Habitat structure and food resources for wildlife across successional stages in a tropical forest

Habitat structure and food resources for wildlife across successional stages in a tropical forest

Forest Ecology and Management 283 (2012) 119–127 Contents lists available at SciVerse ScienceDirect Forest Ecology and Management journal homepage: ...

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Forest Ecology and Management 283 (2012) 119–127

Contents lists available at SciVerse ScienceDirect

Forest Ecology and Management journal homepage: www.elsevier.com/locate/foreco

Habitat structure and food resources for wildlife across successional stages in a tropical forest Bruno T. Pinotti a, Camilla P. Pagotto b, Renata Pardini a,⇑ a b

Universidade de São Paulo, Instituto de Biociências, Departamento de Zoologia, Rua do Matão, Travessa 14, 101, 05508-900 São Paulo, Brazil Universidade de São Paulo, Instituto de Biociências, Departamento de Ecologia, Rua do Matão, Travessa 14, 101, 05508-900 São Paulo, Brazil

a r t i c l e

i n f o

Article history: Received 13 April 2012 Received in revised form 12 July 2012 Accepted 13 July 2012 Available online 10 August 2012 Keywords: Food availability Habitat structure Wildlife recovery Restoration Forest succession Secondary forest

a b s t r a c t Tropical forests are experiencing an increase in the proportion of secondary forests as a result of the balance between the widespread harvesting of old-growth forests and the regeneration in abandoned areas. The impacts of such a process on biodiversity are poorly known and intensely debated. Recent reviews and multi-taxa studies indicate that species replacement in wildlife assemblages is a consistent pattern, sometimes stronger than changes in diversity, with a replacement from habitat generalists to old-growth specialists being commonly observed during tropical forest regeneration. However, the ecological drivers of such compositional changes are rarely investigated, despite its importance in assessing the conservation value of secondary forests, and to support and guide management techniques for restoration. By sampling 28 sites in a continuous Atlantic forest area in Southeastern Brazil, we assessed how important aspects of habitat structure and food resources for wildlife change across successional stages, and point out hypotheses on the implications of these changes for wildlife recovery. Old-growth areas presented a more complex structure at ground level (deeper leaf litter, and higher woody debris volume) and higher fruit availability from an understorey palm, whereas vegetation connectivity, ground-dwelling arthropod biomass, and total fruit availability were higher in earlier successional stages. From these results we hypothetize that generalist species adapted to fast population growth in resource-rich environments should proliferate and dominate earlier successional stages, while species with higher competitive ability in resource-limited environments, or those that depend on resources such as palm fruits, on higher complexity at the ground level, or on open space for flying, should dominate older-growth forests. Since the identification of the drivers of wildlife recovery is crucial for restoration strategies, it is important that future work test and further develop the proposed hypotheses. We also found structural and functional differences between old-growth forests and secondary forests with more than 80 years of regeneration, suggesting that restoration strategies may be crucial to recover structural and functional aspects expected to be important for wildlife in much altered ecosystems, such as the Brazilian Atlantic forest. Ó 2012 Elsevier B.V. All rights reserved.

1. Introduction Tropical forests have already lost about 6 million km2 (35%) of their area due to human action (Wright, 2010). Although natural regeneration occurs in a significant extension of cleared areas, deforestation of old-growth forests is still widespread, leading to an increase in the proportion of secondary forests (FAO, 2010; Wright, 2010). Today over half of the remaining tropical forests are secondary or otherwise degraded, and the proportion of these secondary vegetation continues to increase (Wright, 2010). However, the impacts of such a process on wildlife are poorly known, ⇑ Corresponding author. Address: Department of Zoology, Institute of Biosciences, University of São Paulo, Rua do Matão, Travessa 14, 101, CEP 05508-900 São Paulo, SP, Brazil. Tel.: +55 11 30917510; fax: +55 11 30917513. E-mail address: [email protected] (R. Pardini). 0378-1127/$ - see front matter Ó 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.foreco.2012.07.020

since the value of secondary forests to wildlife conservation and long-term maintenance of ecological processes is still poorly understood (Bowen et al., 2007; Chazdon et al., 2009a; Gardner et al., 2007), and strongly debated. Some authors argue that natural regeneration could mitigate the effects of old-growth forest loss (Dent and Wright, 2009; Wright and Muller-Landau, 2006a, 2006b), while others suggest that secondary forests do not have the same conservation value of old-growth forests (Brook et al., 2006; Gardner et al., 2007; Gibson et al., 2011; Laurance, 2007). Studies assessing wildlife recovery during tropical forest regeneration indicate that the most common species at the beginning of this process are habitat generalists or species typical of open physiognomies or biomes, which are rare or absent in old-growth forests (Bowen et al., 2007; Lawton et al., 1998; Pardini et al., 2009). On the other hand, a large number of species that are common in old-growth forests are rare or absent in secondary vegeta-

