Fluctuating Environments and Phytoplankton Community Structure: A Stochastic Model

200

180

Adding fluctuations to models of resource competition provides one solution to Hutchinson's paradox. Fundamental to fluctuation dependent mechanisms of coexistence are differential species' responses to environmental fluctuations through time. The covariance between the environment and competition this generates leads to compensatory population dynamics, now thought to be important for community stability. We tested this theory experimentally using freshwater diatoms. We measured the growth of Cyclotella pseudostelligera and Fragilaria crotonensis over a range of temperatures. Cyclotella showed a higher growth rate and higher silicate assimilation rate between 6°C and 18°C, while Fragilaria showed greater competitive ability above 18°C. These results were used to parameterize a resource competition model. Numerical simulation of this model predicted stable coexistence and compensatory dynamics under fluctuating but not constant temperature. A long‐term competition experiment with Cyclotella and Fragilaria confirmed the predictions of the model. These results demonstrate the role abiotic fluctuations may play in maintaining the diversity of natural communities.

Section: Introductionmentioning

confidence: 99%

Stable Coexistence in a Fluctuating Environment: An Experimental Demonstration

Descamps‐Julien

Gonzalez

2005

200

180

“…However, when considering the response of communities to gradients in a more mechanistic way, one might expect that a functional approach that considers how species differ ecologically in their responses to resources will be more informative. For example, the resource-heterogeneity hypothesis explains diversity patterns by employing feeding and life history characteristics of species (Abrams 1984, Anderies andBeisner 2000), and thus, the theory assumes that species differences in a functional, and not simply a taxonomic sense, are the ones that enable coexistence. Along environmental gradients of resource variability, it is the number of different niches that should change with habitat variability, and species numbers will not matter unless additional species are functionally complementary and able to occupy vacant niches (Walker et al 1999).…”

Section: Introductionmentioning

confidence: 99%

Zooplankton Biodiversity and Lake Trophic State: Explanations Invoking Resource Abundance and Distribution

Barnett¹,

Beisner²

2007

136

While empirical studies linking biodiversity to local environmental gradients have emphasized the importance of lake trophic status (related to primary productivity), theoretical studies have implicated resource spatial heterogeneity and resource relative ratios as mechanisms behind these biodiversity patterns. To test the feasibility of these mechanisms in natural aquatic systems, the biodiversity of crustacean zooplankton communities along gradients of total phosphorus (TP) as well as the vertical heterogeneity and relative abundance of their phytoplankton resources were assessed in 18 lakes in Quebec, Canada. Zooplankton community richness was regressed against TP, the spatial distribution of phytoplankton spectral groups, and the relative biomass of spectral groups. Since species richness does not adequately capture ecological function and life history of different taxa, features which are important for mechanistic theories, relationships between zooplankton functional diversity (FD) and resource conditions were examined. Zooplankton species richness showed the previously established tendency to a unimodal relationship with TP, but functional diversity declined linearly over the same gradient. Changes in zooplankton functional diversity could be attributed to changes in both the spatial distribution and type of phytoplankton resource. In the studied lakes, spatial heterogeneity of phytoplankton groups declined with TP, even while biomass of all groups increased. Zooplankton functional diversity was positively related to increased heterogeneity in cyanobacteria spatial distribution. However, a smaller amount of variation in functional diversity was also positively related to the ratio of biomass in diatoms/chrysophytes to cyanobacteria. In all observed relationships, a greater variation of functional diversity than species richness measures was explained by measured factors, suggesting that functional measures of zooplankton communities will benefit ecological research attempting to identify mechanisms behind environmental gradients affecting diversity.

“…Despite the ubiquity of disturbance and other, less catastrophic sources of temporal variability in ecology (e.g., environmental stochasticity), most models of simple communities still make the blanket assumption that temporal variation in the environment has sufficiently little influence on coexistence that this source of variation can be ignored when studying the process of interest (e.g., competition, predation, mutualism). This trend has only recently begun to change (e.g., Ives 1995, Ives and Jansen 1998, Ripa et al 1998, Anderies and Beisner 2000, Kilpatrick and Ives 2003, although the approach for incorporating temporal variability into community models has been available for some time (May 1973, Nisbet andGurney 1982).…”

Section: Introductionmentioning

confidence: 99%

Stochasticity, Predator–prey Dynamics, and Trigger Harvest of Nonnative Predators

Sabo

2005

Environmental stochasticity is one of the premier features of population models used to forecast population persistence; however, most population viability analyses ignore interactions with other species. By contrast, theory in community ecology draws from a tradition of determinism: focusing on the processes (competition, predation) that promote the coexistence of groups of species (i.e., coexistence), while generally ignoring environmental variation in these processes (process noise or environmental stochasticity). Here I considered the role of deterministic and stochastic sources of variation in a predator– prey interaction on the probability of prey population extinction. Using discrete‐time stochastic versions of a classic predator–prey model I show that even low levels of predator process noise increase the probability of prey extinction. Surprisingly, predator process noise has stronger negative effects on prey persistence than stochasticity in the population growth rate of the prey species itself. This conclusion is robust across a wide range of deterministic model behaviors, suggesting that the effects of stochastic variation in predator abundance on prey persistence may be pervasive. I then applied these insights to the management of endangered prey populations threatened by a nonnative predator and examined the relative efficacy of three predator control techniques at reviving declining prey populations. These techniques included immediate eradication and reduction of predator populations through either “proportional” harvest or “trigger” harvest. Under a proportional harvest regime, a constant proportion of predators were removed each year, with little attention paid to the natural variation experienced by predator populations. By contrast, I implemented trigger harvest by reducing predator abundance to a fixed threshold abundance level only in years in which the predator exceeds this harvest threshold. Trigger harvest reduces both the relative abundance and variance of predators, whereas proportional harvest only reduces the abundance of these species. My analysis suggests that the reduction of predator variance through trigger harvest can revive declining prey populations and, in some cases, revive them more effectively than a proportional reduction of predator abundance: the default control strategy when eradication cannot be achieved.