Small-scale Variability Research Articles

The spatial distribution of tree-tree interaction effects on soil microbial biomass and respiration.

The capacity of forests to sequester carbon in both above- and belowground compartments is a crucial tool to mitigate rising atmospheric carbon concentrations. Belowground carbon storage in forests is strongly linked to soil microbial communities that are the key drivers of soil heterotrophic respiration, organic matter decomposition and thus nutrient cycling. However, the relationships between tree diversity and soil microbial properties such as biomass and respiration remain unclear with inconsistent findings among studies. It is unknown so far how the spatial configuration and soil depth affect the relationship between tree richness and microbial properties. Here, we studied the spatial distribution of soil microbial properties in the context of a tree diversity experiment by measuring soil microbial biomass and respiration in subtropical forests (BEF-China experiment). We sampled soil cores at two depths at five locations along a spatial transect between the trees in mono- and hetero-specific tree pairs of the native deciduous species Liquidambar formosana and Sapindus saponaria. Our analyses showed decreasing soil microbial biomass and respiration with increasing soil depth and distance from the tree in mono-specific tree pairs. We calculated belowground overyielding of soil microbial biomass and respiration - which is higher microbial biomass or respiration than expected from the monocultures - and analysed the distribution patterns along the transect. We found no general overyielding across all sampling positions and depths. Yet, we encountered a spatial pattern of microbial overyielding with a significant microbial overyielding close to L. formosana trees and microbial underyielding close to S. saponaria trees. We found similar spatial patterns across microbial properties and depths that only differed in the strength of their effects. Our results highlight the importance of small-scale variations of tree-tree interaction effects on soil microbial communities and functions and are calling for better integration of within-plot variability to understand biodiversity-ecosystem functioning relationships.

Technical note: Challenges in detecting free tropospheric ozone trends in a sparsely sampled environment

Abstract. High-quality long-term observational records are essential to ensure appropriate and reliable trend detection of tropospheric ozone. However, the necessity of maintaining high sampling frequency, in addition to continuity, is often under-appreciated. A common assumption is that, so long as long-term records (e.g., a span of a few decades) are available, (1) the estimated trends are accurate and precise, and (2) the impact of small-scale variability (e.g., weather) can be eliminated. In this study, we show that the undercoverage bias (e.g., a type of sampling error resulting from statistical inference based on sparse or insufficient samples, such as once-per-week sampling frequency) can persistently reduce the trend accuracy of free tropospheric ozone, even if multi-decadal time series are considered. We use over 40 years of nighttime ozone observations measured at Mauna Loa, Hawaii (representative of the lower free troposphere), to make this demonstration and quantify the bias in monthly means and trends under different sampling strategies. We also show that short-term meteorological variability remains a cause of an inflated long-term trend uncertainty. To improve the trend precision and accuracy due to sampling bias, two remedies are proposed: (1) a data variability attribution of colocated meteorological influence can efficiently reduce estimation uncertainty and moderately reduce the impact of sparse sampling, and (2) an adaptive sampling strategy based on anomaly detection enables us to greatly reduce the sampling bias and produce more accurate trends using fewer samples compared to an intense regular sampling strategy.

Enrichment of prime-edited mammalian cells with surrogate PuroR reporters

Prime editing is a programmable genetic method that can precisely generate any desired small-scale variations in cells without requiring double-strand breaks and DNA donors. However, higher editing efficiency is greatly desirable for wide practical applications. In this study, we developed a target-specific prime editing reporter (tsPER) and a universal prime editing reporter (UPER) to facilitate rapid selection of desired edited cells through puromycin screening. The modification efficiency of HEK3_i1CTT_d5G in HEK293T cells improved from 36.37 % to 64.84 % with the incorporation of tsPER. The target sequence of interested genes could be custom inserted into a selection cassette in tsPER to establish personalized reporters. The UPER demonstrated PE3 editing efficiency up to 74.49 % on HEK3_i1CTT_d5G and 73.52 % on HEK3_i1His6, achieved through co-selection with an additional pegRNA (puro) to repair the mutant PuroR cassette. Overall, tsPER and UPER robustly improved the efficiency of prime editing. Both of these approaches expand enrichment strategies for genomically modified cells and accelerate the generation of genetically modified models.

