Complex patterns of temperature sensitivity, not ecological traits, dictate diverse species responses to climate change
Bibliographic record
Abstract
Despite widespread interest in describing and forecasting the impacts of climate change on species distributions, poor understanding of the climate variables that shape distributions and conflicting perspectives on the role that species traits play in mediating shifts have limited our ability to interpret and project changes in species distributions. Using standardized survey data along the northeast US continental shelf, we assessed the historical exposure and sensitivity of 81 species of marine chordates, arthropods, and molluscs to 24 sea surface temperature (SST) variables in two seasons. By comparing temperature trends in geographies available to species against temperature trends in geographies used by them we were able to identify which variables species track consistently through space and time. Logistic regression analyses were then used to assess whether species traits affected the likelihood of niche tracking while accounting for the season and temporal window in which temperatures were summarized and methodological constraints that might have limited our ability to detect tracking responses. A slight majority of species (52%) clearly shifted their distributions to track at least one temperature variable through space and time. Tracking rates were much lower on a per variable basis (5.1% of 3432 variables), despite widespread exposure to changing temperatures (89.2% of 3432 variables). None of the twelve ecological traits we investigated – including traits related to dispersal ability, ecological specialization, reproductive capacity, and commercial harvest – accounted for differences in tracking responses across species even after accounting for differences in climate exposure. Our results suggest widespread behavioral or physiological flexibility among our study species, or ongoing genetic adaptation to changing temperatures. They also suggest that divergent selection on climate sensitivities of close relatives may limit the utility of ecological traits for predicting distributional responses to future climate change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.047 | 0.001 |
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; both teacher heads agree on what is shown here.
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".