Invasive trees impact leaf litter carbon and nitrogen pools in temperate forests

Invasive trees impact leaf litter carbon and nitrogen pools in temperate forests | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 22 July 2025 V1 Latest version Share on Invasive trees impact leaf litter carbon and nitrogen pools in temperate forests Authors : Paweł Horodecki [email protected] , Andrzej Jagodziński , and Marcin K. Dyderski Authors Info & Affiliations https://doi.org/10.22541/au.175316740.00565263/v1 280 views 134 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Invasive tree species may modify habitats to ensure their ecological success. We aimed to assess the impact of Prunus serotina , Quercus rubra , and Robinia pseudoacacia on litter mass and its carbon and nitrogen pools in temperate forests of Wielkopolska National Park. We predicted that P. serotina will reduce litter mass and litter element pools in poor habitats oppositely to fresh ones; Q. rubra will accumulate more litter than comparable broadleaved forests; while R. pseudoacacia will have no effect on studied features. For our study we used a set of study plots divided into nine types according to tree stand species composition and soil fertility. We measured the biometric characteristics of trees, the share of every species in total stand basal area, and then we calculated foliage production and used it as a base for leaf features community-weighted means (CWM) used in further calculations. We compared them with decomposition rates and litterfall assessed on sample plots. Using linear mixed-effects models we showed that forest type explains from 32.7% (litter mass) to 39.2% (litter N pool) of variability. P. serotina affected studied features according to our expectations, while Q. rubra and R. pseudoacacia had no statistically significant effects. PCA revealed a gradient of soil, litter, and foliage characteristics along the axis from broadleaf to poor conifers, which explains 53.7% of the variability. The studied invasive species affect litter accumulation and nutrient pools in temperate forests. They potentially improve habitat conditions, however, mainly for their ecological success. Their presence, at the expense of native species, threatens biological diversity. Invasive trees impact leaf litter carbon and nitrogen pools in temperate forests Paweł Horodecki a,1*§ , Andrzej M. Jagodziński a,b,2 , Marcin K. Dyderski a,3§ a Institute of Dendrology, Polish Academy of Sciences, Parkowa 5, Kórnik 62-035, Poland b Department of Game Management and Forest Protection, Faculty of Forestry and Wood Technology, Poznań University of Life Sciences, Wojska Polskiego 71d, Poznań 60-625, Poland * Corresponding author. E-mail address: [email protected] (P. Horodecki) 1 ORCID: 0000-0002-7789-8723; e-mail address: [email protected] . 2 ORCID: 0000-0001-6899-0985; e-mail address: [email protected] . 3 ORCID: 0000-0003-4453-2781; e-mail address: [email protected] . § These authors contributed equally to this work Abstract: Invasive tree species may modify habitats to ensure their ecological success. We aimed to assess the impact of Prunus serotina , Quercus rubra , and Robinia pseudoacacia on litter mass and its carbon and nitrogen pools in temperate forests of Wielkopolska National Park. We predicted that P. serotina will reduce litter mass and litter element pools in poor habitats oppositely to fresh ones; Q. rubra will accumulate more litter than comparable broadleaved forests; while R. pseudoacacia will have no effect on studied features. For our study we used a set of study plots divided into nine types according to tree stand species composition and soil fertility. We measured the biometric characteristics of trees, the share of every species in total stand basal area, and then we calculated foliage production and used it as a base for leaf features community-weighted means (CWM) used in further calculations. We compared them with decomposition rates and litterfall assessed on sample plots. Using linear mixed-effects models we showed that forest type explains from 32.7% (litter mass) to 39.2% (litter N pool) of variability. P. serotina affected studied features according to our expectations, while Q. rubra and R. pseudoacacia had no statistically significant effects. PCA revealed a gradient of soil, litter, and foliage characteristics along the axis from broadleaf to poor conifers, which explains 53.7% of the variability. The studied invasive species affect litter accumulation and nutrient pools in temperate forests. They potentially improve habitat conditions, however, mainly for their ecological success. Their presence, at the expense of native species, threatens biological diversity. Keywords: decomposition, litterfall, nutrients cycling, biological invasions, tree species effect, invasive species impact [1]¿p#1 Introduction Leaves, once shed, are the major source of organic matter that drives nutrient cycling on the forest floor (although fine root and herb layer biomass turnover also play important roles; da Silva et al. , 2018; Gilliam, 2007; Rawlik et al. , 2022). For instance, it can reach as much as 41% of forest net primary production in the tropics (Zheng et al. , 2019 and the literature cited therein). Nevertheless, the magnitude of litter production depends, among others, on geographical zone, habitat quality, and stand species composition, age and density (Starr et al. , 2005; Zheng et al. , 2019). Due to these variables, it differs significantly between particular forests. Under the same climatic and environmental conditions, stand species composition is of the highest importance in organic matter accumulation and element cycling rates (Hansen et al. , 2009; Mazza et al. , 2021). Forests of various species characterized by similar crown complementarity, produce optimal leaf mass that is shed every year (Zheng et al. , 2019). However, in the process of litter buildup, not only its production volume (Berg et al. , 2001), but also its quality (Cornwell et al. , 2008) and the stand disturbance history are the key factors (Wardle et al. , 1997b). Furthermore, litter buildup and overall circulation of nutrients also depend on the rate of litter mineralization and humification (Berg & McClaugherty, 2020). These