# Climate-growth relationships in the introduced dominant woody tree Neltuma juliflora in Santiago Island, Cabo Verde Archipelago (Eastern Atlantic)

Daniel Semedo, Diogo C. Pavão, Lurdes Borges Silva, Guilherme Roxo, Roberto Resendes, Maria Romeiras, Monica Moura, Luis Silva

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Daniel Semedo, Diogo C. Pavão, Lurdes Borges Silva, Guilherme Roxo, Roberto Resendes, et al. Climate-growth relationships in the introduced dominant woody tree Neltuma juliflora in Santiago Island, Cabo Verde Archipelago (Eastern Atlantic). Journal of Arid Environments, 2026, 233, pp.105532. 10.1016/j.jaridenv.2025.105532. hal-05488014

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# Climate-growth relationships in the introduced dominant woody tree *Neltuma juliflora* in Santiago Island, Cabo Verde Archipelago (Eastern Atlantic)

Daniel Semedo<sup>a,b,*</sup>, Diogo C. Pavão<sup>c</sup>, Lurdes Borges Silva<sup>a,d</sup>, Guilherme Roxo<sup>a</sup>, Roberto Resendes<sup>a</sup>, Maria Romeiras<sup>b</sup>, Mónica Moura<sup>a</sup>, Luís Silva<sup>a</sup>

## ARTICLE INFO

**Keywords:**

*Neltuma juliflora*

Dendrochronology

Radial growth

Climatic sensitivity

Cabo verde

Semi-arid ecosystems

## ABSTRACT

*Neltuma juliflora* (syn. *Prosopis juliflora*) is the dominant woody species in Cabo Verde, introduced through extensive reforestation efforts to combat land degradation. Despite its ecological and socio-economic relevance, little is known about its growth dynamics and climatic sensitivity in this region. This study investigates growth ring formation and climate-growth relationships of *N. juliflora* on Santiago Island. We sampled 165 trees across 11 blocks, grouped into four bioclimatic zones. Tree-ring widths were measured using standard dendrochronological techniques. Generalised Linear Models (GLMs) were applied to assess the influence of climatic variables, with multicollinearity controlled using the Variance Inflation Factor (VIF). Although false rings were present, distinct growth boundaries were observed. Mean radial growth varied from 1.53 to 2.11 mm/year across bioclimatic zones. The best GLMs suggest that higher precipitation outside of the usual rainy season could negatively affect growth, while mean annual temperature could have a marginally positive effect. Significant differences in tree-ring growth rate were observed between trees sampled at the four bioclimatic areas, likely reflecting the influence of climate and elevation, and of other factors such as soil and tree density. These results highlight the ecological plasticity of *N. juliflora* and support its potential use in dendroclimatic research in semi-arid tropical regions. These findings have significant implications for ecosystem-based adaptation strategies and sustainable land management practices in Cabo Verde, particularly in the context of climate change and land degradation mitigation. The study underscores the need for longer chronologies and expanded geographic sampling across the archipelago in future work.

## 1. Introduction

Tree-ring analyses are a widely used method for determining tree age and estimating radial growth rates, providing valuable insights into the relationship between tree growth and environmental factors that

influence tree-ring characteristics, such as tree-ring width (TRW) and the presence or absence of ring boundaries (Haines et al., 2016). The analysis of tree growth rings have been extensively used in temperate climate regions to study tree development and reconstruct past climatic conditions. This field of research can also be widely applied to analyse

<footnote><sup>a</sup> CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, InBIO Laboratório Associado, BIOPOLIS Program in Genomics, Biodiversity and Land Planning; UNESCO Chair – Land Within Sea: Biodiversity & Sustainability in Atlantic Islands, Faculty of Sciences and Technology, University of the Azores, Rua da Mãe de Deus, 9500-321, Ponta Delgada, Portugal</footnote>

<footnote><sup>b</sup> Linking Landscape, Environment, Agriculture and Food (LEAF), Associated Laboratory TERRA, Instituto Superior de Agronomia (ISA), Universidade de Lisboa, Tapada da Ajuda, 1340-017, Lisboa, Portugal</footnote>

<footnote><sup>c</sup> CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, InBIO Laboratório Associado, BIOPOLIS Program in Genomics, Biodiversity and Land Planning, Universidade do Porto, Vairão, Portugal</footnote>

<footnote><sup>d</sup> CBGP, INRAE, Institut Agro Montpellier, Université de Montpellier, Montpellier, France</footnote>

<footnote>* Corresponding authorCIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, InBIO Laboratório Associado, BIOPOLIS Program in Genomics, Biodiversity and Land Planning; UNESCO Chair – Land Within Sea: Biodiversity & Sustainability in Atlantic Islands, Faculty of Sciences and Technology, University of the Azores, Rua da Mãe de Deus, 9500-321, Ponta Delgada, Portugal.

*E-mail addresses:* daniel.at.semedo@uac.pt (D. Semedo), diogo.c.pavao@uac.pt (D.C. Pavão), lurdes.cb.silva@uac.pt (L.B. Silva), guilherme.g.roxo@uac.pt (G. Roxo), roberto.resendes@uac.pt (R. Resendes), mmromeiras@isa.ulisboa.pt (M. Romeiras), monica.mt.moura@uac.pt (M. Moura), luis.fd.silva@uac.pt (L. Silva).</footnote>
tree-ring characteristics, including width, density, and isotopic composition, as well as their relationship with environmental factors such as climate, wildfires, insect outbreaks, and pollution (Navrátil et al., 2018). However, despite advances in the tropical region, Cabo Verde Islands remain virtually unexplored in this field, particularly regarding the species *Neltuma juliflora* (Sw.) Raf. (Syn. *Prosopis juliflora* (Sw.) DC.).

