Research Article |
Corresponding author: Ingo Kowarik ( kowarik@tu-berlin.de ) Academic editor: Gerhard Karrer
© 2021 Andreas Lemke, Sascha Buchholz, Ingo Kowarik, Uwe Starfinger, Moritz von der Lippe.
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:
Lemke A, Buchholz S, Kowarik I, Starfinger U, von der Lippe M (2021) Interaction of traffic intensity and habitat features shape invasion dynamics of an invasive alien species (Ambrosia artemisiifolia) in a regional road network. NeoBiota 64: 55-175. https://doi.org/10.3897/neobiota.64.58775
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Road corridors are important conduits for plant invasions, and an understanding of the underlying mechanisms is necessary for efficient management of invasive alien species in road networks. Previous studies identified road type with different traffic volumes as a key driver of seed dispersal and abundance of alien plants along roads. However, how the intensity of traffic interacts with the habitat features of roadsides in shaping invasion processes is not sufficiently understood. To elucidate these interactions, we analyzed the population dynamics of common ragweed (Ambrosia artemisiifolia L.), a common non-indigenous annual species in Europe and other continents, in a regional road network in Germany. Over a period of five years, we recorded plant densities at roadsides along four types of road corridors, subject to different intensities of traffic, and with a total length of about 300 km. We also classified roadsides in regard to habitat features (disturbance, shade). This allowed us to determine corridor- and habitat-specific mean population growth rates and spatial-temporal shifts in roadside plant abundances at the regional scale. Our results show that both traffic intensity and roadside habitat features significantly affect the population dynamics of ragweed. The combination of high traffic intensity and high disturbance intensity led to the highest mean population growth whereas population growth in less suitable habitats (e.g. shaded roadsides) declined with decreasing traffic intensity. We conclude that high traffic facilitates ragweed invasion along roads, likely due to continued seed dispersal, and can compensate partly for less suitable habitat features (i.e. shade) that decrease population growth along less trafficked roads. As a practical implication, management efforts to decline ragweed invasions within road networks (e.g. by repeated mowing) should be prioritized along high trafficked roads, and roadside with disturbed, open habitats should be reduced as far as possible, e.g. by establishing grassland from the regional species pool.
Disturbance, habitat type, human-mediated dispersal, interaction, population dynamics, road ecology, seed dispersal, shading
Plant invasions are a global phenomenon closely linked to human activities and related transportation network infrastructures (
Traffic volume has also been shown to trigger abiotic parameters like pollutant load (e.g.
While several vectors of human-mediated dispersal at roadsides are well understood, their effects on regional population dynamics of alien plants along roads and their interplay with habitat quality and adjacent land use have hardly been studied. A better understanding of interactions between traffic-related and habitat-related drivers of plant invasions along roads would also support the early detection of alien species, their control and related management measures – if necessary – in management at local or regional scales (
We use Ambrosia artemisiifolia L. (henceforth common ragweed) as a model species to elucidate relationships between traffic- and habitat-related features of road corridors. Common ragweed is an annual ruderal plant species that is well adapted to roadside habitats (
The spread of common ragweed is limited by low natural dispersal rates (barochory;
Based on our multi-annual approach we test the following hypotheses: (a) traffic intensity affects the expansion and densification of common ragweed populations along roads, resulting in growth rates linked to the corridor type (from high to low traffic intensity); (b) habitat type affects the population growth of common ragweed resulting in higher plant densities on disturbed roadsides compared to undisturbed roadsides and lower plant densities for shaded compared to un-shaded habitats; and (c) depending on the corridor type, the interaction between traffic- and habitat-related factors leads to changes in population growth in similar habitat types.
Common ragweed (Ambrosia artemisiifolia L.) was chosen as a model species because its spatial distribution patterns are closely related to human activities – especially to transportation corridors, with rail or road traffic likely functioning as major dispersal vectors (
Common ragweed grows in a range of open and disturbed habitats like wastelands, old fields or agricultural areas and along transportations corridors (
The study region (35 km × 35 km; road network of 300 km) is embedded in the historical region of Niederlausitz (Federal State of Brandenburg, Germany) and one of the hotspots of common ragweed in Germany (
Within the road network analyzed in this study (Fig.
The study area (c) is situated in the south east of the Federal State of Brandenburg, Germany (b). The mapped road network consists of four road types (black line: a-road, black dotted line: state road; grey line: district road; grey dotted line: parish road). Based on official and additional vehicle counts we pooled a-road and state road to high traffic intensity roads and district road and parish road to low traffic intensity roads. Settlements are displayed in red.
Our field survey included five semi-quantitative mappings of common ragweed roadside populations in the years 2008 to 2012. In each of these five years, we conducted a census in summer (July to August) when common ragweed was best visible along the roads and easy to distinguish from the other roadside vegetation by its characteristic greenish color. The total length of mapped roadsides was about 300 km. We classified four plant density categories of common ragweed (Fig.
