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Can human change its morphology or anatomy due to ecological changes?

Can human change its morphology or anatomy due to ecological changes?


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According to Charles Darwin, as the surrounding environment changes, so changes the anatomy or morphology of a specific organism.

But nowadays, humans have become very advanced in the technology and when the surrounding temperature changes, humans in turn change the surrounding temperature (e.g. the use of ACs), and maintain the surrounding as they like.

What I want to emphasize is can human being evolve with these scopes in mind?


Ecological Changes in Coyotes (Canis latrans) in Response to the Ice Age Megafaunal Extinctions

Coyotes (Canis latrans) are an important species in human-inhabited areas. They control pests and are the apex predators in many ecosystems. Because of their importance it is imperative to understand how environmental change will affect this species. The end of the Pleistocene Ice Age brought with it many ecological changes for coyotes and here we statistically determine the changes that occurred in coyotes, when these changes occurred, and what the ecological consequences were of these changes. We examined the mandibles of three coyote populations: Pleistocene Rancho La Brean (13–29 Ka), earliest Holocene Rancho La Brean (8–10 Ka), and Recent from North America, using 2D geometric morphometrics to determine the morphological differences among them. Our results show that these three populations were morphologically distinct. The Pleistocene coyotes had an overall robust mandible with an increased shearing arcade and a decreased grinding arcade, adapted for carnivory and killing larger prey whereas the modern populations show a gracile morphology with a tendency toward omnivory or grinding. The earliest Holocene populations are intermediate in morphology and smallest in size. These findings indicate that a niche shift occurred in coyotes at the Pleistocene/Holocene boundary – from a hunter of large prey to a small prey/more omnivorous animal. Species interactions between Canis were the most likely cause of this transition. This study shows that the Pleistocene extinction event affected species that did not go extinct as well as those that did.

Citation: Meachen JA, Janowicz AC, Avery JE, Sadleir RW (2014) Ecological Changes in Coyotes (Canis latrans) in Response to the Ice Age Megafaunal Extinctions. PLoS ONE 9(12): e116041. https://doi.org/10.1371/journal.pone.0116041

Editor: Benjamin Lee Allen, University of Queensland, Australia

Received: September 29, 2014 Accepted: November 30, 2014 Published: December 31, 2014

Copyright: © 2014 Meachen et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The authors confirm that all data underlying the findings are fully available without restriction. The raw TPS file from this study has been deposited to Dryad (http://dx.doi.org/10.5061/dryad.vn413).

Funding: JAM and ACJ were supported by start-up funds (JAM) and student mentored research funds (ACJ) from Des Moines University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.


Human Characteristics: Humans Change the World

For millions of years all humans, early and modern alike, had to find their own food. They spent a large part of each day gathering plants and hunting or scavenging animals. Then, within just the past 12,000 years, our species, Homo sapiens, made the transition to producing food and changing our surroundings. We have been so successful that we have inadvertently created a turning point in the history of life on Earth.

Modern Humans Evolve in Africa

During a time of dramatic climate change, modern humans (Homo sapiens) evolved in Africa. Like early humans, modern humans gathered and hunted food. They evolved behaviors that helped them respond to the challenges of survival.

The first modern humans shared the planet with at least three species of early humans. Over time, as modern humans spread around the world, the other three species became extinct. We became the sole survivors in thehuman family tree.

Modern humans collect and cook shellfish

Modern humans exchange resources over long distances

Modern humans make special tools for fishing

Between 80,000 and 60,000 years ago

Modern humans spread to Asia

Modern humans record information on objects

Near-extinction!

Modern humans almost become extinct as a result of extreme climate changes, the population may have been reduced to about 10,000 adults of reproductive age.

Homo erectus becomes extinct

Modern humans create permanent drawings

Modern humans reach Australia

Modern humans reach Europe

Neanderthals (Homo neanderthalensis) become extinct

Homo floresiensis becomes extinct, leavingmodern humans (Homo sapiens) as the sole survivor in the once diverse human family tree

Modern humans reach the Americas

The Turning Point

Eventually, humans found they could control the growth and breeding of certain plants and animals. This discovery led to farming and herding animals, activities that transformed Earth’s natural landscapes—first locally, then globally.

As humans invested more time in producing food, they settled down. Villages became towns, and towns became cities. With more food available, the human population began to increase dramatically.

Figs cultivated in Lower Jordan Valley, Middle East

Jericho, West Bank, begins to grow into a city

Cows domesticated in Africa and Middle East

Squash cultivated in Central America

Wheat cultivated in Middle East

Çatalhöyük, Turkey, begins to grow into a city

Sheep domesticated in Middle East

Corn cultivated in North America

8,000 years ago

Chickens domesticated in Southeast Asia

Potatoes cultivated in South America

Bananas cultivated in Southeast Asia

Horses domesticated in Eurasia

Caral, Peru, begins to grow into a city

Cacao (chocolate) cultivated in Central America

Athens, Greece, begins to grow into a city

Xi’an, China, begins to grow into a city

Rome, Italy, begins to grow into a city

Smallpox kills millions of citizens in ancient Rome

Coffee cultivated in Africa

Bubonic plague kills up to 10,000 people a day in Europe, North Africa, and the Near East

Bubonic plague (“The Plague”) kills at least a third of Europe’s population

Influenza kills up to 40 million people worldwide, about 5% of the entire human population.

Humans Change the World: Today

Modern humans have spread to every continent and grown to huge numbers. Producing our own food, rather than tracking it down daily, has freed us to enrich our lives in many ways—to become artists, inventors, scientists, politicians, and more.

We have altered the world in ways that benefit us greatly. But this transformation has unintended consequences for other species as well as for ourselves, creating new survival challenges.

By 1995, at least 83% of Earth’s land surface had been directly affected by humans.

In 2004, the International Union for Conservation of Nature (IUCN) reported that current bird, mammal, and amphibian extinction rates were at least 48 times greater than natural extinction rates—possibly 1,024 times higher.

As of 2005, humans had built so many dams that nearly six times as much water was held in storage as flowed freely in rivers.

Benefits and Costs of Our Success

By settling down and producing our own food, we created:

●enough food to feed billions of people and respond to catastrophes

●buildings that protect us from extreme weather

●technologies that enable us to extend our lives, communicate worldwide, and venture into space

●time to think, create, play, socialize, and much more.

By settling down and producing our own food, we created:

●piles of waste that form natural breeding grounds for contagious diseases

large concentrations of people, enabling diseases to spread and become epidemics

●domesticated landscapes that displace wild habitats

●loss of wild species that depend on natural habitats.

Changing the World:

Great Moments in Food Technology

63 BCE - Water-powered grist mill

9500 BCE - Grain storehouse

Changing the World:

Animal Domestication

FACT: From 1961 to 2004, the population of cattle, pigs, sheep, and goats increased from 2.7 to 4.1 billion. The number of domesticated fowl grew from 3 to 16 billion.

FACT: Of the estimated 15,000 species of mammals and birds, only about 30–40 have been used for food.

FACT: Fewer than 14 species of animals account for 90% of global livestock production today.

