Filtern
Erscheinungsjahr
Dokumenttyp
Gehört zur Bibliographie
- ja (102)
Schlagworte
- functional traits (7)
- community ecology (6)
- population dynamics (6)
- coexistence (5)
- lake (5)
- biodiversity (4)
- functional diversity (4)
- phytoplankton (4)
- plankton (4)
- Coadaptation (3)
Institut
- Institut für Biochemie und Biologie (102) (entfernen)
Trophic transfer efficiency (TTE) is usually calculated as the ratio of production rates between two consecutive trophic levels. Although seemingly simple, TTE estimates from lakes are rare. In our review, we explore the processes and structures that must be understood for a proper lake TTE estimate.
We briefly discuss measurements of production rates and trophic positions and mention how ecological efficiencies, nutrients (N, P) and other compounds (fatty acids) affect energy transfer between trophic levels and hence TTE.
Furthermore, we elucidate how TTE estimates are linked with size-based approaches according to the Metabolic Theory of Ecology, and how food-web models can be applied to study TTE in lakes.
Subsequently, we explore temporal and spatial heterogeneity of production and TTE in lakes, with a particular focus on the links between benthic and pelagic habitats and between the lake and the terrestrial environment.
We provide an overview of TTE estimates from lakes found in the published literature. Finally, we present two alternative approaches to estimating TTE. First, TTE can be seen as a mechanistic quantity informing about the energy and matter flow between producer and consumer groups.
This approach is informative with respect to food-web structure, but requires enormous amounts of data. The greatest uncertainty comes from the proper consideration of basal production to estimate TTE of omnivorous organisms.
An alternative approach is estimating food-chain and food-web efficiencies, by comparing the heterotrophic production of single consumer levels or the total sum of all heterotrophic production including that of heterotrophic bacteria to the total sum of primary production.
We close the review by pointing to a few research questions that would benefit from more frequent and standardized estimates of TTE in lakes.
The shape of a defense-growth trade-off governs seasonal trait dynamics in natural phytoplankton
(2020)
Theory predicts that trade-offs, quantifying costs of functional trait adjustments, crucially affect community trait adaptation to altered environmental conditions, but empirical verification is scarce. We evaluated trait dynamics (antipredator defense, maximum growth rate, and phosphate affinity) of a lake phytoplankton community in a seasonally changing environment, using literature trait data and 21 years of species-resolved high-frequency biomass measurements. The trait data indicated a concave defense-growth trade-off, promoting fast-growing species with intermediate defense. With seasonally increasing grazing pressure, the community shifted toward higher defense levels at the cost of lower growth rates along the trade-off curve, while phosphate affinity explained some deviations from it. We discuss how low fitness differences of species, inferred from model simulations, in concert with stabilizing mechanisms, e.g., arising from further trait dimensions, may lead to the observed phytoplankton diversity. In conclusion, quantifying trade-offs is key for predictions of community trait adaptation and biodiversity under environmental change.
Seagrass beds are important habitats in coastal areas but increasingly decline in area and quality, thus conservation measures are urgently needed. Quantitative food webs, describing the biomass distribution and energy fluxes among trophic groups, reveal structural and functional aspects of ecosystems. Their knowledge can improve ecological conservation. For the recently discovered large warm-temperate seagrass (Zostera japonica) habitat in China's Yellow River Delta wetland, we used delta C-13 and delta N-15 measurements and a Bayesian isotope mixing model to construct its food web diagram with quantitative estimations of consumer diet compositions, comprising detritus and 14 living trophic groups from primary producers to fish. We then estimated the quantitative food web fluxes based on biomass measurements and calculated corresponding ecosystem functions. Pelagic producers were significantly C-13-depleted compared to benthic sources. Consumers (except zooplankton) were increasingly C-13-depleted with increasing trophic positions even though the consumed benthic production surpassed the pelagic one. Bivalves dominated consumer biomasses and fluxes and were the first to connect the pelagic and benthic pathways, whereas zooplankton and gastropods were specialized on the two pathways, respectively. We found flat biomass and production pyramids indicating low trophic transfer efficiencies. Generally, the energetic structure of the quantitative food web was consistent with the stable isotope analysis, and the estimated net primary production and most estimated production to biomass ratios of the trophic groups fell within literature ranges. This study provides a systematical understanding of the quantitative trophic ecology of a seagrass bed and facilitates synergistic knowledge on management, conservation, and restoration.
