Supporting Figure Legends (figures as separate files)
Supporting Figure S1. Rates of phenological change (1969-2008) across three trophic levels
in a) the North Basin and b) the South Basin of Windermere, UK. Each bar represents the
slope of a linear trend fitted to data on the seasonal timing of a particular phenological metric
± the standard error of the estimate. Bars are shaded according to trophic level as indicated.
Metrics are: the first day of the year on which chlorophyll concentrations/zooplankton
abundance exceed 0.1 mg C L-1 or 5 individuals per L (0.1CL, 5L), the onset and peak dates
derived from a fitted Weibull function (WO, WP), the first day on which cumulative spring
abundance exceeds 25% or 50% of the spring total (C25%, C50%), the first day on which
abundance/biomass exceeds 50% of the spring maximum (P50%), the day of maximum
abundance (DOMa), the peak derived from a fitted generalised additive model (GAM) and the
centre of gravity of the spring population (CofG). Lines under the abbreviated metric names
indicate whether metrics indicate onset (solid line) or peak/mid-point (dashed line) type
events. The statistical significance of each trend is indicated: ns = P>0.05, * = P<0.05, ** =
P<0.01, *** = P<0.001.
Supporting Figure S2. Examples of long-term change in the seasonal time difference (in
days) between events at different trophic levels in the North and South basins of Windermere,
using three common metrics. Top panels compare phytoplankton and zooplankton and bottom
panels compare zooplankton and fish. Metrics included are the day of maximum abundance
(DOMa, open circles, dotted line), centre of gravity (CofG, black circles, solid line) and day
of 50% cumulative abundance (C50%, grey circles, dashed line). See also Supporting Table
S4.
Supporting Figure S3. Long-term variations in potential physical drivers of phenological
change. Shown are monthly mean water temperatures for (a) the North and (b) the South
Basin, the day of year of on which a range of Schmidt stability thresholds were attained in (c)
the North and (d) the South Basin, the January-May mean bright sunshine hours per day (e)
and the January- May mean River Leven discharge (f).
Supporting Figure S4. Long-term variations in potential chemical and biological drivers of
phenological change. Shown are winter mean concentrations of soluble reactive phosphorus
and silicate for (a) the North and (b) the South Basin, the over-winter inoculum of chlorophyll
and Cladocera in (c) the North and (d) the South Basin and the median length of perch in (e)
the North and (f) the South Basin.
Supporting Table S1. The phenological metrics used to quantify the seasonal timing of
plankton development and perch spawning, grouped by conceptual class, and with named
examples of their application.
Trophic level
Metrics, by class
Phytoplankton
Onset
Example references
(chlorophyll a) & Absolute threshold (5 Daphnia L-1 Romare et al. (2005); Hampton et al.
zooplankton
(Daphnia
or 0.1 mg C L-1*)
and 25% cumulative abundance
filter-paper counts) 50% peak abundance
Weibull onset
(2006); Thackeray et al (2012)
Greve et al. (2005)
Thackeray et al. (2010)
Rolinski et al. (2007).
Peak
Day of maximum abundance
Winder & Schindler (2004); Thackeray
et al.(2008)
Weibull peak
Rolinski et al. (2007)
GAM peak
Ferguson et al. (2008); Thackeray et al.
(2012)
Mid-growing season
Centre of gravity
Edwards
&
Richardson
(2004);
Thackeray et al.(2008)
50% cumulative abundance
Fish
Peak
(perch)
Day of maximum abundance
Greve et al. (2005)
Winder & Schindler (2004); Thackeray
et al.(2008)
Mid-point
Centre of gravity
Edwards
&
Richardson
(2004);
Thackeray et al.(2008)
50% cumulative abundance
Greve et al. (2005)
*Based upon estimating a carbon concentration from the observed chlorophyll a
concentration (Reynolds, 2006).