Changing the World:

Agriculture

FACT: About a quarter of Earth’s surface is used to grow crops.

FACT: Fewer than 20 plant species produce most of the world’s food.

FACT: Most of the world’s population is dependent on 4 main crops: wheat, corn, rice, and potatoes.

Changing the World:

Growing Numbers of People

FACT: Between 1959 and 1999, just 40 years, the human population doubled from 3 billion to 6 billion people.

FACT: Today the population continues to grow by over 90 million people a year.

FACT: By 2042, the world population may reach 9 billion, an increase of 50% in 43 years.

Changing the World:

Unintended Consequences

FACT: A cholera pandemic that began in 1961 is still ongoing in Asia, Africa, and the Americas. The number of cases reported in 2006 was 79% more than in 2005.

FACT: Every year between 3 and 5 million people get “the flu,” and between 250,000 and 500,000 people die from it.

FACT: A child dies of malaria every 30 seconds. About 40% of the world’s population is at risk of malaria.

FACT: Every second someone in the world is infected with tuberculosis. One-third of the world’s population is infected.


Global diversity of the human microbiome

Although the 6.5 meter human digestive tract consists of three organs—the stomach, small intestine, and large intestine—most human microbiome research focuses on the microbial community (the microbiota) of the large intestine as read out through the stool. This community harbors by far the greatest microbial biomass of any organ or surface of the human body. Each milliliter of the large intestine holds approximately 10 11 microbial cells compared to 10 8 cells in the small intestine [4]. Typically, researchers turn to non-invasive fecal samples as proxies for the distal colon microbiome. These samples contain a mixture of microbes and human colonocytes from along the length of the digestive tract and have a similar, albeit distinct, composition to intestinal biopsies [5, 6].

Zooming in to the microbiome of a single individual, an estimated 150 to 400 species reside in each person’s gut based on culture-dependent and -independent techniques [7]. Typically, most of these species belong to the Bacteroidetes, Firmicutes, Actinobacteria, and Proteobacteria phyla. The relative proportions of each of these taxa vary dramatically [7] between individuals [8] and even within an individual over the course of their lives [9,10,11]. Although each person’s microbiome is unique, several trends emerge when we examine microbiomes of populations around the globe (Fig. 1a). Most of what we know of the microbiome comes from studies that examine individuals from highly industrialized and developed (“westernized”) nations, including both medical microbiome research and major microbiome surveys, like the United States focused Human Microbiome Project [8] and the European MetaHIT [12]. However, Western microbiomes differ in several ways from the non-Western microbiomes profiled to date [13,14,15,16,17,18,19,20,21,22].

The human gut microbiome within the context of populations and deeper evolutionary landscapes. a The microbiomes of different human populations are distinct from each other, especially between industrialized populations such as in the USA and remote, non-industrialized populations such as Malawians or the Guahibo and Yanomami people of the Amazon [14, 17]. b Within the context of the greater primates lineage, these differences between human populations become smaller and a connection between humans and captive populations of non-human primates can be seen. c Zooming out to include other vertebrate lineages further diminishes those differences, as the effects of deep evolutionary splits between host species and lifestyle characteristics on the gut microbiome become evident. Methods: All data were drawn from publically available studies in Qiita (https://qiita.ucsd.edu/ studies 850, 894, 940, 963, 1056, 1696, 1734, 1736, 1747, 1773, 2182, 2259, 2300, 10052, 10171, 10315, 10376, 10407, 10522). Sequence data for all samples were generated using the same protocol [134] and sequenced on an Illumina MiSeq or HiSeq platform. Sequence data were trimmed to 100 nucleotides and OTUs were picked using the deblur method [135]. Up to five samples per species were randomly selected, rarefied to 10,000 sequences per sample, and unweighted UniFrac [136] distances between samples were computed using Qiime 1.9.1 [137]. The non-metric multidimensional scaling ordination technique was employed in R 3.3.3 [138] to visualize these distances. Silhouettes of the running woman, primate, bird, and bat in c are designed by Vexels.com and reproduced with permission

First, Western microbiomes consist of 15 to 30% fewer species than non-Western microbiomes [14, 18, 19]. One proposal, the disappearing microbiome hypothesis, puts forth that technological and cultural changes accompanying industrialization lead to a “disappearing microbiome” [23]. In lieu of building a time machine, the one way to evaluate this hypothesis is to turn to ancient DNA (Box 2).

Second, Western microbiomes lack certain species that consistently occur in non-Western microbiomes. The most striking example is the spiral bacteria in the genus Treponema, which appears in the stool of numerous non-Western populations who utilize different subsistence strategies, including hunting and gathering and agriculture [13, 15, 19].

Finally, the relative abundances of common phyla shift between Western and non-Western microbiomes. Western microbiomes generally bear a greater amount of Bacteroides, while non-Western microbiomes generally contain greater amounts of Firmicutes and Proteobacteria [15, 17], although exceptions to this trend exist [13]. Taken together, these studies point to the fact that there is no single “human microbiome”, but rather a wide range of configurations that our commensal microbiomes assume.

Given this observation, two key questions arise. First, why do these differences exist between populations? One explanation points to cultural and environmental factors. Diets in particular vary dramatically between cultures and continents. In general, the increased fiber and decreased sugar, fat, and meat in non-Western diets is thought to promote bacterial richness in the gut [13,25,, 24–26]. Additionally, differences in hygiene and medicine likely contribute, including exposure to animals and other septic environments, overuse of antibiotics early in life [23, 27, 28], and differences in enteric parasite carriage in Western populations [20, 29, 30]. Alternatively, portions of the microbiome may simply have diverged along with human populations as they expanded around the globe. For example, the modern day distribution of Helicobacter pylori strains aligns with known human migrations [9, 31].

The second question that arises is: Do these differences between populations matter? Are they larger than expected for a species that eats diets and lives in environments as variable as our own? To answer this, it is useful to compare human microbiomes to our close evolutionary relatives, the non-human primates.


Skinny Individuals Wanted

After contorting themselves 40 feet down the narrow chute in the Rising Star cave, Tucker and Rick Hunter had dropped into another pretty chamber, with a cascade of white flowstones in one corner. A passageway led into a larger cavity, about 30 feet long and only a few feet wide, its walls and ceiling a bewilderment of calcite gnarls and jutting flowstone fingers. But it was what was on the floor that drew the two men’s attention. There were bones everywhere. The cavers first thought they must be modern. They weren’t stone heavy, like most fossils, nor were they encased in stone—they were just lying about on the surface, as if someone had tossed them in. They noticed a piece of a lower jaw, with teeth intact it looked human.

Berger could see from the photos that the bones did not belong to a modern human being. Certain features, especially those of the jawbone and teeth, were far too primitive. The photos showed more bones waiting to be found Berger could make out the outline of a partly buried cranium. It seemed likely that the remains represented much of a complete skeleton. He was dumbfounded. In the early hominin fossil record, the number of mostly complete skeletons, including his two from Malapa, could be counted on one hand. And now this. But what was this? How old was it? And how did it get into that cave?

