L'Institut Agro

The capacity of fishes to cope with environmental variation is considered to be a main determinant of their fitness and is partly determined by their stress physiology. By 2100, global ocean temperature is expected to rise by 1-4°C, with potential consequences for stress physiology. Global warming is affecting animal populations worldwide through chronic temperature increases and an increase in the frequency of extreme heatwave events. As ectotherms, fishes are expected to be particularly vulnerable to global warming. Although little information is available about the effects of global warming on stress physiology in nature, multiple studies describe the consequences of temperature increases on stress physiology in controlled laboratory conditions, providing insight into what can be expected in the wild. Chronic temperature increase constitutes a physiological load that can alter the ability of fishes to cope with additional stressors, which might compromise their fitness. In addition, rapid temperature increases are known to induce acute stress responses in fishes and might be of ecological relevance in particular situations. This review summarizes knowledge about effects of temperature increases on the stress physiology of fishes and discusses these in the context of global warming.

Soils are essential for supporting food production and providing ecosystem services but are under pressure due to population growth, higher food demand, and land use competition. Because of the effort to ensure the sustainable use of soil resources, demand for current, updatable soil information capable of supporting decisions across scales is increasing. Digital soil mapping (DSM) addresses the drawbacks of conventional soil mapping and has been increasingly used for delivering soil information in a time- and cost-efficient manner with higher spatial resolution, better map accuracy, and quantified uncertainty estimates. We reviewed 244 articles published between January 2003 and July 2021 and then summarised the progress in broad-scale (spatial extent >10,000 km2) DSM, focusing on the 12 mandatory soil properties for GlobalSoilMap. We observed that DSM publications continued to increase exponentially; however, the majority (74.6%) focused on applications rather than methodology development. China, France, Australia, and the United States were the most active countries, and Africa and South America lacked country-based DSM products. Approximately 78% of articles focused on mapping soil organic matter/carbon content and soil organic carbon stocks because of their significant role in food security and climate regulation. Half the articles focused on soil information in topsoil only (<30 cm), and studies on deep soil (100–200 cm) were less represented (21.7%). Relief, organisms, and climate were the three most frequently used environmental covariates in DSM. Nonlinear models (i.e. machine learning) have been increasingly used in DSM for their capacity to manage complex interactions between soil information and environmental covariates. Soil pH was the best predicted soil property (average R2 of 0.60, 0.63, and 0.56 at 0–30, 30–100, and 100–200 cm). Other relatively well-predicted soil properties were clay, silt, sand, soil organic carbon (SOC), soil organic matter (SOM), SOC stocks, and bulk density, and coarse fragments and soil depth were poorly predicted (R2 < 0.28). In addition, decreasing model performance with deeper depth intervals was found for most soil properties. Further research should pursue rescuing legacy data, sampling new data guided by well-designed sampling schemas, collecting representative environmental covariates, improving the performance and interpretability of advanced spatial predictive models, relating performance indicators such as accuracy and precision to cost-benefit and risk assessment analysis for improving decision support; moving from static DSM to dynamic DSM; and providing high-quality, fine-resolution digital soil maps to address global challenges related to soil resources.

Data generated by analytical instruments, such as spectrometers, may contain unwanted variation due to measurement mode, sample state and other external physical, chemical and environmental factors. Preprocessing is required so that the property of interest can be predicted correctly. Different correction methods may remove specific types of artefacts while still leaving some effects behind. Using multiple preprocessing in a complementary way can remove the artefacts that would be left behind by using only one technique. This article summarizes the recent developments in new data preprocessing strategies and specifically reviews the emerging ensemble approaches to preprocessing fusion in chemometrics. A demonstration case is also presented. In summary, ensemble preprocessing allows the selection of several techniques and their combinations that, in a complementary way, lead to improved models. Ensemble approaches are not limited to spectral data but can be used in all cases where preprocessing is needed and identification of a single best option is not easily done.

Local biodiversity trends over time are likely to be decoupled from global trends, as local processes may compensate or counteract global change. We analyze 161 long-term biological time series (15-91 years) collected across Europe, using a comprehensive dataset comprising ~6,200 marine, freshwater and terrestrial taxa. We test whether (i) local long-term biodiversity trends are consistent among biogeoregions, realms and taxonomic groups, and (ii) changes in biodiversity correlate with regional climate and local conditions. Our results reveal that local trends of abundance, richness and diversity differ among biogeoregions, realms and taxonomic groups, demonstrating that biodiversity changes at local scale are often complex and cannot be easily generalized. However, we find increases in richness and abundance with increasing temperature and naturalness as well as a clear spatial pattern in changes in community composition (i.e. temporal taxonomic turnover) in most biogeoregions of Northern and Eastern Europe.

