Large-scale Drivers Research Articles

The 2019–21 drought in southern Madagascar

Two consecutive failed rainy seasons in the southern part of Madagascar in 2019–21 had devastating impacts on the population, including an amplification of the ongoing food insecurity in the area. The drought events were second in severity only to the 1990–92 drought and were estimated in a previous study to have a return period of 135 years. In this study, the physical mechanisms that led to these consecutive drought events are investigated.We found that the anomalously cold sea surface temperatures (SSTs) that persisted to the south of Madagascar between December 2019 and December 2020 led to a decrease in the transport of moist air over land. These cold SST anomalies were the most negative anomalies in the past four decades and intensified the rainfall deficit resulting from a negative Subtropical Indian Ocean Dipole (SIOD) mode during the rainy season of December 2019 to March 2020 and during December 2020. We also found that the rainfall response to the SST anomaly south of Madagascar was three times greater than that of a canonical SIOD.A weak Mozambique Channel Trough and a strong Angola low system, on the other hand, modulated the expected above-normal rainfall from a La Niña event in January–February 2021. Our study demonstrates how local factors can modulate the impacts of large-scale drivers, and that both local and global drivers, and their interactions, should be considered when producing seasonal forecasts and advisories, as well as climate change adaptation and mitigation plans for southern Madagascar.

Using the Observed Variations of the Start Date of the Rainy Season over Central America for Its Reliable Seasonal Outlook

Abstract We introduce a simple method to define the start and the end of the rainiest part of the year as the first and the last day of the year when the daily rain rate is more or less than the annual mean climatological rain rate for a region or at a given grid point of the rainfall analysis, respectively. A novelty of this work is the adoption of a perturbation technique to generate a total of 1001 ensemble members to account for observational and analysis uncertainties. This allows for a probabilistic estimate of the start and retreat dates of the rainy season at the granularity of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), version 6, rainfall analysis over Central America. The seasonal cycle of the IMERG rainfall analysis is also found to verify with in situ observations in the region. Many large-scale climate drivers affect regional rainfall, often with complex interactions that affect the onset date, retreat date, and magnitude of the seasonal rainfall cycle, making it difficult to predict the length or total quantity of seasonal rainfall using climate drivers alone. Once an onset date is established, however, this metric alone can be more indicative of both the length and total seasonal rainfall anomaly than predicting how the climate drivers will interact to affect the quantity and duration of upcoming seasonal rainfall. The local relationships of the start date with seasonal length and rainfall anomaly are leveraged to produce effective seasonal outlooks of the rainy season for the region by just monitoring the start date variations. Significance Statement The start date and retreat date of the rainy season in Central America are defined every year, precisely to a specific date from our proposed definition of it. This is possible because the region exhibits a strong seasonality of the rainfall. As a result, the year-to-year (interannual) variation of the seasonal rainfall during the rainy season is also determined by the variations in the length of the season that are usually overlooked in fixed calendar seasons. We define the start or retreat date of the rainy season as the first or the last day of the year when the daily rain rate exceeds or falls below the average daily rainfall from 2001 to 2022, respectively. We also find that both start and retreat date variations independently influence the length and total seasonal rainfall of the rainy season. Consequently, these relationships are leveraged to provide an outlook of the forthcoming rainy season from the diagnosis of the variations of its onset date, which is shown to be an effective predictor with significant useful seasonal prediction skills.

