Invasive plant species should be evaluated and prioritized for management according to their impacts, which include reduction in native diversity, changes to nutrient pools, and alteration of fire regimes. However, the impacts of most invasive species have not been quantified and, when measured, those impacts are based on a limited number of response metrics. As a result, invasion ecology has been overwhelmed by speculation and bias regarding the ecological consequences of invasive plants. We propose a quantitative mathematical framework that integrates any number of impact metrics as a function of groundcover and geographic extent. By making relative comparisons between invaded and uninvaded landscapes at the population scale, which results in a percent change for each metric, we overcome previous limitations that confounded the integration of metrics based on different units. Our model offers a quantitative approach to ecological impact that may allow identification of the transition from benign introduction to impactful invader, while also allowing empirical comparisons at the species and population levels that will be useful for management prioritization.

Mast‐fruiting trees represent a pulsed resource that both supports and destabilizes consumer populations. Whereas a reliable resource is abundant on average and with limited variation in time and space, masting is volatile and localized, and that variability ramifies throughout food‐webs. Theory is developed to evaluate how the space–time structure of masting interacts with consumers who exploit alternative hosts, forage widely in space, and store reserves in time. We derive the space–time–species covariance in resource supply and combine it with the space–time–diet breadth of consumers, or ambit. Direct connection to data is made possible with Mast Inference and Forecasting (MASTIF), a state‐space autoregressive model that fits seed‐trap and canopy observations and predicts resource availability within the canopy and on the forest floor with full uncertainty. A resource score can be assigned to each consumer–habitat combination that integrates the benefits of a high mean supply weighed against the variance cost. As the consumer ambit increases, the volatility of an unreliable resource shifts from a variance cost to a mean benefit. Consumers foraging in the canopy (arboreal arthropods and rodents, song birds) experience space‐time covariance between host trees. Consumers on the forest floor (seed and damping‐off fungi, arthropods, rodents, ground‐nesting birds, mammals) experience instead a redistribution of that covariance by dispersal. For consumers lacking mobility, demographic storage in the form of episodic birth cohorts following mast years is important for population persistence. Consumers additionally compensate volatility with diet breadth. Depending on the dominant masting strategies of host tree species in the diet, habitats differentially limit consumers depending on the misalignment between consumer ambit and spatiotemporal covariance of hosts. The impact of adding or subtracting a diet item can be gauged with the standard error (SE) rule or the benefit of an added diet item balanced against the variance cost, both of which depend on the existing diet, the abundance of the new host, and the consumer's foraging ambit. Results rank habitats by their capacities to support wildlife and other consumers from a resource perspective. Results are connected directly to data, with full uncertainty, by MASTIF.

Elucidating how organismal survival depends on the environment is a core component of ecological and evolutionary research. To reconcile high-frequency covariates with lower-frequency demographic censuses, many statistical tools involve aggregating environmental conditions over long periods, potentially obscuring the importance of fluctuating conditions in driving mortality. Here, we introduce a flexible model designed to infer how survival probabilities depend on changing environmental covariates. Specifically, the model (1) quantifies effects of environmental covariates at a higher frequency than the census intervals, and (2) allows partitioning of environmental drivers of individual survival into acute (short-term) and chronic (accumulated) effects. By applying our method to a long-term observational data set of eight annual plant species, we show we can accurately infer daily survival probabilities as temperature and moisture levels change. Next, we show that a species' water use efficiency, known to mediate annual plant population dynamics, is positively correlated with the importance of "chronic stress" inferred by the model. This suggests that model parameters can reflect underlying physiological mechanisms. This method is also applicable to other binary responses (hatching, phenology) or systems (insects, nestlings). Once known, environmental sensitivities can be used for ecological forecasting even when the frequency or variability of environments are changing.

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