestion and absorption of food. The total energy (toughness, in Joules per cm3 ) required to break a unit volume of food increases with the strength, compliance and notch-resistance of the food. Even if

materials require equal amounts of energy for fracture, this may result from different properties, such as strength in mollusc shells, compliance in worms, or notch-resistance in fibrous plants. Aquatic food items are evaluated for strength, compliance, fibre content and toughness (Table 1). Since most biomaterials, and certainly whole organisms, are composites, only the most demanding components are considered (e.g., shells in molluscs, exoskeletons in arthropods, skin in fish). Few data on aquatic food types are available from the literature (Jeronimidis, 1991). Chemical composition The contrast in chemical composition is most conspicuous between animal and plant material. Plant materials have a high carbon/nitrogen ratio (C/N), a high content of structural polymers (fibres), and thick cell walls of cellulose. Such properties impose high demands (low pH, lignases, cellulases, time) on chemical breakdown by hydrolysis in digestion (Hofer, 1991). Soft animal tissues, with low C/N ratio, require mainly proteases, less aggressive digestive conditions, and less processing time for exposing their nutrients. Food types Plant and animal materials contrast most conspicuously in chemical composition and mobility. Several food categories of plant origin are distinguished in view of feeding by fish. Phytoplankton includes diatoms and minute, non-fibrous, free-floating pelagic algae. Most sessile algae grow on rocks or plants. Macrophytes are large, often rooted plants in a wide variety of (often elongate) shapes. The larger and emergent species are often fibrous and, for utilisation as food, animals break them down both at macro (body and organ) level and at micro (tissue and cell) level. Seeds have strong and stiff capsules and are dispersed in benthic and littoral areas. Detritus is a mixture of decaying plant material, microorganisms and bottom ooze (organic and inorganic). It offers a habitat for small benthos (e.g., ostracods). Animals consist of readily digestible soft tissues, largely composed of proteins, but they have antipredator capacities like swimming. Zooplankton includes pelagic cladocerans and copepods, for large predators micro-crustaceans. Macro-crustaceans (such as amphipods and isopods) vary in size and velocity. The exoskeleton of decapods (crayfish and crabs) is stiff. Benthic insect larvae and worms (chironomid larvae and oligochaetes) are sedentary, soft-skinned,

>5 >5

Fish

++

0

+

2–10

Molluscs

0

0.5–2

+

>2

Benthic larvae /worms Macro-insects

0 0/+

3 m) with rocky substratum (open triangles), and deep with muddy / sandy substratum (open circles). The Blue Nile is the only outflowing river.

during the rainy season (July–October). The barbs of Lake Tana grow to a large size (modus circa 32 cm, maximum 80 cm fork length) and show a wide range of morphological and ecological diversity (Nagelkerke et al., 1994). Fourteen species of large Lake Tana barbs (B. acutirostris, B. brevicephalus, B. crassibarbis, B. dainellii, B. gorgorensis, B. gorguari, B. longissimus, B. macrophtalmus, B. megastoma, B. nedgia, B. platydorsus, B. surkis, B. truttiformis, and B. tsanensis; Nagelkerke and Sibbing, 1997, 2000) were collected. Samples were taken between October 1992

and November 1995, at 30 stations covering five types of habitat: (1) littoral, close to papyrus beds, (2) shallow (depth < 3 m), rocky substratum, (3) shallow, muddy / sandy substratum, (4) deep (depth > 3 m), rocky substratum, and (5) deep, muddy / sandy substratum (Figure 3). All habitats were sampled with bottom gill nets (stretched mesh sizes: 16, 25, 32, 44, 60, 80, 100 mm). The muddy / sandy stations were also sampled by bottom trawling (stretched mesh size of the cod-end 20 or 40 mm). Nets were set at least bimonthly, both at day and night time, to account for seasonal and diurnal variation.

410 Measuring trophic morphology in Barbus Measurements were taken on 1,307 barbs (fork length > 15 cm): circa 95% was freshly caught and circa 5% had previously been frozen, and thawed prior to measuring. Branchial sieve parameters were measured on material preserved in 4% formaldehyde. Pharyngeal jaws were measured after maceration in water and preservation in 1% formaldehyde. All morphometric parameters that were measured are explained in Table 3, referring to the original papers describing them, and shown in Figure 2. For new parameters a description is given. Taste buds were stained in situ, using the method by Kiyohara et al., (1984). However, instead of freshly killed fish we used frozen specimens to remove mucus. Fish were thawed and the taste bud areas gently rinsed prior to staining. Analysis of gut contents and available food resources Gut contents were taken from 4,711 barbs (> 15 cm fork length) including all species. Within a single catch, gut contents of a maximum of 10 fish were pooled per species. This yields a total of 1,247 gut content samples for analysis. The number of samples varied from 27 for B. gorgorensis to 195 for B. tsanensis. Samples were preserved in a 4% formaldehyde solution within three hours of fish collection, and analysed by microscopy. For each particular food type we estimated the volume-percentage. Food categories are phytoplankton, sessile algae, macrophytes, seeds, detritus / substratum, microcrustaceans, macro-crustaceans, insect larvae / worms, macro-insects, molluscs, and fish. Some food types are difficult to identify (e.g., parts of insect larvae and macro-insects) or more easily digested than others (e.g., worms), resulting in an underestimate in the gut contents. Ideally, only freshly ingested food should be considered. Since cyprinids lack a discrete stomach, we sampled the anterior part of the intestine. Occasionally, partly digested samples further down the gut were included to increase sample size. Therefore, the diet analysis inevitably shows some bias. The presence of food resources was sampled every two months in all habitats. Plankton samples were vertically netted (net diameter 15 cm; mesh-size 80 µm) at least every month. At muddy / sandy stations 5 bottom-grabs were taken, using an EckmanBirg bottom-sampler, covering circa 0.1 m2 up to a depth of 5 cm. The substratum was sieved (0.1 mm mesh) and organisms were collected. At rocky stations, organisms were scraped from circa 0.5 m2

of rock surface, using a brush attached to a 80 µm mesh-sized net (Hydrobios, Kiel, Germany). Among vegetation, a sample of plants (covering 0.5–1 m2 ) and the associated organisms were collected. All samples were stored in a 4% formaldehyde solution. We identified food organisms to the lowest possible taxonomic level (i.e., genus or species for zooplankton; order or family for most other organisms) and pooled them into one of the 11 food categories described above. Processing of morphometric data for trophic predictions We predict the potential food niches of the Lake Tana barbs by comparing the sets of 35 parameter values derived for food specialists, the SVS (Table 2), with measurements of the same parameters on the 14 Barbus species: the BVS (Table 4). Since not all characters were measured on each barb specimen, the resulting dataset contains missing values. As a consequence, it is not possible to perform multivariate analysis such as principal component analysis (PCA) on the original raw data. Therefore, we use the mean value for each parameter per species. For each parameter we statistically test the differences between means among species and assign numerical ranking values according to the generalised gapcoding procedure (Simon, 1983; Archie, 1985). The procedure includes: (1) Scaling the BVS. As we are interested in the relative size and shape of structures, we scale metric parameter values by converting them into ratios of body length or body mass. Angles need no scaling. (2) Testing differences. Differences in the means of normally distributed ratios and angles among species are tested using the Tukey-Kramer method for multiple, unplanned comparisons (Sokal and Rohlf, 1995), using a significance level of 0.05. Non-normally distributed parameters are tested non-parametrically using a Mann-Whitney U-test for two samples (Sokal and Rohlf, 1995). The significance level for multiple comparisons among all 14 species (i.e., 14 × (14 – 1) / 2 = 91), was adjusted to 1 – (1 – 0.05)1/91 = 0.00055 (Dunn-Šidák method: Sokal and Rohlf, 1995). (3) Gap-coding the BVS. The gap-coding procedure (Simon, 1983; Archie, 1985) is performed for each parameter separately and explained in Figure 4. Gap-codes rank mean parameter values and simul-

411

Figure 4. Gap-coding procedure for morphometric data (adapted from Simon, 1983). X and Y are different parameters, measured from fish structures. For each parameter, its mean values for individual species (suffixes A, B, C . . . I) are sorted in ascending order. Lines connect species with mean parameter values that are not significantly different. Species that belong to the group with the lowest mean parameter values are assigned code 1, species belonging to the next group are assigned code 2 etc. If a species belongs to several groups (overlapping lines) it is assigned the average code value of all groups it belongs to.

taneously take into account whether these values are significantly different. This procedure results in a matrix with a ranking code for each measured parameter for each species: the gapcoded data set (Table 4). The average parameter values are listed for comparison with other studies and fish groups. Comparison of SVS and BVS to predict potential food niches The ranges of the SVS and the BVS are different (from –2 to 2 for the SVS: Table 2; from 1 to a maximum of 8 for the gap-coded BVS: Table 4). For comparison we therefore standardise both datasets, by subtracting the mean value of a parameter from each individual parameter value, and dividing it by its standard deviation. Next, we calculate for each species the correlation of its BVS with each of the SVSs. An example of the calculation is given in Figure 5. The resulting correlation coefficients summarise the match between a barb species and each food specialist (Figure 7). The size and sign of the correlations are used as a quantitative prediction of how important a particular food type will be for a barb species, according to its structural characteristics. High positive correlations are interpreted as a high capability for a barb species to utilise a particular food type. Visualisation of trophic predictions In order to visualise the trophic predictions in a single plot (Figure 9), a PCA is performed on the correlation matrix between food specialists and barb species (Figure 7), saving as much of the original variation as possible. The proximity of species in this plot reflects their similarity in trophic predictions. This means that species that are close in the plot, potentially depend

on the same food types. A cluster analysis, using the unweighted pair-group method with arithmetic averages (UPGMA: Rohlf, 1993) on the same correlation matrix results in a tree of species (Figure 8a), in which clusters can be interpreted as the potential trophic groups among the barbs: the predicted trophic hierarchy. Processing of gut contents data To investigate diet composition for a species, average volume-percentages of all food types in its gut are tested for statistical differences (rows in Table 5). As the volume-percentages are distributed non-normally, they are transformed using the van der Waerden method (SAS Institute Inc., 1989), giving an approximately normal distribution. These data are now tested according to the Tukey-Kramer method. Mean volume-percentages of the food types are sorted in ascending order and coded numerically (GCd in Table 5), according to the generalised gap-coding method (Simon, 1983; Archie, 1985; explained in Figure 4). To investigate resource use among the species (food partitioning), the volume-percentages are similarly tested for significant differences per food type. Now, volume-percentages of a particular food type are ranked in the samples of all species (columns in Table 5), giving different gap-codes (GCp ). This procedure provides a matrix containing the average volume-percentages and the two different gap-codes for each food type-species combination. Comparison of predicted versus actual diet spectra and food partitioning The predicted diet spectra are tested by calculating the correlations between the quantitative diet predictions of a species (Figure 7) and its actual diet gap-codes (GCd in Table 5). Predicted food partitioning is evaluated by calculating the correlations between the same quantitative predictions and the partitioning gap-codes of the gut contents data (GCp in Table 5). The correlation coefficients indicate how well predictions and actual data match. Examples are given in Figure 6. Since the FFM is a comparative approach, the values of the correlation coefficients between predictions and gut data are compared rather than taken as absolute measures of matching. To evaluate how well a particular prediction and gut data match, we calculate the relative fit. The relative fit expresses the correlation coefficient as a percentage of its whole range for all

412

Figure 5. Correlations of the BVS of Barbus nedgia with the SVS of a specialised detritus eater (circles), and a specialised fish eater (pursuit hunter, squares). Each data point represents a different parameter: its X-value is the gap-coded and standardised barb value, and its Y-value is the required value for a food specialist (FFM). The data points for the oral gape axis and the gut length of Barbus nedgia are printed large. The detritus specialist has a lower value for its oral gape axis than the fish eating specialist (Y-axis), but a higher gut length value. B. nedgia also has a small angle of its oral gape axis and a long gut (X-axis), which means that for these two parameters this species is more similar to a detritus eater than to a fish eater. Regression of all data points (i.e., all parameters) for a particular combination of a food specialist with a Barbus species indicate how well a species can utilise that particular food type. The larger the positive correlation, the larger the predicted ability of a fish species to utilise this food type. Negative correlations show serious limitations of a fish species to feed on a certain food type. This example predicts that B. nedgia is a poor fish-eater (r = –0.614, dotted line) and good at eating detritus (r = 0.618, solid line). Correlations between all barb species and all food specialists are given in Figure 7.

species’ gut data. For example, the correlation coefficient of the predicted and actual diet of B. longissimus has a value of 0.574 (Table 6). The correlation coefficients with the actual diets of all species (column in Table 6) range from –0.756 to 0.772. The relative fit between the diet prediction and the actual diet of B. longissimus is calculated as the relative position of its correlation coefficient in this range, i.e., (0.574 – [–0.756]) / (0.772 – [–0.756]) × 100% = 87%. Alternatively, the relative fit of the actual diet of B. longissimus can be compared to the diet predictions of all species (rows in Table 6). In this case the correlation coefficients range from –0.582 to 0.574. This obviously results in a relative fit for B. longissimus of (0.574 – [–0.582]) / (0.574 – [–0.582]) × 100% = 100%. Its overall relative fit (94%) was calculated by taking the mean of both relative fits. The evaluation of

the match of predicted and actual partitioning of food types among the species (Table 7) can be performed in a similar way. A relative fit value of 50% indicates that a prediction is equally distant from the worst and the best predictions for particular dietary data. Therefore, only predictions with relative fit larger than 50% are considered to be successful. If relative fits are 70–90% the predictions are considered fair, if the relative fits are larger than 90%, the predictions are considered good. UPGMA-clustering (Rohlf, 1993) is performed on the gap-codes of volume percentages of food types, as we did for the trophic predictions, to evaluate the matching between actual and predicted food utilisation visually (Figure 8b). This independent method shows the actual hierarchy in trophic relationships. If both

413

Figure 6. Correlations between the predicted and actual diet spectra (a) and between the predicted and actual food partitioning (b). In case of the diet spectra (a) each data-point represents a different food type. The regression of all data-points for the predicted and actual diet of B. tsanensis (dots, data-points for detritus and fish taken by ambush are printed large) gives a high correlation coefficient (r = 0.681), indicating a good diet prediction. In contrast, the diet of B. crassibarbis (squares, data-points for micro-crustaceans (townet) and macro-crustaceans are printed large) is poorly predicted (r = –0.062). All correlations between predicted and actual diets are listed in Table 6. In case of the food partitioning (b) each data-point represents a different species. The correlation between the predicted and actual partitioning of fish taken by pursuit (dots, data-points for B. nedgia and B. acutirostris are printed large) is high (r = 0.834), indicating a good prediction of the partitioning of this food type. In contrast, the partitioning of macro-insects (squares, data-points for B. brevicephalus and B. truttiformis are printed large) is poorly predicted (r = –0.493). All correlations between predicted and actual food partitioning are listed in Table 7.

ways of evaluating the match, by UPGMA clustering and by the relative fits, corroborate, our predictions are robust. Univariate statistics are performed with Statistical Analysis Software (SAS). All multivariate and

clustering techniques are performed with NTSYSpc, version 1.80 software (Exeter Software, Setauket, New York, http://www.exetersoftware.com).

Intake

Approach

Search and detection

No. specimens

HyL / LJSL

HyL / FL

HL

LJin / LJout

LJL

ProtL

900 -OGAx

AFiAr

CPD

OGAr / BAr

BD / BW

BD

ED

ABaL

3 65.8 1.5 0.041 5 0.109 1 0.355 5 0.271 2.5 0.105 2 0.964

1 0.219 2.5 1.99 5.5 0.272 1 0.085 1 1.07

1 0.028 6.5 0.048

B. acutirostris 10–45

1 50.6 2 0.040 1.5 0.077 . . 1 0.206 . . . .

4 0.238 3 2.14 1.5 0.111 6.5 0.099 2.5 1.21

3 0.037 7 0.049

B. brevicephalus 9–26

1 46.9 4 0.066 5 0.108 1 0.340 4 0.250 1.5 0.096 1 0.920

4.5 0.243 2 1.93 3.5 0.218 7.5 0.103 3 1.24

7 0.058 4.5 0.041

B. crassibarbis 8–21

1 46.2 3.5 0.051 5 0.110 1 0.343 5 0.274 3 0.110 3 0.994

1 0.215 1.5 1.87 6 0.358 1.5 0.088 2.5 1.19

3.5 0.038 3 0.039

B. dainellii 7–35

1.5 52.7 2.5 0.047 2 0.082 . . 2 0.222 . . . .

6.5 0.269 2.5 1.99 2.5 0.115 7 0.102 4 1.32

6 0.050 1.5 0.036

B. gorgorensis 7–15

2.5 63.5 3 0.047 5 0.107 1 0.354 5 0.267 2.5 0.105 3.5 1.014

2 0.224 1 1.71 5.5 0.282 2.5 0.091 2.5 1.19

2.5 0.035 4.5 0.043

B. gorguari 10–26

4 77.6 1.5 0.038 5 0.109 1 0.330 4 0.250 3 0.110 4 1.053

1.5 0.221 1.5 1.82 4.5 0.241 5.5 0.098 1.5 1.12

1 0.023 1 0.035

B. longissimus 9–16

Table 4. Measured fish parameters (abbreviations in Table 3), their gap-codes (1-8), and the average parameter values for all species. Gap-codes rank the average parameter values among species and indicate whether the values are statistically different from the values for other species (Figure 4). All values are to be compared within a row. Underlined values indicate which species show extreme (minimum or maximum) values. The range of specimen numbers for individual parameters is indicated below the species names. The bottom of the table lists the number of parameters measured for each species, and the percentage of extreme values per species, calculated over parameters with gap-codes that range at least 1–3

414

1 1.00 1 0 6.5 0.512

PJM / FL3 (∗ 108 ) PTIntDig

Pharyngeal mastication

PTA2out / PJL

3.5 0.0122

1 48485 3 0.309

4 0.082 2 0.052 2 0.0061 2 0.0053 1.5 1.89

7 1.66 4 1.24 1 1.09

PLOW

100∗PalOAr / FL2

PalTBD ∗ FL

GiRP

GiIRD

GiRL

PhGW

OGD

GiAR

OpAr

POrL / OpD


Transport

Taste selection

Size selection

No. specimens

Table 4. Continued

. . . . . .

1.5 0.0097

2.5 76421 1 0.266

1 0.060 . . 3 0.0068 1 0.0039 5 4.75

1.5 1.15 . . 4.5 1.61


3 1.50 1 0 4 0.456

3 0.0121

. . . .

3 0.076 1.5 0.043 1 0.0054 2 0.0052 1 1.20

3 1.24 1 0.76 1 1.00


2 1.12 1 0 5 0.477

1 0.0077

. . . .

6.5 0.090 1.5 0.049 1 0.0053 2.5 0.0054 1.5 1.50

6 1.49 3 1.15 1 0.98

B. dainellii 7–35

2 1.39 1 0 3.5 0.455

4 0.0151

2.5 0.0119 4 5.49 1 0 1 0.392

. . . .

7 0.090 1.5 0.052 3 0.0070 2.5 0.0055 1.5 1.40

5.5 1.45 4 1.27 2 1.22

B. gorguari 10–26

. . . .

1.5 0.065 1.5 0.043 2 0.0060 1.5 0.0046 3 2.67

2 1.20 . . 2.5 1.29


2 1.15 1 0 7 0.527

4 0.0134

. . . .

6 0.086 1.5 0.045 1.5 0.0056 2 0.0051 1 1.22

5.5 1.43 1 0.79 1 1.01


415

Approach

Search and detection

No. specimens

Digestion

No. specimens

Table 4. Continued

CPD

OGAr / BAr

BD / BW

BD

ED

4.5 0.239 2.5 2.00 3.5 0.209 4.5 0.095

1.5 0.029 8 0.059 1 0.215 1.5 1.90 5.5 0.285 2 0.090

1 0.029 4 0.041

B. megastoma 10–39

B. macrophtalmus 10–29 ABaL

23 28

33 48

No. measured No. extremes (%)

4 2.05

. . . . . . . .

1.5 1.70

1 0.175 1.5 2.62 2 1.00 1 0.93


GuL

PTA2W (∗ 100)

PJM / PJL3 (∗ 105 ) PTA2Hook

PJSymL / PJL


3 0.236 1.5 1.91 2 0.112 6.5 0.100

6 0.051 4.5 0.042

B. nedgia 8–52

31 31

5 2.27

2 0.198 1 2.18 2 0.78 1 1.00


4.5 0.243 1.5 1.88 4 0.231 3.5 0.093

4.5 0.043 5.5 0.044

B. platydorsus 10–47

31 41

1 1.52

1 0.176 1 2.166 2 1.00 1 0.98

B. dainellii 7–35

7 0.270 3 2.14 1 0.083 6 0.098

3 0.038 5.5 0.045

B. surkis 9–45

28 21

7 3.4

4 0.271 3 6.64 1 0.20 2 1.93


4.5 0.242 1.5 1.83 5 0.248 8 0.106

1.5 0.029 2 0.037

B. truttiformis 10–28

31 24

1.5 1.66

1.5 0.192 1.5 2.77 2 0.92 1 0.93

B. gorguari 10–26

5.5 0.253 2.5 1.99 2.5 0.145 7 0.102

5.5 0.047 5.5 0.044

B. tsanensis 8–47

31 38

2 1.71

2 0.194 1.5 2.65 2 1.00 1 0.96


416

Size selection

Intake

No. specimens

Table 4. Continued

GiIRD

GiRL

PhGW

OGD

GiAR

OpAr

POrL / OpD

HyL / LJSL

HyL / FL

HL

LJin / LJout

LJL

4.5 0.084 1.5 0.050 3 0.0075 2 0.0052

3 68.0 3 0.048 4.5 0.104 1 0.357 4 0.251 2.5 0.103 2 0.966 3.5 1.26 2 0.95 3 1.39

900 -OGAx ProtL

3.5 1.28

AFiAr

B. macrophtalmus 10–29

4.5 0.083 1.5 0.048 1 0.0056 . .

4 79.6 1.5 0.039 5 0.110 1 0.362 4 0.248 2 0.100 1.5 0.942 5.5 1.45 2 0.91 1 1.06

2 1.13


2.5 0.073 1 0.043 3 0.0066 2 0.0047

1 47.5 3.5 0.051 3 0.092 . . 4 0.247 . . . . 2 1.18 . . 3.5 1.43

3 1.25

B. nedgia 8–52

5.5 0.086 1.5 0.051 2.5 0.0066 2.5 0.0053

3 65.8 1.5 0.040 4 0.100 1 0.345 4 0.253 3 0.111 4 1.054 4.5 1.33 3.5 1.19 1.5 1.19

3 1.24


1 0.057 . . 2.5 0.0065 1 0.0039

2 58.7 1.5 0.037 1 0.075 . . 1 0.205 . . . . 1 1.11 . . 5 1.64

3 1.23

B. surkis 9–45

1 49.6 3.5 0.051 3 0.090 1 0.347 3 0.237 1 0.092 3.5 1.008 3 1.23 1.5 0.89 4 1.45 2.5 0.071 1 0.043 3 0.0070 2 0.0049

4.5 0.084 1.5 0.050 3 0.0071 3 0.0061

2.5 1.19

B. tsanensis 8–47

3 67.1 1 0.031 3 0.095 1 0.330 3 0.238 2.5 0.102 3.5 1.024 5.5 1.41 2 0.90 1.5 1.13

4 1.30


417

1.5 62011 2 0.307

PalTBD ∗ FL

Digestion

2 1.10 1 0 4 0.461 1.5 0.190 1 2.36 2 1.00 1 0.92

PJM/FL3 (∗ 108 ) PTIntDig

Pharyngeal mastication

2.5 1.82 33 10

GuL

# measured # extremes (%)

PTA2W (∗ 100)

PJM/PJL3 (∗ 105 ) PTA2Hook

PJSymL/PJL

PTA2out/PJL

1.5 0.0098

PLOW

100 ∗ PalOAr / FL2

3.5 3.25

GiRP

B. macrophtalmus 10–29

Transport

Taste selection

No. specimens

Table 4. Continued

6 2.41 30 17

28 24

2.5 1.40 1 0 3.5 0.454 2.5 0.218 1.5 2.73 2 0.96 1 1.03

2 0.0103

4 110077 3 0.314

3.5 3.10

B. nedgia 8–52

3 1.91

2 1.09 1 0 6 0.493 2 0.194 1 2.36 2 1.00 1 0.92

. .

