Correlations between tool use and evolution

Correlations between tool use and evolution

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We humans would be nearly defenseless against large predators without weapons. Even small mammals can outrun humans. Our canines have degenerated to the point where they're almost useless as weapons. A young chimpanzee is far stronger than a human.

So how could our ancestors have survived before the invention of tools and weapons?

Apparently, our species didn't necessarily invent these things at all. As I understand it, more ancient hominids were using tools and weapons even before our species evolved. I would therefore assume that we essentially inherited tools and weapons from the species we evolved from.

At any rate, we are incredibly weak and defenseless compared to chimpanzees and other apes. One might speculate that big canines and physical strength became more irrelevant as our ancestors began to rely on artificial weapons for protection.

Are you aware of any research that supports this? Do our ancestors exhibit a trend towards smaller canines or a decrease in physical strength?

This is a very confusing question! You have many sentences that end in question marks but then you answer them yourself, ending with a final question mark which is basically "are there references that show I'm right" but which is phrased in a way that makes the answer rather trivial (yes, of course our ancestors show a decrease in canine size, I'm not sure how else you would expect to get from there to here. Strength is harder to measure in fossils I think).

I'll address a few points. First I think this question makes humans to be much more hapless and vulnerable than we are:

We humans would be nearly defenseless against large predators without weapons.

Humans are large predators. It's a nitpick because your other points about the relative strength of great apes and how we use weapons are correct, but I think it's relevant to the "oh us poor squishy defenseless humans" trope. We tend to think of our size as normal but we're actually among the biggest animals.

Even small mammals can outrun humans.

Not for long they can't. One hypothesis that seems to have some credence in the field of human evolution today is the Endurance running hypothesis, which points out that almost no other animal shares our capacity to run for a very long time and that hunting stragegies based on this may have been a significant aspect of our evolution, that would explain many aspects of our physiology.

Of course this kind of hunting would likely involve weapons, but as you point out humans and proto-humans have been using tools for a long time.

You seem to have an image of weak proto-humans inventing weapons to turn the tables on the large predators that would eat them, but how would they have survived before? I had a similar one about hairlessness and clothes; probably we tend to think of human bodies as immutable human attributes, and all our tools as optional. When the truth, as you said yourself, is that humans as a species aren't separable from our tools and intelligence; our bodies, minds and behaviors all co-evolved. There never was a weak, naked human with no tools; before tools our ancestors weren't weak or naked and with tools of course we are neither. Also, intelligence and social behavior count for a lot; a single human might be vulnerable to a large predator where a coordinated band of hominids, probably armed with sticks and stones and some understanding of how to use them, would be another story.

But you want actual references and evidence, which is harder. I linked to the Wikipedia page on the evolution of canine size but strength isn't so easily measured in the fossil record. I think this paper might be the closest thing:

Body mass and encephalization in Pleistocene Homo

Relevant quote from the abstract:

On the basis of an analysis of 163 individuals, body mass in Pleistocene Homo averaged significantly (about 10%) larger than a representative sample of living humans.

That's assuming body mass is a good proxy for strength, which isn't clear to me.

The following paper confirms that humans are indeed weaker than other apes and that the genes related to skeletal musculature have changed a lot since our common ancestor with them but doesn't give a timeline:

Exceptional Evolutionary Divergence of Human Muscle and Brain Metabolomes Parallels Human Cognitive and Physical Uniqueness

That paper goes with the hypothesis that loss of strength was caused by diverting resources to our brains, but other papers suggest there could be a trade-off between strength and fine motor control, like here:

The strength of great apes and the speed of humans

More than 50 years ago, Maynard Smith and Savage (1956) showed that the musculoskeletal systems of mammals can be adapted for strength at one extreme and speed at the other but not both. Great apes are adapted for strength--chimpanzees have been shown to be about four times as strong as fit young humans when normalized for body size. The corresponding speed that human limb systems gain at the expense of power is critical for effective human activities such as running, throwing, and manipulation, including tool making. The fossil record can shed light on when the change from power to speed occurred. I outline a hypothesis that suggests that the difference in muscular performance between the two species is caused by chimpanzees having many fewer small motor units than humans, which leads them, in turn, to contract more muscle fibers earlier in any particular task.

(note that this paper mostly seems to be proposing a hypothesis and not demonstrating it, and it isn't very cited… but a lot of the papers citing it look interesting. Including this entry in the "could pre-humans hunt with weapons" column:

Clavicle length, throwing performance and the reconstruction of the Homo erectus shoulder

These data (… ) suggest that the capacity for high speed throwing dates back nearly two million years.


I think the question of how human strength evolved in their evolutionary history is probably very answerable with modern genetics, I've been looking for papers along the line of this one, which is about jaw muscles not upper-body strength:

Myosin gene mutation correlates with anatomical changes in the human lineage

Using the coding sequence for the myosin rod domains as a molecular clock, we estimate that this mutation appeared approximately 2.4 million years ago, predating the appearance of modern human body size and emigration of Homo from Africa.

but I haven't found any; it's possible this specific question just hasn't been answered yet.

