Abstract
Context
Understanding how spatial pattern changes with scale can provide insights into its relationship with ecological processes. In riverine landscapes, spatial pattern could scale differently from other well-studied landscapes because of their dendritic form.
Objectives
The objectives of this study were (1) to assess how spatial pattern of hydrogeomorphic habitat patches (HGP) change with spatial extent, grain size, and thematic resolution, and (2) to quantify how spatial pattern in river networks varies across the contiguous United States (CONUS).
Methods
We identified hydrogeomorphic patches in river networks located in different ecoclimatic domains. We then quantified spatial pattern within each river network using a suite of landscape metrics and investigated scaling relationships for each component of scale. We also assessed whether watershed area, river network length, and drainage density were related to spatial pattern among river networks and explored regional differences in the hydrologic, geomorphologic, and climatic variables that differentiate HGP types.
Results
When predictable, scaling relationships within river networks followed either linear, logarithmic, or power functions. Among river networks, spatial pattern was related to total network length, catchment area and drainage density. Rarely were HGP types in different networks characterized by the same suite of hydrologic, geomorphologic and climatic variables.
Conclusions
In riverine landscapes, there are a variety of relationships between spatial pattern and scale. The scaling functions we present can provide a concise description of scale dependency in these landscapes and improve our ability to synthesize information across scales.
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Data availability
The datasets generated during the current study are available at https://github.com/dkopp3/HydrogeomorphicPatches and from the corresponding author on reasonable request.
References
Baldwin DJB, Weaver K, Schnekenburger F, Perera AH (2004) Sensitivity of landscape pattern indices to input data characteristics on real landscapes: implications for their use in natural disturbance emulation. Landsc Ecol 19:255–271
Benda L, Poff NL, Miller D et al (2004) The network dynamics hypothesis: how channel networks structure riverine habitats. Bioscience 54:413. https://doi.org/10.1641/0006-3568(2004)054[0413:TNDHHC]2.0.CO;2
Benstead JP, Leigh DS (2012) An expanded role for river networks. Nat Geosci 5:678–679. https://doi.org/10.1038/ngeo1593
Bieger K, Rathjens H, Allen PM, Arnold JG (2015) Development and evaluation of bankfull hydraulic geometry relationships for the physiographic regions of the United States. J Am Water Resour Assoc 51:842–858. https://doi.org/10.1111/jawr.12282
Borcard D, Gillet F, Legendre P (2018) Numerical Ecology with R, 2nd. Springer Nature, Cham, Switzerland
Buyantuyev A, Wu J (2007) Effects of thematic resolution on landscape pattern analysis. Landsc Ecol 22:7–13. https://doi.org/10.1007/s10980-006-9010-5
Campbell Grant EH (2011) Structural complexity, movement bias, and metapopulation extinction risk in dendritic ecological networks. J North Am Benthol Soc 30:252–258. https://doi.org/10.1899/09-120.1
Collins SE, Thoms MC, Flotemersch JE (2014) Hydrogeomorphic zones characterize riverbed sediment patterns within a river network. River Syst 21:203–213. https://doi.org/10.1127/1868-5749/2014/0084
Collins SE, Matter SF, Buffam I, Flotemersch JE (2018) A patchy continuum? Stream processes show varied responses to patch- and continuum-based analyses. Ecosphere. https://doi.org/10.1002/ecs2.2481
Comte L, Olden JD (2018) Fish dispersal in flowing waters: A synthesis of movement- and genetic-based studies. Fish Fish 19:1063–1077. https://doi.org/10.1111/faf.12312
Cote D, Kehler DG, Bourne C, Wiersma YF (2009) A new measure of longitudinal connectivity for stream networks. Landsc Ecol 24:101–113. https://doi.org/10.1007/s10980-008-9283-y
Cushman SA, Littell J, McGarigal K (2010) The Problem of Ecological Scaling in Spatially Complex, Nonequilibrium Ecological Systems. In: Cushman SA, Huettmann F (eds) Spatial Complexity, Informatics, and Wildlife Conservation. Springer, Tokyo, pp 1–458
Datry T, Pella H, Leigh C et al (2016) A landscape approach to advance intermittent river ecology. Freshw Biol N/a-N/a. https://doi.org/10.1111/fwb.12645
