Biological complexity in rapid biostimulant screening across multiple seasons

Biological complexity in rapid biostimulant screening across multiple seasons | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Biological complexity in rapid biostimulant screening across multiple seasons Iida-Maria Rantanen, Janne Isojärvi, Erik Chovancek, Martina Jokel, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6648660/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Microalgae are a diverse group of photosynthetic microorganisms and offer a sustainable source of bioactive compounds for next generation plant biostimulants. In this study we screened the biostimulant effect of 15 aqueous microalgal extracts, obtained by maceration and centrifugation, using Arabidopsis thaliana root growth assays. Due to significant variations in root growth in treated and untreated (control) seedlings, even under controlled conditions, an integrative statistical strategy, incorporating dimensional reduction approaches, was employed to address this complexity. This analysis revealed significant seasonal variations in root growth, likely sensed by endogenous plants mechanisms, despite controlled growth conditions. By employing statistical methods and accounting for seasonal effects, Porphyridium purpureum and Chlorococcum sp. were identified as potent strains, consistently stimulating root growth by 15–45%. To the best of authors' knowledge, no prior studies have explicitly addressed this issue in biostimulant assays. The observed circannual rhythm suggests that results from in vitro assays may vary significantly depending on the season in which they are conducted, and that future studies should carefully consider this factor. These findings provide valuable insights for optimizing rapid root/germination screening assays and guiding extract selection for evaluating biostimulant potential. Microalgae Biostimulant Algal extracts In-vitro assays Seasonal variation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Increasing demand for food, feed, energy, and chemicals as a result of population growth and development necessitates the search for alternative resources. Consequently, agricultural practices are rapidly transitioning towards more sustainable methods, with bio-based products playing a key role in promoting crop growth, soil health, and natural pest control (Velasco-Muñoz et al., 2021; Donate & Frederico, 2019). Plant biostimulants have emerged as an effective innovation to enhance crop resilience against various stressors, stimulate growth and productivity, and enhance nutrient use efficiency (EU, 2019). These biostimulants may include beneficial microorganisms, and a range of natural bioactive compounds, as well as derivatives from both natural and synthetic sources. Among them, microalgae have gained attention as one of the most promising sources of biostimulants. Since the 1960s research has demonstrated that algae can enhance plant nutrition by supplying essential micronutrients and bioactive metabolites that support growth and stress tolerance (Booth, 1969; Dmytryk & Chojnacka, 2018). In addition, algae-based treatments have been reported to improve soil structure and fertility by enriching it with macro- and micronutrients, as well as various bioactive compounds (Sharma et al., 2014; Alvarez et al., 2021). Particular attention has been given to algal derivatives such as phytohormones, hormone-like compounds, poly- and oligosaccharides, and phenolic compounds (Mazhar et al., 2013; Gheda and Ahmed, 2015; Behera et al., 2021; Parmar et al., 2023). The diversity of derivatives from photosynthetic microorganisms is vast, reflecting their extensive natural biodiversity. Although the term microalgae is commonly used in biotechnology to broadly describe photosynthetic microorganisms, including prokaryotic cyanobacteria and various eukaryotic taxa such as green algae, euglenoids, and diatoms, it is important to recognize that cyanobacteria and microalgae are phylogenetically distinct, belonging to separate bacterial and eukaryotic lineages, respectively. For simplicity, we will refer to both microalgae and cyanobacteria as microalgae in this study, unless specified otherwise. Due to their diversity and metabolic flexibility, microalgae are a potent source of high-value metabolites, including proteins, amino acids, enzymes, pigments, polyunsaturated fatty acids, polysaccharides, vitamins, antioxidants, and phytohormones (Parmar et al., 2023). Consequently, microalgae are recognized for their potential to act as growth promoters and to mitigate both biotic and abiotic stress in plants (Ferreira et al., 2023). In vitro assays have been developed to facilitate and accelerate the rapid screening of algal extracts. These methods typically involve germination or root elongation assays performed under sterile conditions (e.g., in Petri dishes) in a controlled environment, where growth parameters can be precisely modulated and monitored. Extracts are added to either solid or liquid medium at varying concentrations to evaluate dose-response effects. These experimental conditions enable rapid screening using plant models, eliminating the influence of soil and other environmental parameters, including microbial competition (Povero et al., 2016). However, while seasonality is widely acknowledged as a significant factor e.g., in seed germination and crop experiments, its influence on in vitro bioassays is often overlooked. Specifically, when in vitro assays are performed over extended periods spanning different seasons, the potential impacts of seasonality remain largely unconsidered, due to the assumption that controlled environmental conditions fully mitigate such effects. Numerous studies have documented the ability of many different organisms to sense seasonal changes, even under constant and highly controlled conditions. Such circannual cycles persist without external time cues across a diverse range of taxa (Helm et al., 2013). In plants, several examples have been reported, including the germination capacity of seeds stored in a dried state, the water-binding capacity of seeds, metabolic and growth changes in duckweed under continuous conditions, and the production of winter shoots in Spirodela (Franceschi et al., 2019, Spruyt et al., 1987, Ziegler, 2024). Additionally, significant seasonal variations in oat growth rates have been observed (Abidin, Z. U. 1956). Current research on Arabidopsis thaliana focuses primarily on circadian rhythms (Cervela-Cardona et al., 2021) as well as the seasonal changes regulating flowering and their underlying endogenous mechanisms (Freytes et al., 2021). Despite these findings, in plants there are relatively few studies focused on rhythms that are sustained under constant environmental conditions, indicating their endogenous nature. In this study we evaluated the biostimulant potential of 15 microalgae strains by conducting Arabidopsis thaliana root growth elongation assays using extracts obtained by maceration (Chovancek et al. 2023). As a result, we identified several microalgal strains exhibiting biostimulant effects. Our findings also demonstrated that A. thaliana root growth varies over time across different seasons. The statistical analysis revealed temporal patterns in root growth, with varying levels of stimulation or inhibition observed throughout the year. To the best of our knowledge, no prior study has explicitly addressed this issue in biostimulant assays, pointing out that A. thaliana root growth showed a circannual rhythm even under controlled growth conditions. This finding suggests that results from in vitro assays may not be fully comparable if performed in different seasons, and highlights the need for future studies to investigate this factor. These insights are valuable for improving the design, interpretation and reproducibility of rapid root/germination screening assays and for guiding extract selection in biostimulant development. Materials and methods 2.1. Growth of microalgal strains and biomass preparation The 15 microalgal species (Table 1) were acquired from the Norwegian Culture Collection of Algae (NORCCA) and the Microbial Domain Biological Resource Centre (HAMBI) and maintained according to the culture collection instructions. Pre-cultures (50 mL) were inoculated in BG11 medium (pH = 7.5) (Stanier et al., 1971), with seawater and vitamins added when required according to strain specific guidelines. Cultures were maintained at room temperature under continuous light (50 μmol m⁻² s⁻¹ photosynthetic photon flux density; PPFD). After four weeks, 2 L of fresh BG11 medium was inoculated with pre-cultures to a final concentration of 0.1 ± 0.05 g/L (n = 3). The experimental cultures were grown at 21°C under 200 μmol photons m⁻² s⁻¹, with a 14/10 h light-dark cycle and continuous bubbling with 1.5% CO₂. Cultures were harvested during the early stationary phase (supplementary information S1) by centrifugation (6000 g, 15 min, 18°C; Beckman, USA). The resulting biomass was freeze-dried (74 Pa, -53°C; Christ, Germany) and stored at -20°C. The two biological replicates of the cultures, replicate 1 (R1) and replicate 2 (R2), were both cultivated during the summer season. Table 1. List of microalgal strains employed in this research. Strains H1-H8 were obtained from HAMBI, while N1-N20 are from NORCCA. Genus/Species Order # Phylum Culture Collection Catalog # Nostoc sp. Nostocales H1 Cyanobacteria UHCC0252 Nostoc sp. H2 UHCC0360 Nostoc sp. H6 UHCC0268 Anabaena variabilis H7 PCC7937 Nodularia sp. H8 HAN37/1 Selenastrum sp. Sphaeropleales N1 Green algae, Chlorophyta K-1877 Scenedesmus sp. N5 NIVA-CHL99 Tetradesmus obliquus N6 NIVA-CHL107 Monoraphidium contortum N7 NIVA-CHL100 Haematococcus lacustris Chlamydomonadales N9 Green algae, Chlorophyta K-0084 Dunaliella tertiolecta N13 NIVA-CHL26 Dunaliella salina N20 K-1830 Chlorococcum sp. Chlorococcales N3 Green algae, Chlorophyta NIVA-CHL131 Apatococcus lobatus Chlorellales N8 Green algae, Chlorophyta NIVA-CHL144/1 Porphyridum purpureum Porphyridiales N12 Red algae, Rhodophyta NIVA-1/92 2.2. Microalgal extract preparation The extract was prepared utilizing a short period of mild thermo-maceration (Chovanček et al. 2024). A stock extract with 15 g L -1 concentration was prepared by weighing 150 mg microalgal biomass, homogenizing it shortly with mortar and pestle, and diluting it to 10 ml of deionized water. The mixture of microalgal biomass and water was shaken (120 rpm) for 3 hours at +30 °C and then centrifuged (6000 g, 10 min, 4 °C). The supernatant was collected and sterile-filtered (0.45 µm) before diluting into working concentrations (0.1 g L -1 , 0.3 g L -1 , and 0.5 g L -1 ) and applying to the agar-based plates. 2.3. A. thaliana root tip elongation assay To assess the biostimulant potential of the microalgal extracts, a root assay (Chovanček et al. 2023) was used. In this method, the seeds of Arabidopsis thaliana (Col-0) were sterilized with chlorine gas and planted on agar plates with ½ MS media (Murashige & Skoog), 1% sucrose, and 0.8% agar. The plates were placed first to +4 °C for 48 h for stratification and then vertically in the growth chamber (120 µmol m -2 s -1 PPFD, 16/8h, 23 °C, ca 40% humidity) for 5 days. After this, 15 seedlings with root lengths between 16-20 mm were transferred to agar plates (½ MS plates + 0.8% agar) with algae extract in different concentrations (0.1 g L -1 , 0.3 g L -1 , and 0.5 g L -1 ) as treatment groups. For the control group, seedlings were transferred to similar plates without any extract. The seedlings were aligned in one line with 0.7 mm spaces in between, and the position of the seedlings' root tip was marked with a permanent marker. The plates were placed back into the growth chamber, and the root tip elongation was measured on the 7th day from RGB images analyzed with ImageJ. The lateral roots were counted on the 5th day from RGB images. For one biological replicate of the microalgae biomass, one A. thaliana root assay with 15 plants was performed. Normalized root stimulation (RS) was calculated by comparing the average primary root tip elongation of the treated plants (RE) to the average primary root tip elongation of the control plants (AEC) as follows: RS = (RE / AEC) * 100%. 