An old genetic tool in plant biology still has value

Scientific tools for plant genetics research continuously fade away as newer methods evolve. However, researchers at Mississippi State University have found that one older method, the use of fragmented chloroplast DNA sequences, still stands strong amidst modern technologies.

Chloroplast simple sequence repeats or microsatellites (cpSSRs) are short, repeating fragments of DNA that mark specific locations in a plant's genome. cpSSR markers are used to study plant evolution, such as plant breeding and hybridization in agricultural species and the genetic diversity of plants of conservation concern. They are especially useful for distinguishing plant groups and resolving their evolutionary relationships.

Gregory Wheeler, Associate Professor Lisa Wallace, and colleagues found that plant studies that use cpSSRs are on the rise. The number of plant studies using cpSSRs has doubled in the past ten years. Since 1995, cpSSRs have been used to study wild and cultivated plants from 85 different plant families--the most common being the history of pine trees (Pinaceae family) through the latest ice age.

Many plant research labs are turning to the latest next-generation sequencing methods to collect molecular genetic data because these methods allow for a more complete "fingerprint" of plant DNA. However, as Wallace points out, "There are still a lot of labs that do not have the financial or genomic resources to make next-generation sequencing methods feasible."

The published review detailing the status of cpSSRs in plant genetics is published in a recent issue of Applications in Plant Sciences (available for free viewing at http://www. bioone. org/ doi/ pdf/ 10. 3732/ apps. 1400059).

Because cpSSRs remain a popular method, Wheeler and colleagues explored their risks and benefits to uncover the most suitable and informative scientific questions that cpSSRs can answer in future studies.

The most prevalent problem with cpSSRs, which less than 33 percent of studies tested for, is called size homoplasy. Size homoplasy occurs when mutations in the DNA arise independently, causing DNA from different plants to falsely appear similar by evolutionary descent. Size homoplasy can lead scientists to overestimate plant relatedness.

To illustrate the risks of size homoplasy, Wheeler and colleagues pulled from their own data on the plant genus Acmispon, a member of the pea family found throughout California, USA. Four of the nine loci tested exhibited size homoplasy within or between species. "I was surprised to find a lack of testing of homoplasy in cpSSR studies given how commonly we detected it in our own data set," comments Wallace.

For future studies, Wallace and colleagues suggest using cpSSRs that were developed for specific plant species. This approach can help avoid problems of size homoplasy and answer questions related to genetic conservation and variability of single species, such as those that are economically and ecologically important.

"There have been major transitions towards NGS techniques in the past decade," says Wallace, "but our paper shows that cpSSRs are still a useful type of marker for many research groups in basic and applied plant sciences." As the number of genetic tools for plant research expands, Wallace notes that the availability of new study systems will continue to grow, providing new opportunities for the use of cpSSRs in plant biology.

Gregory L. Wheeler, Hanna E. Dorman, Alenda Buchanan, Lavanya Challagundla, and Lisa E. Wallace. A review of the prevalence, utility, and caveats of using chloroplast simple sequence repeats for studies of plant biology. Applications in Plant Sciences 2(12): 1400059. doi:10.3732/apps.1400059.

Applications in Plant Sciences (APPS) is a monthly, peer-reviewed, open access journal focusing on new tools, technologies, and protocols in all areas of the plant sciences. It is published by the Botanical Society of America, a nonprofit membership society with a mission to promote botany, the field of basic science dealing with the study and inquiry into the form, function, development, diversity, reproduction, evolution, and uses of plants and their interactions within the biosphere. APPS is available as part of BioOne's Open Access collection .

For further information, please contact the APPS staff at [email protected]

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.


Phylogenetic comparative approaches can complement other ways of studying adaptation, such as studying natural populations, experimental studies, and mathematical models. [6] Interspecific comparisons allow researchers to assess the generality of evolutionary phenomena by considering independent evolutionary events. Such an approach is particularly useful when there is little or no variation within species. And because they can be used to explicitly model evolutionary processes occurring over very long time periods, they can provide insight into macroevolutionary questions, once the exclusive domain of paleontology. [4]

Phylogenetic comparative methods are commonly applied to such questions as:

Example: do canids have larger hearts than felids?

Example: do carnivores have larger home ranges than herbivores?

Example: where did endothermy evolve in the lineage that led to mammals?

Example: where, when, and why did placentas and viviparity evolve?

  • Does a trait exhibit significant phylogenetic signal in a particular group of organisms? Do certain types of traits tend to "follow phylogeny" more than others?

Example: are behavioral traits more labile during evolution?

Example: why do small-bodied species have shorter life spans than their larger relatives?

Felsenstein [1] proposed the first general statistical method in 1985 for incorporating phylogenetic information, i.e., the first that could use any arbitrary topology (branching order) and a specified set of branch lengths. The method is now recognized as an algorithm that implements a special case of what are termed phylogenetic generalized least-squares models. [8] The logic of the method is to use phylogenetic information (and an assumed Brownian motion like model of trait evolution) to transform the original tip data (mean values for a set of species) into values that are statistically independent and identically distributed.

