MotivationThe BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community‐led open‐source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene.Main types of variables includedThe database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of 2 years, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology and contextual information about each record.Spatial location and grainBioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km2 (158 cm2) to 100 km2 (1,000,000,000,000 cm2).Time period and grainBioTIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year.Major taxa and level of measurementBioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates.Software format.csv and .SQL.

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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.

216 words 12Body: 4012 words 13References: 27 14 15 peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/040360 doi: bioRxiv preprint first posted online Feb. 19, 2016; 2 Abstract 16Understanding why so many species are rare yet persistent remains a significant 17 challenge for both theoretical and empirical ecologists. Yenni, Adler, and Ernest (2012) proposed 18 that strong negative frequency dependence causes species to be rare while simultaneously 19 buffering them against extinction. This hypothesis predicts that, on average, rare species should 20 experience stronger negative frequency dependence than common species. However, it is 21 unknown if ecological communities generally show this theoretical pattern, or if rarity is 22 primarily determined by other processes that overwhelm the effects of strong negative frequency 23 dependence. We discuss the implications of this mechanism for natural communities, and 24 develop a method to test for a non-random relationship between negative frequency dependence 25 and relative abundance, using species abundance data from 90 communities across a broad range 26 of environments and taxonomic groups. To account for biases introduced by measurement error, 27we compared the observed correlation between species relative abundance and the strength of 28 frequency dependence against expectations from a randomization procedure. In approximately 29 half of the analyzed communities, rare species showed disproportionately strong negative 30 frequency dependence compared to common species. Specifically, we found a pattern of 31 increasingly strong negative frequency dependence with decreasing relative abundance. Our 32 results suggest that strong negative frequency dependence is a signature of both rarity and 33 persistence for many species in many communities. 34

The Smith-Fretwell model for optimal offspring size assumes the existence of an inverse proportional relationship (i.e., trade-off) between the number of offspring and the amount of resources invested in an individual offspring; virtually all of the many models derived from theirs make the same trade-off assumption. Over the last 30 years it has become apparent that the predicted proportionality is often not observed when evaluated across species. We develop a general allometric approach to correct for size-related differences in the resources available for reproduction. Using data on mammals, we demonstrate that the predicted inverse proportional relationship between number of offspring and offspring size is closely approached after correcting for allocation, though there is a slight curvature in the relationship. We discuss applications for this approach to other organisms, possible causes for the curvature, and the usefulness of allometries for estimating life-history variables that are difficult to measure.