Human-induced high densities of large wild herbivores may pose a threat to natural ecosystems, modifying biodiversity and ecosystem functioning. Although the negative impacts on individual taxa and processes are well documented, a comprehensive understanding of how ecosystem integrity responds to high herbivore densities remains limited. This study addresses this gap through an extensive herbivore-density manipulation experiment conducted in two Mediterranean woodlands. We established two scenarios of high red deer (Cervus elaphus) density, representative of some protected areas (~30 individuals·km2) and intensively managed hunting estates (~90 individuals·km2). We monitored 38 metrics encompassing a broad spectrum of biodiversity and functional responses to synthesize the short-term impacts of high herbivore densities. We revealed an overall 13% decline in ecosystem integrity after one year of exposure to high herbivore densities, with particularly widespread impacts on functional responses and detrimental effects on biodiversity components. We detected rapid declines in plant diversity and a positive response of epigeic invertebrate orders. Conversely, ants (analyzed separately), birds, and small mammals remained mostly unaffected. Functional responses were consistently impaired, including declines in plant regeneration and physiological performance, simplified pollination networks, degraded soil structure, and increased disease vectors. These findings show that human-induced high herbivore densities rapidly create conditions that disrupt ecosystem integrity, reveal early signs of ecological degradation, and underscore the urgent need for population regulation.

Temperate river floodplains present a significant challenge for archaeologists, as cultural and palaeoenvironmental remains are often difficult to locate but can be exceptionally well preserved, especially where groundwater levels are high. In these alluvial environments, the deposition of thick, fine-grained sediments has potential to deeply bury rich archaeological archives that can be used to reconstruct past environments, but these deposits also render conventional forms of archaeological prospection largely ineffective. Consequently, subsurface mapping techniques have been developed to determine the three-dimensional spatial distribution of archaeological remains and their relationship to sediment architecture within alluvial environments. These can be generated using a combination of intrusive (boreholes, trial pits, etc.) and nonintrusive (e.g., geophysical survey) investigations augmented by other geological and topographical datasets. Although lidar and other passive remote sensing methods such as multispectral imagery and aerial photography have been utilized to investigate floodplain landscapes, the spaceborne capabilities of Synthetic Aperture Radar (SAR) have yet to be explored within the context of geoarchaeological prospection. This contribution, therefore, examines the capacity of SAR to reconstruct and map landform assemblages within temperate river floodplains by analysing images in a 6-year time series of (COSMO-SkyMed) SAR data across two valleys in Herefordshire, United Kingdom. The results demonstrate that SAR can be used to record the spatial extent of recent flood events to outline surface topographic complexity and water table levels to achieve a detailed understanding of subsurface complexity across temperate river floodplains. This information can, in turn, be used to form a 'model' of the likely distribution and potential preservation conditions of archaeological resources. Although higher resolution topographic datasets (e.g., lidar, if available) may often be more effective, the integration of SAR within geoarchaeological investigations provides an alternative data source for the reconstruction of alluvial landscapes.

This study investigates the role of seagrass habitats in carbon sequestration by quantifying sediment carbon and seagrass coverage in Bahrain’s coastal waters. Recognised as significant carbon sinks, seagrass ecosystems are crucial for marine biodiversity and climate change mitigation. The study uses a twofold approach: assessing sediment carbon content variation across sampling points and estimating seagrass cover with remote sensing techniques using Sentinel-2 and Landsat 8 data. The accuracy of remote sensing-based models for estimating sediment carbon in seagrass is evaluated. Findings show higher sediment carbon concentrations in southern sites and an increase in average sediment carbon content in 2019, despite declines in seagrass coverage and stored sediment carbon. The Landsat 8-based model proved more accurate than the Sentinel-2-based model. This research highlights the importance of preserving seagrass habitats for carbon sequestration, contributing valuable data to global blue carbon reservoirs and informing future conservation strategies and climate change mitigation efforts.

