Spatial Variability Research Articles

Evaluating the utility of weather generators in crop simulation models for in-season yield forecasting

CONTEXTCrop yield forecasting is crucial for ensuring food security and adapting to the impacts of climate change, as it provides early insights into potential harvest outcomes and helps farmers and policymakers make informed decisions in the face of changing environmental conditions. The accuracy of the crop model–based yield forecasting frameworks is affected by the uncertainty in future weather data, which is often substituted with synthetic weather realizations generated by stochastic weather generators. OBJECTIVEThis study aims to assess the performance of three recent stochastic weather generators—Global Weather Generator (GWGEN), WeatherGEN, and R Multi-Sites Autoregressive Weather GENerator (RMAWGEN) — in producing synthetic weather realizations that accurately represent regional climate variations and their impact on winter wheat yield forecasting. METHODSWe utilized historical weather data from Daymet, an interpolation of daily meteorological observations that produces gridded datasets with a spatial resolution of 1 km. This data was used both as an input for the weather generators and for evaluating the performance of the generated weather realizations. Furthermore, the weather realizations generated by these weather generators across multiple winter wheat field sites in Kansas were employed in the calibrated Environmental Policy Integrated Climate (EPIC) crop model to assess the potential impact of variations in weather generators on the accuracy of crop yield forecasts. RESULTS AND CONCLUSIONSRMAWGEN and WeatherGEN excelled in accurately simulating rainy days and precipitation amounts, with WeatherGEN particularly effective in wet months and RMAWGEN performing best in dry months, showcased their proficiency in diverse weather conditions. RMAWGEN consistently showed lowest error across all variables, including precipitation, solar radiation, and both maximum and minimum temperatures. Except for GWGEN, both RMAWGEN and WeatherGEN demonstrate good agreement with Daymet in replicating spatial variability patterns. RMAWGEN notably outperformed other weather generators, particularly during the forecasting period. Consequently, it showed superior capabilities in forecasting crop yields closely matching the simulated results with Daymet data. SIGNIFICANCEThe findings of this study are crucial for selecting accurate weather data estimates for crop yield forecasting. Utilizing alternative sources such as ensembles of multiple weather generators or outputs from sub-seasonal multi-model forecast systems may further enhance the accuracy of crop yield forecasts.

Characterizing the distribution pattern of traffic-related air pollutants in near-road neighborhoods.

In near-road neighborhoods, residents are more frequently exposed to traffic-related air pollution (TRAP), and they are increasingly aware of pollution levels. Given this consideration, this study adopted portable air pollutant sensors to conduct a mobile monitoring campaign in two near-road neighborhoods, one in an urban area and one in a suburban area of Shanghai, China. The campaign characterized spatiotemporal distributions of fine particulate matter (PM2.5) and black carbon (BC) to help identify appropriate mitigation measures in these near-road micro-environments. The study identified higher mean TRAP concentrations (up to 4.7-fold and 1.7-fold higher for PM2.5 and BC, respectively), lower spatial variability, and a stronger inter-pollutant correlation in winter compared to summer. The temporal variations of TRAP between peak hour and off-peak hour were also investigated. It was identified that district-level PM2.5 increments occurred from off-peak to peak hours, with BC concentrations attributed more to traffic emissions. In addition, the spatiotemporal distribution of TRAP inside neighborhoods revealed that PM2.5 concentrations presented great temporal variability but almost remained invariant in space, while the BC concentrations showed notable spatiotemporal variability. These findings provide valuable insights into the unique spatiotemporal distributions of TRAP in different near-road neighborhoods, highlighting the important role of hyperlocal monitoring in urban micro-environments to support tailored designing and implementing appropriate mitigation measures.

Chromophoric dissolved organic matter traces seasonally changing coastal processes in a river-influenced region of the western Bay of Bengal.

