Photo of a small waterfall on Nevergo Creek, Willamette River basin that forms a barrier to fish passage, marking the upper extent of fish in a stream. The “last fish” would be found in the pool below the waterfall.

The Upper Limit of Fish

Mapping the upper extent of fish in streams through modeling

Land-Water Connections

In western North America, the regulations governing the management of forests and streams highlight the desire to protect and utilize the ecosystem goods and services provided by both land and water resources. For example, forest harvest practices are regulated to protect fish, streams, and clean water which are valued socially and economically, as is the timber. Balancing the protection and production of these sometimes competing goods and services has contributed to a rich evolution of research, regulation, and management.

Photo of a small waterfall on Muletail Creek in the Nestucca River basin in the Oregon Coast Range that forms a barrier to fish passage, marking the upper extent of fish in a stream. The “last fish” would be found in the pool below the waterfall.
Photo of a small waterfall on Muletail Creek in the Nestucca River basin in the Oregon Coast Range that forms a barrier to fish passage, marking the upper extent of fish in a stream. The “last fish” would be found in the pool below the waterfall.

The uppermost fish in Muletail Creek in the Nestucca River basin in the Oregon Coast Range is found in the pool below the small falls.

Illustration of ecosystem services at the upper limit of fish. Forest-freshwater ecosystems jointly produce benefits from nature, also known as ecosystem services, such as carbon storage, greenhouse gases, and climate regulation, aquatic and terrestrial biodiversity and habitat, mitigation of soil erosion and floods, timber and nontimber products, and management of water quality and quantity. Ecosystem services are jointly produced, thus when forests are harvested, the trajectory of ecosystem services associated with forest-freshwater ecosystems may change. Riparian buffer regulations for forest harvests depend on the upper limit of fish. Forest management practices near the upper extent of fish affect the levels of co-produced ecosystem services associated with the riparian buffer.

Identifying the upper distribution of fish within a stream network is key to contemporary forest and fisheries management. Stream reaches with fish receive more protection than reaches without fish. These protections include harvest restrictions on lands adjacent to fish-bearing reaches and wider riparian buffers.   Regulations around timber harvest and other forest management practices are designed to protect fish and their habitats while benefitting other species and protecting water quality.

A shared map that offers a visual context for where fish are, where they are not, and where their distributions end would help facilitate management of multiple resources and inform decisionmakers.

Header: Why focus on the upper extent of fish?

The upper distribution boundary for fish in forested streams receives special attention because fish-bearing portions of streams are managed differently and are more protected than fishless portions.

Two sliding images compare an illustration of fish distributions in a watershed where the image on the left has fish in the streams and consequently it also has additional protections, including wider riparian buffers. The image on the right does not have any fish in the streams shown in the watershed and accordingly there are narrower riparian buffers along the streams.

Example map of predictions by Fransen et al. (2006) from Panther Creek, Oregon showing a distribution of where fish are predicted and where they aren’t.

Example of predictions by Fransen et al. (2006) from Panther Creek, Oregon showing a distribution of where fish are predicted and where they aren’t.

Most fish distribution maps come from models based on occurrence information or habitat features. They may also include information from mechanistic, process-based, and correlative models. Distribution maps can also be based on direct observations, often from electrofishing, trapping, or snorkeling. However, data collected through direct observation can be labor intensive, rely on taxonomic expertise, and are influenced by both seasonal flow and the life cycle of the fish. Sampling every stream reach across a region is almost impossible. Slopes of 20 percent are recommended as the cutoff for the uppermost extent of fish across various western states and provinces of the United States and Canada. Another model that predicts the upper extent of trout is the optimal Fransen model (Fransen et al. 2006), which is a logistic regression model that was developed on stream layers derived from the National Hydrography Dataset (NHD) for 10-m reaches on private lands in western Washington.

Photograph of coastal cutthroat trout being netted in Mack Creek, McKenzie River the H.J. Andrews Experimental Forest.

Coastal cutthroat trout being netted in Mack Creek, McKenzie River at the H.J. Andrews Experimental Forest.

