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).

1. 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.

1. 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.

1. 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](../../resources/external_tools.md) — experimental, observational, and modelling toolchains
• [../../resources/official-source-map.md](../../resources/official-source-map.md) — GCB scope (molecular-to-biome, aquatic/terrestrial)