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  1. Abstract

    Directly observing autotrophic biomass at ecologically relevant frequencies is difficult in many ecosystems, hampering our ability to predict productivity through time. Since disturbances can impart distinct reductions in river productivity through time by modifying underlying standing stocks of biomass, mechanistic models fit to productivity time series can infer underlying biomass dynamics. We incorporated biomass dynamics into a river ecosystem productivity model for six rivers to identify disturbance flow thresholds and understand the resilience of primary producers. The magnitude of flood necessary to disturb biomass and thereby reduce ecosystem productivity was consistently lower than the more commonly used disturbance flow threshold of the flood magnitude necessary to mobilize river bed sediment. The estimated daily maximum percent increase in biomass (a proxy for resilience) ranged from 5% to 42% across rivers. Our latent biomass model improves understanding of disturbance thresholds and recovery patterns of autotrophic biomass within river ecosystems.

     
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  3. Abstract

    To cope with uncertainty and variability in their environment, plants evolve distinct life‐history strategies by allocating different fractions of energy to growth, survival and fecundity. These differences in life‐history strategies could potentially influence ecosystem‐level dynamics, such as the sensitivity of primary production to resource fluctuations. However, linkages between evolutionary and ecosystem dynamics are not well understood.

    We used an annual plant population model to ask, when might differences in plant life‐history strategies produce differences in the sensitivity of primary production to resource fluctuations?

    Consistent with existing theory, we found that a highly variable and unpredictable environment led to the evolution of a conservative strategy characterized by relatively low and invariant germination fractions, while a variable but predictable environment favoured a riskier strategy featuring more variable germination fractions. Unexpectedly, we found that the influence of life‐history strategy on the sensitivity of production to resource fluctuations depended on competitive interactions, specifically the rate at which production saturates with the number of competing individuals. Rapid saturation overwhelms the influence of life‐history strategy, but when production saturates more slowly, the risky strategy translated to high sensitivity, whereas the conservative strategy translated to low sensitivity.

    Empirical estimates from Sonoran Desert annual plant populations indicate that production saturates relatively rapidly with the number of individuals for most species, suggesting that life‐history differences are unlikely to alter sensitivity of production to resource fluctuations, at least in this community.

    Synthesis. Our modelling results imply that research to understand the sensitivity of primary production to resource fluctuations should focus more on the intraspecific competitive interactions shaping the density–yield relationship than on the life‐history strategies that determine temporal risk‐spreading.

     
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  4. Abstract

    Structured demographic models are among the most common and useful tools in population biology. However, the introduction of integral projection models (IPMs) has caused a profound shift in the way many demographic models are conceptualized. Some researchers have argued that IPMs, by explicitly representing demographic processes as continuous functions of state variables such as size, are more statistically efficient, biologically realistic, and accurate than classic matrix projection models, calling into question the usefulness of the many studies based on matrix models. Here, we evaluate how IPMs and matrix models differ, as well as the extent to which these differences matter for estimation of key model outputs, including population growth rates, sensitivity patterns, and life spans. First, we detail the steps in constructing and using each type of model. Second, we present a review of published demographic models, concentrating on size‐based studies, which shows significant overlap in the way IPMs and matrix models are constructed and analyzed. Third, to assess the impact of various modeling decisions on demographic predictions, we ran a series of simulations based on size‐based demographic data sets for five biologically diverse species. We found little evidence that discrete vital rate estimation is less accurate than continuous functions across a wide range of sample sizes or size classes (equivalently bin numbers or mesh points). Most model outputs quickly converged with modest class numbers (≥10), regardless of most other modeling decisions. Another surprising result was that the most commonly used method to discretize growth rates for IPM analyses can introduce substantial error into model outputs. Finally, we show that empirical sample sizes generally matter more than modeling approach for the accuracy of demographic outputs. Based on these results, we provide specific recommendations to those constructing and evaluating structured population models. Both our literature review and simulations question the treatment of IPMs as a clearly distinct modeling approach or one that is inherently more accurate than classic matrix models. Importantly, this suggests that matrix models, representing the vast majority of past demographic analyses available for comparative and conservation work, continue to be useful and important sources of demographic information.

     
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