Journal of Applied Ecology 2010, 47, 701–709
doi: 10.1111/j.1365-2664.2010.01816.x
Contrasting spatial pattern and pattern-forming processes in natural vs. restored shrublands Ben P. Miller1,2,3*, George L. W. Perry4, Neal J. Enright5,2 and Byron B. Lamont2 1
Botanic Gardens and Parks Authority, West Perth, WA 6005, Australia; 2Centre for Ecosystem Diversity and Dynamics, Department of Environment and Agriculture, Curtin University, Perth, WA 6845, Australia; 3School of Plant Biology, University of Western Australia, Nedlands, WA 6009, Australia; 4School of Environment, The University of Auckland, Private Bag 92019, Auckland, New Zealand; and 5School of Environmental Sciences, Murdoch University, Murdoch, WA 6150 Australia
Summary 1. Variation in the spatial arrangement of plants can lead to differences in the rates and trajectories of change in the composition, structure and function of plant populations and communities. While the ecological significance of spatial interactions has proven difficult to unravel in natural communities, restored ecosystems provide a simplified context for studying this problem. Examining spatial pattern in restoration is therefore useful for both restoration management and ecological theory. 2. We compared 87 plant species patterns at two sites restored after sand-mining in southwestern Australia with 233 patterns at four nearby, species-rich, natural shrubland communities, examining a total of 193 species. Spatial tests were performed for all stems, and for all species with ‡20 individuals in each site, using Ripley’s K and pair correlation functions with three null spatial models to characterize observed patterns. 3. The processes and time-scales creating spatial pattern differ between restored and natural vegetation, so we hypothesized that, relative to natural sites, restored sites would have fewer gaps and less aggregation among all stems, fewer species with aggregated patterns, and a lower ratio of cluster- to gradient-aggregated patterns. We also hypothesized differences in pattern between species groups defined by seed size, dispersal, seed bank and regeneration traits, and that these would vary between natural and restored sites. 4. However, relative to natural sites, restored vegetation had lower stem densities, disproportionately higher gap cover and similar patch attributes and mean nearest-neighbour distances. Most species were aggregated, and the fraction of aggregated species in restored sites fell within the range of natural sites. The frequency of aggregation varied little between species groups in restored sites, but strongly in natural sites, being rarer among serotinous and resprouting species. 5. Synthesis and applications. Our spatial results show an important role for gap-creating processes in shrubland restoration development; that particular species groups may be susceptible to disproportionate decline under restoration; and how pattern-creating processes in natural vegetation vary with plant traits. Recommendations for restoration practice include compensatory supplementation of these species groups, research into the development of gaps and awareness of the potential for spatial pattern to influence outcomes. Key-words: gap formation, pair correlation function, plant communities, point pattern analysis, post-mining restoration, Ripley’s K-function, Western Australia
Introduction Although viewed as an ideal context for testing ecological theory (Palmer, Ambrose & Poff 1997; Lake, Bond & Reich 2007), post-disturbance restoration projects are rarely used as *Correspondence author. E-mail:
[email protected]
such. As the ecological processes that create spatial pattern in natural plant communities (e.g. dispersal, competition) are fundamental, analysis of pattern should enable the determination of the relative contributions of these key processes within communities (Mcintire & Fajardo 2009). However, while the multiple processes leading to spatial pattern are not easily disentangled in natural communities (Moravie & Robert 2003;
2010 The Authors. Journal compilation 2010 British Ecological Society
702 B. P. Miller et al. Perry et al. 2008), restored plant communities are simplified systems within which this question may be approached more effectively. Spatial pattern in turn also influences ecological processes in plant communities, and hence their developmental trajectories, making it a potentially important field of investigation within restoration ecology. In natural systems, processes operating across different life stages and spatio-temporal scales, contribute singly, sequentially or simultaneously to the creation of spatial pattern. This complexity confounds demonstration that particular patterns arise from particular processes or combinations of processes (Cale, Henebry & Yeakley 1989). It has been suggested that limited dispersal capability, heterogeneity in resource availability and variable disturbance history (e.g. herbivory, gap-phase dynamics) contribute to aggregated patterns, while biotic interactions (e.g. facilitation, competition, host-specific predators) enhance or suppress them (e.g. Harper, Williams & Sagar 1965; Janzen 1970; Lamont, Witkowski & Enright 1993; Thomson et al. 1996; Barot, Gignoux & Menaut 1999; Seidler & Plotkin 2006). Studies in species-rich natural systems show that most species have aggregated patterns (Condit et al. 2000; Upadhaya et al. 2003; Perry et al. 2008). Patterns indistinguishable from random are also common, whereas regular (over-dispersed) patterns are rare. Patterns exist among all individuals in a community irrespective of species, as well as among individuals within species, and these need not be congruent. The presence of gaps in semi-arid shrubland communities are well-documented examples of pattern among all individuals, and may arise from feedback interactions among biotic and abiotic processes (Dunkerley & Brown 1995; Ludwig & Tongway 1995; Puigdefa´bregas 2008; Rietkerk & van de Koppel 2008). Within-generation abiotic interactions may contribute to pattern formation in restored communities, but the shorter development times and prescribed initial conditions of these communities should enable the roles of biotic, abiotic and dispersal processes in pattern formation to be distinguished more readily. Ecological processes create spatial pattern, but conversely, the spatial arrangement of individuals affects almost all biophysical processes operating in plant communities, e.g. herbivory, pollination, seed dispersal ⁄ predation, fuel structure, fire behaviour and surface movement of water, wind, sediment, litter, nutrients and propagules (Kalabokidis & Omi 1992; Callaway, DeLuca & Belliveau 1999; Jones et al. 2003; Bullock & Moy 2004; Bell, Karron & Mitchell 2005). Theoretically, the most significant consequence of spatial pattern is its role in mediating competitive and facilitative interactions between individuals as these determine population dynamics, species coexistence and community diversity (Shmida & Ellner 1984; Pacala & Silander 1987; Silvertown et al. 1992; Rees, Grubb & Kelly 1996; Brooker & Callaghan 1998; Purves & Law 2002; Turnbull et al. 2007). This view is confirmed in controlled experiments (Eccles et al. 2001; Stoll & Prati 2001; Maestre et al. 2003; Fehmi, Rice & Laca 2004; Monzeglio & Stoll 2005; Seabloom et al. 2005; Hart & Marshall 2009). However, field studies in natural species-rich plant communities are more equivocal concerning the importance of species interactions in
