Recolonization of native and invasive plants after large-scale clearance of a temperate coastal dunefield

Applied Geography 109 (2019) 102030

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Recolonization of native and invasive plants after large-scale clearance of a temperate coastal dunefield

T

Daehyun Kima,∗, Jung-Yun Leeb, Jongcheol Seoc, Insang Songa a

Department of Geography, Institute for Korean Regional Studies, Seoul National University, Seoul, 08826, South Korea Ecology & Spatial Information Institute, Sejong, 30130, South Korea c Department of Geography Education, Daegu Catholic University, Gyeongsan, 38430, South Korea b

A R T I C LE I N FO

A B S T R A C T

Keywords: Biological invasions Coastal dune management Spatial distribution Ecological-niche factor analysis Sindu

In the management of alien invasive plants in coastal dunes, plot-based approaches have generally been adopted: researchers establish a set of experimental (often topographically homogeneous) plots of a given size where the plants are removed, and recovery patterns are monitored for a period of time. Therefore, the literature still lacks a detailed understanding of where (i.e., under what topographic circumstances) native and invasive species are likely to recolonize after clearance of a large dunefield. In this study, we report on an unprecedented case from the Sindu dunefield in western Korea in which both native and invasive plants had been thoroughly removed to bare sand over a vast area (ca. 11.0 ha), followed by in situ exhaustive mapping of regeneration patterns throughout the entire cleared zone twelve times within four years. The results showed that, after removal, natives and invaders increased to occupy larger (> 50%) areas than those in the pre-removal state. Furthermore, Ecological-Niche Factor Analysis revealed that these two vegetation types exhibited markedly and significantly contrasting regeneration hotspots: invasive plants expanded primarily in low-lying sites that were close to trails. These findings indicate that the recolonization of invasive species was not a spatially random process but rather concentrated along the trails through which local employees transported removed plant material, inadvertently dropping invader propagules. We conclude that removal is often costly, and if executed without a careful plan for the movement of workers, equipment, and plant debris, these efforts may even increase the extent of invasions beyond the initial state.

1. Introduction Landscape scientists and planners continue to study biological invasions with emphasis on the patterns (e.g., rate and pathway) of colonization by alien species (D'Antonio & Vitousek, 1992; Wang et al., 2011), the driving factors that influence invasion success (Barney & Whitlow, 2008; Colautti, Grigorovich, & MacIsaac, 2006), the impacts of invasion on ecosystem structure, functioning, and services (Mooney & Cleland, 2001; Walsh, Carpenter, & Vander Zanden, 2016), and the strategies to mitigate such impacts and develop sustainable solutions for conservation biology (Hobbs & Huenneke, 1992; McGeoch et al., 2016; Sitzia, Campagnaro, Kowarik, & Trentanovi, 2016). Coastal dunes have long been subjected to this line of research. Located at the edge of the land, these systems are characterized by a unique mixture of indigenous species that are adapted to the strong effects of the nearby marine environment (e.g., wind, salt spray, and storm surges), as well as to the edaphic conditions conferred by predominantly sandy

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substrates (Martínez & Psuty, 2008). Biological invasions are serious threats to many coastal dunes worldwide in this era of human development pressure. Most typically, the propagules of alien plants are inadvertently transported into a dunefield by tourists, off-road vehicles, and heavy-duty vehicles that are related to reclamation activities and/ or recreational resort building (Kim, 2005; Wiedemann & Pickart, 1996). The subsequent establishment of these species often leads to negative consequences in both ecological and socioeconomic terms, changing the original view, functioning, biodiversity, and ecosystem services of the dune landscape formerly occupied by native plants (Dangremond, Pardini, & Knight, 2010; Martínez & Psuty, 2008; Seabloom, Ruggiero, Hacker, Mull, & Zarnetske, 2013; Zarnetske, Seabloom, & Hacker, 2010). In active response to these problems, removing alien invasive plants with the aim to protect and restore natural habitats has perhaps been the most intuitive and widely adopted initial strategy (Pickart, Miller, & Duebendorfer, 1998; Reid, Morin, Downey, French, & Virtue, 2009;

Corresponding author. E-mail addresses: [email protected] (D. Kim), [email protected] (J.-Y. Lee), [email protected] (J. Seo), [email protected] (I. Song).

https://doi.org/10.1016/j.apgeog.2019.05.007 Received 28 October 2018; Received in revised form 17 May 2019; Accepted 24 May 2019 Available online 14 June 2019 0143-6228/ © 2019 Elsevier Ltd. All rights reserved.

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Fig. 1. (a) The location (36°50'46.12" N, 126°11'50.89" E) of the Sindu coastal dunefield in South Korea. The “STUDY DUNE” is detailed in (b), (c), and (d). (b) The green zone is where extensive clearance of the whole vegetation cover occurred in fall 2012. (c) This shows the overall regeneration patterns throughout the study period. The red zone indicates the area which has been occupied by an alien invasive species at least once, and thereafter, not replaced by another type of vegetation cover during the study period. The light blue zone represents the area which has been occupied by a native dune species, and thereafter, not replaced by any other type of vegetation. (d) A three-dimensional visualization of (c). The thick lines indicate the interface between the foredune edge and the sand beach. The thinner lines indicate the trails through which tourists and local employees walked or drove occasionally.

