In highly biodiverse tropical mountains with thermally specialist biota, natural elevationally-linked temperature gradients strongly determine the distribution of species. In addition to this natural gradient, anthropogenic habitat change creates new abiotic conditions by (a) shifting existing thermal gradients upwards, and (b) through the creation of manmade habitats such as agriculture and degraded forest, which are hotter and climatically more variable than natural forest. Climate change is already causing rapid range shifts of many species to higher elevations, but how species adapt to changes in the abiotic environment because of the interactive effects of climate change and land-use change remains largely unknown. Recent evidence indicates that populations of some species undergo morphological changes over decades in response to warming temperatures. Further, in birds, environmental stressors (including thermal stress) trigger the hypothalamus-pituitary-adrenal axis (HPA axis) leading to the release of corticosterone, the primary stress hormone in birds. While blood corticosterone levels rise transiently in response to stress, feathers sequester corticosterone, providing a long-term picture of stress faced by birds. Prolonged stress also leads to an altered immune system, leading to changes in the composition of the gut, crucial for nutrient assimilation and detoxification. Unless morphological changes or range shifts minimise thermal stress, alterations in corticosterone levels and gut microbiota because of habitat degradation and climate change should affect fitness in birds. For three widely distributed bird species (abundant at all elevations and found in degraded forest; Schoeniparus castaneceps, Actinodura egertoni, and Trochalopteron erythrocephalum)that vary greatly in body size, I aim to understand how natural and man-made temperature gradients affect morphology, stress, the composition of gut microbiota, and survival in Eastern Himalayan montane bird species.

Species interactions are known to shape biological communities. While antagonistic interactions like competition and predation are well known, cooperative interactions have received comparatively less attention. Mixed-species foraging behaviour is a common phenomenon seen across various taxa, including fish, birds, and mammals, where different species form groups and forage together. Unlike symbiotic associations, these interactions are more dynamic and include a much larger subset of species of the community. We sampled mixed-species groups (MSG) of reef fish in the Lakshadweep islands, off the west coast of India. The data was gathered over four years following a mass-bleaching event which led to massive loss of coral in Lakshadweep in 2010. Though not widely reported, we discovered that mixed-species grouping is a common occurrence in the reef ecosystem. Around 130 of the 305 commonly observed species of fish in the Lakshadweep were seen participating in groups to some extent. Using a cluster analysis on species composition, we categorised the groups that were observed in Lakshadweep into nine compositional categories, which also exhibited variation in behaviour, habitat affinity and group cohesion. We then examined variation in grouping propensity, species richness, species evenness as well as species composition across space, time and habitat for the most commonly observed compositional categories. We found that invertivores tended to form smaller attendant groups, with clear nuclear-follower relationships, and likely form for direct foraging benefits. Herbivorous fish on the other hand formed large shoaling associations indicating benefits gained from increasing group size. We found evidence of the effect of the mass-bleaching event and subsequent ecosystem recovery on the formation of some groups. Reef fish MSGs are thus important components of these ecosystems and can both affect and be impacted by reef structure and function.

Honeybees are a well known example of self-organized collective systems. Individuals perform tasks and coordinate their behavior in a way that translates to the colony-level organization. Stressful situations such as high temperatures are common in the environment. Specifically, during heat stress periods individuals show enhanced behaviors such as fanning and spreading water. During such conditions, it is not understood how individuals in the colony vary in their behavior, what factors determine changes in behavior, and how these translate to the colonylevel response. We examine the honey bee heat stress response by introducing multiple agematched cohorts (i.e. several thousand tagged bees) into an observation hive, and analyzing their movement behavior. We use the behavior over time for each individual to extract the dominant modes of response, and furthermore ask how previous behavior predicts how an individual responds during heat stress. Broadly, such large scale colony-level analysis can reveal if there are general principles of reorganization that honeybees adhere to when encountered with sudden changes or stress.

Collective behavior is observed at different scales, from cells to animal groups to societies. Recent technological advancements have enabled an unprecedented increase in our ability to collect data for how individuals behave in a group. I will show results from a long-term tracking study of honeybees, where 5000+ bees were individually tracked using barcodes over their entire lives. This data provides a detailed picture of daily behavioral differences, and reveals lifetime differences in motion and task-switching behavior among bees. Using the same framework we introduced reproductive male drones into the hive, and analysis of their in-nest motion reveals synchronized periods of high-speed “hyperactive” motion that coincide with trips outside. By comparing with other collective systems we have analyzed – including fish, rats, and cellular collectives – I will highlight basic ways how “big data” can be used to describe biological variation, and discuss ongoing work which seeks to connect observed differences to group function and environmental characteristics.

