Process-based Phenology Models Research Articles

Overcoming mechanistic limitations of process-based phenological models: A data clustering method for large-scale applications

Process-based phenological models use thermal requirement (TR) defined by planting date, temperature and photoperiod to predict crop developmental stages. The TR value for a specific developmental stage for a given variety is often presumed to be constant regardless of environmental conditions. We calibrated and compared 12 phenological models using 27-year of observation data of one unique rice (Oryza sativa L.) variety ('Shanyou63') from 46 sites in southern China. Our findings indicated that shifts in environmental conditions significantly affected TR values, e.g., standard deviations of TR at physiological maturation ranged from 83 to 167 °C d. Clustering sites together minimized environmental heterogeneity, and thus minimized the differences in TR for different phenological stages. When the increased from 1 to 24, simulation errors for the 12 models showed a significant decrease across all developmental stages, from 1.8 to 1.4 days for tillering, from 5.3 to 3.7 days for jointing, from 5.6 to 3.9 days for booting, from 4.7 to 3.3 days for heading, and from 6.6 to 3.7 days for physiological maturation. Furthermore, our findings indicate that models featuring a three-segment piecewise linear temperature response function provide a more precise prediction. In contrast, models incorporating a Beta temperature response function have not performed well. This difference is attributed to different mechanisms used to describe the response of crop development rate to temperature, particularly at non-optimum temperatures. The impact of the photoperiod response function on prediction accuracy became significant with the expansion of scale. Our results demonstrate that clustering method effectively compensates for the lack of crop adaptation processes in common phenological models, leading to significantly improved phenological prediction accuracy in regions with environmental diversity.

Soil freeze–thaw cycles affect spring phenology by changing phenological sensitivity in the Northern Hemisphere

The use of frozen soil-vegetation feedback for predictive models is undergoing enormous changes under rapid climate warming. However, the influence of soil freeze–thaw (SFT) cycles on vegetation phenology and the underlying mechanisms remain poorly understood. By synthesizing a variety of satellite-derived data from 2002 to 2021 in the Northern Hemisphere (NH), we demonstrated a widespread positive correlation between soil thawing and the start of the growing season (SOS). Our results also showed that the SFT cycles had a significant impact on vegetation phenology mainly by altering the phenological sensitivities to daytime and nighttime temperatures, solar radiation and precipitation. Moreover, the effects of SFT cycles on the sensitivity of the SOS were more pronounced than those on the sensitivity of the end of the growing season (EOS) and the length of growing season (LOS). Furthermore, due to the degradation of frozen soil, the changes in phenological sensitivity in the grassland and tundra biomes were significantly larger than those in the forest. These findings highlighted the importance of incorporating the SFT as an intermediate process into process-based phenological models.

Lengthening height-growth duration in Smith fir as onset becomes more synchronous across elevations under climate warming scenarios

Accurate projections of growing-season duration of trees are crucial in evaluating the capacity of forests to mitigate climate warming through growth and CO2 uptake. However, little is known on how and to what extent the growing season of tree height growth will change under future warming. Herein, we projected the height-growth phenology of Smith fir (Abies georgei var. smithii) along two altitudinal gradients on the southeastern Tibetan Plateau under CMIP6 climate scenarios by using process-based phenology models. The models performed well when simulating timings of height-growth onset and cessation on the plot level, with root mean square errors of less than 5.8 and 3.8 days, respectively. From the 2020s to 2090s, the onset of height growth (i.e. bud swelling date) was predicted to advance by 10 ± 6 days under four warming scenarios. The elevational gradients of onset of bud swelling would significantly decrease from 3–4 to 1–2 days 100m−1 under the warmest climate scenario, suggesting future warming may result in more uniform onset of height growth along elevational gradients. Given the temporal variation of thermal requirements, the projected cessation of height growth was delayed by 3 ± 1 days in most plots. Overall, the height-growth duration was predicted to lengthen by 4–22 days by the end of the century. The extended duration of height growth predicted for Smith fir could enhance primary growth, forest productivity, and CO2 sequestration on the southeastern Tibetan Plateau.

