Seasonal Partitioning Research Articles

Unfixed Seasonal Partition Based on Symbolic Aggregate Approximation for Forecasting Solar Power Generation Using Deep Learning

Solar energy is an important alternative energy source, and it is essential to forecast solar power generation for efficient power management. Due to the seasonal characteristics of weather features, seasonal data partition strategies help develop prediction models that perform better in extreme weather-related situations. Most existing studies rely on fixed season partitions, such as meteorological and astronomical, where the start and end dates are specific. However, even if the countries are in the same Northern or Southern Hemisphere, seasonal changes can occur due to abnormal climates such as global warming. Therefore, we propose a novel unfixed seasonal data partition based on Symbolic Aggregate Approximation (SAX) to forecast solar power generation. Here, symbolic representations generated by SAX are used to select seasonal features and obtain seasonal criteria. We then employ two-layer stacked LSTM and combine predictions from various seasonal features and partitions through ensemble methods. The datasets used in the experiments are from real-world solar panel plants such as in Gyeongju, South Korea; and in California, USA. The results of the experiments show that the proposed methods perform better than non-partitioned or fixed-partitioned solar power generation forecasts. They outperform them by 2.2% to 3.5%; and 1.6% to 6.5% in the Gyeongju and California datasets, respectively.

Regional and seasonal partitioning of water and temperature controls on urban dryness variability in China

Rapid urban expansion can significantly alter urban microclimate. In recent years, the phenomenon of urban dry islands (UDIs) has attracted increasing attention, but there remains a lack of understanding regarding the impact of urbanization on UDIs in different urban agglomerations (UAs). In this study, vapor pressure deficit (VPD) was used as the humidity indicator, and meteorological data from 20 UAs were used to explore how atmospheric temperature and humidity control urban dryness variability in China. Our analysis revealed that VPD trends are increasing with a median rate of 0.121 hPa/decade and 0.103 hPa/decade for urban and rural areas, respectively. The urbanization effect is more significant in coastal UAs. Furthermore, the difference in VPD between urban and rural areas obviously varied on a seasonal scale, with the largest difference observed in summer (i.e., −0.63 hPa on average and up to 2.66 hPa). Notably, our findings suggest that changes in local atmospheric water content primarily control the response of VPD to urbanization. In summer, the average contribution of temperature is 0.118 hPa, while the average contribution of relative humidity is 0.723 hPa. Overall, these results have important implications for our understanding of the impacts of urbanization on local climate.

Invasion of Bythotrephes longimanus and Cercopagis pengoi in Lake Champlain: Impacts on the Native Zooplankton Community

The zooplankton community of Lake Champlain has been altered over the past decade due to the introduction of two predatory zooplankton species. Bythotrephes longimanus Leydig, 1860 was first detected in Lake Champlain in August 2014, and Cercopagis pengoi (Ostroumov, 1891) was detected in August 2018. Monitoring for both invasive species at 15 lake sites using whole water tow sampling has been ongoing since 2010 with no detection of either species until 2014. Utilizing data from the Lake Champlain long-term monitoring program, we assessed pre- and post-invasion population dynamics of both invasive species on the native zooplankton community. Our results showed shifts in community structure following invasion, including a reduction in Diacyclops thomasi (Forbes, 1882) populations by both invaders and a reduction in Daphnia retrocurva Forbes, 1882 following the introduction of B. longimanus. Other cyclopoids, bosminids, and rotifers were also reduced, corresponding to both introduced species. The native large-bodied plankton predator Leptodora kindtii (Focke, 1844) appears to be unable to coexist with either invasive predatory cladoceran, displaying seasonal partitioning between L. kindtii and the Cercopagidae. These findings suggest that the invasion of both large predators in Lake Champlain have impacted pelagic zooplankton community structure.

