Showing papers by "Wageningen University and Research Centre published in 2023"

Sex differences in skeletal muscle-aging trajectory: same processes, but with a different ranking

Abstract: Sex differences in muscle aging are poorly understood, but could be crucial for the optimization of sarcopenia-related interventions. To gain insight into potential sex differences in muscle aging, we recruited young (23 ± 2 years, 13 males and 13 females) and old (80 ± 3.5 years, 28 males and 26 females) participants. Males and females in both groups were highly matched, and vastus lateralis muscle parameters of old versus young participants were compared for each sex separately, focusing on gene expression. The overall gene expression profiles separated the sexes, but similar gene expression patterns separated old from young participants in males and females. Genes were indeed regulated in the same direction in both sexes during aging; however, the magnitude of differential expression was sex specific. In males, oxidative phosphorylation was the top-ranked differentially expressed process, and in females, this was cell growth mediated by AKT signaling. Findings from RNA-seq data were studied in greater detail using alternative approaches. In addition, we confirmed our data using publicly available data from three independent human studies. In conclusion, top-ranked pathways differ between males and females, but were present and altered in the same direction in both sexes. We conclude that the same processes are associated with skeletal muscle aging in males and females, but the differential expression of those processes in old vs. young participants is sex specific.

Current insights in ICU nutrition: tailored nutrition

Abstract: Purpose of review To summarize recent research on critical care nutrition focusing on the optimal composition, timing, and monitoring of enteral feeding strategies for (post)-ICU patients. We provide new insights on energy and protein recommendations, feeding intolerance, and describe nutritional practices for coronavirus disease 2019 ICU patients. Recent findings The use of indirect calorimetry to establish individual energy requirements for ICU patients is considered the gold standard. The limited research on optimal feeding targets in the early phase of critical illness suggests avoiding overfeeding. Protein provision based upon the absolute lean body mass is rational. Therefore, body composition measurements should be considered. Body impedance analysis and muscle ultrasound seem reliable, affordable, and accessible methods to assess body composition at the bedside. There is inadequate evidence to change our practice of continuous enteral feeding into intermittent feeding. Finally, severe acute respiratory syndrome coronavirus 2 patients are prone to underfeeding due to hypermetabolism and should be closely monitored. Summary Nutritional therapy should be adapted to the patient's characteristics, diagnosis, and state of metabolism during ICU stay and convalescence. A personalized nutrition plan may prevent harmful over- or underfeeding and attenuate muscle loss. Despite novel insights, more research is warranted into tailored nutrition strategies during critical illness and convalescence.

Inference and reconstruction of the heimdallarchaeial ancestry of eukaryotes

Abstract: Abstract In the ongoing debates about eukaryogenesis, the series of evolutionary events leading to the emergence of the eukaryotic cell from prokaryotic ancestors, members of the Asgard archaea play a key role as the closest archaeal relatives of eukaryotes. However, the nature and phylogenetic identity of the last common ancestor of Asgard archaea and eukaryotes remain unresolved. Here, we analyze distinct phylogenetic marker datasets of an expanded genomic sampling of Asgard archaea and evaluate competing evolutionary scenarios using state-of-the-art phylogenomic approaches. We find that eukaryotes are placed, with high confidence, as a well-nested clade within Asgard archaea, as a sister lineage to Hodarchaeales, a newly proposed order within Heimdallarchaeia. Using sophisticated gene tree/species tree reconciliation approaches, we show that, in analogy to the evolution of eukaryotic genomes, genome evolution in Asgard archaea involved significantly more gene duplication and fewer gene loss events compared to other archaea. Finally, we infer that the last common ancestor of Asgard archaea likely was a thermophilic chemolithotroph, and that the lineage from which eukaryotes evolved adapted to mesophilic conditions and acquired the genetic potential to support a heterotrophic lifestyle. Our work provides key insights into the prokaryote-to-eukaryote transition and the platform for the emergence of cellular complexity in eukaryotic cells.

