植物物候学研究进展

植物物候变化在研究陆地生态系统对气候变化的响应时被誉为"矿井中的金丝雀"，全球气候变化愈演愈烈，重新引起了人们对植物物候研究的广泛关注。随着观测技术的发展，在各种空间和生态尺度上收集到的物候观测数据迅速累积，尽管已经在多个尺度上（物种、群落和景观尺度）观察到物候变化，但物候变化的机理仍然没有得到很好的理解。回顾了国内外植物物候研究的发展历程；总结了物候数据收集技术进展和全球物候变化的主要趋势；归纳了植物物候变化的机理与驱动因素；探讨了物候模型研究及物候对气候变化响应研究的主要方向。随着物候观测技术在不同尺度上应用的增加，物候研究进入了一个新的阶段。未来物候研究需要制定跨区域标准化观测指南，融合所有相关学科，改进物候模型，拓展研究区域；同时融合有效的历史物候资料，采用新技术和长期收集的物候数据为大数据时代植物物候学研究提供基础。     

Variational learning for Generalized Associative Functional Networks in modeling dynamic process of plant growth

This paper presents a new statistical techniques — Bayesian Generalized Associative Functional Networks (GAFN), to model the dynamical plant growth process of greenhouse crops. GAFNs are able to incorporate the domain knowledge and data to model complex ecosystem. By use of the functional networks and Bayesian framework, the prior knowledge can be naturally embedded into the model, and the functional relationship between inputs and outputs can be learned during the training process. Our main interest is focused on the Generalized Associative Functional Networks (GAFNs), which are appropriate to model multiple variable processes. Three main advantages are obtained through the applications of Bayesian GAFN methods to modeling dynamic process of plant growth. Firstly, this approach provides a powerful tool for revealing some useful relationships between the greenhouse environmental factors and the plant growth parameters. Secondly, Bayesian GAFN can model Multiple-Input Multiple-Output (MIMO) systems from the given data, and presents a good generalization capability from the final single model for successfully fitting all 12 data sets over 5-year field experiments. Thirdly, the Bayesian GAFN method can also play as an optimization tool to estimate the interested parameter in the agro-ecosystem. In this work, two algorithms are proposed for the statistical inference of parameters in GAFNs. Both of them are based on the variational inference, also called variational Bayes (VB) techniques, which may provide probabilistic interpretations for the built models. VB-based learning methods are able to yield estimations of the full posterior probability of model parameters. Synthetic and real-world examples are implemented to confirm the validity of the proposed methods.     

Quantitative modeling of Arabidopsis development

We present an empirical model of Arabidopsis (Arabidopsis thaliana), intended as a framework for quantitative understanding of plant development. The model simulates and realistically visualizes development of aerial parts of the plant from seedling to maturity. It integrates thousands of measurements, taken from several plants at frequent time intervals. These data are used to infer growth curves, allometric relations, and progression of shapes over time, which are incorporated into the final three-dimensional model. Through the process of model construction, we identify the key attributes required to characterize the development of Arabidopsis plant form over time. The model provides a basis for integrating experimental data and constructing mechanistic models.     

Using functional–structural plant models to study, understand and integrate plant development and ecophysiology

Functional–structural plant models (FSPMs) explore and integrate relationships between a plant’s structure and processes that underlie its growth and development. In recent years, the range of topics being addressed by scientists interested in functional–structural plant modelling has expanded greatly. FSPM techniques are now being used to dynamically simulate growth and development occurring at the microscopic scale involving cell division in plant meristems to the macroscopic scales of whole plants and plant communities. The plant types studied also cover a broad spectrum from algae to trees. FSPM is highly interdisciplinary and involves scientists with backgrounds in plant physiology, plant anatomy, plant morphology, mathematics, computer science, cellular biology, ecology and agronomy. This special issue of Annals of Botany features selected papers that provide examples of comprehensive functional–structural models, models of key processes such as partitioning of resources, software for modelling plants and plant environments, data acquisition and processing techniques and applications of functional–structural plant models for agronomic purposes.     

