Creating multithemed ecological regions for macroscale ecology: Testing a flexible,repeatable, and accessible clustering method

Understanding broad‐scale ecological patterns and processes often involves accounting for regional‐scale heterogeneity. A common way to do so is to include ecological regions in sampling schemes and empirical models. However, most existing ecological regions were developed for specific purposes, using a limited set of geospatial features and irreproducible methods. Our study purpose was to: (1) describe a method that takes advantage of recent computational advances and increased availability of regional and global data sets to create customizable and reproducible ecological regions, (2) make this algorithm available for use and modification by others studying different ecosystems, variables of interest, study extents, and macroscale ecology research questions, and (3) demonstrate the power of this approach for the research question—How well do these regions capture regional‐scale variation in lake water quality? To achieve our purpose we: (1) used a spatially constrained spectral clustering algorithm that balances geospatial homogeneity and region contiguity to create ecological regions using multiple terrestrial, climatic, and freshwater geospatial data for 17 northeastern U.S. states (~1,800,000 km²); (2) identified which of the 52 geospatial features were most influential in creating the resulting 100 regions; and (3) tested the ability of these ecological regions to capture regional variation in water nutrients and clarity for ~6,000 lakes. We found that: (1) a combination of terrestrial, climatic, and freshwater geospatial features influenced region creation, suggesting that the oft‐ignored freshwater landscape provides novel information on landscape variability not captured by traditionally used climate and terrestrial metrics; and (2) the delineated regions captured macroscale heterogeneity in ecosystem properties not included in region delineation—approximately 40% of the variation in total phosphorus and water clarity among lakes was at the regional scale. Our results demonstrate the usefulness of this method for creating customizable and reproducible regions for research and management applications.     

基于多源地理数据精细尺度的武汉市人居环境新型冠状病毒肺炎疫情传播风险评估

新型冠状病毒肺炎的迅速传播和扩散警示着疾病风险评估的重要性。但现有的风险评估方法受数据限制,缺少实时性和准确性。此外,多数研究以行政统计单元作为分析尺度,存在可变面元问题。为解决这些问题,耦合精细尺度下武汉市疫情数据及多源地理数据,基于随机森林算法构建社区尺度的市域疫情传播风险评估模型并进行了疫情风险制图。模型测试精度达到0.85,Kappa系数达到0.70。此外,本研究还建立基于随机森林算法的社区及场所尺度的"空间变量-感染风险"模型,评估了不同场所设施疫情传播的风险程度。研究表明,（1）武汉中心区域感染风险最高并呈现出向外围递减的趋势;（2）感染风险排名前五的一级场所类型分别为购物服务、医疗服务、金融服务、交通设施以及公共设施;（3）小学、中学的疫情传播风险较低,而高等院校传播风险较高;（4）社区尺度下的疫情风险程度,预测购物场所与交通场所是疫情传播风险最高的驱动因子。本研究基于精细尺度提出风险评估新方法,可为未来疾病风险评估提供新思路,为疫情防控提供决策支持,人民群众提供安全保障。     

Comparing predicted yield and yield stability of willow and Miscanthus across Denmark

To achieve the goals of energy security and climate change mitigation in Denmark and the EU, an expansion of national production of bioenergy crops is needed. Temporal and spatial variation of yields of willow and Miscanthus is not known for Denmark because of a limited number of field trial data. The semi‐mechanistic crop model BioCro was used to simulate the production of both short‐rotation coppice (SRC) willow and Miscanthus across Denmark. Predictions were made from high spatial resolution soil data and weather records across this area for 1990–2010. The potential average, rain‐fed mean yield was 12.1 Mg DM ha^?1 yr^?1 for willow and 10.2 Mg DM ha^?1 yr^?1 for Miscanthus. Coefficient of variation as a measure for yield stability was poorest on the sandy soils of northern and western Jutland, and the year‐to‐year variation in yield was greatest on these soils. Willow was predicted to outyield Miscanthus on poor, sandy soils, whereas Miscanthus was higher yielding on clay‐rich soils. The major driver of yield in both crops was variation in soil moisture, with radiation and precipitation exerting less influence. This is the first time these two major feedstocks for northern Europe have been compared within a single modeling framework and providing an important new tool for decision‐making in selection of feedstocks for emerging bioenergy systems.     

