聲景生態學在最近的十年蓬勃發展，關注在地景上聲音的組成，用以討論人為聲音的干擾和生物聲音多樣性隨時間改變等議題，提供另一種在生物群集尺度監測多樣性改變、自然擾動與人為影響的可能。由於近幾年錄音工具的快速發展，促使長期且大量收集聲景錄音的研究漸漸增加，也陸續證明聲景在生物群集的層級上能有效反映生物多樣性的變化。然而，長期監測的聲景研究間，並未有一致的錄音方法，且愈來愈大量的錄音資料在儲存與分析所需的軟、硬體資源上皆造成負擔。因此，本研究希望藉由臺灣北部聲景一整年的監測資料來找出最具有成本效益的錄音取樣方法，並探討錄音頻度與錄音覆蓋率對錄音取樣代表性的影響。我選定關渡濕地、陽明山天然林與東眼山人工林三種棲地樣點，架設SM4自動錄音機，自2018年7月到2019年6月每3分鐘取樣錄音1分鐘收集整年聲景監測資料，同時從2019年4月到8月收集每個月各十天的每日完整的聲景錄音。此研究以六種聲音指數數值在每日尺度下的五個百分位數（第5、25、50、75和95百分位數）量化每日聲景特徵，再分別比較利用19種錄音取樣與利用最密集錄音所量化得到的聲景特徵間的差異，評估各錄音取樣方法的代表性。我先計算每分鐘錄音檔的六種聲音指數，並在不同錄音取樣方法下計算各指數的各百分位數，再藉由bootstrapped resampling的方法，以單一指數的單一百分位數在各取樣與最密集錄音間的差異為重取單位，以一千次重取平均值的九十五信賴區間與0重疊與否，判斷各錄音方法和最密集錄音之間的聲景特徵量化結果是否一致。最後以30個指數百分位數（6個指數x 5個百分位數）中結果一致的數量，作為錄音取樣方法代表性的測量值，再分析取樣代表性隨錄音覆蓋率與錄音頻度的變化情形。除了整體的聲景比較外，我也分別針對單一棲地、季節，以及聲音群集的聲景進行相同的分析。整體聲景的研究結果顯示，隨錄音覆蓋率的降低，錄音取樣的代表性愈低，每小時錄音1次的錄音頻度相對較佳。在特定棲地、季節或聲音群集的聲景分析中，錄音覆蓋率愈高則取樣代表性有愈高的趨勢，而錄音頻度在各特定聲景間沒有一致的影響。各取樣方法的代表性在單一季節中較跨季節要高；在單一聲音群集的聲景，則不比跨群集分析擁有較高的代表性；單一棲地則與跨棲地相似。雖然很多因素可能影響長期聲景監測之錄音取樣方法代表性，本研究建議應避免過低的錄音覆蓋率，愈高的錄音覆蓋率原則上愈具聲景代表性，但為有效利用資源，可考量對監測目標之聲景進行前測，並在短期前測中考量季節的影響，避免單一季節的前測低估長期、跨季節監測下的最佳覆蓋率，在聲景資料收集、儲存、分析、研究或管理目標取捨下，找出符合一個地區的最佳錄音覆蓋率與錄音頻度。本研究透過長期且系統性的資料收集，發展具代表性錄音取樣的測試方法，找出長期聲景監測錄音取樣方法的代表性、提供特定聲景監測下的取樣建議，將有利於未來長期且自動排程錄音的聲景監測工作。

Soundscape ecology, which studies the composition of sound at landscape scale, has flourished in the last decade. Soundscape research focuses on potential impacts of anthropogenic noise as well as temporal change of biological sound. At the community scale, soundscape provides another dimension of biodiversity and is useful in tracking biodiversity changes due to natural disturbances and human influences. With the rapid development of recording tools in recent years, it is becoming easier to conduct long-term soundscape projects, which generate big data. Such data has allowed researchers to demonstrate that soundscape metrics can be effective in monitoring biodiversity changes. At the same time, it also poses two challenges: 1) recording methods often vary among projects, making comparasion or synthesis difficult; and 2) the increasing amount of data demands more funds and manpower for storage and analysis. This study aims to find the most cost-effective temporal sampling schemes based on a full-year soundscape data set in northern Taiwan. The cost-effectiveness is evaluated by the ability of a given recording frequency and/or recording coverage (i.e. temporal sampling scheme) to represent the soundscape characterized with the full-year data set. I included three habitat types in this study: a wetland (Guandu), a natural deciduous forest (Yangmingshan National Park) and a tree plantation (Dongyanshan Forest Recreation Area). I collected the full-year data by recording 1 minute for every 3-minute interval from July 2018 to June 2019. In addition, I also collected near-complete coverage data for 10 days each month from April to August 2019. Daily soundscape was characterized using six acoustic indices, each with their five percentiles (5th, 25th, 50th, 75th, and 95th percentiles). A total of 19 temporal sampling schemes were evaluated. Each acoustic index was calculated based on 1-minute recording, which gives many index values for a given day. For each day, the five percentiles of each index were then calculated from those 1-minute values. Therefore, a total of 30 index percentiles (6 indexes x 5 percentiles) can be generated daily for each of the 19 temporal sampling schemes, as well as for the full-year and near-complete data sets. The difference in each index percentile between a given temporal sampling scheme and the full-year (or near-complete coverage) data was calculated for each day. The daily differences for a given index percentile were bootstrapped to estimate its 95% confidence interval. If the 95% confidence interval includes zero, the index percentile is treated as being significantly different between a temporal sampling scheme and the full-year (or near-complete coverage) data. The