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Giam, X.

Publications and source records attributed to Giam, X..

2 recordsLinked to original sources

Degradation of fish food webs in the anthropocene

The Anthropocene is marked by profound changes in biodiversity and the ecosystems in which species live 1-4. A primary signature of this change is the often rapid change in species composition through time (i.e., species turnover) rather than changes in the numbers of species per se 5-7. Less well known, however, is which types of species are winning and which are losing as ecosystems change through time 8,9, as well as whether and how these changes influence higher-level processes in the food webs in which species are embedded 10,11. Here, we combine a compilation of long-term observations of [~]15,000 freshwater and marine fish communities surveyed for 1949-2019 years, together with information about their diets and trophic status in order to evaluate how the food webs in which these fish communities are embedded are changing through time. We found widespread alteration to fish food web topology and functioning. This includes an increase in connectance and generalism in food webs, which has led to greater predation pressure, as indicated by higher diet overlap and increased prey vulnerability. We also identified a decrease in modularity, which has reduced the compartmentalization within local networks. These changes extend across the trophic structure of food webs, causing a cascading shift in the proportion of species across trophic levels. Our study highlights the complex responses of biodiversity change of fish food webs in the Anthropocene, which can ultimately influence the functions of these ecosystems and human well-being 12,13.

ecology↗

Counteracting estimation bias and social influence to improve the wisdom of crowds

Aggregating multiple non-expert opinions into a collective estimate can improve accuracy across many contexts. However, two sources of error can diminish collective wisdom: individual estimation biases and information sharing between individuals. Here we measure individual biases and social influence rules in multiple experiments involving hundreds of individuals performing a classic numerosity estimation task. We first investigate how existing aggregation methods, such as calculating the arithmetic mean or the median, are influenced by these sources of error. We show that the mean tends to overestimate, and the median underestimate, the true value for a wide range of numerosities. Quantifying estimation bias, and mapping individual bias to collective bias, allows us to develop and validate three new aggregation measures that effectively counter sources of collective estimation error. In addition, we present results from a further experiment that quantifies the social influence rules that individuals employ when incorporating personal estimates with social information. We show that the corrected mean is remarkably robust to social influence, retaining high accuracy in the presence or absence of social influence, across numerosities, and across different methods for averaging social information. Utilizing knowledge of estimation biases and social influence rules may therefore be an inexpensive and general strategy to improve the wisdom of crowds.

animal behavior and cognition↗