Data Processing for Predicting DNA Damaging Properties of Complex UV Sources.

A growing number of experimental evidence emphasizes that photobiological phenomena are not always the sum of the effect of individual wavelengths present in the emission spectrum of light sources. Unfortunately, tools are missing to identify such non-additive effects and predict effects of various exposure conditions. In the present work, we addressed these points for the formation of pyrimidine dimers in DNA upon co-exposure to UVC, UVB and UVA radiation. We first applied a combination index approach to determine whether mixtures of theses UV ranges exhibited additive, inhibitory or synergistic effects on the formation of cyclobutane pyrimidine dimers, (6-4) photoproducts and Dewar valence isomers. A predictive approach based on an experimental design strategy was then used to quantify the contribution of each wavelength range to the formation of DNA photoproducts. The obtained models allowed us to accurately predict the level of pyrimidine dimers in DNA irradiated under different conditions. The data were found to be more accurate than those obtained with the simple additive approach underlying the use of action spectra. Experimental design thus appears as an attractive concept that could be widely applied in photobiology even for cellular experiments.

Discovering non-additive heritability using additive GWAS summary statistics.

LD score regression (LDSC) is a method to estimate narrow-sense heritability from genome-wide association study (GWAS) summary statistics alone, making it a fast and popular approach. In this work, we present interaction-LD score (i-LDSC) regression: an extension of the original LDSC framework that accounts for interactions between genetic variants. By studying a wide range of generative models in simulations, and by re-analyzing 25 well-studied quantitative phenotypes from 349,468 individuals in the UK Biobank and up to 159,095 individuals in BioBank Japan, we show that the inclusion of a cis-interaction score (i.e. interactions between a focal variant and proximal variants) recovers genetic variance that is not captured by LDSC. For each of the 25 traits analyzed in the UK Biobank and BioBank Japan, i-LDSC detects additional variation contributed by genetic interactions. The i-LDSC software and its application to these biobanks represent a step towards resolving further genetic contributions of sources of non-additive genetic effects to complex trait variation.

Utilizing genomic prediction to boost hybrid performance in a sweet corn breeding program.

Sweet corn breeding programs, like field corn, focus on the development of elite inbred lines to produce commercial hybrids. For this reason, genomic selection models can help the in silico prediction of hybrid crosses from the elite lines, which is hypothesized to improve the test cross scheme, leading to higher genetic gain in a breeding program. This study aimed to explore the potential of implementing genomic selection in a sweet corn breeding program through hybrid prediction in a within-site across-year and across-site framework. A total of 506 hybrids were evaluated in six environments (California, Florida, and Wisconsin, in the years 2020 and 2021). A total of 20 traits from three different groups were measured (plant-, ear-, and flavor-related traits) across the six environments. Eight statistical models were considered for prediction, as the combination of two genomic prediction models (GBLUP and RKHS) with two different kernels (additive and additive + dominance), and in a single- and multi-trait framework. Also, three different cross-validation schemes were tested (CV1, CV0, and CV00). The different models were then compared based on the correlation between the estimated breeding values/total genetic values and phenotypic measurements. Overall, heritabilities and correlations varied among the traits. The models implemented showed good accuracies for trait prediction. The GBLUP implementation outperformed RKHS in all cross-validation schemes and models. Models with additive plus dominance kernels presented a slight improvement over the models with only additive kernels for some of the models examined. In addition, models for within-site across-year and across-site performed better in the CV0 than the CV00 scheme, on average. Hence, GBLUP should be considered as a standard model for sweet corn hybrid prediction. In addition, we found that the implementation of genomic prediction in a sweet corn breeding program presented reliable results, which can improve the testcross stage by identifying the top candidates that will reach advanced field-testing stages.

Genotype diversity enhances invasion resistance of native plants via soil biotic feedbacks.

