Implementing climate‐change refugia conservation
Notice bibliographique
Résumé
The past decade has seen major advances in the study of climate-change refugia (Morelli et al., In Press), defined as areas on the landscape relatively buffered from contemporary climate change over time that enable the persistence of valued physical, ecological, and socio-cultural resources (Morelli et al., 2016). From its inception in paleoecology to its application in modern climate adaptation (Keppel et al., 2015), the refugia concept has grown enormously and expanded its focus beyond mapping to conservation implementation, with several recent syntheses providing comprehensive overviews (e.g., Keppel et al., 2024; Morelli et al., 2020). Today, as climate adaptation increasingly emphasizes on-the-ground action, so too is refugia science evolving from conceptual exploration to practical application. In this special issue, we highlight recent advances in climate-change refugia conservation and management across a diverse set of ecosystems while conveying the current state of refugia science. This collection of papers showcases the latest contributions of work along the climate-change refugia science to implementation spectrum from a diversity of perspectives, methods, and geographies. We show that refugia science is being applied to guide on-the-ground management decisions and the investment of resources, even as it continues to evolve and expand to incorporate new methodologies and perspectives. The individual studies seek to address questions about where, how, and for how long refugia may support conservation of biodiversity in the face of climate change, and they provide examples of how early adopters are incorporating refugia into climate adaptation, conservation, and landscape planning in a changing world. They illustrate how practitioners are increasingly integrating refugia science into their work to guide on-the-ground management decisions and the investment of resources worldwide. As refugia science and conservation have matured, the nuance and complexity of the methods have increased. Advances in spatiotemporal data availability have led to the development of high-resolution, large-extent products based on a variety of remotely sensed inputs and predictive models (Krawchuk et al., In Press; Stralberg et al., In Press). These advances have been leveraged to record abiotic factors as well as to estimate species occurrence and abundance (Cavalieri et al., In Press; Dykema et al., In Press). Nevertheless, an often overlooked key step to refugia conservation is validating refugia hypotheses (Barrows et al. 2020). Such validation may require fine-scale, in-depth study to understand the mechanism by which coarse-scale relationships are built (Bentze et al., 2025). Nadeau et al. (In Press) present a case study for this process, illustrating how independent field data collection and experiments can verify refugia hypotheses. Refugia characteristics are also being identified for a variety of ecosystems and landscape features whose climate buffering characteristics are more subtle and often hydrologically mediated (Phillips et al., 2025; Słowińska et al., In Press; Zuckerberg et al., In Press). The identification of refugia from wildfire and other disturbance events has become an important component of land management and conservation activities, leading to more nuanced tools, frameworks, and conservation targets (Hohwieler et al., In Press; Krawchuk et al., In Press). As refugia science develops, so does the recognition of a need to connect these advances to existing conservation and planning initiatives and to foster relationships of mutual respect between partners (Kehm et al., In Press). Clear frameworks are needed to balance competing conservation and land-use goals in the presence of climate change. This also involves the enumeration of management actions to be taken within and outside of these buffered areas (Jennings et al., In Press; Stralberg et al., In Press). Increasingly, there are calls for intensive management and even creation of refugia in highly suitable areas (Zuckerberg et al., In Press). Recent refugia studies have incorporated extensive coproduction efforts and decision science (Mozelewski et al., In Press) for efficient conservation and restoration investments given limited resources. Decision support tools have been developed to help land managers assess refugia potential across landscapes and regions (Dreiss & Rice, 2025; John et al., In Press). The incorporation of refugia concepts into planning efforts has also involved increasing recognition of local and Indigenous knowledge and values. Coproduction with Indigenous communities enables local values to be central to the decision process, which can lead to more credible and enduring plans (Kehm et al., In Press). Focus on climate-change refugia identification is notably shifting to their protection and management (Caven & Pearse 2025; Mozelewski et al., In Press); as mapping and validation of refugia have become more sophisticated and widespread, implementation has become the next challenge. However, implementation requires expertise and attention in a multitude of areas and is challenged by many factors, including data limitations and the complexities of operationalizing refugia conservation across scales (Morelli et al., In Press). As refugia science matures, additional nuanced ways allow integration of refugia into conservation planning and prioritization. Although it is no panacea to the global shocks that ecological and cultural resources are experiencing, refugia management is finding its place among the toolkit of adaptation and conservation actions. Moreover, refugia science will be most impactful if embedded in existing paradigms like Resist-Accept-Direct (RAD; Lynch et al., 2021) and if considered with other priorities like connectivity. The studies highlighted throughout this special issue demonstrate practical applications of refugia science, showcasing its potential to leverage coproduction in a way that can greatly improve the efficiency and efficacy of conservation action in a changing climate. To successfully advance climate adaptation, the principles of collaboration and innovation (Enquist et al., 2017), alongside rigor and validation, will need to be foundational to refugia conservation. In coming years, harnessing the broad base of physical, ecological, and statistical science knowledge can inform local and regional actions, aided by frameworks recently developed in this special issue and elsewhere. There are no data associated with this paper.
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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 | 0,004 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,002 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».