Resilience and Stability of Ecological and Social Systems PDF
Preview Resilience and Stability of Ecological and Social Systems
István Karsai Thomas Schmickl George Kampis Resilience and Stability of Ecological and Social Systems Resilience and Stability of Ecological and Social Systems István Karsai (cid:129) Thomas Schmickl (cid:129) George Kampis Resilience and Stability of Ecological and Social Systems IstvánKarsai ThomasSchmickl DepartmentofBiologicalSciences DepartmentofZoology EastTennesseeStateUniversity Karl-Franzens-Universitat JohnsonCity,TN,USA Graz,Austria GeorgeKampis EotvosUniversityBudapest Budapest,Hungary ISBN978-3-030-54559-8 ISBN978-3-030-54560-4 (eBook) https://doi.org/10.1007/978-3-030-54560-4 ©SpringerNatureSwitzerlandAG2020 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpartof thematerialisconcerned,specificallytherightsoftranslation,reprinting,reuseofillustrations,recitation, broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,andtransmissionorinformation storageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilarmethodology nowknownorhereafterdeveloped. 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Foreword Climate change has been always affecting planet Earth, but what is different this time is that the human society became an important element of the causational network of these changes. Ecology and climate science are on the frontline of the research to decipher this network. Generating predictions of events are especially importantforunderstandingandmanagingtheprocessesofthesecomplexsystems. In this endeavor, modeling is an important skill set and tool to understand and predict. Unfortunately, however, the use of these tools is not widespread in biology because the training of biologists is generally different from the training of physicists or chemists, who are more accustomed to models and modeling. It seems that much of the existing excellent material (both books and articles) on either modeling or mathematical biology is not readily accessible to the students of biology. Mathematicians who are interested in modeling biological system are important to be inspired and communicated biology in ways what meaningful for themtoproducerelevantbiologicalmodelsorbeabletocollaboratewithbiologists. Addressing the series of challenges and their combinations, humanity faces the increasingneedforhierarchicalthinking.Phenomenaatthelevelofpopulationsand societies,ecosystemsandbiomesaretightlylinkedandinteract.Weneedbothdata andmodelsthatlinkupverticallythehorizontallevelsoforganization,inorderto betterunderstandpartsandwholesinnatureandsociety. This monograph written by a trio of long-term collaborators (István Karsai, GeorgeKampis,andThomasSchmickl)aimstopromotetheinterdisciplinaryfield of mathematical biology. Their approach is focusing on accessibility. Instead of educating the reader in a textbook form, they show examples from their own research. A central theme is the resilience of ecological and social systems. Resilienceisacentralprobleminclimatechange,sustainability,ecology,economy, andthesocialsciences.Buttheauthorsdidnotaimtowriteatextbookorareview ofthefieldinabookformat,butratheracarefullypickedseriesoftheirresearchis turnedintoamonograph. The first part of the book starts with a simple population growth model and leadsthereaderstowardsmoreandmorecomplexproblems.Stabilityofinteracting species,turningfragmentedhabitatsintoamorestablestate,oranalyzingtheeffect vii viii Foreword of fire to ecosystems remains accessible to the readers because the models stay simple and the authors make these models accessible to the readers—encouraging themtodotheirownexperiments. Thesecondhalfofthebookisfocusingonthestabilityandresilienceofinsect societies. These societies are the model system of ecological, economical, and societalsystems.Insomeways,theyaresimplerthanecologicalsystemsandhuman societies,andtheyaremoreaccessible.Thesesystemsarealsoeasytomanipulate with different perturbations; therefore, the theories on stability and resilience can betestedonthemexperimentally.Thesesystemsarealsothefruitsofevolutionand theyevolvedseveraltimesindependently.Wecanthuslearnalotfromthesesystems on how we can help stabilizing system and managing resources. For example, the authorsaskasimplequestionwhybeesdonothoardpollen,aresourcewhichcould clearlybehoardedandinfactcommonlybecomescarce(forexample,duringrainy weather)inthecolony.Thelackofpollenwillelicitcannibalismoflarvae,slowing downthedevelopmentofthousandsoflarvaeandalloftheseeventsarehappening several times a year. This all could be avoided if only the bees would store a bit morepollen.Sowhydon’ttheydothat? Beyond these concrete examples and focused questions, the authors also con- tribute to the state of the art of the theory of stability research. Analyzing task allocationandmaterialflowofdifferentinsectsocieties,theydiscoveredanewtype ofregulatorynetwork,whichhasnotbeendiscoveredelsewhereabovethecelllevel. Thekeyfindingthatensuresstabilityisthatmultiplefeedbacksgothroughabuffer node. While simple theory would claim that a simple negative feedback is able to provide stability, this would not work in real systems. Real systems need multiple feedback loops to ensure reliability and buffers to avoid unnecessary reactions. Current biological systems have been operating and evolving for a long time and they are not following the bare bone minimal theoretical structures, but they have redundancies.Theauthorsshowthatnotonlytheseredundanciesareimportantbut alsothattheyareeasilyaccessibleviatheory. In biology, we practically have no master equations to describe systems math- ematically. We use mostly empirical functions, fitted data, or we use agent-based approach to model these systems. The authors show examples on how we can use master equations from physics to describe a biological system and how we can understand biology better by using these master equations developed originally to describeelectriccircuits. Thus the monograph offers a wide spectrum of approaches, each