Fisheries and Wildlife Protection and Restoration Strategies

Editor's Note: This section is in outline form except for the Discussion of Harvest Management

1. Potential Strategies: Habitats

i. Irrigation diversion by-passes and intake mortalities

ii. Excessive groundwater removals that reduce stream flows

2. Potential Strategies for Hydropower and other major dams

3. Potential Strategies for Hatcheries

4. Harvest - Puget Sound Salmon Harvest Management as a Restoration Strategy

Harvest management is one of the four “Hs” essential for the recovery of Puget Sound Salmon (Shared Strategy 2007). Harvest management is critical because it determines both the number of spawners that reach spawning habitat as well as the number of fish available for harvest.

We suggest six interrelated harvest management strategies that could be applied to Pacific salmon restoration in Puget Sound. If simultaneously implemented, they will set the stage for improved escapement of spawners to the freshwater habitat, ultimately leading to improved run sizes (assuming the other Hs are well-managed). These strategies are: 1) improved estimates of salmon carrying capacity, 2) improved run-size forecasting, 3) improved accuracy of in-season harvest management, 4) avoidance of genetic alteration of stock structure and diversity via harvest, 5) fully functional, realistic tools for harvest management decisions, and 6) monitoring of escapement and harvests.

Understanding salmon carrying capacity is a key component of salmonid restoration. Because of chronic overfishing, habitat degradation and, more recently, habitat restoration, salmon managers have lost the baseline reference points for freshwater and estuarine production (Pauley 1995). Furthermore, awareness is increasing about the critical nature of carrying capacity in the nearshore marine and oceanic habitats (e.g., Ruggerone et al. 2003, 2005) and the relative importance of early marine survival (Farley et al. 2007, Van Doornik et al. 2007). However, without a more complete understanding of these limitations, it is difficult to assess whether restoration of salmon populations is working. Better habitat-based benchmarks are needed from which to manage the restoration process.

It is important to note that the recovery benchmarks of the Endangered Species Act (ESA) recovery plans are not necessarily the same goals for full restoration. This is because the ESA is designed to ensure that populations do not go extinct, rather than ensure that they attain their full production capacity which, when restored, will in turn support healthy aquatic ecosystems and tribal, commercial, recreational fisheries.

The science of salmon capacity estimation has only been partially developed. For decades, capacity for many salmon stocks was estimated using retrospective statistical models of the relationship between the number of spawners and the subsequent count of returning adults (Ricker 1958, 1975; Beverton-Holt 1957). While these models perform adequately under ideal conditions, they have been shown to be fraught with numerous technical weaknesses (Hilborn and Walters 1992, Knudsen 2000). More recently, the science of salmon capacity has been expanding based on observations of the numbers of juveniles produced per spawner and per habitat area and expressed in various models (e.g., EDT – Mobrand et al. 1997 , SHIRAZ – Scheuerell et al. 2006, Ripple – Dietrich and Ligon 2008, UCM – e.g., Cramer and Ackerman 2009). Research and development is expanding on life history-based models that incorporate both habitat and capacity in the relationship of salmon to their environment, as it ultimately influences survival.

Some recent progress has been made in estimating salmon carrying capacity and related modeling to support harvest management as a salmon restoration strategy. However, strategic implementation of these techniques requires further scientific advancements as suggested by Hilborn (2009) and Knudsen and Michael (2009). Some of the remaining challenges are 1) determining how many fish should be produced per habitat, 2) better ascertaining how the environment influences salmon survival and production, 3) developing functional, realistic simulations that can be used for more precise management decisions, 4) accounting for the interactive effects of habitat, hatcheries, and other salmon species, and 5) correcting for the lack of accurate and/or long-term data (by stock).

Salmon restoration also depends on improving both pre-season and in-season run size forecasts so that decisions about harvest management can be tuned to the number of adults expected to return. Successful forecasting is extremely challenging because the number of returning fish depends on dynamic and complex interaction between the often unknown number of smolts entering the ocean and the highly variable ocean environment. Pre-season forecasting is important for management decisions for determining expected escapements and, by subtraction, opening or closing the various fisheries. Current run forecasting is generally relatively inaccurate. For example, Puget Sound Chinook pre-season forecasts of escapement were only within 10% of the actual escapement values for 12% of the forecasts between 2001 and 2006 (PSIT and WDFW 2008). Therefore, the current strategy for managers working to rebuild depleted runs is to set harvest rates relatively low to account for the highly variable returns (e.g., PSIT and WDFW 2009). There is increasing evidence, however, that forecasts can be improved by additional research into the relationships between salmon survival and environmental drivers (Beamish et al. 2009, Noakes and Beamish 2009). When managers have more accurate forecasts, they will be able to refine harvest management decisions.

Short-term forecasting could be improved by increasing the frequency and accuracy of in-season fisheries-dependent sampling of open fisheries, in test fisheries in closed areas, and by monitoring in-river escapement with weirs, traps, and/or sonar (e.g. Clark et al. 2006). Research is gradually revealing technical methods that will make in-season predictions more reliable, such as the ability to use coded-wire tag information to refine run predictions (e.g. Holt et al. 2009), but more research is needed.