. . . .

2.5 2.46


31 10

2.5 1.83

2 1.33 1 0 4 0.462 1.5 0.191 1 2.58 2 1.00 1 0.99

2.5 0.0111

. . . .

2 2.30


29 38

7 2.90

3 1.50 1 0 1.5 0.421 3 0.223 2 3.32 2 1.00 1 1.04

1.5 0.0096

3.5 91863 3 0.321

4 3.93

B. surkis 9–45

31 24

3 1.92

1.5 1.02 1 0 5.5 0.484 1 0.180 1 2.37 2 1.00 1 0.95

3.5 0.0123

. . . .

3 2.50


33 24

5.5 2.28

2 1.39 1 0 2.5 0.433 2.5 0.215 1.5 2.59 2 0.86 1 0.97

1 0.0092

4 110974 3 0.338

4 3.57

B. tsanensis 8–47

418

419

Figure 7. Correlations between the BVS of each barb species and the SVS for each food specialist, based on all measured parameters (see regression lines in Figure 5). The larger the positive correlation values, the higher the capability of that species to utilise a particular food type. The larger the negative correlations, the higher the inability of a species to utilise a food type. Note the differences in correlation-ranges for the species. For example, the large range in B. nedgia predicts conspicuous specialisations as well as limitations in food utilisation, whereas the small range in B. truttiformis predicts a generalised feeding potential. Depending on the number of missing values in the data set, the significant correlation level ranges from 0.355 (for B. acutirostris: 33 parameters) to 0.434 (for B. brevicephalus: 22 parameters). An average significance level of 0.4 is indicated by horizontal lines.

Results Trophic morphology in Lake Tana Barbus The differentiation in trophic morphology among the Lake Tana Barbus species is demonstrated by their varied BVS, expressed by the corresponding gapcodes (Table 4). The maximum gap-code for a particular parameter indicates the number of statistically

significant differences among the species, and is therefore a measure of differentiation. Many parameters (67%) have maximum gap-codes of at least 4, the eye diameter even has a maximum gap-code of 8. Two characters do not show any differentiation among the barb species: no single species has interdigitating pharyngeal teeth and all have a similar kinematic (and force) efficiency in closing the lower jaw. Species with extreme (either minimum, or maximum) gap-code

420

Figure 8. Comparison of predicted versus actual trophic hierarchy in Lake Tana barbs. UPGMA-clustering of the 14 Barbus species is based on (a) diet predictions (see Figure 7) (cophenetic value: 0.89, indicating good fit) and (b) the gap-codes of the actual volume-percentages of the food types (Table 5) (cophenetic value: 0.82, indicating good fit). Numbers indicate trophic groups. Abbreviations: Det, detritus / substratum; Ins, macro-insects; Lar, insect larvae and worms; Micru, micro-crustaceans; Mol, molluscs; Mph, macrophytes; Sal, sessile algae; See, seeds.

values for many structural parameters are considered to have a more specialised feeding morphology (such as B. acutirostris and B. surkis, with 45% extremes in their structural parameters). Species with few extreme gap-codes have a more generalised trophic morphology (such as B. macrophtalmus, with 15% extreme values). Predicting diets from the barb data sets An example of calculating the correlation coefficients between the BVS of Barbus nedgia and two SVSs (detritus feeder, pursuit hunting piscivore) is given in Figure 5. The complete correlation matrix of all food type specialist – barb species combinations is presented in Figure 7. Some barbs are predicted as trophic generalists (B. gorguari, B. truttiformis, B. platydorsus) showing neither significant specialisations nor limitations in feeding, while other species (B. nedgia) show conspicuous specialisations as well as limitations. The FFM predicts typical pursuit hunters

(B. acutirostris and B. megastoma) and typical ambush hunters (B. platydorsus), whereas other piscivores (B. dainellii, B. gorguari, B. longissimus) are predicted to combine these two feeding strategies. The UPGMA-clustering of diet predictions (Figure 8a), based on all correlations from Figure 7, shows a predicted trophic hierarchy with two major groups, largely piscivores and non-piscivores, each with several subgroups. Food type characteristics, added to the tree, show which properties dominate the divergences in the tree. The fish species roughly cluster into: (1) pursuit hunting piscivores (B. acutirostris, B. megastoma, B. longissimus, B. dainellii, B. gorguari), of which the latter three species also have abilities for ambush hunting; (2) facultative piscivores, the typical ambush hunting B. platydorsus and B. truttiformis, which are predicted to feed on insects and macrophytes as well; (3) specialists in seeds and molluscs (B. gorgorensis), and in sessile algae and macrophytes (B. surkis); (4)

51

58

27

B. crassibarbis

B. dainellii

B. gorgorensis

123

107

B. brevicephalus

B. gorguari

121

B. acutirostris

No. samples

310

33

81

84

972

238

No. specimens % GCd GCp % GCd GCp % GCd GCp % GCd GCp % GCd GCp % GCd GCp

3.2 1.5 1.5 7.4 4 1.5 10.4 2.5 1.5 2.1 2 1.5 8.5 2 1.5 6.9 3.5 1.5

Phytoplankton 0.1 1 1 0.2 2 1 0.3 2 1 0.0 1 1 0.1 1.5 1 0.2 1.5 1

Sessile algae 2.0 1.5 1 14.6 6 3.5 0.7 2 1.5 8.5 3 2.5 31.6 4 4 15.8 5 3.5

Macrophytes 0.0 1 1 0.0 1.5 1 0.3 2 1.5 0.0 1 1 0.1 1 1.5 0.7 1 1

Seeds

2.6 2.5 1.5 3.9 4.5 1.5 28.2 5 5 1.0 2 1 14.6 3 2.5 3.4 3 1.5

Detritus/ substratum 0.6 1.5 1 34.0 7 4 8.8 3.5 3 0.0 1 1 0.0 1 1 1.4 2 1

Microcrustaceans 0.0 1 1 0.0 1 1 0.0 1 1 2.4 2 1 3.3 1.5 1 2.4 2 1

Macrocrustaceans 7.9 3 1.5 9.4 5.5 1.5 28.8 5 3 5.7 2.5 1.5 1.6 2.5 1.5 6.5 4.5 1.5

Larvae/ worms

3.5 2 1.5 23.2 7 4 7.3 3 2 7.3 2 2 0.0 1 1.5 1.2 2.5 1.5

Macroinsects

0.7 1 1 5.2 3 1 11.8 4 2 1.6 1.5 1 27.0 3.5 2 3.6 2.5 1

Molluscs

76.0 4 4 0.2 1.5 1 2.1 1.5 1 69.2 4 3.5 11.5 2.5 1 55.4 6 2.5

Fish

Table 5. Average percentages by volume of food types in gut contents of fish larger than 15 cm fork length (%, top line) and the gap-codes within (diet spectra, GCd , middle line) and among (food partitioning, GCp , bottom line) species. A gap-code ranks the percentage value of a food type in the gut contents and indicates whether this value is statistically different from other percentage values within species (GCd , rows), or among species (GCp , columns). The number of samples and specimens per species are indicated. The sum of percentages does not always equal 100%, because some minor food categories (e.g., fish eggs) are omitted

421

38

87

76

152

84

90

38

195

B. macrophtalmus

B. megastoma

B. nedgia

B. platydorsus

B. surkis

B. truttiformis

B. tsanensis

No. samples

B. longissimus

Table 5. Continued

1555

52

268

141

417

130

356

74

No. specimens % GCd GCp % GCd GCp % GCd GCp % GCd GCp % GCd GCp % GCd GCp % GCd GCp % GCd GCp

3.8 1.5 1.5 9.6 2 1.5 1.3 1.5 1 5.0 1 1.5 9.4 1.5 1.5 7.9 2.5 2 4.2 1 1.5 6.3 4 1.5

Phytoplankton 0.1 1 1 1.8 1.5 1 0.1 1.5 1 0.3 1 1 0.0 1 1 0.3 1.5 1 0.0 1 1 0.2 2 1

Sessile algae 12.9 2 3 8.4 3 2.5 13.9 3 3 4.7 1 2 10.3 2.5 3 70.9 4 5 3.3 1 2 4.6 4.5 2

Macrophytes 0.7 1 1.5 0.3 2 1.5 0.0 1 1 0.3 1 1.5 0.0 1 1 0.6 1 1 0.0 1 1 0.8 3 2

Seeds

2.9 1.5 1 3.2 3.5 1.5 0.7 1.5 1 18.9 4 4.5 3.2 1.5 1.5 2.8 1.5 1 3.7 1 1.5 14.2 6 3.5

Detritus/ substratum 0.0 1 1 6.5 3.5 2.5 1.3 1.5 1 3.2 1 2 1.2 1 1.5 5.2 2 1.5 2.8 1 1.5 6.2 5 2.5

Microcrustaceans 2.6 1 1 0.5 1 1 0.3 1.5 1 1.1 1 1 0.1 1 1 0.2 1 1 5.1 1 1 0.0 1 1

Macrocrustaceans 2.5 1.5 1 13.0 4 1.5 6.8 1.5 1.5 32.5 5 3 17.6 3 2 4.9 2.5 1.5 1.3 1 1 42.6 7 4

Larvae/ worms 0.1 1 1 10.4 3 2 6.9 2 2 10.8 2 3 3.6 1.5 2 4.3 3 2 0.0 1 1 4.9 5 2.5

Macroinsects

0.8 1 1 0.3 1.5 1 0.3 1.5 1 16.3 3 2 4.0 1.5 1 0.4 1 1 1.6 1 1 17.7 6 2

Molluscs

71.0 3 3.5 45.3 5 2 66.8 4 3.5 5.6 1 1 49.0 4 2.5 2.1 1 1 72.3 2 4 1.3 1.5 1

Fish

422

423 Table 6. Evaluation of the match between predicted and actual diet spectra for each Barbus species. Values are correlations between predicted (Figure 7) and actual diet spectra (gap-codes GCd in Table 5) within the Barbus species (explained in Figure 6a). Columns list the correlation coefficients of the predicted diet of a particular species with the actual diets of all species. Rows list the correlation coefficients of the actual diet of a particular species with the predicted diets of all species (values between brackets are the correlation coefficients of the predicted diet of a species with its own actual diet). Italics indicate minimum and maximum values per row; minimum and maximum values per column are underlined. Note that only bold correlation coefficients (> 0.532) are significant. Abbreviations in the top row refer to the species listed in the first column Diet spectra predictions

Gut data

B. acutirostris B. brevicephalus B. crassibarbis B. dainellii B. gorgorensis B. gorguari B. longissimus B. macrophtalmus B. megastoma B. nedgia B. platydorsus B. surkis B. truttiformis B. tsanensis

Ac

Br

Cr

Da

Go

Gu

Lo

[0.524] –0.094 –0.563 0.360 –0.308 0.376 0.545 0.294 0.409 –0.439 0.447 –0.135 0.398 –0.576

–0.623 [0.630] 0.464 –0.609 0.054 –0.346 –0.414 –0.226 –0.474 –0.144 –0.583 0.594 –0.444 0.549

0.261 –0.486 [–0.062] 0.346 0.156 0.075 –0.070 –0.017 0.086 0.587 0.225 –0.508 0.020 –0.144

0.765 –0.530 –0.425 [0.455] –0.278 0.309 0.434 0.398 0.265 0.192 0.649 –0.649 0.331 –0.496

–0.456 –0.149 0.628 –0.352 [0.333] –0.467 –0.275 –0.175 –0.292 0.343 –0.329 –0.114 0.068 0.625

0.667 –0.307 –0.525 0.488 –0.377 [0.337] 0.568 0.313 0.441 –0.232 0.527 –0.384 0.608 –0.587

0.342 –0.507 –0.756 0.748 0.235 0.570 [0.574] 0.056 0.772 –0.394 0.551 –0.206 0.484 –0.719

Diet spectra predictions


Ma

Me

Ne

Pl

Su

Tr

Ts

–0.132 0.554 0.020 –0.179 –0.422 –0.144 0.043 [0.000] –0.123 –0.452 –0.297 0.428 0.192 0.081

0.180 –0.015 –0.545 0.419 –0.080 0.333 0.417 0.013 [0.460] –0.532 0.222 0.125 0.393 –0.461

–0.280 0.316 0.698 –0.606 –0.067 –0.397 –0.582 –0.121 –0.681 [0.490] –0.466 0.102 –0.600 0.605

0.486 0.081 –0.543 0.017 –0.337 0.243 0.370 0.492 0.301 –0.387 [0.550] 0.057 –0.062 –0.466

–0.782 0.092 0.507 –0.197 0.615 –0.268 –0.519 –0.545 –0.255 0.260 –0.600 [0.259] –0.281 0.520

–0.031 0.085 –0.492 0.151 0.193 0.338 0.111 0.035 0.400 –0.437 0.227 0.387 [–0.290] –0.377

–0.410 0.448 0.742 –0.671 –0.095 –0.452 –0.569 –0.155 –0.722 0.338 –0.584 0.237 –0.525 [0.681]

a specialist in eating micro-crustaceans (by pumpfilterfeeding) and sessile algae (B. brevicephalus); (5) true benthivores feeding on detritus and insect larvae (B. nedgia, B. tsanensis); (6) a specialist in benthic insect larvae, detritus and macro-insects (B. crassibarbis); and (7) a pump-filterfeeder on micro-crustaceans (B. macrophtalmus).

