Accounting for albedo change to identify climate-positive tree cover restoration

Restoring tree cover changes albedo, which is the fraction of sunlight reflected from the Earth’s surface. In most locations, these changes in albedo offset or even negate the carbon removal benefits with the latter leading to global warming. Previous efforts to quantify the global climate mitigation benefit of restoring tree cover have not accounted robustly for albedo given a lack of spatially explicit data. Here we produce maps that show that carbon-only estimates may be up to 81% too high. While dryland and boreal settings have especially severe albedo offsets, it is possible to find places that provide net-positive climate mitigation benefits in all biomes. We further find that on-the-ground projects are concentrated in these more climate-positive locations, but that the majority still face at least a 20% albedo offset. Thus, strategically deploying restoration of tree cover for maximum climate benefit requires accounting for albedo change and we provide the tools to do so

Priority science can accelerate agroforestry as a natural climate solution

The expansion of agroforestry could provide substantial climate change mitigation (up to 0.31 Pg C yr−1), comparable to other prominent natural climate solutions such as reforestation. Yet, climate-focused agroforestry efforts grapple with ambiguity about which agroforestry actions provide mitigation, uncertainty about the magnitude of that mitigation and inability to reliably track progress. In this Perspective, we define agroforestry as a natural climate solution, discuss current understanding of the controls on farm-scale mitigation potential and highlight recent innovation on emergent, high-resolution remote sensing methods to enable detection, measurement and monitoring. We also assess the status of agroforestry in the context of global climate ambitions, highlighting regions of underappreciated expansion opportunity and identifying priorities for policy and praxis

Geonomics: Forward-Time, Spatially Explicit, and Arbitrarily Complex Landscape Genomic Simulations.

Understanding the drivers of spatial patterns of genomic diversity has emerged as a major goal of evolutionary genetics. The flexibility of forward-time simulation makes it especially valuable for these efforts, allowing for the simulation of arbitrarily complex scenarios in a way that mimics how real populations evolve. Here, we present Geonomics, a Python package for performing complex, spatially explicit, landscape genomic simulations with full spatial pedigrees that dramatically reduces user workload yet remains customizable and extensible because it is embedded within a popular, general-purpose language. We show that Geonomics results are consistent with expectations for a variety of validation tests based on classic models in population genetics and then demonstrate its utility and flexibility with a trio of more complex simulation scenarios that feature polygenic selection, selection on multiple traits, simulation on complex landscapes, and nonstationary environmental change. We then discuss runtime, which is primarily sensitive to landscape raster size, memory usage, which is primarily sensitive to maximum population size and recombination rate, and other caveats related to the model's methods for approximating recombination and movement. Taken together, our tests and demonstrations show that Geonomics provides an efficient and robust platform for population genomic simulations that capture complex spatial and evolutionary dynamics