Age-related differences in prey-handling efficiency and feeding habitat utilization of Squalius carolitertii (Cyprinidae) according to prey trait analysis

Abstract

Multivariate prey trait analysis is a functional approach to understand predator-prey relationships. Here, seven macroinvertebrate ecological traits have been used for the analysis of trophic ecology of co-occurring age classes of Northern Iberian chub Squalius carolitertii, a cyprinid fish species. The present study identified several key factors in the handling efficiency and habitat utilization for feeding of S. carolitertii that may have a wider application, particularly for other cyprinid species. The results revealed a remarkable similarity in the feeding behaviour among age classes, suggesting a foraging behaviour convergence among them in both prey-handling efficiency and feeding habitat utilization. Nevertheless, some age classes showed clear preferences for particular categories of ecological trait; for example, age 1 showed a clear ability to feed on flattened prey items, whereas ages 2 and 3 were able to feed on preys with different body shape due to their general distribution in the fuzzy principal component analysis (FPCA). Finally, this study shows how multivariate approaches can complement traditional diet analyses, and the method has wide applicability across life-stages of cyprinid species.

A from‐benchtop‐to‐desktop workflow for validating HTS data and for taxonomic identification in diet metabarcoding studies

“…Diet analyses are critical to gain a better understanding of prey/habitat relationships and feeding habitats (e.g., Corse et al., ; Sanchez‐Hernandez, ), especially when a fine and accurate taxonomic identification of prey can be achieved (e.g., Adamczuk & Mieczan, ). In this study, stringent wet‐laboratory conditions, carefully selected primer sets, PCR replicates and nonarbitrary filtering thresholds based on control samples led to the validation of a robust data set dedicated to the study of the diet of a critically endangered fish species.…”

One‐locus‐several‐primers: A strategy to improve the taxonomic and haplotypic coverage in diet metabarcoding studies

“…Diet studies are critical to the understanding of species interactions, trophic structures, and trophic dynamics (Nielsen, Clare, Hayden, Brett, & Kratina, ). They have been applied to a vast set of issues in ecology, evolution, and conservation, such as predator/prey interactions and habitat use (Corse et al, ; Sánchez‐Hernández, ), trophic niche partitioning (Kartzinel et al, ; Trevelline et al, ), and the delineation of habitats for guiding species conservation (Quéméré et al, ), management (Chivers et al, ), and habitat restoration (Motte & Libois, ). Diet studies have also proved critical in interfacing agriculture and ecology by assessing the effects of agricultural practices or policies on the trophic behaviors of species (Llaneza & López‐Bao, ; Mollot et al, ) and by evaluating the ecosystem services of wild species, such as in the control of crop pests (Aizpurua et al, ; McCracken et al, ).…”

Using biological traits to assess diet selection: the case of the Pyrenean Desman

“…We assigned biological traits to available and consumed prey taxa based on De Crespin and Usseglio-Polatera (2002) and Sánchez-Hernández et al (2014) . We examined nine traits potentially affecting desman selection ( Table 1 ), each trait having 2–5 categories with affinity scores (from 0 = low affinity, to 5 = high affinity) expressed using fuzzy coding ( Supplementary Data SD2 ).…”

“…We transformed affinities into relative affinities by dividing each value by the sum of affinities for each trait of each taxon. For taxonomic levels with no trait data (e.g., Chironomidae), the average value of all the lower taxonomic levels was computed ( Sánchez-Hernández 2014 ; Supplementary Data SD2 ). Taxa with no data (annelids, Lepidoptera, and some terrestrial Coleoptera, all rarely consumed; Esnaola et al 2021 ) were excluded from the analyses ( Supplementary Data SD2 ).…”

“…Supplementary Data SD2. —Affinity scores of each taxon for each trait category expressed through fuzzy coding ( Chevenet et al 1994 ; Sánchez-Hernández 2014 ). Taxa with no data were given the average value of all the lower taxonomic levels (gray) or were excluded from the analyses (“-”).…”