# Nutrient Enrichment by Poultry Biochar Reshapes Soil Microbial Networks, Reducing Interaction Complexity in Tropical Sandy Soils

Avete Vieira Lima, Diogo Paes da Costa, Gustavo Pereira Duda, Jamilly Alves de Barros, Rafaela da Conceição Santos, Sabrina Esposito Oliveira da Mota, José Romualdo de Sousa Lima, Vanilson Pedro da Silva, Márcio Henrique Leal Lopes, Claude Hammecker, et al.

► To cite this version:

Avete Vieira Lima, Diogo Paes da Costa, Gustavo Pereira Duda, Jamilly Alves de Barros, Rafaela da Conceição Santos, et al. Nutrient Enrichment by Poultry Biochar Reshapes Soil Microbial Networks, Reducing Interaction Complexity in Tropical Sandy Soils. Journal of Soil Science and Plant Nutrition, In press, 10.1007/s42729-026-03005-1 ird-05498845

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# Nutrient Enrichment by Poultry Biochar Reshapes Soil Microbial Networks, Reducing Interaction Complexity in Tropical Sandy Soils

Avete Vieira Lima¹ ID · Diogo Paes da Costa² ID · Gustavo Pereira Duda² ID · Jamilly Alves de Barros² ID ·

Rafaela da Conceição Santos¹ ID · Sabrina Esposito Oliveira da Mota² ID · José Romualdo de Sousa Lima² ID ·

Vanilson Pedro da Silva² ID · Márcio Henrique Leal Lopes² ID · Claude Hammecker³ ID ·

Arthur Prudêncio de Araujo Pereira⁴ ID · Lucas William Mendes⁵ ID · Ademir Sergio Ferreira Araujo⁶ ID ·

Erika Valente de Medeiros² ID

Received: 11 July 2025 / Accepted: 31 December 2025

© The Author(s) 2026

## Abstract

**Purpose** This study evaluated how poultry manure-derived biochar influences the structure, diversity, and co-occurrence networks of soil bacterial communities in nutrient-poor tropical sandy soils. We examined the trade-off between improved soil fertility and potential simplification of microbial networks, testing the hypothesis that high biochar rates would enhance nutrient availability but reduce microbial complexity and diversity.

**Methods** A field experiment was conducted over two years in sandy soils of northeastern Brazil, applying poultry biochar. We used six treatments: (a) control (no amendments), (b) low-rate biochar (Bio_10t=10 t ha⁻¹), (c) high-rate biochar (Bio_40t=40 t ha⁻¹), (d) chicken manure (E.Av=5 t ha⁻¹), (e) synthetic mineral fertilizer (Mineral=NPK), and (f) poultry manure (C.Av=10 t ha⁻¹). Soil chemical properties were assessed, and bacterial communities were analyzed using 16 S rRNA gene sequencing. Network analyses were performed using SparCC and NetCoMi, and multivariate statistics determined key environmental drivers.

**Results** High biochar inputs increased organic C, P, and K levels, leading to a distinct shift in microbial community structure. However, this nutrient enrichment reduced amplicon sequence variants richness by 22.4% (from 3,250 to 2,521) and

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

Erika Valente de Medeiros
erika.valente@ufape.edu.br

Avete Vieira Lima
avete.lima@ufrpe.br

Diogo Paes da Costa
diogopaes1@gmail.com

Gustavo Pereira Duda
gustavo.duda@ufape.edu.br

Jamilly Alves de Barros
jamillybarroscos@gmail.com

Rafaela da Conceição Santos
rafaela.conceicao@ufrpe.br

Sabrina Esposito Oliveira da Mota
sabrisposito22@gmail.com

José Romualdo de Sousa Lima
romualdo.lima@ufape.edu.br

Vanilson Pedro da Silva
vanilson.silva@ufape.edu.br

Márcio Henrique Leal Lopes
mhll9785@gmail.com

Claude Hammecker
claude.hammecker@gmail.com

Arthur Prudêncio de Araujo Pereira
arthur.prudencio@ufc.br

Lucas William Mendes
lucaswmendes@gmail.com

Ademir Sergio Ferreira Araujo
asfaruaj@yahoo.com.br

¹ Federal Rural University of Pernambuco, Recife, PE 52171-900, Brazil

² Semiarid(bio)tech group, Federal University of Agreste Pernambuco, Garanhuns, PE 55292-270, Brazil

³ Soil-Agrosystem-Hydrosystem Interaction Lab-LISAH, Place Pierre Viala, 2, Montpellier 34060, France

⁴ Soil Science Department, Federal University of Ceará, Fortaleza, CE, Brazil

⁵ Center for Nuclear Energy in Agriculture, University of São Paulo, Piracicaba, SP, Brazil

⁶ Agricultural Science Center, Federal University of Piauí, Teresina, PI 64049-550, Brazil
Shannon diversity by 3.6% (from 7.16 to 6.90) relative to the control. Network analysis showed substantial simplification of microbial interactions: the control treatment had 3,116 co-occurrence edges, which fell to 1,423 under low biochar (Bio_10t) and 1,849 under high biochar (Bio_40t)—reductions of 54% and 40%, respectively.

