Spatial Variability Research Articles

APPLICATION OF SUPPORT VECTOR MACHINE AND ORDINARY KRIGING (SVM-OK) TO SPATIAL INTERPOLATION OF POROSITY ATTRIBUTE OF AGBABU BITUMEN DEPOSIT

The lack of geostatistical modelling of volumetric characteristics of Nigeria's long-discovered heavy oil and bitumen deposits in Agbabu was accountable for the discrepancies in the hydrocarbon-in-place calculations. The absence of spatial variability evaluation in such efforts is responsible for the inconsistency and divergence that define the impacts of the existing attempts to estimate the porosity attribute. Due to its optimum features and simplicity of estimate, Kriging is reported as the most generally used approach to evaluate the connection between variables (inputs and output). However, Kriging reveals that the relationship between variables requires a linear form, which is frequently not the case. Thus, the necessity for a procedure with no fixed shape is built based on information acquired directly from the data. Consequently, support vector regression, a form of the support vector machine, is employed as a choice in contrast to kriging. This research is a comparative application of support vector machine and kriging (SVM-OK) to the spatial interpolation of the porosity property of the Agbabu bitumen field. The porosity values from existing data are trained, cross-validated, and verified with the help of the support vector machine, and they are interpolated by ordinary kriging. The geographical patterns of the predictions have been checked visually using graphs based on these three diagnostics: variance of the error terms and correlation of the error terms. The findings of this study illustrate the performance of the combination of the Support vector machine and conventional kriging in the spatial interpolation of volumetric characteristics in the Agbabu bitumen deposit. The approach exhibited great predictive performance. SVM-kriging will be employed in reservoir characterisation with the least mean absolute error (MAE) and mean squared error (MSE).

Statistical Relationship between Satellite observed Land Surface Temperature and in-situ measured Surface Air Temperature over the Indian Region: An Exploratory Study

This paper presents an investigation of spatio-temporal estimation of daily minimum (Tmin) and maximum (Tmax) surface air temperature using satellite (INSAT and MODIS) derived Land surface temperature and in-situ observational data over the Indian region. Least Absolute Shrinkage and Selection Operator (LASSO) regression technique is used to identify influencing neighboring stations. To capture spatial and temporal variability of surface air temperature of a particular IMD station, Linear Regression and Auto-Regressive Integrated Moving Average, which is known as ARIMA, are used respectively. These models are statistically ensembled using stack generalization. Obtained station-by-station relationship is validated on an independent test data using Root Mean Squared Error (RMSE) to check the validity of the model under consideration. Results show ensembled model outperform (has lowest RMSE) the traditional methods for prediction of Tmin and Tmax with RMSE within a range of [1, 2] range for most of the regions and seasons.

Using Commercial Satellite Imagery to Reconstruct 3 m and Daily Spring Snow Water Equivalent

AbstractSnow water equivalent (SWE) distribution at fine spatial scales (≤10 m) is difficult to estimate due to modeling and observational constraints. However, the distribution of SWE throughout the spring snowmelt season is often correlated to the timing of snow disappearance. Here, we show that snow cover maps generated from PlanetScope's constellation of Dove Satellites can resolve the 3 m date of snow disappearance across seven alpine domains in California and Colorado. Across a 5‐year period (2019–2023), the average uncertainty in the date of snow disappearance, or the period of time between the last date of observed snow cover and the first date of observed snow absence, was 3 days. Using a simple shortwave‐based snowmelt model calibrated at nearby snow pillows, the PlanetScope date of snow disappearance could be used to reconstruct spring SWE. Relative to lidar SWE estimates, the SWE reconstruction had a spatial coefficient of correlation of 0.75, and SWE spatial variability that was biased by 9%, on average. SWE reconstruction biases were then improved to within 0.04 m, on average, by calibrating snowmelt rates to track the spring temporal evolution of fractional snow cover observed by PlanetScope, including fractional snow cover over the full modeling domain, and across domain subsections where snowmelt rates may differ. This study demonstrates the utility of fine‐scale and high‐frequency optical observations of snow cover, and the simple and annually repeatable connections between snow cover and spring snow water resources in regions with seasonal snowpack.

