Importance of Terrain and Climate for Predicting Soil Organic Carbon Is Highly Variable across Local to Continental Scales.

Soil organic carbon (SOC) plays a vital role in global carbon cycling and sequestration, underpinning the need for a comprehensive understanding of its distribution and controls. This study explores the importance of various covariates on SOC spatial distribution at both local (up to 1.25 km) and continental (USA) scales using a deep learning approach. Our findings highlight the significant role of terrain attributes in predicting SOC concentration distribution with terrain, contributing approximately one-third of the overall prediction at the local scale. At the continental scale, climate is only 1.2 times more important than terrain in predicting SOC distribution, whereas at the local scale, the structural pattern of terrain is 14 and 2 times more important than climate and vegetation, respectively. We underscore that terrain attributes, while being integral to the SOC distribution at all scales, are stronger predictors at the local scale with explicit spatial arrangement information. While this observational study does not assess causal mechanisms, our analysis nonetheless presents a nuanced perspective about SOC spatial distribution, which suggests disparate predictors of SOC at local and continental scales. The insights gained from this study have implications for improved SOC mapping, decision support tools, and land management strategies, aiding in the development of effective carbon sequestration initiatives and enhancing climate mitigation efforts.

Soil erosion modelling: A bibliometric analysis.

Soil erosion can present a major threat to agriculture due to loss of soil, nutrients, and organic carbon. Therefore, soil erosion modelling is one of the steps used to plan suitable soil protection measures and detect erosion hotspots. A bibliometric analysis of this topic can reveal research patterns and soil erosion modelling characteristics that can help identify steps needed to enhance the research conducted in this field. Therefore, a detailed bibliometric analysis, including investigation of collaboration networks and citation patterns, should be conducted. The updated version of the Global Applications of Soil Erosion Modelling Tracker (GASEMT) database contains information about citation characteristics and publication type. Here, we investigated the impact of the number of authors, the publication type and the selected journal on the number of citations. Generalized boosted regression tree (BRT) modelling was used to evaluate the most relevant variables related to soil erosion modelling. Additionally, bibliometric networks were analysed and visualized. This study revealed that the selection of the soil erosion model has the largest impact on the number of publication citations, followed by the modelling scale and the publication's CiteScore. Some of the other GASEMT database attributes such as model calibration and validation have negligible influence on the number of citations according to the BRT model. Although it is true that studies that conduct calibration, on average, received around 30% more citations, than studies where calibration was not performed. Moreover, the bibliographic coupling and citation networks show a clear continental pattern, although the co-authorship network does not show the same characteristics. Therefore, soil erosion modellers should conduct even more comprehensive review of past studies and focus not just on the research conducted in the same country or continent. Moreover, when evaluating soil erosion models, an additional focus should be given to field measurements, model calibration, performance assessment and uncertainty of modelling results. The results of this study indicate that these GASEMT database attributes had smaller impact on the number of citations, according to the BRT model, than anticipated, which could suggest that these attributes should be given additional attention by the soil erosion modelling community. This study provides a kind of bibliographic benchmark for soil erosion modelling research papers as modellers can estimate the influence of their paper.

Impact of invasive alien plants on native plant communities and Natura 2000 habitats: State of the art, gap analysis and perspectives in Italy.

