Ecological networks: Linking structure to dynamics in food webs. (Oxford University Press, 2006).
Adaptive food webs: Stability and transitions of real and model ecosystems. (Cambridge University Press, 2018).
Pimm, S. L. Food Webs (Springer, 1982).
Google Scholar
Adaptive Food Webs: Stability and Transitions of Real and Model Ecosystems. (Cambridge University Press, 2017). doi:https://doi.org/10.1017/9781316871867.
da Mata, A. S. Complex Networks: A Mini-review. Braz. J. Phys. 50, 658–672 (2020).
Google Scholar
Zhang, W. Fundamentals of Network Biology. (World Scientific (Europe), 2018). https://doi.org/10.1142/q0149.
Reichman, O. J., Jones, M. B. & Schildhauer, M. P. Challenges and opportunities of open data in ecology. Science 331, 703–705 (2011).
Google Scholar
Farley, S. S., Dawson, A., Goring, S. J. & Williams, J. W. situating ecology as a big-data science: Current advances, challenges, and solutions. Bioscience 68, 563–576 (2018).
Google Scholar
Osawa, T. Perspectives on biodiversity informatics for ecology. Ecol. Res. 34, 446–456 (2019).
Google Scholar
Shin, N. et al. Toward more data publication of long-term ecological observations. Ecol. Res. 35, 700–707 (2020).
Google Scholar
Pringle, R. M. & Hutchinson, M. C. Resolving food-web structure. Annu. Rev. Ecol. Evol. Syst. 51, 55–80 (2020).
Google Scholar
Derocles, S. A. P. et al. Biomonitoring for the 21st Century: Integrating Next-Generation Sequencing Into Ecological Network Analysis. in Advances in Ecological Research vol. 58 1–62 (Elsevier, 2018).
Vacher, C. et al. Learning ecological networks from next-generation sequencing data. in Advances in Ecological Research vol. 54, 1–39 (Elsevier, 2016).
Evans, D. M., Kitson, J. J. N., Lunt, D. H., Straw, N. A. & Pocock, M. J. O. Merging DNA metabarcoding and ecological network analysis to understand and build resilient terrestrial ecosystems. Funct. Ecol. 30, 1904–1916 (2016).
Google Scholar
Pocock, M. J. O. et al. A vision for global biodiversity monitoring with citizen science. in Advances in Ecological Research vol. 59, 169–223 (Elsevier, 2018).
Sultana, M. & Storch, I. Suitability of open digital species records for assessing biodiversity patterns in cities: A case study using avian records. J. Urban Ecol. 7, juab014 (2021).
Google Scholar
Amano, T., Lamming, J. D. L. & Sutherland, W. J. Spatial gaps in global biodiversity information and the role of citizen science. Bioscience 66, 393–400 (2016).
Google Scholar
Chandler, M. et al. Contribution of citizen science towards international biodiversity monitoring. Biol. Conserv. 213, 280–294 (2017).
Google Scholar
Fontaine, C. et al. The ecological and evolutionary implications of merging different types of networks: Merging networks with different interaction types. Ecol. Lett. 14, 1170–1181 (2011).
Google Scholar
Martinson, H. M. & Fagan, W. F. Trophic disruption: A meta-analysis of how habitat fragmentation affects resource consumption in terrestrial arthropod systems. Ecol. Lett. 17, 1178–1189 (2014).
Google Scholar
Marczak, L. B., Thompson, R. M. & Richardson, J. S. Meta-analysis: Trophic level, Habitat, and productivity shape the food web effects of resource subsidies. Ecology 88, 140–148 (2007).
Google Scholar
McCary, M. A., Mores, R., Farfan, M. A. & Wise, D. H. Invasive plants have different effects on trophic structure of green and brown food webs in terrestrial ecosystems: A meta-analysis. Ecol. Lett. 19, 328–335 (2016).
Google Scholar
Cirtwill, A. R., Stouffer, D. B. & Romanuk, T. N. Latitudinal gradients in biotic niche breadth vary across ecosystem types. Proc. R. Soc. B Biol. Sci. 282, 20151589 (2015).
Google Scholar
Fortuna, M. A., Ortega, R. & Bascompte, J. The Web of Life. ArXiv14032575 Q-Bio (2014).
Brose, U. et al. Predator traits determine food-web architecture across ecosystems. Nat. Ecol. Evol. 3, 919–927 (2019).
Google Scholar
Mace, G. M., Norris, K. & Fitter, A. H. Biodiversity and ecosystem services: A multilayered relationship. Trends Ecol. Evol. 27, 19–26 (2012).
Google Scholar
Keyes, A. A., McLaughlin, J. P., Barner, A. K. & Dee, L. E. An ecological network approach to predict ecosystem service vulnerability to species losses. Nat. Commun. 12, 1586 (2021).