The search for path-dependency mechanisms using physically-based soil-landscape modelling of landslides

Abstract: The spatial pattern of landslide susceptibility is a key input for decision making by many natural hazard agencies. Therefore, the estimation of landslide susceptibility maps has received much attention in the last decades. Increasingly, such maps are produced by statistical methods that relate the locations of observed landslides to geofactors such as slope steepness or vegetation density. Almost without exception, these susceptibility assessments are entirely spatial. At the same time, recent studies of large multitemporal landslide datasets have shown empirically that landslide susceptibility changes over time as well as space, as a result of the impact of recent nearby landslides. In at least two study sites, places near previous landslides are temporarily more susceptible to landsliding, sometimes substantially so. Several candidate mechanisms underlie this form of complexity (called path-dependency) in the landslide system, and targeted field measurements in landslide-prone study sites should be recorded to fully understand which mechanism is most important.Awaiting such measurements, physically-based mechanistic modelling of landslide impacts in the soil-landscape system can help explore the possible mechanisms. Here, we report on our development of landslide simulation capabilities in soil-landscape evolution model LORICA. In this model, landslides affect not only surface elevation, but also local soil and vegetation properties. Since other processes in the model also affect these properties, the impact of landslides is not permanent. Applied to a hypothetical soil-landscape, this model allows us to explore whether a) local topographic effects such as oversteepening, b) temporarily changed soil hydraulic parameters, or c) disruption of vegetation and roots, are the most likely mechanisms behind landslide path-dependency.

NH-SWE: Northern Hemisphere Snow Water Equivalent dataset based on in-situ snow depth time series

Abstract: Abstract. Ground-based datasets of observed Snow Water Equivalent (SWE) are scarce, while gridded SWE estimates from remote-sensing and climate reanalysis are unable to resolve the high spatial variability of snow on the ground. Long-term ground observations of snow depth, in combination with models that can accurately convert snow depth to SWE, can fill this observational gap. Here, we provide a new SWE dataset (NH-SWE) that encompasses 11,071 stations in the Northern Hemisphere, and is available at https://doi.org/10.5281/zenodo.7515603 (Fontrodona-Bach et al., 2023). This new dataset provides daily time series of SWE, varying in length between one and seventy-three years, spanning the period 1950–2022 and covering a wide range of snow climates including many mountainous regions. At each station, observed snow depth was converted to SWE using an established snow-depth-to-SWE conversion model, with excellent model performance using regionalised parameters based on climate variables. The accuracy of the model after parameter regionalisation is comparable to that of the calibrated model. The key advantages and strengths of the regionalised model presented here are its transferability across climates and the high performance in modelling daily SWE dynamics in terms of peak SWE, total snowmelt and duration of the melt season, as assessed here against a comparison model. This dataset is particularly useful for studies that require accurate time series of SWE dynamics, timing of snowmelt onset, and snowmelt totals and duration. It can e.g. be used for climate change impact analyses, water resources assessment and management, validation of remote sensing of snow, hydrological modelling and snow data assimilation into climate models.

Mapping of the wind erodible fraction of soil by bidirectional gated recurrent unit (BiGRU) and bidirectional recurrent neural network (BiRNN) deep learning models

Abstract: The destructive consequences of wind erosion have been reported in many studies, but accurate assessment of wind erosion is still a challenge, especially on large scales. Our research introduces two deep learning (DL) algorithms consisting of bidirectional gated recurrent unit (BiGRU), and bidirectional recurrent neural network (BiRNN) for spatial mapping of wind-erodible fraction of the soil (EF). EF was measured in 508 soil samples using the Chepil method. 15 key factors controlling EF including: soil, topography, and meteorology parameters were mapped. The performance of the most efficient DL model was interpreted by Game theory. The uncertainty of the DL models was quantified by deep quantile regression (DQR). Results showed that both DL models were performed very well with the BiRNN performing slightly better than BiGRU. The aggregate mean weight diameter (MWD) was a key variable for the mapping of soil susceptibility to wind erosion. Based on the BiRNN model, most of the study region was moderately and highly susceptible to wind erosion regarding the EF value (between 32 and 98). This indicates the urgent need for soil conservation measures in the region. The DQR results showed that the observed values of EF fell within the EF values predicted by the model. Overall, the suggested methodology has proven to be helpful in mapping wind erosion susceptibility on a large scale.

