Coarse Spatial Scales Research Articles

Quantifying Night Sky Brightness as a Stressor for Coastal Ecosystems in Moreton Bay, Queensland

Growing light pollution is increasingly studied in terrestrial environments. However, research on night lights in coastal ecosystems is limited. We aimed to complement spaceborne remote sensing with ground-based hemispheric photos to quantify the exposure of coastal habitats to light pollution. We used a calibrated DSLR Canon camera with a fisheye lens to photograph the night sky in 24 sites in the rapidly developing area of Moreton Bay, Queensland, Australia, extracting multiple brightness metrics. We then examined the use of the LANcubeV2 photometer and night-time satellite data from SDGSAT-1 for coastal areas. We found that the skies were darker in less urbanized areas and on islands compared with the mainland. Sky brightness near the zenith was correlated with satellite observations only at a coarse spatial scale. When examining light pollution horizontally above the horizon (60–80° degrees below the zenith), we found that the seaward direction was brighter than the landward direction in most sites due to urban glow on the seaward side. These findings emphasize the importance of ground measurements of light pollution alongside satellite imagery. In order to reduce the exposure of coastal ecosystems to light pollution, actions need to go beyond sites with conservation importance and extend to adjacent urban areas.

Resource selection of den sites for bobcats (Lynx rufus) in the Northern Great Plains, United States

Abstract Dens play a crucial role in the survival of young and can ultimately affect population dynamics for felid carnivores. However, little is known about Bobcat (Lynx rufus) den site selection. Therefore, this study aimed to identify environmental correlates of Bobcat den sites in the Black Hills, South Dakota, United States. Our objectives for this study were to compare environmental characteristics of Bobcat dens to random sites at coarse (3.14 ha) and local (0.05 ha) spatial scales. Female bobcats were radio-collared (n = 35) and dens (n = 27) were located using ground telemetry. Bobcats selected for high horizontal cover and terrain ruggedness at the coarse scale, as well as high horizontal cover at the local scale, which could provide concealment from predators. In less rugged areas land management practices that promote increased tree and shrub habitat as horizontal cover may provide habitat for Bobcat dens and ensure the survival of this ecologically and culturally important species.

A matter of scale: Identifying the best spatial and temporal scale of environmental variables to model the distribution of a small cetacean.

The importance of scale when investigating ecological patterns and processes is recognised across many species. In marine ecosystems, the processes that drive species distribution have a hierarchical structure over multiple nested spatial and temporal scales. Hence, multi-scale approaches should be considered when developing accurate distribution models to identify key habitats, particularly for populations of conservation concern. Here, we propose a modelling procedure to identify the best spatial and temporal scale for each modelled and remotely sensed oceanographic variable to model harbour porpoise (Phocoena phocoena) distribution within the Irish Exclusive Economic Zone. Harbour porpoise sightings were recorded during dedicated line-transect aerial surveys conducted in the summers of 2016, 2021 and 2022. Binary generalised additive models were used to assess the relationships between porpoise presence and oceanographic variables at different spatial (5-40 km) and temporal (daily, monthly and across survey period) scales. Selected variables included sea surface temperature, thermal fronts, chlorophyll-a, sea surface height, mixed layer depth and salinity. A total of 30,514 km was covered on-effort with 216 harbour porpoise sightings recorded. Overall, the best spatial scale corresponded to the coarsest resolution considered in this study (40 km), while porpoise presence showed stronger association with oceanographic variables summarised at a longer temporal scale. Habitat models including covariates at coarse spatial and temporal scales may better reflect the processes driving availability and abundance of resources at these large scales. These findings support the hypothesis that a multi-scale approach should be applied when investigating species distribution. Identifying suitable spatial and temporal scale would improve the functional interpretation of the underlying relationships, particularly when studying how a small marine predator interacts with its environment and responds to climate and ecosystem changes.

