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Additional file 3. Figure S1. The breeding and wintering areas of two subspecies of Common Swift and the random sample points used in this study.

Additional file 7. Figure S2. Map showing the tracks away from breeding site and across the Junggar Basin.

Additional file 1. Table S1. Distribution of data over years. Table S2. Key phenological parameters of migration of different sex individuals of A. a. pekinensis breeding in Beijing. Table S3. Results of comparative analysis of characteristic parameters between female and male pekinensis using t-test. Table S4. Results of comparative analysis of characteristic parameters between pekinensis from Beijing (N=25) and nominate apus from Sweden (N=25) using t-test. Table S5. Monthly and annual precipitation (mm) at several sites reported in the breeding areas of both subspecies.

Additional file 8. Figure S3. Maps showing the migration routes of five individuals in two years. The pentagram represents the bird breeding site and fieldwork location -Beijing. The dotted lines indicate the lack of data in the two weeks before/after the autumn/spring equinoxes. The yellow lines represent the autumn routes, the blue line represent the spring routes.

为研究不同有机物料对喀斯特石灰土元素有效性的影响，采用40 d的室内培养实验，比较了单独添加不同比例(1%、3%、5%)的生物质炭、鸡粪肥、羊粪肥对喀斯特石灰土有效N、Fe、Zn含量的影响。结果表明：添加生物质炭提高了喀斯特石灰土的pH值，而添加鸡粪肥和羊粪肥降低了喀斯特石灰土的pH值；添加3种有机物料均增加了喀斯特石灰土的有机质含量，大小关系为：生物质炭>鸡粪肥>羊粪肥，但添加鸡粪肥和羊粪肥土壤有机质的化学活性和微生物活性更高。受pH、有机质活性、碳氮比等因素的影响，添加鸡粪肥和羊粪肥能增加土壤有效N含量，但两种有机肥对土壤有效N的提高效果相差不大，而添加生物质炭反而降低了土壤有效N的含量；3种有机物料均能提高土壤的有效Fe和有效Zn含量，其中鸡粪肥效果最佳，其次为羊粪肥和生物质炭。当3种有机物料的添加比例为5%时，生物质炭处理土壤的有效N、Fe、Zn含量分别是对照处理的0.92、1.13、1.21倍；鸡粪肥处理土壤的有效N、Fe、Zn含量分别是对照处理的1.22、1.63和3.39倍；羊粪肥处理土壤的有效N、Fe、Zn含量分别是对照处理的1.27、1.34和2.59倍。所以，相对于生物质炭，有机粪肥对喀斯特地区的石灰土有更好的改良效果。

This paper proposes a method to assign the on-the-spot scores of the syllable level to the initial, final and tone level in PSC Test Corpus by integrating the statistic data with a small amount of experts' annotation information (ISDEAI method hereafter). Comparing with the result of previous data-driven statistic method (DDS), the results of ISDEAI are more consistent with the actual pronunciation features of the provincial Putonghua in China. This method can be applied to assign the errors and defects semi-automatically in PSC Test corpus and thus benefit to the research for establishing a large-scale speech corpus with the pronunciation errors annotations.

Automatic pronunciation evaluation (APE) often works in noise environment. With MFCC based automatic speech recognizer (ASR) embedded, APE's noise robustness is not usually satisfied. Since it improves anti-noise performances of ASR, power normalized cepstrum coefficient (PNCC) [1] is introduced i n t o APE in this paper. Experiment of noise environment shows improvement in both APE and recognition performances. In order to discriminate mispronunciation APE needs standard “golden” acoustic models. The speaker adaptation models of APE need to be trained by standard pronounced utterances of target speaker. But standard pronunciation is not usually expected in APE. In this paper a new speaker adaptation strategy is suggested. A recognizer of speaker independent (SI) is used to selected correct pronounced utterances for adaptation training. The adapted system achieved better score correlation with subjective scores.