Genetic adaptation to urban living: molecular DNA analyses of wild boar populations in Budapest and surrounding area

Studies of wild boar, Sus scrofa Linnaeus 1758, in urban and suburban areas of Budapest, Hungary, have indicated that these populations do not have continuous contact. Based on the assumption that the city has a discrete population, we hypothesized that the urban wild boar would differ genetically from those in suburban areas. Analysis of single-nucleotide polymorphism (SNP) data using the GeneSeek Genomic Profiler (GGP) Porcine 50 K system (Neogen, Scotland, UK) differentiated three populations: Buda (B) from the Western bank of the Danube; Buda Surrounding (BS); and Valkó (V) from the Eastern bank of the Danube. The coefficient of genetic differentiation (FST) for the B and BS populations was low. The inbreeding coefficients of the populations BS and V were close to zero, while population B had a high positive value reflecting the influence of founders and the inbreeding of the continuous urban population. The genome regions that were most differentiated between the B and BS populations were analyzed based on the FST values of the SNP markers using a mixed linear multi-locus model and BayeScan software. The most differentiated marker, WU_10.2_18_56278226, was found on chromosome 18. The surrounding region contained several candidate genes that could play important roles in adaptations related to human-induced stress. Two of these, encoding the adenylate cyclase 1 (ADCY1) and inhibin beta A chain precursor (INHBA) genes, were sequenced. While IHBA gene did not display variation, the allele distribution of the ADCY1 gene in the B population was significantly different from that of the BS population supporting the parapatric differentiation of wild boar.

Spread of new mutations through space

The terms population size and population density are often used interchangeably, when in fact they are quite different. When viewed in a spatial landscape, density is defined as the number of individuals within a square unit of distance, while population size is simply the total count of a population. In discrete population genetics models, the effective population size is known to influence the interaction between selection and random drift with selection playing a larger role in large populations while random drift has more influence in smaller populations. Using a spatially explicit simulation software we investigate how population density affects the flow of new mutations through a geographical space. Using population density, selectional advantage, and dispersal distributions, a model is developed to predict the speed at which the new allele will travel, obtaining more accurate results than current diffusion approximations provide. We note that the rate at which a neutral mutation spreads begins to decay over time while the rate of spread of an advantageous allele remains constant. We also show that new advantageous mutations spread faster in dense populations.

Formulas, Algorithms and Examples for Binomial Distributed Data Confidence Interval Calculation: Excess Risk, Relative Risk and Odds Ratio

Medical studies often involve a comparison between two outcomes, each collected from a sample. The probability associated with, and confidence in the result of the study is of most importance, since one may argue that having been wrong with a percent could be what killed a patient. Sampling is usually done from a finite and discrete population and it follows a Bernoulli trial, leading to a contingency of two binomially distributed samples (better known as 2×2 contingency table). Current guidelines recommend reporting relative measures of association (such as the relative risk and odds ratio) in conjunction with absolute measures of association (which include risk difference or excess risk). Because the distribution is discrete, the evaluation of the exact confidence interval for either of those measures of association is a mathematical challenge. Some alternate scenarios were analyzed (continuous vs. discrete; hypergeometric vs. binomial), and in the main case—bivariate binomial experiment—a strategy for providing exact p-values and confidence intervals is proposed. Algorithms implementing the strategy are given.

Acoustic Monitoring of a Bottlenose Dolphin (Tursiops truncatus) Population: Trends in Presence and Foraging beyond the Limits of the Lower River Shannon SAC

The Shannon dolphins are a population of bottlenose dolphins resident year round within the Lower River Shannon SAC, Ireland, which has been designated to protect this relatively small, genetically discrete population. Although trends in habitat use and foraging have been studied within the estuary, little is known about the movements of the Shannon dolphins outside the boundaries of the SAC, and whether any other foraging hotspots exist for this population outside of the estuary. The purpose of this study was to explore the presence and foraging behavior of these dolphins in adjacent waters located 20–30 km to the southwest of the Lower River Shannon SAC. Static acoustic monitoring was carried out with C-PODs deployed in Ballyheigue Bay, Brandon Bay, and around the Maharees between May and November 2013. A GEE-GLM modelling approach was then used to analyze potential significant environmental predictors of presence and foraging by bottlenose dolphins at these sites. Brandon Bay was found to be a site of particular importance for the Shannon population, where dolphins were present on 92% of days monitored and foraging occurring on 20% of all monitored hours. The results of this study indicate that Brandon Bay is a potentially important habitat for the Shannon dolphins and further support designation of this site as a candidate SAC. However, long-term acoustic monitoring should be conducted at all sites to identify relative use of the areas at year-round and inter-annual scales.

A Cluster-based Model of COVID-19 Transmission Dynamics

Many countries have manifested COVID-19 trajectories where extended periods of constant and low daily case rate suddenly transition to epidemic waves of considerable severity with no correspondingly drastic relaxation in preventive measures. Such solutions are outside the scope of classical epidemiological models. Here we construct a deterministic, discrete-time, discrete-population mathematical model which can explain these non-classical phenomena. Our key hypothesis is that with partial preventive measures in place, viral transmission occurs primarily within small, closed groups of family members and friends, which we call clusters. Inter-cluster transmission is infrequent compared to intra-cluster transmission but it is the key to determining the course of the epidemic. If inter-cluster transmission is low enough, we see stable plateau solutions. Above a cutoff level however, such transmission can destabilize a plateau into a huge wave even though its contribution to the population-averaged spreading rate still remains small. We call this the cryptogenic instability. We also find that stochastic effects when case counts are very low may result in a temporary and artificial suppression of an instability; we call this the critical mass effect. Both these phenomena are absent from conventional infectious disease models and militate against the successful management of the epidemic.