A Solution to Treat Mixed-Type Human Datasets from Socio-Ecological Systems
Main Article Content
Abstract
Coupled human and natural systems (CHANS) are frequently represented by large datasets with varied data including continuous, ordinal, and categorical variables. Conventional multivariate analyses cannot handle these mixed data types. In this paper, our goal was to show how a clustering method that has not before been applied to understanding the human dimension of CHANS: a Gower dissimilarity matrix with partitioning around medoids (PAM) can be used to treat mixed-type human datasets. A case study of land managers responsible for invasive plant control projects across rivers of the southwestern U.S. was used to characterize managers’ backgrounds and decisions, and project properties through clustering. Results showed that managers could be classified as “federal multitaskers” or as “educated specialists”. Decisions were characterized by being either “quick and active” or “thorough and careful”. Project goals were either comprehensive with ecological goals or more limited in scope. This study shows that clustering with Gower and PAM can simplify the complex human dimension of this system, demonstrating the utility of this approach for systems frequently composed of mixed-type data such as CHANS. This clustering approach can be used to direct scientific recommendations towards homogeneous groups of managers and project types.
Downloads
Article Details
x
Funding data
-
National Science Foundation
Grant numbers Dynamics of Coupled Natural and Human Systems award (project number 1617463)
References
Akhanli, S.E., Hennig, C. 2017. Some Issues in Distance Construction for Football Players Performance Data. Archives of Data Science 2(1). DOI: 10.5445/KSP/1000058749/09
Arunachalam, D., Kumar, N. 2018. Benefit-based consumer segmentation and performance evaluation of clustering approaches: An evidence of data-driven decision-making. Expert Systems with Applications 111, 11–34. DOI: 10.1016/j.eswa.2018.03.007
Bateman, H.L., Paxton, E.H., Longland, W.S. 2013. Tamarix as Wildlife Habitat. In Sher, A.A., Quigley, M.T. (Eds.) Tamarix: A Case Study of Ecological Change in the American West. Oxford University Press, New York, 168–188. DOI: 10.1093/acprof:osobl/9780199898206.003.0010
Bean, D., Dudley, T. 2018. A synoptic review of Tamarix biocontrol in North America: tracking success in the midst of controversy. BioControl 63(3), 361–376. DOI: 10.1007/s10526-018-9880-x
Bernhardt, E.S., Sudduth, E.B., Palmer, M.A., Allan, J.D., Meyer, J.L., Alexander, G., Follastad-Shah, J., Hassett, B., Jenkinson, R., Lave, R., Rumps, J., Pagano, L. 2007. Restoring rivers one reach at a time: Results from a survey of U.S. river restoration practitioners. Restoration Ecology 15, 482–493. DOI: 10.1111/j.1526-100X.2007.00244.x
Bohensky, E.L., Kirono, D.G.C., Butler, J.R.A., Rochester, W., Habibi, P., Handayani, T., Yanuartati, Y. 2016. Climate knowledge cultures: Stakeholder perspectives on change and adaptation in Nusa Tenggara Barat, Indonesia. Climate Risk Management 12, 17–31. DOI: 10.1016/j.crm.2015.11.004
Borcard, D., Gillet, F., Legendre, P. 2011. Numerical Ecology with R. Springer, New York.
Canul-Reich, J., Hernández-Torruco, J., Frausto-Solis, J., Méndez Castillo, J.J. 2015. Finding relevant features for identifying subtypes of Guillain-Barré Syndrome using Quenching Simulated Annealing and Partitions Around Medoids. International Journal of Combinatorial Optimization Problems and Informatics 6(2), 11–27.
