Book contents
- Quantitative Analysis of Ecological Networks
- Quantitative Analysis of Ecological Networks
- Copyright page
- Contents
- Preface
- 1 Ecological Processes and Network Systems
- 2 Structural Properties of Networks
- 3 Quantitative Analysis of Dynamic Networks
- 4 Multilayer, -type, and -level Networks
- 5 Tying It All Together
- Glossary
- References
- Index
- Plate Section (PDF Only)
- References
References
Published online by Cambridge University Press: 05 April 2021
Book contents
- Quantitative Analysis of Ecological Networks
- Quantitative Analysis of Ecological Networks
- Copyright page
- Contents
- Preface
- 1 Ecological Processes and Network Systems
- 2 Structural Properties of Networks
- 3 Quantitative Analysis of Dynamic Networks
- 4 Multilayer, -type, and -level Networks
- 5 Tying It All Together
- Glossary
- References
- Index
- Plate Section (PDF Only)
- References
Summary
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- Information
- Quantitative Analysis of Ecological Networks , pp. 201 - 218Publisher: Cambridge University PressPrint publication year: 2021
References
Abrams, P. A. (1995). Implications of dynamically variable traits for identifying, classifying, and measuring direct and indirect effects in ecological communities. American Naturalist, 146, 112–134.Google Scholar
Ackerly, D. D., & Stuart, S. A. (2009). Physiological ecology: Plants. In Levin, S. A. (ed.), The Princeton Guide to Ecology, pp. 20–26. Princeton, NJ: Princeton University Press.Google Scholar
Addicott, J. F. (1996). Cheaters in yucca/moth mutualism. Nature, 380, 114–115.CrossRefGoogle Scholar
Ahn, Y.-Y., Bagrow, J. P., & Lehman, S. (2010). Link communities reveal multiscale complexity in networks. Nature, 466 (7307), 761–764.Google Scholar
Ahuja, R. K., Magnanti, T. L., & Orlin, J. B. (1993). Network Flows: Theory, Algorithms, and Applications. Upper Saddle River, NJ: Prentice-Hall.Google Scholar
Airoldi, E., Blei, D. M., Fienberg, S. E., Goldenberg, A., Xing, E. P., & Zheng, A. X. (eds.) (2007). Statistical Network Analysis: Models, Issues, and New Directions. Berlin: Springer.CrossRefGoogle Scholar
Allesina, S., & Pascual, M. (2008). Network structure, predator-prey models, and stability in large food webs. Theoretical Ecology, 1, 55–64.Google Scholar
Altaf-Ul-Amin, M., Saito, A., Koma, T., et al. (2003). Prediction of protein function based on k-cores of protein-protein interaction networks and amino acid sequences. Genome Informatics, 14, 498–499.Google Scholar
Alvarez-Vega, M. (2011). Graph Kernels and Applications in Bioinformatics. PhD dissertation, Utah State University, https://digitalcommons.usu.edu/etd/1185.Google Scholar
Anderson, T. K., & Sukhdeo, M. V. K. (2011). Host centrality in food web networks determines parasite diversity. PLoS ONE, 6(10), e26798.Google Scholar
Aparício, D., Ribiero, P., & Silva, F. (2015). Network comparison using directed graphlets. arXiv:1511.01964v1[cs.SI].Google Scholar
Aref, S., & Wilson, M. C. (2018). Measuring partial balance in signed networks. Journal of Complex Networks, 6, 566–595.Google Scholar
Baiser, B., Elesha, R., & Kahveci, T. (2015). Motifs in the assembly of food web networks. Oikos, 125, 480–491.Google Scholar
Bang-Jensen, J., & Gutin, G. (2009). Digraphs: Theory, Algorithms and Applications, 2nd ed. London: Springer.CrossRefGoogle Scholar
Barrat, A., Barthélemy, M., & Vespignani, A. (2008). Dynamical Processes on Complex Networks. Cambridge: Cambridge University Press.Google Scholar
Bastolla, U., Fortuna, M. A., Pascual-Garcia, A., Ferrera, A., Luque, B., & Bascompte, J. (2009). The architecture of mutualistic networks minimizes competition and increases biodiversity. Nature, 458, 1018–1021.Google Scholar
Bate, A. M., & Hilker, F. M. (2012). Rabbits protecting birds: Hypopredation and limitations of hyperpredation. Journal of Theoretical Biology, 297, 103–115.Google Scholar
Batista-Foguet, J. M., Coenders, G., Saris, W. E., & Bisbe, J. (2004). Simultaneous estimation of indirect and interaction effects using structural equation models. Metodoloski Zvezki, 1, 163–184.Google Scholar
Battiston, F., Nicosia, V., Chavez, M., & Latora, V. (2016). Multilayer motif analysis of brain networks. Chaos, 27, 047404.Google Scholar
Batushansky, A., Toubiana, D., & Fait, A. (2016). Correlation-based network generation, visualization, and analysis as a powerful tool in biological studies: A case study in cancer cell metabolism. BioMed Research International, 2016, 8313272.Google Scholar
Baur, M., & Benkert, M. (2005). Network comparison. In Brandes, U. & Erlebach, T. (eds.), Network Analysis: Methodological Foundations, pp. 318–340. Berlin: Springer-Verlag.CrossRefGoogle Scholar
Bedord, T, & Cooke, R. M. (2004). Vines: A new graphical model for dependent random variables. The Annals of Statistics, 30, 1031–1068.Google Scholar
Bell, T. (2009). Ecology of microbial populations. In Levin, S. A. (ed.), The Princeton Guide to Ecology, pp. 239–246. Princeton, NJ: Princeton University Press.Google Scholar
Bender, E., & Canfield, E. (1978). The asymptotic number of labeled graphs with given degree sequence. Journal of Combinatorial Theory, A, 24, 296–307.Google Scholar
Benedek, Z., Jordán, F., & Báldi, A. (2007). Topological keystone species complexes in ecological interaction networks. Community Ecology, 8, 1–7.Google Scholar
Bianconi, G. (2018). Multilayer Networks: Structure & Function. Oxford: Oxford University Press.Google Scholar
Blanchet, F. G., Legendre, P., & Borcard, D. (2008). Modelling directional spatial processes in ecological data. Ecological Modelling, 215, 325–336.Google Scholar
Blanchet, F. G., Legendre, P., Maranger, R., Monti, D., & Pepin, P. (2011). Modelling the effect of directional spatial ecological processes at different scales. Oecologia, 166, 357–368.Google Scholar
Bliss, C. A., Danforth, C. M., & Dodds, P. S. (2014). Estimation of global network statistics from incomplete data. PLoS ONE, 9, e108471.Google Scholar
Blüthgen, N. (2010). Why network analysis is often disconnected from community ecology: A critique and an ecologist’s guide. Basic & Applied Ecology, 11, 185–195.Google Scholar
Boccaletti, S., Bianconi, G., Criado, R., et al. (2014). The structure and dynamics of multilayer networks. Physics Reports, 544, 1–122.Google Scholar
Bode, M., Burrage, K., & Possingham, H. (2008). Using complex network metrics to predict the persistence of metapopulations with asymmetric connectivity patterns. Ecological Modelling, 214, 201–209.Google Scholar
Bonald, T., Hollocou, A., & Lelarge, M. (2018). Weighted spectral embedding of graphs. arXiv:1809.11115v2.Google Scholar
Bonchev, D., & Buck, G. A. (2007). Quantitative measures of network complexity. In Bonchev, D & Rouvray, D. H. (eds.), Complexity in Chemistry, Biology, and Ecology, pp. 191–235. New York: Springer Science+Business Media.Google Scholar
Bonchev, D., & Rouvray, D. H. (eds.) (2007). Complexity in Chemistry, Biology, and Ecology. New York: Springer Science+Business Media.Google Scholar
Borcard, D., Legendre, P., Avois-Jacquet, C., & Tuomisto, H. (2004). Dissecting the spatial structure of ecological data at multiple scales. Ecology, 85, 1826–1832.CrossRefGoogle Scholar
Borgwardt, K. M., Petri, T., Vishwanathan, S. V. N., & Kriegel, H.-P. (2007). An efficient sampling scheme for comparison of large graphs. In Proceedings of Conference on Mining and Learning with Graphs, Florence, Italy, 1–3 August 2007.Google Scholar
Borrelli, J. J. (2015). Selection against instability: Stable subgraphs are most frequent in empirical food webs. Oikos, 124, 1583–1588.Google Scholar
