560 lines
58 KiB
BibTeX
560 lines
58 KiB
BibTeX
@book{ottawafield-naturalistsclubCanadianFieldnaturalist1976,
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title = {The {{Canadian}} Field-Naturalist},
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author = {Ottawa Field-Naturalists' Club and Club, Ottawa Field-Naturalists'},
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date = {1976},
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volume = {90},
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pages = {1--568},
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publisher = {Ottawa Field-Naturalists' Club},
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location = {Ottawa},
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issn = {0008-3550},
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url = {https://www.biodiversitylibrary.org/item/89149},
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pagetotal = {568},
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file = {/home/polarolouis/Zotero/storage/DFN9BYBR/28045499.html}
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}
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@online{yumpu.comInsectPollinatorsMer,
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title = {Insect Pollinators of the {{Mer Bleue}} Peat Bog of {{Ottawa}} - {{Biodiversity}} ...},
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author = {Yumpu.com},
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url = {https://www.yumpu.com/en/document/view/11762821/insect-pollinators-of-the-mer-bleue-peat-bog-of-ottawa-biodiversity-},
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urldate = {2023-08-06},
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abstract = {Insect pollinators of the Mer Bleue peat bog of Ottawa - Biodiversity ...},
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langid = {english},
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organization = {yumpu.com},
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file = {/home/polarolouis/Zotero/storage/DIXT2PYL/insect-pollinators-of-the-mer-bleue-peat-bog-of-ottawa-biodiversity-.html}
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}
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@article{hubertComparingPartitions1985,
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title = {Comparing Partitions},
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author = {Hubert, Lawrence and Arabie, Phipps},
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date = {1985-12-01},
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journaltitle = {Journal of Classification},
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volume = {2},
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number = {1},
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pages = {193--218},
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issn = {1432-1343},
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doi = {10.1007/BF01908075},
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url = {https://doi.org/10.1007/BF01908075},
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urldate = {2023-07-04},
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abstract = {The problem of comparing two different partitions of a finite set of objects reappears continually in the clustering literature. We begin by reviewing a well-known measure of partition correspondence often attributed to Rand (1971), discuss the issue of correcting this index for chance, and note that a recent normalization strategy developed by Morey and Agresti (1984) and adopted by others (e.g., Miligan and Cooper 1985) is based on an incorrect assumption. Then, the general problem of comparing partitions is approached indirectly by assessing the congruence of two proximity matrices using a simple cross-product measure. They are generated from corresponding partitions using various scoring rules. Special cases derivable include traditionally familiar statistics and/or ones tailored to weight certain object pairs differentially. Finally, we propose a measure based on the comparison of object triples having the advantage of a probabilistic interpretation in addition to being corrected for chance (i.e., assuming a constant value under a reasonable null hypothesis) and bounded between ±1.},
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langid = {english},
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keywords = {Consensus indices,Measures of agreement,Measures of association},
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file = {/home/polarolouis/Zotero/storage/7TKW7HEM/Hubert et Arabie - 1985 - Comparing partitions.pdf}
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}
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@online{AccueilMIAParisSaclay,
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title = {Accueil | {{MIA Paris-Saclay}}},
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url = {https://mia-ps.inrae.fr/},
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urldate = {2023-07-03},
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file = {/home/polarolouis/Zotero/storage/I7FWTZC3/mia-ps.inrae.fr.html}
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}
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@article{biernackiAssessingMixtureModel2000,
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title = {Assessing a Mixture Model for Clustering with the Integrated Completed Likelihood},
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author = {Biernacki, C. and Celeux, G. and Govaert, G.},
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date = {2000-07},
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journaltitle = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
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volume = {22},
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number = {7},
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pages = {719--725},
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issn = {1939-3539},
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doi = {10.1109/34.865189},
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abstract = {We propose an assessing method of mixture model in a cluster analysis setting with integrated completed likelihood. For this purpose, the observed data are assigned to unknown clusters using a maximum a posteriori operator. Then, the integrated completed likelihood (ICL) is approximated using the Bayesian information criterion (BIC). Numerical experiments on simulated and real data of the resulting ICL criterion show that it performs well both for choosing a mixture model and a relevant number of clusters. In particular, ICL appears to be more robust than BIC to violation of some of the mixture model assumptions and it can select a number of dusters leading to a sensible partitioning of the data.},
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eventtitle = {{{IEEE Transactions}} on {{Pattern Analysis}} and {{Machine Intelligence}}},
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keywords = {Bayesian methods,Context modeling,Gaussian distribution,Numerical simulation,Probability distribution,Robustness},
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file = {/home/polarolouis/Zotero/storage/MK9H446U/Biernacki et al. - 2000 - Assessing a mixture model for clustering with the .pdf}
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}
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@article{aubertModelbasedBiclusteringOverdispersed2021,
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title = {Model-Based Biclustering for Overdispersed Count Data with Application in Microbial Ecology},
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author = {Aubert, Julie and Schbath, Sophie and Robin, Stéphane},
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date = {2021},
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journaltitle = {Methods in Ecology and Evolution},
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volume = {12},
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number = {6},
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pages = {1050--1061},
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issn = {2041-210X},
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doi = {10.1111/2041-210X.13582},
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url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13582},
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urldate = {2023-06-22},