Arbuscular mycorrhizal fungi (AMF) establish symbiotic associations with most terrestrial plants. These soil microorganisms enhance the plant’s nutrient uptake by extending the root absorbing area. In return, the symbiont receives plant carbohydrates for the completion of its life cycle. AMF also helps plants to cope with biotic and abiotic stresses such as salinity, drought, extreme temperature, heavy metal, diseases, and pathogens. For abiotic stresses, the mechanisms of adaptation of AMF to these stresses are generally linked to increased hydromineral nutrition, ion selectivity, gene regulation, production of osmolytes, and the synthesis of phytohormones and antioxidants. Regarding the biotic stresses, AMF are involved in pathogen resistance including competition for colonization sites and improvement of the plant’s defense system. Furthermore, AMF have a positive impact on ecosystems. They improve the quality of soil aggregation, drive the structure of plant and bacteria communities, and enhance ecosystem stability. Thus, a plant colonized by AMF will use more of these adaptation mechanisms compared to a plant without mycorrhizae. In this review, we present the contribution of AMF on plant growth and performance in stressed environments.

Abstract As of 2020, the world has an estimated 290 million ha of planted forests and this number is continuously increasing. Of these, 131 million ha are monospecific planted forests under intensive management. Although monospecific planted forests are important in providing timber, they harbor less biodiversity and are potentially more susceptible to disturbances than natural or diverse planted forests. Here, we point out the increasing scientific evidence for increased resilience and ecosystem service provision of functionally and species diverse planted forests (hereafter referred to as diverse planted forests) compared to monospecific ones. Furthermore, we propose five concrete steps to foster the adoption of diverse planted forests: (1) improve awareness of benefits and practical options of diverse planted forests among land‐owners, managers, and investors; (2) incentivize tree species diversity in public funding of afforestation and programs to diversify current maladapted planted forests of low diversity; (3) develop new wood‐based products that can be derived from many different tree species not yet in use; (4) invest in research to assess landscape benefits of diverse planted forests for functional connectivity and resilience to global‐change threats; and (5) improve the evidence base on diverse planted forests, in particular in currently under‐represented regions, where new options could be tested.

Abstract To respect the Paris agreement targeting a limitation of global warming below 2°C by 2100, and possibly below 1.5°C, drastic reductions of greenhouse gas emissions are mandatory but not sufficient. Large‐scale deployment of other climate mitigation strategies is also necessary. Among these, increasing soil organic carbon (SOC) stocks is an important lever because carbon in soils can be stored for long periods and land management options to achieve this already exist and have been widely tested. However, agricultural soils are also an important source of nitrous oxide (N 2 O), a powerful greenhouse gas, and increasing SOC may influence N 2 O emissions, likely causing an increase in many cases, thus tending to offset the climate change benefit from increased SOC storage. Here we review the main agricultural management options for increasing SOC stocks. We evaluate the amount of SOC that can be stored as well as resulting changes in N 2 O emissions to better estimate the climate benefits of these management options. Based on quantitative data obtained from published meta‐analyses and from our current level of understanding, we conclude that the climate mitigation induced by increased SOC storage is generally overestimated if associated N 2 O emissions are not considered but, with the exception of reduced tillage, is never fully offset. Some options (e.g. biochar or non‐pyrogenic C amendment application) may even decrease N 2 O emissions.

