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Vol. 24. Issue 3.
Pages 247-362 (July - September 2026)
Research Letters
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Landscape heterogeneity buffers soundscape degradation in human-modified landscapes

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133
Júlio Cesar Lima de Araújoa,*
Corresponding author
jcl.araujo@unesp.br

Corresponding author. Present address: São Paulo State University (Unesp), Institute of Biosciences, Av. 24 A,1515, Bela Vista, Rio Claro, SP, Brazil.
, Vinicius de Avelar São Pedrob, Alexandre Uezuc, Alexandre Camargo Martensenb
a Programa de Pós Graduação em Ciências Ambientais – Centro de Ciências Biológicas e da Saúde, Universidade Federal de São Carlos, Rod. Washington Luís, km 235 - SP-310, São Carlos, SP, Brazil
b Centro de Ciências da Natureza, Universidade Federal de São Carlos, Rod. Lauri Simões de Barros, km 12 - SP-189, Bairro Aracaçu, Buri, SP, Brazil
c Instituto de Pesquisas Ecológicas (IPÊ), Rod. Dom Pedro I, km 47, Nazaré Paulista, SP, Brazil
Highlights

  • Heterogeneity sustains acoustic complexity in low habitat amount tropical landscapes

  • Acoustic complexity declines with landscape homogenization, even without habitat loss

  • In intensified landscapes, more habitat is needed to maintain biodiversity

  • Findings support planning that balances land use, habitat amount and biodiversity

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Table 1. Candidate models explaining variation in acoustic indices for each analyzed period, ordered by Akaike’s Information Criterion (AIC).
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Abstract

Land-use intensification reshapes tropical landscapes, altering the spatial structure of habitats and affecting acoustically active biodiversity. We assessed how habitat amount and landscape heterogeneity jointly influence soundscapes in 72 Atlantic Forest landscapes spanning 5–85% native vegetation. We calculated acoustic diversity (ADI, Distribution of acoustic energy across frequency bands) and acoustic complexity (ACI, Variability in acoustic amplitude) based on autonomous audio recordings for diurnal, nocturnal, and combined periods. ADI decreased with increasing habitat amount, likely reflecting the dominance of generalist and insect-driven acoustic signatures in more altered landscapes. In contrast, ACI increased with habitat amount, but this effect depended strongly on landscape heterogeneity. Heterogeneous landscapes maintained high ACI even at <40% habitat cover, acting as a buffer against soundscape degradation, whereas homogeneous landscapes required more than 40–50% habitat to reach comparable levels of acoustic complexity. These findings show that, under ongoing intensification and simplification of human-modified landscapes, maintaining the same proportion of native vegetation may still lead to biodiversity loss if heterogeneity declines. Integrating habitat amount and heterogeneity into conservation planning is therefore essential for sustaining the structure and function of acoustic communities in human-modified tropical regions.

Keywords:
Acoustic complexity
Habitat amount
Agricultural intensification
Atlantic Forest.
Graphical abstract
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Introduction

The global demand for food, fiber, and biofuels has intensified land use and accelerated the loss and fragmentation of native habitats (Tilman et al., 2011). In human-modified regions, these processes reorganize spatial patterns and erode ecological integrity, driving biodiversity decline across multiple taxa (Newbold et al., 2020). As landscapes simplify, native vegetation becomes restricted to marginal areas (Mantero et al., 2020), and ecological communities shift toward generalist and disturbance-tolerant species (Gámez-Virués et al., 2015), reducing functional diversity and disrupting ecosystem services (Le Provost et al., 2023).

Landscape heterogeneity, encompassing both land-cover diversity and spatial configuration, has emerged as a key factor modulating the effects of habitat loss (Priyadarshana et al., 2024). Heterogeneous landscapes tend to support higher biodiversity by providing complementary resources and reducing isolation (Fletcher et al., 2024), whereas homogenized landscapes promote biotic and functional homogenization (Gossner et al., 2023), with cascading ecological impacts (Wang et al., 2021). Understanding how habitat amount and landscape heterogeneity jointly shape biodiversity is therefore essential for managing multifunctional landscapes.