**Conclusions** Our findings underscore a trade-off: while poultry biochar effectively enhances soil fertility, excessive application may disrupt microbial diversity and interaction networks, potentially compromising long-term soil resilience. These results highlight the need to optimize biochar rates to balance immediate fertility gains with preservation of microbial ecological stability in fragile sandy soils.

**Graphical Abstract**

<figure><img src="image_3.png" /></figure>

**Keywords** Biocarbon · Phaseolus vulgaris · Soil microbiome · Organic amendments · Co-occurrence networks · Microbial diversity

**Abbreviations**

ASV amplicon sequence variant

PCoA Principal coordinates analysis

CCA canonical correlation analysis

SparCC Sparse Correlations for Compositional data

ctrl control

Bio_10t lowest rate of biochar

Bio_40t highest rate of biochar

E.Av chicken manure

Mineral NPK fertilizer

C.Av Poultry litter

TOC total organic carbon

**1 Introduction**

Soil fertility and microbial community stability are fundamental for sustainable agriculture, especially in fragile, nutrient-poor sandy ecosystems where maintaining productivity and ecological balance is challenging (Costa et al. 2022). Soil microbial communities drive nutrient cycling and organic matter decomposition, forming intricate co-occurrence networks that underpin ecosystem resilience (Zhan 2024). These networks function as ecological insurance, buffering ecosystem processes against environmental perturbations through redundant functional pathways and
complementary resource use strategies (King et al. 2023). Biochars derived from organic wastes such as poultry manure are increasingly applied to enhance soil fertility and promote waste valorization. Yet their impacts on soil microbial communities remain highly variable depending on biochar properties and application rates (Dominchin et al. 2025; Sharma et al. 2025).

Biochar amendments can significantly alter soil chemical properties, such as pH, organic carbon, and nutrient availability, which in turn influence the structure, diversity, and function of soil microbial communities (Deinert et al. 2024; Xu et al. 2021). Poultry manure biochar specifically increases soil pH and nutrient content while affecting key microbial taxa abundance, including Chloroflexi and Proteobacteria populations (Dominchin et al. 2025). However, these effects are rate-dependent and can be either beneficial or detrimental depending on application rate and soil context (Li et al. 2020a, 2020b; Xu et al. 2021). Recent evidence suggests that excessive nutrient inputs from high biochar rates may trigger ecological filtering mechanisms that favor fast-growing copiotrophic taxa while suppressing slower-growing oligotrophic specialists, fundamentally altering community assembly processes (Hu et al. 2024).

Despite evidence that biochar can enhance soil fertility and microbial biomass, there is a critical need to understand the trade-offs between nutrient enrichment and the stability of microbial networks, especially in sandy soils where microbial diversity and network complexity are already vulnerable (Li et al. 2020a, 2020b; Yan et al. 2021). High rates of biochar may simplify microbial networks and reduce diversity, potentially undermining long-term soil resilience (Yan et al. 2021). Sandy soils present unique challenges due to their low water and nutrient retention capacity, making microbial communities particularly sensitive to input-induced disturbances that can disrupt established interaction networks (Yu et al. 2024). Therefore, evaluating the impacts of poultry manure-derived biochar on both soil fertility and microbial ecological stability is essential for developing sustainable land management strategies in tropical sandy environments.

Indeed, analysis of co-occurrence network and diversity indices provide critical insights into the complexity of microbial interactions and the stability of soil ecosystems (Oña et al. 2025). While biochar amendments have demonstrated potential to stimulate microbial biomass, diversity,

and network connectivity, these effects are highly context-dependent, influenced by soil type, biochar characteristics, and especially application rates (Li et al. 2020b). Importantly, the impacts of rates of poultry manure-derived biochar on bacterial community composition and interaction networks in tropical sandy soils remain poorly understood. This gap is particularly relevant given the dual potential of biochar to enrich soil nutrient status while simultaneously altering microbial ecological dynamics.

We hypothesized that high rates of poultry manure biochar would increase soil fertility but simplify microbial networks and reduce community complexity, posing risks to long-term soil resilience. To test this hypothesis, we assessed how low (10 t ha⁻¹) and high (40 t ha⁻¹) application rates of poultry manure biochar influence the structure, diversity, and co-occurrence networks of soil bacterial communities in nutrient-poor tropical sandy soils over a two-year field experiment.

# 2 Materials and Methods

## 2.1 Biochar Production and Characterization

Poultry manure from 42-day-old birds was mixed with sugarcane bagasse (Saccharum officinarum L.) serving as an absorbent and subjected to slow pyrolysis at 400–500 °C for 12 h in a controlled chamber, yielding biochar at 43.2% (Medeiros et al. 2020). After cooling, the biochar was sieved to <2 mm and homogenized in a concrete mixer to ensure uniformity. The poultry manure-derived biochar, alongside the raw poultry litter and chicken manure, was analyzed for key chemical properties, including pH, total organic carbon (TOC), total nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sodium (Na), and C/N ratio (Table 1).

