# Tree mortality by ecological groups in the amazon region: Influence of forest structure, soil characteristics, and extreme climatic events after logging

Jorge Luis Reategui-Betancourt, Guido Vicente Briceno Castillo, Rodrigo Geroni Mendes Nascimento, Alba Valéria Rezende, Lucas Jose Mazzei de Freitas

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Jorge Luis Reategui-Betancourt, Guido Vicente Briceno Castillo, Rodrigo Geroni Mendes Nascimento, Alba Valéria Rezende, Lucas Jose Mazzei de Freitas. Tree mortality by ecological groups in the amazon region: Influence of forest structure, soil characteristics, and extreme climatic events after logging. Trees, Forests and People, 2024, 18, pp.100670. 10.1016/j.tfp.2024.100670. hal-05183091

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# Tree mortality by ecological groups in the amazon region: Influence of forest structure, soil characteristics, and extreme climatic events after logging

Jorge Luis Reategui-Betancourt<sup>a, *</sup>, Guido Briceño<sup>b</sup>, Rodrigo Geroni Mendes Nascimento<sup>c</sup>, Alba Valéria Rezende<sup>a</sup>, Lucas José Mazzei de Freitas<sup>a,d, *</sup>

## ARTICLE INFO

### Keywords:

- Forest structure

- Soil characteristics

- Extreme climatic events

- Forest phases

## ABSTRACT

Mortality of remaining trees tends to increase after logging, and adapting forest management to climate change is another significant challenge to ensure the sustainability of forests, timber production, and provision of ecosystem services. This study investigated mortality in remaining trees (DBH ≥ 20 cm) in different ecological groups (pioneer, light-demanding, and shade-tolerant) during different phases of forest monitoring (initial, intermediate, and final), as well as their relationship with structural, climatic, and environmental characteristics. The research was conducted in five managed areas in the Brazilian Amazon (Jari, Km114, Km67, Peteco, and Moju) with continuous monitoring over six to 33 years. We analyzed a total of 212 permanent plots containing 62 pioneer species, 250 light-demanding species, and 304 shade-tolerant species. The mortality rate for the three monitoring phases in logged forests was 1.27 %, 1.49 %, and 0.75 % year⁻¹, respectively, while the rate in unlogged forests was 1.04 %, 1.33 %, and 0.50 % year⁻¹. In post-logging forests, mortality was high for pioneer and light-demanding species, especially in the initial phase. For shade-tolerant species, mortality rate was influenced by soil silt and sand content, presence of phosphorus, and total carbon content. In unlogged forests, forest structure variables such as average height and total basal area, soil characteristics like phosphorus content, and climate factors such as intensity of dry months were most important for pioneer and light-demanding species. In the intermediate and final monitoring phases in logged forests, tree mortality was related to soil characteristics like sand and clay content, total carbon, and cation exchange capacity. Extreme climatic events such as severe drought, El Niño, and extreme temperatures also influenced tree mortality, along with time elapsed after logging. These findings highlight the complex interaction between soil characteristics, climate, and forest structure and tree mortality and ecology. These correlations are crucial to understand mortality processes in logged and unlogged forests, underscoring the importance of these factors for managing and conserving these ecosystems in the face of extreme climatic events and forest management practices.

## 1. Introduction

Understanding processes of change in the richness, structure, and function of forests over time during and after natural and anthropogenic disturbances is essential for activities that involve using and conserving tropical forests. The effects of competition on tree growth and mortality, along with geographic variations of these effects, fundamentally affect

the global terrestrial carbon balance (Rozendaal et al., 2020) after drought or prolonged dry spells (Allen et al., 2015; Bugmann et al., 2019; Esquivel-Muelbert et al., 2020; Law et al., 2019). Within the context of changing climatic conditions, there is growing concern that tree mortality could lead to rapid and significant changes in the richness, structure, and function of tropical forests (Archambeau et al., 2020; Rasche et al., 2013; Rutishauser et al., 2015).

<footnote><sup>a</sup> Department of Forest Sciences, University of Brasília, Campus Universitário Darcy Ribeiro W/N, Brasília 70910-900, DF, Brazil</footnote>

<footnote><sup>b</sup> CIRAD, Forêts et Sociétés, Montpellier F-34398, France</footnote>

<footnote><sup>c</sup> Federal Rural University of the Amazon, Belém, Laboratory of Forest Resources, Measurement and Management (LabFor), Presidente Tancredo Neves, N° 2501, Belém, PA 66077-830, Brazil</footnote>

<footnote><sup>d</sup> EMBRAPA Eastern Amazon, Tv. Dr. Enéas Pinheiro s/n, Belém, PR 66095-903, Brazil</footnote>

<footnote>* Corresponding authors.</footnote>

<footnote>E-mail addresses: jorgereategui91@gmail.com (J.L. Reategui-Betancourt), lucas.mazzei@embrapa.br (L.J.M. de Freitas).</footnote>
Tree mortality is an important ecological and demographic process that controls long-term dynamics in forest ecosystems. (Chao et al., 2009, 2008; Dietze and Moorcroft, 2011; Law et al., 2019; Ruiz-Benito et al., 2013). Locally, competition among trees affects growth and survival of individuals and is a key factor in mortality (Kunstler et al., 2016; Rozendaal et al., 2020), and understanding these relationships is fundamental to comprehend forest dynamics as well as define conservation strategies. Studies have found mortality to be determined by individual growth, wood density, and tree size (Ruiz-Benito et al., 2013); slow growth, low wood density, and large size are three factors that elevate the probability of tree mortality (Brienen et al., 2015; Esquivel-Muelbert et al., 2020; Rozendaal et al., 2020).

