75 Article Modelling phytoplankton dynamics in fresh waters: affirmation of the PROTECH approach to simulation J. Alex Elliott1, Anthony E. Irish2,3 and Colin S. Reynolds2,4 1 Centre for Ecology and Hydrology (CEH) Lancaster, Library Avenue, Bailrigg, Lancaster, LA1 4AP, UK. Email: [email protected] 2 Formerly of CEH and Freshwater Biological Association, The Ferry Landing, Ambleside, LA22 0LP, UK 3 3 Lake Road, Ambleside, LA22 0AD, UK 4 18 Applerigg, Kendal, LA9 6EA, UK Received 13 November 2009; accepted 3 March 2010; published 21 June 2010 Abstract Twenty years after model equations describing the in situ growth rates of phytoplankton were first devised and eight since their successful incorporation into a computer simulation was first published, we set out to affirm the general validity and utility of PROTECHP ( hytoplankton RespOnses To Environmental Change). Elaborated originally for commercial purposes, PROTECH has been shown to be capable of simulating simultaneous seasonal fluctuations in the standing crops of several contrasting species of alga, making it attractive for testing the impacts of various simulated regimes for managing the growth conditions. These have been sufficiently convincing to persuade us to use PROTECH as a research tool; over a number of years, it has been used to simulate such ‘traditional’ problems of ecology as succession, competitive exclusion and species diversity, in the context of intermediate disturbance. In this paper, we review critically the workings of the model, especially how complex but consistent outcomes emerge in compliance with simple trait-based rules of community assembly. We affirm that temperature-specific growth rates of algae are strongly influenced by algal morphology, that slender species are tolerant of low average light exposure and that periodicity is related to species-specific characteristics of motility and buoyant behaviour. The results of some applications of PROTECH are presented, simulating responses of the phytoplankton community to adjustments in nutrient loading, light penetration and hydrological flushing rates; an explicit investigation of the sensitivity of population responses of Cyanobacteria to eutrophication is also reported, in the context of varying availabilities of combined inorganic nitrogen. Considering future developments of PROTECH, we affirm the virtues of its central growth equations; we anticipate that future applications will mostly depend upon improved representation of the physical environments it seeks to simulate and that these may more frequently relate to aquatic systems other than the lakes and reservoirs for which it was originally devised. Keywords: Phytoplankton; modelling; lake; climate change; functional groups; morphology. DOI: 10.1608/FRJ-3.1.4 Freshwater Reviews (2010) 3, pp. 75-96 © Freshwater Biological Association 2010 76 Elliott, J.A., Irish, A.E. & Reynolds, C.S. Introduction With the availability of modern computing power, there is no obvious reason why these model approaches There has long been an implicit desire among limnologists should not be combined, but there have been few attempts to be able to construct reasonable models that simulate to do this. While there is a foundation of understanding of the dynamics of phytoplankton populations in lakes and the mechanisms by which natural communities are both reservoirs. In recent years, various directives on protecting selected and assembled, there remains a conceptual hiatus ecological quality and providing guidance on the maximum between the basic scientific knowledge of processes and levels of cyanobacterial toxins to be permitted in water the ability to apply them to the practical requirements of supplied for drinking has turned desire into an explicit managers, regulators and legislators for critical information. requirement. Yet ‘significant barriers to progress’ (Reynolds In 2001, we published a paper (Reynolds et al., et al., 2001) in formulating suitable models persist: despite 2001) intimating an alternative approach to modelling a widespread and comprehensive understanding of the phytoplankton dynamics. We had already been anabolic processes supporting phytoplankton growth – clear using for commercial purposes a family of models examples include the relationship between photosynthesis under the collective name, PROTECH (Phytoplankton and underwater light intensity, duration and penetration RespOnses To Environmental CHange). Essentially, (Talling, 1971) and the requirement and rates of uptake of these programmes had been built around the equations essential nutrients (Dugdale, 1967; Droop, 1974; Tilman of Reynolds (1989), which relate the growth dynamics et al., 1982) - few workers have been able satisfactorily of various phytoplankton species, grown in the to extrapolate likely actual rates of cell replication and laboratory under controlled idealised conditions, to their population recruitment. Population assembly is countered morphological characterisation in terms of cell volume by losses, measurement of which is tedious and prone to and surface area. In PROTECH, these relationships are wide inaccuracies (Jassby & Goldman, 1974). As to the applied, in defined sequence, to simulate the potential forces governing the selection, dynamic variation and the rates of change in biomass of each of these species, as well diversity of emergent assemblages, the general appreciation as others of given size and shape, in a contrived, virtual has probably altered little since Tilman (1996) declared that habitat. The major novelty in its construction was to ‘largely, they remain mysteries’. assume that the phytoplankton will always recruit new The component processes resulting in recognisable generations of individuals in natural environments, at outcomes or patterns are acknowledged to be complicated. the potential rates observed consistently in the laboratory, There is currently available an array of modelling under the identical conditions of temperature, insolation approaches that have been devised to rationalise, and resource availability. It was further assumed that the describe and predict the behaviour of populations. rates of biomass losses (through mortality, sedimentation, Mostly, these have been found not to be altogether consumption by grazing zooplankton) apply at rates also helpful: according to Reynolds’ (1999) overview, the determined experimentally on captive populations in large models then on offer fell among three categories: enclosures (Reynolds et al., 1982; see also later). In this way, • those that simulate as far as possible precise inputs environmental constraints, where they apply, detract from and responses but, thus, lack generality; optimal species-specific performances. Where manifestly • those that focus on particular or isolated processes they do not apply, the maximum attainable growth rate is and whose outputs may be precise but limited in assumed to be maintained for so long as it is sustainable. application; and Thus, it is not necessary to know the cell-specific rates of • those that require limited information inputs to derive photosynthesis or of nutrient uptake: it is self-evident that general, principled outputs but give little information the anabolic requirements of the self-replicating cells are about any particular site. simultaneously fulfilled. It is important, however, that © Freshwater Biological Association 2010 DOI: 10.1608/FRJ-3.1.4 Modelling phytoplankton dynamics in fresh waters 77 the model is able to recognise the onset of critical resource a short résumé of its uses as a commercial tool and as an aid depletion and the points where light fluxes or nutrient to understanding the current and projected behaviour of availabilities might fail to saturate the maximum rates of natural waters in the face of eutrophication and of climate assimilative deployment. In other words, the achievable change. We look again at the functions in the model which rates of specific growth become subject to control by are crucial to its ability to discriminate among the properties the resource supply; that is, they become ‘limited’ by of individual species that favour their dominance under it. Applying these principles to the dynamic changes in particular environmental conditions, or at particular times of species-specific phytoplankton populations, the logic of the year. We shall look at some of the versions of PROTECH PROTECH is that it subtracts losses and the deficiencies and of the success of one of its derivations, the Swedish of poor performance from a verifiable maximum growth PROTBAS model (PROTech-Based Algal Simulations). rate, thereby avoiding the extrapolation of recruitment We consider the application of PROTECH’s outputs to rates from component processes (photosynthetic rate, issues of fundamental ecological significance, including nutrient uptake rate) that may well over-saturate competition, alternative dominance, succession, diversity considerably the actual growth rate achievable. and disturbance. Finally, we remark upon how, with a The model has continued to be applied, not just to an growing awareness of its capabilities through numerous ever-increasing range of real management problems at and varied studies, confidence in the authenticity and specific world-wide locations but also to the investigation reliability of PROTECH outputs to simulate the dynamics of a number of more general ecological problems. It of freshwater phytoplankton has steadily accrued. has been used to simulate actual, observed variations