Problem Set 4: The Measure of the World

IV.1.

(A)

Five conclusions that surprised me:

“We estimate that the contribution of reptiles and amphibians to the total chordate biomass is negligible” - You mean the 18,500 species (and counting) that are spread across nearly every major continent? I might not have enough background knowledge in this topic, but this certainly doesn’t feel like a negligible quantity to me For example: the biomass of all red-backed salamanders?
Marine ecosystems have an inverted trophic level/biomass relationship compared to terrestrial habitats
Only ~30% of marine biomass comes from fish and arthropods
Global prokaryote biomass estimates actually decreased after an increased diversity of sampling locations
Arthropods make up 1 FULL GtC of the 2 Gtcs estimated for all animals

Also, while not a conclusion made in this paper, I was curious: How is airborne living biomass accounted for in this calculation? Is it significant enough to influence the data set in a statistically significant way?

(B)

Figure 3 diagrams the overall procedure of how the paper translates biological samples into overall estimates of biomass. How are next-generation sequencing, remote sensing, and taxonomic levels related to this overall flow diagram?

For starters, both remote sensing and next-gen sequencing allow us to collect data (aka samples) much more rapidly and effectively, which in the context of this paper means we can gather a more robust data set of biomass samples than we could have 10 years ago. However, there are still significant technological barriers that prevent us from sampling certain biomasses in specific habitats; I thought the paper did a good job of noting that deep-sea and undergorund biomass can be especially difficult to estimate, and we still have lots of room for growth in how we can gather more robust and holistic data sets for these areas in particular.

(C)

The paper largely uses fold-change to capture uncertainties because of the vast diﬀerence between measurements (for different taxa). What stages in the procedure outlined in Figure 3 do you think contribute to variation in estimates for diﬀerent taxonomic groups? When thinking about accuracy and the variation in global biomass estimates, the very first step seems to be the most problamatic in my opinion; aligning sample biomasses for specific taxa with a specific preferred environmental parameter feels like a complicated process that has lots of room for variation in accuracy.

For example, we know that sun, water, CO2 and other nutrient elements are important for plant growth, but there are other variables that can cause different reactions depending on what other parameters in the environment they react with. This means that even if a warmer temperature stimulates more vegetative growth in a year with plenty of precipitation (aka increasing overall living plant biomass for that region), that same temperature could cause drought and a die-off of living plant biomass in a year when less water is available.

You could also argue that due to the seasonality of many temperate habitats across the globe can contribute to this variation. Depending on the time of year sampling, the abundance of living plant or insect biomass could dramatically shift, and this happens on a yearly basis. Not to mention that many microrganisms (such as marine diatoms dinoflagellates, or other algae) have extremely short life cycles that can cause rapid boom-and-bust population cycles over the span of a couple months, or even a couple weeks and days, also causing a significant variation in the living local biomass for that day.

I also think that biomass correlation to environmental parameters is still a space that has room for more research. Even if these parameters fluctuate on a day-to-day basis (also adding variation), living organisms would have different reaction times to actually cause a significant change in the living biomass of their population.

(D)

dataset <-read.csv("biomass.csv")


Fold.change <-dataset$Fold.change
Est.Biomass <-dataset$Mass.GtC.

plot(Est.Biomass,Fold.change, col="blue", pch=19)

Argument: While I can’t necessarily support my argument of seasonal plant biomass based off of this plot, I can show that the outlying data value on the right (Likely plants) had the highest biomass with the lowest fold change. It also clearly shows the weight of plant biomass in making these global biomass calculations, as they far outweigh the biomass of every other major taxa group.

(E)

dataset <- read.csv("biomass.csv")

dataset$Group <- c("Plants","Bacteria", "Fungi", "Archae", "Protists", "Animals", "Viruses")


Fold.change <- dataset$Fold.change 
Est.Biomass <- dataset$Mass.GtC.
Groups <- dataset$Group   


colors <- rainbow(length(unique(Groups)))        
group.colors <- colors[as.numeric(factor(Groups))]  


plot(Est.Biomass, Fold.change, 
     col = group.colors, 
     pch = 19,
     xlab = "Estimated Biomass (GtC)", 
     ylab = "Fold Change",
     main = "Fold-change Against Estimated Biomass")

legend("topright", 
       legend = levels(factor(Groups)), 
       col = colors, 
       pch = 19, 
       title = "Taxonomic Group")

IV.3

dataset <-read.csv("asteroids.csv")

q <- dataset$q
a <- dataset$a
w <- dataset$w

r <-(a+q)/2
hist(r,breaks=500)

w <- 2*pi*w/360

x <- r*cos(w)
y <-r*sin(w)

plot(x,y,col="blue", pch=19, cex=1/10)

Most Salient Features of the Histogram: There are two major peaks located at ~2.4 r and ~2.6 r, or roughly 2-3. There’s also a small cluster around r=5.

Observations of the Scatterplot: The Donut shape shows a visual representation of the organization of the asteroids, which essentially mirrors the previous histogram: There’s an empty space (where the sun is) followed by an extremely dense belt of values (Likely the asteroid belt), ending with a large empty space and a small ring of values again at the end of the data set (the outer reaches of the solar system, maybe the Oort cloud?).

Problem Set 4: The Measure of the World

Bay Nadeau

2025-09-25

IV.1.

(A)

(B)

(C)

(D)

(E)

IV.2

(a)

(b)

(c)

IV.3

IV.4

IV.5