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 socio-environmental factors can lead to variable outcomes. A critical knowledge gap to inform forest restoration planning is how to predict where natural forest regrowth is likely to recover high levels of biodiversity, as an indicator of conservation value and potential for provisioning of diverse ecosystem services. In this study, we predicted and mapped landscape-scale biodiversity 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 that quantified the extent to which recovery of species richness and total abundance of vertebrates, invertebrates, and plants in second-growth forests deviated from biodiversity values in reference old-growth forests found within the same landscape. We then employed a machine learning algorithm and a comprehensive set of socio-environmental factors to spatially predict and map this landscape-scale deviation. We found that landscape-scale biodiversity recovery in second-growth forests: (i) can be spatially predicted using socio-environmental landscape factors (human demography, land use/cover, anthropogenic/natural disturbances, ecosystem productivity, and topography/soil); (ii) was higher for species richness than for total abundance for vertebrates and invertebrates but not for plants (which showed a similar recovery for both metrics); and (iii) was positively correlated for total abundance of plant and vertebrate species. Our approach can help identify tropical and subtropical forest landscapes with high potential for biodiversity recovery through natural forest regrowth

Macroecological patterns of forest structure and allometric scaling in mangrove forests

Mangrove wetlands span broad geographical gradients, resulting in functionally diverse tree communities. We asked whether latitudinal variation, allometric scaling relationships and species composition influence mangrove forest structure and biomass allocation across biogeographical regions and distinct coastal morphologies. We built the largest field‐based dataset on mangrove forest structure and biomass to date (c. 2,800 plots from 67 countries) to address macroecological questions pertaining to structural and functional diversity of mangroves spanning biogeographical and coastal morphology gradients. We used frequentist inference statistics and machine learning models to determine environmental drivers that control biomass allocation within and across mangrove communities globally. Allometric scaling relationships and forest structural complexity were consistent across biogeographical and coastal morphology gradients, suggesting that mangrove biomass is controlled by regional forcings rather than by latitude or species composition. For instance, nearly 40% of the global variation in biomass was explained by regional climate and hydroperiod, revealing nonlinear thresholds that control biomass accumulation across broad geographical gradients. Furthermore, we found that ecosystem‐level carbon stocks (average 401 ± 48 MgC/ha, covering biomass and the top 1 m of soil) varied little across diverse coastal morphologies, reflecting regional bottom‐up geomorphic controls that shape global patterns in mangrove biomass apportioning. Our findings reconcile views of wetland and terrestrial forest macroecology. Similarities in stand structural complexity and cross‐site size–density relationships across multiscale environmental gradients show that resource allocation in mangrove ecosystems is independent of tree size and invariant to species composition or latitude. Mangroves follow a universal fractal‐based scaling relationship that describes biomass allocation for several other terrestrial tree‐dominated communities. Understanding how mangroves adhere to these universal allometric rules can improve our ability to account for biomass apportioning and carbon stocks in response to broad geographical gradients