Acoustic indices offer a promising approach by summarizing the distribution, variability, and complexity of sounds, providing a cost-effective way to infer biodiversity patterns across large spatial and temporal scales (Bradfer-Lawrence et al., 2023, 2024). Despite ongoing methodological refinement, these indices show strong associations with species richness and activity across taxa (Dröge et al., 2024) and respond sensitively to habitat loss and land-use intensification (Scarpelli et al., 2021; Burivalova et al., 2022). Because acoustically active taxa exhibit strong behavioral responses to environmental variation, soundscapes can serve as indicators of ecological integrity in human-modified systems (Burivalova et al., 2022).

Despite growing interest in soundscape monitoring, few studies evaluate the combined influence of habitat amount and landscape heterogeneity on acoustic patterns. Research often treats these drivers separately, limiting our understanding of whether heterogeneity can buffer soundscape degradation in landscapes with reduced habitat. Moreover, although habitat amount is a key biodiversity driver in fragmented landscapes (Watling et al., 2020), its interaction with landscape composition (Fahrig et al., 2011) remains poorly explored for acoustically active taxa, particularly in tropical regions undergoing rapid land-use transitions. The Atlantic Forest of Brazil provides an ideal context for addressing these knowledge gaps, given its extensive habitat loss, strong land-use gradients, and marked variation in landscape configuration (Vancine et al., 2024).

Here, we investigate how native vegetation amount and landscape heterogeneity jointly influence soundscapes in the Alto Paranapanema watershed, southeastern Brazil. We focus on acoustically active taxa (vertebrates and insects), particularly birds and anurans, given their distinct circadian activity peaks and sensitivity to habitat change (De Araújo et al., 2024). We sampled 72 landscapes spanning a full gradient of habitat amount and heterogeneity, calculated acoustic indices and evaluated five alternative hypotheses: (i) if soundscapes are primarily driven by habitat amount, acoustic indices should increase with the proportion of native vegetation; (ii) landscape heterogeneity may enhance soundscape indices by increasing the diversity and availability of habitats; (iii) both factors may act independently, producing additive effects; (iv) landscape heterogeneity may modify the influence of habitat amount on soundscape indices through an interaction between the two factors; and (v) these relationships differ between diel periods due specific biological groups activity and different acoustic patterns. This framework allows us to evaluate how landscape composition shapes soundscape patterns and their implications for landscape management in human-modified tropical landscapes.

MethodsStudy area

The study was conducted in the central portion of the Alto Paranapanema watershed, São Paulo State, southeastern Brazil, a region comprising a mosaic of native Atlantic Forest remnants, pasturelands, croplands and silviculture (Fig. 1). The climate is subtropical, with mean annual temperatures of 18–20 °C and ∼1,200 mm of rainfall concentrated between September and March. Elevation ranges from 600 to 800 m. To minimize biotic heterogeneity unrelated to land use, all sampling points were located in forest remnants of intermediate to advanced successional stages (see Supplementary Materials - Study area for details).

Fig. 1.

Land use and cover (MapBiomas Collection 3.1) of the central portion of the Alto Paranapanema watershed. The red shape represents the São Paulo state and the red box represents the sampling area. The dots represent the sampled points.

Landscape selection

Landscape selection was based on two key metrics: habitat amount and landscape heterogeneity, calculated using a squared moving window of 1,592 hectares (3,990 × 3,990 m) applied across a refined 30 m land-use/cover raster map from MapBiomas Collection 3.1. This spatial extent is consistent with the scale of effect at which organisms respond to landscape (Jackson and Fahrig, 2015) and has been widely used in studies in the Atlantic Forest (Boscolo and Metzger, 2009; Melo et al., 2024). Within each window, heterogeneity was calculated using the Shannon–Weaver index based on land-cover diversity and habitat amount was estimated as the proportion of native vegetation pixels. Landscapes were stratified into high, medium, and low heterogeneity using ±1 SD within each 10% habitat-amount class (except for the 65–85% class) to ensure full gradient representation (Supplementary Materials - Fig. S1). Heterogeneity was classified into three levels to account for its non-linear relationship with habitat amount and to distinguish landscape configurations relative to the proportion of native vegetation, enabling evaluation of how variation in heterogeneity across these gradients affects the soundscape. From all candidate sites separated by ≥2 km, we selected 72 landscapes spanning 5–85% native vegetation and all heterogeneity classes. Sampling points were positioned at least 100 m from large artificial water bodies when possible (see Supplementary Materials - Landscape selection for details).