The optical characteristics of colored dissolved organic matter (CDOM) serve as a convenient tool for evaluating coastal processes, e.g., river runoff, anthropogenic inputs, primary production, and bacterial/photochemical processes. We conducted a study on the seasonal and spatial variability of absorbance and fluorescence characteristics of CDOM and nutrients in the coastal waters near the Gauthami estuary of River Godavari, the largest peninsular river of India, for a year. The surface aCDOM(350) showed a significant inverse relation with salinity in the coastal region, indicating a conservative mixing of marine and terrestrial end members. The aCDOM(350) was not conservative in the offshore (100m isobath) waters due to enrichment by secondary sources. Seasonal variability in optical properties indicated diverse sources for CDOM, as revealed by principal component analysis. The excitation-emission matrix (EEM) spectra followed by parallel factor analysis (EEM-PARAFAC) revealed four distinct fluorophores. The tyrosine (B) fluorophore showed a predominant increase in the post-monsoon season (October to January), while tryptophan (T) was relatively more enriched, coincident with nutrient enrichment and transparency increase during the early monsoon phase (July). The biological index (BIX), which reflects recent photosynthetic activity, also displayed relatively higher values during the early monsoon. The humic fluorophores A and M, and humification index (HIX) were relatively enriched during the later phase of monsoon (July-October). HIX was > 4 in a few samples of the offshore region (100-m isobath) and indicated a probable contamination from drill-mud (bentonite) used in hydrocarbon exploration. During the monsoon, the relationship between T and B with CDOM was not evident due to the masking of B fluorescence in intact protein. However, during the post-monsoon (POM) and pre-monsoon (PRM) periods, this masking effect was not observed, likely due to protein degradation via bacterial and photochemical processes, respectively. Temporal variability in nutrients indicated that high ammonium levels were produced during POM (OM bacterial degradation), and high nitrite levels were observed during PRM (due to primary production). This study provides foundational insights into the use of CDOM for understanding the impact of diverse environmental, river discharge, and anthropogenic factors on coastal ecosystems.

Spatial variability of rainfall: deciphering flood characteristics and model precision

ABSTRACT This study investigates the intricate role of spatial variability in rainfall (SVR) concerning flood characteristics and its impact on refining flood prediction models. A spatial variability index was used to classify rainfall events into two categories: spatially homogeneous (Class A) and heterogeneous (Class B). The analysis of historical flood events suggests that the SVR influences flood peaks. This research introduces a novel approach to assess SVR’s role in calibrating hydrological models, subsequently improving model selection. By separately calibrating Class A and B events within both lumped and distributed models, the models yield superior results compared to the conventional approach. For the catchments considered, the lumped models demonstrated heightened performance for Class A events, while the distributed models outperformed in Class B events. This study underscores not only the influence of SVR on flood dynamics but also the efficacy of event-based classification in refining hydrological models for superior flood prediction accuracy.

Study of charged particle multiplicity with centrality in Au+Au collisions at SNN = 200 GeV

We have carried out methods of study to find the correlation between charged particle multiplicity and collision centrality of Au+Au collisions at SNN = 200 GeV. We have studied the open data from the ALICE experiment and arrived at the conclusion that central collisions produced a greater number of charged particles than peripheral collisions. This is done by reconstructing the three silicon layers inside the inner tracking system of the detector and studying the change in two spatial variables between layers: pseudorapidity with range || < 1 and azimuthal angle with acceptance , which can then be input into the sideband method to find the final multiplicity of each event. The study provides an insight into the transient nature of the Quark Gluon Plasma (QGP) produced in collisions by exploring how collision aftermaths vary with different conditions of the partons, which hopefully adds to the understanding of matter under the strong force at extreme energy density.

Aircraft-derived CH4 emissions from surface and in-situ mining activities in the Alberta oil sands region

The identification and reduction of methane sources is considered an important part of the fight to stem greenhouse gas (GHG) emissions to the atmosphere. One of the largest industrial contributors to national GHG emissions in Canada is the Alberta Oil Sands Region. To quantify and investigate the spatial distribution and temporal variability of methane emissions from this region, airborne measurements were conducted in 2017 and 2018 with three aircraft. 59 flights were conducted in total to assess emissions for both open-pit and in-situ facilities, in both cold and warm seasons. Derived emission rates were higher than those reported in national inventories by 30%–96% depending on the facility. In-situ facilities had emission rates an order of magnitude lower than surface mining operations and differed significantly from inventory estimates. No statistical differences in CH4 emissions between cold and warm seasons were observed, substantiating the use of simple upscaling to annual emissions within inventories. Rather than confirming a reported decrease in emissions between 2013 and 2018, the measurements suggest essentially no change from the 18 t h−1 for the region observed in 2013. Overall, the results suggest that current methods of CH4 emission determination within the oil sands region, for use in reporting, require improvement.

