The flaws in estimation process may produce bias derived from overestimation of the mean (Bird et al., 2014). Samples obtained from lower survey effort often leads to overestimation of the mean, such as lower duration, fewer individuals. The results showed that nearly three times (3.25) of eBird checklists were reported a bias >1 after Chao1 species richness estimation at 60-minutes. Among species richness estimations, the Chao1 estimator is especially sensitive to the number of singletons from a reference sample. When restricting duration of between 36 to 60 minutes from both BBS and eBird datasets, the median of percentage of singleton was 21.4 and 26.2, respectively (Figure S10). Percentage of singletons in the eBird dataset was significantly higher than in the BBS dataset (W = 1688300, p < 0.001) (Figure S10).
It has been found that a low sampling effort may result in more singletons than larger sampling effort (Lopez et al., 2012). Chao1 estimator specifies the number of singletons in a sample with rare or undetected species (Chao & Chiu, 2014). This might result in biased estimation when a large number of singleton species appear in a reference sample. I investigated the relationship between the number of singletons and bias from the eBird dataset. The results showed that as the number of singletons increased, the outcome value of bias increased as a response (Figure S11 and Table S4). This confirms that the number of singletons may determine the probability of overestimation by the Chao1 estimator. Therefore, Chao1 may overestimate the true species richness when singleton species are abundant.
The number of singletons is likely to present an issue, especially in unstructured citizen science. Soroye et al. (2018) explored the accuracy of the species richness estimation derived from unstructured citizen science – eButterfly. When using eButterfly to predict the regional species richness in which rare species were excluded, species richness estimation was more accurate than including rare species (Soroye et al., 2018).
A reliable estimate needs to take the effect of the number of singletons into account, particularly in unstructured citizen science.
One way to decrease the number of singleton species is by increasing the sampling intensity and sampling effort (Lopez et al., 2012). This would reduce the possibility of overestimating the true species richness. Therefore, I applied a linear regression analysis to examine the relationship between duration and percentage of singleton species derived from each eBird checklist. The percentage of singleton species had a significant negative relationship with duration (Table S3). In other words, as duration increased, the
analysis. The results showed that the value of bias had a significant positive relationship with percentage of singleton species, indicating as percentage of singleton species increased, the value of bias increased as a response (Table S4 and Figure S11). Generally, large sampling efforts will produce more accurate predictions than small sampling efforts (de Caprariis et al., 1981).
Although non-parametric approaches of species richness estimation methods make no assumption on distribution of species abundance, variable species abundance distributions present in samples can still affect the performance of these estimators (Bunge & Fitzpatrick, 1993; Soberón & Llorente, 1993). This is probably also due to the number of singleton species. As mentioned above, the number of singleton species affects the value of bias. In addition, the survey duration over which samples are collected might influence the shape of species abundance distributions (Magurran, 2007). Low-duration samples have an increased probability of containing singleton species, which will in-turn influence the shape of species abundance distributions. Finally, I suggest the future use of species richness estimation on unstructured citizen science data should increase sampling effort (e.g., duration, number of individuals), to decrease the bias in estimates of species richness. Another way to increase power and reduce the uncertainty around associated results is to combine datasets or checklists. Additional observations may improve our ability to detect more individuals of a species and species count within the data (Soroye et al., 2018). Thus, we may compile more than one eBird checklist, or combine them with BBS dataset to decrease bias in the results.
Nevertheless, insufficient checklists will still be common in some inaccessible or distant areas (Tulloch & Szabo, 2012; Klemann-Junior et al., 2017). And further, checklists collected in unstructured citizen science exhibit a considerable spatial bias towards more densely populated regions or interesting sites (Boakes et al., 2010; Lin et
al., 2015; Kamp et al., 2016). In this study, I set up a minimum requirement that each BBS site contained at least six eBird checklists. From a total of 457 BBS sites, only 204 BBS sites (45%) met the requirement. Thus, more than half of the BBS sites were located in places that eBird volunteers appeared unwilling or uninterested in visiting. This clearly complicates the strategy of using species richness estimates from eBird to make up for missing BBS data.