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abstract = {Different studies have shown that microbial communities living in animals (humans included), in or around plants have a significant impact on health and disease of their host and on various services, such as adaptation under stressing environment. The basic input data to study microbiomes is a matrix representing abundance data of micro-organisms across different sampling units. Such a matrix typically corresponds to taxonomic profiles derived from the high-throughput sequencing of environmental samples. Biclustering is one way to study the interactions between the structure of micro-organism communities and the environmental samples they come from. We propose a latent block model (LBM) and an associated inference procedure for the biclustering of rows and columns of abundance matrices. The LBM assumes that micro-organisms (rows) and environmental samples (columns) can both be clustered into groups characterizing preferential interaction or avoidance. We use the Poisson–Gamma distribution to model the overdispersion observed in microbial abundance data and introduce row and column effects to account for the sequencing effort in each sample and the mean abundance of each micro-organism. Because the latent variables are not independent conditionally on the observed ones, classical maximum likelihood inference is intractable. We then derive a variational-based inference algorithm and propose a strategy to select the number of biclusters. We illustrate the flexibility and performance of our approach both on a simulation study and on three ecological datasets. The model-based framework allows us to adapt to peculiarities of microbial ecological abundance data and allows us to explore relationships between entities of two different natures. We implemented our method in the cobiclust R package available on the CRAN and built a website with example of usage (https://julieaubert.github.io/cobiclust/cobiclust-example1.html).},
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langid = {english},
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keywords = {count data,latent block model,metabarcoding,microbial interactions,model-based biclustering,Poisson–Gamma distribution,variational EM algorithm},
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file = {/home/polarolouis/Zotero/storage/A4V9MJAF/Aubert et al. - 2021 - Model-based biclustering for overdispersed count d.pdf}
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}
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@dataset{thebaultelisaDatabasePlantpollinatorNetworks2020,
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title = {A Database of Plant-Pollinator Networks},
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author = {Thébault, Elisa and Fontaine, Colin},
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date = {2020-12-01},
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publisher = {Zenodo},
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doi = {10.5281/ZENODO.4300427},
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url = {https://zenodo.org/record/4300427},
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urldate = {2023-06-21},
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abstract = {This database assembles different published datasets of observed interaction networks between plants and pollinators, which were extracted from articles, theses and existing online databases. Each row in the data table corresponds to an interaction between a plant and a pollinator species reported at a given site by a given publication.},
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version = {1},
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keywords = {data,diversity,flower visitors,mutualistic network,plant-pollinator,plant-pollinator interaction}
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}
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@dataset{thebaultelisaDatabasePlantpollinatorNetworks2022,
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title = {A Database of Plant-Pollinator Networks},
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author = {Thébault, Elisa and Fontaine, Colin},
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namea = {Doré, Maël and Parra, Santiago},
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nameatype = {collaborator},
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date = {2022-06-10},
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publisher = {Zenodo},
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doi = {10.5281/ZENODO.4300426},
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url = {https://zenodo.org/record/4300426},
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urldate = {2023-06-21},
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abstract = {This database assembles different published datasets of observed interaction networks between plants and pollinators, which were extracted from articles, theses and existing online databases. Each row in the data table corresponds to an interaction between a plant and a pollinator species reported at a given site by a given publication.},
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version = {2},
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keywords = {data,diversity,flower visitors,mutualistic network,plant-pollinator,plant-pollinator interaction}
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}
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@article{maeldoreMaelDorePollination_networksScripts2020,
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title = {{{MaelDore}}/{{Pollination}}\_networks: {{R}} Scripts for {{Doré}} et al., 2020 - {{Relative}} Effects of Anthropogenic Pressures, Climate, and Sampling Design on the Structure of Pollination Networks at the Global Scale},
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shorttitle = {{{MaelDore}}/{{Pollination}}\_networks},
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author = {MaelDore},
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date = {2020-11-25},
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publisher = {Zenodo},
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doi = {10.5281/ZENODO.4290503},
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url = {https://zenodo.org/record/4290503},
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urldate = {2023-06-21},
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abstract = {R scripts for Doré et al., 2020 - Relative effects of anthropogenic pressures, climate, and sampling design on the structure of pollination networks at the global scale},
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keywords = {data,plant-pollinator}
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}
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@dataset{thebaultelisaDatabasePlantpollinatorNetworks2022a,
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title = {A Database of Plant-Pollinator Networks},
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author = {Thébault, Elisa and Fontaine, Colin},
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namea = {Doré, Maël and Parra, Santiago},
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nameatype = {collaborator},
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date = {2022-06-10},
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publisher = {Zenodo},
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doi = {10.5281/ZENODO.6630184},
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url = {https://zenodo.org/record/6630184},
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urldate = {2023-06-21},
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abstract = {This database assembles different published datasets of observed interaction networks between plants and pollinators, which were extracted from articles, theses and existing online databases. Each row in the data table corresponds to an interaction between a plant and a pollinator species reported at a given site by a given publication.},
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version = {2},
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keywords = {data,diversity,flower visitors,mutualistic network,plant-pollinator,plant-pollinator interaction}
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}
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@dataset{thebaultDatabasePlantpollinatorNetworks2020,
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title = {A Database of Plant-Pollinator Networks},
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author = {Thébault, Elisa and Fontaine, Colin},
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date = {2020-12-01},
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publisher = {Zenodo},
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doi = {10.5281/zenodo.4300427},
