# Mosaic of Size-Dependent Mortality in Three Ecologically and Economically Important Pine Species Reveals Patterns Across Space and Time

David Bauman, Sean M Mcmahon, Daniel J Johnson

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David Bauman, Sean M Mcmahon, Daniel J Johnson. Mosaic of Size-Dependent Mortality in Three Ecologically and Economically Important Pine Species Reveals Patterns Across Space and Time. Global Ecology and Biogeography, 2025, 34 (10), pp.e70141. 10.1111/geb.70141. hal-05361114

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RESEARCH ARTICLE

OPEN ACCESS

# Mosaic of Size-Dependent Mortality in Three Ecologically and Economically Important Pine Species Reveals Patterns Across Space and Time

David Bauman¹,² ID | Sean M. McMahon³,⁴ | Daniel J. Johnson⁵

¹AMAP, Univ Montpellier, IRD, CIRAD, CNRS, INRAE, Montpellier, France | ²Laboratoire d'Écologie Végétale et Biogéochimie, Faculté Des Sciences, Université Libre de Bruxelles, Brussels, Belgium | ³Smithsonian Environmental Research Center, Edgewater, Maryland, USA | ⁴Forest Global Earth Observatory, Smithsonian Tropical Research Institute, Panama City, Panama | ⁵School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, Florida, USA

**Correspondence:** David Bauman (david.bauman@ird.fr)

**Received:** 1 October 2024 | **Revised:** 23 September 2025 | **Accepted:** 8 October 2025

**Handling Editor:** Alejandro Ordonez

**Funding:** This work was supported by the Belgian American Educational Foundation, National Institute of Food and Agriculture (006480) and H2020 Marie Skłodowska-Curie Actions (895799).

**Keywords:** Bayesian multilevel models | Forest inventory and analysis | gap dynamics | insects | mortality causes | spatial analysis | spatial and temporal patterns | survival analysis | temperate forests | tree demography

## ABSTRACT

**Aim:** Global forests face increasing stresses from novel climate states, altered disturbance regimes and the spread of pests and pathogens. Understanding where and when mortality occurs across species' ranges—and identifying associated mortality agents—can improve predictions of forest shifts, inform management and enhance vegetation model carbon cycle components.

**Location:** USA.

**Time Period:** 2003–2023.

**Taxa Studied:** *Pinus elliottii*, *Pinus palustris* and *Pinus taeda*.

**Methods:** Using inventory data on 129,970 trees from 14,517 forest plots censused two to five times, recorded agents of mortality (competition, weather events, fire, animals, insects and pathogens), and applied Bayesian size-dependent survival models, multinomial regressions and spatial eigenvector-based analyses to assess spatial and temporal variation in the survival of three major US pine species.

**Results:** Species varied ontogenetically, spatially and temporally in survival. Most interspecific differences occurred in saplings and small trees (< 10 cm diameter), and near species' maximum stature. *Pinus taeda* showed a marked decline in survival across the natural populations over the last two decades. Spatially, survival patterns were significant for all three species; *P. taeda* showed consistent spatial structuring at all three spatially examined resolutions, with mortality associated primarily with weather, insects and competition. *Pinus elliottii* and *P. palustris* showed spatial structures at one resolution each. As expected, competition dominated in low-mortality areas, consistent with background mortality from succession and gap-phase dynamics. High mortality patches were dominated by disturbance agents such as weather events and insects.

**Main Conclusions:** Exploring range-wide demography while jointly accounting for ontogenetic, spatial and temporal variations is essential in determining species vulnerability to environmental and anthropogenic changes. Models to predict future trends in

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2025 The Author(s). *Global Ecology and Biogeography* published by John Wiley & Sons Ltd. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.
mortality need to include mechanisms beyond climate envelopes (such as fire, storms, pests and pathogens) in order to properly
capture species' whole-range responses to global change.

# 1 | Introduction

Globally, forests are threatened by shifting climate, especially increased temperatures, disturbance frequency and intensity, pests and pathogens, and other drivers of forest dynamics (Bauman, Fortunel, Delhaye, et al. 2022; Hartmann et al. 2022; Hubau et al. 2020; McDowell et al. 2020). Threats manifest through changes in demography, specifically the growth and mortality rates of affected populations (Bauman, Fortunel, Cernusak, et al. 2022; Bauman, Fortunel, Delhaye, et al. 2022; Locosselli et al. 2020; Peltier et al. 2022). Studies of forest demography, especially mortality, have observed important spatial and temporal patterns that emerge across a broad range of regions, species and biomes (Andrus et al. 2021; Bauman, Fortunel, Delhaye, et al. 2022; Brienen et al. 2015; Carnicer et al. 2011; Hubau et al. 2020; McDowell et al. 2020), with implications for carbon cycling, community composition and biodiversity. A challenge in interpreting mortality patterns of species is that observed increases in mortality may only indicate changes in parts of the geographic range.

The forests of the southeastern United States are known as the ‘wood basket’ of the country (Prestemon and Abt 2002), and that reputation stems from the tremendous volume of wood that comes from the region. Three major pine species are responsible for the bulk of the volume of trees in the region: *Pinus elliottii*, *Pinus palustris* and *Pinus taeda*—commonly known as slash pine, longleaf pine, and loblolly pine, respectively. Although these species are congeneric and have overlapping ranges, they differ in important ways. *Pinus taeda* is the most abundant of the three species because of its widespread use in forestry plantations, often under intense management (Fox et al. 2007). *Pinus taeda* also naturally occurs in wetter sites and has a colonising habit that has led to invasions in non-native habitats (Simberloff et al. 2010). *Pinus palustris* has a long history of use for naval stores (Harley et al. 2023) and was once much more abundant than currently, having been removed by land use change as first agriculture and then urbanisation swept through the region (Frost 2006). *Pinus palustris* tends to occupy drier upland sites as opposed to the two other pine species being investigated here and tends to be the most fire-tolerant of the three species (Burns et al. 1990). *Pinus elliottii* is more restricted in its range but occupies a large area of the forests in Florida and southern Georgia, where it is an important timber species that thrives in moist flatwood sites (Dickens et al. 2004). Spatio-temporal trends in mortality in these three important pine species could critically change these forested ecosystems with wide-ranging ecological and economic implications.

