scholarly journals Topology and habitat assortativity drive neutral and adaptive diversification in spatial graphs

2021 ◽  
Author(s):  
Victor Boussange ◽  
Loic Pellissier
Keyword(s):  
Habitat Heterogeneity ◽  
Landscape Connectivity ◽  
Spatial Dynamics ◽  
Evolutionary Model ◽  
Population Level ◽  
Landscape Features ◽  
Adaptive Diversification ◽  
Modelling Framework ◽  
Spatial Graphs ◽  
Connectivity Patterns

Biodiversity results from differentiation mechanisms developing within biological populations. Such mechanisms are influenced by the properties of the landscape over which individuals interact, disperse and evolve. Notably, landscape connectivity and habitat heterogeneity constrain the movement and survival of individuals, thereby promoting differentiation through drift and local adaptation. Nevertheless, the complexity of landscape features can blur our understanding of how they drive differentiation. Here, we formulate a stochastic, eco-evolutionary model where individuals are structured over a graph that captures complex connectivity patterns and accounts for habitat heterogeneity. Individuals possess neutral and adaptive traits, whose divergence results in differentiation at the population level. The modelling framework enables an analytical underpinning of emerging macroscopic properties, which we complement with numerical simulations to investigate how the graph topology and the spatial habitat distribution affect differentiation. We show that in the absence of selection, graphs with high characteristic length and high heterogeneity in degree promote neutral differentiation. Habitat assortativity, a metric that captures habitat spatial auto-correlation in graphs, additionally drives differentiation patterns under habitat-dependent selection. While assortativity systematically amplifies adaptive differentiation, it can foster or depress neutral differentiation depending on the migration regime. By formalising the eco-evolutionary and spatial dynamics of biological populations in complex landscapes, our study establishes the link between landscape features and the emergence of diversification, contributing to a fundamental understanding of the origin of biodiversity gradients.

scholarly journals The role of habitat configuration in shaping animal population processes: a framework to generate quantitative predictions

Oecologia ◽  
2021 ◽  
Author(s):  
Peng He ◽  
Pierre-Olivier Montiglio ◽  
Marius Somveille ◽  
Mauricio Cantor ◽  
Damien R. Farine
Keyword(s):  
Population Level ◽  
R Package ◽  
Habitat Configuration ◽  
Modelling Framework ◽  
Alternative Habitat ◽  
Animal Populations ◽  
Research Questions ◽  
Habitat Networks ◽  
Animal Habitat

AbstractBy shaping where individuals move, habitat configuration can fundamentally structure animal populations. Yet, we currently lack a framework for generating quantitative predictions about the role of habitat configuration in modulating population outcomes. To address this gap, we propose a modelling framework inspired by studies using networks to characterize habitat connectivity. We first define animal habitat networks, explain how they can integrate information about the different configurational features of animal habitats, and highlight the need for a bottom–up generative model that can depict realistic variations in habitat potential connectivity. Second, we describe a model for simulating animal habitat networks (available in the R package AnimalHabitatNetwork), and demonstrate its ability to generate alternative habitat configurations based on empirical data, which forms the basis for exploring the consequences of alternative habitat structures. Finally, we lay out three key research questions and demonstrate how our framework can address them. By simulating the spread of a pathogen within a population, we show how transmission properties can be impacted by both local potential connectivity and landscape-level characteristics of habitats. Our study highlights the importance of considering the underlying habitat configuration in studies linking social structure with population-level outcomes.

scholarly journals Folding Stabilizes the Evolution of Catalysts

2004 ◽  
Vol 10 (1) ◽  
pp. 23-38 ◽  
Author(s):  
Stephan Altmeyer ◽  
Rudolf M. Füchslin ◽  
John S. McCaskill
Keyword(s):  
Dimensional Space ◽  
Spatial Dynamics ◽  
Heterogeneous Systems ◽  
Evolutionary Model ◽  
Complete Sequence ◽  
Infinite Dimensional ◽  
Spatially Resolved ◽  
Catalytic Function ◽  
Sequence Recognition ◽  
Intermolecular Recognition

