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Abstract Understanding trophic cascades in terrestrial wildlife communities is a major challenge because these systems are difficult to sample properly. We show how a tradition of non‐random sampling has confounded this understanding in a textbook system (Yellowstone National Park) where carnivore [Canis lupus(wolf)] recovery is associated with a trophic cascade involving changes in herbivore [Cervus canadensis(elk)] behaviour and density that promote plant regeneration. Long‐term data indicate a practice of sampling only the tallest young plants overestimated regeneration of overstory aspen (Populus tremuloides) by a factor of 4–7 compared to random sampling because it favoured plants taller than the preferred browsing height of elk and overlooked non‐regenerating aspen stands. Random sampling described a trophic cascade, but it was weaker than the one that non‐random sampling described. Our findings highlight the critical importance of basic sampling principles (e.g. randomisation) for achieving an accurate understanding of trophic cascades in terrestrial wildlife systems. 

Abstract Different modes of non‐genetic inheritance are expected to affect population persistence in fluctuating environments. We here analyseCaenorhabditis elegansdensity‐independent per capita growth rate time series on 36 populations experiencing six controlled sequences of challenging oxygen level fluctuations across 60 generations, and parameterise competing models of non‐genetic inheritance in order to explain observed dynamics. Our analysis shows that phenotypic plasticity and anticipatory maternal effects are sufficient to explain growth rate dynamics, but that a carryover model where ‘epigenetic’ memory is imperfectly transmitted and might be reset at each generation is a better fit to the data. We further find that this epigenetic memory is asymmetric since it is kept for longer when populations are exposed to the more challenging environment. Our analysis suggests that population persistence in fluctuating environments depends on the non‐genetic inheritance of phenotypes whose expression is regulated across multiple generations. 

Abstract Primary production, a key regulator of the global carbon cycle, is highly responsive to variations in climate. Yet, a detailed, continental‐scale risk assessment of climate‐related impacts on primary production is lacking. We combined 16 years of MODIS NDVI data, a remotely sensed proxy for primary production, with observations from 1218 climate stations to derive values of ecosystem sensitivity to precipitation and aridity. For the first time, we produced an empirically‐derived map of ecosystem sensitivity to climate across the conterminous United States. Over this 16‐year period, annual primary production values were most sensitive to precipitation and aridity in dryland and grassland ecosystems. Century‐long trends measured at the climate stations showed intensifying aridity and climatic variability in many of these sensitive regions. Dryland ecosystems in the western US may be particularly vulnerable to reductions in primary production and consequent degradation of ecosystem services as climate change and variability increase in the future. 

Abstract Experiments and models suggest that climate affects mosquito‐borne disease transmission. However, disease transmission involves complex nonlinear interactions between climate and population dynamics, which makes detecting climate drivers at the population level challenging. By analysing incidence data, estimated susceptible population size, and climate data with methods based on nonlinear time series analysis (collectively referred to as empirical dynamic modelling), we identified drivers and their interactive effects on dengue dynamics in San Juan, Puerto Rico. Climatic forcing arose only when susceptible availability was high: temperature and rainfall had net positive and negative effects respectively. By capturing mechanistic, nonlinear and context‐dependent effects of population susceptibility, temperature and rainfall on dengue transmission empirically, our model improves forecast skill over recent, state‐of‐the‐art models for dengue incidence. Together, these results provide empirical evidence that the interdependence of host population susceptibility and climate drives dengue dynamics in a nonlinear and complex, yet predictable way. 

Abstract Predicting whether, how, and to what degree communities recover from disturbance remain major challenges in ecology. To predict recovery of coral communities we applied field survey data of early recovery dynamics to a multi‐species integral projection model that captured key demographic processes driving coral population trajectories, notably density‐dependent larval recruitment. After testing model predictions against field observations, we updated the model to generate projections of future coral communities. Our results indicated that communities distributed across an island landscape followed different recovery trajectories but would reassemble to pre‐disturbed levels of coral abundance, composition, and size, thus demonstrating persistence in the provision of reef habitat and other ecosystem services. Our study indicates that coral community dynamics are predictable when accounting for the interplay between species life‐history, environmental conditions, and density‐dependence. We provide a quantitative framework for evaluating the ecological processes underlying community trajectory and characteristics important to ecosystem functioning. 

Abstract Climate change is shifting the environmental cues that determine the phenology of interacting species. Plant–pollinator systems may be susceptible to temporal mismatch if bees and flowering plants differ in their phenological responses to warming temperatures. While the cues that trigger flowering are well‐understood, little is known about what determines bee phenology. Using generalised additive models, we analyzed time‐series data representing 67 bee species collected over 9 years in the Colorado Rocky Mountains to perform the first community‐wide quantification of the drivers of bee phenology. Bee emergence was sensitive to climatic variation, advancing with earlier snowmelt timing, whereas later phenophases were best explained by functional traits including overwintering stage and nest location. Comparison of these findings to a long‐term flower study showed that bee phenology is less sensitive than flower phenology to climatic variation, indicating potential for reduced synchrony of flowers and pollinators under climate change.