adequate in its place, which together show the real complexity of biological systems that can nevertheless be understandable. This book is recommended for students and researchers interested in complex biological systems, from mathematicians to biologistsandfromengineerstosocialscientists. Tihany,Hungary FerencJordán January2020 Preface This book focuses on the way how mathematics helps us understand the ecology and how ecology will provide a new way of thinking in mathematics. While ecology has always been one of the most “mathematical” disciplines in the life sciences,aninterestingschismhasbeenevidentintheliterature.Experimentaland field studies were carried out mostly by biologists, while theoretical work came frommathematicians.Cross-referencesbetweenthetwoapproachesweresporadic. Fortunately,thishasbeenchangedinthelastdecadesandaplethoraofworkcame out in this interdisciplinary field. So why do we need another book? What is so specificaboutthisbook? Our goal here is to provide neither a textbook nor a review of the field. Our goal was to write a monograph on the subject on the basis of our own research in this field. Such an approach is essentially biased, opinionated, and may generate morequestionsthananswers;allarepropertiesofanongoingscientificinquiry.We are very passionate about this field and we would like to radiate our enthusiasm for inspiring both mathematicians and biologists to venture into this field and to investigate. These days, it is not as hard as it seems. Cross-education is finally catchingupandmoreandmorepeopleareeducatedininterdisciplinaryfieldssuch as data science, quantitative biology, applied mathematics, computational biology, and so on. However, there are other ways to get involved in this interdisciplinary field:collaboration.Currently,thereexistseveralmodelingplatformswherepeople ofdifferingbackground couldworktogether usingtheirown specific skillsetsand producesomethingverynew.Thesedayseveryonehasthepossibilityandaccessto thefreetoolsandtutorialstocarryoutthesetypesofresearch. Why do we emphasize modeling and simulations? What are these tools good for?Oneofthemostimportantpracticalbenefitsbythemisinthedecreaseofthe complexity of the problem. In physics and chemistry, with a minimum ignorance, we can safely say that the units are the same. In biology, even two clones are not the same. The enormous number of variables and their even larger number of interactionsmakebiologicalproblemshighlyintractable.Themainstepinmodeling is the decreasing of the number and complexity of variables to the minimum. ix x Preface This step translates the theoretically intractable original biological problem into a manageable abstract system, where all variables and their interactions are known. Also,transferringabiologicalsystemintoquantitativemodelsremovesambiguities of terms and makes assumptions explicit and clear. Generally, this step sharpens the goals and the questions and makes it impossible to sweep things under the carpet, so to speak. This is the hardest and essential step that requires both scientific (biological) and mathematical thinking. Choosing key variables and relationships (functions) and neglecting others lead obviously to a simplification. Is this simplification a problem? Yes and no. Yes, because we are actually not studying the biological phenomenon itself, but only its simplified mockup. No, because we can understand this mockup much easier than the real system. We can get an insight on how the original system works. With different models, we gain different insights. This is the main reason that there is no definitive model or modelingtechniqueforagivenbiologicalproblem.Thisisalsothereasonwhyin thisbookwestresstheimportanceofdifferentmodelingtechniquesandaniterative approachofmodeling. The title of the book is “Resilience and Stability of Ecological and Social Systems,”whichcovers verywellthecontent ofthebook.However, ourscientific workinthelast2decadeswasnotfocusedonjuststudyingstabilityitself.Thiswas ameta-resultofaseriesoffocusedresearcheswehadbeenconducting.Originally, we simply wanted to understand how some ecological and social systems work. Our goal was to describe a system mathematically and try to understand its basic properties. As we studied several of these specific systems, a common theme emerged. These systems all are self-organized and a network of feedback loops ensurestheirresilienceandstability.Forthisbook,wepickedthoseworksfromour studies where we have something new to say about a specific biological system, buttheycanalsobeusedasgoodexamplesofthegeneraltheme.Wealsohaveas a main goal that this book should be accessible for students of nature (even for those whose background is sporadic in modeling). To achieve this, we start the book with a simple case of population growth, then we continue with different types of population interactions and finally we reach the more complex topics of the functioning of insect societies. Without going into textbook level details, we believe the structure of the book makes easy to access it for both mathematicians andbiologists. Toenhancethisaccessibilityofourstudies,werefertoouroriginalpapersand wemakeallcomputationalmodelsavailable.1Thusthereaderhastheoptiontouse ourmodelsforher/hisowninvestigations.Whenwedescribeasysteminthisbook, wefocusonlyonthecoreofthemodelandforallfurtherdetails,suchasthoseof sub-models, parametrization, and so on, we refer to the original technical papers. We also provide a short appendix on modeling techniques. We provide a series of models that will run on free or open-source modeling platforms. These platforms 1https://sites.google.com/site/springerbook2020/home. Preface xi havedetailedtutorialsandtheyareveryaccessibleevenforundergraduatestudents. Wewantyoutoexperiment,rewriteourmodels,havenewideas,findshortcomings inourwork,andhaveanever-endingadventureinthefieldofscience. JohnsonCity,TN,USA IstvánKarsai Graz,Austria ThomasSchmickl Budapest,Hungary GeorgeKampis Kaiserslautern,Germany
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