This study explores the veracity of remote sensing-based classification of Bahrain's coastal water habitats, referencing data from three prior studies conducted in Bahrain waters. The objective is to illuminate the limitations of remote sensing for habitat mapping and evaluate the proficiency of Maximum Likelihood, Support Vector Machines, and SoftMax Regression classifiers using expanded pre-processing methodologies. Our reanalysis yielded Maximum Likelihood accuracies ranging from 45 to 61

The monitoring of grape quality parameters within viticulture using airborne remote sensing is an increasingly important aspect of precision viticulture. Airborne remote sensing allows high volumes of spatial consistent data to be collected with improved efficiency over ground-based surveys. Spectral data can be used to understand the characteristics of vineyards, including the characteristics and health of the vines. Within viticultural remote sensing, the use of cover-crop spectra for monitoring is often overlooked due to the perceived noise it generates within imagery. However, within viticulture, the cover crop is a widely used and important management tool. This study uses multispectral data acquired by a high-resolution uncrewed aerial vehicle (UAV) and Sentinel-2 MSI to explore the benefit that cover-crop pixels could have for grape yield and quality monitoring. This study was undertaken across three growing seasons in the southeast of England, at a large commercial wine producer. The site was split into a number of vineyards, with sub-blocks for different vine varieties and rootstocks. Pre-harvest multispectral UAV imagery was collected across three vineyard parcels. UAV imagery was radiometrically corrected and stitched to create orthomosaics (red, green, and near-infrared) for each vineyard and survey date. Orthomosaics were segmented into pure cover-cropuav and pure vineuav pixels, removing the impact that mixed pixels could have upon analysis, with three vegetation indices (VIs) constructed from the segmented imagery. Sentinel-2 Level 2a bottom of atmosphere scenes were also acquired as close to UAV surveys as possible. In parallel, the yield and quality surveys were undertaken one to two weeks prior to harvest. Laboratory refractometry was performed to determine the grape total acid, total soluble solids, alpha amino acids, and berry weight. Extreme gradient boosting (XGBoost v2.1.1) was used to determine the ability of remote sensing data to predict the grape yield and quality parameters. Results suggested that pure cover-cropuav was a successful predictor of grape yield and quality parameters (range of R2 = 0.37–0.45), with model evaluation results comparable to pure vineuav and Sentinel-2 models. The analysis also showed that, whilst the structural similarity between the both UAV and Sentinel-2 data was high, the cover crop is the most influential spectral component within the Sentinel-2 data. This research presents novel evidence for the ability of cover-cropuav to predict grape yield and quality. Moreover, this finding then provides a mechanism which explains the success of the Sentinel-2 modelling of grape yield and quality. For growers and wine producers, creating grape yield and quality prediction models through moderate-resolution satellite imagery would be a significant innovation. Proving more cost-effective than UAV monitoring for large vineyards, such methodologies could also act to bring substantial cost savings to vineyard management.

Lidar has become an essential tool for the mapping and interpretation of natural and archaeological features within the landscape. It is also increasingly integrated and visualized within geoarchaeological deposit models, providing valuable topographic and stratigraphic control from the contemporary ground surface downwards. However, there is a wide range of methods available for the visualization of lidar elevation models and a review of existing research suggests that it remains unclear which are most appropriate for geoarchaeological applications. This paper addresses this issue by providing an overview and quantitative evaluation of these techniques with examples from archaeologically resource-rich alluvial environments. Owing to the relatively low-relief nature of the terrain within these temperate lowland flood plain environments, the results show that there is a small number of visualization methods that demonstrably improve the detection of geomorphological landforms that can be related to the variable distribution of archaeological resources. More specifically, a combination of Relative Elevation Models combined with Simple Local Relief Models offered an optimal approach that subsequently allows integration with deposit models. Whilst the presented examples are from a flood plain setting, deposit models are pertinent to a range of landscape contexts and the methodology applied here has wider applicability.

On 16th October 2017 ex-hurricane Ophelia passed over the UK, bringing with it a unique mixture of particulates which caused the sky to turn a dramatic red colour.