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tion (Barlow et al., 2007a; Bowen et al., 2007; Chazdon et al., 2009a; Dunn, 2004; Pardini et al., 2009) and are commonly restricted to forested biomes at larger spatial scales (Pardini et al., 2009). Therefore, although species richness in young forests may often be comparable to that in old-growth areas, species composition changes substantially during regeneration (Dunn, 2004; Pardini et al., 2009). Nevertheless, the ecological processes underlying changes in composition or structure of wildlife assemblages during succession have been rarely investigated (Gardner et al., 2009). These processes should be related to the structural, functional, and floristic changes that occur during regeneration, such as the increase in plant biomass (linked to the increase in basal area and canopy height), the decrease in net primary productivity, and changes in plant species composition (Brown and Lugo, 1990; Clark, 1996; Guariguata and Ostertag, 2001), which should lead to changes in habitat structure and food resources for wildlife (e.g. Jayapal et al., 2009). In particular, vegetation structure (e.g. Dial et al., 2006; Hopkins, 2011; Lambert et al., 2006; Malcolm, 1997; Pardini et al., 2005), as well as the structure of the leaf litter (e.g. Naxara et al., 2009; Sayer et al., 2010; Van Sluys et al., 2007; Vonesh, 2001) and woody debris (e.g. Grove, 2002; Lambert et al., 2006; Naxara et al., 2009; Vonesh, 2001) are important habitat features to several wildlife groups. Besides these structural aspects, the availability of arthropods and fruits – important food resources to several vertebrates (e.g. Caldwell and Vitt, 1999; Durães and Marini, 2005; Herrera et al., 2002; Pinotti et al., 2011; Tutin et al., 1997) – should also change during regeneration. Thus information on how regeneration changes aspects of habitat structure and food availability, although scarce (but see DeWalt et al., 2003), is essential to clarify the ecological processes underpinning the changes usually observed in wildlife composition, as well as to support restoration techniques aiming at the conservation or recovery of wildlife (Prach and Walker, 2011; Walker et al., 2007). This type of information is of particular importance in severely exploited tropical forests, such as the Brazilian Atlantic forest. The second largest South American rainforest, the Atlantic forest is inserted in the most populous Brazilian region, the Atlantic coast, home to 61% of the Brazilian population in an area of about 15% of the country (SOS Mata Atlântica and INPE, 2009). It has been intensely exploited since the arrival of Europeans in the 16th century (Dean, 1995), and currently only 11.4–16% of its original area remains, mostly in small fragments of secondary forest (Ribeiro et al., 2009). Recently, efforts are being made for the restoration of altered landscapes in this biome (e.g. Wuethrich, 2007). However, for these initiatives to be effective also for wildlife, information on the recovery of important aspects of habitat structure and food availability is essential. Here we investigate the existence of consistent patterns of variation in habitat structure and food availability in a mosaic of forests in different successional stages inserted in the largest continuous tract of Brazilian Atlantic forest, comparing 28 sampling grids in mid secondary, late secondary, and old-growth forest patches. We discuss our findings pointing out hypotheses on the implications for commonly observed changes in composition of animal assemblages during tropical forest regeneration, and for the recovery of wildlife through natural regeneration in intensively exploited ecosystems.

2. Materials and methods 2.1. Study area Our study was carried out in the Morro Grande Forest Reserve (23°390 –23°480 S, 47°010 –46°550 W), located in the Cotia

