Spatial variation in plant diversity has been attributed to heterogeneity in resource availability for many ecosystems. However, urbanization has resulted in entire landscapes that are now occupied by plant communities wholly created by humans, in which diversity may reflect social, economic, and cultural influences in addition to those recognized by traditional ecological theory. Here we use data from a probability-based survey to explore the variation in plant diversity across a large metropolitan area using spatial statistical analyses that incorporate biotic, abiotic, and human variables. Our prediction for the city was that land use, along with distance from urban center, would replace the dominantly geomorphic controls on spatial variation in plant diversity in the surrounding undeveloped Sonoran desert. However, in addition to elevation and current and former land use, family income and housing age best explained the observed variation in plant diversity across the city. We conclude that a functional relationship, which we term the ''luxury effect,'' may link human resource abundance (wealth) and plant diversity in urban ecosystems. This connection may be influenced by education, institutional control, and culture, and merits further study.

Winter conditions are rapidly changing in temperate ecosystems, particularly for those that experience periods of snow and ice cover. Relatively little is known of winter ecology in these systems, due to a historical research focus on summer 'growing seasons'. We executed the first global quantitative synthesis on under-ice lake ecology, including 36 abiotic and biotic variables from 42 research groups and 101 lakes, examining seasonal differences and connections as well as how seasonal differences vary with geophysical factors. Plankton were more abundant under ice than expected; mean winter values were 43.2% of summer values for chlorophyll a, 15.8% of summer phytoplankton biovolume and 25.3% of summer zooplankton density. Dissolved nitrogen concentrations were typically higher during winter, and these differences were exaggerated in smaller lakes. Lake size also influenced winter-summer patterns for dissolved organic carbon (DOC), with higher winter DOC in smaller lakes. At coarse levels of taxonomic aggregation, phytoplankton and zooplankton community composition showed few systematic differences between seasons, although literature suggests that seasonal differences are frequently lake-specific, species-specific, or occur at the level of functional group. Within the subset of lakes that had longer time series, winter influenced the subsequent summer for some nutrient variables and zooplankton biomass.

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.

Spatial variation in plant diversity has been attributed to heterogeneity in resource availability for many ecosystems. However, urbanization has resulted in entire landscapes that are now occupied by plant communities wholly created by humans, in which diversity may reflect social, economic, and cultural influences in addition to those recognized by traditional ecological theory. Here we use data from a probability-based survey to explore the variation in plant diversity across a large metropolitan area using spatial statistical analyses that incorporate biotic, abiotic, and human variables. Our prediction for the city was that land use, along with distance from urban center, would replace the dominantly geomorphic controls on spatial variation in plant diversity in the surrounding undeveloped Sonoran desert. However, in addition to elevation and current and former land use, family income and housing age best explained the observed variation in plant diversity across the city. We conclude that a functional relationship, which we term the ''luxury effect,'' may link human resource abundance (wealth) and plant diversity in urban ecosystems. This connection may be influenced by education, institutional control, and culture, and merits further study.

Summary1. New analytical tools applied to long-term data demonstrate that ecological communities are highly dynamic over time. We developed an R package, library("codyn"), to help ecologists easily implement these metrics and gain broader insights into ecological community dynamics. 2. library("codyn") provides temporal diversity indices and community stability metrics. All functions are designed to be easily implemented over multiple replicates. 3. Temporal diversity indices include species turnover, mean rank shifts and rate of community change over time. Community stability metrics calculate overall stability and patterns of species covariance and synchrony over time, and include a null-modelling method to test significance. 4. Finally, library("codyn") contains vignettes that describe methods and reproduce figures from published papers to help users contextualize and apply functions to their own data.