3.3.1 Aquatic Habitat Barriers


Goal: C. Protect and enhance landscape and habitats structure and processes to benefit ecosystem and watershed functions

Objective: 1. Protect and enhance aquatic habitat connectivity

WAF Attribute: Hydrology/Geomorphology

What is it?

The connectivity of aquatic habitat is interrupted by man-made structures which include road crossings, dams, weirs and other management structures (Baxter, Frissell et al. 1999; Gibson, Haedrich et al. 2005). The interruptions vary in nature from complete barriers (a dam with no fish ladder or other accessible passage), to changes that have little or no impact on habitat (Warren and Pardew 1998). This indicator is a count of all stream and river crossings by roads and other barriers (collectively referred to as barriers) per linear kilometer of river in the regions of interest. This provides a statistic that summarizes the amount of interaction between barriers and rivers for each area. Included in these data are barriers generated by the analysis of road and river datasets as well as barriers catalogued by the Fish Passage Assessment.

Why is it Important?

Locations where roads cross waterways change the natural shape of the river and how it is allowed to flow through the barrier. This can affect sediment transport and deposition and the movement and migration of aquatic species (Forman and Alexander 1998; Warren and Pardew 1998). Natural processes are altered by crossings and higher barrier frequency has a negative impact on many aspects of waterway health. Specifically, increases in the water velocity due to the configuration of a road crossing are inversely proportional to fish movement (Warren and Pardew 1998). A similar metric is used in assessing logging road impacts on fish habitat. In this case, road density is used with the assumption that all crossings have a similar configuration and are proportional to road density (Baxter, Frissell et al. 1999).

What is the target or desired condition?

The desired condition of the landscape, from an ecological health standpoint, is to have no barriers in the aquatic habitat. This could mean all road crossings or other structures are configured such that they generate no impact on the habitat or movement of aquatic species. Some planning watersheds do not have roads and therefore are the benchmark for scoring the watersheds that do have barriers. Watersheds that have no barriers have a score of 100.

What can influence or stress condition?

Alteration of road crossings or installation of new crossings (or other barriers) that do not specifically address the need for habitat-friendly design will negatively impact aquatic connectivity. Likewise, the construction of any roads in a watershed can negatively impact connectivity due to increases in runoff, total suspended solids and water chemistry (Byron and Goldman 1989; Forman and Alexander 1998; Ahearn, Sheibley et al. 2005).

What did we find out/How are we doing?

The barrier scores for each of the 11 subwatersheds ranged between 67 and 82 (Table 1). This score was calculated by comparing the density of barriers within each planning watershed with the highest density measured in the Feather River Watershed (2.99 barriers/km stream), which resulted in a proportional value, which was in turn converted to a score. Reduction in road crossings or other barriers in all regions will improve environmental health and reduce the negative impacts on aquatic habitat connectivity. These data will need to be recalculated as the data sources are updated to provide an indication of temporal trend. Road building, especially in rural areas, is likely to increase the barriers per kilometer of river. No trend was calculated because of the lack of historical information

Table 1. Aquatic barrier scores for subwatersheds

Goal Measurable Objective Subwatershed Score
C. Protect and enhance landscape and habitats structure and processes to benefit ecosystem and watershed functions 1. Protect and enhance aquatic habitat connectivity NFF 82
MFF 81
LF 82
NY 82
MY 76
SY 79
DC 69
LY 77
UB 67
LB 79

Figure 1. Distribution of aquatic barriers score across subwatersheds

Temporal and spatial resolution

River and road data were sourced from the highest-resolution datasets available. Ephemeral streams were removed from the dataset because of inconsistent representations of this stream type data across the watersheds. Road data were combined from two sources, the state of California and the USFS. The USFS data had higher resolution and more detail within the boundaries of the national forests and were used in place of state data. Temporal resolution is unknown as these datasets are irregularly updated.

How sure are we about our findings
(Things to keep in mind)

The road crossings combined with barriers cataloged by DFG provide a detailed dataset from which to derive information. However, no field validation of the spatial data were performed. As such, an assumption was made that each barrier is equally detrimental to fish movement and no barrier completely blocks fish movement. Future analyses of this nature could be further detailed to boost confidence in the indicator. For instance, the different types of road crossing (e.g. culvert, open box, fording and others) exhibit differential impacts on fish movement (Warren and Pardew 1998). Culverts tend to accelerate the water through the crossing, which makes the crossing more of a barrier to fish movement. The configuration of each of the crossings in this analysis is not known and data of this nature would improve the analysis.

