Complex effects of landscape, habitat and reservoir operation on riparian vegetation across multiple scales in a human-dominated landscape

Ecological Indicators 94 (2018) 482–490

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Original Articles

Complex effects of landscape, habitat and reservoir operation on riparian vegetation across multiple scales in a human-dominated landscape ⁎

⁎

T

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Chundi Chena, , Maohua Maa, Shengjun Wua, , Junsong Jiab, , Yuncai Wangc a

Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University, Nanchang, Jiangxi 330022, China c Tongji University, Shanghai, China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Biodiversity Landscape gradient model Large dam Manipulated flooding Surface metrology Yangtze River

Riparian ecosystems associated with dam reservoirs, are subjected to a wide variety of stressors that alter their plant community composition and diversity patterns and affect riparian ecosystem services delivery. To enhance riverine and regional ecosystem management, the different influences of dam-induced environmental change and anthropogenic factors within and around the reservoir need to be understood across multiple scales. To elucidate the relative importance of these factors, this study examines riparian vegetation composition and structure along the Three Gorges Reservoir (China) and their responses to surrounding landscapes, habitat quality and reservoir management (three groups of influences). We recorded vegetation data in 5 × 1 m quadrats embedded in three stratified elevational zones 145–155 m, 155–165 m, and 165–175 m. Correspondence analyses were used to partition the contribution of various influences in explaining vegetation patterns at three organisational scales: site, community and individual species. Vascular plants totaled 150 species in 130 genera from 56 families. Overall, vegetation responses were idiosyncratic across the analysed scales. At the site-scale, landscape was most important in structuring communities. However, localised habitat and reservoir flooding influences have higher explanatory power at the community-scale, regardless of elevational divergence. Overall, there seem to be a general trend in the importance of variables across all analysed scales. Among the landscape factors, variables based on a landscape gradient model were much more influential than those based on the conventional patch mosaic model. Given the heavily inter-correlated nature of many variables at varying scales of human altered landscapes, the best way forward for reservoir riparian vegetation management strategies is to develop multi-scale, synthetic and location-specific approaches that may optimise conservation efforts.

1. Introduction By 2015, more than half of the world’s major rivers have been regulated by over 57,000 large dams (ICOLD, 2017). With growing demands for clean energy, more rivers are likely to be altered, especially in developing countries (DOE, 2004; Grumbine and Pandit, 2013). Compared with natural free-flowing rivers with long-established, and comparatively stable riverine plant communities, dam associated riparian ecosystems are subjected to a wider variety of stressors that alter vegetation composition, structure and diversity patterns and affect their functions, such as bank stability, water purification and biodiversity support (Kellogg and Zhou, 2014; Nilsson et al., 2005). Spontaneous, opportunistic, non-native or invasive species rapidly

colonise and spread because of the abundance of exposed ground caused by hydrological alteration (Caruso, et al., 2013; Nilsson, et al., 2005). A comparative study of pre-dam (in 2001) and post-dam (in 2009) riparian vegetation along the Three Gorges Reservoir (TGR) shows that reservoir operation has significantly changed plant community composition and richness. Vascular plant species were reduced by 43% and the dominant plant life forms changed significantly from perennial herbs and shrubs to annual herbs, with woody species dramatically decreasing from 108 to 39 (Yang et al., 2012). It is undisputed that the composition, structure and dynamics of riparian vegetation are strongly and directly influenced by localised interactions and feedbacks among habitat properties altered by dam construction and the hydrological regime (Garssen et al., 2015; You

⁎ Corresponding authors at: Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, No. 266 Fangzheng Avenue, Shuitu Town, BeiBei, Chongqing, China (C. Chen, S. Wu). Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University, Nanchang, Jiangxi 330022, China (J. Jia). E-mail addresses: [email protected] (C. Chen), [email protected] (M. Ma), [email protected] (S. Wu), [email protected] (J. Jia), [email protected] (Y. Wang).

https://doi.org/10.1016/j.ecolind.2018.04.040 Received 21 January 2017; Received in revised form 12 April 2018; Accepted 16 April 2018 1470-160X/ © 2018 Published by Elsevier Ltd.

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Fig. 1. Location of study area and sample sites.

