Design of optimal ecosystem monitoring networks: hotspot detection and biodiversity patterns

  • Matteo Convertino
  • Rafael Muñoz-Carpena
  • Gregory A. Kiker
  • Stephen G. Perz
Original Paper

Abstract

Effective monitoring of ecosystems is crucial for assessing and possibly anticipating shifts, quantifying ecosystem services, and optimal decision making based on shifts and services. The selection of monitoring sites is typically suboptimal following local stakeholder or research interests that do not allow to capture ecosystem patterns and dynamics as a whole. The design of optimal monitoring network is crucial for the accurate determination of biodiversity patterns of ecosystems. A novel model for the design of optimal monitoring networks for biodiversity based on the concept of the value of information (VoI) is proposed. The VoI is assigned to species richness that is the economically and ecologically valuable metric. As a case study the trinational frontier ecosystem among Brazil, Peru, and Bolivia is considered for the model. A multiresolution texture-based model estimates species richness and turnover on satellite imagery calibrated on different sets of information coming from forest plot data organized in network topologies. The optimal monitoring network is the network that minimizes the integrated VoI defined as the variation of the VoI in the 28 years considered. This is equivalent to minimize the sum of the species turnover of the ecosystem. The small world network is identified as the optimal and most resilient monitoring network whose nodes are the hotspots of species richness. The hotspots are identified as the sites whose VoI is the highest for the whole period considered. Hence, the hotspots are the most valuable communities for inferring biodiversity patterns and the most ecologically/economically valuable according to the richness—resilience hypothesis. Most hotspots are honored by the small world network that can be thought as the ”backbone” ecological network of the ecosystem. The small world monitoring network has an accuracy ~50 % higher than other network topologies in predicting biodiversity patterns. This network has the highest VoI at any time step and scale considered; thus, it guarantees to track changes of ecosystems in space and time. The network that results from the optimal trade-off between data value with their uncertainty and relevance, has deep implications for understanding ecosystem function and for management decisions. The model allows to include preferences for ecosystem communities by using differential weights on the VoI of these communities, and economic constraints that limit the extension of the network. Because of the optimal integration of environmental, social, and economical factors the model allows a sustainable monitoring and planning of biodiversity for the future.

Keywords

Value of spatial information Monitoring network Species richness Multiresolution texture Kullback–Leibler divergence 

Supplementary material

477_2014_999_MOESM1_ESM.pdf (1.5 mb)
Supplementary material 1 (pdf 1459 KB)

References

  1. Abida R, Bocquet M, Vercauteren N, Isnard O (2008) Design of a monitoring network over france in case of a radiological accidental release. Atmos Environ 42(21):5205–5219. doi:10.1016/j.atmosenv.2008.02.065 CrossRefGoogle Scholar
  2. Albert R, Barabási A-L (2002) Statistical mechanics of complex networks. Rev Mod Phys 74:47–97. doi:10.1103/RevModPhys.74.47 CrossRefGoogle Scholar
  3. Albert R, Jeong H, Barabasi A-L (2000) Error and attack tolerance of complex networks. Nature 406(6794):378–382. doi:10.1038/35019019. ISSN 1476–4687