Invasive alien plants are a major threat to biodiversity and they contribute to the unfavourable conservation status of habitats of interest to the European Community. In order to favour implementation of European Union Regulation no. 1143/2014 on invasive alien species, the Italian Society of Vegetation Science carried out a large survey led by a task force of 49 contributors with expertise in vegetation across all the Italian administrative regions. The survey summed up the knowledge on impact mechanisms of invasive alien plants in Italy and their outcomes on plant communities and the EU habitats of Community Interest, in accordance with Directive no. 92/43/EEC. The survey covered 241 alien plant species reported as having deleterious ecological impacts. The data collected illustrate the current state of the art, highlight the main gaps in knowledge, and suggest topics to be further investigated. In particular, the survey underlined competition as being the main mechanism of ecological impact on plant communities and Natura 2000 habitats. Of the 241 species, only Ailanthus altissima was found to exert an ecological impact on plant communities and Natura 2000 habitats in all Italian regions; while a further 20 species impact up to ten out of the 20 Italian administrative regions. Our data indicate that 84 out of 132 Natura 2000 Habitats (64%) are subjected to some degree of impact by invasive alien plants. Freshwater habitats and natural and semi-natural grassland formations were impacted by the highest number of alien species, followed by coastal sand dunes and inland dunes, and forests. Although not exhaustive, this research is the first example of nationwide evaluation of the ecological impacts of invasive alien plants on plant communities and Natura 2000 Habitats.

Spatial Bayesian belief networks as a planning decision tool for mapping ecosystem services trade-offs on forested landscapes.

An integrated methodology, based on linking Bayesian belief networks (BBN) with GIS, is proposed for combining available evidence to help forest managers evaluate implications and trade-offs between forest production and conservation measures to preserve biodiversity in forested habitats. A Bayesian belief network is a probabilistic graphical model that represents variables and their dependencies through specifying probabilistic relationships. In spatially explicit decision problems where it is difficult to choose appropriate combinations of interventions, the proposed integration of a BBN with GIS helped to facilitate shared understanding of the human-landscape relationships, while fostering collective management that can be incorporated into landscape planning processes. Trades-offs become more and more relevant in these landscape contexts where the participation of many and varied stakeholder groups is indispensable. With these challenges in mind, our integrated approach incorporates GIS-based data with expert knowledge to consider two different land use interests - biodiversity value for conservation and timber production potential - with the focus on a complex mountain landscape in the French Alps. The spatial models produced provided different alternatives of suitable sites that can be used by policy makers in order to support conservation priorities while addressing management options. The approach provided provide a common reasoning language among different experts from different backgrounds while helped to identify spatially explicit conflictive areas.

Plant invasion risk inside and outside protected areas: Propagule pressure, abiotic and biotic factors definitively matter.

Invasive alien species are among the main global drivers of biodiversity loss posing major challenges to nature conservation and to managers of protected areas. The present study applied a methodological framework that combined invasive Species Distribution Models, based on propagule pressure, abiotic and biotic factors for 14 invasive alien plants of Union concern in Italy, with the local interpretable model-agnostic explanation analysis aiming to map, evaluate and analyse the risk of plant invasions across the country, inside and outside the network of protected areas. Using a hierarchical invasive Species Distribution Model, we explored the combined effect of propagule pressure, abiotic and biotic factors on shaping invasive alien plant occurrence across three biogeographic regions (Alpine, Continental, and Mediterranean) and realms (terrestrial and aquatic) in Italy. We disentangled the role of propagule pressure, abiotic and biotic factors on invasive alien plant distribution and projected invasion risk maps. We compared the risk posed by invasive alien plants inside and outside protected areas. Invasive alien plant distribution varied across biogeographic regions and realms and unevenly threatens protected areas. As an alien's occurrence and risk on a national scale are linked with abiotic factors followed by propagule pressure, their local distribution in protected areas is shaped by propagule pressure and biotic filters. The proposed modelling framework for the assessment of the risk posed by invasive alien plants across spatial scales and under different protection regimes represents an attempt to fill the gap between theory and practice in conservation planning helping to identify scale, site, and species-specific priorities of management, monitoring and control actions. Based on solid theory and on free geographic information, it has great potential for application to wider networks of protected areas in the world and to any invasive alien plant, aiding improved management strategies claimed by the environmental legislation and national and global strategies.

Ethnomedicinal and Ethnobotanical Survey in the Aosta Valley Side of the Gran Paradiso National Park (Western Alps, Italy).

