Oikos 120: 1838–1846, 2011 doi: 10.1111/j.1600-0706.2011.19433.x © 2011 The Authors. Oikos © 2011 Nordic Society Oikos Subject Editor: Ben Chapman. Accepted 5 September 2011
Interplay between temperature, fish partial migration and trophic dynamics Jakob Brodersen, Alice Nicolle, P. Anders Nilsson, Christian Skov, Christer Brönmark and Lars-Anders Hansson J. Brodersen (
[email protected]), A. Nicolle, P. A. Nilsson, C. Brönmark and L.-A. Hansson, Dept of Ecology / Limnology, Ecology Building, Lund Univ., SE-223 62 Lund, Sweden. Present address for JB: Dept of Fish Ecology and Evolution, EAWAG Swiss Federal Inst. of Aquatic Science and Technology, Center of Ecology, Evolution and Biochemistry, Seestrasse 79, CH-6047 Kastanienbaum, Switzerland. – C. Skov, Technical Univ. of Denmark, National Inst. of Aquatic Resources (DTU-Aqua), Section for Freshwater Fisheries Ecology, Vejlsøvej 39, DK-8600 Silkeborg, Denmark.
Whereas many studies have addressed the mechanisms driving partial migration, few have focused on the consequences of partial migration on trophic dynamics, and integrated studies combining the two approaches are virtually nonexistent. Here we show that temperature affects seasonal partial migration of cyprinid fish from lakes to predation refuges in streams during winter and that this migration in combination with temperature affects the characteristics and phenology of lower trophic levels in the lake ecosystem. Specifically, our six-year study showed that the proportion of fish migrating was positively related to lake temperature during the pre-migration growth period, i.e. during summer. Migration from the lake occurred later when autumn water temperatures were high, and timing of return migration to the lake occurred earlier at higher spring water temperatures. Moreover, the winter mean size of zooplankton in the lake increased with the proportion of fish being away from the lake, likely as a consequence of decreased predation pressure. Peak biomass of phytoplankton in spring occurred earlier at higher spring water temperatures and with less fish being away from the lake. Accordingly, peak zooplankton biomass occurred earlier at higher spring water temperature, but relatively later if less fish were away from the lake. Hence, the time between phyto- and zooplankton peaks depended only on the amount of fish being away from the lake, and not on temperature. The intensity of fish migration thereby had a major effect on plankton spring dynamics. These results significantly contribute to our understanding of the interplay between partial migration and trophic dynamics, and suggest that ongoing climate change may significantly affect such dynamics.
Animal migration is a widespread phenomenon that has fascinated and affected humans throughout history. This fascination has lead to a vast amount of research in multiple disciplines, which has significantly increased our understanding of the causes and consequences of migration. The mechanisms driving migration have received much attention and we therefore have gained much knowledge on why, how and where animals migrate (Swingland and Greenwood 1984, Berthold 1996). Research in this field has made it increasingly clear that patterns of migration are to a great extent shaped by the dynamics of the surrounding ecosystem (Forchhammer et al. 2002). Further, the ecological and evolutionary consequences of migration are currently receiving increased attention. For instance, meta-population studies have indicated the consequences of spatial connectivity and migration on population dynamics (Hanski 1998, McCann et al. 2005), and migrating animals have been identified as important nutrient reallocation vectors between ecosystems (Post et al. 1998, Vanni 2002, Schindler et al. 2005). Recent studies on the consequences of migration on the trophic dynamics in ecosystems also suggest that migration may have a major influence on ecosystem trophic structure and stability (Brodersen et al. 2008a, Post et al. 1838
2008). However, research that considers both causes and consequences of migration simultaneously has hitherto been absent, although much needed (Nathan et al. 2008). This is especially notable as factors driving ecosystem dynamics may also drive migration patterns, which in turn can feedback into trophic dynamics by either strengthening or dampening the primary effects of such driving factors. Migration of keystone species can influence the structure and dynamics of lower trophic levels (Post et al. 2008). However, such studies have hitherto only focused on scenarios where all individuals in a population either migrate or stay resident. A number of studies have shown that there is considerable variation in the extent of migration within species or populations, where only a subset of the individuals migrate, i.e. they show partial migration (Swingland and Greenwood 1984, Berthold 1996, Chapman et al. 2011). Both timing of migration and the resident/migratory fraction in partially migratory populations are likely to vary between years and between populations (Mueller et al. 1977, Metcalfe and Thorpe 1990, Quinn et al. 2000, Cagnacci et al. 2011, Mysterud et al. 2011). Such variation may theoretically affect trophic dynamics and stability (Brodersen et al. 2008a), but empirical studies of these effects are rare (see however Hansson et al. 2007).
