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Bibliografias temáticas / Multidimensional scaling

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Literatura científica selecionada sobre o tema "Multidimensional scaling"

Autor: Grafiati

Publicado: 4 de junho de 2021

Última modificação: 9 de junho de 2025

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Artigos de revistas sobre o assunto "Multidimensional scaling"

1

Gower, J. C., F. Cox, and M. A. A. Cox. "Multidimensional Scaling." Journal of the Royal Statistical Society. Series A (Statistics in Society) 159, no. 1 (1996): 184. http://dx.doi.org/10.2307/2983485.

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Jeffers, J. N. R., and Mark L. Davison. "Multidimensional Scaling." Statistician 34, no. 2 (1985): 257. http://dx.doi.org/10.2307/2988171.

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Lee, In-Soon. "Multidimensional Scaling." Journal of Korean Medical Library Association 19, no. 1 (June 1992): 1–6. http://dx.doi.org/10.69528/jkmla.1992.19.1.1.

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4

Jolliffe, Ian. "Multidimensional Scaling." Technometrics 38, no. 4 (November 1996): 403–4. http://dx.doi.org/10.1080/00401706.1996.10484556.

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Mugavin, Marie E. "Multidimensional Scaling." Nursing Research 57, no. 1 (January 2008): 64–68. http://dx.doi.org/10.1097/01.nnr.0000280659.88760.7c.

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Hout, Michael C., Megan H. Papesh, and Stephen D. Goldinger. "Multidimensional scaling." Wiley Interdisciplinary Reviews: Cognitive Science 4, no. 1 (October 8, 2012): 93–103. http://dx.doi.org/10.1002/wcs.1203.

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Aflalo, Y., and R. Kimmel. "Spectral multidimensional scaling." Proceedings of the National Academy of Sciences 110, no. 45 (October 9, 2013): 18052–57. http://dx.doi.org/10.1073/pnas.1308708110.

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Venna, Jarkko, and Samuel Kaski. "Local multidimensional scaling." Neural Networks 19, no. 6-7 (July 2006): 889–99. http://dx.doi.org/10.1016/j.neunet.2006.05.014.

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Spence, Ian, and Stephan Lewandowsky. "Robust multidimensional scaling." Psychometrika 54, no. 3 (September 1989): 501–13. http://dx.doi.org/10.1007/bf02294632.

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de Leeuw, Jan, and Patrick J. F. Groenen. "Inverse Multidimensional Scaling." Journal of Classification 14, no. 1 (January 1, 1997): 3–21. http://dx.doi.org/10.1007/s003579900001.

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Teses / dissertações sobre o assunto "Multidimensional scaling"

1

Bell, Paul W. "Statistical inference for multidimensional scaling." Thesis, University of Newcastle Upon Tyne, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.327197.

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CUGNATA, FEDERICA. "Bayesian three-way multidimensional scaling." Doctoral thesis, Università Bocconi, 2012. https://hdl.handle.net/11565/4054285.

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Jones, Synthia S. "Multidimensional scaling of user information satisfaction." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1993. http://handle.dtic.mil/100.2/ADA277230.

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Thesis (M.S. in Information Technology Management) Naval Postgraduate School, December 1993.<br>Thesis advisor(s): William J. Haga ; Kishore Sengupta. "December 1993." Bibliography: p. 108-110. Also available online.

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Ingram, Stephen F. "Multilevel multidimensional scaling on the GPU." Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/409.

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We present Glimmer, a new multilevel visualization algorithm for multidimensional scaling designed to exploit modern graphics processing unit (GPU) hard-ware. We also present GPU-SF, a parallel, force-based subsystem used by Glimmer. Glimmer organizes input into a hierarchy of levels and recursively applies GPU-SF to combine and refine the levels. The multilevel nature of the algorithm helps avoid local minima while the GPU parallelism improves speed of computation. We propose a robust termination condition for GPU-SF based on a filtered approximation of the normalized stress function. We demonstrate the benefits of Glimmer in terms of speed, normalized stress, and visual quality against several previous algorithms for a range of synthetic and real benchmark datasets. We show that the performance of Glimmer on GPUs is substantially faster than a CPU implementation of the same algorithm. We also propose a novel texture paging strategy called distance paging for working with precomputed distance matrices too large to fit in texture memory.

