Rates of Evolution – Palaeontology’s greatest ever graphs

Tags

evolution, paleontology, rates of evolution

The Palaeontological Association has been running a fun series of essays in its newsletter entitled “Palaeontology’s greatest ever graphs”. I was kindly invited by the editor Emilia Jarochowska to write the latest essay, which featured this iconic graph published by Phil Gingerich in 1983 (wonderfully recreated and cartoonified by Ratbot Comics). The figure compiled data on measured rates of morphological evolution, plotting them against the interval of time over which the rate was measured. In other words, say I measured the rate of evolution of a species of snail every generation, and generations last one year, I would then plot those rates against one year. When Gingerich compiled rates ranging over intervals spanning days to millions of years, he got the inverse relationship (negative slope) shown in the figure. In other words, the longer the interval of time, the slower the measured rate! Debate raged over whether this was an actual biological feature, wherein rates at different times could differ greatly, or whether it was some sort of mathematical, or even psychological artifact. Well, here’s my take on it. And if you like the figure, head over to Ratbot Comics where you will find some truly fun stuff from the artist, Ellis Jones. Enjoy.

Inverse relationship of evolutionary rates and interval of time over which rates were measured

The rates at which morphological evolution proceeds became a central palaeontological contribution to development of the neo-synthetic theory of evolution in the mid-twentieth century (Simpson, 1944; Haldane, 1949). Many decades later we can say retrospectively that three questions must qualify the study of those rates. First, how is rate being measured? Second, at which level or for what type of biological organization is rate being measured, e.g. within a species or within a clade (Roopnarine, 2003)? Third, why do we care about rate? In other words, what might we learn from knowing the speed of morphological evolution?

The figure presented here illustrates a compilation of rates of morphological evolution calculated within species, or within phyletic lineages of presumed relationships of direct ancestral- descendant species (Gingerich, 1983). The most obvious feature is the inverse relationship between rate and the interval of time over which a rate is measured. The compilation included data derived from laboratory experiments of artificial selection, historical events such as biological invasions, and the fossil record. The rates are calculated in units of “darwins”, i.e., the proportional difference between two measures divided by elapsed time standardized to units of 1 million years (Haldane, 1949). This makes 1 darwin roughly equivalent to a proportional change of 1/1000 every 1,000 years. Haldane’s interest in rates was to determine how quickly phenotypically expressed mutations could become fixed in a population, and he expected the fossil record to be a potential source of suitable data. Later, Bjorn Kurtén (1959), pursuing this line of thinking was, I believe, the first to note that morphological change, and hence rate, decreased as the interval of time between measured points increased. Kurtén, who was measuring change in lineages of mammals from the Tertiary, Pleistocene and Holocene, throughout which rates increased progressively, suggested two alternative explanations for the inverse relationship: (1) increasing rates reflected increasingly variable climatic conditions as one approached the Holocene, or (2) the trend is a mathematical artifact. Philip Gingerich compiled significantly more data and suggested that the decline of rates as measurement interval increases might indeed be an artifact, yet a meaningful one. To grasp the significance of Gingerich’s argument, we must dissect both the figure and Kurtén’s second explanation.

The displayed rates vary by many orders of magnitude, and Gingerich divided them subjectively into four domains, with the fastest rates occupying Domain I and coming from laboratory measures of change on generational timescales. The slowest rates, Domain IV, hail exclusively from the fossil record. Kurtén suggested that, as the geological span over which a rate is calculated increases, the higher the frequency of unobserved reversals of change. Thus change that might have accumulated during an interval could be negated to some extent during a longer interval, leading to a calculated rate that is slower than true generational rates. Gingerich regressed his compiled rates against the interval of measurement, and not only validated Kurtén’s observation for a much broader set of data, but additionally asserted that if one then scales rate against interval, the result is unexpectedly uniform. This implies no difference between true generational rates, rates of presumed adaptation during historical events, and phyletic changes between species on longer timescales. It is a simple leap from here to Gingerich’s main conclusion, that the process or evolutionary mode operating within the domains is a single one, and there is thus no mechanistic distinction between microevolutionary and macroevolutionary processes.