municipality, São Paulo State, Brazil (Fig. S1-A). The Reserve encompasses 9400 ha of continuous Atlantic forest, comprising patches in different successional stages after clear cut and smallscale agriculture and cattle ranching, and by old-growth patches that were not clear cut during the period of more intensive exploitation in the region, from the 17th century until the 1930s, when the area was expropriated and cleared patches could start regenerating (Metzger et al., 2006). In its southern portion, the Reserve is connected to the largest continuous area of Atlantic forest in Brazil, the Serra do Mar sub-region (Ribeiro et al., 2009). The altitude varies from 860 to 1075 m, the climate is Cfb, temperate warm and wet (Köppen, 1948), and the average monthly temperature and precipitation vary from 16.5 to 20.5 °C and 43 to 77 mm in the cool–dry season (April to September), and from 20.7 to 23.5 °C and 125 to 196 mm in the warm–wet season (October to March) (Metzger et al., 2006). The forest is classified as ‘‘Dense Mountain Rain Forest’’, and the most representative botanic families are Myrtaceae, Lauraceae, Fabaceae and Rubiaceae (Catharino et al., 2006). Detailed abiotic and historic data of the study area are found in Metzger et al. (2006). 2.2. Sampling design Through the interpretation of aerial photographs and groundtruthing through the checking of 64 sites in the field, we mapped land cover in a buffer of 1 km from the access roads in the Reserve, classifying the native vegetation into successional stages as defined by Brazilian law (Supplementary data). From the total area mapped, 62.4% is in mid secondary stage, 12.3% in patches in late secondary stage, and 10.1% in patches of old-growth forest. The last are represented by areas that were not clear cut since the 17th century, and were considered control areas (see also Supplementary data). Sampling grids were located in the eight largest late secondary patches (6.1–164.8 ha), in the ten largest old-growth patches (7.4–90.8 ha), and randomly in 10 points in mid secondary stage (since forests at this stage are connected and not fragmented in patches), totaling 28 grids. We kept a minimum distance of 500 m among grids, of 100 m from each grid to open areas or areas in other successional stages, and of 50 m from each grid to water bodies. For each successional stage, half of the grids were located in the north and half in the south of the Reserve (Fig. S1-A). Detailed information about the mapping of successional stages and the location of sampling grids is found in the Supplementary data. 2.3. Data collection Grids were 100  70 m (0.7 ha, Fig. S1-B), an appropriate size to encompass small-scale heterogeneity within successional stages, while still maintaining at least a 100-m distance to forests at different successional stages. All datasets were collected in the warm–wet season, and for each of them, all grids were sampled simultaneously or within a short period of time (maximum 25 days), in order to prevent bias due to seasonal variation across sampling grids. Data on habitat structure were collected in January 2008, on ground-dwelling arthropods in three sampling sessions in the warm–wet season 2007–2008 (November–December 2007, February–March 2008 and March–April 2008, in each of which all grids were sampled, except three grids in the last session, see below), on trunk-dwelling arthropods in February 2009, and on fruits in January 2009. In three of the 28 grids (one in each successional stage, Fig. S1-A), we were unable to complete samples after mid March 2008 for security reasons. Thus, data from one of the three planned samplings of ground-dwelling arthropods, as well as data from the sampling of trunk-dwelling arthropods and fruits are missing in these three grids.

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2.3.1. Habitat structure 2.3.1.1. Vegetation stratification and connectivity. In 24 points in each sampling grid, located at both ends of 12 15-m long transects (Fig. S1-B), we used a 3-m pole to help establishing an imaginary vertical column with 15 cm in diameter, in which the height of the lower and upper limits of all vegetation strips was estimated with the help of a rangefinder. To estimate vegetation stratification, we calculated the mean among the 24 points of the length occupied by vegetation in seven vertical strata (0–1 m, 1–5 m, 5– 10 m, 10–15 m, 15–20 m, 20–25 m and >25 m) (modified from Malcolm, 1995; Pardini et al., 2005). To estimate vegetation connectivity, we calculated the mean among the 24 points of the maximum length without vegetation. We multiplied the results by 1, so that the greater the value, the smaller the space unfilled, and the greater the vegetation connectivity. 2.3.1.2. Litter biomass and depth, woody debris volume and number. To quantify the biomass and depth of the litter (including leaves and twigs smaller than 2 cm in diameter), we collected 12 50  50-cm sub-samples in each grid (Fig. S1-B). We measured the litter depth with a ruler in four points within these 0.25-m2 sub-samples before its removal, and the depth of each sub-sample was given by the mean among these four points. The litter collected was dried at 70 °C and weighted to obtain a biomass estimate. The values for each grid are the means among the 12 subsamples. To quantify the volume and number of woody debris (the dead wood on the forest floor, including fallen logs, branches and twigs larger than 2 cm in diameter), we used 12 15-m long transects, totaling 180 m in each grid (Fig. S1-B). We measured the circumference of all dead wood crossing the transects up to 1 m in height. The volume of woody debris was calculated by the formula: v = p2Rd2/8l, where v = volume of woody debris per unit area, d = diameter of the branch (=circumference/p), and l = length of the transect (Van Wagner, 1968). 2.3.2. Food resources 2.3.2.1. Arthropod biomass. In each grid, we sampled ground-dwelling arthropods in three 5-day sessions. In each session, we installed 400-ml pitfall traps with 150 ml of ethanol 96% in eight of the 24 ends of 12 15-m long transects (Fig. S1-B). Among the three sessions, the location of the eight traps was changed in order to sample all 24 points of each grid. The sub-samples collected in each trap were screened to remove debris, dried at 60 °C, and weighed to obtain a biomass estimate. For each grid and session, we computed the mean among the eight sub-samples, and then the mean among sessions. To sample trunk-dwelling arthropods, we used downward funnel crawl traps (modified from Bar-Ness, 2005), made with two PET plastic bottles (2, 2.5 or 3 l) washed in soapy water. The bottles were joined mouth to mouth, one of them cut to form a collecting funnel, and the other filled with 200 ml of formaldehyde 5%. Traps were fixed with strings to tree trunks with 35–45 cm in circumference, about 50 cm above ground. We installed eight traps at each grid (Fig. S1-B), which remained in the field for 23 days. These sub-samples were screened, dried and weighed, and the values of biomass for each grid are the means among the eight sub-samples. 2.3.2.2. Number of species and individuals that were fruiting. We quantified fruit availability in the understorey along 12 transects (2-m wide  2-m high  15-m long) at each grid (Fig. S1-B). We quantified the number of plant species that were fruiting and the number of individuals that were fruiting in total and separately for the three most abundant species, which represent 80% of the individuals that were fruiting. We included in the count all plants in which at least one branch with fruits reached the sampled transects.