percentage of the 30 index percentiles that are not significantly different from the full-year (or near-complete coverage) data is used as a measure of the representativeness. In addition to the pooled data (pooled across all habitats, seasons and acoustic communities), I also performed the same analysis on specific soundscapes of a single habitat, season, and acoustic community. The results suggest that soundscape representativeness decreased with reduced recording coverage of a given temporal sampling scheme, and the once per hour recording frequency yielded high representativeness. For the analyses on specific soundscapes, the representativeness also decreased with reduced recording coverage, but recording frequency did not have a consistent effect on the representativeness. The representativeness was generally higher in single-season analyses than in pooled-season analysis across all sampling schemes. On the other hand, for the analyses of a single acoustic community or a single habitat, the representativeness across all sampling schemes was similar to that of pooled-data (pooled across all acoustics communities or all habitats) analyses. The findings of this study suggests that low recording coverage should generally be avoided. Furthermore, it is recommended that a pre-testing protocol be implemented prior to a long-term soundscape monitoring project, particularly for correcting potential underestimation of recording coverage that was determined based on a single-season data. The most cost-effective temporal sampling scheme (i.e. recording coverage and recording frequency) for any given study will ultimately be a balance between research/management goals and logistic constrains. This study demonstrated how a pre-testing can be done to find the most representative sampling scheme for long-term soundscape monitoring, and where the same acoustic indices as I used here are involved, the representativeness of the temporal sampling schemes evaluated in this study might be highly applicable.

Adams, A. M., McGuire, L. P., Hooton, L.A., & Fenton, M. B. (2015). How high is high? Using percentile thresholds to identify peak bat activity. Canadian Journal of Zoology, 93(4), 307–313. https://doi.org/10.1139/cjz-2014-0230
Aide, T. M., Hernández-Serna, A., Campos-Cerqueira, M., Acevedo-Charry, O., & Deichmann, J. L. (2017). Species richness (of insects) drives the use of acoustic space in the tropics. Remote Sensing, 9(11), 1096. https://doi.org/10.3390/rs9111096
Aletta, F., & Xiao, J. (2018). What are the Current Priorities and Challenges for (Urban) Soundscape Research? Challenges, 9(1), 16. https://doi.org/10.3390/challe9010016
Barlow, K. E., Briggs, P. A., Haysom, K. A., Hutson, A. M., Lechiara, N. L., Racey, P. A. et al. (2015). Citizen science reveals trends in bat populations: The National Bat Monitoring Programme in Great Britain. Biological Conservation, 182, 14–26. https://doi.org/10.1016/j.biocon.2014.11.022
Bertucci, F., Parmentier, E., Lecellier, G., Hawkins, A. D., & Lecchini, D. (2016). Acoustic indices provide information on the status of coral reefs : an example from Moorea Island in the South Pacific. Scientific Reports, 6, 33326. https://doi.org/10.1038/srep33326
Bobryk, C. W., Rega-Brodsky, C. C., Bardhan, S., Farina, A., He, H. S., & Jose, S. (2015). A rapid soundscape analysis to quantify conservation benefits of temperate agroforestry systems using low-cost technology. Agroforestry Systems. https://doi.org/10.1007/s10457-015-9879-6
Boelman, N. T., Asner, G. P., Hart, P. J., & Martin, R. E. (2007). Multi-Trophic Invasion Resistance in Hawaii: Bioacoustics, Field Surveys, and Airborne Remote Sensing. Ecological Applications, 17(8), 2137–2144. https://doi.org/10.1890/07-0004.1
Bolzan, A. M. R., Garey, M.V., Hartmann, P. A., & Hartmann, M. T. (2019). Too cold for dating: Temporal distribution of the calling activity of an austral anuran assemblage. Herpetology Notes, 12, 961–968.
