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Spatial Structure of Coastal Cutthroat Trout Distribution in Stream Networks

Explore fine- and coarse-scale patterns of coastal cutthroat trout distribution and abundance in stream networks. Understand the influence of landscape characteristics on habitat conservation and population dynamics.

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Spatial Structure of Coastal Cutthroat Trout Distribution in Stream Networks

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  1. Distribution and abundance Age 1+ coastal cutthroat trout 0 1 km (Gresswell, Torgersen, and Bateman 2006, Influence of landscape on stream habitats and biological assemblages)

  2. Fine- and coarse-scale patterns 12 Pool 9 7 Riffle/rapid Cascade 3 0 350 m (Torgersen et al. 2004, GIS in Fisheries and Aquatic Sciences)

  3. (Ganio et al. 2005)

  4. (Ganio et al. 2005)

  5. Hardy Creek Cascade Range Random, incorrect sampling scale (?) 0 1 km

  6. E. Fork Laying Creek Cascade Range Exponential, or linear increase 0 1 km

  7. Miller Creek Cascade Range Nested - patchy with a gradient 0 1 km

  8. Glenn Creek Coast Range Range = 1099 m 0 1 km

  9. N. Fork Ecola Coast Range Range = 272 m 0 0.5 km

  10. Spatial scale Population dynamics, patch size, sampling/monitoring,habitat conservation. Relative abundance of coastal cutthroat trout

  11. Bedrock geology vs. rock stability 1:500K-scale geologic map (Walker and MacLeod 1991) Sedimentary, basalt, intrusive Weak, intermediate, resistant (Ricks and Swanson, unpublished data)

  12. Spatial variability in fish distribution and landscape characteristics Range is often inter-preted as the average patch size.

  13. Predicting the spatial scale of variation in trout distribution • Weak rock (+) • Resistantrock (-) • Mean distance between tributaries (+) Multiple linear regression Adjusted R 2 = 0.78 Predicted scale (m) Observed scale (m)

  14. Conclusions • Coastal cutthroat trout distribution exhibited strong spatial structuring at scales of 200-1000 m. • Empirical variograms provided an effective means to compare spatial structure among watersheds. • Scale of variation (patch size) in trout distribution corresponded with landscape characteristics.

  15. Broader implications • Geostatistical analysis can be applied in stream networks but requires specific statistical considerations. • Existing geospatial and statistical software can be used (with minor modifications) to describe spatial pattern in networks. • Explicit incorporation of network spatial structure has much to offer ecology and hydrology.

  16. The bottom line about geostatistics… Use semivariograms and correlograms to: • Evaluate spatial autocorrelation before conducting standard statistical analysis (ANOVA, etc.) • Describe spatial structure after conducting descriptive studies. • Develop spatially explicit predictive models (e.g., kriging and spatial regression.

  17. Using semi-variograms to infer processes from spatial patterns

  18. Networks unleashed… • Fisher (1997). Creativity, idea generation, and the functional morphology of streams. JNABS. • FLoWS Tools • - SSN R package • Ver Hoef et al. (2006); Peterson and Ver Hoef (2010); Ver Hoef and Peterson (2010)

  19. Hubbard Brook Valley-Wide Survey Area = 4,000 ha Length = 14 km

  20. Hubbard Brook Valley-Wide Survey Spring 2001: n = 625 Fall 2001: n = 761 Likens and Buso (2006), Biogeochemistry, 78:1-30.

  21. Objectives • Quantify spatial variability of streamwater chemistry at multiple scales within the stream network. • Differentiate between patterns of spatial dependence for flow-connected versus “flow-unconnected” locations. • Explore this approach for linking patterns and processes of streamwater chemistry across scales.

  22. Data collection pH, acid-neutralizing capacity, Ca2+, Mg2+, K+, Na+, SO42-, NO3-, Cl-, dissolved silica, Aln+, dissolved organic carbon, dissolved inorganic carbon, and specific conductance Time period: October – early December, 2001. Likens and Buso (2006), Biogeochemistry, 78:1-30.

  23. ? Distance, connectedness, and flow direction Euclidean vs. instream distance

  24. ? Distance, connectedness, and flow direction Euclidean vs. instream distance

  25. ? Distance, connectedness, and flow direction Flow- connected vs. “flow-unconnected”

  26. ? Distance, connectedness, and flow direction Flow- connected vs. “flow-unconnected”

  27. Conclusions • New tools enable modeling and extrapolation in networks, but underlying variability is still poorly understood. • Exploratory analysis of spatial dependence can provide insights into processes operating at multiple scales. • Upstream and downstream effects are complex and scale dependent, and thus may be difficult to generalize.

  28. Wavelets: A gentle introduction

  29. 1-dimensional transect 0 1 km

  30. Nested structure at two spatial scales. Nested structure at two spatial scales.

  31. Nested structure at one spatial scale? (Torgersen et al. 2004, GIS in Fisheries and Aquatic Sciences)

  32. Examples from Interactive Wavelet Plot http://ion.researchsystems.com/

  33. References Fisher, S. G. 1997. Creativity, idea generation, and the functional morphology of streams. Journal of the North American Benthological Society 16:305-318. Ganio, L. M., C. E. Torgersen, and R. E. Gresswell. 2005. A geostatistical approach for describing spatial pattern in stream networks. Frontiers in Ecology and Environment 3:138-144. Gresswell, R. E., C. E. Torgersen, D. S. Bateman, T. J. Guy, S. R. Hendricks, and J. E. B. Wofford. 2006. A spatially explicit approach for evaluating relationships among coastal cutthroat trout, habitat, and disturbance in small Oregon streams. Pages 457-471 in R. M. Hughes, L. Wang, and P. W. Seelbach, editors. Landscape influences on stream habitats and biological assemblages. American Fisheries Society, Bethesda, Maryland. Likens, G. E., and D. C. Buso. 2006. Variation in streamwater chemistry throughout the Hubbard Brook Valley. Biogeochemistry 78:1-30. Peterson, E. E., and J. M. Ver Hoef. 2010. A mixed-model moving-average approach to geostatistical modeling in stream networks. Ecology 91:644-661. Torgersen, C. E., R. E. Gresswell, and D. S. Bateman. 2004. Pattern detection in stream networks: Quantifying spatial variability in fish distribution. Pages 405-420 in T. Nishida, P. J. Kailola, and C. E. Hollingworth, editors. GIS/Spatial Analyses in Fishery and Aquatic Sciences (Vol. 2). Fishery-Aquatic GIS Research Group, Saitama, Japan. Ver Hoef, J., E. Peterson, and D. M. Theobald. 2006. Spatial statistical models that use flow and stream distance. Environmental and Ecological Statistics 13:449-464. Ver Hoef, J. M., and E. E. Peterson. 2010. A moving average approach for spatial statistical models of stream networks. Journal of the American Statistical Association 105:6-18. FLOWS Tools and SSN R scripts: http://www.fs.fed.us/rm/boise/AWAE/projects/SpatialStreamNetworks.shtml

  34. Course Evaluations Instructor: Torgersen (Lawler) Course: SEFS 521 B Section: ---- Date: 3-Dec-2012

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