Spatial stream-network models

SSN Bibliography

Below is a chronological bibliography of work relevant to SSN model development and applications. Please see the SSN/STARS website for full details on using these models, the latest software versions, tutorials, and example datasets. If you are part of a group that has published a new study using the SSN models and would like to have it added to this bibliography, please contact us regarding the possibility.

Ver Hoef, J, EE Peterson, and D Theobald. 2006. Spatial statistical models that use flow and stream distance. Environmental and Ecological Statistics 13:449–464.

Peterson, EE, AA Merton, DM Theobald, and NS Urquhart. 2006. Patterns of spatial autocorrelation in stream water chemistry. Environmental Monitoring and Assessment 121:569–594.

Peterson, EE, and NS Urquhart. 2006. Predicting water quality impaired stream segments using landscape-scale data and a regional geostatistical model: a case study in Maryland. Environmental Monitoring and Assessment 121:615–638.

Gardner K, and B McGlynn. 2009. Seasonality in spatial variability and influence of land use/land cover and watershed characteristics on stream water nitrate concentrations in a developing watershed in the Rocky Mountain West. Water Resources Research 45, DOI: 10.1029/2008WR007029.

Ver Hoef J.M. and Peterson E.E. 2010. A moving average approach to spatial statistical models of stream networks. The Journal of the American Statistical Association 489: 6-18.

Ver Hoef J.M. and Peterson E.E. 2010. A moving average approach to spatial statistical models of stream networks: Rejoinder. Journal of the American Statistical Association 105: 22-24.

Peterson E.E. and Ver Hoef J.M. 2010. A mixed-model moving-average approach to geostatistical modeling in stream networks. Ecology 91: 644-651.

Garreta, V, Monestiez P, and Ver Hoef JM. 2010. Spatial modeling and prediction on river networks: up model, down model or hybrid? Environmentrics 21:439-456.

Isaak D., Luce, C., Rieman B., Nagel D., and Peterson E., Horan D., Parkes S., and G. Chandler. 2010. Effects of climate change and wildfire on salmonid thermal habitats within a mountain stream network. Ecological Applications 20:1350-1371.

Ruesch, A. S., Torgersen, C. E., Lawler, J. J., Olden, J. D., Peterson, E. E., Volk, C. J., & Lawrence, D. J. 2012. Projected Climate‐Induced Habitat Loss for Salmonids in the John Day River Network, Oregon, USA. Conservation Biology 26:873-882.

Jones, L. A., Muhlfeld, C. C., Marshall, L. A., McGlynn, B. L., & Kershner, J. L. 2013. Estimating thermal regimes of bull trout and assessing the potential effects of climate warming on critical habitats. River Research and Applications 30:204-216.

Peterson, EE.; Ver Hoef, J.M.; Isaak, DJ.; Falke, JA.; Fortin, MJ; Jordan, CE.; McNyset, K; Monestiez, P; Ruesch, AS.; Sengupta, A; Som, N; Steel, EA; Theobald, DM.; Torgersen, CE.; Wenger, SJ. 2013. Modelling dendritic ecological networks in space: An integrated network perspective. Ecology Letters. 16:707-719.

Peterson E.E. and Ver Hoef J.M. 2014. STARS: An ArcGIS toolset used to calculate the spatial information needed to fit spatial statistical models to stream network data. Journal of Statistical Software 56(2).

Ver Hoef J.M., Peterson E.E., Clifford D., and Shah R. 2014. SSN: An R package for spatial statistical modeling on stream networks. Journal of Statistical Software 56(3).

Isaak, D.J., E. Peterson, J. V. Hoef, S. Wenger, J. Falke, C. Torgersen, C. Sowder, A. Steel, M.J. Fortin, C. Jordan, A. Reusch, N. Som, P. Monestiez. 2014. Applications of spatial statistical network models to stream data. Wiley Interdisciplinary Reviews - Water 1:277-294.

Falk, MG, JM McGree, and AN Pettitt. 2014. Sampling designs on stream networks using the pseudo-Bayesian approach. Environmental and Ecological Statistics 21 : 751-773

Som N.A., Monestiez P., Ver Hoef J.M., Zimmerman D.L., and Peterson E.E. 2014. Spatial sampling on streams: Principles for inference on aquatic networks. Environmetrics 25: 306-323.