et al., 2015). Almost all landscape metrics are based on the patch mosaic model (PMM), where landscape is categorised as mosaics of discrete patches. Although such a categorical model applies well for landscape encompassing patches with clearly defined boundaries by natural or anthropogenic disturbances, it cannot satisfy landscapes with continuous environmental gradients since it homogenises inter-patch differences (Cushman et al., 2010; McGarigal et al., 2009; St-Louis et al., 2009). An alternative to address these uncertainties is the use of unclassified satellite imagery; that may directly derive measures for habitat heterogeneity in terms of physical and biological diversity (Macfadyen et al., 2016; Rocchini et al., 2010; St-Louis et al., 2009). Landscape gradient model (GM) and associated surface metrology metrics introduced by McGarigal et al. (2009) are promising tools. GM is mainly based on a continuous range of satellite reflectance values that can reflect ecological attributes such as the Normalised Difference Vegetation Index (NDVI). This is represented by a 3D surface that can capture horizontal composition and configuration, as well as vertical features of a landscape (McGarigal et al., 2009; Moniem and Holland, 2013; Pielech, 2015). Though most studies adopt a binary PMM to characterise landscape heterogeneity and understanding relationships between patterns and species dynamics, applications of these newly proposed 3D surface metrics in vegetation patterns remain underused. This work examines riparian vegetation composition and structure along the newly formed Three Gorges Reservoir and attempts to disentangle vegetation responses to surrounding landscapes, habitat quality and reservoir management across site, community and individual species scales. Surrounding land use and landscape structures are of particular interest to reservoir managers since they can be managed to preserve species diversity in riparian habitats. Apart from

et al., 2015). However, more recent studies draw attention to complex environmental heterogeneity at larger spatial scales that represent different levels of anthropogenic disturbances (González-Moreno et al., 2013; Kumar et al., 2006; Malavasi et al., 2014; Méndez-Toribio et al., 2014). Adjacent land use and landscape structures affect physical features such as the availability of solar energy, water, nutrients and pollutants, soil texture and moisture condition; and biological processes, such as species pools, species dispersal, humans movement and other ecological flows (Brudvig et al., 2009; Douda 2010; Leach et al., 2017; Vandvik et al., 2016; Vilà and Ibáñez, 2011; Wang et al., 2013). Studies show that riparian areas surrounded by fragmented landscapes are subjected to non-native species (e.g. Basnou et al., 2015; Malavasi et al., 2014). Some particular landscape elements, such as edges and corridors, play a unique role in shaping species abundance patterns in human-dominated landscapes (Eldegard et al., 2015; Ribeiro et al., 2016). With the emergence of more theories about functional responses of organisms to landscapes, such as biodiversity “spillover” effect (Brudvig et al., 2009) and environmental continuity (Ayram et al., 2015), it is believed that a biological community pattern observed at a site is the result of these multiple filters and obstacles operating in a hierarchy of spatial scales (Sydenham et al., 2014; König et al., 2017). Phenomena at each level of the scale tend to have their own emergent characters and patterns, suggesting a scale-dependent relationship. The complex system must be analysed simultaneously at multiple scales to prevent bias in determining the potential influential factors. Extensive studies document that landscape-based metrics are reliable and powerful in depicting the hierarchical and multi-scale relationships between landscape heterogeneity and biodiversity (BanksLeite et al., 2013; Gabrielsen et al., 2016; McGarigal et al., 2009; You

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the conventional PMM, we also adopt the continuous GM and associated surface metrics to evaluate landscape heterogeneity. Given the studies cited previously, we hypothesised that (1) the importance of landscape, habitat and reservoir influences vary across site, community and individual species scales, and (2) there is no general trend in the importance of variables across all analysed scales, indicating riparian vegetation at different scales are differentially successful in passing environmental filters. Our work could further facilitate the understanding of the forces that (re)shape and determine the structural and functional attributes of reservoir riparian environments under anthropogenic impacts, and can inform adjacent land use and landscape management and development in the conservation of novel ecosystems.

Fig. 2. Schematic diagram showing stratified sample strategies: quadrats (Q) within the black frame were averaged for site-scale analysis, and quadrats within the dotted-box represent the community data at one elevational zone for community-scale analysis.

2. Materials and methods 2.1. Study area

communities do not have many species and all species are easily seen in one or two strata, the area of each species was estimated and further divided by the quadrat area of 5 m2. The abundance of each plant species both for each quadrat and the entire sampling site (average of all quadrats within a site) was used as the response variable. All species information was estimated by one botanist to avoid bias. Nomenclature and species attributes follows the Flora of China (http://frps.eflora.cn/) and the local Sichuan Flora (CCFS, 2012). Each variety and subspecies was treated as a full species. Soil data were collected for the top to 20 cm in the centre of each quadrat and sent to the soil laboratory of Southwest University (Chongqing) for analyses of pH, soil organic matter (SOM), soil moisture (SM), alkaline nitrogen (AN), available phosphorus (AP), available potassium (AK) and slow-release potassium (SK) concentrations relevant to plant growth (Liu et al., 2016; Stubbs and Pyke, 2005). The other physical variables, potentially affecting vegetation patterns, were recorded: elevation, slope, aspect, bank morphology (straight/convex/concave), presence of animals (goat/cattle faeces and human rubbish), moss occupation, mud proportion, water distance (distance between the quadrat and water line) and geographical position: X (latitude), Y (longitude), X2, XY, Y2, Y3, X3, X2Y and XY2 were constructed from X and Y coordinates (see e.g. Jones et al., 2008; Pino et al., 2005). The geographical position can illustrate patterns that may be caused by some spatially contagious biological or environmental processes but cannot be substituted for by other environmental data (De Reu et al., 2013). The hydrological regime is regarded as the most critical factor in shaping riparian vegetation. We chose timing (autumn or winter), flood duration and frequency within one year as proxies to characterise the hydrological influences for each surveyed quadrat (Nilsson and Svedmark, 2002). All calculations were based on daily water level data for 2014 and 2015 from the nearest gauging station in Wanzhou (CQWRB, 2015), see Fig. B in Appendix). Land use and landscape data for each sample watershed were derived from the Landsat ETM + image (30 m resolution) acquired 17 July, 2015 (orbit sequence numbers 127, 38) to coincide with the mature season and vegetation survey time. All images were corrected by standard pro-processing in ENVI 5.0. The land use/cover classification was based on the latest Second National Land Survey in China. Two types of landscape variables derived from different landscape models were calculated as follows (Table 1).