Browning, E., Gibb, R., Glover-Kapfer, P., & Jones, K. E. (2017). Passive acoustic monitoring in ecology and conservation. WWF Conservation Technology Series, 1(2). https://doi.org/10.13140/RG.2.2.18158.46409
Burivalova, Z., Game, E. T., & Butler, R. A. (2019). The sound of a tropical forest. Science, 363(6422), 28–29. https://doi.org/10.1126/science.aav1902
Burivalova, Z., Towsey, M., Boucher, T., Truskinger, A., Apelis, C., Roe, P., & Game, E. T. (2018). Using soundscapes to detect variable degrees of human influence on tropical forests in Papua New Guinea. Conservation Biology, 32(1), 205–215. https://doi.org/10.1111/cobi.12968
Buxton, R. T., Agnihotri, S., Robin, V.V, Goel, A., & Balakrishnan, R. (2018). Acoustic indices as rapid indicators of avian diversity in different land-use types in an Indian biodiversity hotspot. Journal of Ecoacoustics, 2, #GWPZVD. https://doi.org/10.22261/JEA.GWPZVD
Campos-Cerqueira, M., & Aide, T. M. (2017). Changes in the acoustic structure and composition along a tropical elevational gradient Changes in the acoustic structure and composition along a tropical elevational gradient. Journal of Ecoacoustics, 1, #PNCO7I. https://doi.org/10.22261/JEA.PNCO7I
Cook, A., & Hartley, S. (2018). Efficient sampling of avian acoustic recordings: Intermittent subsamples improve estimates of single species prevalence and total species richness. Avian Conservation and Ecology, 13(1), 21. https://doi.org/10.5751/ACE-01221-130121
Deichmann, J. L., Acevedo-Charry, O., Barclay, L., Burivalova, Z., Campos-Cerqueira, M., d’Horta, F. et al. (2018). It’s time to listen: there is much to be learned from the sounds of tropical ecosystems. Biotropica, 50(5), 713–718. https://doi.org/10.1111/btp.12593
Deichmann, J. L., Hernández-serna, A., Delgado C., J. A., Campos-cerqueira, M., & Aide, T. M. (2017). Soundscape analysis and acoustic monitoring document impacts of natural gas exploration on biodiversity in a tropical forest. Ecological Indicators, 74, 39–48. https://doi.org/10.1016/j.ecolind.2016.11.002
Depraetere, M., Pavoine, S., Jiguet, F., Gasc, A., Duvail, S., & Sueur, J. (2012). Monitoring animal diversity using acoustic indices: Implementation in a temperate woodland. Ecological Indicators, 13(1), 46–54. https://doi.org/10.1016/j.ecolind.2011.05.006
Derryberry, E. P., Danner, R. M., Danner, J. E., Derryberry, G. E., Phillips, J. N., Lipshutz, S. E. et al. (2016). Patterns of Song across Natural and Anthropogenic Soundscapes Suggest That White-Crowned Sparrows Minimize Acoustic Masking and Maximize Signal Content. PLoS ONE, 11(4), e0154456. https://doi.org/10.1371/journal.pone.0154456
Digby, A., Towsey, M., Bell, B. D., & Teal, P. D. (2013). A practical comparison of manual and autonomous methods for acoustic monitoring, (Charif 2008), 675–683. https://doi.org/10.1111/2041-210X.12060
Dornelas, M., Gotelli, N. J., McGill, B., Shimadzu, H., Moyes, F., Sievers, C., & Magurran, A. E. (2014). Assemblage Time Series Reveal Biodiversity Change but Not Systematic Loss. Science, 344(6181), 296–299. https://doi.org/10.1126/science.1248484
Ehnes, M., Dech, J. P., & Foote, J. R. (2018). Seasonal changes in acoustic detection of forest birds. Journal of Ecoacoustics, 2, QVDZO7. https://doi.org/10.22261/JEA.QVDZO7
Eiseman, J., Vonhof, M., & Gill, S. (2018). Assessing the Performance of Different Sampling Schedules in Capturing the Temporal Complexity of Soundscapes. In US Regional Association of the International Association for Landscape Ecology Symposium, Chicago, IL, USA.: US-IALE, 2018-04-09 ~ 2018-04-11.
Eldridge, A., Casey, M., Moscoso, P., & Peck, M. (2016). A new method for ecoacoustics? Toward the extraction and evaluation of ecologically-meaningful soundscape components using sparse coding methods. PeerJ, 4, e2108. https://doi.org/10.7717/peerj.2108
Fairbrass, A. J., Rennett, P., Williams, C., Titheridge, H., & Jones, K. E. (2017). Biases of acoustic indices measuring biodiversity in urban areas. Ecological Indicators, 83, 169–177. https://doi.org/10.1016/j.ecolind.2017.07.064
Fairbrass, A. J., Titheridge, H., Firman, M., Williams, C., Brostow, G. J., & Jones, K. E. (2018). CityNet — Deep learning tools for urban ecoacoustic assessment, 2019(10), 186–197. https://doi.org/10.1111/2041-210X.13114
Farina, A. (2014). Soundscape Ecology: Principles, Patterns, Methods and Applications. Springer Dordrecht Heidelberg New York London. https://doi.org/10.1007/978-94-007-7374-5
Farina, A., Gage, S. H., & Salutari, P. (2018). Testing the ecoacoustics event detection and identification (EEDI) approach on Mediterranean soundscapes. Ecological Indicators, 85, 698–715. https://doi.org/10.1016/j.ecolind.2017.10.073
Farina, A., & James, P. (2016). The acoustic communities: Definition, description and ecological role. BioSystems, 147, 11–20. https://doi.org/10.1016/j.biosystems.2016.05.011
Farina, A., & Pieretti, N. (2012). The soundscape ecology: A new frontier of landscape research and its application to islands and coastal systems. Journal of Marine and Island Cultures, 1(1), 21–26. https://doi.org/10.1016/j.imic.2012.04.002
Farina, A., Pieretti, N., & Piccioli, L. (2011). The soundscape methodology for long-term bird monitoring : A Mediterranean Europe case-study. Ecological Informatics, 6, 354–363. https://doi.org/10.1016/j.ecoinf.2011.07.004
Ferreira, L. M., Oliveira, E. G., Lopes, L. C., Brito, M. R., Baumgarten, J., Rodrigues, F. H., & Sousa-Lima, R. S. (2018). What do insects, anurans, birds, and mammals have to say about soundscape indices in a tropical savanna. Journal of Ecoacoustics, 2, PVH6YZ. https://doi.org/10.22261/JEA.PVH6YZ
Frommolt, K. H., & Tauchert, K. H. (2014). Applying bioacoustic methods for long-term monitoring of a nocturnal wetland bird. Ecological Informatics, 21, 4–12. https://doi.org/10.1016/j.ecoinf.2013.12.009