Invasive rodents have a detrimental impact on terrestrial ecosystem functioning, this is often exacerbated on small islands. Rat eradication campaigns are often used to deal with this environmental perturbation given their classification as invasive species. Studies assessing the effects of rodent control at ecosystem scale are scarce and thus little is known about the subsequent functional response of vegetation subsequent to rat control. In this work, we use remote sensing to assess the effects of black rat (Rattus rattus) eradication on Mediterranean vegetation productivity in the Sa Dragonera Islet, Mallorca (Spain). Rats feed on seeds, sprouts, and leaves of woody vegetation and hence we expect primary production to increase nine years after the rodenticide campaign. The Break Detection approach for additive season and trend (BFAST method) was adopted to examine changes in vegetation density before and after the eradication campaign in Sa Dragonera Islet (Balearic Islands), using a temporal series of monthly NDVI data extracted from Landsat imagery. The same temporal trends were examined for a control zone where no rat eradication took place, in order to control for weather-driven changes. The results of this study revealed changes across the 21-year monthly NDVI time series. However, the dates, magnitude, and trend of these changes could not be explicitly attributed to the action of rats, when compared to the historical changes on the islet and the changes found to co-occur within the control zone. These finding could, perhaps, be explained by the high resilience of Mediterranean shrubs to browsing including that of rat invasion. However, the results from the study appear to show that rat damage on specific plant species, with little contribution to global NDVI values, would be overshadowed by the effects of broader environmental factors in this remote sensing approach. The results suggest that the current passive restoration scheme imposed following eradication is not sufficient for effective ecosystem restoration.

Temperate river systems cover large regions of the earth's surface and provide challenging environments for the detection of archaeological remains. The deposition of alluvium above, and interbedded with, archaeological deposits means conventional forms of archaeological prospection such as geophysical survey or aerial photography are often ineffective. Consequently, archaeologists have turned to the construction of deposit models to evaluate the archaeological potential of various landforms. However, despite the high capacity of satellite multispectral data to identify alluvial landforms and archaeological deposits, the contribution that it can make towards deposit models has only received limited attention. This research assesses the capability of higher resolution satellite multispectral imagery to identify alluvial landform assemblages and subsequently facilitate the modelling of geoarchaeological resources. As image enhancements are often required to improve interpretation or classification, it includes a quantitative evaluation of 45 different processes, comprising two spectral separability measures on paired regions of interest (ROIs) from the Lower Lugg Valley, Herefordshire, UK. The results define optimal image processing techniques for the detailed geoarchaeological interpretation of alluvial environments and demonstrate that relatively simple visualisation methods (e.g. composite images) and summative techniques (Principal Components Analysis) offer some of the most effective approaches.

Prehistoric carvings on the stone surfaces of Stonehenge may lie undiscovered beneath shrubby layers of lichen. We assess the suitability of terahertz time domain imaging as a subsurface imaging technique for archaeologists to identify carvings obscured by lichen. In a lab setting, we show that lichen at low water content does not completely obscure carvings on stone using terahertz imagery, and that the fainter returns from the stone surface can still be leveraged to give a basic view of the stone topography beneath lichen.

Temperate grasslands are considered the most endangered terrestrial ecosystem worldwide; the existent areas play a key role in biodiversity conservation. The Aguapey Valuable Grassland Area (VGA), one of the most well-preserved temperate grassland areas within Argentina, is currently threatened by the anthropogenic expansion of exotic tree plantations. Little is known about the impacts of afforestation over temperate grassland landscape structures; therefore, the aim of this study is to characterize Aguapey VGA landscape structural changes between 1999 and 2020 based on remotely sensed data. This involves the generation of land cover maps for four annual periods based on unsupervised classification of Landsat 5 TM and 8 OLI images, the estimation of landscape metrics, and the transition analysis between land cover types and annual periods. The area covered by temperate grassland is shown to have decreased by almost 22% over the 20 year-period studied, due to the expansion of tree plantation cover. The afforestation process took place mainly between 1999 and 2007 in the northern region of the Aguapey VGA, which led first to grassland perforation and subsequently to grassland attrition; however, Aguapey’s cultural tradition of cattle ranching could have partially inhibited the expansion of exotic trees over the final years of the study. The evidence of grassland loss and fragmentation within the Aguapey VGA should be considered as an early warning to promote the development of sustainable land use policies, mainly focused towards the Aguapey VGA’s southern region where temperate grassland remains the predominant land cover type.

