Optimization methodology for a river temperature monitoring network for the characterization of fish thermal habitat

Abstract

A methodology for planning an optimized river water temperature monitoring network is presented. The methodology is based on sampling of the physio-climatic variability of the region to be monitored. Physio-climatic metrics are selected to describe the study region, based on principal component analysis. The sites to be monitored are then identified based on a k-means clustering in the multidimensional space defined by the selected metrics. The methodology is validated on an existing dense water temperature network in Haute-Savoie, France. Different configurations of more or less dense network scenarios are evaluated by assessing their ability to estimate water temperature indices at ungauged locations. An optimized network containing 83 sites is found to provide satisfactory estimations for seven ecologically and biologically meaningful thermal indices defined to characterize brown trout thermal habitat

Thermal Regimes of Perennial Rivers and Streams in the Western United States

“…Different, however, is the grassroots nature of temperature databases, which are growing because declining sensor costs are democratizing data acquisition efforts. Temperature sensors with multi‐year data logging capacities cost U.S. $20–200, for example, are available from several manufacturers, and are easily deployed using standard protocols (Stamp et al 2014), which has spawned an array of local monitoring networks by natural resource agencies and watershed councils in many countries (e.g., Trumbo et al 2014; Daigle et al 2016; Jackson et al 2016; Mauger et al 2016).…”

“…Comparable knowledge about thermal regimes is needed, but has lagged because temperature data are not recorded at most flow gauges and datasets of annual records have been difficult to obtain from more than a few sites or watersheds (Orr et al 2015; Isaak et al 2018b). In recent years, data limitations have begun to ease with the advent of inexpensive, reliable temperature sensors and grassroots monitoring efforts are becoming common throughout much of Europe and North America (e.g., Hilderbrand et al 2014; Trumbo et al 2014; Nusslé et al 2015; Daigle et al 2016; Jackson et al 2016; Mauger et al 2016).…”

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

“…Sensor technologies for measuring stream temperatures have proliferated in recent decades (Dugdale, ; Ebersole et al, ; Quilty & Moore, ; Selker et al, ; Torgersen et al, ; Vaccaro & Maloy, ) but the most popular have been inexpensive sensors that record measurements at user‐specified intervals and create time series of recordings (Angilletta & Krochmal, ; Dunham et al, ; Stamp et al, ). Inexpensive sensors democratized the collection of temperature data beginning in the early 1990s, which resulted in extensive, albeit largely uncoordinated, monitoring efforts throughout North America and Europe (Daigle et al, ; DeWeber & Wagner, ; Dunham et al, ; Hannah & Garner, ; Hilderbrand et al, ; Isaak et al, ; Isaak & Hubert, ; Jackson et al, ; Johnson & Wilby, ; Mauger et al, ; McKenna et al, ; Molinero et al, ; Moore et al, ; Trumbo et al, ; Wehrly et al, ). Sensors deployed in those efforts sometimes record data only for short periods (e.g., 1–3 months or years) but viewed collectively, constitute a massive distributed monitoring array that provides measurements from thousands of sites.…”

Genotype‐by‐environment interactions drive the maintenance of genetic variation in a Salmo trutta L. hybrid zone

“…In the present study, we investigated patterns of offspring survival at egg stage in Salmo trutta , based on parental genotypes—using markers related to original MED and ATL lineages—in interaction with river temperature to detect potential GxE interactions. Selection can indeed act very strongly during early stages of development in salmonid species, notably as a function of temperature (Huuskonen et al, 2003 ; Ojanguren & Braña, 2003 ; Régnier et al, 2013 ), a factor that can show contrast at small scales (Brown & Hannah, 2008 ; Daigle et al, 2016 ). Offspring from ATL lineage are expected to be adapted to relatively warm temperatures (8–10°C) for prehatching survival as already demonstrated (Jungwirth & Winkler, 1984 ; Ojanguren & Braña, 2003 ; Régnier et al, 2013 ), a thermal range often encountered in their distribution area.…”