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## Hot answers tagged angular-momentum

3

First, the electron isn't actually spinning. Physical objects made up of collections of electrons and protons (and neutrons) can have angular momentum because they rotate; the electron does not get its angular momentum for the same reason. Second, the magnetic moment of an object with angular momentum L is proportional to $$\mu \propto \frac{qL}{M}$$ ...

2

You seem to be saying that friction couldn't speed it up, because nothing else is moving that fast. Well, how fast is it moving? We can imagine the gyroscope axis parallel to the z axis, and the casing to be aligned such that the x axis goes through it. If the casing is tipped slightly, the gyroscope resists that turning and one side of the shaft has firm ...

2

Not all galaxies are disk-shaped, but some certainly are. (Some others are spiral, etc.) For one thing, we see a lot of galaxies, and several of them look exactly like they would if they were disk-shaped and we were just seeing them from different angles. Some seem circular because we are seeing them head-on, while others seem more linear or elliptical ...

1

1) I don't understand your questions really well. Consider rephrasing it to make it a bit clearer. What I can tell you is the following: The mathematical definition of the principal axes of inertia is that they are the eigenvectors of the inertia tensor. The physical interpretation of the principal axes of inertia is that they represent the directions ...

1

For the relation between the abstract position basis and the $L^2$ spaces, I refer you to my answer here (read the other answers too, they're good ;) ) You are quite close with your understanding of the representations, but not quite there: First of all, for the 2-dim spin-$\frac{1}{2}$ Hilbert space $\mathcal{H}_{\uparrow\downarrow}$, the set of ...

1

There seems to be no way to proceed unless more information is given. In fact, from what you have above (correcting the typo pointed out by Bernhard and ticster): $$\vec{j}=\vec{l}+\vec{s} \quad \Rightarrow \quad \vec{j}^2=\big(\vec{l}+\vec{s}\big)^2 = \vec{l}^2+\vec{s}^2 + 2\, \vec{l}\cdot\vec{s}\,,$$ meaning that knowledge of the eigenvalues of ...

1

$$D(\hat{X}_i):= \left\{ \psi \in L^2(K, d^nx)\:\left|\: \int_K x_i^2|\psi(x)|^2 d^nx< \right.+\infty \right\} = L^2(K, d^nx)$$ where the last identity holds true if $K$ is bounded (in particular compact) because $x_i^2$ is bounded as well thereon (in this case the operator is bounded, too). Moreover D(\hat{J}^2):= \left\{\psi \in L^2(\mathbb S^2, ...

1

As the previous post mentioned, forget about the concept that the electron is actually spinning. Spin, like rest mass and electric charge, is an intrinsic property of subatomic particles. Yes, it's angular momentum. No, nothing is spinning. Although many physicists today do not like this explanation, special relativity introduces a useful analogy with mass. ...

1

Regarding the second question: The parallel axis theorem states, that if you have rotation around another axis than a body's center of mass, you only have to add the inertia tensor of a point mass at the location of the body's center of mass. And because of your setup, you will probably not be able to measure more than one component of the person's inertia ...

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Article | Open

# Evidence for mutual assessment in a wild primate

• Scientific Reports 7, Article number: 2952 (2017)
• doi:10.1038/s41598-017-02903-w
Accepted:
Published online:

## Abstract

In aggressive interactions, game theory predicts that animals should assess an opponent’s condition relative to their own prior to escalation or retreat. Despite the benefits of such mutual assessment, few studies have been able to reject simpler assessment strategies. Here we report evidence for mutual assessment in a wild primate. Gelada (Theropithecus gelada) males have conspicuous loud calls that may function as a signal of male quality. “Leader” males with harems putatively use loud calls to deter challenges from non-reproductive “bachelor” males. By contrast, leader males pose no threat to each other and congregate in large groups for a dilution effect against bachelors. In playback experiments and natural observations, gelada males responded to loud calls according to both their own and their opponent’s attributes. Although primates routinely classify others relative to themselves using individual attributes, this represents some of the first direct evidence for mutual assessment in primate signaling contests.

## Introduction

Limited resources lead animals into contests. Because aggressive contests are costly, game theory predicts that contestants will assess the costs and benefits of a particular contest before escalating1, 2. Contestants with a high ability to compete (i.e., high resource holding potential – RHP) should escalate the contest, while those with a low ability to compete (low RHP) should withdraw. Despite the simplicity of this prediction, there is enormous debate about how animals make these decisions3. It stands to reason that a contestant should gather information about their opponent’s condition and compare that to their own (mutual assessment2). However, many empirical studies find it difficult to reject “simpler” assessment strategies4 such as self-assessment (relying solely on one’s own condition5), or opponent-only assessment (relying solely on a rival’s condition6). For example, when an inferior contestant withdraws from an aggressive contest with an opponent, the contestant may indeed be using mutual assessment, or they may simply be withdrawing because the damage incurred was too high. Therefore, a “cumulative” self-assessment strategy is difficult to distinguish from a “sequential” mutual assessment one4.

Non-contact displays – such as those involving animal signals – avoid this problem entirely7. Contestants do not accumulate sufficient costs during displays for a cumulative assessment strategy to operate. Therefore, measuring receiver responses based on the relative quality of the signaler and the receiver makes for a strong test of mutual assessment. However, within the vast literature documenting receiver responses to putative signals based on the quality of the signaler8,9,10,11,12,13,14,15,16,17,18,19, only a handful of studies also examined receiver responses based on the relative quality of the signaler and the receiver7, 19,20,21,22. Thus, the current evidence for many signals is only sufficient for identifying opponent-only assessment. In practice, however, it is likely that many of these taxa may be using mutual assessment.

In non-human primates (hereafter, “primates”), there is very little evidence for mutual assessment based solely on the content encoded in a signal for two reasons. First, experimental manipulations are necessary. In one of the few captive experiments that sought to examine mutual assessment (e.g., manipulated scrotal color and staged contests in male vervet monkeys, Chlorocebus pygerythrus), the results were inconclusive possibly because it is near impossible to recreate realistic scenarios that males typically encounter in the wild22. Second, primates tend to rely predominantly on social information that derives from individual recognition (not signals) to guide their interactions23. The use of this social information can be quite sophisticated (e.g., eavesdropping)24 or quite simple (e.g., requiring nothing more than recognizing an individual)23. For example, in primate systems with linear dominance hierarchies, the dominance rank of an individual determines the outcome of most social interactions25,26,27. Typically, these interactions are not thought to be based on the relative Resource Holding Potential (RHP) of contestants at the time of the interaction, but rather based on social knowledge of contestants derived from a recent history of interactions with them28. However, results from previous research on mutual assessment have not been able to entirely distinguish whether primate subjects use individual identity or quality signals as the underlying basis for assessment, even in cases where playback experiments were able to manipulate the individual and/or the signal.

For example, playback experiments in chacma baboons (Papio ursinus) have shown that subjects respond to loud call displays based on the relative RHP of contestants19, 25, 26. Specifically, based on simulated loud call contests, chacma males were more likely to enter vocal contests if the opponent’s rank was similar to their own26, they were able to discriminate between the relative ranks of “third party” contestants25, and they were more interested in calls that were manipulated to suggest a higher quality male19. Combined, these three studies suggest that mutual assessment may be occurring in chacma baboons. However, the first two studies25, 26 and half of the third study19 were conducted entirely with familiar rivals. In a parallel study on chacma male grunts (soft vocalizations that are unlikely to carry information about the sender’s competitive ability), researchers also observed that the strongest responses occurred between similarly-ranked males29. Therefore, because known males were used, it is impossible to distinguish whether chacma male assessment hinged on individual recognition (knowing who is calling) or signal strength (knowing the quality of the caller). In the only experiment to use unknown callers, the subjects did not alter their responses according to their own rank – they all attended more strongly to the higher quality loud call19. Therefore, although these results are certainly suggestive, they fall just short of providing clear evidence of mutual assessment in the context of animal signals. Here we examined whether a close relative to chacma baboons, the gelada (Theropithecus gelada), uses mutual assessment when hearing loud calls from other males.