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url = {https://zenodo.org/record/4300427},
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urldate = {2023-06-21},
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abstract = {This database assembles different published datasets of observed interaction networks between plants and pollinators, which were extracted from articles, theses and existing online databases. Each row in the data table corresponds to an interaction between a plant and a pollinator species reported at a given site by a given publication.},
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version = {1},
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keywords = {diversity,flower visitors,mutualistic network,plant-pollinator interaction}
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}
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@article{doreRelativeEffectsAnthropogenic2021,
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title = {Relative Effects of Anthropogenic Pressures, Climate, and Sampling Design on the Structure of Pollination Networks at the Global Scale},
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author = {Doré, Maël and Fontaine, Colin and Thébault, Elisa},
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date = {2021},
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journaltitle = {Global Change Biology},
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volume = {27},
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number = {6},
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pages = {1266--1280},
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issn = {1365-2486},
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doi = {10.1111/gcb.15474},
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url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.15474},
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urldate = {2023-06-21},
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abstract = {Pollinators provide crucial ecosystem services that underpin to wild plant reproduction and yields of insect-pollinated crops. Understanding the relative impacts of anthropogenic pressures and climate on the structure of plant–pollinator interaction networks is vital considering ongoing global change and pollinator decline. Our ability to predict the consequences of global change for pollinator assemblages worldwide requires global syntheses, but these analytical approaches may be hindered by variable methods among studies that either invalidate comparisons or mask biological phenomena. Here we conducted a synthetic analysis that assesses the relative impact of anthropogenic pressures and climatic variability, and accounts for heterogeneity in sampling methodology to reveal network responses at the global scale. We analyzed an extensive dataset, comprising 295 networks over 123 locations all over the world, and reporting over 50,000 interactions between flowering plant species and their insect visitors. Our study revealed that anthropogenic pressures correlate with an increase in generalism in pollination networks while pollinator richness and taxonomic composition are more related to climatic variables with an increase in dipteran pollinator richness associated with cooler temperatures. The contrasting response of species richness and generalism of the plant–pollinator networks stresses the importance of considering interaction network structure alongside diversity in ecological monitoring. In addition, differences in sampling design explained more variation than anthropogenic pressures or climate on both pollination networks richness and generalism, highlighting the crucial need to report and incorporate sampling design in macroecological comparative studies of pollination networks. As a whole, our study reveals a potential human impact on pollination networks at a global scale. However, further research is needed to evaluate potential consequences of loss of specialist species and their unique ecological interactions and evolutionary pathways on the ecosystem pollination function at a global scale.},
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langid = {english},
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keywords = {anthropogenic pressures,climate,connectance,data,generalism,human impacts,plant-pollinator,pollination networks,richness,sampling effects,specialization},
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file = {/home/polarolouis/Zotero/storage/89ZXBJQP/10.1111@gcb.15474.pdf.pdf;/home/polarolouis/Zotero/storage/IVR6RGG7/Doré et al. - 2021 - Relative effects of anthropogenic pressures, clima.pdf;/home/polarolouis/Zotero/storage/WSJ4DV98/gcb.html}
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}
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@online{larousseDefinitionsBipartiBipartite,
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title = {Définitions : biparti, bipartite - Dictionnaire de français Larousse},
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shorttitle = {Définitions},
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author = {Larousse, Éditions},
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url = {https://www.larousse.fr/dictionnaires/francais/biparti/9503},
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urldate = {2023-06-17},
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abstract = {biparti, bipartite - Définitions Français : Retrouvez la définition de biparti, bipartite, ainsi que les difficultés... - synonymes, homonymes, difficultés, citations.},
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langid = {french},
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file = {/home/polarolouis/Zotero/storage/MA2VH6NX/9503.html}
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}
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@online{WebLifeEcological,
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title = {Web of {{Life}}: Ecological Networks Database},
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url = {https://www.web-of-life.es/map.php},
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urldate = {2023-06-17},
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keywords = {networks,site},
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file = {/home/polarolouis/Zotero/storage/9WZE8QLQ/map.html}
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}
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@article{govaertEMAlgorithmBlock2005,
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title = {An {{EM}} Algorithm for the Block Mixture Model},
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author = {Govaert, G. and Nadif, M.},
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date = {2005-04},
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journaltitle = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
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volume = {27},
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number = {4},
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pages = {643--647},
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issn = {1939-3539},
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doi = {10.1109/TPAMI.2005.69},
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abstract = {Although many clustering procedures aim to construct an optimal partition of objects or, sometimes, of variables, there are other methods, called block clustering methods, which consider simultaneously the two sets and organize the data into homogeneous blocks. Recently, we have proposed a new mixture model called block mixture model which takes into account this situation. This model allows one to embed simultaneous clustering of objects and variables in a mixture approach. We have studied this probabilistic model under the classification likelihood approach and developed a new algorithm for simultaneous partitioning based on the classification EM algorithm. In this paper, we consider the block clustering problem under the maximum likelihood approach and the goal of our contribution is to estimate the parameters of this model. Unfortunately, the application of the EM algorithm for the block mixture model cannot be made directly; difficulties arise due to the dependence structure in the model and approximations are required. Using a variational approximation, we propose a generalized EM algorithm to estimate the parameters of the block mixture model and, to illustrate our approach, we study the case of binary data by using a Bernoulli block mixture.},
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eventtitle = {{{IEEE Transactions}} on {{Pattern Analysis}} and {{Machine Intelligence}}},
