# Unlocking Tropical Forest Complexity: How Tree Assemblages in Secondary Forests Boost Biodiversity Conservation

Maïri Souza Oliveira, Maxime Lenormand, Sandra Luque, Nelson Zamora, Samuel Alleaume, Adriana Aguilar Porras, Marvin Castillo, Eduardo Chacón-Madrigal, Diego Delgado, Luis Gustavo Hernández Sánchez, et al.

<figure><img src="image_1.png" /></figure>

## To cite this version:

Maïri Souza Oliveira, Maxime Lenormand, Sandra Luque, Nelson Zamora, Samuel Alleaume, et al. "Unlocking Tropical Forest Complexity: How Tree Assemblages in Secondary Forests Boost Biodiversity Conservation. Ecology and Evolution, 2025, 15, pp.e72428. 10.1002/ece3.72428. hal-05358705

HAL Id: hal-05358705

https://hal.inrae.fr/hal-05358705v1

Submitted on 11 Nov 2025

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_3.png" /></figure>

RESEARCH ARTICLE

OPEN ACCESS

# Unlocking Tropical Forest Complexity: How Tree Assemblages in Secondary Forests Boost Biodiversity Conservation

Maïri Souza Oliveira<fnref n="1" />,² ID | Maxime Lenormand<fnref n="1" /> | Sandra Luque<fnref n="1" /> | Nelson A. Zamora³ |

Samuel Alleaume<fnref n="1" /> | Adriana C. Aguilar Porras⁴ | Marvin U. Castillo³ | Eduardo Chacón-Madrigal⁵ ID |

Diego Delgado⁶ | Luis Gustavo Hernández Sánchez⁷ | Marie-Ange Ngo Bieng² ID | Ruperto Quesada-Monge³ |

Gilberth S. Solano⁸ | Pedro M. Zúñiga⁹

**Correspondence:** Maïri Souza Oliveira (mairi.souza-oliveira@inrae.fr)

**Received:** 4 April 2025 | **Revised:** 30 September 2025 | **Accepted:** 20 October 2025

**Funding:** This work was supported by Centre National d'Etudes Spatiales, Centre de Coopération Internationale en Recherche Agronomique pour le Développement, and Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement.

**Keywords:** forest ecosystem | GBF 2030 targets | hierarchical clustering | network analysis | random forest | Sentinel-2

## ABSTRACT

Secondary forests now dominate tropical landscapes and play a crucial role in achieving COP15 conservation objectives. This study develops a replicable national approach to identifying and characterising forest ecosystems, with a focus on the role of secondary forests. We hypothesised that dominant tree species in the forest canopy serve as reliable indicators for delineating forest ecosystems and untangling biodiversity complexity. Using national inventories, we identified in situ clusters through hierarchical clustering based on dominant species abundance dissimilarity, determined using the Importance Variable Index. These clusters were characterised by analysing species assemblages and their interactions. We then applied object-oriented Random Forest modelling, segmenting the national forest cover using NDVI to identify the forest ecosystems derived from in situ clusters. Freely available spectral (Sentinel-2) and environmental data were used in the model to delineate and characterise key forest ecosystems. We finished with an assessment of the distribution of secondary and old-growth forests within ecosystems. In Costa Rica, 495 dominant tree species defined in situ clusters, with 7 main clusters successfully modelled. The modelling (F1-score: 0.73, macro F1-score: 0.58) and species-based characterisation highlighted the main ecological trends of these ecosystems, which are distinguished by specific species dominance, topography, climate, and vegetation dynamics, aligning with local forest classifications. The analysis of secondary forest distribution provided an initial assessment of ecosystem vulnerability by evaluating their role in forest maintenance and dynamics. This approach also underscored the major challenge of in situ data acquisition.

This is an open access article under the terms of the [Creative Commons Attribution License](https://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2025 The Author(s). *Ecology and Evolution* published by British Ecological Society and John Wiley & Sons Ltd.

<footnote n="1">¹INRAE, National Research Institute on Agriculture, Food & the Environment, TETIS Research Unit, Maison de la Télédétection, Montpellier, France | ²CIRAD, Centre for International Cooperation in Agricultural Research for Development, Forests and Societies Research Unit, Montpellier, France | ³ITCR, Instituto Tecnológico de Costa Rica, Escuela de Ingeniería Forestal, Cartago, Costa Rica | ⁴Departamento Conservación y Uso Sostenible de la Biodiversidad y los Servicios Ecosistémicos, MINAE, SINAC, Sistema Nacional de Áreas de Conservación, San José, Costa Rica | ⁵Herbario Nacional, Museo Nacional de Costa Rica & Herbario Luis Fournier Origgi, Centro de Investigación en Biodiversidad y Ecología Tropical, Universidad de Costa Rica, San José, Costa Rica | ⁶CATIE, Centro Agronómico Tropical de Investigación y Enseñanza, Turrialba, Costa Rica | ⁷Instituto de Investigación y Servicios Forestales, UNA, Universidad Nacional, Heredia, Costa Rica | ⁸CODEFORSA, Comisión de Desarrollo Forestal de San Carlos, San Carlos, Costa Rica | ⁹FUNDECOR, Fundación Para el Desarrollo de la Cordillera Volcánica Central, San José, Costa Rica</footnote>
Tropical forests play a crucial role in preserving global biodi-
versity, harbouring a significant portion of terrestrial diver-
sity, often estimated to be more than half of existing species
(Myers et al. 2000). They are central to conservation strategies
to achieve the Convention on Biological Diversity (COP15)
goals, in particular the identification of priority ecosystems re-
quired to achieve the target of protecting 30% of land by 2030
(Mrema et al. 2020; CBD 2022; Pendrill et al. 2022). However,
their degradation and deforestation remain among the lead-
ing drivers of global biodiversity loss (Bourgoin et al. 2024).
As a result, many forest species are constrained to persist in
human-modified landscapes, where forests survive within a
matrix that varies significantly in its capacity to support bio-
diversity (Arroyo-Rodríguez et al. 2020). This raises critical
questions about whether these anthropogenically influenced
forested landscapes can sustain ambitious conservation objec-
tives and at what spatial scale this support might be feasible
(Perino et al. 2022).

In this context, secondary forests (SF), typically resulting
from natural regeneration following anthropogenic pressures,
primarily develop on fallow lands abandoned after agricul-
tural use (Brown and Lugo 1990; Chazdon 2014). Today, SF
constitutes the majority of tropical forest cover, while old-
growth forests (OGF) are increasingly restricted to inacces-
sible and non-arable areas (Edwards et al. 2019). While OGF,
recognised for their complex structure and key role in sustain-
ing biodiversity (Clark and Clark 1996; Chazdon 2014), SF are
still often perceived as degraded systems (Pain et al. 2021).
Although their species richness can recover relatively quickly,
their species composition converges with that of OGF only over
several centuries (Rozendaal et al. 2019; Poorter et al. 2021).
Their regeneration dynamics are influenced by multiple in-
teracting factors operating across spatial scales, contributing
to the uncertainty of their trajectories (Walker et al. 2010;
Arroyo-Rodríguez et al. 2017; Balvanera et al. 2021). As such,
SF cannot replace OGF (Gibson et al. 2011), reinforcing the
justification for prioritising OGF in conservation policies,
often through “land-sparing” strategies (Mertz et al. 2021).
However, this approach contributes to SF marginalisation,
leaving them vulnerable to conversion into more econom-
ically lucrative land uses and undermining their potential
contribution to long-term conservation efforts (Laurance
et al. 2014; Reid et al. 2019). Located within human-modified
tropical landscapes, SF are often cleared during the early
stages of succession and struggle to fully restore ecosystem
functions and biodiversity (Arroyo-Rodríguez et al. 2017).
The limited recognition of the biodiversity value of these early
stages and the uncertainty surrounding their trajectories,
combined with conflicts between conservation and human
land use, highlight a gap in conservation strategies regarding
the integration of SF (Vieira et al. 2014; Sandoval et al. 2019;
Shebitz et al. 2023). Nevertheless, despite the increase in pro-
tected forest areas, mainly OGF, contributing to biodiversity
conservation, this measure remains insufficient to halt its
rapid decline (Jenkins and Joppa 2009; Gibson et al. 2011;
Barber et al. 2014). Disturbances in the surrounding land-
scape continue to affect species within protected areas, call-
ing into question the effectiveness of conservation strategies

focused on OGF, particularly threatened by deforestation
(Chazdon 2014; Arroyo-Rodríguez et al. 2020; Pain et al.
2021). In this context, SF are crucial as biological corridors,
linking OGF fragments and facilitating genetic flow and spe-
cies movement (Arroyo-Rodríguez et al. 2017). However, do
SF have the capacity to preserve the specific flora composition
that characterises tropical ecosystems, thereby complement-
ing conservation efforts in OGF?

To address this question and better understand the role of SFs in conserving tropical forest ecosystems and their contribution to international objectives, it is essential to develop robust and reproducible indicators and methods at the national scale capable of guiding the identification of priority ecosystems and supporting the implementation of conservation strategies aligned with global targets (Carroll and Noss 2022; Eckert et al. 2023). Nevertheless, despite numerous initiatives at international, continental, regional, and local levels to develop such tools, their lack at the national scale remains a significant challenge (Perino et al. 2022; Shen et al. 2023).

Segmenting the forest cover into coherent ecosystems can guide conservation plans by integrating ecological and biological specificities unique to each forest ecosystem. Since the 1970s, studies on vegetation interactions with various environmental factors have shown that they reflect the complexity of biophysical processes (Monteith 1972; Droissart et al. 2018). Ecological, environmental, and historical filtering contribute to the distribution and spatial organisation of distinct species assemblages, representing large-scale biogeographical structures (Kreft and Jetz 2010; Araújo and Rozenfeld 2014), which influence the structure of ecosystems. Preserving the diversity of these ecosystems is essential to prevent the homogenisation of plant communities that structure them and to combat biodiversity loss (Jakovac et al. 2022). This approach, applied to tropical forests, is documented in the scientific literature at different spatial scales, including regional (e.g., Moonlight et al. 2020), national (e.g., Pérez Chaves et al. 2020) and continental (e.g., Jakovac et al. 2022). However, it faces several limitations. The high complexity of elevated biodiversity and intricate species interactions in tropical forest ecosystems makes comprehensive biodiversity identification challenging (Pérez Chaves et al. 2020; Yang et al. 2023; Aguirre-Gutiérrez et al. 2025). In addressing this challenge, the trees that compose the forest canopy, as key structural elements of forest ecosystems, provide crucial insights into large-scale patterns of forest biodiversity (Rüger et al. 2020; Keppel et al. 2021; van Tiel et al. 2024). The specific composition of dominant tree species assemblages in the forest canopy, in terms of abundance and structure, evolves based on forest succession stages, disturbance levels, and variations in environmental and historical factors (Chazdon 2008; Crouzeilles et al. 2016; Rosenfield et al. 2023). In tropical landscapes under anthropogenic pressures, the heterogeneity and turnover of these assemblages between plots are key factors determining forest resilience and stability (Lohbeck et al. 2016).

While ecological network analysis has mainly focused on
species interactions at local scales (Schmid et al. 2020), meth-
odological tools to assess interactions between ecosystems
at national scales, particularly in tropical forests, remain
underdeveloped. The approach proposed by Lenormand et al. (2019), applying species contributions and network theory to relationships between bioregions provides an innovative avenue to address this gap by quantifying ecosystem interactions and specificity, thereby informing more integrated conservation strategies.

At the national scale, ecosystem delimitation is hindered by the lack of sufficient and reliable field data. The approach based on multispectral remote sensing, widely implemented through pixel-based classifications (e.g., Sentinel-2, Random Forest), has therefore become the dominant approach, with in situ data largely confined to calibration and validation roles (Massey et al. 2023; Saim and Aly 2025). However, the ecological value of the maps remains limited, constraining the information available for conservation (Pang et al. 2023). Multi-source approaches, combining in situ data with modelling based on spectral information, generate more interpretable classes that can reflect species assemblages, thereby enhancing their usefulness for management and conservation planning (Pérez Chaves et al. 2020; Pang et al. 2023). The integration of multi-source data, including environmental variables, in this multi-source approach allows the limitations of spectral information alone—which under tropical conditions is often affected by cloud cover—to be overcome (Ferrer Velasco et al. 2022; Silveira et al. 2023). The use of object-oriented modelling based on spectral segmentation of the forest canopy enables the integration of data sources at different spatial resolutions and produces ecologically meaningful classes, while automating large-scale mapping (Waśniewski et al. 2020). This approach thus facilitates the operational delimitation of ecosystems at the national scale by incorporating ecological and environmental filters into precise and interpretable maps (Flores-Tolentino et al. 2021).