Reconstructing eutrophication trends of a shallow lake environment using biomarker dynamics and sedimentary sterols

Major extractable lipid biomarkers (hydrocarbons, sterols and fatty acids) were assessed in Lake Seeburg, a shallow eutrophic lake that has increasingly been suffering from cyanobacterial blooms due to continued anthropogenic nutrient inputs over the last decades. Over the course of one year (2018/19), the distributions of these compounds were analyzed in the inflow, the lake water, and the topmost sediments (0–2 cm) to assess their origin and transfer into the lake deposits. Principal component analysis (PCA) was used to cluster the studied biomarkers into 5 groups with similar characteristics. These groups were comprised of (i) compounds delivered from external sources via the inflow, (ii) autochthonous compounds formed in the lake by eukaryotes or (iii) bacteria, (iv) compounds accumulating in the surface sediment, and (v) C27 to C29 stenols together with their degradation products, C27 to C29 5α(H)-stanols. Their seasonal partition clearly revealed that C27 stenols mainly derived from autochthonous sources within the lake, whereas C29 stenols largely reflect allochthonous material reaching the lake via the inflow. Analysis of stenol plus stanol concentrations with depth in two lake sediment cores (≈30 and 50 cm) found highest C27 to C29 ratios in surface sediments with lowest ratios at depth. These signals are interpreted to reflect the increasing trend of eutrophication of Lake Seeburg and, thus, enhanced autochthonous organic matter production in the lake over the last decades. The abundances of C27 vs. C29 stenols, summed with their respective degradation products, 5α(H)-stanols, are considered as suitable molecular indicators to qualitatively reconstruct historical eutrophication trends.

Critical thermal limits and temperature-dependent walking speed may mediate coexistence between the native winter ant (Prenolepis imparis) and the invasive Argentine ant (Linepithemahumile)

Comparing the thermal tolerance and performance of native and invasive species from varying climatic origins may explain why some native and invasive species can coexist. We compared the thermal niches of an invasive and native ant species. The Argentine ant (Linepithema humile) is an invasive species that has spread to Mediterranean climates worldwide, where it is associated with losses in native arthropod biodiversity. In northern California, long-term surveys of ant biodiversity have shown that the winter ant (Prenolepis imparis) is the native species best able to coexist with Argentine ants. Both species tend hemipteran scales for food, and previous research suggests that these species’ coexistence may depend on seasonal partitioning: winter ants are active primarily in the colder winter months, while Argentine ants are active primarily in the warmer months in northern California.We investigated the physiological basis of seasonal partitioning in Argentine and winter ants by a) measuring critical thermal limits, and b) comparing how ant walking speed varies with temperature. While both species had similar CTmax values, we found differences between the two species' critical thermal minima that may allow winter ants to remain functional at ecologically relevant temperatures between 0 and 2.5 °C. We also found that winter ants' walking speeds are significantly less temperature-dependent than those of Argentine ants. Winter ants walk faster than Argentine ants at low temperatures, which may allow the winter ants to remain active and forage at lower winter temperatures. These results suggest that partitioning based on differences in temperature tolerance promotes the winter ant's continued occupation of areas invaded by the Argentine ant.

Pattern recognition describing spatio-temporal drivers of catchment classification for water quality