References for Supporting Table S1
Edwards M, Richardson AJ (2004) Impact of climate change on marine pelagic phenology
and trophic mismatch. Nature, 430, 881-884.
Ferguson CA, Carvalho L, Scott EM, Bowman AW, Kirika A (2008) Assessing ecological
responses to environmental change using statistical models. Journal of Applied
Ecology, 45, 193-203.
Greve W, Prinage S, Zidowitz H, Nast J, Reiners F (2005) On the phenology of north sea
ichthyoplankton. Ices Journal of Marine Science, 62, 1216-1223.
Hampton SE, Romare P, Seiler DE (2006) Environmentally controlled Daphnia spring
increase with implications for sockeye salmon fry in Lake Washington, USA. Journal
of Plankton Research, 28, 399-406.
Reynolds CS (2006) Ecology of phytoplankton, Cambridge, Cambridge University Press, 535
pp.
Rolinski S, Horn H, Petzoldt T, Paul L (2007) Identifying cardinal dates in phytoplankton
time series to enable the analysis of long-term trends. Oecologia, 153, 997-1008.
Romare P, Schindler DE, Scheuerell MD, Scheuerell JM, Litt AH, Shepherd JH (2005)
Variation in spatial and temporal gradients in zooplankton spring development: the
effect of climatic factors. Freshwater Biology, 50, 1007-1021.
Thackeray SJ, Jones ID, Maberly SC (2008) Long-term change in the phenology of spring
phytoplankton: species-specific responses to nutrient enrichment and climatic change.
Journal of Ecology, 96, 523-535.
Thackeray SJ, Sparks TH, Frederiksen M et al. (2010) Trophic level asynchrony in rates of
phenological change for marine, freshwater and terrestrial environments. Global
Change Biology, 16, 3304-3313.
Thackeray SJ, Henrys PA, Jones ID, Feuchtmayr H (2012) Eight decades of phenological
change for a freshwater cladoceran: what are the consequences of our definition of
seasonal timing? Freshwater Biology, 57, 345-359.
Winder M, Schindler DE (2004) Climate change uncouples trophic interactions in an aquatic
ecosystem. Ecology, 85, 2100-2106.
Supporting Table S2. Driving variables used in the analyses of phenological change, with justification for their inclusion and supporting
references.
Driver, by trophic level
Hypothesis and rationale
Supporting
references
Phytoplankton
Water temperature (Jan – May H: Spring blooms will occur earlier when water temperatures are higher. Reynolds (1989)
monthly means, °C)
Phytoplankton replication rate is temperature sensitive. Higher spring water
temperatures could lead to more rapid population growth, and an earlier
attainment of maximal population size.
Timing
of
thermal H: Spring blooms will occur earlier when thermal stratification occurs earlier.
stratification (day of
when
Schmidt
Diehl et al. (2002);
year Earlier stratification will lead to earlier reductions in turbulence and mixing Huisman
&
stability depth. This may 1) lead to earlier improvement in the underwater light climate Sommeijer (2002);
exceeds threshold values)
and, hence, a higher replication rate early in the season, or 2) allow Huisman
et
al.
phytoplankton replication rates in the upper water column to exceed (1999);
(downward) turbulent mixing rates earlier in the year. Furthermore, earlier
Berger
et
al.
reductions in turbulent mixing intensity and mixing depth could lead to earlier (2010)
phytoplankton bloom collapse as a result of increased sinking losses.
Incident solar radiation (Jan – H: Higher spring mean irradiance will lead to earlier blooms.
Reynolds
(1989);
May mean, bright sunshine Changes in irradiance affect phytoplankton replication rate under nutrient Neale et al. (1991)
hour d-1)
replete conditions and thus affect population growth rate, thereby influencing
the time taken to reach the maximal population size.
Hydrological flushing (Jan – H: Higher rates of hydrological flushing will lead to later phytoplankton Jones et al. (2011)
May
mean
River
discharge, m3 s-1)
Leven blooms.
