Table Of ContentIstván 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
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abetterworld.
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
