Detecting steps in spatial genetic data: Which diversity measures are best?

Accurately detecting sudden changes, or steps, in genetic diversity across landscapes is important for locating barriers to gene flow, identifying selectively important loci, and defining management units. However, there are many metrics that researchers could use to detect steps and little information on which might be the most robust. Our study aimed to determine the best measure/s for genetic step detection along linear gradients using biallelic single nucleotide polymorphism (SNP) data. We tested the ability to differentiate between linear and step-like gradients in genetic diversity, using a range of diversity measures derived from the q-profile, including allelic richness, Shannon Information, G_ST, and Jost-D, as well as Bray-Curtis dissimilarity. To determine the properties of each measure, we repeated simulations of different intensities of step and allele proportion ranges, with varying genome sample size, number of loci, and number of localities. We found that alpha diversity (within-locality) based measures were ineffective at detecting steps. Further, allelic richness-based beta (between-locality) measures (e.g., Jaccard and Sørensen dissimilarity) were not reliable for detecting steps, but instead detected departures from fixation. The beta diversity measures best able to detect steps were: Shannon Information based measures, G_ST based measures, a Jost-D related measure, and Bray-Curtis dissimilarity. No one measure was best overall, with a trade-off between those measures with high step detection sensitivity (G_ST and Bray-Curtis) and those that minimised false positives (a variant of Shannon Information). Therefore, when detecting steps, we recommend understanding the differences between measures and using a combination of approaches.

Information-based summary statistics for spatial genetic structure inference

The measurement of biodiversity at all levels of organisation is an essential first step to understand the ecological and evolutionary processes that drive spatial patterns of biodiversity. Ecologists have explored the use of a large range of different summary statistics and have come to the view that information-based summary statistics, and in particular so-called Hill numbers, are a useful tool to measure biodiversity. Population geneticists, on the other hand, have largely focused on summary statistics based on heterozygosity and allelic richness measures. However, recent studies proposed the adoption of information-based summary statistics in population genetics studies. Here, we performed a comprehensive assessment of the power of this family of summary statistics to inform about genetic diversity spatial patterns and we compared it with that of traditional population genetics approaches, namely measures based on allelic richness and heterozygosity. To give an unbiased evaluation, we used three machine learning methods to test the performance of different sets of summary statistics to discriminate between spatial scenarios. We defined three distinct sets, (a) one based on allelic richness measures which included the Jaccard index, (b) a set based on heterozygosity that included F_ST , and (c) a set based on Hill numbers derived from Shannon entropy, which included the recently proposed Shannon differentiation, ΔD. The results showed that the latter performed as well or, under some specific spatial scenarios, even better than the traditional population genetics measures. Interestingly, we found that a rarely or never used genetic differentiation measure based on allelic richness, Jaccard dissimilarity (J), showed the highest discriminatory power to discriminate among spatial scenarios, followed by Shannon differentiation ΔD. We concluded, therefore, that information-based measures as well as Jaccard dissimilarity, represent excellent additions to the population genetics toolkit.

Real-World Data Difficulty Estimation with the Use of Entropy

In the era of the Internet of Things and big data, we are faced with the management of a flood of information. The complexity and amount of data presented to the decision-maker are enormous, and existing methods often fail to derive nonredundant information quickly. Thus, the selection of the most satisfactory set of solutions is often a struggle. This article investigates the possibilities of using the entropy measure as an indicator of data difficulty. To do so, we focus on real-world data covering various fields related to markets (the real estate market and financial markets), sports data, fake news data, and more. The problem is twofold: First, since we deal with unprocessed, inconsistent data, it is necessary to perform additional preprocessing. Therefore, the second step of our research is using the entropy-based measure to capture the nonredundant, noncorrelated core information from the data. Research is conducted using well-known algorithms from the classification domain to investigate the quality of solutions derived based on initial preprocessing and the information indicated by the entropy measure. Eventually, the best 25% (in the sense of entropy measure) attributes are selected to perform the whole classification procedure once again, and the results are compared.

The Introduction of Entropy and Information Methods to Ecology by Ramon Margalef

In ecology and evolution, entropic methods are now used widely and increasingly frequently. Their use can be traced back to Ramon Margalef's first attempt 70 years ago to use log-series to quantify ecological diversity, including searching for ecologically meaningful groupings within a large assemblage, which we now call the gamma level. The same year, Shannon and Weaver published a generally accessible form of Shannon's work on information theory, including the measure that we now call Shannon-Wiener entropy. Margalef seized on that measure and soon proposed that ecologists should use the Shannon-Weiner index to evaluate diversity, including assessing local (alpha) diversity and differentiation between localities (beta). He also discussed relating this measure to environmental variables and ecosystem processes such as succession. Over the subsequent decades, he enthusiastically expanded upon his initial suggestions. Finally, 2019 also would have been Margalef's 100th birthday.