  4. Aldecoa R, Marín I (2013) Surprise maximization reveals the community structure of complex networks. Sci Rep. doi:10.1038/srep01060
  5. Alessa LN, Kliskey AA, Brown G (2008) Social-ecological hotspots mapping: a spatial approach for identifying coupled social-ecological space. Landsc Urban Plan 85:27–39. doi:10.1016/j.landurbplan.2007.09.007. ISSN 01692046
  6. Alfonso L, Price R (2012) Coupling hydrodynamic models and value of information for designing stage monitoring networks. Water Resour Res 48(8):W08530. doi:10.1029/2012WR012040 CrossRefGoogle Scholar
  7. Arinaminpathy N, Kapadia S (2012) Size and complexity in model financial systems. Proc Natl Acad Sci USA 109(45):18338–18343. doi:10.1073/pnas.1213767109 CrossRefGoogle Scholar
  8. Banavar JR, Colaiori F, Flammini A, Maritan A, Rinaldo A (2001) Scaling, optimality, and landscape evolution. J Stat Phys 104:1–48. doi:10.1023/A:1010397325029. ISSN 0022–4715
  9. Beale N, Rand DG, Battey H, Croxson K, May RM, Nowak MA (2011) Individual versus systemic risk and the regulator’s dilemma. Proc Natl Acad Sci USA 108(31):12647–12652. doi:10.1073/pnas.1105882108. http://www.pnas.org/content/108/31/12647.abstract
  10. Bekessy SA, Wintle BA, Lindenmayer DB, Mccarthy MA, Burgman MA, Possingham HP (2010) The biodiversity bank cannot be a lending bank. Conserv Lett 3(3):151–158. doi:10.1111/j.1755-263X.2010.00110.x CrossRefGoogle Scholar
  11. Bhattacharjya D, Eidsvik J, Mukerji T (2010) The value of information in spatial decision making. Math Geosci 42(2):141–163. doi:10.1007/s11004-009-9256-y. ISSN 1874–8961
  12. Boakes EH, McGowan PJK, Fuller RA, Chang-qing D, Clark NE, O’Connor K, Mace GM (2010) Distorted views of biodiversity: spatial and temporal bias in species occurrence data. PLoS Biol 8(6):e1000385. doi:10.1371/journal.pbio.1000385
  13. Borisova T, Shortle J, Horan RD, Abler D (2005) Value of information for water quality management. Water Resour Res. doi:10.1029/2004WR003576. ISSN 1944–7973
  14. Caers J (2011) Value of information. Wiley, New York, pp 193–213. doi:10.1002/9781119995920.ch11. ISBN 9781119995920
  15. Cohen R, Havlin S (2003) Scale-free networks are ultrasmall. Phys Rev Lett 90:058701. doi:10.1103/PhysRevLett.90.058701
  16. Colizza V, Banavar JR, Maritan A, Rinaldo A (2004) Network structures from selection principles. Phys Rev Lett 92:198701. doi:10.1103/PhysRevLett.92.198701
  17. Convertino M, Baker KM, Vogel JT, Lu C, Suedel B, Linkov I (2013) Multi-criteria decision analysis to select metrics for design and monitoring of sustainable ecosystem restorations. Ecol Indicat 26:76–86. doi:10.1016/j.ecolind.2012.10.005 CrossRefGoogle Scholar
  18. Convertino M, Mangoubi RS, Linkov I, Lowry NC, Desai M (2012a) Inferring species richness and turnover by statistical multiresolution texture analysis of satellite imagery. PLoS One 7(10):10. doi:10.1371/journal.pone.0046616
  19. Convertino M, Welle P, Munoz-Carpena R, Kiker GA, Chu-Agor ML, Fischer RA, Linkov I (2012b) Epistemic uncertainty in predicting shorebird biogeography affected by sea-level rise. Ecol Model 240:1–15. doi:10.1016/j.ecolmodel.2012.04.012. ISSN 0304–3800
  20. Cordeiro NJ, Howe HF (2003) Forest fragmentation severs mutualism between seed dispersers and an endemic african tree. Proc Natl Acad Sci USA 100(24):14052–14056. doi:10.1073/pnas.2331023100 CrossRefGoogle Scholar
  21. Costello C, Rassweiler A, Siegel D, de Leo G, Micheli F, Rosenberg A (2010) Marine reserves special feature: the value of spatial information in MPA network design. Proc Natl Acad Sci USA 107:18294–18299. doi:10.1073/pnas.0908057107 CrossRefGoogle Scholar