The algorithm involves computing values at internal nodes as an intermediate step, but they are generally not used for inferences by themselves. An exception occurs for the basal (root) node, which can be interpreted as an estimate of the ancestral value for the entire tree (assuming that no directional evolutionary trends [e.g., Cope's rule] have occurred) or as a phylogenetically weighted estimate of the mean for the entire set of tip species (terminal taxa). The value at the root is equivalent to that obtained from the "squared-change parsimony" algorithm and is also the maximum likelihood estimate under Brownian motion. The independent contrasts algebra can also be used to compute a standard error or confidence interval.

Probably the most commonly used PCM is phylogenetic generalized least squares (PGLS). [8] [9] This approach is used to test whether there is a relationship between two (or more) variables while accounting for the fact that lineage are not independent. The method is a special case of generalized least squares (GLS) and as such the PGLS estimator is also unbiased, consistent, efficient, and asymptotically normal. [10] In many statistical situations where GLS (or, ordinary least squares [OLS]) is used residual errors ε are assumed to be independent and identically distributed random variables that are assumed to be normal

whereas in PGLS the errors are assumed to be distributed as

where V is a matrix of expected variance and covariance of the residuals given an evolutionary model and a phylogenetic tree. Therefore, it is the structure of residuals and not the variables themselves that show phylogenetic signal. This has long been a source of confusion in the scientific literature. [11] A number of models have been proposed for the structure of V such as Brownian motion [8] Ornstein-Uhlenbeck, [12] and Pagel's λ model. [13] (When a Brownian motion model is used, PGLS is identical to the independent contrasts estimator. [14] ). In PGLS, the parameters of the evolutionary model are typically co-estimated with the regression parameters.

PGLS can only be applied to questions where the dependent variable is continuously distributed however, the phylogenetic tree can also be incorporated into the residual distribution of generalized linear models, making it possible to generalize the approach to a broader set of distributions for the response. [15] [16] [17]

Intralocus tactical conflict: genetic correlations between fighters and sneakers of the dung beetle Onthophagus taurus

Correspondence: Bruno A. Buzatto, Centre for Evolutionary Biology, School of Animal Biology (M092), The University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia.

Tel.: (+61 8) 64882699 fax: (+61 8) 64881029

Department of Biological and Environmental Science, University of Jyväskylä, Jyväskylä, Finland

Centre for Evolutionary Biology, School of Animal Biology (M092), The University of Western Australia, Crawley, WA, Australia

Centre for Evolutionary Biology, School of Animal Biology (M092), The University of Western Australia, Crawley, WA, Australia

Centre for Evolutionary Biology, School of Animal Biology (M092), The University of Western Australia, Crawley, WA, Australia

Correspondence: Bruno A. Buzatto, Centre for Evolutionary Biology, School of Animal Biology (M092), The University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia.

Tel.: (+61 8) 64882699 fax: (+61 8) 64881029

Department of Biological and Environmental Science, University of Jyväskylä, Jyväskylä, Finland

Centre for Evolutionary Biology, School of Animal Biology (M092), The University of Western Australia, Crawley, WA, Australia

Centre for Evolutionary Biology, School of Animal Biology (M092), The University of Western Australia, Crawley, WA, Australia


Males and females differ in their phenotypic optima for many traits, and as the majority of genes are expressed in both sexes, some alleles can be beneficial to one sex but harmful to the other (intralocus sexual conflict ISC). ISC theory has recently been extended to intrasexual dimorphisms, where certain alleles may have opposite effects on the fitness of males of different morphs that employ alternative reproductive tactics (intralocus tactical conflict ITC). Here, we use a half-sib breeding design to investigate the genetic basis for ISC and ITC in the dung beetle Onthophagus taurus. We found positive heritabilities and intersexual genetic correlations for almost all traits investigated. Next, we calculated the intrasexual genetic correlation between males of different morphs for horn length, a sexually selected trait, and compared it to intrasexual correlations for naturally selected traits in both sexes. Intrasexual genetic correlations did not differ significantly between the sexes or between naturally and sexually selected traits, failing to support the hypothesis that horns present a reduction of intrasexual genetic correlations due to ITC. We discuss the implications for the idea of developmental reprogramming between male morphs and emphasize the importance of genetic correlations as constraints for the evolution of dimorphisms.

The human gut microbiome in the context of our closest evolutionary ancestors: non-human primates

Despite the evolutionary relevance, surprisingly few studies systematically compare human and non-human primate gut microbiomes. Directly comparing primate and human microbiomes offers insights into what factors shaped our microbiome throughout our evolutionary past. The data that exist demonstrate that, in contrast to our most recent common ancestors, African apes, humans have lower gut microbiota diversity, increased relative abundances of Bacteroides, and reduced relative abundances of Methanobrevibacter and Fibrobacter [32, 33]. Many of these traits are associated with carnivory in other mammals, suggesting that a human dietary shift toward meat-eating over evolutionary timescales may have been accompanied by associated gut microbial shifts [34, 35]. Comparing primate and human microbiomes also provides an indication of how quickly the human microbiome is changing. The human gut microbiome composition appears to have diverged from the ancestral state at an accelerated pace compared to that of the great apes [33]. Some of the hallmarks of human evolution and history potentially responsible include cooked food, the advent of agriculture, population size and density increases, and physiological changes such as the human-specific loss of N-glycolylneuraminic acid (Neu5Gc).