Dodds WK, Gido K, Whiles MR et al (2015) The Stream Biome Gradient Concept: factors controlling lotic systems across broad biogeographic scales. Freshw Sci 34:1–19. https://doi.org/10.1086/679756
Dodds WK, Bruckerhoff L, Batzer D et al (2019) The freshwater biome gradient framework: Predicting macroscale properties based on latitude, altitude, and precipitation. Ecosphere. https://doi.org/10.1002/ecs2.2786
Erős T, Lowe WH (2019) The landscape ecology of rivers: from patch-based to spatial network analyses. Curr Landsc Ecol Reports 4:103–112. https://doi.org/10.1007/s40823-019-00044-6
Fausch KD, Torgersen CE, Baxter CV, Li HW (2002) Landscapes to riverscapes: Bridging the gap between research and conservation of stream fishes. Bioscience 52:483–498. https://doi.org/10.1641/0006-3568(2002)052[0483:LTRBTG]2.0.CO;2
Fullerton AH, Burke BJ, Lawler JJ et al (2017) Simulated juvenile salmon growth and phenology respond to altered thermal regimes and stream network shape. Ecosphere. https://doi.org/10.1002/ecs2.2052
Hadwen WL, Fellows CS, Westhorpe D et al (2010) Longitudinal trends in river functioning: Patterns of nutrient and carbin processing in three Australian rivers. River Res Appl 26:1129–1152. https://doi.org/10.1002/rra
Hargrove WW, Hoffman FM (2004) Potential of multivariate quantitative methods for delineation and visualization of ecoregions. Environ Manage 34:39–60. https://doi.org/10.1007/s00267-003-1084-0
Harris C, Thoms M, Scown M (2009) The ecohydrology of stream networks Ecohydrol Surf Groundw Depend Syst Concepts. Methods Recent Dev 328:127–136
Hubler S, Huff DD, Edwards P, Pan Y (2016) The Biological Sediment Tolerance Index: Assessing fine sediments conditions in Oregon streams using macroinvertebrates. Ecol Indic 67:132–145. https://doi.org/10.1016/j.ecolind.2016.02.009
Jackson HB, Fahrig L (2015) Are ecologists conducting research at the optimal scale? Glob Ecol Biogeogr 24:52–63. https://doi.org/10.1111/geb.12233
Le Pichon C, Gorges G, Baudry J et al (2007) Spatial metrics and methods for riverscapes: quantifying variability in riverine fish habitat patterns. Environmetrics 18:697–712. https://doi.org/10.1002/env
Lehner B, Verdin K, Jarvis A (2008) New global Hydrography derived from spaceborne elevation data. Eos Trans 89:94–94
Li H, Wu J (2004) Use and misuse of landscape indices. Landsc Ecol 19:389–399. https://doi.org/10.1023/B:LAND.0000030441.15628.d6
Maasri A, Thorp JH, Gelhaus JK et al (2019) Communities associated with the Functional Process Zone scale: A case study of stream macroinvertebrates in endorheic drainages. Sci Total Environ 677:184–193. https://doi.org/10.1016/j.scitotenv.2019.04.394
Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M., Hornik, K. (2019). cluster: Cluster Analysis Basics and Extensions. R package version 2.1.0.
McGarigal K, Cushman S, Ene E (2012) FRAGSTATS v4: Spatial Pattern Analysis Program for Categorical and Continuous Maps. https://www.umass.edu/landeco/research/fragstats/fragstats.html
McKay L, Bondelid T, Dewald T, et al (2012) NHD Plus Version 2: User Guide. 1–179
Moore JW, Beakes MP, Nesbitt HK et al (2015) Emergent stability in a large, free-flowing watershed. Ecology 96:340–347. https://doi.org/10.1890/14-0326.1
Moore RB, McKay L, Rea A et al (2019) User’s guide for the national hydrography dataset plus (NHDPlus) high resolution. US Geol Surv. https://doi.org/10.3133/ofr20191096
O’Neill RV, Krummel JR, Gardner RH et al (1988) Indices of Landscape Pattern. Landsc Ecol 1:153–162
Oksanen J, Blanchet FG, Friendly M, Kindt R., Legendre P., McGlinn D., Minchin P., O'Hara R.B., Simpson G.L., Solymos P., Stevens M.H., Szoecs E., and Wagner H. (2019). “vegan”: Community Ecology Package. R package version 2.5-7
Qiu Y, Teng SN, Zhang Y et al (2019) The resolution-dependent role of landscape attributes in shaping macro-scale biodiversity patterns. Glob Ecol Biogeogr 28:767–778. https://doi.org/10.1111/geb.12889
Rüegg J, Dodds WK, Daniels MD et al (2016) Baseflow physical characteristics differ at multiple spatial scales in stream networks across diverse biomes. Landsc Ecol 31:119–136. https://doi.org/10.1007/s10980-015-0289-y
Schmera D, Árva D, Boda P et al (2018) Does isolation influence the relative role of environmental and dispersal-related processes in stream networks? An empirical test of the network position hypothesis using multiple taxa. Freshw Biol 63:74–85. https://doi.org/10.1111/fwb.12973
Shangguan W, Hengl T, Mendes de Jesus J et al (2017) Mapping the global depth to bedrock for land surface modeling. J Adv Model Earth Syst 9:65–88. https://doi.org/10.1002/2013MS000282.Received
Shen W, Jenerette GD, Wu J, Gardner RH (2004) Evaluating empirical scaling relations of pattern metrics with simulated landscapes. Ecography (Cop) 27:459–469. https://doi.org/10.1111/j.0906-7590.2004.03799.x
Šímová P, Gdulová K (2012) Landscape indices behavior: A review of scale effects. Appl Geogr 34:385–394. https://doi.org/10.1016/j.apgeog.2012.01.003
Thoms MC, Parsons M (2002) Eco-geomorphology: an interdisciplinary approach to river science, vol 276. International Association of Hydrological Sciences, pp 113–120
Thoms M, Scown M, Flotemersch J (2018) Characterization of River Networks: A GIS Approach and Its Applications. J Am Water Resour Assoc 54:899–913. https://doi.org/10.1111/1752-1688.12649
Thorp JH, Thoms MC, Delong MD (2006) The riverine ecosystem synthesis: Biocomplexity in river networks across space and time. River Res Appl 22:123–147. https://doi.org/10.1002/rra.901
Thorp J, Thoms M, Michael D (2008) The Riverine Ecosystem Synthesis: towards a conceptual cohesiveness in river science. Elsevier, Oxford, UK
Thorp JH, Flotemersch JE, Delong MD et al (2010) Linking Ecosystem Services, Rehabilitation, and River Hydrogeomorphology. Bioscience 60:67–74. https://doi.org/10.1525/bio.2010.60.1.11
Tonkin JD, Altermatt F, Finn D et al (2017) The role of dispersal in river network metacommunities: Patterns, processes, and pathways. Freshw Biol. https://doi.org/10.1111/fwb.13037
Turner MG, Gardner RH (2015) Landscape Ecology in Theory and Practice. Springer-Verlag, New York, Second
Turner MG, O’Neill RV, Gardner RH, Milne BT (1989) Effects of changing spatial extent on landscape pattern analysis. Landsc Ecol 3:153–162
Viger RJ, Rea A, Simley JD, Hanson KM (2016) NHDPlusHR: A National Geospatial Framework for Surface-Water Information. J Am Water Resour Assoc 52:901–905. https://doi.org/10.1111/1752-1688.12429
Walling DE (1999) Linking land use, erosion and sediment yields in river basins. Hydrobiologia 410:223–240. https://doi.org/10.1023/A:1003825813091
Ward JV, Malard F, Tockner K (2002) Landscape Ecology: a Framework for Integrating Pattern and Process in River Corridors. Landsc Ecol 17:35–45
Wiens JA (1989) Spatial scaling in ecology. Funct Ecol 3:385–397
Williams BS, D’Amico E, Kastens JH et al (2013) Automated riverine landscape characterization: GIS-based tools for watershed-scale research, assessment, and management. Environ Monit Assess 185:7485–7499. https://doi.org/10.1007/s10661-013-3114-6
Wu J (2004) Effects of changing scale on landscape pattern analysis: Scaling relations. Landsc Ecol 19:125–138. https://doi.org/10.1023/B:LAND.0000021711.40074.ae
Wu J, Li H (2006) Concepts of scale and scaling. Scaling Uncertain Anal Ecol Methods Appl. https://doi.org/10.1007/1-4020-4663-4_1
Wu J, Loucks O (1995) From Balance of Nature to Hierarchical Patch Dynamics: A Paradigm Shift in Ecology. Q Rev Biol 70:439–466
Wu J, Shen W, Sun W, Tueller PT (2002) Empirical patterns of the effects of changing scale on landscape metrics. Landsc Ecol 17:761–782
Xu C, Zhao S, Liu S (2020) Spatial scaling of multiple landscape features in the conterminous United States. Landsc Ecol 35:223–247. https://doi.org/10.1007/s10980-019-00937-1
Acknowledgements
We would like to thank J. Wu, J. Kelly, T. Neeson, M. Kaspari and two anonymous reviewers for comments on previous versions of the manuscript. We would also like to thank D. Cote for assistance with the dendritic connectivity index. This research is part of a dissertation at the University of Oklahoma.
Funding
This work was supported by a Grant from the National Science Foundation (1802872) and DA was supported by additional NSF funding (1754389).
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Conceptualization: DK and DA; Formal analysis and investigation: DK; Writing—original draft preparation: DK; Writing – review and editing: DK and DA; Funding acquisition: DA.
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Kopp, D., Allen, D. Scaling spatial pattern in river networks: the effects of spatial extent, grain size and thematic resolution. Landscape Ecol 36, 2781–2794 (2021). https://doi.org/10.1007/s10980-021-01270-2
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DOI: https://doi.org/10.1007/s10980-021-01270-2