Forest canopy perforation in time and space in Amazonian Ecuador

Acta Oecologica 21 (4-5) (2000) 285−291 / © 2000 Éditions scientifiques et médicales Elsevier SAS. All rights reserved S1146609X00010869/FLA

Forest canopy perforation in time and space in Amazonian Ecuador David Salvador-Van Eysenrode*, Jan Bogaert, Piet Van Hecke, Ivan Impens Research Group of Plant and Vegetation Ecology, Department of Biology, University of Antwerp, Universiteitsplein 1, B-2610 Antwerpen, Belgium

Received 10 February 1999; revised 10 July 2000; accepted 10 September 2000

Abstract — The spatial and temporal distribution of canopy gaps was analysed during 26 months in a permanent sample plot of Amazonian terra firme forest. Using nearest neighbour analysis based on Monte Carlo simulations, and circular statistics, we concluded that gaps occur clustered in space but random in time. The temporal and spatial dispersion patterns of gap formation should influence the structure and species composition of forest areas. © 2000 Éditions scientifiques et médicales Elsevier SAS Circular statistics / dispersion / Monte Carlo test / nearest neighbour / random pixel clusters / terra firme rainforests

1. INTRODUCTION The falling of trees, branches and vines in tropical rainforests usually leads to the formation of gaps in the forest canopy (e.g. [4, 19]). This process is known as perforation [21]. In places where gaps are formed, a free space for forest regeneration is created, suddenly enhancing light penetration to the understory [12, 45]. The resulting changes and environmental heterogeneity in and around gaps affect the presence, growth, development and diversity of plants (e.g. [18, 20, 23, 36]), through destructive and/or constructive effects for both the already established and the potential recruiting vegetation [14]. Plants respond mainly to light [27], and thus two general functional groups or guilds are recognized: shade intolerants (pioneer plants, whose seeds generally germinate in gaps, fast growing, and present during the first part of the succession) and shade tolerants (climax species, able to grow in shade and dominant in the latter stages of succession) [8, 11, 29, 42]. Other shade-tolerance classes are in between these two extremes [33]. A gap can vary in its physical features, frequency of occurrence, intensity, predictability and distribution * Corresponding author (fax: +32 3 820 22 71). E-mail address: [email protected] (D. Salvador-Van Eysenrode).

within and between forests [2, 7, 19], and all of these factors interact to affect the species composition of the regenerating forest sub-community [2, 11, 17]. We expect forest areas with frequent gap occurrence, and/or with large gap sizes, to be composed of more heliophile plants than other forest areas (hence causing heliophile species and individuals to show a clustered spatial dispersion pattern) and to have a structure similar to that of young forest patches in the building phase [35, 37]. The interaction of some properties of the gap-maker elements (e.g. tree age, size, architecture, epiphyte load, rooting system and diseases) with the environmental factors (e.g. wind, rainfall, soil conditions) influence the formation of gaps [7, 8, 14, 28]. In this paper, we focus on the spatial and temporal distribution of canopy gaps in a tropical rainforest. The assumption that rainfall is one of the main factors influencing gap occurrence [8, 31] is reviewed and the ecological implications of the observed spatial and temporal distribution patterns of gaps are discussed.

2. MATERIALS AND METHODS The study was carried out at the Tiputini Biodiversity Station (TBS, Universidad San Francisco de Quito, in a joint venture with Boston University),

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Figure 1. Average monthly rainfall (mm, 1981–1998) at Coca Airport (0°27’2 S, 76°59’8 W). The rainfall pattern is typically bimodal for Ecuadorian Amazonian lowland rainforests. Source: Dirección de Aviación Civil.