Mechanically and accurately calculate river width in vegetation areas by coupling Sentinel-1 and -2 imageries within land-water-mixed pixels

Accurately measuring river width has been one of greatest challenges due to the presence of mixed land–water pixels intersecting river boundaries. Therefore, this study proposed a novel mechanical method (RW-vebasud), instead of traditionally empirical models, to estimate river width within a pixel in vegetation areas based on time series analysis of Sentinel-1 and Sentinel-2 spaceborne multispectral images. We initially explored the mechanism of variation in backscatter intensity (σ) with enhanced vegetation index (EVI) whereby we successfully removed noises in σ–EVI relationship resulted from vegetation growth. Then, for the first time a smooth functional relationship between water area proportion and backscatter intensity within a ROI (or region of interest) was constructed. Consequently, subpixel water–land separation based on the mechanism process was realized. The novel method could not only work at large-scaled rivers (satellite-visible) but perform well at small-scaled rivers within a water-land mixed pixel (satellite-invisible). A total of 197 measurements for river widths in China during 2016 ∼ 2021 were used for model verification, demonstrating a positive correlation between EVI and σ, with R2 ranging from 0.16 to 0.69 (P<0.05). The RW-vebasud exhibited superior accuracy in calculating river width compared to the widely used MNDWI (modified normalized difference water index). The Root Mean Square Error (RMSE) decreased by 4.32 ∼ 6.65 m when the river width was less than 90 m and by 66.12 % when it exceeded 90 m, compared to MNDWI. Remarkably, RW-vebasud maintains satisfactorily high accuracy (the Nash-Sutcliffe efficiency coefficient: NSE=0.70 and RMSE=3.19) even at the spatial scale less than 3 times the image resolution, breaking the internationally accepted limit that river width extraction can only be accurate when the river width is greater than 3 times the satellite resolution. Moreover, the accuracy of this method is better than that with the currently well-known global river width datasets GRWL and MERIT Hydro. For the RW-Vebasud/GRWL/MERIT Hydro datasets, the NSE=0.99 /0.93/0.87, the RMSE=5.99/42.33/54.27, and the R2 = 0.99/0.91/0.74, respectively. The application of RW-vebasud in China shows that river widths in wet and dry seasons exhibited an increasing trend over the previous six years (2016–2021), as global warming accelerated glacier melting and increased rainfall quantity, with an average growth rate of 2.26 m/year (wet, P<0.05) and 2.17 m/year (dry, P<0.05), respectively. In response to the summer/winter Asian monsoons, most rivers widen in summer. The largest river width occurs in the Yellow River Basin (YLRB, 155.28 m on average), while the smallest occurs in the Hai River Basin (HARB, 22.99 m on average). The method proposed in this study can provide efficient techniques for surface river-width reconstruction which can greatly facilitate global resource and environmental modelling.

A Tale of Two Novembers: Confounding Influences on La Niña’s Relationship with Rainfall in Australia

Abstract Despite common background La Niña conditions, Australia was very dry in November 2020 and wet in November 2021. This paper aims to provide an explanation for this difference. Large-scale drivers of Australian rainfall, including El Niño–Southern Oscillation, Indian Ocean dipole, Southern Annular Mode, and Madden–Julian oscillation, were examined but did not provide obvious clues for the differences. We found that the absence (in 2020) or presence (in 2021) of an enhanced thermal wind and subtropical jet over the Australian continent contributed to the rainfall anomalies. In general, La Niña sets up warm sea surface temperatures around northern Australia, which enhances the meridional temperature gradient over the continent and hence thermal wind and subtropical jet. In November 2021, these warm sea surface temperatures, coupled with a persistent midlatitude trough, which advected cold air over the Australian continent, led to an enhanced meridional temperature gradient and subtropical jet over Australia. The enhanced jet provided favorable conditions for the development of rain-bearing weather systems across Australia. In 2020, the continent was warm, displacing the latitude of maximum meridional temperature gradient south of the continent, resulting in fewer instances of the subtropical jet over Australia, and little development of weather systems over the continent. We highlight that although La Niña tilts the odds to wetter conditions for Australia, in any given month, variability in temperatures over the continent can contribute to subtropical jet variability and resulting rainfall in ways which confound the normal expectation from La Niña. Significance Statement Forecasts of El Niño–Southern Oscillation are eagerly awaited, as the state of this climate driver has profound impacts on the likelihood of rainfall in regions around the world. While El Niño and La Niña do change rainfall likelihoods, the actual outcomes of these events are sometimes counter to expectation. This work explores one of the confounding factors to those expectations in the Australian context—the role of the meridional temperature gradient over the continent in modifying the storm track over Australia, which can disrupt the expected El Niño and La Niña teleconnections. We present case studies for two La Niña springs, highlighting that the Australian continent can help shape its own weather toward wetter or drier outcomes.