Resilience, Food Security and Food Systems: Setting the Scene

Abstract: Abstract The aim of this introduction chapter is twofold. First it will set the scene, frame the overarching problem and present the central question of this volume: How does the concept of resilience help in improving our general understanding of the development process, in particular around the issue of food (in)security, and how does it influence the way development interventions around this question of food security are now programmed and implemented? To address this ambitious question, the entire series of chapters will adopt a food system approach. The second part of the introduction chapter will then ‘kick-start’ the discussion, first by providing some initial element of definition for the three concepts under consideration and then by highlighting some of the main discussions, debates or even contradictions that emerge in the literature around the definition, interpretations and application of those concepts.

The genome of <i>Lactuca saligna</i> , a wild relative of lettuce, provides insight into non‐host resistance to the downy mildew <i>Bremia lactucae</i>

Abstract: Lactuca saligna L. is a wild relative of cultivated lettuce (Lactuca sativa L.), with which it is partially interfertile. Hybrid progeny suffer from hybrid incompatibility (HI), resulting in reduced fertility and distorted transmission ratios. Lactuca saligna displays broad-spectrum resistance against lettuce downy mildew caused by Bremia lactucae Regel and is considered a non-host species. This phenomenon of resistance in L. saligna is called non-host resistance (NHR). One possible mechanism behind this NHR is through the plant–pathogen interaction triggered by pathogen recognition receptors, including nucleotide-binding leucine-rich repeat (NLR) proteins and receptor-like kinases (RLKs). We report a chromosome-level genome assembly of L. saligna (accession CGN05327), leading to the identification of two large paracentric inversions (>50 Mb) between L. saligna and L. sativa. Genome-wide searches delineated the major resistance clusters as regions enriched in NLRs and RLKs. Three of the enriched regions co-locate with previously identified NHR intervals. RNA-seq analysis of Bremia-infected lettuce identified several differentially expressed RLKs in NHR regions. Three tandem wall-associated kinase-encoding genes (WAKs) in the NHR8 interval display particularly high expression changes at an early stage of infection. We propose RLKs as strong candidates for determinants of the NHR phenotype of L. saligna.

Ten years of 1 Hz solar irradiance observations at Cabauw, the Netherlands, with cloud observations, variability classifications, and statistics

Abstract: Abstract. Surface solar irradiance varies on scales down to seconds, of which detailed, long-term observational datasets are rare but in high demand. Here, we present an observational dataset of global, direct, and diffuse solar irradiance sampled at 1 Hz over a period of 10 years, from the Baseline Surface Radiation Network (BSRN) station at Cabauw, the Netherlands. The dataset is complemented with irradiance variability classifications, clear-sky irradiance and aerosol reanalysis, information about the solar position, observations of clouds and sky type, and wind measurements up to 200 meters above ground level. Statistics of variability derived from all time series include approximately 185,000 detected events of both cloud enhancement and cloud shadows. The Cabauw measurement site has additional observations freely available at the open data platform of the Royal Netherlands Meteorological Institute. This paper describes the observational site, quality control, classification algorithm with validation, and the processing method of complementary products. These observations and derived statistics provide detailed information to aid research into how clouds and atmospheric composition influence solar irradiance variability, and to help validate models that are starting to resolve variability at higher fidelity. The main datasets are available at https://doi.org/10.5281/zenodo.7093164 (Knap and Mol, 2022) and https://doi.org/10.5281/zenodo.7092058 (Mol et al., 2022b), see the data availability section for a complete list.