Trait variation among heterospecific and conspecific organisms may substantially affect community and food web dynamics. While the relevance of competition and feeding traits have been widely studied for different consumer species, studies on intraspecific differences are more scarce, partly owing to difficulties in distinguishing different clones of the same species. Here, we investigate how intraspecific trait variation affects the competition between the freshwater ciliates Euplotes octocarinatus and Coleps hirtus in a nitrogen-limited chemostat system. The ciliates competed for the microalgae Cryptomonas sp. (Cry) and Navicula pelliculosa (Nav), and the bacteria present in the cultures over a period of 33 days. We used monoclonal Euplotes and three different Coleps clones (Col 1, Col 2, and Col 3) in the experiment that could be distinguished by a newly developed rDNA-based molecular assay based on the internal transcribed spacer (ITS) regions. While Euplotes feeds on Cry and on bacteria, the Coleps clones cannot survive on bacteria alone but feed on both Cry and Nav with clone-specific rates. Experimental treatments comprised two-species mixtures of Euplotes and one or all of the three different Coleps clones, respectively. We found intraspecific variation in the traits "selectivity" and "maximum ingestion rate" for the different algae to significantly affect the competitive outcome between the two ciliate species. As Nav quickly escaped top-down control and likely reached a state of low food quality, ciliate competition was strongly determined by the preference of different Coleps clones for Cry as opposed to feeding on Nav. In addition, the ability of Euplotes to use bacteria as an alternative food source strengthened its persistence once Cry was depleted. Hence, trait variation at both trophic levels codetermined the population dynamics and the outcome of species competition.
A large number and wide variety of lake ecosystem models have been developed and published during the past four decades. We identify two challenges for making further progress in this field. One such challenge is to avoid developing more models largely following the concept of others ('reinventing the wheel'). The other challenge is to avoid focusing on only one type of model, while ignoring new and diverse approaches that have become available ('having tunnel vision'). In this paper, we aim at improving the awareness of existing models and knowledge of concurrent approaches in lake ecosystem modelling, without covering all possible model tools and avenues. First, we present a broad variety of modelling approaches. To illustrate these approaches, we give brief descriptions of rather arbitrarily selected sets of specific models. We deal with static models (steady state and regression models), complex dynamic models (CAEDYM, CE-QUAL-W2, Delft 3D-ECO, LakeMab, LakeWeb, MyLake, PCLake, PROTECH, SALMO), structurally dynamic models and minimal dynamic models. We also discuss a group of approaches that could all be classified as individual based: super-individual models (Piscator, Charisma), physiologically structured models, stage-structured models and traitbased models. We briefly mention genetic algorithms, neural networks, Kalman filters and fuzzy logic. Thereafter, we zoom in, as an in-depth example, on the multi-decadal development and application of the lake ecosystem model PCLake and related models (PCLake Metamodel, Lake Shira Model, IPH-TRIM3D-PCLake). In the discussion, we argue that while the historical development of each approach and model is understandable given its 'leading principle', there are many opportunities for combining approaches. We take the point of view that a single 'right' approach does not exist and should not be strived for. Instead, multiple modelling approaches, applied concurrently to a given problem, can help develop an integrative view on the functioning of lake ecosystems. We end with a set of specific recommendations that may be of help in the further development of lake ecosystem models.
A large number and wide variety of lake ecosystem models have been developed and published during the past four decades. We identify two challenges for making further progress in this field. One such challenge is to avoid developing more models largely following the concept of others ('reinventing the wheel'). The other challenge is to avoid focusing on only one type of model, while ignoring new and diverse approaches that have become available ('having tunnel vision'). In this paper, we aim at improving the awareness of existing models and knowledge of concurrent approaches in lake ecosystem modelling, without covering all possible model tools and avenues. First, we present a broad variety of modelling approaches. To illustrate these approaches, we give brief descriptions of rather arbitrarily selected sets of specific models. We deal with static models (steady state and regression models), complex dynamic models (CAEDYM, CE-QUAL-W2, Delft 3D-ECO, LakeMab, LakeWeb, MyLake, PCLake, PROTECH, SALMO), structurally dynamic models and minimal dynamic models. We also discuss a group of approaches that could all be classified as individual based: super-individual models (Piscator, Charisma), physiologically structured models, stage-structured models and traitbased models. We briefly mention genetic algorithms, neural networks, Kalman filters and fuzzy logic. Thereafter, we zoom in, as an in-depth example, on the multi-decadal development and application of the lake ecosystem model PCLake and related models (PCLake Metamodel, Lake Shira Model, IPH-TRIM3D-PCLake). In the discussion, we argue that while the historical development of each approach and model is understandable given its 'leading principle', there are many opportunities for combining approaches. We take the point of view that a single 'right' approach does not exist and should not be strived for. Instead, multiple modelling approaches, applied concurrently to a given problem, can help develop an integrative view on the functioning of lake ecosystems. We end with a set of specific recommendations that may be of help in the further development of lake ecosystem models.