This study proposes a national-level approach for assessing the potential of SF in maintaining national tropical forest ecosystems. Our goal is to unravel the complexity of biodiversity by analysing the assemblages of tree species that shape and structure the forest canopy. The Costa Rica case study serves as the demonstrator for this approach. To achieve this, we identified and characterised high-level forest ecosystems and evaluated the distribution of OGF and SF across these ecosystems through four main steps: (i) using forest inventory data and botanical expertise, we identified clusters based on the dominant tree species of sites, (ii) we then characterised the clusters based on species distribution by identifying the associated assemblages of contributive species and analysing the interactions between these clusters, (iii) we modelled these clusters at the national forest cover level, using freely available spectral and environmental information to delineate and characterise key forest ecosystems defined by dominant species assemblages, and (iv) we assessed the distribution of forest types (OGF and SF) within these ecosystems. We hypothesised (i) that the dominant tree species in the forest canopy serve as reliable indicators for delineating forest ecosystems at a national scale and (ii) that the interactions between forest ecosystems and the presence of SF are influenced by anthropogenic factors, linked to the specific environmental characteristics of each ecosystem. The developed pipeline for this multi-source approach is designed to be reproducible, innovative by relying on open multisource data (Sentinel-2, global environmental databases) and on well-established methods, but

integrated in a novel way to enable the operational delineation of forest ecosystems at the national scale. To our knowledge, Pérez Chaves et al. (2020) also applied a multi-step approach to map floristic patterns at the national scale in Peru. However, the originality of the present study lies in its delineation and characterisation of ecosystems based on canopy-dominant species assemblages, as well as its analysis of interactions among ecosystems. This approach is designed to translate ecosystem delimitation into operational information for national conservation planning, grounded in the floristic patterns that structure the canopy, while incorporating ecological filters, local knowledge, and alignment with international conservation frameworks.

# 2 | Materials and Methods

## 2.1 | A National-Level Demonstrator

Costa Rica, located in Central America, represents an exceptional world biodiversity hotspot on just 0.03% of the Earth's surface, due to significant topographic and environmental gradients, as well as the ecological transition undertaken since the 1990s (Myers et al. 2000). The country is traversed by a central mountain range, composed of four main cordilleras (Guanacaste, Tilarán, Central, and Talamanca), creating a substantial altitudinal gradient, ranging from sea level to 3820 m above sea level and essentially two main slopes, Pacific and Caribbean (Figure 1b). Costa Rica also exhibits a broad environmental spectrum, ranging from the dry, highly seasonal biome of the northwestern Pacific coast to the very humid, seasonal biome of the southern Pacific coast and the nearly perennially wet conditions of the Caribbean slope. The major climatic gradient is along the Pacific slope, characterised by annual precipitation ranging from 1475 mm in the northwestern to 5070 mm in the southwestern (Figure 1c). Costa Rica has transitioned from a deforestation pioneer in the 1970s–1980s to a reforestation pioneer in the tropics by the 2000s (Redo et al. 2012). The current forest cover in Costa Rica reaches 52%, with 36% being SF (Figure 1a—Stan and Sanchez-Azofeifa 2019). However, this forest cover remains highly fragmented and mixed, with the landscape dominated by agricultural areas, pastures, and secondary forests (Stan and Sanchez-Azofeifa 2019).

## 2.2 | Data

### 2.2.1 | Tree Species Forest Data

We compiled data from the national inventory of Costa Rica (Programa REDD/CCAD-GIZ—SINAC, 2015) along with data from regional and local forest research projects. The resulting dataset includes 364 georeferenced plots (Figure 1a), ranging from 0.1 to 1.6 ha, carried out between 2004 and 2021. To standardise the data across the different inventories, we retained only trees with a DBH greater than 10 cm (DBH=diameter at breast height, at 1.3 m). Taxonomic identification was performed at the species level in all the selected plots. For the current study, we only accounted for individuals identified to the species level with confirmed taxonomic validation by botanical expertise. Consequently, our final database consists of 58,773 trees belonging to 1333 species.
<figure><img src="image_4.png" /><figcaption>**FIGURE 1** | Presentation and environmental characteristics of the study area, Costa Rica: (a) Spatial distribution of 364 forest inventory plots across the national forest cover, (b) elevation (m), (c) Mean annual precipitation (mm/year), (d) Normalised Difference Water Index (NDWI) calculated as the median of the time series over the most stable months of the 2018 dry season (February and April).</figcaption></figure>

### 2.2.2 | Environmental Data

To define and characterise the identified forest ecosystems, we selected variables representing vegetation dynamics, topography, soil and climatic conditions. We represented vegetation dynamics during the dry and wet seasons by computing several vegetation spectral indices. These indices were computed, using multitemporal images from the Copernicus Sentinel-2 L2A satellite with 10 m and 20 m spatial resolution (CEOS 2024). These data were processed through Planetary Computer, which includes atmospheric correction using Sen2Cor (Main-Knorn et al. 2017). The vegetation indices were calculated by median over the multitemporal optical image series from 2018. To reduce spectral variability and ensure proper representation of the different seasons, two three-month periods were used: November to January for the wet season (Figure 1d) and February to April for the dry season. These vegetation indices are available as open access ("costarica-sentinel-2-l2-spectral-indices"). Elevation, along with several associated topographic metrics was derived from the NASADEM Merged DEM Global 1 Arc-Second V001, with a spatial resolution of 30 m (NASA JPL 2020). Climate variables, with a spatial resolution of 1 km, were obtained from CHELSA V1.0 (Karger et al. 2017), while soil variables, with a

spatial resolution of 250 m, were sourced from SoilGrids v0.5.5 (Hengl et al. 2017). The full set of variables used is presented in Table 1. We also used the 2021 forest-type map of Costa Rica, produced by SINAC at a spatial resolution of 10 m. This map was generated through a classification of Sentinel-2 and Sentinel-1 image mosaics (SINAC 2021) and is available as open access ("SNIT"). From this dataset, we selected SF and OGF forest types (Figure 1a).

## 2.3 | Biogeographical Network Analysis of In Situ Data

### 2.3.1 | Identification of the Dominant Tree Species in the Forest Canopy

Our objective was to identify the global floristic patterns of the forest canopy across the country. Due to the complexity of tropical forest diversity and the local biodiversity richness, some species were expected to have low prevalence, which could bias the delineation of forest ecosystems. To avoid this issue, we used the Importance Value Index (IVI) to determine which species contributed most to each plot's composition, structure, and
<table><caption>TABLE 1 | Description of variables set used for this study.</caption><thead><tr><th>Type</th><th>Variable</th><th>Unit</th></tr></thead><tbody><tr><td rowspan="5">Topography</td><td>DEM</td><td>Elevation</td><td>m</td></tr><tr><td>Slope</td><td>—</td><td>degrees (°)</td></tr><tr><td>Aspect</td><td>—</td><td>degrees (°)</td></tr><tr><td>TWI</td><td>Topographic Wetness Index</td><td>—</td></tr><tr><td>TRI</td><td>Terrain Ruggedness Index</td><td>m</td></tr><tr><td rowspan="3">Soil (0-30 cm, 30-200 cm, 0-200 cm)</td><td>TPI</td><td>Topographic Position Index</td><td>—</td></tr><tr><td>C</td><td>Soil Organic Carbon</td><td>dg/kg</td></tr><tr><td>CEC</td><td>Cation Exchange Capacity</td><td>mmolc/kg</td></tr><tr><td rowspan="5">Climate</td><td>Clay</td><td>—</td><td>g/kg</td></tr><tr><td>N</td><td>—</td><td>cg/kg</td></tr><tr><td>pH</td><td>—</td><td>—</td></tr><tr><td>Silt</td><td>—</td><td>g/kg</td></tr><tr><td>anPR</td><td>Mean annual precipitation</td><td>mm/year</td></tr><tr><td rowspan="4">Vegetation dynamics (Wet and Dry seasons)</td><td>MAT</td><td>Mean annual temperature</td><td>°C</td></tr><tr><td>Prsea</td><td>Precipitation seasonality (CV)</td><td>%</td></tr><tr><td>Tsea</td><td>Temperature seasonality (SD)</td><td>°C</td></tr><tr><td>CCI</td><td>Canopy Chlorophyll Content Index</td><td>—</td></tr><tr><td rowspan="2">NDVI</td><td>NDVI</td><td>Normalised Difference Vegetation Index</td><td>—</td></tr><tr><td>NDWI</td><td>Normalised Difference Water Index</td><td>—</td></tr></tbody></table>

dynamics of the tree communities (Figure 2a). The IVI combines three parameters: (i) relative density, measured by abundance, (ii) relative dominance, measured by basal area, and (iii) relative frequency, which represents the proportion of subplots where the species are present (Curtis and McIntosh 1951).

We set an IVI threshold of 5% to select the dominant species in each forest plot, which was then used for the forest ecosystems delineation. We performed a sensitivity analysis to evaluate the impact of selecting a 5% IVI threshold on the delineation of forest ecosystems. For each plot, we calculated the cumulative IVI captured by species exceeding thresholds of 5% and 10%,

and found that the 5% threshold retained on average 60% of the cumulative IVI, whereas the 10% threshold retained only 47% (Figure S1). We also assessed the stability of the forest cluster composition using pairwise comparisons (Adjusted Rand Index, ARI) between clustering results obtained with thresholds of 0%, 5%, and 10%. The ARI values showed high similarity between the 5% and 10% thresholds (ARI=0.64), indicating that the overall cluster structure is robust to variations in the threshold. These results support the choice of a 5% threshold for selecting dominant species, as it captures the majority of canopy dominance, balances the inclusion of the most influential species, ensures stability of the clustering results, and avoids excluding moderately abundant species.

### 2.3.2 | Hierarchical Clustering of Sites

In this study, we considered forest inventory plots as sites. The delineation of forest ecosystems was based on automatic discrimination of tree species assemblages, aiming to minimise the taxonomic turnover rate within ecosystems while maximising it between them (Kreft and Jetz 2010). We performed hierarchical clustering based on the dominant tree species assemblages to cluster the sites according to their floristic similarities in an unsupervised approach (i.e., without incorporating environmental variables) (Figure 2a). To achieve this, we applied the UPGMA (Unweighted Pair Group Method with Arithmetic Mean) agglomerative algorithm using the turnover component of the Bray-Curtis dissimilarity index (β BrayTurn). β BrayTurn specifically focuses on changes in species composition by quantifying the minimal difference in presence and abundance. This approach emphasises the species unique to each site (Equation 1) (Baselga 2013). This metric is particularly suitable for our data because it allows the analysis of variations in species composition without being influenced by abundance differences related to site size.

$$ \beta \text{BrayTurn} = 1 - \frac{\min(B, C)}{A + \min(B, C)} \quad (1) $$

In Equation (1), A represents the sum of the minimum abundances of species shared between two sites, while B and C correspond to the abundances of species present exclusively on each site of the pair. The function min(B, C) measures the turnover between the two sites, that is, the total number of individuals of species present only on one of the two sites.

The UPGMA method has proven more effective for detecting consistent biogeographical patterns than other hierarchical classification methods and does not weight clusters based on their size (Kreft and Jetz 2010). Since this method is influenced by site order in the distance matrix, we performed 1000 randomisations of the order in the dissimilarity matrix (Dapporto et al. 2013). To quantitatively assess each resulting tree, we calculated the cophenetic correlation coefficient, which measures the correlation between the distance at which the sites are connected in the tree and the distance between the sites in the initial dissimilarity matrix (Sokal and Rohlf 1962).

The optimal number of clusters was selected using the Elbow Method based on the pc_distance metric, in order to maximise
<figure><img src="image_5.png" /><figcaption>**FIGURE 2** | Schematic workflow used in this study, illustrating the key methodological steps.</figcaption></figure>

the dissimilarity between clusters. This metric calculates the ratio between the sum of $\beta$ dissimilarities between clusters and the total sum of $\beta$ dissimilarities for the entire dissimilarity matrix (Holt et al. 2013).

Single-site clusters were further assessed for their representativeness using pairwise similarity tests based on species composition (Simpson's distance with Bonferroni correction) between the isolated sites and the main clusters. A 50% similarity threshold, supported by botanical expertise, was used to determine whether isolated sites represented genuine floristic specificity or simply exhibited a high rate of unique species due to random sampling and could therefore be associated with existing clusters (see Table S1). Isolated plots deemed not representative of

broader floristic patterns were excluded from further analysis, as we did not have a sufficient number of plots to adequately represent these specific floras in the modelling.