## 2.2 Field Localization and Experimental Design

The field experiment was conducted from 2021 to 2022 in São João, Pernambuco, Brazil (08°50'24.9" S, 36°22'49.8" W; elevation 716 m; Fig. 1). The soil was classified as Eutrophic Regolithic Neosol with sandy loam texture. The climate is dry tropical, with an average annual precipitation of 753 mm, concentrated between April and August.

<table><caption>**Table 1** Chemical characterization of biochar, poultry litter and chicken manure</caption><thead><tr><th></th><th>pH</th><th>TOC</th><th>N</th><th>P</th><th>K</th><th>Na</th><th>Ca</th><th>Mg</th><th>C/N</th></tr></thead><tbody><tr><td>Poultry litter biochar</td><td>10.2</td><td>14.7</td><td>1.53</td><td>0.96</td><td>2.82</td><td>0.54</td><td>nr</td><td>nr</td><td>9.6</td></tr><tr><td>Poultry litter</td><td>7.6</td><td>33.0</td><td>2.25</td><td>0.63</td><td>1.20</td><td>nr</td><td>0.99</td><td>0.25</td><td>14.7</td></tr><tr><td>Chicken manure</td><td>8.9</td><td>23.7</td><td>2.40</td><td>2.09</td><td>3.36</td><td>nr</td><td>1.50</td><td>1.00</td><td>9.9</td></tr></tbody></table>

TOC=total organic carbon (%); N=total nitrogen (%); P=phosphorus (%); K=potassium (%); Ca=calcium (%); Mg=magnesium (%); C/N=carbon nitrogen (ratio); nr, not reported
a

<figure><img src="image_4.png" /><figcaption>**Fig. 1** Bacterial beta and alpha diversity in soils under different rates of poultry manure-derived biochar and other amendments. (a) Principal coordinates analysis (PCoA) based on Bray-Curti's dissimilarity. Statistics of the Shannon diversity index (b) and amplicon sequence variants (ASV) richness (c). Treatments connected by one or more asterisks differed significantly according to the Wilcoxon test (p-value:</figcaption></figure>

The experiment followed a randomized block design with four replicates. Each plot measured 9 m² (3 × 3 m), with a 2.0 m² effective area designated for soil sampling. Six treatments were established: (a) control (no amendments), (b) low-rate biochar (Bio_10t=10 t ha⁻¹), (c) high-rate biochar (Bio_40t=40 t ha⁻¹), (d) chicken manure (E.Av=5 t ha⁻¹), (e) synthetic mineral fertilizer (Mineral=NPK), and (f) poultry manure (C.Av=10 t ha⁻¹). Biochar was uniformly incorporated into the soil to a depth of 20 cm one day prior to sowing, at the specified rates. Chicken manure and poultry manure were also incorporated to 20 cm depth at rates commonly used by local farmers (5 t ha⁻¹ and 10 t ha⁻¹, respectively). Mineral fertilizers were applied at 10 cm depth at rates of 17.7 kg ha⁻¹ Ca, 13.3 kg ha⁻¹ S, 20 kg ha⁻¹ P₂O₅, and 20.4 kg ha⁻¹ K₂O. A top-dressing of 40 kg ha⁻¹ N was applied 30 days after planting (DAP).

Common bean (*Phaseolus vulgaris* L. cv. BRS Netuno) was sown with six seeds per hole, spaced 0.5 m between rows and 0.2 m between plants. Thinning at 13 DAP (days after planting) left three plants per hole, achieving a final stand of approximately 240,000 plants ha⁻¹. All other crop management practices followed regional standards for bean cultivation.

b

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c

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0.001 < ** < 0.01 < * 0.05). Treatments: control (ctrl); synthetic mineral fertilizer (Mineral); chicken manure (E.Av=5 t ha⁻¹); poultry manure (C.Av=10 t ha⁻¹); low-rate poultry manure-derived biochar (Bio_10t=10 t ha⁻¹); high-rate poultry manure-derived biochar (Bio_40t=40 t ha⁻¹)

## 2.3 Soil Sampling and Chemical Analysis

The field experiment ran for two consecutive growing seasons (2021–2022). Soil samples for microbial community analysis were collected at the end of the second crop cycle (August 2022) to capture cumulative treatment effects on established microbial communities. Although the experimental design included four spatial replicates per treatment, three replicates were randomly selected for 16 S rRNA gene sequencing (n=18 total). This sampling strategy allowed biochar-induced changes to stabilize beyond initial perturbation effects, providing insight into longer-term community restructuring. From each plot, five soil subsamples (0–20 cm depth) were randomly collected, combined into composite samples, homogenized, and sieved to <2 mm. Three of the four field replicates per treatment were randomly selected for 16 S rRNA gene sequencing (n=18 total), a sample size consistent with microbiome studies balancing statistical power and sequencing costs (sufficient for detecting large effect sizes with α=0.05, power>0.90).

