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1. Understanding the importance of biotic interactions in driving the distribution and abundance of species is a central goal of plant ecology. Early vascular plants likely colonized land occupied by biocrusts — photoautotrophic, surface-dwelling soil communities comprised of cyanobacteria, bryophytes, lichens and fungi — suggesting biotic interactions between biocrusts and plants have been at play for some 2,000 million years. Today, biocrusts coexist with plants in dryland ecosystems worldwide, and have been shown to both facilitate or inhibit plant species performance depending on ecological context. Yet, the factors that drive the direction and magnitude of these effects remain largely unknown. 2. We conducted a meta-analysis of plant responses to biocrusts using a global data-set encompassing 1,004 studies from six continents. 3. Meta-analysis revealed there is no simple positive or negative effect of biocrusts on plants. Rather, plant responses differ by biocrust composition and plant species traits and vary across plant ontogeny. Moss-dominated biocrusts facilitated, while lichen-dominated biocrusts inhibited overall plant performance. Plant responses also varied among plant functional groups: C₄ grasses received greater benefits from biocrusts compared to C₃ grasses, and plants without N-fixing symbionts responded more positively to biocrusts than plants with N-fixing symbionts. Biocrusts decreased germination but facilitated growth of non-native plant species. 4. Synthesis. Results suggest that interspecific variation in plant responses to biocrusts, contingent on biocrust type, plant traits, and ontogeny can have strong impacts on plant species performance. These findings have important implications for understanding biocrust contributions to plant productivity and community assembly processes in ecosystems worldwide.

Landscapes can lead to different emotions towards nature that in turn shape people’s environmental behavior and decision processes. This study explores the role of emotions that Mediterranean landscapes foster in people and to what extent these emotions are associated with human–nature connectedness (HNC). We conducted 176 face-to-face surveys to explore HNC and the diversity of emotions associated with a suite of landscapes in Southeast Spain. Results revealed that Marine and Coastal Protected Areas received the highest number of positive emotions, whereas Greenhouses and Non-Protected Littoral were linked to negative emotions. We propose a framework for classifying emotional landscapes according to four groups: emotionally positive, negative, polarized or neutral. Results showed that emotions might play a key role in shaping HNC in Spanish Mediterranean landscapes and may be used as a common ground for understanding roots underpinning human decisions and actions that lead to sustainable management or landscape degradation.

Reactive nitrogen species have a strong influence on atmospheric chemistry and climate, tightly coupling the Earth’s nitrogen cycle with microbial activity in the biosphere. Their sources, however, are not well constrained, especially in dryland regions accounting for a major fraction of the global land surface. Here, we show that biological soil crusts (biocrusts) are emitters of nitric oxide (NO) and nitrous acid (HONO). Largest fluxes are obtained by dark cyanobacteria-dominated biocrusts, being ∼20 times higher than those of neighboring uncrusted soils. Based on laboratory, field, and satellite measurement data, we obtain a best estimate of ∼1.7 Tg per year for the global emission of reactive nitrogen from biocrusts (1.1 Tg a⁻¹ of NO-N and 0.6 Tg a⁻¹ of HONO-N), corresponding to ∼20% of global nitrogen oxide emissions from soils under natural vegetation. On continental scales, emissions are highest in Africa and South America and lowest in Europe. Our results suggest that dryland emissions of reactive nitrogen are largely driven by biocrusts rather than the underlying soil. They help to explain enigmatic discrepancies between measurement and modeling approaches of global reactive nitrogen emissions. As the emissions of biocrusts strongly depend on precipitation events, climate change affecting the distribution and frequency of precipitation may have a strong impact on terrestrial emissions of reactive nitrogen and related climate feedback effects. Because biocrusts also account for a large fraction of global terrestrial biological nitrogen fixation, their impacts should be further quantified and included in regional and global models of air chemistry, biogeochemistry, and climate.