Many animal migrations are triggered by seasonal changes in environmental temperature (Forchhammer et al. 2002). However, as migration and temperature may have concurrent effects on ecosystem processes, there may be both direct and indirect effects on trophic dynamics. In order to study such direct and indirect effects, both trophic dynamics and migration have to be monitored accurately and simultaneously. Freshwater ecosystems are thoroughly investigated and their trophic dynamics are relatively well understood (Carpenter and Kitchell 1993, Scheffer 1998) and they thereby serve as valuable model ecosystems for studying complex interactions between climate, behaviour, and trophic dynamics. Size-selective predation by fish commonly affects both biomass and size-structure of zooplankton (Brooks and Dodson 1965). Changes in zooplankton biomass and size structure affect grazing rates (Brooks and Dodson 1965) and thereby phytoplankton biomass, creating a trophic cascade from fish to phytoplankton (Carpenter et al. 1987, Hansson 1992, Jeppesen et al. 2003). Cyprinid fish in particular have been shown to be of key importance for structuring plankton communities in temperate lakes (Cryer et al. 1986, Jeppesen et al. 2003). Moreover, several cyprinid species perform seasonal migrations out of lakes during winter (Skov et al. 2008, Brönmark et al. 2010). These migrations appear to be driven by seasonal changes in the habitat-specific predation risk/growth rate tradeoffs, with streams providing a refuge from predation, but lower food supply compared to lakes (Brönmark et al. 2008, 2010). One well studied cyprinid fish system is that of the roach Rutilus rutilus (Brönmark et al. 2008, Brodersen et al. 2008b), a species occurring throughout most of Europe and Asia (Kottelat and Freyhof 2007). The roach also has an important role structuring lake ecosystems (Cryer et al. 1986), since it is often a dominant size-selective zooplanktivore (Hansson et al. 2007). Here, we employ an integrated approach to study how temperature affects partial migration and concurrently how lake trophic dynamics is affected by both temperature and migration. Temperature has documented and great impact on both migration (Brönmark et al. 2008) and lake ecosystem processes (Adrian et al. 1999, Winder and Schindler 2004), which is why we here focus on effects of temperature on the interplay between migration and trophic functions. Specifically, we evaluate the interplay between water temperature, plankton dynamics and roach migration during six consecutive years. In general, we expect that migration can both amplify and dampen temperature effects on trophic dynamics. Specifically, we expect that fish migration will counteract temperature effects on zooplankton phenology, but amplify temperature effects on phytoplankton phenology through a trophic cascade (Fig. 1).
Material and methods Study system The study was conducted in Lake Krankesjön, a 3.4 km2, shallow (mean depth 0.7 m, maximum depth 3.0 m), macrophyte rich, eutrophic lake in southern Sweden (for lake description, see Hansson et al. 2007, Brodersen et al. 2008b). Roach is the most abundant fish species in Lake Krankesjön,
and parts of the population are known to overwinter in either of two tributaries or in the outlet stream (Skov et al. 2008). Fish migration We quantified migration of roach (size range: 120–268 mm total length) by passive telemetry. Fish were captured by electrofishing in the lake between 22 September and 15 October (for 2003 until 23 November) each year between 2003 and 2008. After capture fish were held in net enclosures over night and tagging took place on the following day. After tagging and a short recovery, all fish were released into the lake at the approximate area of capture. Between years 2003 and 2007, we tagged 479 to 696 fish per year. In 2008, only 98 fish were tagged, due to low catch efficiency during electrofishing. Apart from tagging, no fish in this study were manipulated, i.e. were not subjected to any experimental treatment. After being weighed to nearest 0.1 g and measured to nearest mm (total length), each fish was tagged according to Skov et al. (2005) by surgically implanting a passive integrated transponder-tag (PIT-tag) (134 kHz, 23.1 mm long, 3.85 mm diameter, 0.6 g in air) into the body cavity of the fish. PIT tags are passive telemetry tags that are activated when exposed to an electromagnetic field produced by antennae. When energized and activated, the tags emit a unique code enabling identification of tagged individuals. Migration of fish between the lake and the inlet and outlet streams was monitored from October 2003 to June 2009 by passive bio-telemetry using a modified PIT-tag antenna system with two sequential loop antennas in each stream, allowing determination of migratory direction (details in Brodersen et al. 2008b). PIT-tagged fish were recorded with high accuracy at the moment they pass the antenna. An Spring temperature 1
Time of fish return migration
2 3
4
Zooplankton phenology 5 6
Phytoplankton phenology
Figure 1. Potential effects of temperature on migration and combined effects of temperature and migration on phytoplankton and zooplankton spring phenology in lake ecosystems. Positive effects are indicated by solid arrows and negative effects by dashed arrows. Increased temperature is expected to advance spring return migration (1) and to accelerate zooplankton (2) and phytoplankton spring phenology (3). However, earlier return of fish will likely slow down zooplankton spring phenology (4), counteracting the effects of temperature on zooplankton phenology (2). Whereas zooplankton negatively affects phytoplankton phenology through grazing effects (5), phytoplankton will advance zooplankton phenology through resource availability (6). The predation from returned fish on zooplankton is however likely to decrease zooplankton grazing on phytoplankton, which creates a trophic cascade accelerating phytoplankton spring phenology.