2.4. Statistical analysis All the statistical analyses were done with RStudio, R version 4.3.1 (2023-06-16) -- "Beagle Scouts". To assess the statistical significance of normalized root stimulation (p < 0.05), normality of the data was first analyzed with the Shapiro-Wilk test. As the data did not meet the assumptions of normality, non-parametric analysis was conducted using Kruskal-Wallis test followed by Dunn’s post hoc test with Bonferroni correction. This analysis was applied to the normalized root stimulation calculated from a dataset containing root tip elongation results for all plants across different treatment groups (0.1 g L⁻¹, 0.3 g L⁻¹ and 0.5 g L⁻¹) in both replicates (R1 and R2), as well as to a subset where the two replicates were combined for the control group and the 0.5 g L⁻¹ treatment group. Dimensionality reduction-based techniques were applied to represent the data in a lower dimensional space with preserving the key information. Among linear methods, multidimensional scaling (MDS) and principal component approaches were applied. MDS was used to visualize the relationships among samples in a two-dimensional space. For MDS, a dataset containing primary root tip elongation measurements from all plants in the different treatment groups (0.1 g L⁻¹, 0.3 g L⁻¹, and 0.5 g L⁻¹), as well as the control group, with data combined from both replicates (R1 and R2), was used. First, to determine the optimal number of clusters, the Elbow and Silhouette methods were applied using the factoextra package (version 1.0.7). Based on these results, K-means clustering was performed using the base R stats package ( version 4.3.1 ) to assess the distribution of the two biological replicates of microalgae. A distance matrix was calculated using Euclidean distance, and classical MDS (metric MDS) was performed using the cluster (version 2.1.6) and factorextra (version 1.0.7) packages. Ordination plots were generated using the ggpubr package (version 0.6.0) to highlight treatment group clustering. Different clusters were color-coded to facilitate visual interpretation. Principal Component Analysis (PCA) was conducted to extract the most prominent patterns and simplify the dataset, focusing on key factors affecting root tip elongation while minimizing the impact of seasonality and reducing associated noise. The analysis was conducted using the same primary root tip elongation dataset described above, with PCA performed on both the combined dataset (including data from replicates R1 and R2) and separately for each replicate. The analyses were carried out in R using the FactoMineR (version 2.11) and factoextra packages (version 1.0.7). An eigenvalue analysis was used to select the principal components for further analysis based on the proportion of variance explained. Eigenvalues were calculated by squaring the standard deviations from the PCA results. The percentage of variance explained by each component was then computed, and a bar plot was generated to visualize the distribution of variance. Samples were color-coded by treatment group in the PCA plots, with confidence ellipses added to represent the variability within each treatment group to facilitate visual interpretation of clustering and group separation. To detect seasonal variation in primary root tip elongation and the number of lateral roots, a time series analysis was performed. The analysis of primary root tip elongation was conducted using the same primary root tip elongation dataset described above, while a separate dataset, containing the number of lateral roots across the same treatment groups, was used for the analysis of lateral root development. Both datasets were analyzed using the ggplot2 package (version 3.5.1) to create boxplots over time (month), with each treatment group color-coded, and LOESS smoothing curves fitted to each treatment to highlight temporal trends. Rectangular background shading and annotations were used to visually distinguish the two biological replicates. To highlight temporal dependencies, the autocorrelation function (ACF) was calculated. Results 3.1. Phenotypic variability in A. thaliana root responses between two microalgal biomass replicates We examined the root elongation response to varying doses of aqueous extracts from 15 microalgal strains, using two biological replicates (R1 and R2) per strain. A detailed analysis of primary root elongation showed significantly more pronounced effects (both inhibitory and stimulatory) in replicate 2 (R2) than replicate 1 (R1) (Fig 1). Most strains in R1 showed some degree of primary root stimulation at the applied concentrations of 0.1 and 0.3 g L -1 . Particularly, H2 - Nostoc sp . , N3 - Chlorococcum sp., N5 - Scenedesmus sp., N7 - Monoraphidium contortum , and N20 - Dunaliella salina showed stimulation above 10% in these concentrations. Although three strains (N3 - Chlorococcum sp., N8 - Apatococcus lobatus , and N12 - Porphyridium purpureum ) showed up to 30% stimulation at 0.5 g L -1 , only the effect observed for N12 - Porphyridium purpureum was statistically significant. In contrast, R2 showed statistically significant effects, with strains N3 – Chlorococcum sp. and N7 - Monoraphidium contortum, showing remarkable stimulation across all tested concentrations. The strongest stimulation was observed with strain N3 – Chlorococcum sp. at 0.5 g L -1 and N7- Monoraphidium contortum at 0.1 g L -1 , both above 50% root elongation. In contrast, cyanobacterial strains ( Nostoc sp. -strains, Anabaena variabilis and Nodularia sp.) mostly inhibited root growth, with the inhibitory effects being more statistically pronounced in R2. In R1, the maximum inhibition was 40% with H8 - Nodularia sp., while in R2, the maximum inhibition was 61%, observed with H2 - Nostoc sp. Among the studied strains, three are standing out with repeatedly stimulating effects. N3 – Chlorococcum sp. consistently stimulated the root growth by +20-50% across both replicates and all concentrations, demonstrating a stable and notable effect. N7 - Monoraphidium contortum significantly stimulated root elongation (+50%) in R2, but only moderately in R1 (+10%). N12 - Porphyridium purpureum had a clear stimulating effect on the root growth in R1 but only moderate stimulation in the R2 highest concentration. Furthermore, we observed a dose-dependent root stimulation or inhibition in response to increasing concentration of aqueous microalgal extracts. This pattern was particularly noticeable in strain N5 – Scenedesmus sp., where the highest stimulation in R1 occurred at 0.1 g L -1 (+15%), while in R2 it peaked at 0.3 g L -1 (+15 %). These results suggest that the optimal concentration for this extract likely falls between those concentrations. 3.2. Synchronised performance of A. thaliana root elongation within microalgal extract replicates The root assay results showed consistent trends in stimulatory or inhibitory effects across two independently grown replicates (R1 and R2) of 15 microalgal strains. This consistency led us to further investigate, whether the observed trends were statistically significant. To better understand the patterns in our data, we used multidimensional scaling (MDS) (Fig 2), which enabled us to reconstruct the data topology and define similarity matrices within the root assays, thereby clarifying the underlying trends. Initially, the optimum number of the clusters was determined based on Elbow and Silhouette methods and root tip elongation levels are optimally represented by two clusters (supplementary data S2). Once the optimal number of clusters has been determined, K-means clustering method and MDS were applied to the combined dataset. This dataset included root tip elongation data from the plants treated with different microalgal extracts and their respective controls. After dimensional reduction, the data from two biological replicates showed distinct clustering patterns. Approximately 93% of the R1 samples clustered within Group 2 (yellow), whereas 77% of the R2 samples clustered in Group 1 (blue). This separation suggests a clear difference between R1 and R2, although a slight overlap between clusters likely reflects similar root tip elongation responses in the control plants. 3.3. Seasonal variation patterns of A. thaliana root length under controlled conditions After confirming the clear difference between R1 and R2, to examine if the timing of the root assays (screening period) is correlated with the root growth responses, we applied a time series analysis to the dataset from all plants including controls. Figure 3a shows monthly root tip elongation presented as boxplots grouped by applied extract concentrations to visualize potential seasonal dynamics. The red trend lines illustrate overall trends over time. Analysis of root tip elongation boxplots across all treatments and controls, along with the four trendlines representing each treatment and control group, demonstrates that the root tip elongation was more stable during summer months (July, August), with the trendlines providing the clearest illustration of this pattern. This stability is further reflected in the relatively uniform root tip lengths, with only slight variation across measurements. A minor increase in root tip elongation was observed towards September (Fig 3a). The primary root elongation decreased during autumn and winter (October, November, January). As the season shifted to late winter (February), root tip elongation increased once more (Fig 3a). Fig 3b clearly demonstrates this trend, with a seasonal pattern becoming apparent: the first two images (July and August) show a more stable growth period, while the latter two images (November and February) correspond to periods of either significantly reduced or enhanced root tip elongation in control plants. A seasonal pattern, similar to that observed in primary root elongation, was also evident in lateral root development (Fig 3c), supporting the occurrence of circannual growth in A. thaliana. Minor differences in controlled growth conditions (light, temperature, and humidity) of A. thaliana seeds were observed between R1 (summer) and R2 (autumn/winter), with R2 showing slightly higher temperatures (< 0.75 °C) and slightly lower humidity (< 5%) (supplementary information S3). However, these variations remained within a narrow, stable range and are unlikely to have influenced A. thaliana root growth. Notably, the sealed MS media plates provided a stable microclimate, supporting the conclusion that short-term seasonal spikes in root tip elongation observed in September and February were not correlating by external environmental variations. The root tip elongation assays with microalgal biomass extract from R1 were performed between July and September, whereas the assays with biomass extract from R2 took place during October to February period. This introduces potential seasonal bias into the analysis of biological replicates. However, all microalgal biomass replicates were grown under controlled summer conditions, and harvested at matching growth stages to maximize comparability (supplementary information S1). Therefore, the observed seasonal variation in root elongation is more likely attribute to the A. thaliana plants themselves rather than differences in the microalgal biomass. To assess potential temporal dependencies in the root development data, autocorrelation function (ACF) was calculated for primary root tip elongation and lateral root count (Fig 4). The ACF plot for primary root elongation (Fig 4a) shows statistically significant autocorrelations (p < 0.05) at lags 1 to 5, with values exceeding the 95% confidence interval (dotted horizontal line). The gradual decay in autocorrelation indicates a time-dependent structure in the data, and alternating positive and negative peaks suggests the presence of seasonality. A similar trend was observed for lateral root amount (Fig. 4b), with autocorrelation gradually decaying up to lag 7, although the absence of negative values points to a weaker seasonal effect in lateral root formation compared to primary root elongation. 