Ecology

Differences in environmental factorsEnvironmental factors showed value differences between forest types, while the significance of differences differed among variables, which were both found with corrected values and original measurements (Fig. 1).Figure 1The differences in environmental factors between naturally regenerating forests (NF) and planted forests (PF) in China. The environmental factors include three annual climatic factors (a–c), three seasonal temperature factors (d–f), three seasonal precipitation factors (g–i), three biotic factors (j–l), and two soil factors (m,n). Three annual climatic factors include mean annual air temperature (MAT, a), mean annual precipitation (MAP, b), and aridity index (AI, c) defined as the ratio of MAP to annual potential evapotranspiration. Three seasonal temperature factors include the temperature of the warmest month (Tw, d), the temperature of the coldest month (Tc, e), temperature annual range (TR, f). Three seasonal precipitation factors include precipitation of the wettest month (Pw, g), precipitation of the driest month (Pd, h), and precipitation seasonality (Ps, i) defined as the standard deviation of monthly precipitation during the measuring year. Three biological factors include the mean annual leaf area index (LAI, j), the maximum leaf area index (MLAI, k), and stand age (SA, l). Two soil factors include soil organic carbon content (SOC, m) and soil total nitrogen content (STN, n). The differences are tested for each variable with one-way analysis of variance (ANOVA), where * and ** indicate significant differences between forest types at significance levels of α = 0.05 and α = 0.01, respectively. The corrected values are mean values during 2003–2019 after correcting the original measurements with the interannual trend (See methods), which are listed in each panel, while original measurements are mean values during the measuring period of each ecosystem, which are not shown in each panel.Full size imageFor annual climatic factors, the significant difference between NF and PF only appeared in MAT (Fig. 1a). The mean MAT of NF was 10.50 ± 7.81 °C, which was significantly lower than that of PF (15.65 ± 6.23 °C) (p  0.05) (Fig. 2c). Even considering the significant effects of MAT on ER, ANCOVA results obtained by fixing MAT as a covariant also suggested that ER values did not significantly differ between forest types (F = 0.01, p  > 0.05). Fixing other variables as a covariant also drew a similar result.Therefore, NF showed a lower NEP resulting from the lower GPP than PF, while their differences were not statistically significant (Fig. 2).Differences in NEP latitudinal patternsCarbon fluxes showed divergent latitudinal patterns between NF and PF, while their latitudinal patterns varied among carbon fluxes, which were both found with corrected values and original measurements (Fig. 3).Figure 3The latitudinal patterns of carbon fluxes over Chinese naturally regenerating forests (NF) and planted forests (PF). The carbon fluxes include net ecosystem productivity (NEP, a,b), gross primary productivity (GPP, c,d), and ecosystem respiration (ER, e,f). Each panel is drawn with the corrected values (blue points) and original measurements (grey points), respectively. The blue and black lines represent the regression lines calculated from the corrected values and original measurements, respectively, with their regression statistics listed in blue and black letters. Only the regression slope (Sl) and R2 of each regression are listed. The grey lines represent the regressions between carbon fluxes added by random errors and latitude. Only significant (p  0.05).The ER of NF showed a significant decreasing latitudinal pattern (Fig. 3e), while that of PF exhibited no significant latitudinal pattern (Fig. 3f). The increasing latitude caused the ER of NF to significantly decrease. Each unit increase in latitude led to a 28.71 gC m−2 year−1 decrease in ER, with an R2 of 0.31. However, the increasing latitude contributed little to the ER spatial variation of PF (p  > 0.05).In addition, the latitudinal patterns of carbon fluxes and their differences between forest types were also obtained with the original measurements (Fig. 3, grey points). The latitudinal patterns of random error adding carbon fluxes were comparable to those of our corrected carbon fluxes (Fig. 3), which confirmed that the latitudinal patterns of carbon fluxes and their differences between forest types would not be affected by the uncertainties in generating the corrected carbon fluxes.Therefore, among NFs, the similar decreasing latitudinal patterns of GPP and ER meant that NEP showed no significant latitudinal pattern, while the