Google Scholar
Peng, J. et al. Linking ecosystem services and circuit theory to identify ecological security patterns. Sci. Total Environ. 644, 781–790 (2018).
Google Scholar
Su, Y. et al. Modeling the optimal ecological security pattern for guiding the urban constructed land expansions. Urban For. Urban Green. 19, 35–46 (2016).
Google Scholar
Kowarik, I. Novel urban ecosystems, biodiversity, and conservation. Environ. Pollut. 159, 1974–1983 (2011).
Google Scholar
Di Marco, M., Watson, J. E. M., Venter, O. & Possingham, H. P. Global biodiversity targets require both sufficiency and efficiency. Conserv. Lett. 9, 395–397 (2016).
Google Scholar
Kim, K.-H. & Pauleit, S. Landscape character, biodiversity and land use planning: The case of Kwangju City Region, South Korea. Land Use Policy 24, 264–274 (2007).
Google Scholar
Young, J. et al. Towards sustainable land use: Identifying and managing the conflicts between human activities and biodiversity conservation in Europe. Biodivers. Conserv. 14, 1641–1661 (2005).
Google Scholar
Dardonville, M., Urruty, N., Bockstaller, C. & Therond, O. Influence of diversity and intensification level on vulnerability, resilience and robustness of agricultural systems. Agric. Syst. 184, 102913 (2020).
Google Scholar
Oliver, T. H. et al. Biodiversity and resilience of ecosystem functions. Trends Ecol. Evol. 30, 673–684 (2015).
Google Scholar
Lau, M. K., Borrett, S. R., Baiser, B., Gotelli, N. J. & Ellison, A. M. Ecological network metrics: Opportunities for synthesis. Ecosphere 8, e01900 (2017).
Google Scholar
Newman, M. E. J. Networks. (Oxford University Press, 2018).
Levine, S. Several measures of trophic structure applicable to complex food webs. J. Theor. Biol. 83, 195–207 (1980).
Google Scholar
Guimarães, P. R. The structure of ecological networks across levels of organization. Annu. Rev. Ecol. Evol. Syst. 51, 433–460 (2020).
Google Scholar
Dormann, C. F., Frund, J., Bluthgen, N. & Gruber, B. Indices, graphs and null models: Analyzing bipartite ecological networks. Open Ecol. J. 2, 7–24 (2009).
Google Scholar
Jordán, F., Benedek, Z. & Podani, J. Quantifying positional importance in food webs: A comparison of centrality indices. Ecol. Model. 205, 270–275 (2007).
Google Scholar
Jordán, F., Liu, W. & Davis, A. J. Topological keystone species: Measures of positional importance in food webs. Oikos 112, 535–546 (2006).
Google Scholar
Jordán, F., Okey, T. A., Bauer, B. & Libralato, S. Identifying important species: Linking structure and function in ecological networks. Ecol. Model. 216, 75–80 (2008).
Google Scholar
Jiang, L. Determination of keystone species in CSM food web: A topological analysis of network structure. Netw. Biol. 5, 13 (2015).
Abarca-Arenas, L. G., Franco-Lopez, J., Peterson, M. S., Brown-Peterson, N. J. & Valero-Pacheco, E. Sociometric analysis of the role of penaeids in the continental shelf food web off Veracruz. Mexico Based By-catch Fish. Res. 87, 46–57 (2007).
Abascal-Monroy, I. M. et al. Functional and structural food web comparison of Terminos Lagoon, Mexico in Three Periods (1980, 1998, and 2011). Estuaries Coasts 39, 1282–1293 (2016).
Google Scholar
McDonald-Madden, E. et al. Using food-web theory to conserve ecosystems. Nat. Commun. 7, 10245 (2016).
Google Scholar
Windsor, F. M. et al. Identifying plant mixes for multiple ecosystem service provision in agricultural systems using ecological networks. J. Appl. Ecol. 58, 2770–2782 (2021).
Google Scholar
Klaise, J. & Johnson, S. The origin of motif families in food webs. Sci. Rep. 7, 16197 (2017).
Google Scholar
Estrada, E. Characterization of topological keystone species. Ecol. Complex. 4, 48–57 (2007).
Google Scholar
Thompson, R. M. & Townsend, C. R. Impacts on stream food webs of native and exotic forest: An intercontinental comparison. Ecology 84, 145–161 (2003).
Google Scholar
Bascompte, J., Melian, C. J. & Sala, E. Interaction strength combinations and the overfishing of a marine food web. Proc. Natl. Acad. Sci. 102, 5443–5447 (2005).
Google Scholar
Dunne, J. A. et al. The roles and impacts of human hunter-gatherers in North Pacific marine food webs. Sci. Rep. 6, 21179 (2016).
