Towards an ecosystem model of infectious disease

Increasingly intimate associations between human society and the natural environment are driving the emergence of novel pathogens, with devastating consequences for humans and animals alike. Prior to emergence, these pathogens exist within complex ecological systems that are characterized by trophic interactions between parasites, their hosts and the environment. Predicting how disturbance to these ecological systems places people and animals at risk from emerging pathogens-and the best ways to manage this-remains a significant challenge. Predictive systems ecology models are powerful tools for the reconstruction of ecosystem function but have yet to be considered for modelling infectious disease. Part of this stems from a mistaken tendency to forget about the role that pathogens play in structuring the abundance and interactions of the free-living species favoured by systems ecologists. Here, we explore how developing and applying these more complete systems ecology models at a landscape scale would greatly enhance our understanding of the reciprocal interactions between parasites, pathogens and the environment, placing zoonoses in an ecological context, while identifying key variables and simplifying assumptions that underly pathogen host switching and animal-to-human spillover risk. As well as transforming our understanding of disease ecology, this would also allow us to better direct resources in preparation for future pandemics

Publisher Correction: Towards an ecosystem model of infectious disease

Correction to: Nature Ecology & Evolution https://doi.org/10.1038/s41559-021-01454-8, published online 17 May 2021

Host plant quality, spatial heterogeneity, and the stability of mite predator–prey dynamics

Population dynamics models suggest that both the over-all level of resource productivity and spatial variability in productivity can play important roles in community dynamics. Higher productivity environments are predicted to destabilize consumer–resource dynamics. Conversely, greater heterogeneity in resource productivity is expected to contribute to stability. Yet the importance of these two factors for the dynamics of arthropod communities has been largely overlooked. I manipulated nutrient availability for strawberry plants in a multi-patch experiment, and measured effects of overall plant quality and heterogeneity in plant quality on the stability of interactions between the phytophagous mite Tetranychus urticae and its predator Phytoseiulus persimilis. Plant size, leaf N content and T. urticae population growth increased monotonically with increasing soil nitrogen availability. This gradient in plant quality affected two correlates of mite population stability, population variability over time (i.e., coefficient of variation) and population persistence (i.e., proportion of plant patches colonized). However, the highest level of plant quality did not produce the least stable dynamics, which is inconsistent with the “paradox of enrichment”. Heterogeneity in plant productivity had modest effects on stability, with the only significant difference being less variable T. urticae densities in the heterogeneous compared to the corresponding homogeneous treatment. These results are generally congruent with metapopulation theory and other models for spatially segregated populations, which predict that stability should be governed largely by relative movement rates of predators and prey—rather than patch quality

The Integrative Taxonomic Approach Reveals Host Specific Species in an Encyrtid Parasitoid Species Complex

Integrated taxonomy uses evidence from a number of different character types to delimit species and other natural groupings. While this approach has been advocated recently, and should be of particular utility in the case of diminutive insect parasitoids, there are relatively few examples of its application in these taxa. Here, we use an integrated framework to delimit independent lineages in Encyrtus sasakii (Hymenoptera: Chalcidoidea: Encyrtidae), a parasitoid morphospecies previously considered a host generalist. Sequence variation at the DNA barcode (cytochrome c oxidase I, COI) and nuclear 28S rDNA loci were compared to morphometric recordings and mating compatibility tests, among samples of this species complex collected from its four scale insect hosts, covering a broad geographic range of northern and central China. Our results reveal that Encyrtus sasakii comprises three lineages that, while sharing a similar morphology, are highly divergent at the molecular level. At the barcode locus, the median K2P molecular distance between individuals from three primary populations was found to be 11.3%, well outside the divergence usually observed between Chalcidoidea conspecifics (0.5%). Corroborative evidence that the genetic lineages represent independent species was found from mating tests, where compatibility was observed only within populations, and morphometric analysis, which found that despite apparent morphological homogeneity, populations clustered according to forewing shape. The independent lineages defined by the integrated analysis correspond to the three scale insect hosts, suggesting the presence of host specific cryptic species. The finding of hidden host specificity in this species complex demonstrates the critical role that DNA barcoding will increasingly play in revealing hidden biodiversity in taxa that present difficulties for traditional taxonomic approaches

Keratan sulphate in the tumour environment

Keratan sulphate (KS) is a bioactive glycosaminoglycan (GAG) of some complexity composed of the repeat disaccharide D-galactose β1→4 glycosidically linked to N-acetyl glucosamine. During the biosynthesis of KS, a family of glycosyltransferase and sulphotransferase enzymes act sequentially and in a coordinated fashion to add D-galactose (D-Gal) then N-acetyl glucosamine (GlcNAc) to a GlcNAc acceptor residue at the reducing terminus of a nascent KS chain to effect chain elongation. D-Gal and GlcNAc can both undergo sulphation at C6 but this occurs more frequently on GlcNAc than D-Gal. Sulphation along the developing KS chain is not uniform and contains regions of variable length where no sulphation occurs, regions which are monosulphated mainly on GlcNAc and further regions of high sulphation where both of the repeat disaccharides are sulphated. Each of these respective regions in the KS chain can be of variable length leading to KS complexity in terms of chain length and charge localization along the KS chain. Like other GAGs, it is these variably sulphated regions in KS which define its interactive properties with ligands such as growth factors, morphogens and cytokines and which determine the functional properties of tissues containing KS. Further adding to KS complexity is the identification of three different linkage structures in KS to asparagine (N-linked) or to threonine or serine residues (O-linked) in proteoglycan core proteins which has allowed the categorization of KS into three types, namely KS-I (corneal KS, N-linked), KS-II (skeletal KS, O-linked) or KS-III (brain KS, O-linked). KS-I to -III are also subject to variable addition of L-fucose and sialic acid groups. Furthermore, the GlcNAc residues of some members of the mucin-like glycoprotein family can also act as acceptor molecules for the addition of D-Gal and GlcNAc residues which can also be sulphated leading to small low sulphation glycoforms of KS. These differ from the more heavily sulphated KS chains found on proteoglycans. Like other GAGs, KS has evolved molecular recognition and information transfer properties over hundreds of millions of years of vertebrate and invertebrate evolution which equips them with cell mediatory properties in normal cellular processes and in aberrant pathological situations such as in tumourogenesis. Two KS-proteoglycans in particular, podocalyxin and lumican, are cell membrane, intracellular or stromal tissue–associated components with roles in the promotion or regulation of tumour development, mucin-like KS glycoproteins may also contribute to tumourogenesis. A greater understanding of the biology of KS may allow better methodology to be developed to more effectively combat tumourogenic processes

Extraction of Spatio-Temporal Data for Social Networks

Abstract. It is often possible to better understand group change over time through examining social network data in a spatial and temporal context. Providing that context from a text analysis perspective requires identifying locations and associating them with people. This paper presents our GeoRef algorithm to automatically do this person-to-place mapping. It involves the identification of location, and uses syntactic proximity of words in the text to link location to person’s name. We describe an application using the algorithm based upon a small set of data from the Sudan Tribune divided into three periods in 2006 for the Darfur crisis. Contributions of this paper are (1) techniques to mine for location from text (2) techniques to mine for social network edges (associations between location and person), (3) use of the mined data to make spatio-temporal maps, and (4) use of the mined data to perform social network analysis