Spatial Scales Research Articles

Predicting the responses of European grassland communities to climate and land cover change.

European grasslands are among the most species-rich ecosystems on small spatial scales. However, human-induced activities like land use and climate change pose significant threats to this diversity. To explore how climate and land cover change will affect biodiversity and community composition in grassland ecosystems, we conducted joint species distribution models (SDMs) on the extensive vegetation-plot database sPlotOpen to project distributions of 1178 grassland species across Europe under current conditions and three future scenarios. We further compared model accuracy and computational efficiency between joint SDMs (JSDMs) and stacked SDMs, especially for rare species. Our results show that: (i) grassland communities in the mountain ranges are expected to suffer high rates of species loss, while those in western, northern and eastern Europe will experience substantial turnover; (ii) scaling anomalies were observed in the predicted species richness, reflecting regional differences in the dominant drivers of assembly processes; (iii) JSDMs did not outperform stacked SDMs in predictive power but demonstrated superior efficiency in model fitting and predicting; and (iv) incorporating co-occurrence datasets improved the model performance in predicting the distribution of rare species. This article is part of the theme issue 'Ecological novelty and planetary stewardship: biodiversity dynamics in a transforming biosphere'.

Application of multiplatform remote sensing data over East Asia Ocean: aerosol characteristics and aerosol types.

It is important to explore the characteristics and rules of atmospheric aerosol in the East Asian Sea for monitoring and evaluating atmospheric environmental quality. Based on Aerosol Robot Network (AERONET), Visible Infrared Imaging Radiometer (VIIRS), and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data, the temporal and spatial variation characteristics and differences of aerosol parameters and types in the East Asian Sea were studied by using figure classification method (FIGCM), aerosol optical depth (AOD)440-Angstrom exponent (AE)440-870 method (AA1M), and AOD550-AE490-670 method (AA2M). The results show that the seasonal variation trend of aerosol characteristics and types is obvious in East Asia Sea. AOD, volume concentration (Cv), and aerosol effective radius (reff) in the Bohai-Yellow Sea and the Sea of Japan in autumn are lower than those in other seasons, and the occurrence frequency of ocean-type aerosols is high. Different from the Bohai-Yellow Sea and Sea of Japan, human activities in winter, summer, and autumn seriously affect the air quality in the East China Sea and South China Sea. Especially at the Taipei CWB site, from aerosol parameters and high biomass burning/urban industrial (BB/UI) aerosol, human activity is an important factor for high pollution at the Taipei CWB site. Aerosol types of AA1M, FIGCM, AA2M, and CALIPSO were compared at Anmyon and Yonsei University sites in the Bohai-Yellow Sea in March 2020. The results show that aerosol types based on threshold classification methods generally have higher mixed aerosol results, and the marine (MA) results of AA1M, FIGCM, and AA2M are close to the clean marine aerosol results of CALIPSO. Comparing the results of AA 2M and CALIPSO on a spatial scale, it is found that the clean marine aerosol proportion identified by CALIPSO (0.38, 0.48, 0.82) is consistent with the MA proportion identified by AA 2M (0.43, 0.46, 0.97) in the East China Sea, South China Sea, and Western Pacific Ocean.

Stability in plant-pollinator communities across organizational levels: present, gaps, and future

Abstract The study of ecological stability continues to fill the pages of scientific journals almost seven decades after the first ecologists initiated this line of research. The many advances in this field have focused on understanding the stability of populations, communities or functions within single guilds or trophic levels, with less research conducted across multiple trophic levels and considering the different interactions that relate species to each other. Here, we review the recent literature on the multiple dimensions of ecological stability specifically within plant-pollinator communities. We then focus on one of stability´s dimensions, temporal invariability, and adapt an existing partitioning framework that bridges invariability and synchrony measures across spatial scales and organizational levels to accommodate interactions between plants and their pollinators. Finally, we use this framework to analyze temporal invariability in plant reproductive success, partitioning it on invariability and synchrony components across plant and pollinator populations and communities, as well as their interactions, using a well-resolved dataset that encompasses data for two years. Our review of the literature points to several significant gaps in our current knowledge, with simulation studies clearly overrepresented in the literature as opposed to experimental or empirical approaches. Our quantitative approach to partitioning invariability shows similar patterns of decreasing temporal invariability across increasing organizational levels driven by asynchronous dynamics amongst populations and communities, which overall stabilize ecosystem functioning (plant reproductive success). This study represents a first step towards a better comprehension of temporal invariability in ecosystem functions defined by interactions between species and provides a blueprint for the type of spatially replicated multi-year data that needs to be collected in the future to further our understanding of ecological stability within multi-trophic communities.

