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Article

The Accuracy of the Step Point Vegetation Sampling Method for Herbaceous Layer Monitoring in South African Savannas

by
Armand A. Biko’o
1,*,
Willem J. Myburgh
1 and
Brian K. Reilly
2
1
Department of Nature Conservation, Tshwane University of Technology, Private Bag X680, Pretoria 0001, South Africa
2
Department of Genetics, Faculty of Natural and Agricultural Sciences, University of Free State, P.O. Box 339, Bloemfontein 9300, South Africa
*
Author to whom correspondence should be addressed.
Diversity 2025, 17(3), 146; https://doi.org/10.3390/d17030146
Submission received: 15 August 2024 / Revised: 11 February 2025 / Accepted: 18 February 2025 / Published: 21 February 2025
(This article belongs to the Special Issue Biodiversity and Ecology of African Vegetation)
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Abstract

:
Robust monitoring techniques, capable of showing change in the savanna when change has occurred, are a prerequisite for better managing this ecosystem. The Step Point Method is a well-established technique in South African range surveys (Short and Morris 2016). However, it is often considered inaccurate in describing vegetation dynamics in the savanna herbaceous layer due primarily to issues with sample sizes and cover estimates, its inability to capture the spatial heterogeneity and patchy distribution typical of these ecosystems, sensitivity to observer bias, reliance on relative values and poor inclusion of sparse or less common species. This study aimed to test the effectiveness and accuracy of the Step Point Method for monitoring the herbaceous layer of savanna by comparison to absolute densities of plants. The results show that the Step Point Method only recorded 41–50% of species richness. It overestimated the relative species richness of grasses by 17.4% while underestimating that of forbs by 13.8% on average. The relative abundance of grasses was overestimated by 32.4%, while that of forbs was underestimated on average by 28.4%. Dominance was overestimated by 115.5% on average, and species diversity was underestimated by 15%. Considering these shortcomings, the Step Point Method should be used with extreme caution in studies focusing on monitoring temporal and spatial changes in veld condition and for biodiversity management.

Graphical Abstract

1. Introduction

Vegetation monitoring is important for understanding the health and productivity of ecosystems, as well as for assessing the impacts of environmental drivers and/or stressors such as grazing, browsing, fire, climate change, land use and invasive species. This is especially necessary in South Africa due to its unique and diverse conservation estate, rangelands and ecosystems under pressure from agriculture, human population growth and lack of economic development.
Field-based vegetation monitoring techniques are commonly used to collect data on vegetation composition, structure and dynamics. These techniques can be grouped into area-based, semi-quantitative and point-based methods [1,2,3]. These methods provide detailed information on the vegetation at a specific location but are limited in their ability to capture spatial patterns and changes over larger areas and longer time periods.
Point-based vegetation monitoring techniques have been adopted and variously modified in South Africa, starting with the development of the wheel point technique [4], the nearest plant method [5], the benchmark method [6], the foot-point method [7] and the key species method [8]. The most applied techniques in South Africa routinely make use of 200 points spaced at constant intervals along a line or randomly in a plot or paddock [7,9,10,11]. However, the current trend is to use a larger sample of over 1000 points adjusted based on topographical units or vegetation types, and the survey objectives determine the type of information collected at each point [12,13].
The Step Point Method [5] is a well-established technique in South Africa [11] and is often the applied method for herbaceous vegetation monitoring since it is efficient in terms of time and lend itself readily to statistical analyses [11,14,15]. However, since this technique relies on relative values and makes use of relatively small sample sizes (200 points in general), its ability to show change with high precision has been questioned due primarily to issues with basal cover estimates [6,16], the inability to capture the spatial heterogeneity and patchy distribution [17,18], sensitivity to observer bias, poor inclusion of the less frequently encountered plants [9,19,20,21] and statistical power [15].
This has served the purpose of providing information from a purely pasture science perspective to inform decision making on providing for grazing ungulates but is lacking where biodiversity and more natural systems approaches are required. The less frequently encountered plants may also be indicators of impending vegetation change as, in most instances, neither the fidelity nor constancy of the species is known and may also be geographically variable. Knowledge of plant identification is crucial for accurately collecting data using the step point method [5] because it relies on identifying plant species at specific points along a transect. Misidentification can lead to errors and compromise study reliability [1,22]. Proper identification ensures the ecological analysis accurately reflects plant community composition and aids in monitoring vegetation changes and assessing rangeland health [23]. This study aimed to test the effectiveness and accuracy of the Step Point Method (SPM) of Foran et al. [5] for monitoring the herbaceous layer of South African savannas by comparison to absolute densities of plants derived from total counts.