Frieden J.C., Peterson E.E., Webb J.A., and Negus P.M. 2014. Improving the predictive power of spatial statistical models of stream macroinvertebrates using weighted autocovariance functions. Environmental Modelling and Software 60: 320-330.

McGuire, K. J., Torgersen, C. E., Likens, G. E., Buso, D. C., Lowe, W. H., & Bailey, S. W. 2014. Network analysis reveals multiscale controls on streamwater chemistry. Proceedings of the National Academy of Sciences 111: 7030-7035.

Rushworth A.M., Peterson E.E., Ver Hoef J.M., and Bowman A.W. 2015. Validation and comparison of geostatistical and spline models for spatial stream networks. Environmetrics doi: 10.1002/env.2340.

Lois, S., Cowley, D. E., Outeiro, A., San Miguel, E., Amaro, R., & Ondina, P. 2015. Spatial extent of biotic interactions affects species distribution and abundance in river networks: the freshwater pearl mussel and its hosts. Journal of Biogeography 42:229-240.

Golden, H. E., Sander, H. A., Lane, C. R., Zhao, C., Price, K., D'Amico, E., & Christensen, J. R. 2015. Relative effects of geographically isolated wetlands on streamflow: a watershed‐scale analysis. Ecohydrology. DOI: 10.1002/eco.1608.

Falke, Jeffrey A., Dunham, Jason B., Hockman-Wert, David, and Pahl, Randy. 2016. A Simple Prioritization Tool to Diagnose Impairment of Stream Temperature for Coldwater Fishes in the Great Basin. North American Journal of Fisheries Management 36:147–160.

Turschwell, Mischa P., Peterson, Erin E., Balcombe, Stephen R, Sheldon, Fran. 2016. To aggregate or not? Capturing the spatio-temporal complexity of the thermal regime. Ecological Indicators, 67 39-48. doi:10.1016/j.ecolind.2016.02.014.

Brennan, S.R., C.E. Torgersen, J.P. Hollenbeck, D.P. Fernandez, C.K. Jensen, D.E. Schindler. 2016. Dendritic network models improve isoscapes and quantify influence of landscape and in-stream processes on strontium isotopes in rivers. Geophysical Research Letters. 43: 5043-5051. DOI: 10.1002/2016GL068904.

Steel, E.A., C. Sowder, and E.E. Peterson. 2016. Spatial and Temporal Variation of Water Temperature Regimes on the Snoqualmie River Network. Journal of the American Water Resources Association. 52:769-787. DOI: 10.1111/1752-1688.12423.

Dobos, Marika E., Matthew P. Corsi, Daniel J. Schill, Joseph M. DuPont, and Michael C. Quist. 2016. Influences of Summer Water Temperatures on the Movement, Distribution, and Resource Use of Fluvial Westslope Cutthroat Trout in the South Fork Clearwater River Basin. North American Journal of Fisheries Management. 36:549–567.

Detenbeck, N.E., A. Morrison, R.W. Abele, D. Kopp. 2016. Spatial Statistical Network Models for Stream and River Temperature in New England, USA. Water Resources Research. 52: 6018-6040. DOI: 10.1002/2015WR018349.

Filipe, A., L. Quaglietta, M. Ferreira, M. Magalhaes, and P. Beja. 2017. Geostatistical distribution modelling of two invasive crayfish across dendritic stream networks. Biological Invasions. DOI 10.1007/s10530-017-1492-3.

Gallacher K, Miller C, Scott EM, Willows R, Pope L, Douglass J. 2017. Flow-directed PCA for monitoring networks. Environmetrics. e2434. doi:10.1002/env.2434.

Isaak, Daniel J.; Ver Hoef, Jay M.; Peterson, Erin E.; Horan, Dona L.; Nagel, David E. 2017. Scalable population estimates using spatial-stream-network (SSN) models, fish density surveys, and national geospatial database frameworks for streams. Canadian Journal of Fisheries and Aquatic Sciences. 74: 147-156.

Scown, M.W., M.G. McManus, J.H. Carson Jr., and C.T. Nietch. 2017. Improving Predictive Models of In-Stream Phosphorus Concentration Based on Nationally-Available Spatial Data Coverages. Journal of the American Water Resources Association.  DOI: 10.1111/1752-1688.12543.

Zimmerman, D.L., and J.M. VerHoef. 2017. The Torgegram for fluvial variography: characterizing spatial dependence on stream networks. Journal of Computational and Graphical Statistics. Vol 26. No 2, 253-264. DOI: 10.1080/10618600.2016.1247006.

