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Seabirds have increasingly encountered offshore wind farms (OWFs) in European waters in the past 10 years, resulting in potential conflicts with offshore foraging areas. During the breeding season, seabirds are restricted in their choice of foraging habitat and are under increased pressure to find enough prey to raise their offspring. However, information on the individual reactions of seabirds towards OWFs during the breeding season is lacking. Three OWFs located 23–35 km north of the island of Helgoland have operated since October 2015. We studied their possible effects on locally breeding common guillemots (Uria aalge) using GPS tracking. GPS tags were deployed on 12 breeding guillemots from Helgoland for 8–26 days during 2016–2017. Most individuals avoided the OWFs, but one individual in each year briefly entered the OWFs on two or three occasions. Using a point process model, we revealed a 63% reduction in the resource selection of the OWF areas compared with the surroundings (lower confidence interval (CI) = 79% reduction, upper CI = 36% reduction). Furthermore, OWF avoidance was increased to 75% when the turbine blades were rotating (lower CI = 93% reduction, upper CI = 11% reduction). Guillemots mainly approached the OWFs from their eastern edge when resting or diving, and rarely approached the areas when commuting. These results provide a detailed description of guillemot reactions to OWFs during the breeding season, and the first comprehensive analysis of OWF effects on this species based on telemetry data. The strong avoidance effect for guillemots during the breeding season indicates the need to consider the presence of OWFs when interpreting future trends in the abundance and breeding success of this species.

Turing patterns are commonly understood as specific instabilities of a spatially homogeneous steady state, resulting from activator–inhibitor interaction destabilized by diffusion. We argue that this view is restrictive and its agreement with biological observations is problematic. We present two alternatives to the classical Turing analysis of patterns. First, we employ the abstract framework of evolution equations to enable the study of far-from-equilibrium patterns. Second, we introduce a mechano-chemical model, with the surface on which the pattern forms being dynamic and playing an active role in the pattern formation, effectively replacing the inhibitor. We highlight the advantages of these two alternatives vis-à-vis the classical Turing analysis, and give an overview of recent results and future challenges for both approaches. This article is part of the theme issue ‘Recent progress and open frontiers in Turing’s theory of morphogenesis’.

The North Sea is a key area worldwide for the installation of offshore wind farms (OWFs). We analysed data from multiple sources to quantify the effects of OWFs on seabirds from the family Gaviidae (loons) in the German North Sea. The distribution and abundance of loons changed substantially from the period before to the period after OWF construction. Densities of loons were significantly reduced at distances of up to 9–12 km from the OWF footprints. Abundance declined by 94% within the OWF + 1 km zone and by 52% within the OWF + 10 km zone. The observed redistribution was a large-scale effect, with birds aggregating within the study area at large distances from the OWFs. Although renewable energies will be needed to provide a large share of our energy demands in the future, it is necessary to minimize the costs in terms of less-adaptable species, to avoid amplifying the biodiversity crisis.

Chemical and mechanical pattern formation is fundamental during embryogenesis and tissue development. Yet, the underlying molecular and cellular mechanisms are still elusive in many cases. Most current theories assume that tissue development is driven by chemical processes: either as a sequence of chemical patterns each depending on the previous one, or by patterns spontaneously arising from specific chemical interactions (such as \"Turing-patterns\"). Within both theories, mechanical patterns are usually regarded as passive by-products of chemical pre-patters. However, several experiments question these theories, and an increasing number of studies shows that tissue mechanics can actively influence chemical patterns during development. In this study, we thus focus on the interplay between chemical and mechanical processes during tissue development. On one hand, based on recent experimental data, we develop new mechanochemical simulation models of evolving tissues, in which the full 3D representation of the tissue appears to be critical for obtaining a realistic mechanochemical behaviour. The presented modelling approach is flexible and numerically studied using state of the art finite element methods. Thus, it may serve as a basis to combine simulations with new experimental methods in tissue development. On the other hand, we apply the developed approach and demonstrate that even simple interactions between tissue mechanics and chemistry spontaneously lead to robust and complex mechanochemical patterns. Especially, we demonstrate that the main contradictions arising in the framework of purely chemical theories are naturally and automatically resolved using the mechanochemical patterning theory.

