Structure and inferred dynamics of a large grove of Microberlinia bisulcata trees in central African rain forest: the possible role of periods of multiple disturbance events

A 272-ha grove of dominant Microberlinia bisulcata (Caesalpinioideae) adult trees greater than or equal to 50 cm stem diameter was mapped in its entirety in the southern part of Korup National Park, Cameroon. The approach used an earlier-established 82.5-ha permanent plot with a new surrounding 50-m grid of transect lines. Tree diameters were available from the plot but trees on the grid were recorded as being greater than or equal to 50 cm. The grove consisted of 1028 trees in 2000. Other species occurred within the grove. including the associated subdominants Tetraberlinia bifoliolata and T. korupensis. Microberlinia bisulcata becomes adult at a stein diameter of c. 50 cm and at an estimated age of 50 y. Three oval-shaped subgroves with dimensions c. 8 50 in x 13 50 in (90 ha) were defined. For two of them (within the plot) tree diameters were available. Subgroves differed in their scales and intensities of spatial tree patterns, and in their size frequency distributions, these suggesting differing past dynamics. The modal scale of clumping was 40-50 m. Seed dispersal by pod ejection (to c. 50 in) was evident from the semi-circles of trees at the grove's edge and from the many internal circles (100-200 m diameter). The grove has the capacity. therefore, to increase at c. 100 m per century. To form its present extent and structure. it is inferred that it expanded and infilled from a possibly smaller area of lower adult-tree density. This possibly happened in three waves of recruitment, each one determined by a period of several intense disturbances. Climate records for Africa show that 1740-50 and 1820-30 were periods of drought, and that 1870-1895 was also regionally very dry. Canopy openings allow the light-demanding and fast-growing ectomycorrhizal M. bisulcata to establish, but successive releases are thought to be required to achieve effective recruitment. Nevertheless, in the last 50 y there were no major events and recruitment in the grove was very poor. This present study leads to a new hypothesis of the role of periods of multiple extreme events being the driving factor for the population dynamics of many large African tree species such as M. bisulcata.

Improved methods for measuring forest landscape structure: LiDAR complements field-based habitat assessment

Conservation and monitoring of forest biodiversity requires reliable information about forest structure and composition at multiple spatial scales. However, detailed data about forest habitat characteristics across large areas are often incomplete due to difficulties associated with field sampling methods. To overcome this limitation we employed a nationally available light detection and ranging (LiDAR) remote sensing dataset to develop variables describing forest landscape structure across a large environmental gradient in Switzerland. Using a model species indicative of structurally rich mountain forests (hazel grouse Bonasa bonasia), we tested the potential of such variables to predict species occurrence and evaluated the additional benefit of LiDAR data when used in combination with traditional, sample plot-based field variables. We calibrated boosted regression trees (BRT) models for both variable sets separately and in combination, and compared the models’ accuracies. While both field-based and LiDAR models performed well, combining the two data sources improved the accuracy of the species’ habitat model. The variables retained from the two datasets held different types of information: field variables mostly quantified food resources and cover in the field and shrub layer, LiDAR variables characterized heterogeneity of vegetation structure which correlated with field variables describing the understory and ground vegetation. When combined with data on forest vegetation composition from field surveys, LiDAR provides valuable complementary information for encompassing species niches more comprehensively. Thus, LiDAR bridges the gap between precise, locally restricted field-data and coarse digital land cover information by reliably identifying habitat structure and quality across large areas.