Gauging genetic diversity of generalists: A test of genetic and ecological generalism with RNA virus experimental evolution

Generalist viruses, those with a comparatively larger host range, are considered more likely to emerge on new hosts. The potential to emerge in new hosts has been linked to viral genetic diversity, a measure of evolvability. However, there is no consensus on whether infecting a larger number of hosts leads to higher genetic diversity, or whether diversity is better maintained in a homogeneous environment, similar to the lifestyle of a specialist virus. Using experimental evolution with the RNA bacteriophage phi6, we directly tested whether genetic generalism (carrying an expanded host range mutation) or environmental generalism (growing on heterogeneous hosts) leads to viral populations with more genetic variation. Sixteen evolved viral lineages were deep sequenced to provide genetic evidence for population diversity. When evolved on a single host, specialist and generalist genotypes both maintained the same level of diversity (measured by the number of single nucleotide polymorphisms (SNPs) above 1%, P = 0.81). However, the generalist genotype evolved on a single host had higher SNP levels than generalist lineages under two heterogeneous host passaging schemes (P = 0.001, P < 0.001). RNA viruses’ response to selection in alternating hosts reduces standing genetic diversity compared to those evolving in a single host to which the virus is already well-adapted.

Entropy, or Information, Unifies Ecology and Evolution and Beyond

This article discusses how entropy/information methods are well-suited to analyzing and forecasting the four processes of innovation, transmission, movement, and adaptation, which are the common basis to ecology and evolution. Macroecologists study assemblages of differing species, whereas micro-evolutionary biologists study variants of heritable information within species, such as DNA and epigenetic modifications. These two different modes of variation are both driven by the same four basic processes, but approaches to these processes sometimes differ considerably. For example, macroecology often documents patterns without modeling underlying processes, with some notable exceptions. On the other hand, evolutionary biologists have a long history of deriving and testing mathematical genetic forecasts, previously focusing on entropies such as heterozygosity. Macroecology calls this Gini-Simpson, and has borrowed the genetic predictions, but sometimes this measure has shortcomings. Therefore it is important to note that predictive equations have now been derived for molecular diversity based on Shannon entropy and mutual information. As a result, we can now forecast all major types of entropy/information, creating a general predictive approach for the four basic processes in ecology and evolution. Additionally, the use of these methods will allow seamless integration with other studies such as the physical environment, and may even extend to assisting with evolutionary algorithms.

Information theory, evolutionary innovations and evolvability

How difficult is it to 'discover' an evolutionary adaptation or innovation? I here suggest that information theory, in combination with high-throughput DNA sequencing, can help answer this question by quantifying a new phenotype's information content. I apply this framework to compute the phenotypic information associated with novel gene regulation and with the ability to use novel carbon sources. The framework can also help quantify how DNA duplications affect evolvability, estimate the complexity of phenotypes and clarify the meaning of 'progress' in Darwinian evolution.This article is part of the themed issue 'Process and pattern in innovations from cells to societies'.

Information Theory Broadens the Spectrum of Molecular Ecology and Evolution

Information or entropy analysis of diversity is used extensively in community ecology, and has recently been exploited for prediction and analysis in molecular ecology and evolution. Information measures belong to a spectrum (or q profile) of measures whose contrasting properties provide a rich summary of diversity, including allelic richness (q=0), Shannon information (q=1), and heterozygosity (q=2). We present the merits of information measures for describing and forecasting molecular variation within and among groups, comparing forecasts with data, and evaluating underlying processes such as dispersal. Importantly, information measures directly link causal processes and divergence outcomes, have straightforward relationship to allele frequency differences (including monotonicity that q=2 lacks), and show additivity across hierarchical layers such as ecology, behaviour, cellular processes, and nongenetic inheritance.

Poliovirus intrahost evolution is required to overcome tissue-specific innate immune responses

RNA viruses, such as poliovirus, have a great evolutionary capacity, allowing them to quickly adapt and overcome challenges encountered during infection. Here we show that poliovirus infection in immune-competent mice requires adaptation to tissue-specific innate immune microenvironments. The ability of the virus to establish robust infection and virulence correlates with its evolutionary capacity. We further identify a region in the multi-functional poliovirus protein 2B as a hotspot for the accumulation of minor alleles that facilitate a more effective suppression of the interferon response. We propose that population genetic dynamics enables poliovirus spread between tissues through optimization of the genetic composition of low frequency variants, which together cooperate to circumvent tissue-specific challenges. Thus, intrahost virus evolution determines pathogenesis, allowing a dynamic regulation of viral functions required to overcome barriers to infection.

RNA viruses, such as polioviruses, have a great evolutionary capacity and can adapt quickly during infection. Here, the authors show that poliovirus infection in mice requires adaptation to innate immune microenvironments encountered in different tissues.

What we still don't know about invasion genetics

Publication of The Genetics of Colonizing Species in 1965 launched the field of invasion genetics and highlighted the value of biological invasions as natural ecological and evolutionary experiments. Here, we review the past 50 years of invasion genetics to assess what we have learned and what we still don't know, focusing on the genetic changes associated with invasive lineages and the evolutionary processes driving these changes. We also suggest potential studies to address still-unanswered questions. We now know, for example, that rapid adaptation of invaders is common and generally not limited by genetic variation. On the other hand, and contrary to prevailing opinion 50 years ago, the balance of evidence indicates that population bottlenecks and genetic drift typically have negative effects on invasion success, despite their potential to increase additive genetic variation and the frequency of peak shifts. Numerous unknowns remain, such as the sources of genetic variation, the role of so-called expansion load and the relative importance of propagule pressure vs. genetic diversity for successful establishment. While many such unknowns can be resolved by genomic studies, other questions may require manipulative experiments in model organisms. Such studies complement classical reciprocal transplant and field-based selection experiments, which are needed to link trait variation with components of fitness and population growth rates. We conclude by discussing the potential for studies of invasion genetics to reveal the limits to evolution and to stimulate the development of practical strategies to either minimize or maximize evolutionary responses to environmental change.