Classification using spatial data is foundational for hydrological modelling, particularly for ungauged areas. However, models developed from classified land use drivers deliver inconsistent water quality results for the same land uses and hinder decision-making guided by those models. This paper explores whether the temporal variation of water quality drivers, such as season and flow, influence inconsistency in the classification, and whether variability is captured in spatial datasets that include original vegetation to represent the variability of biotic responses in areas mapped with the same land use. An Artificial Neural Network Pattern Recognition (ANN-PR) method is used to match catchments by Dissolved Inorganic Nitrogen (DIN) patterns in water quality datasets partitioned into Wet vs Dry Seasons and Increasing vs Retreating flows. Explainable artificial intelligence approaches are then used to classify catchments via spatial feature datasets for each catchment. Catchments matched for sharing patterns in both spatial data and DIN datasets were corroborated and the benefit of partitioning the observed DIN dataset evaluated using Kruskal Wallis method. The highest corroboration rates for spatial data classification with DIN classification were achieved with seasonal partitioning of water quality datasets and significant independence (p < 0.001 to 0.026) from non-partitioned datasets was achieved. This study demonstrated that DIN patterns fall into three categories suited to classification under differing temporal scales with corresponding vegetation types as the indicators. Categories 1 and 3 included dominance of woodlands in their datasets and catchments suited to classify together change depending on temporal scale of the data. Category 2 catchments were dominated by vineforest and classified catchments did not change under different temporal scales. This demonstrates that including original vegetation as a proxy for differences in DIN patterns will help guide future classification where only spatially mapped data is available for ungauged catchments and will better inform data needs for water modelling.

Development of a lucerne model in APSIM next generation: 3 Biomass accumulation and partitioning for different fall dormancy ratings

Modelling lucerne growth requires response functions that represent seasonal partitioning of biomass into above-ground and below-ground organs. An additional challenge is to parameterize perennial organ responses across contrasting fall dormancy (FD) genotypes. Current models use empirical approaches to simulate biomass accumulation and partitioning. This research integrated knowledge of lucerne biomass accumulation and partitioning into the Agricultural Production Systems sIMulator (APSIM) next generation (APSIM NextGen) model framework. Biomass supply was calculated from light interception and total radiation use efficiency (RUEtotal), and then allocated based on the relative demand of each organ. Leaf biomass demand was parameterized as a function of specific leaf area (SLA). Stem biomass demand was parameterized as a positive power function of shoot biomass. Root biomass (taproots and crowns) showed a strong seasonal pattern. The observed decrease of root biomass in periods of an increasing photoperiod (mid-winter to mid-summer) was assumed as remobilization to shoots and carbon loss from maintenance respiration. Periods of decreasing photoperiod showed increased biomass of root caused by greater carbon partitioning to this organ. To capture this, a model optimization approach was used to fit required parameters. Fitted parameters included a remobilization coefficient (percentage of storage biomass per day) of 0.01common to all FD cultivars tested (FD5, FD2 and FD10). The regrowth coefficient (remobilization duration) remained constant at 0.01 post-defoliation until 250 °Cd for FD5, 200 °Cd for FD2 and 300 °Cd FD 10, and then declined to 0 after another 50 °Cd. The model was parameterized to have maximal root demand in a decreasing photoperiod to capture carbon partitioning. The model had good prediction of shoot biomass (NSE=0.70) and fair prediction (NSE=0.60) of root biomass for 42 day defoliation treatments. It was less accurate for predictions of shoot biomass under a frequent (28 day) defoliation regime. This highlights the importance to include the response to limitations caused by depleted root N reserves in future model versions. The APSIM NextGen lucerne model provided a mechanistic framework to model perennial organ biomass dynamics with structural and storage components, root maintenance respiration, remobilization in spring, partitioning in autumn and the regrowth effect. This framework accounted for differences in fall dormancy of genotypes and provided a methodology that can be integrated into models of other perennial crops.

Regional and seasonal partitioning of water and temperature controls on global land carbon uptake variability

Global fluctuations in annual land carbon uptake (NEEIAV) depend on water and temperature variability, yet debate remains about local and seasonal controls of the global dependences. Here, we quantify regional and seasonal contributions to the correlations of globally-averaged NEEIAV against terrestrial water storage (TWS) and temperature, and respective uncertainties, using three approaches: atmospheric inversions, process-based vegetation models, and data-driven models. The three approaches agree that the tropics contribute over 63% of the global correlations, but differ on the dominant driver of the global NEEIAV, because they disagree on seasonal temperature effects in the Northern Hemisphere (NH, >25°N). In the NH, inversions and process-based models show inter-seasonal compensation of temperature effects, inducing a global TWS dominance supported by observations. Data-driven models show weaker seasonal compensation, thereby estimating a global temperature dominance. We provide a roadmap to fully understand drivers of global NEEIAV and discuss their implications for future carbon–climate feedbacks.