In the Amazon, extensive forest areas are managed with varying levels of disturbance (Asner et al., 2002; Tritsch et al., 2016). Mortality tends to increase following logging operations (de Avila et al., 2017; de Oliveira et al., 2019), especially in the first few years after logging (de Oliveira et al., 2019; Rutishauser et al., 2015). Felling of large trees, log dragging, and infrastructure development raises the risk of mortality for remaining individuals (D'Arace et al., 2019; de Avila et al., 2017; Lima et al., 2018; Rutishauser et al., 2015).

Prognosing tree mortality is important to assess future structure in forests affected by climate change or management practices, for example (Bugmann et al., 2019), but this process is partially hindered by enormous temporal and spatial variations and their relationship with individual tree characteristics (size, health, competition, biotic and abiotic relationships, etc.) (McDowell et al., 2018). Models for predicting individual tree mortality are typically produced using regression methods (P.C.M. Reis et al., 2016; Reis et al., 2018), and are crucial for accurate forecasting of tree numbers, basal area, diameter distribution, and long-term forest productivity (Reis et al., 2018).

Tree mortality is often correlated with water stress events (Esquivel-Muelbert et al., 2020), and in these cases mortality is higher for fast-growing early succession species and those with lower wood density (Chao et al., 2008). Extreme climatic events like droughts, intense heat, windstorms and heavy rainfall, and El Niño and La Niña (D'Oliveira et al., 2017) also boost the probability of tree mortality for at least two years following the event; pioneer species and those with low wood density are more susceptible to extreme events (Aleixo et al., 2019). Canopy opening (from logging or natural causes) exposes trees to wind, a natural reason trees fall in tropical forests (Canham et al., 2001; Fontes et al., 2018; Nelson et al., 1994).

Recent climate trends raise concerns about mortality in humid tropical forests (McDowell et al., 2018). Quantitative studies on tree mortality remain scarce (Bugmann et al., 2019), particularly regarding forests under management and climate change.

This study investigated the mortality rate of remaining trees in managed forests at different monitoring phases after logging (initial, intermediate, and final) and across different ecological groups in order to establish a relationship between mortality and local climatic and environmental characteristics. Our hypotheses for this study were: a) Mortality in the initial phase of logged forests is higher than in unlogged forests and during intermediate and final phases, because logging is a significant disturbance that makes the remaining trees more vulnerable to physical damage, microclimatic changes, and environmental stressors; b) In the first phase after logging, mortality is higher in remaining shade-tolerant species than in unlogged forests and pioneer and light-demanding species, since shade-tolerant species are less resistant to sudden changes in light and temperature exposure and physical damage caused by logging; c) Environmental and ecological variables that directly influence tree mortality affect each ecological group differently, because different species groups have distinct ecological requirements and environmental tolerance (light availability, soil composition, and climatic conditions, for example), leading to specific mortality drivers.

## 2. Methodology

### 2.1. Description and location of the study area

The study areas are in five experimental forest sites managed by Embrapa Eastern Amazon, namely Jari, Km114, Km67, Moju, and Peteco (Fig. 1).

The Jari experimental area (0°54'19.97"S, 52°11'25.76"W) is situated within forests owned by Orsa Florestal in the municipality of Vitória do Jari (AP); total experimental area is 500 ha, at an average altitude of 136 m. The climate is classified as Köppen Am, with average annual precipitation of 2234 mm, a rainy season from December to May, and average annual temperature of 25.8 °C (de Azevedo et al., 2011). The vegetation is considered dense submontane ombrophile forest (IBGE, 2012), and the soils are predominantly dystrophic yellow latosols with a heavy clay texture (de Azevedo et al., 2011).

The Km114 (3°17'19.79"S, 54°55'39.36"W) and Km67 (2°53'0.90"S, 54°55'27.00"W) sites are located in the Tapajós National Forest in the municipalities of Belterra and Aveiro (PA), at altitudes of 135 m and 195 m, respectively. The climate is classified as Am, with average annual precipitation of 2100 mm; most rain falls between December and May, while precipitation drops below 60 mm during July and August (Costa et al., 2002). Forests here are dense submontane ombrophile (IBGE, 2012), and soils are yellow latosols and yellow argisols with a clayey texture (Rodrigues et al., 2003). The average annual temperature is 26 °C (Oliveira et al., 2006).

The Moju site (2°8'14.00"S, 48°47'34.29"W) is located in Moju, PA, at an average altitude of 36 m, with total area of 1050 ha. The climate is classified as Am, with annual precipitation of 2500 mm (L.P. Reis et al., 2016) and average annual temperature of 26 °C. The soils are dystrophic yellow latosols with varying textures and include red-yellow argisols, gleisols, and plinthosols; the vegetation is considered dense ombrophile lowland forest.

The Peteco site (3°31'11.63"S, 48°47'26.19"W) is located within an area managed by CKBV Florestal Ltda. (part of the Keilla Group) in Paragominas, PA, at an average altitude of 115 m. The climate is classified as Aw, with average annual precipitation of 1800 mm and a well-defined dry season from July to September, average annual temperature of 26.3 °C, and relative humidity of 81 % (Alvares et al., 2013). The soils are classified as yellow latosols and yellow argisols, with the presence of plinthosols, gleisols, and neosols (Rodrigues et al., 2003), and the vegetation is considered dense submontane ombrophile forest.

### 2.2. Experimental design

We analyzed 212 permanent plots: 166 in experimental areas with logging activities and 46 plots in areas without logging (Table 1). The experimental sites have a history of continuous monitoring that ranges from six to 33 years (Fig. 2).

Based on observations in previous studies on initial tree mortality in logged forests (Alder et al., 2002; Alder and Silva, 2000; de Avila et al., 2017; Mazzei et al., 2010; Rutishauser et al., 2015; Silva et al., 1995), in this study we classified the monitoring periods into three phases: initial (0–4 years after logging), intermediate (5–12 years), and final (13+ years) (Fig. 2).