The study area is within the Chongqing section of TGR region (105°49′–110°12′E, 28°28′–31°44′N, accounting for 80% of TGR, Fig. 1), including part of the mainstream of the Yangtze River, and one of largest tributaries (Pengxi River). The water levels of TGR are operated between 145 m during summer and 175 m above sea level (asl) during winter, which has created a completely new riparian zone encircling the TGR. This seasonally flooded land has an area of 348.9 km2, most of which encompasses good alluvial soil and was previously used for agriculture (43.7%) and forest/grass (35.9%). This region is characterised by diverse topography of low-elevation river valleys, foothills, and mountain ranges. It has a northern subtropical humid monsoonal climate with an average annual precipitation of 1200 mm, 60–80% concentrated between April and September. The mean air temperature is 18.2 °C; there are fewer than 20 frost days per year and only seldom is there snow above 1000 m asl on the mountains. The prevailing soil types are yellow brown soils and purple soils that support subtropical broad-leaved evergreen forests as the regional climax vegetation. The rivers meander through intensively developed cities and towns, intertwining with adjacent environmental and anthropogenic impacts. Over 33 million people live in this region; 40% are farmers. Our study area is representative of contemporary riparian landscape, influenced both by natural and anthropogenic gradients and disturbances. Since the Hanfeng Lake dam was constructed on the Pengxi River within Kaixian Town, only the section of Hanfeng Lake dam - Yunyang is fully controlled by the Three Gorges Dam (Fig. 1), with water fluctuating between 150 m in summer and 175 m in winter. Water fluctuation has generated a 55.47 km2 drawdown zone, ranking largest among all tributaries of TGR (Yuan et al., 2013). 2.2. Vegetation survey and data collection Data were collected in August and September, 2014 and 2015, when reservoir water levels were low around 150 m. We specifically focused on the left bank with a comparatively gentle slope and deep soil. The small watersheds along the river were delineated in ArcGIS 10 (details see Fig. A in Appendix). Within watersheds, sample sites were placed in comparatively natural areas without obvious sign of recent management activities, and slopes > 30°. Considering accessibility, 30 sites were selected, including six along the Yangtze River and 24 along the Pengxi River. At each site, we stratified the riparian zone into three zones of 145–155 m, 155–165 m, 165–175 m, representing the high, medium and low flooding disturbances, respectively. In each zone of each site, we randomly laid 1–2 rectangle quadrats (5 m long and 1 m wide with the long axis parallel to the river) (Fig. 2). All sample plots were geographically positioned with 1 m accuracy using a GPS recorder. A total of 113 quadrats were sampled. We inventoried all vascular plants species by name and coverage. Since our riparian herbaceous

2.3. Landscape gradient model We created three gradient surfaces using multi-spectral bands of satellite images and a Digital Elevation Model (DEM, 30 m resolution) following the approach of Moniem and Holland (2013). DEM indicates the geophysical properties of land that may influence plant community dynamics (Sebastiá, 2004). Availability of solar energy is pivotal to maintain whole ecosystems and biodiversity. Based on DEM, we 484

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standardised by Hellinger transformation and used as the response variable. In the preliminary detrended correspondence analysis (DCA), the largest gradient length of the first DCA axis was greater than 4 which indicated canonical correspondence analysis (CCA) was appropriate for our data. There were 76 explanatory variables in three data groups: landscape, habitat and reservoir operation. All were log10 (X + 1)-transformed before analysis (Jongman et al., 1995). The correlations of individual variable to the two most explained gradients of CCA ordination were used to indicate the importance of these variables to the vegetation composition and structure. We then summarised the occurrence frequency of all these significant variables across all scales in order to infer the overall importance of variables in shaping vegetation patterns (Table I in Appendix). Forward selection procedures were applied to variable selection (P ≤ 0.05). Partial CCA was executed to determine the independent effect of each variable group. The shared effects were calculated following the detailed work of Cushman and McGarigal (2002). This variation partitioning approach allowed both the important variables within each group of variables to be identified and permitted comparisons of the relative importance of the different influences. Monte Carlo permutation tests with 999 permutations were performed to evaluate the significance of variables separately (TerBraak and Smilauer, 2012). At the site-scale, average coverage of species of all quadrats within a site was used to represent the site’s species abundance. We could analyse only the landscape and habitat variables since there is no variation in reservoir management at this scale. The analysis procedure was similar to that for the community-scale. At the species-scale, species with over 10 occurrences among all the quadrats were selected for analysis. Redundancy analysis (RDA) and partial RDA were used for variation partitioning since the largest gradient length of the first DCA axis of species-scale data was less than 3. The analysis procedure was similar to CCA. All analyses were completed in the “vegan” package of R 3.1.3 (Oksanen et al., 2015). Statistical significance was at ≤0.05 level for all analyses.

