Drivers Of Dynamics Research Articles

Using local knowledge to reconstruct climate‐mediated changes in disease dynamics and yield—A case study on Arabica coffee in its native range

Societal Impact StatementAdapting agriculture to climate change requires an understanding of the long‐term relationship between climate, disease dynamics, and yield. While some countries have monitored major crop diseases for decades or centuries, comparable data is scarce or non‐existent for many countries that are most vulnerable to climate change. For this, a novel approach was developed to reconstruct climate‐mediated changes in disease dynamics and yield. Here, a case study on Arabica coffee in its area of origin demonstrates how to combine local knowledge, climate data, and spatial field surveys to reconstruct disease and yield time series and to postulate and test hypotheses for climate–disease–yield relationships.Summary While some countries have monitored crop diseases for several decades or centuries, other countries have very limited historical time series. In such areas, we lack data on long‐term patterns and drivers of disease dynamics, which is important for developing climate‐resilient disease management strategies. We adopted a novel approach, combining local knowledge, climate data, and spatial field surveys to understand long‐term climate‐mediated changes in disease dynamics in coffee agroforestry systems. For this, we worked with 58 smallholder farmers in southwestern Ethiopia, the area of origin of Arabica coffee. The majority of farmers perceived an increase in coffee leaf rust and a decrease in coffee berry disease, whereas perceptions of changes in coffee wilt disease and Armillaria root rot were highly variable among farmers. Climate data supported farmers' understanding of the climatic drivers (increased temperature, less rainy days) of these changes. Temporal disease‐climate relationships were matched by spatial disease‐climate relationships, as expected with space‐for‐time substitution. Understanding long‐term disease dynamics and yield is crucial to adapt disease management to climate change. Our study demonstrates how to combine local knowledge, climate data and spatial field surveys to reconstruct disease time series and postulate hypotheses for disease‐climate relationships in areas where few long‐term time series exist.

High response diversity and conspecific density-dependence, not species interactions, drive dynamics of coral reef fish communities.

Species-to-species and species-to-environment interactions are key drivers of community dynamics. Disentangling these drivers in species-rich assemblages is challenging due to the high number of potentially interacting species (the 'curse of dimensionality'). We develop a process-based model that quantifies how intraspecific and interspecific interactions, and species' covarying responses to environmental fluctuations, jointly drive community dynamics. We fit the model to reef fish abundance time series from 41 reefs of Australia's Great Barrier Reef. We found that fluctuating relative abundances are driven by species' heterogenous responses to environmental fluctuations, whereas interspecific interactions are negligible. Species differences in long-term average abundances are driven by interspecific variation in the magnitudes of both conspecific density-dependence and density-independent growth rates. This study introduces a novel approach to overcoming the curse of dimensionality, which reveals highly individualistic dynamics in coral reef fish communities that imply a high level of niche structure.

Dominant Drivers of Current Account Dynamics

Dominant Drivers of Current Account Dynamics

Interplay of crises: Unpacking intraday spillovers in oil and European equities in the shadow of the COVID-19 and the Ukraine-Russia war

This study investigates the time-varying frequency of spillovers between European stock markets and oil during the COVID-19 pandemic and the Russia-Ukraine war. Using the spillover index by Diebold & Yilmaz, 2012 and Baruník & Křehlík, 2018, we analyze high-frequency data at a 5-min interval to analyze the interplay between crude oil market returns and the Stoxx 600 index returns, including sectors such as auto, basic material, banks, chemicals, food and beverage, health, industrials, insurance, oil and gas, retail, real estate, technology (tech), telecommunication (telecom), and utilities. The sample period is January 3, 2022, to March 25, 2022. Our findings reveal a substantial degree of connectedness within this financial network, with specific sectors—auto, chemicals, food, industrials, insurance, real estate, retail, tech, and telecom—acting as net transmitters of shocks, while other sectors assume the role of net receivers. The Russia-Ukraine war is a significant driver of these interconnected dynamics. Spillovers are most prevalent in the medium-term horizon, with the time dimension affecting the role of sectors and oil. Furthermore, our research highlights the potential benefits of adding oil assets to portfolios, enhancing risk-adjusted performance. However, ongoing risk management remains crucial due to the dynamic hedge ratios. This study offers practical insights for investors and policy makers, aiding risk management and investment strategies in turbulent markets.