Most pressing of all: how to get it out again, and quickly, before some other amateurs found their way into that chamber. (It was clear from the arrangement of the bones that someone had already been there, perhaps decades before.) Tucker and Hunter lacked the skills needed to excavate the fossils, and no scientist Berger knew—certainly not himself—had the physique to squeeze through that chute. So Berger put the word out on Facebook: Skinny individuals wanted, with scientific credentials and caving experience must be “willing to work in cramped quarters.” Within a week and a half he’d heard from nearly 60 applicants. He chose the six most qualified all were young women. Berger called them his “underground astronauts.”

With funding from National Geographic (Berger is also a National Geographic explorer-in-residence), he gathered some 60 scientists and set up an aboveground command center, a science tent, and a small village of sleeping and support tents. Local cavers helped thread two miles of communication and power cables down into the fossil chamber. Whatever was happening there could now be viewed with cameras by Berger and his team in the command center. Marina Elliott, then a graduate student at Simon Fraser University in British Columbia, was the first scientist down the chute.

“Looking down into it, I wasn’t sure I’d be OK,” Elliott recalled. “It was like looking into a shark’s mouth. There were fingers and tongues and teeth of rock.”

Elliott and two colleagues, Becca Peixotto and Hannah Morris, inched their way to the “landing zone” at the bottom, then crouched into the fossil chamber. Working in two-hour shifts with another three-woman crew, they plotted and bagged more than 400 fossils on the surface, then started carefully removing soil around the half-buried skull. There were other bones beneath and around it, densely packed. Over the next several days, while the women probed a square-yard patch around the skull, the other scientists huddled around the video feed in the command center above in a state of near-constant excitement. Berger, dressed in field khakis and a Rising Star Expedition cap, would occasionally repair to the science tent to puzzle over the accumulating bones—until a collective howl of astonishment from the command center brought him rushing back to witness another discovery. It was a glorious time.

The bones were superbly preserved, and from the duplication of body parts, it soon became clear that there was not one skeleton in the cave, but two, then three, then five . then so many it was hard to keep a clear count. Berger had allotted three weeks for the excavation. By the end of that time, the excavators had removed some 1,200 bones, more than from any other human ancestor site in Africa—and they still hadn’t exhausted the material in just the one square yard around the skull. It took another several days digging in March 2014 before its sediments ran dry, about six inches down.

There were some 1,550 specimens in all, representing at least 15 individuals. Skulls. Jaws. Ribs. Dozens of teeth. A nearly complete foot. A hand, virtually every bone intact, arranged as in life. Minuscule bones of the inner ear. Elderly adults. Juveniles. Infants, identified by their thimble-size vertebrae. Parts of the skeletons looked astonishingly modern. But others were just as astonishingly primitive—in some cases, even more apelike than the australopithecines. “We’ve found a most remarkable creature,” Berger said. His grin went nearly to his ears.


Evolutionary Responses to Global Change

What might the long-term outcome be of evolution under novel environmental conditions? For one possibility, let us consider again, but on an evolutionary time scale, the effects on a low-N terrestrial plant community of a large increase in the regional rate of N deposition. This could cause light and dispersal ability to become major limiting factors, as illustrated in Fig. 1C. As already discussed, the immediate effect of a high rate of N deposition would be dominance by a few formerly rare, fast-growing, rapidly dispersing plant species. These species would rapidly spread and overtop low-N-adapted species and thus out-compete them for light. However, a large portion of the viable trait space of this community would be empty, as in Fig. 1C. Assuming that N deposition is occurring on a geographically large region, or that habitat fragmentation or other dispersal barriers prevent colonization by suitable superior light competitors, or that the region has experienced other environmental changes (e.g., Ca leaching, soil acidification, invasion by pathogens) that make it inhospitable for otherwise suitable superior light competitors, its longer-term dynamics would be driven as much, or more, by internal evolutionary processes than by colonization.

The evolutionary dynamics of such systems have been explored for situations in which it is assumed that there is a strict trade-off between competitive ability and dispersal ability (36, 73, 74). Let us ask what might happen to a weedy plant species that was the initial dominant of a formerly N-poor habitat that experienced elevated N deposition, as shown in Fig. 1C. Numerical solutions to a partial differential equation model (36) show that, within the initially dominant weedy species (species 1 of Fig. 3A), those individuals that are better light competitors have greater fitness than those that are better dispersers. This causes the weedy species to evolve into a progressively better light competitor (acquiring such traits as a larger proportion of biomass in stem, greater height, and larger seed), but to produce fewer seeds and/or allocate less to vegetative spread. Thus, species 1 evolves to the right in Fig. 3A. As species 1 evolves into a better local competitor (and thus a poorer disperser), it occupies fewer sites in the spatial habitat. After this has progressed sufficiently far, an interesting phenomenon occurs. Individuals at the far end of the range of phenotypes, which are good dispersers but poor light competitors, are also favored (species 2 of Fig. 3B). These individuals are poor light competitors, and thus do not competitively inhibit species 1. However, they are good dispersers, which allows them to live in the sites not occupied by species 1.

Numerical solutions of evolutionary change in a weedy species growing in a spatially implicit habitat in which fitness is limited both by dispersal ability and by competitive ability, based on a model of phenotypic diffusion (36). (A) Given this trade-off, an initially weedy species, species 1, undergoes evolutionary change, with its peak shown moving to the right. (B) After 50,000 years, species 1 has evolved into a much better competitor, but a much poorer disperser than it originally was, and a new species, species 2, has appeared. Species 2 is a superior disperser, but an inferior competitor. It survives in vacant sites in this spatial habitat. (C) Species 1 and 2 each evolve toward being superior competitors. After some time a third species appears that is a poor competitor, but excellent disperser. This third species evolves into a superior competitor and a fourth species appears, etc. Shown here is the result after 475,000 years, at which time 21 peaks of abundance appear, each peak representing a different phenotype, thus corresponding with different species.

In essence, there is a bimodal selective pressure created by competition in a spatial habitat and by an analytical limit to similarity for coexistence of organisms with traits at different points on the trade-off curve (36). This leads to two peaks on the trade-off curve, each peak corresponding to an incipient species (Fig. 3B). Such peaks appear even when all phenotypes are initially rare, and result from the interplay of selection, mutation/recombination, and the competitive limit to similarity. Within each of these peaks, those individuals that are superior light competitors but inferior dispersers are favored, causing the peaks to move to the right in Fig. 3B. Once the second peak, incipient species 2, moves sufficiently far to the right, a third peak appears. It also evolves toward the right, and a fourth peak appears, etc. In numerical solutions of the underlying reaction-diffusion model, after a 475,000 year period, a single weedy species had speciated into 21 species (Fig. 3C) that spanned the empty niche space of Fig. 1C. Such speciation processes would occur within each of the original weedy species, and eventually would yield a local flora as species-rich as occurred before N deposition.

In total, this process suggests that the imposition of novel environmental constraints would lead to the eventual diversification of the flora of a region, with the new flora filling in the empty niches created by novel human-caused environmental conditions. The process by which this is predicted to occur is one in which the ancestral progenitors of this new flora are small, fast-growing, weedy species. Interestingly, this is just what has been suggested to have occurred during the evolution of the angiosperms, during diversification in corals, and during the diversification of terrestrial mammals.


