As ecologists increasingly adopt stochastic models over deterministic ones, the question arises: when is this a positive development and when is this an unnecessary complication? While deterministic models -- like the Lotka-Volterra model -- provide straightforward predictions about competitive outcomes, they are often unrealistic. Stochastic models are more realistic, but their complexity can limit their usefulness in explaining coexistence. Here, we investigate the relative importance of deterministic and stochastic processes in competition between two flour beetle species, Tribolium castaneum and Tribolium confusum. Specifically, we use highly-replicated one-generation experiments (784 microcosms) to parameterize a mechanistic model. Both the full stochastic model and the underlying deterministic skeleton exhibit priority effects, where one species excludes the other, but the identity of the winning species depends on initial abundances. Stochasticity makes the identity of the winner less predictable, but deterministic dynamics still make reliable predictions (94% accuracy across a range of reasonable initial abundances). We conclude that deterministic population dynamics are sufficient to account for patterns of coexistence (or lack thereof), a potentially general finding that is supported by recent field studies. Additionally, we resolve longstanding issues in flour beetle research by identifying selective egg predation as the mechanism for priority effects, demonstrating the primacy of demographic stochasticity (compared to environmental stochasticity), and reinterpreting classic competition experiments to show that apparent coexistence often represents long-term transient dynamics.
{"title":"Determinism vs. stochasticity in competitive flour beetle communities","authors":"Evan C. Johnson, Tad Dallas, Alan Hastings","doi":"arxiv-2409.05317","DOIUrl":"https://doi.org/arxiv-2409.05317","url":null,"abstract":"As ecologists increasingly adopt stochastic models over deterministic ones,\u0000the question arises: when is this a positive development and when is this an\u0000unnecessary complication? While deterministic models -- like the Lotka-Volterra\u0000model -- provide straightforward predictions about competitive outcomes, they\u0000are often unrealistic. Stochastic models are more realistic, but their\u0000complexity can limit their usefulness in explaining coexistence. Here, we\u0000investigate the relative importance of deterministic and stochastic processes\u0000in competition between two flour beetle species, Tribolium castaneum and\u0000Tribolium confusum. Specifically, we use highly-replicated one-generation\u0000experiments (784 microcosms) to parameterize a mechanistic model. Both the full\u0000stochastic model and the underlying deterministic skeleton exhibit priority\u0000effects, where one species excludes the other, but the identity of the winning\u0000species depends on initial abundances. Stochasticity makes the identity of the\u0000winner less predictable, but deterministic dynamics still make reliable\u0000predictions (94% accuracy across a range of reasonable initial abundances). We\u0000conclude that deterministic population dynamics are sufficient to account for\u0000patterns of coexistence (or lack thereof), a potentially general finding that\u0000is supported by recent field studies. Additionally, we resolve longstanding\u0000issues in flour beetle research by identifying selective egg predation as the\u0000mechanism for priority effects, demonstrating the primacy of demographic\u0000stochasticity (compared to environmental stochasticity), and reinterpreting\u0000classic competition experiments to show that apparent coexistence often\u0000represents long-term transient dynamics.","PeriodicalId":501044,"journal":{"name":"arXiv - QuanBio - Populations and Evolution","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Martin Frohn, Niels Holtgrefe, Leo van Iersel, Mark Jones, Steven Kelk
Semi-directed networks are partially directed graphs that model evolution where the directed edges represent reticulate evolutionary events. We present an algorithm that reconstructs binary $n$-leaf semi-directed level-1 networks in $O( n^2)$ time from its quarnets (4-leaf subnetworks). Our method assumes we have direct access to all quarnets, yet uses only an asymptotically optimal number of $O(n log n)$ quarnets. Under group-based models of evolution with the Jukes-Cantor or Kimura 2-parameter constraints, it has been shown that only four-cycle quarnets and the splits of the other quarnets can practically be inferred with high accuracy from nucleotide sequence data. Our algorithm uses only this information, assuming the network contains no triangles. Additionally, we provide an $O(n^3)$ time algorithm that reconstructs the blobtree (or tree-of-blobs) of any binary $n$-leaf semi-directed network with unbounded level from $O(n^3)$ splits of its quarnets.