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McQuaid, Michael J., Thian-Huat Ong, Hsinchun Chen, and Jay F. Nunamaker. "Multidimensional scaling for group memory visualization." Elsevier, 1999. http://hdl.handle.net/10150/105458.

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Artificial Intelligence Lab, Department of MIS, University of Arizona<br>We describe an attempt to overcome information overload through information visualization â in a particular domain, group memory. A brief review of information visualization is followed by a brief description of our methodology. We . discuss our system, which uses multidimensional scaling MDS to visualize relationships between documents, and which . we tested on 60 subjects, mostly students. We found three important and statistically significant differences between task performance on an MDS-generated display and on a randomly generated display. With some qualifications, we conclude that MDS speeds up and improves the quality of manual classification of documents and that the MDS display agrees with subject perceptions of which documents are similar and should be displayed together.

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Tulabandula, Sridhar. "Localization of wireless sensor networks using multidimensional scaling." Diss., Columbia, Mo. : University of Missouri-Columbia, 2007. http://hdl.handle.net/10355/4986.

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Thesis (M.S.)--University of Missouri-Columbia, 2007.<br>The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on April 17, 2008) Includes bibliographical references.

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Sun, Jiang. "Extending the metric multidimensional scaling with bregman divergences." Thesis, University of the West of Scotland, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.556070.

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Mohd, Yunus Mohd Yusri. "Multivariate statistical process monitoring using classical multidimensional scaling." Thesis, University of Newcastle upon Tyne, 2012. http://hdl.handle.net/10443/1495.

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A new Multivariate Statistical Process Monitoring (MSPM) system, which comprises of three main frameworks, is proposed where the system utilizes Classical Multidimensional Scaling (CMDS) as the main multivariate data compression technique instead of using the linearbased Principal Component Analysis (PCA). The conventional method which usually applies variance-covariance or correlation measure in developing the multivariate scores is found to be inappropriately used especially in modelling nonlinear processes, where a high number of principal components will be typically required. Alternatively, the proposed method utilizes the inter-dissimilarity scales in describing the relationships among the monitored variables instead of variance-covariance measure for the multivariate scores development. However, the scores are plotted in terms of variable structure, thus providing different formulation of statistics for monitoring. Nonetheless, the proposed statistics still correspond to the conceptual objective of Hotelling’s T2 and Squared Prediction Errors (SPE). The first framework corresponds to the original CMDS framework, whereas the second utilizes Procrustes Analysis (PA) functions which is analogous to the concept of loading factors in PCA for score projection. Lastly, the final framework employs dynamic mechanism of PA functions as an alternative for enhancing the procedures of the second approach. A simulated system of Continuous Stirred Tank Reactor with Recycle (CSTRwR) has been chosen for the demonstration and the fault detection results were comparatively analyzed to the outcomes of PCA on the grounds of false alarm rates, total number of detected cases and also total number of fastest detection cases. The last two performance factors are obtained through fault detection time. The overall outcomes show that the three CMDS-based systems give almost comparable performances to the linear PCA based monitoring systemwhen dealing the abrupt fault events, whereas the new systems have demonstrated significant improvement over the conventional method in detecting incipient fault cases. More importantly, this monitoring accomplishment can be efficiently executed based on lower compressed dimensional space compared to the PCA technique, thus providing much simpler solution. All of these evidences verified that the proposed approaches are successfully developed conceptually as well as practically for monitoring while complying fundamentally with the principles and technical steps of the conventional MSPM system.

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9

Williams, Michelle A. "A factor structure with means confirmatory factor analytic approach to multitrait-multimethod models." Diss., Columbia, Mo. : University of Missouri-Columbia, 2007. http://hdl.handle.net/10355/5010.

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Thesis (M.A.)--University of Missouri-Columbia, 2007.<br>The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on November 6, 2007) Includes bibliographical references.

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Jansson, Mattias, and Jimmy Johansson. "Interactive Visualization of Statistical Data using Multidimensional Scaling Techniques." Thesis, Linköping University, Department of Science and Technology, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1716.

