gcb-study-design
GitHub指导全球变化生物学（GCB）论文的研究设计，涵盖操纵实验、观测研究及模型模拟。提供设计选择、权衡建议及针对伪重复、外推性等常见审稿问题的诊断与强化策略，旨在提升因果推断的严谨性。
Trigger Scenarios
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill gcb-study-design -g -y
SKILL.md
Frontmatter
{
"name": "gcb-study-design",
"description": "Use when designing the study behind a Global Change Biology (GCB) manuscript — manipulative experiments, observational\/gradient studies, or process modelling of biological responses to global change. GCB reviewers probe scale, replication, realism, and causal inference. Guides design choices; it does not collect or simulate data."
}
Study Design (gcb-study-design)
GCB reviewers are experts in ecology, biogeochemistry, and ecosystem/Earth-system modelling. They
will probe whether the design can actually support a driver → biological-response claim at the
stated scale. This skill covers design choices and their tradeoffs; analysis lives in
gcb-data-analysis.
When to trigger
- Designing a warming / eCO2 / drought / N-addition experiment or a gradient/observational study
- Setting up a process-model or species-distribution-model experiment (Technical Advance or analysis)
- Justifying scale, replication, controls, and the realism of the manipulation
- A reviewer questioned confounding, pseudoreplication, or extrapolation
Design families and what GCB expects
- Manipulative experiments (OTC/infrared warming, FACE/eCO2, rainfall manipulation, N addition, reciprocal transplants). Report dose, duration, replication, and the realism gap versus real-world change; avoid pseudoreplication (treatment confounded with plot/chamber).
- Observational / gradient & long-term studies (space-for-time, latitudinal/elevational gradients, LTER/NEON time series). State confounders and the limits of space-for-time substitution; use design or covariates to address them.
- Process / ecosystem & distribution models (DGVMs, soil-C, crop, SDM/niche). Document version, forcing, spin-up, parameterization, and evaluation against observations; prefer ensembles and report structural vs parameter vs scenario uncertainty.
- Evidence synthesis / meta-analysis. Pre-specify the search protocol (PRISMA-style), inclusion criteria, effect size, and heterogeneity/publication-bias plan.
Cross-cutting design principles
- Match scale to claim. Plot-scale results do not automatically scale to ecosystem or biome.
- Replicate at the level of inference, and state the experimental unit explicitly.
- Define controls and baselines appropriate to the driver (ambient, pre-treatment, counterfactual run).
- Plan for uncertainty up front, not as an afterthought.
Design-weakness diagnostic
GCB reviewers probe whether the design can bear the weight of the global-change claim. Use this to locate the soft spot before a referee does and to choose the strengthening move.
| Design soft spot | Reviewer phrasing | Strengthening move |
|---|---|---|
| Treatment confounded with unit | "Pseudoreplication" | Replicate at the inference level; state the unit |
| Dose far above realistic change | "Unrealistic forcing" | Add a realism gap statement or a dose gradient |
| Space-for-time as causal | "Gradient is not an experiment" | Add covariates or a confounder model |
| Single model run | "No structural uncertainty" | Move to an ensemble; partition uncertainty |
| Unstated search protocol | "Synthesis not reproducible" | Pre-register a PRISMA-style protocol |
Worked micro-example (illustrative)
A team plans an open-top-chamber warming experiment to test a soil-respiration feedback. A weak design warms one large chamber and samples it 30 times, then treats those as 30 replicates — pseudoreplication a GCB referee will flag immediately. The strengthened design uses six warmed and six control plots (illustrative n), warming each by an ecologically realistic +2 C rather than +6 C, and pre-commits to a mixed model with plot as the random unit. Power analysis (illustrative) suggests this detects a 15% efflux change. The realism gap and the scaling limit to ecosystem level are stated up front. Numbers illustrative.
Referee pushback patterns and the design fix
- "Correlative gradient presented as mechanistic" → pair the gradient with a manipulation or a process-model test of the mechanism.
- "Cannot scale this plot result to the biome" → design the sampling or modelling to carry scaling uncertainty, and bound rather than assert the larger claim.
- "Controls inadequate" → specify ambient, pre-treatment, or counterfactual baselines matched to the driver.
Anti-patterns
- Pseudoreplication: a single warmed plot/chamber treated as many independent replicates
- Over-extrapolating a short, high-dose manipulation to gradual real-world change
- Space-for-time substitution presented as if it were a controlled experiment
- A model run with no evaluation against observations and no uncertainty
- A meta-analysis with no pre-specified protocol or bias assessment
Output format
【Design family】experiment / gradient-observational / model / synthesis
【Driver & response】manipulated/measured at what scale
【Replication & unit】level of inference; pseudoreplication ruled out? [Y/N]
【Realism / confounding】dose-duration realism or confounder plan
【Uncertainty plan】measurement + model + scenario
【Next】gcb-data-analysis
Supplementary resources
../../resources/external_tools.md— experimental, observational, and modelling toolchains../../resources/official-source-map.md— GCB scope (molecular-to-biome, aquatic/terrestrial)
Version History
- 1839142 Current 2026-07-05 13:16
