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Emerging Land-Use Cross-Scale Patterns and the Pirsig’s Monkey Trap

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Law and Agroecology

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Abstract

We want to draw the attention to some emerging land-use cross-scale patterns resulting from social-economic factors and associated with an historical characteristic sequence of different land-use regimes that could indicate overregulation in social-ecological landscapes (SELs). We postulate that these emerging patterns with clearly defined spatial areas with fixed rules and increasing merging and enlargements of specific functions in some SEL locations are early warning signal of regime shifts and can be typical in many different human-dominated parts of the world. This current overall tendency could make in fact land administration inflexible, and planning may reinforce rigidity, erode resilience, and promote regime shifts and collapse in SELs instead of the adaptability required to counter surprises due, for instance, to climate change. The problem we presently face is how a “static” and “ordered” landscape condition in SELs, provided by the cross-scale intersections of land use, plans, and norms can be made sustainable in face of unpredictable disturbance and change. If we don’t have proper mechanisms to monitor and predict changes and if we are not able to adapt through feedback mechanisms to changes in the environment, we might get stuck in a rigidity trap like the Pirsig’s monkey and we are at high risk for failing. We show that a potential way to address such issues is to look at recent trends of different land-use regimes, along with a simple framework to interpret resulting spatial patterns across scales. We provide examples of this approach and discuss what a cross-scale land-use pattern could mean, what it tells about the condition of SELs, and what the effects could be of changing observed conditions in SELs because of, for instance, climate change. We exercise the approach for the Apulia region in southern Italy taking advantage of recent historical trends observed in main drivers and of the rich information provided by cross-scale pattern analysis in the pattern transition space provided by classic neutral landscape models. We suggest that the degree to which the observed pattern departs from a particular neutral model can indicate whether major constraints or organizing structure has been placed on the landscape and how those landscapes might evolve/react to additional variation due to land use and climate change. The degree of overregulation provided by cross-scale patterns of land use is a warning to planners and managers that the problem is becoming widespread and can no longer be addressed simply with short-term and local-scale solutions. To manage a transition toward more environmentally efficient and, therefore, more sustainable land use, we should design and manage landscape elements and structure to create less contagious and more heterogeneous landscapes. Nevertheless, we have to change societal values at the root of overregulation and rigidity. We have to be aware that we might get stuck in a rigidity trap to appreciate the similarity of our common condition and to start real cooperation.

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Notes

  1. 1.

    Marsh (2005).

  2. 2.

    Nassauer and Corry (2004), pp. 343–356.

  3. 3.

    Kato and Ahern (2008), pp. 543–559.

  4. 4.

    Owens and Cowell (2011).

  5. 5.

    Levin (1999) and Gunderson and Holling (2002).

  6. 6.

    Kato and Ahern (2008).

  7. 7.

    Berkes and Folke (1998); Zaccarelli et al. (2008), p. 26.

  8. 8.

    Dijst and Schenkel (2002), pp. 1–18.

  9. 9.

    Gunderson and Holling (2002); Zaccarelli et al. (2008); Petrosillo et al. (2010), pp. 359–367.

  10. 10.

    Slezak (1999), pp. 3–22.

  11. 11.

    Zurlini et al. (2013), pp. 1161–1173.

  12. 12.

    Booth (2003).

  13. 13.

    Black et al. (2003), pp. 51–67; Millennium Ecosystem Assessment (MEA) (2005).

  14. 14.

    Foley et al. (2005), pp. 570–574.

  15. 15.

    Lambin et al. (2001), pp. 261–269; MEA (2005).

  16. 16.

    Foley et al. (2005).

  17. 17.

    Holling and Meffe (1996), pp. 328–337; Allison and Hobbs (2004), p. 3; Anderies et al. (2006), pp. 865–878.

  18. 18.

    Diamond (1995), pp. 131–138.

  19. 19.

    Ahern (1999), pp. 175–201.

  20. 20.

    Turner et al. (2007), pp. 20666–20671; MEA (2005).

  21. 21.

    Lambin and Meyfroidt (2011), pp. 3465–3472.

  22. 22.

    Zurlini et al. (2013).

  23. 23.

    Scheffer and Westley (2007), p. 36.

  24. 24.

    Carpenter and Brock (2008), p. 40.

  25. 25.

    Dakos et al. (2010), pp. 163–174.

  26. 26.

    Scheffer et al. (2009), pp. 53–59.

  27. 27.

    Zurlini et al. (2006), pp. 119–128; Zurlini et al. (2007), pp. 705–721; Zaccarelli et al. (2008); Petrosillo et al. (2010).

  28. 28.

    Gardner et al. (1987), pp. 19–28; Gardner and Urban (2007), pp. 15–29.

  29. 29.

    Zaccarelli et al. (2008).

  30. 30.

    Zaccarelli et al. (2008).

  31. 31.

    Zaccarelli et al. (2008) and Petrosillo et al. (2010).

  32. 32.

    Istituto Nazionale di Statistica (ISTAT) (2012).

  33. 33.

    Puglia, Regional Law 14/2007; Regione Puglia (2012).

  34. 34.

    Tilman (1999), pp. 5995–6000.

  35. 35.

    Kapur et al. (2010), pp. 1470–1478.

  36. 36.

    Gualdi et al. (2011); Salvati et al. (2011), pp. 1216–1227.

  37. 37.

    Regione Puglia (2012).

  38. 38.