The marine area of Bahrain comprises 91% of the total area of the country, the management of which is crucial for decision-makers, as it contains the country’s most valuable resources. It is also ecologically important supporting such fauna as, sea dugong, dolphins, green turtles, and 70+ species of fish, and such flora as seagrass beds and algae which provide essential ecosystem services. Providing current benthic habitat maps using remote methods is vital for efficient management and monitoring of these dynamic resources. In this threefold study, remotely sensed Landsat 8/OLI and Sentinel-2 imagery, combined with field survey (176 points), are used to investigate, classify, and map benthic habitats in light of varying spatial and spectral image resolutions while also assessing the role sunglint correction methods perform. Two widely applied methodologies proposed by Hedley et al. (2005) and Lyzenga (2006) for sunglint correction in the water column are examined to assess their role in creating accurate classification maps in this region. Sunglint is an issue in Bahrain due to its shallow waters, long summer and clear skies. The results using unsupervised classification indicate the effectiveness of both correction methods, demonstrating comparable results of high classification accuracy using either 3 (Blue, Green and Red) or 4 (Coastal Aerosol, Blue, Green and Red) spectral band combinations. Maximum accuracy using Hedley was 74% (4 bands) for Landsat 8 and 80% (3 bands) for Sentinel-2 while for Lyzenga 74% (4 bands) for Landsat 8 and 80% (3 bands) for Sentinel 2. The outputs generated were all >68%, with the introduction of more spectral bands associated with higher accuracy for Landsat 8 but inversely for Sentinel 2.

The assessment of landscape condition for large herbivores, also known as foodscapes, is fast gaining interest in conservation and landscape management programs worldwide. Although traditional approaches are now being replaced by satellite imagery, several technical issues still need to be addressed before full standardization of remote sensing methods for these purposes. We present a low-cost method, based on the use of a modified blue/green/near-infrared (BG-NIR) camera housed on a small-Unmanned Aircraft System (sUAS), to create foodscapes for a generalist Mediterranean ungulate: the Iberian Ibex (Capra pyrenaica) in Northeast Spain. Faecal cuticle micro-histological analyses were used to assess the dietary preferences of ibexes and then individuals of the most common plant species (n = 19) were georeferenced to use as test samples. Because of the seasonal pattern in vegetation activity, based on the NDVI (Smooth term Month = 21.5, p-value < .01, R2 = 43%, from a GAM), images were recorded in winter and spring to represent contrasting vegetation phenology using two flight heights above ground level (30 and 60 m). Additionally, the range of image pixel sizes was 3.5-30 cm with the smallest pixel size representing the highest resolution. Boosted Trees were used to classify plant taxa based on spectral reflectance and create a foodscape of the study area. The number of target species, the sampling season, the height of flight and the image resolution were analysed to determine the accuracy of mapping the foodscape. The highest classification error (70.66%) was present when classifying all plant species using a 30 cm pixel size from acquisitions at 30 m height. The lowest error (18.7%), however, was present when predicting plants preferred by ibexes, at 3.5 cm pixel size acquired at 60 m height. This methodology can help to successfully monitor food availability and seasonality and to identify individual species.

Keywords: browsing, carbon cycle, global change, grazing, zoogeochemistry. DOI: 10.7325/Galemys.2020.F1

