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
1---2name: gcb-study-design3description: 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.4---56# Study Design (gcb-study-design)78GCB reviewers are experts in **ecology, biogeochemistry, and ecosystem/Earth-system modelling**. They9will probe whether the design can actually support a **driver → biological-response** claim at the10stated scale. This skill covers design choices and their tradeoffs; analysis lives in11`gcb-data-analysis`.1213## When to trigger1415- Designing a warming / eCO2 / drought / N-addition experiment or a gradient/observational study16- Setting up a process-model or species-distribution-model experiment (Technical Advance or analysis)17- Justifying scale, replication, controls, and the realism of the manipulation18- A reviewer questioned confounding, pseudoreplication, or extrapolation1920## Design families and what GCB expects21221. **Manipulative experiments** (OTC/infrared warming, FACE/eCO2, rainfall manipulation, N addition,23 reciprocal transplants). Report **dose, duration, replication, and the realism gap** versus real-world24 change; avoid **pseudoreplication** (treatment confounded with plot/chamber).252. **Observational / gradient & long-term studies** (space-for-time, latitudinal/elevational gradients,26 LTER/NEON time series). State **confounders** and the limits of **space-for-time substitution**; use27 design or covariates to address them.283. **Process / ecosystem & distribution models** (DGVMs, soil-C, crop, SDM/niche). Document **version,29 forcing, spin-up, parameterization, and evaluation against observations**; prefer **ensembles** and30 report **structural vs parameter vs scenario uncertainty**.314. **Evidence synthesis / meta-analysis.** Pre-specify the **search protocol** (PRISMA-style), inclusion32 criteria, effect size, and heterogeneity/publication-bias plan.3334## Cross-cutting design principles3536- **Match scale to claim.** Plot-scale results do not automatically scale to ecosystem or biome.37- **Replicate at the level of inference**, and state the experimental unit explicitly.38- **Define controls and baselines** appropriate to the driver (ambient, pre-treatment, counterfactual run).39- **Plan for uncertainty** up front, not as an afterthought.4041## Design-weakness diagnostic4243GCB reviewers probe whether the design can bear the weight of the global-change claim. Use this to44locate the soft spot before a referee does and to choose the strengthening move.4546| Design soft spot | Reviewer phrasing | Strengthening move |47|------------------|-------------------|--------------------|48| Treatment confounded with unit | "Pseudoreplication" | Replicate at the inference level; state the unit |49| Dose far above realistic change | "Unrealistic forcing" | Add a realism gap statement or a dose gradient |50| Space-for-time as causal | "Gradient is not an experiment" | Add covariates or a confounder model |51| Single model run | "No structural uncertainty" | Move to an ensemble; partition uncertainty |52| Unstated search protocol | "Synthesis not reproducible" | Pre-register a PRISMA-style protocol |5354## Worked micro-example (illustrative)5556A team plans an open-top-chamber warming experiment to test a soil-respiration feedback. A weak design57warms one large chamber and samples it 30 times, then treats those as 30 replicates — pseudoreplication58a GCB referee will flag immediately. The strengthened design uses six warmed and six control plots59(illustrative n), warming each by an ecologically realistic +2 C rather than +6 C, and pre-commits to a60mixed model with plot as the random unit. Power analysis (illustrative) suggests this detects a 15%61efflux change. The realism gap and the scaling limit to ecosystem level are stated up front. Numbers62illustrative.6364## Referee pushback patterns and the design fix6566- "Correlative gradient presented as mechanistic" → pair the gradient with a manipulation or a67 process-model test of the mechanism.68- "Cannot scale this plot result to the biome" → design the sampling or modelling to carry scaling69 uncertainty, and bound rather than assert the larger claim.70- "Controls inadequate" → specify ambient, pre-treatment, or counterfactual baselines matched to the71 driver.7273## Anti-patterns7475- Pseudoreplication: a single warmed plot/chamber treated as many independent replicates76- Over-extrapolating a short, high-dose manipulation to gradual real-world change77- Space-for-time substitution presented as if it were a controlled experiment78- A model run with no evaluation against observations and no uncertainty79- A meta-analysis with no pre-specified protocol or bias assessment8081## Output format8283```84【Design family】experiment / gradient-observational / model / synthesis85【Driver & response】manipulated/measured at what scale86【Replication & unit】level of inference; pseudoreplication ruled out? [Y/N]87【Realism / confounding】dose-duration realism or confounder plan88【Uncertainty plan】measurement + model + scenario89【Next】gcb-data-analysis90```9192## Supplementary resources9394- [`../../resources/external_tools.md`](../../resources/external_tools.md) — experimental, observational, and modelling toolchains95- [`../../resources/official-source-map.md`](../../resources/official-source-map.md) — GCB scope (molecular-to-biome, aquatic/terrestrial)
brycewang-stanford