### 2.3.3 | Test Value Matrix

To analyse the contribution of species to each cluster and characterise the associated species assemblages, we used test values $\rho$, which measures the under- or over-representation of species in the clusters (Lebart et al. 2000) (Figure 2b). The test value $\rho$ for a species $i$ in a cluster $j$ quantifies the difference between the mean abundance $X \mu_{ij}$ of species $i$ in $n_j$ samples from the cluster $j$ and its mean abundance $\mu$ in $n$ samples from
the entire study area, standardised to assess whether the species is over- or under-represented in this cluster compared to all samples (Equation 2). Since this quantity depends on the size of the clusters ($n_j$), it is normalised by the standard deviation associated with the expected mean abundance if the variability in the cluster $j$ were comparable to that of the total population represented by {$n, \mu, \sigma^2$}, taking into account the difference in cluster sizes.

$$ \rho_{ij} = \frac{\mu_{ij} - \mu}{\sqrt{\frac{n - n_j}{n - 1} \times \frac{\sigma^2(X)}{n_j}}} \quad (2) $$

To define the representative species in the assemblages associated with the clusters, we qualified the species that contributed positively and significantly to one or more clusters by introducing a significance threshold $\delta$ applied to the test values $\rho$ corresponding to a one-tailed significance level of 2.5% in a Gaussian distribution, that is, $\delta=1.96$. Thus, the matrix of test values $\rho$ highlights the sets of species that best characterise the clusters (Lenormand et al. 2019).

### 2.3.4 | Interaction Network Between Clusters

To quantify the relationships between the clusters, we examined the distribution among clusters of species with contributions were significantly positive ($\rho_{ij} \ge 1.96$) (Lenormand et al. 2019). $\rho^+$ that represents these significantly positive contributions of species to the clusters (Figure 2b). To obtain the relative contribution $\hat{\rho}_{ij}^+$ of a species $i$ to a cluster $j$, we normalised the contributions $\rho^+$ by the sum of $\rho^+$ for all clusters $k$ (Equation 3).

$$ \hat{\rho}_{ij}^{+} = \frac{\rho_{ij}^{+}}{\sum_{k} \rho_{ik}^{+}} \quad (3) $$

Subsequently, we calculated for each cluster $j$ the mean fraction of contribution to the cluster from species that also contribute significantly to the cluster $j'$, denoted as $\lambda_{ij'}$, based on Equation (4). In this equation, $A_j$ represents the set of species for which the contribution to cluster $j$ is significant, i.e., those for which $\rho_{ij} \ge 1.96$. The normalisation of contributions by $|A_j|$ ensures that the similarity measure is independent of the size of $A_j$. Thus, $\lambda_{ij}$ expresses the specificity of cluster $j$. These fractions are expressed as percentages with a vector $\lambda_j$ for a given cluster that sums to 1. Therefore, the values of $\lambda_j$ provide measures of both specificity and connectivity between the clusters, based on the representative species they share.

$$ \lambda_{ij'} = \frac{1}{|A_j|} \sum_{i \in A_j} \hat{\rho}_{ij'}^{+} \quad (4) $$

We then used the values from the matrix to construct an interaction network illustrating the relationships between site clusters and the associated species assemblages. This network was then projected into a two-dimensional space, using altitude derived from the DEM and annual precipitation, taking the median of these variables for each cluster, in order to explore the main environmental gradients of the country.

## 2.4 | Delimitation of Forest Ecosystems Using Spectral and Environmental Data

### 2.4.1 | Data Standardisation

To address the difference in spatial resolution between floristic and environmental data, we used segmentation to create an object-oriented approach (Figure 2c). This process divided the forest cover, derived from the national forest-type map, into spectrally uniform segments, helping to standardise the data for ecosystem modelling and validating environmental factors. The segmentation was based on an NDVI mosaic derived from the median of Sentinel-2 images from the dry season of 2018, using the Large Scale Generic Region Merging (LSGRM) algorithm from the Moringa Land Cover Toolbox (Gaetano et al. 2019). Subsequently, for each segment, we performed statistical zoning by extracting the 25th (q25), 50th (q50), and 75th (q75) percentiles for each numerical explanatory variable. This approach reduced biases from local variability and captured environmental trends representative of each segment, aiding in the characterisation of forest ecosystems. Each segment containing a site adopted the cluster value and species assemblage of that site, based on the assumption that it accurately represents the entire segment.

### 2.4.2 | Modelling Clusters at the National Scale

A Random Forest model (Breiman 2001) was developed using the clustering results from the sites to predict the clusters within the Costa Rica forest cover, aiming to produce a map of the main forest ecosystems (Figure 2c). For the modelling, we selected only the main clusters with a minimum of 15 sites. Most of the 34 predictive variables were highly correlated. To limit overfitting, we applied a two-step variable selection procedure. First, we used a recursive feature elimination (RFE) procedure with a Random Forest model, optimising the F1-score to identify the best-performing subsets of predictors (sizes between 5 and 30 variables were tested). The best-performing subset was then examined for multicollinearity using Pearson's correlation coefficients. Variables with $|r| > 0.7$ were considered redundant and manually removed based on ecological relevance and interpretability. Model evaluation was performed using 10-fold cross-validation, with performance assessed through the F1-score and the macro F1-score. The macro F1-score, calculated as the arithmetic mean of the F1 scores for all classes, which treats all classes equally, is mainly well suited to unbalanced classes (Sokolova and Lapalme 2009). For each predicted forest ecosystem, we assessed the variable importance using 100 permutations per variable (Ramosaj and Pauly 2023), as well as the marginal effects of the most important variables by constructing partial dependence plots (B. M. Greenwell 2017). This final step allows better characterisation of these ecosystems and establishes links with botanical expertise.

## 2.5 | Analysis of the Distribution of Forest Types Within Predicted Forest Ecosystems

We calculated the proportions of SF and OGF in the in situ clusters and the derived forest ecosystems to assess the concordance
<figure><img src="image_6.png" /><figcaption>**FIGURE 3** | Geographical distribution of the 10 identified site clusters, determined through hierarchical clustering based on dissimilarity in species composition.</figcaption></figure>

between the prediction results and clustering results and to analyse their distribution across different forest ecosystems (Figure 2d). To do this, the vector layer of the main Costa Rican forest ecosystems was converted into a raster with a spatial resolution of 10 m to be compatible with the forest cover raster of the same resolution. The proportions of forest types were then calculated based on the number of corresponding pixels.

All analyses were performed using R version 4.3.3 (R Core Team 2024), with several specific packages, including “bioregion” (Denelle et al. 2025) for biogeographical network analysis, “caret” (Kuhn et al. 2024) for variable selection, training and prediction of the Random Forest model, and “rfPermute” (Archer 2023) for variable importance assessment, and finally the “pdp” package (B. M. Greenwell 2024) for calculating the marginal effects of the variables.

# 3 | Results

## 3.1 | Biogeographical Network Analysis

We identified 18 optimal clusters from the hierarchical clustering analysis applied to the forest inventory data. This analysis was based on the distribution of 495 dominant tree species selected

from the 1333 species present at the sites, using a threshold IVI of 5%. These clusters explained 85.74% of the observed variation in species composition (Figure S2a). However, 8 of these clusters were not usable, represented by a single site, and were either merged with the main clusters if they showed sufficient floristic similarity, or excluded when they did not meet the representativeness threshold (details in Table S1). As a result, we retained 10 clusters for subsequent analysis (Figure 3a) that explained 69.77% of the observed variation (Figure S2). The cluster sizes ranged from 4 to 101 sites. These clusters, although highly unbalanced (Figure 4b), were obtained using the UPGMA algorithm, which preserves the structure of the dissimilarity matrix.

According to the significance threshold $\delta$ set at 1.96, 71% of the species contributed to a single cluster, and 18% contributed to two clusters. The maximum number of species contributing to only one cluster was reached at the specified threshold, indicating that the majority of the dominant canopy species were mainly associated with a single cluster (Figure 4a). As a result, the species assemblages associated with the clusters included between 20 and 88 contributive species, i.e., species that contributed positively and significantly to the clusters (Figure 4b). Details of the contributive species and their contribution values $\rho_{ij}$ are available in Table S2. In total, 322 species were identified as contributing among the 495 species dominating the forest canopy. The interaction network
<figure><img src="image_7.png" /><figcaption>**FIGURE 4** | Analysis of species contribution to identified clusters: (a) Fraction of species contributing positively and significantly to a given number of clusters (from 0 to 5 or more) as a function of the significance threshold. The vertical line represents the significance threshold $\delta = 1.96$. (b) Barplot of the numbers of contributive species ($\rho_{ij} \ge 1.96$) and sites according to identified clusters.</figcaption></figure>

between clusters, based on the fraction of contributing species ($\lambda_{ij''}$), validated the strong specificity of the clusters, with a mean of 79.2%, ranging from 63% for cluster C9 to 98% for cluster C3 (Figure 5). Despite these variations in specificity, the network revealed weak interactions between clusters, with the maximum sharing of contributing species reaching 13% for $\lambda_{6,1}$. Clusters C5, C6, C9, and C10 appear as transition zones between clusters associated with the high, medium, and low-altitude wet biomes. In contrast, cluster C3, isolated, seems representative of the low-altitude

dry biome. All $\lambda_{ij''}$ interactions and shared species are presented respectively in Tables S3 and S4.

**3.2 | Delimitation and Characterisation of Forest Ecosystems Using Spectral and Environmental Data**

Only clusters C1 to C7 had a sufficient number of plots to be modelled across the forest cover. The random forest classification
models showed optimal performance when they included only the variables selected through a recursive feature elimination (RFE) procedure combined with manual removal of collinearity (Figure S3). The model achieved an overall F1-score of 0.73 and a macro F1-score of 0.58, although performance varied significantly across the clusters. Significant classification errors were observed for clusters C5, C7, and particularly C6, with respective

error rates of 52.7%, 66.7%, and 83.3% (Table 2). Frequent classification errors observed in the confusion matrix, particularly from C1 to C2, from C6 to C1, from C4 to C5, as well as from C7 to C5 and C4, align with the cluster interactions highlighted in previous analyses. A notable proportion of classification errors was also observed from C5 to C1. In contrast, clusters C2 and C3 were distinguished with high accuracy by the model.

<figure><img src="image_8.png" /><figcaption>**FIGURE 5** | Interaction network illustrating the relationships between clusters and the associated contributive species assemblages. $\lambda_{j\mu}$, expressed as a percentage, represents the mean fraction of contribution to the cluster j of species that also contribute significantly to the cluster j'. Only interaction with a value $\lambda_{j\mu}$ higher than 5% are shown. The network is represented in two-dimensional space, with altitude (in m) and Mean Annual Precipitation (in mm/year). The median of these variables is used for each cluster.</figcaption></figure>

<table><caption>**TABLE 2** | Assessment of random forest model performance: Confusion matrix expressed in percentages and F1-Score by class for the best model with a global F1-Score of 0.73 and a macro F1-score of 0.58.</caption><thead><tr><th></th><th>C1</th><th>C2</th><th>C3</th><th>C4</th><th>C5</th><th>C6</th><th>C7</th><th>F1-score</th></tr></thead><tbody><tr><th>C1</th><td>63.74</td><td>10.53</td><td>7.60</td><td>0.00</td><td>5.85</td><td>7.02</td><td>5.26</td><td>0.60</td></tr><tr><th>C2</th><td>7.04</td><td>86.38</td><td>5.63</td><td>0.00</td><td>0.94</td><td>0.00</td><td>0.00</td><td>0.83</td></tr><tr><th>C3</th><td>7.50</td><td>6.67</td><td>81.67</td><td>0.00</td><td>0.83</td><td>2.50</td><td>0.83</td><td>0.81</td></tr><tr><th>C4</th><td>1.59</td><td>0.00</td><td>0.00</td><td>69.84</td><td>22.22</td><td>0.00</td><td>6.35</td><td>0.74</td></tr><tr><th>C5</th><td>17.20</td><td>9.68</td><td>9.68</td><td>6.45</td><td>47.31</td><td>6.45</td><td>3.23</td><td>0.50</td></tr><tr><th>C6</th><td>58.33</td><td>0.00</td><td>12.50</td><td>0.00</td><td>12.50</td><td>16.67</td><td>0.00</td><td>0.19</td></tr><tr><th>C7</th><td>9.52</td><td>9.52</td><td>11.90</td><td>14.29</td><td>14.29</td><td>7.14</td><td>33.33</td><td>0.38</td></tr></tbody></table>
This classification model allowed to define, characterise, and delineate the forest ecosystems of Costa Rica based on the seven main clusters modelled. The seven explanatory variables selected for the model input show varying levels of discrimination depending on the clusters (Table 3). According to Mean Decrease Accuracy, which measures the impact of a variable on model accuracy by randomly permuting its values, all variables are globally significant. Altitude (q50.DEM) is the most discriminant variable on average, followed by precipitation seasonality (q25.PRSea), the NDWI index during the wet season (q25.NDWIw), and annual precipitation (q75.anPR). The least discriminant variables on average are pH (q50.pH 30), CEC (q50.CEC30) of the topsoil layer, and slope (q25.slope). According to Mean Decrease Gini, which measures the reduction in node impurity in decision trees when a variable is used for splitting, only four variables are significantly discriminant, precipitation seasonality (q25.PRSea) and altitude (q50.DEM), followed by annual precipitation (q75.anPR) and soil pH (q50.pH 30). The variable importance for each cluster highlights the model's difficulty in predicting clusters C5 to C7 using topographic, environmental, and seasonal vegetation dynamics variables.

