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- Materials and methods
One of the main goals of systematic conservation planning is to encapsulate the complexity of biodiversity across different spatial scales and geographical regions when delineating a protected area (Margules & Pressey 2000; Pressey et al. 2007). To address this complexity, many biodiversity conservation tools have been developed. These focus on designating a protected area using different species and habitat heterogeneity concepts (Schulte et al. 2006). Of these, fine- and coarse-filter operational scales are often used to delineate networks of protected areas (Noss 1987; Schwartz 1999). Protected areas are either designated for a specific species, usually a flagship one, or around a set geographical area, for example, 1000 km2 of a certain ecosystem (Noss 1987). However, both these fine- and coarse-filter operational scales have their shortcomings.
Fine-filter approaches usually entail the use of surrogates of biodiversity through concepts such as umbrella species, focal species or even guilds (Marcot & Flather 2007). However, congruency issues arise when these surrogates do not adequately represent targeted taxa or overall biodiversity (van Jaarsveld et al. 1998; Lindenmayer et al. 2002). This means that using focal species as a proxy to protect other taxa could be problematic, as species-specific requirements towards habitat conditions, and their response towards threats, are highly variable in space and time (Lindenmayer et al. 2002). Also, areas that are poorly surveyed might lead to false absence of a species and consequently be mistakenly excluded from protected areas (Ferrier 2002). Therefore, in many circumstances, fine-filter conservation is not the appropriate approach, because what is needed is to select surrogates (and subsequently protected areas) in such a way that it will also ensure that spatial autecological requirements of most species are met (Margules & Pressey 2000).
In contrast, coarse-filter reserve selection is theoretically more directed towards including multiple ecosystem types or cover types. However, the problem with coarse-filter approaches is that in most cases, a lack of knowledge may lead to protected areas not being truly representative of natural ecosystems (Margules, Nichous & Pressey 1988) and, in doing so, fail systematic conservation planning. Therefore, for many protected areas to persist, they often need to be expanded into the surrounding matrix to encompass these spatial autecological deficiencies. This can be problematic due to ongoing human infrastructure development (Maiorano, Falcucci & Boitani 2008).
To address this disparity in conservation planning, Hunter (2005) developed a new operational scale for biodiversity conservation – the mesofilter. Broadly, the mesofilter can be defined as specified ecosystem elements, or features, which are important for the maintenance of certain species within an area. The mesofilter complements the coarse filter by helping conservation planners to delineate those physical features of the landscape that are known to be associated with, and promote, a higher diversity of species (Hunter, Jacobsen & Webb 1988). Furthermore, the conservation significance of using this complementary approach to conservation planning is highlighted, as many mesofilters could also endure over long periods, despite climate change (Hunter, Jacobsen & Webb 1988). Therefore, this mesofilter approach at least partly overcomes the flaw in fine-filter conservation, by focusing on those ecosystem elements that are easier to survey and map than single species. Conversely, instead of using biotic components as surrogates for other biota, the emphasis here is on the use of abiotic elements as surrogates for biota (Carroll 1998). The mesofilter ensures that protected area selection, as well as selecting conservancies outside protected areas, incorporates multiple environmental elements within the geographical area to ensure more comprehensive conservation of biodiversity, compared with an area adjacent or nearby which lacks these elements.
However, the mesofilter concept has not to date received much attention as an operational scale in conservation planning. Many studies have shown certain habitat elements or landscape features to be important indicators of diversity, emphasizing that conservation of these elements leads to protection of a diversity of species (Armstrong, van Hensbergen & Geertsema 1994; Armstrong & van Hensbergen 1999; Wessels, Freitag & van Jaarsveld 1999; Hewitt et al. 2005; Overton, Schmitz & Casazza 2006; Barton et al. 2009; Overton, Casazza & Coates 2010). Barton et al. (2009), for example, showed that woody logs in a reserve area had specific associations with many beetle species. These logs increased the biodiversity of the area, so delineating beetle biodiversity hotspots. This is important for protected area design and management, as incorporating these logs as part of the conservation planning will increase biodiversity at the landscape level. Therefore, the mesofilter provides a practical approach to inventorying landscape features of increased biodiversity value, to which subsequent management could be directed (Lindenmayer et al. 2008). Similarly, should a new protected area network be designed, identifying habitat elements that provide a characteristic assemblage of species would prove a vital addition to the design of the conservation network. The efficacy of using a similar complementary approach when designating biodiversity hotspots within a protected area has been shown (Noss et al. 2002). Recognizing mesofilter conservation per se, as posited by Hunter (2005), therefore needs to be explored, particularly as it shows promise as a valuable new operational scale in the biodiversity and conservation planning toolbox (Schulte et al. 2006; Samways, McGeoch & New 2010).