A meta-analysis of non-human primate and human gut microbiome datasets currently available in the Qiita public repository provides some additional insight (see Fig. 1). Human inter-population differences appear similar to the inter-species differences in non-human primates (Fig. 1b). Human inter-population differences are commonly attributed to diet [14, 15]. Similarly, non-human primate gut microbiomes change in response to host habitat and season [36,37,38,39,40,41,42,43], effects which appear to be most strongly linked to spatial and temporal variation in diet. However, differences in gut microbiome composition among non-human primates mirrors host phylogenetic relationships, a pattern known as phylosymbiosis, and this signal of host phylogeny persists across a range of timescales, regardless of diet [43]. The human microbiome also exhibits signs of phylosymbiosis. Across primates, human microbiome composition is most similar to Old World monkeys and apes, and distinct from the gut microbiome of New World primates and lemurs (Fig. 1b). Nevertheless, more extensive sampling of non-human primate populations would help determine if the range of variation in human microbiomes is similar to that of non-human primates and if patterns of phylosymbiosis are truly differentially resistant to host environmental context. These data would offer insight into whether unique and/or divergently evolved aspects of human physiology and environments resulted in human-specific gut microbiome traits and whether non-human primates represent a model for understanding dietary transitions and their impact on the microbiome over human evolutionary history.

In this sense, studies of captive primates with artificially manipulated diets provide helpful context for understanding human dietary transitions. Several studies including our own find that captive primates consume less diverse, lower-fiber diets compared to their wild counterparts [42, 44, 45], mirroring the gradual transition to low-fiber diets over the course of human evolution and the stark contrast of modern Western and non-Western diets. One study reports that the low-fiber captive diet provided to howler monkeys and douc langurs results in a “humanization” of the gut microbiome, as characterized by the loss of microbial diversity [41, 44]. However, even with the low-fiber diet, the howler and douc microbiomes were more similar to non-Western than Western human microbiomes, indicating that the relationship between host diet and the gut microbiota differs between human and non-human primates when considering specific microbial taxa. Our own study comparing the gut microbiomes of vervets and humans consuming both high- and low-fiber diets reports similar results [41]. In contrast to observations of Western versus non-Western human populations [14], Bacteroides relative abundances are lower in captive animals with low-fiber diets [44], while Prevotella relative abundances are higher. These data indicate that closely related microbial taxa may have evolved to encode different metabolic functions in humans and non-human primate microbiomes. Given the overall similarities among primate microbiomes, targeting these related but contrasting lineages for more detailed genomic and functional characterization offers unique opportunities for understanding both the overall function of the human microbiome as well as how evolution of its constituents impacts human health.


Our results point to a strong association between angiosperm diversification and rates of seed size evolution irrespective of analytical method or timescale, with weaker evidence for a link between macroevolutionary dynamics and absolute seed size. In the first instance, we calculated rates of speciation (λ), extinction (μ) and seed size evolution by using Bayesian Analysis of Macroevolutionary Mixtures (BAMM) [19]. BAMM models rate heterogeneity through time and lineages, and accounts for incomplete taxon sampling. We used a phylogenetic tree that contained 29,703 angiosperm species for the speciation/extinction analysis. The tree was subset to 13,577 species with seed size data for phenotypic evolution analysis. As expected, given the high degree of taxonomic imbalance observed in the angiosperm phylogeny, we found strong support for more than 500 shifts in the rates of diversification. There was also marked heterogeneity in the rates of seed size evolution (Fig 1), which varied over 3 orders of magnitude (S1 Fig).

Phylogenetic tree of 13,577 species of flowering plants with seed mass, rate of seed mass change, and speciation (λ), extinction (μ), and net diversification (r) rates estimated by BAMM. Seed mass and rate data were standardised to Z-scores so that variation could be directly compared. λ, μ, and r were calculated with a larger, 29,703-species tree. Photo credits (Fig 1): Laitche, Kaldari, A.Orcram, Acatkiller, Vihljun, John Tann, Patrick Verdier and Hans Braxmeier. See for data.

We then estimated whether shifts in macroevolutionary dynamics (λ, μ, and r) estimated with BAMM were significantly correlated with absolute seed size and tip-specific rates of seed size evolution by comparing the empirical correlations to a null distribution generated using STructured Rate Permutations on Phylogenies (STRAPP), which is robust to phylogenetic pseudoreplication (see Materials and methods for details) [20]. We were able to link major differences in diversity across angiosperm clades with both the present rate of phenotypic evolution and the absolute value of trait itself. Specifically, increased speciation was associated with a faster rate of seed size evolution (Spearman’s ρ = 0.55, p-value < 0.0001 Fig 2A). Increased extinction rates were similarly associated with higher evolvability (ρ = 0.44, p-value < 0.0001 Fig 2B), but given the weaker effect, the net outcome of λ−μ was that diversification rates were positively correlated with phenotypic change (ρ = 0.49, p-value < 0.0001 Fig 2C). We also identified an association between seed size and both speciation (ρ = −0.17, p-value = 0.003 Fig 2D), and extinction rates (ρ = −0.17, p-value = 0.003, Fig 2E). As the correlations with speciation and extinction were in the same direction and of comparable magnitude, and estimates of extinction rates were relatively variable (Fig 2E), net diversification rates did not change with seed size (ρ = −0.12, p-value = 0.077 Fig 2F). Generally, the observed correlations arose from many phenotypically fast-evolving clades distributed across the phylogeny (S1 Fig) and were robust to prior choice in the BAMM analyses (S2 Fig).