Historically, a prevailing view in dendrochronology held that tropical trees were unsuitable for dendrochronological studies, based on the assumption that the perceived lack of strong temperature seasonality would prevent the formation of distinct, annual growth rings (Worbes, 2002). However, this paradigm has been progressively challenged. A growing body of research has demonstrated that hundreds of tropical species do, in fact, form reliable annual rings, particularly in environments with pronounced climatic seasonality, such as seasonally dry or flooded forests (Brienen et al., 2006; Quesada-Román et al., 2022). The key mechanism is not temperature, but rather any predictable environmental factor that induces a period of cambial dormancy or reduced activity, providing the necessary environmental cue for ring formation. Despite these advances, dendrochronology in the tropics remains more complex than in temperate regions due to the larger number of species, variety of habitats, variable cambial activity, and the complex phenology throughout the year (Rozendaal and Zuidema, 2011). This has also been partly confirmed in regions with climates transitioning between temperate and subtropical, such as the Azores archipelago (Pavão et al., 2023).

Recent advances in tropical dendrochronology have demonstrated that the number of tropical chronologies has rapidly increased, with approximately 400 chronologies added over the past two decades alone (Groenendijk et al., 2025). This expansion has been facilitated by the recognition that many tropical and subtropical trees exhibit repeated anatomical changes in their secondary xylem as a reaction to seasonal environmental conditions that limit, rather than entirely halt, growth. This process, which often results in the formation of annual growth rings, is primarily driven by climatic seasonality, such as variations in precipitation or flood pulses, which induce periods of cambial dormancy or reduced activity, thereby confirming the existence of annual rings for hundreds of species in seasonally dry and inundated forests.

In the Macaronesian region (i.e. Azores, Madeira, Canary Islands and Cabo Verde), the southernmost islands - Cabo Verde - are located in an ecological transition zone between the Sahara Desert and the Sudanese savannas, characterised by a highly complex tropical climate that is both arid and semi-arid (Pio et al., 2014). This climate is influenced by regional atmospheric factors, including the oscillations of high- and low-pressure zones, which significantly impact precipitation and temperature patterns. The high interannual and seasonal variability of these patterns profoundly impacts natural processes, particularly when changes occur at a rate that exceeds the adaptive capacity of species (IPCC, 2018; Varela et al., 2022). Cabo Verde's vulnerability to climate change is heightened by its small territorial size, frequent exposure to natural hazards, limited economic resilience, and scarce human and technological resources necessary to mitigate or adapt to these impacts effectively. In this context, the archipelago serves as a crucial case study within the global framework of arid tropical regions, offering valuable insights into the impacts of climate change on fragile and transitional ecosystems.

The genus *Neltuma* Raf. (Fabaceae) includes 43 accepted species, several of which have played a crucial role in reforestation programmes and efforts to mitigate desertification, significantly contributing to the restoration of degraded areas (Felker and Moss, 1996). Although its native range extends from Central U.S.A. to Tropical and Subtropical Americas (Nogueira et al., 2019), this genus is adapted to arid and semi-arid environments, and is currently distributed across Asia, Africa (Kleinjan et al., 2021), and South Australia. A distinctive characteristic of the *Neltuma* genus is its remarkable tolerance to extreme climatic conditions and low-fertility soils (Pasiecznik et al., 2001).

The *Neltuma* genus has been instrumental in dendrochronological research throughout tropical and subtropical regions (Ambite et al., 2022), offering a significant potential for assessing climate-growth relationships in arid and semi-arid areas. Its occurrence in extreme habitats provides an opportunity to study environmental events, including wildfires and changes in water resources. Extensive studies on various *Neltuma* species emphasise the strong influence of regional factors, such as groundwater availability, on tree ring growth, highlighting the need for further research into the genus' dendroclimatic potential.

Currently, *Neltuma juliflora* is the most abundant and dominant tree in Cabo Verde, covering 82.9 % of the forested area (Rego et al., 2021). This species plays a crucial role in the local economy, supplying wood for fuel, particularly in rural areas, and providing pods that are used as livestock feed. It also plays a crucial role in dendrochronological research across tropical and subtropical regions (Andreu-Hayles et al., 2023).

However, limited information is available about its dendroecological characteristics in Cabo Verde. Therefore, assessing the relationship between the growth patterns of this species and the climate of Cabo Verde would contribute to addressing a critical gap in ecological knowledge. This study aims to examine this relationship by focusing on three main questions: i) Are synchronous growth rings discernible on *N. juliflora* wood samples from Santiago Island (Cabo Verde)? ii) What relations exist between climatic variables and growth ring patterns? iii) How do these relationships in Cabo Verde compare to findings from other regions, including the species' native range?

Based on the premise that the growth rings of *N. juliflora* in Santiago reflect local climatic conditions, this study was designed to deepen the understanding of the relationships between tree growth and environmental factors in arid and semi-arid regions. To this end, a comprehensive analysis was conducted across distinct climatic zones of the island, facilitating a systematic investigation of the influence of climatic variables on growth ring formation. Specifically, the study aimed to quantify the effects of environmental conditions on species growth, identify interannual variation patterns and their potential constraints, and ultimately explore the applicability of dendrochronology in reconstructing climatic variability in semi-arid ecosystems.

