We used simple regression models to demonstrate an association between land use and parr survival of chinook salmon Oncorhynchus tshawytscha from overwintering areas in the Snake River drainage of Idaho and Oregon to the first main‐stem dam encountered during emigration to the Pacific Ocean. We used data on tagged (passive integrated transponder tags) releases of naturally produced Snake River spring–summer chinook parr and subsequent tag detections, as well as indices of land use, vegetation, and road density. We spot‐checked the land‐use and vegetation indices in a field survey of spawning and rearing areas in the summer of 1999, and we believe that they are reliable indicators of land‐use patterns. The models also employed month of release, length of parr at release, and a drought index as independent variables. The models were developed and tested using parr tagged from 1992 through 1998. Age‐0 parr that reared in wilderness areas (a land‐use category; not necessarily federally designated Wilderness Areas) had the highest survival during their last 6–9 months of freshwater residence. In contrast, parr that reared in young, dry forests (typically, intensively managed timber lands) had the lowest survival. Similarly, parr that reared in areas of low road density had substantially higher survival than those in areas of high road density. We concluded that in the area studied there is a close association between land‐use indices and survival of chinook salmon parr during their last 6–9 months of freshwater residence. This analysis suggests that road‐building and associated land‐use activities in the region may have a detrimental effect on the survival of juvenile chinook salmon and that mitigative changes in these activities could be warranted because Snake River spring–summer chinook salmon are listed as threatened under the Endangered Species Act.
We used 11 years of parr-to-smolt survival estimates from 33 Snake River sites to demonstrate that despite a number of confounding factors higher numbers of past habitat remediation or enhancement actions are associated with higher parr-to-smolt survival of endangered wild Snake River springϪsummer (stream-type) Chinook salmon Oncorhynchus tshawytscha. Information-theoretic weights were applied to help distinguish between statistical models based on their relative plausibility. In the models with the highest estimated weights, actions taken to improve fish habitat showed a positive association with increased parr-to-smolt survival. However, because the actions were not sited randomly on the landscape, and because the actions may also have influenced other potentially important covariates, it is difficult to separate habitat action effects from effects due to other important factors.
For salmon populations in the Columbia River basin, many of which are listed under the U.S. Endangered Species Act of 1973, reliable estimation of the proportion of hatchery‐origin spawners in spawning areas (p) is needed to make inferences about their status and potential for interbreeding with wild‐origin adults, which may reduce the genetic fitness of subsequent generations. Despite its importance, accurate and precise techniques for estimating p are lacking, especially when there are spawner inputs from multiple hatcheries in a single spawning area. To identify hatchery‐origin spawners, some hatchery releases are given visible marks, some are tagged with coded wire tags (CWTs), and some are marked in both ways. However, different marking fractions are used at different hatcheries and this complicates the problem of estimating p. To handle this situation, we derived a new generalized least‐squares estimator (GLSE) of p and another, less computationally intensive estimator that uses CWT recoveries alone (SMME). We conducted Monte Carlo simulations using both estimators to compare their precision and statistical accuracy. We then applied the estimators to 2010 carcass survey data for fall Chinook salmon Oncorhynchus tshawytscha in the Hanford Reach of the Columbia River. An incremental change away from identical marking fractions for all source hatchery releases reduced precision, increased bias, and complicated estimation. The GLSE had greater precision than the SMME. Statistical bias shrank as the true spawning population size, the fraction of hatchery‐origin fish, or the CWT fraction increased. The GLSE estimate of p in the Hanford Reach was 0.077, while the SMME estimate was 0.041. To maximize the accuracy and precision of the estimates of p, we recommend that identical marking fractions (preferably 100%) be used for all hatchery releases; barring this, we recommend that the CWT fractions be as large as possible.
The rapid decline of the Snake River spring-summer-run Chinook salmon Oncorhynchus tshawytscha evolutionarily significant unit (ESU) in the 1990s led a group of scientists to develop the Plan for Analyzing and Testing Hypotheses (PATH). Under this plan, researchers used spawner-recruit (SR) data to estimate the survival of out-migrating smolts through eight dams of the Federal Columbia River Power System (FCRPS). Direct measurements of survival during out-migration, known as passage survival, were not available, so the PATH scientists estimated survival for index populations using trends from Ricker-type SR models. This modeling framework had the advantage of estimating both the direct and indirect (or latent) effects (FCRPS-related mortalities that do not occur until the smolts have passed the FCRPS dams) on the life cycle survival of the populations. We evaluated the SR model used by the PATH scientists by examining how changes in model structure affected important inferences. We calculated condition indexes as measures of the sensitivity of the model results to perturbations in the SR data and model structure, finding that the results were highly sensitive to certain assumptions. In particular, we found that changing the Ricker a term from a population-specific parameter to a parameter common to all of the populations in the ESU changed total passage survival from 9% to 56% and latent mortality from one-half the total passage mortality to a value that is not significantly different from zero. Therefore, the condition indexes revealed high potential sensitivities of the SR model results to perturbations in data and model structure. Although information criteria indicated that the population-specific model had a poorer fit than lower-parameter models, it was impossible to resolve the question as to whether there was latent mortality.
Abstract.-Because many stocks of Pacific salmon Oncorhynchus spp. are listed under the U.S. Endangered Species Act (ESA), research has focused on predicting the future population dynamics for these low-abundance stocks. One method used to make predictions is known as population viability analysis. Pacific salmon populations exhibit much higher apparent variability than other ESA-listed vertebrates, and high variability increases the probability of extinction. If the high variability is primarily due to counting methods, it could be reduced in model predictions by using methods that correct for measurement error, sampling error, or both. Using data from British Columbia pink salmon O. gorbuscha and Snake River springor summer-run Chinook salmon O. tshawytscha and several modeling approaches (Ricker, Dennis, and statespace models), we compared repeated counts of the same population (e.g., spawner and fry, dam and redd counts). We applied the methods to the first half of the time series and compared the predictions with the last half of the time series. The results demonstrated that having counts of all life stages of a Pacific salmon population is no guarantee that variability will be markedly reduced. Measurement error is not the primary cause of high variability in empirical estimates of abundance or in predicted future abundance for the stocks analyzed. The very wide bounds on predicted abundance limit the utility of the model predictions for making management decisions. Furthermore, obtaining more accurate or complete measurements of population abundance is unlikely to reduce the wide error bounds in predictions of future abundances.Over the past 15 years many stocks of Pacific salmon Oncorhynchus spp. have been listed as threatened or endangered under the U.S. Endangered Species Act (ESA) (NOAA Fisheries 2005). This has focused researchers' attention on a difficult problem: how best to predict the future course of population dynamics for listed stocks whose abundances are often greatly reduced from historic levels. One method used to make such predictions is known as population viability analysis (PVA). Several PVA methods have been used for modeling listed salmon populations, beginning with spawner-recruit (SR) models familiar to many fisheries biologists (e.g., Marmorek et al. 1998;Paulsen and Hinrichsen 2002). More recently, researchers, drawing on methods used for other ESAlisted vertebrate species (e.g., northern spotted owl Strix occidentalis caurina, loggerhead sea turtle Caretta caretta, snail kite Rostrhamus sociabilis, and Bachman's sparrow Aimophifa aestivulis; Morris et al. 2002), have employed diffusion approximation methods (e.g., Holmes 2004;Holmes and Semmens 2004). These methods have also been applied for long-term projections by management agencies at the evolutionarily significant unit (ESU) and population levels (NOAA 2000). Unlike spawner-recruit models diffusion approximation methods do not require information on the age of returning progeny (recruits) and, therefore, can be employed for...
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