Engel, Michael S, Ceríaco, Luis M P, Daniel, Gimo M, Dellapé, Pablo M, Löbl, Ivan, Marinov, Milen, Reis, Roberto E, Young, Mark T, Dubois, Alain, Agarwal, Ishan, Lehmann A., Pablo, Alvarado, Mabel, Alvarez, Nadir, Andreone, Franco, Araujo-Vieira, Katyuscia, Ascher, John S, Baêta, Délio, Baldo, Diego, Bandeira, Suzana A, Barden, Phillip, Barrasso, Diego A, Bendifallah, Leila, Bockmann, Flávio A, Böhme, Wolfgang, Borkent, Art, Brandão, Carlos R F, Busack, Stephen D, Bybee, Seth M, Channing, Alan, Chatzimanolis, Stylianos, Christenhusz, Maarten J M, Crisci, Jorge V, D'elía, Guillermo, Da Costa, Luis M, Davis, Steven R, De Lucena, Carlos Alberto S, Deuve, Thierry, Fernandes Elizalde, Sara, Faivovich, Julián, Farooq, Harith, Ferguson, Adam W, Gippoliti, Spartaco, Gonçalves, Francisco M P, Gonzalez, Victor H, Greenbaum, Eli, Hinojosa-Díaz, Ismael A, Ineich, Ivan, Jiang, Jianping, Kahono, Sih, Kury, Adriano B, Lucinda, Paulo H F, Lynch, John D, Malécot, Valéry, Marques, Mariana P, Marris, John W M, Mckellar, Ryan C, Mendes, Luis F, Nihei, Silvio S, Nishikawa, Kanto, Ohler, Annemarie, Orrico, Victor G D, Ota, Hidetoshi, Paiva, Jorge, Parrinha, Diogo, Pauwels, Olivier S G, Pereyra, Martín O, Pestana, Lueji B, Pinheiro, Paulo D P, Prendini, Lorenzo, Prokop, Jakub, Rasmussen, Claus, Rödel, Mark-Oliver, Rodrigues, Miguel Trefaut, Rodríguez, Sara M, Salatnaya, Hearty, Sampaio, Íris, Sánchez-García, Alba, Shebl, Mohamed A, Santos, Bruna S, Solórzano-Kraemer, Mónica M, Sousa, Ana C A, Stoev, Pavel, Teta, Pablo, Trape, Jean-François, Dos Santos, Carmen Van-Dúnem, Vasudevan, Karthikeyan, Vink, Cor J, Vogel, Gernot, Wagner, Philipp, Wappler, Torsten, Ware, Jessica L, Wedmann, Sonja, Zacharie, Chifundera Kusamba (2021): EDITORIAL The taxonomic impediment: a shortage of taxonomists, not the lack of technical approaches. Zoological Journal of the Linnean Society 193 (2): 381-387, DOI: 10.1093/zoolinnean/zlab072, URL: https://academic.oup.com/zoolinnean/article/193/2/381/6374389

Phenylpropanoids and flavonoids are specialized metabolites frequently reported as involved in plant defense to biotic or abiotic stresses. Their biosynthetic accumulation may be constitutive and/or induced in response to external stimuli. They may participate in plant signaling driving plant defense responses, act as a physical or chemical barrier to prevent invasion, or as a direct toxic weapon against microbial or insect targets. Their protective action is described as the combinatory effect of their localization during the host's interaction with aggressors, their sustained availability, and the predominance of specific compounds or synergy with others. Their biosynthesis and regulation are partly deciphered; however, a lot of gaps in knowledge remain to be filled. Their mode of action on microorganisms and insects probably arises from an interference with important cellular machineries and structures, yet this is not fully understood for all type of pests and pathogens. We present here an overview of advances in the state of the art for both phenylpropanoids and flavonoids with the objective of paving the way for plant breeders looking for natural sources of resistance to improve plant varieties. Examples are provided for all types of microorganisms and insects that are targeted in crop protection. For this purpose, fields of phytopathology, phytochemistry, and human health were explored.

Aquatic ecosystems are under increasing stress from global anthropogenic and natural changes, including climate change, eutrophication, ocean acidification, and pollution. In this critical review, we synthesize research on the microbiota of aquatic vertebrates and discuss the impact of emerging stressors on aquatic microbial communities using two case studies, that of toxic cyanobacteria and microplastics. Most studies to date are focused on host-associated microbiomes of individual organisms, however, few studies take an integrative approach to examine aquatic vertebrate microbiomes by considering both host-associated and free-living microbiota within an ecosystem. We highlight what is known about microbiota in aquatic ecosystems, with a focus on the interface between water, fish, and marine mammals. Though microbiomes in water vary with geography, temperature, depth, and other factors, core microbial functions such as primary production, nitrogen cycling, and nutrient metabolism are often conserved across aquatic environments. We outline knowledge on the composition and function of tissue-specific microbiomes in fish and marine mammals and discuss the environmental factors influencing their structure. The microbiota of aquatic mammals and fish are highly unique to species and a delicate balance between respiratory, skin, and gastrointestinal microbiota exists within the host. In aquatic vertebrates, water conditions and ecological niche are driving factors behind microbial composition and function. We also generate a comprehensive catalog of marine mammal and fish microbial genera, revealing commonalities in composition and function among aquatic species, and discuss the potential use of microbiomes as indicators of health and ecological status of aquatic ecosystems. We also discuss the importance of a focus on the functional relevance of microbial communities in relation to organism physiology and their ability to overcome stressors related to global change. Understanding the dynamic relationship between aquatic microbiota and the animals they colonize is critical for monitoring water quality and population health.