Improved precision in spatial and temporal management is a strategy to separate capture of abundant stocks, such as plentiful wild fish or those from hatcheries, from “incidentally” captured depleted or jeopardized stocks (NRC 1996, SSDC 2007). There are two major types of suggested improvements in-season harvest management: 1) techniques that make harvest management decisions more precise, and 2) harvest techniques that separate harvestable from non-harvestable fish. Clearly, if these two kinds of techniques can be improved, harvest managers will be able to more carefully determine which and how many fish may be harvested and which fish may escape to spawn (Knudsen and Doyle 2006).

The more precise the in-season harvest management, the more likely abundant stocks, such as hatchery or abundant wild fish, can be harvested without harming wild populations that can only withstand a much lower harvest rate. The major features of such a scheme include identifying the relative abundance of each stock present in each fishing area at any given time, and then opening or closing the fishing area as necessary, as described and preliminarily modeled by Newman (2000). An important component is real-time stock separation within each fishing area. Recent advancements in genetic stock identification are increasingly improving the technical ability for accuracy and precision of stock separation (e.g., Smith et al 2005, Dann et al. 2009), although these techniques have not yet been widely applied for Puget Sound stocks. Currently, stock mixtures are determined for chinook and coho from coded-wire-tag data of representative hatchery stocks. Under ideal management, as the fish move sequentially through the management areas, decisions could be made to protect weak stocks and/or allow harvest of abundant stocks(e.g., Newman 1998). For example, increasing the accuracy of in-season harvest management, has been shown to be effective in maintaining healthy Alaskan salmon runs, even though they are routinely subjected to moderate to heavy fishing (e.g., Clark et al. 2006). Further research as well as dedication to in-season sampling is needed for such real-time decision-making to be effective in Puget Sound salmon management.

Selective fishing methods and gears allow release of incidentally captured non-targeted stocks to escape unharmed.Because of a lack of external indicators of stock origin, selective salmon fisheries can only be applied to hatchery versus wild stocks by externally marking all hatchery fish (e.g., HSRG 2004, Kostow et al. 2009, McHugh et al. 2009). Some fishing gears are better suited to selective fisheries. For example, fish wheels, purse seines, and traps allow non-target fish to be released unharmed, while gill nets tend to preclude live release (e.g., Copes 2000). Both recreationally and commercially caught salmon and steelhead can be released unharmed (e.g., Vander Haegen et al. 2004, Cowen et al. 2007), although there can be various amounts of delayed mortality and/or failures to spawn due to catch and release, or escapement from gears (e.g., Wertheimer, 1988, Baker and Schindler 2009). Additional research is needed to advance the science and management of selective fisheries.

In summary, the status of applying precision forecasting and in-season harvest techniques to Puget Sound salmon harvest management is mixed. While in some cases the techniques described above are being partially applied, in many cases additional technical advancements are needed. For example, Puget Sound Chinook salmon harvests (hence escapements) are managed primarily by setting a relatively low harvest rate (PSIT & WDFW) in part to accommodate the fact that there is insufficient information to manage the in-season run precisely.

To sustain the productivity of harvested populations, there are important genetic considerations for harvest management which havethe potential to cause three types of genetic change: 1) alteration of population subdivision; 2) loss of genetic variation; and 3) selective genetic changes (Allendorf et al. 2008). Population subdivision occurs through changes in the metapopulation structure of Pacific salmonids that coincides with the network of natal river populations (Policansky and Magnuson 1998, Gustafson et al. 2007). This also includes the subpopulation diversity represented by the variety of intraspecific life history types. Loss of genetic variation occurs when a single population is reduced to too few spawners (Waples 1990). Allendorf et al. (2008) also suggest that, as the population size decrease, there can be a loss of fitness selection. Selective genetic changes in salmon populations can be induced by harvests. Hard et al. (2007) reported strong evidence that selection intensity and genetic variability in salmon fitness traits from fishing can cause detectable evolution within ten or fewer generations. Salmon body size and run timing are two heritable traits, among others, that have been demonstrated to be affected by fishing (e.g., Hamon et al. 2000, Quinn et al. 2002).

Allendorf et al. (2008) recommended recognizing that some genetic change due to harvest is inevitable and that harvest management plans should be developed by applying basic genetic principles combined with molecular genetic monitoring to minimize harmful genetic change. These issues need further study in Puget Sound so that harvest management plans can be refined to reduce fisheries-induced genetic selection.

We suggest that ideal salmon and steelhead population management consists of monitoring two population variables: adult run size and smolt production. For successful long-term harvest management and future planning, total salmon run sizes should be estimated after each season. This requires accurate monitoring of the harvest, attributed to each population, plus the escapement of each population. In Washington, harvest estimates are made via fish sales tickets for commercial harvests and by the sport catch record card system for sport harvests (SSDC 2007), each of which has inherent inaccuracies. Assignment of the harvest to the river of origin is a key component of the final estimates, the accuracy of which depends upon the type of fishery (e.g., marine harvests tend to be mixed while freshwater harvest are more likely to be assigned to the correct river of origin). Catches of chinook, coho, and chum from mixed stock fishery areas are separated post-season by some combination of coded-wire-tag (CWT) recovery data and genetic baseline information (e.g., Johnson et al. 1997, PSIT and WDFW 2008). Once estimates are assigned to their rivers of origin, cohort reconstructions enable estimation of exploitation rates, which may be compared to results from the fishery regulation and assessment model (FRAM) estimates (PSIT and WDFW 2004, PFMC 2007). This process is variably imprecise depending on the species, available data, and model used (e.g., Starr and Hilborn 1988, Johnson et al. 1997).