Monthly and Interannual Variations in Upwelling and Chlorophyll off the Vietnam Coast in Summer Based on Deep Learning

AbstractThe deep learning technique is used to automatically identify upwelling regions off the Vietnam coast using Sea Surface Temperature (SST) data from satellite imagery. After delineating these upwelling areas, this study is pioneering in deriving the upwelling probability off the Vietnam coast, pinpointing two significant upwelling hotspots. To deepen the understanding of the individual impacts of wind and sea surface height anomalies on monthly and spatial variations in upwelling probability and chlorophyll distribution, the Empirical Orthogonal Function method was employed to analyze this data set. The upwelling probability demonstrates a close relationship with wind and sea surface height anomalies, while chlorophyll concentration correlates with the strength of the southwesterly wind. The southwesterly wind, Ekman pumping, eddy dipole, and northeastward jet have been identified as key drivers of the formation and spatial variability of upwelling in summer, albeit with varying contributions to different parts of the coastal region. The distribution of upwelling probability correlates with chlorophyll variation off the northern Vietnam coast. High chlorophyll concentration off the southern Vietnam coast is primarily influenced by the southwesterly wind that carries the Mekong River plume eastward. Furthermore, two abnormal scenarios in upwelling and chlorophyll concentration during 2010 and 2018 were analyzed, attributed to the abnormal southwesterly monsoon and coastal circulation influenced by an El Niño event and a weak La Niña event with a positive Indian Ocean Dipole, respectively. This study elucidates the dynamic processes underlying the intricate chlorophyll distribution over the continental shelf, which is influenced by both the river plume and upwelling.

Sedimentary organic matter signature hints at the phytoplankton-driven biological carbon pump in the central Arabian Sea

Abstract. The central Arabian Sea, a unique tropical basin, is profoundly impacted by monsoon wind reversal affecting its surface circulation and biogeochemistry. Phytoplankton blooms associated with high biological productivity and particle flux occur in the northern part of the central Arabian Sea due to summer-monsoon-induced open-ocean upwelling and winter convection. The core oxygen minimum zone (OMZ) at intermediate water depths is another important feature of the northern central Arabian Sea and fades southward. In this study, we attempt to interlink how these factors collectively impact phytodetrital export to the sediment. Short sediment core-top (1 cm) samples representing the recent particle flux signatures were analysed from five locations (21 to 11° N; 64° E) in the central Arabian Sea. Previously, we used core-top (0–0.5 cm) samples and observed a trend between diatom frustule abundance and diversity with bulk sedimentary parameters indicating a spatial variability in phytodetrital export to the sediment. To verify this observation further, lipid biomarkers of key phytoplankton groups and a sea surface temperature (SST) proxy have been analysed in addition to diatom frustules. The C37 alkenone-based SST proxy indicated cooler SST (27.6 ± 0.25 °C) in the north (21–15° N) mostly due to upwelling (summer) and convective mixing (winter). Warmer SSTs (+0.4 °C) are measured in the south, which usually remains nutrient-poor. This trend was consistent with satellite-derived average SST values (2017–2020). Lipid biomarker analysis suggests that dinoflagellates were likely to be the highest contributor, as indicated by dinosterol and its degradative product dinostanol, followed by brassicasterol and C37 alkenone, likely representing diatoms and coccolithophores, respectively. The north, which largely experiences periodic phytoplankton blooms and is influenced by the thick OMZ, revealed the highest contents of organic matter, diatom frustules (diversity and abundance), dominated by large, thickly silicified cells (e.g. Coscinodiscus and Rhizosolenia) and phytoplankton lipid biomarkers, as well as lower contents of zooplankton biomarkers (cholesterol and cholestanol). In contrast, relatively smaller chain-forming centric (e.g. Thalassiosira) and pennate (e.g. Pseudo-nitzschia, Nitzschia, Thalassionema) diatom frustules along with lower phytoplankton lipid biomarker contents were found in the south, where zooplankton biomarkers and silicious radiolarians were more abundant. The possible impacts of the OMZ on particle flux related to the phytoplankton community, including zooplankton grazing and other factors, have been discussed.

Using a Bayesian analytic approach to identify county-level ecological factors associated with survival among individuals with early-onset colorectal cancer.

