James J. Anderson

Deep water renewal in Saanich Inlet, an intermittently anoxic basin

Abstract The seasonal renewal of deep water in Saanich Inlet was investigated. Origin and temperature-salinity characteristics of the deep water and of the water that renews the deep water were identified. Renewals equivalent to 64 and 33% of the deep-water volume, calculated from nitrate budgets, occurred in 1962 and in 1969. The time for 37% renewal of the deep water was estimated to be about 12 days. A bolus-type flushing mechanism is postulated.

A model for coupled sulfate reduction and methane oxidation in the sediments of Saanich Inlet

Abstract A methane-sulfate coupled reaction diffusion model has been developed to describe the inverse relationship commonly observed between methane and sulfate concentrations in the pore waters of anoxic marine sediments. The sediment column was divided into two zones; an upper zone where diagenetic reaction rates are limited by the concentration of oxidizable organic matter and a lower zone in which reaction rates are limited by the concentration of oxidizing agent—sulfate. For each zone differential equations describing the distribution of methane and sulfate were derived. The boundary conditions used to solve these equations resulted in a set of four coupled equations. When fit to data from Saanich Inlet (B.C., Canada) and Skan Bay (Alaska) the model not only reproduces the observed methane and sulfate pore water concentration profiles but also accurately predicts the methane oxidation and sulfate reduction rates. Maximum methane oxidation rates occur at the transition boundary from the upper to the lower layer. In Saanich Inlet sediments from 23 to 40% of the downward sulfate flux is consumed in methane oxidation while in Skan Bay this value is only about 12%.

A model for nitrate distributions in oceanic oxygen minimum zones

Abstract The vertical distributions of nitrate and nitrate deficit (nitrate consumed during denitrification) in oxygen minimum zones are modeled using a recycling mechanism incorporating bacterially mediated reaction and diffusion. At the core of the oxygen minimum zone bacteria reduce nitrate to nitrite and part of the nitrte to molecular nitrogen (denitrification). The remaining nitrite diffuses out of the layer, is oxidized to nitrate by nitrifying bacteria, and diffuses back into the layer to complete the cycle. The peak nitrite and nitrate deficit concentrations and the amount of recycling depend on two parameters: θ, the ratio of the sum of the nitrate and nitrate reduction rates to the diffusion coefficient, and λ, the ratio of the nitrate reduction rate to the sum of the nitrate and nitrite reduction rates. We estimate from peak concentrations that for oxygen minimum zones in the Arabian Sea, the eastern tropical North Pacific, and the coastal waters of Peru that the nitrogen production rate is between 39 and 60% of the nitrate reduction rate, with the difference in rates equaling the recycling rate between the denitrifying and nitrifying layers. A dependency of θ and λ on organic substrates available to denitrifying bacteria is suggested from independent chemostat studies and the primary productivity overlying the oxygen minimum zones. The peak concentrations of nitrite and nitrate deficit are near the mid-depth of the denitrifying layer, and from this characteristic we estimate the thickness of the denitrifying layers to be between 30 and 70% of the thickness of the oxygen minimum zones.

The vitality model: A way to understand population survival and demographic heterogeneity

A four-parameter model describing mortality as the first passage of an abstract measure of survival capacity, vitality, is developed and used to explore four classic problems in demography: (1) medfly demographic paradox, (2) effect of diet restriction on longevity, (3) cross-life stage effects on survival curves and (4) mortality plateaus. The model quantifies the sources of mortality in these classical problems into vitality-dependent and independent parts, and characterizes the vitality-dependent part in terms of initial and evolving heterogeneities. Three temporal scales express the balance of these factors: a time scale of death from senescence, a time scale of accidental mortality and a crossover time between evolving vs. initial heterogeneity. The examples demonstrate how the first-passage approach provides a unique and informative perspective into the processes that shape the survival curves of populations.

A VITALITY‐BASED MODEL RELATING STRESSORS AND ENVIRONMENTAL PROPERTIES TO ORGANISM SURVIVAL

A survivorship curve is shaped by the differential survivability of the or- ganisms within the population, and a change in a survivorship curve with a stressor reflects the differential response of the organisms to the stressor. Quantifying this linkage in a simple, rigorous way is valuable for characterizing the response of populations to stressors and ultimately for understanding the evolutionary selection of individuals exposed to stress- ors. To quantify this stressor-individual-population linkage with as few parameters as possible, I present a simple mechanistic model describing organism survival in terms of age-dependent and age-independent mortality rates. The age-independent rate is represented by a Poisson process. For the age-dependent rate, a concept of vitality is defined, and mortality occurs when an organisms vitality is exhausted. The loss of vitality over age is represented by a continuous Brownian-motion process, the Weiner process; vitality-related mortality occurs when the random process reaches the boundary of zero vitality. The age at which vitality-related mortality occurs is represented by the Weiner-process probability distribution for first-arrival time. The basic model has three rate parameters: the rate of accidental mortality, the mean rate of vitality loss, and the variability in the rate of vitality loss. These rates are related to body mass, environmental conditions, and xenobiotic stress- ors, resulting in a model that characterizes intrinsic and extrinsic factors that control a populations survival and the distribution of vitality of its individuals. The model assumes that these factors contribute to the rate parameters additively and linearly. The model is evaluated with case studies across a range of species exposed to natural and xenobiotic stressors. The mean rate of vitality loss generally is the dominant factor in determining the shape of survival curves under optimal conditions. Xenobiotic stressors add to the mean rate in proportion to the strength of the stressor. The base, or intrinsic, vitality loss rate is proportional to the - 1/3 power of adult body mass across a range of iteroparous species. The increase in vitality loss rate with a xenobiotic stressor can be a function of body mass according to the allometric relationship of the organism structures affected by the stressor. The models applicability to dose-response studies is illustrated with case studies including natural stressors (temperature, feeding interval, and population density) and xenobiotic stressors (organic and inorganic toxicants). The model provides a way to extrapolate the impact of stressors measured in one environment to another envi- ronment; by characterizing how stressors alter the vitality probability distribution, it can quantify the degree to which a stressor differentiates members of a population.