The five most frequently harvested species at each site, listed in descending order of importance, were:

a) Jari: *Manilkara elata* (Allemão ex Miq.) Monach., *Goupia glabra* Aubl., *Hymenaea courbaril* L., *Dipteryx odorata* (Aubl.) Willd., and *Qualea albiflora* Warm.

b) Km114: *Carapa guianensis* Aubl., *Manilkara elata* (Allemão ex Miq.) Monach., *Lecythis lurida* (Miers) S.A. Mori, *Goupia glabra* Aubl., and *Bertholletia excelsa* Bonpl.

c) Km67: *Carapa guianensis* Aubl., *Couratari stellata* A.C. Sm., *Bixa arborea* Huber, *Alexa grandiflora* Ducke, and *Erisma uncinatum* Warm.
<figure><img src="image_4.png" /><figcaption>Fig. 1. Experimental monitoring sites.</figcaption></figure>

<table><caption>**Table 1**
Experimental design.</caption><thead><tr><th rowspan="2">Site</th><th colspan="2">Number of plots</th><th rowspan="2">Plot size</th><th rowspan="2">Monitoring period after logging (years)</th></tr><tr><th>Logged</th><th>Unlogged</th></tr></thead><tbody><tr><td>Jari</td><td>36 (36 ha)</td><td>4 (4 ha)</td><td>1 ha (100 m x 100 m)</td><td>Logged forest: 26 years<br>Unlogged forest: 26 years</td></tr><tr><td>Km114</td><td>48 (12 ha)</td><td>12 (3 ha)</td><td>0.25 ha (50 m x 50 m)</td><td>Logged forest: 30 years<br>Unlogged forest: 30 years</td></tr><tr><td>Km67</td><td>36 (9 ha)</td><td>18 (4.5 ha)</td><td>0.25 ha (50 m x 50 m)</td><td>Logged forest: 33 years<br>Unlogged forest: 6 years</td></tr><tr><td>Moju</td><td>22 (11 ha)</td><td></td><td>0.5 ha (50 m x 100 m)</td><td>Logged forest: 18 years</td></tr><tr><td>Peteco</td><td>24 (6 ha)</td><td>12 (3 ha)</td><td>0.25 ha (50 m x 50 m)</td><td>Logged forest: 8 years<br>Unlogged forest: 8 years</td></tr></tbody></table>

d) Moju: *Manilkara elata* (Allemão ex Miq.) Monach., *Caryocar glabrum* (Aubl.) Pers., Maquira sp., *Terminalia amazonia* (J.F. Gmel.) Exell, and *Parkia gigantocarpa* Ducke.

e) Peteco: *Manilkara elata* (Allemão ex Miq.) Monach., *Pseudopiptadenia suaveolens* (Miq.) J.W. Grimes, *Manilkara paraensis* (Huber) Standl., *Laetia procera* (Poepp.) Eichler, and *Lecythis pisonis* Cambess.

## 2.3. Data collection

In the logged plots, commercial trees with DBH ≥ 45 cm were felled at intensities of 22–72 m³/ha. Before and after logging, a continuous forest inventory was conducted at 100 % intensity, considering all trees with DBH ≥ 20 cm. In the permanent plots, all trees were botanically identified in situ; when identification was unclear, samples were collected for posterior identification at Embrapa Eastern Amazon’s IAN Herbarium. Within each quadrant, all trees were also marked with aluminum tags and GPS points or X and Y coordinates. For each new forest inventory, the DBH of each tree was measured at a painted measurement point.

<figure><img src="image_5.png" /><figcaption>Fig. 2. Monitoring evaluation phases according to inventory periods.</figcaption></figure>
## 2.4. Data analysis

Species names were verified using the R 4.1.0 software Taxonstand package (Cayuela et al., 2012), World Flora Online (http://www. worldfloraonline.org), and the Virtual Herbarium for the Knowledge and Conservation of Brazilian Flora (REFLORA) (http://floradobrasil.jbrj.go v.br/).

The species were classified into three ecological groups based on previous publications (Alder et al., 2002; Carvalho, 1992; Swaine and Whitmore, 1988; Whitmore, 1989a, 1989b): pioneers (P), light-demanding (LD), and shade-tolerant (ST). Pioneers rapidly colonize open areas and are adapted to disturbed environments, light-demanding species require high light levels for growth and are typically found in canopy gaps, and shade-tolerant species can thrive with low light, often dominating the understory of mature forests. The dataset from all experimental sites includes 62 pioneer species, 250 light-demanding species, and 304 shade-tolerant species. For our study, all trees with DBH ≥ 20 cm were selected.

Annualized mortality rates were calculated according to Sheil's equation (Sheil et al., 1995; Sheil and May, 1996),

$$ \text{Mortality} (M) = \left[ 1 - \left[ \frac{N_0 - N_m}{N_0} \right]^{\frac{1}{t}} \right] \cdot 100 \quad (1) $$

where:

t = time elapsed between two measurements;

N₀ = number of living individuals at the beginning of the monitored period;

Nm = number of individuals that died within the monitored period.

To facilitate comparisons between sites with different inventory intervals, we standardized our estimates using the correction factor λ<sub>adjusted</sub> = λ·t<sup>0.0759</sup>, where λ represents the rate to be corrected and t is the time elapsed between measurements in years, after Lewis et al. (2004). We initially compared the means between forest types (logged and unlogged) across the different post-logging monitoring phases (initial, intermediate, and final): we also compared mortality rates among the ecological groups and forest types for each phase. These analyses were conducted using ANOVA, and Tukey's post hoc test was used to compare means at 5 % significance.

The relationship between tree mortality and various environmental and ecological factors was studied using 23 variables that describe forest structure, logging intensity, post-logging forest resilience, soil characteristics, and climatic conditions (Table 2).

