Volume estimation of Cryptomeria japonica logs in southern Brazil using artificial intelligence models

This study aimed to test taper functions and artificial intelligence (AI) models in order to estimate merchantable volumes of Japanese cedar (Cryptomeria japonica) trees in a homogenous plantation in southern Brazil. A total of 30 individuals were rigorously scaled and their total volumes were calculated, including those of the following log assortments: veneer, sawn, pulp and energy. Three AI models, i.e. two variants of k-nearest neighbours (KNN) instance-based classification (one and three nearest neighbours) and an artificial neural network (ANN) approach, were compared with three traditional taper models: fifth-order polynomial, fractional powers and the Garay model. The estimated volumes were compared with the actual volumes by means of the standard error (S_yx), bias, precision and accuracy. Total volume estimates proved to be unbiased (maximum bias 5.42%), precise (maximum precision 9.28%) and accurate (maximum accuracy 10.79%) with all of the investigated models. The tested models tended to give lower bias, better precision and accuracy in the middle portion of the stems, but worse estimates at the base and tip (maximum bias ?12.41%). In general, the KNN models improved merchantable volume estimation, particularly KNN1, which is a straightforward and simple method. We conclude that AI techniques have appeal for application in forest inventories and that KNN is a particularly interesting alternative for tree volume estimation.     

Salish Sea Chinook salmon exhibit weaker coherence in early marine survival trends than coastal populations

Identifying factors that influence anadromous Pacific salmon (Oncorhynchus spp.) population dynamics is complicated by their diverse life histories and large geographic range. Over the last several decades, Chinook salmon (O. tshawytscha) populations from coastal areas and the Salish Sea have exhibited substantial variability in abundance. In some cases, populations within the Salish Sea have experienced persistent declines that have not rebounded. We analyzed a time series of early marine survival from 36 hatchery Chinook salmon populations spanning ocean entry years 1980–2008 to quantify spatial and temporal coherence in survival. Overall, we observed higher inter‐population variability in survival for Salish Sea populations than non‐Salish Sea populations. Annual survival patterns of Salish Sea populations covaried over smaller spatial scales and exhibited less synchrony among proximate populations relative to non‐Salish Sea populations. These results were supported by multivariate autoregressive state space (MARSS) models which predominantly identified region‐scale differences in survival trends between northern coastal, southern coastal, Strait of Georgia, and Puget Sound population groupings. Furthermore, Dynamic Factor Analysis (DFA) of regional survival trends showed that survival of southern coastal populations was associated with the North Pacific Gyre Oscillation, a large‐scale ocean circulation pattern, whereas survival of Salish Sea populations was not. In summary, this study demonstrates that survival patterns in Chinook salmon are likely determined by a complex hierarchy of processes operating across a broad range in spatial and temporal scales, presenting challenges to the management of mixed‐stock fisheries.