Visualization of the four population density classes of common ragweed mapped in the road network survey. Note that the minimum length for a change in recorded plant density during the mapping was different for ‘scattered populations’ (200 m) compared to the other three density classes (100 m). Dominant populations differed from line-like populations in their lateral expansion perpendicular to the roadway.
We classified four road types according to traffic volume (Fig.
In 2012, we conducted an additional roadside habitat mapping from a slowly moving car to capture habitat features that we expected to potentially limit ragweed populations in road corridors. The underlying classification of roadside habitats was chosen after a thorough inspection of the different characteristics of roadsides in the field. First, we differentiated shaded from un-shaded (i.e. sunny) habitats. We assigned all roadsides to the category “shaded” that were shaded most of the day by close tree lines, tree stretches or single trees, or by an adjacent forest canopy, given that all of these elements clearly reduce light availability on the ground – which we hypothesized to limit common ragweed establishment. Since we expected a dense vegetation cover to limit the population establishment of common ragweed as well, we further differentiated the sunny roadside sections into disturbed and undisturbed sections according to a vegetation cover of <50% and of >50%, respectively, assuming that most kinds of disturbance lead to open soil patches along roads. We were not able to differentiate the shaded roadsides into disturbed and undisturbed sections as these sections were generally characterized by a sparse vegetation layer that made it impossible to visually distinguish disturbances from the slowly mowing car. However, we argue that in this habitat type recruitment is limited by shade rather than by the limitation of safe sites as the share of open soil was high throughout the road network. This classification resulted in three well distinguishable habitat types with recurring vegetation elements. The undisturbed roadsides frequently contained species of ruderal grasslands like Poa pratensis, Lolium perenne, Bromus hordeaceus, Achillea millefolium or Rumex crispus. In disturbed roadsides, these species gradually drop out and typical disturbance indicators appear such as Poa annua, Stellaria media, Conyza canadensis and Matricaria discoidea. Along the shaded roadsides, some of the competitive grassland species of the undisturbed sections still occur, supplemented by some species of shade-tolerant ruderal vegetation, such as Geum urbanum, Chaerophyllum temulum or Chelidonium majus.
We used a geographic information system (ArcMap of ArcGIS 10.3.1, ESRI, Redlands, California) to integrate the density distribution mappings of common ragweed as roadside line features to a digital form of the regional road network (OpenStreetMap and contributors, CC-BY-SA). In a second step, we split this network into 3-m-long spatial units resulting in 198,327 single road cells of 3 m length longitudinal to road direction. Each cell contained information about road type, traffic direction, roadside habitat and plant density for the seasons 2008, 2009, 2010, 2011 and 2012.
Road sections: To better understand how common ragweed interacts with variations in roadside habitats and traffic intensity within the road network at a regional scale, we split the entire network into 49 road sections based on existing junctions and t-junctions (a-road: n = 14, state road: n = 16, district road: n = 8, parish road: n = 11). We defined these sections as our main investigation units for analyses on temporal and spatial changes in the number of road cells colonized by common ragweed. Within their boundaries, each of the units is homogeneous in relation to the type of corridor or traffic intensity.
Road cells: To get a deeper insight into the roadside population dynamics we used road cells as a second level unit within our main unit road section in the sense of a high-precision investigation of the interactions between traffic intensity and habitat feature at the local scale. As we wanted to uncover plant density variations individually for each road cell, we encoded the mapped plant densities (none, scattered, patchy, line-like, dominant) into numbers (0, 1, 2, 3, 4) and calculated the differences in population density between the years (n = 4 sub sets of periods, i.e. 2008/2009, 2009/2010, 2010/2011, 2011/2012). Each road cell now included information about plant density in the prior season, the difference between the two seasons (change in population density) and the plant density in the post season. To cover only those road cells where change in ragweed density could potentially be observed, we filtered the road cells so that either the prior season or the post season had to be non-zero.
Binomial generalized linear mixed model: To assess the effects of road type and year on the invaded portion of the road sections, we performed a binomial generalized linear mixed model (‘glmer’ from the r-package lme4) with the counts of invaded and uninvaded road cells in each section as a dichotomous response vector. We used road type (a-road, state road, district road, parish road) and year (2008, 2009, 2010, 2011, 2012) and their interaction as fixed effects and the nesting of road sections in year as a random factor.
Linear mixed-effects model: To unravel the effects of traffic and habitat on the dynamics of ragweed density, we used a linear mixed-effects model (‘lmer’ from the r-package lme4) with the categorical variables roadside habitat (disturbed, undisturbed, shaded) and traffic intensity (high traffic, low traffic) as fixed effects. As random effects we used again road sections nested in year to account for temporal and spatial nesting of the road cells. Here, year is used as a covariate and no longer as a fixed effect, as we were interested in the interactions between habitat feature and traffic intensity independently of annual dynamics. As response we used the change in population density based on the encoded population density categories (see paragraph on roadside cells). This vector ranged from “-4” (population in the road cell decreases from level ‘4’ to ‘0’ [‘dominant’ to ‘uninvaded’]) to “+4” (population in the road cell increases from level ‘0’ to ‘4’). A vector value of “+1” for example would display an increase of plant density in the road cell by a whole factor level (e.g. 0 to 1, 1 to 2, 2 to 3, 3 to 4). This variable showed a normal distribution and hence a linear model was chosen.