The massive improvement in food production, as a result of effective genetic selection combined with advancements in farming practices, has been one of the greatest achievements of modern agriculture. For instance, the dairy cattle industry has more than doubled milk production over the past five decades, while the total number of cows has been reduced dramatically. This was achieved mainly through the intensification of production systems, direct genetic selection for milk yield and a limited number of related traits, and the use of modern technologies (e.g., artificial insemination and genomic selection). Despite the great betterment in production efficiency, strong drawbacks have occurred along the way. First, across-breed genetic diversity reduced dramatically, with the worldwide use of few common dairy breeds, as well as a substantial reduction in within-breed genetic diversity. Intensive selection for milk yield has also resulted in unfavorable genetic responses for traits related to fertility, health, longevity, and environmental sensitivity. Moving forward, the dairy industry needs to continue refining the current selection indexes and breeding goals to put greater emphasis on traits related to animal welfare, health, longevity, environmental efficiency (e.g., methane emission and feed efficiency), and overall resilience. This needs to be done through the definition of criteria (traits) that (a) represent well the biological mechanisms underlying the respective phenotypes, (b) are heritable, and (c) can be cost-effectively measured in a large number of animals and as early in life as possible. The long-term sustainability of the dairy cattle industry will also require diversification of production systems, with greater investments in the development of genetic resources that are resilient to perturbations occurring in specific farming systems with lesser control over the environment (e.g., organic, agroecological, and pasture-based, mountain-grazing farming systems). The conservation, genetic improvement, and use of local breeds should be integrated into the modern dairy cattle industry and greater care should be taken to avoid further genetic diversity losses in dairy cattle populations. In this review, we acknowledge the genetic progress achieved in high-yielding dairy cattle, closely related to dairy farm intensification, that reaches its limits. We discuss key points that need to be addressed toward the development of a robust and long-term sustainable dairy industry that maximize animal welfare (fundamental needs of individual animals and positive welfare) and productive efficiency, while also minimizing the environmental footprint, inputs required, and sensitivity to external factors.

Projections of climate change impacts on marine ecosystems have revealed long-term declines in global marine animal biomass and unevenly distributed impacts on fisheries. Here we apply an enhanced suite of global marine ecosystem models from the Fisheries and Marine Ecosystem Model Intercomparison Project (Fish-MIP), forced by new-generation Earth system model outputs from Phase 6 of the Coupled Model Intercomparison Project (CMIP6), to provide insights into how projected climate change will affect future ocean ecosystems. Compared with the previous generation CMIP5-forced Fish-MIP ensemble, the new ensemble ecosystem simulations show a greater decline in mean global ocean animal biomass under both strong-mitigation and high-emissions scenarios due to elevated warming, despite greater uncertainty in net primary production in the high-emissions scenario. Regional shifts in the direction of biomass changes highlight the continued and urgent need to reduce uncertainty in the projected responses of marine ecosystems to climate change to help support adaptation planning.

Abstract We present a literature review of the role played by agricultural cooperatives in influencing farm sustainability. We first focus on the theoretical literature to highlight the various economic behaviours of cooperatives. Then we investigate all three dimensions of sustainability in developing and developed countries. We aim at linking the empirical findings to the theoretical understanding of cooperatives, in particular members’ heterogeneity. This paper shows that cooperatives play a non‐negligible role in farm economic sustainability and in the adoption of environmentally friendly practices, suggesting that both public policies and private initiatives in cooperatives may be complementary. As regards social sustainability, there are only a few studies existing on the role of agricultural cooperatives. The trade‐off between economic and environmental sustainability in cooperatives would need to be further investigated.