2. Materials and Methods

2.1. Study Area

This study was conducted at three different study areas representing three major South African savanna bioregions: The Central Bushveld, Mopane and Lowveld [24].

2.1.1. Letlapa Pula Game Reserve

The Letlapa Pula Game Reserve (LPGR) is situated in the Waterberg area near Koedoeskop, south of Thabazimbi, in the Limpopo Province of South Africa. Its climate is classified as region Cwa, denoting summer rainfall areas with hot summers [25,26]. The Letlapa Pula Game Reserve falls in the Central Bushveld Bioregion and represents two vegetation units, namely the Western Sandy Bushveld (SVcb 16) and the Waterberg Mountain Bushveld (SVcb 17), as described by Mucina and Rutherford [24].

2.1.2. Selati Game Reserve

The Selati Game Reserve (SGR) is situated south of the small town of Gravelotte in the Limpopo Province of South Africa. The Selati Game Reserve falls within the hot, arid steppe climate zone (BSh), which is a region that tends to have hot summers and warm to cool winters with an evaporation rate that exceeds the precipitation rate, leading to a negative nett water balance year on year [25,26]. The Selati Game Reserve falls largely in the Mopane Bioregion of the Savanna Biome and is dominantly characterised by the vegetation unit Phalaborwa-Timbavati Mopaneveld (SVmp 7) of Mucina and Rutherford [24].

2.1.3. Kempiana Nature Reserve

The Kempiana Nature Reserve forms part of the greater Kruger National Park and is located approximately 50 km southeast of the town of Hoedspruit. Kottek et al. [26] classify the area as Bsh, meaning it is an arid area with hot average annual temperatures of more than 18 °C. Mucina and Rutherford [24] described three vegetation units on KNR, namely the Phalaborwa-Timbavati Mopane veld (SVmp 7), which is part of the Mopane Bioregion, the Granite Lowveld (SVl 3) and the Gabbro Grassy Bushveld (SVl6), which are part of the Lowveld Bioregion. The data for this project were collected on the Granite Lowveld (SVl 3) only.

2.2. Methods

2.2.1. Sampling Framework

A relatively homogenous study site was chosen in each study area to ensure that the sampling sites were representative of the vegetation unit. Each selected study site was subdivided into three terrain (hillslope) units, viz. crest, mid-slope and foot-slope. In each of these terrain units, a 40 × 40 m quadrat, representing a sampling site, was placed using tape measures. In each of these sampling sites, data were collected using the Total Count Quadrats (TCQ) as control and the Step Point Method (SPM) [5]. The results of the SPM were compared to the absolute densities for each species from the TCQ.

2.2.2. Total Count Quadrats: Control Population Parameters

A grid was overlaid on each sample site, subdivided into 2 × 2 m subplots, providing 400 possible subplots per sample site. A random number generator was then used to select thirty 2 × 2 m subplots within each 40 × 40 m sample site in order to obviate risks of pseudo-replication. Each of the thirty randomly selected 2 × 2 m subplots was demarcated, and all the herbaceous plants were identified and recorded. Data recorded in each included all herbaceous species, growth form and the number of individuals for each species.