Tree size is often the strongest predictor of mortality patterns (Arellano et al. 2019; Johnson et al. 2018; Zuleta et al. 2022). Mortality probability as a function of size is often ‘bathtub’ shaped, where minimum mortality occurs in intermediate size classes and mortality is higher at the smallest and largest size

classes (Johnson et al. 2018; Lines et al. 2010; Metcalf et al. 2009). Size dependence in mortality likely derives from multiple mechanisms that act on early or late ontogenetic stages (Bennett et al. 2015; Zuleta et al. 2022): asymmetric competition, diseases and herbivory in small-sized trees or saplings (Canham and Murphy 2017; Magee, LaManna, et al. 2024), the accumulation of damages, weather events and harvesting in large-sized trees (Wang et al. 2022). Accurately understanding variation in mortality in space and time, and responses to external drivers requires explicitly accounting for nonlinear ontogenetic survival changes (Johnson et al. 2018; Leite et al. 2024; Needham et al. 2018; Vieilledent et al. 2009).

Rarely has the species' ontogenetic trajectory of mortality and their spatio-temporal variation across the entire range been examined. Most studies are restricted to a subset of a species' range because of logistical and data constraints (e.g., Bauman, Fortunel, Delhaye, et al. 2022; Johnson et al. 2018). Range-wide information offers insights into potential large-scale climate responses of a species and can indicate fundamental vulnerabilities at the margins of ranges to gradients in altered temperature or precipitation (Anderegg et al. 2019; Zhu et al. 2012). Species may, however, be affected by climate-driven anomalies that do not map to large gradients, such as shifts in disturbance regimes or the introduction of novel pathogens. Gaining a range-wide perspective on a species' mortality rate across scales and with a focus on local patterns as well as range margins may identify better predictions of species mortality under novel climates and disturbance regimes.

Analogous, but independently important to spatial variation in size-dependent mortality, is variation in time. Pulses in mortality can inform demographic sensitivity to particular extreme abiotic or biotic events (Condit et al. 2017; Lloret and Kitzberger 2018; Mueller et al. 2005), whereas more gradual shifts in baseline survival have allowed highlighting more fundamental changes in forest and carbon dynamics across a range of biomes, in response to climate change in particular (Andrus et al. 2021; Bauman, Fortunel, Delhaye, et al. 2022; Brienen et al. 2015; Carnicer et al. 2011; Hubau et al. 2020). Variation in mortality across time can provide a window into the long-term trends in forest change that is not achievable with short-term analyses (McMahon et al. 2019). To move from pattern to process, understanding how the relative importance of causes of mortality vary in time will be important for projecting species dynamics into the future (Allen et al. 2010; Hartmann et al. 2022; McDowell et al. 2020; Zuleta et al. 2022).

Here, we model the rate of survival using data collected over the last two decades for three major pine species across the southeastern United States. We also use extensive temporal and spatial data covering their entire range, as well as assignments of mortality causes to build a statistical understanding
of whole-range mortality patterns. Using the US Forest Service Forest Inventory and Analysis (FIA) database, we combine mortality from saplings to large trees with advanced modelling techniques to ask three questions: (1) How do the three pine species vary in their size-dependent mortality? (2) Does survival show spatial patterns of variation across the species' ranges, and has mortality changed over the past 20 years? (3) Is spatio-temporal variation in mortality related to changes in the relative prevalence of different causes of mortality?

## 2 | Methods

### 2.1 | Study System

The survival of three southern US pine species, *P. elliottii* (slash pine), *P. palustris* (longleaf pine) and *P. taeda* (loblolly pine), was examined using data from the FIA database spanning the years from 2003 to 2023 (Bechtold and Patterson 2005; Burrill et al. 2021). Data were downloaded from the FIA datamart on 5 May 2025. We analysed data from trees in Phase 2 forest inventory plots (visited ground-based samples) of natural stand origin and excluded trees in plantations. Although human-caused mortality is common in the southeastern United States (Wang et al. 2022), we excluded all observations from individuals whose mortality agent was marked as being human-caused (n = 89,304 across all three species) to focus on human-independent survival as much as possible. FIA field crew assign human-caused mortality when trees are obviously killed by humans through mechanical or chemical means. We included all individuals of the three species ≥ 2.54 cm in diameter at breast height (1.37 m). Applying these filters, we had 213,131 observations, 129,970 individual trees and 25,823 death events to analyse (see Table 1). We divided the ranges of these species into 1250 km² (25 × 25 km), 2500 km² (50 × 50 km) and 10,000 km² (100 × 100 km) cells to provide adequate data to estimate species survival locally and to give a spatial grid to examine variation in survival across the range of the species where grid cells contained enough trees to provide a robust estimate of survival and examine differences in inference from varying spatial grain (see grids and distribution of the FIA plots into grid cells in Figure 2a,c,e (50 × 50 km), Figure S5a,c,e (100 × 100 km) and Figure S6a,c,e (25 × 25 km)). Survival analyses (described below) were run at all three spatial grains, which yielded similar results. For simplicity, and to ensure spatially robust survival parameter estimates while retaining a statistical power as high as possible for spatial analyses, we focus on results from the 50-km grain throughout the main text (see Supporting Information for all results and figures from the 25- and 100-km grain analyses).

### 2.2 | Survival Monitoring and Modelling

#### 2.2.1 | Estimation of Size-Dependent Tree Survival Across Space and Time

Note that our models estimate parameters for tree survival (the inverse of mortality), and so we use the term 'survival' when explaining and presenting these models and their results. We use 'mortality' in reference to concepts, agents that might influence survival rates and broader theoretical inference. Therefore, the basis of all analyses was the estimation of the probability of survival per stem per year per spatial grid cell obtained from fitting a survival function for each species separately. We used a Bayesian size-dependent multilevel model of survival probability. The model fits five parameters defining a function formed by an S- and an inverse S-shaped logistic function of survival probability, joined by a common plateau K characterising annual survival probability throughout most of the species' life (detailed description and illustration in Figure S1, and model equations in Methods S1, equation 1) (Bauman, Fortunel, Delhaye, et al. 2022; Johnson et al. 2018; Needham et al. 2018). The multilevel structure comes as varying intercepts associated with the 'plateau parameter' K, for both 'cell' (space) and 'year' (time) (details in Figure S1 and equation 1 in Methods S1).