3.4. Principal component analysis separates A . thaliana root elongation data into distinct clusters To simplify the dataset, reduce noise and identify underlying growth patterns, given the seasonal variability impacting root tip elongation, we performed a Principal Component Analysis (PCA) on both the combined R1 and R2 datasets and the individual replicates, R1 and R2 separately. When grouping the combined data of R1 and R2 based on extract concentration (Fig 5a), the different concentrations of microalgal extract treatments formed distinct clusters at a 95% confidence interval. We focused our analysis on the first two principal components (PCs) based on their eigenvalues (supplementary data S4). In this context, PC1 and PC2 combined explained most of the variation in the dataset (28 %). While the 0.1 g L -1 and 0.3 g L -1 treatments showed overlapping indicating no apparent distinction between the groups based on the principal components. They also partially overlap with the control group, however the cluster associated with the control group remains slightly shifted along PC2 and somewhat distinct suggesting subtle differences. Separate PCAs were performed for each replicate, R1 and R2 (Fig 5b and 5c), revealing both consistent patterns and some discrepancies between the replicates. Following eigenvalue analysis (supplementary data S4), we focused on the first two PCs which explained 26% of the variance in R1, and 30% in R2. A consistent form across both R1 and R2 was noticed as distinct clustering of the 0.5 g L -1 treatment, indicating the same subset pattern also in the level of replicates. Another similarity between the PCAs from the separate replicates is the overlapping clusters formed by 0.1 g L -1 and 0.3 g L -1 treatments, suggesting a similar response pattern at these lower concentrations. However, the replicates differed from each other in the clustering of the control group. In R2, the control group formed a more distinct and clearly separated cluster, whereas in R1, it overlaps partially with the 0.1 g L -1 and 0.3 g L -1 treatment clusters. The key difference between R1 and R2 was the spatial separation of all the clusters. R2 showed greater inter-clustering distances, suggesting a potential treatment effect or a more pronounced distinction of the response profiles. This distinction in R2 might be caused by variations in growth seasons, which could have amplified treatment-related differences and led to more distinct data patterns. This analysis showed that the 0.1 g L -1 and 0.3 g L -1 treatments cannot be analyzed in a statistically significant manner as they could not be reliably distinguished from the control due to overlapping values. However, the 0.5 g L -1 treatment consistently formed a distinct cluster, both in the combined and separate replicate analysis. This clear separation allowed us to pool and further analyze the biological replicates from 0.5 g L -1 treatment even in the presence of seasonal growth variation. 3.5. Despite seasonal variation the screening consistently revealed two promising candidates for future biostimulant assessment As demonstrated by PCA, root tip elongation data from R1 and R2 for the 0.5 g L -1 treatment group can be combined despite seasonal variation. The data pooling allows a larger sample size, and thus provides a more robust and generalized view of the effect of treatments. Figure 6 displays the stimulation and/or inhibition observed in the 0.5 g L -1 treatment group, calculated as previously described. All the cyanobacterial strains belonging to the Nostocales order (strains H1 - Nostoc sp., H2 - Nostoc sp., H6 - Nostoc sp., H7 - Anabaena variabilis and H8 - Nodularia sp.) have a strong plant growth inhibiting effect (from -20% to -40%). Strains in the Sphaeropleales order (N1 - Selenastrum sp., N5 - Scenedesmus sp., N6 - Tetradesmus obliquus and N7 Monoraphidium contortum ) mainly inhibit plant growth (from -12% to -30%), except for one strain, N7 Monoraphidium contortum , which has a slightly stimulating effect (+5%). The strains in Chlamydomonadales order (N9 - Haematococcus lacustris , N13 - Dunaliella tertiolecta and N20 - Dunaliella salina ) have similar effects as the strains in the Sphaeropleales order (from +3% to -30%). There is slight stimulation with strains from Chlorellales (N8 - Apatococcus lobatus , +8%) but the strains that have the strongest plant growth stimulating effect belong to the Porphyridiales (N12 - Porphyridium purpureum with +15%) and Chlorococcales (N3 - Chlorococcum sp. with +45%) orders. Discussion Diversity provides options. The vast diversity of microalgae presents considerable potential for the development of plant biostimulants that support more sustainable agricultural practices (Ronga et al., 2019). To harness this potential efficiently, rapid screening approaches are essential for evaluating the bioactive properties of diverse strains. In this study, we identified Chlorococcum sp. and Porphyridum purpureum as promising strains for biostimulant formulations, as they consistently showed positive results across both independent experiments (R1 and R2 extracts) and at all tested concentrations. While Monoraphidium contortum and Apatococcus lobatus showed promising trends, statistical validation was hindered by seasonal variability. However, more in-depth analysis may reveal their full biostimulant potential. Both of the identified top-performing species have previously been reported to display plant biostimulant properties, supporting their potential observed in this study. For example, Chlorococcum vacuolatum improved tomato germination and root length (Kolman et al., 2024) and a Chlorococcum sp. extract enhances spinach germination (Rupawalla et al., 2022), likely due to high concentrations of cytokinins and gibberellins, hormones known to promote root cell division and elongation (Takahashi et al., 2013; Ubeda-Tomás et al., 2009). Since Chlorococcum sp. (Alhattab et al., 2018) has a relatively resilient cellulose-based cell wall, complete extraction of intracellular compounds is unlikely with the maceration method applied in this study. Unlike harsher methods such as high-pressure homogenization and enzymatic hydrolysis, mild thermo-maceration is a gentle method that avoids mechanical or chemical disruption of microalgae cells. Instead, it primarily yields compounds with high water solubility, such as amino acids, mono- and polysaccharides, phenols, flavonoids, antioxidants and phytohormones (Chovancek et al., 2024; Jamshidi‐Kia et al. 2024), all of which have potential biostimulant activity (Ferreira et al., 2023). Therefore, the observed stimulation may result from water-soluble bioactive molecules, including hormones, and their synergistic effects. Following the positive effects observed for Chlorococcum sp., P. purpureum has likewise been associated with plant growth promotion, including enhanced A. thaliana root tip elongation and increased lettuce yield (Chovancek et al., 2023). Also, extracts from Porphyridium sp. have been reported to improve tomato growth by increasing root and shoot dry weight, and shoot length (Mutale-joan et al., 2020; Rachidi et al., 2020). P. purpureum lacks traditional cell wall structure and is instead encapsulated by a complex and robust polysaccharide capsule, which limits extraction to only easily water-soluble molecules during maceration (Keidan et al., 2009). Among these molecules exopolysaccharides (EPS) are a plausible source of the observed biostimulant effect (Medina-Cabrera et al., 2021). Algal polysaccharides have been reported to support root growth and enhance photosynthesis (Chanda et al., 2019), and are likely present in macerated extracts due to their solubility in warm water (Caetano et al., 2022). Our study also revealed strains that had an inhibiting effect on plant growth. Notably, the strongest inhibition was observed with strains belonging to the Nostocales order. This finding contrasts with previous research, as Nostocales have been reported to function as potential biostimulants (Silambarasan et al., 2021; Santini et al., 2022). Accounting for seasonal variability is essential for rapid biostimulant screening in long-term experiments. Biological replicates across different seasons often show similar trends but varying values, weakening conclusions and complicating large dataset analysis. We addressed this by employing an integrative approach combining multivariate statistical techniques with time series analysis, ultimately revealing an underlying seasonal pattern. This strategy is valuable in long-term studies with multiple independent biological replicates. Seasonal variation of root growth is linked to environmental factors, nutrient availability, life cycle, and presence of plant hormones (Walter et al., 2009; Jung & McCouch, 2013). However, despite maintaining stable growth conditions (light, temperature, and humidity) within a narrow range throughout our assays, we observed noticeable differences related to seasonal variations in untreated A. thaliana roots. These variations are possibly influenced by an internal circannual clock, independent of external environmental factors. This endogenous mechanism has been well documented in Arabidopsis, in other plants species such as Dendrocalamus strictus (Rosb.), Mesembryanthemum nodiflorum L. ( Aizoaceae ) and some orchids species (Rawal, 2003; Gutterman, 2005; Durán-Mendoza et al., 2025); and various organisms (Helm & Lincoln, 2017; Lincoln, 2019) even in the unicellular protist dinoflagellate Gonyaulax tamarensis (Andersen & Keafer, 1987). Despite being well-documented, this mechanism can be easily overlooked in screening approaches conducted in long-term screenings, where statistical tools have shown to be profoundly helpful in deriving relevant data from complex datasets. These results suggest that minimizing seasonal effects requires careful consideration in experimental design, prioritizing experiments during the natural growing season. Additionally, performing all replicates within the same season will mitigate these pronounced seasonal variations, as growing seasons follow a distinct spatiotemporal pattern. Beyond the observed seasonal pattern, the extended storage time of R2 microalgal biomass, is also an important factor to consider, as it might be potentially contributing to the observed outcomes. Storage of Chlorella vulgaris biomass for over a year has been shown to enhance root promotion in mung bean ( Vigna radiata ) due to cell wall degradation and the increased release of bioactive compounds (Stirk et al., 2021). In our study, the freeze-dried biomass R1 was stored for 14 to 16 months, while R2 was stored for a longer period of 15 and 19 months. It is possible that similar degradation processes contributed to the stronger effects seen with R2 extracts. Data analysis tools facilitate seasonality assessment. Exploratory data analysis (EDA) is considered an important preliminary step before formal statistical modeling or hypothesis testing, as it helps uncover patterns, relationships, and potential anomalies within datasets. PCA and MDS, universal tools for EDA, effectively evaluate biological replicates. Replicates coded with the same colors should cluster together along the same dimensions, indicating consistency and reproducibility. Furthermore, time series analysis methods such as ACF plots are important for detecting temporal patterns, including seasonality. Our results highlight the usefulness of ACF plots in identifying seasonal effects, suggesting that similar analyses could reveal hidden seasonal trends in other long-term datasets as well. Together, these EDA tools enhance the reliability and interpretation of complex biological experiments. Conclusion This study demonstrated that aqueous microalgal extracts from Chlorococcum sp. (N3) and P. purpureum (N12) at a concentration of 0.5 g L⁻¹ significantly enhanced root elongation in A. thaliana , while also revealing significant seasonal variations most possibly sensed by plants via endogenous mechanisms even under controlled growth conditions. By acknowledging and addressing this variability, it is possible to improve the robustness and reproducibility of fast screening root/germination assays. Future studies could benefit from applying time series analysis with the autocorrelation function (AFC) when long-term experiments require extended timelines, as this method provides a valuable tool for mitigating seasonal effects and enhancing data reliability. Declarations The authors have no competing interests to declare that are relevant to the content of this article. Funding This research was financially supported by the European Union Horizon Europe IA project REALM (grant agreement # 101060991). Availability of data and material The data that support the findings of this study are available from the corresponding author upon reasonable request. Authors' contributions: I.