Assessing the Relative Importance of Factors at Multiple Spatial Scales Affecting Terrestrial and Aquatic Wildlife

  • Joshua J. LawlerEmail author
  • Christian E. Torgersen
Spatial Scale-Measurement, Influence, and Integration (A Martin and J Holland, Section Editors)
Part of the following topical collections:
  1. Topical Collection on Spatial Scale-Measurement, Influence, and Integration


Purpose of Review

We reviewed recent studies focused on assessing the relative roles of factors operating at different scales in shaping animal populations, species, communities, and individual behaviors. Our goal was to summarize the current state of the science by documenting trends and advances in approaches used to weigh the relative impact of drivers at different scales.

Recent Findings

We identify several recent advances in remote sensing–based data collection, such as unmanned aerial vehicles and terrestrial laser scanning, that have the potential to increase the range of scales over which more detailed measurements of the composition and structure of environments can be made. We also highlight the promise of experimental studies and specific statistical approaches for providing a more solid understanding of the relative importance of factors operating at different spatial scales.


We found that after nearly three decades of studies focused on the relative importance of factors operating at different scales, no general pattern has emerged. There is no clear evidence that one scale or one set of scales consistently plays a larger role than others. Nonetheless, it is clear from this research that ecological processes are indeed affected by processes operating at multiple spatial scales. We conclude that a more productive line of questioning might focus not on the relative importance of factors operating at different scales, but on understanding which factors affect a given process, at what scales they operate, and how they interact.


Multiscale Hierarchy Habitat Scale Fish Wildlife 



We are grateful for discussions with D. C. Schneider for broadening our perspectives and increasing the depth of our understanding of scale in ecology. Constructive reviews from N. Schumaker and an anonymous reviewer on an earlier version of this manuscript helped improve the clarity and precision of the ideas presented in this paper.

Compliance with Ethical Standards

Conflict of Interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.


Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. government.

Supplementary material

40823_2019_47_MOESM1_ESM.docx (19 kb)
ESM 1 (DOCX 18 kb)


Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. 1.
    Schneider DC. The rise of the concept of scale in ecology. Bioscience. 2001;51:545–53.CrossRefGoogle Scholar
  2. 2.
    West GB. Scale: the universal laws of life, growth, and death in organisms, cities, and companies. New York: Penguin Books; 2018.Google Scholar
  3. 3.
    Blei DM, Smyth P. Science and data science. Proc Natl Acad Sci. 2017;114:8689–92.PubMedCrossRefGoogle Scholar
  4. 4.
    Allen FH, Hoekstra TW. Toward a unified ecology. New York: Columbia University Press; 1992.Google Scholar
  5. 5.
    Allen TFH, Starr TB. Hierarchy: perspectives for ecological complexity. Chicago: University of Chicago Press; 1982.Google Scholar
  6. 6.
    Urban DL. On scale and pattern. Bull Ecol Soc Am. 2014;95:124–5.CrossRefGoogle Scholar
  7. 7.
    Wiens JA. Spatial scaling in ecology. Funct Ecol. 1989;3:385–97.CrossRefGoogle Scholar
  8. 8.
    Levin SA. The problem of pattern and scale in ecology: the Robert H. MacArthur Award Lecture. Ecology. 1992;73:1943–67.CrossRefGoogle Scholar
  9. 9.
    Giller PS, Hildrew AG, Raffaelli DG. Aquatic ecology: scale, pattern and process. Oxford: Blackwell Science Ltd.; 1994.Google Scholar
  10. 10.
    Schneider DC. Quantitative ecology: spatial and temporal scaling. San Diego: Academic Press; 1994.Google Scholar
  11. 11.
    Peterson DL, Parker VT. Ecological scale: theory and applications. New York: Columbia University Press; 1998.Google Scholar
  12. 12.
    O’Neill RV, DeAngelis DL, Waide JB, Allen TFH. A hierarchical concept of ecosystems, monographs in population biology 23. Princeton University Press: Princeton; 1986.Google Scholar
  13. 13.
    Kotliar NB, Wiens JA. Multiple scales of patchiness and patch structure: a hierarchical framework for the study of heterogeneity. Oikos. 1990;59:253–60.CrossRefGoogle Scholar
  14. 14.
    King AW. Considerations of scale and hierarchy. In: Woodley S, Kay J, editors. Ecological Integrity and the Management of Ecosystems. CRC Press; 1993.Google Scholar
  15. 15.
    Salthe SN. Evolving hierarchical systems: their structure and representation. New York: Columbia University Press; 1985.CrossRefGoogle Scholar
  16. 16.
    O’Neill RV, Johnson AR, King AW. A hierarchical framework for the analysis of scale. Landsc Ecol. 1989;3:193–205.CrossRefGoogle Scholar
  17. 17.
    McGarigal K, Wan HY, Zeller KA, Timm BC, Cushman SA. Multi-scale habitat selection modeling: a review and outlook. Landsc Ecol. 2016;31:1161–75.CrossRefGoogle Scholar
  18. 18.
    Holland JD, Yang S. Multi-scale studies and the ecological neighborhood. Curr Landscape Ecol Rep. 2016;1:135–45.CrossRefGoogle Scholar
  19. 19.
    Mayor SJ, Schneider DC, Schaefer JA, Mahoney SP. Habitat selection at multiple scales. Écoscience. 2009;16:238–47.CrossRefGoogle Scholar
  20. 20.
    Wheatley M, Johnson C. Factors limiting our understanding of ecological scale. Ecol Complex. 2009;6:150–9.CrossRefGoogle Scholar
  21. 21.
    • Martin AE, Fahrig L. Measuring and selecting scales of effect for landscape predictors in species–habitat models. Ecological applications. 2012;22:2277–92 The authors used a large range of spatial extents in their exploration of the relative explanatory power of distance-based and composition-based variables and single-scale versus multiscale models.PubMedCrossRefGoogle Scholar
  22. 22.
    Samu F, Szaboky C, Horavth A, Neidert D, Toth M. Traits in Lepidoptera assemblages are differently influenced by local and landscape scale factors in farmland habitat islands. Community Ecol. 2016;17:28–39.CrossRefGoogle Scholar
  23. 23.
    • Chiavacci SJ, Benson TJ, Ward MP. Linking landscape composition to predator-specific nest predation requires examining multiple landscape scales. Journal of applied ecology. 2018;55:2082–92 This study found that the scales that best explained the relationship between landscape composition and predation rates were predator species specific.CrossRefGoogle Scholar
