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Record W4387893618 · doi:10.1111/brv.13028

How seasonality influences the thermal biology of lizards with different thermoregulatory strategies: a meta‐analysis

2023· article· en· W4387893618 on OpenAlexaff
Danilo Giacometti, Alexandre V. Palaoro, Laura C. Leal, Fábio Cury de Barros

Bibliographic record

VenueBiological reviews/Biological reviews of the Cambridge Philosophical Society · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsBrock University
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsEctothermThermoregulationSeasonalityNicheEcologyHabitatBiologyContext (archaeology)Range (aeronautics)Climate changeAtmospheric sciences

Abstract

fetched live from OpenAlex

ABSTRACT Ectotherms that maintain thermal balance in the face of varying climates should be able to colonise a wide range of habitats. In lizards, thermoregulation usually appears as a variety of behaviours that buffer external influences over physiology. Basking species rely on solar radiation to raise body temperatures and usually show high thermoregulatory precision. By contrast, species that do not bask are often constrained by climatic conditions in their habitats, thus having lower thermoregulatory precision. While much focus has been given to the effects of mean habitat temperatures, relatively less is known about how seasonality affects the thermal biology of lizards on a macroecological scale. Considering the current climate crisis, assessing how lizards cope with temporal variations in environmental temperature is essential to understand better how these organisms will fare under climate change. Activity body temperatures ( T b ) represent the internal temperature of an animal measured in nature during its active period (i.e. realised thermal niche), and preferred body temperatures ( T pref ) are those selected by an animal in a laboratory thermal gradient that lacks thermoregulatory costs (i.e. fundamental thermal niche). Both traits form the bulk of thermal ecology research and are often studied in the context of seasonality. In this study, we used a meta‐analysis to test how environmental temperature seasonality influences the seasonal variation in the T b and T pref of lizards that differ in thermoregulatory strategy (basking versus non‐basking). Based on 333 effect sizes from 137 species, we found that T b varied over a greater magnitude than T pref across seasons. Variations in T b were not influenced by environmental temperature seasonality; however, body size and thermoregulatory strategy mediated T b responses. Specifically, larger species were subjected to greater seasonal variations in T b , and basking species endured greater seasonal variations in T b compared to non‐basking species. On the other hand, the seasonal variation in T pref increased with environmental temperature seasonality regardless of body size. Thermoregulatory strategy also influenced T pref , suggesting that behaviour has an important role in mediating T pref responses to seasonal variations in the thermal landscape. After controlling for phylogenetic effects, we showed that T b and T pref varied significantly across lizard families. Taken together, our results support the notion that the relationship between thermal biology responses and climatic parameters can be taxon and trait dependent. Our results also showcase the importance of considering ecological and behavioural aspects in macroecological studies. We further highlight current systematic, geographical, and knowledge gaps in thermal ecology research. Our work should benefit those who aim to understand more fully how seasonality shapes thermal biology in lizards, ultimately contributing to the goal of elucidating the evolution of temperature‐sensitive traits in ectotherms.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.296
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.005
Bibliometrics0.0000.002
Science and technology studies0.0000.004
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.211
GPT teacher head0.330
Teacher spread0.119 · 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 teacher head, not a consensus.

Study designObservational
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

Citations41
Published2023
Admission routes1
Has abstractyes

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