  22. Dasgupta P, Kinzig A (2011) The value of biodiversity. Technical report, University of Cambridge, Cambridge. http://www.econ.cam.ac.uk/faculty/dasgupta/pubs11/Dasgupta-Kinzig-Perrings-EOB.pdf
  23. Davidson EA, de Araujo AC, Artaxo P, Balch JK, Brown IF, Bustamante MMC, Coe MT, DeFries RS, Keller M, Longo M, Munger JW, Schroeder W, Soares-Filho BS, Souza CM, Wofsy SC (2012) The amazon basin in transition. Nature 481(7381):321–328. doi:10.1038/nature10717. ISSN 0028–0836
  24. Doak DF, Bigger D, Harding EK, Marvier MA, O’Malley RE, Thomson D (1998) The statistical inevitability of stability-diversity relationships in community ecology. Am Nat 151(3):264–276Google Scholar
  25. Emmert-Streib F, Dehmer M (2012) Exploring statistical and population aspects of network complexity. PLoS One 7(5):e34523. doi:10.1371/journal.pone.0034523 CrossRefGoogle Scholar
  26. Farnsworth KD, Lyashevska O, Fung T (2012) Functional complexity: the source of value in biodiversity. Ecol Complex 11:46–52. doi:10.1016/j.ecocom.2012.02.001. ISSN 1476–945X
  27. Feeley KJ, Silman MR (2009) Extinction risks of amazonian plant species. Proc Natl Acad Sci USA 106 (30):12382–12387. doi:10.1073/pnas.0900698106. http://www.pnas.org/content/106/30/12382.abstract
  28. Friedman A, Yakubu A (2012) Host demographic allee effect, fatal disease, and migration: persistence or extinction. SIAM J Appl Math 72(5):1644–1666. doi:10.1137/120861382 CrossRefGoogle Scholar
  29. Gaines SD, White C, Carr MH, Palumbi SR (2010) Designing marine reserve networks for both conservation and fisheries management. Proc Natl Acad Sci USA 107 (43):18286–18293. doi:10.1073/pnas.0906473107. http://www.pnas.org/content/107/43/18286.abstract
  30. Haldane AG, May RM (2011) Systemic risk in banking ecosystems. Nature 469:351–355. doi:10.1038/nature09659 CrossRefGoogle Scholar
  31. Halpern BS, Longo C, Hardy D, McLeod KL, Samhouri JF, Katona SK, Kleisner K, Lester SE, O/’Leary J, Ranelletti M, Rosenberg AA, Scarborough C, Selig ER, Best BD, Brumbaugh DR, Stuart Chapin F, Crowder LB, Daly KL, Doney SC, Elfes C, Fogarty MJ, Gaines SD, Jacobsen KI, Karrer LB, Leslie HM, Neeley E, Pauly D, Polasky S, Ris B, St Martin K, Stone GS, Sumaila UR, Zeller D (2012) An index to assess the health and benefits of the global ocean. Nature 488:615–620. doi:10.1038/nature11397
  32. Haynes-Young R, Potschin M (2009) The links between biodiversity, ecosystem services and human well-being. In: Raffaelli D, Frid C (eds) BES ecological reviews series, vol 6. Cambridge University, CambridgeGoogle Scholar
  33. Humphries MD, Gurney K (2008) Network ‘small-world-ness’: a quantitative method for determining canonical network equivalence. PLoS One 3(4):e0002051. doi:10.1371/journal.pone.0002051
  34. Janssen MA, Bodin Ö, Anderies JM, Elmqvist T, Ernstson H, Mcallister RRJ, Olsson P, Ryan P (2006) Toward a network perspective of the study of resilience in social-ecological systems. Ecol Soc 11(1):15CrossRefGoogle Scholar
  35. Kefi S, Rietkerk M, Roy M, Franc A, de Ruiter PC, Pascual M (2011) Robust scaling in ecosystems and the meltdown of patch size distributions before extinction. Ecol Lett, 14(1):29–35. doi:10.1111/j.1461-0248.2010.01553.x. ISSN 1461–0248
  36. Keisler J (2004) Value of information in portfolio decision analysis. Decis Anal 1(3):177–189. doi:10.1287/deca.1040.0023. http://da.journal.informs.org/content/1/3/177.abstract
  37. Kim IY, de Weck OL (2005) Adaptive weighted-sum method for bi-objective optimization: Pareto front generation. Struct Multidiscip Optim 29:149–158. doi:10.1007/s00158-004-0465-1