Previous research in geladas reported that leader males that display more frequently were less likely to be targeted by bachelors, suggesting that the quantity of these displays serves to deter rivals33. But, in addition to the quantity of loud calls produced, recent evidence also suggests that the quality of these loud calls is important for rival assessment37. Specifically, the males with the highest RHP in gelada society (e.g., prime-aged, high-status males) utter the most calls per bout, produce calls that are the lowest in overall frequency measures, and exhibit the greatest vocal range37. Thus, the loud calls themselves appear to be honest signals of male RHP, and bachelor males could use RHP information encoded in these calls for identifying relatively low-quality males (e.g., old males, low-status males, exhausted males)37.

By contrast, leader males do not assess bachelors. Leader males are always on defense, never offence, from bachelor males. However, leader males do have the potential to assess other leader males via these calls37. If indeed leader males rely on a putative dilution effect to avoid being challenged by bachelor males, then each leader’s position is secure only if they, themselves, have a higher RHP than the other leader males around them. Thus, leader males can use RHP information encoded in these calls for identifying situations when they are surrounded by relatively strong males (and are, thus, weaker by comparison).

We used a playback experiment as well as observations of natural behavior in wild geladas to investigate the rival assessment strategy used by males. We examined male responses to both experimental and natural loud calls of varying quality. If gelada males rely only on self-assessment in male contests, we predicted that neither leaders nor bachelors would respond differently to low- and high-quality calls (Fig. 1a). If gelada males rely on opponent-only assessment, we predicted that all subjects would attend more strongly to high-quality calls than low-quality calls regardless of their own status (Fig. 1b; note that the direction of this response could also be reversed). However, if gelada males rely on mutual assessment in male contests (Fig. 1c), we predicted that male subjects would respond to loud calls based on the combined information about themselves (i.e., their own status and/or RHP) and the quality of their rival (i.e., call quality). Specifically, we expected: (1) bachelor males to attend more to low-quality calls (a weak rival) because this represents a prime opportunity for a takeover; (2) leader males to attend more to high quality calls (a situation that weakens their relative security in the group, and one that indicates they might soon be challenged) – (3) particularly if they themselves have high RHP; (4) high RHP leader males to not just attend to high quality calls, but to advertise their own quality by subsequently participating in the display; and (5) females to not discriminate between call quality (because loud calls are used in male-male competition rather than female choice).

## Results

### Do males attend differently to high- and low-quality calls based on their own status?

We conducted a playback experiment on 60 adult geladas (20 females, 20 leader males, and 20 bachelor males) using previously recorded loud calls obtained during naturally-occurring displays between adult males (7 high-quality bouts and 7 low-quality bouts were used to construct 10 playback sets each containing a unique combination of one high- and one low-quality loud call bout from different males). Each subject heard both a high-quality loud call (one caller) and a low-quality loud call (a different caller). We visually recorded each subject’s response to each call type (randomized for order of presentation) and examined six response variables (look duration, approach duration, latency to look, latency to approach, approach distance, and time to resume activity), which were reduced using factor analysis. The factor analysis resulted in two latent factors, (1) an “approach” response, and (2) a “look” response, with Eigenvalues >1, together explaining 90.68% of the total variance. Factor 1 (“approach response”) accounted for 64.18% of the variance and loaded heavily on approach duration, approach distance, and latency to approach. Factor 2 (“look response”) accounted for 26.50% of the variance and loaded heavily on look duration, latency to look, and time to resume activity (Table 1).

To examine whether social status and/or call quality (high or low) determined a subject’s response, we constructed two Linear Mixed Models (LMMs) with each factor score as the dependent variable. In each model, we included social status (leader, bachelor, or female), call quality (high or low), and an interaction between them as predictors and controlled for call order (fixed) and subject (random). To further assess if bachelors and leaders differentiated between call-quality, we conducted additional pairwise contrasts and adjusted the p-value accordingly.

For Factor 1 (“approach response”), we found a significant interaction between bachelors and call quality (β = 0.25, s.e. = 0.11, t = 2.202, p = 0.032; Table 2). Bachelors approached low-quality calls significantly more than females and leaders (Fig. 2a). Bachelors were also more likely to approach low-quality calls than high-quality calls (β = −0.27, s.e. = 0.08, t = −3.353, p = 0.014; Table 2), but neither leaders (β = 0.02, s.e. = 0.08, t = 0.199, p = 0.843) nor females (β = 0.02, s.e. = 0.08, t = 0.234, p = 0.816) differed in whether they approached either call type (Table 2). In fact, females rarely approached the speaker (Table S1).

By contrast, leaders were more likely to look (Factor 2) towards high-quality calls than low-quality calls (β = 0.52, s.e. = 0.18, t = 2.856, p = 0.006; Fig. 2b). Yet, neither bachelors (β = 0.18, s.e. = 0.18, t = 1.000, p = 0.321) nor females (β = −0.19, s.e. = 0.18, t = −1.042, p = 0.302) distinguished between call type in terms of looking time (Table 2). In general, both bachelors (β = 0.99, s.e. = 0.29, t = 3.389, p = 0.001) and leader males (β = 0.61, s.e. = 0.29, t = 2.110, p = 0.038) spent more time looking towards the speaker than females did. We found no effect of call order in either model (Factor 1; β = 0.05, s.e. = 0.05, t = 1.145, p = 0.257: Factor 2; β = −0.06, s.e. = 0.11, t = −0.568, p = 0.572).

In addition to the Factor Analysis, we further examined overall response time, a measure of how long each individual spent investigating the source of the call (look duration + move duration), to assess whether status or call type affected the overall strength of a male’s response. Supporting the previous results, we found a significant effect of call quality for bachelors and a significant interaction between social status and call quality. Bachelors spent more time oriented towards loud call bouts of low-quality (low quality; β = 10.66, s.e. = 3.82, t = 2.787, p = 0.008; Fig. 3a,b) while leader males spent more time oriented towards loud call bouts of high-quality (leader x low-quality; β = −16.35, s.e. = 5.41, t = −3.024, p = 0.004; Fig. 3a,b). In general, bachelors and leaders did not differ in their overall total orientation time to high-quality loud calls (β = 0.93, s.e. = 5.92, t = 0.157, p = 0.876; Fig. 3).

### Do males attend differently to high- and low-quality calls based on their own quality?

Males’ responses to differences in call quality were based on their own categorical differences in status as a leader or bachelor. We additionally wanted to examine whether males further differentiated playback stimuli based on their own “quality” (i.e., using the quality of their own loud calls as a proxy for overall “quality”37). The sample for this analysis (N = 11) was only a subset of the leader males used for the first analysis (we did not have recordings from all subjects, and generally only leader males produce loud calls33). Note that because low-quality males rarely produce loud calls, the leader males included in this analysis disproportionately comprise males whose loud calls are mid- to high-quality. We predicted that the previous result was due mainly to the high-quality leader males responding to the high-quality call type. We established a call quality score for each subject’s loud calls in the same way that we determined high- and low-quality calls for the playback experiment.

For each call type (low, high), we compared each subject’s overall response time to his own call quality score. In response to the simulated low-quality calls, we found no relationship between the subject’s call quality score and his overall response time (rs = 0.489, p = 0.127). However, in response to the simulated high-quality calls, leader males with high call quality scores themselves, responded more strongly than those with low quality scores (rs = 0.752, p = 0.008; Fig. 4).

### Are males more likely to join a loud call display when they hear calls of similar quality to their own?

The previous two results suggest that we can predict a male’s response to the quality of a loud call by using experimental stimuli – bachelor males responded strongly to simulated low-quality calls and leader males responded strongly to simulated high-quality calls. Within leader males, high-quality leaders responded strongest to high-quality calls suggesting that leaders attend to both the quality of the caller and their own quality. Next, we wanted to determine if these same results hold true in natural observations of male contests. Loud call displays often serve as a catalyst for other unit males to join in with loud calls of their own. We predicted that high-quality leader males will be more likely to enter a loud call display when the display includes other males of high quality. To test these predictions, we used behavioral observations and loud call recordings from 20 unit males (16 leader males, 4 follower males) across 291 loud call displays, recording 423 loud calls from all 20 males.

We then examined whether male A (subject) was more likely to participate in a display given that male B also displayed (binomial distribution). We included relative call quality (the difference between the call quality scores of both males), caller “familiarity” (using social network analysis), and leader/follower status in the models as fixed effects; and we included the identification of both males as random effects.