2.2.3. The Step Point Method: Relative Population Parameters

In each of the 40 × 40 m sampling sites, data were also collected using the Step Point Method [5]. A total of 200 points were systematically placed along straight-line transects to cover the whole quadrat.
Tape measures were used to create ten straight-line transects in each sampling site, each 40 m in length and 4 m apart. A spoke was placed vertically on the ground at every 2 m mark along each line transect, and the nearest plant to the spoke was identified, giving 20 points per line with a total of 200 points for the ten lines. At each point, the first herbaceous plant hit by the point was recorded. If no rooted part of an herbaceous plant was hit, the pin was pushed into the ground, and the plant nearest to the pin was recorded.

2.3. Data Analysis

Accuracy is defined as the relationship between “reality” and the result of a survey. In this case, the relationship between the Total Count Quadrats (TCQ), “reality”, and the mean result of the SPM—hence accuracy. The vegetation structure parameters described below were derived from the TCQ and SPM, and the results were compared to check for statistical differences.
Species richness: Species richness was taken as the total number of species recorded in a sampling site or in a study site. The recorded species were grouped into growth forms (grasses, forbs and sedges), thus forming different species cohorts. A statistical estimate of total species richness, Smax, was calculated using a nonparametric method developed by Chao and associates [27]. The statistical package Species Prediction and Diversity Estimation (SPADE) [28] was used for the calculations.
Species abundance: Species abundance is the number of individuals per species [29], and relative abundance refers to the evenness of distribution of individuals among species in a community [30]. Both the species abundance and relative species abundance were calculated. Relative abundance was taken as the percentage composition of a species cohort relative to the total number of species in the sampling or study site.
Dominance and evenness: The Berger–Parker index was calculated as an indicator of species dominance in each sampling site [31], while the Pielou J index was used as an indicator of evenness. This measure of equitability compares the observed Shannon–Wiener index against the distribution of individuals among the observed species, which would maximise diversity. The statistical package Species Diversity and Richness 4.1.2 (SDR) [32] was used to calculate all the species composition values of each sampling site.
Species diversity: Alpha diversity was calculated for each of the methods at each of the sampling sites using the Shannon–Wiener Diversity Index. β diversity was also assessed for each method using Whittaker βw.
Similarity between the sampling sites: The spatial variations between sampling sites were assessed using a compositional similarity analysis. The Morista Similarity Index was used, and pairwise comparisons were conducted.

3. Results

3.1. Central Bushveld

The results of herbaceous species richness, abundance, dominance, diversity and evenness at LPGR are summarised in Table 1 and show that the SPM, when compared to TCQ, demonstrated the following results:
  • Recorded 48.1% of herbaceous species;
  • Underestimated the species richness of grasses, forbs and sedges by 22.2%, 65.2% and 100%, respectively.
  • Overestimated the relative species richness of grasses by 20.5% while underestimating that of forbs by 17.7%;
  • Overestimated the relative abundance of grasses by up to 25% while underestimating that of forbs by 18.1%;
  • Underestimated the herbaceous species diversity by 12% while overestimating evenness by 5% overall;
  • Showed an underestimation in diversity by 4.6% and 14% for grasses and forbs, respectively;
  • Showed an overestimation of evenness by 2.5% and 14.5% for grasses and forbs, respectively;
  • Did not record any sedge species.
Further results show that the SPM overestimated dominance by 75% overall, an overestimation of 12.5% and 54.5% for the dominance of grasses and forbs, respectively.
The SPM also underestimated the absolute species richness Smax by 49.6%. Whittaker’s βw was underestimated by 5.7%, and the Morista Similarity Index was underestimated by 79.2%.

