Colors in Nature can be produced either chemically, by the selective light absorption by pigments, or physically, by light interference from biophotonic nanostructures. Intriguingly, there are almost no known violet, blue or green pigments in animals. And yet these structurally produced colors are ubiquitous in nature and constitute an important aspect of the overall appearance of organisms, as they are frequently used in camouflage, and in social and sexual communication. As the underlying biophotonic nanostructures are overwhelmingly diverse in form and function, their structural and optical characterization has hitherto remained challenging despite centuries of research, which is where I have made rapid and significant contributions. Although there is a burgeoning interest on structural colors from biologists, physicists and engineers, we currently lack an explicit comparative framework, which is essential to understand how these biological signals function, and evolve in organisms. Moreover, the mechanisms controlling the morphogenesis of these complex, biologically patterned nanostructures are much too large to be described by conventional cell or molecular biology, and much too small to be captured by traditional developmental biology. As a consequence, we know very little about the development of photonic nanostructures within cells, beyond the realisation that they are self-assembled intra-cellularly by mechanobiological, phase separation and micro-phase separation like processes. Biophotonic nanostructures are also of broader interest to materials science and engineering, since the facile synthetic fabrication of three-dimensional photonic nanostructures at these rather large optical length scales (200-500 nm) is challenging. Organismal structural colors that have evolved over millions of years to function in a variety of signalling contexts are an ideal source to look for naturally optimized solutions to technological problems in sensing, photonics, etc. In this talk, I will summarise our current knowledge about the structure, function and morphogenesis of biophotonic nanostructures and how this can be leveraged for the biomimetic or bio-inspired synthesis of next generation photonic meta-materials and devices.

In this talk, I will give an introduction to Topological Data Analysis (TDA), which uses techniques in Topology to uncover hidden patterns in data cloud. At the heart of TDA lies the philosophy that data has a shape, and shape carries meaning. In other words, data cloud can be thought of as points distributed over a smooth manifold. TDA focuses on understanding the shape of the manifold by suitably projecting the data to two dimensions.

I will focus on certain case studies and talk about the merits of TDA in gaining a qualitative understanding of data.

These workshops are intended to serve as an introduction/refresher to commonly used advanced statistical models. The workshops will consist of lectures on how the different statistical models work, accompanied by hands-on sessions in R, where we apply these models to ecological data-sets and become familiar with fitting and interpreting them.
The detailed schedule for the workshops is available here:
https://docs.google.com/document/d/1h6dCLtXBxTRhVi4_MV-3VOLTREP9AcRa81of...

Topic 1: Generalised Linear Models (GLM)

10th January (Friday) 10am - 1pm: Discrete data problems - Prof Nagaraja

13th January (Monday) 10am - 1pm: GLMs with examples of binary and proportion data - Prof. Nagaraja

14th January (Tuesday) 10am - 1pm: R Session: Applying GLMs to ecological data sets - Kavita

(15th Jan 2020 is a HOLIDAY)

16th January (Thursday) 10am - 1 pm: GLMs with examples of count data - Prof. Nagaraja

17th Jan (Friday) 10am - 1pm: R Session: Applying GLMs to ecological data sets - Kavita

Topic 2: Mixed-effects Models

27th Jan (Monday): 10am - 1pm: GLM With examples of zero inflation + R session (by Prof Nagaraja)

28th Jan (Tuesday): 10am - 1pm: Linear mixed-effects models - Prof. Nagaraja

29th Jan (Wednesday): 10am - 1pm: R Session: Applying LMMs to ecological data sets - Kavita

30th Jan (Thursday): 10am - 1pm: 11th Jan 10am - 1pm: Generalised Linear Mixed-effects Models GLMMs - Prof. Nagaraja

31st Jan (Friday): 10 am - 1pm: R Session: Applying GLMMs to ecological data sets - Kavita

*Attendance only with Registration.
**Pre-requisites: Since these workshops focus on advanced statistical models, familiarity with basic statistics (statistical hypothesis testing, one and two-sample problems, simple linear regression, one-way ANOVA) and basic math and probability (functions, distributions) is expected. Since we will be working in R/RStudio, some familiarity with R & R-studio is also expected.

Many complex ecological and social-ecological systems are capable of nonlinear feedbacks. These can result in abrupt and unanticipated shifts in the dynamical regime of a system, as environmental conditions move the system beyond a tipping point. Research in early warning signals of tipping points focusses on ways to predict these tipping points ahead of time by looking for telltale signatures of noise in the data before the tipping point is reached. In this talk, I will describe some research research in our group that (1) characterizes how conventional early warning signals in ecological systems change in the face of social-ecological feedbacks, and (2) explores new types of early warning signals that predict not only the presence, but also the type, of tipping point that is being approached. I will also discuss some opportunities to find early warning signals of social-ecological transitions in social media data, such as tweets on climate change and vaccines.