Unpacking satellite pixels: UAVs reveal fine-scale drivers of land surface phenology in a winter rainfall shrubland

Land surface phenology (LSP) can reveal important connections between vegetation dynamics and climate but remains poorly understood in evergreen winter-rainfall shrublands globally. Field-based studies have indicated diverse plant functional strategies in shrublands, but further work is required to link LSP to vegetation functional composition in these regions. We analysed time-series of the normalised difference vegetation index (NDVI) in fynbos shrublands of South Africa using multi-spectral imagery from satellites and unmanned aerial vehicles (UAVs). We investigated the climate drivers of seasonal vegetative phenology and long-term NDVI trends at multiple spatial scales ranging from the landscape to individual species. At coarse spatial resolutions, NDVI time-series indicated rainfall-driven vegetation dynamics in fynbos, both at inter and intra-annual time scales. However, high-resolution time-series from UAVs exposed an underlying divergence in vegetative phenology and long-term NDVI trends between shallow and deep-rooted growth forms. Phenophases and NDVI trends of isolated, deep-rooted, overstory shrubs were decoupled from rainfall relative to dense overstory patches and shallow-rooted understory growth forms. Variations in growth form phenology were not detected at coarse spatial scales due to scaling and competitive effects based on the functional composition of the vegetation.

Integrating Farmers’ Perspectives into Earth System Model Development: Interviews with End Users in the Willamette Valley, Oregon, to Guide Actionable Science

Abstract This paper analyzes findings from semistructured interviews and focus groups with 31 farmers in the Willamette Valley in which farmers were asked about their needs for climate data and about the usability of a range of outputs from the Community Earth System Model, version 2 (CESM2), for their soil management practices. Findings indicate that climate and soils data generated from CESM and other Earth system models (ESMs), despite their coarse spatial scale resolutions, can inform farmers’ long-term decisions, but that the data would be more usable if the outputs were provided in a format that allowed farmers to choose the variables and thresholds relevant to their particular needs and if ESMs incorporated farmer practices including residue removal, cover cropping, and tillage levels into the model operations so that farmers could better understand the impacts of their decisions. Findings also suggest that although there is a significant gap in the spatial resolution at which these global ESMs generate data and the spatial resolution needed by farmers to make most decisions, farmers are adept at making scalar adjustments to apply coarse-resolution data to the specifics of their own farm’s microclimate. Thus, our findings suggest that, to support agricultural decision-making, development priorities for ESMs should include developing better representations of agricultural management practices within the models and creating interactive data dashboards or platforms.

Total CO2 budget estimate and degassing dynamics for an active stratovolcano: Turrialba Volcano, Costa Rica

Distributions and concentrations of carbon dioxide being emitted from active volcanoes elucidate the subsurface controls on gas ascent from the source and provide important information regarding the extent and state of the magmatic system. The main goal of this study was to determine if degassing followed open- or closed-system dynamics, and to define a baseline for eruption monitoring of degassing across the volcanic edifice through the CO2 budget estimate from the combined results of two CO2 gas emission surveys from 2021 and 2022 on Turrialba volcano, Costa Rica. This was accomplished by utilizing a new method for estimating total carbon flux on and around this persistently degassing and intermittently erupting volcano by integrating fine and coarse spatial scales of measurements; including an analysis of carbon isotopes to determine the source contributions to the gas emissions. Approximately 99% (2287 ± 1719 t CO2 day−1) of magma-derived degassing activity is advective and concentrated at the summit crater, with a smaller, continuous component of ∼1% (23.73 ± 6.65 tonnes CO2 day−1) flank diffuse soil degassing. As the majority of the gas emissions from Turrialba are concentrated in the summit plume, the system is likely experiencing open-system degassing dynamics through one dominant degassing pathway. Though at relatively low levels, the locations and distributions of diffuse degassing on the volcanic flanks allow us to delineate subsurface features that likely reveal the extent of the magmatic system of the volcano. Volcanic CO2 outputs at Turrialba primarily concentrate along faults and fractures near the summit and across the flanks where permeable zones allow gas ascent, and with limited emissions elsewhere. The results of this study provide a baseline for monitoring future changes in the Turrialba magmatic system and demonstrate the potential for applying this method to other volcanic complexes, particularly those that are poorly monitored or where there is a greater prevalence of diffuse and distal degassing.