unique—probably unprecedented—opportunity for conservation biology: both native dune and alien invasive plants were thoroughly removed to completely bare sand over a vast portion (ca. 11.0 ha) of the dunefield in fall 2012. The aim was to facilitate sand movement and satisfy tourists who expected to see a desert-like landscape for the first time in their lives (see section 2.2). After removal, the spatial distribution of these two regenerating vegetation types was exhaustively mapped in situ throughout the entire cleared area by the same researcher using a consistent protocol in the spring, summer, and fall of 2013, 2014, 2015, and 2016 (i.e., 12 times). We believe that the substantial amount of these spatial and temporal vegetation data, combined with multiple topographic variables extracted from the digital elevation model (DEM) of the dunefield (see 2.4. Data analysis), will allow us to evaluate the success of the large-scale vegetation removal, identify recolonization hotspots in relation to, for example, surface elevation, slope angle, and distance to the coastline, and finally, visualize these hotspots using species distribution modeling (SDM) (Bazzichetto et al., 2018; Guisan & Thuiller, 2005; Václavík & Meentemeyer, 2009). Accomplishing these overall objectives could potentially support the planning and design for the sustainability of Sindu and other similar ecosystems. External forces, whether natural or anthropogenic, can drive an ecosystem toward a tipping point at which the system undergoes an abrupt change in its property and function from one state to another qualitatively different state (Andersen, Carstensen, Hernández-García, & Duarte, 2009; Folke et al., 2010; Gunderson, 2010; Lenton, 2011; Scheffer et al., 2009). This regime shift is often profound enough to

Zavaleta, Hobbs, & Mooney, 2001). Much progress has been made on this topic in terms of refining clearance methods (Van Hook, 1985), monitoring the post-removal recovery of native species and the degree of reinvasion as a means to assess restoration success (Marchante, Freitas, & Hoffmann, 2011), and guiding management and planning decisions (Szitár et al., 2014). There remains, however, a paucity of studies that conduct extensive dunefield clearance at the landscape scale (e.g., > 10 ha), followed by intensive mapping of recolonizing native dune and invasive plants within the cleared landscape over multiple years. Many previous researchers indeed employed plot-based approaches, meaning that they defined the location and size of plots a priori and then examined the patterns of post-removal vegetation recovery only in these plots (generally ≤ 100 m2; e.g., Marchante et al., 2011; Pickart et al., 1998). Alternatively, others performed opportunistic removals in which patches of invasive plants were uprooted when encountered and buried in sand within a given experimental area (e.g., Kollmann, Brink-Jensen, Frandsen, & Hansen, 2011; Wiedemann & Pickart, 1996). While these methods have made important contributions to the field of biological invasions, one possible limitation herein is that our knowledge is inevitably restricted to the plots or patches of interest, with the areas beyond left unexplored. As a result, we are currently far from understanding where—e.g., under what topographic conditions—native and invasive species are likely or unlikely to recolonize a large bare dune surface after clearance. This limitation can further undermine our efforts to predict the regeneration hotspots of invaders and develop proactive management strategies accordingly. The Sindu coastal dunefield in western Korea provides a 2

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Vries, Klijn, and Kros (1994) who attributed the expansion of bushgrass to increased input of acidic materials (e.g., SOx, NOx, and NHx) from the atmosphere. A possible consequence herein is the conversion of a nutrient-poor, dry dune into a dune with nitrogen-rich and acidic soils, which further facilitates the establishment of plants from the Poaceae family (e.g., bushgrass) and alien species as well. Additionally, a concern was raised from a geomorphological standpoint: due to the everincreasing vegetation cover and terrestrialization, the Sindu dunefield has become almost completely static without active movement of blown sands and development of characteristic erosional landforms (i.e., blowouts). Given this background, an extensive vegetation removal—regardless of native or invasive species—across the Sindu dunefield was first approved by the local government in early 2012. In fact, there was another important, but somewhat implicit, justification that drove such a decision: the local government and planners occasionally heard that many tourists, expecting to see a desert-like landscape for the first time in their lives, were disappointed when encountering the dense vegetation at Sindu. In summary, the large-scale massive clearance of both native and invasive plants across an 11-ha dunefield, which had probably never been witnessed in the history of coastal zone management and planning, was a by-product of the combined socioecological perspectives on how to better attract tourists and how to better maintain dunes. In early fall 2012, an excavator was hired to grab and rake both tree and herbaceous species across a substantial portion of Sindu for three months. This treatment removed both aerial parts and rhizomes of all species to a depth of at least 30 cm (e.g., Wiedemann & Pickart, 1996). Subsequently, a number of local residents collected the removed materials and transported them via two major trails around the dunefield (Fig. 1b). Because some invasive plants regenerated relatively quickly (especially, evening primrose; see section 3.1), the residents conducted additional removals to manually remove them in the fall of 2013, 2014, and 2015 (see Appendix S1 for photos taken from the field). Certainly, there was some disagreement regarding these drastic plans for vegetation removal. First, the abovementioned vegetation processes could be considered important parts of the course of the overall, long-term dynamics of Sindu under the changing regimes of climate and human activities. Although Sindu might be losing its traditional scenic landscape as a true dunefield with a bare surface, we could still accept a new view (i.e., dense vegetation) as its alternate phase, as long as native plants are not seriously outcompeted by invasive counterparts. Second, with the clearance, we were not only eliminating plants but also the associated animals and microorganisms, which are certainly essential elements, but often neglected, components of dune ecology (Fitoussi, Pen-Mouratov, & Steinberger, 2016; McLachlan, 1991; Roy-Bolduc, Laliberté, & Hijri, 2016). The Sindu dunefield used to be well recognized for its endangered animal species, such as boreal digging frog (Kaloula borealis), pond frog (Pelophylax chosenicus), colubrid snake of northeast Asia (Elaphe schrenckii), and Mongolian racerunner (Eremias argus) (Do et al., 2017; Kim, Kim, Kim, Ra, & Park, 2011). Third, to the best of our knowledge, clearing a large dunefield (> 10 ha) for management and planning purposes has rarely been attempted in the past, given its radical nature and, more practically, the substantial budget and labor required. A critical question was whether the ecological and socioeconomic situation of Sindu was desperate enough to justify such unprecedented vegetation removal. Last, but not least, it remained uncertain if the removal would be a success in the end, that is, if the dune landscape, after the anthropogenic regime shift toward bare ground (i.e., vegetation removal), would continue as it is, allowing active sand movement and geomorphic dynamism. There was no assured pathway, such as the regeneration of native plants, reinvasion or continued bareness. The present work was conducted in order to address these questions.