In summary, unstructured citizen science has become a prominent mechanism for collecting biodiversity information in recent decades. But, the results from my study showed that eBird surveys failed to record the same number of species as BBS. This discrepancy might result from the number of BBS survey points located in various habitats, from weather conditions, from surveyor skill levels, and from the time of day that samples were taken. Chao1 performed the best among all estimators examined, and increased the number of recorded species from 66% to 86% in the eBird dataset. I also found that the number of singletons present in a dataset may bias estimates of species richness. Finally, I conclude that species richness estimates derived from unstructured citizen science studies should always account for imperfect detection probability. When applying Chao1 estimation in the eBird dataset, more attention should be paid to the biased result derived from the number of singletons, particularly in the low-effort samples.
Once the species richness is estimated, and the effect of singletons are dealt with, better conservation strategies can be established for the areas where biodiversity has been impacted.
References
Arrhenius, O. (1921). Species and area. Journal of Ecology, 9(1), 95-99.
Bean, W. T., Stafford, R., & Brashares, J. S. (2012). The effects of small sample size and sample bias on threshold selection and accuracy assessment of species distribution models. Ecography, 35(3), 250-258.
Bird, T. J., Bates, A. E., Lefcheck, J. S., Hill, N. A., Thomson, R. J., Edgar, G. J., Stuart-Smith, R. D., Wotherspoon, S., Krkosek, M., & Stuart-Smith, J. F.
(2014). Statistical solutions for error and bias in global citizen science datasets.
Biological Conservation, 173, 144-154.
Boakes, E. H., McGowan, P. J., Fuller, R. A., Chang-qing, D., Clark, N. E., O'Connor, K., & Mace, G. M. (2010). Distorted views of biodiversity: spatial and temporal bias in species occurrence data. PLoS Biology, 8(6), e1000385.
Bonter, D. N., & Cooper, C. B. (2012). Data validation in citizen science: a case study from Project FeederWatch. Frontiers in Ecology and the Environment, 10(6), 305-307.
Bunge, J., & Fitzpatrick, M. (1993). Estimating the number of species: a review.
Journal of the American Statistical Association, 88(421), 364-373.
Burnham, K. P., & Overton, W. S. (1978). Estimation of the size of a closed population when capture probabilities vary among animals. Biometrika, 65(3), 625-633.
Callaghan, C., Lyons, M., Martin, J., Major, R., & Kingsford, R. (2017). Assessing the reliability of avian biodiversity measures of urban greenspaces using eBird citizen science data. Avian Conservation and Ecology, 12(2).
Cardinale, B. J., Duffy, J. E., Gonzalez, A., Hooper, D. U., Perrings, C., Venail, P., Narwani, A., Mace, G. M., Tilman, D., & Wardle, D. A. (2012). Biodiversity loss and its impact on humanity. Nature, 486(7401), 59-67.
Casanovas, P., Lynch, H. J., & Fagan, W. F. (2014). Using citizen science to estimate lichen diversity. Biological Conservation, 171, 1-8.
Chao, A. (1984). Nonparametric estimation of the number of classes in a population.
Scandinavian Journal of Statistics, 265-270.
Chao, A., & Chiu, C. H. (2014). Species richness: estimation and comparison. Wiley StatsRef: Statistics Reference Online, 1-26.
Chao, A., & Lee, S.-M. (1992). Estimating the number of classes via sample coverage.
Journal of the American Statistical Association, 87(417), 210-217.
Chazdon, R. L., Colwell, R. K., Denslow, J. S., & Guariguata, M. R. (1998). Statistical methods for estimating species richness of woody regeneration in primary and secondary rain forests of northeastern Costa Rica.
Clavero, M., Brotons, L., Pons, P., & Sol, D. (2009). Prominent role of invasive species in avian biodiversity loss. Biological Conservation, 142(10), 2043-2049.
