scholarly journals Population demography maintains biogeographic boundaries

2022 ◽  
Author(s):  
Chloé Schmidt ◽  
Gabriel Muñoz ◽  
Lesley T Lancaster ◽  
Jean-Philippe Lessard ◽  
Katharine A Marske ◽  
...  
Keyword(s):  
Adaptive Capacity ◽  
Environmental Gradients ◽  
Mammalian Species ◽  
Population Level ◽  
Data Sets ◽  
Population Demography ◽  
Effective Population ◽  
Transition Zones ◽  
Biogeographic Regions ◽  
Global Biodiversity

Global biodiversity is organized into biogeographic regions that comprise distinct biotas. The contemporary factors maintaining differences in species composition between biogeographic regions are poorly understood. Given the evidence that populations with sufficient genetic variation can adapt to fill new habitats, it is surprising that we do not see more homogenization of species assemblages among regions. Theory suggests that the expansion of populations across biogeographic transition zones could be limited by environmental gradients that affect population demography in ways that could limit adaptive capacity, but this has not been empirically explored. Using three independently curated data sets describing continental patterns of mammalian demography and population genetics, we show that populations closer to biogeographic transition zones have lower effective population sizes and genetic diversity, and are more genetically differentiated. These patterns are consistent with reduced adaptive capacity near biogeographic transition zones. The consistency of these patterns across mammalian species suggests they are stable, predictable, and generalizable in their contribution to long-term limits on expansion and homogenization of biodiversity across biogeographic transition zones. Understanding the contemporary processes acting on populations that maintain differences in the composition of regional biotas is crucial for our basic understanding of the current and future organization of global biodiversity. The importance of contemporary, population-level processes on the maintenance of global biogeographic regions suggests that biogeographic boundaries are susceptible to environmental perturbation associated with human-caused global change.

scholarly journals AlleleShift: an R package to predict and visualize population-level changes in allele frequencies in response to climate change

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11534
Author(s):  
Roeland Kindt
Keyword(s):  
Climate Change ◽  
Environmental Gradients ◽  
Additive Model ◽  
Population Level ◽  
R Package ◽  
Allele Frequencies ◽  
Data Sets ◽  
Climate Data ◽  
Link Type ◽  
And Migration

Background At any particular location, frequencies of alleles that are associated with adaptive traits are expected to change in future climates through local adaption and migration, including assisted migration (human-implemented when climate change is more rapid than natural migration rates). Making the assumption that the baseline frequencies of alleles across environmental gradients can act as a predictor of patterns in changed climates (typically future but possibly paleo-climates), a methodology is provided by AlleleShift of predicting changes in allele frequencies at the population level. Methods The prediction procedure involves a first calibration and prediction step through redundancy analysis (RDA), and a second calibration and prediction step through a generalized additive model (GAM) with a binomial family. As such, the procedure is fundamentally different to an alternative approach recently proposed to predict changes in allele frequencies from canonical correspondence analysis (CCA). The RDA step is based on the Euclidean distance that is also the typical distance used in Analysis of Molecular Variance (AMOVA). Because the RDA step or CCA approach sometimes predict negative allele frequencies, the GAM step ensures that allele frequencies are in the range of 0 to 1. Results AlleleShift provides data sets with predicted frequencies and several visualization methods to depict the predicted shifts in allele frequencies from baseline to changed climates. These visualizations include ‘dot plot’ graphics (function shift.dot.ggplot), pie diagrams (shift.pie.ggplot), moon diagrams (shift.moon.ggplot), ‘waffle’ diagrams (shift.waffle.ggplot) and smoothed surface diagrams of allele frequencies of baseline or future patterns in geographical space (shift.surf.ggplot). As these visualizations were generated through the ggplot2 package, methods of generating animations for a climate change time series are straightforward, as shown in the documentation of AlleleShift and in the supplemental videos. Availability AlleleShift is available as an open-source R package from https://cran.r-project.org/package=AlleleShift and https://github.com/RoelandKindt/AlleleShift. Genetic input data is expected to be in the adegenet::genpop format, which can be generated from the adegenet::genind format. Climate data is available from various resources such as WorldClim and Envirem.

scholarly journals AlleleShift: An R package to predict and visualize population-level changes in allele frequencies in response to climate change