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<p>This study has been carried out in cooperation with Unilever and partly with the EC founded project, Smartdoc IST-2000-28137. </p><p>In areas of statistics and image processing, both the amount of data and the dimensions are increasing rapidly and an interactive visualization tool that lets the user perform real-time analysis can save valuable time. Real-time cropping and drill-down considerably facilitate the analysis process and yield more accurate decisions. </p><p>In the Smartdoc project, there has been a request for a component used for smart filtering in multidimensional data sets. As the Smartdoc project aims to develop smart, interactive components to be used on low-end systems, the implementation of the self-organizing map algorithm proposes which dimensions to visualize. </p><p>Together with Dr. Robert Treloar at Unilever, the SOM Visualizer - an application for interactive visualization and analysis of multidimensional data - has been developed. The analytical part of the application is based on Kohonen’s self-organizing map algorithm. In cooperation with the Smartdoc project, a component has been developed that is used for smart filtering in multidimensional data sets. Microsoft Visual Basic and components from the graphics library AVS OpenViz are used as development tools.</p>

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Mais fontes

Livros sobre o assunto "Multidimensional scaling"

1

A, Cox Michael A., ed. Multidimensional scaling. 2nd ed. Boca Raton: Chapman & Hall/CRC, 2001.

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2

Davison, Mark L. Multidimensional scaling. Malabar, Fla: Krieger Pub. Co., 1992.

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3

Borg, Ingwer, Patrick J. F. Groenen, and Patrick Mair. Applied Multidimensional Scaling. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-31848-1.

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Borg, Ingwer, and Patrick Groenen. Modern Multidimensional Scaling. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4757-2711-1.

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F, Groenen Patrick J., Mair Patrick, and SpringerLink (Online service), eds. Applied Multidimensional Scaling. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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6

Biela, Adam. Skalowanie wielowymiarowe jako metoda badań naukowych. Lublin: Tow. Naukowe Katolickiego Uniwersytetu Lubelskiego, 1992.

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7

Schubert, Leo. Lösungsansätze der mehrdimensionalen Skalierung mit Berücksichtigung unterschiedlicher Datenniveaus. Königstein/Ts: A. Hain, 1985.

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8

Borg, Ingwer, Patrick J. F. Groenen, and Patrick Mair. Applied Multidimensional Scaling and Unfolding. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73471-2.

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9

J, Carmone Frank, and Smith Scott M, eds. Multidimensional scaling: Concepts and applications. Boston, MA: Allyn and Bacon, 1989.

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10

Green, Paul E. Multidimensional scaling: Concepts and applications. Boston: Allyn and Bacon, 1989.

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Capítulos de livros sobre o assunto "Multidimensional scaling"

1

Härdle, Wolfgang Karl, and Zdeněk Hlávka. "Multidimensional Scaling." In Multivariate Statistics, 289–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-36005-3_17.

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2

Härdle, Wolfgang, and Léopold Simar. "Multidimensional Scaling." In Applied Multivariate Statistical Analysis, 373–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05802-2_15.

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3

Everitt, Brian S., and Graham Dunn. "Multidimensional Scaling." In Applied Multivariate Data Analysis, 93–124. West Sussex, United Kingdom: John Wiley & Sons, Ltd,., 2013. http://dx.doi.org/10.1002/9781118887486.ch5.

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4

Shen, Heng Tao. "Multidimensional Scaling." In Encyclopedia of Database Systems, 1–2. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_548-2.

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du Toit, S. H. C., A. G. W. Steyn, and R. H. Stumpf. "Multidimensional Scaling." In Springer Texts in Statistics, 105–75. New York, NY: Springer New York, 1986. http://dx.doi.org/10.1007/978-1-4612-4950-4_6.

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Härdle, Wolfgang Karl, and Léopold Simar. "Multidimensional Scaling." In Applied Multivariate Statistical Analysis, 455–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-45171-7_17.

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Mukherjee, S. P., Bikas K. Sinha, and Asis Kumar Chattopadhyay. "Multidimensional Scaling." In Statistical Methods in Social Science Research, 113–22. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2146-7_11.