Prediction of streamflow in Chalakudy River Basin, Kerala, by integrating teleconnection patterns of large-scale atmospheric circulations

ABSTRACT In this study, the relationship between large-scale climatic drivers and streamflow of the Chalakudy River Basin, Kerala, was analysed using methods such as bivariate wavelet coherence (BWC), multiple wavelet coherence (MWC), and partial wavelet coherence (PWC) analysis. The four prominent global climate indices chosen are the Indian Ocean Dipole (IOD), North Atlantic Oscillation (NAO), El Niño Southern Oscillation (ENSO), and Pacific Decadal Oscillation (PDO), along with other local climate drivers. The BWC analysis showed that streamflow and rainfall had a very strong in-phase relationship, whereas the maximum temperature and average temperature showed an anti-phase relationship. In the case of global climate drivers, ENSO has a significant impact on the streamflow of the Chalakudy River Basin. The average wavelet coherence (AWC), which quantifies the teleconnections, confirms the observation with a high coherency of 0.75 between streamflow and rainfall and 0.51 for streamflow and ENSO. Streamflow prediction models were developed using random forest (RF) and artificial neural network (ANN) techniques by considering the influence of significant global and local climatic drivers. It was observed that the RF model performed slightly better than the ANN model, with R = 0.875, NSE = 0.766, RMSE = 23.468, and RSR = 0.524.

Unravelling large-scale patterns and drivers of biodiversity in dry rivers

More than half of the world’s rivers dry up periodically, but our understanding of the biological communities in dry riverbeds remains limited. Specifically, the roles of dispersal, environmental filtering and biotic interactions in driving biodiversity in dry rivers are poorly understood. Here, we conduct a large-scale coordinated survey of patterns and drivers of biodiversity in dry riverbeds. We focus on eight major taxa, including microorganisms, invertebrates and plants: Algae, Archaea, Bacteria, Fungi, Protozoa, Arthropods, Nematodes and Streptophyta. We use environmental DNA metabarcoding to assess biodiversity in dry sediments collected over a 1-year period from 84 non-perennial rivers across 19 countries on four continents. Both direct factors, such as nutrient and carbon availability, and indirect factors such as climate influence the local biodiversity of most taxa. Limited resource availability and prolonged dry phases favor oligotrophic microbial taxa. Co-variation among taxa, particularly Bacteria, Fungi, Algae and Protozoa, explain more spatial variation in community composition than dispersal or environmental gradients. This finding suggests that biotic interactions or unmeasured ecological and evolutionary factors may strongly influence communities during dry phases, altering biodiversity responses to global changes.

A systematic evaluation of high-cloud controlling factors

Abstract. Clouds strongly modulate the top-of-the-atmosphere energy budget and are a major source of uncertainty in climate projections. “Cloud controlling factor” (CCF) analysis derives relationships between large-scale meteorological drivers and cloud radiative anomalies, which can be used to constrain cloud feedback. However, the choice of meteorological CCFs is crucial for a meaningful constraint. While there is rich literature investigating ideal CCF setups for low-level clouds, there is a lack of analogous research explicitly targeting high clouds. Here, we use ridge regression to systematically evaluate the addition of five candidate CCFs to previously established core CCFs within large spatial domains to predict longwave high-cloud radiative anomalies: upper-tropospheric static stability (SUT), sub-cloud moist static energy, convective available potential energy, convective inhibition, and upper-tropospheric wind shear (ΔU300). We identify an optimal configuration for predicting high-cloud radiative anomalies that includes SUT and ΔU300 and show that spatial domain size is more important than the selection of CCFs for predictive skill. We also find an important discrepancy between the optimal domain sizes required for predicting locally and globally aggregated radiative anomalies. Finally, we scientifically interpret the ridge regression coefficients, where we show that SUT captures physical drivers of known high-cloud feedbacks and deduce that the inclusion of SUT into observational constraint frameworks may reduce uncertainty associated with changes in anvil cloud amount as a function of climate change. Therefore, we highlight SUT as an important CCF for high clouds and longwave cloud feedback.

