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Aim: Climate limits the potential distribution ranges of species. Establishment and growth of individuals at range margins is assumed to be more limited by extreme events such as drought or frost events than in the centre of their range. We explore whether the growth of beech is more sensitive to drought towards the dry distribution margin and more sensitive to frost towards the cold distribution margin. Furthermore, we aim to gain insight into the adaptive potential of beech towards both the dry and cold distribution margins. Location: European gradient from the dry (Spain) to the cold (Poland, Sweden) distribution margin of beech. Taxon: European beech (Fagus sylvatica L.). Methods: We applied a range‐wide dendroecological study to analyse spatial and temporal trends in climate–growth relationships. We further investigated negative growth anomalies and growth synchrony towards the range margins. Results: We found beech to be drought sensitive across its whole range, except at the dry distribution margin. Furthermore, sensitivity to winter temperature was not found in the centre or at the cold distribution margin, but at the southern distribution margin. Growth synchrony was lower at the dry than at the cold distribution margin. Main conclusions: Beech seems to be adapted to drought at the dry distribution margin with a high adaptive potential indicated by the lowest growth synchrony along the gradient. At the cold distribution margin, cold events in winter and spring were less important for growth than drought. Still, the importance of spring frost for beech growth appears to increase in recent decades. Considering a projected north‐eastward shift of the distribution range, beech is likely facing drought stress in combination with spring frost risk at the cold margin which could lead to a hampered range expansion. [ABSTRACT FROM AUTHOR]

The land surface temperature/vegetation index feature space has important application in quantitative soil remote sensing inversion and drought monitoring, water resources management, such as soil water content, evapotranspiration. However, the study of its feature space construction method is still relatively lacking. In this study, we take the Oklahoma state of the United States as an example, the fitting method of the dry and wet edges of the land surface temperature/vegetation index feature space is carried out, and the linear and index, logarithm, polynomial, and power functions are used to fit the dry and wet edges, respectively, and the fitting results were evaluated by the measured soil water content data. We found that the results by polynomial function fitting, r-squared is the highest in the five fitting modes, and r-squared is more than 0.66 in the dry and wet edges of the feature space; and the water content of soil surface was compared with that of soil moisture content, and the root mean square error value is the smallest. In conclusion, these results strongly suggest that the polynomial function fitting the dry and the wet edges is the best way to construct the land surface temperature/vegetation index feature space. [ABSTRACT FROM AUTHOR]

Land surface temperature and fractional vegetation coverage (LST/FVC) space is a classical model for estimating evapotranspiration, soil moisture, and drought monitoring based on remote sensing. One of the key issues in its utilization is to determine its boundaries, i.e., the dry and wet edges. In this study, we revisited and compared three methods that were presented by Moran et al. (1994), Long et al. (2012), and Sun (2016) for calculating the dry and wet edges theoretically. Results demonstrated that: (1) for the dry edge, the Sun method is equal to the Long method and they have greater vegetation temperature than that of the Moran method. (2) With respect to the wet edge, there are greater differences among the three methods. Generally, Long's wet edge is a horizontal line equaling air temperature. Sun's wet edge is an oblique line and is higher than that of the Long's. Moran's wet edge intersects them with a higher soil temperature and a lower vegetation temperature. (3) The Sun and Long methods are simpler in calculation and can circumvent some complex parameters as compared with the Moran method. Moreover, they outperformed the Moran method in a comparison of estimating latent heat flux (LE), where determination coefficients varied between 0.45 ~ 0.66 (Sun), 0.47 ~ 0.68 (Long), and 0.39 ~ 0.57 (Moran) among field stations. [ABSTRACT FROM AUTHOR]

