Ants Do Trigonometry: A Problem for Darwinism
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Ants Do Trigonometry: A Problem for Darwinism
Editor’s note: The following is an excerpt from the newly released book, Animal Algorithms: Evolution and the Mysterious Origin of Ingenious Instincts, from Discovery Institute Press. Don’t miss the upcoming webinar with Eric Cassell and Casey Luskin, Thursday, December 9, from 4 to 5:30 Pacific time. Register here.
As
we have seen, a wide variety of animal navigation and migration
patterns strongly suggest complex programmed behaviors. The migration
and navigation strategies used by most animals are far more
sophisticated than initially supposed when scientists first started
studying them. The more scientists learn about them, the more complex
they appear in many animals.
One
way of demonstrating the level of engineering involved in these
behaviors is by examining modern aircraft navigation systems. As shown
in Figure 3.4, aircraft navigation systems use multiple redundant
sensors (inertial, GPS, ground-based systems) along with other essential
components.
The
Figure 3.4 illustration is, of course, a gross oversimplification.
Having been personally involved in the engineering design of several
systems, including aircraft navigation systems, I can attest that a
structured engineering process is essential. The process must be
top-down, where the overall concept must first be defined, as
illustrated in Figure 3.5.
The
process is as follows. The first step is to define the overall goal,
including the purpose of the system. The second step is to develop a
concept for implementing the system including the primary functions.
Next is performing an analysis of potential design options. This
includes assessing the options and evaluating the trade-offs, which for
man-made systems include performance, complexity, and cost. Once a
design option is chosen the next step is to define the specific
requirements. Following that, the system can be manufactured.
Top-Down and Structured, but Why?
There
are numerous reasons why this process must be top-down and structured.
One is that since it is a complex system there are a lot of
interdependencies, and therefore the design requirements must take this
into account to ensure the components and operation function coherently.
If all of the functions are not integrated correctly, system
performance is significantly degraded.
The
overall design of aircraft navigation systems requires thousands of
hours of design work and development by engineers. The goal is a system
that provides the optimum navigation information to the pilots by
selecting the navigation source that provides the best performance for
specific phases of flight — takeoff, en route, oceanic, airport terminal
area, approach, and landing. The functions include the various sensors
that provide this information, each of which is itself extremely
complex.
As
with all modern systems, it includes a combination of hardware (radio
receivers, computer processors, cockpit displays) and software
containing the computer algorithms for processing aircraft position
data, navigation selection logic, map and route information, and display
interface. All navigation methods require an integrated and coherent
combination of physical elements, programmed algorithms, and other
related information. The same is true for all migrating animals.
How the Elements Interact
Referring
to Figure 2.2 regarding animal navigation systems, recall how all of
these elements interact. Or to take one of the specific examples from
above, we see this in desert ants (Figure 3.1). While the sensors,
brain, algorithm, and physiology can be viewed as separate subsystems,
they must work as an integrated system. Recall that desert ants employ
three methods of navigation. Notice the similarity to aircraft
navigation systems in Figure 3.4. The odometer and polarized light
compass in desert ants are the information sources for path integration,
while landmarks are the other information source. The central control
function uses this information, in addition to the cues available from
external conditions, to make a programmed decision of which source to
use and to compute the correct navigation route. As we can see from all
of this, the control function is a complex algorithm.
And
it only gets more complicated. Referring again to Figure 3.3, the
information defining these elements likely resides in different parts of
the genome. Further, the information defining these elements is of a
very different nature. For example, genes that control physiology bear
no relationship to genes that define navigation or migration algorithms.
Even the genes that determine sensor physiology (compass sensors, etc.)
are very different from genes that determine migratory physiology
(flight characteristics, etc.). All told, the development of navigation
and migration behaviors requires the independent origin of the physical
traits and information necessary for five separate groups of genes and
other genetic information in the genome. As discussed above, the
likelihood of obtaining even a few coordinated genetic changes is very
low. The likelihood of obtaining an unknown (but likely large) number of
coordinated genetic changes in five different parts of the genome is
extremely improbable. Also required is the development of novel traits
(of which there are numerous related to navigation and migration), which
will be discussed in Chapter 4.
Two Incidents
I
wrote an initial draft of this section shortly after there were two
incidents in the news where commercial aircraft landed at airports that
were not the intended destination. This is an example of pilots either
failing to refer to their alternate navigation systems, or actual
failure of the system. In either case, the incidents illustrate how
crucial it is to have backup navigation strategies, and how challenging
it is to program systems that consistently work properly. It is a
fundamental concept in engineering that when the availability and
reliability of a system are critical, then it’s best to design a backup
system. A given cue may not be available at certain times, and at other
times it may give ambiguous information. Having an alternate source of
information can also help detect erroneous cues. We find this rule of
thumb followed in desert ants and in many other migratory creatures.
Those
who program computer algorithms for a living are especially well
situated to appreciate just how complex an algorithm would need to be to
function as well as the desert ant’s navigational algorithm. In this
case, the algorithm consists of several logical decisions based on the
information detected. Once the algorithm makes a decision concerning
which source of navigation to use based on incoming data and
environmental conditions, the control function then must compute the
course. In some instances the course is computed based on combining the
information from two navigation methods. When the ant is using the path
integrator it keeps track of its movement through the odometer and the
angular movements determined by the compass, which are both stored in
memory. It then can compute the direct path to its home nest. This is a
complex process that involves trigonometry.
When Ants Do Trigonometry
While
we can write a reasonably simple segment of computer code to do this
computation, in this case it must be programmed into the ant’s brain and
integrated with the many other essential elements of the navigation
algorithm. The question is twofold: (1) How can a trigonometric
mathematical computation be programmed into the brain of an ant through a
neo-Darwinian process of genetic mutation and natural selection? The
programming, keep in mind, likely involves a neural circuit, and one of
considerable sophistication. (2) How could a neo-Darwinian process
manage to do so while simultaneously building other essential subsystems
of the larger integrated navigation system? The trigonometric
calculation alone would seem to be difficult to evolve in a stepwise
manner, but without these other subsystems in place, the most beautiful
algorithm in the world for calculating a trigonometric mathematical
computation is useless to the ant, and therefore not available for being
seized upon and preserved by natural selection. This raises the
chicken-or-egg problem: Which evolved first — the physical systems or
the behavior algorithms?
Serial
gradualistic evolution does not seem plausible, as each characteristic
by itself is not useful. On the other hand, simultaneous evolution of
all of the physical characteristics and behaviors is not plausible due
to its extreme improbability.
