How the ladybird got its spots : effects of resource limitation on the honesty of aposematic signals

Prey species often possess defences (e.g. toxins) coupled with warning signals (i.e. aposematism). There is growing evidence that the expression of aposematic signals often varies within species and correlates with the strength of chemical defences. This has led to the speculation that such signals may be 'honest', with signal reliability ensured by the costliness of producing or maintaining aposematic traits. We reared larval seven-spot ladybirds (Coccinella septempunctata) on a Low or High aphid diet and measured the effects on warning signal expression (elytral carotenoid pigmentation, conspicuousness, spot size), levels of defensive alkaloids (precoccinelline, coccinelline), and relationships between these traits. High-diet individuals had greater total precoccinelline levels, and elytra carotenoid concentrations at adulthood which was detectable to a typical avian predator. However, larval diet did not significantly affect adult body mass or size, spot size or coccinelline levels. Elytra carotenoid concentrations correlated positively with total precoccinelline levels in both diet groups and sexes. However, the relationship between elytra carotenoid concentrations and total levels of coccinelline depended on sex: in both diet groups, elytra carotenoids and coccinelline levels were positively correlated in females, but negatively correlated in males. Spot size and coccinelline levels correlated positively in Low-diet individuals, but negatively in High-diet individuals. These results point to physiological linkages between components of aposematism, which are modulated by resource (i.e. food) availability and affect the honesty of signals. Developmental diet, but also sex, influenced the relationships between signals and toxin levels. Ladybirds are sexually size dimorphic, and thus in comparison with males, females may be more susceptible to resource limitation and more likely to be honest signallers. © 2012 The Authors. Functional Ecology © 2012 British Ecological Society.

Response to comment on "Illusions promote mating success in great bowerbirds"

Borgia et al. raise some questions about our recent study showing that great bowerbirds create visual illusions that are used in mate choice. We address them by providing further details about our methods and results. We also provide detailed descriptions of our geometric calculations to address their measurement and analysis questions.

Illusions promote mating success in great bowerbirds

Sexual selection studies normally compare signal strengths, but signal components and sensory processing may interact to create misleading or attention-capturing illusions. Visual illusions can be produced by altering object and scene geometry in ways that trick the viewer when seen from a particular direction. Male great bowerbirds actively maintain size-distance gradients of objects on their bower courts that create forced-perspective illusions for females viewing their displays from within the bower avenue. We show a significant relationship between mating success and the female's view of the gradient; this view explains substantially more variance in mating success than the strength of the gradients. Illusions may be widespread in other animals because males of most species display to females with characteristic orientation and distance, providing excellent conditions for illusions.

Females use multiple mating and genetically loaded sperm competition to target compatible genes

Individuals in socially monogamous species may participate in copulations outside of the pair bond, resulting in extra-pair offspring. Although males benefit from such extra-pair behavior if they produce more offspring, the adaptive function of infidelity to females remains elusive. Here we show that female participation in extra-pair copulations, combined with a genetically loaded process of sperm competition, enables female finches to target genes that are optimally compatible with their own to ensure fertility and optimize offspring viability. Such female behavior, along with the postcopulatory processes demonstrated here, may provide an adaptive function of female infidelity in socially monogamous animals.

Event extraction using behaviors of sentiment signals and burst structure in social media

Significant world events often cause the behavioral convergence of the expression of shared sentiment. This paper examines the use of the blogosphere as a framework to study user psychological behaviors, using their sentiment responses as a form of ‘sensor’ to infer real-world events of importance automatically. We formulate a novel temporal sentiment index function using quantitative measure of the valence value of bearing words in blog posts in which the set of affective bearing words is inspired from psychological research in emotion structure. The annual local minimum and maximum of the proposed sentiment signal function are utilized to extract significant events of the year and corresponding blog posts are further analyzed using topic modeling tools to understand their content. The paper then examines the correlation of topics discovered in relation to world news events reported by the mainstream news service provider, Cable News Network, and by using the Google search engine. Next, aiming at understanding sentiment at a finer granularity over time, we propose a stochastic burst detection model, extended from the work of Kleinberg, to work incrementally with stream data. The proposed model is then used to extract sentimental bursts occurring within a specific mood label (for example, a burst of observing ‘shocked’). The blog posts at those time indices are analyzed to extract topics, and these are compared to real-world news events. Our comprehensive set of experiments conducted on a large-scale set of 12 million posts from Livejournal shows that the proposed sentiment index function coincides well with significant world events while bursts in sentiment allow us to locate finer-grain external world events.

Novel watermarking methods for copyright protection of audio signals

This research presented improved watermarking methods for mono and stereo audio signals. To enhance the performance, novel methods are developed using echo hiding techniques and patchwork-based algorithms. The superior performances of the proposed methods are demonstrated by theoretical analysis and simulation examples, in comparison with the existing methods.

Feature extraction and classification of metal detector signals using the wavelet transform and the fuzzy ARTMAP neural network

In this paper, the Fuzzy ARTMAP (FAM) neural network is used to classify metal detector signals into different categories for automated target discrimination. Feature extraction of the metal detector signals is conducted using a wavelet transform technique. The FAM neural network is then employed to classify the extracted features into different target groups. A series of experiments using individual FAM networks and a voting FAM network is conducted. Promising classification accuracy rates are obtained from using individual and voting FAM networks, respectively. The experimental outcomes positively demonstrate the effectiveness of the generated features, and of the FAM network in classifying metal detector signals for automated target discrimination tasks.