Soundscape survey

We deployed autonomous recorders (AudioMoth v1.1.0) between October 2021 and April 2022, recording at 48 kHz in a 1-min on / 9-min off cycle over 24 h. Devices remained active for at least 21 days per site. Because recording effort varied among sites (average of 4,666 ± 1,807 min), analyses used a standardized 3,150 min per site, corresponding to the first 21 sampling days, totaling 242,550 min (see Supplementary Materials - Soundscape survey for details).

Biologically meaningful time windows were extracted following De Araújo et al. (2024): Day (05:00–09:00, peak bird activity), Night (19:00–23:00, peak anuran activity) and Both (combined Day + Night dataset).

Soundscape metrics

We calculate five acoustic indices (Acoustic Diversity, Acoustic Evenness, Total Entropy, Acoustic Complexity, Bioacoustic, Bradfer-Lawrence et al., 2023, 2025a), but retained only Acoustic Diversity Index (ADI) and Acoustic Complexity Index (ACI) due to high correlation among indices (r > 0.8, Supplementary Materials - Table S1). Indices were computed in R using seewave and soundecology, restricted to 0–10,000 Hz. Background noise was attenuated using the rmnoise function (see Supplementary Materials - Soundscape metrics for details).

Data analysis

We fitted Linear Mixed Models (LMMs) to evaluate the effects of habitat amount and landscape heterogeneity on ADI and ACI. Four models were tested: (i) habitat amount; (ii) heterogeneity; (iii) additive effects; (iv) interaction. Habitat amount and heterogeneity class were included as fixed effects, and month of deployment as a random effect to account for potential seasonal variation (October 2021–April 2022). Model selection was based on AIC (ΔAIC < 2), and model fit assessed using marginal and conditional R². Day–Night correlations were evaluated using Spearman’s test (see Supplementary Materials - Data analysis for details), to assess whether soundscapes recorded in different diel periods responded similarly to landscape variables.

ResultsAcoustic Diversity (ADI) models

Across the 72 sampled landscapes, ADI decreased with increasing habitat amount. For the Day and Both (Day + Night) periods, the best-supported models included only habitat amount, showing a significant negative relationship with native vegetation cover. During the Night period, models including habitat amount or landscape heterogeneity received similar support, but neither was statistically significant. Overall, the strongest and most consistent pattern was a reduction in ADI as habitat amount increased, particularly in the diurnal soundscape (Fig. 2, Table 1).

Fig. 2.

Results from the Linear Mixed Models (LMM) of the mean ADI by periods (circle for Day, triangle for Night and square for Both) for each location as a function of habit amount for the best and best supported models selected (a) Day period (05 h to 09 h) best model, (b) Night period (19 h to 23 h) best model, (c) Both period (05 h-09 h and 19 h - 23 h) best model, (d) Night period (19 h to 23 h) best supported mode.

Table 1.

Candidate models explaining variation in acoustic indices for each analyzed period, ordered by Akaike’s Information Criterion (AIC).

