Spatial Scales Research Articles

Environmental heterogeneity caused by large-scale cultivation of Pyropia haitanensis shapes multi-group biodiversity distribution in coastal areas

The response of marine biodiversity to mariculture has long been a research focus in marine ecology. However, the effects of seaweed cultivation on biological community assembly are poorly understood, especially in diverse communities with distinct ecological characteristics. In this study, we used environmental DNA metabarcoding to investigate the spatial distribution patterns of bacterial, protistan, and metazoan diversity, aiming to reveal the mechanisms of community assembly in the Pyropia haitanensis cultivation zone along the Fujian coast, China. We found that, compared with the biological communities in control zones, those in P. haitanensis cultivation zones exhibited stronger geographic distance-decay patterns and displayed more complex and stable network structures. Deterministic processes (environmental selection) played a more important role in the assembly of bacterial, protistan, and metazoan communities in P. haitanensis cultivation zones, especially metazoan communities. Variance partitioning analysis showed that environmental variables made greater contributions to the diversity of the three types of communities within the P. haitanensis cultivation zones than in the control zones. Partial least squares path modeling analysis identified nitrate‑nitrogen (NO3-N), pH, particulate organic carbon (POC), and dissolved organic carbon (DOC) as the key environmental variables affecting biodiversity. Overall, the environmental heterogeneity caused by the large-scale cultivation of P. haitanensis could be the crucial factor influencing the composition and structure of various biological communities. Our results highlight the importance of the responses of multi-group organisms to the cultivation of seaweed, and provide insights into the coexistence patterns of biodiversity at the spatial scale.

A framework for multivariate analysis of compound extremes based on correlated hydrologic time series

While hydrologic design has primarily relied on use of annual maxima values, many events of hydrologic interest such as active and break spells in monsoonal rains, heat waves, flash flooding from snowmelt, etc. manifest at time scales of days to weeks, and require daily (or finer resolution) data for proper characterization. Often combinations of several hydrologic variables herald highly impactful events and are labelled as compound extremes. Using a time-varying interval-censored estimation method of copula models, a novel multivariate approach for dealing with compound extremes under temporal dependence amongst variables and when data contain significant ties is developed here to enable the determination of design magnitudes and associated risk. The efficacy of this method is demonstrated over the Godavari River Basin, India, using daily precipitation and temperature data from a recent period (1977 to 2020) during the monsoon season. A conservative approach is proposed for estimating the design magnitudes of hydrologic variables in multivariate settings. The significance of ties and temporal dependence amongst precipitation and temperature data in estimation of design magnitudes of cold-wet compound extremes at specified probabilities of exceedance is explored at various spatial scales. Ties and temporal dependence are both shown to have a profound influence on design estimates. Since ties and temporal variation in dependence amongst hydrologic variables are ubiquitous features of most hydrologic data, this framework would be applicable for characterization of other compound extremes in hydrology.

Climate- and scale-dependent sex ratio in threatened Araucaria forests

Male and female dioecious trees often differ in their sensitivity to environmental stress, resulting in unbalanced sex ratios that can affect forest regeneration. Dioecious species are thus especially vulnerable to climate change, which can intensify their sexual bias. We investigated the sex ratio in endangered pehuén (Araucaria araucana) forests to determine whether there is an imbalance in ratio at different spatial scales, and if so, whether this is climate-dependent. To assess deviations in sex ratios on local and regional scales, we quantified the proportion of males along 37 transects within 11 patches in two populations. To assess the sex–climate relationship, we used WorldClim data; for the biogeographic scale, we added published sex ratio information from other populations. We found a bias towards males both locally and regionally. On a local scale, this bias was related to heterogeneous temperature conditions, especially in patches with particularly wet or hot summers. On a biogeographic scale, this bias increased in warmer, humid and thermally more homogeneous conditions. The male bias we found implies a direct reduction in the effective size of these already threatened forests. Considering climate change projections for Patagonia, predicting an increase in temperatures and their anomalies, our results suggest that males would be less affected than females; this points to an intensification of the male bias and thus to increased vulnerability of these already endangered pehuén forests. This study provides evidence of climate-dependent sex bias in threatened dioecious forests and discusses its importance in the context of climate change.

Sharing the burden: Cabbage stem flea beetle pest pressure and crop damage are lower in rapeseed fields surrounded by other rapeseed crops

The cabbage stem flea beetle (Psylliodes chrysocephala) is a significant pest of rapeseed (Brassica napus). Feeding by adult P. chrysocephala can cause severe leaf damage and larval infestation can reduce stem strength, both of which impact crop growth and development, causing substantial yield losses and economic damage. The structure of the agricultural landscape can regulate herbivorous pest populations through top-down and bottom-up processes. This has shown promise in regulating the populations of other herbivorous pests, but remains relatively unexplored for P. chrysocephala. Here we investigate how the structure of the agricultural landscape influences P. chrysocephala abundance (pest pressure) and associated crop damage. We also examine the effect of the landscape on natural enemies and their ability to regulate P. chrysocephala populations. We show that P. chrysocephala populations are primarily regulated through bottom-up processes. We identify adjacency to another rapeseed crop and the total proportion of rapeseed grown in the landscape as key factors influencing beetle pressure, crop damage, and larval infestation, but find no effect of host crop proportions grown in the previous year at the examined scales up to 1 km surrounding focal crops. We also observe positive effects of crop heterogeneity and semi-natural habitat proportions on natural enemy abundance and diversity; however, these increases had no direct impact on P. chrysocephala. Bottom-up processes appear to contribute to herbivorous pest regulation by diluting beetles in the landscape, and could represent an important mechanism for sustainably managing pest populations by adapting the proportions and neighbourhoods of rapeseed crops at small to large spatial scales.