Water and sediment chemistry drivers of chlorophyll-a dynamics within a Ramsar declared floodplain pan wetland system.

Floodplain pans are hydrologically dynamic in nature and characterised by variables such as chlorophyll-a (chl-a), water, and sediment chemistry over their hydroperiods. The present study investigated the spatio-temporal variations in water and sediment physico-chemical, and chlorophyll-a concentration characteristics of six floodplain pans found in the Ramsar declared Makuleke wetlands, Kruger National Park, South Africa. The water and sediment physico-chemical variable values were generally elevated during the high-water period, whereas chlorophyll-a concentrations varied across pans and hydroperiod. Benthic chl-a concentration significantly varied across pans with concentrations ranging from 161 to 1036.2mg m2. The two-way ANOVA showed significant differences in benthic chl-a concentration among hydroperiods, and no significant differences were observed in pelagic chl-a across pans and hydroperiods. Generally, pelagic and benthic chl-a concentration increased as water and sediment chemistry variables increased. Furthermore, three sediment variables, i.e. pH, calcium, and magnesium, and water conductivity were found to be significant in structuring benthic chlorophyll-a dynamics in pans. However, none of the sediment and water variables had a significant effect on pelagic chl-a. Hydroperiod had a significant effect on influencing chl-a concentration, with high and low water level periods being characterised by low and high chl-a concentration, respectively. The n-MDS results showed strong overlaps in chl-a biomass among the Makuleke floodplain pans across hydroperiods. The increasing chl-a concentration in these floodplain pans due to potential bioturbation effects as a result of large mammals could potentially lead to eutrophication, which in turn could affect the system's primary productivity and aquatic biota. Therefore, it is important to establish a continuous monitoring programme on these pans to inform sound management decisions.

Monitoring and influencing factors of grassland livestock overload in Xinjiang from 1982 to 2020.

It is crucial to estimate the theoretical carrying capacity of grasslands in Xinjiang to attain a harmonious balance between grassland and livestock, thereby fostering sustainable development in the livestock industry. However, there has been a lack of quantitative assessments that consider long-term, multi-scale grass-livestock balance and its impacts in the region. This study utilized remote sensing and empirical models to assess the theoretical livestock carrying capacity of grasslands. The multi-scale spatiotemporal variations of the theoretical carrying capacity in Xinjiang from 1982 to 2020 were analyzed using the Sen and Mann-Kendall tests, as well as the Hurst index. The study also examined the county-level grass-livestock balance and inter-annual trends. Additionally, the study employed the geographic detector method to explore the influencing factors. The results showed that: (1) The overall theoretical livestock carrying capacity showed an upward trend from 1982 to 2020; The spatial distribution gradually decreased from north to south and from east to west. In seasonal scale from large to small is: growing season > summer > spring > autumn > winter; at the monthly scale, the strongest livestock carrying capacity is in July. The different grassland types from largest to smallest are: meadow > alpine subalpine meadow > plain steppe > desert steppe > alpine subalpine steppe. In the future, the theoretical livestock carrying capacity of grassland will decrease. (2) From 1988 to 2020, the average grass-livestock balance index in Xinjiang was 2.61%, showing an overall increase. At the county level, the number of overloaded counties showed an overall increasing trend, rising from 46 in 1988 to 58 in 2020. (3) Both single and interaction factors of geographic detectors showed that annual precipitation, altitude and soil organic matter were the main drivers of spatiotemporal dynamics of grassland load in Xinjiang. The results of this study can provide scientific guidance and decision-making basis for achieving coordinated and sustainable development of grassland resources and animal husbandry in the region.