Comprehensive risk assessment of typhoon disasters in China's coastal areas based on multi-source geographic big data

Typhoons can bring substantial casualties and economic ramifications, and effective prevention strategies necessitate a comprehensive risk assessment. Nevertheless, existing studies on its comprehensive risk assessment are characterized by coarse spatial scales, limited incorporation of geographic big data, and rarely considering disaster mitigation capacity. To address these problems, this study combined multi-source geographic big data to develop the Comprehensive Risk Assessment Model (CRAM). The model integrated 17 indicators from 4 categories of factors, including exposure, vulnerability, hazard, and mitigation capacity. A subjective-objective combination weighting method was introduced to generate the indicator weights, and comprehensive risk index of typhoon disasters was calculated for 987 counties along China's coastal regions. Results revealed a pronounced spatial heterogeneity of the comprehensive typhoon risk, which exhibited an overall decreasing trend from the southeast coastal areas toward the northwest inland territories. 61.7 % of the counties exhibited a medium-to-high level of comprehensive risk, and counties with very-high risks are predominantly concentrated in the Shandong Peninsula, Yangtze River Delta, Hokkien Golden Triangle, Greater Bay Area, Leizhou Peninsula, and Hainan Province, mainly due to high exposure and hazard factors. The correlation coefficient between the risk assessment results and typhoon-induced direct economic losses reached 0.702, indicating the effectiveness and reliability of the CRAM. Meanwhile, indicators from intrinsic attributes of typhoons and geographic big data had pronounced importance, and regional mitigation capacity should be improved. Our proposed method can help to scientifically understand spatial patterns of comprehensive risk and mitigate the effects of typhoon disasters in China's coastal regions.

Integrating wastewater and randomised prevalence survey data for national COVID surveillance

During the COVID-19 pandemic, studies in a number of countries have shown how wastewater can be used as an efficient surveillance tool to detect outbreaks at much lower cost than traditional prevalence surveys. In this study, we consider the utilisation of wastewater data in the post-pandemic setting, in which collection of health data via national randomised prevalence surveys will likely be run at a reduced scale; hence an affordable ongoing surveillance system will need to combine sparse prevalence data with non-traditional disease metrics such as wastewater measurements in order to estimate disease progression in a cost-effective manner. Here, we use data collected during the pandemic to model the dynamic relationship between spatially granular wastewater viral load and disease prevalence. We then use this relationship to nowcast local disease prevalence under the scenario that (i) spatially granular wastewater data continue to be collected; (ii) direct measurements of prevalence are only available at a coarser spatial resolution, for example at national or regional scale. The results from our cross-validation study demonstrate the added value of wastewater data in improving nowcast accuracy and reducing nowcast uncertainty. Our results also highlight the importance of incorporating prevalence data at a coarser spatial scale when nowcasting prevalence at fine spatial resolution, calling for the need to maintain some form of reduced-scale national prevalence surveys in non-epidemic periods. The model framework is disease-agnostic and could therefore be adapted to different diseases and incorporated into a multiplex surveillance system for early detection of emerging local outbreaks.

Unravelling the role of tropical cyclones in shaping present species distributions.

Driven by climate change, tropical cyclones (TCs) are predicted to change in intensity and frequency through time. Given these forecasted changes, developing an understanding of how TCs impact insular wildlife is of heightened importance. Previous work has shown that extreme weather events may shape species distributions more strongly than climatic averages; however, given the coarse spatial and temporal scales at which TC data are often reported, the influence of TCs on species distributions has yet to be explored. Using TC data from the National Hurricane Center, we developed spatially and temporally explicit species distribution models (SDMs) to examine the role of TCs in shaping present-day distributions of Puerto Rico's 10 Anolis lizard species. We created six predictor variables to represent the intensity and frequency of TCs. For each occurrence of a species, we calculated these variables for TCs that came within 500 km of the center of Puerto Rico and occurred within the 1-year window prior to when that occurrence was recorded. We also included predictor variables related to landcover, climate, topography, canopy cover and geology. We used random forests to assess model performance and variable importance in models with and without TC variables. We found that the inclusion of TC variables improved model performance for the majority of Puerto Rico's 10 anole species. The magnitude of the improvement varied by species, with generalist species that occur throughout the island experiencing the greatest improvements in model performance. Range-restricted species experienced small, almost negligible, improvements but also had more predictive models both with and without the inclusion of TC variables compared to generalist species. Our findings suggest that incorporating data on TCs into SDMs may be important for modeling insular species that are prone to experiencing these types of extreme weather events.