Google Scholar
Gauzens, B., Legendre, S., Lazzaro, X. & Lacroix, G. Food-web aggregation, methodological and functional issues. Oikos 122, 1606–1615 (2013).
Google Scholar
Patonai, K. & Jordán, F. Aggregation of incomplete food web data may help to suggest sampling strategies. Ecol. Model. 352, 77–89 (2017).
Google Scholar
Thompson, R. M. & Townsend, C. R. Is resolution the solution?: The effect of taxonomic resolution on the calculated properties of three stream food webs. Freshw. Biol. 44, 413–422 (2000).
Google Scholar
Abarca-Arenas, L. G. & Ulanowicz, R. E. The effects of taxonomic aggregation on network analysis. Ecol. Model. 149, 285–296 (2002).
Google Scholar
Jordán, F. & Osváth, G. The sensitivity of food web topology to temporal data aggregation. Ecol. Model. 220, 3141–3146 (2009).
Google Scholar
European Commission. Communication from the commission to the european parliament, the council, the european economic and social committee and the committee of the regions: EU Biodiversity Strategy for 2030 Bringing nature back into our lives. Preprint at https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52020DC0380 (2020).
European Parliament. European Parliament resolution of 9 June 2021 on the EU Biodiversity Strategy for 2030: Bringing nature back into our lives (P9_TA(2021)0277). Preprint at https://www.europarl.europa.eu/doceo/document/TA-9-2021-0277_EN.html (2021).
Felson, A. J. & Ellison, A. M. Designing (for) Urban Food Webs. Front. Ecol. Evol. 9, 582041 (2021).
Google Scholar
Warren, P. et al. Urban food webs: Predators, prey, and the people who feed them. Bull. Ecol. Soc. Am. 87, 387–393 (2006).
Google Scholar
De Montis, A., Ganciu, A., Cabras, M., Bardi, A. & Mulas, M. Comparative ecological network analysis: An application to Italy. Land Use Policy 81, 714–724 (2019).
Google Scholar
Poisot, T. et al. Mangal—making ecological network analysis simple. Ecography 39, 384–390 (2016).
Google Scholar
Morris, Z. B., Weissburg, M. & Bras, B. Ecological network analysis of urban–industrial ecosystems. J. Ind. Ecol. 25, 193–204 (2021).
Google Scholar
Chamberlain, S. A. & Szöcs, E. taxize: Taxonomic search and retrieval in R. F1000 Research 2, 191 (2013).
Google Scholar
Hagberg, A. A., Schult, D. A. & Swart, P. J. Exploring network structure, dynamics, and function using networkX. in Proceedings of the 7th Python in Science Conference (eds. Varoquaux, G., Vaught, T. & Millman, J.) 11–15 (2008).
Scotti, M. & Jordán, F. Relationships between centrality indices and trophic levels in food webs. Community Ecol. 11, 59–67 (2010).
Google Scholar
Gouveia, C., Móréh, Á. & Jordán, F. Combining centrality indices: Maximizing the predictability of keystone species in food webs. Ecol. Indic. 126, 107617 (2021).
Google Scholar
Allesina, S. & Pascual, M. Googling Food Webs: Can an Eigenvector Measure Species’ Importance for Coextinctions?. PLoS Comput. Biol. 5, e1000494 (2009).
Google Scholar
Patro, S. G. K. & Sahu, K. K. Normalization: A preprocessing stage. https://doi.org/10.48550/ARXIV.1503.06462(2015).
Reback, J. et al. pandas-dev/pandas: Pandas 1.2.3. (Zenodo, 2021). 10.5281/ZENODO.4572994.
Hunter, J. D. Matplotlib: A 2D graphics environment. Comput. Sci. Eng. 9, 90–95 (2007).
Google Scholar
Waskom, M. et al. mwaskom/seaborn: v0.11.1 (December 2020). (Zenodo, 2020). 10.5281/ZENODO.4379347.
Girvan, M. & Newman, M. E. J. Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99, 7821–7826 (2002).
Google Scholar
Rosvall, M., Axelsson, D. & Bergstrom, C. T. The map equation. Eur. Phys. J. Spec. Top. 178, 13–23 (2009).
Google Scholar
Gao, P. & Kupfer, J. A. Uncovering food web structure using a novel trophic similarity measure. Ecol. Inform. 30, 110–118 (2015).
Google Scholar
Gauzens, B., Thébault, E., Lacroix, G. & Legendre, S. Trophic groups and modules: Two levels of group detection in food webs. J. R. Soc. Interface 12, 20141176 (2015).
Google Scholar
Rudiger, P. et al. holoviz/holoviews: Version 1.14.2. (Zenodo, 2021). 10.5281/ZENODO.4581995.
Pedregosa, F. et al. Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
Google Scholar
Source: Ecology - nature.com