5. Development and Evolution

The relationships that obtain between development and evolution are complicated and under ongoing investigation (for a review, see Love 2015). Two main axes dominate within a loose conglomeration of research programs (Raff 2000 Müller 2007): (a) the evolution of development, or inquiry into the pattern and processes of how ontogeny varies and changes over time and, (b) the developmental basis of evolution, or inquiry into the causal impact of ontogenetic processes on evolutionary trajectories&mdashboth in terms of constraint and facilitation. Two examples where the concepts and practices of developmental and evolutionary biology intersect are treated here: the problematic appeal to functional homology in developmental genetics that is meant to underwrite evolutionary generalizations about ontogeny (Section 5.1) and the tension between using normal stages for developmental investigation and determining the evolutionary significance of phenotypic plasticity (Section 5.2). These cases expose some of the philosophical issues inherent in how development and evolution can be related to one another.

5.1 Functional Homology in Developmental Genetics

The conserved role of Hox genes in axial patterning is referred to as functionally homologous across animals (Manak and Scott 1994), over and above the relation of structural homology that obtains between DNA sequences. And yet &ldquofunctional homology&rdquo is a contradiction in terms (Abouheif et al. 1997) because the definition of a homologue is &ldquothe same organ in different animals under every variety of form and function&rdquo (Owen 1843: 379)&mdashthe descendant, evolutionary distinction between homology (structure) and analogy (function) is founded on this recognition. Therefore, the idea of functional homology appears theoretically confused and there is a conceptual tension in its use by molecular developmental biologists.

Figure 6: Vertebrate wings are homologous as forelimbs they are derived by common descent from the same structure. The function of vertebrate wings (i.e., flight) is analogous although the wings fulfill similar functions, their role in flight has evolved separately.

The reference to &ldquoorgan&rdquo in Owen&rsquos definition is indicative of a structure (an entity) found in an organism that may vary in its shape and composition (form) or what it is for (function) in the species where it occurs. Translated into an evolutionary context, sameness is cashed out by reference to common ancestry. Since structures also can be similar by virtue of natural selection operating in similar environments, homology is contrasted with analogy. Homologous structures are the same by virtue of descent from a common ancestor, regardless of what functions these structures are involved in, whereas analogous structures are similar by virtue of selection processes favoring comparable functional outcomes, regardless of common descent (Figure 6).

This is what makes similarity of function an especially problematic criterion of homology (Abouheif et al. 1997). Because functional similarity is the appropriate relation for analogy, it is not necessary for analogues to have the same function as a consequence of common ancestry&mdashsimilarity despite different origins suffices (Ghiselin 2005). Classic cases of analogy involve taxa that do not share a recent common ancestor that exhibits the structure, such as the external body morphology of dolphins and tuna (Pabst 2000). Thus, functional homology seems to be a category error because what a structure does should not enter into an evaluation of homologue correspondence and similarity of function is often the result of adaptation via natural selection to common environmental demands, not common ancestry.

Although we might be inclined to simply prohibit the terminology of functional homology, its widespread use in molecular and developmental biology should at least make us pause. [18] While it is important to recognize this pervasive practice, some occurrences may be illicit. Swapping structurally homologous genes between species to rescue mutant or null phenotypes is not a genuine criterion of functional homology, especially when there is little or no attention to establishing a phylogenetic context. This makes a number of claims of functional homology suspect. To not run afoul of the conceptual tension, explicit attention must be given to the meaning of &ldquofunction.&rdquo Biological practice harbors at least four separate meanings of function (Wouters 2003, 2005): activity (what something does), causal role (contribution to a capacity), fitness advantage or viability (value of having something), and selected effect or etiology (origination and maintenance via natural selection). Debate has raged about which of them (if any) is most appropriate for different aspects of biological and psychological reasoning or most general in scope (i.e., what makes them all function concepts?) (see discussion in Garson 2016). Here the issue is whether we can identify a legitimate concept of homology of function.

If we are to avoid mixing homology and analogy, then the appropriate notion of function cannot be based on selection history, which is allied with the concept of analogy and concerns a particular variety of function. Similarly, viability interpretations concentrate on features where the variety of function is critical because of conferred survival advantages. Any interpretation of function that relies on a particular variety of function (because it was selected or because it confers viability) clashes with the demand that homology concern something &ldquounder every variety of form and function.&rdquo A causal role interpretation emphasizes a systemic capacity to which a function makes a contribution. It too focuses on a particular variety of function, though in a way different from either selected effect or viability interpretations. Only an activity interpretation (&lsquowhat something does&rsquo) accents the function itself, apart from its specific contribution to a systemic capacity and position in a larger context. Therefore, the most appropriate meaning to incorporate into homology of function is &ldquoactivity-function&rdquo because it is at least possible for activity-functions to remain constant under every variety. An evaluation of sameness due to common ancestry is made separately from the role the function plays (or its use), whether understood in terms of a causal role, a fitness advantage, or a history of selection. [19] Activity-functions can be put to different uses while being shared via common descent (i.e., homologous). More precisely, homology of function can be defined as the same activity-function in different animals under every variety of form and use-function (Love 2007). This unambiguously removes the tension that plagued functional homology.

Careful discussions of regulatory gene function in development and evolution recognize something akin to the distinction between activity- and use-function (i.e., between what a gene does and what it is for in some process within the organism).

When studying the molecular evolution of regulatory genes, their biochemical and developmental function must be considered separately. The biochemical function of PAX-6 and eyeless are as general transcription factors (which bind and activate downstream genes), but their developmental function is their specific involvement in eye morphogenesis (Abouheif 1997: 407).

The biochemical function is the activity-function and the developmental function is the use-function. This distinction helps to discriminate between divergent evolutionary trajectories. Biochemical (activity-functions) of genes are often conserved (i.e., homologous), while simultaneously being available for co-option to make causal role contributions (use-functions) to distinct developmental processes. The same regulatory genes are evolutionarily stable in terms of activity-function and evolutionarily labile in terms of use-function. [20] By implication, claims about use-function homology for genes qua developmental function are suspect compared to those concerning activity-function homology for genes qua biochemical function because developmental functions are more likely to have changed as phylogenetic distance increases.

The distinction between biochemical (activity) function and developmental (use) function is reinforced by the hierarchical aspects of homology (Hall 1994). A capacity defining the use-function of a regulatory gene at one level of organization, such as axial patterning, must be considered as an activity-function itself at another level of organization, such as the differentiation of serially repeated elements along a body axis. (Note that &ldquolevel of organization&rdquo need not be compositional and thus the language of &ldquohigher&rdquo and &ldquolower&rdquo levels may be inappropriate.) The developmental roles of Hox genes in axial patterning may be conserved by virtue of their biochemical activity-function homologies but Hox genes are not use-function homologues because of these developmental roles. Instead of focusing on the activity of a gene component and its causal role in axial patterning, we shift to the activity of axial patterning and its causal role elsewhere (or elsewhen) in embryonic development.

