The results reported here indicate the usefulness of codominant markers for future studies of the population genetics of P. nakaii. In addition, the markers are useful for further phytogeographic and speciation studies in P. fasciculus, P. macrophyllus var. macrophyllus, and P. macrophyllus var. maki, which are closely related species.

Aims Climate shapes forest types on our planet and also drives the differentiation of zonal vegetation at regional scale. A climate‐based ecological model may provide an effective alternative to the traditional approach for assessing limitations, thresholds, and the potential distribution of forests. The main objective of this study is to develop such a model, with a machine‐learning approach based on scale‐free climate variable estimates and classified vegetation plots, to generate a fine‐scale predicted vegetation map of Taiwan, a subtropical mountainous island. Location Taiwan. Methods A total of 3,824 plots from 13 climate‐related forest types and 57 climatic variable estimates for each plot were used to build an individual ecological niche model for each forest type with random forest (RF). A predicted vegetation map was developed through the assemblage of RF predictions for each forest type at the spatial resolution of 100 m. The accuracy of the ensemble RF model was evaluated by comparing the predicted forest type with its original classification by plot. Results The climate environment of regions higher than 100 m above sea level in Taiwan was classified into potential habitats of 13 forest types by using model predictions. The predicted vegetation map displays a distinct altitudinal zonation from subalpine to montane cloud forests, followed by the latitudinal differentiation of subtropical mountain forests in the north and tropical montane forests in the south, with an average mismatch rate of 6.59%. An elevational profile and 3D visualization demonstrate the excellence of the model in estimating a fine, precise, and topographically corresponding potential distribution of forests. Conclusions The machine‐learning approach is effective for handling a large number of variables and to provide accurate predictions. This study provides a statistical procedure integrating two sources of training data: (a) the locations of field sampling plots; and (b) their corresponding climate variable estimates, to predict the potential distribution of climate‐related forests.

Objectives: Allergic rhinitis (AR) is associated with increased risk of major depression in the general population, however, no previous study has evaluated its role among pregnant women. We aimed to investigate the potential impact of AR during pregnancy on the development of postpartum depression (PPD). Methods: This is a population-based case-control study. Data were retrieved from the National Health Insurance Research Database (NHIRD). Medical records of a total of 199 470 deliveries during 2000 and 2010 were identified. Among which, 1416 women with PPD within 12 months after delivery were classified as the case group, while 198 054 women without PPD after delivery formed the control group. Univariate and multivariate regression analyses were conducted to determine the associations between AR during pregnancies and other study variables with PPD. Results: AR during pregnancy was found in 9.53% women who developed PPD and 5.44% in women without PPD. After adjusting for age at delivery, income level, various pregnancy and delivery-related conditions, asthma, atopic dermatitis and other medical comorbidities in the multivariate analysis, AR was significantly associated with increased odds of PPD (aOR: 1.498, 95% CI: 1.222-1.836). Conclusion: AR during pregnancy was independently and significantly associated with an approximately 50% increased risk of PPD among women giving birth. Closely monitoring of AR is warranted in the future in order to optimize mother and child outcomes after delivery.

Dioecy is a rather rare sexual expression system guarantees outcrossing to avoid the deleterious effects of inbreeding. The incidence of dioecy varied among local floras and suggested inclining to tropical and oceanic environments, but its ecocorrelates received little research attention. In this article, we explored geographical patterns and variations in sexual expression systems of angiosperms in mountainous environments of Taiwan, a subtropical island in East Asia. A comprehensive geo-database of vegetation inventories and herbarium specimens were used to identify eco-correlates causing variations in the horizontal geographical extent and along a large elevational gradient of more than 3,500 m. We found the average incidence of dioecy in the flora of Taiwan to be 8.2%, but it exhibits geographical variations from islets in the Taiwan Strait to the Pacific Ocean. Detailed studies on the main island of Taiwan revealed that the incidence of dioecy varied among land cover types and elevational zones. An apparent two-step decreasing pattern of dioecy percentages with elevation was found, with the highest proportion in the lowlands (0-600 m; 23.96%), followed by middle elevations (600-2,700 m; 20.87%) and subalpine regions (2,700-3,900 m; with a range of 11.38-0%). We found that spatial variations of dioecy were associated with eco-correlates of land cover, elevation, woodiness, species richness, and mean annual temperature. Results of this study partially support Bawa's hypothesis of a higher incidence of dioecy on oceanic islands, and is consistent with Baker and Cox's observations of richer dioecious species on high-mountain islands in the tropics and subtropics. K E Y W O R D Sdioecy, elevational gradient, sexual expression system, subtropics, Taiwan

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