For a transition from a linear, ‘take-make-dispose’ economy to a sustainable usage of all constituents of renewable resources in cascading and circular pathways, new business models valorising streams that are currently considered as waste are needed. The aim of this article is to understand critical success and risk factors of eco-innovative business models that contribute to a circular economy via agricultural unavoidable waste or by-products valorisation. 39 cases were studied focusing on agricultural side stream conversion into valuable products. Semi-structured interviews were performed and secondary data collected. Cases were analysed according to types of initiatives, main objectives, resources and valorisation pathways, as well as external and internal factors that have influenced the businesses over time. Following success and risk factor categories are identified: (1) technical and logistic, (2) economic, financial and marketing, (3) organisational and spatial, (4) institutional and legal, (5) environmental, social and cultural. Herein, specific factors for the agricultural sector are innovative conversion technologies, flexible in and out logistics, joint investments in R&D, price competitiveness for bio-based products, partnerships with research organisations, space availability, subsidies, agricultural waste management regulations, local stakeholder involvement and acceptance of bio-based production processes. Insights from this study can help farmers and agribusiness managers by defining and adapting their strategies within their local contexts. They also show that for shifting from linear agro-food chains to a circular system, individual businesses need to evolve towards more dynamic and integrated business models, in which the macro-environment sets the boundary conditions for successful operations.

Abstract The priming effect (PE) is a key mechanism contributing to the carbon balance of the soil ecosystem. Almost 100 years of research since its discovery in 1926 have led to a rich body of scientific publications to identify the drivers and mechanisms involved. A few review articles have summarised the acquired knowledge; the last major one was published in 2010. Since then, knowledge on the soil microbial communities involved in PE and in PE + C sequestration mechanisms has been considerably renewed. This article reviews current knowledge on soil PE to state to what extent new insights may improve our ability to understand and predict the evolution of soil C stocks. We propose a framework to unify the different concepts and terms that have emerged from the international scientific community on this topic, report recent discoveries and identify key research needs. Seventy per cent of the studies on the soil PE were published in the last 10 years, illustrating a renewed interest for PE, probably linked to the increased concern about the importance of soil carbon for climate change and food security issues. Among all the drivers and mechanisms proposed along with the different studies to explain PE, some are named differently but actually refer to the same object. This overall introduces ‘artificial’ complexity for the mechanistic understanding of PE, and we propose a common, shared terminology. Despite the remaining knowledge gaps, consistent progress has been achieved to decipher the abiotic mechanisms underlying PE, together with the role of enzymes and the identity of the microbial actors involved. However, including PE into mechanistic models of SOM dynamics remains challenging as long as the mechanisms are not fully understood. In the meantime, empirical alternatives are available that reproduce observations accurately when calibration is robust. Based on the current state of knowledge, we propose different scenarios depicting to what extent PE may impact ecosystem services under climate change conditions. Read the free Plain Language Summary for this article on the Journal blog.

Deep learning (DL) is emerging as a new tool to model spectral data acquired in analytical experiments. Although applications are flourishing, there is also much interest currently observed in the scientific community on the use of DL for spectral data modelling. This paper provides a critical and comprehensive review of the major benefits, and potential pitfalls, of current DL tecnhiques used for spectral data modelling. Although this work focuses on DL for the modelling of near-infrared (NIR) spectral data in chemometric tasks, many of the findings can be expanded to cover other spectral techniques. Finally, empirical guidelines on the best practice for the use of DL for the modelling of spectral data are provided.

Dietary diversity has long been recognized as a key component of diet quality and many dietary diversity indicators (DDIs) have been developed. This systematic scoping review aimed to present a comprehensive inventory of DDIs and summarize evidence linking DDIs and dietary adequacy or health outcomes in adolescents and adults. Two search strategies were developed to identify peer-reviewed articles published in English up until June 2018 and were applied to Medline, Web of Science, and Scopus. A 2-stage screening process was used to select the studies to be reviewed. Four types of DDIs were identified among 161 articles, the majority of them belonging to the food group-based indicator type (n = 106 articles). Fifty studies indicated that DDIs were proxies of nutrient adequacy, but there was a lack of evidence about their relation with nutrients to limit. Associations between DDIs and health outcomes were largely inconsistent among 137 studies, especially when the outcomes studied were body weight (n = 60) and noncommunicable diseases (n = 41). We conclude that the ability of DDIs to reflect diet quality was found to be principally limited to micronutrient adequacy and that DDIs do not readily relate to health outcomes. These findings have implications for studies in low- and lower-middle-income economies where DDIs are often used to assess dietary patterns and overall diet quality.

