If you would like the goby data in the emdbookx package , please contact me bolker at ufl. These have some gory details, but should reproduce exactly the code chunks and figures in the text. Note that you may also need to install the Hmisc package from CRAN for generating some of the tabular output. You may also want chapskel. R , a "chapter skeleton" file with miscellaneous setup code. Most of the R code for doing things in the book is now in the two packages bbmle also available in a development version and emdbook , both available from R archive CRAN or via install.
Once a user makes a request through the web browser or command line utilities, the scientific workflow takes charge of triggering and executing corresponding tasks, be it pulling data from a remote server, running a particular ecological model, automating forecasting, or making the result easily understandable to users Figs. With the workflow, the system is agnostic to operation system, environment, and programming language and is built to horizontally scale to meet the demands of the model and the end-user community. Data are an important component of EcoPAD v1.
These datasets might have high temporal resolutions, such as those from real-time ecological sensors, or the display of spatial information from remote sensing sources and data stored in the geographic information system GIS.
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These datasets may also include, but are not limited to, inventory data, laboratory measurements, FLUXNET databases, or data from long-term ecological networks Baldocchi et al. Such data contain information related to environmental forcing e. Datasets in EcoPAD v1.
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These datasets are first described and stored with appropriate metadata via either manual operation or scheduled automation from sensors. Each project has a separate folder where data are stored. Data are generally separated into two categories. One is used as boundary conditions for modeling and the other category is related to observations that are used for data assimilation. Scheduled sensor data are appended to existing data files with prescribed frequency. Attention is then given to how the particular dataset varies over space x , y and time t.
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When the spatiotemporal variability is understood, it is then placed in metadata records that allow for query through its scientific workflow. Linkages among the workflow, data assimilation system, and ecological model are based on messaging.
For example, the data assimilation system generates parameters that are passed to ecological models. The state variables simulated from ecological models are passed back to the data assimilation system. Models may have different formulations. The common practice makes use of observations to develop or calibrate models to make predictions, while the EcoPAD v1.
Data and model are iteratively integrated through its data assimilation systems to improve forecasting. Its near-real-time forecasting results are shared among research groups through its web interface to guide new data collections. The scientific workflow enables web-based data transfer from sensors, model simulation, data assimilation, forecasting, result analysis, visualization, and reporting, encouraging broad user—model interactions, especially for experimenters and the general public with a limited background in modeling.
The original TECO model has four major submodules canopy, soil water, vegetation dynamics, and soil carbon and nitrogen and is further extended to incorporate methane biogeochemistry and snow dynamics Huang et al. Leaf photosynthesis and stomatal conductance are based on the common scheme from Farquhar et al. Transpiration and associated latent heat losses are controlled by stomatal conductance, soil water content, and the rooting profile. Evaporation losses of water are balanced between the soil water supply and the atmospheric demand based on the difference between saturation vapor pressure and the actual atmospheric vapor pressure.
Soil moisture in different soil layers is regulated by water influxes e. Vegetation dynamic tracks processes such as growth, allocation, and phenology. The soil carbon and nitrogen module tracks carbon and nitrogen through processes such as litterfall, soil organic matter SOM decomposition, and mineralization. SOM decomposition modeling follows the general form of the Century model Parton et al. SOM is divided into pools with different turnover times the inverse of decomposition rates , which are modified by environmental factors such as the soil temperature and moisture. Data assimilation is growing in importance as process-based ecological models, despite largely simplifying the real systems, need to be complex enough to address sophisticated ecological issues.
These ecological issues are composed of an enormous number of biotic and abiotic factors interacting with each other. Data assimilation techniques provide a framework to combine models with data to estimate model parameters Shi et al. Under the Bayesian paradigm, data assimilation techniques treat the model structure and the initial and parameter values as priors that represent our current understanding of the system. As new information from observations or data becomes available, model parameters and state variables can be updated accordingly.
The posterior distributions of estimated parameters or state variables are imprinted with information from the model, observations, and data as the chosen parameters act to reduce mismatches between observations and model simulations.
Future predictions benefit from such constrained posterior distributions through forward modeling Fig. S1 in the Supplement. As a result, the probability density function of predicted future states through data assimilation normally has a narrower spread than that without data assimilation when everything else is equal Niu et al.
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MCMC is a class of sampling algorithms to draw samples from a probability distribution obtained through constructed Markov chains to approximate the equilibrium distribution. The Bayesian-based MCMC method takes into account various uncertainty sources that are crucial in interpreting and delivering forecasting results Clark et al.
In the application of MCMC, the posterior distribution of a parameter for given observations is proportional to the prior distribution of that parameter and the likelihood function linked to the fit or match or cost function between model simulations and observations. For simplicity, we assume uniform distributions in priors and Gaussian or multivariate Gaussian distributions in observational errors, which can be operationally expanded to other specific distribution forms depending on the available information. A detailed description is available in Xu et al. Workflow is a relatively new concept in the ecology literature but is essential to realize real- or near-real-time forecasting.
Thus, we describe it in detail below. The essential components of the scientific workflow of EcoPAD v1. The workflow system of EcoPAD v1. Datasets can be placed and queried in EcoPAD v1. Calls for good management of current large and heterogeneous ecological datasets are common Vitolo et al. Kepler Ludascher et al.
Cool Class: Ecological Modeling and Data in R
Similarly to these systems, EcoPAD v1. The EcoPAD v1. Through MongoDB, measured datasets can be easily fed into ecological models for various purposes such as to initialize the model, calibrate model parameters, evaluate model structure, and drive model forecasts. For datasets from real-time ecological sensors that are constantly updating, EcoPAD v1. Once a user makes a request, such as through clicking on relevant buttons from a web browser, the request is passed through the RESTful API to trigger specific tasks.
Hence, a user can incorporate summary data from EcoPAD v1. Simplicity, ease of use, and interoperability are among the main advantages of this API, which enables web-based modeling. The workflow wraps ecological models and data assimilation algorithms with the docker containerization platform. Tasks are managed through the asynchronous task queue, Celery. Tasks can be executed concurrently on a single or more worker servers across different scalable IT infrastructures.
The task queue i. Celery communicates through messages, and EcoPAD v1. These messages may trigger different tasks, which include but are not limited to pulling data from a remote server where original measurements are located, accessing data through a metadata catalog, running model simulations with user-specified parameters, conducting data assimilation that recursively updates model parameters, forecasting future ecosystem status, and post-processing model results for visualization. The broker inside Celery receives task messages and handles out tasks to available Celery workers that perform the actual tasks Fig.
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Celery workers are in charge of receiving messages from the broker, executing tasks, and returning task results. The worker can be a local or remote computation resource e. Each worker can perform different tasks depending on the tools installed in each worker.
One task can also be distributed to different workers. In such a way, the EcoPAD v1. Another key feature that makes EcoPAD v1.