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evaluation of PIT-tag marking techniques showed that the method used resulted in no significant effect on fish wellbeing, including condition (Skov et al. 2005). The study complies with the current laws in Sweden; ethical concerns on care and use of experimental animals were followed under permissions (M14-04 and M165-07) from the Malmö/Lund Ethical Committee. Lake trophic dynamics Phytoplankton and zooplankton were sampled in Lake Krankesjön every second week during 2003–2009. Samples were taken at a fixed position at the deepest part of the lake. A 1 m plexiglass tube (35 mm diameter) was used for sampling of both phyto- and zooplankton. For zooplankton sampling 10 l of water was filtered through a 45 μm net and the remaining animals were preserved in Lugols solution. Zooplankton were determined to genus level with the exception of copepods, which were divided into cyclopoids, calanoids and nauplii. Individuals were counted and measured to determine density, mean length and biomass. For analysis of chlorophyll-a, which was used as a proxy for phytoplankton biomass, 300 ml of water was filtered through a Whatman GF/C filter. In the laboratory, filters were put into test tubes with 10 ml of ethanol and stored in darkness for 20 h. The extract was then cleared by centrifugation, and absorbance of the supernatant was measured at 665 and 750 nm. Lake water temperature was monitored with an temperature logger every four h from December 2003 to June 2009 in the outlet of Lake Krankesjön. Data treatment For analyses of timing of migration and plankton phenology, we used each year as an independent replicate. For analyses of zooplankton size and phytoplankton biomass during the migration period, we used every sampling date as an independent replicate. The number of individual fish being away from the lake was evaluated for each hour and mean values per day were used in the analyses. The proportion of migrants during winter was calculated as the proportion of tagged fish that at some point were recorded in any of the streams. When the proportion of migratory fish was used as a dependent variable, data was arcsine√x transformed. Since tagging of fish could only take place at relatively low water temperatures to avoid detrimental effects related to surgery, the tagging was carried out when the first few fish had already initiated their winter migration. For analyses of timing of migration from the lake, we therefore only used data from fish that were tagged in previous years, as using data from fish tagged the same year would lead to a bias towards later mean migration dates by missing early migrants. To estimate the proportion of fish being out of the lake at a given time, we multiplied the proportion of fish tagged the previous year being away from the lake at time t (M⫺1(t)) with the total proportion of the fish tagged the same year being away from the lake at any time during the migration period (M0,tot) divided by the total proportion of the fish tagged the previous year being away from the lake at any time during the migration period (M⫺1,tot), using the following equation: 1840
M est (t ) ⫽ M⫺1(t )
M 0,tot M⫺1,tot
where Mest(t) is estimated proportion of the fish population away from the lake at time t. This method takes advantage of the accuracy in measurements of timing of migration for fish tagged in the previous year and of the accuracy in measurements of the proportion of migratory individuals for fish tagged in the same year to give the most accurate estimate of migratory pattern for the population. For calculation of return timing, we only used data from fish whose last record was on the antennae closest to the lake, i.e. representing a return migration to the lake. Daily means of temperature were used in all analyses. For calculation of seasonal temperature profiles, we used mean deviation during the period of interest, calculated from polynomial regression with temperature as a function of date. Multiple linear regressions were performed using backward selection with p ⫽ 0.1 as a selection criteria. Phytoplankton biomass and mean size of zooplankton during the migration period were evaluated by analysis of covariance (ANCOVA) with season, i.e. autumn and spring (Supplementary material Appendix 1 Table A1), as a factor, and temperature and proportion of fish being out of the lake as covariates. When used as an independent variable, the phytoplankton