Vegetation generally appears scattered in drylands. Its structure, composition and spatial patterns are key controls of biotic interactions, water, and nutrient cycles. Applying segmentation methods to very high-resolution images for monitoring changes in vegetation cover can provide relevant information for dryland conservation ecology. For this reason, improving segmentation methods and understanding the effect of spatial resolution on segmentation results is key to improve dryland vegetation monitoring. We explored and analyzed the accuracy of Object-Based Image Analysis (OBIA) and Mask Region-based Convolutional Neural Networks (Mask R-CNN) and the fusion of both methods in the segmentation of scattered vegetation in a dryland ecosystem. As a case study, we mapped Ziziphus lotus, the dominant shrub of a habitat of conservation priority in one of the driest areas of Europe. Our results show for the first time that the fusion of the results from OBIA and Mask R-CNN increases the accuracy of the segmentation of scattered shrubs up to 25% compared to both methods separately. Hence, by fusing OBIA and Mask R-CNNs on very high-resolution images, the improved segmentation accuracy of vegetation mapping would lead to more precise and sensitive monitoring of changes in biodiversity and ecosystem services in drylands.

Sea level rise has accelerated during recent decades, exceeding rates recorded during the previous two millennia, and as a result many coastal habitats and species around the globe are being impacted. This situation is expected to worsen due to anthropogenically induced climate change. However, the magnitude and relevance of expected increase in sea level rise (SLR) is uncertain for marine and terrestrial species that are reliant on coastal habitat for foraging, resting or breeding. To address this, we showcase the use of a low-cost approach to assess the impacts of SLR on sea turtles under various Intergovernmental Panel on Climate Change (IPCC) SLR scenarios on different sea turtle nesting rookeries worldwide. The study considers seven sea turtle rookeries with five nesting species, categorized from vulnerable to critically endangered including leatherback turtles ( Dermochelys coriacea ), loggerhead turtles ( Caretta caretta ), hawksbill turtles ( Eretmochelys imbricata ), olive ridley turtles ( Lepidochelys olivacea ) and green turtles ( Chelonia mydas ). Our approach combines freely available digital elevation models for continental and remote island beaches across different ocean basins with projections of field data and SLR. Our case study focuses on five of the seven living sea turtle species. Under moderate climate change scenarios, by 2050 it is predicted that at some sea turtle nesting habitats 100% will be flooded, and under an extreme scenario many sea turtle rookeries could vanish. Overall, nesting beaches with low slope and those species nesting at open beaches such as leatherback and loggerheads sea turtles might be the most vulnerable by future SLR scenarios.

The Mediterranean region is experiencing a stronger warming effect than other regions, which has generated a cascade of negative impacts on productivity, biodiversity, and stability of the ecosystem. To monitor ecosystem status and dynamics, aboveground biomass (AGB) is a good indicator, being a surrogate of many ecosystem functions and services and one of the main terrestrial carbon pools. Thus, accurate methodologies for AGB estimation are needed. This has been traditionally done by performing direct field measurements. However, field-based methods, such as biomass harvesting, are destructive, expensive, and time consuming and only provide punctual information, not being appropriate for large scale applications. Here, we propose a new non-destructive methodology for monitoring the spatiotemporal dynamics of AGB and green biomass (GB) of M. tenacissima L. plants by combining structural information obtained from terrestrial laser scanner (TLS) point clouds and spectral information. Our results demonstrate that the three volume measurement methods derived from the TLS point clouds tested (3D convex hull, voxel, and raster surface models) improved the results obtained by traditional field-based measurements. (Adjust-R2 = 0.86–0.84 and RMSE = 927.3–960.2 g for AGB in OLS regressions and Adjust-R2 = 0.93 and RMSE = 376.6–385.1 g for AGB in gradient boosting regression). Among the approaches, the voxel model at 5 cm of spatial resolution provided the best results; however, differences with the 3D convex hull and raster surface-based models were very small. We also found that by combining TLS AGB estimations with spectral information, green and dry biomass fraction can be accurately measured (Adjust-R2 = 0.65–0.56 and RMSE = 149.96–166.87 g in OLS regressions and Adjust-R2 = 0.96–0.97 and RMSE = 46.1–49.8 g in gradient boosting regression), which is critical in heterogeneous Mediterranean ecosystems in which AGB largely varies in response to climatic fluctuations. Thus, our results represent important progress for the measurement of M. tenacissima L. biomass and dynamics, providing a promising tool for calibration and validation of further studies aimed at developing new methodologies for AGB estimation at ecosystem regional scales.