Lights, location, action: Shade avoidance signalling over spatial scales.

Plants growing in dense vegetation stands need to flexibly position their photosynthetic organs to ensure optimal light capture in a competitive environment. They do so through a suite of developmental responses referred to as the shade avoidance syndrome. Belowground, root development is also adjusted in response to aboveground neighbour proximity. Canopies are dynamic and complex environments with heterogenous light cues in the far-red, red, blue and UV spectrum, which can be perceived with photoreceptors by spatially separated plant tissues. Molecular regulation of plant architecture adjustment via PHYTOCHROME-INTERACTING FACTOR (PIF) transcription factors and growth-related hormones such as auxin, gibberellic acid, brassinosteroids and abscisic acid were historically studied without much attention to spatial or tissue-specific context. Recent developments and technologies have, however, sparked strong interest in spatially explicit understanding of shade avoidance regulation. Other environmental factors such as temperature and nutrient availability interact with the molecular shade avoidance regulation network, often depending on the spatial location of the signals, and the responding organs. Here, we aim to review recent advances in how plants respond to heterogenous light cues and integrate these with other environmental signals.

The Spatial Scale and Spread of Child Victimization.

Previous research shows that large, densely populated urban areas have higher rates of child victimization that have persisted over time. However, few investigations have inquired about the processes that produce and sustain hot and cold spots of child victimization. As a result, the mechanisms that produce the observed spatial clustering of child victimization, and hence "why" harms against children tend to cluster in space, remains unknown. Does the likelihood of being a victim of violence in one location depend on a similar event happening in a nearby location within a specified timeframe? Rather, are child victims of violence more likely to reside in suboptimal neighborhood conditions? This paper aims to present an analytical and theoretical framework for distinguishing between these locational (point) processes to determine whether the empirical spatial patterns undergirding child victimization are more reflective of the "spread" via contagion (i.e., dependency) or whether they are produced by neighborhood structural inequality resulting from spatial heterogeneity. To detect spatial dependence, we applied the inhomogeneous K-function to Los Angeles Medical Examiner data on child homicide victim locations while controlling for regional differences in victimization events (i.e., heterogeneity). Our analysis found strong evidence of spatial clustering in child victimization at small spatial scales but inhibition at larger scales. We further found limited support for the spatiotemporal clustering of child victimization indicative of a contagion effect. Overall, our results support the role of neighborhood structural vulnerability in the underlying mechanisms producing patterns of child victimization across Los Angeles County. We conclude by discussing the policy implications for understanding this spatial patterning in geographical context and for developing effective and targeted preventive interventions.

Scale effects and implications of the stochastic structure of customer water demands

ABSTRACT The effect of different temporal (from seconds to months) and spatial aggregation scales (from individual users to full urban areas) on water demand behavior has been explored to a limited degree. The effort described here extends those works by evaluating the scale effects of residential water consumption in a unique US data set that covers 10,000 households with a 1-gallon (3.79 L) hourly resolution over 2 years. A preliminary data analysis and a sequential Principal Component Analysis (PCA) is carried out to assess the effect of different temporal (weekly, daily, hourly) and spatial aggregation (individual meters and groups every 10, 100 and 1,000 m) levels on demand. Results show that individual users act very differently from each other, and individual consumer variability is only canceled out when a significant number of households are aggregated. The implications of this finding are assessed from a hydraulic modeling perspective as the spatiotemporal scale of measurements may condition the type of analysis that can be carried out in practice. However, additional work is needed to explore the point at which it may be worth to embrace a micro (per fixture/household) or a macro (per node/network) approach for different purposes.

