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their.life.cycles,.and.(2).in.strict.coevolutionary.relationships,.such.as.the.butterly.
Baronia brevicornis .and.its.exclusive.food.plant. Acacia cochliacantha .(Soberón.and.
Peterson.2005)..In.these.cases,.information.regarding.geographic.distributions.of.all.
species.involved.is.necessary.to.achieve.accurate.prediction.(Nakazawa.et.al..2007;.
Peterson.2008).
However,. recent. work. suggests. that. even. in. biotic. interactions. that. are. more.
relaxed. (e.g.,. interspeciic. competition,. mutualism),. the. inclusion. of. distributional.
information. on. interacting. species. improves. distribution. models. of. target. species.
(Anderson. et. al.. 2002).. For. instance,. Araújo. and. Luoto. (2007). tested. whether. the.
distribution.of.three.food-plant.species.of.the.clouded.Apollo.butterly.( Parnassius
mnemosyne ).inluenced.the.butterly's.current.distribution.and.predictions.of.the.spe-
cies'.shifted.range.under.climate.change..Their.results.indicate.a.signiicant.role.of.
biotic.interactions.in.all.species'.distributions.both.in.the.present.and.in.the.future,.
suggesting.that.climatic.variables.are.not.the.only.drivers.of.species'.ranges.at.macro-
ecological.scales,.as.has.often.been.assumed..Analogously,.Gotelli.et.al..(2010).found.
that. competitive. interactions. among. ecologically. similar. bird. species. in. Denmark.
can.be.detected.at.spatial.resolutions.up.to.four.orders.of.magnitude.larger.than.that.
of.individual.territories..Accordingly,.the.authors.concluded.that.biotic.interactions.
should. be. incorporated. into. the. modeling. process,. in. order. to. reine. distributional.
predictions.
s ourCes of e rror and u nCerTainTy in enm
In.addition.to.the.limitations.and.biases.produced.by.complexities.inherent.in.bio-
logical.systems,.ENMs.are.also.affected.by.problems.in.source.data.and.analytical.
implementations..As.mentioned.previously,.ENMs.require.two.sources.of.primary.
data:.species.occurrences.(presence.or.presence.and.absence.data),.and.environmen-
tal. variables. in. the. form. of. GIS. layers.. The. latter. variables. also. require. a. parallel.
set.of.environmental.layers.for.hindcasts.to.the.past.or.projections.to.the.future..In.
addition.to.these.data.sources,.ENMs.also.require.modeling.techniques.(Figure 4.1)..
Each. of. these. three. elements. contains. its. own. biases. that. affect. the. accuracy. and.
uncertainty.of.distributional.models.to.varying.degrees.
Occurrence. data. come. from. a. variety. of. sources,. such. as. scientiic. collections.
(museums,.herbaria),.observational.surveys.(e.g.,.Breeding.Bird.Surveys.in.different.
countries),.and.ield.studies..From.these,.the.most.taxonomically.comprehensive.and.
widely. available. data. comes. from. scientiic. collections. (e.g.,. see. the. GBIF. portal:.
http://www.gbif.org/).. Data. drawn. from. collections. have. both. taxonomic. and. posi-
tional.inaccuracies.that.affect.niche.modeling;.thus,.a.iltering.process.is.necessary.
before. using. them. (Newbold. 2010).. Nonetheless,. positional. uncertainty. remains. in.
georeferenced. collecting. locations. even. after. dubious. records. have. been. removed.
(Guo.et.al..2008)..For.example,.a.typical.locality.description.in.a.specimen.voucher.
could.be.“20.mi.W.Lawrence,.Kansas,.USA.”.Because.Lawrence,.Kansas,.is.a.town.
(polygon),.uncertainty.exists.whether.distance.from.the.town.has.to.be.taken.from.
the.centroid.of.the.polygon.or.from.its.margin..Furthermore,.ecologists.seldom.have.
additional. information. to. decide. whether. the. 20. miles. described. in. the. tag. were.
measured.on.a.map.or.in.the.ield,.and.whether.the.distance.was.simply.rounded.to.
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