render it impossible for the system to transition back to its original state. In the present research, we test these ideas of multiple state equilibrium, irreversibility, and system resilience, addressing the following research questions: Question 1 (Q1): Has the cleared area remained largely as a bare surface, indicating a low possibility of regeneration across the dunefield? Would this condition indicate that the ecosystem has entered a whole new state that is irreversible to its former phase? Question 2 (Q2): Do invaders tend to colonize again the habitats they had previously occupied before removal? Question 3 (Q3): Do natives and invaders tend to recolonize—thus, are likely to prefer—different habitat types? What are the major factors that influence these recolonization hotspots? Question 4 (Q4): Do all of these recolonization patterns (or preferences) change over time? Or, are there any major factors that are consistently important for explaining the regeneration hotspots throughout the study period? Question 5 (Q5): Following up on these questions, do invaders and natives show contrasting patterns of recolonization over space? Which type exhibits more specialized niches? Or, do they exhibit more or less overlapping recolonization hotspots? Question 6 (Q6): Overall, do invaders and natives require different types of management strategies? What are the possible ways to enhance and suppress the regeneration of natives and invaders at Sindu, respectively? 2. Materials and methods 2.1. Study area The Sindu coastal dunefield is located on the coast of the Yellow Sea, South Korea (36°50′46.12″ N, 126°11′50.89″ E; Fig. 1a), spanning ca. 4 km along the coastline and 0.5–1.5 km inland. The regional climate is generally temperate with an annual mean temperature and total precipitation of 11.9 °C and 952.8 mm, respectively ([dataset] Korea Meteorological Administration (KMA), 2018). The annual ranges of these factors are rather wide (24.8 °C and 221.6 mm), indicating that the winter is extremely cold and very dry, whereas the summer is very hot and humid. Notably, more than half (56%) of the annual rainfall occurs only between July and September. Public access to the dunefield had long been prohibited for military purposes until the early 1990s. Moreover, there existed few disturbance agents (e.g., storm overwash, wildfire, grazing, recreational activities) in this ecosystem, except for sand burial during winter seasons when a large amount of sand is transported by the winds generated from the Siberian high-pressure system from the northwest (Kim, Yu, & Park, 2008). Hence, Sindu has been famous for its well-conserved natural landscapes, showing geomorphological features (e.g., foredune ridges and swales) and plant species (e.g., Elymus mollis (Trin.) Pilg., Rosa rugosa Thunb., and Calamagrostis epigeios (L.) Roth) that are typical of Korean dunes (Kim & Shin, 2016; Kim & Yu, 2009). These facts, combined with the efforts of scientists and the National Trust of Korea, helped to designate this ecosystem in 2001 as the first (and still the only) Natural Monument (#431) among all dunes in the country. 2.2. Vegetation removal – background, justification, and controversy The Sindu dunefield has undergone a rapid expansion of vegetation cover at least since the mid-1990s. In particular, Kim and Yu (2009) considered the gradual dominance of bushgrass (Calamagrostis epigejos (L.) Roth) in low-lying areas as indicative of the increased atmospheric deposition (i.e., fine materials are supplied from the atmosphere and accumulate on Earth surface), originating from the nearby coastal regions where several coal-fired power stations were being operated. A similar situation had been reported previously in the Netherlands by De 3

