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This is the author’s final version of the work, as accepted for publication following peer review but without the publisher’s layout or pagination. The definitive version is available at http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2664.2012.02152.x/full
Craig, M.D, Hardy, G.E.St.J., Fontaine, J.B., Garkaklis, M.J., Grigg, A.H., Grant, C.D., Fleming, P.A. and Hobbs, R.J. (2012) Identifying unidirectional and dynamic habitat filters to faunal recolonisation in restored mine-pits. Journal of Applied Ecology, 49 (4). pp. 919-928.
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Running title: Filters to faunal recolonisation in restoration
Title: Identifying habitat filters to faunal recolonisation in restored mine-pits
List of Authors: Michael D. Craig1,7, Giles E. St J. Hardy1, Joseph B. Fontaine2, Mark J. Garkakalis1,5, Andrew
H. Grigg3, Carl D. Grant3, Patricia A. Fleming4 & Richard J. Hobbs2,6
1Address: School of Biological Sciences and Biotechnology, Murdoch University, Murdoch, WA 6150,
Australia
2Address: School of Environmental Science, Murdoch University, Murdoch, WA 6150, Australia
3Address: Alcoa World Alumina Australia, Pinjarra, WA 6208, Australia
4Address: School of Veterinary and Biomedical Sciences, Murdoch University, Murdoch, WA 6150, Australia
5Present Address: Astron Environmental Services, Level 1, 620 Newcastle Street, Leederville WA 6007,
Australia
6Present address: School of Plant Sciences, University of Western Australia, Nedlands, WA 6907, Australia
7Address correspondence to Michael D. Craig
Email: [email protected]
Phone: 08 9360 2605
FAX: 08 9360 6303
Pages: 28 (including tables and figures)
Tables: 3
Figures: 4
References: 49
Word count: 6948 (Summary – 350 words, Main text – 4920 words, Acknowledgments – 86 words,
References – 1308 words, Table legends – 80 words, Figure legends – 204 words)
21
1 Summary
2 1. There is increasing evidence that passive faunal recolonisation of restored areas can take decades or even
3 centuries, reducing benefits to biodiversity from restoration. Thus, there is a need to develop restoration and
4 management strategies that facilitate and accelerate faunal recolonisation. This requires identification of habitat
5 features that act as filters to slow or prevent recolonisation and whether those filters are temporally unidirectional
6 or dynamic.
7 2. We investigated successional patterns of reptiles and mammals in restored mine-pits in south-western
8 Australia to identify potential filters to faunal recolonisation. We sampled reptiles and small mammals using 30
9 trapping grids across each of four restoration ages (4, 8, 12 and 17-years old) and unmined forest, and assessed
10 vegetation structure to identify animal-habitat relationships.
11 3. Mammal communities in restored areas converged on unmined forest communities as restoration matured,
12 and all species recolonised rapidly, indicating there were no filters to mammal recolonisation. In contrast, reptile
13 communities did not converge in the same way indicating there were filters that slowed or prevented reptile
14 recolonisation. We identified three reptile species that were slow, or failed, to recolonise restored areas and a
15 fourth species that rapidly recolonised but disappeared as restoration matured. We identified low coarse woody
16 debris (CWD) volumes and high overstorey stem densities as likely filters; the former is a unidirectional filter that
17 will decrease gradually over long timeframes, possibly centuries, while the latter is a dynamic filter that fluctuates
18 in its intensity over short timeframes of years.
19 4. Synthesis and applications. Our study adds to growing evidence that filters to faunal recolonisation may be
20 widespread in restored areas, with important implications for restoration practices. Firstly, examining individual
21 species may more effectively identify filters than examining community successional patterns. Secondly filters
22 can persist over long timeframes, possibly centuries, so management, such as the provision and accelerated
23 development of CWD, may need to occur over similar timeframes. Lastly, filters can be dynamic and repeated
24 management interventions, such as thinning, may be required to overcome these filters. The growing evidence for
25 filters suggests that facilitating faunal recolonisation is more complex than simply returning vegetation to
26 restoration sites.
22 27 Keywords: restoration, succession, mammal, reptile, lizard, mining, coarse woody debris, vegetation structure,
28 forest, jarrah, Australia
29 Introduction
30 Given past and current levels of habitat clearance and degradation, restoration is likely to become increasingly
31 important in conserving global biodiversity (Hobbs & Harris, 2001). However, given that fauna contribute most of
32 the biodiversity in ecosystems, restored areas will need to support most of the original fauna if they are to be
33 effective (e.g. Thomas, 2009). Despite this, many restoration projects simply revegetate and assume that animals
34 will naturally recolonize; this assumption is called the “Field of Dreams” hypothesis, which states that “if you
35 build it they will come” (Palmer, Ambrose & Poff, 1997). However, evidence is increasing that in some systems
36 not all fauna recolonise restored areas even after several decades (e.g. Martin et al., 2004; Kanowski et al., 2006).
37 Species that fail to recolonise are often specialised and are most vulnerable to habitat clearance and fragmentation
38 (Ryan, 1999; Vesk et al., 2008). Thus, it is likely these species will be lost from landscapes unless restoration can
39 provide suitable habitat within the first few decades. If restoration is to be effective in helping preserve global
40 biodiversity, it should aim to facilitate, accelerate and maintain faunal recolonisation rather than relying on
41 passive recolonisation.
