Table Of Contentno'. "iT DOCUMENTS
UMASS/AMHERST Tiat are Compiiiiter Models and What
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Waquoit Bay National Estuarine Research Reserve Science and Policy Bulletin Series
What a Model Anyway?
is shows how we thinknitrogen moves on the Earth:
Imagineatwelve yearoldchildbuilding rocketsoutofwood, 1. nitrogenmovesfromtheatmosptieretoplants,
glue, and cardboard. The models are supposed to look like protists, and bacteria.
realrockets; a few ofthem are built to scale - one inchon the 2. nitrogen movesfromdeadorganismstosediments.
model isequivalent to somany feeton the actual rocket. The 3. nitrogenmovesfromplantsto herbivoresto
companies that sell die model kits also sell engines that shoot carnivores.
the models high into the air. 4. nitrogenmovesfromanimalwastetoplants.
So these model rockets look sort oflike real rockets and be- Anotlier type of conceptual model, which integrates these
have sort of like real rockets. And that is all that any rules, is a flow diagram. In this case, tlie diagram would
modelis-arepresentationofsomethingthatisreal. show the different places the nitrogen could be, and itwould
have arrows showing nitrogen moving from one place to
Folded anotlier. If we thought tliat it moved from the land into the
parachute atmosphere faster than from the atmosphere into the oceans,
Flame-resistant we could make tlie arrows going from land to atmosphere
wadding thick, toindicate afasterrate ofmovement.
Shock cord
Todescribe more precisely the way we thinknitrogen moves,
Bodytube we could use specific rates (eg pounds per year). We might
holder Launch lug alsotry to show how thedifferentratesofmovementdepend
Engine
_ Fins
Igniter
Amodel isa representationofsomethingthatisreal
A physical model of tlie
Earth might be a large,
sealed terrarium with soil,
plants, a pond, bacteria,
fish, insects, and so on.
This would be a very sim-
plified representation of
theEarth. Ifwe wanted to
understand the way nitro-
genmoves between tlieat-
mosphere, the land, and
the ocean, we couldmeas-
ure tlie amountofnitrogen
in each part of our model
Earthatdifferenttimes. Aflowdiagram isanothertypeof model
Asimplemodelofthebiosphere upon one anotlier. Soon, instead ofa seriesofsentences, our
modelwouldconsistofaseries ofequations.
In addition to physical models, scientists use conceptual mod-
els; hke physical models, tliey represent real systems.
One example of a conceptual model is a list of rules tliat
How
are Models Developed?
Alas, for the unwary individual, taking this view ofmodels
leads inevitably to crestfallen frowns. All models are over-
Toexplore how amodel is developed and how itcan be used, simplifications of the "real things" that they are supposed to
letusexaminethemodeling projectatWaquoit Bay. represent. Indeed,ecologicalmodels are considerablyless like
the "real thing" than scalemodels ofrockets. Thatisprimari-
So, thinks the modeler, I want to understand how land use in ly fortwo reasons.
the watershed affects nutrient concentrations and the ecosys-
tem in Waquoit Bay. Hmm. Well, I had better divide this First, rocket builders understand how the machine is put to-
problem into parts, study tlie parts individually, and then put gether (afterall, theyhavedesigned it),even though theymay
the pieces back together. Some ofthe pieces I need to study be wrong about how it will behave. Second, a rocket seems
are: complex, but it actually has far fewer parts and works in a
muchmore straight-forward fashion than thebiosphere.
1 Fromwheredoesgroundwaterflowinto WaquoitBay?
.
2. How do different land uses in this area (singlefamily Weare tlius led toask; "Ifmodelsare so farremoved fromre-
homes, agriculture, andindustry)affectthegroundwater? ality, why bother? Wliy produce these sets ofrules andequa-
3. Howfastdoesthegroundwatermove? tions? Why should we care about them? What can these
4. Howdoes the algae in the bay respond to different levels models tell us, and will that information help us to do our
ofnutrients? jobsbetter?"
Doesthisdependupon thetime ofyear?
Does itdepend upon the amountoflight? What Can Models Tell Us?
Onthetemperature?