Clark, L.B., Henry, A.L., Lave, R., Sayre, N.F., González, E., Sher, A.A. 2019. Successful information exchange between restoration science and practice. Restoration Ecology 27(6), 1241–1250. DOI: 10.1111/rec.12979
Curtis, A., de Lacy, T. 1998. Landcare, stewardship and sustainable agriculture in Australia. Environmental Values 7, 59–78. DOI: https://www.jstor.org/stable/30302269
Friedman, J.M., Auble, G.T., Shafroth, P.B., Scott, M.L., Merigliano, M.F., Freehling, M.D., Griffin, E.R. 2005. Dominance of non-native riparian trees in western USA. Biological Invasions 7(4), 747–751. DOI: 10.1007/s10530-004-5849-z
García-Llorente, M., Martín-López, B., Nunes, P.A.L.D., González, J.A., Alcorlo, P., Montes, C. 2011. Analyzing the Social Factors That Influence Willingness to Pay for Invasive Alien Species Management Under Two Different Strategies: Eradication and Prevention. Environmental Management 48(3), 418–435. DOI: 10.1007/s00267-011-9646-z
Gellynck, X., Kühne, B., Weaver, R.D. 2011. Relationship quality and innovation capacity of chains: the case of the traditional food sector in the EU. Proceedings in Food System Dynamics 2(1), 1–22. DOI: 10.22004/ag.econ.100498
González, E., Sher, A.A., Anderson, R.M., Bay, R.F., Bean, D.W., Bissonnete, G.J., Bourgeois, B., Cooper, D.J., Dohrenwend, K., Eichhorst, K.D., El Waer, H., Kennard, D.K., Harms-Weissinger, R., Henry, A.L., Makarick, L.J., Ostoja, S.M., Reynolds, L.V., Robinson, W.W., Shafroth, P.B. 2017a. Vegetation response to invasive Tamarix control in southwestern U.S. rivers: A collaborative study including 416 sites. Ecological Applications 27(6), 1789–1804. DOI: 10.1002/eap.1566
González, E., Sher, A.A., Anderson, R.M., Bay, R.F., Bean, D.W., Bissonnete, G.J., Cooper, D.J., Dohrenwend, K., Eichhorst, K.D., El Waer, H., Kennard, D.K., Harms-Weissinger, R., Henry, A.L., Makarick, L.J., Ostoja, S.M., Reynolds, L.V., Robinson, W.W., Shafroth, P.B., Tabacchi, E. 2017b. Secondary invasions of noxious weeds associated with control of invasive Tamarix are frequent, idiosyncratic and persistent. Biological Conservation 213, 106–114. DOI: 10.1016/j.biocon.2017.06.043
González, E., Sher, A. A., Tabacchi, E., Masip, A., Poulin, M. 2015. Restoration of riparian vegetation: a global review of implementation and evaluation approaches in the international, peer-reviewed literature. Journal of Environmental Management 158, 85-94. DOI: 10.1016/j.jenvman.2015.04.033
Gower, J.C. 1971. A general coefficient of similarity and some of its properties. Biometrics 27(4), 857–871. DOI: 10.2307/2528823
Hagger, V., Dwyer, J., Wilson, K. 2017. What motivates ecological restoration? Restoration Ecology 25, 832–843. DOI: 10.1111/rec.12503
Han, S., Sung, K.R., Lee, K.S., Hong, J.W. 2014. Outcomes of laser peripheral iridotomy in angle closure subgroups according to anterior segment optical coherence tomography parameters. Investigative Ophthalmology & Visual Science 55, 6795–6801. DOI: 10.1167/iovs.14-14714
Hennig, C. 2013. fpc: Flexible procedures for clustering. R package version 21-5.
Hennig, C., Liao, T.F. 2013. How to find an appropriate clustering for mixed-type variables with application to socio-economic stratification. Journal of the Royal Statistical Society. Series C: Applied Statistics 62, 309–369. DOI: 10.1111/j.1467-9876.2012.01066.x
Higuera, D., Martín-López, B., Sánchez-Jabba, A. 2013. Social preferences towards ecosystem services provided by cloud forests in the neotropics: Implications for conservation strategies. Regional Environmental Change 13, 861–872. DOI: 10.1007/s10113-012-0379-1
Hummel, M., Edelmann, D., Kopp-Schneider, A. 2017. Clustering of samples and variables with mixed-type data. PloS ONE 12(11), 1–24. DOI: 10.1371/journal.pone.0188274
Iparraguirre, J., Gentry, T., Pena, D. 2013. Vulnerability of Primary Care Organizations to the National Health Service Reform in England. Applied Economic Perspectives and Policy 35(4), 634–660. DOI: 10.1093/aepp/ppt021
Kallis, G., Kiparsky, M., Norgaard, R. 2009. Collaborative governance and adaptive management: Lessons from California’s CALFED Water Program. Environmental Science & Policy 12, 631–643. DOI: 10.1016/j.envsci.2009.07.002
Kaufman, L., Rousseeuw, P. 1990. Finding Groups in Data: And Introduction to Cluster Analysis. Wiley seri. John Wiley and Sons Inc.
