Above and belowground functional trait response to biochar addition in seedlings of six tropical dry forest tree species

Above and belowground functional trait response to biochar addition in seedlings of six tropical dry forest tree species | 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 Above and belowground functional trait response to biochar addition in seedlings of six tropical dry forest tree species Oscar R. Lanuza, Josep Peñuelas, Josep M. Espelta, Guille Peguero This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4078094/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Nov, 2024 Read the published version in New Forests → Version 1 posted 10 You are reading this latest preprint version Abstract The addition of biochar as a soil amendment has great potential for ecological restoration and long-term carbon (C) storage. However, few studies have evaluated the functional trait responses of tree seedlings to increasing application rates of biochar and almost no information is available for tropical dry forests (TDF). Here, we conducted a greenhouse experiment to quantify effects of rates of biochar (0, 5, 10, 20, and 40 t/ha) on demographic and functional traits of six tree species used in TDF restoration programs. After 100 days of growth, we found no negative effects of biochar on seedling survival and only in two of the species the highest dose applied slightly reduced the final biomass. The addition of biochar increased leaf chlorophyll content (LCC) and specific leaf area (SLA) of all species. Greater variation in above-and below-ground trait responses to biochar was due more to inter-specific (52%) and intra-specific (36%) differences than main effects of biochar across species (11%), although we found that 81% of the variation in the LCC was due to the addition of biochar. We found a positive effect of biochar on morphological traits related to C gain and physiological tolerance to drought (higher dry mass content of root, leaf, and stem, LCC, SLA, and leaf area ratio). Therefore, we suggest that applications of biochar between 5 to 30 t/ha do not compromise the early growth of the seedlings of the studied species, and even may improve their growth capacity and drought resistance during their establishment in the field. biochar biomass allocation intraspecific trait variability plasticity soil amendment tropical dry forests Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. INTRODUCTION Reducing the risks of a 1.5°C rise in global temperatures requires a drastic reduction in greenhouse gas emissions along with greater sequestration of excess atmospheric carbon dioxide (CO 2 ) (IPCC 2022), and restoration and regeneration of natural forests can help to achieve this challenge (Chazdon et al. 2016 ; Griscom et al. 2017 ; Lewis et al. 2019 ; Bastin et al. 2019 ; Cook-Patton et al. 2020 ). Given soils represent the largest terrestrial reservoir of organic carbon (C), with a high storage capacity (Georgiou et al. 2022 ), the development of priority actions for the management of C in forest ecosystems is likely to be key to the long-term mitigation of impacts of climate change. Biochar has been used as a soil amendment in agricultural ecosystems to increase productivity, with positive impacts on soil C stocks (Biederman and Harpole 2013 ; Liu et al. 2013 ); as a result, it has been suggested that soil applications of biochar may improve the success rates of forest restoration projects (Lehmann and Joseph 2015 ; Thomas and Gale 2015 ; Wang et al. 2016 ; Irfan 2017 ). For example, biochar may contribute to soil C sequestration, due to its potential negative emissions of approximately 0.7 Gt C-eq/yr (Smith 2016 ) and CO 2 capture equivalents of 1.8–11.9 Gt CO2-eq/year (Lee et al. 2010 ; Woolf et al. 2010 ). There is evidence for long-term effects of applications of biochar sequestration of C in soil (Wang et al. 2016 ), due to length of residence time and content of available C (108 days and 3%, respectively) and recalcitrant C (556 years and 97%, respectively) (Wang et al. 2016 ). In addition, there are reports of extreme mean residence times for soil sequestration of recalcitrant C from biochar of > 1000 years (Cheng et al. 2008 ; Lehmann et al. 2021 ). However, residence time of C depends on a range of factors, including raw materials and pyrolysis temperatures used in biochar production, soil type, and climate conditions (Amoah-Antwi et al. 2020 ). The addition of biochar to agricultural systems has been shown to lead to 10–30% increases in crop biomass (Biederman and Harpole 2013 ; Liu et al. 2013 ), with greater increases reported for pioneer herbaceous plant species (30–37%; (Gale et al. 2017 ) and woody plants (c. 41%; (Thomas and Gale 2015 ). These impacts on productivity are likely due to effects of biochar on soil and rhizosphere conditions, such as increases in available phosphorous (P) and microbial biomass of agricultural soils (Gao et al. 2019 ). Other effects associated with biochar are greater cation exchange capacity, pH, content of total and organic C, and total nitrogen (N), and C:N ratios in agricultural soils a global scale (Dai et al. 2020 ), as well as increases in annual plant root P concentrations, and numbers of root-associated microbes and root nodules (Xiang et al. 2017 ). However, there is evidence for inconsistency in these positive effects of biochar across soil types, climate, and plant strategies; for example, addition of biochar to acidic, low fertility tropical soils increased crop yields by 25%, whereas there were no impacts of applications to neutral pH, high fertility temperate soils (Jeffery et al. 2017 ), while inter-specific variation in direction of responses have been reported for pioneer herbaceous plants (Gale et al. 2017 ). Nevertheless, it is possible that the addition of biochar to soils of reforestation, afforestation and forest restoration projects may elicit positive impacts on soil fertility, particularly in tropical ecosystems where degradation of soils has led to high levels of nutrient deficiency, and also in non-degraded tropical soils given the pervasive P limitation (Thomas and Gale 2015 ). Growth and litter production of 4-year old trees from two species tropical have been shown to be unaffected by biochar application (Gonzalez Sarango et al. 2021 ). Despite that, several studies have shown that simultaneous additions of biochar and inorganic fertilizer increase height, diameter, and above- and below-ground biomass, including leaf production, in forest plant species (Lefebvre et al. 2019 ). Thus, biochar applications have led to greater increases in tree seedling quality than applications of inorganic fertilizer (Fagbenro et al. 2015 ) and, in soils with high levels of salinity, addition of biochar improves productivity of tree seedlings (Drake et al. 2016 ). However, the measurement of the impacts on plants derived from the addition of biochar should be carried out by applying an approach based on functional traits and not only on demographic (i.e. survival, growth and reproduction) measures. This would facilitate an understanding of the response mechanisms of plants and allow an improved selection of species for forest restoration programs according to prevailing environmental conditions (Werden et al. 2018 ). For example, functional traits of leaf mass fraction and specific leaf area are predictors of photosynthetic capacity, as they are related to light interception and water loss through transpiration (Markesteijn and Poorter 2009 ). Stem characteristics, such as diameter and the ratio of height to diameter, are predictors of survival and growth, as they are related to light capture, water transport, and resistance to pest and weather damage (Poorter 1999 ; Haase 2008 ). On the other hand, root traits, such as root mass fraction and specific root length are related to water and nutrient uptake that are important for the capture and storage of water, nutrients, and seedling support (Poorter and Markesteijn 2008 ). While the ratio of roots to shoots biomass reflects the balance between water loss by transpiration (shoot) and water grain through absorption (root) and dry matter content of leaf, stem and root tissues tend to be related to physiological drought tolerance (Hacke et al. 2001 ; Jacobsen et al. 2005 ). However, quantification of plant demographics and functional trait responses to biochar addition to threatened ecosystems in which restoration programs are urgently needed, such as tropical dry forests (TDFs) and particularly from the Neotropics, is currently lacking. The aim of our study was to test for biochar-mediated increases on seedling growth, survival, and functional trait expression on TDF tree species. We conducted a greenhouse experiment to test for demographic and trait responses to increasing rates of biochar addition in seedlings of six tree species commonly used in TDF restoration programs. We also measured intraspecific trait variability (ITV) because it is supposed to predict better seedling survival and growth than species-level responses to the environment (Poorter et al. 2018 ). This may help to assess whether biochar affects species’ functional trait expression, and also if phenotypic plasticity may be a useful indicator of suitability of target species for forest restoration programs (Lanuza et al. 2020 ). 2. METHODS 2.1. Study Site The experiment was conducted in a greenhouse at the National Autonomous University of Nicaragua-Managua El Limón Experimental Station (13°03'044″ N, 86°21'057″ W), located at 888 m a.s.l. in northwestern Nicaragua. The dry tropical climate of the region is characterized by an average annual temperature and rainfall of 23.1°C and 892 mm per year, respectively, and an annual water deficit of -385.4 mm per year (Ruiz Gómez et al. 2021 ). 2.2. Experimental Design Dry wood feedstock (< 5 cm diameter) (90% Vachellia pennatula Schltdl. & Cham. Seigler & Ebinger; 10% mix of other tree species locally used as firewood) was pyrolyzed in a top-lit updraft gasifier reactor (TLUD) at 700–1000°C. The reactor was built with a 200 L steel barrel perforated with 300 9–10 mm diameter holes in the bottom. A 25 cm high crown made from the bottom of another barrel was placed on the reactor which had four triangular holes (15 × 20 cm) in the upper part and four in the lower part (10 × 13 cm) and a chimney 1.20 m high in the center of the crown. The pyrolysis time was 70 minutes, and once the biochar had cooled to room temperature, it was crushed using a manual mill and screened using a 2-mm sieve prior to use. We then determined the pH of the biochar on 5 replicates using 1:20 (v:v) biochar to water solutions (e.g., 2 mL of biochar to 40 mL water) with a pH probes LAQUAtwin pH-11. The pH of the biochar produced was 11.57. We collected seeds of six, locally abundant TDF tree species ( Crescentia alata Kunth, Cordia alliodora (Ruiz & Pav.) Oken, Cedrela odorata L., Swietenia humilis Zucc., Tabebuia rosea Bertol. DC., and Guazuma ulmifolia Lam.) from local single mother trees to reduce genotypic differences and minimize intraspecific trait variability and germinated the seeds over 20–25 days in a homogenous substrate. All the species selected are strictly confined to TDF in our study area although some of them show wider distributions elsewhere. We transplanted single seedlings in polyethylene nursery bags with a total surface area of approximately 700.4 cm 2 (1208.7 cm 3 volume). The bags were filled with a homogeneous mixture of local soil (up to ~ 15 cm depth, 0.5 cm sieve) and biochar. The local soil used in the experiment was an eutric vertisol due to the presence of more than 30% of expanding clay (a soft phyllosilicate mineral of the smectite type, probably montmorillonite) in the first horizon (up to 20–30 cm depth). This soil has an optimum neutral to slightly acidic pH (6.8 in water with a potential pH of 5.7 in KCl). It has, however, a poor content of organic matter and a rather low concentration of nitrogen (< 10 kg N-NO 3 ha − 1 ), but a quite high level of phosphorus, potassium and calcium (200, 400 and 1000 ppm, respectively). For growing plants with this soil, it is recommended the use of a general fertilizer to supplement the potential deficit of nitrogen. The biochar treatments were equivalent to 0, 5, 10, 20, and 40 t/ha on the average weight of the bags filled only with soil (1406 g). The nursery bags were arranged in a factorial design (6 species × 5 treatments) with 20 replicates, to account for variation in light and temperature conditions. The seedlings were irrigated at field capacity (350 ml) twice a week and 3g of NPK (12-30-10) fertilizer was added 30 days after transplantation. After 100 days, we removed the seedlings from the nursery bags and gently washed the roots in tap water to remove substrate, prior to analysis of trait data. Since seedlings were grown for 100 days and therefore no longer depended on their seed reserves upon harvest, we acknowledge they should be formally considered saplings, although we refer to them as seedlings for simplicity. 2.3. Trait Measurement and Calculation Mass (g) of seedling leaf, stem, and root material was measured fresh and following drying in an oven at 60°C for 48 h, or until constant weight, using an analytical balance with 0.001g precision. We calculated the mass fraction of leaf, stem, and root material (LMF, SMF, RMF, respectively) as the dry mass of each component/total seedling dry mass (g/g) (Poorter et al. 2012 ; Amissah et al. 2021 ) and leaf, stem, and root dry mass content (LDMC, SDMC, RDMC, respectively) was calculated as respective tissue dry/fresh mass (mg/g). Specific root length (SRL) was calculated as the length of fine roots/root dry mass (cm/g), and root-to-shoot ratio was calculated as root mass/stem + leaf mass. Stem diameter (mm) immediately below the cotyledon scar was measured using calipers, seedling height (cm) was measured from the cotyledon scar to the base or tip of the terminal bud, or the end of the growing tip, if no bud was formed, and seedling robustness was calculated as the ratio between height and diameter at the root neck (Haase 2008 ). Leaf chlorophyll content (LCC) (g/m 2 ) was measured from 10 seedlings per species using a chlorophyll content meter (CCM-200 Plus, Opti-Sciences, USA) between 08:00 and 14:00 hrs. We then transformed instrument measures into LCC applying the regression equation from (Brown et al. 2022 ). The mean leaf area of fresh leaves from 10 seedlings per species that were digitized using a desktop scanner (HP Scanjet 5590, USA) was calculated using Image J software (Schneider et al. 2012 ). We calculated leaf area ratio (LAR) as total leaf area/plant mass (cm 2 /g) and specific leaf area (SLA) as leaf area/leaf mass (cm 2 /g) (Poorter et al. 2012 ; Pérez-Harguindeguy et al. 2013 ). 2.4. Data Analysis We calculated median, range (5th to 95th percentiles), and coefficients of variation for all measured demographic (i.e. survival and growth) and functional traits across species and treatments (Table 1 ). Species trait responses to biochar were tested using general linear models, with species and biochar treatment as fixed-effects terms, and trait responses across species were tested using general mixed-effects models, with treatment as a fixed-effect term and species as a random factor; analyses were conducted using the lme4 R package (Bates et al. 2015 ) and quality of model fit was evaluated using the performance R package (Lüdecke et al. 2021 ). Table 1 Median values, 5–95th percentile ranges, and coefficients of variation (CV) of traits in seedlings of six tropical dry forest tree species grown with the addition of biochar. Traits Abbreviation Units Median Range (5–95th percentile) CV Height Height cm 36 15–77 46.91 Stem diameter Stemd mm 6.6 4.16–10.16 25.67 Leaf chlorophyll content LCC g m 2 0.87 0.45–1.12 21.86 Stem dry mass SDM g 2.19 0.36–4.8 58.53 Root dry mass RDM g 2.31 0.36–5.16 58.39 Leaf dry mass LDM g 3.08 0.75–4.91 41.49 Total dry mass TDM g 8.15 1.5–13.13 44.25 Dry matter content DMC % 0.29 0.19–0.41 23.09 Roof mass fraction RMF g g − 1 0.28 0.17–0.53 34.47 Stem mass fraction SMF g g − 1 0.28 0.18–0.39 23.55 Leaf mass fraction LMF g g − 1 0.41 0.26–0.57 24.42 Leaf dry matter content LDMC mg g − 1 0.28 0.18–0.39 22.6 Stem dry matter content SDMC mg g − 1 0.3 0.18–0.42 26.11 Root dry matter content RDMC mg g − 1 0.33 0.21–0.51 30.15 Leaf area Leaf_area cm 2 752.14 184.73–1569.3 54.19 Leaf area ratio LAR cm 2 g − 1 109.57 45.01–182.37 36.97 Specific leaf area SLA cm 2 g − 1 247.1 166.52–457.3 33.15 Specific root length SRL cm g − 1 10.8 4.65–65.27 128.07 Root to shoot ratio R:S ratio n/a 0.38 0.21–1.11 57.12 Height to diameter ratio H:D ratio n/a 4.79 2.73–12.72 52.99 For each species, we calculated a simple plasticity index (PI), defined as the highest phenotypic value divided by the lowest value (Poorter et al. 2012 ), to summarize the relationship between trait variability and response to biochar treatment, where PI = 1 indicates no change in response to rate of biochar application. We conducted a variance partitioning analysis using a series of nested linear mixed-effects models to estimate intra- and inter-specific variation in trait responses to biochar application rates, where separate linear mixed-effects models were fitted for each trait, with species as a random factor (for intra- and inter-specific trait variation), followed by treatment nested within species as a random factor (within species variation in trait response to biochar rate) (Vilà-Cabrera et al. 2015 ). All statistical analyses were conducted using R version 4.1.1 (R Core Team 2021 ). 