Two-dimensional imaging of elastic properties of rock core samples from measurements of angle-dependent ultrasonic reflection coefficients

Laboratory and field measurements often fail to identify small-scale variations in rock elastic properties. Due to limited spatial resolution, conventional laboratory methods cannot properly describe rock formations exhibiting a high degree of heterogeneity, thereby masking differences between stiff and compliant layers. Continuous sample measurements can mitigate this problem but are not widely used by the industry. We build upon our previous work and apply laboratory angle-dependent ultrasonic reflection coefficient (ADURC) measurements to achieve detailed two-dimensional descriptions of the elastic properties of complex rock samples. This method successfully yields high-resolution information on P- and S-wave velocities, as well as bulk density, across the surface of rock samples. Elastic properties are estimated using a nonlinear inversion algorithm that matches laboratory measurements with numerical simulations. ADURC data acquired at various sample locations enable detailed rock descriptions, where the effective measurement area is determined by the size of the receiver, measurement frequency, and incidence angle. Consequently, the sampling area is smaller compared with triaxial loading and acoustic transmission tests, for which the resolution is controlled by sample size. Measurements conducted on samples exhibiting different levels of spatial complexity validate the capability of the ADURC method to identify small-scale heterogeneities. For the reported experiments, variations in angle-dependent reflectivity give rise to corresponding variations in the estimated P- and S-wave velocities and density, which can exceed 60%. These small-scale variations across heterogeneous rock samples often are overlooked by conventional laboratory methods.

The turbulent variability of accretion discs observed at high energies

ABSTRACT We use numerical stochastic-viscosity hydrodynamic simulations and new analytical results from thin disc theory to probe the turbulent variability of accretion flows, as observed at high energies. We show that the act of observing accretion discs in the Wien tail exponentially enhances small-scale temperature variability in the flow, which in a real disc will be driven by magnetohydrodynamic turbulence, to large-amplitude luminosity fluctuations (as predicted analytically). In particular, we demonstrate that discs with more spatially coherent turbulence (as might be expected of thicker discs), and relativistic discs observed at larger inclinations, show significant enhancement in their Wien tail variability. We believe that this is the first analysis of relativistic viewing angle effects on turbulent variability in the literature. Using these results, we argue that tidal disruption events represent particularly interesting systems with which to study accretion flow variability, and may in fact be the best astrophysical probes of small-scale disc turbulence. This is a result of a typical tidal disruption event disc being naturally observed in the Wien tail and likely having a somewhat thicker disc and cleaner X-ray spectrum than other sources. We argue for dedicated X-ray observational campaigns of tidal disruption events, with the aim of studying accretion flow variability.

Characterization of hypoxic events in Paracas bay (Peru, 13.8°S) through intensity and biological effect indexes

Over the past decade, there has been a significant increase in low oxygen conditions within marine coastal areas, profoundly impacting ecosystem processes and living coastal resources. Coastal bays in highly productive upwelling regions, where hypoxia occurs naturally, are special areas affected by both local and adjacent shelf-related processes. Paracas Bay (13.8°S) is a traditional shellfish fishing and intense farming area highly influenced by one of the most active upwelling centers of the Peruvian coast. Despite the small dimensions of the bay (35 km2), a key feature is its complex physical dynamics and high environmental variability. Recently, important efforts have been made in the study of both the spatial and temporal oxygen concentration variability, nevertheless, information regarding the ecological and biological impact of hypoxic events is still lacking. In this study, the spatial and temporal distribution of hypoxic events was analyzed across Paracas bay at different depths by means of high-frequency hourly dissolved oxygen records collected by data-loggers deployed across the bay during the periods September 2012 – February 2013 and March 2015 – February 2017. To study the ecological impact of hypoxic events, we developed a hypoxia intensity index, while the biological impact was studied through the development of a hypoxia biological effect index using as model species the Peruvian scallop (Argopecten purpuratus). Our results showed that hypoxic events have an intrinsic variability across the bay. The deeper areas of the bay, towards the northwest and center, were characterized by long, intense, and lethal events, while the southeast and southwest, shallower areas, were characterized by shorter events of low intensity and either sublethal or innocuous. We propose that the observed variability is not only related to the large-scale environmental context in which the events occurred, but also to small-scale variability linked to local circulation, biological activity, and sediment biogeochemistry. We expect that our research will be useful not only for scientific purposes, but also for coastal resource management and aquaculture, underlining the importance of developing high-resolution oxygen monitoring systems in coastal bays.

