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The sensitivity of tropical forest carbon to climate is a key uncertainty in predicting global climate change. Although short-term drying and warming are known to affect forests, it is unknown if such effects translate into long-term responses. Here, we analyze 590 permanent plots measured across the tropics to derive the equilibrium climate controls on forest carbon. Maximum temperature is the most important predictor of aboveground biomass (−9.1 megagrams of carbon per hectare per degree Celsius), primarily by reducing woody productivity, and has a greater impact per °C in the hottest forests (>32.2°C). Our results nevertheless reveal greater thermal resilience than observations of short-term variation imply. To realize the long-term climate adaptation potential of tropical forests requires both protecting them and stabilizing Earth’s climate.

The Red List Categories and the accompanying five criteria developed by the International Union for Conservation of Nature (IUCN) provide an authoritative and comprehensive methodology to assess the conservation status of organisms. Red List criterion B, which principally uses distribution data, is the most widely used to assess conservation status, particularly of plant species. No software package has previously been available to perform large‐scale multispecies calculations of the three main criterion B parameters [extent of occurrence (EOO), area of occupancy (AOO) and an estimate of the number of locations] and provide preliminary conservation assessments using an automated batch process. We developed ConR, a dedicated R package, as a rapid and efficient tool to conduct large numbers of preliminary assessments, thereby facilitating complete Red List assessment. ConR (1) calculates key geographic range parameters (AOO and EOO) and estimates the number of locations sensu IUCN needed for an assessment under criterion B; (2) uses this information in a batch process to generate preliminary assessments of multiple species; (3) summarize the parameters and preliminary assessments in a spreadsheet; and (4) provides a visualization of the results by generating maps suitable for the submission of full assessments to the IUCN Red List. ConR can be used for any living organism for which reliable georeferenced distribution data are available. As distributional data for taxa become increasingly available via large open access datasets, ConR provides a novel, timely tool to guide and accelerate the work of the conservation and taxonomic communities by enabling practitioners to conduct preliminary assessments simultaneously for hundreds or even thousands of species in an efficient and time‐saving way.

Tropical forests store 40-50% of terrestrial vegetation carbon 1 . Spatial variations in aboveground live tree biomass carbon (AGC) stocks remain poorly understood, in particular in tropical montane forests 2 . Owing to climatic and soil changes with increasing elevation 3 , AGC stocks are lower in tropical montane compared to lowland forests 2 . Here we assemble and analyse a dataset of structurally intact old-growth forests (AfriMont) spanning 44 montane sites in 12 African countries. We find that montane sites in the AfriMont plot network have a mean AGC-stock of 149.4 Mg C ha -1 (95% CI 137.1-164.2), comparable to lowland forests in the African Tropical Rainforest Observation Network 4 and about 70 per cent and 32 per cent higher than averages from plot networks in montane 2,5,6 and lowland 7 forests in the Neotropics, respectively. Notably, our results are two-thirds higher than the IPCC default values for these forests in Africa 8 . We find that the low stem density and high abundance of large trees of African lowland forests 4 is mirrored in the montane forests sampled. This carbon store is endangered: we estimate that 0.8 million ha of old-growth African montane forest have been lost since 2000. We provide country-specific montane forest AGC stock estimates modelled from our plot network to help guide forest conservation and reforestation interventions. Our findings highlight the need for conserving these biodiverse 9,10 and carbon-rich ecosystems.

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