42 A number of recolonisation strategies have been proposed (e.g. Thomas, 2009; Lindenmayer et al., 2010) but their
43 incorporation into restoration planning is still not widespread (Brennan, Nichols & Majer, 2005). This may be due
44 to the lack of a conceptual framework through which to interpret barriers to faunal recolonisation and examine
45 generalities across studies. However, community ecology based assembly rules, which seek to understand how
46 communities assemble and why some species occur in particular places (e.g. Wallem et al., 2010), provide a
47 fertile framework through which to interpret faunal recolonisation of restored areas (e.g. Summerville, Conoan &
48 Steichen, 2006). In particular, trait-filter models, which predict how species’ traits limit species presence within
49 assemblages (e.g. Purcell et al., 2009), could improve our understanding of why some species move from regional
50 species pools into restored areas whereas others do not.
23 51 Although trait-filter models have been widely applied to restored plant communities (e.g. Hough-Snee et al.,
52 2011), they have been infrequently used in understanding faunal community assembly (but see Summerville,
53 Conoan & Steichen, 2006). However, the mobility of most terrestrial fauna means that they can respond more
54 rapidly to filters than plants, which are sessile and, often, long-lived. This means filters to faunal recolonisation
55 can be both unidirectional (decreasing gradually over time) or dynamic (fluctuating in intensity over time)
56 depending on whether filters develop over long timeframes (e.g. coarse woody debris) or change rapidly (e.g.
57 canopy cover). Typically, it has been assumed that filters are unidirectional and that once a species has
58 recolonised restored areas they will persist there. However, if filters are dynamic, species may recolonise restored
59 areas but fail to persist. The temporal nature of filters has important implications for management because
60 reducing the effect of unidirectional filters may require only single management inputs, whereas reducing the
61 effects of dynamic filters is likely to require on-going and repeated management inputs.
62 Alcoa of Australia has mined bauxite in the jarrah forest of Western Australia since 1963 and restored its mine-
63 pits since 1966 (Koch, 2007). Long-term research indicates that most vertebrate species in unmined jarrah forest
64 recolonise restored sites (Nichols & Nichols, 2003; Nichols & Grant, 2007), although some species are either
65 unrecorded or recorded very rarely in restored areas, indicating there may be filters to recolonisation. Most of
66 these species are reptiles, which are the slowest vertebrate group to recolonise restored areas (Nichols & Nichols,
67 2003). However, the filters that prevent, or slow, their recolonisation have not been identified so restoration
68 practices cannot be modified to facilitate their return. It is also important to identify successional patterns within
69 restored areas so we can determine whether vertebrates that recolonise persist in restored areas or whether there
70 are temporally dynamic filters. This will, in turn, inform the type, and timeframe, of management strategies in
71 restored areas.
72 Our study examined reptile and small mammal communities in restored mine-pits of varying ages and adjacent
73 unmined forest to investigate vertebrate successional patterns and use them to identify filters to recolonisation.
74 We analysed vertebrate successional patterns at both community and individual species levels to identify potential
75 filters. At the community level, we concluded that filters to recolonisation were present if communities failed to
76 converge on those in unmined forest as restoration matured. At the species level, we concluded that filters to
24 77 recolonisation were present if species were absent from restoration, or were present in young restoration but
78 disappeared as restoration matured, and showed relationships with habitat variables that were also absent from the
79 restored area, or differed greatly in value between restoration and unmined forest. Our study objectives were to:
80 (1) determine if habitat filters were present; (2) if so, identify them and their temporal nature (unidirectional or
81 dynamic); (3) examine the efficacy of community metrics or individual species in identifying filters; and (4) make
82 recommendations for restoration practices and management to facilitate faunal recolonisation.
83 Materials and methods
84 STUDY AREA
85 This study was conducted on Alcoa of Australia’s Jarrahdale and Huntly mines in south-western Australia.
86 Jarrahdale mine (32°17'S, 116°07'E), 10 km north of Karnet, was mined from 1963 to 1998 with mine-pit
87 restoration completed by 2001. Huntly mine (32°36'S, 116°06'E), 10 km north of Dwellingup, has been mined
88 since 1969, with mining still continuing. The climate at both Karnet and Dwellingup is Mediterranean with cool,
89 wet winters and warm, dry summers. Rainfall averages 1164 mm yr-1 at Karnet and 1249 mm yr-1 at Dwellingup
90 with >75% falling between May and September at both localities.
91 The original vegetation at both mines was jarrah forest that was logged at varying intensities several times in the
92 past century (Havel, 1989). Jarrah forest is a eucalypt forest with a canopy up to 30 m tall consisting almost
93 exclusively of two eucalypts, jarrah Eucalyptus marginata Smith and marri Corymbia calophylla (Lindley) K. D.
94 Hill & L. A. S.Johnson. Following mining, both mines consist of a mosaic of unmined jarrah forest and restored
95 mine pits of varying ages. Restored mine pits have similar species compositions to unmined jarrah forest,
96 although some grass and sedge species are less common. For details of restoration procedures see Koch (2007).
