New theory on the origin of primates

A new model for primate origins is presented in Zoologica Scripta, published by the Norwegian Academy of Science and Letters and The Royal Swedish Academy of Sciences. The paper argues that the distributions of the major primate groups are correlated with Mesozoic tectonic features and that their respective ranges are congruent with each evolving locally from a widespread ancestor on the supercontinent of Pangea about 185 million years ago.

Michael Heads, a Research Associate of the Buffalo Museum of Science, arrived at these conclusions by incorporating, for the first time, spatial patterns of primate diversity and distribution as historical evidence for primate evolution. Models had previously been limited to interpretations of the fossil record and molecular clocks.

"According to prevailing theories, primates are supposed to have originated in a geographically small area (center of origin) from where they dispersed to other regions and continents" said Heads, who also noted that widespread misrepresentation of fossil molecular clocks estimates as maximum or actual dates of origin has led to a popular theory that primates somehow crossed the globe and even rafted across oceans to reach America and Madagascar.

In this new approach to molecular phylogenetics, vicariance, and plate tectonics, Heads shows that the distribution ranges of primates and their nearest relatives, the tree shrews and the flying lemurs, conforms to a pattern that would be expected from their having evolved from a widespread ancestor. This ancestor could have evolved into the extinct Plesiadapiformes in north America and Eurasia, the primates in central-South America, Africa, India and south East Asia, and the tree shrews and flying lemurs in South East Asia.

Divergence between strepsirrhines (lemurs and lorises) and haplorhines (tarsiers and anthropoids) is correlated with intense volcanic activity on the Lebombo Monocline in Africa about 180 million years ago. The lemurs of Madagascar diverged from their African relatives with the opening of the Mozambique Channel (160 million years ago), while New and Old World monkeys diverged with the opening of the Atlantic about 120 million years ago.

"This model avoids the confusion created by the center of origin theories and the assumption of a recent origin for major primate groups due to a misrepresentation of the fossil record and molecular clock divergence estimates" said Michael from his New Zealand office. "These models have resulted in all sorts of contradictory centers of origin and imaginary migrations for primates that are biogeographically unnecessary and incompatible with ecological evidence".

The tectonic model also addresses the otherwise insoluble problem of dispersal theories that enable primates to cross the Atlantic to America, and the Mozambique Channel to Madagascar although they have not been able to cross 25 km from Sulawesi to Moluccan islands and from there travel to New Guinea and Australia.

Heads acknowledged that the phylogenetic relationships of some groups such as tarsiers, are controversial, but the various alternatives do not obscure the patterns of diversity and distribution identified in this study.

Biogeographic evidence for the Jurassic origin for primates, and the pre-Cretaceous origin of major primate groups considerably extends their divergence before the fossil record, but Heads notes that fossils only provide minimal dates for the existence of particular groups, and there are many examples of the fossil record being extended for tens of millions of years through new fossil discoveries.

The article notes that increasing numbers of primatologists and paleontologists recognize that the fossil record cannot be used to impose strict limits on primate origins, and that some molecular clock estimates also predict divergence dates pre-dating the earliest fossils. These considerations indicate that there is no necessary objection to the biogeographic evidence for divergence of primates beginning in the Jurassic with the origin of all major groups being correlated with plate tectonics.

The Buffalo Museum of Science is the non-profit educational institution dedicated to the study and interpretation of the natural and physical sciences. Its extensive collections of over 700,000 specimens and artifacts represent all facets of the natural world with an emphasis on Western New York as well as man-made objects spanning the globe. Based at 1020 Humboldt Parkway and anchoring Buffalo's East Side in Olmsted-designed Martin Luther King, Jr. Park since 1929, the Museum presents a wide variety of programs and services for children, teachers, families, adults, and community organizations throughout each year. The Museum also operates Tifft Nature Preserve in South Buffalo, a 264-acre urban wetland preserve on reclaimed former industrial land and seasonally sponsors archaeological exploration at the Hiscock Site in nearby Genesee County, NY, one of North America's richest Ice Age sites.

For further information on the Museum and its upcoming activities, call 716-896-5200 or visit www.sciencebuff.org.

Engineers and scientists, despite what they have been habituated to say, could never design anything by the process of evolution; it is mathematically impossible.

