Cover and density of southwestern ponderosa pine understory plants in permanent chart quadrats (2002-2020).

This data set consists of 101 permanent 1 m × 1 m (1-m2 ) quadrats located within southwestern ponderosa pine ecosystems near Flagstaff, Arizona, USA. Individual plants in these quadrats were identified and mapped annually for 19 years (2002-2020). The original chart quadrats were established between 1912 and 1927 to determine the effects of domestic livestock grazing on herbaceous plants and pine seedlings. Today these data provide opportunities to examine the effects of climate and land-use change on plant demography, population dynamics, and community processes. We provide the following data and data formats: (1) digitized maps of all plant locations in shapefile and geodatabase format, (2) shapefiles showing annual locations of each individual plant species, (3) annual maps of each quadrat in TIFF and PDF format, (4) annual basal area of each species per quadrat for species mapped as polygons, (5) tabular representation of polygon areas and centroid locations for plant species mapped as polygons, (6) tabular representation of point locations for plant species mapped as points, (7) plot-scale 20 m × 20 m overstory tree canopy cover, tree basal area, parent material, and elevation, (8) quadrat-scale information (e.g., quadrat site and number, coordinates in UTM Zone 12 and latitude/longitude, and descriptive comments for each quadrat), (9) plant species list, (10) summary of plant species observed in each quadrat, (11) summary of quadrats mapped by site and year, and (12) data formatted for use in Integral Projection Models (IPM) and plant population analyses. There are no copyright restrictions; please cite this paper and the associated data set when data are used in publications.

An experimental test of the Community Assembly by Trait Selection (CATS) model.

The Community Assembly by Trait Selection (CATS) model of community assembly predicts species abundances along environmental gradients in relatively undisturbed vegetation. Here we ask whether this model, when calibrated with data from natural plant communities, can predict the abundances of five dominant grass species (Bouteloua gracilis, Elymus elymoides, Festuca arizonica, Muhlenbergia montana, and Poa fendleriana) in a greenhouse experiment that manipulated light and soil properties. To address this question, we used generalized additive models (GAMs) to model community-weighted mean (CWM) seed mass, mean Julian flowering date, and specific root length (SRL) as non-linear functions of two environmental variables (soil pH and pine basal area) in natural vegetation. The model-fitted CWM traits were then used as constraints in the CATS model to predict the relative abundance of the five grass species that were seeded in a mixture at equal densities into a 2×2 factorial experiment with soil parent material and light level as crossed factors. Light was the most important factor influencing seedling community composition, especially the abundances of Bouteloua gracilis and Poa fendleriana. The model-predicted relative abundances were significantly correlated with the observed relative abundances, and the model accurately predicted the dominant species in every treatment. P. fendleriana was correctly predicted to be the most abundant species in both shade treatments and the sun-basalt treatment, and B. gracilis was correctly predicted to be the most abundant species in the sun-limestone treatment. Our results provide experimental evidence that environmental filtering of the species pool occurs in the early stages of community assembly (including germination, emergence, and early growth), and that trait-based models calibrated with data from natural plant communities can be used to predict the outcome of the early stages of community assembly under experimental conditions.

Survival rates indicate that correlations between community-weighted mean traits and environments can be unreliable estimates of the adaptive value of traits.

Correlations between community-weighted mean (CWM) traits and environmental gradients are often assumed to quantify the adaptive value of traits. We tested this assumption by comparing these correlations with models of survival probability using 46 perennial species from long-term permanent plots in pine forests of Arizona. Survival was modelled as a function of trait × environment interactions, plant size, climatic variation and neighbourhood competition. The effect of traits on survival depended on the environmental conditions, but the two statistical approaches were inconsistent. For example, CWM-specific leaf area (SLA) and soil fertility were uncorrelated. However, survival was highest for species with low SLA in infertile soil, a result which agreed with expectations derived from the physiological trade-off underpinning leaf economic theory. CWM trait-environment relationships were unreliable estimates of how traits affected survival, and should only be used in predictive models when there is empirical support for an evolutionary trade-off that affects vital rates.