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keywords = {Approximation algorithms,Classification algorithms,Clustering algorithms,Clustering methods,Data mining,EM algorithm,Index Terms- Block mixture model,Maximum likelihood estimation,Parameter estimation,Partitioning algorithms,Self organizing feature maps,Sparse matrices,variational approximation.},
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file = {/home/polarolouis/Zotero/storage/6IG45HH2/govaert2005.pdf.pdf;/home/polarolouis/Zotero/storage/TL8M3XRF/Govaert et Nadif - 2005 - An EM algorithm for the block mixture model.pdf;/home/polarolouis/Zotero/storage/2Y48IB26/1401917.html}
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}
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@article{daudinMixtureModelRandom2008,
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title = {A Mixture Model for Random Graphs},
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author = {Daudin, J.-J. and Picard, F. and Robin, S.},
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date = {2008-06-01},
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journaltitle = {Stat Comput},
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volume = {18},
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number = {2},
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pages = {173--183},
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issn = {1573-1375},
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doi = {10.1007/s11222-007-9046-7},
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url = {https://doi.org/10.1007/s11222-007-9046-7},
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urldate = {2023-06-16},
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abstract = {The Erdös–Rényi model of a network is simple and possesses many explicit expressions for average and asymptotic properties, but it does not fit well to real-world networks. The vertices of those networks are often structured in unknown classes (functionally related proteins or social communities) with different connectivity properties. The stochastic block structures model was proposed for this purpose in the context of social sciences, using a Bayesian approach. We consider the same model in a frequentest statistical framework. We give the degree distribution and the clustering coefficient associated with this model, a variational method to estimate its parameters and a model selection criterion to select the number of classes. This estimation procedure allows us to deal with large networks containing thousands of vertices. The method is used to uncover the modular structure of a network of enzymatic reactions.},
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langid = {english},
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keywords = {Mixture models,Random graphs,Variational method},
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file = {/home/polarolouis/Zotero/storage/439HK27B/Daudin et al. - 2008 - A mixture model for random graphs.pdf;/home/polarolouis/Zotero/storage/HVVF5MNY/daudin2007.pdf.pdf}
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}
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@article{hollandStochasticBlockmodelsFirst1983,
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title = {Stochastic Blockmodels: {{First}} Steps},
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shorttitle = {Stochastic Blockmodels},
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author = {Holland, Paul W. and Laskey, Kathryn Blackmond and Leinhardt, Samuel},
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date = {1983-06-01},
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journaltitle = {Social Networks},
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volume = {5},
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number = {2},
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pages = {109--137},
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issn = {0378-8733},
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doi = {10.1016/0378-8733(83)90021-7},
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url = {https://www.sciencedirect.com/science/article/pii/0378873383900217},
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urldate = {2023-06-15},
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abstract = {A stochastic model is proposed for social networks in which the actors in a network are partitioned into subgroups called blocks. The model provides a stochastic generalization of the blockmodel. Estimation techniques are developed for the special case of a single relation social network, with blocks specified a priori. An extension of the model allows for tendencies toward reciprocation of ties beyond those explained by the partition. The extended model provides a one degree-of-freedom test of the model. A numerical example from the social network literature is used to illustrate the methods.},
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langid = {english},
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file = {/home/polarolouis/Zotero/storage/6F8YT8AD/holland1983.pdf.pdf;/home/polarolouis/Zotero/storage/7DSZ3KD9/Holland et al. - 1983 - Stochastic blockmodels First steps.pdf;/home/polarolouis/Zotero/storage/DUL2RV8Q/holland1983.pdf.pdf;/home/polarolouis/Zotero/storage/G9KZBG9W/0378873383900217.html}
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}
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@article{snijdersEstimationPredictionStochastic1997,
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title = {Estimation and {{Prediction}} for {{Stochastic Blockmodels}} for {{Graphs}} with {{Latent Block Structure}}},
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author = {Snijders, Tom A.B. and Nowicki, Krzysztof},
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date = {1997-01-01},
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journaltitle = {J. of Classification},
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volume = {14},
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number = {1},
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pages = {75--100},
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issn = {1432-1343},
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doi = {10.1007/s003579900004},
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url = {https://doi.org/10.1007/s003579900004},
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urldate = {2023-06-15},
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abstract = {blockmodeling for graphs is proposed. The model assumes that the vertices of the graph are partitioned into two unknown blocks and that the probability of an edge between two vertices depends only on the blocks to which they belong. Statistical procedures are derived for estimating the probabilities of edges and for predicting the block structure from observations of the edge pattern only. ML estimators can be computed using the EM algorithm, but this strategy is practical only for small graphs. A Bayesian estimator, based on the Gibbs sampling, is proposed. This estimator is practical also for large graphs. When ML estimators are used, the block structure can be predicted based on predictive likelihood. When Gibbs sampling is used, the block structure can be predicted from posterior predictive probabilities. A side result is that when the number of vertices tends to infinity while the probabilities remain constant, the block structure can be recovered correctly with probability tending to 1.},
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langid = {english},
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keywords = {Bayesian Estimator,Block Structure,Gibbs Sampling,Large Graph,Statistical Procedure},
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file = {/home/polarolouis/Zotero/storage/2GYRASW5/snijders1997.pdf.pdf;/home/polarolouis/Zotero/storage/JJNQV32Y/Snijders et Nowicki - 1997 - Estimation and Prediction for Stochastic Blockmode.pdf;/home/polarolouis/Zotero/storage/LXGG9SRP/snijders1997.pdf.pdf}
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}
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@article{pavlopoulosBipartiteGraphsSystems2018,
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title = {Bipartite Graphs in Systems Biology and Medicine: A Survey of Methods and Applications},
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shorttitle = {Bipartite Graphs in Systems Biology and Medicine},
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author = {Pavlopoulos, Georgios A and Kontou, Panagiota I and Pavlopoulou, Athanasia and Bouyioukos, Costas and Markou, Evripides and Bagos, Pantelis G},
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date = {2018-04-01},
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journaltitle = {GigaScience},