Joint action of phase mixing and nonlinear effects in MHD waves propagating in coronal loops

The evolution of Alfv\'en waves in cylindrical magnetic flux tubes, which represent a basic model for loops observed in the solar corona, can be affected by phase mixing and turbulent cascade. Phase mixing results from transverse inhomogeneities in the Alfv\'en speed, causing different shells of the flux tube to oscillate at different frequencies, thus forming increasingly smaller spatial scales in the direction perpendicular to the guide field. Turbulent cascade also contributes to the dissipation of the bulk energy of the waves through the generation of smaller spatial scales. Both processes present characteristic timescales. Different regimes can be envisaged according to how those timescales are related and to the typical timescale at which dissipation is at work. We investigate the interplay of phase mixing and the nonlinear turbulent cascade in the evolution and dissipation of Alfv\'en waves using compressible magnetohydrodynamic numerical simulations. We consider perturbations in the form of torsional waves, both propagating and standing, or turbulent fluctuations, or a combination of the two. The main purpose is to study how phase mixing and nonlinear couplings jointly work to produce small scales in different regimes. We conducted a numerical campaign to explore the typical parameters, such as the loop length, the amplitude and spatial profile of the perturbations, and the dissipative coefficients. A pseudo-spectral code was employed to solve the three-dimensional compressible magnetohydrodynamic equations, modeling the evolution of perturbations propagating in a flux tube corresponding to an equilibrium configuration with cylindrical symmetry. We find that phase mixing takes place for moderate amplitudes of the turbulent component even in a distorted, nonaxisymmetric configuration, building small scales that are locally transverse to the density gradient. The dissipative time decreases with increasing the percentage of the turbulent component. This behavior is verified both for propagating and standing waves. Even in the fully turbulent case, a mechanism qualitatively similar to phase mixing occurs: it actively generates small scales together with the nonlinear cascade, thus providing the shortest dissipative time. General considerations are given to identify this regime in the parameter space. The turbulent perturbation also distorts the background density, locally increasing the Alfv\'en velocity gradient and further contributing to accelerating the formation of small scales. Our campaign of simulations is relevant for the coronal plasma where Reynolds and Lundquist numbers are extremely high. For sufficiently low perturbation amplitudes, phase mixing and turbulence work synergically, speeding up the dissipation of the perturbation energy: phase mixing dominates at early times and nonlinear effects at later times. We find that the dissipative time is shorter than those of phase mixing and the nonlinear cascade when individually considered.

The Long-Term Agroecosystem Research cropland common experiment at Northern Plains.

Cropland agriculture in the northern Great Plains is challenged by variable weather, agricultural intensification, and competing use for energy development. Innovative cropland practices that address these challenges are needed to ensure regional agriculture can sustainably meet future food, fuel, and fiber demand. In response to this need, the Northern Plains Long-Term Agroecosystem Research Network site established a cropland experiment in 2019 that contrasts prevailing and alternative practices at plot and field scales over a proposed 30-year time frame. The experimental site is located on the Area IV Soil Conservation Districts Cooperative Research Farm near Mandan, ND. Cropping practices for the first 6 years of the experiment were developed with input from stakeholders and include a 3-year crop rotation of spring wheat (Triticum aestivum L.), corn (Zea mays L.), and soybean (Glycine max L.) with cover crops (alternative practice) and without (prevailing practice). The prevailing practice also involves the removal of crop residue, while a second alternative practice of perennial forages is included in the plot-scale experiment. Biophysical measurements are made at both spatial scales at frequencies aligned with approved methods for each agronomic and environmental metric. Findings from the first 6 years of the experiment will help identify tradeoffs associated with cover crop use and residue removal in dryland cropping systems. In the future, the experiment will adopt a knowledge co-production approach whereby researchers and stakeholders will work collaboratively to identify problems, implement research, and interpret results.

Environmental filtering mediates desert ant community assembly at two spatial scales.