Small-scale variation of atmospheric dynamics applying chaos theory, case study

Characterization and knowledge of the variability of atmospheric dynamics on a small scale in the city of Riobamba, Ecuador, are achieved through the chaos theory. Meteorological data is taken every hour during four years, including variables such as wind speed, wind direction, incident radiation, temperature, and humidity, from the ESPOCH, SAN JUAN, and QUIMIAG weather stations in the canton of Riobamba. The van Ulden and Hostlang models are used to calculate the Obukhov length, surface heat fluxes, and latent heat flux. The chaos theory is applied to study the variation of atmospheric microdynamics. The Lyapunov coefficients, Kolmogorov-Sinai entropy, and Kaplan-Yorke fractal dimension are determined. Before analysis, noise reduction is necessary due to the lack of correlation, especially in the Obukhov length. This research follows a longitudinal design and employs quantitative and explanatory methods based on data analysis, statistical-mathematical techniques, and inductive-deductive approaches. The results indicate a highly variable system, reflected in a high number of Lyapunov coefficients, fractional dimensions, and entropy variations. The microdynamic parameters exhibit hyperchaotic behavior, as indicated by the presence of more than one positive Lyapunov coefficient. The variables also demonstrate a fractional fractal dimension, highlighting the irregularity in the geometric representation of the system.

Snow mechanical property variability at the slope scale – implication for snow mechanical modelling

Abstract. Snow avalanches represent a natural hazard to infrastructure and backcountry recreationists. Risk assessment of avalanche hazard is difficult due to the sparse nature of available observations informing on snowpack mechanical and geophysical properties and overall stability. The spatial variability of these properties also adds complexity to decision-making and route finding in avalanche terrain for mountain users. Snow cover models can simulate snow mechanical properties with good accuracy at fairly good spatial resolution (around 100 m). However, monitoring small-scale variability at the slope scale (5–50 m) remains critical, since slope stability and the possible size of an avalanche are governed by that scale. To better understand and estimate the spatial variability at the slope scale, this work explores links between snow mechanical properties and microtopographic indicators. Six spatial snow surveys were conducted in two study areas across Canada. Snow mechanical properties, such as snow density, elastic modulus and shear strength, were estimated from high-resolution snow penetrometer (SMP) profiles at multiple locations over several studied slopes, in Rogers Pass, British Columbia, and Mt. Albert, Québec. Point snow stability metrics, such as the skier crack length, critical propagation crack length and a skier stability index, were derived using the snow mechanical properties from SMP measurements. Microtopographic indicators, such as the topographic position index (TPI), vegetation height and proximity, wind-exposed slope index, and potential radiation index, were derived from unoccupied aerial vehicle (UAV) surveys with sub-metre resolution. We computed the variogram and the fractal dimension of the snow mechanical properties and stability metrics and compared them. The comparison showed some similarities in the correlation distances and fractal dimensions between the slab thickness and the slab snow density and also between the weak layer strength and the stability metrics. We then spatially modelled snow mechanical properties, including point snow stability, using spatial generalized additive models (GAMs) with microtopographic indicators as covariates. The use of covariates in GAMs suggested that microtopographic indicators can be used to adequately estimate the variation in the snow mechanical properties but not the stability metrics. We observed a difference in the spatial pattern between the slab and the weak layer that should be considered in snow mechanical modelling.

The vegetation–topography heterogeneity coupling in the Loess Plateau, China

As a result of adaptation to the environment, the great environmental spatial heterogeneity leads to the high spatial heterogeneity of vegetation status. This coupling may be more apparent in water-limited drylands, where topography is the main determinant of small-scale variation in water availability and energy. Metrics describing this coupling may contribute to the detection of the extension of vegetation reshaped by human intervention and other driven forces. In this study, the heterogeneity index of coupling (HIC) was developed to indicate the coupling between spatial heterogeneity of vegetation status (Hv) and the spatial heterogeneity of topography (HT) in the Loess Plateau in northern China. The 16-day composed MODIS normalized vegetation index (NDVI) with a resolution of 250 m and SRTM DEM were employed to quantify the heterogeneity of vegetation status and the topographical heterogeneity. The results show that HIC varies among geomorphic zones, land cover types, and land cover change categories. Among all land cover types, HIC of sandy areas was the largest, followed by the HIC of the forest, shrub, farmland, and grassland. Among geomorphic zones, the highest HIC value appeared in plains with dense residential areas, followed by sandy land that is frequently reshaped by wind, rocky mountainous areas, hilly and gully loess plateaus, and loess tableland. It was revealed that the alternation of vegetation by human activities and natural disturbances shaped greater HIC. Results of this study approved the effectiveness of the HIC in reflecting the coupling of the vegetation status with topography at regional scale.

