Intraspecific variability drives diversity in food webs

Biodiversity theories neglect individual-level variability in ecological interactions even though empirical work has revealed considerable genetic and phenotypic variation among individuals in natural populations. This impedes assessing the impact of individual-level variability on biodiversity in multi-trophic ecosystems. Here we use a density-dependent and individual-based food web model, tested against the largest individual-based food web to date, to show that non-random intraspecific variation in prey selection alters species diversity in food webs. Predators consuming many prey increase diversity by preferentially selecting common prey; predators consuming few prey inhibit diversity by preferentially selecting rare prey, putting them at risk of extinction. Thus species-level patterns cannot be explained by species-level averages, but instead must consider individual-level variation in prey selection. Individual-level variation occurs in many biological and social contexts, suggesting that analyses of individual-level interaction data will be relevant in a wide range of fields

Environmental and anthropogenic drivers affect the abundance of anchovy and mysids in the Guadalquivir Estuary (SW Spain)

Natural drivers, acting at various spatio-temporal scales firstly determine the distribution and abundance of species. On top of this natural forcing we usually find anthropogenic effects. Disentangling the relative influence of these two sources of variability has always been a challenge in ecology, and particularly in fisheries science. The Guadalquivir Estuary (SW Spain) serves as nursery ground for several commercial species in the Gulf of Cadiz. This study aims at quantifying the relative influence of biological (predator-prey effects), environmental (e.g. temperature, winds) and anthropogenic (dam discharges) effects on this nursery function with the focus on an important species, anchovy. We used data from a monitoring programme consisting of monthly records since 1997 at two sites: Tarfia (32 km) and Bonanza (8 km) (distance from the river mouth). Nonparametric models (GAM) were fit to the data to estimate the partial effects of the various covariates. We found positive and linear effects of temperature and mysids on anchovy abundance in both stations, while turbidity, winds and freshwater input had a negative effect, reducing fish abundance. A dam, 110 km upstream from the Guadalquivir mouth regulates freshwater discharges, directly influencing the estuarine habitat quality and extent, as captured by our models. In order to separate the anthropogenic effects from natural variability we further ran the models on a number of scenarios combining a range of dam discharges and environmental conditions. Water management stands out as a key node where potentially conflicting interests (irrigators, electric power, shipping, aquaculture, fisheries) converge. By focussing on the consequences that the effects of these activities ultimately have on the anchovy fishery, through this nursery function, our study aims to contribute to the process of making the ecosystem approach operational in the Gulf of Cadiz

Decentralized and collaborative machine learning framework for IoT

Decentralized machine learning has recently been proposed as a potential solution to the security issues of the canonical federated learning approach. In this paper, we propose a decentralized and collaborative machine learning framework specially oriented to resource-constrained devices, usual in IoT deployments. With this aim we propose the following construction blocks. First, an incremental learning algorithm based on prototypes that was specifically implemented to work in low-performance computing elements. Second, two random-based protocols to exchange the local models among the computing elements in the network. Finally, two algorithmics approaches for prediction and prototype creation. This proposal was compared to a typical centralized incremental learning approach in terms of accuracy, training time and robustness with very promising results.Axencia Galega de Innovación | Ref. 25/IN606D/2021/2612348Agencia Estatal de Investigación | Ref. PID2020-113795RB-C3

The nursery role of the Guadalquivir estuary for marine fish. A long-term ecological research

Trabajo presentado en el 23rd Biennial CERF Conference (Grand challenges in coastal and Estuarine Science: securin our future), celebrado en Portland (Oregón, US) del 8 al 12 de noviembre de 2015.N

A hybrid analysis of LBSN data to early detect anomalies in crowd dynamics

Undoubtedly, Location-based Social Networks (LBSNs) provide an interesting source of geo-located data that we have previously used to obtain patterns of the dynamics of crowds throughout urban areas. According to our previous results, activity in LBSNs reflects the real activity in the city. Therefore, unexpected behaviors in the social media activity are a trustful evidence of unexpected changes of the activity in the city. In this paper we introduce a hybrid solution to early detect these changes based on applying a combination of two approaches, the use of entropy analysis and clustering techniques, on the data gathered from LBSNs. In particular, we have performed our experiments over a data set collected from Instagram for seven months in New York City, obtaining promising results.Ministerio de Economía y Competitividad | Ref. TEC2014-54335-C4-2-RMinisterio de Economía y Competitividad | Ref. TEC2014-54335-C4-3-RAgencia Estatal de Investigación | Ref. TEC2017-84197-C4-2-RAgencia Estatal de Investigación | Ref. TEC2017-84197-C4-3-