Males were more likely to display with males of similar call quality scores to their own (β = −0.62, s.e. = 0.31, z = −2.042, p = 0.041; Table 3), with caller familiarity having little effect (β = 0.25, s.e. = 0.14, z = 1.808, p = 0.071; Table 3).

## Discussion

In simulated signal displays, gelada males, but not females, discriminated between loud calls based on the acoustic quality of the signal as well as their own status and quality. Specifically, bachelor males – males that must compete to gain reproductive access to females – exhibited a stronger response to low-quality loud calls, while leader males exhibited a stronger response to high-quality loud calls. Furthermore, within leader males from whom we had loud calls (a sample biased towards mid- to high-RHP males), we found that higher-RHP males themselves (based on their call quality) responded more strongly to the high-quality stimuli than did lower-quality males. Finally, in natural observations, leader males were more likely to join loud call displays when their own calls were of similar quality to the other males involved in the display. In all three cases, a male’s response to other males’ loud calls was based on both their own RHP and that of the caller (coded into the quality of the signal). Taken together, these findings support the hypothesis that gelada males use a mutual assessment strategy, rather than a self- or opponent-based one. These data provide some of the first evidence for a mutual assessment strategy using signals for a non-human primate.

Although bachelor males attended to both high- and low-quality loud call bouts (Fig. 2b), they only approached the hidden speaker (“escalated”) when they were played the low-quality call (Fig. 2a). In playback studies, approach behaviors (e.g., approach distance, approach rate, latency to approach) represent more “intense” measures of interest in the signal than looking time alone38,39,40,41. This is especially true in the study of aggressive signals where approaching the source of the call is a relatively high-cost response, as it implies an interest in engaging the caller42, 43. In support of this, males that approached the speaker reached (or passed) the source of the call (mean approach distance 32.7 m, mean speaker distance 28.9 m), and 73% of these approaches were accompanied with visual and vocal threats. When confronted with a potentially weak rival, bachelors may benefit from an escalated response (i.e., an approach) because successful challenges can result in reproductive access to females33, 44, 45. By contrast, when confronted with a potentially strong rival, bachelors may suffer severe (and possibly fatal) costs from an escalated response32. Our results suggest that bachelor males assess the quality of leader males by attending to these loud calls, and they use information gleaned from these calls to make decisions about which males to challenge and which to avoid.

By contrast, leader males rarely approached the speaker regardless of call quality (Fig. 2a). A strong approach is especially risky for a leader male as it requires him to leave his female unattended with bachelor males in close proximity. However, leader males did spend more time attending to the high-quality calls compared to the low-quality ones (Fig. 2b), with the strongest responses deriving from the leader males exhibiting the highest-RHP (as measured by their own loud call quality, Fig. 4). The motivation for leader males to attend to (and, engage in) call displays presumably derives from the need to showcase their own quality in the midst of bachelors. The large aggregations of geladas (sometimes numbering over 1200 individuals) have been hypothesized to create a “dilution effect” against predators35, but also against bachelors36. Indeed, at least one feature of loud call displays (how often a leader male participated in displays33) was negatively associated with his likelihood of takeover. Therefore, leader males should broadcast their loud calls when they “compare well” to displaying males around them. In support of this, leader males were more likely to participate in natural loud call displays when their call quality was similar to the males calling around them.

Similar results have been reported for chacma baboons (Papio ursinus) where males were more likely to loud call with males of similar dominance rank25. However, the male baboons were likely using social knowledge (monitoring other males’ ranks and attending to acoustic cues of identity) to assess one another, not signals19, 25. Indeed, mutual assessment using social knowledge appears routine among primates. In primate societies, interactions are structured by dyadic properties such as relative rank or kinship that require animals to account for the behavior of other individuals in relation to themselves. The novelty of our finding is in demonstrating that primates can use signals to perform mutual assessments while interacting with completely unfamiliar individuals. This use of signals to guide interactions is particularly useful in geladas where their large groups and fission-fusion social system46 require males to consistently monitor the quality of unfamiliar males.

Importantly, bachelors, by successfully defeating the resident harem-holding male, become leaders. As males transition from bachelors to leaders, the information an individual pays attention to is likely to change with this change in status. Other primate species have been shown to monitor changes in other individuals’ dominance ranks and social relationships over time47. In chacma baboons, for example, males track temporary changes in the status of other males’ consortship but, once again, the results of this study were likely based on identity information, not signals48. In the case of geladas, information acquired from quality signals may be the only way to successfully navigate such large social groups of unknown conspecifics.

Unlike males, gelada females did not differentiate between high- and low- quality calls. Indeed, they rarely attended to either call (Fig. 2a,b). There has been considerable debate as to whether loud calls in primates evolved to attract mates or to deter competitors49, 50. One of the strengths of this study is that both females and males were tested within the same design. Our results indicate that gelada loud calls evolved as a signal for assessing rivals and not attracting mates.

One promising avenue for future research will be to assess how group dynamics influence assessment strategies in social animals in a natural context23. For example, if the composition of social groups is dynamic, we might expect males to rely on information gleaned from signals rather than individual recognition and social knowledge when assessing rivals. More studies that combine experiments with natural observations of assessment behavior are necessary to understand the role of assessment strategies in social animals.

## Methods

### Study site and subjects

Research was conducted on a population of wild geladas living in the Simien Mountains National Park, Ethiopia from Feb-Dec 2013. The University of Michigan Gelada Research Project has been collecting long-term behavioral and demographic data on this population since January 2006. All males were individually recognizable and habituated to observers on foot (approach distance <3 m). Methods include a combination of playback experiments and behavioral observations. We have adhered to the Guidelines for the Use of Animals in Research and the Institutional Animal Care and Use Committee guidelines at the University of Michigan and all field research was conducted with permissions from the appropriate offices in Ethiopia.

### Do males attend differently to high- and low-quality calls based on their own status?

We conducted a playback experiment on 60 adult geladas (20 females, 20 leader males, and 20 bachelor males). To increase our sample size, we included both known and unknown individuals in this experiment. All unknown individuals were identified using morphological features to ensure they were not used in subsequent experiments.

### Playback stimuli

Playback stimuli comprised previously-recorded loud calls obtained during naturally-occurring signaling contests between adult males. Loud calls were recorded using a Sennheiser ME-66 directional microphone and a Marantz PMD 660 digital recorder. Loud call bouts were only used as playback stimuli if they were complete (no calls were missed during the recording) and devoid of background noise and interruptions. We audibly and visually inspected calls using Avisoft SASLab Pro (Avisoft Bioacoustics, Berlin, Germany) acoustic software for acoustic disturbances (e.g., background noise). At the time of the experiment, we had 157 loud call bouts from 50 prime-age males that fit this criteria (e.g., free of background noise).

For geladas, loud call bouts generally consist of a series of two-syllable “ee-yow” calls (2–9 calls per bout). Previously, we found support for the hypothesis that the entire bout (and not just the individual calls within the bout) functions as a quality signal37. We selected a total of 14 loud call bouts as playback stimuli: 7 high-quality bouts and 7 low-quality bouts, to construct 10 playback sets, each containing a unique combination of one high- and one low-quality loud call bout from two different males. We determined call quality by comparing calls along several parameters we had previously found to differ with age and status, fundamental frequency and number of calls per bout37. To assess these parameters, we conducted a spectrogram analysis in Avisoft with a fast Fourier transformation size of 1024 points (frequency range: 22 kHZ; frequency resolution: 43 Hz time resolution: 2.903 ms; 100% frame). For the 157 loud call bouts, we examined the distribution for both parameters and chose loud call bouts that were at the extremes of these distributions.

### Playback design

We presented each subject with one of the playback sets comprising both a high- and a low-quality loud call bout. Because natural occurrences of loud calls in geladas generally occur when leader males encounter bachelor males, experiments were only conducted when both bachelors and leader males were present on a given day. To simulate a natural loud call contest, the calls were played from the direction of the bachelors (when the subject was a unit individual) or from the direction of the units (when the subject was a bachelor male).