Soil pH was measured in a 1:2.5 soil-to-water suspension. Total organic carbon (TOC) was determined by colorimetry following oxidation with potassium dichromate (K₂Cr₂O₇) (Yeomans and Bremner 1988). Available phosphorus (P),
sodium (Na⁺), and potassium (K⁺) were extracted using the
Mehlich-1 solution; P was quantified by spectrophotome-
try, while Na⁺ and K⁺ were measured by flame photometry
(Teixeira et al. 2017). Total nitrogen (N) was analyzed by
wet oxidation.

**2.4 DNA Extraction and Sequencing**

Total genomic DNA was extracted from 0.4 g of each
composite soil sample using the DNeasy® PowerSoil® Kit
(QIAGEN Inc., Valencia, CA, USA) following the manu-
facturer's protocol. DNA quality and concentration were
then quantified using a NanoDrop® 2000 spectrophotometer
(Thermo Fisher Scientific Inc., Waltham, MA, USA).

The sequencing libraries were constructed by amplifying the V3-V4 region of the 16 S rRNA gene using primers 341 F (5'- CCT.AYG.GGR.BGC.ASC.AG-3') and 806R (5'-GGA.CTA.CNN.GGG.TAT.CTA.AT-3') (Caporaso et al. 2012). Along with the primers, the amplicon sequencing library of the 16 S rRNA gene was generated using Herkulase II Fusion DNA polymerase (© Agilent Technologies, Inc., Santa Clara, CA, USA) and the Nextera XT v2 Index Kit (© Illumina, Inc., San Diego, CA, USA), following the manufacturer's guidelines at Novogene in Sacramento, USA. Sequencing was performed on an Illumina® MiSeq® using a v3 flow cell. A library concentration of 3 pM was loaded, with a 30% increase of Illumina® PhiX library control DNA, following the manufacturer's guidelines. The binary base call (BCL) files, which are the raw data files generated by Illumina sequencers, were converted into sequence data in FASTQ format using bcl2fastq v2.20 software (© Illumina). Subsequently, the data were demultiplexed, and the primers and barcodes were removed for the next step of quality control and processing of raw data.

The subsequent quality control process was divided into
two steps. Initially, using the software FLASH (Fast Length
Adjustment of SHort reads, v.1.2.11), raw paired-end (PE)
complementary reads were merged into a single-end tag of
amplified region length (V3-V4 of the 16 S rRNA gene).
Next, the tags underwent quality control through the fastp
software (v.0.23.4), removing sequences with low qual-
ity, excess bases, and high error rate (N). The sequencing
error rate at a single base position was less than 1%, and the
maximum did not exceed 6%. After these procedures, the
pre-treated data libraries, with merged forward and reverse
reads, totaled 4,018,262 sequences.

The sequences were then processed in the R environ-
ment (v.4.3.1) using the RStudio interface 2023.06.1 (Build
524). In total, 20 libraries of 16S rRNA amplicons were con-
structed (one sample was lost during the sequencing step).
Metagenomic analyses were continued using the “DADA2”
analysis package (v.1.28.0) in the R environment (Callahan

et al. 2016). The sequences were filtered again using the
'filterAndTrim' algorithm, with the following parameters:
minLen=150; maxLen=409; maxN=0; maxEE=2; and
truncQ=2. Error rates for each base position in the sequenc-
ing reads were estimated by the 'learnErrors' algorithm using
default settings. Next, amplicon sequence variants (ASVs)
were inferred using the "dada2" algorithm, grouping samples
using the "pooling" method to increase the resolution of rare
variants that are seldom observed in individual samples but
frequent across samples. Finally, chimeric sequences were
filtered again using the 'removeBimeraDenovo' algorithm
with the "pooling" method. Only fragments between 400 and
409 bp were used, as the abundance of other lengths was neg-
ligible, resulting in 1,419,111 high-quality sequences.

All sequences were aligned with the current Silva refer-
ence alignment database (Silva v.138) for classification up
to the genus level. The R packages ‘DECIPHER’ (Wright
2016) and ‘phangorn’ (Schliep 2011) were used to create an
alignment and a phylogenetic tree of the ASVs. Sequences
identified as mitochondria, chloroplasts, and non-bacterial
sequences were removed from the analysis, resulting in
1,310,916 sequences ready for rarefaction procedures and
further downstream analyses. Additional analyses were
performed in R using the ‘phyloseq’ (McMurdie 2013) and
‘vegan’ (Dixon 2003; Oksanen 2010) packages.

**2.5 Statistical Analysis**

All statistical analyses were performed in the R environment
(v.4.3.1). Alpha diversity metrics (ASV Richness and Shannon
Index) were calculated, and significant differences between
treatments were assessed using the non-parametric Wilcoxon
signed-rank test. Multivariate analyses, including Principal
Coordinate Analysis (PCoA) based on Bray-Curtis dissimilar-
ity and Canonical Correspondence Analysis (CCA), were con-
ducted using the ‘vegan’ and ‘microeco’ packages to visualize
community structure and identify key environmental drivers.

