Study Sites

Three NCF patches in KwaZulu-Natal (KZN,SA), located within the Indian Ocean Coastal Belt (IOCB) biome, were selected for investigation. Northern Coastal Forest is a subtropical forest type that has been under increasing pressure from agriculture, timber plantations, urban development, heavy metal mining and illegal clearing over the last few decades (Mucina et al., 2006). The three forest patches selected for investigation were the uMdoni, Hawaan and uMdloti forests (Fig. 1; Table 1).

uMdoni Forest is a primary forest with high levels of disturbance (primary highly disturbed [PHD]), due to a surrounding golf course and its associated activities. Parts of the forest that have expanded beyond the borders of the golf course are subject to uncontrolled clearing, harvesting and burning, illegal access and informal pathways, making these portions clearly distinguishable from the access-controlled primary forest (Kambaj et al., 2018).

Hawaan Forest is a primary forest with low levels of disturbance (primary least disturbed [PLD]). It has been fully fenced and access controlled for approximately 50 years, with regular clearing of alien vegetation over the last decade. However, small parts (almost exclusively the fringes) of the forest are subject to illegal access, informal pathways, and some clearing and harvesting (Kambaj et al., 2018).

uMdloti Forest is a transitional forest that contains a mosaic of primary and secondary forest with high levels of disturbance (transitional highly disturbed [THD]). A transitional forest is classified as a forest that experiences increasing forest cover following a period of decreased forest cover, due primarily to land use changes (Mather, 1992). Previously, the uMdloti Forest experienced unrestricted access resulting in the creation of footpaths and informal settlements within the forest. A portion of primary forest is now access controlled through fencing (for c. 5 years).

 

Figure 1.  Location of the three forests PLD: primary least disturbed; PHD: primary highly disturbed; THD: transitional highly disturbed) in KwaZulu-Natal, South Africa.

 

 

 

 

Gap selection and characterisation

At each forest twelve gaps of varying size classes (‘small’ gaps < 25 m2, ‘medium’ gaps between 25 - 45 m2; ‘large’ gaps > 45 m2) were sampled during the summer season (November-December 2016). Canopy gaps were defined as an opening in the canopy due to complete or partial loss of trees extending to the forest undergrowth (Obiri & Lawes, 2004). The gaps were selected via parallel walked transects (three to five that were > 75 m apart) within the physiognomically mature (primary) parts of the forests. The boundary and centre point of each gap was recorded using a handheld GPS (Garmin Etrex 20x, Garmin, Kansas, United States of America). Gaps were placed into different size classes based on area. Area (A) was calculated following Runkle (1992), as follows: A = πLW/4, where L = gap length (longest distance between two gap edges) and W = gap width (longest distance perpendicular to length). A total of 15 small gaps, 12 medium gaps and nine large gaps were sampled across the forests, ensuring sufficient variability in gap sizes sampled.

Air temperature, relative humidity and wind speed were measured in all gaps using a 4500 Pocket Weather Tracker (Kestrel, Birmingham, United Kingdom) at midday. Soil moisture content was also measured for each gap using a soil moisture meter (HH2 Moisture Meter, Delta-T Devices, Cambridge, United Kingdom).

 

Table 1. Site characteristics of the uMdoni (PHD), Hawaan (PLD) and uMdloti (THD) forests

* Perimeter partially fenced with access controlled primary forest **Perimeter fully fenced and access controlled

 

 

 

Vegetation surveys

Plant species abundance was recorded for each gap (n =12 gaps for each forest) and within the intact canopy (n = 15 × 225 m2 quadrats per forest) in all three forests. Individuals were placed into growth stage (seedling, sapling, shrub, and tree) and life form (graminoid, climber, herb, shrub, and tree) categories. Diameter at breast height (DBH) was measured for each tree present in the gaps. Voucher specimens of each species were deposited at the Ward Herbarium (University of KwaZulu-Natal, Westville Campus, Durban). Specimens were identified to species level. In a few cases, seedlings were very young and could only be identified to genus level. Comprehensive checklists of all plant species identified within the gaps of each forest were compiled. Indigenous species names and authorships follow the South African National Biodiversity Institute’s (SANBI) online Plants of southern Africa (New POSA) (SANBI, 2020a). The conservation status of indigenous species was based on SANBI’s Red List of South African Plants (SANBI, 2020b). Alien taxa were categorised according to the National Environmental Management: Biodiversity Act (NEMBA) (10 of 2004) (Department of Environmental Affairs [DEA], 2004) and the alien and invasive species list (DEA, 2016). Plants were also classified according to demographic size classes: individuals < 10 cm tall were classified as seedlings, individuals > 10 cm tall showing no reproductive traits were classified as saplings and individuals showing reproductive traits (e.g. production of buds and flowers) were classified as mature individuals.

