Linking organismal physiology and the landscape to predict vulnerability to climate change
Notice bibliographique
Résumé
Global temperatures continue to increase at unprecedented rates, both in mean and in variance. Thus, a major challenge for scientists of the 21st century is to predict whether species will persist through these changes. One way to partly assess vulnerability to climate change is to investigate the relationships between the environment and traits that are either particularly sensitive to temperature or may confer resilience against thermal changes. In ectotherms, external temperatures dictate their physiology, thus thermal physiological traits may be key to understanding ectothermic persistence. Although population variation is integral to the evolvability of thermal physiological traits, most studies using these traits to infer vulnerability extrapolate data from one or few populations to represent the species. Furthermore, many studies also use coarse metrics of environmental temperatures which may not fully capture the variation experienced by the organism. Here, using a cold-water frog system, I demonstrate the relationships between thermal physiological traits and local environmental temperatures among populations. In my first chapter, I provide a brief overview of ectothermic physiology, environmental thermal landscapes, and the ecology of the two species of tailed frogs that I investigated. In my second chapter, I show that populations of tailed frogs vary in their critical thermal limit (CTmax) plasticity, which impacts species-level assessments of vulnerability. I also demonstrate the methodological impacts of ignoring acute responses to temperature when estimating plasticity in this trait. For my third chapter, I demonstrate relationships between CTmax and local thermal environments, including temporal and spatial variability in temperature, among populations of tailed frogs. These results show that tailed frogs have limited opportunity for behavioural avoidance of warm temperatures, and that populations of one tailed frog species show a positive relationship between CTmax and maximum stream temperature while populations of the other species does not. In my fourth chapter, I test the critical assumption that CTmax is related to fitness, specifically mortality in ecologically relevant temperatures. My results show that populations with higher estimates of CTmax experience less mortality from thermal stress in temperatures experienced in nature, demonstrating the link between CTmax and fitness. Lastly, in my fifth chapter, I return to the plasticity in CTmax results and demonstrate the relationship between this trait and local thermal environments, showing that populations experiencing greater temperature fluctuations have greater estimates of plasticity in CTmax. Overall, these results underscore the importance of sampling widely among populations when inferring vulnerability to climate changes from physiological traits. The population variation in CTmax and its plasticity that I uncovered demonstrate the differing trends in vulnerability to climate change for the two species investigated. This work also highlights the importance of quantifying local thermalscapes and highlight how similar environments can differentially shape physiological tolerance and patterns of vulnerability among populations, in turn impacting vulnerability to future warming.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».