Quantifying the capacity for contemporary trait changes to drive intermittent predator-prey cycles
(2022)
A large and growing body of theory has demonstrated how the presence of trait variation in prey or predator populations may affect the amplitude and phase of predator-prey cycles. Less attention has been given to so-called intermittent cycles, in which predator-prey oscillations recurrently disappear and re-appear, despite such dynamics being observed in empirical systems and modeling studies. A comprehensive understanding of the conditions under which trait changes may drive intermittent predator-prey dynamics, as well as their potential ecological implications, is therefore missing. Here we provide a first systematic analysis of the eco-evolutionary conditions that may give rise to intermittent predator-prey cycles, investigating 16 models that incorporate different types of trait variation within prey, predators, or both. Our results show that intermittent dynamics often arise through predator-prey coevolution, but only very rarely when only one trophic level can adapt. Additionally, the frequency of intermittent cycles depends on the source of trait variation (genetic variation or phenotypic plasticity) and the genetic architecture (Mendelian or quantitative traits), with intermittency occurring most commonly through Mendelian evolution, and very rarely through phenotypic plasticity. Further analysis identified three necessary conditions for when trait variation can drive intermittent cycles. First, the intrinsic stability of the predator-prey system must depend on the traits of prey, predators, or both. Second, there must be a mechanism causing the recurrent alternation between stable and unstable states, leading to a "trait" cycle superimposed on the population dynamics. Finally, these trait dynamics must be significantly slower than the predator-prey cycles. We show how these conditions explain all the abovementioned patterns. We further show an important unexpected consequence of these necessary conditions: they are most easily met when intraspecific trait variation is at high risk of being lost. As trait diversity is positively associated with ecosystem functioning, this can have potentially severe negative consequences. This novel result highlights the importance of identifying and understanding intermittent cycles in theoretical studies and natural systems. The new approach for detecting and quantifying intermittency we develop here will be instrumental in enabling future study.
It is well known that functional diversity strongly affects ecosystem functioning. However, even in rather simple model communities consisting of only two or, at best, three trophic levels, the relationship between multitrophic functional diversity and ecosystem functioning appears difficult to generalize, because of its high contextuality. In this study, we considered several differently structured tritrophic food webs, in which the amount of functional diversity was varied independently on each trophic level. To achieve generalizable results, largely independent of parametrization, we examined the outcomes of 128,000 parameter combinations sampled from ecologically plausible intervals, with each tested for 200 randomly sampled initial conditions. Analysis of our data was done by training a random forest model. This method enables the identification of complex patterns in the data through partial dependence graphs, and the comparison of the relative influence of model parameters, including the degree of diversity, on food-web properties. We found that bottom-up and top-down effects cascade simultaneously throughout the food web, intimately linking the effects of functional diversity of any trophic level to the amount of diversity of other trophic levels, which may explain the difficulty in unifying results from previous studies. Strikingly, only with high diversity throughout the whole food web, different interactions synergize to ensure efficient exploitation of the available nutrients and efficient biomass transfer to higher trophic levels, ultimately leading to a high biomass and production on the top level. The temporal variation of biomass showed a more complex pattern with increasing multitrophic diversity: while the system initially became less variable, eventually the temporal variation rose again because of the increasingly complex dynamical patterns. Importantly, top predator diversity and food-web parameters affecting the top trophic level were of highest importance to determine the biomass and temporal variability of any trophic level. Overall, our study reveals that the mechanisms by which diversity influences ecosystem functioning are affected by every part of the food web, hampering the extrapolation of insights from simple monotrophic or bitrophic systems to complex natural food webs.
The shape of a defense-growth trade-off governs seasonal trait dynamics in natural phytoplankton
(2020)
Theory predicts that trade-offs, quantifying costs of functional trait adjustments, crucially affect community trait adaptation to altered environmental conditions, but empirical verification is scarce. We evaluated trait dynamics (antipredator defense, maximum growth rate, and phosphate affinity) of a lake phytoplankton community in a seasonally changing environment, using literature trait data and 21 years of species-resolved high-frequency biomass measurements. The trait data indicated a concave defense-growth trade-off, promoting fast-growing species with intermediate defense. With seasonally increasing grazing pressure, the community shifted toward higher defense levels at the cost of lower growth rates along the trade-off curve, while phosphate affinity explained some deviations from it. We discuss how low fitness differences of species, inferred from model simulations, in concert with stabilizing mechanisms, e.g., arising from further trait dimensions, may lead to the observed phytoplankton diversity. In conclusion, quantifying trade-offs is key for predictions of community trait adaptation and biodiversity under environmental change.