  24. 24.
    Mitchell R, Urpeth H, Britton A, Black H, Taylor A. Relative importance of local- and large-scale drivers of alpine soil microarthropod communities. Oecologia. 2016;182:913–24.PubMedCrossRefGoogle Scholar
  25. 25.
    Seidl R, Müller J, Hothorn T, Bässler C, Heurich M, Kautz M. Small beetle, large-scale drivers: how regional and landscape factors affect outbreaks of the European spruce bark beetle. J Appl Ecol. 2016;53:530–40.CrossRefGoogle Scholar
  26. 26.
    Ranius T, Johansson V, Schroeder M, Caruso A. Relative importance of habitat characteristics at multiple spatial scales for wood-dependent beetles in boreal forest. Landsc Ecol. 2015;30:1931–42.CrossRefGoogle Scholar
  27. 27.
    Millette KL, Keyghobadi N. The relative influence of habitat amount and configuration on genetic structure across multiple spatial scales. Ecology and Evolution. 2015;5:73–86.PubMedCrossRefGoogle Scholar
  28. 28.
    Northrup JM, Anderson CR, Hooten MB, Wittemyer G. Movement reveals scale dependence in habitat selection of a large ungulate. Ecol Appl. 2016;26:2746–57.CrossRefGoogle Scholar
  29. 29.
    Laforge MP, Vander Wal E, Brook RK, Bayne EM, McLoughlin PD. Process-focussed, multi-grain resource selection functions. Ecol Model. 2015;305:10–21.CrossRefGoogle Scholar
  30. 30.
    Belmaker J, Zarnetske P, Tuanmu M, Zonneveld S, Record S, Strecker A, et al. Empirical evidence for the scale dependence of biotic interactions. Glob Ecol Biogeogr. 2015;24:750–61.CrossRefGoogle Scholar
  31. 31.
    Frissell CA, Liss WJ, Warren CE, Hurley MD. A hierarchical framework for stream habitat classification: viewing streams in a watershed context. Environ Manag. 1986;10:199–214.CrossRefGoogle Scholar
  32. 32.
    Allan JD. Landscapes and riverscapes: the influence of land use on stream ecosystems. Annu Rev Ecol Evol Syst. 2004;35:257–84.CrossRefGoogle Scholar
  33. 33.
    Peterson JT, Dunham JB. Scale and fishery management. In: Hubert W, Quist M, editors. Inland fisheries management. Bethesda: American Fisheries Society; 2010. p. 81–105.Google Scholar
  34. 34.
    Audino LD, Murphy SJ, Zambaldi L, Louzada J, Comita LS. Drivers of community assembly in tropical forest restoration sites: role of local environment, landscape, and space. Ecol Appl. 2017;27:1731–45.PubMedCrossRefGoogle Scholar
  35. 35.
    Murray TE, Fitzpatrick Ú, Byrne A, Fealy R, Brown MJF, Paxton RJ, et al. Local-scale factors structure wild bee communities in protected areas. J Appl Ecol. 2012;49:998–1008.CrossRefGoogle Scholar
  36. 36.
    Barnagaud J-Y, Barbaro L, Hampe A, Jiguet F, Archaux F. Species’ thermal preferences affect forest bird communities along landscape and local scale habitat gradients. Ecography. 2013;36:1218–26.CrossRefGoogle Scholar
  37. 37.
    Wendt CA, Johnson MD. Multi-scale analysis of barn owl nest box selection on Napa Valley vineyards. Agric Ecosyst Environ. 2017;247:75–83.CrossRefGoogle Scholar
  38. 38.
    Stoner KJL, Joern A. Landscape vs. local habitat scale influences to insect communities from Tallgrass prairie remnants. Ecol Appl. 2004;14:1306–20.CrossRefGoogle Scholar
  39. 39.
    • Morante-Filho JC, Arroyo-Rodríguez V, de Souza Pessoa M, Cazetta E, Faria D. Direct and cascading effects of landscape structure on tropical forest and non-forest frugivorous birds. Ecological applications. 2018;28:2024–32 This study uses structural equation modeling to explore the relative importance of landscape context and local vegetation structure for both forest and non-forest birds.PubMedCrossRefGoogle Scholar
  40. 40.
    Vierling KT, Vierling LA, Gould WA, Martinuzzi S, Clawges RM. Lidar: shedding new light on habitat characterization and modeling. Front Ecol Environ. 2008;6:90–8.CrossRefGoogle Scholar