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volume = {7},
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number = {4},
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pages = {giy014},
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issn = {2047-217X},
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doi = {10.1093/gigascience/giy014},
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url = {https://doi.org/10.1093/gigascience/giy014},
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urldate = {2023-06-15},
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||
abstract = {The latest advances in high-throughput techniques during the past decade allowed the systems biology field to expand significantly. Today, the focus of biologists has shifted from the study of individual biological components to the study of complex biological systems and their dynamics at a larger scale. Through the discovery of novel bioentity relationships, researchers reveal new information about biological functions and processes. Graphs are widely used to represent bioentities such as proteins, genes, small molecules, ligands, and others such as nodes and their connections as edges within a network. In this review, special focus is given to the usability of bipartite graphs and their impact on the field of network biology and medicine. Furthermore, their topological properties and how these can be applied to certain biological case studies are discussed. Finally, available methodologies and software are presented, and useful insights on how bipartite graphs can shape the path toward the solution of challenging biological problems are provided.},
|
||
file = {/home/polarolouis/Zotero/storage/2KJFL3SB/Pavlopoulos et al. - 2018 - Bipartite graphs in systems biology and medicine .pdf;/home/polarolouis/Zotero/storage/A2Y2EGPA/pavlopoulos2018.pdf.pdf;/home/polarolouis/Zotero/storage/UK2MK5FW/pavlopoulos2018.pdf.pdf;/home/polarolouis/Zotero/storage/XP7G4PZF/4875933.html}
|
||
}
|
||
|
||
@article{ramos-jilibertoTopologicalChangeAndean2010,
|
||
title = {Topological Change of {{Andean}} Plant–Pollinator Networks along an Altitudinal Gradient},
|
||
author = {Ramos-Jiliberto, Rodrigo and Domínguez, Daniela and Espinoza, Claudia and López, Gioconda and Valdovinos, Fernanda S. and Bustamante, Ramiro O. and Medel, Rodrigo},
|
||
date = {2010-03-01},
|
||
journaltitle = {Ecological Complexity},
|
||
volume = {7},
|
||
number = {1},
|
||
pages = {86--90},
|
||
issn = {1476-945X},
|
||
doi = {10.1016/j.ecocom.2009.06.001},
|
||
url = {https://www.sciencedirect.com/science/article/pii/S1476945X09000622},
|
||
urldate = {2023-06-15},
|
||
abstract = {Pollination interaction networks exhibit structural regularities across a wide range of natural environments. Long-tailed degree distribution, nestedness, and modularity are the most prevalent topological patterns found in most bipartite networks analyzed up to day. In this work we evaluate the variation of these topological properties along an altitudinal gradient. To this end, we examined four plant–pollinator networks from the Chilean Andes at 33°S, in range from 1800 to 3600m elevation. Our results indicate that network topology is strongly and systematically affected by elevation. At increasing altitude, the number of potential visitors per plant decreased, and species’ degree distributions are closer to random expectations. On the other hand, the nested structure of mutualistic interactions systematically decreased with elevation, and network modularity was significantly higher than random expectations over the entire altitudinal range. In addition, at increasing elevations the pollination networks were organized in fewer and more strongly connected modules. Our results suggest that the severe abiotic conditions found at increased elevations translate into less organized pollination networks.},
|
||
langid = {english},
|
||
keywords = {bipartite-example,Chile,Complexity,Degree distribution,Modularity,Mutualistic networks,Nestedness,Power law},
|
||
file = {/home/polarolouis/Zotero/storage/ATY3ZP2X/Ramos-Jiliberto et al. - 2010 - Topological change of Andean plant–pollinator netw.pdf;/home/polarolouis/Zotero/storage/HPBGUP65/ramos-jiliberto2010.pdf.pdf;/home/polarolouis/Zotero/storage/I33MZQQ7/ramos-jiliberto2010.pdf.pdf;/home/polarolouis/Zotero/storage/YJX8XBNW/S1476945X09000622.html}
|
||
}
|
||
|
||
@article{kaszewska-gilasGlobalStudiesHostParasite2021,
|
||
title = {Global {{Studies}} of the {{Host-Parasite Relationships}} between {{Ectoparasitic Mites}} of the {{Family Syringophilidae}} and {{Birds}} of the {{Order Columbiformes}}},
|
||
author = {Kaszewska-Gilas, Katarzyna and Kosicki, Jakub Ziemowit and Hromada, Martin and Skoracki, Maciej},
|
||
date = {2021-12},
|
||
journaltitle = {Animals},
|
||
volume = {11},
|
||
number = {12},
|
||
pages = {3392},
|
||
publisher = {Multidisciplinary Digital Publishing Institute},
|
||
issn = {2076-2615},
|
||
doi = {10.3390/ani11123392},
|
||
url = {https://www.mdpi.com/2076-2615/11/12/3392},
|
||
urldate = {2023-06-15},
|
||
abstract = {The quill mites belonging to the family Syringophilidae (Acari: Prostigmata: Cheyletoidea) are obligate ectoparasites of birds. They inhabit different types of the quills, where they spend their whole life cycle. In this paper, we conducted a global study of syringophilid mites associated with columbiform birds. We examined 772 pigeon and dove individuals belonging to 112 species (35\% world fauna) from all zoogeographical regions (except Madagascan) where Columbiformes occur. We measured the prevalence (IP) and the confidence interval (CI) for all infested host species. IP ranges between 4.2 and 66.7 (CI 0.2–100). We applied a bipartite analysis to determine host–parasite interaction, network indices, and host specificity on species and whole network levels. The Syringophilidae–Columbiformes network was composed of 25 mite species and 65 host species. The bipartite network was characterized by a high network level specialization H2′ = 0.93, high nestedness N = 0.908, connectance C = 0.90, and high modularity Q = 0.83, with 20 modules. Moreover, we reconstructed the phylogeny of the quill mites associated with columbiform birds on the generic level. Analysis shows two distinct clades: Meitingsunes + Psittaciphilus, and Peristerophila + Terratosyringophilus.},
|
||
issue = {12},
|
||
langid = {english},
|
||
keywords = {Acari,biodiversity,bipartite-example,network,pigeons and doves,quill mites},
|
||
file = {/home/polarolouis/Zotero/storage/VXVQ5CPH/Kaszewska-Gilas et al. - 2021 - Global Studies of the Host-Parasite Relationships .pdf}
|
||
}
|
||
|
||
@article{desjardins-proulxEcologicalInteractionsNetflix2017,
|
||
title = {Ecological Interactions and the {{Netflix}} Problem},
|
||
author = {Desjardins-Proulx, Philippe and Laigle, Idaline and Poisot, Timothée and Gravel, Dominique},
|
||
date = {2017-08-10},
|
||
journaltitle = {PeerJ},
|
||
volume = {5},
|
||
pages = {e3644},
|
||
publisher = {PeerJ Inc.},
|
||
issn = {2167-8359},
|
||
doi = {10.7717/peerj.3644},
|
||
url = {https://peerj.com/articles/3644},
|
||
urldate = {2023-06-15},
|
||
abstract = {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.},
|
||
langid = {english},
|
||
file = {/home/polarolouis/Zotero/storage/3L7JALP4/Desjardins-Proulx et al. - 2017 - Ecological interactions and the Netflix problem.pdf}
|
||
}
|
||
|
||
@article{govaertLatentBlockModel2010,
|
||
title = {Latent {{Block Model}} for {{Contingency Table}}},
|
||
author = {Govaert, Gérard and Nadif, Mohamed},
|
||
date = {2010-01-13},
|
||
journaltitle = {Communications in Statistics - Theory and Methods},
|
||
volume = {39},
|
||
number = {3},
|
||
pages = {416--425},
|
||
publisher = {Taylor \& Francis},
|
||
issn = {0361-0926},
|
||
doi = {10.1080/03610920903140197},
|
||
url = {https://doi.org/10.1080/03610920903140197},
|
||
urldate = {2023-06-15},
|
||
abstract = {Although many clustering procedures aim to construct an optimal partition of objects or, sometimes, variables, there are other methods, called block clustering methods, which simultaneously consider the two sets and organize the data into homogeneous blocks. This kind of method has practical importance in a wide variety of applications such as text and market basket data analysis. Typically, the data that arise in these applications are arranged as a two-way contingency table. Using Poisson distributions, a latent block model for these data is proposed and, setting it under the maximum likelihood approach and the classification maximum likelihood approach, various algorithms are provided. Their performances are evaluated and compared to a simple use of EM or CEM applied separately on the rows and columns of the contingency table.},
|
||