2021 ◽  
Author(s):  
Roeland Kindt
Keyword(s):  
Climate Change ◽  
Environmental Gradients ◽  
Additive Model ◽  
Population Level ◽  
R Package ◽  
Allele Frequencies ◽  
Data Sets ◽  
Climate Data ◽  
Constrained Ordination ◽  
And Migration

AbstractBackgroundAt any particular location, frequencies of alleles in organisms that are associated with adaptive traits are expected to change in future climates through local adaption and migration, including assisted migration (human-implemented when climate change is more rapid than natural migration rates). Making the assumption that the baseline frequencies of alleles across environmental gradients can act as a predictor of patterns in changed climates (typically future but possibly paleo-climates), a methodology is provided by AlleleShift of predicting changes in allele frequencies at populations’ locations.MethodsThe prediction procedure involves a first calibration and prediction step through redundancy analysis (RDA), and a second calibration and prediction step through a generalized additive model (GAM) with a binomial family. As such, the procedure is fundamentally different to an alternative approach recently proposed to predict changes in allele frequencies from canonical correspondence analysis (CCA). My methodology of AlleleShift is also different in modelling and predicting allele counts through constrained ordination (not frequencies as in the CCA approach) and modelling both alleles for a locus (not solely the minor allele as in the CCA method; both methods were developed for diploid organisms where individuals are homozygous (AA or BB) or heterozygous (AB)). Whereas the GAM step ensures that allele frequencies are in the range of 0 to 1 (negative values are sometimes predicted by the RDA and CCA approaches), the RDA step is based on the Euclidean distance that is also the typical distance used in Analysis of Molecular Variance (AMOVA). The AlleleShift::amova.rda enables users to verify that the same ‘mean-square’ values are calculated by AMOVA and RDA, and gives the same final statistics with balanced data.ResultsBesides data sets with predicted frequencies, AlleleShift provides several visualization methods to depict the predicted shifts in allele frequencies from baseline to changed climates. These include ‘dot plot’ graphics (function shift.dot.ggplot), pie diagrams (shift.pie.ggplot), moon diagrams (shift.moon.ggplot), ‘waffle’ diagrams (shift.waffle.ggplot) and smoothed surface diagrams of allele frequencies of baseline or future patterns in geographical space (shift.surf.ggplot). As these were generated through the ggplot2 package, methods of generating animations for a climate change time series are straightforward, as shown in the documentation of AlleleShift and in the supplementary materials. In addition, graphical methods are provided of showing shifts of populations in environmental space (population.shift) and to assess how well the predicted frequencies reflect the original frequencies for the baseline climate (freq.ggplot).AvailabilityAlleleShift is available as an open-source R package from https://github.com/RoelandKindt/AlleleShift. Genetic input data is expected to be in the adegenet::genpop format, which can be generated from the adegenet::genind format. Climate data is available from various resources such as WorldClim and Envirem.

scholarly journals Determinants of SARS-CoV-2 transmission to guide vaccination strategy in a city

2020 ◽  
Author(s):  
Sarah C. Brüningk ◽  
Juliane Klatt ◽  
Madlen Stange ◽  
Alfredo Mari ◽  
Myrta Brunner ◽  
...  
Keyword(s):  
Vaccination Strategy ◽  
Population Level ◽  
Data Sets ◽  
Epidemic Dynamics ◽  
Modelling Analysis ◽  
City Population ◽  
Senior Population ◽  
Spatio Temporal ◽  
Socioeconomic Data ◽  
The City

Transmission chains within cities provide an important contribution to case burden and economic impact during the ongoing COVID-19 pandemic, and should be a major focus for preventive measures to achieve containment. Here, at very high spatio-temporal resolution, we analysed determinants of SARS-CoV-2 transmission in a medium-sized European city. We combined detailed epidemiological, mobility, and socioeconomic data-sets with whole genome sequencing during the first SARS-CoV-2 wave. Both phylogenetic clustering and compartmental modelling analysis were performed based on the dominating viral variant (B.1-C15324T; 60% of all cases). Here we show that transmissions on the city population level are driven by the socioeconomically weaker and highly mobile groups. Simulated vaccination scenarios showed that vaccination of a third of the population at 90% efficacy prioritising the latter groups would induce a stronger preventive effect compared to vaccinating exclusively senior population groups first. Our analysis accounts for both social interaction and mobility on the basis of molecularly related cases, thereby providing high confidence estimates of the underlying epidemic dynamics that may readily be translatable to other municipal areas.

