DOCSLIB.ORG

Demographic Stochasticity in Evolutionary Biology

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Demographic Stochasticity in Evolutionary Biology Demographic Stochasticity in Evolutionary Biology by Yen Ting Lin A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Physics) in The University of Michigan 2013 Doctoral Committee: Professor Charles R. Doering, Chair Post-Doc Assistant Professor Arash Fahim Assistant Professor Emanuel Gull Professor Mark E. J. Newman Professor John C. Schotland 5/1, 2013 at Navy Pier, Chicago. c Yen Ting Lin 2013 All Rights Reserved To Chia-Hui and Amber ii ACKNOWLEDGEMENTS I met Prof. Charles Doering, who prefers to be called \Charlie", in my very first class|Complex System 541|here at the UM. Immediately I was attractive to his teaching charisma|until then I had not realized the meaning of the famous saying \teaching is an art". Charlie really masters the \art". After 541 I took his Asymptotic Analysis offered by the Department of Mathematics and learned a great deal about asymptotic analysis, which turned out to be one of the main tools for my later research. If I can only thank one person in this Acknowledgement, Charlie is the one. Without him, I would probably never finish my Ph. D. study. He kindly accepted me transferring from Prof. Leonard Sander's group, and has been a wonderful mentor to me since then. He reshaped my \thinking" so that I can perform a more mathematical research, pushed me to take graduate courses in mathematics to broaden my view, encouraged me to attend to conferences (with financial support!) and supported me| financially and mentally|to work on subjects that I really enjoy. Charlie was the one who taught me everything about research|from performing theoretical analysis, comparing numerical data to theoretical conjectures, organizing literature reviews, drafting the dissertation, to refining the articles (he also carefully polished almost every sentences of my lame English writing). This dissertation would never exist without his guide. In every step I learned from him the rigor of scientific research, and he certainly set up a high-standard paradigm for me to follow in the future. Most important of all, as a well-respected Professor with a happy family, Charlie showed me iii that taking care of my family never contradicts the excellence in scientific research. Words cannot express my gratitude to Charlie. He has made this journey a won- derful experience in my life. I am very fortunate to have him as my mentor, and I sincerely hope to become the scientist he tried to shape. I would also like to thank to Prof. Gull, who joined the Department of Physics last year. He taught me a lot about computational physics, which had never been organized so well in the curriculum of the Department before he joined. He also made substantial suggestions to this dissertation; with his advice the dissertation turned out to be much more complete. In addition, I would like to thank him for the wonderful referral to his previously-affiliated Max Planck Institute for the Physics of Complex Systems. With his and certainly with Charlie's recommendations, I am very blessed to have the opportunity to work at such a world-class research institute in the near future. Starting from the beginning of this project, Dr. Hyejin Kim and I have been working closely together as a group. I would like to thank Hyejin for a wonderful experience of interdisciplinary research. Particularly, I enjoy our discussions, in which I gained a lot of knowledge in mathematics. Hyejin was always so patient to explain rigorous mathematics to me. Aside from the research, in the past few years our families have become very close friends, especially her 2-year-old son Minjoon and our 1-year-old daughter Amber. I certainly hope this friendship will last in the future. I appreciate Dr. Arash Fahim for giving me many excellent suggestions in the final revision of the dissertation. I also enjoy very much his Math 526, in which he showed me a comprehensive picture about general stochastic processes. Thanks very much to Prof. Mark Newman and Prof. John Schotland for serving as my committee members with a very short notice. Many of my friends helped me in the process of earning the Ph. D. Here I thank my friends Paul Bierdz, Tim Saucer, and Ran Lu for helping me improve the writing iv in my job applications and the dissertation. My groupmates in the Sweetland writing group, especially Ben Yee, also helped me to write a lot. I would also like to thank the Department of Physics for offering teaching positions several times to support me when the funding is short. Finally, special thanks to my dear wife Chia-Hui for being with me all the time and raising our lovely daughter Amber almost independently. Both Chia-Hui and I thank all of our family members who emotionally support us in Taiwan. At this time I would like to share the joy and the honor of earning this Ph. D. with all the people who ever helped|without any of them this would never be possible. v TABLE OF CONTENTS DEDICATION :::::::::::::::::::::::::::::::::: ii ACKNOWLEDGEMENTS :::::::::::::::::::::::::: iii LIST OF FIGURES ::::::::::::::::::::::::::::::: ix LIST OF TABLES :::::::::::::::::::::::::::::::: xvi LIST OF APPENDICES :::::::::::::::::::::::::::: xvii ABSTRACT ::::::::::::::::::::::::::::::::::: xviii CHAPTER I. Introduction ..............................1 1.1 General introduction . .1 1.2 Literature review . .4 1.3 Outline of the dissertation . .8 II. Features of Fast Living: On the Weak Selection for Longevity in Degenerate Birth-Death Processes ............... 