Conservation planning and sustainable environmental management have limited resources for implementation owing to competition with other socio-economic imperatives (Heywood & Iriondo 2003; Shankaran 2009; Schneider et al. 2011). Prioritisation of areas for conservation, through systematic conservation planning (SCP), has become a key tool in conservation resource allocation (Heywood & Iriondo 2003; Margules & Pressey, 2000; Pressey, Cowling & Rouget 2003). These algorithm-based planning tools use available data on habitats, species and ecosystem services to select priority sites based on important criteria (Margules & Pressey 2000). However, SCP outputs are only as good as the input data (Heywood & Iriondo 2003; Margules & Pressey 2000; Smith, Goodman & Matthews 2006), and conservation plans are often based on limited data, which requires conservation planners to define different environmental features by using environmental breaks or changes (termed mesofilters; Crous, Samways & Pryke 2013). These ‘surrogate’ habitats are then assumed to be representative of a set of species known to occur within them (Crous et al. 2013; Smith et al. 2006). Incorrect selection of surrogates may result in inaccurate SCP that does not sufficiently represent reality, leading to potentially wasted resource expenditure on marginal areas (Crous et al. 2013) and inappropriate protection for the species that need it most (Coppolillo et al., 2004).

The eThekwini Municipal Area (EMA), which includes the metropolitan city of Durban, is located on the central-southern coastal region of KwaZulu-Natal (KZN) Province and includes 99 728 ha of the original extent of the KwaZulu-Natal Coastal Belt Biome (KZNCB; Mclean, Ground & Boon 2014; Rutherford et al. 2006). This municipality is a hub of development, and the remaining grassland vegetation within the municipality currently represents less than 25% of its historical extent (eThekwini Municipality SDF 2013; Mclean et al. 2014; O’Conner 2005; Scott-Shaw & Styles 2012; Uys, Bond & Everson 2004). The EMA has distinct variations in climatic conditions, with subtropical to temperate conditions at higher elevations (high rainfall seasonality, high temperature seasonality, lower mean temperatures) and subtropical to tropical conditions at lower elevations approaching sea level (lower rainfall seasonality, lower temperature seasonality, higher mean temperatures) owing to climatic regulation from the warm Mozambique ocean current (Rutherford et al. 2006). This variation has created a heterogeneous region that supports tropical, sub-tropical and temperate mist-belt species (Eeley, Lawes & Piper 1999; Rutherford et al. 2006).

The grassland ecosystems of KZN have, until recently, had fairly limited scientific attention (O’Conner 2005; Scott-Shaw & Styles 2012; Uys et al. 2004; Zaloumis & Bond 2011), which imposes the risk of poor selection and representation of critical biodiversity areas (Crous et al., 2013). Graminoides make up the majority of the biomass of a grassland system and have historically been the main focus of scientific studies (Zaloumis & Bond 2011). Conversely, the non-graminoid herbaceous component (commonly termed forbs, which include geophytes) contributes most to overall grassland species richness (Uys et al. 2004; Zaloumis & Bond 2011).

The scale of transformation in the EMA is likely to have affected ecosystem integrity and associated functioning. Fragmentation will have led to the disruption of natural spatial disturbance patterns, particularly fire and grazing, critical for the maintenance of the successional state of grasslands (Boakye et al. 2013; Snyman 2006; Uys et al. 2004). Management (including block burning, grazing regulation, alien plant clearing) is currently the primary method for ensuring simulation of natural disturbance. It is in these managed grasslands where one would expect to find higher species richness and a more representative species composition (comparative to unmanaged grassland; Uys et al. 2004; Van Wilgen et al. 2014). The discrepancy between species assemblages within and outside managed areas adds an additional anthropogenic-derived level of heterogeneity to an already complex ecosystem (Uys et al. 2004).

Understanding the primary drivers of species heterogeneity within a grassland ecosystem is critical in ensuring the development of both adequate and accurate conservation targets, and associated conservation site selection (Crous et al. 2013; Pressey et al. 2003; Uys, Hamer & Slotow 2009). Scale is an important factor when investigating the mechanisms explaining species or ecosystem heterogeneity, as various factors have differing levels of influence at different scales (Gonzalez-Megias, Gomez & Sanchez-Pinero 2007; Huston, 1999). What may affect a particular site or area may not be as significant at a regional scale; similarly, a locally dominant species may be rare regionally, or a species recorded throughout a region may be sporadic in its local distribution (Gonzalez-Megias et al. 2007; Honnay & Hermy 2004; Hanski, 1994). These distributional differences may be caused by site-specific anthropogenic disturbances, or might be explained by environmental heterogeneity. Knowing the drivers of plant species abundance (including rarity) will significantly improve SCP output accuracy (Crous et al. 2013).

The coastal and near-coastal grasslands of the EMA are predominantly included in the Durban Metropole Grassland vegetation type (KZN2), Northern Coastal Grassland (KZN16), Ngongoni Veld (SVs4), or KZN Sandstone Sourveld (SVs5; Rutherford et al. 2006). These grasslands are nationally recognised as either vulnerable (SVs4), endangered (SVs5, KZN16) or critically endangered (KZN2), respectively (Rutherford et al. 2006). The percentage of these grassland types that are formally conserved, ranges between 1% and 4% (Rutherford et al. 2006), and the total remaining area of coastal and coastal-hinterland grassland within the EMA is 13 534 ha, which is only 12% of the pre-1850 distribution (Mclean et al. 2014). These grassland ecosystems are, therefore, under significant threat and require urgent scientific attention to optimise their conservation planning and management within the EMA spatial development plan (Rutherford et al. 2006; Uys et al. 2009).

