Intra-annual Data Research Articles

Quantifying terminal white bands in Salix from the Yenisei river, Siberia and their relationship to late-season flooding

Key MessageWood fiber cell wall thickness best characterizes white bands found at the end of certain growth rings in Salix alba. Evidence suggests these features are related to late-season hydrology.Recent, record-breaking discharge in the Yenisei River, Siberia, is part of a larger trend of increasing river flow in the Arctic driven by Arctic Amplification. These changes in magnitude and timing of discharge can lead to increased risk of extreme flood events, with implications for infrastructure, ecosystems, and climate. To better understand the effect of these changes on riparian tree growth along the lower reaches of the Yenisei River, we collected white willow (Salix alba) cross sections from a fluvial fill flat terrace that occasionally floods when water levels are extremely high. These samples displayed bands of lighter colored wood at the end of certain annual growth rings that we hypothesized were related to flood events. To identify the characteristics and causes of these features, we use an approach known as quantitative wood anatomy (QWA) to measure variation in fiber cell dimensions across tree rings, particularly fiber lumen area (LA) and cell wall thickness (CWT). We investigate (1) which cell parameters and method to extract intra-annual data from annual tree rings best capture terminal white bands identified in Salix, and (2) if these patterns are related to flood magnitude and/or duration. We find that fiber CWT best captures terminal white bands found in Salix rings. Time series derived from CWT measurements correlate with July water-level durations, but at levels too low to be labeled flooding. Although both terminal white bands and July flooding have reduced since 1980, questions remain as to the cause of terminal white bands. Understanding how riparian vegetation responds to changes in hydrology can help us better manage riparian ecosystems and understand the impacts of a changing Arctic hydrological regime.

Nudibranch predation boosts sponge silicon cycling

Diatoms play a key role in the marine silica cycle, but recent studies have shown that sponges can also have an important effect on this dynamic. They accumulate large stocks of biogenic silica within their bodies over long periods, which are thought to vary little on an intra-annual scale. The observation of an abrupt decline in sponge biomass in parallel with large increases in abundance of a spongivorous nudibranch (Doris verrucosa) led us to conduct a year-long study on the effect of nudibranch predation on the silicon budget of a sponge (Hymeniacidon perlevis) population. After 5 months of predation, the abundance of sponge individuals did not change but their biomass decreased by 95%, of which 48% was explained by nudibranch predation. About 97% of sponge spicules ingested by nudibranchs while feeding was excreted, most of them unbroken, implying a high rate of sponge silica deposition in the surrounding sediments. After predation, sponges partially recovered their biomass stocks within 7 months. This involved a rapid growth rate and large assimilation of dissolved silicon. Surprisingly, the highest rates of silicon absorption occurred when dissolved silicon concentration in seawater was minimal (< 1.5 µM). These findings suggest that the annual sponge predation-recovery cycle triggers unprecedented intra-annual changes in sponge silicon stocks and boosts the cycling of this nutrient. They also highlight the need for intra-annual data collection to understand the dynamics and resilience of sponge ecosystem functioning.

Ocean acidification in the Western Pacific: Boron isotopic composition recorded in a tropical massive coral core from Lanyu Islet SE Taiwan

Boron (B) and B isotopic compositions (δ11B) in biogenic carbonates are useful proxies for pH reconstruction in the ocean. However, high-resolution archives are scarce due to associated sampling and analytical difficulty. In this study, a modern long-lived massive coral skeleton (Porites lobata) from Lanyu Islet off southeast Taiwan was drilled and used for high-resolution major/trace element analyses, including trace elements B and δ11B, as well as oxygen and carbon isotopes, to investigate the associated environmental changes during 1991–1997. To avoid complicated biological influence, the top-most tissue layer was excluded in this study. The coralline records show a clear temporal trend in metal/Ca-based sea surface temperatures (SSTs) on annual and monthly timescales. In particular, the Mg/Ca-SSTs, the most sensitive temperature proxy at the site, show a significant warming trend (+0.2°C year−1) during the study period. On the other hand, subtle changes in the annual δ11B record were identified, corresponding to ~0.2 pH unit, which is comparable with other coral records in the Pacific, e.g., the South China Sea (SCS), Guam Island, Flinders, and Arlington Reef, as well as the in-situ seawater pH measurement at Hawaii station. This corresponds to an acidification rate of ~0.25 pH unit 100 year−1, similar to other coralline data, in-situ pH/pCO2 measurement, or model predictions, and emphasizes the importance of ocean acidification due to anthropogenic activities. Combined with the Mg/Ca-SST, the intra-annual data show a clear seasonal cycle with higher pH in winter, consistent with the pCO2 at the oceanic surface. These chemical and isotopic results in corals conclude that marine biogenic carbonates are informative for oceanic pH reconstruction and can provide new insights into the relationships between climate changes and environmental responses on the coast of Taiwan.