determining structure (Lieberman & Lieberman 2007; Wiegand, Gunatilleke & Gunatilleke 2007; Perry et al. 2009). ‘Ecological’ restoration aims to produce resilient communities that conserve biodiversity and allow natural ecological processes to operate (SERI 2004). While the spatial pattern of individuals is rarely counted among the important attributes of restored communities (SERI 2004), a small number of studies have begun to explore its significance. Bartha et al. (2004) review the increasing consideration given to pattern in the practice of ecological restoration, noting that like most ecological research, ecological restoration research is usually designed to avoid spatial dependencies. Valladares & Gianoli (2007) show how recent understanding of spatial interactions, both between species and with environmental heterogeneity, could guide changes in restoration practices. In a unique empirical study, Blignaut & Milton (2005) aimed to increase restoration success by manipulating spatial structure of three succulent Karoo species but with ambiguous results. However, empirical studies of pattern in the abiotic attributes of restored communities, either affecting, or as evidence of, restoration trajectories are more common (Dhillion 1999; Lane & BassiriRad 2005). It seems unlikely that restored communities would (initially, at least) display the same spatial arrangement of species and individuals as natural communities since restoration procedures influence initial pattern, and ecological processes may operate at different scales and intensity. However, to our knowledge, no assessment of the fine-scale spatial pattern resulting from restoration techniques has been published. Spatial pattern may be created in various ways in post-mining restoration: (i) restored environments may be spatially organized as a result of restoration treatments (e.g. deep ripping); (ii) respread topsoils are likely to have their soil-seedbanks homogenized; (iii) seeds for some species are planted; (iv) or broadcast; (v) nursery-raised seedlings of others are spot planted; and (vi) added mulched vegetation may provide additional propagules (Roche, Koch & Dixon 1997; Bakler 2000; Rokich et al. 2000). Subsurface soil physical and chemical properties may also be more uniform than in natural ecosystems due to the mixing of layers during processing, with subsequent effects on water relations of plants (Enright & Lamont 1992). Such processes are likely to result in uniform-to-random patterns among species in restoration at establishment (and through to maturity if post-establishment processes are not spatially structured) rather than the aggregated-to-random patterns common in natural ecosystems. While the consequences of these differences have not been investigated before, they may be critical to the successful development of restored ecosystems with the functional attributes of natural vegetation. We have previously described spatial pattern in four natural, species-rich Mediterranean-type shrubland communities on the Eneabba sandplain in southwestern Australia (Perry et al. 2008, 2009). Here, we compare spatial pattern both among all stems and within species, in these four natural shrubland sites with the spatial pattern in two examples of vegetation restored following sand-mining in the same system. We assess the pattern of all sufficiently abundant species, classifying each into one of four spatial patterns: random, first-order aggregation
2010 The Authors. Journal compilation 2010 British Ecological Society, Journal of Applied Ecology, 47, 701–709
Spatial pattern in restored shrublands 703 (reflecting changes in density across sites; i.e. gradients), second-order aggregation (reflecting interactions between plants; i.e. clusters) and regular. We test the following hypotheses: 1. Restored vegetation has less intense aggregation of stems and less bare area than natural vegetation, as pattern in natural vegetation arises from multi-generational feedback processes that are not fully developed in restored vegetation. 2. A smaller fraction of species has aggregated patterns in restored sites than in natural sites due to differences in pattern-creating processes. 3. Species patterns in restored sites have a higher ratio of first- to second-order aggregated patterns as they are more likely to result from a variable density of propagules than interactions between individuals. 4. Groups of species with contrasting life-history traits differ in spatial pattern within natural and restored vegetation as the processes leading to their formation differ. For example pattern in restoration among soil-seedbank species and small-seeded species may arise from random processes (topsoil spreading, seed broadcasting) but in large-seeded, and canopy-stored seedbank (serotinous) species, from regular processes (e.g. direct planting). Similarly, species with different dispersal syndromes may differ in natural but not restored sites. Resprouting and non-sprouting species are likely to differ in natural (with less aggregation in resprouters) but not restored sites. Establishment of resprouters in natural vegetation occurs over multiple (fire) events but non-sprouters establish as a single cohort after fire, while in restoration, establishment is a single event for both. Analyses of the effect of dispersal mode on plant spatial pattern are limited to forests and results are contradictory (Armesto, Mitchell & Villagran 1986; Seidler & Plotkin 2006), and while resprouting ability has been related to differences in spatial pattern in a model-based study (Pausas & Lloret 2007), field evidence for differences in pattern among species with varying fire response, seedbank storage strategy or seed size, to our knowledge, has not been previously described.
