Modelling and Mapping Total and Bioaccessible Arsenic and Lead in Stoke-on-Trent and Their Relationships with Industry

This study was based on a geochemical soil survey of Stoke-on-Trent in the UK of 747 surface soil samples analysed for 53 elements. A subset of 50 of these soil samples were analysed for their bioaccessible As and Pb content using the Unified Barge Method. Random Forest modelling, using the total element data as predictor variables, was used to predict bioaccessible As and Pb for all 747 samples. Random Forest modelling, using inverse distance weighed predictors and bedrock and superficial geology, was also used to map both total and bioaccessible As and Pb on a 400 × 400 spatial prediction grid with a 50 m resolution. The predicted bioaccessible As ranged from ca. 1 to 8 mg/kg and the total As ca. 8 to 45 mg/kg. The bioaccessible Pb and the total Pb both covered the range ca. 16–1200 mg/kg, with the highest values for both forms of Pb showing similar spatial distributions. Predictor variable importance and information on past industry suggest that the source of both of these elements is driven by anthropogenic causes.

Random forest modelling of neuropathological features identifies microglial activation as an accurate pathological classifier of C9orf72-related amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are regarded as two ends of a pathogenetic spectrum, termed ALS-frontotemporal spectrum disorder (ALS-FTSD). However, it is currently difficult to predict where on the spectrum an individual will lie, especially for patients with C9orf72 hexanucleotide repeat expansions (HRE), a mutation associated with both ALS and FTD. It has been shown that both inflammation and protein misfolding influence aspects of ALS and ALS-FTSD disease pathogenesis, such as the manifestation or severity of motor or cognitive symptoms. Previous studies have highlighted markers which may influence C9orf72-associated disease presentation in a targeted fashion, though there has yet to be a systematic and quantitative assessment of common immunohistochemical markers to investigate the significance of these pathways in an unbiased manner. Here we report the first extensive digital pathological assessment with random forest modelling of pathological markers often used in neuropathology practice. This study profiles glial activation and protein misfolding in a cohort of deeply clinically profiled post-mortem tissue from patients with a C9orf72 HRE, who either met the criteria for a diagnosis of ALS or ALS-FTSD. We show that microglial immunohistochemical staining features, both morphological and spatial, are the best independent classifiers of disease status and that clinicopathological associations exist between microglial activation status and cognitive dysfunction in ALS-FTSD patients with C9orf72 HRE. Furthermore, we show that spatially resolved changes in FUS staining are also an accurate predictor of disease status, implying that liquid-liquid phase shift of this aggregation-prone RNA-binding protein may be important in ALS caused by a C9orf72 HRE. Our findings provide further support to the hypothesis of dysfunctional immune regulation and proteostasis in the pathogenesis of C9orf72 ALS and provide a framework for digital analysis of commonly used neuropathological stains as a tool to enrich our understanding of clinicopathological associations between cohorts.

Identification of cerebrospinal fluid biomarkers for parkinsonism using a proteomics approach

The aim of our study was to investigate cerebrospinal fluid (CSF) tryptic peptide profiles as potential diagnostic biomarkers for the discrimination of parkinsonian disorders. CSF samples were collected from individuals with parkinsonism, who had an uncertain diagnosis at the time of inclusion and who were followed for up to 12 years in a longitudinal study. We performed shotgun proteomics to identify tryptic peptides in CSF of Parkinson’s disease (PD, n = 10), multiple system atrophy patients (MSA, n = 5) and non-neurological controls (n = 10). We validated tryptic peptides with differential levels between PD and MSA using a newly developed selected reaction monitoring (SRM) assay in CSF of PD (n = 46), atypical parkinsonism patients (AP; MSA, n = 17; Progressive supranuclear palsy; n = 8) and non-neurological controls (n = 39). We identified 191 tryptic peptides that differed significantly between PD and MSA, of which 34 met our criteria for SRM development. For 14/34 peptides we confirmed differences between PD and AP. These tryptic peptides discriminated PD from AP with moderate-to-high accuracy. Random forest modelling including tryptic peptides plus either clinical assessments or other CSF parameters (neurofilament light chain, phosphorylated tau protein) and age improved the discrimination of PD vs. AP. Our results show that the discovery of tryptic peptides by untargeted and subsequent validation by targeted proteomics is a suitable strategy to identify potential CSF biomarkers for PD versus AP. Furthermore, the tryptic peptides, and corresponding proteins, that we identified as differential biomarkers may increase our current knowledge about the disease-specific pathophysiological mechanisms of parkinsonism.