A PCA on the data from Figure 7, taking the correlations of a species with all food specialists as parameters, visualises the predicted trophic segregation among the barbs in a single plot with the food specialists as a reference (Figure 9). The vectors indicate the factor loadings of the food specialists on the axes. The PCA shows that structural demands

424 Table 7. Evaluation of the match between predicted and actual food partitioning among the 14 Barbus species (listed in Table 6). Values are correlations between predicted (Figure 7) and actual food partitioning (gap-codes GCp in Table 5) among all species (explained in Figure 6b). Columns list the correlation coefficients of the predicted partitioning of a particular food type with the actual partitioning of all food types. Rows list the correlation coefficients of the actual partitioning of a particular food type with the predicted partitioning of all food types. Values between brackets indicate the correlation coefficient of the predicted partitioning of a particular food type with the actual partitioning of the same food type. Italics indicate minimum and maximum values per row; minimum and maximum values per column are underlined. Note that only bold correlation coefficients are significant (> 0.576). Note that for phytoplankton, micro-crustaceans, and fish, predictions for two different feeding modes are distinguished Food partitioning predictions

Gut data

Phytoplankton Sessile algae Macrophytes Seeds Detritus/substratum Micro-crustaceans Macro-crustaceans Larvae/worms Macro-insects Molluscs Fish

Phyto-plankton (townet)

Phytoplankton (pump)

Sessile algae

Macrophytes

Seeds

Detritus/ substratum

Microcrustaceans (townet)

[–0.056] 0.034 0.099 0.035 –0.541 0.040 0.034 –0.194 0.346 –0.491 0.349

[–0.282] –0.551 0.036 0.203 0.272 0.610 –0.551 0.265 0.518 0.121 –0.708

–0.043 [–0.124] 0.096 0.209 0.197 0.311 –0.124 0.100 0.216 0.352 –0.577

0.294 0.362 [0.088] –0.231 –0.319 0.093 0.362 –0.368 –0.167 –0.244 0.281

0.026 0.015 0.333 [0.442] 0.275 –0.100 0.015 –0.006 –0.104 0.687 –0.597

–0.357 –0.392 –0.404 0.116 [0.762] 0.288 –0.392 0.665 0.314 0.641 –0.799

0.026 0.046 0.132 –0.056 –0.622 [–0.017] 0.046 –0.263 0.246 –0.624 0.510

Food partitioning predictions

Phytoplankton Sessile algae Macrophytes Seeds Detritus/substratum Micro-crustaceans Macro-crustaceans Larvae/worms Macro-insects Molluscs Fish

Microcrustaceans (pump)

Macrocrustaceans

Larvae / worms

Macro-insects

Molluscs

Fish (pursuit)

Fish (ambush)

–0.249 –0.641 0.037 –0.030 0.025 [0.729] –0.641 0.212 0.702 –0.328 –0.476

0.344 0.562 0.111 0.165 –0.230 –0.385 [0.562] –0.387 –0.561 –0.058 0.432

–0.274 –0.349 –0.453 –0.019 0.746 0.120 –0.349 [0.681] 0.167 0.564 –0.599

0.128 0.284 –0.045 –0.274 –0.253 –0.346 0.284 –0.301 [–0.493] –0.390 0.751

0.069 0.142 0.325 0.419 0.261 –0.203 0.142 –0.018 –0.154 [0.697] –0.544

0.164 0.424 –0.067 –0.194 –0.410 –0.468 0.424 –0.297 –0.381 –0.386 [0.834]

0.269 0.256 0.029 –0.350 –0.286 –0.467 0.256 –0.262 –0.459 –0.378 [0.746]

for efficiently feeding on fish and macro-insects are predicted to be incompatible (vectors have opposing directions) with those for pump-filterfeeding of phytoplankton, with those for feeding on sessile algae and also with those for feeding on benthos. Feeding on macro-crustaceans is predicted to be incompatible with pump-filterfeeding on micro-crustaceans or phytoplankton and with feeding on macrophytes. The relative specialisation of each barb species to utilise a particular food type, can be read from its proximity to

the arrowhead of the food specialist vectors (Figure 9). The first two principal components account for 72% of the total variance among the barbs. Predicting resource partitioning from the barb data sets The predicted resource partitioning is analysed in more detail from the correlations between SVS and BVS (Figure 7). Phytoplankton is predicted to be utilised effectively by B. tsanensis and, somewhat less

425 B. brevicephalus, and much less by B. tsanensis. None of the barbs is predicted to feed effectively on macrocrustaceans and only B. crassibarbis, and (to a lesser extent) most piscivores, can utilise macro-insects. However, B. crassibarbis, together with B. nedgia and B. tsanensis, are predicted to feed more successfully on insect larvae and worms. Fish is predicted to be the most widely exploited prey (by B. acutirostris, B. dainellii, B. longissimus, B. megastoma, and B. platydorsus: r > 0.4). Actual diet of the Lake Tana Barbus species

Figure 9. Principal component analysis (PCA) of all BVS-SVS correlation values from Figure 7. The PC-scores of the species on the first two principal components (dots) and the factor loadings of the food specialists (arrows) are plotted. The ability of a species to utilise a particular food type can be derived by estimating its relative position along that food type vector. For example, the relative positions of B. brevicephalus and B. platydorsus projected on the micro-crustaceans (townet) vector (dashed lines), show that the former species is better at townetting micro-crustaceans. Note that PC1 and PC2 do not account for all variance among the barbs (72.3%), so for detailed predictions of the feeding capabilities of species, Figure 7 should be read. Abbreviations of species: Ac, B. acutirostris; Br, B. brevicephalus; Cr, B. crassibarbis; Da, B. dainellii; Go, B. gorgorensis; Gu, B. gorguari; Ma, B. macrophtalmus; Me, B. megastoma; Ne, B. nedgia; Lo, B. longissimus; Pl, B. platydorsus; Su, B. surkis; Tr, B. truttiformis; Ts, B. tsanensis. Abbreviations food types: Cru, macro-crustaceans; Det, detritus / substratum; Fia, fish (by ambush); Fip, fish (by pursuit); Ins, macro-insects; Lar, insect larvae / worms; Micrp, micro-crustaceans (by pumping); Micrt, micro-crustaceans (by townetting); Mol, molluscs; Mph, macrophytes; Php, phytoplankton (by pumping); Pht, phytoplankton (by townetting); Sal, sessile algae; See, seeds.

effectively (r < 0.4), by B. brevicephalus and B. nedgia. Sessile algae are predicted to be exploited by B. surkis and to a lesser extent by B. gorgorensis, B. tsanensis, B. brevicephalus, and B. nedgia. Only B. truttiformis is predicted to feed effectively on macrophytes and only B. gorgorensis is able to feed highly efficiently on seeds and molluscs. Detritus is predicted to be utilised most effectively by B. nedgia and B. tsanensis, and to a lesser extent by B. crassibarbis. Micro-crustaceans are predicted to be exploited successfully only by B. macrophtalmus and

From the gut contents it appears that some food types are much more utilised by the barbs than others (Table 5, Figure 10); fish (average percentage by volume 37.7%); macrophytes (14.4%); and larvae / worms (12.9%). The average volume-percentages of seeds (0.3%), sessile algae (0.3%) and macrocrustaceans (1.3%) show that these are least eaten. This might be due to their scarcity in the environment, but possibly also to intrinsic food characters (e.g., low quality and the lack of barbs able to cope with their properties). Little-utilised food types show a narrow range of gap-codes among species (Table 5), meaning that their contribution to the diet of the barbs does not differ widely. Sessile algae and macro-crustaceans do not show any significant differences among species at all. We distinguish the following trophic groups among the barbs, based on the original gut contents data (Figure 10) and on the UPGMA-clustering of the gap-codes of the dietary data, the GCp values from Table 5 (Figure 8b). 1. Piscivores (> 65% fish), subdivided in: i. Almost obligate piscivores that feed on other food types only to a minor extent (< 8% for each food type other than fish): B. acutirostris (76% fish) and B. truttiformis (72% fish). ii. Predominant piscivores that also feed on other food types, of which macrophytes are dominant (> 8%): B. longissimus (71% fish – 13% macrophytes), B. dainellii (69% fish – 8% macrophytes) and B. megastoma (67% fish – 14% macrophytes). 2. Facultative piscivores that also feed on other food types in appreciable quantities, especially macrophytes and larvae/worms: B. gorguari (55% fish – 16% macrophytes – 7% larvae/worms) and B. platydorsus (49% fish – 10% macrophytes – 18% larvae/worms).

426

Figure 10. Gut contents of 14 Lake Tana Barbus species (specimens > 15 cm fork length), given as percentages by volume of food type (Table 5). The number of analysed guts is indicated at the right side of the bars.

3. Herbivores/molluscivores/detritivores. This group includes the almost exclusive macrophytivore B. surkis (71% macrophytes – 8% phytoplankton), and B. gorgorensis (32% macrophytes – 27% molluscs – 15% detritus/substratum), the most molluscivorous among the barbs. 4. Polyphagous barbs. These species feed on most food categories, such as macrophytes, larvae/ worms, and macro-insects, in intermediate quantities (5–25%). In addition, B. brevicephalus also feeds on a large amount of micro-crustaceans (34%) and B. macrophtalmus on fish (45%). 5. Benthivores, mainly feeding on larvae/worms, detritus/substratum, and molluscs: B. nedgia (33% larvae/worms – 19% detritus/substratum – 16% molluscs), B. tsanensis (43% larvae/worms – 14% detritus/substratum – 18% molluscs), and B. crassibarbis (29% larvae/worms – 28% detritus/substratum – 12% molluscs). Matching between predicted and actual gut contents The matching between predicted and actual diets depends on the densities of food types in the ecosystem. We have no quantitative data on the

relative abundance of all food types, but a consistent qualitative pattern is apparent from the fieldwork. Phytoplankton, micro-crustaceans, detritus, small fish (< 9 cm FL) and insect larvae and worms, were omnipresent and relatively abundant. Macrophytes (and their associated epiphytic algae, molluscs, and macro-insects) are patchily distributed and mostly limited to shallow areas. The abundant rocks provide a substratum for encrusting algae and molluscs. Seeds and macro-crustaceans have hardly been found in Lake Tana. Predicted versus actual diet spectra Predicted and actual diets are compared by the correlation coefficients (Figure 6a), for all combinations summarized in Table 6. The correlations of predicted and actual diet spectra are positive in most cases and never significantly negative. Significantly positive correlations (r > 0.532) are found for B. brevicephalus, B. longissimus, B. platydorsus and B. tsanensis. However, since we are mainly interested in a comparison within the Lake Tana Barbus group, the relative correlation values are of more interest to us than the absolute values. Therefore we evaluate the match of predictions and actual gut data by calculating a

427 Table 8. Correlations and overall relative fits of the predicted and actual diet spectra for the 14 Barbus species (A) and of the predicted and actual food partitioning among the 14 Barbus species (B). Relative fits are calculated as the percentage of the total correlation range for a particular prediction – gut data combination. Minimum and maximum values per column are underlined A


Diet spectra Correlation

Relative fit

0.524 0.630 –0.062 0.455 0.333 0.337 0.574 0.000 0.460 0.490 0.550 0.259 –0.290 0.681