significant decreasing latitudinal pattern of GPP and no significant latitudinal pattern of ER caused NEP to show a decreasing latitudinal pattern among PFs.Differences in the environmental effects on NEP spatial variationsEnvironmental factors, including the annual climatic factors, seasonal temperature factors, seasonal precipitation factors, biological factors, and soil factors, exerted divergent effects on the spatial variations of NEP and its components, which also differed between forest types (Table 1). No factor was found to affect that the spatial variation of NEP among NFs, while most annual and seasonal climatic factors were found to affect that among PFs. The spatial variations of GPP and ER among NFs were both affected by most annual and seasonal climatic factors and LAI, while those among PFs were primarily shaped by most annual and seasonal climatic factors. Though LAI showed no significant effect on GPP and ER spatial variations among PFs, SA exerted a significant negative effect. In addition, the spatial variations of soil variables contributed little to the spatial variations of carbon fluxes. Therefore, among NFs, most annual and seasonal climatic factors and LAI were found to affect GPP and ER spatial variations, while no factor was found to significantly influent the NEP spatial variation. However, among PFs, most annual and seasonal climatic factors were found to affect the spatial variations of NEP and its components, while LAI showed no significant effect. Using the original measurements also generated the similar correlation coefficients (Supplementary Table S1).Table 1 Correlation coefficients between carbon fluxes and environmental factors in naturally regenerating forests (NF) and planted forests (PF).Full size tableGiven the high correlations among annual climatic factors and seasonal climatic factors (Supplementary Table S2), the partial correlation analysis was applied to determine which factors should be employed to reveal the mechanisms underlying the spatial variations of NEP. Partial correlation analysis showed that MAT and MAP exerted the most important roles in spatial variations of NEP and its components (Table 2). After controlling MAT (or MAP), other factors seldom showed significant correlation with carbon fluxes, especially fixing MAT (Table 2). In addition, MAT and MAP exerted similar effects on the spatial variations of NEP and its components (Table 1). Using the original measurements also generated the similar partial correlation coefficients (Supplementary Table S3). Therefore, we only presented the effects of MAT on carbon flux spatial variations and their differences between forest types in detail.Table 2 Partial correlation coefficients between carbon fluxes and environmental factors in naturally regenerating forests (NF) and planted forests (PF) with fixing mean annual air temperature (MAT) or mean annual precipitation (MAP).Full size tableThe increasing MAT increased carbon fluxes, while the increasing rates differed between forest types (Fig. 4). The increasing MAT contributed little to the NEP spatial variation of NF but raised the NEP of PF (Fig. 4a,b). Each unit increase in MAT caused the NEP of PF to increase at a rate of 27.77 gC m−2 year−1, with an R2 of 0.31 (Fig. 4b). The increasing MAT significantly raised GPP in NF and PF (Fig. 4c,d). For NF, each unit increase in MAT increased GPP at a rate of 43.76 gC m−2 year−1, with an R2 of 0.49 (Fig. 4c), while each unit increase in MAT increased the GPP of PF at a rate of 69.18 gC m−2 year−1, with an R2 of 0.57 (Fig. 4d). The GPP increasing rates did not significantly differ between NF and PF (F = 1.52, p  > 0.05). The increasing MAT also raised ER in both NF and PF (Fig. 4e,f), whose increasing rates were 38.97 gC m−2 year−1 (Fig. 4e) and 36.79 gC m−2 year−1 (Fig. 4f), respectively, while their differences were not statistically significant (F = 0.01, p  > 0.05). In addition, using the original measurements also generated the similar spatial variations and their differences between forest types (Fig. 4). Furthermore, the random error adding carbon fluxes responded similarly to those of our correcting carbon fluxes (Fig. 4), indicating that the effects of MAT on carbon fluxes would not be affected by the uncertainties in our correcting carbon fluxes. Therefore, the similar responses of GPP and ER to MAT made MAT contribute little to NEP spatial variations among NFs, while GPP and ER showed divergent response rates to MAT, which made NEP increase with MAT among PFs.Figure 4The effects of mean annual air temperature (MAT) on the spatial variations of carbon fluxes over Chinese naturally regenerating forests (NF) and planted forests (PF). The carbon fluxes include net ecosystem productivity (NEP, a,b), gross primary productivity (GPP, c,d), and ecosystem respiration (ER, e,f). Each panel is drawn with the corrected values (blue points) and original measurements (grey points), respectively. The blue and black lines represent the regression lines calculated from the corrected values and original measurements, respectively, with their regression statistics listed in blue and black letters. Only the regression slope (Sl) and R2 of each regression are listed. The grey lines represent the regressions between carbon fluxes added by random errors and latitude. Only significant (p  More

