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.

 

 

 

 

Is the past a key to the future?

Tags

extinction, food webs, paleo-food web, paleontology, Permian-Triassic extinction

Time travel anyone? (BBC)

One of the main motivations for our most recent paper (available here) was to gain insight into how modern ecosystems might behave in the future as they are subjected to increasing human-driven stresses. “Global change” biology is an emerging field that seeks to understand how the biosphere will change in response to factors such as ongoing climate change, habitat loss, landscape transformation, and so forth. Much of the work in this area rightfully focuses on measuring change, working to understand how modern ecosystems work, and projecting how they might respond in the future. The effort is ongoing, and includes theoretical work, controlled experiments, and uncontrolled impacts on natural systems. A limitation of these efforts, however, is the magnitude of the changes that are available for study. For example, we can observe how species are moving in space right now in response to rising environmental temperatures, or how they are adapting (or not) to drought conditions, but we cannot observe how they will respond in the future as those stressors continue to increase in magnitude. No one realistically expects the responses to increase linearly; we fully expect nonlinear, hard-to-predict, surprises. That was the message of an earlier paper, and a focus of a lot of current work on critical ecosystem transitions. One way to address this concern, and the one that we’ve taken, is to look back into Earth’s past, to times when the planet was similarly undergoing major changes. Those were natural experiments; times when ecosystems were subjected to extreme environmental stresses. The problem there of course is that we don’t have a Tardis, and all our information has to come from evidence that has been preserved in the geological record, and our ability to interpret it. Yes, the natural experiments were performed, but as I like to say, either no one kept notes, or the notebook was chewed up by the family dog before anyone had a chance to read it.

So where does that leave us? It leaves us with an incomplete record yes, but it’s also the only record of how the biosphere has responded to truly dire circumstances. Our challenge is to take this incomplete record, and to extract from it data and ideas that are useful for forecasting how the biosphere might respond to future dire circumstances. In the case of our present study, we were able to take advantage of first-rate field paleontology, first-rate organismal paleontology, recent developments in theoretical ecology, and to combine those with our own methods for reconstructing paleo-food webs. And the main question which we were interested in was, “How would those food webs (important parts of the paleoecosystems) have responded to everyday types of disturbances, on the short-term, as the planet was busily falling apart?”

And the planet really was in trouble at the end of the Permian 252 million years ago. Siberia had opened up in one of the most magnificent episodes of volcanism in the last half billion years. Recent dating suggests the volcanism started about 300,000 years before the marine extinctions, and may have continued intermittently for another 500,000 years after. The knock-on effects probably included greenhouse warming, sulphurous atmosphere, ocean acidification and reductions of oceanic oxygen concentrations. In southern Africa, the location of the terrestrial ecosystem which we studied, the stage was set for a catastrophe of global proportions.

A new paper on the Permian-Triassic mass extinction

Tags

biodiversity, extinction, food webs, modeling, paleo-food web, paleontology, Permian-Triassic extinction, Scientific models

Dicynodon, an ancient relative of mammals, at the end of the Permian. (Marlene Hill Donnelly).

Yesterday, Ken Angielczyk and I published our most recent paper on the Permian-Triassic mass extinction (PTME) in the journal Science. In a nutshell, we examined a series of paleocommunities spanning the extinction, from the Late Permian to the Middle Triassic, and modelled the stability of their food webs. We compared the models to hypothetical alternatives, where we varied parameters such as how species are divided among guilds, or ecological “jobs”, and the numbers of interactions that species have. One of our very interesting discoveries is that the real food webs were always the most stable, or amongst the most stable of the models, even during the height of the extinction! That’s remarkable, given the devastating loss of species at the end of the Permian. Our other discovery is that the ability to remain highly stable during the extinction stemmed from the more rapid extinction of small, terrestrial vertebrate species. That’s not something we would predict given our experience with modern and ongoing extinctions, where larger vertebrate species are considered to be at greater risk. And finally, our last interesting observation is that the early recovery, the immediate aftermath during the Early Triassic, was an exception to the above. That community was not particularly stable, which seems to have been the result of the rapid evolutionary diversification of the extinction survivors, and the arrival of immigrants from neighbouring regions.

Some aspects of the paper are quite technical, and take advantage of fantastic new paleontological data and recent developments in theoretical ecology. Therefore, over the next few posts I’ll go through what we did, and how we did it, using a more “plain language” approach. In the meanwhile, the paper was covered by a number of news outlets, and here’s my favourite!

“5 things we learned from the mass extinction study that’s “the first of its kind”“, The Irish Examiner.

Upcoming lecture

Tags

extinction, food webs, modeling, paleo-food web, paleontology, Scientific models

I will be giving a lecture on Tuesday, April 28th, at Swarthmore College in the Department of Mathematics and Statistics. If you’re in the area, please stop by!

RESILIENCE AND STABILITY OF PERMO-TRIASSIC KAROO BASIN COMMUNITIES

Tags

biodiversity, extinction, food webs, modeling, paleo-food web, paleontology, simulations, trophic guild

Late Permian community dynamics

This is the second presentation that I made at the Annual Conference of the Geological Society of America in Vancouver last month. The presentation was part of a special session, “Extreme Environmental Conditions and Biotic Responses during the Permian-Triassic Boundary Crisis and Early Triassic Recovery”, co-organized my myself, Tom Algeo, Hugo Bucher and Arne Winguth. The session, spanning two days, was excellent, outstanding, and a lot of fun! I came away with the firm conviction that we are beginning to really understand the massive Permo-Triassic mass extinction, from its causes to consequences to recovery. It truly was a watershed “moment” in the history of the biosphere. The full program for both days can be found here and here. And, here is the abstract. An online copy of the presentation is available here.