located in the new province of Orellana (0°40’ S, 76°20’ W, altitude ca. 300 m). The annual rainfall follows a bimodal pattern, totalling ca. 3 000 mm·yr–1, with no month with less than 141 mm on average (figure 1). Yearly mean temperatures are above 25 °C, with a relative air humidity above 80 % (source: ‘Dirección de Aviación Civil’). Soils at TBS are alluvial and clayey [1]. A 13.5-ha permanent sample plot was demarcated on a tract of old-growth terra firme forest [26, 39]. The plot consists of a platform with a small discontinuity in the middle (ca. 4 m) dividing it into two stands, and is surrounded by swamps and an oxbow lake. Using the definition of gap of Brokaw [9], “a hole in the forest extending through all levels down to an average of two m above the ground”, but to a minimal size of 4 m2, all gaps created in the plot between October 1996 and December 1998 were localized and their formation date recorded. When necessary, to determine the age of some of these gaps, we used other gaps (inside or outside the plot) with known age and compared the state of vitality of the gap-maker element(s), the freshness of the snap(s) or uprooting(s), and the state of the damaged and grown vegetation in the gap. The time resolution used for gap age was 1 month. A permanent metallic stake was put in the approximate centre (centroid) of each gap. This position was

D. Salvador-Van Eysenrode et al.

determined and marked on a map of the plot to the nearest metre. The distance from the stake to the edge of the gap (ground projection of the surrounding tree-crowns) was measured along eight radii at 45° intervals, starting from the north. Gap size was estimated by adding the areas of all octants. For the analysis of the spatial distribution of the gaps, a nearest neighbour analysis [15] combined with a Monte Carlo simulation technique was used to test whether the gaps departed from a random distribution. The basics of this test [5] have been enhanced and used in the study of the spatial distribution of canopy gaps in Ivory Coast [35] and French Guyana [44]. The distance from each stake to its nearest neighbour was measured. All distances were ranked in ascending order and converted to cumulative series. Using a FORTRAN-77 programme, 1 000 maps of the plot were generated. Each map contained random generated pixel clusters [6], each cluster representing a gap. The random clusters were generated keeping constant (a) the number of gaps observed in the field, (b) the total gap area and (c) the plot geometry, and considering the smallest gap size used (4 m2 = 2 × 2 pixels). A discriminant distance between gap edges in the simulations was set at 1 m (one pixel, 4-connectivity), although the minimum distance between two centroids observed in the field was 5 m. For all cluster pixels, the distances to all the other pixels of the same cluster were calculated and the pixel characterized by the overall minimum average of these distances was assigned as the central pixel of the gap. In every map, the nearest neighbour distances between the central pixels were measured and ranked from smallest to largest. Each of the 1 000 ranked series was transformed to a cumulative series, and all of them were sorted again from smallest to largest (sorted rows). For each successive point of the cumulative series separately, the 1 000 simulations were ranked (sorted columns). The 26th and 975th simulations were plotted on a graph together with the cumulative probability (Cum P) in order to construct a two-sided 95 % confidence interval [35, 44]. The series of the field distances was plotted on the same graph. If the values of the distribution function of the distances of gaps were found within the boundaries of the confidence interval, gap dispersion was considered as random. This analysis was performed twice, the first time for the gap status as of October 1996 until March 1998, and the second for the status up to December 1998 (whole study time). The different length of these two periods is related to the dates when the field trips Acta Oecologica

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were performed. Because an additional field trip was possible after March 1998, we considered it as valid to include the additional information not only to expand the dataset, but also to give information on the possible changes in the spatial dispersion pattern when more gaps were included in the original dataset. Gap distribution in time was assessed using circular statistics [3], but was restricted to two complete cycles (October 1996–September 1998) in order to have an equal representation of months. Each gap event represents an observation (point) in its respective month, and each month constitutes an arc with opposite angle q, where

q = 360o /k with k = 12 periods.