Alpha diversity differences among communitiesNematode gut microbiomes were assigned into their respective species categories of E. antarcticus and P. murrayi based on 18S host data that was consistent with morphology (see Methods “Microinvertebrate haplotypes”). In contrast, due to recovery of three undiscernible 18S tardigrade haplotypes, the gut microbiomes were assigned to Tardigrada. Mat bacterial communities were significantly (Tukey’s HSD, P  0.65, χ2(1)  0.38, χ2(3)  More

As humans warm the planet, biodiversity is plummeting. These two global crises are connected in multiple ways. But the details of the intricate feedback loops between biodiversity decline and climate change are astonishingly under-studied.It is well known that climate extremes such as droughts and heatwaves can have devastating impacts on ecosystems and, in turn, that degraded ecosystems have a reduced capacity to protect humanity against the social and physical impacts of such events. Yet only a few such relationships have been probed in detail. Even less well known is whether biodiversity-depleted ecosystems will also have a negative effect on climate, provoking or exacerbating weather extremes.For us, a group of researchers living and working mainly in Central Europe, the wake-up call was the sequence of heatwaves of 2018, 2019 and 2022. It felt unreal to watch a floodplain forest suffer drought stress in Leipzig, Germany. Across Germany, more than 380,000 hectares of trees have now been damaged (see go.nature.com/3etrrnp; in German), and the forestry sector is struggling with how to plan restoration activities over the coming decades1. What could have protected these ecosystems against such extremes? And how will the resultant damage further impact our climate?
Nature-based solutions can help cool the planet — if we act now
In June 2021, the Intergovernmental Panel on Climate Change (IPCC) and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) published their first joint report2, acknowledging the need for more collaborative work between these two domains. And some good policy moves are afoot: the new EU Forest Strategy for 2030, released in July 2021, and other high-level policy initiatives by the European Commission, formally recognize the multifunctional value of forests, including their role in regulating atmospheric processes and climate. But much more remains to be done.To thoroughly quantify the risk that lies ahead, ecologists, climate scientists, remote-sensing experts, modellers and data scientists need to work together. The upcoming meeting of the United Nations Convention on Biological Diversity in Montreal, Canada, in December is a good opportunity to catalyse such collaboration.Buffers and responsesWhen lamenting the decline in biodiversity, most people think first about the tragedy of species driven to extinction. There are more subtle changes under way, too.For instance, a study across Germany showed that over the past century, most plant species have declined in cover, with only a few increasing in abundance3. Also affected is species functionality4 — genetic diversity, and the diversity of form and structure that can make communities more or less efficient at taking up nutrients, resisting heat or surviving pathogen attacks.When entire ecosystems are transformed, their functionality is often degraded. They are left with less capacity to absorb pollution, store carbon dioxide, soak up water, regulate temperature and support vital functions for other organisms, including humans5. Conversely, higher levels of functional biodiversity increase the odds of an ecosystem coping with unexpected events, including climate extremes. This is known as the insurance effect6.The effect is well documented in field experiments and modelling studies. And there is mounting evidence of it in ecosystem responses to natural events. A global synthesis of various drought conditions showed, for instance, that forests were more resilient when trees with a greater diversity of strategies for using and transporting water lived together7.

