Variability In Seasonal Patterns Research Articles

Analysis of trends and abrupt changes in groundwater and meteorological droughts in the United Kingdom

In recent years, the escalating impact of climate change, coupled with growing concerns regarding groundwater drought, has underscored the critical importance of analyzing hydrological trends. This is a foundational step in advancing effective water resource planning and management. This study presents a regional investigation of groundwater and meteorological droughts in the UK, utilizing the Standardized Groundwater Index (SGI) and the Standardized Precipitation Index (SPI). The analysis encompasses drought duration and an evaluation of the correlation between the SGI and SPI. Subsequently, the seasonal Mann-Kendall (MK) test and the Bayesian Changepoint Detection and Time Series Decomposition (BEAST) algorithm were employed to assess overall trends for both SGI and SPI. Additionally, these methods were utilized to pinpoint trend and seasonal abrupt changes within the time series of both indices.The seasonal MK test revealed statistically significant trends in many SGI stations across the UK, spanning from Northern Scotland down to the South of England, encompassing both increasing and decreasing trends. Additionally, the MK test showed slightly positive trends at the low SPI time scale, though these were not statistically significant. In contrast, more consistent and significant increasing trends were observed throughout the entire UK, except for Northern Scotland, which exhibited negative trends. Despite indications of a progressively more humid UK, the BEAST analysis enabled the detection of decreasing abrupt changes in trends. These changes result in sudden transitions from wet to dry conditions, which can be challenging to identify using the MK test alone. However, BEAST revealed both positive and negative abrupt changes across the entire UK, as well as positive or negative variations in seasonality. This suggests a high degree of variability in seasonal patterns. In summary, this study provides a detailed assessment of groundwater and meteorological drought trends and abrupt changes, considering the impact of climate change on various regions of the UK.

When a Year Is Not Enough: Further Study of the Seasonality of Planktonic Protist Communities Structure in an Ice-Free High Arctic Fjord (Adventfjorden, West Spitsbergen)

As a contribution to understanding the ecological framework of protistan seasonal succession patterns, we present the weekly-to-monthly (January–October) light microscopy-based study of nano- and microplanktonic protist communities of Adventfjorden waters in 2013. In general, protist dynamics corresponded to the classic paradigm for the Arctic ice-free waters with extremely low abundance and diversity in winter, with the main abundance and chlorophyll-a peak in April-May, followed by a diverse but low abundant community during summer/autumn. However, the reference of the obtained data to the previously conducted year-round research in 2012 allows us to observe substantial variability in seasonal patterns between the two consecutive years. The most striking difference concerned the spring bloom composition and abundance, with clear domination of Phaeocystis pouchetii in Atlantified fjord waters in 2012 and Bacillariophyceae-dominated (mainly Fragilariopsis, Thalassiosira nordenskioeldii, and, in a lesser extent, also Pseudo-nitzschia seriata) bloom in 2013 when local water prevailed. On the other hand, a surprisingly high share of spring bloom taxa persisted throughout the summer/autumn of 2013 when they co-occurred with typical summer taxa (dinoflagellates and other small flagellates). Their extended growth could, at least in part, result from scarce Ciliophora throughout the season, which, in turn, can be attributed to the high grazing pressure of very numerous meroplankton and mesozooplankton. In light of this, our results may be relevant in discussions proposed for the West Spitsbergen waters link between the Atlantic water inflow and the spring bloom composition, as well as its further progression in the productive season. They also highlight the strong need for further high-resolution monitoring of annual plankton cycles and great caution when looking for phenological patterns within a single year or when interpreting short-term data.

Seasonal variability of cephalopod populations: a spatio‐temporal approach in the Western Mediterranean Sea

AbstractCephalopod populations show wide temporal fluctuations in abundance, which have usually been investigated at inter‐annual scales related to environmental variability. However, cephalopods are also strongly linked to seasonal environmental fluctuations owing to their short life cycles and single seasonal breeding. Therefore, population abundance critically depends on the success of breeding and recruitment from the previous year and the optimization of resources in a narrow period of favorable conditions. This adaptation of population dynamics is paramount in marked oligotrophic systems, such as the western Mediterranean Sea. We used monthly landings per unit effort (LPUE) to explore the spatio‐temporal variability in seasonal patterns of three cephalopod species (Illex coindetii, Eledone cirrhosa and Octopus vulgaris). Common trends across the study area were characterized for each species. In all cases, seasonal patterns were geographically aggregated in relation to differences in local environment (i.e., primary production and surface hydro‐climatology). Variability in the mean seasonal pattern over time was also investigated under contrasting environmental or population regimes. The mean seasonal trend was more pronounced in regimes of high‐population densities, suggesting a density‐dependent control that can modify the strength of the environmental forcing in the seasonal patterns. Our study also evidences a spatial synchrony in the seasonal fluctuations of LPUEs. Scales of synchrony ranged from 70 to 200 km, indicating a patchy‐aggregated spatial pattern as a part of complex population structures in the western Mediterranean. Improving our understanding of seasonal dynamics of cephalopods across temporal and spatial scales may lead to improved forecasts and management strategies.

