Fruit production in three masting tree species does not rely on stored carbon reserves

Fruiting is typically considered to massively burden the seasonal carbon budget of trees. The cost of reproduction has therefore been suggested as a proximate factor explaining observed mast-fruiting patterns. Here, we used a large-scale, continuous 13C labeling of mature, deciduous trees in a temperate Swiss forest to investigate to what extent fruit formation in three species with masting reproduction behavior (Carpinus betulus, Fagus sylvatica, Quercus petraea) relies on the import of stored carbon reserves. Using a free-air CO2 enrichment system, we exposed trees to 13C-depleted CO2 during 8 consecutive years. By the end of this experiment, carbon reserve pools had significantly lower δ13C values compared to control trees. δ13C analysis of new biomass during the first season after termination of the CO2 enrichment allowed us to distinguish the sources of built-in carbon (old carbon reserves vs. current assimilates). Flowers and expanding leaves carried a significant 13C label from old carbon stores. In contrast, fruits and vegetative infructescence tissues were exclusively produced from current, unlabeled photoassimilates in all three species, including F. sylvatica, which had a strong masting season. Analyses of δ13C in purified starch from xylem of fruit-bearing shoots revealed a complete turn-over of starch during the season, likely due to its usage for bud break. This study is the first to directly demonstrate that fruiting is independent from old carbon reserves in masting trees, with significant implications for mechanistic models that explain mast seeding.

Evaluating the surveillance for swine dysentery and progressive atrophic Rhinitis in closed multiplier herds using Scenario tree modelling

Background: The Swiss pig population enjoys a favourable health situation. To further promote this, the Pig Health Service (PHS) conducts a surveillance program in affiliated herds: closed multiplier herds with the highest PHS-health and hygiene status have to be free from swine dysentery and progressive atrophic rhinitis and are clinically examined four times a year, including laboratory testing. Besides, four batches of pigs per year are fattened together with pigs from other herds and checked for typical symptoms (monitored fattening groups (MF)). While costly and laborious, little was known about the effectiveness of the surveillance to detect an infection in a herd. Therefore, the sensitivity of the surveillance for progressive atrophic rhinitis and swine dysentery at herd level was assessed using scenario tree modelling, a method well established at national level. Furthermore, its costs and the time until an infection would be detected were estimated, with the final aim of yielding suggestions how to optimize surveillance. Results: For swine dysentery, the median annual surveillance sensitivity was 96.7 %, mean time to detection 4.4 months, and total annual costs 1022.20 Euro/herd. The median component sensitivity of active sampling was between 62.5 and 77.0 %, that of a MF between 7.2 and 12.7 %. For progressive atrophic rhinitis, the median surveillance sensitivity was 99.4 %, mean time to detection 3.1 months and total annual costs 842.20 Euro. The median component sensitivity of active sampling was 81.7 %, that of a MF between 19.4 and 38.6 %. Conclusions: Results indicate that total sensitivity for both diseases is high, while time to detection could be a risk in herds with frequent pig trade. From all components, active sampling had the highest contribution to the surveillance sensitivity, whereas that of MF was very low. To increase efficiency, active sampling should be intensified (more animals sampled) and MF abandoned. This would significantly improve sensitivity and time to detection at comparable or lower costs. The method of scenario tree modelling proved useful to assess the efficiency of surveillance at herd level. Its versatility allows adjustment to all kinds of surveillance scenarios to optimize sensitivity, time to detection and/or costs.