As hypothesized, traffic intensity was related to the expansion of common ragweed populations within the road network. At the landscape scale, common ragweed colonized a portion of between 0.24 to 0.51 (a-road), 0.41 to 0.53 (state road), 0.29 to 0.35 (district road) and 0.46 to 0.36 (parish road) of all road cells in the respective corridor type between 2008 and 2012 (Fig.
Proportion of roadsides in different types of road corridors, colonized by Ambrosia artemisiifolia in five years. Mean values are based on road section specific proportion of colonized road cells (n = 49 road sections in the road network).
In the binomial generalized linear mixed model the year of investigation has the strongest effect on the number of colonized road cells while road type alone is not a significant predictor (Table
Effects of a) Road type and year on the proportion of roadside cells colonized by Ambrosia artemisiifolia and b) Traffic intensity and roadside habitat on annual change in population density of Ambrosia artemisiifolia in 49 roadside sections.
Model | χ² | df | p | |
---|---|---|---|---|
a) | Colonized roadside cells (binomial glmm) | |||
Road Type | 7.19 | 3 | 0.066 | |
Year | 14.68 | 4 | 0.0054 | |
Road Type:Year | 21.32 | 12 | 0.046 | |
b) | Annual Change in population density (linear mixed model) | |||
Traffic intensity | 7.62 | 1 | 0.006 | |
Roadside habitat | 88.53 | 2 | < 0.001 | |
Traffic intensity:Roadside habitat | 120.33 | 2 | < 0.001 |
Population density in already invaded road sections increased over the entire road network during the study but this process was strongly modulated by both traffic intensity and habitat type. Based on the linear-mixed model, both factors, roadside habitat and traffic intensity, significantly affect the mean change in population density of common ragweed in the road cells (Table
The interaction plot in Fig.
Mean change in population density of Ambrosia artemisiifolia between subsequent years in invaded road corridors with different traffic intensities. High traffic is merged from the corridor types a-road and state road, low traffic is merged from district road and parish road. Mean values are based on road cell specific population density categories (n = 303994 road cells in the road network).
Plant invasions at higher spatial scales are shaped by highly complex dispersal pathways which interact with landscape characteristics (
In general, a multitude of dispersal vectors shape plant dispersal in road networks (
As a main insight from this study, traffic intensity seems to modulate the inter-annual variation of the spatial occupancy patterns of common ragweed within the regional road network. In the observation period 2009–2012, the percentage of colonized road cells was increasing or rather stable along high traffic roads (a-road, state road; Fig.
The sudden increase in the overall occupancy of common ragweed in the road network after the first observation period (2008–2009, Fig.
Preferences for disturbed, open habitats correspond to the pioneer character of common ragweed (
Our linear mixed model revealed significant interactions between habitat features and traffic intensity and the population dynamics of common ragweed in the regional road network. However, the effect of habitat was considerably larger than that of traffic (Table
Bullock and colleagues (2018) propose to differentiate dispersal processes in spatial networks into “human-vectored dispersal” (HVD) and “human-altered dispersal” (HAD). Local impacts on population dynamics (e.g. by traffic-mediated dispersal) can thereby be identified as aspects of HVD. Still, the network-wide distribution of ragweed can be related to HAD as it is affected by the predominant land-use structures. A next step would be to concurrently analyze short-term and long-term changes in landscape patterns (e.g. temporary change in land use, construction works) in regard to spatiotemporal invasion patterns. While
The drivers of roadside invasions by common ragweed are not yet fully understood, although there is increasing evidence of the separated effects of dispersal by traffic and road maintenance and habitat features (
In our study, population growth of common ragweed proceeded even on roadsides with less suitable habitat conditions – but only along high-traffic roads. This indicates seed dispersal by vehicles and by road maintenance can compensate, at least partly, for less favorable habitat conditions. As a future direction, a threshold in traffic intensity and maintenance for a continued population growth along roadsides should be identified, based on more detailed data.
Our results on the interaction between traffic, roadside habitats and population dynamics have practical implications for habitat and population management to halt Ambrosia invasions along the road network. Depending on traffic intensity, colonized roadsides can serve both as a stepping stone habitat and as conduit for common ragweed invasions in road networks as already indicated by
The present study was conducted at the Technische Universität Berlin. We thank Konstantin Etling for mapping the roadside habitats and for digitalizing the data set and Gerhard Karrer and two anonymous reviewers for helpful comments on a previous version. We also thank Kelaine Vargas for improving the English. AL acknowledges funding from the Julius Kühn Institute, Braunschweig for part of the work presented here. SB received funding from the German Federal Ministry of Education and Research BMBF within the Collaborative Project “Bridging in Biodiversity Science-BIBS” (funding number 01LC1501A-H). We acknowledge support by the Open Access Publication Fund of TU Berlin.