Sexual selection in polygynous mating systems has largely been studied in the context of female mate choice and competition in males. There is empirical evidence on how males strategize their investment in costly displays. More recently, studies have observed sexual signalling in females, however, our understanding of how females invest in such costly signals is limited. Male Peninsular rock agama Psammophilus dorsalis show diverse displays in the context of competitions and courtship. Recent studies indicate that females also use complex displays during the breeding season. Here we test the hypothesis that females invest more in sexual signalling when there is reduced availability of mating opportunities with high-quality males. We also test whether females increase their sexual signalling through the breeding season to maximize terminal-mating opportunities. We caught and tagged wild female lizards and released them back into their territories. On these lizards, we carried out model presentation trials, where 3D printed models of a male lizard in breeding and non-breeding colours were presented following which, the initial response of the focal lizard was recorded. We have conducted and recorded 129 trials across three breeding seasons between 2019 and 2021. Preliminary analysis shows that females display using dynamic colour change and body postures in response to male models in breeding colour. This paper investigates how female Peninsular rock agama strategizes their investment in sexual signals based on various intrinsic factors, social conditions and life-history trade-offs.

Due to the absence of physical barriers, the open-nesting giant honeybee Apis dorsata has evolved a spectacular collective defence behaviour – known as “shimmering” – against predators, which is characterised by travelling waves generated by individual bees flipping their abdomens in a coordinated and sequential manner across the bee curtain. We examined if shimmering is visually-mediated by presenting moving stimuli of varying sizes and contrasts to the background (dark or light) in bright and dim ambient light conditions. Shimmering was strongest under bright ambient light, and its strength declined in dim-light. A. dorsata shimmered only when presented with the darkest stimulus against a light background, but not when this condition was reversed (light stimulus against dark background). We suggest that this is an effective anti-predatory strategy in open-nesting A. dorsata colonies, exposed to high ambient light, as flying predators are more easily detected when they appear as dark moving objects against a bright sky. Moreover, the stimulus detection threshold (smallest visual angular size) is much smaller in this anti-predatory context (1.6° - 3.4°) than in the context of foraging (5.7°), indicating that ecological context affects visual detection threshold.

Many biological systems, from flocks of birds, swarms of locusts, shoals of fish, to crowds of humans show collective behaviours which are emergent properties that manifest only at the level of a group. In each of these cases, local, individual-level interactions give rise to highly coordinated and synchronized emergent patterns that are observable at the group level. Theoretical, computational, and empirical research in this field have been used to address questions about various aspects of collective behaviour, from characterizing its structural and dynamical properties to deciphering how the individual behaviour produces emergent group patterns. However, most of these studies examine the group-level properties in terms of their mean values and do not attempt to characterize the variability present in the collective properties which arise due to the stochastic fluctuations. In real groups, stochastic fluctuations in the group-level properties arise due to the probabilistic interactions between finite number of individuals. As a result, this intrinsic noise present in collective systems can produce non-intuitive and non-trivial behaviours. Real groups in finite space will likely have some interaction with the boundary, and the observed collective dynamics may also include effects of interaction with the boundary. To gain comprehensive understanding of collective dynamics in real groups, examining the effect of interaction with the boundary is necessary. However, whether boundary interactions confound analyses of inferred interaction rules have not been investigated rigorously. In the current study, we examine how the parameters of the boundary conditions affect the simulated collective dynamics. We performed stochastic simulations of two data-inspired spatial models developed by Jhawar et al. that have contrasting collective properties: (i) where individuals show pairwise interactions and collective order is driven stochastically (ii) where individuals show ternary interactions and collective order is driven deterministically. We characterize intrinsic noise in the data generated from the simulations and examine the susceptibility of the results to the presence of boundary. In these data-inspired spatial models, we show that the essential features of the group-level dynamics obtained from the simulated time-series do not change because of boundary interactions. Furthermore, our inference of local interactions also remains unaffected by the boundary conditions – at least within the model framework and the context we investigated, which were parameterized to the experimental conditions of our laboratory.