Table 4 presents the identification and characterisation of the predicted forest ecosystems by combining (i) the significant variable importance (Table 3) and the marginal effects of key ecological variables retained by the model, whose log-odds define the main ecological trends (Figure S4), (ii) the analysis of contributive species associated with the clusters (Table S2), (iii) the mean values of each explanatory variable for each cluster (Table S5), and (iv) the distribution of the seven forest ecosystems across Costa Rica (Figure 6a). Although the model's performance is relatively weak for clusters C5 to C7, it still captured certain ecological gradients that contributed to the definition of the associated forest ecosystems: PMC-C, TWPE-P, and PMC-P.

## 3.3 | Analysis of the Distribution of Forest Types Within Predicted Forest Ecosystems

The analysis of the distribution of OGF and SF across the clusters and derived forest ecosystems revealed notable inversions in the proportions of OGF and SF for ecosystems LWE-C and PMC-P between the results of the two methods (Table 5). Overall, all main clusters and associated forest ecosystems exhibit a dominance of one forest type, with an average difference in OGF and SF proportions of 63% ± 23% in the clustering results and 58% ± 22% in those from Random Forest. A mean difference of 26% ± 22% in the proportions of forest types is observed between the clustering approach and the Random Forest approach, with this variation primarily due to the inverse predictions for two ecosystems, LWE-C and PMC-P.

## 4 | Discussion

The objective of this study was to propose a replicable national approach to evaluate the potential of SF in maintaining dominant tree species structuring the forest ecosystems. Our approach was applied to Costa Rica. For that, we analysed dominant canopy species assemblages to better understand their contribution to the preservation and dynamics of forest ecosystems. Based

<table><caption>TABLE 3 | Variable importance in the random forest of the seven variables selected as model inputs with (i) the variable importance value from the permutation test with its statistical significance (p-value) for each cluster, and (ii) the Mean decrease accuracy and Mean decrease gini metrics with their statistical significance (p) for the overall model.</caption><thead><tr><th rowspan="2">Variables</th><th colspan="2">C1</th><th colspan="2">C2</th><th colspan="2">C3</th><th colspan="2">C4</th><th colspan="2">C5</th><th colspan="2">C6</th><th colspan="2">C7</th><th colspan="2">Mean decrease accuracy</th><th colspan="2">Mean decrease gini</th></tr><tr><th>Imp</th><th>p</th><th>Imp</th><th>p</th><th>Imp</th><th>p</th><th>Imp</th><th>p</th><th>Imp</th><th>p</th><th>Imp</th><th>p</th><th>Imp</th><th>p</th><th>Value</th><th>p</th><th>Value</th><th>p</th></tr></thead><tbody><tr><td>q50.DEM</td><td>15.10</td><td>0.01*</td><td>25.66</td><td>0.01*</td><td>11.77</td><td>0.01*</td><td>35.50</td><td>0.01*</td><td>17.08</td><td>0.01*</td><td>2.76</td><td>0.14</td><td>13.65</td><td>0.01*</td><td>41.87</td><td>0.01*</td><td>38.16</td><td>0.01*</td></tr><tr><td>q25.PRSea</td><td>15.42</td><td>0.01*</td><td>35.39</td><td>0.01*</td><td>20.23</td><td>0.01*</td><td>14.42</td><td>0.01*</td><td>4.61</td><td>0.04*</td><td>1.19</td><td>0.26</td><td>6.08</td><td>0.02*</td><td>37.66</td><td>0.01*</td><td>40.64</td><td>0.01*</td></tr><tr><td>q25.NDWIw</td><td>18.37</td><td>0.01*</td><td>24.36</td><td>0.01*</td><td>7.42</td><td>0.07</td><td>1.22</td><td>0.31</td><td>10.53</td><td>0.01*</td><td>5.55</td><td>0.02*</td><td>8.97</td><td>0.01*</td><td>30.25</td><td>0.01*</td><td>30.38</td><td>1.00</td></tr><tr><td>q75.anPR</td><td>17.36</td><td>0.01*</td><td>13.18</td><td>0.01*</td><td>21.07</td><td>0.01*</td><td>11.48</td><td>0.01*</td><td>5.49</td><td>0.02*</td><td>1.70</td><td>0.20</td><td>4.93</td><td>0.06</td><td>27.24</td><td>0.01*</td><td>37.20</td><td>0.01*</td></tr><tr><td>q50.pH 30</td><td>15.19</td><td>0.01*</td><td>15.37</td><td>0.01*</td><td>18.45</td><td>0.01*</td><td>15.48</td><td>0.01*</td><td>-3.43</td><td>0.79</td><td>0.06</td><td>0.39</td><td>8.04</td><td>0.01*</td><td>25.37</td><td>0.01*</td><td>34.79</td><td>0.05*</td></tr><tr><td>q50.CEC30</td><td>6.71</td><td>0.03*</td><td>18.79</td><td>0.01*</td><td>11.76</td><td>0.01*</td><td>4.43</td><td>0.05*</td><td>6.99</td><td>0.03*</td><td>0.28</td><td>0.38</td><td>4.77</td><td>0.04*</td><td>22.45</td><td>0.01*</td><td>26.93</td><td>1.00</td></tr><tr><td>q25.slope</td><td>5.46</td><td>0.07</td><td>20.89</td><td>0.01*</td><td>-3.57</td><td>0.91</td><td>11.83</td><td>0.01*</td><td>-2.60</td><td>0.79</td><td>-0.59</td><td>0.46</td><td>7.08</td><td>0.01*</td><td>20.03</td><td>0.01*</td><td>24.10</td><td>1.00</td></tr></tbody></table>

Note: Stars (*) indicate statistically significant importance values (p < 0.05); shading reflects the relative importance (Imp) of each variable, with darker colours representing higher importance.
<table><caption>**TABLE 4** | Correspondence between clusters, modelled forest ecosystem names, and associated acronyms, with characterisation based on key ecological variables and contributive species assemblages: Only ecological variables retained by the model with a significant importance score ($p \le 0.05$) were considered for characterisation of each ecosystem.</caption><thead><tr><th rowspan="2">Clusters</th><th rowspan="2">Ecosystems</th><th rowspan="2">Acronyms</th><th rowspan="2">Contributive species</th><th rowspan="2">Tropography</th><th colspan="4">Characterisation</th></tr><tr><th>Climate</th><th>Vegetation dynamic</th><th>Soil</th><th>Location</th></tr></thead><tbody><tr><td>C1</td><td>Wet Seasonal Evergreen forest</td><td>WSE</td><td>Majority of evergreen species (predominated by <i>Elaeoloma glabrescens</i>, <i>Symphonia globulifera</i>, and <i>Garcinia madruno</i>)</td><td>Broad median elevation ranges from low to intermediate (504 ± 361 m)</td><td>Maximum mean annual precipitation of 3611 ± 707 mm/year. Minimum precipitation seasonality variation of 51% ± 13%.</td><td>Minimum NDWI range during the wet season of 0.31 ± 0.11</td><td>Median pH of 5.21 ± 0.62<br>Median CEC of 134 ± 44 mmolc/kg</td><td>Southern Pacific and Caribbean coasts</td></tr><tr><td>C2</td><td>Lowland Wet Evergreen forest of the Caribbean slope</td><td>LWE-C</td><td>Evergreen species (predominated by <i>Pentaclethra macroloba</i>)</td><td>Low median elevation (≤ 346 m)<br>Low minimal slope (4° ± 4°)</td><td>Maximum mean annual precipitation of 3522 ± 726 mm/year. Minimum precipitation seasonality variation of 34% ± 9%</td><td>Minimum NDWI range during the wet season of 0.31 ± 0.07</td><td>Median pH of 5.27 ± 0.62<br>Median CEC of 132 ± 49 mmolc/kg</td><td>Caribbean coast</td></tr><tr><td>C3</td><td>Lowland Dry-to-Moist Deciduous-to-Semi-deciduous forest</td><td>LDM-DS</td><td>Mainly composed of deciduous to semi-deciduous species (predominated by <i>Handroanthus ochraceus</i>, <i>Bursera simaruba</i>, and <i>Spondias mombin</i>)</td><td>Low median elevation (≤ 519 m)</td><td>Maximum mean annual precipitation of 2308 ± 542 mm/year. Minimum precipitation seasonality variation of 75% ± 14%</td><td>Minimum NDWI range during the wet season of 0.22 ± 0.09</td><td>Median pH of 5.73 ± 0.69<br>Median CEC of 212 ± 60 mmolc/kg</td><td>Mainly on the southern Pacific coast</td></tr><tr><td>C4</td><td>Mountain Oak Rainforest</td><td>MOR</td><td>Predominantly composed of oak species (dominated by <i>Quercus sapotifolia</i>)</td><td>High median elevation (2269 ± 463 m)<br>High minimal slope (20° ± 8°)</td><td>Maximum mean annual precipitation of 3804 ± 614 mm/year. Minimum precipitation seasonality variation of 45% ± 10%</td><td>—</td><td>Median pH of 5.12 ± 0.17<br>Median CEC of 192 ± 39 mmolc/kg</td><td>Talamanca cordillera</td></tr></tbody></table>

(Continues)
<table><caption>TABLE 4 | (Continued)</caption><thead><tr><th rowspan="2">Clusters</th><th rowspan="2">Ecosystems</th><th rowspan="2">Acronyms</th><th rowspan="2">Contributive species</th><th rowspan="2">Tropography</th><th rowspan="2">Climate</th><th colspan="3">Characterisation</th></tr><tr><th>Vegetation dynamic</th><th>Soil</th><th>Location</th></tr></thead><tbody><tr><td>C5</td><td>Premontane-to-mountain<br>Mixed-to-evergreen<br>Cloud<br>forest of the<br>Caribbean<br>slope</td><td>PMC-C</td><td>Mix of evergreen and deciduous species with a large altitudinal gradient (predominated by <i>Ruagea glabra</i>, <i>Elaeagia auriculata</i>, <i>Salacia petenensis</i>, <i>Cecropia angustifolia</i> and <i>Inga oerstediana</i>)</td><td>Intermediate median elevation (1142 ± 361 m)</td><td>Maximum mean annual precipitation of 3616 ± 547 mm/year. Minimum precipitation seasonality variation of 48% ± 13%</td><td>Minimum NDWI range during the wet season of 0.37 ± 0.05</td><td>Median CEC of 171 ± 45 mmolc/kg</td><td>Caribbean slope</td></tr><tr><td>C6</td><td>Transitional wet<br>premontane<br>evergreen<br>forest of the<br>Pacific slope</td><td>TWPE-P</td><td>Evergreen species (predominated by <i>Otoba novogranatensis</i>)</td><td>—</td><td>—</td><td>Minimum NDWI range during the wet season of 0.39 ± 0.03</td><td>—</td><td>Southern Pacific coast</td></tr><tr><td>C7</td><td>Premontane-to-mountain<br>Mixed-to-evergreen<br>Cloud forest<br>of Pacific<br>slope</td><td>PMC-P</td><td>Mix of evergreen and deciduous species with a large altitudinal gradient (predominated by <i>Saurauia montana</i> and <i>Inga punctata</i>)</td><td>Intermediate median elevation (1360 ± 372)<br>High minimal slope (16° ± 8°)</td><td>Minimum precipitation seasonality variation of 55% ± 12%</td><td>Minimum NDWI range during the wet season of 0.26 ± 0.08</td><td>Median pH of 5.29 ± 0.31<br>Median CEC of 162 ± 33</td><td>Pacific slope</td></tr></tbody></table>
<figure><img src="image_9.png" /><figcaption>**FIGURE 6** | Map of Costarican forest ecosystems based on a classification model of seven main clusters, derived from dissimilarity between inventoried forest sites in terms of dominant tree species composition, using a random forest model. WSE: Wet seasonal evergreen forest. LWE-C: Lowland wet evergreen forest of the Caribbean slope. LDM-DS: Lowland dry-to-moist deciduous-to-semi-deciduous forest. MOR: Mountain oak rainforest. PMC-C: Premontane-to-mountain mixed-to-evergreen cloud forest of the Caribbean slope. TWPE-P: Transitional wet premontane evergreen forest of the Pacific slope. PMC-P: Premontane-to-mountain mixed-to-evergreen cloud forest of Pacific slope. The table provides the names of ecosystems and their acronyms. a, b, and c represent three areas where forests were characterised at the local scale, and the results will be interpreted in the discussion section.</figcaption></figure>

on 364 plots, we identified and modelled seven major forest eco-
systems in Costa Rica. These results relied on an approach that
combined accessible data with local expertise. By integrating
free environmental geodata sources, such as satellite spectral
information, global spatial environmental databases, and open-
access digital elevation model (DEM), we produced a consistent
forest mapping. Our approach achieved robust performance
with an overall F1-score of 0.73 and a macro F1-score of 0.58,
demonstrating the reliability of the model despite the imbalance
between clusters. The challenge of differing spatial resolutions
between floristic and environmental data was addressed using
a segmentation method, dividing forest cover into spectrally
homogeneous segments. This oriented objects approach was
completed by statistical zoning based on percentiles, effectively
capturing ecological trends specific to each segment and ensur-
ing better representation of forest variations. The integration of
expert knowledge was crucial for interpreting these data, refin-
ing models, and validating clusters through field campaigns.
This method offers the advantage of being highly replicable; any
country with local expertise and national forest inventory data

can adopt it. Furthermore, the use of open-source software such
as R and QGIS enhances its accessibility and global applicability.

