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Predicting food web connectivity Phylogenetic scope, evidence thresholds, and intelligent agents. Cynthia Sims Parr Ecological Society of America Memphis, TN August 8, 2006. ELVIS: Ecosystem Localization, Visualization, and Information System. Oreochromis niloticus Nile tilapia. Bacteria

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Predicting food web connectivity phylogenetic scope evidence thresholds and intelligent agents l.jpg

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

Cynthia Sims Parr

Ecological Society of America Memphis, TN August 8, 2006


Elvis ecosystem localization visualization and information system l.jpg

ELVIS: Ecosystem Localization, Visualization, and Information System

Oreochromis niloticus

Nile tilapia

Bacteria

Microprotozoa

Amphithoe longimana

Caprella penantis

Cymadusa compta

Lembos rectangularis

Batea catharinensis

Ostracoda

Melanitta

Tadorna tadorna

Food web constructor

Species list constructor

?

. . .


Elvis s food web constructor predicts basic network structure l.jpg

ELVIS’s Food Web Constructor predicts basic network structure

Prelude to systems models


Slide4 l.jpg

G

taxon

S

taxon

Food Web

node

S

link

G

Evolutionary tree

A

step


Evolutionary distance weighting l.jpg

Evolutionary Distance Weighting

  • Set distance thresholds

  • 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

  • For each found link, compute weight based on distance

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


Food web database l.jpg

Food web database

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 l.jpg

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 correct89%

    Precision percentage of predicted links that are correct55%

    Recall percentage of actual links that are predicted47%


Choosing parameters l.jpg

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 threshold 2 steps up and 4 steps down l.jpg

Evolutionary distance threshold2 steps up and 4 steps down

recall

precision

steps down

steps up

steps up


Evolutionary direction penalty not very sensitive l.jpg

ancestor

descendent

siblings

Evolutionary direction penalty not very sensitive


Negative evidence discount is sensitive l.jpg

Negative evidence discount is sensitive


Results over all webs l.jpg

Results over all webs


Is evolutionary distance weighting better than strict database search l.jpg

***

Database search

Evolutionary distance weighting

***

%

***

Paired T-tests

df=251

***p<0.001

Is evolutionary distance weighting better than strict database search?

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


Older webs contribute l.jpg

Older webs contribute

Recall percentage of actual links that are predicted47%  48% with no EcoWEB data

Precision percentage of predicted links that are correct55% 39% with no EcoWEB data


Slide15 l.jpg

large webs have fewer unknown “taxa”

recent webs are bigger

large webs have better taxonomic resolution

…but large webs are harder to predict


Some phyla are easier to predict than others l.jpg

Some phyla are easier to predict than others


Slide17 l.jpg

How can we do better predicting links?

Trait space distance weighting

Euclidean distance in natural history

N-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


Ethan evolutionary trees and natural history ontology l.jpg

Animal Diversity Web

http://www.animaldiversity.org

geographic range

habitats

physical description

reproduction

lifespan

behavior and trophic info

conservation status

Triples

“Esox lucius” hasMaxMass “1.4 kg”

“Esox lucius” isSubclassOf “Esox”

“Esox” eats “Actinopterygii”

ETHANEvolutionary Trees and Natural History ontology


Umbc triple shop query what are body masses of fishes that eat fishes l.jpg

UMBC Triple ShopQueryWhat are body masses of fishes that eat fishes?

Enter a SPARQL query

SELECT 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 dataset find semantic web docs that can answer query l.jpg

Esox_lucius.owl

webs_publisher.php?

published_study=11

Actinopterygii.owl

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

http://swoogle.umbc.edu


Umbc triple shop get results apply query to dataset with semantic reasoning l.jpg

UMBC Triple ShopGet results Apply query to dataset with semantic reasoning.

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


Slide22 l.jpg

Summary

  • 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


Slide23 l.jpg

http://spire.umbc.edu

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


Slide24 l.jpg

Others

ETHANworkflow

KeywordsHTML

Keywords

OWL

XSLT

template

Filters

ETHAN

Taxon

acct

OWL

ADW

taxon acct

HTML

ADW

database

MySQL

Acct

data

tabular

text

Animal

name tree

Taxon Path

OWL

ITIS

Plants, etc.

Phylum-sized

ET chunk

OWL

Evolutionary

Tree side

of ontology

OWL

SPIRE taxon database

MySQL


Slide25 l.jpg

UMBC

Info. Retrieval Agents

Food Web Constructor

Evidence Provider

U Maryland

Semantic Prototypes In

Ecoinformatics

UC Davis

Semantic Web Tools

Species List constructor

NASA

Goddard

Rocky Mtn

Bio Lab

Invasive Species Forecasting System

Remote Sensing Data

Food Webs

Ecological Interaction

Ontologies


Food web constructor example nile tilapia in st marks l.jpg

Food Web Constructor example Nile Tilapia in St. Marks

http://spire.umbc.edu/fwc

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

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


Food web constructor generates possible links l.jpg

Food Web Constructor generates possible links


Evidence provider gives details l.jpg

Evidence provider gives details


Slide29 l.jpg

Nile tilapia – what organisms could be impacted?


Implications parameterized functions l.jpg

Implications: parameterized functions

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

  • 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


Distance weighting options l.jpg

3 changes

2 steps

X

Y

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


Ontologies l.jpg

has-a

has-a

TaxonA

HigherTaxon

TaxonB

is-a

is-a

is-a

is-a

Breeding Season

Reproductive Characteristic

Breeding Duration

Sexual maturity

Age of Sexual Maturity

Ontologies

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

“Higher Taxon” lives in “Australia”

“Taxon A” lives in “Australia”

“Taxon A” hasAgeOfSexualMaturity “1 year”

“TaxonA” hasBreedingDuration “5 months”

“Taxon B” lives in “Australia”


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