  41. 41.
    Müller J, Vierling K. Assessing biodiversity by airborne laser scanning. In: Maltamo M, Næsset E, Vauhkonen J, editors. Forestry applications of airborne laser scanning: concepts and case studies [internet]. Dordrecht: springer Netherlands; 2014 [cited 2019 may 12]. p. 357–74. Available from: Scholar
  42. 42.
    Wang H, Glennie C. Fusion of waveform lidar data and hyperspectral imagery for land cover classification. ISPRS J Photogramm Remote Sens. 2015;108:1–11.CrossRefGoogle Scholar
  43. 43.
    Sankey T, Donager J, McVay J, Sankey JB. UAV lidar and hyperspectral fusion for forest monitoring in the southwestern USA. Remote Sens Environ. 2017;195:30–43.CrossRefGoogle Scholar
  44. 44.
    Luo S, Wang C, Xi X, Pan F, Peng D, Zou J, et al. Fusion of airborne lidar data and hyperspectral imagery for aboveground and belowground forest biomass estimation. Ecol Indic. 2017;73:378–87.CrossRefGoogle Scholar
  45. 45.
    Olsoy PJ, Forbey JS, Rachlow JL, Nobler JD, Glenn NF, Shipley LA. Fearscapes: mapping functional properties of cover for prey with terrestrial lidar. BioScience. 2015;65:74–80.CrossRefGoogle Scholar
  46. 46.
    Ashcroft MB, Gollan JR, Ramp D. Creating vegetation density profiles for a diverse range of ecological habitats using terrestrial laser scanning. Kriticos D, editor. Methods Ecol Evol 2014;5:263–72.Google Scholar
  47. 47.
    Eitel JUH, Höfle B, Vierling LA, Abellán A, Asner GP, Deems JS, et al. Beyond 3-D: the new spectrum of lidar applications for earth and ecological sciences. Remote Sens Environ. 2016;186:372–92.CrossRefGoogle Scholar
  48. 48.
    Anderson K, Gaston KJ. Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Front Ecol Environ. 2013;11:138–46.CrossRefGoogle Scholar
  49. 49.
    Hodgson JC, Baylis SM, Mott R, Herrod A, Clarke RH. Precision wildlife monitoring using unmanned aerial vehicles. Sci Rep. 2016;6:22574.PubMedPubMedCentralCrossRefGoogle Scholar
  50. 50.
    Cunliffe AM, Brazier RE, Anderson K. Ultra-fine grain landscape-scale quantification of dryland vegetation structure with drone-acquired structure-from-motion photogrammetry. Remote Sens Environ. 2016;183:129–43.CrossRefGoogle Scholar
  51. 51.
    Duffy JP, Shutler JD, Witt MJ, DeBell L, Anderson K. Tracking fine-scale structural changes in coastal dune morphology using kite aerial photography and uncertainty-assessed structure-from-motion photogrammetry. Remote Sens. 2018;10:1494.CrossRefGoogle Scholar
  52. 52.
    Ettema CH, Wardle DA. Spatial soil ecology. Trends in Ecology and Evolution. 2002;17:177–183.CrossRefGoogle Scholar
  53. 53.
    Fausch KD. Preface: a renaissance in stream fish ecology. In: Gido KB, Jackson DA, editors. Community ecology of stream fishes: concepts, approaches, and techniques. Bethesda, MD: American Fisheries Society; 2010. p. 199–206.Google Scholar
  54. 54.
    Thurow RF, Dolloff CA, Marsden JE. Visual observation of fishes and aquatic habitat. In: Zale AV, Parrish DL, Sutton TM, editors. Fisheries techniques. Bethesda: American Fisheries Society; 2012. p. 781–817.Google Scholar
  55. 55.
    Carbonneau PE, Fonstad MA, Marcus WA, Dugdale SJ. Making riverscape real. Geomorphology. 2012;137:74–86.CrossRefGoogle Scholar
  56. 56.
    Fullerton AH, Torgersen CE, Lawler JJ, Faux RN, Steel EA, Beechie TJ, et al. Rethinking the longitudinal stream temperature paradigm: region-wide comparison of thermal infrared imagery reveals unexpected complexity of river temperatures. Hydrol Process. 2015;29:4719–37.CrossRefGoogle Scholar
  57. 57.
    Dugdale SJ. A practitioner’s guide to thermal infrared remote sensing of rivers and streams: recent advances, precautions and considerations. WIREs Water. 2016;3:251–68.CrossRefGoogle Scholar
  58. 58.