11 2.1 The models . 12 2.1.1 Deterministic dynamics . 12 2.1.2 Stochastic dynamics . 15 2.2 Drift and diffusion along the coexistence line . 19 2.2.1 A physically motivated asymptotic analysis . 19 2.2.2 Truly degenerate case: γ =1............. 23 2.2.3 Summary . 25 2.3 Numerical simulations and asymptotic verification . 26 2.4 Asymptotic analysis for arbitrary γ ............... 29 2.5 Summary and discussion . 32 vi III. Demographic Stochasticity and Evolution of Dispersal in Ho- mogeneous Environments ...................... 35 3.1 The two-patch model . 35 3.1.1 The model . 35 3.1.2 Asymptotic analysis . 40 3.1.3 Detailed computation of the physically motivated asymp- totic analysis . 45 3.1.4 Simulations and numerical computations . 55 3.2 The many-patch model . 58 3.2.1 The model . 58 3.2.2 Asymptotic analysis . 62 3.2.3 Detailed computation of the physically motivated asymp- totic analysis . 67 3.2.4 Simulations and numerical computations . 72 3.3 Discussion and conclusion . 75 IV. Nonlinear Dynamics of Heterogeneous Patchy Models .... 77 4.1 The deterministic two-patch model . 78 4.2 The deterministic many patch model . 85 4.3 Discussion and conclusion . 93 V. Demographic Stochasticity and Evolution of Dispersal in Het- erogeneous Environments ...................... 94 5.1 Stochastic two-patch model . 95 5.1.1 The model . 95 5.1.2 Physically motivated asymptotic analysis . 98 5.2 Stochastic many patch model . 104 5.2.1 The model . 104 5.2.2 Physically motivated asymptotic analysis . 106 5.3 Simulations and numerical computations . 108 5.3.1 Stochastic two-patch model . 108 5.3.2 Stochastic many-patch model . 114 5.4 Summary . 118 VI. Bifurcation Analysis ......................... 122 6.1 Analysis of the effective drifts in the center manifold . 124 6.2 Identifying the sets in the parameter space . 130 6.2.1 f$(qright; µx; µy; ρ, ξ) ≤ 0g and f$(qright; µx; µy; ρ, ξ) ≥ 0g ............................ 130 6.2.2 f$(qleft; µx; µy; ρ, ξ) ≥ 0g and f$(qleft; µx; µy; ρ, ξ) ≤ 0g ............................ 134 vii 6.2.3 fqmax 2 (qleft; qright)g and fqmax 2= (qleft; qright)g .... 135 6.2.4 f$max ≥ 0g and f$max ≤ 0g ............. 141 6.3 Properties of the sets in the parameter space . 145 6.4 Bifurcation of the Landscape in Parameter Space and Evolu- tionarily Stable Dispersal Rate . 150 VII. Discussion, Conclusion, and Future Work ............ 159 7.1 Discussion and conclusion . 159 Development of the physical asymptotic analysis . 159 Demographic stochasticity in passive dispersal models . 161 Demographic stochasticity and the emergence of a evolution- arily stable dispersal rate . 163 Analytical predictions . 164 7.2 Future Work . 166 Population dynamics . 166 General mathematical biology . 168 Applied mathematics . 169 APPENDICES :::::::::::::::::::::::::::::::::: 170 A.1 The problem . 172 A.2 Change of variable . 174 A.3 Asymptotic upper bound . 175 α A.3.1 Set 1 S1 := fy : y < 2/µg .............. 175 α A.3.2 Set 2 S2 := fy : y > 2/µg .............. 176 A.4 Asymptotic solution of the upper bound as µ ! 1 ...... 178 A.4.1 n > 4.......................... 180 A.4.2 n =3.......................... 181 A.5 Small µ approximation . 183 A.6 Numerical verification and discussion . 183 C.1 The model . 190 C.1.1 Asymptotic analysis when α =0........... 192 C.2 Effective dynamics, dynamical interpretation, and numerical simulations . 198 BIBLIOGRAPHY :::::::::::::::::::::::::::::::: 200 viii LIST OF FIGURES Figure 1.1 Outline of dissertation. 10 2.1 Deterministic trajectories and the (dashed) line of fixed points for Eq.(2.2) with γ = 10. 13 2.2 Schematic diagram of the stochastic processes and the corresponding per capita rate. The random populations are Xt = n and Yt = m at this time. 14 2.3 Mechanism for demographic fluctuation-induced drift and diffusion along deterministic coexistence line. From (x0; y0)|solid dot|the system fluctuates to (x0; y0)|indicated by the open circles|and sub- sequently relaxes back to corresponding (x0−ξ; y0+ξ) point|indicated by the filled squares|on the coexistence line along a deterministic trajectory. 20 2.4 Probability of domination of the Y -species over the X-species starting from position z on the coexistence line, i.e., u(z) from (2.56), for life- cycle ratios γ = :1;:5; 1; 2; 10 (solid lines bottom to top) as a function of starting position z on the coexistence line.