scholarly journals dDocent: a RADseq, variant-calling pipeline designed for population genomics of non-model organisms

2014 ◽  
Author(s):  
Jonathan Puritz ◽  
Christopher M. Hollenbeck ◽  
John R. Gold
Keyword(s):  
Population Genomics ◽  
De Novo ◽  
Variant Calling ◽  
Population Level ◽  
Model Organisms ◽  
Effective Population ◽  
Reduction Techniques ◽  
Indel Polymorphisms ◽  
Indel Calling ◽  
Population Sizes

Restriction-site associated DNA sequencing (RADseq) has become a powerful and useful approach for population genomics. Currently, no software exists that utilizes both paired-end reads from RADseq data to efficiently produce population-informative variant calls, especially for organisms with large effective population sizes and high levels of genetic polymorphism but for which no genomic resources exist. dDocent is an analysis pipeline with a user-friendly, command-line interface designed to process individually barcoded RADseq data (with double cut sites) into informative SNPs/Indels for population-level analyses. The pipeline, written in BASH, uses data reduction techniques and other stand-alone software packages to perform quality trimming and adapter removal, de novo assembly of RAD loci, read mapping, SNP and Indel calling, and baseline data filtering. Double-digest RAD data from population pairings of three different marine fishes were used to compare dDocent with Stacks, the first generally available, widely used pipeline for analysis of RADseq data. dDocent consistently identified more SNPs shared across greater numbers of individuals and with higher levels of coverage. This is most likely due to the fact that dDocent quality trims instead of filtering and incorporates both forward and reverse reads in assembly, mapping, and SNP calling, thus enabling use of reads with Indel polymorphisms. The pipeline and a comprehensive user guide can be found at (http://dDocent.wordpress.com).

scholarly journals Parasite induced mortality is context dependent in Atlantic salmon: insights from an individual-based model

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Knut Wiik Vollset
Keyword(s):  
Atlantic Salmon ◽  
Environmental Gradients ◽  
Population Level ◽  
Relative Effect ◽  
Host Population ◽  
Sea Lice ◽  
Fish Farms ◽  
Individual Based Model ◽  
Parasite Infestation ◽  
The Impact

AbstractAn individual-based model was parameterized to explore the impact of a crustacean ectoparasite (sea louse, Lepeophtheirus salmonis & Caligus spp.) on migrating Atlantic salmon smolt. The model explores how environmental and intrinsic factors can modulate the effect of sea lice on survival, growth and maturation of Atlantic salmon at sea. Relative to other effects, the parasite infestation pressure from fish farms and the encounter process emerge as the most important parameters. Although small variations in parasite-induced mortality may be masked by variable environmental effects, episodes of high infestation pressure from fish farms should be observable in wild populations of Atlantic salmon if laboratory studies accurately reflect the physiological effects of sea lice. Increases in temperature in the model negatively influenced fish survival by affecting the development time of the parasite at a rate that was not compensated for by the growth of the host. Discharge from rivers was parameterized to increase migration speed and influenced parasite induced mortality by decreasing time spent in areas with increased infestation pressure. Initial size and growth of the host was inversely related to the impact of the parasite because of size-dependent parasite-induced mortality in the early phase of migration. Overall, the model illustrates how environmental factors modulate effects on the host population by impacting either the parasite load or the relative effect of the parasite. The results suggest that linking population-level effects to parasite infestation pressure across climatic and environmental gradients may be challenging without correctly accounting for these effects.

