Research Article |
Corresponding author: Andrzej M. Jagodziński ( amj@man.poznan.pl ) Academic editor: Uwe Starfinger
© 2019 Andrzej M. Jagodziński, Marcin K. Dyderski, Paweł Horodecki, Kathleen S. Knight, Katarzyna Rawlik, Janusz Szmyt.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Jagodziński AM, Dyderski MK, Horodecki P, Knight KS, Rawlik K, Szmyt J (2019) Light and propagule pressure affect invasion intensity of Prunus serotina in a 14-tree species forest common garden experiment. NeoBiota 46: 1-21. https://doi.org/10.3897/neobiota.46.30413
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Experiments testing multiple factors that affect the rate of invasions in forests are scarce. We aimed to assess how the biomass of invasive Prunus serotina changed over eight years and how this change was affected by light availability, tree stand growth, and propagule pressure. The study was conducted in Siemianice Experimental Forest (W Poland), a common garden forest experiment with 14 tree species. We investigated aboveground biomass and density of P. serotina within 53 experimental plots with initial measurements in 2005 and repeated in 2013. We also measured light availability and distance from seed sources. We used generalized additive models to assess the impact of particular predictors on P. serotina biomass in 2013 and its relative change over eight years. The relative biomass increments of P. serotina ranged from 0 to 22,000-fold. The success of P. serotina, expressed as aboveground biomass and biomass increment, varied among different tree species stands, but was greater under conifers. Total biomass of P. serotina depended on light and propagule availability while biomass increment depended on the change in tree stand biomass, a metric corresponding to tree stand maturation. Our study quantified the range of invasion intensity, expressed as biomass increment, in a forest common garden experiment with 14 tree species. Canopy cover was the most important variable to reduce susceptibility to invasion by P. serotina. Even a modest decrease of overstory biomass, e.g. caused by dieback of coniferous species, may be risky in areas with high propagule pressure from invasive tree species. Thus, P. serotina control may include maintaining high canopy closure and supporting natural regeneration of tree species with high leaf area index, which shade the understory.
Allometric equations, biomass, invasion dynamics, light availability, natural regeneration, tree species effect
Although invasive trees and shrubs are a worldwide problem (
There are three crucial elements shaping invasion success: ecosystem invasibility, propagule pressure, and species invasiveness (
Few studies of invasive trees and shrubs have quantified the density and size of different life stages during the initial stages of invasion. Because seedlings are more vulnerable to environmental conditions (e.g. drought, frosts or herbivory) than adult trees (
Due to different invasion metrics used in fully controlled experiments and those used in field assessments, there is a gap in transferability of results between these two types of studies. Experiments in fully controlled conditions have measured invader success as relative growth rate or biomass (e.g.
We took advantage of the invasion of Prunus serotina Ehrh. into an experimental forest common garden comprised of different tree species. The wide range of ecological characteristics of the cultivated species, similar soil parent material, climate and site history, and presence of alien species, compromise an ideal experimental design for invasion ecology studies (
Our study addresses the following questions: (1) how did invasion intensity, expressed as total biomass of P. serotina, change over eight years? and (2) how was this change connected with changes in light availability, tree stand maturation and seed source availability?
One of the most common invasive species of trees and shrubs in Europe is P. serotina (black cherry). It is a tree species from the Rosaceae family, with a natural range in the eastern part of North America, where it occurs in a wide range of habitats (
Black cherry was most frequently introduced in intermediate-fertile habitats of mixed-coniferous sites, forests of poor site quality with Quercus spp., and in Pinus sylvestris stands where it occurs the most frequently (
The study was conducted in the Siemianice Experimental Forest in Poland (51°14.87'N, 18°06.35'E, elev. 180 m a.s.l.). The mean annual temperature is 8.2 °C, the mean annual precipitation is 579 mm and the growing season (considered as the number of days with mean temperature ≥ 5 °C) length is 213 days (
Prunus serotina appeared within the experimental plots before 2005, as the tallest specimen measured in 2005 was 285 cm tall (
Distribution of experimental plots in Siemianice Experimental Forest and locations of fruiting specimens of Prunus serotina in 2013 in the nearest neighborhood of plots (up to 50 m). Labels are abbreviations using the first two letters of genus and species names, e.g. Acer pseudoplatanus = AcPs.
