Is hearing believing? Patterns of bird voice misidentification in an online quiz
Tóm tắt
This study aims to uncover patterns of species identification error in bioacoustic surveys of central Amazon birds. To quantify errors, we developed an on-line quiz based on vocalizations of an undisclosed set of 41 antbird (Thamnophilidae) and woodcreeper (Dendrocolaptinae) species. We invited experts to answer the quiz and obtained 820 answers from 20 participants. The answers were compared to the results of a binomial experiment with a success probability of 0.5; i.e. we examined whether participants identified species correctly more often than expected by the toss of a coin with a 50% chance of producing the right identification. We also examined whether species were correctly identified more often than expected under a similar coin toss experiment. Quiz answers were compiled in a triangular matrix showing species ranked by taxonomic order on both axes. From the triangular matrix we can ask whether closely-related species were mistaken for each other, i.e. confused, more often than distantly-related species. We tested this hypothesis with a null model approach that compared the mean taxonomic distance between confused species in the observed matrix to the distribution of mean taxonomie distances between confused species in 10,000 randomized matrices. Finally, we drew a dendrogram to represent the similarity between species with regard to the distribution of identification errors. The 20 participants who took the quiz showed substantial variation in their ability to identify species correctly. Fourteen species were correctly identified more often than expected at random, while only one was misidentified more often than expected at random. The observed mean distance between confused species was smaller than all of the mean distances from the randomized, null-model matrices, indicating that confusions are more frequent between closely related species than between distant ones.
Tài liệu tham khảo
Cerqueira, M. C.; Cohn-Haft, M.; Vargas, C. F.; Nader, C. E.; Andretti, C. B.; Costa, T. V. V.; Sberze, M.; Hines, J. E.; Ferraz, G. & Burgman, M. 2013. Rare or elusive? A test of expert knowledge about rarity of Amazon Forest birds. Diversity and Distributions, 19: 710–721.
Cohn-Haft, M.; Whittaker, A. & Stouffer, P. C. 1997. A new look at the “species-poor” central Amazon: the avifauna north of Manaus, Brazil. Ornithological Monographs, 48: 205–235.
Donovan, J. J. & Radosevich, D. J. 1999. A meta-analytic review of the distribution of practice effect: now you see it, now you don’t. Journal of Applied Psychology, 84: 795–805.
Farmer, R. G.; Leonard, M. L. & Horn, A. G. 2012. Observer effects and avian-call-count survey quality: rare-species biases and overconfidence. Auk, 129: 76–86.
Fristrup, K. M. & Mennitt, D. 2012. Bioacustical monitoring in terristrial environments. Acoustics Today, 8: 15–24.
Gotelli, N. J. & Graves, G. R. 1996. Null models in ecology. Washington: Smithsonian Institution Press.
Irestedt, M.; Fjeldså, J.; Nylander, J. A. & Ericson, P. G. 2004. Phylogenetic relationships of typical antbirds (Thamnophilidae) and test of incongruence based on Bayes factors. BMC Evolutionary Biology, 4: 23.
Jurman, G.; Riccadonna, S.; Vislntalner, R. & Furlanello, C. 2009. Canberra distance on ranked lists. Proceedings, Advances in Ranking - NIPS 09 Workshop 22-27.
Lance, G. N. & Williams, W. T. 1966. Computer programs for hierarchical polythetic classification (“similarity analyses”). Computer Journal, 9: 60–64.
Lance, G. N. & Williams, W. T. 1967. Mixed-data classificatory programs I - agglomerative systems. Australian Computer Journal, 1: 15–20.
Lees, A. C.; Naka, L. N.; Aleixo, A.; Cohn-Haft, M.; Piacentini, V. Q.; Santos, M. P. D.; & Silveira, L. F. 2014. Conducting rigorous avian inventories: Amazonian case studies and a roadmap for improvement. Revista Brasileira de Ornitologia, 22: 107–120.
MacKenzie, D. I.; Nichols, J. D.; Lachman, G. B.; Droege, S.; Royle, J. A. & Langtimm, C. A. 2002. Estimating site occupancy rates when detection probabilites are less than one. Ecology, 83: 2248–2255.
McClintock, B. T.; Bailey, L. L.; Pollock, K. H. & Simons, T. R. 2010. Experimental investigation of observation error in anuran call surveys. Journal of Wildlife Management, 74: 1882–1893.
Miller, D. A.; Nichols, J. D.; Gude, J. A.; Rich, L. N.; Podruzny, K. M.; Hines, J. E. & Mitchell, M. S. 2013. Determining occurrence dynamics when false positives occur: estimating the range dynamics of wolves from public survey data. PLoS One, 8:e65808.
Miller, D. A.; Nichols, J. D.; McClintock, B. T.; Grant, E. H. C.; Bailey, L. L. & Weir, L. A. 2011. Improving occupancy estimation when two types of observational error occur: non-detection and species misidentification. Ecology, 92: 1422–1428.
Moyle, R. G.; Chesser, R. T.; Brumfield, R. T.; Tello, J. G.; Marchese, D.J. & Cracraft, J. 2009. Phylogenyandphylogenetic classification of the antbirds, ovenbirds, woodcreepers, and allies (Aves: Passeriformes: infraorder Furnariides). Cladistics, 25: 386–405.
Naka, L. N.; Stouffer, P. C.; Cohn-Haft, M.; Marantz, C. A.; Whittaker, A. & Bierregaard, R. O. J. 2008. Voices of the Brazilian Amazon, v. 1. Birds of the terra firme forests north of Manaus: Guianan area of endemism. Manaus: Editora INPA.
R Development Core Team. 2013. A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. Version 3.0.2.http://www.R-project.org/
Remsen, J. V. J.; Cadena, C. D.; Jaramillo, A.; Nores, M.; Pacheco, J. F.; Pérez-Emán, J.; Robbins, M. B.; Stiles, F. G.; Stotz, D. F. & Zimmer, K. J. 2014. A classification of the bird species of South America. American Ornithologists’ Union. Version [20/05/2014]http://museum.lsu.edu/-Remsen/SACCBaseline.html
Somervuo, P.; Koskela, S.; Pennanen, J.; Nilsson, R. H. & Ovaskainen, O. 2016. Unbiased probabilistic taxonomie classification for DNA barcoding. Bioinformatics, 32: 2920–2927.
Sousa-Lima, R. S.; Norris, T. F.; Oswald, J. N. & Fernandes, D. P. 2013. A review and inventory of fixed autonomous recorders for passive acoustic monitoring of marine mammals. Aquatic Mammals, 39: 23–53.