 

Data analysis

Species accumulation curves were constructed for gap and intact canopy data (EstimateS 9.0, Colwell, 2013) to determine sampling effort. Sampling effort was calculated using the Jack1 and Chao2 estimators. An 80% sampling effort was achieved for the intact canopy (Kambaj et al., 2018). The finite number of gaps present in each forest did not allow for sampling to continue until an 80% sampling effort was achieved. Consequently, the Jack1 and Chao2 estimators provided only a modelled species estimate for the gap dataset. Graminoids were excluded from the assessment of sampling effort as abundance data could not be collected due to their clonal habit (Kambaj et al., 2018). Species richness, Shannon’s exponential and Simpson’s inverse (denoted as 0H, 1H and 2H, respectively in the Hill numbers nomenclature) were also computed (EstimateS 9.0, Colwell, 2013; RStudio, Version 1.2.1335, RStudio Inc., 2019) to compare alpha diversities across forest sites (all taxa, including graminoids). Species richness (all taxa) at each site was compared between intact canopy and canopy gaps using the Jaccard and Sørenson indices, based on presence-absence data (after Sershen et al., 2019).

A one-way analysis of variance (ANOVA) was used to test for significant differences in floristic and abiotic parameters across forests (data were parametric). The relationship between gap area and species richness was assessed using Pearson’s correlation analysis. Pearson’s correlations (parametric data) or Spearman’s rank correlation analysis (non-parametric data) were also used to assess for relationships between selected abiotic (air temperature and soil moisture) and floristic (species richness, abundance and density of indigenous and alien species) parameters. Pearson’s correlation analysis (parametric data) was also used to assess the relationship between indigenous seedlings and saplings, alien seedlings and saplings, and between mature indigenous alien plants.

Cluster and ordination analyses were used to assess the similarity among the three forests (n = 3) and gaps of different size classes (with data from all three sites pooled; n = 36). The presence-absence data were used to generate a similarity matrix based on the Bray-Curtis index and clustering was then performed using the unweighted pair group method with arithmetic mean (UPGMA). A non-metric multi-dimensional scaling (NMDS) ordination was also conducted on square root-transformed species abundance data, in three dimensions, using the Bray-Curtis index for gaps of all three forests combined (n = 36). The ordination was evaluated using the ‘stress’ coefficients (a measure of disagreement between values in ordination space). Stress co-efficients < 0.20 were considered well-represented interpretations (Kruskal 1964; Clarke, 1993). Graminoids were excluded from the NMDS analysis (given the absence of abundance data) but included in the UPGMA cluster analysis. All statistical analyses were performed using RStudio (Version 1.2.1335, RStudio Inc., 2019) and PAST (Version 3.25, Paleontological Statistics, 2019) (Hammer et al., 2001).

 

ResultsTop

Canopy gap floristics

The floristic survey across all three forests canopy gaps collectively identified 198 species belonging to 63 families (Table S1 [suppl.]). The most species rich forest was the primary highly disturbed forest (PHD) with 136 species (56 families). One hundred and fifteen species (44 families) were identified in the primary least disturbed forest (PLD), and 54 species (35 families) in the transitional forest with high levels of disturbance (THD) (Fig. 2). Thirty species and 29 families were shared amongst all three forests.

The three most dominant families when data for all three forests were pooled were Rubiaceae, Fabaceae and Asteraceae with 23, 21 and 18 species, respectively. Rubiaceae (12), Asteraceae (10) and Fabaceae (9) were the most speciose in the PHD forest. At the PLD forest, the top three families in terms of species richness were Fabaceae (8), Euphorbiaceae (7) and Rubiaceae (7), while at the THD forest, Fabaceae (4) and Rubiaceae (4) were the top two families. Several families, viz. Asteraceae, Malvaceae, Poaceae, Rutaceae and Vitaceae, ranked third at the THD forest, each with three species.

One taxa of conservation concern taxa, Atalaya natalensis R.A.Dyer (Sapindaceae, listed as ‘Near Threatened’) (SANBI, 2020b), occurred in the gaps and the PHD forest only. Collectively, 15 alien plant species were identified across the three forests: 11 Invasive Alien Plant Species (IAPs), of which 10 species are classified as Category 1b (invasive species that must be controlled and wherever possible, removed and destroyed; trading and planting is strictly prohibited) and one species is Category 3 (invasive species that are allowed to remain in prescribed areas; trading and planting is prohibited) (DEA, 2016). The remaining four species are classified as naturalised exotic species (SANBI, 2020a). Thirteen alien species were found at the PHD (nine Category 1b IAPs; one Category 3 IAPs; three naturalised exotics), six at the PLD (four Category 1b IAPs and two naturalised exotics), and eight at the THD (six Category 1b IAPs and two naturalised exotics). The THD had the highest (15% of total number of species) level of alien species representation, followed by the PHD (10% of total number of species) and the PLD (5% of total number of species).