When a blackbird swallows an earthworm, there is little doubt about the nature of the interaction. Sadly, few microbial interactions are this clear. This is particularly true when bacteria reside inside eukaryotes. Who gains, who loses, and the role of evolution are large questions in the biology of microbial interactions. Many look at the mechanisms of interaction, but here we take a different approach. First we explore the landscape of interactions across the eastern US, determining how commonly specific bacteria interact with amoebae, beginning with the obligate endosymbionts Amoebophilis and Neoclamydia and the eukaryote host Dictyostelium discoideum. We image the interactions and measure health effects on the host. Then we turn to a novel facultative endosymbiont, Paraburkholderia spp. where geographic distribution and health effects can be measured for both host and endosymbiont. We name three new species, two of which are highly dependent on the host and then use experimental evolution techniques to reveal how tightly they have co-evolved. Combining the tools of landscape surveys, microscopy, fitness assays, genomics, and experimental evolution allow a much deeper understanding of host-pathogen or host-mutualist interactions.

This study investigates beta diversity and its partitions to quantify the influence of different processes that drive spatial and temporal variation in ground-dwelling arthropod assemblages. First, ant assemblages across Goa, India, were studied to quantify how different species and functional groups, and human land use contribute to beta diversity over large spatial scales, and whether invasive species have a disproportionate influence on beta diversity. Human land use strongly influenced diversity and distribution of ant assemblages. Human land use spared local species richness, but not functional groups. A small number of invasive species exerted negative influence even in a very speciose community. Second, intra-annual variation in beta diversity and its partitions of ant communities was studied across three seasons in Bhagwan Mahaveer Wildlife Sanctuary, Goa, India, to quantify how loss and gain of species leads to functional redundancy. Ant community composition was highly variable at seasonal scales. But ecological roles were maintained across seasons by species with redundant functional traits. Third, effect of human altered land use on temporal beta diversity and its partitions of ground active arthropods was quantified in the coupled human-natural Trans-Himalayan ecosystem in Spiti, northern India. Human land use altered seasonal trajectories of community dynamics and influenced beta diversity at the taxonomic level. But functional roles were spared due to species replacement and redundancy in traits. Together, the three chapters of this thesis show that community composition rather than species richness is a better indicator of how arthropods respond to human land use. They also establish functional redundancy to be an important feature of ecological resilience and resistance that can be affected by human land use.

Many biological systems, from flocks of birds, swarms of locusts, shoals of fish, to crowds of humans show collective behaviours which are emergent properties that manifest only at the level of a group. In each of these cases, local, individual-level interactions give rise to highly coordinated and synchronized emergent patterns that are observable at the group level. Theoretical, computational, and empirical research in this field have been used to address questions about various aspects of collective behaviour, from characterizing its structural and dynamical properties to deciphering how the individual behaviour produces emergent group patterns. However, most of these studies examine the group-level properties in terms of their mean values and do not attempt to characterize the variability present in the collective properties which arise due to the stochastic fluctuations. In real groups, stochastic fluctuations in the group-level properties arise due to the probabilistic interactions between finite number of individuals. As a result, this intrinsic noise present in collective systems can produce non-intuitive and non-trivial behaviours. Real groups in finite space will likely have some interaction with the boundary, and the observed collective dynamics may also include effects of interaction with the boundary. To gain comprehensive understanding of collective dynamics in real groups, examining the effect of interaction with the boundary is necessary. However, whether boundary interactions confound analyses of inferred interaction rules have not been investigated rigorously. In the current study, we examine how the parameters of the boundary conditions affect the simulated collective dynamics. We performed stochastic simulations of two data-inspired spatial models developed by Jhawar et al. that have contrasting collective properties: (i) where individuals show pairwise interactions and collective order is driven stochastically (ii) where individuals show ternary interactions and collective order is driven deterministically. We characterize intrinsic noise in the data generated from the simulations and examine the susceptibility of the results to the presence of boundary. In these data-inspired spatial models, we show that the essential features of the group-level dynamics obtained from the simulated time-series do not change because of boundary interactions. Furthermore, our inference of local interactions also remains unaffected by the boundary conditions – at least within the model framework and the context we investigated, which were parameterized to the experimental conditions of our laboratory.