Extracted volume was calculated using a formula developed for the Paragominas region, based on precise measurements of 1153 trees taken by the company Grupo CIKEL Ltda. company in 2011:

$$ V = 10^{(1.93 \cdot \text{Log10DAP}) - 2.96} \quad (2) $$

where:

V = commercial volume

DBH = diameter at breast height

Soil data were obtained from surveys conducted at the different experimental sites which are part of the Embrapa Eastern Amazon database. Climate data such as precipitation and temperature were sourced from the CRU-TS 4.03 dataset (Harris et al., 2014) and reduced in WorldClim 2.1 (Fick and Hijmans, 2017). The number of rainy months (Mc) and dry months (Ms) was determined through a temporal analysis of precipitation for each site over 36 years (1981–2017). Mc was defined as months when precipitation exceeded the 85th percentile, and Ms as months below the 15th percentile. Finally, the total number of months obtained for each site was divided according to the measurement period between inventories in each phase: initial, intermediate, and final (Fig. S1).

For strong and very strong El Niño and La Niña events, average Oceanic Niño Index (ONI) values were considered, with SE and VSE

<table><caption>**Table 2**

Environmental and ecological variables.</caption><thead><tr><th colspan="2">Explanatory variables</th></tr></thead><tbody><tr><td>Final tree abundance at each phase</td><td>Abundance (Arv·ha<sup>-1</sup>)</td></tr><tr><td>Basal area at the end of each phase</td><td>G (m<sup>2</sup>·ha<sup>-1</sup>)</td></tr><tr><td>Average tree height at the end of each phase</td><td>Hm (m)</td></tr><tr><td>Maximum tree height at the end of each phase</td><td>Hmax (m)</td></tr><tr><td>Logging intensity</td><td>IEV (m<sup>3</sup>·ha<sup>-1</sup>)</td></tr><tr><td>Number of logged trees</td><td>AE (Arv·ha<sup>-1</sup>)</td></tr><tr><td>Basal area of trees killed by logging</td><td>ABP (Gm<sup>2</sup>·ha<sup>-1</sup>)</td></tr><tr><td>Total basal area lost to logging (dead trees + logged trees)</td><td>RAB (Gm<sup>2</sup>·ha<sup>-1</sup>)</td></tr><tr><td>Soil sand content</td><td>Sand (g·kg<sup>-1</sup>)</td></tr><tr><td>Soil silt content</td><td>Silt (g·kg<sup>-1</sup>)</td></tr><tr><td>Soil clay content</td><td>Clay (g·kg<sup>-1</sup>)</td></tr><tr><td>Total carbon</td><td>CT (g·kg<sup>-1</sup>)</td></tr><tr><td>Soil aluminum saturation</td><td>Al (cmolc/dm<sup>3</sup>)</td></tr><tr><td>Extractable phosphorus (Bray)</td><td>P·Bray (mg/dm<sup>3</sup>)</td></tr><tr><td>Total exchangeable bases</td><td>TEB (cmolc/dm<sup>3</sup>)</td></tr><tr><td>Cation exchange capacity</td><td>CEC (cmol/kg)</td></tr><tr><td>Rainy months</td><td>Mc (number of months)</td></tr><tr><td>Dry months</td><td>Ms (number of months)</td></tr><tr><td>Strong El Niño/La Niña events</td><td>El Niño/La Niña (Months/ periods)</td></tr><tr><td>Minimum temperature</td><td>Tmin (°C)</td></tr><tr><td>Maximum temperature</td><td>Tmax (°C)</td></tr><tr><td>Time after logging</td><td>TE (years)</td></tr></tbody></table>

indices for El Niño and SL index for La Niña (https://ggweather.com/ens o/oni.htm). The total number of months during which each event occurred was consequently counted and evenly divided according to the measurement period between inventories in each phase (Fig. S1).

Principal component analysis (PCA) was performed with the selected variables, which were previously normalized by range using the scale function (Becker, 2018). The purpose of PCA is to reduce dimensionality (the number of predictor or dependent variables) in order to facilitate interpretation and ensure that variables are not correlated; scores for the first two principal components were extracted to run linear regression models with the mortality rate for each ecological species group across different forest phases and types. The significance of each PCA axis was determined at 5 % significance, and predictor variables with coefficients > 0.7 in PCA correlation were identified as having the most influence on mortality rates. The analyses were conducted using the FactoMineR (Le et al., 2008) and factoextra (Kassambara and Mundt, 2017) packages in R 4.1.2 software (Core, 2021).

## 3. Results

### 3.1. Tree mortality in the amazon and ecological groups

Mortality rates were found to differ between forest phases and types, whether logged or unlogged (p < 0.001). The highest mortality rates were observed in the initial and intermediate phases, regardless of logging. During the initial phase, mortality rates were 1.27 % year<sup>-1</sup> for logged forests and 1.04 % year<sup>-1</sup> for unlogged forests, and in the intermediate phase they were 1.49 % year<sup>-1</sup> for logged forests and 1.33 % per year for unlogged forests. In the final phase, however, mortality rates differed significantly, dropping to 0.75 % year<sup>-1</sup> in logged forests and 0.50 % year<sup>-1</sup> in unlogged forests (Fig. 3).

Mortality rates also differed significantly in ecological groups during different monitoring phases. In the initial stage, the highest mortality was observed in light-demanding species in unlogged forests (1.54 % year<sup>-1</sup>), while in logged forests this rate was 1.25 % year<sup>-1</sup>. Pioneer species exhibited mortality rates of 0.81 % and 1.11 % year<sup>-1</sup>, respectively, and in shade-tolerant species the mortality rates were 1.10 % and 1.01 % year<sup>-1</sup>, respectively (Fig. 4).