Abstract 2750: Leveraging machine-learning approaches to dissect drivers of clinical metastatic dynamics in lung adenocarcinoma

Abstract Background: Non-small cell lung cancer (NSCLC) survival is closely linked to metastatic progression. 5-year survival rates drop from >65% when localized to ~9% when distant metastases occur. Despite this impact on mortality, drivers of overall metastasis and site-specific organotropism are not well understood in NSCLC. To investigate these questions, we focused on Lung adenocarcinoma (LUAD), the most common subtype of NSCLC. Although previous efforts have been made to examine LUAD organotropism, we sought to expand upon longitudinal modeling approaches and explore associations in an independent large clinical and genomic annotated cohort. Methods: Using the artificial intelligence model from (Kehl et al. 2021), we annotated a clinico-genomic cohort of 2777 patients with lung adenocarcinoma, resulting in 59,177 annotated imaging reports. This resulted in time to metastatic site annotations for each patient, with median of 2 years follow up. Metastatic sites included brain, bone, adrenal, liver, lymph node, and mesentery. All patients were evaluated at the Dana-Farber Cancer Institute, and each patient had at least one tumor biopsy sequenced with targeted panel sequencing of 400+ cancer related genes. To infer genomic and clinical correlates of site-specific metastasis, we performed cause-specific and Fine-Gray hazard modeling in this cohort. We used the recurrent event Andersen-Gill model to infer predictors of longitudinal metastasis rates. Results: Site-specific analyses yielded genes whose mutant status were significantly associated with a change in risk and/or rate of certain metastatic sites. We found that NOTCH1 is significantly associated with an increased rate and risk of brain metastases (HR=2.46, P<0.001). SETD2 is associated with decreased incidence of brain (HR=0.42, P<0.001). SMARCA4 is significantly associated with increased incidence and rate of adrenal metastasis (HR=1.64, P<0.01), while NF1 is significantly associated with decreased rate of adrenal metastasis (HR=0.72, P<0.01). In a multivariable model, Female patients and younger patients had a lower rate of new metastatic sites. As previously seen, TP53 and SMARCA4 mutations associated with higher rates of new metastatic sites (HR=1.17, P<0.001; HR=1.36, P<0.001 respectively), and SETD2 is associated with a lower rate of new metastatic sites (HR=0.71, P<0.001). Conclusions: By using time-to-event analysis with longitudinal metastatic annotations, we identify potential drivers of overall metastasis and site-specific organotropism in LUAD patients. Identification of new associations and concordance of our results with previous studies also supports the utility of artificial-intelligence-annotated datasets, allowing for larger cohorts without the need for manual annotation. Citation Format: Tyler Aprati, Michael Manos, Giuseppe Tarantino, Marc Glettig, Maryclare Griffin, Alexander Gusev, Kenneth Kehl, David Liu. Leveraging machine-learning approaches to dissect drivers of clinical metastatic dynamics in lung adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2750.

Synoptic and planetary-scale dynamics modulate antarctic atmospheric river precipitation intensity

Although rare, atmospheric rivers substantially influence the interannual variability of Antarctic surface mass balance. Here we use MERRA-2 reanalysis to identify characteristics unique to atmospheric river environments by comparing (1) Analog (environments that feature high-low pressure couplets, similar to Atmospheric River environments, but no Atmospheric River), (2) Atmospheric River, and (3) Top Atmospheric River (highest precipitation) timesteps during 1980–2019 around Antarctica. We find significant differences between Atmospheric River and Analog environments including more intense and poleward-shifted mid-tropospheric geopotential height couplets as well as larger atmospheric moisture anomalies. We find similar significant enhancement in synoptic-scale dynamic drivers of Top Atmospheric Rivers compared to all Atmospheric River environments, but no significant difference in local integrated water vapor anomalies. Instead, our results highlight the importance of large-scale dynamic drivers during Top Atmospheric River timesteps, including amplified Rossby waves excited by tropical convection.

Exploring the Driving Forces of Vegetation Greening on the Loess Plateau at the County Scale

