Biology subjects

Schielzeth, H.

Publications and source records attributed to Schielzeth, H..

14 recordsLinked to original sources

bioRxiv2025

Plant diversity promotes aboveground arthropods and associated functions despite arthropod loss over time

Arthropods comprise the majority of terrestrial biodiversity and play key roles in ecosystem functioning. Biodiverse grasslands support many arthropods, yet such ecosystems have declined due to land conversion and management changes. While restoration aims to conserve species-rich grasslands, long-term effects of plant richness on arthropod communities and associated functions remain underexplored. We addressed this gap by quantifying arthropods, herbivory, and predation over 13 years (2010-2022) across 80 grassland plots with varying plant richness. We examined (1) temporal trends in arthropod communities, herbivory and predation and whether changes depended on plant richness, (2) whether plant richness effects varied or strengthened over time, and (3) whether arthropod changes affected associated functions. Arthropod metrics declined over time across all plant richness levels, with average losses mainly being more pronounced in species-poor mixtures. Plant richness consistently had a positive effect on arthropods and their functions, although this effect varied between years without a consistent temporal trend. Notably, temporal changes in arthropod community metrics did not predict shifts in associated functions. Our findings highlight the dynamic interplay between plant richness and arthropods. From a conservation perspective, we can conclude that diversification in grasslands- the increase in plant diversity- directly supports arthropods and associated functions. Additionally, first trends indicate that the maintenance and protection of diverse semi-natural grasslands over a long period might mitigate the arthropod loss driven by environmental changes. In other words, diverse grasslands may buffer against the ongoing arthropod loss, though this effect may take years to become apparent. This again emphasizes the long-term nature of conservation efforts.

ecology↗

bioRxiv2025

Phenotypic plasticity, heritability, and genotype-by-environment interactions in an insect dispersal polymorphism

Evolutionary fitness is determined by the match between an organisms phenotype and its local environment. When mismatched, individuals may disperse to more suitable habitats. For flightless insects, however, the range of dispersal is typically limited. Numerous flightless species have, therefore, evolved a dispersal dimorphism, that is, some individuals in otherwise short-winged populations develop long wings. Wing development may be genetically or environmentally determined, but these two drivers have rarely been analysed together. We studied the inheritance and density-dependent plasticity in the dispersal dimorphism of the meadow grasshopper Pseudochorthippus parallelus. Using a full-sib half-sib breeding design, we found that the development of long wings strongly depends on rearing density, with tactile stimulation being the most likely proximate cause. Additionally, we found heritable variation in the development of long wings, both in the propensity to produce long wings and in response to density (genotype-by-environment interactions). While at high and low densities, the environmental effect dominates, genetic variation takes effect mostly at intermediate densities. Our results have implications for the phenotype-environment match and ultimately the evolution of individualized niches. Induced dimorphisms represent a form of niche conformance (or adaptive phenotypic plasticity) and both genetic and induced dispersal dimorphisms facilitate niche choice in allowing individuals to sample a greater range of environments. Our study shows that niche-related polymorphisms can evolve via selection on the sensitivity threshold.

evolutionary biology↗

bioRxiv2025

Three species, three labs, three experiments: testing the reproducibility of ecological studies on insect behaviour in a multi-laboratory setting

The reproducibility of studies involving insect species is an underexplored area in the broader discussion about poor reproducibility in science. Our study addresses this gap by conducting a systematic multi-laboratory investigation into the reproducibility of ecological studies on insect behaviour. We implemented a 3x3 experimental design, incorporating three study sites, and three independent experiments on three insect species from different orders: the turnip sawfly (Athalia rosae, Hymenoptera), the meadow grasshopper (Pseudochorthippus parallelus, Orthoptera) and the red flour beetle (Tribolium castaneum, Coleoptera). Using random-effect meta-analysis, we compared the consistency and accuracy of treatment effects on insect behavioural traits across replicate experiments. We successfully reproduced the overall statistical treatment effect in 83% of the replicate experiments, but overall effect size replication was achieved in only 66% of the replicates. Thus, though demonstrating sufficient reproducibility in some measures, this study also provides first experimental evidence for cases of poor reproducibility in insect experiments. Our findings further show that reasons causing poor reproducibility established in rodent research also hold for other study organisms and research questions. We believe that a rethinking of current best practices is required to face reproducibility issues in insect studies, but also across disciplines. Specifically, we advocate for adopting open research practices and the implementation of methodological strategies that reduce bias and problems arising from over-standardization. With respect to the latter, the introduction of systematic variation through multi-laboratory or heterogenized designs may contribute to improved reproducibility in studies involving any living organisms.