CAGECAT: The CompArative GEne Cluster Analysis Toolbox for rapid search and visualisation of homologous gene clusters

Abstract: ABSTRACT Background Co-localized sets of genes that encode specialized functions are common across microbial genomes and occur in genomes of larger eukaryotes as well. Important examples include Biosynthetic Gene Clusters (BGCs) that produce specialized metabolites with medicinal, agricultural, and industrial value (e.g. antimicrobials). Comparative analysis of BGCs can aid in the discovery of novel metabolites by highlighting distribution and identifying variants in public genomes. Unfortunately, gene-cluster-level homology detection remains inaccessible, time-consuming and difficult to interpret. Results The comparative gene cluster analysis toolbox (CAGECAT) is a rapid and user-friendly platform to mitigate difficulties in comparative analysis of whole gene clusters. The software provides homology searches and downstream analyses without the need for command-line or programming expertise. By leveraging remote BLAST databases, which always provide up-to-date results, CAGECAT can yield relevant matches that aid in the comparison, taxonomic distribution, or evolution of an unknown query. The service is extensible and interoperable and implements the cblaster and clinker pipelines to perform homology search, filtering, gene neighbourhood estimation, and dynamic visualisation of resulting variant BGCs. With the visualisation module, publication-quality figures can be customized directly from a web-browser, which greatly accelerates their interpretation via informative overlays to identify conserved genes in a BGC query. Conclusion Overall, CAGECAT is an extensible software that can be interfaced via a standard web-browser for whole region homology searches and comparison on continually updated genomes from NCBI. The public web server and installable docker image are open source and freely available without registration at: https://cagecat.bioinformatics.nl

Investigating the Diurnal Radiative, Turbulent, and Biophysical Processes in the Amazonian Canopy‐Atmosphere Interface by Combining LES Simulations and Observations

Abstract: We investigate the atmospheric diurnal variability inside and above the Amazonian rainforest for a representative day during the dry season. To this end, we combine high-resolution large-eddy simulations that are constrained and evaluated against a comprehensive observation set, including CO2 concentrations, gathered during GoAmazon2014/15. We design systematic numerical experiments to quantify whether a multilayer approach in solving the explicit canopy improves our canopy-atmosphere representation. We particularly focus on the relationship between photosynthesis and plant transpiration, and their distribution at leaf and canopy scales. We found the variability of photosynthesis drivers like vapor pressure deficit and leaf temperature to be about 3 times larger for sunlit leaves compared to shaded leaves. This leads to a large spread on leaf stomatal conductance values with minimum and maximum values varying more than 100%. Regarding the turbulent structure, we find wind-driven stripe-like shapes at the canopy top and structures resembling convective cells at the canopy. Wind-related variables provide the best spatiotemporal agreement between model and observations. The potential temperature and heat flux profiles agree with an observed decoupling near the canopy top interface, although with less variability and cold biases of up to 3 K. The increasing complexity on the biophysical processes leads to the largest disagreements for evaporation, CO2 plant assimilation and soil efflux. The model is able to capture the correct dependences and trends with the magnitudes still differing. We finally discuss the need to revise leaf and soil models and to complete the observations at leaf and canopy levels.

The Time Scale of Shallow Convective Self‐Aggregation in Large‐Eddy Simulations Is Sensitive to Numerics

Abstract: Numerical simulations of the tropical mesoscales often exhibit a self-reinforcing feedback between cumulus convection and shallow circulations, which leads to the self-aggregation of clouds into large clusters. We investigate whether this basic feedback can be adequately captured by large-eddy simulations (LESs). To do so, we simulate the non-precipitating, cumulus-topped boundary layer of the canonical "BOMEX" case over a range of numerical settings in two models. Since the energetic convective scales underpinning the self-aggregation are only slightly larger than typical LES grid spacings, aggregation timescales do not converge even at rather high resolutions (<100 m). Therefore, high resolutions or improved sub-filter scale models may be required to faithfully represent certain forms of trade-wind mesoscale cloud patterns and self-aggregating deep convection in large-eddy and cloud-resolving models, and to understand their significance relative to other processes that organize the tropical mesoscales.

