Carbon payments can cost-effectively improve logging sustainability in the Amazon

Selective logging is pervasive across the tropics and unsustainable logging depletes forest biodiversity and carbon stocks. Improving the sustainability of logging will be crucial for meeting climate targets. Carbon-based payment for ecosystem service schemes, including REDD+, give economic value to standing forests and can protect them from degradation, but only if the revenue from carbon payments is greater than the opportunity cost of forgone or reduced logging. We currently lack understanding of whether carbon payments are feasible for protecting Amazonian forests from logging, despite the Amazon holding the largest unexploited timber reserves and an expanding logging sector. Using financial data and inventories of >660,000 trees covering 52,000 ha of Brazilian forest concessions, we estimate the carbon price required to protect forests from logging. We estimate that a carbon price of $7.90 per tCO2 is sufficient to match the opportunity costs of all logging and fund protection of primary forest. Alternatively, improving the sustainability of logging operations by ensuring a greater proportion of trees are left uncut requires only slightly higher investments of$7.97–10.45 per tCO2. These prices fall well below the current compliance market rate and demonstrate a cost-effective opportunity to safeguard large tracts of the Amazon rainforest from further degradation

Sparing old-growth maximises conservation outcomes within selectively logged Amazonian rainforest

Timber extraction threatens a vast area of tropical ecosystems, making it vital to design productive harvesting operations that limit biodiversity declines. Contrasting management options span a continuum from less-intensive, land-sharing logging applied over a larger area to land-sparing operations that combine intensive harvesting with the preservation of old-growth forest. Combining company-reported extraction rates with dung beetle surveys along an Amazonian logging gradient, we explore how individual species' abundances, geometric mean population sizes, functional diversity, and trait characteristics vary across simulated logging concessions and production targets. We substantially extend previous studies by evaluating 8000 mixed-harvesting scenarios and by assessing the profitability of contrasting practices. Simply maximising old-growth protection delivers the highest species' abundances and population sizes for species negatively affected by logging. Maximising old-growth also supports communities with a functional trait dissimilarity (FDis, RaoQ) and functional structure of nesting guilds, biomass, pronotum volume, front leg area, and front:back leg ratio traits that closely resembles old-growth forest. Functional evenness (FEve), richness (FRic), and divergence (FDiv) did not vary across logging strategies. Some 3 % of mixed approaches outperform extreme sparing (which maximises old-growth retention through intensive logging) but still involve substantial sparing, enabled by intensified logging elsewhere. However more-extensive business-as-usual harvesting is up to 90 % more profitable than extreme sparing, suggesting active policy mechanisms, standards, or regulations would be needed to make spatially-concentrated logging operations (which benefit biodiversity) more commercially attractive. Old-growth sparing appears key to limiting biodiversity declines within tropical timber concessions, but would require payments to compensate for reduced profits

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Ecology: A few species dominate forest tree abundance pan-tropically.

An analysis of over 1 million old-growth tropical forest trees reveals that ∼2.2% of species comprise 50% of the individuals in Africa, Amazonia, and Southeast Asia, suggesting that the ecological mechanisms underpinning tree community assembly are ubiquitous across the tropics

Global hotspots of traded phylogenetic and functional diversity.

Wildlife trade is a multibillion-dollar industry1 targeting a hyperdiversity of species2 and can contribute to major declines in abundance3. A key question is understanding the global hotspots of wildlife trade for phylogenetic (PD) and functional (FD) diversity, which underpin the conservation of evolutionary history4, ecological functions5 and ecosystem services benefiting humankind6. Using a global dataset of traded bird and mammal species, we identify that the highest levels of traded PD and FD are from tropical regions, where high numbers of evolutionary distinct and globally endangered species in trade occur. The standardized effect size (ses) of traded PD and FD also shows strong tropical epicentres, with additional hotspots of mammalian ses.PD in the eastern United States and ses.FD in Europe. Large-bodied, frugivorous and canopy-dwelling birds and large-bodied mammals are more likely to be traded whereas insectivorous birds and diurnally foraging mammals are less likely. Where trade drives localized extinctions3, our results suggest substantial losses of unique evolutionary lineages and functional traits, with possible cascading effects for communities and ecosystems5,7. Avoiding unsustainable exploitation and lost community integrity requires targeted conservation efforts, especially in hotspots of traded phylogenetic and functional diversity