Observation and modelling of infragravity waves at a large meso-tidal inlet and lagoon

The role of infragravity waves (IG waves) in beach and dune erosion or in flood hazard has been extensively studied on open beaches. In contrast, the detailed characterization of IG waves and their contribution to the Total Water Level (TWL) along the shore of inlets received little attention so far. In such environment, there is a real lack of in situ observations of waves and hydrodynamics conditions at appropriate spatial and temporal coverage to study the role of infragravity (IG) waves (long waves of frequency typically ranging between 0.004 Hz and 0.04–0.05 Hz) on coastal hazards. This contribution is based on field observations collected at the Arcachon Lagoon, a shallow semi-enclosed lagoon connected to the ocean by a large tidal inlet, located in southwest France. Analyses combine observations made at several locations during storm events within the inlet and the lagoon with numerical simulation with the XBeach surfbeat model to explore the spatial variability of IG waves and simulate observed, historical, and idealized storm conditions. The results show that IG waves are substantial during typical winter storms at the inlet and range from Hm0 = 0.8 to over 1 m across the ebb delta and about 0.4–0.6 m in the inner part of the inlet. At the lagoon entrance, IG waves remain substantial (about 0.1–0.2 m) and decrease to a few centimeters at the lagoon shore. The spatial variability and magnitude of IG waves along the inlet coast, simulated for the historical storms, are quite comparable to those observed during classical winter, and do not increase linearly with offshore wave energy. However, both observations and simulations reveal local amplifications of IG waves in the inner part of the inlet, especially along the sheltered coast were IG waves dominate the variance of free surface elevation, reaching about 0.6–0.7 m during common storms and more than 1 m for an extreme storm scenario. A numerical experiment indicates that IG wave reflection from one coast to the other contributes up to 35–40% of the measured IG wave height at a hot spot located along the sheltered coast. Finally, the contribution of IG waves to TWL at the shore on both sides of the inlet has been estimated to be about 0.4–0.6 m for a common storm and 0.6–0.9 m for an extreme scenario, locally peaking at 0.74 and 1.1 m respectively and overpassing the contribution of wave-induced setup. This work provides new insights into the contribution of IG waves to TWL and its implications for overtopping flooding hazard and overwash processes at large inlets, highlighting the need to consider IG waves in Early Warning Systems or hazard mapping for flood prevention plans in these environments.

Forecasting Oligochaeta Abundance using the Apparent Electrical Conductivity of Irrigated Soils with Reclaimed Wastewater: Evidence from Corso, Algeria

Background: The main objective of this paper is to study the possibility of using the measurement of apparent electrical conductivity (ECa) to estimate Oligochaeta (earthworm) density in irrigated soils treated with reclaimed wastewater. Additionally, the study sought to analyze the impact of using reclaimed wastewater for irrigation on earthworm density in the study area. Methods: Earthworm sampling was carried out at thirty emplacements within the study area during two measurement campaigns. Soil ECa was measured by (EM38). Result: The results showed that irrigation with reclaimed wastewater promotes the abundance of earthworms (earthworm density). Indeed, sites irrigated with reclaimed wastewater have higher earthworm densities compared to sites irrigated with conventional water. The results also show a strong correlation between ECa and earthworm density (R2 greater than 0.7). The Wilcoxon test indicates a non-significant difference between measured earthworm densities and those predicted by ECa (p greater than 0.5). This result confirms the effectiveness of the EMI technique as a tool to evaluate the spatial variability of soil biological parameters. These results collectively suggest that ECa represents a real possibility of reducing the number of earthworm samplings, whil ensuring the reliability of the estimates of real values of earthworm density and their surface spatial distribution. ECa could therefore be a useful tool to spatialize and predict the presence of earthworms in soil irrigated by reclaimed wastewater.