5. How do thefish and shellfish respond to changes in the
algaepopulations? Models as a Reseeirch Tool
One way in which tlie model is extremely useful is as a tool
With this information, I can build a model (a relatively sim- foraresearch group to give the members ideas about whether
phstic description ofareal system) thatsays: so-and-so many tliey are on tlie right track, to suggest ways in which they
houses combined witli so-and-so many farms and so-and-so should modify theirefforts, and to suggest fruitful avenues of
much light industry in tliese-and-tliose places should lead to cooperation.
groundwater wiUi this concentration ofnutrients entering the
bay. Tlie algae should then grow at tlius-and-such a rate, and During tlieir study, scientists measure the quantities tJiat the
we should find this-and-tliat much algae in tlie bay at this-or- model predicts (eg, amount ofalgae in different parts of the
that time of year. Under those conditions, with weatlier pat- bay). Suppose the model's predictions differ from observa-
terns like these-or-those, we should find oxygen concentra- tions made in the bay. That tells the modeler and his col-
tions in the bay hke tliis-and-that. leagues tliat tliey need to revise tlieir ideas ofhow tlie system
works. Perhapsa piece oftlie puzzle they thought was unim-
What ifthismodel couldreally describe asystem accurately? portant in factneeds tobe considered.
Ahh, what bliss. Say we want to know how the system will
respond if we tweak it a little bit, change just one thing, or Models as a Planning Tool
two diings. Well, that would be no problem. We wouldjust Tlie model can also be useful to people who make decisions
look at our list of rules or equations, and they would tell us about land useregulation iuid llierefore need infonnationabout
what wouldhappen. Hence tlie allure, the enticement, tlie lip- how different land use patterns would affect the ecosystem in
smacking sweemess thatmodelsexude. tlie bay. It is in tliis secondarea Uiat misunderstandingsabout
Giventhis information The model predictsthese outcomes
land usepatterns whatnutrientconcentrationsshould bein groundwaterentering
(numbersandlocationsofhouses, farms bogs industries, etc.) the bay at different points
weather patterns whatdissolved oxygenconcentrationsshould be in different
(temperature, cloudcover, rainfall, windspeedanddirection) parts ofthe bay
howalgae respondtodifferentlevelsofnutrients howmuchalgaeshould bein differentpartsofthebay
(howfastdifferentspeciesgrow, howmuchoxygentheyproduce
duringtheday)
Simplifiedsummaryofinformation needed byamodeland predictionsgenerated bythemodel
tlie abilitiesandconstraints ofmodelsoften arise. The model does, however, have a number of
strengths that enable it to give managersvaluable
Consider the question: "what will happen to Waquoit Bay if informationand insightabout thegeneralrelation-
we build two hundred single family houses in these loca- shipoflandusetothe stateofthebay. Itwillenable
tions?" It is easy to understand the attraction ofmodels that us tocompare predictionsfrom different land use
predicthowdifferentlandusescenarios would impact thebay. scenarios,tocomparethe effects ofa single change
Suppose the model were to predict that this development within an internally consistent framework, and
would cause die phytoplankton population to double. The thereisagoodchancethatthemodelwillaccurately
critical point, and the one which is easy tomiss, is the neces- predict differences in resulting trends. So, while a
sity of thinking about: "what do these predictions mean?" predictionthatphytoplanktonproductionwilldouble
The answer to this question is not straight-forward, and its in response to a particular development scenario
subtleties contribute to modelers' concerns about their prod- maynotbe quantitativelyprecise, it isnevertheless
uctsbeing misused. ausefulresultbecausethe generalpatternofsignif-
icant phytoplankton increase is probablya sound
There is certainly a chance that building these houses would prediction.
cause the ecosystem to respond as the model predicts. It is
difficult to define this probability precisely; is it 80%, 90%, The reason thatthemodel's predictionsareparticularly valua-
95% ? Ofcourse,thechancethatthemodel'spredic- ble is tliat tlie model affords us the ability to look at interac-
tions are generally (or qualitatively) correct is tions and feedback loops widiin the system - checks and bal-
muchgreaterthanthechancethattheypredictpre- ances, positives andnegatives which can lead to non-intuitive
cisely how much algae will occur in a particular effects.
placeonaparticulardate.