In South African montane grasslands, Armstrong, van Hensbergen & Geertsema (1994) provided some evidence that rockier landscapes had higher plant and butterfly species richness. Here, we assess the value of mesofilters for conservation planning by looking at this rocky mesofilter. To achieve this, we explore whether percentage rockiness in this case (juxtaposed to elevation as a proxy for microclimatic variation) can predict patterns of varying plant, butterfly and grasshopper species richness at the landscape scale, and in addition to species richness, determine the influence of these habitat characteristics on the similarity of species assemblages across this landscape.
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- Materials and methods
Since the inception of the mesofilter concept (Hunter 2005), little research has been carried out to explore this as a practical field tool. Moreover, little research has been carried out to explore the relationship between physical ecosystem features and species richness and composition for practical conservation planning. Here, we tested the use of rockiness as a mesofilter as described by Hunter (2005). This physical landscape feature had significant interactions in species richness and composition with all three focal taxa. This interaction illustrates how we can apply environmental data using a mesofilter to help optimize design of conservation plans, and thus management of biodiversity, at a landscape scale.
Overall, the percentage of rockiness is an important driver of the variation observed here for species richness across all studied taxa. This result was true whether using either continuous data or our experimental categories. In fact, using a specific delineation of higher or lower than 10% rockiness, it was sites with <10% rockiness where all taxa had lower species richness. Moreover, it is clear that plant and grasshopper species richness were also influenced by elevation. We could thus infer that rockiness and elevation are important variables in delineating biodiversity hotspots for plant and grasshoppers. However, species richness alone is a poor indicator of biodiversity as a whole (Purvis & Hector 2000). For example, if one area had ten species and another had twenty, by using only species richness one might argue that the area with twenty species is more important to conservation. However, if the ten species found in the other site were significantly dissimilar in composition to the other area, both areas are indeed important for biodiversity conservation. In that sense, we also measured similarity/dissimilarity in species composition and whether this difference could be a function of the rockiness and/or elevation mesofilters, and what this would mean for biodiversity planning. Both flora and grasshoppers showed significantly different species composition for elevation, but not rockiness. Specifically, for both taxa, it was the combination of >1280 m a.s.l. and <10% habitat rockiness that influenced this observed assemblage difference. Essentially, for flora and grasshoppers at a landscape scale, we can more readily predict biodiversity ‘microhotspots’ (Grant & Samways 2011) as the rockiness mesofilter was strong enough to delineate biodiversity hotspots across two taxa. Furthermore, these results also emphasize the significance of both rockiness and elevation as mesofilters for delineating areas of conservation concern for plants and grasshoppers within this montane landscape.
The real question underlying these results is why these taxa would respond to the rocky mesofilter. Within grasslands, variable levels of fire disturbance are known to structure plant communities (Bond & Keeley 2005). Rocks within a landscape are implicated in lessening the severity of fires and are thus creating refugia for certain fire-sensitive species (Signell & Abrams 2006). Furthermore, rocks may also provide barriers against ground-dwelling herbivores that eat bulbous plants, again promoting the longevity of certain plants in rocky landscapes (Thomson et al. 1996). In grasshoppers, rocks are important structures that aid in thermoregulatory processes (Chappell 1983). Essentially, processes such as fire, predation and thermoregulation, which occur across many ecosystems, could all be seen as confounding variables in the response of taxa to rockiness. However, as this was not explicitly tested here, it remains to be fully explored for this grassland landscape.
Interestingly, in this montane landscape, elevation had no significant influence on butterfly species richness. Grill et al. (2005) also found moderate elevation differences to have no relationship with butterfly species richness and suggested that increased butterfly richness is in response to variation in favourable floral composition and structure. In contrast, variation in butterfly species richness has been shown to be a function of elevation and topographical heterogeneity (Mac Nally et al. 2003; Gutiérrez Illán, Gutiérrez & Wilson 2010). Overall, it seems that the factors influencing butterfly species richness might be a complex interaction between land cover heterogeneity, climate and topography (Kerr, Southwood & Cihlar 2001; Gutiérrez Illán, Gutiérrez & Wilson 2010). This means that diversity in land cover (measured at the large spatial scale) influences species composition in space, owing to different species inherently being associated with specific conditions (Fleishman et al. 2001). This would then ultimately explain the variation in species richness when measured at a small scale (Kerr, Southwood & Cihlar 2001). Consequently, butterfly species richness is a weak measure for delineating biodiversity hotspots at a small spatial scale, owing to high species turnover across a heterogeneous landscape.