Assessing the Atmospheric Response to Subgrid Surface Heterogeneity in the Single‐Column Community Earth System Model, Version 2 (CESM2)

AbstractLand‐atmosphere interactions are central to the evolution of the atmospheric boundary layer and the subsequent formation of clouds and precipitation. Existing global climate models represent these connections with bulk approximations on coarse spatial scales, but observations suggest that small‐scale variations in surface characteristics and co‐located turbulent and momentum fluxes can significantly impact the atmosphere. Recent model development efforts have attempted to capture this phenomenon by coupling existing representations of subgrid‐scale (SGS) heterogeneity between land and atmosphere models. Such approaches are in their infancy and it is not yet clear if they can produce a realistic atmospheric response to surface heterogeneity. Here, we implement a parameterization to capture the effects of SGS heterogeneity in the Community Earth System Model (CESM2), and compare single‐column simulations against high‐resolution Weather Research and Forecasting (WRF) large‐eddy simulations (LESs), which we use as a proxy for observations. The CESM2 experiments increase the temperature and humidity variances in the lowest atmospheric levels, but the response is weaker than in WRF‐LES. In part, this is attributed to an underestimate of surface heterogeneity in the land model due to a lack of SGS meteorology, a separation between deep and shallow convection schemes in the atmosphere, and a lack of explicitly represented mesoscale secondary circulations. These results highlight the complex processes involved in capturing the effects of SGS heterogeneity and suggest the need for parameterizations that communicate their influence not only at the surface but also vertically.

A multiscale characterization of cortical shape asymmetries in early psychosis.

Psychosis has often been linked to abnormal cortical asymmetry, but prior results have been inconsistent. Here, we applied a novel spectral shape analysis to characterize cortical shape asymmetries in patients with early psychosis across different spatial scales. We used the Human Connectome Project for Early Psychosis dataset (aged 16-35), comprising 56 healthy controls (37 males, 19 females) and 112 patients with early psychosis (68 males, 44 females). We quantified shape variations of each hemisphere over different spatial frequencies and applied a general linear model to compare differences between healthy controls and patients with early psychosis. We further used canonical correlation analysis to examine associations between shape asymmetries and clinical symptoms. Cortical shape asymmetries, spanning wavelengths from about 22 to 75 mm, were significantly different between healthy controls and patients with early psychosis (Cohen's d = 0.28-0.51), with patients showing greater asymmetry in cortical shape than controls. A single canonical mode linked the asymmetry measures to symptoms (canonical correlation analysis r = 0.45), such that higher cortical asymmetry was correlated with more severe excitement symptoms and less severe emotional distress. Significant group differences in the asymmetries of traditional morphological measures of cortical thickness, surface area, and gyrification, at either global or regional levels, were not identified. Cortical shape asymmetries are more sensitive than other morphological asymmetries in capturing abnormalities in patients with early psychosis. These abnormalities are expressed at coarse spatial scales and are correlated with specific symptom domains.

Novel quantitative methods to enable multispectral identification of high-purity water ice exposures on Mars using High Resolution Imaging Science Experiment (HiRISE) images

Reliable detection and characterization of water ice on the Martian surface is pivotal to not only understand its present and past climate, but to also provide valuable information on in-situ resource availability and distribution for future human exploration missions. Ice-rich features are currently identified with visible/near-IR (VNIR), thermal IR and radar data. However, their coarse spatial scale sometimes limits confident characterization of small (i.e., meter-scale) icy exposures resulting from recent activity like new impacts. Water ice bearing materials possess weaker spectral characteristics at wavelengths shorter than ∼1030 nm that may be resolved by VNIR imaging instruments like the High Resolution Imaging Science Experiment (HiRISE) and the Colour and Stereo Surface Imaging System (CaSSIS). Our study assesses the spectral capability of HiRISE colour observations to help distinguish high purity water ice exposures from ice-poor materials. We report detailed methodologies for reliable colour characterization of icy surface using unfiltered HiRISE images. We present the first quantitative approach to uniquely characterize high-purity ice-rich materials through spectral shape and spectral parameterization methods at high spatial resolution (∼50 cm/pixel). We also present three spectral parameters to aid detection of pure water ice features, while also providing statistical constraints to enable a quantitative interpretation scheme. Our methods are observed to work well in characterizing and separating ice-rich features uniquely from ice-poor and ferrous materials. However, we do observe that these methods have a lower grain size detection limit of ∼250–300 μm, and may not be able to uniquely separate frosts from ground ice exposures. We also apply these methods to better constrain the composition of bright materials exposed by recent impacts identified in previous surveys, where substantial evidence for ice-bearing materials was previously unavailable. Overall, our work proposes HiRISE colour-based methods as a novel approach for high-resolution multispectral characterization of ice-rich features on the Martian surface, which is of particular value since the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) has ceased operations.