In the intermediate phase, the highest mortality rates were observed in light-demanding species, regardless of forest type, with respective
<figure><img src="image_6.png" /><figcaption>Fig. 3. Mortality rates by monitoring phase (initial, intermediate, final); letters indicate significant difference according to the Tukey test at 5 % significance.</figcaption></figure>

rates of 1.87 % and 1.65 % year⁻¹. For pioneer species, the mortality rate was higher in logged forests at 1.62 % year⁻¹, compared to 0.45 % year⁻¹ in unlogged forests. Mortality of shade-tolerant species was 0.94 % and 0.96 % year⁻¹ in unlogged and logged forests, respectively (Fig. 4).

In the final phase, pioneer species in unlogged and logged forests had the highest mortality rates, 1.23 % and 1.08 % year⁻¹, respectively. Light-demanding and shade-tolerant species exhibited lower mortality rates, between 0.23 % and 0.60 % year⁻¹ (Fig. 4).

## 3.2. Potential drivers of mortality

In addition to distinct characteristics at all sites (Tables S1, S2, and S3), soil variables such as clay, sand, and silt content were most strongly related to tree mortality rates, regardless of site or monitoring phase; other soil variables such as aluminum content or phosphorus were important in specific sites and phases. Among forest structure and logging variables, only average tree height and basal area were found to be significant, and only in certain phases and forest types. All climatic variables were correlated with mortality rates in at least one monitoring

phase and forest type (Table 3, Fig. 5).

In the initial monitoring phase of logged forests, four soil variables (sand, clay, aluminum, and cation exchange capacity) and three climatic variables (La Niña and extreme minimum and maximum temperatures) comprised the first principal component of the PCA; the second was represented by silt content and total carbon. These nine variables explained 51.50 % of the data variability (Table 3, Fig. 5, Fig. S2). In the intermediate phase, the analysis was mainly represented by 10 variables including new aspects such as phosphorus content (replacing soil aluminum saturation, which lost importance) and other climatic variables like dry months and strong El Niño events (replacing La Niña occurrences). Together, these variables explained 54.50 % of the data variability. For the final monitoring phase, 13 variables were most important, combining variables from the initial and intermediate phases as well as the variable time since logging, and explained 60.60 % of variability (Table 3, Fig. 5, Fig. S2).

For unlogged forests in the initial phase, the same variables as in logged forests (including average tree height) explained 63.10 % of the variation in the two main PCA axes. This same pattern was observed in the intermediate phase, with 10 variables most prominent, also

<figure><img src="image_7.png" /><figcaption>Fig. 4. Mean mortality rates by ecological groups: pioneer (P), light-demanding (LD), shade-tolerant (ST) in logged and unlogged forests across different monitoring phases. Different letters indicate significantly different mortality rates according to the Tukey test at 5 % significance.</figcaption></figure>
<caption>**Table 3**</caption>

Loadings and explained variance of the two principal axes of variation recorded in logged and unlogged forests, using explanatory variables related to structure, logging, climate, and soil.