Computational models of plant development and form

The use of computational techniques increasingly permeates developmental biology, from the acquisition, processing and analysis of experimental data to the construction of models of organisms. Specifically, models help to untangle the non-intuitive relations between local morphogenetic processes and global patterns and forms. We survey the modeling techniques and selected models that are designed to elucidate plant development in mechanistic terms, with an emphasis on: the history of mathematical and computational approaches to developmental plant biology; the key objectives and methodological aspects of model construction; the diverse mathematical and computational methods related to plant modeling; and the essence of two classes of models, which approach plant morphogenesis from the geometric and molecular perspectives. In the geometric domain, we review models of cell division patterns, phyllotaxis, the form and vascular patterns of leaves, and branching patterns. In the molecular-level domain, we focus on the currently most extensively developed theme: the role of auxin in plant morphogenesis. The review is addressed to both biologists and computational modelers.     

Validation of species–climate impact models under climate change

Increasing concern over the implications of climate change for biodiversity has led to the use of species–climate envelope models to project species extinction risk under climate‐change scenarios. However, recent studies have demonstrated significant variability in model predictions and there remains a pressing need to validate models and to reduce uncertainties. Model validation is problematic as predictions are made for events that have not yet occurred. Resubstituition and data partitioning of present‐day data sets are, therefore, commonly used to test the predictive performance of models. However, these approaches suffer from the problems of spatial and temporal autocorrelation in the calibration and validation sets. Using observed distribution shifts among 116 British breeding‐bird species over the past ～20 years, we are able to provide a first independent validation of four envelope modelling techniques under climate change. Results showed good to fair predictive performance on independent validation, although rules used to assess model performance are difficult to interpret in a decision‐planning context. We also showed that measures of performance on nonindependent data provided optimistic estimates of models' predictive ability on independent data. Artificial neural networks and generalized additive models provided generally more accurate predictions of species range shifts than generalized linear models or classification tree analysis. Data for independent model validation and replication of this study are rare and we argue that perfect validation may not in fact be conceptually possible. We also note that usefulness of models is contingent on both the questions being asked and the techniques used. Implementations of species–climate envelope models for testing hypotheses and predicting future events may prove wrong, while being potentially useful if put into appropriate context.     

Improving prediction of chickpea wilt severity using machine learning coupled with model combination techniques under field conditions

Accurate estimation of disease severity in the field is a key to minimize the yield losses in agriculture. Existing disease severity assessment methods have poor accuracy under field conditions. To overcome this limitation, this study used thermal and visible imaging with machine learning (ML) and model combination (MC) techniques to estimate plant disease severity under field conditions. Field experiments were conducted during 2017–18, 2018–19 and 2021–22 to obtain RGB and thermal images of chickpea cultivars with different levels of wilt resistance grown in wilt sick plots. ML models were constructed using four different datasets created using the wilt severity and image derived indices. ML models were also combined using MC techniques to assess the best predictor of the disease severity. Results indicated that the Cubist was the best ML model, while the KNN model was the poorest predictor of chickpea wilt severity under field conditions. MC techniques improved the prediction accuracy of wilt severity over individual ML models. Combining ML models using the least absolute deviation technique gave the best predictions of wilt severity. The results obtained in the present study showed the MC techniques coupled with ML models improved the prediction accuracies of plant disease severity under field conditions.     

GLM versus CCA spatial modeling of plant species distribution

Despite the variety of statistical methods available for static modeling of plant distribution, few studies directly compare methods on a common data set. In this paper, the predictive power of Generalized Linear Models (GLM) versus Canonical Correspondence Analysis (CCA) models of plant distribution in the Spring Mountains of Nevada, USA, are compared. Results show that GLM models give better predictions than CCA models because a species-specific subset of explanatory variables can be selected in GLM, while in CCA, all species are modeled using the same set of composite environmental variables (axes). Although both techniques can be readily ported to a Geographical Information System (GIS), CCA models are more readily implemented for many species at once. Predictions from both techniques rank the species models in the same order of quality; i.e. a species whose distribution is well modeled by GLM is also well modeled by CCA and vice-versa. In both cases, species for which model predictions have the poorest accuracy are either disturbance or fire related, or species for which too few observations were available to calibrate and evaluate the model. Each technique has its advantages and drawbacks. In general GLM will provide better species specific-models, but CCA will provide a broader overview of multiple species, diversity, and plant communities.     