Sequence folding is known to determine the spatial structure and catalytic function of proteins and nucleic acids. We show here that folding also plays a key role in enhancing the evolutionary stability of the intermolecular recognition necessary for the prevalent mode of catalytic action in replication, namely, in trans, one molecule catalyzing the replication of another copy, rather than itself. This points to a novel aspect of why molecular life is structured as it is, in the context of life as it could be: folding allows limited, structurally localized recognition to be strongly sensitive to global sequence changes, facilitating the evolution of cooperative interactions. RNA secondary structure folding, for example is shown to be able to stabilize the evolution of prolonged functional sequences, using only a part of this length extension for intermolecular recognition, beyond the limits of the (cooperative) error threshold. Such folding could facilitate the evolution of polymerases in spatially heterogeneous systems. This facilitation is, in fact, vital because physical limitations prevent complete sequence-dependent discrimination for any significant-size biopolymer substrate. The influence of partial sequence recognition between biopolymer catalysts and complex substrates is investigated within a stochastic, spatially resolved evolutionary model of trans catalysis. We use an analytically tractable nonlinear master equation formulation called PRESS (McCaskill et al., Biol. Chem. 382: 1343–1363), which makes use of an extrapolation of the spatial dynamics down from infinite dimensional space, and compare the results with Monte Carlo simulations.

scholarly journals Origins of cell-to-cell variability, kinetic proof-reading and the robustness of MAPK signal transduction

2015 ◽  
Author(s):  
Sarah Filippi ◽  
Chris Barnes ◽  
Paul Kirk ◽  
Takamasa Kudo ◽  
Siobhan McMahon ◽  
...  
Keyword(s):  
Image Cytometry ◽  
Population Level ◽  
Statistical Modelling ◽  
Population Heterogeneity ◽  
Dominant Factor ◽  
Cellular Signalling ◽  
Modelling Framework ◽  
Erk Phosphorylation ◽  
Proof Reading ◽  
Cell To Cell Variability

Cellular signalling processes can exhibit pronounced cell-to-cell variability in genetically identical cells. This affects how individual cells respond differentially to the same environmental stimulus. However, the origins of cell-to-cell variability in cellular signalling systems remain poorly understood. Here we measure the temporal evolution of phosphorylated MEK and ERK dynamics across populations of cells and quantify the levels of population heterogeneity over time using high-throughput image cytometry. We use a statistical modelling framework to show that upstream noise is the dominant factor causing cell-to-cell variability in ERK phosphorylation, rather than stochasticity in the phosphorylation/dephosphorylation of ERK. In particular, the cell-to-cell variability during sustained phosphorylation stems from random fluctuations in the background upstream signalling processes, while during transient phosphorylation, the heterogeneity is primarily due to noise in the intensity of the upstream signal(s). We show that the core MEK/ERK system uses kinetic proof-reading to faithfully and robustly transmits these variable inputs. The MAPK cascade thus propagates cell-to-cell variability at the population level, rather than attenuating or increasing it.

scholarly journals A whole-genome scan for association with invasion success in the fruit fly Drosophila suzukii using contrasts of allele frequencies corrected for population structure

2019 ◽  
Author(s):  
Laure Olazcuaga ◽  
Anne Loiseau ◽  
Hugues Parrinello ◽  
Mathilde Paris ◽  
Antoine Fraimout ◽  
...  
Keyword(s):  
Population Structure ◽  
Genome Wide Association Study ◽  
Evolutionary Model ◽  
Population Level ◽  
Allele Frequencies ◽  
Invasion Success ◽  
Nucleotide Polymorphisms ◽  
Drosophila Suzukii ◽  
A Genome ◽  
Invasive Status