Characterizing Water Composition with an Autonomous Robotic Team Employing Comprehensive In Situ Sensing, Hyperspectral Imaging, Machine Learning, and Conformal Prediction

Inland waters pose a unique challenge for water quality monitoring by remote sensing techniques due to their complicated spectral features and small-scale variability. At the same time, collecting the reference data needed to calibrate remote sensing data products is both time consuming and expensive. In this study, we present the further development of a robotic team composed of an uncrewed surface vessel (USV) providing in situ reference measurements and an unmanned aerial vehicle (UAV) equipped with a hyperspectral imager. Together, this team is able to address the limitations of existing approaches by enabling the simultaneous collection of hyperspectral imagery with precisely collocated in situ data. We showcase the capabilities of this team using data collected in a northern Texas pond across three days in 2020. Machine learning models for 13 variables are trained using the dataset of paired in situ measurements and coincident reflectance spectra. These models successfully estimate physical variables including temperature, conductivity, pH, and turbidity as well as the concentrations of blue–green algae, colored dissolved organic matter (CDOM), chlorophyll-a, crude oil, optical brighteners, and the ions Ca2+, Cl−, and Na+. We extend the training procedure to utilize conformal prediction to estimate 90% confidence intervals for the output of each trained model. Maps generated by applying the models to the collected images reveal small-scale spatial variability within the pond. This study highlights the value of combining real-time, in situ measurements together with hyperspectral imaging for the rapid characterization of water composition.

Spatial variation in the rocky temperate benthic mesophotic communities of the Wellington region in New Zealand

ABSTRACT Temperate mesophotic ecosystems (TMEs) have only recently been recognised as distinct ecosystems and generally occur between 30 and 150 m at the limit of photosynthetic light penetration. In this study, we surveyed the benthic communities inhabiting rocky deep-water features in two regions at the south of New Zealand’s North Island to assess small (1–2 km) and larger (10–100 km) scale variation in the benthic communities they support: the Wellington South Coast (WSC) and the Kāpiti Coast (KC). We deployed Remotely Operated Vehicles at seventeen sites, three on the WSC and 14 on the KC, covering a depth range of 26 to 72 m. There was significant variation in the benthic community composition between sites, at both spatial scales, and with depth. Across all sites, sponges and bryozoans were by far the most abundant organisms, covering around 30% of the substrate, with all other groups having <5% cover. The sponges and bryozoans created complex three-dimensional structure on the seafloor, providing habitat and refuge for other mobile organisms. Our work provides an important baseline against which any future changes can be compared and will support future spatial management in this region.

Diel vertical migration and tidal influences on plankton densities in dynamic coastal systems

Recent increased application of optical imaging devices have facilitated efficient capture of plankton abundance and community composition, enabling the study of plankton distribution in situ and at a high spatio-temporal resolution. In this study, we aim to investigate how the abundances and distribution patterns of plankton taxa relate over 24-h periods, covering tidal and diel cycles, in the southern North Sea using data from a WP2 net and a Video Plankton Recorder. In the highly dynamic southern North Sea, we document diel vertical migration patterns in the pelagic zone of both pelagic and hyperbenthic taxa, including Calanoida (Copepoda), Amphipoda, Annelida, and Cumacea. In addition, the densities of plankton taxa showed significant small-scale geographical variation over a 24-h period for which tidal currents played an important role, a source of considerable variation that is typically not accounted for. This study adds to the current understanding of plankton distribution and behaviour, particularly in the context of coastal areas characterised by strong tidal cycles and currents, by using in situ imaging techniques.