Spearman correlations were calculated between speciation (λ), extinction (μ), and net diversification (r), and each of (a) present-day rate of seed mass change and (b) seed mass. Coloured lines are correlations for each of one sample of the BAMM posterior distribution, bold line is the median. The insets show the density plots of the absolute difference between the observed and null correlation calculated across 1,000 structured permutations of the evolutionary rates on the phylogenetic tree (myr, million years). See for data.

Given recent discussion on the reliability of BAMM for estimating diversification rates ([21], but see [22]), we tested the robustness of our results by using alternative methodologies to infer macroevolutionary dynamics across clades at different timescales. Ten, 2 million year-wide time slices from the present up to 20 million years (myr) ago were defined. These time slices were used to identify the most inclusive monophyletic clades of at least 4 species in which we had estimated at least a 70% probability of recovering the correct crown age node of the clade (see Materials and methods). For each resulting clade in each time slice, we calculated diversification rates using a method-of-moments estimator, which assumes rates are constant over time [23]. We also fitted a series of time-dependent diversification models to each clade with R: Phylogenetic ANalyses of DiversificAtion (RPANDA), which uses a maximum likelihood approach to estimate speciation and extinction and allows for incomplete taxon sampling [24] (see S1 Table for a summary of the best fitting models for each time slice). Rates of seed size evolution were estimated within each clade that also had at least 4 species with seed size data by fitting both Brownian motion (BM) and early burst (EB) or accelerating decelerating [25] models of trait evolution. Mirroring the BAMM results, we found a positive correlation between the rate of seed size evolution and speciation rates that was consistent across time slices (Fig 3A, S3A Fig). As expected given the weaker association between seed size and speciation found in our BAMM analyses, correlations were generally weaker and nonsignificant (Fig 3B), except for 1 of the time slices (S3B Fig).

Correlation of (a) rate of seed mass evolution and (b) seed mass with speciation rate (λ) estimated by using R: Phylogenetic ANalyses of DiversificAtion (RPANDA) in the clade-based analysis. The strength of correlations is shown as phylogenetic generalised least squares (PGLS) slopes and were calculated using mean clade-level seed mass across 10 time slices in our species-level phylogenetic tree. The size of the circles represents the number of clades in each time slice plotted at the median age of the time slice. Colour indicates the significance of the slope. A detailed representation of the results in each time slice is given in S13 and S14 Figs. Correlations calculated with speciation rates obtained with the method-of-moments estimator are given in S3 Fig. See for data.

We also found limited evidence that other traits that covary with seed size (S2 and S3 Tables) better explained our results. If our results were explained by seed size being a proxy of some other phenotypic trait that ultimately influenced speciation, we would expect this other phenotypic trait to be strongly correlated with seed size. We would also expect a significantly stronger correlation of speciation with both this trait and its rate of evolution when compared with seed size and its rate of evolution. By comparing the effects of genome size, life cycle, plant height, and woodiness across a subset of 1,007 species in our dataset, we found that only the distinctions between woody and herbaceous and annual and perennial species were more strongly correlated with macroevolutionary dynamics than absolute seed size. The rate of phenotypic evolution of all the continuous traits (genome size, seed size, and plant height) was strongly and similarly correlated with speciation (S4 Fig, S4 Table). Importantly, however, neither the rate of evolution nor the absolute values of both genome size and plant height were more strongly associated with speciation than seed size or its rate of evolution (S4 Fig). These results therefore suggest that the correlation between macroevolutionary dynamics and both seed size and its rate of evolution is not simply mediated by other phenotypic traits.

Phylogenetic Tools for Comparative Biology

As part of a project with colleagues, I just added some features to the phytools function evol.vcv and did a major, major overhaul of the related function evolvcv.lite .

evol.vcv , for those that don't know it, fits, the variable evolutionary correlation model that I developed with David Collar and originally published in 2009.

The idea of this method is pretty straightforward &ndash it takes a data matrix containing two or more quantitative traits plus a phylogeny with mapped 'regimes', and then it fits two models: one in which the Brownian motion evolutionary rates and the evolutionary correlations between traits are constant across all the edges of the tree and a second in which the rates and correlations can differ according to our different mapped regimes.

The idea here might be, for instance, that our regimes represent different constraints on multivariate evolution between our two or more traits, as reflected by a tendency for our traits to coevolve (or not).

The major feature updates that I added to evol.vcv yesterday is that it can now take as input not only the trait data, but sampling variances and (crucially) covariances for each set of species means for each taxon in our phylogeny. (Sampling variances are just the square of the standard error of the mean for each species. Sampling covariances are computed in the same way.) I'll try to get into the reasons this could be important in another post.

We can supply this through the 'hidden' function argument error_vcv which should be a list of m × m matrices for each of our N species in the tree.

