Heterogeneous Spatial Patterns Research Articles

New insight into bacterial communities of chicken manure composting under increased carbon to nitrogen ratios: Spatial heterogeneity in diversity, networks, and assembly processes

Ammonia is emitted from compost piles with inherent spatial heterogeneity, while adjusting carbon to nitrogen (C/N) ratios can mitigate ammonia emissions. However, the role of spatial heterogeneity in composting bacterial communities mediating nitrogen conversion remains unclear. This study explored bacterial spatial heterogeneity at different locations in piles and its links to ammonia emission during chicken manure composting under increased C/N ratios. Compared with manure-only composting (C/N ratio 7), composting under increased C/N ratios of 16 reduced ammonia emissions by 57.60 % and 22.62 % by adding pine wood and peanut shell, respectively. Meanwhile, significant spatial variations in Sobs, Shannon, and node numbers (P < 0.05) during manure-only composting became insignificant due to C/N ratio increases. Null model and neutral community model showed that stochasticity strongly drove community assembly in all treatments. Assembly process balancing became spatially consistent under increased C/N ratios, shaping the corresponding reduced spatial heterogeneity in alpha-diversity and networks. The variation patterns of spatial heterogeneity in Sobs, node numbers, and dispersal limitation ratios were similar to that of cumulative ammonia emission decreasing with increasing C/N ratio. Additionally, cumulative ammonia emission was positively associated with spatial heterogeneity in Sobs (R2 = 0.49, P < 0.05) and dispersal limitation ratios (R2 = 0.53, P < 0.05), while the heterogeneity explained 60.33 % of ammonia variations. This study demonstrated that reduced bacterial spatial heterogeneity was associated with ammonia emission reductions under increased C/N ratios. The improved understanding of bacterial spatial heterogeneity during composting will provide a new perspective for indicating and regulating ammonia emissions.

Machine learning provides insights for spatially explicit pest management strategies by integrating information on population connectivity and habitat use in a key agricultural pest.

Insect pests have garnered increasing interest because of anthropogenic global change, and their sustainable management requires knowledge of population habitat use and spread patterns. To enhance this knowledge for the prevalent tea pest Empoasca onukii, we utilized a random forest algorithm and a bivariate map to develop and integrate models of its habitat suitability and genetic connectivity across China. Our modeling revealed heterogeneous spatial patterns in suitability and connectivity despite the common key environmental predictor of isothermality. Analyses indicated that tea cultivation in areas surrounding the Tibetan Plateau and the southern tip of China may be at low risk of population outbreaks because of their predicted low suitability and connectivity. However, regions along the middle and lower reaches of the Yangtze River should consider the high abundance and high recolonization potential of E. onukii, and thus the importance of control measures. Our results also emphasized the need to prevent dispersal from outside regions in the areas north of the Yangtze River and highlighted the effectiveness of internal management efforts in southwestern China and along the southeastern coast. Further projections under future conditions suggested the potential for increased abundance and spread in regions north of the Yangtze River and the southern tip of China, and indicated the importance of long-term monitoring efforts in these areas. These findings highlighted the significance of combining information on habitat use and spread patterns for spatially explicit pest management planning. In addition, the approaches we used have potential applications in the management of other pest systems and the conservation of endangered biological resources. © 2024 Society of Chemical Industry.

Spatiotemporal Variations of COVID-19 Variants in 100 Countries: Exploring Spatial Patterns and Time-Lag Effects

Learning from the COVID-19 pandemic is vital for future global health crises. Forecasting pandemic spread across countries in the presence of viral mutations, such as SARS-CoV-2 variant strains, remains a challenge. Previous studies indicate spatial and temporal regularities in SARS-CoV-2 variant distribution, yet limitations persist: (1) static geographical patterns overlook dynamic changes and their causes, and (2) the association between temporal variations in SARS-CoV-2 lineages and epidemic outbreaks is underexplored. To address these gaps, we propose an analysis framework using more than 10 million SARS-CoV-2 genome sequence metadata from 100 countries. Our framework identifies spatial heterogeneity patterns in the relative frequency of SARS-CoV-2 variants through clustering, examines factors influencing spatial heterogeneity using explainable machine learning, and analyzes the time lag effect of temporal variation on COVID-19 infection waves based on spatial patterns. Our findings demonstrate the following: (1) The distribution of SARS-CoV-2 variant strains among 100 countries exhibits a spatial pattern characterized by geographic proximity, with observed dynamic changes. (2) Bilateral distance consistently dominates, and factors related to globalization and response policy contribute to nongeographic proximity and temporal variation of the spatial pattern. (3) Leveraging this spatial pattern allows for the forecast of time lags (mainly ranging from twenty to thirty-eight days) between the emergence of new strains and subsequent infection waves. Our study clarifies the spatial pattern and time lag effect of the dynamic distribution of SARS-CoV-2 variant strains. It offers a spatial basis and temporal reference for early warning in future global public health crises from a geographical perspective.