97 EXPERIMENTAL DESIGN AND TERRESTRIAL VERTEBRATE AND VEGETATION SAMPLING
98 We investigated terrestrial vertebrate successional patterns in restored mine-pits using a chronosequence
99 experiment with five treatments: unmined forest (the reference community) and four ages of restoration; 4, 8, 12
100 and 17-years old. The oldest restoration age was chosen as it was the first year when current restoration
25 101 prescriptions were implemented; other ages were chosen because they represented different stages of vegetation
102 succession within restored mine-pits (Norman et al., 2006). For each treatment we selected six sites, four at
103 Huntly and two at Jarrahdale, and treatments were interspersed, except the 17-year old restored sites at Huntly
104 which were a few kilometres apart from the remaining sites (Craig et al., 2007).
105 We sampled terrestrial vertebrates using trapping grids consisting of nine pit-traps (four 850 ml plastic take-away
106 containers, three 20 L plastic buckets and two 40 cm long 15 cm diameter PVC tubing) located every 3 m along
107 29 m aluminium fly-wire drift-fences; two paired funnel traps placed along drift fences 7 m from each end; four
108 Elliott traps placed 5 m either side of the two end pit-traps; and Sheffield cage-traps placed at one end of drift
109 fences (Craig et al., 2007). These trapping grids have been shown to provide accurate estimates of relative
110 abundance across a range of restored and unmined forests, precluding the need to correct for detectability (Craig
111 et al., 2009). Due to the number of sites, we conducted trapping sessions over 2 weeks and sampled half the sites
112 each week (two sites at Huntly and one at Jarrahdale in each treatment). Vertebrates were sampled for 4 nights
113 each week in spring (17 to 21 and 23 to 28 October 2005), summer (2 to 6 and 9 to 13 January 2006), autumn (20
114 to 24 and 27 to 31 March 2006) and winter (15 to 19 and 22 to 26 May 2006) totalling 1728 trap nights treatment-1
115 or 8640 trap nights overall. All vertebrates captured were individually marked and released at their capture site.
116 We conducted vegetation assessments between 19 January and 30 June 2006 to quantify treatment differences and
117 explore relationships between terrestrial vertebrates and habitat structure. To collect data on coarse woody debris
118 (CWD) and overstorey and understorey composition we set up 100 m2 square plots with bottom edges centred 2 m
119 from the end of trapping grid drift–fences. CWD (defined as >5cm diameter at largest end) volume was calculated
120 from lengths and end diameters of all CWD on plots. Where CWD extended outside plots, we only measured
121 sections within plots. Overstorey and understorey stem densities were calculated by recording the height of all
122 overstorey plants (>3 m in height) present in plots and, due to greater densities, all understorey plants (0.6 to 3 m
123 in height) in the bottom right-hand quarters of these plots. Canopy height was the average of the 5 tallest
124 overstorey plants on each plot. We also collected structural data from 0.25 m2 plots located 5 and 10 m on one
125 side and 5, 10 and 15 m on the other side of each 20 L bucket pit-trap on grids, with plots falling along lines
126 running perpendicular to drift-fences. In each plot, we estimated overall vegetation cover in three strata (0 to 1, 1
26 127 to 2 and 2 to 5 m), canopy cover using densiometers while facing away from drift-fences and percentage cover of
128 bare ground and litter.
129 STATISTICAL ANALYSIS
130 To investigate community level patterns, we first created resemblance matrices between sites using a Bray-Curtis
131 similarity measure on untransformed data for reptile and mammal communities separately. For each community
132 we then used the resemblance matrix to (1) visually represent site differences using principal coordinates analysis
133 (PCO) and overlaid vectors representing species relationships with PCO axes; (2) analyse treatment (unmined or
134 restoration) and location (Huntly or Jarrahdale) differences using two-way permutational ANOVA
135 (PERMANOVA), with 9999 permutations, and conducted post-hoc pairwise tests between all ages of restoration
136 and unmined forest; (3) analyse difference in similarities with unmined sites for each treatment (all ages of
137 restoration and unmined forest) using a one-way ANOVA; and (4) examine the relationship between community
138 composition and habitat structure using distance based linear models (DISTLM), after removing highly correlated
139 structural variables, namely bare ground (r28 = -0.96 with litter cover) and cover from 1 to 2 m (r28 = 0.75 and -
140 0.81 with cover from 2 to 5 m and canopy height respectively), and normalising structural variables. For mammal
141 analyses, we excluded one site where no mammals were trapped. To investigate changes in vegetation structure,
142 we first created a resemblance matrix between sites using a Euclidean similarity measure on normalised variables,
143 then conducted analyses (1) and (2) described above.
144 To identify differences in vertebrate abundances between restoration and unmined forest, we analysed individual
145 species using two-way ANOVAs, with location (Jarrahdale or Huntly) and treatment (unmined or restoration) as
146 factors and also analysed community metrics (number and species richness of reptiles and mammals) to
147 supplement community analyses. To investigate successional patterns, we conducted univariate regressions
148 between restoration age (i.e. excluding unmined sites) and abundance with the same variables. To examine
149 structural differences between unmined forest and restoration and understand structural changes with restoration
150 succession, we conducted the same analyses on the ten structural variables measured.