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2.4. Data analysis We used the length occupied by vegetation in each of the seven vertical strata to perform a Principal Component Analysis (PCA) in a correlation matrix using the program Canoco (Canoco for Windows version 4.5, Plant Research International, 2002). The scores of the sampling grids on the first axis of this analysis (PCA1) were used as a synthetic variable representing the changes in vegetation stratification across grids. Vegetation stratification is one of the structural aspects that most clearly changes during succession, as plant biomass accumulates during regeneration in tropical forests (Brown and Lugo, 1990; Faria et al., 2009; Guariguata and Ostertag, 2001; Pardini et al., 2005). Therefore, in addition to the successional stages, the PCA1 was also used as an independent variable, i.e. a way to quantify succession on a continuous basis. For this reason, the PCA was run twice, once considering all 28 grids, and another using only the 25 grids where data on trunk-dwelling arthropods and fruits were quantified. For the continuous dependent variables (vegetation connectivity, litter biomass and depth, woody debris volume, biomass of ground-dwelling and trunk-dwelling arthropods), as well as for the number of plant species that were fruiting, all of which fitted normal distribution, we built general linear models, while for the other discrete dependent variables (woody debris number, and the variables of number of individuals that were fruiting), we built generalized linear models with negative binomial errors (Bolker, 2007). We built three models for each dependent variable, one with the categorical independent variable successional stages (mid secondary, late secondary and old-growth), one with the continuous independent variable vegetation stratification (PCA1), and a constant model (intercept-only model). We compared these three models using the Akaike Information Criterion modified for small samples (AICc), and considered equally supported by the data those models for which differences in AICc to the first-ranked model (DAICc) were lower than 2 (Burnham and Anderson, 2002). We also calculated 95% confidence intervals for the means of the dependent variables in each of the three successional stages to check for differences among them. Analyses were run in the R environment (R.2.8.1, The R Foundation for Statistical Computing, 2008).

3. Results The first axis of the Principal Component Analysis with the 28 sampling grids explained 39.4% of the variation in vegetation distribution among vertical forest strata. This axis represents a gradient from sites with higher vegetation density in the strata from 0 to 15 m (left side of the graph) to sites with higher vegetation density in the strata above 15 m (right side of the graph) (Fig. 1). Mid secondary areas are concentrated on the left side of the graph, and old-growth areas on the right side, while late secondary areas are distributed in the center, with greater overlap with mid secondary areas (Fig. 1). Indeed, the model with PCA1 as a function of the successional stages was better than the constant model, with confidence intervals indicating differences in vegetation stratification between the old-growth areas and both the mid and late secondary areas, while the secondary areas did not differ from each other (Table 1, Fig. 2). The result of the PCA for the 25 grids where trunkdwelling arthropods and fruits were sampled followed the same pattern, with a strong correlation among grid scores in the two analyses (Pearson: r = 0.9969; p < 0.001). For six of the 12 dependent variables, the model with vegetation stratification (for litter depth, biomass of ground-dwelling arthropods, and number of fruiting individuals of Geonoma pohliana), the model with successional stages (for number of fruiting

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Fig. 1. (A) Biplot of the first and second axes of the Principal Component Analysis on vegetation density in seven vertical forest strata in 28 sampling grids. (B) Projections of grid scores on the first axis (PCA1). White circles – grids in mid secondary successional stage; grey circles – grids in late secondary successional stage; black circles – grids in old-growth successional stage.