Fuller, S., Axel, A. C., Tucker, D., & Gage, S. H. (2015). Connecting soundscape to landscape: Which acoustic index best describes landscape configuration? Ecological Indicators, 58, 207–215. https://doi.org/10.1016/j.ecolind.2015.05.057
Gage, S. H., & Axel, A. C. (2014). Ecological Informatics Visualization of temporal change in soundscape power of a Michigan lake habitat over a 4-year period. Ecological Informatics, 21, 100–109. https://doi.org/10.1016/j.ecoinf.2013.11.004
Gage, S. H., Wimmer, J., Tarrant, T., & Grace, P. R. (2017). Acoustic patterns at the Samford Ecological Research Facility in South East Queensland, Australia: The Peri-Urban SuperSite of the Terrestrial Ecosystem Research Network. Ecological Informatics, 38, 62–75. https://doi.org/10.1016/j.ecoinf.2017.01.002
Gasc, A., Gottesman, B. L., Francomano, D., Jung, J., Durham, M., Mateljak, J., & Pijanowski, B. C. (2018). Soundscapes reveal disturbance impacts: biophonic response to wildfire in the Sonoran Desert Sky Islands. Landscape Ecology, 33(8), 1399–1415. https://doi.org/10.1007/s10980-018-0675-3
Gasc, A., Pavoine, S., Lellouch, L., Grandcolas, P., & Sueur, J. (2015). Acoustic indices for biodiversity assessments : Analyses of bias based on simulated bird assemblages and recommendations for field surveys. Biological Conservation, 191, 306–312. https://doi.org/10.1016/j.biocon.2015.06.018
Gasc, A., Sueur, J., Pavoine, S., Pellens, R., & Grandcolas, P. (2013). Biodiversity Sampling Using a Global Acoustic Approach: Contrasting Sites with Microendemics in New Caledonia. PLoS ONE, 8(5). https://doi.org/10.1371/journal.pone.0065311
Gibb, R., Browning, E., Glover-Kapfer, P., & Jones, K. E. (2019). Emerging opportunities and challenges for passive acoustics in ecological assessment and monitoring. Methods in Ecology and Evolution, 10(2), 169–185. https://doi.org/10.1111/2041-210X.13101
Gómez, W. E., Isaza, C.V., & Daza, J. M. (2018). Identifying disturbed habitats: A new method from acoustic indices. Ecological Informatics, 45, 16–25. https://doi.org/10.1016/j.ecoinf.2018.03.001
Gottesman, B. L., Francomano, D., Zhao, Z., Bellisario, K., Ghadiri, M., Broadhead, T. et al. (2018). Acoustic monitoring reveals diversity and surprising dynamics in tropical freshwater soundscapes. Freshwater Biology, 1–16. https://doi.org/10.1111/fwb.13096
Greif, S., Zsebők, S., Schmieder, D., & Siemens, B. M. (2017). Acoustic mirrors as sensory traps for bats. Science, 1047(September), 1045–1047. https://doi.org/10.1126/science.aam7817
Hagens, S.V, Rendall, A. R., & Whisson, D. A. (2018). Passive acoustic surveys for predicting species’ distributions: Optimising detection probability. PLoS ONE, 13(7), e0199396. https://doi.org/10.1371/journal.pone.0199396
Halfwerk, W., & Slabbekoorn, H. (2015). Pollution going multimodal: the complex impact of the human-altered sensory environment on animal perception and performance. Biology Letters, 11(4), 20141051. https://doi.org/10.1098/rsbl.2014.1051
Harris, S. A., Shears, N. T., & Radford, C. A. (2016). Ecoacoustic indices as proxies for biodiversity on temperate reefs. Methods in Ecology and Evolution, 7(6). https://doi.org/10.1111/2041-210X.12527
Heinicke, S., Kalan, A. K., Wagner, O. J. J., & Mundry, R. (2015). Assessing the performance of a semi-automated acoustic monitoring system for primates. Methods in Ecology and Evolution, 6, 753–763. https://doi.org/10.1111/2041-210X.12384
Honrado, J. P., Pereira, H. M., & Guisan, A. (2016). Fostering integration between biodiversity monitoring and modelling. Journal of Applied Ecology, 53(5), 1299–1304. https://doi.org/10.1111/1365-2664.12777
Hughes, A. C., Satasook, C., Bates, P. J. J., Soisook, P., Sritongchuay, T., Jones, G., & Bumrungsri, S. (2010). Echolocation Call Analysis and Presence-Only Modelling as Conservation Monitoring Tools for Rhinolophoid Bats in Thailand. Acta Chiropterologica, 12(2), 311–327. https://doi.org/10.3161/150811010X537891
Izaguirre, M. I. R., Ramírez-alán, O., & Castro, J. D.la. (2018). Acoustic indices applied to biodiversity monitoring in a Costa Rica dry tropical forest. Journal of Ecoacoustics, 2, #TNW2NP. https://doi.org/10.22261/JEA.TNW2NP
Jones, K. E., Russ, J. A., Bashta, A. T., Bilhari, Z., Catto, C., Csosz, I. et al. (2013). Indicator Bats Program: A System for the Global Acoustic Monitoring of Bats. Biodiversity Monitoring and Conservation: Bridging the Gap between Global Commitment and Local Action. https://doi.org/10.1002/9781118490747.ch10
Joo, W., Gage, S. H., & Kasten, E. P. (2011). Analysis and interpretation of variability in soundscapes along an urban-rural gradient. Landscape and Urban Planning, 103(3–4), 259–276. https://doi.org/10.1016/j.landurbplan.2011.08.001
Jorgea, F. C., Machadob, C. G., Nogueira, S. S. da C., & Nogueira-Filho, S. L. G. (2018). The effectiveness of acoustic indices for forest monitoring in Atlantic rainforest fragments. Ecological Indicators, 91(April), 71–76. https://doi.org/10.1016/j.ecolind.2018.04.001
Kasten, E. P., Gage, S. H., Fox, J., & Joo, W. (2012). The remote environmental assessment laboratory’s acoustic library: An archive for studying soundscape ecology. Ecological Informatics, 12, 50–67. https://doi.org/10.1016/j.ecoinf.2012.08.001
Krause, B., & Farina, A. (2016). Using ecoacoustic methods to survey the impacts of climate change on biodiversity. BIOC, 195, 245–254. https://doi.org/10.1016/j.biocon.2016.01.013
Kuehne, L. M., Padgham, B. L., & Olden, J. D. (2013). The Soundscapes of Lakes across an Urbanization Gradient. PLoS ONE, 8(2), e55661. https://doi.org/10.1371/journal.pone.0055661
Lamond, A. (2016). Can Soundscape Indices Be Used To Reflect Biodiversity In An Ecuadorian Andean Tropical Montane Habitat? University of Sussex MPhil Thesis.