Most of traditional knowledge about plants and their uses is fast disappearing because of socio-economic and land use changes. This trend is also occurring in bio-cultural refugia, such as mountain areas. New data on Traditional Ethnobotanical Knowledge (TEK) of Italian alpine regions were collected relating to three valleys (Cogne, Valsavarenche, Rhêmes) of the Gran Paradiso National Park. Extensive dialogues and semi-structured interviews with 68 native informants (30 men, 38 women; mean age 70) were carried out between 2017 and 2019. A total of 3918 reports were collected, concerning 217 taxa (including 10 mushrooms, 1 lichen) mainly used for medicinal (42%) and food (33%) purposes. Minor uses were related to liquor making (7%), domestic (7%), veterinary (5%), forage (4%), cosmetic (1%) and other (2%). Medicinal plants were used to treat 14 ailment categories, of which the most important were respiratory (22%), digestive (19%), skin (13%), musculoskeletal (10%) and genitourinary (10%) diseases. Data were also evaluated by quantitative ethnobotanical indexes. The results show a rich and alive traditional knowledge concerning plants uses in the Gran Paradiso National Park. Plants resources may provide new opportunities from the scientific point of view, for the valorization of local products for health community and for sustainable land management.

Soil erosion modelling: A global review and statistical analysis.

To gain a better understanding of the global application of soil erosion prediction models, we comprehensively reviewed relevant peer-reviewed research literature on soil-erosion modelling published between 1994 and 2017. We aimed to identify (i) the processes and models most frequently addressed in the literature, (ii) the regions within which models are primarily applied, (iii) the regions which remain unaddressed and why, and (iv) how frequently studies are conducted to validate/evaluate model outcomes relative to measured data. To perform this task, we combined the collective knowledge of 67 soil-erosion scientists from 25 countries. The resulting database, named 'Global Applications of Soil Erosion Modelling Tracker (GASEMT)', includes 3030 individual modelling records from 126 countries, encompassing all continents (except Antarctica). Out of the 8471 articles identified as potentially relevant, we reviewed 1697 appropriate articles and systematically evaluated and transferred 42 relevant attributes into the database. This GASEMT database provides comprehensive insights into the state-of-the-art of soil- erosion models and model applications worldwide. This database intends to support the upcoming country-based United Nations global soil-erosion assessment in addition to helping to inform soil erosion research priorities by building a foundation for future targeted, in-depth analyses. GASEMT is an open-source database available to the entire user-community to develop research, rectify errors, and make future expansions.

The potential for modelling peatland habitat condition in Scotland using long-term MODIS data.

Globally, peatlands provide an important sink of carbon in their near natural state but potentially act as a source of gaseous and dissolved carbon emission if not in good condition. There is a pressing need to remotely identify peatland sites requiring improvement and to monitor progress following restoration. A medium resolution model was developed based on a training dataset of peatland habitat condition and environmental covariates, such as morphological features, against information derived from the Moderate Resolution Imaging Spectroradiometer (MODIS), covering Scotland (UK). The initial, unrestricted, model provided the probability of a site being in favourable condition. Receiver operator characteristics (ROC) curves for restricted training data, limited to those located on a peat soil map, resulted in an accuracy of 0.915. The kappa statistic was 0.8151, suggesting good model fit. The derived map of predicted peatland condition at the suggested 0.56 threshold was corroborated by data from other sources, including known restoration sites, areas under known non-peatland land cover and previous vegetation survey data mapped onto inferred condition categories. The resulting locations of the areas of peatland modelled to be in favourable ecological condition were largely confined to the North and West of the country, which not only coincides with prior land use intensity but with published predictions of future retraction of the bioclimatic space for peatlands. The model is limited by a lack of spatially appropriate ground observations, and a lack of verification of peat depth at training site locations, hence future efforts to remotely assess peatland condition will require more appropriate ground-based monitoring. If appropriate ground-based observations could be collected, using remote sensing could be considered a cost-efficient means to provide data on changes in peatland habitat condition.