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because coastal dunes are strongly affected by the winds blowing predominantly from the sea. The index uses the direction (azimuth °) of the prevailing wind as the input parameter—in this paper, 337.5° (NNW) according to the most recent 10-year record from the KMA—and it ranges between 0.7 and 1.3. Values below and above 1 indicate a cell being leeward (wind-shadows) and windward (exposed to wind), respectively (Gerlitz, Conrad, & Böhner, 2015). Exposure to wind was calculated for each cell using an open source geospatial analysis software, SAGA GIS 6.2.0 (Conrad et al., 2015). Because slope aspect was a circular variable, it was linearized with the following cosine transformation to scale the values to between 0 and 2 (facing approximately seaward and landward, respectively): cosine [(45° – azimuth) × 3.141592/180] + 1 (Kim, 2018; Kupfer & Farris, 2007). Only the slope angle was normalized with a square-root transformation because the other variables generally showed normality in their frequency distribution (data not shown). To address Q3 (Do natives and invaders tend to recolonize—thus, are likely to prefer—different habitat types? What are the major factors that influence these recolonization hotspots?), we first developed a single vegetation layer, combining the distribution of both native dune and alien invasive species throughout the entire study period (Fig. 1c). In this overall map, the dune and invaded zones indicated which cells were occupied by a dune and an invasive species at least once, respectively, and thereafter, were not replaced by another type of vegetation cover. For each environmental variable, we conducted independent samples ttests to evaluate whether a variable mean was significantly different between the native and invasive plants zones at the p < 0.05 level. Next, Ecological-Niche Factor Analysis (ENFA), developed by Hirzel, Hausser, Chessel, and Perrin (2002), was employed as a means for evaluating which environmental factors affect the recolonization hotspots for native and invasive plants. The fundamental concept underlying ENFA is ecological niche (Begon, Harper, & Townsend, 1996; Hutchinson, 1957), commonly defined as a hypervolume in the multidimensional space of eco-geographical variables (EGVs) within which a species has a reasonable probability to occur. The ENFA approach compares the species distribution within the EGV space against the overall conditions of the EGVs across a reference area. As a factorial analysis, this method extracts a set of axes, with the first axis representing the marginality of the focal species and the following axes indicating specialization. Marginality is a measure of the departure of the realized niche from the average available habitat. Hence, a large marginality value should indicate a strong preference of native or invasive plants for specific environmental conditions among the whole set of possibilities (Basille, Calenge, Marboutin, Andersen, & Gaillard, 2008). Specialization, defined as “the ratio of variance in the global distribution to that in the species distribution” (Hirzel et al., 2002; Reutter, Helfer, Hirzel, & Vogel, 2003), indicates how restricted a species's niche is compared to the whole reference area. As specialization increases, therefore, the niche should become narrower. In this paper, the six predictor variables introduced previously were treated as EGVs. The entire area in which vegetation removal occurred was considered the reference area for ENFA. We examined whether any two predictor variables were strongly correlated, and thus, redundant (i.e., issue of multicollinearity; Graham, 2003). Because there was no correlation coefficient exceeding 0.7, we decided to retain all of the original six predictor variables (e.g., Galparsoro, Borja, Bald, Liria, & Chust, 2009; Medley, 2010). ENFA was conducted with the R-package “adehabitatHR” (Calenge, 2017; R Core Team, 2018; see Appendix S4 for the associated code). Different from previous applications of ENFA (e.g., Hemery et al., 2011; Hill & Terblanche, 2014; Valle, Borja, Chust, Galparsoro, & Garmendia, 2011), we did not intend to perform any predictive modeling (e.g., habitat suitability modeling), followed by validating model outcomes. Instead, we employed this method primarily for an explorative purpose to address Q3, namely, identifying major factors that explained where native dune and alien invasive plants were likely to recolonize (see also Dolgener et al., 2014). This

2.3. Vegetation survey In summer 2012, we carefully examined a pre-removal aerial image of Sindu to digitize different vegetation zones using AutoCAD 2010 (Autodesk, Inc., 2009). It is a powerful drafting software system that has been widely utilized by architects, engineers, graphic designers, and to some degree, geographers since 1982. We considered the system highly useful because the drawing files (*.dwg) produced by AutoCAD are easily transferred to and manipulated by ArcMap 10.3 (Environmental Systems Research Institute, 2014) (see next section). Next, one of the authors (J.-Y. Lee) meticulously walked across the study area to identify the species occupying each zone and to detect micro-scale patches that could not be discerned from the aerial photo. The spatial coordinates of these additional patches were recorded and also digitized. Beginning in 2013 (i.e., after removal), the same researcher revisited the cleared area every spring, summer, and fall until 2016 (i.e., 12 times) to exhaustively draw regenerating vegetation patches, which were later digitized by AutoCAD (see Appendix S2 for all digitized maps). 2.4. Data analysis We converted the computer-aided design (CAD) drawings into feature classes, which are compatible with either GIS software or R. At this step, we fixed topological errors in drawings derived from human works, such as dangling vertices and incomplete polygons. To analyze our data in a raster format, we rasterized polygons through the Feature to Raster tool in ArcMap, in which smooth polygon boundaries are tessellated into square pixels, in 1-m resolution in our case, under the default setting that a possible pixel covering multiple attributes (e.g., two vegetation classes at a pixel) is assigned to the attribute with the largest area. In each time period, we merged all species into four broad categories: dune plants, xerophytes, hydrophytes, and trees (Appendix S3), as the focus of this paper was the spatial and temporal dynamics of the main vegetation types, rather than each particular species in detail. To address Q1 (Has the cleared area remained largely as a bare surface?), we plotted the number of cells pertaining to these categories over the seasons. We were particularly interested in the temporal variation of dune plants and xerophytes, as the latter included the most invasive species. For Q2 (Do invaders tend to colonize again the habitats they had previously occupied before removal?), we extracted the cells occupied by invasive plants in summer 2012 (i.e., just before removal) from the entire dunefield and examined how many were invaded again after removal at least once until fall 2016. Questions 3–5 were all spatially explicit inquiries since we broadly asked under what environmental circumstances native dune and invasive plants were likely or unlikely to colonize across the entire cleared area. Hence, ArcMap was utilized again to create six environmental layers from the DEM of Sindu (National Geographic Information Institute, 2014), which were treated as predictor variables in the subsequent analyses: surface elevation (m), slope angle (°), slope aspect (azimuth °), wind exposure (unitless), distance to the coastline (m), and distance to the trails (m) (Fig. 2). We selected these environmental variables in this research because they were known to be correlated with the composition and presence of plant species in many previous ecological investigations (e.g., Bazzichetto et al., 2018; Franklin, 2010; Kim & Shin, 2016; Kim et al., 2008). The original DEM resolution was 5 m × 5 m, but we resampled the DEM to 1 m × 1 m using bilinear interpolation to match the resolution of the vegetation layer. While such downscaling should involve the issue of data uncertainty (Pradhan, Tachikawa, & Takara, 2006; Srivastava, Han, Rico-Ramirez, & Islam, 2014), we believed that this would not be a critical problem in this study, given Sindu's highly smooth surface topography (Kim & Zheng, 2011). Exposure to wind, originally known as the windward/ leeward index (Böhner & Antonić, 2009), was considered herein 4

