Plant phenological modeling and its application in global climate change research: overview and future challenges

Abstract

Plants interact to the seasonality of their environments, and changes in plant phenology have long been regarded as sensitive indicators of climatic change. Plant phenology modeling has been shown to be the simplest and most useful tool to assess phenol–climate shifts. Temperature, solar radiation, and water availability are assumed to be the key factors that control plant phenology. Statistical, mechanistic, and theoretical approaches have often been used for the parameterization of plant phenology models. The statistical approaches correlate the timing of phenological events to environmental factors or heat unit accumulations. The approaches have the simplified calculation procedures, correct phenological mechanism assumptions, but limited applications and predictive abilities. The mechanistic approaches describe plant phenology with the known or assumed “cause–effect relationships” between biological processes and key driving variables. The mechanistic approaches have the improved parameter processes, realistic assumptions, broad applications, and effective predictions. The theoretical approaches assume cost–benefit tradeoff strategies in trees. These methods are capable of capturing and quantifying the potential impacts and consequences of global climate change and human activity. However, certain limitations still exist related to our understanding of phenological mechanisms in relation to (1) interactions between plants and their specific climates, (2) the integration of both field observational and remote sensing data with plant phenology models across taxa and ecosystem type, (3) amplitude clarification of scale-related sensitivity to global climate change, and (4) improvements in parameterization processes and the overall reduction of modeling uncertainties to forecast impacts of future climate change on plant phenological dynamics. To improve our capacity in the prediction of the amplitude of plant phenological responses with regard to both structural and functional sensitivity to future global climate change, it is important to refine modeling methodologies by applying long-term and large-scale observational data. It is equally important to consider other less used but critical factors (such as heredity, pests, and anthropogenic drivers), apply advanced model parameterization and data assimilation techniques, incorporate process-based plant phenology models as a dynamic component into global vegetation dynamic models, and test plant phenology models against long-term ground observations and high-resolution satellite data across different spatial and temporal scales.

Variability and Changes in Climate, Phenology, and Gross Primary Production of an Alpine Wetland Ecosystem

“…Vegetation phenology is a fundamental determinant affecting the processes of carbon, water, and energy exchange in wetland ecosystems [6,7]. Phenology determines the timing and duration of the photosynthetically active canopy and drives annual carbon uptake in wetland ecosystems [8][9][10].…”

Trends in the Start of the Growing Season in Fennoscandia 1982–2011

“…Xu et al [1] also showed that the onset of the growing season was significantly correlated with the spring temperatures in the circumpolar and circumboreal areas. So far, there are three types of approaches for detecting the start of the growing season for vegetation [18]: (a) the conventional phenology approach observing the phenology of individual plants or tree stands [12,13,17,19]; (b) the land surface phenology approach using satellite-derived growing season metrics retrieved from various vegetation index data, such as the Normalized Difference Vegetation Index (NDVI) data from, e.g., the Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) [9,[20][21][22]; (c) temperature, solar radiation and water availability are assumed to be the key factors that control plant phenology. Statistical, mechanistic and theoretical approaches have often been used for the parameterization of plant phenology models describing the response to these key factors [18].…”

Modeling the Influence of Genetic and Environmental Variation on the Expression of Plant Life Cycles across Landscapes

“…Because genotypes differ measurably in environmental sensitivities, different model parameterizations can represent allelic variation in how organisms respond to diverse environmental factors (Morin et al 2007;Wilczek et al 2009;Zhao et al 2013). As such, this modeling approach supplies an extremely flexible tool for predicting the reaction norms of particular genotypes in response to complex and variable environments (Buckley and Kingsolver 2012).…”