RESEARCH ARTICLE
Indicators of restoration success in riparian tropical forests using multiple reference ecosystems Marcio S. Suganuma1,2,3 , Giselda Durigan1 Forest restoration by planting trees often accelerates succession, but the trajectories toward reference ecosystems have rarely been evaluated. Using a chronosequence (4–53 years) of 26 riparian forest undergoing restoration in the Brazilian Atlantic Forest, we modeled how the variables representing forest structure, tree species richness and composition, and the proportion of plant functional guilds change through time. We also estimated the time required for these variables to reach different types of reference ecosystems: old-growth forest (OGF), degraded forest, and secondary forest. Among the attributes which follow a predictable trajectory over time are: the basal area, canopy cover, density and tree species richness, as well as proportions of shade tolerant and slow growing species or individuals. Most of the variation in density of pteridophythes, lianas, shrubs and phorophythes, proportion of animal-dispersed individuals, rarefied richness and floristic similarity with reference ecosystems remain unexplained. Estimated time to reach the reference ecosystems is, in general, shorter for structural attributes than for species composition or proportion of functional guilds. The length of this time varies among the three types of reference ecosystems for most attributes. For instance, tree species richness and proportion of shade tolerant or slow growing individuals become similar to secondary forests in about 40 years, but is estimated to take 70 years or more to reach the OGF. Of all the variables considered, canopy cover, basal area, density, and richness of the understory—by their ecological relevance and predictability—are recommended as ecological indicators for monitoring tropical forest restoration success. Key words: assembly rules, chronosequence, ecological indicators, forest structure, functional guilds, linear models, monitor-
ing, semideciduous tropical forest
Implications for practice • The expected values of most plant community attributes for each age of forest restoration plantings can be predicted by modeling on the basis of a chronosequence, facilitating monitoring of intermediate goals. • The best indicators for monitoring forest communities being restored are: canopy cover, basal area, density of saplings, and richness of the understory. • Reference ecosystems for setting final goals of restoration projects should represent the entire range of histories and regimes of disturbance within a same ecological region. • Within the same ecological region, forest restoration by planting trees result in a standard trajectory, predictable for most attributes of structure, richness, and functional guilds, which should guide the establishment of targets for restoration. Floristic composition, however, is not predictable. Introduction Interventions aiming to restore tropical forests generally accelerate the recovery of plant communities in areas degraded by human occupation. The intentional planting of trees (restoration plantings) allows the forest structure to quickly recover and thereby provides a suitable habitat for colonization by late successional species (Chapman & Chapman 1996; Aide et al. 2000;
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Souza & Batista 2004; Chazdon 2008; Holl & Aide 2011). Even in highly degraded environments, like most pasture areas (Florentine & Westbrooke 2004; Sansevero et al. 2011) or mining areas (Parrotta et al. 1997; Parrotta & Knowles 1999), the planting of tree species facilitates the process of recolonization by native species, which originate from the regional species pool (Lugo 1992; Parrotta et al. 1997; Ruiz-Jaen & Aide 2005b). However, both the understanding and prediction of successional trajectories of forest restoration through planting is understudied (Souza & Batista 2004; McClain et al. 2011; Sansevero et al. 2011; Suganuma et al. 2014). In forest restoration there are different theories about the outcome of succession from a disturbed state to the final state of the ecosystem. From the different theories, one can expect Author contributions: GD conceived the research; MSS, GD designed the research and collected the data; MSS organized and analyzed the data; both interpreted the results, and wrote and edited the manuscript. 1 Forestry Institute of São Paulo State, Assis State Forest, PO Box 104, 19802-970, Assis, São Paulo Brazil 2 University of São Paulo/EESC, Environmental Engineer Science (PPG-SEA), São Carlos, São Paulo Brazil 3 Address correspondence to Marcio S. Suganuma, email
[email protected]
© 2014 Society for Ecological Restoration doi: 10.1111/rec.12168 Supporting information at: http://onlinelibrary.wiley.com/doi/10.1111/rec.12168/suppinfo
Restoration Ecology Vol. 23, No. 3, pp. 238–251
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Indicators of riparian forests restoration success
that forest restoration (1) will lead the ecosystem being restored to a state similar to natural ecosystems attributes (White & Walker 1997; Society for Ecological Restoration International Science and Policy Working Group [SER] 2004) or (2) will lead to successions of alternative stable states which are different from the natural ecosystems in their functional and structural aspects (Hobbs et al. 2009), or even, (3) will lead to ecosystems that are different in species composition, although functionally similar to the original forest (Hobbs & Norton 2004; Hobbs et al. 2006; Lugo 2010). Studies on secondary tropical forests undergoing succession point to the third alternative (Liebsch et al. 2008; Dent & Wright 2009), but there is no evidence that this is also true for forests being recovered by active restoration intervention. In addition to there being no consensus on the final result of the restoration, there are also different theories about the pathways that can lead to this final state. To be able to recognize when an ecosystem requires restoration interventions depends on the ability to predict the effect of interventions and non-interventions on the successional trajectories (Suding and Hobbs 2009; Holl & Aide 2011), to ensure that there will be no unnecessary waste of labor, time and money (Ruiz-Jaen & Aide 2005a; Florentine 2008). Three possible trajectories for ecosystem restoration were proposed by Suding and Hobbs (2009): (1) the gradual continuous model, where changes in the environment and the successional trajectory of the community gradually drift, becoming similar to the ecosystem before the disturbance; (2) the stochastic model, where there is an imbalance between changes in the ecosystem and changes in the environment without any predictable trajectory; and (3) the