In:
Landscape Ecology, Springer Science and Business Media LLC, Vol. 38, No. 9 ( 2023-09), p. 2307-2321
Abstract:
Naturally recovering secondary forests are frequently re-cleared before they can recover to pre-disturbance conditions. Identifying landscape factors associated with persistence success will help planning cost-efficient and effective forest restoration. Objectives The ability of secondary forest to persist is an often undervalued requisite for long-term ecosystem restoration. Here we identify the landscape context for naturally regenerated forests to persist through time within central Panama. Methods We developed a random forest classification (RFC) calibration method to identify areas with high (≥ 90%) and low ( 〈 90%) likelihood of forest persistence success based on their spatial relation with nine landscape explanatory variables. Results The RFC model discriminated between secondary forests areas that persisted and did not persisted with an error rate of 2%. By tuning, we obtained a precision of 0.94 (94%) in the validation test. The two most important explanatory variables involved in the persistence dynamic were elevation and distance to the nearest rural area. Naturally regenerated forests lasted longer in patches that were closer to both Gatun and Alajuela Lakes as to protected areas, but further from rural communities, roads, urban areas and in patches with higher elevation and steeper slopes. Conclusion By tracking remote sensed, landscape context metrics of easy collection, we developed a prediction map of central Panama areas with high (≥ 90%) and low ( 〉 90%) probability of natural forest regeneration and persistence success within the next 30 years. This map represents a basis for management decisions and future investigations for effective, long-term forest-landscape restoration.
Type of Medium:
Online Resource
ISSN:
0921-2973
,
1572-9761
DOI:
10.1007/s10980-023-01694-y
Language:
English
Publisher:
Springer Science and Business Media LLC
Publication Date:
2023
detail.hit.zdb_id:
2016200-5
SSG:
12
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