Colwell, R. K., Chao, A., Gotelli, N. J., Lin, S.-Y., Mao, C. X., Chazdon, R. L., &
Longino, J. T. (2012). Models and estimators linking individual-based and sample-based rarefaction, extrapolation and comparison of assemblages. Journal of Plant Ecology, 5(1), 3-21.
Colwell, R. K., & Coddington, J. A. (1994). Estimating terrestrial biodiversity through extrapolation. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 345(1311), 101-118.
Crall, A. W., Newman, G. J., Stohlgren, T. J., Holfelder, K. A., Graham, J., & Waller, D. M. (2011). Assessing citizen science data quality: an invasive species case study. Conservation Letters, 4(6), 433-442.
de Caprariis, P., Lindemann, R., & Haimes, R. (1981). A relationship between sample size and accuracy of species richness predictions. Journal of the International Association for Mathematical Geology, 13(4), 351-355.
Dickinson, J. L., Zuckerberg, B., & Bonter, D. N. (2010). Citizen science as an ecological research tool: challenges and benefits. Annual Review of Ecology, Evolution, and Systematics, 41, 149-172.
Ding, T.-S., C.-S. Juan, R.-S. Lin, Y.-J. Tsai, J.-L. Wu, J. Wu and Y.-H. Yang. 2020.
The 2020 CWBF Checklist of the Birds of Taiwan. Chinese Wild Bird Federation. Taipei, Taiwan.
Fahrig, L. (2003). Effects of habitat fragmentation on biodiversity. Annual Review of Ecology, Evolution, and Systematics, 34(1), 487-515.
Farmer, R. G., Leonard, M. L., & Horn, A. G. (2012). Observer effects and avian-call-count survey quality: rare-species biases and overconfidence. The Auk, 129(1), 76-86.
Flather, C. (1996). Fitting species–accumulation functions and assessing regional land use impacts on avian diversity. Journal of Biogeography, 23(2), 155-168.
Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements of statistical learning (Vol. 1): Springer series in statistics New York.
Gardiner, M. M., Allee, L. L., Brown, P. M., Losey, J. E., Roy, H. E., & Smyth, R. R.
(2012). Lessons from lady beetles: accuracy of monitoring data from US and UK citizen‐science programs. Frontiers in Ecology and the Environment, 10(9), 471-476.
Geoghegan, H., Dyke, A., Pateman, R., West, S., & Everett, G. (2016). Understanding motivations for citizen science. Final report on behalf of UKEOF, University of Reading, Stockholm Environment Institute (University of York) and University of the West of England.
Gideon, S. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461-464.
Gómez-Martínez, C., Aase, A. L. T., Totland, Ø., Rodríguez-Pérez, J., Birkemoe, T., Sverdrup-Thygeson, A., & Lázaro, A. (2020). Forest fragmentation modifies the composition of bumblebee communities and modulates their trophic and
competitive interactions for pollination. Scientific Reports, 10(1), 1-15.
Gotelli, N. J., & Colwell, R. K. (2001). Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecology Letters, 4(4), 379-391.
Hsieh, T., Ma, K., & Chao, A. (2016). iNEXT: an R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods in Ecology and Evolution, 7(12), 1451-1456.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112): Springer.
Jarzyna, M. A., & Jetz, W. (2016). Detecting the multiple facets of biodiversity. Trends in Ecology & Evolution, 31(7), 527-538.
Kamp, J., Oppel, S., Heldbjerg, H., Nyegaard, T., Donald, P. F., & Schröder, B. (2016).
Unstructured citizen science data fail to detect long-term population declines of common birds in Denmark. Diversity and Distributions, 22(10), 1024-1035.
doi:10.1111/ddi.12463
Kellner, K. F., & Swihart, R. K. (2014). Accounting for imperfect detection in ecology:
a quantitative review. Plos One, 9(10).
