Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).
Google Scholar
Schumaker, N. H. Using landscape indices to predict habitat connectivity. Ecology 77, 1210–1225 (1996).
Google Scholar
Pacifici, M. et al. Assessing species vulnerability to climate change. Nat. Clim. Chang. 5, 215–224 (2015).
Google Scholar
Fahrig, L. Effects of habitat fragmentation on biodiversity. Annu. Rev. Ecol. Evol. Syst. 34, 487–515 (2003).
Google Scholar
Clavero, M., Brotons, L., Pons, P. & Sol, D. Prominent role of invasive species in avian biodiversity loss. Biol. Conserv. 142, 2043–2049 (2009).
Google Scholar
Soroye, P., Ahmed, N. & Kerr, J. T. Opportunistic citizen science data transform understanding of species distributions, phenology, and diversity gradients for global change research. Glob. Change Biol. 24, 5281–5291 (2018).
Google Scholar
Gotelli, N. J. & Colwell, R. K. Quantifying biodiversity: Procedures and pitfalls in the measurement and comparison of species richness. Ecol. Lett. 4, 379–391 (2001).
Google Scholar
Dickinson, J. L., Zuckerberg, B. & Bonter, D. N. Citizen science as an ecological research tool: Challenges and benefits. Annu. Rev. Ecol. Evol. Syst. 41, 149–172 (2010).
Google Scholar
Kelling, S. et al. Using semistructured surveys to improve citizen science data for monitoring biodiversity. Bioscience 69, 170–179 (2019).
Google Scholar
Steen, V. A., Elphick, C. S. & Tingley, M. W. An evaluation of stringent filtering to improve species distribution models from citizen science data. Divers. Distrib. 25, 1857–1869 (2019).
Google Scholar
Crall, A. W. et al. Assessing citizen science data quality: An invasive species case study. Conserv. Lett. 4, 433–442 (2011).
Google Scholar
Bird, T. J. et al. Statistical solutions for error and bias in global citizen science datasets. Biol. Conserv. 173, 144–154 (2014).
Google Scholar
MacKenzie, D. I. et al. Estimating site occupancy rates when detection probabilities are less than one. Ecology 83, 2248–2255 (2002).
Google Scholar
Kellner, K. F. & Swihart, R. K. Accounting for imperfect detection in ecology: A quantitative review. PLoS ONE 9(10), E111436 (2014).
Google Scholar
Weisshaupt, N., Lehikoinen, A., Mäkinen, T. & Koistinen, J. Challenges and benefits of using unstructured citizen science data to estimate seasonal timing of bird migration across large scales. PLoS ONE 16, e0246572 (2021).
Google Scholar
Kéry, M. & Schmid, H. Estimating species richness: Calibrating a large avian monitoring programme. J. Appl. Ecol. 43, 101–110 (2006).
Google Scholar
Chao, A. & Chiu, C. H. Species richness: Estimation and comparison 1–26 (Wiley StatsRef: Statistics Reference Online, 2014).
Walther, B. A. & Moore, J. L. 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, 815–829 (2005).
Google Scholar
Chao, A. & Lee, S.-M. Estimating the number of classes via sample coverage. J. Am. Stat. Assoc. 87, 210–217 (1992).
Google Scholar
Walther, B. A. & Morand, S. Comparative performance of species richness estimation methods. Parasitology 116, 395–405 (1998).
Google Scholar
Walther, B. A. & Martin, J. L. Species richness estimation of bird communities: How to control for sampling effort?. Ibis 143, 413–419 (2001).
Google Scholar
Walther, B. A., Cotgreave, P., Price, R., Gregory, R. & Clayton, D. H. Sampling effort and parasite species richness. Parasitol. Today 11, 306–310 (1995).
Google Scholar
Colwell, R. K. & Coddington, J. A. Estimating terrestrial biodiversity through extrapolation. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 345, 101–118 (1994).
Google Scholar
Bean, W. T., Stafford, R. & Brashares, J. S. The effects of small sample size and sample bias on threshold selection and accuracy assessment of species distribution models. Ecography 35, 250–258 (2012).
Google Scholar
Flather, C. Fitting species–accumulation functions and assessing regional land use impacts on avian diversity. J. Biogeogr. 23, 155–168 (1996).
Google Scholar
White, P. E. et al. A comparison of the species–time relationship across ecosystems and taxonomic groups. Oikos 112, 185–195 (2006).
Google Scholar
McGlinn, D. J. & Palmer, M. W. Modeling the sampling effect in the species–time–area relationship. Ecology 90, 836–846 (2009).
Google Scholar
Isaac, N. J. et al. Statistics for citizen science: Extracting signals of change from noisy ecological data. Method Ecol. Evol. 5, 1052–1060 (2014).
Google Scholar
Ding, T. et al. The 2020 CWBF checklist of the birds of Taiwan (Chinese Wild Bird Federation, 2020).
Lin, M.-M. et al. Bird records database of a Taiwanese non-governmental organization, the Chinese wild bird federation, from 1972 to 2017. TW. J. Biodivers. 21, 83–101 (2019).
Dokter, A. M., Desmet, P., Van Hoey, S. (2022) bioRad: Biological analysis and visualization of weather radar data: v0. 6.0
Strimas-Mackey, M. et al. (2020) Best practices for using eBird Data. Version 1.0. Cornell Laboratory of Ornithology, Ithaca, New York, 10.5281/zenodo.3620739
Robinson, O. J. et al. Using citizen science data in integrated population models to inform conservation. Biol. Conserv. 227, 361–368 (2018).
Google Scholar
Callaghan, C. T., Martin, J. M., Major, R. E. & Kingsford, R. T. Avian monitoring–comparing structured and unstructured citizen science. Wildl. Res. 45, 176–184 (2018).