Perhaps unsurprisingly, the first notable response to Gingerich’s claim was made by Stephen Gould (1984), a founder of the Theory of Punctuated Equilibrium, and a major force in the then developing macroevolutionary programme. A major tenet of that programme is that there exists a discontinuity between microevolutionary processes that operate during the temporal span of a species, and macroevolutionary processes that are responsible for speciation events and other phenomena which occur beyond the level of populations, such as species selection. Gould objected strongly to Gingerich’s argument, and presented two non-exclusive alternative explanations. Appearing to initially accept a constancy of rate across scales, Gould argued that the inverse relationship between rate and interval must require the amount of morphological change to also be constant. He found Gingerich’s calculated slope of 1.2, however, to be suspiciously close to 1, pointing towards two psychological artifacts. First, very small changes are rarely noticed and hence reported, essentially victims of the bias against negative results. Second, instances of very large change tend to be overlooked because we would not recognize the close phyletic relationship between the taxa. This second explanation strikes directly from the macroevolutionist paradigm. Gould proposed that the very high rates measured at the shortest timescales (Domain I) are a biased sample that ignores the millions of extant populations that exhibit very low rates. This bias creates an incommensurability with rates measured from the fossil record, which would be low if morphological stasis is the dominant mode of evolution on the long term.

Gingerich and Gould, observing the same data, arrived at opposing explanations. Neither party, unfortunately, were free of their own a priori biases concerning the evolutionary mechanism(s) responsible for the data. A deeper consideration of the underlying mathematics reveals a richer framework behind the data and figure than either worker acknowledged. Fred Bookstein (1987) provided the first insight by modelling unbiased or symmetric random walks as null models of microevolutionary time series. Bookstein pointed out that for such series, the frequency of reversals is about equal to the number of changes in the direction of net evolution between any two points. In other words, if a species trait increased by a factor of x when measured at times t₁ and t₂, the number of generations for which the trait increased is roughly equal to the number of generations for which it decreased (in the limit as series length approaches infinity). “Rate” becomes meaningless for such a series beyond a single generational step! In one fell swoop, Bookstein rendered the entire argument moot, unless one could reject the hypothesis that the mode of trait evolution conformed to a random walk. He also, however, opened the door to a better understanding of the inverse relationship: measures of morphological change over intervals greater than two consecutive generations cannot be interpreted independently of the mode or modes of evolution that operated during the intervals.

In order to understand this, imagine a time series of morphological trait evolution generated by an unbiased random walk. That is, for any given generation the trait’s value, logarithmically transformed, can decrease or increase with equal probability by a factor k, the generational rate of evolution. The expected value of the trait after N generations will be x₀±kN^0.5 (see Berg, 2018 for an accessible explanation), where x₀ is the initial value of the trait. Selecting any two generations in the series and calculating an interval rate then yields (x_N-x₀)/N = kN^0.5/N = kN^-0.5. Logarithmic transformation of the interval, as done in the figure, will thus yield a slope of -0.5. Alternatively, suppose the mode of evolution was incrementally directional (a biased random walk), then the expected rate would simply be kN^a, where a>-0.5. The expected rate generated by a perfectly directional series would be kN⁰, yielding a slope of rate versus interval of 0 (I’ll leave the proof to readers; or see Gingerich, 1993 or Roopnarine, 2003). And finally, a series that was improbably constrained in a manner often envisioned by Gould and others as stasis (Roopnarine et al., 1999), would yield a slope close to -1. Gingerich (1993) exploited these relationships, using all the available observations from a stratophenetic series to classify the underlying mode, and presumably test the frequency with which various modes account for observed stratophenetic series: slope 0 – directional, ~-0.5 – random, -1 – stasis. The method suffered complications arising from the regression of a ratio on its denominator (Gould, 1984; Sheets and Mitchell, 2001; Roopnarine, 2003), and the fact that the statistics of evolutionary series converge to those of unbiased random walks as preservational incompleteness increases (Roopnarine et al., 1999). It is nevertheless intimately related to the appropriate mathematics (Roopnarine, 2001).