Exploring the Impact of Multimodal Access on Property and Land Economies in Shanghai’s Inner Ring Districts: Leveraging Advanced Spatial Analysis Techniques

This study explores the impact of accessibility on property pricing and land economies by advanced spatial analysis techniques, focusing on Shanghai as a representative metropolis. Despite the impact of metro systems on residential property values, which has been frequently assessed, a research gap exists in understanding this phenomenon in Asian, particularly Chinese, urban contexts. Addressing this gap is crucial for shaping effective urban land use policy and improving the land economy rationally in China and similar settings facing urban challenges. To assess the impact of metro station accessibility on property prices in Shanghai, with extensive rail transit, and to deeply explore the overall impact of land value varieties driven by metro on urban development, we conducted a comprehensive analysis, with discussion about future aspirations for land planning and management along with landscape and facility design, and measures to improve land economy. The procedures involved creating neighborhood centroids to represent accessibility and using the Euclidean distance analysis to determine the shortest paths to metro stations. Our evaluation incorporated a hedonic pricing model, considering variables like neighborhood characteristics, housing attributes, and socio-economic factors. Advanced spatial analysis encompassing Ordinary Least Squares (OLS) regression and XGBoost analysis were employed to explore spatial effects, and Geographically Weighted Regression (GWR) helped examine spatial patterns and address autocorrelation challenges. Results revealed a negative association between distance to metro station and property prices, indicating a non-linear and spatially clustered relationship and heterogeneous spatial pattern. We dissected the non-linear results in detail, which complemented the conclusion in existing research. This study provides valuable insights into the dynamic interplay between metro accessibility and housing market behaviors in a significant Asian urban context, offering targeted suggestions for urban planners and governors to decide on more reasonable land use planning and management strategies, along with landscape and infrastructure design, to promote not only the healthy growth of the real estate market but also the sustainable urban development in China and similar regions.

Influence of Spatial Heterogeneity in Sea Surface Temperature on Tropical Cyclone Intensity Over the Western North Pacific

AbstractThis study explores the impact of sea surface temperature (SST) spatial heterogeneity on tropical cyclone (TC) intensity through a combination of observations and simulations, aiming to provide a reference for further improving TC intensity forecasting skills. Two distinct patterns of SST spatial heterogeneity are identified based on a statistical analysis of observational data, when the SST at the TC center is above and below 29.3°C, respectively. One is a warm‐core pattern (WCP) with a warm peak SST at the TC center decreasing centrifugally which favors TC development, and the other one is a poleward‐decreasing pattern (PDP) with a warm SST at the south decreasing poleward which suppresses TC development. The numerical simulations confirm the opposite influence of the WCP and the PDP on TC intensity. The WCP intensifies TC intensity by strengthening TC secondary circulation, increasing the conversion from ocean heat energy to TC kinetic energy, and compacting TC structure. In contrast, the PDP weakens TC intensity by inducing opposing responses of these processes. The magnitude of TC intensity change caused by SST spatial heterogeneity is comparable to those caused by a 1°C change in SST at the TC center. These findings offer valuable insights into the role of SST spatial heterogeneity in TC development and provide a new perspective to improve TC intensity forecasting by incorporating SST spatial heterogeneity into statistical‐dynamic models.

