Cues versus proximate drivers: testing the mechanism behind masting behavior

Abstract

Masting, the intermittent and synchronized production of seeds, is a common and important phenomenon throughout the plant kingdom. Surprisingly, the proximate mechanisms by which populations of masting plants synchronize their seed sets have been relatively unexplored. We examined how temperature influences the acorn crop of the valley oak Quercus lobata, a masting species common in California, USA, over 33 years in order to assess whether temperature acts directly on acorn crop as a cue or whether it acts instead through intermediate steps indicative of a direct mechanistic connection to acorn production. Compared to several alternatives, the difference in temperature during the spring flowering period over the prior two years (Δt) was a good predictor of annual acorn crop in valley oak, as proposed recently by Kelly et al. Significantly, Δt correlates positively with temperatures the previous April, a likely driver of pollination success in valley oak, and negatively with the previous year's acorn crop, which is in turn negatively correlated with the current year's acorn crop, presumably due to resource limitation. Thus, the success of Δt is not as a cue but rather explained by its close relationship to the proximate drivers that have a direct, mechanistic relationship with acorn crop size.

Nature beyond Linearity: Meteorological Variability and Jensen's Inequality Can Explain Mast Seeding Behavior

“…The variability in the meteorological variables in these cases is always similar in magnitude to the variability of fruit production, as our simulation also suggested ( Table 1). Our second simulation used log-transformed data for fruit production (exponential function), the most common analysis performed in masting studies (e.g., Sork et al, 1993;Koenig et al, 1994;Kelly et al, 2013;Pearse et al, 2014), in which Jensen's inequality already plays a role.…”

“…Hypotheses accounting for proximate causes, though, are focussed on the mechanisms by which plants produce fruits (Norton and Kelly, 1988;Kelly and Sork, 2002). These hypotheses are being intensely debated, and many studies have investigated the potential causes of the high interannual variability of fruit production: stored carbohydrates (Hoch et al, 2013;Ichie et al, 2013), reproductive costs (Sala et al, 2012), availability of nutrients (Crone et al, 2009;Fernández-Martínez et al, 2016), pollination efficiency Bogdziewicz et al, 2017a,b), or weather, either as a cue prompting reproduction (Kelly et al, 2013;Pearse et al, 2014;Bogdziewicz et al, 2017b) or by its effect on plant productivity either by enhancing photosynthesis (Fernández-Martínez et al, 2015, 2017 or by its interaction with nutrient availability (Smaill et al, 2011). Amongst proximate causes, meteorological variability may be an inseparable component of variable seed production , because weather is the most evident source of interannual variability that can affect plant productivity.…”

“…Second, meteorological variables are rarely bimodally distributed, but fruit production sometimes is (Norton and Kelly, 1988;Herrera et al, 1998). These arguments, though, are only correct when linear relationships between seed production and weather are assumed, even though a large number of masting studies have relied on logarithmic transformations for seed production to normalize model residuals (Sork et al, 1993;Koenig et al, 1994;Kelly et al, 2013;Pearse et al, 2014). This transformation linearises the relationship between crop size and weather, but the true relationship remains unequivocally non-linear and is thus within the domain of Jensen's inequality.…”

Roles of the Environment in Plant Life-History Trade-offs

“…The difference in temperature from one growing season to the next effectively predicted the occurrence of mast years [52][53][54]. Considering yearly climatic variability, Kelly et al [55] developed a model based on temperature differentials over multiple seasons to predict seed yield [55] and this model was further validated by Pearse et al [56]. The robustness of these models is emanated from the hypothesized correlation between seed mass and the environment and, in turn, lends support to the crucial role of climate in seed mass modulations.…”

Assessing the efficacy of population-level models of mast seeding

“…Mast seeding is the "synchronous highly variable seed production among years by a population of plants" (Kelly 1994), observed in a wide range of plant species including Fagus, Fuscospora, Quercus, Chionochloa, Dipterocarp and pistachio (Lyles et al 2009;Pearse et al 2014;Sakai 2002;Schauber et al 2002). Total annual seed production is usually continuous rather than bi-modal, making it difficult (and sometimes foolhardy: (Kelly 1994)) to categorize individual years as mast or non-mast.…”