Air temperature suitability for Plasmodium falciparum malaria transmission in Africa 2000-2012: a high-resolution spatiotemporal prediction.

BackgroundTemperature suitability for malaria transmission is a useful predictor variable for spatial models of malaria infection prevalence. Existing continental or global models, however, are synoptic in nature and so do not characterize inter-annual variability in seasonal patterns of temperature suitability, reducing their utility for predicting malaria risk.MethodsA malaria Temperature Suitability Index (TSI) was created by first modeling minimum and maximum air temperature with an eight-day temporal resolution from gap-filled MODerate Resolution Imaging Spectroradiometer (MODIS) daytime and night-time Land Surface Temperature (LST) datasets. An improved version of an existing biological model for malaria temperature suitability was then applied to the resulting temperature information for a 13-year data series. The mechanism underlying this biological model is simulation of emergent mosquito cohorts on a two-hour time-step and tracking of each cohort throughout its life to quantify the impact air temperature has on both mosquito survival and sporozoite development.ResultsThe results of this research consist of 154 monthly raster surfaces that characterize spatiotemporal patterns in TSI across Africa from April 2000 through December 2012 at a 1 km spatial resolution. Generalized TSI patterns were as expected, with consistently high values in equatorial rain forests, seasonally variable values in tropical savannas (wet and dry) and montane areas, and low values in arid, subtropical regions. Comparisons with synoptic approaches demonstrated the additional information available within the dynamic TSI dataset that is lost in equivalent synoptic products derived from long-term monthly averages.ConclusionsThe dynamic TSI dataset presented here provides a new product with far richer spatial and temporal information than any other presently available for Africa. As spatiotemporal malaria modeling endeavors evolve, dynamic predictor variables such as the malaria temperature suitability data developed here will be essential for the rational assessment of changing patterns of malaria risk.

Indexing butterfly abundance whilst accounting for missing counts and variability in seasonal pattern

Summary Volunteer‐based ‘citizen science’ schemes now play a valuable role in deriving biodiversity indicators, both aiding the development of conservation policies and measuring the success of management. We provide a new method for analysing such data based on counts of invertebrate species characterised by highly variable numbers within a season combined with a substantial proportion of proposed survey visits not made. Using the UK Butterfly Monitoring Scheme (UKBMS) for illustration, we propose a two‐stage model that makes more efficient use of the data than previous analyses, whilst accounting for missing values. Firstly, generalised additive models were applied separately to data from each year to estimate the annual seasonal flight patterns. The estimated daily values were then normalised to estimate a seasonal pattern that is the same across sites but differs between years. A model was then fitted to the full set of annual counts, with seasonal values as an offset, to estimate annual changes in abundance accounting for the varying seasonality. The method was tested and compared against the current approach and a simple linear interpolation using simulated data, parameterised with values estimated from UKBMS data for three example species. The simulation study demonstrated accurate estimation of linear time trends and improved power for detecting trends compared with the current model. Comparison of indices for species covered by the UKBMS under the various model approaches showed similar predicted trends over time, but confidence intervals were generally narrower for the two‐stage model. In addition to creating more robust trend estimates, the new method allows all volunteer records to contribute to the indices and thus incorporates data from more populations within the geographical range of a species. On average, the current model only enables data from 60% of 10 km2 grid squares with monitored sites to be included, whereas the two‐stage model uses all available data and hence provides full coverage at least of the monitored area. As many invertebrate species exhibit similar patterns of emergence or voltinism, our two‐stage method could be applied to other taxa.

Phytoplankton photocompensation from space‐based fluorescence measurements

Recent satellite‐derived observations linked global scale phytoplankton fluorescence variability with iron stress and hinted at photophysiological responses associated with changing light levels. These photocompensation reactions, the sum of photoacclimation and photoadaptation, were examined with climatological data for the Gulf of Maine. Significant seasonal variability was observed in the fluorescence quantum yield that was unrelated to patterns of biomass. Up to 89% of the variability in the fluorescence quantum yield was explained by a physiology‐based photocompensation model. Spatial variability in seasonal patterns was associated with differing hydrodynamic regimes. This variability in the quantum yield demonstrates that satellite‐based fluorescence is inappropriate for phytoplankton biomass determinations. More importantly, the work presented here provides the modeling foundation for fluorescence‐based investigations of temporal and spatial variability in phytoplankton physiology associated with growth irradiance. These space‐based physiological observations have the potential to decrease uncertainties in future ocean color derived primary productivity estimates.