4.1 | Defining Forest Ecosystems Through
Dominant Tree Assemblages

The delineation of forest ecosystems was carried out using
higher-level clusters to ensure a good balance between sampling
size and satisfactory modelling performance across the entire
forest cover. The classification performance of clusters was in-
fluenced by sampling biases in terms of accessibility and alti-
tude (Table S6), with only 4% of plots between 1000 and 1500 m
and 14% between 500 and 1000 m. This primarily affected the
classification of ecosystems PMC-C, TWPE-P, and PMC-P
within these ranges. Despite these limitations, the overall forest-
type characterisation remains robust, supported by local-scale
studies. Three well-documented sites in Costa Rica, located in
ecologically diverse areas, confirm this consistency and demon-
strate the effectiveness of our approach in complex contexts.
<table><caption>**TABLE 5** | Comparison of the proportions of forest types (Old-Growth Forests—OGF and Secondary Forests—SF) derived from the clustering of forest inventory plots and Random Forest modelling across the entire forest cover.</caption><thead><tr><th rowspan="2">Ecosystems</th><th rowspan="2">Forest types</th><th colspan="2">Proportion (%)</th></tr><tr><th>Clustering</th><th>Random forest</th></tr></thead><tbody><tr><td rowspan="2">WSE</td><td>OGF</td><td>0.79</td><td>0.69</td></tr><tr><td>SF</td><td>0.21</td><td>0.31</td></tr><tr><td rowspan="2">LWE-C</td><td>OGF</td><td>0.74</td><td>0.25</td></tr><tr><td>SF</td><td>0.26</td><td>0.75</td></tr><tr><td rowspan="2">LDM-DS</td><td>OGF</td><td>0.23</td><td>0.14</td></tr><tr><td>SF</td><td>0.77</td><td>0.86</td></tr><tr><td rowspan="2">MOR</td><td>OGF</td><td>1</td><td>0.95</td></tr><tr><td>SF</td><td>0</td><td>0.05</td></tr><tr><td rowspan="2">PMC-C</td><td>OGF</td><td>0.66</td><td>0.85</td></tr><tr><td>SF</td><td>0.34</td><td>0.15</td></tr><tr><td rowspan="2">TWPE-P</td><td>OGF</td><td>0.93</td><td>0.63</td></tr><tr><td>SF</td><td>0.07</td><td>0.37</td></tr><tr><td rowspan="2">PMC-P</td><td>OGF</td><td>0.18</td><td>0.79</td></tr><tr><td>SF</td><td>0.82</td><td>0.21</td></tr></tbody></table>

In the San Juan-La Selva Biological Corridor in northeastern Costa Rica, two forest ecosystems (WSE and LWE-C) coexist (Figure 6b). Three lowland forest types have been identified (Sesnie et al. 2008): *Pentaclethra macroloba* forests, dominant in LWE-C; *Qualea paraensis*, *Vochysia ferruginea*, and *Couma macrocarpa* forests, characteristic of WSE; and *Dialium guianense*, *Brosimum alicastrum*, and *Tachigali costaricensis* forests, spanning WSE and LWE-C. Additionally, piedmont and pre-montane species like *Vochysia allenii* and *Macrohasseltia macroterantha* are abundant in WSE.

In the rainforests of the Talamanca Cordillera, where ecosystems WSE, MOR, and PMC-C coexist along an altitudinal gradient up to 2520 m (Figure 6c), three main forest types have been characterised (Veintimilla et al. 2019): Lowland forests (440–1120 m) are dominated by *Pourouma bicolor*, *Vochysia allenii*, and *Calophyllum brasiliense* (WSE); intermediate forests (1400–1660 m) host both lowland and montane species, primarily *Oreomunnea mexicana* (MOR), alongside *Billia rosea* (MOR), *Alchornea latifolia*, and *Cecropia insignis* (PMC-C); montane forests (2150–2950 m) define MOR, dominated by *Quercus bumeiodes*, *Drimys granadensis*, *Ocotea austinii*, and *Weinmannia pinnata* (MOR).

The Osa Peninsula, home to WSE, LWE-C, LDM-DS, TWPE-P, and PMC-P (Figure 6d), is among Costa Rica's most biodiverse regions due to its environmental and topographical variability (Hofhansl et al. 2020). With 28 forest types, no single canopy species dominates (Gilbert and Kappelle 2016). Despite high endemism and wet forest dominance, lowland forests also

include species from drier and moister habitats of Nicoya and the Central Valley, explaining LDM-DS continuity into the peninsula (Zamora et al. 2004). Some moist and wet lowland forest species of the Osa Peninsula also occur on the Caribbean coast, indicating a floristic and climatic affinity across the Talamanca Range, consistent with WSE, LWE-C, and TWPE-P distributions (Table S7).

The analysis of these examples shows that the local heterogeneity of forests is well represented by forest ecosystems based on dominant canopy species. It highlights the key role of oligarchic species in structuring assemblages across diverse, sometimes discontinuous, regions within the same ecosystem type. Specifically, *Vochysia* spp. and *Pourouma bicolor* define the WSE ecosystem, *Goethalsia meiantha* characterises LWE-C, and *Cordia alliodora*, *Spondias* spp., and *Brosimum* spp. dominate LDM-DS (Sesnie et al. 2008; Gilbert and Kappelle 2016). The distribution of oligarchic species is strongly correlated with geographical and topographical variables, particularly in the lowland forests of the Osa Peninsula (Hofhansl et al. 2019; Morera-Beita et al. 2019). These findings enhance the understanding of the regional spatial complex distribution of ecosystems at large spatial scales (Figure 6). The contribution values of species are available in Table S2.

## 4.2 | Importance of Spectral and Environmental Factors in Ecosystem Characterisation and Their Interactions

Our approach identifies contributive species assemblages that significantly define forest ecosystems, enabling analysis of their interactions and specificities. Using abundance data from forest inventories, we calculate the average fraction of species contributing to cluster j that also contribute to cluster j' (Lenormand et al. 2019). In Costa Rica, assemblages show greater specificity in lowland (WSE, LWE-C, LDM-DS) and in high-mountain (MOR) forests but are less distinct in intermediate ecosystems (PMC-C, TWPE-P, PMC-P). Low interaction rates between ecosystems at the national level reflect abiotic filters shaping habitat diversity, which drives high biodiversity (Araújo and Rozenfeld 2014).

Forest ecosystems are mostly structured by environmental gradients that directly influence the contribution of dominant canopy species, which have played a key role in their definition. Through Random Forest modelling, we observe that topography, followed by climate and the NDWI index—used here to estimate water content in vegetation and, consequently, water stress—are the most determining factors in the turnover of tree species composition that structure ecosystems. However, edaphic properties also play an important role, as evidenced by the selection of pH and CEC among the seven variables chosen for the modelling. Our results are consistent with previous studies conducted in tropical forests across South and Central America, where tree species turnover has been associated with these environmental variables, as well as canopy reflectance, at spatial scales ranging from regional to continental (e.g., Pérez Chaves et al. 2020; Bañares-de-Dios et al. 2022; Jakovac et al. 2022; Nuñez et al. 2024). The integration of these environmental determinants with the analysis of
contributive species assemblages allowed for the characteri-
sation and naming of forest ecosystems derived from in situ
clusters (Table 4). This designation is based on two national,
well-established ecological-vegetation mapping systems: life
zones (Holdridge 1967), defined by bioclimatic factors, and
phytogeographic units (Zamora 2008), determined by floristic
composition associated with topographic, climatic, and soil
type factors. A detailed description of these ecosystems, inte-
grating the contribution of key environmental variables in the
modelling as well as the assemblages of species that signifi-
cantly characterise them, is provided in Table S8.

## 4.3 | Contribution of Secondary Forests to the Dynamics and Conservation of National Forest Ecosystems

Anthropogenic pressures shape forest ecosystems by influenc-
ing species composition, distribution, and interactions. The
spread of SF, driven by past and present disturbances (Arroyo-
Rodríguez et al. 2017), helps assess human impact. While SF
distribution differs minimally between in situ clusters and
model-derived ecosystems, LWE-C and PMC-P show inverse
trends: modelling predicts more SF in LWE-C and more OGF
in PMC-P, contrasting with clustering results obtained from
in situ data. This may stem from sampling bias favouring OGF
plots in accessible, lower-altitude areas, particularly in LWE-C.
However, for PMC-P, botanical expertise and national distur-
bance records confirm its accuracy. In Costa Rica, colonisation
history has influenced land use, shaping the distribution of OGF
and SF (Redo et al. 2012; Aide et al. 2013; Shaver et al. 2015).
Since pre-Columbian times, human activity on the northern
Pacific coast and central highlands (LDM-DS) converted dry
forests mainly into cattle pastures, later abandoned and replaced
by SF mosaics. During colonial times, deforestation expanded to
pre-mountain Pacific areas (PMC-P) due to their proximity to
the central valley, mild climate, and fertile soils, ideal for coffee
cultivation, which fuelled SF expansion. On the Caribbean coast
(LWE-C), intense deforestation (1960s–1980s) in particular for
banana and pineapple monocultures fragmented OGF, restrict-
ing it to inaccessible high-altitude zones (PMC-C, MOR). In Osa
Peninsula lowlands, about half the forest remains OGF (WSE
and TWPE-P), though degradation from logging and oil palm
plantations has driven SF expansion in LWE-C, LDM-DS, and
TWPE-P. These findings align with Random Forest modelling,
which corrected OGF bias in LWE-C using ecological variables.
However, for PMC-P, model limitations persisted, reflected in
a low F1-score (0.38). The SF proportion in each ecosystem re-
flects the level of anthropogenic pressure reshaping the forested
landscape, playing a crucial role in floristic dynamics and sig-
nificantly influencing the evolution of forest floras.

Analysis of SF contributions to forest ecosystems revealed their
limited ability to maintain OGF-specific tree assemblages, iden-
tified through clustering and Random Forest modelling. In
Costa Rica, OGF dominates WSE, MOR, PMC-C, and TWPE-P,
while SF prevails in LWE-C, LDM-DS, and PMC-P. This sug-
gests SF, in its current stages, remains compositionally distinct
from OGF (Rozendaal et al. 2019; Mertz et al. 2021) especially
in MOR, where OGF fully dominates due to inaccessibility.
However, SF ecosystems like LWE-C and LDM-DS should not

be seen as degraded but as distinct ecosystems with their own
species assemblages (Pain et al. 2021). OGF and SF distinctions
blur at the canopy level, as both share many species, though
one tends to dominate per ecosystem. Some plots classified as
OGF are likely forests that have undergone a certain degree of
human intervention, which explains the significant presence of
species typical of SF or fast-growing species. In OGF-dominated
landscapes with low-intensity land use, SF could follow optimal
successional paths, helping preserve native flora (Rosenfield
et al. 2023).

This study provides a first assessment of forest ecosystem vul-
nerability at the national level. In the case of Costa Rica, MOR
appears the most threatened, with its contributive species
strictly confined to high altitudes (> 2150 m) and a 90% speci-
ficity rate. Its high vulnerability arises from SF's inability to
sustain itself and the potential loss of its ecological niche due to
climate change, despite low anthropogenic pressure. In contrast,
PMC-C seems the least vulnerable, dominated by OGF with an
85% specificity rate and benefiting from natural protection due
to inaccessibility. Its species adapt to a wider altitudinal range
(< 1300 m), enhancing resilience to climate change. Lowland and
mid-mountain ecosystems (WSE, LWE-C, LDM-DS, TWPE-P,
PMC-P) face high anthropogenic pressures, with specificity
rates of 78%-99%. In these areas, young SF are particularly vul-
nerable, often seen as fallow lands (Reid et al. 2019), threatening
their regeneration potential and long-term ecosystem stability.