Greenness of moss biocrusts derived from RGB image is a reliable indicator to estimate biocrust carbon-fixation rates in drylands

Biocrusts are common living covers found across drylands worldwide, and their photosynthesis substantially contributes to carbon input in these ecosystems. However, direct monitoring of biocrusts’ photosynthetic carbon fixation is challenging due to their scattered distribution, dark pigments, and lower CO2 exchange rate. Current studies have limited monitoring frequency and the results are difficult to extend to a broader spatial scale. Greenness typically functions as an indicator of both vegetation cover and plant photosynthesis. Monitoring biocrust greenness is expected to be able to provide insights into their environmental responses and estimate carbon fixation rates, particularly for moss biocrusts. Here, we monitored greenness, soil moisture and temperature, and gross primary productivity (GPP) of moss biocrusts, as well as climatic factors over two years in the northern China’s Loess Plateau. The results indicated that moss biocrust greenness was highly sensitive to changing environmental conditions and exhibited significant temporal variability. Moss biocrust greenness exhibited a notably higher value in wet season (0.384) than in dry season (0.259). According to these greenness values, we proposed moss biocrust activity and classified it into high (>0.403), medium (0.334–0.403), low (0.271–0.333), and dormancy (<0.270). Soil moisture, relative humidity, and wind speed were the three main determinates of moss biocrust greenness. Higher soil moisture and relative humidity were linked to increased greenness, whereas elevated wind speed led to a reduction. In dry seasons, soil moisture contributed 23.4 % to the variation in moss biocrust greenness, while in wet seasons relative humidity contributed 16.1 %. A significant and positive correlation between moss biocrust greenness and GPP was found, and annual GPP prediction based on greenness using machine learning performed well. Our study highlights the significance of moss biocrust greenness and their potential as indicators for moss biocrust photosynthetic carbon fixation rates and possibly other ecological functions in drylands.

Effects of the freeze–thaw process on sources and pathways of subsurface flow in an alpine hillslope on the northeastern Qinghai-Tibet Plateau

The impact of the freeze–thaw process on the active layer is reflected in the changed subsurface flow (SSF) process in cold alpine regions. Identifying sources and pathways of SSF in the freeze–thaw process is critical but difficult, and the related dominant factors and mechanisms are still unknown. In this paper, the effective identification and analysis of SSF are promoted based on field sampling data from the thawing (June) to freezing (September) period of 2022 in the Qinghai Lake basin on the northeastern Qinghai-Tibetan Plateau. By the proposed method with a high sampling frequency and refined sampling spatial scale, the sources and pathways of SSF are clearly identified. The results are as follows: (1) The soil temperature is considered the most fundamental factor affecting the SSF pathways, it influences water infiltration to the deep layer and the effect is extended to the saprolite and weathered bedrock layers. (2) Thawing promotes water to infiltrating into deep layer. 30 cm soil water contributes the most to SSF (2 %–86 %) in the thawing period, while the contribution difference of the water from the 30 cm, 60 cm, and 90 cm layers is small (ranging from 32 %–33 %, 24 %–26 %, and 32 %–35 %, respectively) in the thawed period. (3) Meanwhile, the soil water from different slope positions contribute differently to SSF, and the SSF from deep soil layer is transit in prolong paths and depths. It is caused by the out-of-sync water transit process in the hillslope. With continuing climate warming, we propose that the differences in the water sources of SSF across soil layers may decrease, while the differences in the transit processes of SSF across soil layers may increase.

Geographical match of objective and subjective measures of well-being at an intra-city scale

Despite human well-being serving as a crucial indicator for measuring social development and progress, the measurement of well-being faces great challenges. This study constructs a multi-level statistical model to investigate the geographical relationship between subjective well-being measure derived from a large-scale social survey and objective well-being measure derived by using a non-compensatory indicator aggregation technique and a variety of urban geospatial data. We find that a consistent resemblance between the subjective and objective measures of well-being at both the community (r = 0.61 with a p < 0.01) and sub-district scales (r = 0.62 with a p < 0.01) within a city. This finding is further supported by the estimation results from models that controls for a variety of covariate effects. Drawing on the close resemblance between the subjective and objective measures of human well-being, we develop a statistical model that links these two measurements to create a composite measure of well-being. Our results extend the understanding on the geographical match between subjective and objective measures of well-being at an intra-city spatial scale, and provide a new insight on how to leverage the emerging urban geospatial data and social survey when devising indicators of well-being.

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.