3. RESULTS Biochar addition did not change seedling mortality in none of the studied species ( F = 1.29; P = 0.31). We found that the effects of biochar addition on all functional traits measured strongly varied among species (biochar treatment × species interaction for all traits measured with P < 0.05 and R 2 ranging from 0.24 up to 0.81. See Table S1 for further details). For instance, while the application of the highest rate of biochar (40 t/ha) led to a decrease in the total dry mass of the seedlings of C. alliodora and C. odorata , this negative effect was absent in the other four species tested. Actually, this high rate increased LMF in C. alata , decreased the SMF in C. alliodora and T. rosea , while increased the RMF in T. rosea (Fig. 1). The addition of biochar at 40 t/ha also increased SLA in C. odorata and G. ulmifolia (Fig. 2A), and the addition of biochar, even at the lowest rate increased the concentration of chlorophyll in the leaves of the seedlings of all six species (Fig. 2B). Overall, this variability stressed the different species-specific responses of the seedlings, in particular concerning to their pattern of biomass allocation during early growth. When controlling for this high species-specific variability, the addition of biochar led to moderate, but statistically significant, increases in SDMC, RMF, LDMC, DMC, RDMC, LAR, SLA, and LCC, while to a small decrease in SDM and SMF or simply no effects on TDM, LDM, RDM, LMF, Stem, Height, or SRL (Fig. 3; Table S2). 3.1. Plasticity of Trait Responses There was wide variation in ranges of functional traits (Table 1 ). For example, SDM varied c. 13-fold (0.36–4.8 g), leaf area varied 8.5-fold (184.7–1,569.3 cm 2 ), LAR varied 4.1-fold (45.0–182.3 cm 2 /g 2 ), the SLA and LCC varied c. 3-fold (166.5–457.3 cm2/g2, 0.45–1.12 g/m2). There was greater variation in belowground than aboveground traits, including 14-fold variations in RDM and SRL (0.36–5.16 g and 4.65–65.3 cm/g, respectively). There was species variation in plasticity of trait responses to the addition of biochar, where those of G. ulmifolia tended to be least plastic, and LDM, SDM and LCC were the most plastic aboveground traits, while RDM and the SRL were the most plastic belowground traits across the other five species (Fig. 4). Inter- and intra-specific variation in trait responses to the addition of biochar were greater than across-species variation in trait responses to biochar (Fig. 5). We found that an average of 52% of the variation in trait responses to the addition of biochar were due to inter-specific differences, ranging from 9.5% for LCC to 78% for height and H:D ratios, while intra-specific differences accounted for an average of 36% of variation in trait responses and main effects of biochar accounted for an average of 11% of the variation in species trait responses, ranging to up to 81% for LCC, thus confirming that this trait was particularly sensitive to the addition of biochar. 4. DISCUSSION We quantified demographic and functional trait responses in seedlings of six TDF tree species to increasing application rates of biochar and found contrasting impacts between the two trait types. While there were no effects on seedling survival and limited impacts on growth in two species under applications of 40 t/ha of biochar, seedling functional traits were more sensitive to the addition of biochar. Soil addition of biochar increased LCC, LAR, and SLA, thus indicating an improvement in the photosynthetic capacity of the seedlings (Markesteijn and Poorter 2009 ; Qian et al. 2021 ), while there were moderate increases in DMC of root, stem, and leaf material, possibly indicating an improvement in physiological tolerance to drought conditions (Hacke et al. 2001 ; Jacobsen et al. 2005 ). A large proportion of the variation in trait values was explained by inter-specific differences in trait responses (52%), while differences in intra-species trait responses explained on average 36% of the variation trait values. Despite this potential for adaptive phenotypic plasticity the experimental addition of biochar only accounted for a 11% of trait variability on average, with the notable exception of the LCC where up to 81% of its variation was due to biochar. Hence, biochar addition did not negatively affect the growth or the functional trait expression patterns of the seedlings of the six TDF species studied. Altogether, our findings suggest that there is a wide range of biochar addition schemes (e.g. from 5 up to 30 tons per ha) that either improve some parameters of most of the species, or at least do not negatively affect the performance of the most sensitive species. Hence, in our opinion biochar could be incorporated in large-scale tropical dry forest (TDF) restoration programs both at the seedling nursery level and at the field establishment stage without compromising seedling survival and growth, thereby potentially contributing to long-term C sequestration in the soil. 4.1. Overall Effects of Biochar on Tree Seedling Demographic and Functional Traits We found positive main effects of biochar on key growth-related morphological traits (RDMC, SLA, LAR, RMF, LDMC, SDMC, DMC and RDMC) across species, this suggests that the addition of biochar as a soil amendment may improve the physiological tolerance to drought (Hacke et al. 2001 ; Jacobsen et al. 2005 ). The positive effects of biochar on the foliar traits (LCC, SLA and LAR) and root traits (RMF) may also improve the establishment of the seedlings in the field, as these traits are related to light capture, photosynthetic capacity, control of water losses through transpiration, and the capture and storage of water, nutrients, and seedling support, respectively (Markesteijn and Poorter 2009 ). The addition of biochar has a strong impact on soil nitrogen dynamics, increasing its adsorption and mitigating its leaching losses (Clough et al. 2013 ). This potential increase in nutrient availability may help to explain the higher capacity of the plants to synthesize and accumulate more chlorophyll in their leaves. However, we did not find main effects of biochar on seedling survival, TDM, LDM, RDM, LMF, Stem, Height, or SRL, and moderately negative effects on SDM and SMF. This is in contrast to the prevailing pattern of mostly positive plant growth responses to biochar additions. This general increase of growth has been found on various crops (Biederman and Harpole 2013 ; Liu et al. 2013 ), on woody plants (Thomas and Gale 2015 ), and also on pioneer herbaceous species (Gale et al. 2017 ). Despite that, prior studies have also found neutral or negative responses (e.g., Spokas et al. 2012 ; Gale & Thomas 2019 ; Gonzalez Sarango et al. 2021 ). The reasons for such disparities can be the great variability of biochars and soil properties, the range of rates applied, and also the specific responses of the plant species assessed due to their different ecological strategies. In our study, the response to biochar was dose-dependent, only arising a slightly negative growth after adding 40 t/ha and only in two out of the six species tested. This observation is in line with previous works reporting moderately negative effects of biochar on aboveground plant biomass production from 40 t/ha onwards (Gale and Thomas 2019 ). The fact that this negative effect only affected two of the species tested, however, points out to the highly variable and species-specific plant responses to biochar. With all, these results highlight the wide margin (e.g. from 5 up to 30 tons per ha) for a safe application of biochar in TDF restoration programs before producing negative effects on tree seedlings growth even on the most sensitive species. 4.2. Species Responses to Biochar Our data show inter-specific variation in trait responses to addition of biochar, where there was greater allocation to aboveground biomass (high TDM, SDM, SLA) in G. ulmifolia , while in C. alata there were reductions in RMF, SMF, and increases in LMF and SLA. The allocation of biomass to plant organs varies with species, ontogeny, and environmental conditions (Poorter and Nagel 2000 ). Dry forest species, such as C. alata , limit water losses through reductions in amount of transpiration tissues (lower Leaf_area, SLA, LAR) and improved access to water in deeper soil layers (higher RMF) (Poorter and Markesteijn 2008 ), whereas fast-growing species, such as G. ulmifolia , are characterized by acquisitive foliar traits and greater allocation of biomass to aboveground structures under high levels of nutrient availability and greater allocation of biomass to belowground structures under nutrient limitation (Lanuza et al. 2020 ). Our data show that seedlings of TDF species modulate biomass allocation depending on the biochar rate applied, and this suggests that they did so in response to shifts in resource availability as has been observed in previous studies (Lanuza et al. 2020 ). Our results indicate that greater variation in above- and below-ground trait responses to biochar was due to inter-specific, rather than intra-specific (ITV) differences and we found that addition of biochar increased the plasticity index of above- and below-ground functional traits (SRL, SDM, RDM, LDM, LCC). We found that ITV of our experiment was slightly higher than that reported by Poorter et al. ( 2018 ); Siefert et al. ( 2015 ), and similar to levels for three dry forest species subjected to contrasting levels of nutrients, irrigation, and herbivory (Lanuza et al. 2020 ), indicating these species may show high level of adaptability to shifts in environmental conditions (Poorter et al. 2018 ). We found that addition of biochar accounted for an average of 11% variation in trait responses across species, yet explained 81% of species variation in LCC, while the overall proportion of variation in LCC explained by biochar was 1.3%, indicating the sensitivity of this functional trait to water stress, given drought affects photosynthesis (LI et al. 2006 ). Biochar improves water retention capacity due to its internal porosity and by increasing the interpore volume of soils (Liao and Thomas 2019 ), and applications of biochar have been shown to improve water use efficiency of pioneer herbaceous seedlings by 44% (Gale et al. 2017 ), but also reduce leaf N content and LCC in tomato seedlings (Akhtar et al. 2014 ). The proportions of inter-specific and intra-specific variation in trait responses to biochar addition ranged between 9.5 and 8.7–78%, respectively. Traits related to tissue quality and toughness (DMC of root, stem, leaf) are expected to express low levels of ITV, as they tend to be phylogenetically conservative (Chave et al. 2006 ); this was evident in our study for SDMC and RDMC, but was higher (61%) for LDMC. We found a low ITV for SLA, supporting findings reported by Poorter et al. ( 2018 ), but in contrast to controlled studies that show marked responses in leaf traits to shifts in ambient light levels, to enhance light capture (Poorter et al. 2009 ; Sterck et al. 2013 ). Our data show high levels LMF, SRL, and LAR in T. rosea , C. odorata , and S. humilis , that are coupled with low levels of DMC of leaf, stem, and roots, being the latter traits typically correlated with the physiological tolerance to drought (Hacke et al. 2001 ; Jacobsen et al. 2005 ). Thus, it is likely that these species may be susceptible to water deficit, in contrast to G. ulmifolia that was characterized by high levels of DMC, height and robustness (H:D ratio), indicating adaptations for light capture, water transport, support, and tolerance to wind damage and drought conditions (Poorter 1999 ; Haase 2008 ). We found that above-ground (LMF) and below-ground (RMF) trait responses to biochar mirrored relative allocations of biomass, as reported by Lanuza et al. ( 2020 ) for dry forest seedlings subjected to contrasting levels of fertilization and that are similar to responses to drought conditions, where species tend to reduce biomass allocation of LAR and LMF and increase allocation to RMF (Poorter and Markesteijn 2008 ; Markesteijn and Poorter 2009 ). Competition for above-ground and below-ground resources tends to be dynamic during the seedling stage, when acquisition of sufficient water, nutrients, and light is essential for sustained growth (McMurtrie et al. 2008 ; Poorter et al. 2012 ; Ågren et al. 2012 ; Fatichi et al. 2014 ). Previous research has shown limited effects of RMF and LMF on below- and above-ground resource foraging, respectively (Poorter and Nagel 2000 ). Our study showed that limited investment in root biomass (low RMF), as found for C. odorata , S. humilis and T. rosea , may be offset by cost-effective root growth, as indicated by large root length per unit of biomass invested (high SRL), whereas low biomass investment in leaf material (low LMF), as found for G. ulmifolia , may be offset by large leaf area per unit of leaf biomass invested (high SLA). This compensation strategy in above- and below-ground biomass allocation has been demonstrated in response to drought conditions (Markesteijn and Poorter 2009 ). Conclusions We don’t find no negative effects of biochar on seedling survival and growth even at the highest dose applied. The variation in above-and below-ground trait responses to biochar were due more to inter- and intra-specific differences than to the main effects of biochar across species, indicating strong inherited effects of species. The application of biochar at the nursery stage improves the allocation of biomass towards traits related to growth and has positive effects on traits related to drought tolerance. However, given the potential importance of biochar applications on the metrics of demographic and functional traits of seedlings of species suitable for dry forest reforestation projects, long-term field studies with different application rates of biochar produced from different feedstocks are needed to generalize their effects. Declarations Acknowledgements We would like to thank González-Zamora, J.M; López-Cruz, R.E; Gutiérrez-Cruz T.U for their invaluable assistance with the fieldwork. Funding No funding was received for conducting this study. Author Contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Oscar Lanuza, with supervision of Guille Peguero. The first draft of the manuscript was written by Oscar Lanuza and Guille Peguero with contributions and ideas of Josep M. Espelta and Josep Peñuelas. All authors edited, critically reviewed, and finally approved the manuscript. Competing Interests The authors have no competing interests to declare that are relevant to the content of this article. Data availability statement The data supporting the results of this study is published under CC by 4.0 license at the figshare repository: https://doi.org/10.6084/m9.figshare.25013753.v1 References Ågren GI, Wetterstedt JÅM, Billberger MFK (2012) Nutrient limitation on terrestrial plant growth – modeling the interaction between nitrogen and phosphorus. New Phytol 194:953–960. https://doi.org/10.1111/j.1469-8137.2012.04116.x Akhtar SS, Li G, Andersen MN, Liu F (2014) Biochar enhances yield and quality of tomato under reduced irrigation. Agric Water Manag 138:37–44. https://doi.org/10.1016/j.agwat.2014.02.016 Amissah L, Mohren GMJ, Bongers F, et al (2021) Plant traits shape tree species drought survival and distribution along a rainfall gradient in Ghana. Ghana J For 37:1–30 Amoah-Antwi C, Kwiatkowska-Malina J, Thornton SF, et al (2020) Restoration of soil quality using biochar and brown coal waste: A review. Sci Total Environ 722:137852. https://doi.org/10.1016/j.scitotenv.2020.137852 Bastin J-F, Finegold Y, Garcia C, et al (2019) The global tree restoration potential. Science (80- ) 365:76–79. https://doi.org/10.1126/science.aax0848 Bates D, Mächler M, Bolker B, Walker S (2015) Fitting Linear Mixed-Effects Models Using lme4. J Stat Softw 67:. https://doi.org/10.18637/jss.v067.i01 Biederman LA, Harpole WS (2013) Biochar and its effects on plant productivity and nutrient cycling: a meta‐analysis. GCB Bioenergy 5:202–214. https://doi.org/10.1111/gcbb.12037 Brown LA, Williams O, Dash J (2022) Calibration and characterisation of four chlorophyll meters and transmittance spectroscopy for non-destructive estimation of forest leaf chlorophyll concentration. Agric For Meteorol 323:109059. https://doi.org/10.1016/j.agrformet.2022.109059 Chave J, Muller-Landau HC, Baker TR, et al (2006) Regional and phylogenetic variation of wood density across 2456 neotropical tree species. Ecol Appl 16:2356–2367 Chazdon RL, Broadbent EN, Rozendaal DMA, et al (2016) Carbon sequestration potential