Ultimately, we can use these relationships to understand that the inverse proportionality between rates and their temporal intervals is compelled to be negative because of mathematics, and only mathematics. Given that no measures of morphology are free of error, and that it is highly improbable that any lineage will exhibit perfect monotonicity of evolutionary mode during its geological duration, then all slopes of the relationship must lie between -1 and 0. Furthermore, the distribution of data within and among the four domains by itself tells us very little about mode, for it consists of point measures taken from entire histories, and those measures cannot inform us of the modes that generated them. Gingerich’s method (1983, 1993) might have been problematic, but it provided a foundation for further developments that demonstrated the feasibility of recovering evolutionary mode from stratophenetic series (Roopnarine, 2001; Hunt, 2007). Perhaps it is time to circle back to this iconic figure and broadly reassess the distribution of evolutionary rates in the fossil record (Voje, 2016).

References

BERG, H. C. 2018. Random walks in biology. Princeton University Press, 152 pp.
BOOKSTEIN, F. L. 1987. Random walk and the existence of evolutionary rates. Paleobiology, 13 (4), 446–464.
GINGERICH, P. D. 1983. Rates of evolution: effects of time and temporal scaling. Science, 222 (4620), 159–161.
GINGERICH, P. D. 1984. Response: Smooth curve of evolutionary rate: A psychological and mathematical artifact. Science, 226 (4677), 995–996.
GOULD, S. J. 1984. Smooth curve of evolutionary rate: a psychological and mathematical artifact. Science, 226 (4677), 994–996.
HALDANE, J. B. S. 1949. Suggestions as to quantitative measurement of rates of evolution. Evolution, 3 (1), 51–56.
HUNT, G. 2007. The relative importance of directional change, random walks, and stasis in the evolution of fossil lineages. Proceedings of the National Academy of Sciences, 104 (47), 18404–18408.
KURTÉN, B. 1959. Rates of evolution in fossil mammals. 205–215. In: Cold Spring Harbor Symposia on Quantitative Biology Vol. 24. Cold Spring Harbor Laboratory Press.
ROOPNARINE, P. D. 2001. The description and classification of evolutionary mode: a computational approach. Paleobiology, 27 (3), 446–465.
ROOPNARINE, P. D. 2003. Analysis of rates of morphologic evolution. Annual Review of Ecology, Evolution, and Systematics, 34 (1), 605–632.
ROOPNARINE, P. D., BYARS, G. and FITZGERALD, P. 1999. Anagenetic evolution, stratophenetic patterns, and random walk models. Paleobiology, 25 (1), 41–57.
SHEETS, H. D. and MITCHELL, C. E. 2001. Uncorrelated change produces the apparent dependence of evolutionary rate on interval. Paleobiology, 27 (3), 429–445.
SIMPSON, G. G. 1944. Tempo and Mode in Evolution. Columbia University Press, 217 pp.
VOJE, K. L. 2016. Tempo does not correlate with mode in the fossil record. Evolution, 70 (12), 2678–2689.

Systems Paleoecology – Introduction

Tags

dynamics, ecology, evolution, modeling, networks, paleoecology, paleontology

WHAT IS ECOLOGICAL STABILITY ? In 2019 I posed this question informally to colleagues, using Twitter, a professional workshop that I lead, and a conference. Respondents on Twitter consisted mostly of ecological scientists, but the workshop included paleontologists, biologists, physicists, applied mathematicians, and an array of social scientists, including sociologists, anthropologists, economists, archaeologists, political scientists, historians and others. And this happened…

We have a capacity for imagining situations that are not implied by the data. . . Lee Smolin

The concept of “stability” in science is an evolving one, partly because of the advent of systems approaches to multiple disciplines. To the extent that the 20th century was the century of the small (the atom, the gene, the bit), we can claim the 21st century to be the century of systems: ecological, genomic, socio-eco-economic, information, and so on. In the end I don’t think that we yet have a complete understanding of stability, or perhaps we do not yet fully know what it is that we need to understand.