Although native species diversity is frequently reported to enhance invasion resistance, within-species diversity of native plants can also moderate invasions. While the positive diversity-invasion resistance relationship is often attributed to competition, indirect effects mediated through plant-soil feedbacks can also influence the relationship. We manipulated the genotypic diversity of an endemic species, Scirpus mariqueter, and evaluated the effects of abiotic versus biotic feedbacks on the performance of a global invader, Spartina alterniflora. We found that invader performance on live soils decreased non-additively with genotypic diversity of the native plant that trained the soils, but this reversed when soils were sterilized to eliminate feedbacks through soil biota. The influence of soil biota on the feedback was primarily associated with increased levels of microbial biomass and fungal diversity in soils trained by multiple-genotype populations. Our findings highlight the importance of plant-soil feedbacks mediating the positive relationship between genotypic diversity and invasion resistance.

Testing for synergistic effects of natural and anthropogenic disturbance on ecological communities at a landscape scale.

Context: Anthropogenic and natural disturbances may interact synergistically, magnifying their individual effects on biodiversity. However, few studies have measured responses of ecological communities to multiple stressors at landscape scales. Objectives: We use a long-term dataset to test for synergistic effects of anthropogenic and natural disturbance on plant community diversity and composition in a large protected area. Methods: We quantified changes in plant communities over two decades in 98 plots in Waterton Lakes National Park, Canada. Fifty-three plots burned in a wildfire in the interim. We modeled the effects of wildfire, proximity to trails or roads, and their interaction on changes in species richness, community composition, relative abundance of disturbance-associated species, and colonization by exotic species. Results: Interactions between wildfire and proximity to roads and trails affected all metrics except species richness. Only one interaction was synergistic: the relative abundance of disturbance-associated species following wildfire was magnified closer to recreational corridors. The other community metrics showed unexpected patterns. For example, plots with no exotic species in the baseline survey that burned in the wildfire were more likely to gain exotic species than unburned plots only when they were distant from recreational corridors. Conclusions: Our study demonstrates interactive effects of natural and anthropogenic disturbance at landscape scales within a protected area. Plant community response to wildfire was influenced by proximity to recreational corridors, sometimes in surprising ways. As the frequency and severity of anthropogenic and natural disturbances both continue to rise, documenting the prevalence and magnitude of interactions between them is key to predicting long-term effects and designing mitigation strategies. Supplementary Information: The online version contains supplementary material available at 10.1007/s10980-024-01844-w.

Optimising clonal performance in sugarcane: leveraging non-additive effects via mate-allocation strategies.

Mate-allocation strategies in breeding programs can improve progeny performance by harnessing non-additive genetic effects. These approaches prioritise predicted progeny merit over parental breeding value, making them particularly appealing for clonally propagated crops such as sugarcane. We conducted a comparative analysis of mate-allocation strategies, exploring utilising non-additive and heterozygosity effects to maximise clonal performance with schemes that solely consider additive effects to optimise breeding value. Using phenotypic and genotypic data from a population of 2,909 clones evaluated in final assessment trials of Australian sugarcane breeding programs, we focused on three important traits: tonnes of cane per hectare (TCH), commercial cane sugar (CCS), and Fibre. By simulating families from all possible crosses (1,225) with 50 progenies each, we predicted the breeding and clonal values of progeny using two models: GBLUP (considering additive effects only) and extended-GBLUP (incorporating additive, non-additive, and heterozygosity effects). Integer linear programming was used to identify the optimal mate-allocation among selected parents. Compared to breeding value-based approaches, mate-allocation strategies based on clonal performance yielded substantial improvements, with predicted progeny values increasing by 57% for TCH, 12% for CCS, and 16% for fibre. Our simulation study highlights the effectiveness of mate-allocation approaches that exploit non-additive and heterozygosity effects, resulting in superior clonal performance. However, there was a notable decline in additive gain, particularly for TCH, likely due to significant epistatic effects. When selecting crosses based on clonal performance for TCH, the inbreeding coefficient of progeny was significantly lower compared to random mating, underscoring the advantages of leveraging non-additive and heterozygosity effects in mitigating inbreeding depression. Thus, mate-allocation strategies are recommended in clonally propagated crops to enhance clonal performance and reduce the negative impacts of inbreeding.