animal behavior and cognition↗

bioRxiv2023

Plant diversity stabilizes soil temperature

Extreme weather events are occurring more frequently, and research has shown that plant diversity can help mitigate impacts of climate change by increasing plant productivity and ecosystem stability1,2. Although soil temperature and its stability are key determinants of essential ecosystem processes related to water and nutrient uptake3 as well as soil respiration and microbial activity4, no study has yet investigated whether plant diversity can buffer soil temperature fluctuations. Using 18 years of a continuous dataset with a resolution of 1 minute ([~]795,312,000 individual measurements) from a large-scale grassland biodiversity experiment, we show that plant diversity buffers soil temperature throughout the year. Plant diversity helped to prevent soil heating in hot weather, and cooling in cold weather. Moreover, this effect of plant diversity increased over the 18-year observation period with the aging of experimental communities and was even stronger under extreme conditions, i.e., on hot days or in dry years. Using structural equation modelling, we found that plant diversity stabilized soil temperature by increasing soil organic carbon concentrations and, to a lesser extent, by increasing the plant leaf area index. We suggest that the diversity-induced stabilization of soil temperature may help to mitigate the negative effects of extreme climatic events such as soil carbon release, thus slow global warming.

ecology↗

bioRxiv2022

State-dependency of behavioural traits is a function of the life-stage in a holometabolous insect

State variables, such as body condition, are important predictors of behavioural traits. Depending on the state of an individual, the costs and benefits associated with different behavioural decisions can vary. An individuals state could affect its average behavioural response and also the behavioural repeatability. Moreover, even for the same state, different behavioural strategies may be adaptive depending on the individuals life-stage. Here, we manipulated the body condition in larvae and adults of a holometabolous insect species, Athalia rosae, using starvation. We assessed the effects of starvation on the behavioural traits of post-contact immobility (PCI) and activity levels and tested their repeatability. Our results show state-dependency of behaviour, although the effect varied with life-stage. Starved larvae exhibited shorter PCI duration and higher activity levels, whereas starved adults were less active than non-starved individuals. Moreover, although most behavioural repeatability estimates were significant in both life-stages, we did not find any significant effect of starvation on the estimates. Next, we calculated standardised effect sizes to compare starvation effects across life stages. We found that starvation had a larger and opposite effect in the larval stage than during the adult stage for all behavioural traits. Finally, we conducted microcosm and no-choice bioassay experiments to examine the benefits and costs, respectively, of the behaviour elicited by starvation in the larval stage. We observed that starved larvae located food faster than non-starved larvae but were also attacked sooner by a predator, possibly due to their higher activity levels. Together, our results demonstrate that behavioural state-dependence is a function of the life-stage of an individual. Moreover, the behavioural strategy exhibited can be adaptive for a specific life-stage with respect to certain functions, like foraging, but also carry costs, like risk of predation.

animal behavior and cognition↗

bioRxiv2022

Abundant Phenotypic Parallelism, Incomplete Lineage Sorting, and Introgression in Open-Habitat Chats