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Adachi, Kohei. "Multidimensional Scaling." In Matrix-Based Introduction to Multivariate Data Analysis, 247–58. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4103-2_16.

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Härdle, Wolfgang Karl, and Léopold Simar. "Multidimensional Scaling." In Applied Multivariate Statistical Analysis, 397–412. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-17229-8_16.

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Jacoby, William G., and David J. Ciuk. "Multidimensional Scaling." In The Wiley Handbook of Psychometric Testing, 375–412. Chichester, UK: John Wiley & Sons, Ltd, 2018. http://dx.doi.org/10.1002/9781118489772.ch14.

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Trabalhos de conferências sobre o assunto "Multidimensional scaling"

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Dennig, Frederik L., Lucas Joos, Patrick Paetzold, Daniela Blumberg, Oliver Deussen, Daniel A. Keim, and Maximilian T. Fischer. "The Categorical Data Map: A Multidimensional Scaling-Based Approach." In 2024 IEEE Visualization in Data Science (VDS), 25–34. IEEE, 2024. http://dx.doi.org/10.1109/vds63897.2024.00008.

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2

Mackute-Varoneckiene, Ausra, Antanas Zilinskas, and Audrius Varoneckas. "Multidimensional scaling." In the International Conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1731740.1731805.

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Runkler, Thomas A., and James C. Bezdek. "Multidimensional scaling with multiswarming." In 2014 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2014. http://dx.doi.org/10.1109/cec.2014.6900225.

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4

Zhang, Xi, Hao Huang, Klaus Mueller, and Shinjae Yoo. "Streaming Classical Multidimensional Scaling." In 2018 New York Scientific Data Summit (NYSDS). IEEE, 2018. http://dx.doi.org/10.1109/nysds.2018.8538942.

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Kumar, Sandeep, and Ketan Rajawat. "Velocity-assisted multidimensional scaling." In 2015 IEEE 16th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). IEEE, 2015. http://dx.doi.org/10.1109/spawc.2015.7227102.

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Di Franco, Carmelo, Enrico Bini, Mauro Marinoni, and Giorgio C. Buttazzo. "Multidimensional scaling localization with anchors." In 2017 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC). IEEE, 2017. http://dx.doi.org/10.1109/icarsc.2017.7964051.

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Vu, Viet Minh, Adrien Bibal, and Benoit Frenay. "iPMDS: Interactive Probabilistic Multidimensional Scaling." In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9534425.

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Qu, Taiguo, and Zixing Cai. "A fast multidimensional scaling algorithm." In 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2015. http://dx.doi.org/10.1109/robio.2015.7419726.

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Qu, Taiguo, and Dong Wang. "A Fast Multidimensional Scaling Algorithm." In ICEITSA 2023: The 3rd International Conference on Electronic Information Technology and Smart Agriculture. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3641343.3641431.

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Rajan, Raj Thilak, Geert Leus, and Alle-Jan van der Veen. "Relative velocity estimation using multidimensional scaling." In 2013 IEEE 5th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). IEEE, 2013. http://dx.doi.org/10.1109/camsap.2013.6714023.

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Relatórios de organizações sobre o assunto "Multidimensional scaling"

1

Cvetkovski, Andrej, and Mark Crovella. Multidimensional Scaling in the Poincare Disk. Fort Belvoir, VA: Defense Technical Information Center, May 2011. http://dx.doi.org/10.21236/ada585960.

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2

Oh, Man-Suk, and Adrian E. Raftery. Bayesian Multidimensional Scaling and Choice of Dimension. Fort Belvoir, VA: Defense Technical Information Center, August 2000. http://dx.doi.org/10.21236/ada458817.

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3

Kearsley, Anthony J., Richard A. Tapia, and Michael W. Trosset. The Solution of the Metric STRESS and SSTRESS Problems in Multidimensional Scaling Using Newton's Method. Fort Belvoir, VA: Defense Technical Information Center, January 1995. http://dx.doi.org/10.21236/ada445621.

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4

Koven, Charles, R. Knox, R. Fisher, Forrest Hoffman, Trevor Keenan, D. Lawrence, M. Longo, and B. Sanderson. Modular hybrid modeling to increase efficiency, explore structural uncertainty, andallow multidimensional complexity scaling in land surface models. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1769750.