Higher rates of hydrological flushing will increase the flux of phytoplankton
from the lake, slowing net population growth, and delaying the attainment of
the maximum population size.
Nutrient concentrations (mean H: Earlier spring blooms will occur in years with higher concentrations of Lund
concentration
of
soluble soluble reactive phosphorus, and lower concentrations of dissolved silicate.
1950b);
(1950a,
Reynolds
reactive
phosphorus
and Under phosphorus limitation, higher phosphorus concentrations support a (1990, 1997)
silicate in first 10 weeks of longer period of rapid population growth early in the year, allowing the
current year, mg m-3)
maximal population size to be reached earlier. For the diatoms that dominate
the spring phytoplankton bloom, maximal population size will be dependent
upon silicate concentration. Lower silicate availability would support a smaller
maximum population size which, all else being equal, would be attained earlier
in the year.
Overwintering population size H: Earlier spring blooms will occur following winters with higher Maberly
(mean
chlorophyll
a overwintering phytoplankton populations.
et
al.
(1994); Gerten &
concentration in last 10 weeks If the over-wintering population (inoculum) present at the start of the year is Adrian (2000)
of previous year, mg m-3)
higher, then any given abundance threshold will be attained earlier in the year.
Timing of spring zooplankton H: Earlier spring blooms will occur in years when zooplankton population Lampert
maximum
(zooplankton development is also earlier.
(1986);
et
al.
Tirok
&
metrics in Supporting Table Zooplankton grazing can be an important loss process for phytoplankton Gaedke (2006)
S1)
populations, and can drive the collapse of the spring phytoplankton bloom.
Thus, earlier zooplankton population development would lead to earlier
phytoplankton bloom collapse, shifting the phytoplankton bloom as a whole
earlier by curtailing is later limit.
Zooplankton
Water temperature (Jan – Jun H: Spring population development will occur earlier when water temperatures Hall (1964); Munro
monthly means, °C)
are higher.
& White (1975);
The time taken to reach reproductive maturity, egg development time and the Vijverberg (1980);
interval between the release of successive clutches of eggs, are all reduced at Weetman
&
higher temperatures (provided temperature optima are not exceeded). As a Atkinson (2004)
result, population growth should be more rapid in warm water, all else being
equal.
Incident solar radiation (Jan – H: Spring population development will occur earlier when mean incident light Pancella & Stross
Jun mean, bright sunshine intensity is higher.
hour d-1)
For some zooplankton species, hatching success from resting eggs can be
(1963)
affected by underwater light intensity.
Hydrological flushing (Jan – H: Spring population development will be later when rates of hydrological Dirberger
Jun
mean
River
Leven flushing are higher.
discharge, m3 s-1)
Threlkeld
&
(1986);
Pelagic zooplankton have a limited ability to maintain position in the face of Richardson (1992)
rapid hydrological flushing. If this is the case, rates of spring population
increase might be restricted when flushing rates are high.
Overwintering population size H: Earlier spring population development will occur following winters with Romare
(mean zooplankton abundance higher overwintering populations.
et
al.
(2005); Hampton et
in last 10 weeks of previous If the over-wintering population present at the start of the year is higher, then al (2006)
year, individuals L-1)
Timing
of
phytoplankton
(Chlorophyll
any given abundance threshold will be attained earlier in the year.
spring H: Zooplankton spring population development will occur earlier in years Lampert
bloom when phytoplankton blooms occur earlier.
a
metrics
Guisande
in Clutch sizes and the proportion of egg-bearing females in a population will Gliwicz
(1978);
&
(1992);
Supporting Table S1)
increase in response to increased food availability, leading to an increase in George
&
population growth. If the seasonal food increase occurs earlier, then rapid Reynolds (1997)
zooplankton population growth will also occur earlier.