Insights into the processes underpinning convergent evolution advance our understanding of the contributions of ancestral, introgressed, and novel genetic variation to phenotypic evolution. Phylogenomic analyses characterizing genome-wide gene tree heterogeneity can provide first clues about the extent of ILS and of introgression and thereby into the potential of these processes or (in their absence) the need to invoke novel mutations to underpin convergent evolution. Here, we were interested in understanding the processes involved in convergent evolution in open-habitat chats (wheatears of the genus Oenanthe and their relatives). To this end, based on whole-genome resequencing data from 50 taxa of 44 species, we established the species tree, characterized gene tree heterogeneity, and investigated the footprints of ILS and introgression within the latter. The species tree corroborates the pattern of abundant convergent evolution, especially in wheatears. The high levels of gene tree heterogeneity in wheatears are explained by ILS alone only for 30% of internal branches. For multiple branches with high gene tree heterogeneity, D-statistics and phylogenetic networks identified footprints of introgression. Finally, long branches without extensive ILS between clades sporting similar phenotypes provide suggestive evidence for a role of novel mutations in the evolution of these phenotypes. Together, our results suggest that convergent evolution in open-habitat chats involved diverse processes and highlight that phenotypic diversification is often complex and best depicted as a network of interacting lineages.

evolutionary biology↗

bioRxiv2022

Strength of stabilizing selection on the amino-acid sequence is associated with the amount of non-additive variance in gene expression.

Contemporary populations are unlikely to respond to natural selection if much of their genetic variance is non-additive. Understanding the evolutionary and genomic factors that drive amounts of non-additive variance in natural populations is therefore of paramount importance. Here, we use a quantitative genetic breeding design to separate the additive from the non-additive components of expression variance in 17,657 gene transcripts of the outcrossing plant Arabidopsis lyrata. We partition the expressed genes according to their predominant variance components in a set of half- and full-sib families obtained by crossing individuals from different populations. As expected, a population-genetic simulation model shows that when divergent alleles segregate in the population, our ability to detect non-additive genetic variance is enhanced. Variation in its relative contribution can thus be analyzed and compared across transcribed genes. We find that most of the genetic variance in gene expression represents non-additive variance, especially among long genes or genes involved in epigenetic gene regulation. Genes with the most non-additive variance in our design not only display markedly lower rates of synonymous variation, they have also been exposed to stronger purifying selection compared to genes with high additive variance. Our study demonstrates that both the genomic architecture and the past history of purifying selection impacts the composition of genetic variance in gene expression.

genetics↗

bioRxiv2020

Novelty at second glance: A critical appraisal of the novel-object paradigm based onmeta-analysis.

The study of consistent individual differences in behaviour has become an important focus in research on animal behaviour. Behavioural phenotypes are typically measured through standardized testing paradigms and one frequently used paradigm is the novel object test. In novel object tests, animals are exposed to new (unknown) objects and their reaction is quantified. When repeating trials to assess the temporal consistency of individual differences, researchers face the dilemma of whether to use the same or different novel objects, since the same stimulus can result in habituation, while exposure to different objects can result in context-dependent responses. We performed a quantitative assessment of 254 effect sizes from 113 studies on novel-object trials to evaluate the properties of this testing paradigm, in particular the effect of object novelty and time interval between novel-object trials on estimates of individual consistency. We found an increase of sample sizes and an increase of estimates of repeatabilities with time. The vast majority of short-term studies ( one month) used either the same or different novel objects about equally often. The average estimate for individual consistency was r = 0.47 (short-term r = 0.52, long-term r = 0.44). Novelty, time interval between trials and their interaction together explained only 3% of the total heterogeneity. Overall, novelobject trials reliably estimate individual differences in behaviour, but results were very heterogeneous even within the same study species, suggesting susceptibility to unknown details in testing conditions. Most studies that measure novel-object responses in association with food label the trait as neophobia, while novel-object trials in a neutral context are labelled variously as boldness/shyness, exploratory behaviour or neophobia/neophilia. Neophobia/neophilia is also the term most specific to novel object presentations. To avoid ambiguity, we suggest object neophobia/neophilia as the most specific label for novel-object responses.