rates differ significantly due to many factors, among which litter quality plays a crucial role (Cornelissen, 1996; Sun et al. , 2022). Leaf-litter quality per se , but also general habitat quality, that is highly determined by a number of factors such as the sources of organic material, soil origin, and climate, determine the rate of mineralization and humification processes (Horodecki & Jagodziński, 2017), and thus the litter accumulation rate (Berg, 2018). Invasive species can significantly alter nutrient cycling via acceleration or delaying litter decomposition (Jo et al. , 2016). However, the effects of invasive trees strongly depend on environmental context (Ahmad et al. , 2021; Ulyshen et al. , 2020), especially concerning the reference point (Sapsford et al. , 2020). Very often, due to the disparate quality and palatability of their leaf litter (but also other ecological features), invasive species may modify habitats to reach their own ecological success (Richardson et al. , 2000). This impact has only recently become a hot topic (Kumschick & Richardson, 2013) and still needs to be examined, especially since many scholars drew contradictory conclusions (Wohlgemuth et al. , 2022). For instance, some authors stated that leaves of invasive species decompose relatively faster than native ones and they may also hasten decomposition of post-invasion litter, accelerating nutrient cycling (Ashton et al. , 2005; Liao et al. , 2008; but see Rothstein et al. , 2004). In contrast, other studies showed rather inhibitory effects of invasive tree species on litter decomposition, which hamper the release of nutrients (Bonifacio et al. , 2015; Godoy et al. , 2010). In light of these contradictory conclusions, the statement closest to the truth appears to be that the susceptibility to decomposition of both native or non-native species is largely species- and habitat-specific (Ahmad et al. , 2021; Horodecki & Jagodziński, 2017; Jo et al. , 2016). Therefore, questions arise not only about the direction, but also about the magnitude, of changes in inputs to nutrient pools caused by alien tree species invasions (Wohlgemuth et al. , 2022). The mentioned authors synthesized the results of seven alien tree species effects on soil properties in their second range in Europe, found in 103 publications (Wohlgemuth et al., 2022). Although they did not include litter mass accumulation beneath the canopies or the community-weighted mean of decomposition rates, they identified 223 records of variables related to soil N, which were influenced by all of the investigated alien tree species. However, the three most frequent invasive tree species in Europe: P. serotina , Q. rubra , and R. pseudoacacia (Wagner et al. , 2017), accounted for only 50 records. Moreover, these species were irregularly represented as most of the records concerned R. pseudoacacia (80%), with only a few records for P. serotina (14%) and Q. rubra (6%). This, along with the lack of results regarding the litter characteristics examined in our study, keeps the questions posed above open. Thanks to its association with symbiotic rhizobia, R. pseudoacacia may contribute relatively high nitrogen inputs to the forest floor (Cierjacks et al. , 2013). However, this input does not necessarily translate into increased nitrogen availability for the biotic community, as the species’ leaf traits and biochemical composition may limit both the quantity of litterfall and its decomposability compared to those of native species it outcompetes in invaded stands (Castro-Díez et al. , 2009; Poblador et al. , 2019). Compared to native competitors, Quercus rubra produces a large amount of litterfall annually (Reich et al. , 2005). Due to its low leaf-litter decomposition rate, this results in the formation of a thick litter layer on the forest floor (Dobrylovská, 2001; Reich et al. , 2005). This, in combination with low light availability under the Q. rubra canopy (Jagodziński et al. , 2018) inhibits the abundant germination of understory plants. Only species adapted to such harsh conditions are able to survive and develop. Acorns and seedlings of Q. rubra itself demonstrate such adaptation (Dyderski & Jagodziński, 2018; Jagodziński et al. , 2018). Prunus serotina , in turn, produces litter that decomposes at a relatively high rate (Horodecki & Jagodziński, 2017), which enables its fast-growing seedlings (Jagodziński et al. , 2019) to access released nutrients immediately after germination. Thus, through litter decomposition, this species may modify habitat conditions, potentially increasing the likelihood of its own ecological success (Aerts et al. , 2017). Therefore, to answer these questions, we assessed the impacts of three invasive tree species – Prunus serotina Ehrh., Quercus rubra L., and Robinia pseudoacacia L. – on litter mass and its carbon and nitrogen pools in mixed temperate forest stands. These were compared with stands composed of diverse native competitor species. Following the mass ratio hypothesis (Grime, 1998), which posits that ecosystem functioning is primarily driven by the traits of the most biomass-dominant species, we hypothesized that (1) the presence and relative dominance of invasive tree species would significantly affect litter mass and its chemical composition (C and N pools), with effects depending on the species’ leaf traits and decomposition rates. (2) The magnitude and direction of these effects would vary among invasive species and site types – with faster-decomposing invaders such as P. serotina expected to reduce litter accumulation (pronounced in poor sites), and slower-decomposing species such as Q. rubra likely to increase it, compared to native-dominated stands. Additionally, the high nitrogen content associated with R. pseudoacacia may not be reflected in an increased nitrogen pool, due to its relatively low litter input and the limited decomposability of its leaf-litter. Study design We conducted our study in Wielkopolska National Park (WNP; W Poland; 52º16’N, 16º48’E). The climate of WNP is temperate, with a mean annual temperature of 8.4°C and mean annual precipitation of 521 mm, for the years 1951-2010. WNP was established in 1957, to conserve the forest landscape. However, WNP forest ecosystems were