and zooplankton biomasses were averaged by dividing the area under the curve with the number of days for the time period of interest. We used temperature as explanatory variable for explaining outmigration (proportion and timing) and both temperature and timing of outmigration to explain timing of return migration (Supplementary material Appendix 1 Table A2). The definition of, and rationale behind, time periods used in analyses are shown in Supplementary material Appendix 1 Table A1. Multiple defined time periods are necessary because we are interested in effects at different times of the year and because biological variables, such as plankton biomass and migration intensity, are used in analyses both as independent and dependent variables. Also timing of migration, both away from the lake and returning to the lake, was divided into two different time periods, i.e. early and late migration. The rationale behind this was based on the observation that most outmigrating fish either left the lake during late autumn/early winter, defined as early outmigration or during spring, i.e. late outmigration, whereas returning fish either returned scattered throughout the winter, i.e. early return migration, or during a relatively brief period during spring, i.e. late return migration (Supplementary material Appendix 1 Fig. A1). In general, changes in the definition of time periods had very little effect on the output of the statistical analyses. The conclusions of the study do therefore not rest on the exact definition of time periods.
Results Effects of temperature on migration The proportion of the roach population leaving the lake in winter varied significantly between years (χ2-test; χ2 ⫽ 32.1, p ⬍ 0.001). This proportion was positively related to the
Figure 2. The overall percentage of the tagged roach population that was recorded in the either of the three streams of Lake Krankesjön during early outmigration period (1 September–20 March) as a function of the mean deviation from mean of temperatures in the pre-migration growth period, i.e. during summer, during five consecutive years (2004/05–2008/09).
22-Apr
17-Apr Mean return date
temperature deviation during the pre-migration growth period, i.e. more fish migrated after relatively warm summers (linear regression on arc-sine transformed data; R2 ⫽ 0.924, p ⫽ 0.009, Fig. 2). In all years, fish left the lake primarily during autumn or early winter with 93.6% of migrants leaving the lake before 1 January. The mean time of departure varied significantly among years (ANOVA; F ⫽ 16.1, p ⬍ 0.001). Subsequent post hoc tests showed that this difference was caused by one particular year (2006), where fish left the lake later than in other years (Tukey post hoc test; p ⬍ 0.001 for all). In 2006, water temperatures during autumn (September through December) were unusually high (mean 11.5°C) as compared to other years (means 7.8–9.0°C). Individual fish returned to the lake throughout winter and spring. The proportion of migratory individuals returning to the lake before 20 March, i.e. the cut date for early and late return migration, varied significantly between years (Pearson χ2-test; χ2 ⫽ 54.2, p ⬍ 0.001). However, this proportion was neither related to early-winter temperature, nor to timing of outmigration (multiple linear regression; p ⬎ 0.5 for both). For early returning fish, i.e. before 20 March (Supplementary material Appendix 1 Table A1), return date varied considerably between years (ANOVA; F ⫽ 17.6, p ⬍ 0.001). The mean return date showed a trend to be positively related to timing of out-migration (multiple linear regression; R2 ⫽ 0.70, p ⫽ 0.079), but not to early-winter temperature deviation (multiple linear regression; p ⫽ 0.69). Also, the return date of late-returning fish varied considerably between years (ANOVA; F ⫽ 52.4, p ⬍ 0.001), with the mean return date of late-returning fish being related to the day when the water temperature exceeded 10°C for the first time during the season (multiple linear regression; R2 ⫽ 0.894, p ⫽ 0.004; Fig. 3), but not to timing of outmigration (multiple linear regression; p ⬎ 0.2).
12-Apr
07-Apr
02-Apr 26-Mar
02-Apr
09-Apr
16-Apr
23-Apr
Date of 10ºC
Figure 3. The mean return date of fish that returned late, i.e. after 20 March, to Lake Krankesjön from winter migration to streams plotted against date where water temperature first reached 10°C. The six points on the graph represent data from six consecutive years (2004–2009).