Enhanced surface nanoanalytics of transient biomolecular processes

Abstract: Fundamental knowledge of the physical and chemical properties of biomolecules is key to understanding molecular processes in health and disease. Bulk and single-molecule analytical methods provide rich information about biomolecules but often require high concentrations and sample preparation away from physiologically relevant conditions. Here, we present the development and application of a lab-on-a-chip spray approach that combines rapid sample preparation, mixing, and deposition to integrate with a range of nanoanalytical methods in chemistry and biology, providing enhanced spectroscopic sensitivity and single-molecule spatial resolution. We demonstrate that this method enables multidimensional study of heterogeneous biomolecular systems over multiple length scales by nanoscopy and vibrational spectroscopy. We then illustrate the capabilities of this platform by capturing and analyzing the structural conformations of transient oligomeric species formed at the early stages of the self-assembly of α-synuclein, which are associated with the onset of Parkinson's disease.

Open-ended questions in sensory testing practice

Abstract: This chapter provides an overview about open-ended questions in Sensory and Consumer Research. The different formats and applications are described, together with the strengths and weaknesses at the different stages (data collection, analyses, interpretation). This chapter also provides a guideline on how to treat the data obtained, and helps researchers make informed decisions on the use of open-ended questions considering their business problem at hand. Finally, the directions of open-ended questions in the future are also discussed.

Effective root responses to salinity stress include maintained cell expansion and carbon allocation

Abstract: Acclimation of root growth is vital for plants to survive salt stress. Halophytes are great examples of plants that thrive even under severe salinity, but their salt tolerance mechanisms, especially those mediated by root responses, are still largely unknown. We compared root growth responses of the halophyte Schrenkiella parvula with its glycophytic relative species Arabidopsis thaliana under salt stress and performed transcriptomic analysis of S. parvula roots to identify possible gene regulatory networks underlying their physiological responses. Schrenkiella parvula roots do not avoid salt and experience less growth inhibition under salt stress. Salt-induced abscisic acid levels were higher in S. parvula roots compared with Arabidopsis. Root transcriptomic analysis of S. parvula revealed the induction of sugar transporters and genes regulating cell expansion and suberization under salt stress. 14C-labeled carbon partitioning analyses showed that S. parvula continued allocating carbon to roots from shoots under salt stress while carbon barely allocated to Arabidopsis roots. Further physiological investigation revealed that S. parvula roots maintained root cell expansion and enhanced suberization under severe salt stress. In summary, roots of S. parvula deploy multiple physiological and developmental adjustments under salt stress to maintain growth, providing new avenues to improve salt tolerance of plants using root-specific strategies.

Application of Electric Field Technologies in the Manufacture of Food Powders and the Retention of Bioactive Compounds

Abstract: Electric fields have been used in the manufacturing of powders in a number of ways, including to enhance drying rates and retain heat-sensitive materials. Electrohydrodynamic drying and electrostatic spray drying use electric fields to accelerate the evaporation of liquid from a surface, resulting in faster drying times and improved product quality. These technologies are used in the food and pharmaceutical industries to manufacture powders from liquid feed materials. In addition to enhancing drying rates, the use of electric fields in powder manufacturing can also help to retain the bioactivity of compounds in the final product. Many bioactive compounds are sensitive to heat and can be degraded or destroyed during conventional drying processes. By using electric fields to dry powders, it is possible to reduce the amount of heat applied and therefore preserve the bioactive compounds in the final product. This article reviews the different mechanisms of various electric field assisted technologies, i.e., electrohydrodynamic atomization, electrohydrodynamic drying, pulsed electric fields and a new approach of electrostatic spray drying, along with their potential food industry applications.

Comment on “Low-cost solutions to global warming, air pollution, and energy insecurity for 145 countries” by M. Z. Jacobson, A. von Krauland, S. J. Coughlin, E. Dukas, A. J. H. Nelson, F. C. Palmer and K. R. Rasmussen, <i>Energy Environ. Sci.</i>, 2022, <b>15</b>, 3343–3359

Abstract: According to Jacobson et al. the energy transition from fossil fuels (Business as Usual, BAU) to energy using wind, water and sun (WWS) can be completed by the year 2050, even without nuclear energy.