AbstractEvidence is accumulating that evolutionary changes are not only common during biological invasions but may also contribute directly to invasion success. The genomic basis of such changes is still largely unexplored. Yet, understanding the genomic response to invasion may help to predict the conditions under which invasiveness can be enhanced or suppressed. Here we characterized the genome response of the spotted wing drosophila Drosophila suzukii during the worldwide invasion of this pest insect species, by conducting a genome-wide association study to identify genes involved in adaptive processes during invasion. Genomic data from 22 population samples were analyzed to detect genetic variants associated with the status (invasive versus native) of the sampled populations based on a newly developed statistic, we called C2, that contrasts allele frequencies corrected for population structure. This new statistical framework has been implemented in an upgraded version of the program BayPass. We identified a relatively small set of single nucleotide polymorphisms (SNPs) that show a highly significant association with the invasive status of populations. In particular, two genes RhoGEF64C and cpo, the latter contributing to natural variation in several life-history traits (including diapause) in Drosophila melanogaster, contained SNPs significantly associated with the invasive status in the two separate main invasion routes of D. suzukii. Our methodological approaches can be applied to any other invasive species, and more generally to any evolutionary model for species characterized by non-equilibrium demographic conditions for which binary covariables of interest can be defined at the population level.

scholarly journals A modelling framework to assess the likely effectiveness of facemasks in combination with ‘lock-down’ in managing the COVID-19 pandemic

2020 ◽  
Vol 476 (2238) ◽  
pp. 20200376 ◽  
Author(s):  
Richard O. J. H. Stutt ◽  
Renata Retkute ◽  
Michael Bradley ◽  
Christopher A. Gilligan ◽  
John Colvin
Keyword(s):  
Mobile Apps ◽  
Population Level ◽  
Disease Spread ◽  
Contact Tracing ◽  
Lower Efficiency ◽  
The Public ◽  
Molecular Tests ◽  
Modelling Framework ◽  
Increased Risk ◽  
The Impact

COVID-19 is characterized by an infectious pre-symptomatic period, when newly infected individuals can unwittingly infect others. We are interested in what benefits facemasks could offer as a non-pharmaceutical intervention, especially in the settings where high-technology interventions, such as contact tracing using mobile apps or rapid case detection via molecular tests, are not sustainable. Here, we report the results of two mathematical models and show that facemask use by the public could make a major contribution to reducing the impact of the COVID-19 pandemic. Our intention is to provide a simple modelling framework to examine the dynamics of COVID-19 epidemics when facemasks are worn by the public, with or without imposed ‘lock-down’ periods. Our results are illustrated for a number of plausible values for parameter ranges describing epidemiological processes and mechanistic properties of facemasks, in the absence of current measurements for these values. We show that, when facemasks are used by the public all the time (not just from when symptoms first appear), the effective reproduction number, R e , can be decreased below 1, leading to the mitigation of epidemic spread. Under certain conditions, when lock-down periods are implemented in combination with 100% facemask use, there is vastly less disease spread, secondary and tertiary waves are flattened and the epidemic is brought under control. The effect occurs even when it is assumed that facemasks are only 50% effective at capturing exhaled virus inoculum with an equal or lower efficiency on inhalation. Facemask use by the public has been suggested to be ineffective because wearers may touch their faces more often, thus increasing the probability of contracting COVID-19. For completeness, our models show that facemask adoption provides population-level benefits, even in circumstances where wearers are placed at increased risk. At the time of writing, facemask use by the public has not been recommended in many countries, but a recommendation for wearing face-coverings has just been announced for Scotland. Even if facemask use began after the start of the first lock-down period, our results show that benefits could still accrue by reducing the risk of the occurrence of further COVID-19 waves. We examine the effects of different rates of facemask adoption without lock-down periods and show that, even at lower levels of adoption, benefits accrue to the facemask wearers. These analyses may explain why some countries, where adoption of facemask use by the public is around 100%, have experienced significantly lower rates of COVID-19 spread and associated deaths. We conclude that facemask use by the public, when used in combination with physical distancing or periods of lock-down, may provide an acceptable way of managing the COVID-19 pandemic and re-opening economic activity. These results are relevant to the developed as well as the developing world, where large numbers of people are resource poor, but fabrication of home-made, effective facemasks is possible. A key message from our analyses to aid the widespread adoption of facemasks would be: ‘my mask protects you, your mask protects me’.