Unraveling the spatial imprint of hominin and carnivore accumulations in Early Pleistocene African sites

Reconstructions of palimpsest formation and dynamics in Early Pleistocene African archaeological deposits have undergone significant advances thanks to taphonomic research. However, the spatial imprint of different agents implicated in most of these accumulations still needs to be addressed. We hypothesize that different site formation dynamics may yield diverse spatial distributions of archaeological remains, reflecting the intervention of different agents (i.e., hominins, felids, hyaenids) in palimpsests. This study aims to investigate the spatial patterns of archaeological remains in a selected sample of Early Pleistocene accumulations with the goal of understanding and characterizing their spatial dynamics. Building on previous taphonomic interpretations of twelve paradigmatic archaeological deposits from Olduvai Bed I (FLK Zinj 22 A, PTK 22 A, DS 22B, FLK N 1–2 to 5, FLK NN 3, DK 1–3) and Koobi Fora (FxJj50, FxJj20 East and FxJj20 Main), we explore the spatial patterns of remains statistically and use hierarchical clustering on principal components analysis (HCPC) to group the highest-density spots at these sites based on a number of spatial variables. The results of this approach show that despite sharing a similar inhomogeneous pattern, anthropogenic sites and assemblages where carnivores played the main role display fundamentally different spatial features. Both types of spatial distributions also show statistical differences from modern hunter-gatherer campsites. Additional taphonomic particularities and differing formation processes of the analyzed accumulations also appear reflected in the classifications. This promising approach reveals crucial distinctions in spatial imprints related to site formation and agents’ behavior, prompting further exploration of advanced spatial statistical techniques for characterizing archaeological intra-site patterns.

Effects of Surface Area and Water Level Variability on Fisheries Production in Lake Naivasha, Kenya

Lake Naivasha is a freshwater, a Ramsar site and an Important Bird Area. It provides multiple services to the surrounding communities such as Fishing, Tourism and Agriculture. Lake Naivasha has been experiencing a water level variation cycle over the years as a result of it being shallow and experiencing high evaporation. Variation in water levels leads to flooding, and receding which disrupts the Lake ecosystem thus causing direct and indirect effects on fishing. The study was conducted to establish the relationship between Surface area and water level variability and fish yield and revenue in Lake Naivasha. Primary data was collected using a semi-structured questionnaire, and Key informant interviews. Secondary data was collected using Landsat images that provided the temporal and spatial water level variability and documentary analysis between 1989 to 2022. Both descriptive statistics and inferential statistics were used for data analysis. The results show a positive correlation between Surface area Variability and fish yield (r= 0.6261, R2= 0.3920) and fish revenue (r= 0.5219, R2= 0.2724). Surface area variability accounts for 39% of the variation in total fish yield, and 27% of the fish revenue. The study recommends continuous monitoring and surveillance of the lake level, and water quality by the relevant government agencies, as well as watershed management and conservation measures for the sustainability of the Lake’s fishery.

The signature of the main modes of climatic variability as revealed by the Jenkinson‐Collison classification over Europe

AbstractThe Jenkinson‐Collison Weather Typing (JC‐WT) method uses sea‐level pressure gradients to create 27 types based on the geostrophic flow and vorticity around any extratropical target location. Typically, JC‐WTs are applied over specific locations or limited domains, thus hampering the understanding of the impact of large‐scale mechanisms on regional climate. This study explores the links between regional climate variability, as represented by the JC‐WTs, and large‐scale phenomena, to describe the synoptic‐scale variability in the North Atlantic‐European region and evaluate the JC‐WT methodology. Large‐scale circulation is here characterized by major atmospheric low‐frequency modes, namely the North Atlantic Oscillation, the East Atlantic and the Scandinavian teleconnection indices, and by atmospheric blockings. Results show that JC‐WTs coherently capture the spatial and temporal variability of the large‐scale modes and yields a characteristic response to blocking events. Overall, our results underpin the exploratory potential of this method for the analysis of the near‐surface circulation. These findings endorse the use of JC‐WTs and support the reliability and utility of the JC‐WT classification for process‐based model assessments and model selection, a crucial task for climate impact studies.

