Antecedent climatic conditions spanning several years influence multiple land-surface phenology events in semi-arid environments

Abstract

Ecological processes are complex, often exhibiting non-linear, interactive, or hierarchical relationships. Furthermore, models identifying drivers of phenology are constrained by uncertainty regarding predictors, interactions across scales, and legacy impacts of prior climate conditions. Nonetheless, measuring and modeling ecosystem processes such as phenology remains critical for management of ecological systems and the social systems they support. We used random forest models to assess which combination of climate, location, edaphic, vegetation composition, and disturbance variables best predict several phenological responses in three dominant land cover types in the U.S. Northwestern Great Plains (NWP). We derived phenological measures from the 25-year series of AVHRR satellite data and characterized climatic predictors (i.e., multiple moisture and/or temperature based variables) over seasonal and annual timeframes within the current year and up to 4 years prior. We found that antecedent conditions, from seasons to years before the current, were strongly associated with phenological measures, apparently mediating the responses of communities to current-year conditions. For example, at least one measure of antecedent-moisture availability [precipitation or vapor pressure deficit (VPD)] over multiple years was a key predictor of all productivity measures. Variables including longer-term lags or prior year sums, such as multi-year-cumulative moisture conditions of maximum VPD, were top predictors for start of season. Productivity measures were also associated with contextual variables such as soil characteristics and vegetation composition. Phenology is a key process that profoundly affects organism-environment relationships, spatio-temporal patterns in ecosystem structure and function, and other ecosystem dynamics. Phenology, however, is complex, and is mediated by lagged effects, interactions, and a diversity of potential drivers; nonetheless, the incorporation of antecedent conditions and contextual variables can improve models of phenology.

Infrastructure to factorially manipulate the mean and variance of precipitation in the field

“…Second, greater variance can increase the frequency, magnitude, and/or duration of climate extremes that cause ecological tipping points, force transitions to new ecological states, slow the rate of recovery from disturbance, or alternatively, promote community or ecotone stability (Chesson, 2000; Doak & Morris, 2010; Lynch et al, 2014; Peters et al, 2006; Scheffer et al, 2015; Zinnert et al, 2021). Third, as climate variance increases, differences in climate between consecutive years become more dramatic, increasing the potential for antecedent effects, in which current biological responses depend on responses to past perturbations (Liu et al, 2019; Ogle et al, 2015; Wood et al, 2022). For instance, current primary production may be lessened if the prior year is drier than average (Reichmann et al, 2013).…”