<table><thead><tr><th rowspan="3">Variables</th><th colspan="4">Initial</th><th colspan="4">Intermediate</th><th colspan="4">Final</th></tr><tr><th colspan="2">Logged</th><th colspan="2">Unlogged</th><th colspan="2">Logged</th><th colspan="2">Unlogged</th><th colspan="2">Logged</th><th colspan="2">Unlogged</th></tr><tr><th>Dim.1</th><th>Dim.2</th><th>Dim.1</th><th>Dim.2</th><th>Dim.1</th><th>Dim.2</th><th>Dim.1</th><th>Dim.2</th><th>Dim.1</th><th>Dim.2</th><th>Dim.1</th><th>Dim.2</th></tr></thead><tbody><tr><td>Abundance</td><td>-0.20</td><td>-0.30</td><td>-0.42</td><td>0.53</td><td>-0.23</td><td>0.41</td><td>0.30</td><td></td><td>-0.41</td><td></td><td>0.60</td><td></td></tr><tr><td>G</td><td>-0.37</td><td>0.25</td><td>-0.36</td><td>0.64</td><td>-0.37</td><td></td><td>0.32</td><td>0.36</td><td></td><td></td><td></td><td><strong>0.72</strong></td></tr><tr><td>Hm</td><td>-0.17</td><td>0.54</td><td></td><td><strong>0.74</strong></td><td></td><td>-0.61</td><td></td><td>0.50</td><td></td><td>0.48</td><td></td><td><strong>0.74</strong></td></tr><tr><td>Hmax</td><td>-0.30</td><td>0.40</td><td></td><td>0.42</td><td>-0.26</td><td>-0.44</td><td>0.35</td><td></td><td>-0.23</td><td>0.41</td><td>0.69</td><td></td></tr><tr><td>IEv</td><td>0.44</td><td></td><td></td><td></td><td>0.37</td><td>0.33</td><td></td><td></td><td>0.36</td><td>-0.26</td><td></td><td></td></tr><tr><td>AE</td><td>0.48</td><td></td><td></td><td></td><td>0.39</td><td>0.37</td><td></td><td></td><td>0.36</td><td>-0.29</td><td></td><td></td></tr><tr><td>ABP</td><td>-0.24</td><td></td><td></td><td></td><td>-0.17</td><td></td><td></td><td></td><td>-0.23</td><td>0.21</td><td></td><td></td></tr><tr><td>RAB</td><td>-0.41</td><td></td><td></td><td></td><td>-0.30</td><td>-0.35</td><td></td><td></td><td>-0.35</td><td>0.33</td><td></td><td></td></tr><tr><td>Sand</td><td><strong>-0.70</strong></td><td>-0.63</td><td><strong>-0.97</strong></td><td></td><td><strong>-0.80</strong></td><td>0.39</td><td><strong>0.96</strong></td><td></td><td><strong>-0.94</strong></td><td>-0.23</td><td><strong>-0.92</strong></td><td></td></tr><tr><td>Silt</td><td>0.29</td><td><strong>0.88</strong></td><td><strong>0.88</strong></td><td></td><td>0.56</td><td><strong>-0.77</strong></td><td><strong>-0.81</strong></td><td></td><td>0.62</td><td><strong>0.71</strong></td><td><strong>1.00</strong></td><td></td></tr><tr><td>Clay</td><td><strong>0.74</strong></td><td>0.56</td><td><strong>0.95</strong></td><td></td><td><strong>0.81</strong></td><td>-0.31</td><td><strong>-0.96</strong></td><td></td><td><strong>0.96</strong></td><td></td><td><strong>0.86</strong></td><td></td></tr><tr><td>TC</td><td>0.57</td><td><strong>0.79</strong></td><td><strong>0.86</strong></td><td></td><td><strong>0.76</strong></td><td>-0.59</td><td><strong>-0.78</strong></td><td></td><td><strong>0.84</strong></td><td>0.51</td><td><strong>0.97</strong></td><td></td></tr><tr><td>Al</td><td><strong>0.70</strong></td><td>-0.28</td><td>-0.63</td><td>-0.52</td><td>0.58</td><td>0.44</td><td>0.66</td><td>-0.48</td><td>0.59</td><td>-0.54</td><td><strong>-0.73</strong></td><td></td></tr><tr><td>P_Bray</td><td>0.64</td><td>-0.66</td><td>-0.47</td><td>-0.67</td><td>0.37</td><td><strong>0.82</strong></td><td>0.42</td><td>-0.34</td><td>0.33</td><td><strong>-0.87</strong></td><td><strong>-0.89</strong></td><td></td></tr><tr><td>TEB</td><td>0.43</td><td></td><td>0.33</td><td></td><td>0.27</td><td>0.22</td><td>-0.31</td><td>0.58</td><td>0.42</td><td>-0.35</td><td></td><td><strong>-0.70</strong></td></tr><tr><td>CEC</td><td><strong>0.85</strong></td><td>0.47</td><td>0.69</td><td>-0.36</td><td><strong>0.92</strong></td><td>-0.19</td><td>-0.62</td><td>-0.64</td><td><strong>0.96</strong></td><td></td><td><strong>0.88</strong></td><td></td></tr><tr><td>Mc</td><td>0.68</td><td>-0.17</td><td>-0.68</td><td>0.55</td><td>-0.46</td><td><strong>0.76</strong></td><td></td><td><strong>0.92</strong></td><td>-0.74</td><td>-0.65</td><td><strong>-0.97</strong></td><td></td></tr><tr><td>Ms</td><td>0.62</td><td>-0.51</td><td></td><td><strong>-0.86</strong></td><td><strong>0.90</strong></td><td>0.35</td><td><strong>0.93</strong></td><td></td><td><strong>0.95</strong></td><td></td><td><strong>0.97</strong></td><td></td></tr><tr><td>El_Niño</td><td>0.36</td><td>0.17</td><td></td><td>0.40</td><td><strong>0.91</strong></td><td>-0.44</td><td>0.23</td><td></td><td>0.32</td><td><strong>0.83</strong></td><td><strong>0.97</strong></td><td></td></tr><tr><td>La_Niña</td><td><strong>-0.93</strong></td><td>0.30</td><td><strong>0.89</strong></td><td></td><td><strong>0.80</strong></td><td><strong>-0.96</strong></td><td></td><td><strong>-0.80</strong></td><td><strong>-0.97</strong></td><td></td><td><strong>0.97</strong></td><td></td></tr><tr><td>Tmin</td><td><strong>-0.88</strong></td><td></td><td></td><td></td><td><strong>0.81</strong></td><td>-0.34</td><td></td><td><strong>0.83</strong></td><td><strong>-0.79</strong></td><td>0.50</td><td><strong>0.97</strong></td><td></td></tr><tr><td>Tmax</td><td><strong>-0.74</strong></td><td>0.45</td><td>0.59</td><td>0.61</td><td><strong>-0.81</strong></td><td>-0.34</td><td></td><td></td><td><strong>0.88</strong></td><td>-0.37</td><td></td><td></td></tr><tr><td>TE</td><td>-0.56</td><td>-0.27</td><td></td><td></td><td>0.33</td><td>0.63</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Pv (%)</td><td><strong>51.50</strong></td><td></td><td><strong>63.10</strong></td><td></td><td><strong>54.50</strong></td><td></td><td><strong>63.10</strong></td><td></td><td><strong>60.60</strong></td><td></td><td><strong>83.10</strong></td><td></td></tr></tbody></table>

G=basal area, Hm=mean tree height, Hmax=maximum tree height, IEv=logging intensity, AE=number of logged trees, ABP=basal area of trees killed by logging, RAB=total basal area loss due to logging (dead trees + logged trees), CT=total carbon, Al=aluminum, P_Bray=extractable phosphorus (Bray), TEB=total exchangeable bases, CEC=cation exchange capacity, Mc=rainy months, Ms=dry months, El_Niño=strong El Niño events, La_Niña=strong La Niña events, Tmin=minimum temperature, Tmax=maximum temperature, TE=time after logging, Pv (%)=proportion of variance.

explaining 63.10 % of the variation. In the final phase, all variables were important and explained 83.10 % of the variation in the two axes (Table 3, Fig. 5, Fig. S2).

When species mortality rates were correlated by ecological group with the scores for the first two principal components from the PCA, not all correlations were found to be significant (Fig. 6). In the initial monitoring phase of logged forests, models showed the mortality rate of shade-tolerant species to be correlated with the second PCA axis. In this case, the influencing variables were soil silt content and total carbon content in the soil (Fig. 6, Table 3). In unlogged forests, during the same initial monitoring stage mortality rates for pioneer and light-demanding species were significantly related to the second PCA axis, influenced by mean tree height and number of dry months (Table 3, Fig. 6).

