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Combining Bayesian genetic clustering and ecological niche modeling: Insights into wolf intraspecific genetic structure.

ABSTRACT: The distribution of intraspecific genetic variation and how it relates to environmental factors is of increasing interest to researchers in macroecology and biogeography. Recent studies investigated the relationships between the environment and patterns of intraspecific genetic variation across species ranges but only few rigorously tested the relation between genetic groups and their ecological niches. We quantified the relationship of genetic differentiation (F _ST) and the overlap of ecological niches (as measured by n-dimensional hypervolumes) among genetic groups resulting from spatial Bayesian genetic clustering in the wolf (Canis lupus) in the Italian peninsula. Within the Italian wolf population, four genetic clusters were detected, and these clusters showed different ecological niches. Moreover, different wolf clusters were significantly related to differences in land cover and human disturbance features. Such differences in the ecological niches of genetic clusters should be interpreted in light of neutral processes that hinder movement, dispersal, and gene flow among the genetic clusters, in order to not prematurely assume any selective or adaptive processes. In the present study, we found that both the plasticity of wolves-a habitat generalist-to cope with different environmental conditions and the occurrence of barriers that limit gene flow lead to the formation of genetic intraspecific genetic clusters and their distinct ecological niches.

SUBMITTER: Milanesi P

PROVIDER: S-EPMC6262746 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Project description:Background and aimsNiche divergence between polyploids and their lower ploidy progenitors is one of the primary mechanisms fostering polyploid establishment and adaptive divergence. However, within-species chromosomal and reproductive variability have usually been neglected in community ecology and biodiversity analyses even though they have been recognized to play a role in the adaptive diversification of lineages.MethodsWe used Paspalum intermedium, a grass species with diverging genetic systems (diploidy vs. autopolyploidy, allogamy vs. autogamy and sexuality vs. apomixis), to recognize the causality of biogeographic patterns, adaptation and ecological flexibility of cytotypes. Chromosome counts and flow cytometry were used to characterize within-species genetic systems diversity. Environmental niche modelling was used to evaluate intraspecific ecological attributes associated with environmental and climatic factors and to assess correlations among ploidy, reproductive modes and ecological conditions ruling species' population dynamics, range expansion, adaptation and evolutionary history.Key resultsTwo dominant cytotypes non-randomly distributed along local and regional geographical scales displayed niche differentiation, a directional shift in niche optima and signs of disruptive selection on ploidy-related ecological aptitudes for the exploitation of environmental resources. Ecologically specialized allogamous sexual diploids were found in northern areas associated with higher temperature, humidity and productivity, while generalist autogamous apomictic tetraploids occurred in southern areas, occupying colder and less productive environments. Four localities with a documented shift in ploidy and four mixed populations in a zone of ecological transition revealed an uneven replacement between cytotypes.ConclusionsPolyploidy and contrasting reproductive traits between cytotypes have promoted shifts in niche optima, and increased ecological tolerance and niche divergence. Ecologically specialized diploids maintain cytotype stability in core areas by displacing tetraploids, while broader ecological preferences and a shift from sexuality to apomixis favoured polyploid colonization in peripheral areas where diploids are displaced, and fostered the ecological opportunity for autotetraploids supporting range expansion to open southern habitats.

Project description:BACKGROUND:Emerging and understudied pathogens often lack information that most commonly used analytical tools require, such as negative controls or baseline data; thus, new analytical strategies are needed to analyze transmission patterns and drivers of disease emergence. Zoonotic infections with Vaccinia virus (VACV) were first reported in Brazil in 1999, VACV is an emerging zoonotic Orthopoxvirus, which primarily infects dairy cattle and farmers in close contact with infected cows. Prospective studies of emerging pathogens could provide critical data that would inform public health planning and response to outbreaks. By using the location of 87-recorded outbreaks and publicly available bioclimatic data, we demonstrate one such approach. Using an ecological niche model (ENM) algorithm, we identify the environmental conditions under which VACV outbreaks have occurred, and determine additional locations in two affected countries that may be susceptible to transmission. Further, we show how suitability for the virus responds to different levels of various environmental factors and highlight the most important factors in determining its transmission. METHODS:A literature review was performed and the geospatial coordinates of 87 molecularly confirmed VACV outbreaks in Brazil were identified. An ENM was generated using MaxENT software by combining principal component analysis results of 19 bioclim spatial layers, and 25 randomly selected subsets of the original list of 87 outbreaks. RESULTS:The final ENM predicted all areas where Brazilian outbreaks occurred, one out of five of the Colombian outbreak regions and identified new regions within Brazil that are suitable for transmission based on bioclimatic factors. Further, the most important factors in determining transmission suitability are precipitation of the wettest quarter, annual precipitation, mean temperature of the coldest quarter and mean diurnal range. CONCLUSION:The analyses here provide a means by which to study patterns of an emerging infectious disease and identify regions that are potentially suitable for its transmission, in spite of the paucity of high-quality critical data. Policy and methods for the control of infectious diseases often use a reactionary model, addressing diseases only after significant impact on human health has ensued. The methodology used in the present work allows the identification of areas where disease is likely to appear, which could be used for directed intervention.

Project description:A recent advancement in modeling was the development of quantum Bayesian networks (QBNs). QBNs generally differ from BNs by substituting traditional Bayes calculus in probability tables with the quantum amplification wave functions. QBNs can solve a variety of problems which are unsolvable by, or are too complex for, traditional BNs. These include problems with feedback loops and temporal expansions; problems with non-commutative dependencies in which the order of the specification of priors affects the posterior outcomes; problems with intransitive dependencies constituting the circular dominance of the outcomes; problems in which the input variables can affect each other, even if they are not causally linked (entanglement); problems in which there may be >1 dominant probability outcome dependent on small variations in inputs (superpositioning); and problems in which the outcomes are nonintuitive and defy traditional probability calculus (Parrondo's paradox and the violation of the Sure Thing Principle). I present simple examples of these situations illustrating problems in prediction and diagnosis, and I demonstrate how BN solutions are infeasible, or at best require overly-complex latent variable structures. I then argue that many problems in ecology and evolution can be better depicted with ecological QBN (EcoQBN) modeling. The situations that fit these kinds of problems include noncommutative and intransitive ecosystems responding to suites of disturbance regimes with no specific or single climax condition, or that respond differently depending on the specific sequence of the disturbances (priors). Case examples are presented on the evaluation of habitat conditions for a bat species, representing state-transition models of a boreal forest under disturbance, and the entrainment of auditory signals among organisms. I argue that many current ecological analysis structures-such as state-and-transition models, predator-prey dynamics, the evolution of symbiotic relationships, ecological disturbance models, and much more-could greatly benefit from a QBN approach. I conclude by presenting EcoQBNs as a nascent field needing the further development of the quantum mathematical structures and, eventually, adjuncts to existing BN modeling shells or entirely new software programs to facilitate model development and application.

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Combining Bayesian genetic clustering and ecological niche modeling: Insights into wolf intraspecific genetic structure.

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