Theory, method and application advance of isotopic flux partitioning of ecosystem photosynthesis and respiration

Photosynthesis and respiration are two important components of net ecosystem exchange (NEE). NEE can be directly observed by eddy covariance (EC) technique, and statistically separated into ecosystem assimilation and respiration based on the statistical flux partitioning of temperature response function or light-response curves. However, these methods would result in auto-correlation between assimilation and respiration, and overestimate daytime respiration. Recently-developed isotope ratio infrared spectroscopy permits high-resolution measurement of atmospheric CO2 and its stable carbon isotope composition (δ13C) under field conditions, and achieves diurnal and seasonal partitioning of ecosystem photosynthesis and respiration by matching with NEE measurements from EC. We expounded the fundamental theories and assumptions of isotopic flux partitioning of ecosystem photosynthesis and respiration, elaborated the development and application advance of techniques in isotopic flux measurement, summarized the advance of isotopic flux partitioning to provide new insight into the assimilation and respiration processes, and prospected the uncertainty of isotopic flux partitioning theory and the necessity of comparative researches of various methods.

Bacterioplankton Community Profiling of the Surface Waters of Kuwait

The marine waters of Kuwait are highly dynamic and strongly influenced by extreme environmental conditions and high levels of anthropogenic activities. This study explored the bacterioplankton community composition and diversity in Kuwait Bay and at four offshore sites close to urbanized coastal areas during two seasons, autumn (October) and spring (April). 16S rRNA amplicon sequencing detected higher bacterial diversity and richness in spring than in autumn. Also, bacterial community beta diversity revealed a significant seasonal partitioning between the two sampling periods but no clear spatial variation could be depicted. Alphaproteobacteria, Gammaproteobacteria, Cyanobacteria, Flavobacteriia, and Acidimicrobiia dominated the seasonal samples, and SARII clade Ia, SARII clade II, Synechococcus, and SUP05 cluster were among the most abundant taxa in the seasonal samples. However, the strong temporal shift in bacterial community composition was related to Synechococcus abundance. The prevailing physicochemical parameters displayed a significant influence on the bacterioplankton community composition, which was mainly driven by shifts in temperature and nutrient inputs. Furthermore, functional prediction by PICRUSt analysis revealed a relatively stable conserved functional profile in Kuwait’s waters. However, functional genes related to membrane transport were enriched in eutrophic spring waters, while photosynthetic genes were enriched in autumn. Our results provide in-depth insights into the temporal and spatial variations of bacterioplankton dynamics in Kuwait waters and highlight the strong seasonal influence of natural and anthropogenic stressors on their composition and predicted functional capabilities.

Seasonal Partitioning of Rainfall in Second-Growth Evergreen Temperate Rainforests in Chiloé Island, Southern Chile

