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Comparison of Population Monitoring Methods for Breeding Forest Birds in Korean Temperate Mixed Forests

국내 온대 혼효림에 서식하는 산림성 조류의 번식기 개체군 모니터링 방법에 대한 비교

  • Nam, Hyun-Young (School of Biological Sciences, Seoul National University) ;
  • Choi, Chang-Yong (Research Institute of Agriculture and Life Sciences, Seoul National University) ;
  • Park, Jin-Young (National Migratory Bird Research Center, National Institute of Biological Resources) ;
  • Hur, Wee-Haeng (Division of Animal Resources, National Institute of Biological Resources)
  • 남현영 (서울대학교 생명과학부) ;
  • 최창용 (서울대학교 농업생명과학연구원) ;
  • 박진영 (국립생물자원관 국가철새연구센터) ;
  • 허위행 (국립생물자원관 동물자원과)
  • Received : 2019.08.08
  • Accepted : 2019.10.04
  • Published : 2019.12.31

Abstract

Birds are effective ecological indicators but there is no national protocol in place to monitor population dynamics of forest birds in Korea. To support the establishment of future monitoring protocols, we compared the results of two generally used monitoring methods for forest bird surveys in two temperate mixed forests in central Korea. There was no statistical difference in the number of species and individuals detected per unit survey effort when comparing line transects and point counts. The number of species and individuals were higher in a five-minute count than in a three-minute point count, but the total accumulated number of expected observed species showed no difference between the two count durations. The number of observed species and individuals increased in both methods as plot radius or transect width increased, suggesting that multi-layer or multi-band surveys may be useful for quantitative and qualitative objectives. The decreasing number of observed species and individuals after sunrise suggested that bird monitoring should be conducted earlier in the morning, within four hours after sunrise. To detect 70% of the total number of species, 7.0 to 7.6 survey hours, equivalent to 42 three-minute counts (95% confidence interval [CI]: 26 to 61) or 33 five-minute counts (95% CI: 19 to 53) were needed for unlimited radius point counts. On the other hand, 4.8 survey hours, equivalent to 26 line transect counts (95% CI: 15 to 45) using 200-m transects with unlimited width, were required to achieve the same level of species detection. Therefore, the line transect method may be more effective than the point count method, at least in terms of local species richness assessment. For national forest bird monitoring, our data indicated that one or both survey methods can be selected as a basic protocol, based on the goals and scales of monitoring, forest types, and the conditions of the target areas.

조류는 포획과 채집에 의존하지 않고 관찰 조사를 통해 서식 현황을 평가할 수 있는 유용한 생태계 지표이다. 그러나 우리나라의 산림성 조류의 개체군 변동을 파악하기 위한 통일된 모니터링 지침이 아직 없으며 이를 위한 자료도 부족한 실정이다. 따라서 본 연구는 이를 위한 기초 자료를 제공하기 위해 실시되었으며, 중부의 온대혼효림 두 곳에 서식하는 번식기 산림성 조류를 대상으로 가장 일반적으로 사용되는 선조사 및 조사시간이 다른 정점조사를 적용하여 그 조사 결과를 비교하였다. 단위 조사노력당 관찰되는 종수 및 개체수는 선조사와 정점조사간에 유의한 차이를 보이지 않았으나, 서로 다른 연구지역의 조류상 차이를 파악할 수 있는 것으로 나타났다. 정점조사에서는 단위 정점당 조사 시간이 길수록 종과 개체수가 많이 관찰되었으나, 조사횟수를 누적하면 뚜렷한 차이를 보이지 않았다. 정점조사와 선조사 모두 조사반경이 커질수록 단위 정점 또는 구간 내에서 더 많은 종과 개체수가 관찰되었으며, 정량적 또는 정성적인 목적에 따라 활용할 수 있도록 거리에 따라 구분하여 다층구조로 결과를 기록할 필요가 있다. 또한 조류의 관찰률은 일출 후 시간이 경과하면서 점차 감소하므로, 조사는 일출 후 4시간 이내의 가급적 이른 오전에 수행해야 하는 것으로 나타났다. 특정 지역의 산림성 조류의 전체 종 풍부도의 70%를 파악하기 위해서는 7.0-7.6시간이 소요되는 42회(95% 신뢰한계: 26-61회)의 3분 정점조사 또는 33회(95% 신뢰한계: 19-53회)의 5분 정점조사가 필요한 것으로 예측되었다. 반면 동일한 수준의 종 풍부도 파악을 위해서는 26회(95% 신뢰구간: 15-45회)의 200 m 세부구간에 대한 선조사가 필요하며, 이는 약 4.8시간이 소요되는 것으로 평가되었다. 따라서 선조사는 정점조사에 비해 대상 지역의 전체적인 종 풍부도를 파악하는데에는 보다 효율적인 방법으로 나타났다. 향후 산림성 조류조사의 목적과 규모, 현장상황 등에 따라 본 연구에서 확인된 조사방법을 택일하거나 병행하는 방법을 고려할 수 있다.