## 2. Materials and methods

### 2.1. Study area and target species

#### 2.1.1. Study area characterisation

The Cabo Verde archipelago is situated in the north-eastern Atlantic Ocean, approximately 570 km off the western coast of Africa. As part of the Macaronesian biogeographical region (Fernández-Palacios et al., 2024), the archipelago comprises ten principal islands and five islets, all of volcanic origin. The climate is characterised by pronounced aridity, with strong spatial gradients determined by topography and altitude. Mean annual temperatures remain relatively stable at approximately 25 °C owing to oceanic influence, whilst precipitation exhibits marked spatial variation, generally increasing from coastal zones towards the mountainous interior (Monteiro et al., 2020).

Santiago Island was selected as the focal site for sampling in this study on account of its marked environmental heterogeneity and its representativeness within the bioclimatic context of the Cabo Verde archipelago. Geographically, the island is located between latitudes 14°50' and 15°00'N and longitudes 23°20' and 23°50'W. Santiago possesses a semi-arid climate, characterised by strong winds during the dry season and a mean annual precipitation of 321 mm, concentrated in torrential rainfall between August and October (Cabral Pinto et al., 2017). The island's rugged topography, a consequence of its volcanic origin, features elevations up to 1394 m at Pico da Antónia, significantly influencing the rainfall regime, which varies from less than 100 mm annually at sea level to in excess of 500 mm in the higher mountainous areas. The dry season extends from November to July, marked by prolonged periods without precipitation, whilst the rainy season is short
and irregular.

### 2.1.2. Species description (Neltuma juliflora)

In arid and semi-arid regions, such as Santiago Island, where precipitation is exceedingly limited, *Neltuma juliflora* has emerged as the primary species for plantations owing to its remarkable capacity for adaptation and survival under adverse environmental conditions (Villagra et al., 2010). This species exhibits extraordinary resilience to elevated temperatures, low precipitation, and nutrient-poor soils, including those with high salinity or alkalinity levels (Pasiecznik et al., 2001). Its invasive nature enables it to spread in grazing areas, frequently threatening native vegetation (Romeiras et al., 2016). The versatility of *Neltuma juliflora* is reflected in its ability to thrive in areas with significant precipitation variability, ranging from less than 100 mm to 1500 mm annually, and at altitudes reaching up to 1700 m levels (Pasiecznik et al., 2001). This species performs optimally at temperatures between 20 and 32 °C, though it can tolerate extremes ranging from -1.5 to in excess of 50 °C. In Cabo Verde, N. juliflora was extensively planted in arid and semi-arid zones, playing a crucial role in soil protection and the maintenance of vegetative cover (see Fig. 1).

## 2.2. Sampling design and data collection

### 2.2.1. Field sampling

Fieldwork was conducted on Santiago Island between March and June 2023. The island was subdivided into 11 sampling blocks, each comprising five 1-km² UTM grid cells (Fig. 2A). Within each grid cell, three trees were selected to ensure broad spatial coverage across the island's environmental gradients. In compliance with national conservation legislation prohibiting the felling of mature trees, a research permit was obtained from the Directorate of the Environment. Sampling was therefore directed towards the main trunks of standing individuals, specifically those that had not been recently harvested. In *Neltuma juliflora*, such trees are morphologically characterised by multiple low-ramified stems emerging close to the base—a growth form well documented in the National Forest Inventory of Cabo Verde and consistent with descriptions of the species in arid regions (Pasiecznik et al., 2001). These multi-stemmed, non-harvested individuals generally represent the longest-lived specimens, thereby maximising the likelihood of

recovering the longest possible ring series. From each tree, two wood discs were extracted from separate primary stems of the trunk system, preferentially selecting the stems with the largest diameters. Discs were sampled as close as possible to the basal section to increase the number of growth rings obtained. This procedure yielded a total of 330 discs from 165 trees (i.e., 15 trees per block).

To avoid pseudoreplication, the four radii measured per tree (two per disc) were first cross-dated and subsequently averaged to produce a single tree-level series, ensuring that each tree contributed only one mean chronology to the analyses. This sampling design provided robust statistical power for dendrochronological analysis whilst accommodating the logistical constraints of the island's topography and the legal and morphological restrictions associated with the target species.

### 2.2.2. Bioclimatic grouping of sampling sites

In order to evaluate the bioclimatic relationships amongst the 11 sampling blocks, a hierarchical cluster analysis was performed on the 19 bioclimatic variables extracted from the CHELSA database for the coordinates of each block (see Table S1 for the complete list of variables). All analyses were performed using the R software version 3.3.4 (R Core Team, 2017), with packages for multivariate analysis¹. We applied several classical distance metrics for quantitative data, namely Euclidean Distance, Chord Distance, and Hellinger Distance (Legendre and Legendre, 1998). For each dissimilarity coefficient, we tested several agglomeration methods, namely complete linkage, centroid distance, unweighted pair-group method with arithmetic mean (UPGMA), and Ward's minimum variance clustering. To determine the optimal combination of dissimilarity measure and agglomeration method, we calculated the cophenetic correlation value between the region's distance matrix and the dendrogram representation. We followed the guidelines defined in Numerical Ecology with R (Borcard et al., 2011) for hierarchical clustering approaches. For each dendrogram, the putative number of groups formed by the target blocks was estimated using both the Rousseeuw quality index, which determines the optimal number of clusters according to silhouette widths (Rousseeuw, 1987), and the Mantel statistic (Legendre and Legendre, 1998).

