Predicting landscape-scale biodiversity recovery by natural tropical forest regrowth.

Autor: Prieto PV; Rio Conservation and Sustainability Science Centre, Department of Geography and the Environment, Pontifícia Universidade Católica, Rio de Janeiro, Brazil., Bukoski JJ; The Betty and Gordon Moore Center for Science, Conservation International, Arlington, Virginia, USA.; Department of Environmental Science, Policy, and Management, University of California, Berkeley, California, USA., Barros FSM; International Institute for Sustainability Australia, Canberra, Australian Capital Territory, Australia.; Centro de Referencia en Tecnologías de la Información para la Gestión con Software Libre (CeRTIG+SoL), Universidad Nacional de Misiones (UNaM), Misiones, Argentina.; Departamento de Geografía, Instituto Superior Antonio Ruiz de Montoya, Misiones, Argentina.; Instituto Misionero de Biodiversidad, Posadas, Misiones, Argentina., Beyer HL; International Institute for Sustainability Australia, Canberra, Australian Capital Territory, Australia.; Global Change Institute, University of Queensland, Brisbane, Queensland, Australia., Iribarrem A; Rio Conservation and Sustainability Science Centre, Department of Geography and the Environment, Pontifícia Universidade Católica, Rio de Janeiro, Brazil.; International Institute for Sustainability, Rio de Janeiro, Brazil., Brancalion PHS; Department of Forest Sciences, 'Luiz de Queiroz' College of Agriculture, University of São Paulo, Piracicaba, Brazil., Chazdon RL; International Institute for Sustainability Australia, Canberra, Australian Capital Territory, Australia.; Department of Ecology and Evolutionary Biology, University of Connecticut, Storrs, Connecticut, USA.; Tropical Forests and People Research Centre, University of the Sunshine Coast, Sunshine Coast, Queensland, Australia., Lindenmayer DB; Sustainable Farms, Fenner School of Environment and Society, The Australian National University, Canberra, Australian Capital Territory, Australia., Strassburg BBN; Rio Conservation and Sustainability Science Centre, Department of Geography and the Environment, Pontifícia Universidade Católica, Rio de Janeiro, Brazil.; International Institute for Sustainability, Rio de Janeiro, Brazil.; Programa de Pós Graduação em Ecologia, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil., Guariguata MR; Center for International Forestry Research, Lima, Perú., Crouzeilles R; Rio Conservation and Sustainability Science Centre, Department of Geography and the Environment, Pontifícia Universidade Católica, Rio de Janeiro, Brazil.; International Institute for Sustainability Australia, Canberra, Australian Capital Territory, Australia.; International Institute for Sustainability, Rio de Janeiro, Brazil.; Programa de Pós Graduação em Ecologia, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.; Mestrado Profissional em Ciências do Meio Ambiente, Universidade Veiga de Almeida, Rio de Janeiro, Brazil.
Jazyk: angličtina
Zdroj: Conservation biology : the journal of the Society for Conservation Biology [Conserv Biol] 2022 Jun; Vol. 36 (3), pp. e13842. Date of Electronic Publication: 2022 Apr 20.
DOI: 10.1111/cobi.13842
Abstrakt: 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.
(© 2021 Society for Conservation Biology.)
Databáze: MEDLINE
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