Load more

Recommended publications
  • Accepted Version
    This document is the accepted manuscript version of the following article: Giometto, A., Rinaldo, A., Carrara, F., & Altermatt, F. (2014). Emerging predictable features of replicated biological invasion fronts. Proceedings of the National Academy of Sciences of the United States of America PNAS, 111(1), 297-301. https://doi.org/10.1073/pnas.1321167110 Emerging predictable features of replicated biological invasion fronts Andrea Giometto ∗ y, Andrea Rinaldo ∗ z Francesco Carrara ∗ , and Florian Altermatt y ∗Laboratory of Ecohydrology, School of Architecture, Civil and Environmental Engineering, Ecole´ Polytechnique F´ed´eralede Lausanne,CH-1015 Lausanne, Switzerland,yDepartment of Aquatic Ecology, Eawag: Swiss Federal Institute of Aquatic Science and Technology, CH-8600 D¨ubendorf, Switzerland, and zDipartimento di Ingegneria Civile, Edile ed Ambientale, Universit`adi Padova, I-35131 Padova, Italy Submitted to Proceedings of the National Academy of Sciences of the United States of America Biological dispersal shapes species' distribution and affects their co- logical range expansions is fundamental to the study of inva- existence. The spread of organisms governs the dynamics of invasive sive species dynamics [1{10], shifts in species ranges due to species, the spread of pathogens and the shifts in species ranges due climate or environmental change [11{14] and, in general, the to climate or environmental change. Despite its relevance for fun- spatial distribution of species [3, 15{18]. Dispersal is the key damental ecological processes, however, replicated experimentation agent that brings favorable genotypes or highly competitive on biological dispersal is lacking and current assessments point at inherent limitations to predictability, even in the simplest ecologi- species into new ranges much faster than any other ecological cal settings.
    [Show full text]
  • Global-Scale Episodes of Transoceanic Biological Dispersal During the Cenozoic
    Global-scale episodes of transoceanic biological dispersal during the Cenozoic Daniel Viete ( [email protected] ) Johns Hopkins University Siobhán Cooke Johns Hopkins School of Medicine Brief Communication Keywords: biological dispersal, transoceanic rafting, Cenozoic Posted Date: November 20th, 2020 DOI: https://doi.org/10.21203/rs.3.rs-104248/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License 1 Global-scale episodes of transoceanic biological dispersal during the Cenozoic 2 Daniel R. Viete1 & Siobhán B. Cooke2 3 1. Department of Earth & Planetary Sciences, Johns Hopkins University; 2. Center for 4 Functional Anatomy & Evolution, Johns Hopkins School of Medicine 5 Since formalization of plate tectonic theory in the 1960s, vicariance has been the 6 dominant model for interpretation in biogeography. However, modern research 7 suggests transoceanic ‘rafting’ is also an important process in biological dispersal. 8 Here we show that Cenozoic occurrences of rafting-associated colonization of new 9 lands by terrestrial biota are not randomly distributed in time, but cluster at c. 40 10 million years ago (Ma) and c. 15 Ma. These excursions in rafting activity are reflected 11 across taxonomic groups and ocean basins. The global scale and ~25 million-year (My) 12 wavelength of the fluctuations suggest they are associated with planetary-scale 13 changes and/or events. 14 Simpson [1] argued that differences in the terrestrial faunal assemblages of Africa and 15 Madagascar could be explained by an adventitious process of faunal exchange; migration 16 from Africa was limited to fauna whose physiology could, on rare attempt, allow survival of an 17 extended oceanic journey then establishment of a viable Malagasy population.