China has implemented several ecological projects in the Loess Plateau region to address severe land degradation and soil erosion. Accurately assessing ecological restoration and its driving factors remains challenging. Previous studies in this area concentrated on driving factors have mainly focused on natural factors at the regional or watershed scale, with limited consideration of socioeconomic factors at the county scale. In this study conducted in Huanglong County on the Loess Plateau, the focus was to fill the gaps in previous research and provide insights into the socioeconomic driving forces behind vegetation greening. Remote sensing image data (NDVI) from 1999 to 2019 were used to analyze vegetation greenness dynamics in the region. Five socioeconomic variables were considered, including afforestation intensity, deforestation intensity, agricultural intensity, village intensity, and road intensity layers, to characterize the impact of afforestation, agriculture, and urbanization policies. The RESTREND (residual trends) method was employed to assess the relative importance of climate and human activities on vegetation dynamics. This study found that temperature–NDVI relationships are more suitable for building RESTREND models than precipitation–NDVI relationships. Human activity was the main driver of vegetation dynamics, contributing 62% compared to 38% from climate change. Agricultural practices and afforestation were found to have a positive impact on ecological restoration, while deforestation and urbanization had no significant impact. These findings highlight a conceptual framework for understanding the intricate relationship between ecological restoration, climatic factors, and human activity on the Loess Plateau. This study suggests that significant progress has been made in ecological restoration through human efforts in combating land degradation. However, it emphasizes the need to strengthen natural conservation efforts and gradually transition toward restoration processes driven by natural forces for sustainable socioeconomic development. The methodology used in this study can be applied to explore the driving forces of ecological restoration in other regions facing human-driven land degradation.

A general, resource‐based explanation for density dependence in populations of large herbivores

AbstractThe discipline of ecology seeks to understand how ecosystems, communities, and populations are regulated. A ubiquitous mechanism of population regulation of consumers is that capturing energy and nutrients in sufficient quantities for survival and reproduction becomes more difficult as population density increases. Extensive evidence has revealed that populations of large herbivores are often regulated by density dependence, defined as the reduction in the per‐capita population growth rate that occurs as populations grow large. Diminished body mass of individuals has been repeatedly observed in high‐density populations, implicating compromised nutrition as the primary cause of density dependence. However, there is no general explanation for why these nutritional deficiencies occur. Recent work demonstrated that reduced food intake rates resulting from the functional response of herbivores to depleted plant biomass does not provide a sensible explanation for density dependence because rates of food intake of herbivores are often insensitive to changes in plant biomass. A new model of feedbacks from plant biomass to herbivores shows how reduced nutrition of herbivores can result from increased dilution of nutrients in the plant tissue they consume as populations grow, even when their rate of consumption of plants remains constant. The model contains parameters that can be scaled to body mass, allowing unusually general predictions. The model shows that convex, concave, and linear relationships between the per‐capita growth rate and population density can arise from the effects of depletion of plant biomass by herbivore foraging. The model is the first to explicitly include spatial variance in the nutritional quality of plants as a general driver of herbivore population dynamics. I show how regulation of herbivore abundance by plant nutrients can occur, even when a large fraction of the consumable plant biomass remains uneaten, providing a simple, mechanistic explanation for bottom‐up control of population dynamics of primary consumers in a “green world.”

A Comprehensive Analysis of Vegetation Dynamics and Their Response to Climate Change in the Loess Plateau: Insight from Long-Term kernel Normalized Difference Vegetation Index Data

The Loess Plateau (LP) is a typical climate-sensitive and ecologically delicate area in China. Clarifying the vegetation–climate interaction in the LP over 40+ years, particularly pre- and post-Grain to Green Program (GTGP) implementation, is crucial for addressing potential climate threats and achieving regional ecological sustainability. Utilizing the kernel Normalized Difference Vegetation Index (kNDVI) and key climatic variables (precipitation (PRE), air temperature (TEM), and solar radiation (SR)) between 1982 and 2022, we performed an extensive examination of vegetation patterns and their reaction to changes in climate using various statistical methods. Our findings highlight a considerable and widespread greening on the LP from 1982 to 2022, evidenced by a kNDVI slope of 0.0020 yr−1 (p < 0.001) and a 90.9% significantly increased greened area. The GTGP expedited this greening process, with the kNDVI slope increasing from 0.0009 yr−1 to 0.0036 yr−1 and the significantly greened area expanding from 39.1% to 84.0%. Over the past 40 years, the LP experienced significant warming (p < 0.001), slight humidification, and a marginal decrease in SR. Post-GTGP implementation, the warming rate decelerated, while PRE and SR growth rates slightly accelerated. Since the hurst index exceeded 0.5, most of the vegetated area of the LP is expected to be greening, warming, and humidification in the future. In the long term, 75% of the LP vegetated area significantly benefited from the increase in PRE, especially in relatively dry environments. In the LP, 61% of vegetated areas showed a positive correlation between kNDVI and TEM, while 4.9% exhibited a significant negative correlation, mainly in arid zones. SR promoted vegetation growth in 23% of the vegetated area, mostly in the eastern LP. The GTGP enhanced the sensitivity of vegetation to PRE, increasing the area corresponding to a significant positive correlation from 15.3% to 59.9%. Overall, PRE has emerged as the dominant climate driver for the vegetation dynamics of the LP, followed by TEM and SR. These insights contribute to a comprehensive understanding of the climate-impact-related vegetation response mechanisms, providing guidance for efforts toward regional sustainable ecological development amid the changing climate.