Temporal and spatial dynamics of predation in a robber fly (Efferia staminea) population (Diptera: Asilidae)

1992 ◽  
Vol 70 (8) ◽  
pp. 1546-1552 ◽  
Author(s):  
Kevin M. O'Neill
Keyword(s):  
Prey Availability ◽  
Spatial Scales ◽  
Large Population ◽  
Spatial Dynamics ◽  
Population Level ◽  
Management Program ◽  
Small Scale ◽  
Generalist Predator ◽  
Robber Flies ◽  
Temporal And Spatial

To determine the effect of short-term temporal and small-scale spatial variation in availability of specific prey groups, field studies of prey use by a population of the robber fly Efferia staminea were undertaken. In one study, the appearance of mating swarms of winged males of the ant Formica subpolita was associated with a rapid increase in the proportion of E. staminea observed feeding, and an increase in the proportion of these ants taken as prey. The change in diet occurred over the same time scale as the change in the activity of the ants. When the swarms were absent from the same area, the fewer E. staminea observed feeding utilized a greater diversity of prey taxa and sizes. The proportion of conspecifics in prey records during swarms of F. subpolita was only one-tenth of that during non-swarm intervals, suggesting that high alternative prey availability decreases the incidence of cannibalism in this species. In the second study, E. staminea used a wider diversity of prey on an area of grassland with native vegetation than on a nearby area of grassland that had been reseeded with the grass Agropyron intermedium as part of a range-management program. In the latter area, a large population of crambine moths supplied a major portion of the robber flies' diet. The results of this population-level study illustrate the fine scale over which the composition of the diet of E. staminea varies, and show that the diet of a generalist predator is a function of the temporal and spatial scales over which sampling occurs. The implications of the data for interpreting the composition of the diet, population dynamics, and impact upon prey communities of robber flies are discussed.

scholarly journals Effects of time and isolation on plant diversity: testing island biogeography theory with an eco-evolutionary model

2017 ◽  
Author(s):  
Juliano Sarmento Cabral ◽  
Robert J. Whittaker ◽  
Kerstin Wiegand ◽  
Holger Kreft
Keyword(s):  
Species Richness ◽  
Endemic Species ◽  
Island Biogeography ◽  
Temporal Trends ◽  
Evolutionary Model ◽  
Simulation Models ◽  
Population Level ◽  
Species Number ◽  
Dispersal Ability ◽  
Extinction Rates

abstractAimsThe General Dynamic Model of oceanic island biogeography (GDM) predicts how biogeographical rates, species richness, and endemism vary depending on island age, area, and isolation, based on the interplay of colonization, extinction, and speciation. Here, we used a simulation model to test whether GDM predictions may arise from individual- and population-level processes.LocationHypothetical hotspot islands.MethodsOur model (i) considers an idealized island ontogeny, (ii) metabolic constraints, and (iii) stochastic, spatially-explicit, and niche-based processes at the level of individuals and populations (plant demography, dispersal, competition, mutation, and speciation). Isolation scenarios involved varying dispersal ability and distances to mainland.ResultsHumped temporal trends were obtained for species richness, endemic richness, proportion of cladogenetic endemic species, number of radiating lineages, number of species per radiating lineage, and biogeographical rates. The proportion of anagenetic endemics and of all endemics steadily increased over time. Extinction rates of endemic species peaked later than for non-endemic species. Species richness and the number of anagenetic endemics decreased with isolation as did rates of colonization, anagenesis, and extinction. The proportion of all endemics and of cladogenetic endemics, the number of cladogenetic endemics, of radiating lineages, and of species per radiating lineage, and the cladogenesis rate all increased with isolation.Main conclusionsThe results confirm most GDM predictions related to island ontogeny and isolation, but predict an increasing proportion of endemics throughout the experiment: a difference attributable to diverging assumptions on late island ontogeny. New insights regarding the extinction trends of endemics further demonstrate how simulation models focusing on low ecological levels provide tools to test biogeographical-scale predictions and to develop more detailed predictions for further empirical tests.