of second-growth forest regeneration in the Latin American tropics. Sci Adv 2:e1501639. https://doi.org/10.1126/sciadv.1501639 Cheng C, Lehmann J, Thies JE, Burton SD (2008) Stability of black carbon in soils across a climatic gradient. J Geophys Res Biogeosciences 113:G02027. https://doi.org/10.1029/2007JG000642 Clough T, Condron L, Kammann C, Müller C (2013) A Review of Biochar and Soil Nitrogen Dynamics. Agronomy 3:275–293. https://doi.org/10.3390/agronomy3020275 Cook-Patton SC, Leavitt SM, Gibbs D, et al (2020) Mapping carbon accumulation potential from global natural forest regrowth. Nature 585:545–550. https://doi.org/10.1038/s41586-020-2686-x Dai Y, Zheng H, Jiang Z, Xing B (2020) Combined effects of biochar properties and soil conditions on plant growth: A meta-analysis. Sci Total Environ 713:136635. https://doi.org/10.1016/j.scitotenv.2020.136635 Drake JA, Cavagnaro TR, Cunningham SC, et al (2016) Does Biochar Improve Establishment of Tree Seedlings in Saline Sodic Soils? L Degrad Dev 27:52–59. https://doi.org/10.1002/ldr.2374 Fagbenro JA, Oshunsanya SO, Oyeleye BA (2015) Effects of Gliricidia Biochar and Inorganic Fertilizer on Moringa Plant Grown in an Oxisol. Commun Soil Sci Plant Anal 46:619–626. https://doi.org/10.1080/00103624.2015.1005222 Fatichi S, Leuzinger S, Körner C (2014) Moving beyond photosynthesis: from carbon source to sink‐driven vegetation modeling. New Phytol 201:1086–1095. https://doi.org/10.1111/nph.12614 Gale N V, Halim MA, Horsburgh M, Thomas SC (2017) Comparative responses of early‐successional plants to charcoal soil amendments. Ecosphere 8:e01933. https://doi.org/10.1002/ecs2.1933 Gale N V, Thomas SC (2019) Dose-dependence of growth and ecophysiological responses of plants to biochar. Sci Total Environ 658:1344–1354. https://doi.org/10.1016/j.scitotenv.2018.12.239 Gao S, DeLuca TH, Cleveland CC (2019) Biochar additions alter phosphorus and nitrogen availability in agricultural ecosystems: A meta-analysis. Sci Total Environ 654:463–472. https://doi.org/10.1016/j.scitotenv.2018.11.124 Georgiou K, Jackson RB, Vindušková O, et al (2022) Global stocks and capacity of mineral-associated soil organic carbon. Nat Commun 13:3797. https://doi.org/10.1038/s41467-022-31540-9 Gonzalez Sarango EM, Valarezo Manosalvas C, Mora M, et al (2021) Biochar amendment did not influence the growth of two tree plantations on nutrient‐depleted Ultisols in the south Ecuadorian Amazon region. Soil Sci Soc Am J 85:862–878. https://doi.org/10.1002/saj2.20227 Griscom BW, Adams J, Ellis PW, et al (2017) Natural climate solutions. Proc Natl Acad Sci 114:11645–11650. https://doi.org/10.1073/pnas.1710465114 Haase DL (2008) Understanding forest seedling quality: measurements and interpretation. Tree Plant Notes 52:24–30 Hacke UG, Sperry JS, Pockman WT, et al (2001) Trends in wood density and structure are linked to prevention of xylem implosion by negative pressure. Oecologia 126:457–461. https://doi.org/10.1007/s004420100628 IPCC [Intergovernmental Panel on Climate Change] (2022) Summary for Policymakers. In: Masson-Delmotte V, Zhai P, Pörtner H-O, et al. (eds) Global Warming of 1.5°C. Cambridge University Press, Cambridge, UK and New York, NY, USA, pp 1–24 Irfan M (2017) Potential value of biochar as a soil amendment: A review. Pure Appl Biol 6:1494–1502. https://doi.org/10.19045/bspab.2017.600161 Jacobsen AL, Ewers FW, Pratt RB, et al (2005) Do Xylem Fibers Affect Vessel Cavitation Resistance? Plant Physiol 139:546–556. https://doi.org/10.1104/pp.104.058404 Jeffery S, Abalos D, Prodana M, et al (2017) Biochar boosts tropical but not temperate crop yields. Environ Res Lett 12:053001. https://doi.org/10.1088/1748-9326/aa67bd Lanuza OR, Espelta JM, Peñuelas J, Peguero G (2020) Assessing intraspecific trait variability during seedling establishment to improve restoration of tropical dry forests. Ecosphere 11:e03052. https://doi.org/10.1002/ecs2.3052 Lee JW, Hawkins B, Day DM, Reicosky DC (2010) Sustainability: the capacity of smokeless biomass pyrolysis for energy production, global carbon capture and sequestration. Energy Environ Sci 3:1695. https://doi.org/10.1039/c004561f Lefebvre D, Román-Dañobeytia F, Soete J, et al (2019) Biochar Effects on Two Tropical Tree Species and Its Potential as a Tool for Reforestation. Forests 10:678. https://doi.org/10.3390/f10080678 Lehmann J, Cowie A, Masiello CA, et al (2021) Biochar in climate change mitigation. Nat Geosci 14:883–892. https://doi.org/10.1038/s41561-021-00852-8 Lehmann J, Joseph S (2015) Biochar for environmental management: an introduction. In: Lehmann J, Joseph S (eds) Biochar for environmental management, 2nd edn. Routledge, London, pp 1–13 Lewis SL, Wheeler CE, Mitchard ETA, Koch A (2019) Restoring natural forests is the best way to remove atmospheric carbon. Nature 568:25–28. https://doi.org/10.1038/d41586-019-01026-8 LI R, GUO P, Michael B, et al (2006) Evaluation of Chlorophyll Content and Fluorescence Parameters as Indicators of Drought Tolerance in Barley. Agric Sci China 5:751–757. https://doi.org/10.1016/S1671-2927(06)60120-X Liao W, Thomas S (2019) Biochar Particle Size and Post-Pyrolysis Mechanical Processing Affect Soil pH, Water Retention Capacity, and Plant Performance. Soil Syst 3:14. https://doi.org/10.3390/soilsystems3010014 Liu X, Zhang A, Ji C, et al (2013) Biochar’s effect on crop productivity and the dependence on experimental conditions—a meta-analysis of literature data. Plant Soil 373:583–594. https://doi.org/10.1007/s11104-013-1806-x Lüdecke D, Ben-Shachar M, Patil I, et al (2021) performance: An R Package for Assessment, Comparison and Testing of Statistical Models. J Open Source Softw 6:3139. https://doi.org/10.21105/joss.03139 Markesteijn L, Poorter L (2009) Seedling root morphology and biomass allocation of 62 tropical tree species in relation to drought‐ and shade‐tolerance. J Ecol 97:311–325. https://doi.org/10.1111/j.1365-2745.2008.01466.x McMurtrie RE, Norby RJ, Medlyn BE, et al (2008) Why is plant-growth response to elevated CO2 amplified when water is limiting, but reduced when nitrogen is limiting? A growth-optimisation hypothesis. Funct Plant Biol 35:521. https://doi.org/10.1071/FP08128 Pérez-Harguindeguy N, Díaz S, Garnier E, et al (2013) New handbook for standardised measurement of plant functional traits worldwide. Aust J Bot 61:167. https://doi.org/10.1071/BT12225 Poorter H, Nagel O (2000) The role of biomass allocation in the growth response of plants to different levels of light, CO2, nutrients and water: a quantitative review. Funct Plant Biol 27:1191. https://doi.org/10.1071/PP99173_CO Poorter H, Niinemets Ü, Poorter L, et al (2009) Causes and consequences of variation in leaf mass per area (LMA): a meta‐analysis. New Phytol 182:565–588. https://doi.org/10.1111/j.1469-8137.2009.02830.x Poorter H, Niklas KJ, Reich PB, et al (2012) Biomass allocation to leaves, stems and roots: meta‐analyses of interspecific variation and environmental control. New Phytol 193:30–50. https://doi.org/10.1111/j.1469-8137.2011.03952.x Poorter L (1999) Growth responses of 15 rain‐forest tree species to a light gradient: the relative importance of morphological and physiological traits. Funct Ecol 13:396–410. https://doi.org/10.1046/j.1365-2435.1999.00332.x Poorter L, Castilho C V, Schietti J, et al (2018) Can traits predict individual growth performance? A test in a hyperdiverse tropical forest. New Phytol 219:109–121. https://doi.org/10.1111/nph.15206 Poorter L, Markesteijn L (2008) Seedling Traits Determine Drought Tolerance of Tropical Tree Species. Biotropica 40:321–331. https://doi.org/10.1111/j.1744-7429.2007.00380.x Qian X, Liu L, Croft H, Chen J (2021) Relationship Between Leaf Maximum Carboxylation Rate and Chlorophyll Content Preserved Across 13 Species. J Geophys Res Biogeosciences 126:e2020JG006076. https://doi.org/10.1029/2020JG006076 R Core Team (2021) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing Ruiz Gómez VL, Savé Monserrat R, Lanuza Lanuza OR, et al (2021) Evolución de la temperatura y precipitación en cuatro estaciones meteorológicas, ubicadas en la región Norcentral de Nicaragua, Centroamérica. Rev Científica FAREM-Estelí 38:197–212. https://doi.org/10.5377/farem.v0i38.11952 Schneider CA, Rasband WS, Eliceiri KW (2012) NIH Image to ImageJ: 25 years of image analysis. Nat Methods 9:671–675. https://doi.org/10.1038/nmeth.2089 Siefert A, Violle C, Chalmandrier L, et al (2015) A global meta‐analysis of the relative extent of intraspecific trait variation in plant communities. Ecol Lett 18:1406–1419. https://doi.org/10.1111/ele.12508 Smith P (2016) Soil carbon sequestration and biochar as negative emission technologies. Glob Chang Biol 22:1315–1324. https://doi.org/10.1111/gcb.13178 Spokas KA, Cantrell KB, Novak JM, et al (2012) Biochar: A Synthesis of Its Agronomic Impact beyond Carbon Sequestration. J Environ Qual 41:973–989. https://doi.org/10.2134/jeq2011.0069 Sterck FJ, Duursma RA, Pearcy RW, et al (2013) Plasticity influencing the light compensation point offsets the specialization for light niches across shrub species in a tropical forest understorey. J Ecol 101:971–980. https://doi.org/10.1111/1365-2745.12076 Thomas SC, Gale N (2015) Biochar and forest restoration: a review and meta-analysis of tree growth responses. New For 46:931–946. https://doi.org/10.1007/s11056-015-9491-7 Vilà‐Cabrera A, Martínez‐Vilalta J, Retana J (2015) Functional trait variation along environmental gradients in temperate and Mediterranean trees. Glob Ecol Biogeogr 24:1377–1389. https://doi.org/10.1111/geb.12379 Wang J, Xiong Z, Kuzyakov Y (2016) Biochar stability in soil: meta‐analysis of decomposition and priming effects. GCB Bioenergy 8:512–523. https://doi.org/10.1111/gcbb.12266 Werden LK, Alvarado J. P, Zarges S, et al (2018) Using soil amendments and plant functional traits to select native tropical dry forest species for the restoration of degraded Vertisols. J Appl Ecol 55:1019–1028. https://doi.org/10.1111/1365-2664.12998 Woolf D, Amonette JE, Street-Perrott FA, et al (2010) Sustainable biochar to mitigate global climate change. Nat Commun 1:56. https://doi.org/10.1038/ncomms1053 Xiang Y, Deng Q, Duan H, Guo Y (2017) Effects of biochar application on root traits: a meta-analysis. GCB Bioenergy 9:1563–1572. https://doi.org/10.1111/gcbb.12449 Ågren GI, Wetterstedt JÅM, Billberger MFK (2012) Nutrient limitation on terrestrial plant growth – modeling the interaction between nitrogen and phosphorus. New Phytol 194:953–960. https://doi.org/10.1111/j.1469-8137.2012.04116.x Akhtar SS, Li G, Andersen MN, Liu F (2014) Biochar enhances yield and quality of tomato under reduced irrigation. Agric Water Manag 138:37–44. https://doi.org/10.1016/j.agwat.2014.02.016 Amissah L, Mohren GMJ, Bongers F, et al (2021) Plant traits shape tree species drought survival and distribution along a rainfall gradient in Ghana. Ghana J For 37:1–30 Amoah-Antwi C, Kwiatkowska-Malina J, Thornton SF, et al (2020) Restoration of soil quality using biochar and brown coal waste: A review. Sci Total Environ 722:137852. https://doi.org/10.1016/j.scitotenv.2020.137852 Bastin J-F, Finegold Y, Garcia C, et al (2019) The global tree restoration potential. Science (80- ) 365:76–79. https://doi.org/10.1126/science.aax0848 Bates D, Mächler M, Bolker B, Walker S (2015) Fitting Linear Mixed-Effects Models Using lme4. J Stat Softw 67:. https://doi.org/10.18637/jss.v067.i01 Biederman LA, Harpole WS (2013) Biochar and its effects on plant productivity and nutrient cycling: a meta‐analysis. GCB Bioenergy 5:202–214. https://doi.org/10.1111/gcbb.12037 Brown LA, Williams O, Dash J (2022) Calibration and characterisation of four chlorophyll meters and transmittance spectroscopy for non-destructive estimation of forest leaf chlorophyll concentration. Agric For Meteorol 323:109059. https://doi.org/10.1016/j.agrformet.2022.109059 Chave J, Muller-Landau HC, Baker TR, et al (2006) Regional and phylogenetic variation of wood density across 2456 neotropical tree species. Ecol Appl 16:2356–2367 Chazdon RL, Broadbent EN, Rozendaal DMA, et al (2016) Carbon sequestration potential of second-growth forest regeneration in the Latin American tropics. Sci Adv 2:e1501639. https://doi.org/10.1126/sciadv.1501639 Cheng C, Lehmann J, Thies JE, Burton SD (2008) Stability of black carbon in soils across a climatic gradient. J Geophys Res Biogeosciences 113:G02027. https://doi.org/10.1029/2007JG000642 Clough T, Condron L, Kammann C, Müller C (2013) A Review of Biochar and Soil Nitrogen Dynamics. Agronomy 3:275–293. https://doi.org/10.3390/agronomy3020275 Cook-Patton SC, Leavitt SM, Gibbs D, et al (2020) Mapping carbon accumulation potential from global natural forest regrowth. Nature 585:545–550. https://doi.org/10.1038/s41586-020-2686-x Dai Y, Zheng H, Jiang Z, Xing B (2020) Combined effects of biochar properties and soil conditions on plant growth: A meta-analysis. Sci Total Environ 713:136635. https://doi.org/10.1016/j.scitotenv.2020.136635 Drake JA, Cavagnaro TR, Cunningham SC, et al (2016) Does Biochar Improve Establishment of Tree Seedlings in Saline Sodic Soils? L Degrad Dev 27:52–59. https://doi.org/10.1002/ldr.2374 Fagbenro JA, Oshunsanya SO, Oyeleye BA (2015) Effects of Gliricidia Biochar and Inorganic Fertilizer on Moringa Plant Grown in an Oxisol. Commun Soil Sci Plant Anal 46:619–626. https://doi.org/10.1080/00103624.2015.1005222 Fatichi S, Leuzinger S, Körner C (2014) Moving beyond photosynthesis: from carbon source to sink‐driven vegetation modeling. New Phytol 201:1086–1095. https://doi.org/10.1111/nph.12614 Gale N V, Halim MA, Horsburgh M, Thomas SC (2017) Comparative responses of early‐successional plants to charcoal soil amendments. Ecosphere 8:e01933. https://doi.org/10.1002/ecs2.1933 Gale N V, Thomas SC (2019) Dose-dependence of growth and ecophysiological responses of plants to biochar. Sci Total Environ 658:1344–1354. https://doi.org/10.1016/j.scitotenv.2018.12.239 Gao S, DeLuca TH, Cleveland CC (2019) Biochar additions alter phosphorus and nitrogen availability in agricultural ecosystems: A meta-analysis. Sci Total Environ 654:463–472. https://doi.org/10.1016/j.scitotenv.2018.11.124 Georgiou K, Jackson RB, Vindušková O, et al (2022) Global stocks and capacity of mineral-associated soil organic carbon. Nat Commun 13:3797. https://doi.org/10.1038/s41467-022-31540-9 Gonzalez Sarango EM, Valarezo Manosalvas C, Mora M, et al (2021) Biochar amendment did not influence the growth of two tree plantations on nutrient‐depleted Ultisols in the south Ecuadorian Amazon region. Soil Sci Soc Am J 85:862–878. https://doi.org/10.1002/saj2.20227 Griscom BW, Adams J, Ellis PW, et al (2017) Natural climate solutions. Proc Natl Acad Sci 114:11645–11650. https://doi.org/10.1073/pnas.1710465114 Haase DL (2008) Understanding forest seedling quality: measurements and interpretation. Tree Plant Notes 52:24–30 Hacke UG, Sperry JS, Pockman WT, et al (2001) Trends in wood density and structure are linked to prevention of xylem implosion by negative pressure. Oecologia 126:457–461. https://doi.org/10.1007/s004420100628 IPCC [Intergovernmental Panel on Climate Change] (2022) Summary for Policymakers. In: Masson-Delmotte V, Zhai P, Pörtner H-O, et al. (eds) Global Warming of 1.5°C. Cambridge University Press, Cambridge, UK and New York, NY, USA, pp 1–24 Irfan M (2017) Potential value of biochar as a soil amendment: A review. Pure Appl Biol 6:1494–1502. https://doi.org/10.19045/bspab.2017.600161 Jacobsen AL, Ewers FW, Pratt RB, et al (2005) Do Xylem Fibers Affect Vessel Cavitation Resistance? Plant Physiol 139:546–556. https://doi.org/10.1104/pp.104.058404 Jeffery S, Abalos D, Prodana M, et al (2017) Biochar boosts tropical but not temperate crop yields. Environ Res Lett 12:053001. https://doi.org/10.1088/1748-9326/aa67bd Lanuza OR, Espelta JM, Peñuelas J, Peguero G (2020) Assessing intraspecific trait variability during seedling establishment to improve restoration of tropical dry forests. Ecosphere 11:e03052. https://doi.org/10.1002/ecs2.3052 Lee JW, Hawkins B, Day DM, Reicosky DC (2010) Sustainability: the capacity of smokeless biomass pyrolysis for energy production, global carbon capture and sequestration. Energy Environ Sci 3:1695. https://doi.org/10.1039/c004561f Lefebvre D, Román-Dañobeytia F, Soete J, et al (2019) Biochar Effects on Two Tropical Tree Species and Its Potential as a Tool for Reforestation. Forests 10:678. https://doi.org/10.3390/f10080678 Lehmann J, Cowie A, Masiello CA, et al (2021) Biochar in climate change mitigation. Nat Geosci 14:883–892. https://doi.org/10.1038/s41561-021-00852-8 Lehmann J, Joseph S (2015) Biochar for environmental management: an introduction. In: Lehmann J, Joseph S (eds) Biochar for environmental management, 2nd edn. Routledge, London, pp 1–13 Lewis SL, Wheeler CE, Mitchard ETA, Koch A (2019) Restoring natural forests is the best way to remove atmospheric carbon. Nature 568:25–28. https://doi.org/10.1038/d41586-019-01026-8 LI R, GUO P, Michael B, et al (2006) Evaluation of Chlorophyll Content and Fluorescence Parameters as Indicators of Drought Tolerance in Barley. Agric Sci China 5:751–757. https://doi.org/10.1016/S1671-2927(06)60120-X Liao W, Thomas