King, M.L., Hering, A.S., Aguilar, O.M. 2016. Building predictive models of counterinsurgent deaths using robust clustering and regression. Journal of Defense Modeling and Simulation 13(4), 449–465. DOI: 10.1177/1548512916644074
Knight, A. T., Cowling, R. M., Difford, M., & Campbell, B. M. 2010. Mapping human and social dimensions of conservation opportunity for the scheduling of conservation action on private land. Conservation Biology 24(5), 1348–1358. DOI: https://www.jstor.org/stable/40864035
Krichen, L., Martins, J.M.S., Lambert, P., Daaloul, A., Trifi-Farah, N., Marrakchi, M., Audergon, J.M. 2008. Using AFLP Markers for the Analysis of the Genetic Diversity of Apricot Cultivars in Tunisia. Journal of the American Society for Horticultural Science 133(2), 204–212. DOI: 10.21273/JASHS.133.2.204
Kühne, B., Vanhonacker, F., Gellynck, X., Verbeke, W. 2010. Innovation in traditional food products in Europe: Do sector innovation activities match consumers’ acceptance? Food Quality and Preference 21(6), 629–638. DOI: 10.1016/j.foodqual.2010.03.013
Legendre, P., Legendre, L. 2012. Numerical Ecology. 3rd English. Elsevier Science, Amsterdam.
Lismont, J., Vanthienen, J., Baesens, B., Lemahieu, W. 2017. Defining analytics maturity indicators: A survey approach. International Journal of Information Management 37, 114–124. DOI: 10.1016/j.ijinfomgt.2016.12.003
Liu, J., Dietz, T., Carpenter, S.R., Alberti, M., Folke, C., Moran, E., Pell, A.N., Deadman, P. 2007. Complexity of Coupled Human and Natural Systems. Science 317, 1513–1516. DOI: 10.1126/science.1144004
Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M., Hornik, K. 2018. Cluster Analysis Basics and Extensions. R package version 2.0.7-1.
Maione, C., Nelson, D.R., Barbosa, R.M. 2018. Research on social data by means of cluster analysis. Applied Computing and Informatics 15(2), 153–162. DOI: 10.1016/j.aci.2018.02.003
Martin-Lopez, B., Montes, C., Benayas, J. 2007. The non-economic motives behind the willingness to pay for biodiversity conservation. Biological Conservation 139(1-2), 67–82. DOI: 10.1016/j.biocon.2007.06.005
Merritt, D.M., Poff, N.L. 2010. Shifting dominance of riparian Populus and Tamarix along gradients of flow alteration in western North American rivers. Ecological Applications 20(1), 135–152. DOI: 10.1890/08-2251.1
Morandi, B., Piégay, H., Lamouroux, N., Vaudor, L. 2014. How is success or failure in river restoration projects evaluated? Feedback from French restoration projects. Journal of Environmental Management 137, 178–188. DOI: 10.1016/j.jenvman.2014.02.010
Ohrtman, M.K., Lair, K.D. 2013. Tamarix and Salinity: An Overview. In: Sher, A.A., Quigley, M.T. (Eds.) Tamarix: A Case Study of Ecological Change in the American West. Oxford University Press, New York, 123–145. DOI: 10.1093/acprof:osobl/9780199898206.003.0008
Oppenheimer, J.D., Beaugh, S.K., Knudson, J.A., Mueller, P., Grant-Hoffman, N., Clements, A., Wight, M. 2015. A collaborative model for large-scale riparian restoration in the western United States. Restoration Ecology 23(2), 143–148. DOI: 10.1111/rec.12166
Padgett, D.D., Imani, N.O. 1999. Qualitative and quantitative assessment of land-use managers’ attitudes towards environmental justice. Environmental Management 24(4), 509–515. DOI: 10.1007/s002679900250