The continuing global expansion of electricity networks increases the risk of bird collisions with power lines. Several field studies have demonstrated that this risk can be reduced by marking lines with flight diverters. A before‐after control‐impact (BACI) design is currently the suggested approach for evaluating the effectiveness of these diverters and is generally assumed to give unbiased results. Using systematic flight survey data, we demonstrate that the assumptions underlying the BACI approach are frequently violated, leading to biased effectiveness estimates. We present an alternative field and statistical design in which the number of bird strike victims is directly related to bird flight intensity (“fusion design”), instead of estimating it indirectly using a control site. The presented design is validated based on simulations. We demonstrate that the presented method is unbiased and shows an approximately 3‐fold higher statistical power compared with BACI, even under ideal/unbiased data conditions, with similar field‐experimental effort. Moreover, this approach can provide a direct analysis of bird reactions/collisions, estimation of collision rates, and the possibility of conducting the required fieldwork within a single season. Our presented method can be used to standardize and improve future studies on diverter effectiveness, for example, by supporting the acquisition of a more detailed picture of species‐, diverter type‐, and habitat‐specific estimates. We compare and extend methods to analyze bird collision frequency at power lines. We present a new field and statistical approach (“fusion approach”) and demonstrate that this new method is unbiased and shows an approximately 3‐fold higher statistical power compared with previous methods.

Frequency-domain electromagnetic induction (FDEMI) methods are frequently used in non-invasive, area-wise mapping of the subsurface electromagnetic soil properties. A crucial part of data analysis is the geophysical inversion of the data, resulting in either conductivity and/or magnetic susceptibility subsurface distributions. We present a novel 1D stochastic optimization approach that combines dimension-adapting reversible jump Markov chain Monte Carlo (MCMC) with artificial bee colony (ABC) optimization for geophysical inversion, with specific application to frequency-domain electromagnetic induction (FDEMI) data. Several solution models of simplified model geometry and a variable number of model knots, which are found by the inversion method, are used to create re-sampled resulting average models. We present synthetic test inversions using conductivity models based on 14 direct-push (DP) EC logs from Greece, Italy, and Germany, as well as field data applications using multi-coil FDEMI devices from three sites in Azerbaijan and Germany. These examples show that the method can effectively lead to solutions that resemble the known DP input models or image reasonable stratigraphic and archaeological features in the field data. Neighboring 1D solutions on field data examples show high coherence along profiles even though each 1D inversion is independently handled. The computational effort for one 1D inversion is less than 120,000 forward calculations, which is much less than usually needed in MCMC inversions, whereas the resulting models show more plausible solutions due to the dimension-adapting properties of the inversion method.

Protecting threatened elasmobranch species despite limited data on their distribution and abundance is a critical challenge, particularly in the context of increasing human impacts on marine ecosystems. In the northeastern Atlantic, species such as the leafscale gulper shark, Portuguese dogfish, spurdog, and spotted ray are facing pressures from overfishing, bycatch, habitat degradation, and climate change. The OSPAR Commission has listed these species as threatened and/or declining and aims to protect them by reliably identifying suitable habitats and integrating these areas into Marine Protected Areas (MPAs). In this study, we present a spatial modelling framework using regression-based approaches to identify suitable habitats for these four species. Results show that suitable habitats of the spotted ray (25.8%) and spurdog (18.8%) are relatively well represented within existing MPAs, while those of the deep-water sharks are underrepresented (6.0% for leafscale gulper shark, and 6.8% for Portuguese dogfish). Our findings highlight the need for additional MPAs in deep-sea continental slope areas, particularly west and northwest of Scotland and Ireland. Such expansions would support OSPAR’s goal to protect 30% of its maritime area by 2030 and could benefit broader deep-sea biodiversity, including other vulnerable demersal species and benthic communities.

