Southeast Alaska Fish-Habitat
Landscape-level ecosystem assessment using best-available science paired with remote sensing to support aquatic conservation.
Romey Riverscape Science provides a unique combination of quantitative ecology, remote sensing, and fish biology for the design, management, and analysis of ecological field studies. We are committed to developing a wide range of scientifically defensible collaborative solutions to help solve natural resource problems, and help balance the growing need for ecosystem services with aquatic conservation.
We have helped pioneer a new era of aquatic conservation in Southeast Alaska by creating models for salmonids that include habitat intrinsic potential (HIP), habitat suitability index (HSI), end of resident (EoF) and anadromous salmonids (EoA), natural carrying capacity (NCC), stock-recruit, and life-cycle. Some of these models are being used to support the development of natural resource conservation plans and watershed assessments for multiple regions, while others are being used by agencies and organizations for the following objectives:
- Identify and prioritize stream restoration sites
- Evaluate amount and quality of habitat upstream of road-stream crossings and natural barriers
- Assess cost benefit for barrier enhancement
- Predict natural carrying capacity for entire riverscape
- Predict population viability thresholds for at risk basins
- Identify core habitat for maintaining viable populations
- Prioritize field efforts
- Assess cost benefit for conducting ground surveys
- Post restoration baseline comparison
- Limiting factor analysis
Agencies and organizations across Southeast Alaska are taking a landscape-level approach for evaluating riverscapes to identify important aquatic habitat for conservation, management, and restoration objectives (Johnson et al. 2020)
Due to the remote and rugged landscapes of Southeast Alaska current fish and associated habitat information for some regions is sparse or missing completely. In order for natural resource managers to make well-informed decision for maintaining viable fish populations a complete and accurate coverage of their habitat requirements is needed.
A complete landscape coverage is possible using models based on high-resolution remote sensing data. High resolution Light Detection and Ranging (LiDAR) is used to derive stream reach- and basin-scale geomorphic and hydrologic attributes which are associated with fish distribution and habitat quality (Benda et al. 2007).
LiDAR
Developing fish-habitat models from remote sensing data starts with the acquisition of the LiDAR point cloud. The point cloud is then classified into ground (brown), and vegetation (green) returns. A digital terrain model (DTM) and canopy height model (CHM) are then created from the ground and vegetation returns.
LiDAR point cloud acquisition
Synthetic Stream
With a high-resolution LiDAR DTM (1-m pixel), a synthetic stream network is created ( TerrainWorks-NetMap ) and habitat attributes are associated with every reach (e.g, gradient, mean annual flow, channel confinement, basin area - Benda et al. 2007).
Karst river feature on Kupearnof Island
Modeling Fish-Habitat
The final step is to collect a random sample of fish population field data that can be paired with synthetic habitat metrics to predict fish occupancy and habitat quality for the entire landscape.
For example, the Coho Habitat Intrinsic Potential (HIP) map of the KKCFP project area landscape near Kake shows the spatial distribution and amount of important habitat required to sustain viable populations (Romey 2018). HIP models are a powerful tool to identify habitat conservation areas, or potential restoration sites when compared with past land use (Burnett et al. 2007).
Collecting information for predicting Coho summer rearing habitat near Hoonah, Alaska
Upstream & Downstream Assessment
Road-stream crossing have the potential to block migration of adult and juvenile salmonids. End of fish and habitat quality models (HIP) can be used to identify the amount and quality of inaccessible habitat upstream and downstream of a road crossing.
The upstream assessment map shows all water crossing across the entire HNFP project area landscape indicating the amount of potential Coho Salmon habitat area upstream of a crossing. Decision makers can leverage this information to help prioritize field efforts or evaluate the cost/benefit of replacing a structure blocking fish migration.
Perched culvert blocking fish migration.
Upper Limit of Resident Salmonid Distribution
State and federal regulations governing land use activity near streams in Southeast Alaska differ depending on whether a reach of stream supports anadromous or resident fish. With a lack of resident fish information for large regions of Southeast Alaska, a landscape-level model using LiDAR derived habitat metrics can be used to predict resident fish reach occupancy with > 98% accuracy and an average error distance of < 67 meters (Romey & Martin 2021).
The comparison maps show probable resident salmonid relative fish occupancy for the entire HNFP landscape compared to the current anadromous waters catalog (AWC) for protected streams. This complete coverage showing population boundaries can inform conservation, management, and restoration objectives.
Southeast Alaska local community workforce collecting fish abundance, habitat, and channel morphology data in support of aquatic conservation
References
- Anlauf, K. J., W. Gaeuman, and K. K. Jones. 2011. Detection of Regional Trends in Salmonid Habitat in Coastal Streams, Oregon. Transactions of the American Fisheries Society 140:52–66.
- Benda, L., D. Miller, K. Andras, P. Bigelow, G. Reeves, and D. Michael. 2007. NetMap: a new tool in support of watershed science and resource management. Forest Science 53(2):206–219.
- Bryant, M. D., and R. D. Woodsmith. 2009. The Response of Salmon Populations to Geomorphic Measurements at Three Scales. North American Journal of Fisheries Management 29(3):549–559.
- Burnett, K. M., G. H. Reeves, D. J. Miller, S. Clarke, K. Vance-Borland, and K. Christiansen. 2007. Distribution of salmon-habitat potential relative to landscape characteristics and implications for conservation. Ecological Applications 17(1):66–80.
- Johnson, I., S. Savell, and editors. 2020. Hoonah Native Forest Partnership: An Interdisciplinary, Collaborative Approach to Watershed Assessment and Resource Planning. Page 258. Hoonah Indian Association, Technical Report, Hoonah, Alaska.
- Romey, B., and D. Martin. 2018. Landscape-Level Model for Predicting Juvenile Coho Salmon Rearing Habitat in Southeast Alaska. Page 37. RRS Technical Report 18-2, Longview, WA.
- Romey, B. T. 2018, March. Modeling Spawning Habitat Potential for Chum (Oncorhynchus keta) and Pink Salmon (O. gorbuscha) in Relation to Landscape Characteristics in Coastal Southeast Alaska . MS Thesis, Portland State University, Portland, Oregon.
- Romey, B. T., and M. Douglas. 2021. Landscape-Level Extent of Resident Fish Occupancy in the Alexander Archipelago; Predicting resident salmonid population boundaries with variable-length reach habitat attributes derived from high resolution LiDAR. Page 38. RRS, Technical Report 21–03, Longview, WA.