## 4.4 | Limits and Perspectives

The approach used has limitations related to the quantity and
distribution of available field data. With only 364 sampling plots
at the national scale, the classification of forest ecosystems in
Costa Rica faced a sampling power imbalance between clusters,
impacting the modelling performance in particularly for clus-
ters C5 to C7. This imbalance is partly due to sampling biases
related to the accessibility and altitudinal distribution of the
plots, favouring forests located at low and medium altitudes.
Moreover, the variability of the plots by forest types (OGF and
SF) in the sampling also affects the species composition in each
assemblage. Classification effectiveness depends on the quantity
and representativeness of available data. Well-sampled clusters
(C1–C3) show clear ecological trends and high performance,
while underrepresented clusters (C5–C7) have broader, harder-
to-distinguish trends. Cluster C4 is an exception—despite being
undersampled, its strict ecological characteristics enable better
classification. In Costa Rica, these findings underscore the need
for greater financial investment in forest inventories to enhance
model accuracy by increasing plot numbers and improving rep-
resentation of mid- and high-altitude forests, which are harder
to access.

Data acquisition remains a major challenge, highlighting coun-
tries' difficulties in collecting essential biodiversity data for mon-
itoring, tracking, and modelling. Significant disparities exist
in generating in situ data due to high costs, lack of standard-
isation, and coordination challenges. These limitations hinder
comprehensive data collection, affecting national biodiversity
strategies and global assessments (Chapman et al. 2024). As a re-
sult, global biodiversity data remains highly uneven, hindering
accurate assessments of ecosystem status, extent, and distribu-
tion (Gonzalez and Londoño 2022). This data gap weakens ef-
forts to guide conservation actions and monitor progress toward
protecting 30% of land by 2030, a core target of the Kunming-
Montreal Global Biodiversity Framework (GBF) adopted in
2022 (CBD 2022). The GBF emphasises ecosystem integrity and
connectivity, yet lacks explicit targets for national biodiversity
monitoring systems (Perino et al. 2022). The Ecosystem Extent
Indicator, a key metric for tracking ecosystem loss and degrada-
tion at the global level, highlights the urgency of strengthening
standardised monitoring frameworks to assess ecosystem trends
effectively. However, disparities in data availability, financial re-
sources, and technical capacity create major obstacles for many
countries. International financial and technical support is there-
fore essential to enable comprehensive biodiversity monitoring,
ecosystem extent assessments, and effective conservation plan-
ning (Gonzalez and Londoño 2022; Cardona Santos et al. 2023).
Without these investments, achieving the GBF 2030 targets and
reversing biodiversity loss will remain a significant challenge.
In the absence of such funding, the deployment of cost-effective
monitoring systems relies on integrating remote sensing with
biodiversity surveillance models at national scales, complement-
ing in situ data to enhance spatial representativeness (Fernández
et al. 2020). These large-scale extrapolated quantitative models
must be incorporated into participatory approaches that engage
both expert and local knowledge to strengthen the effective im-
plementation of the GBF at the national level (Rosa et al. 2017;
Xu et al. 2021).

In this context, the approach developed and tested in this
study—designed to be operational and replicable in other trop-
ical countries—aims to enhance the identification and assess-
ment of tropical forest ecosystems by analysing the contribution
of SF. Importantly, this operational framework represents a crit-
ical step toward prioritising tropical forest conservation at the
national level, directly supporting the goal of protecting 30% of
terrestrial land. By integrating national forest inventories, spec-
tral data, and global environmental databases, the approach
enables the identification and characterisation of forest eco-
systems based on contributive species assemblages, ecosystem
interactions, and ecological specificity. Moreover, it provides
key insights into the role of SF in maintaining and shaping for-
est dynamics. Besides, the network of interactions (Figure 5)
highlights the specificity of different clusters or bioregions in
terms of species assemblages and their interrelationships. This
approach allows rapid identification of highly specific clusters
as well as transition areas, providing a concise summary of
ecological relationships across the country. While the primary
focus of this study is on mapping and characterising species as-
semblages, these network patterns offer valuable insights for fu-
ture conservation planning. In particular, key network metrics
(e.g., λᵢⱼ) could help identify priority areas and guide biodiversity
management at regional and national scales, representing an
important avenue for subsequent research.

From a conservation prioritisation perspective, developing a vul-
nerability categorisation for ecosystems facing anthropogenic
pressures and climate change (Guariguata and Ostertag 2001;
Edwards et al. 2019) is essential. This can be achieved using
vulnerability indices (Kumar et al. 2021; Roshani et al. 2024)

that incorporate ecosystem specificity, interaction strength, and
detailed analyses of how contributive species respond to distur-
bances (Fremout et al. 2020; Pang et al. 2023). Future research
should aim to explicitly link the identified tree assemblages
to key ecosystem functions and biodiversity metrics (Lohbeck
et al. 2016; Fichtner and Härdtle 2021; Zhang and Zang 2021;
Cooper et al. 2024). Such analyses would help clarify whether
different assemblages contribute in complementary or redundant
ways to ecosystem multifunctionality, thereby strengthening the
conservation implications of the delineated forest ecosystems. To
further reduce sampling biases, the integration of citizen science
data, such as those available through GBIF, should be considered
in future studies (Fraisl et al. 2022). Additionally, a fully remote
sensing-based approach could be employed to generate spectral
clusters that capture spectral diversity, which may strongly cor-
relate with species richness, beta diversity, and functional diver-
sity derived from in situ observations (Féret and de Boissieu 2020;
Chraibi et al. 2021; Perrone et al. 2023; Lenormand et al. 2025).
These perspectives represent a first step toward improving under-
standing of the role of secondary forests in conserving tropical
forest ecosystems at the national scale.

Author Contributions

**Maïri Souza Oliveira:** conceptualization (lead), data curation (lead), formal analysis (lead), investigation (equal), methodology (equal), validation (equal), visualization (equal), writing – original draft (equal), writing – review and editing (equal). **Maxime Lenormand:** investigation (equal), methodology (equal), software (lead), writing – review and editing (equal). **Sandra Luque:** conceptualization (equal), funding acquisition (equal), investigation (equal), methodology (equal), project administration (lead), supervision (lead), visualization (equal), writing – review and editing (equal). **Nelson A. Zamora:** conceptualization (equal), investigation (equal), methodology (equal), resources (equal), validation (equal), writing – review and editing (equal). **Samuel Alleaume:** methodology (equal), resources (equal), writing – review and editing (equal). **Adriana C. Aguilar Porras:** investigation (equal), resources (equal). **Marvin U. Castillo:** resources (equal). **Eduardo Chacón-Madrigal:** resources (equal), writing – review and editing (equal). **Diego Delgado:** investigation (equal), resources (equal). **Luis Gustavo Hernández Sánchez:** resources (equal). **Marie-Ange Ngo Bieng:** conceptualization (equal), funding acquisition (equal), investigation (equal), supervision (equal), writing – review and editing (equal). **Ruperto Quesada-Monge:** resources (equal). **Gilberth S. Solano:** resources (equal). **Pedro M. Zúñiga:** resources (equal).

Acknowledgments

This research was partially funded by a PhD fellowship from INRAE and CIRAD, with additional support from CNES (National Space Agency, France) TOSCA Project CODEX for fieldwork and data validation. We sincerely thank SINAC, CATIE, ITCR, Fundecor, UNA, and CODEFORSA for granting access to forest inventory databases. We are grateful to Mona Bonier, Florian de Boissieu, Raffaele Gaetano, Cassio Fraga Dantas, Jean-Baptiste Féret, and Dino Ienco (TETIS) for their valuable support in various aspects of data analysis and expertise. Thanks also to Sergio Vilchez-Mendoza (CATIE) for his advice and to Daniel Villavicencio (SINAC) and Eliott Maurent (CIRAD) for providing the data and explanations, which were essential to the project. Special thanks to CATIE and SINAC for their hospitality during fieldwork, Luc Villain (CIRAD) for administrative support, and the SINAC park guards and technicians and Vicente Herra for their essential field assistance during our in situ validation campaign.
## Conflicts of Interest

The authors declare no conflicts of interest.

## Data Availability Statement

The in situ data used in this study are the property of Costa Rican institutions and were made available exclusively for the purposes of this research. For any requests regarding access to these data, please contact the co-authors affiliated with the relevant Costa Rican institutions. All other data used to develop the various variables, as well as the code and data necessary to assess the study's conclusions, are referenced in the manuscript or included in the Supporting Information.

## References

Aguirre-Gutiérrez, J., S. W. Rifai, X. Deng, et al. 2025. "Canopy Functional Trait Variation Across Earth's Tropical Forests." *Nature* 641: 129–136. https://www.nature.com/articles/s41586-025-08663-2.

Aide, T. M., M. L. Clark, H. R. Grau, et al. 2013. "Deforestation and Reforestation of Latin America and the Caribbean (2001–2010)." *Biotropica* 45: 262–271. https://doi.org/10.1111/j.1744-7429.2012.00908.x.

Araújo, M. B., and A. Rozenfeld. 2014. "The Geographic Scaling of Biotic Interactions." *Ecography* 37: 406–415. https://doi.org/10.1111/j.1600-0587.2013.00643.x.

Archer, E. 2023. "rfPermute: Estimate Permutation p-Values for Random Forest Importance Metrics." https://cran.r-project.org/web/packages/rfPermute/index.html.

Arroyo-Rodríguez, V., L. Fahrig, M. Tabarelli, et al. 2020. "Designing Optimal Human-Modified Landscapes for Forest Biodiversity Conservation." *Ecology Letters* 23: 1404–1420. https://doi.org/10.1111/ele.13535.

Arroyo-Rodríguez, V., F. P. L. Melo, M. Martínez-Ramos, et al. 2017. "Multiple Successional Pathways in Human-Modified Tropical Landscapes: New Insights From Forest Succession, Forest Fragmentation and Landscape Ecology Research." *Biological Reviews* 92: 326–340. https://doi.org/10.1111/brv.12231.

Balvanera, P., H. Paz, F. Arreola-Villa, et al. 2021. "Social Ecological Dynamics of Tropical Secondary Forests." *Forest Ecology and Management* 496: 119369. https://www.sciencedirect.com/science/article/pii/S0378112721004576.

Bañares-de-Dios, G., M. J. Macía, G. M. de Carvalho, G. Arellano, and L. Cayuela. 2022. "Soil and Climate Drive Floristic Composition in Tropical Forests: A Literature Review. Frontiers in Ecology and Evolution." *Frontiers* 10: 866905. https://doi.org/10.3389/fevo.2022.866905/full.

Barber, C. P., M. A. Cochrane, C. M. Souza, and W. F. Laurance. 2014. "Roads, Deforestation, and the Mitigating Effect of Protected Areas in the Amazon." *Biological Conservation* 177: 203–209. https://linkinghub.elsevier.com/retrieve/pii/S000632071400264X.

Baselga, A. 2013. "Separating the Two Components of Abundance-Based Dissimilarity: Balanced Changes in Abundance vs. Abundance Gradients." *Methods in Ecology and Evolution* 4: 552–557. https://doi.org/10.1111/2041-210X.12029.

Bourgoin, C., G. Ceccherini, M. Girardello, et al. 2024. "Human Degradation of Tropical Moist Forests Is Greater Than Previously Estimated." *Nature* 631: 570–576. https://www.nature.com/articles/s41586-024-07629-0.

Breiman, L. 2001. "Random Forests." *Machine Learning* 45: 5–32. https://doi.org/10.1023/A:1010933404324.

Brown, S., and A. E. Lugo. 1990. "Tropical Secondary Forests." *Journal of Tropical Ecology* 6: 1–32. https://www.cambridge.org/core/product/identifier/S0266467400003989/type/journal_article.

Cardona Santos, E. M., F. Kinniburgh, S. Schmid, et al. 2023. "Mainstreaming Revisited: Experiences From Eight Countries on the Role of National Biodiversity Strategies in Practice." *Earth System Governance* 16: 100177. https://www.sciencedirect.com/science/article/pii/S2589811623000149.

Carroll, C., and R. F. Noss. 2022. "How Percentage-Protected Targets Can Support Positive Biodiversity Outcomes." *Conservation Biology* 36: e13869. https://doi.org/10.1111/cobi.13869.

CBD. 2022. *Kunming-Montreal Global Biodiversity Framework*. Convention on Biological Diversity, COP15. https://www.cbd.int/doc/decisions/cop-15/cop-15-dec-04-en.pdf.

CEOS. 2024. *Tropical Forests: Costa Rican Ecosystem Extent Demonstrator*. Committee on Earth Observation Satellites (CEOS). https://ceos.org/news/tropical-forests-costa-rica/.

