The rise of big data in disease ecology

Geographic distribution of bacterial communities of inland waters in China

Microorganisms are integral to freshwater ecological functions and, reciprocally, their activity and diversity are shaped by the ecosystem state. Yet, the diversity of bacterial community and its driving factors at a large scale remain elusive. To bridge this knowledge gap, we delved into an analysis of 16S RNA gene sequences extracted from 929 water samples across China. Our analyses revealed that inland water bacterial communities showed a weak latitudinal diversity gradient. We found 530 bacterial genera with high relative abundance of hgcI clade. Among them, 29 core bacterial genera were identified, that is strongly linked to mean annual temperature and nutrient loadings. We also detected a non-linear response of bacterial network complexity to the increasing of human pressure. Mantel analysis suggested that MAT, HPI and P loading were the major factors driving bacterial communities in inland waters. The map of taxa abundance showed that the abundant CL500-29 marine group in eastern and southern China indicated high eutrophication risk. Our findings enhance our understanding of the diversity and large-scale biogeographic pattern of bacterial communities of inland waters and have important implications for microbial ecology.

An update and review of arthropod vector sensory systems: Potential targets for behavioural manipulation by parasites and other disease agents

For over a century, vector ecology has been a mainstay of vector-borne disease control. Much of this research has focused on the sensory ecology of blood-feeding arthropods (black flies, mosquitoes, ticks, etc.) with terrestrial vertebrate hosts. Of particular interest are the cues and sensory systems that drive host seeking and host feeding behaviours as they are critical for a vector to locate and feed from a host. An important yet overlooked component of arthropod vector ecology are the phenotypic changes observed in infected vectors that increase disease transmission. While our fundamental understanding of sensory mechanisms in disease vectors has drastically increased due to recent advances in genome engineering, for example, the advent of CRISPR-Cas9, and high-throughput "big data" approaches (genomics, proteomics, transcriptomics, etc.), we still do not know if and how parasites manipulate vector behaviour. Here, we review the latest research on arthropod vector sensory systems and propose key mechanisms that disease agents may alter to increase transmission.

Footprint of war: integrating armed conflicts in disease ecology

War is an understudied and yet significant contributor to disease outbreaks, necessitating approaches incorporating conflicts into disease studies. We discuss mechanisms by which war affects disease dynamics, and supply an illustrative example. Lastly, we provide relevant data sources and pathways for incorporating metrics of armed conflict into disease ecology.

Sensitivity of bipartite network analyses to incomplete sampling and taxonomic uncertainty

Bipartite network analysis is a powerful tool to study the processes structuring interactions in ecological communities. In applying the method, it is assumed that the sampled interactions provide an accurate representation of the actual community. However, acquiring a representative sample may be difficult as not all species are equally abundant or easily identifiable. Two potential sampling issues can compromise the conclusions of bipartite network analyses: failure to capture the full range of interactions (sampling completeness) and use of a taxonomic level higher than species to evaluate the network (taxonomic resolution). We asked how commonly used descriptors of bipartite antagonistic communities (modularity, nestedness, connectance, and specialization [H₂ ']) are affected by reduced host sampling completeness, parasite taxonomic resolution, and their crossed effect, as they are likely to co-occur. We used a quantitative niche model to generate weighted bipartite networks that resembled natural host-parasite communities. The descriptors were more sensitive to uncertainty in parasite taxonomic resolution than to host sampling completeness. When only 10% of parasite taxonomic resolution was retained, modularity and specialization decreased by ~76% and ~12%, respectively, and nestedness and connectance increased by ~114% and ~345% respectively. The loss of taxonomic resolution led to a wide range of possible communities, which made it difficult to predict its effects on a given network. With regards to host sampling completeness, standardized nestedness, connectance, and specialization were robust, whereas modularity was sensitive (~30% decrease). The combination of both sampling issues had an additive effect on modularity. In communities with low effort for both sampling issues (50%-10% of sampling completeness and taxonomic resolution), estimators of modularity, and nestedness could not be distinguished from those of random assemblages. Thus, the categorical description of communities with low sampling effort (e.g., if a community is modular or not) should be done with caution. We recommend evaluating both sampling completeness and taxonomic certainty when conducting bipartite network analyses. Care should also be exercised when using nonrobust descriptors (the four descriptors for parasite taxonomic resolution; modularity for host sampling completeness) when sampling issues are likely to affect a dataset.

Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities

The unprecedented generation of large volumes of biodiversity data is consistently contributing to a wide range of disciplines, including disease ecology. Emerging infectious diseases are usually zoonoses caused by multi-host pathogens. Therefore, their understanding may require the access to biodiversity data related to the ecology and the occurrence of the species involved. Nevertheless, despite several data-mobilization initiatives, the usage of biodiversity data for research into disease dynamics has not yet been fully leveraged. To explore current contribution, trends, and to identify limitations, we characterized biodiversity data usage in scientific publications related to human health, contrasting patterns of studies citing the Global Biodiversity Information Facility (GBIF) with those obtaining data from other sources. We found that the studies mainly obtained data from scientific literature and other not aggregated or standardized sources. Most of the studies explored pathogen species and, particularly those with GBIF-mediated data, tended to explore and reuse data of multiple species (>2). Data sources varied according to the taxa and epidemiological roles of the species involved. Biodiversity data repositories were mainly used for species related to hosts, reservoirs, and vectors, and barely used as a source of pathogens data, which was usually obtained from human and animal-health related institutions. While both GBIF- and not GBIF-mediated data studies explored similar diseases and topics, they presented discipline biases and different analytical approaches. Research on emerging infectious diseases may require the access to geographical and ecological data of multiple species. The One Health challenge requires interdisciplinary collaboration and data sharing, which is facilitated by aggregated repositories and platforms. The contribution of biodiversity data to understand infectious disease dynamics should be acknowledged, strengthened, and promoted.