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Fig. 2. The spatial patterns of the environmental variables included in this research. The red line indicates the area (ca. 11 ha) in which massive clearance of vegetation occurred in fall 2012. The blue lines represent the trails through which tourists and local employees walked or drove occasionally. All original values of slope aspect have been linearized (see 2.4. Data analysis), so the variable is now unitless, ranging from 0 to 2.

identification was achieved by comparing the marginality and specialization coefficients—these are equivalent to factor loadings in factor analysis—of the six predictor variables. Question 4 (Do all of these recolonization patterns (or preferences) change over time? Or, are there any major factors that are consistently important for explaining the regeneration hotspots throughout the study period?) was examined by conducting all of these same analyses (t-tests and ENFA) separately for each season of the vegetation survey after removal from spring 2013 to fall 2016 (i.e., 12 times in total). After ranking all predictor variables based on the magnitude (absolute value) of their marginality coefficients for native and invasive plants, we studied whether that ranking remained more or less the same (i.e., consistent importance of particular variables) or changed notably over time. Finally, we tested Q5 (Do invaders and natives show contrasting patterns of recolonization over space? Which type exhibits more specialized niches?) by visually comparing the spatial patterns of recolonizing native and invasive plants over time and by comparing the overall marginality and specialization values of the two vegetation types. In addition, the temporal changes of the variance (%) explained by the marginality of native and invasive species were investigated, followed by a paired-samples t-test to determine if there was a significant difference in the mean variance between the two vegetation types. As explained above, marginality herein indicates the first axis of factor analysis, extracted during the ENFA procedure. 3. Results 3.1. Question 1: continued bare surface? Or regenerated vegetation? The pre-removal vegetation survey in summer 2012 revealed that trees (black locust and Japanese black pine; see Appendix S3) had been the most dominant vegetation type occurring in ca. 48% (53,220 cells) of the study dune, followed in order by xerophytes, dune plants, and hydrophytes (Fig. 3a). These trees, however, minimally regenerated (up

Fig. 3. (a) The areal changes of different vegetation types within the 2012 cleared area of the Sindu coastal dunefield, South Korea. The class “Xerophytes” includes alien invasive species that are detailed in (b).

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mean values between the areas occupied by native and invasive species throughout the post-removal vegetation survey (except for slope aspect in fall 2013; Table 3). The natives and invaders, therefore, tended to recolonize—i.e., were likely to prefer—different habitat types in each season examined. The relative importance of these variables to the distribution of the two vegetation groups, however, was not always consistent over time: only distance to the trails was invariably the most important to native plants with consistently positive influences on them (Table 4; Fig. 5a), whereas the other five variables exhibited notable changes in their rankings for both native and invasive plants. Surface elevation—the second most important variable in the preceding section where the overall distribution map combining all seasons (Fig. 1c) was of interest—became least or second least important on several occasions. Elevation always negatively influenced invader recolonization, but its effect was not consistently positive for natives (Fig. 5b).

to only 403 cells) after removal throughout the research period. In contrast, the recolonization of xerophytes was so rapid that, as of fall 2013 (i.e., just one year after clearance), they occupied 53% more cells than in summer 2012 (29,876 → 45,758). Native dune species gradually and steadily expanded throughout the post-removal surveys, eventually becoming the most dominant vegetation type in fall 2016 with a total of 43,583 colonized cells. This result indicated that the number of cells colonized by the natives increased by approximately 68% compared to the pre-clearance state when they occupied 25,980 cells. We identified seven alien invasive species from the xerophyte group: horseweed, daisy fleabane, flax-leaf fleabane, common ragweed, Carolina horsenettle, marshpepper knotweed, and evening primrose (Appendix S3). Before removal, horseweed had been most abundant among these, accounting for approximately 90% of all invaders, but the proportion reduced to 17% in fall 2016, giving way to evening primrose (Fig. 3b). Since spring 2014, primrose has occupied a dominant portion (> 50%) of all invasive plants, even reaching 92% in spring 2015. The occasional decreases of its cover in fall 2014, 2015, and 2016 were caused by the additional clearance campaigns introduced previously (see 2.2. Vegetation removal).

3.5. Question 5: Of natives and invaders, which exhibited more specialized niches? The results presented thus far indicate that native dune and alien invasive species had contrasting spatial niches of recolonization, which rarely overlapped (Fig. 4). In particular, invasive plants were determined to exhibit greater overall marginality (1.26 vs. 0.54) and. specialization (0.73 vs. 0.47) values than native counterparts (i.e., stronger preferences for specific environmental conditions and narrower niches selected by the invaders than the natives). In addition, except for summer 2015, invasive species always showed larger variances (%) explained by the marginality (i.e., more specialized niches throughout the study period) than native counterparts (Fig. 6), and this difference was statistically significant (paired-samples t-test t = 4.714, p < 0.01).