Chlorophyll a concentration (Chla) is a well-proven proxy of biocrust development, photosynthetic organisms’ status, and recovery monitoring after environmental disturbances. However, laboratory methods for the analysis of chlorophyll require destructive sampling and are expensive and time consuming. Indirect estimation of chlorophyll a by means of soil surface reflectance analysis has been demonstrated to be an accurate, cheap, and quick alternative for chlorophyll retrieval information, especially in plants. However, its application to biocrusts has yet to be harnessed. In this study we evaluated the potential of soil surface reflectance measurements for non-destructive Chla quantification over a range of biocrust types and soils. Our results revealed that from the different spectral transformation methods and techniques, the first derivative of the reflectance and the continuum removal were the most accurate for Chla retrieval. Normalized difference values in the red-edge region and common broadband indexes (e.g., normalized difference vegetation index (NDVI)) were also sensitive to changes in Chla. However, such approaches should be carefully adapted to each specific biocrust type. On the other hand, the combination of spectral measurements with non-linear random forest (RF) models provided very good fits (R2 > 0.94) with a mean root mean square error (RMSE) of about 6.5 µg/g soil, and alleviated the need for a specific calibration for each crust type, opening a wide range of opportunities to advance our knowledge of biocrust responses to ongoing global change and degradation processes from anthropogenic disturbance.

Cryptogamic organisms such as bryophytes and lichens cover most surfaces within tropical forests, yet their impact on the emission of biogenic volatile organic compounds is unknown. These compounds can strongly influence atmospheric oxidant levels as well as secondary organic aerosol concentrations, and forest canopy leaves have been considered the dominant source of these emissions. Here we present cuvette flux measurements, made in the Amazon rainforest between 2016–2018, and show that common bryophytes emit large quantities of highly reactive sesquiterpenoids and that widespread lichens strongly uptake atmospheric oxidation products. A spatial upscaling approach revealed that cryptogamic organisms emit sesquiterpenoids in quantities comparable to current canopy attributed estimates, and take up atmospheric oxidation products at rates comparable to hydroxyl radical chemistry. We conclude that cryptogamic organisms play an important and hitherto overlooked role in atmospheric chemistry above and within tropical rainforests.

Drylands are ecohydrologically-coupled ecosystems whose functioning depends on the interplay between hydrological connectivity between runoff source areas and the capacity of vegetation to retain water fluxes and associated resources. In this study we present a new easily applicable methodology for the ecohydrological characterization of dryland ecosystem functioning grounded in the balance between these two strongly interrelated processes using easily obtainable remote sensing data (e.g. UAV and SENTINEL-2 images), the BalanCR method (Balance between Connectivity and potential Water Retention Capacity). This methodology was first tested on synthetic hillslopes representing different configurations of the patchy and heterogenic distribution of vegetation in drylands. The analysis of these synthetic vegetation spatial patterns involving different vegetation patch densities, sizes, and fractional coverage values showed that BalanCR properly characterizes the expected ecohydrological interactions between potential conditions of runoff connectivity and water retention by plants operating in drylands. In a second step, we applied the BalanCR method on four semiarid hillslopes along an altitudinal aridity gradient covered by Mediterranean alpha steppes at very detailed spatial resolution (0.2 m) and at medium resolution (10 m). The obtained results were validated based on soil moisture data and vegetation greening and clearly recognized the four study sites as functional ecosystems, with very low water resource losses, and a pattern of increasing water redistribution processes as vegetation coverage declines. However, the sensitivity of methodology depends on the resolution of the input data (vegetation map and Digital Elevation Model; DEM), and the expected positive effect of small vegetation structures (vegetation patches smaller than the pixel size) on water redistribution is underestimated. Even in this case, the functionality and connectivity of the analyzed sites is correctly characterized as ecosystems showed similar values of both components for the methodology BalanC (hydrological connectivity component) and BalanR (potential water retention capacity component) than those obtained at very detailed scale, with a similar pattern of water allocation values in response to increased aridity. Thus, the proposed metric represents a promising tool for the proper evaluation of dryland conditions and to incorporate hillslope processes in climate change models, which is one of the main gaps to better understand the drylands response upon ongoing climate change.