Dead trees near Iserlohn, Germany, in April 2020 (left) and after felling in June 2021 (right).Credit: Ina Fassbender/AFP via Getty

However, biodiversity cannot protect all ecosystems against all kinds of impacts. In a study this year across plots in the United States and Canada, for example, mortality was shown to be higher in diverse forest ecosystems8. The proposed explanation for this unexpected result was that greater biodiversity could also foster more competition for resources. When extreme events induce stress, resources can become scarce in areas with high biomass and competition can suddenly drive mortality, overwhelming the benefits of cohabitation. Whether or not higher biodiversity protects an ecosystem from an extreme is highly site-specific.Some plants respond to drought by reducing photosynthesis and transpiration immediately; others can maintain business as usual for much longer, stabilizing the response of the ecosystem as a whole. So the exact response of ecosystems to extremes depends on interactions between the type of event, plant strategies, vegetation composition and structure.Which plant strategies will prevail is hard to predict and highly dependent on the duration and severity of the climatic extreme, and on previous extremes9. Researchers cannot fully explain why some forests, tree species or individual plants survive in certain regions hit by extreme climate conditions, whereas entire stands disappear elsewhere10. One study of beech trees in Germany showed that survival chances had a genomic basis11, yet it is not clear whether the genetic variability present in forests will be sufficient to cope with future conditions.And it can take years for ecosystem impacts to play out. The effects of the two consecutive hot drought years, 2018 and 2019, were an eye-opener for many of us. In Leipzig, tree growth declined, pathogens proliferated and ash and maple trees died. The double blow, interrupted by a mild winter, on top of the long-term loss of soil moisture, led to trees dying at 4–20 times the usual rate throughout Germany, depending on the species (see go.nature.com/3etrrnp; in German). The devastation peaked in 2020.Ecosystem changes can also affect atmospheric conditions and climate. Notably, land-use change can alter the brightness (albedo) of the planet’s surface and its capacity for heat exchange. But there are more-complex mechanisms of influence.Vegetation can be a source or sink for atmospheric substances. A study published in 2020 showed that vegetation under stress is less capable of removing ozone than are unstressed plants, leading to higher levels of air pollution12. Pollen and other biogenic particles emitted from certain plants can induce the freezing of supercooled cloud droplets, allowing ice in clouds to form at much warmer temperatures13, with consequences for rainfall14. Changes to species composition and stress can alter the dynamics of these particle emissions. Plant stress also modifies the emission of biogenic volatile organic gases, which can form secondary particles. Wildfires — enhanced by drought and monocultures — affect clouds, weather and climate through the emission of greenhouse gases and smoke particles. Satellite data show that afforestation can boost the formation of low-level, cooling cloud cover15 by enhancing the supply of water to the atmosphere.Research prioritiesAn important question is whether there is a feedback loop: will more intense, and more frequent, extremes accelerate the degradation and homogenization of ecosystems, which then, in turn, promote further climate extremes? So far, we don’t know.One reason for this lack of knowledge is that research has so far been selective: most studies have focused on the impacts of droughts and heatwaves on ecosystems. Relatively little is known about the impacts of other kinds of extremes, such as a ‘false spring’ caused by an early-season bout of warm weather, a late spring frost, heavy rainfall events, ozone maxima, or exposure to high levels of solar radiation during dry, cloudless weather.Researchers have no overview, much less a global catalogue, of how each dimension of biodiversity interacts with the full breadth of climate extremes in different combinations and at multiple scales. In an ideal world, scientists would know, for example, how the variation in canopy density, vegetation age, and species diversity protects against storm damage; and whether and how the diversity of canopy structures controls atmospheric processes such as cloud formation in the wake of extremes. Researchers need to link spatiotemporal patterns of biodiversity with the responses of ecosystem processes to climate extremes.