{"title":"Reconstructing semi-directed level-1 networks using few quarnets","authors":"Martin Frohn, Niels Holtgrefe, Leo van Iersel, Mark Jones, Steven Kelk","doi":"arxiv-2409.06034","DOIUrl":"https://doi.org/arxiv-2409.06034","url":null,"abstract":"Semi-directed networks are partially directed graphs that model evolution\u0000where the directed edges represent reticulate evolutionary events. We present\u0000an algorithm that reconstructs binary $n$-leaf semi-directed level-1 networks\u0000in $O( n^2)$ time from its quarnets (4-leaf subnetworks). Our method assumes we\u0000have direct access to all quarnets, yet uses only an asymptotically optimal\u0000number of $O(n log n)$ quarnets. Under group-based models of evolution with\u0000the Jukes-Cantor or Kimura 2-parameter constraints, it has been shown that only\u0000four-cycle quarnets and the splits of the other quarnets can practically be\u0000inferred with high accuracy from nucleotide sequence data. Our algorithm uses\u0000only this information, assuming the network contains no triangles.\u0000Additionally, we provide an $O(n^3)$ time algorithm that reconstructs the\u0000blobtree (or tree-of-blobs) of any binary $n$-leaf semi-directed network with\u0000unbounded level from $O(n^3)$ splits of its quarnets.","PeriodicalId":501044,"journal":{"name":"arXiv - QuanBio - Populations and Evolution","volume":"46 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The mountain pine beetle (MPB), a destructive pest native to Western North America, has recently extended its range into Alberta, Canada. Predicting the dispersal of MPB is challenging due to their small size and complex dispersal behavior. Because of these challenges, estimates of MPB's typical dispersal distances have varied widely, ranging from 10 meters to 18 kilometers. Here, we use high-quality data from helicopter and field-crew surveys to parameterize a large number of dispersal kernels. We find that fat-tailed kernels -- those which allow for a small number of long-distance dispersal events -- consistently provide the best fit to the data. Specifically, the radially-symmetric Student's t-distribution with parameters {nu} = 0.012 and {rho} = 1.45 stands out as parsimonious and user-friendly; this model predicts a median dispersal distance of 60 meters, but with the 95th percentile of dispersers travelling nearly 5 kilometers. The best-fitting mathematical models have biological interpretations. The Student's t-distribution, derivable as a mixture of diffusive processes with varying settling times, is consistent with observations that most beetles fly short distances while few travel far; early-emerging beetles fly farther; and larger beetles from larger trees exhibit greater variance in flight distance. Finally, we explain why other studies have found such a wide variation in the length scale in MPB dispersal, and we demonstrate that long-distance dispersal events are critical for modelling MPB range expansion.
{"title":"Stratified dispersal explains mountain pine beetle's range expansion in Alberta","authors":"Evan C. Johnson, Micah Brush, Mark A. Lewis","doi":"arxiv-2409.05320","DOIUrl":"https://doi.org/arxiv-2409.05320","url":null,"abstract":"The mountain pine beetle (MPB), a destructive pest native to Western North\u0000America, has recently extended its range into Alberta, Canada. Predicting the\u0000dispersal of MPB is challenging due to their small size and complex dispersal\u0000behavior. Because of these challenges, estimates of MPB's typical dispersal\u0000distances have varied widely, ranging from 10 meters to 18 kilometers. Here, we\u0000use high-quality data from helicopter and field-crew surveys to parameterize a\u0000large number of dispersal kernels. We find that fat-tailed kernels -- those\u0000which allow for a small number of long-distance dispersal events --\u0000consistently provide the best fit to the data. Specifically, the\u0000radially-symmetric Student's t-distribution with parameters {nu} = 0.012 and\u0000{rho} = 1.45 stands out as parsimonious and user-friendly; this model predicts\u0000a median dispersal distance of 60 meters, but with the 95th percentile of\u0000dispersers travelling nearly 5 kilometers. The best-fitting mathematical models\u0000have biological interpretations. The Student's t-distribution, derivable as a\u0000mixture of diffusive processes with varying settling times, is consistent with\u0000observations that most beetles fly