For each trial, we placed a Bose Roommate II portable speaker approximately 25–50 m (M = 28.96 m, SD = 7.95 m) from the subject. The speaker was hidden behind a physical barrier (i.e., tree, rock, or bush), and completely obscured from the subject’s view. All subjects were observed for 15 minutes prior to the start of the playback experiment; and experiments were only conducted if (1) the subject was sitting (e.g., feeding or resting) for at least 2 minutes prior to the start of each call, and (2) the subject was oriented away from the speaker.

The experiment used a within-subjects design, in which subjects heard both a high-quality loud call bout and a low-quality loud call bout in each trial (to simulate a loud call contest between many males). The second call was played 5 minutes after the first call to allow subjects to return to an initial resting state. Subjects generally returned to an initial resting state within 1 minute after hearing the first call. We played each set of calls (n = 10 unique sets) to 6 subjects each: two bachelors, two leaders, and two females. To combat any order effect, we counterbalanced the order in which the high- and low-quality bouts were played across leaders, bachelors, and females. No subject heard any of the calls in the set prior to his or her experimental trial, and each trial was separated by at least 10 days for individuals in the same band.

Prior to all trials, we noted the identity of the subject, the location of the speaker relative to the subject, the subject’s initial state (feeding or resting), the experimental playback set used, and the order of calls heard. During each trial, one observer played the loud calls from a loudspeaker using an MP3 player (Apple ipod touch 3rd generation). A second experimenter with a Kodax PlaySport (Z × 5) HD video camera, positioned herself 5–10 m in front of the subject, with the speaker hidden to the left or right of the subject. All subjects were video-recorded continuously from 15 seconds prior to the first call to 5 minutes after the second call. For each individual, we matched his or her state (feeding or resting) and distance to the speaker between the first and second call – in some cases, moving the speaker to a new hiding spot the appropriate distance away.

For playback trials on unit individuals (leaders and females), we pre-designated two different subjects prior to the start of the trial: a unit male from one unit and a unit female from a different unit at least 40 m away. In such cases, we placed the speaker between the two subjects (from the direction of the bachelors) to ensure their visual trajectories towards the stimuli were not overlapping. Each subject was filmed and scored independently. Experimenters were in contact via two-way radios, and if any of the conditions were not met, we aborted the experiment immediately. We conducted a total of 60 successful playback trials and an additional 22 trials were aborted prior to completion.

### Playback responses

All videos were scored on a computer with a frame-by-frame analysis using Adobe Premier (Adobe Systems, Inc.) by two independent observers. Prior to video analyses, playback videos were cut to contain only the response to one loud call bout within a set. All files were then renamed and randomized such that observers were blind to the identity of the subject (i.e., whether he was a unit male or a bachelor male – it was impossible to hide whether the subject was a female) and the condition (i.e., whether it was a high- or low-quality bout). Reliability for all measurements between the two observers was greater than 95% (M = 97%, SD = 1.2%).

We measured 6 different response variables: (1) duration of time spent looking towards the speaker (look duration), (2) duration of time spent moving towards the speaker (approach duration), (3) latency of look response (latency to look), (4) latency of approach response (latency to approach), (5) total distance moved towards the speaker (approach distance), and (6) total time to return to initial resting state (resume activity).

Look duration measured the time a subject spent oriented toward the speaker while stationary. When a subject oriented toward the speaker while moving towards it, we recorded the response as approach duration. For both duration responses, we measured the total duration of all responses until the subject returned to their initial state for up to 1 minute after playback onset. Time to return to initial state was assessed once an individual spent at least 15 seconds feeding or resting without orienting towards the speaker. We did not record responses after subjects returned to their initial state as such responses were overly influenced by other individuals within the group (unit or bachelor group). We subtracted any time spent looking or moving towards the direction of the speaker during the 15 seconds prior to the onset of the trial. Latency to look and/or latency to approach were measured as the time from the onset of the playback stimuli until the onset of the subject’s first look and/or movement towards the speaker. Due to the high mobility of the group during feeding, if a subject did not look or move within the first minute after the onset of the stimulus, we assigned the subject’s latency as 60 seconds. We also recorded approach distance for all movement toward the speaker in the first minute after the onset of each playback stimulus.

### Playback analyses

To remove redundancy between response variables, we reduced response variables into latent factors using Factor Analyses (FA51) with a varimax rotation with SPSS (v.22.0.0.0). We accepted all factors with eigenvalues greater than 1.0, which produced two factors associated with approach and looking behavior (Table 1).

To assess differences due to call quality and status, we constructed two LMMs, one for each factor score from the FA as the dependent variable with status (leader, bachelor, or female), call quality (high or low), and an interaction between both as predictor variables. In each model, we controlled for call order (fixed effect) and subject (random intercept). To assess whether bachelor’s or leaders differed in their response to high and low-quality calls, we conducted two additional a priori contrast (Table 2). We corrected for multiple testing using a Bonferroni adjusted alpha level of 0.025 (0.05/2).

In addition to the FA, we examined whether males differed in overall response towards high and low-quality calls. We calculated “overall response time” for each male by summing the time he spent looking and approaching the speaker (look duration + approach duration). We constructed a third LMM with overall response time as the dependent variable, subject as a random factor, status and call quality as fixed effects and an interaction between status and call quality. Since call order was not a significant factor in any of the previous LMMs, we excluded it from this model.

For these models (and all subsequent models), we determined the statistical significance of the full model by comparing its fit using likelihood tests with that of a null model including only the intercept and the random effect (Table 2). We conducted all model analyses in R v.3.2.0 using the “lmer” function in the lme4 packages v.1.1–1152 and contrasts using the lsmeans package v. 2.5–553. We visually inspected each model using a Q-Q plot, histogram of residuals, and scatter plot of fitted versus residual values. Residual values for all models were normally distributed.

### Do males attend differently to high- and low-quality calls based on their own quality?

We assigned each male a call quality score by examining 12 acoustic parameters related to frequency (e.g., fundamental frequency) and temporal measures (e.g., call duration). Given our previous results that lower frequency calls are energetically-costly to produce37, we established a call quality score based on the factor analysis (i.e., Factor 1, spectral measures). We focused our analysis on the first calls given within a bout (n = 122) as these calls are the lowest in frequency measures (and presumably the highest quality). We calculated a mean for the spectral measure scores to establish a call quality score for each male. Because calls that are lower in spectral measures were higher in quality, we multiplied the call quality score by −1, so that a high call quality score represents a high-quality call. We then ran two Spearman’s rank-order correlations, comparing each male’s response time to the high- and low-quality playback stimuli to his own call quality score.

### Are males more likely to join a loud call display when they hear calls of similar quality to their own?

We collected all-occurrence behavioral sampling and recorded 423 loud calls across 291 different loud call displays from 20 males across the study period. For all displays, we recorded the identity of all known males that participated in the display as well as the males present in the group that did not participate. We conducted acoustic analyses on all calls in the same way as described above. Again, we focused our analysis on the first calls given within a bout (n = 122) and established a call quality score for each male.

To control for a subject’s “familiarity” with the caller, we used all proximity data between the subject and the caller in a social network analyses34. We constructed an undirected, weighted network based on male-male association. In this network, males were represented by nodes and the edge weight was given by an association index. This index was calculated as:

$A s s o c i a t i o n i n d e x m a l e s A , B = # o f t i m e s m a l e A s e e n w i t h B ̲ m i n i m u m # o f t i m e s m a l e A o r m a l e B s e e n$

where the numerator is the total number of times males A and B were seen together in the same group, divided by the minimum number of times we observed either A or B in the same group34. The association index ranges from 0 (if two individuals were never seen together) to 1 (if they were always seen together). From this network, we used the Louvain community identification algorithm to assign males to “cliques” within their social network. Males associated into two distinct cliques (N = 15 and N = 21 males respectively) with a modularity coefficient of 0.011. Males were considered to be “familiar” with each other if they were assigned to the same clique, and “not familiar” if assigned to different cliques.