Understanding the mechanisms that maintain species coexistence and determine patterns of community assembly are fundamental goals of ecology. Quantifying the relationship between species traits and stress gradients is a necessary step to disentangle assembly processes and to be able to predict the outcome of environmental change. We examined the hypothesis that desert ant communities are assembled by niche-based processes i.e., environmental filtering and limiting similarity. First, we used population-level morphological trait measurements to study the functional structure of ant communities along a dryland environmental stress gradient. Second, we developed species distribution models for each species to quantify large-scale climatic niche overlap between species. Body, femur, antennal scape, and head lengths were correlated with environmental gradients. Regionally, the ant community was significantly and functionally overdispersed in terms of morphological traits which suggests the importance of competition to ant community structure. Ant community assembly was also strongly influenced by environmental factors as the degree of functional trait divergence, but not phylogenetic divergence, decreased with increasing environmental stress. Thus, environmental stress likely mediates limiting similarity in these desert ecosystems. Species with lower climatic niche overlap were more dissimilar in morphological traits. This suggests that environmental filtering on ant functional traits is important at the scale of species distributions in addition to regional scales. This study shows that environmental and biotic filtering (i.e., niche-based assembly mechanisms) are jointly and non-independently structuring the ant community.

Monitoring Volcanic Plumes and Clouds Using Remote Sensing: A Systematic Review

Volcanic clouds pose significant threats to air traffic, human health, and economic activity, making early detection and monitoring crucial. Accurate determination of eruptive source parameters is crucial for forecasting and implementing preventive measures. This review article aims to identify the most common remote sensing methods for monitoring volcanic clouds. To achieve this, we conducted a systematic literature review of scientific articles indexed in the Web of Science database published between 2010 and 2022, using multiple query strings across all fields. The articles were reviewed based on research topics, remote sensing methods, practical applications, case studies, and outcomes using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our study found that satellite-based remote sensing approaches are the most cost-efficient and accessible, allowing for the monitoring of volcanic clouds at various spatial scales. Brightness temperature difference is the most commonly used method for detecting volcanic clouds at a specified temperature threshold. Approaches that apply machine learning techniques help overcome the limitations of traditional methods. Despite the constraints imposed by spatial and temporal resolution and optical limitations of sensors, multiplatform approaches can overcome these limitations and improve accuracy. This study explores various techniques for monitoring volcanic clouds, identifies research gaps, and lays the foundation for future research.

Trends in prescribed fire weather windows from 2000 to 2022 in California

As increasing wildfire activity puts pressure on wildland fire suppression resources both nationally and within the state of California, further development of programs and infrastructure that emphasize preventative fuels treatments, e.g. prescribed burning, is critical for mitigating the impacts of wildfire at large spatial scales. Among many factors that limit the use of prescribed fire, weather and fuel moisture conditions are among the most critical. We analyzed a 2-km gridded hourly surface weather dataset over a 23-yr period to explore the relationship between climatological trends and prescribed fire weather windows. Pairing this dataset with burn prescription parameters provided by experienced regional fire practitioners, we seek to identify the timing and extent of changes in weather-related opportunities for prescribed fire in two distinct geographic regions within California. We found an increasing trend in opportunities for prescribed fire use in Sonoma County, a representative coastal Mediterranean region of CA, and a decreasing trend in Plumas County, a montane region that extends through the Northern Sierra Nevada. Seasonally, we see more nuances—increased winter opportunities in both counties, as well as increased summer opportunities in Sonoma. Most notably, we see great variation spatially in the occurrence of suitable weather windows for prescribed burning. Fire management resource availability and air quality regulations further constrain burn windows. We observed a greater influence of these factors in Sonoma County vs. Plumas. Resource availability is the greatest constraint in the Summer and Fall, during wildfire season, and air quality regulations are a greater constraint in the Winter. Our findings provide information to decision-makers and regulators at the county and other government levels to more effectively support use of prescribed fire to achieve land management and fuels reduction goals.

Evaluation of Essential Dynamics and Fixed-Length Coarse Graining for Multidomain Proteins.