S (2019) Biochar Particle Size and Post-Pyrolysis Mechanical Processing Affect Soil pH, Water Retention Capacity, and Plant Performance. Soil Syst 3:14. https://doi.org/10.3390/soilsystems3010014 Liu X, Zhang A, Ji C, et al (2013) Biochar’s effect on crop productivity and the dependence on experimental conditions—a meta-analysis of literature data. Plant Soil 373:583–594. https://doi.org/10.1007/s11104-013-1806-x Lüdecke D, Ben-Shachar M, Patil I, et al (2021) performance: An R Package for Assessment, Comparison and Testing of Statistical Models. J Open Source Softw 6:3139. https://doi.org/10.21105/joss.03139 Markesteijn L, Poorter L (2009) Seedling root morphology and biomass allocation of 62 tropical tree species in relation to drought‐ and shade‐tolerance. J Ecol 97:311–325. https://doi.org/10.1111/j.1365-2745.2008.01466.x McMurtrie RE, Norby RJ, Medlyn BE, et al (2008) Why is plant-growth response to elevated CO2 amplified when water is limiting, but reduced when nitrogen is limiting? A growth-optimisation hypothesis. Funct Plant Biol 35:521. https://doi.org/10.1071/FP08128 Pérez-Harguindeguy N, Díaz S, Garnier E, et al (2013) New handbook for standardised measurement of plant functional traits worldwide. Aust J Bot 61:167. https://doi.org/10.1071/BT12225 Poorter H, Nagel O (2000) The role of biomass allocation in the growth response of plants to different levels of light, CO2, nutrients and water: a quantitative review. Funct Plant Biol 27:1191. https://doi.org/10.1071/PP99173_CO Poorter H, Niinemets Ü, Poorter L, et al (2009) Causes and consequences of variation in leaf mass per area (LMA): a meta‐analysis. New Phytol 182:565–588. https://doi.org/10.1111/j.1469-8137.2009.02830.x Poorter H, Niklas KJ, Reich PB, et al (2012) Biomass allocation to leaves, stems and roots: meta‐analyses of interspecific variation and environmental control. New Phytol 193:30–50. https://doi.org/10.1111/j.1469-8137.2011.03952.x Poorter L (1999) Growth responses of 15 rain‐forest tree species to a light gradient: the relative importance of morphological and physiological traits. Funct Ecol 13:396–410. https://doi.org/10.1046/j.1365-2435.1999.00332.x Poorter L, Castilho C V, Schietti J, et al (2018) Can traits predict individual growth performance? A test in a hyperdiverse tropical forest. New Phytol 219:109–121. https://doi.org/10.1111/nph.15206 Poorter L, Markesteijn L (2008) Seedling Traits Determine Drought Tolerance of Tropical Tree Species. Biotropica 40:321–331. https://doi.org/10.1111/j.1744-7429.2007.00380.x Qian X, Liu L, Croft H, Chen J (2021) Relationship Between Leaf Maximum Carboxylation Rate and Chlorophyll Content Preserved Across 13 Species. J Geophys Res Biogeosciences 126:e2020JG006076. https://doi.org/10.1029/2020JG006076 R Core Team (2021) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing Ruiz Gómez VL, Savé Monserrat R, Lanuza Lanuza OR, et al (2021) Evolución de la temperatura y precipitación en cuatro estaciones meteorológicas, ubicadas en la región Norcentral de Nicaragua, Centroamérica. Rev Científica FAREM-Estelí 38:197–212. https://doi.org/10.5377/farem.v0i38.11952 Schneider CA, Rasband WS, Eliceiri KW (2012) NIH Image to ImageJ: 25 years of image analysis. Nat Methods 9:671–675. https://doi.org/10.1038/nmeth.2089 Siefert A, Violle C, Chalmandrier L, et al (2015) A global meta‐analysis of the relative extent of intraspecific trait variation in plant communities. Ecol Lett 18:1406–1419. https://doi.org/10.1111/ele.12508 Smith P (2016) Soil carbon sequestration and biochar as negative emission technologies. Glob Chang Biol 22:1315–1324. https://doi.org/10.1111/gcb.13178 Spokas KA, Cantrell KB, Novak JM, et al (2012) Biochar: A Synthesis of Its Agronomic Impact beyond Carbon Sequestration. J Environ Qual 41:973–989. https://doi.org/10.2134/jeq2011.0069 Sterck FJ, Duursma RA, Pearcy RW, et al (2013) Plasticity influencing the light compensation point offsets the specialization for light niches across shrub species in a tropical forest understorey. J Ecol 101:971–980. https://doi.org/10.1111/1365-2745.12076 Thomas SC, Gale N (2015) Biochar and forest restoration: a review and meta-analysis of tree growth responses. New For 46:931–946. https://doi.org/10.1007/s11056-015-9491-7 Vilà‐Cabrera A, Martínez‐Vilalta J, Retana J (2015) Functional trait variation along environmental gradients in temperate and Mediterranean trees. Glob Ecol Biogeogr 24:1377–1389. https://doi.org/10.1111/geb.12379 Wang J, Xiong Z, Kuzyakov Y (2016) Biochar stability in soil: meta‐analysis of decomposition and priming effects. GCB Bioenergy 8:512–523. https://doi.org/10.1111/gcbb.12266 Werden LK, Alvarado J. P, Zarges S, et al (2018) Using soil amendments and plant functional traits to select native tropical dry forest species for the restoration of degraded Vertisols. J Appl Ecol 55:1019–1028. https://doi.org/10.1111/1365-2664.12998 Woolf D, Amonette JE, Street-Perrott FA, et al (2010) Sustainable biochar to mitigate global climate change. Nat Commun 1:56. https://doi.org/10.1038/ncomms1053 Xiang Y, Deng Q, Duan H, Guo Y (2017) Effects of biochar application on root traits: a meta-analysis. GCB Bioenergy 9:1563–1572. https://doi.org/10.1111/gcbb.12449 Additional Declarations No competing interests reported. Supplementary Files SUPPLEMENTARYMATERIAL.docx Cite Share Download PDF Status: Published Journal Publication published 02 Nov, 2024 Read the published version in New Forests → Version 1 posted Editorial decision: Revision requested 26 Aug, 2024 Reviews received at journal 14 Aug, 2024 Reviewers agreed at journal 09 Jul, 2024 Reviewers agreed at journal 08 Jul, 2024 Reviews received at journal 05 Jul, 2024 Reviewers agreed at journal 18 Jun, 2024 Reviewers invited by journal 24 May, 2024 Submission checks completed at journal 12 Mar, 2024 Editor assigned by journal 12 Mar, 2024 First submitted to journal 11 Mar, 2024 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4078094","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":278830642,"identity":"82057976-ae8b-4aaa-a4b3-503328f486a6","order_by":0,"name":"Oscar R. Lanuza","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYDACdgglB+UyAzFjA34tzBDKmHQtiQ0oXHyAn5n52WOeP3bpG243P3vAUGGd2CDdjN8WyWY2c2MenuTcDXeOmRswnElPbJA5iF+LwWEGM2keCebcDTcSzCQY2w4nNkgkEtLC/k2ax6A+3eBG+jcJxn9EaeEB2pJwOMHgRg7QlgYitEg285RJzjlw3HDmjZwyiYRj6cZthLTws7dvk3jzp1qe70b6NokPNday/RLpD/BqQQUJQMxGgvpRMApGwSgYBTgAAMHrQAYh7pVOAAAAAElFTkSuQmCC","orcid":"","institution":"Universidad Nacional Autónoma de Nicaragua (UNAN-Managua / CUR- Estelí)","correspondingAuthor":true,"prefix":"","firstName":"Oscar","middleName":"R.","lastName":"Lanuza","suffix":""},{"id":278830643,"identity":"a84af706-32dc-4af9-9111-40d9130b5317","order_by":1,"name":"Josep Peñuelas","email":"","orcid":"","institution":"CREAF","correspondingAuthor":false,"prefix":"","firstName":"Josep","middleName":"","lastName":"Peñuelas","suffix":""},{"id":278830644,"identity":"a301e4e3-1921-42fd-b820-e4a656b311fc","order_by":2,"name":"Josep M. Espelta","email":"","orcid":"","institution":"CREAF","correspondingAuthor":false,"prefix":"","firstName":"Josep","middleName":"M.","lastName":"Espelta","suffix":""},{"id":278830645,"identity":"82d6d1b9-00a2-48aa-9333-f0a8ce2aa4b5","order_by":3,"name":"Guille Peguero","email":"","orcid":"","institution":"Universitat de Barcelona","correspondingAuthor":false,"prefix":"","firstName":"Guille","middleName":"","lastName":"Peguero","suffix":""}],"badges":[],"createdAt":"2024-03-11 21:20:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4078094/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4078094/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11056-024-10074-6","type":"published","date":"2024-11-02T16:20:07+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":52713675,"identity":"624f427e-18ef-42ce-864b-93379ee48188","added_by":"auto","created_at":"2024-03-14 20:39:47","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":208440,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of rates of biochar addition on total dry mass (A) and masses of leaf (B), stem (C), and root (D) of seedlings of six tropical dry forest tree species\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4078094/v1/136e546de69784739870a5a8.jpg"},{"id":52713680,"identity":"5f2d6061-782d-4589-a7fb-1a5963d39281","added_by":"auto","created_at":"2024-03-14 20:39:48","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":137832,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of rate of biochar addition on specific leaf area (A) and leaf chlorophyll concentrations (B) of seedlings of six tropical dry forest tree species\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4078094/v1/9e61d7c19d2334b0feeb8017.jpg"},{"id":52713679,"identity":"3118cb65-5a33-4d51-b731-76cbad0a3e0b","added_by":"auto","created_at":"2024-03-14 20:39:48","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":141891,"visible":true,"origin":"","legend":"\u003cp\u003eLinear mixed-effects model estimates of overall effects of biochar addition on seedling functional traits, averaged across six tropical dry forest tree species. Triangles and circles indicate biochar effects at \u003cem\u003eP \u0026lt; \u003c/em\u003e0.05 and \u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05, respectively. Color gradients of symbols indicate variance explained by the addition of biochar (marginal \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e). Data are presented in two panels for clarity, due to contrasting ranges of effect estimates. X-axes show the biochar effect estimate ± standard error according to general and generalized linear mixed-effect models. See Table 1 for trait descriptions and data analyses for further details on the modelling approach\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4078094/v1/228df564332b53a8d06a34a0.jpg"},{"id":52713963,"identity":"652a8760-a518-4e3c-b7fd-ab905e7d3ab5","added_by":"auto","created_at":"2024-03-14 20:47:48","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":341339,"visible":true,"origin":"","legend":"\u003cp\u003eSeedling trait plasticity indexes for six tropical dry forest tree species in response to overall effects of biochar addition. See Table 1for trait descriptions\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4078094/v1/f024ae428bf1829cfac063ea.jpg"},{"id":52713677,"identity":"eb16119b-2182-4680-b016-f7bc9f3d8eb0","added_by":"auto","created_at":"2024-03-14 20:39:48","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":185796,"visible":true,"origin":"","legend":"\u003cp\u003eLinear mixed-effects model analysis of inter-specific (black), intra-specific (dark gray), and a cross-species (light gray) variation in seedling trait responses to biochar addition. See Table 1 for trait descriptions\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4078094/v1/0722d47de712e8ba1bc1ec06.jpg"},{"id":68207060,"identity":"916c44d6-1330-4a50-ad5b-eddd74d10cfc","added_by":"auto","created_at":"2024-11-04 16:34:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1603887,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4078094/v1/85a67e29-bdc8-49a7-b4e9-e2e493e23566.pdf"},{"id":52713676,"identity":"7dd9a8d2-5bf9-418b-877c-2602bafeecd2","added_by":"auto","created_at":"2024-03-14 20:39:47","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19877,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYMATERIAL.docx","url":"https://assets-eu.researchsquare.com/files/rs-4078094/v1/4577e434a3b3c66b9e4f7fd8.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Above and belowground functional trait response to biochar addition in seedlings of six tropical dry forest tree species","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eReducing the risks of a 1.5\u0026deg;C rise in global temperatures requires a drastic reduction in greenhouse gas emissions along with greater sequestration of excess atmospheric carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e) (IPCC 2022), and restoration and regeneration of natural forests can help to achieve this challenge (Chazdon et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Griscom et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Lewis et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Bastin et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Cook-Patton et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Given soils represent the largest terrestrial reservoir of organic carbon (C), with a high storage capacity (Georgiou et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the development of priority actions for the management of C in forest ecosystems is likely to be key to the long-term mitigation of impacts of climate change.\u003c/p\u003e \u003cp\u003eBiochar has been used as a soil amendment in agricultural ecosystems to increase productivity, with positive impacts on soil C stocks (Biederman and Harpole \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2013\u003c/span\u003e); as a result, it has been suggested that soil applications of biochar may improve the success rates of forest restoration projects (Lehmann and Joseph \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Thomas and Gale \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Irfan \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For example, biochar may contribute to soil C sequestration, due to its potential negative emissions of approximately 0.7 Gt C-eq/yr (Smith \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and CO\u003csub\u003e2\u003c/sub\u003e capture equivalents of 1.8\u0026ndash;11.9 Gt CO2-eq/year (Lee et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Woolf et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). There is evidence for long-term effects of applications of biochar sequestration of C in soil (Wang et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), due to length of residence time and content of available C (108 days and 3%, respectively) and recalcitrant C (556 years and 97%, respectively) (Wang et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In addition, there are reports of extreme mean residence times for soil sequestration of recalcitrant C from biochar of \u0026gt;\u0026thinsp;1000 years (Cheng et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Lehmann et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, residence time of C depends on a range of factors, including raw materials and pyrolysis temperatures used in biochar production, soil type, and climate conditions (Amoah-Antwi et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe addition of biochar to agricultural systems has been shown to lead to 10\u0026ndash;30% increases in crop biomass (Biederman and Harpole \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), with greater increases reported for pioneer herbaceous plant species (30\u0026ndash;37%; (Gale et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and woody plants (c. 41%; (Thomas and Gale \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These impacts on productivity are likely due to effects of biochar on soil and rhizosphere conditions, such as increases in available phosphorous (P) and microbial biomass of agricultural soils (Gao et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Other effects associated with biochar are greater cation exchange capacity, pH, content of total and organic C, and total nitrogen (N), and C:N ratios in agricultural soils a global scale (Dai et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), as well as increases in annual plant root P concentrations, and numbers of root-associated microbes and root nodules (Xiang et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, there is evidence for inconsistency in these positive effects of biochar across soil types, climate, and plant strategies; for example, addition of biochar to acidic, low fertility tropical soils increased crop yields by 25%, whereas there were no impacts of applications to neutral pH, high fertility temperate soils (Jeffery et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), while inter-specific variation in direction of responses have been reported for pioneer herbaceous plants (Gale et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Nevertheless, it is possible that the addition of biochar to soils of reforestation, afforestation and forest restoration projects may elicit positive impacts on soil fertility, particularly in tropical ecosystems where degradation of soils has led to high levels of nutrient deficiency, and also in non-degraded tropical soils given the pervasive P limitation (Thomas and Gale \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGrowth and litter production of 4-year old trees from two species tropical have been shown to be unaffected by biochar application (Gonzalez Sarango et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Despite that, several studies have shown that simultaneous additions of biochar and inorganic fertilizer increase height, diameter, and above- and below-ground biomass, including leaf production, in forest plant species (Lefebvre et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Thus, biochar applications have led to greater increases in tree seedling quality than applications of inorganic fertilizer (Fagbenro et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and, in soils with high levels of salinity, addition of biochar improves productivity of tree seedlings (Drake et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, the measurement of the impacts on plants derived from the addition of biochar should be carried out by applying an approach based on functional traits and not only on demographic (i.e. survival, growth and reproduction) measures. This would facilitate an understanding of the response mechanisms of plants and allow an improved selection of species for forest restoration programs according to prevailing environmental conditions (Werden et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For example, functional traits of leaf mass fraction and specific leaf area are predictors of photosynthetic capacity, as they are related to light interception and water loss through transpiration (Markesteijn and Poorter \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Stem characteristics, such as diameter and the ratio of height to diameter, are predictors of survival and growth, as they are related to light capture, water transport, and resistance to pest and weather damage (Poorter \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Haase \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). On the other hand, root traits, such as root mass fraction and specific root length are related to water and nutrient uptake that are important for the capture and storage of water, nutrients, and seedling support (Poorter and Markesteijn \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). While the ratio of roots to shoots biomass reflects the balance between water loss by transpiration (shoot) and water grain through absorption (root) and dry matter content of leaf, stem and root tissues tend to be related to physiological drought tolerance (Hacke et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Jacobsen et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). However, quantification of plant demographics and functional trait responses to biochar addition to threatened ecosystems in which restoration programs are urgently needed, such as tropical dry forests (TDFs) and particularly from the Neotropics, is currently lacking.