Phenotypic plasticity can increase individual fitness when environmental conditions change over time. Inducible defences are a striking example, allowing species to react to fluctuating predation pressure by only expressing their costly defended phenotype under high predation risk. Previous theoretical investigations have focused on how this affects predator–prey dynamics, but the impact on competitive outcomes and broader community dynamics has received less attention. Here we use a small food web model, consisting of two competing plastic autotrophic species exploited by a shared consumer, to study how the speed of inducible defences across three trade-off constellations affects autotroph coexistence, biomasses across trophic levels, and temporal variability. Contrary to the intuitive idea that faster adaptation increases autotroph fitness, we found that higher switching rates reduced individual fitness as it consistently provoked more maladaptive switching towards undefended phenotypes under high predation pressure. This had an unexpected positive impact on the consumer, increasing consumer biomass and lowering total autotroph biomass. Additionally, maladaptive switching strongly reduced autotroph coexistence through an emerging source-sink dynamic between defended and undefended phenotypes. The striking impact of maladaptive switching on species and food web dynamics indicates that this mechanism may be of more critical importance than previously recognized.
Phenotypic plasticity can increase individual fitness when environmental conditions change over time. Inducible defences are a striking example, allowing species to react to fluctuating predation pressure by only expressing their costly defended phenotype under high predation risk. Previous theoretical investigations have focused on how this affects predator–prey dynamics, but the impact on competitive outcomes and broader community dynamics has received less attention. Here we use a small food web model, consisting of two competing plastic autotrophic species exploited by a shared consumer, to study how the speed of inducible defences across three trade-off constellations affects autotroph coexistence, biomasses across trophic levels, and temporal variability. Contrary to the intuitive idea that faster adaptation increases autotroph fitness, we found that higher switching rates reduced individual fitness as it consistently provoked more maladaptive switching towards undefended phenotypes under high predation pressure. This had an unexpected positive impact on the consumer, increasing consumer biomass and lowering total autotroph biomass. Additionally, maladaptive switching strongly reduced autotroph coexistence through an emerging source-sink dynamic between defended and undefended phenotypes. The striking impact of maladaptive switching on species and food web dynamics indicates that this mechanism may be of more critical importance than previously recognized.
Trait-based approaches have broadened our understanding of how the composition of ecological communities responds to environmental drivers. This research has mainly focussed on abiotic factors and competition determining the community trait distribution, while effects of trophic interactions on trait dynamics, if considered at all, have been studied for two trophic levels at maximum. However, natural food webs are typically at least tritrophic. This enables indirect interactions of traits and biomasses among multiple trophic levels leading to underexplored effects on food web dynamics. Here, we demonstrate the occurrence of mutual trait adjustment among three trophic levels in a natural plankton food web (Lake Constance) and in a corresponding mathematical model. We found highly recurrent seasonal biomass and trait dynamics, where herbivorous zooplankton increased its size, and thus its ability to counter phytoplankton defense, before phytoplankton defense actually increased. This is contrary to predictions from bitrophic systems where counter-defense of the consumer is a reaction to prey defense. In contrast, counter-defense of carnivores by size adjustment followed the defense of herbivores as expected. By combining observations and model simulations, we show how the reversed trait dynamics at the two lower trophic levels result from a "trophic biomass-trait cascade" driven by the carnivores. Trait adjustment between two trophic levels can therefore be altered by biomass or trait changes of adjacent trophic levels. Hence, analyses of only pairwise trait adjustment can be misleading in natural food webs, while multitrophic trait-based approaches capture indirect biomass-trait interactions among multiple trophic levels.