The workshop took place at the Leibniz Center in Berlin, during May, 2019.

In this blog series I will outline my own current views on what stability means in paleocology — the study of the ecological aspects of the history of life. Although stability is a multi-disciplinary concept, my discussion will be biased heavily toward ecological and paleoecological systems as those are my areas of expertise. However, the concepts and discussion are hopefully general enough to be of multi- and trans-disciplinary interest. In instances where they are not, or fall short of being applicable in another discipline, I urge others working in those areas to formulate terms and definitions as needed so that in the end we have a comprehensible and comprehensive terminology, and can truly understand what stability means in all the dynamic systems that we are dealing with today.

Paleoecological concepts

Ecology, including paleoecology, is a fundamentally observational discipline for which a large and broad array of explanatory principles and theories has been developed, e.g. the principle of competitive exclusion (Gause’s law, Grinnell’s principle), the Theory of Island Biogeography, and Hubbell’s Unified Neutral Theory of Biodiversity. These laws, principles and theories differ from foundational theories in other scientific disciplines, such as General Relativity, quantum mechanics, evolution by natural selection, and population genetics, in being limited in the numerical capabilities or precision of their predictions. E.g., many species that compete for resources will coexist in the wild without exclusion, and assemblages of species competing for resources often do not behave neutrally. Despite this, there is an underlying strength to predictive ecological theories and models when they are based on sound inductive reasoning, for the limits of their applicabilities to the real world or inconsistencies with empirical data expose the sheer complexity and high dimensionality of ecological systems — competitors may coexist because of differing life history traits (e.g. dynamics of birth-death rates), incomplete or intermittent resource overlap, spatial and temporal refuges from superior competitors, pressure from predators, and so forth. This complexity of ecological systems is in turn driven by four main factors: the geosphere, evolution on short timescales, history on long timescales, and emergent properties.

The geosphere, atmosphere and hydrosphere, including tectonic, oceanographic and atmospheric processes, affect ecological systems on multiple spatio-temporal scales. Geospheric dynamics determine the appearance and disappearance of islands, the erection and removal of barriers to dispersal and isolation, patterns and rates of ocean circulation and mixing, climate, and weather. The mechanisms of genetic variation and natural selection determine whether, how and how quickly populations of organisms can acclimatize or adapt to their ever-changing, dynamic environments. Those accommodations in turn feedback to their abiotic and biotic environments. No ecological system, however, is solely or even largely a product of processes occurring on generational, ecological, or contemporaneous timescales, for the collection of species that occupy a particular place and time — a community — arrived at that point via path-dependent histories. What you see now depends very much on what came before. Those histories are themselves a cumulative set of past responses of populations, species and communities to their abiotic and biotic environments. And those populations of multiple species, when interacting, are complex systems with emergent properties such as stability. Emergent properties can act as additional drivers of population and community dynamics in feedback loops that both expand and contract the scope within which ecological dynamics deviate from the pure predictions of principle-based theories and models.