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Whitecloud, Simone, Kevin Philley, J. Minkin, Anna Antrim, Franz Lichtner, Nicole Wuerslin, Natalie Barker, et al. Assessing the Validity and Accuracy of Wetland Indicator Status Ratings for Eight Species in Alaska Subregions. Engineer Research and Development Center (U.S.), January 2025. https://doi.org/10.21079/11681/49509.

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Preexisting ecological information and plant species occurrence data were used to determine the accuracy and validity of the present regional and subregional wetland indicator status ratings for eight species: Andromeda polifolia, Arctous rubra, Carex canescens, Rhododendron tomentosum, Rubus arcticus, Salix arctica, Salix pulchra, and Viola palustris. Technical documentation was developed to either (1) support the current National Wetland Plant List (NWPL) subregion boundaries and wetland indicator status ratings for the NWPL Alaska Region or (2) support a proposed change to the subregions or wetland indicator status ratings for the NWPL Alaska Region, for inclusion into the next NWPL update. The project developed repeatable, quantitative methods for assignment of wetland indicator status rating. Analyses included multiple correspondence analysis (MCA), analysis of similarities (ANOSIM), nonmetric multidimensional scaling (NMDS), and principal component analysis (PCA). Prevalence index (PI) was used as a numeric approximation of wetland status for comparing observations across subregions. A pilot study on S. pulchra data evaluated regional assignments by machine learning and assessed the feasibility of correlation network analysis and Louvain clustering for wetland indicator status rating assignment as dictated by co-occurring species. The methods developed for this Alaska-specific study may be applied to any future regional or subregional updates to the NWPL.

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Coscia, Jordan, Wendy Cass, and Leighton J. Evaluation of 20 years of vegetation change in the Big Meadows landscape of Shenandoah National Park in response to mowing and burning: Application of historic data to identify best practices for future meadow management. National Park Service, 2025. https://doi.org/10.36967/2312599.

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The Big Meadows landscape is an open habitat that supports unique biodiversity not found elsewhere in Shenandoah National Park. To support this biodiversity and to maintain the Meadow’s historic landscape characteristics, Park staff have managed the meadows using prescribed burns, mowing, and the targeted cutting and removal of woody plants since 1998. To assess the impacts of these management actions on the plant community in Big Meadows and to guide future land management recommendations, we visualized changes in the plant community composition between 1998 and 2022 using non-metric multidimensional scaling and used generalized linear mixed-effects regression to model the impacts of climate and land management actions on eight plant community composition variables. Ordination revealed that the plant community across Big Meadows has moved in the direction of undesired increases in Rubus and Celastrus presence between 2004 and 2022 (stress = 0.13, non-metric R2 = 0.98, linear R2 = 0.91), a direction correlated with longer intervals between prescribed burns (R2 = 0.36, P = 0.01). Our models suggest that changes in the plant community were correlated with both changes in climate and changes in management frequency, providing strong evidence that longer intervals between prescribed burns were associated with undesired increases in woody plant cover (estimate ± SE = 0.19 ± 0.06, P < 0.001) and decreases in desired forb cover (estimate ± SE = −0.20 ± 0.04, P < 0.001 and moderate evidence that later spring freezes were associated with increases in desired graminoid cover (estimate ± SE = 0.11 ± 0.04, P = 0.01) and decreases in undesired Celastrus cover (estimate ± SE = −1.02 ± 0.48 , P = 0.04). Overall, though targeted experimentation will be needed to identify a management interval that balances the costs and effects of changes in management frequencies, our results suggest that increases in the frequency of prescribed burns and other management actions may assist in the maintenance of desired landscape conditions in Big Meadows.

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Ramm-Granberg, Tynan, F. Rocchio, Catharine Copass, Rachel Brunner, and Eric Nelsen. Revised vegetation classification for Mount Rainier, North Cascades, and Olympic national parks: Project summary report. National Park Service, February 2021. http://dx.doi.org/10.36967/nrr-2284511.

