Prediction modeling of isoscapes for stable isotopes in China’s rice based on environmental similarity

Rice quality is directly related to human health, so it is important to have traceability systems that can trace inferior or contaminated rice back to its geographical origin. This ensures farming practices in substandard regions become better regulated to improve rice quality, origin labelling and consumer trust. However, tracing the origin of rice on the marketplace requires an accurate database benchmarking the isotope distribution over areas of rice production. Large stable isotope data sets can be used to determine the geographical origin of rice through predictive isoscape models. This study presents the first rice isoscape based on environmental similarity to predict the geospatial distribution of δ13C, δ2H and δ18O values of Chinese rice and provides uncertainty at every location such prediction is made. For this study, 794 rice samples were collected in 2017 from primary rice production regions of China. An independent verification shows that the predicted isotope distribution from this new approach is of high accuracy, with a root mean squared error (RMSE) of 0.51‰, 7.09‰ and 2.06‰ for δ13C, δ2H and δ18O values respectively. In addition, uncertainty in the spatial distribution of isotopes can be used to indicate the prediction accuracy and to guide future sampling. Our results indicate that an isoscape prediction method based on environmental similarity is effective to predict the spatial distribution of stable isotope in rice, and is an effective tool for building isotope distribution in rice over large areas with complex environment. This method could also be used to predict potential isotopic variations in future years due to climate change.

Environment-Specific Genomic Prediction Ability in Maize Using Environmental Covariates Depends on Environmental Similarity To Training Data

Technology advances have made possible the collection of a wealth of genomic, environmental, and phenotypic data for use in plant breeding. Incorporation of environmental data into environment-specific genomic prediction (GP) is hindered in part because of inherently high data dimensionality. Computationally efficient approaches to combining genomic and environmental information may facilitate extension of GP models to new environments and germplasm, and better understanding of genotype-by-environment (G × E) interactions. Using genomic, yield trial, and environmental data on 1,918 unique hybrids evaluated in 59 environments from the maize Genomes to Fields project, we determined that a set of 10,153 SNP dominance coefficients and a 5-day temporal window size for summarizing environmental variables were optimal for GP using only genetic and environmental main effects. Adding marker-by-environment variable interactions required dimension reduction, and we found that reducing dimensionality of the genetic data while keeping the full set of environmental covariates was best for environment-specific GP of grain yield, leading to an increase in prediction ability of 2.7% to achieve a prediction ability of 80% across environments when data were masked at random. We then measured how prediction ability within environments was affected under stratified training-testing sets to approximate scenarios commonly encountered by plant breeders, finding that incorporation of marker-by-environment effects improved prediction ability in cases where training and test sets shared environments, but did not improve prediction in new untested environments. The environmental similarity between training and testing sets had a greater impact on the efficacy of prediction than genetic similarity between training and test sets.

Environmental Stratification in Trials of Unbalanced Multiyear Soybean (Glycine max (l.) Merril) via the Integration of GGE Biplot Graphs and Networks of Environmental Similarity

Genotype x enviroment (GE) interaction can difficult soybean breeding programs to atieve the aim of obtain more productive cultivars. Enviroment stratification is a way to circunvent this problem. This work aimed to gather GGE Biplot graphs of a network of trials unbalance multiyear soybean via matrices of coincidence and networks of enviroment to optimize environmental stratification. Data from an experimental network of 43 trials was used, these experiments were implanted during the crop seasons of 2011/12, 2012/13, 2013/14 and 2015/16 in Brazil. The GE interaction were statistically significant for all 43 trials. The step by step of our analses was: GGE Biplots graphs were obtained; the enviroment coincidence matrices were calculated; the values of matrices were used for to obtain the networks of environmental similarity. The study demonstrated that by the method was possible to identify, using unbalanced multiyear data, the formation of four mega-environments. Therefore, integrating GGE Biplot graphs and networks of environmental similarity is an efficient method to optimize a soybean program by environment stratification.