    Marotz BL, Lorang MS. Pallid sturgeon larvae: the drift dispersion hypothesis. Journal of Applied Ichthyology. 2017;34: Scholar
  59. 59.
    Hugue F, Lapointe M, Eaton BC, Lepoutre A. Satellite-based remote sensing of running water habitats at large riverscape scales: tools to analyze habitat heterogeneity for river ecosystem management. Geomorphology. 2016;253:353–69.CrossRefGoogle Scholar
  60. 60.
    Woodget AS, Carbonneau PE, Visser F, Maddock IP. Quantifying submerged fluvial topography using hyperspatial resolution UAS imagery and structure from motion photogrammetry. Earth Surf Process Landf. 2015;40:47–64.CrossRefGoogle Scholar
  61. 61.
    Dugdale SJ, Kelleher CA, Malcolm IA, Caldwell S, Hannah DM. Assessing the potential of drone-based thermal infrared imagery for quantifying river temperature heterogeneity. Hydrol Process. 2019;33:1152–63.CrossRefGoogle Scholar
  62. 62.
    Fausch KD, Torgersen CE, Baxter CV, Li HW. Landscapes to riverscapes: bridging the gap between research and conservation of stream fishes. Bioscience. 2002;52:483–98.CrossRefGoogle Scholar
  63. 63.
    Brenkman SJ, Duda JJ, Torgersen CE, Welty E, Pess GR, Peters R, et al. A riverscape perspective of Pacific salmonids and aquatic habitats prior to large-scale dam removal in the Elwha River, Washington. USA Fisheries Management and Ecology. 2012;19:36–53.CrossRefGoogle Scholar
  64. 64.
    McGuire KJ, Torgersen CE, Likens GE, Buso DC, Lowe WH, Bailey SW. Network analysis reveals multiscale controls on streamwater chemistry. Proc Natl Acad Sci. 2014;111:7030–5.PubMedCrossRefGoogle Scholar
  65. 65.
    Isaak D, Wenger SJ, Peterson EE, Hoef JMV, Nagel DE, Luce CH, et al. The NorWeST summer stream temperature model and scenarios for the western U.S.: a crowd-sourced database and new geospatial tools foster a user community and predict broad climate warming of rivers and streams. Water Resources Research. 2017;53:9181–205.CrossRefGoogle Scholar
  66. 66.
    Briggs MA, Harvey JW, Hurley ST, Rosenberry DO, McCobb T, Werkema D, et al. Hydrogeological controls on brook trout spawning habitats in a coastal stream. Hydrol Earth Syst Sci. 2018;22:6383–98.PubMedPubMedCentralCrossRefGoogle Scholar
  67. 67.
    Duan M, Liu Y, Yu Z, Li L, Wang C, Axmacher JC. Environmental factors acting at multiple scales determine assemblages of insects and plants in agricultural mountain landscapes of northern China. Agric Ecosyst Environ. 2016;224:86–94.CrossRefGoogle Scholar
  68. 68.
    Johnson DH. The comparison of usage and availability measurements for evaluating resource preference. Ecology. 1980;61:65–71.CrossRefGoogle Scholar
  69. 69.
    Hutto RL. Habitat selection by nonbreeding, migratory, land birds. In: Cody ML, editor. Habitat selection in birds. San Diego: Academic Press; 1985. p. 455–76.Google Scholar
  70. 70.
    Hildén O. Habitat selection in birds: a review. Ann Zool Fenn. 1965;2:53–75.Google Scholar
  71. 71.
    Klaassen B, Broekhuis F. Living on the edge: multiscale habitat selection by cheetahs in a human-wildlife landscape. Ecology and Evolution. 2018;8:7611–23.PubMedPubMedCentralCrossRefGoogle Scholar
  72. 72.
    Battin J, Lawler JJ. Cross-scale correlations and the design and analysis of avian habitat selection studies. Condor. 2006;108:59–70.CrossRefGoogle Scholar
  73. 73.
    Whittaker J. Model interpretation from the additive elements of the likelihood function. Appl Stat. 1984;33:52–64.CrossRefGoogle Scholar
  74. 74.
    Borcard D, Legendre P, Drapeau P. Partialling out the spatial component of ecological variation. Ecology. 1992;73:1045–55.CrossRefGoogle Scholar
  75. 75.
    Cushman SA, McGarigal K. Hierarchical, multi-scale decomposition of species-environment relationships. Landsc Ecol. 2002;17:637–46.CrossRefGoogle Scholar
  76. 76.