Introducing a conceptually legitimate idea of homology of activity-function is not about keeping the ideas of developmental biology tidy. It assists in the interpretation of evidence and circumscribes the inferences drawn. For example, NK-2 genes are involved in mesoderm specification, which underlies muscle morphogenesis. In Drosophila, the expression of a particular NK-2 gene (tinman) is critical for both cardiac and visceral mesoderm development. If tinman is knocked out and transgenically replaced with its vertebrate orthologue, Nkx2-5, only visceral mesoderm specification is rescued the regulation of cardiac mesoderm is not (Ranganayakulu et al. 1998). A region of the vertebrate protein near the 5&prime end of the polypeptide differs enough to prevent appropriate regulation in cardiac morphogenesis. The homeodomains (stretches of sequence that confer DNA binding) for vertebrate Nkx2-5 and Drosophila tinman are interchangeable. The inability of Nkx2-5 to rescue cardiac mesoderm specification is not related to the activity-function of differential DNA binding. One component of the orthologous (homologous) proteins in both species retains an activity-function homology related to visceral mesoderm specification but another component (not the homeodomain) has diverged. This homeobox gene does not have a single use-function (as expected), but it also does not have a single activity-function. Any adequate evaluation of these cases must recognize a more fine-grained decomposition of genes into working units to capture genuine activity-function conservation. We can link activity-function homologues directly to structural motifs within a gene, but there is not necessarily a single activity-function for an entire open reading frame.

Defusing the conceptual tensions between developmental and evolutionary biology with respect to homology of function has a direct impact on the causal generalizations and inferences made from model organisms (Section 4). Activity-function homology directs our attention to the stability or conservation of activities. This conservation is indicative of when the study of mechanisms in model organisms will produce robust and stable generalizations (Section 1.3). The widespread use of functional homology in developmental biology is aimed at exactly this kind of question, which explains its persistence in experimental biology despite conceptual ambiguities. Generalizations concerning molecular signaling cascades are underwritten by the coordinated biochemical activities in view, not the developmental roles (though sometimes they may coincide). Thus, activity-function details about a signaling cascade gleaned from a model organism can be generalized via homology to other unstudied organisms even if the developmental role varies for the activity-function in other species.

5.2 Normal Stages and Phenotypic Plasticity

All reasoning strategies combine distinctive strengths alongside of latent weaknesses. For example, decomposing a system into its constituents to understand the features manifested by the system promotes a dissection of the causal interactions of the localized constituents, while downplaying interactions with elements external to the system (Wimsatt 1980 Bechtel and Richardson 1993). Sometimes the descriptive and explanatory practices of the sciences are successful precisely because they intentionally ignore aspects of natural phenomena or use a variety of approximation techniques. Idealization is one type of reasoning strategy that scientists use to describe, model, and explain that purposefully departs from features known to be present in nature. For example, the interior space of a cell is often depicted as relatively empty even though intracellular space is known to be crowded (Ellis 2001) the variable of cellular volume takes on a value that is known to be false (i.e., relatively empty). Idealizations involve knowingly ignoring variations in properties or excluding particular values for variables, in a variety of different ways, for descriptive and explanatory purposes (Jones 2005 Weisberg 2007).

&ldquoNormal development&rdquo is conceptualized through strategies of abstraction that manage variation inherent within and across developing organisms (Lowe 2015, 2016). The study of ontogeny in model organisms (Section 4) is usually executed by establishing a set of normal stages for embryonic development (see Other Internet Resources). A developmental trajectory from fertilized zygote to fully-formed adult is broken down into distinct temporal periods by reference to the occurrence of major events, such as fertilization, gastrulation, or metamorphosis (Minelli 2003: ch. 4 see Section 1.2). This enables researchers in different laboratory contexts to have standardized comparisons of experimental results (Hopwood 2005, 2007). They are critical to large communities of developmental biologists working on well-established models, such as chick (Hamburger and Hamilton 1951) or zebrafish (Kimmel et al. 1995): &ldquoEmbryological research is now unimaginable without such standard series&rdquo (Hopwood 2005: 239). These normal stages are a form of idealization because they intentionally ignore kinds of variation in development, including variation associated with environmental variables. While facilitating the study of particular causal relationships, this means that specific kinds of variation in developmental features that might be relevant to evolution are minimized in the process of rendering ontogeny experimentally tractable (Love 2010).

Phenotypic plasticity is a ubiquitous biological phenomenon. It involves the capacity of a particular genotype to generate phenotypic variation, often in the guise of qualitatively distinct phenotypes, in response to differential environmental cues (Pigliucci 2001 DeWitt and Scheiner 2004 Kaplan 2008 Gilbert and Epel 2009). One familiar example is seasonal caterpillar morphs that depend on different nutritional sources (Greene 1989). Some of the relevant environmental variables include temperature, nutrition, pressure/gravity, light, predators or stressful conditions, and population density (Gilbert and Epel 2009). The reaction norm is a summary of the range of phenotypes, whether quantitatively or qualitatively varying, exhibited by organisms of a given genotype for different environmental conditions. When the reaction norm exhibits discontinuous variation or bivalent phenotypes (rather than quantitative, continuous variation), it is often labeled a polyphenism (Figure 7).

Figure 7: A color polyphenism in American Peppered Moth caterpillars that represents an example of phenotypic plasticity.

Phenotypic plasticity has been of recurring interest to biological researchers and controversial in evolutionary theory. Extensive study of phenotypic plasticity has occurred in the context of quantitative genetic methods and phenotypic selection analyses, where the extent of plasticity in natural populations has been demonstrated and operational measures delineated for its detection (Scheiner 1993 Pigliucci 2001). Other aspects of plasticity require different investigative methods to ascertain the sources of plasticity during ontogeny, the molecular genetic mechanisms that encourage plasticity, and the kinds of mapping functions that exist between the genotype and phenotype (Pigliucci 2001 Kirschner and Gerhart 2005: ch. 5). These latter aspects, the origin of phenotypic variation during and after ontogeny, are in view at the intersection of development and evolution: How do molecular genetic mechanisms produce (or reduce) plasticity? What genotype-phenotype mapping functions are prevalent or rare? Does plasticity contribute to the origination of evolutionary novelties (Moczek et al. 2011 West-Eberhard 2003)?

In order to evaluate these questions experimentally, researchers need to alter development through the manipulation of environmental variables and observe how a novel phenotype can be established within the existing plasticity of an organism (Kirschner and Gerhart 2005: ch. 5). This manipulation could allow for the identification of patterns of variation through the reliable replication of particular experimental alterations within different environmental regimes. However, without measuring variation across different environmental regimes, you cannot observe phenotypic plasticity. These measurements are required to document the degree of plasticity and its patterns for a particular trait, such as qualitatively distinct morphs. An evaluation of the significance of phenotypic plasticity for evolution requires answers to questions about where plasticity emerges, how molecular genetic mechanisms are involved in the plasticity, and what genotype-phenotype relations obtain.

