The European catfish
Silurus glanis
L. is a large, adaptable predator whose range continues to expand rapidly in Europe, posing potential environmental, ecological and socio-economic impacts on freshwater ecosystems. Its success results from a combination of species-specific traits and extrinsic abiotic and anthropogenic drivers, which together enhance its ecological adaptability and competitive advantage. While some studies report substantial impacts on fish communities, others find minimal effects, likely due to differences in timing of data collection, lack of baseline data, control sites and assessments of biotic and abiotic interactions. In this review, we: (i) explore the main factors contributing to the invasion success of
S. glanis
, including both species-specific traits (e.g. trophic plasticity, reproductive strategies, environmental tolerance) and extrinsic drivers, such as propagule pressure, human-assisted dispersal and habitat alterations; (ii) assess its ecological effects across freshwater ecosystems, based on available studies; and (iii) review the methods commonly used for detecting
S. glanis
, highlighting their pros and cons for species detection. In each section, we highlight key knowledge gaps, such as the lack of experimental studies, limited understanding of long-term impacts on native species and insufficient data on context-dependent effects. These gaps emphasise the urgent need for coordinated research efforts to improve impact assessment and guide effective management strategies.
Stefano Brignone, F. Cicala, Luca Minazzi et al.· NeoBiota· 0 citations
While traditional evolutionary algorithms hard-code reproduction, self-replication can emerge spontaneously within digital ``primordial soups''. This paper investigates the coevolution of such emergent self-replication alongside problem-solving capabilities. We initialize a population of random 32-byte Z80 assembly programs, requiring self-replication to arise purely through random assembly-level mutations and pairwise program interactions. To couple computation with reproduction, we introduce a task-based validation step: correctly evaluating a polynomial raises a program's interaction probability above a baseline rate. Our experiments yield four primary findings. First, self-replication and mathematical problem-solving successfully coevolve from initial randomness. Second, the pressure to compute accelerates the emergence of compact, robust reproductive architectures that preserve memory for task execution. Third, applying metabolic constraints that penalize runtime promotes the emergence of sophisticated conditional execution patterns that reduce energy use. Finally, partitioning programs into interconnected task niches generates an emergent learning curriculum that utilizes simple solutions as stepping stones toward more complex tasks. Altogether, these results demonstrate an interactive feedback loop: environmental task demands actively shape the physical architecture of self-replication, while spontaneous replication alters the evolutionary trajectory of functional problem-solving.
F. Cicala, Eyvind Niklasson, E. Randazzo et al.· 0 citations
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