animal behavior and cognition↗

bioRxiv2020

partR2: Partitioning R2 in generalized linear mixed models

The coefficient of determination R2 quantifies the amount of variance explained by regression coefficients in a linear model. It can be seen as the fixed-effects complement to the repeatability R (intra-class correlation) for the variance explained by random effects and thus as a tool for variance decomposition. The R2 of a model can be further partitioned into the variance explained by a particular predictor or a combination of predictors using semi-partial (part) R2 and structure coefficients, but this is rarely done due to a lack of software implementing these statistics. Here, we introduce partR2, an R package that quantifies part R2 for fixed effect predictors based on (generalized) linear mixed-effect model fits. The package iteratively removes predictors of interest from the model and monitors the change in the variance of the linear predictor. The difference to the full model gives a measure of the amount of variance explained uniquely by a particular predictor or a set of predictors. partR2 also estimates structure coefficients as the correlation between a predictor and fitted values, which provide an estimate of the total contribution of a fixed effect to the overall prediction, independent of other predictors. Structure coefficients can be converted to the total variance explained by a predictor, here called inclusive R2, as the square of the structure coefficients times total R2. Furthermore, the package reports beta weights (standardized regression coefficients). Finally,partR2 implements parametric bootstrapping to quantify confidence intervals for each estimate. We illustrate the use of partR2 with real example datasets for Gaussian and binomial GLMMs and discuss interactions, which pose a specific challenge for partitioning the explained variance among predictors.

bioinformatics↗

bioRxiv2020

Phylogenetic, geographic and ecological distribution of a green-brown polymorphisms in European Orthopterans

The green-brown polymorphism among polyneopteran insects represents one of the most penetrant color polymorphisms in any group of organisms. Yet systematic overviews are lacking. I here present analyses of the phylogenetic, geographic and habitat distribution of the green-brown polymorphism across the complete European orthopteran fauna. Overall, 30% of European orthopterans are green-brown polymorphic. Polymorphic species are scattered across the entire phylogenetic tree, including roughly equal proportions of Ensifera and Caelifera. A few taxonomic groups, however, include only brown species. Polymorphic species occur more frequently in clades that contain monomorphic green species than in those without green species. The relative abundance of color morphs in polymorphic species is skewed towards green, and in particular rare/exceptional brown morphs are more common in predominantly green species than rare/exceptional green morphs in predominantly brown species. The patterns of abundances support the hypothesis that loss-of-function mutations play a role in creating polymorphic populations from green species. Polymorphic species are particularly common in moist to mesic grasslands, alpine and arboreal habitats. Dry, open, rocky and cave habitats as well as nocturnal lifestyles are dominated by monomorphic brown species. The proportion of polymorphic species increases from southern to northern latitudes. These marked habitat-dependencies also show that coloration is affected by natural selection and/or environmental filtering. Overall, the results illustrate that the occurrence of the polymorphism is phylogenetically, geographically and ecologically widespread and they suggest that polymorphism is thus potentially in mutation-selection balance across a large number of species.

evolutionary biology↗

bioRxiv2020

Conditional repeatability and the variance explained by reaction norm variation in random slope models

Individuals differ in average phenotypes and in sensitivity to environmental variation. Such context-sensitivity can be modelled as random-slope variation. Random-slope variation implies that the proportion of between-individual variation varies across the range of a covariate (environment/context/time/age) and has thus been called conditional repeatability. We propose to put conditional repeatabilities in perspective of the total phenotypic variance and suggest a way of standardization using the random-slope coefficient of determination [Formula]. Furthermore, we illustrate that the marginalized repeatability Rmar averaged across an environmental gradient offers a biologically relevant description of between-individual variation. We provide simple equations for calculating key descriptors of conditional repeatabilities, clarify the difference between random-intercept variation and average between-individual variation and make recommendations for comprehensive reporting. While we introduce the concept with individual variation in mind, the framework is equally applicable to other type of between-group/cluster variation that varies across some (environmental) gradient.

animal behavior and cognition↗

bioRxiv2020

Linked-read sequencing enables haplotype-resolved resequencing at population scale