Species differences in the green-up date of typical vegetation in Inner Mongolia and climate-driven mechanism based on process-based phenology models

Different species within the same community may exhibit distinct phenological responses to climate change, so it is necessary to study species differences in the green-up date among abundant species within a wide area, and a suitable phenology model should be introduced to explain the associated climate-driven mechanism. Although various models have been developed, very few studies have aimed to compare their efficiency and robustness, and the relative contributions of climate driving factors have not been sufficiently examined. We analyzed phenology data for 12 species across 17 stations in Inner Mongolia and found that essential spatiotemporal and interspecies differences existed in the green-up date. Five process-based models were established for each species and their performance was comprehensively evaluated. The two-phase models (sequential model, parallel model, unified model and unified model combined with precipitation driving) generally performed better than the one-phase model (thermal time model), and the model considering precipitation performed the best, which indicates that it is necessary to introduce the chilling effect and precipitation driving effect to improve the model accuracy in arid environments. We proposed a method to estimate the contribution rates of various climate driving factors, and significant differences in the relative demand for the various climate driving factors among different species were clearly revealed. The results indicated that for natural vegetation in Inner Mongolia, the need for the chilling and temperature driving is relatively high, and the precipitation driving is very important for herbaceous vegetation, which leads to considerable spatial and interspecies differences in green-up date. We demonstrated the feasibility of quantitatively evaluating the contributions of different climate driving factors with a process-based model, and the contradiction in phenological changes among different studies may eventually be clarified.

Species Difference in the Green-Up Date of Typical Vegetation in Inner Mongolia and Climate Driving Mechanism Based on Process-Based Phenology Models

Species Difference in the Green-Up Date of Typical Vegetation in Inner Mongolia and Climate Driving Mechanism Based on Process-Based Phenology Models

Spring phenology in subtropical trees: Developing process-based models on an experimental basis

Process-based phenological models are currently used for assessing the effects of climatic warming on the timing of spring phenological events, such as leafout and flowering, in trees. However, the biological realism of the models may be undermined by the practices of often formulating the models solely on the basis of observational records of the phenological event rather than addressing the physiological processes actually modelled. Here we introduce a framework for developing process-based phenological models on the basis of experiments explicitly designed for this purpose and apply the framework to developing process-based models for leafout in four subtropical tree species. Our method is based on a hypothetico-deductive approach, where the air temperature responses of the simulated processes are inferred from their implications for the occurrence and timing of leafout in the experimental conditions. That approach has only rarely been taken with boreal or temperate trees, and to the best of our knowledge, never before with subtropical trees. Big differences were found between the four species in the air temperature responses modelled, and these differences implied major differences in the dormancy dynamics predicted for the four species. Together with the results of a recent modelling study based on observational data, our results highlight the importance of experimental studies for the development of biologically realistic process-based models of spring phenology in trees. The framework developed in the present study can be applied to developing such models for all tree species that show the phenomena of rest (endodormancy) and chilling requirement, no matter whether the trees are boreal, temperate, or subtropical.

Diverse effects of climate at different times on grassland phenology in mid-latitude of the Northern Hemisphere

• SOS/EOS/LGS has significantly advanced/delayed/prolonged in dominated areas. • Temperature and precipitation showed stronger control on SOS and EOS, respectively. • The acting time of water condition on SOS is earlier and longer than temperature. • EOS primarily depends on spring thermal conditions and autumn water availability. Studying grassland phenology and its relationships to climate would deepen our understanding of vegetation-air interactions under global climate change. To date, however, our knowledge of the responses of grassland phenology to climatic factors is still limited at the continental scale. In this study, we retrieved the start (SOS) and end (EOS) of the growing season for mid-latitude (30°N–55°N) grasslands of the Northern Hemisphere during 1981–2014, and investigated their relations with previous temperature, rainfall, and snowfall (only for SOS) through trends analysis and time window analysis. Results illustrated a predominant significant advancing/delaying trend of SOS/EOS in 23.2%/20.5% of the study region. They jointly resulted in a primarily significant prolongation trend of growing season length in 22.7% of the study region. Next, a dominated negative correlation between air temperature/rainfall and SOS was found in 62.4%/57.6% of areas. Snowfall showed converse effects (positive/negative) among different grasslands. The time window opening date for air temperature to start to affect SOS was identified as the day 1–90 before the multi-year average SOS in 76.1% of areas, while the time window opening date for the effect of rainfall/snowfall on SOS was relatively evenly distributed between the 1st and 180th day before the multi-year average SOS. EOS was found to be significantly negatively/positively correlated with air temperature/precipitation in 74.8%/83.7% of areas. The time window opening date for the effect of air temperature on EOS was identified as the 90–180th day before the multi-year average EOS in 66.9% of areas, while the time window opening date for the effect of precipitation on EOS was mainly concentrated on the 60-120th day before the multi-year average EOS in 51.5% of areas. Overall, this study highlights the distinctly different time windows for the thermal-moisture effects on grassland vegetation phenology and this should be considered when establishing process-based phenological models.