To assess if relative call quality or caller familiarity influenced the likelihood that a male would participate in these vocal displays, we conducted a GLMM with a binomial distribution. For each subject, we examined the dyadic calling relationship with other males. The outcome variable in our model was the likelihood that male A participated in a display, given that male B also displayed. This was modeled as the count of successes, the number of times male A and male B displayed together, offset by the count of failures, the total number of times male A or B displayed (but not both), given that both males could have displayed (e.g., were both present in the group on that day). We included relative call quality and caller familiarity in the models as fixed effects. Relative call quality was calculated for each dyad by taking the absolute value of the difference between the call quality scores of both males. The smaller the difference between the two call quality scores, the closer the males were in relative call quality. Caller familiarity was established for each dyad from the social network analysis. Males were considered to be “familiar” with each other if they were assigned to the same clique, and “not familiar” if assigned to different cliques. We controlled for the identity of both males by including their identification as random variables in the model. Although the majority of calls were given by leader males, occasionally subordinate follower males engaged in these displays. Because leaders are more likely to display than followers, we controlled for status of both males in the model. We compared the full model to a null model, which included only the intercept and random effects. The social network analysis and GLMM were conducted in R 3.2.4 using igraph54 and lme452 packages respectively.

### Data availability

Datasets and R scripts generated and analyzed during the current study are available from the corresponding author on reasonable requests. Playback videos are also available upon request.

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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## Acknowledgements

We thank the Ethiopian Wildlife Conservation Authority (EWCA) as well as the wardens and staff of the Simien Mountain National Park for permission and support in conduction research on geladas. We are extremely grateful to M. Gomery for her assistance with data collection, K. Kingma for helping with video coding, and J. Mitani, A. Marshall, and B. Dantzer for their feedback on drafts of this manuscript. We also thank all the members of the University of Michigan Gelada Research Project for assistance in data collection and valuable insight on analyses and interpretation. This project was funded by the National Science Foundation (IOS-1255974, BCS-0715179, BCS-1231790), the NSF Graduate Research Fellowship Program, the Leakey Foundation, and the University of Michigan.

## Author information

### Affiliations

1. #### Department of Psychology, Language Research Center, Georgia State University, Atlanta, Georgia, USA

• Marcela E. Benítez
2. #### Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, USA

• David J. Pappano
3. #### Department of Anthropology, University of Michigan, Ann Arbor, Michigan, USA

• Jacinta C. Beehner
4. #### Department of Psychology, University of Michigan, Ann Arbor, Michigan, USA

• Jacinta C. Beehner
•  & Thore J. Bergman
5. #### Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, Michigan, USA

• Thore J. Bergman

### Contributions

M.B. conducted all the data collection, analysis, and wrote the text and prepared figures. J.B., T.J. helped with writing and editing. D.J. conducted statistical analyses and contributed to the writing of methods and results. All authors reviewed the manuscript.

### Competing Interests

The authors declare that they have no competing interests.

### Corresponding author

Correspondence to Marcela E. Benítez.

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# Introgression obscures and reveals historical relationships among the American live oaks

Introgression obscures and reveals historical relationships among the American live oaks

Deren Eaton , Antonio Gonzalez-Rodriguez , Andrew Hipp , Jeannine Cavender-Bares
doi: http://dx.doi.org/10.1101/016238

Introgressive hybridization challenges the concepts we use to define species and our ability to infer their evolutionary relationships. Methods for inferring historical introgression from the genomes of extant species are now widely used, however, few guidelines have been articulated for how best to interpret their results. Because these tests are inherently comparative, we show that they are sensitivite to the effects of missing data (unsampled species) and to non-independence (hierarchical relationships among species). We demonstrate this using genomic RAD data sampled from populations across the geographic ranges of all extant species in the American live oaks (Quercus series Virentes), a group notorious for hybridization. By considering all species in the clade, and their phylogenetic relationships, we were able to distinguish true hybridizing lineages from those that falsely appear admixed due to phylogenetic structure among hybridizing relatives. Six of seven species show evidence of admixture, often with multiple other species, but which can be explained by hybrid introgression among few related lineages where they occur in close proximity. We identify the Cuban oak as a highly admixed lineage and use an information-theoretic model comparison approach to test alternative scenarios for its origin. Hybrid speciation is a poor fit compared to a model in which a population from Central America colonized Cuba and received subsequent gene flow from Florida. The live oaks form a continuous ring-like distribution around the Gulf of Mexico, connected in Cuba, across which they could effectively exchange alleles. However, introgression appears to remain localized to areas of sympatry, suggesting that oak species boundaries, and their geographic ranges have remained relatively stable over evolutionary time.

# The Spatial Mixing of Genomes in Secondary Contact Zones

The Spatial Mixing of Genomes in Secondary Contact Zones
Alisa Sedghifar , Yaniv Brandvain , Peter L. Ralph , Graham Coop
doi: http://dx.doi.org/10.1101/016337

Recent genomic studies have highlighted the important role of admixture in shaping genome-wide patterns of diversity. Past admixture leaves a population genomic signature of linkage disequilibrium (LD), reflecting the mixing of parental chromosomes by segregation and recombination. The extent of this LD can be used to infer the timing of admixture. However, the results of inference can depend strongly on the assumed demographic model. Here, we introduce a theoretical framework for modeling patterns of LD in a geographic contact zone where two differentiated populations are diffusing back together. We derive expressions for the expected LD and admixture tract lengths across geographic space as a function of the age of the contact zone and the dispersal distance of individuals. We develop an approach to infer age of contact zones using population genomic data from multiple spatially sampled populations by fitting our model to the decay of LD with recombination distance. We use our approach to explore the fit of a geographic contact zone model to three human population genomic datasets from populations along the Indonesian archipelago, populations in Central Asia and populations in India.

# Eight thousand years of natural selection in Europe

Eight thousand years of natural selection in Europe
Iain Mathieson , Iosif Lazaridis , Nadin Rohland , Swapan Mallick , Bastien Llamas , Joseph Pickrell , Harald Meller , Manuel A. Rojo Guerra , Johannes Krause , David Anthony , Dorcas Brown , Carles Lalueza Fox , Alan Cooper , Kurt W. Alt , Wolfgang Haak , Nick Patterson , David Reich
doi: http://dx.doi.org/10.1101/016477

The arrival of farming in Europe beginning around 8,500 years ago required adaptation to new environments, pathogens, diets, and social organizations. While evidence of natural selection can be revealed by studying patterns of genetic variation in present-day people, these pattern are only indirect echoes of past events, and provide little information about where and when selection occurred. Ancient DNA makes it possible to examine populations as they were before, during and after adaptation events, and thus to reveal the tempo and mode of selection. Here we report the first genome-wide scan for selection using ancient DNA, based on 83 human samples from Holocene Europe analyzed at over 300,000 positions. We find five genome-wide signals of selection, at loci associated with diet and pigmentation. Surprisingly in light of suggestions of selection on immune traits associated with the advent of agriculture and denser living conditions, we find no strong sweeps associated with immunological phenotypes. We also report a scan for selection for complex traits, and find two signals of selection on height: for short stature in Iberia after the arrival of agriculture, and for tall stature on the Pontic-Caspian steppe earlier than 5,000 years ago. A surprise is that in Scandinavian hunter-gatherers living around 8,000 years ago, there is a high frequency of the derived allele at the EDAR gene that is the strongest known signal of selection in East Asians and that is thought to have arisen in East Asia. These results document the power of ancient DNA to reveal features of past adaptation that could not be understood from analyses of present-day people.

# Efficient computation of the joint sample frequency spectra for multiple populations

Efficient computation of the joint sample frequency spectra for multiple populations

John A. Kamm, Jonathan Terhorst, Yun S. Song
(Submitted on 3 Mar 2015)

A wide range of studies in population genetics have employed the sample frequency spectrum (SFS), a summary statistic which describes the distribution of mutant alleles at a polymorphic site in a sample of DNA sequences. In particular, recently there has been growing interest in analyzing the joint SFS data from multiple populations to infer parameters of complex demographic histories, including variable population sizes, population split times, migration rates, admixture proportions, and so on. Although much methodological progress has been made, existing SFS-based inference methods suffer from numerical instability and high computational complexity when multiple populations are involved and the sample size is large. In this paper, we present new analytic formulas and algorithms that enable efficient computation of the expected joint SFS for multiple populations related by a complex demographic model with arbitrary population size histories (including piecewise exponential growth). Our results are implemented in a new software package called momi (MOran Models for Inference). Through an empirical study involving tens of populations, we demonstrate our improvements to numerical stability and computational complexity.

