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
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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).
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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.
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Evidence synthesis / meta-analysis. Pre-specify the search protocol (PRISMA-style), inclusion
criteria, effect size, and heterogeneity/publication-bias plan.
Cross-cutting design principles
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Match scale to claim. Plot-scale results do not automatically scale to ecosystem or biome.
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Replicate at the level of inference, and state the experimental unit explicitly.
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Define controls and baselines appropriate to the driver (ambient, pre-treatment, counterfactual run).
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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