In addition to this I also performed a major overhaul of the closely related function evolvcv.lite .

evolvcv.lite fits the same two models as evol.vcv , but also fit two other intermediate models &ndash a model in which the rates differ between our regimes, but not the evolutionary correlation and a second in which the evolutionary correlation differs, but not the rates. The main limitation of evolvcv.lite is that it can be used for only trait data consisting of two traits (though it works for an arbitrary number of different regimes).

In addition to adding sampling error to evolvcv.lite I also added a set of other intermediate models between the two extremes of identiy and no common structure. These are as follows: model 2b. different rates for trait 1 only, common correlation model 2c. different rates for trait 2 only, common correlation model 3b. different rates for trait 1 only, different correlations and model 3c. different rates for trait 2 only, different correlation.

Whereas before the user was required to fit all four of the base models, now evolvcv.lite allows user control of which of these 8 models to fit.

Let's see an example using the sunfish example of our original study. (*Note that for multiple reasons these are slightly different data, and a slightly different phylogeny, then in our publication!)

First, we can plot the tree:

phylomorphospace(sunfish.tree,[,2:3],colors=cols, ftype="off",bty="n",lwd=3,node.size=c(0,1.5),,xlab="relative gape width", ylab="relative buccal length") legend("topleft",c("non-piscivorous","piscivorous"), pch=21,pt.cex=2,,bty="n") title(main="Phylomorphospace plot of Centrarchidae",font.main=3)

OK, now we're ready to fit our models.

We can start with the four models of the original evolvcv.lite function. This is a relatively small dataset, so they should run pretty fast.

We can see from this result that all four models seem to have converged, and the best supported model (taking into account complexity) is model 4: the no common structure model.

But let's try all 8 possible models, and see if the result changes. I hope it does!

Neat. Now our result changes a tiny bit. Instead of the best supported model being the no common structure model, our model that is best supported by the data, taking into account model complexity, is model 3c &ndash the 'different rates (trait 2 only), different correlation model.' (Although, to be fair, the &DeltaAIC between the two best-fitting models is pretty small. Still, since our preferred model is the simpler model also, I think the principle of parsimony would tell us to go with it!)

I've always found correlations a bit easier to interpret than covariances. Let's pull out our evolutionary covariance matrices (sometimes called Brownian 'rate matrices') and compute evolutionary correlations between the two traits for each one.

This can be done using the R base function cov2cor as follows:

Neat. In the best fitting model we go from basically an effective correlation of 0 on the blue branches, to 0.85 on the red branches of the phylogeny.

Just for fun, let's use simulation to generate data in which the sign of the correlation flips completely depending on the regime, the rate of trait evolution for trait 2 changes, but the rate for trait 1 stays the same &ndash and then see what we get.

For simulation, I will use the sunfish tree, and the following matrices:

This tells us &ldquoModel 4: no common structure&rdquo is the one best supported by our data. Let's try all eight models, including the new four, instead:

One of the neat things about this optimization now is that it survived what could have been a fatal error ( system is computationally singular. ) and continued on.

Now, compared to before, the picture changes. Model 3c, &ldquodifferent rates (trait 2), different correlation&rdquo is the best fitting. This is precisely the model we simulated under. Great!

Human Teeth Likely Shrank Due to Tool Use

Wisdom teeth may have shrunk during human evolution as part of changes that started with human tool use, according to a new study.

The research behind this finding could lead to a new way of figuring out how closely related fossil species are to modern humans, scientists added.

Although modern humans are the only surviving members of the human family tree, other species once lived on Earth. However, deducing the relationships between modern humans and these extinct hominins&mdashhumans and related species dating back to the split from the chimpanzee lineage&mdashis difficult because fossils of ancient hominins are rare. [Image Gallery: Our Closest Human Ancestor]

Teeth are the hominin fossils most often found because they are the hardest parts of the human body. "Teeth are central to how a fossil ancestor lived, and can tell us about which species they belonged to, how they are related to other species, what they ate, and how quickly or slowly they developed during childhood," said lead study author Alistair Evans, an evolutionary biologist at Monash University in Melbourne, Australia.

Hominin teeth have shrunk in size throughout evolution, a trend perhaps most clearly seen with the wisdom teeth located at the back of the mouth, the researchers said. In modern humans, wisdom teeth are often very small or do not even develop, while in many other hominin species they were huge, with chewing surfaces two to four times larger than those of their modern human counterparts.

Previous research suggested this profound shrinking in modern human wisdom tooth size was due to the advent of cooking or other changes in diet unique to modern humans. However, Evans and his colleagues now suggest this shift may have begun much earlier in human evolution.

The scientists analyzed tooth size in modern humans and fossil hominins. They found that hominin teeth fell into two major groups. One group was composed of the genus Homo, which includes both modern humans and extinct human relatives. The other group was made up of early hominins preceding Homo, such as the australopiths, the first primates to walk on two feet.

In australopiths and other early hominins, the scientists found that teeth tended to get bigger toward the back of the mouth, with proportions that stayed constant regardless of the overall size of the teeth. However, in the genus Homo, the smaller all the teeth were, the smaller the teeth were toward the back of the mouth.