Measuring prairie snow water equivalent with combined UAV-borne gamma spectrometry and lidar

Abstract. Despite decades of effort, there remains an inability to measure snow water equivalent (SWE) at high spatial resolutions using remote sensing. Passive gamma ray spectrometry is one of the only well-established methods to reliably remotely sense SWE, but airborne applications to date have been limited to observing kilometre-scale areal averages. Noting the increasing capabilities of unoccupied aerial vehicles (UAVs) and miniaturization of passive gamma ray spectrometers, this study tested the ability of a UAV-borne gamma spectrometer and concomitant UAV-borne lidar to quantify the spatial variability of SWE at high spatial resolutions. Gamma and lidar observations from a UAV (UAV-gamma and UAV-lidar) were collected over two seasons from shallow, wind-blown, prairie snowpacks in Saskatchewan, Canada, with validation data collected from manual snow depth and density observations. A fine-resolution (0.25 m) reference dataset of SWE, to test UAV-gamma methods, was developed from UAV-lidar snow depth and snow survey snow density observations. The ability of UAV-gamma to resolve the areal average and spatial variability of SWE was promising with appropriate flight characteristics. Survey flights flown at a velocity of 5 m s−1, altitude of 15 m, and line spacing of 15 m were unable to capture the average or spatial variability of SWE within the uncertainty of the reference dataset. Slower, lower, and denser flight lines at a velocity of 4 m s−1, altitude of 8 m, and line spacing of 8 m were able to successfully observe areal average SWE and its variability at spatial resolutions greater than 22.5 m. Using a combination of UAV-based gamma SWE and UAV-based lidar snow depth improved the spatial representation of SWE substantially and permitted estimation of SWE at a spatial resolution 0.25 m with a ± 14.3 mm error relative to the reference SWE dataset. UAV-borne gamma spectrometry to estimate SWE is a promising and novel technique that has the potential to improve the measurement of shallow prairie snowpacks, and when combined with UAV-borne lidar snow depths, can provide fine-resolution, high-accuracy estimates of prairie SWE. Research on optimal hardware, data processing, and interpolation techniques is called for to further improve this remote sensing product and explore its application in other environments.

Anthropogenic Drivers of Spatial Trends in Groundwater Quality in the Upper Athi River Basin of Kenya, East Africa

Rapid urbanization and population growth in the upper Athi River basin in Kenya have increased the strain on the sub catchments water supply and sanitation situation. Due to increasing demand, inadequate supply of drinking water and lack of sanitation facilities, people in the sub-catchment are increasingly reliant on groundwater as a primary or supplementary water source. However, the use of on-site wastewater systems and agricultural pollution, mainly from runoff containing fertilizers, pesticides, herbicides, and faeces, pose a threat to groundwater in the sub catchment. Subsequently, the current study was conducted in the Thiririka sub catchment, Kiambu County, Kenya, to determine the safety of groundwater sources and to examine the factors influencing groundwater quality in the catchment area. This study assessed the influence of anthropogenic activities on the physical, chemical, and bacteriological quality of groundwater in the Upper Athi River basin of Kenya between April and June 2022. Twenty variables were analyzed and compared with water quality standards to determine hydro chemical characteristics, evidence of contamination, and suitability of groundwater. Shallow wells (SW) had higher concentrations of major ions and key parameters than boreholes (BH), such as alkalinity (7%), turbidity (96%), nitrates (92%), sulfates (48%), phosphates (93%), chlorides (77%), potassium (84%), sodium (30%) and fecal coliforms (99%) significant at p < 0.01. Concentrations of eleven water quality variables however were comparable in both systems. Farming, animal husbandry, and pit latrines were negatively but significantly correlated with the water quality of SW explaining substantial amounts of variation (≤ 45%) in concentrations of water quality variables. Ionic and coliform levels increased with decreasing distance and vice versa. IDW interpolation maps were generated in ArcGIS software to determine the spatial variability of groundwater quality in the basin. Anthropogenic activities such as pit latrines and animal husbandry impaired the quality of groundwater which in most cases was not potable.

Developing Water Quality Formulations for a Semi-Distributed Rainfall–Runoff Model

Hydrological modeling can be challenging due to significant data requirements and computational complexities. Hydrological models must be sufficiently complex to describe physical processes yet simple enough to use. This paper describes the development of a simplified watershed-scale input–output model to simulate runoff quantity and quality during a storm event. This work builds upon an existing semi-distributed rainfall–runoff model by adding calculations for pollutant concentrations based on simplified mass balance equations. The model was tested against various watershed examples of increasing complexity. The results show the change in peak flow and pollutant concentration in different areas of the watershed, demonstrating the model’s ability to account for the dynamics of runoff movement through the watershed. This paper advances watershed management by addressing data scarcity through the development of a simplified hydrological model that effectively incorporates spatial variability within a watershed while requiring minimal data input.