In 2005 and in September 2013, all specimens of P. serotina were investigated within all 53 experimental plots. We measured the root collar diameter (RCD) and height (H) of each tree and we determined its location within the plot with 0.25 m accuracy. Following
Biomass, as a function of both the dimensions and the density of plants, directly results from of invasive species growth, thus providing an approximation of invasion success. To assess the biomass of P. serotina within the experimental plots we harvested a subsample 59 trees (from a total population of 2339) for biomass estimation. We randomly selected 58 trees to harvest from the database of all measured trees, ordered by increasing RCD and H, plus the tallest tree, which was chosen to ensure coverage of the entire range of dimensions (n = 59; Suppl. material
To assess propagule pressure, we used a GPS receiver to map fruiting trees up to 50 m from each plot, as the probability of occurrence of P. serotina natural regeneration is highest within this distance (
We calculated relative increments of biomass for tree stands and for P. serotina as (B2013-B2005)/B2005, where B2005 – biomass in 2005 and B2013 – in 2013. We used relative increments to account for the effect of initial biomass from population dynamics within the study period. Tree stand biomass change was used as a proxy for tree stand maturation, describing increments of tree quantity in the ecosystem. In the case of negative values, biomass change may reflect quantity of trees destroyed during disturbances. Tree stand maturation is also connected with decrease of light availability (
Within the 53 experimental plots, the number of P. serotina specimens increased from 556 in 2005 to 2339 in 2013. Density of P. serotina in experimental plots in 2013 ranged from 0 ind. ha-1 (in a plot with A. alba and a plot with Q. rubra) to 7895 and 8471 ind. ha-1 in plots with P. abies (Suppl. material
Mean +SE density (ind. ha-1) of Prunus serotina within experimental plots of 14 tree species in 2005 and in 2013. Asterisks mark significance levels of differences between inventories in 2005 and 2013 (* – p < 0.05, ** – p < 0.01), based on Student’s t-test. Letters under bars represent significance of differences among tree species in 2013, based on Kruskal-Wallis tests; there are no statistically significant differences between values marked by the same letter.
During the study period, almost all tree stands increased their biomass, with the exception of P. abies stands (–13.9 ± 4.7 Mg ha-1), where mortality exceeded growth (Suppl. material
Biomass of P. serotina increased in almost all plots (except six; Suppl. material
Relationship between light availability, expressed by DIFN, and aboveground biomass of P. serotina, in two classes of propagule pressure, expressed by the number of fruiting P. serotina trees up to 50 m from the plot (open dots and dashed line :≤ 20; solid dots and line: > 20; p < 0.001, R2 = 0.32). Threshold of 20 trees was chosen according to median and mean for all plots (18 and 18.49, respectively). Modification given by class of propagule pressure was statistically significant (p < 0.01). Note log-transformation of the y axis. Shading around regression lines indicate range of model SE.
Responses of percentage increment of Prunus serotina biomass and total biomass in 2013 to the particular predictors. Lines were fitted using General Additive Models (see Table
General Additive Models describing predictors of percentage increment of Prunus serotina biomass and total biomass in 2013. The best model was chosen according to AIC. AIC of null model refers to model with an intercept only, to express a final model inertia; edf – estimated degree of freedom, Ref.df – reference degree of freedom, used for F test for p-value computation
Estimated variable | Percentage increment of P. serotina biomass | Biomass of P. serotina in 2013 | ||||||
Parametric coefficients: | Estimate | SE | t | p | Estimate | SE | t | p |
(Intercept) | 1032.700 | 401.300 | 2.573 | 0.014 | 91.070 | 20.720 | 4.396 | <0.001 |
Approximate significance of smooth terms: | edf | Ref.df | F | p | edf | Ref.df | F | p |
change of tree stand biomass | 3.456 | 4.282 | 3.316 | 0.001 | – | – | – | – |
number of fruiting P. serotina up to 50 m | 1.095 | 1.182 | 3.134 | 0.090 | 5.191 | 6.249 | 4.608 | 0.001 |
min distance to fruiting P. serotina | – | – | – | – | 3.426 | 4.064 | 15.952 | <0.001 |
DIFN in 2013 | – | – | – | – | 5.469 | 6.520 | 2.633 | 0.028 |
random effect (block) | <0.001 | 1.000 | 0.000 | 0.659 | <0.001 | 1.000 | 0.000 | 0.768 |
Model parameters | R2 | Deviance explained | AIC | AIC of null model | R2 | Deviance explained | AIC | AIC of null model |
0.254 | 31.9% | 1018.2 | 1027.7 | 0.696 | 77.9% | 700.9 | 748.2 |
We found the highest intensities of invasion, expressed as relative biomass change, within plots with low P. serotina density in 2005. In these plots, invasion accelerated later, which was indicated by the high proportion of seedlings compared to older plants (Fig.