individuals of Psychotria suterella) or both models (for vegetation connectivity, and total number of fruiting individuals) were better than the constant model, indicating a consistent pattern of variation due to forest regeneration (Figs. 2 and 3). For the remaining six dependent variables, the constant model was better than (for litter biomass, biomass of trunk-dwelling arthropods, and number of fruiting individuals of Pleurostachys cf. urvillei) or equally good to (for woody debris volume, woody debris number, and number of species that were fruiting) at least one of the models containing the independent variables quantifying succession (Table 1). For vegetation connectivity, two models were selected, the one with vegetation stratification, followed by that with successional stages (Table 1). The models indicate that connectivity decreases from earlier to later forests, i.e. as vegetation becomes concentrated in the upper forest strata (Fig. 3), and is higher in mid and late secondary stages in relation to old-growth forest (Table 1, Fig. 2). Among the variables associated with the structure at ground level, the only selected model for litter depth contains vegetation stratification, and indicates an increase from earlier to later forests, i.e. as vegetation concentrates in the upper forest strata (Table 1, Fig. 3). The first-ranked model for woody debris volume was that with vegetation stratification, followed by the model with successional stages, but both were equally good to the constant model (Table 1). According to the first-ranked model, the woody debris volume increases from earlier to later forests, where vegetation concentrates in the upper strata (Fig. 3). On the other hand, for litter biomass and woody debris number, the first-ranked model was the constant model, with no clear relationship between both variables and regeneration quantified either as a categorical or continuous variable (Table 1, Figs. 2 and 3). Among the variables related to food availability, the only selected model for ground-dwelling arthropod biomass was that with vegetation stratification (Table 1), which indicates a decrease from earlier to later forests, i.e. as vegetation concentrates in the

upper forest strata (Fig. 3). The first-ranked model for the total number of fruiting individuals was also that containing vegetation stratification, with that containing successional stages being equally good (Table 1). The models indicate that understorey fruit availability decreases from earlier to later forests, as vegetation concentrates in the upper forest strata (Fig. 3), and is higher in mid and late secondary forests compared to old-growth areas (Table 1, Fig. 2). This result is largely due to the most abundant species (Psychotria suterella, Rubiaceae), since the only selected model for the number of fruiting individuals of this species was that with successional stages, and indicates that this resource was more abundant in mid and late secondary forests than in old-growth forests (Table 1, Fig. 2). On the other hand, for the number of fruiting individuals of the third most abundant species (the understorey palm Geonoma pohliana, Arecaceae), the only selected model (containing vegetation stratification, Table 1) indicates that this resource increases from earlier to later forests, where vegetation concentrates in the upper forest strata (Fig. 3). There were no consistent patterns of variation for trunk-dwelling arthropod biomass, number of plant species that were fruiting, and number of fruiting individuals of the second most abundant species (Pleurostachys cf. urvillei, Cyperaceae), since in all these cases the constant model was the first-ranked one (Table 1, Figs. 2 and 3). 4. Discussion Most studies on regeneration in tropical forests are based on the sampling of chronosequences (e.g. Brady and Noske, 2010; Piotto et al., 2009). Recently, however, criticism to this space-for-time substitution approach has been increasing (Johnson and Miyanishi, 2008), given that the rhythm and trajectory of regeneration do not depend only on the time since abandonment, but also on features such as distance from propagule sources and land use intensity before abandonment (Chazdon et al., 2007; Guariguata and Ostertag, 2001). Our study area is a good example of the fact that the time

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Table 1 Results of the selection of models of habitat structure and food availability as a function of forest regeneration (represented by the continuous variable vegetation stratification – PCA1, and the categorical variable successional stages), including the confidence intervals for the means of the dependent variables in the three successional stages. K – number of parameters; AICc – Akaike Information Criterion modified for small samples; DAICc – difference to the AICc of the best model; Best (selected) models, equally supported by the data (DAICc < 2). Model

K

AICc

DAICc

95% Confidence intervals around mean in each stage Mid

Late

Old-growth

1.188 to 0.204

0.819 to 0.282

0.419 to 1.403

Vegetation stratification – PCA1

Stages Constant

4 2

70.4 83.9

0.0 13.6

Vegetation connectivity

PCA 1 Stages Constant

3 4 2

87.4 88.3 110.0

0.0 0.9 22.6

5.966 to 4.610

7.322 to 5.806

8.924 to 7.568

Constant Stages PCA 1

2 4 3

243.6 246.0 246.1

0.0 2.3 2.5

117.3 to 139.9

105.8 to 131.1

120.4 to 143.0

PCA 1 Stages Constant

3 4 2

29.7 37.5 42.0

0.0 7.8 12.3

2.590 to 3.137

2.498 to 3.111

3.095 to 3.643

PCA 1 Stages Constant

3 4 2

245.4 245.6 246.4

0.0 0.2 1.0

8.784 to 31.291

24.249 to 49.413

25.508 to 48.015

Woody debris number

Constant PCA 1 Stages

2 3 4

238.9 240.2 243.2

0.0 1.3 4.4

76.42 to 95.55

81.40 to 104.15

76.97 to 96.21

Biomass of ground-dwelling arthropods (g)