Index  Period  Model  AIC  ΔAIC  wAIC  R² cond  R² marg  β0 (intercept)  β (slope) 
ADIDay (05 h - 09 h)Proportion  56.70  0.00  0.95  0.26  0.11  1.98  −0.01 
Heterogeneity  63.76  7.05  0.02  –  –  –  – 
Proportion + Heterogeneity  64.10  7.40  0.03  0.27  0.15  h = 1.98, m = 2.05, l = 1.87  h = -0.01, m = -0.01, l = -0.01 
Proportion * Heterogeneity  72.34  15.64  0.0004  0.27  0.16  h = 2.08, m = 1.97, l = 1.94  h = -0.01, m = -0.008, l = -0.004 
Night (19 h–23 h)Heterogeneity  140.74  0.00  0.64  –  –  –  – 
Proportion  142.44  1.66  0.28  0.19  0.05  1.65  −0.01 
Proportion + Heterogeneity  145.09  4.30  0.07  0.34  0.08  h = 1.71, m = 1.39, l = 1.72  h = -0.004, m = -0.004, l = -0.004 
Proportion * Heterogeneity  150.30  9.51  0.005  0.40  0.10  h = 1.58, m = 1.27, l = 2.05  h = -0.0003, m = -0.001, l = 0.013 
Both (05 h-09 h 19 h–23 h)Proportion  79.98  0.00  0.93  0.18  0.14  1.87  −0.01 
Heterogeneity  85.74  5.76  0.05  –  –  –  – 
Proportion + Heterogeneity  88.34  8.36  0.01  0.22  0.11  h = 1.87, m = 1.77, l = 1.83  h = -0.01, m = -0.01, l = -0.01 
Proportion * Heterogeneity  95.81  15.83  0.0003  0.28  0.11  h = 1.84, m = 1.65, l = 2.00  h = -0.01, m = -0.003, l = -0.01 
ACIDay (05 h - 09 h)Proportion * Heterogeneity  508.01  0.00  0.98  0.29  0.23  h = 527.15, m = 518.81, l = 515.58  h = -0.01, m = 0.22, l = 0.3 
Proportion + Heterogeneity  516.74  8.74  0.01  0.21  0.15  h = 519,64, m = 520.19, l = 519.47  h = 0.18, m = 0.18, l = 0.18 
Proportion  520.25  12.25  0.002  0.22  0.15  519.87  0.18 
Heterogeneity  524.59  16.58  0.0002  –  –  –  – 
Night (19 h–23 h)Proportion * Heterogeneity  613.12  0.00  0.99  0.45  0.18  h = 544.48, m = 548.61, l = 517.06  h = -0.03, m = 0.24, l = 0.59 
Proportion + Heterogeneity  622.95  9.84  0.007  0.34  0.17  h = 533.75, m = 545.69 l = 527.82  h = 0.26, m = 0.26, l = 0.26 
Heterogeneity  627.69  14.58  0.0007  –  –  –  – 
Proportion  637.95  24.84  0.000004  0.16  0.11  534.77  0.35 
Both (05 h-09 h 19 h–23 h)Proportion * Heterogeneity  545.99  0.00  0.99  0.52  0.20  h = 537.02, m = 535.21, l = 516.74  h = -0.05, m = 0.19, l = 0.43 
Proportion + Heterogeneity  556.59  10.60  0.005  0.39  0.17  h = 527.44, m = 524.66, l = 534,11  h = 0.20, m = 0.20, l = 0.20 
Heterogeneity  562.14  16.15  0.0003  –  –  –  – 
Proportion  567.99  22.00  0.00001  0.26  0.14  528.33  0.25 

For models including the combined effects of habitat amount and heterogeneity class, β₀ represents the intercept and β the slope for each heterogeneity class (h = high, m = medium, l = low). Models with ΔAIC <2 are considered to have substantial support and are highlighted in bold.

Acoustic Complexity (ACI) models

ACI increased with habitat amount, but this effect depended on landscape heterogeneity (Fig. 3). For all periods (Day, Night, Both), the best-supported models included an interaction between habitat amount and heterogeneity class (Table 1). In homogeneous landscapes, ACI increased steeply with habitat amount, indicating higher acoustic complexity with increasing forest cover. In contrast, heterogeneous landscapes maintained relatively high ACI even at low habitat amounts (<40%) during the Day period, suggesting that heterogeneity can buffer reductions in acoustic complexity when native vegetation is scarce (Fig. 3; Table 1). Similar patterns were observed across periods, although the Night period showed a more consistent increase in ACI with habitat amount across all heterogeneity classes, with curves converging around ∼50% habitat.

Fig. 3.

Results from the Linear Mixed Models (LMM) of the mean ACI by periods (circle for Day, triangle for Night and square for Both) for each location as a function of habitat amount for the best models selected (a) Day period (05 h to 09 h) best model, (b) Night period (19 h to 23 h) best model, (c) Both period (05 h-09 h and 19 h - 23 h) best model.

Correlation between Day and Night soundscapes

Soundscapes measured during Day and Night periods were positively correlated for both indices. ADI showed a significant moderate correlation (ρ ≈ 0.31, p < 0.01), while ACI exhibited a stronger and significant relationship (ρ ≈ 0.54, p < 0.01), indicating broadly consistent acoustic patterns across circadian cycles (Fig. 3; see Supplementary Materials - Fig. S3 for details).

