More studies are needed on the mechanism and effective prediction of bird diversity in various habitats. The primary purpose of this study was to explore the difference in the species richness and evenness of various habitats. The secondary purpose was to explore which habitat types and compositions predict a high bird diversity. The 2010-2016 Taiwan Breeding Bird Survey was used to analyze the relationship between landscape habitat and bird ecology. Landscape habitat type was divided into seven categories and 26 sub-types: forestland, farmland, grassland, freshwater wetland, aquaculture pond and saltpan, coastland, and building area. Four ecological indexes were used: the number of bird individuals, the number of species, the Margalef Richness Index, and the Pielou Evenness Index. The result indicated that forestland decreased bird numbers, except in a windbreak forest. Natural and farmland-related habitats increased bird species richness. Similarly, the natural habitat increased species evenness. Urban greenspace could not replace the effect of forestland on species richness and evenness. Conifer forest, bamboo forest, windbreak forest, mixed tree, tall grassland, and orchard were important habitats for promoting higher species richness and evenness.The relationship between various environments and bird diversity has been a critical issue. A large number of studies have explored the variety of bird diversity in urban and rural areas 1-8 , farmland 9-12 , and forestland [13][14][15][16] . Some studies indicated that a higher ecological diversity not only benefits species survival but is also an important indicator of human well-being 17,18 . The promotion of bird diversity is a useful method for generating human psychological benefits 19 . Therefore, the mechanism and effective prediction of bird diversity in various habitats should be understood.The mechanisms relating to various habitats and bird diversity with human activity are still not clear. Species richness and species evenness are two common concepts to measure species diversity 20 . The number of breeding bird species increases from urban to suburban, rural, and natural areas 2,4,12,21 . A few studies indicated that urbanization did not reduce bird species richness (i.e., the number of bird species) due to an abundant food supply, but rather increased the number of birds in a few dominant bird species 22 . One of the main characteristics of urban areas is the numerical dominance of a few abundant bird species 21 , which means a lower species evenness. This observation corresponds to the primary purpose of the study: species evenness may demonstrate a dissimilarity in bird diversity between natural and urban environments. Few studies have separately explored bird species richness and evenness to answer this question in various bird habitats.Some habitat types and characteristics have been studied and have shown positive results for higher bird diversity. The presence of forest is a positive environmental characteristic for bird diversity in various envi...

Altitudinal migration is a common and important but understudied behavior in birds. Difficulty in characterizing avian altitudinal migration has prevented a comprehensive understanding of this behavior. To address this, we investigated the altitudinal migration patterns and explored potential drivers for a major proportion (~70%) of the entire resident bird community along an almost 4000 m elevational gradient on the main island of Taiwan. Based on the occurrence records collected by citizen scientists, we examined the seasonal shifts in the center and the upper and lower boundaries of elevational distributions for 104 individual species. We then built phylogeny‐controlled regression models to investigate the associations between the birds’ seasonal distribution shifts and seven of their traits, and examined whether the observed shifts can be explained by three main hypotheses on potential drivers. Results showed that at least 60 species (58%) seasonally changed their distributions along elevations. While most of them (42 species) tended to move downhill in winter, a considerable number of species (14) tended to move uphill. While the species breeding at high or low elevations tended to move downhill in winter, those breeding at medium‐low elevations tended to move or extend their distributions to higher elevations. Our regression models suggested that seasonal variations in climates and food availability could be major drivers of the behavior. However, the three hypotheses can only partially explain the observed downhill migration patterns and none of them can well explain the uphill patterns, indicating an important knowledge gap. This study investigated avian altitudinal migration from a new perspective with a novel and generalizable approach, and revealed interesting patterns that could be difficult to identify with conventional approaches. It demonstrated the power of citizen science data to provide new insights into this behavior by characterizing the general patterns and mechanisms across a large number of species.

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Species traits affect how a species interacts with the environment and other species and thus determine the role of the species in an ecosystem. They affect not only population dynamics of a species across space and over time, but also community structure and function through their key role in the community assembly processes. Information on species traits is also highly relevant for conservation planning as they determine the adaptive ability of a species in the face of environmental changes. However, information on species traits is usually scarce and sparsely distributed amongst diverse types of literature and sources. Difficulty in accessing comprehensive information on species traits has formed an essential knowledge gap, limiting our understanding of biodiversity patterns and ecosystem functioning and preventing effective conservation. Even for birds, a well-studied taxon, comprehensive trait information is still unavailable or distributed across different sources for many species. In this study, we compiled information from a variety of sources on 23 traits for all breeding birds, including 157 resident and 14 summer visiting species, in Taiwan and surrounding islands. The 23 traits include those related to the movement patterns, morphology, geographic distributions, activity patterns, feeding behaviour, habitat use, and breeding behaviour and strategies of the species. The trait information was obtained, not only from published literature and datasets, but also from unpublished banding records and specimen measurements. The dataset also contains derived traits, including the elevation and temperature boundaries of species distribution ranges in Taiwan. In addition, structured information on nest characters, which is seldom compiled in trait datasets, has been made available, for the first time, for the breeding birds in Taiwan. Therefore, the most comprehensive trait dataset to date on breeding birds in Taiwan will allow trait-based research and applications in diverse topics and thus enhance our understanding of the patterns and dynamics of breeding bird diversity and its functions in Taiwan.

We used non-metric multidimensional-scaling analysis (NMDS) to analyze the variation and associations of abiotic and biotic variables among and within three constructed wetlands in metropolitan Taipei. Abiotic variables included NH 4 -N, biochemical oxygen demand, total phosphorus, suspended solids, dissolved oxygen, water temperature, pH, and electrical conductivity and were sampled monthly from April to October 2007. Biotic variables included zooplankton and phytoplankton communities and were sampled in May and August 2007. We sampled front-compartment, mid-compartment, and effluent portions of each wetland. BOD, NH 4 -N, TP, DO, SS, and pH differed significantly (p<0.05) among the three wetlands. Phytoplankton communities in each wetland were unique. Zooplankton differed between the two wetlands (Hsin-Hai Bridge I and Fu-Zhou Bridge). Overall, the three wetlands supported unique abiotic and biotic communities. Zooplankton and phytoplankton communities were correlated to TP, NH 4 -N, DO, and pH levels.