Water-related ecosystem services interactions and their natural-human activity drivers: Implications for ecological protection and restoration

Sustainable development faces the crucial challenge of safeguarding water-related ecosystem services, particularly in arid regions. However, scale-dependent interactions and their influencing factors remain unclear. This study addresses this local gap on the regional level by focusing on ecologically vulnerable mountain areas, employing a comprehensive quantitative and spatial analysis approach, utilizing Spearman coefficient, trade-off/synergy index, and trade-off/synergy criterion, to examine water-related ecosystem services interactions across scales in arid area. Additionally, a Geographical detector was used to identify dominant natural and human activity factors. Finally, we determined ecologically optimal and worst areas and proposed spatial planning and management recommendations for ecological protection and restoration. Key results indicate that: (1) From 1995 to 2015, water yield and nutrient delivery ratio exhibited a declining trend, while soil retention showed an increasing trend, with the weakest nutrient delivery ratio function in the reserve. (2) At the grid scale, there were 2 trade-offs among water-related ecosystem services in 1995, which decreased to 1 trade-off in 2005 and 2015. The synergistic was most prominent near Qinghai Lake, while the trade-off was most obvious in the western mountainous areas. Conversely, the county scale demonstrated synergy. (3) NDVI, slope, and precipitation dominantly influence the spatial heterogeneity patterns of soil retention_water yield, soil retention_nutrient delivery ratio, and water yield_nutrient delivery ratio, respectively, with natural factors outweighing human activities in impacting water-related ecosystem services. This study contributes to the improvement and optimization of ecological environment management decisions.

Assessing the spatial coherence of forest cover indicators from different data sources: A contribution to sustainable development reporting

The accuracy of forest area estimates has improved over time as a result of field/cadastral surveys, enhanced remote sensing techniques, and the effectiveness of algorithms for automatical recognition of land cover types. However, forest statistics seem to be less accurate in disaggregated spatial domains such as small administrative units. To evaluate the contribution of different data sources to small-area forest cover estimation in Europe, we compared seven indicators with different spatial coverage and resolution. The analysis considered multiple information sources from innovative initiatives, such as the Copernicus Land monitoring scheme, and traditional (national) surveys. More specifically, the study examined the spatial coherence of these indicators at the municipal scale in Italy to achieve two objectives: (i) assessing the overall precision of forest cover rates and (ii) identifying spatial variations in forest cover rates associated with the technical characteristics of each data source. A spatial econometric approach was used to identify the sources of spatial divergence in forest cover rates and determine the data providers best suited to meet the information requirements of environmental reporting at the desired spatial scale. The results reveal that the selected indicators show varying degrees of internal coherence, with some indices displaying strong correlations and others delineating heterogeneous spatial patterns. Our study highlights the importance of choosing the right information source assessing forest area at the municipal level and provides a valuable approach quantifying the coherence and reliability of environmental indicators in monitoring key aspects of sustainable development.

What drives historical and future changes in photovoltaic power production from the perspective of global warming?

We investigate the drivers of global and regional changes in the potential for photovoltaic (PV) power production from the pre-industrial (1850) to present-day (1985–2014) and until the end of the century (2071–2100), based on output from the Coupled Model Intercomparison Project phase six (CMIP6). Our assessment separates regional contributions from changes in clouds, humidity, temperature, aerosols, and wind speed to the changes in PV power potentials for the first time. Present-day PV power potentials are adversely affected by anthropogenic aerosols compared to the pre-industrial, with a global decrease of the PV power potential by −1.3%. Our results highlight a globally averaged decrease in future PV power potentials primarily driven by temperature and humidity increases by −1.2% to more than −3.5%, depending on the scenario. Regionally different contributions of changes in clouds and aerosols cause heterogeneous spatial patterns in changes of PV potentials, with typically stronger (weaker) influences from clouds (aerosols) in SSP5-8.5 compared to SSP1-2.6. Our results imply that the uncertain response of clouds to warming and aerosol effects are hurdles in quantifying changes in the regional potentials for PV power production.