-M.R. performed experiments, curated and analyzed data, and drafted the manuscript. J.I. supervised and validated the bioinformatics analysis; E.C. provided methodological support for screening experiments; M.J. contributed to writing and editing the manuscript; S.S. conceptualized and supervised the research; Y.A. conceived and supervised the research and acquired funding. All authors contributed to the discussion and revision of the manuscript draft. S.S. and Y.A. finalized the paper with input from all authors. All authors read and approved of the published version of the manuscript. Acknowledgements The authors would like to thank the Norwegian Culture Collection of Algae (NORCCA) at the NIVA (Norway) and the HAMBI Culture Collection at the University of Helsinki (Finland) for providing the algal strains. We also thank Pragya Karmacharya and Joni Vuorinen for their assistance in preparing the microalgal cultivations. References Abidin, Z. ul. (1956). Über die Beeinflussung der Samenkeimung durch den Temperaturwechsel. Zeitschrift Für Botanik , 44 , 207–220. 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Supplementary Files IMR2025Supplementaryinformation.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 24 Jul, 2025 Reviews received at journal 01 Jun, 2025 Reviewers agreed at journal 23 May, 2025 Reviewers agreed at journal 16 May, 2025 Reviewers invited by journal 16 May, 2025 Editor assigned by journal 14 May, 2025 Submission checks completed at journal 14 May, 2025 First submitted to journal 12 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6648660","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":457841046,"identity":"3cd3efa0-2bee-40d7-9e5b-1d6a4642ec2f","order_by":0,"name":"Iida-Maria Rantanen","email":"","orcid":"","institution":"University of Turku","correspondingAuthor":false,"prefix":"","firstName":"Iida-Maria","middleName":"","lastName":"Rantanen","suffix":""},{"id":457841047,"identity":"1155e234-6853-4615-b0bf-f24f8764ec34","order_by":1,"name":"Janne Isojärvi","email":"","orcid":"","institution":"Turku University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Janne","middleName":"","lastName":"Isojärvi","suffix":""},{"id":457841048,"identity":"327ba1b5-6ec3-4e63-ba63-d557d39d3510","order_by":2,"name":"Erik Chovancek","email":"","orcid":"","institution":"University of Turku","correspondingAuthor":false,"prefix":"","firstName":"Erik","middleName":"","lastName":"Chovancek","suffix":""},{"id":457841049,"identity":"3943d1c0-e1f2-4275-9dc6-a9718b2f2913","order_by":3,"name":"Martina Jokel","email":"","orcid":"","institution":"University of Turku","correspondingAuthor":false,"prefix":"","firstName":"Martina","middleName":"","lastName":"Jokel","suffix":""},{"id":457841050,"identity":"d3901577-3fd6-4ab2-9629-bd25356c1ae1","order_by":4,"name":"Sema Sirin","email":"","orcid":"","institution":"University of Turku","correspondingAuthor":false,"prefix":"","firstName":"Sema","middleName":"","lastName":"Sirin","suffix":""},{"id":457841051,"identity":"79e88410-9789-4ddf-a226-f0fda5f352dc","order_by":5,"name":"Yagut Allahverdiyeva","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqklEQVRIiWNgGAWjYDACduYGZiAlB+ZUGBCjhZkRrMWYgQ1IniFFS2IDWAsxOvibGRs/F7bZpG+432PAcKCACC0ShxmbpWe2peVuOMYD1EKUww4zNkjznDkM1sL8gRgt8kBbfvOc+Z9uQLQtBocZ26R5Kg4kEK/FEKjFmqci2XDmsbSCA0RpkTvefPg2j4GdPN/hwxsfHPhDhBYUcIBUDaNgFIyCUTAKcAAAVjI0v87dPd0AAAAASUVORK5CYII=","orcid":"","institution":"University of Turku","correspondingAuthor":true,"prefix":"","firstName":"Yagut","middleName":"","lastName":"Allahverdiyeva","suffix":""}],"badges":[],"createdAt":"2025-05-12 16:53:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6648660/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6648660/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83171808,"identity":"207ffefd-aa13-4832-adce-4bee2fd6b7e7","added_by":"auto","created_at":"2025-05-20 17:50:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":284318,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect of various microalgal extracts on \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eA. thaliana\u003c/strong\u003e\u003c/em\u003e \u003cstrong\u003eroots. \u003c/strong\u003e\u0026nbsp;\u003cem\u003eA. thaliana\u003c/em\u003e root growth response was tested using 15 aqueous microalgal extracts, prepared from two independent cultivations and applied at three concentrations (0.1 g L\u003csup\u003e-1\u003c/sup\u003e, 0.3 g L\u003csup\u003e-1\u003c/sup\u003e and 0.5 g L\u003csup\u003e-1\u003c/sup\u003e). The normalization was carried out by comparing the root tip elongation of the plants treated with microalgal extract to that of control plants and the results were expressed as a percentage. The asterisks indicate the statistical significance after Kruskal-Wallis test and Dunn’s test with Bonferroni adjustment (* = p-value \u0026lt; 0,05; ** = p-value \u0026lt; 0,01; *** = p-value \u0026lt; 0,001).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6648660/v1/c36fa6e7d30787185b8fc37c.png"},{"id":83171806,"identity":"07781462-39a2-4af2-9973-65e777adda7b","added_by":"auto","created_at":"2025-05-20 17:50:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":20870,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe multidimensional scaling (MDS) plot of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eA. thaliana\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e root tip elongation data\u003c/strong\u003e. MDS was performed with K-means clustering on a dataset containing plants treated with different microalgal extracts and their respective controls. The optimal number of clusters was determined to be two, based on the Elbow and Silhouette methods. Most samples from R1 fall into the group 2 (yellow) while most samples from R2 fall into the group 1 (blue).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6648660/v1/17dbfb982dc988bbbc7e14a8.png"},{"id":83172523,"identity":"36a76513-0fb7-4035-81d0-a761fb3e8760","added_by":"auto","created_at":"2025-05-20 17:58:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":767983,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSeasonal dynamics of primary root elongation and amount of lateral roots of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eA. thaliana\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e in response to extracts. (a)\u003c/strong\u003e Boxplot illustrating seasonal variation in primary root tip elongation (mm) across treatments Treatment concentrations are color-coded (green shades), with the control in light yellow. LOESS curves (red) show temporal trends. The vertical shaded areas indicate the two replicates (R1 with blue and R2 with purple). \u003cstrong\u003e(b) \u003c/strong\u003eRGB images of \u003cem\u003eA. thaliana\u003c/em\u003e control seedlings at day 7. Images of seedlings taken in November and February reflect differences observed over time compared to those taken in July and August. Scale bar = 20 mm. \u003cstrong\u003e(c)\u003c/strong\u003e Boxplot showing seasonal variation in amount of lateral roots across treatments. The interpretation of the figure follows the same approach as the time series analysis from primary root tip elongation.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6648660/v1/04c6556d130d629d6d228a6b.png"},{"id":83171811,"identity":"f8bba8a6-3698-44a1-a51f-3a2c91ff1404","added_by":"auto","created_at":"2025-05-20 17:50:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":41469,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAutocorrelation function (ACF) plot of (a) primary root tip elongation over time and (b) amount of lateral roots over time.\u003c/strong\u003e The x-axis represents the time lags (number of time steps between observations), and the y-axis shows the autocorrelation values. Significant correlations are indicated by values outside the 95% confidence interval (dashed horizontal line). Both plots reveal a gradual decay in autocorrelation, suggesting a dependence over time in the data. Potential seasonal pattern indicated by alternating positive and negative values is stronger in the plot containing data from primary roots.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6648660/v1/c13c69a9dd7a9678904f1b81.png"},{"id":83171814,"identity":"674113dc-6bc3-473c-8abb-748831391923","added_by":"auto","created_at":"2025-05-20 17:50:13","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":157809,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrincipal Component Analysis (PCA) of root tip elongation. PCA performed using (a) the combined dataset from both replicates, and (b) the dataset from R1, and (c) R2 separately.\u003c/strong\u003e Samples are grouped by extract concentration, and ellipses represent 95% confidence intervals for each group. When combining data from both replicates (a) the clusters corresponding to 0.1 g L⁻¹ (red dots) and 0.3 g L⁻¹ (green triangles) showed partial overlap with each other and with the control group (purple crosses), while the 0.5 g L⁻¹ (blue squares) cluster appeared more distinct. A similar clustering pattern was observed in the separate analyses of replicate 1 (b) and replicate 2 (c).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6648660/v1/feeb8a471e82b561e91215eb.png"},{"id":83171815,"identity":"40c779b0-3522-4f72-82b7-ef47aa7c46b8","added_by":"auto","created_at":"2025-05-20 17:50:13","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":99788,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffects of 0.5 g L\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e-1 \u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eextract treatment on \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e. \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ethaliana\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e roots based on combined data from both replicates.\u003c/strong\u003e The normalized effect in root growth is calculated by comparing the root tip elongation of the plants treated with microalgal extract to control plants and reported as percentage. Colours of the bars indicate the order of algae strains. PCA supports the feasibility of data merging, which helps mitigate seasonal effects. The asterisks indicate the statistical significance after Kruskal-Wallis test and Dunn’s test with Bonferroni adjustment (* = p-value \u0026lt; 0,05; ** = p-value \u0026lt; 0,01; *** = p-value \u0026lt; 0,001; ns = not significant)\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6648660/v1/2f6bd4b788b354eb3b9140d0.png"},{"id":83173224,"identity":"95690785-2db0-4af6-8775-85fc0c73d14f","added_by":"auto","created_at":"2025-05-20 18:14:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2550702,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6648660/v1/061eb5a2-a4c5-4451-859a-4e2990669bcd.pdf"},{"id":83172836,"identity":"5c2bba87-7f9b-496e-8020-553d1add9c85","added_by":"auto","created_at":"2025-05-20 18:06:13","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":305443,"visible":true,"origin":"","legend":"","description":"","filename":"IMR2025Supplementaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-6648660/v1/737e8038acbb7bfb0adb9029.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Biological complexity in rapid biostimulant screening across multiple seasons ","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIncreasing demand for food, feed, energy, and chemicals as a result of population growth and development necessitates the search for alternative resources. Consequently, agricultural practices are rapidly transitioning towards more sustainable methods, with bio-based products playing a key role in promoting crop growth, soil health, and natural pest control (Velasco-Mu\u0026ntilde;oz et al., 2021;\u0026nbsp;Donate \u0026amp; Frederico, 2019).\u003c/p\u003e\n\u003cp\u003ePlant biostimulants have emerged as an effective innovation to enhance crop resilience against various stressors, stimulate growth and productivity, and enhance nutrient use efficiency (EU, 2019). These biostimulants may include beneficial microorganisms, and a range of natural bioactive compounds, as well as derivatives from both natural and synthetic sources. Among them, microalgae have gained attention as one of the most promising sources of biostimulants.