# Digging into the dissappearance of nature's land-dwelling vertebrates¶

During one of my many re-reads of the amazing (seriously, I cannot recommend it enough!) essay, "What can a technologist do about climate change", by Brett Victor, I came across this incredible, provacative, and upsetting graphic:

This graph shows another example of how over the last 150 years or so, humans have come to dominate the earth. Like many other hockey stick graphs we've seen, the exponential growth of our impact is hard to wrap your head around.

But this graph also raises so many more questions. How did this happen? That is, what was the biggest cause of this change? Can it be explained by the massive human population growth alone; or is it more about the radical death of wild land vertebrates; or more to do with an explosion in the numbers of our livestock from an increasingly meat-eating population?

And finally, and perhaps most importantly, what can be done about it? How much would we have to change in order to get back closer to a natural world?

In this post, I dig into the source of the data for the above graph, and find out the answers to those questions!

Brett Victor lists the source for the above plot as this 1998 TED talk from Paul MacCready. The graph in MacCready's talk is the same as the plot from the WorryDream essay, but unfortunately doesn't provide any more detail. Here's a screenshot:

Fortunately, through some internet searching, I was able to find a more detailed version of this plot in a 2004 chapter MacCready wrote for "The Hydrogen Energy Transition", titled "The Case for Battery Electric Vehicles" (Google Books link to page 230). This one breaks down the changes into their component parts, "Humans + Livetock + Pets", "Humans Only", "Wildlife Only".

Here is the graph cropped out of that page:

MacCready, Paul, 2004, 'The Case for Battery Electric Vehicles.' In Daniel Sperling and James Cannon, eds., The Hydrogen Energy Transition New York: Academic Press, pp. 227-33

Surprisingly, I think this is not quite the same data as was used for the 1998 presentation, as we will see below. It seems likely that since this data was newer (2004 vs 1998), that it was updated with newer information, and so I'm guessing it's probably more accurate. Either way, it's similar to the original data, and it's got the kind of break-down I was looking for.

The rest of this post is a jupyter notebook, a way of combining code and results into a document, like a lab notebook. Feel free to skip any of the code sections and just read the text and graphics, but for the curious, all the analysis done is included right inline in this page.

In [1]:
# Setting up this notebook for plotting our data analysis in python.
from matplotlib import pyplot as plt
import numpy as np
%pylab inline
matplotlib.rcParams['axes.xmargin'], matplotlib.rcParams['axes.ymargin'] = [0,0]  # wrap the plots tightly without margins.

Populating the interactive namespace from numpy and matplotlib


So first, we need to collect the raw data. I eyeballed points off of the curves above to trace the plot from MacCready's book:

In [2]:
# Eyeballed datapoints collected from "The Case for Battery Electric Vehicles" p230
hu_only_data  = {1850:60, 1875:70, 1900:90, 1910:95, 1925:105, 1940:125, 1950:135, 1962:170, 1975:210, 2000:290, 2025:420, 2050:570}
hu_plus_data  = {1850:95, 1875:110, 1900:135, 1910:155, 1925:200, 1940:255, 1950:315, 1962:445, 1975:665, 2000:1200, 2025:1935, 2050:2700}
wildlife_data = {1850:205, 1875:190, 1900:175, 1910:165, 1925:140, 1940:130, 1950:115, 1962:90, 1975:75, 2000:30, 2025:20, 2050:10}

hu_only  = sorted(hu_only_data.items())    # Turn them into sorted lists of
hu_plus  = sorted(hu_plus_data.items())    #  pairs to make plotting easier.
wildlife = sorted(wildlife_data.items())
years = sorted(wildlife_data.keys())       # Just the years (the X-axis points).

In [3]:
# Verify that the eyeballed datapoints are correct by plotting them overlaid on the original graphic.
fig, axes = plt.subplots(figsize=(15, 10))

axes.imshow(im, aspect='auto', extent=(1850,2050,0,2500), alpha=0.5, zorder=-1,
origin='upper')

axes.plot(*zip(*hu_plus), alpha=1.0)
axes.plot(*zip(*wildlife), alpha=1.0)
axes.plot(*zip(*hu_only), alpha=1.0)
axes.axvline(2000, linestyle='dotted')

Out[3]:
<matplotlib.lines.Line2D at 0x10edc87b8>
In [4]:
# We can calculate the values for only Livestock+Pets by subtracting humans out of Humans+Livestock+Pets
livestock_and_pets_data = {y : hu_plus_data[y]-hu_only_data[y] for y in years}
livestock_and_pets = sorted(livestock_and_pets_data.items())

In [5]:
# Reuse axes from above to plot Livestock+Pets along with the other lines.
axes.plot(*zip(*livestock_and_pets), alpha=1.0)
axes.text(1977, 830, 'Livestock+Pets Only', size=13, rotation=40)
fig

Out[5]:

It's interesting that the three lines cross around 1940. It's probably a coincedence, but it's interesting.

