Context 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.
Context Secondary forests are frequently re-cleared before they can recover to pre-disturbance conditions. The identification of factors associated with passive regeneration persistence success would help planning cost-efficient forest restoration. Objectives In this paper we investigated the role that the landscape context of naturally regenerated forest patches plays for their chances to mature and persist in time in central Panama. Maturation and persistence of secondary forests are concepts often undervalued representing, however, essential requisites for an effective and long-term restoration of the ecosystem processes. Methods A unique data set of land-cover maps of central Panama was used to identify the forest patches that naturally recovered and persisted between 1990 and 2020. We developed a Random Forest Classification (RFC) calibration method to identify areas with higher likelihood of forest persistence success. Results The RFC model discriminated between areas that naturally recovered and persisted in time and areas that did not persisted with an error rate of 2%. By tuning, we obtained a precision of 0.94 (94%) in the validation test. Based on the model, we developed a prediction map of central Panama areas with higher probability (≥ 90%) of secondary forests persistence success within the next 20 years. Conclusions Tracking simple landscape and socio-economic metrics allowed for a deeper understanding of the underlying mechanisms of secondary forest persistence in central Panama. Through the development of RFC calibration method, this study maximized the reliability of the patches identified as suitable to persistence success, representing a basis for management decisions and future investigations for a successful, long-term forest-landscape restoration.
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