Increasing monsoon precipitation extremes in relation to large-scale climatic patterns in Pakistan

This study comprehensively evaluates monsoon variability and its large-scale drivers in Pakistan, a country often affected by extreme monsoon events leading to destructive flooding. Utilizing the Monotonic Mann-Kendall and Sen's Slope test, we analyzed trends in seven indicators of extreme precipitation events (EPEs) from 1961 to 2017 across Pakistan and its homogeneous climatic regions. The wavelet coherence method further allowed us to probe the connection between EPEs and key climate indices such as the Atlantic Multidecadal Oscillation (AMO), North Atlantic Oscillation (NAO), El Niño–Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Pacific Decadal Oscillation (PDO), Indian summer monsoon (ISM), and Southern Oscillation Index (SOI) in the time-frequency domain. Our findings indicate a significant increase in all EPEs across Pakistan, particularly in the monsoon region (R-II) from 1961 to 2017, with a noticeable decline post-1980s. The findings revealed a significant increase in EPEs in the whole country from 1961 to 1989 to 1990–2017, with a dominant change in the monsoon region during 1990–2017. In terms of climate indices combinations, IOD + ISM showed the highest coherences (>0.65), while ENSO+IOD + ISM and ENSO+IOD + PDO displayed coherences >0.81 and > 0.79, respectively, across the entire country. The multi-linear regression (MLR) model analysis drivers indicate that IOD and ENSO are the leading drivers of changes in monsoon EPEs in the whole country, whereas the core monsoon region of Pakistan also showed a strong influence from AMO and NAO in tandem with ENSO and IOD. The overall increase in EPEs over Pakistan are mainly associated with the indices from the Pacific Ocean, especially IOD, ENSO and PDO and with their various combinations. Our study reveals the variations in EPEs across Pakistan, yielding insights that could inform water resource management strategies and disaster prevention measures.

Variability in flood frequency in sub-Saharan Africa: The role of large-scale climate modes of variability and their future impacts

Sub-Saharan Africa (SSA) is strongly affected by flood hazards, endangering human lives and economic stability. However, the role of internal climate modes of variability in driving fluctuations in SSA flood occurrence remains poorly documented and understood. To address this gap, we quantify the relative and combined contribution of large-scale climate drivers to seasonal and regional flood occurrence using a new 65-year daily streamflow dataset, sea-surface temperatures derived from observations, and 12 Single Model Initial-condition Large Ensembles (SMILEs) from the Coupled Model Intercomparison Project Phases 5 and 6. We find significant relationships between floods and large-scale climate variability across SSA, with climatic drivers accounting for 30–90 % of the variability in floods. Notably, western, central, and the summer-rain region of southern Africa display stronger teleconnections to large-scale climate variability in comparison to East Africa and the winter-rain region of South Africa, where regional circulation patterns and human activities may play a more important role. In southern and eastern Africa, floods are mainly influenced by teleconnections with the Pacific and Indian Oceans, while in western and central Africa, teleconnections with the Atlantic Ocean and Mediterranean Sea play a larger role. We also find that the number of floods is projected to fluctuate by ± 10–50 % during the 21st century in response to different sequences of key modes of climate variability. We also note that the relative contributions of large-scale climate variability to future flood risks are generally consistent across all SMILEs. Our findings thus provide valuable information for long-term disaster risk reduction and management.

