Skip to main content
Log in

Emerging trajectories for spatial pattern analysis in landscape ecology

  • Research Article
  • Published:
Landscape Ecology Aims and scope Submit manuscript

Abstract

Context

Landscape ecology is an interdisciplinary field, drawing on theories and methods from across the physical, natural, and social sciences. Spatial pattern analysis was built on this foundation of interdisciplinarity, and these connections continue to foster new trajectories in the field.

Objectives

Using the Isserman Curve (i.e., the innovation-adoption or cumulative knowledge curve) as a framing device, this paper examines how interdisciplinary perspectives continue to help de-lock from periods of incremental improvement in spatial pattern analysis and launch new, transformative directions for describing and analyzing spatial patterns.

Results

Examples of interdisciplinary perspectives from three fields are discussed alongside the promising trajectories being launched. These include: (1) microscopy and surface metrology, which are contributing methods for analyzing spatial patterns in gradient surfaces, (2) thermodynamics and information theory, which contribute a foundation for measuring entropy and an understanding of how landscape patterns are governed by the central organizing principles of nature, and (3) regional studies, which utilizes alternative conceptualizations of proximity that may be applied to graph-based approaches to better incorporate functional connectivity.

Conclusions

Landscape ecology’s interdisciplinary roots have been instrumental for developing innovative approaches to spatial pattern analysis, and outside perspectives continue to add richly to development efforts today. During periods of incremental improvement, landscape ecologists have drawn from other disciplines to create new seedbeds for ideas. While many trajectories may emerge, there is no rule that only one must become dominant. Blending multiple perspectives and ideas together into mutually supportive structures is helping the field move beyond the status quo.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

References

  • Abler RF (1987) What shall we say? To who should we speak. Ann Assoc Am Geogr 77:511–524

    Google Scholar 

  • Autant-Bernard C, Billand P, Frachisse D, Massard N (2007) Social distance versus spatial distance in R&D cooperation: empirical evidence from European collaboration choices in micro and nanotechnologies. Papers Reg Sci 86(3):495–519

    Google Scholar 

  • Baerwald T (2010) Prospects for geography as an interdisciplinary discipline. Ann Assoc Am Geogr 100(3):493–501

    Google Scholar 

  • Baerwald T (2013) The legacy of Andrew Isserman at the US National Science Foundation. Int Reg Sci Rev 36(1):29–35

    Google Scholar 

  • Balland PA (2012) Proximity and the evolution of collaboration networks: evidence from research and development projects within the global navigation satellite system (GNSS) industry. Reg Stud 46(6):741–756

    Google Scholar 

  • Batt M (1974) Spatial entropy. Geogr Anal 6(1):1–31

    Google Scholar 

  • Batty M (1972) Entropy and spatial geometry. Area 4:230–236

    Google Scholar 

  • Bonczak B, Kontokosta CE (2019) Large-scale parameterization of 3D building morphology in complex urban landscapes using aerial LiDAR and city administrative data. Comput Environ Urban Syst 73:126–142

    Google Scholar 

  • Boschma R (2005) Proximity and innovation: a critical assessment. Reg Stud 39(1):61–74

    Google Scholar 

  • Casquilho JP, Rego FC (2017) Discussing landscape compositional scenarios generated with maximization of non-expected utility decision models based on weighted entropies. Entropy 19:66

    Google Scholar 

  • Costanza J, Riitters K, Wickham J, Vogt P (Forthcoming, this issue). Landscape Ecol

  • Cushman SA (2015) Thermodynamics in landscape ecology: the importance of integrating measurement and modeling of landscape entropy. Landscape Ecol 30(1):7–10

    Google Scholar 

  • Cushman SA (2016) Calculating the configurational entropy of a landscape mosaic. Landscape Ecol 31(3):481–489

    Google Scholar 

  • Cushman SA (2018) Editorial: entropy in Landscape Ecology. Entropy 20(5):314

    Google Scholar 

  • Cushman SA, Gutzweiler K, Evans JS, McGarigal K (2010) The gradient paradigm: a conceptual and analytical framework for landscape ecology. In: Cushman SA, Huettmann F (eds) Spatial complexity, informatics, and wildlife conservation. Springer, Tokyo, pp 83–108

    Google Scholar 

  • Dale MRT, Fortin MJ (2010) From graphs to spatial graphs. Annu Rev Ecol Syst 41:21–38

    Google Scholar 

  • Davids M, Frenken K (2018) Proximity, knowledge base and the innovation process: towards an integrated framework. Reg Stud 52(1):23–34

    Google Scholar 

  • Dramstad WE (2009) Spatial metrics—useful indicators for society or mainly fun tools for landscape ecologists? Nor J Geogr 63(4):246–254

    Google Scholar 

  • Farm Service Agency USDA (2018) Conservation Reserve Program. https://www.fsa.usda.gov/programs-and-services/conservation-programs/conservation-reserve-program/. Accessed 15 May 2019

