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Record W4400892461 · doi:10.1242/jeb.248197

Expanding the scope: integrating costs of digestive metabolism and growth into estimates of maximum oxygen uptake in fishes

2024· article· en· W4400892461 on OpenAlexafffund
Timothy Fernandes, Shi‐Jian Fu, David J. McKenzie, Shaun S. Killen

Bibliographic record

VenueJournal of Experimental Biology · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaCompany of BiologistsNatural Environment Research CouncilSight Research UK
KeywordsScope (computer science)MetabolismOxygenOxygen metabolismBiologyBiochemistryChemistryComputer science

Abstract

fetched live from OpenAlex

The recent paper by Rees et al. (2024) provided a comprehensive overview of various methods for estimating the maximum rate of oxygen uptake in fishes (ṀO2,max), as measured during physical activity. ṀO2,max is a critical physiological trait in animals that, when combined with the basal rate of oxygen uptake, allows estimation of the aerobic scope for in situ organismal performance. When measured in fish, ṀO2,max is often equated to maximum metabolic rate (MMR) and thus marks the apparent upper limit on aerobic metabolism, thought to be important for survival and performance during various life history events and tolerance to environmental stressors. When attempting to measure ‘true’ ṀO2,max, an appreciation for how methodological and experimental procedures can influence results is critical, as well highlighted by Rees et al. (2024) in their Commentary on estimating maximum oxygen uptake of fishes during either swimming (peak ṀO2,swim) or following exhaustive chase (peak ṀO2,recovery). As discussed by Rees et al. (2024), ṀO2,swim and ṀO2,recovery measure different physiological processes, yet are both used to estimate ṀO2,max in fishes. In addition to ṀO2,swim and ṀO2,recovery, mounting evidence suggests that consideration for the costs of digestion and growth, both alone and in combination with activity, may be required for understanding and estimating ‘true’ ṀO2,max across species, and, therefore, in understanding ‘true’ MMR.Although often overlooked, research in some fish species suggests that MMR may only be measured during the digestion and processing of a meal, or when exercise and digestion occur simultaneously. During and following digestion in some fish species, apparent specific dynamic action (SDA) – the cost of digestion, assimilation of nutrients and post-absorptive processes (e.g. protein synthesis and deposition) following feeding (Goodrich et al., 2024) – involves considerably greater oxygen uptake than during or following exhaustive physical activity (Fu et al., 2022; Steell et al., 2019). Existing work in some fish species has already leveraged this phenomenon by combining meal provision with activity when attempting to elicit ṀO2,max in laboratory settings (e.g. Couturier et al., 2013). Although peak oxygen uptake during SDA (peak ṀO2,SDA) does not always match ṀO2,swim or ṀO2,recovery across species, the cost and prioritisation of SDA appear to depend on a species' lifestyle and constitutive capacity for swimming performance. Indeed, species with more sedentary lifestyles appear more likely to achieve ṀO2,max during SDA as compared with during locomotor activity (Fu et al., 2009, 2022). Fish species may therefore exist along a continuum of metabolic types, spanning those prioritising digestive processes and that achieve ṀO2,max through SDA, to those that prioritise locomotor activity and attain ṀO2,max during or following physical exercise (i.e. visceral-type to locomotor-type continuum; Fu et al., 2022). Moreover, when SDA is combined with swimming activity by feeding and exercising animals simultaneously, values of ṀO2,max exceeding peak ṀO2,swim and ṀO2,recovery have been observed in many species (Jourdan-Pineau et al., 2010). Even in the absence of physical activity, elevated temperatures and seasonal conditions may also drive peak ṀO2,SDA to approach or exceed peak ṀO2,swim or ṀO2,recovery (Sandblom et al., 2014), possibly even in species that may otherwise prioritise locomotion. Notably, however, peak oxygen uptake during digestion has not been widely recorded across fish species, limiting our understanding of how the cost of SDA may vary with meal size and environmental conditions, as well as how the cost of SDA then compares with ṀO2,recovery and ṀO2,swim. Therefore, available evidence suggests that ṀO2,max may be achieved following feeding in some species and this phenomenon may be more widespread among fishes than is currently appreciated.By further incorporating the costs of SDA into our understanding of ṀO2,max and MMR, we would also be better positioned to apply knowledge of MMR and aerobic scope in an ecological context. The digestive system represents the interface between an organism's internal and external environments through foraging and digestion, directly mediating the capacity for and aerobic cost of energy acquisition and growth. It is also among the most dynamic and expensive organ systems, receiving a disproportionate share of resting cardiac output (∼25–40%; Thorarensen et al., 1993) and circulating oxygen (11–25% of total whole-animal oxygen uptake; Brijs et al., 2018). Analogous to the critical periods of high-performance swimming fuelled by peak ṀO2,swim and ṀO2,recovery, values of peak ṀO2,SDA may reflect the capacity for resource acquisition and allocation to growth during critical life history events, or be linked to key aspects of behaviour due to constraints on available aerobic scope (McLean et al., 2018). Given the potential for seasonal plasticity in the digestive tract to drive >2-fold changes in the mass of digestive machinery in at least some fish species (Fernandes et al., 2024), values of in situ SDA during periods of digestive up-regulation are likely to far exceed those measured in the lab. Thus, true ṀO2,max or MMR in some species may be seasonally dynamic, responding to plastic changes in the form and function of digestive tissues, independent of locomotor ability. In such species, times of the year when energy acquisition has a disproportionate impact on survival and organismal fitness (e.g. during reproductive tissue development, peak seasonal growth windows, or energy accumulation prior to overwinter quiescence) may represent seasonal windows of elevated peak ṀO2,SDA that directly shape reproductive potential and survival probability. Conversely, during periods of energy conservation and reduced feeding, the digestive tract can experience substantial down-regulation (Middleton et al., 2024). Corresponding reductions in peak ṀO2,SDA during these periods could shift the true ceiling for whole-animal ṀO2,max to occur in response to locomotor activity, while also providing information about the capacity for flexibility in the costs of digestion and growth. As such, whether true ṀO2,max or MMR can be measured through SDA or in response to physical activity may not only depend on a species' lifestyle, but also on its dynamic responses to ecological and environmental factors that are yet to be thoroughly studied.Rees et al. (2024) present an excellent and timely perspective on the benefits of understanding the physiological relevance of estimates of ṀO2,max, as determined using two widespread approaches involving physical activity, introducing peak ṀO2,swim and ṀO2,recovery as useful updates to terminology in the field. Here, we suggest that further incorporating ṀO2,SDA into estimates of ṀO2,max offers an additional and fruitful suite of opportunities for understanding fish ecophysiology. We also encourage additional research on a wider diversity of fish species to further our understanding of the processes underlying why some species appear to engage in peak ṀO2 during digestion and post-absorptive processes as opposed to during or following physical activity. This information will then reveal whether the most appropriate measure of ṀO2,max varies within species in response to feeding history or environmental context.The authors are grateful for constructive feedback from several anonymous colleagues.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.044
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.044
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0010.004
Scholarly communication0.0030.006
Open science0.0030.006
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.286
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations3
Published2024
Admission routes2
Has abstractyes

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