High-resolution dispersion modelling of PM2.5, PM10, NOx and NO2 exposure in metropolitan areas in Sweden 2000‒2018 – large health gains due to decreased population exposure

Ambient air pollution remains the major environmental cause of disease. Accurate assessment of population exposure and small-scale spatial exposure variations over long time periods is essential for epidemiological studies. We estimated annual exposure to fine and coarse particulate matter (PM2.5, PM10), and nitrogen oxides (NOx, NO2) with high spatial resolution to examine time trends 2000‒2018, compliance with the WHO Air Quality Guidelines, and assess the health impact. The modelling area covered six metropolitan areas in Sweden with a combined population of 5.5 million. Long-range transported air pollutants were modelled using a chemical transport model with bias correction, and locally emitted air pollutants using source-specific Gaussian-type dispersion models at resolutions up to 50 × 50 m. The modelled concentrations were validated using quality-controlled monitoring data. Lastly, we estimated the reduction in mortality associated with the decrease in population exposure. The validity of modelled air pollutant concentrations was good (R2 for PM2.5 0.84, PM10 0.61, and NOx 0.87). Air pollution exposure decreased substantially, from a population weighted mean exposure to PM2.5 of 12.2 µg m−3 in 2000 to 5.4 µg m−3 in 2018. We estimated that the decreased exposure was associated with a reduction of 2719 (95% CI 2046–3055) premature deaths annually. However, in 2018, 65%, 8%, and 42% of residents in the modelled areas were still exposed to PM2.5, PM10, or NO2 levels, respectively, that exceeded the current WHO Air Quality Guidelines for annual average exposure. This emphasises the potential public health benefits of reductions in air pollution emissions.

Simple bulk pigment analysis suggests microphytobenthos contributions to food webs may be underestimated due to isotopic contamination by remineralized wetland carbon

Stable isotope studies have revealed the importance of microphytobenthos (MPB) in coastal food webs. Microalgae typically have δ13C values between depleted C3 wetland/terrestrial macrophytes, and enriched C4 macrophytes and seagrasses. However, the challenges of obtaining clean samples of microalgae from sediments means they are often represented by limited sampling in many food web studies; consequently, we have a limited understanding of spatial and temporal variation in their δ13C values. We tested a simple technique to measure the δ13C of bulk pigments extracted from surficial sediments to represent MPB and applied it to quantify fine scale spatial variation in MPB δ13C around salt marshes. The bulk extraction method is logistically simple, and drives substantial but relatively consistent fractionation in δ13C of −3.5 ± 0.13 ‰ (mean ± 1 S.E., range = 2.3–4.4 ‰, n = 18 paired comparisons) compared to whole cell values. The consistency in fractionation suggests that spatial and temporal δ13C patterns measured in field samples should reflect real variation in source values, and that measured values could be corrected and incorporated into isotope mixing models. In 88 MPB samples among four marsh sites over two summers, MPB δ13C in marsh creeks was lower by an average of 4.4 ± 0.72 ‰ and up to 8.4 ‰ compared to sites along the outer marsh-open water fringe 10's of m away. Few food web studies incorporate this magnitude of variation in their MPB source estimates into mixing models. Over three weekly samplings at one marsh creek site, low tide dissolved inorganic carbon (DIC) δ13C was similarly lower by 4.8 ± 0.36 ‰ and up to 6.2 ‰ compared to high tide and adjacent open water DIC values. The significant small-scale variability in MPB δ13C appears to be driven by remineralized marsh carbon which depletes the DIC δ13C in the marsh creeks, a phenomenon that has long been recognized but is rarely considered in food web studies. Mixing models that assume a narrower range in MPB source values will erroneously attribute isotopically variable MPB contributions to end-member production sources thereby clouding our understanding of energy flows through coastal seascapes.

A low-cost and open-source approach for supraglacial debris thickness mapping using UAV-based infrared thermography

Abstract. Debris-covered glaciers exist in many mountain ranges and play an important role in the regional water cycle. However, modelling the surface mass balance, runoff contribution and future evolution of debris-covered glaciers is fraught with uncertainty as accurate observations on small-scale variations in debris thickness and sub-debris ice melt rates are only available for a few locations worldwide. Here we describe a customised low-cost unoccupied aerial vehicle (UAV) for high-resolution thermal imaging of mountain glaciers and present a complete open-source pipeline that facilitates the generation of accurate surface temperature and debris thickness maps from radiometric images. First, a radiometric orthophoto is computed from individual radiometric UAV images using structure-from-motion and multi-view-stereo techniques. User-specific calibration and correction procedures can then be applied to the radiometric orthophoto to account for atmospheric and environmental influences that affect the radiometric measurement. The thermal orthophoto reveals distinct spatial variations in surface temperature across the surveyed debris-covered area. Finally, a high-resolution debris thickness map is derived from the corrected thermal orthophoto using an empirical or inverse surface energy balance model that relates surface temperature to debris thickness and is calibrated against in situ measurements. Our results from a small-scale experiment on the Kanderfirn (also known as Kander Neve) in the Swiss Alps show that the surface temperature and thickness of a relatively thin debris layer (ca. 0–15 cm) can be mapped with high accuracy using an empirical or physical model. On snow and ice surfaces, the mean deviation of the mapped surface temperature from the melting point (∼ 0 ∘C) was 0.6 ± 2.0 ∘C. The root-mean-square error of the modelled debris thickness was 1.3 cm. Through the detailed mapping, typical small-scale debris features and debris thickness patterns become visible, which are not spatially resolved by the thermal infrared sensors of current-generation satellites. The presented approach paves the way for comprehensive high-resolution supraglacial debris thickness mapping and opens up new opportunities for more accurate monitoring and modelling of debris-covered glaciers.

