18 research outputs found

    The spatial scaling of food web structure across European biogeographical regions

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    The species–area relationship (SAR) is one of the most well-established scaling patterns in ecology. Its implications for understanding how communities change across spatial gradients are numerous, including the effects of habitat loss on biodiversity. However, ecological communities are not mere collections of species. They are the result of interactions between these species forming complex networks that tie them together. Should we aim to grasp the spatial scaling of biodiversity as a whole, it is fundamental to understand the changes in the structure of interaction networks with area. In spite of a few empirical and theoretical studies that address this challenge, we still do not know much about how network structure changes with area, or what are the main environmental drivers of these changes. Here, using the meta-network of potential interactions between all terrestrial vertebrates in Europe (1140 species and 67 201 feeding interactions), we analysed network–area relationships (NARs) that summarize how network properties scale with area. We do this across ten biogeographical regions, which differ in environmental characteristics. We found that the spatial scaling of network complexity strongly varied across biogeographical regions. However, once the variation in SARs was accounted for, differences in the shape of NARs vanished. On the other hand, the proportion of species across trophic levels remained remarkably constant across biogeographical regions and spatial scales, despite the great variation in species richness. Spatial variation in mean annual temperature and habitat clustering were the main environmental determinants of the shape of both SARs and NARs across Europe. Our results suggest new avenues in the exploration of the effects of environmental factors on the spatial scaling of biodiversity. We argue that NARs can provide new insights to analyse and understand ecological communities

    Linking differences in microbial network structure with changes in coral larval settlement

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    Coral cover and recruitment have decreased on reefs worldwide due to climate change-related disturbances. Achieving reliable coral larval settlement under aquaculture conditions is critical for reef restoration programmes; however, this can be challenging due to the lack of reliable and universal larval settlement cues. To investigate the role of microorganisms in coral larval settlement,we undertook a settlement choice experiment with larvae of the coral Acropora tenuis and microbial biofilms grown for different periods on the reef and in aquaria. Biofilm community composition across conditioning types and time was profiled using 16S and 18S rRNA gene sequencing. Co-occurrence networks revealed that strong larval settlement correlated with diverse biofilm communities, with specific nodes in the network facilitating connections between modules comprised of low- vs high-settlement communities. Taxa associated with high-settlement communities were identified as Myxoccales sp., Granulosicoccus sp., Alcanivoraceae sp., unassigned JTB23 sp. (Gammaproteobacteria), and Pseudovibrio denitrificans. Meanwhile, taxa closely related to Reichenbachiella agariperforans, Pleurocapsa sp., Alcanivorax sp., Sneathiella limmimaris, as well as several diatom and brown algae were associated with low settlement. Our results characterise high-settlement biofilm communities and identify transitionary taxa that may develop settlement-inducing biofilms to improve coral larval settlement in aquaculture

    Joint effects of species traits and environmental preferences on range edge shifts of British birds

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    Aim: Despite the strong evidence of species range shifts as a response to environmental change, attempts to identify species traits that modulate those shifts have been equivocal. We investigate the role of species traits and environmental preferences on birds' range shifts in Great Britain, an island where dispersal is limited by the English Channel and the North Sea. Location: Great Britain (England, Scotland and Wales). Taxa: Birds (Aves). Time Period: 1968–2011. Methods: Using 404,949 occurrence records from two time periods, we investigated the potential drivers of leading and rear range edge shifts of breeding birds using phylogenetic linear mixed models. We hypothesized that shifts are influenced by species' trophic and morphological traits, dispersal abilities and environmental preferences, but also by the geographical boundaries of Great Britain. Results: Geographical boundaries—the distance from the northern or southern boundaries of Britain—accounted for most of the variability in range edge shifts. Species traits and environmental preferences emerged as relevant drivers of range shifts only for northern and Passeriform species. Northern habitat specialist, those with more predators and those sensitive to precipitation were more likely to shift their rear edge poleward. For Passeriformes, habitat generalists, species with smaller dispersal capabilities, under higher predatory pressure or associated with forest and grassland were more likely to shift their rear edge poleward. Main Conclusions: While geographical boundaries impose constraints on range shifts in British birds, the subtle effects of species traits and environmental preferences emerge as relevant predictors for Northern and passeriform species' rear edge shifts. This highlights the importance of accounting for geographical boundaries when predicting species responses to global change. Differential range shifts of species across different trophic levels could result in the reorganization of biotic interactions, with consequences for ecosystem structure and stability

    Managing a digital business ecosystem using a simulation tool

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    Simple ecological rules yield complex agent networks

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    On the implementation of a DBE to improve SME collaboration

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    Ecological network complexity scales with area

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    MADBE: A Multi-Agent Digital Business Ecosystem

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    Chimps: An evolutionary reinforcement learning approach for soccer agents

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    An ecologically inspired simulation tool for managing digital ecosystems

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