\u003c/p\u003e \u003cp\u003eThe aim of our study was to test for biochar-mediated increases on seedling growth, survival, and functional trait expression on TDF tree species. We conducted a greenhouse experiment to test for demographic and trait responses to increasing rates of biochar addition in seedlings of six tree species commonly used in TDF restoration programs. We also measured intraspecific trait variability (ITV) because it is supposed to predict better seedling survival and growth than species-level responses to the environment (Poorter et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This may help to assess whether biochar affects species\u0026rsquo; functional trait expression, and also if phenotypic plasticity may be a useful indicator of suitability of target species for forest restoration programs (Lanuza et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e"},{"header":"2. METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Study Site\u003c/h2\u003e\n \u003cp\u003eThe experiment was conducted in a greenhouse at the National Autonomous University of Nicaragua-Managua El Lim\u0026oacute;n Experimental Station (13\u0026deg;03\u0026apos;044\u0026Prime; N, 86\u0026deg;21\u0026apos;057\u0026Prime; W), located at 888 m a.s.l. in northwestern Nicaragua. The dry tropical climate of the region is characterized by an average annual temperature and rainfall of 23.1\u0026deg;C and 892 mm per year, respectively, and an annual water deficit of -385.4 mm per year (Ruiz G\u0026oacute;mez et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. Experimental Design\u003c/h2\u003e\n \u003cp\u003eDry wood feedstock (\u0026lt;\u0026thinsp;5 cm diameter) (90% \u003cem\u003eVachellia pennatula\u003c/em\u003e Schltdl. \u0026amp; Cham. Seigler \u0026amp; Ebinger; 10% mix of other tree species locally used as firewood) was pyrolyzed in a top-lit updraft gasifier reactor (TLUD) at 700\u0026ndash;1000\u0026deg;C. The reactor was built with a 200 L steel barrel perforated with 300 9\u0026ndash;10 mm diameter holes in the bottom. A 25 cm high crown made from the bottom of another barrel was placed on the reactor which had four triangular holes (15 \u0026times; 20 cm) in the upper part and four in the lower part (10 \u0026times; 13 cm) and a chimney 1.20 m high in the center of the crown. The pyrolysis time was 70 minutes, and once the biochar had cooled to room temperature, it was crushed using a manual mill and screened using a 2-mm sieve prior to use. We then determined the pH of the biochar on 5 replicates using 1:20 (v:v) biochar to water solutions (e.g., 2 mL of biochar to 40 mL water) with a pH probes LAQUAtwin pH-11. The pH of the biochar produced was 11.57.\u003c/p\u003e\n \u003cp\u003eWe collected seeds of six, locally abundant TDF tree species (\u003cem\u003eCrescentia alata\u003c/em\u003e Kunth, \u003cem\u003eCordia alliodora\u003c/em\u003e (Ruiz \u0026amp; Pav.) Oken, \u003cem\u003eCedrela odorata\u003c/em\u003e L., \u003cem\u003eSwietenia humilis\u003c/em\u003e Zucc., \u003cem\u003eTabebuia rosea\u003c/em\u003e Bertol. DC., and \u003cem\u003eGuazuma ulmifolia\u003c/em\u003e Lam.) from local single mother trees to reduce genotypic differences and minimize intraspecific trait variability and germinated the seeds over 20\u0026ndash;25 days in a homogenous substrate. All the species selected are strictly confined to TDF in our study area although some of them show wider distributions elsewhere. We transplanted single seedlings in polyethylene nursery bags with a total surface area of approximately 700.4 cm\u003csup\u003e2\u003c/sup\u003e (1208.7 cm\u003csup\u003e3\u003c/sup\u003e volume). The bags were filled with a homogeneous mixture of local soil (up to ~\u0026thinsp;15 cm depth, 0.5 cm sieve) and biochar. The local soil used in the experiment was an eutric vertisol due to the presence of more than 30% of expanding clay (a soft phyllosilicate mineral of the smectite type, probably montmorillonite) in the first horizon (up to 20\u0026ndash;30 cm depth). This soil has an optimum neutral to slightly acidic pH (6.8 in water with a potential pH of 5.7 in KCl). It has, however, a poor content of organic matter and a rather low concentration of nitrogen (\u0026lt;\u0026thinsp;10 kg N-NO\u003csub\u003e3\u003c/sub\u003e ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), but a quite high level of phosphorus, potassium and calcium (200, 400 and 1000 ppm, respectively). For growing plants with this soil, it is recommended the use of a general fertilizer to supplement the potential deficit of nitrogen. The biochar treatments were equivalent to 0, 5, 10, 20, and 40 t/ha on the average weight of the bags filled only with soil (1406 g). The nursery bags were arranged in a factorial design (6 species \u0026times; 5 treatments) with 20 replicates, to account for variation in light and temperature conditions. The seedlings were irrigated at field capacity (350 ml) twice a week and 3g of NPK (12-30-10) fertilizer was added 30 days after transplantation. After 100 days, we removed the seedlings from the nursery bags and gently washed the roots in tap water to remove substrate, prior to analysis of trait data. Since seedlings were grown for 100 days and therefore no longer depended on their seed reserves upon harvest, we acknowledge they should be formally considered saplings, although we refer to them as seedlings for simplicity.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3. Trait Measurement and Calculation\u003c/h2\u003e\n \u003cp\u003eMass (g) of seedling leaf, stem, and root material was measured fresh and following drying in an oven at 60\u0026deg;C for 48 h, or until constant weight, using an analytical balance with 0.001g precision. We calculated the mass fraction of leaf, stem, and root material (LMF, SMF, RMF, respectively) as the dry mass of each component/total seedling dry mass (g/g) (Poorter et al. \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Amissah et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) and leaf, stem, and root dry mass content (LDMC, SDMC, RDMC, respectively) was calculated as respective tissue dry/fresh mass (mg/g).\u003c/p\u003e\n \u003cp\u003eSpecific root length (SRL) was calculated as the length of fine roots/root dry mass (cm/g), and root-to-shoot ratio was calculated as root mass/stem\u0026thinsp;+\u0026thinsp;leaf mass. Stem diameter (mm) immediately below the cotyledon scar was measured using calipers, seedling height (cm) was measured from the cotyledon scar to the base or tip of the terminal bud, or the end of the growing tip, if no bud was formed, and seedling robustness was calculated as the ratio between height and diameter at the root neck (Haase \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e). Leaf chlorophyll content (LCC) (g/m\u003csup\u003e2\u003c/sup\u003e) was measured from 10 seedlings per species using a chlorophyll content meter (CCM-200 Plus, Opti-Sciences, USA) between 08:00 and 14:00 hrs. We then transformed instrument measures into LCC applying the regression equation from (Brown et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). The mean leaf area of fresh leaves from 10 seedlings per species that were digitized using a desktop scanner (HP Scanjet 5590, USA) was calculated using Image J software (Schneider et al. \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). We calculated leaf area ratio (LAR) as total leaf area/plant mass (cm\u003csup\u003e2\u003c/sup\u003e/g) and specific leaf area (SLA) as leaf area/leaf mass (cm\u003csup\u003e2\u003c/sup\u003e/g) (Poorter et al. \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; P\u0026eacute;rez-Harguindeguy et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4. Data Analysis\u003c/h2\u003e\n \u003cp\u003eWe calculated median, range (5th to 95th percentiles), and coefficients of variation for all measured demographic (i.e. survival and growth) and functional traits across species and treatments (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Species trait responses to biochar were tested using general linear models, with species and biochar treatment as fixed-effects terms, and trait responses across species were tested using general mixed-effects models, with treatment as a fixed-effect term and species as a random factor; analyses were conducted using the \u003cem\u003elme4\u003c/em\u003e R package (Bates et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e) and quality of model fit was evaluated using the \u003cem\u003eperformance\u003c/em\u003e R package (L\u0026uuml;decke et al.\u0026nbsp;\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMedian values, 5\u0026ndash;95th percentile ranges, and coefficients of variation (CV) of traits in seedlings of six tropical dry forest tree species grown with the addition of biochar.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTraits\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAbbreviation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnits\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003cp\u003e(5\u0026ndash;95th percentile)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCV\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u0026ndash;77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStem diameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStemd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.16\u0026ndash;10.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeaf chlorophyll content\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eg m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u0026ndash;1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStem dry mass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.36\u0026ndash;4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRoot dry mass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.36\u0026ndash;5.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeaf dry mass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u0026ndash;4.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal dry mass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5\u0026ndash;13.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDry matter content\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDMC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19\u0026ndash;0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRoof mass fraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eg g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u0026ndash;0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStem mass fraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eg g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18\u0026ndash;0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeaf mass fraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eg g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26\u0026ndash;0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeaf dry matter content\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDMC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18\u0026ndash;0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStem dry matter content\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSDMC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18\u0026ndash;0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRoot dry matter content\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRDMC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u0026ndash;0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeaf area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeaf_area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecm\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e752.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e184.73\u0026ndash;1569.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeaf area ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecm\u003csup\u003e2\u003c/sup\u003e g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e109.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.01\u0026ndash;182.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpecific leaf area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSLA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecm\u003csup\u003e2\u003c/sup\u003e g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e247.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e166.52\u0026ndash;457.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpecific root length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSRL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecm g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.65\u0026ndash;65.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e128.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRoot to shoot ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR:S ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u0026ndash;1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeight to diameter ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH:D ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.73\u0026ndash;12.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eFor each species, we calculated a simple plasticity index (PI), defined as the highest phenotypic value divided by the lowest value (Poorter et al. \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e), to summarize the relationship between trait variability and response to biochar treatment, where PI\u0026thinsp;=\u0026thinsp;1 indicates no change in response to rate of biochar application. We conducted a variance partitioning analysis using a series of nested linear mixed-effects models to estimate intra- and inter-specific variation in trait responses to biochar application rates, where separate linear mixed-effects models were fitted for each trait, with species as a random factor (for intra- and inter-specific trait variation), followed by treatment nested within species as a random factor (within species variation in trait response to biochar rate) (Vil\u0026agrave;-Cabrera et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). All statistical analyses were conducted using R version 4.1.1 (R Core Team \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cp\u003eBiochar addition did not change seedling mortality in none of the studied species (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.29; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.31). We found that the effects of biochar addition on all functional traits measured strongly varied among species (biochar treatment \u0026times; species interaction for all traits measured with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e ranging from 0.24 up to 0.81. See Table S1 for further details). For instance, while the application of the highest rate of biochar (40 t/ha) led to a decrease in the total dry mass of the seedlings of \u003cem\u003eC. alliodora\u003c/em\u003e and \u003cem\u003eC. odorata\u003c/em\u003e, this negative effect was absent in the other four species tested. Actually, this high rate increased LMF in \u003cem\u003eC. alata\u003c/em\u003e, decreased the SMF in C. \u003cem\u003ealliodora\u003c/em\u003e and \u003cem\u003eT. rosea\u003c/em\u003e, while increased the RMF in \u003cem\u003eT. rosea\u003c/em\u003e (Fig.