With the goal of model fitting species abundance distribution patterns of the tree, shrub and herb layers of the natural Toona ciliata community in Xingdoushan Nature Reserve, Enshi Autonomous Prefecture, Hubei Province, we used the data collected from the field survey and employed different ecological niche models. The models tested were the broken stick model (BSM), the overlapping niche model (ONM) and the niche preemption model (NPM), as well as three statistic models, the log-series distribution model (LSD), the log-normal distribution model (LND) and the Weibull distribution model (WDM). To determine the fitted model most suitable to each layer, the fitting effects were judged by criteria of the lowest value of Akaike Information Criterion (AIC), Chi-square and the K-S values with no significant difference (P>0.05) between the theoretical predictions and observed species abundance distribution values. The result showed: (1) The fitting suitability and goodness of fit of the tree, shrub and herb layers by using the three ecological niche models were ranked as: NPM>BSM>ONM. Of the three statistical models, by accepting the fitting results of the three layers, WDM was the best fitting model, followed by LND. By rejecting the fitting tests of the herb layer, LSD had the worst fitting effect. The goodness of the statistical models was ranked as: WDM>LND>LSD. In general, the statistical models had better fitting results than the ecological models. (2) T. ciliata was the dominant species of the tree layer. The species richness and diversity of the herb layer were much higher than those of either the tree layer or the shrub layer. The species richness and diversity of the shrub layer were slightly higher than those of the tree layer. The community evenness accorded to the following order: herb>shrub>tree. Considering the fitting results of the different layers, different ecological niche models or statistical models with optimal goodness of fit and ecological significance can be given priority to in studying the species abundance distribution patterns of T. ciliata communities.

Near-surface remote sensing (e.g., digital cameras) has played an important role in capturing plant phenological metrics at either a focal or landscape scale. Exploring the relationship of the digital image-based greenness index (e.g., Gcc, green chromatic coordinate) with that derived from satellites is critical for land surface process research. Moreover, our understanding of how well Gcc time series associate with environmental variables at field stations in North American prairies remains limited. This paper investigated the response of grass Gcc to daily environmental factors in 2018, such as soil moisture (temperature), air temperature, and solar radiation. Thereafter, using a derivative-based phenology extraction method, we evaluated the correspondence between key phenological events (mainly including start, end and length of growing season, and date with maximum greenness value) derived from Gcc, MODIS and VIIRS NDVI (EVI) for the period 2015-2018. The results showed that daily Gcc was in good agreement with ground-level environmental variables. Additionally, multivariate regression analysis identified that the grass growth in the study area was mainly affected by soil temperature and solar radiation, but not by air temperature. High frequency Gcc time series can respond immediately to precipitation events. In the same year, the phenological metrics retrieved from digital cameras and multiple satellites are similar, with spring phenology having a larger relative difference. There are distinct divergences between changing rates in the greenup and senescence stages. Gcc also shows a close relationship with growing degree days (GDD) derived from air temperature. This study evaluated the performance of a digital camera for monitoring vegetation phenological metrics and related climatic factors. This research will enable multiscale modeling of plant phenology and grassland resource management of temperate prairie ecosystems.