Genetic and chemodiversity in native populations of Schinus terebinthifolia Raddi along the Brazilian Atlantic forest

Schinus terebinthifolia is a species native to different ecoregions in the Brazilian Atlantic Forest. The plant is listed on the National Relation of Medicinal Plants and recommended as phytomedicine, however while extractive exploitation prevails as the main route of raw material a significant variation of compounds will be detected. To assure the expansion of productive chain it is important to start by studying population diversity and chemical variations. We used SSR markers for studies of genetic structure among populations from dense ombrophilous forest (ES); the deciduous seasonal forest (SM); the savanna (DOU) and the sandbanks (ITA and MSP), and compared the results to their chemical profiles of essential oil. Genetic structure revealed differences among populations and significant fixation rates. Pairwise studies and Bayesian analysis showed similarities between ITA and SM and between DOU and MSP, proving that the patterns of distribution for the species do not follow the isolation by distance or similarity by environmental conditions. The comparison between PCA of genotypes and chemodiversity reinforces the unique profile for each population despite the environmental similarity observed and genetic analysis. The most divergent genotype and chemical group was found at the ombrophilous forest, strong evidence that we should undertake conservation efforts to prevent losses of biodiversity in that area.

Enviromic Assembly Increases Accuracy and Reduces Costs of the Genomic Prediction for Yield Plasticity in Maize

Quantitative genetics states that phenotypic variation is a consequence of the interaction between genetic and environmental factors. Predictive breeding is based on this statement, and because of this, ways of modeling genetic effects are still evolving. At the same time, the same refinement must be used for processing environmental information. Here, we present an “enviromic assembly approach,” which includes using ecophysiology knowledge in shaping environmental relatedness into whole-genome predictions (GP) for plant breeding (referred to as enviromic-aided genomic prediction, E-GP). We propose that the quality of an environment is defined by the core of environmental typologies and their frequencies, which describe different zones of plant adaptation. From this, we derived markers of environmental similarity cost-effectively. Combined with the traditional additive and non-additive effects, this approach may better represent the putative phenotypic variation observed across diverse growing conditions (i.e., phenotypic plasticity). Then, we designed optimized multi-environment trials coupling genetic algorithms, enviromic assembly, and genomic kinships capable of providing in-silico realization of the genotype-environment combinations that must be phenotyped in the field. As proof of concept, we highlighted two E-GP applications: (1) managing the lack of phenotypic information in training accurate GP models across diverse environments and (2) guiding an early screening for yield plasticity exerting optimized phenotyping efforts. Our approach was tested using two tropical maize sets, two types of enviromics assembly, six experimental network sizes, and two types of optimized training set across environments. We observed that E-GP outperforms benchmark GP in all scenarios, especially when considering smaller training sets. The representativeness of genotype-environment combinations is more critical than the size of multi-environment trials (METs). The conventional genomic best-unbiased prediction (GBLUP) is inefficient in predicting the quality of a yet-to-be-seen environment, while enviromic assembly enabled it by increasing the accuracy of yield plasticity predictions. Furthermore, we discussed theoretical backgrounds underlying how intrinsic envirotype-phenotype covariances within the phenotypic records can impact the accuracy of GP. The E-GP is an efficient approach to better use environmental databases to deliver climate-smart solutions, reduce field costs, and anticipate future scenarios.

“Reconstructions of the past distribution of Testudo graeca mitochondrial lineages in the Middle East and Transcaucasia support multiple refugia since the Last Glacial Maximum”: A response to Turkozan et al. (2021)

Species distribution models (SDMs) are frequently used to characterise current, past or future realised environmental niches. Two recent studies applied different approaches to infer range dynamics in eastern subspecies of the spur-thighed tortoise Testudo graeca Linnaeus, 1758. We discuss differences in the conclusions of the two papers and use multivariate environmental similarity surface (MESS) analyses to show that the results of the study by Turkozan et al. (2021), recently published in the Herpetological Journal, are compromised by extrapolation and therefore have to be interpreted with caution.

Proximal CA3 is the primary locus of age-related pattern separation deficits in rats

Age-related deficits in pattern separation have been postulated to bias the output of hippocampal memory processing toward pattern completion, which can cause deficits in accurate memory retrieval. While the CA3 region of the hippocampus is often conceptualized as a homogeneous network involved in pattern completion, growing evidence demonstrates a functional gradient in CA3 along the transverse axis, with proximal CA3 supporting pattern separation and distal CA3 supporting pattern completion. We examined the neural representations along the CA3 transverse axis in young (Y), aged memory-unimpaired (AU), and aged memory-impaired (AI) rats when different changes were made to the environment. When the environmental similarity was high (e.g., altered cues or altered environment shapes in the same room), Y and AU rats showed more orthogonalized representations in proximal CA3 than in distal CA3, consistent with prior studies showing a functional dissociation along the transverse axis of CA3. In contrast, AI rats showed less orthogonalization in proximal CA3 than Y and AU rats but showed more normal (i.e., generalized) representations in distal CA3, with little evidence of a functional gradient. When the environmental similarity was low (e.g., recordings were done in different rooms), representations in proximal and distal CA3 remapped in all rats, showing that AI rats are able to dissociate representations when inputs show greater dissimilarity. These results provide evidence that the aged-related bias towards pattern completion is due to the loss in AI rats of the normal transition from pattern separation to pattern completion along the CA3 transverse axis and, furthermore, that proximal CA3 is the primary locus of this age-related dysfunction in neural coding.

