Climate change vulnerability assessment of forest plants in the Credit River watershed: An application of NatureServe's CCVI tool
Notice bibliographique
Résumé
Climate change is the most important conservation challenge of our lifetime. Conservation organizations and researchers can no longer assume stable climate averages and need to account for climate vulnerabilities when planning management and conservation activities. Climate vulnerability assessments allow for fine-scale information on species natural history and functional traits to be considered. The three pillars of this type of assessment are 1) exposure to climate change, 2) sensitivity to these changes, and 3) the adaptive capacity of a species or system. There are several methods for assigning climate vulnerability, among which the NatureServe Climate Change Vulnerability Index (CCVI) is the most widely used. In the present study, I used the NatureServe CCVI to assess 30 forest plant species in the Credit River watershed including forbs, ferns, shrubs, and trees. Credit Valley Conservation (CVC) is responsible for protecting and managing approximately 1000 km2 of land in southern Ontario. The land within CVC’s jurisdiction is largely fragmented and encompasses several municipalities and major cities. As climate change becomes a real issue, CVC needs a way to understand its effects on the natural heritage that exists within its watershed. My objectives were to: 1) conduct a climate change vulnerability assessment of forest plants within the Credit river watershed, using the CCVI tool; 2) identify the key factors contributing to species’ vulnerabilities; 3) use existing bioclimatic envelope models for several tree species within the Credit River watershed and rankings from other CCVI projects in nearby areas, to support or dispute species ranks; and 4) weigh the benefits and limitations of the CCVI tool, and provide recommendations on how it could be used by organizations like CVC in the future. Future climate conditions under Representative Concentration Pathway (RCP) 4.5 indicate overall drying from 38-58 mm as indicated by the Hamon (AET:PET) moisture metric, and a 3°C increase in mean annual temperature by 2050. Upon ranking each species, 13% are “low vulnerability”, 43% are “moderately vulnerable”, 37% are “highly vulnerable” and 7% are “extremely vulnerable”. The factors that contributed most to vulnerability were historical and physiological hydrological niche, history of pathogens or natural enemies, dispersal and movement capabilities, history of genetic bottlenecks, and genetic variation, consecutively.Theses results align with previous CCVI assessments in nearby geographic regions including the Ontario Great Lakes basin, Michigan, and West Virginia. Additionally, the rankings generally agree with bioclimatic envelope modelling for tree species in the Credit River watershed under climate change. Moving forward, I recommend that CVC: 1) Conduct more detailed assessments using the CCVI, and work with other organizations on larger-scale assessments; 2) Develop a plan for assisted migration of species with more southerly seed zones, and Carolinian species; 3) Conduct a study to determine whether phenological mismatch is affecting spring ephemerals; and 4) Develop public education campaigns centred around climate change impacts on natural heritage.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».