27 151 As ecological knowledge of jarrah forest vertebrates is practically non-existent, we posed general a priori
152 hypotheses describing three candidate response types (linear, threshold, quadratic) to examine relationships
153 between vertebrate abundance and structural variables. After removing both correlated structural variables (see
154 above), we then confronted vertebrate abundances (species captured ≥4 times) with three forms of the remaining
155 eight variables plus an intercept-only model, resulting in 25 models per species. For threshold models, we applied
156 a pseudo-threshold form to save parameter estimates (Franklin et al., 2000). We did not employ more complex,
157 multi-predictor models owing to limited data and the lack of a priori predictions of species responses. We ranked
158 models using AICc, or chose the form with highest AICc where two forms of a predictor were supported, and
159 retained models with weights >0.1 (Burnham & Anderson, 2002). To determine which variables were well-
160 supported, we summed the model weights of all models containing each variable and considered summed weights
161 >0.4 to be well-supported (Converse, White & Block, 2006).
162 All analyses were conducted using PERMANOVA+ for Primer (Primer-E, 2008) Statistica 6.0 (StatSoft, 2001)
163 and JMP 3.2.5 (SAS, 1999) and R 2.13 (R Development Core Team, 2011).
164 Results
165 IDENTIFYING FILTERS THROUGH COMMUNITY SUCCESSION
166 Reptile communities did not differ between locations (Pseudo-F1,20 = 1.25, P = 0.272) but did differ between
167 treatments (Pseudo-F4,20 = 1.67, P = 0.020) although there was no interaction (Pseudo-F4,20 = 1.12, P = 0.314).
168 Post-hoc tests revealed unmined sites differed from all restoration ages (all P < 0.016) but no restoration age
169 differed from another (0.131 < P < 0.868). Resemblances to unmined forest differed significantly between
170 treatments (F4,25 = 4.80, P = 0.005), with unmined sites being more similar to each other than to any restoration
171 age (P < 0.05; Fig. 1). PCOs also indicated restored sites were not converging on unmined sites as they matured,
172 with Hemiergis initialis and Cryptoblepharus buchananii the species most associated with unmined sites (Fig. 2).
173 Neither overall reptile abundance, nor species richness, differed between locations or restored and unmined sites,
174 or were related to restoration age (Table 1).
28
175 Mammal communities differed significantly between locations (Pseudo-F1,20 = 13.80, P < 0.001), with Jarrahdale
176 having more Trichosurus vulpecula and less Antechinus flavipes than Huntly, and treatments (Pseudo-F4,20 = 4.47,
177 P < 0.001), although there was no interaction (Pseudo-F4,20 = 1.18, P = 0.377). Treatment differences were
178 complex, but were primarily between young restoration and older restoration and unmined forest combined.
179 Resemblances to unmined forest did not differ between treatments (F4,25 = 2.20, P = 0.098) (Fig. 1) and PCOs
180 indicated restored sites were converging on unmined sites as they matured, with Mus musculus strongly associated
181 with young restoration (Fig. 2). Overall mammal abundance did not differ between unmined forest and restoration
182 or between locations and was not related to restoration age (Table 1). Mammal species richness did not differ
183 between locations and was not related to restoration age, but was significantly greater on restored than unmined
184 sites (2.4 ± 0.2 vs 1.5 ± 0.4 site-1) (Table 1). When analyses were restricted to native mammals, the only
185 differences were that overall native mammal species richness did not differ between restored and unmined sites
186 and both native mammal abundance and species richness were positively correlated with restoration age (r28 =
187 0.64 and 0.46, P = 0.001 and 0.025 respectively).
188 IDENTIFYING FILTERS THROUGH INDIVIDUAL SPECIES SUCCESSIONAL PATTERNS
189 We recorded 20 reptile species across all sites (Table 1). Three species (Christinus marmoratus, C. buchananii
190 and Egernia napoleonis) were significantly more abundant in unmined forest (Table 1) and appeared to be late
191 successional, with the first two species only recorded in unmined forest (Fig. 3). Two species (Diplodactylus
192 polyophthalmus and Morethia obscura) appeared to prefer open habitats with low canopy cover (see Table 2),
193 being most abundant in 4-year old restoration and unmined forest (Fig. 3). M. obscura was more abundant in
194 unmined forest than restoration (3.3 ± 0.5 vs 0.8 ± 0.3 individuals site-1) but D. polyophthalmus was not;
195 abundances of both species were negatively related to restoration age (Table 1). Hemiergis initialis appeared to be
196 a successional stage increaser, recolonising rapidly and becoming more abundant as restoration matured (Fig. 3).
197 Five species (Ctenotus labilladieri, Lerista distinguenda, Menetia greyii, Tiliqua rugosa and Pogona minor)
198 appeared to be generalists, being found in low abundance in most treatments (Table 1; Fig. 3). One species
199 (Acritoscincus trilineatus) appeared most abundant in 8, 12 and 17-year old restoration, but there was little
200 variation between treatments at Huntly with the overall pattern being driven by Jarrahdale (Fig. 3), so we consider
29 201 it a generalist. The remaining species were captured too infrequently (Table 1) to determine their successional
202 response. C. buchananii was the only species that differed in abundance between locations and showed a
203 significant interaction (Table 1). This resulted from two individuals being captured at Jarrahdale and one at