Keywords

References

  1. Andren, H. 1994. Effects of habitat fragmentation on birds and mammals in landscapes with different proportions of suitable habitat: a review. Oikos 71: 355-366. https://doi.org/10.2307/3545823
  2. Bart, J. and Herrick, J. 1984. Diurnal timing of bird surveys. Auk 101: 384-387. https://doi.org/10.1093/auk/101.2.405
  3. Bibby, C.J., Burgess, N.D., Hill, D.A. and Mustoe, S.H. 2000a. Bird census techniques. Academic Press. London, UK. pp. 302.
  4. Bibby, C., Jones, M. and Marsden, S. 2000b. Expedition field techniques - bird survey. Bird Life International. Cambridge, UK. pp. 137.
  5. British Trust for Ornithology (BTO). 2019. BTO/JNCC/RSPB breeding bird survey instructions. https://www.bto.org/our-science/projects/bbs/taking-part/download-forms-instructions. British Trust for Ornithology [cited 29 May 2019].
  6. Buckland, S.T., Anderson, D.R., Burnham, K.P., Laake, J.L., Borchers, D.L. and Thomas, L. 2001. Introduction to distance sampling. Oxford University Press. Oxford, UK. pp. 448.
  7. Canterbury, G.E., Martin, T.E., Petit, D.R., Petit, L.J. and Bradford, D.F. 2000. Bird communities and habitat as ecological indicators of forest condition in regional monitoring. Conservation Biology 14(2): 544-558. https://doi.org/10.1046/j.1523-1739.2000.98235.x
  8. Choi, C.-Y., Lee, E.-J., Nam, H.-Y. and Lee, W.-S. 2007. Effects of postfire logging on bird populations and communities in burned forests. Journal of Korean Forest Society 96(1): 115-123.
  9. Choi, C.-Y., Lee, E.-J., Nam, H.-Y., Lee, W.-S. and Lim, J.-H. 2014. Temporal changes in the breeding bird community caused by post-fire treatments after the Samcheok forest fire in Korea. Landscape and Ecological Engineering 10(1): 203-214. https://doi.org/10.1007/s11355-012-0203-6
  10. Clark, R.G. 2016. Statistical efficiency in distance sampling. PLoS ONE 11(3): e0149298. https://doi.org/10.1371/journal.pone.0149298
  11. Cody, M.L. 1981. Habitat selection in birds: The roles of vegetation structure, competitors, and productivity. BioScience 31(2): 107-113. https://doi.org/10.2307/1308252
  12. Colwell, R.K. 2018. EstimateS: Statistical estimation of species richness and shared species from samples. Version 9. User's guide and application. http://purl.oclc.org/estimates.
  13. Colwell, R.K., Chao, A., Gotelli, N.J., Lin, S.-Y., Mao, C.X., Chazdon, R.L. and 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. https://doi.org/10.1093/jpe/rtr044
  14. Farnsworth, G.L., Pollock, K.H., Nichols, J.D., Simons, T.R., Hines, J.E. and Sauer, J.R. 2002. A removal model for estimating detection probabilities from point-count surveys. Auk 119(2): 414-425. https://doi.org/10.1093/auk/119.2.414
  15. Gill, J.A. and Sutherland, W.J. 2000. Predicting the consequences of human disturbance from behavioural decisions. pp. 51-64. In: Gosling, L.M. and Sutherland, W.J. (Eds.). Behaviour and conservation. Cambridge University Press. Cambridge, UK.
  16. Gregory, R.D., Gibbons, D.W. and Donald, P.F. 2004. Bird census and survey techniques. pp. 17-56. In : Sutherland, W.J., Newton, I. and Green, R. (Eds.) Bird ecology and conservation: a handbook of techniques. Oxford University Press. New York, USA.
  17. Gregory, R.D., Willis, S.G., Jiguet, F., Vorisek, P., Klvanova, A., van Strien, A., Huntley, B., Collingham, Y.C., Couvet, D. and Green, R.E. 2009. An indicator of the impact of climatic change on European bird populations. PLoS ONE 4(3): e4678. https://doi.org/10.1371/journal.pone.0004678
  18. Haselmayer, J. and Quinn, J.S. 2000. A comparison of point counts and sound recording as bird survey methods in Amazonian southeast Peru. Condor 102(4): 887-893. https://doi.org/10.1650/0010-5422(2000)102[0887:ACOPCA]2.0.CO;2