The Boreal–Arctic Wetland and Lake Dataset (BAWLD)

Methane emissions from boreal and arctic wetlands, lakes, and rivers are expected to increase in response to warming and associated permafrost thaw. However, the lack of appropriate land cover datasets for scaling field-measured methane emissions to circumpolar scales has contributed to a large uncertainty for our understanding of present-day and future methane emissions. Here we present the Boreal–Arctic Wetland and Lake Dataset (BAWLD), a land cover dataset based on an expert assessment, extrapolated using random forest modelling from available spatial datasets of climate, topography, soils, permafrost conditions, vegetation, wetlands, and surface water extents and dynamics. In BAWLD, we estimate the fractional coverage of five wetland, seven lake, and three river classes within 0.5 × 0.5∘ grid cells that cover the northern boreal and tundra biomes (17 % of the global land surface). Land cover classes were defined using criteria that ensured distinct methane emissions among classes, as indicated by a co-developed comprehensive dataset of methane flux observations. In BAWLD, wetlands occupied 3.2 × 106 km2 (14 % of domain) with a 95 % confidence interval between 2.8 and 3.8 × 106 km2. Bog, fen, and permafrost bog were the most abundant wetland classes, covering ∼ 28 % each of the total wetland area, while the highest-methane-emitting marsh and tundra wetland classes occupied 5 % and 12 %, respectively. Lakes, defined to include all lentic open-water ecosystems regardless of size, covered 1.4 × 106 km2 (6 % of domain). Low-methane-emitting large lakes (>10 km2) and glacial lakes jointly represented 78 % of the total lake area, while high-emitting peatland and yedoma lakes covered 18 % and 4 %, respectively. Small (<0.1 km2) glacial, peatland, and yedoma lakes combined covered 17 % of the total lake area but contributed disproportionally to the overall spatial uncertainty in lake area with a 95 % confidence interval between 0.15 and 0.38 × 106 km2. Rivers and streams were estimated to cover 0.12 × 106 km2 (0.5 % of domain), of which 8 % was associated with high-methane-emitting headwaters that drain organic-rich landscapes. Distinct combinations of spatially co-occurring wetland and lake classes were identified across the BAWLD domain, allowing for the mapping of “wetscapes” that have characteristic methane emission magnitudes and sensitivities to climate change at regional scales. With BAWLD, we provide a dataset which avoids double-accounting of wetland, lake, and river extents and which includes confidence intervals for each land cover class. As such, BAWLD will be suitable for many hydrological and biogeochemical modelling and upscaling efforts for the northern boreal and arctic region, in particular those aimed at improving assessments of current and future methane emissions. Data are freely available at https://doi.org/10.18739/A2C824F9X (Olefeldt et al., 2021).

The Relationship between Satellite-Derived Vegetation Indices and Live Weight Changes of Beef Cattle in Extensive Grazing Conditions

The live weight (LW) and live weight change (LWC) of cattle in extensive beef production is associated with pasture availability and quality. The remote monitoring of pastures and cattle LWC can be achieved with a combination of satellite imagery and walk-over-weighing (WoW) stations. The objective of the present study is to determine the association, if any, between vegetation indices (VIs) (pasture availability) and the LWC of beef cattle in an extensive breeding operation in Northern Australia. The study also tests a suite of VIs along with variables such as rainfall and Julian day to predict the LWC of breeding cows. The VIs were calculated from Sentinel-2 satellite imagery over a 2-year period from a paddock with 378 cattle. Animal LW was measured remotely using a weighing scale at the water point. The relationship between VIs, the LWC, and LW was assessed using linear mixed-effects regression models and random forest modelling. Findings demonstrate that all VIs calculated had a significant positive relationship with the LWC and LW (p < 0.001). Machine learning predictive modelling showed that the LWC of breeding cows could be predicted from VIs, Julian day, and rainfall information, with a Lin’s Concordance Correlation Coefficient of 0.62 when using the leave-one-month-out cross-validation. The LW and LWC were greater during the wet season when VIs were higher compared to the dry season (p < 0.001). Results suggest that the remote monitoring of pasture availability, the LWC and LW is possible under extensive grazing conditions. Further, the use of VIs and other readily available data such as rainfall can be used to predict the LWC of a breeding herd in extensive conditions. Such information could be used to increase the productivity and land management in extensive beef production. The integration of these data streams offers great potential to improve the monitoring, management, and productivity of grazing or cropping enterprises.