Interpreting floodplain heterogeneity: Using field data to understand unsupervised floodplain classifications

The dynamic environment of natural river floodplains creates spatial heterogeneity that influences floodplain functions. Diverse human activities have homogenized natural floodplains and reduced their functions across many river networks in the temperate latitudes. Consequently, quantification of floodplain heterogeneity is needed to understand patterns of spatial heterogeneity on diverse floodplains and to inform floodplain restoration. We use a novel approach of spatially connecting field and remotely sensed data in order to interpret the output of, and build upon, a previous unsupervised classification workflow. We apply the method to three rivers in the US Pacific Northwest and the Altamaha River in the southeastern US and compare our results to a previous study. We find that field classifications, relative topography, and NDVI are useful for interpreting results from the unsupervised classification workflow. The interpretations are visually interesting, but we propose that it is the heterogeneity within the groups that is vital to floodplain functioning. Natural floodplains in the Pacific Northwest and coastal Southeast have moderate to high evenness, moderate to high intermixing, and moderate aggregation; and aggregation and evenness similar to rivers in Colorado and Oklahoma, USA, but lower intermixing. We attribute lower intermixing at the Altamaha River to slower rates of lateral channel migration, and lower intermixing at the Hoh River to the different hydrologic and sediment regimes and less stable braided planform. The results show that the larger rivers in this study (Altamaha, Hoh, and Sol Duc Rivers) have spatial heterogeneity similar to beaver-modified and shortgrass prairie rivers in Colorado, whereas the more inland and smaller river (Lookout Creek) has spatial heterogeneity similar to the tallgrass prairie site (Sand Creek). From the results of an ad hoc sensitivity analysis, we suggest using the highest spatial resolution topographic data available, using aerial imagery/mosaics from the same sensor, and removing largest patch index from the suite of comparable indices. The metrics reveal similarities and differences between rivers in the United States, and indicate that discernable trends may arise from a meta study comparing heterogeneity from more rivers across the country.

Spatial Pattern of Large-Scale Agricultural Land and Spatial Heterogeneity of Influencing Factors in the Mountainous Areas of Western China—Wuling Mountains as an Example

The scaling of agricultural land is a trend in land use transformation and is important for modernizing agriculture. Therefore, the reasons for large-scale agricultural land formation should be explored. The spatial distribution of large-scale agricultural land and the factors for its formation vary between different regions. Currently, the exploration of the formation mechanism of large-scale agricultural land from the perspective of heterogeneity is not yet sufficient. Therefore, the main objectives of this article are as follows: first, analyze the spatial pattern characteristics of large-scale agricultural land; second, explore the spatial heterogeneity characteristics of influencing factors from both global and local perspectives; third, explore the mechanism of the formation of large-scale agricultural land from the perspective of heterogeneity. The results indicate the following: (1) The large-scale agricultural land distribution pattern in the Wuling Mountains area was high in the east and low in the west. (2) Natural conditions, production factors, and location conditions all significantly impacted large-scale agricultural land, but with differences in their degree of influence. From a local perspective, the influences of various factors in different regions also exhibited spatial heterogeneity. These two types of heterogeneity can be attributed to the differences in regional development stages. (3) Natural conditions, location conditions, and production factors had negative, positive, and positive effects on the agricultural land scale, respectively, but the influence of the first two decreased with the improvement in the regional development stages. The influence of different factors on production factors was related to the regional development stage, and production factors that were suitable for the regional development stage had a greater impact. The conclusion can provide differentiated policy support for regional land use in practice.

Spatiotemporal Patterns of Hospitalizations Among Older Adults With Co-Presence of Cancer and Dementia in US Counties: 2013-2018.

We assessed the spatiotemporal patterns of hospitalization with comorbid cancer and dementia. Using the 2013-2018 inpatient claims data for Medicare fee-for-service (FFS) beneficiaries, we calculated hospitalization rates by dividing the total admissions from individuals with the co-presence of a major cancer (breast, prostate, lung, and colorectal) and dementia diagnoses with the total counts of FFS beneficiaries aged 65 or older. We identified 22 hotspots with high hospitalization rates that showed heterogeneous spatial and temporal utilization patterns. The odds of a county being a hotspot increased significantly with the county-level percentage of dual Medicare-Medicaid beneficiaries (aOR 1.05; 95% CI: 1.04-1.07) and the prevalence of cancer (aOR 1.73; 95% CI: 1.59-1.89), while decreased significantly with increasing degree of rurality (aOR .82; 95% CI: .79-.85) and decreased yearly over time (aOR .72; 95% CI: .68-.75). The identified hotspots and factors at the county-level may help understand healthcare utilization patterns and assess resource allocation for this unique patient group.

