Large-scale reforestation can increase water yield and reduce drought risk for water-insecure regions in the Asia-Pacific.

Large-scale reforestation can potentially bring both benefits and risks to the water cycle, which needs to be better quantified under future climates to inform reforestation decisions. We identified 477 water-insecure basins worldwide accounting for 44.6% (380.2 Mha) of the global reforestation potential. As many of these basins are in the Asia-Pacific, we used regional coupled land-climate modeling for the period 2041-2070 to reveal that reforestation increases evapotranspiration and precipitation for most water-insecure regions over the Asia-Pacific. This resulted in a statistically significant increase in water yield (p < .05) for the Loess Plateau-North China Plain, Yangtze Plain, Southeast China, and Irrawaddy regions. Precipitation feedback was influenced by the degree of initial moisture limitation affecting soil moisture response and thus evapotranspiration, as well as precipitation advection from other reforested regions and moisture transport away from the local region. Reforestation also reduces the probability of extremely dry months in most of the water-insecure regions. However, some regions experience nonsignificant declines in net water yield due to heightened evapotranspiration outstripping increases in precipitation, or declines in soil moisture and advected precipitation.

Conservation opportunities through improved management of recently established protected areas in Southeast Asia.

Protected areas (PAs) play a crucial role in biodiversity conservation and climate change mitigation.1,2 However, ineffective management can lead to biodiversity loss and carbon emissions from deforestation.3,4,5,6 To address this issue and explore viable solutions, we assessed the impact of PA establishment on avoided deforestation in 80 Southeast Asian PAs using the synthetic control approach.7,8 Our results show that 36 PAs successfully prevented 78,910 ha of deforestation. However, the remaining 44 PAs lost 72,497 ha of forest, impacting the habitat of 226 threatened bird and mammal species. Effective management of these reserves could have potentially avoided up to 2.07 MtCO2e yr-1 in carbon emissions. We estimate that at least $17 million USD per year in additional funding is required to better manage these 44 ineffective PAs and reduce future emissions. Furthermore, we demonstrate that carbon markets have the potential to generate these funds by reducing carbon emissions from deforestation within protected areas. Our findings emphasize that improving PA management is an essential nature-based solution for conserving biodiversity and mitigating climate change.

Climate co-benefits of tiger conservation.

Biodiversity conservation is increasingly being recognized as an important co-benefit in climate change mitigation programmes that use nature-based climate solutions. However, the climate co-benefits of biodiversity conservation interventions, such as habitat protection and restoration, remain understudied. Here we estimate the forest carbon storage co-benefits of a national policy intervention for tiger (Panthera tigris) conservation in India. We used a synthetic control approach to model avoided forest loss and associated carbon emissions reductions in protected areas that underwent enhanced protection for tiger conservation. Over a third of the analysed reserves showed significant but mixed effects, where 24% of all reserves successfully reduced the rate of deforestation and the remaining 9% reported higher-than-expected forest loss. The policy had a net positive benefit with over 5,802 hectares of averted forest loss, corresponding to avoided emissions of 1.08 ± 0.51 MtCO2 equivalent between 2007 and 2020. This translated to US$92.55 ± 43.56 million in ecosystem services from the avoided social cost of emissions and potential revenue of US$6.24 ± 2.94 million in carbon offsets. Our findings offer an approach to quantitatively track the carbon sequestration co-benefits of a species conservation strategy and thus help align the objectives of climate action and biodiversity conservation.

Deep learning for environmental conservation.

The last decade has transformed the field of artificial intelligence, with deep learning at the forefront of this development. With its ability to 'self-learn' discriminative patterns directly from data, deep learning is a promising computational approach for automating the classification of visual, spatial and acoustic information in the context of environmental conservation. Here, we first highlight the current and future applications of supervised deep learning in environmental conservation. Next, we describe a number of technical and implementation-related challenges that can potentially impede the real-world adoption of this technology in conservation programmes. Lastly, to mitigate these pitfalls, we discuss priorities for guiding future research and hope that these recommendations will help make this technology more accessible to environmental scientists and conservation practitioners.