Combined high-resolution genotyping and geospatial analysis reveals modes of endemic urban typhoid fever transmission

Typhoid is a systemic infection caused by Salmonella Typhi and Salmonella Paratyphi A, human-restricted bacteria that are transmitted faeco-orally. Salmonella Typhi and S. Paratyphi A are clonal, and their limited genetic diversity has precluded the identification of long-term transmission networks in areas with a high disease burden. To improve our understanding of typhoid transmission we have taken a novel approach, performing a longitudinal spatial case-control study for typhoid in Nepal, combining single-nucleotide polymorphism genotyping and case localization via global positioning. We show extensive clustering of typhoid occurring independent of population size and density. For the first time, we demonstrate an extensive range of genotypes existing within typhoid clusters, and even within individual households, including some resulting from clonal expansion. Furthermore, although the data provide evidence for direct human-to-human transmission, we demonstrate an overwhelming contribution of indirect transmission, potentially via contaminated water. Consistent with this, we detected S. Typhi and S. Paratyphi A in water supplies and found that typhoid was spatially associated with public water sources and low elevation. These findings have implications for typhoid-control strategies, and our innovative approach may be applied to other diseases caused by other monophyletic or emerging pathogens.     

Scale criticality in estimating ecosystem carbon dynamics

Scaling is central to ecology and Earth system sciences. However, the importance of scale (i.e. resolution and extent) for understanding carbon dynamics across scales is poorly understood and quantified. We simulated carbon dynamics under a wide range of combinations of resolution (nine spatial resolutions of 250 m, 500 m, 1 km, 2 km, 5 km, 10 km, 20 km, 50 km, and 100 km) and extent (57 geospatial extents ranging from 108 to 1 247 034 km²) in the southeastern United States to explore the existence of scale dependence of the simulated regional carbon balance. Results clearly show the existence of a critical threshold resolution for estimating carbon sequestration within a given extent and an error limit. Furthermore, an invariant power law scaling relationship was found between the critical resolution and the spatial extent as the critical resolution is proportional to Aⁿ (n is a constant, and A is the extent). Scale criticality and the power law relationship might be driven by the power law probability distributions of land surface and ecological quantities including disturbances at landscape to regional scales. The current overwhelming practices without considering scale criticality might have largely contributed to difficulties in balancing carbon budgets at regional and global scales.     

Modelling of landscape variables at multiple extents to predict fine sediments and suitable habitat for Tubifex tubifex in a stream system

1. Aggregations of fine sediments are a suitable proxy for the presence and abundance of Tubifex tubifex, one of the obligate hosts in the parasitic life cycle that causes salmonid whirling disease (Myxobolus cerebralis). 2. To determine and evaluate practical approaches to predict fine sediments (<2 mm diameter) that could support Tubifex spp. aggregations, we measured habitat features in a catchment with field measures and metrics derived from digital data sets and geospatial tools at three different spatial extents (m²) within a hierarchical structure. 3. We used linear mixed models to test plausible candidate models that best explained the presence of fine sediments measured in stream surveys with metrics from several spatial extents. 4. The percent slow water habitat measured at the finest extent provided the best model to predict the likely presence of fine sediments. The most influential models to predict fine sediments using landscape metrics measured at broader extents included variables that measure the percentage land cover in conifer or agriculture, specifically, decreases in conifer cover and increases in agriculture. 5. The overall best‐fitting model of the presence of fine sediments in a stream reach combined variables measured and operating at different spatial extents. 6. Landscape features modelled within a hierarchical framework may be useful tools to evaluate and prioritise areas with fine sediments that may be at risk of infection by Myxobolus cerebralis.     

Crassulacean acid metabolism (CAM) offers sustainable bioenergy production and resilience to climate change

Biomass production on low‐grade land is needed to meet future energy demands and minimize resource conflicts. This, however, requires improvements in plant water‐use efficiency (WUE) that are beyond conventional C3 and C4 dedicated bioenergy crops. Here we present the first global‐scale geographic information system (GIS)‐based productivity model of two highly water‐efficient crassulacean acid metabolism (CAM) candidates: Agave tequilana and Opuntia ficus‐indica. Features of these plants that translate to WUE advantages over C3 and C4 bioenergy crops include nocturnal stomatal opening, rapid rectifier‐like root hydraulic conductivity responses to fluctuating soil water potential and the capacity to buffer against periods of drought. Yield simulations for the year 2070 were performed under the four representative concentration pathway (RCPs) scenarios presented in the IPCC's 5th Assessment Report. Simulations on low‐grade land suggest that O. ficus‐indica alone has the capacity to meet ‘extreme’ bioenergy demand scenarios (>600 EJ yr^?1) and is highly resilient to climate change (?1%). Agave tequilana is moderately impacted (?11%). These results are significant because bioenergy demand scenarios >600 EJ yr^?1 could be met without significantly increasing conflicts with food production and contributing to deforestation. Both CAM candidates outperformed the C4 bioenergy crop, Panicum virgatum L. (switchgrass) in arid zones in the latitudinal range 30°S–30°N.     

