Mountain streams provide important habitats for many species, but their faunas are especially vulnerable to climate change because of ectothermic physiologies and movements that constrained linear networks easily fragmented. Effectively conserving biodiversity in these systems requires accurate downscaling climatic trends local habitat conditions, is difficult complex terrains given diverse microclimates mediation stream heat budgets by conditions. We compiled a temperature database (n =...

Abstract Thermal regimes are fundamental determinants of aquatic ecosystems, which makes description and prediction temperatures critical during a period rapid global change. The advent inexpensive temperature sensors dramatically increased monitoring in recent decades, although most is done by individuals for agency‐specific purposes, collectively these efforts constitute massive distributed sensing array that generates an untapped wealth data. Using the framework provided National...

The imminent demise of montane species is a recurrent theme in the climate change literature, particularly for aquatic that are constrained to networks and elevational rather than latitudinal retreat as temperatures increase. Predictions widespread losses, however, have yet be fulfilled despite decades change, suggesting trends much weaker anticipated may too subtle detection given use sparse water temperature datasets or imprecise surrogates like elevation air temperature. Through...

Abstract Ecological data often exhibit spatial pattern, which can be modeled as autocorrelation. Conditional autoregressive (CAR) and simultaneous (SAR) models are network‐based (also known graphical models) specifically designed to model spatially autocorrelated based on neighborhood relationships. We identify discuss six different types of practical ecological inference using CAR SAR models, including: (1) selection, (2) regression, (3) estimation autocorrelation, (4) other connectivity...

In this article we use moving averages to develop new classes of models in a flexible modeling framework for stream networks. Streams and rivers are among our most important resources, yet with autocorrelated errors spatially continuous networks have been described only recently. We based on distance rather than Euclidean distance. Spatial autocovariance developed may not be valid when using begin by describing topology. then build several streams. Various derived depending whether the...

Abstract Streams and rivers host a significant portion of Earth's biodiversity provide important ecosystem services for human populations. Accurate information regarding the status trends stream resources is vital their effective conservation management. Most statistical techniques applied to data measured on networks were developed terrestrial applications are not optimized streams. A new class spatial model, based valid covariance structures networks, can be used with many common types...

Spatial autocorrelation is an intrinsic characteristic in freshwater stream environments where nested watersheds and flow connectivity may produce patterns that are not captured by Euclidean distance. Yet, many common autocovariance functions used geostatistical models statistically invalid when distance replaced with hydrologic We use simple worked examples to illustrate a recently developed moving‐average approach construct two types of valid based on distances. These were designed...

The SSN package for R provides a set of functions modeling stream network data. can import geographic information systems data or simulate new as 'SpatialStreamNetwork', object class that builds on the spatial sp classes. Functions are provided fit linear models (SLMs) 'SpatialStreamNetwork' object. covariance matrix SLMs use distance metrics and geostatistical unique to networks; these account distances topological configuration networks, including volume direction flowing water. In...

This paper describes the STARS ArcGIS geoprocessing toolset, which is used to calcu- late spatial information needed fit statistical models stream network data using SSN package. The toolset designed for use with a landscape (LSN), topological model produced by FLoWS toolset. An overview of LSN structure and few particularly useful tools also provided so that users will have clear understanding underlying struc- ture depends on. document may be as an introduction new users. methods calculate...

Catchment and riparian degradation has resulted in declining ecosystem health of streams worldwide. With restoration a priority many regions, there is an increasing interest the scale at which land use influences stream health. Our goal was to substantial data set collected as part monitoring program (the Southeast Queensland, Australia, Ecological Health Monitoring Program set, 116 sites over six years) identify spatial use, or combination scales, that most strongly overall In addition, we...

1. Biodiversity, water quality and ecosystem processes in streams are known to be influenced by the terrestrial landscape over a range of spatial temporal scales. Lumped attributes (i.e. per cent land use) often used characterise condition catchment; however, they not spatially explicit do account for disproportionate influence located near stream or connected overland flow. 2. We compared seven representation metrics determine whether accounting proximity hydrological effects use can...

Summary 1. Geostatistical models based on Euclidean distance fail to represent the spatial configuration, connectivity, and directionality of sites in a stream network may not be ecologically relevant for many chemical, physical biological studies freshwater streams. Functional measures, such as symmetric asymmetric hydrologic distance, more accurately transfer organisms, material energy through networks. However, calculating distances large study area remains challenging substituting...

Abstract: Climate change will likely have profound effects on cold‐water species of freshwater fishes. As temperatures rise, fish distributions may shift and contract in response. Predicting the projected stream warming networks is complicated by generally poor correlation between water temperature air temperature. Spatial dependencies are complex because geography processes governed dimensions flow direction network structure. Therefore, forecasting climate‐driven range shifts biota has...

Anomaly detection (AD) in high-volume environmental data requires one to tackle a series of challenges associated with the typical low frequency anomalous events, broad-range possible anomaly types, and local nonstationary conditions, suggesting need for flexible statistical methods that are able cope unbalanced problems. Here, we aimed detect anomalies caused by technical errors water-quality (turbidity conductivity) collected automated situ sensors deployed contrasting riverine estuarine...

Abstract Anticipating future changes of an ecosystem's dynamics requires knowledge how its key communities respond to current environmental regimes. The Great Barrier Reef (GBR) is under threat, with rapid reef‐building hard coral (HC) community structure already evident across broad spatial scales. While several underlying relationships between HC and multiple disturbances have been documented, responses other benthic are not well understood. Here we used statistical modelling explore the...

Abstract Although mean temperatures change annually and are highly correlated with elevation, the entire thermal regime on Snoqualmie River, Washington, USA does not simply shift elevation or season. Particular facets of have unique spatial patterns river network at particular times year. We used a spatially temporally dense temperature dataset to generate 13 metrics representing popular summary measures (e.g., minimum, mean, maximum temperature) wavelet variances over each seven time...

We investigated invasive group A Streptococcus epidemiology in Idaho, USA, during 2008-2019 using surveillance data, medical record review, and emm (M protein gene) typing results. Incidence increased from 1.04 to 4.76 cases/100,000 persons 2008-2019. 1, 12, 28, 11, 4 were the most common types, 2 outbreaks identified. examined changes distribution of clinical syndrome, patient demographics, risk factors by comparing 2008-2013 baseline with 2014-2019 data. was higher among all age groups...

The SSN2 R package provides tools for spatial statistical modeling, parameter estimation, and prediction on stream (river) networks.SSN2 is the successor to SSN (Ver Hoef, Peterson, Clifford, & Shah, 2014), which was archived alongside broader changes in

Population size estimates for stream fishes are important conservation and management, but sampling costs limit the extent of most to small portions river networks that encompass 100s–10 000s linear kilometres. However, advent large fish density data sets, spatial-stream-network (SSN) models benefit from nonindependence among samples, national geospatial database frameworks streams provide components create a broadly scalable approach population estimation. We demonstrate such an with...

A particular focus of water-quality monitoring is the concentrations sediments and nutrients in rivers, constituents that can smother biota cause eutrophication. However, physical economic constraints manual sampling prohibit data collection at frequency required to capture adequately variation through time. Here, we developed models predict total suspended solids (TSS) oxidized nitrogen (NOx) based on high-frequency time series turbidity, conductivity river level from low-cost situ sensors...