The present study therefore attempts to improve the understanding of coastal and near-coastal grasslands within the central region of KZN, with a particular focus on key anthropogenic and environmental factors influencing species richness, species composition, species frequency, and species turnover (or beta diversity). For this purpose, we sampled sites situated in or near to the urban/peri-urban matrix of the EMA region of KZN, which contained primary coastal and near-coastal grassland. The main objectives of the study were: (1) to quantify the species richness and identify the level of variation in forb species composition at plot, site and regional scales of central KZN coastal grasslands; (2) to determine which disturbance, or biogeographic or environmental factors, may be responsible for any observed variation in forb species richness and composition; and (3) to understand the spatial distribution and frequency of occurrence of species at different spatial scales.

The EMA’s coastal and near-coastal grasslands had a high number of forb species when compared with similar South African grassland studies. Between 30 and 44 forb taxa per plot were recorded within 1000 m² Whittaker plots in natural dolerite and sandstone grasslands of the Drakensberg, KZN (O’Conner 2005), and 8 – 30 graminoid and forb taxa per 16 m² plots in urban Bloemfontein, Karoo-Supergroup underlain grasslands (Dingaan & Dupreez 2013). Closer to the central KZN region, 118 taxa were recorded from 27 (100 m²) mesic grassland plots within the rural midlands of KZN (840 metres above sea level (m.a.s.l.); Uys et al. 2004). Whilst the level of species richness we recorded is expected at a coastal and near-coastal region, it is high, given the location of our sites within an urban and peri-urban landscape (Dingaan & Dupreez 2013; Knight, Crowe & Siegfried 1982; et al. 2004). This finding, combined with the absence of disturbance as a factor influencing species composition, highlights the ongoing conservation value of these threatened grasslands within this global biodiversity hotspot (Maurer, Durka & Stocklin 2003; Steenkamp et al. 2004).

The high beta diversity and nestedness observed, both within and across sites, is in accord with other KZN grasslands studies, which showed significant correlation between environmental and climatic mesofilter types and species composition and richness variation (Crous et al. 2013; Uys et al., 2004; Pausas & Austin, 2001). That plots within a site were more similar to plots in other sites, and that there was a biological basis to this pattern, is an important result which indicates that grassland patches within close proximity are not necessarily the same, and that stratified conservation planning, which incorporates quantified determinants of species variation, and uses the boundaries or thresholds of these determinants for defining subtypes, is needed to ensure adequate species variation selection (mesofilter determination; Crous et al. 2013; Laliberte et al., 2009 Gabriel et al., 2006; Coppolillo et al., 2004).

The species frequency curves, however, suggest that these grassland systems are not entirely unaffected by urbanisation-associated impacts. The observed higher number of dominant species, and the relatively higher frequencies of those dominant species in Giba East and Sherwood (both subjected to sporadic management or periods of no management), may be an outcome of modified disturbance and patch size patterns resulting in the diminishing competitiveness of certain species, as well as the entire exclusion of other species from the grassland (as shown by the significant influence of disturbance and size factors on total species richness; O’Conner & Hurt, 2009). These patterns are consistent with observations made in other grassland disturbance response studies (Menge & Sutherland 1987; Pooley et al. 2005; Zaloumis & Bond 2011).

Reserve managers should therefore strive to create an environment where natural disturbance is maintained at historical levels, and anthropogenic disturbance is excluded as far as possible, to ensure maintenance and improvement of species diversity within these grasslands (Connell 1978; Roberts & Gilliam 1995; Tilman & Lehman 2001). The size of a contiguous grassland patch, its distance to the traditional medicine market, and the shape of the grassland patch, were the biggest drivers of species richness variation. Special management attention should therefore be given to (1) the maintenance and expansion of grassland patch size, and the reduction of perimeter-to-area ratios by controlling bush encroachment and restoring appropriate adjacent areas to grassland; and (2) managing public access to and activities within grassland sites (O’Conner 2005; O’Conner & Hurt 2009; Scott-Shaw & Morris 2014; Zaloumis & Bond 2011). Considering the limitations of plant seed dispersal, reintroduction of key species, based on local ecological requirements, would assist in species richness improvement (Soons & Heil 2002; Soons et al. 2005).

The outcomes of the present study highlight the often underappreciated importance of conservation efforts in urban and peri-urban landscapes. Considering that global biodiversity hotspots often have above average population densities, urban and peri-urban conservation, through protection of relatively small (when compared with traditional ‘game reserves’ such as Kruger National Park and Hluhluwe Imfolozi Park) high biodiversity areas cannot be ignored in the pursuit of global conservation goals (Miller & Hobbs 2002). Broad-scale systematic conservation plans cannot be solely relied on, and urban and peri-urban systematic conservation plans will need to be developed with a focus on finer-scale, local-level variances in ecosystem types (Knight et al. 2006). Urban and peri-urban systematic conservation plans should consider variables such as aspect, altitude, and climate as potential indicators of fine-scale variation in ecosystem types (Crous et al. 2013). These fine-scale variations will, in turn, provide a more robust basis for development decisions (Crous et al. 2013). Without these efforts, urban biodiversity hotspots are likely to suffer continued degradation and associated species losses (Malcolm et al. 2005).