Perch
Water temperature (Jan – May H: Perch spawning will occur earlier with increases in water temperature.
monthly means, °C)
Eriksson
(1978);
Perch activity levels are temperature-sensitive. Spawning behaviour can be Craig
(2000);
induced by increases in temperature. Critical temperatures will be reached Winfield
et
earlier in warmer years, and so spawning will occur earlier.
al.
(2004); Gillet &
Dubois (2007)
Incident solar radiation (Jan – H: Perch spawning will occur earlier with decreases in incident solar Craig
May mean, bright sunshine radiation.
hour d-1)
Alabaster & Stott
Perch activity levels are strongly light-sensitive, affecting their “catchability” (1978)
in perch traps. Reduced levels of activity at higher levels of incident solar
radiation should delay the detection of aggregations of fish at the spawning
grounds.
(1977);
Median length of spawning H: Perch spawning will occur later when the median length of the spawning Gillet
fish (cm)
fish is greater.
Field evidence suggests that larger perch have a tendency to spawn later than
smaller perch, so we would expect that spawning time would be later when the
median length of the spawning population is greater.
(2007)
&
Dubois
References for Supporting Table S2
Alabaster JS, Stott B (1978) Swimming activity of perch, Perca fluviatilis L. Journal of Fish
Biology, 12, 587-591.
Berger SA, Diehl S, Stibor H, Trommer G, Ruhenstroth M (2010) Water temperature and
stratification depth independently shift cardinal events during plankton spring
succession. Global Change Biology, 16, 1954-1965.
Craig JF (1977) Seasonal changes in the day and night activity of adult perch, Perca fluviatilis
L. Journal of Fish Biology, 11, 161-166.
Craig JF (2000) Percid Fishes: Systematics, Ecology and Exploitation, Oxford, Blackwells,
352 pp.
Diehl S, Berger SA, Ptacnik R, Wild A (2002) Phytoplankton, light, and nutrients in a
gradient of mixing depths: field experiements. Ecology, 83, 399-411.
Dirnberger JM, Threlkeld ST (1986) Advective effects of a reservoir flood on zooplankton
abundance and dispersion. Freshwater Biology, 16, 387-396.
Eriksson L-O (1978) A laboratory study of diel and annual activity rhythms and vertical
distribution in the perch, Perca fluviatilis, at the Arctic circle. Environmental Biology
of Fishes, 3, 301-307.
George DG, Reynolds CS (1997) Zooplankton-phytoplankton interactions: the case for
refining methods, measurements and models. Aquatic Ecology, 31, 59-71.
Gerten D, Adrian R (2000) Climate-driven changes in spring plankton dynamics and the
sensitivity of shallow polymictic lakes to the North Atlantic Oscillation. Limnology
and Oceanography, 45, 1058-1066.
Gillet C, Dubois JP (2007) Effect of water temperature and size of females on the timing of
spawning of perch Perca fluviatilis L. in Lake Geneva from 1984 to 2003. Journal of
Fish Biology, 70, 1001-1014.
Guisande C, Gliwicz ZM (1992) Egg size and clutch size in two Daphnia species growth at
different food levels. Journal of Plankton Research, 14, 997-1007.
Hall DJ (1964) An experimental approach to the dynamics
of a natural population of
Daphnia galeata mendotae. Ecology, 45, 94-112.
Hampton SE, Romare P, Seiler DE (2006) Environmentally controlled Daphnia spring
increase with implications for sockeye salmon fry in Lake Washington, USA. Journal
of Plankton Research, 28, 399-406.
Huisman J, Sommeijer B (2002) Maximal sustainable sinking velocity of phytoplankton.
Marine Ecology-Progress Series, 244, 39-48.
Huisman J, Van Oostveen P, Weissing FJ (1999) Critical depth and critical turbulence: two
different mechanisms for the development of phytoplankton blooms. Limnology and
Oceanography, 44, 1781-1787.
Jones ID, Page T, Elliott JA, Thackeray SJ, Heathwaite AL (2011) Increases in lake
phytoplankton biomass caused by future climate-driven changes to seasonal river
flow. Global Change Biology, 17, 1809-1820.