Leach, E. C., Burwell, C. J., Ashton, L. A., Jones, D. N., & Kitching, R. L. (2016). Comparison of point counts and automated acoustic monitoring: detecting birds in a rainforest biodiversity survey. Emu-Austral Ornithology, 116(3), 305–309. https://doi.org/10.1071/MU15097
Lee, B. P. Y.-H., Davies, Z. G., & Struebig, M. J. (2017). Smoke pollution disrupted biodiversity during the 2015 El Niño fires in Southeast Asia. Environmental Research Letters, 12(9), 094022. https://doi.org/10.1088/1748-9326/aa87ed
Lee, J.-S., & Hsu, P.-H. (2010). The History of Forest Management and Recreation Development in Taiwan After World War Ⅱ. Quarterly Journal of Forest Research, 32(1), 87–96. https://doi.org/10.29898/SHBQ.201003.0006
Lehmann, G. U. C., Frommolt, K.-H., Lehmann, A. W., & Riede, K. (2014). Baseline data for automated acoustic monitoring of Orthoptera in a Mediterranean landscape , the Hymettos , Greece. Journal of Insect Conservation, 18(5), 909–925. https://doi.org/10.1007/s10841-014-9700-2
Lex Brown, A. (2012). A review of progress in soundscapes and an approach to soundscape planning. International Journal of Acoustics and Vibrations, 17(2), 73–81. https://doi.org/10.20855/ijav.2012.17.2302
Ligges, U., Krey, S., Mersmann, O., & Schnackenberg, S. (2018). {tuneR}: Analysis of Music and Speech. R package version 1.3.2. Retrieved from https://cran.r-project.org/package=tuneR
Lin, H., Chu, L., & Wang, Y.-H. (2019). Asian Soundscape. Retrieved from http://soundscape.twgrid.org/
Lindenmayer, D. B., Burns, E. L., Tennant, P., Dickman, C. R., Green, P. T., Keith, D. A. et al. (2015). Contemplating the future: Acting now on long-term monitoring to answer 2050’s questions. Austral Ecology, 40(3), 213–224. https://doi.org/10.1111/aec.12207
Lindseth, A.V. (2019). Determining Temporal Recording Schemes for Underwater Acoustic Monitoring Studies. Master Thesis, University of Colorado.
Lindseth, A.V., & Lobel, P. S. (2018). Underwater Soundscape Monitoring and Fish Bioacoustics: A Review. Fishes, 3(36). https://doi.org/10.3390/fishes3030036
Lomolino, M.V., Pijanowski, B. C., & Gasc, A. (2015). The silence of biogeography. Journal of Biogeography, 42(7), 1187–1196. https://doi.org/10.1111/jbi.12525
Lynch, E., Joyce, D., & Fristrup, K. (2011). An assessment of noise audibility and sound levels in U.S. National Parks. Landscape Ecology, 26, 1297–1309. https://doi.org/10.1007/s10980-011-9643-x
Machado, R. B., Aguiar, L., & Jones, G. (2017). Do acoustic indices reflect the characteristics of bird communities in the savannas of Central Brazil ? Landscape and Urban Planning, 162, 36–43. https://doi.org/10.1016/j.landurbplan.2017.01.014
Mammides, C., Goodale, E., Dayananda, S. K., Kang, L., & Chen, J. (2017). Do acoustic indices correlate with bird diversity ? Insights from two biodiverse regions in Yunnan Province, south China. Ecological Indicators, 82, 470–477. https://doi.org/10.1016/j.ecolind.2017.07.017
Marques, T. A., Thomas, L., Martin, S. W., Mellinger, D. K., Ward, J. A., Moretti, D. J. et al. (2013). Estimating animal population density using passive acoustics. Biological Reviews, 88(2), 287–309. https://doi.org/10.1111/brv.12001
Meyer, C. F. J., Aguiar, L. M. S., Aguirre, L. F., Baumgarten, J., Clarke, F. M., Cosson, J. F. et al. (2010). Long-term monitoring of tropical bats for anthropogenic impact assessment: Gauging the statistical power to detect population change. Biological Conservation, 143(11), 2797–2807. https://doi.org/10.1016/j.biocon.2010.07.029
Moreno-gómez, F. N., Bartheld, J., Silva-escobar, A. A., Briones, R., Márquez, R., & Penna, M. (2019). Evaluating acoustic indices in the Valdivian rainforest , a biodiversity hotspot in South America. Ecological Indicators, 103, 1–8. https://doi.org/10.1016/j.ecolind.2019.03.024
Moreno-Gómez, F. N., Bartheld, J., Silva-Escobar, A. A., Briones, R., Márquez, R., & Penna, M. (2019). Evaluating acoustic indices in the Valdivian rainforest, a biodiversity hotspot in South America. Ecological Indicators, 103(June 2018), 1–8. https://doi.org/10.1016/j.ecolind.2019.03.024
Mullet, T. C., Gage, S. H., Morton, J. M., & Huettmann, F. (2016). Temporal and spatial variation of a winter soundscape in south-central Alaska. Landscape Ecology, 31(5), 1117–1137. https://doi.org/10.1007/s10980-015-0323-0
Munro, J., Williamson, I. A. N., & Fuller, S. (2018). Traffic noise impacts on urban forest soundscapes in south-eastern Australia. Austral Ecology, 43(2), 180–190. https://doi.org/10.1111/aec.12555
Parijs, S. M., Smith, J. N., & Corkeron, P. (2002). Using calls to estimate the abundance of inshore dolphins : a case study with Pacific humpback dolphins Sousa chinensis. Journal of Applied Ecology, 39(5), 853–864. https://doi.org/10.1046/j.1365-2664.2002.00756.x