# Quality assessment for different haplotyping methods and GWAS sensitivity to phasing errors

Quality assessment for different haplotyping methods and GWAS sensitivity to phasing errors

Giovanni Busonera , Marco Cogoni , Gianluigi Zanetti
doi: http://dx.doi.org/10.1101/015669

In this report we present a multimarker association tool (Flash) based on a novel algorithm to generate haplotypes from raw genotype data. It belongs to the entropy minimization class of methods and is composed of a two stage deterministic – heuristic part and of a optional stochastic optimization. This algorithm is able to scale up well to handle huge datasets with faster performance than the competing technologies such as BEAGLE and MACH while maintaining a comparable accuracy. A quality assessment of the results is carried out by comparing the switch error. Finally, the haplotypes are used to perform a haplotype-based Genome-wide Association Study (GWAS). The association results are compared with a multimarker and a single SNP association test performed with Plink. Our experiments confirm that the multimarker association test can be more powerful than the single SNP one as stated in the literature. Moreover, Flash and Plink show similar results for the multimarker association test but Flash speeds up the computation time of about an order of magnitude using 5 SNP size haplotypes.

# Differential Evolution Approach to Detect Recent Admixture

Differential Evolution Approach to Detect Recent Admixture

Konstantin Kozlov , Dmitry Chebotarov , Mehedi Hassan , Petr Triska , Martin Triska , Pavel Flegontov , Tatiana V Tatarinova
doi: http://dx.doi.org/10.1101/015446

The genetic structure of human populations is extraordinarily complex and of fundamental importance to studies of anthropology, evolution, and medicine. As increasingly many individuals are of mixed origin, there is an unmet need for tools that can infer multiple origins. Misclassification of such individuals can lead to incorrect and costly misinterpretations of genomic data, primarily in disease studies and drug trials. We present an advanced tool to infer ancestry that can identify the biogeographic origins of highly mixed individuals. reAdmix can incorporate individual’s knowledge of ancestors (e.g. having some ancestors from Turkey or a Scottish grandmother). reAdmix is an online tool available at http://chcb.saban-chla.usc.edu/reAdmix/.

# A Spatial Framework for Understanding Population Structure and Admixture.

A Spatial Framework for Understanding Population Structure and Admixture.
Gideon Bradburd, Peter L. Ralph, Graham Coop
doi: http://dx.doi.org/10.1101/013474

Geographic patterns of genetic variation within modern populations, produced by complex histories of migration, can be difficult to infer and visually summarize. A general consequence of geographically limited dispersal is that samples from nearby locations tend to be more closely related than samples from distant locations, and so genetic covariance often recapitulates geographic proximity. We use genome-wide polymorphism data to build “geogenetic maps”, which, when applied to stationary populations, produces a map of the geographic positions of the populations, but with distances distorted to reflect historical rates of gene flow. In the underlying model, allele frequency covariance is a decreasing function of geogenetic distance, and nonlocal gene flow such as admixture can be identified as anomalously strong covariance over long distances. This admixture is explicitly co-estimated and depicted as arrows, from the source of admixture to the recipient, on the geogenetic map. We demonstrate the utility of this method on a circum-Tibetan sampling of the greenish warbler (Phylloscopus trochiloides), in which we find evidence for gene flow between the adjacent, terminal populations of the ring species. We also analyze a global sampling of human populations, for which we largely recover the geography of the sampling, with support for significant histories of admixture in many samples. This new tool for understanding and visualizing patterns of population structure is implemented in a Bayesian framework in the program SpaceMix.

# Scaling probabilistic models of genetic variation to millions of humans

Scaling probabilistic models of genetic variation to millions of humans

Prem Gopalan, Wei Hao, David M. Blei, John D. Storey
doi: http://dx.doi.org/10.1101/013227

A major goal of population genetics is to quantitatively understand variation of genetic polymorphisms among individuals. Researchers have developed sophisticated statistical methods to capture the complex population structure that underlies observed genotypes in humans. The number of humans that have been densely genotyped across the genome has grown significantly in recent years. In aggregate about 1M individuals have been densely genotyped to date, and if we could analyze this data then we would have a nearly complete picture of human genetic variation. Existing state-of-the-art methods, however, cannot scale to data of this size. To this end, we have developed TeraStructure. TeraStructure is a new algorithm to fit Bayesian models of genetic variation in human populations on tera-sample-sized data sets (1012 observed genotypes, e.g., 1M individuals at 1M SNPs). It is a principled approach to approximate Bayesian inference that iterates between subsampling locations of the genome and updating an estimate of the latent population structure. On real and simulated data sets of up to 10K individuals, TeraStructure is twice as fast as existing methods and recovers the latent population structure with equal accuracy. On genomic data simulated at the tera-sample-size scales, TeraStructure continues to be accurate and is the only method that can complete its analysis.

# A new FST-based method to uncover local adaptation using environmental variables

A new $F_{\text{ST}}$-based method to uncover local adaptation using environmental variables
Pierre de Villemereuil, Oscar E. Gaggiotti
Comments: 18 pages, 5 figures, Supplementary Information at the end of the document
Subjects: Populations and Evolution (q-bio.PE)

Genome-scan methods are used for screening genome-wide patterns of DNA polymorphism to detect signatures of positive selection. There are two main types of methods: (i) “outlier” detection methods based on $F_{\text{ST}}$ that detect loci with high differenciation compared to the rest of the genomes and, (ii) environmental association methods that test the association between allele frequencies and environmental variables. In this article, we present a new $F_{\text{ST}}$-based genome scan method, BayeScEnv, which incorporates environmental information in the form of “environmental differentiation”. It is based on the F model but as opposed to existing approaches it considers two locus-specific effects, one due to divergent selection and another due to other processes such as differences in mutation rates across loci or background selection. Simulation studies showed that our method has a much lower false positive rate than an existing $F_{\text{ST}}$-based method, BayeScan, under a wide range of demographic scenarios. Although it had lower power, it leads to a better compromise between power and false positive rate. We apply our method to Human and Salmon datasets and show that it can be used successfully to study local adaptation. The method was developped in C++ and is avaible at this http URL

# Visualizing spatial population structure with estimated effective migration surfaces

Visualizing spatial population structure with estimated effective migration surfaces
Desislava Petkova, John Novembre, Matthew Stephens
doi: http://dx.doi.org/10.1101/011809

Genetic data often exhibit patterns that are broadly consistent with “isolation by distance” – a phenomenon where genetic similarity tends to decay with geographic distance. In a heterogeneous habitat, decay may occur more quickly in some regions than others: for example, barriers to gene flow can accelerate the genetic differentiation between groups located close in space. We use the concept of “effective migration” to model the relationship between genetics and geography: in this paradigm, effective migration is low in regions where genetic similarity decays quickly. We present a method to quantify and visualize variation in effective migration across the habitat, which can be used to identify potential barriers to gene flow, from geographically indexed large-scale genetic data. Our approach uses a population genetic model to relate underlying migration rates to expected pairwise genetic dissimilarities, and estimates migration rates by matching these expectations to the observed dissimilarities. We illustrate the potential and limitations of our method using simulations and data from elephant, human, and Arabidopsis thaliana populations. The resulting visualizations highlight important features of the spatial population structure that are difficult to discern using existing methods for summarizing genetic variation such as principal components analysis.


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# Characters Tables¶

These tables deal with records that make up the player and NPCs. Together we’ll refer to them as characters.

## Skills¶

Characters in OpenMW can perform various actions and skills define how successful a character is in performing these actions. The skills themselves are hardcoded and cannot be added through the editor but can have some of their parameters adjusted.

Attribute
Each skill is governed by an attribute. The value of the attribute will contribute or hinder your success in using a skill.
Specialization
A skill can belong to combat, magic, or stealth category. If the player specializes in the same category, they get a +5 starting bonus to the skill.
Use Value 1, 2, 3, 4
Use Values are the amount of experience points the player will be awarded for successful use of this skill. Skills can have one or multiple in-game actions associated with them and each action is tied to one of the Use Values. For example, Block skill has Use Value 1 = 2,5. This means that on each successful blocked attack, the player’s block skill will increase by 2,5 experience points. Athletics have Use Value 1 = 0,02, and Use Value 2 = 0,03. For each second of running the player’s athletics skill will be raised by 0,02 experience points, while for each second of swimming by 0,03 points. Note that not all of the skills use all of the Use Values and OpenMW-CS currently doesn’t tell what action a skill’s Use Value is associated with.
Description
Flavour text that appears in the character information window.