  19. Jarvinen, O., Vaisanen, R.A. and Haila, Y. 1977. Bird census results in different years, stages of the breeding season and times of the day. Ornis Fennica 54: 108-118.
  20. Ko, J.C., Fan, M.W., Lin, R.S., Lee, P.F. and Tsai, S.P. 2017. Point count sampling data from the Taiwan Breeding Bird Survey. Taiwan Journal of Biodiversity 19(4): 243-254.
  21. Koskimies, P. and Vaisanen, R.A. 1991. Monitoring bird populations - a manual of methods applied in Finland. https://www.luomus.fi/en/methods-bird-monitoring. Helsinki, Finland: Zoological Museum, Finnish Museum of Natural History [cited 29 May 2019].
  22. Kulaga, K. and Budka, M. 2019. Bird species detection by an observer and an autonomous sound recorder in two different environments: Forest and farmland. PLoS ONE 14(2): e0211970. https://doi.org/10.1371/journal.pone.0211970
  23. National Institute of Biological Resources (NIBR). 2017. 2016-2017 winter waterbird census of Korea. National Institute of Biological Resources. Incheon, Korea.
  24. National Institute of Ecology (NIE). 2017. Data book of national ecosystem survey. National Institute of Ecology. Seocheon, Korea.
  25. National Institute of Environmental Research (NIER). 2012. 4th national ecosystem survey manual. National Institute of Environmental Research. Incheon, Korea.
  26. National Institute of Forest Science (NIFS). 2017. Climate change effect assessment manual on the forestry and forest sciences. National Institute of Forest Science. Seoul, Korea.
  27. National Park Research Institute (NPRI). 2018. Annual Report on the Bird Research. National Park Research Institute. Wonju, Korea.
  28. O'connell, T.J., Jackson, L.E. and Brooks, R.P. 2000. Bird guilds as indicators of ecological condition in the central Appalachians. Ecological Applications 10(6): 1706-1721. https://doi.org/10.1890/1051-0761(2000)010[1706:BGAIOE]2.0.CO;2
  29. Paillet, Y. et al. 2009. Biodiversity differences between managed and unmanaged forests: meta-analysis of species richness in Europe. Conservation Biology 24(1): 101-112. https://doi.org/10.1111/j.1523-1739.2009.01399.x
  30. Ralph, C.J. and Scott, J.M. 1981. Estimating numbers of terrestrial birds. Allen Press Inc. Lawrence, USA.
  31. Richards, D.G. 1981. Environmental acoustics and censuses of singing birds. Studies in Avian Biology 6: 297-300.
  32. Robbins, C.S. 1981a. Bird activity levels related to weather. Studies in Avian Biology 6: 301-310.
  33. Robbins, C.S. 1981b. Effect of time of day on bird activity. Studies in Avian Biology 6: 275-286.
  34. Rosenstock, S.S., Anderson, D.R., Giesen, K.M., Leukering, T. and Carter, M.F. 2002. Landbird counting techniques: current practices and an alternative. Auk 119(1): 46-53. https://doi.org/10.1093/auk/119.1.46
  35. SAS Institute Inc. 2011. SAS/STAT(R) 9.3 user's guide. SAS Institute Inc., Cary, USA.
  36. Stephen, P.A. et al. 2016. Consistent response of bird populations to climate change on two continents. Science 352(6281): 84-87. https://doi.org/10.1126/science.aac4858
  37. Sung, Y.-H., Tse, I.W.-L. and Yu, Y.-T. 2018. Population trends of the Black-faced Spoonbill Platalea minor: analysis of data from international synchronised censuses. Bird Conservation International 28(1): 157-167. https://doi.org/10.1017/S0959270917000016
  38. Sutherland, W.J. 2006. Ecological census techniques. 2nd Ed. Cambridge University Press. New York, USA.
  39. Thompson, W.L. 2002. Towards reliable bird surveys: accounting for individuals present but not detected. Auk 119(1): 18-25. https://doi.org/10.1093/auk/119.1.18
  40. US Geological Survey (USGS). 2018. Instructions for conducting the North American Breeding Bird Survey. https://www.pwrc.usgs.gov/bbs/participate/index.html [cited 27 Mar 2019].
  41. Vorisek, P., Klvanova, A., Wotton, S. and Gregory, R.D. 2008. A best practice guide for wild bird monitoring schemes. CSO/RSPB. Trebon, Czech Republic.