Losing ground: projections of climate-driven bloom shifts and their implications for the future of California's almond orchards.

Climate change is expected to impact the spring phenology of perennial trees, potentially altering the suitability of land for their cultivation. In this study, we investigate the effects of climate change on the bloom timing of almond orchards, focusing on California, the world's leading region for almond production. By analyzing historical climatic data, employing a model that considers hourly temperatures and fall non-structural carbohydrates to predict bloom dates, and examining various Coupled Model Intercomparison Project Phase 6 (CMIP6) scenarios, we assess the potential impacts of climate shifts on plant phenology and, consequently, on land suitability for almond farming. Our findings reveal that, within the next 30 years, the land suitable for almond production will not undergo significant changes. However, under unchanged emission scenarios, the available land to support almond orchard farming could decline between 48 to 73% by the end of the century. This reduction corresponds with an early shift in bloom time from the average Day of Year (DOY) 64 observed over the past 40 years to a projected earlier bloom between DOY 28-33 by 2100. These results emphasize the critical role climate shifts have in shaping future land use strategies for almond production in Central Valley, California. Consequently, understanding and addressing these factors is essential for the sustainable management and preservation of agricultural land, ensuring long-term food security and economic stability in the face of a rapidly changing climate.

Satellite-based assessment of water use and leaf area efficiencies of dryland conifer forests along an aridity gradient.

Dryland forests worldwide are increasingly threatened by drought stress due to climate change. Understanding the relationships between forest structure and function is essential for managing dryland forests to adapt to these changes. We investigated the structure-function relationships in four dryland conifer forests distributed along a semiarid to subhumid climatic aridity gradient. Forest structure was represented by leaf area index (LAI) and function by gross primary productivity (GPP), evapotranspiration (ET), and the derived efficiencies of water use (WUE = GPP/ET) and leaf area (LAE = GPP/LAI). Estimates of GPP and ET were based on the observed relationships between high-resolution vegetation indices from VENµS and Sentinel-2A satellites and flux data from three eddy covariance towers in the study regions between November 2015 to October 2018. The red-edge-based MERIS Terrestrial Chlorophyll Index (MTCI) from VENµS and Sentinel-2A showed strong correlations to flux tower GPP and ET measurements for the three sites (R2cal > 0.91, R2val > 0.84). Using our approach, we showed that as LAI decreased with decreasing aridity index (AI) (i.e., dryer conditions), estimated GPP and ET decreased (R2 > 0.8 to LAI), while WUE (R2 = 0.68 to LAI) and LAE increased. The observed global-scale patterns are associated with a variety of forest vegetation characteristics, at the local scale, such as tree species composition and density. However, our results point towards a canopy-level mechanism, where the ecosystem-LAI and resultant proportion of sun-exposed vs. shaded leaves are primary determinants of WUE and LAE along the studied climatic aridity gradient. This work demonstrates the importance of high-resolution (spatially and spectrally) remote sensing data conjugated with flux tower data for monitoring dryland forests and understanding the intricate structure-function interactions in their response to drying conditions.

Detection of goat herding impact on vegetation cover change using multi-season, multi-herd tracking and satellite imagery.

The frequency and severity of Mediterranean forest fires are expected to worsen as climate change progresses, heightening the need to evaluate understory fuel management strategies as rigorously as possible. Prescribed small-ruminant foraging is considered a sustainable, cost-effective strategy, but demonstrating a link between animal presence and vegetation change is challenging. This study tested whether the effect of small-ruminant herd presence in Mediterranean woodlands can be detected by integrating remote sensing and herd tracking at the landscape scale. The daily foraging routes of seven shepherded goat herds that exploited a 100-km2 forested area of the Judean Hills, Israel, were tracked over six years using GPS (Global Positioning System) collars. Herd locations were converted to stocking rates, with units of animal-presence-days per unit area per defined time period, and mapped at a spatial resolution of 10 m. We estimated pixel-level vegetation cover change based on a time series of 63 monthly Landsat-8 images expressed as the normalized soil-adjusted vegetation index (SAVI). Spatiotemporal trend analysis assessed the magnitude and direction of change, and a random forest machine-learning algorithm estimated the relative impact on vegetation cover change of environmental factors as well as the herd-related factors of stocking rate that accrued over six years and distance to the closest corral. The last two factors were among the most influential factors determining vegetation cover change in the regional and individual-herd analyses. In some respects, the permanent herds differed in their spatial pattern of stocking rate from the mobile herds that periodically relocated their night corral throughout the year, but stocking rate scaled logarithmically for all herds individually and combined. The combination of multi-season GPS tracking, remote sensing, and machine-learning techniques, applied at a regional scale, detected herd impacts on vegetation cover trends, consistent with livestock foraging being an effective tool for fuel reduction in Mediterranean woodlands.