Lampert W (1978) A field study on the dependence of the fecndity of Daphnia spec. on food
concentration. Oecologia, 36, 363-369.
Lampert W, Fleckner W, Rai H, Taylor BE (1986) Phytoplankton control by grazing
zooplankton: A study on the spring clear-water phase. Limnology and Oceanography,
31, 478-490.
Lund JWG (1950a) Studies on Asterionella formosa Hass II. Nutrient depletion and the spring
maximum. Part I. Observations on Windermere, Esthwaite Water and Blelham Tarn.
Journal of Ecology, 38, 1-14.
Lund JWG (1950b) Studies on Asterionella formosa Hass II. Nutrient depletion and the spring
maximum. Part II. Discussion. Journal of Ecology, 38, 15-32.
Maberly SC, Hurley MA, Butterwick C et al. (1994) The rise and fall of Asterionella formosa
in the south basin of Windermere: analysis of a 45-year series of data. Freshwater
Biology, 31, 19-34.
Munro IG, White RWG (1975) Comparison of the Influence of Temperature on the Egg
Development and Growth of Daphnia longispina O.F. Müller (Crustacea:Cladocera)
from Two Habitats in Southern England. Oecologia, 20, 157-165.
Neale PJ, Talling JF, Heaney SI, Reynolds CS, Lund JWG (1991) Long time series from the
English Lake District:irradiance-dependent phytoplankton dynamics during the spring
maximum. Limnology and Oceanography, 36, 751-760.
Pancella JR, Stross RG (1963) Light induced hatching of Daphnia resting eggs. Chesapeake
Science, 4, 135-140.
Reynolds CS (1989) Physical determinants of phytoplankton succession. In: Plankton
Ecology. (ed Sommer U) pp 9-56. Madison, Brock-Springer.
Reynolds CS (1990) Temporal scales of variability in pelagic environments and the response
of phytoplankton. Freshwater Biology, 23, 25-53.
Reynolds CS (1997) Successional development, energetics and diversity in planktonic
communities. In: Biodiversity: An ecological perspective. (eds Abe T, Levin SR,
Higashi M) pp 167-202. New york, Springer.
Richardson WB (1992) Microcrustacea in flowing water: experimental analysis of washout
times and a field test. Freshwater Biology, 28, 217-230.
Romare P, Schindler DE, Scheuerell MD, Scheuerell JM, Litt AH, Shepherd JH (2005)
Variation in spatial and temporal gradients in zooplankton spring development: the
effect of climatic factors. Freshwater Biology, 50, 1007-1021.
Tirok K, Gaedke U (2006) Spring weather determines the relative importance of ciliates,
rotifers and crustaceans for the initiation of the clear-water phase in a large, deep lake.
Journal of Plankton Research, 28, 361-373.
Vijverberg J (1980) Effect of temperature in laboratory studies on development and growth of
Cladocera and Copepoda from Tjeukemeer, The Netherlands. Freshwater Biology, 10,
317-340.
Weetman D, Atkinson D (2004) Evaluation of alternative hypotheses to explain temperatureinduced life history shifts in Daphnia. Journal of Plankton Research, 26, 107-116.
Winfield IJ, Fletcher JM, Hewitt DP, James JB (2004) Long-term trends in the timing of the
spawning season of Eurasian perch (Perca fluviatilis) in the north basin of
Windermere, U.K. In: Proceedings of Percis III: The Third International Percid Fish
Symposium. (eds Barry TP, Malison JA) pp 95-96, University of Wisconsin Sea Grant
Institute, Madison.
Supporting Table S3. Comparison of phenological metrics derived using filter count and
microscope data for Daphnia. The conceptual class of each calculated metric is indicated
(Metric class) as well as the Pearson correlation coefficient when comparing metric values
calculated from the two data sets (r). For each metric, differences in the long-term
phenological trend among data sets have been assessed by including a data set*year
interaction in a regression model of seasonal timing against year (see text). The F statistic (F)
and degrees of freedom (df) is shown for each of these comparisons. GAM = General
Additive Model.