Neill, A.J., D. Tetzlaff, N. James, C. Strachanc, R.L. Hough, L.M. Avery, H. Watson, and C. Soulsby. 2017. Using spatial-stream-network models and long-term data to understand and predict dynamics of faecal contamination in a mixed land-use catchment. Science of the Total Environment. https://doi.org/10.1016/j.scitotenv.2017.08.151.

Paz-Vinas, I., G. Loot, V. Hermoso, C. Veyssiere, N. Poulet, G. Grenouillet, and S. Blanchet. 2017. The systematic conservation planning for intraspecific genetic diversity. Proceedings of the Royal  Society  B 285 : 20172746. http://dx.doi.org/10.1098/rspb.2017.2746.

Marsha, A., E.A. Steel, A.H. Fullerton, and C. Sowder. 2017. Monitoring riverine thermal regimes on stream networks: Insights into spatial sampling designs from the Snoqualmie River, WA. Ecological Indicators 84: 11-26.

Lois, S. and Cowley, D.E., 2017. Conservation of interacting species in network‐constrained environments. Diversity and Distributions. DOI: 10.1111/ddi.12623.

Isaak, D., S. Wenger, E. Peterson, J. Ver Hoef, D. Nagel, C. Luce, S. Hostetler, J. Dunham, B. Roper, S. Wollrab, G. Chandler, D. Horan, S. Parkes-Payne. 2017. 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, 53: 9181-9205. https://doi.org/10.1002/2017WR020969.

Ver Hoef, J. 2018. Kriging models for linear networks and non-Euclidean distances: Cautions and solutions. Methods in Ecology and Evolution doi: 10.1111/2041-210X.12979.

ElSaadani, M., W.F. Krajewski, and D.L. Zimmerman. 2018. River network based characterization of errors in remotely sensed rainfall products in hydrological applications. Remote Sensing Letters 9:743–752.

Quaglietta, L., J. Pauperio, F. M. S. Martins, P. C. Alves, and P. Beja. 2018. Recent range contractions in the globally threatened Pyrenean desman highlight the importance of stream headwater refugia. Animal Conservation doi:10.1111/acv.12422.

Post, C.J., Cope, M.P., Gerard, P.D., Masto, N.M., Vine, J.R., Stiglitz, R.Y., Hallstrom, J.O., Newman, J.C. and Mikhailova, E.A. 2018. Monitoring spatial and temporal variation of dissolved oxygen and water temperature in the Savannah River using a sensor network. Environmental Monitoring and Assessment 190:272.

Rodriguez-Castillo, T., A. Estevez, M. Gonza´lez-Ferreras, and J. Barquın. 2018. Estimating ecosystem metabolism to entire river networks. Ecosystems https://doi.org/10.1007/s10021-018-0311-8

Bruckerhoff, L.A., Leasure, D.R. and Magoulick, D.D. 2018. Flow‐ecology relationships are spatially structured and differ among flow regimes. Journal of Applied Ecology. doi: 10.1111/1365-2664.13297.

Ver Hoef, J.M., E.E. Peterson, and D.J. Isaak. 2019. Spatial statistical models for stream networks. Pages 423-444 in A.E. Gelfand, M. Fuentes, J.A. Hoeting, and R.L. Smith, editors. Handbook of Environmental and Ecological Statistics. Chapman and Hall, CRC Press.

Ciepiela, L.R., and A.W. Walters. 2019. Quantifying 87Sr/86Sr temporal stability and spatial heterogeneity for use in tracking fish movement. Canadian Journal of Fisheries and Aquatic Sciences dx.doi.org/10.1139/cjfas-2018-0124.

Rodriguez-Gonzalez et al. 2019. A spatial stream-network approach assists in managing the remnant genetic diversity of riparian forests. Scientific Reports 9:6741.

Segura, C., D. Noone, D. Warren, J. Jones, J. Tenny, and L. Ganio. 2019. Climate, landforms, and geology affect baseflow sources in a mountain catchment. Water Resource Research doi: 10.1029/2018WR023551.

Kaylor, M.J., S.M. White,W.C. Saunders, and D.R. Warren. 2019. Relating spatial patterns of stream metabolism to distributions of juveniles salmonids at the river network scale. EcoSphere 10: e02781

Larsen, S., M.C. Brunoa, I.P. Vaughanc, and G. Zolezzia. 2019. Testing the River Continuum Concept with geostatistical stream-network models. Ecological Complexity
39: 100773.