strongly transformed by former forest management, especially by replacement of mixed and broadleaved forests with Scots pine monocultures. Moreover, before 1957 numerous alien trees and shrubs had been introduced in WNP, resulting in the highest number of alien woody species among all national parks in Poland (Gazda & Szwagrzyk, 2016). Currently, scattered but distinct monocultures or dominant patches of R. pseudoacacia and Q. rubra exist within the park, and P. serotina is a common understory component, particularly beneath P. sylvestris stands. For our study we used 168 permanent stand structure sample plots that were described in details by Dyderski and Jagodziński (2018, 2021a). Study plots were distributed across 21 blocks ( Fig. 1 ), surrounding R. pseudoacacia or Q. rubra monocultures or P. sylvestris stands with P. serotina dominance in the understory. Each block was designed to cover a homogeneous stand with a high abundance of the target invasive species, with four plots placed at the edges of the invaded stand and four located 30 m from the edge. This design enabled comparisons between invaded stands and the most frequently co-occurring types, representing the dominant vegetation types in the region. The area of study plots varied from 150 to 2000 m 2 (mean 667±26 m 2 ) and was limited by canopy homogeneity – we aimed to cover the largest possible area within the stands studied, to stabilize the stand structure measurements. This system of study plots was designed to study invasive tree species regeneration (Dyderski & Jagodziński, 2018, see 2021a), not the effects of invasive trees on ecosystems. We assume that this is an advantage as systematic design greatly decreases the subjectivity of study plot establishment. Also, our study design is unbalanced, as we focused on sampling forest types adjacent to central plots. Nevertheless, the large number and diversity of study plots provide unique insights into the continuity of various forest types and associated environmental gradients. [1]¿p#1 Fig. 1. The scheme of a block of experimental plots in the field. Each of the 21 blocks covers a set of 18 square plots (100 m 2 ), established for natural regeneration assessment (Dyderski & Jagodziński, 2018), marked as grey squares. These plots were embedded within stand structure plots (bold rectangles), established to cover homogenous forests. For that reason, a single study plot covers two or four regeneration plots and four or eight litter collection sites. Study plots represent nine categories of forests ( Table 1 ), distinguished in previous studies according to tree stand species composition and soil fertility (Dyderski & Jagodziński, 2020, 2021b, 2021a). The division follows the phytosociological variability of invaded and uninvaded vegetation: Fagus sylvatica dominated forest refers to Deschampsio flexuosae-Fagetum , an acidophilous beech forest; Quercus petraea -dominated forest refers to Calamagrostio arundinaceae-Quercetum petraeae, an acidophilous oak forest; and Quercus-Acer-Tilia forest refers to Galio sylvatici-Carpinetum , a fertile broadleaved forest. Pinus sylvestris dominated forests represented two groups: poor (occupying mainly mesic sites of Leucobryo-Pinetum and Calamagrostio arundinaceae-Quercetum petraeae on podzols and brunic soils), and plantations (on nutrient-rich luvisols and cambisols soils, which replaced Galio sylvatici-Carpinetum ). In each of these two P. sylvestris groups we distinguished a variant invaded by P. serotina, that spontaneously colonized both types of forests. We assumed that plots with more than 500 ind. ha -1 of P. serotina were invaded at the time they were surveyed (Dyderski & Jagodziński, 2021a). Table 1 Overview of soil characteristics, biomass, and species composition of forest types included in the study. Notation for soil pH, C:N ratio, and litter mass shows mean±SE (min-max). The types with ‘- Ps’ indicates Scots pine stands under Prunus serotina invasion. Fagus 9 4.06±0.10 (3.64-4.64) 40.85±2.94 (29.89-58.55) 19.99±1.33 (15.16-28.67) 45.29±4.96 (29.20-78.20) Fagus sylvatica (73.3±7.6%), Quercus petraea (12.6±2.6%), Pinus sylvestris (11.4±6.6%) Pinus plantation 46 4.31±0.10 (3.71-6.65) 38.71±0.91 (26.42-57.15) 21.25±1.39 (7.98-48.39) 41.61±2.38 (18.22-89.26) P. sylvestris (57.2±3.5%) , Q. petraea (11.2±2.7%), Acer pseudoplatanus (5.3±1.1%) , Acer platanoides (5.0±1.2%) Pinus plantation - Ps 19 4.13±0.06 (3.78-4.86) 37.31±1.04 (29.43-48.93) 27.37±2.28 (10.31-50.45) 36.59±1.23 (24.13-46.28) P. sylvestris (77.4±5.1%) , P. serotina (5.1±1.7%) Pinus poor 7 3.93±0.04 (3.76-4.16) 40.93±1.27 (34.75-47.57) 38.64±3.50 (26.06-54.48) 49.44±9.57 (17.82-83.71) P. sylvestris (80.6±5.6%), Betula pendula (4.1±2.3%), F. sylvatica (5.1±2.9%) Pinus poor - Ps 23 4.20±0.07 (3.78-5.06) 43.08±1.25 (31.91-54.44) 27.27±2.54 (20.06-54.59) 36.33±2.74 (14.49-76.72) P. sylvestris (80.9±5.8%), Q. petraea (10.4±4.6%), P. serotina (6.4±1.9%) Q. petraea 22 3.91±0.04 (3.56-4.32) 42.77±1.16 (30.26-51.32) 17.54±1.28 (10.26-40.62) 36.09±3.12 (12.83-89.91) Q. petraea (44.7±5.3%), P. sylvestris (42.9±5.4%), F. sylvatica (6.3±2.6%) Q. rubra 12 4.21±0.12 (3.67-5.16) 48.52±2.26 (32.75-61.92) 16.38±1.79 (9.40-29.81) 35.05±1.46 (27.76-44.74) Q. rubra (77.2±7.7%), P. sylvestris (10.6±4.8%), Q. petraea (8.7±4.8%) Quercus-Acer-Tilia 20 5.64±0.20 (3.95-7.10) 36.77±1.13 (26.10-47.06) 8.95±0.64 (4.56-16.25) 35.64±3.87 (13.13-80.02) Q. robur (25.3±6.0%) , A. platanoides (16.6±4.5%) , Q. petraea (12.0±3.8%), Tilia cordata (9.9±4.4%) , A. pseudoplatanus (8.7±2.4%) Robinia 10 5.66±0.36 (3.79-6.89) 29.19±1.24 (25.38-35.49) 10.46±1.47 (5.48-19.34) 40.91±3.78 (15.48-59.61) Robinia pseudoacacia (66.9±4.4%) , Acer platanoides (8.8±4.0%), Ulmus minor (8.4±3.1%) Data collection In August 2014 and 2015 we recorded all trees and shrubs with heights (DBH) ≥5 cm, including bark. For individuals with DBH <5 cm we recorded the number of individuals and we assumed their DBH to be 2.5 cm, as this is the mid-point of the non-measured interval (0–4.9 cm), for further analyses (Dyderski & Jagodziński, 2019, 2021a). Within each of 168 study plots, in March 2017, we collected a set of samples of litter accumulated on the forest floor using a circular frames (0.16 m 2 ). The number of samples (four or eight – depending on plot area) were distributed systematically on special subplot borders (Dyderski & Jagodziński, 2018; natural regeneration plots on Fig. 1). Therefore, the higher spatial variability of larger plots was covered by a higher number of litter samples – eight. We collected 756 accumulated litter samples in total. Accumulated litter samples included leaf litter and woody debris with a diameter <1 cm. We dried the samples in the oven at 65°C and weighed them with an accuracy of 1 g. Then, we ground representative subsamples and determined C and N contents using an ECS CHNS−O 4010 Elemental Combustion System (Costech Instruments, Italy/USA) and a CHNS/O Analyser 2400 Series II (Perkin Elmer, USA). To compare the proportion of species-specific leaf litterfall with leaf-litter accumulated on the forest floor, we installed a total of 84 litter traps (0.36 m 2 each) across a part of 14 stands with varying species composition, placing six traps in each stand. Freshly shed litter was collected monthly during the four months with the most intense leaf-fall (September-December). To catch occasional leaf-fall (after strong wind, etc.) we also emptied traps at the end of April and August. From the collected material we separated leaves by species and then, after drying in the oven at 65°C, we weighed them on a balance with accuracy to 0.01 g. Data analysis We analyzed our data using R software (R Core Team, 2021). For calculations of community-weighted means (CWM) values of leaf functional traits used in the study (i.e., leaf C and N content, specific leaf area (SLA), and decomposition rate (k)) we estimated foliage biomass as the weight of particular tree and shrub species abundance. For this purpose, we calculated foliage biomass using allometric models found in the literature (see Dyderski & Jagodziński, 2019). For each tree species, we found published allometric models ( Table S1, S2 ). In cases where trees exceeded the maximum diameter of sample trees used to develop a particular allometric model by >20%, or in cases of no species-specific allometric model, we used the general model for broadleaved trees (Forrester et al. , 2017). However, this problem was noted in single tree species, usually in the top 5% of diameters. In the case of coniferous species with evergreen leaves we assumed one-third of the calculated mass, as the mean lifespan of needles in Poland is estimated to be ca. three years (Jankowski et al. , 2017; Reich et al. , 1996). We used the estimated foliage biomass to calculate CWMs as an available proxy for species contribution to the litter layer. This approach was chosen due to the high labor intensity of species-specific litterfall sorting from litter traps, if we had decided to install them under the canopy of all study plots (with at least four replications). While we recognize that the accumulation of litter is affected not only by production (i.e., litterfall; see Introduction ), we assumed that foliage biomass may serve as a first-order approximation of species’ representation in the litter layer, as also applied in previous studies (e.g., Garnier et al. , 2004; Quested et al. , 2007). This simplification allowed us to examine a wide set of study plots. We compared our calculations with data from litter traps installed in 14 study plots in 2019 (see Data collection ), to show how much estimates differ from the amount of litterfall measured in plots. Mean annual leaf-necromass shed on the forest floor ranged from 26% (±1) to 72% (±4) of estimated foliage biomass in the full growing season ( Table S4 ). These differences were expected, as allometric models provide information about foliage mass during the full growing season, while foliage mass and nutrient content during litterfall decreases due to the retranslocation process that takes place in the leaves before they are shed (Vergutz et al. , 2012). We obtained data about leaf C and leaf N content from the BIEN database (Enquist et al. , 2016). We also collected published data on the decomposition constant k of the species present within study plots ( Table 1 ). These constants come from the TRY database (Kattge et al. , 2020), our previous research (Horodecki et al. , 2019; Horodecki & Jagodziński, 2017, 2019), and data found in the literature (see Table S3 in Supplementary materials). K constant is a measure commonly used in the literature that indicates the share of litter that may be decomposed during a common period (usually year -1 ). We used data on SLA collected for a previous study (Dyderski & Jagodziński, 2019) and described there in detail. The data come from three main data sources: LEDA (Kleyer et al. , 2008), BIEN (Enquist et al. , 2016) and Forrester et al. (2017). Using mentioned sources, we obtained SLA for 88.1%, leaf C for 74.6%, leaf N for 67.8%, and k for 40.7% of species ( Table 2 ). To cover all species with values, we used phylogenetic imputation to compute missing values based on known traits values and phylogenetic eigenvectors (the first ten explained 72.6% of variability), following Penone et al. (2014) and Dyderski and Jagodziński (2021a). The imputation based on the random forest method and included all known trait values and the first 15 phylogenetic eigenvectors. For imputation we used the missForest package (Stekhoven & Bühlmann, 2012). The normalized RMSE of imputation was 0.138, indicating a very low error rate ( Fig. S1 ). Table 2. Summary statistics of traits used in analyses and their completeness. SLA [cm 2 g -1 ] 51.77 402.6007 170.0425 37.2 88.13 C [mg g -1 ] 394.12 558.5661 467.6731 06.9 74.58 N [mg g -1 ] 11.35 38.17 23.49 25.1 67.80 k [year -1 ] 0.19 2.4525 0.90901 63.7 40.68 We explored relationships between leaf, litter, and soil characteristics using principal components analysis (PCA) in the vegan package (Oksanen et al. , 2018). For PCA we standardized all variables by scaling (dividing by SD) and centering (subtracting mean), to overcome differences in variable ranges. We compared litter mass and mass of litter C and N among forest types using linear mixed-effects models, accounting for spatial dependence among blocks by random effects. Due to skewness, we log-transformed these variables, obtaining normal distributions confirmed by