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Institutional explanation of satellite chlorophyll as a proxy for near-surface phytoplankton.

Chlorophyll a occurs in nearly all oxygen-producing phytoplankton groups and is central to their photosynthesis. Its concentration can be measured reproducibly by pigment extraction or calibrated optical methods, and its absorption affects ocean colour, making it observable over large areas. It is therefore a practical common index of photosynthetic standing stock across diverse communities, unlike a pigment limited to one group.

It is a proxy, not a fixed conversion to phytoplankton biomass or primary production. The carbon-to-chlorophyll ratio varies with species, light acclimation, nutrient supply and physiological condition; low-light cells often contain more pigment per unit carbon. Dead pigment and methodological differences can also affect interpretation, and nonphotosynthetic plankton are not represented. A pigment increase need not mean the same proportional increase in living carbon biomass.^[ref]

Phytoplankton are drifting aquatic organisms that carry out photosynthesis, including many algae. They can supply food to zooplankton in an ecosystem.

The standing stock of living phytoplankton, often expressed as carbon mass per water volume. Chlorophyll a is an accessible proxy, but its ratio to carbon is variable.

A pigment found in phytoplankton, including light-harvesting and photoprotective components. The assemblage spectrum depends on its pigment composition and intracellular packaging.

Plankton are aquatic organisms whose large-scale movement is strongly influenced by water currents. The group is defined by ecological mode rather than a single evolutionary origin: phytoplankton include photosynthetic forms, whereas zooplankton include drifting consumers.

Many plankton can swim locally or change depth, so drifting does not mean complete immobility. Vertical movement can change access to light, nutrients and predators, linking behaviour to transport by currents and to an aquatic food web.

Replication in experimental design repeats a treatment on multiple independent experimental units, so that variation between those units can inform a treatment contrast and its standard error. Repeated measurements on one experimental unit can improve measurement precision without providing additional independent treatment replication. The experimental unit is identified by where the treatment is applied, rather than by how many responses are recorded.^[ref]

For a constructed example, grow one plant in each of eight separate pots. Apply treatment A to four pots and treatment B to four pots, without sharing drainage water between pots. Measure the length of three leaves per pot. The pot is the experimental unit; the leaf is the observational unit. There are four treatment replicates per group, eight experimental units overall, and $8\cdot 3=24$ measurements. The three leaves in one pot are nested measurements, because they share the same plant and treatment application. The setup assumes that pots do not affect one another; simply putting them in separate containers does not establish statistical independence.

Randomization chooses which experimental units receive which treatments; replication supplies multiple experimental units receiving the same treatment. These solve different problems. In the balanced allocation above, choose the four A pots uniformly among the $\left(\genfrac{}{}{0ex}{}{8}{4}\right)=70$ possible subsets. A particular pot has probability $1/2$ of receiving A. Given that one pot receives A, a second receives A with conditional probability $3/7$, rather than $1/2$. Thus the assignments are not independent random variables, even though the experimental units can supply independent responses conditional on those assignments in a suitable statistical model.

An actual ecology experiment illustrates why the distinction matters. In Stuart H. Hurlbert's account, twelve experimental ponds initially received zero, fifty or 450 fish, with five, four and three ponds respectively. Preparation included assessing variation between ponds before introducing fish; allocation considered both location and existing differences between ponds. The zero-fish ponds supplied the scientific control. These were repeated treatment applications to ponds, not twelve samples from one pond.^[ref]

The original article followed the southern California ponds for ten months. Its readouts included zooplankton populations and total phytoplankton; it reported decreases in some zooplankton populations and increased total phytoplankton.^[ref] Repeated sampling within a pond helps describe its response through time, while ponds supply treatment replication. Unequal group sizes and initial pond variation matter for the statistical model; the treatment counts alone do not establish equal variances or independent responses.