Geodiversity impacts plant community structure in a semi-arid region.

Geodiversity refers to the variety of geological and physical elements as well as to geomorphological processes of the earth surface. Heterogeneity of the physical environment has an impact on plant diversity. In recent years, the relations between geodiversity and biodiversity has gained attention in conservation biology, especially in the context of climate change. In this study, we assessed the spatial and temporal change in plant's community structure in a semi-arid region, Sayeret Shaked Long Term Ecosystem Research (LTER) station, Israel. Vegetation surveys were conducted on different hillslopes, either with or without rock covers in order to study the spatial trends of hillslope geodiversity. The surveys were conducted for two consecutive years (2016 and 2017), of which the second year was drier and hotter and therefore permitted to investigate the temporal change of plant's community structure. The results of the spatial trends show that (1) geodiversity increases vegetation biodiversity and promotes perennial plants and those of the temporal change show that (2) the positive effect of geodiversity on plants' community structure and species richness is greater in the drier year than that in a wetter year. The main insight is that in these drylands, hillslopes with higher geodiversity appear to buffer the effect of drier years, and supported a more diverse plant community than lower geodiversity hillslopes.

A novel approach for long-term spectral monitoring of desert shrubs affected by an oil spill.

Crude oil pollution is a global environmental concern since it persists in the environment longer than most conventional carbon sources. In December 2014, the hyper-arid Evrona Nature Reserve, Israel, experienced large-scale contamination when crude oil spilled. The overarching goal of the study was to investigate the possible changes, caused by an accidental crude oil spill, in the leaf reflectance and biochemical composition of four natural habitat desert shrubs. The specific objectives were (1) to monitor the biochemical properties of dominant shrub species in the polluted and control areas; (2) to study the long-term consequences of the contamination; (3) to provide information that will assist in planning rehabilitation actions; and (4) to explore the feasibility of vegetation indices (VIs), along with the machine learning (ML) technique, for detecting stressed shrubs based on the full spectral range. Four measurement campaigns were conducted in 2018 and 2019. Along with the various stress indicators, field spectral measurements were performed in the range of 350-2500 nm. A regression analysis to examine the relation of leaf reflectance to biochemical contents was carried out, to reveal the relevant wavelengths in which polluted and control plants differ. Vegetation indices applied in previous studies were found to be less sensitive for indirect detection of long-term oil contamination. A novel spectral index, based on indicative spectral bands, named the "normalized blue-green stress index" (NBGSI), was established. The NBGSI distinguished significantly between shrubs located in the polluted and in the control areas. The NBGSI showed a strong linear correlation with pheophytin a. Machine learning classification algorithms obtained high overall prediction accuracy in distinguishing between shrubs located in the oil-polluted and the control sites, indicating internal component differences. The findings of this study demonstrate the efficacy of indirect and non-destructive spectral tools for detecting and monitoring oil pollution stress in shrubs.