In this study, soil sampling, vegetation analysis, and remotely sensed indices are used to devise a framework for monitoring impact of oil pollution on Mangrove forests. Mangroves are under threat from resource extraction and associated degradation. As a result of their inter-tidal location, Mangroves provide habitat for terrestrial and aquatic organisms and are important components of coastal ecosystems, providing a range of naturally available ecosystem services. Despite the widely accepted and documented range of ecosystem services provided by mangroves, they have nevertheless, experienced a worldwide degradation resulting from various anthropogenic activities including oil exploitation.This research is conducted in the Niger Delta where the largest spatial extent of Mangrove forests in Africa is located, consisting of 7% of global stock. Hydrocarbon exploitation in the Niger Delta region is one of several resource extractions undertaken in the area and as a result associated environmental pollution has caused a drastic decline in the region’s biodiversity and ecological resources. Of interest to this study is the effect of associated oil spills on the Mangrove forest ecosystem and their detection.This study undertook a detailed field exercise over three seasons across the Niger Delta within close proximity to recorded oil spills; as noted in the NOSDRA (National Oil Spill Detection & Response Agency) archive. Soil sampling and laboratory analyses were conducted to establish the level and nature of contamination and supported by complementary vegetation structure analysis evaluating Leaf Area Index (LAI) from ground (LAI2200C) and spaceborne (Landsat archive) systems. Levels of soil contamination were significant with respect to control areas regarding both presence and concentration of heavy metal pollutants (Cr, Mn, Fe, Zn, Pb, Al and Hg). Additionally, negative structural impacts were detected on the local soil via Bulk Density reductions, known to impact soil function, as high as 0.566 g/cm3 when comparing control Estuarine with high polluted locations, and Soil Organic Matter (SOM) reductions indicated by a mean percentage difference to the control of 11% for high polluted Fringing locations. These results highlight the immediate harm from spills, with degraded areas visually recorded and validated via ground measurements with mean LAI in high polluted Estuarine locations recording 1.8 higher. Linking vegetation structure in the Mangrove system with soil contamination allows the use of remote sensing to identify areas of degradation and subsequently to model the level and nature of contamination. The correlation between ground and spaceborne measurements of LAI (eg. r=0.62 p<0.005 for fringing low pollution locations), allows machine learning approaches to be used to model LAI given the presence of contaminants and to provide a framework for supporting the detection and recording of areas at risk. Success will be expanded upon through use of GEDI lidar waveforms in the near future to improve the remotely derived description of forest structure.

Background: Podoconiosis is a form of leg swelling, which arises when individuals are exposed over time to red clay soil formed from alkaline volcanic rock. The exact causal agent of the disease is unknown. This study investigates associations between podoconiosis disease data and ground-sampled soil data from North West Cameroon. Methods: The mineralogy and elemental concentrations were measured in the soil samples and the data were spatially interpolated. Mean soil values were calculated from a 3 km buffer region around the prevalence data points to perform statistical analysis. Analysis included Spearman's rho correlation, binary logistic regression and principal component analysis (PCA). Results: Six elements, barium, beryllium, potassium, rubidium, strontium and thallium, as well as two minerals, potassium feldspar and quartz, were identified as statistically related to podoconiosis. PCA did not show distinct separation between the spatial locations with or without recorded cases of podoconiosis, indicating that other factors such as shoe-wearing behaviour and genetics may significantly influence podoconiosis occurrence and prevalence in North West Cameroon. Conclusion: Several soil variables were statistically significantly related to podoconiosis. To further the current study, future investigations will look at the inflammatory pathway response of cells after exposure to these variables.

Eusocial bees are likely to be ecologically important competitors for floral resources, although competitive effects can be difficult to quantify in wild pollinator communities. To investigate this, we excluded honeybees (HBE treatment), bumblebees (BBE) or both (HB&BBE) from wild-growing patches of bramble, Rubus fruticosus L. agg., flowers in two eight-day field trials at separate locations, with complementary mapping of per-site local floral resource availability. Exclusions increased per-flower volume of nectar and visitation rates of non-excluded bees, compared to control patches with no bee exclusions (CON). There was a large increase in average nectar standing crop volume both at Site 1 (+ 172%) and Site 2 (+ 137%) in HB&BBE patch flowers, and no significant change in HBE or BBE, compared to CON patches. Foraging bee responses to exclusion treatments were more pronounced at Site 2, which may be due to lower local floral resource availability, since this is likely to increase the degree of exploitative competition present. Notably, at Site 2, there was a 447% increase in larger-bodied solitary (non-Apis/Bombus) bees visiting HB&BBE patches, suggesting ecological release from competition. Hoverflies showed no response to bee removals. Numbers of other non-bee insect groups were very small and also showed no clear response to exclusions. Our findings reveal patterns of competitive exclusion between pollinator groups, mediated by resource depletion by eusocial bees. Possible long-term implications of displacement from preferred flowers, particularly where alternative forage is reduced, are discussed. Significance statement Understanding patterns of exploitative competition and displacement is necessary for pollinator conservation, particularly for vulnerable or threatened species. In this research, experimental methods reveal underlying patterns of resource competition exerted by eusocial bees in a wild pollinator community. We show that honeybees and bumblebees competitively displace each other and particularly solitary (non-Apis/Bombus) bees from bramble, an important native nectar and pollen source. Effects were stronger where local floral resource availability was identified to be limited. Notably, following experimental exclusion of both honey- and bumblebees from flowers, visitation by solitary bees increased by up to 447%, strongly suggesting ecological release from competition. These results highlight the need for informed landscape management for pollinator wellbeing, including appropriate honeybee stocking densities and improved floral resource availability.