PCA 1 Constant Stages

3 2 4

22.2 19.9 17.8

0.0 2.3 4.4

0.251 to 0.455

0.202 to 0.430

0.132 to 0.336

Biomass of trunk-dwelling arthropods (g)



Constant PCA 1 Stages

2 3 4

150.0 147.6 145.9

0.0 2.3 4.0

0.0305 to 0.0463

0.0337 to 0.0516

0.0283 to 0.0441

Number of species that were fruiting

Constant PCA 1 Stages

2 3 4

129.9 131.6 134.4

0.0 1.7 4.5

5.410 to 9.701

3.853 to 8.718

5.410 to 9.701

Total number of fruiting individuals

PCA 1 Stages Constant

3 4 2

221.0 222.0 228.8

0.0 1.0 7.8

50.01 to 80.51

42.38 to 73.06

25.04 to 41.40

Fruiting individuals of Psychotria suterella (Rubiaceae)

Stages PCA 1 Constant

4 3 2

204.7 213.5 221.4

0.0 8.8 16.6

28.38 to 68.27

22.63 to 61.61

3.69 to 10.12

Fruiting individuals of Pleurostachys cf. urvillei (Cyperaceae)

Constant PCA 1 Stages

2 3 4

156.7 159.2 161.7

0.0 2.6 5.0

2.556 to 18.420

3.769 to 34.782

3.799 to 26.725

Fruiting individuals of Geonoma pohliana (Arecaceae)

PCA 1 Stages Constant

3 4 2

128.5 134.5 140.3

0.0 6.0 11.8

0.422 to 4.319

0.781 to 9.298

3.505 to 26.717

Litter biomass (g)

Litter depth (cm)

Woody debris volume (m3/ha)

since abandonment may be an inadequate variable for accessing successional stages. Although all secondary forests in the Reserve have been at least 80 years in regeneration, many areas still show features of early successional stages, given the long and intense history of fragmentation and land use (Metzger et al., 2006). Indeed, differences between late secondary areas and old-growth forests were larger than between mid and late secondary areas for six of the 13 analyzed variables (vegetation stratification and connectivity, litter depth, total number of fruiting individuals, number of fruiting individuals of P. suterella and G. pohliana), indicating a long-lasting residual effect of deforestation on structural and functional features of the forest. Therefore, to quantify regeneration we chose variables that are based on structural features. On the one hand, the categorical variable successional stages has the advantage of allowing the mapping of large areas through photo interpretation, and is based on the Brazilian environmental law, allowing more direct application in public policies. On the other hand, continuous variables measured in the field, as vegetation stratification, allow a more refined ordering of the sampling sites and thus a more robust analysis. Indeed, although there is correspondence between the results of

these two ways of quantifying regeneration, overall the models with the continuous variable vegetation stratification were better in explaining the variation in habitat structure and food availability. The gradient observed in vegetation stratification among the forests of the study area is a recurrent pattern in tropical forests in different successional stages (Brown and Lugo, 1990; Faria et al., 2009; Guariguata and Ostertag, 2001; Pardini et al., 2005), reflecting an important structural change that occurs during regeneration, associated with the biomass accumulation and the decrease in light availability within the forest. In contrast to younger forests, older and well structured forests have taller canopy and the upper strata limit the amount of light, and thus the vegetation development, in the lower strata.

4.1. Habitat structure Our results indicate that the main changes in habitat structure during regeneration are an increase in the complexity at ground level (due to an increase in litter depth and a trend of increasing

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Fig. 2. Observed (circles) and estimated (maximum likelihood estimates of the parameters of the models for stages in Table 1 – points connected by lines) values of habitat structure and food availability in three successional stages: mid secondary (MS), late secondary (LS) and old-growth (OG).