To obtain the total estimated run size, annual escapement of spawners are added to the estimated harvest numbers. Escapements are monitored by a variety of methods, but not all streams/populations are monitored and the data quality of those that are monitored is highly variable (Knudsen 2000). For example, there is no escapement information on summer steelhead and escapement estimates are unavailable for four winter steelhead runs (PSSTRT 2005). The Salmonid Stock Inventory (SaSI) by WDFW is a standard process for monitoring and recording the escapements or indices of escapement, also used to assess the overall status of the stocks¹. However, it was last updated in 2002 and, at that time, there was insufficient data for 28% of known Puget Sound stocks¹. Assessment of smolt production is also important for optimal harvest management. Having both adult and smolt metrics for a given population allows the discernment of both freshwater and marine survival. WDFW presently uses the Intensively Monitored Watershed (IMW) program to monitor smolt production for selected species (Bilby et al. 2004), including six locations in Puget Sound. The basic concept is that these watersheds represent a sampling of all watersheds and that IMW observations on smolt production may be expanded to other similar watersheds (Bilby et al. 2004). Coho smolts are also monitored in several Puget Sound streams by WDFW.

Currently, run forecasting tools generally consist of past years’ run reconstructions, combined with observations on brood-year survival conditions, and, in some cases, observed smolt production, to estimate predicted run returns (e.g., PSIT and WDFW 2008). The primary harvest management modeling tool for Chinook and coho is FRAM (PFMC 2007). Current salmon run forecasting is a highly variable science (Adkison and Peterman 2000, Beamish et al. 2009). For example Puget Sound Chinook forecasts ranged between -403% to +88% of the subsequently observed run sizes (PSIT and WDFW 2008).

Full salmon restoration will require progress on all the topics described above plus the concomitant development of improved computer-based, decision-making tools as described by Knudsen and Michael (2009). There are a number of possible scenarios for how modeling tools could be improved, but perhaps the most the most promising approach is exemplified when the all-H analyzer (AHA) is used to evaluate the management options (e.g., Kaje et al. 2008). Inputs on habitat-based recovery goals are obtained from SHIRAZ (Scheuerell and Hilborn (2006) and/or EDT (Mobrand et al. 1997). AHA then allows the user to concurrently model alternative scenarios for habitat, harvest, and hatcheries. However, there are many opportunities for improvement to the AHA model. In regard to improving AHA, for example, habitat is modeled as a simple production curve and harvest is modeled as a fixed exploitation rate. The model could be made much more useful by incorporating a model of the relationship of life-stage-specific survivals to habitat conditions (Hilborn 2009), perhaps through modifications of SHIRAZ. This also has the advantage of being able to incorporate interactions between hatchery and wild fish at a number of life stages, as recommended by Hilborn (2009). Another necessary improvement is to incorporate more detailed harvest management modeling, such as by including key outputs of the FRAM model currently used for fishery management. This would allow evaluation of the effects of selective fisheries and/or the impacts of different fishery plans on different life history types (i.e., diversity) (Hilborn 2009). AHA is currently focused on the degradation of wild stock productivity due to the presence of hatchery fish, mainly arising from deleterious genetic effects (Michael et al. 2009). However, with incorporation of improved habitat and harvest modules, the model could also include a number of other hatchery effects that are currently ignored in AHA. Additionally, modifications to make the AHA model stochastic are needed (Hilborn 2009). Lastly, further model developmentis necessary to include the interactions of multiple species (e.g., Greene and Pess 2009).

To date, harvest management restoration strategies have included relatively unknown habitat capacity, harvest management information inaccuracies, lack of in-season management techniques, and therefore complicated negotiations, often contentious because of the uncertainty of run-sizes. Such strategies have only been partly effective. Overall, the scientific basis for harvest-related salmon restoration could be considered to be “developing” in that much of the science, especially the basic biology of the species, is reasonably advanced, but certain critical information is still lacking. At this point in the development of salmon harvest management science, we can articulate some ways to improve accuracy and precision:

• improved methods for estimating salmon and steelhead carrying capacity,
• better run-size forecasting,
• improved accuracy and precision of in-season harvest management,
• better ways to avoid genetic alteration of stock structure and diversity,
• increased monitoring of escapement, harvests, and smolts, and
• advanced tools for harvest management decisions

Outline for the rest of Section 5.2

A. Habitat protection and restoration (as described in Chapter 3 and in the next chapter), but specifically:

Footnotes

¹ see http://wdfw.wa.gov/conservation/fisheries/sasi/

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