Table 1 All factors considered as possible explanatory variables. The significant variables (P ≤ 0.05) shown after CCA analysis using forward selection are marked in bold. For abbreviations see the text or Appendix. Landscape factors (respond to vegetation across site, community and individual species scales) GM metrics* (DEM_Sa, DEM_S10z, DEM_Ssk, DEM_Sku, DEM_Sbi, DEM_Sdr, DEM_Std, DEM_Stdi, DEM_Srwi, DEM_Sfd, Solar_Sa, Solar_S10z, Solar_Ssk, Solar_Sku, Solar_Sbi, Solar_Sdr, Solar_Std, Solar_Stdi, Solar_Srwi, Solar_Sfd, NDVI_Sa, NDVI_S10z, NDVI_Ssk, NDVI_Sku, NDVI_Sbi, NDVI_Sdr, NDVI_Std, NDVI_Stdi, NDVI_Srwi, NDVI_Sfd); PMM metrics (NP, PD, ED, AREA_MN, AREA_CV, SHAPE_MN, FRAC_MN, PARA_MN, PAFRAC, ENN_MN, IJI, CONNECT, PRD, SHDI, AI, Na_P, Ag_P, Bu_P, watershed area) Habitat factors (respond to vegetation across site, community and individual species scales) Soil properties (pH, SOM, SM, AN, AP, AK, SK); elevation, slope, aspect, bank morphology, presence of animals, moss occupation, mud proportion, water distance and geographical positions (X, Y, X2, XY, Y2, Y3, X3, X2Y and XY2) Reservoir operation factors (respond to vegetation across community and individual species scales) Flood timing, duration and frequency

* DEM_Sa means surface roughness value of DEM surface; the rest of GM metrics are explained in the same way.

calculated the annual solar radiation by heat load index following McCune and Keon (2002). The NDVI, as an indication of species biodiversity and species pools in the adjacent landscapes (Parviainen et al. 2010), was derived from the Landsat images according to Tucker (1979). For 90 continuous data sets (three gradient surfaces × 30 sample sites), we measured 10 surface metrics of ecological interests and minimum possible redundancy to characterise the 3D surface texture (McGarigal et al. 2009). We used the Scanning Probe Image Processor (SPIP) software to calculate the chosen metrics, including surface kurtosis (Sku), surface skewness (Ssk), surface area ratio (Sdr), Surface roughness (Sa), 10-point height (S10z), surface dominant texture direction (Std), texture direction (Stdi), radial wavelength (Srwi), fractal dimension (Sfd), and surface bearing (Sbi) (Table A, B in Appendix).

3. Results

2.4. Landscape patch mosaic model

3.1. Species occurrence and community types

We calculated 19 metrics at class and landscape levels relevant to riparian ecosystems using Fragstats 3.3 (McGarigal et al., 2002). They include the watershed area, percentage of natural land (Na_P), percentage of built-up area (Bu_P), and landscape Shannon’s Diversity Index (SHDI) (Table C in Appendix, Fernandes et al., 2011; GonzálezMoreno et al., 2013). These metrics are commonly used to describe landscape heterogeneity in terms of composition and configuration variations.

We recorded 150 vascular plant species belonging to 130 genera of 56 families. The most common species were Cynodon dactylon (L.) Pers., Paspalum thunbergii Kunth ex Steud, Xanthium sibiricum Patrin ex Widder, Bidens pilosa L., and Eclipta prostrata (L.) L., which occurred in 68.7%, 58.5%, 55.1%, 55.1% and 47.5% of all quadrats, respectively (Table D in Appendix). Seventy-four species were rare and were removed for analysis. Most species are annual herbaceous, confirming that our study area is severely disturbed and at an early succession stage. In the 145–155 m zone, TWINSPAN results included the least and simplest groupings of species (Fig. 3). There was small amount of overlap in the ordination diagram implying that discrete compositional groups existed along the environmental gradient. One group comprises solely Kyllinga brevifolia Rottb. (KYBR in Fig. 3), which commonly occurred in wet, sandy places. It occurred in a few quadrats close to the lowest waterside, or on the inside bend of the river, where the sandy sediments easily deposit on the top soil. With increasing altitude, more diverse species groups occurred. In the 155–165 m zone, TWINSPAN identified four major species groups, but the ordinations showed much overlap, suggesting these groups had similar environmental needs. In the 165–175 m zone, there was more variation along both axes 1 and 2, indicating distinctive communities along the variable gradients

