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Improving collaborations between empiricists and modelers to advance grassland community dynamics in ecosystem models.

Kevin J Wilcox,K. Komatsu,Meghan L. Avolio

Published 2020 in New Phytologist

ABSTRACT

Climate change, increasing atmospheric CO2, and land use change havealteredbiogeochemical andhydrologic cyclesworld-wide,with grassland systems being particularly vulnerable to resulting vegetation shifts (Komatsu et al., 2019). Therefore, incorporating plant community dynamics into ecosystemmodels is critical for accurate forecasting of ecosystem responses to global change (Levine, 2016). Process-based ecosystem models, which simulate the biogeochemical transfers of mass and energy among biota, the subsurface, and atmosphere, require representation of dynamic composition of organisms within ecosystems. For example, these models simulate leaf and plant-level characteristics, such as electron transport rate and allometry of carbon (C) allocation, to predict how net primary productivity and other ecosystem processes respond to abiotic drivers. These models are particularly useful in scaling from organismal to ecosystem levels but are still underdeveloped in their ability to capture community change, especially in grassland ecosystems. To represent compositional changes, these models must simulate competition, mortality, establishment, and reproduction of plant populations within communities. Yet, current ecosystem modeling approaches to forecast plant community change have derived from studies of forested systems and are either too coarse to capture fine-scale community dynamics (e.g. dynamic global vegetation models (DGVMs)) or too complex to be used at large spatial scales (e.g. forest gap models). Community ecology often relies on statistical models describing population dynamics or the abundance/frequency of individual species to identify linkages between community dynamics and ecosystem processes. For example, there is a vast literature linking species richness to ecosystem function using statistical models (Hooper et al., 2005; Cardinale et al., 2006). Yet, statistical models are rarely able to scale leaf and plant-level characteristics to ecosystem levels owing to data constraints and the fact that they do not incorporate process knowledge. Process-based ecosystem models are thus needed to predict whole ecosystem function, especially under novel environmental conditions. Yet, as already mentioned, these models often struggle to link plant characteristics to local-scale community dynamics. To better represent community dynamics in ecosystem models, scientists must identify: (1) how physiological and morphological traits of plant species or functional types, and their diversity, can drive changes in community structure (Fig. 1, arrow 1); and (2) how community dynamics alter the distribution of traits across the entire community (Fig. 1, arrow 2); leading to (3) improved ability to simulate shifts in community structure and their concurrent effects on ecosystem functioning (Fig. 1, arrows 6 and 7). Additionally, ecosystem processes, as forced by abiotic drivers, should alter the competitive balance in community dynamics that are sensitive to the coupling between their physiological/morphological traits and the abiotic drivers (Fig. 1, grey arrow). Integration across organismal, community, and ecosystem ecology, as well as between empirical and process-based modeling approaches, is necessary to address this issue. Toward this end, we (K. Komatsu, M. Avolio, K. Wilcox) led a working group funded by the LongTerm Ecological Research Network from 2017 to 2019, where we gathered scientists from diverse fields (C2E Consortium) to identify challenges and formulate directions for better integration of community dynamics in land surface models (LSMs).

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