Predicting landscape-scale biodiversity recovery by natural tropical forest regrowth

Natural forest regrowth is a cost-effective, nature-based solution for biodiversity recovery, yet different socioenvironmental factors can lead to variable outcomes. A critical knowledge gap in forest restoration planning is how to predict where natural forest regrowth is likely to lead to high levels of biodiversity recovery, which is an indicator of conservation value and the potential provisioning of diverse ecosystem services. We sought to predict and map landscape-scale recovery of species richness and total abundance of vertebrates, invertebrates, and plants in tropical and subtropical second-growth forests to inform spatial restoration planning. First, we conducted a global meta-analysis to quantify the extent to which recovery of species richness and total abundance in second-growth forests deviated from biodiversity values in reference old-growth forests in the same landscape. Second, we employed a machine-learning algorithm and a comprehensive set of socioenvironmental factors to spatially predict landscape-scale deviation and map it. Models explained on average 34% of observed variance in recovery (range 9–51%). Landscape-scale biodiversity recovery in second-growth forests was spatially predicted based on socioenvironmental landscape factors (human demography, land use and cover, anthropogenic and natural disturbance, ecosystem productivity, and topography and soil chemistry); was significantly higher for species richness than for total abundance for vertebrates (median range-adjusted predicted deviation 0.09 vs. 0.34) and invertebrates (0.2 vs. 0.35) but not for plants (which showed a similar recovery for both metrics [0.24 vs. 0.25]); and was positively correlated for total abundance of plant and vertebrate species (Pearson r = 0.45, p = 0.001). Our approach can help identify tropical and subtropical forest landscapes with high potential for biodiversity recovery through natural forest regrowth.     

Conceptual Stormwater Quality Models by Alternative Linear and Non-linear Formulations: an Event-Based Approach

Environmental Modeling & Assessment - Different linear transfer functions (TFs) models are compared to the traditional non-linear rating curve (RC) model in a novel event-based exploratory...     

Global rarity of intact coastal regions

Management of the land–sea interface is essential for global conservation and sustainability objectives because coastal regions maintain natural processes that support biodiversity and the livelihood of billions of people. However, assessments of coastal regions have focused strictly on either the terrestrial or marine realm. Consequently, understanding of the overall state of Earth's coastal regions is poor. We integrated the terrestrial human footprint and marine cumulative human impact maps in a global assessment of the anthropogenic pressures affecting coastal regions. Of coastal regions globally, 15.5% had low anthropogenic pressure, mostly in Canada, Russia, and Greenland. Conversely, 47.9% of coastal regions were heavily affected by humanity, and in most countries (84.1%) >50% of their coastal regions were degraded. Nearly half (43.3%) of protected areas across coastal regions were exposed to high human pressures. To meet global sustainability objectives, all nations must undertake greater actions to preserve and restore the coastal regions within their borders.