Klemann-Junior, L., Villegas Vallejos, M. A., Scherer-Neto, P., & Vitule, J. R. S.
(2017). Traditional scientific data vs. uncoordinated citizen science effort: A review of the current status and comparison of data on avifauna in Southern Brazil. Plos One, 12(12), e0188819. doi:10.1371/journal.pone.0188819 Lee, C. (1995). A comparison of bird communities between conifer plantation and
natural broadleaf forest. Master Thesis, National Taiwan University, Taipei, Taiwan, ROC (in Chinese).
Lin, Y.-P., Deng, D., Lin, W.-C., Lemmens, R., Crossman, N. D., Henle, K., &
Schmeller, D. S. (2015). Uncertainty analysis of crowd-sourced and professionally collected field data used in species distribution models of Taiwanese moths. Biological Conservation, 181, 102-110.
Lin M, Chen W, Lin D, Ko C, Lin R, Ding T (2020). Chinese Wild Bird Federation Bird Records Database. Version 1.5. Taiwan Endemic Species Research Institute. Sampling event dataset https://doi.org/10.15468/blpygb accessed via GBIF.org on 2020-06-20.
Lopez, L. C. S., de Aguiar Fracasso, M. P., Mesquita, D. O., Palma, A. R. T., & Riul, P.
(2012). The relationship between percentage of singletons and sampling effort: a new approach to reduce the bias of richness estimates. Ecological Indicators, 14(1), 164-169.
MacKenzie, D. I., Nichols, J. D., Lachman, G. B., Droege, S., Andrew Royle, J., &
Langtimm, C. A. (2002). Estimating site occupancy rates when detection probabilities are less than one. Ecology, 83(8), 2248-2255.
Magurran, A. E. (2007). Species abundance distributions over time. Ecology Letters, 10(5), 347-354.
Magurran, A. E., & McGill, B. J. (Eds.). (2011). Biological diversity: frontiers in measurement and assessment. Oxford University Press.
Mazerolle, M. J., & Mazerolle, M. M. J. (2019). Package ‘AICcmodavg’. R package version 2.2-2.
Newson, S. E., Woodburn, R. J., Noble, D. G., Baillie, S. R., & Gregory, R. D. (2005).
Evaluating the Breeding Bird Survey for producing national population size and density estimates. Bird Study, 52(1), 42-54.
Oksanen, J., Blanchet, F., Kindt, R., Legendre, P., Minchin, P., O’Hara, R., Simpson, G., Solymos, P., Stevens, M., & Wagner, H. (2016). vegan: community ecology package. R package version 2.2–1. 2015.
Pacifici, K., Simons, T. R., & Pollock, K. H. (2008). Effects of vegetation and background noise on the detection process in auditory avian point-count surveys. The Auk, 125(3), 600-607.
Pacifici, M., Foden, W. B., Visconti, P., Watson, J. E., Butchart, S. H., Kovacs, K. M., Scheffers, B. R., Hole, D. G., Martin, T. G., & Akçakaya, H. R. (2015).
Assessing species vulnerability to climate change. Nature Climate Change, 5(3), 215-224.
Preston, F. W. (1962). The canonical distribution of commonness and rarity: Part I.
Ecology, 43(2), 185-215.
Ratnieks, F. L., Schrell, F., Sheppard, R. C., Brown, E., Bristow, O. E., & Garbuzov, M.
(2016). Data reliability in citizen science: learning curve and the effects of training method, volunteer background and experience on identification accuracy of insects visiting ivy flowers. Methods in Ecology and Evolution, 7(10), 1226-1235.
Robbins, C. S. (1981). Effect of time of day on bird activity. Studies in Avian Biology, 6(3), 275-286.
Schumacher, F. (1939). A new growth curve and its application to timber yield studies.
Journal of Forestry, 37(10), 819-820.
Schumaker, N. H. (1996). Using landscape indices to predict habitat connectivity.
Ecology, 77(4), 1210-1225.
Shmueli, G. (2010). To explain or to predict? Statistical Science, 25(3), 289-310.