\u0026nbsp;1). The addition of biochar at 40 t/ha also increased SLA in \u003cem\u003eC. odorata\u003c/em\u003e and \u003cem\u003eG. ulmifolia\u003c/em\u003e (Fig.\u0026nbsp;2A), and the addition of biochar, even at the lowest rate increased the concentration of chlorophyll in the leaves of the seedlings of all six species (Fig.\u0026nbsp;2B). Overall, this variability stressed the different species-specific responses of the seedlings, in particular concerning to their pattern of biomass allocation during early growth. When controlling for this high species-specific variability, the addition of biochar led to moderate, but statistically significant, increases in SDMC, RMF, LDMC, DMC, RDMC, LAR, SLA, and LCC, while to a small decrease in SDM and SMF or simply no effects on TDM, LDM, RDM, LMF, Stem, Height, or SRL (Fig.\u0026nbsp;3; Table S2).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Plasticity of Trait Responses\u003c/h2\u003e \u003cp\u003eThere was wide variation in ranges of functional traits (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For example, SDM varied c. 13-fold (0.36\u0026ndash;4.8 g), leaf area varied 8.5-fold (184.7\u0026ndash;1,569.3 cm\u003csup\u003e2\u003c/sup\u003e), LAR varied 4.1-fold (45.0\u0026ndash;182.3 cm\u003csup\u003e2\u003c/sup\u003e/g\u003csup\u003e2\u003c/sup\u003e), the SLA and LCC varied c. 3-fold (166.5\u0026ndash;457.3 cm2/g2, 0.45\u0026ndash;1.12 g/m2). There was greater variation in belowground than aboveground traits, including 14-fold variations in RDM and SRL (0.36\u0026ndash;5.16 g and 4.65\u0026ndash;65.3 cm/g, respectively).\u003c/p\u003e \u003cp\u003eThere was species variation in plasticity of trait responses to the addition of biochar, where those of \u003cem\u003eG. ulmifolia\u003c/em\u003e tended to be least plastic, and LDM, SDM and LCC were the most plastic aboveground traits, while RDM and the SRL were the most plastic belowground traits across the other five species (Fig.\u0026nbsp;4).\u003c/p\u003e \u003cp\u003eInter- and intra-specific variation in trait responses to the addition of biochar were greater than across-species variation in trait responses to biochar (Fig.\u0026nbsp;5). We found that an average of 52% of the variation in trait responses to the addition of biochar were due to inter-specific differences, ranging from 9.5% for LCC to 78% for height and H:D ratios, while intra-specific differences accounted for an average of 36% of variation in trait responses and main effects of biochar accounted for an average of 11% of the variation in species trait responses, ranging to up to 81% for LCC, thus confirming that this trait was particularly sensitive to the addition of biochar.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eWe quantified demographic and functional trait responses in seedlings of six TDF tree species to increasing application rates of biochar and found contrasting impacts between the two trait types. While there were no effects on seedling survival and limited impacts on growth in two species under applications of 40 t/ha of biochar, seedling functional traits were more sensitive to the addition of biochar. Soil addition of biochar increased LCC, LAR, and SLA, thus indicating an improvement in the photosynthetic capacity of the seedlings (Markesteijn and Poorter \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Qian et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), while there were moderate increases in DMC of root, stem, and leaf material, possibly indicating an improvement in physiological tolerance to drought conditions (Hacke et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Jacobsen et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). A large proportion of the variation in trait values was explained by inter-specific differences in trait responses (52%), while differences in intra-species trait responses explained on average 36% of the variation trait values. Despite this potential for adaptive phenotypic plasticity the experimental addition of biochar only accounted for a 11% of trait variability on average, with the notable exception of the LCC where up to 81% of its variation was due to biochar. Hence, biochar addition did not negatively affect the growth or the functional trait expression patterns of the seedlings of the six TDF species studied. Altogether, our findings suggest that there is a wide range of biochar addition schemes (e.g. from 5 up to 30 tons per ha) that either improve some parameters of most of the species, or at least do not negatively affect the performance of the most sensitive species. Hence, in our opinion biochar could be incorporated in large-scale tropical dry forest (TDF) restoration programs both at the seedling nursery level and at the field establishment stage without compromising seedling survival and growth, thereby potentially contributing to long-term C sequestration in the soil.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Overall Effects of Biochar on Tree Seedling Demographic and Functional Traits\u003c/h2\u003e \u003cp\u003eWe found positive main effects of biochar on key growth-related morphological traits (RDMC, SLA, LAR, RMF, LDMC, SDMC, DMC and RDMC) across species, this suggests that the addition of biochar as a soil amendment may improve the physiological tolerance to drought (Hacke et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Jacobsen et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The positive effects of biochar on the foliar traits (LCC, SLA and LAR) and root traits (RMF) may also improve the establishment of the seedlings in the field, as these traits are related to light capture, photosynthetic capacity, control of water losses through transpiration, and the capture and storage of water, nutrients, and seedling support, respectively (Markesteijn and Poorter \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The addition of biochar has a strong impact on soil nitrogen dynamics, increasing its adsorption and mitigating its leaching losses (Clough et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This potential increase in nutrient availability may help to explain the higher capacity of the plants to synthesize and accumulate more chlorophyll in their leaves.\u003c/p\u003e \u003cp\u003eHowever, we did not find main effects of biochar on seedling survival, TDM, LDM, RDM, LMF, Stem, Height, or SRL, and moderately negative effects on SDM and SMF. This is in contrast to the prevailing pattern of mostly positive plant growth responses to biochar additions. This general increase of growth has been found on various crops (Biederman and Harpole \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), on woody plants (Thomas and Gale \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and also on pioneer herbaceous species (Gale et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Despite that, prior studies have also found neutral or negative responses (e.g., Spokas et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Gale \u0026amp; Thomas \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Gonzalez Sarango et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The reasons for such disparities can be the great variability of biochars and soil properties, the range of rates applied, and also the specific responses of the plant species assessed due to their different ecological strategies. In our study, the response to biochar was dose-dependent, only arising a slightly negative growth after adding 40 t/ha and only in two out of the six species tested. This observation is in line with previous works reporting moderately negative effects of biochar on aboveground plant biomass production from 40 t/ha onwards (Gale and Thomas \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The fact that this negative effect only affected two of the species tested, however, points out to the highly variable and species-specific plant responses to biochar. With all, these results highlight the wide margin (e.g. from 5 up to 30 tons per ha) for a safe application of biochar in TDF restoration programs before producing negative effects on tree seedlings growth even on the most sensitive species.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Species Responses to Biochar\u003c/h2\u003e \u003cp\u003eOur data show inter-specific variation in trait responses to addition of biochar, where there was greater allocation to aboveground biomass (high TDM, SDM, SLA) in \u003cem\u003eG. ulmifolia\u003c/em\u003e, while in \u003cem\u003eC. alata\u003c/em\u003e there were reductions in RMF, SMF, and increases in LMF and SLA. The allocation of biomass to plant organs varies with species, ontogeny, and environmental conditions (Poorter and Nagel \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Dry forest species, such as \u003cem\u003eC. alata\u003c/em\u003e, limit water losses through reductions in amount of transpiration tissues (lower Leaf_area, SLA, LAR) and improved access to water in deeper soil layers (higher RMF) (Poorter and Markesteijn \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), whereas fast-growing species, such as \u003cem\u003eG. ulmifolia\u003c/em\u003e, are characterized by acquisitive foliar traits and greater allocation of biomass to aboveground structures under high levels of nutrient availability and greater allocation of biomass to belowground structures under nutrient limitation (Lanuza et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Our data show that seedlings of TDF species modulate biomass allocation depending on the biochar rate applied, and this suggests that they did so in response to shifts in resource availability as has been observed in previous studies (Lanuza et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur results indicate that greater variation in above- and below-ground trait responses to biochar was due to inter-specific, rather than intra-specific (ITV) differences and we found that addition of biochar increased the plasticity index of above- and below-ground functional traits (SRL, SDM, RDM, LDM, LCC). We found that ITV of our experiment was slightly higher than that reported by Poorter et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); Siefert et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and similar to levels for three dry forest species subjected to contrasting levels of nutrients, irrigation, and herbivory (Lanuza et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), indicating these species may show high level of adaptability to shifts in environmental conditions (Poorter et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe found that addition of biochar accounted for an average of 11% variation in trait responses across species, yet explained 81% of species variation in LCC, while the overall proportion of variation in LCC explained by biochar was 1.3%, indicating the sensitivity of this functional trait to water stress, given drought affects photosynthesis (LI et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Biochar improves water retention capacity due to its internal porosity and by increasing the interpore volume of soils (Liao and Thomas \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and applications of biochar have been shown to improve water use efficiency of pioneer herbaceous seedlings by 44% (Gale et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), but also reduce leaf N content and LCC in tomato seedlings (Akhtar et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe proportions of inter-specific and intra-specific variation in trait responses to biochar addition ranged between 9.5 and 8.7–78%, respectively. Traits related to tissue quality and toughness (DMC of root, stem, leaf) are expected to express low levels of ITV, as they tend to be phylogenetically conservative (Chave et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2006\u003c/span\u003e); this was evident in our study for SDMC and RDMC, but was higher (61%) for LDMC. We found a low ITV for SLA, supporting findings reported by Poorter et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), but in contrast to controlled studies that show marked responses in leaf traits to shifts in ambient light levels, to enhance light capture (Poorter et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Sterck et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur data show high levels LMF, SRL, and LAR in \u003cem\u003eT. rosea\u003c/em\u003e, \u003cem\u003eC. odorata\u003c/em\u003e, and \u003cem\u003eS. humilis\u003c/em\u003e, that are coupled with low levels of DMC of leaf, stem, and roots, being the latter traits typically correlated with the physiological tolerance to drought (Hacke et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Jacobsen et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Thus, it is likely that these species may be susceptible to water deficit, in contrast to \u003cem\u003eG. ulmifolia\u003c/em\u003e that was characterized by high levels of DMC, height and robustness (H:D ratio), indicating adaptations for light capture, water transport, support, and tolerance to wind damage and drought conditions (Poorter \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Haase \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). We found that above-ground (LMF) and below-ground (RMF) trait responses to biochar mirrored relative allocations of biomass, as reported by Lanuza et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for dry forest seedlings subjected to contrasting levels of fertilization and that are similar to responses to drought conditions, where species tend to reduce biomass allocation of LAR and LMF and increase allocation to RMF (Poorter and Markesteijn \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Markesteijn and Poorter \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Competition for above-ground and below-ground resources tends to be dynamic during the seedling stage, when acquisition of sufficient water, nutrients, and light is essential for sustained growth (McMurtrie et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Poorter et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Ågren et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Fatichi et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrevious research has shown limited effects of RMF and LMF on below- and above-ground resource foraging, respectively (Poorter and Nagel \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Our study showed that limited investment in root biomass (low RMF), as found for \u003cem\u003eC. odorata\u003c/em\u003e, \u003cem\u003eS. humilis\u003c/em\u003e and \u003cem\u003eT. rosea\u003c/em\u003e, may be offset by cost-effective root growth, as indicated by large root length per unit of biomass invested (high SRL), whereas low biomass investment in leaf material (low LMF), as found for \u003cem\u003eG. ulmifolia\u003c/em\u003e, may be offset by large leaf area per unit of leaf biomass invested (high SLA). This compensation strategy in above- and below-ground biomass allocation has been demonstrated in response to drought conditions (Markesteijn and Poorter \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWe don’t find no negative effects of biochar on seedling survival and growth even at the highest dose applied. The variation in above-and below-ground trait responses to biochar were due more to inter- and intra-specific differences than to the main effects of biochar across species, indicating strong inherited effects of species. The application of biochar at the nursery stage improves the allocation of biomass towards traits related to growth and has positive effects on traits related to drought tolerance. However, given the potential importance of biochar applications on the metrics of demographic and functional traits of seedlings of species suitable for dry forest reforestation projects, long-term field studies with different application rates of biochar produced from different feedstocks are needed to generalize their effects.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank González-Zamora, J.M; López-Cruz, R.E; Gutiérrez-Cruz T.U for their invaluable assistance with the fieldwork.