keywords = {62H17,62H30,Block clustering,Block Poisson mixture model,CEM algorithm,Contingency table,EM algorithm},
|
||
file = {/home/polarolouis/Zotero/storage/PPHP33Z9/Govaert et Nadif - 2010 - Latent Block Model for Contingency Table.pdf;/home/polarolouis/Zotero/storage/UT8TARCX/govaert2010.pdf.pdf}
|
||
}
|
||
|
||
@online{anakokDisentanglingStructureEcological2022,
|
||
title = {Disentangling the Structure of Ecological Bipartite Networks from Observation Processes},
|
||
author = {Anakok, Emre and Barbillon, Pierre and Fontaine, Colin and Thebault, Elisa},
|
||
date = {2022-11-29},
|
||
eprint = {2211.16364},
|
||
eprinttype = {arXiv},
|
||
eprintclass = {stat},
|
||
url = {http://arxiv.org/abs/2211.16364},
|
||
urldate = {2023-06-14},
|
||
abstract = {The structure of a bipartite interaction network can be described by providing a clustering for each of the two types of nodes. Such clusterings are outputted by fitting a Latent Block Model (LBM) on an observed network that comes from a sampling of species interactions in the field. However, the sampling is limited and possibly uneven. This may jeopardize the fit of the LBM and then the description of the structure of the network by detecting structures which result from the sampling and not from actual underlying ecological phenomena. If the observed interaction network consists of a weighted bipartite network where the number of observed interactions between two species is available, the sampling efforts for all species can be estimated and used to correct the LBM fit. We propose to combine an observation model that accounts for sampling and an LBM for describing the structure of underlying possible ecological interactions. We develop an original inference procedure for this model, the efficiency of which is demonstrated in simulation studies. The practical interest in ecology of our model is highlighted on a large dataset of plant-pollinator network.},
|
||
langid = {english},
|
||
pubstate = {prepublished},
|
||
keywords = {Statistics - Methodology},
|
||
file = {/home/polarolouis/Zotero/storage/LQ3FINZG/Anakok et al. - 2022 - Disentangling the structure of ecological bipartit.pdf}
|
||
}
|
||
|
||
@article{celisseConsistencyMaximumlikelihoodVariational2012,
|
||
title = {Consistency of Maximum-Likelihood and Variational Estimators in the Stochastic Block Model},
|
||
author = {Celisse, Alain and Daudin, Jean-Jacques and Pierre, Laurent},
|
||
date = {2012-01},
|
||
journaltitle = {Electronic Journal of Statistics},
|
||
volume = {6},
|
||
pages = {1847--1899},
|
||
publisher = {{Institute of Mathematical Statistics and Bernoulli Society}},
|
||
issn = {1935-7524, 1935-7524},
|
||
doi = {10.1214/12-EJS729},
|
||
url = {https://projecteuclid.org/journals/electronic-journal-of-statistics/volume-6/issue-none/Consistency-of-maximum-likelihood-and-variational-estimators-in-the-stochastic/10.1214/12-EJS729.full},
|
||
urldate = {2023-06-06},
|
||
abstract = {The stochastic block model (SBM) is a probabilistic model designed to describe heterogeneous directed and undirected graphs. In this paper, we address the asymptotic inference in SBM by use of maximum-likelihood and variational approaches. The identifiability of SBM is proved while asymptotic properties of maximum-likelihood and variational estimators are derived. In particular, the consistency of these estimators is settled for the probability of an edge between two vertices (and for the group proportions at the price of an additional assumption), which is to the best of our knowledge the first result of this type for variational estimators in random graphs.},
|
||
issue = {none},
|
||
keywords = {62E17,62G05,62G20,62H30,Concentration inequalities,consistency,maximum likelihood estimators,Random graphs,Stochastic block model,variational estimators},
|
||
file = {/home/polarolouis/Zotero/storage/JNWRIYKG/celisse2012.pdf.pdf;/home/polarolouis/Zotero/storage/XG463B5I/Celisse et al. - 2012 - Consistency of maximum-likelihood and variational .pdf}
|
||
}
|
||
|
||
@online{chabert-liddellLearningCommonStructures2023,
|
||
type = {article},
|
||
title = {Learning Common Structures in a Collection of Networks. {{An}} Application to Food Webs},
|
||
author = {Chabert-Liddell, Saint-Clair and Barbillon, Pierre and Donnet, Sophie},
|
||
date = {2023-03-27},
|
||
eprint = {2206.00560},
|
||
eprinttype = {arXiv},
|
||
eprintclass = {stat},
|
||
doi = {10.48550/arXiv.2206.00560},
|
||
url = {http://arxiv.org/abs/2206.00560},
|
||
urldate = {2023-05-22},
|
||
abstract = {Let a collection of networks represent interactions within several (social or ecological) systems. We pursue two objectives: identifying similarities in the topological structures that are held in common between the networks and clustering the collection into sub-collections of structurally homogeneous networks. We tackle these two questions with a probabilistic model based approach. We propose an extension of the Stochastic Block Model (SBM) adapted to the joint modeling of a collection of networks. The networks in the collection are assumed to be independent realizations of SBMs. The common connectivity structure is imposed through the equality of some parameters. The model parameters are estimated with a variational Expectation-Maximization (EM) algorithm. We derive an ad-hoc penalized likelihood criterion to select the number of blocks and to assess the adequacy of the consensus found between the structures of the different networks. This same criterion can also be used to cluster networks on the basis of their connectivity structure. It thus provides a partition of the collection into subsets of structurally homogeneous networks. The relevance of our proposition is assessed on two collections of ecological networks. First, an application to three stream food webs reveals the homogeneity of their structures and the correspondence between groups of species in different ecosystems playing equivalent ecological roles. Moreover, the joint analysis allows a finer analysis of the structure of smaller networks. Second, we cluster 67 food webs according to their connectivity structures and demonstrate that five mesoscale structures are sufficient to describe this collection.},
|
||
pubstate = {prepublished},
|
||
keywords = {Statistics - Applications,Statistics - Methodology},
|
||
file = {/home/polarolouis/Zotero/storage/M74TXGCF/Chabert-Liddell et al. - 2023 - Learning common structures in a collection of netw.pdf;/home/polarolouis/Zotero/storage/A35M8KNP/2206.html}
|
||
}
|
||
|
||
@article{keribinEstimationSelectionLatent2015,
|
||
title = {Estimation and Selection for the Latent Block Model on Categorical Data},
|
||
author = {Keribin, Christine and Brault, Vincent and Celeux, Gilles and Govaert, Gérard},
|
||
date = {2015-11-01},
|
||
journaltitle = {Stat Comput},
|
||
volume = {25},
|
||
number = {6},
|
||
pages = {1201--1216},
|
||
issn = {1573-1375},
|
||
doi = {10.1007/s11222-014-9472-2},
|
||
url = {https://doi.org/10.1007/s11222-014-9472-2},
|
||
urldate = {2024-05-15},
|
||
abstract = {This paper deals with estimation and model selection in the Latent Block Model (LBM) for categorical data. First, after providing sufficient conditions ensuring the identifiability of this model, we generalise estimation procedures and model selection criteria derived for binary data. Secondly, we develop Bayesian inference through Gibbs sampling and with a well calibrated non informative prior distribution, in order to get the MAP estimator: this is proved to avoid the traps encountered by the LBM with the maximum likelihood methodology. Then model selection criteria are presented. In particular an exact expression of the integrated completed likelihood criterion requiring no asymptotic approximation is derived. Finally numerical experiments on both simulated and real data sets highlight the appeal of the proposed estimation and model selection procedures.},
|
||
langid = {english},
|
||
keywords = {Bayesian inference,BIC criterion,EM algorithm,Gibbs sampling,Integrated completed likelihood,Stochastic EM,Variational approximation},
|
||
file = {/home/polarolouis/Zotero/storage/49IKUHMA/s11222-014-9472-2.pdf.pdf;/home/polarolouis/Zotero/storage/VXKAK359/Keribin et al. - 2015 - Estimation and selection for the latent block mode.pdf}
|
||
}
|
||
|
||
@article{pichonTellingMutualisticAntagonistic2024,
|
||
title = {Telling Mutualistic and Antagonistic Ecological Networks Apart by Learning Their Multiscale Structure},
|
||
author = {Pichon, Benoît and Le Goff, Rémy and Morlon, Hélène and Perez-Lamarque, Benoît},
|
||
date = {2024},
|
||
journaltitle = {Methods in Ecology and Evolution},
|
||
volume = {15},
|
||
number = {6},
|
||
pages = {1113--1128},
|
||
issn = {2041-210X},
|
||
doi = {10.1111/2041-210X.14328},
|
||
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.14328},
|
||
urldate = {2024-06-17},
|
||