Morphological variation of the kelp Ecklonia radiata in northeastern New Zealand and across its Australasian range

Understanding the drivers of morphological plasticity, a key mechanism allowing species to flourish under a range of conditions, can provide important information on how a species will adapt to climate-change. The kelp Ecklonia radiata is the dominant canopy-forming macroalgae in temperate Australasia, occurring across a wide environmental range. Previous assessments of morphological variation across Australasia have not included northeastern New Zealand (NENZ) populations, where E. radiata exhibits an anomalous long-stipe morphology. Morphology in NENZ E. radiata over nine locations was quantified to examine variability and its relationship with environmental drivers: depth, wave exposure, turbidity and temperature. Published literature was then reviewed to assess variation across Australasia in relation to similar large-scale environmental drivers. In NENZ, morphology was driven by depth, wave exposure, and turbidity, but not temperature. Thalli had short stipes and relatively long lamina at shallow depths (< 2 m) and across depths at highly wave exposed sites. Stipe length increased with depth and the long stipe morphology dominated deeper depths (4–12 m) at sheltered to moderately exposed sites. However, this relationship varied in relation to turbidity, with more turbid sites having shorter stipes across all depths. Regional variation in morphology across Australasia was most strongly related to wave climate rather than temperature with the long-stipe morphology characterising regions with low energy wave climates such as NENZ. This study highlights the high levels of variability in E. radiata morphology and its complex relationship with environmental stress. If Australasia’s wave climate is to increase in severity, our findings suggest morphological variability expressed across Australasian E. radiata populations will shrink.

Spatially varying impacts of pacific and southern ocean climate modes on tidal residuals in New South Wales, Australia

Still water levels along the New South Wales (NSW) coast often vary from those predicted by harmonic analysis based purely on astronomical forcing. This variation can lead to unexpected inundation episodes along the coast and in estuaries. This study investigates the large-scale ocean-atmosphere drivers that can contribute to these unexpected water levels. Time series regressions and cross- and coherence-wavelet transforms were used to investigate relationships between six tidal residual datasets from NSW ocean gauges and three indices of large-scale climate modes: canonical El Niño Southern Oscillation (ENSO), ENSO Modoki, and Southern Annular Mode (SAM). Significant relationships were identified between canonical ENSO and SAM and the tidal residual datasets; however, ENSO Modoki did not have a significant impact. Collectively, SAM and canonical ENSO account for up to 45% of the variance in tidal residuals along the NSW coast, with impacts increasing southwards. Wavelet coherences showed that tidal residuals covary significantly with SAM and ENSO, with the largest peaks in the two- and eight-year frequency bands. These results demonstrate a strong dependence of tidal residuals on the phasing of SAM and canonical ENSO. With continued improvements in seasonal forecasts of climate modes, including canonical ENSO and SAM, this research presents important new insights that may contribute to improved management of inundation along the NSW coast. The research also provides a baseline of natural variability on which climate change projections can be considered, given that inundation events are projected to become more frequent.

Leaf area predicts conspecific spatial aggregation of woody species

AbstractAimAddressing how woody plant species are distributed in space can reveal inconspicuous drivers that structure plant communities. The spatial structure of conspecifics varies not only at local scales across co‐existing plant species but also at larger biogeographical scales with climatic parameters and habitat properties. The possibility that biogeographical drivers shape the spatial structure of plants, however, has not received sufficient attention.LocationGlobal synthesis.Time Period1997–2022.Major Taxa StudiedWoody angiosperms and conifers.MethodsWe carried out a quantitative synthesis to capture the interplay between local scale and larger scale drivers. We modelled conspecific spatial aggregation as a binary response through logistic models and Ripley's L statistics and the distance at which the point process was least random with mixed effects linear models. Our predictors covered a range of plant traits, climatic predictors and descriptors of the habitat.ResultsWe hypothesized that plant traits, when summarized by local scale predictors, exceed in importance biogeographical drivers in determining the spatial structure of conspecifics across woody systems. This was only the case in relation to the frequency with which we observed aggregated distributions. The probability of observing spatial aggregation and the intensity of it was higher for plant species with large leaves but further depended on climatic parameters and mycorrhiza.Main ConclusionsCompared to climate variables, plant traits perform poorly in explaining the spatial structure of woody plant species, even though leaf area is a decisive plant trait that is related to whether we observe homogenous spatial aggregation and its intensity. Despite the limited variance explained by our models, we found that the spatial structure of woody plants is subject to consistent biogeographical constraints and that these exceed beyond descriptors of individual species, which we captured here through leaf area.