Developmental stages intentionally ignore variation associated with phenotypic plasticity. Animals and plants are raised under stable environmental conditions so that stages can be reproduced in different laboratory settings and variation is often viewed as noise that must be reduced or eliminated if one is to understand how development works (Frankino and Raff 2004). This practice also encourages the selection of model organisms that exhibit less plasticity (Bolker 1995). The laboratory domestication of a model organism may also reduce the amount or type of observable phenotypic variation (Gu et al. 2005), though laboratory domestication also can increase variation (e.g., via inbreeding). Despite attempts to reduce variation by controlling environmental factors, some of it always remains (Lowe 2015) and is displayed by the fact that absolute chronology is not a reliable measure of time in ontogeny, and neither is the initiation or completion of its different parts (Mabee et al. 2000 Sheil and Greenbaum 2005). Developmental stages allow this recalcitrant variation to be effectively ignored by judgments of embryonic typicality. Normal stages also involve assumptions about the causal connections between different processes across sequences of stages (Minelli 2003: ch. 4). Once these stages have been constructed, it is possible to use them as a visual standard against which to recognize and describe variation as a deviation from the norm (DiTeresi 2010 Lowe 2016). But, more typically, variation ignored in the construction of these stages is also ignored in the routine consultation of the stages in day-to-day research contexts (Frankino and Raff 2004).

Normal stages fulfill a number of goals related to descriptive and explanatory endeavors that developmental biologists engage in (Kimmel et al. 1995). They yield a way to measure experimental replication, enable consistent and unambiguous communication among researchers, especially if stages are founded on commonly observable morphological features, facilitate accurate predictions of developmental phenomena, and aid in making comparisons or generalizations across species. As idealizations of ontogeny, normal stages allow for a classification of developmental events that is comprehensive with suitably sized and relatively homogeneous stages, reasonably sharp boundaries between stages, and stability under different investigative conditions (Dupré 2001), which encourages more precise explanations within particular disciplinary approaches (Griesemer 1996). Idealizations also can facilitate abstraction and generalization, both of which are a part of extrapolating findings from the investigative context of a model organism to other domains (Steel 2008 see Section 4 and 5.1).

There are various weaknesses associated with normal stages that accompany the fulfillment of these investigative and explanatory goals. Key morphological indicators sometimes overlap stages, terminology that is useful for one purpose may be misleading for another, particular terms can be misleading in cross-species comparisons, and manipulation of the embryo for continued observation can have a causal impact on ontogeny. Avoiding variability in stage indicators can encourage overlooking the significance of this variation, or at least provide a reason to favor its minimization.

Thus, there are good reasons for adopting normal stages to periodize model organism ontogeny, and these reasons help to explain why their continued use yields empirical success. However, similar to other standard (successful) practices in science, normal stages are often taken for granted, which means their biasing effects are neglected (Wimsatt 1980), some of which are relevant to evolutionary questions (e.g., systematically underestimating the extent of variation in a population). This is critical to recognize because the success of a periodization is not a function of the eventual ability to relax the idealizations periodizations are not slowly corrected so that they become less idealized. Instead, new periodizations are constructed and used alongside the existing ones because different idealizations involve different judgments of typicality that serve diverse descriptive and explanatory aims. In addition to the systematic biases involved in developmental staging, most model organisms are poorly suited to inform us about how environmental effects modulate or combine with genetic or other factors in development&mdashthey make it difficult to discover details about mechanisms underlying reaction norms. Short generation times and rapid development are tightly correlated with insensitivity to environmental conditions through various mechanisms such as prepatterning (Bolker 1995).

The tension between the specific practice of developmental staging in model organisms and uncovering the relevance of variation due to phenotypic plasticity for evolution can be reconstructed as an argument.

  1. Variation due to phenotypic plasticity is a normal feature of ontogeny.
  2. The developmental staging of model organisms intentionally downplays variation in ontogeny associated with the effects of environmental variables (e.g., phenotypic plasticity) by strictly limiting the range of values for environmental variables and by removing variation in characters utilized to establish the comprehensive periodization.
  3. Therefore, using model organisms with specified developmental stages will make it difficult, if not impossible, to observe patterns of variation due to phenotypic plasticity.

Although this tension obtains even if the focus is not on evolutionary questions, sometimes encouraging developmental biologists to interpret absence of evidence as evidence of the developmental insignificance of phenotypic plasticity, it is exacerbated for evolutionary researchers. The documentation of patterns of variation is precisely what is required to gauge the evolutionary significance of phenotypic plasticity. Practices of developmental staging in model organisms can retard our ability to make either a positive or negative assessment. Developmental staging, in conjunction with the properties of model organisms, tends to encourage a negative assessment of the evolutionary importance of phenotypic plasticity because the variation is not manifested and documented, and therefore is unlikely to be reckoned as substantive. Idealizations involving normal stages discourage a robust experimental probing of phenotypic plasticity, which is an obstacle to determining its evolutionary significance.

The consequences of this tension for the intersection of development and evolution are two-fold. First, the most powerful experimental systems for studying development are set up to minimize variation that may be critical to comprehending how evolutionary processes occur in nature. Second, if evolutionary investigations revolve around a character that was assessed for typicality to underwrite the temporal partitions that we call stages, then much of the variation in this character was conceptually removed as a part of rendering the model organism experimentally tractable. [21]

The identification of drawbacks that accompany strategies of idealization used to study development invites consideration of ways to address the liabilities identified (Love 2006). We can construct a principled perspective on how to address these liabilities by adding three further premises:

  1. Reasoning strategies involving idealization, such as (2), are necessary to the successful prosecution of biological investigations of ontogeny.
  2. Therefore, compensatory tactics should be chosen in such a way as to specifically redress the blind spots arising from the kind of idealizations utilized.
  3. Given (1)&ndash(3), compensatory tactics must be related to the effects of ignoring variation due to phenotypic plasticity that result from the developmental staging of model organisms.

At least two compensatory tactics can promote observations of variation due to phenotypic plasticity that is ignored when developmental stages are constructed for model organisms: the employment of diverse model organisms and the adoption of alternate periodizations.

Variation often will be observable in non-standard model organisms because experimental organisms that do not have large communities built around them are less likely to have had their embryonic development formally staged, and thus the effects of idealization on phenotypic plasticity are not operative. In turn, researchers are sensitized to the ways in which these kinds of variation are being muted in the study of standard models. Stages can be used then as visual standards to identify variation as deviations from a norm and thereby characterize patterns of variability. [22]

A second compensatory tactic is the adoption of alternative periodizations. This involves choosing different characters to construct new temporal partitions, thereby facilitating the observation of variation with respect to characteristics previously stabilized in the normal stage periodization. These alternative periodizations often divide a subset of developmental events according to processes or landmarks that differ from those used to construct the normal stages, and they may not map one-one onto the existing normal stages, especially if they encompass events beyond the trajectory from fertilization to a sexually mature adult. This lack of isomorphism between periodizations also will be manifested if different measures of time are utilized, whether sequence (event ordering) or duration (succession of defined intervals), and whether sequences or durations are measured relative to one another or against an external standard, such as absolute chronology (Reiss 2003 Colbert and Rowe 2008). These incompatibilities prevent assimilating the alternative periodizations into a single, overarching staging scheme. In all of these cases, idealization is involved and therefore each new periodization is subject to the liabilities of ignoring kinds of variation. However, alternative periodizations require choosing different characters to stabilize and typify when defining its temporal partitions, which means different kinds of variation will be exposed than were previously observable. [23]