Soberón, J. M., & Llorente, J. B. (1993). The use of species accumulation functions for the prediction of species richness. Conservation Biology, 7(3), 480-488.
Soroye, P., Ahmed, N., & Kerr, J. T. (2018). Opportunistic citizen science data transform understanding of species distributions, phenology, and diversity gradients for global change research. Global Change Biology, 24(11), 5281-5291.
Sorte, F. A. L., & Somveille, M. (2020). Survey completeness of a global citizen‐
science database of bird occurrence. Ecography, 43(1), 34-43.
Steen, V. A., Elphick, C. S., & Tingley, M. W. (2019). An evaluation of stringent filtering to improve species distribution models from citizen science data.
Diversity and Distributions, 25(12), 1857-1869.
Sullivan, B. L., Aycrigg, J. L., Barry, J. H., Bonney, R. E., Bruns, N., Cooper, C. B., Damoulas, T., Dhondt, A. A., Dietterich, T., Farnsworth, A., Fink, D.,
Fitzpatrick, J. W., Fredericks, T., Gerbracht, J., Gomes, C., Hochachka, W. M., Iliff, M. J., Lagoze, C., La Sorte, F. A., Merrifield, M., Morris, W., Phillips, T.
B., Reynolds, M., Rodewald, A. D., Rosenberg, K. V., Trautmann, N. M., Wiggins, A., Winkler, D. W., Wong, W.-K., Wood, C. L., Yu, J., & Kelling, S.
Sullivan, B. L., Wood, C. L., Iliff, M. J., Bonney, R. E., Fink, D., & Kelling, S. (2009).
eBird: A citizen-based bird observation network in the biological sciences.
Biological Conservation, 142(10), 2282-2292.
Swanson, A., Kosmala, M., Lintott, C., & Packer, C. (2016). A generalized approach for producing, quantifying, and validating citizen science data from wildlife images. Conservation Biology, 30(3), 520-531.
Theobald, E. J., Ettinger, A. K., Burgess, H. K., DeBey, L. B., Schmidt, N. R.,
Froehlich, H. E., Wagner, C., HilleRisLambers, J., Tewksbury, J., & Harsch, M.
(2015). Global change and local solutions: Tapping the unrealized potential of citizen science for biodiversity research. Biological Conservation, 181, 236-244.
Tingley, M. W., Nadeau, C. P., & Sandor, M. E. (2020). Multi‐species occupancy models as robust estimators of community richness. Methods in Ecology and Evolution.
Tulloch, A. I., & Szabo, J. K. (2012). A behavioural ecology approach to understand volunteer surveying for citizen science datasets. Emu-Austral Ornithology, 112(4), 313-325.
Tyre, A. J., Tenhumberg, B., Field, S. A., Niejalke, D., Parris, K., & Possingham, H. P.
(2003). Improving precision and reducing bias in biological surveys: estimating false‐negative error rates. Ecological Applications, 13(6), 1790-1801.
Ulrich, W. (2006). Decomposing the process of species accumulation into area dependent and time dependent parts. Ecological Research, 21(4), 578-585.
Walther, B. A., Cotgreave, P., Price, R., Gregory, R., & Clayton, D. H. (1995).
Sampling effort and parasite species richness. Parasitology Today, 11(8), 306-310.
Walther, B. A., & Martin, J. L. (2001). Species richness estimation of bird communities: how to control for sampling effort? Ibis, 143(4), 413-419.
Walther, B. A., & Moore, J. L. (2005). The concepts of bias, precision and accuracy, and their use in testing the performance of species richness estimators, with a literature review of estimator performance. Ecography, 28(6), 815-829.
Walther, B. A., & Morand, S. (1998). Comparative performance of species richness estimation methods. Parasitology, 116(4), 395-405.
Zeide, B. (1993). Analysis of growth equations. Forest Science, 39(3), 594-616.
