Cryptic phenology in plants: Case studies, implications, and recommendations

Abstract

Plant phenology—the timing of cyclic or recurrent biological events in plants—offers insight into the ecology, evolution, and seasonality of plant‐mediated ecosystem processes. Traditionally studied phenologies are readily apparent, such as flowering events, germination timing, and season‐initiating budbreak. However, a broad range of phenologies that are fundamental to the ecology and evolution of plants, and to global biogeochemical cycles and climate change predictions, have been neglected because they are “cryptic”—that is, hidden from view (e.g., root production) or difficult to distinguish and interpret based on common measurements at typical scales of examination (e.g., leaf turnover in evergreen forests). We illustrate how capturing cryptic phenology can advance scientific understanding with two case studies: wood phenology in a deciduous forest of the northeastern USA and leaf phenology in tropical evergreen forests of Amazonia. Drawing on these case studies and other literature, we argue that conceptualizing and characterizing cryptic plant phenology is needed for understanding and accurate prediction at many scales from organisms to ecosystems. We recommend avenues of empirical and modeling research to accelerate discovery of cryptic phenological patterns, to understand their causes and consequences, and to represent these processes in terrestrial biosphere models.

Defensive Traits during White Spruce (Picea glauca) Leaf Ontogeny

“…These cryptic traits are increasingly under molecular and genomic investigation [ 17 , 18 ], and studies need to consider the phenology of these traits. Investigation of cryptic phenologies, like the toughness, chemical and nutritional content traits covered here, is essential to understand plant-mediated effects on herbivores, including the spruce budworm [ 81 ].…”

A new field instrument for leaf volatiles reveals an unexpected vertical profile of isoprenoid emission capacities in a tropical forest

“…PORCO has the portability and speed of online detection required to observe how emissions respond to temporally dynamic sun exposure beneath the canopy surface. The ease of deployment of PORCO can enable repeat measurements to disentangle the effects of leaf age and environment (Alves et al, 2014;Ü Niinemets et al, 2010) that contribute to a 'cryptic phenology' of emissions from evergreen forests (Albert et al, 2019;Alves et al, 2016Alves et al, , 2018. Understanding emission responses to leaf age, macro and microenvironmental variation, and the mechanisms underpinning species distributions will aid in scaling efforts based on physiological emissions models (Morfopoulos et al, 2014;Unger et al, 2013) and remote sensing (Zheng et al, 2015(Zheng et al, , 2017 in order to better resolve the role of emissions in biosphere-anthroposphere-climate interactions (Unger et al, 2017).…”

Accurate Simulation of Both Sensitivity and Variability for Amazonian Photosynthesis: Is It Too Much to Ask?

“…( 2018 ) found that the optimal temperature for EBF in one MsTMIP model (Figure 7 ) is too low. Mismatches in GPP seasonal timing (Figure 8 ; Text S5 ; Albert et al., 2018 ; Albert et al., 2019 , Borchert et al., 2002 ; Doughty & Goulden, 2008 ; Morton et al., 2016 ; Samanta et al., 2012 ; Wilson et al., 2001 ; Wu et al., 2016 ), consistent with the findings of Poulter, Hattermann, et al. ( 2010 ), suggest that during the dry season, actual plants experience less water stress than is modeled.…”