To identify differentially abundant taxa, an indicator spe-
cies analysis was performed using the ‘trans_diff’ class of the
‘microeco’ package. Significant differences were assessed
using the Wilcoxon Rank Sum test, with p-values adjusted
for multiple comparisons using the False Discovery Rate
(FDR<0.05). Additionally, a correlation heatmap (Fig. 2b
and c) was generated to visualize relationships between the
relative abundance of the main bacterial classes and soil
chemical properties using Pearson’s correlation method.

“Co-occurrence network analysis was conducted to
elucidate the structure of microbial interactions. Separate
networks for each treatment were constructed using the Net-
CoMi package (v. 1.1.0). Associations between Amplicon
Sequence Variants (ASVs) were calculated using the SparCC
correlation method, which is robust for compositional data.
<figure><img src="image_7.png" /><figcaption>**Fig. 2** Relationships between soil environmental factors and bacterial community structure. (a) Canonical correspondence analysis (CCA) biplot showing the influence of soil chemical properties on bacterial communities under different biochar application rates. The scores of each sample were indicated by colored points while the chemical variables were demonstrated by vectors with lengths proportional to their importance in the multivariate model. The significance of the variables in the CCA model was evaluated by the Mantel test. (b) Heatmap showing Pearson correlations between the relative abundance of</figcaption></figure>

dominant bacterial classes and soil chemical/enzymatic properties. (c) Heatmap showing Pearson correlations between alpha diversity indices and soil properties. Asterisks indicate significant correlations (*p*<0.05, **p**<0.01, ***p**<0.001). Treatments: control (ctrl); synthetic mineral fertilizer (Mineral); chicken manure (E.Av=5 t ha⁻¹); poultry manure (C.Av=10 t ha⁻¹); low-rate poultry manure-derived biochar (Bio_10t=10 t ha⁻¹); high-rate poultry manure-derived biochar (Bio_40t=40 t ha⁻¹)

Prior to analysis, ASVs were filtered to retain only those with a relative abundance greater than 0.001%. The networks were constructed considering only strong and statistically significant correlations, defined as a SparCC correlation coefficient magnitude (|r|)>0.95 and a p-value<0.01, with p-values adjusted for multiple comparisons using the False Discovery Rate (FDR) method. Keystone taxa were identified based on their network topology, specifically Within-module connectivity (zᵢ) and Among-module connectivity (Pᵢ). Nodes were classified as Module Hubs (zᵢ > 2.5), Connectors (Pᵢ > 0.62), or Network Hubs. Topological properties were calculated for each network to compare complexity and connectivity across treatments and the final network was visualized using Gephi software (v. 0.10.1).

# 3 Results

## 3.1 Soil Properties

The application of amendments significantly influenced soil chemical properties (Fig. S1). Notably, the high-rate biochar treatment (Bio_40t) resulted in the most substantial

changes, leading to significant increases in Total Organic Carbon (TOC, *p*<0.05), Phosphorus (P, *p*<0.01), and Potassium (K, *p*<0.01) compared to the control. Additionally, enzymatic activities (Urease, Phosphatase) showed distinct patterns correlated with nutrient availability, reinforcing the biological response to the altered soil fertility status.

## 3.2 Bacterial Community Structure, Richness, and Diversity

These changes in soil chemistry, particularly the nutrient enrichment under the high-rate biochar treatment (Bio_40t), were accompanied by significant shifts in the bacterial community structure (Fig. 1a). Principal coordinate analysis (PCoA) revealed that the first two axes explained 38.4% of the total variation (27.2% and 11.2% on axes 1 and 2, respectively), clearly separating the high biochar treatment from the control and other treatments. In contrast, the low biochar rate (Bio_10t) did not produce a distinct clustering, indicating minimal changes in community composition.

Alpha diversity analyses revealed that Bio_40t significantly reduced both bacterial richness and Shannon
<figure><img src="image_8.png" /><figcaption>**Fig. 3** Relative abundance of the ten most dominant bacterial classes in soils amended with different rates of biochar and other treatments. The values in parentheses indicate the global relative frequency of each class. Treatments: control (ctrl); synthetic mineral fertilizer (Mineral); chicken manure (E.Av=5 t ha⁻¹); poultry manure (C.Av=10 t ha⁻¹); low-rate poultry manure-derived biochar (Bio_10t=10 t ha⁻¹); high-rate poultry manure-derived biochar (Bio_40t=40 t ha⁻¹)</figcaption></figure>

diversity (Fig. 1b and c; Table 1). Compared to the con-
trol, ASV richness in Bio_40t decreased by 22.4% (from
3,250 to 2,521), and the Shannon index declined by 3.6%
(from 7.16 to 6.90). Similar reductions occurred relative
to mineral fertilizer treatment: 18.8% decrease in ASV
richness (from 3,103 to 2,521) and 3.5% decline in Shan-
non diversity (from 7.15 to 6.90). In contrast, the low

biochar application (Bio_10t) did not significantly alter
richness or diversity compared to control or mineral
treatments, indicating that moderate biochar inputs exert
minimal selective pressure on microbial assemblages.