For multiscale modeling of biomolecules, reliable coarse-grained (CG) models can offer great potential to simulate larger temporal and spatial scales than traditional all-atom (AA) models. In this study, we explore the essential dynamics coarse graining (EDCG) and fixed-length coarse graining (FLCG) approaches for constructing highly coarse-grained models for multidomain proteins (MDPs), with 1 to 10 amino acid residues per CG site. In the studies of 13 MDPs, our data indicate that both EDCG and FLCG can preserve the protein dynamics of MDPs. FLCG, which restricts an equal number of residues in each CG site, represents an excellent approximation to EDCG and a straightforward approach for coarse-graining MDPs. Furthermore, FLCG is tested with a class B G-protein-coupled receptor protein, and the agreement with prior experiments suggests its general application to various MDPs in different environments or conditions. Finally, we demonstrate another application of FLCG through progressive backmapping, showcasing the ability to recover from lower-resolution CG models (6 residues/CG site) to higher-resolution ones (1 residue/CG site). These promising outcomes underscore the broad applicability of FLCG to construct highly or ultra-coarse-grained models of complex biomolecules for multiscale simulations.

Estimating scale-specific and localized spatial patterns in allele frequency.

Characterizing spatial patterns in allele frequencies is fundamental to evolutionary biology because these patterns contain evidence of underlying processes. However, the spatial scales at which gene flow, changing selection, and drift act are often unknown. Many of these processes can operate inconsistently across space, causing non-stationary patterns. We present a wavelet approach to characterize spatial pattern in allele frequency that helps solve these problems. We show how our approach can characterize spatial patterns in relatedness at multiple spatial scales, i.e. a multi-locus wavelet genetic dissimilarity. We also develop wavelet tests of spatial differentiation in allele frequency and quantitative trait loci (QTL). With simulation we illustrate these methods under different scenarios. We also apply our approach to natural populations of Arabidopsis thaliana to characterize population structure and identify locally-adapted loci across scales. We find, for example, that Arabidopsis flowering time QTL show significantly elevated genetic differentiation at 300 to 1300 km scales. Wavelet transforms of allele frequencies offer a flexible way to reveal geographic patterns and underlying evolutionary processes.

Multi-scale, open-system magmatic and sub-solidus processes contribute to the chemical and isotopic characteristics of the Jurassic Guadalupe Igneous Complex, Sierra Nevada, California, USA

The chemical and isotopic characteristics of a solidified pluton represent the integration of magmatic and sub-solidus processes operating across a range of spatial and temporal scales during pluton construction, crystallization, and cooling. Disentangling these processes and understanding where chemical and isotopic signatures were acquired requires the combination of multiple tools tracing processes at different time and length scales. We combine whole-rock oxygen and Sr-Nd isotopes, zircon oxygen isotopes and trace elements, and mineral compositions with published high-precision U-Pb zircon geochronology to evaluate differentiation within the bimodal Guadalupe Igneous Complex, Sierra Nevada, California (USA). The complex was constructed in ~300 k.y. between 149 and 150 Ma. Felsic magmas crystallized as centimeter- to meter-sized segregations in gabbros in the lower part of the complex and as granites and granophyres structurally above the gabbros. A central mingling zone separates the mafic and felsic units. Pluton-wide δ18O(whole-rock), δ18O(zircon), and Sr-Nd isotopic ranges are too large to be explained by in situ, closed-system differentiation, instead requiring open-system behavior at all scales. Low δ18O(whole-rock) and δ18O(zircon) values indicate assimilation of hydrothermally altered marine host rocks during ascent and/or emplacement. In situ differentiation processes operated on a smaller scale (meters to tens of meters) for at least ~200 k.y. via (1) percolation and segregation of chemically and isotopically diverse silicic interstitial melt from a heterogeneous gabbro mush; (2) crystal accumulation; and (3) sub-solidus, high-temperature, hydrothermal alteration at the shallow roof of the complex to modify the chemical and isotopic characteristics. Whole-rock and mineral chemistry in combination with geochronology allows deciphering open-system differentiation processes at the outcrop to pluton scale from magmatic to sub-solidus temperatures over time scales of hundreds of thousands to millions of years.