The optimal combination of distance metric and agglomeration schedule corresponded to Chord Distance with UPGMA (cophenetic

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Geographical location of the study area in the Eastern Atlantic. The right panel shows the position of the Cabo Verde Archipelago in relation to West Africa and the Iberian Peninsula. The left panel provides a detailed view of Santiago Island, highlighted in black, where dendrochronological sampling of *Neltuma juliflora* was conducted.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Bioclimatic grouping of sampling sites. (A) Map of Santiago Island showing the 11 sampling blocks (numbered). The green shading represents the administrative boundaries of the island's municipalities, included to contextualise the spatial distribution of the sampling blocks. (B) Dendrogram from a hierarchical cluster analysis (Ward's method) based on 19 bioclimatic variables, illustrating the partitioning of the 11 blocks into four distinct bioclimatic groups used in the climate-growth analysis. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

correlation = 0.8475194). The resulting four groups, indicated both by the Rousseeuw quality index and the Mantel statistic, represent distinct climatic zones across the island, characterised by different patterns of precipitation, temperature, and altitude. This approach enables a more robust analysis of large-scale climate-growth relationships by accounting for local environmental heterogeneity.

### 2.2.3. Climate data

Monthly climate data (precipitation and mean temperature) for the period 1979–2023 were obtained from the CHELSA-CRUTS database (Karger and Zimmermann, 2018), which provides high-resolution climate time series suitable for ecological analyses. Prior to analysis, the climate data were homogenised using standard climatological procedures to correct for potential biases and ensure temporal consistency across the study period.

## 2.3. Dendrochronological analysis

### 2.3.1. Sample preparation and image acquisition

Standard dendrochronological protocols were followed for sample preparation (Speer, 2010). The collected wood discs were air-dried and subsequently polished using sandpaper of progressively finer grades (from 120 to 1200 grit) until the tree-ring boundaries were clearly visible under a stereomicroscope (Fig. S2A). Images for measuring the growth rings were captured using a LEICA S91 Stereozoom stereomicroscope (Fig. S3), equipped with an integrated 10 M.P. camera. Throughout the image capture process, a 50 % overlap between sequential images was maintained to ensure reliable stitching, accomplished using open-source image processing software.

### 2.3.2. Ring-width measurement, cross-dating, and chronology building

Tree-ring width (TRW) measurements were carried out on two selected radii per disc (Fig. S2B) using dendrochronological measurement software, with a precision of 0.01 mm. Visual cross-dating of all measurement series was conducted using specialised software, with statistical verification using Gleichläufigkeit (GLK), the *t*-test, and Pearson's correlation coefficient (Fritts, 1976; Schweingruber, 1988). To construct the tree-level chronologies whilst avoiding pseudorleplication, the four resulting measurement series from each tree (two radii from each of the two discs) were cross-dated and then averaged to create a single, robust mean series for each individual tree. False rings and missing rings were identified through anatomical discontinuities and inconsistencies in vessel arrangement patterns. Samples that exhibited poor cross-dating statistics or contained anatomical features that rendered reliable ring identification impossible were systematically excluded from subsequent analyses to minimise data noise and maximise the climatic signal.

### 2.3.3. Standardisation and chronology quality control

To remove age-related growth trends and non-climatic influences, all raw tree-ring width series underwent standardisation. The detrending process was implemented using standard dendrochronological methods, employing smoothing spline curves with a 50 % frequency cut-off (f = 0.5) and a 10-year window to optimise signal preservation (Cook and Kairiukstis, 1990). The resulting dimensionless tree-ring width indices (TRWi) were then used to build a master chronology for each of the four bioclimatic groups.

To validate the quality of each master chronology, several standard dendrochronological statistics were examined (Wigley et al., 1984). The mean inter-series correlation (r-bar) was calculated to quantify the extent to which individual trees share a common growth signal. We also computed the Subsample Signal Strength (SSS), which evaluates how well a chronology derived from a finite set of trees approximates the theoretical population chronology. Following conventional dendrochronological standards, we adopted a threshold of SSS ≥0.75, meaning that the sample chronology captures at least 75 % of the variance expected in an infinitely replicated chronology, thus ensuring an adequate signal-to-noise ratio and reliability for climate-growth analysis. The common period used in figures and modelling corresponds to the first year in which each group reaches SSS ≥0.75 (Fig. 2B). In addition, first-order autocorrelation (AC1) was calculated to assess the persistence of growth from one year to the next.

## 2.4. Statistical analysis

### 2.4.1. Climate data and climate-growth relationships

The climatic data used to assess climate-growth relationships included mean, minimum, and maximum temperature, as well as precipitation, extracted from the CHELSA Climate database (Karger and Zimmermann, 2018) for the period 1979–2018. For each of the four bioclimatic groups, monthly climate variables were calculated by averaging the data from the grid cells corresponding to the blocks within that group.

To comprehensively analyse climate-growth interactions, we correlated the master chronology of each group with monthly climatic variables from both the year of ring formation (current year, t) and the immediately preceding year (t-1). This strategy is fundamental in dendroclimatological studies to account for carry-over effects, whereby the climate of the previous year influences physiological processes and conditions subsequent growth (Fritts, 1976). Annual mean growth rates were also compared across the four groups to assess spatial variation via ANOVA followed by Tukey's post-hoc tests.

### 2.4.2. Generalised Linear Models (GLM)

To refine the structure of the models and enhance their predictive
capacity, we applied a stepwise model selection procedure using the step
() function, based on Akaike's Information Criterion (AIC). This iterative
forward and backward selection process allowed us to identify the most
parsimonious model, retaining only statistically significant predictors
(Burnham and Anderson, 2002; Pavão et al., 2022).

