Seasonal Variability in Soil Inorganic Nitrogen Across Borders Between Woodland and Farmland in the Songnen Plain of Northeast China

Pedosphere 23(4): 472–481, 2013 ISSN 1002-0160/CN 32-1315/P c 2013 Soil Science Society of China  Published by Elsevier B.V. and Science Press

Seasonal Variability in Soil Inorganic Nitrogen Across Borders Between Woodland and Farmland in the Songnen Plain of Northeast China∗1 LIN Chang-Cun1,2 , FU Yao3,4 , LIU Li3 , WANG Kun2,∗2 and WANG De-Li3 1 The

Academy of Forestry, Beijing Forestry University, Beijing 100083 (China) of Grassland Science, China Agricultural University, Beijing 100193 (China) 3 Institute of Grassland Science, Key Laboratory of Vegetation Ecology, Northeast Normal University, and Key Laboratory of Ecological Restoration and Ecosystem Management of Jilin Province, Changchun 130024 (China) 4 Chinese Research Academy of Environmental Sciences, Beijing 100012 (China) 2 Department

(Received August 5, 2012; revised May 22, 2013)

ABSTRACT To study the seasonal variability of soil inorganic nitrogen (N) across borders at the woodland-farmland ecotone and potential mechanisms, contents of soil inorganic N were measured during the dry season (May 20 and June 30) and the rainy season (August 10 and September 20) of 2006 in the Songnen Plain of Northeast China. The borders between farmland and woodland were determined by a border-and-ecotone detection analysis (BEDA). The ecotone limits, often referred to as the depth-of-edge influence (DEI), are critical for determining the scale at which edge effect operates. The results showed that the soil inorganic N border between the woodland and farmland was located further toward the woodland interior during the rainy season (DEI = 53.4 ± 8.7 m, August 10) than during the dry season (DEI = 35.0 ± 12.6 m, May 20). The seasonal variability in the soil inorganic N border was found to be associated with seasonal changes of deposition flux of N (the correlation coefficients between them for the dry season and rainy season were 0.61 and 0.67, respectively), which resulted from foliation patterns of trees and crops. Accordingly, the leaf area index at woodland edges was lower than that in the woodland interior, so woodland edges captured large amounts of atmosphere nitrogen deposition. The average DEI was 44.1 m, which was in accordance with the values of other temperate forest boundaries in literatures; therefore, BEDA was an appropriate method to estimate the borders of ecotones. Key Words:

depth-of-edge influence, ecotone, semi-arid, woodland edge

Citation: Lin, C. C., Fu, Y., Liu, L., Wang, K. and Wang, D. L. 2013. Seasonal variability in soil inorganic nitrogen across borders between woodland and farmland in the Songnen Plain of Northeast China. Pedosphere. 23(4): 472–481.

INTRODUCTION Human-induced woodland borders are common and significant consequences of fragmentation in many woodland landscapes. Therefore, the borders are important measurement for ecological studies and natural resource management. Many research have shown that soil nutrient conditions will likely be a key determinant of edge vegetation composition along associated ecological gradients, and a considerable amount of edges within the remaining woodland are generated by variations in the Kieldahl N (F¨ olster et al., 2001), organic matter (Marchand and Houle, 2006), and inorganic N (Peters, 2002; Piessens et al., 2006). Woodland edges can be thought of as buffer zones across which environmental conditions progressively change with distance, with significant impact on woodland structure and dy∗1 Supported

namics (Matlack, 1993; Jose et al., 1996). The edges are composed of hard edges and soft edges (Forman, 1995; Harper and Macdonald, 2001). A hard edge creates an abrupt transition between the two cover types with very limited penetration of edge effect into the adjacent cover type; whereas a soft edge is more permeable to edge effects, which penetrate farther into the adjacent cover type (Forman, 1995; Voller and Harrison, 1998; Harper et al., 2005). Indeed, there are indications for both woody and herbaceous plants that changes occur in the woodland community from a few meters to 23 m from the border (Fox et al., 1997; Honnay et al., 2002). Respective knowledge of the corresponding penetration depth of edge effects towards the woodland interior, the so-called depth-of-edge influence (DEI), can be used to approximate the woodlandcore area (Fern´andez et al., 2002). And the size of

by the Fundamental Research Funds for the Central Universities of China (No. BLX2012037), the China Postdoctoral Science Foundation (No. 20100470411), and the National Natural Science Foundation of China (Nos. 30571318, 30600427 and 30590382). ∗2 Corresponding author. E-mail: [email protected].