2.5. Statistical analyses and modeling Vegetation composition was analysed using a combination of the divisive classification method of Two-Way INdicator SPecies ANalysis (TWINSPAN) and Correspondence Analysis (CA) (Hill, 1979). TWINSPAN is a statistical procedure that divides communities into two groups, iteratively subdividing each group into smaller subgroups based on species coverage. Values of cut levels were set at 0, 10, 20, 30, 40, 50, 60, 70 and 80 corresponding to percentage data. The Chi-square test was used to determine the division when two subdivided communities were not significantly different (P > 0.05). The species-environment relationship was revealed by constrained gradient analysis methods commonly used in quantitative ecology. Analyses were performed at three scales: site, community and individual species. At the community-scale, the whole study area and three elevational zones as aforementioned were analysed separately. Species occurring in fewer than two of all quadrats were removed to reduce the influence of rare species. Species coverage data were

3.2. Description of the variables The magnitude and quality of variables of three group influences 485

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Fig. 3. Community types distinguished by TWINSPAN on DCA ordination at each elevational zone (abbreviations of species names refer to Appendix; first two DCA ordination axes are shown).

For the zone of 145–155 m, the selected significant variables included DEM_Sdr, Solar_Stdi, and SHDI from the landscape group; and bank morphology, moss, SM, AN, AP, AK and SK from the habitat group. Unsurprisingly, no flooding variable was selected since flooding variables have narrow range of variation in this zone. The total explained variation was 61.87%, ranking highest among all the analysed scales. In the 155–165 m zone, variables Solar_Sa, Solar_S10z, and Solar_Stdi based on GM explained 9.25% of species variation. No variables based on PMM passed the forward selection. The combination of elevation, water distance, mud, pH and SM explained the highest variation. Flooding frequency and duration had an independent effect of 4.68%. The total explained variation was 38.9%, which was the lowest among the three zones. This might imply the 155–165 m zone was affected by more complex, unknown influences not included in this study. In the 165–175 m zone, variables Solar_Stdi, NDVI_Stdi, NDVI_S10z, and DEM_Sfd based on GM, and SHDI and area based on PMM made the highest independent contribution to species abundance (20.78%) compared with the other two zones. The habitat influence comprising elevation, water distance, mud, pH, AP, and SM explained most of the variability (21.75%). There were no significant flooding variables identified in this zone.

varied considerably among the 30 sites (Table B, C in Appendix). The two most dominant land uses were urban (maximum 74% built-up area) and agriculture (maximum 65% agricultural land). The wide range of landscape metrics values indicates significant heterogeneity of the study area. Among all the quadrats, variables of habitat and reservoir operation are summarised in Table J (Appendix). Available phosphorus (AP) and Available potassium (AK) have the highest Coefficient of Variation (CV), suggesting high divergence of these elements in the soil. 3.3. Site-scale analysis Forward selection shows that only four variables (DEM_Sdr, DEM_Sfd, Solar_Stdi, and NDVI_S10z) based on GM were significant for further analysis. Surprisingly, only one variable (Na_P) based on PMM show a correlation. Among the habitat influences, pH and SM from soil properties, have a significant relationship with species composition. The full model including the variables from landscape and habitat explained 41.27% of total species variation. Partial CCA results showed that the largest explanation was contributed by the independent landscape variables (26.13%). The local variable group made the smallest pure contribution (10.9%). The joint effect was little (4.24%), indicating most of the variance was contributed by independent effects (Table F in Appendix).

3.5. Species-scale analysis In particular, 22 species with ≥10 occurrence were used for speciesscale analysis. As expected, species presented a wide range of individual responses to various influences. Full models including all significant variables explained variance ranging from 0 to 45.06%. Partitioning showed that many species were strongly affected by independent effects from particular variables, as indicated by larger independent explanation in general (Table G, H in Appendix). Nonetheless, species responses were so distinctive that there was not a clear overall trend of the “most” important variable(s).

3.4. Community-scale analysis For the whole species from 145 to 175 m, forward selection suggested the significant variables (Table 2): (1) DEM_Sdr, DEM_Stdi, DEM_Sfd, Solar_Stdi, NDVI_S10z and NDVI_Stdi based on GM captured 9.28%. (2) aspect, elevation, water distance, animal presence, moss, mud, pH, SM, SOM and AK from the habitat group contributed 23.41%; (3) flooding duration representing reservoir operational influence accounted for 3.64%, but this portion was shared with habitat variables. The explanation shared among the three groups was small (0.69%), reflecting a weak correlation among these influences. Compared with site-scale analysis, habitat factors appeared more powerful in explaining community variation.