Feeding patterns of Atlantic salmon (Salmo salar) parr are better explained by local drivers than by macroecological drivers.

We modeled feeding of the Atlantic salmon (Salmo salar) parr to understand the role of global (cross-continental) and regional (river) spatial scales for delineating feeding patterns. The diet composition differed between Eurasia and North America populations. Geographic location (latitude and elevation) had an influence for the most common prey (Ephemeroptera, Diptera and Plecoptera). The random factors (sampling location and river) had a strong explanatory power in our models, suggesting that local drivers may override the effects of large-scale drivers.

STOCHASTICITY, SCALE, AND REFRACTION: DETERMINANTS OF POPULATION SYNCHRONY IN PARASITES OF CENTRARCHIDS IN J. STROM THURMOND LAKE, SOUTH CAROLINA.

Among-deme asynchrony has the potential to influence community richness and diversity by increasing the likelihood of regional persistence for a species. Parasites of Lepomis spp. collected from 4 localities at J. Strom Thurmond Lake, South Carolina over a 1-yr period were used to evaluate patterns of parasite population synchrony. Localities were separated by approximately 5 km to increase the likelihood that the parasites sampled represented different demes. Tylodelphys scheuringi and Crinicleidus longus, exhibited negative covariation between synchrony and among-locality distances. The degree of synchrony exhibited by Neoechinorhynchus cylindratis, Crepidostomum cornutum, and Clavunculus bifurcatus was associated with the degree of similarity in habitat structure between localities. Patterns of synchrony for Posthodiplostomum minimum and Spinitectus sp. were not associated with any of the habitat variables examined. The influence of habitat structure on parasite population synchrony, possibly through the refraction of large-scale environmental drivers, has the potential to produce asynchronous dynamics that are independent of the distance between demes, thereby promoting regional persistence by increasing the likelihood of rescue effects.

Human activities shape global patterns of decomposition rates in rivers.

Rivers and streams contribute to global carbon cycling by decomposing immense quantities of terrestrial plant matter. However, decomposition rates are highly variable and large-scale patterns and drivers of this process remain poorly understood. Using a cellulose-based assay to reflect the primary constituent of plant detritus, we generated a predictive model (81% variance explained) for cellulose decomposition rates across 514 globally distributed streams. A large number of variables were important for predicting decomposition, highlighting the complexity of this process at the global scale. Predicted cellulose decomposition rates, when combined with genus-level litter quality attributes, explain published leaf litter decomposition rates with high accuracy (70% variance explained). Our global map provides estimates of rates across vast understudied areas of Earth and reveals rapid decomposition across continental-scale areas dominated by human activities.

Improving dynamical-statistical subseasonal precipitation forecasts using deep learning: A case study in Southwest China

