Biodiversity experimental design

What is biodiversity experimental design?

It is the design of experiments to investigate issues relating to the drivers of biodiversity or consequences of changes in biodiversity. For example, experiments can be conducted on the effects of different levels of biodiversity on some environmental variable of interests.

For example, cold-water tanks can be set up in a laboratory to mimic freshwater ponds. If there is room for 12 organisms, a tank could have 12 of one species, or 6 each of two different species, or 4 each of three different species, or 3 each of four different species, and so on. A design question might be “Which of these possibilities will give the investigator most power to detect the effect they are interested in?”

In another experiment, mixtures of species of intertidal beach detrivores (animals that feed on dead plants or animals) were placed on a beach in California and the quantity of kelp devoured was measured. The investigators were interested in whether species consume food at a given rate irrespective of the presence of other species, or whether biodioversity affects feeding rate.

What species are surveyed using these methods?

In principle, biodiversity experimental design methods apply to any species, but in practice they are limited to species and situations in which the experimenter can set or control the design variables of interest (e.g. number of organisms per volume of water, or amount of food available to detrivores).

Who in CREEM works on these methods?

Prof. R. A. Bailey

A few relevant publications by CREEM staff

Daniel M. Perkins, R. A. Bailey, Matteo Dossena, Lars Gamfeldt, Julia Reiss, Mark Trimmer and Guy Woodward: Higher biodiversity is required to sustain multiple ecosystem processes across temperature regimes. Global Change Biology, 21 (2015), 396–406.
doi: 10.1111/gcb.12688

Lorea Flores, R. A. Bailey, Arturo Elosegi, Aitor Larrañaga and Julia Reiss: Habitat complexity in aquatic micorcosms affects processes driven by detrivores. PLoS ONE, 11 (11):e0165065, 2016.
doi: 10.1371/journal.pone.0165065

Julia Reiss, R. A. Bailey and Daniel M. Perkins: Design and analysis of laboratory experiments on aquatic plant litter decomposition. Chapter 20 in The Ecology of Plant Litter Decomposition in Stream Ecosystems (eds. Christopher M. Swan, Luz Boyero and Cristina Canhoto), Springer, 2021, pp. 455–482.
doi: 10.1007/978-3-030-72854-0_20

Kyle A. Emery, Jenifer E. Dugan, R. A. Bailey and Robert J. Miller: Species identity drives ecosystem function in a subsidy-dependent coastal system. Oecologia, 196 (2021), 1195–1206.
doi: 10.1007/s00442-021-05002-w

The image is primarily text, as follows:

Family of expectation models (subspaces): dimensions shown in red

There is a diagram of six connected dots.

The first three dots connect in a line:

  1. Assemblage identity (with a dimension of 41).
    • The subspace at the bottom of a line is contained in the subspace at the top of it.
  2. Richness multiplied by Type (with a dimension of 18).
    • βi can change with each level of richness but does not depend on what else is present.
  3. Richness plus Type (with a dimension of 8).
    • Add a different constant for each level of richness.

The ‘Richness plus Type’ dot connects to the next three dots in a loop:

  1. Type (with a dimension of 6)
  2. Constant (with a dimension of 1)
  3. Richness (with a dimension of 3)

The image concludes with the following text:

Success: an ecology journal published:

  • Hasse diagram of family models
  • statement that each row of an ANOVA table is for a difference between models.

Bailey, Experiments in biodiversity, RSS, Belfast, 2019, 7/29