Predicting Flowering of Rhizome Johnsongrass (Sorghum halepense) Populations Using a Temperature-dependent Model

Weed Science ◽  
1996 ◽  
Vol 44 (2) ◽  
pp. 266-272 ◽  
Author(s):  
David L. Holshouser ◽  
James M. Chandler
Keyword(s):  
Rate Equation ◽  
Model Performance ◽  
Population Level ◽  
Weibull Function ◽  
Data Sets ◽  
Temperature Dependent ◽  
Independent Field ◽  
Dependent Model ◽  
Improved Model ◽  
Temperature Dependent Model

Research was conducted to formulate a temperature-dependent population-level model for rhizome johnsongrass flowering. A nonlinear poikilotherm rate equation was used to describe development as a function of temperature and a temperature-independent Weibull function was used to distribute development times for the population. Johnsongrass flowering data were collected under constant temperature conditions to parameterize the poikilotherm rate equation and Weibull function. Coupling the poikilotherm rate equation with the Weibull function resulted in a population level temperature-dependent model. The model was validated against independent field data sets. The model accurately predicted rhizome johnsongrass flowering from plants emerging in the spring. The model performed poorly for plants emerging in summer. Adjustments to the high-temperature inhibition parameter of the poikilotherm rate equation improved model performance in the summer without affecting spring predictions.

A Population Level Temperature-Dependent Model of Seedling Johnsongrass (Sorghum halepense) Flowering

Weed Science ◽  
1989 ◽  
Vol 37 (3) ◽  
pp. 471-477 ◽  
Author(s):  
David C. Bridges ◽  
James M. Chandler
Keyword(s):  
Water Availability ◽  
Nitrogen Availability ◽  
Nitrogen Concentration ◽  
Population Level ◽  
Effect Of Temperature ◽  
Weibull Function ◽  
Data Sets ◽  
Data Set ◽  
Independent Field ◽  
Temperature Dependent Model

A population level, two-compartment, temperaturedependent model that predicts date of seedling johnsongrass flowering was formulated. The model consisted of a fourparameter poikilotherm rate equation to describe development rate as a function of temperature and a temperature-independent Weibull function to distribute flowering times for the population. Experiments were conducted to determine the effect of temperature, nitrogen availability, and water availability on development of seedling johnsongrass. Development was most sensitive to temperature while the effect of nitrogen concentration and water availability was minimum and inconsistent. The model was tested against three independent field data sets and provided accurate prediction of flowering dates for each data set.

scholarly journals Predicting how populations decline to extinction

2011 ◽  
Vol 366 (1577) ◽  
pp. 2577-2586 ◽  
Author(s):  
Ben Collen ◽  
Louise McRae ◽  
Stefanie Deinet ◽  
Adriana De Palma ◽  
Tharsila Carranza ◽  
...  
Keyword(s):  
At Risk ◽  
Extinction Risk ◽  
Mammalian Species ◽  
Local Population ◽  
Population Decline ◽  
Scale Up ◽  
Population Level ◽  
Biological Traits ◽  
Population Declines ◽  
Trend Data

Global species extinction typically represents the endpoint in a long sequence of population declines and local extinctions. In comparative studies of extinction risk of contemporary mammalian species, there appear to be some universal traits that may predispose taxa to an elevated risk of extinction. In local population-level studies, there are limited insights into the process of population decline and extinction. Moreover, there is still little appreciation of how local processes scale up to global patterns. Advancing the understanding of factors which predispose populations to rapid declines will benefit proactive conservation and may allow us to target at-risk populations as well as at-risk species. Here, we take mammalian population trend data from the largest repository of population abundance trends, and combine it with the PanTHERIA database on mammal traits to answer the question: what factors can be used to predict decline in mammalian abundance? We find in general that environmental variables are better determinants of cross-species population-level decline than intrinsic biological traits. For effective conservation, we must not only describe which species are at risk and why, but also prescribe ways to counteract this.

Preservation of spatial and environmental gradients by death assemblages

Paleobiology ◽  
2009 ◽  
Vol 35 (1) ◽  
pp. 119-145 ◽  
Author(s):  
Adam Tomašových ◽  
Susan M. Kidwell
Keyword(s):  
Community Structure ◽  
Community Composition ◽  
Environmental Gradients ◽  
Compositional Variation ◽  
Time Averaging ◽  
Data Sets ◽  
Square Root ◽  
Spatial Gradients ◽  
Death Assemblages ◽  
The Impact