threshold model, in which small changes in environmental conditions lead to abrupt changes in the ecosystem from one stable state to another. Data obtained from chronosequences have been used to build predictive models of community changes over time, using the gradual continuous model pattern in attributes such as forest structure, sapling density, richness and biomass (Chazdon 2003; Chazdon et al. 2007; Letcher & Chazdon 2009; Maza-Villalobos et al. 2011). The majority of these studies were performed to model the successional trajectory of secondary forests (SFs) (Chazdon 2003; Chazdon et al. 2007; Liebsch et al. 2007, 2008; Letcher & Chazdon 2009; Lebrija-Trejos et al. 2010; Maza-Villalobos et al. 2011). In the case of restoration planting, only a few studies on succession exist (Reay & Norton 1999; Souza & Batista 2004; Ruiz-Jaen & Aide 2005b; McClain et al. 2011; Suganuma et al. 2014) and they are insufficient to assess either the effectiveness of this technique in triggering successional processes or in predicting whether the restoration goals can be achieved. Monitoring the evolution of communities requires good ecological indicators, which remains a challenge in evaluating the results of restoration interventions (Ruiz-Jaen & Aide 2005a; Suding 2011). Specifically, this requires the selection of a maneuverable set of ecological indicators that together meet the following criteria: (i) the variables are easily measured, (ii) sensitive to stresses on the system, (iii) have predictable and known responses to disturbances or anthropogenic stresses, (iv)
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are anticipatory and can predict changes that can be prevented by management actions, and (v) are integrative (Dale & Beyeler 2001). A large set of variables have been used as ecological indicators for tropical forests undergoing restoration, such as stem density, basal area, aerial biomass, plant species richness and relative abundance of plant life forms other than trees, woody plants in natural regeneration, soil properties, and even richness and abundance of faunal groups (Aide et al. 2000; Ruiz-Jaen & Aide 2005a; Letcher & Chazdon 2009). Little evidence exists, however, of the predictive power of these variables as indicators of ecosystem recovery over time. For the Brazilian Atlantic Forest, especially in riparian areas, restoration through planting of seedlings has increased in recent decades (Rodrigues et al. 2009), providing an unusual opportunity to study successional trajectories. We constructed a chronosequence using these existing forest restorations and compared with a set of natural forest ecosystems as references to reply the following questions: (1) Do the attributes of the forests being restored follow predictable trajectories over time? (2) How long will it take for each attribute to reach the reference level? This allowed us to: (1) build predictive models that represent changes in each attribute of the plant community over time in this ecological region and select good indicators for monitoring progress toward restoration; and (2) estimate, by using the most robust models, the time required for the attributes of a forest under restoration to reach the same values as that of reference ecosystems. Based on the premise that native forests with distinct histories of disturbances differ in their attributes (Hobbs & Norton 1996; Choi 2004), and that using as reference a broad spectrum of natural ecosystems has been recommended (Brinson & Rheinhardt 1996), we used three different categories of reference forests for this estimation. Methods Study region
All 35 study areas are located in an extensive region previously covered by Semideciduous Tropical Forest—STF, one of the subtypes of the Brazilian Atlantic Forest, which is characterized by climate seasonality. These areas are in the state of São Paulo, Paraná and Mato Grosso do Sul, spanning an area of approximately 80,000 km2 (Fig. 1). The average annual temperature in this region ranges between 21∘ C and 24∘ C, with average minimum temperatures around 10∘ C and average maximum of 30∘ C. The average annual rainfall varies between 1,150 and 1,630 mm, and may be less than 40 mm in the driest months which occurs in the winter (Sentelhas et al. 2003). Specifically, we were interested in the restoration technique of planting nursery-raised tree seedlings. We selected 26 sites in which the restoration technique was applied between 4 and 53 years ago. The sites varied in the size of the restored area (0.5–135.3 ha), composition and number of species planted (1 to more than 100), proportion of native species (45–100%), density of seedlings (625–2,667 seedlings per ha), soil fertilization, after-care (1 to more than 3 years), distance from seed sources (0–5,700 m), previous land use (pasture or agriculture). Importantly, these selected sites represented different climate and soil
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Indicators of riparian forests restoration success
Figure 1. Location of the 35 study sites in the states of São Paulo, Paraná and MatoGrosso do Sul, Brazil.
conditions, landscapes and the wide spectrum of techniques used for forest restoration by seedling planting in the Atlantic Forest region. (Detailed information about the 26 restoration sites are presented as Table S1, Supporting Information.) For the different reference ecosystems, we selected nine riparian forests in patches of different sizes, different soil types and climates within the STF. These sites were categorized according to their ecological integrity as: (1) Old-growth Forest—OGF: three primary forests within large areas (376–34,000 ha), without any evidence of disturbance; (2) Degraded Forest—DF: three small fragments (3–20 ha) of primary forests experiencing edge effects, the presence of cattle or selective wood harvesting until the 1970s; (3) SF: three forests in the process of natural regeneration (3–10 ha) with an estimated time of abandonment between 35 and 60 years, in areas previously used as pastures (Fig. 1). (Detailed information on the nine reference ecosystems can be found in Table S2.) Plant community sampling
For each of the 35 sites, we sampled a total area of 1,000 m2 . In order to capture variability throughout the forest, this area was subdivided into 10 plots of 20 × 5 m, parallel to the border of the water. Plots were randomly distributed between 0 and 50 m away from the water body, and above the flooding level, stretching at least 200 m and no more than 700 m. In every plot, all individuals of tree species of height (h) equal or greater than 0.5 m were counted and identified. The sampled individuals were categorized according to their diameter at breast height (measured at 1.3 m) into three size classes: (1) understory with dbh