    Lawler JJ, Edwards TC. A variance-decomposition approach to investigating multiscale habitat associations. Condor. 2006;108:47–58.CrossRefGoogle Scholar
  77. 77.
    Martins da Silva P, Berg M, Silva A, Dias S, Leitão P, Chamberlain D, et al. Soil fauna through the landscape window: factors shaping surface-and soil-dwelling communities across spatial scales in cork-oak mosaics. Landscape Ecology. 2015;30:1511–26.CrossRefGoogle Scholar
  78. 78.
    Mitsuo Y. Determining the relative importance of catchment- and site-scale factors in structuring fish assemblages in small coastal streams. Knowl Manag Aquat Ecosyst. 2017;57.Google Scholar
  79. 79.
    Krajenbrink HJ, Acreman M, Dunbar MJ, Hannah DM, Laizé CLR, Wood PJ. Macroinvertebrate community responses to river impoundment at multiple spatial scales. Sci Total Environ. 2019;650:2648–56.PubMedCrossRefGoogle Scholar
  80. 80.
    Arnaud C, Selma M, Mouillot D, Troussellier M, Bernard C. Patterns and multi-scale drivers of phytoplankton species richness in temperate peri-urban lakes. Sci Total Environ. 2016;559:74–83.CrossRefGoogle Scholar
  81. 81.
    Chust G, Pretus J l., Ducrot D, Bedòs a, Deharveng L. Response of soil fauna to landscape heterogeneity: determining optimal scales for biodiversity modeling. Conserv Biol 2003;17:1712–1723.CrossRefGoogle Scholar
  82. 82.
    Graham MH. Confronting multicollinearity in ecological multiple regression. Ecology. 2003;84:2809–15.CrossRefGoogle Scholar
  83. 83.
    Mcmahon SM, Diez JM. Scales of association: hierarchical linear models and the measurement of ecological systems. Ecol Lett. 2007;10:437–52.PubMedCrossRefGoogle Scholar
  84. 84.
    Cuffney TF, Kashuba R, Qian SS, Alameddine I, Cha YK, Lee B, et al. Multilevel regression models describing regional patterns of invertebrate and algal responses to urbanization across the USA. J N Am Benthol Soc. 2011;30:797–819.CrossRefGoogle Scholar
  85. 85.
    Fenoglio MS, Werenkraut V, Morales JM, Salvo A. A hierarchical multi-scale analysis of the spatial relationship between parasitism and host density in urban habitats. Austral Ecology. 2017;42:732–41.CrossRefGoogle Scholar
  86. 86.
    Grace JB, Anderson TM, Olff H, Scheiner SM. On the specification of structural equation models for ecological systems. Ecol Monogr. 2010;80:67–87.CrossRefGoogle Scholar
  87. 87.
    Shipley B. Cause and correlation in biology. A user’s guide to path analysis, structural equations and causal inference. Cambridge: Cambridge University Press; 2000.CrossRefGoogle Scholar
  88. 88.
    Villeneuve B, Piffady J, Valette L, Souchon Y, Usseglio-Polatera P. Direct and indirect effects of multiple stressors on stream invertebrates across watershed, reach and site scales: a structural equation modelling better informing on hydromorphological impacts. Sci Total Environ. 2018;612:660–71.PubMedCrossRefGoogle Scholar
  89. 89.
    Chacin DH, Stallings CD. Disentangling fine- and broad- scale effects of habitat on predator–prey interactions. J Exp Mar Biol Ecol. 2016;483:10–9.CrossRefGoogle Scholar
  90. 90.
    Haynes K, Dillemuth F, Anderson B, Hakes A, Jackson H, Elizabeth Jackson S, et al. Landscape context outweighs local habitat quality in its effects on herbivore dispersal and distribution. Oecologia. 2007;151:431–41.PubMedCrossRefGoogle Scholar
  91. 91.
    • Frey D, Vega K, Zellweger F, Ghazoul J, Hansen D, Moretti M. Predation risk shaped by habitat and landscape complexity in urban environments. Journal of applied ecology. 2018;55:2343–53. This study combined an experimental approach with detailed lidar-based measurement of woody vegetation heterogeneity to explore drivers of predation at multiple scales by two types of predators. They found that cross-scale interactions were the main drivers of predation. CrossRefGoogle Scholar
  92. 92.