91 100 44 79 73 76 94 49 90 88 96 74 24 98

relative fit value (Table 8a) (see Materials and Techniques). The diets of B. acutirostris, B. brevicephalus, B. longissimus, B. megastoma, B. platydorsus, and B. tsanensis are predicted well (relative fit > 90%). The diets of B. dainellii, B. gorgorensis, B. gorguari, B. nedgia, and B. surkis are predicted fairly well (70% < relative fit < 90%), and the diets of B. macrophtalmus, B. crassibarbis, and B. truttiformis are predicted poorly (relative fit < 70%). Predicted versus actual food partitioning Predicted and actual resource partitioning is compared by the correlation coefficients (Figure 6b), for all combinations summarized in Table 7. The correlations between predicted and actual food partitioning among species are also mostly positive, and never significantly negative. Detritus/substratum, micro-crustaceans (by pump-filterfeeding), macrocrustaceans, larvae/worms, molluscs, and fish (by pursuit and ambush hunting) all show significantly positive correlations (r > 0.576). Phytoplankton (by townet- and pump-filterfeeding), sessile algae, microcrustaceans (only by townet-filterfeeding) and macroinsects show (non-significant) negative correlations. All food types that are relatively abundant in the lake, with the exception of macrophytes, have a

B

Food partitioning

Phytoplankton (townet) Phytoplankton (pump) Sessile algae Macrophytes Seeds Detritus/substratum Micro-crustaceans (townet) Micro-crustaceans (pump) Macro-crustaceans Larvae/worms Macro-insects Molluscs Fish (pursuit) Fish (ambush)

Correlation

Relative fit

–0.056 –0.282 –0.124 0.088 0.442 0.762 –0.017 0.729 0.562 0.681 –0.493 0.697 0.834 0.746

54 32 49 62 81 100 54 100 100 95 0 100 100 100

high predictability, evaluated from their relative fit (Table 8b). The partitioning of detritus/substratum, micro-crustaceans (by pump-filterfeeding), macrocrustaceans, larvae/worms, molluscs, and fish (by pursuit and ambush hunting) are predicted well, the partitioning of seeds fairly well. In contrast, the partitioning of phytoplankton (by townet- and pumpfilterfeeding), sessile algae, macrophytes, microcrustaceans (by townet-filterfeeding), and macroinsects are predicted poorly. A complementary and informative way of evaluating the match of predicted and actual food partitioning is by visual comparison of the predicted and the actual trophic hierarchy (Figure 8). Most species groups are consistent in both hierarchies. Groups 1 and 2 include the piscivores, B. acutirostris, B. megastoma, B. gorguari, B. dainellii, B. longissimus, and B. platydorsus. Note that B. platydorsus is accurately predicted to be a facultative piscivore. Group 3 includes the macrophytivores-molluscivores B. gorgorensis and B. surkis. Group 4 consists of the polyphagous B. brevicephalus, which is also the most zooplanktivorous barb species. Finally, group 5 includes the benthivores B. tsanensis and B. nedgia. A major difference between predictions and actual gut contents is found for B. truttiformis, which is

428 predicted to be a facultative piscivore, whereas it feeds almost exclusively on fish. B. macrophtalmus is predicted to feed on micro-crustaceans, whereas it actually feeds on a wide food spectrum (including fish, insect larvae and worms, insects and phytoplankton). B. crassibarbis appears to be even more benthivorous than predicted, closely resembling B. tsanensis and B. nedgia.

Discussion Strength and weakness of the FFM The FFM is unique in accounting for all processes involved in feeding, and in its conception of food properties. It provides a universal framework, independent of a specific ecosystem. The advantage of applying the FFM to a particular system is that it narrows down the food resources in that system to potential diets of its diversified fish community. Once the composition of resources or fish in a system changes use of the FFM also enables the prediction of niche shifts in a community, according to the predicted trophic hierarchy. In general, both diet spectra and food partitioning are well predicted for the 14 barb species. The use of gap-codes, representing statistically significant differences in the original data, gives conservative and reliable estimates of the differences among species parameter values and their gut contents. The predictions of the FFM have a high resolution. Among the 14 barb species 6 major trophic groups are distinct by predictions and actual diet. However, some methodological (see materials and methods) and biological factors constrain the matching of predicted and actual food partitioning. Biological factors affecting the test of the FFM, not its predictions, are: (a) The predicted food partitioning does not account for the actual abundance of food types and fish species. For example, Barbus crassibarbis has the potential to utilise macro-insects. However, since these are scarce in the sampled lake area, this species may need to shift to other resources, according to the predicted trophic hierarchy. Also, other predators, including birds and man, may deplete some fish species or food types. The avoidance of predators may affect the encounter rate between food types and fish species.

(b) Competition for resources and food quality. Competition is a precondition for testing predictions of diet spectra and food partitioning. For example, even for a species that specialises in feeding on detritus, consumption of large zooplankton may be more profitable in conditions of abundance. In Lake Tana, many barb species (including the herbivorous B. surkis) feed on locally discarded fish offal. A high abundance of food resources solves Liem’s paradox (Liem, 1980) of specialists which forage as generalists. In such cases behavioral flexibility allows for a wider resource use than predicted from morphological specialisation. The poor food condition in Lake Tana (oligo-mesotrophic; Rzóska, 1976; Dejen, unpublished results) increases competition and thereby the predictability of individual diet spectra. (c) Phylogenetic constraints. The development of specialisations in a fish family is constrained by its phylogenetic history, whereas the demands imposed by the food types remain the same. For example, all cyprinid fish lack oral teeth, which excludes oral biting at mega food items. Also the absence of townet-filterfeeders in cyprinids may be due to constraints in their evolutionary design. Phylogenetic constraints differ widely among the species compared (Strauss, 1987). The comparison among Lake Tana barbs for testing the FFM has the major advantage of their recent common ancestry, almost excluding phylogenetic constraints. This may even imply that evolutionary time has been too short for extreme adaptations comparable to those in evolutionary ‘old’ cyprinid specialists. For example, the pharyngeal jaws are much less differentiated than expected from the varied mechanical properties of food types. In other cyprinids they are highly varied (Chu, 1935). In conclusion, the prediction of food types is best when we compare species with close phylogenetic affinities, when all food types are available in the system, and when fish meet competitive conditions. Food properties evoking resource partitioning: incompatible feeding modes and food types Specialisation for utilising some food types limits the potential for efficiently utilising others. Such trade-offs have been demonstrated previously, for example between suction feeding and biting in cichlid

429 fish (Bouton et al., 1998). Based on the FFM, the food property with highest impact on structures is relative prey velocity (large in prey fish and macroarthropods). Relative food size, habitat, mechanical properties and chemical properties have gradually decreasing impact (compare Figure 8). This does not corroborate with feeding guilds such as herbivores and carnivores. The hierarchy is largely based on the different, often incompatible, demands that food properties impose on fish. For example, the conflicting demands for utilising suspensions of small particles and large evasive prey render the efficient utilisation of both resources by one species impossible. This is illustrated by the PCA (Figure 9) in which these food types have opposing vectors. Fish features that optimise piscivory (fast swimming, or voluminous and fast suction, short widely spaced gill rakers, slender pharyngeal jaws with a short symphysis and hooked pharyngeal teeth for tearing, and a short gut length) all oppose the demands for suspension-feeding on plankton. The fine branchial sieve in filterfeeders has a high flow resistance, which impedes the rapid outflow of water as required in piscivores. The demands for feeding on benthos, detritus and substratum are incompatible with those for suspension feeding as well as with those for piscivory. Mechanical properties of food evoke food partitioning due to specialised pharyngeal jaws, since the mechanical breakdown of stiff-strong food types and compliant prey are highly incompatible. Which of the measured characters are redundant? Repeating the analysis after addition or deletion of some measuring parameters shows similar predicted patterns, suggesting that we measured redundant parameters in the Lake Tana barbs. It should be noted, however, that all parameters carry an amount of ‘noise’ which cannot be interpreted functionally, and which is possibly due to their integration with other structures into one construction (‘constructional constraints’; Barel et al., 1989; Liem, 1991). Measuring a large set of characters will amplify the functional signal rather than the random noise. To increase the resolution within trophic groups, even more characters are probably needed. Any character may hold the key to trophic segregation, e.g., small, but consistent differences in eye size may render some fish day-time and others night-time feeders. A priori reducing the number of parameters can therefore reduce

the resolution and predictive power of the FFM. If the only objective is to distinguish between major trophic groups, for example piscivores and non-piscivores in fisheries research, focussing on contrasting parameter values of incompatible food types is adequate. Behavioral options to improve feeding efficiency Behavioral flexibility enables fish to switch between feeding modes in response to environmental changes, e.g., abundance of prey, competitors or predators (Janssen, 1976; Lammens and Hoogenboezem, 1991; Sibbing, 1991a). An example is switching from filterfeeding to particulate feeding, which is more profitable at decreasing zooplankton densities (Hoogenboezem et al., 1992). Since both feeding modes are performed by the same system, one can expect structural compromises. The FFM predicts that even specialised Lake Tana piscivores are flexible; B. longissimus, B. dainellii and B. gorguari may switch from ambush hunting in complex environments to pursuit hunting in open water (Figure 7). Since requirements for either of these feeding strategies are conflicting, flexibility must come at the cost of their effectiveness separately. Switching between crushing and grinding in common carp, depending on food properties, is another example of behavioral flexibility (Sibbing et al., 1986). Aquarium experiments have shown that once bream deplete the large zooplankton, they instantaneously reduce their mesh to half-size by activating special gill rakers (van den Berg et al., 1994; Hoogenboezem et al., 1991). Experimental studies linking functional morphology, behavioral and ecological performance are needed to trace and evaluate trophic adaptations. A change in feeding mode can also be expected during growth. For example, zooplankton may be macro-prey for small larvae, but for adult fish they are micro-prey and often too small to be retained. In bream, branchial mesh size increases isometrically with fish size, so growing fish need to shift their diet towards larger prey (van den Berg et al., 1992). Most fish not only increase in size, but they also change the shape and specialisation of their structures (‘allometric growth’ in Osse, 1990). Allometric growth and diet shifts are often linked (Olson et al., 1995; Persson et al., 1998; Lundvall et al., 1999; Sanderson and Kupferberg, 1999; Piet, 1998). Fish pass through ‘ontogenetic niches’ (Werner and Gilliam, 1984).

430 In conclusion, adaptive behavior often requires compromises in underlying structures. It enables animals to cope with environmental changes, and makes them opportunistic. Specialising the feeding apparatus limits switching between feeding modes, but optimises the use of fewer resources. In larvae and juvenile fish allometric growth allows for changing of feeding modes and diets as size increases. The FFM can predict such ontogenetic niche shifts by comparing different size classes of the same species. Specialists versus generalists We compare the barbs by structures and gut contents. The definition of specialists and generalists is often ambiguous (Gerking, 1994) due to the varied criteria used. They may involve feeding structures, feeding actions, foraging strategies, and diets, which are all linked. As a quantitative measure for structural specialisation of species we take the occurrence of extreme anatomical structures, indicated by the minimum and maximum gap-codes (Table 4). The highest percentage of extreme gap codes is measured for the piscivorous B. acutirostris (48%), B. dainellii (41%), B. longissimus (38%), and for the herbivorous B. surkis (38%). We consider these species as structural specialists. They contrast with structural generalists such as B. macrophtalmus (10%), B. platydorsus (10%) and B. nedgia (17%). Trophic specialists and generalists are evaluated from their gut contents. Trophic specialists, with > 65% of volume for a single food type in their gut, are the piscivorous B. acutirostris, B. dainellii, B. longissimus, B. megastoma, B. truttiformis, and the herbivorous B. surkis. So structural and trophic specialisation coincide, although B. megastoma and B. truttiformis appear to be far more piscivorous (Figure 10) than expected from their intermediate percentage of structural extremes (both species 24%). The remarkably high number of piscivorous specialists among the Lake Tana barbs, by consequence, lowers the percentage of extreme structures in B. megastoma and B. truttiformis. They are second best. Trophic generalists, with all food types contributing < 35% of volume in the gut contents, are B. brevicephalus, B. crassibarbis, B. nedgia, and B. gorgorensis. Except B. nedgia, these species do not all show few extreme structures (28%, 31%, 17% and 21% respectively). This is probably due to some demanding food

types (e.g., molluscs and macrophytes) in their varied diet. Feeding behavior is characterised by feeding modes. Generalists may also be defined by a large flexibility in feeding behavior, readily switching between feeding modes. The use of different feeding modes cannot be reconstructed from gut contents, but will cause structural compromises. Behavioral flexibility is predicted for B. macrophtalmus and B. platydorsus, feeding mostly on prey fish and insect larvae. Structural compromises will inevitably result in a low number of structural extremes (10% for both species, Table 4). For predators, the attack and capture may be specialised (Christensen, 1996). For others, the internal processing may be specialised. Foraging modes primarily relate to size, velocity and habitat of a food type (e.g., pursuit hunters and filterfeeders), whereas food processing modes relate to material properties of food types (e.g., crushers and laceraters). Behavioral specialisation in foraging is far more conspicuous than in processing of food, rendering processing modes underestimated from behavioral observations. Yet, definitions by behavior are attractive since they integrate aspects of both food and forager. In practice, however, we are mostly faced with structures and gut contents and often lack observations on behavior. Experimental studies identifying feeding techniques and quantifying performances under different environmental conditions are of the utmost importance. In conclusion, structural specialists are generally trophic specialists and vice versa. The combined arguments presented from fish structures, gut contents and behavior are more informative than just assigning a specialist or generalist status to a species. Perspectives This study encourages a wider use of the present research method. We developed the FFM for cyprinid fish, by far the largest family of freshwater fish with more than 2,000 species (Nelson, 1994), and intensively used in aquaculture and fisheries. The FFM approach may also work for other groups of fish, after adjusting the selected parameter set to include the important feeding characters of their evolutionary design. In fact, the demands that food types impose on feeding mechanisms are so universal, that this approach is appropriate for any animal group.