We also calculated the following variables: (i) monthly precipitation variables (e.g., Prec10, Prec5, Prec1), representing the total precipita- tion of specific months; (ii) aggregated precipitation indices, including the cumulative precipitation from July to October, hereafter denoted as total precipitation (TP = sum of Prec7 to Prec10); (iii) a water- availability index (IDM), defined as the ratio between precipitation accumulated from July to October and the mean annual temperature plus a constant, (i.e., IDM = (Prec7 + Prec8 + Prec9 + Prec10)/(MAT + 10)); and (iv) mean annual temperature (MAT), together with a cate- gorical factor representing the bioclimatic group (Code).

3. Results

3.1. *Characterisation of the block group chronologies*

The bioclimatic groups identified in this study (Fig. 2B) provided a
detailed characterisation of the environmental variability across the
study sites on Santiago Island, highlighting bioclimatic differences that
may influence the growth of *Neltuma juliflora*. These variations are
crucial for dendrochronological interpretation, as tree-ring formation
reflects water availability, soil properties, and atmospheric humidity, all
of which are key factors in shaping the species' response to local envi-
ronmental conditions.

Across the period covered by the chronologies, radial growth in *Neltuma juliflora* differs significantly among the four bioclimatic areas. In relative terms, G2 and G3 exhibit higher mean growth rates, whereas

<figure><img src="image_5.png" /><figcaption>Fig. 3. Spaghetti plots of ring-width indices (TRWi) for the four groups (G1–G4) on Santiago Island, Cabo Verde. Grey lines show individual tree series and the thick black line represents the group mean chronology; the horizontal line at 1 marks the index baseline. Dashed vertical lines indicate quality thresholds: red marks the first year in which the Subsample Signal Strength (SSS) reaches SSS ≥0.75, the standard criterion for adequate chronology reliability; blue denotes the modelling cut-off year; and purple shows where both coincide. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>
G1 and G4 show more constrained growth. Inter-annual variability mirrors this pattern: years of exceptionally favourable growth occur chiefly in G2 (followed by G3), while the lowest values cluster in G4. Despite these contrasts, most series fluctuate moderately around each group's mean, indicating broadly stable year-to-year behaviour punctuated by occasional extremes.

Chronology quality metrics support this interpretation. The rbar value is higher in G3, indicating a more cohesive common signal among trees in that group and, hence a stronger representation of shared environmental forcing. By contrast, G1 shows a lower rbar, consistent with greater within-group heterogeneity (e.g., microhabitat or age-structure effects). First-order autocorrelation (AC1) is low to moderate across groups, implying limited persistence from one year to the next after standardisation; accordingly, biological memory does not dominate residual variability, and the response to climate in the current year is likely the most informative component.

The individual ring-width series within each group display strong convergence towards their respective mean chronologies, indicating coherent growth patterns despite local variability (Fig. 3). The baseline index highlights relative departures from average growth, while the onset of segments with adequate signal strength is marked by the point at which the Subsample Signal Strength (SSS) surpasses the operational threshold (SSS ≥0.75). These segments define the common period used for modelling and ensure temporal comparability across groups. Overall, the chronologies exhibit sufficiently robust signal quality for climate—growth analysis within this common interval. Group G3 shows the strongest and most internally consistent environmental signal, whereas G1 displays higher heterogeneity, reflecting greater site-level stress or microsite variability. Groups G2 and G4 occupy intermediate positions in both growth rate and signal coherence.

## 3.2. Comparison of radial growth between bioclimatic groups

The analysis of annual ring growth revealed significant variation in radial increment among the four bioclimatic groups across Santiago Island (see Table 1). Statistical analysis demonstrated a clear hierarchical growth pattern, with Group G2 exhibiting the highest mean radial increment, followed in descending order by Groups G3, G1, and G4. Analysis of variance (ANOVA) followed by Tukey's post-hoc test revealed significant differences between specific group pairs (Table 2). Group G2 demonstrated significantly superior growth performance compared to the baseline Group G1. Conversely, Group G4 consistently exhibited significantly reduced radial increment relative to all other groups, establishing it as the poorest-performing bioclimatic

**Table 1**

Mean radial growth data and dendrochronology statistics for *N. juliflora* at four distinct sampling sites (G1-G4). For each site, the table provides the chronology time span, sample depth (number of trees, rings, and cores), mean annual radial growth increment (AGR) with standard deviation (SD), and minimum/maximum values. Key quality-control statistics are also included: the mean inter-series correlation (rbar), which measures the common growth signal, and first-order autocorrelation (AC1), which quantifies growth persistence from the previous year.

<table><thead><tr><th></th><th>G1</th><th>G2</th><th>G3</th><th>G4</th></tr></thead><tbody><tr><td>Chronology Time Span</td><td>1985–2022</td><td>1978–2022</td><td>1996–2022</td><td>1985–2022</td></tr><tr><td>Number of trees</td><td>26</td><td>37</td><td>20</td><td>27</td></tr><tr><td>Number of rings</td><td>4960</td><td>3465</td><td>1664</td><td>3648</td></tr><tr><td>Number of cores</td><td>52</td><td>74</td><td>40</td><td>54</td></tr><tr><td>Mean annual radial increment ± SD (mm)</td><td>1.53 (0.248)</td><td>2.11 (0.36)</td><td>2.00 (0.22)</td><td>1.63 (0.29)</td></tr><tr><td>Minimum and maximum values (mm)</td><td>1.22–2.25</td><td>1.15–2.81</td><td>1.54–2.36</td><td>0.77–2.12</td></tr><tr><td>rbar</td><td>0.12</td><td>0.23</td><td>0.40</td><td>0.17</td></tr><tr><td>AC1</td><td>0.19</td><td>0.25</td><td>0.13</td><td>0.18</td></tr></tbody></table>

**Table 2**

Pairwise comparisons of mean radial growth (TRWi) between bioclimatic groups of *Neltuma juliflora* on Santiago Island, Cabo Verde.