In this study, we sought to investigate species-specific ontological, spatial and temporal changes in the annual survival probability plateau (K), between 2003 and 2023, as well as the variation in mortality agents to explain the spatial and temporal heterogeneity of survival across the species' range. Our Bayesian framework allowed estimating a posterior distribution of latent survival probability per tree per cell per year (K_L, on the logit scale; see equation 1 in Methods S1, and Figure S1). We extracted the median of the K_L posteriors for each of these latent observations and used these posterior medians to test for and visualise spatial patterns of survival across the species' range, to investigate temporal changes, and to test for associations with the different mortality agents.

#### 2.2.2 | Spatial Analysis of Estimated Survival Across Species' Range

To test for spatial patterns of survival across the species' range and quantify their contribution to the overall variation in K_L, we used Moran's eigenvector maps (MEMs; Dray et al. 2006), a powerful spatially constrained multivariate approach allowing the detection and quantification of multiscale spatial patterns. We compared a small number of contrasted graph-based spatial weighting matrices to balance the optimisation of spatial pattern

<table><caption>TABLE 1 | Observations and individual trees used in the study's analysis. The number of cells corresponds to the 50-km/100-km/25-km grids used to cluster FIA forest plots into spatial units providing stable estimates of survival probabilities.</caption><thead><tr><td>Species</td><td>Observations</td><td>Individuals</td><td>Deaths</td><td>Nb plots</td><td>Nb cells</td></tr></thead><tbody><tr><td>Pinus elliottii</td><td>26,451</td><td>14,532</td><td>2764</td><td>2040</td><td>209/78/546</td></tr><tr><td>Pinus palustris</td><td>16,654</td><td>9107</td><td>976</td><td>1516</td><td>212/77/510</td></tr><tr><td>Pinus taeda</td><td>185,026</td><td>107,059</td><td>22,083</td><td>10,961</td><td>509/158/1628</td></tr></tbody></table>
detection accuracy with keeping enough statistical power, while controlling for inflation of type I error rate, following recommendations by Bauman, Drouet, Dray, and Vlemminckx (2018) and Bauman, Drouet, Fortin, and Dray (2018) (see details in Methods S2), using R package 'adespatial' (Dray et al. 2025). The presence of a statistically significant spatial structure in a species' survival (i.e., cell-level $K_L$ averaged over time) across its distribution range was tested by permutation, adjusting p-values for multiple comparisons (details in Methods S2).

If a clear (i.e., statistically significant) spatial pattern was detected, an adjusted coefficient of determination ($R^2_{adj}$; Legendre and Legendre 2012) was calculated to determine the strength of the spatial signature, and the fitted linear regression of the cell $K_L$ (averaged over the years) against the selected spatial eigenvectors—that is, the spatially structured variation in $K_L$—was mapped to visualise the survival patterns (Figure S7).

Finally, for the species showing a significant spatial pattern, we ran a new Bayesian linear model to test for associations between the spatially structured $K_L$ variation (i.e., fitted $K_L$ values from the regression of $K_L$ against the spatial eigenvectors) and the cells' dominant mortality agents estimated from the spatial multinomial models (see next section). Further details on this model and associated tests are provided in Methods S3. Contrasts between the predicted response (spatial KL variation) under 'Competition' as the main mortality agent and the predicted response under 'Weather', 'Insects', 'Disease', 'Fire' and 'Unknown' as main mortality agents were then generated from the fitted model to test whether the significantly spatially structured variation in $K_L$ was associated with a prevalence of disturbance-related agents relative to 'Competition', used as baseline.

### 2.2.3 | Assessing the Effect of Mortality Agents on Spatial and Temporal Survival Variations

When encountering a recently dead tree, FIA field crews assign a mortality agent based on the visual clues and expert knowledge of species ecology, using post-mortem evidence and stand context (snags/boles and surrounding vegetation) for the time since the last inventory. The mortality agents are broad categories based on observable obvious symptoms that attempt to characterise ultimate causes of death (Bechtold and Patterson 2005; USDA Forest Service 2023). We recognise that tree death can be an accumulation of many forest health issues (Aleixo et al. 2019; Lloret and Kitzberger 2018; Zuleta et al. 2022), but this approach gives broad insight into the dominant drivers of tree mortality across the range of the species as suggested by expert observers. The agents of death include Weather, Competition, Insects, Disease, Fire and Animals, as well as Unknown, for unidentifiable or interacting causes (USDA Forest Service 2023; USDA Forest Service, Pacific Northwest Research Station (PNW-FIA) 2021). Typical FIA-documented signs (and dead-tree analogues) used in this attribution include fungal conks/fruiting bodies and perennial cankers or extensive butt/root rot (Disease); bark-beetle galleries under loosened bark, small round exit holes and residual boring dust (frass) in bark crevices or at the base (Insects); deep bole or root-collar char with heat-killed cambium, often with bark sloughing and checks in the exposed outer wood (Fire); windthrow with root-plate mounding, stem snap with

fresh fibre pull, flood/landslide sediment lines or abrasion and stand-level die-off consistent with drought in the absence of other agents (Weather); girdling or basal bark removal with diagnostic tooth marks (e.g., beaver/ungulates) (Animals); and overtopped/ suppressed position of the dead stem at remeasurement—often with heavy vine load—and absence of other causative signs (Competition) (USDA Forest Service, Pacific Northwest Research Station (PNW-FIA) 2021). We used the tree death observations and their attributed mortality agent in Bayesian multinomial hierarchical regressions ('softmax regression') to test for the association between the prevalence of different agents of mortality (the response variable) and either spatial or temporal changes in the latent survival probability ($K_L$) (see Methods S4 for additional information on multinomial models). We estimated how survival ($K_L$) changes influenced the probability of the different mortality agents over space and time by including cell and year through varying intercepts and by using varying $K_L$ slopes in separate models for space and time (a combined space by time analysis was not feasible because of insufficient sample size). We estimated the spatial and temporal softmax regression coefficients for each species separately with a varying intercept and varying $K_L$ slope for either cell or year using the R package *brms* (Bürkner 2017), allowing us to estimate a 'grand' $K_L$ slope (the slope hyperparameter) across all cells or years (i.e., how the probability of a given mortality agent changes across cells or years, relative to 'Competition' as baseline), as well as deviations from the grand slope for each cell or year (regional and temporal sensitivities of agent probabilities to KL changes). To interpret and present the results of the softmax regressions, we extracted the marginal effects of each mortality agent using the R package 'marginaleffects' (Arel-Bundock 2024), and used the generative nature of Bayesian models to generate the implied probability of each mortality agent under a range of low to high survival ($K_L$ corresponding to annual survival of 0.92–0.97). We then reported the most probable mortality agent under the low and high survival conditions (0.92 and 0.97) for each spatial location, or year, for the spatial and temporal softmax model, respectively.