Bossenbroek, J. M., Kraft, C. E., & Nekola, J. C. (2001). Prediction of long-distance dispersal using gravity models: Zebra mussel invasion of inland lakes. Ecological Applications, 11, 1778–1788.Google Scholar
Bray, J. R., & Curtis, J. T. (1957). An ordination of upland forest communities of southern Wisconsin. Ecological Monographs, 27, 325–349.CrossRefGoogle Scholar
Bressan, M., Chierichetti, F., Kumar, R., Leucci, S., & Panconesi, A. (2017). Counting graphlets: Space vs time. In Conference on Web Search & Data Mining, 6–10 February 2017. Association for Computing Machinery, 557–566.Google Scholar
Brouwer, A. E., & Haemers, W. H. (2012). Spectra of Graphs. New York: Springer Science+Business Media.Google Scholar
Brualdi, R. A. (2010). Spectra of digraphs. Linear Algebra and its Applications. 432, 2181–2213.Google Scholar
Brüggemann, R., & Carlsen, L. (2006a). Introduction to partial order theory exemplified by the evaluation of sampling sites. In Brüggemann, R. & Carlsen, L. (eds), Partial Order in Environmental Sciences and Chemistry, pp. 61–110. Berlin: Springer-Verlag.Google Scholar
Brüggemann, R., & Carlsen, L. (2006b). Partial Order in Environmental Sciences and Chemistry. Berlin: Springer-Verlag.Google Scholar
Buck, J. C., Rohr, J. R., & Blausten, A. R. (2016). Effects of nutrient supplementation on host-pathogen dynamics of the amphibian chytrid fungus: A community approach. Freshwater Biology, 61, 110–120.Google Scholar
Burkle, L. A., Marlin, J. C., & Knight, T. M. (2013). Plant-pollinator interactions over 120 years: Loss of species, co-occurrence, and function. Science, 339, 1611–1615.Google Scholar
Burnham, K. P., & Anderson, D. R. (2004). Multimodel inference: Understanding AIC and BIC in model selection. Sociological Methods & Research, 33, 261–304.CrossRefGoogle Scholar
Byrnes, J. E., Reed, D. C., Cardinale, B. J., Cavanaugh, K. C., Holbrooks, S. J., & Schmitts, R. J. (2011). Climate-driven increases in storm frequency simplify kelp forest food webs. Global Change Biology, 17, 2513–2524.Google Scholar
Chao, A. (1987). Estimating the population size for capture-recapture data with unequal catchability. Biometrics, 43, 783–791.Google Scholar
Chave, J. (2009). Competition, neutrality and community organization. In Levin, S. A. (ed.), The Princeton Guide to Ecology, pp. 264–273. Princeton, NJ: Princeton University Press.Google Scholar
Chen, H.-W., Liu, W-c., Davis, A. J., Jordán, F., Hwang, M-J., & Shao, K-T. (2008). Network position of hosts in food webs and their parasite diversity. Oikos, 117, 1847–1855.Google Scholar
Chisholm, C., Lindo, Z., and Gonzalez, A. (2010). Metacommunity diversity depends on connectivity and patch arrangement in heterogeneous habitat networks. Ecography, 34, 415–424.Google Scholar
Chuine, I., & Régnière, J. (2017). Process-based models of phenology for plants and animals. Annual Review of Ecology Evolution & Systematics, 48, 159–182.Google Scholar
Chung, F. R. K. (1997). Spectral Graph Theory. Providence, RI: American Mathematical Society.Google Scholar
Clauset, A., Moore, C., & Newman, M. E. J. (2007). Structural inference of hierarchies in networks. In Airoldi, E., Blei, D. M., Fienberg, S. E., Goldenberg, A., Xing, E. P., & Zheng, A. X. (eds.), Statistical Network Analysis: Models, Issues, and New Directions, pp. 1–13. Berlin: Springer.Google Scholar
Connor, N., Barberán, A. & Clauset, A. (2017). Using null models to infer microbial co-occurrence networks. PLoS ONE, 12, e0176751.Google Scholar
Constantini, G., & Perugini, M. (2014). Generalization of clustering coefficients to signed correlation networks. PLoS ONE, 9, e88669.Google Scholar
Costa, L. S. da F., Rodrigues, F. A., Travieso, G., & Villas Boas, P.R. (2007). Characterization of complex networks: A survey of measurements. Advances in Physics, 56, 167–242. arXiv:cond-mat/0505185.Google Scholar
Coux, C., Rader, R., Bartomeus, I., & Tylianakis, J. M. (2016). Linking species functional roles to their network roles. Ecological Letters, 19, 762–770.Google Scholar
Cozzo, E., Kivelä, M., De Domenico, M., et al. (2015). Structure of triadic relations in multiplex networks. New Journal of Physics, 17, 073029.Google Scholar
Craft, M. E., & Caillaud, D. (2010). Network models: An underutilized tool in wildlife epidemiology. Interdisciplinary Perspectives on Infectious Diseases. 2011, Article ID 676949. DOI: 10.1155/2011/676949.Google Scholar
Craft, M. E., Volz, E., Packer, C., & Meyers, L.A. (2010). Disease transmission in territorial populations: The small-world network of Serengeti lions. Journal of the Royal Society Interface, 8, 776–786.Google Scholar
Crane, H. (2018). Probabilistic Foundations of Statistical Network Analysis. Boca Raton, FL: CRC Press.Google Scholar
Cummings, G. (2016). Heterarchies: Reconciling networks and hierarchies. Trends in Ecology & Evolution, 31, 622–632.Google Scholar
Dale, M. R. T. (1985). Graph theoretical methods for comparing phytosociological structures. Vegetatio, 63, 79–88.Google Scholar
Dale, M. R. T. (1999). Spatial Pattern Analysis in Plant Ecology. Cambridge: Cambridge University Press.Google Scholar
Dale, M. R. T. (2017). Applying Graph Theory in Ecological Research. Cambridge: Cambridge University Press.Google Scholar
Dale, M. R. T., & Fortin, M.-J. (2009). Spatial autocorrelation and statistical tests: Some solutions. Journal of Agricultural, Biological, and Environmental Statistics, 14, 188–206.CrossRefGoogle Scholar
Dale, M. R. T., & Fortin, M.-J. (2010). From graphs to spatial graphs. Annual Review of Ecology, Evolution, and Systematics, 41, 21–38.CrossRefGoogle Scholar
Dale, M. R. T., & Fortin, M.-J. (2014). Spatial analysis. A guide for ecologists, 2nd ed. Cambridge: Cambridge University Press.Google Scholar
Daniel, C. J., Frid, L., Sleeter, B. M., & Fortin, M.-J. (2016). State-and-transition simulation models: A framework for forecasting landscape change. Methods in Ecology & Evolution, 7, 1413–1423.CrossRefGoogle Scholar
Daniel, C. J., Sleeter, B. M., Frid, L., & Fortin, M.-J. (2018). Integrating continuous stocks and flows into state-and-transition simulation models of landscape change. Methods in Ecology and Evolution, 9, 1133–1143.Google Scholar
Danon, L., Ford, A.P., House, T., etb al. (2011). Networks and the epidemiology of infectious disease. Interdisciplinary Perspectives on Infectious Diseases, 2011, Article ID 284909. DOI: 10.1155/2011/284909.Google Scholar
de Aguiar, M. A. M., Newman, E. A., Pires, M. M., et al. (2019). Revealing biases in the sampling of ecological interaction networks. PeerJ. e7566. https://doi.org/10.7717/peerj.7566Google Scholar
De Domenico, M., Solé-Ribalta, A., Cozzo, E., et al. (2013). Mathematical formulation of multilayer networks. Physical Review X, 3, 041022.Google Scholar
De Domenico, M., Solé-Ribalta, A., Ormodei, E., Gomez, S., & Arenas, A. (2015). Ranking in interconnected multilayer networks reveals versatile nodes. Nature Communications, 6: 6868.Google Scholar
DeFord, D. R., & Pauls, S. D. (2017). Spectral clustering methods for multiplex networks. arXiv:1703.05355v1.Google Scholar
Dehmer, M., & Emmert-Strieb, F. (2015). Quantitative Graph Theory. Boca Raton, FL: CRC Press.Google Scholar
Delmas, E., Besson, M., Brice, M.-H., et al. (2019). Analyzing ecological networks of species interactions. Biological Reviews, 94, 16–36.Google Scholar
Dempsey, K., & Ali, H. (2011). Evaluation of essential genes in correlation networks using measures of centrality. In Proceedings of IEEE International Conference on Bioinformatics and Biomedicine Workshops.Google Scholar
Deng, Y., Jiang, Y. H., Yang, Y., He, Z., Luo, F., & Zhou, J. (2012). Molecular ecological network analysis. BMC Bioinformatics, 13, 113.Google Scholar
