The conversion of forest to agriculture continues to contribute to the loss and fragmentation of remaining orang‐utan habitat. There are still few published estimates of orang‐utan densities in these heavily modified agricultural areas to inform range‐wide population assessments and conservation strategies. In addition, little is known about what landscape features promote orang‐utan habitat use. Using indirect nest count methods, we implemented surveys and estimated population densities of the Northeast Bornean orang‐utan (Pongo pygmaeus morio) across the continuous logged forest and forest remnants in a recently salvage‐logged area and oil palm plantations in Sabah, Malaysian Borneo. We then assessed the influence of landscape features and forest structural metrics obtained from LiDAR data on estimates of orang‐utan density. Recent salvage logging appeared to have a little short‐term effect on orang‐utan density (2.35 ind/km 2), which remained similar to recovering logged forest nearby (2.32 ind/km 2). Orang‐utans were also present in remnant forest patches in oil palm plantations, but at significantly lower numbers (0.82 ind/km 2) than nearby logged forest and salvage‐logged areas. Densities were strongly influenced by variation in canopy height but were not associated with other potential covariates. Our findings suggest that orang‐utans currently exist, at least in the short‐term, within human‐modified landscapes, providing that remnant forest patches remain. We urge greater recognition of the role that these degraded habitats can have in supporting orang‐utan populations, and that future range‐wide analyses and conservation strategies better incorporate data from human‐modified landscapes.

Logging, pervasive across the lowland tropics, affects millions of hectares of forest, yet its influence on nutrient cycling remains poorly understood. One hypothesis is that logging influences phosphorus (P) cycling, because this scarce nutrient is removed in extracted timber and eroded soil, leading to shifts in ecosystem functioning and community composition. However, testing this is challenging because P varies within landscapes as a function of geology, topography and climate. Superimposed upon these trends are compositional changes in logged forests, with species with more acquisitive traits, characterized by higher foliar P concentrations, more dominant. It is difficult to resolve these patterns using traditional field approaches alone. Here, we use airborne light detection and ranging‐guided hyperspectral imagery to map foliar nutrient (i.e. P, nitrogen [N]) concentrations, calibrated using field measured traits, over 400 km2 of northeastern Borneo, including a landscape‐level disturbance gradient spanning old‐growth to repeatedly logged forests. The maps reveal that canopy foliar P and N concentrations decrease with elevation. These relationships were not identified using traditional field measurements of leaf and soil nutrients. After controlling for topography, canopy foliar nutrient concentrations were lower in logged forest than in old‐growth areas, reflecting decreased nutrient availability. However, foliar nutrient concentrations and specific leaf area were greatest in relatively short patches in logged areas, reflecting a shift in composition to pioneer species with acquisitive traits. N:P ratio increased in logged forest, suggesting reduced soil P availability through disturbance. Through the first landscape scale assessment of how functional leaf traits change in response to logging, we find that differences from old‐growth forest become more pronounced as logged forests increase in stature over time, suggesting exacerbated phosphorus limitation as forests recover.

A new framework linking economics with remote-sensing means impermanent carbon reduction can be directly compared with permanent drawdown.

Efforts to avert dangerous climate change by conserving and restoring natural habitats are hampered by widespread concerns over the credibility of methods used to quantify their net long-term benefits. We develop a novel, flexible framework for estimating the long-run social benefit of impermanent carbon credits generated by nature-based interventions which integrates three substantial advances: the conceptualisation of the permanence of a project’s impact as its additionality over time (relative to a statistically-derived counterfactual); the risk-averse estimation of the social cost of future reversals of carbon gains; and the deployment of post-credit monitoring to correct for errors in deliberately pessimistic release forecasts. Our framework generates incentives for safeguarding already-credited carbon while enabling would-be investors to make like-for-like comparisons of diverse carbon projects. Preliminary comparisons suggest that after fully adjusting for the impermanence of their effects, nature-based interventions may offer less costly ways of reducing climate damages than more technological solutions.

We make a case for planetary computing: accessible, interoperable and extensible end-to-end systems infrastructure to process petabytes of global remote-sensing data for the scientific analysis of environmental action. We discuss some pressing scientific scenarios, survey existing solutions and find them incomplete, and present directions for systems research to help reverse the climate and biodiversity crises.

Efforts to avert dangerous climate change by conserving and restoring natural habitats are hampered by widespread concerns over the credibility of methods used to quantify their net long-term benefits. We develop a novel, flexible framework for estimating the long-run social benefit of impermanent carbon credits generated by nature-based interventions which integrates three substantial advances: the conceptualisation of the permanence of a project’s impact as its additionality over time (relative to a statistically-derived counterfactual); the risk-averse estimation of the social cost of future reversals of carbon gains; and the deployment of post-credit monitoring to correct for errors in deliberately pessimistic release forecasts. Our framework generates incentives for safeguarding already-credited carbon while enabling would-be investors to make like-for-like comparisons of diverse carbon projects. Preliminary comparisons suggest that after fully adjusting for the impermanence of their effects, nature-based interventions may offer less costly ways of reducing climate damages than more technological solutions.

Efforts to avert dangerous climate change by conserving and restoring natural habitats are hampered by widespread concerns over the credibility of methods used to quantify their net long-term benefits. We develop a novel, flexible framework for estimating the long-run social benefit of impermanent carbon credits generated by nature-based interventions which integrates three substantial advances: the conceptualisation of the permanence of a project’s impact as its additionality over time (relative to a statistically-derived counterfactual); the risk-averse estimation of the social cost of future reversals of carbon gains; and the deployment of post-credit monitoring to correct for errors in deliberately pessimistic release forecasts. Our framework generates incentives for safeguarding already-credited carbon while enabling would-be investors to make like-for-like comparisons of diverse carbon projects. Preliminary comparisons suggest that after fully adjusting for the impermanence of their effects, nature-based interventions may offer less costly ways of reducing climate damages than more technological solutions.

Efforts to avert dangerous climate change by conserving and restoring natural habitats are hampered by widespread concerns over the credibility of methods used to quantify their net long-term benefits. We develop a novel, flexible framework for estimating the long-run social benefit of impermanent carbon credits generated by nature-based interventions which integrates three substantial advances: the conceptualisation of the permanence of a project’s impact as its additionality over time (relative to a statistically-derived counterfactual); the risk-averse estimation of the social cost of future reversals of carbon gains; and the deployment of post-credit monitoring to correct for errors in deliberately pessimistic release forecasts. Our framework generates incentives for safeguarding already-credited carbon while enabling would-be investors to make like-for-like comparisons of diverse carbon projects. Preliminary comparisons suggest that after fully adjusting for the impermanence of their effects, nature-based interventions may offer less costly ways of reducing climate damages than more technological solutions.