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woodland-core areas is a central issue in the protection of woodland-interior species at the landscape scale in both natural woodland islands and anthropogenic woodland fragments (Drake et al., 2002; Fahrig, 2003). Sustainable management efforts of the forest aim to adjust and improve forestry practices towards patterns and processes that are close to those generated by natural disturbance regimes (Anglestam, 1998; Bergeron et al., 2004). Thus, it is essential to determining whether the ecological changes are different and how they affect the ecological factors on both sides of the edge. From a landscape conservation perspective, measuring edge effects on species associated with interior woodland conditions will better assess the impact of silvicultural practices on the landscape and organisms (Baker et al., 2007). Most studies on border estimation were on the basis of simple and handy data, such as microclimate data, however these data were often influenced unexpectedly by environmental turbulent flow. The border estimation on the basis of comparatively steady soil inorganic N data was carried through accurately. Soil inorganic N is considered to be the most limiting nutrient to plant growth in most forest ecosystems, especially in arid and semiarid regions (Lajtha and Whitford, 1989; Ferris et al., 1998). From soil nutrient data measured over short time periods, Kouno et al. (1999) showed that soil inorganic N conditions varied along woodland edge-to-interior gradients. However, many boundary studies have lacked a uniform method to estimate edge effects, which should receive more attention for their potential value in ecology (Murcia, 1995; Zhou and Peng, 2008). In addition, many boundary studies failed to select appropriate transect replicates (Murcia, 1995). This ongoing problem is especially true for soil inorganic N border studies which are constrained by the long term and high cost of measurement. In this study, we used the border and ecotone detection analysis (BEDA) based on non-linear regression (Ewers and Didham, 2006; Hennenberg et al., 2008), which has several advantages over other methods that include a generalized linear model, analysis of variance (Honnay et al., 2002), nested analysis of covariance (Rheault et al., 2003), randomization (Harper and Macdonald, 2001), and piecewise linear regression (Toms and Lesperance, 2003) as follows: the other methods use transect replicates as replicates in the analysis itself and for all transects the DEI is only given as a single value; whereas the BEDA provides more information about borders than other methods. Moreover, the BEDA is generally restricted to data where the errors can be assumed to be normally distributed, like

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measurements of soil nutrition, whereas it would be desirable to generalize the approach to non-normally distributed data such as proportional data or count data (Hennenberg et al., 2008). It will then be instructive to adopt the BEDA to delineate the location of borders and confidence intervals. On the western Songnen Plain of Northeast China, a transitional belt between the agricultural and pastoral regions can be found. The regions have been undergoing severe soil erosion, degradation and desertification because of inappropriate land use and overgrazing by livestock (Xu et al., 2009). To sustain agricultural productivity, massive anthropogenic woodlands were planted along the edge of the farmland as farmland shelter-belts in 1995, and nascent mosaics of anthropogenic woodland islands and farmland still occur. In this study we delineated the boundary between the farmlands and woodland using a BEDA analytical approach. It was hypothesized that edge environmental conditions during the dry season were different from those during the rainy season, and consequently different DEIs on soil inorganic N associated with edges were created between the dry and rainy seasons. More specifically, the aim of this study was to confirm our hypothesis that i) environmental variables along edges result in changes more abruptly in rainy season than in dry season and ii) there is a larger DEI for soil inorganic N across borders in rainy season than that in dry season. MATERIALS AND METHODS Study area Field work was conducted at the Grassland Ecological Research Station of Northeast Normal University, Jilin Province, China (44◦ 40 –44◦ 44 N, 123◦ 44 – 123◦ 47 E). The study area is located in a northeastern transitional belt between the agricultural region and the pastoral region on the western Songnen Plain. The region has a semi-arid, continental climate with a frost-free period of about 140 days. Annual mean temperature ranges from 4.6 to 6.4 ◦ C, varying from −16 ◦ C in January to 25 ◦ C in July. Annual precipitation is 350–450 mm and mostly falls in July, August and September. Annual potential evapotranspiration is approximately three times as much as the mean annual precipitation (Gao et al., 2008). Topography of this area is characterized by a gently undulating relief with slope inclinations of normally less than 5%. Soils are classified as chestnut (Haplic Kastanozem, FAO), meadow (Eutric Vertisol, FAO) and aeolian (Arenosol, FAO) (Wang et al., 2009).