Importance of Weighting High-Resolution Proxy Data From Bivalve Shells to Avoid Bias Caused by Sample Spot Geometry and Variability in Seasonal Growth Rate

Shells of bivalve mollusks serve as archives for past climates and ecosystems, and human-environmental interactions as well as life history traits and physiology of the animals. Amongst other proxies, data can be recorded in the shells in the form of element chemical properties. As demonstrated here with measured chemical data (10 elements) from 12 Arctica islandica specimens complemented by numerical simulations, mistakes during sclerochronological data processing can introduce significant bias, adding a further source of error to paleoenvironmental or biological reconstructions. Specifically, signal extraction from noisy LA-ICP-MS (Laser Ablation—Inductively Coupled Plasma—Mass Spectrometry) data generated in line scan mode with circular LA spots requires a weighted rather than an arithmetic moving average. Otherwise, results can be in error by more than 41%. Furthermore, if variations of seasonal shell growth rate remain unconsidered, arithmetic annual averages of intra-annual data will be biased toward the fast-growing season of the year. Actual chemical data differed by between 3.7 and 33.7% from weighted averages. Numerical simulations not only corroborated these findings, but indicated that arithmetic annual means can overestimate or underestimate the actual environmental variable by nearly 40% relative to its seasonal range. The magnitude and direction of the error depends on the timing and rate of both seasonal shell growth and environmental change. With appropriate spatial sampling resolution, weighting can reduce this bias to almost zero. On average, the error reduction attains 80% at a sample depth of 10, 92% when 20 samples were analyzed and nearly 100% when 100 samples were taken from an annual increment. Under some exceptional, though unrealistic circumstances, arithmetic means can be superior to weighted means. To identify the presence of such cases, a numerical simulation is advised based on the shape, amplitude and phase relationships of both curves, i.e., seasonal shell growth and the environmental quantity. To assess the error of the offset induced by arithmetic averaging, Monte Carlo simulations should be employed and seasonal shell growth curves randomly generated based on observed variations.

Annual CO2 Budget Estimation From Chamber-Based Flux Measurements on Intensively Drained Peat Meadows: Effect of Gap-Filling Strategies

Estimating annual CO2 budgets on drained peatlands is important in understanding the significance of CO2 emissions from peatland degradation and evaluating the effectiveness of mitigation techniques. The closed-chamber technique is widely used in combination with gap-filling of CO2 fluxes by parameter fitting empirical models of ecosystem respiration (Reco) and gross primary production (GPP). However, numerous gap-filling strategies are available which are suitable for different circumstances and can result in large variances in annual budget estimates. Therefore, a need for guidance on the selection of gap-filling methodology and its influence on the results exists. Here, we propose a framework of gap-filling methods with four Tiers following increasing model complexity at structural and temporal levels. Tier one is a simple parameter fitting of basic empirical models on an annual basis. Tier two adds structural complexity by including extra environmental factors such as grass height, groundwater level and drought condition. Tier three introduces temporal complexity by separation of annual datasets into seasons. Tier four is a campaign-specific parameter fitting approach, representing highest temporal complexity. The methods were demonstrated on two chamber-based CO2 flux datasets, one of which was previously published. Performance of the empirical models were compared in terms of error statistics. Annual budget estimates were indirectly validated with carbon export values. In conclusion, different gap-filling methodologies gave similar annual estimates but different intra-annual CO2 fluxes, which did not affect the detection of the treatment effects. The campaign-wise gap-filling at Tier four gave the best model performances, while Tier three seasonal gap-filling produced satisfactory results throughout, even under data scarcity. Given the need for more complete carbon balances in drained peatlands, our four-Tier framework can serve as a methodological guidance to the handling of chamber-measured CO2 fluxes, which is fundamental in understanding emissions from degraded peatlands and its mitigation. The performance of models on intra-annual data should be validated in future research with continuous measured CO2 flux data.

Using Bayesian Models to Estimate Humpback Whale Entanglements in the United States West Coast Sablefish Pot Fishery

