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  1. Abstract

    While climate warming is widely predicted to reduce body size of ectotherms, evidence for this trend is mixed. Body size depends not only on temperature but also on other factors, such as food quality and intraspecific competition. Because temperature trends or other long‐term environmental factors may affect population size and food sources, attributing trends in average body size to temperature requires the separation of potentially confounding effects. We evaluated trends in the body size of the midgeTanytarsus gracilentusand potential drivers (water temperature, population size, and food quality) between 1977 and 2015 at Lake Mývatn, Iceland. Although temperatures increased at Mývatn over this period, there was only a slight (non‐significant) decrease in midge adult body size, contrary to theoretical expectations. Using a state‐space model including multiple predictors, body size was negatively associated with both water temperature and midge population abundance, and it was positively associated with13C enrichment of midges (an indicator of favorable food conditions). The magnitude of these effects were similar, such that simultaneous changes in temperature, abundance, and carbon stable isotopic signature could counteract each other in the long‐term body size trend. Our results illustrate how multiple factors, all of which could be influenced by global change, interact to affect average ectotherm body size.

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  2. Abstract

    Many statistical models currently used in ecology and evolution account for covariances among random errors. Here, I address five points: (i) correlated random errors unite many types of statistical models, including spatial, phylogenetic and time‐series models; (ii) random errors are neither unpredictable nor mistakes; (iii) diagnostics for correlated random errors are not useful, but simulations are; (iv) model predictions can be made with random errors; and (v) can random errors be causal?

    These five points are illustrated by applying statistical models to analyse simulated spatial, phylogenetic and time‐series data. These three simulation studies are paired with three types of predictions that can be made using information from covariances among random errors: predictions for goodness‐of‐fit, interpolation, and forecasting.

    In the simulation studies, models incorporating covariances among random errors improve inference about the relationship between dependent and independent variables. They also imply the existence of unmeasured variables that generate the covariances among random errors. Understanding the covariances among random errors gives information about possible processes underlying the data.

    Random errors are caused by something. Therefore, to extract full information from data, covariances among random errors should not just be included in statistical models; they should also be studied in their own right. Data are hard won, and appropriate statistical analyses can make the most of them.

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  3. Abstract

    Population cycles can be caused by consumer–resource interactions. Confirming the role of consumer–resource interactions, however, can be challenging due to an absence of data for the resource candidate. For example, interactions between midge larvae and benthic algae likely govern the high‐amplitude population fluctuations ofTanytarsus gracilentusin Lake Mývatn, Iceland, but there are no records of benthic resources concurrent with adult midge population counts. Here, we investigate consumer population dynamics using the carbon stable isotope signatures of archivedT. gracilentusspecimens collected from 1977 to 2015, under the assumption that midge δ13C values reflect those of resources they consumed as larvae. We used the time series for population abundance and δ13C to estimate interactions between midges and resources while accounting for measurement error and possible preservation effects on isotope values. Results were consistent with consumer–resource interactions: high δ13C values preceded peaks in the midge population, and δ13C values tended to decline after midges reached high abundance. One interpretation of this dynamic coupling is that midge isotope signatures reflect temporal variation in benthic algal δ13C values, which we expected to mirror primary production. Following from this explanation, high benthic production (enriched δ13C values) would contribute to increased midge abundance, and high midge abundance would result in declining benthic production (depleted δ13C values). An additional and related explanation is that midges deplete benthic algal abundance once they reach peak densities, causing midges to increase their relative reliance on other resources including detritus and associated microorganisms. Such a shift in resource use would be consistent with the subsequent decline in midge δ13C values. Our study adds evidence that midge–resource interactions driveT. gracilentusfluctuations and demonstrates a novel application of stable isotope time‐series data to understand consumer population dynamics.

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  4. Many consumers depend on the contemporaneous growth of their food resources. For example,Tanytarsus gracilentusmidges feed on algae, and because midge generation time is much longer than that of algae, individual midges benefit not just from the standing stock but also from the growth of algae during their lifespans. This implies that an intermediate consumption rate maximizes midge somatic growth: low consumption rates constrain midge growth because they do not fully utilize the available food, whereas high consumption rates suppress algal biomass growth and consequently limit future food availability. An experiment manipulating midge presence and initial algal abundance showed that midges can suppress algal growth, as measured by changes in algal gross primary production (GPP). We also found a positive relationship between GPP and midge growth. A consumer–resource model fit to the experimental data showed a hump‐shaped relationship between midge consumption rates and their somatic growth. In the model, predicted midge somatic growth rates were only positively associated with GPP when their consumption rate was below the value that optimized midge growth. Therefore, midges did not overexploit algae in the experiment. This work highlights the balance that consumers which depend on contemporaneous resource growth might have to strike between short‐term growth and future food availability, and the benefits for consumers when they ‘manage' their resources well.

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    Free, publicly-accessible full text available December 1, 2024
  5. Free, publicly-accessible full text available November 1, 2024
  6. Quantifying temporal variation in demographic rates is a central goal of population ecology. In this study, we analyzed a multidecadal age-structured time series of Arctic char (Salvelinus alpinus) abundance in Lake Mývatn, Iceland, to infer the time-varying demographic response of the population to reduced harvest in the wake of the fishery’s collapse. Our analysis shows that while survival probability of adults increased following the alleviation of harvesting pressure, per capita recruitment consistently declined over most of the study period, until the final three years when it began to increase. The countervailing demographic trends resulted in only limited directional change in the total population size and population growth rate. Rather, the population dynamics were dominated by large interannual variability and a shift towards an older age distribution. Our results are indicative of a slow recovery of the population after its collapse, despite the rising number of adults following relaxed harvest. This underscores the potential for heterogeneous demographic responses to management efforts due to the complex ecological context in which such efforts take place.

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