Tracing boundaries in Eastern Antarctica: Multi-scale drivers of soil microbial communities across the hyperarid Vestfold Hills

Microorganisms are key to sustaining core ecosystem processes across terrestrial Antarctica but they are rarely considered in conservation frameworks. Whilst greater advocacy has been made towards the inclusion of microbial data in this context, there is still a need for better tools to quantify multispecies responses to environmental change. Here, we extend the scope of Gradient Forest modelling beyond macroorganisms and small datasets to the comprehensive polar soil microbiome encompassing >17, 000 sequence variants for bacteria, micro-eukarya and archaea throughout the hyperarid Vestfold Hills of Eastern Antarctica. Quantification of microbial diversity against 79 physiochemical variables revealed that whilst rank-order importance differed, predictors were broadly consistent between domains, with greatest sharing occurring between bacteria and micro-eukarya. Moisture was identified as the most robust predictor for shaping the regional soil microbiome, with highest compositional turnover or “splits” occurring within the 10 – 12 % moisture content range. Often the most responsive taxa were rarer lineages of bacteria and micro-eukarya with phototrophic and nutrient-cycling capacities such as Cyanobacteria (up to 61.81 % predictive capacity), Chlorophyta (62.17 %) and Ochrophyta (57.81 %). These taxa groups are thus at greater risk of biodiversity loss or gain to projected climate trajectories, which will inevitably disturb current ecosystem dynamics. Better understanding of these threshold tipping points will positively aid conservation efforts across Eastern Antarctica. Furthermore, the successful implementation of an improved Gradient Forest model also presents an exciting opportunity to broaden its use on microbial systems globally.

Soil-Related Predictors for Distribution Modelling of Four European Crayfish Species

One of the most critical challenges in species distribution modelling is testing and validating various digitally derived environmental predictors (e.g., remote-sensing variables, topographic variables) by field data. Therefore, here we aimed to explore the value of soil properties in the spatial distribution of four European indigenous crayfish species. A database with 473 presence and absence locations in Romania for Austropotamobius bihariensis, A. torrentium, Astacus astacus and Pontastacus leptodactylus was used in relation to eight digitalised soil properties. Using random forest modelling, we found a preference for dense soils with lower coarse fragments content together with deeper sediment cover and higher clay values for A. astacus and P. leptodactylus. These descriptors trigger the need for cohesive soil river banks as the microenvironment for building their burrows. Conversely, species that can use banks with higher coarse fragments content, the highland species A. bihariensis and A. torrentium, prefer soils with slightly thinner sediment cover and lower density while not influenced by clay/sand content. Of all species, A. astacus was found related with higher erosive soils. The value of these soil-related digital descriptors may reside in the improvement of approaches in crayfish species distribution modelling to gain adequate conservation measures.

Comprehensive Examination of the Determinants of Damage to Houses in Two Wildfires in Eastern Australia in 2013

Wildfires continue to destroy houses, but an understanding of the complex mix of risk factors remains elusive. These factors comprise six themes: preparedness actions (including defensible space), response actions (including defence), house construction, landscape fuels, topography and weather. The themes span a range of spatial scales (house to region) and responsible agents (householders through government to entirely natural forces). We conducted a statistical analysis that partitions the contribution of these six themes on wildfire impact to houses, using two fires that destroyed 200 houses in New South Wales (Australia) in October 2013 (the Linksview and Mt York fires). We analysed 85 potential predictor variables using Random Forest modelling. The best predictors of impact were whether the house was defended and distance to forest toward the direction of fire spread. However, predictors from all four of the other themes had some influence, including distance to the nearest other burnt house (indicating house-to-house transmission) and vegetation cover up to 40 m from the house. The worst-placed houses (undefended, without adequate defensible space, with burnt houses nearby and with a westerly aspect) were 10 times more likely to be impacted than the best-placed houses in our study. The results indicate that householders are the agents most able to mitigate risk in the conditions experienced in these fires through both preparation and active defence.