Plant trait responses to grazing – a global synthesis

Herbivory by domestic and wild ungulates is a major driver of global vegetation dynamics. However, grazing is not considered in dynamic global vegetation models, or more generally in studies of the effects of environmental change on ecosystems at regional to global scale. An obstacle to this is a lack of empirical tests of several hypotheses linking plant traits with grazing. We, therefore, set out to test whether some widely recognized trait responses to grazing are consistent at the global level. We conducted a meta‐analysis of plant trait responses to grazing, based on 197 studies from all major regions of the world, and using six major conceptual models of trait response to grazing as a framework. Data were available for seven plant traits: life history, canopy height, habit, architecture, growth form (forb, graminoid, herbaceous legume, woody), palatability, and geographic origin. Covariates were precipitation and evolutionary history of herbivory. Overall, grazing favoured annual over perennial plants, short plants over tall plants, prostrate over erect plants, and stoloniferous and rosette architecture over tussock architecture. There was no consistent effect of grazing on growth form. Some response patterns were modified by particular combinations of precipitation and history of herbivory. Climatic and historical contexts are therefore essential for understanding plant trait responses to grazing. Our study identifies some key traits to be incorporated into plant functional classifications for the explicit consideration of grazing into global vegetation models used in global change research. Importantly, our results suggest that plant functional type classifications and response rules need to be specific to regions with different climate and herbivory history.     

Effect of plant interaction on wind-induced crop motion

Plant motion due to wind affects plant growth, a phenomenon called thigmomorphogenesis. Despite intensive studies of the turbulence over plant canopies, the study of plant motion induced by wind has often been limited to individual trees or cereal plants. Few models of global canopy motions are available. Moreover the numerical analysis of models that are based on individual stems becomes time consuming when dealing with crops. A model of motion within the canopies is proposed here using a wave propagation equation within a homogenized continuous medium, and a forcing function representing turbulent gusts advected over the canopy. This model is derived from a discrete model of a set of plant shoots represented as individual oscillators, including elastic contacts between shoots. Such contacts induce nonlinearities into the wave equation. A new experimental method to measure stem dynamical properties and elastic collision properties is presented with an illustration on alfalfa stems. Results obtained modeling plant motions in an alfalfa crop are presented.     

Plant respiration and photosynthesis in global‐scale models: incorporating acclimation to temperature and CO2

To realistically simulate climate feedbacks from the land surface to the atmosphere, models must replicate the responses of plants to environmental changes. Several processes, operating at various scales, cause the responses of photosynthesis and plant respiration to temperature and CO₂ to change over time of exposure to new or changing environmental conditions. Here, we review the latest empirical evidence that short‐term responses of plant carbon exchange rates to temperature and CO₂ are modified by plant photosynthetic and respiratory acclimation as well as biogeochemical feedbacks. We assess the frequency with which these responses have been incorporated into vegetation models, and highlight recently designed algorithms that can facilitate their incorporation. Few models currently include representations of the long‐term plant responses that have been recorded by empirical studies, likely because these responses are still poorly understood at scales relevant for models. Studies show that, at a regional scale, simulated carbon flux between the atmosphere and vegetation can dramatically differ between versions of models that do and do not include acclimation. However, the realism of these results is difficult to evaluate, as algorithm development is still in an early stage, and a limited number of data are available. We provide a series of recommendations that suggest how a combination of empirical and modeling studies can produce mechanistic algorithms that will realistically simulate longer term responses within global‐scale models.     

