Ecological research increasingly considers integrative relationships among phenomena at broad spatial and temporal domains. However, such large‐scale inferences are commonly confounded by changing properties in the processes that govern phenomena (termed nonstationarity), which can violate assumptions underlying standard analytical methods. Changing conditions are fundamental and pervasive features in ecology, but their influence on ecological inference and prediction increases with larger spatial and temporal domains for a host of factors. Fortunately, tools for identifying and accommodating potentially confounding spatial or temporal trends are available, and new methods are being rapidly developed. Here, we provide guidance for gaining a better understanding of nonstationarity, its causes, and how it can be addressed. Acknowledging and addressing non‐constant trends in ecological patterns and processes is key to conducting large‐scale research and effectively translating findings to local policies and practices.

Tree rings provide an invaluable long‐term record for understanding how climate and other drivers shape tree growth and forest productivity. However, conventional tree‐ring analysis methods were not designed to simultaneously test effects of climate, tree size, and other drivers on individual growth. This has limited the potential to test ecologically relevant hypotheses on tree growth sensitivity to environmental drivers and their interactions with tree size. Here, we develop and apply a new method to simultaneously model nonlinear effects of primary climate drivers, reconstructed tree diameter at breast height (DBH), and calendar year in generalized least squares models that account for the temporal autocorrelation inherent to each individual tree's growth. We analyze data from 3811 trees representing 40 species at 10 globally distributed sites, showing that precipitation, temperature, DBH, and calendar year have additively, and often interactively, influenced annual growth over the past 120 years. Growth responses were predominantly positive to precipitation (usually over ≥3‐month seasonal windows) and negative to temperature (usually maximum temperature, over ≤3‐month seasonal windows), with concave‐down responses in 63% of relationships. Climate sensitivity commonly varied with DBH (45% of cases tested), with larger trees usually more sensitive. Trends in ring width at small DBH were linked to the light environment under which trees established, but basal area or biomass increments consistently reached maxima at intermediate DBH. Accounting for climate and DBH, growth rate declined over time for 92% of species in secondary or disturbed stands, whereas growth trends were mixed in older forests. These trends were largely attributable to stand dynamics as cohorts and stands age, which remain challenging to disentangle from global change drivers. By providing a parsimonious approach for characterizing multiple interacting drivers of tree growth, our method reveals a more complete picture of the factors influencing growth than has previously been possible.

Ecological forecasting provides a powerful set of methods for predicting short‐ and long‐term change in living systems. Forecasts are now widely produced, enabling proactive management for many applied ecological problems. However, despite numerous calls for an increased emphasis on prediction in ecology, the potential for forecasting to accelerate ecological theory development remains underrealized. Here, we provide a conceptual framework describing how ecological forecasts can energize and advance ecological theory. We emphasize the many opportunities for future progress in this area through increased forecast development, comparison and synthesis. Our framework describes how a forecasting approach can shed new light on existing ecological theories while also allowing researchers to address novel questions. Through rigorous and repeated testing of hypotheses, forecasting can help to refine theories and understand their generality across systems. Meanwhile, synthesizing across forecasts allows for the development of novel theory about the relative predictability of ecological variables across forecast horizons and scales. We envision a future where forecasting is integrated as part of the toolset used in fundamental ecology. By outlining the relevance of forecasting methods to ecological theory, we aim to decrease barriers to entry and broaden the community of researchers using forecasting for fundamental ecological insight.

The response of understory trees to climate variability is key to understanding current and future forest dynamics. However, analyses of climatic effects on tree growth have primarily focused on the upper canopy, leaving understory dynamics unresolved. We analyzed differences in climate sensitivity based on canopy position of four common tree species (Acer rubrum,Fagus grandifolia,Quercus rubra, andTsuga canadensis) using growth information from 1,084 trees across eight sites in the northeastern United States. Effects of canopy position on climate response varied, but were significant and often nonlinear, for all four species. Compared to overstory trees, understory trees showed stronger reductions in growth at high temperatures and varied shifts in precipitation response. This contradicts the prevailing assumption that climate responses, particularly to temperature, of understory trees are buffered by the overstory. Forest growth trajectories are uncertain in compositionally and structurally complex forests, and future demography and regeneration dynamics may be misinferred if not all canopy levels are represented in future forecasts.