3.2. Question 2: Invaders likely to reoccupy previous habitat? Among the 3826 cells in which alien invasive plants had been found before removal, only 491 cells (12.8%) were reoccupied by one or more invaders after removal. 3.3. Question 3: Significant predictors of the overall recolonization? In the analysis of the overall distribution map (Fig. 1c), all environmental variables were determined to have significantly different mean values between the zones colonized by native dune and alien invasive plants (Table 1). That is, a significant habitat differentiation was detected such that, in comparison with natives, invaders tended to establish in the sites that had lower elevation and lower slope angle and that were more perpendicular to the coastline, more sheltered from the prevailing NNW wind, closer to the coastline, and closer to the trails. These findings notably corresponded to the ENFA results in that all marginality coefficients for dune and invasive plants were positive and negative, respectively (Table 2). For both vegetation types, distance to the trails and elevation proved to be the two most important factors influencing niche selection.

4. Discussion In natural resources management—especially when dealing with a landscape recognized as an important treasure nationally or internationally—planners often juggle several different strategies, some of which are moderate in nature while others are radical (De Jong, Keijsers, Riksen, Krol, & Slim, 2014; French, 2001; Phillips & Jones, 2006; Turner, 2009). In many cases, radical programs are unprecedented, large-scale, and costly with uncertain success rates, and therefore, their implementation generally involves much controversy and concern. Constructing seawalls, for example, has been employed with an aim to mitigate the worldwide tendency toward coastal erosion, but the outcome is not always beneficial because the action occasionally proves to aggravate the situation (e.g., Anfuso, Pranzini, & Vitale, 2011; Betzold & Mohamed, 2017; Cooper & Pilkey, 2012; De Sousa, Loureiro, & Ferreira, 2018; Nunn, 2013; Pourkerman et al., 2018). The present findings from Sindu reflect similar stories as above. On one hand, the massive vegetation clearance across the dunefield, at first glance, appears successful given the low probability (12.8%) of reinvasion of the areas that had been previously invaded before clearance. This positive outcome indicates that the methods for removal were adequately planned and carefully conducted such that the propagules of invasive plants were not inadvertently left behind in the former habitat. There are indeed not many empirical studies that can tell us the probability of reinvasion at the same habitat after removal. Pickart (2013), for example, reported that the cover of Ammophila arenaria (an invasive plant in the Ma-le'l dunes of northern California) was less than 1%, 5 years after clearance. However, it is unknown whether the species regenerated at the same zone it had formerly occupied or a nearby location. Therefore, a direct comparison of our findings with those of other studies is not a straightforward task (see also Thomas et al., 2018). On the other hand, the invaders have actually expanded well beyond (i.e., 53%) the pre-clearance state within only one year, especially

3.4. Question 4: consistently significant predictors present across multiple seasons? By conducting t-tests and ENFA for each season (Fig. 4), we first detected that all environmental variables had significantly different Table 1 Comparison of the mean values of topographic variables between the zones colonized by native dune species (DUNE) and alien invasive species (INVADED) within the 2012 cleared area of the Sindu coastal dunefield, South Korea.

Distance to the trails (m) Surface elevation (m) Exposure to wind (unitless) Distance to the coastline (m) Slope angle (°) Slope aspect (unitless)a

Mean of DUNE

Mean of INVADED

t-value

p-value

155.7 10.1 0.99

58.3 8.1 0.97

227.1 139.7 66.0

< 0.001 < 0.001 < 0.001

284.4

241.4

51.7

< 0.001

2.2 0.98

1.6 0.92

42.1 9.8

< 0.001 < 0.001

a All values in azimuth angle have been linearized (see 2.4. Data analysis), so the variable is now unitless.

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Table 2 Comparison of the Ecological-Niche Factor Analysis results (marginality and specialization coefficients) between the zones colonized by native dune species and alien invasive species within the 2012 cleared area of the Sindu coastal dunefield, South Korea. The amount of marginality (Mar.) and specialization (Spec.) accounted for is presented in parentheses in each column heading. Native dune plants

Invasive plants

Mar.

Spec. 1

Spec. 2

Spec. 3

Spec. 4

Mar.

Spec. 1

Spec. 2

Spec. 3

Spec. 4

EGV

(29%)

(20%)

(19%)

(17%)

(16%)

(44%)

(30%)

(16%)

(6%)

(4%)

Dist. trails Elevation Dist. coastline Exp. to wind Slope aspect Slope angle

0.81 0.42 0.38 0.13 0.05 0.01

−0.52 0.44 0.70 −0.19 −0.03 −0.10

0.16 −0.23 0.07 −0.47 0.17 −0.82

−0.09 0.47 −0.46 0.22 0.60 −0.40

−0.01 0.59 −0.55 −0.09 −0.32 −0.49

−0.69 −0.58 −0.23 −0.29 −0.04 −0.22

0.58 −0.52 0.35 −0.50 0.03 −0.15

−0.18 0.66 −0.09 −0.70 −0.15 −0.15

0.04 0.35 −0.56 0.17 0.03 −0.73

0.22 −0.27 0.12 −0.01 −0.92 0.11

a

EGVs (ecogeographical variables) are sorted by decreasing absolute values of the marginality coefficients for “Native dune plants.” See Table 1 for the variables' full names and units. a