RESILIENCE AND STABILITY OF PERMO-TRIASSIC KAROO BASIN COMMUNITIES: THE IMPORTANCE OF SPECIES RICHNESS AND FUNCTIONAL DIVERSITY TO ECOLOGICAL STABILITY AND ECOSYSTEM RECOVERY

ROOPNARINE, Peter, Invertebrate Zoology and Geology, California Academy of Sciences, 55 Music Concourse Dr, Golden Gate Park, San Francisco, CA 94118, proopnarine@calacademy.org and ANGIELCZYK, Kenneth D., Department of Geology, The Field Museum, 1400 South Lake Shore Drive, Chicago, IL 60605

A central question of the P/Tr extinction is the manner in which Permian ecological communities collapsed and E. Triassic ones were built. The end Permian Dicynodon Assemblage Zone (DAZ) has recently been resolved into 3 phases of the extinction spanning ~120ky, followed by the E. Triassic (Induan) Lystrosaurus Assemblage Zone (LAZ), offering an opportunity to examine the ecological dynamics of extinction and recovery in enhanced detail. We do this with 2 modelling approaches.

The first model assumes that populations exist in an energetic balance between consumption and predation. Communities are modelled as stochastic variants sampled from a space defined by species richness and functional diversity. Paleoenvironmental data from the DAZ indicate an increasingly seasonal, arid and drought-prone environment. The models were perturbed by simulated reductions of primary productivity. Results show that DAZ Phase 0 (Ph0) was a robust community resistant to low-moderate levels of perturbation with a well-defined collapse threshold. DAZ Ph1 and Ph2, however, exhibit highly variable responses and are significantly less resistant. LAZ similarly exhibits highly variable responses across minor variation of model configurations.

The second model assumes that communities are locally stable, i.e. minor perturbations are followed by asymptotic returns to equilibrium. During this return, however, communities can exhibit transient behavior during which perturbations can be greatly amplified. Amplification is likely to be important in unstable environments when the frequency of perturbations is shorter than the return time to equilibrium. Applying this model to DAZ and LAZ communities shows that the Karoo ecosystem became more limited in its responses to perturbation as the P/Tr boundary was approached, with Ph1 and Ph2 communities exhibiting very little transient behavior. LAZ in contrast exhibits increased transience.

The energetics and stability models are reconcilable in a history where the Karoo ecosystem became more ecologically stable as the extinction unfolded, yet more sensitive to cascading effects of species extinction and reductions of productivity. The Induan ecosystem was an unrecovered one, sensitive to both extinction and minor ecological disturbances.

Modern and paleocommunity analogues

Tags

connectance, coral reef, food webs, marine communities, modeling, Network theory, networks, paleo-food web, paleontology, real world networks, Scientific models, trophic guild, trophic level

Last week I gave a keynote presentation at the annual conference of the Geological Society of America in Vancouver. Here is the abstract, and a link to the presentation (pdf file).

ANCIENT AND MODERN COMMUNITIES AS RECIPROCAL ANALOGUES OF PERSISTENCE AND STABILITY

ROOPNARINE, Peter, Invertebrate Zoology and Geology, California Academy of Sciences, 55 Music Concourse Dr, Golden Gate Park, San Francisco, CA 94118, proopnarine@calacademy.org
Paleocommunities are spatio-temporally averaged communities structured by biotic interactions and abiotic factors. The best data on paleocommunity structures are estimates of species richness, number of biotic interactions and the topology of interactions. These provide insights into paleoecological dynamics if modern communities are used as analogs; e.g., the recent lionfish invasion of the western Atlantic is the first modern invasion of a marine ecosystem by a high trophic-level predator and serves as an analog for the invasion of paleocommunities by new predators during the Mesozoic Marine Revolution. Despite the invader’s broad diet, it targets very specific parts of the invaded food web. This will lead to non-uniform escalation on evolutionary timescales.

Theoretical ecology provides a rich framework for exploring dynamics of community persistence. Persistence–the stability of species richness and composition on geological timescales–is central to paleoecology. Ecological stability, a community’s return to stability after perturbation, is not necessary for geological persistence. However, it does dictate a community’s response to perturbation, and thus a species’ persistence or extinction. What then is the relationship between paleoecological richness/composition and ecological stability? How do communities respond to losses of species richness or ecological function? Questions of stability and diversity loss are addressed with an examination of transient responses and species deletion stability analyses of end-Permian terrestrial paleocommunities of the Karoo Basin. Transience is measured as the degree to which a perturbation is amplified over ecological time, even as a community returns asymptotically to stability. Transience during times of frequent perturbation, as during times of environmental crises, decreases the likelihood of a persistently stable community. Species deletion stability measures the dynamic response of a community to the loss of single species. It is an open question whether communities become more vulnerable or more resistant during environmental crises. That process, which has occurred repeatedly in the geological past, is important to the fate of threatened modern communities.

Chat with an Academy scientist: Paleontology

Tags

biodiversity, california academy of sciences, food webs, paleontology

Chat with an Academy scientist, this time with a paleontologist (me!). Not at my most articulate though (not the interviewer’s fault). If you make it to the end, I stayed to read the book, twice, to the young man.

Science Today: Studying paleo-food webs

Tags

california academy of sciences, extinction, food webs, paleo-food web, paleontology

One of the most important ways species interact in an ecosystem? Food Webs. Learn how researchers study paleo and present-day food webs.
From the California Academy of Sciences.

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.