Are preferences for soil-based ecosystem services driven by spatial phenomena?

Agricultural soils provide multiple ecosystem services that affect human well-being. Soils’ potential to provide these ecosystem services varies spatially. Socio-demographic and other drivers of environmental preferences are also spatially variable. Therefore, preferences for soil-based ecosystem services are likely to be spatially heterogeneous, which may result in different policy priorities across locations. Understanding this spatial heterogeneity of preferences is therefore essential to guide public policy to protect healthy soils. We present a study that combines explorative and hypothesis-driven approaches to understand the spatial heterogeneity of preferences for four soil-based ecosystem services: climate regulation, clean water provision, drought protection and flood protection. Based on the results of a discrete choice experiment conducted on a representative sample of the German public, we first use global and local spatial autocorrelation measures to test whether there are any obvious patterns in the spatial distribution of preferences. Second, we use spatial lag models to test a number of hypotheses to explain the observed preference heterogeneity. We particularly focus on the spatial variability of relevant phenomena such as floods, droughts or nitrate pollution of groundwater, and their effects on the studied preferences. Lastly, we compare the results from both approaches in order to see whether the identified patterns are consistent with each other. We find weak patterns of spatial heterogeneity, but our hypotheses are all rejected. This suggests that salience of relevant phenomena and individual affectedness do not have an effect on preferences for soil-based ecosystem services.

Consistently heterogeneous structures observed at multiple spatial scales across fire-intact reference sites

Yellow pine and mixed-conifer (YPMC) forests of California’s Sierra Nevada have experienced widespread fire suppression for over a century, resulting in ingrowth and densification of trees, heavy fuel accumulation, and shifts in species composition. Under warmer and drier climates, these forests are primed for stand-replacing fires and severe drought mortality, requiring management interventions to improve their resilience and mitigate future impacts. Observations from functioning frequent-fire systems (e.g., contemporary reference sites) can provide key insights about pattern-process relationships in fire-intact systems, which can be used to inform regional management efforts. In this study, we used airborne lidar data to quantify and compare forest structure at multiple spatial scales between contemporary reference sites (i.e., forests with a restored frequent, low-intensity fire regime) and control sites (i.e., typical fire-suppressed forests). We evaluated structures at the neighborhood- (∼1 ha), site- (∼100–1,000 ha), and among-site- (∼10,000–100,000 ha) levels. In reference sites, high proportions of individual trees, small clumps of 2–4 trees, and open space formed mostly open canopy structures at the neighborhood-level, and patches of these neighborhood-level structures were arranged in heterogeneous spatial patterns within sites. We observed low variability in site-level structures among reference sites, indicating a stabilizing effect of frequent, low-intensity fire across broad, ecosystem scales. In fire-suppressed control sites, edaphic factors and other non-fire disturbances occasionally produced heterogeneity at the neighborhood- and site-level, but the degree of heterogeneity was not consistent across sites. Structural patterns in contemporary reference sites suggest improved resilience to future disturbances and climate change, and increased provisioning of ecosystem services relative to control sites. We suggest applying these metrics to help inform multi-scale and multi-resource management in Sierra Nevada forests.

Characterizing and Measuring the Environmental Amenities of Urban Recreation Leisure Regions Based on Image and Text Fusion Perception: A Case Study of Nanjing, China

Quantitative evaluation of the environmental amenities (EAs) in urban recreation and leisure regions (URLRs) can provide stronger support for the government to enhance the quality of urban leisure space and improve the well-being of urban residents. Considering the diversity of leisure spaces and the complexity of environmental perception perspectives, this study proposes a comprehensive environmental measurement framework based on image and text fusion perception, which utilizes big data to perceive and quantify the EA features of URLRs comprehensively and efficiently. The study of the URLRs in Nanjing, China, was conducted as an empirical study. The results indicate the following: (1) When it comes to leisure environments, the top concerns for most people are service, hygiene, reputation, and walkability. (2) The EA level of URLRs in Nanjing generally decreases from the center to the outside and shows regional differentiation. (3) EA features in Nanjing’s URLRs exhibit a spatial pattern of similarity in the center and at each district’s edges. This study enhances our understanding of leisure regions’ environmental features that contribute to quality. The measurement results support understanding the spatial heterogeneity patterns of urban leisure activities and vibrancy. Furthermore, valuable urban planning and policy suggestions are made to promote sustainable urban development.