Metric
Metric class
r
F (df)
25% cumulative
Onset
0.76***
2.56 (1,46) ns
0.61**
1.01 (2,44) ns
abundance
50%
peak Onset
abundance
Weibull onset
Onset
0.61**
0.14 (1,46) ns
Day of maximum
Peak
0.61**
0.17 (1,46) ns
Weibull peak
Peak
0.76***
0.32 (1,46) ns
GAM peak
Peak
0.90***
0.03 (1,45) ns
Centre of gravity
Mid-growing
0.87***
0.91 (1,46) ns
0.88***
2.04 (2,44) ns
0.82***
-
season
50% cumulative
Mid-growing
abundance
season
All metrics
All
*** = P<0.001, ** = P<0.01, * = P<0.05 and ns = not significant
Supporting Table S4. Summaries of linear models of long-term changes in the seasonal time
difference between phenological events at adjacent trophic levels. For each lake basin the
difference in timing between phytoplankton and zooplankton population development, and
between zooplankton population development and fish spawning, was calculated based upon
the three common metrics. Shown are the F statistic and associated degrees of freedom (Fdf),
P value and slope and associated standard error [b (s.e.)] of each model.
Comparison, by basin
Metric
b (s.e.)
Fdf
P
Day of maximum
0.04 (0.32)
0.021,38
0.904
Centre of gravity
-0.55 (0.14)
15.31,38
<0.001
50% cumulative abundance
-0.46 (0.16)
8.61,38
0.006
Day of maximum
-0.37 (0.23)
2.61,38
0.114
Centre of gravity
-0.34 (0.11)
9.41,38
0.004
50% cumulative abundance
-0.52 (0.13)
15.41,38
<0.001
Day of maximum
-0.20 (0.30)
0.51,38
0.500
Centre of gravity
-0.49 (0.18)
7.81,38
0.008
50% cumulative abundance
-0.35 (0.17)
4.51,38
0.041
Day of maximum
-0.58 (0.24)
5.91,36
0.020
Centre of gravity
-0.42 (0.14)
9.61,36
0.004
50% cumulative abundance
-0.59 (0.14)
17.41,36
<0.001
North basin
Zooplankton-Phytoplankton
Zooplankton-Fish
South basin
Zooplankton-Phytoplankton
Zooplankton-Fish
Supporting Table S5: Correlation matrix for predictors included in the hierarchical modelling of phenological change at three trophic levels. For
each pair of predictors a bivariate Pearson correlation coefficient is given. Correlations are given separately for data from the North Basin (N:)
and the South Basin (S:). When a particular combination of predictors did not occur in any model “na” is shown. For temperature, correlations
along the diagonal indicate the correlations among monthly mean temperatures. For thermal stratification, the correlations along the diagonal
indicate the correlations among data on the seasonal timings of the different Schmidt Stability thresholds (see text).