bare state. In other words, the human-driven regime shift did not remain irreversible, but has transitioned into a new alternative phase, in which both native and invasive plants regenerated rapidly (i.e., high resilience of the system). These regeneration patterns appear to have been accelerated by the ever-increasing atmospheric deposition around the study area in the last two decades (De Vries et al., 1994; Kim & Song, 2017; Oh et al., 2015; Remke et al., 2009; Sival & Strijkstra-Kalk, 1999). Located in the western part of South Korea, the dunefield has at least two distinct sources of atmospheric deposition: coal-fired power stations in the nearby coastal regions and industrial factories in eastern China. We believe that the resulting eutrophication, to which Kim and Yu (2009) earlier attributed the overall expansion of vegetation cover across Sindu since the 1990s (see section 2.2), is still occurring within the cleared zone, and thereby accelerates the establishment of plant species, whether native or invasive. From the standpoint of landscape planning, it is encouraging that post-clearance reinvasion of non-native plants has occurred by one dominant species (i.e., evening primrose; Fig. 3b). Therefore, management strategies focusing on the life history traits of that species will help to implement an early control measure on the current biological invasions at the Sindu dunefield (e.g., Kim, Lee, & Myeong, 2015). Primrose, belonging to the Onagraceae family, is a biennial plant, remaining as a rosette in its first year. This plant flowers and grows up to 60–120 cm in the second year. In fact, the species had once shown an extensive distribution at Sindu regardless of, for example, surface elevation, slope angle, and slope aspect in the early 2000s (Appendix S5). This indicates that primrose individuals have a high tolerance of a wide range of environmental conditions, and hence, without being drastically disturbed by an external force, they are potentially able to become prevalent across the dunefield (Kim & Yu, 2009). In this sense, it is also encouraging that the hotspots of early reinvasion are, as indicated by the high marginality and specialization indices for invasive plants (see 3.5. Question 5), largely restricted to the low-lying areas close to the trails. Invaders, however, have probably not yet colonized all suitable sites (Hirzel et al., 2002). As alien species are anticipated to eventually expand beyond the current extent, local practitioners are advised to continue immediate additional removal of them in the future. From a methodological perspective, these discussions indicate that, in our vegetation data, the absence of invasive plants does not necessarily imply a poor-quality habitat. Such a problem of “false (or pseudo-) absences” has long been regarded as a source of considerable bias in SDM (e.g., Barbet-Massin, Jiguet, Albert, & Thuiller, 2012; Lobo, Jiménez-Valverde, & Hortal, 2010; Renner et al., 2015). Given that the absence of invaders is an unlikely equivalent to true habitat unsuitability at Sindu, we believe that our choice of ENFA, which does not require absence data and only includes presence data in its procedure (Basille et al., 2008; Hirzel et al., 2002), is highly appropriate compared to employing traditional approaches, such as logistic regressions,

Fig. 4. The spatial distribution of native (light blue) and invasive (red) plant species during the study period within the 2012 cleared area of the Sindu coastal dunefield, South Korea. The thin blue lines indicate the trails through which tourists and local employees walked or drove occasionally. These maps, combined, constitute the overall map in Fig. 1c.

in the low-lying areas that are relatively close to the trails (Tables 1 and 2). In addition, the considerably increased cover of native species (i.e., by 68% since fall 2012) is somewhat worrisome because the expanding vegetation restricts aeolian sand movement and reduces the sandy areas at Sindu. These effects can further decrease the geomorphic dynamism across the dunefield and the number of tourists who expect and travel to see a desert-like landscape (see 2.2. Vegetation removal). Addressing Question 1, we note that the vegetation clearance, despite its comprehensiveness, did not convert Sindu into a permanently 7

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Table 3 The temporal variation of the significance (p-values) of the mean differences1 between the zones colonized by native dune species and invasive species within the 2012 cleared area of the Sindu coastal dunefield, South Korea.

Table 4 The temporal variation of the ranking of each ecogeographical variable (EGV) identified by Ecological-Niche Factor Analysis, indicating the relative importance of EGVs to the spatial distribution of native dune and invasive plants within the 2012 cleared area of the Sindu coastal dunefield, South Korea. Native dune plants

2013

EGVa

Su

Fa

Sp

Su

F

Sp

Su

F

Sp

Su

F

Average ranking

Average coefficientc

Distance to the trails Surface elevation Distance to the coastline Exposure to wind Slope aspect Slope angle

1 4 3 5 2 6

1 3 4 6 5 2

1 5 3 6 4 2

1 4 5 6 3 2

1 4 5 6 3 2

1 5 3 6 4 2

1 2 5 4 3 6

1 2 4 6 3 5

1 3 6 4 2 5

1 6 4 3 2 5

1 5 4 2 3 6

1.0 3.9 4.2 4.9 3.1 3.9

0.80 0.18 0.15 0.13 0.30 0.24

Invasive plants

2013

b

2014

2015

2014

2016

2015

2016

EGV

Su

Fa

Sp

Su

F

Sp

Su

F

Sp

Su

F

Average ranking

Average coefficientc

Distance to the trails Surface elevation Exposure to wind Distance to the coastline Slope angle Slope aspect

3 1 4 2 5 6

2 1 5 3 4 6

3 2 4 1 5 6

4 1 3 2 5 6

3 1 4 2 5 6

1 5 4 2 3 6

2 1 4 3 6 5

4 1 3 2 6 5

2 3 4 1 5 6

3 2 4 1 5 6

1 5 4 3 6 2

2.5 2.1 3.9 2.0 5.0 5.5

0.47 0.50 0.37 0.53 0.18 0.10

a b c

EGVs are sorted by decreasing absolute values of the marginality coefficients for “Native dune plants” as in Table 2. EGVs are sorted by decreasing absolute values of the marginality coefficients for “Invasive plants” as in Table 2. Averaged absolute values of the marginality coefficients for each EGV.