All analyses were run in R (R Core Team 2025). The data, commented R code, model outputs and figures can be found in an associated repository (https://doi.org/10.5281/zenodo.17045292).

## 3 | Results

### 3.1 | Differences in Ontological Survival Trajectories Among Species

All three pine species reach a plateau of high annual survival probability ($K$ parameter) that they maintain throughout most of their life (Figure 1, Figure S4). This plateau lies at an annual survival probability of 0.945 [0.930, 0.968] (median and 90% highest posterior density interval—90% HPDI)—of posterior probability distribution), 0.963 [0.957, 0.970] and 0.962 [0.955, 0.969] for *Pinus elliottii*, *P. palustris* and *P. taeda*, respectively, and is reached at DBHs of approximately 25 cm for *P. elliottii* and *P. palustris*, and 30 cm for *P. taeda* (Figure 1, Figure S4). Small saplings (DBH of 2.54 cm) of the three species also presented very different annual survival probabilities of 0.551 [0.490, 0.610], 0.732 [0.662, 0.771] and 0.481 [0.439, 0.498] for *P. elliottii*, *P. palustris* and *P. taeda*,
<figure><img src="image_3.png" /><figcaption>**FIGURE 1** | Size-dependent annual survival probability trajectories of *Pinus elliottii* (slash pine), *Pinus palustris* (longleaf pine) and *Pinus taeda* (loblolly pine). Red lines, dark and light grey areas are the median, 50% and 90% credibility intervals of the annual survival posteriors predicted from each species' model fit, between their minimum and maximum observed DBH (vertical dashed line). Figure based on analyses at the spatial grain of 50×50 km (see Figure S4 for corresponding figure at the grains of 25×25 km and 100×100 km).</figcaption></figure>

while at a DBH of 10 cm these probabilities increase to 0.835 [0.742, 0.883], 0.920 [0.835, 0.945] and 0.803 [0.732, 0.828], respectively. Such ontogenetic differences in survival trajectories become more evident, quantitatively, through posterior distributions of survival probability differences between pairs of species across different DBHs (i.e., contrasts). The contrasts among species show that the clearest differences in survival trajectories among the three species occur below 10 cm DBH (Figure S2).

## 3.2 | Spatial Patterns of Pine Species Survival Across Their Ranges

The multilevel nature of our Bayesian survival models provided cell-level estimates of annual survival across the distribution area of the three species (Figure 2a,c,e). Visual assessment of the mapped survival outputs at the cell level across the species' range suggests aggregated spatial patterns of survival structured at multiple spatial scales (Figure 2a,c,e). We tested for evidence of a spatial structuring of species' estimates of survival plateau (K) and quantified the importance of spatial structure relative to the overall survival variation using MEMs, a spatial eigenvector-based multivariate approach to detect complex spatial patterns (see Section 2). We found that the spatial

variation in survival of trees in the three studied species is not random and presents significant spatial aggregation patterns at varying scales across their distribution ranges (Figure S7). At a spatial grain of 50 km, we found clear evidence of multi-scale aggregation spatial patterns in the survival probability of *P. taeda*, corresponding to a strongly structuring pattern (adjusted *p*: 0.0004, adjusted $R^2$: 0.438), and of *P. palustris*, with a relatively weaker level of spatial structure (*p*: 0.0226, $R^2$: 0.222; Figure S7b,e, respectively), pointing to underlying ecological processes imprinting their spatial structures into the species' survival across their ranges. Clear evidence of spatial structuring in *K* was also found for *P. taeda* based on plot aggregation at the spatial grains of both 25 and 100 km, revealing similarly looking patterns (Figure S7a,c). Although we did not detect clear evidence of spatial structure of survival in *P. elliottii* at the 50-km grain (*p*: 0.197, $R^2$: 0.124), we did find evidence of a weak spatial structure for that species at the grain of 25 km (*p*: 0.0004, $R^2$: 0.178) (Figure S7d), likely partly due to the combination of weak structuring strength of the pattern and the higher statistical power at that grain (Table 1; Bauman, Drouet, Fortin, and Dray 2018).

Aggregations of survival plateau ($K_L$) values in *P. taeda* ranged from two to several 50-km grid cells, showing consistently high
Species' annual survival variation across their range

<figure><img src="image_4.png" /></figure>

**FIGURE 2** | Maps of the grid-level median estimated survival probability plateau across years 2004 to 2023 for each species (K in equation 1.3; see Methods S1), and associated maps of most likely agents of mortality (spatial softmax regression model), using the 50×50 km grid. (a, c, e) Circles are FIA plots where species were monitored; circle colours are proportional to grid-level median estimated survival probability across the years during which each plot was monitored (bounded between 0 and 1). Note that all plots within the same 50×50 km grid cell are attributed the same grid-level median K value, and therefore colour, for this figure. Light grey squares delineate the 50-km grid cells used as effective spatial units in the multilevel survival models (K_P in equation 1.2; see Methods S1). (b, d, f) Colours indicate the predicted dominant agent of mortality associated with each grid cell, obtained from cell-level posterior predictions of mortality agents under the cell-specific average survival plateau K (shown in a, c and e). Figures S5 and S6 present the corresponding figure for the size-dependent survival models and the spatial softmax regressions using spatial grains 100×100 km and 25×25 km, respectively, showing essentially the same patterns and dominant agents as with the grain of 50×50 km.

or low survival probabilities across bands of the species' range (Figure 2, Figure S7b), with particularly lower average survival in two clusters: portions of Alabama and Mississippi, and eastern North Carolina. We found clusters of high survival in the