Models

The following work will make extensive use of mathematical models, because I believe that they are useful and somewhat underutilized in paleoecology, and because I like them. One guide to understanding the utility of model-based approaches in ecology and paleoecology is to question the soundness of their underlying assumptions, and to explore why those assumptions might appear to be inaccurate when a particular approach is applied to the real world. And both ecological and paleoecological theories are laden with assumptions, sometimes explicit, but often implicit. Ask yourself the following questions: Do real populations ever attain carrying capacity? Are the sometimes complex dynamics predicted by intrinsic rates of population growth ever realized in nature? Are populations ever in equilibrium? What are the relative contributions of intrinsic and extrinsic processes to a population’s dynamics? Are communities stable? If they are, is stability a function of species properties, or of community structure, and if the latter, where did that structure come from? Is community stability always a result of a well-defined set of general properties, or is the set wide-ranging, variable, and idiosyncratic? And, are the answers to these questions based on laws that have remained immutable throughout the history of life on our planet, or have the laws themselves evolved or varied in response to a dynamic and evolving biogeosphere? In the posts that follow, I will introduce basic concepts that are essential to understanding ecological stability, and to equip us to further explore more extensive and sophisticated models that are beyond the scope of the blog. I will attempt to build the concept of stability along steps of hierarchical levels of ecological organization, and to relate each of those steps to paleoecological settings, concepts and studies. This will not be a series on analytical methods. It is about concepts and conceptual models. There are already rich resources and texts for paleoecological methodologies.

The Series

The posts will be divided into parts, each successive part building on the previous one by expanding the complexity of the systems and the levels of organization under consideration. Part I deals with isolated populations, an unrealistic situation perhaps, but an idealization fundamental to understanding systems of multiple species. And, it is populations that become extinct. This part contains a lot of introductory material, but it is essential for laying groundwork for later sections that both deal with more advanced and original material. Advanced readers might wish to skip over these posts, but there is original matter in there, and I welcome feedback! Part II addresses community stability, with an emphasis on paleoecological models and applications. Part III explores the evolutionary and historical roots of ecological stability, including the origination of hierarchical structure and community complexity, stability as an agent of natural selection, and the selection and evolution of communities and ecosystems.

Caveat lector!

The discussion will be technical in some areas, because “systems” is a technical concept. Mathematical models are used extensively because I have found them to be a more accessible way to understand the necessary ecological concepts, sometimes in contrast to actual ecological narratives. Ecological systems are complicated and complex, and models offer a way for us to focus on specific
questions, distilling features of interest. Useful models are in my opinion simple, and they can serve as essential guides to constructing narratives and theories of larger and more complete systems. I will therefore taken great care to outline and explain basic concepts and models (Do not fear the equations! But feel free to ignore them..). Examples of real-world data and analyses will be included in many sections. Additionally, code for many of the models will also included. I use the Julia programming language exclusively (but I have also used C++, Octave and Mathematica extensively in the past, and recommend them highly). I regard R‘s power with awe, but I am not a fan of its syntax.

My hope is that the series will successfully build on concepts and details progressively, and that at no point will readers find themselves unable to continue. I don’t think that a technical mastery is at all necessary, but it can deepen one’s qualitative grasp significantly. And one should never underestimate the power to impress at a party if you can explain mathematical attractors and chaos!

And finally, what follows is unlikely to comprise my final opinions on this topic.

 

 

 

 

The more things change…

Tags

ecology, evolution, simulations

The Paleozoic Marine Evolutionary Faunas. Shown above are Sheehan’s revision of Boucot’s Ecologic Evolutionary Units. (Drawn by Ashley Dineen).

Every paleontologist, and hopefully everyone who has taken a paleontology course in the past 25 years, recognizes this figure. The main part of it shows one version of the famous Sepkoski curves. By analyzing occurrences of marine invertebrates through the Phanerozoic (past 540 million years or so), Sepkoski noted that the history of marine invertebrates can be divided into three temporally successive, but overlapping “faunas”, named in order the Cambrian, Paleozoic and Modern faunas. Each one is characterized by two features: First, they tend to be dominated by particular types of animals. For example, trilobites are a dominant Cambrian group, brachiopods a dominant Paleozoic group, and bivalved molluscs a dominant Modern group. “Dominant” itself is applied because species of a fauna tend to be present in most of the communities of the fauna, and are often numerically dominant, meaning that they are among the most abundant type of fossil organism in the community. The second feature is the longevity of a fauna; these things persisted for at least tens of millions of years, in the case of the Cambrian Fauna, to hundreds of millions of years in the case of the Paleozoic and Modern! Identification of the faunas, in my opinion, is one of the pillars of the revolution that took place in paleontology between the late 1970’s to early 1980’s, setting paleontology onto its current course, the mainstream of which seeks nothing less than the geobiological processes that explain the history of life on Earth.

