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Plot (調査区)

Mount Usu / Sarobetsu post-mined peatland
From left: Crater basin in 1986 and 2006. Cottongrass / Daylily

Sample-based (incidence) data: A common form of data in biodiversity surveys. The data set consists of a set of sampling units (such as plots, quadrats, traps, and transect lines). The incidence or presence of each species is recorded for each sampling unit.
Plot: a measured area of land → rectangular plot = quadrat (s.s.)

Quadrat (方形区): a square (sensu stricto) or rectangle (sensu lato), marked by stakes and/or frames to measure abundance within a given area.

Quantitative evaluation: plot size is one of the most important decisions for the surveys of plant communities, because the size influences the analyses and interpretations.
  • point quadrat
  • quadrat (方形区): frame that is laid down to mark out a specific area of the community to be sampled
  • belt transect

Quadrat methods

Estimation of abundance, such as cover, density and height within quadrats. By monitoring quadrats, we can detect temporal changes in vegetaiton (LTER).
When we set up quadrats, we have to mention three characteristics.

[ permanent plot | LTER | field equipment ]

  • alignment of quadrats (random, regular, or arbitrary)
  • quadrat size (and shape)
  • number of quadrats

Toposequence and chronosequence

  1. Toposequence
    Vegetation changes over space - assumed to mimic time
    Ex. glacier, riverside, bogs, and sand dunes
  2. Chronosequence
    Sample in habitats of different ages or inferred ages
    array data by ages
    array data by species composition - indirect ordination
    array data by other methods (e.g., environmental conditions)
    repeatedly sample same habitat by non-disturbance methods

Optimal size of quadrats (in Hokkaido)

These values are for vegetation survey. When we have different reseach objectives, the size should be changed depending on the purposes and costs.

Theoretical decision

making species-area curve

Empirical decision (for vegetation survey)

Roughly saying, the size is consistent with the vegetation height.
Physiognomy                 Vegetation   Size (m)
                            height (m)

Moss / Lichens              < 0.05       0.1 × 0.1
Short grassland             < 1          1 × 1
(annual grassland)
Tall grassland              < 2          2 × 2
(perennial grassland)
Shrub                       < 4          5 × 5
Young forest (sub-forest)   < 8          10 × 10
Mature forest               ≶ 8          20 × 20


Belt transect (帯状区)

Belt transect on Mount Soranuma
Belt transect (bisect method): a Betula ermanii forest near Masumi Pond on Mount Soranuma
belt transect
ag: Acer ginnala var. aidzuense, am: Acer mono, eo: Euonymus oxyphyllus, fm: Fraxinus mandshurica, kp: Kalopanax pictus, mb: Morus bombycis, mk: Magnolia kobus var. borealis, pa: Phellodendron amurense, qm: Quercus mongolica var. grosseserrata, sr: Sambucus racemosa ssp. kamtschatica, ud: Ulmus davidiana, vw: Viburnum wrightii
Keiteki Wood surveyed in 1984.
Belt transect in taiga
Bisect mode: Piceetum montaum - an European alpine spruce forest, Piceetum hudsonianum - a taiga in northern Canada (lichen forest), Piceetum marianae - Piceetum ericaceum on a peatland in northern Canada. [left] community structure, [right] reproduction (Dansereau & Lems 1957)

Permanent plot (永久調査区)

Why do we need permanent plots in the study of long-term vegetation dynamics? (Bakker et al. 1996)
Obtaining external and internal causes of succession
Confirming the pathways of succession directly

Chronosequence (クロノシークエンス) is not enough to understand successional change because site history is so often important (Pickett 1989)

Nature conservation
The geographical distribution of permanent plots (quadrats) in the Netherlands (Smits et al. 2002) → 70 years of permanent plot research in the Netherlands
Most long-term ecolgocial researches (LTER) have been conducted by permanent plots.

Permanent plots I have monitored

Long-Term Ecological Research, LTER (長期生態学研究)

1980: US LTER Network was founded in USA.
1993: International LTER (ILTER) Network was founded in 1993, following LTER and others.


  1. Necessity of databse for long-term researches
  2. Necessity of intercommunion on the database
Open-access database of biodiversity time series, following the guiding principles of FAIR data (Dornelas et al. 2017)