The feasibility to sequence entire genomes of virtually any organism provides unprecedented insights into the evolutionary history of populations and species. Nevertheless, many population genomic inferences - including the quantification and dating of admixture, introgression and demographic events, and the inference of selective sweeps - are still limited by the lack of high-quality haplotype information. In this respect, the newest generation of sequencing technology now promises significant progress. To establish the feasibility of haplotype-resolved genome resequencing at population scale, we investigated properties of linked-read sequencing data of songbirds of the genus Oenanthe across a range of sequencing depths. Our results based on the comparison of downsampled (25x, 20x, 15x, 10x, 7x, and 5x) with high-coverage data (46-68x) of seven bird genomes suggest that phasing contiguities and accuracies adequate for most population genomic analyses can be reached already with moderate sequencing effort. At 15x coverage, phased haplotypes span about 90% of the genome assembly, with 50 and 90 percent of the phased sequence located in phase blocks longer than 1.25-4.6 Mb (N50) and 0.27-0.72 Mb (N90), respectively. Phasing accuracy reaches beyond 99% starting from 15x coverage. Higher coverages yielded higher contiguities (up to about 7 Mb/1Mb (N50/N90) at 25x coverage), but only marginally improved phasing accuracy. Finally, phasing contiguity improved with input DNA molecule length; thus, higher-quality DNA may help keeping sequencing costs at bay. In conclusion, even for organisms with gigabase-sized genomes like birds, linked-read sequencing at moderate depth opens an affordable avenue towards haplotype-resolved genome resequencing data at population scale.

evolutionary biology↗

bioRxiv2017

Fixed effect variance and the estimation of the heritability: Issues and solutions

Linear mixed effects models are frequently used for estimating quantitative genetic parameters, including the heritability, of traits of interest. Heritability is an important metric, because it acts as a filter that determines how efficiently phenotypic selection translates into evolutionary change. As a quantity of biological interest, it is important that the denominator, the phenotypic variance, actually reflects the amount of phenotypic variance in the relevant ecological stetting. The current practice of quantifying heritability from mixed effects models frequently deprives the heritability of variance explained by fixed effects (often leading to upward-bias) and it has been suggested to omit fixed effects when estimating heritabilities. We advocate an alternative option of fitting complex models incorporating all relevant effects, while including the variance explained by fixed effects into the estimation of heritabilities. The approach is easily implemented (an example is provided) and allows corrections for the estimation of heritability, such as the exclusion of variance arising from experimental design effects while still including all biologically relevant sources of variation. We explore the complications arising depending on the nature of the covariates included as fixed effects (e.g. biological or experimental origin, characteristics of biological covariates). Furthermore, we discuss fixed effects in non-linear and generalized linear models when fixed effects. In these cases, the variance parameters depend on the location of the intercept and hence on the scaling of the fixed effects. Integration over the biologically relevant range of fixed effects offers a preferred solution in those situations.

evolutionary biology↗

bioRxiv2016

Coefficient of determination R2 and intra-class correlation coefficient ICC from generalized linear mixed-effects models revisited and expanded

O_LIThe coefficient of determination R2 quantifies the proportion of variance explained by a statistical model and is an important summary statistic of biological interest. However, estimating R2 for (generalized) linear mixed models (GLMMs) remains challenging. We have previously introduced a version of R2 that we called R2GLMM for Poisson and binomial GLMMs, but not for other distributional families.\nC_LIO_LISimilarly, we earlier discussed how to estimate intra-class correlation coefficients ICC (also known as repeatability in the field of ecology and evolution) using Poisson and binomial GLMMs, but not for other distributional families. ICC is related to R2 because they are both ratios of variance components.\nC_LIO_LIIn this article we expand our method to additional non-Gaussian distributions, namely quasi-Poisson, negative binomial and gamma GLMMs. However, in theory, our extension could be applied to any distribution and we include an explanatory calculation for the Tweedie distribution.\nC_LIO_LIWhile expanding our approach, we highlight two useful concepts, Jensens inequality and the delta method, both of which help in understanding the properties of GLMMs. Jensens inequality has important implications for the interpretation GLMMs while the delta method allows a general derivation of distribution-specific variances. We also discuss some special considerations for binomial GLMMs with binary or proportion data.\nC_LIO_LIWe illustrate the implementation of our extension by worked examples in the R environment. However, our method can be used regardless of statistical packages and environments. We finish by referring to two alternative methods to our approach along with a cautionary note.\nC_LI

ecology↗