The Notch-signalling pathway plays an important role in pattern formation in Hydra . Using pharmacological Notch inhibitors (DAPT and SAHM1), it has been demonstrated that HvNotch is required for head regeneration and tentacle patterning in Hydra . HvNotch is also involved in establishing the parent-bud boundary and instructing buds to develop feet and detach from the parent. To further investigate the functions of HvNotch, we successfully constructed NICD (HvNotch intracellular domain)-overexpressing and HvNotch-knockdown transgenic Hydra strains. NICD-overexpressing transgenic Hydra showed a pronounced inhibition on the expression of predicted HvNotch-target genes, suggesting a dominant negative effect of ectopic NICD. This resulted in a “Y-shaped” phenotype, which arises from the parent-bud boundary defect seen in polyps treated with DAPT. Additionally, “multiple heads”, “two-headed” and “ectopic tentacles” phenotypes were observed. The HvNotch-knockdown transgenic Hydra with reduced expression of HvNotch exhibited similar, but not identical phenotypes, with the addition of a “two feet” phenotype. Furthermore, we observed regeneration defects in both, overexpression and knockdown strains. We integrated these findings into a mathematical model based on long-range gradients of signalling molecules underlying sharply defined positions of HvNotch-signalling cells at the Hydra tentacle and bud boundaries.

A differentiated understanding of how regional human activities affect the spatial distribution and abundance of animals is of great ecological importance. However, estimating these effects from empirical data is challenging, as human activities influence animals in different ways and on various spatial and temporal scales. Additionally, spatio‐temporal animal abundance is often shaped by intrinsic and extrinsic factors, which can confound impact assessments. To separate these influences, we combined regression and mechanistic modelling. First, we used partial differential equations to simulate potential animal redistribution patterns driven by regional human activities. These patterns were then incorporated as predictors into regression‐based species distribution models, alongside other anthropogenic and environmental covariates. This allowed us to estimate and predict human‐induced changes by jointly accounting for pressure‐driven large‐scale redistribution and local changes in expected abundance, while controlling for additional environmental influences. We applied this approach to assess the impact of offshore wind farms (OWF) on common murres (Uria aalge) in the German North Sea during autumn. OWF constructed by 2019 reduced common murre numbers within German waters by 18.3%. If the planned OWF priority and reservation areas outlined in the German Marine Spatial Plan are implemented, the predicted net reduction within German waters would increase to 77.7%. Importantly, these predictions do not account for additional anthropogenic activities or OWF expansion in surrounding waters, which could further affect common murre abundance beyond the scenarios considered here. By comparing predicted animal numbers and distributions under hypothetical scenarios with and without specific human pressures, our method enables the quantification and prediction of human‐induced effects on regional trends and large‐scale redistribution. The framework provides a transparent way to disentangle large‐scale redistribution and cumulative effects from local responses in spatially bounded management areas, supporting impact assessments under ongoing expansion of offshore renewables. We combine regression‐based species distribution models with a mechanistic redistribution component to assess how regional human activities affect animal distribution and abundance across spatial scales. Applied to common murres (Uria aalge) in the German North Sea, the framework indicates scenario‐based reductions in numbers within German waters of 18.3% under the 2019 offshore wind farm configuration, increasing to 77.7% if currently planned areas are implemented. By explicitly distinguishing local avoidance from large‐scale redistribution, the approach supports transparent ecological impact assessments under phased development of renewable energy infrastructure.

Motivated by recent experimental findings, we propose a novel mechanism of embryonic pattern formation based on coupling of tissue curvature with diffusive signaling by a chemical factor. We derive a new mathematical model using energy minimization approach and show that the model generates a variety of morphogen and curvature patterns agreeing with experimentally observed structures. The mechanism proposed transcends the classical Turing concept which requires interactions between two morphogens with a significantly different diffusivity. Our studies show how biomechanical forces may replace the elusive long-range inhibitor and lead to formation of stable spatially heterogeneous structures without existence of chemical prepatterns. We propose new experimental approaches to decisively test our central hypothesis that tissue curvature and morphogen expression are coupled in a positive feedback loop.