Molecular and functional characterization of the Drosophila melanogaster conserved smORFome

Short polypeptides encoded by small open reading frames (smORFs) are ubiquitously found in eukaryotic genomes and are important regulators of physiology, development, and mitochondrial processes. Here, we focus on a subset of 298 smORFs that are evolutionarily conserved between Drosophila melanogaster and humans. Many of these smORFs are conserved broadly in the bilaterian lineage, and ∼182 are conserved in plants. We observe remarkably heterogeneous spatial and temporal expression patterns of smORF transcripts-indicating wide-spread tissue-specific and stage-specific mitochondrial architectures. In addition, an analysis of annotated functional domains reveals a predicted enrichment of smORF polypeptides localizing to mitochondria. We conduct an embryonic ribosome profiling experiment and find support for translation of 137 of these smORFs during embryogenesis. We further embark on functional characterization using CRISPR knockout/activation, RNAi knockdown, and cDNA overexpression, revealing diverse phenotypes. This study underscores the importance of identifying smORF function in disease and phenotypic diversity.

Retrospective analysis of the pelagic ecosystem of the Western Mediterranean Sea: Drivers, changes and effects

In the Western Mediterranean Sea, forage fishes have changed in abundance, body condition, growth, reproduction, and distribution in the last decades. Different hypotheses have been proposed to explain these changes, including increase in fishing mortality; changes in environmental conditions affecting species fitness, and planktonic productivity and quality; recovery of top predators; and increase in competitors. We investigated the main drivers and changes of the pelagic ecosystem and their effects using an ecosystem-based modelling approach. Specifically, we (1) quantified the potential historical contribution of various drivers of change, (2) investigated changes in temporal trends and spatial distributions of main ecosystem components, and (3) identified ecological consequences of these changes in top predator and competitors, their fisheries and ecosystem traits during 2000–2020. We updated an established Ecopath food-web model representing the Spanish and French Mediterranean sub-areas (GSA06 and GSA07) in 2000 with recent available data. We applied the temporal dynamic Ecosim module, and tested historical time series of fishing effort, fishing mortality and environmental factors as potential drivers. Observed biomass and landings of key species were used to validate model projections. A spatial-temporal Ecospace model was developed to project species distribution changes. Results showed historical biomass and catch changes driven by a combination of high fishing pressure and environmental change (i.e. increase in temperature and salinity, and decline in primary productivity). Small pelagic fish showed significant temporal changes and predicted shifts in their distributions, following a latitudinal gradient. Predators and competitors showed changes as well, displaying heterogeneous spatial patterns, while fisheries landings declined. Overall, results matched observations (e.g., decline of sardine, fluctuations of anchovy and increases in bluefin tuna) and illustrated the need to complement traditional assessments with integrative frameworks to move towards an ecosystem-based approach in the Mediterranean. They also highlighted important knowledge gaps to guide future research in the region.

Socio-spatial Inequities of Fire and Rescue Services in Sweden: An Analysis of Real and Estimated Response Times

Lowering the response times of fire and rescue services (FRS) can reduce costs and the risk of fatalities in emergencies. Ensuring everyone can be reached by the FRS as quickly as possible is therefore a key objective for planners, who often rely on estimated response times to know where response times are long. To be reliable, such estimations need to be validated against real response times. Therefore, this study investigates the spatial patterns of FRS in Sweden in 2018, and compares how estimated and real response times correspond, as well as what factors explain under- or overestimation of the real times. Network analysis performed in a Geographical Information System (GIS) and regression modelling underpin the methodology. In most FRS events (81.3%), estimated response times are shorter than real response times with an average of 83.6 s. Both real and estimated response times are highest in rural municipalities. However, on average, underestimations are larger in urban municipalities. Response times also tend to be underestimated in municipalities with formalized cooperation agreements. Joint organization of FRS resources is thus not necessarily an effective way to reduce response times, with implications for the way FRS is delivered. This study highlights the heterogeneous spatial patterns of difference between estimated and real response times, and shows the importance of being event and area specific when planning the FRS system.