Process-based models outcompete correlative models in projecting spring phenology of trees in a future warmer climate

Many phenology models have been developed to explain historical trends in plant phenology and to forecast future ones. Two main types of model can be distinguished: correlative models, that statistically relate descriptors of climate to the date of occurrence of a phenological event, and process-based models that build upon explicit causal relationships determined experimentally. While process-based models are believed to provide more robust projections in novel conditions, it is still unclear whether this assertion always holds true and why. In addition, the efficiency and robustness of the two model categories have rarely been compared. Here we aimed at comparing the efficiency and the robustness of correlative and process-based phenology models with contrasting levels of complexity in both historical and future climatic conditions. Models were calibrated, validated and compared using budburst dates of five tree species across the French Alps collected during 8 years by a citizen-science program. Process-based models were less efficient, yet more robust than correlative models, even when their parameter estimates relied entirely on inverse modeling, i.e. parameter values estimated using observed budburst dates and optimization algorithms. Their robustness further slightly increased when their parameter estimates relied on forward estimation, i.e. parameter values measured experimentally. Our results therefore suggest that the robustness of process-based models comes both from the fact that they describe causal relationships and the fact that their parameters can be directly measured. Process-based models projected a reduction in the phenological cline along the elevation gradient for all species by the end of the 21st century. This was due to a delaying effect of winter warming at low elevation where conditions will move away from optimal chilling conditions that break bud dormancy vs an advancing effect of spring warming at high elevation where optimal chilling conditions for dormancy release will persist even under the most pessimistic emissions scenario RCP 8.5. These results advocate for increasing efforts in developing process-based phenology models as well as forward modelling, in order to provide accurate projections in future climatic conditions.

A simpler way to predict flowering and full bloom dates of cherry blossoms by self-organizing maps

Knowledge of flowering and leaf flush phenology is important for a deep understanding of the responses of ecosystem functions, ecosystem services, and biodiversity to climate change. Process-based phenology models, which account statistically for chilling accumulation for endodormancy release and thermal accumulation after endodormancy release, can predict flowering and leaf flush dates, but they are difficult to develop for poorly studied species. A simpler approach is needed. We propose a model of the blooming phenology of Yoshino cherry trees (Cerasus ×yedoensis) based on bidirectional self-organizing maps (SOM). SOM, a data mining approach, is a tool for categorizing patterns in n-dimensional observation data by forming a two-dimensional lattice. The bi-directional SOM was applied to the data of daily mean temperature, using flowering (or full blooming) dates as teacher signals. We inputted daily mean air temperatures during the thermal accumulation period (mainly from February to just before flowering) into multiple input vectors and obtained flowering or full bloom dates into an output vector. The mean absolute errors between predicted and observed dates at 42 locations in Japan ranged from 2.8 to about 4.5 days for flowering and full bloom. The bidirectional SOM approach had a slightly higher error than the process-based phenology model approach, but it has an advantage in predicting flowering and leaf flush dates of poorly studied species under future warming conditions.

Sensitivity analysis of tree phenology models reveals increasing sensitivity of their predictions to winter chilling temperature and photoperiod with warming climate

The phenology of plants is a major driver of agro-ecosystem processes and biosphere feedbacks to the climate system. Phenology models are classically used in ecology and agronomy to project future phenological changes. With our increasing understanding of the environmental cues affecting bud development, phenology models also increase in complexity. But, we expect these cues, and the underlying physiological processes, to have varying influence on bud break date predictions depending on the specific weather patterns in winter and spring. Here, we evaluated the parameter sensitivity of state-of-the-art process-based phenology models that have been widely used to predict forest tree species phenology. We used sensitivity analysis to compare the behavior of models with increasing complexity under specific climatic conditions. We thus assessed whether the influence of the parameters and modeled processes on predictions varies with winter and spring temperatures. We found that the prediction of the bud break date was mainly affected by the response to forcing temperature under current climatic conditions. However, the impact of the parameters driving the response to chilling temperatures and to photoperiod on the prediction of the models increased with warmer winter and spring temperatures. Interaction effects between parameters played an important role on the prediction of models, especially for the most complex models, but did not affect the relative influence of parameters on bud break dates. Our results highlighted that a stronger focus should be given to the characterization of the reaction norms to both forcing and chilling temperature to predict accurately bud break dates in a larger range of climatic conditions and evaluate the evolutionary potential of phenological traits with climate change.

