Predicting food web connectivityPhylogenetic scope, evidence thresholds, and intelligent agents

Cynthia Sims ParrEcological Society of America Memphis, TN August 8, 2006

Bacteria

Microprotozoa

Amphithoe longimana

Caprella penantis

Cymadusa compta

Lembos rectangularis

Batea catharinensis

Ostracoda

Melanitta

Tadorna tadorna

ELVIS: Ecosystem Localization, Visualization, and Information System

Oreochromis niloticusNile tilapia

??

. . .

Species list constructor

Food web constructor

ELVIS’s Food Web Constructor predicts basic network structure

Prelude to systems models

Food Web

G

S

node

link

Evolutionary tree

step

G

taxonS

taxon

A

Evolutionary Distance Weighting1. Set distance thresholds

2. Find relatives of target nodes X, Y with known link status

E.g. relative A is close to X, relative B close to Y

where Link Value between A and B is known

3. For each found link, compute weight based on distance

4. Compute certainty index for a predicted link by combining weighted link values, with a discount for negative evidence

AB

XA XA YB

1

1 ( ) ( )YB

WeightDistance Penalty Distance Penalty

XY

1

( )i

Ni

i

weightCertaintyIdx LinkValue

discount

Food web database

Source Webs Nodes Links

Animal Diversity Web n/a 711 2165

EcoWEB 212 4503 11967

Webs on the Web 19 1373 12056

Interaction Web DB 26 2139 9882

Tuesday Lake 2 101 510

Total 259 8827 36580

4600 distinct taxa

Food web data: Cohen 1989, Dunne et al. 2006, Vazquez 2006, Jonsson et al. 2005

Evolutionary tree: Parr et al. 2004. + plants from ITIS + hierarchy of non-taxonomic nodes

Testing the algorithm Take each web out of the database Attempt to predict its links Compare prediction with actual data

Accuracy percentage of all predictions that are correct 89%

Precision percentage of predicted links that are correct 55%

Recall percentage of actual links that are predicted 47%

Choosing parameters

30 web subsample Representative of habitats, years, # nodes,

percent identified to species Iterate over parameter settings Tradeoff between

Precision percentage of predicted links that are correct

Recall percentage of actual links that are predicted

Evolutionary distance threshold2 steps up and 4 steps down

1 2 3 4S1

S40.3

0.35

0.4

0.45

0.5

1 2 3 4S1

S30.4

0.45

0.5

0.55

0.6

steps up

steps down

precision

steps up

recall

Evolutionary direction penalty not very sensitive

AB

XA XA YB

1

1 ( ) ( )YB

WeightDistance Penalty Distance Penalty

ancestor

descendent

siblings

Negative evidence discount is sensitive

XY

1

( )i

Ni

i

weightCertaintyIdx LinkValue

discount

0.2

0.3

0.4

0.5

0.6

0.7

0 25 50 75 100

Negative evidence discount

Recall

Precision

Results over all webs

Is evolutionary distance weighting better than strict database search?

Paired T-testsdf=251

***p<0.001

Database searchEvolutionary distance weighting

%

***

***

***

Database search is more precise, but evolutionary distance wt has better recall.

Older webs contribute

Recall percentage of actual links that are predicted 47% 48% with no EcoWEB data

Precision percentage of predicted links that are correct 55% 39% with no EcoWEB data

…but large webs are harder to predict

0

0.2

0.4

0.6

0.8

1

1.2

0 50 100 150 200

Number of taxa

Recall r

ate

large webs have better taxonomic resolution

0

20

40

60

80

100

120

0 50 100 150 200

Number of taxa

% id

enti

fied

to

sp

ecie

s

recent webs are bigger

0

20

40

60

80

100

120

140

160

180

1910 1930 1950 1970 1990 2010

Year of study

nu

mb

er

of

taxa

large webs have fewer unknown “taxa”

0

10

20

30

40

50

60

70

80

90

0 50 100 150 200

Number of Taxa

% t

axa

un

kno

wn

Some phyla are easier to predict than others

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Annelida

Arthro

poda

Bacill

ario

phyta

Chordata

Mollu

sca

Phylum

Re

ca

ll ra

te

Trait space distance weighting

Euclidean distance in natural historyN-space

Parameterize functions from the literature that might predict links using characteristics of taxa. For example, size or stoichiometry.

LinkStatusAB= ƒ(α, sizeA, sizeB), ƒ(β, stoichA, stoichB) …

…need more data

How can we do better predicting links?