Correlation analyses on density data for all forests combined revealed significant negative correlations between indigenous seedlings and saplings and alien seedlings and saplings (r = -0.999; p < 0.05) and between mature indigenous alien plants (r = -0.998; p < 0.05).

 

Figure 2.  Venn diagram showing number of species (black numbers) and families (grey numbers) found in canopy gaps (n = 12 gaps per forest) at the three forests (PLD: primary least disturbed; PHD: primary highly disturbed; THD: transitional highly disturbed), and the number of species shared between forests.

 

 

Canopy gap and intact canopy floristic similarity

Collectively, 217 species belonging to 70 families were found in the intact canopy of all forests, while 198 species belonging to 63 families were found in gaps. Shared species between canopy gaps and intact canopy in PHD included 81 of the 199 total species found (41%). Shared species between canopy gaps and intact canopy in PLD amounted to 76 of the 149 total species found (51%). Shared species between canopy gaps and intact canopy in THD amounted to 53 of the 64 total species found (83%). This together with the results of similarity analyses (Table 2) illustrates that proportion of taxa shared between gap and intact canopy can vary greatly across forests.

The three most abundant species in terms of density in PHD gaps were Trifolium repens L. (9.18% of total density), Kedrostis foetidissima (Jacq.) Cogn. (6.94% of total density) and Cyperus albostriatus Schrad. (6.02% of total density) while Englerophytum natalense (Sond.) T.D.Penn. (10.93% of total density), Monanthotaxis caffra (Sond.) Verdc. (6.70% of total density) and Drypetes arguta (Müll.Arg.) Hutch. (5.79% of total density) were the most abundant species in the PHD intact canopy. In PLD gaps, Asparagus plumosus Baker. (11.66% of total density), Hibiscus cannabinus L. (8.64% of total density) and Achyranthes aspera L. (7.95% of total density) were the three most abundant species in gaps while Cola natalensis Oliv. (3.90% of total density), Uvaria caffra E. Mey. ex Sond. (3.78% of total density) and Achyranthes aspera L. (3.65% of total density), were the most abundant in intact canopy within this forest. The most abundant species in THD gaps were Ageratum houstonianum Mill. (22.49% of total density), Laportea peduncularis (Wedd.) Chew. (13.1% of total density) and Lantana camara L. (12.58% of total density) while Vitellariopsis marginata (N.E.Br.) Aubrév. (11.89% of total density), Kedrostis foetidissima (Jacq.) Cogn. (9.13% of total density) and Laportea peduncularis (Wedd.) Chew. (8.69% of total density) were the most abundant species in the THD intact canopy. It should be noted here that L. camara and A. houstonianum are both Category 1b IAPs. The lack of common taxa in gaps and closed canopy con-firms the stochastic nature of species recruitment often expected in gaps (Obiri & Lawes, 2004).

Shannon’s exponential index (1H) provides an indi-cation of the equivalent number of commonly occurring species while the Simpson’s inverse index (2H) reflects the equivalent number of abundant species found at a site (Chao et al., 2014). Finally, Chao2 index provides an in-dication of the maximum number of species that could be found at a site with the maximum sampling effort. Hill numbers have not been used in species assemblages stu-dies until recently (particularly in forest gap studies). The PHD forest gaps displayed the highest number of possi-ble species (Chao2 index = 188.15) as well as the highest number of species, commonly occurring species and abundant species (0H = 136; 1H = 51.71 and 2H = 30.34). PLD forest gaps reached intermediate values for the num-ber of possible species (Chao2 index = 140.01) as well as the number of species, commonly occurring species and abundant species (0H = 115.6; 1H = 42.85 and 2H = 23.18). The THD forest gaps displayed lowest values for the number of possible species (Chao2 index = 93.71) and the number of species, commonly occurring species and abundant species (0H = 59; 1H = 17.53 and 2H = 10.31). Calculation of these indices offers a congruent compara-tive of the plant diversity status in the gaps of the three forest sampled (Fig. 3).