\u003c/p\u003e\n\u003cp\u003eSince the 1960s research has demonstrated that algae can enhance plant nutrition by supplying essential micronutrients and bioactive metabolites that support growth and stress tolerance (Booth, 1969; Dmytryk \u0026amp; Chojnacka, 2018). In addition, algae-based treatments have been reported to improve soil structure and fertility by enriching it with macro- and micronutrients, as well as various bioactive compounds (Sharma et al., 2014;\u0026nbsp;Alvarez et al., 2021). Particular attention has been given to algal derivatives such as phytohormones, hormone-like compounds, poly- and oligosaccharides, and phenolic compounds (Mazhar et al., 2013; Gheda and Ahmed, 2015; Behera et al., 2021; Parmar et al., 2023).\u003c/p\u003e\n\u003cp\u003eThe diversity of derivatives from photosynthetic microorganisms is vast, reflecting their extensive natural biodiversity. Although the term microalgae is commonly used in biotechnology to broadly describe photosynthetic microorganisms, including prokaryotic cyanobacteria and various eukaryotic taxa such as green algae, euglenoids, and diatoms, it is important to recognize that cyanobacteria and microalgae are phylogenetically distinct, belonging to separate bacterial and eukaryotic lineages, respectively. For simplicity, we will refer to both microalgae and cyanobacteria as \u003cem\u003emicroalgae\u003c/em\u003e in this study, unless specified otherwise. Due to their diversity and metabolic flexibility, microalgae are a potent source of high-value metabolites, including proteins, amino acids, enzymes, pigments, polyunsaturated fatty acids, polysaccharides, vitamins, antioxidants, and phytohormones (Parmar et al., 2023). Consequently, microalgae are recognized for their potential to act as growth promoters and to mitigate both biotic and abiotic stress in plants (Ferreira et al., 2023).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eIn vitro\u003c/em\u003e assays have been developed to facilitate and accelerate the rapid screening of algal extracts. These methods typically involve germination or root elongation assays performed under sterile conditions (e.g., in Petri dishes) in a controlled environment, where growth parameters can be precisely modulated and monitored. Extracts are added to either solid or liquid medium at varying concentrations to evaluate dose-response effects. These experimental conditions enable rapid screening using plant models, eliminating the influence of soil and other environmental parameters, including microbial competition (Povero et al., 2016). However, while seasonality is widely acknowledged as a significant factor e.g., in seed germination and crop experiments, its influence on \u003cem\u003ein vitro\u003c/em\u003e bioassays is often overlooked. Specifically, when \u003cem\u003ein vitro\u003c/em\u003e assays are performed over extended periods spanning different seasons, the potential impacts of seasonality remain largely unconsidered, due to the assumption that controlled environmental conditions fully mitigate such effects. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNumerous studies have documented the ability of many different organisms to sense seasonal changes, even under constant and highly controlled conditions. Such circannual cycles persist without external time cues across a diverse range of taxa (Helm et al., 2013). In plants, several examples have been reported, including the germination capacity of seeds stored in a dried state, the water-binding capacity of seeds, metabolic and growth changes in duckweed under continuous conditions, and the production of winter shoots in \u003cem\u003eSpirodela\u0026nbsp;\u003c/em\u003e(Franceschi et al., 2019, Spruyt et al., 1987, Ziegler, 2024). Additionally, significant seasonal variations in oat growth rates have been observed (Abidin, Z. U. 1956). Current research on \u003cem\u003eArabidopsis thaliana\u003c/em\u003e focuses primarily on circadian rhythms (Cervela-Cardona et al., 2021) as well as the seasonal changes regulating flowering and their underlying endogenous mechanisms (Freytes et al., 2021). Despite these findings, in plants there are relatively few studies focused on rhythms that are sustained under constant environmental conditions, indicating their endogenous nature.\u003c/p\u003e\n\u003cp\u003eIn this study we evaluated the biostimulant potential of 15 microalgae strains by conducting \u003cem\u003eArabidopsis thaliana\u0026nbsp;\u003c/em\u003eroot growth elongation assays using extracts obtained by maceration (Chovancek et al. 2023). As a result, we identified several microalgal strains exhibiting biostimulant effects. Our findings also demonstrated that \u003cem\u003eA. thaliana\u003c/em\u003e root growth varies over time across different seasons. The statistical analysis revealed temporal patterns in root growth, with varying levels of stimulation or inhibition observed throughout the year. To the best of our knowledge, no prior study has explicitly addressed this issue in biostimulant assays, pointing out that \u003cem\u003eA. thaliana\u003c/em\u003e root growth showed a circannual rhythm even under controlled growth conditions. This finding suggests that results from \u003cem\u003ein vitro\u003c/em\u003e assays may not be fully comparable if performed in different seasons, and highlights the need for future studies to investigate this factor. These insights are valuable for improving the design, interpretation and reproducibility of rapid root/germination screening assays and for guiding extract selection in biostimulant development.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003e2.1. Growth of microalgal strains and biomass preparation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 15 microalgal species (Table 1) were acquired from the Norwegian Culture Collection of Algae (NORCCA) and the Microbial Domain Biological Resource Centre (HAMBI) and maintained according to the culture collection instructions. Pre-cultures (50 mL) were inoculated in BG11 medium (pH = 7.5) (Stanier et al., 1971), with seawater and vitamins added when required according to strain specific guidelines. Cultures were maintained at room temperature under continuous light (50 \u0026mu;mol m⁻\u0026sup2; s⁻\u0026sup1; photosynthetic photon flux density; PPFD). After four weeks, 2 L of fresh BG11 medium was inoculated with pre-cultures to a final concentration of 0.1 \u0026plusmn; 0.05 g/L (n = 3). The experimental cultures were grown at 21\u0026deg;C under 200 \u0026mu;mol photons m⁻\u0026sup2; s⁻\u0026sup1;, with a 14/10 h light-dark cycle and continuous bubbling with 1.5% CO₂. Cultures were harvested during the early stationary phase (supplementary information S1) by centrifugation (6000 g, 15 min, 18\u0026deg;C; Beckman, USA). The resulting biomass was freeze-dried (74 Pa, -53\u0026deg;C; Christ, Germany) and stored at -20\u0026deg;C. The two biological replicates of the cultures, replicate 1 (R1) and replicate 2 (R2), were both cultivated during the summer season.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eList of microalgal strains employed in this research. Strains H1-H8 were obtained from HAMBI, while N1-N20 are from NORCCA.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"643\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cstrong\u003eGenus/Species\u0026nbsp;\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cstrong\u003eOrder\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cstrong\u003e#\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cstrong\u003ePhylum\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cstrong\u003eCulture Collection Catalog #\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cem\u003eNostoc\u0026nbsp;\u003c/em\u003esp.\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"5\"\u003eNostocales\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eH1\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"5\"\u003eCyanobacteria\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eUHCC0252\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cem\u003eNostoc\u0026nbsp;\u003c/em\u003esp.\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eH2\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eUHCC0360\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cem\u003eNostoc\u0026nbsp;\u003c/em\u003esp.\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eH6\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eUHCC0268\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cem\u003eAnabaena variabilis\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eH7\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003ePCC7937\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cem\u003eNodularia\u0026nbsp;\u003c/em\u003esp.\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eH8\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eHAN37/1\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cem\u003eSelenastrum\u0026nbsp;\u003c/em\u003esp.\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"4\"\u003eSphaeropleales\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eN1\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"4\"\u003eGreen algae, Chlorophyta\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eK-1877\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cem\u003eScenedesmus\u0026nbsp;\u003c/em\u003esp.\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eN5\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eNIVA-CHL99\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cem\u003eTetradesmus obliquus\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eN6\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eNIVA-CHL107\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cem\u003eMonoraphidium contortum\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eN7\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eNIVA-CHL100\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cem\u003eHaematococcus lacustris\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003eChlamydomonadales\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eN9\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003eGreen algae, Chlorophyta\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eK-0084\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cem\u003eDunaliella tertiolecta\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eN13\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eNIVA-CHL26\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cem\u003eDunaliella salina\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eN20\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eK-1830\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cem\u003eChlorococcum\u0026nbsp;\u003c/em\u003esp.\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eChlorococcales\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eN3\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eGreen algae, Chlorophyta\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eNIVA-CHL131\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cem\u003eApatococcus lobatus\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eChlorellales\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eN8\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eGreen algae, Chlorophyta\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eNIVA-CHL144/1\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cem\u003ePorphyridum purpureum\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003ePorphyridiales\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eN12\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003eRed algae, Rhodophyta\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eNIVA-1/92\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e2.2. Microalgal extract preparation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe extract was prepared utilizing a short period of mild thermo-maceration (Chovanček et al. 2024). A stock extract with 15 g L\u003csup\u003e-1\u003c/sup\u003e concentration was prepared by weighing 150 mg microalgal biomass, homogenizing it shortly with mortar and pestle, and diluting it to 10 ml of deionized water. The mixture of microalgal biomass and water was shaken (120 rpm) for 3 hours at +30 \u0026deg;C and then centrifuged (6000 g, 10 min, 4 \u0026deg;C). The supernatant was collected and sterile-filtered (0.45 \u0026micro;m) before diluting into working concentrations (0.1 g L\u003csup\u003e-1\u003c/sup\u003e, 0.3 g L\u003csup\u003e-1\u003c/sup\u003e, and 0.5 g L\u003csup\u003e-1\u003c/sup\u003e) and applying to the agar-based plates.