Predicting landscape-scale biodiversity recovery by natural tropical forest regrowth
© 2021 Society for Conservation Biology.
| Veröffentlicht in: | Conservation biology : the journal of the Society for Conservation Biology. - 1999. - 36(2022), 3 vom: 15. Juni, Seite e13842 |
|---|---|
| 1. Verfasser: | |
| Weitere Verfasser: | , , , , , , , , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2022
|
| Zugriff auf das übergeordnete Werk: | Conservation biology : the journal of the Society for Conservation Biology |
| Schlagworte: | Journal Article Meta-Analysis Research Support, U.S. Gov't, Non-P.H.S. Research Support, Non-U.S. Gov't bosque aleatorio bosque secundario forest restoration meta-analysis metaanálisis modelos predictivos mehr... |
| Zusammenfassung: | © 2021 Society for Conservation Biology. 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 |
|---|---|
| Beschreibung: | Date Completed 30.05.2022 Date Revised 08.07.2022 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1523-1739 |
| DOI: | 10.1111/cobi.13842 |