Genetic factors linked to aberrant neural activity of individuals with substance use disorder phenotypes: A systematic review and meta-analysis of EEG studies.

Background: Alterations in EEG activity have been considered valid endophenotypes of substance use disorders (SUDs). Empirical evidence has supported the association between genetic factors (e.g., genes, single nucleotide polymorphisms [SNPs]) and SUDs, considering both clinical samples and individuals with a positive family history of SUDs [F+SUD]). Nevertheless, the relationship between genetic factors and intermediate phenotypes (i.e., altered EEG activity) among individuals with SUD phenotypes remains unclear.Objective(s): The current study aims at summarizing genetic factors linked to aberrant EEG activity among individuals with SUDs and those with F+SUD.Methods: Sixteen studies (5 [N = 986] + 11 from the Collaborative Studies On Genetics of Alcoholism [COGA] sample [432 ≤ N ≤ 8810]) were included for a qualitative systematic review. Thirteen studies (5 + 8 studies from the COGA sample) were used for multi-level meta-analytic procedures.Results: Qualitative analyses highlighted a multivariate genetic architecture linked to alterations in EEG waves among individuals with SUD phenotypes (i.e., augmented resting-state beta waves; reduced resting-state alpha waves; reduced resting-state and task-dependent theta waves). The most recurrent genetic factors were involved in cellular energy homeostasis, modulation of inhibitory and excitatory neural activity together with neural cell growth. Meta-analytic results showed a moderate association between genetic factors and altered resting-state and task-dependent EEG activity. Meta-analytic results also suggested non-additive genetic effects on altered EEG activity.Conclusions: Complex genetic interactions mediating neural activity and brain development might constitute a causal pathway toward intermediate phenotypes associated with phenotypic features, which in turn are linked to SUDs.

Maize/peanut intercropping has greater synergistic effects and home-field advantages than maize/soybean on straw decomposition.

Introduction: The decomposition of plant litter mass is responsible for substantial carbon fluxes and remains a key process regulating nutrient cycling in natural and managed ecosystems. Litter decomposition has been addressed in agricultural monoculture systems, but not in intercropping systems, which produce species-diverse litter mass mixtures. The aim here is to quantify how straw type, the soil environment and their combined effects may influence straw decomposition in widely practiced maize/legume intercropping systems. Methods: Three decomposition experiments were conducted over 341 days within a long-term intercropping field experiment which included two nitrogen (N) addition levels (i.e. no-N and N-addition) and five cropping systems (maize, soybean and peanut monocultures and maize/soybean and maize/peanut intercropping). Experiment I was used to quantify litter quality effects on decomposition; five types of straw (maize, soybean, peanut, maize-soybean and maize-peanut) from two N treatments decomposed in the same maize plot. Experiment II addressed soil environment effects on root decomposition; soybean straw decomposed in different plots (five cropping systems and two N levels). Experiment III addressed 'home' decomposition effects whereby litter mass (straw) was remained to decompose in the plot of origin. The contribution of litter and soil effects to the home-field advantages was compared between experiment III ('home' plot) and I-II ('away' plot). Results and discussions: Straw type affected litter mass loss in the same soil environment (experiment I) and the mass loss values of maize, soybean, peanut, maize-soybean, and maize-peanut straw were 59, 77, 87, 76, and 78%, respectively. Straw type also affected decomposition in the 'home' plot environment (experiment III), with mass loss values of maize, soybean, peanut, maize-soybean and maize-peanut straw of 66, 74, 80, 72, and 76%, respectively. Cropping system did not affect the mass loss of soybean straw (experiment II). Nitrogen-addition significantly increased straw mass loss in experiment III. Decomposition of maize-peanut straw mixtures was enhanced more by 'home-field advantage' effects than that of maize-soybean straw mixtures. There was a synergistic mixing effect of maize-peanut and maize-soybean straw mixture decomposition in both 'home' (experiment III) and 'away' plots (experiment I). Maize-peanut showed greater synergistic effects than maize-soybean in straw mixture decomposition in their 'home' plot (experiment III). These findings are discussed in terms of their important implications for the management of species-diverse straw in food-production intercropping systems.