431 Trophic interactions among fish species in an aquatic ecosystem can be analysed by sampling programs over years, accounting for trophic, diurnal, seasonal and spatial variation of fish and their resources. Such studies analyse the present food web, but have little predictive value once environmental conditions change (Sibbing et al., 1994). The FFM provides a tool to analyse potential trophic interactions with less effort and enables us to predict long term shifts in species composition and density, due to natural or man-induced environmental changes such as those resulting from over-fishing or from introduction of species to fill unoccupied niches (Mills et al., 1987; Pet et al., 1996). By predicting the cascading effects at the level of interacting organisms, and their trophic hierarchy, including larvae and juveniles, this approach may help in the rational management for sustainable fisheries and in explaining the role of biodiversity in an ecosystem (Lévêque, 1995).

This sequence of food types reflects the impact in their demands on feeding fish and overrules taxonomic affinity. (4) Switching between feeding modes is a feature of structural generalists, identified by the low occurrence of extreme anatomical structures. Trophic specialists are structural specialists as well. Defining generalists and specialists by behavior (feeding modes) accounts best for both food and fish characteristics. (5) Alternative feeding modes cannot be reconstructed from gut contents. Behavioral experiments measuring feeding performances under varied size, density and habitat conditions of prey are required to evaluate efficiencies of feeding adaptations. (6) The FFM provides a formalised tool for the analysis of complex feeding relationships in fish communities, concerning both juveniles and adults. It offers good perspectives for use on other animal groups.

Conclusions Acknowledgements (1) The FFM is based on functional morphology and on experiments linking food and fish properties. It provides us with a character set which covers widely varied aspects of foraging and internal food processing, and predicts potential diets and resource partitioning among the Lake Tana barbs, including feeding modes. Evaluation of the match between the predicted and actual trophic hierarchy shows a good fit. (2) Quantitative research on adaptive feeding strategies of fish and their underlying structural diversity, as well as quantifying food types by size, shape, habitat, velocity, mechanical and chemical properties, are needed to further increase the resolution of the model at lower levels of trophic segregation. (3) Structural specialisations for feeding on different, very demanding resources are incompatible. Trophic specialists have to ‘choose’ between feeding on: (a) fast macro-prey, (b) suspended micro-particles, (c) benthic organisms, (d) macrophytes, and (e) stiff and strong encapsulated items.

We thank the Ministry of Agriculture in Addis Ababa for its co-operation, as well as the Amhara NR State Agriculture Bureau in Bahar Dar for co-operation in the local implementation of our work. We also acknowledge all the people who helped collecting in the field, Geert van Snik, Michiel Helmes, Hans van Oostenbrugge, Sander Kranenbarg, Arnold van den Burg, and Suzanne van Gaans. Furthermore, we thank Albert Otten for his help with the statistics. Jan Osse is acknowledged for his critical input during the project and Johan van Leeuwen for suggestions on improving the manuscript. We would also like to thank two anonymous reviewers for improving a previous version of the manuscript. This study was financed by the Netherlands Organisation for the Advancement of Tropical Research (NWO / WOTRO), project W88 – 176, the Interchurch Foundation Ethiopia/Eritrea (ISE, Urk, The Netherlands), and the Interchurch Co-ordination Commission for Development Projects (ICCO, Zeist, The Netherlands).

432 Appendix Optimisation of fish structures for the effective use of different food types, according to different sources. Feeding action and parameters Prey detection ABaL

ED

Approach of prey BD / FL BD / BW

% white/ red muscle fibers Aspect ratio caudal fin CPD, AFiAr

Intake of prey ProtL

LJin / LJout for jaw closing

Optimisation

Fish species investigated

Source

Fish from dark, muddy waters have conspicuously more taste buds than their cognates in clear waters.

Extrarius, Macrohybopsis, Platygobio, Erimystax Hybopsis

Moore, 1950

Davis and Miller, 1967

Ictalurus natalis

Atema, 1971

Haplochromine cichlids Diversity of teleosts

Van der Meer and Anker, 1984 Fernald, 1988

Tropical fish community

Piet, 1998

14 species from 8 families in Lake Opinicon, Ontario

Keast and Webb, 1966

Tropical fish assemblage

Piet, 1998

Turbid water species have longer barbels than clear water species of the same genus. Long barbels increase the number of taste buds (density > 25 mm−2 , maximum in skin 9 mm−2 ). Eye size increases visual sensitivity, as well as visual acuity. Deep-sea fish have large eyes to increase photon collection, surface dwelling fish to achieve greater acuity and contrast sensitivity. Different retinal organisations affect these properties. Benthic species have larger eyes.

Body depth ranges 12-52% SL and increases with sucking and manoeuvring life styles. BD / BW ranges 1.2–2.8 and caudal peduncle length ranges 23–49% SL. Both parameters decrease with swimming life styles. Species that forage low in the water column have higher BD. In the trunk, white muscle fibers dominate in sprinters, red fibers in cruisers and stayers. Species that swim during long periods have a high aspect ratio of their caudal fin, suction feeding species have low ratios. Deep caudal peduncles and a large anal fin area increase thrust at strike. In swimmers they are small to avoid drag.

Upper jaw protrusion ranges between 0–58% of preorbital length. Protrusion serves to direct and increase the velocity of water flow into the mouth, decreasing prey-predator distance. In substratum feeders, protrusion with closed mouth allows oral expansion for resuspension. Carp penetrates up to 12 cm into a silt substratum (deeper than roach or rudd).

Biting species have a large ratio (0.40) for their in/out lever in jaw closing, increasing force transmission. Suction feeders have low ratios (0.17), increasing kinematic efficiency.

Boddeke et al., 1959 Webb, 1984, 1988

Webb, 1984, 1988

14 species from 8 families in Lake Opinicon, Ontario Diversity of teleosts


Cyprinus carpio

Sibbing et al., 1986

Cyprinus carpio Rutilus rutilus Scardinius erythrophthalmus Micropterus salmoides Lepomis macrochirus

Nikolsky, 1963

Motta, 1984 Osse, 1985

Wainwright and Richard, 1995

433 Feeding action and parameters

Optimisation


Source

HyL / LJSL

Optimisation of mouth volume increase for suction is achieved by a ratio of 0.71 between hyoid bar and lower jaw-suspensorium bar. Values range between 0.11 and 1.00 Increasing the ratio opercular length/depth increases the volume that opercula can displace per unit of surface (volume capacity), further enhanced by increasing the total opercular area. Ram-feeders open the opercular valves early after mouth opening, volume sucking fish open it late.

Diversity of teleosts

Muller, 1989


Elshoud, 1986


Van Leeuwen, 1984

14 species from 8 families in Lake Opinicon, Ontario 34 species of Caribbean reef fishes Abramis brama Blicca bjoerkna Rutilus rutilus


POrL / OpD, OpAr

Size selection OGD

PhGW

GiRL, GiIRD

GiRP

Taste selection PalTBD

PalOAr ProtL

Oral gapes range between 4–17% SL. Ontogenetic diet changes occur at similar oral gape diameter rather than body size. Bream and white bream are oral gape limited. From bream, white bream to roach, maximum (47%, 80%, 90% OGD) and optimum mussel size (30%, 65%, 70% OGD) increase. Maximum and mean prey size increase with predator size, due to increasing oral gape size, in 27 piscivorous species. OGD 9% SL, PhGW 4% SL in carp. Data suggest that PhGW rather than OGD limits prey size. PhGW appoximates 0.5 OGD in fish > 11 cm SL. Among bream, white bream, and roach, only roach is pharyngeal gape limited. Bream reduces its branchial sieve meshwidth 50% after depleting the large zooplankters. Bream switches from particulate feeding to filterfeeding at high prey densities. Gill raker length and inter-raker distance increase isometrically with fish size. Only bream is capable of reducing mesh-width to half-size instantaneously. Inter-raker distance increases at niche shift from zooplankton to benthic invertebrates. Elaborate secondary raker profiles decrease the meshwidth of the branchial sieve.

In carp feeding on buried organisms, the indigestible substratum fraction amounts only 3% of its gut contents. Maximum taste bud densities on the palatal organ range from 60 mm−2 in piscivorous asp, to 820 mm−2 in benthivorous carp. The capacity for internal sorting of food from substratum increases with the area of the palatal organ. Closed protrusion serves oral resuspension of a foodsubstratum mixture prior to sorting.

Wainwright and Richard, 1995 Nagelkerke and Sibbing, 1996


Persson et al., 1998

Cyprinus carpio Micropterus salmoides Lepomis macrochirus Abramis brama Blicca bjoerkna Rutilus rutilus Abramis brama

Sibbing et al., 1986 Wainwright and Richard, 1995 Nagelkerke and Sibbing, 1996

Abramis brama

Hoogenboezem et al., 1991 Hoogenboezem et al., 1992 van den Berg et al., 1993

Abramis brama Blicca bjoerkna Rutilus rutilus Rastrineobola argentea

Wanink and Witte, 2000


Zander, 1906

Cyprinus carpio

Uribe-Zamora, 1975

Abramis brama, Aspius aspius Cyprinus carpio Rutilus rutilus Cyprinids

Osse et al., 1997

Cyprinus carpio

Sibbing et al., 1986

Sibbing, 1991a

434 Feeding action and parameters Transport PLOW

Mastication PJM, PTIpA

PTIntDig

PTA2out PJSymL

PTA2W

Optimisation


Source

The branchial sieve area decreases and postlingual organ width increases with food size.

Cyprinids

Sibbing, 1988

Roach feeding on mussels have heavier PJs (0.19% body weight) and larger impact angles of their teeth (80◦ ) than white bream (0.18%, 71◦ ) and bream (0.14%, 56◦ ) with gradually less mussels in their diet. Interdigitating teeth serve tearing and shearing, and are not effective in crushing, splitting, piercing, and grinding. Long output levers of teeth increase excursions but reduce force. A long symphysis between left and right PJ stabilises their movements, but limits their excursions, thus optimising crushing and splitting. Wide tooth bases are needed to absorb high reactive stresses, involved in chewing tough food. Grasscarp shear plant material and need to produce forces of 1 Newton per mm2 .

Abramis brama Blicca bjoerkna Rutilus rutilus

Nagelkerke and Sibbing, 1996

Diverse cyprinids

Sibbing, 1991b

Diverse cyprinids

Sibbing, 1991b

Cyprinus carpio

Sibbing, 1982

Diverse cyprinids

Sibbing, 1991b

Ctenopharyngodon idella

Vincent 1992

No cellulases are produced by cyprinids.