In that sense, Sepkoski’s discovery cemented a feature of that history that has been recognized since we started to organize the fossil record back in the days of Cuvier and Lyell, and it is this: Whereas the mechanisms that generate Life’s history, namely variation of the geosphere (climate, tectonics, etc.) and Darwinian evolution, are mechanisms of inexorable change, Life’s history itself is one of fits and starts. By this I mean that at many evolutionary and ecological levels, change simply does not happen for very long spans of time. Steve Gould‘s hypothesis of Punctuated Equilibrium will immediately come to the minds of many readers, and indeed morphological stasis is a real and frequent phenomenon among fossil species. But it goes much further than that. If we examine Sepkoski’s faunas closely, we begin to realize that we can subdivide each one repeatedly into units that are smaller in size (diversity), and shorter in duration, but which also persist relatively unchanged for what are ecologically and evolutionarily long periods of geological time. Look at the figure again, and you will see a scale at the top entitled “EEUs”, or Ecologic Evolutionary Units. These were first described by the paleontologist Art Boucot (later revised by Peter Sheehan), to describe communities that at the genus level remained unchanged for tens of millions of years. EEU’s are not the end, however, and can be subdivided yet again into smaller, shorter units, such as Bambach and Bennington’s “community types” (see below). The bottom line, regardless of scale, is this: In spite of underlying mechanisms of change, ecological communities are organized into a nested hierarchy characterized by non-change.

So, when we combine dominance and the absence of change, one could accuse the fossil record, at those evolutionary and ecological levels, of being rather boring! But of course it isn’t (personal biases aside), and one huge reason for this, whether you’ve recognized it previously or not, is that the non-change of the ecological units serve to emphasize, highlight, “punctuate” the times when things do change! Dramatic changes? The history of life has those in spades: evolution of multicellularity, evolution of photosynthesis, evolution of skeletonized bodies, invasion of the land; all certainly mark the beginning of new ecologic-evolutionary epics. And, of course, things also have endings, and the endings of our units are marked by extinctions. The bigger the extinction, the bigger the unit lost. Or perhaps, the bigger the unit lost the bigger the extinction? The decline and fall of Sepkoski’s first two faunas are marked by mass extinctions, the standout being the Permian-Triassic mass extinction 251 million years ago. To paraphrase Dave Jablonski, mass extinctions remove dominance. Is there a mechanistic relationship between our EEUs and extinction? How are EEUs made, and how do they persist unchanged? Can only extinction bring them to an end? And if so, how are new EEUs made and why are they different?

To me, these are among the most interesting and important questions in paleontology today, and not only because they touch on big pieces of the fossil record. Our modern world is composed of the latest EEUs, which range in age from the end of the last ice age, to a couple million years (when the current ice ages began). Our modern world might also be teetering on the brink of a new, sixth global mass extinction. I therefore believe that answering the preceding questions are very important to the preservation of our modern biosphere, and a sustainable relationship between it and our own complex society. Toward that end, my colleagues and I have been working for several years on the relationship between large fossil ecosystems and mass extinctions, and we recently published a paper in which we present a new hypothesis to explain not only how EEUs arise, but also how they are transformed by mass extinctions. I had a lot of fun writing this paper, but it is probably a bit dry and technical (if I am being honest). So, over the next few weeks I intend to take interested readers through the paper, bit by bit, in a series of posts on this blog. I’ll wrap it up here for now, but will leave you with both a link to a free, unpublished version of the paper, and a figure as a preview (with very little explanation! For now). It’s a “mathematical space” illustrating the relative global stabilities of two alternative type of communities.