204 Huntly which, with fewer Jarrahdale sites, led to a result with statistical, but no biological, significance.
205 We recorded six mammal species across all sites (Table 1). Mus musculus was early successional (Fig. 4). Its
206 abundance was negatively related to restoration age, but did not differ between restored and unmined sites due to
207 its rarity in older restoration (Table 1). Trichosurus vulpecula, which was more abundant at Jarrahdale than
208 Huntly (1.9 ± 0.4 vs 0.5 ± 0.3 site-1), appeared to be a seral stage increaser (Fig. 4) whose abundance was
209 positively related to restoration age but did not differ between restored sites and unmined forest (Table 1). Two
210 species (Antechinus flavipes and Cercatetus concinnus) appeared to be generalists and their abundances did not
211 differ between restoration and unmined forest (Table 1). Antechinus flavipes was least common in young
212 restoration (Fig. 4), and was more abundant at Huntly than Jarrahdale (2.2 ± 0.5 vs 0.5 ± 0.2 individuals site-1),
213 while C. concinnus was recorded in all restoration ages with no obvious pattern (Fig. 4). Sminthopsis griseoventer
214 and Rattus rattus were captured too infrequently to determine their successional pattern.
215 IDENTIFYING FILTERS THROUGH ANIMAL-HABITAT RELATIONSHIPS
216 Vegetation structure differed between locations (Pseudo-F1,20 = 3.20, P = 0.004) although, univariately, only
217 understorey stem densities differed (F1,26 = 4.30, P = 0.048), being greater at Huntly than Jarrahdale (10 800 ±
-1 218 1316 vs 5200 ± 950 stems ha ). Vegetation structure also differed between treatments (Pseudo-F1,20 = 8.67, P <
219 0.001), and changed as restoration matured. All ages of restoration were significantly different from one another
220 and unmined forest (all P < 0.028), except 8 and 12-year (t8 = 1.48, P = 0.086) and 12 and 17-year old restoration
221 (t8 = 1.02, P = 0.419). Unmined forest had less cover from 1 to 2 and 2 to 5 m, lower overstorey stem densities,
222 taller canopies and more CWD than restored areas, but other structural variables did not differ between forest
223 types (Appendix S1). As restoration matured it developed more canopy and litter cover, taller canopies and less
224 bare ground and cover from 1 to 2 m, but the remaining structural variables did not change (Appendix S1). The
30 225 last four variables gradually changed as restoration matured, but canopy cover increased from 4 to 8-year old
226 restoration then remained relatively constant.
227 Structural variables explained little variation in reptile communities (adjusted r2 of best model = 0.13) and
228 marginal tests from the DISTLM revealed only canopy height explained significant amounts of this variation
229 (Pseudo-F1,28 = 3.03, P = 0.001) and then only 9.8%. Structural variables also explained little variation in reptile
230 community metrics with no variables well-supported (Table 2). At the individual species level, there were well-
231 supported variables for five species with Pogona minor showing a linear increase with understorey density,
232 Egernia napoleonis a linear increase with CWD volume, Morethia obscura a decreasing threshold response to
233 overstorey stem density, Hemiergis initialis a linear increase with canopy height and Menetia greyii a trough-
234 shaped quadratic relationship with CWD volume (Table 2).
235 Structural variables explained much of the variation in mammal communities (adjusted r2 of best model = 0.42)
236 and marginal tests from the DISTLM revealed that understorey density (Pseudo-F1,28 = 6.74, P < 0.001), litter
237 cover (Pseudo-F1,28 = 5.02, P = 0.002), canopy height (Pseudo-F1,28 = 4.28, P = 0.006) and canopy cover (Pseudo-
238 F1,28 = 3.09, P = 0.032) all univariately explained significant amounts of this variation; 20.0, 15.7, 13.7 and 10.3%
239 respectively. Conversely, structural variables explained little variation in mammal community metrics with no
240 variable well supported (Table 2). At the individual species level, there were well-supported variables for three
241 species with both Trichosurus vulpecula and Cercatetus concinnus showing a decreasing threshold relationship
242 with understorey density and Mus musculus a trough-shaped quadratic relationship with canopy cover (Table 2).
243 Discussion
244 Mammal communities in restored areas converged towards those in unmined forest as restoration matured, but
245 reptile communities did not. This strongly indicates that restoration contains filters that slow or prevent
246 recolonisation by reptiles; however, the community level approach was poor at identifying those filters. Our
247 analyses did not identify any structural variables that were well-supported as being related to reptile community
248 metrics, while analyses on overall reptile communities identified canopy height as the only variable related to
249 community composition. However, we suspect the relationship with canopy height is correlative rather than
31 250 causal. Many studies have found no evidence that canopy height is important in structuring reptile communities
251 (e.g. Taylor & Fox, 2001a; Craig et al., 2010) and canopy height become more similar to unmined forest as
252 restoration matured, but reptile communities did not. There were significant relationships between mammal
253 community composition and four structural variables, but we know that mammal communities converged towards
254 unmined communities as restoration matured, therefore these variables could not be acting as filters. We propose
255 that community-level analyses were not effective at detecting filters because individual species displayed a range
256 of successional patterns that counteracted one another thereby obscuring relationships with vegetation structure at
257 a community level. While studies do use community-level analyses to identify filters to recolonisation (e.g.
