Climate change vulnerability assessment of forest plants in the Credit River watershed: An application of NatureServe's CCVI tool
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".