Simulating the Effects of Intensifying Silviculture on Desired Species Yields across a Broad Environmental Gradient

In the past two decades, forest management has undergone major paradigm shifts that are challenging the current forest modelling architecture. New silvicultural systems, guidelines for natural disturbance emulation, a desire to enhance structural complexity, major advances in successional theory, and climate change have all highlighted the limitations of current empirical models in covering this range of conditions. Mechanistic models, which focus on modelling underlying ecological processes rather than specific forest conditions, have the potential to meet these new paradigm shifts in a consistent framework, thereby streamlining the planning process. Here we use the NEBIE (a silvicultural intervention scale that classifies management intensities as natural, extensive, basic, intensive, and elite) plot network, from across Ontario, Canada, to examine the applicability of a mechanistic model, ZELIG-CFS (a version of the ZELIG tree growth model developed by the Canadian Forest Service), to simulate yields and species compositions. As silvicultural intensity increased, overall yield generally increased. Species compositions met the desired outcomes when specific silvicultural treatments were implemented and otherwise generally moved from more shade-intolerant to more shade-tolerant species through time. Our results indicated that a mechanistic model can simulate complex stands across a range of forest types and silvicultural systems while accounting for climate change. Finally, we highlight the need to improve the modelling of regeneration processes in ZELIG-CFS to better represent regeneration dynamics in plantations. While fine-tuning is needed, mechanistic models present an option to incorporate adaptive complexity into modelling forest management outcomes.

The Boreal-Arctic Wetland and Lake Dataset (BAWLD)

Methane emissions from boreal and arctic wetlands, lakes, and rivers are expected to increase in response to warming and associated permafrost thaw. However, the lack of appropriate land cover datasets for scaling field-measured methane emissions to circumpolar scales has contributed to a large uncertainty for our understanding of present-day and future methane emissions. Here we present the Boreal-Arctic Wetland and Lake Dataset (BAWLD), a land cover dataset based on an expert assessment, extrapolated using random forest modelling from available spatial datasets of climate, topography, soils, permafrost conditions, vegetation, wetlands, and surface water extents and dynamics. In BAWLD, we estimate the fractional coverage of five wetland, seven lake, and three river classes within 0.5 × 0.5° grid cells that cover the northern boreal and tundra biomes (17 % of the global land surface). Land cover classes were defined using criteria that ensured distinct methane emissions among classes, as indicated by a co-developed comprehensive dataset of methane flux observations. In BAWLD, wetlands occupied 3.2 × 106 km2 (14 % of domain) with a 95 % confidence interval between 2.8 and 3.8 × 106 km2. Bog, fen, and permafrost bog were the most abundant wetland classes, covering ~28 % each of the total wetland area, while the highest methane emitting marsh and tundra wetland classes occupied 5 and 12 %, respectively. Lakes, defined to include all lentic open-water ecosystems regardless of size, covered 1.4 × 106 km2 (6 % of domain). Low methane-emitting large lakes (> 10 km2) and glacial lakes jointly represented 78 % of the total lake area, while high-emitting peatland and yedoma lakes covered 18 and 4 %, respectively. Small (< 0.1 km2) glacial, peatland, and yedoma lakes combined covered 17 % of the total lake area, but contributed disproportionally to the overall spatial uncertainty of lake area with a 95 % confidence interval between 0.15 and 0.38 × 106 km2. Rivers and streams were estimated to cover 0.12 × 106 km2 (0.5 % of domain) of which 8 % was associated with high-methane emitting headwaters that drain organic-rich landscapes. Distinct combinations of spatially co-occurring wetland and lake classes were identified across the BAWLD domain, allowing for the mapping of “wetscapes” that will have characteristic methane emission magnitudes and sensitivities to climate change at regional scales. With BAWLD, we provide a dataset which avoids double-accounting of wetland, lake and river extents, and which includes confidence intervals for each land cover class. As such, BAWLD will be suitable for many hydrological and biogeochemical modelling and upscaling efforts for the northern Boreal and Arctic region, in particular those aimed at improving assessments of current and future methane emissions. Data is freely available at https://doi.org/10.18739/A2C824F9X (Olefeldt et al., 2021).