Google Scholar
Robinson, W. D., Hallman, T. A. & Hutchinson, R. A. Benchmark bird surveys help quantify counting accuracy in a citizen-science database. Front. Ecol. Evol. 9, 568278 (2021).
Google Scholar
Neate-Clegg, M. H., Horns, J. J., Adler, F. R., Aytekin, M. Ç. K. & Şekercioğlu, Ç. H. Monitoring the world’s bird populations with community science data. Biol. Conserv. 248, 108653 (2020).
Google Scholar
Chao, A. Nonparametric estimation of the number of classes in a population. Scand. J. Stat 1, 265–270 (1984).
Hsieh, T., Ma, K. & Chao, A. iNEXT: An R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods Ecol. Evol. 7, 1451–1456 (2016).
Google Scholar
Team, R. C. (2013).R: A language and environment for statistical computing.
James, G., Witten, D., Hastie, T. & Tibshirani, R. An Introduction to Statistical Learning Vol. 112 (Springer, 2013).
Google Scholar
Magurran, A. E. & McGill, B. J. Biological diversity: Frontiers in measurement and assessment (OUP Oxford, 2010).
Spiess, A.-N. (2018) Package ‘propagate’
RC Team, C Worldwide. The R stats package (R Foundation for Statistical Computing, 2002).
Guralnick, R. & Van Cleve, J. Strengths and weaknesses of museum and national survey data sets for predicting regional species richness: Comparative and combined approaches. Divers. Distrib. 11, 349–359 (2005).
Google Scholar
Dar, T. A. et al. Bird community structure in Phakot and Pathri Rao watershed areas in Uttarakhand. India. Int. J. Environ. Sci. 34, 193–205 (2008).
Azevedo, G. H. et al. Effectiveness of sampling methods and further sampling for accessing spider diversity: A case study in a Brazilian Atlantic rainforest fragment. Insect. Conserv. Divers. 7, 381–391 (2014).
Google Scholar
Bonter, D. N. & Cooper, C. B. Data validation in citizen science: A case study from project feederwatch. Front. Ecol. Environ. 10, 305–307 (2012).
Google Scholar
Gómez-Martínez, C. et al. Forest fragmentation modifies the composition of bumblebee communities and modulates their trophic and competitive interactions for pollination. Sci. Rep. 10, 1–15 (2020).
Google Scholar
Sullivan, B. L. et al. eBird: A citizen-based bird observation network in the biological sciences. Biol. Conserv. 142, 2282–2292 (2009).
Google Scholar
Newson, S. E., Woodburn, R. J., Noble, D. G., Baillie, S. R. & Gregory, R. D. Evaluating the breeding bird survey for producing national population size and density estimates. Bird Study 52, 42–54 (2005).
Google Scholar
Robbins, C. S. Effect of time of day on bird activity. Stud. Avian Biol. 6, 275–286 (1981).
Farmer, R. G., Leonard, M. L. & Horn, A. G. Observer effects and avian-call-count survey quality: Rare-species biases and overconfidence. Auk 129, 76–86 (2012).
Google Scholar
Gardiner, M. M. et al. Lessons from lady beetles: Accuracy of monitoring data from US and UK citizen-science programs. Front. Ecol. Environ. 10, 471–476 (2012).
Google Scholar
Swanson, A., Kosmala, M., Lintott, C. & Packer, C. A generalized approach for producing, quantifying, and validating citizen science data from wildlife images. Conserv. Biol. 30, 520–531 (2016).
Google Scholar
Ratnieks, F. L. et al. 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 Ecol. Evol. 7, 1226–1235 (2016).
Google Scholar
Lopez, L. C. S., de Aguiar Fracasso, M. P., Mesquita, D. O., Palma, A. R. T. & Riul, P. The relationship between percentage of singletons and sampling effort: A new approach to reduce the bias of richness estimates. Ecol. Indicators 14, 164–169 (2012).
Google Scholar
Bunge, J. & Fitzpatrick, M. Estimating the number of species: A review. J. Am. Stat. Assoc. 88, 364–373 (1993).
SoberónM, J. & LlorenteB, J. The use of species accumulation functions for the prediction of species richness. Conserv. Biol. 7, 480–488 (1993).
Google Scholar
Magurran, A. E. Species abundance distributions over time. Ecol. Lett. 10, 347–354 (2007).
Google Scholar
de Caprariis, P., Lindemann, R. & Haimes, R. A relationship between sample size and accuracy of species richness predictions. J. Int. Assoc. Math. Geol. 13, 351–355 (1981).
Google Scholar
Klemann-Junior, L., Villegas Vallejos, M. A., Scherer-Neto, P. & Vitule, J. R. S. 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, e0188819. https://doi.org/10.1371/journal.pone.0188819 (2017).
Google Scholar
Tulloch, A. I. & Szabo, J. K. A behavioural ecology approach to understand volunteer surveying for citizen science datasets. Emu 112, 313–325 (2012).
Google Scholar
Boakes, E. H. et al. Distorted views of biodiversity: Spatial and temporal bias in species occurrence data. PLoS Biol. 8, e1000385 (2010).
Google Scholar
Kamp, J. et al. Unstructured citizen science data fail to detect long-term population declines of common birds in Denmark. Divers. Distrib. 22, 1024–1035. https://doi.org/10.1111/ddi.12463 (2016).
Google Scholar
Lin, Y.-P. et al. Uncertainty analysis of crowd-sourced and professionally collected field data used in species distribution models of Taiwanese moths. Biol. Conserv. 181, 102–110 (2015).
Google Scholar
Fletcher, R. J. Jr. et al. A practical guide for combining data to model species distributions. Ecology 100, e02710 (2019).
Google Scholar
Source: Ecology - nature.com