DeepDive: estimating global biodiversity patterns through time using deep learning

Understanding how biodiversity has changed through time is a central goal of evolutionary biology. However, estimates of past biodiversity are challenged by the inherent incompleteness of the fossil record, even when state-of-the-art statistical methods are applied to adjust estimates while correcting for sampling biases. Here we develop an approach based on stochastic simulations of biodiversity and a deep learning model to infer richness at global or regional scales through time while incorporating spatial, temporal and taxonomic sampling variation. Our method outperforms alternative approaches across simulated datasets, especially at large spatial scales, providing robust palaeodiversity estimates under a wide range of preservation scenarios. We apply our method on two empirical datasets of different taxonomic and temporal scope: the Permian-Triassic record of marine animals and the Cenozoic evolution of proboscideans. Our estimates provide a revised quantitative assessment of two mass extinctions in the marine record and reveal rapid diversification of proboscideans following their expansion out of Africa and a >70% diversity drop in the Pleistocene.

Quantifying marine larval dispersal to assess MPA network connectivity and inform future national and transboundary planning efforts

A marine protected area (MPA) network of multiple reserves can protect biodiversity across space, but to be effective, network configuration should support dispersal among MPAs as well as spillover to unprotected habitats. The ability of MPAs to function as an interacting network of populations connected by dispersal, however, is difficult to estimate at broad spatial scales, and therefore, connectivity is often not integrated in MPA design. Here, we simulate passive larval dispersal using a biophysical model to estimate potential transboundary network connectivity of MPAs in western Canada and United States. Drift time was varied to represent generic functional groups of nearshore species with planktonic larvae and sedentary adult stages. We found that MPAs potentially act as an interacting network and meet connectivity design criteria for nearshore invertebrate species, many MPAs (65%–90%) possibly exchange individuals, and a third of larvae from MPAs (25%–32%) source areas of the unprotected coast. This analysis provides a first approximation of multispecies connectivity to inform ongoing transboundary MPA design, and it can be used as a foundation for future model development.

Aerosols in the central Arctic cryosphere: satellite and model integrated insights during Arctic spring and summer

Abstract. The central Arctic cryosphere is influenced by the Arctic amplification (AA) and is warming faster than the lower latitudes. AA affects the formation, loss, and transport of aerosols. Efforts to assess the underlying processes determining aerosol variability are currently limited due to the lack of ground-based and space-borne aerosol observations with high spatial coverage in this region. This study addresses the observational gap by making use of total aerosol optical depth (AOD) datasets retrieved by the AEROSNOW algorithm over the vast cryospheric region of the central Arctic during Arctic spring and summer. GEOS-Chem (GC) simulations combined with AEROSNOW-retrieved data are used to investigate the processes controlling aerosol loading and distribution at different temporal and spatial scales. For the first time, an integrated study of AOD over the Arctic cryosphere during sunlight conditions was possible with the AEROSNOW retrieval and GC simulations. The results show that the spatial patterns observed by AEROSNOW differ from those simulated by GC. During spring, which is characterized by long-range transport of anthropogenic aerosols in the Arctic, GC underestimates the AOD in the vicinity of Alaska in comparison with AEROSNOW retrieval. At the same time, it overestimates the AOD along the Bering Strait, northern Europe, and the Siberian central Arctic sea-ice regions, with differences of −12.3 % and 21.7 %, respectively. By contrast, GC consistently underestimates AOD compared with AEROSNOW in summer, when transport from lower latitudes is insignificant and local natural processes are the dominant source of aerosol, especially north of 70° N. This underestimation is particularly pronounced over the central Arctic sea-ice region, where it is −10.6 %. Conversely, GC tends to overestimate AOD along the Siberian and Greenland marginal sea-ice zones by 19.5 % but underestimates AOD along the Canadian Archipelago by −9.3 %. The differences in summer AOD between AEROSNOW data products and GC-simulated AOD highlight the need to integrate improved knowledge of the summer aerosol process into existing models in order to constrain its effects on cloud condensation nuclei, on ice nucleating particles, and on the radiation budget over the central Arctic sea ice during the developing AA period.