Use of near-infrared spectroscopy for the classification of medicinal cannabis cultivars and the prediction of their cannabinoid and terpene contents.

Cannabis sativa L. is used to treat a wide variety of medical conditions, in light of its beneficial pharmacological properties of its cannabinoids and terpenes. At present, the quantitative chemical analysis of these active compounds is achieved through the use of laborious, expensive, and time-consuming technologies, such as high-pressure liquid-chromatography- photodiode arrays, mass spectrometer detectors (HPLC-PDA or MS), or gas chromatography-mass spectroscopy (GC-MS). Hence, we aimed to develop a simple, accurate, fast, and cheap technique for the quantification of major cannabinoids and terpenes using Fourier transform near infra-red spectroscopy (FT-NIRS). FT-NIRS was coupled with multivariate classification and regression models, namely partial least square-discriminant analysis (PLS-DA) and partial least squares regression (PLS-R) models. The PLS-DA model yielded an absolute major class separation (high-THC, high-CBD, hybrid, and high-CBG) and perfect class prediction. Using only three latent variables (LVs), the cross-validation and prediction model errors indicated a low probability of over-fitting the data. In addition, the PLS-DA model enabled the classification of chemovars with genetic-chemical similarities. The classification of high-THCA chemovars was more sensitive and more specific than the classifications of the remaining chemovars. The prediction of cannabinoid and terpene concentrations by PLS-R yielded 11 robust models with high predictive capabilities (R2CV and R2pred > 0.8, RPD >2.5 and RPIQ >3, RMSECV/RMSEC ratio <1.2) and additional 15 models whose performance was acceptable for initial screening purposes (R2CV > 0.7 and R2pred < 0.8, RPD >2 and RPIQ <3, 1.2 < RMSECV/RMSEC ratio <2). Our results confirm that there is sufficient information in the FT-NIRS to develop cannabinoid and terpene prediction models and major-cultivar classification models.

Multivariate classification of cannabis chemovars based on their terpene and cannabinoid profiles.

Cannabis is used to treat various medical conditions, and lines are commonly classified according to their total concentrations of Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD). Based on their ratio of total THC to total CBD, cannabis cultivars are commonly classified into high-THC, high-CBD, and hybrid classes. While cultivars from the same class have similar compositions of major cannabinoids, their levels of other cannabinoids and their terpene compositions may differ substantially. Therefore, a more comprehensive and accurate classification of medicinal cannabis cultivars, based on a large number of cannabinoids and terpenes is needed. For this purpose, three different chemometric-based classification models were constructed using three sets of chemical profiles. We examined those models to determine which provides the most accurate "chemovar" classification. This was done by analyzing profiles of cannabinoids, terpenes, and the combination of these substances using the partial least square-discriminant analysis multivariate (PLS-DA) technique. The chemical profiles were selected from the three major classes of medicinal cannabis that are most commonly prescribed to patients in Israel: high-THC, high-cannabigerol (CBG), and hybrid. We studied the correlations between cannabinoids and terpenes to identify major bio-indicators representing the plant's terpene and cannabinoid content. All three PLS-DA models provided highly accurate classifications, utilizing six to nine latent variables with an overall accuracy ranging from 2 to 11% CV. The PLS-DA model applied to the combined cannabinoid-and-terpene profile did the best job of differentiating between the chemovars in terms of misclassification error, sensitivity, specificity, and accuracy. The combined cannabinoid-and-terpene PLS-DA profile had cross-validation and prediction misclassification errors of 4% and 0%, respectively. This is the first study to demonstrate the highly accurate classification of samples of medicinal cannabis based on their cannabinoid and terpene profiles, as compared to cannabinoid profiles alone. Furthermore, our correlation analysis indicated that 11 cannabinoids and terpenes might serve as bio-indicators for 32 different active compounds. These findings suggest that the use of multivariate statistics could assist in breeding studies and serve as a tool for minimizing the mislabeling of cannabis inflorescences.

What mediates tree mortality during drought in the southern Sierra Nevada?

Severe drought has the potential to cause selective mortality within a forest, thereby inducing shifts in forest species composition. The southern Sierra Nevada foothills and mountains of California have experienced extensive forest dieback due to drought stress and insect outbreak. We used high-fidelity imaging spectroscopy (HiFIS) and light detection and ranging (LiDAR) from the Carnegie Airborne Observatory (CAO) to estimate the effect of forest dieback on species composition in response to drought stress in Sequoia National Park. Our aims were (1) to quantify site-specific conditions that mediate tree mortality along an elevation gradient in the southern Sierra Nevada Mountains, (2) to assess where mortality events have a greater probability of occurring, and (3) to estimate which tree species have a greater likelihood of mortality along the elevation gradient. A series of statistical models were generated to classify species composition and identify tree mortality, and the influences of different environmental factors were spatially quantified and analyzed to assess where mortality events have a greater likelihood of occurring. A higher probability of mortality was observed in the lower portion of the elevation gradient, on southwest- and west-facing slopes, in areas with shallow soils, on shallower slopes, and at greater distances from water. All of these factors are related to site water balance throughout the landscape. Our results also suggest that mortality is species-specific along the elevation gradient, mainly affecting Pinus ponderosa and Pinus lambertiana at lower elevations. Selective mortality within the forest may drive long-term shifts in community composition along the elevation gradient.

Drivers of woody canopy water content responses to drought in a Mediterranean-type ecosystem.

Severe droughts increase physiological stress in woody plant species, which can lead to mortality, fundamentally altering the composition, structure, and biogeography of forests in many regions. Little is known, however, about the factors determining the physiological response of woody plants to drought at landscape scales. Our objective was to understand woody plant species responses to ongoing changes in climate, using remotely sensed canopy water content (CWC) as an indicator of plant physiological and phenological status. We used fused imaging spectroscopy and light detection and ranging from the Carnegie Airborne Observatory to quantify the factors affecting species compositional changes in CWC in a diverse Mediterranean-type ecosystem (Jasper Ridge Biological Preserve, California, USA) between 2013 and 2015. Mapped CWC was spatially variable in both of the observation years, and proved to be most closely tied to species composition and distribution across the landscape. The secondary predictors of CWC were elevation and soil substrate. In contrast, we found that CWC change was much more related to environmental factors than to the species composition. We suggest that the effect of environment on CWC change is mediated through species resistance and resilience to drought. Monitoring CWC change with imaging spectroscopy is a powerful approach to identifying species-level responses to climatic events and long-term change, which may provide support for policy decisions and conservation at large spatial scales.

Multiscale mapping of species diversity under changed land use using imaging spectroscopy.

Land use changes are one of the most important factors causing environmental transformations and species diversity alterations. The aim of the current study was to develop a geoinformatics-based framework to quantify alpha and beta diversity indices in two sites in Israel with different land uses, i.e., an agricultural system of fruit orchards, an afforestation system of planted groves, and an unmanaged system of groves. The framework comprises four scaling steps: (1) classification of a tree species distribution (SD) map using imaging spectroscopy (IS) at a pixel size of 1 m; (2) estimation of local species richness by calculating the alpha diversity index for 30-m grid cells; (3) calculation of beta diversity for different land use categories and sub-categories at different sizes; and (4) calculation of the beta diversity difference between the two sites. The SD was classified based on a hyperspectral image with 448 bands within the 380-2500 nm spectral range and a spatial resolution of 1 m. Twenty-three tree species were classified with high overall accuracy values of 82.57% and 86.93% for the two sites. Significantly high values of the alpha index characterize the unmanaged land use, and the lowest values were calculated for the agricultural land use. In addition, high values of alpha indices were found at the borders between the polygons related to the "edge-effect" phenomenon, whereas low alpha indices were found in areas with high invasion species rates. The beta index value, calculated for 58 polygons, was significantly lower in the agricultural land use. The suggested framework of this study succeeded in quantifying land use effects on tree species distribution, evenness, and richness. IS and spatial statistics techniques offer an opportunity to study woody plant species variation with a multiscale approach that is useful for managing land use, especially under increasing environmental changes.