Canonical correlations analysis (CCA) further con-
firmed the separation between treatments, highlighting
TOC, P, and K as the primary drivers (*P*<0.01) of bacte-
rial community shifts, particularly under the high biochar
rate (Fig. 2).

**3.3 Composition of the Bacterial Community**

Across all treatments, the soil bacterial community was dominated by Actinobacteriota (47.8%), Proteobacteria (33.9%), Chloroflexi (13.2%), and Acidobacteria (3.6%) when considering taxa with relative abundances exceeding 1% (Table S1). At the class level, Actinobacteria and Alphaproteobacteria were the most prevalent groups (Fig. 3).

Differential abundance analysis revealed notable shifts under the high biochar application. While many dominant taxa were shared across treatments, soils amended with Bio_40t were uniquely enriched with members of the Sphingomonadaceae, Microbacteriaceae, and Xanthobacteriaceae families. In contrast, treatments with lower nutrient inputs, including the control and low-rate biochar, maintained a higher relative abundance of groups like Streptomycetaceae and Micrococcaceae (Fig. 4).

<figure><img src="image_9.png" /><figcaption>**Fig. 4** Heatmap of differentially abundant ASVs across different bio-
char application rates and controls. The hierarchical clustering of
samples was based on the relative abundance of amplicon sequence
variants (ASVs) and Bray-Curtis dissimilarity. Only ASVs with a sig-
nificant difference (FDR < 0.05) and a substantial fold change (|logFC|</figcaption></figure>

> = 2) between treatments are displayed. Treatments: control (ctrl); synthetic mineral fertilizer (Mineral); chicken manure (E.Av=5 t ha⁻¹); poultry manure (C.Av=10 t ha⁻¹); low-rate poultry manure-derived biochar (Bio_10t=10 t ha⁻¹); high-rate poultry manure-derived biochar (Bio_40t=40 t ha⁻¹)
## 3.4 Bacterial Co-occurrence Networks

Both biochar rates substantially altered soil bacterial co-occurrence networks (Fig. 5a). The control treatment showed 3,116 co-occurrence edges, which dropped to 1,423 edges under low biochar (Bio_10t) and 1,849 edges under high biochar (Bio_40t). This reduction in network complexity suggests that biochar application, regardless of the rate, simplifies microbial interactions compared to the control. Beyond reducing the total number of edges, Bio_40t also decreased the proportion of positive correlations from 67.3% in the control to 58.9% (Table S2), pointing to a shift from cooperative interactions toward more competitive or antagonistic relationships. Despite these changes in connectivity, the total number of nodes remained similar across treatments (ranging from 387 to 412 nodes), indicating that biochar influenced how taxa interact rather than which taxa were present.

Analysis of keystone taxa revealed that Proteobacteria consistently served as central hubs in all networks, followed by Acidobacteria. However, under the high biochar application, Chloroflexi emerged alongside Proteobacteria as primary keystone groups, while Acidobacteria's centrality diminished (Fig. 5b). This indicates that excessive biochar inputs not only reduced network complexity but also restructured the dominant microbial interactions in the soil.

## 4 Discussion

In this study, we investigated the dual role of poultry manure-derived biochar in tropical sandy soils, simultaneously acting as a potent nutrient source and a selective force reshaping soil microbial community. Our findings revealed that high biochar applications significantly increased TOC, N, P, and K, underscoring its effectiveness in enhancing fertility in inherently poor, fragile soils. This substantial enrichment highlights the strong fertilizing potential of poultry biochar in poor and fragile sandy soils. These improvements are likely due to the high nutrient content of poultry manure and the physicochemical properties of biochar, such as its porosity and surface area, which enhance nutrient retention and availability (Agbede et al. 2024). The high TOC indicates increased carbon inputs and greater organic matter stabilization, both of which can improve soil structure and stimulate microbial activity (Moreno-Barriga et al. 2017). Similarly, the rise in P and K suggests that poultry biochar functions as a slow-release nutrient source (Wang et al. 2015), potentially enhancing cation exchange capacity and nutrient cycling. However, despite these chemical benefits, the high nutrient load may exert selective pressure on

microbial communities, favoring fast-growing copiotrophs and leading to reduced microbial diversity.

However, this substantial nutrient enrichment came with an important ecological shift. High biochar application (Bio_40t) fundamentally altered the bacterial community, as shown by the distinct clustering in PCoA ordination, which was driven by elevated TOC, P, and K levels (Fig. 2). This shift resulted in notable declines in both ASV richness (22.4%) and Shannon diversity (3.6%). Rather than reflecting ecosystem degradation or a simple ecological cost, these changes appear to stem from strong selective pressure that favored copiotrophic taxa better suited to nutrient-rich environments (Hu et al. 2024; Xu et al. 2022). The important takeaway is that despite lower diversity metrics, the core ecosystem functions were likely preserved through functional redundancy within the enriched copiotrophic community. This demonstrates the soil microbiome's capacity to maintain essential functions even under nutrient-enriched conditions.

