Herein, we document changes in the Lake Michigan food web between 1970 and 2000 and identify the factors responsible for these changes. Control of sea lamprey (Petromyzon marinus) and alewife (Alosa pseudoharengus) populations in Lake Michigan, beginning in the 1950s and 1960s, had profound effects on the food web. Recoveries of lake whitefish (Coregonus clupeaformis) and burbot (Lota lota) populations, as well as the buildup of salmonine populations, were attributable, at least in part, to sea lamprey control. Based on our analyses, predation by salmonines was primarily responsible for the reduction in alewife abundance during the 1970s and early 1980s. In turn, the decrease in alewife abundance likely contributed to recoveries of deepwater sculpin (Myoxocephalus thompsoni), yellow perch (Perca flavescens), and burbot populations during the 1970s and 1980s. Decrease in the abundance of all three dominant benthic macroinvertebrate groups, including Diporeia, oligochaetes, and sphaeriids, during the 1980s in nearshore waters ([Formula: see text]50 m deep) of Lake Michigan, was attributable to a decrease in primary production linked to a decline in phosphorus loadings. Continued decrease in Diporeia abundance during the 1990s was associated with the zebra mussel (Dreissena polymorpha) invasion, but specific mechanisms for zebra mussels affecting Diporeia abundance remain unidentified.

We compare two approaches to designing and analyzing monitoring studies to assess chronic, local environmental impacts. Intervention Analysis (IA) compares Before and After time series at an Impact site; a special case is Before-After, Control-Impact (BACI), using comparison sites as covariates to reduce extraneous variance and serial correlation. IVRS (impact vs. reference sites) compares Impact and Control sites with respect to Before-After change, treating the sites as experimental units. The IVRS estimate of an ''effect'' is the same as that of the simplest BACI (though not of others), but IVRS estimates error variance by variation among sites, while IA and BACI estimate it by variation over time.These approaches differ in goals, design, and models of the role of chance in determining the data. In IA and BACI, the goal is to determine change at the specific Impact site, so no Controls are needed. IA does not have controls and BACI's are not experimental controls, but covariates, deliberately chosen to be correlated with the Impact site. The goal given for IVRS is to compare hypothetical Impact and Control ''populations,'' so the Controls are essential and are randomly chosen, perhaps with restrictions to make them independent of each other and (presumably) of Impact. IA and BACI inferences are model based: uncertainty arises from sampling error and natural temporal processes causing variation in the variable of concern (e.g., a species' abundance); these processes are modeled as the results of repeatable chance setups. IVRS inferences are design based: uncertainty arises from variation among sites, as well as the other two sources, and is modeled by the assumed random selection of Impact and Control sites, like the drawing of equiprobable numbers from a hat.We outline the formal analyses, showing that IVRS is simpler, and BACI more complex, than usually supposed. We then describe the principles and assumptions of IA and BACI, defining an ''effect'' as the difference between what happened after the impact and what would have happened without it, and stressing the need to justify chance models as reasonable representations of human uncertainty. We respond to comments on BACI, some of which arise from misunderstanding of these principles.IVRS's design-based justification is almost always invalid in real assessments: the Impact site is not chosen randomly. We show that ''as if random'' selection by ''Nature'' is untenable and that an approximation to this, while a possibly useful guide, cannot be used for inference. We argue that, without literal random assignment of treatments to sites, IVRS can only be model based. Its design and analyses will then be different, using and allowing for correlation between sites. It is likely to have low power and requires strong assumptions that are difficult to check, so should be used only when IA or BACI cannot be used, e.g., when there are no Before data.

We address the task of determining the effects, on mean population density or other parameters, of an unreplicated perturbation, such as arises in environmental assessments and some ecosystem—level experiments. Our context is the Before—After—Control—Impact—Pairs design (BACIP): on several dates Before and After the perturbation, samples are collected simultaneously at both the Impact site and a nearby "Control." One approach is to test whether the mean of the Impact—Control difference has changed from Before to After the perturbation. If a conventional test is used, checks of its assumptions are an important and messy part of the analysis, since BACIP data do not necessarily satisfy them. It has been suggested that these checks are not needed for randomization tests, because they are insensitive to some of these assumptions and can be adjusted to allow for others. A major aim of this paper is to refute this suggestion: there is no panacea for the difficult and messy technical problems in the analysis of data from assessments or unreplicated experiments. We compare the randomization t test with the standard t test and the modified (Welch—Satterthwaite—Aspin) t test, which allows for unequal variances. We conclude that the randomization t test is less likely to yield valid inferences than is the Welch t test, because it requires identical distributions for small sample sizes and either equal variances or equal sample sizes for larger ones. The formal requirement of Normality is not crucial to the Welch t test. Both parametric and randomization tests require that time and location effects be additive and the Impact—Control differences on different dates be independent. These assumptions should be tested; if they are seriously wrong, alternative analyses are needed. This will often require a long time series of data. Finally, for assessing the importance of a perturbation, the P value of a hypothesis test is rarely as useful as an estimate of the size of the effect. Especially if effect size varies with time and conditions, flexible estimation methods with approximate answers are preferable to formally exact P values.