Simulation-based methods such as approximate Bayesian computation (ABC) are well-adapted to the analysis of complex scenarios of populations and species genetic history. In this context, supervised machine learning (SML) methods provide attractive statistical solutions to conduct efficient inferences about scenario choice and parameter estimation. The Random Forest methodology (RF) is a powerful ensemble of SML algorithms used for classification or regression problems. Random Forest allows conducting inferences at a low computational cost, without preliminary selection of the relevant components of the ABC summary statistics, and bypassing the derivation of ABC tolerance levels. We have implemented a set of RF algorithms to process inferences using simulated data sets generated from an extended version of the population genetic simulator implemented in DIYABC v2.1.0. The resulting computer package, named DIYABC Random Forest v1.0, integrates two functionalities into a user-friendly interface: the simulation under custom evolutionary scenarios of different types of molecular data (microsatellites, DNA sequences or SNPs) and RF treatments including statistical tools to evaluate the power and accuracy of inferences. We illustrate the functionalities of DIYABC Random Forest v1.0 for both scenario choice and parameter estimation through the analysis of pseudo-observed and real data sets corresponding to pool-sequencing and individual-sequencing SNP data sets. Because of the properties inherent to the implemented RF methods and the large feature vector (including various summary statistics and their linear combinations) available for SNP data, DIYABC Random Forest v1.0 can efficiently contribute to the analysis of large SNP data sets to make inferences about complex population genetic histories.

Abstract. Plant transpiration links physiological responses of vegetation to water supply and demand with hydrological, energy, and carbon budgets at the land–atmosphere interface. However, despite being the main land evaporative flux at the global scale, transpiration and its response to environmental drivers are currently not well constrained by observations. Here we introduce the first global compilation of whole-plant transpiration data from sap flow measurements (SAPFLUXNET, https://sapfluxnet.creaf.cat/, last access: 8 June 2021). We harmonized and quality-controlled individual datasets supplied by contributors worldwide in a semi-automatic data workflow implemented in the R programming language. Datasets include sub-daily time series of sap flow and hydrometeorological drivers for one or more growing seasons, as well as metadata on the stand characteristics, plant attributes, and technical details of the measurements. SAPFLUXNET contains 202 globally distributed datasets with sap flow time series for 2714 plants, mostly trees, of 174 species. SAPFLUXNET has a broad bioclimatic coverage, with woodland/shrubland and temperate forest biomes especially well represented (80 % of the datasets). The measurements cover a wide variety of stand structural characteristics and plant sizes. The datasets encompass the period between 1995 and 2018, with 50 % of the datasets being at least 3 years long. Accompanying radiation and vapour pressure deficit data are available for most of the datasets, while on-site soil water content is available for 56 % of the datasets. Many datasets contain data for species that make up 90 % or more of the total stand basal area, allowing the estimation of stand transpiration in diverse ecological settings. SAPFLUXNET adds to existing plant trait datasets, ecosystem flux networks, and remote sensing products to help increase our understanding of plant water use, plant responses to drought, and ecohydrological processes. SAPFLUXNET version 0.1.5 is freely available from the Zenodo repository (https://doi.org/10.5281/zenodo.3971689; Poyatos et al., 2020a). The “sapfluxnetr” R package – designed to access, visualize, and process SAPFLUXNET data – is available from CRAN.

In recent years, multi-modal measurements of process and product properties have become widely popular. Sometimes classical chemometric methods such as principal component analysis (PCA) and partial least squares regression (PLS) are not adequate to analyze this kind of data. In recent years, several multi-block methods have emerged for this purpose; however, their use is largely limited to chemometricians, and non-experts have little experience with such methods. In order to deal with this, the present review provides a brief overview of the multi-block data analysis concept, the various tasks that can be performed with it and the advantages and disadvantages of different techniques. Moreover, basic tasks ranging from multi-block data visualization to advanced innovative applications such as calibration transfer will be briefly highlighted. Finally, a summary of software resources available for multi-block data analysis is provided.