Subseasonal precipitation forecasting is challenging but critical for water management, energy supply, and disaster prevention. To improve regional subseasonal precipitation prediction, previous studies have proposed a dynamical-statistical projection model (DSPM). In this study, we develop a new method that combines the DSPM and deep learning (DL), called the DL-DSPM. The DSPM is developed using the observed relationships between large-scale atmospheric circulations and regional precipitation, and the dynamical forecasted atmospheric circulations from the European Centre for Medium-Range Weather Forecasts (ECMWF) model. The DL-DSPM improves upon the DSPM by correcting biases in atmospheric circulation forecasts from the ECMWF model using two DL models, namely, residual network and U-Net models. In the case of Southwest China (SWC), DL models can improve atmospheric circulation forecasts at lead times beyond 5 pentads, including large-scale drivers of SWC precipitation variability. The DL-DSPM outperforms the ECMWF model and DSPM forecasts in predicting precipitation anomalies beyond 4 and 5 pentads over most SWC regions, respectively. In addition, the DL-DSPM is more skillful than the ECMWF model and DSPM in predicting extreme precipitation events more than 4 pentads in advance. The successful combination of DL and the DSPM provides a new possible direction for DL applications in subseasonal precipitation forecasting.

Invertebrate Responses to Large- and Small-Scale Drivers in Coastal Phragmites australis Beds in the Northern Baltic Sea

Phragmites australis is a common helophyte, covering much of the sheltered and shallow soft bottoms along the coasts of the Baltic Sea. Despite the expansion of P. australis over the past decades, there is little information on aquatic macroinvertebrates within P. australis beds. In this study, we examined the effect of large-scale (wave exposure, nutrients) and small-scale (distance from the seaward edge, live and dead stalk density, epiphyte and rhizome biomass) drivers on the density, taxa richness, diversity, and community structure of epifauna and infauna in monospecific P. australis beds around the Åland Islands and the Archipelago Sea. We found that higher wave exposure and nutrient levels generally supported higher epi- and infauna abundance and taxa richness. The effects on Shannon–Wiener diversity were less evident apart from an increase of the infauna diversity in the Archipelago Sea with increasing nutrient levels. On a local scale, the distance from the seaward edge, live and dead stalk density, and epiphyte biomass had varying effects on both epi- and infauna communities in the different regions. Rhizome biomass had no effect on either the epi- or infauna abundance, taxa richness, or diversity. Furthermore, according to existing studies, other habitats, e.g., Zostera marina meadows, Fucus vesiculosus belts, and vegetated soft-bottomed shallow bays, are generally characterized by more abundant fauna, except for the infauna, which had a higher density in P. australis beds than in vegetated soft-bottomed shallow bays. P. australis are a widespread, expanding, and understudied habitat with an important role in supporting coastal biodiversity.

Influence of global teleconnections on long-term variability in flood seasonality across peninsular India

Climate change and natural climate variability impact not only the frequency and magnitude of floods but also flood seasonality. However, limited to no study has investigated the seasonality in flood occurrence in peninsular Indian rivers. The Central Water Commission opening its long-term records of river stage and discharge gathered from many monitoring stations offers a unique opportunity to analyse flood seasonality. The primary aims of this study are to identify the time of the year when floods are most likely, investigate whether the occurrence of floods has changed over time due to the influence of climate change and natural climate variability, and determine the most significant large-scale and local climate drivers of flood seasonality. Stage and discharge data from 281 gauging stations across ten peninsular river basins are considered to identify the annual maximum gauge level for a gauging point while retaining the corresponding Julian day (also known as the date of occurrence or DO) for further analysis. Several attributes related to circular statistics are calculated from the DO series to find that 68% of the gauging stations experience floods during August, indicating the dominance of the monsoon system on DO. Preliminary analysis confirms that while most stations experience a non-stationarity in the DO series, a few stations exhibit a non-linear trend. Furthermore, our study develops a machine learning model with DO as predictand and 36 local and large-scale predictors to identify the dominant predictors of DO. The predictor importance metric shows that the Pacific Decadal Oscillation (PDO) and the El Niño and Southern Oscillation (ENSO) explain DO variability better than other drivers. Moreover, since ENSO and PDO are teleconnected with the onset of monsoon and annual maximum rainfall (Pradhan et al 2017 Sci. Rep. 7 14229; Choudhury et al 2021a Q. J. R. Meteorol. Soc. 147 3111–23), flood seasonality demonstrates a connection with both PDO and ENSO.