Payn, T., Carnus, J.-M., Freer-Smith, P., Kimberley, M., Kollert, W., Liu, S. et al. (2015). Changes in planted forests and future global implications. Forest Ecology and Management, 352, 57–67. https://doi.org/10.1016/j.foreco.2015.06.021
Pereira, H. M., & Cooper, H. D. (2006). Towards the global monitoring of biodiversity change. Trends in Ecology and Evolution, 21(3), 123–129. https://doi.org/10.1016/j.tree.2005.10.015
Phillips, Y. F. (2018). Analysis and Visualisation of Very-Long-Duration Acoustic Recordings of the Natural Environment. PhD thesis, Queensland University of Technology.
Phillips, Y. F., Towsey, M., & Roe, P. (2018). Revealing the ecological content of long-duration audio-recordings of the environment through clustering and visualisation. PLoS ONE, 13(3), e0193345. https://doi.org/10.1371/journal.pone.0193345
Pieretti, N., Duarte, M. H. L., Sousa-Lima, R. ., Rodrigues, M., Young, R. J., & Farina, A. (2015). Determining temporal sampling schemes for passive acoustic studies in different tropical ecosystems. Tropical Conservation Science, 8(1), 215–234.
Pieretti, N., Farina, A., & Morri, D. (2011). A new methodology to infer the singing activity of an avian community: The Acoustic Complexity Index (ACI). Ecological Indicators, 11(3), 868–873. https://doi.org/10.1016/j.ecolind.2010.11.005
Pieretti, N., Martire, M.Lo, Farina, A., & Danovaro, R. (2017). Marine soundscape as an additional biodiversity monitoring tool: A case study from the Adriatic Sea (Mediterranean Sea). Ecological Indicators, 83(July), 13–20. https://doi.org/10.1016/j.ecolind.2017.07.011
Pijanowski, B. C., Farina, A., Gage, S. H., Dumyahn, S. L., & Krause, B. L. (2011). What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology, 26(9), 1213–1232. https://doi.org/10.1007/s10980-011-9600-8
Pijanowski, B. C., Villanueva-Rivera, L. J., Dumyahn, S. L., Farina, A., Krause, B. L., Napoletano, B. M. et al. (2011). Soundscape Ecology: The Science of Sound in the Landscape. BioScience, 61(3), 203–216. https://doi.org/10.1525/bio.2011.61.3.6
Pijanowski, B., Gasc, A., Bellisario, K., Ghadiri, M., Harris, M., Doucette, J., & Beatty, B. (2019). Center for global soundscapes Purdue University, IN. Retrieved from https://centerforglobalsoundscapes.org/vanishing-soundscapes/
R Core Team. (2014). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. (version 3.5.0). Retrieved from http://www.rdproject.org
Rajan, S. C., Jaishanker, K. A. R., Sarojkumar, N. P. S.V, Athira, K., Jaishanker, R., Sooraj, N. P., & Sarojkumar, V. (2019). Rapid assessment of biodiversity using acoustic indices. Biodiversity and Conservation, 28(8–9), 2371–2383. https://doi.org/10.1007/s10531-018-1673-0
Rayment, W., Webster, T., Brough, T., Jowett, T., & Dawson, S. (2018). Seen or heard? A comparison of visual and acoustic autonomous monitoring methods for investigating temporal variation in occurrence of southern right whales. Marine Biology, 165, 12. https://doi.org/10.1007/s00227-017-3264-0
Resources, N. (2015). Application of Autonomous Recording Units to Monitor Gobbling Activity by Wild Turkey. Wildlife Society Bulletin, 39(4), 757–763. https://doi.org/10.1002/wsb.577
Ritts, M., Gage, S. H., Picard, C. R., Dundas, E., & Dundas, S. (2016). Collaborative research praxis to establish baseline ecoacoustics conditions in Gitga ’ at Territory. Global Ecology and Conservation, 7, 25–38. https://doi.org/10.1016/j.gecco.2016.04.002
Rodriguez, A., Gasc, A., Pavoine, S., Grandcolas, P., Gaucher, P., & Sueur, J. (2014). Temporal and spatial variability of animal sound within a neotropical forest. Ecological Informatics, 21, 133–143. https://doi.org/10.1016/j.ecoinf.2013.12.006
Ross, S. R. P.- J., Friedman, N. R., Dudley, K. L., Yoshimura, M., Yoshida, T., & Economo, E. P. (2018). Listening to ecosystems: data-rich acoustic monitoring through landscape-scale sensor networks. Ecological Research, 33(1), 135–147. https://doi.org/10.1007/s11284-017-1509-5
RStudio. (2014). RStudio: Integrated development environment for R (Version 1.1.442) [Computer Software]. Boston, MA. Retrieved from http://www.rstudio.org
Russo, D., Ancillotto, L., & Jones, G. (2017). Bats are still not birds in the digital era: echolocation call variation and why it matters for bat species identification. Canadian Journal of Zoology. https://doi.org/10.1139/cjz-2017-0089
Sankupellay, M., Towsey, M., Truskinger, A., & Roe, P. (2015). Visual fingerprints of the acoustic environment: The use of acoustic indices to characterise natural habitats. In IEEE.
