Evaluation of the effect of the 2011 Tsunami on coastal forests by means of multiple isotopic analyses of tree-rings

The what, how, why, and when of dendrochemistry: (paleo)environmental information from the chemical analysis of tree rings

The chemical analysis of tree rings has attracted the interest of researchers in the past five decades in view of the possibility of exploiting this biological indicator as a widely available, high-resolution environmental archive. Information regarding the surrounding environment can be derived either by directly measuring environmental variables (nutrient availability, presence of pollutants, etc.) or by exploiting proxies (e.g. paleoclimatic and paleoenvironmental reconstructions). This review systematically covers the topic and provides a critical view on the reliability of dendrochemical information. First, we introduce the determinable chemical species, such as major elements, trace metals, isotopic ratios, and organic compounds, together with a brief description of their uptake mechanisms and functions in trees. Subsequently, we present the possibilities offered by analytical techniques in the field of tree ring analysis, focusing on direct methods and recent developments. The latter strongly improved the details of the accessible information, enabling the investigation of complex phenomena associated with plant life and encouraging the direct analysis of new analytes, particularly minor organic compounds. With regard to their applications, dendrochemical proxies have been used to trace several processes, such as environmental contamination, paleoclimate reconstruction, global environmental changes, tree physiology, extreme events, ecological trends, and dendroprovenance. Several case studies are discussed for each proposed application, with special emphasis on the reliability of tracing each process. Starting from the reviewed literature data, the second part of the paper is devoted to the critical assessment of the reliability of tree ring proxies. We provide an overview of the current knowledge, discuss the limitations of the inferences that may be drawn from the dendrochemical data, and provide recommendations for the best practices to be used for their validation. Finally, we present the future perspectives related to the advancements in analytical instrumentation and further extension of application fields.

Computer Vision and Deep Learning Techniques for the Analysis of Drone-Acquired Forest Images, a Transfer Learning Study

Unmanned Aerial Vehicles (UAV) are becoming an essential tool for evaluating the status and the changes in forest ecosystems. This is especially important in Japan due to the sheer magnitude and complexity of the forest area, made up mostly of natural mixed broadleaf deciduous forests. Additionally, Deep Learning (DL) is becoming more popular for forestry applications because it allows for the inclusion of expert human knowledge into the automatic image processing pipeline. In this paper we study and quantify issues related to the use of DL with our own UAV-acquired images in forestry applications such as: the effect of Transfer Learning (TL) and the Deep Learning architecture chosen or whether a simple patch-based framework may produce results in different practical problems. We use two different Deep Learning architectures (ResNet50 and UNet), two in-house datasets (winter and coastal forest) and focus on two separate problem formalizations (Multi-Label Patch or MLP classification and semantic segmentation). Our results show that Transfer Learning is necessary to obtain satisfactory outcome in the problem of MLP classification of deciduous vs evergreen trees in the winter orthomosaic dataset (with a 9.78% improvement from no transfer learning to transfer learning from a a general-purpose dataset). We also observe a further 2.7% improvement when Transfer Learning is performed from a dataset that is closer to our type of images. Finally, we demonstrate the applicability of the patch-based framework with the ResNet50 architecture in a different and complex example: Detection of the invasive broadleaf deciduous black locust (Robinia pseudoacacia) in an evergreen coniferous black pine (Pinus thunbergii) coastal forest typical of Japan. In this case we detect images containing the invasive species with a 75% of True Positives (TP) and 9% False Positives (FP) while the detection of native trees was 95% TP and 10% FP.

Hydraulic Constraints to Whole-Tree Water Use and Respiration in Young Cryptomeria Trees under Competition

Although extensive studies have focused on carbon and water balance from aboveground measurements, the link between the belowground and aboveground processes deserves greater attention. In this context, the aim of this work was to assess the bi-directional feedback between whole-plant respiration and transpiration. The study was performed on 25 saplings of Sugi (Japanese cedar, Cryptomeria japonica D. Don), including dominant and suppressed individuals (total fresh weight ranging between 0.2 and 8.0 kg). During one week, the integrated water use (WU) was determined using the Deuterium dilution method. After this, the trees were uprooted and the root, stem, and leaf respiration were measured using incubation chambers and CO2 infrared sensors. The stem and root respiration followed a power response to mass (power exponent b < 1), implying a decline in mass-specific respiration with size. Conversely, the leaf respiration followed a near-linear increase with size (power exponent b ≈ 1), but was negatively affected by the stem density, indicating the hydraulic limitations of the leaf metabolism. The water use followed a power response with the tree size (b < 1), showing a decline in the transpiration per leaf mass with the tree size, but was also negatively correlated with the stem density. Our results indicate that dominant trees are more efficient in the use of water, and highlight the role of hydraulic limitations to leaf metabolism in suppressed trees.