Using poststratification to improve abundance estimates from multispecies surveys: a study of juvenile flatfishes

Population assessments seldom incorporate habitat information or use previously observed distributions of fish density. Because habitat affects the spatial distribution of fish density and overall abundance, the use of habitat information and previous estimates of fish density can produce more precise and less biased population estimates. In this study, we describe how poststratification can be applied as an unbiased estimator to data sets that were collected under a probability sampling design, typical of many multispecies trawl surveys. With data from a multispecies survey of juvenile flatfish, we show how poststratification can be applied to a data set that was not collected under a probability sampling design, where both the precision and the bias are unknown. For each of four species, three estimates of total abundance were compared: 1) unstratified; 2) poststratified by habitat; and 3) poststratified by habitat and fish density (high fish density and low fish density) in nearby years. Poststratification by habitat gave more precise and (or) less design-biased estimates than an unstratified estimator for all species in all years. Poststratification by habitat and fish density produced the most precise and representative estimates when the sample size in the high fish-density and low fish-density strata were sufficient (in this study, n≥20 in the high fish-density stratum, n≥9 in the low fish-density stratum). Because of the complexities of statistically testing the annual stratified data, we compared three indices of abundance for determining statistically significant changes in annual abundance. Each of the indices closely approximated the annual differences of the poststratified estimates. Selection of the most appropriate index was dependent upon the species’ density distribution within habitat and the sample size in the different habitat areas. The methods used in this study are particularly useful for estimating individual species abundance from multispecies surveys and for retrospective s

Evaluating signals of oil spill impacts, climate, and species interactions in Pacific herring and Pacific salmon populations in Prince William Sound and Copper River, Alaska

<div><p>The <i>Exxon Valdez</i> oil spill occurred in March 1989 in Prince William Sound, Alaska, and was one of the worst environmental disasters on record in the United States. Despite long-term data collection over the nearly three decades since the spill, tremendous uncertainty remains as to how significantly the spill affected fishery resources. Pacific herring (<i>Clupea pallasii</i>) and some wild Pacific salmon populations (<i>Oncorhynchus spp</i>.) in Prince William Sound declined in the early 1990s, and have not returned to the population sizes observed in the 1980s. Discerning if, or how much of, this decline resulted from the oil spill has been difficult because a number of other physical and ecological drivers are confounded temporally with the spill; some of these drivers include environmental variability or changing climate regimes, increased production of hatchery salmon in the region, and increases in populations of potential predators. Using data pre- and post-spill, we applied time-series methods to evaluate support for whether and how herring and salmon productivity has been affected by each of five drivers: (1) density dependence, (2) the EVOS event, (3) changing environmental conditions, (4) interspecific competition on juvenile fish, and (5) predation and competition from adult fish or, in the case of herring, humpback whales. Our results showed support for intraspecific density-dependent effects in herring, sockeye, and Chinook salmon, with little overall support for an oil spill effect. Of the salmon species, the largest driver was the negative impact of adult pink salmon returns on sockeye salmon productivity. Herring productivity was most strongly affected by changing environmental conditions; specifically, freshwater discharge into the Gulf of Alaska was linked to a series of recruitment failures—before, during, and after EVOS. These results highlight the need to better understand long terms impacts of pink salmon on food webs, as well as the interactions between nearshore species and freshwater inputs, particularly as they relate to climate change and increasing water temperatures.</p></div

Time series of total run and escapement (or spawning biomass, herring).

<p>Total population size and escapement (salmon, in numbers of fish) or total population biomass and spawning stock biomass (spawning herring, in metric tons) for the six populations and four species in our analysis. Harvest for each population can be interpreted as the difference between total (black) and spawning (grey) lines. Red vertical lines are used to indicate 1989 (corresponding to the year of the EVOS event).</p

Gulf of Alaska freshwater discharge (Royer 1982, IMS 2016) as a driver of Pacific herring productivity.

<p>Shown are (a) the total freshwater discharge (m³ s^-1) and (b) log of observed age-3 recruits per spawning biomass (mt)—log(recruits/SSB)—in grey circles, and the model predicted log(recruits/SSB) using freshwater discharge as a covariate (R² = 0.55). High discharge events correspond to reduced productivity (fewer recruits to the population as three year olds). For historical reference, the discharge time series starting in 1931 is shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0172898#pone.0172898.s002" target="_blank">S2 Fig</a>. R = millions of mature and immature age-3 herring, SSB = spawning stock biomass in metric tons.</p

Map of Prince William Sound, and the adjacent Copper River Alaska.

<p>Triangles indicate the location of wild salmon stocks included in our analyses, circles show towns, and the asterisk shows where the <i>Exxon Valdez</i> ran aground in 1989.</p

Table of delta-AIC values used for model selection (S1–S5 Tables include raw values).

<p>Table of delta-AIC values used for model selection (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0172898#pone.0172898.s006" target="_blank">S1</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0172898#pone.0172898.s010" target="_blank">S5</a> Tables include raw values).</p

Relationships between spawners (salmon) or spawning stock biomass (herring, in metric tons) and recruits-per-spawner.

<p>Raw data are shown for the years included in our analysis, with each year assigned a unique color. R = recruits, S = spawners, SSB = spawning stock biomass, age-3 recruits = millions of mature and immature age-3 herring, and PWS.</p

Models of potential impacts (pulse, press, and pulse/recovery) associated with the <i>Exxon Valdez</i> oil spill.

<p>Impacts here are shown for a time lag of 0, however lag-1 and lag-2 equivalents were also considered as predictors in our models.</p