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The woodland islands in the study area can be classified as a less diverse variant of dense deciduous woodlands dominated by Populus simonii and Populus tomentosa trees, and generally the trees defoliate in October every year. The understory vegetation was considerably scarce because of uncontrolled grazing, especially for legume plants preferred by livestock; however, as a result of a prohibition on the practice, uncontrolled grazing greatly decreased in recent years. Historically, the baseline vegetation of the woodland and farmland was grassland. During the last few decades, the grasslands were largely converted to farmlands, so the soil texture over the entire area was homogeneous. In the study area, maize (Zea mays L.) is the typical crop in the farmland. Maize was generally sown during May 5– 7, and harvested during October 5–10. Management practices in the farmland were the same as the local agricultural management as follows. The spacings between rows and plants were 65 and 30 cm, respectively. The farmland received base fertilizer (30 000 kg ha−1 of organic fertilizer, 100 kg ha−1 of urea, 220 kg ha−1 of diammonium phosphate, and 300 kg ha−1 of potassium sulfate) on May 1, and topdressing urea (200 kg ha−1 ) on July 5. The farmland was not irrigated. The woodland island is located on the south of the farmland, and from north to south, the width of it extends 300 m, and from east to west, the length extends 1 300 m. The width of farmland extends 500 m from north to south, and the length extends 2 000 m from east to west. The distance between the study area and any major mega cities is more than 150 km, so there is no significant difference of atmospheric N-deposition among all sampling sites of the study area.

difference between crop and tree species composition completely. Distance measurements refer to this borderline as the origin of coordinate (0 m). Towards the woodland interior, a second recognizable borderline was used for distinguishing the ecotone belt and closed woodland. This borderline location was the result that the present study wanted to acquire. The length of transects was 210 m (90 m farmland, and 120 m woodland; Fig. 1). Sampling sites were selected at 5-m intervals along the four transects. Soil inorganic N was determined in five soil replicates collected from each site to a depth of 10 cm using metal cylinders with 5 cm diameter. Each collected soil sample was oven dried (65 ◦ C) and then passed through a 2-mm mesh sieve for chemical analyses. Soil inorganic N concentration was measured by extracting 10 g fresh-sieved soil with 50 mL of 2 mmol L−1 KCl for 1 hour on a reciprocal shaker. The filtered − soil extracts were analyzed for NH+ 4 -N and NO3 -N usplus ing a segment flow analyzer (Scalar SAN , Skalar, the Netherlands). Soil samples were collected on May 20, June 30, August 10, and September 20, 2006.

Sampling design and soil measurements

Nitrogen deposition collection was conducted from June 1 to 30 and from August 1 to 30 in 2007. The wet/dry deposition collectors were equipped at the ground surface for the collection of rain and dry deposited samples. Collectors were installed at a 10-m interval along the four transects. Each collector contained a round-bottomed polyethylene bucket (30 cm diameter). Dry deposition (dust) samples were recovered by rinsing the collection bucket with 100 mL of

Four parallel transects, with the distance between transects > 200 m, were established perpendicular to the woodland-farmland border, moving from the farmland towards the woodland interior. Each transect was divided into three segments: farmland, ecotone belt and closed woodland (Fig. 1). The borderline between farmland and ecotone belt was clearly identified by

Leaf area index measurements Leaf area indexes were measured every 10 m along the four transects with an LAI-2000 plant canopy analyzer (LI-COR Inc., Lincoln, USA) on May 28 (in the dry season) and August 26 (in rainy season). The measurement height was fixed to 0.2 m above the ground, paralleling to the ground, to maximize the contribution of the understory and crops. N deposition flux measurements

Fig. 1 Positioning of the samples along the intensely studied transects. Each transect was divided into sections of farmland, ecotone belt, and closed woodland. The width of ecotone belt always changes with seasonal variability.