3.2. Mopane Veld

The results of herbaceous species richness, abundance, dominance, diversity and evenness at SGR are summarised in Table 2 and show that the SPM when compared to TCQ, demonstrated the following results:
  • Recorded 41.67% of herbaceous species;
  • Underestimated the species richness of grasses, forbs and sedges by 35.71%, 59.26% and 100%, respectively;
  • Overestimated the relative species richness of grasses by 15.8% while underestimating that of forbs by 11.6%;
  • Overestimated the relative abundance of grasses by 33.7% while underestimating that of forbs by 31.8%;
  • Underestimated the herbaceous species diversity by 14.3% while overestimating evenness by 6.25% overall;
  • Showed an underestimation of diversity by 7.8% and 18.7% for grasses and forbs, respectively;
  • Showed an overestimation of evenness by 6.2% and 9.5% for grasses and forbs, respectively;
  • Did not record any sedge species.
Further results for SGR show that the SPM overestimated dominance by 71.4% overall, an overestimation of 13.3% and 118.2% for the dominance of grasses and forbs, respectively. The SPM also underestimated the absolute species richness Smax by 52%. Whittaker’s βw was overestimated by 6.6% and underestimated the Morista Similarity Index by 12.8%.

3.3. Lowveld

The results of herbaceous species richness, abundance, dominance, diversity and evenness at SGR are summarised in Table 3 and show that the SPM demonstrated the following results:
  • Recorded 50% of herbaceous species;
  • Underestimated the species richness of grasses, forbs and sedges by 20%, 58.82% and 100%, respectively;
  • Overestimated the relative species richness of grasses by 16.2% while underestimating that of forbs by 12.1%;
  • Overestimated the relative abundance of grasses by 38.6% while underestimating that of forbs by 35.4%;
  • Underestimated the herbaceous species diversity and evenness by 26.12% and 12.05% overall, respectively;
  • Showed an underestimation of diversity by 18.6% and 8.4% for grasses and forbs, respectively;
  • Showed an underestimation of the evenness of grasses by 12.2% and an overestimation of that of forbs by 17.7%;
  • Did not record any sedge species.
Further results for SGR show that the SPM overestimated dominance by 200% overall, an overestimation in dominance of 94.7% for grasses and an underestimation of 38.9% for forbs. The SPM also underestimated the absolute species richness Smax by 48%. Whittaker’s βw was overestimated by 92.3% and underestimated the Morista Similarity Index by 34.4%.

4. Discussion

4.1. Species Richness

The results of this study show that the SPM, as described by Foran et al. [5], has a limited ability to determine species richness accurately. This was valid for all three savanna types in which this study was conducted. The SPM recorded 41–50% of herbaceous species on average. Inferences made about species richness of the herbaceous layer of South African savannas on the basis of this technique should therefore be taken with caution, especially given that it primarily focuses on point data and does not readily provide detailed information on the spatial distribution of species or vegetation patches within the herbaceous layer.
Accurately determining species richness is crucial for biodiversity conservation because it is a fundamental measure of biodiversity, serves as an indicator of ecosystem health and provides a baseline for understanding ecosystem functioning and resilience [33,34]. High species richness often indicates robust and resilient ecosystems capable of withstanding environmental stressors [35] and providing essential services like pollination, nutrient cycling and climate regulation [34]. It also helps identify biodiversity hotspots and keystone species, enabling targeted and prioritised conservation efforts [36]. Furthermore, accurate species richness data are essential for monitoring biodiversity loss, evaluating the success of conservation policies and fulfilling international commitments like the Convention on Biological Diversity [37]. The 2010 Biodiversity Target of the Convention on Biological Diversity (CBD) states that there should be a significant reduction in the current rate of biodiversity loss. Magurran and McGill [30] are of the opinion that this has put the focus on quantifying biodiversity trends within regions or countries. Detectability therefore becomes an important concept to address. Unless the sampling plots are so small that all species and individuals can be detected and so numerous that all or nearly all species occur on at least one sampling plot [38].
However, this is often not the case in vegetation studies and was certainly not the case for the SPM as used in this study. If detectability of individuals of a species is very low, or if a species is so rare that few, if any, occur in the sampled sites or plots, then the species may be undetected [30,38]. It can be reasonably assumed that the number of undetected species in an ecosystem is impacted by the accuracy of the technique used. Techniques with low accuracy will often miss many species, leading to incorrect inferences about species richness and biodiversity in an ecosystem. This is a situation to be avoided as much as possible in plant studies, especially those aimed at detecting biodiversity trends.