Enhancing the WorldClim data set for national and regional applications.

Climatic change in the last few decades has had a widespread impact on both natural and human systems, observable on all continents. Ecological and environmental models using climatic data often rely on gridded data, such as WorldClim. The main aim of this study was to devise and evaluate a computationally efficient approach to produce new high resolution (100m) estimates of current and future climatic variables to be used at the national and regional scale. The test area was Great Britain, where local data are available and of good quality. Present and future climate surfaces were produced. For the present, the approach involved the integration, via spatial interpolation, of local climate information and WorldClim to reduce bias. For future climate scenarios the approach involved spatially downscaling of WorldClim (1km) to a finer resolution of 100m. The main advantages of the proposed approach are: 1. finer resolution, 2. locally adapted to the study area with use of higher number of meteorological stations and improved accuracy and bias, and 3. computationally efficient while making use of the existing resources provided by WorldClim. Two applications were presented to illustrate the practical consequences of improvements obtained with this method. The first is a measure of rainfall intensity, i.e. the R-factor, widely applied in erosion and catchment-scale studies. The second is an application to species distribution modelling, involving a range of bioclimatic variables. The results highlighted the importance of considering the spatial variability and structure of the data integrated in the modelling, and using data adapted to the geographical extent of the analysis, whenever possible. The results of the applications showed the advantage of using enhanced climatic data in applications such as the estimation of soil erosion, species range shift, carbon stocks and the provision of ecosystem services.

Spatial assessment of land degradation through key ecosystem services: The role of globally available data.

Land degradation is a serious issue especially in dry and developing countries leading to ecosystem services (ESS) degradation due to soil functions' depletion. Reliably mapping land degradation spatial distribution is therefore important for policy decisions. The main objectives of this paper were to infer land degradation through ESS assessment and compare the modelling results obtained using different sets of data. We modelled important physical processes (sediment erosion and nutrient export) and the equivalent ecosystem services (sediment and nutrient retention) to infer land degradation in an area in the Ethiopian Great Rift Valley. To model soil erosion/retention capability, and nitrogen export/retention capability, two datasets were used: a 'global' dataset derived from existing global-coverage data and a hybrid dataset where global data were integrated with data from local surveys. The results showed that ESS assessments can be used to infer land degradation and identify priority areas for interventions. The comparison between the modelling results of the two different input datasets showed that caution is necessary if only global-coverage data are used at a local scale. In remote and data-poor areas, an approach that integrates global data with targeted local sampling campaigns might be a good compromise to use ecosystem services in decision-making.

Assimilation of optical and radar remote sensing data in 3D mapping of soil properties over large areas.

Soil is very important for many land functions. To achieve sustainability it is important to understand how soils vary over space in the landscape. Remote sensing data can be instrumental in mapping and spatial modelling of soil properties, resources and their variability. The aims of this study were to compare satellite sensors (MODIS, Landsat, Sentinel-1 and Sentinel-2) with varying spatial, temporal and spectral resolutions for Digital Soil Mapping (DSM) of a set of soil properties in Scotland, evaluate the potential benefits of adding Sentinel-1 data to DSM models, select the most suited mix of sensors for DSM to map the considered set of soil properties and validate the results of topsoil (2D) and whole profile (3D) models. The results showed that the use of a mixture of sensors proved more effective to model and map soil properties than single sensors. The use of radar Sentinel-1 data proved useful for all soil properties, improving the prediction capability of models with only optical bands. The use of MODIS time series provided stronger relationships than the use of temporal snapshots. The results showed good validation statistics with a RMSE below 20% of the range for all considered soil properties. The RMSE improved from previous studies including only MODIS sensor and using a coarser prediction grid. The performance of the models was similar to previous studies at regional, national or continental scale. A mix of optical and radar data proved useful to map soil properties along the profile. The produced maps of soil properties describing both lateral and vertical variability, with associated uncertainty, are important for further modelling and management of soil resources and ecosystem services. Coupled with further data the soil properties maps could be used to assess soil functions and therefore conditions and suitability of soils for a range of purposes.