Introduction: Podoconiosis, a form of non-filarial elephantiasis, is a geochemical disease associated with individuals exposed to red clay soil from alkalic volcanic rock. It is estimated that globally 4 million people suffer from the disease, though the exact causal agent is unknown. This study is the first analysis in Cameroon to compare high resolution ground-sampled geochemical soil variables and remote sensing data in relation to podoconiosis prevalence and occurrence. Aim: To investigate the associations of soil mineralogical and element variables in relation to podoconiosis prevalence and occurrence in Cameroon. Methods: In this study, exploratory statistical and spatial data analysis were conducted on soil and spatial epidemiology data associated with podoconiosis. The studied soil data was comprised of 194 samples from an area of 65 by 45 km, containing 19 minerals and 55 elements. Initial proximal analysis included a spatial join between the prevalence data points and the closest ground-sampled soil variables. In addition, the soil variables were interpolated to create a continuous surface. At each prevalence data point, soil values from the interpolated surfaces were extracted. Correlation and logistic regression analysis were carried out on both the proximal analysis data set and the interpolated soil variables. The interpolated soil variables were also analysed using principal component analysis, to identify any patterns or clusters, regarding podoconiosis occurrence. Results: Bivariate analysis of the proximal and interpolated data set identified several statistically significant soil variables associated with podoconiosis. Correlation analysis identified several soil variables with a statistically significant positive Spearman rho value in relation to podoconiosis prevalence. Logistic regression analysis identified several statistically significant soil variables with odds ratio values greater than 1, with respect to the podoconiosis occurrence data. The significant variables included barium, beryllium, potassium, sodium, rubidium, strontium, thallium, potassium feldspar, mica and quartz. Barium, beryllium, potassium, sodium, quartz, mica and potassium feldspar have been previously identified in the literature in relation to podoconiosis occurrence. The PCA biplots showed no definite groupings of soil compositions with respect to podoconiosis occurrence. However, the envelope of the 95% confidence ellipse, representing prevalence data with at least one case of podoconiosis, does begin to separate as the soil variables suggested to be associated with podoconiosis occurrence increase and reach maximal values. Conclusion: The findings suggest that the key minerals and elements identified in this study may play a role in the pathogenesis of podoconiosis or could be disease covariates. These significant results have led to ongoing research within this project to examine the utilisation of medium and high-resolution hyperspectral methods to identify if podoconiosis-associated soil variables, such as quartz, are detectable remotely. Data can then be used to predict areas at risk using multivariate machine learning techniques theorising a link between prevalence, presence and combinations of multiple soil related variables. This study is supported by the National Institute for Health Research (NIHR) Global Health Research Unit on NTDs at BSMS (16/136/29). The views expressed are those of the author and not necessarily those of the NIHR or the Department of Health and Social Care.

There is an urgent need to quantify anthropogenic influence on forest carbon stocks. Using satellite-based radar imagery for such purposes has been challenged by the apparent loss of signal sensitivity to changes in forest aboveground volume (AGV) above a certain ‘saturation’ point. The causes of saturation are debated and often inadequately addressed, posing a major limitation to mapping AGV with the latest radar satellites. Using ground- and lidar-measurements across La Rioja province (Spain) and Denmark, we investigate how various properties of forest structure (average stem height, size and number density; proportion of canopy and understory cover) simultaneously influence radar backscatter. It is found that increases in backscatter due to changes in some properties (e.g. increasing stem sizes) are often compensated by equal magnitude decreases caused by other properties (e.g. decreasing stem numbers and increasing heights), contributing to the apparent saturation of the AGV-backscatter trend. Thus, knowledge of the impact of management practices and disturbances on forest structure may allow the use of radar imagery for forest biomass estimates beyond commonly reported saturation points.