To understand how biodiversity responds to global change, we need to connect research across scales, from molecules to landscapes. We show how integrating research disciplines can further a comprehensive understanding of biodiversity, resource-efficient conservation research, and management planning. Using a probabilistic modeling approach, Latent Dirichlet Allocation, we find common features within disparate datasets and present a framework to analyze data about landscape vegetation patterns, plant chemicals, and bacteria in the digestive tracts of sagebrush herbivores. Our study demonstrates how an interdisciplinary approach can aid conservation strategies and how generative models for detecting communities can provide a common language across many types of ecological data. SynthesisBiodiversity science encompasses multiple disciplines and biological scales from molecules to landscapes. Nevertheless, biodiversity data are often analyzed separately with discipline-specific methodologies, constraining resulting inferences to a single scale. To overcome this, we present a topic modeling framework to analyze community composition in cross-disciplinary datasets, including those generated from metagenomics, metabolomics, field ecology and remote sensing. Using topic models, we demonstrate how community detection in different datasets can inform the conservation of interacting plants and herbivores. We show how topic models can identify members of molecular, organismal and landscape-level communities that relate to wildlife health, from gut microbes to forage quality. We conclude with a future vision for how topic modeling can be used to design cross-scale studies that promote a holistic approach to detect, monitor and manage biodiversity.

Pests, such as parasites and pathogens, persist throughout time and space as threats to public health and food security. The need for novel and sustainable approaches to managing these threats are in high demand. The current approach of discovering and developing chemical treatments to manage pests is tedious, not efficient, and often outpaced by traits of resistance in pests. Here, we propose a new approach to discovering new chemical pest management solutions by observing chemical coping behaviors in wildlife. We define a chemical coping behavior as the exploitation of naturally occurring chemicals within a host's environment to manage pests. Specifically, the use of greenery in nests by avian species may provide clues to plants that can deter ectoparasites. Plants use chemical defenses to cope with their own parasites, pathogens, and herbivores, which avian hosts can exploit to combat pests in nests. A local host-pest-plant interaction was investigated to discover the potential chemical diversity and bioactivity of greenery found in nests of golden eagles (Aquila chrysaetos). We found that each plant offered unique chemicals, but that the plant species underrepresented in nests compared to availability in the landscape provided greater diversity in volatile chemicals whereas overrepresented plant species provided greater diversity in water-soluble chemicals compared to other plants. Furthermore, we tested how concentration and diversity of volatile and water-soluble chemicals in plant species found in nests of golden eagles affected the behavior of a hematophagous parasite (Cimex lectularius, the common bed bug). We found that bed bugs spent less time resting and transitioned from grooming to exploration at an increased frequency with high concentration and diversity of volatiles from plants found in nests of golden eagles. Observing the chemical coping behaviors in the wild could provide a sustainable framework for discovering diverse and robust sources of chemicals and modes of action that can used to manage pests of human concern.