3. RESULTS AND DISCUSSION 3.1. Spatial pattern The Monte Carlo test revealed that gaps were non-randomly dispersed in the period between October 1996 and March 1998 (figure 2a). The data have fewer short and fewer long inter-gap distances relative to the simulations. This pattern was more pronounced considering all the gaps formed during the whole study time (figure 2b, until December 1998). A comparison of the gap status between the partial and whole study time is given in table I. An index of dispersion ([30], p. 58) confirmed clumping for both times (I = 2.46 and 2.71 respectively, P ≤ 0.05, based on 100 random points). This is in accordance with other studies on gap dispersion in tropical moist-and-rain forests [31, 35, 44]. For both time periods, the majority of distances to the nearest gap was found to be between 20 and 50 m (75.8 and 83.3 % respectively). This suggests a typical gap interdistance between these two values, which was also found in Ivory Coast [35], and gives an insight in the likelihood that a given gap may influence the formation of another. It should be noted that these distances are between gap centroids, so gap edges are even closer together. The number of distances to the nearest gap falling outside the 95 % confidence interval is less in the first (partial) than in the whole study period, which shows a more intense clustering for the whole study time. The majority of interdistances outside the confidence interval is found between 40 and 46 m for the first time period and between 20 and 30 m for the Vol. 21 (4-5) 2000

second (figure 2). However, when considering the whole study period there is another large group of such interdistances lying between 40 and 47 m. The clustering could result from the tendency of gaps to form in the neighbourhood of previously formed gaps, due to increased wind turbulence [10], similar soil properties [28], topography [35], architectural changes of the trees surrounding the gaps [47], or biotic factors (e.g. zonal fungus attack) [43]. The temporal-structural pattern of the forest itself may also influence gap clustering: if actual gaps are spatially clustered, then these gaps may become mature patches at a given time, still spatially clustered. If such mature forest patches reach an age such that they are ready to become gaps again, then it can be expected that these gaps will occur roughly at the same time and place, i.e. keeping the original clustered spatial pattern of one forest cycle before. The clumped spatial pattern of gaps could have implications for the population dynamics of many species depending on their growth strategy, which will also affect the structure and composition of a given forest zone [24]. Pioneer species, those able to germinate and/or to grow only in gaps, may be expected to show a clumped spatial distribution, regardless of other factors affecting the spacing dynamics of plants, such as density dependent factors (e.g. predation, pest attacks) [13, 24, 25, 46]. The overall shape of the curves representing field distances follows roughly those of the confidence interval (figures 2a, b); however, some segments are vertically oriented, i.e. more interdistances having the same length are observed. If the dispersion pattern is uniform, it would be represented by a vertical line. This tendency could also be expected for gaps created by logging [21, 22], assuming that (a) once a gap is opened, its area will not become forest again, (b) there is no overlap between old and new gaps, and (c) the logging area remains constant. In that case, gap interdistances will become shorter with time. 3.2. Temporal pattern Looking at the 43 gaps formed during the 24-month period, we found that their occurrence was uniform along the year (n = 43, α = 24° January, r = 0.04, P non-significant, Rayleigh test [3]; figure 3a), with no concentration of gap events around a particular month. Grouping gap events functionally by type of gap starter (uprooted and snapped trees together, and branch and vinefalls together), we found that gap formation by uprooted and snapped trees was also random (n = 29, α = 36° February, r = 0.26, P nonsignificant, Rayleigh test; figure 3b). The same was found for gap formation by branch and vinefalls,

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Figure 2. Cumulative distribution functions of simulated (solid lines) and observed (dots) nearest-gap distances at the Tiputini Biodiversity Station, and their cumulative probabilities (Cum P), for the period between October 1996 and March 1998 (a), and between October 1996 and December 1998 (b). The central dashed lines represent the expected distances for a random distribution, found within a 95 % confidence interval based on 1 000 simulations for each time period. Arrows point out distances lying outside the 95 % confidence envelope. See text for further explanation.

according to both the Rayleigh test (n = 14, α = 218° August, r = 0.41, P non-significant) and the χ2 test (χ2[13df] = 13.4, P non-significant; figure 3c). For the latter, because it is hard to say anything with a Rayleigh test having only n = 14, the χ2 test was preferred. However, this should still be interpreted with caution, and a larger n would give more certainty about the spatial distribution of this particular type of gap starters. A preliminary inspection of the observed monthly gap frequencies suggested that gaps opened by uprooted and snapped trees occurred mainly in a period

in the year, while branch and vinefalls concentrated in another period. However, there was no statistical evidence for concentration of gap occurrence in any of the three cases shown in figure 3. Gaps created by uprooted and snapped trees tend to have larger areas because these gap-makers are larger (than branches and vines) and are more likely to cause domino effects [40]. This means that if a concentration of gaps created by uprooted or snapped trees was found in a given period of the year, then the largest gaps may have been expected to occur during such a period. Unlike other findings [10, 31], neither the Acta Oecologica