Shapiro-Wilk tests. We assumed that models with the difference in Akaike Information Criterion, with small sample size correction (AICc) <2, were equally important. We calculated AICc using the MuMIn package (Bartoń, 2017). Then we calculated marginal means from models (i.e. mean values excluding random effects) for each forest type and compared them using Tukey posteriori tests with multiple hypothesis testing adjustment, using the emmeans package (Lenth, 2019). In the results section we reported marginal mean values followed by SE of estimation. To assess relationships between CWMs and litter mass (log-transformed) we used linear mixed-effect models, accounting for CWMs of leaf N, SLA, and k . We did not assess the impact of CWM leaf C, as analyzing variable inflation factors, we found high collinearity with CWM leaf N, reported by value of factor factors were 3.65, 3.37, and 1.18, respectively. We tested the multivariate model with all three variables, to show how a particular CWM affected the outcome, as well as univariate models showing particular relationships. We assessed the AICc of the models and their marginal (R 2 m ) and conditional (R 2 c ) coefficients of determination, describing the amount of variance explained by fixed effects only and both fixed and random effects, respectively (Nakagawa & Schielzeth, 2013), using the MuMIn package (Bartoń, 2017). We showed marginal responses of the model using the ggeffects package (Lüdecke, 2018). These responses assume mean values of all other fixed effects and exclude random effects from estimates of regression curves, to show the response of the model. Results Differences in litter mass and nutrient pools among forest types We found differences among forest types in litter mass, C and N pools, and CWM k ( Fig. 2 , Table 3 ). Forest type explained from 32.7% (litter mass) to 39.2% (litter N pool) of variability. We found the highest litter mass in non-invaded P. sylvestris poor forest (marginal mean ±SE 30.3±4.2 Mg ha -1 ). Although the difference between P. serotina invaded and non-invaded P. sylvestris poor forest was statistically insignificant, it was biologically relevant, comprising 8.3 Mg ha -1 , i.e., 27.4% of litter mass in non-invaded stands. In P. sylvestris plantations there were no statistically significant differences between P. serotina invaded and non-invaded stands (7.2%). Stands invaded by R. pseudoacacia and Q. rubra had the lowest litter masses, statistically significantly lower than non-invaded poor P. sylvestris stands: by 18.7 Mg ha -1 (61.9%) and 16.2 Mg ha -1 (53.3%), respectively. The difference between Q. rubra stands and non-invaded P. sylvestris plantations was statistically insignificant but biologically relevant: 5.4 Mg ha -1 (27.8%). In contrast, litter mass in R. pseudoacacia stands was statistically significantly lower than in non-invaded P. sylvestris plantations, by 8.1 Mg ha -1 (41.0%). Comparing Q. rubra invaded forests we found statistically insignificant but biologically relevant differences from Quercus-Acer-Tilia forests (4.0 Mg ha -1 higher, 39.5%) and from F. sylvatica forests (5.9 Mg ha -1 lower, 29.6%). Similarly, R. pseudoacacia forests had statistically insignificant but biologically relevant lower litter mass than Q. petraea forests (5.7 Mg ha -1 , 33.1%), but statistically significantly lower than F. sylvatica forests (8.5 Mg ha -1 , 42.8%). We observed similar trends in litter C and N pools. CWM k also differed among studied forest types, reaching from 0.38±0.06 in Q. rubra forests to 0.94±0.05 in Quercus-Acer-Tilia forests. We did not find an effect of P. serotina on CWM k in P. sylvestris fresh stands, or a difference between non-invaded P. sylvestris fresh stands and R. pseudoacacia forests. Q. rubra forests had lower CWM k than almost all native forest types. Fig. 2. Marginal mean (+SE) litter mass, litter C and N pools, and CWMs k for studied forest types, estimated using linear mixed-effect models ( Table 3 ). Table 3. Summary of linear mixed-effects models describing differences in litter mass, litter C and N pools and CWM k among studied forest types. Litter mass [Mg ha -1 ] (Intercept) 9.9095 0.1287 148.96 76.973 < 0.0001 Block RE SD type= Pinus plantation -0.0248 0.1316 169.50 -0.188 0.8509 0.2138 type= Pinus plantation – Ps 0.0811 0.1529 175.78 0.531 0.5964 Residuals RE SD type= Pinus poor 0.4110 0.1821 176.85 2.257 0.0252 0.3545 type= Pinus poor – Ps 0.0912 0.1534 176.94 0.594 0.5530 AICc=211.2 type= Q. petraea -0.1522 0.1471 176.20 -1.035 0.3023 AICc 0 =231.9 type= Q. rubra -0.3506 0.1595 164.58 -2.199 0.0293 R 2 m =0.327, R 2 c =0.507 type= Quercus-Acer-Tilia -0.6838 0.1503 176.97 -4.551 < 0.0001 Log-transformed type= Robinia -0.5549 0.1705 171.43 -3.255 0.0014 Litter N pool [Mg ha -1 ] (Intercept) -1.4102 0.1340 159.78 -10.524 < 0.0001 Block RE SD type= Pinus plantation 0.0486 0.1418 175.64 0.342 0.7324 0.1587 type= Pinus plantation – Ps 0.2756 0.1631 175.50 1.690 0.0928 Residuals RE SD type= Pinus poor 0.5825 0.1925 166.25 3.025 0.0029 0.3892 type= Pinus poor – Ps 0.1474 0.1623 167.96 0.908 0.3653 AICc=234.1 type= Q. petraea -0.1533 0.1569 175.63 -0.977 0.3299 AICc 0 =268.7 type= Q. rubra -0.4581 0.1728 170.62 -2.650 0.0088 R 2 m =0.392, R 2 c =0.479 type= Quercus-Acer-Tilia -0.7109 0.1595 172.08 -4.457 < 0.0001 Log-transformed type= Robinia -0.3384 0.1834 176.16 -1.845 0.0667 Litter C pool [Mg ha -1 ] (Intercept) 2.1439 0.1340 153.86 16.004 < 0.0001 Block RE SD type= Pinus plantation 0.0014 0.1392 171.95 0.010 0.9920 0.1954 type= Pinus plantation – Ps 0.1191 0.1611 176.95 0.739 0.4607 Residuals RE SD type= Pinus poor 0.5193 0.1913 174.48 2.715 0.0073 0.3776 type= Pinus poor – Ps 0.1672 0.1612 175.08 1.037 0.3010 AICc=229.3 type= Q. petraea -0.1351 0.1550 176.99 -0.872 0.3845 AICc 0 =258.4 type= Q. rubra -0.3162 0.1690 166.30 -1.871 0.0631 R 2 m= 0.386, R 2 c =0.516 type= Quercus-Acer-Tilia -0.7719 0.1580 176.33 -4.884 < 0.0001 Log-transformed type= Robinia -0.6078 0.1802 173.48 -3.373 0.0009 CWM k [year -1 ] (Intercept) 0.4769 0.0622 158.28 7.672 < 0.0001 Block RE SD type= Pinus plantation 0.1846 0.0642 172.52 2.878 0.0045 0.0963 type= Pinus plantation – Ps 0.1100 0.0744 176.70 1.479 0.1409 Residuals RE SD type= Pinus poor 0.0464 0.0884 176.25 0.525 0.6004 