Uncovering the Drivers of Responsive Ionospheric Dynamics to Severe Space Weather Conditions: A Coordinated Multi‐Instrumental Approach

AbstractSpace‐weather conditions can often have a detrimental impact on satellite communications and limited experimental data has made it challenging to understand the complex processes that occur in the upper atmosphere. To overcome this challenge, we utilized a coordinated multi‐instrumental dataset consisting of GNSS airglow remote sensing, ionosonde, magnetometer, and in‐situ satellite data to investigate plasma depletions. We present a case study focused on the geomagnetic storm that occurred on 27 February 2014. During the storm, GNSS positioning errors exceeded undisturbed levels by at least 2 times, and ionospheric corrections reached amplitudes of up to ±20 m at the Rabat station. We identified 3 large depletions that were most likely generated by sudden vertical ionospheric drifts that began at approximately 17:00 UTC at sunset in Morocco and the southern regions of Spain. These drifts reached ∼500 m/s and lasted until 22:00 UTC. The observed depletions propagated to the northeast, as seen through ionosonde echoes and ground‐based airglow images. Satellite limb‐images revealed an ionospheric uplift of about 100 km due to the storm, consistent with ionosondes in Spain. The observed local anomalies may be influenced by variations in equatorial electric current flows, which are correlated with fluctuations in ground‐based magnetometer data. These variations are likely a result of the effects of the inner radiation belt on the development of plasma bubbles in the African longitude sector. Sudden enhancements in upward E × B drift caused ionospheric uplift to higher altitudes, enhancing the “fountain effect” and shifting the Equatorial Ionospheric Anomaly crests to higher latitudes.

Expansion of apple cultivation increases the abundance of codling moth (Cydia pomonella) in agricultural landscapes of China.

Agricultural land-use change is an important driver of pest population dynamics, and can alter source-sink dynamics and the concentration-dilution effects of the landscape. Understanding the effects of land use on pests at both landscape and regional levels is essential for the development of sustainable pest management strategies given the large changes occurring in cropping systems in China. At the landscape level, we investigated the impacts of landscape composition and edge density on pheromone trap catch of codling moth (Cydia pomonella) in apple orchards, in Aksu, Xinjiang, China. At the regional scale, we conducted a meta-analysis using data from studies performed across the Aksu area in recent decades, to assess the relationship between trends in codling moth abundance and the area of apple cultivation. Both extensive planting of apple and large areas of annual crops in the landscape increased the abundance of codling moth, whereas the presence of secondary host plants (peach, pear, walnut, plum, and apricot) had a negative effect. Seminatural habitats and landscape edge density did not significantly affect codling moth abundance. The responses of different generations of codling moth to landscape factors were varied. At the regional level, codling moth occurrence was positively correlated with the expansion of apple production areas. Expansion of apple cultivation increases the abundance of codling moth in agricultural landscapes. We recommend decreasing the area devoted to monocultures of apple when designing agricultural landscapes and increasing plantings of secondary host crops to dilute and reduce the abundance of codling moth. © 2024 Society of Chemical Industry.

Evaluating top-down, bottom-up, and environmental drivers of pelagic food web dynamics along an estuarine gradient.