    [Show full text]
  • Evolutionarily Stable and Convergent Stable Strategies in Reaction-Diffusion Models for Conditional Dispersal
    EVOLUTIONARILY STABLE AND CONVERGENT STABLE STRATEGIES IN REACTION-DIFFUSION MODELS FOR CONDITIONAL DISPERSAL KING-YEUNG LAM AND YUAN LOU Abstract. We consider a mathematical model of two competing species for the evolution of conditional dispersal in a spatially varying but temporally constant environment. Two species are different only in their dispersal strategies, which are a combination of random dispersal and biased movement upward along the resource gradient. In the absence of biased movement or advection, Hastings showed that the mutant can invade when rare if and only if it has smaller random dispersal rate than the resident. When there is a small amount of biased movement or advection, we show that there is a positive random dispersal rate that is both locally evolutionarily stable and convergent stable. Our analysis of the model suggests that a balanced combination of random and biased movement might be a better habitat selection strategy for populations. Keywords: Conditional dispersal; Evolutionarily stable strategy; Convergent stable strategy; Reaction- diffusion-advection; Competition. AMS Classification: 92D35, 92D40, 35K57, 35J55. 1. Introduction Biological dispersal refers to the movement of organisms from one location to another within a habitat. Dispersal of organisms has important consequence on population dynamics, disease spread and distribution of species [3, 32, 36, 37]. Unconditional dispersal refers to the movement of organisms in which the probability of movement is independent of the local environment, while conditional dispersal refers to those that are contingent on the local environmental cues. Understanding the evolution of dispersal is an important topic in ecology and evolutionary biology [9, 35]. In the following, we limit our discussion to phenotypic evolution under clonal reproduction, where the importance of genes and sex are ignored.
    [Show full text]
  • Dispersal of Microalgae of Dispersal the Role of Biological and Physical Barriers Barriers Physical and of Biological the Role
    JosefinSefbom Dispersal of Microalgae The role of Biological and Physical Barriers DispersalBiologicalMicroalgaeof RolePhysicalTheofand Barriers – Josefin Sefbom Ph.D. thesis Department of Marine Sciences University of Gothenburg 2015 ISBN 978-91-85529-83-4 Printed by Aidla Trading AB Dispersal of Microalgae The role of Biological and Physical Barriers Doctoral Thesis Josefin Sefbom Department of Marine Sciences Faculty of Science 2015 Front cover photograph by Josefin Sefbom & Susanna Gross Printed by Kompendiet, Aidla Trading AB, Gothenburg, Sweden © Josefin Sefbom, 2015 ISBN 978-91-85529-83-4 2 Abstract Microalgae are only a few micrometres to a millimetre in size, yet they constitute the base for aquatic food webs and are extremely important drivers in elemental cycling. Long it has been assumed that small organisms (<1 mm) that occur in high abundances, have global dispersal potential facilitated by winds, currents and vectors e.g. birds and insects. A growing body of evidence is however portraying a different story; that several planktonic microalgal species exhibit genetically differentiated populations at regional geographic scales, and that populations can also differ in environmentally important phenotypic traits. In this thesis I present results from field studies and experiments designed to test various physical and biological dispersal barriers to explain why, despite the high dispersal potential, microalgae exhibit reduced gene flow (exchange of genetic material) between populations. A marine species Skeletonema marinoi (Bacillariophyceae) and a freshwater species Gonyostomum semen (Raphidophyceae) were used to test the function of geographic distance and hydrographic connectivity (physical barriers) as well as local adaptation and priority effects (biological barriers) on gene flow between populations.