short distances while few travel far;\u0000early-emerging beetles fly farther; and larger beetles from larger trees\u0000exhibit greater variance in flight distance. Finally, we explain why other\u0000studies have found such a wide variation in the length scale in MPB dispersal,\u0000and we demonstrate that long-distance dispersal events are critical for\u0000modelling MPB range expansion.","PeriodicalId":501044,"journal":{"name":"arXiv - QuanBio - Populations and Evolution","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Evolution is often understood through genetic mutations driving changes in an organism's fitness, but there is potential to extend this understanding beyond the genetic code. We propose that natural products - complex molecules central to Earth's biochemistry can be used to uncover evolutionary mechanisms beyond genes. By applying Assembly Theory (AT), which views selection as a process not limited to biological systems, we can map and measure evolutionary forces in these molecules. AT enables the exploration of the assembly space of natural products, demonstrating how the principles of the selfish gene apply to these complex chemical structures, selecting vastly improbable and complex molecules from a vast space of possibilities. By comparing natural products with a broader molecular database, we can assess the degree of evolutionary contingency, providing insight into how molecular novelty emerges and persists. This approach not only quantifies evolutionary selection at the molecular level but also offers a new avenue for drug discovery by exploring the molecular assembly spaces of natural products. Our method provides a fresh perspective on measuring the evolutionary processes both, shaping and being read out, by the molecular imprint of selection.
{"title":"Mapping Evolution of Molecules Across Biochemistry with Assembly Theory","authors":"Sebastian Pagel, Abhishek Sharma, Leroy Cronin","doi":"arxiv-2409.05993","DOIUrl":"https://doi.org/arxiv-2409.05993","url":null,"abstract":"Evolution is often understood through genetic mutations driving changes in an\u0000organism's fitness, but there is potential to extend this understanding beyond\u0000the genetic code. We propose that natural products - complex molecules central\u0000to Earth's biochemistry can be used to uncover evolutionary mechanisms beyond\u0000genes. By applying Assembly Theory (AT), which views selection as a process not\u0000limited to biological systems, we can map and measure evolutionary forces in\u0000these molecules. AT enables the exploration of the assembly space of natural\u0000products, demonstrating how the principles of the selfish gene apply to these\u0000complex chemical structures, selecting vastly improbable and complex molecules\u0000from a vast space of possibilities. By comparing natural products with a\u0000broader molecular database, we can assess the degree of evolutionary\u0000contingency, providing insight into how molecular novelty emerges and persists.\u0000This approach not only quantifies evolutionary selection at the molecular level\u0000but also offers a new avenue for drug discovery by exploring the molecular\u0000assembly spaces of natural products. Our method provides a fresh perspective on\u0000measuring the evolutionary processes both, shaping and being read out, by the\u0000molecular imprint of selection.","PeriodicalId":501044,"journal":{"name":"arXiv - QuanBio - Populations and Evolution","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Leon Alexander Valencia, Ph. D, Jorge Mario Ramirez Osorio, Jorge Andres Sanchez
This paper explores a stochastic Gause predator-prey model with bounded or sub-linear functional response. The model, described by a system of stochastic differential equations, captures the influence of stochastic fluctuations on predator-prey dynamics, with particular focus on the stability, extinction, and persistence of populations. We provide sufficient conditions for the existence and boundedness of solutions, analyze noise-induced extinction events, and investigate the existence of unique stationary distributions for the case of Holing Type I functional response. Our analysis highlights the critical role of noise in determining long-term ecological outcomes, demonstrating that even in cases where deterministic models predict stable coexistence, stochastic noise can drive populations to extinction or alter the system's dynamics significantly.