Discussion

Our results reveal contrasting responses of acoustic diversity (ADI) and acoustic complexity (ACI) to landscape composition. ADI decreased with increasing habitat amount, whereas ACI showed an opposite pattern. Importantly, the positive effect of habitat amount on ACI was strongly modulated by landscape heterogeneity. At lower habitat proportions (<40%), heterogeneous landscapes maintain relatively high ACI, while homogeneous landscapes exhibited marked reductions. Only when habitat cover exceeded ∼40−50% did homogeneous landscapes converge toward higher ACI levels. This indicates that heterogeneity buffers soundscape degradation, sustaining complex acoustic communities even where native vegetation is limited, whereas homogeneous landscapes rely more on high habitat availability to maintain similar acoustic complexity.

The negative association between ADI and habitat amount contrasts with general expectations from habitat–biodiversity relationships (Watling et al., 2020) and may reflect the influence of dominant or continuous acoustic signals, indicating that ADI does not necessarily track species diversity directly. Although previous studies have reported positive relationships between ADI and species richness in less intensive landscapes (Drodge et al., 2024), we found no relationship between heterogeneity and ADI at the landscape scale. A plausible explanation is that altered landscapes are dominated by generalist or edge species (Gámez-Virués et al., 2015; Hendershot et al., 2020) and insects (Burilova et al., 2022), producing broad and continuous acoustic signatures (Gasc et al., 2018). Additionally, higher levels of anthropophony (e.g., machinery and agricultural activities) and geophony (e.g., wind) may increase spectral variability and inflate ADI values beyond those generated by biophony alone. This suggests that low-habitat landscapes may exhibit higher acoustic saturation despite reduced biodiversity, potentially hindering heterogeneity effects. In contrast, more forested landscapes harbor acoustically complex communities, particularly birds and amphibians (Gasc et al., 2015; Alcocer et al., 2022), supporting the positive relationship between habitat amount and ACI.

ACI increased with habitat amount, consistent with evidence that more intact forests sustain complex vocal communities with greater behavioral diversity and acoustic variability (Gasc et al., 2015). The moderating role of heterogeneity suggests that landscape configuration influences not only species occurrence but also the structure of acoustic communities (Barbaro et al., 2022). Heterogeneous landscapes can enhance resource complementarity (Priyadarshana et al., 2024), reduce isolation (Fletcher et al., 2024), and maintain microhabitat diversity, allowing acoustically complex taxa to persist even when habitat cover is low. This aligns with broader ecological evidence showing that land-use homogenization drives biotic simplification (Wang et al., 2021; Gossner et al., 2023) and impairs ecosystem functioning (Newbold et al., 2020), whereas heterogeneous landscapes increase resilience (Estrada-Carmona et al., 2022).

Homogeneous landscapes required substantially more habitat to maintain ACI values comparable to heterogeneous landscapes with <40% native vegetation. This pattern echoes threshold ranges identified in the Atlantic Forest for multiple taxa, typically between 30% and 50% habitat cover (Martensen et al., 2012; Melo et al., 2018; Anunciação et al., 2021). The convergence ∼40–50%, where homogeneous landscapes recover higher ACI, is particularly noteworthy given the broad recommendation of maintaining at least 40% habitat to prevent rapid biodiversity loss (Arroyo-Rodríguez et al., 2020). However, as landscapes become increasingly simplified under intensification, larger amounts of native habitat may be required to sustain biodiversity. Given that many tropical landscapes retain <50% native vegetation, maintaining habitat alone may be insufficient if heterogeneity declines. Our results indicate that heterogeneity can partially buffer these effects by maintaining diverse habitat conditions.

The buffering effect of landscape heterogeneity is especially relevant in regions where agricultural intensification and land consolidation reduce complexity (Kennedy et al., 2019; Morteo-Montiel et al., 2021). In such settings, even landscapes with moderate habitat amounts may experience rapid declines in ecological integrity if heterogeneity is lost (Kennedy et al., 2019). Conversely, heterogeneous mosaics with mixed land-uses and varied vegetation structure can sustain acoustically complex communities even when habitat is scarce. This aligns with growing evidence that heterogeneity supports multifunctional landscapes capable of reconciling biodiversity conservation and production (Perfecto and Vandermeer, 2010; Tscharntke et al., 2021; Barbaro et al., 2022).