    See, e.g., Li and Reynolds (1994), pp. 2446–2455; Riitters et al. (2000), pp. 27–56; Neel et al. (2004), pp. 435–455; Zurlini et al. (2006, 2007); Proulx and Fahrig (2010), pp. 1479–1487.

  39. 39.

    Gardner et al. (1987).

  40. 40.

    Neel et al. (2004).

  41. 41.

    See, e.g., Jones et al. (2013), pp. 1175–1192.

  42. 42.

    Neel et al. (2004); Li et al. (2004), pp. 137–148.

  43. 43.

    Riitters et al. (1995), pp. 23–39.

  44. 44.

    Riitters et al. (1995).

  45. 45.

    Riitters et al. (2000), Zurlini et al. (2006), Zaccarelli et al. (2008), and Petrosillo et al. (2010).

  46. 46.

    Gardner (1999), pp. 43–62.

  47. 47.

    Zurlini et al. (2006, 2007).

  48. 48.

    Zurlini et al. (2007).

  49. 49.

    Zurlini et al. (2006).

  50. 50.

    Zaccarelli et al. (2008).

  51. 51.

    See, e.g., Milne (1991), pp. 199–235; Li (2000), pp. 33–50; Halley et al. (2004), pp. 254–271; Zurlini et al. (2007).

  52. 52.

    Zurlini et al. (2007).

  53. 53.

    Metzger et al. (2006), pp. 69–85.

  54. 54.

    Foley et al. (2005).

  55. 55.

    Metzger et al. (2006).

  56. 56.

    Briassoulis (2003).

  57. 57.

    Briassoulis (2003).

  58. 58.

    Petrosillo et al. (2013), pp. 609–620.

  59. 59.

    Perini et al. (2009), pp. 45–55.

  60. 60.

    Parise and Pascali (2003), pp. 247–256; Briassoulis (2003).

  61. 61.

    Gualdi et al. (2011).

  62. 62.

    Gualdi et al. (2011).

  63. 63.

    Kapur et al. (2010).

  64. 64.

    Acosta-Michlik et al. (2008), pp. 151–160.

  65. 65.

    Adger (2006), pp. 268–281.

  66. 66.

    Pielke (2005), pp. 1625–1626.

  67. 67.

    Tilman et al. (2002), pp. 671–677.

  68. 68.

    Holling (2001), pp. 390–405; Carpenter and Brock (2008).

  69. 69.

    Lambin and Meyfroidt (2011).

  70. 70.

    Scheffer and Westley (2007).

  71. 71.

    Armson (2011).

  72. 72.

    Schusler et al. (2003), pp. 309–326.

  73. 73.

    Repetto and Allen (2006), pp. 110–136.

  74. 74.

    Walters (1986); Vernier et al. (2009), pp. 3–14; Cushman and McKelvey (2010), pp. 111–130.

  75. 75.

    Scheffer et al. (2001), pp. 591–596.

  76. 76.

    See, e.g., Olsson et al. (2004), pp. 75–90; Folke et al. (2005), pp. 441–473; Musacchio (2009), pp. 993–1013; Opdam et al. (2009), pp. 715–721; Ostrom (2009), pp. 419–422; Benayas and Bullock (2012), pp. 883–889; Zurlini et al. (2013); Jones et al. (2013).

  77. 77.

    Lambin and Meyfroidt (2011).

  78. 78.

    FAO (2009).

  79. 79.

    Kiers et al. (2008), pp. 320–321.

  80. 80.

    Jones et al. (2013).

  81. 81.

    Benayas and Bullock (2012).

  82. 82.

    Benayas and Bullock (2012).

  83. 83.

    Jones et al. (2010), pp. 1261–1275.

  84. 84.

    Kiers et al. (2008).

  85. 85.

    Lindenmayer and Likens (2009), pp. 482–486.

  86. 86.

    Ahern (1999) and Jones et al. (2013).

  87. 87.

    Wu and Hobbs (2002), pp. 355–365; Naidoo et al. (2008), pp. 9495–9500.

  88. 88.

    Walker et al. (2002), p. 14; Olsson et al. (2004).

  89. 89.

    Butler and Goldstein (2010), p. 21.

  90. 90.

    Fischer et al. (2013), pp. 10229–10233.

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Authors and Affiliations

  1. Landscape Ecology Laboratory, Department of Biological and Environmental Sciences and Technologies (DISTEBA), University of Salento, Lecce, Italy

    G. Zurlini, T. Semeraro, R. Aretano, M. R. Pasimeni, A. De Marco, I. Castorini, N. Zaccarelli & I. Petrosillo

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Corresponding author

Correspondence to G. Zurlini .

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Editors and Affiliations

  1. Dipartimento di Scienze Giuridiche, Università del Salento, Lecce, Italy

    Massimo Monteduro

  2. Dipartimento di Scienze Giuridiche; Institute for the History of Law, Università del Salento; University of Münster, Münster, Germany, Lecce, Italy

    Pierangelo Buongiorno

  3. Dipartimento di Scienze Giuridiche, Università del Salento, Lecce, Italy

    Saverio Di Benedetto

  4. Dipartimento di Storia, Societ a e Studi sull’Uomo, Università del Salento, Lecce, Italy

    Alessandro Isoni

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Zurlini, G. et al. (2015). Emerging Land-Use Cross-Scale Patterns and the Pirsig’s Monkey Trap. In: Monteduro, M., Buongiorno, P., Di Benedetto, S., Isoni, A. (eds) Law and Agroecology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46617-9_17

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