clearance). Such season-specific stochastic factors can yield a habitat occupied by a distinct set of species that are often transient, that is, they are likely to be replaced by another set of plants depending on the new particular environmental conditions of the next season. This reason is why no single topographic factor tested was invariably the most important for the distribution of regenerating plants during the study period (except distance to the trails for native species). Therefore, a few seasons may be needed to observe the complete establishment of plants that are optimally adapted to the habitat. In summary, we consider that analyzing an overall map (Fig. 1c), combining the observations over multiple years, is recommended as a proper means to search for indicators of post-clearance regeneration patterns.

discriminant analysis, and artificial neural networks (Giam & Olden, 2015; Lek & Guégan, 1999; Manel, Dias, & Ormerod, 1999). Last, but not least, the results indicate that separate ENFA for each season of vegetation surveying (Fig. 4) will hardly help to identify the key factors that influence the overall regeneration patterns of native and invasive plants. We attribute the considerable amount of variation in the ranking of environmental variables through time (Table 4) to season-specificity and time-lag effects (Alexander et al., 2018; D'Antonio & Flory, 2017; Rouget et al., 2016; Seebens, Essl, & Blasius, 2017). In each season, propagules and seedlings are subject to different conditions in terms of temperature, precipitation, herbivory, sand burial, abrupt deflation, and management campaigns (e.g., additional 8

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findings report a potential aggravation of biological invasions by the effort itself designed to solve the situation. In short, this research can be considered reminiscent of one of the seminal works by Wiedemann and Pickart (1996) who argued that “… control of eradication may be feasible over small areas for specific purposes such as habitat restoration, but it is impractical as a means of slowing or reversing natural cyclic processes.” Second, in addressing Question 6 (Do invaders and natives require different types of management strategies?), we suggest that additional clearance efforts should be continued locally, especially in low-lying areas that are close to the trails where evening primrose quickly reinvades. Our experience indicates that a two-step approach would be optimal (1) intense removal (e.g., complete uprooting or cutting at ground level): between April and June and (2) additional removal between July and September immediately before flowering or fruiting (see also Kim et al., 2015). Third, the methods for removing alien invasive plants can be implemented in the current format (and certainly applied to other dunes), but workers are advised to be careful not to mistakenly drop propagules when transporting the removed plant material along the trails or to introduce propagules from outside areas. This paper is the first to present the background, implementation, and outcomes of an unprecedented large-scale program for coastal dune management. This radical program proves to be not entirely successful in a few aspects. However, we believe that even such failure, together with some positive results, can provide both academia and the public with useful insight and baseline information, potentially serving as a benchmark upon which future research can build in other dunes. In particular, we call for additional studies that investigate the post-removal recolonization of both native dune and alien invasive plants in a spatially explicit way as in the present work. Fig. 5. The temporal variation of the marginality coefficients for (a) distance to the trails and (b) surface elevation during the study period within the 2012 cleared area of the Sindu coastal dunefield, South Korea.

Acknowledgments This study was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2017R1C1B5076922) and the Research Resettlement Fund for the new faculty of Seoul National University. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.apgeog.2019.05.007. References Alexander, J. M., Chalmandrier, L., Lenoir, J., Burgess, T. I., Essl, F., Haider, S., et al. (2018). Lags in the response of mountain plant communities to climate change. Global Change Biology, 24(2), 563–579. https://doi.org/10.1111/gcb.13976. Andersen, T., Carstensen, J., Hernández-García, E., & Duarte, C. M. (2009). Ecological thresholds and regime shifts: Approaches to identification. Trends in Ecology & Evolution, 24(1), 49–57. https://doi.org/10.1016/j.tree.2008.07.014. Anfuso, G., Pranzini, E., & Vitale, G. (2011). An integrated approach to coastal erosion problems in northern tuscany (Italy): Littoral morphological evolution and cell distribution. Geomorphology, 129(3–4), 204–214. https://doi.org/10.1016/J. GEOMORPH.2011.01.023. Autodesk, Inc (2009). AutoCAD 2010: User's guide. USA: San Rafael, California. Barbet-Massin, M., Jiguet, F., Albert, C. H., & Thuiller, W. (2012). Selecting pseudo-absences for species distribution models: How, where and how many? Methods in Ecology and Evolution, 3(2), 327–338. https://doi.org/10.1111/j.2041-210X.2011. 00172.x. Barney, J. N., & Whitlow, T. H. (2008). A unifying framework for biological invasions: The state factor model. Biological Invasions, 10(3), 259–272. https://doi.org/10. 1007/s10530-007-9127-8. Basille, M., Calenge, C., Marboutin, É., Andersen, R., & Gaillard, J.-M. (2008). Assessing habitat selection using multivariate statistics: Some refinements of the ecologicalniche factor analysis. Ecological Modelling, 211(1–2), 233–240. https://doi.org/10. 1016/J.ECOLMODEL.2007.09.006. Bazzichetto, M., Malavasi, M., Barták, V., Acosta, A. T. R., Moudrý, V., & Carranza, M. L. (2018). Modeling plant invasion on mediterranean coastal landscapes: An integrative approach using remotely sensed data. Landscape and Urban Planning, 171, 98–106. https://doi.org/10.1016/J.LANDURBPLAN.2017.11.006. Begon, M., Harper, J. L., & Townsend, C. R. (1996). Ecology : Individuals, populations, and

Fig. 6. The temporal variation of the variance (%) explained by the marginality of native dune and invasive plants during the study period within the 2012 cleared area of the Sindu coastal dunefield, South Korea.

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