Maguire, T.J., and S.O.C. Mundle. 2020. Citizen Science Data Show Temperature-Driven Declines in Riverine Sentinel Invertebrates. Environmental Science and Technology Letters https://dx.doi.org/10.1021/acs.estlett.0c00206

Mota-Ferreira, M. and P. Beja. 2020. Combining geostatistical and biotic interaction model to predict amphibian refuges under crayfish invasion across dendritic stream networks. Diversity and Distributions DOI: 10.1111/ddi.13047.

McGill, L.M., E.A. Steel, J.R. Brooks, R.T. Edwards, and A.H. Fullerton. 2020. Elevation and spatial structure explain most surface-water isotopic variation across five Pacific Coast basins. Journal of Hydrology 583: 124610.

McManus, M.G., E. D’Amico, E.M. Smith, R. Polinsky, J. Ackerman, and K. Tyler. 2020. Variation in stream network relationships and geospatial predictions of watershed conductivity. Freshwater Science DOI: 10.1086/710340.

Kattwinkel, M., Szöcs, E., Peterson, E. and Schäfer, R.B., 2020. Preparing GIS data for analysis of stream monitoring data: The R package openSTARS. Plos One 15(9), p.e0239237.

Gendaszek, A.S., Dunham, J.B., Torgersen, C.E., Hockman-Wert, D.P., Heck, M.P., Thorson, J., Mintz, J. and Allai, T., 2020. Land-cover and climatic controls on water temperature, flow permanence, and fragmentation of Great Basin stream networks. Water 12(7), doi:10.3390/w12071962.

Wang, Y., Moe, C.L., Dutta, S., Wadhwa, A., Kanungo, S., Mairinger, W., Zhao, Y., Jiang, Y. and Teunis, P.F., 2020. Designing a typhoid environmental surveillance study: a simulation model for optimum sampling site allocation. Epidemics 31, https://doi.org/10.1016/j.epidem.2020.100391.

Pearse, A.R., McGree, J.M., Som, N.A., Leigh, C., Maxwell, P., Ver Hoef, J.M. and Peterson, E.E., 2020. SSNdesign—An R package for pseudo-Bayesian optimal and adaptive sampling designs on stream networks. PloS One 15(9), p.e0238422.

Figary, S., Detenbeck, N. and O'Donnell, C. 2020. Guiding riparian management in a transboundary watershed through high resolution spatial statistical network models. Journal of Environmental Management 278: 111585.

French, D.W., D.E. Schindler, S.R. Brennan, and D. Whited. 2020. Headwater Catchments Govern Biogeochemistry in America's Largest Free‐Flowing River Network. Journal of Geophysical Research – Biogeosciences 10.1029/2020JG005851.

Figary, S., N. Detenbeck, and C. O’Donnell. 2021. Guiding riparian management in a transboundary watershed through high resolution spatial statistical network models. Journal of Environmental Management 278:111585.

Larsen, S., B. Majone, P. Zulian, E. Stella, A. Bellin, M.C. Bruno, and G. Zolezzi. 2021. Combining Hydrologic Simulations and Stream-network Models to Reveal Flow-ecology Relationships in a Large Alpine Catchment. Water Resources Research 10.1029/2020WR028496.

Sharma, A., V.K. Dubey, J.A. Johnson, Y.K. Rawal, and K. Sivakumar. 2021. Dendritic prioritization through spatial stream network modeling informs targeted management of Himalayan riverscapes under brown trout invasion. DOI: 10.1111/1365-2664.13997. 

Kaylor, M.J., Justice, C., Armstrong, J.B., Staton, B.A., Burns, L.A., Sedell, E. and White, S.M., 2021. Temperature, emergence phenology and consumption drive seasonal shifts in fish growth and production across riverscapes. Journal of Animal Ecology, 90(7), pp.1727-1741.

McGill, L.M., Brooks, J.R. and Steel, E.A., 2021. Spatiotemporal dynamics of water sources in a mountain river basin inferred through  δ²H and δ¹⁸O  of water. Hydrological processes, 35(3), p.e14063.

