To analyze an animal’s movement trajectory, a basic model is required that satisfies the following conditions: the model must have an ecological basis and the parameters used in the model must have ecological interpretations, a broad range of movement patterns can be explained by that model, and equations and probability distributions in the model should be mathematically tractable. Random walk models used in previous studies do not necessarily satisfy these requirements, partly because movement trajectories are often more oriented or tortuous than expected from the models. By improving the modeling for turning angles, this study aims to propose a basic movement model. On the basis of the recently developed circular auto-regressive model, we introduced a new movement model and extended its applicability to capture the asymmetric effects of external factors such as wind. The model was applied to GPS trajectories of a seabird (Calonectris leucomelas) to demonstrate its applicability to various movement patterns and to explain how the model parameters are ecologically interpreted under a general conceptual framework for movement ecology. Although it is based on a simple extension of a generalized linear model to circular variables, the proposed model enables us to evaluate the effects of external factors on movement separately from the animal’s internal state. For example, maximum likelihood estimates and model selection suggested that in one homing flight section, the seabird intended to fly toward the island, but misjudged its navigation and was driven off-course by strong winds, while in the subsequent flight section, the seabird reset the focal direction, navigated the flight under strong wind conditions, and succeeded in approaching the island.
Matrix models have been widely used to investigate the population dynamics of plant species. To make use of this method, we first divide individuals into groups and estimate transition probabilities per pair of groups. When a continuous variable, such as plant size, is used for grouping, there is often a trade-off: if the class intervals are narrow each group will only include a small number of samples, but if the intervals are wider, this may obscure some changes. This paper introduces a new matrix model in which we no longer have to divide individuals into arbitrarily defined size classes. The methodology is based on the Bayesian non-parametric binary regression. We first divide the data into 'very fine' intervals. For estimating transition probabilities in a 'large' matrix, we do not use the observed transition rate per class directly, but we smooth neighboring observed rates and select the most appropriate degree of smoothing using an information criterion called the Akaike Bayesian Information Criterion (ABIC). Our approach is illustrated using longterm forest monitoring data from an old-growth, warm-temperate evergreen forest, in which we examined the population dynamics of four evergreen subcanopy tree species. Transition probabilities allowed us to represent d.b.h.-related growth and mortality patterns graphically, and matrix analysis provided stable size distributions, reproductive values and elasticity that vary smoothly for trees of different sizes. The quantitative approach makes it possible to determine characteristic patterns of population dynamics for qualitatively similar species.
Question: Abrupt increments in tree radial growth chronology are associated with gap formations derived from disturbances. If a forest has been primarily controlled by fine‐scale disturbances such as single tree‐fall, do these release events spatio‐temporally synchronize at a fine scale such as 10 m and 5 years? Is it possible to quantify spatio‐temporal patterns of synchronicity from tree rings and long‐term inventories, and associate them with spatial forest patch dynamics? How and to what extent can we reconstruct the fine‐scale synchronized growth and spatio‐temporal forest patch dynamics from currently available information?
Location: Cores were taken from Abies sachalinensis trees in a coniferous/deciduous mixed forest in the Shiretoko Peninsula, Hokkaido, northern Japan.
Methods: We first eliminated short‐term fluctuations and highlighted growth trends over the mid‐term using a time‐series smoothing technique. This helped identify release events, we then conducted fine‐scale spatial analyses on released A. sachalinensis primarily with cluster analysis.
Results: We specified the unit scale of synchronicity at 10 m, and classified released A. sachalinensis trees into spatially separated regions. Only once during the recent 50 years was extensive synchronicity over 40 m found. Most of the released A. sachalinensis were isolated, with non‐released A. sachalinensis present in nearby, implying imperfect synchronization. The ambiguous 20–30 m A. sachalinensis patches present in the current forest were the result of connected and overlapping patches smaller than 10 m associated with different disturbances and different responses of understorey trees.
Conclusion: Tree‐ring series, long‐term census and fine‐scale spatio‐temporal analyses revealed that this forest community has been controlled by two types of disturbance: frequent small disturbances such as single tree‐fall and less frequent multiple tree‐falls.
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