What constrains food webs? A maximum entropy framework for predicting their structure with minimal biases.
Food webs are complex ecological networks whose structure is both ecologically and statistically constrained, with many network properties being correlated with each other. Despite the recognition of these invariable relationships in food webs, the use of the principle of maximum entropy (MaxEnt) in...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-09-01
|
| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011458&type=printable |
| _version_ | 1797674074285015040 |
|---|---|
| author | Francis Banville Dominique Gravel Timothée Poisot |
| author_facet | Francis Banville Dominique Gravel Timothée Poisot |
| author_sort | Francis Banville |
| collection | DOAJ |
| description | Food webs are complex ecological networks whose structure is both ecologically and statistically constrained, with many network properties being correlated with each other. Despite the recognition of these invariable relationships in food webs, the use of the principle of maximum entropy (MaxEnt) in network ecology is still rare. This is surprising considering that MaxEnt is a statistical tool precisely designed for understanding and predicting many types of constrained systems. This principle asserts that the least-biased probability distribution of a system's property, constrained by prior knowledge about that system, is the one with maximum information entropy. MaxEnt has been proven useful in many ecological modeling problems, but its application in food webs and other ecological networks is limited. Here we show how MaxEnt can be used to derive many food-web properties both analytically and heuristically. First, we show how the joint degree distribution (the joint probability distribution of the numbers of prey and predators for each species in the network) can be derived analytically using the number of species and the number of interactions in food webs. Second, we present a heuristic and flexible approach of finding a network's adjacency matrix (the network's representation in matrix format) based on simulated annealing and SVD entropy. We built two heuristic models using the connectance and the joint degree sequence as statistical constraints, respectively. We compared both models' predictions against corresponding null and neutral models commonly used in network ecology using open access data of terrestrial and aquatic food webs sampled globally (N = 257). We found that the heuristic model constrained by the joint degree sequence was a good predictor of many measures of food-web structure, especially the nestedness and motifs distribution. Specifically, our results suggest that the structure of terrestrial and aquatic food webs is mainly driven by their joint degree distribution. |
| first_indexed | 2024-03-11T21:54:51Z |
| format | Article |
| id | doaj.art-8d8545db8aaf4898af8f0943ea7cd6eb |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-03-11T21:54:51Z |
| publishDate | 2023-09-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-8d8545db8aaf4898af8f0943ea7cd6eb2023-09-26T05:30:47ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582023-09-01199e101145810.1371/journal.pcbi.1011458What constrains food webs? A maximum entropy framework for predicting their structure with minimal biases.Francis BanvilleDominique GravelTimothée PoisotFood webs are complex ecological networks whose structure is both ecologically and statistically constrained, with many network properties being correlated with each other. Despite the recognition of these invariable relationships in food webs, the use of the principle of maximum entropy (MaxEnt) in network ecology is still rare. This is surprising considering that MaxEnt is a statistical tool precisely designed for understanding and predicting many types of constrained systems. This principle asserts that the least-biased probability distribution of a system's property, constrained by prior knowledge about that system, is the one with maximum information entropy. MaxEnt has been proven useful in many ecological modeling problems, but its application in food webs and other ecological networks is limited. Here we show how MaxEnt can be used to derive many food-web properties both analytically and heuristically. First, we show how the joint degree distribution (the joint probability distribution of the numbers of prey and predators for each species in the network) can be derived analytically using the number of species and the number of interactions in food webs. Second, we present a heuristic and flexible approach of finding a network's adjacency matrix (the network's representation in matrix format) based on simulated annealing and SVD entropy. We built two heuristic models using the connectance and the joint degree sequence as statistical constraints, respectively. We compared both models' predictions against corresponding null and neutral models commonly used in network ecology using open access data of terrestrial and aquatic food webs sampled globally (N = 257). We found that the heuristic model constrained by the joint degree sequence was a good predictor of many measures of food-web structure, especially the nestedness and motifs distribution. Specifically, our results suggest that the structure of terrestrial and aquatic food webs is mainly driven by their joint degree distribution.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011458&type=printable |
| spellingShingle | Francis Banville Dominique Gravel Timothée Poisot What constrains food webs? A maximum entropy framework for predicting their structure with minimal biases. PLoS Computational Biology |
| title | What constrains food webs? A maximum entropy framework for predicting their structure with minimal biases. |
| title_full | What constrains food webs? A maximum entropy framework for predicting their structure with minimal biases. |
| title_fullStr | What constrains food webs? A maximum entropy framework for predicting their structure with minimal biases. |
| title_full_unstemmed | What constrains food webs? A maximum entropy framework for predicting their structure with minimal biases. |
| title_short | What constrains food webs? A maximum entropy framework for predicting their structure with minimal biases. |
| title_sort | what constrains food webs a maximum entropy framework for predicting their structure with minimal biases |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011458&type=printable |
| work_keys_str_mv | AT francisbanville whatconstrainsfoodwebsamaximumentropyframeworkforpredictingtheirstructurewithminimalbiases AT dominiquegravel whatconstrainsfoodwebsamaximumentropyframeworkforpredictingtheirstructurewithminimalbiases AT timotheepoisot whatconstrainsfoodwebsamaximumentropyframeworkforpredictingtheirstructurewithminimalbiases |