    [Show full text]
  • Diffusion of Point Source and Biological Dispersal
    Chapter 3 Diffusion of point source and biological dispersal 3.1 Diffusion of a point source Suppose that a new species is introduced to an existing environment, then naturally the individuals of the species will disperse from the release point. That would similar to the diffusion when a drop of ink enter a jar of pure water. Reaction-diffusion equation can be used to describe an ideal growth and spatial-diffusion phenomena. The simplest way is to consider the dispersion in an infinite domain–we first consider the simplest one dimensional problem. Suppose that P (t,x) is the population density function of this species, t 0 and x ( , ). Suppose that at t = 0, we ≥ ∈ −∞ ∞ import a group of M individuals to a point (say, x = 0). Let u0(x) be the initial distribution of the population. Then ∞ u0(x)dx = M. (3.1) Z−∞ However u (x) = 0 for any x = 0 since before the dispersion, the species does not exist in the 0 6 environment yet. On the other hand the population density at x = 0 is given by the following limit x x u0(x)dx u0(0) = lim − = . (3.2) x 0+ 2x ∞ → R Thus u0(x) is an unusual function can be characterized by if x = 0, ∞ u0(x)= ∞ and u0(x)dx = M. (3.3) 0 otherwise, ( Z−∞ This function is called a δ-function (delta function) in physics, and we denote it by δ0(x) (a function which is infinity at a would be δa(x).) Although this function is not a function in the normal sense, we can check that the function M x2 Φ(t,x)= e− 4Dt (3.4) √4πDt 37 38 CHAPTER 3.
    [Show full text]
  • Biological Advection and Cross-Diffusion with Parameter
    AIMS Mathematics, 4(6): 1721–1744. DOI:10.3934/math.2019.6.1721 Received: 17 October 2018 Accepted: 27 November 2019 http://www.aimspress.com/journal/Math Published: 23 December 2019 Research article Biological advection and cross-diffusion with parameter regimes Jaywan Chung1, Yong-Jung Kim2;∗, Ohsang Kwon3 and Chang-Wook Yoon4 1 Thermoelectric Conversion Research Center, Korea Electrotechnology Research Institute, 12, Bulmosan-ro 10 beon-gil, Changwon-si, Gyeongsangnam-do, 51543, Korea 2 Department of Mathematical Sciences, KAIST, 291 Daehak-ro, Yuseong, Daejeon 305-701, Korea 3 Department of Mathematics, Chungbuk National University, Chungdae-ro 1, Seowon-gu, Cheongju, Chungbuk 362-763, Korea 4 College of Science & Technology, Korea University, Sejong 30019, Republic of Korea * Correspondence: Email: [email protected]; Tel: +82423502739; Fax: +82423505710. Abstract: Advection and cross-diffusion terms are obtained as dispersal strategies of biological species. The focus of the paper is their connection to a given population dynamics. In particular, meaningful parameter regimes as biological dispersal are obtained. Eventually, we obtain a systematic approach to construct an advection or a cross-diffusion term from a given population dynamics and find meaningful parameter regimes as biological advection and cross-diffusion. Keywords: cross diffusion; chemotaxis; biological dispersal; Lotka-Volterra equations; evolution of dispersal Mathematics Subject Classification: 35K57, 35Q92 1. Introduction A realistic dispersal of biological organisms should take account the interaction between individuals and the response to the environmental variations. The importance of having such a diffusion model has been emphasized by many researchers (see Skellam [25,26] and Okubo & Levin [24, Chapter 5]). In a reaction-diffusion equation, such interactions and responses are given in the reaction term and hence it is important to appropriately design the dispersal part depending on the reaction term.
    [Show full text]
  • An Abstract of the Dissertation Of
    AN ABSTRACT OF THE DISSERTATION OF Daniel Farber for the degree of Doctor of Philosophy in Botany and Plant Pathology presented on October 27, 2016 Title: The Primary Disease Gradient of Wheat Stripe Rust (Puccinia striiformis f.sp. tritici) Across Spatial Scales Abstract approved: ______________________________________________________ Christopher C. Mundt Wheat stripe rust (WSR), also called yellow rust of wheat (Tricitum spp.), causal agent Puccinia striiformis f. sp. tritici (Pst), is a foliar disease of major economic importance on wheat, especially grown in temperate locations. WSR causes major losses of wheat yield, estimated at nearly $ 1 billion per year, and has been found in upwards of sixty countries throughout the world, particularly in temperate regions. WSR, like many diseases of widely planted agricultural crops, has been managed largely by breeding resistant cultivars of wheat and by applying fungicide, but these strategies have resulted in periodic intensifications of WSR epidemics due to Pst mutations overcoming resistance or becoming resistant to fungicides. However, these strategies can be augmented to control WSR and other plant diseases by understanding the epidemiology, including the dispersal patterns of their causal pathogens, factors affecting the pathogen’s infection efficiency and reproduction rate, allowing managers to make better informed decisions. In this dissertation, I have examined the spread of WSR, bridging theoretical dispersal and disease ecology with plant epidemiology to provide analyses that have direct applicability to managing the spread of diseases caused by foliar wind-dispersed plant pathogens, as well as increase our understanding of basic biological processes. To investigate the dispersal gradient of Pst, I inoculated single wheat leaves with Pst urediniospores, and sampled all wheat leaves within two intersecting 0.3 m × 3.0 m transects in eight replicates over three years.
