RESUMO
Microorganisms are vital in mediating the earth's biogeochemical cycles; yet, despite our rapidly increasing ability to explore complex environmental microbial communities, the relationship between microbial community structure and ecosystem processes remains poorly understood. Here, we address a fundamental and unanswered question in microbial ecology: 'When do we need to understand microbial community structure to accurately predict function?' We present a statistical analysis investigating the value of environmental data and microbial community structure independently and in combination for explaining rates of carbon and nitrogen cycling processes within 82 global datasets. Environmental variables were the strongest predictors of process rates but left 44% of variation unexplained on average, suggesting the potential for microbial data to increase model accuracy. Although only 29% of our datasets were significantly improved by adding information on microbial community structure, we observed improvement in models of processes mediated by narrow phylogenetic guilds via functional gene data, and conversely, improvement in models of facultative microbial processes via community diversity metrics. Our results also suggest that microbial diversity can strengthen predictions of respiration rates beyond microbial biomass parameters, as 53% of models were improved by incorporating both sets of predictors compared to 35% by microbial biomass alone. Our analysis represents the first comprehensive analysis of research examining links between microbial community structure and ecosystem function. Taken together, our results indicate that a greater understanding of microbial communities informed by ecological principles may enhance our ability to predict ecosystem process rates relative to assessments based on environmental variables and microbial physiology.
RESUMO
Dynamic modelling of hydrochemistry is a valuable tool to study and predict the recovery of surface waters from acidification, and to assess the effects of confounding factors (such as delayed soil response and changing climate) that cause hysteresis during reversal from acidification. The availability of soil data is often a limitation for the regional application of dynamic models. Here we present a method to upscale site-specific soil properties to a regional scale in order to circumvent that problem. The method proposed for upscaling relied on multiple regression models between soil properties and a suite of environmental variables used as predictors. Soil measurements were made during a field survey in 13 catchments in the Pyrenees (NW Spain). The environmental variables were derived from mapped or remotely sensed topographic, lithological, land-cover, and climatic information. Regression models were then used to model soil parameters, which were supplied as input for the biogeochemical model MAGIC (Model for Acidification of Groundwater In Catchments) in order to reconstruct the history of acidification in Pyrenean lakes and forecast the recovery under a scenario of reduced acid deposition. The resulting simulations were then compared with model runs using field measurements as input parameters. These comparisons showed that regional averages for the key water and soil chemistry variables were suitably reproduced when using the modelled parameters. Simulations of water chemistry at the catchment scale also showed good results, whereas simulated soil parameters reflected uncertainty in the initial modelled estimates.