Materials and methods In a previous study, we mapped four natural shrubland communities, characteristic of different substrate types occurring near Eneabba (29 51¢ S 115 16¢ E; Fig. 1), 275 km north of Perth, Australia (Perry et al. 2008, 2009). Here, we add two examples of species-rich, maturephase shrubland vegetation that was restored using a mix of local species in 1981 and 1989, respectively (‘R1981’ and ‘R1989’) on heavymineral sand-mine tailings. The natural sites were located close to the mine on deep and shallow sands (dune crest and swale substrates) and on shallow sands overlying laterite and limestone (Fig. 1). At the time of mapping, all natural sites supported mature vegetation last burnt ‡15 years previously. The preponderance of second-order aggregation among species in these sites (Perry et al. 2008) suggests that local dispersal – potentially interacting with fine-scale environmental variation (e.g. microtopography, patchiness of fire: Lamont et al. 1993; Enright et al. 2007; but not soil resources, Perry et al. 2008) – is the principal pattern-forming process. Descriptions of the sandplain ecosystem and the restored sites are provided in Appendix S1 (Supporting information) and elsewhere (Enright et al. 2007; Perry et al. 2008;
Fig. 1. Study location, showing natural reference and restored sites (closed ⁄ open squares), Eneabba sandplain (light grey) and mine footprint (dark grey).
Herath et al. 2009). Detailed descriptions of the restoration methods and their likely implications for spatial pattern formation are also given in Appendix S1 (Supporting information). Restored-site soils are no more fertile than those of natural sites (Herath et al. 2009; Table 1) but are almost twice as hard, potentially contributing to the reduced seedling establishment rates observed for some species (Enright & Lamont 1992; Herath et al. 2009). Excluding dormant geophytes and plants 2 mg), dispersal (wind, ant, vertebrate, unassisted) and fireresponse (non-sprouting, resprouting) traits. These traits were determined from our own observations (e.g. Enright et al. 2007) and from the literature (Berg 1975). We measured seed weights for a subset of 75 species and used these to guide classification in relation to the 2 mg cut-off for the remainder. We modelled a binary pattern variable (aggregated ⁄ random) using GLM with a binomial error structure in R (R Development Core Team 2008) with log-transformed abundance (of individuals contributing to each pattern), site-type and the four categorical trait variables (in turn) as predictors. Spatial analyses were performed using SpPack (Perry 2004) and the R libraries Splancs (Rowlingson & Diggle 1993) and Spatstat (Baddeley & Turner 2005). Bartlett tests were employed to confirm homogeneity of variances and data were log-transformed to ensure normality for anova of patch size parameters.
1·0 0
5 Distance (m)
10
0
5 Distance (m)
10
Fig. 2. K ) [L(r)] function and PCF [g(r)] analysis of all stems in restored vegetation plots. CSR confidence envelopes (grey lines) estimated at a = 0Æ01. See Perry et al. (2008) for natural site comparison.
that cluster models fit best in all sites. These cluster models suggest that patches were smaller at the Limestone and R1981 sites, of intermediate size at R1989, Crest and Swale (averaging 10–15 plants per patch and 1Æ5–1Æ7 m radii) and larger at the Laterite site (Table 1). Mean nearest-neighbour distances within restored sites were similar to those in the two less-dense natural sites, and 1Æ5 times larger than the two densest sites (Table 1). Percentage bare ground varied greatly between natural and restored sites. In restored sites, 6–7% of 1 m grid points was >0Æ56 m from a mapped stem (corresponding to a circular bare area of >1 m2) while natural sites had 0Æ01; Fig. 5). Restored sites were similar to the most strongly aggregated natural site, in terms of their distribution of patterns with 86% of species aggregated in both R1989 and Limestone, and 82% in R1981. Overall, of the natural site species’ patterns that were analysed, 63% were aggregated, significantly fewer than in restored sites (v2 = 10Æ11, d.f. = 1, P = 0Æ0015). Within sites, 78–92% of species with aggregated patterns were best described by cluster rather than by gradient models (Fig. 5), and this trend was independent of site-type (v2 = 6Æ52; d.f. = 5, P = 0Æ26). Mean patch radius (of patterns described by cluster models) did not vary significantly between sites (log-transformed, F5 = 0Æ717, P = 0Æ398; anova), with restored sites fitting between the extremes of Crest (3Æ4 m) and Limestone (2Æ5 m; Fig. 4a). While between-site differences in mean plants per patch were significant (log-transformed, P < 0Æ001, F5 = 11Æ73), restored sites again fell midway between the extremes of Laterite and Swale (Fig. 4).