    [Show full text]
  • A Stochastic Model of Maize Gene Dispersal David G
    Iowa State University Capstones, Theses and Retrospective Theses and Dissertations Dissertations 2003 A stochastic model of maize gene dispersal David G. Ripplinger Iowa State University Follow this and additional works at: https://lib.dr.iastate.edu/rtd Part of the Agricultural and Resource Economics Commons, Agricultural Economics Commons, Economics Commons, and the Plant Breeding and Genetics Commons Recommended Citation Ripplinger, David G., "A stochastic model of maize gene dispersal" (2003). Retrospective Theses and Dissertations. 17194. https://lib.dr.iastate.edu/rtd/17194 This Thesis is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Retrospective Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. A stochastic model of maize gene dispersal by David G. Ripplinger A thesis submitted to the graduate faculty in partial fulfi1lment of the requirements for the degree of MASTER OF SCIENCE Major: Agricultural Economics Program of Study Committee: Dermot Hayes (Major Professor) Kendall Lamkey Sergio Lenee Stephen Vardeman Iowa State University Ames, Iowa 2003 11 Graduate College Iowa State University This is to certify that the master's thesis of David G. Ripplinger has met the thesis requirements of Iowa State University Signatures have been redacted for privacy lll TABLE OF CONTENTS LIST OF FIGURES iv LIST OF TABLES v ACKNOWLEDGEMENTS ~ ABSTRACT vii CHAPTER l. INTRODUCTION 1 CHAPTER 2. REVIEW OF LITERATURE 4 Pollen and Gene Dispersal in MaiZe 4 Modeling Spore and Pollen Movement 14 Preventing Gene Movement 20 CHAPTER 3.
    [Show full text]
  • Microbial Colonization in Marine Environments: Overview of Current Knowledge and Emerging Research Topics
    Journal of Marine Science and Engineering Review Microbial Colonization in Marine Environments: Overview of Current Knowledge and Emerging Research Topics Gabriella Caruso Institute of Polar Sciences, National Reasearch Council (ISP-CNR), Spianata S. Raineri 86, 98122 Messina, Italy; [email protected]; Tel.: +39-090-6015423; Fax: +39-090-669007 Received: 13 December 2019; Accepted: 22 January 2020; Published: 24 January 2020 Abstract: Microbial biofilms are biological structures composed of surface-attached microbial communities embedded in an extracellular polymeric matrix. In aquatic environments, the microbial colonization of submerged surfaces is a complex process involving several factors, related to both environmental conditions and to the physical-chemical nature of the substrates. Several studies have addressed this issue; however, more research is still needed on microbial biofilms in marine ecosystems. After a brief report on environmental drivers of biofilm formation, this study reviews current knowledge of microbial community attached to artificial substrates, as obtained by experiments performed on several material types deployed in temperate and extreme polar marine ecosystems. Depending on the substrate, different microbial communities were found, sometimes highlighting the occurrence of species-specificity. Future research challenges and concluding remarks are also considered. Emphasis is given to future perspectives in biofilm studies and their potential applications, related to biofouling prevention (such as cell-to-cell communication by quorum sensing or improved knowledge of drivers/signals affecting biological settlement) as well as to the potential use of microbial biofilms as sentinels of environmental changes and new candidates for bioremediation purposes. Keywords: biofilms; microbes; colonization; substrates; temperate areas; polar regions 1. Introduction Biofilm formation on substrate surfaces is the first step in biofouling formation [1–5].