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Field crews recently collected more than 10 years of classification and mapping data in support of the North Coast and Cascades Inventory and Monitoring Network (NCCN) vegetation maps of Mount Rainier (MORA), Olympic (OLYM), and North Cascades (NOCA) National Parks. Synthesis and analysis of these 6000+ plots by Washington Natural Heritage Program (WNHP) and Institute for Natural Resources (INR) staff built on the foundation provided by the earlier classification work of Crawford et al. (2009). These analyses provided support for most of the provisional plant associations in Crawford et al. (2009), while also revealing previously undescribed vegetation types that were not represented in the United States National Vegetation Classification (USNVC). Both provisional and undescribed types have since been submitted to the USNVC by WNHP staff through a peer-reviewed process. NCCN plots were combined with statewide forest and wetland plot data from the US Forest Service (USFS) and other sources to create a comprehensive data set for Washington. Analyses incorporated Cluster Analysis, Nonmetric Multidimensional Scaling (NMS), Multi-Response Permutation Procedure (MRPP), and Indicator Species Analysis (ISA) to identify, vet, and describe USNVC group, alliance, and association distinctions. The resulting revised classification contains 321 plant associations in 99 alliances. A total of 54 upland associations were moved through the peer review process and are now part of the USNVC. Of those, 45 were provisional or preliminary types from Crawford et al. (2009), with 9 additional new associations that were originally identified by INR. WNHP also revised the concepts of 34 associations, wrote descriptions for 2 existing associations, eliminated/archived 2 associations, and created 4 new upland alliances. Finally, WNHP created 27 new wetland alliances and revised or clarified an additional 21 as part of this project (not all of those occur in the parks). This report and accompanying vegetation descriptions, keys and synoptic and environmental tables (all products available from the NPS Data Store project reference: https://irma.nps.gov/DataStore/Reference/Profile/2279907) present the fruit of these combined efforts: a comprehensive, up-to-date vegetation classification for the three major national parks of Washington State.

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Nikula, Blair, and Robert Cook. Status and distribution of Odonates at Cape Cod National Seashore. National Park Service, 2024. http://dx.doi.org/10.36967/2303254.

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Odonates are significant components of most wetland habitats and important indicators of their health. At Cape Cod National Seashore (CACO), we compiled odonate records dating back to the 1980s and, based partly on that data, identified 41 wetland sites for sampling, representing six freshwater habitats (kettle pond, inter-dune pond, dune slack, riparian marsh, vernal pool, and bog). We surveyed these sites for adult odonates during the 2016?2018 field seasons. Ten sites were surveyed all three years (total 19-20 surveys/site); all ten had at least some historical data. The remaining 31 sites were surveyed for one field season, a total of 6-8 times each. We conducted 391 surveys, recording 53,435 individuals and 74 species (45 dragonflies and 29 damselflies); not all individuals were identified to species. Abundance and species richness varied significantly between habitats. For all individuals recorded, abundance was greatest at vernal pools and kettle ponds. Riparian sites had the lowest abundance. Species richness was highest at kettle ponds, including several species of conservation concern, two listed as Threatened by the state of Massachusetts. Riparian marshes and dune slacks had relatively low richness. Among the 10 sites surveyed three years, we found significant annual variation in abundance and species richness. There was significant and generally greater between-site variation in abundance within a year than between years at sites. Community analysis found pond depth, habitat type, and presence of predaceous fish were significant factors explaining between-site variation in community composition. Habitats also differed significantly in community composition. Multidimensional scaling showed sites tend to cluster together by habitat type. Vernal ponds have the highest average community similarity to all other habitats (53.5%), with dune slack (52.9%), bog (52.0%) and inter-dune (51.5%) close behind. In contrast, riparian sites (46.3%) and kettle ponds (39.5%) are least similar to other habitats. Overall, 86 species of odonates have been recorded at CACO, a rich and diverse assemblage reflecting the variety and quality of freshwater habitats present. Although these habitats are relatively well-protected, stressors include climate change, nutrient inflow from adjacent development, road runoff, and trampling of emergent vegetation. A plan for monitoring is beyond the scope of this project. Ideally, it would be best to use the insight into odonate variation obtained from these surveys to develop a monitoring program designed to meet standards of statistical confidence and power currently employed in NPS monitoring programs.

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