PATTERN IN RELATION TO SPECIES TRAITS
Swale
Laterite
Crest
Limestone
R89
R81
Swale
Laterite
Crest
Limestone
R89
R81
Fig. 4. Pattern descriptors in natural and restored sites. Mean radius and plants per patch for all clustered species. Bare ground shows observed (bars), and simulated (crosses), percentage of a grid of circles >1 m2 that include no stems. Stem density includes site total and (with standard error bars) mean of analysed species. PATTERN WITHIN SPECIES
Aggregated patterns were dominant in all but the Swale site (where 44% of species were aggregated), and no regular
The likelihood that a species was aggregated was influenced by the abundance of individuals contributing to its pattern (Table 2). GLM analyses of pattern (aggregated vs. random) varying with species trait and site-type (natural vs. restored) among species with ‡20 individuals and including abundance as a covariate found no significant role for dispersal mode in differentiating pattern. Nonetheless, species with no apparent dispersal adaptation were more frequently aggregated than ant- or wind-dispersed species, and less aggregated than vertebrate-dispersed species in natural sites. This pattern was reversed in restored sites (Table 2). The biggest difference between site-types was found among wind-dispersed species, of which 58% were aggregated in natural sites, compared with 95% in restored sites. Species with no dispersal adaptation were almost equally aggregated in natural and restored sites (Table 2). There was also no significant effect of seed size on
2010 The Authors. Journal compilation 2010 British Ecological Society, Journal of Applied Ecology, 47, 701–709
706 B. P. Miller et al. Table 2. (a) Percentage of species patterns aggregated in natural and restored sites by species traits (n, number of patterns compared). (b) Significance of GLM terms: models of pattern type (aggregated or not) varying with site-type (ST: natural vs. restored) and trait (Tr), incorporating abundance (Ab) as a covariate (a)
Dispersal Nil Ant Wind Vertebrate Seed size 2 mg Fire response Non-sprouting Resprouting Seedbank Soil Canopy Overall
Natural
Restored
n
70 53 58 93
75 86 95 67
91, 57, 71, 14,
72 57
76 88
98, 38 135, 49
88 55
85 81
58, 33 175, 54
69 48 63
79 92 82
175, 61 58, 26 233, 87
40 22 22 3
(b) Trait Dispersal Seed size Fire response Seedbank Overall
Tr
* *
ST
*
Tr:ST
*** *
Ab *** *** *** *** ***
Ab:Tr
Ab:ST * *
** * *
***P < 0Æ001; **P < 0Æ01; *P < 0Æ05.
pattern, although large-seeded species were much less frequently aggregated (i) in natural vegetation than in restoration, and (ii) than small-seeded species. Fire response and seedbank strategy influenced spatial pattern significantly, and this effect varied between site-types. The fraction of soil-seedbank and non-sprouting species that were aggregated did not differ between restored and natural sites. But differences were large within serotinous and resprouting species, both of which were more aggregated in restored than in natural sites. In natural sites, 55% of resprouter patterns and 88% of non-sprouters were aggregated, compared with 81% and 85% (respectively) of these species groups in restoration (Table 2). There were no significant differences in patch size or plants per patch between trait groups, nor interactions of these with site-type (two-way anova of patterns best fit by cluster models, P > 0Æ05; data not shown).
Discussion VEGETATION SPATIAL STRUCTURE
We hypothesized that natural plant communities would contain more gaps and more intense clustering of patches than
restored sites due to the contrasting processes leading to their establishment. Further, because structure in vegetation develops from multi-generation feedback processes, this structure may not yet have had time to develop in restored vegetation. Instead, the range of variation in patterns in the distribution of stems in natural sites encompassed that of restored sites, and bare ground was 5–10 times more abundant in restored than natural communities. Total stem density was 25–50% lower in restored sites, so differences in bare area would be expected by random pattern and varying density alone. However, simulations show that gap incidence in restored sites is higher even after taking this into account. Finally, and despite differences in density, mean nearest-neighbour distances were similar among site-types. These results suggest that spatial pattern does not vary between natural and restored sites – in patches where vegetation does occur – but that sites do differ as patches are separated by more and ⁄ or larger gaps in restored sites. Feedbacks underpin the processes believed to contribute to the creation of bare patches in natural shrublands – competition and facilitation, water runoff and infiltration, surface erosion and nutrient cycling (Dunkerley & Brown 1995; Rietkerk & van de Koppel 2008). Maestre et al. (2003) found that finescale heterogeneity in compaction, bare soil and sand content led to spatially variable mortality among planted Pistacia lentiscus L. seedlings – and hence gap initiation – over a few years in a degraded semi-arid ecosystem. Variation in surface and soil attributes following restoration, such as hydrophobicity and infiltration rates, may additionally interact to create variation in plant survival. This could allow space for the related fluvial and aeolian processes believed to create gaps in natural vegetation (Ludwig & Tongway 1995). The restoration sites we studied were 16 and 24 years old, so if post-establishment spatial processes were important, they would have had just this time to develop. However, this rapid formation of gaps in restored vegetation suggests that shrubland ‘gap processes’ may be more dynamic than previously expected. Interestingly, Seabloom et al. (2005) also remark on the surprising rapidity of pattern entrenchment in grasslands system they studied.