Effects of migration on trophic dynamics The zooplankton community in Lake Krankesjön during the six years of sampling was dominated by cyclopoid copepods, accounting for 57.0% of the total zooplankton biomass, whereas Bosmina spp. and calanoid copepods each accounted for 15.7% and 15.5%, respectively. Bosmina spp. and cyclopoid copepods were the most abundant zooplankton taxa with means of 38.2 and 35.1 individuals l⫺1, respectively. During the migration period, the size of all three dominant zooplankton taxa showed a positive relationship with the proportion of fish being out of the lake (ANCOVA; cyclopoid copepods: F1,49 ⫽ 5.8, p ⫽ 0.020, calanoid copepods: F1,51 ⫽ 4.5, p ⫽ 0.039; Bosmina spp.: F1,37 ⫽ 10.1, p ⫽ 0.003; Fig. 4). Moreover, the size of cyclopoid and calanoid copepods were negatively related to water temperature (ANCOVA; cyclopoid copepods: F1,49 ⫽ 4.4, p ⫽ 0.041, calanoid copepods: F1,51 ⫽ 12.3, p ⫽ 0.001). The factor ‘season’ also had an effect on the size of zooplankton (ANCOVA; cyclopoid copepods: F1,49 ⫽ 15.4, p ⬍ 0.001, calanoid copepods: F1,51 ⫽ 8.4, p ⫽ 0.006), with zooplankton generally being larger in spring than in autumn. Zooplankton and phytoplankton biomass followed distinct seasonal patterns both with peaks and subsequent collapses in late spring (Fig. 5). Phytoplankton biomass over the whole migration period was significantly higher in spring than in autumn (ANCOVA; F1,86 ⫽ 34.5, p ⬍ 0.001), but was not directly related to either the proportion of fish being out of the lake or temperature (ANCOVA; p ⬎ 0.5 for both). Date of peak phytoplankton biomass in spring ranged between 19 March and 25 April, whereas date of peak zooplankton biomass ranged between 26 April and 27 May, on average 29 days later than the phytoplankton peak. Peak phytoplankton abundance occurred earlier at higher spring temperatures, but later when more fish were away from the lake (Table 1, Fig. 6A). The peak in zooplankton biomass 1841
0.50
1.6
0.45 1.4 0.40 0.35 1.0
0.30 0.25
0.8
0.20 0.6 0.15
Mean size Bosmina spp. (mm)
Mean size copepods (mm)
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0.4 0.10 0.2
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Percentage of fish out of lake
Figure 4. The mean size of zooplankton and regression lines (cyclopoid copepods [Δ; broken line], calanoid copepods [; stippled line] and Bosmina spp.[; solid line]) for each sampling day during four consecutive winters in Lake Krankesjön plotted against the percentage of the roach population being away from the lake at each particular day.
occurred earlier in years with high spring temperatures and with more fish being away from the lake (Table 1, Fig. 6B). The time between phytoplankton- and zooplankton peaks was shorter when more fish were away from the lake, but was neither related to spring temperature nor to phytoplankton biomass (p ⬎ 0.3 for both; Table 1, Fig. 7).
Discussion In this study we illustrated that partial migration patterns that respond to an external factor, here temperature, can both enhance and dampen the direct effects of the same external factor on trophic dynamics. The study thereby illustrates the
Figure 5. Total zooplankton biomass (log μg l⫺1) and phytoplankton biomass (chlorophyll a: log mg l⫺1) ⫾ respective standard deviations (blue and green areas for zooplankton and phytoplankton respectively) for six consecutive years in Lake Krankesjön. There is a reduction in zooplankton biomass between late May and late June following the spring clear-water phase with decreasing densities of phytoplankton.