Identification of the key biotic and abiotic drivers within food webs is important for understanding species abundance changes in ecosystems, particularly across ecotones where there may be strong variation in interaction strengths. Using structural equation models (SEMs) and four decades of integrated data from the San Francisco Estuary, we investigated the relative effects of top-down, bottom-up, and environmental drivers on multiple trophic levels of the pelagic food web along an estuarine salinity gradient and at both annual and monthly temporal resolutions. We found that interactions varied across the estuarine gradient and that the detectability of different interactions depended on timescale. For example, for zooplankton and estuarine fishes, bottom-up effects appeared to be stronger in the freshwater upstream regions, while top-down effects were stronger in the brackish downstream regions. Some relationships (e.g., bottom-up effects of phytoplankton on zooplankton) were seen primarily at annual timescales, whereas others (e.g., temperature effects) were only observed at monthly timescales. We also found that the net effect of environmental drivers was similar to or greater than bottom-up and top-down effects for all food web components. These findings can help identify which trophic levels or environmental factors could be targeted by management actions to have the greatest impact on estuarine forage fishes and the spatial and temporal scale at which responses might be observed. More broadly, this study highlights how environmental gradients can structure community interactions and how long-term data sets can be leveraged to generate insights across multiple scales.

Quantifying the impacts of management and herbicide resistance on regional plant population dynamics in the face of missing data

Abstract A key challenge in the management of populations is to quantify the impact of interventions in the face of environmental and phenotypic variability. However, accurate estimation of the effects of management and environment, in large‐scale ecological research is often limited by the expense of data collection, the inherent trade‐off between quality and quantity, and missing data. In this paper we develop a novel modelling framework, and demographically informed imputation scheme, to comprehensively account for the uncertainty generated by missing population, management, and herbicide resistance data. Using this framework and a large dataset (178 sites over 3 years) on the densities of a destructive arable weed (Alopecurus myosuroides) we investigate the effects of environment, management, and evolved herbicide resistance, on weed population dynamics. In this study we quantify the marginal effects of a suite of common management practices, including cropping, cultivation, and herbicide pressure, and evolved herbicide resistance, on weed population dynamics. Using this framework, we provide the first empirically backed demonstration that herbicide resistance is a key driver of population dynamics in arable weeds at regional scales. Whilst cultivation type had minimal impact on weed density, crop rotation, and earlier cultivation and drill dates consistently reduced infestation severity. Synthesis and applications: As we demonstrate that high herbicide resistance levels can produce extremely severe weed infestations, monitoring herbicide resistance is a priority for farmers across Western Europe. Furthermore, developing non‐chemical control methods is essential to control current weed populations, and prevent further resistance evolution. We recommend that planning interventions that centre on crop rotation and incorporate spring sewing and cultivation to provide the best reductions in weed densities. More generally, by directly accounting for missing data our framework permits the analysis of management practices with data that would otherwise be severely compromised.

The cyclicality of on-the-job search

On-the-job search is increasingly recognized as an important potential driver of labor market dynamics over the business cycle. Using the UK Labor Force Survey, we find robust empirical evidence that on-the-job search is countercyclical and that the cyclical fluctuations have important repercussions for labor market dynamics. We also find that the cyclical pattern is not explained by precautionary search motives but rather appears to be driven by job-ladder-motivated searches. This finding is surprising because, as we confirm, the expected returns to on-the-job search are procyclical. We find evidence that three features of search behavior may contribute to this finding: greater search effort in response to lower job-to-job transition probabilities, a prevalence of non-pecuniary motivated searches that are less affected by lower expected wage gains, and procyclicality in average match quality, which has a significant impact on the search behavior of new hires over the business cycle.

Kenyan Free-Tailed Bats Demonstrate Seasonal Birth Pulse Asynchrony with Implications for Virus Maintenance

Ecological information on wildlife reservoirs is fundamental for research targeting prevention of zoonotic infectious disease, yet basic information is lacking for many species in global hotspots of disease emergence. We provide the first estimates of synchronicity, magnitude, and timing of seasonal birthing in Mops condylurus, a putative ebolavirus host, and a co-roosting species, Mops pumilus (formerly Chaerephon pumilus). We show that population-level synchronicity of M. condylurus birthing is wide (~ 8.5 weeks) and even wider in M. pumilus (> 11 weeks). This is predicted to promote the likelihood of filovirus persistence under conditions of bi-annual birthing (two births per year). Ecological features underlying the magnitude of the birth pulse—relative female abundance (higher than expected for M. condylurus and lower for M. pumilus, based on literature) and reproductive rate (lower than expected)—will have countering effects on birthing magnitude. Species-specific models are needed to interpret how identified birth pulse attributes may interact with other features of molossid ebolavirus ecology to influence infection dynamics. As a common feature of wildlife species, and a key driver of infection dynamics, detailed information on seasonal birthing will be fundamental for future research on these species and will be informative for bat-borne zoonoses generally.

