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.