Genomic prediction through machine learning and neural networks for traits with epistasis.

Genomic wide selection (GWS) is one contributions of molecular genetics to breeding. Machine learning (ML) and artificial neural networks (ANN) methods are non-parameterized and can develop more accurate and parsimonious models for GWS analysis. Multivariate Adaptive Regression Splines (MARS) is considered one of the most flexible ML methods, automatically modeling nonlinearities and interactions of the predictor variables. This study aimed to evaluate and compare methods based on ANN, ML, including MARS, and G-BLUP through GWS. An F2 population formed by 1000 individuals and genotyped for 4010 SNP markers and twelve traits from a model considering epistatic effect, with QTL numbers ranging from eight to 480 and heritability ( h 2 ) of 0.3, 0.5 or 0.8 were simulated. Variation in heritability and number of QTL impacts the performance of methods. About quantitative traits (40, 80, 120, 240, and 480 QTLs) was observed highest R2 to Radial Base Network (RBF) and G-BLUP, followed by Random Forest (RF), Bagging (BA), and Boosting (BO). RF and BA also showed better results for traits to h 2 of 0.3 with R 2 values 16.51% and 16.30%, respectively, while MARS methods showed better results for oligogenic traits with R 2 values ranging from 39,12 % to 43,20 % in h 2 of 0.5 and from 59.92% to 78,56% in h 2 of 0.8. Non-additive MARS methods also showed high R2 for traits with high heritability and 240 QTLs or more. ANN and ML methods are powerful tools to predict genetic values in traits with epistatic effect, for different degrees of heritability and QTL numbers.

Insect pest damage increases faba bean (Vicia faba) yield components but only in the absence of insect pollination.

Identifying and quantifying crop stressors interactions in agroecosystems is necessary to guide sustainable crop management strategies. Over the last 50 years, faba bean cropping area has been declining, partly due to yield instabilities associated with uneven insect pollination and herbivory. Yet, the effect of interactions between pollinators and a key pest, the broad bean beetle Bruchus rufimanus (florivorous and seed predating herbivore) on faba bean yield has not been investigated. Using a factorial cage experiment in the field, we investigated how interactions between two hypothesized stressors, lack of insect pollination by bumblebees and herbivory by the broad bean beetle, affect faba bean yield. Lack of bumblebee pollination reduced bean weight per plant by 15%. Effects of the broad bean beetle differed between the individual plant and the plant-stand level (i.e., when averaging individual plant level responses at the cage level), likely due to high variation in the level of herbivory among individual plants. At the individual plant level, herbivory increased several yield components but only in the absence of pollinators, possibly due to plant overcompensation and/or pollination by the broad bean beetle. At the plant-stand level, we found no effect of the broad bean beetle on yield. However, there was a tendency for heavier individual bean weight with bumblebee pollination, but only in the absence of broad bean beetle herbivory, possibly due to a negative effect of the broad bean beetle on the proportion of legitimate flower visits by bumblebees. This is the first experimental evidence of interactive effects between bumblebees and the broad bean beetle on faba bean yield. Our preliminary findings of negative and indirect associations between the broad bean beetle and individual bean weight call for a better acknowledgment of these interactions in the field in order to understand drivers of crop yield variability in faba bean.

An optimal kernel-based multivariate U-statistic to test for associations with multiple phenotypes.