Hill numbers and the Chao 2 index were also calcula-ted for gap size classes, by pooling the canopy gap data for the three forests (Fig. 4). As expected, calculations revealed that size class 3 (‘large’ gaps > 45 m2) displa-yed the highest number of possible species (Chao2 in-dex = 194.73) and highest Hill numbers (0H=140, 1H = 114.82 and 2H = 94.34).The size class 2 (‘medium’ gaps between 25 - 45 m2) values were intermediate for the Chao2 index (= 166.38) and Hill numbers (0H=127; 1H = 95.67 and 2H = 76.97), and size class 1 (‘small’ gaps < 25 m2) displayed the lowest values for the Chao2 index (= 148.02) and Hill numbers (0H=122; 1H = 92.35 and 2H = 73.47).

Sørenson and Jaccard similarity indices were used to compare the canopy gaps across the three forests. Both indices indicate differing levels of similarity in terms of species richness between intact canopy and canopy gaps across the three forests. In general, the gap and canopy similarity for each forest was highest when compared to the gaps and canopies of the other two forest. The THD forest showed the highest levels of similarity between in-tact canopy and canopy gaps while the PLD and PHD fo-rests showed markedly lower similarities between intact canopy and canopy gaps (for both indices). The PHD and PLD forest gaps displayed the highest levels of similarity to each other in terms of species richness, while the hi-ghest levels of dissimilarity were between the PHD and THD forest gaps (Table 2).

 

Table 2. Sørenson and Jaccard similarity values for comparisons between canopy gaps and intact canopies within each forest investigated and between canopy gaps of different forests (PLD: primary least disturbed; PHD: primary highly disturbed; THD: transitional highly disturbed).

 

Figure 3.  Equivalent number of species by Hill numbers (0H = species richness; 1H = Shannon’s exponential; 2H = Simpson’s inverse) and predicted species using the Chao2 in-dex, for canopy gaps in the primary highly disturbed (PHD), primary least disturbed (PLD), and transitional highly disturbed (THD) forests investigated.

 

Figure 4.  Equivalent number of species by Hill numbers (0H = species richness; 1H = Shannon’s exponential; 2H = Simpson’s inverse) and predicted species using the Chao2 index, for canopy gap size classes (SC) based on pooled data from the three forests investigated (SC 1: gaps < 25 m2; SC 2: gaps between 25 - 45 m2; SC 3: gaps > 45 m2).

 

 

Relationship between species richness and canopy gap area

Pearson’s correlation analyses were used to assess the relationship between species richness and gap size class for each forest. There was a trend for species richness to be higher in larger gaps in the PLD forest; however in a few cases across all three forests, smaller gaps had greater species richness than larger gaps (Fig. 5a). There was a significant positive relationship between species richness and gap area for the PHD forest (Fig. 5b). No clear relationship between species richness and gap area was observed for the THD forest (Fig. 5c). A significant positive correlation between species richness and canopy gap area when data for all three forests were pooled for analysis, was also revealed, i.e. species richness was higher in larger gaps (Fig. 5d).

 

Floristic similarity of canopy gaps

The UPGMA cluster analysis was used to determine f loristic similarity of canopy gaps among forests, and was based on presence/absence data for the 198 species (including graminoids) found in all gaps across the three forests. There are three main clusters (THD, PHD and PLD) at approximately 0.27 similarity (Fig. 6). The clusters are forest-specific and appear to reflect the differences in disturbance history across the three forests: the highly disturbed forests (THD and PHD) grouped together, indicating that these forests are more similar to each other than to the PLD forest. It is also worth noting that gaps of the same size class did not always group within a forest, i.e. gap size did not influence floristic similarity (not shown). The NMDS analysis also recovered the same results (Fig. S1 [suppl.]).

 

 

Figure 5.  Correlation analyses for species richness versus canopy area for the, a) Primary least disturbed (n = 12; r = 0.461; p > 0.05), b) Primary highly disturbed (n = 12; r = 0.752; p < 0.05) and c) Transitional highly disturbed forests investigated (n = 12; r = 0.423; p > 0.05); d) data for all forests pooled (n = 36; r = 0.521; p < 0.05).

 

Figure 6.  Unweighted pair group method with arithmetic mean (UPGMA) phenogram for the THD (transitional highly disturbed) (n = 12 gaps), PHD (primary highly disturbed) (n = 12 gaps) and PLD (primary least disturbed) (n = 12 gaps) forests. Bars denote the forest specific clusters.

 

 

Relationship between floristic and abiotic factors in canopy gaps

When canopy gap data for all forests were pooled there was a significantly negative correlation between species richness and canopy gap air temperature (r = -0.582; p < 0.05), as well as between canopy gap plant density and air temperature (r = -0.505; p < 0.05). Species richness was significantly positively correlated with soil moisture content (r = 0.424; p < 0.05). When these relationships were tested within each forest, there was a strong positive correlation between species richness and soil moisture content for the PHD forest only (r = 0.676; p < 0.05).