Spatial variations in the dominance of mortality agents

<figure><img src="image_5.png" /></figure>

western and north central portions of the range (Figure S7a). At the 25-km cell size, we found *P. elliottii* had significant clustering of high survival in southeastern Georgia and southern Mississippi, whereas lower survival was clustered in central
<table><caption>**TABLE 2** | Effects of increasing survival (grand $K_L$ slopes) on the probability of each mortality agent being more likely than ‘Competition’, based on the spatial softmax regression (grain of 50 km). Main and bracketed values are grand $K_L$ slope posteriors median and 90% highest posterior density intervals (90% HPDI). Bold values indicate clear differences (90% intervals not encompassing zero). Negative values indicate a decrease in the probability of the corresponding mortality agent in comparison with ‘Competition’, as survival probability ($K_L$) increases, that is indicate that agent to be more prevalent when mortality is higher, whereas positive values point to the agent being more probable under higher survival plateau $K_L$, that is in low mortality regions.</caption><thead><tr><th>Mortality agent</th><th>Pinus elliottii</th><th>Pinus palustris</th><th>Pinus taeda</th></tr></thead><tbody><tr><td>Disease</td><td><strong>-0.48</strong> [-0.77, -0.23]</td><td>-0.57 [-1.4, 0.21]</td><td><strong>-0.35</strong> [-0.6, -0.14]</td></tr><tr><td>Fire</td><td><strong>-0.54</strong> [-1.02, -0.11]</td><td>0.15 [-0.88, 1.18]</td><td><strong>-0.95</strong> [-1.73, -0.27]</td></tr><tr><td>Insect</td><td><strong>-0.32</strong> [-0.65, -0.02]</td><td><strong>-0.66</strong> [-1.28, -0.02]</td><td><strong>-0.43</strong> [-0.65, -0.21]</td></tr><tr><td>Unknown</td><td>0.01 [-0.32, 0.34]</td><td><strong>-2.27</strong> [-3.09, -1.54]</td><td>0.2 [-0.19, 0.59]</td></tr><tr><td>Weather</td><td><strong>-0.45</strong> [-0.67, -0.21]</td><td><strong>-1.08</strong> [-1.64, -0.54]</td><td><strong>-0.38</strong> [-0.61, -0.15]</td></tr><tr><td>Animal</td><td>-2.08 [-5.68, 0.99]</td><td>-4.26 [-10.25, 0.76]</td><td>-0.03 [-1.71, 1.55]</td></tr></tbody></table>

Georgia and southern Florida (Figure S7d). At the 50-km cell grain, we found that *P. palustris* had higher survival in southwestern Georgia and the panhandle of Florida, whereas the lowest survival pattern was in central Alabama and southern Louisiana (Figure S7e).

The spatial distribution of species' average survival probability over most of their life cycle (K) across their range likely partly results from the prevalence of different dominant agents of mortality in different regions of their range. We next investigated whether any particular mortality agent recorded in the FIA protocol was associated with lower/higher survival and, in the cases of *P. taeda* and *P. palustris* (i.e., species with significant patterns at the 50-km grain), whether the spatial variation of mortality agent prevalence (Figure 2d,f) was associated with the spatially structured variation in the species' survival (Figure S7b,e).

We used a multinomial (softmax) multilevel Bayesian model to detect trends in mortality and focused on competition for resources, weather events (storms, droughts), insects, pathogens and fire as main mortality agents with an agent of unknown origin that can signify either interacting agents or unknown causes.

Competition was a key agent of mortality when survival was high relative to all other agents of mortality, in particular for *P. taeda* (Figure 2f), indicating potential prevalence of gap-phase dynamics. Insects also appeared as a dominating mortality agent associated with spatial aggregation patterns in some regions of all three species (Figure 2b,d,f), in particular forming a north-south band in southern Georgia and northern Florida. Weather also had a dominant role in some regions for all three species, near the coasts likely indicating cyclonic storm impacts (Figure 2b,d,f). When survival decreased towards certain regions, different species had different agents of mortality predicted to become more important relative to Competition (corresponding to a negative grand $K_L$ slope coefficient; Table 2). In regions of lower survival, *P. elliottii*'s increasing mortality was more associated with Fire, Disease and Weather as the agents of mortality, compared to Competition (Table 2). For *P. palustris*, regions of lower average survival were associated with increasing chances of Insect, Unknown and Weather being the agents of mortality. In *P. taeda*, regions where average survival

decreased were more associated with Fire, Insects, Weather and Disease, relative to Competition (Table 2).

Interestingly, the spatial distribution of dominant mortality agents under higher average mortality (Insect, Weather, Disease, Unknown) (Figure 2f) appeared to match the spatially organised variation in *P. taeda* survival (Figure S7b). We tested those associations (see Section 2) and found clear evidence that spatial clusters of higher mortality in *P. taeda* (Figure S7b) were indeed more associated with Weather and Insects—but not Disease and Unknown—than with Competition (Competition being more associated with patches of higher survival; Figure S8a). No clear association between the significant spatial pattern of *P. palustris* (Figure S7e) and dominant mortality agents was found (Figure S8b).

## 3.3 | Survival Changes Over Time

*Pinus elliottii* and *P. palustris* showed no evidence of an overall temporal trend in survival (K), whereas *P. taeda*'s annual survival showed a decreasing trend from 2004 to 2014, when survival stabilised at and varied around a lower level, going from 0.968 [90% HPDI: 0.959, 0.975] in the baseline period 2007–2009 (estimates for years <2007 should be taken with caution; see Section 4) to 0.950 [0.939, 0.961] in the period 2014–2023 (Figure 3). The survival decline in *P. taeda* was consistently found at the 25- and 100-km grains too (Figure S9), and was such that there was nearly no overlap between the 90% HPDI of the predicted annual survival of periods 2007–2009 and 2014–2023, with an average survival decline of ca. 0.02 from the first to the second period (Figure S3c).

