Your SlideShare is downloading. ×
Thackeray ehfi sefs8
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×

Saving this for later?

Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime - even offline.

Text the download link to your phone

Standard text messaging rates apply

Thackeray ehfi sefs8

136
views

Published on

Published in: Technology

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
136
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
0
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. Disentangling long-term responses of crustacean zooplankton to multiple stressors Stephen J. Thackeray (sjtr@ceh.ac.uk), Peter Smyntek, Heidrun Feuchtmayr, Ian J. Winfield, Ian D. Jones & Stephen C. Maberly Lake Ecosystems Group, Centre for Ecology & Hydrology
  • 2. Multiple stressors • Lake ecosystems are affected by many internal and external factors • External factors:  climate change  eutrophication  acidification  species introduction • Operate at different (local – regional) scales and may interact.
  • 3. Top-down and bottom-up effects Maberly & Elliott (2012) Freshwater Biology, 57, 233-243 • Stressors may act upon:  physical properties and basal resources – “bottom up”.  predator/consumer populations – “top down”. • Relative importance of these pathways and associated stressors will vary among ecosystems, and over time.
  • 4. Windermere, as a model system Mean winter SRP (mg m -3 ) 0 5 10 15 20 25 30 1950 1960 1970 1980 1990 2000 2010 Year North Basin South Basin 6 8 10 12 1950 1970 1990 2010 Year Mean surface temperature (oC) North Basin South Basin Nutrient enrichment Warming
  • 5. Windermere, as a model system 0 1000 2000 3000 4000 5000 6000 1990 1995 2000 2005 2010 Abundance(fishha-1) Year Expansion of non-native species
  • 6. Focus on crustacean zooplankton PredatorsGrazersFood/Temperature • Effects on grazers of long-term changes in:  Temperature  Food (algae)  Predators (invertebrate)  Predators (fish) • Is it possible to detect these effects on the long-term dynamics of grazer populations?
  • 7. Drivers of zooplankton change • Fortnightly data,1991-2010 • Response data: • Crustacean zooplankton abundance • Driving data: • Water temperature • Phytoplankton biomass, (Chlorophyll a) • Predatory zooplankton (Bythotrephes, Leptodora, Cyclops) • Fish abundance (monthly)
  • 8. A proxy for zooplanktivory 0 1000 2000 3000 4000 5000 6000 1990 1995 2000 2005 2010 Abundance(fishha-1) Year 6 8 10 12 1950 1970 1990 2010 Year Mean surface temperature (oC) North Basin South Basin Meansurfacetemperature(˚C) Maximum consumption rate (Cmax) = 0.016 x Weight (g)-0.16 x e0.133 x Temperature (˚C) Hölker & Haertel (2004) Journal of Applied Icthyology, 20, 548-550
  • 9. Statistical methods • Seasonality:  Focus on long-term (not seasonal) change.  Induces correlation among driving variables.  Therefore, removed smooth seasonal “trend” from original data using generalised additive models (GAMs). • Lagged effects:  Response at time t related to drivers at time t-1. • Seasonal shifts in drivers:  Drivers can vary (interact) with month-of- year. • Linear models with different predictor combinations compared by AIC. °C Food Fish
  • 10. Patterns of change: Eudiaptomus
  • 11. Correlates of change: Eudiaptomus • “Top” model (by AIC): “effects” of chlorophyll (food) and planktivory by fish
  • 12. Correlates of change: Eudiaptomus November - March data 1991 1994 1997 2000 2003 2006 2009 -0.20.00.2 Seasonally-detrended log chlorophyll concentration Year 1991 1994 1997 2000 2003 2006 2009 -0.50.00.5 Seasonally-detrended log fish consumption Year 1991 1994 1997 2000 2003 2006 2009 -1.5-1.0-0.50.00.51.0 Seasonally-detrended log Eudiaptomus abundance Year 1991 1994 1997 2000 2003 2006 2009 -1.5-1.0-0.50.00.51.0 Model prediction Year
  • 13. Summary and next steps • Can detect a likely effect of increased planktivory upon Eudiaptomus, though much unexplained variation. • Further exploration of the zooplanktivory “effect”  sensitivity to parameter choice  can we apportion planktivory among fish species?  is magnitude sufficient to cause observed population change? • What about other species? • Can we see a cascade to the phytoplankton? • Independent process modelling studies.
  • 14. Acknowledgements • This work was funded by NERC Grant NE/H000208/1: “Whole lake responses to species invasion mediated by climate change” (http://www.windermere- science.org.uk/). • Many thanks to everyone involved in maintaining the Cumbrian Lakes long-term monitoring programme, past and present. • Thank you for your attention!