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e2.3. \u003cem\u003eA. thaliana\u0026nbsp;\u003c/em\u003eroot tip elongation assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the biostimulant potential of the microalgal extracts, a root assay (Chovanček et al. 2023) was used. In this method, the seeds of \u003cem\u003eArabidopsis thaliana\u003c/em\u003e (Col-0) were sterilized with chlorine gas and planted on agar plates with \u0026frac12; MS media (Murashige \u0026amp; Skoog), 1% sucrose, and 0.8% agar. The plates were placed first to +4 \u0026deg;C for 48 h for stratification and then vertically in the growth chamber (120 \u0026micro;mol\u0026thinsp;m\u003csup\u003e-2\u003c/sup\u003e\u0026thinsp;s\u003csup\u003e-1\u003c/sup\u003e PPFD, 16/8h, 23 \u0026deg;C, ca 40% humidity) for 5 days. After this, 15 seedlings with root lengths between 16-20 mm were transferred to agar plates (\u0026frac12; MS plates + 0.8% agar) with algae extract in different concentrations (0.1 g L\u003csup\u003e-1\u003c/sup\u003e, 0.3 g L\u003csup\u003e-1\u003c/sup\u003e, and 0.5 g L\u003csup\u003e-1\u003c/sup\u003e) as treatment groups. For the control group, seedlings were transferred to similar plates without any extract. The seedlings were aligned in one line with 0.7 mm spaces in between, and the position of the seedlings\u0026apos; root tip was marked with a permanent marker. The plates were placed back into the growth chamber, and the root tip elongation was measured on the 7th day from RGB images analyzed with ImageJ. The lateral roots were counted on the 5th day from RGB images. For one biological replicate of the microalgae biomass, one \u003cem\u003eA. thaliana\u0026nbsp;\u003c/em\u003eroot assay with 15 plants was performed. Normalized root stimulation (RS) was calculated by comparing the average primary root tip elongation of the treated plants (RE) to the average primary root tip elongation of the control plants (AEC) as follows:\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;RS = (RE / AEC) * 100%.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e2.4. Statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the statistical analyses were done with RStudio, R version 4.3.1 (2023-06-16) -- \u0026quot;Beagle Scouts\u0026quot;.\u0026nbsp;To assess the statistical significance of normalized root stimulation (p \u0026lt; 0.05), normality of the data was first analyzed with the Shapiro-Wilk test. As the data did not meet the assumptions of normality, non-parametric analysis was conducted using Kruskal-Wallis test followed by Dunn\u0026rsquo;s post hoc test with Bonferroni correction. This analysis was applied to the normalized root stimulation calculated from a dataset containing root tip elongation results for all plants across different treatment groups (0.1 g L⁻\u0026sup1;, 0.3 g L⁻\u0026sup1; and 0.5 g L⁻\u0026sup1;) in both replicates (R1 and R2), as well as to a subset where the two replicates were combined for the control group and the 0.5 g L⁻\u0026sup1; treatment group.\u003c/p\u003e\n\u003cp\u003eDimensionality reduction-based techniques were applied to represent the data in a lower dimensional space with preserving the key information. Among linear methods, multidimensional scaling (MDS) and principal component approaches were applied. MDS was used to visualize the relationships among samples in a two-dimensional space. For MDS, a dataset containing primary root tip elongation measurements from all plants in the different treatment groups (0.1 g L⁻\u0026sup1;, 0.3 g L⁻\u0026sup1;, and 0.5 g L⁻\u0026sup1;), as well as the control group, with data combined from both replicates (R1 and R2), was used. First, to determine the optimal number of clusters, the Elbow and Silhouette methods were applied using the factoextra package (version 1.0.7). Based on these results, K-means clustering was performed using the base R stats package ( version 4.3.1 ) to assess the distribution of the two biological replicates of microalgae. A distance matrix was calculated using Euclidean distance, and classical MDS (metric MDS) was performed using the cluster (version 2.1.6) and factorextra\u0026nbsp; (version 1.0.7) packages. Ordination plots were generated using the ggpubr package (version 0.6.0) to highlight treatment group clustering. Different clusters were color-coded to facilitate visual interpretation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Principal Component Analysis (PCA) was conducted to extract the most prominent patterns and simplify the dataset, focusing on key factors affecting root tip elongation while minimizing the impact of seasonality and reducing associated noise. The analysis was conducted using the same primary root tip elongation dataset described above, with PCA performed on both the combined dataset (including data from replicates R1 and R2) and separately for each replicate. The analyses were carried out in R using the FactoMineR (version 2.11) and factoextra packages (version 1.0.7). An eigenvalue analysis was used to select the principal components for further analysis based on the proportion of variance explained. Eigenvalues were calculated by squaring the standard deviations from the PCA results. The percentage of variance explained by each component was then computed, and a bar plot was generated to visualize the distribution of variance. Samples were color-coded by treatment group in the PCA plots, with confidence ellipses added to represent the variability within each treatment group to facilitate visual interpretation of clustering and group separation.\u003c/p\u003e\n\u003cp\u003eTo detect seasonal variation in primary root tip elongation and the number of lateral roots, a time series analysis was performed. The analysis of primary root tip elongation was conducted using the same primary root tip elongation dataset described above, while a separate dataset, containing the number of lateral roots across the same treatment groups, was used for the analysis of lateral root development. Both datasets were analyzed using the ggplot2 package (version 3.5.1) to create boxplots over time (month), with each treatment group color-coded, and LOESS smoothing curves fitted to each treatment to highlight temporal trends. Rectangular background shading and annotations were used to visually distinguish the two biological replicates. To highlight temporal dependencies, the autocorrelation function (ACF) was calculated. \u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e3.1. Phenotypic variability in \u003cem\u003eA. thaliana\u003c/em\u003e root responses between two microalgal biomass replicates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe examined the root elongation response to varying doses of aqueous extracts from 15 microalgal strains, using two biological replicates (R1 and R2) per strain. A detailed analysis of primary root elongation showed significantly more pronounced effects (both inhibitory and stimulatory) in replicate 2 (R2) than replicate 1 (R1) (Fig 1).\u003c/p\u003e\n\u003cp\u003eMost strains in R1 showed some degree of primary root stimulation at the applied concentrations of 0.1 and 0.3 g L\u003csup\u003e-1\u003c/sup\u003e. Particularly, H2 - \u003cem\u003eNostoc\u0026nbsp;\u003c/em\u003esp\u003cem\u003e.\u003c/em\u003e, N3 - \u003cem\u003eChlorococcum\u003c/em\u003e sp., N5 - \u003cem\u003eScenedesmus\u003c/em\u003e sp., N7 - \u003cem\u003eMonoraphidium contortum\u003c/em\u003e, and N20 - \u003cem\u003eDunaliella salina\u003c/em\u003e showed stimulation above 10% in these concentrations. Although three strains (N3 - \u003cem\u003eChlorococcum\u0026nbsp;\u003c/em\u003esp., N8 - \u003cem\u003eApatococcus lobatus\u003c/em\u003e, and N12 - \u003cem\u003ePorphyridium purpureum\u003c/em\u003e) showed up to 30% stimulation at 0.5 g L\u003csup\u003e-1\u003c/sup\u003e, only the effect observed for N12 - \u003cem\u003ePorphyridium purpureum\u003c/em\u003e was statistically significant. In contrast, R2 showed statistically significant effects, with strains N3 \u0026ndash; \u003cem\u003eChlorococcum\u003c/em\u003e sp. and N7 - \u003cem\u003eMonoraphidium contortum,\u003c/em\u003e showing remarkable stimulation across all tested concentrations. The strongest stimulation was observed with strain N3 \u0026ndash; \u003cem\u003eChlorococcum\u003c/em\u003e sp. at 0.5 g L\u003csup\u003e-1\u003c/sup\u003e and N7- \u003cem\u003eMonoraphidium contortum\u003c/em\u003e at 0.1 g L\u003csup\u003e-1\u003c/sup\u003e, both above 50% root elongation. In contrast, cyanobacterial strains (\u003cem\u003eNostoc\u003c/em\u003e sp. -strains, \u003cem\u003eAnabaena\u003c/em\u003e \u003cem\u003evariabilis\u003c/em\u003e and \u003cem\u003eNodularia\u003c/em\u003e sp.) mostly inhibited root growth, with the inhibitory effects being more statistically pronounced in R2. In R1, the maximum inhibition was 40% with H8 - \u003cem\u003eNodularia\u003c/em\u003e sp., while in R2, the maximum inhibition was 61%, observed with H2 - \u003cem\u003eNostoc\u003c/em\u003e sp.\u003c/p\u003e\n\u003cp\u003eAmong the studied strains, three are standing out with repeatedly stimulating effects. N3 \u0026ndash; \u003cem\u003eChlorococcum\u003c/em\u003e sp. consistently stimulated the root growth by +20-50% across both replicates and all concentrations, demonstrating a stable and notable effect. N7 \u003cem\u003e- Monoraphidium contortum\u003c/em\u003e significantly stimulated root elongation (+50%) in R2, but only moderately in R1 (+10%). N12 - \u003cem\u003ePorphyridium purpureum\u0026nbsp;\u003c/em\u003ehad a clear stimulating effect on the root growth in R1 but only moderate stimulation in the R2 highest concentration. Furthermore, we observed a dose-dependent root stimulation or inhibition in response to increasing concentration of aqueous microalgal extracts. This pattern was particularly noticeable in strain N5 \u0026ndash; \u003cem\u003eScenedesmus\u003c/em\u003e sp., where the highest stimulation in R1 occurred at 0.1 g L\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003e(+15%), while in R2 it peaked at 0.3 g L\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003e(+15 %). These results suggest that the optimal concentration for this extract likely falls between those concentrations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2. Synchronised performance of \u003cem\u003eA. thaliana\u003c/em\u003e root elongation within microalgal extract replicates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe root assay results showed consistent trends in stimulatory or inhibitory effects across two independently grown replicates (R1 and R2) of 15 microalgal strains. This consistency led us to further investigate, whether the observed trends were statistically significant. To better understand the patterns in our data, we used multidimensional scaling (MDS) (Fig 2), which enabled us to reconstruct the data topology and define similarity matrices within the root assays, thereby clarifying the underlying trends.