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Table 1. Standardized regression coefficients from stepwise multiple linear regressions on the effects of temperature, migration and plankton biomass on the time of maximum phytoplankton and zooplankton biomass during spring and on the time difference between these two. Regression coefficients (p-values within brackets) are included for all variables not excluded from the model (selection criteria p ⱕ 0.1). Excluded variables are indicated by a hyphen. Phytoplankton biomass was not used in regression analysis on time of phytoplankton peak biomass. Likewise zooplankton biomass was not used in regression analysis on time of zooplankton peak biomass or time difference between time of phytoplankton- and zooplankton peak biomasses. Time of phytoplankton peak biomass ⫺0.99 (0.009) 0.37 (0.063) NA –
Temperature Migration Phytoplankton biomass Zooplankton biomass
need for an integrated approach, including previously separated disciplines on migratory mechanisms (Brodersen et al. 2008b, Boyle 2011), consequences of migration (Brodersen et al. 2008a, Post et al. 2008) and external control of trophic dynamics, as seen in e.g. climate change research (Adrian et al. 1999, Winder and Schindler 2004). Among winter-migrating fish, the variation in migration, both in terms of proportion of fish migrating and timing of migration, is primarily explained by temperature. We found
Day of phytoplankton peak
A 5
6
0
10
7
Temperature 8 9
10
11
20 30 40 50 Percentage of migratory fish
60
9-May 29-Apr 19-Apr 9-Apr 30-Mar 20-Mar 10-Mar
B 9
10
11
Temperature 12 13
14
15
16
Day of zooplankton peak
29-May 19-May 9-May 29-Apr 19-Apr 0
10 20 30 40 50 Percentage of migratory fish
60
Figure 6. Timing of spring phyto- (A) and zooplankton peaks (B) as a function of preceding water temperature and percentage of the roach population being out of the lake. The lines show results of multiple regression analyses, where mean proportion of fish being out of the lake (31.3% and 30.9% for phytoplankton- and zooplankton peaks respectively) was used for prediction of the effects of temperature alone and mean temperature (7.4°C and 11.8°C for phytoplankton- and zooplankton peaks respectively) was used to for prediction of effects of percentage of fish being out of the lake.
Time of zooplankton peak biomass
Time difference
⫺0.74 (0.008) ⫺0.68 (0.009) – NA
– ⫺0.94 (0.019) – NA
that a higher temperature during summer lead to a higher proportion of migrating individuals. Likely explanations are that more fish had been able to accumulate sufficient energy stores to make winter migration a beneficial strategy (Brodersen et al. 2008b), or that high temperature-dependent predation risk had led more fish to reach a perceived predation risk/growth rate threshold that would initiate migration (Brönmark et al. 2008, 2010). Also the timing of migrations, both away from and back to the lake, was affected by water temperature. The lake temperature during autumn affected the timing of migration from the lake, which can be explained by variation in the predation risk/growth rate development for individual fish (Brönmark et al. 2008, 2010). Return migration in spring appeared to be related to when water temperatures reached a threshold level of about 10°C, which is close to the reported minimum temperature necessary for somatic growth in roach (Van Dijk et al. 2002). This suggests that roach may postpone return migration until they are physically capable of using lake resources for growth. The migratory pattern found here, with migrants adjusting the timing of migration to immediate changes in their surrounding ecosystem, is a general pattern found in several species of short-distance migrants across species (Jenni and Kéry 2003). Interestingly, whereas timing of late return migration, i.e. returning during spring, was affected by lake temperature, timing of early return migration, i.e. return during the winter, was affected by day of out migration. It is expected that the most optimal time of return migration is during spring, when the ratio between predation risk and potential growth rate changes in favor of the lake habitat directly linked to temperature (Brönmark et al. 2008). However, some individual fish may not possess the energy stores for a long winter stay in the streams, where food availability is low, and may be forced to leave the streams during the winter to avoid starvation (Brodersen et al. 2008b). Fish returning early can thus be expected not to have had sufficient resources for the long winter stay in the streams. The time that these fish spend in the streams before returning appears to be relatively fixed, which explains why the date at which they return is dependent on the timing of outmigration. However, environmental temperature not only affects migration. Spring plankton phenology was also affected by water temperature; an effect previously documented in several lakes (George and Harris 1985, Adrian et al. 1999, Straile 2002). Accordingly, we found that timing of spring peak biomass for both phyto- and zooplankton occurred earlier in years with higher water temperatures. In a homogenous 1843
Figure 7. The number of days between day of phytoplankton peak abundance and zooplankton peak abundance in spring plotted against the mean of the percentage of the roach population estimated to be away from Lake Krankesjön during 20 March to the end of April.