<table><thead><tr><th>Comparison</th><th>Difference</th><th>Lower Limit (95 %)</th><th>Upper Limit (95 %)</th><th>Adjusted p-value</th></tr></thead><tbody><tr><td>G2-G1</td><td>0.261</td><td>0.063</td><td>0.458</td><td>0.0045**</td></tr><tr><td>G3-G1</td><td>0.143</td><td>-0.087</td><td>0.372</td><td>0.3701</td></tr><tr><td>G4-G1</td><td>-0.227</td><td>-0.439</td><td>-0.015</td><td>0.00306*</td></tr><tr><td>G3-G2</td><td>-0.119</td><td>-0.332</td><td>0.096</td><td>0.48</td></tr><tr><td>G4-G2</td><td>-0.488</td><td>-0.683</td><td>-0.293</td><td>&lt;0.0001***</td></tr><tr><td>G4-G3</td><td>-0.370</td><td>-0.598</td><td>-0.142</td><td>0.0003 ***</td></tr></tbody></table>

Note: ***p < 0.001; **p < 0.01; *p < 0.05; ns = not significant.

environment. Groups G1 and G3 showed statistically equivalent growth rates, as did Groups G2 and G3, indicating comparable growth performance within these pairs. The pairwise comparisons revealed that Group G4 was significantly differentiated from all other groups, while the remaining groups formed two statistically homogeneous subsets: G2-G3 and G1-G3, with G3 serving as an intermediate group showing no significant differences with either G1 or G2 (Table 2).

## 3.3. Generalised Linear Models

The generalised linear model (GLM) fitted to explain the variability in annual relative growth (TRWi) demonstrated substantial explanatory power, accounting for approximately half of the observed variation in radial growth. The model's statistical validity was confirmed by a markedly lower AICc compared to the null model AICc, indicating strong support for the inclusion of the selected predictors.

Spatial effects within the GLM corroborated the patterns identified in the analysis of variance (Table 3). Relative to the reference Group G1, Group G2 exhibited significantly enhanced growth performance, while Group G4 demonstrated significantly reduced radial increment. Group G3 showed no statistically significant deviation from the baseline conditions represented by G1.

Climatic variables revealed distinct patterns of association with radial growth. The proportional distribution of annual precipitation during specific months showed consistent negative relationships with tree growth. October, May, and January precipitation ratios all demonstrated statistically significant negative associations with radial increment, with May precipitation exhibiting the strongest negative coefficient. Mean annual temperature displayed a positive trend with radial growth, though this relationship did not achieve statistical significance at the conventional alpha level. The model performance metrics indicated robust predictive capability, with the selected combination of spatial and climatic predictors providing substantial improvement over baseline predictions. The statistical significance patterns revealed that spatial grouping effects were generally more

**Table 3**

Estimated coefficients, standard errors, t-values, and statistical significance of the generalised linear model (GLM) fitted to annual relative growth (TRWi) of *Neltuma juliflora* on Santiago Island, Cabo Verde.

<table><thead><tr><th>Variable</th><th>Coefficient</th><th>Standard error</th><th>t-value</th><th>p-value</th><th>Significance</th></tr></thead><tbody><tr><td>G2</td><td>0.31</td><td>0.07</td><td>4.72</td><td>&lt;0.001</td><td>***</td></tr><tr><td>G3</td><td>0.11</td><td>0.08</td><td>1.46</td><td>0.147</td><td>ns</td></tr><tr><td>G4</td><td>-0.24</td><td>0.07</td><td>-3.32</td><td>0.001</td><td>**</td></tr><tr><td>Prec10/TP</td><td>-0.49</td><td>0.22</td><td>-2.2</td><td>0.03</td><td>*</td></tr><tr><td>Prec5/TP</td><td>-16.38</td><td>5.3</td><td>-3.09</td><td>0.003</td><td>**</td></tr><tr><td>Prec1/TP</td><td>-2.32</td><td>0.82</td><td>-2.84</td><td>0.005</td><td>**</td></tr><tr><td>MT</td><td>0.15</td><td>0.08</td><td>1.97</td><td>0.052</td><td>ns</td></tr></tbody></table>

Note: The model was fitted for n = 110 observations (R² = 0.495; AICc = 23.71; null model AICc = 83.23). **p < 0.001; *p < 0.01; p < 0.05; ns = not significant. G1 was the reference group. MT = mean annual temperature; TP = total annual precipitation; Prec10, Prec5, and Prec1 refer to monthly precipitation in October, May, and January, respectively, expressed as a proportion of TP.
pronounced than individual climatic variables, with the exception of the
seasonal precipitation distribution variables.

These results indicate a strong influence of both local and climatic
factors on the growth of *N. juliflora* in the study region. The differential
growth patterns between bioclimatic groups may be attributed to site-
specific variations in soil characteristics, microclimate, and water
availability. Particularly notable is the negative effect of the seasonal
distribution of precipitation (in January, May, and October) on the
radial growth of this species.

