How coral reefs survive as oases of life in low-productivity oceans has puzzled scientists for centuries. The answer may lie in internal nutrient cycling and/or input from the pelagic zone. Integrating meta-analysis, field data, and population modeling, we show that the ocean’s smallest vertebrates, cryptobenthic reef fishes, promote internal reef fish biomass production through extensive larval supply from the pelagic environment. Specifically, cryptobenthics account for two-thirds of reef fish larvae in the near-reef pelagic zone despite limited adult reproductive outputs. This overwhelming abundance of cryptobenthic larvae fuels reef trophodynamics via rapid growth and extreme mortality, producing almost 60% of consumed reef fish biomass. Although cryptobenthics are often overlooked, their distinctive demographic dynamics may make them a cornerstone of ecosystem functioning on modern coral reefs.

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.

Energy flow and nutrient cycling dictate the functional role of organisms in ecosystems. Fishes are key vectors of carbon (C), nitrogen (N) and phosphorus (P) in aquatic systems, and the quantification of elemental fluxes is often achieved by coupling bioenergetics and stoichiometry. While nutrient limitation has been accounted for in several stoichiometric models, there is no current implementation that permits its incorporation into a bioenergetics approach to predict ingestion rates. This may lead to biased estimates of elemental fluxes. Here, we introduce a theoretical framework that combines stoichiometry and bioenergetics with explicit consideration of elemental limitations. We examine varying elemental limitations across different trophic groups and life stages through a case study of three trophically distinct reef fishes. Further, we empirically validate our model using an independent database of measured excretion rates. Our model adequately predicts elemental fluxes in the examined species and reveals species‐ and size‐specific limitations of C, N and P. In line with theoretical predictions, we demonstrate that the herbivore Zebrasoma scopas is limited by N and P, and all three fish species are limited by P in early life stages. Further, we show that failing to account for nutrient limitation can result in a greater than twofold underestimation of ingestion rates, which leads to severely biased excretion rates. Our model improved predictions of ingestion, excretion and egestion rates across all life stages, especially for fishes with diets low in N and/or P. Due to its broad applicability, its reliance on many parameters that are well‐defined and widely accessible, and its straightforward implementation via the accompanying r‐package fishflux, our model provides a user‐friendly path towards a better understanding of ecosystem‐wide nutrient cycling in the aquatic biome. A free Plain Language Summary can be found within the Supporting Information of this article.

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DNA metabarcoding is an increasingly popular technique to investigate biodiversity; however, many methodological unknowns remain, especially concerning the biases resulting from marker choice. Regions of the cytochrome c oxidase subunit I (COI) and 18S rDNA (18S) genes are commonly employed “universal” markers for eukaryotes, but the extent of taxonomic biases introduced by these markers and how such biases may impact metabarcoding performance is not well quantified. Here, focusing on macroeukaryotes, we use standardized sampling from autonomous reef monitoring structures (ARMS) deployed in the world's most biodiverse marine ecosystem, the Coral Triangle, to compare the performance of COI and 18S markers. We then compared metabarcoding data to image‐based annotations of ARMS plates. Although both markers provided similar estimates of taxonomic richness and total sequence reads, marker choice skewed estimates of eukaryotic diversity. The COI marker recovered relative abundances of the dominant sessile phyla consistent with image annotations. Both COI and the image annotations provided higher relative abundance estimates of Bryozoa and Porifera and lower estimates of Chordata as compared to 18S, but 18S recovered 25% more phyla than COI. Thus, while COI more reliably reflects the occurrence of dominant sessile phyla, 18S provides a more holistic representation of overall taxonomic diversity. Ideal marker choice is, therefore, contingent on study system and research question, especially in relation to desired taxonomic resolution, and a multimarker approach provides the greatest application across a broad range of research objectives. As metabarcoding becomes an essential tool to monitor biodiversity in our changing world, it is critical to evaluate biases associated with marker choice.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Organismal metabolic rates (MRs) are the basis of energy and nutrient fluxes through ecosystems. In the marine realm, fishes are some of the most prominent consumers.However, their metabolic demand in the wild (field MR [FMR]) is poorly documented, because it is challenging to measure directly. Here, we introduce a novel approach to estimating the component of FMR associated with voluntary activity (i.e., the field active MR [AMR field ]). Our approach combines laboratory-based respirometry, swimming speeds, and field-based stereo-video systems to estimate the activity of individuals.We exemplify our approach by focusing on six coral reef fish species, for which we quantified standard MR and maximum MR (SMR and MMR, respectively) in the laboratory, and body sizes and swimming speeds in the field. Based on the relationships between MR, body size, and swimming speeds, we estimate that the activity scope (i.e., the ratio between AMR field and SMR) varies from 1.2 to 3.2 across species and body sizes. Furthermore, we illustrate that the scaling exponent for AMR field varies across species and can substantially exceed the widely assumed value of 0.75 for SMR. Finally, by scaling organismal AMR field estimates to the assemblage level, we show the potential effect of this variability on community metabolic demand. Our approach may improve our ability to estimate elemental fluxes mediated by a critically

1The diversity of life on our planet has produced a remarkable variety of biological traits that 2 characterize different species. Such traits are widely employed instead of taxonomy to increase 3 our understanding of biodiversity and ecosystem functioning. However, for species' trophic 4 niches, one of the most critical aspects of organismal ecology, a paucity of empirical information 5 has led to inconsistent definitions of trophic guilds based on expert opinion. Using coral reef 6 fishes as a model, we show that experts often disagree on the assignment of trophic guilds for the 7 same species. Even when broad categories are assigned, 60% of the evaluated trait schemes 8 disagree on the attribution of trophic categories for at least 20% of the species. This 9 disagreement greatly hampers comparability across studies. Here, we introduce a quantitative, 10 unbiased, and fully reproducible framework to define species' trophic guilds based on empirical 11 data. First, we synthesize data from community-wide visual gut content analysis of tropical coral 12 reef fishes, resulting in trophic information from 13,961 individuals belonging to 615 reef fish 13 species across all ocean basins. We then use network analysis to cluster the resulting global 14 bipartite food web into distinct trophic guilds, resulting in eight trophic guilds, and employ a 15Bayesian phylogenetic model to predict trophic guilds based on phylogeny and maximum body 16 size. Our model achieved a misclassification error of 5%, indicating that our approach results in 17 a quantitative and reproducible trophic categorization scheme, which can be updated as new 18 information becomes available. Although our case study is for reef fishes, the most diverse 19 vertebrate consumer group, our approach can be applied to other organismal groups to advance 20 reproducibility in trait-based ecology. As such, our work provides an empirical and conceptual 21 advancement for trait-based ecology and a viable approach to monitor ecosystem functioning in 22 our changing world. 23