Determination of Water Quality for Ground Water Near Municipal Dumpsite in Ibadan Southwest Nigeria

Access to clean drinking water is crucial for human health and survival. In vicinities near municipal dumpsites, ensuring groundwater quality is especially vital to safeguard the ecosystem and human health. This study utilized the Entropy Weighted Water Quality Index (EWQI), a novel assessment tool, to evaluate groundwater near the Awotan dumpsite in southwestern Nigeria. Forty-five groundwater samples were collected during rainy and dry seasons and explored for groundwater quality using EWQI. The physicochemical parameters fall within permissible limits for domestic purposes during both seasons. Nonetheless, 2% of the samples surpassed the recommended nitrate limit of 45 mg/L, and all samples had iron levels above the 0.3 mg/L threshold. The calculated EWQI values for these samples ranged from 20 to 528, with 2% deemed excellent, 16% good, 51% medium, 18% poor and 13% extremely poor in terms of water quality for domestic use. The spatial variability in water quality levels means that there are localised impacts of the dumpsite and thus there is need for targeted management strategies. We recommend the government undertake sustainable retrofitting to enhance landfill design, incorporating an efficient leachate collection system to lessen groundwater pollution. Additionally, nearby residents should implement water treatment methods to improve water quality and minimize health risks.

The role of citizen science in assessing the spatiotemporal pattern of rainfall events in urban areas: a case study in the city of Genoa, Italy

Abstract. Climate change in the Mediterranean region is manifesting itself as an increase in average air temperature and a change in the rainfall regime: the value of cumulative annual rainfall generally appears to be constant, but the intensity of annual rainfall maxima, between 1 and 24 h, is increasing, especially in the period between late summer and early autumn. The associated ground effects in urban areas consist of flash floods and pluvial floods, often in very small areas, depending on the physical-geographical layout of the region. In the context of global warming, it is therefore important to have an adequate monitoring network for rain events that are highly concentrated in space and time. This research analyses the meteo-hydrological features of the 27 and 28 August 2023 event that occurred in the city of Genoa, Italy, just 4 d after the record maximum air temperature was recorded: between 19:00 and 02:00 UTC almost 400 mm of rainfall was recorded in the eastern sector of the historic centre of Genoa, with significant ground effects such as flooding episodes and the overflowing of pressurised culverts. Rainfall observations and estimates were made using both official or “authoritative” networks (rain gauges and meteorological radar) and rain gauge networks inspired by citizen science principles. The combined analysis of observations from authoritative and citizen science networks reveals, for the event analysed, a spatial variability of the precipitation field at an hourly and a sub-hourly timescale that cannot be captured by the current spatial density of the authoritative measurement stations (which have one of the highest densities in Italy). Monthly total rainfall and short-duration annual maximum time series recorded by the authoritative rain gauge network of the Genoa area are then analysed. The results show significant variation even at distances of less than 2 km in the average rainfall depth accumulated over sub-hourly duration. Extreme weather monitoring activity is confirmed as one of the most important aspects in terms of flood prevention and protection in urban areas. The integration between authoritative and citizen science networks can prove to be a valid contribution to the monitoring of extreme events.

Spatial and Temporal Trends in Precipitation and Temperature and their Influence on Groundwater in Ballia

A study examined the impact of long-term pre- and post-monsoon precipitation and temperature on groundwater level fluctuations in 17 blocks of Ballia District. The study used 100 years of daily precipitation data (1917-2016) and 62 years of daily temperature data (1951-2012) from the Indian Meteorological Department. Seasonal analysis was performed using pre-monsoon, monsoon, post-monsoon, and winter data. The Mann-Kendall method and Sen's slope were applied. The spatial variability of rainfall and temperature was mapped using the IDW technique in ArcGIS. Significant annual precipitation decreases were found in Murlichapra and Sear blocks. Maniar and Pandah blocks showed precipitation increases. An increasing pre-monsoon trend was observed in several blocks, while post-monsoon trends varied. Groundwater levels fluctuated across Ballia, with a notable decrease of 2.1 m in Bairia Block from 2006 to 2016.