6. Future Prospects

Arguably, discussions of reductionism in biology are becoming more rather than less philosophically interesting. This is a consequence of recognizing the diverse conceptual landscape carved out over past decades, one that is much larger than that conceived of when only Nagelian theory reduction was in view. Biological science is now more specialized than ever, but disciplinary proliferation brings with it issues relevant to any analysis of reduction: using the same terms differently, disparate methodologies, distinct explanatory norms, and divergent interests in &ldquolevels&rdquo of biological organization. It also includes an acknowledgment that certain enduring biological topics like development still engender difficult questions about reductionism and new fields of inquiry reinvigorate these discussions (e.g., synthetic biology&rsquos appeal to molecular components as Lego-like building blocks in the Registry of Standard Biological Parts). Philosophers need to recognize that a criterion of adequacy on accounts of reductionism in biology involves interpreting why scientists make pronouncements about the failure of reductionism in different areas of life science (see, e.g., De Backer et al. 2010). [16]

There also is increasing contact between neuroscience and psychologically oriented investigations (Bechtel 2008, Boogerd et al. 2002, Craver 2007, Sullivan 2016). A potential prospect related to the contact between molecular neurobiology and psychology is the injection of epistemic considerations into philosophy of mind discussions (Godfrey-Smith 2008). More attention to issues of representation, decomposition, and temporality could alter the nature of these debates. At the same time, a more explicit evaluation of metaphysical components imported from philosophy of mind into philosophy of biology is warranted. Mediated by the work of Jaegwon Kim, Rosenberg incorporated the concept of supervenience from Davidson four decades ago (Rosenberg 1978). [17] Rosenberg subsequently redeployed Kim&rsquos (1998) causal exclusion argument in support of reductionism in biology (Rosenberg 2006 cf. Eronen 2011, Sachse 2007). Discussions of the status of downward causation and realization in biological systems are relevant for a better understanding of the intersection between epistemological and metaphysical components of reduction (Craver and Bechtel 2007, Love 2012, Robinson 2005, Wilson and Craver 2007). For example, an interest in the context sensitivity of realization in philosophy of mind (Wilson 2004, ch. 6) invokes issues pertaining to the context objection, individuation, temporality (especially causation versus constitution), and intrinsicality. These analyses can then be imported into biological contexts to argue for or against particular aspects of reductionism (see, e.g., Wilson 2005).

More work is needed in how to relate different kinds of hierarchies to questions of reductionism. Biological hierarchies are diverse (Grene 1987, Korn 2002, 2005), and some areas of biological research, such as paleontology and systematics (Valentine and May 1996, see also Grantham 1999, 2004a, 2004b, 2007), have been ignored when reductionism comes into view. Paying attention to temporality encourages the exploration of functional or control hierarchies in more detail (Salthe 1985, 1993, Wimsatt 2002). The limitations of representing biological systems in terms of hierarchies should be explored in more depth as well (Brooks 2014, O&rsquoMalley et al. 2014, Potochnik and McGill 2012).

All of these possibilities suggest more scrutiny of disciplinary heterogeneity in biological investigation relevant to reductionism. Although ecology has received some treatment (Dupré 1993, ch. 5 Mikkelson 2004), issues of decomposition and representation have not yet received wide attention (Lafrançois 2006). Other topics connected to explanatory reduction and mechanisms in experimental research are cancer (Bertolaso 2013, 2016, Bizzarri et al. 2008, Moss 2002, Plutynski 2013, Soto and Sonnenschein 2005, 2006) and stem cells (Fagan 2013, Laplane 2016). Because of the diversity of explanations found in these different disciplines, the nature of scientific explanation returns with a vengeance (Schaffner 2006). Although only a few reductionists demand that explanation be strictly nomological (Rosenberg 2006, Weber 2005), the subtle interplay between explanation and reduction must be treated when ranging over diverse biological subdisciplines. Additionally, as more attention is given to the diversity of investigative reasoning (or &ldquoscientific practice&rdquo) in and among these disciplinary contexts, the interplay between different aspects of methodological and epistemic reduction will become more salient.

A final prospect concerns whether discussions of reduction in different sciences will interact fruitfully. Although questions about reduction in philosophy of physics have largely diverged from those in philosophy of biology, some connections have been drawn (Hüttemann and Love 2016). For example, Sarkar has approached quantum mechanics using his account of reductionism that was forged in a biological context (Jaeger and Sarkar 2003). A potential intersection between these discussions arises around the relations between parts and wholes or temporality, which have come under scrutiny from philosophers focused on the physical sciences (Hüttemann 2004, Love and Hüttemann 2011, Rueger 2000, Rueger and McGivern 2010). Another area worthy of more attention is reductive explanation in chemistry, which has been ignored in large part by philosophers of biology (see, e.g., Bishop 2010, LePoidevin 2005, Ramsey 1997, Scerri 1994, 2000).

In all these cases it seems clear that debates about reductionism in biology have not reached a denouement but rather portend vigorous philosophical discussion as the heterogeneity of issues related to its ontological, epistemological, and methodological types are brought to bear on perennial biological topics. The task of philosophers focused on reductionism in biology will be to analyze these promiscuities of reasoning and seek to develop accounts of reduction that capture what scientists actually do and contribute to more general perspectives on biological knowledge and scientific inquiry.


Materials and Methods

We collected morphometric data (Table 2 and Appendix S1) from 140 leporid skulls spanning 16 taxa (Table 1) housed in the departments of Mammalogy at the American Museum of Natural History (AMNH) and the Los Angeles County Museum of Natural History (LACM). Care was made to use only adult specimens, characterized by fully fused occipital sutures (Hoffmeister & Zimmerman, 1967). Ten linear measurements (Table 3 and Fig. 3) were recorded per specimen using digital calipers by three authors (BPK, MW, and NB), and a repeatability analysis (consisting of 10 specimens measured 3 times, results not presented) was performed to ensure there was no intercollector bias introduced. The ten cranial measurements were analyzed using the log-shape ratios approach (Mosimann, 1970 Mosimann & James, 1979). For each specimen, size was computed as the geometric mean of all measurements, and then each measurement was divided by size to obtain the shape ratios. We then used the log of this quantity as raw data for the subsequent analyses.