abstract = {Characterizing and understanding the processes that shape the structure of ecological networks, which represent who interacts with whom in a community, has many implications in ecology, evolutionary biology and conservation. A highly debated question is whether and how the structure of a bipartite ecological network differs between antagonistic (e.g. herbivory) and mutualistic (e.g. pollination) interaction types. Here, we tackle this question by using a multiscale characterization of network structure, machine learning tools, and a large database of empirical and simulated bipartite networks. Contrary to previous studies focusing on global structural metrics, such as nestedness and modularity, which concluded that antagonistic and mutualistic networks cannot be told apart from only their structure, we find that they can be told apart by combining a meso-scale characterization of their structure and supervised machine learning. Motif frequencies appear particularly informative, with an over-representation of densely connected motifs in antagonistic networks and of motifs with asymmetrical specialization in mutualistic networks. These structural properties can be used to predict the type of interaction with relatively good confidence. Beyond this classical mutualism/antagonism dichotomy, we also find significant structural uniqueness linked to specific ecologies (e.g. pollination, parasitism). Our results clarify structural differences between antagonistic and mutualistic networks and suggest the investigation of the structural uniqueness of specific ecologies as a promising approach for characterizing interactions beyond the coarse antagonistic/mutualistic dichotomy.},
|
||
langid = {english},
|
||
keywords = {ecological interactions,interaction classification,machine learning,motif frequency,network structure},
|
||
file = {/home/polarolouis/Zotero/storage/9DFZFNV7/Pichon et al. - 2024 - Telling mutualistic and antagonistic ecological ne.pdf;/home/polarolouis/Zotero/storage/RZXQ6LCV/2041-210X.html}
|
||
}
|
||
|
||
@article{chabert-liddellLearningCommonStructures2024a,
|
||
title = {Learning Common Structures in a Collection of Networks. {{An}} Application to Food Webs},
|
||
author = {Chabert-Liddell, Saint-Clair and Barbillon, Pierre and Donnet, Sophie},
|
||
date = {2024-06},
|
||
journaltitle = {The Annals of Applied Statistics},
|
||
volume = {18},
|
||
number = {2},
|
||
pages = {1213--1235},
|
||
publisher = {Institute of Mathematical Statistics},
|
||
issn = {1932-6157, 1941-7330},
|
||
doi = {10.1214/23-AOAS1831},
|
||
url = {https://projecteuclid.org/journals/annals-of-applied-statistics/volume-18/issue-2/Learning-common-structures-in-a-collection-of-networks-An-application/10.1214/23-AOAS1831.full},
|
||
urldate = {2024-07-01},
|
||
abstract = {Let a collection of networks represent interactions within several (social or ecological) systems. We pursue two objectives: identifying similarities in the topological structures that are held in common between the networks and clustering the collection into subcollections of structurally homogeneous networks. We tackle these two questions with a probabilistic model-based approach. We propose an extension of the stochastic block model (SBM) adapted to the joint modeling of a collection of networks. The networks in the collection are assumed to be independent realizations of SBMs. The common connectivity structure is imposed through the equality of some parameters. The model parameters are estimated with a variational expectation-maximization (EM) algorithm. We derive an ad hoc penalized likelihood criterion to select the number of blocks and to assess the adequacy of the consensus found between the structures of the different networks. This same criterion can also be used to cluster networks on the basis of their connectivity structure. It thus provides a partition of the collection into subsets of structurally homogeneous networks. The relevance of our proposition is assessed on two collections of ecological networks. First, an application to three stream food webs reveals the homogeneity of their structures and the correspondence between groups of species in different ecosystems playing equivalent ecological roles. Moreover, the joint analysis allows a finer analysis of the structure of smaller networks. Second, we cluster 67 food webs according to their connectivity structures and demonstrate that five mesoscale structures are sufficient to describe this collection.},
|
||
keywords = {clustering,ecology,latent variable models,networks,Stochastic block model},
|
||
file = {/home/polarolouis/Zotero/storage/9XBNTTWB/Chabert-Liddell et al. - 2024 - Learning common structures in a collection of netw.pdf}
|
||
}
|
||
|
||
@article{baldockDailyTemporalStructure2011,
|
||
title = {Daily Temporal Structure in {{African}} Savanna Flower Visitation Networks and Consequences for Network Sampling},
|
||
author = {Baldock, Katherine C. R. and Memmott, Jane and Ruiz-Guajardo, Juan Carlos and Roze, Denis and Stone, Graham N.},
|
||
date = {2011},
|
||
journaltitle = {Ecology},
|
||
volume = {92},
|
||
number = {3},
|
||
pages = {687--698},
|
||
issn = {1939-9170},
|
||
doi = {10.1890/10-1110.1},
|
||
url = {https://onlinelibrary.wiley.com/doi/abs/10.1890/10-1110.1},
|
||
urldate = {2024-07-02},
|
||
abstract = {Ecological interaction networks are a valuable approach to understanding plant–pollinator interactions at the community level. Highly structured daily activity patterns are a feature of the biology of many flower visitors, particularly provisioning female bees, which often visit different floral sources at different times. Such temporal structure implies that presence/absence and relative abundance of specific flower–visitor interactions (links) in interaction networks may be highly sensitive to the daily timing of data collection. Further, relative timing of interactions is central to their possible role in competition or facilitation of seed set among coflowering plants sharing pollinators. To date, however, no study has examined the network impacts of daily temporal variation in visitor activity at a community scale. Here we use temporally structured sampling to examine the consequences of daily activity patterns upon network properties using fully quantified flower–visitor interaction data for a Kenyan savanna habitat. Interactions were sampled at four sequential three-hour time intervals between 06:00 and 18:00, across multiple seasonal time points for two sampling sites. In all data sets the richness and relative abundance of links depended critically on when during the day visitation was observed. Permutation-based null modeling revealed significant temporal structure across daily time intervals at three of the four seasonal time points, driven primarily by patterns in bee activity. This sensitivity of network structure shows the need to consider daily time in network sampling design, both to maximize the probability of sampling links relevant to plant reproductive success and to facilitate appropriate interpretation of interspecific relationships. Our data also suggest that daily structuring at a community level could reduce indirect competitive interactions when coflowering plants share pollinators, as is commonly observed during flowering in highly seasonal habitats.},
|
||
langid = {english},
|
||
keywords = {Africa,competition,ecological networks,facilitation,Kenya,mutualism,pollination,savanna,temporal structure,visitation webs},
|
||
file = {/home/polarolouis/Zotero/storage/4ALS9Y6W/10-1110.1.pdf.pdf;/home/polarolouis/Zotero/storage/4YSLVYC5/Baldock et al. - 2011 - Daily temporal structure in African savanna flower.pdf;/home/polarolouis/Zotero/storage/7PEDTWU9/10-1110.html}
|
||
}
|
||
|
||
@article{baldockSystemsApproachReveals2019a,
|
||
title = {A Systems Approach Reveals Urban Pollinator Hotspots and Conservation Opportunities},
|
||
author = {Baldock, Katherine C. R. and Goddard, Mark A. and Hicks, Damien M. and Kunin, William E. and Mitschunas, Nadine and Morse, Helen and Osgathorpe, Lynne M. and Potts, Simon G. and Robertson, Kirsty M. and Scott, Anna V. and Staniczenko, Phillip P. A. and Stone, Graham N. and Vaughan, Ian P. and Memmott, Jane},
|
||
date = {2019-03},
|
||
journaltitle = {Nat Ecol Evol},
|
||
volume = {3},
|
||
number = {3},
|
||
eprint = {30643247},
|
||
eprinttype = {pmid},
|
||
pages = {363--373},
|
||
issn = {2397-334X},
|
||
doi = {10.1038/s41559-018-0769-y},
|
||