woody debris volume), and a decrease in vegetation connectivity. Both are important habitat features for different wildlife groups. Vegetation connectivity was higher in earlier forests, in which canopy is lower, allowing higher light availability and greater vegetation development inside the forest. This habitat characteristic should influence the locomotion of animals that move on the vegetation or fly. Higher connectivity should facilitate movement for animals like arboreal small mammals, which rarely jump from one branch to another (see Cunha and Vieira, 2002). On the other hand, higher connectivity may hinder the flight of animals like bats (Spada et al., 2008). Indeed, some studies in the neotropics show that the understorey vegetation density, or direct measurements of vegetation connectivity, are negatively related to the richness and abundance of bats (Faria, 2002; Pardini et al., 2009), but positively related to the richness and abundance of non-volant small mammals (Fonseca and Robinson, 1990; Grelle, 2003; Lambert et al., 2006; Malcolm, 1995; Pardini et al., 2005). Although we have not observed consistent changes in litter biomass with regeneration (see also Barlow et al., 2007b; NegreteYankelevich et al., 2007), litter depth was higher in older-growth areas. In addition, woody debris volume also tended to increase in these later forests, as found in other studies (e.g. DeWalt et al., 2003; Saldarriaga et al., 1988). The abundance, composition, or richness of ground and soil fauna in tropical forests, either invertebrates or vertebrates, are positively related to both litter depth (e.g. Sayer et al., 2010; Van Sluys et al., 2007) and woody debris volume (e.g. Grove, 2002; Lambert et al., 2006), which are considered important for moisture retention, provision of food, foraging sites, and shelter, or guiding locomotion.

4.2. Food resources Our results indicate that despite the fact that some specific resources, such as palm fruits, are more abundant in older-growth forests, earlier forests present higher total food availability regarding arthropods and fruits, which should influence many insectivorous and frugivorous species. The biomass of ground-dwelling arthropods (but not trunkdwelling) was related to changes in vegetation stratification, being greater in earlier forests. Higher arthropod biomass or abundance on the ground or understorey of tropical forests were also previously found in younger or disturbed forests and in those subjected to edge effect (e.g. Lambert et al., 2006; Malcolm, 1997; Zurita and Zuleta, 2009), all of which are expected to present higher net primary productivity compared to well-preserved old-growth forests (Guariguata and Ostertag, 2001; Montagnini and Jordan, 2005). This indicates that the higher arthropod availability in disturbed or yourger forests may be the result of the higher primary productivity in those areas, i.e. a result of a bottom-up effect of the increased food resources for these arthropods provided by the higher primary productivity (Hunter and Price, 1992; e.g. Yang et al., 2007). On the other hand, several studies found an increased abundance of terrestrial arthropods associated with higher litter (e.g. Sayer et al., 2010; Yang et al., 2007) and woody debris (e.g. Jabin et al., 2004) depth, volume or biomass. However, since litter depth and woody debris volume were higher in older-growth forests, while ground-dwelling arthropod biomass was higher in earlier forests, our results suggest that differences in arthropod availability among successional stages are more associated with

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Fig. 3. Estimated relationships (maximum likelihood estimates of the parameters of the models for PCA1 in Table 1 – lines) of habitat structure and food availability with vegetation stratification (grid scores on the first axis of the Principal Component Analysis on vegetation stratification – PCA1). White circles – observed values for grids in mid secondary successional stage; grey circles – observed values for grids in late secondary successional stage; black circles – observed values for grids in old-growth successional stage.

changes in the vertical structure of the forest (possibly associated with changes in net primary productivity) than with structural aspects at ground level. Increased total fruit availability in the understorey of earlier forests as we observed was also found in other neotropical forest localities (e.g. DeWalt et al., 2003; Levey, 1988), and should also be related to the increased net primary productivity in the understorey due to the higher light availability. In our study area, much of this increased fruit availability was due to Psychotria suterella, a very common shrub/treelet in secondary Atlantic forest areas (Carvalho et al., 2000; Catharino et al., 2006). Despite the presence of alkaloids (Pasquali et al., 2006), the fruits of Psychotria species are consumed by various animal species (e.g. Almeida et al., 2006; Lessa and Costa, 2010; Poulin et al., 1999; Richard and Rada, 2006). On the other hand, we found a higher number of fruiting individuals of the understorey palm Geonoma pohliana in the older-growth forests, which agrees with the increased palm abundance usually found in old-growth neotropical forests (DeWalt et al., 2003; Guariguata et al., 1997). Therefore, species that depend on palm fruits, which are considered key resources in tropical forests (Snow, 1981; Terborgh, 1986), should benefit in older-growth areas.