Biodiversity needs every tool in the box: use OECMs
Creating such a catalogue is a huge challenge, particularly given the more frequent occurrence of extremes with little or no precedent16. Scientists will also need to account for the increasing likelihood of pile-ups of climate stressors. The ways in which ecosystems respond to compound events17 could be quite different. Researchers will have to study which facets of biodiversity (genetic, physiological, structural) are required to stabilize ecosystems and their functions against these onslaughts.There is at least one piece of good news: tools for data collection and analysis are improving fast, with huge advances over the past decade in satellite-based observations for both climate and biodiversity monitoring. The European Copernicus Earth-observation programme, for example — which includes the Sentinel 1 and 2 satellite fleet, and other recently launched missions that cover the most important wavelengths of the electromagnetic spectrum — offer metre-scale resolution observations of the biochemical status of plants and canopy structure. Atmospheric states are recorded in unprecedented detail, vertically and in time.Scientists must now make these data interoperable and integrate them with in situ observations. The latter is challenging. On the ground, a new generation of data are being collected by researchers and by citizen scientists18. For example, unique insights into plant responses to stress are coming from time-lapse photography of leaf orientation; accelerometer measures of movement patterns of stems have been shown to provide proxies for the drought stress of trees19.High-quality models are needed to turn these data into predictions. The development of functional ‘digital twins’ of the climate system is now in reach. These models replicate hydrometeorological processes at the metre scale, and are fast enough to allow for rapid scenario development and testing20. The analogous models for ecosystems are still in a more conceptual phase. Artificial-intelligence methods will be key here, to study links between climate extremes and biodiversity.Researchers can no longer afford to track global transformations of the Earth system in disciplinary silos. Instead, ecologists and climate scientists need to establish a joint agenda, so that humanity is properly forewarned: of the risks of removing biodiversity buffers against climate extremes, and of the risk of thereby amplifying these extremes. More

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The structure and microstructure of the fibresThe surfaces of the natural fibres are presented from Figs. 6, 7, 8, 9, 10 and of the synthetic fibres are presented in Figs. 11 and 12.Figure 6SEM of jute fibre [Fot.M.Kurpińska].Full size imageFigure 7SEM of bamboo fibre [Fot.M.Kurpińska].Full size imageFigure 8SEM of sisal fibre [Fot.M.Kurpińska].Full size imageFigure 9SEM of cotton fibre [Fot.M.Kurpińska].Full size imageFigure 10SEM of ramie fibre [Fot.M.Kurpińska].Full size imageFigure 11SEM of polymer fibre [Fot.M.Kurpińska].Full size imageFigure 12SEM of polypropylene (PP) fibre [Fot.M.Kurpińska].Full size imageThe basic components of natural fibres influencing their properties are cellulose, hemicellulose, lignin, waxes, oils, and pectin. Cellulose is mainly composed of three elements such as carbon, hydrogen, and oxygen, and it is the material basis that forms the cell wall natural fibre. Typically, cellulose remains in the form of micro-fibrils within the cell wall of a plant. Cellulose is the main factor affecting the tensile strength along natural fibre and the cellulose content is closely related to the plant’s age and content decreases with the increasing age of the plant6.Hemicellulose is an amorphous substance offering a low degree of polymerization and it exists between fibres. Hemicellulose is a complex polysaccharide with xylan as the predominant chain, and the branches mainly include 4-O-methyl-D-glucuronic acid, L-arabinose, and D-xylose. Lignin is a kind of polymer with complex structures and of many types. The basic units of lignin include: guaiacyl, syringyl monomers, and p-hydroxyphenyl monomers. The structural units in lignin are mainly connected by ether bonds and carbon–carbon single bonds. Usually, lignin is not evenly distributed in the plant fibre wall9.In addition to three main components, lignin often contains various sugars, fats, protein substances, and a small amount of ash elements. These chemical compositions affect not only the properties of natural fibres, but also the possibility of a specific application of fibre. The composition of individual natural fibres and their properties are presented in Table 1. Figure 6a–c shows longitudinal and cross-sectional views of the untreated jute fibre. Externally, the fibre is smooth and shiny. The presence of hemicellulose influences the high hygroscopicity of jute fibres. The structure of the jute fibre shows that the fibre swells when it absorbs water. Possible swelling of the fibre in the cross-section by approx. 