Compound drought and heat waves variation and association with SST modes across China

Compound drought and heatwaves (CDH) have garnered increasing attention because concurrent extreme events can exacerbate the harmful impacts caused by univariate extremes. However, various severities in CDH events and their relationships with sea surface temperature (SST) variations in China remain little understood. Here, we accurately identify CDH events and multi-aspect of characteristics using the standardized precipitation evapotranspiration index (SPEI) and the excess heat factor (EHF) during the extended summer (May–September) of 1961–2017. The evolution of multifaceted characteristics of CDH and their association with SST variation are further explored. The results suggest that the number, frequency, duration and intensity of regional CDH events show heterogeneous spatial patterns, with a significant increasing trend. A consistent abrupt transition in CDH characteristics averaged over China occurred in the period of 1993–1996. Mild and moderate CDHs occur more commonly in Northwest and North China, whereas severe CDHs are mainly found in central and eastern regions. Mild and moderate CDHs are more susceptible to SST modes than severe CDH, and there are strong positive correlations between mild and moderate CDH characteristics and SST variations in the northwest and northern regions. Compared to El Niño-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) plays a dominant role in the intensifications of mild and moderate CDH events. Regionally, the northwest and north have experienced longer, more frequent and severe CDH events during the positive phase of IOD. These findings reveal the divergent evolutions in CDH characteristics with various severities and inconsistent impacts of different SST modes on the compound events.

From the severity patch to the landscape: Wildfire and spatial heterogeneity in northern Sierra Nevada conifer forests

AbstractAimsProlonged fire suppression in conifer forests of the Sierra Nevada Mountain range, California, USA, has led to ingrowth of conifer seedlings, converting the open heterogeneous structure into uniformly dense and layered forest. The threat of a stand‐replacing fire has increased because of fuel buildup in combination with rising drought and extreme heat frequency caused by climate change. With such high severity fire, there is also rising concern regarding conifer forest converting to shrublands as severe fire favors the establishment of large shrub patches altering landscape vegetation pattern and heterogeneity. However, a clear understanding of the effects of increased fire severity, size, and frequency on landscape‐scale heterogeneity and postfire patch dynamics is lacking, which is critical in implementing restoration and forest management activities. Our aim was to understand multiscale dynamics and spatial heterogeneity patterns of conifer forests and chaparral shrublands due to repeated mixed‐severity fire.LocationA mosaic of burned and unburned patches spanning the boundary of Lassen and Plumas National Forests, CA, USA.MethodsWe used secondary geospatial landcover data classified by cover type before modern fires (1999) and after eight modern fires (2014). We calculated various landscape diversity and fragmentation metrics at patch and landscape scales using FRAGSTATS for comparison before and after fires.ResultsAt the fire severity patch scale, high‐severity fire reduced vegetation cover type heterogeneity by half, but reburning at low to moderate severity nearly doubled cover type heterogeneity. At the full landscape scale mixed‐severity fire, including all burn severities, increased vegetation cover type heterogeneity. Fragmentation indexes confirmed that fire created larger patches of shrub and fragmented patches of conifer forest.ConclusionsThe effects of frequent large fire events on vegetation pattern and heterogeneity vary with the scale of analysis. Hence, heterogeneity and vegetation pattern change need to be evaluated at more than one scale to understand past and future ecological processes before prioritizing management actions for the conifer forests of the Sierra Nevada Mountain range.

DNA-barcoded signal amplification for imaging mass cytometry enables sensitive and highly multiplexed tissue imaging

Imaging mass cytometry (IMC) is a highly multiplexed, antibody-based imaging method that captures heterogeneous spatial protein expression patterns at subcellular resolution. Here we report the extension of IMC to low-abundance markers through incorporation of the DNA-based signal amplification by exchange reaction, immuno-SABER. We applied SABER-IMC to image the tumor immune microenvironment in human melanoma by simultaneous imaging of 18 markers with immuno-SABER and 20 markers without amplification. SABER-IMC enabled the identification of immune cell phenotypic markers, such as T cell co-receptors and their ligands, that are not detectable with IMC.