Spatially explicit predictions of food web structure from regional-level data.

Knowledge about how ecological networks vary across global scales is currently limited given the complexity of acquiring repeated spatial data for species interactions. Yet, recent developments in metawebs highlight efficient ways to first document possible interactions within regional species pools. Downscaling metawebs towards local network predictions is a promising approach to using the current data to investigate the variation of networks across space. However, issues remain in how to represent the spatial variability and uncertainty of species interactions, especially for large-scale food webs. Here, we present a probabilistic framework to downscale a metaweb based on the Canadian mammal metaweb and species occurrences from global databases. We investigated how our approach can be used to represent the variability of networks and communities between ecoregions in Canada. Species richness and interactions followed a similar latitudinal gradient across ecoregions but simultaneously identified contrasting diversity hotspots. Network motifs revealed additional areas of variation in network structure compared with species richness and number of links. Our method offers the potential to bring global predictions down to a more actionable local scale, and increases the diversity of ecological networks that can be projected in space. This article is part of the theme issue 'Connected interactions: enriching food web research by spatial and social interactions'.

Neighborhood Diversity Promotes Tree Growth in a Secondary Forest: The Interplay of Intraspecific Competition, Interspecific Competition, and Spatial Scale.

Understanding the biodiversity-productivity relationship (BPR) is crucial for biodiversity conservation and ecosystem management. While it is known that diversity enhances forest productivity, the underlying mechanisms at the local neighborhood level remain poorly understood. We established a 9.6 ha dynamic forest plot to study how neighborhood diversity, intraspecific competition, and interspecific competition influence tree growth across spatial scales using linear mixed-effects models. Our analysis reveals a significant positive correlation between neighborhood species richness (NSR) and relative growth rate (RGR). Notably, intraspecific competition, measured by conspecific neighborhood density and resource competition, negatively impacts RGR at finer scales, indicating intense competition among conspecifics for limited resources. In contrast, interspecific competition, measured by heterospecific density and resource competition, has a negligible impact on RGR. The relative importance of diversity and intra/interspecific competition in influencing tree growth varies with scale. At fine scales, intraspecific competition dominates negatively, while at larger scales, the positive effect of NSR on RGR increases, contributing to a positive BPR. These findings highlight the intricate interplay between local interactions and spatial scale in modulating tree growth, emphasizing the importance of considering biotic interactions and spatial variability in studying BPR.

In vitro quantification of stent-graft behaviour during chimney thoracic endovascular aortic repair.

To quantify dynamic gutter phenomena and endograft deformations during double chimney thoracic endovascular aortic repair (ch-TEVAR) in a physiological model of the thoracic aorta subjected to pulsatile haemodynamic conditions. Two in vitro procedures revascularizing the brachiocephalic trunk and left common carotid artery were performed representing both balloon-expandable (BE, Ankura-BeGraft) and self-expandable (SE, Ankura-Viabahn) double ch-TEVAR configurations. Retrospectively gated computed tomography (CT) was used to evaluate endograft behaviour. Device interactions were characterised according to gutter volume, gutter surface deviation, and endograft deformation (D-ratio) at end-diastolic and peak-systolic aortic pressure. Use of BE chimney grafts resulted in three times total gutter volume compared to SE chimney grafts. Gutter volumes were observed to vary dynamically between the end-diastolic and peak-systolic phases of the cardiac cycle, with the most substantial change associated with the BE configuration. Chimney graft deformations were dependent on device type, with SE devices exhibiting up to twice the deformation as BE devices. When adjacent, SE chimney grafts were observed to support each other, and thus tended towards a more consistently circular shape. Gutter and chimney graft behaviour were dependent on device type, and exhibited both spatial and temporal variability. This study emphasises notable differences between BE and SE double ch-TEVAR configurations which should be considered when evaluating risk of endoleak. The findings reported here also support the use of gated CT to better identify device-related complications with ch-TEVAR, and can be used in the design of next generation devices.