Ls Land Issue Ls Models 05 Meadow =LINK=

In this study, we provide evidence that spatial structure is distinct even when species occur in well-mixed multispecies meadows, and we suggest that size-dependent plant traits have a strong influence on the distribution and maintenance of tropical marine plant communities. This study offers a contrast from previous spatial models of seagrasses which have largely focused on monospecific temperate meadows.

ls land issue ls models 05 meadow

Fine-scale drivers were considerably more difficult to identify because of the wide range in distances. However, the type of variogram models used is informative about patch characteristics. The exponential model in the along-shore (Table 3) indicates that patches have an irregular extent usually attributed to stochastic processes [48]. Irregular gap formation at this scale could have been caused by the grazing of mega fauna, sediment erosion and deposition both natural and caused by boat movement and anchoring, and surface runoff outlets from land which cause changes in salinity and nutrient input. In the across-shore direction, the spherical model provided the best fit. Thus, drivers in this direction resulted in species patches that were fairly regular.

In the across-shore direction, the depth gradient (light gradient) is a possible broad-scale driver. At the landward meadow edge where water depth is around 3 m, photosynthetically active radiation (PAR) averages 37% of surface irradiance. The meadow slopes down gradually to around 10 m depth where PAR averages 15% of surface irradiance, beyond which no seagrasses occur [29]. Because Halophila spp and H. uninervis possess small rhizomes, they have small respiratory demands [49] and they do not integrate resources as well as larger species such as S. isoetifolium and C. serrulata [40], [50]. These are size-specific traits which result in small species having greater sensitivity to environmental changes that occur over the broad spatial scale, such as a reduction in light along a gradient of water depth. Light and seasonal environmental variability have previously been shown to affect resource availability for small species [51].

The Z component includes: all land uses and types (forest, wood, woodland, woodlot, park land, terrestrial system, agricultural land, cropland, pasture, grazing land, savanna (woody and herbaceous), grassland, wetland, meadow, swamp, marsh, agroforestry, agroecosystem, bog, shrubs, trees, biomes, peatland, fen, and all other land) in the form of:

In most of the studies, only one model is compared to the data, with no comparison of the fit of different models. No single model can be considered as the gold standard against which others are compared. The R2 and error estimates are collected from studies applying models to forest and cropland ecosystem flux data. The review will only consider studies where models are applied to independent data excluding calibration sites. Based on the measures reported for accuracy and precision in the papers the models can be ranked in their performance under the assumption the flux data are the independent variable.

Abstract: Plant photosynthesis is the fundamental driver of all the biospheric functions. Alpine meadow on the Tibetan Plateau is sensitive to rapid climate change, and thus can be considered an indicator for the response of terrestrial ecosystems to climate change. However, seasonal variations in photosynthetic parameters, including the fraction of photosynthetically active radiation by canopy (FPAR), the light extinction coefficient (k) through canopy, and the leaf area index (LAI) of plant communities, are not known for alpine meadows on the Tibetan Plateau. In this study, we used field measurements of radiation components and canopy structure from 2009 to 2011 at a typical alpine meadow on the northern Tibetan Plateau to calculate these three photosynthetic parameters. We developed a satellite-based (NDVI and EVI) method derived from the Beer-Lambert law to estimate the seasonal dynamics of FPAR, k ,and LAI, and we compared these estimates with the Moderate Resolution Imaging Spectroradiometer (MODIS) FPAR (FPAR_MOD) and LAI product (LAI_MOD). The results showed that the average daily FPAR was 0.33, 0.37 and 0.35, respectively, from 2009 to 2011, and that the temporal variations could be explained by all four satellite-based FPAR estimations, including FPAR_MOD, an FPAR estimation derived from the Beer-Lambert law with a constant k (FPAR_LAI), and two FPAR estimations from the nonlinear functions between the ground measurements of FPAR (FAPRg) and NDVI/EVI (FPAR_NDVI and FPAR_EVI). We found that FPAR_MOD seriously undervalued FPARg by over 40%. Tower-based FPAR_LAI also significantly underestimated FPARg by approximately 20% due to the constant k (0.5) throughout the whole growing seasons. This indicated that using FPAR_LAI to validate the FPAR_MOD was not an appropriate method in this alpine meadow because the seasonal variation of k ranged from 0.19 to 2.95 in this alpine meadow. Thus, if the seasonal variation of k was taken into consideration, both FPAR_NDVI and FPAR_EVI provided better descriptions, with negligible overestimates of less than 5% of FAPRg (RMSE=0.05), in FPARg estimations than FPAR_MOD and FPAR_LAI. Combining the satellite-based (NDVI and EVI) estimations of seasonal FPAR and k, LAI_NDVI and LAI_EVI derived from the Beer-Lambert law also provided better LAIg estimations than LAI_MOD (less than 30% of LAIg). Therefore, this study concluded that satellite-based models derived from the Beer-Lambert law were a simple and efficient method for estimating the seasonal dynamics of FPAR, k and LAI in this alpine meadow.