Optimising long‐term monitoring projects for species distribution modelling: how atlas data may help

Long‐term biodiversity monitoring data are mainly used to estimate changes in species occupancy or abundance over time, but they may also be incorporated into predictive models to document species distributions in space. Although changes in occupancy or abundance may be estimated from a relatively limited number of sampling units, small sample size may lead to inaccurate spatial models and maps of predicted species distributions. We provide a methodological approach to estimate the minimum sample size needed in monitoring projects to produce accurate species distribution models and maps. The method assumes that monitoring data are not yet available when sampling strategies are to be designed and is based on external distribution data from atlas projects. Atlas data are typically collected in a large number of sampling units during a restricted timeframe and are often similar in nature to the information gathered from long‐term monitoring projects. The large number of sampling units in atlas projects makes it possible to simulate a broad gradient of sample sizes in monitoring data and to examine how the number of sampling units influences the accuracy of the models. We apply the method to several bird species using data from a regional breeding bird atlas. We explore the effect of prevalence, range size and habitat specialization of the species on the sample size needed to generate accurate models. Model accuracy is sensitive to particularly small sample sizes and levels off beyond a sufficiently large number of sampling units that varies among species depending mainly on their prevalence. The integration of spatial modelling techniques into monitoring projects is a cost‐effective approach as it offers the possibility to estimate the dynamics of species distributions in space and over time. We believe our innovative method will help in the sampling design of future monitoring projects aiming to achieve such integration.     

The ecology and evolution of host-plant resistance to insects

Genetic techniques have yielded new insights into plant-herbivore coevolution. Quantitative genetic tests of herbivory theory reveal that in some cases insect herbivores impose selection on resistance traits. Also, some resistance traits are costly while others appear not to be, and genetic models can explain these results. Genetic variation in plant resistance influences insect community structure by modifying interactions of herbivores with competitors and natural enemies. Therefore, models of multispecies coevolution are more realistic than pairwise coevolutionary models. Ecological genetics will facilitate further theoretical and empirical exploration of multispecies coevolution of plants and herbivores.     

Integral projection models perform better for small demographic data sets than matrix population models: a case study of two perennial herbs

1. Matrix population models are widely used to describe population dynamics, conduct population viability analyses and derive management recommendations for plant populations. For endangered or invasive species, management decisions are often based on small demographic data sets. Hence, there is a need for population models which accurately assess population performance from such small data sets.
2. We used demographic data on two perennial herbs with different life histories to compare the accuracy and precision of the traditional matrix population model and the recently developed integral projection model (IPM) in relation to the amount of data.
3. For large data sets both matrix models and IPMs produced identical estimates of population growth rate (λ). However, for small data sets containing fewer than 300 individuals, IPMs often produced smaller bias and variance for λ than matrix models despite different matrix structures and sampling techniques used to construct the matrix population models.
4. Synthesis and applications . Our results suggest that the smaller bias and variance of λ estimates make IPMs preferable to matrix population models for small demographic data sets with a few hundred individuals. These results are likely to be applicable to a wide range of herbaceous, perennial plant species where demographic fate can be modelled as a function of a continuous state variable such as size. We recommend the use of IPMs to assess population performance and management strategies particularly for endangered or invasive perennial herbs where little demographic data are available.     

Nonmanipulative determination of plant community dynamics

Knowledge of the strengths of interactions between species in plant communities is of fundamental importance to our understanding of how communities are structured, although they are notoriously difficult to quantify. Techniques have recently been developed that allow the detailed enumeration of the strength of interactions between plant species within unmanipulated multispecies communities. Nonlinear regression analysis is used to fit competition models to long-term census data using natural variations in plant densities in lieu of manipulation. The models generated have been used to infer the intensity and importance of interactions as well as to analyse the effects of spatial and temporal variability. Theoretical work has begun to look at how different techniques for measuring competition perform in a range of systems, highlighting the importance of spatial scale. The lessons learned from applying these methods will enable improved estimation of the strength of competition in natural communities.     

Structural models of primary cell walls in flowering plants: consistency of molecular structure with the physical properties of the walls during growth

Advances in determination of polymer structure and in preservation of structure for electron microscopy provide the best view to date of how polysaccharides and structural proteins are organized into plant cell walls. The walls that form and partition dividing cells are modified chemically and structurally from the walls expanding to provide a cell with its functional form. In grasses, the chemical structure of the wall differs from that of all other flowering plant species that have been examined. Nevertheless, both types of wall must conform to the same physical laws. Cell expansion occurs via strictly regulated reorientation of each of the wall's components that first permits the wall to stretch in specific directions and then lock into final shape. This review integrates information on the chemical structure of individual polymers with data obtained from new techniques used to probe the arrangement of the polymers within the walls of individual cells. We provide structural models of two distinct types of walls in flowering plants consistent with the physical properties of the wall and its components.     