Continuous Plant-Based and Remote Sensing for Determination of Fruit Tree Water Status

Climate change poses significant challenges to agricultural productivity, making the efficient management of water resources essential for sustainable crop production. The assessment of plant water status is crucial for understanding plant physiological responses to water stress and optimizing water management practices in agriculture. Proximal and remote sensing techniques have emerged as powerful tools for the non-destructive, efficient, and spatially extensive monitoring of plant water status. This review aims to examine the recent advancements in proximal and remote sensing methodologies utilized for assessing the water status, consumption, and irrigation needs of fruit tree crops. Several proximal sensing tools have proved useful in the continuous estimation of tree water status but have strong limitations in terms of spatial variability. On the contrary, remote sensing technologies, although less precise in terms of water status estimates, can easily cover from medium to large areas with drone or satellite images. The integration of proximal and remote sensing would definitely improve plant water status assessment, resulting in higher accuracy by integrating temporal and spatial scales. This paper consists of three parts: the first part covers current plant-based proximal sensing tools, the second part covers remote sensing techniques, and the third part includes an update on the on the combined use of the two methodologies.

A JVLA, LOFAR, e-Merlin, VLBA, and EVN study of RBS 797: can binary supermassive black holes explain the outburst history of the central radio galaxy?

The multifaceted central radio galaxy of the cluster RBS 797 shows several episodes of jet activity in multiple directions. We wish to understand the causes behind these dramatic misalignments and measure the timescales of the successive outbursts. We present a multifrequency (144 MHz -- 9 GHz) and multiscale (5 pc -- $50$ kpc) investigation of the central radio galaxy in RBS 797, by means of JVLA, LOFAR (with international stations), e-Merlin, VLBA, and EVN data. We investigate the morphological and spectral properties of the radio lobes, the jets, and the active core. We confirm the co-spatiality of the radio lobes with the four perpendicular X-ray cavities previously discovered. The radiative ages of the east-west lobes ($31.4 Myr) and of the north-south lobes ($32.1 Myr) support a coeval origin of the perpendicular outbursts, which also have similar active phase duration (sim 12 Myr). Based on the analysis of the inner north-south jets (on scales of $ kpc), we (a) confirm the S-shaped jet morphology; (b) show the presence of two hotspots per jet with a similar spectral index; and (c) estimate the age of the twisting north-south jets to be less than $ Myr. Based on these results, we determine that jet precession, with a period sim 9 Myr, half-opening angle sim circ $, and jet advance speed sim 0.01$c$, can explain the properties of the north-south jets. We also find that the synchrotron injection index has steepened from the large, older outbursts ($ to the younger S-shaped jets ($ possibly due to a transition from FR I-like to FR II-like activity. The e-Merlin, VLBA, and EVN data reveal a single, compact core at the heart of RBS 797, surrounded by extended radio emission whose orientation depends on the spatial scale sampled by the data. We explore several engine-based scenarios to explain these results. Piecing together the available evidence, we argue that RBS 797 likely hosts (or hosted) binary active supermassive black holes (SMBHs). The detection of a single component in the VLBA and EVN data is still consistent with this interpretation, since the predicted separation of the binary SMBHs (leq 0.6 pc) is an order of magnitude smaller than the resolution of the available radio data (5 pc).

Landscape drives zoonotic malaria prevalence in non-human primates.

Zoonotic disease dynamics in wildlife hosts are rarely quantified at macroecological scales due to the lack of systematic surveys. Non-human primates (NHPs) host Plasmodium knowlesi, a zoonotic malaria of public health concern and the main barrier to malaria elimination in Southeast Asia. Understanding of regional P. knowlesi infection dynamics in wildlife is limited. Here, we systematically assemble reports of NHP P. knowlesi and investigate geographic determinants of prevalence in reservoir species. Meta-analysis of 6322 NHPs from 148 sites reveals that prevalence is heterogeneous across Southeast Asia, with low overall prevalence and high estimates for Malaysian Borneo. We find that regions exhibiting higher prevalence in NHPs overlap with human infection hotspots. In wildlife and humans, parasite transmission is linked to land conversion and fragmentation. By assembling remote sensing data and fitting statistical models to prevalence at multiple spatial scales, we identify novel relationships between P. knowlesi in NHPs and forest fragmentation. This suggests that higher prevalence may be contingent on habitat complexity, which would begin to explain observed geographic variation in parasite burden. These findings address critical gaps in understanding regional P. knowlesi epidemiology and indicate that prevalence in simian reservoirs may be a key spatial driver of human spillover risk.