Cyprinids

Gutlength ranges from 0.7–0.9 SL in specialised carnivores, 1.2–2.2 SL in omnivores to 5–29 SL in specialised herbivores.

21 species from Panama forest streams

Barnard, 1973 Hofer, 1991 Kramer and Bryant, 1995

and

Sibbing,

Digestion

GuL

References Archie, J.W. (1985) Methods for coding variable morphological features for numerical taxonomic analysis. Syst. Zool. 34, 326– 345. Atema, J. (1971) Structures and functions of the sense of taste in catfish (Ictalurus natalis). Brain, Behav. Evol. 4, 273–294. Barel, C.D.N., Anker, G.C., Witte, F., Hoogerhoud, R.J.C. and Goldschmidt, T. (1989) Constructional constraints and its ecomorphological implications. Acta Morphol. Neerl.-Scand. 27, 83–109. Barnard, E.A. (1973) Biochemical adaptations to diet. In: Prosser, C.L. (ed.), Comparative Animal Physiology. Saunders, Philadelphia, pp. 147–152. Boddeke, R., Slijper, E.J. and van der Stelt, A. (1959) Histological characteristics of the body-musculature of fishes in connection with their mode of life. Proceedings of the Koninklijke Nederlandse Akademie van Wetenschappen, Series C 62, 576–588. Bond, C.E. (1979) Biology of Fishes. Saunders, Philadelphia, 514 pp. Bouton, N., van Os, N. and Witte, F. (1998) Feeding performance of Lake Victoria rock cichlids: testing predictions from morphology. J. Fish Biology, Suppl. A 53, 118–127. Christensen, B. (1996) Predator foraging capabilities and prey antipredator behaviors: pre- versus postcapture constraints on size-dependent predator-prey interactions. Oikos 76, 368–380.

Chu, Y.T. (1935) Comparative studies on the scales and on the pharyngeals and their teeth in Chinese cyprinids, with particular reference to taxonomy and evolution. Biological Bulletin of St. John’s University Shanghai 2, 1–225. Crowder, L.B. (1984) Character displacement and habitat shift in a native cisco in southeastern Lake Michigan: evidence for competition? Copeia 1984, 878–883. Dabrowski, K.R. (1982) The influence of light intensity on feeding of fish larvae and fry. II. Rutilus rutilus L. and Perca fluviatilis L. Zoologische Jahrbücher, Abteilung für Physiologie 86, 353– 360. Davis, B.J. and Miller, R.J. (1967) Brain patterns in minnows of the genus Hybopsis in relation to feeding habits and habitat. Copeia 1967, 1–39. de Visser, J. and Barel, C.D.N. (1996) Architectonic constraints on the hyoid’s optimal starting position for suction feeding of fish. J. Morphol. 228, 1–18. Douglas, M.E. and Matthews, W.J. (1992) Does morphology predict ecology? Hypothesis testing within a freshwater stream fish assemblage. Oikos 65, 231–244. Elshoud, G.C.A. (1986) Fish and Chips, Computer Models and Functional Morphology of Fishes. PhD Dissertation, University Leiden, Netherlands, 122 pp. Evans, H.E. (1952) The correlation of brain pattern and feeding habits in four species of cyprinid fishes. J. Comp. Neurol. 97, 133–142.

435 Felley, J.D. (1984) Multivariate identification of morphologicalenvironmental relationships within the Cyprinidae (Pisces). Copeia 1984, 442–455. Fernald, R.D. (1988) Aquatic adaptations in fish eyes. In: Atema, J., Fay, R.R., Popper, N.N. and Tavolga, W.N. (eds.), Sensory Biology of Aquatic Animals. Springer Verlag, New York, pp. 435–466. Finger, T.E. (1988) Sensorimotor mapping in oropharyngeal reflexes in goldfish, Carassius auratus. Brain, Behav. Evol. 31, 17–24. Gatz, A.J, Jr. (1979a) Community organization in fishes as indicated by morphological features. Ecology 60, 11–718. Gatz, A.J., Jr. (1979b) Ecological morphology of freshwater stream fishes. Tulane Studies in Zoology and Botany 21, 91–124. Gerking, S.D. (1994) Feeding Ecology of Fish. Academic Press, London, 416 pp. Gordon, J.E. (1976) The New Science of Strong Materials: or Why You Don’t Fall through the Floor. Pelican Books, Harmondsworth, Middlesex, 287 pp. Gosline, W.A. (1973) Considerations regarding the phylogeny of cypriniform fishes, with special reference to structures associated with feeding. Copeia 1973, 761–776. Goulding, M. (1985) Forest fishes of the Amazon. In: Prance, G.T. and Lovejoy, T.E. (eds.), Key Environments of Amazonia. Pergamon Press, New York, pp. 267–276. Grossman, G.D. (1986) Food resource partitioning in a rocky intertidal fish assemblage. J. Zool., Lond. 1, 317–355. Hambright, K.D. (1991) Experimental analysis of prey selection by largemouth bass: role of predator mouth width and prey body depth. Trans. Am. Fish. Soc. 120, 501–508. Hofer, R. (1991) Digestion. In: Winfield, I.J. and Nelson, J.S. (eds.), Cyprinid Fishes: Systematics, Biology and Exploitation. Chapman and Hall, London, pp. 413–425. Holˇcik, J., B˘an˘arescu, P. and Evans, D. (1989) A general introduction to fishes. In: Holˇcik, J. (ed.), General Introduction to Fishes. Acipenseriformes (The fresh water fishes of Europe), Vol. 1, part 2. Aula Verlag, Wiesbaden, Germany, pp. 18–147. Hoogenboezem, W., van den Boogaart, J.G.M., Sibbing, F.A., Lammens, E.H.R.R., Terlouw, A. and Osse, J.W.M. (1991) A new model of particle retention and branchial sieve adjustment in filter-feeding bream (Abramis brama, Cyprinidae). Can. J. Fish. Aquat. Sci. 48, 7–18. Hoogenboezem, W., Lammens, E.H.R.R., van Vugt, Y. and Osse, J.W.M. (1992) A model for switching between particulatefeeding and filter-feeding in the common bream, Abramis brama. Env. Biol. Fish. 33, 13–21. Hoyle, J.A. and Keast, A. (1987) The effects of prey morphology and size on handling time in a piscivore, the largemouth bass (Micropterus salmoides). Can. J. Zool. 65, 1972–1977. Hoyle, J.A. and Keast, A. (1988) Prey handling time in two piscivores, (Esox americanus vermiculatus) and (Micropterus salmoides), with contrasting mouth morphologies. Can. J. Zool. 66, 540–542. Hubbs, C.L. and Lagler, K.F. (1947) Fishes of the Great Lakes Region. University of Michigan Press, Ann Arbor, Michigan, 213 pp. Hugueny, B. and Pouilly, M. (1999) Morphological correlates of diet in an assemblage of West-African freshwater fishes. J. Fish Biol. 54, 1310–1325. Janssen, J. (1976) Feeding modes and prey size selection in the alewife (Alosa pseudoharengus). J. Fish. Res. Bd Can. 33, 1972–1975. Jeronomidis, G. (1991) Mechanical and fracture properties of cellular and fibrous material. In: Vincent, J.F.V. and Lillford, P.J.

(eds.), Feeding and the Texture of Food (SEB Seminar Series 44). Cambridge University Press, Cambridge, pp. 1–17. Keast, A. and Webb, D. (1966) Mouth and body form relative to feeding ecology in the fish fauna of a small lake, Lake Opinicon, Ontario. J. Fish. Bd Can. 23, 1845–1874. Kiyohara, S., Yamashita, S. and Kitoh, J. (1984) Rapid location of fish taste buds by a selective surface staining method. Bull. Japan. Soc. Sci. Fish. 50, 1293–1297. Kotrschal, K. (1989) Trophic ecomorphology in eastern Pacific blennioid fishes: character transformation of oral jaws and associated change of their biological role. Env. Biol. Fish. 24, 199– 218. Kotrschal, K., Brandstätter, R., Gomahr, A., Junger, H., Palzenberger, M. and Zaunreiter, M. (1991) Brain and sensory systems. In: Winfield, I.J. and Nelson, J.S. (eds.), Cyprinid Fishes: Systematics, Biology and Exploitation. Chapman and Hall, London, pp. 284–331. Kramer, D.L. and Bryant, M.J. (1995) Intestine length in the fishes of a tropical stream: 2. Relationships to diet – the long and short of a convoluted issue. Env. Biol. Fish. 42, 129–141. Lammens, E.H.R.R. and Hoogenboezem, W. (1991) Diets and feeding behavior. In: Winfield, I.J. and Nelson, J.S. (eds.), Cyprinid Fishes: Systematics, Biology and Exploitation. Chapman and Hall, London, pp. 353–376. Lauder, G.V. (1980) Hydrodynamics of prey capture by teleost fishes. In: Schneck, D. (ed.), Biofluid Mechanics, volume 2. Plenum Press, New York, pp. 161–181. Lazzaro, X. (1987) A review of planktivorous fishes: their evolution, feeding behaviors, selectivities, and impacts. Hydrobiologia 146, 97–167. Lévêque, C. (1995) Role and consequences of fish diversity in the functioning of African freshwater ecosystems: a review. Aquat. Living Resour. 8, 59–78. Liem, K.F. (1980) Adaptive significance of intra- and interspecific differences in the feeding repertoires of cichlid fishes. Amer. Zool. 20, 295–314. Liem, K.F. (1990) Aquatic versus terrestrial feeding modes: possible impacts on the trophic ecology of vertebrates. Amer. Zool. 30, 209–221. Liem, K.F. (1991) A functional approach to the development of the head of teleosts: implications on constructional morphology and constraints. In: Schmidt-Kittler, N. and Vogel, K. (eds.), Constructional Morphology and Evolution. Springer Verlag, Berlin, pp. 231–249. Lindsey, C.C. (1981) Stocks are chameleons: plasticity in gill rakers of coregonid fishes. Can. J. Fish. Aquat. Sci. 38, 1497–1506. Lundvall, D., Svanbaeck, R., Persson, L. and Bystroem, P. (1999) Size-dependent predation in piscivores: interactions between predator foraging and prey avoidance abilities. Can. J. Fish. Aquat. Sci. 56, 1285–1292. Matthes, H. (1963) A comparative study of the feeding mechanisms of some African Cyprinidae (Pisces, Cypriniformes). Bijdragen tot de Dierkunde, Amsterdam 33, 1–35. Mills, E.L., Forney, J.L. and Wagner, K.J. (1987) Fish predation and its cascading effects on the Oneida Lake food chain. In: Kerfoot, W.C. and Sih, A. (eds.), Predation: Direct and Indirect Impacts on Aquatic Communities. University Press of New England, Hanover, pp. 118–131. Mittelbach, G.G. and Persson, L. (1998) The ontogeny of piscivory and its ecological consequences. Can. J. Fish. Aquat. Sci. 55, 1454–1465. Mohr, P.A. (1962) The Geology of Ethiopia. Addis Ababa University Press, Addis Ababa, 268 pp.