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Table I. Comparison of inter-gap distances between the two survey times. Status of the distances to the nearest gap up to March 1998 and up to December 1998 in the 13.5-ha plot of terra firme forest at the Tiputini Biodiversity Station. The maximum interdistance becomes shorter as the number of gaps increases, but the typical interdistances remain similar. See text for further explanation. Nearest neighbour distances

March 1998

December 1998

Number Outside the confidence interval Groups outside the interval Shortest Longest Mean (± SD) Median

33 10 (30.3 %)

48 30 (62.5 %)

6

6

5m 92 m 35.5 (± 19.2) m 33 m

5m 47 m 29.9 (± 10.9) m 30.5 m

number of gaps, nor the proportion of plot area opened to gaps was found to be correlated with rainfall during the study period (n = 18 months, respectively r = 0.07 and r = 0.23, P non-significant, Spearman correlation),

suggesting that gap formation at the Tiputini Biodiversity Station is not synchronous with rainfall. Moreover, this confirms that there is no preferential month for gap formation. Gap size, and the presence of different gap zones (root, bole or crown) associated with the potential for differential resource utilization by responding plants, with rainfall or with the availability of (seed) dispersal vectors, can determine the presence and overall performance of species in gaps [32, 34, 38]. However, the randomness found in the temporal availability of different types of gaps (and hence gap zones) suggests that chance should be far more important in shaping gap community structure and composition, instead of a pattern in the availability of a number of gaps of a certain type during the year. Although the spatial and temporal scale of gap occurrence may partially explain the structure and composition of forest patches in any of their phases (gap - building - mature - gap [37]), the role of the pre-gap presence of species should also be considered [16]. Studies on spatial and temporal distribution of gaps should be complemented with studies on the effects that both may have on plants for a better understanding of the functioning of forest regeneration and canopy dynamics. 4. CONCLUSIONS

Figure 3. Frequency of gap events between October 1996 and September 1998: all gaps together (A, n = 43), gaps formed by uprooted and snapped trees (B, n = 29) and branch and vinefalls (C, n = 14). The central line points to the mean month (mean vector length (A) r = 0.04, α = January; (B) r = 0.26, α = February; (C) r = 0.41, α = August). The dots represent the number of gaps formed per month. See text for further explanation. Vol. 21 (4-5) 2000

The technique used to analyse spatial gap dispersion has been improved by integrating other spatial features of gaps and the plot in the simulations, such as the plot area in gap stage – thus considering gaps as surfaces – and the assignation of centroids, instead of maps with only random-generated points. With this, the simulations resemble more closely gaps in the field. The tests revealed a clumped spatial distribution of gaps at the Tiputini Biodiversity Station, which is in accordance with the observed pattern in other tropical forests. However, few studies on Amazonian canopy gaps have focused on spatial dispersion. The clumping of gaps can be the result of a combination of soil, wind and age factors affecting the gap-maker elements in the places where gaps are found. The concept of cycles in the assessment of the temporal occurrence of gaps has also been integrated, using an appropriate tool, i.e. circular statistics. Temporal gap formation was found to occur randomly throughout the year. Not only gap dispersion, but also the overall gap formation regime [41] should be studied in order to understand the forest cycle and aspects of its structure and species composition.

290 Acknowledgments We thank Dr K. Swing (Universidad San Francisco de Quito Boston University) for permission to stay at TBS, the station’s staff for assistance and F. Kockelbergh for technical support. M. Heuer kindly reviewed and commented on this manuscript. R. Rousseau is acknowledged for help on statistical interpretations. Jan Bogaert is a research assistant of the Fund for Scientific Research (F.W.O., Flanders, Belgium).

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