\u003c/p\u003e\n\u003cp\u003eInitially, the optimum number of the clusters was determined based on Elbow and Silhouette methods and root tip elongation levels are optimally represented by two clusters (supplementary data S2). Once the optimal number of clusters has been determined, K-means clustering method and MDS were applied to the combined\u0026nbsp;dataset. This dataset included root tip elongation data from the plants treated with different microalgal extracts and their respective controls. After dimensional reduction, the data from two biological replicates showed distinct clustering patterns. Approximately 93% of the R1 samples clustered within Group 2 (yellow), whereas 77% of the R2 samples clustered in Group 1 (blue). This separation suggests a clear difference between R1 and R2, although a slight overlap between clusters likely reflects similar root tip elongation responses in the control plants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3. Seasonal variation patterns of \u003cem\u003eA. thaliana\u003c/em\u003e root length under controlled conditions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter confirming the clear difference between R1 and R2, to examine if the timing of the root assays (screening period) is correlated with the root growth responses, we applied a time series analysis to the dataset from all plants including controls. Figure 3a shows monthly root tip elongation presented as boxplots grouped by applied extract concentrations to visualize potential seasonal dynamics. The red trend lines illustrate overall trends over time. Analysis of root tip elongation boxplots across all treatments and controls, along with the four trendlines representing each treatment and control group, demonstrates that the root tip elongation was more stable during summer months (July, August), with the trendlines providing the clearest illustration of this pattern. This stability is further reflected in the relatively uniform root tip lengths, with only slight variation across measurements. A minor increase in root tip elongation was observed towards September (Fig 3a). The primary root elongation decreased during autumn and winter (October, November, January). As the season shifted to late winter (February), root tip elongation increased once more (Fig 3a). Fig 3b clearly demonstrates this trend, with a seasonal pattern becoming apparent: the first two images (July and August) show a more stable growth period, while the latter two images (November and February) correspond to periods of either significantly reduced or enhanced root tip elongation in control plants. A seasonal pattern, similar to that observed in primary root elongation, was also evident in lateral root development (Fig 3c), supporting the occurrence of circannual growth in \u003cem\u003eA. thaliana.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMinor differences in controlled growth conditions (light, temperature, and humidity) of \u003cem\u003eA. thaliana\u003c/em\u003e seeds were observed between R1 (summer) and R2 (autumn/winter), with R2 showing slightly higher temperatures (\u0026lt; 0.75 \u0026deg;C) and slightly lower humidity (\u0026lt; 5%) (supplementary information S3). However, these variations remained within a narrow, stable range and are unlikely to have influenced \u003cem\u003eA. thaliana\u003c/em\u003e root growth. Notably, the sealed MS media plates provided a stable microclimate, supporting the conclusion that short-term seasonal spikes in root tip elongation observed in September and February were not correlating by external environmental variations.\u003c/p\u003e\n\u003cp\u003eThe root tip elongation assays with microalgal biomass extract from R1 were performed between July and September, whereas the assays with biomass extract from R2 took place during October to February period. This introduces potential seasonal bias into the analysis of biological replicates. However, all microalgal biomass replicates were grown under controlled summer conditions, and harvested at matching growth stages to maximize comparability (supplementary information S1). Therefore, the observed seasonal variation in root elongation is more likely attribute to the \u003cem\u003eA. thaliana\u003c/em\u003e plants themselves rather than differences in the microalgal biomass. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;To assess potential temporal dependencies in the root development data, autocorrelation function (ACF) was calculated for primary root tip elongation and lateral root count (Fig 4). The ACF plot for primary root elongation (Fig 4a) shows statistically significant autocorrelations (p \u0026lt; 0.05) at lags 1 to 5, with values exceeding the 95% confidence interval (dotted horizontal line). The gradual decay in autocorrelation indicates a time-dependent structure in the data, and alternating positive and negative peaks suggests the presence of seasonality. A similar trend was observed for lateral root amount (Fig. 4b), with autocorrelation gradually decaying up to lag 7, although the absence of negative values points to a weaker seasonal effect in lateral root formation compared to primary root elongation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4. Principal component analysis separates \u003cem\u003eA\u003c/em\u003e. \u003cem\u003ethaliana\u003c/em\u003e root elongation data into distinct clusters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo simplify the dataset, reduce noise and identify underlying growth patterns, given the seasonal variability impacting root tip elongation, we performed a Principal Component Analysis (PCA) on both the combined R1 and R2 datasets and the individual replicates, R1 and R2 separately.\u003c/p\u003e\n\u003cp\u003eWhen grouping the combined data of R1 and R2 based on extract concentration (Fig 5a), the different concentrations of microalgal extract treatments formed distinct clusters at a 95% confidence interval. We focused our analysis on the first two principal components (PCs) based on their eigenvalues (supplementary data S4). In this context, PC1 and PC2 combined explained most of the variation in the dataset (28 %). While the 0.1 g L\u003csup\u003e-1\u003c/sup\u003e and 0.3 g L\u003csup\u003e-1\u003c/sup\u003e treatments showed overlapping indicating no apparent distinction between the groups based on the principal components. They also partially overlap with the control group, however the cluster associated with the control group remains slightly shifted along PC2 and somewhat distinct suggesting subtle differences.\u003c/p\u003e\n\u003cp\u003eSeparate PCAs were performed for each replicate, R1 and R2 (Fig 5b and 5c), revealing both consistent patterns and some discrepancies between the replicates. Following eigenvalue analysis (supplementary data S4), we focused on the first two PCs which explained 26% of the variance in R1, and 30% in R2. A consistent form across both R1 and R2 was noticed as distinct clustering of the 0.5 g L\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003etreatment, indicating the same subset pattern also in the level of replicates. Another similarity between the PCAs from the separate replicates is the overlapping clusters formed by 0.1 g L\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003eand 0.3 g L\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003etreatments, suggesting a similar response pattern at these lower concentrations. \u0026nbsp;However, the replicates differed from each other in the clustering of the control group. In R2, the control group formed a more distinct and clearly separated cluster, whereas in R1, it overlaps partially with the 0.1 g L\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003eand 0.3 g L\u003csup\u003e-1\u003c/sup\u003e treatment clusters. The key difference between R1 and R2 was the spatial separation of all the clusters. R2 showed greater inter-clustering distances, suggesting a potential treatment effect or a more pronounced distinction of the response profiles. This distinction in R2 might be caused by variations in growth seasons, which could have amplified treatment-related differences and led to more distinct data patterns. \u0026nbsp;This analysis showed that the 0.1 g L\u003csup\u003e-1\u003c/sup\u003e and 0.3 g L\u003csup\u003e-1\u003c/sup\u003e treatments cannot be analyzed in a statistically significant manner as they could not be reliably distinguished from the control due to overlapping values. However, the 0.5 g L\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003etreatment consistently formed a distinct cluster, both in the combined and separate replicate analysis. This clear separation allowed us to pool and further analyze the biological replicates from 0.5 g L\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003etreatment even in the presence of seasonal growth variation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5. Despite seasonal variation the screening consistently revealed two promising candidates for future biostimulant assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs demonstrated by PCA, root tip elongation data from R1 and R2 for the 0.5 g L\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003etreatment group can be combined despite seasonal variation. The data pooling allows a larger sample size, and thus provides a more robust and generalized view of the effect of treatments. Figure 6 displays the stimulation and/or inhibition observed in the 0.5 g L\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003etreatment group, calculated as previously described.\u003c/p\u003e\n\u003cp\u003eAll the cyanobacterial strains belonging to the Nostocales order (strains H1 - \u003cem\u003eNostoc\u0026nbsp;\u003c/em\u003esp., H2 - \u003cem\u003eNostoc\u0026nbsp;\u003c/em\u003esp., H6 - \u003cem\u003eNostoc\u0026nbsp;\u003c/em\u003esp., H7 - \u003cem\u003eAnabaena variabilis\u0026nbsp;\u003c/em\u003eand H8 - \u003cem\u003eNodularia\u0026nbsp;\u003c/em\u003esp.) have a strong plant growth inhibiting effect (from -20% to -40%). Strains in the Sphaeropleales order (N1 - \u003cem\u003eSelenastrum\u0026nbsp;\u003c/em\u003esp., N5 - \u003cem\u003eScenedesmus\u0026nbsp;\u003c/em\u003esp., N6 - \u003cem\u003eTetradesmus obliquus\u0026nbsp;\u003c/em\u003eand N7 \u003cem\u003eMonoraphidium contortum\u003c/em\u003e) mainly inhibit plant growth (from -12% to -30%), except for one strain, N7 \u003cem\u003eMonoraphidium contortum\u003c/em\u003e, which has a slightly stimulating effect (+5%). The strains in Chlamydomonadales order (N9 - \u003cem\u003eHaematococcus lacustris\u003c/em\u003e, N13 - \u003cem\u003eDunaliella tertiolecta\u0026nbsp;\u003c/em\u003eand N20 - \u003cem\u003eDunaliella salina\u003c/em\u003e) have similar effects as the strains in the Sphaeropleales order (from +3% to -30%). There is slight stimulation with strains from Chlorellales (N8 - \u003cem\u003eApatococcus lobatus\u003c/em\u003e, +8%) but the strains that have the strongest plant growth stimulating effect belong to the Porphyridiales (N12 - \u003cem\u003ePorphyridium purpureum\u0026nbsp;\u003c/em\u003ewith +15%) and Chlorococcales (N3 - \u003cem\u003eChlorococcum\u0026nbsp;\u003c/em\u003esp. with +45%) orders.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003eDiversity provides options.\u003c/strong\u003e The vast diversity of microalgae presents considerable potential for the development of plant biostimulants that support more sustainable agricultural practices (Ronga et al., 2019). To harness this potential efficiently, rapid screening approaches are essential for evaluating the bioactive properties of diverse strains. In this study, we identified \u003cem\u003eChlorococcum\u0026nbsp;\u003c/em\u003esp. and \u003cem\u003ePorphyridum\u003c/em\u003e\u003cem\u003e\u0026nbsp;purpureum\u003c/em\u003e as promising strains for biostimulant formulations, as they consistently showed positive results across both independent experiments (R1 and R2 extracts) and at all tested concentrations. While \u003cem\u003eMonoraphidium contortum\u003c/em\u003e and \u003cem\u003eApatococcus lobatus\u003c/em\u003e showed promising trends, statistical validation was hindered by seasonal variability. However, more in-depth analysis may reveal their full biostimulant potential.\u003c/p\u003e\n\u003cp\u003eBoth of the identified top-performing species have previously been reported to display plant biostimulant properties, supporting their potential observed in this study. For example, \u003cem\u003eChlorococcum vacuolatum\u003c/em\u003e improved tomato germination and root length (Kolman et al., 2024) and a \u003cem\u003eChlorococcum\u0026nbsp;\u003c/em\u003esp. extract enhances spinach germination (Rupawalla et al., 2022), likely due to high concentrations of cytokinins and gibberellins, hormones known to promote root cell division and elongation (Takahashi et al., 2013; Ubeda-Tom\u0026aacute;s et al., 2009). Since \u003cem\u003eChlorococcum\u0026nbsp;\u003c/em\u003esp.\u003cem\u003e\u0026nbsp;\u003c/em\u003e(Alhattab et al., 2018) has a relatively resilient cellulose-based cell wall, complete extraction of intracellular compounds is unlikely with the maceration method applied in this study. Unlike harsher methods such as high-pressure homogenization and enzymatic hydrolysis, mild thermo-maceration is a gentle method that avoids mechanical or chemical disruption of microalgae cells. Instead, it primarily yields compounds with high water solubility, such as amino acids, mono- and polysaccharides, phenols, flavonoids, antioxidants and phytohormones (Chovancek et al., 2024; Jamshidi‐Kia et al. 2024), all of which have potential biostimulant activity (Ferreira et al., 2023). Therefore, the observed stimulation may result from water-soluble bioactive molecules, including hormones, and their synergistic effects.