本文探讨了一个具有有界或亚线性功能响应的随机高斯捕食者-猎物模型。该模型由随机微分方程系统描述，捕捉了随机波动对捕食者-猎物动力学的影响，尤其关注种群的稳定性、灭绝和存续。我们提供了解的存在性和有界性的充分条件，分析了噪声诱发的灭绝事件，并研究了霍林 I 型功能响应情况下唯一静态分布的存在性。我们的分析凸显了噪声在决定长期生态结果中的关键作用，表明即使在确定性模型预测稳定共存的情况下，随机噪声也会导致种群灭绝或显著改变系统的动态。
{"title":"The Stochastic Gause predator-prey model: noise-induced extinctions and invariance","authors":"Leon Alexander Valencia, Ph. D, Jorge Mario Ramirez Osorio, Jorge Andres Sanchez","doi":"arxiv-2409.05237","DOIUrl":"https://doi.org/arxiv-2409.05237","url":null,"abstract":"This paper explores a stochastic Gause predator-prey model with bounded or\u0000sub-linear functional response. The model, described by a system of stochastic\u0000differential equations, captures the influence of stochastic fluctuations on\u0000predator-prey dynamics, with particular focus on the stability, extinction, and\u0000persistence of populations. We provide sufficient conditions for the existence\u0000and boundedness of solutions, analyze noise-induced extinction events, and\u0000investigate the existence of unique stationary distributions for the case of\u0000Holing Type I functional response. Our analysis highlights the critical role of\u0000noise in determining long-term ecological outcomes, demonstrating that even in\u0000cases where deterministic models predict stable coexistence, stochastic noise\u0000can drive populations to extinction or alter the system's dynamics\u0000significantly.","PeriodicalId":501044,"journal":{"name":"arXiv - QuanBio - Populations and Evolution","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Characterising the intelligence of biological organisms is challenging. This work considers intelligent algorithms developed evolutionarily within neural systems. Mathematical analyses unveil a natural equivalence between canonical neural networks, variational Bayesian inference under a class of partially observable Markov decision processes, and differentiable Turing machines, by showing that they minimise the shared Helmholtz energy. Consequently, canonical neural networks can biologically plausibly equip Turing machines and conduct variational Bayesian inferences of external Turing machines in the environment. Applying Helmholtz energy minimisation at the species level facilitates deriving active Bayesian model selection inherent in natural selection, resulting in the emergence of adaptive algorithms. In particular, canonical neural networks with two mental actions can separately learn transition mappings of multiple Turing machines. These propositions were corroborated by numerical simulations of algorithm implementation and neural network evolution. These notions offer a universal characterisation of biological intelligence emerging from evolution in terms of Bayesian model selection and belief updating.
{"title":"Evolutionary emergence of biological intelligence","authors":"Takuya Isomura","doi":"arxiv-2409.04928","DOIUrl":"https://doi.org/arxiv-2409.04928","url":null,"abstract":"Characterising the intelligence of biological organisms is challenging. This\u0000work considers intelligent algorithms developed evolutionarily within neural\u0000systems. Mathematical analyses unveil a natural equivalence between canonical\u0000neural networks, variational Bayesian inference under a class of partially\u0000observable Markov decision processes, and differentiable Turing machines, by\u0000showing that they minimise the shared Helmholtz energy. Consequently, canonical\u0000neural networks can biologically plausibly equip Turing machines and conduct\u0000variational Bayesian inferences of external Turing machines in the environment.\u0000Applying Helmholtz energy minimisation at the species level facilitates\u0000deriving active Bayesian model selection inherent in natural selection,\u0000resulting in the emergence of adaptive algorithms. In particular, canonical\u0000neural networks with two mental actions can separately learn transition\u0000mappings of multiple Turing machines. These propositions were corroborated by\u0000numerical simulations of algorithm implementation and neural network evolution.\u0000These notions offer a universal characterisation of biological intelligence\u0000emerging from evolution in terms of Bayesian model selection and belief\u0000updating.","PeriodicalId":501044,"journal":{"name":"arXiv - QuanBio - Populations and Evolution","volume":"8 3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nathan O. Silvano, João Valeriano, Emilio Hernández-García, Cristóbal López, Ricardo Martinez-Garcia
Populations very often self-organize into regular spatial patterns with important ecological and evolutionary consequences. Yet, most existing models neglect the effect that external biophysical drivers might have both on pattern formation and the spatiotemporal population dynamics once patterns form. Here, we investigate the effect of environmental flows on pattern formation and population dynamics using a spatially nonlocal logistic model (or Fisher-Kolmogorov equation) coupled to a simple shear and a Rankine vortex flow. We find that, whereas population abundance generally decreases with increasing flow intensity, the effect of the flow on the pattern instability depends on the spatial structure of the flow velocity field. This result shows that the velocity field interacts with the spatial feedbacks responsible for pattern formation in non-trivial ways, leading to a variety of spatiotemporal population dynamics regimes in which the total population abundance can exhibit either regular oscillations with a characteristic frequency or more erratic dynamics without a well-defined period. More generally, the diversity of spatiotemporal population dynamics caused by the interplay between self-organizing feedbacks and environmental flows highlights the importance of incorporating environmental and biophysical processes when studying both ecological pattern formation and its consequences.