30%. The microscope scans of indicate the succinylated regions. This is due to the chemical bonding of the succinic anhydride molecule with the hydroxyl group of the cellulose present in the fibre. The encircled region in the top side shows an unsuccinylated region with naturally waxy impurities16.Figure 7a shows the scanning electron micrograph (SEM) of the bamboo fibre. According to the SEM analysis, the microstructure of bamboo is anisotropic. At the Fig. 7b–c it can be recognized that the orientation of cellulose fibrils was placed almost along the fibre axis which may affect to maximize the modulus of elasticity. Factors affect the mechanical properties of bamboo fibres are the chemical composition and structure of bamboo fibres, moisture content, age of bamboo, etc. In addition, the age of the plant affects the chemical composition and structure of fibre. These factors and the natural humidity influence their change of mechanical properties. The hemicellulose content directly influences the tensile strength. This parameter increases with the decrease in the hemicellulose content in the bamboo fibre18.The cell structure of bamboo fibres is complex, and the middle layer of the cell wall has a multi-layer structure. The lignification of the thin and thick layers in the multilayer structure varies. The multi-layered cell wall structure leads to better fracture resistance and promotes internal sliding between the cell wall layers during tension. The angle of the microfiber alignment is also an important factor influencing the mechanical properties of the fibre. Typically, the tensile strength and modulus of elasticity of a fibre increase as the angle between the interposition of the microfibers decreases. Hence, the smaller microfibril angle is an important factor that contributes to the good mechanical properties of bamboo fibre. Large voids between bamboo fibre molecules can be seen, which impact good hygroscopicity19. The moisture content is an important factor affecting the mechanical properties of bamboo fibres. Figure 8a–c shows the morphology of the sisal fibre. The surface of the sisal fibre has higher roughness, and it increases the bonding area between the fibre and cement paste. This leads to increase the mechanical properties of the composites38.Figure 9a–c shows images of the cotton fibres. At the microscope image, a cotton fibre looks like a twisted ribbon or a collapsed and twisted tube. These twists are called convolutions: there are about 60 convolutions per centimetre. The weaves give the cotton an uneven surface of the fibres, which increases the friction between the fibres, but at the same time they can prevent fibres from evenly dispersing in the cement matrix. The outer layer, the cuticle is a thin film of mostly fats and waxes. Figure 9b shows the waxy layer surface with some smooth grooves. The waxy layer forms a thin sheet over the primary wall that forms grooves on the cotton surface19. The cotton fibre surface comprises non-cellulosic materials and amorphous cellulose in which the fibrils are arranged in a criss-cross pattern. Owing to the non-structured orientation of cellulose and non-cellulosic materials, the wall surface is unorganized and open. This gives flexibility to the fibre. The basic ingredients, responsible for the complicated interconnections in the primary wall, are cellulose, hemicelluloses, pectin, proteins, and ions. In the core of fibre, only the crystalline cellulose is present, what is highly ordered and has a compact structure with the cellulose fibrils lying parallel to one another18.SEM micrograph of the surface and cross section of the ramie fibre are shown at Fig. 10a–c. The surface of the ramie fibres is dense but porous. There are many micropores and continuous bubbles in the porous structure of a single bundle of a ramie fibre Fig. 10c. This structure has some effect for low absorption of water, moreover, it is also related to the fibre distribution in the cement composites. In case of the short ramie fibre, due to its random distribution in composites, the strength of the composite may be affected. Cellulose, lignin, and hemicellulose weight materials can form a dense layer on the surface of the ramie fibres, so the water absorptivity is low. This special structure of the fibre with a dense matrix, and at the same time, with a characteristic pore arrangement has an influence on the adhesion of the cement matrix and the strength of the cement composite18.The surface and cross section of multifilament macrofibre is demonstrated at Fig. 11a–c. From the chemical point of view, this type of fibres belongs to the polymers from the group of polyolefins, composed of units