Appendixes
Table S1 Summary of a total of 204 BBS sites from this study, including the number of points, time of visits, and total time of duration recorded from 2009 to 2017. “A” denotes sites located in low-elevation (<1000 meters a.s.l.); “B” denotes sites located in mid-elevation (1000–2500 meters a.s.l.); “C” denotes sites located in high-mid-elevation (>2500 meters a.s.l).
Table S1 (continued)
Table S1 (continued)
Table S1 (continued)
Table S1 (continued)
Table S1 (continued)
Table S2 Bird species reported from the Breeding Bird Survey Taiwan (BBS) and eBird datasets. I included BBS dataset recorded from 2009 to 2017; and included eBird dataset recorded from 2008 to 2018.
*Note: Where “1” represents the species reported from the datasets, “NA” represents the species that were not reported from the datasets.
Common Name Scientific Name Chinese
Common Name BBS eBird
Barred Buttonquail Turnix suscitator 棕三趾鶉 1 1
Long-tailed Shrike Lanius schach 棕背伯勞 1 1
White-bellied Erpornis Erpornis zantholeuca 綠畫眉 1 1
Large Cuckooshrike Coracina macei 花翅山椒鳥 1 1
Gray-chinned Minivet Pericrocotus solaris 灰喉山椒鳥 1 1
Taiwan Yellow Tit Machlolophus holsti 黃山雀 1 1
Gray-throated Martin Riparia chinensis 棕沙燕 1 1
Table S2 (continued)
Common Name Scientific Name Chinese
Common Name BBS eBird
Bronzed Drongo Dicrurus aeneus 小卷尾 1 1
Black Drongo Dicrurus macrocercus 大卷尾 1 1
Black-naped Monarch Hypothymis azurea 黑枕藍鶲 1 1
Japanese
Paradise-Flycatcher Terpsiphone atrocaudata 紫綬帶 1 1
Rufous-capped Babbler Cyanoderma ruficeps 山紅頭 1 1
Black-necklaced
Woodpecker Yungipicus canicapillus 小啄木 1 1
Common Kingfisher Alcedo atthis 翠鳥 1 1
Crested Myna Acridotheres cristatellus 八哥 1 1
Oriental Skylark Alauda gulgula 小雲雀 1 1
Taiwan Barwing Actinodura morrisoniana 紋翼畫眉 1 1
Morrison's Fulvetta Alcippe morrisonia 繡眼畫眉 1 1
Taiwan Hwamei Garrulax taewanus 臺灣畫眉 NA 1
White-eared Sibia Heterophasia auricularis 白耳畫眉 1 1
Table S2 (continued)
Common Name Scientific Name Chinese
Common Name BBS eBird Rusty Laughingthrush Ianthocincla
poecilorhyncha 棕噪眉 1 1
Rufous-crowned
Laughingthrush Ianthocincla ruficeps 臺灣白喉噪眉 1 1
Steere's Liocichla Liocichla steerii 黃胸藪眉 1 1
Slaty-legged Crake Rallina eurizonoides 灰腳秧雞 1 1
Ruddy-breasted Crake Zapornia fusca 緋秧雞 1 1
Taiwan Yuhina Yuhina brunneiceps 冠羽畫眉 1 1
Swinhoe's White-eye Zosterops simplex 斯氏繡眼 1 1
Lowland White-eye Zosterops meyeni 低地繡眼 1 1
Greater Painted-Snipe Rostratula benghalensis 彩鷸 1 1
Taiwan Barbet Psilopogon nuchalis 五色鳥 1 1
Rufous-faced Warbler Abroscopus albogularis 棕面鶯 1 1 Yellowish-bellied Bush
