Instream barriers, such as dams, culverts, and diversions, alter hydrologic processes and aquatic habitat. Removing uneconomical and aging instream barriers is increasingly used for river restoration. Historically, selection of barrier removal projects used score‐and‐rank techniques, ignoring cumulative change and the spatial structure of stream networks. Likewise, most water supply models prioritize either human water uses or aquatic habitat, failing to incorporate both human and environmental water use benefits. Here, a dual‐objective optimization model identifies barriers to remove that maximize connected aquatic habitat and minimize water scarcity. Aquatic habitat is measured using monthly average streamflow, temperature, channel gradient, and geomorphic condition as indicators of aquatic habitat suitability. Water scarcity costs are minimized using economic penalty functions while a budget constraint specifies the money available to remove barriers. We demonstrate the approach using a case study in Utah's Weber Basin to prioritize removal of instream barriers for Bonneville cutthroat trout, while maintaining human water uses. Removing 54 instream barriers reconnects about 160 km of quality‐weighted habitat and costs approximately US$10 M. After this point, the cost‐effectiveness of removing barriers to connect river habitat decreases. The modeling approach expands barrier removal optimization methods by explicitly including both economic and environmental water uses.
Generalizable methods that identify suitable aquatic habitat across large river basins and regions are needed to inform resource management. Habitat suitability models intersect environmental variables to predict species occurrence, but are often data intensive and thus are typically developed at small spatial scales. This study estimated mean monthly aquatic habitat suitability throughout Utah (USA) for Bonneville Cutthroat Trout (
- Award ID(s):
- 1653452
- NSF-PAR ID:
- 10379876
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- JAWRA Journal of the American Water Resources Association
- Volume:
- 59
- Issue:
- 1
- ISSN:
- 1093-474X
- Page Range / eLocation ID:
- p. 107-126
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
Abstract -
more » « less
Abstract Understanding how populations respond to spatially heterogeneous habitat disturbance is as critical to conservation as it is challenging. Here, we present a new, free, and open‐source metapopulation model: Dynamic Habitat Disturbance and Ecological Resilience (Dy
HDER ), which incorporates subpopulation habitat condition and connectivity into a population viability analysis framework. Modeling temporally dynamic and spatially explicit habitat disturbance of varying magnitude and duration is accomplished through the use of habitat time‐series data and a mechanistic approach to adjusting subpopulation vital rates. Additionally, DyHDER uses a probabilistic dispersal model driven by site‐specific habitat suitability, density dependence, and directionally dependent connectivity. In the first application of DyHDER , we explore how fragmentation and projected climate change are predicted to impact a well‐studied Bonneville cutthroat trout metapopulation in the Logan River (Utah,USA ). The DyHDER model predicts which subpopulations are most susceptible to disturbance, as well as the potential interactions between stressors. Further, the model predicts how populations may be expected to redistribute following disturbance. This information is valuable to conservationists and managers faced with protecting populations of conservation concern across landscapes undergoing changing disturbance regimes. The DyHDER model provides a valuable and generalizable new tool to explore metapopulation resilience to spatially and temporally dynamic stressors for a diverse range of taxa and ecosystems. -
Abstract. Watershed-scale stream temperature models are often one-dimensional because they require fewer data and are more computationally efficient than two- or three-dimensional models. However, one-dimensional models assume completely mixed reaches and ignore small-scale spatial temperature variability, which may create temperature barriers or refugia for cold-water aquatic species. Fine spatial- and temporal-resolution stream temperature monitoring provides information to identify river features with increased thermal variability. We used distributed temperature sensing (DTS) to observe small-scale stream temperature variability, measured as a temperature range through space and time, within two 400 m reaches in summer 2015 in Nevada's East Walker and main stem Walker rivers. Thermal infrared (TIR) aerial imagery collected in summer 2012 quantified the spatial temperature variability throughout the Walker Basin. We coupled both types of high-resolution measured data with simulated stream temperatures to corroborate model results and estimate the spatial distribution of thermal refugia for Lahontan cutthroat trout and other cold-water species. Temperature model estimates were within the DTS-measured temperature ranges 21 % and 70 % of the time for the East Walker