Table 2 Relative effects of landscape, habitat and reservoir operation on species abundance at the community-scale. Zones (m) 145–175 145–155 155–165 165–175

L(a)

H(b) *

9.28 16.11** 9.25** 20.78**

R(c) **

23.41 32.51** 21.41** 21.75***

**

0 NA 4.68 NA

L × H(d)

L × R(e)

**

**

9.16 13.25** 0.8** 7.21**

**

0 NA −0.41 NA

L: Landscape variable group; H: Habitat variable group; R: Reservoir variable group (*P ≤ 0.05; 486

R × H(f) 3.64 NA 2.86** NA

**

P ≤ 0.01;

***

P ≤ 0.001).

L × H × R(g) **

0.69 NA 0.3** NA

Unexplained 53.82** 38.13*** 61.1* 50.26***

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4. Discussion

transformed to forest land because of environmental policies (e.g. the ongoing national projects of “Ecological Barrier Zone of TGR” and “Grain for Green”). However, they are still categorised as agricultural land on maps and therefore distort the analysis. Additionally, unlike studies of coastal dune landscapes or urban landscapes that have clear boundaries with other land use/cover types (Basnou et al., 2015), it is too hard to define the boundaries of landscapes in the urban–rural mixed hilly environment. In contrast, satellite reflectance data have a great advantage, especially for hilly areas (Zhang et al., 2013). Among all surface metrics, nine variables from the DEM, NDVI and Solar layers have a significant influence on riparian vegetation patterns (see Table I in Appendix). Six of the nine variables belong to the Configuration Metric family that combines horizontal and vertical characteristics of the surface topology to measure aspects of landscape configuration; only three belong to the Amplitude Metric family that reflects the overall non-spatial features of the surface to measure aspects of landscape composition (McGarigal et al., 2009; see Appendix). In total, these nine variables presented 28 times within 25 analyses including site, community and species scales. Variables derived from Solar surface layer were the most frequently identified variables (11 occurrences), followed by those from NDVI (9) and DEM (8) surface layers (Table I in Appendix). The variable Solar_Stdi (texture direction index derived from Solar layer) appeared to be most influential since it presented nine times throughout all scales. In previous studies, the role of solar radiation has been proven in structuring various types of temperate floodplain forests (Pielech et al., 2015; Slezák et al., 2017). However, most of studies measure solar radiation in quantity, i.e. how much potential solar radiation this area receives (wh/m2). In comparison, our study finding revealed that the structural aspect of solar radiation surface also has effect and relevance with riparian vegetation. This spatial heterogeneity of solar radiation should not be ignored in the further study. Overall, the metric (Stdi) derived from the other two layers also showed significant correlation with vegetation patterns. This metric belongs to the Configurational Metrics family and measures the relative dominance of surface texture direction over all other texture directions. A high value implies a high contrast in the concerned attributes for landscapes. Although there is no direct research on the relationship between vegetation and landscape surface metrics, a study on beetle movement showed that such metrics (e.g. Stdi) explained much of the differences in lepturine beetle species and provided new insights into landscape heterogeneity analysis (Moniem and Holland, 2013). One reason is that more contrasting landscapes may contain local higher quality habitats that are valuable to supply and enrich native plant communities. Other configurational metrics, including Std, Sfd and Sdr, are somewhat analogous to spatial measures in the patch mosaic paradigm, whereby greater variability in spatial arrangement of surface peaks and valleys equates to a more complex landscape. Our results also emphasise two amplitude metrics that provided compositional information about landscapes. The S10z (10-point height) and Sa (surface roughness) metrics are analogous to the diversity metrics based on the PMM. However, the S10z and Sa had much higher explanatory power. Larger values of these two metrics represent a wide range of variation within the surface property (akin to increasing patch richness, see Table A in Appendix) and an increasing spread in the distribution of area among levels (heights) of the surface attribute (akin to increasing patch evenness) (McGarigal et al., 2009). Furthermore, the NDVI_S10z (S10z of NDVI layer) shows positive correlations with riparian vegetation patterns across scales (see Table I in Appendix). A general consensus regards NDVI as a good biodiversity predictor (Krishnaswamy et al., 2009; Parviainen et al., 2010). Our finding indicates that NDVI related structural metrics also hold great potentiality in predicting spatial patterns of biodiversity at different scales. By comparison, eight of 19 variables from the PMM have comparatively weak correlations with the vegetation. They presented 13