Rainfall partitioning in secondary forests from southern Chile is relevant in the climate change scenario, in which a 30% reduction in summer precipitation has been projected for the temperate region. Logging and degradation of old-growth forests has resulted in extensive secondary forests, over large areas of the Chiloé Archipelago as well as the mainland. These secondary forests are simple tree communities, dominated by two broad-leaved tree species, evergreen Drimys winteri and Nothofagus nitida, and have the potential to provide multiple benefits to society, including water provision, soil protection, and wood-derived products. Here, we ask how southern South American secondary rainforests modulate rainwater redistribution considering precipitation partitioning. We evaluated the seasonality of throughfall and stemflow components of precipitation, to assess ecohydrological processes for water regulation in a climate change context, where summer droughts have been more frequent in the last decade. The partitioning of gross rainfall (TP) into throughfall (TH), stemflow (ST), and canopy interception (IN) in relation to forest structure, was assessed in four forest plots (400 m2 each) in Senda Darwin Biological Station, Chiloé. TH and ST were measured seasonally for 35 rainfall events from 2019 to 2021. IN water losses were estimated from the mass balance equation. Results indicate that the secondary rainforest intercepts 33% of TP (990 mm of the total monitored), where 59% of the volume corresponds to TH and 7% to ST, which taken together account for nearly 100% the rainwater that reaches the forest floor. Canopy IN varied seasonally from 25 to 40% of total rainfall, with maximum values occurring in the growing season (spring-summer). We found no statistical relation between ST and forest structural parameters (DBH, Basal Area). We explored the contribution of the two dominant tree species to ST and discuss the results in a climate change context. Finally, we propose to incorporate this hydrologic knowledge into adaptive forest management strategies to maximize ecosystem benefits to people. If these ecosystems were properly managed, they have the potential to provide multiple benefits to society within this century, such as water provision and soil protection in addition to carbon sequestration in biomass.

Unique utilisation pattern responses of five sympatric ungulates to local phenological gradients

Context Ungulate life-history strategies and behaviour are driven by two dominant factors, namely, acquisition of nutrients and avoidance of predation. Although many studies have investigated single species resource/habitat selection, there are still gaps in our understanding of multi-species systems and resource partitioning. Aim We explored the habitat selection of five sympatric resident ungulates in relation to seasonal phenological gradients in a temperate grassland (Northern Great Plains) system. We identified and compared habitat-utilisation strategies across the vegetation-growing season and related to established forage acquisition hypotheses. Methods We collected ungulate detection data during two field seasons (July–October 2018, May–September 2019) across 202 sites and deployed 1202 cameras (29 284 camera-nights). We modelled ungulate detections in relation to normalised difference vegetation index (NDVI) and NDVI slope (rate and direction of change) to learn about ungulate resource selection in four habitat types (grass, prairie dog colonies, forest and riparian). Results We found unique foraging strategies for each of the focal species throughout the growing season (spring, summer, early autumn), which resulted in seasonal partitioning of resources among sympatric ungulate species. Conclusions The temporal and spatial patterns observed emphasise that multiple species can experience the same vegetation phenology and respond differently in their movements and foraging behaviour. At local scales, some ungulates selected for higher forage quantity (selection of higher NDVI) and some for higher forage quality (selection of positive NDVI slope or greening up). Implications The unique foraging strategies presented here indicated that generalisations are problematic when managing wildlife resources and emphasise the need to conduct multi-species studies to understand resource utilisation at local scales. Furthermore, understanding variation in foraging strategies of resident sympatric species can direct management planning where decreased connectivity or loss of migration pathways alters traditional behaviours.

Effects of Spatiotemporal Constraints on Geostatistical Analysis of Groundwater Depth.

Geostatistical evaluation of the groundwater depth (GWD) in California's South Coast hydrologic region, and its sensitivity to different spatiotemporal assumptions, is presented in this paper. We obtain a pseudo-stationary representation of the groundwater depth, using the publicly available, online database from the GAMA GeoTracker project, while tracking the associated uncertainty throughout the process. We create nine different sub-datasets, using different temporal constraints, such as seasonal partitioning and different long-term variability filtering criteria. The geostatistical analysis and comparison between the different maps highlight the trade-off between spatial and temporal accuracy. For example, when moving to stricter filtering criteria, despite removing a large number of sites from the interpolation, the root mean squared error (RMSE) calculated in the analysis either decreased or only slightly increased. This suggests that the long-term variability filter is a good representation of the GWD accuracy and that the cross-validation RMSE captures both the stability effect as well as spatial density of the measurement points. We further find that the point-specific standard error is strongly correlated with the associated GWD prediction and that the mean relative error is approximately 60% of the prediction. Hence, it is highly recommended to account for such error in a forward-engineering application, by introducing a GWD distribution rather than a single value into the analysis. Finally, we analyze seasonal fluctuations in the study region and find that they are on average 2.5m with a standard deviation of 8m.