Protected species bycatch can be rare, making it difficult for fishery managers to develop unbiased estimates of fishing-induced mortality. To address this problem, we use Bayesian time-series models to estimate the bycatch of humpback whales (Megaptera novaeangliae), which have been documented only twice since 2002 by fishery observers in the United States West Coast sablefish pot fishery, once in 2014 and once in 2016. This model-based approach minimizes under- and over-estimation associated with using ratio estimators based only on intra-annual data. Other opportunistic observations of humpback whale entanglements have been reported in United States waters, but, because of spatio-temporal biases in these observations, they cannot be directly incorporated into the models. Notably, the Bayesian framework generates posterior predictive distributions for unobserved entanglements in addition to estimates and associated uncertainty for observed entanglements. The United States National Marine Fisheries Service began using Bayesian time-series to estimate humpback whale bycatch in the United States West Coast sablefish pot fishery in 2019. That analysis resulted in estimates of humpback whale bycatch in the fishery that exceeded the previously anticipated bycatch limits. Those results, in part, contributed to a review of humpback whale entanglements in this fishery under the United States Endangered Species Act. Building on the humpback whale example, we illustrate how the Bayesian framework allows for a wide range of commonly used distributions for generalized linear models, making it applicable to a variety of data and problems. We present sensitivity analyses to test model assumptions, and we report on covariate approaches that could be used when sample sizes are larger. Fishery managers anywhere can use these models to analyze potential outcomes for management actions, develop bycatch estimates in data-limited contexts, and guide mitigation strategies.

Relationship between wood anatomy, tree-ring widths and wood density of Pinus sylvestris L. and climate at high latitudes in northern Sweden

In this study, wood anatomy, tree-ring width and wood density of Pinus sylvestris at the northern timberline in Fennoscandia were used to identify relationships among the parameters and to screen them for their climatic signals. Furthermore we investigated the influence of the juvenile wood section for all parameters developed. The measurements of wood anatomy were conducted with confocal laser scanning microscopy (CLSM) while the density profiles were produced using an Itrax MultiScanner. We developed chronologies of ring width, wood density and anatomy for a period between 1940 and 2010. Correlations between wood density and wood anatomy were strong in the latewood part. For some wood anatomy and density chronologies youth trends were found in the juvenile part. Wood density decreased from the pith up to the 9th ring and stabilized afterwards, while cell lumen diameter and lumen area increased simultaneously up to the 15th ring. All chronologies contained strong summer temperature signals. The wood anatomical variables provided additional information about seasonal precipitation which could not be found in wood density and tree-ring widths. Our study confirmed previous results stating that the parameter maximum density contains the strongest climate signal, that is, summer temperatures at the northern timberline. Nevertheless, the intra-annual data on tracheid dimensions showed good potential to supply seasonal climatic information and improve our understanding of climatic effects on tree growth and wood formation.

A quantitative analysis of temporal turnover in aquatic species assemblages across ecosystems

Recent meta-analyses have shown that beta diversity through space is jointly driven by species traits, geographical gradients, and ecosystem properties. Spatial variation is, however, only one aspect of beta diversity. The other component is variation in species assemblages through time, that is, temporal turnover. We examined the decrease of assemblage similarity in time in aquatic ecosystems in relation to several ecological, physical, and geographical factors using an extensive data set derived from the literature. The data set was first divided into intra-annual and interannual studies depending on the temporal extent of the studies. Sampling duration was one the most significant variables affecting the degree of temporal turnover, and we found that turnover was faster in studies with shorter temporal extent. Our results further suggested that the rate of temporal turnover increased with increasing ecosystem size, thus contradicting the general species-time-area relationship. Temporal turnover also varied among the ecosystem types: lake assemblages showed faster turnover than stream or marine assemblages in the interannual data set. We found that temporal turnover exhibited large-scale geographical variation, as there was a latitudinal gradient in turnover. Turnover was faster in the tropics in the intra-annual data set, but the pattern was reversed in the interannual data set, where turnover was faster at high latitudes. Finally, we found that the degree of temporal turnover was related to organism characteristics, as larger organisms with active mobility showed slower temporal turnover than smaller organisms. Our results suggest that the degree of species turnover in time is jointly driven by several ecological, physical, and geographical factors in aquatic ecosystems and that the turnover is not uniform across taxonomic groups. Our findings have important consequences for understanding how different biotic assemblages track temporal changes in the environment and how resilient assemblages are toward such changes.

Short-term monitoring of the Spanish government balance

AbstractWe construct multivariate, state-space mixed-frequencies models for the main components of the Spanish General Government sector made up of blocks for each one of its subsectors: Central Government, Social Security and aggregate of Regional and Local government sectors. Each block is modelled through its total revenue and expenditure categories, and encompasses a number of indicators, depending on data availability. The mixed-frequencies approach is particularly relevant for the case of Spain, given its institutional set-up and the specific data availability for the different subsectors. All in all, we provide models detailed enough in coverage, while at the same time manageable, to be used: (1) for real-time monitoring of fiscal policies with a focus on quarterly developments of the General Government sector, (2) for the monitoring of general government sub-sectors for which intra-annual data coverage is limited (Regional and Local governments), (3) to bridge (translate) into National Accounts available monthly information for the subsectors of the general government.