Donetti, L., Neri, F., & Munoz, M. A. (2006). Optimal network topologies: Expanders, Cages, Ramanujan graphs, entangled networks and all that. Journal of Statistical Mechanics, 8, P08007. arXiv:cond-mat/0605565v2.Google Scholar
Dong, X., Frossard, P., Vandergheynst, P., & Nefedov, N. (2011). Clustering with multi-layer graphs: A spectral perspective. IEEE Transactions on Signal Processing, 60, 5820–5831.Google Scholar
Doreian, P., & Mrvar, A. (2009). Partitioning signed social networks. Social Networks, 31, 1–11.Google Scholar
Dormann, C. F., Fründ, J., & Schaefer, H. M. (2017). Identifying causes of patterns in ecological networks: Opportunities and limitations. Annual Review of Ecology, Evolution, and Systematics, 48, 559–584.Google Scholar
Doyle, P. G., & Snell, J. L. (1984). Random Walks and Electric Networks. Washington, DC: Mathematical Association of America.Google Scholar
Dray, S., Legendre, P., & Peres-Neto, P. R. (2006). Spatial modelling: A comprehensive framework for principal coordinate analysis of neighbour matrices (PCNM). Ecological Modelling, 196, 483–493.Google Scholar
Dray, S., Pélissier, R., Couteron, P., et al.(2012). Community ecology in the age of multivariate multiscale spatial analysis. Ecological Monographs, 82, 257–275.Google Scholar
Duboscq, J., Romano, V., Sueur, C., & MacIntosh, A. J. J. (2015). Network centrality and seasonality interact to predict lice load in a social primate. Scientific Reports, 6, 22095.Google Scholar
Dunne, J. A., Williams, R. J., & Martinez, N. D. (2002). Network structure and biodiversity loss in food-webs: Robustness increases with connectance. Ecology Letters, 5, 558–567.Google Scholar
Duran-Pinedo, A. E., Paster, B., Teles, R., & Frias-Lopez, J. (2011). Correlation network analysis applied to complex biofilm communities. PLoS ONE, 6, e28438.Google Scholar
Dutilleul, P. (1993). Modifying the t test for assessing the correlation between two spatial processes. Biometrics, 49, 305–314.Google Scholar
El Maftouhi, A., Harutyunyan, A., & Manoussakis, Y. (2015). Weak balance in random signed graphs. Internet Mathematics, 11, 143–154.Google Scholar
Ellington, E. H., Bastille, A., Rousseau, G., et al. (2015). Using multiple imputation to estimate missing data in meta-regression. Methods in Ecology and Evolution, 6, 153–163.Google Scholar
Emer, C., Galetti, M., Pizo, M. A., Guimaraes Jr, P. R., Moraes, S., Piratelli, A., & Jordano, P. (2018). Seed‐dispersal interactions in fragmented landscapes–a metanetwork approach. Ecology Letters, 21, 484–493.Google Scholar
Erickson, R. O. (1943). Population size and geographical distribution of Clematis fremontii var. riehlii. Annals of the Missouri Botanical Garden, 30, 63–68.Google Scholar
Estrada, E. (2007). Characterization of topological keystone species: Local, global and ‘meso-scale’ centralities in food webs. Ecological Complexity, 4, 46–57.Google Scholar
Estrada, E. (2012). The Structure of Complex Networks: Theory and Applications. Oxford: Oxford University Press.Google Scholar
Estrada, E., & Bodin, Ö. (2008). Using network centrality measures to manage landscape connectivity. Ecological Applications, 18, 1810–1825.Google Scholar
Estrada, E., & Rodriguez-Velazquez, J. A. (2006). Subgraph centrality and clustering in complex hyper-networks. Physica A, 364, 581–594.Google Scholar
Facchetti, G., Iacono, G., & Altafini, C. (2011). Computing global structural balance in large-scale signed social networks. Proceedings of the National Academy of Science, 108, 20953–20958.CrossRefGoogle ScholarPubMed
Fall, A., Fortin, M.-J., Kneeshaw, D. D., et al.(2004). Consequence of various landscape-scale ecosystem management strategies and fire cycles on age-class structure and harvest in boreal forests. Canadian Journal of Forest Research, 34, 310–322.Google Scholar
Fall, A., Fortin, M.-J., Manseau, M., & O’Brien, D. (2007). Spatial graphs: Principles and applications for habitat connectivity. Ecosystems, 10, 448–461.Google Scholar
Fan, Y., Chen, J., Shirkey, G., John, R., Wu, S. R., Park, H., & Shao, C. (2016). Applications of structural equation modeling (SEM) in ecological studies: An updated review. Ecological Processes, 5, 19.Google Scholar
Fausett, L. V. (1994). Fundamentals of Neural Networks: Architectures, Algorithms & Applications. Upper Saddle River, NJ: Prentice-Hall.Google Scholar
Faust, K., & Raes, J. (2012). Microbial interactions: From networks to models. Nature Reviews, 10, 538–550.Google Scholar
Fernandez, M., Riveros, J. D., Campos, M., Mathe, K., & Narasimhan, G. (2015). Microbial “social networks.” BioMedCentral Genomics, 16, Suppl. 11, S6.Google Scholar
Finn, K. R., Silk, M. J., Porter, M. A., & Pinter-Wollman, N. (2017). Novel insights into animal sociality from multilayer networks. arXiv:1712.01790v1.Google Scholar
Fischer, M. M. (2009). Neural networks for spatial data analysis. In Fotheringham, A. S. & Rogerson, P. A. (eds.), The SAGE Handbook of Spatial Analysis, pp. 375–396. Los Angeles: SAGE.Google Scholar
Fisher, N. I. (1997). Copulas. In Encyclopedia of Statistical Sciences, Update Vol. 1, pp. 159–163. New York: John Wiley & Sons.Google Scholar
Fletcher, R. J., Revell, A., Reichert, B. E., Kitchens, W. M., Dixon, J. D., & Austin, J. D. (2013). Network modularity reveals critical scales for connectivity in ecology and evolution. Nature Communications, 4, 2572.Google Scholar
Fontaine, C., Guimaraes, P. R., Kéfi, S., et al. (2011). The ecological and evolutionary implications of merging different types of networks. Ecology Letters, 14, 1170–1181.Google Scholar
Friedman, J., & Alm, E. J. (2012). Inferring correlation networks from genomic survey data. PLoS Computational Biology, 9, e1002687.Google Scholar
Gabow, H. N., & Myers, E. W. (1978). Finding all spanning trees of directed and undirected graphs. SIAM Journal of Computing, 7, 280–287.Google Scholar
Gago, S., Hurajova, J. C., & Madaras, T. (2015). Betweenness centrality in graphs. In Dehmer, M. & Emmert-Streib, F. (ed.), Quantitative Graph Theory, pp. 233–257. Boca Raton, FL: CRC Press.Google Scholar
Gallier, J. (2013). Notes on elementary spectral graph theory. Applications to graph clustering using normalized cuts. ResearchGate 258374324.Google Scholar
Gallier, J. (2017). Spectral theory of unsigned and signed graphs. Applications to graph clustering: a survey. arXiv:1601.04692v1.Google Scholar
Gao, J., Barzel, B., & Barabási, A.-L. (2016). Universal resilience patterns in complex networks. Nature, 530, 307–312.Google Scholar
Gao, J., Buldyrev, S. V., Stanley, H. E., & Havlin, S. (2012). Networks formed from interdependent networks. Nature Physics, 8, 40–48.Google Scholar
Gao, J., Li, D., & Havlin, S. (2014). From a single network to a network of networks. National Science Review, 1, 346–356.Google Scholar
García-Callejas, D., Malowny-Horas, R., & Araújo, M. B. (2018). Multiple interactions networks: Toward more realistic descriptions of the web of life. Oikos, 127, 5–22.Google Scholar
Gärtner, T. (2003). A survey of kernels for structured data. ACM SIGKDD Explorations Newsletter, 5, 49–58.Google Scholar
Gaüzère, B., Brun, L., & Willemin, D. (2015). Graph kernels in chemoinformatics. In Dehmer, M. & Emmert-Streib, F., (eds), Quantitative Graph Theory, pp. 425–469. Boca Raton, FL: CRC Press.Google Scholar
Geng, H., Tran-Gyamfi, M. B., Lane, T. W., Sale, K. L., & Yu1, E.T. (2016). Changes in the structure of the microbial community associated with Nannochloropsis salina following treatments with antibiotics and bioactive compounds. Frontiers in Microbiology, 7, Article 1155. https://doi.org/10.3389/fmicb.2016.01155.Google Scholar
Gera, R., Alonso, L., Crawford, B., House, J., Mendez-Bermudez, J. A., Knuth, T., & Miller, R. (2018). Identifying network structure similarity using spectral graph theory. Applied Network Science, 3, 2.Google Scholar
Gerstmann, H., Doktor, D., Glässer, C., & Möler, M. (2016). PHASE: A geostatistical model for the Kriging-based spatial prediction of crop phenology using public phenological and climatological observations. Computers and Electronics in Agriculture, 127, 726–738.Google Scholar
Ghosh, S., Das, N., Gonçalves, T., Quaresma, P., & Kundu, M. (2017). The journey of graph kernels through two decades. Computer Science Review, 27, 88–111.Google Scholar
Gilarranz, L. J., & Bascompte, J. (2012). Spatial network structure and metapopulation persistence. Journal of Theoretical Biology, 297, 11–16.Google Scholar
Gilarranz, L. J., Hastings, A., & Bascompte, J. (2014). Inferring topology from dynamics in spatial networks. Theoretical Ecology, 8, 15–21.Google Scholar
Gilbert, J. A., Steele, J. A., Caporaso, J. G., et al. (2012). Defining seasonal marine microbial community dynamics. ISME Journal, 6, 298–308.Google Scholar
Girvan, M., & Newman, M. E. J. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences of the USA, 99, 7821–7826.Google Scholar
Glaz, J., Naus, J., & Wallenstein, S. (2001). Scan Statistics. New York: Springer Science+Business Media.Google Scholar
Godfrey, S. S. (2013). Networks and the ecology of parasite transmission: A framework for wildlife parasitology. International Journal for Parasitology: Parasites and Wildlife, 2, 235–245.Google Scholar
Godfrey, S. S., Moore, J. A., Nelson, N. J., & Bull, C. M. (2010). Social network structure and parasite infection patterns in a territorial reptile, the Tuatara (Sphenodon punctatus). International Journal of Parasitology, 40, 1575–1585.Google Scholar
Godsil, C., & Royle, G. (2001). Algebraic Graph Theory. New York: Springer Science+Business Media.Google Scholar
Goldreich, O. (2010). P, NP, and NP-Completeness. Cambridge: Cambridge University Press.Google Scholar
Golubski, A. J., & Abrams, P. A. (2011). Modifying the modifiers: What happens when interspecific interactions interact? Journal of Animal Ecology, 80, 1097–1108.Google Scholar
Golubski, A. J., Westlund, E. E., Vandermeer, J., & Pascual, M. (2016). Ecological networks over the edge: Hypergraph trait-mediated indirect interaction (TMII) structure. Trends in Ecology and Evolution, 31, 344–354.Google Scholar
Gomez, S., Diaz-Guilera, A., Gomez-Gardeñes, J., Perez-Vicente, C. J., Moreno, Y., & Arenas, A. (2013). Diffusion dynamics on multiplex networks. Physical Review Letters, 110, 028701.Google Scholar
Grace, J. B. (2006). Structural Equation Modeling and Natural Systems. Cambridge: Cambridge University Press.Google Scholar
Grace, J. B., & Irvine, K. M. (2019). Scientist’s guide to developing explanatory statistical models using causal analysis principles. Extended pre-publication draft of Grace & Irvine (2020).Google Scholar
Grace, J. B., & Irvine, K. M. (2020). Scientist’s guide to developing explanatory statistical models using causal analysis principles. Ecology, 101, e02962.Google Scholar
Gravel, D., Massol, F., & Leibold, M. A. (2016). Stability and complexity in model meta-ecosystems. Nature Communications, 7, 12457.Google Scholar
Gray, M., Stansberry, M. J., Lynn, J. S., Williams, C. F., White, T. E., & Whitney, K. D. (2018). Consistent shifts in pollinator-relevant floral coloration along Rocky Mountain elevation gradients. Journal of Ecology, 106, 1910–1924.Google Scholar
Gross, T., & Blasius, B. (2008). Adaptive coevolutionary networks: A review. Journal of the Royal Society Interface, 5, 259–271.Google Scholar
Gross, T., & Sayama, H., eds. (2009). Adaptive Networks. Cambridge, MA: NECSI and Springer.Google Scholar
Gu, S., Johnson, J., Faisal, F. E., & Milenković, T. (2018). From homogeneous networks to heterogeneous network alignment via colored graphlets. Nature Scientific Reports, 8, 12524.Google Scholar
Gunderson, L. H. (2000). Ecological resilience- on theory and application. Annual Review of Ecology & Systematics, 31, 425–439.Google Scholar
Haegeman, B., Arnoldi, J.-F., Wang, S., de Mazancourt, C., Montoya, J. M., & Loreau, M. (2016). Resilience, invariability, and ecological stability across levels of organization. bioRxiv, DOI: http://dx.doi.org/10.1101/085852.Google Scholar
Hamede, R. K., Bashford, J., McCallum, H., & Jones, M. (2009). Contact networks in a wild Tasmanian devil (Sarcophilus harrisii) population: Using social network analysis to reveal seasonal variability in social behaviour and its implications for transmission of devil facial tumour disease. Ecology Letters, 12, 1–11.Google Scholar
Harary, F. (1953). On the notion of balance of a signed graph. Michigan Mathematical Journal, 2, 143–146.Google Scholar
Harary, F. (1959). On the measurement of structural balance. Behavioral Science, 4, 316–323.Google Scholar
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed. New York: Springer Science+Business Media.Google Scholar
Hastings, A. (2001). Transient dynamics and the persistence of ecological systems. Ecological Letters, 4, 215–220.Google Scholar
Hastings, A. (2004). Transients: The key to long-term ecological understanding. Trends in Ecology and Evolution, 19, 39–45.Google Scholar
Hastings, A., Abbott, K. C., Cuddington, K., et al. (2018). Transient phenomena in ecology. Science, 361, aat6412.Google Scholar
Heckathorn, D. D. (1997). Respondent-driven sampling: A new approach to the study of hidden populations. Social Problems, 44, 174–199.Google Scholar
Helm, D. (2006). Evaluation of biomonitoring data. In Brüggemann, R. & Carlsen, L, (eds.), Partial Order in Environmental Sciences and Chemistry, pp. 285–307. Berlin: Springer-Verlag.Google Scholar
Holling, C. (1973). Resilience and stability of ecological systems. Annual Review of Ecology & Systematics, 4, 1–23.Google Scholar
Horn, H. S. (1975). Markovian properties of forest succession. In Cody, M. L. & Diamond, J. M. (eds.), Ecology and Evolution of Communities, pp. 196–211. Cambridge, MA: Belknap.Google Scholar
Horn, H. S. (1976). Succession. In May, R. M. (ed.), Theoretical Ecology: Principles and Applications, pp. 187–204. Oxford: Blackwell.Google Scholar
Horvath, S. (2011). Weighted Network Analysis: Application in Genomics and Systems Biology. New York: Springer.Google Scholar
Hsieh, H.-Y., Liere, H., Soto, E. J., & Perfecto, J. (2012). Cascading trait-mediated interactions induced by ant pheromones. Ecology & Evolution, 2, 2181–2191.Google Scholar
Hulovatyy, Y., Chen, H., & Milenković, T. (2015). Exploring the structure and function of temporal networks with dynamic graphlets. Bioinformatics, 31, 1171–1180.Google Scholar
Ilany, A., Barocas, A., Koren, L., Kam, M., & Geffen, E. (2013). Structural balance in the social networks of a wild mammal. Animal Behaviour, 85, 1397–1405.Google Scholar
Ings, T. C., Montoya, J. M., Bascompte, J., et al. (2009). Ecological networks – beyond food webs. Journal of Animal Ecology, 78, 253–269.Google Scholar
Ives, A. R., & Carpenter, S. R. (2007). Stability and diversity in ecosystems. Science, 317, 58–62.Google Scholar
Jiang, L. Q., & Zhang, W. J. (2015). Determination of keystone species in CSM food web: A topological analysis of network structure. Network Biology, 5, 13–33.Google Scholar
Joe, H. (1997). Multivariate Models and Dependence Concepts. Boca Raton, FL, CRC Press.Google Scholar
Johansson, J., Kristensen, N. P., Nilsson, J.-A., & Jonz, N. (2015). The eco-evolutionary consequences of interspecific phenological asynchrony—a theoretical perspective. Oikos, 124, 102–112.Google Scholar
Jordán, F. (2009). Keystone species and food webs. Philosophical Transactions of the Royal Society, B, 364, 1733–1741.Google Scholar
Jordán, F., Takaács-Sánta, A., & Molnár, I. (1999). A reliability theoretical quest for keystones. Oikos, 86, 453–462.Google Scholar
Jordano, P. (2016). Sampling networks of ecological interactions. Functional Ecology, 30, 1883–1893.Google Scholar
Junker, B. H., & Schreiber, F. (2008). Analysis of Biological Networks. Hoboken, NJ: John Wiley & Sons.Google Scholar
Kamisinski, A., Cholda, P., & Jajszczyk, A. (2015). Assessing the structural complexity of computer and communication networks. ACM Computing Surveys, 47, Article 66. DOI: 10.1145/2755621.Google Scholar
Kao, T.-C., & Porter, M. A. (2017). Layer communities in multiplex networks. arXiv:706.04147v1.Google Scholar
Kashtan, N., Itzkovitz, S., Milo, R., & Alon, U. (2004). Topological generalizations of network motifs. Physical Review E, 70, 031909.Google Scholar
Kéfi, S., Berlow, E. L., Wieters, E. A., et al. (2015). Network structure beyond food webs: Mapping non-trophic and trophic interactions on Chilean rocky shores. Ecology, 96, 291–303.Google Scholar
Kéfi, S. Thébault, E., Eklöf, A., et al. (2018). Toward multiplex ecological networks: Accounting for multiple interaction types to understand community structure and dynamics. In Moore, J. C., de Ruiter, P. C., McCann, K. S., & Wolters, V (eds). Adaptive Food Webs: Stability and Transitions of Real and Model Ecosystems, pp. 74 –87. Cambridge: Cambridge University Press.Google Scholar
Kelly, W. P., Thorne, T., & Stumpf, M. P. H. (2010). Statistical null models for biological network analysis. In Stumpf, M. P. H., & Wiuf, C (eds.), Statistical and Evolutionary Analysis of Biological Networks, pp. 145–165. London: Imperial College Press.Google Scholar
Kemp, J. E., Evans, D. M., Augustyn, W. J., & Ellis, A. G. (2017). Invariant antagonistic network structure despite high spatial and temporal turnover of interactions. Ecography, 40, 1315–1324.Google Scholar
Kepaptsoglou, K., Karlaftis, M. G., & Tsambloulas, D. (2010). The gravity model specification for modeling international trade flows and free trade agreement effects: A 10–year review of empirical studies. The Open Economics Journal, 3, 1–13.Google Scholar
Kim, H. J., & Kim, J. M. (2005). Cyclic topology in complex networks. Physical Review E, 72, 036109.Google Scholar
Kim, J., & Lee, J.-G. (2015). Community detection in multi-layer graphs: A survey. SIGMOD Record, 44, 37–48.Google Scholar
Kivelä, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2, 203–271.Google Scholar
Klau, G. W., & Weiskercher, R. (2005). Robustness and resilience. In Brandes, U., & Erlebach, T (eds.) Network Analysis: Methodological Foundations, pp. 417–437. Berlin: Springer-Verlag.Google Scholar
Kolaczyk, E. D., & Csárdi, G. (2014). Statistical Analysis of Network Data with R. New York: Springer Science+Business Media.Google Scholar
Kondor, R. I., & Lafferty, J. (2002). Diffusion kernels on graphs and other discrete input spaces. In Proceedings of the International Conference on Machine Learning (ICML), Sydney, 2002.Google Scholar
Koschützki, D. (2008). Network centralities. In Junker, B. H., & Schreiber, F., (eds.), Analysis of Biological Networks, pp. 65–84. Hoboken, NJ: John Wiley & Sons.Google Scholar
Krivitsky, P. N., & Kolaczyk, E.D. (2015). On the question of the effective sample size in network modelling: An asymptotic inquiry. Statistical Science, 30, 184–198.Google Scholar
Kunegis, J. (2014). Applications of structural balance in signed social networks. arXiv:1402.6865v1.Google Scholar
Kunegis, J., Schmidt, S., Lommatzsch, A., Lerner, J, De Luca, E. W., & Albyrak, S. (2010). Spectral analysis of signed graphs for clustering, prediction and visualization. In Proceedings of the SIAM International Conference on Data Mining, Columbus, 2010, pp. 559–570.Google Scholar
Kurowicka, D., & Cooke, R. M. (2002). The vine copula method for representing high dimensional dependent distributions: Application to continuous belief nets. In Yücesan, E., Chen, C. H, Snowdon, J. L., & Charnes, J. M. (eds.), Proceedings of the 2002 Winter Simulation Conference, San Diego, pp. 270–278.Google Scholar
Lasserre, J., Chung, H. R., & Vingron, M. (2013). Finding associations among histone modifications using sparse partial correlation networks. PLoS Computational Biology, 9, e1003168.Google Scholar
Lau, M. K., Borrett, S. R., Baiser, B., Gotelli, N. J., & Ellison, A. M. (2017). Ecological network metrics: Opportunities for synthesis. Ecosphere, 8, p.e01900.Google Scholar
Layeghifard, M., Hwang, D. M., & Guttman, D. S. (2017). Disentangling interactions in the microbiome: A network perspective. Trends in Microbiology, 25, 217–228.Google Scholar
Lazega, E., Jourda, M.-T., Mounier, L., & Stofer, R. (2008). Catching up with big fish in the big pond? Multi-level network analysis through linked design. Social Networks, 30, 159–176.Google Scholar
Lee, A. B., Luca, D., & Roeder, K. (2010). A spectral graph approach to discovering genetic ancestry. The Annals of Applied Statistics, 4, 179–202.Google Scholar
Lee, K.-M., Miu, B., & Goh, K.-I. (2015). Towards real-world complexity: An introduction to multiplex networks. European Physics Journal, arXiv:1502.03909v1.Google Scholar
Lee, S. H., Kim, P.-J., & Jeong, H. (2006). Statistical properties of sampled networks. Physical Review, E. 016102. arXiv:cond-mat/0500232v4.Google Scholar
Lefcheck, J. S. (2016). Piecewise structural equation modelling in R for ecology, evolution and systematics. Methods in Ecology and Evolution, 7, 573–579.Google Scholar
Legendre, P., & Legendre, L. (2012). Numerical Ecology, 3rd English ed. Amsterdam: Elsevier.Google Scholar
Levin, S. A. (1998). Ecosystems and the biosphere as complex adaptive systems. Ecosystems, 1, 431–436.Google Scholar
Li, B., Springer, J., Bebis, G., & Gunes, M. H. (2013). A survey of network flow applications. Journal of Network and Computer Applications, 36, 567–581.Google Scholar
Li, Y., Wu, X., & Lu, A. (2016). On spectral analysis of directed signed graphs. International Journal of Data Science and Analytics, 6, 147–162.Google Scholar
Libralato, S., Christensen, V., & Pauly, D. (2006). A method for identifying keystone species in food web models. Ecological Modelling, 195, 153–171.Google Scholar
Little, R. J. A., & Rubin, D. B. (1987). Statistical Analysis with Missing Data. New York: John Wiley & Sons.Google Scholar
Lugo-Martinez, J., & Radivojac, P. (2014). Generalized graphlet kernels for probabilistic inference in sparse graphs. Network Science, 2, 254–276.Google Scholar
Lupatini, M., Suleiman, A. K. A., Jacques, R. J. S., et al. (2014). Network topology reveals high connectance levels and few key microbial genera within soils. Frontiers in Environmental Science, 2, 10.Google Scholar
Lurgi, M., Galiana, N., Lopez, B. C., Joppa, L. N., & Montoya, J. M. (2014). Network complexity and species traits mediate the effects of biological invasions on dynamic food webs. Frontiers in Ecology and Evolution, 2, 36.Google Scholar
Malmros, J., Masuda, N., & Britton, T. (2013). Random walks on directed networks: Inference and respondent-driven sampling. Journal of Official Statistics, 32, 433–459.Google Scholar
Malod-Dognin, N., & Pržulj, N. (2019). Network alignment. In Pržulj, N. (ed.) Analyzing Network Data in Biology and Medicine, pp. 369–413. Cambridge: Cambridge University Press.Google Scholar
Manley, B. J. F. (2006). Randomization, Bootstrap and Monte Carlo Methods in Biology, 3rd ed. Boca Raton, FL: CRC Press.Google Scholar
Marcus, D. A. (2008). Graph Theory: A Problem Oriented Approach. Washington, DC: Mathematical Association of America.Google Scholar
Marsh, M. K., McLeod, S. R., Hutchings, M. R., & White, P. C. L. (2011). Use of proximity loggers and network analysis to quantify social interactions in free-ranging wild rabbit populations. Wildlife Research, 38, 1–12.Google Scholar
Martensen, A. C., Saura, S., & Fortin, M.-J. (2017). Spatio‐temporal connectivity: Assessing the amount of reachable habitat in dynamic landscapes. Methods in Ecology and Evolution, 8, 1253–1264.Google Scholar
Martinez, N. D. (1992). Constant connectance in community food webs. American Naturalist, 139, 1208–1281.Google Scholar
Matsui, T. (1993). A flexible algorithm for generating all the spanning trees in undirected graphs. Algorithmica, 18, 530–543.Google Scholar
May, R. M. (1973). Stability and Complexity in Model Ecosystems. Princeton, NJ: Princeton University Press.Google Scholar
Maynard, D. S., Crowther, T. W., & Bradford, M. A. (2017). Competitive network determines the direction of the diversity-function relationship. PNAS, 114, 11464–11469.Google Scholar
McRae, B. H., Dickson, B. G., Keitt, T. H., & Shah, V. B. (2008). Using circuit theory to model connectivity in ecology, evolution, and conservation. Ecology, 89, 2712–2724.Google Scholar
Menge, B. A. (1995). Indirect effects in a marine rocky intertidal interaction webs: Patterns and importance. Ecological Monographs, 65, 21–74.Google Scholar
Miki, T., & Jacquet, S. (2010). Indirect interactions in the microbial world: Specificities and similarities to plant-insect systems. Population Ecology, 52, 475–483.Google Scholar
Milenkovic, T., & Pržulj, N. (2008). Uncovering biological network function via graphlet degree signatures. Cancer Informatics, 6, 257–273.Google Scholar
Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., & Alon, U. (2002). Network motifs: Simple building blocks of complex networks. Science, 298, 824–827.Google Scholar
Minor, E. S., & Lookingbill, T. R. (2010). A multiscale network analysis of protected-area connectivity for mammals in the United States. Conservation Biology, 24, 1549–1558.Google Scholar
Minor, E. S., & Urban, D. L. (2007). Graph theory as a proxy for spatially explicit population models in conservation planning. Ecological Applications, 17, 1771–1782.Google Scholar
Mondragon, R. J., Iacovacci, J., & Bianconi, G. (2018). Multilink communities of multiplex networks. PLoS ONE, 13, e0193821.Google Scholar
Moore, J. C., de Ruiter, P. C., McCann, K. S., & Wolters, V., eds. (2018). Adaptive Food Webs: Stability and Transitions of Real and Model Ecosystems. Cambridge: Cambridge University Press.Google Scholar
Moore, C., & Mertens, S. (2011). The Nature of Computation. Oxford: Oxford University Press.Google Scholar
Mora, B. B., Gravel, D., Gilarranz, L. J., Poisot, T., & Stouffer, D. B. (2018). Identifying a common backbone of interactions underlying food webs from different ecosystems. Nature Communications, 9, 2603.Google Scholar
Mowshowitz, A., & Dehmer, M. (2012). Entropy and the complexity of graphs revisited. Entropy, 14, 559–570.Google Scholar
Mowshowitz, A., & Mitsou, V. (2009). Entropy, orbits and spectra of graphs. In Dehmer, M. & Emmert-Streib, F. (eds.), Analysis of Complex Networks: From Biology to Linguistics, pp. 1–22. Weinheim: Wiley-VCH Verlag.Google Scholar
Murphy, M. A., Dezzani, R., Pilliod, D. S., & Storfers, A. (2010). Landscape genetics of high mountain frog metapopulations. Molecular Ecology, 19, 3634–3649.Google Scholar
Naujokaitis-Lewis, I., Rico, Y., Lovell, J., Fortin, M.-J., & Murphy, M. (2013). Implications of incomplete networks on estimation of landscape genetic connectivity. Conservation Genetics, 14, 287–298.Google Scholar
Nelson, G. A. (2014). Cluster sampling: A pervasive, yet little recognized survey design in fisheries research. Transactions of the American Fisheries Society, 143, 926–938.Google Scholar
Nelsen, R. B. (1992). On measures of association as measures of positive dependence. Statistics & Probability Letters, 14, 269–274.Google Scholar
Neuhaus, M., & Bunke, H. (2006). A random walk kernel derived from graph edit distance. Structural, Syntactic and Statistical Pattern Recognition, Hong Kong, August 2006. Proceedings, pp. 191–199. DOI: 10.1007/11815921_20.Google Scholar
Newaz, K., & Milenković, T. (2019). Graphlets in network science and computational biology. In Pržulj, N. (ed.), Analyzing Network Data in Biology and Medicine, pp. 193–240. Cambridge: Cambridge University Press.Google Scholar
Newman, M. E., Watts, D. J., & Strogatz, S. H. (2002). Random graph models of social networks. PNAS, 99, 2566–2572.Google Scholar
Newman, M. E. J., & Park, J. (2003). Why social networks are different from other types of networks. Physics Review E, 68, 036122.Google Scholar
Nicosia, V., Tang, J., Mascolo, C., Musolesi, M., & Latora, V. (2013). Graph metrics for temporal networks. In Holme, P. & Saramäki, J. (eds.), Temporal Networks, pp. 15–40. Berlin: Springer-Verlag.Google Scholar
Okabe, A., & Satoh, T. (2009). Spatial analysis on a network. In Fotheringham, A. S. & Rogerson, P. A. (eds.), The SAGE Handbook of Spatial Analysis, pp. 443–464. Los Angeles: SAGE.Google Scholar
Okuyama, T., & Bolker, B. M. (2007). On quantitative measures of indirect interactions. Ecological Letters, 10, 264–271.Google Scholar
Opgen-Rhein, R., & Strimmer, K. (2007). From correlation to causation networks: A simple approximate learning algorithm and its application to high-dimensional plant gene expression data. BioMed Central Systems Biology, 1, 37.Google Scholar
Paerl, H. W., & Pinckney, J. L. (1996). A mini-review of microbial consortia: Their roles in aquatic production and geochemical cycling. Microbial Ecology, 31, 225–247.Google Scholar
Park, J., & Barabási, A.-L. (2007). Distribution of node characteristics in complex networks. Proceedings of the National Academy of Sciences of the USA, 104, 17916–17920.Google Scholar
Paulau, P. V., Feenders, C., & Blasius, B. (2015). Motif analysis in directed ordered networks and applications to food webs. Scientific Reports, 5, 11926.Google Scholar
Pearl, J. (2009). Causality: Models, Reasoning and Inference, 2nd ed. Cambridge: Cambridge University Press.Google Scholar
Pearl, J., Glymour, M., & Jewell, N. P. (2016). Causal Inference in Statistics. Chichester: John Wiley & Sons.Google Scholar
Pedersen, A. B., & Fenton, A. (2006). Emphasizing the ecology in parasite community ecology. Trends in Ecology and Evolution, 22, 133–138.Google Scholar
Pellissier, L., Albouy, C., Bascompte, J., et al. (2018). Comparing species interaction networks along environmental gradients. Biological Reviews, 93, 785–800.Google Scholar
Perfecto, I., Vandermeer, J., & Philpott, S. M. (2014). Complex ecological interactions in the coffee agroecosystem. Annual Review of Ecology, Evolution, and Systematics, 45, 137–158.Google Scholar
Peterson, E. E., Ver Hoef, J. M., Isaak, D. J., (2013). Modeling dendritic ecological networks in space: An integrated network perspective. Ecology Letters, 16, 707–719.Google Scholar
Peyrard, N., Pellegrin, F., Chadoeuf, J., & Nandris, D. (2006). Statistical analysis of the spatio-temporal dynamics of rubber tree (Hevea brasiliensis) trunk phloem necrosis: No evidence of pathogen transmission. Forest Pathology, 36, 360–371.Google Scholar
Phillips, J. D. (2011). The structure of ecological state transitions: Amplification, synchronization, and constraints in response to environmental change. Ecological Complexity, 8, 336–346.Google Scholar
Phillips, S. T., Williams, P., Midgeley, G., & Archer, A. (2008). Optimizing dispersal corridors for the Cape Proteaceae using network flow. Ecological Applications, 18, 1200–1211.Google Scholar
Pilosof, S., Porter, M. A., Pascual, M., & Kéfi, S. (2017). The multilayer nature of ecological networks. Nature Ecology & Evolution, 1, 0101.Google Scholar
Piraveenan, M., Propokenko, M., & Zomaya, A. Y. (2008). Local assortativeness in scale-free networks. Europhysics Letters, 84, 28002.Google Scholar
Piraveenan, M., Propokenko, M., & Zomaya, A. Y. (2010). Classifying complex networks using unbiased local assortativity. Proceedings of the Alife XII Conference, Odense, Denmark.Google Scholar
Pocock, M. J. O., Evans, D. M., & Memmott, J. (2012). The robustness and restoration of a network of ecological networks. Science, 335, 973–977.Google Scholar
Poisot, T., Canard, E., Mouillot, D., Mouquet, N., & Gravel, D. (2012). The dissimilarity of species interaction networks. Ecology Letters, 15, 1353–1361.Google Scholar
Poisot, T., Cirtwill, A. R., Cazelles, K., Gravel, D., Fortin, M. J., & Stouffer, D. B. (2016a). The structure of probabilistic networks. Methods in Ecology and Evolution, 7, 303–312.Google Scholar
Poisot, T., Stouffer, D. B., & Kéfi, S. (2016b). Describe, understand and predict: Why do we need networks in ecology? Functional Ecology, 30, 1878–1882.Google Scholar
Porter, M. A., & Gleeson, J. P. (2015). Dynamical systems on networks: A tutorial. arXiv:1403.7663v2 [nlin.AO].Google Scholar
Potapov, A., Muirhead, J. R., Lele, S. R., & Lewis, M. A. (2011). Stochastic gravity models for modeling lake invasions. Ecological Modelling, 222, 964–972.Google Scholar
Pržulj, K., Wigle, D. A., & Jurisica, I. (2004). Functional topology in a network of protein interactions. Bioinformatics, 20, 340–348.Google Scholar
Pržulj, N. (2007). Biological network comparison using graphlet degree distribution. Bioinformatics, 23, 177–183.Google Scholar
Pržulj, N., & Malod-Dognin, N. (2016). Network analytics in the age of big data. Science, 353, 123–124.Google Scholar
Radicchi, F., & Bianconi, G. (2017). Redundant interdependencies boost the robustness of multiplex networks. Physical Review X, 7, 011013.Google Scholar
Rayfield, B., Fortin, M.-J., & Fall, A. (2011). Connectivity for conservation: A framework to classify network measures. Ecology, 92, 847–858.Google Scholar
Reshef, D. N., Reshef, Y. A., Finucane, H. K., et al. (2011). Detecting novel associations in large data sets. Science, 334, 1518–1524.Google Scholar
Roesch, F. A. (1993). Adaptive cluster sampling for forest inventories. Forest Science, 39, 655–669.Google Scholar
Rohr, R. P., Naisbit, R. E., Mazza, C., & Bersier, L. F. (2018). Statistical approaches for inferring and predicting food-web architecture. In Moore, J. C., Ruiter, P. C., McCann, K. S, & Wolters, V (eds.), Adaptive Food Webs: Stability and Transitions of Real and Model Ecosystems, pp. 178–192. Cambridge: Cambridge University Press.Google Scholar
Rohr, R. P., Scherer, H., Kehrli, P., Mazza, C., & Bersier, L. F. (2010). Modeling food webs: Exploring unexplained structure using latent traits. American Naturalist, 173, 170–177.Google Scholar
Ruan, Q., Dutta, D., Schwalbach, M. S., Steele, J. A., Fuhrman, J. A., & Sun, F. (2006). Local similarity analysis reveals unique associations among marine bacterioplankton species and environmental factors. Bioinformatics, 22, 2532–2538.Google Scholar
Ruiz, E. J. L., Mozo, H. G., Vilches, E. D., & Galán, C. (2012). The use of geostatistics in the study of floral phenology of Vulpia geniculata (L.) Link. The Scientific World Journal, 624247.Google Scholar
Sarajlić, A., Malod-Dognin, N., Yaveroglu, O. N., & Pržulj, N. (2016). Graphlet-based characterization of directed networks. Scientific Reports, 6, 35098.Google Scholar
Sayama, H., Pestov, I., Schmidt, J., Bush, B. J., Wong, C., Yamanoi, J., & Gross, T. (2013). Modeling complex systems with adaptive networks. Computers and Mathematics with Applications, 65, 1645–1664.Google Scholar
Scheffer, M. (2009). Alternative stable states and regime shifts in ecosystems. In Levin, S. A. (ed.), The Princeton Guide to Ecology, pp. 395–406. Princeton, NJ: Princeton University Press.Google Scholar
Schneider, D. W., Ellis, C. D., & Cummings, K. S. (1998). A transportation model assessment of the risk to native mussel communities from zebra mussel spread. Conservation Biology, 12, 788–800.Google Scholar
Schölkopf, B., Tsuda, K., & Vert, J.-P. (2004). Kernel Methods on Computational Biology. Cambridge, MA: MIT Press.Google Scholar
Schröder, W., Schmidt, G., & Schönrock, S. (2014). Modelling and mapping of plant phenological stages as bio-meteorological indicators for climate change. Environmental Sciences Europe, 26, 5.Google Scholar
Schwöbbermeyer, H. (2008). Network motifs. In Junker, B. H., & Schreiber, F. (eds.), Analysis of Biological Networks, pp. 85–111. Hoboken, NJ: John Wiley & Sons.Google Scholar
Shalizi, C. R. (2006). Methods and techniques of complex systems science: An overview. In Deisboeck, T. & Kresh, J. Y. (eds.), Complex Systems Science in Biomedicine, pp. 33–114. Boston, MA: Springer.Google Scholar
Shao, J., Zhou, X., Luo, Y., et al. (2016) Direct and indirect effects of climatic variations on the interannual variability in net ecosystem exchange across terrestrial ecosystems. Tellus B, DOI: http://dx.doi.org/10.3402/tellusb.v68.30575Google Scholar
Shervashidze, N., Schweitzer, P., van Leeuwen, E. J., Mehlhorn, K., & Borgwardt, K. M. (2011). Weisfeiler-Lehman graph kernels. Journal of Machine Learning Research, 12, 2539–2561.Google Scholar
Shervashidze, N., Vishwanathan, S. V. N, Petri, T., Mehlhorn, K., & Borgwardt, K. (2009). Efficient graphlet kernels for large graph comparison. In Proceedings of the 12th International Conference on Artificial Intelligence and Statistics (AISTATS) 2009, pp. 488–495.Google Scholar
Shipley, B. (2016). Cause and Correlation in Biology, 2nd ed. Cambridge: Cambridge University Press.Google Scholar
Shizuka, D., & McDonald, D. B. (2012). A social network perspective on measurements of dominance hierarchies. Animal Behaviour, 83, 925–934.Google Scholar
Shtileman, E., & Stone, L. (2015). The effects of connectivity on metapopulation persistence: Network symmetry and degree correlations. Proceedings of the Royal Society B: Biological Sciences, 282, 20150203.Google Scholar
Silk, M. J., Croft, D. P., Delahay, R. J., et al. (2017). Using social network measures in wildlife disease ecology, epidemiology, and management. BioScience, 67, 245–257.Google Scholar
Silvertown, J. W., & Lovett-Doust, J. (1993). Introduction to Plant Population Biology. Oxford: Blackwell.Google Scholar
Simmons, B. I., Cirtwill, A. R., Baker, N. J., Dicks, L. V., Stouffer, D. B., & Sutherland, W. I. (2018). Uncovering indirect interactions in bipartite ecological networks. bioRxiv, DOI: 10.1101/315010.Google Scholar
Smith, D. R., Conroy, M. J., & Brakhage, D. H. (1995). Efficiency of adaptive cluster sampling for estimating density of wintering waterfowl. Biometrics, 51, 777–788.Google Scholar
Snijders, T. A., & Bosker, R. (2012). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modelling, 2nd ed. London: SAGE.Google Scholar
Solé, R. V., & Montoya, J. M. (2001). Complexity and fragility in ecological networks. Proceedings of the Royal Society London B: Biological Sciences, 268, 2039–2045.Google Scholar
Solé-Ribalta, A., De Demenico, M., Kouvaris, N. E., Diaz-Guilera, A., Gomez, S., & Arenas, A. (2013). Spectral properties of the Laplacian of multiplex networks. Physical Review E, 88(3), p.032807.Google Scholar
Sørensen, P. B., Lerche, D. B., & Thomsen, M. (2006). Developing decision support based on field data and partial order theory. In Brüggemann, R., & Carlsen, L., (eds.), Partial Order in Environmental Sciences and Chemistry, pp. 259–283. Berlin: Springer-Verlag.Google Scholar
Sotomayor, D. A., & Lortie, C. J. (2015). Indirect interactions in terrestrial plant communities: Emerging patterns and research gaps. Ecosphere, 6, Article 103.Google Scholar
Šourek, G., O Kuzelka, O., & Zelezný, F. (2013). Predicting top-k trends on twitter using graphlets and time features. ILP, Late Breaking Papers, 52.Google Scholar
Spielman, D. (2012). Spectral graph theory. In Naumann, U., & Schenk, O.(eds.), Combinatorial Scientific Computing, pp. 495–523. Boca Raton, FL: Chapman & Hall/CRC.Google Scholar
Staniczenko, P. P. A., Kopp, J. C., & Allesina, S. (2012). The ghost of nestedness in ecological networks. Nature Communications, 4, 1391.Google Scholar
Stella, M., Selakovic, S., Antonioni, A., & Andrazzi, C.S. (2018). Ecological multiplex interactions determine the role of species for parasite spread amplification. eLife, e32814.Google Scholar
Stumpf, M. P. H., & Wiuf, C. (2010). Statistical and Evolutionary Analysis of Biological Networks, London: Imperial College Press.Google Scholar
Stumpf, M. P. H., Wiuf, C., & May, R. M. (2005). Subnets of scale-free networks are not scale-free: Sampling properties of networks. Proceedings of the National Academy of Sciences of the USA, 102, 4221–4224.Google Scholar
Tang, W., Lu, Z., & Dhillon, S. (2009). Clustering with multiple graphs. In Proceedings of the 9th International Conference on Data Mining, 1016–1–21, Miami.Google Scholar
Thébault, R. (2013). Identifying compartments in presence-absence matrices and bipartite networks: Insights into modularity measures. Journal of Biogeography, 40, 759–768.Google Scholar
Thébault, E., & Fontaine, C. (2010). Stability of ecological communities and the architecture of mutualistic and trophic networks. Science, 329, 853–856.Google Scholar
Thébault, E., Sauve, A. M. C., & Fontaine, C. (2018). Merging antagonistic and mutualistic bipartite webs: A first step to integrate interaction diversity into network approaches. In Moore, J. C., de Ruiter, P. C., McCann, K. S., & Wolters, V (eds.), Adaptive Food Webs: Stability and Transitions of Real and Model Ecosystems, pp. 62–73. Cambridge: Cambridge University Press.Google Scholar
Thompson, R. M., & Williams, R. (2017). Unpacking resilience in food webs: An emergent property or a sum of the parts? In Moore, J. C., de Ruiter, P. C., McCann, K. S., & Wolters, V (eds.) Adaptive Food Webs: Stability and Transitions of Real and Model Ecosystems, pp. 88–103. Cambridge: Cambridge University Press.Google Scholar
Thompson, S. K. (2011). Adaptive network and spatial sampling. Survey Methodology, 37, 183–196. (Statistics Canada, Catalogue No. 12–001–X.)Google Scholar
Timóteo, S., Correia, M., Rodriguez-Echeverria, S., Freitas, H., & Heleno, R. (2018). Multilayer networks reveal the spatial structure of seed-dispersal interactions across the Great Rift landscapes. Nature Communications, 9, 140.Google Scholar
Tomczak, M. T., Heymans, J. J., Yletyinen, J., Niiranen, S., Otto, S. A., & Blenckner, T. (2013). Ecological network indicators of ecosystem status and change in the Baltic Sea. PLoS ONE, 8, e75439.Google Scholar
Trøjelsgaard, K., & Olesen, J. M. (2016). Ecological networks in motion: Micro- and macroscopic variability across scales. Functional Ecology, 30, 1926–1935.Google Scholar
Trpevski, I., Dimitrova, T., Boshkovski, T., Stikov, N., & Kocarev, L. (2016). Graphlet characteristics in directed networks. Scientific Reports, 6, 37057.Google Scholar
Trussell, G. C., Matassa, C. M., & Ewanchuk, P.J. (2017). Moving beyond linear food chains: Trait-mediated indirect interactions in a rocky intertidal food web. Proceedings of the Royal Society B: Biological Sciences, 284, 20162590.Google Scholar
Ulrich, W. (2009). Ecological interaction networks: Prospects and pitfalls. Ecological Questions, 11, 17–25.Google Scholar
Urban, D. L., Minor, E. S., Treml, E. A., & Schick, R. S. (2009). Graph models of habitat mosaics. Ecology Letters, 12, 260–273Google Scholar
Vacic, V., Iakoucheva, L. M., Lonardi, S., & Radivjac, P. (2010). Graphlet kernels for prediction of functional residues in protein structures. Journal of Computational Biology, 17, 55–72.Google Scholar
Valdovinos, F. S. (2019). Mutualistic networks: Moving closer to a predictive theory. Ecology Letters, 22, 1517–1534.Google Scholar
Vile, D., Shipley, B., & Garnier, E. (2006). A structural equation model to integrate changes in functional strategies during old-field succession. Ecology, 87, 504–517.Google Scholar
Vishwanathan, S. V. N., Schraudolph, N. N., Kondor, R., & Borgwardt, K. M. (2010). Graph kernels. Journal of Machine Learning Research, 11, 1201–1242.Google Scholar
Wang, P., Robins, G., Pattison, P., & Lazega, E. (2013). Exponential random graph models for multilevel networks. Social Networks, 35, 96–115.Google Scholar
Warton, D. I., Blanchet, F. G., O’Hara, R. B., et al. (2015). So many variables: Joint modelling in community ecology. Trends in Ecology and Evolution, 30, 766–779.Google Scholar
Wasserman, S., & Faust, K. (1994). Social Network Analysis. Cambridge: Cambridge University Press.Google Scholar
Weiss, S., et al. (16 others). (2016). Correlation detection strategies in microbial data sets vary widely in sensitivity and precision. The ISME Journal, 10, 1669–1681.Google Scholar
Whalen, M. A., Duffy, J. E., & Grace, J. B. (2013). Temporal shifts in top-down vs. bottom-up control of epiphytic algae in a seagrass ecosystem. Ecology, 94, 510–520.Google Scholar
White, S. R., Tannas, S., Bao, T., Bennet, J. A., Bork, E. W., & Cahill, J. F. Jr. (2013). Using structural equation modelling to test the passenger, driver and opportunist concepts in a Poa pratensis invasion. Oikos, 122, 377–384.Google Scholar
Williams, M. J., & Musolesi, M. (2016). Spatio-temporal networks: Reachability, centrality and robustness. Royal Society Open Science, 3(6), p.160196.Google Scholar
Williams, R. J., & Purves, D. W. (2011). The probabilistic niche model reveals substantial variation in the niche structure of empirical food webs. Ecology, 92, 1849–1857.Google Scholar
Williams, R. J., Purves, D. W., Williams, R. J., Howe, A., & Hofmokel, K. S. (2014). Demonstrating microbial co-occurrence pattern analyses within and between ecosystems. Frontiers in Microbiology, 5, 358.Google Scholar
Wills, P., & Meyer, F. G. (2020). Metrics for graph comparison: A practitioner’s guide. PLoS ONE, 15, p.e0228728.Google Scholar
Wilson, R. C., & Zhu, P. (2008). A study of graph spectra for comparing graphs and trees. Pattern Recognition, 41, 2833–2841.Google Scholar
Windels, S. F. L., Malod-Dognin, N., & Pržulj, N. (2018). Graphlet Laplacians: graphlet-based neighbourhoods highlight topology-function and topology-disease relationships. bioRxiv, DOI:10.1101/460964.Google Scholar
Wootton, J. T. (1994). Predicting direct and indirect effects: An integrated approach using experiments and path analysis. Ecology, 75, 151–165.Google Scholar
Yaveroğlu, Ö. N., Malod-Dognin, N., Davis, D., et al. (2014). Revealing the hidden language of complex networks. Scientific Reports, 4, 4547.Google Scholar
Yletyinen, J., Bodin, Ö., Weigel, B., Nordström, M. C., Bonsdorff, E., & Blenker, T. (2015). Regime shifts in marine communities: A complex systems perspective on food web dynamics. Proceedings of the Royal Society B: Biological Sciences, 283, 20152569.Google Scholar
Zaborain-Mason, J., Russ, G. R., Abesamis, R. A., Bucol, A. A., & Connolly, S. R. (2017). Network theory and metapopulation persistence: Incorporating node self-connections. Ecology Letters, DOI :10.1111/ele12784.Google Scholar
Zhang, B., & Horvath, S. (2005). A general framework for weighted gene co-expression network analysis. Statistical Applications in Genetics and Molecular Biology, 4, a7.Google Scholar
Zhang, Q., & Shi, X. (2017). A mixture copula Bayesian network model for multimodal genomic data. Cancer Informatics, 16, 1-11.Google Scholar