In the United States (US), incidence of early age of onset colorectal cancer (EOCRC, diagnosed <50 years of age) has been increasing. Using a Bayesian analytic approach, we evaluated the association between county-level ecological factors and survival among individuals with EOCRC and identified hotspot and coldspot counties with unexplained low and high survival, respectively. Principal component (PC) analysis was used to reduce dimensionality of 36 county-level social, behavioral, and preventive factors from the Centers for Disease Control and Prevention data. Survival information was derived from the Surveillance, Epidemiology, and End Results Program data from January 1, 2000 to December 31, 2019. The association between the identified PCs and survival was evaluated using multivariable spatial generalized linear mixed models. Counties with residual low and high survival (i.e., unexplained by the PCs) were classified as hotspots and coldspots, respectively. Four PCs were used to explain the spatial variability in 5-year survival among 75,215 individuals with EOCRC: PC1) poverty, chronic disease, health risk behaviors (β = -0.03, 95% credible interval (CrI): -0.04, -0.03); PC2) younger age, chronic disease-free, minority status (β = -0.01, 95% CrI: -0.02, 0.00); PC3) urban environment, preventive services (β = 0.02, 95% CrI: 0.00, 0.03); and PC4) older age (-0.04, 95% CrI: -0.06, -0.02). Among individuals with distant malignancies, the residual spatial variability remained high for two US counties: 1) Salt Lake County, UT residents experiencing 26.5% (95% CrI: 1.5%, 47.8%) lower odds of survival [hotspot], and 2) Riverside County, CA residents experiencing 37% (95% CrI: 7.97%, 78.8%) higher odds survival [coldspot] after adjustment for county-level factors. County-level ecological factors are strongly associated with survival among individuals with EOCRC. Yet there is some evidence of survival disparities among individuals with distant malignancies that remain unexplained by the included factors.

Spatial pattern and differential expression analysis with spatial transcriptomic data.

The emergence of spatial transcriptomic technologies has opened new avenues for investigating gene activities while preserving the spatial context of tissues. Utilizing data generated by such technologies, the identification of spatially variable (SV) genes is an essential step in exploring tissue landscapes and biological processes. Particularly in typical experimental designs, such as case-control or longitudinal studies, identifying SV genes between groups is crucial for discovering significant biomarkers or developing targeted therapies for diseases. However, current methods available for analyzing spatial transcriptomic data are still in their infancy, and none of the existing methods are capable of identifying SV genes between groups. To overcome this challenge, we developed SPADE for spatial pattern and differential expression analysis to identify SV genes in spatial transcriptomic data. SPADE is based on a machine learning model of Gaussian process regression with a gene-specific Gaussian kernel, enabling the detection of SV genes both within and between groups. Through benchmarking against existing methods in extensive simulations and real data analyses, we demonstrated the preferred performance of SPADE in detecting SV genes within and between groups. The SPADE source code and documentation are publicly available at https://github.com/thecailab/SPADE.

Scaling and Depth Variability of Source Parameters in Central and Southern Italy Using Regional Attenuation Models

ABSTRACT We study the scaling between seismic moment and corner frequency, and the spatial variability of the stress drop, in central and southern Italy. We analyze a data set generated by 28,943 earthquakes that occurred between 2005 and 2023 recorded by 1045 temporary or permanent stations. Considering the trade-off between source and propagation effects, we develop a spectral decomposition approach in which several attenuation models are derived for different subregions, and we perform an iterative decomposition to propagate the site amplification constraint to the whole data set. The use of multiple attenuation models has a significant impact on the source parameter estimation, resulting in stress-drop ratios on the order of a factor of 10 compared to values obtained without considering the attenuation differences. Once the attenuation variability is taken into account, the scaling between seismic moment and corner frequency shows different slopes for different subregions, with different degrees of self-similarity breakdown. Furthermore, we observe a clear trend in the slopes with depth for several subregions. Finally, the spatial variability of the stress-drop anomalies with respect to the average values expected for a given seismic moment shows a large lateral variability, which makes it difficult to detect increasing stress-drop trends with depth.

Variability of Biodegradation Rates of Commercial Chemicals in Rivers in Different Regions of Europe.

Biodegradation is one of the most important processes influencing the fate of organic contaminants in the environment. Quantitative understanding of the spatial variability in environmental biodegradation is still largely uncharted territory. Here, we conducted modified OECD 309 tests to determine first-order biodegradation rate constants for 97 compounds in 18 freshwater river segments in five European countries: Sweden, Germany, Switzerland, Spain, and Greece. All but two of the compounds showed significant spatial variability in rate constants across European rivers (ANOVA, P < 0.05). The median standard deviation of the biodegradation rate constant between rivers was a factor of 3. The spatial variability was similar between pristine and contaminated river segments. The longitude, total organic carbon, and clay content of sediment were the three most significant explanatory variables for the spatial variability (redundancy analysis, P < 0.05). Similarities in the spatial pattern of biodegradation rates were observed for some groups of compounds sharing a given functional group. The pronounced spatial variability presents challenges for the use of biodegradation simulation tests to assess chemical persistence. To reflect the variability in the biodegradation rate, the modified OECD 309 test would have to be repeated with water and sediment from multiple sites.

