Low-dimensional trade-offs fail to explain richness and structure in species-rich plant communities

Abstract

Mathematical models and ecological theory suggest that low-dimensional life history trade-offs (i.e. negative correlation between two life history traits such as competition vs. colonisation) may potentially explain the maintenance of species diversity and community structure. In the absence of trade-offs, we would expect communities to be dominated by 'super-types' characterised by mainly positive trait expressions. However, it has proven difficult to find strong empirical evidence for such trade-offs in species-rich communities. We developed a spatially explicit, rule-based and individual-based stochastic model to explore the importance of low-dimensional trade-offs. This model simulates the community dynamics of 288 virtual plant functional types (PFTs), each of which is described by seven life history traits. We consider trait combinations that fit into the trade-off concept, as well as super-types with little or no energy constraints or resource limitations, and weak PFTs, which do not exploit resources efficiently. The model is parameterised using data from a fire-prone, species-rich Mediterranean-type shrubland in southwestern Australia. We performed an exclusion experiment, where we sequentially removed the strongest PFT in the simulation and studied the remaining communities. We analysed the impact of traits on performance of PFTs in the exclusion experiment with standard and boosted regression trees. Regression tree analysis of the simulation results showed that the trade-off concept is necessary for PFT viability in the case of weak trait expression combinations such as low seed production or small seeds. However, species richness and diversity can be high despite the presence of super-types. Furthermore, the exclusion of super-types does not necessarily lead to a large increase in PFT richness and diversity. We conclude that low-dimensional trade-offs do not provide explanations for multi-species co-existence contrary to the prediction of many conceptual models.

Seed Size, Fecundity and Postfire Regeneration Strategy Are Interdependent in Hakea

“…Further, the two traits have coevolved, as revealed by the Bayesian MCMC analysis. Given that both more and larger seeds may be adaptive among nonsprouters, as they regenerate solely from seeds after fire and their seedlings establish in nutrient-impoverished environments [ 27 , 24 ], this fire-response type opts for more rather than larger seeds. Extensive demographic studies on hakeas and related woody species in SWA have shown that postfire recruitment patterns conform to biased lotteries, with the demographic component paramount followed by biotic components, such as seedling size [ 58 ].…”

“…H 1 : Nonsprouters produce more seeds or larger seeds than resprouters [ 24 ], and have greater investment in seeds [ 13 , 25 , 30 , 29 ];…”

“…In addition, there is much support for a trade-off between fecundity and seed size [ 21 , 22 , 23 ], though this relationship must be set in the context of other life-history traits. Working in fire-prone sclerophyll shrublands, Esther et al [ 24 ] showed that the two most important interactions affecting population viability were seed size–seed production and seed size–regeneration strategy. Resprouters always did well, but the success of nonsprouters depended on their having many or large seeds.…”

Integrating trait‐based empirical and modeling research to improve ecological restoration

“…Although various trait‐based simulation models of Mediterranean‐type ecosystems exist and have been used, for example, to assess the impact of climate and fire on vegetation composition or performance, none of these models can currently fully assist restoration efforts toward multifunctional and resilient ecosystems. For example, several model approaches neglect soil water and nutrient dynamics, as well as their feedbacks to vegetation dynamics (e.g., Esther et al., ; Moore & Noble, ; Pausas, ) and are therefore too simplified to assess the impact of global change. Other models explicitly consider water dynamics, but neglect nitrogen dynamics (e.g., Fyllas & Troumbis, ; Mouillot, Rambal, & Lavorel, ) and thus cannot account for the effects of nutrient deposition, for example, on invasive species or on ecosystem functions such as dissolved and particulate leaching and gaseous nutrient loss.…”

“…For example, several model approaches neglect soil water and nutrient dynamics, as well as their feedbacks to vegetation dynamics (e.g., Esther et al, 2011;Moore & Noble, 1990;Pausas, 1999) and are therefore too simplified to assess the impact of global change.…”