Partitioning of trace elements in the tissues of Mediterranean mussels (Mytilus galloprovincialis) sampled from industrial sites along the Algerian coast

This research was aimed at evaluating the seasonal partitioning of Zn, Se, As, Cu, and Co in the tissues of Mytilus galloprovincialis sampled at two industrial sites along the Algerian coast.Adult mussels were seasonally collected from two sites over the course of a whole year. The gills, digestive glands, gonads, and remaining soft tissues were analyzed through ICP-MS.The observations led to identifying metals ranges (μg g−1Dry Weight) of 67.17–395.51 (Zn), 2.18–12.74 (Se), 7.81–28.61 (As), 3.32–155.91 (Cu), and 0.10–3.59 (Co) in the various tissues.The highest concentrations were found in the digestive glands and gills as compared to the gonads and remaining soft tissues. Distinct patterns of metals partitioning were found: indeed, As and Co concentrations were higher in the digestive glands, while Se and Zn concentrations were higher in the gills. Many of the mussels samples resulted contaminated, therefore potentially posing a considerable health risk to consumers.

Partitioning resources through the seasons: abundance and phenology of carrion beetles (Silphidae) in southeastern Ontario, Canada

The coexistence of ecologically similar species is thought to require resource partitioning to minimize competition. Phenological, seasonal differences in activity may provide an important axis for resource partitioning. Here, we test for evidence of seasonal differences in activity within a diverse guild of carrion beetles (Silphidae) in a habitat preserve on the Frontenac Arch, southeastern Ontario, Canada, using a large-scale survey during their active seasons (April to October). We then used generalized additive models to test for differences in seasonal abundance among eight co-occurring carrion beetle species, including five species of burying beetles (Nicrophorinae: Nicrophorus Fabricius, 1775) and three species from the Silphinae subfamily. Consistent with previous work, all species showed seasonal variation in abundance, with peak abundance of most species occurring between June and August. All but one species (Nicrophorus sayi Laporte, 1840) showed positive relationships between abundance and temperature. We find evidence consistent with seasonal partitioning of resources among Nicrophorus habitat generalists that could potentially reduce competition for limited carrion resources. In contrast, we find little evidence for seasonal differences in abundance among Nicrophorus habitat specialists, which instead may partition resources spatially. Overall, our results provide evidence consistent with an important role for seasonal resource partitioning among carrion beetle species that show higher levels of spatial (habitat) overlap within a temperate beetle guild.

Breeding latitude is associated with the timing of nesting and migration around the annual calendar among Purple Martin (Progne subis) populations

Timing is critical for species that migrate seasonally, as timing mismatches can lead to reduced reproductive success and population declines. We investigated the effect of breeding latitude on the migration timing of a long-distance migratory songbird (Purple Martin, Progne subis subis) by using light-level geolocators deployed across breeding sites between 26° and 53° latitude (n = 329). We used nesting data (1st egg dates; n = 28,165) to explore latitudinal patterns in the timing of breeding. Using generalized linear mixed models (GLMMs) we found that breeding latitude was highly associated with timing around the calendar, explaining population-level timing across both spring and fall migration. Populations breeding at more northern latitudes migrated later in fall (mean days/degree latitude = 2), in spring (mean days/degree latitude = 3), and nested later (days/degree latitude = 3) than populations breeding at more southern latitudes. Generalized additive mixed models (GAMMs) revealed significant non-linearity between breeding latitude and spring and fall migration timing, and the timing of nesting. In spring, the most northern breeders (> 40 degrees N) were more synchronous in their timing, departing their nonbreeding sites and arriving earlier at their breeding sites relative to their breeding latitude. These most northern breeders also had similar nest timing and fall migration departure timing. Breeding latitude was also associated with seasonal partitioning of migratory and sedentary periods. The most northern breeders spent 10% more time on fall migration, 2% more at wintering grounds, 1% more on spring migration, and 13% less time at their breeding sites. Our results support the notion that selection for timing at breeding latitudes can have year-round effects on the timing of seasonal events in long-distance migratory songbirds. Future studies should examine whether these timing patterns associated with latitude limit phenotypic responses to climate change.