Pimenta, V., Barroso, I., Boitani, L., Beja, P. 2017. Wolf predation on cattle in Portugal: Assessing the effects of husbandry systems. Biological Conservation 207, 17–26. DOI: 10.1016/j.biocon.2017.01.008
Raymond, C.M., Brown, G. 2011. Assessing conservation opportunity on private land: socio-economic, behavioral, and spatial dimensions. Journal of Environmental Management 92(10), 2513–2523. DOI: 10.1016/j.jenvman.2011.05.015
R Core Team. 2017. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Online available at: http://www.R-project.org/
Roche, L.M., Schohr, T.K., Derner, J.D., Lubell, M.N., Cutts, B.B., Kachergis, E., Eviner, V.T., Tate, K.W. 2015. Sustaining Working Rangelands: Insights from Rancher Decision Making. Rangeland Ecology and Management 68(5), 383–389. DOI: 10.1016/j.rama.2015.07.006
Sander, U., Lubbe, N. 2018. The potential of clustering methods to define intersection test scenarios: Assessing real-life performance of AEB. Accident Analysis & Prevention 113, 1–11. DOI: 10.1016/j.aap.2018.01.010
SAS Institute, I. 2014. JMP version 13.0.
Sher, A.A. 2013. Introduction to the Paradox Plant. In: Sher, A.A. and Quigley, M.T. (Eds.) Tamarix: A Case Study of Ecological Change in the American West. Oxford University Press, New York:1–18. DOI: 10.1093/acprof:osobl/9780199898206.003.0001
Sher, A.A., Clark, L., Henry, A.L., Goetz, A.R.B., González, E., Tyagi, A., Simpson, I., Bourgeois, B. 2020. The Human Element of Restoration Success: Manager Characteristics Affect Vegetation Recovery Following Invasive Tamarix Control. Wetlands. DOI: 10.1007/s13157-020-01370-w
Silva, F., Teixeira, B., Pinto, T., Santos, G., Vale, Z., Praça, I. 2016. Generation of realistic scenarios for multi-agent simulation of electricity markets. Energy 116, 128–139. DOI: 10.1016/j.energy.2016.09.096
Sogge, M. K., E. H. Paxton, Van Riper, C. 2013. Tamarisk in Riparian Woodlands: A Bird’s Eye View’. In: Sher, A.A., Quigley, M.T. (Eds.) Tamarix: A Case Study of Ecological Change in the American West. Oxford University Press, New York, 189–206. DOI: 10.1093/acprof:osobl/9780199898206.003.0011
Stefani, F.O.P., Jones, R.H., May, T.W. 2014. Concordance of seven gene genealogies compared to phenotypic data reveals multiple cryptic species in Australian dermocyboid Cortinarius (Agaricales). Molecular Phylogenetics and Evolution 71, 249–260. DOI: 10.1016/j.ympev.2013.10.019
Strudley, S., Dalin, P. 2013. Tamarix as Invertebrate Habitat’. In: Sher, A.A. Quigley, M.T. (eds.) Tamarix: A Case Study of Ecological Change in the American West. Oxford University Press, New York, 207–224. DOI: 10.1093/acprof:osobl/9780199898206.003.0012
Vangen, S., Huxham, C. 2003. Nurturing Collaborative Relations: Building Trust in Interorganizational Collaboration. The Journal of Applied Behavioral Science 39(1), 5–31. DOI: 10.1177/0021886303039001001
Williams, J.N., Hollander, A.D., Geen, A.T.O., Thrupp, L.A., Hanifin, R., Steenwerth, K., Mcgourty, G., Jackson, L.E. 2011. Assessment of carbon in woody plants and soil across a vineyard-woodland landscape. Carbon Balance and Management 6(1), 1–14. DOI: 10.1186/1750-0680-6-11