Chapman, M., B. R. Goldstein, C. J. Schell, et al. 2024. "Biodiversity Monitoring for a Just Planetary Future." *Science* 383: 34–36. https://doi.org/10.1126/science.adh8874.

Chazdon, R. L. 2008. "Beyond Deforestation: Restoring Forests and Ecosystem Services on Degraded Lands." *Science* 320: 1458–1460. https://doi.org/10.1126/science.1155365.

Chazdon, R. L. 2014. *Second Growth: The Promise of Tropical Forest Regeneration in an Age of Deforestation*. University of Chicago Press. https://press.uchicago.edu/ucp/books/book/chicago/S/bo17407876.html.

Chraibi, E., H. Arnold, S. Luque, A. Deacon, A. Magurran, and J.-B. Féret. 2021. "A Remote Sensing Approach to Understanding Patterns of Secondary Succession in Tropical Forest." *Remote Sensing* 13: 2148. https://www.mdpi.com/2072-4292/13/11/2148.

Clark, D. B., and D. A. Clark. 1996. "Abundance, growth and mortality of very large trees in neotropical lowland rain forest." *Forest Ecology and Management* 80, no. 1-3: 235–244. https://doi.org/10.1016/0378-1127(95)03607-5.

Cooper, D. L. M., S. L. Lewis, M. J. P. Sullivan, et al. 2024. "Consistent Patterns of Common Species Across Tropical Tree Communities." *Nature* 625: 728–734. https://www.nature.com/articles/s41586-023-06820-z.

costarica-sentinel-2-l2-spectral-indices. https://api.stac.teledetection.fr.

Crouzeilles, R., M. Curran, M. S. Ferreira, D. B. Lindenmayer, C. E. V. Grelle, and J. M. Rey Benayas. 2016. "A Global Meta-Analysis on the Ecological Drivers of Forest Restoration Success." *Nature Communications* 7: 11666. https://www.nature.com/articles/ncomm s11666.

Curtis, J. T., and R. P. McIntosh. 1951. "An Upland Forest Continuum in the Prairie-Forest Border Region of Wisconsin." *Ecology* 32: 476–496. https://www.jstor.org/stable/1931725.

Dapporto, L., M. Ramazzotti, S. Fattorini, G. Talavera, R. Vila, and R. L. H. Dennis. 2013. "Recluster: An Unbiased Clustering Procedure for Beta-Diversity Turnover." *Ecography* 36: 1070–1075. https://doi.org/10.1111/j.1600-0587.2013.00444.x.

Denelle, P., B. Leroy, and M. Lenormand. 2025. "Bioregionalization Analyses With the Bioregion R Package." *Methods in Ecology and Evolution* 16, no. 3: 496–506. https://doi.org/10.1111/2041-210X.14496.

Droissart, V., G. Dauby, O. J. Hardy, et al. 2018. "Beyond Trees: Biogeographical Regionalization of Tropical Africa." *Journal of Biogeography* 45: 1153–1167. https://doi.org/10.1111/jbi.13190.

Eckert, I., A. Brown, D. Caron, F. Riva, and L. J. Pollock. 2023. "30×30 Biodiversity Gains Rely on National Coordination." *Nature Communications* 14: 7113. https://www.nature.com/articles/s41467-023-42737-x.

Edwards, D. P., J. B. Socolar, S. C. Mills, Z. Burivalova, L. P. Koh, and D. S. Wilcove. 2019. "Conservation of Tropical Forests in the
Anthropocene.” *Current Biology* 29: R1008–R1020. https://linkinghub.elsevier.com/retrieve/pii/S0960982219310759.

Féret, J.-B., and F. de Boissieu. 2020. “biodivMapR: An R Package for α- and β-Diversity Mapping Using Remotely Sensed Images.” *Methods in Ecology and Evolution* 11: 64–70. https://doi.org/10.1111/2041-210X.13310.

Fernández, N., S. Ferrier, L. M. Navarro, and H. M. Pereira. 2020. “Essential Biodiversity Variables: Integrating In-Situ Observations and Remote Sensing Through Modeling.” In *Remote Sensing of Plant Biodiversity*, edited by J. Cavender-Bares, J. A. Gamon, and P. A. Townsend, 485–501. Springer International Publishing. https://doi.org/10.1007/978-3-030-33157-3_18.

Ferrer Velasco, R., M. Lippe, F. Tamayo, et al. 2022. “Towards Accurate Mapping of Forest in Tropical Landscapes: A Comparison of Datasets on How Forest Transition Matters.” *Remote Sensing of Environment* 274: 112997. https://www.sciencedirect.com/science/article/pii/S0034425722001110.

Fichtner, A., and W. Härdtle. 2021. “Forest Ecosystems: A Functional and Biodiversity Perspective.” In *Perspectives for Biodiversity and Ecosystems*, edited by C. Hobohm, 383–405. Springer International Publishing. https://doi.org/10.1007/978-3-030-57710-0_16.

Flores-Tolentino, M., L. Beltrán-Rodríguez, J. Morales-Linares, et al. 2021. “Biogeographic Regionalization by Spatial and Environmental Components: Numerical Proposal.” *PLoS One* 16: e0253152. https://doi.org/10.1371/journal.pone.0253152.

Fraisl, D., G. Hager, B. Bedessem, et al. 2022. “Citizen Science in Environmental and Ecological Sciences.” *Nature Reviews Methods Primers* 2: 64. https://www.nature.com/articles/s43586-022-00144-4.

Fremout, T., E. Thomas, H. Gaisberger, et al. 2020. “Mapping Tree Species Vulnerability to Multiple Threats as a Guide to Restoration and Conservation of Tropical Dry Forests.” *Global Change Biology* 26: 3552–3568. https://doi.org/10.1111/gcb.15028.

Gaetano, R., S. Dupuy, V. Lebourgeois, G. Le Maire, A. Jolivot, and A. Bégué. 2019. *The MORINGA Processing Chain: Automatic Object-Based Land Cover Classification of Tropical Agrosystems Using Multi-Sensor Satellite Imagery*, 3. Italian Space Agency. https://agritrop.cirad.fr/594650/1/Living%20Planet_abSTRACT2019_Gaetano.pdf.

Gibson, L., T. Lee, L. Koh, et al. 2011. “Primary Forests Are Irreplaceable for Sustaining Tropical Biodiversity.” *Nature* 478: 378–381. https://www.nature.com/articles/nature10425.

Gilbert, L., and M. Kappelle. 2016. “The Southern Pacific Lowland Evergreen Moist Forest of the Osa Region.” In *Costa Rican Ecosystems First*, edited by M. Kappelle, 360–411. University of Chicago Press. https://www.researchgate.net/publication/308777833_The_Southern_Pacific_Lowland_Evergreen_Moist_Forest_of_the_Osa_Region.

Gonzalez, A., and M. C. Londoño. 2022. “Monitor Biodiversity for Action.” *Science* 378: 1147. https://doi.org/10.1126/science.adg1506.

Greenwell, B. M. 2017. “pdp: An R Package for Constructing Partial Dependence Plots.” *R Journal* 9: 421. https://journal.r-project.org/archive/2017/RJ-2017-016/index.html.

Greenwell, B. M. 2024. “pdp: Partial Dependence Plots.” https://cran.r-project.org/web/packages/pdp/index.html.

Guariguata, M. R., and R. Ostertag. 2001. “Neotropical Secondary Forest Succession: Changes in Structural and Functional Characteristics.” *Forest Ecology and Management* 148: 185–206. https://www.sciencedirect.com/science/article/pii/S0378112700005351.

Hengl, T., J. M. de Jesus, G. B. M. Heuvelink, et al. 2017. “SoilGrids250m: Global Gridded Soil Information Based on Machine Learning.” *PLoS One* 12: e0169748. https://doi.org/10.1371/journal.pone.0169748.

Hofhansl, F., E. Chacón-Madrigal, L. Fuchslueger, et al. 2020. “Climatic and Edaphic Controls Over Tropical Forest Diversity and Vegetation

Carbon Storage.” *Scientific Reports* 10: 5066. https://www.nature.com/articles/s41598-020-61868-5.

Hofhansl, F., E. Chacón-Madrigal, A. Morera-Beita, et al. 2019. “Diversity and Composition of Tropical Forest Plant Communities in the Golfo Dulce Region.” *Acta ZooBot Austria* 156: 31–46.

Holdridge, L. R. 1967. *Life Zone Ecology*. Tropical Science Center. https://wiki.neotropicos.org/images/8/8e/LR_Holdridge_1966_life_zone_ecology.pdf.

Holt, B. G., J.-P. Lessard, M. K. Borregaard, et al. 2013. “An Update of Wallace’s Zoogeographic Regions of the World.” *Science* 339: 74–78. https://doi.org/10.1126/science.1228282.

Jakovac, C. C., J. A. Meave, F. Bongers, et al. 2022. “Strong Floristic Distinctiveness Across Neotropical Successional Forests.” *Science Advances* 8: eabn1767. https://doi.org/10.1126/sciadv.abn1767.

Jenkins, C. N., and L. Joppa. 2009. “Expansion of the Global Terrestrial Protected Area System.” *Biological Conservation* 142: 2166–2174. https://www.sciencedirect.com/science/article/pii/S0006320709001980.

Karger, D. N., O. Conrad, J. Böhner, et al. 2017. “Climatologies at High Resolution for the Earth’s Land Surface Areas.” *Scientific Data* 4: 170122. https://www.nature.com/articles/sdata2017122.

Keppel, G., D. Craven, P. Weigelt, et al. 2021. “Synthesizing Tree Biodiversity Data to Understand Global Patterns and Processes of Vegetation.” *Journal of Vegetation Science* 32: e13021. https://doi.org/10.1111/jvs.13021.

Kreft, H., and W. Jetz. 2010. “A Framework for Delineating Biogeographical Regions Based on Species Distributions.” *Journal of Biogeography* 37: 2029–2053. https://doi.org/10.1111/j.1365-2699.2010.02375.x.

Kuhn, M., J. Wing, S. Weston, et al. 2024. “Caret: Classification and Regression Training.” https://cran.r-project.org/web/packages/caret/index.html.

Kumar, M., N. Kalra, H. Singh, et al. 2021. “Indicator-Based Vulnerability Assessment of Forest Ecosystem in the Indian Western Himalayas: An Analytical Hierarchy Process Integrated Approach.” *Ecological Indicators* 125: 107568. https://www.sciencedirect.com/science/article/pii/S1470160X21002338.

Laurance, W. F., J. Sayer, and K. G. Cassman. 2014. “Agricultural Expansion and Its Impacts on Tropical Nature.” *Trends in Ecology & Evolution* 29: 107–116. https://linkinghub.elsevier.com/retrieve/pii/S0169534713002929.

Lebart, L., A. Morineau, and M. Piron. 2000. *Statistique Exploratoire Multidimensionnelle*. Dunod. https://horizon/documentation.ird.fr/exl-doc/pleins_textes/2022-03/010029478.pdf?trk=public_post_comme nt-text.

Lenormand, M., J.-B. Féret, G. Papuga, S. Alleaume, and S. Luque. 2025. “Coupling In Situ and Remote Sensing Data to Assess α- and β-Diversity Over Biogeographic Gradients.” *Ecography* 2025: e07479. https://doi.org/10.1111/ecog.07479.

Lenormand, M., G. Papuga, O. Argagnon, et al. 2019. “Biogeographical Network Analysis of Plant Species Distribution in the Mediterranean Region.” *Ecology and Evolution* 9: 237–250. https://doi.org/10.1002/ecr3.4718.

Lohbeck, M., F. Bongers, M. Martinez-Ramos, and L. Poorter. 2016. “The Importance of Biodiversity and Dominance for Multiple Ecosystem Functions in a Human-Modified Tropical Landscape.” *Ecology* 97: 2772–2779. https://doi.org/10.1002/ecy.1499.

Main-Korn, M., B. Pflug, J. Louis, V. Debaecker, U. Müller-Wilm, and F. Gascon. 2017. “Sen2Cor for Sentinel-2.” In *Image and Signal Processing for Remote Sensing XXIII*, edited by L. Bruzzone, F. Bovolo, and J. A. Benediktsson, 3. SPIE. https://doi.org/10.1117/12.2278218.full.
Massey, R., L. T. Berner, A. C. Foster, S. J. Goetz, and U. Vepakomma. 2023. "Remote Sensing Tools for Monitoring Forests and Tracking Their Dynamics." In *Boreal Forests in the Face of Climate Change: Sustainable Management*, edited by M. M. Girona, H. Morin, S. Gauthier, and Y. Bergeron, 637–655. Springer International Publishing. https://doi.org/10.1007/978-3-031-15988-6_26.