Process-Based Models of Phenology for Plants and Animals

Phenology is a key aspect of plant and animal life strategies that determines the ability to capture seasonally variable resources. It defines the season and duration of growth and reproduction and paces ecological interactions and ecosystem functions. Phenology models have become a key component of models in agronomy, forestry, ecology, and biogeosciences. Plant and animal process-based phenology models have taken different paths that have so far not crossed. Yet, they share many features because plant and animal annual cycles also share many characteristics, from their stepwise progression, including a resting period, to their dependence on similar environmental factors. We review the strengths and shortcomings of these models and the divergences in modeling approaches for plants and animals, which are mostly due to specificities of the questions they tackle. Finally, we discuss the most promising avenues and the challenges phenology modeling needs to address in the upcoming years.

Delayed response of spring phenology to global warming in subtropics and tropics

Climate drivers of plant phenology in the subtropics and tropics are still unclear, which significantly hinders accurate prediction of climate change impacts on vegetation growth and carbon balance in these unique ecoregions. The basic hypothesis of process-based phenology models is that spring tree phenology is regulated by temperature and triggered by chilling temperatures during the dormancy period, followed by forcing temperatures during the growth period. That is, trees require cool temperatures during a chilling period to break endodormancy, and then enter the phase of ecodormancy during which the rate of ontogenetic development increases with increasing air temperature. Therefore, insufficient chilling requirements may slow bud growth and consequently delay budburst. Many studies have shown that chilling requirement is at present sufficient to release bud dormancy fully in temperate regions, and thus forcing temperatures play a dominant role in triggering spring tree phenology. To identify differences in mechanisms of spring tree phenology responses to air temperature between the subtropics/tropics and the temperate zone, and their possible effects on future phenological trends under global warming, we used leaf unfolding and flowering data from a tree species of tropical origin, Melia azedarach, and output daily mean air temperature data from a regional climate model (HadGEM3-RA) for the period 1981–2005 at 42 stations in southeastern China to fit unified forcing and chilling phenology models. Then, we selected optimum models for each phenophase at each station. Moreover, we predicted leafing and flowering dates across the research region over 2021–2100 under global climate warming scenarios. The results show a previously unreported phenological phenomenon: chilling will often be insufficient to break bud dormancy in the northern tropical zone and may become a crucial factor limiting leafing and flowering responses to spring warming. However, chilling will still be sufficient to break bud dormancy in the warm temperate zone, and thus may not limit leafing and flowering responses to spring warming there. Consequently, predicted leafing and flowering dates both will be delayed in the northern tropical zone but will advance in the warm temperate zone from 2021 to 2100. In the subtropical zone, the effect of chilling temperature on spring phenology will be reduced gradually from earlier to later phenophases. Thus, predicted leafing and flowering dates show decreased delaying trends and increased advancing trends from earlier to later phenophases in the subtropical zone. Our findings suggest that insufficient chilling temperature accumulation during the dormancy period may counteract the forcing temperature accumulation during the growth period in the northern tropical zone and parts of the subtropical zone, resulting in delayed leafing and flowering dates.

Will tree species experience increased frost damage due to climate change because of changes in leaf phenology?

In temperate zones, trees tend to unfold their leaves earlier due to climate warming. However, changes in the timing of the bud development also affect the dynamics of the cold-hardening process, which may increase frost injuries endured by trees because new leaves unfold at a period when frost events can still occur. This possible increase in frost damage in response to climate change is known as the “frost-damage hypothesis”. In this study, we have tested this hypothesis by forcing a process-based frost-injury model with process-based phenological models for 22 North American species with two Intergovernmental Panel on Climate Change storylines. Using a simplified parameterization of the frost-injury model, we found that risk of frost injury changed with climate change for all species. In fact, frost injury decreased for the vast majority of the species, but this trend varied across species and throughout each species’ distribution. We further explored the variability of response among species using their phenological and geographic characteristics. The interspecific trends depicted here show what could be the implications of climate change on the ecophysiology of boreal and temperate trees and highlight the importance of process-based models in studying the complexity of long-term impacts of climate change on species biology.