  38. Kujala H, Burgman MA, Moilanen A (2012) Treatment of uncertainty in conservation under climate change. Conserv Lett doi:10.1111/j.1755-263X.2012.00299.x. ISSN 1755–263X
  39. Laurance WF, Luizao RCC (2007) Driving a wedge into the amazon. Nature 448(7152):409–410. doi:10.1038/448409a. ISSN 0028–0836
  40. Li L, Wang J, Cao Z, Zhong E (2008) An information-fusion method to identify pattern of spatial heterogeneity for improving the accuracy of estimation. Stoch Environ Res Risk Assess 22:689–704. doi:10.1007/s00477-007-0179-1
  41. Malhi Y, Roberts JT, Betts RA, Killeen TJ, Li W, Nobre CA (2008) Climate change, deforestation, and the fate of the amazon. Science 319(5860):169–172. doi:10.1126/science.1146961. ISSN 1095–9203
  42. Mumby PJ, Elliott IA, Eakin CM, Skirving W, Paris CB, Edwards HJ, Enriquez S, Iglesias-Prieto R, Cherubin LM, Stevens JR (2011) Reserve design for uncertain responses of coral reefs to climate change. Ecol Lett 14(2):132–140. doi:10.1111/j.1461-0248.2010.01562.x. ISSN 1461–0248
  43. Muneepeerakul R, Rinaldo A, Levin SA, Rodriguez-Iturbe I (2008) Signatures of vegetational functional diversity in river basins. Water Resour Res. doi:10.1029/2007WR006153. ISSN 1944–7973
  44. May RM (1973) Stability and complexity in model ecosystems. Princeton University Press, PrincetonGoogle Scholar
  45. Newman M (2003) The structure and function of complex networks. SIAM Rev 45(2):167–256. doi:10.1137/S003614450342480
  46. Nonaka E, Petter H (2007) Agent-based model approach to optimal foraging in heterogeneous landscapes: effects of patch clumpiness. Ecography 30(6):777–788. doi:10.1111/j.2007.0906-7590.05148.x. ISSN 0906–7590
  47. Oakley JE (2009) Decision-theoretic sensitivity analysis for complex computer models. Technometrics 51(2):121–129. doi:10.1198/TECH.2009.0014
  48. Pandit A, Crittenden JC (2012) Index of network resilience (inr) for urban water distribution systems. http://www.tisp.org/index.cfm?cdid=12519&pid=10261
  49. Park J, Seager TP, Rao PSC, Convertino M, Linkov I (2012) Integrating risk and resilience approaches to catastrophe management in engineering systems. Risk Anal. doi:10.1111/j.1539-6924.2012.01885.x. ISSN 1539–6924
  50. Perz SG, Cabrera L, Carvalho LA, Castillo J, Chacacanta R, Cossio RE, Solano YF, Hoelle J, Perales LM, Puerta I, Cspedes DR, Camacho IR, Silva AC (2012a) Regional integration and local change: road paving, community connectivity, and socialecological resilience in a tri-national frontier, southwestern amazonia. Reg Environ Change 12:35–53. doi:10.1007/s10113-011-0233-x. ISSN 1436–3798
  51. Perz SG, Shenkin A, Barnes G, Cabrera L, Carvalho LA, Castillo J (2012b) Connectivity and resilience: a multidimensional analysis of infrastructure impacts in the southwestern amazon. Soc Indicat Res 106:259–285. doi:10.1007/s11205-011-9802-0
  52. Peterson G, Allen CE, Holling CS (1998) Ecological resilience, biodiversity, and scale. Ecosystems 1(1):6–18. doi:10.1007/s100219900002. ISSN 1432–9840
  53. Pinto PC, Thiran P, Vetterli M (2012) Locating the source of diffusion in large-scale networks. Phys Rev Lett 109:068702. doi:10.1103/PhysRevLett.109.068702
  54. Rassweiler A, Costello C (2012) Marine protected areas and the value of spatially optimized fishery management. Proc Natl Acad Sci USA 109(29):11884–11889. doi:10.1073/pnas.1116193109 CrossRefGoogle Scholar
  55. Reinmoeller P, van Baardwijk N (2005) The link between diversity and resilience. Technical report, MIT Sloan Review. www.forestplots.net.
