diff --git a/modules/meta.analysis/R/approx.posterior.R b/modules/meta.analysis/R/approx.posterior.R
index 451f9d29467..ca66f183b4f 100644
--- a/modules/meta.analysis/R/approx.posterior.R
+++ b/modules/meta.analysis/R/approx.posterior.R
@@ -67,8 +67,8 @@ approx.posterior <- function(trait.mcmc, priors, trait.data = NULL, outdir = NUL
         if (!is.null(trait.data)) {
           graphics::rug(trait.data[[trait]]$Y, lwd = 2, col = "purple")
         }
-        graphics::lines(x, dbeta(x, fit$estimate[1], fit$estimate[2]), lwd = 2, type = "l")
-        graphics::lines(x, dbeta(x, pparm[1], pparm[2]), lwd = 3, type = "l", col = 3)
+        graphics::lines(x, stats::dbeta(x, fit$estimate[1], fit$estimate[2]), lwd = 2, type = "l")
+        graphics::lines(x, stats::dbeta(x, pparm[1], pparm[2]), lwd = 3, type = "l", col = 3)
         graphics::legend("topleft", 
                legend = c("data", "prior", "post", "approx"), 
                col = c("purple", 3, 2, 1), lwd = 2)
@@ -93,7 +93,7 @@ approx.posterior <- function(trait.mcmc, priors, trait.data = NULL, outdir = NUL
       dist.names <- dist.names[!failfit.bool]
       
       fparm <- lapply(fit, function(x) { as.numeric(x$estimate) })
-      fAIC <- lapply(fit, AIC) 
+      fAIC <- lapply(fit, stats::AIC)
       
       bestfit <- which.min(fAIC)
       posteriors[ptrait, "distn"] <- dist.names[bestfit]
diff --git a/modules/meta.analysis/R/jagify.R b/modules/meta.analysis/R/jagify.R
index ab8c1dd0d86..10ecdc5c95b 100644
--- a/modules/meta.analysis/R/jagify.R
+++ b/modules/meta.analysis/R/jagify.R
@@ -34,15 +34,15 @@ jagify <- function(result, use_ghs = TRUE) {
   r <- PEcAn.DB::assign.treatments(r)
   r <- PEcAn.utils::summarize.result(r)
   r <- subset(transform(r, 
-                        stat = as.numeric(stat), 
-                        n = as.numeric(n), 
-                        site_id = as.integer(factor(site_id, unique(site_id))), 
-                        greenhouse = as.integer(factor(greenhouse, unique(greenhouse))),
+                        r$stat <- as.numeric(r$stat), 
+                        r$n <- as.numeric(r$n), 
+                        r$site_id <- as.integer(factor(r$site_id, unique(r$site_id))), 
+                        r$greenhouse <- as.integer(factor(r$greenhouse, unique(r$greenhouse))),
                         mean = mean, 
-                        citation_id = citation_id,
-                        ghs = greenhouse,
-                        site = site_id,
-                        trt_name = name), 
+                        r$citation_id <- r$citation_id,
+                        ghs = r$greenhouse,
+                        site = r$site_id,
+                        trt_name = r$name), 
               select = c("stat", "n", "site_id", "trt_id", "mean", "citation_id", "greenhouse", 
                          "ghs", "treatment_id", "site", "trt_name")) # original versions of greenhouse, treatment_id, site_id, and name