Land surface temperature / vegetation index feature space has important applications in quantitative retrieval of water content in soil and crop evapotranspiration. However, at present, the research on the fitting of the dry and wet edges of the land surface temperature/vegetation index feature space was relatively lacking. In the tradition, for dry edge of the model, a simple linear negative correlation was adopted to fit the parameters, and wet edge was considered as a simplified treatment parallel to the coordinate axis. Whether it is appropriate is the focus of this paper that needs to be discussed. The study area was located in Oklahoma, the United States. Based on Landsat TM5 image data, land surface temperature (LST) and normalized difference vegetation index (NDVI) were calculated, and LST was calculated by radiation equation model and NDVI by pixel dichotomy model. And the fitting of dry edges and wet edges of LST/NDVI feature space was carried out with different functions, which included linear function, exponential function, logarithm function, power function and polynomial function. All of them were used to fit dry edges and wet edges respectively, and the results were evaluated by the measured data of water content in soil. The results showed that for the fitting of 5 different functions, r2 value as a whole was between 0.4 and 0.7, and there were some differences in the fitting precision between different fitting methods. For the fitting of dry edges, r2 value of exponential function fitting was 0.64, r2 value of linear function fitting was 0.60, r2 value of logarithm function fitting was 0.41, r2 value of power function fitting was 0.43, and r2 value of polynomial function fitting was 0.67 which was the best fitting way for dry edges. For the fitting of wet edges, r2 value of exponential function fitting was 0.59, r2 value of linear function fitting was 0.63, r2 value of logarithm function fitting was 0.67, r2 value of power function fitting was 0.69, and r2 value of polynomial function fitting was 0.70 which was the best fitting way for wet edges. For the fitting of dry edges and wet edges, polynomial function was the best method. And the results of 5 kinds of function fitting were compared with those from the soil moisture stations in the study area. Root mean square error (RMSE) and mean absolute error (MAE) were calculated, and 5, 25 and 60 cm depth were selected. In the 3 different depths, RMSE and MAE of polynomial function were the smallest. RMSE at 5 cm depth was 0.29, RMSE at 25 cm depth was 0.27, and RMSE at 60 cm depth was 0.28; MAE at 5 cm depth was 0.26, MAE at 25 cm depth was 0.23, and MAE at 60 cm depth was 0.25. The results indicated that the LST/NDVI feature space inversion based on dry edges and wet edges fitting with the polynomial function was the most accurate for the soil surface water content in this study area, and it was most sensitive to water content at 25 cm depth in soil. For an optimal fitting, it must be an optimal solution between fitting accuracy and fitting efficiency. In the process of this study, only small amount of data were involved, so the main consideration was the accuracy of dry edges and wet edges fitting, not taking into account the time cost of computer computing process. But for the large amount of data operations in the actual application process, the time efficiency still needs to be considered. [ABSTRACT FROM AUTHOR]

Plants can respond to climate change by either migrating, adapting to the new conditions or going extinct. Relict plant species of limited distribution can be especially vulnerable as they are usually composed of small and isolated populations, which may reduce their ability to cope with rapidly changing environmental conditions. The aim of this study was to assess the vulnerability of Cneorum tricoccon L. (Cneoraceae), a Mediterranean relict shrub of limited distribution, to a future drier climate. We evaluated population differentiation in functional traits related to drought tolerance across seven representative populations of the species' range. We measured morphological and physiological traits in both the field and the greenhouse under three water availability levels. Large phenotypic differences among populations were found under field conditions. All populations responded plastically to simulated drought, but they differed in mean trait values as well as in the slope of the phenotypic response. Particularly, dry-edge populations exhibited multiple functional traits that favored drought tolerance, such as more sclerophyllous leaves, strong stomatal control but high photosynthetic rates, which increases water use efficiency (iWUE), and an enhanced ability to accumulate sugars as osmolytes. Although drought decreased RGR in all populations, this reduction was smaller for populations from the dry edge. Our results suggest that dry-edge populations of this relict species are well adapted to drought, which could potentially mitigate the species' extinction risk under drier scenarios. Dry-edge populations not only have a great conservation value but can also change expectations from current species' distribution models. [ABSTRACT FROM AUTHOR]