By plotting the data as a stackplot instead of as lines, we can see a bit more easily which changes had the greatest effect on this overall phenonemon.

In [6]:
from IPython.core.pylabtools import figsize
figsize(12, 6)  # Set the plot size for the rest of the notebook

In [7]:
stacked_values = [[y for _,y in line] for line in [hu_only,livestock_and_pets,wildlife]]
plt.stackplot(years, stacked_values, baseline="zero", labels=['Humans Only', 'Livestock+Pets', 'Wildlife'])
plt.axvline(2000, linestyle='dotted',color='darkgray')
legend = plt.legend(loc='upper center', fontsize=12)
plt.show()

In [8]:
def find_change(datapoints, end_point):
return (datapoints[end_point] / datapoints[1850])
print("Humans Only is now {0:0.0f}% of original value".format(find_change(hu_only_data, 2000)*100.0))
print("Livestock+Pets is now {0:0.0f}% of original value".format(find_change(livestock_and_pets_data, 2000)*100.0))
print("Wildlife is now {0:0.0f}% of original value".format(find_change(wildlife_data, 2000)*100.0))

Humans Only is now 483% of original value
Livestock+Pets is now 2600% of original value
Wildlife is now 15% of original value


## So we have our answers.¶

It seems that the largest change has happened in our livestock, which has exploded. It's grown by ~25 times! The next biggest change is the loss of wildlife, which has shrunk by ~6 times. Finally, humans have grown by ~5 times, which is still an incredibly large change.

Those changes are even more exagurated if you follow MacCready's predictions out to 2050:

In [9]:
print("Humans Only becomes {0:0.0f}% of original value".format(find_change(hu_only_data, 2050)*100.0))
print("Livestock+Pets becomes {0:0.0f}% of original value".format(find_change(livestock_and_pets_data, 2050)*100.0))
print("Wildlife becomes now {0:0.0f}% of original value".format(find_change(wildlife_data, 2050)*100.0))

Humans Only becomes 950% of original value
Livestock+Pets becomes 6086% of original value
Wildlife becomes now 5% of original value


### As percentages¶

Finally, we can reproduce the original graphic as seen on worrydream using this data. The graph is slightly less of a hockey-stick than the plot from MacCready's 1998 talk, but it's still pretty flooring.

In [10]:
total_mass_by_year = [hu_plus[i][1] + wildlife[i][1] for i in range(0,len(wildlife))]
hu_plus_percent_graph = [hu_plus[i][1] / total_mass_by_year[i] for i in range(0,len(wildlife))]
wildlife_percent_graph = [wildlife[i][1] / total_mass_by_year[i] for i in range(0,len(wildlife))]
plt.stackplot(years, *[hu_plus_percent_graph, wildlife_percent_graph], colors=['#187797', '#7cd3f5'])
plt.axvline(2000, linestyle='dotted',color='darkgray')
plt.text(1870, 0.7, 'natural land-dwelling\nvertebrates', color='white', family='Verdana', fontsize=18)
plt.text(1965, 0.25, 'humans,\n livestock,\n & pets', color='white', family='Verdana', fontsize=18)
plt.show()

In [11]:
hu_only_percent_graph = [hu_only[i][1] / total_mass_by_year[i] for i in range(0,len(wildlife))]
livestock_percent_graph = [livestock_and_pets[i][1] / total_mass_by_year[i] for i in range(0,len(wildlife))]
plt.stackplot(years, *[hu_only_percent_graph, livestock_percent_graph, wildlife_percent_graph], colors=['#245064', '#187797', '#7cd3f5'])
plt.axvline(2000, linestyle='dotted',color='darkgray')
plt.text(1870, 0.7, 'natural land-dwelling\nvertebrates', color='white', family='Verdana', fontsize=18)
plt.text(1945, 0.5, 'livestock & pets', color='white', family='Verdana', fontsize=18)
plt.text(1960, 0.12, 'humans only', color='white', family='Verdana', fontsize=18)
plt.show()