abstract = {Urban areas are often perceived to have lower biodiversity than the wider countryside, but a few small-scale studies suggest that some urban land uses can support substantial pollinator populations. We present a large-scale, well-replicated study of floral resources and pollinators in 360 sites incorporating all major land uses in four British cities. Using a systems approach, we developed Bayesian network models integrating pollinator dispersal and resource switching to estimate city-scale effects of management interventions on plant-pollinator community robustness to species loss. We show that residential gardens and allotments (community gardens) are pollinator 'hotspots': gardens due to their extensive area, and allotments due to their high pollinator diversity and leverage on city-scale plant-pollinator community robustness. Household income was positively associated with pollinator abundance in gardens, highlighting the influence of socioeconomic factors. Our results underpin urban planning recommendations to enhance pollinator conservation, using increasing city-scale community robustness as our measure of success.},
|
||
langid = {english},
|
||
pmcid = {PMC6445365},
|
||
keywords = {Bayes Theorem,Biodiversity,Cities,Conservation of Natural Resources,England,Pollination,Scotland,Systems Analysis},
|
||
file = {/home/polarolouis/Zotero/storage/BSGKKFLX/s41559-018-0769-y.pdf.pdf;/home/polarolouis/Zotero/storage/NZR8WPUA/Baldock et al. - 2019 - A systems approach reveals urban pollinator hotspo.pdf}
|
||
}
|
||
|
||
@online{rebafkaModelbasedClusteringMultiple2023,
|
||
title = {Model-Based Clustering of Multiple Networks with a Hierarchical Algorithm},
|
||
author = {Rebafka, Tabea},
|
||
date = {2023-11-06},
|
||
eprint = {2211.02314},
|
||
eprinttype = {arXiv},
|
||
eprintclass = {math, stat},
|
||
doi = {10.48550/arXiv.2211.02314},
|
||
url = {http://arxiv.org/abs/2211.02314},
|
||
urldate = {2024-07-22},
|
||
abstract = {The paper tackles the problem of clustering multiple networks, directed or not, that do not share the same set of vertices, into groups of networks with similar topology. A statistical model-based approach based on a finite mixture of stochastic block models is proposed. A clustering is obtained by maximizing the integrated classification likelihood criterion. This is done by a hierarchical agglomerative algorithm, that starts from singleton clusters and successively merges clusters of networks. As such, a sequence of nested clusterings is computed that can be represented by a dendrogram providing valuable insights on the collection of networks. Using a Bayesian framework, model selection is performed in an automated way since the algorithm stops when the best number of clusters is attained. The algorithm is computationally efficient, when carefully implemented. The aggregation of clusters requires a means to overcome the label-switching problem of the stochastic block model and to match the block labels of the networks. To address this problem, a new tool is proposed based on a comparison of the graphons of the associated stochastic block models. The clustering approach is assessed on synthetic data. An application to a set of ecological networks illustrates the interpretability of the obtained results.},
|
||
pubstate = {prepublished},
|
||
keywords = {Mathematics - Statistics Theory},
|
||
file = {/home/polarolouis/Zotero/storage/B9C8S8WQ/Rebafka - 2023 - Model-based clustering of multiple networks with a.pdf;/home/polarolouis/Zotero/storage/GG7C6CNM/2211.html}
|
||
}
|
||
|
||
@article{erdosRandomGraphs1959,
|
||
title = {On Random Graphs. {{I}}.},
|
||
author = {Erdős, P. and Rényi, A.},
|
||
date = {1959},
|
||
journaltitle = {Publ. Math. Debrecen},
|
||
volume = {6},
|
||
number = {3-4},
|
||
pages = {290--297},
|
||
issn = {00333883},
|
||
doi = {10.5486/PMD.1959.6.3-4.12},
|
||
url = {https://publi.math.unideb.hu/load_doi.php?pdoi=10_5486_PMD_1959_6_3_4_12},
|
||
urldate = {2024-08-09},
|
||
abstract = {Semantic Scholar extracted view of "On random graphs. I." by P. Erdos et al.},
|
||
file = {/home/polarolouis/Zotero/storage/WRSY3FZV/Erdős et Rényi - 2022 - On random graphs. I..pdf}
|
||
}
|
||
|
||
@article{devotoUnderstandingPlanningEcological2012,
|
||
title = {Understanding and Planning Ecological Restoration of Plant–Pollinator Networks},
|
||
author = {Devoto, Mariano and Bailey, Sallie and Craze, Paul and Memmott, Jane},
|
||
date = {2012},
|
||
journaltitle = {Ecology Letters},
|
||
volume = {15},
|
||
number = {4},
|
||
pages = {319--328},
|
||
issn = {1461-0248},
|
||
doi = {10.1111/j.1461-0248.2012.01740.x},
|
||
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1461-0248.2012.01740.x},
|
||
urldate = {2024-08-20},
|
||
abstract = {Ecology Letters (2012) 15: 319–328 Abstract Theory developed from studying changes in the structure and function of communities during natural or managed succession can guide the restoration of particular communities. We constructed 30 quantitative plant–flower visitor networks along a managed successional gradient to identify the main drivers of change in network structure. We then applied two alternative restoration strategies in silico (restoring for functional complementarity or redundancy) to data from our early successional plots to examine whether different strategies affected the restoration trajectories. Changes in network structure were explained by a combination of age, tree density and variation in tree diameter, even when variance explained by undergrowth structure was accounted for first. A combination of field data, a network approach and numerical simulations helped to identify which species should be given restoration priority in the context of different restoration targets. This combined approach provides a powerful tool for directing management decisions, particularly when management seeks to restore or conserve ecosystem function.},
|
||
langid = {english},
|
||
keywords = {Ecosystem function,functional complementarity,functional redundancy,pine forest,plant–animal interaction,plant–pollinator network,redundancy analysis,restoration,restoration strategy,succession},
|
||
file = {/home/polarolouis/Zotero/storage/XY2INESI/Devoto et al. - 2012 - Understanding and planning ecological restoration of plant–pollinator networks.pdf;/home/polarolouis/Zotero/storage/MWCIJ5TW/j.1461-0248.2012.01740.html}
|
||
}
|
||
|
||
@article{boschPlantPollinatorNetworks2009,
|
||
title = {Plant–Pollinator Networks: Adding the Pollinator’s Perspective},
|
||
shorttitle = {Plant–Pollinator Networks},
|
||
author = {Bosch, Jordi and Martín González, Ana M. and Rodrigo, Anselm and Navarro, David},
|
||
date = {2009},
|
||
journaltitle = {Ecology Letters},
|
||
volume = {12},
|
||
number = {5},
|
||
pages = {409--419},
|
||
issn = {1461-0248},
|
||
doi = {10.1111/j.1461-0248.2009.01296.x},
|
||
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1461-0248.2009.01296.x},
|
||
urldate = {2024-08-20},
|
||
abstract = {Pollination network studies are based on pollinator surveys conducted on focal plants. This plant-centred approach provides insufficient information on flower visitation habits of rare pollinator species, which are the majority in pollinator communities. As a result, pollination networks contain very high proportions of pollinator species linked to a single plant species (extreme specialists), a pattern that contrasts with the widely accepted view that plant–pollinator interactions are mostly generalized. In this study of a Mediterranean scrubland community in NE Spain we supplement data from an intensive field survey with the analysis of pollen loads carried by pollinators. We observed 4265 contacts corresponding to 19 plant and 122 pollinator species. The addition of pollen data unveiled a very significant number of interactions, resulting in important network structural changes. Connectance increased 1.43-fold, mean plant connectivity went from 18.5 to 26.4, and mean pollinator connectivity from 2.9 to 4.1. Extreme specialist pollinator species decreased 0.6-fold, suggesting that ecological specialization is often overestimated in plant–pollinator networks. We expected a greater connectivity increase in rare species, and consequently a decrease in the level of asymmetric specialization. However, new links preferentially attached to already highly connected nodes and, as a result, both nestedness and centralization increased. The addition of pollen data revealed the existence of four clearly defined modules that were not apparent when only field survey data were used. Three of these modules had a strong phenological component. In comparison to other pollination webs, our network had a high proportion of connector links and species. That is, although significant, the four modules were far from isolated.},
|
||
langid = {english},
|
||
keywords = {Apparent specialization,coevolution,generalization,modularity,nestedness,plant–pollinator interactions,pollen analysis,pollination web,sampling effort},
|
||
file = {/home/polarolouis/Zotero/storage/C5TQ6Y49/Bosch et al. - 2009 - Plant–pollinator networks adding the pollinator’s perspective.pdf;/home/polarolouis/Zotero/storage/BHMVU3DU/j.1461-0248.2009.01296.html}
|
||
}
|