258 Longcore, 2003; Lindenmayer et al., 2010), our study suggests this approach will not always effectively identify
259 filters, while acknowledging there is often no alternative in invertebrate communities studies.
260 In contrast, individual species analyses appeared more successful at identifying filters for reptiles. Five species
261 showed significant relationships with structural variables, although only three identified potential filters. Pogona
262 minor increased linearly with understorey density, possibly because it frequently utilises understorey plants (Craig
263 et al., 2007), but this species is more common in restoration than unmined forest so understorey density cannot be
264 acting as a filter. Menetia greyii was most abundant at high and low CWD volumes, implying that CWD cannot
265 act as a filter for this species. Hemiergis initialis was positively related to canopy height, which is a unidirectional
266 filter and slowly decreasing its effect as the restoration matures. Given jarrah growth rates (Koch & Ward, 2005),
267 it will probably cease to be a filter after several decades. However, Craig et al. (2010) found no relationship
268 between H. initialis and canopy height and it is difficult to propose an ecological mechanism whereby canopy
269 height directly influences a litter-inhabiting skink. Morethia obscura showed a decreasing threshold response with
270 overstorey stem density. While the exact mechanism behind the relationship is unclear, it is probably due to
271 vegetation cover and solar insolation influencing the thermal suitability of the habitat (Craig et al., 2010). This
272 species increased when overstorey stem densities in restoration were reduced by thinning and burning (Craig et
273 al., 2010) but, 5 years later, numbers in thinned and burned restoration had decreased (Smith, 2011). This
274 suggests that this filter is dynamic with both M. obscura abundance and vegetation cover increasing and
275 decreasing over short timeframes as succession proceeds within restored areas. Egernia napoleonis linearly
32 276 increased with CWD volume, probably because it utilises CWD extensively (Craig et al., 2011; Christie et al., in
277 press), suggesting low CWD volumes in restoration are a unidirectional filter preventing recolonisation. We
278 would expect the effect of this filter to decrease and E. napoleonis abundance to increase gradually over time. The
279 timeframe over which CWD will develop naturally in restoration is unclear but, given average ages of E.
280 marginata and C. calophylla and average rates of trees fall (Whitford, 2002), it will probably be centuries before
281 CWD accumulates sufficiently to reduce this filter to E. napoleonis recolonisation, particularly as it prefers large
282 CWD (Craig et al., 2011).
283 Studies across a range of ecosystems have shown that species fail to recolonise restored areas, even after several
284 decades (e.g. Martin et al., 2004; Kanowski et al., 2006; Desrochers, Keagy & Cristol, 2008), suggesting that
285 filters to recolonisation may be widespread. The extent of the filters identified in this study is unclear. Canopy
286 height is often related to structural complexity (McElhinny et al., 2005), and several studies have shown that lack
287 of structural complexity can be a filter to recolonisation (Lomov, Keith & Hochuli, 2009), suggesting that canopy
288 height could potentially be an indirect filter in other forest and woodland ecosystems. Vegetation cover affects
289 solar insolation and microclimate (e.g. Martens, Breshears & Meyer, 2000) both of which are critical in
290 influencing habitat use by many fauna species (e.g. Hill et al., 2001; Pike, Webb & Shine, 2011). This suggests
291 that structural variables that relate to vegetation cover, such as canopy, midstorey or understorey cover, or
292 understorey or overstorey stem density, have the potential to act as filters to faunal recolonisation in many forest
293 and woodland ecosystems. Indeed, Taylor & Fox (2001b) found that canopy and understorey cover were major
294 determinants of lizard succession in restored minesites, probably due to differing thermal requirements between
295 species. CWD is a critical component of forested ecosystems and many fauna species are CWD dependent (e.g.
296 Lindenmayer et al., 2002). A lack of CWD is often cited as a factor preventing faunal recolonisation of restored
297 sites across a range of faunal groups (e.g. Martin et al., 2004; Grimbacher & Catterall, 2007), suggesting this filter
298 is likely to be widespread in ecosystems naturally containing CWD (Vesk et al., 2008). While the filters identified
299 in this study are potentially widespread in other forest and woodland systems, the complexity of fauna-habitat
300 relationships means that they almost certainly represent only a small subset of the range of potential filters that
301 could slow or prevent faunal recolonisation. It is likely that filters relate to species traits as well, as found by
33 302 Summerville et al. (2006). Unfortunately, the lack of information on the ecology of jarrah forest reptiles prevents
303 us from examining those relationships in our study, but it is an important area for future research as it would
304 further improve our ability to predict which species might be susceptible to filters.
305 MANAGEMENT IMPLICATIONS
306 Where filters to faunal recolonisation are identified, management actions should focus on reducing the effects of
307 these filters to facilitate faunal recolonisation. In our system, reducing the impact of low CWD volumes requires
308 more CWD, preferably up to 50 logs ha-1 (Christie et al., in press), to be added as part of the restoration process.