ANÁLISE ESPACIAL E TEMPORAL DE EVENTOS DE SECAS NA REGIÃO HIDROGRÁFICA ALTO RIO PARANÁ DE 1990 A 2020

The Paraná River Hydrographic Region is of great socioeconomic relevance for Brazil. Therefore, due to the history of droughts in the region, the aims of the current study were to assess rainfall spatial and temporal variability, as well as to analyze drought features based on using the Normalized Percentage Index (NPI) to contribute to water management processes in Upper Paraná River (UPR). The study area encompasses 14 Planning and Management Units, distributed in Goiás, Mato Grosso do Sul, Minas Gerais and São Paulo states. Climate data were obtained from the National Hydrometeorological Network database. A geostatistical analysis of rainfall was carried out, based on the annual series of 408 stations, and subsequently the calculation of NPI for classifying the droughts severity. All periods analyzed (1990-2020) showed spatial dependence (moderate to high), allowing the elaboration of droughts maps. The dry and rainy periods and areas did not indicate direct influence of El Niño-Southern Oscillation (ENSO). Mato Grosso do Sul and São Paulo units were the main contributors to the identified droughts. The data showed that 2010-2020 was the driest decade of the period, with a greater contribution from the 2019/2020 period, where droughts exceeded 60% of the area for two consecutive years, an event never identified in the last 3 decades. The NPI curves referring to “initial drought” and “mild” allowed verifying years of water crisis when their summed percentages reached at least 50% of the region. The methodology used is versatile, allowing its use in any watershed. Keywords: Rains. Water management. NPI. Kriging.

Assessment of controlled assistance from e-bikes as a mitigation strategy for individual PM exposure

Abstract Background While promoting active mobility and outdoor activities can benefit health (HLT), it also increases Exposure (EXP) to air pollutants, such as particulate matter (PM), due to the increased ventilation rate (VR) under physical activity. Traditional assessments of individual (IND) PM EXP involve costly equipment inaccessible to the public, but real-time low-cost sensors (LCS) are now emerging. This study presents a proof of concept to demonstrate the use of LCS for examining the real-time IND respiratory dose deposited (RDD) to PM cycling across the Brescia province. Methods An e-bike was ridden 33 times between 2022 and 2023 wearing a LCS for PM concentration [PM] linked to a GPS. Respiratory dose deposited(RDD) was calculated for six different locations within each route based on experimental PM and various VR. Results The LCS allows for the study of the spatial and temporal variability of [PM], revealing a significant difference (p &amp;lt; 0.01) according to the heating system operation and no significant difference (p &amp;gt; 0.05) depending on the location. RDD increases with [PM] and VR. Real-time and integrated RDD is calculated for routes with high (&amp;gt; 50 µg/m3), medium (25 µg/m3), and low (&amp;lt;10 µg/m3) [PM2.5]. The e-bike assistance is activated when the WHO interim target 4 of [PM] is reached to reduce VR, which corresponds to a shift to a lower physical activity intensity. The RDD calculated using the e-bike assistance over the path reduces RDD by about 23% and 11% over the high and medium [PM], while it never activates for the low [PM]. Conclusions This study demonstrates that the use of LCS allows the estimation of IND RDD, and the implementation of mitigation strategies based on controlled assistance from e-bikes would enable the reduction of PM individual EXP. Key messages • The integration of HLT data from wearable and air quality LCS enables the promotion of active mobility and outdoor activities controlling the EXP to air pollutants. • Establishing generalized guidelines based on this mitigation strategies for individual exposure reduction may also inform policy recommendations for promoting active and green mobility.

Estimation of Strawberry Canopy Volume in Unmanned Aerial Vehicle RGB Imagery Using an Object Detection-Based Convolutional Neural Network.