The result from the butterfly permanova analysis supports the view that species richness alone is not an accurate indicator of biodiversity as a whole when measured at a small spatial scale. Percentage rockiness showed a strong influence in structuring dissimilar butterfly assemblages across this space. Thus, butterfly biodiversity microhotspots could not be predicted using the mesofilter. Nevertheless, this approach predicted whether a certain butterfly species is present or not. In other words, a certain assemblage of butterflies would be strongly associated with rocks, while another assemblage would be absent from such areas. The reason for this behaviour in butterflies remains to be fully explored. Still, this result has important conservation planning implications at the spatial scale of the landscape, as changes in species composition for butterflies are strongly influenced by rockiness (see Hewitt et al. 2005). This then enables a planning approach where certain landscape features and characteristics, as preferred by different taxa, could be incorporated into the systematic conservation planning process (Margules & Pressey 2000; Lindenmayer et al. 2008).
Subsequently, the biotic surrogacy issues, as raised by Lindenmayer et al. (2002) and Ferrier (2002), could also be addressed through using this rocky mesofilter. Here, the focus was on using abiotic surrogates. Lindenmayer et al. (2002) argued the probable failure of a focal species approach towards surrogacy, as habitat conditions are mostly variable, and therefore, species-specific requirements may also vary. Here, we kept the focal mesofilter constant. When more than one taxon is significantly associated with this mesofilter across space, whether through species richness or composition, as we show here, conservation planners can be more precise in knowing that species-specific requirements are kept constant across an area.
A further point is the importance of developing conservation planning tools, such as surrogates, which are likely to persist across different management regimes or environmental conditions (Hunter, Jacobsen & Webb 1988; Sarkar et al. 2006). In other words, surrogates need to be robust and designed so that they are consistently associated with their target species or taxa irrespective of habitat conditions due to varied management (e.g. between protected areas and unprotected remnant patches). Armstrong, van Hensbergen & Geertsema (1994), studying natural habitats, showed that plant species richness within montane grasslands in South Africa was higher in rocky areas. We have also provided significant evidence for this also being the case in semi-natural montane grassland remnants. In essence, the mesofilter concept, as proposed through this rockiness proxy, fits this recommendation for more accurate surrogates (Sarkar et al. 2006). Moreover, rocks are physical ecosystem features that persist over long periods, despite climate change, again emphasizing the mesofilter concept as a novel complementary approach to modern conservation planning (sensu Hunter, Jacobsen & Webb 1988). This highlights the value of a rockiness mesofilter as a conservation tool for this critically endangered habitat type in South Africa and is likely to have a similar value if explored elsewhere.
An important question remaining is whether abiotic factors are generally important to conservation. The mesofilters of rockiness and elevation studied here suggest that it is, but are not the ‘be all and end all’ for conservation planning, as many other features might also exist within a landscape, which would be as valuable to take into account. For example, different soil types were shown to be an important abiotic variable to take into account for conservation planning in prairie ecosystems in the United States (Wilsey, Martin & Polley 2005). Similarly, logs in Yellow Box–Red Gum grassy woodlands in Australia were shown to have high beetle diversity, which was particularly important towards conservation planning for this taxon (Barton et al. 2009). The importance of abiotic variables in an aquatic environment has also been reported, where piles of shell debris can significantly enhance diversity (Hewitt et al. 2005). Soil type, logs and shell debris are therefore mesofilters within their respective landscapes. Essentially, any ecosystem can be thought of theoretically having many attributes or features that would be of conservation interest, and mesofilters are therefore a way of expressing this attribute to be used in wildlife conservation evaluation (Usher 1986). A particular mesofilter we delineate is therefore an important departure point from which we start conservation planning within a landscape in a rapidly changing environment.
There is an increasing need to understand the determinants of observed spatial heterogeneity in species richness and composition (whether at a large or small spatial scales), as this will greatly optimize conservation planning for both biodiversity maintenance and the movement of species under a changing climate (Gaston 2000). This study presents a mesofilter approach, which adds to our current understanding of species distribution pertaining to certain landscape elements across a small spatial scale. Ultimately, the novelty arose by using an abiotic indicator approach, based on landscape elements that are easy to quantify and map and which are associated with multiple taxa. This would ease land-use decision making in similar areas where species inventories are currently lacking and development is taking place rapidly (Carroll 1998; Fleishman et al. 2001; Mac Nally et al. 2003). We strongly argue the value and relevance of this mesofilter operational scale to be used alongside the currently implemented conservation planning operational scales such as fine- and coarse-filter approaches (sensu Hunter 2005; Schulte et al. 2006).