# 4. Discussion

## 4.1. Growth-ring characteristics and identification challenges in Neltuma juliflora

The variability observed in the formation of growth rings in *Neltuma juliflora* — expressed through the occurrence of false rings and poorly defined anatomical boundaries — represents a classic challenge in tropical dendrochronology. This irregularity arises primarily from the absence of pronounced thermal seasonality, which shifts the main control of cambial activity from temperature-driven cues to moisture availability (Worbes, 2002). In the samples from Santiago Island, growth rings were identified through the alternation between more porous earlywood and denser latewood, frequently delimited by marginal parenchyma. Samples with weak cross-dating statistics or ambiguous anatomical features were systematically excluded to maximise the climatic signal strength. Despite these constraints inherent to dry tropical environments, robust chronologies can be developed when sampling strategies and specimen selection are appropriately adapted to the ecological context (Groenendijk et al., 2025; Quesada-Román et al., 2022).

## 4.2. Radial growth patterns of Neltuma juliflora and climatic influences

The spatial heterogeneity in radial growth of *Neltuma juliflora* documented here provides an empirical quantification of the species' phenotypic plasticity. The strong correspondence between growth patterns and the bioclimatic profiles we delineated indicates that this variability is not idiosyncratic but is directly linked to tangible environmental gradients. The higher growth groups (G2, G3) coincide with higher elevation locations that our analysis identified as more water favoured (higher BIO12 and BIO16). This pattern, whereby topography modulates water availability and in turn growth, is a central principle in dry land ecology (Noy-Meir, 1973; Austin and Van Niel, 2011). Moreover, growth suppression in G1 and G4, driven respectively by lower overall precipitation or by a more pronounced seasonality of drought (low BIO19), underscores that both the volume and the temporal distribution of precipitation are critical. This accords with studies showing that the duration of the dry season can be a stronger predictor of growth in tropical dry lands than annual rainfall totals (Priya and Bhat, 1999; Gebrekirstos et al., 2009).

The modulating role of soils is supported by our chronology di-
agnostics. The interseries correlation (rbar), an indicator of the strength
of the common environmental signal, is notably lower in G1, the most
resource limited group. This statistical pattern is consistent with
ecological theory positing that, under high stress, local competition for
resources such as soil water and nutrients intensifies, such that tree level
responses shaped by edaphic microhabitat can override the regional
climatic signal (Cook and Kairiukstis, 1990; Mendivelso et al., 2016).
Accordingly, the interaction between climate and soil, inferred from our
bioclimatic and statistical evidence, emerges as the mechanism that
explains the magnitude of the observed differences. In sum, this study
shows that differences in *N. juliflora* growth constitute a measurable
response to a complex environmental gradient, resolved through the
synergistic integration of spatial, bioclimatic and dendrochronological
data.

4.3. Climate-growth relationships and the influence of climatic variables

The generalised linear model explained approximately half of the
interannual variability in radial growth, and the emerging pattern
clearly highlights the decisive role of the timing of rainfall. The negative
association between growth and the proportion of precipitation falling
in October, May and January shows that, for *Neltuma juliflora*, the
temporal distribution of rainfall is more influential than the annual total.
This indicates that rainfall events occurring outside the species' adapted
phenological window, especially during the deep dry season, may
introduce substantial physiological disruption. Isolated rainfall during
these periods can trigger premature cambial activation, a metabolically
costly process that cannot be sustained once arid conditions return,
resulting in a net carbon loss rather than structural gain.

Similarly, late rainfall at the end of the wet season may prolong
cambial activity beyond the physiologically safe period, leaving in-
dividuals more vulnerable to subsequent stress, a pattern also docu-
mented in other species from seasonal dry ecosystems (Grogan and
Schulze, 2012). This interpretation suggests that *Neltuma juliflora*
operates within a relatively narrow phenological window, in which
synchrony between water availability and metabolic demand is essential
for effective xylem formation.

The species' root anatomy reinforces this ecological interpretation. Its dimorphic root system, composed of deep taproots accessing stable water reserves and shallow lateral roots that respond rapidly to intermittent rainfall, provides substantial flexibility in water acquisition. However, this flexibility depends on a carefully regulated temporal allocation of carbon. Rainfall occurring outside the optimal period can redirect resources towards exploratory root development, disrupting the balance between hydraulic maintenance and wood production (Shiferaw et al., 2021; Dzikiti et al., 2013). Reduced radial growth may therefore reflect an adaptive physiological adjustment in which hydric security becomes a priority over structural expansion.

Finally, the marginally positive, though non significant, association
between temperature and growth may indicate thermal tolerance in the
species. Moderately warm temperatures can enhance photosynthetic
activity during the wet season when water is not limiting (Ogbaga et al.,
2018). However, under strong hydric stress, high temperatures increase
transpirational demand and intensify drought stress (Hartmann et al.,
2022). This reveals a climate response system that is strongly context
dependent, in which water remains the primary limiting factor and
temperature acts as a secondary modifier that can either support or
constrain physiological performance.

Taken together, these findings show that radial growth in *Neltuma juliflora* is not a simple linear response to climate. Instead, it expresses the behaviour of a finely tuned phenological system whose stability depends on the alignment between rainfall events and the species' metabolic requirements in an environment where water is both unpredictable and frequently limiting.