Water
temperature
Thermal
stratification
Sunlight
Leven
discharge
SRP
concentration
Silica
concentration
Chlorophyll
inoculum
Cladoceran
inoculum
Median
perch length
Water
temperature
Thermal
stratification
Sunlight
Leven
discharge
SRP
Silica
concentration concentration
Chlorophyll
inoculum
Cladoceran
inoculum
Median
perch length
N: 0.27, 0.90
S: 0.25, 0.90
N: -0.31, -0.87
S: -0.09, -0.77
N: 0.46, 0.81
S: 0.17, 0.77
N: 0.01, 0.47
S: 0.00, 0.42
N: -0.12,-0.46
S:-0.14,-0.56
N: 0.07,0.49
S: 0.09,0.51
N: -0.25,-0.38
S: -0.17,-0.36
N: -0.36
S: -0.36
N: 0.17,0.28
S: -0.18,0.10
N: -0.18,-0.31
S: 0.11,0.26
N: -0.12
S: -0.30
N: 0.34
S: 0.00
N: -0.07,0.17
S: 0.00,0.27
N: -0.06,-0.28
S: -0.07,-0.33
N: 0.07
S: 0.23
N: 0.08
S: 0.20
N: -0.02
S: -0.43
N: 0.03
S: -0.10
N: -0.02,0.28
S: -0.11,0.01
na
N: 0.00,-0.29
S: -0.15,-0.39
na
N: -0.23
S: -0.17
N: 0.14
S: 0.23
na
N: 0.18
S: 0.08
na
na
na
na
na
N: -0.16,0.17
S: 0.00,0.34
N: 0.10,0.14
S: -0.03,-0.10
N: 0.11
S: 0.14
N: 0.01
S: 0.14
N:0.09
S: -0.07
na
na
Supporting Information, hierarchical model structure for analysis of drivers
For phytoplankton (in each lake basin) the model structure was:
Phyto class i = α + β1Temp + β2Strat + β3Sun + β4Flush + β5SRP + β6Si + β7Inoc + β8Zoo
where
Temp = β8Jan_Temp + β9Feb_Temp + β10Mar_Temp + β11Apr_Temp + β12May_Temp
and
Strat = β13SS20 + β14SS50+ β15SS150+ β16SS300
and
Zoo = β17Onset + β17PeakMid
For zooplankton (in each lake basin) the model structure was:
Zoo class i = α + β1Temp + β2Sun + β3Flush + β4Inoc + β5Phyto
where
Temp = β6Jan_Temp + β7Feb_Temp + β8Mar_Temp + β9Apr_Temp + β10May_Temp+
β11Jun_Temp
and
Phyto = β12Onset + β13PeakMid
For fish (in each lake basin) the model structure was:
Fish PeakMid= α + β1Temp + β2Sun + β3Median_length
where
Temp = β4Jan_Temp + β5Feb_Temp + β6Mar_Temp + β7Apr_Temp + β8May_Temp+
β9Jun_Temp
In each case, α and βi are the intercept and slope parameters of the fitted models. Therefore,
for each lake basin, the phenology of the three trophic levels (Phyto, Zoo, Fish, see main text)
was modelled as a function of water temperature (Temp), the timing of thermal stratification
(Strat), hours of bright sunshine (Sun), hydrological flushing (Flush), concentrations of
soluble reactive phosphorus and silicate at the start of the growing season (SRP, Si) and overwintering abundance (Inoc). The seasonal timing of phytoplankton and zooplankton
population growth were included as predictors of each other (Phyto, Zoo). Perch spawning
phenology was also modelled as a function of the median length of fish caught on the
spawning grounds (Median_length). Distinct effects of January – June mean temperatures
(Jan_Temp – Jun_Temp) were nested within the higher level temperature terms (Temp), and
the time of year at which Schmidt stability exceeded 20, 50, 150 and 300 J m -2 (SS20 –
SS300) was nested within the higher level stratification term (Strat). In the absence of a priori
information, this allowed identification of key times of year in which variations in water
temperature and water column stability affected seasonal timing. Though certain
combinations of monthly mean temperatures (and stability thresholds) were strongly
correlated, the modelling approach was robust to this colinearity. At each model iteration,
only one monthly mean temperature (or stability threshold) was fitted within the higher level
temperature (and stratification) terms. Through the iterative process, the most optimal
combination of a single monthly mean temperature and stability threshold was derived. For
phytoplankton and zooplankton separate models were run for each metric class (onset vs
peak/mid-point type events), hence the “class i” subscript in the formulae presented above.
Only peak/mid-point type events were available for the fish data. For each year, the
phenology of a specific class of event within a trophic level, is the mean of the metric data
associated with that class-trophic level combination.