We found the field crew-assigned agents of mortality varied among species through time and with respect to the local survival in a given year (Figure 4, Figures S3 and S10–S13 for 100 and 25-km grains). We compared the predicted probability of causal agents under two contrasted levels of the latent survival probability $K_L$ estimates (0.97 and 0.92) to interpret the importance of mortality agents in cases of ‘background’ survival (0.97) and under markedly decreased survival (0.92). We found that, in general, Competition was the dominant agent when survival
<figure><img src="image_6.png" /><figcaption>**FIGURE 3** | Temporal variation and uncertainty in the posterior predictions of annual survival probability (plateau part of the survival curve, $K_L$) for the three *Pinus* species across their entire range. Circle and thick and thin vertical bars are $K_L$ posterior median, and 75% and 90% posterior credibility intervals. Note that the y-axis of (a) encompasses a slightly broader range of values than those of (b) and (c), because of greater interannual survival fluctuations in *Pinus elliottii*. A decline in annual survival is found in *Pinus taeda* (c), with an initial high survival for period 2007–2009, followed by a consistent survival decline until 2014, where survival seems to stabilise and vary around a new baseline. Horizontal dark red lines emphasise those two time periods, 2007–2009 and 2014–2023. The values below those lines summarise the two periods’ posterior predicted survival (median and 90% highest posterior density interval, HPDI), and show how marked the survival decline from 2007–2009 to 2014–2023 (virtually no overlap between the two periods 90% HPDIs). The figure derives from the survival model output using a spatial grain of 50 × 50 km (using a grain of 25 × 25 km or 100 × 100 km yielded very similar year-level survival probabilities; see Figure S9).</figcaption></figure>

was at the 0.97 level (i.e., under low mortality years), especially for *P. taeda*, and for *P. elliottii* for which the difference in probability of Competition as a mortality agent was the largest between high and low survival years (Figure 4, Figure S3). Under low survival years (0.92 level), Weather played a larger role in tree deaths of *P. elliottii* and *P. palustris* for the years 2013–2023, illustrating consistently much higher shares of Weather-related mortality under high mortality years in those species since 2013 (Figure 4). Fire played a consistently greater role as an agent in *P. elliottii* under low-survival years (0.92 level) throughout the years. Fire also played an important role for *P. palustris*, on average, however being more associated with low-survival years from 2005 to 2012, and then with high-survival years from 2016 to 2023. Insects were a slightly more prevalent mortality agent under low-survival years for *P. elliottii* throughout the years, and for *P. taeda* from 2012 to 2023 (Figure 4, Figure S3)

(although Competition remains dominant in that species, even in lower survival years).

4 | Discussion

Leveraging the spatial extent of a national forest inventory, we examined the patterns of mortality in space and time and the major agents of mortality across the entire range of three important *Pinus* species. We found that all species showed size-dependent survival trajectories typical of long-lived tree species. They varied the most in their small-size survival, with *P. taeda* having relatively low survival and *P. palustris* having relatively high survival when small. Small-sized survival is important because it can be a key bottleneck to the populations and a strong filter on canopy tree composition (Chang-Yang et al. 2021; Stone et al. 2025). We
<figure><img src="image_7.png" /><figcaption>**FIGURE 4** | Temporal changes in the probability of the different mortality agents, given either low (red) or high (blue) survival probabilities (inverse-logit of $K_L$ of 0.92 or 0.97, respectively). The figure shows the counterfactual predictions of the expected probabilities of each mortality agent given the $K_L$ of low and high survival, based on the year-level coefficients' posterior probabilities from the three species-specific temporal multi-nomial models (see Methods), using the spatial grain of 50 × 50 km (see Figures S10 and S11 for corresponding figure at the grains of 100 × 100 km and 25 × 25 km). Points and intervals are the posterior predictions' median and 90% credibility intervals. Figure S3 provides additional information by showing the predictions across a continuous range of K between 0.88 and 1 (see Figures S12 and S13 for corresponding figure at grains of 100 × 100 km and 25 × 25 km).</figcaption></figure>

did not find range-edge changes in mortality that would typify a response to shifts in climate means (Zhu et al. 2012). Instead, we found a spatial mosaic of maximum survival rates for all three species, made of non-random spatial structures of multiple scales. There were no temporal trends for *P. elliottii* and *P. palustris*, but *P. taeda* showed both a strong decrease in overall survival from 2007–2009 to 2014–2023 and a highly structured non-random spatial pattern. We also found that the main agents of mortality varied by species across space and time, helping to explain the local mosaic spatial structuring of mortality.

**4.1 | Size-Dependent Survival Variation Across the Species' Ranges**

Survival models of all three species showed similar size-dependent trends over most of their life history, though there were differences at smaller sizes. The survival of small *P. taeda*—and to a lesser extent of *P. elliottii*—was much lower than for *P. palustris*. *Pinus taeda* has the habit of colonising open land at high densities, and

part of the elevated small size class mortality could be due to thinning, as supported by competition as a dominant mortality agent for *P. taeda*. Contrasting the life history strategy of *P. taeda*, *P. palustris* has a more conservative and resilient life history strategy and tends not to colonise at high densities (Magee, Lapalikar, et al. 2024), thus reducing the intraspecific competition in the small size classes. All species survival probabilities converged at high survival by around 20 cm DBH (Figure 1, Figure S2). Using interspecific survival probability contrasts (Figure S2), we found that most of the substantial and clear survival differences occurred in relatively small trees (mostly <10 cm DBH). This result, likely arising from spatio-temporal variation of the size-dependent survival trajectories, highlights how detecting life history strategy differences and demographic trade-offs can be obscured and potentially wrongly lead to conclude an absence of such trade-offs when using data from relatively bigger trees (e.g., > 10 cm DBH), as is often the case in permanent forest plot networks (ForestPlots.net et al. 2021; Malhi et al. 2021). The strong survival response at early life stages is further confirmed by the comparatively less studied seedling and sapling life stages, at which differences in survival
and growth are particularly varied (Chang-Yang et al. 2021; Johnson et al. 2018; Needham et al. 2018).

