Predicting food web connectivity Phylogenetic scope, evidence thresholds, and intelligent agents Cynthia Sims Parr Ecological 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 niloticus Nile 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 taxon S A

Evolutionary Distance Weighting 1. 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

Food web database 4600 distinct taxa Food web data: Cohen 1989, Dunne et al. 2006, Vazquez 2006, Jonsson et al Evolutionary tree: Parr et al 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 threshold 2 steps up and 4 steps down steps up steps down precision steps up recall

Evolutionary direction penalty not very sensitive ancestor descendent siblings

Negative evidence discount is sensitive

Results over all webs

Is evolutionary distance weighting better than strict database search? Paired T-tests df=251 ***p<0.001 Database search Evolutionary 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 large webs have better taxonomic resolution recent webs are bigger large webs have fewer unknown “taxa”

Some phyla are easier to predict than others

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. LinkStatus AB = ƒ(α, size A, size B ), ƒ(β, stoich A, stoich B ) … …need more data How can we do better predicting links?

ETHAN Evolutionary Trees and Natural History ontology Animal Diversity Web 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 Query What 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. Actinopterygii.owl webs_publisher.php? published_study=11 Esox_lucius.owl

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

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

ADW database MySQL XSLT template ADW taxon acct HTML Keywords HTML ETHAN Taxon acct OWL SPIRE taxon database MySQL Evolutionary Tree side of ontology OWL Phylum- sized ET chunk OWL Taxon Path OWL Filters Acct data tabular text Others ITIS ETHAN workflow Plants, etc. Animal name tree Keywords OWL

Semantic Prototypes In Ecoinformatics UMBC U Maryland NASA Goddard NASA Goddard Rocky Mtn Bio Lab Rocky Mtn Bio Lab UC Davis Semantic Web Tools Info. Retrieval Agents Food Web Constructor Evidence Provider Invasive Species Forecasting System Remote Sensing Data Food Webs Ecological Interaction Ontologies Species List constructor

Food Web Constructor example Nile Tilapia in St. Marks Question What are potential predators and prey of Oreochromis niloticus in the St. Marks estuary in Florida? Procedure Submit species list for St. Marks, with Oreochromis niloticus added.

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 LinkPredicted CD = ƒ(α, size C,size D ) + ƒ(β, stoich C,stoich D )

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” Ontologies Richer 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 HigherTaxon is-a TaxonA Age of Sexual Maturity has-a is-a