0.1734 type= Pinus poor – Ps 0.0922 0.0745 176.48 1.238 0.2172 AICc=-44.3 type= Q. petraea 0.2052 0.0715 176.84 2.868 0.0046 AICc 0 =-20.8 type= Q. rubra -0.1007 0.0778 168.57 -1.293 0.1976 R 2 m =0.332, R 2 c =0.490 type= Quercus-Acer-Tilia 0.4622 0.0730 176.91 6.330 < 0.0001 type= Robinia 0.1909 0.0831 173.79 2.298 0.0228 Relationships between foliage characteristics, soil, and litter Principal components analysis revealed a gradient of soil, litter and foliage characteristics along the PC1 axis, representing 53.7% of variability ( Fig. 3 ). Along this axis we found increases of litter mass, litter C content, soil C:N ratio and CWM of leaf C content, as well as decreases of soil and litter pH, CWMs of leaf N content, SLA, and k . Litter N content and litter C:N ratio were not related to this axis. Points representing study plots were arranged in a gradient from R. pseudoacacia and Quercus-Acer-Tilia forests at negative PC1 values, through F. sylvatica , Q. petraea , P. sylvestris fresh, to poor P. sylvestris stands at positive PC1 values. Fig. 3. Result of Principal Components Analysis of litter, soil, and foliage characteristics of 186 study plots (centered and scaled). Univariate models revealed a decrease in litter mass with increasing CWMs of k , SLA, and leaf N, while the multivariate model – with increasing CWMs of k and SLA ( Table 4 , Fig. 4 ). Fixed effects in the multivariate model explained 48.9% of litter mass variability, while univariate models explained from 11.0% (CWM k ) to 40.5% (CWM SLA; Table 4 ). Although CWM k explained the least variability among univariate models, in both models it had a high effect size – decreasing litter mass by 1.81 Mg ha -1 per increase in CWM k from 0.75 to 1.0. Similarly, in the univariate model the decrease was 2.63 Mg ha -1 per similar increase in CWM k . In the final multivariate model, leaf N was not included, as it increased model AICs, while the univariate model showed a high effect size – litter mass decrease was 3.31 Mg ha -1 per increase in leaf N CWM from 2.00 to 2.25%. For SLA CWM, we found decreases of litter mass by 1.59 Mg ha -1 and 1.75 Mg ha -1 per SLA CWM increase from 100 to 110 cm 2 g -1 in the multivariate and univariate models, respectively. Table 4. Summary of multivariate and univariate linear mixed-effects models describing the relationships between CWMs and log-transformed litter mass (AICc 0 =231.9). AICc=150.3 (Intercept) 4.119 0.117 110.3 35.180 <0.001 Block 0.2077 R 2 m =0.489, R 2 c =0.649 CWM SLA -0.008 0.001 182.3 -9.852 <0.001 Residuals 0.3038 CWM k -0.452 0.115 179.9 -3.919 <0.001 AICc=213.1 (Intercept) 3.325 0.114 78.4 29.037 <0.001 Block 0.3100 R 2 m =0.110, R 2 c =0.475 CWM k -0.696 0.137 182.3 -5.086 <0.001 Residuals 0.3720 AICc=173.4 (Intercept) 4.463 0.180 140.3 24.836 <0.001 Block 0.2180 R 2 m =0.361, R 2 c =0.547 CWM leaf N -0.747 0.081 168.1 -9.259 <0.001 Residuals 0.3412 AICc=160.3 (Intercept) 3.912 0.113 107.6 34.760 <0.001 Block 0.2375 R 2 m =0.405, R 2 c =0.619 CWM SLA -0.009 0.001 181.3 -10.665 <0.001 Residuals 0.3169 Figure 4. Relationships between litter mass and CWMs assessed by multivariate (a) and univariate (b) models ( Table 4 ). Points represent measured values, line – marginal prediction, grey area – 95% confidence interval for prediction. Discussion Differences in litter mass and nutrient pools among forest types Our findings support the first hypothesis, stating that the presence and dominance of invasive tree species significantly influence litter mass and its chemical properties. The level of differences in total annual litterfall (freshy shed litter masses from litter traps) between stands, reaches less than 20% in the study area (Horodecki et al. under preparation ). These differences increase to almost 27% when taking into consideration only foliage litter (Horodecki et al. under preparation ). The differences in mean litter mass accumulated on the forest floor among investigated stands are much higher, reaching even 299% ( Table 3 , Fig. 2 ). One of the reasons for these differences is reflected in the CWM k obtained in our study ( Fig. 2 ), that essentially indicates leaf-litter decomposition potential within the investigated stands. The significant presence of relatively easily or hardly decomposable litter on the forest floor may respectively accelerate or hamper the total decomposition process among forest types (Bonifacio et al. , 2015; Jones et al. , 2019). The invasion of P. serotina , that produces easily decomposable leaf-litter (Muys et al. , 1992) into poor P. sylvestris stands resulted in an accumulated litter reduction of more than one fourth in comparison to compositionally similar but non-invaded poor stands, and thus decreases in C and N pools ( Fig. 2 ). Even such a low P. serotina share of total basal area in poor pine stands (6.4%; Table 1 ) caused litter mass reduction of 8.3 Mg ha -1 in comparison to uninvaded poor pine stands ( Fig. 2 ). The effect size has biological implications and is related to overall litter quality improvement via invasion, and thus acceleration of decomposition process. The lack of statistical differences in litter masses (and C pools) between these invaded and non-invaded poor pine stands may stem from limited number of replications. Similar conclusions have been drawn by many authors in recent studies (e.g., Aerts et al. , 2017; Halarewicz et al. , 2017). Some of them have indicated that the admixture of high-quality litter into stands dominated by poor-quality litter not only improves litter and soil properties (Aerts et al. , 2017; Desie et al. , 2020), but may also positively influence tree growth and vitality (Desie et al. , 2023). These effects are less evident in relatively fertile stands, where native shrub species found their ecological niches, thereby limiting space for invasive ones. Consistent with these findings, we did not detect a noticeable effect of P. serotina presence on litter properties in the more fertile P. sylvestris stands (pine plantations) investigated in our study, which partly aligns with our second hypothesis that the impact of this species would be context-dependent and limited in more fertile, diverse systems. Moreover, the C and N pools, as well as litter mass, were similar in pine