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deionized water. Wet deposition (rain) samples were immediately transferred to the laboratory after each rainfall. Contamination from bugs and bird droppings was always checked and removed if necessary. After collection, the samples were transferred to the laboratory, where they were ultrasonically extracted (for 30 min) and filtered through 0.45 μm cellulose membrane filters. The extracts were stored at 4 ◦ C until analysis. Nitrogen concentrations for both wet and dry deposition samples were determined with the Kjeldahl method using a Kjeltes 2300 (FOSS Inc., USA). During the experimental periods, the sum of dry and wet deposition flux was regarded as the deposition flux of nitrogen. Statistical analysis Statistics were computed with SPSS 13.0. Confidence and prediction bands belonging to non-linear regressions were calculated in GraphPad Prism 5.01. Errors reflect the two-tailed 95%-confidence intervals, and the 95%-confidence and 95%-prediction bands, respectively. Borders between the woodland and farmland and borders of the associated ecotones (E1 and E2) were

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determined using the border-and-ecotone detection analysis (BEDA) (Hennenberg et al., 2008). BEDA is based on a four-parameter sigmoidal non-linear function, according to the equation: f (x) = a + (c − a)/{1 + exp[(x − b)/d]}

(1)

where x is the x-coordinate value of sampling site and f (x) is the value of corresponding environmental variable at the site. The coefficients a and c represent the upper and lower asymptotes (Fig. 2) in two adjacent ecosystems, respectively. b is the distance to the inflexion point (B) where the change of a variable is strongest. Location of B can be thought as the border between the two ecosystems. The parameter d characterizes the steepness of curve. We chose the fourparameter sigmoidal non-linear function, because it is a first-order approximation to other possible sigmoidal functions. First, the calculation of the coordinates of E1 and E2 is shown as follows. The first derivative of Eq. 1 is 

f (x) = −(c − a)

e(

x−b d )

d[1 + e(

x−b d ) ]2

(2)

Fig. 2 Contents of soil inorganic N measured along the intensely studied transect during the four measuring days (May 20 and June 30 in dry season; August 10 and September 20 in rainy season). The sigmoidal non-linearmodel (solid lines) and its 95%-prediction bands (dashed lines) are presented. The asymptotes of the model were interpreted as the conditions in farmland and closed woodland. Their variability was approximated by the 95%-prediction bands. Border-and-ecotone-detection analysis revealed the location of border (B) between the woodland and farmland, and the borders of the associated ecotones (E1 and E2). E1 and E2 reflected the depth-ofedge influence towards the farmland and the woodland interior, respectively.

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where f  (x) was the first derivative of f (x). In the nonlinear of Eq. 1, b is the distance to the inflexion point (B), reflecting the x-coordinate of B in Fig. 2. The first derivative at x = b is: f  (b) = (a − c)/(4d) = tanα = −RU/RT

(3)

where f  (b) was the first derivative of the y-coordinate of B in Fig. 2. RU was the difference between the two asymptotes, a and c, RT was the difference between x-coordinates of E1 and E2, and α was the degree of angle at the point of B, we get RT = −4d. RS was the difference between x-coordinates of E1 and B, and ST was the difference between x-coordinates of B and E2. Because the sigmoidal function is point-symmetric in B, RS = ST = RT/2 = −2d. As x-coordinates of E1 and E2 (e1 and e2 , respectively) were e1 = b + 2d and e2 = b − 2d

(4)

concurrently, the y-coordinates of E1 and E2 can be calculated as f (e1 ) and f (e2 ), respectively. The limits of the ecotones, E1 and E2, belonging to the border B (Fig. 2), are equivalent to the DEI towards the two habitat interiors. ˆb and dˆ denote the estimates for b and d. eˆ1 and eˆ2 denote the estimates for e1 and e2 , and can be expressed as eˆ1 = ˆb + 2dˆ and eˆ2 = ˆb − 2dˆ

(5)

Second, the calculation of the confidence intervals of B, E1, and E2 is directly linked to the coefficient estimates ˆ and their covariance matrix [covar(ˆb, d)] ˆ ob(ˆb and d) tained from the sigmoidal non-linear function. The confidence intervals for the x-coordinates of E1 and E2 are derived. The variances (var) of eˆ1 and eˆ2 are ˆ + 4covar(ˆb, d) ˆ var(ˆ e1 ) = var(ˆb) + 4var(d) ˆ − 4covar(ˆb, d) ˆ var(ˆ e2 ) = var(ˆb) + 4var(d)

and (6)

and for the estimates of standard deviations (SD) we may use  

e2 ) = var(ˆ

e1 ) = var(ˆ

e1 ) and SD(ˆ

e2 ) (7) SD(ˆ Assuming a normal distribution for ˆb and dˆ with sufficiently large sample sizes, a confidence interval for e1