Understanding dormancy release in apricot flower buds (Prunus armeniaca L.) using several process-based phenological models

The present study was an initial attempt to calibrate a phenological process-based model of dormancy release with experimental data for apricot flower buds. A large experimental database (88 data points) on dormancy release, concerning several cultivars grown at different geographical sites, was used for the model parameterization. We compared five phenological models. None of them provided accurate prediction of the date of dormancy release at the species level. This inaccuracy appeared to be due to the high variance in dormancy release dates among cultivars. Models fitted for different dormancy release precocity groups provided much more accurate predictions. Parameter estimate analysis of the best model for each cultivar group showed very marked differences in apricot flower bud response to temperature within the species. While in early cultivars dormancy release seemed to be driven by the daily minimum temperature, the daily mean temperature appeared to be the controlling factor in intermediate and late cultivars. Our results show that the apricot dormancy release date cannot be predicted accurately at the species level and that different models should be used for different precocity groups.

Effects of temperature on development, survival and reproduction of insects: Experimental design, data analysis and modeling

The developmental response of insects to temperature is important in understanding the ecology of insect life histories. Temperature-dependent phenology models permit examination of the impacts of temperature on the geographical distributions, population dynamics and management of insects. The measurement of insect developmental, survival and reproductive responses to temperature poses practical challenges because of their modality, variability among individuals and high mortality near the lower and upper threshold temperatures. We address this challenge with an integrated approach to the design of experiments and analysis of data based on maximum likelihood. This approach expands, simplifies and unifies the analysis of laboratory data parameterizing the thermal responses of insects in particular and poikilotherms in general. This approach allows the use of censored observations (records of surviving individuals that have not completed development after a certain time) and accommodates observations from temperature transfer treatments in which individuals pass only a portion of their development at an extreme (near-threshold) temperature and are then placed in optimal conditions to complete their development with a higher rate of survival. Results obtained from this approach are directly applicable to individual-based modeling of insect development, survival and reproduction with respect to temperature. This approach makes possible the development of process-based phenology models that are based on optimal use of available information, and will aid in the development of powerful tools for analyzing eruptive insect population behavior and response to changing climatic conditions.

Predicting the Timing of Cherry Blossoms in Washington, DC and Mid-Atlantic States in Response to Climate Change

Cherry blossoms, an icon of spring, are celebrated in many cultures of the temperate region. For its sensitivity to winter and early spring temperatures, the timing of cherry blossoms is an ideal indicator of the impacts of climate change on tree phenology. Here, we applied a process-based phenology model for temperate deciduous trees to predict peak bloom dates (PBD) of flowering cherry trees (Prunus×yedoensis ‘Yoshino’ and Prunus serrulata ‘Kwanzan’) in the Tidal Basin, Washington, DC and the surrounding Mid-Atlantic States in response to climate change. We parameterized the model with observed PBD data from 1991 to 2010. The calibrated model was tested against independent datasets of the past PBD data from 1951 to 1970 in the Tidal Basin and more recent PBD data from other locations (e.g., Seattle, WA). The model performance against these independent data was satisfactory (Yoshino: r2 = 0.57, RMSE = 6.6 days, bias = 0.9 days and Kwanzan: r2 = 0.76, RMSE = 5.5 days, bias = −2.0 days). We then applied the model to forecast future PBD for the region using downscaled climate projections based on IPCC's A1B and A2 emissions scenarios. Our results indicate that PBD at the Tidal Basin are likely to be accelerated by an average of five days by 2050 s and 10 days by 2080 s for these cultivars under a mid-range (A1B) emissions scenario projected by ECHAM5 general circulation model. The acceleration is likely to be much greater (13 days for 2050 s and 29 days for 2080s ) under a higher (A2) emissions scenario projected by CGCM2 general circulation model. Our results demonstrate the potential impacts of climate change on the timing of cherry blossoms and illustrate the utility of a simple process-based phenology model for developing adaptation strategies to climate change in horticulture, conservation planning, restoration and other related disciplines.

