scholarly journals Ontogenetic dietary changes of coral reef fishes in the mangrove-seagrass-reef continuum: stable isotopes and gut-content analysis

2003 ◽  
Vol 246 ◽  
pp. 279-289 ◽  
Author(s):  
E Cocheret de la Morinière ◽  
BJA Pollux ◽  
I Nagelkerken ◽  
MA Hemminga ◽  
AHL Huiskes ◽  
...  
Keyword(s):  
Stable Isotopes ◽  
Content Analysis ◽  
Coral Reef ◽  
Reef Fishes ◽  
Coral Reef Fishes ◽  
Gut Content Analysis ◽  
Gut Content ◽  
Dietary Changes

scholarly journals Delineating reef fish trophic guilds with global gut content data synthesis and phylogeny

PLoS Biology ◽  
2020 ◽  
Vol 18 (12) ◽  
pp. e3000702
Author(s):  
Valeriano Parravicini ◽  
Jordan M. Casey ◽  
Nina M. D. Schiettekatte ◽  
Simon J. Brandl ◽  
Chloé Pozas-Schacre ◽  
...  
Keyword(s):  
Machine Learning ◽  
Coral Reef ◽  
Reef Fish ◽  
Trophic Interactions ◽  
Reef Fishes ◽  
Trophic Niche ◽  
Coral Reef Fishes ◽  
Misclassification Error ◽  
Trophic Guilds ◽  
Gut Content

Understanding species’ roles in food webs requires an accurate assessment of their trophic niche. However, it is challenging to delineate potential trophic interactions across an ecosystem, and a paucity of empirical information often leads to inconsistent definitions of trophic guilds based on expert opinion, especially when applied to hyperdiverse ecosystems. Using coral reef fishes as a model group, we show that experts disagree on the assignment of broad trophic guilds for more than 20% of species, which hampers comparability across studies. Here, we propose a quantitative, unbiased, and reproducible approach to define trophic guilds and apply recent advances in machine learning to predict probabilities of pairwise trophic interactions with high accuracy. We synthesize data from community-wide gut content analyses of tropical coral reef fishes worldwide, resulting in diet information from 13,961 individuals belonging to 615 reef fish. We then use network analysis to identify 8 trophic guilds and Bayesian phylogenetic modeling to show that trophic guilds can be predicted based on phylogeny and maximum body size. Finally, we use machine learning to test whether pairwise trophic interactions can be predicted with accuracy. Our models achieved a misclassification error of less than 5%, indicating that our approach results in a quantitative and reproducible trophic categorization scheme, as well as high-resolution probabilities of trophic interactions. By applying our framework to the most diverse vertebrate consumer group, we show that it can be applied to other organismal groups to advance reproducibility in trait-based ecology. Our work thus provides a viable approach to account for the complexity of predator–prey interactions in highly diverse ecosystems.

Global gut content data synthesis and phylogeny delineate reef fish trophic guilds

2020 ◽  
Author(s):  
Valeriano Parravicini ◽  
Jordan M. Casey ◽  
Nina M. D. Schiettekatte ◽  
Simon J. Brandl ◽  
Chloé Pozas-Schacre ◽  
...  
Keyword(s):  
Coral Reef ◽  
Reef Fish ◽  
Ecosystem Functioning ◽  
Reef Fishes ◽  
Coral Reef Fishes ◽  
Misclassification Error ◽  
Biological Traits ◽  
Trophic Guilds ◽  
Gut Content ◽  
Viable Approach

AbstractThe diversity of life on our planet has produced a remarkable variety of biological traits that characterize different species. Such traits are widely employed instead of taxonomy to increase our understanding of biodiversity and ecosystem functioning. However, for species’ trophic niches, one of the most critical aspects of organismal ecology, a paucity of empirical information has led to inconsistent definitions of trophic guilds based on expert opinion. Using coral reef fishes as a model, we show that experts often disagree on the assignment of trophic guilds for the same species. Even when broad categories are assigned, 60% of the evaluated trait schemes disagree on the attribution of trophic categories for at least 20% of the species. This disagreement greatly hampers comparability across studies. Here, we introduce a quantitative, unbiased, and fully reproducible framework to define species’ trophic guilds based on empirical data. First, we synthesize data from community-wide visual gut content analysis of tropical coral reef fishes, resulting in trophic information from 13,961 individuals belonging to 615 reef fish species across all ocean basins. We then use network analysis to cluster the resulting global bipartite food web into distinct trophic guilds, resulting in eight trophic guilds, and employ a Bayesian phylogenetic model to predict trophic guilds based on phylogeny and maximum body size. Our model achieved a misclassification error of 5%, indicating that our approach results in a quantitative and reproducible trophic categorization scheme, which can be updated as new information becomes available. Although our case study is for reef fishes, the most diverse vertebrate consumer group, our approach can be applied to other organismal groups to advance reproducibility in trait-based ecology. As such, our work provides an empirical and conceptual advancement for trait-based ecology and a viable approach to monitor ecosystem functioning in our changing world.

Analisis Isi Lambung Ikan Selais Terang Bulan (Kryptopterus bicirrhis, Valenciennes 1840) Di Desa Rantau Kasih Sungai Kampar Kiri Propinsi Riau

Biospecies ◽  
2018 ◽  
pp. 57-63
Author(s):  
Ermi Yeni ◽  
Roza Elvyra
Keyword(s):  
Content Analysis ◽  
Gut Content Analysis ◽  
Gut Content ◽  
Additional Food ◽  
Male And Female ◽  
Carnivorous Fish ◽  
Supplementary Food ◽  
Main Food

The study on gut content of Selais Terang Bulan fish (Kryptopterus bicirrhis) in Rantau Kasih village Kampar Kiri river was conducted from Februari- April 2017. The purpose of this study was to know about gut content of Selais Terang Bulan fish which was categorized as main food, supplementary food and additional food. The analysis is done based on the instruction of Natardjan and Jhingran (1961). The gut content was analized using the Index of preponderance.  The result revealed that  the main food of  Selais Terang Bulan fish in Rantau Kasih village is adult Arthropoda with IP value (78.85%), and supplementary food is caterpillar (25.15%). Male and female fishes at have main food of adult Arthropoda with different percentages are (82.81%) male and female (71.32%). Based on the gut content analysis of Selais Terang Bulan fish was a carnivorous fish.

scholarly journals Comparative gut content analysis of invasive mosquitofish from Italy and Spain

2021 ◽  
Author(s):  
Sara Pirroni ◽  
Laura Pennafort Dezen ◽  
Francesco Santi ◽  
Rüdiger Riesch
Keyword(s):  
Content Analysis ◽  
Gut Content Analysis ◽  
Gut Content
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