5. Implications for management and conservation Our results indicate that changes in vegetation structure, function, and floristics during forest succession lead to consistent changes in habitat structure and food availability, particularly a decrease in vegetation connectivity, an increase in the structural complexity at the ground level, and a decrease in total grounddwelling arthropod and understorey fruit availability. Since these features are important to many wildlife groups (e.g. Grove, 2002; Herrera et al., 2002; Pardini et al., 2005), they could be among the drivers of species replacement from habitat generalists to old-growth specialists commonly observed during tropical forest regeneration (Barlow et al., 2007a; Bowen et al., 2007; Dunn, 2004; Gardner et al., 2007; Jenkins et al., 2003; Lawton et al., 1998; Pardini et al., 2009; Uehara-Prado et al., 2009), as hypothetized below. Generalist species adapted to fast population growth in resource-rich environments should proliferate and dominate earlier successional stages (Amarasekare, 2003), particularly those that require vegetation connectivity for locomotion. On the other hand, species with higher competitive ability in resource-limited environments (Amarasekare, 2003), or those that depend on resources such as palm fruits, on higher complexity at the ground

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level, or on open space for flying, should dominate older-growth forests. Since the identification of the drivers of wildlife recovery is crucial for restoration strategies (Prach and Walker, 2011; Walker et al., 2007), it is important that future work test and further develop the hypotheses presented here. Understanding the drivers of wildlife changes during regeneration is particularly important to guide restoration of highly deforested and degraded forests such as the Brazilian Atlantic forest (Chazdon et al., 2009b). The potential of regeneration in tropical forests is considered high, and about 70 years of regeneration can make structural aspects of the forest resemble that of old-growth forests (DeWalt et al., 2003; Guariguata and Ostertag, 2001; Hughes et al., 1999). However, this is true when conditions are appropriate, with regenerating areas close to propagule sources and subjected to low land use intensity before abandonment (DeWalt et al., 2003; Guariguata and Ostertag, 2001; Hughes et al., 1999). On the contrary, in our study area we found structural and functional differences between old-growth forests and secondary forests with more than 80 years of regeneration. This highlights the fragility of much altered ecosystems, such as the Brazilian Atlantic forest, where soil depletion, associated with isolation of remnants and edge effects, may hinder regeneration, or even trigger a retrogressive succession process, during which remnants acquire features of early successional stages (Tabarelli et al., 2010, 2008; Tilman et al., 1994). Our results suggest that, in such conditions, restoration strategies are essential (Chazdon, 2008; Lamb et al., 2005; Rodrigues et al., 2011, 2009) also for recovering structural and functional aspects expected to be important for wildlife recovery. Acknowledgements We thank A. Pardini for reviewing the English of the manuscript; J.P. Metzger, V.R. Pivello, A.M.G. Teixeira, C. Simonetti, L.R. Tambosi, A. Henderson, R.A.F. Lima, C. Guimarães, P. Rocha, P.I.K.L. Prado, T. Pavão, A. Albuquerque, R.P. Rocha, T.K. Martins, R.G. Pimentel, T. Püttker, C.S. de Barros, N. Rossi, J. de Luca, M. Cardoso-Jr., E. Jeniffer, N. Pineiro, E. Frigeri and many other colleagues for invaluable help during the design and execution of the project; and FAPESP – Fundação de Amparo à Pesquisa do Estado de São Paulo (06/58348-9 and 05/56555-4), CNPq – Conselho Nacional de Desenvolvimento Científico e Tecnológico, BMBF – German Federal Ministry of Education and Research (01 LB 0202) and CAPESPROAP – Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Programa de Apoio à Pós-graduação for grants. Appendix A. Supplementary material Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.foreco.2012. 07.020. References Almeida, E.M., Costa, P.F., Buckeridge, M.S., Alves, M.A.S., 2006. Potential bird dispersers of Psychotria in an area of Atlantic forest on Ilha Grande, RJ, Southeastern Brazil: biochemical analysis of the fruits. Braz. J. Biol. 66 (1A), 1–8. Amarasekare, P., 2003. Competitive coexistence in spatially structured environments: a synthesis. Ecol. Lett. 6, 1109–1122. Barlow, J., Gardner, T.A., Araujo, I.S., Ávila-Pires, T.C., Bonaldo, A.B., Costa, J.E., Esposito, M.C., Ferreira, L.V., Hawes, J., Hernandez, M.I.M., Hoogmoed, M.S., Leite, R.N., Lo-Man-Hung, N.F., Malcolm, J.R., Martins, M.B., Mestre, L.A.M., Miranda-Santos, R., Nunes-Gutjahr, A.L., Overal, W.L., Parry, L., Peters, S.L., Ribeiro-Junior, M.A., da Silva, M.N.F., da Silva Motta, C., Peres, C.A., 2007a. Quantifying the biodiversity value of tropical primary, secondary, and plantation forests. Proc. Natl. Acad. Sci. USA 104, 18555–18560. Barlow, J., Gardner, T.A., Ferreira, L.V., Peres, C.A., 2007b. Litter fall and decomposition in primary, secondary and plantation forests in the Brazilian Amazon. For. Ecol. Manage. 247, 91–97.

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