of the formula: –[CH2CH (CH3)]–. They are obtained by low-pressure polymerization of propylene. They are made of 100% pure co-polymer twisted bundles of multifilament fibres Fig. 11c. Polypropylene is one of two most commonly used plastics, in addition to polyethylene. Polypropylene is a hydrocarbon thermoplastic polymer2.Figure 12a–c shows the structure of a bundle of polypropylene (PP) fibres in the form of a 3D mesh. They are made of isotactic polypropylene, called propylene, CH2=CHCH3 obtained from crude oil. They are one of the finest polypropylene fibres. The surface of the fibres is smooth Fig. 12b 2.The consistency—fluidityThe results of fluidity are shown at Fig. 13. The fluidity of the composite not modified with fibres is 145 mm and is a reference to other test results. The use of bamboo fibres increased the composite fluidity and composite flow by 8.6% (157.5 mm). The use of polymer fibers and jute increased the consistency by about 7%, while the use of sisal fibres by 3%. The use of PP fibres (122.5 mm) had the greatest impact on the loss of consistency by 15.5%. The use of cotton and frame fibres resulted in a reduction of workability and consistency by 13.8% and 3.5%, respectively.Figure 13Results of fluidity test.Full size imageBased on the research results, it was found that in the case of using bamboo fibres characterizing a high absorption of 120–145%, the consistency of composite increased by 8.2% compared to the consistency of composite without fibres. In the case of a change in consistency, the chemical composition of natural fibres, their surface, and the total length in the volume of composite are significant, too. There is a noticeable regularity related to the cellulose content in natural fibres. If the higher cellulose content, it reduces the consistency of the composite. For example, the cellulose content in bamboo fibres is the lowest and amounts to 40–45%, while the cellulose content in cotton fibres is the highest, ranging from 80 to 94%. It can also be recognized that consistency and workability will be influenced by the hemicellulose content.The higher the hemicellulose content, it impacts the higher consistency of the composite. It is similar referring to the content of lignin. It was noticed that the higher the lignin content, the higher the composite consistency was found. Regarding the total length of the fibres, a regularity is apparent that the greater the total length of fibres, e.g., in the case of cotton fibres, the greater decrease in consistency is visible. In the case of polymer and polypropylene (PP) fibres, the consistency is influenced by the surface of the fibre, the number of fibres, and their total length in the volume of the composite. Increasing the total length of PP fibres by approx. 15% resulted in a reduction of the consistency of approx. 20%.Flexural and compressive strengthAssigning mechanical properties of fibre reinforced composite, particular emphasis was placed on the determination of the flexural strength of the composite. This parameter was appointed by the 3-point test. Figure 14. shows the flexural strength of plain composite and 7 groups of different fibre reinforced composites on the 2nd, 7th, 28th, and 56th days.Figure 14Flexural strength test results.Full size imageIt can be seen that the bending strength of composites with the addition of natural fibres, ramie, bamboo, jute, and sisal are similar. The bending strength of composites with PP and polymer fibres is lower. It should be noted that the strength of the cotton fibre-reinforced composite is much lower than that of all the others tested. The reason may be the low tensile strength of the cotton fibres used. When mixing the composites, a tendency to create conglomerates of cotton fibres was also noticed, which may affect the strength of the composites.The test results clearly show that the effectiveness of the added natural fibres depends on the chemical composition and mechanical properties, and above all, on their adhesion to the cement matrix. The adhesion of the natural fibre to the cement matrix has a significant influence on the mechanical properties of the cement composite, in particular on compression and bending strength. The highest bending strength was achieved by cement composites modified with ramie fibres. Ramie fibres are characterized by the highest tensile strength among the tested synthetic and natural fibres, ranging from 400 to 1000 MPa. The results of the compressive strength are shown in Fig. 15.Figure 15Compressive strength test results.Full size imageThe analysis of the test results shows that the use of dispersed fibres reduced the early compressive strength after 2 days from 8.5 to 33%. The exception is the ramie fibres, the use of which increased the early strength by 6.6%. Within 28 days, as in the case of early strength, the use of all types of