PATTERN AMONG SPECIES
Our second hypothesis was that, due to differences in the processes leading to the establishment of plants in each community type, more species would show random or regular patterns in restored than in natural vegetation. Instead, we found fewer random (and no regular) species patterns in restored sites. This probably results from the processes described in relation to species traits described below. We also predicted that, relative to natural sites, more aggregated species would fit gradient rather than cluster models in restored sites. However, gradient models were uncommon at all sites, and the restored sites had respectively the highest and second lowest fractions of these. Our plots were 30 · 30 m or 40 · 40 m, within which pattern was analysed to 10 m, so it is possible that aggregated patterns may comprise gradients of clusters at scales coarser than our analysis (Perry et al. 2008).
2010 The Authors. Journal compilation 2010 British Ecological Society, Journal of Applied Ecology, 47, 701–709
Spatial pattern in restored shrublands 707
PATTERN IN RELATION TO SPECIES TRAITS
Restoration of small-seeded and soil-seedbank species is largely via the presumed random, but perhaps inhomogeneous, procedures of topsoil spreading and broadcast seeding, while restoration of large-seeded or serotinous species is more likely to be via the non-random process of direct planting of seed or nursery stock. Thus, we expected differences in pattern between these species groups in restored but not in natural vegetation. In contrast, we expected differences in natural, but not in restored, sites among dispersal and fire response adaptations, as these are likely to influence spatial processes in natural but not in restored sites. As expected, small-seeded and soil-stored species were less frequently aggregated than larger-seeded and canopy-stored species in restoration, but these differences were not significant. Also non-significant, but trending as expected, differences between species with different dispersal adaptations were smaller in restored than natural sites. More frequent aggregation in natural sites among species with no dispersal adaptation was expected, but among vertebrate-dispersed species was not. This latter may reflect the relatively low reliability of this vector, and (if general) suggests that the benefit of this adaptation is increased maximum, rather than mean, dispersal distance. Differences in the fraction of aggregated patterns within fire response and seedbank strategies were significant, as were their interactions with site-type – with aggregation least frequent among canopy-stored and resprouting species in natural sites. In natural vegetation, differences in pattern between canopy and soil-seedbank species may be due to differences in dispersal timing and cues for germination (Enright et al. 2007). Canopystored seedbanks are released and dispersed in a relatively short time post-fire, while soil-stored seedbanks are cued to germinate by fire, but accumulate from many separate events in the years preceding fire, and are stored at different depths and in a range of spatial patterns (Thompson 1986; Bertiller 1998). Soil-stored seeds respond to the heat of fire and ⁄ or chemicals in smoke (Dixon, Roche & Pate 1995). Vegetation pattern leads to variation in shrubland fuels and fire behaviour, so heat and smoke signals also vary – from lethal to optimal to absent – spatially and with soil depth. This variation in germination cues may lead to increased aggregation among soilstored species. Demographically, natural resprouter populations differ from restored populations in three ways: (i) populations are multi-aged, having established in multiple post-fire recruitment events, while non-sprouters everywhere, and resprouters in restored sites, established in a single recruitment pulse dating to the last fire (or rehabilitation) event. Thus, individuals in natural populations are likely to be (ii) older and (iii) have established in the presence of older conspecific individuals. In natural sites, non-sprouters also establish in the presence of older resprouters, so this last point alone is unlikely to account for differences in pattern. These differences suggest several hypotheses relating to competition and dispersal. First, if competitive interactions sufficient to lead to plant death are variable, episodic or cumulative through time, then greater
longevity may leave resprouters more exposed to their impacts. If these interactions are more likely between near-neighbours, then the greater likelihood of death of resprouter individuals growing in (possibly dispersal-created) aggregations would lead to erosion of these patterns. If abiotic processes impacting on spatial pattern vary temporally over longer time-scales (e.g. extreme droughts; Miriti 2007), then pattern observed among resprouters might reflect events preceding the last fire. Finally, observed pattern may be due to limited dispersal, but, as resprouter populations are multi-aged, and require several firecycles to mature (so cannot act as foci for clusters) they are less likely to form aggregated patterns to start with.