A body of work is emerging wherein simple mathematical models of ecological dynamics are coupled to simple mathematical models of human behaviour to examine long-term sustainability of these systems. There are pros and cons to the use of simple models, as has been argued for decades in science. I will review these pros and cons in the context of several recent and ongoing studies of ours where we examine widely-ranging contemporary human-environment problems including forest pest control, coral reef endangerment, forest-grassland mosaic sustainability, human disease spread, land-use management, and climate change mitigation. Wicked problems such as these require the kind of basic understanding of alternate stable states, feedback strengths, and parameter influences that simple mathematical models can provide. I argue that the value of our models is one of complementarity: strengths of simple models may compensate for weakness of other approaches. Indeed when the level of complexity of the human and environmental submodels that constitute a coupled model are mismatched, then a ‘lower common denominator’ coupled model may be the most parsimonious way to start. Simple models can inform policy and other decision makers by revealing the mechanisms behind emergent properties and critical transitions. I prove examples of such insights from our recent and ongoing case studies.

Animal groups exhibit many emergent properties that are a consequence of local interactions. Linking individual-level behaviour to group-level dynamics has been a question of fundamental interest from both biological and mathematical perspectives. However, most empirical studies have focused on average behaviours ignoring stochasticity at the level of individuals. On the other hand, conclusions from theoretical models are often derived in the limit of infinite systems, in turn neglecting stochastic effects due to finite group sizes. In our study, we use a stochastic framework that accounts for intrinsic-noise in collective dynamics arising due to (a) inherently probabilistic interactions and (b) a finite number of group members. We derive equations of group dynamics starting from individual-level probabilistic rules as well as from real data to understand the effects of such intrinsic noise and the mechanisms underlying collective behaviour.

First, using the chemical Langevin method, we analytically derive models (stochastic differential equations) for group dynamics for a variable m that describes the order/consensus within a group. We assume that organisms stochastically interact and choose between two/four directions. We find that simple pairwise interactions between individuals lead to intrinsic-noise that depends on the current state of the system (i.e. a multiplicative or state-dependent noise). Surprisingly, this noise creates a new ordered state that is absent in the deterministic analogue.

Next, focusing on small-to-intermediate sized groups (10-100), we empirically demonstrate intrinsic-noise induced schooling (polarized or highly coherent motion) in fish groups. The fewer the fish, the greater the intrinsic-noise and therefore the likelihood of alignment. Such empirical evidence is rare, and tightly constrains the possible underlying interactions between fish. Our model simulations indicate that fish align with each other one at a time, ruling out other complex higher-order interactions.

Further, we analyze the method to derive the group-level dynamical equation using simulated data from two different models of collective behaviour. In doing so we resolve important time-scale related issues with deriving the deterministic and stochastic components of the mesoscopic description from the data.

Broadly, our results demonstrate that rather than simply obscuring otherwise deterministic dynamics, intrinsic-noise is fundamental to the characterisation of emergent collective behaviours, suggesting a need to re-appraise aspects of both collective motion and behavioural inference.

The evolution of flamboyant traits in animals is typically attributed to the selective force of sexual selection. However, natural selection can constrain the degree of elaboration of such traits. Therefore, animal signals reflect a balance between natural and sexual selection. I examined the role of these forces in the maintenance of a complex visual signal: dynamic colour change. Males of the Indian rock agama (Psammophilus dorsalis) exhibit rapid dynamic colour changes on their dorsal and lateral body regions during social interactions. The costs, benefits and adaptive significance of this relatively rare signal type is yet unknown.

Using a combination of visual modelling and field experiments, I first examined the predation risk on social colours and found that the courtship signal of males is costlier than the aggression signal. I then tested whether male colours expressed during aggression convey information about individual physiology and performance measures. Apart from a negative association between testosterone levels and the yellow colour expressed during aggression, body size and bite force were correlated, suggesting that body size could be an honest predictor of fighting ability. In the third chapter, I examined differences in health parameters of males and females that occupy dramatically different habitats as a consequence of urbanization. My results suggest that lizards in urban areas appear to have shifted their innate physiology in order to cope with urban stressors. Finally, I examined the response of receivers to different components of the male colour signals by assessing attention paid by conspecific receivers to each signal component independently and together. Both males and females responded equally to all male social colours although females showed difference in response to achromatic signals. Overall, I conclude that dynamic colour change may have evolved in this species to actively balance the costs of predation risk with the benefits of social signalling.

This workshop will be led Prof. Marlene Zuk and will focus on reasons for why women representation in careers in science is so poor, issues faced by women pursuing careers in science, and also what we can all do as individuals, professionals and as institutions to address these issues. The workshop will consist of a presentation by Marlene, small-group and larger group discussions, and examining several common real-life situations and discussing how these can be successfully handled.

A one day symposium on Animal Signals: Functions and Evolution to be held on Thursday, 12th December, at CES, IISc. Prof. Marlene Zuk from University of Minnesota will be delivering the opening talk.

Sexual selection has long been known as the force underlying the evolution of male ornaments and armaments that males use to gain access to mates. However, what is less understood is what happens when females mate with multiple males, causing sperm of different males to compete for fertilization. Selection for sperm that are both more competitive and better able to overcome the challenges of the female reproductive tract has brought about tremendous variation in sperm size and shape. I will discuss recent comparative work on the causes and consequences of variation in sperm form and function, including the evolution of the longest sperm ever measured. I will further discuss how males trade off the allocation of their limited resources between producing copious high-quality sperm and the costly ornaments and armaments to gain mating opportunities in the first place.