No abstract

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. Short time series are common in environmental and ecological studies. For sample sizes of 10 to 50, I examined the performance of methods for adjusting confidence intervals of the mean and parameters of a linear regression for autocorrelation. Similar analyses are common in econometric studies, and serious concerns have been raised about the adequacy of the common adjustment approaches, especially for estimating the slope of a linear regression when the explanatory variable has a time trend. Use of a bias-corrected estimate of the autocorrelation, either in an adjusted t test or in a two-stage approach, outperformed other methods, including maximum likelihood and bootstrap estimators, in terms of confidence interval coverage. The bias correction was, however, sometimes awkward to apply. It was generally better to test for autocorrelation at the 0.5 level and use ordinary least squares if the test was not significant, although this pretesting mainly helped for weak autocorrelation and small sample sizes. For the best methods, the coverage was sometimes still substantially less than the stated 95% when autocorrelation was strong, even for sample sizes as large as 50. This was true for estimates of the mean, the regression intercept, and, when the explanatory variable had a time trend, the slope. Simulation results and an example show that different adjustment methods can produce substantially different estimates and confidence intervals. Cautious interpretation of confidence intervals and hypothesis tests is recommended. Ecological Society of America

Ab~tract. We inve.stigated.size-selective p~edation upon Daphnia pulex and D. magna by the mos~uitofish (Gambusza): T_he mdepende.nt va?ables were the relative size of predator and prey, prey density, and predator satiation. Gambusza actively chose to attack small prey (which we showed to ~ more profitable) .. Gambusia's preference increased monotonically, or decreased, or peaked at an mtermedi~te prey stze, depending upon fish size, prey size, and fish satiation level. These results ~on~st wtth the hypothesis that "gape-limited predators" select the largest prey available and "sizehmited predators" select small or intermediate-sized prey. ' <;">Ptimal diet th~~ry predicts that a predator should drop less profitable prey items from the diet at high~r prey densities. In contrast, Gambusia maintained a mixed diet and its preferences did not ~hang~ m response to changes in_eithe~ the absolute or the relative abundance of prey. Moreover, the u~gest10n rate wo.uld ~ave bee':l higher. Ifless profitable (larger) prey types had been ignored. Gambusia did sh~ly alte~ Its size-selecu:ve feedmg behavior in response to its own satiation level, and we expect that this factor IS correlated wtth prey density in the field. Well-fed fish concentrated their attacks, to a ~ea.ter degr~e than starved fish, on small, more profitable prey. They also ingested less biomass per umt tlme. This last res~lt sugge~ts t~at maximizi~g food intake per unit time may not be the goal of well-fed fish, and that different cntena may determme the profitability of prey at different prey densities.

We quantified piscivory patterns in the main basin of Lake Huron during 1984-2010 and found that the biomass transfer from prey fish to piscivores remained consistently high despite the rapid major trophic shift in the food webs. We coupled age-structured stock assessment models and fish bioenergetics models for lake trout (Salvelinus namaycush), Chinook salmon (Oncorhynchus tshawytscha), walleye (Sander vitreus), and lake whitefish (Coregonus clupeaformis). The model system also included time-varying parameters or variables of growth, length-mass relations, maturity schedules, energy density, and diets. These time-varying models reflected the dynamic connections that a fish cohort responded to year-to-year ecosystem changes at different ages and body sizes. We found that the ratio of annual predation by lake trout, Chinook salmon, and walleye combined with the biomass indices of age-1 and older alewives (Alosa pseudoharengus) and rainbow smelt (Osmerus mordax) increased more than tenfold during 1987-2010, and such increases in predation pressure were structured by relatively stable biomass of the three piscivores and stepwise declines in the biomass of alewives and rainbow smelt. The piscivore stability was supported by the use of alternative energy pathways and changes in relative composition of the three piscivores. In addition, lake whitefish became a new piscivore by feeding on round goby (Neogobius melanostomus). Their total fish consumption rivaled that of the other piscivores combined, although fish were still a modest proportion of their diet. Overall, the use of alternative energy pathways by piscivores allowed the increases in predation pressure on dominant diet species.

We used a long-term series of observations on alewife Alosa pseudoharengus abundance that was based on fall bottom-trawl catches to assess the importance of various abiotic and biotic factors on alewife recruitment in Lake Michigan during 1962-2002. We first fit a basic Ricker spawner-recruit model to the lakewide biomass estimates of age-3 recruits and the corresponding spawning stock size; we then fit models for all possible combinations of the following four external variables added to the basic model: an index of salmonine predation on an alewife year-class, an index for the spring-summer water temperatures experienced by alewives during their first year in the lake, an index of the severity of the first winter experienced by alewives in the lake, and an index of lake productivity during an alewife year-class's second year in the lake. Based on an information criterion, the best model for alewife recruitment included indices of salmonine predation and spring-summer water temperatures as external variables. Our analysis corroborated the contention that a decline in alewife abundance during the 1970s and early 1980s in Lake Michigan was driven by salmonine predation. Furthermore, our findings indicated that the extraordinarily warm water temperatures during the spring and summer of 1998 probably led to a moderately high recruitment of age-3 alewives in 2001, despite abundant salmonines.