1. Most forecasts for the future state of ecological systems are conducted once and never updated or assessed. As a result, many available ecological forecasts are not based on the most up-to-date data, and the scientific progress of ecological forecasting models is slowed by a lack of feedback on how well the forecasts perform. Iterative near-term ecological forecasting involves repeated daily to annual scaleforecasts of an ecological system as new data becomes available and regular assessment of the resulting forecasts. We demonstrate how automated iterative near-term forecasting systems for ecology can be constructed by building one to conduct monthly forecasts of rodent abundances at the Portal Project, a longterm study with over 40 years of monthly data. This system automates most aspects of the six stages of converting raw data into new forecasts: data collection, data sharing, data manipulation, modelling and forecasting, archiving, and presentation of the forecasts.3. The forecasting system uses R code for working with data, fitting models, making forecasts, and archiving and presenting these forecasts. The resulting pipeline is automated using continuous integration (a software development tool) to run the entire pipeline once a week. The cyberinfrastructure is designed for long-term maintainability and to allow the easy addition of new models. Constructing this forecasting system required a team with expertise ranging from field site experience to software development.4. Automated near-term iterative forecasting systems will allow the science of ecological forecasting to advance more rapidly and provide the most up-to-date forecasts possible for conservation and management. These forecasting systems will also accelerate basic science by allowing new models of natural systems to be quickly implemented and compared to existing models. Using existing technology, and teams with diverse skill sets, it is possible for ecologists to build automated forecasting systems and use them to advance our understanding of natural systems. K E Y W O R D Sforecasting, iterative forecasting, mammals, Portal Project, predictionThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Over the past decade, biology has undergone a data revolution in how researchers collect data and the amount of data being collected. An emerging challenge that has received limited attention in biology is managing, working with, and providing access to data under continual active collection. Regularly updated data present unique challenges in quality assurance and control, data publication, archiving, and reproducibility. We developed a workflow for a long-term ecological study that addresses many of the challenges associated with managing this type of data. We do this by leveraging existing tools to 1) perform quality assurance and control; 2) import, restructure, version, and archive data; 3) rapidly publish new data in ways that ensure appropriate credit to all contributors; and 4) automate most steps in the data pipeline to reduce the time and effort required by researchers. The workflow leverages tools from software development, including version control and continuous integration, to create a modern data management system that automates the pipeline.

131. Most forecasts for the future state of ecological systems are conducted once and 14 never updated or assessed. As a result, many available ecological forecasts are not 15 based on the most up-to-date data, and the scientific progress of ecological 16 forecasting models is slowed by a lack of feedback on how well the forecasts 17 perform. 18 2. Iterative near-term ecological forecasting involves repeated daily to annual scale 19 1 forecasts of an ecological system as new data becomes available and regular 20 assessment of the resulting forecasts. We demonstrate how automated iterative 21 near-term forecasting systems for ecology can be constructed by building one to 22 conduct monthly forecasts of rodent abundances at the Portal Project, a long-term 23 study with over 40 years of monthly data. This system automates most aspects of 24 the six stages of converting raw data into new forecasts: data collection, data 25 sharing, data manipulation, modeling and forecasting, archiving, and presentation 26 of the forecasts. 273. The forecasting system uses R code for working with data, fitting models, making 28 forecasts, and archiving and presenting these forecasts. The resulting pipeline is 29 automated using continuous integration (a software development tool) to run the 30 entire pipeline once a week. The cyberinfrastructure is designed for long-term 31 maintainability and to allow the easy addition of new models. Constructing this 32 forecasting system required a team with expertise ranging from field site 33 experience to software development. 34 4. Automated near-term iterative forecasting systems will allow the science of 35 ecological forecasting to advance more rapidly and provide the most up-to-date 36 forecasts possible for conservation and management. These forecasting systems 37 will also accelerate basic science by allowing new models of natural systems to 38 be quickly implemented and compared to existing models. Using existing 39 technology, and teams with diverse skill sets, it is possible for ecologists to build 40 automated forecasting systems and use them to advance our understanding of 41 natural systems. 42 Forecasting the future state of ecological systems is important for management, 45 conservation, and evaluation of how well models capture the processes governing 46 ecological systems (Clark et al., 2001; Tallis & Kareiva, 2006; Díaz et al., 2015; Dietze, 47 2017). In 2001, Clark et al. (2001 called for a more central role of forecasting in 48 ecology. Since then, an increasing number of ecological forecasts are being published 49 that focus on societally important questions from daily to decadal time scales (Dietze et 50 al., 2018). At daily scales, ecological forecasts predict the occurrence of environmental 51 issues like toxic algal blooms (Stumpf et al., 2009) and pollen (Prank et al., 2013). At 52 monthly scales, forecasts are used to predict the stocks of fisheries (NOAA, 2016) and 53 the probability of coral bleaching events (Liu et al., 2018). At decada...

This is a data paper for the Portal Project, a long-term ecological study of rodents, plants, and ants located in southeastern Arizona, U.S.A. This paper contains an overview of methods and information about the structure of the data files and the relational structure among the files. This is a living data paper and will be updated with new information as major changes or additions are made to the data. All data -along with more detailed data collection protocols and site information -is archived at: https://doi

Data management and publication are core components of the research process. An emerging challenge that has received limited attention in biology is managing, working with, and providing access to data under continual active collection. "Living data" present unique challenges in quality assurance and control, data publication, archiving, and reproducibility. We developed a living data workflow for a longterm ecological study that addresses many of the challenges associated with managing this type of data. We do this by leveraging existing tools to: 1) perform quality assurance and control; 2) import, restructure, version, and archive data; 3) rapidly publish new data in ways that ensure appropriate credit to all contributors; and 4) automate most steps in the data pipeline to reduce the time and effort required by researchers. The workflow uses two tools from software development, version control and continuous integration, to create a modern data management system that automates the pipeline.