Mertz, O., T. B. Bruun, M. R. Jepsen, et al. 2021. "Ecosystem Service Provision by Secondary Forests in Shifting Cultivation Areas Remains Poorly Understood." *Human Ecology* 49, no. 3: 271–283. https://doi.org/10.1007/s10745-021-00236-x.

Monteith, J. L. 1972. "Solar Radiation and Productivity in Tropical Ecosystems." *Journal of Applied Ecology* 9: 747–766. https://www.jstor.org/stable/2401901.

Moonlight, P. W., P. L. Silva de Miranda, D. Cardoso, et al. 2020. "The Strengths and Weaknesses of Species Distribution Models in Biome Delimitation." *Global Ecology and Biogeography* 29: 1770–1784. https://doi.org/10.1111/geb.13149.

Morera-Beita, A., D. Sánchez, W. Wanek, et al. 2019. "Beta Diversity and Oligarchic Dominance in the Tropical Forests of Southern Costa Rica." *Biotropica* 51: 117–128. https://doi.org/10.1111/btp.12638.

Mrema, E. M., T. W. Crowther, W. Mathai, et al. 2020. "Ten Years to Restore a Planet." *One Earth* 3: 647–652. https://www.cell.com/one-earth/abstract/S2590-3322(20)30605-9.

Myers, N., R. A. Mittermeier, C. G. Mittermeier, G. A. B. da Fonseca, and J. Kent. 2000. "Biodiversity Hotspots for Conservation Priorities." *Nature* 403: 853–858. https://www.nature.com/articles/35002501.

NASA JPL. 2020. NASADEM Merged DEM Global 1 Arc Second V001. NASA EOSDIS Land Processes Distributed Active Archive Center. https://lpdaac.usgs.gov/products/nasadem_hgtv001.

Nuñez, Y., N. Hoyos, A. Correa-Metrio, et al. 2024. *Influence of Climatic Variables on Biome Transitions in the Colombian and Panamanian Caribbean Region*. Social Science Research Network. https://papers.ssrn.com/abstract=4848671.

Pain, A., K. Marquardt, A. Lindh, and N. J. Hasselquist. 2021. "What Is Secondary About Secondary Tropical Forest? Rethinking Forest Landscapes." *Human Ecology* 49: 239–247. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7734905/.

Pang, S. E. H., J. W. F. Slik, D. Zurell, and E. L. Webb. 2023. "The Clustering of Spatially Associated Species Unravels Patterns in Tropical Tree Species Distributions." *Ecosphere* 14: e4589. https://doi.org/10.1002/ecs2.4589.

Pendrill, F., T. A. Gardner, P. Meyfroidt, et al. 2022. "Disentangling the Numbers Behind Agriculture-Driven Tropical Deforestation." *Science* 377: eabm9267. https://doi.org/10.1126/science.abm9267.

Pérez Chaves, P., G. Zuquim, K. Ruokolainen, et al. 2020. "Mapping Floristic Patterns of Trees in Peruvian Amazonia Using Remote Sensing and Machine Learning." *Remote Sensing* 12: 1523. https://www.mdpi.com/2072-4292/12/9/1523.

Perino, A., H. M. Pereira, M. Felipe-Lucia, et al. 2022. "Biodiversity Post-2020: Closing the Gap Between Global Targets and National-Level Implementation." *Conservation Letters* 15: e12848. https://doi.org/10.1111/conl.12848.

Perrone, M., M. Di Febbraro, L. Conti, et al. 2023. "The Relationship Between Spectral and Plant Diversity: Disentangling the Influence of Metrics and Habitat Types at the Landscape Scale." *Remote Sensing of Environment* 293: 113591. https://www.sciencedirect.com/science/article/pii/S0034425723001426.

Poorter, L., D. Craven, C. C. Jakovac, et al. 2021. "Multidimensional Tropical Forest Recovery." *Science* 374: 1370–1376. https://doi.org/10.1126/science.abh3629.

R Core Team. 2024. *R: The R Project for Statistical Computing*. https://www.r-project.org/.

Ramosaj, B., and M. Pauly. 2023. "Consistent and Unbiased Variable Selection Under Independent Features Using Random Forest Permutation Importance." *Bernoulli* 29: 2101–2118. https://doi.org/10.3150/22-BEJ1534 full.

Redo, D. J., H. R. Grau, T. M. Aide, and M. L. Clark. 2012. "Asymmetric Forest Transition Driven by the Interaction of Socioeconomic Development and Environmental Heterogeneity in Central America." *Proceedings of the National Academy of Sciences* 109: 8839–8844. https://doi.org/10.1073/pnas.1201664109.

Reid, J. L., M. E. Fagan, J. Lucas, J. Slaughter, and R. A. Zahawi. 2019. "The Ephemerality of Secondary Forests in Southern Costa Rica." *Conservation Letters* 12, no. 2. https://doi.org/10.1111/conl.12607.

Rosa, I. M. D., H. M. Pereira, S. Ferrier, et al. 2017. "Multiscale Scenarios for Nature Futures." *Nature Ecology & Evolution* 1: 1416–1419. https://www.nature.com/articles/s41559-017-0273-9.

Rosenfield, M. F., C. C. Jakovac, D. L. M. Vieira, et al. 2023. "Ecological Integrity of Tropical Secondary Forests: Concepts and Indicators." *Biological Reviews* 98: 662–676. https://doi.org/10.1111/brv.12924.

Roshani, S. H., M. H. Rahaman, M. Masroor, Y. Sharma, A. Sharma, and T. K. Saha. 2024. "Vulnerability Assessment of Forest Ecosystem Based on Exposure, Sensitivity and Adaptive Capacity in the Valmiki Tiger Reserve, India: A Geospatial Analysis." *Ecological Informatics* 80: 102494. https://www.sciencedirect.com/science/article/pii/S1574954124000360.

Rozendaal, D. M. A., F. Bongers, T. M. Aide, et al. 2019. "Biodiversity Recovery of Neotropical Secondary Forests." *Science Advances* 5: eaau3114. https://doi.org/10.1126/sciadv.aau3114.

Rüger, N., R. Condit, D. H. Dent, et al. 2020. "Demographic Trade-Offs Predict Tropical Forest Dynamics." *Science* 368: 165–168. https://doi.org/10.1126/science.aaz4797.

Saim, A. A., and M. H. Aly. 2025. "Fusion-Based Approaches and Machine Learning Algorithms for Forest Monitoring: A Systematic Review." *Wild* 2: 7. https://www.mdpi.com/3042-4526/2/1/7.

Sandoval, L., C.-O. Morales, J.-D. Ramírez-Fernández, P. Hanson, L.-R. Murillo-Hiller, and G. Barrantes. 2019. "The Forgotten Habitats in Conservation: Early Successional Vegetation." *Revista de Biología Tropical* 67: S36–S52. https://revistas.ucr.ac.cr/index.php/rbt/article/view/37202.

Schmid, J. S., F. Taubert, T. Wiegand, I.-F. Sun, and A. Huth. 2020. "Network Science Applied to Forest Megaplots: Tropical Tree Species Coexist in Small-World Networks." *Scientific Reports* 10: 13198. https://www.nature.com/articles/s41598-020-70052-8.

Sesnie, S. E., P. E. Gessler, B. Finegan, and S. Thessler. 2008. "Integrating Landsat TM and SRTM-DEM Derived Variables With Decision Trees for Habitat Classification and Change Detection in Complex Neotropical Environments." *Remote Sensing of Environment* 112: 2145–2159. https://www.sciencedirect.com/science/article/pii/S0034425708000345.

Shaver, I., A. Chain-Guadarrama, K. Cleary, et al. 2015. "Coupled Social and Ecological Outcomes of Agricultural Intensification in Costa Rica and the Future of Biodiversity Conservation in Tropical Agricultural Regions." *Global Environmental Change* 32: 74–86. https://linkinghub.elsevier.com/retrieve/pii/S0959378015000254.

Shebitz, D. J., L. P. Agnew, S. Kerns, A. Oviedo, and J. Ha. 2023. "Let It Grow (Back): A Call for the Conservation of Secondary Forests as Medicinal Plant Habitat." *Ethnobiology Letters* 14, no. 2: 37–46. https://ojs.ethnobiology.org/index.php/ebl/article/view/1831.

Shen, X., M. Liu, J. O. Hanson, et al. 2023. "Countries' Differentiated Responsibilities to Fulfill Area-Based Conservation Targets of the Kunming-Montreal Global Biodiversity Framework." *One Earth* 6: 548–559. https://www.sciencedirect.com/science/article/pii/S2590332223001550.
Silveira, E. M. O., V. C. Radeloff, S. Martinuzzi, et al. 2023. "Nationwide Native Forest Structure Maps for Argentina Based on Forest Inventory Data, SAR Sentinel-1 and Vegetation Metrics From Sentinel-2 Imagery." *Remote Sensing of Environment* 285: 113391. https://www.sciencedirect.com/science/article/pii/S0034425722004977.

SINAC. 2021. Mapa de Tipos de Bosque y Otras Tierras de Costa Rica 2021. https://www.sinac.go.cr/ES/bimapas/Paginas/mabosq2021.aspx.

SNIT. https://www.snitcr.go.cr/ico_services_ogc_info?k=bm9kb
zo6NDA=&nombre=SINAC.

Sokal, R. R., and F. J. Rohlf. 1962. "The Comparison of Dendrograms by Objective Methods." *Taxon* 11: 33–40. https://doi.org/10.2307/1217208.

Sokolova, M., and G. Lapalme. 2009. "A Systematic Analysis of Performance Measures for Classification Tasks." *Information Processing & Management* 45: 427–437. https://www.sciencedirect.com/science/article/pii/S0306457309000259.

Stan, K., and A. Sanchez-Azofeifa. 2019. "Tropical Dry Forest Diversity, Climatic Response, and Resilience in a Changing Climate." *Forests* 10: 443. https://www.mdpi.com/1999-4907/10/5/443.

van Tiel, N., F. Fopp, P. Brun, et al. 2024. "Regional Uniqueness of Tree Species Composition and Response to Forest Loss and Climate Change." *Nature Communications* 15: 4375. https://www.nature.com/articles/s41467-024-48276-3.

Veintimilla, D., M. A. Ngo Bieng, D. Delgado, S. Vilchez-Mendoza, N. Zamora, and B. Finegan. 2019. "Drivers of Tropical Rainforest Composition and Alpha Diversity Patterns Over a 2520 m Altitudinal Gradient." *Ecology and Evolution* 9: 5720–5730. https://doi.org/10.1002/ece3.5155.

Vieira, I. C. G., T. Gardner, J. Ferreira, A. C. Lees, and J. Barlow. 2014. "Challenges of Governing Second-Growth Forests: A Case Study From the Brazilian Amazonian State of Pará." *Forests* 5: 1737–1752. https://www.mdpi.com/1999-4907/5/7/1737.

Walker, L. R., D. A. Wardle, R. D. Bardgett, and B. D. Clarkson. 2010. "The Use of Chronosequences in Studies of Ecological Succession and Soil Development." *Journal of Ecology* 98: 725–736. https://doi.org/10.1111/j.1365-2745.2010.01664.x.

Waśniewski, A., A. Hościło, B. Zagajewski, and D. Moukétou-Tarazewicz. 2020. "Assessment of Sentinel-2 Satellite Images and Random Forest Classifier for Rainforest Mapping in Gabon." *Forests* 11: 941. https://www.mdpi.com/1999-4907/11/9/941.

Xu, H., Y. Cao, D. Yu, et al. 2021. "Ensuring Effective Implementation of the Post-2020 Global Biodiversity Targets." *Nature Ecology & Evolution* 5: 411–418.

Yang, Q., M. Y. Bader, G. Feng, J. Li, D. Zhang, and W. Long. 2023. "Mapping Species Assemblages of Tropical Forests at Different Hierarchical Levels Based on Multivariate Regression Trees." *Forest Ecosystems* 10: 100120. https://linkinghub.elsevier.com/retrieve/pii/S2197562023000519.

Zamora, N. 2008. "Unidades Fitogeográficas Para la Clasificación de Ecosistemas Terrestres en Costa Rica." *Catie*. 473154. https://repositorio.catie.ac.cr/handle/11554/6881.

Zamora, N., B. E. Hammel, and M. H. Grayum. 2004. "Vegetation." In *Manual de Plantas de Costa Rica. Vol. I: Introducción*, 91–216. Monographs in Systematic Botany From the Missouri Botanical Garden.

Zhang, S., and R. Zang. 2021. "Tropical Forests Are Vulnerable in Terms of Functional Redundancy." *Biological Conservation* 262: 109326. https://www.sciencedirect.com/science/article/pii/S0006320721003785.

**Supporting Information**

Additional supporting information can be found online in the Supporting Information section. Data S1: ece372428-sup-0001-Supinfo.docx.