  56. Rocco CM, Ramirez-Marquez JE, Salazar DE, Hernandez I (2010) Implementation of multi-objective optimization for vulnerability analysis of complex networks. Proc Inst Mech Eng O 224(2):87–95. doi:10.1243/1748006XJRR274. http://pio.sagepub.com/content/224/2/87.abstract
  57. Rodriguez-Iturbe I, Muneepeerakul R, Bertuzzo E, Levin SA, Rinaldo A (2009) River networks as ecological corridors: a complex systems perspective for integrating hydrologic, geomorphologic, and ecologic dynamics. Water Resour Res. doi:10.1029/2008WR007124. ISSN 1944–7973Google Scholar
  58. Saatchi S, Asefi-Najafabady S, Malhi Y, Aragao LE, Anderson LO, Myneni RB, Nemani R (2013) Persistent effects of a severe drought on amazonian forest canopy. Proc Natl Acad Sci USA 110(2):565–570. doi:10.1073/pnas.1204651110 CrossRefGoogle Scholar
  59. Scheffer M, Carpenter SR, Lenton TM, Bascompte J, Brock W, Dakos V, van de Koppel J, van de Leemput IA, Levin SA, van Nes EH, Pascual M, Vandermeer J, (2012) Anticipating critical transitions. Science 338(6105):344–348. doi:10.1126/science.1225244
  60. Seidler TG, Plotkin JB (2006) Seed dispersal and spatial pattern in tropical trees. PLoS Biol 4(11):e344. doi:10.1371/journal.pbio.0040344 CrossRefGoogle Scholar
  61. Sinha S (2005) Complexity vs. stability in small-world networks. Phys A 346:147–153. doi:10.1016/j.physa.2004.08.062 CrossRefGoogle Scholar
  62. Soares-Filho B, Moutinho P, Nepstad D, Anderson A, Rodrigues H, Garcia R, Dietzsch L, Merry F, Bowman M, Hissa L, Silvestrini R, Maretti C (2010) Role of brazilian amazon protected areas in climate change mitigation. Proc Natl Acad Sci USA 107(24):10821–10826CrossRefGoogle Scholar
  63. Southworth J, Marsik M, Qiu Y, Perz S, Cumming G, Stevens F, Rocha K, Duchelle A, Barnes G (2011) Roads as drivers of change: trajectories across the trinational frontier in map—the southwestern amazon. Rem Sens 3(5):1047–1066. doi:10.3390/rs3051047 CrossRefGoogle Scholar
  64. Steege HT, Pitman NCA, Phillips OL, Chave J, Sabatier D, Duque A, Molino J-F, Prévost M-F, Spichiger R, Castellanos H, von Hildebrand P, Vásquez R (2006) Continental-scale patterns of canopy tree composition and function across amazonia. Nature 443(7110):444–447, 2006. doi:10.1038/nature05134. ISSN 0028–0836
  65. Suzuki S, Caers J (2008) A distance-based prior model parameterization for constraining solutions of spatial inverse problems. Math Geosc 40:445–469. doi:10.1007/s11004-008-9154-8
  66. Taylor K, Brummer T, Taper ML, Wing A, Rew LJ (2012) Human-mediated long-distance dispersal: an empirical evaluation of seed dispersal by vehicles. Divers Distrib 18(9): 942–951. doi:10.1111/j.1472-4642.2012.00926.x. ISSN 1472–4642
  67. Trainor-Guitton WJ, Mukerji T, Knight R (2012) A methodology for quantifying the value of spatial information for dynamic earth problems. Stoch Environ Res Risk Assess 27(4):969–983. doi:10.1007/s00477-012-0619-4. ISSN 1436–3240
  68. von Winterfeldt D, Kavet R, Peck S, Mohan M, Hazen G (2012) The value of environmental information without control of subsequent decisions. Risk Anal 32(12):2113–2132. doi:10.1111/j.1539-6924.2012.01828.x. ISSN 1539–6924