Soil legacy data rescue via GlobalSoilMap and other international and national initiatives.

Legacy soil data have been produced over 70 years in nearly all countries of the world. Unfortunately, data, information and knowledge are still currently fragmented and at risk of getting lost if they remain in a paper format. To process this legacy data into consistent, spatially explicit and continuous global soil information, data are being rescued and compiled into databases. Thousands of soil survey reports and maps have been scanned and made available online. The soil profile data reported by these data sources have been captured and compiled into databases. The total number of soil profiles rescued in the selected countries is about 800,000. Currently, data for 117, 000 profiles are compiled and harmonized according to GlobalSoilMap specifications in a world level database (WoSIS). The results presented at the country level are likely to be an underestimate. The majority of soil data is still not rescued and this effort should be pursued. The data have been used to produce soil property maps. We discuss the pro and cons of top-down and bottom-up approaches to produce such maps and we stress their complementarity. We give examples of success stories. The first global soil property maps using rescued data were produced by a top-down approach and were released at a limited resolution of 1km in 2014, followed by an update at a resolution of 250m in 2017. By the end of 2020, we aim to deliver the first worldwide product that fully meets the GlobalSoilMap specifications.

A GIS-based human health risk assessment for urban green space planning--an example from Grugliasco (Italy).

The need to develop approaches for risk-based management of soil contamination, as well as the integration of the assessment of the human health risk (HHR) due to the soil contamination in the urban planning procedures has been the subject of recent attention of scientific literature and policy makers. The spatial analysis of environmental data offers multiple advantages for studying soil contamination and HHR assessment, facilitating the decision making process. The aim of this study was to explore the possibilities and benefits of spatial implementation of a quantitative HHR assessment methodology for a planning case in a typical urban environment where the soil is contaminated. The study area is located in the city of Grugliasco a part of the Turin (Italy) metropolitan area. The soils data were derived from a site specific soil survey and the land-use data from secondary sources. In the first step the soil contamination data were geo-statistically analysed and a spatial soil contamination data risk modelling procedure designed. In order to spatially assess the HHR computer routines were developed using GIS raster tools. The risk was evaluated for several different land uses for the planned naturalistic park area. The HHR assessment indicated that the contamination of soils with heavy metals in the area is not sufficient to induce considerable health problems due to typical human behaviour within the variety of urban land uses. An exception is the possibility of direct ingestion of contaminated soil which commonly occurs in playgrounds. The HHR evaluation in a planning case in the Grugliasco Municipality confirms the suitability of the selected planning option. The construction of the naturalistic park presents one solution for reducing the impacts of soil contamination on the health of citizens. The spatial HHR evaluation using GIS techniques is a diagnostic procedure for assessing the impacts of urban soil contamination, with which one can verify planning options, and provides an important step in the integration of human health protection within urban planning procedures.

Metals pollution and human bioaccessibility of topsoils in Grugliasco (Italy).

Polluted soils can present a significant health risk especially in an urban environment. Most current legislation and health risk frameworks are based on pseudototal metal content. However, only a fraction of these concentrations is available for plant and human uptake. The aim of this work was to study the diffuse metal contamination in the soils of a municipality in Northern Italy in terms of: (i) metal availability, and (ii) metal accessibility to the human body and its relationship to soil properties, considering lead, copper, zinc, nickel, and chromium. Soil metal content was measured simulating availability conditions. Human bioaccessibility was derived from a modified physiologically-based extraction test. The human bioaccessible content was then estimated taking into account the relationships between pseudototal content and selected soil parameters. For the case study, the prediction of human bioaccessibility based on pseudototal content, organic matter and soil texture produced statistically significant models, with r(2)=0.60 for Cu, r(2)=0.53 for Pb and r(2)=0.42 for Zn.