Estimating canopy volumes of strawberry plants can be useful for predicting yields and establishing advanced management plans. Therefore, this study evaluated the spatial variability of strawberry canopy volumes using a ResNet50V2-based convolutional neural network (CNN) model trained with RGB images acquired through manual unmanned aerial vehicle (UAV) flights equipped with a digital color camera. A preprocessing method based on the You Only Look Once v8 Nano (YOLOv8n) object detection model was applied to correct image distortions influenced by fluctuating flight altitude under a manual maneuver. The CNN model was trained using actual canopy volumes measured using a cylindrical case and small expanded polystyrene (EPS) balls to account for internal plant spaces. Estimated canopy volumes using the CNN with flight altitude compensation closely matched the canopy volumes measured with EPS balls (nearly 1:1 relationship). The model achieved a slope, coefficient of determination (R2), and root mean squared error (RMSE) of 0.98, 0.98, and 74.3 cm3, respectively, corresponding to an 84% improvement over the conventional paraboloid shape approximation. In the application tests, the canopy volume map of the entire strawberry field was generated, highlighting the spatial variability of the plant's canopy volumes, which is crucial for implementing site-specific management of strawberry crops.

Evaluating Factors Affecting Flood Susceptibility in the Yangtze River Delta Using Machine Learning Methods

AbstractFloods are widespread and dangerous natural hazards worldwide. It is essential to grasp the causes of floods to mitigate their severe effects on people and society. The key drivers of flood susceptibility in rapidly urbanizing areas can vary depending on the specific context and require further investigation. This research developed an index system comprising 10 indicators associated with factors and environments that lead to disasters, and used machine learning methods to assess flood susceptibility. The core urban area of the Yangtze River Delta served as a case study. Four scenarios depicting separate and combined effects of climate change and human activity were evaluated using data from various periods, to measure the spatial variability in flood susceptibility. The findings demonstrate that the extreme gradient boosting model outperformed the decision tree, support vector machine, and stacked models in evaluating flood susceptibility. Both climate change and human activity were found to act as catalysts for flooding in the region. Areas with increasing susceptibility were mainly distributed to the northwest and southeast of Taihu Lake. Areas with increased flood susceptibility caused by climate change were significantly larger than those caused by human activity, indicating that climate change was the dominant factor influencing flood susceptibility in the region. By comparing the relationship between the indicators and flood susceptibility, the rising intensity and frequency of extreme precipitation as well as an increase in impervious surface areas were identified as important reasons of heightened flood susceptibility in the Yangtze River Delta region. This study emphasized the significance of formulating adaptive strategies to enhance flood control capabilities to cope with the changing environment.

Characterizing the spatial variability of marine soil properties with site-specific sparse data using a Bayesian data fusion approach

Characterizing the spatial variability of marine soil properties with site-specific sparse data using a Bayesian data fusion approach

Carbon dioxide and evapotranspiration fluxes in an urban area of Krakow, Poland

AbstractUrban areas play an essential role in the global carbon balance, being responsible for more than 70% of anthropogenic carbon emissions, entering the atmosphere mainly in the form of carbon dioxide (CO2). Another crucial component of the carbon balance of the urban atmosphere is CO2 exchange with the biosphere, expressed in both emission and absorption fluxes. The biosphere component of the net CO2 flux is inseparably linked to the water (H2O) balance due to evapotranspiration. This article presents an estimation of CO2 and H2O net fluxes based on eddy covariance (EC) tower measurements and in‐depth analysis of its temporal and spatial variability for a typical urban site at mid‐latitude of the northern hemisphere. The source area was divided into two sectors, one representing dense urban development with sources of CO2 from traffic and households, and the second containing predominantly recreation and sports areas. Temporal variability analysis of evapotranspiration showed a clear seasonal cycle closely correlated with incoming radiation and air temperature, with the seasonal mean ranging from 0.4 mm·day−1 in winter to 1.6 mm·day−1 in summer. As a result of similar green coverage between the urban and green sectors, spatial variability was statistically insignificant. The net CO2 flux over a seasonal cycle was less pronounced for the total source area with a mean of 5.0 g C·m−2·day−1 in summer and 7.5 g C·m−2·day−1 in winter. However, significant variations between urban and green sectors were observed with the highest seasonal mean difference of 4.5 g C·m−2·day−1 in winter. The results confirm that while on the local, short time‐scale urban vegetation has a potential to mitigate the emissions, it is not able to offset them.

High spatial variability in wetland methane fluxes is tied to vegetation patch types

High spatial variability in wetland methane fluxes is tied to vegetation patch types

Temporal and spatial variation in reproductive benefits in a partial migrant.