Understanding the uncertainty of forest aboveground biomass maps derived from satellite observations

Abstract: The role of remote sensing observations in quantifying the biomass of forests is frequently debated because of both their strengths and limitations. Satellite remote sensing is nowadays standard in research activities thanks to missions designed to last over decades. Nonetheless, satellites cannot measure the organic mass stored in trees. As such, indirect approaches are developed that combine multiple observations and mathematical models together with ground-based observations to provide a set of estimates presented in the form of a map.While small-scale studies profit from a strategy that collects observations best suited to estimate biomass, continental and global mapping efforts need to restrict to datasets that have been collected following observation plans and are free of charge. In turn, this increases the demand on the performance of the models selected to link the predictor metrics derived from remote sensing and the response variable biomass. A map of biomass is eventually the result of an interplay between sensitivity of the remote sensing data to response forest variables, the spatial resolution of the sensors, the number of remote sensing observations and the capability of the models to reproduce the relationship between predictors and response variables. A consequence of such interplay is the level of accuracy affecting the biomass estimate, which ultimately is a key parameter to inform user communities on the reliability and efficiency of biomass maps. A comparison of biomass estimates obtained with different predictors and models for the same region provides additional measures to increase our understanding of the uncertainty affecting current biomass maps derived from satellite data.In this presentation, we explore such uncertainties by comparing four maps of forest aboveground biomass (AGB) based on satellite images acquired in 2020 and covering Europe. The maps were based on different predictors (Sentinel-1 and ALOS-2 PALSAR-2, ASCAT, SMOS as well as spaceborne LiDAR metrics) but share the same modelling framework for biomass retrieval. Depending on the spatial resolution of the satellite data, spatial scales ranging between 100 m and 25 km were covered.Validation of each of the datasets indicates that the overall spatial distribution of AGB is well captured even in regions with dense mature forests. However, the maps show substantial discrepancies at the level of individual pixels, regardless of the set of predictors. In addition, the precision of individual AGB estimates is rather low, between 30 and 50% of the estimated value. AGB biases were identified in specific regions and were mostly explained as imperfect modelling of the relationship between predictors and forest variables. The maps&#8217; precision increases with spatial averaging; nonetheless, the spatial correlation of errors implies that the resulting estimates can still be affected by non-negligible uncertainty. These results in turn explain why AGB values from the different maps are highly correlated although the magnitudes can be substantially different. In conclusion, the reliability of biomass maps from satellite data is questionable at the scale of the spatial resolution; their use is instead advised at the landscape scale and for understanding broad spatial patterns.

A Modeller’s Compass: How Modellers Navigate Dozens of Decisions

Abstract: The usage of hydrological models is diverse and omnipresent. For practical purposes, these models are applied to, for example, flood forecasting, water allocation, and climate change impacts. Numerous methods exist to execute any modelling study. Choosing a method creates a narrative behind each model result. This implies that models are not neutral. So, how do modellers make these decisions? We conducted fourteen semi-structured interviews between September and December 2021 with nine modellers from six different water authorities and five modellers from four different consultancy companies in the Netherlands. The interviews were all recorded and transcribed. We executed an inductive content analysis on the transcriptions. We will discuss the motivation modellers have to make choices during the modelling process. With these insights, we aim to contribute to a discussion on how models, despite their unavoidable non-neutrality, can be robust and dependable to support decision making. Standardisation, e.g. automation, can be a way to achieve this. Understanding the social aspects behind the modelling process is necessary to move forward in modelling and modelling workflows, as well as being able to share and reflect on the model results including the narrative behind it.

Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity

Abstract: Abstract. Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data are usually routinely acquired, vegetation can be monitored repeatedly over time; reflecting seasonal vegetation patterns and trends in vegetation productivity metrics. Such metrics include e.g., gross primary productivity, net primary productivity, biomass or yield. To summarize current knowledge, in this paper, we systematically reviewed time series (TS) literature for assessing state-of-the-art vegetation productivity monitoring approaches for different ecosystems based on optical remote sensing (RS) data. As the integration of solar-induced fluorescence (SIF) data in vegetation productivity processing chains has emerged as a promising source, we also include this relatively recent sensor modality. We define three methodological categories to derive productivity metrics from remotely sensed TS of vegetation indices or quantitative traits: (i) trend analysis and anomaly detection, (ii) land surface phenology, and (iii) integration and assimilation of TS-derived metrics into statistical and process-based dynamic vegetation models (DVM). Although the majority of used TS data streams originate from data acquired from satellite platforms, TS data from aircraft and unoccupied aerial vehicles have found their way into productivity monitoring studies. To facilitate processing, we provide a list of common toolboxes for inferring productivity metrics and information from TS data. We further discuss validation strategies of the RS-data derived productivity metrics: (1) using in situ measured data, such as yield, (2) sensor networks of distinct sensors, including spectroradiometers, flux towers, or phenological cameras, and (3) inter-comparison of different productivity products or modelled estimates. Finally, we address current challenges and propose a conceptual framework for productivity metrics derivation, including fully-integrated DVMs and radiative transfer models here labelled as "Digital Twin". This novel framework meets the requirements of multiple ecosystems and enables both an improved understanding of vegetation temporal dynamics in response to climate and environmental drivers and also enhances the accuracy of vegetation productivity monitoring.

An evolutionary explanation of female‐biased sexual size dimorphism in North Sea plaice, <i>Pleuronectes platessa</i> L.

Abstract: Sexual size dimorphism (SSD) is caused by differences in selection pressures and life-history trade-offs faced by males and females. Proximate causes of SSD may involve sex-specific mortality, energy acquisition, and energy expenditure for maintenance, reproductive tissues, and reproductive behavior. Using a quantitative, individual-based, eco-genetic model parameterized for North Sea plaice, we explore the importance of these mechanisms for female-biased SSD, under which males are smaller and reach sexual maturity earlier than females (common among fish, but also arising in arthropods and mammals). We consider two mechanisms potentially serving as ultimate causes: (a) Male investments in male reproductive behavior might evolve to detract energy resources that would otherwise be available for somatic growth, and (b) diminishing returns on male reproductive investments might evolve to reduce energy acquisition. In general, both of these can bring about smaller male body sizes. We report the following findings. First, higher investments in male reproductive behavior alone cannot explain the North Sea plaice SSD. This is because such higher reproductive investments require increased energy acquisition, which would cause a delay in maturation, leading to male-biased SSD contrary to observations. When accounting for the observed differential (lower) male mortality, maturation is postponed even further, leading to even larger males. Second, diminishing returns on male reproductive investments alone can qualitatively account for the North Sea plaice SSD, even though the quantitative match is imperfect. Third, both mechanisms can be reconciled with, and thus provide a mechanistic basis for, the previously advanced Ghiselin–Reiss hypothesis, according to which smaller males will evolve if their reproductive success is dominated by scramble competition for fertilizing females, as males would consequently invest more in reproduction than growth, potentially implying lower survival rates, and thus relaxing male–male competition. Fourth, a good quantitative fit with the North Sea plaice SSD is achieved by combining both mechanisms while accounting for sex-specific costs males incur during their spawning season. Fifth, evolution caused by fishing is likely to have modified the North Sea plaice SSD.

Stop re-inventing the wheel: or how ELSA and RRI can align

Abstract: Ethical, Legal and Social Aspects (ELSA) originated in the 4th European Research Framework Programme (1994) and responsible research and innovation (RRI) from the EC research agenda in 2010. ELSA has received renewed attention in European funding schemes and research. This raises the question of how these two approaches to social responsibility relate to one another and if there is the possibility to align. There is a need to evaluate the relationship/overlap between ELSA and RRI because there is a possibility that new ELSA research will reinvent the wheel if it does not engage with the body of literature already present in RRI research. This provides unneeded extra bureaucracy, reformulations of research agendas, extra investment, and an overabundance of frameworks to implement, and ELSA research does not take advantage of the body of research developed in RRI. This paper evaluates how ELSA and RRI diverge, are complementary, and can be aligned.