Introduction

This report will analyze two data sets that contain information on maple trees in Harvard Forest. This information consists of seed production, sap flow, and other relevant data. The data was collected from two types of trees: sugar maples, which are a masting species, and red maples, which are a non-masting species. Masting means the trees in a forest synchronize to produce a large quantity of seeds at the same time in order to reproduce. The goal of this analysis is to determine if the non-masting species (red maple) has muted dynamics, which is a reduced response to environmental change (often resulting in poor growth/health), compared to the masting species (sugar maple).

The full research report in which the data was collected can be found here: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-hfr&identifier=285&revision=6. We will call the data sets we are working with “maple_sap” and “maple_tap.” The first of these has 8 variables and 9022 observations while the second has 7 variables and 382 observations. We will only be using 3 variables from maple_sap - species, sugar, and sap.wt - and 2 variables from maple_tap - species and dbh. A description of each of these variables can be found below.

species = tree species (ACSA represents sugar maples and ACRU represents red maples)

sugar = sugar concentration (measured in Brixx/weight percent)

sap.wt = weight of sap collected (in kilograms)

dbh = diameter of trunk at 1.4 meters above ground (in centimeters)

Both data sets are displayed here:

library(tidyverse)
library(readr)
library(DT)
maple_tap <- read_csv("hf285-01-maple-tap.csv")
maple_sap <- read_csv("hf285-02-maple-sap.csv")
datatable(maple_sap, options=list(scrollX=TRUE))
datatable(maple_tap, options=list(scrollX=TRUE))

Throughout the report, three packages will be used: tidyverse, readr, and DT. tidyverse provides functions needed to work with data sets and allows us to create visualizations, readr is needed to import the spreadsheets containing the data sets, and DT allows us to work with data tables.

Cleaning the Data

Before we start analyzing the data, we want to look for any extreme outliers that might throw off our work. If we sort the maple_sap data by the sugar variables in order of highest to lowest, we will see that there is one value that is vastly larger than the rest.

arrange(maple_sap, desc(sugar))
## # A tibble: 9,022 × 8
##    date       tree  tap   time   datetime sugar species sap.wt
##    <date>     <chr> <chr> <time> <dttm>   <dbl> <chr>    <dbl>
##  1 2018-03-24 HF33  A        NA  NA        22   ACSA     NA   
##  2 2017-03-08 HF41  B        NA  NA         7.3 ACSA      0.52
##  3 2017-02-24 HF12  A        NA  NA         6   ACSA      1.78
##  4 2017-02-23 HF12  A        NA  NA         5.9 ACSA      1.92
##  5 2017-02-22 HF12  A        NA  NA         5.5 ACSA      2.96
##  6 2017-02-21 HF12  A        NA  NA         5.2 ACSA      0.96
##  7 2017-02-20 HF12  A        NA  NA         5.1 ACSA      1.12
##  8 2017-02-27 HF12  A        NA  NA         5.1 ACSA      3.44
##  9 2015-03-26 HF12  A        NA  NA         5   ACSA      0.74
## 10 2015-03-31 HF41  A        NA  NA         5   ACSA      5.26
## # ℹ 9,012 more rows

When we are working with the data, the outlier value of 22.0 in the sugar column could affect our results and cause them to be misleading. Because of this, it is best to simply remove the row from our data (because the sap.wt value in the same row is NA, we do not have to worry about this affecting our analysis of that variable).

maple_sap <- maple_sap %>% 
  filter(!sugar==22.0)

Sugar Concentration Comparisons

The first variable we will look at is sugar. Sugar concentration in trees is an indication of energy, health, and growth potential. Higher concentration is associated with an increase in these qualities. For trees with muted dynamics, these same qualities would be lacking.

