The ecosystem of a mesotrophic lake-I. Simulating plankton biomass and the timing of phytoplankton blooms

Seip, Knut Lehre

doi:10.1007/bf00877061

Cited by 10 publications

(5 citation statements)

References 63 publications

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“…The N : P concentration ratio (by wt) in lake waters was shown by Downing and McCauley (1992) and Seip (1993) to decrease from -50 in oligotrophic water to -5 in hypereutrophic waters. The surface for the N : P responses of algae to the season-trophy matrix shows that algae relatively abundant in late summer or autumn in eutrophic and hypereutrophic waters have a low optimal N : P ratio of resource requirements (Fig.…”

Section: Discussionmentioning

confidence: 97%

“…Some of these are able to fix nitrogen from the air, indicating that the TN : TP ratio in the water does not have the same significance for these algae as for nonnitrogen-fixing algae. Furthermore, for very eutrophic lakes, TN rather than TP is more likely to become limiting (McCauley et al 1989;Seip 1993).…”

Section: Discussionmentioning

confidence: 99%

“…Total number of species or genera used for the regressions in Table 3 and Schreurs (1992) or calculated from pmax and K,. Optimum N : P ratios (by wt) are from compilations of Seip (1993). For some species or genera, only one or a few characteristics have been found.…”

Section: Methodsmentioning

confidence: 99%

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Phytoplankton functional attributes along trophic gradient and season

Seip

Reynolds

1995

Limnology & Oceanography

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Each of the following phytoplankton functional attributes could be described by significant (P < 0.05) response surfaces defined by trophic status (T) from oligotrophy to hypertrophy and season (S) from early vernal period to late summer: phytoplankton cell volume, growth rate, the ratio between minimum quotas of total N and total P, affinity for P, half-saturation constant for growth with respect to P, and temperature optimum and light optimum for growth. Sinking rate could not be so described. The response surfaces were calculated by multiple regressions, including first-and second-order terms of T and S and the interaction term T. S. Although the data base was rather limited (number of phytoplankton species ranged from 10 to 3 I), the resulting surfaces often reflected a corresponding attribute of the water along T and S (e.g. phytoplankton temperature optimum for growth reflected water temperature). The results support the frequent use of physical factors as explanatory variables for the distribution and abundance of phytoplankton.The abundance of phytoplankton species and the species structure of phytoplankton assemblages vary along trophic and seasonal gradients (Reynolds 1980(Reynolds , 1984b. Because phytoplankton species with similar functional attributes (such as size, maximum growth rate, and optimum temperature for growth) also share similar ecologies (Reynolds 1988), we wanted to find out whether it is possible to give a statistically significant description of the attributes along trophic gradient (T, measured as the level of total P concentration) and season (S, related to day of the year). Discussions of species successions often imply such functional relationships. For example, Sommer (1986, p. 6) wrote that "in eutrophic lakes where P is not limiting during early spring, the higher maximum growth rate makes nanoplanktonic algae such as small Centrales and Rhodomonas dominant." The functional attributes of an alga might also relate directly its position in a seasonal succession of algae. Statement 5 of the PEGmodel of seasonal succession of planktonic events in fresh waters states that "there then (after herbivore grazing) follows a 'clear-water' equilibrium phase which persists until inedible algae species develop in significant numbers" (Sommer et al. 1986, p. 435.) Because inedibility is often related to biovolumes, e.g. volumes > -15 x 1 O3 pm3 (diam, 30 pm, Carpenter et al. 1993), herbivory and not season may be a primary vehicle for the presence of large-size algae or algal colonies. Several more statements about the relationship between algal attributes and species abundance could be listed and some of these would be partly confounding. Our quest, then, is a statistical pattern along trophy and season persisting "through" the web of biotic interactions. Materials and methodsWe use literature values for phytoplankton attributes. Our basic idea is that data should not be screened before the data are included in the analysis; instead, screening should be a consequence of the stati...

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Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%

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Phytoplankton functional attributes along trophic gradient and season

Seip

Reynolds

1995

Limnology & Oceanography

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“…where P i , K i , r i , and d i are biomass, carrying capacity, growth rate and death rates respectively of phytoplankton species i, a 12 and a 21 are competition coefficients. In principle, equations for competition should include both equations for the competing species and for the resources for which they compete, like in many aquatic ecosystem models (Seip et al 1991a). However, such complete forms can, under certain conditions, be shown to reduce to Lotka‐Volterra type equations (Yodzis 1989).…”

Section: Methodsmentioning

confidence: 99%

An anatomy of interactions among species in a seasonal world

Sandvik¹,

Seip²,

Pleym³

2002

Oikos

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Mathematical models merging biological and predictable seasonal dynamics were used to simulate four types of organism interactions: competition, prey‐predation, mutualism and facilitation. By analysing trajectories for biomass in phase portraits (i.e. species 1 biomass plotted against species 2 biomass) graphically and numerically, we found that each of the four interaction types showed characteristic patterns (“fingerprints”) in phase space. All the four interaction types could be distinguished, even though their time trajectories were strongly modified by seasonal forces. For each of the interaction types, we were able to identify characteristics of the interaction that most strongly distinguished it from the others. We could also assess the relative effect of species characteristics and seasonality on each of the four interactions. The simulations indicate that prey‐predation is strongly influenced by seasonal forces and by the characteristics of the predator and its prey. A system variability index got a high value (SVI)=0.167 relative to those of the other interaction types (competition 0.01, mutualism 0.007 and facilitation 0.005). The result was obtained by calculating, angles between successive vectors linking pairs of samples and the positive x‐axis and arranging these as frequency histograms for each of the four quadrants of the phase portrait. In an effort to capture circular motions and other characteristics of the trajectories sampled, angle frequencies were analysed using multivariate statistics (PCA), yielding “characteristic directions” in phase space. We believe that the characteristic patterns identified also will be found in real time series.

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“…with the keyword "phytoplankton" which shows the importance given to the modelling of phytoplankton dynamics (Elliott and Thackeray, 2004;Frisk et al, 1999;Hillmer et al, 2008;Reynolds and Irish, 1997;Rukhovets et al, 2003;Seip, 1991).…”

Section: Phytoplanktonmentioning

confidence: 99%