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Slide 1 of 11 Changes in zooplankton phenology at stn MC 1984-2010 Heather Anne Wright LTER-MC MareChiara meeting 18. October.2012 Changes in zooplankton phenology [email protected]

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Slide 1 of 11

Changes in zooplankton phenology at stn MC 1984-2010

Heather Anne Wright

LTER-MC MareChiara meeting

18. October.2012

Changes in zooplankton phenology [email protected]

Slide 2 of 11

Part I determine phenology 1. plankton taxa selection (20 zooplankton taxa) 2. exploratory data analysis (EDA) 3. identify trends in seasonal and long term abundance 4. determine phenology 5. significant change in phenology by seasons Part II determine environmental drivers 1. Select environmental parameters/data 2. Calculate anomalies 3. Correlate phenological timing with anomalies (by year)

Changes in zooplankton phenology [email protected]

Introduction

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Changes in zooplankton phenology [email protected]

Part I: plankton taxa selection

Mazzocchi et al., 2011. Zooplankton associations in a Mediterranean long-term time-series. JPR 23: 1163–1181.

Heat map analysis L. Dubroca 1984:2006 C. Tortora 1984:2010

Discrimination using: cluster analysis heat map plots

EDA to explore:

abundance frequency (omit rare) season of occurrence

To select taxa:

dominant ecological role

Slide 4 of 11

Part I: zooplankton taxa selection

Changes in zooplankton phenology [email protected]

Slide 5 of 11

Changes in zooplankton phenology [email protected]

Part I: EDA

Season of occurrence + peak AMJ spring JAS JFM and OND

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Changes in zooplankton phenology [email protected]

Part I: identify phenology

p=0.0172

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Changes in zooplankton phenology [email protected]

Part I: changes in phenology SPRING taxa

Acartia clausi ∆ phenology 2° End of season earlier when warmer JAS_2m vs. 75% *p=0.0388 Centropages typicus ≠ phenology Trend in 1° NS End of season earlier when warmer JAS_int vs. 75% *p=0.0281 Oithona similis ∆ phenology 1° end of season earlier when warmer OND_2m vs. 75% *p=0.0399

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Changes in zooplankton phenology [email protected]

Part I: changes in phenology SUMMER taxa

Clausocalanus furcatus ∆ phenology 2° Phenology delayed and shorter Duration shorter when warmer JAS_int vs. Duration **p=0.0014 Peak in September Paracalanus parvus ≠ phenology Trend in 1° NS start of season earlier when warmer MAM_2m vs. 25% *p=0.0174 Peak in peak in August start of season driven by temp

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Changes in zooplankton phenology [email protected]

Part I: changes in phenology AUTUMN taxa

Temora stylifera ∆ phenology End of season earlier Peak in October Calocalanus spp. ∆ phenology 2° Earlier in spring, later in autumn Bimodal peak in April and Nov Correlation with JAS_int Temp. Chaetognaths ∆ phenology 2° Peak in October JAS_2m vs. 75% End of season is later when summer temp is higher

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Changes in zooplankton phenology [email protected]

Oncaeidae ≠ phenology No trend in phenology No correlations with timing vs. Temp bimodal appendicularia ≠ phenology Slight trend in timing vs. Temp [7:12] vs. OND_2m NS Removed 1987 and 1998 outliers bimodal Cirripedia larvae ≠ phenology Slight trend in timing vs. Temp [7:12] vs. JAS_int NS bimodal

Part I: changes in phenology WINTER taxa

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Changes in zooplankton phenology [email protected]

Summary of results (in progress)

Spring taxa: End of season is significantly correlated with temperature anomalies in summer and Autumn. Taxa with a June peak are correlated with JAS (summer) temperature and taxa with a October peak are correlated with OND (autumn) temperature. Summer taxa: Start of season in earlier and duration is shorter when temperature anomalies are above average. Taxa with an August peak are significantly correlated with spring MAM temp. Autumn taxa: End of season is earlier for T.stylifera when temp anomalies are higher, but the end of season later when warmer for other species. Not a consistent trend in Autumn taxa Winter taxa: No significant changes in phenology. All selected winter zooplankton taxa are bimodal.

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Changes in zooplankton phenology [email protected]

Acknowledgements

LTER-MC and MECA – for sampling and data Grazia Mazzocchi, SZN – director of studies Adriana Zingone , SZN– internal advisor Diana Sarno, SZN – phytoplankton taxonomy Francesca Margiotta, SZN-MECA Enzo Saggiomo, SZN-MECA Laurent Dubroca , IFRMER– external collaborator Cristina Tortora, SZN – post-doc, collaborator

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Changes in zooplankton phenology [email protected]

Part I: example of methods

Part II (1995-2010) Start *p=0.011 Middle **p=0.006 End *p=0.013

Cumulative percentile method: start 25% middle 50% end 75%

Timing of central tendency [1:12] or [1:6, 7:12]

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Changes in zooplankton phenology [email protected]

Part II: example of anomalies

What is an anomaly? The term temperature anomaly means a departure from a reference value or long-term average. A positive anomaly indicates that the observed temperature was warmer than the reference value, while a negative anomaly indicates that the observed temperature was cooler than the reference value. Reference: http://www.ncdc.noaa.gov/cmb-faq/anomalies.php