Edge-effects on tree regeneration in the Colombian Andes

Table 2. Effectiveness of discriminant function analyses (DFA) to predict transect membership to edge and interior groups (PTM)
and to determine the depth of edge influence (DEI) of abiotic edge-effects.

Sites Eigen
value
C.C.
Wet Season
Wilk’s
lambda
PTM
(%)
DEI
(m)
Eigen
Value
C.C.
Dry Season

Wilk’s
lambda
PTM
(%)
DEI
(m)
Gironda 3.47 0.88 0.22 ** 100 35 4.01 0.89 0.20 ** 100 5
Aldea 5.47 0.92 0.15 *** 100 15 4.15 0.90 0.19 ** 100 5
Abedules 4.55 0.91 0.18 ** 100 20 n. p. n.p.
Cairo 3.38 0.88 0.23 ** 100 20 98.63 0.99 0.01 *** 100 20
Esperanza 5.45 0.92 0.15 *** 100 20 2.17 0.83 0.31 ** 100 5

Effectiveness of discriminant function analyses: Eigenvalue (ratio of between-groups to within-groups sums of squares), C.C. - Canonical
correlation (degree of association between the discriminant scores and the groups), and Wilk’s lambda (proportion of the total variance in
the discriminant scores not explained by differences among groups) (Norušis 1994). Significance of Wilk’s lambda statistics: ** 0.01 < P
< 0.001, *** P < 0.001.
n. p. – no pattern of group membership as a function of distance from the forest edge.

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Table 3. Number of light-demanding and shade-tolerant tree species in different life stages per site. Species are grouped based on their
increased in abundance towards edge (E) or interior (I) transects, or showed no pattern (NP) as a function of distance from edge. The
species are displayed in ordination plots and had more than 50% of their variation explained by the first two axes.
Regeneration Gironda Aldea Abedules Cairo Esperanza Overall

^habit E I NP E I NP E I NP E I NP E I NP E I NP
Light-demanding
- Seedlings wet 1 2 2 3 2 1 3 (28) 4 (36) 4 (36)
- Seedlings dry 1 1 1 2 1 1 2 (28) 2 (28) 3 (44)
- Juveniles 2 4 2 5 1 2 1 3 3 6 10 (35) 14 (48) 5 (17)
- Adults 3 4 1 3 2 1 2 13 (81) 3 (19) 0
Total 5 5 2 5 8 0 4 3 0 6 3 8 8 4 2 28 (44) 23 (37) 12 (19)
Shade-tolerant
- Seedlings wet 1 1 2 1 3 1 1 2 1 2 (15) 5 (39) 6 (46)
- Seedlings dry 4 1 4 2 2 1 1 3 6 (33) 10 (56) 2 (11)
- Juveniles 5 1 1 2 3 3 4 2 5 1 2 11 (34) 14 (48) 4 (14)
- Adults 4 2 2 2 8 (80) 2 (20) 0
Total 10 2 2 6 8 3 5 7 5 6 7 1 0 7 1 27 (39) 31 (44) 12 (17)
% in parenthesis. Species-specific responses are in Appendix 2.

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Table 4. Multiple regression models for predicting species composition of A. seedlings in
wet season, B. seedlings in dry season, C. juveniles in wet season, and D. juveniles in dry
season, based on abiotic and biotic factors as a function of distance from the forest edge.
Abiotic: abiotic environment in wet or dry season. DEI: depth of edge influence of the
abiotic environment in wet or dry season. Adults: adult composition. Seedlings: seedling
composition in wet or dry season. -: variable not included in a particular model.
Abiotic factors Biotic factors
Site R² Abiotic DEI Adults Seedlings
B B B B
A. Seedlings – wet
Gironda n.s. -
Aldea n.s. -
Abedules 0.31 ** - 0.56 ** -
Cairo n.s. -
Esperanza 0.27 * - 0.52 * -
B. Seedlings – dry
Gironda n.s. -
Aldea n.s. -
_Abedules ^a 0.24 * -0.49 * - -
Cairo 0.63 ** 0.79 *** -
Esperanza n.s. -
C. Juveniles – wet
Gironda 0.30 * -0.55 **
Aldea 0.30 * -0.55 **
Abedules 0.25 * -0.50 *
Cairo 0.67 ** -0.53 * -0.54 *
Esperanza 0.48 *** -0.70 ***
D. Juveniles – dry
Gironda 0.44 ** -0.66 ***
Aldea n.s.
_Abedules ^a 0.25 * -0.50 *
Cairo 0.67 ** -0.53 * -0.54 *
Esperanza 0.28 * -0.53 **

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a

- Models of seedlings and juveniles for Abedules in dry season did not include the
variable DEI because there was not a DEI determined by the discriminant function
analysis.
DEI’s are in meters and were determined by Discriminant Function Analyses (DFA) (see
Table 2).
Models were obtained from stepwise multiple regression analyses, using a Mantel
permutational test to assess the probability of partial regression coefficients (B) and
associated R² (No. of permutations 5000). P < 0.05, ** 0.01 < P < 0.001, *** P < 0.001.
n.s. – not significant model.

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Figure 1. Conceptual model underlying this study. The presence of edges may generate
abiotic edge-effects that are hypothesized to differ seasonally (1). Abiotic edge-effects
may affect forest structure and tree composition in different plant stages (seedlings,
juveniles, adults) (2). Abiotic edge-effects may prevail over biological edge-effects (i.e.,
adults as sources of seedlings and juveniles, and seedlings as sources of juveniles) in
explaining patterns of tree composition in early stages of development (3).

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69

Figure 2. Diagram of the sampling design within a 50 x 50 m plot. Ten transects were
sampled per plot. Within each transect, I collected seedling data in 15 quadrats of 1 x 1m,
juvenile data in 10 quadrats of 2 x 2 m, adult data in one quadrat of 5 x 30 m, and abiotic
and vertical foliage density data in 10 points. In each transect, 10 hemispherical
photographs were taken (only one shown). Diagram is not to scale.

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Figure 3. Plots from PCA’s for the first two axes for abiotic variables in wet and dry
season in five sites. Black dots represent transects and adjacent numbers represent
distance of the transects from the forest edge in meters. Arrows represent the direction
of increase of individual abiotic variables with respect to transects: AT – Air
temperature, ST – Soil temperature, LG – light (%PPFD), SH – Soil humidity, AH –
Air humidity. Circles represent groups with transects closer to the forest edge and
transects closer to the forest interior based on Discriminant Function Analysis (see
Table 2).

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