e1 ), and e2 can be constructed from [ˆ e1 − z1−α/2 × SD(ˆ

e1 )] and [ˆ

e2 ), eˆ2 + eˆ1 + z1−α/2 × SD(ˆ e2 − z1−α/2 × SD(ˆ

e2 )], where z1−α/2 is the (1−α/2)-quantile z1−α/2 × SD(ˆ of the standard normal distribution. In the next step, a linear mixed effects model (nlme-function, R package nlme; Pinheiro et al., 2005) was applied to incorporate the effect of transects in

our models and to assess the differences among the soil samples taken from different transects. A cubic model as an approximation for the sigmoidal model was chosen to approximate transect effects. The linear mixed effects model applied here included measurement date and transect nested in measurement date as random factors. Model selection was performed as described in Hennenberg et al. (2008). The distribution of standardized residuals did not indicate model misspecification for any of the analyses presented here. RESULTS Contents of soil inorganic N decreased from the edge inside the farmland up to the woodland interior (Fig. 2). Except for June 30 (4.06 mg kg−1 ), the differences of soil inorganic N between the farmland and woodland were small on May 20, August 10, and September 20 (2.75, 3.11, and 2.93 mg kg−1 , respectively, Fig. 2). In addition, the confidence intervals of the estimated parameters E1, B, and E2, and the prediction bands were much narrower during the dry season than during the rainy season. For instance, during the dry season, the variability of the sigmoidal nonlinear model was lower (May 20, 1.08 mg kg−1 ; June 30, 1.16 mg kg−1 ) than during the rainy period (August 10, 1.32 mg kg−1 ; September 20, 1.52 mg kg−1 ). As shown in Fig. 2, the location of border (B) was mostly near the borderline (0 m) between the woodland and farmland, which was also clearly visible in the field. Contents of soil inorganic N differed with the distance away from the borders. The smallest divergence between the border (B) and the borderline (0 m) was about 9.4 m on May 20 (9.4 ± 4.6 m), while that increased continuously with precipitation increasing (25.1 ± 2.4, 39.8 ± 3.2, and 39.4 ± 3.0 m on June 30, August 10, and September 20, respectively). Therefore, the border between woodland and farmland was located further towards the farmland during the dry season and further towards the woodland interior during the rainy season. As shown in Fig. 2, the widths of associated ecotones decreased gradually from the dry season (May 20, 51.2 m; June 30, 30.8 m) to the rainy season (August 10, 27.2 m; September 20, 16.6 m). These results agreed with our first prediction as the variables in soil inorganic N were likely to have a sharper edge effect during the rainy season. The values for DEI towards the woodland interior (determined by the position of E2) were higher during the rainy season than during the dry season. A maximum value (53.4 ± 8.7 m) of DEI towards the woodland interior occurred on August 10 and a minimum value (35.0 ± 12.6 m) was detected

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on May 20. These results supported our second prediction that borders between farmland and woodland during the rainy season had a higher DEI than those during dry season. The mean value of DEI toward the woodland interior for soil inorganic N was 44.1 m. During the four sampling days, the associated ecotone on the farmland side moved markedly towards the woodland side from the dry season to the rainy season, and the values for DEI towards the farmland were −16.2 ± 12.6 m on May 20, 9.8 ± 6.5 m on June 30, 26.2 ± 8.7 m on August 10, and 31.0 ± 8.1 m on September 20, respectively. Contents of soil inorganic N differed more sharply between woodland and farmland during the dry season than during the rainy season. This reflected the more distinct edge effects during the dry season. A cubic model was used in the linear mixed effects model as an approximation of the sigmoidal non-linear model. The mean of the cubic model was mostly located within the 95% confidence bands of the sigmoidal model. For the soil inorganic N, model selection presented that nesting transects in sampling time significantly improved the model (the standard deviation of transects nested in measurement day and the residual were 0.0013 and 0.0091, respectively), but only a small amount of variability was associated with this random factor. Sampling time as a random effect explained the mass of the remaining variability. Leaf area indexes in both farmland and woodland were measured during the dry and rainy seasons (Fig. 3). During the two seasons, the leaf area indexes in the woodland were markedly higher than in the farmland. Additionally, there were no detectable differences in leaf area indexes along transects in the far-

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mland, whereas pronounced gradients were found along transects in the woodland. During the dry season, the maximum value occurred near the borderline between the farmland and woodland belt, but during the rainy season the peak value was located further towards the woodland interior. In contrast to leaf area index, the deposition fluxes of N in the woodland were significantly lower than those in the farmland during both the dry and rainy seasons (Fig. 4). Additionally, the deposition fluxes in the farmland during the rainy season were markedly higher than those during the dry season (P = 0.03), however the differences were not significant in woodland (P = 0.11). Soil inorganic N content was positively correlated with nitrogen deposition fluxes, and the correlation coefficients for the dry season and rainy season were 0.61 and 0.67, respectively. Leaf area index was negatively correlated with nitrogen deposition fluxes, and the correlation coefficients during the dry season and rainy season were −0.91 and −0.86, respectively.