Roopnarine, Peter, Kenneth Angielczyk, AllenWeik, and Ashley Dineen. 2018. “Ecological Persistence, Incumbency and Reorganization in the Karoo Basin During the Permian-triassic Transition.” PaleorXiv. November 1. doi:10.1016/j.earscirev.2018.10.014.

An ecological “space” comparing the late Permian Upper Daptocephalus Assemblage Zone of the Karoo Basin, South Africa, to alternative eco-evolutionary histories.

Bambach, Richard K., and J. Bret Bennington. “Do communities evolve? A major question in evolutionary paleoecology.” Evolutionary Paleobiology: University of Chicago Press, Chicago (1996): 123-160.

New paper: Comparing paleo-ecosystems

Tags

dynamics, ecology, evolution, modeling

Modeled ecological dynamics in South Africa 1 million years after the end Permian mass extinction, showing the highly uncertain response of the community to varying losses of primary production.

We have a new paper on paleo-food web dynamics in the Journal of Vertebrate Paleontology! The paper is one in a collection of 13 (and 27 authors), all focused on the “Vertebrate and Climatic Evolution in the Triassic Rift Basins of Tanzania and Zambia”. The collection covers work done in the Luangwa and Ruhuhu Basins of Zambia and Tanzania, surveying the vertebrates who lived there during the Middle Triassic, approximately 245 million years ago (mya). This is a very interesting period in the Earth’s history, being only a few million years after the devastating end Permian mass extinction (251 mya). They are also very interesting places, capturing some of our earliest evidence of the rise of the reptilian groups which would go on to dominate the terrestrial environment for the next 179 million years. The evidence includes Teleocrater, one of the earliest members of the evolutionary group that includes dinosaurs and modern birds.

Our paper, “Comparative Ecological Dynamics Of Permian-Triassic Communities From The Karoo, Luangwa And Ruhuhu Basins Of Southern Africa” is exactly that, a comparison of the ecological communities of southern Africa before, during and after the mass extinction. Most of our knowledge of how the terrestrial world was affected by, and recovered from the mass extinction comes for extensive work on the excellent fossil record in the Karoo Basin of South Africa, but that leaves us wondering how applicable that knowledge is to the rest of the world. We therefore set out to discover how similar or varied the ecosystems were over this large region, comparing both the functional structures (what were the ecological roles and ecosystem functions) and modeling ecological dynamics across the relevant times and spaces of southern Africa. We discovered that during the late Permian, before the extinction, the three regions (South Africa, Tanzania, Zambia) were very similar. In the years leading up to the extinction, however, communities in South Africa were changing, becoming more robust to disturbances, but the change seemed slower to happen further to the north. The record becomes silent during the mass extinction, and for millions of years afterward, but when it does pick up again in the Middle Triassic of Tanzania, the communities in South Africa and Tanzania are quite distinct in their composition. The ecosystem in South Africa was dominated by amphibians and ancient relatives of ours, whereas to the north we see the earliest evidence of the coming Age of Reptiles. Yet, and this is where modeling can become so cool, the two systems seemed to function quite similarly. We believe that this a result of how the regions recovered from the mass extinction. Evolutionarily, they took divergent paths, but the organization of new ecosystems under the conditions which prevailed after the mass extinction lead to two different sets of evolutionary players, in two different geographic regions, playing the same ecological game. As we say in the paper, “This implies that ecological recovery of the communities in both areas proceeded in a similar way, despite the different identities of the taxa involved, corroborating our hypothesis that there are taxon-independent norms of community assembly.”

And finally, this work would not have been possible without the generous support of the United States National Science Foundation’s Earth Life Transitions program.