\u003c/p\u003e\n\u003cp\u003eFollowing the positive effects observed for \u003cem\u003eChlorococcum\u0026nbsp;\u003c/em\u003esp., \u003cem\u003eP. purpureum\u0026nbsp;\u003c/em\u003ehas likewise been associated with plant growth promotion, including enhanced \u003cem\u003eA. thaliana\u0026nbsp;\u003c/em\u003eroot tip elongation and increased lettuce yield (Chovancek et al., 2023). Also, extracts from \u003cem\u003ePorphyridium\u003c/em\u003e sp. have been reported to improve tomato growth by increasing root and shoot dry weight, and shoot length (Mutale-joan et al., 2020;\u0026nbsp;Rachidi et al., 2020).\u0026nbsp;\u003cem\u003eP. purpureum\u0026nbsp;\u003c/em\u003elacks traditional cell wall structure and is instead encapsulated by a complex and robust polysaccharide capsule, which limits extraction to only easily water-soluble molecules during maceration (Keidan et al., 2009). Among these molecules exopolysaccharides\u003cem\u003e\u0026nbsp;\u003c/em\u003e(EPS) are a plausible source of the observed biostimulant effect (Medina-Cabrera et al., 2021). Algal polysaccharides have been reported to support root growth and enhance photosynthesis (Chanda et al., 2019), and are likely present in macerated extracts due to their solubility in warm water (Caetano et al., 2022).\u0026nbsp;Our study also revealed strains that had an inhibiting effect on plant growth. Notably, the strongest inhibition was observed with strains belonging to the Nostocales order. This finding contrasts with previous research, as Nostocales have been reported to function as potential biostimulants (Silambarasan et al., 2021; Santini et al., 2022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAccounting for seasonal variability is essential for rapid biostimulant screening in long-term experiments.\u0026nbsp;\u003c/strong\u003eBiological replicates across different seasons often show similar trends but varying values, weakening conclusions and complicating large dataset analysis. We addressed this by employing an integrative approach combining multivariate statistical techniques with time series analysis, ultimately revealing an underlying seasonal pattern.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThis strategy is valuable in long-term studies with multiple independent biological replicates.\u003c/p\u003e\n\u003cp\u003eSeasonal variation of root growth is linked to environmental factors, nutrient availability, life cycle, and presence of plant hormones (Walter et al., 2009;\u0026nbsp;Jung \u0026amp; McCouch, 2013). However, despite maintaining stable growth conditions (light, temperature, and humidity) within a narrow range throughout our assays, we observed noticeable differences related to seasonal variations in untreated \u003cem\u003eA. thaliana\u003c/em\u003e roots. These variations are possibly influenced by an internal circannual clock, independent of external environmental factors. This endogenous mechanism has been well documented in Arabidopsis, in other plants species such as \u003cem\u003eDendrocalamus strictus\u003c/em\u003e (Rosb.), \u003cem\u003eMesembryanthemum nodiflorum\u003c/em\u003e L. (\u003cem\u003eAizoaceae\u003c/em\u003e) and some orchids species (Rawal, 2003;\u0026nbsp;Gutterman, 2005;\u0026nbsp;Dur\u0026aacute;n-Mendoza et al., 2025); and various organisms (Helm \u0026amp; Lincoln, 2017;\u0026nbsp;Lincoln, 2019)\u0026nbsp;even in the unicellular protist dinoflagellate \u003cem\u003eGonyaulax\u003c/em\u003e \u003cem\u003etamarensis\u003c/em\u003e (Andersen \u0026amp; Keafer, 1987). Despite being well-documented, this mechanism can be easily overlooked in screening approaches conducted in long-term screenings, where statistical tools have shown to be profoundly helpful in deriving relevant data from complex datasets. These results suggest that minimizing seasonal effects requires careful consideration in experimental design, prioritizing experiments during the natural growing season. Additionally, performing all replicates within the same season will mitigate these pronounced seasonal variations, as growing seasons follow a distinct spatiotemporal pattern.\u003c/p\u003e\n\u003cp\u003eBeyond the observed seasonal pattern, the extended storage time of R2 microalgal biomass, is also an important factor to consider, as it might be potentially contributing to the observed outcomes. Storage of \u003cem\u003eChlorella vulgaris\u003c/em\u003e biomass for over a year has been shown to enhance root promotion in mung bean (\u003cem\u003eVigna\u003c/em\u003e \u003cem\u003eradiata\u003c/em\u003e) due to cell wall degradation and the increased release of bioactive compounds (Stirk et al., 2021).\u0026nbsp;In our study, the freeze-dried biomass R1 was stored for 14 to 16 months, while R2 was stored for a longer period of 15 and 19 months. It is possible that similar degradation processes contributed to the stronger effects seen with R2 extracts.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis tools facilitate seasonality assessment.\u003c/strong\u003e Exploratory data analysis (EDA) is considered an important preliminary step before formal statistical modeling or hypothesis testing, as it helps uncover patterns, relationships, and potential anomalies within datasets. PCA and MDS, universal tools for EDA, effectively evaluate biological replicates. Replicates coded with the same colors should cluster together along the same dimensions, indicating consistency and reproducibility. Furthermore, time series analysis methods such as ACF plots are important for detecting temporal patterns, including seasonality. Our results highlight the usefulness of ACF plots in identifying seasonal effects, suggesting that similar analyses could reveal hidden seasonal trends in other long-term datasets as well. Together, these EDA tools enhance the reliability and interpretation of complex biological experiments.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrated that aqueous microalgal extracts from \u003cem\u003eChlorococcum\u003c/em\u003e sp. (N3) and \u003cem\u003eP. purpureum\u003c/em\u003e (N12) at a concentration of 0.5 g L⁻\u0026sup1; significantly enhanced root elongation in \u003cem\u003eA. thaliana\u003c/em\u003e, while also revealing significant seasonal variations most possibly sensed by plants via endogenous mechanisms even under controlled growth conditions. By acknowledging and addressing this variability, it is possible to improve the robustness and reproducibility of fast screening root/germination assays. \u0026nbsp;Future studies could benefit from applying time series analysis with the autocorrelation function (AFC) when long-term experiments require extended timelines, as this method provides a valuable tool for mitigating seasonal effects and enhancing data reliability.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003eThe authors have no competing interests to declare that are relevant to the content of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was financially supported by the European Union Horizon Europe IA project REALM (grant agreement # 101060991).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eI.-M.R.\u003c/strong\u003e performed experiments, curated and analyzed data, and drafted the manuscript. \u003cstrong\u003eJ.I.\u003c/strong\u003e supervised and validated the bioinformatics analysis; \u003cstrong\u003eE.C.\u003c/strong\u003e provided methodological support for screening experiments; \u003cstrong\u003eM.J.\u003c/strong\u003e contributed to writing and editing the manuscript; \u003cstrong\u003eS.S.\u003c/strong\u003e conceptualized and supervised the research; \u003cstrong\u003eY.A.\u003c/strong\u003e conceived and supervised the research and acquired funding. All authors contributed to the discussion and revision of the manuscript draft. \u003cstrong\u003eS.S.\u003c/strong\u003e and \u003cstrong\u003eY.A.\u003c/strong\u003e finalized the paper with input from all authors. All authors read and approved of the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the Norwegian Culture Collection of Algae (NORCCA) at the NIVA (Norway) and the HAMBI Culture Collection at the University of Helsinki (Finland) for providing the algal strains. We also thank Pragya Karmacharya and Joni Vuorinen for their assistance in preparing the microalgal cultivations.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbidin, Z. ul. 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The Developmental Cycle of Spirodela polyrhiza Turions: A Model for Turion-Based Duckweed Overwintering? \u003cem\u003ePlants\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(21), 2993. https://doi.org/10.3390/plants13212993\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"","identity":"journal-of-applied-phycology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"10811","submissionUrl":"https://submission.nature.com/new-submission/10811/3","title":"Journal of Applied Phycology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Microalgae, Biostimulant, Algal extracts, In-vitro assays, Seasonal variation","lastPublishedDoi":"10.21203/rs.3.rs-6648660/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6648660/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMicroalgae are a diverse group of photosynthetic microorganisms and offer a sustainable source of bioactive compounds for next generation plant biostimulants. In this study we screened the biostimulant effect of 15 aqueous microalgal extracts, obtained by maceration and centrifugation, using \u003cem\u003eArabidopsis thaliana\u003c/em\u003e root growth assays. Due to significant variations in root growth in treated and untreated (control) seedlings, even under controlled conditions, an integrative statistical strategy, incorporating dimensional reduction approaches, was employed to address this complexity. This analysis revealed significant seasonal variations in root growth, likely sensed by endogenous plants mechanisms, despite controlled growth conditions. By employing statistical methods and accounting for seasonal effects, \u003cem\u003ePorphyridium purpureum\u003c/em\u003e and \u003cem\u003eChlorococcum\u003c/em\u003e sp. were identified as potent strains, consistently stimulating root growth by 15\u0026ndash;45%. To the best of authors' knowledge, no prior studies have explicitly addressed this issue in biostimulant assays. The observed circannual rhythm suggests that results from \u003cem\u003ein vitro\u003c/em\u003e assays may vary significantly depending on the season in which they are conducted, and that future studies should carefully consider this factor. These findings provide valuable insights for optimizing rapid root/germination screening assays and guiding extract selection for evaluating biostimulant potential.\u003c/p\u003e","manuscriptTitle":"Biological complexity in rapid biostimulant screening across multiple seasons","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-20 17:50:08","doi":"10.21203/rs.3.rs-6648660/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-24T12:55:43+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-02T01:30:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"102727325478490800327681817851398382838","date":"2025-05-23T19:16:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"63135186364515589385812176225764073620","date":"2025-05-17T01:14:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-16T10:29:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-14T11:31:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-14T07:44:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Applied Phycology","date":"2025-05-12T16:41:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"","identity":"journal-of-applied-phycology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"10811","submissionUrl":"https://submission.nature.com/new-submission/10811/3","title":"Journal of Applied Phycology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"706cc7c5-c43b-4f28-8f05-41707f063f69","owner":[],"postedDate":"May 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-10T08:23:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-20 17:50:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6648660","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6648660","identity":"rs-6648660","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}