4.1. Effects of adjacent watershed landscapes across scales Studies have indicated that large-scale, i.e. landscape or regional features, might be even better predictors of vegetation patterns than local-scale influences (Douda 2010; González-Moreno et al., 2013; Malavasi et al., 2014; Méndez-Toribio et al., 2014; Pielech et al., 2015). As hypothesised, landscape, habitat and reservoir influences vary across site, community and individual species scales. At the site-scale, landscape characteristics appeared to be more powerful in determining the longitudinal distribution and composition of plant communities along the reservoir than localised influences from habitat and reservoir management. At the community-scale, overall, landscape factors explained less than other influences. Among the three elevational zones, landscape variables explained more variation in the 165–175 m zone since this zone has fewer flooding disturbances and is close to upland landscapes that may strongly affect communities in the vicinity, the “proximityinfluence” hypothesis (Cushman and McGarigal, 2004; Fernandes et al., 2011). Interestingly, landscape had a stronger impact on 145–155 m than the 155–165 m zone. The 145–155 m zone undergoes submergence for half a year from September to April of the following year, leaving large patches of bare soil especially during the initial 1–2 months of the water receding. This zone has been consistently and overwhelmingly washed over every year; other potential factors, such as influences from land use history, may thus be weakened. We therefore were concerned about the residual effects of present surrounding land uses. Additionally, the open period is the best planting season in China and in such conditions there is little competition for space and resources, so many plant seeds/propagules from the surrounding landscapes could find a better chance to intervene. Then being filtered by limiting factors from local habitats, the desired species would arrive and establish. This may also explain why landscape factors along with habitat variables are the best predictors for this zone (61.87% being explained), and landscape influence has highest interaction with habitat across scales (13.25 % joint effect). At the species-scale, ten of 20 species expressed a connection with the surrounding landscape (Table H in Appendix). Studies argue that dispersal modes determine plant responses to environmental and landscape heterogeneity (Douda 2010; Evju et al., 2015; Marco et al., 2011). Our results indicate that most plants that connected with landscape variables produced light seeds and possibly disperse by wind, e.g. Setaria viridis (L.) Beauv. (coded as SEVI), Acalypha australis L. (ACAU), Eragrostis pilosa (L.) Beauv. (ERPI), or by animals or humans, e.g. Xanthium sibiricum Patrin ex Widder (XASI). Many of our identified species, regarded as agricultural weeds in China, are associated with surrounding anthropogenic influences (Chen et al., 2017). For example, as other studies have indicated, Conyza canadensis (L.) Cronq. is more likely associated with landscape edges and/or disturbed areas (e.g. Brosofske et al., 1999). However, even within the same guild of winddispersed species, we still found different response patterns. Various feedback mechanisms are expected and likely reflect both genetic adaptation of the plant species (e.g. seed mortality, inter- and intraspecies competition), and complex environments (e.g. extra nutrient input by flooding) and human activities (e.g. grazing, and land conversion). Between the two distinct types of landscape metrics based on different models, it was unanticipated that GM based variables appeared to be much more influential than the commonly used PMM based variables, regardless of analysed scales. This is not consistent with most studies drawing conclusions from the conventional PMM approach. The possible reason is that land use/cover maps based on a patch mosaic model in our study area may not fully match reality because of the rapid land use changes. For example, much land claimed as agricultural land in the national land use/cover database actually is abandoned or 487

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145–155 m vegetation since the flooding influence is overwhelming and has little variation within this zone. This expectation was verified by the fact that elevation had no impact on this zone (elevation has a direct correlation with flooding duration). The high zone 165–175 m may also not have differed enough in flood variation to separate species. From the reservoir water fluctuation graph (Appendix), the slope of the 155–165 m zone is the greatest, suggesting a dramatic change within this zone. At the species-scale, 12 species’ variance can be explained by habitat influences, ranging from 0.4% to 37.17%. Except for C. dactylon (CYDA), P. thunbergii (PATH), Eclipta prostrata (L.) L. (ECPR), X. sibiricum (XASI), and B. pilosa (BIPI), many affected species are subordinate species. This finding largely supports the suggestion that habitat (niche) differentiation has more influence on the abundance of subordinate species than on dominant species (Kumordzi et al., 2015; Maire et al., 2012). However, species within the subordinate guild responded idiosyncratically to these habitat variables so we could not detect a general pattern in which particular variable(s) were consistently more important than others. Sixteen habitat variables correlate with these species. Elevation and water distance occurred most frequently. These two variables are inter-related. This reflects the fact that riparian vegetation in hilly areas has a distinct spatial pattern with increasing elevation. In comparison, only five species exhibited a direct response to reservoir flooding, as indexed by flooding duration and frequency. This pattern is somewhat matched the community-scale. This is not saying that reservoir flooding is not an important factor in riparian plant assemblages. A recent comparative study shows that zones with (below 175 m) or without (above 175 m) direct flooding exhibit significant differences in terms of composition, structure and diversity of plant communities (Chen et al., 2017). However, within the zone below 175 m, even though there is a range of flooding duration, submerged depth and frequency, this land experiences extreme disturbance that can deplete plant propagule pools and inhibit plant recovery itself. In this circumstance, only species with strong environmental tolerance and reproductive capacities could survive. These species are not sensitive to flooding variation. Reservoir flooding per se may not impose a direct, strong influence on riparian vegetation. Rather it may show its power through reshaping microhabitats along rivers, such as landform, physical, chemical and biological features. Our results across the analysed scales show that, as a confounding influence, flooding often had a higher shared contribution with habitat variables than its independent effects on vegetation.