## 4.2 | Spatial Patterns in Species' Survival Across Their Ranges

Spatially structured variation in mortality could be an indicator of population-level shifts in response to abiotic or biotic drivers. We found that average survival was spatially structured at multiple scales across the distribution range of *P. taeda* and *P. palustris*, and explained 43.8% and 22.2% of maximum survival (K) spatial variation for those species, respectively (grain of 50 km) (Figure S7b,e). We only found clear evidence of a spatial pattern in *P. elliottii*—though weaker (R² adj: 17.9%)—at the finer grain of 25 km (Figure 2, Figure S7d). The spatially structured variation of *P. taeda*'s maximal survival was such that aggregation patterns of lower survival were more associated with weather events (cyclonic storms) near coastal areas, as expected (Cannon et al. 2023), and with insect-related damages and unknown agents than with competition and gap dynamics, which dominated where survival was higher (Figure S8a). The three species have been shown to have different susceptibility to wind storms that parallels our results, with *P. taeda* being the most susceptible (Johnsen et al. 2009), which may explain the observed spatial pattern. The three studied pines are also somewhat resistant to drought impacts (Klockow et al. 2020).

The relatively weaker degree of spatial autocorrelation in the survival probability of *P. palustris*, and more particularly of *P. elliottii*, was somewhat surprising, given the level of spatial organisation of *P. taeda*'s survival and the intrinsic spatial nature of most ecological processes (Dray et al. 2012; Legendre and Legendre 2012). By using three levels of granularity, we explored a wide range of scales at which mortality agents may be operating, and we found relevant changes in the detection of spatial patterns in survival across the different grains used in the analysis, which suggests that there are pertinent scales at which the mortality experienced by these two species is spatially structured (Figures S5–S8). Fine-scale spatial patterns can remain hidden when they occur at scales finer than those detectable by the sampling design or spatial weighting matrix (Bauman, Drouet, Fortin, and Dray 2018; Dray et al. 2006; Platt and Denman 1975). In our case, the weak spatial structure in *P. elliottii* survival was only evident at the finer 25-km grain (Figure S7d), and we found no clear link between this structure and the spatial variation in mortality agents. Together, these results suggest that the mortality agents driving *P. elliottii*'s spatial survival pattern may themselves be structured at scales finer than our smallest sampling resolution (25 km) (Furniss et al. 2020; Liu et al. 2007). It is also possible that other ecological processes not considered in this study—such as local edaphic or topographic variation—contributed to the spatial patterns observed in survival. This explanation seems particularly plausible for *P. elliottii* and *P. palustris*, where the identified mortality agents did not clearly account for the spatially structured variation in survival.

## 4.3 | Temporal Survival Trends

Temporal correlation in mortality across a population would indicate population growth trends. We found a temporal trend of

decreasing average survival for *P. taeda* across its range. Small numeric changes in annual survival, when maintained, can translate to major demographic changes because of the compounded nature of such changes (Bauman, Fortunel, Delhaye, et al. 2022; McMahon et al. 2019). For example, changing the average annual survival rate from 0.97 to 0.95—as observed in *P. taeda* from 2007–2009 to 2014–2023 (Figure 3)—would lead a 1000-tree cohort to have an expected 218 versus 77 survivors after 50 years. There is uncertainty about what the long-term average survival of *P. taeda* was prior to the first censuses used in this analysis, and whether it was as high as suggested by the earliest 5 years of this analysis, when the survival decline appears to start (Figure 3). Years 2003–2006 had significantly fewer plot inventories, which in addition tended to be spatially clustered (not shown). This may have contributed to the temporal trend of declining survival in *P. taeda* over the years 2004–2006. Subsequent years however had reliable and spatially representative samples across the species' range, and still showed a strong decrease in the survival plateau from 2007 to 2009, taken as stable initial baseline, and the stable 2014–2023 period (Figure S3). This uncertainty and challenge regarding earlier years reflect the typical difficulty in inferring long-term changes in background forest mortality, because of the very high survival of trees and the ensuing need for very extensive data to detect the signal from the noise (Andrus et al. 2021; Bauman, Fortunel, Delhaye, et al. 2022; Das et al. 2016; McMahon et al. 2019).

## 4.4 | Variation in Agents of Mortality

The causes of tree mortality are varied and often complicated (Aleixo et al. 2019; Arellano et al. 2019; Oliva et al. 2014; Zuleta et al. 2022), but understanding the major drivers and symptoms can aid in the conservation and management of a species (Dymond et al. 2014; Goodnow et al. 2008). Studying the prevalence of mortality agents across the entire distributional range of a species and how it relates to spatio-temporal mortality patterns requires enormous amounts of data. Although across the time series there was no clear trend or shift in the prevalence of one agent over another (Figure 4, Figures S3, S8 and S9), we were able to show spatial clusters of predominant mortality agents and how some of these related to the spatially organised mortality patterns across *P. taeda*'s range.

Two of the three species had regions of higher mortality that were more likely to have weather events as the most likely agent of mortality when compared to competition. Future increases in variability of climate extremes will likely reinforce the observed mosaic of survival and increase regional mortality rates (McDowell et al. 2020), as may be occurring in *P. taeda* already. A great deal of forest mortality research focuses on drought as a major driver. For example, Van Mantgem et al. (2009) found a widespread increase of mortality in the western United States (across elevations, tree sizes, genera and past fire history), and they suggest that global warming and increasing hydraulic stress may be the cause (and not competition, as forest density and basal area decreased, nor aging of large trees, as small trees' mortality increased too). The western United States experiences more hydrological climatic extremes compared to the southeastern United States (Hogan et al. 2024), which may explain why we find neither a strong effect of weather (Figure 4)
nor an increasing mortality trend over time for two of the three
species (Figure 3). The southeastern United States is subject to
cyclonic storms that have the potential to cause tree mortality
(e.g., Chambers et al. 2007) and weather emerged from our anal-
ysis as a local agent of mortality for southern pines, as opposed
to a range-wide driver, which would be expected with cyclonic
storms rather than regional droughts. The geographical distinc-
tion between drivers of mortality may be important in translat-
ing range-wide datasets to climate change impacts, as important
events may seem spatially and temporally stochastic, even if the
frequency and intensity of the events are increasing.

We found spatial pockets and temporal peaks of insect-related mortality observed in the data, which likely represent periodic localised outbreaks of bark beetles. Southern pine beetle (*Dendroctonus frontalis*), a native bark beetle, has a long history of impacts on southern pines, particularly *P. taeda* and *P. elliottii* to a lesser extent (Coyle et al. 2015; Friedenberg et al. 2007; Martinson et al. 2007). Management practices, such as thinning, are widely used to prevent beetle outbreaks in plantations, but may be less common in natural forests that are the subject of this study (Asaro et al. 2017; Clarke et al. 2016; Fettig et al. 2007). The observed peak in insect-ascribed mortality for *P. elliottii* may be a case of such an outbreak.

