1. In mixed stands, species complementarity (e.g., facilitation and competition reduction) may enhance forest tree productivity. Although positive mixture effects have been identified in forests worldwide, the majority of studies have focused on two-species interactions in managed systems with high functional diversity. We extended this line of research to examine mixture effects on tree productivity across landscape-scale compositional and environmental gradients in the low functional diversity, fire-suppressed, mixed-conifer forests of the U.S. Interior West.

2. We investigated mixture effects on the productivity of Pinus ponderosa, Pseudotsuga menziesii, and Abies concolor. Using region-wide forest inventory data, we created individual-tree generalized linear mixed models and examined the growth of these species across community gradients. We compared the relative influences of stand structure, age, competition, and environmental stress on mixture effects using multi-model inference. We analyzed growth of neighboring tree species to infer whether a mixture effect in a single species translated to the stand-level.

3. We found support for a positive mixture effect in P. menziesii, although our results were equivocal in light of a weaker but still plausible alternative model. Growth of P. menziesii neighboring species in mixed stands declined or held constant depending on aridity, suggesting that a positive mixture effect in P. menziesii does not necessarily extend to the stand level. We found no evidence for mixture effects in P. ponderosa, A. concolor or their neighboring species.

4. Complementarity appears to have a limited influence on tree growth in the mixed-conifer systems of the U.S. Interior West, reflecting limited functional diversity. Historical changes in stand structure following fire exclusion, particularly high stand densities, may limit the potential for positive species mixture effects. The limited species pool of Interior West forests increases the risk that, without careful management, what functional diversity exists could be lost to compositional changes resulting from stand dynamics or disturbance.

Data for this project were collected by the United States Department of Agriculture, Forest Serivce, Forest Inventory and Analysis Program. These data were collected under a common sampling and measurement protocol, as fully detailed in this agency document valid through the date of database queries: https://www.fs.fed.us/rm/ogden/data-collection/pdf/P2%20Manual_70_Feb2sm.pdf. These data are part of the Public Domain and freely available.

We have provided a workflow for querying FIA plots, applicable both to individual U.S. states as well as the aggregated regional dataset. An SQL script was designed for use with postgreSQL. Many researchers may opt instead to query and summarize the FIA Database with the rFIA R package (Stanke and Finley, 2020; https://cran.r-project.org/web/packages/rFIA/index.html), which does not require SQL querying or other database skills. Please see the link to this R package in "Related Works."

We have also provided the R script for building the analysis files based on our flattened data query. This script details our data filtering steps, such as dropping distrubed or multi-stand plots, not performed in the SQL query. This annotated file details the process metadata used to calculate derived data fields such as competition index and potential evapotranspiration.

Lastly, performing the same query on more up-to-date versions of the FIA database will yield subtly different results. The FIA dataset is continuously updated, correcting minor errors as well as adding additional plot measurements. Based on our criteria, disturbance, harveting, or conversion to non-forest land would be grounds for dropping plots from inclusion in analysis.

In some cases queries may return missing data values for FIA tree records, such as tree height or diameter. In postgreSQL database format, the Interior West FIA Database has blank fields for dead trees. Although we focused this study on live trees, occasional missing values may occur for site class, initial diameter, crown class code, or stand age. These trees were dropped from modeling.

The SQL script is inefficient (John Shaw, personal communication), as an up-front "WHERE" statement would save processing time and greatly reduce file size. Difficulties coding SQL 'WHERE" statements movitated us to filter data in R using subsetting and table joins.