It's done all the time. Genetic Algorithms and Evolutionary Programming have been around for decades now, and are even used by major coporations to solve real world problems. See, for instance:

Genetic Algorithms and Evolutionary Computation (Talk Origins)

This article gives numerous examples. Just a few:

As cited in Begley and Beals 1995, Texas Instruments used a genetic algorithm to optimize the layout of components on a computer chip, placing structures so as to minimize the overall area and create the smallest chip possible. Using a connection strategy that no human had thought of, the GA came up with a design that took 18% less space.
Beasley, Sonander and Havelock 2001 used a GA to schedule airport landings at London Heathrow, the United Kingdom's busiest airport. This is a multiobjective problem that involves, among other things, minimizing delays and maximizing number of flights while maintaining adequate separation distances between planes (air vortices that form in a plane's wake can be dangerous to another flying too closely behind). When compared to actual schedules from a busy period at the airport, the GA was able to reduce average wait time by 2-5%, equating to one to three extra flights taking off and landing per hour - a significant improvement. However, even greater improvements have been achieved: as reported in Wired 2002, major international airports and airlines such as Heathrow, Toronto, Sydney, Las Vegas, San Francisco, America West Airlines, AeroMexico, and Delta Airlines are using genetic algorithms to schedule takeoffs, landings, maintenance and other tasks, in the form of Ascent Technology's SmartAirport Operations Center software (see http://www.ascent.com/faq.html). Breeding and mutating solutions in the form of schedules that incorporate thousands of variables, "Ascent beats humans hands-down, raising productivity by up to 30 percent at every airport where it's been implemented."
As reported in Lemley 2001, United Distillers and Vintners, a Scottish company that is the largest and most profitable spirits distributor in the world and accounts for over one-third of global grain whiskey production, uses a genetic algorithm to manage its inventory and supply. This is a daunting task, requiring the efficient storage and distribution of over 7 million barrels containing 60 distinct recipes among a vast system of warehouses and distilleries, depending on a multitude of factors such as age, malt number, wood type and market conditions. Previously, coordinating this complex flow of supply and demand required five full-time employees. Today, a few keystrokes on a computer instruct a genetic algorithm to generate a new schedule each week, and warehouse efficiency has nearly doubled.
This technique [using genetic algorithms to find optimal routing paths in telecommunications networks] has found real-world applications for similar purposes, as reported in Begley and Beals 1995. The telecommunications company U.S. West (now merged with Qwest) was faced with the task of laying a network of fiber-optic cable. Until recently, the problem of designing the network to minimize the total length of cable laid was solved by an experienced engineer; now the company uses a genetic algorithm to perform the task automatically. The results: "Design time for new networks has fallen from two months to two days and saves US West $1 million to $10 million each" (p.70).

Genetic algorithms not only sometimes hit upon solutions no human designer ever thought of (since they have no biases about what is good or bad design, or what is likely or unlikely to work) but, in at least one instance described in this article, produced an effective and superior result, that humans could not even explain in retrospect!:

A field-programmable gate array, or FPGA for short, is a special type of circuit board with an array of logic cells, each of which can act as any type of logic gate, connected by flexible interlinks which can connect cells. Both of these functions are controlled by software, so merely by loading a special program into the board, it can be altered on the fly to perform the functions of any one of a vast variety of hardware devices.

Dr. Adrian Thompson has exploited this device, in conjunction with the principles of evolution, to produce a prototype voice-recognition circuit that can distinguish between and respond to spoken commands using only 37 logic gates - a task that would have been considered impossible for any human engineer. He generated random bit strings of 0s and 1s and used them as configurations for the FPGA, selecting the fittest individuals from each generation, reproducing and randomly mutating them, swapping sections of their code and passing them on to another round of selection. His goal was to evolve a device that could at first discriminate between tones of different frequencies (1 and 10 kilohertz), then distinguish between the spoken words "go" and "stop".

This aim was achieved within 3000 generations, but the success was even greater than had been anticipated. The evolved system uses far fewer cells than anything a human engineer could have designed, and it does not even need the most critical component of human-built systems - a clock. How does it work? Thompson has no idea, though he has traced the input signal through a complex arrangement of feedback loops within the evolved circuit. In fact, out of the 37 logic gates the final product uses, five of them are not even connected to the rest of the circuit in any way - yet if their power supply is removed, the circuit stops working. It seems that evolution has exploited some subtle electromagnetic effect of these cells to come up with its solution, yet the exact workings of the complex and intricate evolved structure remain a mystery (Davidson 1997).

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