Although only a few studies have explicitly evaluated live-dead agreement of species and community responses to environmental and spatial gradients, paleoecological analyses implicitly assume that death assemblages capture these gradients accurately. We use nine data sets from modern, relatively undisturbed coastal study areas to evaluate how the response of living molluscan assemblages to environmental gradients (water depth and seafloor type; “environmental component” of a gradient) and geographic separation (“spatial component”) is captured by their death assemblages. We find that:1. Living assemblages vary in composition either in response to environmental gradients alone (consistent with a species-sorting model) or in response to a combination of environmental and spatial gradients (mass-effect model). None of the living assemblages support the neutral model (or the patch-dynamic model), in which variation in species abundance is related to the spatial configuration of stations alone. These findings also support assumptions that mollusk species consistently differ in responses to environmental gradients, and suggest that in the absence of postmortem bias, environmental gradients might be accurately captured by variation in species composition among death assemblages. Death assemblages do in fact respond uniquely to environmental gradients, and show a stronger response when abundances are square-root transformed to downplay the impact of numerically abundant species and increase the effect of rare species.2. Species' niche positions (position of maximum abundance) along bathymetric and sedimentary gradients in death assemblages show significantly positive rank correlations to species positions in living assemblages in seven of nine data sets (both square-root-transformed and presence-absence data).3. The proportion of compositional variation explained by environmental gradients in death assemblages is similar to that of counterpart living assemblages. Death assemblages thus show the same ability to capture environmental gradients as do living assemblages. In some instances compositional dissimilarities in death assemblages show higher rank correlation with spatial distances than with environmental gradients, but spatial structure in community composition is mainly driven by spatially structured environmental gradients.4. Death assemblages correctly identify the dominance of niche metacommunity models in mollusk communities, as revealed by counterpart living assemblages. This analysis of the environmental resolution of death assemblages thus supports fine-scale niche and paleoenvironmental analyses using molluscan fossil records. In spite of taphonomic processes and time-averaging effects that modify community composition, death assemblages largely capture the response of living communities to environmental gradients, partly because of redundancy in community structure that is inherently associated with multispecies assemblages. The molluscan data sets show some degree of redundancy as evidenced by the presence of at least two mutually exclusive subsets of species that replicate the community structure, and simple simulations show that between-sample relationships can be preserved and remain significant even when a large proportion of species is randomly removed from data sets.

scholarly journals Population-level deep sequencing reveals the interplay of clonal and sexual reproduction in the fungal wheat pathogen Zymoseptoria tritici

2021 ◽  
Vol 7 (10) ◽  
Author(s):  
Nikhil Kumar Singh ◽  
Petteri Karisto ◽  
Daniel Croll
Keyword(s):  
Fungal Pathogens ◽  
Population Level ◽  
Growing Season ◽  
Stable Gene ◽  
Gene Pools ◽  
Effective Population ◽  
Zymoseptoria Tritici ◽  
Wheat Field ◽  
Global Food Security ◽  
Significant Difference

Pathogens cause significant challenges to global food security. On annual crops, pathogens must re-infect from environmental sources in every growing season. Fungal pathogens have evolved mixed reproductive strategies to cope with the distinct challenges of colonizing growing plants. However, how pathogen diversity evolves during growing seasons remains largely unknown. Here, we performed a deep hierarchical sampling in a single experimental wheat field infected by the major fungal pathogen Zymoseptoria tritici. We analysed whole genome sequences of 177 isolates collected from 12 distinct cultivars replicated in space at three time points of the growing season to maximize capture of genetic diversity. The field population was highly diverse with 37 SNPs per kilobase, a linkage disequilibrium decay within 200–700 bp and a high effective population size. Using experimental infections, we tested a subset of the collected isolates on the dominant cultivar planted in the field. However, we found no significant difference in virulence of isolates collected from the same cultivar compared to isolates collected on other cultivars. About 20 % of the isolate genotypes were grouped into 15 clonal groups. Pairs of clones were disproportionally found at short distances (<5 m), consistent with experimental estimates for per-generation dispersal distances performed in the same field. This confirms predominant leaf-to-leaf transmission during the growing season. Surprisingly, levels of clonality did not increase over time in the field although reproduction is thought to be exclusively asexual during the growing season. Our study shows that the pathogen establishes vast and stable gene pools in single fields. Monitoring short-term evolutionary changes in crop pathogens will inform more durable strategies to contain diseases.

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