Ecosystem Functional Type

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Fig.1: Ecosystem Functional Types (EFT) of temperate South America based on Moderate Resolution Imaging Spectroradiometer Enhanced Vegetation Index (MODIS-EVI) dynamics (Alcaraz-Segura et al. 2013). The map shows the EFTs of the 2001-2008 period. EFTs.PNG
Fig.1: Ecosystem Functional Types (EFT) of temperate South America based on Moderate Resolution Imaging Spectroradiometer Enhanced Vegetation Index (MODIS-EVI) dynamics (Alcaraz-Segura et al. 2013). The map shows the EFTs of the 2001–2008 period.

Ecosystem Functional Type (EFT) is an ecological concept to characterize ecosystem functioning. Ecosystem Functional Types are defined as groups of ecosystems or patches of the land surface that share similar dynamics of matter and energy exchanges between the biota and the physical environment. [1] [2] [3] The EFT concept is analogous to the Plant Functional Types (PFTs) concept, but defined at a higher level of the biological organization. As plant species can be grouped according to common functional characteristics, ecosystems can be grouped according to their common functional behavior.

Contents

One of the most used approaches to implement this concept has been the identification of EFTs from the satellite-derived dynamics of primary production, [1] [2] an essential and integrative descriptor of ecosystem functioning. [4]

History

In 1992, Soriano and Paruelo [5] proposed the concept of Biozones to identify vegetation units that share ecosystem functional characteristics using time-series of satellite images of spectral vegetation indices. Biozones were later renamed to EFTs by Paruelo et al. (2001), [1] using an equivalent definition and methodology. [6] was one of the first authors that used the term EFT as "aggregated components of ecosystems whose interactions with one another and with the environment produce differences in patterns of ecosystem structure and dynamics". Walker (1997) [7] proposed the use of a similar term, vegetation functional types, for groups of PFTs in sets that constitute the different states of vegetation succession in non-equilibrium ecosystems. The same term was applied by Scholes et al. [8] in a wider sense for those areas having similar ecological attributes, such as PFTs composition, structure, phenology, biomass or productivity. Several studies have applied hierarchy and patch dynamic theories [9] [10] [11] for the definition of ecosystem and landscape functional types at different spatial scales, by scaling-up emergent structural and functional properties from patches to regions. Valentini et al. [12] defined land functional units by focusing on patches of the land surface that are able to exchange mass and energy with the atmosphere and show a coordinated and specific response to environmental factors.

Paruelo et al. (2001) [1] and Alcaraz-Segura et al. (2006, 2013) [2] [3] refined the EFT concept and proposed a remote-sensing based methodology to derive them. Since then, several authors have implemented the idea under the same or similar approaches using NOAA-AVHRR, MODIS and Landsat archives. [2] [3] [13] [14] [15] [16] [17] [18] [19] [20] In brief, all these approaches use the seasonal dynamics of spectral indices related to key functional aspects of ecosystems such as primary production, water exchange, heat exchange and radiative balance.

Identification

The functional classification of EFTs developed by Paruelo et al. (2001) [1] and Alcaraz-Segura et al. (2006, 2013) [2] [3] uses time series of spectral vegetation indexes to capture the carbon gains dynamics, the most integrative indicator of ecosystem functioning. [4] To build EFTs, these authors derive three descriptors or metrics from the seasonal dynamics (annual curve) of spectral vegetation indexes (VI) that capture most of the variance in the time series (Fig.2): [1] [2] [3]

Fig.2: Annual curve of spectral vegetation indexes (VI: Enhanced Vegetation Index (MODIS-EVI) AnualDynamics EVI 3vars.png
Fig.2: Annual curve of spectral vegetation indexes (VI: Enhanced Vegetation Index (MODIS-EVI)

The range of values of each VI metric is divided into four intervals, giving the potential number of 4x4x4=64 EFTs. Each EFT is assigned a code of two letters and a number (three characters). The first letter of the code (capital) corresponds to the VI_Mean level, ranging from A to D for low to high (increasing) VI_Mean or productivity. The second letter (small) shows the seasonal CV, ranging from a to d for high (decreasing) to low VI_sCV or seasonality. The numbers refer to DMAX or phenology and indicate the season of maximum VI (1–4: spring, summer, autumn and winter).

Current known applications

Advantages

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References

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