River and main stem Walker River, respectively, and within TIR-measured temperatures 17 %, 5 %, and 5 % of the time for the East Walker, West Walker, and main stem Walker rivers, respectively. DTS, TIR, and modeled stream temperatures in the main stem Walker River nearly always exceeded the 21 ∘C optimal temperature threshold for adult trout, usually exceeded the 24 ∘C stress threshold, and could exceed the 28 ∘C lethal threshold for Lahontan cutthroat trout. Measured stream temperature ranges bracketed ambient river temperatures by −10.1 to +2.3 ∘C in agricultural return flows, −1.2 to +4 ∘C at diversions, −5.1 to +2 ∘C in beaver dams, and −4.2 to 0 ∘C at seeps. To better understand the role of these river features on thermal refugia during warm time periods, the respective temperature ranges were added to simulated stream temperatures at each of the identified river features. Based on this analysis, the average distance between thermal refugia in this system was 2.8 km. While simulated stream temperatures are often too warm to support Lahontan cutthroat trout and other cold-water species, thermal refugia may exist to improve habitat connectivity and facilitate trout movement between spawning and summer habitats. Overall, high-resolution DTS and TIR measurements quantify temperature ranges of refugia and augment process-based modeling.more » « less
-
more » « less
Abstract Reservoirs are sometimes managed to meet agricultural and other water demands, while also maintaining streamflow for aquatic species and ecosystems. In the Henrys Fork Snake River, Idaho (USA), irrigation‐season management of a headwater reservoir is informed by a flow target in a management reach ~95 km downstream. The target is in place to meet irrigation demand and maintain aquatic habitat within the 11.4 km management reach and has undergone four flow target assignments from 1978 to 2021. Recent changes to irrigation‐season management to maximize reservoir carryover warranted investigation into the flow target assignment. Thus, we created a streamflow‐habitat model using hydraulic measurements, habitat unit mapping, and published habitat suitability criteria for Brown Trout (
Salmo trutta ), Rainbow Trout (Oncorhynchus mykiss ), and Mountain Whitefish (Prosopium williamsoni ). We used model output to compare habitat availability across two management regimes (1978–2017 and 2018–2021). We found that efforts to minimize reservoir releases in 2018–2021 did not reduce mean irrigation‐season fish habitat relative to natural flow, but did reduce overall fish habitat variability during the irrigation season compared to streamflow management in 1978–2017. Field observations for this research led to an adjusted flow target in 2020 that moved the target location downstream of intervening irrigation diversions. Using our model output, we demonstrated that moving the location of the target to account for local irrigation diversions will contribute to more consistently suitable fish habitat in the reach. Our study demonstrates the importance of site selection for establishing environmental flow targets. -
Abstract Predicting the edges of species distributions is fundamental for species conservation, ecosystem services, and management decisions. In North America, the location of the upstream limit of fish in forested streams receives special attention, because fish-bearing portions of streams have more protections during forest management activities than fishless portions. We present a novel model development and evaluation framework, wherein we compare 26 models to predict upper distribution limits of trout in streams. The models used machine learning, logistic regression, and a sophisticated nested spatial cross-validation routine to evaluate predictive performance while accounting for spatial autocorrelation. The model resulting in the best predictive performance, termed UPstream Regional LiDAR Model for Extent of Trout (UPRLIMET), is a two-stage model that uses a logistic regression algorithm calibrated to observations of Coastal Cutthroat Trout ( Oncorhynchus clarkii clarkii ) occurrence and variables representing hydro-topographic characteristics of the landscape. We predict trout presence along reaches throughout a stream network, and include a stopping rule to identify a discrete upper limit point above which all stream reaches are classified as fishless. Although there is no simple explanation for the upper distribution limit identified in UPRLIMET, four factors, including upstream channel length above the point of uppermost fish, drainage area, slope, and elevation, had highest importance. Across our study region of western Oregon, we found that more of the fish-bearing network is on private lands than on state, US Bureau of Land Mangement (BLM), or USDA Forest Service (USFS) lands, highlighting the importance of using spatially consistent maps across a region and working across land ownerships. Our research underscores the value of using occurrence data to develop simple, but powerful, prediction tools to capture complex ecological processes that contribute to distribution limits of species.more » « less