Examples of the upper extent of fish from across streams in the Pacific Northwest.

UPRLIMET

Acronym explained: Upstream Regional LiDAR Model for Extent of Trout

UPRLIMET is a spatially explicit and standardized model to predict the upper extent of fish across land ownerships in western Oregon. Using trout occurrence information and a stopping rule, we implemented UPRLIMET as a logistic regression model that uses geophysical aspects of the landscape, including stream size, slope, and elevation. We found this combination resulted in the lowest error.

Generalized development workflow for UPRLIMET, a single logistic regression model fit to trout occurrence observation data with a stopping rule.

It is impractical and inefficient to collect observations across the hundreds of thousands of kilometers of streams in western Oregon. Instead, we can use a few fish sightings and information associated with fish presence that is both less expensive to acquire and available at a broad spatial extent to calibrate a prediction model that allows us to infer where fish are likely to be. This is a complex and extensive process with numerous pitfalls, so we assembled a custom 4-step model development, validation, and prediction framework using R, Python, and ArcGIS software.

UPRLIMET predictions of fish distributions in four HUC12 sub-watersheds, including Coffee Creek (South Umpqua River), Ecola Creek (Coast Range), and Panther Creek (North Umpqua River), and West Fork Smith River (Umpqua River).

Key Findings

Implications

UPRLIMET maps both the probability of trout and the upper limit of trout across multiple ownerships, including private, state, and federal lands. The resulting cross-boundary distribution map may be a useful tool for policymakers and forest managers.

Map of UPRLIMET predictions for the West Fork Smith River (Umpqua River basin) overlaid with landownership layer, an example of how predictions from this model can apply across ownership boundaries in a way that older models that relied on more restricted datasets have difficulty in doing. The checkerboard pattern of private and federal landholdings creates sharply different riparian protections in adjacent reaches of the same stream.

UPRLIMET crosses ownerships.

This work provides a transferable prediction modeling framework for systematically and comprehensively estimating the upper distribution limit of fish, which could be applied to watersheds and fish species around the globe.

Examples of the upper extent of fish from across streams in the Pacific Northwest.

Look for the upper limit of fish in your favorite western Oregon watershed!

Authors

Brooke E. Penaluna  1  , Jonathan D. Burnett  1  , Kelly Christiansen  1  , Ivan Arismendi  2  , Sherri L. Johnson  1  , Kitty Griswold  3  , Brett Holycross  4  , and Sonja H. Kolstoe  5  

  1  U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, 3200 SW Jefferson Way, Corvallis, OR 97331

  2  Oregon State University, Department of Fisheries, Wildlife, and Conservation Sciences, 104 Nash Hall, Corvallis, OR 97331

  3  Idaho State University, Department of Biological Sciences, 921 S. 8th Ave Mail, Stop 8007 | Pocatello, ID 83209-8007

  4  Pacific States Marine Fisheries Commission, 205 SE Spokane St., Portland, OR 97202

  5  U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, 1220 SW 3rd Ave, Suite 1410, Portland, OR 97204

Further Reading

Fransen, B.R.; Duke, S.D.; McWethy, L.G.; Walter, J.K.; Bilby, R.E.  2006. A logistic regression model for predicting the upstream extent of fish occurrence based on geographical information systems data. North American Journal of Fisheries Management. 26: 960–975. https://doi.org/10.1577/M04-187.1.

Penaluna, B.E.; Burnett, J.D.; Christiansen, K.; et al. 2022. UPRLIMET: UPstream Regional LiDAR Model for Extent of Trout in stream networks. Scientific Reports. 12:20266. https://doi.org/10.1038/s41598-022-23754-0.

The uppermost fish in Muletail Creek in the Nestucca River basin in the Oregon Coast Range is found in the pool below the small falls.

Example of predictions by Fransen et al. (2006) from Panther Creek, Oregon showing a distribution of where fish are predicted and where they aren’t.

Coastal cutthroat trout being netted in Mack Creek, McKenzie River at the H.J. Andrews Experimental Forest.

UPRLIMET crosses ownerships.