4.2. Abundance and Dominance

This study has shown that the SPM overestimated the relative abundance of grasses while underestimating that of forbs and sedges, confirming what other authors also found [14,39,40]. These results could negatively influence management decisions and actions. Scientific knowledge of the relative species abundance in an ecosystem is very important. This measure provides insights into ecosystem functioning, stability and health. For instance, shifts in species abundance can signal environmental changes or stressors [29]. It helps prioritise conservation efforts by identifying both dominant and rare species that need protection [41]. Moreover, it is essential for monitoring ecosystem health, managing invasive species and ensuring sustainable resource use [42]. Knowledge about species abundance is also crucial for predicting ecosystem responses to global changes like climate change. Effective conservation strategies thus depend on comprehensive data about species distribution and abundance [43].
This study also shows that the SPM [5] tends to significantly overestimate the overall dominance structure of herbaceous species in all the study sites used. More specifically, the SPM showed a significant overestimation of the dominance structure of grasses and forbs at all sampling sites. These results, together with those seen in relative abundance, suggest that this method should not be used for biodiversity management.
Various authors have already shown that species abundance and dominance patterns are closely linked to patterns of diversity and that shifts in dominant species can have significant impacts on the structure and functioning of communities [29,30,44,45]. The Step Point Method lacks the accuracy required in savanna herbaceous layers for making informed decisions about diversity.

4.3. Alpha Diversity and Evenness

Plant species diversity and evenness are important components of the structure and function of ecosystems [29,30]. Species diversity refers to the number of different plant species in a given area, while evenness refers to the distribution of those species among individuals. Both of these aspects of plant communities can impact the functioning and stability of ecosystems and have important implications for the survival of other species that depend on plants for food, shelter and habitat [46,47]. Higher diversity ensures a greater variety of functional traits and can enhance resource use efficiency, leading to increased primary productivity and biomass [35,48]. Evenness, reflecting a more equitable distribution of individuals among species, contributes to community stability and resilience against disturbances [49]. Together, diversity and evenness ultimately support overall ecosystem functioning and the provision of ecosystem services [35].
This study has shown that the SPM does not have the ability to reliably determine species diversity. This confirms the findings by Panagos [50], Panagos and Zacharias [14], Duelli and Obrist [39] and Panagos and Reilly [40]. The SPM underestimated the herbaceous species diversity by 18% on average. It is therefore suggested that the SPM should not be used for vegetation diversity studies, or at least that when used, care should be taken to adequately interpret the results taking into account their tendency to significantly underestimate species diversity.

4.4. Beta Diversity and Spatial Variation

Soil catenal separation can strongly influence beta diversity in savannas [51]. According to Archibald et al. [52], soils with different water-holding capacities and nutrient availabilities can support different types and densities of vegetation, leading to distinct zones of different types of vegetation. This can result in significant differences in species composition between these zones, leading to higher beta diversity within the savanna landscape [51,53].
In this study, the SPM either grossly underestimated or overestimated beta diversity in all three study sites. This suggests that the SPM should not be used for vegetation studies that seek to accurately determine beta diversity. Whittaker’s beta diversity index is an important measure used in ecology to assess the degree of variation in species composition between different ecosystems or habitats. It measures the change in species diversity from one location to another and is important because it provides insight into the processes that drive biodiversity patterns and the spatial distribution of species [54]. This study has shown that the SPM should be used cautiously for studies that seek to use Whittaker’s beta diversity index as a correlate of species composition and distribution.
Similarly, this study also found that the SPM showed very wide deviations from the TCQ in terms of the Morista Similarity Index (MSI). The Morista Similarity Index is a commonly used ecological index that measures the similarity of species composition between two or more communities. It ranges from 0 to 1, with 1 indicating identical species composition between two communities and 0 indicating no overlap between the communities [29]. The Morista Similarity Index has several advantages over other similarity indices, including its sensitivity to both species richness and abundance and its ability to account for rare species. Additionally, it has been shown to perform well in a variety of ecological settings, including community composition comparisons in terrestrial, freshwater and marine environments [29,55], and this study has demonstrated that the SPM which is a commonly used vegetation survey technique in South Africa currently does not have the ability to accurately determine this index.