scholarly journals Integrating animal movement with habitat suitability for estimating dynamic landscape connectivity

2017 ◽  
Author(s):  
Mariëlle L. van Toor ◽  
Bart Kranstauber ◽  
Scott H. Newman ◽  
Diann J. Prosser ◽  
John Y. Takekawa ◽  
...  
Keyword(s):  
Habitat Suitability ◽  
Disease Ecology ◽  
Animal Movement ◽  
Landscape Connectivity ◽  
Population Level ◽  
Tracking Data ◽  
Migratory Connectivity ◽  
Ecological Processes ◽  
Movement Model ◽  
Movement Data

AbstractContextHigh-resolution animal movement data are becoming increasingly available, yet having a multitude of empirical trajectories alone does not allow us to easily predict animal movement. To answer ecological and evolutionary questions at a population level, quantitative estimates of a species’ potential to link patches or populations are of importance.ObjectivesWe introduce an approach that combines movement-informed simulated trajectories with an environment-informed estimate of the trajectories’ plausibility to derive connectivity. Using the example of bar-headed geese we estimated migratory connectivity at a landscape level throughout the annual cycle in their native range.MethodsWe used tracking data of bar-headed geese to develop a multi-state movement model and to estimate temporally explicit habitat suitability within the species’ range. We simulated migratory movements between range fragments, and calculated a measure we called route viability. The results are compared to expectations derived from published literature.ResultsSimulated migrations matched empirical trajectories in key characteristics such as stopover duration. The viability of the simulated trajectories was similar to that of the empirical trajectories. We found that, overall, the migratory connectivity was higher within the breeding than in wintering areas, corresponding to previous findings for this species.ConclusionsWe show how empirical tracking data and environmental information can be fused for meaningful predictions of animal movements throughout the year and even outside the spatial range of the available data. Beyond predicting connectivity, our framework will prove useful for modelling ecological processes facilitated by animal movement, such as seed dispersal or disease ecology.

scholarly journals On the evolution of dispersal via heterogeneity in spatial connectivity

2015 ◽  
Vol 282 (1803) ◽  
pp. 20142879 ◽  
Author(s):  
Renato Henriques-Silva ◽  
Frédéric Boivin ◽  
Vincent Calcagno ◽  
Mark C. Urban ◽  
Pedro R. Peres-Neto
Keyword(s):  
Evolutionary Ecology ◽  
Landscape Connectivity ◽  
Regional Scale ◽  
Logistic Function ◽  
Evolution Of Dispersal ◽  
Heterogeneous Landscapes ◽  
Fitness Advantage ◽  
Connectivity Patterns ◽  
Open Question

Dispersal has long been recognized as a mechanism that shapes many observed ecological and evolutionary processes. Thus, understanding the factors that promote its evolution remains a major goal in evolutionary ecology. Landscape connectivity may mediate the trade-off between the forces in favour of dispersal propensity (e.g. kin-competition, local extinction probability) and those against it (e.g. energetic or survival costs of dispersal). It remains, however, an open question how differing degrees of landscape connectivity may select for different dispersal strategies. We implemented an individual-based model to study the evolution of dispersal on landscapes that differed in the variance of connectivity across patches ranging from networks with all patches equally connected to highly heterogeneous networks. The parthenogenetic individuals dispersed based on a flexible logistic function of local abundance. Our results suggest, all else being equal, that landscapes differing in their connectivity patterns will select for different dispersal strategies and that these strategies confer a long-term fitness advantage to individuals at the regional scale. The strength of the selection will, however, vary across network types, being stronger on heterogeneous landscapes compared with the ones where all patches have equal connectivity. Our findings highlight how landscape connectivity can determine the evolution of dispersal strategies, which in turn affects how we think about important ecological dynamics such as metapopulation persistence and range expansion.

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