Spatial analysis of urban material stock with clustering algorithms: A Northern European case study

A large share of construction material stock (MS) accumulates in urban built environments. To attain a more sustainable use of resources, knowledge about the spatial distribution of urban MS is needed. In this article, an innovative spatial analysis approach to urban MS is proposed. Within this scope, MS indicators are defined at neighborhood level and clustered with k‐mean algorithms. The MS is estimated bottom‐up with (a) material‐intensity coefficients and (b) spatial data for three built environment components: buildings, road transportation, and pipes, using seven material categories. The city of Gothenburg, Sweden is used as a case study. Moreover, being the first case study in Northern Europe, the results are explored through various aspects (material composition, age distribution, material density), and, finally, contrasted on a per capita basis with other studies worldwide. The stock is estimated at circa 84 million metric tons. Buildings account for 73% of the stock, road transport 26%, and pipes 1%. Mineral‐binding materials take the largest share of the stock, followed by aggregates, brick, asphalt, steel, and wood. Per capita, the MS is estimated at 153 metric tons; 62 metric tons are residential, which, in an international context, is a medium estimate. Denser neighborhoods with a mix of nonresidential and residential buildings have a lower proportion of MS in roads and pipes than low‐density single‐family residential neighborhoods. Furthermore, single‐family residential neighborhoods cluster in mixed‐age classes and show the largest content of wood. Multifamily buildings cluster in three distinct age classes, and each represent a specific material composition of brick, mineral binding, and steel. Future work should focus on megacities and contrasting multiple urban areas and, methodologically, should concentrate on algorithms, MS indicators, and spatial divisions of urban stock.     

Potential for biochar carbon sequestration from crop residues: A global spatially explicit assessment

Global warming necessitates urgent action to reduce carbon dioxide (CO₂) emissions and remove CO₂ from the atmosphere. Biochar, a type of carbonized biomass which can be produced from crop residues (CRs), offers a promising solution for carbon dioxide removal (CDR) when it is used to sequester photosynthetically fixed carbon that would otherwise have been returned to atmospheric CO₂ through respiration or combustion. However, high-resolution spatially explicit maps of CR resources and their capacity for climate change mitigation through biochar production are currently lacking, with previous global studies relying on coarse (mostly country scale) aggregated statistics. By developing a comprehensive high spatial resolution global dataset of CR production, we show that, globally, CRs generate around 2.4 Pg C annually. If 100% of these residues were utilized, the maximum theoretical technical potential for biochar production from CRs amounts to 1.0 Pg C year⁻¹ (3.7 Pg CO₂e year⁻¹). The permanence of biochar differs across regions, with the fraction of initial carbon that remains after 100 years ranging from 60% in warm climates to nearly 100% in cryosols. Assuming that biochar is sequestered in soils close to point of production, approximately 0.72 Pg C year⁻¹ (2.6 Pg CO₂e year⁻¹) of the technical potential would remain sequestered after 100 years. However, when considering limitations on sustainable residue harvesting and competing livestock usage, the global biochar production potential decreases to 0.51 Pg C year⁻¹ (1.9 Pg CO₂e year⁻¹), with 0.36 Pg C year⁻¹ (1.3 Pg CO₂e year⁻¹) remaining sequestered after a century. Twelve countries have the technical potential to sequester over one fifth of their current emissions as biochar from CRs, with Bhutan (68%) and India (53%) having the largest ratios. The high-resolution maps of CR production and biochar sequestration potential provided here will provide valuable insights and support decision-making related to biochar production and investment in biochar production capacity.     

Spatial perspectives enhance modeling of nanomaterial risks

Novel engineered nanomaterials (ENMs) are increasingly being manufactured and integrated into renewable energy generation and storage technologies. Past research estimated the potential impact of this increased demand on environmental systems, due to both the life cycle impact of ENM production and the potential for their direct release into ecosystems. However, many models treat ENM production and use as spatially implicit, without considering the specific geographic location of potential emissions. By not considering geographical context, ENM accumulation or impact may be underestimated. Here, we introduce an integrated predictive model that forecasts likely ENM manufacturing locations and potential emissions to the environment, with a focus on critical environmental areas and freshwater ecosystems. Spatially explicit ENM concentrations are estimated for four case study ENMs that have promising application in lithium‐ion battery production. Results demonstrate that potential ENM exposure from manufacturing locations within buffer zones of sensitive ecosystems would accumulate to levels associated with measured ecotoxicity risk under high release scenarios, underscoring the importance of adding a spatial and temporal perspective to life cycle toxicity impact assessment. This predictive integrated modeling approach is novel to the nanomaterial literature and can be adapted to other regions and material case studies to proactively inform life cycle tradeoffs and decision‐making.