Atmospheric pollen dynamics in Malaga (s. Spain) during 2013-2014. Seasonal trends

Atmospheric pollen dynamics in Malaga (S Spain) during 2013-2014. Seasonal trends Marta RECIO, Antonio PICORNELL, Dorra GHARBI, Inmaculada LOZANOTORELLI, Salvador RUIZ, Baltasar CABEZUDO & María del Mar TRIGO Department of Plant Biology, Faculty of Sciences. University of Malaga, Spain S1#P02# This work has been funded with the aid of the University of Malaga, Campus of Excellence, Andalusia Tech. INTRODUCTION In this work we present the atmospheric pollen results obtained in Malaga, a coastal Mediterranean city situated in southern Spain, throughout years 2013 and 2014. The main objective is to compare the results obtained these years with those registered during the 21 previous years (1992-2012) and detect possible significant trends. METHODOLOGY Air sampling was carried out with the aid of a 7-day recording volumetric spore trap (Hirst, 1952). Pollen grains were counted according to the methodology proposed by the Spanish Aerobiology Network, REA (Domínguez et al. 1991, Galán et al. 2007). To establish the principal main pollen season (MPS) the method proposed by Nilsson and Persson (1981) was used (90% of the total annual for Gramineae and 95% for Quercus). To characterize the phenological behaviour of the atmospheric pollen, the dates of the beginning and end of the MPS and its duration in days (from 1st January) were used. The annual pollen count (pollen index, PI) has been used as indicator of pollen severity. The meteorological data used were annual mean temperature (ºC), total rainfall (mm) and relative humidity (%). The meteorological data were supplied by AEM. The pollen and meteorological data were fitted to a simple linear regression line to observe trends. The slopes of the regression equations, the determination coefficients (R2) and significance levels (p) have been studied. It has been only considers as significant the regression lines whose fitted points as determined from the R2 value, showed a p value of ≤0.05. The SPSS Statistics software was used in all analysis. RESULTS Trends of meteorological data Trends of pollen data Increase by 0.05ºC per year Significant trend Last two years: warm Fluctuate year by year No significant trend Last two years: drought Decreased by 0.25% per year Significant trend Last two years: dry Severity or Pollen Index (PI) Phenology or Main Pollen Season (MPS) Increase Quercus Olea Chenopodiaceae Plantago Cyperaceae Decrease Quercus Gramineae End Duration Start Duration CONCLUSIONS - The trend to increase the annual pollen index of Quercus has already been observed in a previous study (until 2012) whose results have been presented in other congress (Recio et al., 2013). We calculated correlations and think that in the south of the Iberian Peninsula there is a tendency to diminish the relative humidity that in autumn could favour the induction of a greater number of floral buds and, therefore, to produce more future pollen grains during the spring that will be released to the air. Also we observed that the trend to delay the date of end and to increase the duration of Quercus main pollen season in Malaga is caused by tendency to diminish the relative humidity and to increase the temperature (during previous autumn and winter). - The almost significant trend to increase the annual pollen index of Olea can be caused by alternance in the harvest (2013 and 2014 were high production years). Also it could be caused by an increase of its crops. - The trend to decrease the annual pollen index of Chenopodiaceae, Plantago and Cyperaceae (weeds) may be caused by a change of land use (urbanisations, gardens, roads…) that have left less space for wild plants. - The Gramineae phenology behaviour support the conclusions published in Recio et al. (2010): The trend to decrease the duration of Gramineae main pollen season in Malaga may be caused by the tendency to increase spring temperature, leading plants to wilting and parching. The increased rainfall of early spring may be associated with the tendency of pollination to start later. REFERENCES -Domínguez, E., C. Galán, F. Villamandos & F. Infante. 1991. Handling and evaluation of the data from the aerobiological sampling. Monograf. REA/EAN 1, 1–18. -Galán, C., P. Cariñanos, P. Alcázar & E. Domínguez. 2007. Spanish Aerobiology Network (REA). Management and Quality Manual. Servicio de Publicaciones Universidad de Córdoba. -Hirst, J.M. 1952. An automatic volumetric spore-trap. Ann. Appl. Biol., 39: 257-265. -Nilsson, S. & S. Persson. 1981. Tree pollen spectra in the Stockholm region (Sweden), 1973–1980. Grana 20, 179–182. -Recio, M., S. Docampo, J. García-Sánchez, M.M. Trigo, M. Melgar & B. Cabezudo. 2010. Influence of temperature, rainfall and wind trends on grass pollination in Malaga (western Mediterranean coast). Agricultural Forest Meteorol., 150: 931-940. -Recio, M., M.M. Trigo, H. García-Mozo, C. Galán, C. Díaz-De la Guardia, L. Ruíz, S. Docampo & B. Cabezudo. 2013. Quercus airborne pollen tendencies in the south of Iberian Peninsula, its correlations with meteorological trends and possible effect of the climatic change in Mediterranean forest. In: PS. Testillano et al. (eds) Pollen 2013, 2nd International APLE-APLF Congress, Madrid, pp. 158. y=0.053x-88.72 R²=0.490 p=0.000 15 16 17 18 19 20 21 1992 1996 2000 2004 2008 2012 ºC y=-0.252x+571.6 R²=0.382 p=0.002 50 55 60 65 70 75 80 1992 1996 2000 2004 2008 2012 % Annual Mean Temperature Annual Total Rainfall y=1.313x-2101 R²=0.001 p=0.874 00 200 400 600 800 1.000 1.200 1.400 1992 1996 2000 2004 2008 2012 mm Annual Relative Humidity y=119.3x-23410 R²=0.130 p=0.090 0 2000 4000 6000 8000 10000 12000 1992 1996 2000 2004 2008 2012 y=267.1x-52432 R²=0.123 p=0.101 0 5000 10000 15000 20000 25000 1992 1996 2000 2004 2008 2012 y=-35.7x+72847 R²=0.148 p=0.069 0 500 1000 1500 2000 2500 3000 3500 1992 1996 2000 2004 2008 2012 y=-5.5x+11209 R²=0.479 p=0.000 0 50 100 150 200 250 1992 1996 2000 2004 2008 2012 y=-31.9x+65448 R²=0.225 p=0.022 0 500 1000 1500 2000 2500 3000 1992 1996 2000 2004 2008 2012 y=1.676x-3190 R²=0.291 p=0.008 0 50 100 150 200 250 1992 1996 2000 2004 2008 2012 Date y=1.155x-2227 R²=0.143 p=0.074 0 20 40 60 80 100 120 140 160 1992 1996 2000 2004 2008 2012 No. of days y=1.162x-2225 R²=0.308 p=0.006 0 20 40 60 80 100 120 140 1992 1996 2000 2004 2008 2012 Date y=-2.132x+4372 R²=0.161 p=0.057 0 50 100 150 200 250 1992 1996 2000 2004 2008 2012 No. of days Delay Increase Delay Decrease Decrease Decrease Increase Significant trend Almost significant trend Significant trend Almost significant trend Almost significant trend Significant trend Almost significant trend Significant trend Almost significant trend