Fig. 4 Deposition flux of N along the studied transect during the dry season (June 1–June 30) and the rainy season (Augst 1–Augst 30) in 2007. Vertical bars indicate the standard errors of the means.

DISCUSSION

Fig. 3 Leaf area index along the studied transect during the dry season (May 28, 2006) and the rainy season (August 26, 2006). Vertical bars indicate the standard errors of the means.

Soil inorganic N measured along the studied transect revealed that the location of the soil nutrient woodland border shifts over the year. Detected soil inorganic nitrogen borders were located further towards the farmland during the dry season and further towards the woodland interior during the rainy season. Generally, the change of soil inorganic nitrogen in the farmland was influenced by common agricultural practices (such as fertilization), but it was difficult to explain the gradient variation along the s tudied tran-

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sects. The exposed soils were environmental variables which were the part of the primary response to edge effect, while understory structure and species composition were the part of the secondary response (Harper and Macdonald, 2001), which could explain why we observed significant changes in soil inorganic nitrogen across the transects as they are part of the primary response. It is well known that nutrient enrichment and depletion can result from fertilizer addition, atmospheric deposition or water run-off, and erosion of nutrients (Piessens et al., 2006). Although farmlands and woodlands in former grassland areas generally originate from reclamation and afforestation of grassland, respectively, these woodland soils differed from farmland soils (Fu et al., 2010). Also, in the present study, the farmland soil had a greater concentration of soil inorganic N compared with the woodland due to a common agricultural practice such as fertilization. In addition, soil inorganic N changed gradually along the edgeinterior transect, and thus the gradual changes pointed to the existence of a gradual transition zone, incorporating characteristics of the farmland and woodland, rather than a distinct edge zone. NH3 and NO2 are the most abundant gases in the atmosphere. The high rates of atmospheric deposition in woodland edges can also affect the enrichment of the exposed soil nutrients at adjacent woodland and farmland (Weathers et al. 2001; Piessens et al. 2006). In this study, those were confirmed by the positive correlation between nitrogen deposition flux and soil inorganic N (Fig. 3). The changes in foliation patterns of tree species over the year markedly affected exposed soil nutrient. Since the leaf area index of woodland edges was much lower than that of the woodland interior, woodland edge can capture large amounts of inorganic N which was verified by the significantly negative correlation of nitrogen deposition flux with leaf area index (Fig. 4). In addition, the studied woodland island was dominated by deciduous tree species (Populus simonii and Populus tomentosa) that had the highest leaf area index at the adjacent edge during the dry season, whereas during the rainy season, the highest leaf area index was located further toward the woodland interior. As a result, during the rainy season, soil inorganic N in the woodland belt was more similar to those in the farmland while the trees and crops were foliated, and soil inorganic N borders were located further towards the closed woodland. During the dry season, soil inorganic N of the woodland belt was more similar to those in the closed woodland while leaves of crops were small, and the soil inorganic N borders

