Ecological interactions and the Netflix problem
Species interactions are a key component of ecosystems but we generally have an incomplete picture of who-eats-who in a given community. Different techniques have been devised to predict species interactions using theoretical models or abundances. Here, we explore the K nearest neighbour approach, w...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2017-08-01
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| Series: | PeerJ |
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| Online Access: | https://peerj.com/articles/3644.pdf |
| _version_ | 1827608161460158464 |
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| author | Philippe Desjardins-Proulx Idaline Laigle Timothée Poisot Dominique Gravel |
| author_facet | Philippe Desjardins-Proulx Idaline Laigle Timothée Poisot Dominique Gravel |
| author_sort | Philippe Desjardins-Proulx |
| collection | DOAJ |
| description | Species interactions are a key component of ecosystems but we generally have an incomplete picture of who-eats-who in a given community. Different techniques have been devised to predict species interactions using theoretical models or abundances. Here, we explore the K nearest neighbour approach, with a special emphasis on recommendation, along with a supervised machine learning technique. Recommenders are algorithms developed for companies like Netflix to predict whether a customer will like a product given the preferences of similar customers. These machine learning techniques are well-suited to study binary ecological interactions since they focus on positive-only data. By removing a prey from a predator, we find that recommenders can guess the missing prey around 50% of the times on the first try, with up to 881 possibilities. Traits do not improve significantly the results for the K nearest neighbour, although a simple test with a supervised learning approach (random forests) show we can predict interactions with high accuracy using only three traits per species. This result shows that binary interactions can be predicted without regard to the ecological community given only three variables: body mass and two variables for the species’ phylogeny. These techniques are complementary, as recommenders can predict interactions in the absence of traits, using only information about other species’ interactions, while supervised learning algorithms such as random forests base their predictions on traits only but do not exploit other species’ interactions. Further work should focus on developing custom similarity measures specialized for ecology to improve the KNN algorithms and using richer data to capture indirect relationships between species. |
| first_indexed | 2024-03-09T07:06:03Z |
| format | Article |
| id | doaj.art-2bf417db6ed743aebc277fde740d8b98 |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T07:06:03Z |
| publishDate | 2017-08-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-2bf417db6ed743aebc277fde740d8b982023-12-03T09:30:07ZengPeerJ Inc.PeerJ2167-83592017-08-015e364410.7717/peerj.3644Ecological interactions and the Netflix problemPhilippe Desjardins-Proulx0Idaline Laigle1Timothée Poisot2Dominique Gravel3Université de Sherbrooke, Sherbrooke, Quebec, CanadaUniversité de Sherbrooke, Sherbrooke, Quebec, CanadaUniversité de Montréal, Montréal, Quebec, CanadaUniversité de Sherbrooke, Sherbrooke, Quebec, CanadaSpecies interactions are a key component of ecosystems but we generally have an incomplete picture of who-eats-who in a given community. Different techniques have been devised to predict species interactions using theoretical models or abundances. Here, we explore the K nearest neighbour approach, with a special emphasis on recommendation, along with a supervised machine learning technique. Recommenders are algorithms developed for companies like Netflix to predict whether a customer will like a product given the preferences of similar customers. These machine learning techniques are well-suited to study binary ecological interactions since they focus on positive-only data. By removing a prey from a predator, we find that recommenders can guess the missing prey around 50% of the times on the first try, with up to 881 possibilities. Traits do not improve significantly the results for the K nearest neighbour, although a simple test with a supervised learning approach (random forests) show we can predict interactions with high accuracy using only three traits per species. This result shows that binary interactions can be predicted without regard to the ecological community given only three variables: body mass and two variables for the species’ phylogeny. These techniques are complementary, as recommenders can predict interactions in the absence of traits, using only information about other species’ interactions, while supervised learning algorithms such as random forests base their predictions on traits only but do not exploit other species’ interactions. Further work should focus on developing custom similarity measures specialized for ecology to improve the KNN algorithms and using richer data to capture indirect relationships between species.https://peerj.com/articles/3644.pdfFood webEcologySpecies interactions |
| spellingShingle | Philippe Desjardins-Proulx Idaline Laigle Timothée Poisot Dominique Gravel Ecological interactions and the Netflix problem PeerJ Food web Ecology Species interactions |
| title | Ecological interactions and the Netflix problem |
| title_full | Ecological interactions and the Netflix problem |
| title_fullStr | Ecological interactions and the Netflix problem |
| title_full_unstemmed | Ecological interactions and the Netflix problem |
| title_short | Ecological interactions and the Netflix problem |
| title_sort | ecological interactions and the netflix problem |
| topic | Food web Ecology Species interactions |
| url | https://peerj.com/articles/3644.pdf |
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