Set-based analysis that jointly considers multiple predictors in a group has been broadly conducted for association tests. However, their power can be sensitive to the distribution of phenotypes, and the underlying relationships between predictors and outcomes. Moreover, most of the set-based methods are designed for single-trait analysis, making it hard to explore the pleiotropic effect and borrow information when multiple phenotypes are available. Here, we propose a kernel-based multivariate U-statistics (KMU) that is robust and powerful in testing the association between a set of predictors and multiple outcomes. We employed a rank-based kernel function for the outcomes, which makes our method robust to various outcome distributions. Rather than selecting a single kernel, our test statistics is built based on multiple kernels selected in a data-driven manner, and thus is capable of capturing various complex relationships between predictors and outcomes. The asymptotic properties of our test statistics have been developed. Through simulations, we have demonstrated that KMU has controlled type I error and higher power than its counterparts. We further showed its practical utility by analyzing a whole genome sequencing data from Alzheimer's Disease Neuroimaging Initiative study, where novel genes have been detected to be associated with imaging phenotypes.

Increasing cassava root yield: Additive-dominant genetic models for selection of parents and clones.

Genomic selection has been promising in situations where phenotypic assessments are expensive, laborious, and/or inefficient. This work evaluated the efficiency of genomic prediction methods combined with genetic models in clone and parent selection with the goal of increasing fresh root yield, dry root yield, as well as dry matter content in cassava roots. The bias and predictive ability of the combinations of prediction methods Genomic Best Linear Unbiased Prediction (G-BLUP), Bayes B, Bayes Cπ, and Reproducing Kernel Hilbert Spaces with additive and additive-dominant genetic models were estimated. Fresh and dry root yield exhibited predominantly dominant heritability, while dry matter content exhibited predominantly additive heritability. The combination of prediction methods and genetic models did not show significant differences in the predictive ability for dry matter content. On the other hand, the prediction methods with additive-dominant genetic models had significantly higher predictive ability than the additive genetic models for fresh and dry root yield, allowing higher genetic gains in clone selection. However, higher predictive ability for genotypic values did not result in differences in breeding value predictions between additive and additive-dominant genetic models. G-BLUP with the classical additive-dominant genetic model had the best predictive ability and bias estimates for fresh and dry root yield. For dry matter content, the highest predictive ability was obtained by G-BLUP with the additive genetic model. Dry matter content exhibited the highest heritability, predictive ability, and bias estimates compared with other traits. The prediction methods showed similar selection gains with approximately 67% of the phenotypic selection gain. By shortening the breeding cycle time by 40%, genomic selection may overcome phenotypic selection by 10%, 13%, and 18% for fresh root yield, dry root yield, and dry matter content, respectively, with a selection proportion of 15%. The most suitable genetic model for each trait allows for genomic selection optimization in cassava with high selection gains, thereby accelerating the release of new varieties.

Mixing effects of three Eurasian plants on root decomposition in the existence of living plant community in a meadow steppe.

In grasslands, roots of different plant species decay in combination in the presence of living plants, besides, most root decomposition studies are conducted on how roots of plants decomposed alone or in artificial compositions in the absence of living plants. Therefore, we evaluated how roots of different perennial plants induced effects on decomposition process under living plants and their associated mechanisms. By using litter bag technique, we determined the root decomposition process of three perennial plants, Leymus chinensis, Phragmites australis, and Kalimeris integrifolia grown in monocultures, bi- and tri-species mixtures, after 12 months of incubation under living plants and bare soil communities. We found both additive and non-additive effects on decomposition dynamics indicating that root mass losses of compositions cannot be calculated from decaying rates of individual species. The rich-nutrient roots of K. integrifolia in monocultures and in mixtures with other plant species decayed faster. Compared with bare soil, microbial activities were enhanced under living plant communities and hence stimulated decomposition rates. Our results indicated that microbial activities are important but secondary factors to root physico-chemical properties impacting root decomposition rates. In conclusion, the empirical relationships developed here are helpful to better understand the effects of root properties and microbial activities on decay rates.

Understanding the Effectiveness of Genomic Prediction in Tetraploid Potato.