synthetic and natural fibres resulted in a decrease in strength from 4.6 to 26.5%. The exception is the use of ramie fibres, which increased the compressive strength by 7.2% after 28 days. After 56 days, a decrease in strength was noticed in the case of using PP and polymer synthetic fibres as well as natural cotton and bamboo from 5.5 to 11.9%.On the other hand, the increase in compressive strength after 56 days from 5.8 to 16.4% was visible in the case of using fibres such as sisal, jute and ramie. The highest compressive strength was achieved by the composite with a ramie fibre. The fibre of the ramie is characterized by the highest modulus of elasticity ranging from 24.5 to 128 GPa and is over 100% higher than the Young’s modulus of the other fibres.Shrinkage testFigure 16A shows that the samples after demolding showed expansion for about 2 days, and from the third day after demolding, the length of the samples was shortened. The lowest degree of expansion in the first days was shown by samples without fibres and samples containing cotton fibres. In this case, the expansion did not exceed 0.02 mm/m. However, the same samples finally showed the highest shrinkage after 180 days, which was 0.06 mm/m.Figure 16Testing the change in length of samples.Full size imageThe highest expansion within 48 h after deformation was shown by samples containing sisal fibres, while these samples finally after 180 days showed the lowest deformation of the length of the samples, which was 0.001 mm/m. The samples containing the synthetic fibres showed an expansion of about 0.02–0.03 mm/m in 48 h and the final shrinkage after 180 days was 0.03 mm/m for both the polymer and PP fibre samples. The bamboo and ramie fibres initially showed an expansion of 0.04–0.06 mm/m while their final shrinkage was 0.02 mm/m. The samples with jute fibres showed an expansion of 0.04 mm/m and the final shrinkage of the samples was 0.04 mm/m. Figure 16a,b shows the results of testing the change in length of samples over time.After 180 days, the total deformation of the samples was determined. Samples containing sisal fibers showed a slight expansion of about 0.001 mm/m, while the highest deformation (shrinkage) was shown for composite samples without fibers and with cotton fibres, which was 0.06 mm/m. Samples with bamboo, jute, PP, polymer and ramie fibres showed a shrinkage from 0.02 to 0.04 mm/m. Only the samples containing the sisal fibre showed a slight expansion of 0.001 mm/m.Ultimately, the samples containing sisal fibres were characterized by the lowest deformability. This phenomenon is related to the fibre structure and the total length of the fibres in a sample with dimensions of 40 × 40 × 160mm. For example, in a sample containing sisal fibres, their total length is 5856.7 m. Otherwise, a sample containing jute fibres, their total length in the sample is only 7.4 m. Therefore it was found that the fibre structure, its diameter, the cellulose content and the total length of the fibres in the element are important factors of deformation as a result of shrinkage or expansion of the fibre reinforced composite.Water absorption of composite testHigher water absorption (8.5%) compared to the composite without fibres was noticed in the case of using both synthetic fibres and with the exception of the use of ramie fibres, which caused a slight reduction in water absorption to 8.2%. It can be recognized that the water absorption rate of the 8 groups of samples is slightly different, the highest is the polymer fibre-reinforced composite (9.2%); the lowest water absorption rate refers to ramie fibre-reinforced composite (8.2%). The difference in water absorption rates is presented at Fig. 17.Figure 17Water absorption of composite (%).Full size imageExcept for cotton fibre-reinforced composite, the water absorption rate of another plant fibre-reinforced composite is lower than that of synthetic fibre-reinforced composite. Probably because of the fact that ramie, sisal, and jute fibres all have good moisture absorption and release properties. It is commonly known that plant fibre-reinforced cement-based materials have reduced strength and initial properties due to their performance degradation in a humid environment, so their long-term durability could become problematic. Sisal fibres (with noticed absorption of 95–100%) have absorbed more cement slurry on their surface than jute fibres (absorption of fibre 7–12%). This phenomenon could be explained by the fact that the slurry became the impregnation of the fibre. The absorbability of the composite was tested after the composite had completely hardened. Probably a fibre that is characterized by high absorption—sisal is very well “embedded” in the matrix, therefore the bending strength results for composites with sisal fibre were higher by 8–10%. More

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