In the intermediate monitoring stage for logged forests, mortality rates for pioneer, light-demanding, and shade-tolerant species were strongly influenced by the variables in the first PCA axis: soil sand content, clay content, total carbon, cation exchange capacity, occurrence of El Niño, dry months, and minimum as well as maximum temperatures. In unlogged forests, only the mortality rate of pioneer species exhibited a relationship with the variables of the first PCA axis, which include soil sand, silt, clay, and total carbon content, as well as the occurrence of El Niño, dry months, and minimum temperature (Table 3, Fig. 6).

In the final monitoring stage in logged forests, the mortality rates were correlated with the first PCA axis for all ecological groups, pioneer, light-demanding, and shade-tolerant species. The key variables in this relationship were soil sand, clay, and total carbon content, cation exchange capacity, and minimum and maximum temperatures and time after logging. The variables of the second PCA axis, such as El Niño and La Niña events, number of dry months, and soil sand and aluminum content were correlated with mortality rates for light-demanding and shade-tolerant species. In unlogged forests during the same monitoring phase, only mortality for shade-tolerant species was related to the variables of the first PCA axis. With the exception of total exchangeable

bases (TEB), all climatic and soil variables were important to the mortality rate (Table 3, Fig. 6).

## 4. Discussion

Immediately after timber extraction, forests are subject to physiological stress and the impacts of logging can remain visible for decades (Hawthorne et al., 2012; Shenkin et al., 2015). Selective logging typically damages approximately 24.5 % of the remaining trees (Martins et al., 1997); the probability of mortality increases immediately after logging as a result, with trees becoming even more susceptible to natural mortality (D'Oliveira and Braz, 2006). Trees located near logging gaps, storage yards, and roads have a higher risk of mortality (de Oliveira et al., 2019; Gray et al., 2012; Lima et al., 2018), and tall trees with large canopies are more likely to be struck by lightning (Gora et al., 2017) or damaged by storms or other natural causes such as strong winds and heavy rainfall (Aleixo et al., 2019; Canham et al., 2001; Carter et al., 2017; Fontes et al., 2018; Nelson et al., 1994).

In this study, annual mortality rates for logged forests were 1.27 %, 1.49 %, and 0.75 % year⁻¹ in the initial, intermediate, and final monitoring phases, respectively. For unlogged forests, these rates were 1.04 %, 1.33 %, and 0.50 % year⁻¹, respectively (Fig. 3). Mortality rates for undisturbed forests typically range 1–3 % year⁻¹, while rates are higher, exceeding 2.5 % year⁻¹, after timber extraction, fire, or storms (Alder, 1995; Alder et al., 2002). Mortality is generally higher in the initial years post-harvest (de Oliveira et al., 2019; Rutishauser et al., 2015). In managed forests, mortality rates of 2.6 %, 2.4 %, and 2.2 % year⁻¹ have been observed at five, six, and eleven years post-harvest, respectively (Silva et al., 1995). Other studies have reported mortality rates of 2.4 % for low-intensity logging treatments, and up to 4.6 % for high-intensity treatments (Amaral et al., 2019). For trees with DBH ≥ 5 cm, an average mortality rate of 3.2 % year⁻¹ was found four years after logging (D'Oliveira and Braz, 2006). The mortality rates observed in this study are lower than those reported elsewhere.
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Principal component analysis of the explanatory variables in experimental sites in logged and unlogged forests. G = basal area, Hm = mean tree height, Hmax = maximum tree height, IEv = logging intensity, AE = number of logged trees, ABP = basal area of trees killed by logging, RAB = total basal area loss due to logging (dead trees + logged trees), CT = total carbon, Al = aluminum, P_Bray = extractable phosphorus (Bray), TEB = total exchangeable bases, CEC = cation exchange capacity, Mc = rainy months, Ms = dry months, El_Niño = strong El Niño events, La_Niña = strong La Niña events, Tmin = minimum temperature, Tmax = maximum temperature, TE = time after logging.</figcaption></figure>

Our analysis of mortality after logging found higher rates among pioneer and light-demanding species; mortality in pioneer species was higher in the intermediate phase and lower in the final stage (1.11 %, 1.62 %, and 1.08 % year⁻¹, respectively). Similarly, mortality for light-demanding species was highest in the intermediate phase (1.25 %, 1.65 %, and 0.60 % year⁻¹). For shade-tolerant species, mortality rate decreases successively from the initial to the final stage (1.01 %, 0.96 %, and 0.42 % year⁻¹) (Fig. 4). Studies have determined that pioneer species are generally short-lived and fast-growing, and tend to die earlier (Alder et al., 2002; Chao et al., 2008). Manokaran & Kochummen (1987) found mortality rates of 3.8 % year⁻¹ for pioneer and late-climax species. High mortality rates (4.4 % year⁻¹) have also been found for pioneer species in logged areas, followed by understory-tolerant species at 1.9–2.5 % year⁻¹ (Silva et al., 1996). In the forests in this study, pioneer and light-demanding species exhibited higher mortality rates in

the intermediate stage, indicating an effect that persists longer than previously reported in other studies on the impacts of selective logging; this effect is also more pronounced in pioneer and light-demanding species.

