Disentangling long-term responses of
crustacean zooplankton to multiple
stressors
Stephen J. Thackeray ([email protected]),
Peter Smyntek, Heidrun Feuchtmayr, Ian J. Winfield, Ian D. Jones &
Stephen C. Maberly
Lake Ecosystems Group, Centre for Ecology & Hydrology
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.
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.
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 2010Year
Mean surface temperature (oC)
North Basin
South Basin
Nutrient enrichment Warming
Windermere, as a model system
0
1000
2000
3000
4000
5000
6000
1990 1995 2000 2005 2010
Ab
un
dan
ce (
fish
ha
-1)
Year
Expansion of non-native species
Focus on crustacean zooplankton
Pre
dat
ors
G
raze
rs
Foo
d/T
emp
erat
ure
• 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?
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)
A proxy for zooplanktivory
0
1000
2000
3000
4000
5000
6000
1990 1995 2000 2005 2010
Ab
un
dan
ce (
fish
ha
-1)
Year
6
8
10
12
1950 1970 1990 2010Year
Mean surface temperature (oC)
North Basin
South BasinMe
an
su
rfac
e t
em
pe
ratu
re (
˚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
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
Patterns of change: Eudiaptomus
Correlates of change: Eudiaptomus
• “Top” model (by AIC): “effects” of chlorophyll (food) and planktivory by fish
Correlates of change: Eudiaptomus
November -
March data
1991 1994 1997 2000 2003 2006 2009
-0.2
0.0
0.2
Seasonally-detrended log chlorophyll concentration
Year
1991 1994 1997 2000 2003 2006 2009
-0.5
0.0
0.5
Seasonally-detrended log fish consumption
Year
1991 1994 1997 2000 2003 2006 2009
-1.5
-1.0
-0.5
0.0
0.5
1.0
Seasonally-detrended log Eudiaptomus abundance
Year
1991 1994 1997 2000 2003 2006 2009
-1.5
-1.0
-0.5
0.0
0.5
1.0
Model prediction
Year
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.
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!