    Mayor SJ, Schaefer JA. The many faces of population density. Oecologia. 2005;145:275–80.CrossRefGoogle Scholar
  93. 93.
    Grant JWA, Steingrimsson SO, Keeley ER, Cunjak RA. Implications of territory size for the measurement and prediction of salmonid abundance in streams. Can J Fish Aquat Sci. 1998;55(Suppl. 1):181–90.CrossRefGoogle Scholar
  94. 94.
    Laforge MP, Uzal A, Medill SA, McLoughlin PD. Scale-dependent effects of density and habitat on foal survival. J Wildl Manag. 2016;80:347–54.CrossRefGoogle Scholar
  95. 95.
    Welty EZ, Torgersen CE, Brenkman SJ, Duda JJ, Armstrong JB. Multiscale analysis of river networks using the R package linbin. N Am J Fish Manag. 2015;35:802–9.CrossRefGoogle Scholar
  96. 96.
    Torgersen CE, Baxter CV, Li HW, McIntosh BA. Landscape influences on longitudinal patterns of river fishes: spatially continuous analysis of fish-habitat relationships. In: Hughes RM, Wang L, Seelbach PW, editors. Landscape influences on stream habitats and biological assemblages. Bethesda, Maryland: American Fisheries Society; 2006. p. 473–492.Google Scholar
  97. 97.
    Hale R, Colton MA, Peng P, Swearer SE. Do spatial scale and life history affect fish–habitat relationships? J Anim Ecol. 2018;88:439–49.PubMedCrossRefGoogle Scholar
  98. 98.
    Wellemeyer JC, Perkin JS, Jameson ML, Costigan KH, Waters R. Hierarchy theory reveals multiscale predictors of Arkansas darter (Etheostoma cragini) abundance in a Great Plains riverscape. Freshw Biol. 2019:659–70.CrossRefGoogle Scholar
  99. 99.
    Lopez-Lopez P. Individual-based tracking systems in ornithology: welcome to the era of big data. Ardeola. 2016;63:103–6.CrossRefGoogle Scholar
  100. 100.
    Lipsey MK, Naugle DE, Nowak J, Lukacs PM, Albright T. Extending utility of hierarchical models to multi-scale habitat selection. Divers Distrib. 2017;23:783.CrossRefGoogle Scholar
  101. 101.
    Bernhardt ES, Palmer MA. River restoration: the fuzzy logic of repairing reaches to reverse catchment scale degradation. Ecol Appl. 2011;21:1926–31.PubMedCrossRefGoogle Scholar
  102. 102.
    Scholes R. Taking the mumbo out of the jumbo: Progress towards a robust basis for ecological scaling. Ecosystems. 2017;20:4–13.CrossRefGoogle Scholar
  103. 103.
    Chave J. The problem of pattern and scale in ecology: what have we learned in 20 years? Ecol Lett. 2013;16:4–16.PubMedCrossRefGoogle Scholar
  104. 104.
    Pickett STA, Kolasa J, Jones CG. Ecological understanding. New York: Academic Press, Inc.; 1994.CrossRefGoogle Scholar
  105. 105.
    Jackson HB, Fahrig L. Are ecologists conducting research at the optimal scale? Glob Ecol Biogeogr. 2015;24:52–63.CrossRefGoogle Scholar
  106. 106.
    McIntire EJB, Fajardo A. Beyond description: the active and effective way to infer processes from spatial patterns. Ecology. 2009;90:46–56.PubMedCrossRefGoogle Scholar
  107. 107.
    Currens K P. Evolution and risk in conservation of Pacific salmon [Internet] [Dissertation]. [Corvallis, OR]: Oregon State University; 1997 [cited 2019 Aug 9]. Available from:
  108. 108.
    Gresswell RE. Fire and aquatic ecosystems in forested biomes of North America. Trans Am Fish Soc. 1999;128:193–221.CrossRefGoogle Scholar
  109. 109.
    Torgersen CE, Ebersole JL, Keenan DM. Primer for identifying cold-water refuges to protect and restore thermal diversity in riverine landscapes [Internet]. Seattle, WA: U.S. Environmental Protection Agency; 2012 p. 91. Report No.: EPA 910-C-12-001. Available from:

Copyright information

© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2019

Authors and Affiliations

  1. 1.School of Environmental and Forest SciencesUniversity of WashingtonSeattleUSA
  2. 2.Forest and Rangeland Ecosystem Science Center, Cascadia Field StationU.S. Geological SurveySeattleUSA

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