A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Unravelling changing interspecific interactions across environmental gradients using Markov random fields
Tekijät: Clark NJ, Wells K, Lindberg O
Kustantaja: WILEY
Julkaisuvuosi: 2018
Journal: Ecology
Tietokannassa oleva lehden nimi: ECOLOGY
Lehden akronyymi: ECOLOGY
Vuosikerta: 99
Numero: 6
Aloitussivu: 1277
Lopetussivu: 1283
Sivujen määrä: 7
ISSN: 0012-9658
DOI: https://doi.org/10.1002/ecy.2221
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/32046549
Tiivistelmä
Inferring interactions between co-occurring species is key to identify processes governing community assembly. Incorporating interspecific interactions in predictive models is common in ecology, yet most methods do not adequately account for indirect interactions (where an interaction between two species is masked by their shared interactions with a third) and assume interactions do not vary along environmental gradients. Markov random fields (MRF) overcome these limitations by estimating interspecific interactions, while controlling for indirect interactions, from multispecies occurrence data. We illustrate the utility of MRFs for ecologists interested in interspecific interactions, and demonstrate how covariates can be included (a set of models known as Conditional Random Fields, CRF) to infer how interactions vary along environmental gradients. We apply CRFs to two data sets of presence-absence data. The first illustrates how blood parasite (Haemoproteus, Plasmodium, and nematode microfilaria spp.) co-infection probabilities covary with relative abundance of their avian hosts. The second shows that co-occurrences between mosquito larvae and predatory insects vary along water temperature gradients. Other applications are discussed, including the potential to identify replacement or shifting impacts of highly connected species along climate or land-use gradients. We provide tools for building CRFs and plotting/interpreting results as an R package.
Inferring interactions between co-occurring species is key to identify processes governing community assembly. Incorporating interspecific interactions in predictive models is common in ecology, yet most methods do not adequately account for indirect interactions (where an interaction between two species is masked by their shared interactions with a third) and assume interactions do not vary along environmental gradients. Markov random fields (MRF) overcome these limitations by estimating interspecific interactions, while controlling for indirect interactions, from multispecies occurrence data. We illustrate the utility of MRFs for ecologists interested in interspecific interactions, and demonstrate how covariates can be included (a set of models known as Conditional Random Fields, CRF) to infer how interactions vary along environmental gradients. We apply CRFs to two data sets of presence-absence data. The first illustrates how blood parasite (Haemoproteus, Plasmodium, and nematode microfilaria spp.) co-infection probabilities covary with relative abundance of their avian hosts. The second shows that co-occurrences between mosquito larvae and predatory insects vary along water temperature gradients. Other applications are discussed, including the potential to identify replacement or shifting impacts of highly connected species along climate or land-use gradients. We provide tools for building CRFs and plotting/interpreting results as an R package.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
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