Department of Biological Sciences, University of Alabama, P.O. Box 870344, Tuscaloosa, AL, 35487, USA.
Centro de Pesquisa Agroflorestal de Rondônia (Embrapa Rondônia), BR 364, km 5,5, Caixa Postal 127, Porto Velho, Rondônia, CEP 76815-800, Brazil.
Sci Rep. 2021 Jan 28;11(1):2563. doi: 10.1038/s41598-021-81948-4.
Trees in the upper canopy contribute disproportionately to forest ecosystem productivity. The large, canopy-emergent Bertholletia excelsa also supports a multimillion-dollar commodity crop (Brazil nut), harvested almost exclusively from Amazonian forests. B. excelsa fruit production, however is extremely variable within populations and years, destabilizing local harvester livelihoods and the extractive economy. To understand this variability, data were collected in Acre, Brazil over 10 years at two sites with similar climate and forest types, but different fruit production levels, despite their proximity (~ 30 km). One site consistently produced more fruit, showed less individual- and population-level variability, and had significantly higher soil P and K levels. The strongest predictor of fruit production was crown area. Elevation and sapwood area also significantly impacted fruit production, but effects differed by site. While number of wet days and dry season vapor pressure prior to flowering were significant production predictors, no climatic variables completely captured annual observed variation. Trees on the site with higher available P and K produced nearly three times more fruits, and appeared more resilient to prolonged drought and drier atmospheric conditions. Management activities, such as targeted fertilization, may shield income-dependent harvesters from expected climate changes and production swings, ultimately contributing to conservation of old growth forests where this species thrives.
上层树冠中的树木对森林生态系统的生产力有不成比例的贡献。高大的树冠突出的巴西栗也支持着价值数百万美元的商品作物（巴西坚果），这些坚果几乎完全是从亚马逊森林中收获的。然而，巴西栗的果实产量在种群和年份内非常不稳定，这使得当地的采集者生计和采矿业经济不稳定。为了了解这种变异性，在巴西阿克里州的两个具有相似气候和森林类型的地点进行了 10 多年的数据收集，尽管它们相距约 30 公里，但果实产量却不同。一个地点的果实产量始终较高，个体和种群水平的变异性较小，土壤磷和钾水平明显较高。树冠面积是果实产量的最强预测因子。海拔和边材面积也显著影响果实产量，但作用因地点而异。虽然开花前的湿润天数和旱季蒸气压是重要的产量预测因子，但没有任何气候变量完全捕捉到了年度观测到的变化。在磷钾供应较高的地点，树木的果实产量几乎增加了三倍，并且对长时间的干旱和干燥的大气条件表现出更强的适应能力。管理活动，如有针对性的施肥，可能会保护依赖收入的采集者免受预期的气候变化和产量波动的影响，最终有助于保护这种物种茁壮成长的古老森林。