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for conducting this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Oscar Lanuza, with supervision of Guille Peguero. The first draft of the manuscript was written by Oscar Lanuza and Guille Peguero with contributions and ideas of Josep M. Espelta and Josep Peñuelas. All authors edited, critically reviewed, and finally approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\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\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the results of this study is published under CC by 4.0 license at the figshare repository: https://doi.org/10.6084/m9.figshare.25013753.v1 \u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e\u0026Aring;gren GI, Wetterstedt J\u0026Aring;M, Billberger MFK (2012) Nutrient limitation on terrestrial plant growth \u0026ndash; modeling the interaction between nitrogen and phosphorus. New Phytol 194:953\u0026ndash;960. https://doi.org/10.1111/j.1469-8137.2012.04116.x\u003c/li\u003e\n\u003cli\u003eAkhtar SS, Li G, Andersen MN, Liu F (2014) Biochar enhances yield and quality of tomato under reduced irrigation. Agric Water Manag 138:37\u0026ndash;44. https://doi.org/10.1016/j.agwat.2014.02.016\u003c/li\u003e\n\u003cli\u003eAmissah L, Mohren GMJ, Bongers F, et al (2021) Plant traits shape tree species drought survival and distribution along a rainfall gradient in Ghana. Ghana J For 37:1\u0026ndash;30\u003c/li\u003e\n\u003cli\u003eAmoah-Antwi C, Kwiatkowska-Malina J, Thornton SF, et al (2020) Restoration of soil quality using biochar and brown coal waste: A review. Sci Total Environ 722:137852. https://doi.org/10.1016/j.scitotenv.2020.137852\u003c/li\u003e\n\u003cli\u003eBastin J-F, Finegold Y, Garcia C, et al (2019) The global tree restoration potential. Science (80- ) 365:76\u0026ndash;79. https://doi.org/10.1126/science.aax0848\u003c/li\u003e\n\u003cli\u003eBates D, M\u0026auml;chler M, Bolker B, Walker S (2015) Fitting Linear Mixed-Effects Models Using lme4. J Stat Softw 67:. https://doi.org/10.18637/jss.v067.i01\u003c/li\u003e\n\u003cli\u003eBiederman LA, Harpole WS (2013) Biochar and its effects on plant productivity and nutrient cycling: a meta‐analysis. GCB Bioenergy 5:202\u0026ndash;214. https://doi.org/10.1111/gcbb.12037\u003c/li\u003e\n\u003cli\u003eBrown LA, Williams O, Dash J (2022) Calibration and characterisation of four chlorophyll meters and transmittance spectroscopy for non-destructive estimation of forest leaf chlorophyll concentration. Agric For Meteorol 323:109059. https://doi.org/10.1016/j.agrformet.2022.109059\u003c/li\u003e\n\u003cli\u003eChave J, Muller-Landau HC, Baker TR, et al (2006) Regional and phylogenetic variation of wood density across 2456 neotropical tree species. Ecol Appl 16:2356\u0026ndash;2367\u003c/li\u003e\n\u003cli\u003eChazdon RL, Broadbent EN, Rozendaal DMA, et al (2016) Carbon sequestration potential of second-growth forest regeneration in the Latin American tropics. Sci Adv 2:e1501639. https://doi.org/10.1126/sciadv.1501639\u003c/li\u003e\n\u003cli\u003eCheng C, Lehmann J, Thies JE, Burton SD (2008) Stability of black carbon in soils across a climatic gradient. J Geophys Res Biogeosciences 113:G02027. https://doi.org/10.1029/2007JG000642\u003c/li\u003e\n\u003cli\u003eClough T, Condron L, Kammann C, M\u0026uuml;ller C (2013) A Review of Biochar and Soil Nitrogen Dynamics. Agronomy 3:275\u0026ndash;293. https://doi.org/10.3390/agronomy3020275\u003c/li\u003e\n\u003cli\u003eCook-Patton SC, Leavitt SM, Gibbs D, et al (2020) Mapping carbon accumulation potential from global natural forest regrowth. Nature 585:545\u0026ndash;550. https://doi.org/10.1038/s41586-020-2686-x\u003c/li\u003e\n\u003cli\u003eDai Y, Zheng H, Jiang Z, Xing B (2020) Combined effects of biochar properties and soil conditions on plant growth: A meta-analysis. Sci Total Environ 713:136635. https://doi.org/10.1016/j.scitotenv.2020.136635\u003c/li\u003e\n\u003cli\u003eDrake JA, Cavagnaro TR, Cunningham SC, et al (2016) Does Biochar Improve Establishment of Tree Seedlings in Saline Sodic Soils? L Degrad Dev 27:52\u0026ndash;59. https://doi.org/10.1002/ldr.2374\u003c/li\u003e\n\u003cli\u003eFagbenro JA, Oshunsanya SO, Oyeleye BA (2015) Effects of Gliricidia Biochar and Inorganic Fertilizer on Moringa Plant Grown in an Oxisol. Commun Soil Sci Plant Anal 46:619\u0026ndash;626. https://doi.org/10.1080/00103624.2015.1005222\u003c/li\u003e\n\u003cli\u003eFatichi S, Leuzinger S, K\u0026ouml;rner C (2014) Moving beyond photosynthesis: from carbon source to sink‐driven vegetation modeling. New Phytol 201:1086\u0026ndash;1095. https://doi.org/10.1111/nph.12614\u003c/li\u003e\n\u003cli\u003eGale N V, Halim MA, Horsburgh M, Thomas SC (2017) Comparative responses of early‐successional plants to charcoal soil amendments. Ecosphere 8:e01933. https://doi.org/10.1002/ecs2.1933\u003c/li\u003e\n\u003cli\u003eGale N V, Thomas SC (2019) Dose-dependence of growth and ecophysiological responses of plants to biochar. Sci Total Environ 658:1344\u0026ndash;1354. https://doi.org/10.1016/j.scitotenv.2018.12.239\u003c/li\u003e\n\u003cli\u003eGao S, DeLuca TH, Cleveland CC (2019) Biochar additions alter phosphorus and nitrogen availability in agricultural ecosystems: A meta-analysis. Sci Total Environ 654:463\u0026ndash;472. https://doi.org/10.1016/j.scitotenv.2018.11.124\u003c/li\u003e\n\u003cli\u003eGeorgiou K, Jackson RB, Vindu\u0026scaron;kov\u0026aacute; O, et al (2022) Global stocks and capacity of mineral-associated soil organic carbon. Nat Commun 13:3797. https://doi.org/10.1038/s41467-022-31540-9\u003c/li\u003e\n\u003cli\u003eGonzalez Sarango EM, Valarezo Manosalvas C, Mora M, et al (2021) Biochar amendment did not influence the growth of two tree plantations on nutrient‐depleted Ultisols in the south Ecuadorian Amazon region. Soil Sci Soc Am J 85:862\u0026ndash;878. https://doi.org/10.1002/saj2.20227\u003c/li\u003e\n\u003cli\u003eGriscom BW, Adams J, Ellis PW, et al (2017) Natural climate solutions. Proc Natl Acad Sci 114:11645\u0026ndash;11650. https://doi.org/10.1073/pnas.1710465114\u003c/li\u003e\n\u003cli\u003eHaase DL (2008) Understanding forest seedling quality: measurements and interpretation. Tree Plant Notes 52:24\u0026ndash;30\u003c/li\u003e\n\u003cli\u003eHacke UG, Sperry JS, Pockman WT, et al (2001) Trends in wood density and structure are linked to prevention of xylem implosion by negative pressure. Oecologia 126:457\u0026ndash;461. https://doi.org/10.1007/s004420100628\u003c/li\u003e\n\u003cli\u003eIPCC [Intergovernmental Panel on Climate Change] (2022) Summary for Policymakers. In: Masson-Delmotte V, Zhai P, P\u0026ouml;rtner H-O, et al. (eds) Global Warming of 1.5\u0026deg;C. Cambridge University Press, Cambridge, UK and New York, NY, USA, pp 1\u0026ndash;24\u003c/li\u003e\n\u003cli\u003eIrfan M (2017) Potential value of biochar as a soil amendment: A review. Pure Appl Biol 6:1494\u0026ndash;1502. https://doi.org/10.19045/bspab.2017.600161\u003c/li\u003e\n\u003cli\u003eJacobsen AL, Ewers FW, Pratt RB, et al (2005) Do Xylem Fibers Affect Vessel Cavitation Resistance? Plant Physiol 139:546\u0026ndash;556. https://doi.org/10.1104/pp.104.058404\u003c/li\u003e\n\u003cli\u003eJeffery S, Abalos D, Prodana M, et al (2017) Biochar boosts tropical but not temperate crop yields. Environ Res Lett 12:053001. https://doi.org/10.1088/1748-9326/aa67bd\u003c/li\u003e\n\u003cli\u003eLanuza OR, Espelta JM, Pe\u0026ntilde;uelas J, Peguero G (2020) Assessing intraspecific trait variability during seedling establishment to improve restoration of tropical dry forests. Ecosphere 11:e03052. https://doi.org/10.1002/ecs2.3052\u003c/li\u003e\n\u003cli\u003eLee JW, Hawkins B, Day DM, Reicosky DC (2010) Sustainability: the capacity of smokeless biomass pyrolysis for energy production, global carbon capture and sequestration. Energy Environ Sci 3:1695. https://doi.org/10.1039/c004561f\u003c/li\u003e\n\u003cli\u003eLefebvre D, Rom\u0026aacute;n-Da\u0026ntilde;obeytia F, Soete J, et al (2019) Biochar Effects on Two Tropical Tree Species and Its Potential as a Tool for Reforestation. Forests 10:678. https://doi.org/10.3390/f10080678\u003c/li\u003e\n\u003cli\u003eLehmann J, Cowie A, Masiello CA, et al (2021) Biochar in climate change mitigation. Nat Geosci 14:883\u0026ndash;892. https://doi.org/10.1038/s41561-021-00852-8\u003c/li\u003e\n\u003cli\u003eLehmann J, Joseph S (2015) Biochar for environmental management: an introduction. In: Lehmann J, Joseph S (eds) Biochar for environmental management, 2nd edn. Routledge, London, pp 1\u0026ndash;13\u003c/li\u003e\n\u003cli\u003eLewis SL, Wheeler CE, Mitchard ETA, Koch A (2019) Restoring natural forests is the best way to remove atmospheric carbon. Nature 568:25\u0026ndash;28. https://doi.org/10.1038/d41586-019-01026-8\u003c/li\u003e\n\u003cli\u003eLI R, GUO P, Michael B, et al (2006) Evaluation of Chlorophyll Content and Fluorescence Parameters as Indicators of Drought Tolerance in Barley. Agric Sci China 5:751\u0026ndash;757. https://doi.org/10.1016/S1671-2927(06)60120-X\u003c/li\u003e\n\u003cli\u003eLiao W, Thomas S (2019) Biochar Particle Size and Post-Pyrolysis Mechanical Processing Affect Soil pH, Water Retention Capacity, and Plant Performance. Soil Syst 3:14. https://doi.org/10.3390/soilsystems3010014\u003c/li\u003e\n\u003cli\u003eLiu X, Zhang A, Ji C, et al (2013) Biochar\u0026rsquo;s effect on crop productivity and the dependence on experimental conditions\u0026mdash;a meta-analysis of literature data. Plant Soil 373:583\u0026ndash;594. https://doi.org/10.1007/s11104-013-1806-x\u003c/li\u003e\n\u003cli\u003eL\u0026uuml;decke D, Ben-Shachar M, Patil I, et al (2021) performance: An R Package for Assessment, Comparison and Testing of Statistical Models. J Open Source Softw 6:3139. https://doi.org/10.21105/joss.03139\u003c/li\u003e\n\u003cli\u003eMarkesteijn L, Poorter L (2009) Seedling root morphology and biomass allocation of 62 tropical tree species in relation to drought‐ and shade‐tolerance. J Ecol 97:311\u0026ndash;325. https://doi.org/10.1111/j.1365-2745.2008.01466.x\u003c/li\u003e\n\u003cli\u003eMcMurtrie RE, Norby RJ, Medlyn BE, et al (2008) Why is plant-growth response to elevated CO2 amplified when water is limiting, but reduced when nitrogen is limiting? A growth-optimisation hypothesis. Funct Plant Biol 35:521. https://doi.org/10.1071/FP08128\u003c/li\u003e\n\u003cli\u003eP\u0026eacute;rez-Harguindeguy N, D\u0026iacute;az S, Garnier E, et al (2013) New handbook for standardised measurement of plant functional traits worldwide. Aust J Bot 61:167. https://doi.org/10.1071/BT12225\u003c/li\u003e\n\u003cli\u003ePoorter H, Nagel O (2000) The role of biomass allocation in the growth response of plants to different levels of light, CO2, nutrients and water: a quantitative review. Funct Plant Biol 27:1191. https://doi.org/10.1071/PP99173_CO\u003c/li\u003e\n\u003cli\u003ePoorter H, Niinemets \u0026Uuml;, Poorter L, et al (2009) Causes and consequences of variation in leaf mass per area (LMA): a meta‐analysis. New Phytol 182:565\u0026ndash;588. https://doi.org/10.1111/j.1469-8137.2009.02830.x\u003c/li\u003e\n\u003cli\u003ePoorter H, Niklas KJ, Reich PB, et al (2012) Biomass allocation to leaves, stems and roots: meta‐analyses of interspecific variation and environmental control. New Phytol 193:30\u0026ndash;50. https://doi.org/10.1111/j.1469-8137.2011.03952.x\u003c/li\u003e\n\u003cli\u003ePoorter L (1999) Growth responses of 15 rain‐forest tree species to a light gradient: the relative importance of morphological and physiological traits. Funct Ecol 13:396\u0026ndash;410. https://doi.org/10.1046/j.1365-2435.1999.00332.x\u003c/li\u003e\n\u003cli\u003ePoorter L, Castilho C V, Schietti J, et al (2018) Can traits predict individual growth performance? A test in a hyperdiverse tropical forest. New Phytol 219:109\u0026ndash;121. https://doi.org/10.1111/nph.15206\u003c/li\u003e\n\u003cli\u003ePoorter L, Markesteijn L (2008) Seedling Traits Determine Drought Tolerance of Tropical Tree Species. Biotropica 40:321\u0026ndash;331. https://doi.org/10.1111/j.1744-7429.2007.00380.x\u003c/li\u003e\n\u003cli\u003eQian X, Liu L, Croft H, Chen J (2021) Relationship Between Leaf Maximum Carboxylation Rate and Chlorophyll Content Preserved Across 13 Species. J Geophys Res Biogeosciences 126:e2020JG006076. https://doi.org/10.1029/2020JG006076\u003c/li\u003e\n\u003cli\u003eR Core Team (2021) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing\u003c/li\u003e\n\u003cli\u003eRuiz G\u0026oacute;mez VL, Sav\u0026eacute; Monserrat R, Lanuza Lanuza OR, et al (2021) Evoluci\u0026oacute;n de la temperatura y precipitaci\u0026oacute;n en cuatro estaciones meteorol\u0026oacute;gicas, ubicadas en la regi\u0026oacute;n Norcentral de Nicaragua, Centroam\u0026eacute;rica. Rev Cient\u0026iacute;fica FAREM-Estel\u0026iacute; 38:197\u0026ndash;212. https://doi.org/10.5377/farem.v0i38.11952\u003c/li\u003e\n\u003cli\u003eSchneider CA, Rasband WS, Eliceiri KW (2012) NIH Image to ImageJ: 25 years of image analysis. Nat Methods 9:671\u0026ndash;675. https://doi.org/10.1038/nmeth.2089\u003c/li\u003e\n\u003cli\u003eSiefert A, Violle C, Chalmandrier L, et al (2015) A global meta‐analysis of the relative extent of intraspecific trait variation in plant communities. Ecol Lett 18:1406\u0026ndash;1419. https://doi.org/10.1111/ele.12508\u003c/li\u003e\n\u003cli\u003eSmith P (2016) Soil carbon sequestration and biochar as negative emission technologies. Glob Chang Biol 22:1315\u0026ndash;1324. https://doi.org/10.1111/gcb.13178\u003c/li\u003e\n\u003cli\u003eSpokas KA, Cantrell KB, Novak JM, et al (2012) Biochar: A Synthesis of Its Agronomic Impact beyond Carbon Sequestration. J Environ Qual 41:973\u0026ndash;989. https://doi.org/10.2134/jeq2011.0069\u003c/li\u003e\n\u003cli\u003eSterck FJ, Duursma RA, Pearcy RW, et al (2013) Plasticity influencing the light compensation point offsets the specialization for light niches across shrub species in a tropical forest understorey. J Ecol 101:971\u0026ndash;980. https://doi.org/10.1111/1365-2745.12076\u003c/li\u003e\n\u003cli\u003eThomas SC, Gale N (2015) Biochar and forest restoration: a review and meta-analysis of tree growth responses. New For 46:931\u0026ndash;946. https://doi.org/10.1007/s11056-015-9491-7\u003c/li\u003e\n\u003cli\u003eVil\u0026agrave;‐Cabrera A, Mart\u0026iacute;nez‐Vilalta J, Retana J (2015) Functional trait variation along environmental gradients in temperate and Mediterranean trees. Glob Ecol Biogeogr 24:1377\u0026ndash;1389. https://doi.org/10.1111/geb.12379\u003c/li\u003e\n\u003cli\u003eWang J, Xiong Z, Kuzyakov Y (2016) Biochar stability in soil: meta‐analysis of decomposition and priming effects. GCB Bioenergy 8:512\u0026ndash;523. https://doi.org/10.1111/gcbb.12266\u003c/li\u003e\n\u003cli\u003eWerden LK, Alvarado J. P, Zarges S, et al (2018) Using soil amendments and plant functional traits to select native tropical dry forest species for the restoration of degraded Vertisols. J Appl Ecol 55:1019\u0026ndash;1028. https://doi.org/10.1111/1365-2664.12998\u003c/li\u003e\n\u003cli\u003eWoolf D, Amonette JE, Street-Perrott FA, et al (2010) Sustainable biochar to mitigate global climate change. Nat Commun 1:56. https://doi.org/10.1038/ncomms1053\u003c/li\u003e\n\u003cli\u003eXiang Y, Deng Q, Duan H, Guo Y (2017) Effects of biochar application on root traits: a meta-analysis. GCB Bioenergy 9:1563\u0026ndash;1572. https://doi.org/10.1111/gcbb.12449\u003c/li\u003e\n\u003cli\u003e\u0026Aring;gren GI, Wetterstedt J\u0026Aring;M, Billberger MFK (2012) Nutrient limitation on terrestrial plant growth \u0026ndash; modeling the interaction between nitrogen and phosphorus. New Phytol 194:953\u0026ndash;960. https://doi.org/10.1111/j.1469-8137.2012.04116.x\u003c/li\u003e\n\u003cli\u003eAkhtar SS, Li G, Andersen MN, Liu F (2014) Biochar enhances yield and quality of tomato under reduced irrigation. Agric Water Manag 138:37\u0026ndash;44. https://doi.org/10.1016/j.agwat.2014.02.016\u003c/li\u003e\n\u003cli\u003eAmissah L, Mohren GMJ, Bongers F, et al (2021) Plant traits shape tree species drought survival and distribution along a rainfall gradient in Ghana. Ghana J For 37:1\u0026ndash;30\u003c/li\u003e\n\u003cli\u003eAmoah-Antwi C, Kwiatkowska-Malina J, Thornton SF, et al (2020) Restoration of soil quality using biochar and brown coal waste: A review. Sci Total Environ 722:137852. https://doi.org/10.1016/j.scitotenv.2020.137852\u003c/li\u003e\n\u003cli\u003eBastin J-F, Finegold Y, Garcia C, et al (2019) The global tree restoration potential. Science (80- ) 365:76\u0026ndash;79. https://doi.org/10.1126/science.aax0848\u003c/li\u003e\n\u003cli\u003eBates D, M\u0026auml;chler M, Bolker B, Walker S (2015) Fitting Linear Mixed-Effects Models Using lme4. J Stat Softw 67:. https://doi.org/10.18637/jss.v067.i01\u003c/li\u003e\n\u003cli\u003eBiederman LA, Harpole WS (2013) Biochar and its effects on plant productivity and nutrient cycling: a meta‐analysis. GCB Bioenergy 5:202\u0026ndash;214. https://doi.org/10.1111/gcbb.12037\u003c/li\u003e\n\u003cli\u003eBrown LA, Williams O, Dash J (2022) Calibration and characterisation of four chlorophyll meters and transmittance spectroscopy for non-destructive estimation of forest leaf chlorophyll concentration. Agric For Meteorol 323:109059. https://doi.org/10.1016/j.agrformet.2022.109059\u003c/li\u003e\n\u003cli\u003eChave J, Muller-Landau HC, Baker TR, et al (2006) Regional and phylogenetic variation of wood