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The shape of trait distributions may inform about the selective forces that structure ecological communities. Here, we present a new moment-based approach to classify the shape of observed biomass-weighted trait distributions into normal, peaked, skewed, or bimodal that facilitates spatio-temporal and cross-system comparisons. Our observed phytoplankton trait distributions exhibited substantial variance and were mostly skewed or bimodal rather than normal. Additionally, mean, variance, skewness und kurtosis were strongly correlated. This is in conflict with trait-based aggregate models that often assume normally distributed trait values and small variances. Given these discrepancies between our data and general model assumptions we used the observed trait distributions to test how well different aggregate models with first- or second-order approximations and different types of moment closure predict the biomass, mean trait, and trait variance dynamics using weakly or moderately nonlinear fitness functions. For weakly non-linear fitness functions aggregate models with a second-order approximation and a data-based moment closure that relied on the observed correlations between skewness and mean, and kurtosis and variance predicted biomass and often also mean trait changes fairly well and better than models with first-order approximations or a normal-based moment closure. In contrast, none of the models reflected the changes of the trait variances reliably. Aggregate model performance was often also poor for moderately nonlinear fitness functions. This questions a general applicability of the normal-based approach, in particular for predicting variance dynamics determining the speed of trait changes and maintenance of biodiversity. We evaluate in detail how and why better approximations can be obtained.
Allometric trophic network (ATN) models offer high flexibility and scalability while minimizing the number of parameters and have been successfully applied to investigate complex food web dynamics and their influence on food web diversity and stability. However, the realism of ATN model energetics has never been assessed in detail, despite their critical influence on dynamic biomass and production patterns. Here, we compare the energetics of the currently established original ATN model, considering only biomass-dependent basal respiration, to an extended ATN model version, considering both basal and assimilation-dependent activity respiration. The latter is crucial in particular for unicellular and invertebrate organisms which dominate the metabolism of pelagic and soil food webs. Based on metabolic scaling laws, we show that the extended ATN version reflects the energy transfer through a chain of four trophic levels of unicellular and invertebrate organisms more realistically than the original ATN version. Depending on the strength of top-down control, the original ATN model yields trophic transfer efficiencies up to 71% at either the third or the fourth trophic level, which considerably exceeds any realistic values. In contrast, the extended ATN version yields realistic trophic transfer efficiencies 30% at all trophic levels, in accordance with both physiological considerations and empirical evidence from pelagic systems. Our results imply that accounting for activity respiration is essential for consistently implementing the metabolic theory of ecology in ATN models and for improving their quantitative predictions, which makes them more powerful tools for investigating the dynamics of complex natural communities.
Functional groups with diverse responses to environmental factors sum to produce communities with less temporal variability in their biomass than those lacking this diversity. The detection of these compensatory dynamics can be complicated by a spatio-temporal alternation in the environmental factors limiting growth (both abiotic and biotic), which restricts the occurrence of compensatory dynamics to certain periods or locations. Hence, resolving the spatio- temporal scale may uncover important spatial and/or temporal components in community variability. Using long-term data from Lake Constance (Bodensee), we find that a reduction in grazing pressure and relaxed competition for nutrients during winter and spring generates coherent dynamics among edible and less edible phytoplankton. During summer and fall, when both grazing pressure and nutrient limitation are present, edible and less edible phytoplankton exhibit compensatory dynamics. This study supports recent work suggesting that both abiotic and biotic interactions promote compensatory dynamics and to our knowledge, this is the first example of a system where compensatory and coherent dynamics seasonally alternate.
Species richness has been shown to increase biomass production of plant communities. Such overyielding occurs when a community performs better than its component monocultures due to the complementarity or dominance effect and is mostly detected in substrate-bound plant communities (terrestrial plants or submerged macrophytes) where resource use complementarity can be enhanced due to differences in rooting architecture and depth. Here, we investigated whether these findings arc generalizeable for free-floating phytoplankton with little potential for spatial differences in resource use. We performed aquatic microcosm experiments with eight phytoplankton species belonging to four functional groups to determine the manner in which species and community biovolume varies in relation to the number of functional groups and hypothesized that an increasing number of functional groups within a community promotes overyielding. Unexpectedly, we did not detect overyielding in any algal community. Instead. total community biovolume tended to decrease with all increasing, number of functional groups. This underyielding was mainly caused by the negative dominance effect that originated from a trade-off between growth rate and filial biovolume. In monoculture, slow-groing species built up higher biovolumes that fast-growing ones, whereas in mixture a fast-growing but low-productive species monopolized most of the nutrients and prevented competing species from developing high biovolumes expected from monocultures. Our results indicated that the Magnitude of the community biovolume was largely determined by the identify of one species. Functional diversity and resource use complementarity were of minor Importance among free-floating phytoplankton, possibly reflecting the lack of spatially heterogeneous resource distribution. As a consequence, biodiversity-ecosystem functioning relationships may not be easily generalizeable from substrate-bound plant to phytoplankton communities and vice versa.
Ecological communities are complex adaptive systems that exhibit remarkable feedbacks between their biomass and trait dynamics. Trait-based aggregate models cope with this complexity by focusing on the temporal development of the community’s aggregate properties such as its total biomass, mean trait and trait variance. They are based on particular assumptions about the shape of the underlying trait distribution, which is commonly assumed to be normal. However, ecologically important traits are usually restricted to a finite range, and empirical trait distributions are often skewed or multimodal. As a result, normal distribution-based aggregate models may fail to adequately represent the biomass and trait dynamics of natural communities. We resolve this mismatch by developing a new moment closure approach assuming the trait values to be beta-distributed. We show that the beta distribution captures important shape properties of both observed and simulated trait distributions, which cannot be captured by a Gaussian. We further demonstrate that a beta distribution-based moment closure can strongly enhance the reliability of trait-based aggregate models. We compare the biomass, mean trait and variance dynamics of a full trait distribution (FD) model to the ones of beta (BA) and normal (NA) distribution-based aggregate models, under different selection regimes. This way, we demonstrate under which general conditions (stabilizing, fluctuating or disruptive selection) different aggregate models are reliable tools. All three models predicted very similar biomass and trait dynamics under stabilizing selection yielding unimodal trait distributions with small standing trait variation. We also obtained an almost perfect match between the results of the FD and BA models under fluctuating selection, promoting skewed trait distributions and ongoing oscillations in the biomass and trait dynamics. In contrast, the NA model showed unrealistic trait dynamics and exhibited different alternative stable states, and thus a high sensitivity to initial conditions under fluctuating selection. Under disruptive selection, both aggregate models failed to reproduce the results of the FD model with the mean trait values remaining within their ecologically feasible ranges in the BA model but not in the NA model. Overall, a beta distribution-based moment closure strongly improved the realism of trait-based aggregate models.