Biodiversity decline causes a loss of functional diversity, which threatens ecosystems through a dangerous feedback loop: This loss may hamper ecosystems’ ability to buffer environmental changes, leading to further biodiversity losses. In this context, the increasing frequency of human-induced excessive loading of nutrients causes major problems in aquatic systems. Previous studies investigating how functional diversity influences the response of food webs to disturbances have mainly considered systems with at most two functionally diverse trophic levels. We investigated the effects of functional diversity on the robustness, that is, resistance, resilience, and elasticity, using a tritrophic—and thus more realistic—plankton food web model. We compared a non-adaptive food chain with no diversity within the individual trophic levels to a more diverse food web with three adaptive trophic levels. The species fitness differences were balanced through trade-offs between defense/growth rate for prey and selectivity/half-saturation constant for predators. We showed that the resistance, resilience, and elasticity of tritrophic food webs decreased with larger perturbation sizes and depended on the state of the system when the perturbation occurred. Importantly, we found that a more diverse food web was generally more resistant and resilient but its elasticity was context-dependent. Particularly, functional diversity reduced the probability of a regime shift toward a non-desirable alternative state. The basal-intermediate interaction consistently determined the robustness against a nutrient pulse despite the complex influence of the shape and type of the dynamical attractors. This relationship was strongly influenced by the diversity present and the third trophic level. Overall, using a food web model of realistic complexity, this study confirms the destructive potential of the positive feedback loop between biodiversity loss and robustness, by uncovering mechanisms leading to a decrease in resistance, resilience, and potentially elasticity as functional diversity declines.
Biodiversity decline causes a loss of functional diversity, which threatens ecosystems through a dangerous feedback loop: This loss may hamper ecosystems’ ability to buffer environmental changes, leading to further biodiversity losses. In this context, the increasing frequency of human-induced excessive loading of nutrients causes major problems in aquatic systems. Previous studies investigating how functional diversity influences the response of food webs to disturbances have mainly considered systems with at most two functionally diverse trophic levels. We investigated the effects of functional diversity on the robustness, that is, resistance, resilience, and elasticity, using a tritrophic—and thus more realistic—plankton food web model. We compared a non-adaptive food chain with no diversity within the individual trophic levels to a more diverse food web with three adaptive trophic levels. The species fitness differences were balanced through trade-offs between defense/growth rate for prey and selectivity/half-saturation constant for predators. We showed that the resistance, resilience, and elasticity of tritrophic food webs decreased with larger perturbation sizes and depended on the state of the system when the perturbation occurred. Importantly, we found that a more diverse food web was generally more resistant and resilient but its elasticity was context-dependent. Particularly, functional diversity reduced the probability of a regime shift toward a non-desirable alternative state. The basal-intermediate interaction consistently determined the robustness against a nutrient pulse despite the complex influence of the shape and type of the dynamical attractors. This relationship was strongly influenced by the diversity present and the third trophic level. Overall, using a food web model of realistic complexity, this study confirms the destructive potential of the positive feedback loop between biodiversity loss and robustness, by uncovering mechanisms leading to a decrease in resistance, resilience, and potentially elasticity as functional diversity declines.
Predator-prey oscillations are expected to show a 1/4-phase lag between predator and prey. However, observed dynamics of natural or experimental predator-prey systems are often more complex. A striking but hardly studied example are sudden interruptions of classic 1/4-lag cycles with periods of antiphase oscillations, or periods without any regular predator-prey oscillations. These interruptions occur for a limited time before the system reverts to regular 1/4-lag oscillations, thus yielding intermittent cycles. Reasons for this behaviour are often difficult to reveal in experimental systems. Here we test the hypothesis that such complex dynamical behaviour may result from minor trait variation and trait adaptation in both the prey and predator, causing recurrent small changes in attack rates that may be hard to capture by empirical measurements. Using a model structure where the degree of trait variation in the predator can be explicitly controlled, we show that a very limited amount of adaptation resulting in 10-15% temporal variation in attack rates is already sufficient to generate these intermittent dynamics. Such minor variation may be present in experimental predator-prey systems, and may explain disruptions in regular 1/4-lag oscillations.
The green microalga Chlamydomonas acidophila is an important primary producer in very acidic lakes (pH 2.0-3.5), characterized by high concentrations of ferric iron (up to 1 g total Fe L-1) and low rates of primary production. It was previously suggested that these high iron concentrations result in high iron accumulation and inhibit photosynthesis in C. acidophila. To test this, the alga was grown in sterilized lake water and in medium with varying total iron concentrations under limiting and sufficient inorganic phosphorus (Pi) supply, because Pi is an important growth limiting nutrient in acidic waters. Photosynthesis and growth of C. acidophila as measured over 5 days were largely unaffected by high total iron concentrations and only decreased if free ionic Fe3+ concentrations exceeded 100 mg Fe3+ L-1. Although C. acidophila was relatively rich in iron (up to 5 mmol Fe: mol C), we found no evidence of iron toxicity. In contrast, a concentration of 260 mg total Fe L-1 (i.e. 15 mg free ionic Fe3+ L-1), which is common in many acidic lakes, reduced Pi-incorporation by 50% and will result in Pi-limited photosynthesis. The resulting Pi-limitation present at high iron and Pi concentrations was illustrated by elevated maximum Pi-uptake rates. No direct toxic effects of high iron were found, but unfavourable chemical Pi-speciation reduced growth of the acidophile alga.