IMPLICATIONS FOR RESTORATION
Specific values for stem density are a common target for determining restoration success, and these are typically indexed to natural vegetation analogues (Grant & Loneragan 2003). Restored vegetation surveyed here, 16 and 24 years after rehabilitation and subject to density-related completion criteria (EMRC 1996), had stem densities 50–75% lower than the surveyed natural sites, indicating restoration failure based on this criterion. Given that total stem density and gap area differed consistently among sites, but that spatial attributes of non-gap vegetation did not, it seems that this failure is likely to be associated with gap-forming processes. Investigation of which processes are responsible may be a new and important approach to understanding restoration failure. Differences in initial pattern may have a significant influence on the trajectory taken by plant communities (Silvertown et al. 1992). We suggest that the less frequent aggregation of serotinous and resprouting species relative to soil-stored and nonsprouting species, respectively, in natural vegetation result from reduced interactions with environmental heterogeneity and ⁄ or longer exposure to competition between near-neighbours. That these same processes do not apply in the early stages of restoration means that these contrasting species groups share the same (higher) frequency of aggregation in restoration – and this leads to the observed higher overall frequency of aggregation among all species in restored vegetation. The likely impact of increased aggregation of serotinous and resprouting species in restored sites on restoration development is unclear, although elevated mortality among some of these species may be anticipated. Specific management recommendations arising from this work would therefore include supplementation of these species groups in restoration, together with research into processes associated with gap development. As the generality of these results outside of Mediterranean-type shrubland communities is unknown, further research into the role of spatial pattern in restoration outcomes is also recommended.
Acknowledgements This project was funded by a Linkage grant from the Australian Research Council, Iluka Resources Ltd. (WA) and the Minerals & Energy Research Institute of Western Australia to B.B.L. and N.J.E. We thank the many people
2010 The Authors. Journal compilation 2010 British Ecological Society, Journal of Applied Ecology, 47, 701–709
708 B. P. Miller et al. who helped in the mapping of sites, Iluka for logistic support, Joe Fontaine for technical advice and two anonymous reviewers for their helpful comments.
References Armesto, J.J., Mitchell, J.D. & Villagran, C. (1986) A comparison of spatial patterns of trees in some tropical and temperate forests. Biotropica, 18, 1–11. Baddeley, A. & Turner, R. (2005) Spatstat: an R package for analyzing spatial point patterns. Journal of Statistical Software, 12. Available at: http:// www.jstatsoft.org/ Bakler, J.P. (2000) Environmental heterogeneity: effects on plants in restoration ecology. The Ecological Consequences of Environmental Heterogeneity (eds M.J. Hutchings, E.A. John & A.J.A. Stewart), pp. 379–400. Blackwell, Oxford. Barot, S., Gignoux, J. & Menaut, JC. (1999) Demography of a savanna palm tree: predictions from comprehensive spatial pattern analyses. Ecology, 80, 1987–2005. Bartha, S., Campetella, G., Canullo, R., Bo´dis, J. & Mucina, L. (2004) On the importance of fine-scale spatial complexity in vegetation restoration studies. International Journal of Ecology and Environmental Sciences, 30, 101–116. Bell, J.M., Karron, J.D. & Mitchell, R.J. (2005) Interspecific competition for pollination lowers seed production and outcrossing in Mimulus ringens. Ecology, 86, 762–771. Berg, R.Y. (1975) Myrmecochorous plants in Australia and their dispersal by ants. Australian Journal of Botany, 23, 475–508. Bertiller, M.B. (1998) Spatial patterns of the germinable soil seed bank in northern Patagonia. Seed Science Research, 8, 39–46. Blignaut, A. & Milton, S. J. (2005) Effects of multispecies clumping on survival of three succulent plant species translocated onto mine spoil in the succulent Karoo Desert, South Africa. Restoration Ecology, 13, 15–19. Brooker, R.W. & Callaghan, T.V. (1998) The balance between positive and negative plant interactions and its relationship to environmental gradients: a model. Oikos, 81, 196–207. Bullock, J.M. & Moy, I.L. (2004) Plants as seed traps: inter-specific interference with dispersal. Acta Oecologica, 25, 35–41. Cale, W.G., Henebry, G.M. & Yeakley, J.A. (1989) Inferring process from pattern in natural communities. BioScience, 39, 600–605. Callaway, R.M., DeLuca, T.H. & Belliveau, W.M. (1999) Biological-control herbivores may increase competitive ability of the noxious weed Centaurea maculosa. Ecology, 80, 1196–1201. Condit, R., Ashton, P.S., Baker, P., Bunyavejchewin, S., Gunatilleke, S., Gunatilleke, N., Hubbell, S.P., Foster, R.B., Itoh, A., LaFrankie, J.V., Seng Lee, H., Losos, E., Manokaran, N., Sukumar, R. & Yamakura, T. (2000) Spatial patterns in the distribution of tropical tree species. Science, 288, 1414–1418. Dhillion, S.S. (1999) Environmental heterogeneity, animal disturbances, microsite characteristics, and seedling establishment in a Quercus havardii community. Restoration Ecology, 7, 399–406. Diggle, P.J. (2003) Statistical Analysis of Spatial Point Patterns, 2nd edn. Arnold, London. Dixon, P.M. (2002) Nearest neighbour methods. Encyclopaedia of Environmetrics (eds A.H. El-Shaarawi & W.W. Piegorsch), pp. 1370–1383. John Wiley & Sons, New York. Dixon, K.W., Roche, S. & Pate, J.S. (1995) The promotive effect of smoke derived from burnt native vegetation on seed germination of Western Australian plants. Oecologia, 101, 185–192. Dunkerley, D.L. & Brown, K.J. (1995) Runoff and runon areas in a patterned chenopod shrubland, arid western New South Wales, Australia: characteristics and origin. Journal of Arid Environments, 30, 41–55. Eccles, N.S., Lamont, B.B., Esler, K.J. & Lamont, H.C. (2001) Relative performance of clumped vs. experimentally isolated plants in a South African winter-rainfall desert community. Plant Ecology, 155, 219–227. EMRC (1996) A Review of RGC Mineral Sands Flora and Fauna Monitoring Programme at Eneabba. Unpublished report, RGC Mineral Sands, Eneabba, Western Australia. Enright, N.J. & Lamont, B.B. (1992) Survival, growth and water relations of Banksia seedlings in a sand mine rehabilitation site and adjacent scrub-heath sites. Journal of Applied Ecology, 29, 663–671. Enright, N.J., Mosner, E., Miller, B.P., Johnson, N. & Lamont, B.B. (2007) Patterns of soil versus canopy seed storage and plant species coexistence in species-rich shrublands of southwestern Australia. Ecology, 88, 229–2304. Fehmi, J.S., Rice, K.J. & Laca, E.A. (2004) Radial dispersion of neighbors and the small-scale competitive impact of two annual grasses on a native perennial grass. Restoration Ecology, 12, 63–69.
