Plot

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Plot (調査区)

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

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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

Toposequence
Vegetation changes over space - assumed to mimic time
Ex. glacier, riverside, bogs, and sand dunes
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

Plot

Belt transect (帯状区)

Belt transect (bisect method): a Betula ermanii forest near Masumi Pond on Mount Soranuma

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.

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

Site: Period, Research methods (References)
The crater basin of Mount Usu after the 1977-78eruptions: 1983-, 2 m × 5 m quadrats. Expanded to 5 m × 5 m quadrats in 1994 (Tsuyuzaki 2009)
The foot of Mount Usu after the 2000 eruptions: 2000-, Line transect. Total length is 470 m
The summit area of Mount Koma, Hokkaido, Japan, after the 1996 eruptions: 1996-, 400 50 cm × 50 cm quadrats (Tsuyuzaki & Hase 2005)
Southwest slope on Mount Koma dominated by Larix kaempferi forest: 1997-, 16 20 m × 20 m qudartas (3-yr intervals), height and diamter of trees > 30 cm (Akasaka & Tsuyuzaki 2009)
Interior Alaska after wildfire in 2004: 2005-, 96 1 m × 1 m subplots within 16 10 m × 10 m plots (Tsuyuzaki et al. 2014)

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.

Purposes

Necessity of databse for long-term researches
Necessity of intercommunion on the database

BioTIME

Open-access database of biodiversity time series, following the guiding principles of FAIR data (Dornelas et al. 2017)

Ecosphere 2021, 12: e0430-e0434

Results of LTER

Forecasting earth's ecosystems with long-term ecological research

Insights from the U.S. Long Term Ecological Research Network

Bonanza Creek (boreal), Kellogg Biological Station (agricultural and freshwater), Palmer (Antarctica), and Harvard Forest (temperate forest) LTER sites

(Bahlai et al., e0430)

Cascading effects

The stories highlight the need for long-term studies to sort out the complexities of different types of ecological cascades (e.g., trophic cascades including nutrient addition)

Sites: Bonanza Creek (boreal), Kellogg Biological Station (agricultural and freshwater), Palmer (Antarctica), and Harvard Forest (temperate forest)

Def. cascading effects: including a wide array of unforeseen chains of events that result from a variety of actions or changes in a system

Ex. global warming → fire activity (scale, intensity, interval, etc.) →

ecosystem changes (in balck spruce forests) → decrease in permafrost → further ecosystem changes

Ex. human management and disturbance → forest composition
_____________invasion of an exotic pest ↑
_____________________________global warming

(Rastetter et al., e0431)

Time lags

Q. what factors are best indicators of lagged changes? - hard to detect because 100 years are not enough timespan to detect time lags

Sites: Arctic (tundra), California Current Ecosystem (coastal upwelling), Coweeta (montane forests), and Florida Coastal Everglades (freshwater and coastal wetlands)

Multiple element limitation model (MEL) ⇒

C: lag = 20 years
mineral-soil organic matter: lag = 50 years

(Iwaniec, et al., e0432)

Connectivity

(Zinnert et al., e0433)

State changes

Sites: Georgia Coastal (coastal marsh), Konza Prairie (mesic grassland), Luquillo (tropical forest), Sevilleta (arid grassland) and Virginia Coastal (coastal grassland)

Fig. 1. Response of Georgia marshes to changes in freshwater input and temperature. Conceptual model of how variation in freshwater input and temperature leads to four different habitat types in coastal wetlands (Gabler et al. 2017).

(Cowles et al., e0434)

Resilience

= the capacity of a system to maintain structure and processes in the face of disturbance
(1) Resilience research has matured over the past 40 yr of the LTER program
(2) there are many examples of high resilience among the ecosystems in the LTER network
(3) there are also many warning signs of declining resilience of the ecosystems we study

Sites: Baltimore (urban), Hubbard Brook (northern hardwood forest), Andrews (temperate rain forest), Moorea (coral reef), Cedar Creek (grassland), and North Temperate Lakes (lakes)