Table S2 (continued)
Common Name Scientific Name Chinese
Common Name BBS eBird Taiwan
Whistling-Thrush Myophonus insularis 臺灣紫嘯鶇 1 1
Vivid Niltava Niltava vivida 黃腹琉璃 1 1
Plumbeous Redstart Phoenicurus fuliginosus 鉛色水鶇 1 1 White-browed
Bush-Robin Tarsiger indicus 白眉林鴝 1 1
Taiwan Shortwing Brachypteryx
goodfellowi 小翼鶇 1 1
Collared Bush-Robin Tarsiger johnstoniae 栗背林鴝 1 1
Scaly Thrush Zoothera dauma 虎斑地鶇 1 1
Eurasian Wren Troglodytes troglodytes 鷦鷯 1 1
Cattle Egret Bubulcus ibis 黃頭鷺 1 1
Cinnamon Bittern Ixobrychus cinnamomeus 栗小鷺 1 1
Yellow Bittern Ixobrychus sinensis 黃小鷺 1 1
Table S2 (continued)
Common Name Scientific Name Chinese
Common Name BBS eBird
Chinese Crested Tern Thalasseus bernsteini 黑嘴端鳳頭燕
鷗 NA 1
Crested Goshawk Accipiter trivirgatus 鳳頭蒼鷹 1 1
Besra Accipiter virgatus 松雀鷹 1 1
Black-winged Kite Elanus caeruleus 黑翅鳶 1 1
Black Eagle Ictinaetus malaiensis 林鵰 1 1
Black Kite Milvus migrans 黑鳶 1 1
Mountain Hawk-Eagle Nisaetus nipalensis 熊鷹 1 1
Crested Serpent-Eagle Spilornis cheela 大冠鷲 1 1
Pheasant-tailed Jacana Hydrophasianus
chirurgus 水雉 1 1
Russet Sparrow Passer cinnamomeus 山麻雀 1 1
Table S2 (continued)
Common Name Scientific Name Chinese
Common Name BBS eBird
Asian Emerald Dove Chalcophaps indica 翠翼鳩 1 1
Ashy Wood-Pigeon Columba pulchricollis 灰林鴿 1 1
Philippine
Cuckoo-Dove Macropygia tenuirostris 長尾鳩 1 1
Black-chinned
Fruit-Dove Ptilinopus leclancheri 小綠鳩 NA 1
Spotted Dove Streptopelia chinensis 珠頸斑鳩 1 1
Oriental Turtle-Dove Streptopelia orientalis 金背鳩 1 1 Red Collared-Dove Streptopelia
Scaly-breasted Munia Lonchura punctulata 斑文鳥 1 1
White-rumped Munia Lonchura striata 白腰文鳥 1 1
Large-billed Crow Corvus macrorhynchos 巨嘴鴉 1 1
Gray Treepie Dendrocitta formosae 樹鵲 1 1
Eurasian Jay Garrulus glandarius 松鴉 1 1
Eurasian Nutcracker Nucifraga caryocatactes 星鴉 1 1
Taiwan Blue-Magpie Urocissa caerulea 臺灣藍鵲 1 1
Flamecrest Regulus goodfellowi 火冠戴菊鳥 1 1
Golden-headed
Cisticola Cisticola exilis 黃頭扇尾鶯 1 1
Table S2 (continued)
Common Name Scientific Name Chinese
Common Name BBS eBird
Zitting Cisticola Cisticola juncidis 棕扇尾鶯 1 1
Striated Prinia Prinia crinigera 斑紋鷦鶯 1 1
Yellow-bellied Prinia Prinia flaviventris 灰頭鷦鶯 1 1
Plain Prinia Prinia inornata 褐頭鷦鶯 1 1
Black-naped Oriole Oriolus chinensis 黃鸝 1 1
Maroon Oriole Oriolus traillii 朱鸝 1 1
Black-throated Tit Aegithalos concinnus 紅頭山雀 1 1
House Swift Apus nipalensis 小雨燕 1 1
Silver-backed Needletail
Hirundapus
cochinchinensis 灰喉針尾雨燕 1 1
Taiwan Rosefinch Carpodacus formosanus 臺灣朱雀 1 1
Taiwan Rosefinch Carpodacus formosanus 臺灣朱雀 1 1