In partial migrant systems, where residents and migrants coexist within a population, residents are commonly predicted to gain a reproductive advantage over migrants through priority access to high-quality territories and an earlier breeding start. Annual variation in reproductive benefits has been suggested to be important for the coexistence of both strategies in a population, as differences in wintering conditions experienced by the two strategies may result in a periodic reproductive advantage for migrants. However, the importance of spatial environmental variation for reproductive output in partially migrant populations remains largely unexplored. We investigated variation in the reproductive output of migrants and residents in a population of Swiss red kites (Milvus milvus) both temporally, across and within years, and spatially, along an elevational gradient. We gathered 4 years of reproductive data combined with 183 GPS-derived full annual cycles from individuals breeding in the Swiss Alpine foothills. At low, but not high, elevations, residents produced more fledglings than migrants. We also found evidence for annual variation in the reproductive advantage of the two strategies. Furthermore, while reproductive output did decline with a later breeding start, there was no difference in the start of breeding between the two migration strategies. The results of this study suggest that differences in reproductive output between migrants and residents in partial migrant populations can vary both due to the use of spatially distinct overwintering grounds and because the strategies are differently affected by spatial variables in the breeding area, such as elevation. The study emphasizes that spatial and temporal variation in reproductive benefits must be considered when predicting how migratory species will respond to future environmental change.

Projected population exposure to precipitation extremes in China by the end of the twenty-first century: trends, change points, and spatial variability

Projected population exposure to precipitation extremes in China by the end of the twenty-first century: trends, change points, and spatial variability

Longitudinal distributions of CO2-fixing bacteria in forest soils and their potential associations with soil multifunctionality

Autotrophic microorganisms can fix carbon dioxide (CO2) into organic carbon (C), potentially offering a natural mechanism to mitigate global climate change. Forest soils, recognized as vast and critical C repositories with significant microbial CO2 fixation rates, remain understudied, particularly regarding the spatial variations of autotrophic bacteria and their relationship to soil functions in arid regions. In this study, we systematically investigated soil multifunctionality, along with the spatial distribution of autotrophic bacterial communities identified by the RubisCO cbbL and cbbM genes, and the driving factors across a longitudinal gradient in the Loess Plateau forest soils. The investigation spanned an ∼850 km west-east transect with precipitation below 600 mm. The alpha diversity of cbbL-containing bacteria, as measured by the Chao1 index, was correlated with climatic variables such as precipitation and elevation instead of local soil characteristics. In contrast, the alpha diversity of cbbM-containing bacteria was associated with soil properties. The community composition of autotrophic bacteria, based on cbbL and cbbM genes, showed greater similarity in soils from the eastern Loess Plateau and was distinct from those in the western region. The cbbL- and cbbM-containing generalist taxa were subject to differential selection and promotion between the eastern and western regions. Temperature, soil pH and spatial variables were key drivers influencing the community composition of cbbL- and cbbM-containing bacteria. The diversity and communities of soil autotrophic bacteria significantly affected soil multifunctionality. The study demonstrates that soil autotrophic bacteria in forest soils are intricately connected to climatic conditions, soil pH and spatial factors, significantly impacting soil multifunctionality. These insights provide evidence that can be instrumental in predicting and potentially enhancing the functional capacity of forest ecosystems in the Loess Plateau.

Planktonic to sessile: drivers of spatial and temporal variability across barnacle life stages and indirect effects of the Pacific Marine Heatwave

ABSTRACT Barnacles are a foundation species in intertidal habitats. During the Pacific Marine Heatwave (PMH), intertidal barnacle cover increased in the northern Gulf of Alaska (GoA); however, the role of pelagic larval supply in this increase was unknown. Using long-term monitoring data on intertidal benthic (percent cover) and pelagic larval populations (nauplii and cyprid concentrations), we examined potential environmental drivers (temperature, chlorophyll-a, mixed layer depth) of larval concentration and whether including larval concentration at regional and annual scales improved intertidal barnacle percent cover models in two study regions in the GoA. In both regions, larval concentrations were slightly higher following the PMH. Percent cover models were improved by including cyprid concentrations (but not nauplii), and the effect strength varied by site and tidal elevation. This indicates that larval concentration contributes as a bottom–up driver of benthic barnacle abundance. There is little evidence of a direct effect of the PMH on either life stage. Instead, our results may illustrate the positive feedback between life stages, where higher adult benthic abundance increased larval concentrations, which then supplied more new recruits to the benthos. As heatwaves continue to occur, integrating various data types can provide insights into factors influencing both benthic and pelagic communities.