Abbr. Variable Measurement convention
BLD Bulla diameter Maximum diameter (in any direction) of right bulla
BOL Basioccipital length Maximum midsagittal length from anterior basioccipital to foramen magnum
DIL Diastema length Maximum distance between right I2 and M1
IOW Interorbital width Minimum transverse width between dorsal rims of orbits
NL Nasal length Maximum parasagittal length of nasal bones (i.e., orthogonal antero-posterior but not along midline)
NW Nasal width Maximum transverse width across posterior nasal bones
PAL Parietal length Maximum midsagittal length of parietal bones
SCF Splenius capitis fossa Maximum parasagittal length from anterior margin of M. splenius capitis insertion fossa to opisthocranion
SLD Skull length dorsal Maximum midsagittal length from anterior nasal bones to Opisthocranion (just dorsal to incisors) to opisthocranion
SW Skull width Maximum transverse width across zygomatic processes
PC1 PC2 PC3 PC4
Proportion of variance 43.5 24.4 13.2 9.1
BLD 0.85046873 0.02545804 0.016857617 0.272291215
BOL 0.159862063 0.161106564 0.012761305 −0.070830236
DIL −0.246528468 −0.026035317 −0.415136231 0.156169866
IOW −0.249260494 0.331660879 0.681589555 0.259415969
NL −0.175108112 0.062932441 −0.505939657 0.130829626
NW −0.296951523 0.01926985 0.131337875 0.224903873
PAL 0.027064391 0.140782388 0.078947604 −0.866213361
SCF −0.050436754 −0.905402173 0.218089373 −0.039704014
SLD −0.064043373 0.114113325 −0.185926838 0.027064132
SW 0.044933539 0.076114004 −0.032580602 −0.093927069

Facial tilt was measured by photographing each skull in lateral view using a Nikon D80 digital camera (Nikon, Tokyo, Japan). The skulls were placed in a sandbox to ensure that the sagittal plane was orthogonal to the focal direction. Facial tilt angle was acquired from the digital photos within Adobe Photoshop©, measured as the angular difference between the ‘occipital plane’ and a line parallel to the cranial diastema (Fig. 3). Variation among individuals for the cranial variables weas explored using principal components analysis on the covariance matrix of the log-shape ratios shape variables within the statistical software R v3.1.1 (R Core Team, 2014, http://cran.r-project.org/).

Phylogenetically informed analyses

To examine facial tilt angle and cranial shape in a phylogenetic context, we used the phylogenetic relationships among species of Leporidae recently published by Matthee et al. (2004). The original tree was constructed using seven genes (five nuclear and 2 mt) for 25 ingroup taxa. We pruned the tree using Mesquite© (Maddison & Maddison, 2015) to include only the 16 taxa studied here (Fig. 3), and retained the information on branch lengths (details of which are in (Matthee et al., 2004)).

We first examined the amount of phylogenetic signal in the morphometric variables, calculating the K statistic (Blomberg, Garland & Ives, 2003) for facial tilt angle, and the multivariate equivalent Kmult (Adams, 2014a) for all log-shape ratios. The K statistics provide a measure of the strength of phylogenetic signal for univariate and multivariate traits respectively, and in each case provides a single statistic. A value of less than one implies that taxa resemble each other phenotypically less than expected under Brownian motion, while values of more than 1 implies that close relatives are more similar to one another phenotypically than expected under Brownian motion. Significance testing was performed using a permutation procedure whereby the variables are randomized relative to the tree, and 1000 permutations were performed for each test (Blomberg, Garland & Ives, 2003).

Log-shape ratios and facial tilt angle were compared to several key ecological indicators, including locomotor type and burrowing habit (Table 1). Ecological data were obtained from Chapman & Flux (1990) and Stoner, Bininda-Emonds & Caro (2003). We divided leporids into three locomotor categories: generalized or ‘scramble’ locomotors, which tend to be the slowest-moving saltatory or hopping locomotors and fast-moving taxa that practice cursorial (leaping and bounding) locomotion, which is essentially a specialized form of saltation. Regarding burrowing habits, some leporids dig their own burrows (e.g., Oryctolagus and Romerolagus), whereas others simply occupy preexisting burrows excavated by other animals. For the purposes of this study, we refer to leporids as burrowers if they occupy burrows consistently, regardless of whether they dig the burrows.

To test whether or not the degree of facial tilt differs among the three locomotor categories, we performed a one-way Analysis of Variance (ANOVA) in an evolutionary context, under a Brownian motion model of evolution. This was done by using species means of the FT angle in a distance-based phylogenetic generalized least squares analysis (D-PGLS Adams, 2014b). A distance-based approach provides numerically identical estimates of evolutionary patterns to those obtained from standard implementations of PGLS on univariate datasets, and was used here for consistency with analyses below on the log-shape ratios. The statistical significance of each term in the D-PGLS was assessed using 1000 permutations whereby the species means are shuffled among the tips of the phylogeny. We performed a second ANOVA as above to test whether facial tilt differs between taxa that utilize burrows (“burrowing”) and those that do not (“non-burrowing”). Box and whisker plots were used to visualize the individual variation in facial tilt angle among groups.

To test whether or not cranial shape, as represented by ten morphometric variables, differs among the three locomotor types, we performed a multivariate analysis of variance in an evolutionary context under a Brownian motion model of evolution. This was done as a D-PGLS with the species means of the ten log-shape ratios. The D-PGLS performs better than a regular PGLS when the number of variables begins to approach the number of species (Adams, 2014b). The statistical significance of each term in the D-PGLS was assessed using 1,000 permutations of the species means. Similarly, we tested whether or not cranial shape differs between burrowing and non-burrowing taxa using a D-PGLS as above.

Finally, to test whether or not facial tilt is a significant predictor of cranial shape, we performed a multivariate regression in an evolutionary context, under a Brownian motion model of evolution, again using the D-PGLS approach. The statistical significance was assessed using 1000 permutations of the species means of the log-shape ratios. All of the phylogenetically informed analyses were done using the geomorph package (Adams et al., 2014) in the statistical software R v3.1.1 (R Core Team, 2014). The ANOVAs on FT, the MANOVAs on cranial log-shape rations, and the multivariate regression were done using the procD.pgls function, and phylogenetic signal was calculated with the physignal function.


The biology and functional morphology of Mytilaster minimus (Bivalvia: Mytiloidea: Mytilidae) from the intertidal dinaric karst of Croatia (Adriatic Sea)

Mytilaster minimus is widely distributed along the rocky intertidal of much of the Mediterranean's coastline and the Adriatic Sea. Populations are, however, threatened by the Lessepsian invader Brachidontes pharaonis that occupies the same habitat and is more tolerant of environmental extremes. This is the first study of the anatomy of M. minimus in relation to its evolution and adaptations towards an intertidal life on the karsted limestone shores that characterize much of the Mediterranean. In most anatomical respects M. minimus is a typical mytilid but is small (<16 mm shell length) and, post-juvenile, greatly deformed concomitant with its niche of colonizing pits in the karsted rocks. It is thus generally squatter, that is, dorso-ventrally flattened, laterally expanded and antero-posteriorly foreshortened in comparison with M. galloprovincialis . A pair of statocysts has been identified in the visceral mass. Most interest, however, resides in the fact the posterior byssal retractor muscles, like the shell, are foreshortened to comprise one paired block and the posterior pedal retractor muscles are situated beneath these not anterior to them as in other mytilids. These adaptations equip M. minimus for a compressed, squat, life in the intertidal karst. In addition to competition from the introduced B. pharaonis in the Mediterranean, M. minimus is facing competitive exclusion from the native Mytilus galloprovincialis that, as a result of intensive and increasing mariculture, is coming to dominate Croatian shorelines. This study is, therefore, prescient in laying the foundations for future research on what is becoming a threatened native Mediterranean species.