In contrast, low biochar application (Bio_10t) did not significantly alter community structure relative to controls, suggesting that moderate inputs exert minimal selective pressure on microbial assemblages. Canonical correspondence analysis (CCA) confirmed TOC, P, and K as primary drivers of community shifts under high biochar rates (*P*<0.01; Fig. 2). While nutrient enrichment enhances soil fertility, excessive rates may reduce microbial heterogeneity, potentially limiting functional diversity and ecosystem resilience (Yao et al. 2017). This reinforces the rate-dependent nature of biochar effects, with higher concentrations exerting stronger ecological filtering that narrows bacterial communities toward taxa suited to nutrient-enriched conditions (Li et al. 2022; Leff et al. 2015). These findings emphasize the need to calibrate biochar application rates carefully to enhance soil fertility without compromising microbial diversity, as excessive inputs may lead to community homogenization with long-term impacts on soil health and functionality.

Organic amendments often promote copiotrophic microbial groups due to increased nutrient availability, though this may lead to a loss of overall diversity (Guo et al. 2018; Leff et al. 2015). In our study, the high-rate biochar treatment led to a distinct taxonomic composition, notably enriching families such as Sphingomonadaceae, Microbacteriaceae, and Xanthobacteriaceae. Sphingomonadaceae are known for their ability to utilize organic carbon and nitrogen for rapid growth (Souza et al. 2023), while Microbacteriaceae, part of the Actinobacteria phylum, contributes to carbon recycling (Valette et al. 2023). Xanthobacteriaceae have also been associated with soils containing elevated levels of organic carbon (Lu et al. 2023). Although these shifts may enhance
<figure><img src="image_10.png" /><figcaption>b</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>**Fig. 5** Co-occurrence network analysis of soil bacterial communities under different rates of poultry manure biochar. (a) Co-occurrence network of the soil bacterial community. Each node represents a bacterial genus, and node size is proportional to its mean relative abundance. Edges represent strong and significant SparCC correlations (|r| > 0.95, *p*<0.01, FDR-adjusted). Positive correlations are colored according to the treatment with which they are most associated (see Fig. 5a inset),</figcaption></figure>

while negative correlations are shown in gray. (b) Iris plot demonstrating the composition of the five main phyla in each co-occurrence network. Treatments: control (ctrl); synthetic mineral fertilizer (Mineral); chicken manure (E.Av=5 t ha⁻¹); poultry manure (C.Av=10 t ha⁻¹); low-rate poultry manure-derived biochar (Bio_10t=10 t ha⁻¹); high-rate poultry manure-derived biochar (Bio_40t=40 t ha⁻¹)
nutrient turnover, they could reduce microbial functional redundancy and compromise ecosystem resilience. In contrast, the low-rate biochar treatment maintained microbial richness and diversity comparable to the control and mineral fertilizer treatments, indicating that moderate biochar applications can enhance soil fertility without substantially disrupting microbial community balance.

Co-occurrence network analysis showed that biochar application progressively simplified microbial interaction networks as application rates increased. Relative to the control treatment (3,116 edges), network connectivity decreased by 41% under low biochar and 54% under high biochar. At the same time, the proportion of positive correlations declined from 67.3% to 58.9%. This dual pattern, fewer interactions overall and a shift toward more negative associations, points to fundamental changes in how microbial taxa interact with one another. Network complexity and positive co-occurrence are widely recognized as indicators of community stability and functional interdependence, allowing microbial communities to withstand environmental stress through mechanisms like complementary resource use and cross-feeding (Faust and Raes 2012; de Vries et al. 2018). The network simplification we observed under high biochar application may therefore undermine the soil microbiome's ability to maintain stable functioning when faced with future disturbances, even though short-term fertility improved. Equally noteworthy was the shift in keystone taxa from Acidobacteria in the control to Chloroflexi in the 40 t ha⁻¹ treatment, while Proteobacteria remained dominant across treatments. This suggests that excessive nutrient inputs alter not only which species are present, but also which organisms serve as critical hubs that help maintain network stability.

Interestingly, despite the negative impacts on microbial diversity and interaction networks, the high-rate biochar treatment led to an increased abundance of Actinobacteria and Proteobacteria, two groups closely associated with organic matter decomposition and nutrient cycling (Bhatti et al. 2017; Mendes et al. 2015). Actinobacteria are efficient decomposers of complex organic substrates, while Proteobacteria typically dominate during the early stages of residue breakdown due to their ability to utilize simple organic compounds (Fan et al. 2014). Their co-enrichment suggests an acceleration of decomposition processes and a potential short-term benefit for soil fertility and organic matter turnover. While this accelerated decomposition may provide short-term fertility benefits, it risks depleting stable carbon pools and reducing functional redundancy over longer timescales, potentially compromising soil health under future environmental stresses.