Fuller, Matthew R.; Joseph L. Ebersole; Naomi E. Detenbeck; Rochelle Labiosa; Peter Leinenbach; Christian E. Torgersen. 2021. Integrating thermal infrared stream temperature imagery and spatial stream network models to understand natural spatial thermal variability in streams. Journal of Thermal Biology, 100 (2021) 103028. doi:10.1016/j.jtherbio.2021.103028.

Rhea, A.E., T.P. Covino, C.C. Rhoades, and A.C. Brooks. 2022. Use of geostatistical models to evaluate landscape and stream network controls on post-fire stream nitrate concentrations. Hydrological Processes DOI: 10.1002/hyp.14689.

Horvath, I.R., Parolari, A.J., Petrella, S., Stow, C.A., Godwin, C.M., Maguire, T.J., 2022. Volunteer science data show degraded water quality disproportionately burdens areas of high poverty, Journal of Hydrology doi: https://doi.org/10.1016/j.jhydrol.2022.128475.

Frantz, M.W., Wood, P.B. and Latta, S.C., 2022. Spatial stream modeling of Louisiana Waterthrush (Parkesia motacilla) foraging substrate and aquatic prey in a watershed undergoing shale gas development. Food Webs 33: e00249.

Fuller, Matthew R.; Leinenbach, Peter; Detenbeck, Naomi E.; Labiosa, Rochelle; Isaak, Daniel J. 2022. Riparian vegetation shade restoration and loss effects on recent and future stream temperatures. Restoration Ecology. 30(7): e13626.

Warren, D.R., Pett-Ridge, J.C., Segura, C., Kaylor, M.J. and Heaston, E.D., 2022. Extreme drought conditions increase variability of nitrate through a stream network, with limited influence on the spatial patterns of stream phosphate. Biogeochemistry, 160(2), pp.243-258.

Fuller, Matthew R.; Naomi E. Detenbeck, Peter Leinenbach, Rochelle Labiosa, Daniel Isaak. 2023. Spatial and temporal variability in stream thermal regime drivers for three river networks during the summer growing season. Journal of the American Water Resources Association 2023;00:1–22. DOI: 10.1111/1752-1688.13158.

Man, Y., Zhou, F., Wang, Q. and Cui, B., 2023. Quantitative evaluation of sea reclamation activities on tidal creek connectivity. Frontiers in Marine Science, 10, p.1164065.

Ver Hoef, J.M., Dumelle, M., Higham, M., Peterson, E.E. and Isaak, D.J., 2023. Indexing and partitioning the spatial linear model for large data sets. Plos one, 18(11), p.e0291906.

Krochta, M. and Chang, H., 2023. Scales of Connectivity within Stream Temperature Networks of the Clackamas River Basin, Oregon. Annals of the American Association of Geographers, DOI: 10.1080/24694452.2023.2289981

Barbi, C., Menafoglio, A. and Secchi, P., 2023. An object-oriented approach to the analysis of spatial complex data over stream-network domains. Spatial Statistics, 58, p.100784.

 Uğurluoğlu, A. and Uyguçgil, H., 2023. Assessing performance of spatial statistical stream network models in predicting alkalinity: A comparative study of two river basins in Türkiye. Ecological Informatics, 78, p.102358.

Brennan, Kyle Gerard; Gabriel J. Bowen; Diego P. Fernandez; Sean R. Brennan. 2024. Arc Sediments are a Major source of Juvenile Sr in the Northern Cordillera's Taku River Basin: Potential Implications for the Global Sr Cycle. Chemical Geology, 644 (2024) 121877. doi:10.1016/j.chemgeo.2023.121877.

Winkowski, J.J., J.D. Olden, and S. Brown. 2024. Integrating spatial stream network models and environmental DNA to estimate current and future distributions of nonnative Smallmouth Bass. Transactions of the American Fisheries Society 153: https://doi.org/10.1002/tafs.10454.

Struthers, D.P., Gutowsky, L.F., Lucas, T.C., Mochnacz, N.J., Carli, C.M. and Taylor, M.K., 2024. Statistical stream temperature modelling with SSN and INLA: an introduction for conservation practitioners. Canadian Journal of Fisheries and Aquatic Sciences. https://doi.org/10.1139/cjfas-2023-0136.

Wampler, K.A., Bladon, K.D. and Myers-Pigg, A.N., 2024. The influence of burn severity on dissolved organic carbon concentrations across a stream network differs based on seasonal wetness conditions post-fire. EGUsphere https://doi.org/10.5194/egusphere-2024-273.