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were located further towards the farmland. In addition, deposition nitrogen during the dry season was accumulated on the leaves, and then rinsed off with rain, so this would be pulsed with rainfall in the woodland, while nitrogen deposition occurs continuously on the farmland. Meanwhile, sampling time was only one month for measuring atmospheric N deposition, which only partially reflected the effect of N deposition on soil inorganic N. Ecological flows involve the movement of material, organisms or energy between patches (Wiens et al., 1985; Cadenasso et al., 2003). Ecological flows from adjacent habitat patches are a key mechanism underlying the distinction between edge and interior zones. The rate of ecological flows between patches is a function of edge permeability and the degree to which a given flow can penetrate the boundary between two patches. Edges can amplify, attenuate or reflect ecological flows (Strayer et al., 2003). The relative concentrations of energy, materials and organisms on either side of the edge also affect flow rates. Materials that move passively can naturally diffuse to areas of lower concentration. On farmland, where large amount of fertilizer are applied, nitrogen can leach into the ground water, and consequently being transported to neighboring areas (Piessens et al., 2006). The results of this present study supported this mechanism. As a result of low precipitation during the dry season, soil nutrient mapping along transects was not affected by the leaching of soil organic N. In contrast, in the rainy season, because of high canopy density of woodland edges, the organic N flow from leaching occurred only within the short distance to edges. In some cases, edges can act as relatively impermeable barriers that cause the accumulation of materials at the edge (Desrochers and Fortin, 2000). Access is another key potential mechanism that separates the quality of edge habitat from interior zones. Access to spatially separated resources can be enhanced near edges for organisms whose required resources are found in multiple habitat types. When resources are spatially separated between two adjacent patches, edges provide maximum access to both resources (Dunning et al., 1992; McCollin, 1998; Fagan et al., 1999). The ability of soil nutrients and runoff water to form enriched or fertile patches has been well documented for many semiarid landscapes around the world (Whitford et al., 1997; Ludwig et al., 2000). Less edge effects towards the woodland interior were detected for soil inorganic N during the dry season, whereas the maximum DEI occurred during the rainy season. The average DEI for soil inorganic N pa-

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rameters was 44.1 m. The value was in accordance with the values in other literatures for both tropical and temperate forest boundaries (Laurance et al., 2002; Harper et al., 2005). In the woodland of the western Songnen Plain, high DEI values for soil nutrient parameters were found during the rainy season, but the differences between the farmland and closed woodland were rather small and prediction bands of the two habitat types showed a wide overlap. In contrast, differences between the farmland and closed woodland observed during the dry season were much more pronounced. Plant leaf area index along the studied transects reflected DEI values from edge to interior due to edge effect (Piessens et al., 2006). Though it is generally well known that change of leaf area markedly affects atmospheric deposition (Liu et al., 2011), it remains difficult to determine from our data whether atmospheric deposition exerts the crucial impact on soil inorganic N in transects. In general, biological N fixation by legume plants and microorganisms was a potential factor that affected the nitrogen enrichment (Ferris et al., 1998; Drake et al., 2002). In the experiment area, though prohibition on the grazing was implemented, uncontrolled grazing has significantly impacted understory vegetation. Therefore, understory vegetation becames scarce, especially for legume plants which are preferentially grazed by livestock. In addition, the local residents were in the habit of collecting plants litter as fuel. Owing to lack of soil organic matter, the biological N fixation by microorganisms was restrained markedly. Accordingly, the influence of biological N fixation by plants and microorganisms on soil inorganic N could be neglected in this study. The edges can directly influence vegetations by providing a source of nutrients and immigrants across edges, whereas indirect influence of soil nutrient can also occur via the organisms mapping onto changes in the abiotic environment near edges. Generally, the borders will have high vegetation abundance (Cadenasso et al., 2003), which increases the nutrient demands of the vegetation and belowground organisms in the borders. So, the soil nutrients in borders should have a negative edge response (the variables decrease near edges), which is contrary to our study. The main reason is that uncontrolled grazing markedly decreased the understory vegetation, the biomass of existing plants with poor palatability was lower than 48 g m−2 in transects (Wang et al., 2010). So the influence of nutrient demands of the organisms on soil inorganic N can be ignored. Regarding the variability between transects, a ra-