## Classes¶

All characters in OpenMW need to be of a certain class. This table lists existing classes and allows you to create, delete or modify them.

Name
How this class is called.
Attribute 1 and 2
Characters of this class receive a +10 starting bonus on the two chosen atributes.
Specialization
Characters can specialize in combat, magic, or stealth. Each skill that belongs to the chosen specialization receives a +5 starting bonus and is easier to train.
Major Skill 1 to 5
Training these skills contributes to the player leveling up. Each of these skills receives a +25 starting bonus and is easier to train.
Minor Skill 1 to 5
Training these skills contributes to the player leveling up. Each of these skills receives a +10 starting bonus and is easier to train.
Playable
When enabled, the player can choose to play as this class at character creation. If disabled, only NPCs can be of this class.
Description
Flavour text that appears in the character information window.

## Faction¶

Characters in OpenMW can belong to different factions that represent interest groups within the game’s society. The most obvious example of factions are guilds which the player can join, perform quests for and rise through its ranks. Membership in factions can be a good source of interesting situations and quests. If nothing else, membership affects NPC disposition towards the player.

Name
How this faction is called.
Attribute 1 and 2
Two attributes valued by the faction and required to rise through its ranks.
Hidden
When enabled, the player’s membership in this faction will not be displayed in the character window.
Skill 1 to 7
Skills valued by the faction and required to rise through its ranks. Not all of the skill slots need to be filled.
Reactions
Change of NPC disposition towards the player when joining this faction. Disposition change depends on which factions the NPCs belong to.
Ranks
Positions in the hierarchy of this faction. Every rank has a requirement in attributes and skills before the player is promoted. Every rank gives the player an NPC disposition bonus within the faction.

## Races¶

All characters in OpenMW need to be of a certain race. This table list existing races and allows you to create, delete or modify them. After a race is created, it can be assigned to an NPC in the objects table. Race affects character’s starting attributes and skill bonuses, appearance, and voice lines. In addition, a race assigned to the player can affect NPC disposition, quests, dialogues and other things.

Name
How this race is called.
Description
Flavour text that appears in the character information window.
Playable
When enabled, the player can choose to play as this race. If disabled, only the NPCs can belong to it.
Beast Race
When enabled, characters of this race will use alternative animations to depict humanoids of different proportions and movement styles. This is done by using a different animation file compared to the main one and thus a new set of animations needs to be created for this to work properly. In addition, beast races can’t wear closed helmets or boots
Male Weight
Scaling factor of the default body type. Values above 1.0 will make members of this race wider. Values below 1.0 will make them thinner. Applies to males.
Male Height
Scaling factor of the default body type. Values above 1.0 will make members of this race taller. Values below 1.0 will make them shorter. Applies to males.
Female Weight
Scaling factor of the default body type. Values above 1.0 will make members of this race wider. Values below 1.0 will make them thinner. Applies to females.
Female Height
Scaling factor of the default body type. Values above 1.0 will make members of this race taller. Values below 1.0 will make them shorter. Applies to females.

## Birthsigns¶

Birthsigns permanently modify player’s attributes, skills, or other abilities. The player can have a single birthsign which is usually picked during character creation. Modifications to the player are done through one or more spells added to a birthsign. Spells with constant effects modify skills and attributes. Spells that are cast are given to the player in the form of powers.

Name
Name of the birthsign that will be displayed in the interface.
Texture
An image that will be displayed in the birthsigns selection window.
Description
Powers
A list of spells that are given to the player. When spells are added by a birthsign, they cannot be removed from the spell list in-game by the player.

## Body Parts¶

Characters are made from separate parts. Together they form the whole body. Allows customization of the character appearance. Includes heads, arms, legs, torso, hand, armor, pauldrons, chestpiece, helmets, wearables, etc.

## Topics¶

Topics are, in a broader meaning, dialogue triggers. They can take the form of clickable keywords in the dialogue, combat events, persuasion events, and other. What response they produce and under what conditions is then defined by topic infos. A single topic can be used by unlimited number of topic infos. There are four different types of topics.

Greeting 0 to 9
Initial text that appears in the dialogue window when talking to an NPC. Hardcoded.
Persuasion

Persuasion entries produce a dialogue response when using persuasion actions on NPCs. Hardcoded.

• Admire, Bribe, Intimidate Taunt Fail - Text in the dialogue window that the NPC says when the player fails a persuasion action.
• Admire, Bribe, Intimidate Taunt Succeed - Text in the dialogue window that the NPC says when the player succeeds a persuasion action.
• Info Refusal - Text in the dialogue window that the NPC says when the player wishes to talk about a certain topic and the conditions are not met. For example, NPC disposition is too low, the player is not a faction member, etc.
• Service Refusal - Text in the dialogue window that the NPC says when the player wishes a service from the NPC but the conditions are not met.
Topic
A keyword in the dialogue window that leads to further dialogue text, similar to wiki links. These are the foundation to create dialogues from. Entires can be freely added, edited, or removed.
Voice

Voice entries are specific in-game events used to play a sound. Hardcoded.

• Alarm - NPC enters combat state
• Attack - NPC performs an attack
• Flee - NPC loses their motivation to fight
• Hello - NPC addressing the player when near enough
• Hit - When NPCs are hit and injured.
• Idle - Random things NPCs say.
• Intruder
• Thief - When an NPC detects the player steal something.

## Topic Infos¶

Topic infos take topics as their triggers and define responses. Through their many parameters they can be assigned to one or more NPCs. Important to note is that each topic info can take a combination of parameters to accurately define which NPCs will produce a particular response. These parameters are as follow.

Actor
A specific NPC.
Race
All members of a race.
Class
NPCs of a chosen class.
Faction
NPCs belonging to a faction.
Cell
NPCs located in a particular cell.
Disposition
NPC disposition towards the player. This is the 0-100 bar visible in the conversation window and tells how much an NPC likes the player.
Rank
NPC rank within a faction.
Gender
NPC gender.
PC Faction
Player’s faction.
PC Rank
Player’s rank within a faction.

Topic infos when triggered provide a response when the correct conditions are met.

Sound File
Sound file to play when the topic info is triggered
Response
Dialogue text that appears in a dialogue window when clicking on a keyword. Dialogue text that appears near the bottom of the screen when a voice topic is triggered.
Script
Script to define further effects or branching dialogue choices when this topic info is triggered.
Info Conditions.
Conditions required for this topic info to be active.

## Journals¶

Journals are records that define questlines. Entries can be added or removed. When adding a new entry, you give it a unique ID which cannot be edited afterwards. Also to remember is that journal IDs are not the actual keywords appearing in the in-game journal.

## Journal Infos¶

Journal infos are stages of a particular quest. Entries appear in the player’s journal once they are called by a script. The script can be a standalone record or a part of a topic info. The current command is Journal, "Journal ID", "Quest Index"

Quest Status
Finished, Name, None, Restart. No need to use them.
Quest Index
A quest can write multiple entries into the player’s journal and each of these entries is identified by its index.
Quest Description
Text that appears in the journal for this particular stage of the quest.


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Correlation Between a Structural Parameter and the Carboxyl $^{13}C$ Chemical Shielding Tensors in Compounds Containing Carboxylic Carbon as a Functional Group

Jagannathan, NR (1987) Correlation Between a Structural Parameter and the Carboxyl $^{13}C$ Chemical Shielding Tensors in Compounds Containing Carboxylic Carbon as a Functional Group. In: Chemical Physics Letters, 140 (6). pp. 548-552.

 PDF correlation_13C.pdf Restricted to Registered users only Download (442Kb) | Request a copy

Abstract

The influence of local molecular geometry on the carboxyl $^{13}C$ chemical shielding tensors in some organic molecules is investigated. The $\sigma_{22}$ component of the tensor shows a linear variation as a function of the OCO bond angle around the carboxyl carbon. The significance of the symmetry factors calculated for these compounds is discussed.

