Degraded tropical peatlands lack tree cover and are often subject to seasonal flooding and repeated burning. These harsh environments for tree seedlings to survive and grow are therefore challenging to revegetate. Knowledge on species performance from previous plantings represents an important evidence base to help guide future tropical peat swamp forest (TPSF) restoration efforts. We conducted a systematic review of the survival and growth of tree species planted in degraded peatlands across Southeast Asia to examine (1) species differences, (2) the impact of seedling and site treatments on survival and growth and (3) the potential use of plant functional traits to predict seedling survival and growth rates. Planted seedling monitoring data were compiled through a systematic review of journal articles, conference proceedings, reports, theses and unpublished datasets. In total, 94 study‐sites were included, spanning three decades from 1988 to 2019, and including 141 indigenous peatland tree and palm species. Accounting for variable planting numbers and monitoring durations, we analysed three measures of survival and growth: (1) final survival weighted by the number of seedlings planted, (2) half‐life, that is, duration until 50% mortality and (3) relative growth rates (RGR) corrected for initial planting height of seedlings. Average final survival was 62% and half‐life was 33 months across all species, sites and treatments. Species differed significantly in survival and half‐life. Seedling and site treatments had small effects with the strongest being higher survival of mycorrhizal fungi inoculated seedlings; lower survival, half‐life and RGR when shading seedlings; and lower RGR and higher survival when fertilising seedlings. Leaf nutrient and wood density traits predicted TPSF species survival, but not half‐life and RGR. RGR and half‐life were negatively correlated, meaning that slower growing species survived for longer. Synthesis and applications. To advance tropical peat swamp reforestation requires expanding the number and replication of species planted and testing treatments by adopting control vs. treatment experimental designs. Species selection should involve slower growing species (e.g. Lophopetalum rigidum, Alstonia spatulata, Madhuca motleyana) that survive for longer and explore screening species based on functional traits associated with nutrient acquisition, flooding tolerance and recovery from fire.
Agent-based models have been developed and widely employed to assess the impact of disturbances or conservation management on animal habitat use, population development, and viability. However, the direct impacts of canopy disturbance on the arboreal movement of individual primates have been less studied. Such impacts could shed light on the cascading effects of disturbances on animal health and fitness. Orangutans are an arboreal primate that commonly encounters habitat quality deterioration due to land-use changes and related disturbances such as forest fires. Forest disturbance may, therefore, create a complex stress scenario threatening orangutan populations. Due to forest disturbances, orangutans may adapt to employ more terrestrial, as opposed to arboreal, movements potentially prolonging the search for fruiting and nesting trees. In turn, this may lead to changes in daily activity patterns (i.e., time spent traveling, feeding, and resting) and available energy budget, potentially decreasing the orangutan's fitness. We developed the agent-based simulation model BORNEO (arBOReal aNimal movEment mOdel), which explicitly describes both orangutans' arboreal and terrestrial movement in a forest habitat, depending on distances between trees and canopy structures. Orangutans in the model perform activities with a motivation to balance energy intake and expenditure through locomotion. We tested the model using forest inventory data obtained in Sebangau National Park, Central Kalimantan, Indonesia. This allowed us to construct virtual forests with real characteristics including tree connectivity, thus creating the potential to expand the environmental settings for simulation experiments. In order to parameterize the energy related processes of the orangutans described in the model, we applied a computationally intensive evolutionary algorithm and evaluated the simulation results against observed behavioral patterns of orangutans. Both the simulated variability and proportion of activity budgets including feeding, resting, and traveling time for female and male orangutans confirmed the suitability of the model for its purpose. We used the calibrated model to compare the activity patterns and energy budgets of orangutans in both natural and disturbed forests . The results confirm field observations that orangutans in the disturbed forest are more likely to experience deficit energy balance due to traveling to the detriment of feeding time. Such imbalance is more pronounced in males than in females. The finding of a threshold of forest disturbances that affects a significant change in activity and energy budgets suggests potential threats to the orangutan population. Our study introduces the first agent-based model describing the arboreal movement of primates that can serve as a tool to investigate the direct impact of forest changes and disturbances on the behavior of species such as orangutans. Moreover, it demonstrates the suitability of high-performance computing to optimize the calibration of complex agent-based models describing animal behavior at a fine spatio-temporal scale (1-m and 1-s granularity).
<p>Destabilisation of hydrological conditions and associated fire occurrence are the most significant barriers hindering degraded tropical peatland revegetation. For this reason, the monitoring of fires and hydrological conditions is crucial for guiding drained tropical peatland restoration. One of the best tools for large-scale monitoring of the natural environment, especially when access and <em>in situ</em> information are limited, is satellite remote sensing, and fusion of active and passive remote sensing data can provide new insights into dynamic systems such as peatlands. There is usually a relationship between automation, complexity and processing time leading to variations in the method's effectiveness, including reliability and accuracy. The main goal of this work was to develop a rapid method for ease of use by non-specialist users, which has capability to deliver reliable results describing the mapping of the burnt and flooded areas. In this case, two types of data, from multi-spectral passive and microwave active remote sensing sensors, were combined to monitor fires and floods in a 5,000 km&#178; area of tropical peatland of varying land use and level of degradation in Central Kalimantan. Both imaging techniques provide different information. The vegetation index of the differenced Normalised Burn Ratio (dNBR), calculated based on Sentinel-2 and Landsat-8 data, delivers information for mapping burned areas. The backscattering coefficient from Sentinel-1 data can identify permanent and ephemeral water bodies. These methods were effective for detection of burnt areas and water bodies, but there were limitations of the passive sensors' image availability due to cloud cover. In addition, using dNBR and backscattering coefficient separately in some cases caused false positive results (e.g. burnt areas classified as water bodies, or burnt areas detected in the main river bed). The fusion of two data sources increased fire and flood mapping accuracy by eliminating misclassification errors, compared to using them separately, thus indicating their strong complementarity. This combined method allowed analysis of the history of fires and flooding in 2015-2022, and the relationship between these; preliminary results to be presented.</p>
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