environment, temperature generally affects phenology of different organisms in a similar direction although the magnitude of the effect on individual organisms may differ due to species-specific differences in sensitivity to temperature changes (Sommer and Lengfellner 2008). Whether such differences can lead to mismatch scenarios in aquatic systems has been heavily debated (George and Harris 1985, Winder and Schindler 2004). We suggest that this debate would benefit from an increased focus on the effects of other factors, such as migration. The effects of migration on trophic dynamics might be expected to be of only minor importance for short-distance migrations, where the temperature developments in the different alternative habitats generally follow the same phenology, and since both migration and spring phenology of lower trophic levels are temperature dependent. This is to some extent also what we find for the effect of temperature and migration on phytoplankton phenology (Fig. 1). Increased temperature affects phytoplankton both directly by increasing growth rates, and indirectly, through earlier return migration of fish and thereby increased predation pressure on zooplankton grazers (Fig. 1, 3). Together these effects may be predicted to lead to an earlier phytoplankton peak in spring, i.e. the effects of migration can be difficult to distinguish from the effects of temperature. However, with respect to zooplankton, temperature and migration affect spring phenology in opposite directions (Fig. 1). Hence, increased spring temperature brings the time of zooplankton peak biomass forward, whereas earlier return migration of fish delays the time of peak zooplankton biomass. Higher temperature forced both the phyto- and zooplankton peaks to occur earlier in spring, whereas migration affected peaks in opposite direction, which furthermore explains why the time between peaks was affected only by migration and not by temperature. Moreover, the intensity of fish migration affected the size structure of the zooplankton community during the migration 1844
period. This effect is most likely due to size-selective zooplanktivory by roach as previously shown (Jeppesen et al. 2004). However, our study is, to our knowledge, the first to relate this to migration. Since body size of zooplankton affects the efficiency of grazing (Brooks and Dodson 1965), this observation supports the conclusion that fish migration may increase grazing on phytoplankton through a dampened trophic cascade (see also Post et al. 2008, Brodersen et al. 2008a). Our results demonstrate why partial migration should be viewed not only as an interesting phenomenon in itself, but also as a driver of trophic dynamics and structure of ecosystems. Scientists have for the past decades been concerned with the effects of climate change on animal migratory patterns (Sillett et al. 2000, Both and Visser 2001, Both et al. 2006) and on ecosystem dynamics (Walther et al. 2002). Our study exemplifies that it may be difficult to address one of these questions without taking the other into account. Parallel to the mass-migration of planktivorous freshwater fish studied here, examples of migration of secondary consumers are found in both marine and terrestrial ecosystem, e.g. economically important herring migrations (Harden-Jones 1977) or well-known migrations of insectivorous birds (Morse 1971). We therefore suggest that an integrated approach including both how migration is affected by external factors and how trophic dynamics are affected both by migration and external factors in combination, should be applied to these systems as well. It is however generally expected that for migration to have a notable effect on trophic dynamics and/or structure, the migrating species should be either a dominant, like roach in our study system, or a keystone species in its ecosystem (Paine 1969, Mills 1993). Alternatively, migrations of non-keystone species may have effects on trophic dynamics, only when several species migrate and their joint absence creates an effect comparable to that of a dominant or keystone species. We have here only considered migration effects on lower trophic levels, i.e. top–down effects. There may still be a large number of effects on both competitors and predators of the migratory species, both in terms of population dynamics, structure and niche shifts. However, also migrations of primary or tertiary consumers are likely to affect trophic dynamics and structure. The former includes mass migrations of terrestrial herbivores, such as Serengeti ungulates (Fryxell and Sinclair 1988), and winter-migrations of herbivorous birds, such as geese or swans (Nolet et al. 2006), whereas the latter includes cross-continent migrations of raptor birds (Mueller et al. 1977, Strandberg et al. 2009) and spawning migrations of piscivorous fish, such as sharks, tuna and cod (Rose 1993, Block et al. 2005, Bonfil et al. 2005). Furthermore, besides migration effects on trophic dynamics, similar effects can be expected in other scenarios with temporal functional absence of consumers, for example through hibernation or dormancy. We strongly encourage future studies of trophic interactions and climate change to take these aspects into consideration.
Acknowledgements – We are thankful to David M. Post, Jennifer G. Howeth, Matthew R. Walsh and Andrew Jones for valuable comments on the manuscript. The work was supported by the Swedish
Research Council for Environment, Agricultural Sciences and Spatial Planning (FORMAS) and the Swedish Research Council (VR).
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Supplementary material (available as Appendix O19433 at ⬍www.oikosoffice.lu.se⬎). Appendix 1.
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