Found: 33
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A comparison of spatially explicit landscape representation methods and their relationship to stream condition.
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- Freshwater Biology, 2011, v. 56, n. 3, p. 590, doi. 10.1111/j.1365-2427.2010.02507.x
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- Article
Geostatistical modelling on stream networks: developing valid covariance matrices based on hydrologic distance and stream flow.
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- Freshwater Biology, 2007, v. 52, n. 2, p. 267, doi. 10.1111/j.1365-2427.2006.01686.x
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- Article
Spatially structured statistical network models for landscape genetics.
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- Ecological Monographs, 2019, v. 89, n. 2, p. N.PAG, doi. 10.1002/ecm.1355
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Spatial autoregressive models for statistical inference from ecological data.
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- Ecological Monographs, 2018, v. 88, n. 1, p. 36, doi. 10.1002/ecm.1283
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- Article
IDW-Plus: An Arc GIS Toolset for Calculating Spatially Explicit Watershed Attributes for Survey Sites.
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- Journal of the American Water Resources Association, 2017, v. 53, n. 5, p. 1241, doi. 10.1111/1752-1688.12558
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Spatial and Temporal Variation of Water Temperature Regimes on the Snoqualmie River Network.
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- Journal of the American Water Resources Association, 2016, v. 52, n. 3, p. 769, doi. 10.1111/1752-1688.12423
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Modelling imperfect presence data obtained by citizen science.
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- Environmetrics, 2017, v. 28, n. 5, p. n/a, doi. 10.1002/env.2446
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Spatial sampling on streams: principles for inference on aquatic networks.
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- Environmetrics, 2014, v. 25, n. 5, p. 306, doi. 10.1002/env.2284
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Indexing and partitioning the spatial linear model for large data sets.
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- PLoS ONE, 2023, v. 18, n. 11, p. 1, doi. 10.1371/journal.pone.0291906
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Understanding links between water-quality variables and nitrate concentration in freshwater streams using high frequency sensor data.
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- PLoS ONE, 2023, v. 17, n. 6, p. 1, doi. 10.1371/journal.pone.0287640
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Reconstructing Missing and Anomalous Data Collected from High-Frequency In-Situ Sensors in Fresh Waters.
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- International Journal of Environmental Research & Public Health, 2021, v. 18, n. 23, p. 12803, doi. 10.3390/ijerph182312803
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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.
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- Water Resources Research, 2017, v. 53, n. 11, p. 9181, doi. 10.1002/2017WR020969
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- Article
Increasing Trust in New Data Sources: Crowdsourcing Image Classification for Ecology.
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- International Statistical Review, 2024, v. 92, n. 1, p. 43, doi. 10.1111/insr.12542
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- Article
Predicting Water Quality Impaired Stream Segments using Landscape-Scale Data and a Regional Geostatistical Model: A Case Study in Maryland.
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- Environmental Monitoring & Assessment, 2006, v. 121, n. 1-3, p. 613, doi. 10.1007/s10661-005-9163-8
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- Article
Patterns of Spatial Autocorrelation in Stream Water Chemistry.
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- Environmental Monitoring & Assessment, 2006, v. 121, n. 1-3, p. 569, doi. 10.1007/s10661-005-9156-7
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- Article
Forecasting intensifying disturbance effects on coral reefs.
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- Global Change Biology, 2020, v. 26, n. 5, p. 2785, doi. 10.1111/gcb.15059
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Modelling dendritic ecological networks in space: an integrated network perspective.
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- Ecology Letters, 2013, v. 16, n. 5, p. 707, doi. 10.1111/ele.12084
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- Article
A mixed-model moving-average approach to geostatistical modeling in stream networks.
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- Ecology, 2010, v. 91, n. 3, p. 644, doi. 10.1890/08-1668.1
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SSNdesign—An R package for pseudo-Bayesian optimal and adaptive sampling designs on stream networks.
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- PLoS ONE, 2020, v. 15, n. 9, p. 1, doi. 10.1371/journal.pone.0238422
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Using virtual reality and thermal imagery to improve statistical modelling of vulnerable and protected species.
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- PLoS ONE, 2019, v. 14, n. 12, p. 1, doi. 10.1371/journal.pone.0217809
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Predicting sediment and nutrient concentrations from high-frequency water-quality data.
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- PLoS ONE, 2019, v. 14, n. 8, p. 1, doi. 10.1371/journal.pone.0215503
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The Natural Killer–Dendritic Cell Immune Axis in Anti-Cancer Immunity and Immunotherapy.
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- Frontiers in Immunology, 2021, v. 11, p. N.PAG, doi. 10.3389/fimmu.2020.621254
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Identifying the spatial scale of land use that most strongly influences overall river ecosystem health score.
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- Ecological Applications, 2012, v. 22, n. 8, p. 2188, doi. 10.1890/11-1792.1
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Effects of climate change and wildfire on stream temperatures and salmonid thermal habitat in a mountain river network.
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- Ecological Applications, 2010, v. 20, n. 5, p. 1350, doi. 10.1890/09-0822.1
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Riparian restoration offsets predicted population consequences of climate warming in a threatened headwater fish.
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- Aquatic Conservation, 2018, v. 28, n. 3, p. 575, doi. 10.1002/aqc.2864
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Supporting conscious competency: Validation of the Generic Supervision Assessment Tool (GSAT).
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- Psychology & Psychotherapy: Theory, Research & Practice, 2022, v. 95, n. 1, p. 113, doi. 10.1111/papt.12369
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Bayesian design with sampling windows for complex spatial processes.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2024, v. 73, n. 2, p. 378, doi. 10.1093/jrsssc/qlad099
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Correcting misclassification errors in crowdsourced ecological data: A Bayesian perspective.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2021, v. 70, n. 1, p. 147, doi. 10.1111/rssc.12453
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SSNbayes: An R Package for Bayesian Spatio-Temporal Modelling on Stream Networks.
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- R Journal, 2023, v. 15, n. 3, p. 26, doi. 10.32614/rj-2023-061
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A stochastic model of jaguar abundance in the Peruvian Amazon under climate variation scenarios.
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- Ecology & Evolution (20457758), 2020, v. 10, n. 19, p. 10829, doi. 10.1002/ece3.6740
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Giant coral reef fishes display markedly different susceptibility to night spearfishing.
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- Ecology & Evolution (20457758), 2018, v. 8, n. 20, p. 10247, doi. 10.1002/ece3.4501
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Projected Climate-Induced Habitat Loss for Salmonids in the John Day River Network, Oregon, U.S.A.
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- Conservation Biology, 2012, v. 26, n. 5, p. 873, doi. 10.1111/j.1523-1739.2012.01897.x
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Scalable population estimates using spatial-stream-network (SSN) models, fish density surveys, and national geospatial database frameworks for streams.
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- Canadian Journal of Fisheries & Aquatic Sciences, 2017, v. 74, n. 2, p. 147, doi. 10.1139/cjfas-2016-0247
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