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ther small transect effect was found for soil inorganic nitrogen. The variability between transects reflected a sampling effect because sun-facing edges that affected leaf area index in woodland boundaries often showed the highest DEI (Hennenberg et al., 2008). However, these effects on woodland boundaries can be expected to be small in the tropics due to the shorter distance to the equator. More likely, the transect effects were due to the differences of vegetation structure, which were observed in the woodlands. Deng et al. (2009) showed that the structural characteristics of the forest border can be important for the DEI of inorganic N parameters. In semiarid landscapes, excess water from rainfall is usually redistributed as runoff, which is captured by woodland edges with a high abundance of grass species that act as ‘traps’ for soil nutrient runoff (Ludwig et al., 2000). Along the studied transects, the DEI for nitrogen parameters was predominantly determined by vegetation composition and changes in foliation patterns over the year. Foliation patterns can also change the openness of the forest border through affecting leaf area index, which has potential to decrease atmospheric deposition and the DEI for soil inorganic N towards the forest interior during the dry season (Kapos, 1989; Piessens et al., 2006). Woodland edges can be thought of as buffer zones across which environmental conditions progressively change with distance, with significant impacts on woodland structure and dynamics. Edge effects are especially influential when fragments are small or irregularly shaped, or when the gradient between natural and modified habitats is steep (Zheng and Chen, 2000). Accordingly, it is essential to determine core areas of woodland islands, which will provide more realistic delineation of fragmented woodland landscapes for both research purposes and the development of management plans. This study revealed that 71% of woodland surface would remain as the core area with DEI of 44 m. For a DEI of 35 and 53 m, the core area was calculated to be 80% and 62%, respectively. The results of this study indicated that the DEI of nitrogen parameters varied spatio-temporally along the studied woodland-farmland transects, thus the determination of concomitant dynamics of remaining woodland core areas can provide a new perspective for our understanding of woodland landscape structure, landscape pattern, and spatial heterogeneity both in theory and practice. CONCLUSIONS There was pronounced seasonal variability in the soil inorganic N borders and the width of associated

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ecotones between the farmland and woodland. The shift of border location and different ecotone widths resulted from the changes of leaf area of woodland during the dry and rainy seasons that remarkably influenced atmospheric nitrogen deposition. In addition, the ecological flows and access to spatially separated resources were the potential factors that also affected the shift of borders. The average DEI was 44.1 m, which was in accordance with the values of other temperate forest boundaries in literatures; therefore, BEDA was an appropriate method to estimate the borders of ecotones. REFERENCES Anglestam, P. K. 1998. Maintaining and restoring biodiversity in European boreal forests by developing natural disturbance regimes. J. Veg. Sci. 9: 593–602. Baker, S. C., Barmuta, L. A., Mcquillan, P. B. and Richardson, A. M. M. 2007. Estimating edge effects on ground-dwelling beetles at clearfelled non-riparian stand edges in Tasmanian wet eucalypt forest. Forest Ecol. Manag. 239: 92–101. Bergeron, Y., Flannigan, M., Gauthier, S., Leduc, A. and Lefort, P. 2004. Past, current and future fire frequency in the Canadian boreal forest: implications for sustainable forest management. Ambio. 33: 356–360. Cadenasso, M. L., Pickett, S. T. A., Weathers, K. C. and Jones, C. G. 2003. A framework for a theory of ecological boundaries. BioScience. 53: 750–758. Deng, X. W., Han, S. J., Hu, Y. L. and Zhou, Y. M. 2009. Carbon and nitrogen transformations in surface soils under ermans birch and dark coniferous forests. Pedosphere. 19: 230–237. Desrochers, A. and Fortin, M. J. 2000. Understanding avian responses to forest boundaries: a case study with chickadee winter flocks. Oikos. 91: 376–384. Drake, D. R., Mulder, C. P. H., Towns, D. R. and Daugherty, C. H. 2002. The biology of insularity: an introduction. J. Biogeogr. 29: 563–569. Dunning, J. B., Danielson, B. J. and Pulliam, H. R. 1992. Ecological processes that affect populations in complex landscapes. Oikos. 65: 169–175. Ewers, R. M. and Didham, R. K. 2006. Continuous response functions for quantifying the strength of edge effects. J. Appl. Ecol. 43: 527–536. Fagan, W. F., Cantrell, R. S. and Cosner, C. 1999. How habitat edges change species interactions. Am. Nat. 153: 165–182. Fahrig, L. 2003. Effects of habitat fragmentation on biodiversity. Annu. Rev. Ecol. Evol. Syst. 34: 487–515. Fern´ andez, C., Acosta, F. J., Abell´ a, G., L´ opez, F. and D´ıaz, M. 2002. Complex edge effect fields as additive processes in patches of ecological systems. Ecol. Model. 149: 273–283. Ferris, H., Venette, R. C., Van Der Meulen, H. R. and Lau, S. S. 1998. Nitrogen mineralization by bacterial-feeding nematodes: Verification and measurement. Plant Soil. 203: 159–171. F¨ olster, H., Dezzeo, N. and Priess, J. A. 2001. Soil-vegetation relationship in base-deficient premontane moist forest-savanna mosaics of the Venezuelan Guayana. Geoderma. 104: 95– 113. Forman, R. T. T. 1995. Land Mosaics: The Ecology of Landscapes and Regions. Cambridge University Press, Cambridge, UK.

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