{"title":"Shear and transport in a flow environment determine spatial patterns and population dynamics in a model of nonlocal ecological competition","authors":"Nathan O. Silvano, João Valeriano, Emilio Hernández-García, Cristóbal López, Ricardo Martinez-Garcia","doi":"arxiv-2409.04268","DOIUrl":"https://doi.org/arxiv-2409.04268","url":null,"abstract":"Populations very often self-organize into regular spatial patterns with\u0000important ecological and evolutionary consequences. Yet, most existing models\u0000neglect the effect that external biophysical drivers might have both on pattern\u0000formation and the spatiotemporal population dynamics once patterns form. Here,\u0000we investigate the effect of environmental flows on pattern formation and\u0000population dynamics using a spatially nonlocal logistic model (or\u0000Fisher-Kolmogorov equation) coupled to a simple shear and a Rankine vortex\u0000flow. We find that, whereas population abundance generally decreases with\u0000increasing flow intensity, the effect of the flow on the pattern instability\u0000depends on the spatial structure of the flow velocity field. This result shows\u0000that the velocity field interacts with the spatial feedbacks responsible for\u0000pattern formation in non-trivial ways, leading to a variety of spatiotemporal\u0000population dynamics regimes in which the total population abundance can exhibit\u0000either regular oscillations with a characteristic frequency or more erratic\u0000dynamics without a well-defined period. More generally, the diversity of\u0000spatiotemporal population dynamics caused by the interplay between\u0000self-organizing feedbacks and environmental flows highlights the importance of\u0000incorporating environmental and biophysical processes when studying both\u0000ecological pattern formation and its consequences.","PeriodicalId":501044,"journal":{"name":"arXiv - QuanBio - Populations and Evolution","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Predicting horizontal gene transfers often requires comparative sequence data, but recent work has shown that character-based approaches could also be useful for this task. Notably, perfect transfer networks (PTN) explain the character diversity of a set of taxa for traits that are gained once, rarely lost, but that can be transferred laterally. Characterizing the structure of such characters is an important step towards understanding more complex characters. Although efficient algorithms can infer such networks from character data, they can sometimes predict overly complicated transfer histories. With the goal of recovering the simplest possible scenarios in this model, we introduce galled perfect transfer networks, which are PTNs that are galled trees. Such networks are useful for characters that are incompatible in terms of tree-like evolution, but that do fit in an almost-tree scenario. We provide polynomial-time algorithms for two problems: deciding whether one can add transfer edges to a tree to transform it into a galled PTN, and deciding whether a set of characters are galled-compatible, that is, they can be explained by some galled PTN. We also analyze a real dataset comprising of a bacterial species trees and KEGG functions as characters, and derive several conclusions on the difficulty of explaining characters in a galled tree, which provide several directions for future research.