Lightweight high-precision SAR ship detection method based on YOLOv7-LDS.

The current challenges in Synthetic Aperture Radar (SAR) ship detection tasks revolve around handling significant variations in target sizes and managing high computational expenses, which hinder practical deployment on satellite or mobile airborne platforms. In response to these challenges, this research presents YOLOv7-LDS, a lightweight yet highly accurate SAR ship detection model built upon the YOLOv7 framework. In the core of YOLOv7-LDS's architecture, we introduce a streamlined feature extraction network that strikes a delicate balance between detection precision and computational efficiency. This network is founded on Shufflenetv2 and incorporates Squeeze-and-Excitation (SE) attention mechanisms as its key elements. Additionally, in the Neck section, we introduce the Weighted Efficient Aggregation Network (DCW-ELAN), a fundamental feature extraction module that leverages Coordinate Attention (CA) and Depthwise Convolution (DWConv). This module efficiently aggregates features while preserving the ability to identify small-scale variations, ensuring top-quality feature extraction. Furthermore, we introduce a lightweight Spatial Pyramid Dilated Convolution Cross-Stage Partial Channel (LSPHDCCSPC) module. LSPHDCCSPC is a condensed version of the Spatial Pyramid Pooling Cross-Stage Partial Channel (SPPCSPC) module, incorporating Dilated Convolution (DConv) as a central component for extracting multi-scale information. The experimental results show that YOLOv7-LDS achieves a remarkable Mean Average Precision (mAP) of 99.1% and 95.8% on the SAR Ship Detection Dataset (SSDD) and the NWPU VHR-10 dataset with a parameter count (Params) of 3.4 million, a Giga Floating Point Operations Per Second (GFLOPs) of 6.1 and an Inference Time (IT) of 4.8 milliseconds. YOLOv7-LDS effectively strikes a fine balance between computational cost and detection performance, surpassing many of the current state-of-the-art object detection models. As a result, it offers a more resilient solution for maritime ship monitoring.

Identification of Neighborhood Hotspots via the Cumulative Hazard Index: Results From a Community-Partnered Low-Cost Sensor Deployment.

The Strathcona neighborhood in Vancouver is particularly vulnerable to environmental injustice due to its close proximity to the Port of Vancouver, and a high proportion of Indigenous and low-income households. Furthermore, local sources of air pollutants (e.g., roadways) can contribute to small-scale variations within communities. The aim of this study was to assess hyperlocal air quality patterns (intra-neighborhood variability) and compare them to average Vancouver concentrations (inter-neighborhood variability) to identify possible disparities in air pollution exposure for the Strathcona community. Between April and August 2022, 11 low-cost sensors (LCS) were deployed within the neighborhood to measure PM2.5, NO2, and O3 concentrations. The collected 15-min concentrations were down-averaged to daily concentrations and compared to greater Vancouver region concentrations to quantify the exposures faced by the community relative to the rest of the region. Concentrations were also estimated at every 25m grid within the neighborhood to quantify the distribution of air pollution within the community. Using population information from census data, cumulative hazard indices (CHIs) were computed for every dissemination block. We found that although PM2.5 concentrations in the neighborhood were lower than regional Vancouver averages, daily NO2 concentrations and summer O3 concentrations were consistently higher. Additionally, although CHIs varied daily, we found that CHIs were consistently higher in areas with high commercial activity. As such, estimating CHI for dissemination blocks was useful in identifying hotspots and potential areas of concern within the neighborhood. This information can collectively assist the community in their advocacy efforts.

