Seasonal Variation in Bird Species Richness along Elevational Gradients in Taiwan

Seasonal variations in the distribution of bird species richness & community composition along elevational gradients in Taiwan were studied. We compiled bird species richness data through an extensive literature review, & classified the data into breeding season, non-breeding season, & year-round sets. We then examined the avifaunal-environmental relationships using presence/absence data by principle components analysis (PCA), cluster analysis, & detrended correspondence analysis (DCA). With the year-round dataset, species richness appeared roughly constant from sea level to about 1500 m, then declined with increasing elevation; while the richness during the breeding season was non-linearly related to elevation, with a hump-shaped curve which peaked at around 1500 m. By separating migratory landbirds from residents during the non-breeding season, however, we found that the two groups had different patterns along the gradient: residents showed a hump-shaped pattern similar to that of the breeding season, whereas migratory landbirds showed a negative exponential pattern. The results of PCA & cluster analysis demonstrated that the resident assemblages were classified into three major categories: lowlands, mid-elevation domain, & highlands, separated roughly by the elevations of 300 & 2300 m. DCAs indicated that elevation & human disturbance are the major environmental factors contributing to variations in the trend of resident assemblages. The seasonality of data collection strongly influences bird species richness distribution patterns & should be clearly defined to obtain a meaningful result in bird species richness studies

Morphological Characters of Bird Species in Taiwan

We documented body measurements of birds captured during 1987-1995 banding research in Taiwan by Taipei Wild Bird Society. A total of 223 bird species with measurements of body mass, length of body, head, tail, bill & tarsus, & maximum wing span were compiled for the database. Body mass, length of bill, & length of tarsus of 85 species that had sample sizes > 20 were reported. In addition, we also compared our data with that from Dunning (1993). Among the ten species eligible for further analyses, we found that eight species showed significant differences in body mass between our data & that of Dunning (1993). Our results together with our recent studies (Lee et al, 2004; Ding et al., 2005) showed that compiling banding research data contribute to predict ecological parameters of bird studies in community, ecosystem & landscape levels

Morphological Characters of Bird Species in Taiwan

We documented body measurements of birds captured during 1987-1995 banding research in Taiwan by Wild Bird Society Taiwan. A total of 223 bird species with measurements of body mass, length of body, head, tail, bill and tarsus, and maximum wing span were compiled for the database. Body mass, length of bill, and length of tarsus of 85 species that had sample sizes > 20 were reported. In addition, we also compared our data with that from Dunning (1993). Among the ten species eligible for further analyses, we found that eight species showed significant differences in body mass between our data and that of Dunning (1993). Our results together with our recent studies (Lee et al, 2004; Ding et al., 2005) showed that compiling banding research data contribute to predict ecological parameters of bird studies in community, ecosystem and landscape levels

Regional Scale High Resolution δ¹⁸O Prediction in Precipitation Using MODIS EVI

<div><p>The natural variation in stable water isotope ratio data, also known as water isoscape, is a spatiotemporal fingerprint and a powerful natural tracer that has been widely applied in disciplines as diverse as hydrology, paleoclimatology, ecology and forensic investigation. Although much effort has been devoted to developing a predictive water isoscape model, it remains a central challenge for scientists to generate high accuracy, fine scale spatiotemporal water isoscape prediction. Here we develop a novel approach of using the MODIS-EVI (the Moderate Resolution Imagining Spectroradiometer-Enhanced Vegetation Index), to predict δ¹⁸O in precipitation at the regional scale. Using a structural equation model, we show that the EVI and precipitated δ¹⁸O are highly correlated and thus the EVI is a good predictor of precipitated δ¹⁸O. We then test the predictability of our EVI-δ¹⁸O model and demonstrate that our approach can provide high accuracy with fine spatial (250×250 m) and temporal (16 days) scale δ¹⁸O predictions (annual and monthly predictabilities [<em>r</em>] are 0.96 and 0.80, respectively). We conclude the merging of the EVI and δ¹⁸O in precipitation can greatly extend the spatial and temporal data availability and thus enhance the applicability for both the EVI and water isoscape.</p> </div

The fine scale regional δ¹⁸O prediction map of Taiwan in August, 2009.

<p>The inset highlights the fine resolution (250 m) of modeled result.</p

SEM analysis of the relationship among climatic, topographic factors, the iEVI and δ¹⁸O.

<p><b>a,</b> The relationships among the iEVI and δ¹⁸O and other climatic, topographic factors. The “Err” circles represent the parts that were not explained by above factors in both the iEVI and δ¹⁸O, but can be explained by each other. <b>b,</b> The relationship between topographic factors, the iEVI and δ¹⁸O (the climatic factors are replaced by the iEVI). All values are under two tailed t-test, and are significant (<i>p</i><0.001). The values shown here are correlation coefficients (<i>r</i>), unless noted as <i>r²</i> representing the overall variability explained by other factors that connect to the box.</p

The conceptual basis of merging the EVI and δ¹⁸O.

<p>The direction of influence of each factor is represented by the arrows. Functions that are directly influenced by spatial and climatic factors are listed in the parentheses within the EVI and δ¹⁸O boxes. “A” is the constraint of spatial extent for EVI data, where is restricted by the amount of snow cover. “B” is the description of the characteristics of output (δ¹⁸O) including high resolution, large spatial scale, and various temporal scales (16-day, monthly, seasonal or yearly estimation). “C” is the limitation of δ¹⁸O data. It should be monthly data or the model should be modified to a different temporal scale. “D” indicates the long temporal extent of output of the EVI data. Only temperature, precipitation, and altitude are included in our SEM analysis but other topographic and climatic factors may also be included. References for each relationship is as follows (These references are listed in <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045496#pone.0045496.s008" target="_blank">Text S1</a></b>): <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045496#pone.0045496-Cobb1" target="_blank">[3]</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045496#pone.0045496-Bowen2" target="_blank">[7]</a><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045496#pone.0045496-Craig1" target="_blank">[1]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045496#pone.0045496-Hobson1" target="_blank">[8]</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045496#pone.0045496-Worden1" target="_blank">[10]</a><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045496#pone.0045496-Shacklet1" target="_blank">[2]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045496#pone.0045496-Joussaume1" target="_blank">[9]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045496#pone.0045496-Noone1" target="_blank">[11]</a><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045496#pone.0045496-Cobb1" target="_blank">[3]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045496#pone.0045496-Hobson1" target="_blank">[8]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045496#pone.0045496-Worden1" target="_blank">[10]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045496#pone.0045496-Zhang1" target="_blank">[12]</a><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045496#pone.0045496-Benson1" target="_blank">[5]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045496#pone.0045496-Huete1" target="_blank">[13]</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045496#pone.0045496-Peng1" target="_blank">[17]</a><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045496#pone.0045496-Epstein1" target="_blank">[18]</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045496#pone.0045496-Wang1" target="_blank">[20]</a><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045496#pone.0045496-Bowen1" target="_blank">[6]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045496#pone.0045496-Jonsson1" target="_blank">[21]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045496#pone.0045496-MendezBarroso1" target="_blank">[22]</a><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045496#pone.0045496-Huete1" target="_blank">[13]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045496#pone.0045496-Byrne1" target="_blank">[15]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045496#pone.0045496-MendezBarroso1" target="_blank">[22]</a><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045496#pone.0045496-Wolfram1" target="_blank">[23]</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045496#pone.0045496-Frankenberg1" target="_blank">[25]</a>.</p

The precipitation collection site.

<p>Thirteen precipitation collection sites along an elevation gradient in the eastern Taiwan.</p