  • Fenneman NM (1919) The circumference of geography. Ann Assoc Am Geogr 9:3–11

    Google Scholar 

  • Forman RT (1995) Some general principles of landscape and regional ecology. Landscape Ecol 10(3):133–142

    Google Scholar 

  • Fortin MJ, Boots B, Csillag F, Remmel TK (2003) On the role of spatial stochastic models in understanding landscape indices in ecology. Oikos 102(1):203–212

    Google Scholar 

  • Frazier AE (2016) Surface metrics: scaling relationships and downscaling behavior. Landscape Ecol 31(2):351–363

    Google Scholar 

  • Frazier AE, Kedron P (2017) Landscape metrics: past progress and future directions. Curr Landscape Ecol Rep 2(3):63–72

    Google Scholar 

  • Frazier AE, Wikle TA (2017) Renaming and rebranding within US and Canadian geography departments, 1990–2014. Prof Geogr 69(1):12–21

    Google Scholar 

  • Gadelmawla ES, Koura MM, Maksoud TMA, Elewa IM, Soliman HH (2002) Roughness parameters. J Mater Process Technol 123:133

    Google Scholar 

  • Gallardo-Cruz JA, Hernandez-Stefanoni JL, Moser D, Martinez-Yrizar A, Llobet S, Meave JA (2018) Relating species richness to the structure of continuous landscapes: alternative methodological approaches. Ecosphere 9(5):1–15

    Google Scholar 

  • Gao P, Li Z (2019) Computation of the Boltzmann entropy of a landscape: a review and a generalization. Landscape Ecol. https://doi.org/10.1007/s10980-019-00814-x

    Article  Google Scholar 

  • Gleason HA (1926) The individualistic concept of the plant association. Bull Torrey Bot Club 53:7–26

    Google Scholar 

  • Gustafson E (2019) How has the state-of-the-art for quantification of landscape pattern advanced in the twenty-first century? Landscape Ecol. https://doi.org/10.1007/s10980-018-0709-x

    Article  Google Scholar 

  • Kedron PJ, Frazier AE, Ovando-Montejo GA, Wang J (2018) Surface metrics for landscape ecology: a comparison of landscape models across ecoregions and scales. Landscape Ecol 33(9):1489–1504

    Google Scholar 

  • Kedron PJ, Zhao Y, Frazier AE (2019) Three-dimensional (3D) spatial pattern metrics for objects. Landscape Ecol. https://doi.org/10.1007/s10980-019-00861-4

    Article  Google Scholar 

  • Kent M (2009) Biogeography and landscape ecology: the way forward—gradients and graph theory. Prog Phys Geog 33(3):424–436

    Google Scholar 

  • Kupfer JA (2012) Landscape ecology and biogeography: rethinking landscape metrics in a post-FRAGSTATS landscape. Prog Phys Geogr 36(3):400–420

    Google Scholar 

  • Lausch A, Blaschke T, Haase D, Herzog F, Syrbe RU, Tischendorf L, Walz U (2015) Understanding and quantifying landscape structure—a review on relevant process characteristics, data models and landscape metrics. Ecol Model 295:31–41

    Google Scholar 

  • Leopold LB, Langbein WB (1962) The concept of entropy in landscape evolution. US Geol Surv Prof Paper 500-A

  • Li W, Goodchild MF, Church RL (2013) An efficient measure of compactness for 2D shapes and its application in regionalization problems. Int J Geogr Inf Sci 27(6):1227–1250

    Google Scholar 

  • Li H, Wu J (2004) Use and misuse of landscape indices. Landscape Ecol 19(4):389–399

    Google Scholar 

  • Mattes J (2012) Dimensions of proximity and knowledge bases: innovation between spatial and non-spatial factors. Reg Stud 46(8):1085–1099

    Google Scholar 

  • McGarigal K, Cushman S (2005) The gradient concept of landscape structure. In: Wiens JA, Moss MR (eds) Issues and perspectives in landscape ecology. Cambridge University Press, Cambridge, pp 112–119

    Google Scholar 

  • McGarigal K, Marks BJ (1995) FRAGSTATS: spatial pattern analysis program for quantifying landscape structure. Gen Tech Rep PNW-GTR-351. US Department of Agriculture, Forest Service, Pacific Northwest Research Station, Portland, OR

  • McGarigal K, Tagil S, Cushman SA (2009) Surface metrics: an alternative to patch metrics for the quantification of landscape structure. Landscape Ecol 24(3):433–450

    Google Scholar 

  • Moniem HEMA, Holland JD (2013) Habitat connectivity for pollinator beetles using surface metrics. Landscape Ecol 28(7):1251–1267

    Google Scholar 

  • Murphy D, Davidson MW (2001) Differential interference contrast (DIC) microscopy and modulation contrast microscopy. In: Fundamentals of light microscopy and digital imaging. Wiley-Liss, New York, pp 153–168

  • Nowosad J, Stepinski TF (2019) Information theory as a consistent framework for quantification and classification of landscape patterns. Landscape Ecol. https://doi.org/10.1007/s10980-019-00830-x