Modelling the timing of Betula pubescens budburst. I. Temperature and photoperiod: a conceptual model

The main factors triggering and releasing bud dormancy are photoperiod and temperature. Their individual and combined effects are complex and change along a transition from a dormant to a non-dormant state. Despite the number of studies reporting the effects of temperature and photoperiod on dormancy release and budburst, information on the parameters defining these relationships is scarce. The aim of the present study was to investigate the effects and interaction of temperature and photoperiod on the rates of dormancy induction and release in Betula pubescens (Ehrh.) in order to develop a conceptual model of budburst for this species. We performed a series of controlled environment experiments in which temperature and photoperiod were varied during different phases of dormancy in B. pubescens clones. Endodormancy was induced by short days and low temperatures, and released by exposure to a minimal period of chilling temperatures. Photoperiod during exposure to chilling temperatures did not affect budburst. Longer exposure to chilling increased growth capability (growth rate at a given forcing temperature) and decreased the time to budburst. During the forcing phase, budburst was promoted by photoperiods above a critical threshold, which was not constant, but decreased upon longer chilling exposures. These relationships between photoperiod and temperature have, as yet, not been integrated into the commonly used processbased phenological models. We suggest models should account for these relationships to increase the accuracy of their predictions under future climate conditions.

Climate reconstructions from grape harvest dates: Methodology and uncertainties

Several studies have used grape harvest date (GHD) as a proxy for temperature variations of the last centuries in Europe. However, the use of grape harvest dates to reconstruct climate is not straightforward, with four possible causes of major flaws. In this study we identify and evaluate the accuracy of GHD as a proxy to past temperature anomalies, uncertainties in the model used to relate temperature to GHD, identity of the grape varieties cultivated in the past, type of wine produced in the past and cultural practices used in the past. Our analyses are based on several phenological and crop models, and on the most complete data set on grape vine phenology and harvest quality. We show that the two methodologies currently used — linear regression models and process-based phenological models — can be accurate, but process-based phenological models ascertain robustness to be applied confidently in different vineyards and different periods. However, we show that several factors can induce a bias in temperature reconstructions using process-based models. We demonstrate the importance of historical information on the studied areas such as the varieties cultivated, the style of wine produced, the quality sought, the agricultural practices, in order to build the most robust model.

Olive flowering phenology variation between different cultivars in Spain and Italy: modeling analysis

Phenology data are sensitive data to identify how plants are adapted to local climate and how they respond to climatic changes. Modeling flowering phenology allows us to identify the meteorological variables determining the reproductive cycle. Phenology of temperate of woody plants is assumed to be locally adapted to climate. Nevertheless, recent research shows that local adaptation may not be an important constraint in predicting phenological responses. We analyzed variations in flowering dates of Olea europaea L. at different sites of Spain and Italy, testing for a genetic differentiation of flowering phenology among olive varieties to estimate whether local modeling is necessary for olive or not. We build models for the onset and peak dates flowering in different sites of Andalusia and Puglia. Process-based phenological models using temperature as input variable and photoperiod as the threshold date to start temperature accumulation were developed to predict both dates. Our results confirm and update previous results that indicated an advance in olive onset dates. The results indicate that both internal and external validity were higher in the models that used the photoperiod as an indicator to start to cumulate temperature. The use of the unified model for modeling the start and peak dates in the different localities provides standardized results for the comparative study. The use of regional models grouping localities by varieties and climate similarities indicate that local adaptation would not be an important factor in predicting olive phenological responses face to the global temperature increase.

REVIEW OF MODELLING THE DISTRIBUTION OF PLANT SPECIES

Vegetation-climate relationship at the species level has always been a popular research topic in ecology. This review paper summarizes early research and recent research advances on plant species-climate relationship in China and in the world. Especially since the 1980s, when global change study started and rapidly developed, research has focused on relationship between plant species’ geographical distribution and climate. During the past two decades, predictive models on species’ potential geographical distributions have been well developed. These include statistical models (e.g., correlative models including bioclimatic envelope or climatic envelope models, regression models including generalized linear and generalized additive models, rule-based models including classification and regression tree analysis and artificial neural network, as well as ecological niche models and climatic response models) and mechanistic models (e.g., the STASH model on the basis of BIOME1 biogeographical model, FORSKA forest gap model, the process-based phenology model PHENOFIT, and a model based on water balance, temperature and plant phenology). We compare the advantages and disadvantages of different models, synthesize predictions on the future distribution of plant species in different regions and introduce China’s studies about the potential and future distributions of selected plant species. This review provides back ground knowledge in order to more precisely model and predict the future change of plant species under global change.