    [Show full text]
  • Dispersal Evolution Diminishes the Negative Density Dependence in Dispersal
    bioRxiv preprint doi: https://doi.org/10.1101/2020.06.07.138818; this version posted July 15, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 1 Title: Dispersal evolution diminishes the negative density dependence in dispersal 2 3 Running title: Dispersal evolution & density dependence 4 Authors: Abhishek Mishra, Partha Pratim Chakraborty1, Sutirth Dey* 5 Affiliation: 6 Population Biology Laboratory, Biology Division, Indian Institute of Science Education and 7 Research-Pune, Dr. Homi Bhabha Road, Pune, Maharashtra, India, 411 008 8 9 1 Present Address: Department of Biology, University of Ottawa, Ottawa, ON Canada, K1N 10 6N5 11 12 *Corresponding author contact details: 13 Sutirth Dey 14 Professor, Biology Division 15 Indian Institute of Science Education and Research, Pune 16 Dr. Homi Bhabha Road, Pune, Maharashtra, India 411 008 17 Tel: +91-20-25908054 18 Email: [email protected] 19 20 21 Authorship Contributions 22 AM and SD conceived the ideas and designed methodology; AM and PPC collected the data; 23 AM analyzed the data; AM and SD led the writing of the manuscript. All authors contributed 24 critically to the drafts and gave final approval for publication. 25 26 Acknowledgements 27 AM was supported by a Senior Research Fellowship from the Council of Scientific and 28 Industrial Research, Government of India. This study was supported by a research grant 29 (#CRG/2018/001333) from Science and Engineering Research Board, DST, Government of 30 India, and internal funding from IISER-Pune.
    [Show full text]
  • Dispersal Kernel Estimation: a Comparison of Empirical and Modelled Particle Dispersion in a Coastal Marine System
    Estuarine, Coastal and Shelf Science 133 (2013) 11e22 Contents lists available at ScienceDirect Estuarine, Coastal and Shelf Science journal homepage: www.elsevier.com/locate/ecss Dispersal kernel estimation: A comparison of empirical and modelled particle dispersion in a coastal marine system Janelle M. Hrycik a,*, Joël Chassé b, Barry R. Ruddick a, Christopher T. Taggart a a Department of Oceanography, Dalhousie University, 1355 Oxford Street, P.O. Box 15000, Halifax, Nova Scotia, B3H 4R2 Canada b Fisheries and Oceans Canada, Gulf Fisheries Centre, 343 University Avenue, P.O. Box 5030, Moncton, New Brunswick, E1C 9B6 Canada article info abstract Article history: Early life-stage dispersal influences recruitment and is of significance in explaining the distribution and Received 8 April 2013 connectivity of marine species. Motivations for quantifying dispersal range from biodiversity conserva- Accepted 26 June 2013 tion to the design of marine reserves and the mitigation of species invasions. Here we compare estimates Available online 27 July 2013 of real particle dispersion in a coastal marine environment with similar estimates provided by hydro- dynamic modelling. We do so by using a system of magnetically attractive particles (MAPs) and a Keywords: magnetic-collector array that provides measures of Lagrangian dispersion based on the time-integration dispersal kernel of MAPs dispersing through the array. MAPs released as a point source in a coastal marine location Lagrangian particle tracing e empirical-model comparisons dispersed through the collector array over a 5 7 d period. A virtual release and observed (real-time) advection and diffusion environmental conditions were used in a high-resolution three-dimensional hydrodynamic model to marine estimate the dispersal of virtual particles (VPs).
    [Show full text]
  • UNICOR Users Manual
    UNICOR user manual | 1 UNICOR USER MANUAL 2011 Version: 1.0 Authors: E. L. Landguth1, B. K. Hand1, J. M. Glassy1,2, S. A. Cushman3 and R. T. Carlson1 1 - University of Montana, Division of Biological Sciences, Missoula, MT, 59812, USA. 2 - Lupine Logic Inc, Missoula, MT, 59802, USA. 3 - U.S. Forest Service, Rocky Mountain Research Station, 2500 S. Pine Knoll Dr., Flagstaff, AZ 86001, USA UNICOR user manual | 2 Table of Contents 1 Introduction.................................................. 3 1.1 What can UNICOR do.................................... 3 1.2 How does UNICOR work.................................. 3 2 Getting started............................................... 6 2.1 Dependencies.......................................... 6 2.1.1 Baseline requirements............................ 6 2.1.2 Python on non-windows platforms.................. 6 2.1.3 Python on windows................................ 6 2.1.4 Obtaining NumPy and SciPy........................ 6 2.2 Installation.......................................... 6 2.2.1 Installing Python, NumPy, and SciPy.............. 6 2.2.2 Unpack the UNICOR archive........................ 7 2.2.3 Install UNICOR................................... 7 2.2.4 Optional Python extension modules................ 7 2.3 Example run........................................... 7 2.3.1 Command line run................................. 7 3 Input......................................................... 8 3.1 Resistance grid....................................... 8 3.2 XY locations.........................................
    [Show full text]