Climatic data provide clues on the geographic origin of a spider invasion in the Americas

Identifying the source population of invasive species is important to assess the distribution and potential effects in the invaded area. The araneid spider Cyrtophora citricola is widely distributed in Europe, Asia, and Africa; however, in the last twenty years, it has been reported in several countries across the Americas. To date, the geographic origin of the populations established in America remains unclear, but considering the successful colonization after its recent arrival, a high environmental similarity between the invaded and native geographic distributions is expected. In this study, we compared the environmental characteristics of two possible native regions (southern Africa and the Mediterranean) and the invaded region (America), to determine the more likely origin for the populations established in America. We found that the South African populations of C. citricola occupy environments with similar climatic conditions to those of the American populations, and these similarities are greater than the ones shared with the Mediterranean populations. Therefore, our results support a Southern African, rather than a Mediterranean origin for the populations established in America. In addition, our results also show that populations in America are expanding to environments that differ from those of the native populations. Further studies, assessing intrinsic (e.g. physiological tolerances, plasticity, behavior, reproduction) and extrinsic (physical barriers, predator release) factors could provide further information to disentangle the mechanisms behind this expansion.

Enviromic assembly increases accuracy and reduces costs of the genomic prediction for yield plasticity

Quantitative genetics states that phenotypic variation is a consequence of genetic and environmental factors and their subsequent interaction. Here, we present an enviromic assembly approach, which includes the use of ecophysiology knowledge in shaping environmental relatedness into whole-genome predictions (GP) for plant breeding (referred to as E-GP). We propose that the quality of an environment is defined by the core of environmental typologies (envirotype) and their frequencies, which describe different zones of plant adaptation. From that, we derive markers of environmental similarity cost-effectively. Combined with the traditional genomic sources (e.g., additive and dominance effects), this approach may better represent the putative phenotypic variation across diverse growing conditions (i.e., phenotypic plasticity). Additionally, we couple a genetic algorithm scheme to design optimized multi-environment field trials (MET), combining enviromic assembly and genomic kinships to provide in-silico realizations of the future genotype-environment combinations that must be phenotyped in the field. As a proof-of-concept, we highlight E-GP applications: (1) managing the lack of phenotypic information in training accurate GP models across diverse environments and (2) guiding an early screening for yield plasticity using optimized phenotyping efforts. Our approach was tested using two non-conventional cross-validation schemes to better visualize the benefits of enviromic assembly in sparse experimental networks. Results on tropical maize show that E-GP outperforms benchmark GP in all scenarios and cases tested. We show that for training accurate GP models, the genotype-environment combinations' representativeness is more critical than the MET size. Furthermore, we discuss theoretical backgrounds underlying how the intrinsic envirotype-phenotype covariances within the phenotypic records of (MET) can impact the accuracy of GP and limits the potentialities of predictive breeding approaches. The E-GP is an efficient approach to better use environmental databases to deliver climate-smart solutions, reduce field costs, and anticipate future scenarios.

Impact of environmental changes on the dynamics of temporal networks

Dynamics of complex social systems has often been described in the framework of temporal networks, where links are considered to exist only at the moment of interaction between nodes. Such interaction patterns are not only driven by internal interaction mechanisms, but also affected by environmental changes. To investigate the impact of the environmental changes on the dynamics of temporal networks, we analyze several face-to-face interaction datasets using the multiscale entropy (MSE) method to find that the observed temporal correlations can be categorized according to the environmental similarity of datasets such as classes and break times in schools. By devising and studying a temporal network model considering a periodically changing environment as well as a preferential activation mechanism, we numerically show that our model could successfully reproduce various empirical results by the MSE method in terms of multiscale temporal correlations. Our results demonstrate that the environmental changes can play an important role in shaping the dynamics of temporal networks when the interactions between nodes are influenced by the environment of the systems.