The forest sites in this study are in a relatively extensive area in Brazil's central-eastern Amazon, but tree mortality in both logged and unlogged forests was found to be strongly influenced by the same environmental variables, primarily soil physical characteristics and climate. Tropical forests are considered the most dynamic ecosystem, rich in biodiversity, carbon, and productivity compared to other forest biomes (Pan et al., 2011). Any disturbance to this ecosystem consequently has a distinct impact on productivity, which is directly influenced by the site's environmental conditions (Johnson et al., 2016), an effect which was also observed for mortality, although minor changes in important variables were noted between different monitoring stages. It
<figure><img src="image_9.png" /><figcaption>Fig. 6. Linear model representing the relationship between axes 1 and 2 and mortality rate in logged and unlogged forests across different monitoring phases, by ecological group (P: pioneer; LD: light-demanding; ST: shade-tolerant).</figcaption></figure>

is important to note that structural variables (such as average tree height or forest basal area) were only related to tree mortality in the unlogged sites.

In these unlogged forests, mortality of pioneer and light-demanding trees was also influenced by forest structure, particularly the height of the canopies of surrounding trees and basal area. Competition for light, water, and nutrients is one of the main ways neighboring trees may affect mortality (Fargeon et al., 2016; McDowell et al., 2018; Rozendaal et al., 2020): as they grow taller, they may shade out smaller trees, reducing their capacity for photosynthesis (Velázquez and Wiegand, 2020).

Studies also indicate that tree dynamics in tropical forests, particularly mortality, recruitment, and productivity, are correlated with soil characteristics (Quesada et al., 2012). For example, Phillips et al. (2004) indicate that trees die and are recruited twice as quickly in the nutrient-rich soils of southern and western Amazonia as they are in the nutrient-poor soils of eastern and central Amazonia. Variations in forest structure and function in this region have been correlated with variations in soil properties, particularly soil depth, texture, topography, and anoxia (Quesada et al., 2012). High levels of cations and phosphorus in the soil hinder tree growth (Quesada et al., 2010, 2012), adversely affecting function and increasing the probability of mortality (Schoenholtz et al., 2000).

In the logged forests, regardless of monitoring stage, mortality rates among the different ecological groups were primarily influenced by variations in soil sand, clay, and silt content. Generally, sandy soils have low fertility (Laurance et al., 1999; Sanchez, 2019), which also can reduce tree growth (Quesada et al., 2009), but soils with an adequate proportion of sand can be well-drained, especially in areas with heavy rainfall (Kobe et al., 2011; Laurance et al., 1999). Higher tree mortality was associated with lower topography and the predominance of sandy soils, as well as shallow water tables in lowlands and depressions in upland forests, which provide poor adhesion and prevent roots from penetrating deeply into the soil (Madelaine et al., 2007). Even so,

mortality in this type of soil was not related to ecological groups, since all species had the same mortality risk (Arellano et al., 2019).

This study determined that tree mortality across different ecological groups is sensitive to extreme climatic events, such as prolonged dry months, strong El Niño and La Niña events, and extreme temperatures. Previous research has shown tree mortality in tropical forests to be associated with climate change, particularly more intense and longer dry seasons (Adams et al., 2017; Aleixo et al., 2019; Berenguer et al., 2021; Esquivel-Muelbert et al., 2020; Phillips et al., 2010), as well as higher rainfall and temperatures (Allen et al., 2010) and extreme El Niño events (Aleixo et al., 2019; Berenguer et al., 2021; Leitold et al., 2018).

It is important to highlight that in this study, the only variable related to tree mortality in logged forests was time after logging, particularly in the final monitoring stage. Other studies have found mortality to be high in the years immediately following logging (de Avila et al., 2017; de Oliveira et al., 2019; Rutishauser et al., 2015). Mortality among trees damaged by logging is evident immediately afterwards (Mazzei et al., 2010), but remains higher than in undamaged trees even decades later, and surviving trees grow slowly (Shenkin et al., 2015). As we have shown, this phenomenon (along with the other soil and climatic variables that amplify tree mortality across different ecological species groups) indicates that logging has long-term effects on tree mortality.

The complexity of interactions between species richness, soil characteristics, and tree mortality at different forest sites significantly impacts forest dynamics. The forests we studied maintain unique species while also sharing a considerable number, suggesting a common core of biodiversity. For this reason, forest management approaches must consider not only species diversity but also soil characteristics, since both play interconnected roles in maintaining and enhancing the resilience of forest ecosystems. Ultimately, understanding these complex interactions is fundamental for effective biodiversity conservation and sustainable forest management within the context of global environmental changes.
## 5. Conclusions

Tree mortality rates varied across different monitoring stages in both logged and unlogged forests. In logged forests, mortality rates were generally higher during the intermediate phase of monitoring, in contrast with previous findings in the scientific literature.

The interactions between soil characteristics, climate, forest structure, and tree mortality ecology are complex, playing a crucial role in the dynamics of both logged and unlogged forests, and should be considered in the management and conservation of these ecosystems, especially in the face of extreme climatic events and long-term forest management practices.

Our findings provide valuable insights into the processes of tree mortality across different ecological groups over decades of monitoring for managed forests in the central-eastern Brazilian Amazon. These results have significant implications for sustainable forest management and preservation of these vital ecosystems amid climate change and human exploitation.

### CRediT authorship contribution statement

Jorge Luis Reategui-Betancourt: Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Conceptualization. Guido Briceño: Writing – review & editing.

Rodrigo Geroni Mendes Nascimento: Writing – review & editing,

Formal analysis. Alba Valéria Rezende: Writing – review & editing.

Lucas José Mazzei de Freitas: Writing – review & editing, Validation, Supervision, Methodology, Formal analysis, Conceptualization.

### Declaration of competing interest

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

### Data availability

Data will be made available on request.

### Acknowledgements

This study was financed in part by the Brazilian Association for the Improvement of Higher Education Personnel (CAPES), funding code 001. We also wish to thank Embrapa Eastern Amazon for making their study areas available and providing logistical support, along with the University of Brasília (UnB, Brazil) for research support.

### Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.tfp.2024.100670.

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