Pyron’s Puzzling Post Piece

Tags

Alexander Pyron, Conservation, ecology, environment, evolution, extinction, science

(Peter Roopnarine)

Alexander Pyron, a professor of biology at George Washington University, recently wrote an inflammatory op-ed for the Washington Post, entitled “We don’t need to save endangered species. Extinction is part of evolution.” The post outraged many, among them an awful lot of scientists. Needless to say, the piece is a seriously misguided bit of poor reasoning and inaccurate science, particularly with regards to extinction. Myself and colleague Luiz Rocha, also at the California Academy of Sciences, wrote our own response, published several days ago in bioGraphic. Regardless of your opinion on species conservation, Pyron’s article cannot be used as the basis for sound argument, because it is a collection of fundamentally flawed arguments. You can read our own reasoning here: Betting on Conservation.

The image, by the way, shows the fossilized burrows of tiny marine snails in sediments dating to about 250 million years ago. The fossils are from a geological exposure in the mountains of Hubei, China, and is some of the earliest evidence there of the biosphere struggling back from the devastating end Permian mass extinction of 251 million years ago. There are no guarantees in the History of Life.

I’ve edited this post to add a little addendum: While I disagree strongly with Pyron’s opinions, I cannot agree with or support the personal attacks which have been leveled against him by others. The core power of rationalism and modern science is open and free discourse. I think that his science in this case is wrong, and I disagree with his moral stance, but I would not place this in the same category of, for example, charlatan climate change deniers who have alternative and exploitative agendas. So let’s keep the discussion civil.

Reining in the Red Queen

Tags

adaptation, evolution, extinction, paleontology, Red Queen

(The Victorian Web)

Geerat Vermeij and I just published a new paper in Paleobiology, entitled ” Reining in the Red Queen: The dynamics of adaptation and extinction re-examined.” The paper is partly a follow-up to my earlier paper on the Red Queens Hypothesis, reported previously in these posts (here and here), and partly the result of a discussion started between Vermeij and myself while attending the workshop that resulted in this paper on state transitions in the global biosphere. Here we argue that some of the fundamental assumptions of the hypothesis are flawed and that it therefore likely holds only under restricted circumstances. The full reference and abstract follow.

Vermeij, G. J. and P. D. Roopnarine. 2013. Reining in the Red Queen: The dynamics of adaptation and extinction re-examined. Paleobiology 39:560-575.

Abstract

One of the most enduring evolutionary metaphors is Van Valen’s (1973) Red Queen. According to this metaphor, as one species in a community adapts by becoming better able to acquire and defend resources, species with which it interacts are adversely affected. If those other species do not continuously adapt to compensate for this biotically caused deterioration, they will be driven to extinction. Continuous adaptation of all species in a community prevents any single species from gaining a long-term advantage; this amounts to the Red Queen running in place. We have critically examined the assumptions on which the Red Queen metaphor was founded. We argue that the Red Queen embodies three demonstrably false assumptions: (1) evolutionary adaptation is continuous; (2) organisms are important agents of extinction; and (3) evolution is a zero-sum process in which living things divide up an unchanging quantity of resources. Changes in the selective regime need not always elicit adaptation, because most organisms function adequately under many ‘‘suboptimal’’ conditions and often compensate by demonstrating adaptive flexibility. Likewise, ecosystems are organized in such a way that they tend to be robust and capable of absorbing invasions and extinctions, at least up to a point. With a simple evolutionary game involving three species, we show that Red Queen dynamics (continuous adaptation by all interacting species) apply in only a very small minority of possible outcomes. Importantly, cooperation and facilitation among species enable competitors to increase ecosystem productivity and therefore to enlarge the pool and turnover of resources. The Red Queen reigns only under a few unusual circumstances.

New paper: Red queen for a day: models of symmetry and selection in paleoecology

Tags

biodiversity, ecology, evolution, paleoecology, Robustness, top-down cascade

Red queen for a day: models of symmetry and selection in paleoecology . Evolutionary Ecology DOI: 10.1007/s10682-011-9494-6