ETHANEvolutionary Trees and Natural History ontology

Animal Diversity Webhttp://www.animaldiversity.org geographic range habitats physical description reproduction lifespan behavior and trophic info conservation status

“Esox lucius” hasMaxMass “1.4 kg”

“Esox lucius” isSubclassOf “Esox”

“Esox” eats “Actinopterygii”

Triples

UMBC Triple Shop QueryWhat are body masses of fishes that eat fishes?

Enter a SPARQL querySELECT DISTINCT ?predator ?prey ?preymaxmass ?predatormaxmass

WHERE {

?link rdf:type spec:ConfirmedFoodWebLink .

?link spec:predator ?predator .

?link spec:prey ?prey .

?predator rdfs:subClassOf ethan:Actinopterygii .

?prey rdfs:subClassOf ethan:Actinopterygii .

OPTIONAL { ?predator kw:mass_kg_high ?predatormaxmass } . OPTIONAL { ?prey kw:mass_kg_high ?preymaxmass }

}

. . . leaving out the FROM clause

UMBC Triple Shop Create a datasetFind semantic web docs that can answer query.

Actinopterygii.owl

webs_publisher.php?published_study=11

Esox_lucius.owl

http://swoogle.umbc.edu

UMBC Triple Shop Get results Apply query to dataset with semantic reasoning.

http://sparql.cs.umbc.edu/tripleshop2/

Food Web Constructor uses evolutionary approach and large databases

We chose parameters using subsample Explored results over entire database

Evolutionary distance weighting recalls links better than database search

Older webs are useful Large webs harder to predict Some phyla are easier than others to predict

For future algorithms, we can gather and integrate data via ontologies and intelligent agents

Summary

UMBC: Tim Finin, Joel Sachs, Andriy Parafiynyk, Li Ding, Rong Pan, Lushan Han, UMCP: David Wang, RMBL: Neo Martinez, Rich Williams, Jennifer Dunne, UC Davis: Jim Quinn, Allan Hollander

UMMZ Animal Diversity Web: Phil Myers, Roger Espinosa

UMCP: Bill Fagan, Bongshin Lee, Ben Bederson

http://spire.umbc.edu

ADW databaseMySQL

XSLTtemplate

ADW taxon acctHTML

KeywordsHTML

ETHANTaxonacctOWL

SPIRE taxon databaseMySQL

EvolutionaryTree side of ontologyOWL

Phylum-sizedET chunkOWL

Taxon PathOWL

Filters

Acct data tabulartext

Others

ITIS

ETHAN workflow

Plants, etc.

Animal name tree

KeywordsOWL

Semantic Prototypes In

Ecoinformatics

UMBCUMBC

U Maryland U Maryland

NASAGoddard

NASAGoddard

Rocky MtnBio Lab

Rocky MtnBio Lab

UC DavisUC DavisSemantic Web Tools

Info. Retrieval AgentsFood Web ConstructorEvidence Provider

Invasive Species Forecasting System

Remote Sensing Data Food WebsEcological Interaction

Ontologies

Species List constructor

Food Web Constructor example Nile Tilapia in St. Marks

QuestionWhat are potential predators and prey of Oreochromis niloticus in the St. Marks estuary in Florida?

ProcedureSubmit species list for St. Marks, with Oreochromis niloticus added.

http://spire.umbc.edu/fwc

Food Web Constructor generates possible links

Evidence provider gives details

Nile tilapia – what organisms could be impacted?

Implications: parameterized functions

Requires good data for target species Can incrementally add natural history functions to

get better estimate, try different functions from literature or use genetic algorithms

Parameterizing functions: multivariate statistics, machine learning, fuzzy inference

Could use evolutionary info if you localize parameter estimates to clades or taxonomic subsets

LinkPredictedCD = ƒ(α , sizeC,sizeD) + ƒ(β , stoichC,stoichD)

Distance weighting options

Evolutionary Uses phylogeny or classification or

combination of these – assumes related organisms like each other

Distance could be branch length or # steps

Does not need natural history data

2 steps

Y

3 changes

X

“TaxonA” hasBreedingDuration “5 months”

OntologiesRicher way to design databases: instances of concepts that have well-defined meanings and formal relationships.

“Taxon A” hasAgeOfSexualMaturity “1 year”

“Higher Taxon” lives in “Australia”

“Taxon B” lives in “Australia”

“Taxon A” lives in “Australia”

Breeding Season

Reproductive Characteristic

TaxonB

Breeding Duration

is-a

has-a

Sexual maturity

is-a

HigherTaxonis-a TaxonA

Age of Sexual Maturity

has-a

is-a