309 However, CWD in mine-pits may only last 70 years before being consumed by fire (Grigg & Steele, 2011), long
310 before large CWD will develop naturally, so management strategies that aim to kill trees and accelerate CWD
311 formation also need to be considered. Adding CWD has been shown to accelerate faunal recolonisation in other
312 studies (Madden & Fox, 1997; Márquez-Ferrando et al., 2009; Barton et al., 2011), suggesting it is likely to be an
313 effective management strategy across a range of systems. Management strategies to reduce the filters of low
314 canopy heights and high overstorey stem densities will be similar, as reducing overstorey stem densities increases
315 growth rates in jarrah trees (Stoneman et al., 1997). Thinning and burning minesite restoration was an effective
316 strategy for reducing both these filters in our system (Craig et al., 2010) and thinning has also been shown to
317 influence faunal community composition in a range of ecosystems (Hagar, Howlin & Ganio, 2004; Converse,
318 White & Block, 2006). Individual species respond variably to thinning, however (e.g. Converse, White & Block,
319 2006), so use of this management strategy will depend on the species of interest. Furthermore, vegetation cover,
320 which was probably the proximal filter in our system, is a dynamic filter so any benefit of thinning and burning
321 may be short-lived (Smith, 2011). This implies that management strategies that reduce vegetation cover may need
322 to be repeatedly applied if filters are to be reduced to levels that enable populations of species of interest to be
323 maintained. Although our study has focused on habitat filters that are relatively easy to restore compared to some
324 others (e.g. soil water-logging), the effect of all filters should potentially be reducible given unlimited resources,
325 so which filters can be reduced through management in restored sites will be primarily constrained by finances
326 and logistics.
327 CONCLUSIONS
34 328 In our study, both community-level and species-level analyses indicated there were filters to passive reptile
329 recolonisation, but only species-level analyses appeared successful at identifying these filters. Low CWD volumes
330 were identified as a unidirectional filter that could delay passive recolonisation of some species for centuries.
331 Overstorey stem density fluctuates over relatively short timeframes (years) so its filter effect is dynamic. These
332 findings have important implications for restoration practices. Firstly, many species are known to be unable to
333 passively recolonise restored areas (e.g. Kanowski et al., 2006), implying that filters to recolonisation are likely to
334 be widespread in many restored areas, and that community-level analyses might be poor at identifying these
335 filters. Secondly, filters can persist over long timeframes, possibly centuries, so management may also need to
336 occur over similar timeframes. This management will need to ensure that slow-developing habitat resources, such
337 as CWD or tree hollows (Vesk et al., 2008), are provided in restored areas, and their natural development
338 accelerated, to facilitate recolonisation by fauna dependent on these resources. Lastly, filters can be dynamic and
339 repeated management interventions, such as thinning and burning, may be required to overcome these filters and
340 maintain populations of susceptible fauna in restored areas (Craig et al., 2010). The growing evidence for the
341 impact of filters suggests that facilitating faunal recolonisation is more complex than simply returning vegetation
342 to the pre-impacted state at restoration sites.
343 Acknowledgements
344 We thank Anna Whitfield, Dion Trevithick-Harney, Alicia Sparnon, Adam Peck, Angela Mercier, Chantelle
345 Jackson, Kaitlyn Height, Jason Fraser, Finlay Bender and Jane Adcroft who helped install trapping grids and
346 Jonathan Anderson, Angela Mercier and Rod Armistead for help with vegetation surveys. Claire Stevenson from
347 the Western Australian Museum identified the Sminthopsis species. This project was conducted with Department
348 of Environment and Conservation permit SF005179 and Murdoch University Animal Ethics committee approval
349 W1152/05. Financial support was provided by Alcoa of Australia and ARC Linkage Grant LP0455309.
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21 464 TABLE 1. Results of analyses on vertebrate species and community metrics. Location (Jarrahdale vs Huntly) and treatment (unmined vs restoration) report
465 the results of two-way ANOVAs on species abundances and community metrics. Univariate regression reports these abundances regressed against
466 restoration age. n is the number of individuals (or species) captured.
Location (L) Treatment (T) L x T Univariate regression Variable n F1,26 F1,26 F1,26 r22
COMMUNITY METRICS
No. of reptiles 270 1.43 2.73 0.55 -0.12
Reptile species richness 20 0.36 2.18 0.01 -0.29
No. of mammals 146 0.04 2.51 1.16 0.08
No. of mammal species 6 3.99 5.44* 0.44 0.16
REPTILES
Acritoscincus trilineatus (Gray, 1838) 72 1.59 3.12 2.93 0.08
Cryptoblepharus buchananii Gray, 1838 3 20.80*** 57.78*** 20.80***
Ctenotus delli Storr, 1974 2 0.06 0.54 0.06
Ctenotus labilladieri (Duméril & Bibron, 1839) 8 0.23 0.23 0.08 0.14
Egernia napoleonis (J.E. Gray, 1838) 4 0.04 10.00** 0.04 0.29
Hemiergis initialis Werner, 1910 74 0.02 3.67 0.09 0.23
Lerista distinguenda (Werner, 1910) 7 0.33 0.33 0.33 -0.09
Menetia greyii J.E. Gray, 1845 10 1.35 1.35 1.89 -0.12
22 Morethia obscura Storr, 1972 38 0.05 21.38*** 0.05 -0.46*
Tiliqua rugosa (J.E. Gray, 1825) 10 0.29 2.62 0.29 -0.02
Christinus marmoratus (Gray, 1845) 2 1.39 12.48** 1.39
Diplodactylus polyophthalmus Günther, 1867 16 3.91 0.03 0.54 -0.46*
Aprasia pulchella Gray, 1839 4 0.39 0.39 3.47
Lialis burtonis Gray, 1835 2 0.06 0.54 0.06
Pogona minor (Sternfeld, 1919) 8 0.56 1.82 0.56 -0.09
Varanus rosenbergi Mertens, 1957 2 0.25 0.25 0.25
Ramphotyphlops australis (Gray, 1845) 4 2.28 0.82 0.82
Notechis scutatus (Peters, 1861) 1
Parasuta gouldi (Gray, 1841) 1
Parasuta nigriceps (Günther, 1863) 2 0.25 0.25 0.25
MAMMALS
Antechinus flavipes (Waterhouse, 1838) 49 4.37* 0.14 0.14 0.27
Sminthopsis griseoventer Kitchener, Stoddart & Henry, 1984 1
Cercatetus concinnus (Gould, 1845) 20 1.14 1.52 0.17 0.14
Trichosurus vulpecula (Kerr, 1792) 29 12.46** 0.44 3.11 0.65***
Mus musculus# Linnaeus, 1758 45 0.51 3.26 0.51 -0.66***
23 Rattus rattus# (Linnaeus, 1758) 2 1.16 1.16 1.16
467 *P < 0.05, **P < 0.01, ***P < 0.001; #introduced
26
468 TABLE 2. Results of modelling effects of structural variables on community metrics and individual species. Variable, response form and direction, along
469 with model adjusted r2 and model weights are presented.