density across 2456 neotropical tree species. Ecol Appl 16:2356\u0026ndash;2367\u003c/li\u003e\n\u003cli\u003eChazdon RL, Broadbent EN, Rozendaal DMA, et al (2016) Carbon sequestration potential of second-growth forest regeneration in the Latin American tropics. Sci Adv 2:e1501639. https://doi.org/10.1126/sciadv.1501639\u003c/li\u003e\n\u003cli\u003eCheng C, Lehmann J, Thies JE, Burton SD (2008) Stability of black carbon in soils across a climatic gradient. J Geophys Res Biogeosciences 113:G02027. https://doi.org/10.1029/2007JG000642\u003c/li\u003e\n\u003cli\u003eClough T, Condron L, Kammann C, M\u0026uuml;ller C (2013) A Review of Biochar and Soil Nitrogen Dynamics. Agronomy 3:275\u0026ndash;293. https://doi.org/10.3390/agronomy3020275\u003c/li\u003e\n\u003cli\u003eCook-Patton SC, Leavitt SM, Gibbs D, et al (2020) Mapping carbon accumulation potential from global natural forest regrowth. Nature 585:545\u0026ndash;550. https://doi.org/10.1038/s41586-020-2686-x\u003c/li\u003e\n\u003cli\u003eDai Y, Zheng H, Jiang Z, Xing B (2020) Combined effects of biochar properties and soil conditions on plant growth: A meta-analysis. Sci Total Environ 713:136635. https://doi.org/10.1016/j.scitotenv.2020.136635\u003c/li\u003e\n\u003cli\u003eDrake JA, Cavagnaro TR, Cunningham SC, et al (2016) Does Biochar Improve Establishment of Tree Seedlings in Saline Sodic Soils? L Degrad Dev 27:52\u0026ndash;59. https://doi.org/10.1002/ldr.2374\u003c/li\u003e\n\u003cli\u003eFagbenro JA, Oshunsanya SO, Oyeleye BA (2015) Effects of Gliricidia Biochar and Inorganic Fertilizer on Moringa Plant Grown in an Oxisol. Commun Soil Sci Plant Anal 46:619\u0026ndash;626. https://doi.org/10.1080/00103624.2015.1005222\u003c/li\u003e\n\u003cli\u003eFatichi S, Leuzinger S, K\u0026ouml;rner C (2014) Moving beyond photosynthesis: from carbon source to sink‐driven vegetation modeling. New Phytol 201:1086\u0026ndash;1095. https://doi.org/10.1111/nph.12614\u003c/li\u003e\n\u003cli\u003eGale N V, Halim MA, Horsburgh M, Thomas SC (2017) Comparative responses of early‐successional plants to charcoal soil amendments. Ecosphere 8:e01933. https://doi.org/10.1002/ecs2.1933\u003c/li\u003e\n\u003cli\u003eGale N V, Thomas SC (2019) Dose-dependence of growth and ecophysiological responses of plants to biochar. Sci Total Environ 658:1344\u0026ndash;1354. https://doi.org/10.1016/j.scitotenv.2018.12.239\u003c/li\u003e\n\u003cli\u003eGao S, DeLuca TH, Cleveland CC (2019) Biochar additions alter phosphorus and nitrogen availability in agricultural ecosystems: A meta-analysis. Sci Total Environ 654:463\u0026ndash;472. https://doi.org/10.1016/j.scitotenv.2018.11.124\u003c/li\u003e\n\u003cli\u003eGeorgiou K, Jackson RB, Vindu\u0026scaron;kov\u0026aacute; O, et al (2022) Global stocks and capacity of mineral-associated soil organic carbon. Nat Commun 13:3797. https://doi.org/10.1038/s41467-022-31540-9\u003c/li\u003e\n\u003cli\u003eGonzalez Sarango EM, Valarezo Manosalvas C, Mora M, et al (2021) Biochar amendment did not influence the growth of two tree plantations on nutrient‐depleted Ultisols in the south Ecuadorian Amazon region. Soil Sci Soc Am J 85:862\u0026ndash;878. https://doi.org/10.1002/saj2.20227\u003c/li\u003e\n\u003cli\u003eGriscom BW, Adams J, Ellis PW, et al (2017) Natural climate solutions. Proc Natl Acad Sci 114:11645\u0026ndash;11650. https://doi.org/10.1073/pnas.1710465114\u003c/li\u003e\n\u003cli\u003eHaase DL (2008) Understanding forest seedling quality: measurements and interpretation. Tree Plant Notes 52:24\u0026ndash;30\u003c/li\u003e\n\u003cli\u003eHacke UG, Sperry JS, Pockman WT, et al (2001) Trends in wood density and structure are linked to prevention of xylem implosion by negative pressure. Oecologia 126:457\u0026ndash;461. https://doi.org/10.1007/s004420100628\u003c/li\u003e\n\u003cli\u003eIPCC [Intergovernmental Panel on Climate Change] (2022) Summary for Policymakers. In: Masson-Delmotte V, Zhai P, P\u0026ouml;rtner H-O, et al. (eds) Global Warming of 1.5\u0026deg;C. Cambridge University Press, Cambridge, UK and New York, NY, USA, pp 1\u0026ndash;24\u003c/li\u003e\n\u003cli\u003eIrfan M (2017) Potential value of biochar as a soil amendment: A review. Pure Appl Biol 6:1494\u0026ndash;1502. https://doi.org/10.19045/bspab.2017.600161\u003c/li\u003e\n\u003cli\u003eJacobsen AL, Ewers FW, Pratt RB, et al (2005) Do Xylem Fibers Affect Vessel Cavitation Resistance? Plant Physiol 139:546\u0026ndash;556. https://doi.org/10.1104/pp.104.058404\u003c/li\u003e\n\u003cli\u003eJeffery S, Abalos D, Prodana M, et al (2017) Biochar boosts tropical but not temperate crop yields. Environ Res Lett 12:053001. https://doi.org/10.1088/1748-9326/aa67bd\u003c/li\u003e\n\u003cli\u003eLanuza OR, Espelta JM, Pe\u0026ntilde;uelas J, Peguero G (2020) Assessing intraspecific trait variability during seedling establishment to improve restoration of tropical dry forests. Ecosphere 11:e03052. https://doi.org/10.1002/ecs2.3052\u003c/li\u003e\n\u003cli\u003eLee JW, Hawkins B, Day DM, Reicosky DC (2010) Sustainability: the capacity of smokeless biomass pyrolysis for energy production, global carbon capture and sequestration. Energy Environ Sci 3:1695. https://doi.org/10.1039/c004561f\u003c/li\u003e\n\u003cli\u003eLefebvre D, Rom\u0026aacute;n-Da\u0026ntilde;obeytia F, Soete J, et al (2019) Biochar Effects on Two Tropical Tree Species and Its Potential as a Tool for Reforestation. Forests 10:678. https://doi.org/10.3390/f10080678\u003c/li\u003e\n\u003cli\u003eLehmann J, Cowie A, Masiello CA, et al (2021) Biochar in climate change mitigation. Nat Geosci 14:883\u0026ndash;892. https://doi.org/10.1038/s41561-021-00852-8\u003c/li\u003e\n\u003cli\u003eLehmann J, Joseph S (2015) Biochar for environmental management: an introduction. In: Lehmann J, Joseph S (eds) Biochar for environmental management, 2nd edn. Routledge, London, pp 1\u0026ndash;13\u003c/li\u003e\n\u003cli\u003eLewis SL, Wheeler CE, Mitchard ETA, Koch A (2019) Restoring natural forests is the best way to remove atmospheric carbon. Nature 568:25\u0026ndash;28. https://doi.org/10.1038/d41586-019-01026-8\u003c/li\u003e\n\u003cli\u003eLI R, GUO P, Michael B, et al (2006) Evaluation of Chlorophyll Content and Fluorescence Parameters as Indicators of Drought Tolerance in Barley. Agric Sci China 5:751\u0026ndash;757. https://doi.org/10.1016/S1671-2927(06)60120-X\u003c/li\u003e\n\u003cli\u003eLiao W, Thomas S (2019) Biochar Particle Size and Post-Pyrolysis Mechanical Processing Affect Soil pH, Water Retention Capacity, and Plant Performance. Soil Syst 3:14. https://doi.org/10.3390/soilsystems3010014\u003c/li\u003e\n\u003cli\u003eLiu X, Zhang A, Ji C, et al (2013) Biochar\u0026rsquo;s effect on crop productivity and the dependence on experimental conditions\u0026mdash;a meta-analysis of literature data. Plant Soil 373:583\u0026ndash;594. https://doi.org/10.1007/s11104-013-1806-x\u003c/li\u003e\n\u003cli\u003eL\u0026uuml;decke D, Ben-Shachar M, Patil I, et al (2021) performance: An R Package for Assessment, Comparison and Testing of Statistical Models. J Open Source Softw 6:3139. https://doi.org/10.21105/joss.03139\u003c/li\u003e\n\u003cli\u003eMarkesteijn L, Poorter L (2009) Seedling root morphology and biomass allocation of 62 tropical tree species in relation to drought‐ and shade‐tolerance. J Ecol 97:311\u0026ndash;325. https://doi.org/10.1111/j.1365-2745.2008.01466.x\u003c/li\u003e\n\u003cli\u003eMcMurtrie RE, Norby RJ, Medlyn BE, et al (2008) Why is plant-growth response to elevated CO2 amplified when water is limiting, but reduced when nitrogen is limiting? A growth-optimisation hypothesis. Funct Plant Biol 35:521. https://doi.org/10.1071/FP08128\u003c/li\u003e\n\u003cli\u003eP\u0026eacute;rez-Harguindeguy N, D\u0026iacute;az S, Garnier E, et al (2013) New handbook for standardised measurement of plant functional traits worldwide. Aust J Bot 61:167. https://doi.org/10.1071/BT12225\u003c/li\u003e\n\u003cli\u003ePoorter H, Nagel O (2000) The role of biomass allocation in the growth response of plants to different levels of light, CO2, nutrients and water: a quantitative review. Funct Plant Biol 27:1191. https://doi.org/10.1071/PP99173_CO\u003c/li\u003e\n\u003cli\u003ePoorter H, Niinemets \u0026Uuml;, Poorter L, et al (2009) Causes and consequences of variation in leaf mass per area (LMA): a meta‐analysis. New Phytol 182:565\u0026ndash;588. https://doi.org/10.1111/j.1469-8137.2009.02830.x\u003c/li\u003e\n\u003cli\u003ePoorter H, Niklas KJ, Reich PB, et al (2012) Biomass allocation to leaves, stems and roots: meta‐analyses of interspecific variation and environmental control. New Phytol 193:30\u0026ndash;50. https://doi.org/10.1111/j.1469-8137.2011.03952.x\u003c/li\u003e\n\u003cli\u003ePoorter L (1999) Growth responses of 15 rain‐forest tree species to a light gradient: the relative importance of morphological and physiological traits. Funct Ecol 13:396\u0026ndash;410. https://doi.org/10.1046/j.1365-2435.1999.00332.x\u003c/li\u003e\n\u003cli\u003ePoorter L, Castilho C V, Schietti J, et al (2018) Can traits predict individual growth performance? A test in a hyperdiverse tropical forest. New Phytol 219:109\u0026ndash;121. https://doi.org/10.1111/nph.15206\u003c/li\u003e\n\u003cli\u003ePoorter L, Markesteijn L (2008) Seedling Traits Determine Drought Tolerance of Tropical Tree Species. Biotropica 40:321\u0026ndash;331. https://doi.org/10.1111/j.1744-7429.2007.00380.x\u003c/li\u003e\n\u003cli\u003eQian X, Liu L, Croft H, Chen J (2021) Relationship Between Leaf Maximum Carboxylation Rate and Chlorophyll Content Preserved Across 13 Species. J Geophys Res Biogeosciences 126:e2020JG006076. https://doi.org/10.1029/2020JG006076\u003c/li\u003e\n\u003cli\u003eR Core Team (2021) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing\u003c/li\u003e\n\u003cli\u003eRuiz G\u0026oacute;mez VL, Sav\u0026eacute; Monserrat R, Lanuza Lanuza OR, et al (2021) Evoluci\u0026oacute;n de la temperatura y precipitaci\u0026oacute;n en cuatro estaciones meteorol\u0026oacute;gicas, ubicadas en la regi\u0026oacute;n Norcentral de Nicaragua, Centroam\u0026eacute;rica. Rev Cient\u0026iacute;fica FAREM-Estel\u0026iacute; 38:197\u0026ndash;212. https://doi.org/10.5377/farem.v0i38.11952\u003c/li\u003e\n\u003cli\u003eSchneider CA, Rasband WS, Eliceiri KW (2012) NIH Image to ImageJ: 25 years of image analysis. Nat Methods 9:671\u0026ndash;675. https://doi.org/10.1038/nmeth.2089\u003c/li\u003e\n\u003cli\u003eSiefert A, Violle C, Chalmandrier L, et al (2015) A global meta‐analysis of the relative extent of intraspecific trait variation in plant communities. Ecol Lett 18:1406\u0026ndash;1419. https://doi.org/10.1111/ele.12508\u003c/li\u003e\n\u003cli\u003eSmith P (2016) Soil carbon sequestration and biochar as negative emission technologies. Glob Chang Biol 22:1315\u0026ndash;1324. https://doi.org/10.1111/gcb.13178\u003c/li\u003e\n\u003cli\u003eSpokas KA, Cantrell KB, Novak JM, et al (2012) Biochar: A Synthesis of Its Agronomic Impact beyond Carbon Sequestration. J Environ Qual 41:973\u0026ndash;989. https://doi.org/10.2134/jeq2011.0069\u003c/li\u003e\n\u003cli\u003eSterck FJ, Duursma RA, Pearcy RW, et al (2013) Plasticity influencing the light compensation point offsets the specialization for light niches across shrub species in a tropical forest understorey. J Ecol 101:971\u0026ndash;980. https://doi.org/10.1111/1365-2745.12076\u003c/li\u003e\n\u003cli\u003eThomas SC, Gale N (2015) Biochar and forest restoration: a review and meta-analysis of tree growth responses. New For 46:931\u0026ndash;946. https://doi.org/10.1007/s11056-015-9491-7\u003c/li\u003e\n\u003cli\u003eVil\u0026agrave;‐Cabrera A, Mart\u0026iacute;nez‐Vilalta J, Retana J (2015) Functional trait variation along environmental gradients in temperate and Mediterranean trees. Glob Ecol Biogeogr 24:1377\u0026ndash;1389. https://doi.org/10.1111/geb.12379\u003c/li\u003e\n\u003cli\u003eWang J, Xiong Z, Kuzyakov Y (2016) Biochar stability in soil: meta‐analysis of decomposition and priming effects. GCB Bioenergy 8:512\u0026ndash;523. https://doi.org/10.1111/gcbb.12266\u003c/li\u003e\n\u003cli\u003eWerden LK, Alvarado J. P, Zarges S, et al (2018) Using soil amendments and plant functional traits to select native tropical dry forest species for the restoration of degraded Vertisols. J Appl Ecol 55:1019\u0026ndash;1028. https://doi.org/10.1111/1365-2664.12998\u003c/li\u003e\n\u003cli\u003eWoolf D, Amonette JE, Street-Perrott FA, et al (2010) Sustainable biochar to mitigate global climate change. Nat Commun 1:56. https://doi.org/10.1038/ncomms1053\u003c/li\u003e\n\u003cli\u003eXiang Y, Deng Q, Duan H, Guo Y (2017) Effects of biochar application on root traits: a meta-analysis. GCB Bioenergy 9:1563\u0026ndash;1572. https://doi.org/10.1111/gcbb.12449\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":"[email protected]","identity":"new-forests","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nefo","sideBox":"Learn more about [New Forests](http://link.springer.com/journal/11056)","snPcode":"11056","submissionUrl":"https://submission.nature.com/new-submission/11056/3","title":"New Forests","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"biochar, biomass allocation, intraspecific trait variability, plasticity, soil amendment, tropical dry forests","lastPublishedDoi":"10.21203/rs.3.rs-4078094/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4078094/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe addition of biochar as a soil amendment has great potential for ecological restoration and long-term carbon (C) storage. However, few studies have evaluated the functional trait responses of tree seedlings to increasing application rates of biochar and almost no information is available for tropical dry forests (TDF). Here, we conducted a greenhouse experiment to quantify effects of rates of biochar (0, 5, 10, 20, and 40 t/ha) on demographic and functional traits of six tree species used in TDF restoration programs. After 100 days of growth, we found no negative effects of biochar on seedling survival and only in two of the species the highest dose applied slightly reduced the final biomass. The addition of biochar increased leaf chlorophyll content (LCC) and specific leaf area (SLA) of all species. Greater variation in above-and below-ground trait responses to biochar was due more to inter-specific (52%) and intra-specific (36%) differences than main effects of biochar across species (11%), although we found that 81% of the variation in the LCC was due to the addition of biochar. We found a positive effect of biochar on morphological traits related to C gain and physiological tolerance to drought (higher dry mass content of root, leaf, and stem, LCC, SLA, and leaf area ratio). Therefore, we suggest that applications of biochar between 5 to 30 t/ha do not compromise the early growth of the seedlings of the studied species, and even may improve their growth capacity and drought resistance during their establishment in the field.\u003c/p\u003e","manuscriptTitle":"Above and belowground functional trait response to biochar addition in seedlings of six tropical dry forest tree species","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-14 20:39:43","doi":"10.21203/rs.3.rs-4078094/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-26T13:28:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-14T11:14:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"185715670988560776877964013638415882072","date":"2024-07-10T01:30:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"112529072814996287333659729166013579577","date":"2024-07-08T10:32:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-05T06:07:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"165133053175704088099039442568040245406","date":"2024-06-18T17:52:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-24T15:40:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-12T16:13:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-12T16:13:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"New Forests","date":"2024-03-11T20:33:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"new-forests","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nefo","sideBox":"Learn more about [New Forests](http://link.springer.com/journal/11056)","snPcode":"11056","submissionUrl":"https://submission.nature.com/new-submission/11056/3","title":"New Forests","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"57dfdb29-7596-417c-8874-42290d759ff5","owner":[],"postedDate":"March 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-04T16:25:26+00:00","versionOfRecord":{"articleIdentity":"rs-4078094","link":"https://doi.org/10.1007/s11056-024-10074-6","journal":{"identity":"new-forests","isVorOnly":false,"title":"New Forests"},"publishedOn":"2024-11-02 16:20:07","publishedOnDateReadable":"November 2nd, 2024"},"versionCreatedAt":"2024-03-14 20:39:43","video":"","vorDoi":"10.1007/s11056-024-10074-6","vorDoiUrl":"https://doi.org/10.1007/s11056-024-10074-6","workflowStages":[]},"version":"v1","identity":"rs-4078094","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4078094","identity":"rs-4078094","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}