Gcb study design

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

1. 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).
2. 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.
3. 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.
4. 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)