Northeast India, with its variety of geographical features and topography, provides a plethora of niches for species to evolve and thrive. Among a multitude of factors, past climate change is one of the important factors influencing primate speciation in this region. Populations of various species could have risen or declined in response to these climatic fluctuations. Recent advances in climate modelling as well as genomic data analysis has paved the way for understanding how species accumulate at a particular geographic region. We utilized these methods to explore the primate diversity in the unique region of northeast India. To ascertain these population level changes, we inferred demographic history of nine species of primates found in northeast India with MSMC2 and compared it with species distribution models using past climate data of Pliocene and Pleistocene period. Through this study, we are able to give a detailed picture of how past climatic changes have affected the present species diversity and we show that the primate diversity in northeast India is a mix of species either originated there or have dispersed from mainland southeast Asia. We observe that effective population size has decreased for all the species, but distributions are different for all the four genera, Macaca, Trachypithecus, Hoolock and Nycticebus, and this provides important insights pertaining to present primate diversity in the region. It also gives an idea about how each species is affected differently by climate change, and why it should be given emphasis in framing species-wise conservation models for future climate change.

The global decline in biodiversity driven by anthropogenic factors necessitates biomonitoring of ecosystems. However, current approaches are limited to targeted detection of taxa and fail to capture the total biodiversity of ecosystems. We postulated that extracellular environmental DNA (eDNA) represents a genetic repertoire of all the life forms in an ecosystem, which can be retrieved by deep sequencing. The feasibility and effectiveness of this approach were tested through a spatiotemporal study in Chilika Lagoon, a large and biodiverse Ramsar wetland ecosystem in India. Extracellular eDNA was enriched from large-volume filtered water samples using lysis-free methods and PCR-free shotgun sequencing libraries were generated. Based on the saturation of unique k-mers, over 10.96 billion extracellular eDNA fragments were sequenced from 16 libraries and taxonomically classified to the lowest common ancestor of the best hits of the paired-end reads. The results show that organisms from all the domains of life, including the low-abundant non-microbial taxa, can be detected with high sensitivity for taxonomic families with representative genomes. Interestingly, despite Bacteria representing a large proportion (87%) of the taxonomically classified reads, Eukaryotes showed the highest taxonomic diversity (73%). Further, using incidence-based asymptotic richness analysis, the total taxonomic diversity of Chilika was estimated to be 1071 families across the tree of life, comprising approximately 799 families of Eukaryotes, 230 families of Bacteria, 27 families of Archaea, and 13 families of DNA Viruses. We also quantified the compositional changes using Bray-Curtis dissimilarity and showed that extracellular eDNA can resolve the broad-scale spatiotemporal variation of biodiversity across the tree of life. These results demonstrate that PCR-free deep sequencing of extracellular eDNA is an effective approach for taxonomic diversity assessment across the tree of life in large ecosystems. With the increasing genomic resources and decreasing sequencing costs, we foresee its widespread application to monitor future biodiversity loss and support conservation, restoration, and management efforts in the Anthropocene.

In a recent paper, “Environmental DNA: What’s behind the term? Clarifying the terminology and recommendations for its future use in biomonitoring”, Pawlowski et al. argue that the term eDNA should be used to refer to the pool of DNA isolated from environmental samples, as opposed to only extra-organismal DNA from macro-organisms. We agree with this view. However, we are concerned that their proposed two-level terminology specifying sampling environment and targeted taxa is overly simplistic and might hinder rather than improve clear communication about environmental DNA and its use in biomonitoring. Not only is this terminology based on categories that are often difficult to assign and uninformative, but it ignores what is in our opinion the most important distinction within eDNA: the type of DNA (organismal or extra-organismal) from which ecological interpretations are derived.