A substantial amount of the tropical forests of South America and Africa is generated through moisture recycling (i.e., forest rainfall self-reliance). Thus, deforestation that reduces evaporation and dampens the water cycle can further increase the risk of water-stress-induced forest loss in downwind areas, particularly during water scarce periods. However, few studies have investigated dry period forest rainfall self-reliance over longer records and consistently compared the rainforest moisture recycling in both continents. Here, we analyze dry-season anomalies of moisture recycling for mean-years and dry-years, in the South American (Amazon) and African (Congo) rainforests over the years 1980-2013. We find that, in the dry seasons, the reliance of forest rainfall on their own moisture supply (ρfor) increases by 7% (from a mean annual value of 26% to 28%) in the Amazon and up to 30% (from 28% to 36%) in the Congo. Dry years further amplify dry season ρfor in both regions by 4-5%. In both the Amazon and Congo, dry season amplification of ρfor is strongest in regions with a high mean annual ρfor. In the Amazon, forest rainfall self-reliance has declined over time. At the country scale, dry season ρfor can differ drastically from mean annual ρfor. In for example Bolivia and Gabon, mean annual ρfor is ~30% while dry season ρfor is ~50%. The dry period amplification of forest rainfall self-reliance further highlights the role of forests for sustaining their own resilience, and for maintaining downwind rainfall at both regional and national scales.

Emerging technologies based on the detection of electro-magnetic energy offer promising opportunities for sampling biodiversity. We exploit their potential bye showing here how they can be used in bat point counts - a novel method to sample flying bats - to overcome shortcomings of traditional sampling methods, and to maximise sampling coverage and taxonomic resolution of this elusive taxon with minimal sampling bias. We conducted bat point counts with a sampling rig combining a thermal scope to detect bats, an ultrasound recorder to obtain echolocation calls, and a near-infrared camera to capture bat morphology. We identified bats with the first dedicated identification key combining acoustic and morphological features, and compared bat point counts to the standard bat sampling methods of mist netting and automated ultrasound recording in three oil palm plantation sites in Indonesia, over nine survey nights. Based on rarefaction and extrapolation sampling curves, we show that bat point counts were the most time-efficient and effective method for sampling the oil palm species pool. Point counts sampled species that tend to avoid nets and those that are not echolocating, and thus cannot be detected acoustically. We identified some bat sonotypes with near-infrared imagery, and bat point counts revealed strong sampling biases in previous studies using capture-based methods, suggesting similar biases in other regions might exist. While capture-based methods allow to identify bats with absolute and internal morphometry, and unattended ultrasound recorders can effectively sample echolocating bats, bat point counts are a promising, and potentially competitive new tool for sampling all flying bats without bias and observing their behavior in the wild.