the GlobalChangeBiology blog

By averaging in time and/or space, models predict less variable light patterns under tree canopies than in reality. We measured light every minute in 24 positions in a grid under different chestnut orchards, for several clear and overcast days. We also modelled this light with a purposely created 3D, spatially explicit, ray-tracing light interception model, where canopy porosity was calibrated to match measured daily light. Finally, we used both the measured and modeled light patterns transmitted under the tree canopies to estimate the daily net photosynthesis (An) and radiation use efficiency (RUE) of an understory wheat leaf. As expected, modeled light was more uniform than measured light, even at equal daily light. This resulted in large overestimation of daily An and RUE of the understory leaf. Averaging light in time increased the overestimations even further. A sensitivity analysis showed that this overestimation remained substantial over the range of realistic values for leaf ph

Outbreaks of a plant disease in a landscape can be meaningfully modelled using networks with nodes representing individual crop-fields, and edges representing potential infection pathways between them. Their spatial structure, which resembles that of a regular lattice, makes such networks fairly robust against epidemics. Yet, it is well-known how the addition of a few shortcuts can turn robust regular lattices into vulnerable ‘small world’ networks. Although the relevance of this phenomenon has been shown theoretically for networks with nodes corresponding to individual host plants, its real-world implications at a larger scale (i.e. in networks with nodes representing crop fields or other plantations) remain elusive. Focusing on realistic spatial networks connecting olive orchards in Andalusia (Southern Spain), the world’s leading olive producer, we show how even very small probabilities of long distance dispersal of infectious vectors result in a small-world effect that dramatically