Short-Term Monitoring of the Spanish Government Balance with Mixed-Frequencies Models

We construct multivariate, state-space mixed-frequencies models for the main componentsof the Spanish General Government sector made up of blocks for each one of its subsectors: Central Government, Social Security and aggregate of Regional and Local government sectors. Each block is modelled through its total revenue and expenditure categories, and encompasses a number of indicators, depending on data availability. The mixed-frequencies approach is particularly relevant for the case of Spain, given its institutional set-up and the specific data availability for the different subsectors. All in all, we provide models detailed enough in coverage, while at the same time manageable, to be used: (i) for real-time monitoring of fiscal policies with a focus on quarterly developments of the General Government sector; (ii) for the monitoring of general government sub-sectors for which intra-annual data coverage is limited (Regional and Local governments), and (iii) to bridge (translate) into National Accounts available monthly information for the subsectors of the general government.

Focusing modelling on the tracheid development period – An alternative method for treatment of xylogenesis intra-annual data

Intra-annual repeated micro-sampling of the developing tree ring is getting more and more applied in xylogenesis studies. Variability in growth magnitude, notably due to different sampling positions on the stem, encouraged application of standardization and modelling techniques. Among these, methods using Gompertz equation had become widely spread, but tests made with black spruce revealed a frequent occurrence of crossovers between the cumulative number of cells in enlargement and the cumulative number of cells in wall thickening. This was due to a localized problem in the fitting for values close to the asymptote and was a major problem for estimating the timing of each individual cell development phases, which is an interesting application of these data. In this paper, a new method, based on a different approach, has been developed in order to avoid that problem and applied to intra-annual growth curves from four sites in Quebec (Canada). Since tracheid development analysis allows discriminating between active and inactive period of a phase, modelling can be restricted on the active period alone. The new method did not cause crossovers between the fitted curves. Therefore, it has been considered appropriate for estimating the timing for each individual cell in the whole range of data. Since resulting functions are polynomials from degree 1 to 3, possible studies concerning general tendency should be easy to lead. Also, the method has been tested with different sampling frequencies. To do this, number of observations from weekly samplings has been halved to simulate a semi-monthly sampling frequency and a comparison of the results from the new method applied on each version of the datasets has been tested. Generally, the simulated semi-monthly sampled dataset did not give significantly different results from the original weekly sampled dataset, in terms of general tendency and predicted intercept time in the extremities of the data range. This is very encouraging for situations when only semi-monthly sampling is available.

Residential Water Demand in the Northwest of Spain

A panel of monthly aggregate data from the Northwest of Spain is used to estimate domestic water demand functions under linear and non-linear tariffs. Price, billing, climatic, and sociodemographic variables are used as explanatory variables. The use of intraannual data constitutes a relevant contribution in the European context. Overall marginal price elasticity estimates lie between −0.12 and −0.17. Summer-only elasticities and elasticities associated with uses beyond the effectively free allowances seem significantly higher. Climatic variables significantly affect monthly use, although probably less than in other wealthier and drier areas. Domestic water use appears to be a normal good.

Intra- and inter-annual growth rate of modern stalagmites

We measure the factors that determine growth rate (temperature, drip rate, calcium ion concentration) for 31 waters that feed stalagmites within six cave systems throughout Europe. Water samples were collected at a frequency of at least 1 month, to permit the modelling of both inter- and intra-annual growth rate variations, utilising the theory of Wolfgang Dreybrodt (Chem. Geol. 29 (1980) 89–105; Chem. Geol. 32 (1981) 237–245; Dreybrodt, W., 1988. Processes in Karst Systems. Springer-Verlag, Berlin 288 pp.). Inter-annual growth rates were measured using the stalagmites that were associated with the analysed water samples; growth rate was determined from annual lamina counting, specific time markers within the stalagmites, and location of bomb 14C. When compared to theoretically predicted values, a good agreement between theoretical and measured stalagmite growth rates is observed ( R 2=0.69). When compared to site climate and geochemical parameters, a good correlation is observed between measured growth rate and mean annual temperature for five sites ( R 2=0.63) and dripwater calcium content ( R 2=0.61), but not drip rate ( R 2=0.09). The good correlation with both calcium and temperature is due to soil CO 2 production being primarily determined by surface temperature and soil moisture. However, when we compare our data to that in the Grotte de Clamouse, a site that has little soil cover, we observe that the growth rate–temperature relationship breaks down due to either the lack of soil CO 2 production or prior calcite precipitation. Intra-annual data demonstrates that maximum growth rate occurs when calcium concentrations are high, and that this occurs under different seasons depending on the hydrology of each site. Our results demonstrate a stronger dependence of intra-annual stalagmite growth rate on dissolved calcium ion concentrations than drip rate for the range of drip rates investigated here (0.01< t<2drip s −1), but for lower drip rates, this factor becomes important in controlling growth rate. We suggest that for well-monitored and -understood sites, stalagmite growth rate variations can provide useful information for palaeoclimate reconstruction.