Technical Information

Data sources and transformations:

River data was sourced from the USGS National Hydrography Dataset. Ephemeral streams were removed from the dataset prior to analysis. Road data were sourced from both the State of California and from the USFS. All roads within national park boundaries originated from USFS. Additional river barrier locations were obtained from DFG Passage Assessment Database (PAD).

All spatial data were re-projected to Teale Albers NAD 83.


A road dataset was assembled by selecting the USFS road dataset that fell within the Feather River Watershed, then adding data from the State of California road dataset in areas not included in the USFS dataset. Ephemeral streams and shoreline features (for the major lakes) were eliminated from the NHD stream dataset within the watershed. The crossing data points were generated by intersecting roads and streams across all portions of the analysis area. Barriers cataloged in the Passage Assessment Database by the DFG were also added to the road/stream point dataset. For each planning watershed, the ratio of barriers to total river length (located inside the planning watershed) was calculated. The ratio was also calculated for the entire subbasin.

The barrier density for each subwatershed (BRsw) is compared with the maximum observed density of all planning-watersheds in the Feather River Watershed (BRmax, 2.99 barriers/km). The following equation was used to scale the score between 0 and 100: Score = 100 x (1 - (BRsw/BRmax)), where a score of 100 is the highest score and indicates no barriers.

Table 2. Summary statistics for barrier evaluation. “PW in SW” refers to the number of planning watersheds in each subwatershed. “Barrier count” refers to the number of dams and road crossings. Minimum and maximum barriers/km are the lowest and highest densities of barriers in any planning watershed in the subwatershed. “Average barriers/km” and “StDev …” refers to the average and standard deviation of barrier density across all planning watersheds in each subwatershed. “Score” refers to the indicator score.

Subwatershed Name PW in SW (count) Barrier Count (SW) River km Minimum Barriers/km Maximum Barriers/km Average Barriers/km StDev Barriers/km Score
Deer Creek 8 308 332.506 0.65 1.27 0.906 0.2592 69
East Branch North Fork Feather 75 1483 2148.072 0.11 2.99 0.690 0.3829 77
Lower Bear 10 368 597.455 0.40 1.22 0.689 0.2508 79
Lower Feather 19 1435 2676.798 0.00 1.38 0.638 0.3170 82
Lower Yuba 14 409 603.028 0.20 1.44 0.814 0.3459 77
Middle Fork Feather 98 1795 3131.308 0.00 1.60 0.651 0.3413 81
Middle Yuba 20 444 608.727 0.14 1.89 0.799 0.4073 76
North Fork Feather 86 1185 2266.707 0.00 1.92 0.532 0.3779 82
North Yuba 34 594 1115.386 0.15 1.08 0.583 0.2776 82
South Yuba 25 508 822.238 0.00 1.37 0.632 0.3329 79
Upper Bear 21 821 836.948 0.38 1.68 0.992 0.3657 67


Ahearn, D. S., R. W. Sheibley, et al. (2005). “Land use and land cover influence on water quality in the last free-flowing river draining the western Sierra Nevada, California.” Journal of Hydrology 313(3-4): 234-247.

Baxter, C. V., C. A. Frissell, et al. (1999). “Geomorphology, Logging Roads, and the Distribution of Bull Trout Spawning in a Forested River Basin: Implications for Management and Conservation.” Transactions of the American Fisheries Society 128(5): 854-867.

Byron, E. R. and C. R. Goldman (1989). “Land-Use and Water Quality in Tributary Streams of Lake Tahoe, California-Nevada.” J Environ Qual 18(1): 84-88.

Forman, R. T. T. and L. E. Alexander (1998). “Roads and their Major Ecological Effects.” Annual Review of Ecology and Systematics 29(1): 207-231.

Gibson, R. J., R. L. Haedrich, et al. (2005). “Loss of Fish Habitat as a Consequence of Inappropriately Constructed Stream Crossings.” Fisheries 30(1): 10-17.

Warren, M. L. and M. G. Pardew (1998). “Road Crossings as Barriers to Small-Stream Fish Movement.” Transactions of the American Fisheries Society 127(4): 637-644.