Search

Abstract: The impact of climate change, and particularly of climate warming, is being tracked in many physical and biological systems. Plant phenology is seen as one of the most important bio-indicators, since trends can provide considerable temporal and spatial information regarding ongoing changes. Analyses performed at six sites in southern Spain from 1986 to the present have focused on vegetative and overall reproductive phenology in Olea europaea L., and Vitis vinifera L. and in various species of Quercus spp. and Poaceae. Early results suggest that trends in flowering patterns derived from field phenological observations were similar to trends in aerobiological data for most study species, and indicate a trend towards earlier foliation, flowering and fruit ripening. This advance is more evident in arboreal than in herbaceous species. Statistical analysis showed temperature increase was the major factor affecting earlier foliation, flowering and fruit ripening, as well as prompting delayed leaf fall. Herbaceous species were more affected than trees by changes in rainfall records. [Copyright &y& Elsevier]

Given its proximity to northern Africa, southern Spain is regularly affected by high-altitude African intrusions. This determines a well-defined wind dynamics at surface levels. Although this weather event—mainly recorded in spring and summer—coincides with the flowering season of many wind pollinated species, its potential influence on long term airborne pollen transport has been not investigated in detail. We analyse their influence on olive pollen transport at surface level in south Spain. Daily and bi-hourly olive pollen data from 2010 to 2015, recorded at two sites 150 km apart, Málaga (coast) and Córdoba (inland), were analysed together with 1) air masses at 300 m above ground level (m.a.g.l.), 2) surface wind direction and 3) surface wind speed over the same period. Air masses at 3000 m.a.g.l. were used to identify the periods under the influence of African intrusions. The combined analysis has enabled the identification of different pollen patterns and source contributions. In Málaga, hourly pollen peaks were recorded during the early morning coinciding with the arrival of north-westerly winds (developing sea-land breezes), with a minimal impact of local pollen sources; in Córdoba, by contrast, pollen concentrations reflected the joint contribution of local and long term sources, being the maximum concentrations associated with the arrival of southerly air masses in the afternoon. These results help to understand the potential distant sources and back-trajectories of olive pollen detected. In our case pollen from sources located at the west-northwest areas in the case of Malaga, and from the south in Cordoba. These results reinforce the idea that combined studies between synoptic meteorological and aerobiological data together with different atmospheric height air masses data, offer us a better explanation and understanding of the behaviour and the potential sources of recorded airborne data in a given place. [ABSTRACT FROM AUTHOR]

Abstract: Analysis of long-term airborne pollen counts makes it possible not only to chart pollen-season trends but also to track changing patterns in flowering phenology. Changes in higher plant response over a long interval are considered among the most valuable bioindicators of climate change impact. Phenological-trend models can also provide information regarding crop production and pollen–allergen emission. The interest of this information makes essential the election of the statistical analysis for time series study. We analysed trends and variations in the olive flowering season over a 30-year period (1982–2011) in southern Europe (Córdoba, Spain), focussing on: annual Pollen Index (PI); Pollen Season Start (PSS), Peak Date (PD), Pollen Season End (PSE) and Pollen Season Duration (PSD). Apart from the traditional Linear Regression analysis, a Seasonal-Trend Decomposition procedure based on Loess (STL) and an ARIMA model were performed. Linear regression results indicated a trend toward delayed PSE and earlier PSS and PD, probably influenced by the rise in temperature. These changes are provoking longer flowering periods in the study area. The use of the STL technique provided a clearer picture of phenological behaviour. Data decomposition on pollination dynamics enabled the trend toward an alternate bearing cycle to be distinguished from the influence of other stochastic fluctuations. Results pointed to show a rising trend in pollen production. With a view toward forecasting future phenological trends, ARIMA models were constructed to predict PSD, PSS and PI until 2016. Projections displayed a better goodness of fit than those derived from linear regression. Findings suggest that olive reproductive cycle is changing considerably over the last 30years due to climate change. Further conclusions are that STL improves the effectiveness of traditional linear regression in trend analysis, and ARIMA models can provide reliable trend projections for future years taking into account the internal fluctuations in time series. [Copyright &y& Elsevier]