With this in mind, we will compare the average sugar concentration of the non-masting red maples to the average sugar concentration of the masting sugar maples.

maple_sap %>% 
  group_by(species) %>% 
  summarize(avg_sugar_concentration = mean(sugar, na.rm=TRUE),
            count=n())
## # A tibble: 2 × 3
##   species avg_sugar_concentration count
##   <chr>                     <dbl> <int>
## 1 ACRU                       1.84   786
## 2 ACSA                       2.54  7226

We can see that red maples have a lower sugar concentration than sugar maples on average. This would suggest that in comparison to sugar maples, red maples have muted dynamics. However, comparing averages is very vague and does not give us very much information. To get a better look at how the sugar concentrations of the two species compare, we can create a visualization which will show how the values are distributed in more detail.

ggplot(data=maple_sap)+
  geom_boxplot(mapping=aes(x=species, y=sugar))+
  labs(y="sugar concentration",
       title="Sugar Concentration by Tree Species")

This box plot confirms that sugar maples have higher sugar concentrations than red maples. In fact, 50% of the sugar concentration values for sugar maples are greater than 50% of the sugar concentration values for red maples, represented by the interquartile range. It is safe to say that the non-masting red maples display characteristics of muted dynamics when compared to sugar maples when it comes to sugar concentration.

Sap Weight Comparisons

The next variable that could indicate muted dynamics in a species is sap.wt. The heavier a tree’s sap, the healthier and larger it is. This means that lower sap weight could be related to muted dynamics. We will again compare the averages of this variable for each tree species.

maple_sap %>% 
  group_by(species) %>% 
  summarize(avg_sap_weigt = mean(sap.wt, na.rm=TRUE),
            count=n())
## # A tibble: 2 × 3
##   species avg_sap_weigt count
##   <chr>           <dbl> <int>
## 1 ACRU             2.31   786
## 2 ACSA             4.43  7226

As expected, the masting species (sugar maples) have a larger average sap weight than the non-masting species (red maples). We can create another visualization to see how often it is the case that sugar maples have heavier sap than red maples.

ggplot(data=maple_sap)+
  geom_boxplot(mapping=aes(x=species, y=sap.wt))+
  labs(y="weight of sap (kg)",
       title="Sap Weight by Tree Species")

Unlike our last box plot, the interquartile ranges of the two species overlap. However, it is still clear that the sugar maple species has much higher values. The largest sap weight recorded for sugar maples is about 10 kg larger than the largest sap weight recorded for red maples.

The weight of the sap collected from each species also points to muted dynamics in red maples when compared to sugar maples.

Trunk Diameter Comparisons

The final variable we will discuss is trunk diameter. This is also associated with a tree’s health and growth. Smaller than expected width is a sign of muted dynamics.

The results of our analysis so far suggest that sugar maples will have larger diameters than red maples due to their masting quality. We can compare the average diameters of the two species to confirm this.

maple_tap %>% 
  group_by(species) %>% 
  summarize(avg_diameter = mean(dbh, na.rm=TRUE),
            count=n())
## # A tibble: 2 × 3
##   species avg_diameter count
##   <chr>          <dbl> <int>
## 1 ACRU            46.6    36
## 2 ACSA            66.7   346

Our above prediction was correct. However, we will once again create a visualization to compare all the diameter values for each tree species to get a better idea of how often the claim is correct.

ggplot(data=maple_tap)+
  geom_boxplot(mapping=aes(x=species, y=dbh))+
  labs(y="diameter (cm)",
       title="Trunk Diameter by Tree Species")

Our box plot shows that most of the diameter values of sugar maples are larger than most of the diameter values of red maples. This also indicates muted dynamics in the non-masting red maple species.

Conclusion

All three of the variables we looked at, sugar concentration, sap weight, and trunk diameter, are indicators of muted dynamics when they have small values. When comparing these variables between the two tree species - sugar maples, a masting species, and red maples, a non-masting species - we found that they had consistently lower values for the red maples. Because of this, we can claim that, based on this data, the non-masting red maple species showcases muted dynamics in comparison to the masting sugar maple species.

Citation:

Rapp, J., E. Crone, and K. Stinson. 2023. Maple Reproduction and Sap Flow at Harvard Forest since 2011 ver 6. Environmental Data Initiative. https://doi.org/10.6073/pasta/7c2ddd7b75680980d84478011c5fbba9 (Accessed 2025-12-10).