{"title":"Galled Perfect Transfer Networks","authors":"Alitzel López Sánchez, Manuel Lafond","doi":"arxiv-2409.03935","DOIUrl":"https://doi.org/arxiv-2409.03935","url":null,"abstract":"Predicting horizontal gene transfers often requires comparative sequence\u0000data, but recent work has shown that character-based approaches could also be\u0000useful for this task. Notably, perfect transfer networks (PTN) explain the\u0000character diversity of a set of taxa for traits that are gained once, rarely\u0000lost, but that can be transferred laterally. Characterizing the structure of\u0000such characters is an important step towards understanding more complex\u0000characters. Although efficient algorithms can infer such networks from\u0000character data, they can sometimes predict overly complicated transfer\u0000histories. With the goal of recovering the simplest possible scenarios in this\u0000model, we introduce galled perfect transfer networks, which are PTNs that are\u0000galled trees. Such networks are useful for characters that are incompatible in\u0000terms of tree-like evolution, but that do fit in an almost-tree scenario. We\u0000provide polynomial-time algorithms for two problems: deciding whether one can\u0000add transfer edges to a tree to transform it into a galled PTN, and deciding\u0000whether a set of characters are galled-compatible, that is, they can be\u0000explained by some galled PTN. We also analyze a real dataset comprising of a\u0000bacterial species trees and KEGG functions as characters, and derive several\u0000conclusions on the difficulty of explaining characters in a galled tree, which\u0000provide several directions for future research.","PeriodicalId":501044,"journal":{"name":"arXiv - QuanBio - Populations and Evolution","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The level of aggregation in parasite populations is frequently incorporated into ecological studies. It is measured in various ways including variance-to-mean ratio, mean crowding, the $k$ parameter of the negative binomial distribution and indices based on the Lorenz curve such as the Gini index (Poulin's D) and the Hoover index. Assuming the frequency distributions follow a negative binomial, we use contour plots to clarify the relationships between aggregation indices, mean abundance and prevalence. The contour plots highlight the nonlinear nature of the relationships between these measures and suggest that correlations are not a suitable summary of these relationships.
{"title":"A graphical exploration of the relationship between parasite aggregation indices","authors":"R. McVinish, R. J. G. Lester","doi":"arxiv-2409.03186","DOIUrl":"https://doi.org/arxiv-2409.03186","url":null,"abstract":"The level of aggregation in parasite populations is frequently incorporated\u0000into ecological studies. It is measured in various ways including\u0000variance-to-mean ratio, mean crowding, the $k$ parameter of the negative\u0000binomial distribution and indices based on the Lorenz curve such as the Gini\u0000index (Poulin's D) and the Hoover index. Assuming the frequency distributions\u0000follow a negative binomial, we use contour plots to clarify the relationships\u0000between aggregation indices, mean abundance and prevalence. The contour plots\u0000highlight the nonlinear nature of the relationships between these measures and\u0000suggest that correlations are not a suitable summary of these relationships.","PeriodicalId":501044,"journal":{"name":"arXiv - QuanBio - Populations and Evolution","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Human longevity leaders with remarkably long lifespan play a crucial role in the advancement of longevity research. In this paper, we propose a stochastic model to describe the evolution of the age of the oldest person in the world by a Markov process, in which we assume that the births of the individuals follow a Poisson process with increasing intensity, lifespans of individuals are independent and can be characterized by a gamma-Gompertz distribution with time-dependent parameters. We utilize a dataset of the world's oldest person title holders since 1955, and we compute the maximum likelihood estimate for the parameters iteratively by numerical integration. Based on our preliminary estimates, the model provides a good fit to the data and shows that the age of the oldest person alive increases over time in the future. The estimated parameters enable us to describe the distribution of the age of the record holder process at a future time point.
{"title":"Modelling the age distribution of longevity leaders","authors":"Csaba Kiss, László Németh, Bálint Vető","doi":"arxiv-2409.03353","DOIUrl":"https://doi.org/arxiv-2409.03353","url":null,"abstract":"Human longevity leaders with remarkably long lifespan play a crucial role in\u0000the advancement of longevity research. In this paper, we propose a stochastic\u0000model to describe the evolution of the age of the oldest person in the world by\u0000a Markov process, in which we assume that the births of the individuals follow\u0000a Poisson process with increasing intensity, lifespans of individuals are\u0000independent and can be characterized by a gamma-Gompertz distribution with\u0000time-dependent parameters. We utilize a dataset of the world's oldest person\u0000title holders since 1955, and we compute the maximum likelihood estimate for\u0000the parameters iteratively by numerical integration. Based on our preliminary\u0000estimates, the model provides a good fit to the data and shows that the age of\u0000the oldest person alive increases over time in the future. The estimated\u0000parameters enable us to describe the distribution of the age of the record\u0000holder process at a future time point.","PeriodicalId":501044,"journal":{"name":"arXiv - QuanBio - Populations and Evolution","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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