Habitat–trait interactions that control response to climate change: North American ground beetles (Carabidae)
Bibliographic record
Abstract
Abstract Aim As one of the most diverse and economically important families on Earth, ground beetles (Carabidae) are viewed as a key barometer of climate change. Recent meta‐analyses provide equivocal evidence on abundance changes of terrestrial insects. Generalizations from traits (e.g., body size, diets, flights) provide insights into understanding community responses, but syntheses for the diverse Carabidae have not yet emerged. We aim to determine how habitat and trait syndromes mediate risks from contemporary and future climate change on the Carabidae community. Location North America. Time period 2012–2100. Major taxa studied Ground beetles (Carabidae). Methods We synthesized the abundance and trait data for 136 species from the National Ecological Observatory Network (NEON) and additional raw data from studies across North America with remotely sensed habitat characteristics in a generalized joint attribute model. Combined Light Detection and RAnging (LiDAR) and hyperspectral imagery were used to derive habitat at a continental scale. We evaluated climate risks on the joint response of species and traits by expanding climate velocity to response velocity given habitat change. Results Habitat contributes more variations in species abundance and community‐weighted mean traits compared to climate. Across North America, grassland fliers benefit from open habitats in hot, dry climates. By contrast, large‐bodied, burrowing omnivores prefer warm‐wet climates beneath closed canopies. Species‐specific abundance changes predicted by the fitted model under future shared socioeconomic pathways (SSP) scenarios are controlled by climate interactions with habitat heterogeneity. For example, the mid‐size, non‐flier is projected to decline across much of the continent, but the magnitudes of declines are reduced or even reversed where canopies are open. Conversely, temperature dominates the response of the small, frequent flier Agonoleptus conjunctus , causing projected change to be more closely linked to regional temperature changes. Main conclusions Carabidae community reorganization under climate change is being governed by climate–habitat interactions (CHI). Species‐specific responses to CHI are explained by trait syndromes. The fact that habitat mediates warming impacts has immediate application to critical habitat designation for carabid conservation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".