5. Conclusions

For vegetation monitoring to be effective, it is important that the vegetation survey technique used is able to accurately depict all the components of the vegetation. Additionally, it is of utmost importance that such a technique can detect both temporal and spatial changes in vegetation to enable effective management. Relying solely on spatial changes is insufficient because it already implies a change in species composition and, therefore, cannot serve as an early warning system. The lower the species diversity produced by a specific survey method, the smaller the chance of detecting early change. Point sampling methods tend to emphasise dominant species, while low-frequency species may decline unnoticed. However, this is largely influenced by the number of points sampled—an increase in sampled points would likely result in a higher number of recorded species. Unfortunately, this study did not investigate the effect of increasing the number of sample points. A compounding problem observed with the SPM as used in this study is the fact that it very weakly represents forbs despite their richness and abundance [56,57]. The SPM lumps all the forbs into a single category called “forbs” without identifying the species involved despite the fact that in terms of species richness and abundance, savannas comprise more than 60% of forbs [57]. It is therefore not realistic to ignore such a prominent group of plants from management decisions. Forbs play a very important role in the savanna ecosystem.
This study has confirmed the results of other studies that point-based vegetation survey techniques in South African savannas are not sufficiently robust and accurate to be used for monitoring the herbaceous layer in terms of most species parameters. Some of the positive points of these techniques include their simplicity and ease to learn and apply in the field, saving time; they also provide a standardised method for data collection, allowing for easier comparison between different surveys and sites; and the data lend itself to statistical analysis—particularly where rapid approximation is required from a grazing and pasture science perspective. They are also sensitive to sample size as cautioned by Mentis [7] and proven by Reilly and Panagos [15], a fact that seems to have been ignored in application. Despite these positive points, point-based vegetation survey techniques also have many negative points, some of which include their lack of effectiveness in capturing rare or low-abundance species, particularly in heterogeneous or patchy habitats [15,58]. In addition, they also tend to introduce bias if the size or spacing of the points is not appropriate for the vegetation being surveyed, and they do not accurately capture the spatial distribution of species [17,18]. It is therefore recommended that these vegetation survey techniques should be used with extreme caution for studies that seek to monitor and manage biodiversity.

Author Contributions

Conceptualization, A.A.B., W.J.M. and B.K.R.; methodology, A.A.B., W.J.M. and B.K.R.; software, A.A.B.; validation, A.A.B., W.J.M. and B.K.R.; formal analysis, A.A.B.; investigation, A.A.B.; resources, A.A.B. and W.J.M.; data curation, A.A.B., W.J.M. and B.K.R.; writing—original draft preparation, A.A.B.; writing—review and editing, W.J.M. and B.K.R.; visualization, A.A.B.; supervision, W.J.M. and B.K.R.; project administration, W.J.M.; funding acquisition, W.J.M. and B.K.R. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding and The APC was funded by the Faculty of Science, Tshwane University of Technology.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Conflicts of Interest

The authors declare no conflict of interest.