Response of Juvenile Coho and Chinook Salmon to Strobe and Mercury Vapor Lights

Abstract Species-specific responses to flashing (strobe) and nonflashing (mercury vapor) lights were monitored in hatchery-reared juveniles of coho salmon Oncorhynchus kisutch and chinook salmon O. tshawytscha. Fish behaviors were characterized as attraction and avoidance responses, and as active, passive, and hiding behaviors. We investigated how basic fish behavior and activity changed when fish held under a variety of ambient light conditions were exposed to strobe and mercury light. Implications of how these behaviors may influence migrating smolts at a fish bypass system were discussed. Both chinook and coho salmon avoided strobe and full-intensity mercury light, but chinook salmon exhibited an attraction to dim mercury light. Coho and chinook salmon showed different behavior patterns under most conditions when exposed to strobe and mercury light: coho salmon hid 47% of the time, whereas chinook salmon swam actively 74% of the time. The greatest change produced by either of the stimulus lights was at...

Effects of Water Temperature and Flow on Adult Salmon Migration Swim Speed and Delay

Abstract The effects of temperature and flow on the migration of adult Chinook salmon Oncorhynchus tshawytscha and steelhead O. mykiss through the Columbia River hydrosystem were determined with a novel technique that fits a broken linear model of swim speed versus temperature and flow by partitioning data into speed ranks. Using the migration times of passive integrated transponder (PIT)–tagged adult Chinook salmon upstream between Bonneville and Lower Granite dams (462 km) over the years 1998–2002, we found that a maximum swim speed of about 1 body length/s occurred at 16.3°C. Speed was less above and below this optimum temperature. For PIT-tagged steelhead, migration speed uniformly decreased with increasing temperature, suggesting that the fish migrated at temperatures above the optimum. Migration delay was also a unimodal function of temperature, the minimum delay occurring around 16–17°C. The broken linear model was compared with seven alternative models of unimodal and monotonic speed versus temper...

A Model of the Travel Time of Migrating Juvenile Salmon, with an Application to Snake River Spring Chinook Salmon

Abstract We develop a model of the travel time of juvenile salmonids migrating through a river r each. The model is derived from an advection–diffusion equation with an absorbing boundary at the downstream collection site. The resulting travel time distribution is determined by two biologically meaningful parameters: migration rate and the rate of population spreading. The model is applied to travel time distributions for 46 cohorts of juvenile spring chinook salmon Oncorhynchus tshawytscha migrating through the Lower Granite Pool (52 km in length) in the Snake River. Parameters are estimated using maximum likelihood. A Pearson X 2 goodness-of-fit test shows that the model is not rejected (∝ = 0.05) for the majority of cohorts.

Collective motion in animal groups from a neurobiological perspective: the adaptive benefits of dynamic sensory loads and selective attention.

We explore mechanisms associated with collective animal motion by drawing on the neurobiological bases of sensory information processing and decision-making. The model uses simplified retinal processes to translate neighbor movement patterns into information through spatial signal integration and threshold responses. The structure provides a mechanism by which individuals can vary their sets of influential neighbors, a measure of an individuals sensory load. Sensory loads are correlated with group order and density, and we discuss their adaptive values in an ecological context. The model also provides a mechanism by which group members can identify, and rapidly respond to, novel visual stimuli.

Oceanic, riverine, and genetic influences on spring chinook salmon migration timing

Migrating salmonids often return to their spawning habitats in overlapping timing patterns of multiple stocks (populations) collectively called a run that varies in its genetic makeup across and within years. Managers, tasked with developing harvest strategies on these runs, may have preseason estimates of total run size but little information on run timing. Without both it is difficult to assess a runs status in real time. Consequently, to avoid overharvest, managers tend to control the timing of harvest. However, this strategy may inadvertently affect the component stocks disproportionately and therefore the runs diversity. Thus, accurate estimates of run timing are needed to improve management. We developed a model that includes genetic and environmental factors to predict the mean run timing of chinook salmon (Oncorhynchus tshawytscha) at Bonneville Dam on the Columbia River, Oregon, USA. The model predicted mean runtiming (P < 0.00001, r2 = 0.78) by characterizing genetic run timing components from the arrival timing of precocious males returning one year prior to the remainder of the adults and environmental influences of oceanic and riverine flows that impede or advance the run timing. Variations in the relative abundances of the populations in the run explain 62% of the interannual variation in mean run timing while the oceanic and riverine factors combined account for 15.5%. We suggest that when genetic run timing characteristics are preserved in species with multiple maturation strategies the information can be used to improve run time predictions and maintain genetic diversity of harvested species.