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Record W3035021749 · doi:10.1071/an19418

Animal responses to environmental variation: physiological mechanisms in ecological models of performance in deer (Cervidae)

2020· article· en· W3035021749 on OpenAlexaff
N. J. C. Tyler, Pablo Gregorini, Katherine L. Parker, David G. Hazlerigg

Bibliographic record

VenueAnimal Production Science · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsForagingEcologyContext (archaeology)Environmental changeVariation (astronomy)PredationBiologyAnimal behaviorForcing (mathematics)Climate changeZoology

Abstract

fetched live from OpenAlex

Context Proper assessment of the consequences of environmental variation on animals depends on our ability to predict how they will perform under different circumstances. This requires two kinds of information. We need to know which environmental factors influence animal performance and their mode of action, i.e. whether a given factor acts alone or through interaction with other factors, directly or indirectly, instantaneously or after a delay and so on. This essentially correlative process falls within the domain of ecology. We also need to know what determines the direction, amplitude and limits of animal responses to environmental variation and change. This essentially experimental process falls within the domain of physiology. Physiological mechanisms are frequently poorly integrated within the correlative framework of ecological models. This is evident where programmed responses are attributed to environmental forcing and where the effect of environmental factors is evaluated without reference to the physiological state and regulatory capacity of the animal on which they act. Aims Here we examine ways in which the impacts of external (environmental) stimuli and constraints on performance are moderated by the animals (deer) on which they impinge. Key results The analysis shows (1) how trade-offs in foraging behaviour, illustrated by the timing of activity under the threat of predation, are modulated by integration of short-term metabolic feedback and animal emotions that influence the motivation to feed, (2) how the influence of thermal and nutritional challenges on performance, illustrated by the effect of weather conditions during gestation on the body mass of reindeer (Rangifer tarandus) calves at weaning, depends on the metabolic state of the female at the time the challenge occurs and (3) how annual cycles of growth, appetite and reproduction in seasonal species of deer are governed by innate circannual timers, such that their responses to seasonal changes in food supply are anticipatory and governed by rheostatic systems that adjust homeostatic set- points, rather than being purely reactive. Conclusions Concepts like ‘maintenance’ and ‘energy balance’, which were originally derived from non-seasonal domestic ruminants, are unable to account for annual cycles in metabolic and nutritional status in seasonal deer. Contrasting seasonal phenotypes (fat and anoestrous in summer, lean and oestrous in winter) represent adaptive solutions to the predictable challenges presented by contrasting seasonal environments, not failure of homeostasis in one season and its success in another. Implications The analysis and interpretation of responses to environment in terms of interaction between the external stimuli and the internal systems that govern them offer a more comprehensive, multifaceted understanding of the influence of environmental variation on performance in deer and open lines of ecological enquiry defined by non-intuitive aspects of animal function.

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)0.001
metaresearch head score (Gemma)0.001
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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.041
GPT teacher head0.241
Teacher spread0.200 · 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".

Quick stats

Citations23
Published2020
Admission routes1
Has abstractyes

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