Use of genomic prediction (GP) in tetraploid is becoming more common. Therefore, we think it is the right time for a comparison of GP models for tetraploid potato. GP models were compared that contrasted shrinkage with variable selection, parametric vs. non-parametric models and different ways of accounting for non-additive genetic effects. As a complement to GP, association studies were carried out in an attempt to understand the differences in prediction accuracy. We compared our GP models on a data set consisting of 147 cultivars, representing worldwide diversity, with over 39 k GBS markers and measurements on four tuber traits collected in six trials at three locations during 2 years. GP accuracies ranged from 0.32 for tuber count to 0.77 for dry matter content. For all traits, differences between GP models that utilised shrinkage penalties and those that performed variable selection were negligible. This was surprising for dry matter, as only a few additive markers explained over 50% of phenotypic variation. Accuracy for tuber count increased from 0.35 to 0.41, when dominance was included in the model. This result is supported by Genome Wide Association Study (GWAS) that found additive and dominance effects accounted for 37% of phenotypic variation, while significant additive effects alone accounted for 14%. For tuber weight, the Reproducing Kernel Hilbert Space (RKHS) model gave a larger improvement in prediction accuracy than explicitly modelling epistatic effects. This is an indication that capturing the between locus epistatic effects of tuber weight can be done more effectively using the semi-parametric RKHS model. Our results show good opportunities for GP in 4x potato.

Genome-wide epistatic interactions of litter size at birth in Chinese indigenous pigs.

Improving litter size at birth (TNB) and the number of piglets born alive (NBA) are the main breeding goals related to litter traits, which are economically important. A better understanding of genetic architecture underlying TNB and NBA traits could increase pig production efficiency. However, most previous studies on these traits focus on additive genetic effects, while epistatic interactions underlying TNB and NBA traits has not yet been well investigated, which are essential to understand how traits-related genes interact. Herein, we conducted genome scans of epistatic interactions underlying TNB and NBA traits in a total of 150 Chinese indigenous pigs (75 Jinhua and 75 Shengxian Spotted pigs) with high throughput genomic data. Based on SNPs with high interaction values and connectivity scores, we identified eight promising candidate genes (AKT2, TSC1, MTOR, PIK3R5, TIAM1, FGF14, RALB and ROR2) potentially associated with litter traits in pigs. Moreover, the underlying pathways, e.g., calcium ion transport, pointed out their roles in litter size-related traits. Our findings provide new insight into genetic architecture of litter traits in pigs and will benefit economic profits in pig production.

On the sensitivity of food webs to multiple stressors.

Evaluating the effects of multiple stressors on ecosystems is becoming increasingly vital with global changes. The role of species interactions in propagating the effects of stressors, although widely acknowledged, has yet to be formally explored. Here, we conceptualise how stressors propagate through food webs and explore how they affect simulated three-species motifs and food webs of the Canadian St. Lawrence System. We find that overlooking species interactions invariably underestimate the effects of stressors, and that synergistic and antagonistic effects through food webs are prevalent. We also find that interaction type influences a species' susceptibility to stressors; species in omnivory and tri-trophic food chain interactions in particular are sensitive and prone to synergistic and antagonistic effects. Finally, we find that apex predators were negatively affected and mesopredators benefited from the effects of stressors due to their trophic position in the St. Lawrence System, but that species sensitivity is dependent on food web structure. In conceptualising the effects of multiple stressors on food webs, we bring theory closer to practice and show that considering the intricacies of ecological communities is key to assess the net effects of stressors on species.

The Stability Landscape of de novo TIM Barrels Explored by a Modular Design Approach.