Large Carbonaceous Chondrite Parent Bodies Favored by Abundance–Volatility Modeling: A Possible Chemical Signature of Pebble Accretion

Primitive meteorite groups such as the Vigarano, Mighei, and Karoonda carbonaceous chondrites have enigmatic patterns of elemental abundances, with moderately volatile elements—those that transition from vapor to condensate between ∼400 and ∼900 K—defining plateaus of subequal abundances despite a wide range in volatility. In detail, each group defines a plateau with distinctive nonmonotonic “chemical fingerprints” that have been attributed to combinations of mixing, vaporization/condensation, and fluid-mediated metasomatism—but the extent to which these processes can reproduce the observed variability has not been quantified. Starting with primitive Ivuna chondrite, a two-stage, two-component equilibrium condensation–vaporization model—with gravity implemented as Jeans escape—can explain large-scale plateaus in these chondrite groups, as well as more complex, nonmonotonic small-scale variations. For all three chondritic meteorite groups, models favor earlier high-temperature fractionation under low-gravity conditions followed by a low-temperature fractionation event that took place on a protoplanet at least as large as Ceres. The second fractionation event may represent the fractionation of incoming materials to the planetesimal during protracted pebble accretion. Models with only thermally driven volatile loss, gravity, and mixing can explain more than 80% of the observed compositional variability in these meteorite groups. In our five-parameter model, using only five randomly selected elements yields uselessly large ranges of planet sizes and temperatures, ranges that converge with increasing numbers of elements. These results suggest that even simple models are prone to generating inaccurate conclusions when constrained by too few observations, a fault likely held by more complex models as well.

Dynamic, multi-scale analyses indicate site- and landscape-level forest cover drive Yellow-billed and Black-billed Cuckoo interannual turnover.

Studies of habitat use in breeding birds often assume species have relatively stable breeding distributions. Some species, however, display considerable year-to-year variability, complicating efforts to determine suitable or preferred habitats. After returning to their breeding range, Black-billed Cuckoos (Coccyzus erythropthalmus) and Yellow-billed Cuckoos (C. americanus) are thought to range widely before nesting, resulting in high rates of interannual breeding-site turnover, potentially contributing to conflicting habitat associations found in past studies. However, difficulty detecting these rare and declining species could lead to overinflated estimates of interannual turnover. Using broadcast surveys to increase detection probability, we collected detection/non-detection data in 2019 and 2020 at 41 publicly owned sites in Illinois and performed a dynamic, multi-scale occupancy analysis for each species to separate detection probability from potential interannual turnover and determine landscape and small-scale variables driving habitat use and occupancy dynamics. We found strong support for interannual turnover for both species based on poor performance of non-dynamic models and variation in estimated annual occupancy (20% and 21% increase between years for Black-billed and Yellow-billed Cuckoos, respectively). Black-billed Cuckoos persisted at sites with less forest in the surrounding landscape and used areas with denser understory vegetation. Yellow-billed Cuckoos colonized sites with greater canopy cover, avoided developed landscapes, and used areas with a shorter subcanopy layer. The dynamic nature of habitat use in these two cuckoo species suggests the importance of coordinating management and conservation across a broader spatial scale. Managing for larger patches of dense shrubs in less forested landscapes would benefit Black-billed Cuckoos while Yellow-billed cuckoos would benefit from management creating forested areas with open understories in less-developed landscapes.

A new interpolation method to resolve under-sampling of UAV-lidar snow depth observations in coniferous forests

Obtaining accurate snow depth estimates under dense canopies using airborne lidar (light detection and ranging) techniques is challenging due to the under-sampling of ground and snow surfaces. Existing interpolation techniques do not adequately address this problem and they often result in an overestimation of under-canopy snow depths. To address this issue, we introduce and evaluate a new interpolation method that incorporates intra-canopy snow depth variability to provide more accurate estimations at unsampled locations. Four interpolation methods were tested, considering systematic trends (landscape trend, canopy vs. gap trend, and intra-canopy trend) along with spatial interpolation of the residuals. Our results show that spatial interpolation methods without consideration of trends are sufficient to capture and reconstruct the small-scale variability of snow depths below a separation distance of 1 m between sampled and unsampled locations, (i.e., ground surface point density > 1 pt. m−2). However, beyond a separation distance of 2.5–3 m (point density < 0.33–0.40 pt. m−2), spatial interpolation based on proximity alone becomes unreliable because point separation becomes larger than the snow depth spatial correlation scale. Within these limiting distances, the method that incorporates trends along with spatial interpolation techniques can resolve the small-scale variability and thereby reduce the likely overestimation of snow depths under the canopy.