Simulating chlorophyll-a fluorescence changing rate and phycocyanin fluorescence by using a multi-sensor system in Lake Taihu, China

Algal pollution in water sources has posed a serious problem. Estimating algal concentration in advance saves time for drinking water plants to take measures and helps us to understand causal chains of algal dynamics. This paper explores the possibility of building a short-term algal early warning model with online monitoring systems. In this study, we collected high-frequency data for water quality and weather conditions in shallow and eutrophic Lake Taihu by an in situ multi-sensor system (BIOLIFT) combined with a weather station. Extracted chlorophyll-a from water samples and chlorophyll-a fluorescence differentiated according to different algal classeses verified that chlorophyll-a fluorescence continuously measured by BIOLIFT only represent chlorophyll-a of green algae and diatoms. Stepwise linear regression was used to simulate the chlorophyll-a fluorescence changing rate of green algae and diatoms together (ΔChla-f%) and phycocyanin fluorescence concentration (blue-green algae) on the water surface layer (CyanoS). The results show that nutrients (total N, NO3–N, NH4–N, total P) were not necessary parameters for short-term algal models. ΔChla-f % is greatly influenced by the seasons, so seasonal partition of data before modeling is highly recommended. CyanoSmax and ΔChla-f% were simulated by only using multi-sensor and meteorological data (R2 = 0.73; 0.75). All the independent variables (wave, water temperature, relative humidity, depth, cloud cover) used in the model were measured online and predictable. Wave height is the most important independent variable in the shallow lake. This paper offers a new approach to simulate and predict the algal dynamics, which also can be applied in other surface water.

Seasonal partitioning of precipitation between streamflow and evapotranspiration, inferred from end-member splitting analysis

Abstract. The terrestrial water cycle partitions precipitation between its two ultimate fates: “green water” that is evaporated or transpired back to the atmosphere, and “blue water” that is discharged to stream channels. Measuring this partitioning is difficult, particularly on seasonal timescales. End-member mixing analysis has been widely used to quantify streamflow as a mixture of isotopically distinct sources, but knowing where streamwater comes from is not the same as knowing where precipitation goes, and this latter question is the one we seek to answer. Here we introduce “end-member splitting analysis”, which uses isotopic tracers and water flux measurements to quantify how isotopically distinct inputs (such as summer vs. winter precipitation) are partitioned into different ultimate outputs (such as evapotranspiration and summer vs. winter streamflow). End-member splitting analysis has modest data requirements and can potentially be applied in many different catchment settings. We illustrate this data-driven, model-independent approach with publicly available biweekly isotope time series from Hubbard Brook Watershed 3. A marked seasonal shift in isotopic composition allows us to distinguish rainy-season (April–November) and snowy-season (December–March) precipitation and to trace their respective fates. End-member splitting shows that about one-sixth (18±2 %) of rainy-season precipitation is discharged during the snowy season, but this accounts for over half (60±9 %) of snowy-season streamflow. By contrast, most (55± 13 %) snowy-season precipitation becomes streamflow during the rainy season, where it accounts for 38±9 % of rainy-season streamflow. Our analysis thus shows that significant fractions of each season's streamflow originated as the other season's precipitation, implying significant inter-seasonal water storage within the catchment as both groundwater and snowpack. End-member splitting can also quantify how much of each season's precipitation is eventually evapotranspired. At Watershed 3, we find that only about half (44±8 %) of rainy-season precipitation evapotranspires, but almost all (85±15 %) evapotranspiration originates as rainy-season precipitation, implying that there is relatively little inter-seasonal water storage supplying evapotranspiration. We show how results from this new technique can be combined with young water fractions (calculated from seasonal isotope cycles in precipitation and streamflow) and new water fractions (calculated from correlations between precipitation and streamflow isotope fluctuations) to infer how precipitation is partitioned on multiple timescales. This proof-of-concept study demonstrates that end-member mixing and splitting yield different, but complementary, insights into catchment-scale partitioning of precipitation into blue water and green water. It could thus help in gauging the vulnerability of both water resources and terrestrial ecosystems to changes in seasonal precipitation.