"There seems to be a key difference between the two groups of hominins&mdashperhaps one of the things that defines our genus Homo," Evans said in a statement.

Dr. Alistair Evans, Monash University, examines a range of hominin skull casts that were included in the study.
David Hocking

This change in how teeth developed between genus Homo and earlier hominins may have occurred due to the advent of advanced tool use in the genus Homo, Evans said.

"It's always been presumed that sometime in early Homo, we started using more advanced tools," Evans told Live Science. "Tool use meant we didn't need as big teeth and jaws as earlier hominins. This may then have increased evolutionary pressure to spend less energy developing teeth, making our teeth smaller."

In modern humans, tooth-size reduction has reached the point where wisdom teeth are increasingly failing to develop, Evans said. "The advent of cooking made food easier to eat, meaning we didn't need big teeth as much," Evans said.

Prior work suggested there was a lot of variation in how teeth evolved in hominins. "Now we're seeing some very simple, clear patterns in hominin tooth evolution instead," Evans said. [Infographic: Human Origins &ndash How Hominids Evolved]

These patterns could help researchers decide whether ancient hominins were members of genus Homo or not, Evans said.

"It's been suggested a number of times over the past 20 years that maybe Homo habilis, often considered the earliest member of Homo, should be considered an australopith instead," Evans said. "We found Homo habilis tooth proportions followed the australopith rule and not the Homo rule, which supports the argument that Homo habilisshould be reclassified to something like Australopithecus habilis."

This new work builds on previous experiments with mice that suggested teeth could influence each other during development. In this "inhibitory cascade model," teeth that develop early can inhibit the size of teeth that develop later. These new findings suggest this mechanism underlying tooth size in mice and most mammals is seen in hominins as well, Evans said.

These findings suggest that by knowing the size of a single hominin tooth and the group to which it belongs, scientists could infer the size of the hominin's remaining teeth with considerable accuracy. "Sometimes we find only a few teeth in a fossil," Evans said. "With our new insight, we can reliably estimate how big the missing teeth were."

Future research could analyze controversial hominin discoveries such asHomo naledi, recently unearthed in South Africa, Evans said. "It's got an interesting mix of traits, some that look like Homo, some that look australopith," Evans said. "It'd be interesting to examine its teeth and see which pattern it fits best."

The scientists detailed their findings in the Feb. 25 issue of the journal Nature.

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Charles Q. Choi is a frequent contributor to Scientific American. His work has also appeared in The New York Times, Science, Nature, Wired, and LiveScience, among others. In his spare time, he has traveled to all seven continents.

Summary and Conclusions

Research, particularly in the last two decades or so, has shown that a second inheritance system of social learning is widespread among animals, extending to all main classes of vertebrate and also to insects (1, 2). Apes merit a special focus, insofar as they have been subjected to an unmatched diversity and volume of observational and experimental studies by multiple research teams, whose work has revealed what appear to be the richest nonhuman cultural repertoires identified to date (although some cetaceans, e.g., killer whales, may show greater cultural differentiation). This article has attempted to indicate the scope of ape culture research and the key points of its discoveries, particularly with respect to the theme of the present issue: how these cultural phenomena may extend biology and its core evolutionary theory in particular. I have argued that the evidence supports the conclusion that the nature of social learning and its consequences in cultural transmission create new forms of evolution. These new forms echo well the established core principles of organic evolution but also go beyond them in a number of fundamental ways, such as horizontal transmission and inheritance of acquired characteristics, thereby extending the scope of evolutionary processes we must now entertain. Moreover the primary genetically based forms of evolution shaped and are also shaped by the consequences of this second inheritance system in complex ways we are only now starting to uncover.

Correlation and Linear Regression Analysis | Biostatistics

After reading this article you will learn about the correlation and linear regression analysis.


Association between variables or attributes or characteristics at a given time is known as correlation.

(i) The amount of rainfall and yield of a certain crop

(ii) Age of husband and wife

(iii) Height and weight of students and

(iv) Different concentrations of mutagen and their effect on seed germination frequency.

In plant breeding the breeders targets improvement of yield. Relationship between yield and yield related traits (plant height, number of primary branches/ plant total branches/plant, number of capsules/plant, capsule length, seeds/capsule, 100-seed weight, etc.) and between the yield related components can be worked out through correlation studies.

Significant correlation obtained will be helpful for selection and ascertaining the model plant type for the concerned species.

Precisely correlation may be defined as movement of one variable tend to be accompanied by corresponding movements in the other. Such simultaneous movement of two variables can be graphically plotted using value of one variable on x-axis and the other variable along y-axis.

Such representation of variables in­dicates the nature of association between the attributes and is called as scattered diagram or correlation chart.

(a) Positive Correlation:

Increase in plant height is related to increase in number of branches per plant. On the scattered diagram the dots (each pair of obser­vation) representing the variables are in a linear path diagonally across the graph paper from bottom left-hand corner to the top right.

(b) Negative Correlation:

For example, increase in plant height of a species is re­lated to decrease in branch number per plant. The pattern of dots be such as to indicate a straight line path from the upper left-hand corner to the bottom right.

The dots are scattered and do not indicate any straight line.