436 Moore, G.A. (1950) The cutaneous sense organs of barbeled minnows adapted to life in the muddy waters of the Great Plains region. Trans. Am. Microsc. Soc. 69, 69–95. Moriarty, D.J.W. (1973) The physiology of digestion of blue green algae in the cichlid fish, Tilapia nilotica. J. Zool., Lond. 171, 25–39. Motta, P.J. (1984) Mechanics and functions of jaw protrusion in teleost fishes: a review. Copeia 1984, 1–18. Motta, P.J. and Kotrschal, K.M. (1992) Correlative, experimental and comparative evolutionary approaches in ecomorphology. Neth. J. Zool. 42, 400–415. Motta, P.J., Clifton, K.B., Hernandez, P. and Eggold, B.T. (1995) Ecomorphological correlates in ten species of subtropical seagrass fishes: diet and microhabitat utilisation. Env. Biol. Fish. 44, 37–60. Muller, M. (1987) Optimization principles applied to the mechanism of neurocranium levation and mouth bottom depression in bony fishes (Halecostomi). J. theor. Biol. 126, 343–368. Muller, M. (1989) A quantitative theory of expected volume changes of the mouth during feeding in teleost fishes. J. Zool., Lond. 217, 639–662. Muller, M. and Osse, J.W.M. (1984) Hydrodynamics of suction feeding in fish. Trans. Zool. Soc. Lond. 37, 51–135. Muller, M., Osse, J.W.M. and Verhagen, J.H.G. (1982) A quantitative hydrodynamical model of suction feeding in fish. J. Theor. Biol. 95, 49–79. Nagelkerke, L.A.J. and Sibbing, F.A. (1996) Efficiency of feeding on zebra mussel (Dreissena polymorpha) by common bream (Abramis brama), white bream (Blicca bjoerkna), and roach (Rutilus rutilus): the effects of morphology and behavior. Can. J. Fish. Aquat. Sci. 53, 2847–2861. Nagelkerke, L.A.J. and Sibbing, F.A. (1997). A revision of the large barbs (Barbus spp., Cyprinidae, Teleostei) of Lake Tana, Ethiopia, with a description of seven new species. In: Nagelkerke, L.A.J. (ed.), The Barbs of Lake Tana, Ethiopia. Special issue Wageningen University, Wageningen, pp. 105–170. Nagelkerke, L.A.J. and Sibbing, F.A. (2000) The large barbs (Barbus spp., Cyprinidae, Teleostei) of Lake Tana (Ethiopia), with a description of a new species, Barbus osseensis. Neth. Journal of Zoology 50, 179–214. Nagelkerke, L.A.J., Sibbing, F.A., van den Boogaart, J.G.M., Lammens, E.H.R.R. and Osse, J.W.M. (1994) The barbs (Barbus spp.) of Lake Tana: a forgotten species flock? Env. Biol. Fish. 35, 1–22. Nagelkerke, L.A.J., Sibbing, F.A. and Osse, J.W.M. (1995) Morphological divergence during growth in the large barbs (Barbus spp.) of Lake Tana, Ethiopia. Neth. J. Zool. 45, 431–454. Nelson, J.S. (1994) Fishes of the World. John Wiley, New York, 600 pp. Nikolsky, G.V. (1963) The Ecology of Fishes. Academic Press, London, 352 pp. Norton, S.F. (1995) A functional approach to ecomorphological patterns of feeding in cottid fishes. Env. Biol. Fish. 44, 61–78. Norton, S.F. and Brainerd, E.L. (1993) Convergence in the feeding mechanics of ecomorphologically similar species in the Centrarchidae and Cichlidae. J. Exp. Biol. 176, 11–29. Olson, M.H., Mittelbach, G.G. and Osenberg, C.W. (1995) Competition between predator and prey: resource- based mechanisms and implications for stage-structured dynamics. Ecology 76, 1758–1771. Osse, J.W.M. (1969) Functional morphology of the head of the perch (Perca fluviatilis L.): an electromyographic study. Neth. J. Zool. 19, 289–392.

Osse, J.W.M. (1985) Jaw protrusion, an optimization of the feeding apparatus of teleosts? Acta Biotheoretica 34, 219–232. Osse, J.W.M. (1990) Form changes in fish larvae in relation to changing demands of function. Neth. J. Zool. 40, 362–385. Osse, J.W.M., Sibbing, F.A. and van den Boogaart, J.G.M. (1997) Intra-oral food manipulation of carp and other cyprinids: adaptations and limitations. Acta Physiol. Scand. 161 (S 638), 47–57. Otten, E. (1981) Vision during growth of a generalised Haplochromis species: H. elegans Trewavas 1933 (Pisces, Cichlidae). Neth. J. Zool. 31, 650–700. Persson, L., Leonardsson, K., de Roos, A.M., Gyllenberg, M. and Christensen, B. (1998) Ontogenetic scaling of foraging rates and the dynamics of a size-structured consumer-resource model. Theoretical Population Biology 54, 270–293. Pet, J.S., Gevers, G.J.M., van Densen, W.L.T. and Vijverberg, J. (1996) Management options for a more complete utilisation of the biological fish production in Sri Lankan reservoirs. Ecology of Freshwater Fish 5, 1–14. Piet, G.J. (1998) Ecomorphology of a size-structured tropical freshwater fish community. Env. Biol. Fish. 51, 67–86. Ricklefs, R.E. and Miles, D.B. (1994) Ecological and evolutionary inferences from morphology. In: Wainwright, P.C. and Reilly, S.M. (eds.), Ecological Morphology, Integrative Organismal Biology. The University of Chicago Press, Chicago, pp. 13–41. Rohlf, F.J. (1993) NTSYS-pc. Numerical Taxonomy and Multivariate System, version 1.80. Exeter Software, Applied Biostatistics Inc., Setauket, New York. Ross, S.T. (1986) Resource partitioning in fish assemblages: a review of field studies. Copeia 1986, 352–388. Rzóska, J. (1976) Lake Tana, headwaters of the Blue Nile. In: Rzóska, J. (ed.), The Nile, Biology of an Ancient River. Dr. W. Junk Publishers, The Hague, pp. 223–232. Sanderson, S.L. and Kupferberg, S.J. (1999) Development and evolution of aquatic larval feeding mechanisms. In: Hall, B.K. and Wake, M.H. (eds.), The Origin and Evolution of Larval Forms. Academic Press, San Diego, pp. 301–377. Sibbing, F.A. (1982) Pharyngeal mastication and food transport in the carp (Cyprinus carpio L.): a cineradiographic and electromyographic study. J. Morphol. 172, 223–258. Sibbing, F.A. (1987) The role of taste in the feeding mechanism of the carp (Cyprinidae). Annals of the New York Academy of Sciences 510, 612–615. Sibbing, F.A. (1988) Specializations and limitations in the utilization of food resources by the carp (Cyprinus carpio L.): a study of oral food processing. Env. Biol. Fish. 22, 161–178. Sibbing, F.A. (1991a) Food Capture and Oral Processing. In: Winfield, I.J. and Nelson, J.S. (eds.), Cyprinid Fishes: Systematics, Biology and Exploitation. Chapman and Hall, London, pp. 377–412. Sibbing, F.A. (1991b) Food processing by mastication in cyprinid fish. In: Vincent, J.F.V. and Lillford, P.J. (eds.), Feeding and the Texture of Food (SEB Seminar Series 44) Cambridge University Press, Cambridge, pp. 57–92. Sibbing, F.A. and Uribe, R. (1985) Regional specializations in the oro-pharyngeal wall and food processing in the carp (Cyprinus carpio L.). Neth. J. Zool. 35, 377–422. Sibbing, F.A., Osse, J.W.M. and Terlouw, A. (1986) Food handling in the carp (Cyprinus carpio L.): its movement patterns, mechanisms and limitations. J. Zool., Lond.(A) 210, 161–203. Sibbing, F.A., Nagelkerke, L.A.J. and Osse, J.W.M. (1994) Ecomorphology as a tool in fisheries: identification and ecotyping of Lake Tana barbs (Barbus intermedius complex), Ethiopia. Neth. J. Agricult. Sci. 42, 77–85.

437 Sibbing, F.A., Nagelkerke, L.A.J., Stet, R.J.M. and Osse, J.W.M. (1998) Speciation of endemic Lake Tana barbs (Cyprinidae, Ethiopia) driven by trophic resource partitioning; a molecular and ecomorphological approach. Aquatic Ecology 32, 217–227. Simon, C.M. (1983) A new coding procedure for morphometric data with an example from periodical cicada wing veins. In: Felsenstein, J. (ed.), Numerical Taxonomy (Proceedings of the NATO Advanced Study Institute. NATO Adv. Study Institute Ser. G [Ecological Sciences], No.1). Springer-Verlag, Berlin, pp. 378–382. Sokal, R.R. and Rohlf, F.J. (1995) Biometry. Freeman, New York, pp. 1–887. Strauss, R.E. (1987) The importance of phylogenetic constraints in comparisons of morphological structure among fish assemblages. In: Mathews, W.J. and Heins, D.C (eds.), Evolutionary Ecology of North American Stream Fishes. University of Oklahoma Press, Norman, pp. 136–143. Uribe-Zamora, M. (1975) Selection des proies par le filtre branchial de la carpe miroir (Cyprinus carpio). PhD Dissertation, University of Lyon, Lyon, 127 pp. van den Berg, C., Sibbing, F.A., Osse, J.W.M. and Hoogenboezem, W. (1992) Structure, development and function of the branchial sieve of bream (Abramis brama), white bream (Blicca björkna) and roach (Rutilus rutilus). Env. Biol. Fish. 33, 105–124. van den Berg, C., van den Boogaart, J.G.M., Sibbing, F.A., Lammens, E.H.R.R. and Osse, J.W.M. (1993) Shape of zooplankton and retention in filter-feeding: quantitative comparison between industrial sieves and the branchial sieves of common bream (Abramis brama) and white bream (Blicca bjoerkna). Can. J. Fish. Aquat. Sci. 50, 716–724. van den Berg, C., van den Boogaart, J.G.M., Sibbing, F.A. and Osse, J.W.M. (1994) Zooplankton feeding in common bream (Abramis brama), white bream (Blicca bjoerkna) and roach (Rutilus rutilus): experiments, models and energy intake. Neth. J. Zool. 44, 15–42. van Leeuwen, J.L. (1984) A quantitative study of flow in prey capture by rainbow trout, Salmo gairdneri with general consideration of the actinopterygian feeding mechanism. Trans. Zool. Soc. Lond. 37, 171–227. van Leeuwen, J.L. and Muller, M. (1984) Optimum sucking techniques for predatory fish. Trans. Zool. Soc. Lond. 37, 137–169. van der Meer, H.J. and Anker, G.C. (1984) Retinal resolving power and sensitivity of the photopic system in seven haplochromine species (Teleostei, Cichlidae). Neth. J. Zool. 34, 197–209. Verigina, I.A. (1991) Basic adaptations of the digestive system in bony fishes as a function of diet. J. Ichthyol. 31, 8–20.

Vincent, J.F.V. (1990) Structural Biomaterials. Princeton University Press, Princeton, 244 pp. Vincent, J.F.V. (1991) Plants as food. In: Vincent, J.F.V. and Lillford, P.J. (eds.), Feeding and the Texture of Food (SEB Seminar Series 44) Cambridge University Press, Cambridge, pp. 19–33. Vincent J.F.V. and Sibbing F.A. (1992) How the grasscarp (Ctenopharyngodon idella) chooses and chews its food – some clues. J. Zool., Lond. 226, 435–444. Wahl, D.H. and Stein, R.A. (1988) Selective predation by three esocids: the role of prey behavior and morphology. Trans. Am. Fish. Soc. 117, 142–151. Wainwright, P.C. and Reilly, S.M. (eds.) (1994) Ecological Morphology, Integrative Organismal Biology. The University of Chicago Press, Chicago, 367 pp. Wainwright, P.C. and Richard, B.A. (1995) Predicting patterns of prey use from morphology of fishes. Env. Biol. Fish. 44, 97–113. Wanink, J.H. and Witte, F. (2000) Rapid morphological changes following niche shift in the zooplanktivorous cyprinid Rastrineobola argentea from Lake Victoria. Neth. J. Zool. 50, 365–372. Webb, P.W. (1984) Body form, locomotion and foraging in aquatic vertebrates. Amer. Zool. 24, 107–120. Webb, P.W. (1988) Simple physical principles and vertebrate locomotion. Amer. Zool. 28, 709–725. Werner, E.E. (1974) The fish size, prey size, handling time relation in several sunfishes and some implications. J. Fish. Res. Bd Can. 31, 1531–1536. Werner, E.E. and Gilliam, J.F. (1984) The ontogenetic niche and species interactions in size-structured populations. Annu. Rev. Ecol. Syst. 15, 393–426. Wikramanayake, E.D. (1990) Ecomorphology and biogeography of a tropical stream fish assemblage: evolution of assemblage structure. Ecology 71, 1756–1764. Winnemiller, K.O. (1991) Ecomorphological diversification in lowland freshwater fish assemblages from five biotic regions. Ecological Monographs 61, 343–365. Witte, F. and van Oijen, M.J.P. (1990) Taxonomy, ecology and fishery of Lake Victoria haplochromine trophic groups. Zoöl. Verh., Leiden 262, 1–47. Wootton, R.J. (1990) Ecology of Teleost Fishes. Chapman and Hall, London, 416 pp. Zander, E. (1906) Das Kiemenfilter der Teleoster. Zeitschrift für Wissenschaftliche Zoologie 84, 619–713. Zaret, T.M. (1980) Predation and Freshwater Communities. Yale University Press, London, 187 pp.