Shannon, G., McKenna, M. F., Angeloni, L. M., Crooks, K. R., Fristrup, K. M., Brown, E. et al. (2016). A synthesis of two decades of research documenting the effects of noise on wildlife. Biological Reviews, 91, 982–1005. https://doi.org/10.1111/brv.12207
Slabbekoorn, H. (2018). Soundscape Ecology of the Anthropocene. Acoustics Today, 14(1), 42–49.
Southworth, M. F. (1969). The sonic environment of cities. Environment and Behavior, (1), 49–10.
Stanley, C. Q., Walter, M. H., Venkatraman, M. X., & Wilkinson, G. S. (2016). Insect noise avoidance in the dawn chorus of Neotropical birds. Animal Behaviour, 112, 255–265. https://doi.org/10.1016/j.anbehav.2015.12.003
Straile, D., Jochimsen, M. C., & Kümmerlin, R. (2013). The use of long-term monitoring data for studies of planktonic diversity: A cautionary tale from two Swiss lakes. Freshwater Biology, 58(6), 1292–1301. https://doi.org/10.1111/fwb.12118
Sueur, J., Aubin, T., & Simonis, C. (2008). Seewave: a free modular tool for sound analysis and synthesis. Bioacoustics, 18, 213–226. https://doi.org/10.1080/09524622.2008.9753600
Sueur, J., Farina, A., Gasc, A., Pieretti, N., & Pavoine, S. (2014). Acoustic indices for biodiversity assessment and landscape investigation. Acta Acustica United with Acustica, 100(4), 772–781. https://doi.org/10.3813/AAA.918757
Sueur, J., Krause, B., & Farina, A. (2019). Climate Change Is Breaking Earth’s Beat. Trends in Ecology and Evolution, 34(11), 971–973. https://doi.org/10.1016/j.tree.2019.07.014
Sueur, J., Pavoine, S., Hamerlynck, O., Duvail, S., Sueur, J., Pavoine, S. et al. (2008). Rapid acoustic survey for biodiversity appraisal. PLoS ONE, 3(12), e4065. https://doi.org/10.1371/journal.pone.0004065
Sugai, L. S. M., Silva, T. S. F., Ribeiro, J. W., & Llusia, D. (2019). Terrestrial Passive Acoustic Monitoring: Review and Perspectives. BioScience, 69(1), 15–25. https://doi.org/10.1093/biosci/biy147
Thieurmel, B., & Elmarhraoui, A. (2019). suncalc: Compute Sun Position, Sunlight Phases, Moon Position and Lunar Phase (R package version 0.5.0). Retrieved from https://cran.r-project.org/package=suncalc
Tigre, M. A., Tigre, M. A., Buxton, R. T., McKenna, M. F., Mennitt, D., Fristrup, K. et al. (2017). Noise pollution is pervasive in U.S. protected areas. Science, 356(6337), 531–533. https://doi.org/10.1126/science.aah4783
Towsey, M., Wimmer, J., Williamson, I., & Roe, P. (2014). The use of acoustic indices to determine avian species richness in audio-recordings of the environment. Ecological Informatics, 21(100), 110–119. https://doi.org/10.1016/j.ecoinf.2013.11.007
Towsey, M., Zhang, L., Cottman-fields, M., Wimmer, J., Zhang, J., & Roe, P. (2014). Visualization of long - duration acoustic recordings of the environment. Procedia Computer Science, 29, 703–712. https://doi.org/10.1016/j.procs.2014.05.063
Towsey, M., Znidersic, E., Broken-Brow, J., Indraswari, K., Watson, D. M., Phillips, Y. et al. (2018). Long-duration, false-colour spectrograms for detecting species in large audio data-sets. Journal of Ecoacoustics, 2, IUSWUI. https://doi.org/10.22261/jea.iuswui
Tucker, D., Gage, S. H., Williamson, I., & Fuller, S. (2014). Linking ecological condition and the soundscape in fragmented Australian forests. Landscape Ecology, 29(4), 745–758. https://doi.org/10.1007/s10980-014-0015-1
Ulloa, J. S., Gasc, A., Gaucher, P., Aubin, T., Réjou-Méchain, M., & Sueur, J. (2016). Screening large audio datasets to determine the time and space distribution of Screaming Piha birds in a tropical forest. Ecological Informatics, 31, 91–99. https://doi.org/10.1016/j.ecoinf.2015.11.012