Item Type: Journal Article Copyright of this article belongs to Elsevier Science B.V. Division of Chemical Sciences > Solid State & Structural Chemistry Unit 01 Apr 2008 19 Sep 2010 04:44 http://eprints.iisc.ernet.in/id/eprint/13623


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# 4.7: The Common-base Amplifier

The final transistor amplifier configuration (Figure below) we need to study is the common-base amplifiers. This configuration is more complex than the other two, and is less common due to its strange operating characteristics.

Common-base amplifier

## Why is it Called a Common-base Amplifier?

It is called the common-base configuration because (DC power source aside), the signal source and the load share the base of the transistor as a common connection point shown in Figure below.

Common-base amplifier: Input between emitter and base, output between collector and base.

Perhaps the most striking characteristic of this configuration is that the input signal source must carry the full emitter current of the transistor, as indicated by the heavy arrows in the first illustration. As we know, the emitter current is greater than any other current in the transistor, being the sum of base and collector currents. In the last two amplifier configurations, the signal source was connected to the base lead of the transistor, thus handling the least current possible.

## Attenuation of Current in Common-base Amplifiers

Because the input current exceeds all other currents in the circuit, including the output current, the current gain of this amplifier is actually less than 1 (notice how Rload is connected to the collector, thus carrying slightly less current than the signal source). In other words, it attenuates current rather than amplifying it. With common-emitter and common-collector amplifier configurations, the transistor parameter most closely associated with gain was β. In the common-base circuit, we follow another basic transistor parameter: the ratio between collector current and emitter current, which is a fraction always less than 1. This fractional value for any transistor is called the alpha ratio, or α ratio.

## Boosting Signal Voltage in Common-base Amplifiers

Since it obviously can’t boost signal current, it only seems reasonable to expect it to boost signal voltage. A SPICE simulation of the circuit in Figure below will vindicate that assumption.

Common-base circuit for DC SPICE analysis.

Notice in Figure above that the output voltage goes from practically nothing (cutoff) to 15.75 volts (saturation) with the input voltage being swept over a range of 0.6 volts to 1.2 volts. In fact, the output voltage plot doesn’t show a rise until about 0.7 volts at the input, and cuts off (flattens) at about 1.12 volts input. This represents a rather large voltage gain with an output voltage span of 15.75 volts and an input voltage span of only 0.42 volts: a gain ratio of 37.5, or 31.48 dB. Notice also how the output voltage (measured across Rload) actually exceeds the power supply (15 volts) at saturation, due to the series-aiding effect of the input voltage source.

A second set of SPICE analyses (circuit in Figure below) with an AC signal source (and DC bias voltage) tells the same story: a high voltage gain

Common-base circuit for SPICE AC analysis.

As you can see, the input and output waveforms in Figure below are in phase with each other. This tells us that the common-base amplifier is non-inverting.

The AC SPICE analysis in Table below at a single frequency of 2 kHz provides input and output voltages for gain calculation.

Voltage figures from the second analysis (Table above) show a voltage gain of 42.74 (4.274 V / 0.1 V), or 32.617 dB:

Here’s another view of the circuit in Figure below, summarizing the phase relations and DC offsets of various signals in the circuit just simulated.

Phase relationships and offsets for NPN common base amplifier.

. . . and for a PNP transistor: Figure below.

Phase relationships and offsets for PNP common base amplifier.

## Predicting Voltage Gain

Predicting voltage gain for the common-base amplifier configuration is quite difficult, and involves approximations of transistor behavior that are difficult to measure directly. Unlike the other amplifier configurations, where voltage gain was either set by the ratio of two resistors (common-emitter), or fixed at an unchangeable value (common-collector), the voltage gain of the common-base amplifier depends largely on the amount of DC bias on the input signal. As it turns out, the internal transistor resistance between emitter and base plays a major role in determining voltage gain, and this resistance changes with different levels of current through the emitter.

While this phenomenon is difficult to explain, it is rather easy to demonstrate through the use of computer simulation. What I’m going to do here is run several SPICE simulations on a common-base amplifier circuit (Figure previous), changing the DC bias voltage slightly (vbias in Figure below ) while keeping the AC signal amplitude and all other circuit parameters constant. As the voltage gain changes from one simulation to another, different output voltage amplitudes will be noted.

Although these analyses will all be conducted in the “transfer function” mode, each was first “proofed” in the transient analysis mode (voltage plotted over time) to ensure that the entire wave was being faithfully reproduced and not “clipped” due to improper biasing. See “*.tran 0.02m 0.78m” in Figure below, the “commented out” transient analysis statement. Gain calculations cannot be based on waveforms that are distorted. SPICE can calculate the small signal DC gain for us with the “.tf v(4) vin” statement. The output is v(4) and the input as vin.

SPICE net list: Common-base, transfer function (voltage gain) for various DC bias voltages. SPICE net list: Common-base amp current gain; Note .tf v(4) vin statement. Transfer function for DC current gain I(vin)/Iin; Note .tf I(vin) Iin statement.

At the command line, spice -b filename.cir produces a printed output due to the .tf statement: transfer_function, output_impedance, and input_impedance. The abbreviated output listing is from runs with vbias at 0.85, 0.90, 0.95, 1.00 V as recorded in Table below.

A trend should be evident in Table above. With increases in DC bias voltage, voltage gain (transfer_function) increases as well. We can see that the voltage gain is increasing because each subsequent simulation (vbias= 0.85, 0.8753, 0.90, 0.95, 1.00 V) produces greater gain (transfer_function= 37.6, 39.4 40.8, 42.7, 44.0), respectively. The changes are largely due to minuscule variations in bias voltage.

The last three lines of Table above(right) show the I(v1)/Iin current gain of 0.99. (The last two lines look invalid.) This makes sense for β=100; α= β/(β+1), α=0.99=100/(100-1). The combination of low current gain (always less than 1) and somewhat unpredictable voltage gain conspire against the common-base design, relegating it to few practical applications.

Those few applications include radio frequency amplifiers. The grounded base helps shield the input at the emitter from the collector output, preventing instability in RF amplifiers. The common base configuration is usable at higher frequencies than common emitter or common collector. See “Class C common-base 750 mW RF power amplifier” Ch 9 . For a more elaborate circuit see “Class A common-base small-signal high gain amplifier”Ch 9 .

## Review

• Common-base transistor amplifiers are so-called because the input and output voltage points share the base lead of the transistor in common with each other, not considering any power supplies.
• The current gain of a common-base amplifier is always less than 1. The voltage gain is a function of input and output resistances, and also the internal resistance of the emitter-base junction, which is subject to change with variations in DC bias voltage. Suffice to say that the voltage gain of a common-base amplifier can be very high.
• The ratio of a transistor’s collector current to emitter current is called α. The α value for any transistor is always less than unity, or in other words, less than 1.

This page titled 4.7: The Common-base Amplifier is shared under a GNU Free Documentation License 1.3 license and was authored, remixed, and/or curated by Tony R. Kuphaldt (All About Circuits) via source content that was edited to the style and standards of the LibreTexts platform; a detailed edit history is available upon request.


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# How to Calculate Powers of Numbers

••• LongHa2006/iStock/GettyImages
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The powers of numbers are also called exponents. A number, X, to the power of 2 is also referred to as X squared. The number X to the power of 3 is called X cubed. X is called the base number. Calculating an exponent is as simple as multiplying the base number by itself.

Learn to work with positive exponents and positive base numbers. The exponent tells you how many times to multiply the number by itself. For instance, three to the power of four, or 34, will be:

3 × 3 × 3 × 3 = 9 × 9 = 81

Calculate a negative exponent using inversion. When the exponent is a negative number, you are using the inverse of the number. For example, three to the power of negative four, or 3 −4, will be equal to one over three to the power of positive four

\frac{1}{3^4} \text{ or } \frac{1}{3 × 3 × 3 × 3} \text{ or } \frac{1}{81}

Use care when calculating a negative base number. When the base number is negative, you must follow the rules of multiplying negative numbers. This means that if the base number is even, the answer will be positive, and if the base is an odd number, the answer will be negative. For instance, negative 2 to the power of 2, or -22 is:

-2 × -2 = 4

But negative 2 to the power of 3 is:

-2 × -2 × -2 = 4 × -2 = -8

By definition, number to the power of 0 will always equal 1.