4.4. Temporal limitations of the chronologies

A major limitation of this study is the relatively short temporal extent of the chronologies, with most series not exceeding four decades. This constraint is largely unavoidable, as *Neltuma juliflora* was only introduced to Cabo Verde within the last five decades, mainly through post-independence reforestation programmes (Pasiecznik, 1998; Tewari et al., 1998). Consequently, opportunities for long-term dendroclimatic reconstructions and the detection of low-frequency climate variability remain restricted. This challenge is not unique to Cabo Verde, as the development of extended, well-replicated chronologies is a recurrent limitation in tropical and semi-arid regions, often constrained by accelerated growth rates and reduced species longevity (Quesada-Román et al., 2022; Groenendijk et al., 2025; Locosselli et al., 2020). Despite these temporal limitations, the study's strength lies in its spatial coverage across four distinct bioclimatic zones, which provides
valuable ecological insights into contemporary growth-climate relationships. The differential responses observed across these zones highlight the species' capacity to reflect local climatic variability, offering a robust dendroecological contribution for Cabo Verde. Methodological limitations must also be considered. Due to national conservation legislation prohibiting the felling of mature trees, samples were collected from primary branches rather than the main stem. While dictated by legal and morphological constraints, this approach may reduce comparability with conventional trunk-based studies and could influence the strength of detected climate-growth signals. In addition, the absence of systematic edaphic data (e.g., soil texture, nutrient availability, pH, and water-holding capacity) and stand structure variables (e.g., tree density, competition indices) may obscure important drivers of growth variability between sites. Finally, the study was conducted exclusively on Santiago Island, which limits the generalisability of findings across the wider Cabo Verde archipelago, where microclimatic conditions and species performance may vary markedly between islands.

Notwithstanding these constraints, the results provide a critical baseline for understanding *Neltuma juliflora* growth-climate interactions in Cabo Verde. The findings have practical implications for forest management and climate adaptation strategies, particularly in small island developing states. The observed growth differences between bioclimatic groups can guide provenance selection for reforestation programmes, prioritising genotypes with greater tolerance to irregular precipitation and drought stress (Buras et al., 2020). Moreover, the study offers evidence to inform silvicultural practices and ecosystem-based adaptation policies aimed at enhancing resilience to climate variability (Bernier and Schoene, 2009).

## 5. Conclusion

This study presents the first dendroecological assessment of *Neltuma juliflora* on Santiago Island, Cabo Verde, demonstrating its potential as a bioindicator in semi-arid tropical environments. Our results show that radial growth is more sensitive to the seasonal timing of rainfall than to its annual total. A higher proportion of precipitation falling outside the main August-October wet season was negatively associated with growth, likely due to phenological mismatches that disrupt the species' growth-dormancy cycle. Mean annual temperature exerted a secondary, moderate positive influence when water was available. Clear differences among the four bioclimatic groups further indicate that climate-growth relationships are spatially heterogeneous and shaped by local environmental conditions.

These findings provide important baseline evidence for forest planning and provenance selection in restoration programmes, especially under increasing rainfall variability in semi-arid regions. We highlight three priorities for future research: to focus on sub-annual climate variability and ecological timing rather than annual climate totals; to integrate complementary methods such as anatomical microsectioning and radiocarbon dating to confirm annual ring formation; and to examine the role of groundwater availability as a possible modulator of precipitation responses. Adopting these integrated approaches will strengthen dendrochronology in data-scarce tropical drylands and support sustainable land-management strategies under ongoing climate change.

## CRediT authorship contribution statement

**Daniel Semedo:** Writing – review & editing, Writing – original draft, Visualization, Methodology, Formal analysis, Data curation, Conceptualization. **Diogo C. Pavao:** Writing – review & editing, Visualization, Methodology, Formal analysis, Conceptualization. **Lurdes Borges Silva:** Writing – review & editing, Visualization, Methodology. **Guilherme Roxo:** Writing – review & editing, Methodology. **Roberto Resendes:** Methodology. **Maria Romeiras:** Writing – review & editing,

Visualization, Supervision, Methodology, Conceptualization. **Mónica Moura:** Writing – review & editing, Visualization, Supervision, Methodology, Conceptualization. **Luís Silva:** Writing – review & editing, Visualization, Supervision, Project administration, Methodology, Funding acquisition, Formal analysis, Conceptualization.

## Funding

This research is supported by national funds through a doctoral grant (PRT/BD/154333/2022) awarded to D. Semedo by the Foundation for Science and Technology (FCT), under the Ministry of Science, Technology and Higher Education of Portugal. The funding is provided within the scope of the project identified by the DOI: https://doi.org/10.54499/PRT/BD/154333/2022. This research was also supported by national funds through the FCT under. Work funded by National Funds through FCT – Foundation for Science and Technology, under the project UID/50027 – Research Network in Biodiversity and Evolutionary Biology. In case of space limitation, only the reference UID/50027 may be used.

## Declaration of competing interest

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

## Acknowledgments

The authors express their sincere gratitude to Nelson Filomeno Barradas dos Santos dos Reis Freire for his dedicated and competent collaboration during the sampling campaigns conducted across all experimental blocks. His contribution was instrumental to the successful execution of the fieldwork and the methodological development of this study.

We also extend our appreciation to the Instituto Nacional de Investigação e Desenvolvimento Agrário (INIDA), the University of Cape Verde, the University of Trás-os-Montes and Alto Douro, and the Portugal University of the Azores (PRT) – the host institution of this doctoral research – for the logistical, technical, and scientific support provided throughout the investigation.

This research was funded by the Fundação para a Ciência e a Tecnologia (FCT), Portugal, through a doctoral grant [insert grant number, if applicable], whose support was essential for the completion of this study.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jaridenv.2025.105532.

## Data availability

Data will be made available on request.

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