Species redistribution creates unequal outcomes for multispecies fisheries under projected climate change.

Climate change drives species distribution shifts, affecting the availability of resources people rely upon for food and livelihoods. These impacts are complex, manifest at local scales, and have diverse effects across multiple species. However, for wild capture fisheries, current understanding is dominated by predictions for individual species at coarse spatial scales. We show that species-specific responses to localized environmental changes will alter the collection of co-occurring species within established fishing footprints along the U.S. West Coast. We demonstrate that availability of the most economically valuable, primary target species is highly likely to decline coastwide in response to warming and reduced oxygen concentrations, while availability of the most abundant, secondary target species will potentially increase. A spatial reshuffling of primary and secondary target species suggests regionally heterogeneous opportunities for fishers to adapt by changing where or what they fish. Developing foresight into the collective responses of species at local scales will enable more effective and tangible adaptation pathways for fishing communities.

Downscaling satellite night-time lights imagery to support within-city applications using a spatially non-stationary model

For mapping and monitoring socioeconomic activities in cities, night-time lights (NTL) satellite sensor images are used widely, measuring the light intensity during the night. However, the main challenge to mapping human activities in cities using such NTL satellite sensor images is their coarse spatial resolution. To address this drawback, spatial downscaling of satellite nocturnal images is a plausible solution. However, common approaches for spatial downscaling employ spatially stationary models that may not be optimal where the data are spatially heterogeneous. In this research, a geostatistical model termed Random Forest area-to-point regression Kriging (RFATPK) was employed to disaggregate coarse spatial scale VIIRS NTL images (450 m) to a fine spatial scale (100 m). The RF predicts at a coarse resolution from fine spatial resolution variables, such as a Population raster. ATPK then downscales the coarse residuals from the RF prediction. In numerical experiments, RFATPK was compared with three benchmark techniques, including the simple Allocation of pixel values from the coarse resolution NTL data, Machine Learning with Splines and Geographically Weighted Regression. The downscaled results were validated using fine resolution LuoJia 1-01 satellite sensor imagery. RFATPK produced more accurate disaggregated images than the three benchmark approaches, with mean root mean square errors (RMSE) for the year 2018 of 13.89 and 6.74 nWcm−2 sr−1, for Mumbai and New Delhi, respectively. Also, the property of perfect coherence, measured by the Correlation Coefficient, was preserved consistently when applying RFATPK and was almost 1 for all years. The applicability of the disaggregated NTL data to monitor socioeconomic activities at the within-city scale against the reference NTL was illustrated by utilizing them as a proxy for the Gross National Income (GNI) per capita and the Night Light Development Index. The GNI estimation from the downscaled NTL outperformed the coarse resolution NTL when examining their coefficients of determination, with R2 of 0.67 and 0.47 for the GNI estimation using the fine and coarse resolution NTL data, respectively. For the Night Light Development Index (NLDI), the results of the index were compared by measuring their correlation with the Human Development Index (HDI). The NLDI from the downscaled NTL outperformed the coarse resolution NTL when measuring the correlation with the HDI, with Pearson’s correlation coefficients of −0.48 and −0.35 for the NLDI using the fine and coarse resolution NTL data, respectively, for New Delhi. The outcomes indicate that RFATPK provides more accurate predictions than the three benchmark techniques and the downscaled NTL data are more suitable for fine scale socioeconomic applications, as demonstrated by the NLDI and GNI. This research, thus, shows that the RFATPK solution for NTL disaggregation can facilitate data enhancement for fine-scale sub-national applications in social sciences and can be generalized worldwide by including other cities as well as other applications.

The effect of turbulence in brain dynamics information transfer measured with magnetoencephalography

Fast, efficient information transfer is essential for the brain to ensure survival. As recently shown in functional magnetic resonance imaging with high spatial resolution, turbulence appears to offer a fundamental way to facilitate energy and information transfer across spatiotemporal scales in brain dynamics. However, given that this imaging modality is comparably slow and not directly linked with neuronal activity, here we investigated the existence of turbulence in fast whole-brain neural dynamics measured with magnetoencephalography (MEG). The coarse spatial observations in MEG necessitated that we created and validated a empirical measure of turbulence. We found that the measure of edge-centric metastability perfectly detected turbulence in a ring of non-local coupled oscillators where the ground-truth was analytically known, even at a coarse spatial scale of observations. This allowed us to use this measure in the spatially coarse, empirical large-scale MEG data from 89 human participants. We demonstrated turbulence in fast neuronal dynamics and used this to quantify information transfer in the brain. The results demonstrate that the necessary efficiency of brain function is dependent on an underlying turbulent regime.