The density of organisms declines with size, because larger organisms need more energy than smaller ones and energetic losses occur when larger organisms feed on smaller ones. A potential expression of density-size distributions are Normalized Biomass Size Spectra (NBSS), which plot the logarithm of biomass independent of taxonomy within bins of logarithmic organismal size, divided by the bin width. Theoretically, the NBSS slope of multi-trophic communities is exactly - 1.0 if the trophic transfer efficiency (TTE, ratio of production rates between adjacent trophic levels) is 10% and the predator-prey mass ratio (PPMR) is fixed at 10(4). Here we provide evidence from four multi-trophic lake food webs that empirically estimated TTEs correspond to empirically estimated slopes of the respective community NBSS. Each of the NBSS considered pelagic and benthic organisms spanning size ranges from bacteria to fish, all sampled over three seasons in 1 yr. The four NBSS slopes were significantly steeper than -1.0 (range -1.14 to -1.19, with 95% CIs excluding -1). The corresponding average TTEs were substantially lower than 10% in each of the four food webs (range 1.0% to 3.6%, mean 1.85%). The overall slope merging all biomass-size data pairs from the four systems (-1.17) was almost identical to the slope predicted from the arithmetic mean TTE of the four food webs (-1.18) assuming a constant PPMR of 10(4). Accordingly, our empirical data confirm the theoretically predicted quantitative relationship between TTE and the slope of the biomass-size distribution. Furthermore, we show that benthic and pelagic organisms can be merged into a community NBSS, but future studies have yet to explore potential differences in habitat-specific TTEs and PPMRs. We suggest that community NBSS may provide valuable information on the structure of food webs and their energetic pathways, and can result in improved accuracy of TTE-estimates.
Ecoevolutionary feedbacks in predator-prey systems have been shown to qualitatively alter predator-prey dynamics. As a striking example, defense-offense coevolution can reverse predator-prey cycles, so predator peaks precede prey peaks rather than vice versa. However, this has only rarely been shown in either model studies or empirical systems. Here, we investigate whether this rarity is a fundamental feature of reversed cycles by exploring under which conditions they should be found. For this, we first identify potential conditions and parameter ranges most likely to result in reversed cycles by developing a new measure, the effective prey biomass, which combines prey biomass with prey and predator traits, and represents the prey biomass as perceived by the predator. We show that predator dynamics always follow the dynamics of the effective prey biomass with a classic 1/4-phase lag. From this key insight, it follows that in reversed cycles (i.e., -lag), the dynamics of the actual and the effective prey biomass must be in antiphase with each other, that is, the effective prey biomass must be highest when actual prey biomass is lowest, and vice versa. Based on this, we predict that reversed cycles should be found mainly when oscillations in actual prey biomass are small and thus have limited impact on the dynamics of the effective prey biomass, which are mainly driven by trait changes. We then confirm this prediction using numerical simulations of a coevolutionary predator-prey system, varying the amplitude of the oscillations in prey biomass: Reversed cycles are consistently associated with regions of parameter space leading to small-amplitude prey oscillations, offering a specific and highly testable prediction for conditions under which reversed cycles should occur in natural systems.
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.
Resisting annihilation
(2018)
Allelopathic species can alter biodiversity. Using simulated assemblages that are characterised by neutrality, lumpy coexistence and intransitivity, we explore relationships between within-assemblage competitive dissimilarities and resistance to allelopathic species. An emergent behaviour from our models is that assemblages are more resistant to allelopathy when members strongly compete exploitatively (high competitive power). We found that neutral assemblages were the most vulnerable to allelopathic species, followed by lumpy and then by intransitive assemblages. We find support for our modeling in real-world time-series data from eight lakes of varied morphometry and trophic state. Our analysis of this data shows that a lake's history of allelopathic phytoplankton species biovolume density and dominance is related to the number of species clusters occurring in the plankton assemblages of those lakes, an emergent trend similar to that of our modeling. We suggest that an assemblage's competitive power determines its allelopathy resistance.