What Drives House Prices in Europe?

AbstractBoom‐and‐bust cycles in the housing market pose a threat to macroeconomic and financial stability, thus calling for a timely assessment of imbalances. This work sheds light on the drivers of house price dynamics in some euro area economies, investigating the risks of overheating. We show that an Error‐Correction‐Model (ECM) featuring a long‐run relationship between house prices and income and short‐run effects of interest rates and housing supply fits the data well in most cases. We then propose a novel model‐based misalignment indicator and find that extrapolative house price expectations play an important role in the build‐up of speculative bubbles.

Arbuscular mycorrhiza convey significant plant carbon to a diverse hyphosphere microbial food web and mineral-associated organic matter.

Arbuscular mycorrhizal fungi (AMF) transport substantial plant carbon (C) that serves as a substrate for soil organisms, a precursor of soil organic matter (SOM), and a driver of soil microbial dynamics. Using two-chamber microcosms where an air gap isolated AMF from roots, we 13CO2-labeled Avena barbata for 6 wk and measured the C Rhizophagus intraradices transferred to SOM and hyphosphere microorganisms. NanoSIMS imaging revealed hyphae and roots had similar 13C enrichment. SOM density fractionation, 13C NMR, and IRMS showed AMF transferred 0.77 mg C g-1 of soil (increasing total C by 2% relative to non-mycorrhizal controls); 33% was found in occluded or mineral-associated pools. In the AMF hyphosphere, there was no overall change in community diversity but 36 bacterial ASVs significantly changed in relative abundance. With stable isotope probing (SIP)-enabled shotgun sequencing, we found taxa from the Solibacterales, Sphingobacteriales, Myxococcales, and Nitrososphaerales (ammonium oxidizing archaea) were highly enriched in AMF-imported 13C (> 20 atom%). Mapping sequences from 13C-SIP metagenomes to total ASVs showed at least 92 bacteria and archaea were significantly 13C-enriched. Our results illustrate the quantitative and ecological impact of hyphal C transport on the formation of potentially protective SOM pools and microbial roles in the AMF hyphosphere soil food web.

Drivers of heterogeneity in tundra vegetation productivity on the Yamal Peninsula, Siberia, Russia

The direction and magnitude of tundra vegetation productivity trends inferred from the normalized difference vegetation index (NDVI) have exhibited spatiotemporal heterogeneity over recent decades. This study examined the spatial and temporal drivers of Moderate Resolution Imaging Spectroradiometer Max NDVI (a proxy for peak growing season aboveground biomass) and time-integrated (TI)-NDVI (a proxy for total growing season productivity) on the Yamal Peninsula, Siberia, Russia between 2001 and 2018. A suite of remotely-sensed environmental drivers and machine learning methods were employed to analyze this region with varying climatological conditions, landscapes, and vegetation communities to provide insight into the heterogeneity observed across the Arctic. Summer warmth index, the timing of snowmelt, and physiognomic vegetation unit best explained the spatial distribution of Max and TI-NDVI on the Yamal Peninsula, with the highest mean Max and TI-NDVI occurring where summer temperatures were higher, snowmelt occurred earlier, and erect shrub and wetland vegetation communities were dominant. Max and TI-NDVI temporal trends were positive across the majority of the Peninsula (57.4% [5.0% significant] and 97.6% [13.9% significant], respectively) between 2001 and 2018. Max and TI-NDVI trends had variable relationships with environmental drivers and were primarily influenced by coastal-inland gradients in summer warmth and soil moisture. Both Max and TI-NDVI were negatively impacted by human modification, highlighting how human disturbances are becoming an increasingly important driver of Arctic vegetation dynamics. These findings provide insight into the potential future of Arctic regions experiencing warming, moisture regime shifts, and human modification, and demonstrate the usefulness of considering multiple NDVI metrics to disentangle the effects of individual drivers across heterogeneous landscapes. Further, the spatial heterogeneity in the direction and magnitude of interannual covariation between Max NDVI, TI-NDVI, and climatic drivers highlights the difficulty in generalizing the effects of individual drivers on Arctic vegetation productivity across large regions.