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We tested whether shifts in the community‐weighted means of four key functional traits across an environmental gradient in a southern California grassland reflect variation in how these traits affect species' germination and fecundity across the landscape.

We asked whether models that included trait–environment interactions help explain variation in two key vital rates (germination rates and fecundity), as well as an integrative measure of fitness incorporating both vital rates (the product of germination rate and fecundity). To do so, we planted seeds of 17 annual plant species at 16 sites in cleared patches with no competitors, and quantified the lifetime seed production of 1360 individuals. We also measured community composition and a variety of abiotic variables across the same sites. This allowed us to evaluate whether observed shifts in community‐weighted mean traits matched the direction of any trait–environment interactions detected in the plant performance experiment.

We found that commonly measured plant functional traits do help explain variation in species responses to the environment—for example, high‐SLA species had a demographic advantage (higher germination rates and fecundity) in sites with high soil Ca:Mg levels, while low‐SLA species had an advantage in low Ca:Mg soils. We also found that shifts in community‐weighted mean traits often reflect the direction of these trait–environment interactions, though not all trait–environment relationships at the community level reflect changes in optimal trait values across these gradients.

Synthesis. Our results show how shifts in trait–fitness relationships can give rise to turnover in plant phenotypes across environmental gradients, a fundamental pattern in ecology. We highlight the value of plant functional traits in predicting species responses to environmental variation, and emphasise the need for more widespread study of trait–performance relationships to improve predictions of community responses to global change.

We measured specific leaf area (SLA), height and leaf area (LA) of grasses across California, covering 59 species at 230 sampling locations. We asked how these traits change along climate gradients within each species and used machine learning to predict local trait values for any species at any location based on phylogenetic position, local climate and that species’ mean traits. We then examined how much these local predictions alter patterns of assemblage‐level trait variation across the state.

Most species exhibited higher SLA and grew taller at higher temperatures and produced larger leaves in drier conditions. The random forests predicted spatial variation in functional traits very accurately, with correlations up to 0.97. Because trait records were spatially biased towards warmer areas, and these areas tend to have higher SLA individuals within each species, species means of SLA were upwardly biased. As a result, using species means over‐estimates SLA in the cooler regions of the state. Our results also suggest that height may be substantially under‐predicted in the warmest areas.

Synthesis. Using only species mean traits to characterize the functional composition of communities risks introducing substantial error into trait‐based estimates of ecosystem properties including decomposition rates or NPP. The high performance of random forests in predicting local trait values provides a way forward for estimating high‐resolution patterns of ITV without a massive data collection effort.

The new modular metabarcode sequence toolkitAnacapa(https://github.com/limey-bean/Anacapa/) addresses the above needs, allowing users to build comprehensive reference databases and assign taxonomy to raw multilocus metabarcode sequence data. A novel aspect ofAnacapais its database building module, “Creating Reference libraries Using eXisting tools” (CRUX), which generates comprehensive reference databases for specific user‐defined metabarcoding loci. TheQuality Control and ASV Parsingmodule sorts and processes multiple metabarcoding loci and processes merged, unmerged and unpaired reads maximizing recovered diversity.DADA2then detects amplicon sequence variants (ASVs) and theAnacapa Classifiermodule aligns these ASVs toCRUX‐generated reference databases usingBowtie2. Lastly, taxonomy is assigned to ASVs with confidence scores using a Bayesian Lowest Common Ancestor (BLCA) method. TheAnacapa Toolkitalso includes anrpackage,ranacapa, for automated results exploration through standard biodiversity statistical analysis.

Benchmarking tests verify that theAnacapa Toolkiteffectively and efficiently generates comprehensive reference databases that capture taxonomic diversity, and can assign taxonomy to both MiSeq and HiSeq‐length sequence data. We demonstrate the value of theAnacapa Toolkitin assigning taxonomy to seawater eDNA samples collected in southern California.

TheAnacapa Toolkitimproves the functionality of eDNA and streamlines biodiversity assessment and management by generating metabarcode specific databases, processing multilocus data, retaining a larger proportion of sequencing reads and expanding non‐traditional eDNA targets. All the components of theAnacapa Toolkitare open and available in a virtual container to ease installation.