  69. Walker B, Carpenter S, Anderies J, Abel N, Cumming G, Janssen M, Lebel L, Norberg J, Peterson GD, Pritchard R (2002) Resilience management in social-ecological systems: a working hypothesis for a participatory approach. Conserv Ecol 6(1):14CrossRefGoogle Scholar
  70. Wang J-F, A. Stein A, Gao B-B, Ge Y (2012) A review of spatial sampling. Spat Stat 2:1–14. doi:10.1016/j.spasta.2012.08.001. ISSN 2211–6753
  71. Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393:440–442. doi:10.1038/30918 CrossRefGoogle Scholar
  72. Wilhite A (2001) Bilateral trade and ‘small-world’ networks. Comput Econ 18(1):49–64. doi:10.1023/A:1013814511151. ISSN 0927–7099
  73. Wilmers CC (2008) Understanding ecosystem robustness. Trends Ecol Evol 22(10):504–506. doi:10.1016/j.tree.2007.08.008. ISSN 0169–5347
  74. Wintle BA, Runge MC, Bekessy SA (2010) Allocating monitoring effort in the face of unknown unknowns. Ecol Lett 13(11):1325–1337. doi:10.1111/j.1461-0248.2010.01514.x. ISSN 1461–0248
  75. Wintle BA, Bekessy SA, Keith DA, van Wilgen BW, Cabeza M, Schröder B, Carvalho SB, Falcucci A, Maiorano L, Regan TJ, Rondinini C, Boitani L, Possingham HP (2011) Ecological-economic optimization of biodiversity conservation under climate change. Nat Clim Change 1:355–359. doi:10.1038/nclimate1227 CrossRefGoogle Scholar
  76. Xu J, Fischbeck P, Small M, VanBriesen J, Casman E (2008) Identifying sets of key nodes for placing sensors in dynamic water distribution networks. J Water Resour Plan Manag 134(4):378–385. doi:10.1061/(ASCE)0733-9496(2008)134:4(378)
  77. Yeh M-S, Lin Y-P, Chang L-C (2006) Designing an optimal multivariate geostatistical groundwater quality monitoring network using factorial kriging and genetic algorithms. Environ Geol 50:101–121. doi:10.1007/s00254-006-0190-8 CrossRefGoogle Scholar
  78. Yesson C, Brewer PW, Sutton T, Caithness N, Pahwa JS, Burgess M, Gray WA, White RJ, Jones AC, Bisby FA, Culham A (2007) How global is the global biodiversity information facility? PLoS One 2(11):11. doi:10.1371/journal.pone.0001124 CrossRefGoogle Scholar
  79. Ziv G, Baran E, Nam S, Rodrguez-Iturbe I (2012) Trading-off fish biodiversity, food security, and hydropower in the mekong river basin. Proc Natl Acad Sci USA 109(15):5609–5614. doi:10.1073/pnas.1201423109 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.HumNat Lab, School of Public Health, Environmental Health Sciences Division and Public Health Informatics ProgramUniversity of MinnesotaMinneapolisUSA
  2. 2.Institute on the Environment, University of MinnesotaMinneapolisUSA
  3. 3.Institute for Engineering in MedicineUniversity of MinnesotaMinneapolisUSA
  4. 4.Department of Agricultural and Biological Engineering, and Florida Climate InstituteUniversity of FloridaGainesvilleUSA
  5. 5.Department of Sociology, Criminology, and LawUniversity of FloridaGainesvilleUSA
  6. 6.Academic Health CenterUniversity of MinnesotaMinneapolisUSA

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