The ability to design stable proteins with custom-made functions is a major goal in biochemistry with practical relevance for our environment and society. Understanding and manipulating protein stability provide crucial information on the molecular determinants that modulate structure and stability, and expand the applications of de novo proteins. Since the (ß/⍺)8-barrel or TIM-barrel fold is one of the most common functional scaffolds, in this work we designed a collection of stable de novo TIM barrels (DeNovoTIMs), using a computational fixed-backbone and modular approach based on improved hydrophobic packing of sTIM11, the first validated de novo TIM barrel, and subjected them to a thorough folding analysis. DeNovoTIMs navigate a region of the stability landscape previously uncharted by natural TIM barrels, with variations spanning 60 degrees in melting temperature and 22 kcal per mol in conformational stability throughout the designs. Significant non-additive or epistatic effects were observed when stabilizing mutations from different regions of the barrel were combined. The molecular basis of epistasis in DeNovoTIMs appears to be related to the extension of the hydrophobic cores. This study is an important step towards the fine-tuned modulation of protein stability by design.

Non-additive effects of foundation species determine the response of aquatic ecosystems to nutrient perturbation.

Eutrophication is a persistent threat to aquatic ecosystems worldwide. Foundation species, namely those that play a central role in the structuring of communities and functioning of ecosystems, are likely important for the resilience of aquatic ecosystems in the face of disturbance. However, little is known about how interactions among such species influence ecosystem responses to nutrient perturbation. Here, using an array (N = 20) of outdoor experimental pond ecosystems (15,000 L), we manipulated the presence of two foundation species, the macrophyte Myriophyllum spicatum and the mussel Dreissena polymorpha, and quantified ecosystem responses to multiple nutrient disturbances, spread over two years. In the first year, we added five nutrient pulses, ramping up from 10 to 50 µg P/L over a 10-week period from mid-July to mid-October, and in the second year, we added a single large pulse of 50 µg P/L in mid-October. We used automated sondes to measure multiple ecosystems properties at high frequency (15-minute intervals), including phytoplankton and dissolved organic matter fluorescence, and to model whole-ecosystem metabolism. Overall, both foundation species strongly affected the ecosystem responses to nutrient perturbation, and, as expected, initially suppressed the increase in phytoplankton abundance following nutrient additions. However, when both species were present, phytoplankton biomass increased substantially relative to other treatment combinations: non-additivity was evident for multiple ecosystem metrics following the nutrient perturbations in both years but was diminished in the intervening months between our perturbations. Overall, these results demonstrate how interactions between foundation species can cause surprisingly strong deviations from the expected responses of aquatic ecosystems to perturbations such as nutrient additions.

Wavelengths and temporal effects on the response of mammalian cells to UV radiation: Limitations of action spectra illustrated by genotoxicity.

All photobiological events depend on the wavelength of the incident radiation. In real-life situations and in the vast majority of laboratory experiments, exposure always involves sources with various emission spectra spreading over a wide wavelength range. Action spectra are often used to describe the efficiency of a process at different wavelengths and to predict the effects of a given light source by summation of the individual effects at each wavelength. However, a full understanding of the biological effects of complex sources requires more than considering these concomitant events at each specific wavelength. Indeed, photons of different energies may not have additive but synergistic or inhibitory effects on photochemical processes and cellular responses. The evolution of a photobiological response with post-irradiation time must also be considered. These two aspects may represent some limitations to the use of action spectra. The present review, focused on mammalian cells, illustrates the concept of action spectrum and discusses its drawbacks using theoretical considerations and examples taken from the literature. Emphasis is placed on genotoxicity for which wavelength effects have been extensively studied. Other effects of UV exposure are also mentioned.

Hidden layers of density dependence in consumer feeding rates.

Functional responses relate a consumer's feeding rates to variation in its abiotic and biotic environment, providing insight into consumer behaviour and fitness, and underpinning population and food-web dynamics. Despite their broad relevance and long-standing history, we show here that the types of density dependence found in classic resource- and consumer-dependent functional-response models equate to strong and often untenable assumptions about the independence of processes underlying feeding rates. We first demonstrate mathematically how to quantify non-independence between feeding and consumer interference and between feeding on multiple resources. We then analyse two large collections of functional-response data sets to show that non-independence is pervasive and borne out in previously hidden forms of density dependence. Our results provide a new lens through which to view variation in consumer feeding rates and disentangle the biological underpinnings of species interactions in multi-species contexts.