plantations (regardless of P. serotina invasion) and invaded poor pine stands ( Fig. 1 ). This similarity was most likely due to the relatively high proportion of maple trees in the non-invaded pine plantations, which, like P. serotina , produce easily decomposable leaves (Horodecki et al. , 2019). Moreover, the share of P. sylvestris in total stand basal area was ca. one-fourth lower in non-invaded plantations than in invaded ones ( Table 1 ), resulting in lower needle-litter production. Coniferous litter decomposes generally slower than broadleaved litter (e.g., Cornwell et al. , 2008; but see Prescott, 2010), and creates a harsh environment for the faunal community, slowing down decomposition (Urbanowski et al. , 2021). The effect of invasion on litter properties in pine plantations might also be reduced by the higher presence of herbaceous species on the forest floor in comparison with poor pine stands. The herbaceous species decomposition process generally goes much faster than that of tree leaves (Rawlik et al. , 2022), and their abundance in the forest may enhance overall litter quality, accelerating its decomposition (Wang et al. , 2021). The statistically significant differences in accumulated litter mass between pine plantations and R. pseudoacacia stands ( Fig. 2 ) may be explained using the same arguments described above. Moreover, this effect was in line with our assumptions, i.e., estimated litter mass in P. sylvestris plantations was higher than beneath R. pseudoacacia canopies (70% in non-invaded and 89% in invaded stands). In comparison, litter masses in poor pine stands were 163% and 91% higher in non-invaded and invaded stands, respectively, than beneath R. pseudoacacia . The comparison of litter mass between R. pseudoacacia and Quercus-Acer-Tilia stands showed no statistically significant differences. However, slightly lower litter mass on the forest floor of Quercus-Acer-Tilia stands was in line with literature data. R. pseudoacacia leaves, indeed, decompose relatively faster in comparison to Quercus spp. leaves (Horodecki et al. , 2019; Horodecki & Jagodziński, 2017; Tateno et al. , 2007), but slower in comparison to Acer and Tilia (Jacob et al. , 2009). The total share of the latter two species in the stand basal area of Quercus-Acer-Tilia stands was just 2.1% lower than that of oaks ( Table 1 ). Higher CWM k in Quercus-Acer-Tilia , resulted in litter mass reduction relative to R. pseudoacacia stands, even though the mean annual litterfall in the former was more than 8% higher than in the latter ( Table S4 ). The differences found in litter accumulation between Q. rubra forests and all broadleaved stands were statistically insignificant ( Fig. 2 ). Nevertheless, we are conscious that it was again caused by the low number of replications, as the differences in estimated litter mass between Q. rubra and Quercus-Acer-Tilia stands was 4.0 Mg ha -1 ( ca. 39% of the lowest value). This partial inconsistency with literature evidence (Dobrylovská, 2001; Reich et al. , 2005), which often reports high litter accumulation beneath Q. rubra due to its low decomposition rate and high litterfall, may reflect the diversity of co-occurring species in native stands, some of which also contribute slow-decomposing litter. These differences were, however, much lower when comparing only stands that are composed of tree species that produce hardly decomposable litter (excluding Quercus-Acer-Tilia stands), although the much lower litter mass in Q. rubra stands than in F. sylvatica (5.9 Mg ha -1 , 42% higher in F. sylvatica ) was somehow surprising. There is plenty of evidence in the literature proving very high litter production by Q. rubra and its relatively very low decomposition rate. Reich et al. (2005) found in a common garden experiment that Q. rubra mean annual litterfall was 81% higher than F. sylvatica and 150% higher than Q. robur . Moreover, the same authors found its decomposition rate k constants almost twice or almost three times lower than the mentioned species, respectively (Reich et al. , 2005). Similar conclusions came from other studies (e.g., Bonifacio et al. , 2015; Dobrylovská, 2001), however, the differences in discussed aspects were not as remarkable. This more or less unexpected result of the relatively lower litter mass beneath Q. rubra canopy can be explained by the significant share of other tree species that produce decomposition-resistant litter in the compared broadleaved stands ( Table 1 ). Almost three-fourths of basal area in F. sylvatica stands is covered by this species, which also produces hardly decomposable litter (Hättenschwiler & Gasser, 2005). In Q. petraea stands, which create litter with low-rates of decomposition per se (Henneron et al. , 2018), almost half of the basal area was covered by P. sylvestris (42.9%) and F. sylvatica (6.3%) – tree species with even lower litter decomposability. [1]¿p#1 Relationships between foliage characteristics, soil, and litter The negative correlations between litter mass and CMW k , SLA, litter pH, and leaf N found in our study are in line with the conclusions of many recent studies (e.g., Cornelissen et al. , 1999; Zukswert & Prescott, 2017; but see Desie et al. , 2020). In general, leaves with higher SLA are more delicate and thus prone to physical destruction, which in turn facilitates biological decomposition (Cornelissen & Thompson, 1997). Moreover, relatively low SLA is connected with chemical compounds that protect leaf-blades from unfavourable conditions, among others from folivores (while still living) or decomposers (after they are shed; Matsuki & Koike, 2006; Wright & Cannon, 2001). It is also well documented that leaves with high N concentration decompose relatively fast, especially during the first phase of the decomposition process (e.g., Melillo et al. , 1982; Prescott, 2005). However, it should be noted that the decomposition rates cited above are based on single-species litter, while in the case of invaded stands, mixed-species litter may behave differently. Non-additive effects can occur when litter from different species is combined, potentially altering decomposition dynamics. For example, mixtures of high-N and low-N litter types may accelerate decomposition (positive mixing