Abstract. Studying life-history traits within and across taxonomic classifications has revealed many interesting and important patterns, but this approach to life history requires access to large compilations of data containing many different life-history parameters. Currently, life-history data for amniotes (birds, mammals, and reptiles) are split among a variety of publicly available databases, data tables embedded in individual papers and books, and species-specific studies by experts. Using data from this wide range of sources is a challenge for conducting macroecological studies because of a lack of standardization in taxonomic classifications, parameter values, and even in which parameters are reported. In order to facilitate comparative analyses between amniote life-history data, we created a database compiled from peer-reviewed studies on individual species, macroecological studies of multiple species, existing life-history databases, and other aggregated sources as well as published books and other compilations. First, we extracted and aggregated the raw data from the aforementioned sources. Next, we resolved spelling errors and other formatting inconsistencies in species names through a number of computational and manual methods. Once this was completed, subspecies-level data and species-level data were shared via a datasharing algorithm to accommodate the variety of species transformations (taxonomic promotions, demotions, merges, divergences, etc.) that have occurred over time. Finally, in species where multiple raw data points were identified for a given parameter, we report the median value. Here, we report a normalized and consolidated database of up to 29 life-history parameters, containing at least one life-history parameter for 21 322 species of birds, mammals, and reptiles.

A number of different models have been proposed as descriptions of the species-abundance distribution (SAD). Most evaluations of these models use only one or two models, focus on only a single ecosystem or taxonomic group, or fail to use appropriate statistical methods. We use likelihood and AIC to compare the fit of four of the most widely used models to data on over 16,000 communities from a diverse array of taxonomic groups and ecosystems. Across all datasets combined the log-series, Poisson lognormal, and negative binomial all yield similar overall fits to the data. Therefore, when correcting for differences in the number of parameters the log-series generally provides the best fit to data. Within individual datasets some other distributions performed nearly as well as the log-series even after correcting for the number of parameters. The Zipf distribution is generally a poor characterization of the SAD.

Sharing data is increasingly considered to be an important part of the scientific process. Making your data publicly available allows original results to be reproduced and new analyses to be conducted. While sharing your data is the first step in allowing reuse, it is also important that the data be easy understand and use. We describe nine simple ways to make it easy to reuse the data that you share and also make it easier to work with it yourself. Our recommendations focus on making your data understandable, easy to analyze, and readily available to the wider community of scientists.

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A number of different models have been proposed as descriptions of the speciesabundance distribution (SAD). Most evaluations of these models use only one or two models, focus on only a single ecosystem or taxonomic group, or fail to use appropriate statistical methods. We use likelihood and AIC to compare the fit of four of the most widely used models to data on over 16,000 communities from a diverse array of taxonomic groups and ecosystems. Across all datasets combined the logseries, Poisson lognormal, and negative binomial all yield similar overall fits to the data. Therefore, when correcting for differences in the number of parameters the logseries generally provides the best fit to data. Within individual datasets some other distributions performed nearly as well as the log-series even after correcting for the number of parameters. The Zipf distribution is generally a poor characterization of the SAD.