Variably hungry caterpillars: predictive models and foliar chemistry suggest how to eat a rainforest Article Swipe
Simon T. Segar
,
Martin Volf
,
Brus Isua
,
Mentap Sisol
,
Conor Redmond
,
Margaret E Rosati
,
Bradley Gewa
,
Kenneth Molem
,
Chris Dahl
,
Jeremy D. Holloway
,
Yves Basset
,
Scott E. Miller
,
George D. Weiblen
,
Juha‐Pekka Salminen
,
Vojtěch Novotný
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1098/rspb.2017.1803
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1098/rspb.2017.1803
A long-term goal in evolutionary ecology is to explain the incredible diversity of insect herbivores and patterns of host plant use in speciose groups like tropical Lepidoptera. Here, we used standardized food-web data, multigene phylogenies of both trophic levels and plant chemistry data to model interactions between Lepidoptera larvae (caterpillars) from two lineages (Geometridae and Pyraloidea) and plants in a species-rich lowland rainforest in New Guinea. Model parameters were used to make and test blind predictions for two hectares of an exhaustively sampled forest. For pyraloids, we relied on phylogeny alone and predicted 54% of species-level interactions, translating to 79% of all trophic links for individual insects, by sampling insects from only 15% of local woody plant diversity. The phylogenetic distribution of host-plant associations in polyphagous geometrids was less conserved, reducing accuracy. In a truly quantitative food web, only 40% of pair-wise interactions were described correctly in geometrids. Polyphenol oxidative activity (but not protein precipitation capacity) was important for understanding the occurrence of geometrids (but not pyraloids) across their hosts. When both foliar chemistry and plant phylogeny were included, we predicted geometrid–plant occurrence with 89% concordance. Such models help to test macroevolutionary hypotheses at the community level.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1098/rspb.2017.1803
- OA Status
- green
- Cited By
- 28
- References
- 65
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2767559346
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2767559346Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1098/rspb.2017.1803Digital Object Identifier
- Title
-
Variably hungry caterpillars: predictive models and foliar chemistry suggest how to eat a rainforestWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2017Year of publication
- Publication date
-
2017-11-08Full publication date if available
- Authors
-
Simon T. Segar, Martin Volf, Brus Isua, Mentap Sisol, Conor Redmond, Margaret E Rosati, Bradley Gewa, Kenneth Molem, Chris Dahl, Jeremy D. Holloway, Yves Basset, Scott E. Miller, George D. Weiblen, Juha‐Pekka Salminen, Vojtěch NovotnýList of authors in order
- Landing page
-
https://doi.org/10.1098/rspb.2017.1803Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.ncbi.nlm.nih.gov/pmc/articles/5698651Direct OA link when available
- Concepts
-
Lepidoptera genitalia, Biology, Trophic level, Herbivore, Rainforest, Ecology, Arboreal locomotion, Phylogenetics, Phylogenetic tree, Mimicry, Food web, Evolutionary ecology, Host (biology), Habitat, Biochemistry, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
28Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 5, 2023: 2, 2022: 3, 2021: 6, 2020: 6Per-year citation counts (last 5 years)
- References (count)
-
65Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| sustainable_development_goals[0].id | https://metadata.un.org/sdg/15 |
| sustainable_development_goals[0].score | 0.5299999713897705 |
| sustainable_development_goals[0].display_name | Life in Land |
| citation_normalized_percentile.value | 0.94375819 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |