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Martin Gonzalez, A. M.

Publications and source records attributed to Martin Gonzalez, A. M..

3 recordsLinked to original sources

Core-periphery structure in mutualistic networks: an epitaph for nestedness?

The calculation of nestedness has become a routine analysis in the study of ecological networks, as it is commonly associated with community resilience, robustness and species persistence. While meaningful in species distributional patterns, for an interaction matrix to be nested, specialist species must interact with ordered subsets of subsequently more generalized species -- not just with a lower number of species. However, after reviewing 419 papers on mutualistic networks published since nestedness was introduced for the study of species interactions in 2003, we have found that only two theoretical studies considered explicitly ordered subsets. Instead, most studies interpret nestedness as a core of densely connected generalist species, surrounded by a periphery of specialist species attached to this core -- a so-called core-periphery structure. Such a topological feature is generally perceived as a core-periphery structure in network science. Here, we argue that the concept of core-periphery may be more relevant for studies on mutualistic networks than the concept of nestedness, as ecologists are usually not interested in exploring in detail the ordered subsets that characterize nestedness but instead use nestedness to describe a topology with a core of densely linked generalist species surrounded by a sparsely linked periphery of specialists. To illustrate our arguments and the quantification of core-periphery structures, we calculate core-periphery and nestedness in a large publicly available dataset of mutualistic networks. We also describe the calculation of core-periphery structures, its relationship with nestedness, and provide the code inside the R package econetwork for its calculation in mutualistic networks. We hope that our review will help ecologists to move beyond nestedness towards a more explicit representation of the structure of ecological networks.

ecology↗

Ecological networks: Pursuing the shortest path, however narrow and crooked

Representing data as networks cuts across all sub-disciplines in ecology and evolutionary biology. Besides providing a compact representation of the interconnections between agents, network analysis allows the identification of especially important nodes, according to various metrics that often rely on the calculation of the shortest paths connecting any two nodes. While the interpretation of a shortest paths is straightforward in binary, unweighted networks, whenever weights are reported, the calculation could yield unexpected results. We analyzed 129 studies of ecological networks published in the last decade and making use of shortest paths, and discovered a methodological inaccuracy related to the edge weights used to calculate shortest paths (and related centrality measures), particularly in interaction networks. Specifically, 49% of the studies do not report sufficient information on the calculation to allow their replication, and 61% of the studies on weighted networks may contain errors in how shortest paths are calculated. Using toy models and empirical ecological data, we show how to transform the data prior to calculation and illustrate the pitfalls that need to be avoided. We conclude by proposing a five-point check-list to foster best-practices in the calculation and reporting of centrality measures in ecology and evolution studies.

ecology↗

Abundance drives generalisation in hummingbird-plant pollination networks

Abundant pollinators are often more generalised than rare pollinators. This could be because abundance drives generalisation: neutral effects suggest that more abundant species will be more generalised simply because they have more chance encounters with potential interaction partners. On the other hand, generalisation could drive abundance, as generalised species could have a competitive advantage over specialists, being able to exploit a wider range of resources and gain a more balanced nutrient intake. Determining the direction of the abundance-generalisation relationship is therefore a chicken-and-egg dilemma. Here we determine the direction of the relationship between abundance and generalisation in plant-hummingbird pollination networks sampled from a variety of locations across the Americas. For the first time we resolve the direction of the abundance-generalisation relationship using independent data on animal abundance. We find evidence that hummingbird pollinators are generalised because they are abundant, and little evidence that hummingbirds are abundant because they are generalised. Additionally, a null model analysis suggests this pattern is due to neutral processes: most patterns of species-level abundance and generalisation were well explained by a null model that assumed interaction neutrality. These results suggest that neutral processes play a key role in driving broad patterns of generalisation in animal pollinators across large spatial scales.\n\nDeclarationsFunding - BIS is supported by the Natural Environment Research Council as part of the Cambridge Earth System Science NERC DTP [NE/L002507/1]. JVB was funded by CERL - Engineer Research and Development Center. PKM was funded by the Sao Paulo Research Foundation (FAPESP grant #2015/21457-4). PAC was funded by the David Lack studentship from the British Ornithologists Union and Wolfson College, University of Oxford. CL was funded by the ESDEPED-UAT grant. MAM acknowledges the Consejo Nacional para Investigaciones Cientificas y Tecnologicas (Costa Rica), German Academic Exchange Service and the research funding program LOEWE-Landes-Offensive zur Entwicklung Wissenschaftlicho konomischer Exzellenz of Hesses Ministry of Higher Education, Research, and the Arts (Germany). ROP was funded by CONACyT (project 258364). MAR was supported by the State of Sao Paulo Research Foundation (FAPESP) within the BIOTA/FAPESP, The Biodiversity Institute Program (www.biota.org.br) and the Parcelas Permanentes project, as well as by Coordenacao de Pessoal de Nivel Superior (CAPES), Fundo de Apoio ao Ensino e a Pesquisa (FAEP)/Funcamp/Unicamp and The Nature Conservancy (TNC) of Brazil. LCR was supported by CNPq and Capes. MS was funded by CNPq (grant #302781/2016-1). AMMG is supported through a Marie Skodowska-Curie Individual Fellowship (H2020-MSCA-IF-2016-704409). LVD was supported by the Natural Environment Research Council (grants NE/K015419/1 and NE/N014472/1). AMMG, JS, CR and BD thank the Danish National Research Foundation for its support of the Center for Macroecology, Evolution and Climate (grant no. DNRF96). WJS is funded by Arcadia.

ecology↗