Intense competition among individuals of the same sex and species can result in striking, elaborate and costly traits. Such intrasexual competition has been widely studied among males; but it is only in the last few years that intrasexual competition among females has received attention. Recent reviews suggest that competition between females may be widespread; females may compete over a variety of resources including mates, food, nesting sites and safety; and such competition can have important behavioural and evolutionary consequences. However, systematic studies of intrasexual competition among females are scarce. Consequently, our understanding of the form that competition takes in females and the traits that evolve under such competition is limited. Owing to differences in life histories, the patterns and processes acting in female-female competition are expected to be different from those in males. In my thesis, I have focused on studying traits and strategies in intrasexual competition in females in a polygynous species, the tropical rock agama (Psammophilus dorsalis), through observations and experiments in the wild.
The social system plays an important role in establishing the contexts in which various behaviours are played out. Establishing the social system of a population is essential for investigating the ecology and evolution of behavioural and life history traits. Thus, first, I studied the nature of between- and within- sex interactions across the lifetime of individuals in Peninsular rock agama using space use patterns. Individually tagged wild males and females were closely monitored and their home ranges estimated. Male home ranges overlapped multiple female home ranges, but females defended exclusive home ranges, suggesting strong competition.
I then examined signalling traits, which, observations on males suggest, play a key role in conveying information both in direct contest competition and in indirect competition to attract mates. I observed wild individuals every month across their lifetime to study the extent of female signalling and to understand the contexts in which the different signals are used. I also examined whether signalling rates are correlated with proxies of female fitness. I report that females, too, have a complex signalling repertoire. My findings suggest that females may signal both in direct competition in sexual and non-sexual context and to attract mates.
Finally, I examined strategies of female-female competition. Because of their investment in young, the costs of overt competition, such as conspicuous signalling and physical aggression, are expected to be comparatively high for females. Therefore, females should normally signal using relatively inconspicuous traits. They should be sensitive to the perceived threat, more so than males, and escalate to costly signalling and aggression only when the threat is high. I tested this hypothesis using field experiments and by simulating intruder threat on territories of wild female P. dorsalis. I report that in the normal signalling context, females signal using less conspicuous signals, less conspicuous than in males. I also show that females strikingly increase response in the form of signalling and aggression with increasing level of intruder threat. To summarize, I find substantial intrasexual competition in females of a polygynous species. Traits and strategies can be complex and different from that of males. Finally, owing to the cryptic nature of competition, an experimental approach might be key in studying competition between females.

Terrestrial bioacoustics, like many other domains, has recently
witnessed some transformative results from the application of deep
learning and big data (Stowell 2017, Mac Aodha et al. 2018, Fairbrass et
al. 2018, Mercado III and Sturdy 2017). Generalising over specific
projects, which bioacoustic tasks can we consider "solved"? What can we
expect in the near future, and what remains hard to do? What does a
bioacoustician need to understand about deep learning? I will address
these questions, giving a concise summary of recent developments and
ways forward. We build on recent projects and evaluation campaigns led
by the author (Stowell et al. 2015, Stowell et al. 2018), as well as
broader developments in signal processing, machine learning and
bioacoustic applications of these. We will discuss which type of deep
learning networks are appropriate for audio data, how to address
zoological/ecological applications which often have few available data,
and issues in integrating deep learning predictions with existing
workflows in statistical ecology.

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.