Male great bowerbirds create forced perspective illusions with consistently different individual quality

Males often produce elaborate displays that increase their attractiveness to females, and some species extend their displays to include structures or objects that are not part of their body. Such "extended phenotypes" may communicate information that cannot be transmitted by bodily signals or may provide a more reliable signal than bodily signals. However, it is unclear whether these signals are individually distinct and whether they are consistent over long periods of time. Male bowerbirds construct and decorate bowers that function in mate choice. Bower display courts constructed by male great bowerbirds (Ptilonorhynchus nuchalis) induce a visual illusion known as forced perspective for the female viewing the male's display over the court, and the quality of illusion is associated with mating success. We improved the quality of the forced perspective to determine whether males maintained it at the new higher level, decreased the perspective quality back to its original value, or allowed it to decay at random over time. We found that the original perspective quality was actively recovered to individual original values within 3 d.We measured forced perspective over the course of one breeding season and compared the forced perspective of individual males between two successive breeding seasons. We found that differences in the quality of visual illusion among males were consistent within and between two breeding seasons. This suggests that forced perspective is actively and strongly maintained at a different level by each individual male.

A preliminary investigation of adult defence style and physiological reactivity to infant distress signals

Species whose offspring require extended care-giving ought to be predisposed to being biologically responsive to their infant's signalling. This paper examined the interplay between biological and psychological aspects of adult response to an infant's distress. HR (heart rate) and GSR (galvanic skin response) were recorded continuously, while 50 adults listened to white noise and an infant cry audio recording. Participants completed the defence style questionnaire and the state trait anxiety inventory. HR acceleration occurred in response to the control sound, while HR decelerated in response to the infant cry. GSR responsiveness was positively correlated with immature and neurotic defence styles. When controlling for other variables, immature defence was a unique and independent predictor of GSR change in response to infant distress. Defence demonstrated a stronger relationship than self-reported anxiety, than that with physiological responsiveness. Employing defence mechanisms appears to reduce an individual's perceived anxiety, though it has little effect on physiological arousal levels.

Novel Z-Domain precoding method for blind separation of spatially correlated signals

In this paper, we address the problem of blind separation of spatially correlated signals, which is encountered in some emerging applications, e.g., distributed wireless sensor networks and wireless surveillance systems. We preprocess the source signals in transmitters prior to transmission. Specifically, the source signals are first filtered by a set of properly designed precoders and then the coded signals are transmitted. On the receiving side, the Z-domain features of the precoders are exploited to separate the coded signals, from which the source signals are recovered. Based on the proposed precoders, a closed-form algorithm is derived to estimate the coded signals and the source signals. Unlike traditional blind source separation approaches, the proposed method does not require the source signals to be uncorrelated, sparse, or nonnegative. Compared with the existing precoder-based approach, the new method uses precoders with much lower order, which reduces the delay in data transmission and is easier to implement in practice.

Human identification from ECG signals via sparse representation of local segments

This work proposes a novel framework to extract compact and discriminative features from Electrocardiogram (ECG) signals for human identification based on sparse representation of local segments. Specifically, local segments extracted from an ECG signal are projected to a small number of basic elements in a dictionary, which is learned from training data. A final representation is extracted by performing a max pooling procedure over all the sparse coefficient vectors in the ECG signal. Unlike most of existing methods for human identification from ECG signals which require segmentation of individual heartbeats or extraction of fiducial points, the proposed method does not need to segment individual heartbeats or detect any fiducial points. The method achieves an 99.48% accuracy on a 100 subjects dataset constructed from a publicly available database, which demonstrates that both local and global structural information are well captured to characterize the ECG signals.

Extraction of latent patterns and contexts from social honest signals using hierarchical Dirichlet processes

A fundamental task in pervasive computing is reliable acquisition of contexts from sensor data. This is crucial to the operation of smart pervasive systems and services so that they might behave efficiently and appropriately upon a given context. Simple forms of context can often be extracted directly from raw data. Equally important, or more, is the hidden context and pattern buried inside the data, which is more challenging to discover. Most of existing approaches borrow methods and techniques from machine learning, dominantly employ parametric unsupervised learning and clustering techniques. Being parametric, a severe drawback of these methods is the requirement to specify the number of latent patterns in advance. In this paper, we explore the use of Bayesian nonparametric methods, a recent data modelling framework in machine learning, to infer latent patterns from sensor data acquired in a pervasive setting. Under this formalism, nonparametric prior distributions are used for data generative process, and thus, they allow the number of latent patterns to be learned automatically and grow with the data - as more data comes in, the model complexity can grow to explain new and unseen patterns. In particular, we make use of the hierarchical Dirichlet processes (HDP) to infer atomic activities and interaction patterns from honest signals collected from sociometric badges. We show how data from these sensors can be represented and learned with HDP. We illustrate insights into atomic patterns learned by the model and use them to achieve high-performance clustering. We also demonstrate the framework on the popular Reality Mining dataset, illustrating the ability of the model to automatically infer typical social groups in this dataset. Finally, our framework is generic and applicable to a much wider range of problems in pervasive computing where one needs to infer high-level, latent patterns and contexts from sensor data.