Villanueva-Rivera, L. J., & Pijanowski, B. C. (2018). soundecology: Soundscape Ecology. R package version 1.3.3. Retrieved from https://cran.r-project.org/package=soundecology
Villanueva-Rivera, L. J., Pijanowski, B. C., Doucette, J., & Pekin, B. (2011). A primer of acoustic analysis for landscape ecologists. Landscape Ecology, 26(9), 1233–1246. https://doi.org/10.1007/s10980-011-9636-9
Wildlife Acoustics Inc. (2019). Song Meter Sm4 Acoustic Recorder User Guide (Last updated: 2019/08/01). Retrieved from https://www.wildlifeacoustics.com/support/documentation#english
Williams-Guillén, K., & Perfecto, I. (2011). Ensemble composition and activity levels of insectivorous bats in response to management intensification in coffee agroforestry systems. PLoS ONE, 6(1). https://doi.org/10.1371/journal.pone.0016502
Wimmer, J., Towsey, M., Roe, P., Grace, P., & Williamson, I. (2013). Sampling environmental acoustic recordings to determine species richness. Ecological Applications, 23(6), 1419–1428.
Wrege, P. H., Rowland, E. D., Keen, S., & Shiu, Y. (2017). Acoustic monitoring for conservation in tropical forests: examples from forest elephants. Methods in Ecology and Evolution, 8, 1292–1301. https://doi.org/10.1111/2041-210X.12730
Zhang, L., Towsey, M., Xie, J., Zhang, J., & Roe, P. (2016). Using multi-label classification for acoustic pattern detection and assisting bird species surveys. Applied Acoustics, 110, 91–98. https://doi.org/10.1016/j.apacoust.2016.03.027
Zhang, L., Towsey, M., Zhang, J., & Roe, P. (2016). Ecological Informatics Classifying and ranking audio clips to support bird species richness surveys. Ecological Informatics, 34, 108–116. https://doi.org/10.1016/j.ecoinf.2016.05.005
中華民國交通部中央氣象局. (2011). 中央氣象局氣候月平均氣溫、雨量(統計期間: 1981-2010，最後更新: 2011/01). Retrieved from https://www.cwb.gov.tw/V8/C/C/Statistics/monthlymean.html
中華民國陽明山國家公園管理處. (2018). 陽明山國家公園: 業務統計-2018年遊憩據點遊客人數統計 (查詢日期: 2019/11/01). Retrieved from https://www.ymsnp.gov.tw/index.php?option=com_govopen& view=stats& gp=0& Itemid=456
中華民國陽明山國家公園管理處. (2019). 陽明山國家公園: 園區簡介 (最後更新: 2019/11/01). Retrieved from https://www.ymsnp.gov.tw/index.php?option=com_content& view=article& id=18& gp=0& Itemid=231
李桃生, 邱立文, 黃群修, & 吳俊奇. (2015). 第四次全國森林資源調查. 中華民國行政院農業委員會林務局. Retrieved from https://www.forest.gov.tw/0002393
行政院農業委員會林務局. (2018). 林務局全球資訊網: 東眼山國家森林遊樂區 (最後更新: 2018/05/11). Retrieved from https://www.forest.gov.tw/0000180
行政院農業委員會林務局. (2019). 台灣山林悠遊網: 東眼山國家森林遊樂區 (查詢日期: 2019/11/01). Retrieved from https://recreation.forest.gov.tw/Forest/RA?typ=0& typ_id=0200003
林純徵. (2006). Exploring the Ecotourism Policy & Practices of the Forestry Bureau － A case study of Dongyanshan National Forest Recreation Area. https://doi.org/10.6342/NTU.2006.03075
王豫煌, 林誠謙, 嚴漢偉, 林子皓, 陸聲山, 曹昱等 (2019). 亞洲聲景長期監測網. 林業研究專訊, 26(4), 26–30. https://doi.org/10.1017/CBO9781107415324.004
社團法人台北市野鳥學會. (2018). 關渡自然公園: 物種名錄總覽 (最後更新: 2018/05). Retrieved from https://gd-park.org.tw/biolist
社團法人台北市野鳥學會. (2019a). 關渡自然公園: 參觀資訊 (查詢日期: 2019/11/01). Retrieved from https://gd-park.org.tw/guides
社團法人台北市野鳥學會. (2019b). 關渡自然公園: 簡介 (查詢日期: 2019/11/01). Retrieved from https://gd-park.org.tw/about-us
社團法人台灣環境資訊協會. (2011). 台灣濕地網: 關渡自然保留區(關渡濕地)-臺北市唯一自然保留區 (最後更新: 2011/12/27). Retrieved from https://wetland.e-info.org.tw/file/north/74
詹為巽, & 林俊成. (2016). 國內製材業者使用國產木材之現況. 林業研究專訊, 23(6), 114–117.