Predictions mean Predictions do not mean
givenacertain setofconditions (eg weather), defined bythe underanyconditions (egweather),theecosystemwillrespond
user,thereis reasonablechance (orrisk)thattheecosystem exactlyinaccordancewiththe prediction
willrespond inamannernottoodifferentfromthemodel'spre-
dictions
giventwoverydifferentland usescenarios,there isgood chance there isa quantitatively precise riskthata particulardevelop-
thatthekindsofdifferencesseen inthe model's predictionsfor mentscenariowill haveaparticulareffectonthebay'secosystem
thetwoscenarios resemblethekindsofdifferencesthatwould
beobserved intheecosystem
What predictionsfromthemodel meanandwhattheydonotmean
The model does not and will not yield quantitativelyprecise Likeallmodels, theonebeing developedtodescribetheinter-
answersabouthow thebay'secosystem will respond todiffer- actions between land use and ecosystem response in Waquoit
ent land use patterns. Forexample, where tlie model predicts Bay is a simplification ofreality. We may not, and probably
adoubling ofphytoplankton production in response to a par- do not, understand all oftlie components and interactions oc-
ticular development, we will probably see an increase tliat is curring in theWaquoitBayecosystem. Therefore, themodel
less than twiceormore than twice.
Advantages of the model Disadvantages ofttie model
capturesinteractionsand non-intuitivefeedbackeffects impossibletoincludeall aspectsofecosystemfunction
enables usto lood attrends resultingfrom differentland use does notprovideriskanalysesorpatanswers
scenarios
capturesthe most importantfeatures ofthesystem requiresamethodologyforincorporating its resultsintoa risk
analysis
Advantagesand disadvantagesofcomputermodelsas planningtools
(our representation ofhow the actual ecosystem behaves) in- ticulardecision carries with it. "Ifwe build those twohundred
cludesanumberofreasonable assumptions. houses, what is the risk that the amount of algae in the bay
will increase?" Even a fifty percentchancemaybe sufficient
However, the model tries to capture what is most important in todecideagainstbuilding thehouses.
determining the structure and function ofthe ecosystem. Itat-
tempts to show how the whole, complex, dynamic system Ecologist Paul Ehrlich once polled a group of world experts
works by looking at how several of tJie most critical compo- on the subject of global warming. How many of you, he
nents ofthe system workandinteract. asked, think tliere is a ninety percent chance that global tem-
peratures will continue to increase as a result of greenhouse
gas emissions by humans? How many of you think there is
Risk Analysis, Confidence an 80% chance? 70? 60? The most conservative scientists
present indicated that they believed there was at least a 30%
Limits, and Food Poisoning chance thathumanactivities were leading toglobal warming.
Themost liberal estimated90%. Even ifwe were to consider
Thedifficulties facingmanagerswhowouldUke to usemodels only the most conservative estimate, would we be willing to
such as the one being developed by the Waquoit Bay LMER, takea 30% chance? Would we eatin arestaurant ifwe knew
and for scientists advising them on the matter, arises in part there was only a 30% chance thatourmeal would contain ar-
senic?
from subtledifferences between thequestionsresource manag-
ers ask and the questions that research scientists ask. When
Computer models like the one being developed by the Waq-
scientists evaluate the validity of a hypothesis, they look for
95% confidence. That is, dataare considered to supportahy- uoitBayLMER do notprovide riskanalyses orquantitatively
pothesis only if there is less than a 5% chance that the results precise predictions. This particular model may provide the
best available holistic understanding of the ecosystem. The
supporting tliehypothesisoccurredbychance.
challenge isforresourcemanagers tofmdameansforincorpo-
Often, resource managers do not require tliis level of confi- rating information generated by tlie model as a significant
component in making decisions.
dence. Rather, what is relevantis the degreeofrisk thatapar-
No.l Whatare ComputerModels and What Can They Tell UsAbout WaquoitBay?
Waquoit Bay National Estuarine Research Reserve Science and Policy Bulletin Series
TheWaquoit Bay National Esturine Research Reserve is partofthe National Estuarine Research Reserve System, established by section 315ofthe
Coastal Zone ManagmentAct, asamended. Additional information abouttheSystem can beobtainedfromthe Sanctuariesand Reserves Division,
Officeof Ocean and Coastal Resource Management, NationalOceanicandAtmosphericAdministration, U.S. DepartmentofCommence, 1825Con-
necticutAvenue, N.W.,Washington, DC 20235
Editor: Noel Gurwick,WBNERR Designer: Cheryl Ulm, MCZM Sendcommentsandquestionsto: WBNERR, PO Box3092,Waquoit, MA02536
WaquoitBayNationalEstuarineResearchReserve,
^
Massactiusetts DepartmentoC^vironmeo^^i-Manag^mety-
DPiOviBsoixon3o0f92ForestandParks-Reyg.f'e^qA1, ' ^yS -^(^0^ 'tf
MA
Waquoit. 02536
(508) 457-0495