times in 25 analyses. The most significant variables are SHDI and area of each watershed. This result corroborates the findings from the GM based analyses that both metrics are strong correlates of fine-scale vegetation patterns and overall exotic species richness (Chen et al., 2017; Ehrenfeld, 2008; Schindler et al., 2013). Only two variables, IJI and PAFRAC, are landscape configuration metrics. They presented only once in explaining abundance of Eragrostis pilosa (L.) Beauv., and Aeschynomene indica L. It seems difficult to interpret their influences since they are not predictors for most species. This may confirm that individual species have remarkably idiosyncratic preferences for environmental and landscape conditions. 4.2. Effects of habitat and reservoir operation across scales At the site-scale, since each sample site endures the same reservoir operation, the variables related to flooding were not involved in the analysis. Only two soil variables, pH and SM, showed a correlation (10.9%). In contrast to other studies (Ehrenfeld, 2008; You et al., 2015), we found no soil nutrient was associated with vegetation, although there was a wide variation in soil characteristics (Table I in Appendix). One possible explanation is that nutrients are not limiting factors for plants in the flooded riparian zone (Garssen et al., 2016). After the TGR was created, the original narrow river was transformed into a wider lake-like environment, featuring slow water movement, increased standing time, high turbidity and more nutrient deposits both from flooding and upland land uses, and decomposition of dead vegetation (Yan et al., 2015). Additionally, species identified in this study have strategies for efficient nutrient uptake (Yang et al., 2012). These conditions suggest that soil nutrients are not direct stressors in plant species competition here. At the community-scale, habitat factors accounted for largest fraction of the variance, varying from 21.41% to 32.51% across the three zones. The maximum explanation occurred in the 145–155 m zone. We attribute this to the “destructive flooding disturbance” discussed before. Since this zone is possibly the zone with the fewest impacts from previous influences such as land use and potential “soil seed banks” among all three zones, it is more affected by on-going changes in habitat conditions associated with reservoir flooding. The variable bank morphology (straight/convex /concave) could reflect this change. It emerged as a significant correlate of species only in this zone. The bank morphology would govern deposit and distribution of water, sediments and nutrients, and thus further impact vegetation. For example, because of sluggish flow zones, concave banks may harbour comparatively slower water movement, associated nutrient enhancement and more diverse microhabitats than convex and straight banks (Page and Nanson, 1982; Wang et al., 2009). In our studied hilly areas, such banks are often against hill valleys and therefore may receive extra flows from upland watersheds. This would be more favorable to colonisation by both native and exotic species. In comparison, the 155–165 m zone was minimally affected by habitat influence since this zone seems more complex and may be influenced by unexplored reasons. Unlike the site-scale results, soil nutrient factors including AN, AP, and AK showed a strong explanatory power at the community-scale, which is consistent with other studies (Ehrenfeld, 2008; Meisner et al., 2011). No nutrient variable as a significant correlate of overall sitescale vegetation may suggest that site patterns are negotiated results of the individualistic traits of species and the process of environmental filters. Other involved variables include elevation, water distance, aspect, bank morphology, moss, and animals. Except for the first two which occurred frequently, the rest presented only once and had weak connection with vegetation in different elevational zones. This may reflect the complex environments and that the selected variables were not stable predictors of riparian vegetation. Regarding the reservoir flooding influence, only the 155–165 m zone showed significant response (4.68% independent + 3.16% joint effect). It was expected that flooding would not explain the low zone

5. Conclusions This study demonstrates a complex interplay of intrinsic habitat (niche) properties, direct flood disturbance and surrounding landscapes as well as individual traits of species in determining vegetation patterns along a newly formed reservoir. Our results show that: (1) landscape, habitat and reservoir influences dominated differently across site, community and individual species scales. Our results confirm that surface metrics have great potential to build spatially explicit models to predict species abundance (De Reu et al., 2013; Moniem and Holland, 2013; Scown et al. (2015)). The key is to identify the relevant gradient surfaces for the species of interest since the dispersal and colonisation of species are largely determined by environmental suitability (Moniem and Holland, 2013).(2) Only four variables, Solar_Stdi, NDVI_S10z, pH and SM emerged occasionally but through all scales. Although a wide variation of explained power among these variables across the scales, there seem to be a general trend in the importance of variables across all analysed scales. However, this conclusion needs corroboration from further research. Nevertheless, it is important to incorporate a large number of nested, hierarchical variables in analysis models to detect a general trend. Given the heavily inter-correlated nature of many variables at varying scales of human altered landscapes, the best way 488

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forward for reservoir riparian vegetation management strategies is to develop multi-scale, synthetic and location-specific approaches that may optimise conservation efforts.

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