Spatiotemporal Variability of Current and Future Sub‐Daily Rainfall in Japan Using State‐Of‐The‐Art High‐Quality Data Sets

AbstractUsing potentially best available rainfall data sets for the entire country of Japan (spatial scales of 1‐ and 20‐km), we analyze the 1–24 hr and city‐scale (1–400 km2) extreme rainfalls for both current (2006–2020) and future periods (2081–2109) at 1.5 K global warming scenario, complementing previous work that focuses on either coarse spatial and temporal scales or other warming scenarios (e.g., RCP and 2 K warming scenarios). A peak‐over‐threshold (POT)‐based approach is applied to compute areal reduction factor (ARF) for subsequently establishing intensity‐duration‐area‐frequency (IDAF) curves. Our results reveal that ARF values generally decrease with increasing area size and increase with longer duration and are affected by multiple underlying physical phenomena. Moreover, we find a greater increase in the rainfall intensities for shorter durations and higher return periods, ranging from 9.4% (1‐hr) to 6.2% (24‐hr), averaged for all return periods and 8.3% (25‐year) to 7.3% (2‐year), averaged for all rainfall durations. Spatially, extreme rainfall intensities are projected to increase by 8.9% in northern Japan (albeit with a less intense rainfall intensity in the current period), which is greater than the rest of the country (6.8%), underscoring the need to focus more on infrastructure designs in northern Japan. The projected IDAF curves further display an increase in the frequency of extreme events at city‐scale, for example, 25‐year extreme rainfall events in 2006–2020 would likely be 5‐10‐year events in 2081–2109. Our results with the state‐of‐the‐art data and implementable approach can be utilized for policymaking to reduce the warming‐induced risks in Japan and beyond.

A new method for calculating C factor when projecting future soil loss using the Revised Universal soil loss equation (RUSLE) in semi-arid environments

Understanding how reservoir sedimentation rates may evolve due to climate change is essential for projecting future changes in reservoir water storage capacity. The Revised Universal Soil Loss Equation (RUSLE) is commonly used to assess regional soil loss rates because of its suitability for working with the coarse temporal and spatial scale of climate model outputs. Application of the RUSLE for projecting future erosion rates is constrained by the relatively limited number of classes used in projected changes of native vegetation: this typically reduces a wide range of variation in RUSLE cover (C) factors to a single value per class, and these classes may be insensitive to changes in species composition and canopy density with time and space. This paper develops a low-cost, efficient, and objective approach to projecting future C values directly based on widely available Landsat data, downscaled climate model data, and a limited number of terrain variables. Observed C is estimated from Landsat-derived Normalized Difference Vegetation Index (NDVI) and Modified Soil-Adjusted Vegetation Index (MSAVI) values using standard methods. A linear relationship is established between the observed C values and average annual antecedent temperature, average annual antecedent precipitation, latitude, and percent sand (adjusted R2 = 0.75604, 0.7543, and RMSE = 0.09448, 0.07043, respectively). A proof of principal study demonstrates that, unlike when land cover classes are used to estimates C, a RUSLE model driven by the regression-based C provides a correct order-of-magnitude estimate of observed long-term erosion and sediment yield rates (e.g., an estimate of 2.9–3.2 t ha−1 y-1 is obtained where observed rates range from 1.2 to 3.9 t ha−1 y-1 for the same time period, and up to 8 t ha−1 y-1 historically, while standard formulations of RUSLE yield 16.1 and 0.3 t ha−1 y-1). Using climate model data to estimate both precipitation intensity (R factor) and C in the RUSLE model results in basin wide projected end 21st century soil erosion rates for relative concentration pathway 8.5 of 5.5–7.5 t ha−1 y-1, consistent with expectations for accelerated soil loss in the study area as climate change reduces soil moisture while increasing precipitation intensity. The same model driven by land cover class estimates of C produces soil loss rates an order of magnitude smaller (0.3–0.4 t ha−1 y-1), consistent with a much more heavily vegetated landscape than could be supported under the increasingly arid projected climate.