Goreaud, F. & Pe´lissier, R. (1999) On explicit formulas of edge effect correction for Ripley’s K-function. Journal of Vegetation Science, 10, 433–438. Grant, C.D. & Loneragan, W.A. (2003) Using dominance-diversity criteria curves to assess completion criteria after Bauxite mining rehabilitation in Western Australia. Restoration Ecology, 11, 103–109. Harper, J.L., Williams, J.T. & Sagar, G.R. (1965) The behaviour of seeds in soil: I. the heterogeneity of soil surfaces and its role in determining the establishment of plants from seed. Journal of Ecology, 53, 273–286. Hart, S.P. & Marshall, D.J. (2009) Spatial arrangement affects population dynamics and competition independent of community composition. Ecology, 90, 1485–1491. Herath, D., Lamont, B.B., Enright, N.J. & Miller, B.P. (2009) Comparison of postmine rehabilitated and natural shrubland communities in southwestern Australia. Restoration Ecology, 17, 563–749. Janzen, D.H. (1970) Herbivores and the number of tree species in tropical forests. American Naturalist, 104, 501–28. Jones, A.S., Lamont, B.B., Fairbanks, M.M. & Rafferty, C.M. (2003) Kangaroos avoid eating seedlings with or near others with volatile essential oils. Journal of Chemical Ecology, 29, 2621–2635. Kalabokidis, K.D. & Omi, P.N. (1992) Quadrat analysis of wildland fuel spatial variability. International Journal of Wildland Fire, 2, 145–152. Lake, P.S., Bond, N.R. & Reich, P. (2007) Linking ecological theory with stream restoration. Freshwater Biology, 52, 597–615. Lamont, B.B., Witkowski, E.T.F. & Enright, N.J. (1993) Postfire litter microsites – safe for seeds, unsafe for seedlings. Ecology, 74, 501–512. Lane, D.R. & BassiriRad, H. (2005) Diminishing spatial heterogeneity in soil organic matter across a prairie restoration chronosequence. Restoration Ecology, 13, 403–412. Lieberman, M. & Lieberman, D. (2007) Nearest-neighbor tree species combinations in tropical forest: the role of chance, and some consequences of high diversity. Oikos, 116, 377–386. Ludwig, J.A. & Tongway, D.J. (1995) Spatial organization of landscapes and its function in semi-arid woodlands, Australia. Landscape Ecology, 10, 51– 63. Maestre, F.T., Cortina, J., Bautista, S., Bellot, J. & Vallejo, R. (2003) Small-scale environmental heterogeneity and spatiotemporal dynamics of seedling establishment in a semiarid degraded ecosystem. Ecosystems, 6, 630–643. Mcintire, E.J.B. & Fajardo, A. (2009) Beyond description: the active and effective way to infer processes from spatial patterns. Ecology, 90, 46–56. Miriti, M.N. (2007) Twenty years of changes in spatial association and community structure among desert perennials. Ecology, 88, 1177–1190. Monzeglio, U. & Stoll, P. (2005) Spatial patterns and species performances in experimental plant communities. Oecologia, 145, 619–628. Moravie, M.A. & Robert, A. (2003) A model to assess relationships between forest dynamics and spatial structure. Journal of Vegetation Science, 14, 823– 834. Pacala, S.W. & Silander Jr, J.A. (1987) Neighborhood Interference among Velvet Leaf, Abutilon theophrasti, and Pigweed, Amaranthus retroflexus. Oikos, 48, 217–224. Palmer, M.A., Ambrose, R.F. & Poff, N.L. (1997) Ecological theory and community restoration ecology. Restoration Ecology, 5, 291–300. Pausas, J.G. & Lloret, F. (2007) Spatial and temporal patterns of plant functional types under simulated fire regimes. International Journal of Wildland Fire, 16, 484–492. Perry, G.L.W. (2004) SpPack: spatial point pattern analysis in Excel using Visual Basic for Applications (VBA). Environmental Modelling and Software, 19, 559–569. Perry, G.L.W., Miller, B.P. & Enright, N.J. (2006) A comparison of methods for the statistical analysis of spatial point patterns in plant ecology. Plant Ecology, 187, 59–82. Perry, G.L.W., Enright, N.J., Miller, B.P. & Lamont, B.B. (2008) Spatial patterns in species-rich Mediterranean-type shrublands of southwestern Australia. Journal of Vegetation Science, 19, 705–716. Perry, G.L.W., Enright, N.J., Miller, B.P. & Lamont, B.B. (2009) Nearestneighbour species interactions in species-rich shrublands: the roles of abundance, spatial patterns and resources. Oikos, 118, 161–174. Puigdefa´bregas, J. (2008) The role of vegetation patterns in structuring runoff and sediment fluxes in drylands. Earth Surface Processes and Landforms, 30, 133–147. Purves, D.W. & Law., R. (2002) Fine-scale spatial structure in a grassland community: quantifying the plant’s-eye view. Journal of Ecology, 90, 121–129. R Development Core Team (2008) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Available at: http://www.R-project.org.