effects), while chemically recalcitrant litter can slow it down (negative mixing effects; Hättenschwiler et al. , 2005; Wardle et al. , 1997a). As we did not conduct decomposition experiments in situ, we acknowledge this as a limitation. Future research focused on litter mixing and species interactions in invaded stands would help clarify these potential effects. Although the long-term role of leaf-litter N content still remains uncertain (Prescott, 2010), the relatively high values of the mentioned features – such as SLA and leaf N content – generally enhance initial leaf-litter quality, that then influences the overall habitat fertility. This, in turn, determines the abundance of soil organisms, including decomposers, and may support a relatively fast decomposition process (Freschet et al. , 2012; but see Hobbie, 2015) that results in decreasing litter accumulation on the forest floor. Thus, the litter N pool is a product of both CWM leaf N, affecting litter N content, and litter mass. Therefore, although the differences in total litter N pools among the studied stands correspond to litter mass ( Fig. 2 ), the litter N content remains uncorrelated with the primary ecological gradient represented by PC1 in the PCA analysis ( Fig. 3 ). This effect could be heightened by the retranslocation of elements from leaves before they are shed. According to data found in the literature, N concentrations in senesced leaves, for tree species from our study, range from 41% to 57% of the concentrations in living green leaves for P. serotina and for Q. rubra , respectively (e.g., Marchin et al. , 2010; Vergutz et al. , 2012; Yuan & Chen, 2009). The bottom-left concentration of R. pseudoacacia stands on the PCA ( Fig. 3 ) may enhance this evidence. As we know from the literature, due to the ability to absorb atmospheric N (Lazzaro et al. , 2018), this species may shed its leaves with a lower rate of N retranslocation (González et al. , 2020). Litter C pools correlate with both litter mass and CWM leaf C concentration, mainly due to a significantly lower C retranslocation rate from leaves during their senescence (Sariyildiz & Anderson, 2005; Vergutz et al. , 2012) and because this element is the main structural component of compounds that are recalcitrant for decomposition (Berg & McClaugherty, 2020). Conclusions Our study highlights the context-dependent impacts of invasive tree species on litter properties in temperate forest ecosystems. While P. serotina and R. pseudoacacia may locally enhance certain soil or litter characteristics, these effects do not necessarily translate into ecological benefits, particularly given their long-term competitive advantages and the threat they pose to native biodiversity (Wohlgemuth et al. , 2022). In contrast, Q. rubra exerts less influence on litter dynamics but can still affect native vegetation through altered light conditions and structural changes (Jagodziński et al. , 2018). Importantly, our findings support the hypothesis that the ecological effects of invasive species are not uniform across habitats, but are instead shaped by site fertility, the presence of native competitors, and broader climatic trends. This underscores the need to consider local environmental context when assessing invasive species impacts or designing management strategies. Looking forward, invasive species with expanding secondary ranges under future climate scenarios (Dyderski et al. , 2018; Puchałka et al. , 2021, 2023; Segura et al. , 2018) — such as P. serotina and R. pseudoacacia — will require particular attention. Their combination of ecological plasticity, reproductive strategies, and competitive traits makes them especially difficult to control (e.g., Bouteiller et al. , 2023; Engel et al. , 2024; Motta et al. , 2009). In contrast, projected reductions in the spread of Q. rubra (Dyderski et al. , 2018; Puchałka et al. , 2023) offer opportunities for forest restoration and recovery of native species. Overall, our results reinforce the importance of targeted, site-specific management approaches that account for both ecological and climatic factors. Long-term monitoring and proactive interventions — especially those promoting native understory development — are essential for maintaining forest biodiversity and ecosystem resilience in the face of biological invasions. Moreover, litter-related metrics can serve as early and sensitive indicators of invasive species functional impacts, offering valuable insights for timely management interventions. [1]¿p#1 Funding The study was financed by the National Science Centre, Poland, under the project no. 2015/19/N/NZ8/03822 entitled: ‘Ecophysiological and ecological determinants of invasiveness of trees and shrubs with the examples of Padus serotina , Quercus rubra and Robinia pseudoacacia ’ and 2018/31/N/NZ8/01602 entitled: ‘How leaf litter decomposition as well as plant competition affect the ecological success of invasive and native tree species?’. The study was also partially supported by the Institute of Dendrology, Polish Academy of Sciences. Declaration on conflicts of interest and CRediT authorship contribution statement: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. The data are available on request. Paweł Horodecki: writing – original draft, validation, project administration, methodology, investigation, funding acquisition, data curation, conceptualization. Marcin K. Dyderski: writing – original draft, visualization, validation, project administration, methodology, investigation, funding acquisition, formal analysis, data curation, conceptualization. Andrzej M. Jagodziński: writing – review and editing, validation, supervision, methodology, conceptualization. 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Dyderski Instytut Dendrologii Polskiej Akademii Nauk View all articles by this author Metrics & Citations Metrics Article Usage 280 views 134 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Paweł Horodecki, Andrzej Jagodziński, Marcin K. Dyderski. Invasive trees impact leaf litter carbon and nitrogen pools in temperate forests. Authorea . 22 July 2025. DOI: https://doi.org/10.22541/au.175316740.00565263/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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