Overall, these results underscore the dual role of poultry manure-derived biochar as both a soil amendment and

a microbial selective agent. While moderate rates appear beneficial for maintaining microbial diversity and interaction complexity, excessive applications may compromise microbial integrity and long-term soil health. These findings are especially relevant for tropical sandy soils, which are inherently low in fertility and microbial biomass and thus particularly sensitive to input-induced disturbances.

These findings carry important practical implications for biochar management in tropical sandy soils. Our results show that simply increasing biochar application rates does not translate to better outcomes. Although the 40 t ha⁻¹ rate achieved the highest nutrient availability in the short term, it came at the cost of degrading the microbial network architecture that supports long-term soil resilience. In contrast, the 10 t ha⁻¹ rate offers a more balanced approach, maintaining microbial diversity and network complexity at levels comparable to both control and mineral fertilizer treatments while still enhancing soil fertility. For smallholder farmers in northeastern Brazil and similar tropical regions, this lower rate has additional advantages beyond its ecological benefits: it reduces both biochar costs and labor requirements while preserving the microbial foundation needed for sustained productivity under increasingly unpredictable climate conditions. Moving forward, research should examine whether the network simplification we observed at 40 t ha⁻¹ leads to measurable losses in functional resilience, such as reduced capacity to suppress pathogens or slower recovery from drought stress, to better understand the full consequences of excessive biochar application.

## 5 Conclusions

This study demonstrates that poultry manure-derived biochar exerts rate-dependent control over both soil fertility and microbial ecological stability in tropical sandy soils. High poultry manure biochar rates (40 t ha⁻¹) substantially increased total organic carbon, P, and K but triggered a 40% reduction in network connectivity compared to controls (while low rate caused a 54% reduction). This simplified network architecture, characterized by fewer co-occurrence edges and reduced positive correlations, indicates compromised microbial interdependence that may limit resilience to future environmental stresses. In contrast, moderate poultry manure biochar rates (10 t ha⁻¹) maintained microbial diversity and network complexity while providing fertility benefits, representing an optimal balance for sustainable soil management. The enrichment of Actinobacteria and Proteobacteria under high biochar inputs suggests accelerated organic matter decomposition that may provide short-term nutrient cycling benefits but risks long-term depletion of stable carbon pools and functional redundancy. These
findings establish that optimizing biochar rates is essential for achieving fertility goals without sacrificing the microbial network architecture that buffers sandy soils against degradation in tropical agroecosystems.

**Supplementary Information** The online version contains supplementary material available at https://doi.org/10.1007/s42729-026-03005-1.

**Acknowledgments** This work was supported by the “Conselho Nacional de Desenvolvimento Científico e Tecnológico” (CNPq) – Brazil [445579/2024-2; 167922/2022-0; 313421/2021-8]; “Coordenação de Aperfeiçoamento de Pessoal de Nível Superior” (CAPES) – Brazil [assistance Nº. 0360/2018; process Nº 88881.163448/2018-01 and Nº 88887.175812/2018-00]; and “Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco” (FACEPE) – Brazil [grant numbers and process: APQ-1937-2.12/25; APQ-1518-5.01/25; APQ-1582-5.01/24; APQ-1747-5.01/22; APQ-1464-5.01/22; APQ-0431-5.01/17; BCT-0731-5.01/22; and BCT-0026-5.01/18].

**Author Contributions** A.V.L.: performed the field conduction, collection of samples, conducted the laboratory analyses, wrote and revised the manuscript; D.P.C.: performed the collection of samples and conducted the laboratory analyses, performed the data analysis and graphic art, wrote and revised the manuscript; G.P.D.: conducted the laboratory analyses; J.A.B.: conducted the laboratory analyses and analyzed data; J.R.S.L.: analyzed data, wrote and revised the manuscript; V.P.S.: conducted the field analyses and analyzed data; M.H.L.L.: conducted the field analyses and analyzed data; C.H.: revised the manuscript; A.P.A.P.: analyzed data, wrote and revised the manuscript; A.S.F.A.: analyzed data, wrote and revised the manuscript; L.W.M.: analyzed data, wrote and revised the manuscript; E.V.M.: funding acquisition, conceptualization, designed the experimental. All authors reviewed the manuscript.

**Funding** The Article Processing Charge (APC) for the publication of this research was funded by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) (ROR identifier: 00x0ma614).

**Data Availability** The genetic sequences and corresponding BioSample assignments from this study can be accessed in the NCBI repository, under project code PRJNA1123239, in https://drive.google.com/drive/folders/1dyjykgpDD4JxAu90r0Dljdghdj_nXXY?usp=sharing or https://data.mendeley.com/datasets/7c5yxkm6s4. Other data may be made available upon request to the corresponding author.

## Declarations

**Competing Interests** 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.

**Declaration of Generative AI in Scientific Writing** Generative AI and AI-assisted technologies should only be used in the writing process to improve the readability and language of the manuscript.

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