The species-dependent pattern of invasion success resulted from different life history traits of particular species. One of them is growth dynamics, shown by differences in biomass and biomass increment of overstory trees. At the same age, some of them may exhibit different growth stages, due to different costs of growth, connected with specific stem density (
Higher total biomass and increment of P. serotina in stands of coniferous species may also result from habitat modification by these species, i.e. higher nutrient leaching and acidification (
Studies on populations of P. serotina reveal that the proportion of seedlings and specimens in the herb layer (< 50 cm height) is very high. In Rogów Arboretum, within a sample of 20,843 specimens, 76.7% of them were < 50 cm height (
Our study confirmed the sit-and-wait strategy of P. serotina (
Similar to
Our study, for the first time, quantified the range of an invader’s intensity, expressed as biomass increment, in a forest common garden experiment with 14 tree species. Relative biomass increments of P. serotina ranged from 0 to 22,000-fold in eight years. This highlights the urgent need for monitoring even small populations of P. serotina, as this species has the ability for sudden outbreaks. Noticing even small but stable populations of P. serotina that have not reached the dimensions allowing reproduction will provide time for local eradication, which can lower the high cost of P. serotina control (
Our results also recommend prioritized risk assessment for P. serotina, as was stated by
This study was partially supported by the Institute of Dendrology, Polish Academy of Sciences, Kórnik, Poland, and by the General Directorate of State Forests, Warsaw, Poland (research project: ‘Environmental and genetic factors affecting productivity of forest ecosystems on forest and post-industrial habitats’). We are grateful to Dr Lee E. Frelich (Department of Forest Resources, University of Minnesota, USA) for valuable suggestions and linguistic revision of the manuscript. We are also thankful to Dr Uwe Starfinger (Julius Kühn-Institut, Quedlinburg, Germany), Prof. Aníbal Pauchard, Prof. Dr Ingolf Kühn, and the anonymous reviewer for thorough and valuable comments on an earlier draft of the manuscript.
Parameters of tree stands on experimental plots in the Siemianice Experimental Forest
Data type: measurement
Explanation note: Table S1. Parameters of tree stands on experimental plots in the Siemianice Experimental Forest.
Procedure for modelling the allometric relationships between P. serotina dimensions and biomass
Data type: measurement
Explanation note: Table S2. Summary of models of P. serotina biomass. Parameters of the best model were bolded. Figure S1. Distribution of sampled trees compared with root collar diameter and height of all investigated trees.
Allometric equations determining aboveground biomass of particular tree species presented on sample plots
Data type: measurement
Explanation note: Table S3. Allometric equations determining aboveground biomass of particular tree species presented on sample plots. Equations adopted were established for habitat conditions similar to those of this study. Abbreviations: DBH – diameter at breast height; NA – not available.
Changes of P. serotina density, biomass and environmental parameters included in models
Data type: measurement
Explanation note: Table S4. Changes of P. serotina leaf area index, density, biomass and environmental parameters included in models. Abbreviations: Δ – difference 2013-2005; %Δ – percent difference (2013-2005)/2005; LAI – leaf area index; DIFN – diffusive non—interceptance (flight availability expressed as a fraction of the open sky). Figure S2. Aboveground biomass of planted tree species [Mg ha-1] within tree stands: (a) – in 2005; (b) – in 2013; (c) – difference between 2013 and 2005; and light availability level – DIFN: (d) – in 2005; (e) – in 2013; (f) – difference between 2013 and 2005; Figure S3. aboveground biomass of P. serotina [kg ha-1]: (g) – in 2005 (h) – in 2013, (i) – difference between 2013 and 2005 and propagule pressure around the experimental plots: (j) – minimal distance from fruiting specimens [m] and (k) – number of fruiting P. serotina trees up to 50 m from the plot. Black dots in (j) and (k) indicate fruiting specimens of P. serotina.