2010 The Authors. Journal compilation 2010 British Ecological Society, Journal of Applied Ecology, 47, 701–709
Spatial pattern in restored shrublands 709 Rees, M., Grubb, P.J. & Kelly, D. (1996) Quantifying the impact of competition and spatial heterogeneity on the structure and dynamics of a four-species guild of winter annuals. American Naturalist, 147, 1–32. Rietkerk, M. & van de Koppel, J. (2008) Regular pattern formation in real ecosystems. Trends in Ecology and Evolution, 23, 169–175. Roche, S., Koch, J.M. & Dixon, K.W. (1997) Smoke enhanced seed germination for mine rehabilitation in the southwest of Western Australia. Restoration Ecology, 5, 191–203. Rokich, D.P., Dixon, K.W., Sivasithamparam, K. & Meney, K.A. (2000) Topsoil handling and storage effects on woodland restoration in Western Australia. Restoration Ecology, 8, 196–208. Rowlingson, B.S. & Diggle, P.J. (1993) Splancs: spatial point pattern analysis code in S-Plus. Computers & Geosciences, 19, 627–655. Seabloom, E.W., Bjørnstad, O.N., Bolker, B.N. & Reichman, O.J. (2005) Spatial signature of environmental heterogeneity, dispersal, and competition in successional grasslands. Ecological monographs, 75, 199–214. Seidler, T.G. & Plotkin, J.B. (2006) Seed dispersal and spatial pattern in tropical trees. PLoS Biology, 4, e344. SERI (Society for Ecological Restoration International Science & Policy Working Group) (2004) The SER International Primer on Ecological Restoration. SERI, Tucson.Available at: http://www.ser.org (accessed June 2006). Shmida, A. & Ellner, S. (1984) Coexistence of plant species with similar niches. Vegetatio, 58, 29–55. Silvertown, J., Holtier, S., Johnson, J. & Dale, P. (1992) Cellular automaton models of inter-specific competition for space. Journal of Ecology, 80, 527– 34. Stoll, P. & Prati, D. (2001) Intra-specific aggregation alters competitive interactions. Ecology, 82, 319–27. Thompson, K. (1986) Small-scale heterogeneity in the seed bank of an acidic grassland. Journal of Ecology, 74, 733–738. Thomson, J.D., Weiblen, G., Thomson, B.A., Alfaro, S. & Legendre, P. (1996) Untangling multiple factors in spatial distributions: lilies, gophers, and rocks. Ecology, 77, 1698–715.
Turnbull, L.A., Coomes, D. A., Purves, D. W. & Rees, M. (2007) How spatial structure alters population and community dynamics in a natural plant community. Journal of Ecology, 95, 79–89. Upadhaya, K., Pandey, H.N., Law, P.S. & Tripathi, R.S. (2003) Tree diversity in sacred groves of the Jaintia hills in Meghalaya, northeast India. Biodiversity and Conservation, 12, 583–597. Valladares, F. & Gianoli, E. (2007) How much ecology do we need to know to restore Mediterranean ecosystems? Restoration Ecology, 15, 363–368. Wiegand, T., Gunatilleke, S. & Gunatilleke, N. (2007) Species associations in a heterogeneous Sri Lankan Dipterocarp forest. American Naturalist, 170, E77–E95. Received 7 June 2009; accepted 15 March 2010 Handling Editor: Andy Sheppard
Supporting Information Additional supporting information may be found in the online version of this article. Appendix S1. Regional setting and methods used in restoration of study sites and their implications for pattern-forming processes. Appendix S2. Detailed spatial analysis methods. As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials may be re-organized for online delivery, but are not copy-edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.
2010 The Authors. Journal compilation 2010 British Ecological Society, Journal of Applied Ecology, 47, 701–709