Plant–mycorrhizal fungus co‐occurrence network lacks substantial structure

The interactions between plants and arbuscular mycorrhizal fungi (AMF) maintain a crucial link between macroscopic organisms and the soil microbial world. These interactions are of extreme importance for the diversity of plant communities and ecosystem functioning. Despite this importance, only recently has the structure of plant–AMF interaction networks been studied. These recent studies, which used genetic data, suggest that these networks are highly structured, very similar to plant–animal mutualistic networks. However, the assembly process of plant–AMF communities is still largely unknown, and an important feature of plant–AMF interactions has not been incorporated: they occur at an extremely localized scale. Studying plant–AMF networks in a spatial context seems therefore a crucial step. This paper studies a plant–AMF spatial co‐occurrence network using novel methodology based on information theory and a unique set of spatially explicit species‐level data. We apply three null models of which only one accounts for spatial effects. We find that the data show substantial departures from null expectations for the two non‐spatial null models. However, for the null model considering spatial effects, there are few significant co‐occurrences compared with the other two null models. Thus, plant–AMF spatial co‐occurrences seem to be mostly explained by stochasticity, with a small role for other factors related to plant–AMF specialization. Furthermore, we find that the network is not significantly nested or modular. We conclude that this plant–AMF spatial co‐occurrence network lacks substantial structure and, therefore, plants and AMF species do not track each other over space. Thus, random encounters seem more important in the first step of the assembly of plant–AMF communities. Synthesis The symbiotic interaction between plants and arbuscular mycorrhizal fungi (AMF) is crucial for ecosystem functioning. However, the factors affecting the assembly of plant‐AMF communities are poorly understood. An important factor of the assembly of plant‐AMF communities has been overlooked: plant‐AMF interactions occur at a localized spatial scale. Our study investigated the importance of space in the structure of plant‐AMF communities. We studied a plant‐AMF spatial co‐occurrence network using a unique set of spatially explicit data and applied three null models. We found that plant‐AMF spatial co‐occurrences seem to be mostly explained by stochasticity. In particular, our study shows that this plant‐AMF spatial co‐occurrence network lacks substantial structure and, therefore, plants and AMF species do not track each other over space. Thus, random encounters seem to drive the assembly of plant‐AMF communities.     

Biotic interactions boost spatial models of species richness

Biotic interactions are known to affect the composition of species assemblages via several mechanisms, such as competition and facilitation. However, most spatial models of species richness do not explicitly consider inter‐specific interactions. Here, we test whether incorporating biotic interactions into high‐resolution models alters predictions of species richness as hypothesised. We included key biotic variables (cover of three dominant arctic‐alpine plant species) into two methodologically divergent species richness modelling frameworks – stacked species distribution models (SSDM) and macroecological models (MEM) – for three ecologically and evolutionary distinct taxonomic groups (vascular plants, bryophytes and lichens). Predictions from models including biotic interactions were compared to the predictions of models based on climatic and abiotic data only. Including plant–plant interactions consistently and significantly lowered bias in species richness predictions and increased predictive power for independent evaluation data when compared to the conventional climatic and abiotic data based models. Improvements in predictions were constant irrespective of the modelling framework or taxonomic group used. The global biodiversity crisis necessitates accurate predictions of how changes in biotic and abiotic conditions will potentially affect species richness patterns. Here, we demonstrate that models of the spatial distribution of species richness can be improved by incorporating biotic interactions, and thus that these key predictor factors must be accounted for in biodiversity forecasts.     

Computer models of auxin transport: a review and commentary

With the recent proliferation of computer models of auxin transport, it is important that plant biologists understand something about these techniques and how to evaluate them. The paper begins with a brief introduction to the parts of a computer model, followed by a discussion of the limitations of the most common auxin modelling technique. Lastly, several recent models of organ initiation in the shoot apical meristem (i.e. phyllotaxis) are reviewed. The cell and molecular biology of phyllotaxis is now understood well enough that computer models can go beyond a simple 'proof of principle' and start to provide insights into gene function.