Leaf temperatures and environmental conditions predict daily stem radial variations in a temperate coniferous forest

AbstractHourly‐resolved measurements of stem radial variations (SRVs) provide valuable insights into how climate‐induced changes in hydrological regimes affect tree water status and tree stem radial growth. However, while SRVs are easily measured at the individual tree level, currently no methods are available to monitor this phenomenon across broad regions at intra‐annual (daily to weekly) scales. Near‐surface (in situ) thermal remote sensing—with its sensitivity to plant water status—may provide an approach for monitoring intra‐annual SRVs, with the potential for scaling these approaches to the landscape level. Thus, we explored the suitability of in situ thermal remote sensing, in combination with other environmental data, to monitor SRVs in a coniferous forest of the North American Intermountain West. Specifically, we were interested in answering two main questions: Can we use in situ thermal remote sensing by itself and in combination with environmental variables (i.e., photoperiod, photosynthetically active radiation, and soil moisture) to predict (1) daily tree water status and (2) daily tree stem radial growth derived from SRVs? We used data collected by an environmental monitoring network in central Idaho over three growing seasons (2019–2021) to address these questions. Results showed that leaf temperature (TL) in combination with environmental variables explained up to three‐quarters of the SRV‐based variability in daily tree water status (in the form of tree water deficit [TWD]) and approximately one‐half of the variability in daily stem radial growth. The time of day when TL was acquired also appeared to change the strength, shape, and predictive power of the models, with acquisition times in the morning and evening showing stronger relationships with daily SRVs than other times of the day. Overall, these results highlight the promise of utilizing thermal remote sensing data to derive tree hydrological and growth status, and reveal key considerations (e.g., the time of data acquisition) for future observational and modeling efforts. This study also provides a benchmark against which to compare future efforts to test these observed relationships at coarser spatial scales.

Early Holocene plant macrofossils indicate cool refugia for subalpine plant taxa in Acadia National Park, Maine

Identifying refugia— specifically places where species can persist during periods of regionally unsuitable climate— is increasingly important for conservation practitioners and land managers charged with protecting biodiversity in a rapidly warming world. Currently, many researchers assist in this process by building models to predict areas of refugia using climate data projected into the future under different climate scenarios; however, the coarse spatial scale of future climate data can be orders of magnitude larger than the scale of refugia on the landscape. Conservation paleobiology is an emerging field that can contribute to the identification of climate refugia by looking at the macrofossil records contained in sediments to better understand the response of species to past climate change within a small area, and allows us to ground-truth hypotheses about specific areas functioning as climate refugia. Here, we present a conservation paleobiology case study to update vulnerability assessments for subalpine plant species in Acadia National Park and locate potential future refugia on the landscape. We analyzed plant macrofossils in a sediment core from Sargent Mountain Pond in Acadia National Park (Maine, United States) at a fine spatiotemporal resolution to test the hypothesis that the area served as a past climate refugium for the subalpine species it currently hosts. We found that, when compared to a pollen record from a forest hollow core collected on Mount Desert Island, the macrofossils reflect a more stable presence of subalpine taxa throughout the Holocene Climatic Optimum (8,000–5,000 BP) than was observed at lower elevations. Our results indicate the importance of a complementary approach that combines plant macrofossils and pollen in addition to modeling to identify refugia and better understand the vulnerability of species and communities to climate change.

Unsupervised Diffusion and Volume Maximization-Based Clustering of Hyperspectral Images

Hyperspectral images taken from aircraft or satellites contain information from hundreds of spectral bands, within which lie latent lower-dimensional structures that can be exploited for classifying vegetation and other materials. A disadvantage of working with hyperspectral images is that, due to an inherent trade-off between spectral and spatial resolution, they have a relatively coarse spatial scale, meaning that single pixels may correspond to spatial regions containing multiple materials. This article introduces the Diffusion and Volume maximization-based Image Clustering (D-VIC) algorithm for unsupervised material clustering to address this problem. By directly incorporating pixel purity into its labeling procedure, D-VIC gives greater weight to pixels corresponding to a spatial region containing just a single material. D-VIC is shown to outperform comparable state-of-the-art methods in extensive experiments on a range of hyperspectral images, including land-use maps and highly mixed forest health surveys (in the context of ash dieback disease), implying that it is well-equipped for unsupervised material clustering of spectrally-mixed hyperspectral datasets.