Species Variable Form Direction^ Adjusted r2 Model Weight Variable Weight#
COMMUNITY METRICS
Reptile abundance CWD Threshold Increased 0.11* 0.16 0.36
Reptile species richness Canopy Cover Quadratic Peaked 0.21* 0.28 0.35
Canopy Height Quadratic Trough 0.20* 0.22 0.25
Mammal Abundance Canopy Cover Quadratic Trough 0.17* 0.19 0.21
Cover from 0-1m Linear Increased 0.11* 0.14 0.27
Mammal Species Richness CWD Quadratic Trough 0.22** 0.20 0.35
Canopy Height Quadratic Peaked 0.19 0.11 0.17
REPTILES
Acritoscincus trilineatus Understorey Density Threshold Decreased 0.06 0.12 0.25
Ctenotus labilladieri None – Intercept 0.13
Egernia napoleonis CWD Linear Increased 0.43*** 0.48 0.84
Hemiergis initialis Canopy Height Linear Increased 0.19** 0.23 0.51
CWD Threshold Increased 0.15* 0.11 0.23
Lerista distinguenda None – Intercept 0.11
27 Menetia greyii CWD Quadratic Trough 0.16 0.20 0.44
Morethia obscura Overstorey Density Threshold Decreased 0.40*** 0.60 0.75
Canopy Height Quadratic Trough 0.36* 0.12 0.13
Tiliqua rugosa Overstorey Density Linear Increased 0.09 0.16 0.30
Diplodactylus polyophthalmus Leaf Litter Linear Decreased 0.11* 0.17 0.35
Pogona minor Understorey Density Linear Increased 0.38*** 0.63 0.98
MAMMALS
Antechinus flavipes Understorey Density Linear Increased 0.12* 0.19 0.40
Mus musculus Canopy Cover Quadratic Trough 0.44*** 0.35 0.38
Leaf Litter Threshold Decreased 0.40*** 0.29 0.49
Trichosurus vulpecula Understorey Density Threshold Decreased 0.39*** 0.76 0.95
Cercatetus concinnus Understorey Density Threshold Decreased 0.21** 0.43 0.81
470 ^ Peaked = maxima in middle portion of x-axis, Trough = minima in middle portion of x-axis
471 #derived from summing weights of all models containing that variable
26
472
473 FIGURE 1. Mean (± SE) similarity of mammal (top) and reptile communities (bottom) between each treatment
474 and unmined forest (i.e. UM represents similarities between unmined forest plots). Numbers on x-axes represent
475 restoration age.
27
476
477 FIGURE 2. PCOs of reptile communities (top), mammal communities (middle) and vegetation structure (bottom)
478 in 4-year (□), 8-year (∆), 12-year (◊) and 17-year old restoration (○) and unmined forest (●), showing all sites
479 (left) and centroids of each treatment (right). For reptile and mammal communities, vector overlays of species’
480 correlations with PCO axes are shown. The first three letters of each species’ generic and specific name identify
481 vectors.
28
482
-1 483 FIGURE 3. Mean (± SE) abundance (individuals grid ) of Egernia napoleonis, Cryptoblepharus buchananii and Christinus marmoratus (late
484 successional), Hemiergis initialis (seral increaser), Morethia obscura and Diplodactylus polyophthalmus (open habitat specialists) and Acritoscincus
485 trilineatus, Ctenotus labilladieri and Lerista distinguenda (generalists) in each of four ages of restoration and unmined forest. Key is as for FIG. 1.
486 The inset for A. trilineatus shows variation in treatment responses between Jarrahdale (JA) and Huntly (HU).
29
487
-1 488 FIGURE 4. Mean (± SE) abundance (individuals grid ) of Mus musculus, Trichosurus vulpecula, Antechinus
489 flavipes and Cercatetus concinnus in the four restoration ages and unmined forest. Key is as for FIG. 1.