Search for: All records

Growing season length (GSL) is a key unifying concept in ecology that can be estimated from eddy covariance-derived estimates of net ecosystem production (NEP). Previous studies disagree on how increasing GSLs may affect NEP in evergreen coniferous forests, potentially due to the variety of methods used to quantify GSL from NEP. We calculated GSL and GSL-NEP regressions at eleven evergreen conifer sites across a broad climatic gradient in western North America using three common approaches: (1) variable length (3–7 days) regressions of day of year versus NEP, (2) a smoothed threshold approach, and (3) the carbon uptake period, followed by a new approach of a method-averaged ensemble. The GSL and the GSL-NEP relationship differed among methods, resulting in linear relationships with variable sign, slope, and statistical significance. For all combinations of sites and methods, the GSL explained between 6% and 82% of NEP withp-values ranging from 0.45 to < 0.01. These results demonstrate the variability among GSL methods and the importance of selecting an appropriate method to accurately project the ecosystem carbon cycling response to longer growing seasons in the future. To encourage this approach in future studies, we outline a series of best practices for GSL method selection depending on research goals and the annual NEP dynamics of the study site(s). These results contribute to understanding growing season dynamics at ecosystem and continental scales and underscore the potential for methodological variability to influence forecasts of the evergreen conifer forest response to climate variability.

Plants with crassulacean acid metabolism (CAM) are increasing in distribution and abundance in drylands worldwide, but the underlying drivers remain unknown. We investigate the impacts of extreme drought and CO₂enrichment on the competitive relationships between seedlings ofCylindropuntia imbricata(CAM species) andBouteloua eriopoda(C₄grass), which coexist in semiarid ecosystems across the Southwestern United States. Our experiments under altered water and CO₂water conditions show thatC. imbricatapositively responded to CO₂enrichment under extreme drought conditions, whileB. eriopodadeclined from drought stress and did not recover after the drought ended. Conversely, in well‐watered conditionsB. eriopodahad a strong competitive advantage onC. imbricatasuch that the photosynthetic rate and biomass (per individual) ofC. imbricatagrown withB. eriopodawere lower relative to when growing alone. A meta‐analysis examining multiple plant families across global drylands shows a positive response of CAM photosynthesis and productivity to CO₂enrichment. Collectively, our results suggest that under drought and elevated CO₂concentrations, projected with climate change, the competitive advantage of plant functional groups may shift and the dominance of CAM plants may increase in semiarid ecosystems.

Environmental change can result in substantial shifts in community composition. The associated immigration and extinction events are likely constrained by the spatial distribution of species. Still, studies on environmental change typically quantify biotic responses at single spatial (time series within a single plot) or temporal (spatial beta diversity at single time points) scales, ignoring their potential interdependence. Here, we use data from a global network of grassland experiments to determine how turnover responses to two major forms of environmental change – fertilisation and herbivore loss – are affected by species pool size and spatial compositional heterogeneity. Fertilisation led to higher rates of local extinction, whereas turnover in herbivore exclusion plots was driven by species replacement. Overall, sites with more spatially heterogeneous composition showed significantly higher rates of annual turnover, independent of species pool size and treatment. Taking into account spatial biodiversity aspects will therefore improve our understanding of consequences of global and anthropogenic change on community dynamics.