Postmortem submersion interval in human bodies recovered from fresh water in an area of Mediterranean climate. Application and comparison of preexisting models

PurposeThe methods developed in recent years for the assessment of the Postmortem Submersion Interval (PMSI) have proven to be promising, but are strictly related to specific geographical areas or climates. The aim of this study is to assess the suitability of two of the most recent total aquatic decomposition scores (TADS) for the determination of the PMSI in bodies recovered from fresh water in an area of Mediterranean climate in the last 15 years. To do this, the correlation coefficient (r2) between PMSI and Accumulate Degrees Days (ADD) or PMSI and days was studied. MethodsThe correlation between PMSI (expressed in days or in ADD) and the TADS was evaluated considering: (a) bodies recovered during the entire period; (b) bodies recovered during the cold season; (c) bodies recovered during the warm season. A linear regression analysis was set comparing the statistical significance of a model plotting TADS versus ADD, and another model plotting TADS versus PMSI (expressed in days) for both scores. ResultsScores were scarcely applicable when considering the entire casuistry, as regression models showed low r2 values, but both scores showed high correlations after dividing the cases into 2 groups. In fact, after performing the seasonal partition, we observed a strong correlation between PMSI and TADS, using either of the scores. ConclusionThis study helps increase the accuracy, reliability, and validity of PMI estimation in bodies recovered from freshwater in an area of temperate climate, such as Northern Italy, supporting the need to establish regionally-specific equations for estimating PMI in a forensic context.

Partitioning between atmospheric deposition and canopy microbial nitrification into throughfall nitrate fluxes in a Mediterranean forest

Abstract Microbial activity plays a central role in nitrogen (N) cycling, with effects on forest productivity. Although N biotransformations, such as nitrification, are known to occur in the soil, here we investigate whether nitrifiers are present in tree canopies and actively process atmospheric N. This study was conducted in a Mediterranean holm oak (Quercus ilex L.) forest in Spain during the transition from hot dry summer to cool wet winter. We quantified —N and —N fluxes for rainfall (RF) and throughfall (TF) and used δ15N, δ18O and Δ17O to elucidate sources of . Finally, we characterized microbial communities and abundance of nitrifiers on foliage, RF and TF water through metabarcoding and quantitative polymerase chain reaction respectively. NO3—N fluxes at the site were larger in TF than RF, suggesting a contribution from dry deposition, as also supported by δ15N and δ18O. However, Δ17O indicated that about 20% of in TF derived from canopies nitrification in August, after a severe drought, with a lower proportion in September (≈8%). This seasonal partitioning between biologically and atmospherically derived coincided with a decreasing trend of the abundance of archaeal nitrifiers. Tree canopies and TF had more diverse microbial communities than RF. Yet, RF showed higher variability in microbial composition, likely associated with the origin of air masses. Synthesis. Atmospheric N deposition is significantly altered after passing through tree canopies. While nitrification has been proposed as one of the mechanisms responsible for these changes, very few studies directly investigate its occurrence. Here, we showed that nitrification by epiphytic leaf microbes contributed to increasing NO3 in TF and that nitrifiers' activity was reduced going from the dry and hot summer to the cool winter. Overall, these results highlight the power of coupling microbial community analysis, functional gene amplification and stable isotope approaches to examine ecosystem‐scale processes.