    Article  Google Scholar 

  • O’Neill RV, Krummel JR, Gardner RH, Sugihara G, Jackson B, DeAngelis DL, Milne BT, Turner MG, Zygmut B, Christensen SW, Dale VH, Graham RL (1988) Indices of landscape pattern. Landscape Ecol 1(3):153–162

    Google Scholar 

  • Oerlemans LAG, Meeus MTH (2005) Do organizational and spatial proximity impact on firm performance? Reg Stud 39(1):89–104

    Google Scholar 

  • Ponds R, van Oort FG, Frenken K (2007) The geographical and institutional proximity of research collaboration. Pap Reg Sci 86(3):423–443

    Google Scholar 

  • Pratt WK (2001) Digital image processing. Wiley, New York

    Google Scholar 

  • Riitters KH, Coulston JW, Wickham JD (2012) Fragmentation of forest communities in the eastern United States. For Ecol Manage 2012(263):85–93

    Google Scholar 

  • Riitters KH, Wickham JD, O’Neill R, Jones B, Smith E (2000a) Global-scale patterns of forest fragmentation. Conserv Ecol 4(2):3

    Google Scholar 

  • Riitters KH, Wickham JD, Vogelmann JE, Jones B (2000b) National land-cover pattern data. Ecology 81:604

    Google Scholar 

  • Risser, PG, Karr JR, Forman RTT (1984) Landscape ecology: directions and approaches. Special Publication 2, Illinois Natural History Survey, Champaign, IL

  • Romme WH, Knight DH (1982) Landscape diversity: the concept applied to Yellowstone Park. Bioscience 32:664–670

    Google Scholar 

  • Saura S, Torné J (2009) Conefor Sensinode 2.2: a software package for quantifying the importance of habitat patches for landscape connectivity. Environ Modell Softw 24(1):135–139

    Google Scholar 

  • Scown MW, Thoms MC, De Jager NR (2015) Measuring floodplain spatial patterns using continuous surface metrics at multiple scales. Geomorph 245:87–101

    Google Scholar 

  • Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27(3):379–423

    Google Scholar 

  • Steward M (1990) A new approach to the use of bearing area curve. Society of Manufacturing Engineers Technical Papers, International Honing Technologies and Applications, May 1–3, 1990, Novi, MI

  • Turner MG, Gardner RH (eds) (1991) Quantitative methods in landscape ecology. Springer, New York

    Google Scholar 

  • Urban D, Keitt T (2001) Lanscape connectivity: a graph-theoretic perspective. Ecology 82(5):1205–1218

    Google Scholar 

  • Urban DL, O’Neill RV, Shugart HH (1987) Landscape ecology: a hierarchical perspective can help scientists understand spatial patterns. Bioscience 37(2):119–127

    Google Scholar 

  • Vogt P (2019) Patterns in software design. Landscape Ecol. https://doi.org/10.1007/s10980-019-00797-9

    Article  Google Scholar 

  • Vogt P, Riitters K (2017) GuidosToolbox: universal digital image object analysis. Eur J Remote Sens 50(1):352–361

    Google Scholar 

  • Wu J (1999) Hierarchy and scaling: extrapolating information along a scaling ladder. Can J Remote Sens 25(4):367–380

    Google Scholar 

  • Wu J (2006) Landscape ecology, cross-disciplinarity, and sustainability science. Landscape Ecol 21:1–4

    CAS  Google Scholar 

  • Wu J (2013) Landscape sustainability science: ecosystem services and human well-being in changing landscapes. Landscape Ecol 28(6):999–1023

    Google Scholar 

  • Wu J, Hobbs R (2002) Key issues and research priorities in landscape ecology: an idiosyncratic synthesis. Landscape Ecol 17(4):355–365

    Google Scholar 

  • Wu Q, Guo F, Li H, Kang J (2017) Measuring landscape pattern in three dimensional space. Landscape Urban Plan 167:49–59

    Google Scholar 

  • Zhang Z, Zinda JA, Yang Z, Yin M, Ou Z, Xu Q, Yu Q (2018) Effects of topographic attributes on landscape pattern metrics based on redundancy ordination gradient analysis. Landscape Ecol Eng 14(1):67–77

    Google Scholar 

Download references

Acknowledgements

I would like to thank the guest editors, Jennifer Costanza, Kurt Riitters, Jim Wickham, and Peter Vogt, for organizing this special issue for inviting me to contribute. I would also like to thank the handling editor and anonymous reviewers who provided thoughtful comments on an earlier version of this manuscript. Lastly, I am grateful to Peter Kedron and Kurt Riitters, who both provided valuable external perspectives on early drafts that helped me de-lock from several thought plateaus. This work was partially funded by a Grant from the U.S. National Science Foundation (#1561021).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Amy E. Frazier.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Frazier, A.E. Emerging trajectories for spatial pattern analysis in landscape ecology. Landscape Ecol 34, 2073–2082 (2019). https://doi.org/10.1007/s10980-019-00880-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10980-019-00880-1

Keywords

Navigation