A New Method for Characterizing Bark Microrelief Using 3D Vision Systems

Bark water storage capacity influences epiphytic orchid preference for host trees

PREMISE

Of all orchid species described, 70% live on phorophytes. Trees offer a vital space with characteristics that influence the successful establishment and life cycle of orchids. Field inventory and distribution analysis suggest that phorophyte selection is biased to certain tree species that would serve as better hosts. Phorophyte bark is known as an important factor that influences this preference, but the chemical and physical properties of bark that contribute to creating a favorable space for orchids are still poorly understood. In this work, the effect of bark physical characteristics on phorophyte preference of tropical orchids was studied.

METHODS

Orchids and their phorophytes were counted and identified along transects inside two natural reserves in Southeast Mexico. A rhytidome classification was used to describe the bark decoration patterns of the phorophytes. To quantify bark fissuring, we developed a new protocol based on image processing of light micrographs using free-access software. Bark topology characterization was complemented with scanning electronic microscopy. Maximum and minimum water content was also determined.

RESULTS

Analyses of bark decorations and bark fissuring were not enough to explain the preference found for some tropical trees. In contrast, a positive relationship was found among water-storage capacity, bark porosity, and phorophyte preference. The host trees preferred by most orchids have bark with higher pore density and higher water retention after draining.

CONCLUSIONS

Unexpectedly, the phorophytes preferred by orchids are not those with more fissured bark but those with a higher ability to retain minimum water content after draining, which is a bark property positively correlated with higher pore density. Our data indicate that the bark microenvironment, determined by topology and water storage capacity, has a pivotal role in phorophyte specificity, a key factor that affects orchid diversity and distribution in the world.

Nondestructive Estimation of the Above-Ground Biomass of Multiple Tree Species in Boreal Forests of China Using Terrestrial Laser Scanning

Above-ground biomass (AGB) plays a pivotal role in assessing a forest’s resource dynamics, ecological value, carbon storage, and climate change effects. The traditional methods of AGB measurement are destructive, time consuming and laborious, and an efficient, relatively accurate and non-destructive AGB measurement method will provide an effective supplement for biomass calculation. Based on the real biophysical and morphological structures of trees, this paper adopted a non-destructive method based on terrestrial laser scanning (TLS) point cloud data to estimate the AGBs of multiple common tree species in boreal forests of China, and the effects of differences in bark roughness and trunk curvature on the estimation of the diameter at breast height (DBH) from TLS data were quantitatively analyzed. We optimized the quantitative structure model (QSM) algorithm based on 100 trees of multiple tree species, and then used it to estimate the volume of trees directly from the tree model reconstructed from point cloud data, and to calculate the AGBs of trees by using specific basic wood density values. Our results showed that the total DBH and tree height from the TLS data showed a good consistency with the measured data, since the bias, root mean square error (RMSE) and determination coefficient (R2) of the total DBH were −0.8 cm, 1.2 cm and 0.97, respectively. At the same time, the bias, RMSE and determination coefficient of the tree height were −0.4 m, 1.3 m and 0.90, respectively. The differences of bark roughness and trunk curvature had a small effect on DBH estimation from point cloud data. The AGB estimates from the TLS data showed strong agreement with the reference values, with the RMSE, coefficient of variation of root mean square error (CV(RMSE)), and concordance correlation coefficient (CCC) values of 17.4 kg, 13.6% and 0.97, respectively, indicating that this non-destructive method can accurately estimate tree AGBs and effectively calibrate new allometric biomass models. We believe that the results of this study will benefit forest managers in formulating management measures and accurately calculating the economic and ecological benefits of forests, and should promote the use of non-destructive methods to measure AGB of trees in China.