Longitudinal tumor-wide sampling of glioblastoma reveals diverse genomic drivers of the earliest clonal expansion at diagnosis and recurrence.

2009 Background: Despite decades of clinical trials, glioblastoma (GBM) remains a rapidly fatal disease. Much of therapy resistance in GBM is attributed to intratumoral heterogeneity in which subclones rapidly evolve and treatment-resistant cell populations emerge. Understanding heterogeneity at diagnosis and its relationship to the origins of recurrence are critical to selecting therapies that are efficacious across the entire tumor. However, few genomic studies go beyond analyzing single tumor samples per patient. Methods: A spatially oriented, whole tumor sampling approach was used to obtain 43 biopsies from 3 GBMs at diagnosis and recurrence. Pyclone and ClonEvol were applied to whole exome sequencing to reconstruct clonal evolution, FACETs identified copy number variations and HATCHet estimated tumor purity. Candidate driver mutations were evaluated in relation to the Catalogue of Somatic Mutations In Cancer. Results: A single founding clone and multiple subclones were identified for each diagnosis-recurrence pair. Tumor-wide clonal alterations representing initial clonal expansions of these GBMs included both canonical changes (Chr 7 gain, Chr 10 loss, CDKN2A deletion, EGFR amplification) and a diverse set of large-scale copy number variations (Chr 19, 20 gain), driver mutations (PTEN, KDR, CDH11, CNTNAP2), and fusions (LIMCH1::UCHL1, KANK::DOCK8). A second subset of alterations (Chr 8 gain, ATRX mutation), appeared to be tumor wide at diagnosis but were not identified at recurrence. Cancer drivers were also present subclonally, including CDKN2A deletion, MDM2 amplification, and mutations in NF1, and GRM3. Evolutionary trees consisted of 5 generations of clones in Patient 323 (P323), 3 in P454, and 4 in P534. Divergence of the recurrent tumors from their matched primary occurred in the second generation in P454 and P534 and in the third in P323. As a result, an average of 37% of potential drivers of oncogenesis and clonal expansion across the cohort appear after divergence. Furthermore, each recurrent tumor contained at least one tumor wide driver alteration that was subclonal or undetected at diagnosis. Conclusions: Whole-tumor sampling of three GBM patients at both diagnosis and recurrence identified a diversity of genomic drivers and deeper and more complex genetic roots of individual GBM than previously seen in single-biopsy studies. Tumor recurrences consistently arose from a single subclone that diverged early in evolution of the primary tumor and contained clonal drivers either not detected or subclonal in the primary—suggesting a role for these drivers in persistence and expansion. In the age of personalized medicine, our study highlights the clinical potential of tumor wide sampling in identifying therapeutic targets that could avoid heterogeneity-related therapeutic failures.

Unveiling the underlying drivers of Phanerozoic marine diversification.

In investigating global patterns of biodiversity through deep time, many large-scale drivers of diversification have been proposed, both biotic and abiotic. However, few robust conclusions about these hypothesized effectors or their roles have been drawn. Here, we use a linear stochastic differential equation (SDE) framework to test for the presence of underlying drivers of diversification patterns before examining specific hypothesized drivers. Using a global dataset of observations of skeletonized marine fossils, we infer origination, extinction and sampling rates (collectively called fossil time series) throughout the Phanerozoic using a capture-mark-recapture approach. Using linear SDEs, we then compare models including and excluding hidden (i.e. unmeasured) drivers of these fossil time series. We find evidence of large-scale underlying drivers of marine Phanerozoic diversification rates and present quantitative characterizations of these. We then test whether changing global temperature, sea-level, marine sediment area or continental fragmentation could act as drivers of the fossil time series. We show that it is unlikely any of these four abiotic factors are the hidden drivers we identified, though there is evidence for correlative links between sediment area and origination/extinction rates. Our characterization of the hidden drivers of Phanerozoic diversification and sampling will aid in the search for their ultimate identities.