From a management perspective, these findings highlight the need for context-dependent conservation strategies. In landscapes with low habitat cover, maintaining or enhancing heterogeneity is critical to buffer biodiversity loss. In contrast, in homogeneous and intensively managed landscapes, increasing habitat amount becomes necessary to restore soundscape complexity. In landscapes undergoing intensification, conservation should prioritize retaining or promoting heterogeneity, whereas in already simplified landscapes enhance habitat amount.

Positive correlations observed between Day and Night indices, particularly for ACI, suggest that soundscape responses are broadly consistent across taxa active at different times. Despite the distinct ecological and behavioral traits of diurnal birds and nocturnal anurans, both respond similarly to habitat amount and landscape heterogeneity. These results support the use of acoustic indices as indicators of ecological patterns, although they integrate biophony, geophony and anthropophony and should be interpreted cautiously. As highlighted in recent studies (e.g., Alcocer et al., 2020; Bradfer-Lawrence et al., 2025a, 2025b; Sugai and Costa-Pereira, 2025), the ecological interpretation of acoustic metrics can benefit from complementary data sources, such as field-based biodiversity surveys or species-level acoustic identification approaches. Within these limitations, ecoacoustics represents a scalable tool for biodiversity monitoring in rapidly changing tropical landscapes.

Overall, maintaining or enhancing landscape heterogeneity can mitigate losses in acoustic complexity in landscapes with reduced habitat amounts, whereas homogeneous landscapes require substantially higher levels of native vegetation to sustain comparable soundscape structure. These findings bridge landscape ecology and ecoacoustics and provide practical guidance for conservation and land management in the Atlantic Forest and other tropical regions under intensification. In regenerating landscapes, soundscape monitoring may also offer a scalable approach to track ecological recovery and the return of acoustically active taxa over time.

Conclusions

Our findings show that habitat amount and landscape heterogeneity jointly shape soundscape patterns. While ACI increases with habitat cover, heterogeneous landscapes maintain relatively high acoustic complexity even at low habitat amounts (<40%), indicating that heterogeneity can buffer reduced habitat availability, whereas high habitat amounts sustain complexity in homogeneous landscapes. In contrast, ADI showed a different pattern, likely reflecting the influence of dominant or continuous acoustic signals in altered landscapes and highlighting the need for cautious interpretation of acoustic indices. These results indicate that under ongoing land-use intensification in tropical landscapes, maintaining the same proportion of native vegetation may still lead to biodiversity decline if landscape heterogeneity is lost. Effective conservation strategies must therefore integrate habitat amounts with actions that preserve or enhance landscape heterogeneity, to sustain resilient tropical soundscapes.

Author contributions

Júlio Cesar Lima de Araújo: Methodology, Investigation, Formal analysis, Writing – Original Draft, Writing – Review & Editing. Vinicius de Avelar São Pedro: Methodology, Writing – Review & Editing. Alexandre Uezu: Methodology, Writing – Review & Editing. Alexandre Camargo Martensen: Conceptualization, Methodology, Writing – Review & Editing, Funding acquisition, Formal analysis.

Declaration of generative AI and AI-assisted technologies in the manuscript preparation process

During the preparation of this work, the author(s) used ChatGPT tool in order to assist with English translation, spelling and grammar. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.

Funding sources

This work was supported by São Paulo Research Foundation (FAPESP) [grant number 2018/20501-8]; and Coordination of Improvement of Higher Education Personnel - Brazil (CAPES) [grant number001]

Data availability

The datasets generated during and/or analyzed during the current study are available on https://doi.org/10.5281/zenodo.15556586.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This study is part of the project “Governing the Atlantic Forest transition: Improving our knowledge on forest recovery for ecosystem services”, which is supported by the State of São Paulo Research Foundation (FAPESP 2018/20501-8). We thank the landowners for allowing us to work in their properties, and V. Cavelagna for field assistance. A. Peressin and T. Timo helped improve an earlier version of the manuscript and V. Ferrari helped with the english review. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.

Appendix A
Supplementary data

The following are Supplementary data to this article:

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