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Table 1. Differences in species richness, abundance, diversity and evenness between the Step Point Method (SPM) and the Total Counts Quadrats (TCQ) at the Letlapa Pula Game Reserve. In the table, D represents the percentage difference in results between the two methods and N/A means not applicable.
Table 1. Differences in species richness, abundance, diversity and evenness between the Step Point Method (SPM) and the Total Counts Quadrats (TCQ) at the Letlapa Pula Game Reserve. In the table, D represents the percentage difference in results between the two methods and N/A means not applicable.
GrassesForbsSedgesOverall
ResultsD (%)ResultsD (%)ResultsD (%)ResultsD (%)
Species richnessTCQ36−22.269−65.203−100.00108−51.90
SPM2824052
Relative species richness (%)TCQ33.320.5063.9−17.702.8−2.80
SPM53.846.20
Relative abundance
(%)		TCQ48.225.0044.9−18.106.96.90
SPM73.226.80
Species diversity
(H)		TCQ2.84−4.603.21−14.000.8−100.003.76−12.00
SPM2.712.7603.31
Species evenness (He)TCQ0.792.500.7614.500.73N/A0.85.00
SPM0.810.87N/A0.84
Table 2. Differences in species richness, abundance, diversity and evenness between the Step Point Method (SPM) and Total Counts Quadrats (TCQ) at the Selati Game Reserve. In the table, D represents the percentage difference in results between the two methods and N/A means not applicable.
Table 2. Differences in species richness, abundance, diversity and evenness between the Step Point Method (SPM) and Total Counts Quadrats (TCQ) at the Selati Game Reserve. In the table, D represents the percentage difference in results between the two methods and N/A means not applicable.
GrassesForbsSedgesOverall
ResultsD (%)ResultsD (%)ResultsD (%)ResultsD (%)
Species RichnessTCQ28−35.7154−59.264−100.0096−58.33
SPM1822040
Relative species richness (%)TCQ29.215.8066.6−11.604.2−4.20
SPM45550
Relative abundance (%)TCQ3433.7064.1−31.801.9−1.90
SPM67.732.30
Species diversity (H)TCQ2.69−7.813.1−18.710.95−100.003.64−14.29
SPM2.482.5203.12
Species evenness (He)TCQ0.816.170.749.460.69N/A0.86.25
SPM0.860.81N/A0.85
Table 3. Differences in species richness, abundance, diversity and evenness between the Step Point Method (SPM) and the Total Counts Quadrats (TCQ) at the Kempiana Nature Reserve. In the table, D represents the percentage difference in results between the two methods and N/A means not applicable.
Table 3. Differences in species richness, abundance, diversity and evenness between the Step Point Method (SPM) and the Total Counts Quadrats (TCQ) at the Kempiana Nature Reserve. In the table, D represents the percentage difference in results between the two methods and N/A means not applicable.
GrassesForbsSedgesOverall
ResultsD (%)ResultsD (%)ResultsD (%)ResultsD (%)
Species RichnessTCQ20−20.0051−58.823−100.0074−50.00
SPM1621037
Relative species richness (%)TCQ2716.2068.9−12.104.1−4.10
SPM43.256.80
Relative abundance (%)TCQ43.238.6053.6−35.403.2−3.20
SPM81.818.20
Species diversity (H)TCQ2.47−18.623.09−8.410.93−100.003.56−26.12
SPM2.012.8302.63
Species evenness (He)TCQ0.82−12.200.7917.720.84N/A0.83−12.05
SPM0.720.93N/A0.73
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Biko’o, A.A.; Myburgh, W.J.; Reilly, B.K. The Accuracy of the Step Point Vegetation Sampling Method for Herbaceous Layer Monitoring in South African Savannas. Diversity 2025, 17, 146. https://doi.org/10.3390/d17030146

AMA Style

Biko’o AA, Myburgh WJ, Reilly BK. The Accuracy of the Step Point Vegetation Sampling Method for Herbaceous Layer Monitoring in South African Savannas. Diversity. 2025; 17(3):146. https://doi.org/10.3390/d17030146

Chicago/Turabian Style

Biko’o, Armand A., Willem J. Myburgh, and Brian K. Reilly. 2025. "The Accuracy of the Step Point Vegetation Sampling Method for Herbaceous Layer Monitoring in South African Savannas" Diversity 17, no. 3: 146. https://doi.org/10.3390/d17030146

APA Style

Biko’o, A. A., Myburgh, W. J., & Reilly, B. K. (2025). The Accuracy of the Step Point Vegetation Sampling Method for Herbaceous Layer Monitoring in South African Savannas. Diversity, 17(3), 146. https://doi.org/10.3390/d17030146

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