Abstract. The Amazon forest is influenced by strong feedback loops between its biotic and abiotic components. Local forest loss increases CO2 emissions, which, in turn, drives climate change, raising temperatures and reducing rainfall, causing further forest loss. Additionally, forest loss disrupts important forest-rainfall cycles, threatening the overall forest stability. These feedbacks make the system vulnerable to tipping points, where parts of the forest could transition to a degraded state. Slower recovery to short-term disturbances, hereafter named reduced stability, is considered an early warning indicator of such tipping points. However, the role of tree species diversity in regulating this vulnerability remains poorly understood, especially across spatial scales. To examine how tree species diversity impacts tipping point likelihoods across multiple spatial scales, we used modelled tree species diversity data at the alpha (local), beta (asynchrony across local communities), and gamma (regional) scales. We quantified tipping likelihood on the same scales using temporal autocorrelation trends in monthly satellite-derived vegetation productivity time series over 2001–2019. Our findings reveal higher tipping likelihoods at the alpha level (25 km2) compared to the gamma level (209 903 km2), indicating that Amazonian tipping points are more likely to occur locally than regionally or basin-wide. We also observe significant but weak positive linear relationships between tree species diversity and stability at both alpha and beta scales. This emphasizes both the importance of biodiversity conservation at multiple spatial scales and the complexity of understanding the stability of the Amazon forest.
Passel et al. (Wed,) studied this question.