(d) Perfect Correlation:

When the dots lie exactly on a straight line.

In the present example height of plants represented independent variable and on the other hand the variable which changes with the change in the independent variable is called dependent variable (branches/plant).

It is customary to use the horizontal axis (x-axis) for the independent variable and the vertical axis (y-axis) for dependent variable.

The degree of relationship between 2 attributes can be determined by calcu­lating a coefficient called as correlation coefficient. The correlation coefficient is expressed by the letter ‘r’. r varies from 0 to 1 and can be + (positive correlation) or — (negative correlation). Practically, r is never zero or 1 (complete/absolute).

Whenever correlation coefficient analysis is made, r-value ranges from 0 to 1 but it is necessary to compare the calculated r-value with table value at specific de­gree of freedom. If the value is significant, i.e., if the calculated r-value is greater than table value, then only we can say that the two attributes are statistically associated to one another. Degree of significance level has also to be assessed (5%, 1% and 0.1% levels).

where x and y are the variables.

In correlation degree of freedom is n — 1, where n represents pairs of obser­vations.

Ten plants have been assessed in sesame (Til) for plant height (cm) and number of branches per plant. From the given data do you consider that there exist correlation (significant) between the variables?

The calculated value 0.996 for 9 DF is higher than the tabulated value at 5%, 1% and 0.1% levels and hence it can be suggested that the two vari­ables are positively and significantly correlated between them at 0.001 probabil­ity level.

The r-value can be represented as 0.996*** to show the level of significance.

Thus, selection of plants with higher height will facilitate selection of plants with enhanced number of branches.

How to prepare Correlation Table from Experimental Data:

Following data has been given:

a. Plant height and number of primary branches/plant = 0.65

b. Plant height and total branches per plant = 0.57.

c. Height and number of capsules per plant = 0.81**.

e. Primary branches and total branches = 0.35.

f. Primary branches and capsules per plant = 0.80**.

g. Primary branches and yield = 0.87***.

h. Total branches and number of capsules = 0.52.

i. Total branches and yield = 0.43.

j. Capsules per plant and yield = 0.82**.

Interrelationship between four yield related traits and their association with yield have been documented in tabular form. Result indicated positive and significant correlation between height and capsules/plant (1% level), primary branches/plant and capsules/plant (1% level), primary branches and yield (0.1% level) and capsules/plant and yield (1% level).

Thus, plants having higher number of primary branches with enhanced cap­sule number should be the selection indices for higher yield in the plant species.

Simple correlation described so far is of 3 types:

Observable correlation between 2 variables and it includes both genotypic and environmental effects.

Such type of correlation takes into account the inherent association between two variables and it may be the outcome of pleiotropic action of genes or linkage or both.

P cov x. y, G cov x.y and E cov x.y are phenotypic, genotypic and environ­mental, respectively, covariance’s between variables x and y Vx and Vy are variances for x and y variables, respectively.

Partial Correlation:

X1 and X2 estimated by taking into account the effect of a 3rd variable X3 and is denoted as r12.3.

Partial correlation provides better relationship between the two variables X1 and X2 and is given by the formula:

r12, r13 and r23 are the estimates of simple correlation coefficients between the variables X1 and X2, X1 and X3 and X2 and X3, respectively.

Multiple Correlation:

Estimate of joint influence of two or more variables on a dependent variable is called multiple correlation. Such an estimate helps in understanding the dependence of one variable, say x1 on a set of independent variable say X2, X3

The square root of R 2 1.23 is the estimate of multiple correlation coefficient. R 2 1.23 is coefficient of determination.

Linear Regression Analysis:

The statistical analysis employed to find out the exact position of the straight line is known as Linear regression analysis. From simple correlation analysis if there exist relationship between independent variable x and dependent variable y then the relationship can be expressed in a mathematical form known as Re­gression equation.

From regression equation we can work out the actual value of y variable (dependent) based on X variable (independent) and such values plot­ted graphically will give precise nature of the straight line (point of interception to y-axis can be noted).

Simple regression equation Yx = a + bx, where a and b are constant which minimize the residual error of Y. Y is the dependent variable.

The constants a and b can be obtained from the formula:

From the data find out the regression equation and draw a regression line on the graph paper.

Using the regression equation yx = 2.6+1.48x the actual values of dependent variable can be worked out.

Using data of the given example the straight line is drawn but the point of interception to y-axis is lacking and, therefore, precise nature of the straight line is not understood. However, from the straight line it is evident that the variables were significantly and positively correlated between themselves.

These set of values plotted graphically will give a straight and the precise nature of the straight line can be obtained from x = 0, y = 2.6 (point of interception to y- axis can be found out).

Multiple Regression:

The following data giving mean yield (grain), mean ear number per plant and mean grain number per acre of 10 wheat varieties were obtained in low soil condition moisture plots in the experiment conducted at IARI during 2000-01 to study the influence of soil drought on the relation between yield and ear character.

Fit a multiple regression equation giving mean grain yield in terms of mean ear no. per plant and mean grain no. per ear.

Since the calculated value of F in respect of regression is greater than the table value both at 5% and 1% level of significance, the regression is highly significant. Thus, mean grain yield is significantly related to ear characters.