Agents of mortality are broad categories, and there are often
multiple causes involved in the death of a tree (Arellano
et al. 2019; Oliva et al. 2014; Zuleta et al. 2022). The complex
nature of tree mortality may explain the relative frequency
of ‘Unknown’ as a dominant category in the spatial softmax
analysis, as much of this category corresponds to interactions
between multiple agents. For example, competition and insect
outbreaks have both been shown to accentuate tree vulnera-
bility to hydric stress (Anderegg et al. 2015; Huang et al. 2020;
McDowell et al. 2011). Conversely, hydric stress can lead to
lagged decreases in survival that can be attributed to second-
ary causes like insects (Klockow et al. 2018). Importantly, the
FIA methodology focuses on the ultimate agent of death, but
compounded vulnerabilities from different causes over time are
most likely to precede that ultimate cause, particularly in large-
sized trees (Arellano et al. 2019; Franklin et al. 1987; Zuleta
et al. 2022).

Fire plays a particularly important ecological role in the south-
eastern United States. Both *P. palustris*- and *P. elliottii*-dominated
forests experience fire, and the species are well adapted to sur-
vive these. *Pinus palustris*, in particular, is often maintained as a
dominant species by regular fires reducing competition (Gilliam
and Platt 1999). We expected to see a signal of fire as a mortality
agent in *P. taeda*, as it is not as well adapted to fire as the other
two species (Knapp et al. 2011).

5 | Conclusion

In this work, we jointly modelled the ontogenetic, spatial and
temporal patterns of tree survival over the past two decades in
three pine species of major economic and ecological value in
the southeastern United States. By leveraging a national forest

inventory with the flexibility of a size-dependent Bayesian mul-
tilevel survival model, we highlighted the ontogenetic survival
dynamics differences of these species, as well as the multiscale
spatial structure of their maximum survival probability across
their whole range. We did not find any broad-scale (i.e., lati-
tudinal or longitudinal) gradient in mortality rates that might
indicate climate-driven range contraction. Instead, patches of
higher mortality were evident across the range, in particular
for *P. taeda*. We found that spatial variation in survival could
partly be explained by the distribution of the dominant mor-
tality agents. Our approach further identified a clear decrease
in survival over the past 20years in *P. taeda*. This shows that
when looking at the rates of mature trees, changes in local dis-
turbances, such as fire or windstorm frequency and intensity,
as well as invasive pests and pathogens, could drive important
mortality changes in a spatial mosaic.

Acknowledgements

We raise a toast to all the FIA field crews that gave their blood, sweat and tears to collect the data used in this analysis. To the FIA analysts and database maintainers that make these data publicly available, we salute you. D.B. was funded by the Belgian American Educational Foundation (BAEF) and the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 895799. D.J.J. was funded by the US Department of Agriculture under the McIntire Stennis Project FLA-FOR-1007080. Survival models were run on a high-performance computing cluster provided by the Consortium des Équipements de Calcul Intensif (CÉCI), funded by the Fonds de la Recherche Scientifique de Belgique (F.R.S.-FNRS) under grant no. 2.5020.11 and by the Walloon Region. We thank Sergio Marconi for his help running a heavy model on his high-performance computing cluster.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

All data used in these analyses are freely available from the United
States Forest Service (https://www.fs.usda.gov/research/products/
dataandtools/tools/fia-datamart). In addition, a folder containing the
raw and processed datasets, the commented R code underlying all el-
ements of the study, as well as all model outputs and figures, can be
found in the following permanent repository: https://doi.org/10.5281/
zenodo.17045292.

Biosketch

Dr. David Bauman, Dr. Sean M. McMahon and Prof. Daniel J. Johnson are leading experts in tree demography and forest ecology, with a shared focus on forest resilience and management under global change. Dr. Bauman specialises in quantitative ecology, using advanced statistical models to study tree population dynamics and causal drivers of mortality, growth and recruitment in tropical forests. Dr. McMahon's research explores forest resilience, examining how trees respond to environmental stressors like climate change, with a focus on carbon dynamics and long-term forest monitoring. Dr. Johnson integrates tree physiology with demographic modelling to understand forest responses to climate extremes, including drought and temperature shifts. Together, their work advances the understanding of forest ecosystem dynamics, offering insights critical for predicting forest resilience and guiding conservation and management strategies.
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## Supporting Information

Additional supporting information can be found online in the Supporting Information section. **Figure S1:** Illustration of the Bayesian size-dependent survival model. **Figure S2:** Predicted interspecific differences in size-dependent survival. **Figure S3:** Predicted year-level changes in mortality agent probabilities with increasing survival, at the 50-km spatial grain. **Figure S4:** Estimated size-dependent survival trajectories at the spatial grains of 25 and 100 km. **Figure S5:** Maps of the grid-level estimated survival plateau and associated maps of most likely agents of mortality, based on the 100-km spatial grain. **Figure S6:** Maps of the grid-level estimated survival plateau and associated maps of most likely agents of mortality, based on the 25-km spatial grain. **Figure S7:** Significant spatial patterns detected in the survival plateau of the three species, across the period 2004–2023, based on the three spatial grains. **Figure S8:** Tests of association between high mortality location in the spatially structured survival of *Pinus taeda* and *P. palearis* and the dominance of mortality agents. **Figure S9:** Estimated survival per year at the spatial grains of 25 and 100 km. **Figure S10:** Predicted changes in the probability of mortality agents under low and high survival conditions over time, at the 100-km spatial grain. **Figure S11:** Predicted changes in the probability of mortality agents under low and high survival conditions over time, at the 25-km spatial grain. **Figure S12:** Predicted year-level changes in mortality agent probabilities with increasing survival, at the 100-km spatial grain. **Figure S13:** Predicted year-level changes in mortality agent probabilities with increasing survival, at the 25-km spatial grain. **Methods S1.** Equations of the Bayesian size-dependent survival model. **Methods S2.** Details of spatial analyses using Moran's eigenvector maps. **Methods S3.** Model of association between spatial structured survival plateau—KL—and corresponding dominant mortality agent. **Methods S4.** Additional information regarding the multinomial models.