# Too hot for my bugs: mediterranean heatwave disrupts associated microbiomes in the sponge Petrosia ficiformis

Laura Núñez-Pons, Luigi Maria Cusano, Antonia Chiarore, Alice Mirasole, Núria Teixidó, Jana Efremova, Valerio Mazzella

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Laura Núñez-Pons, Luigi Maria Cusano, Antonia Chiarore, Alice Mirasole, Núria Teixidó, et al. Too hot for my bugs: mediterranean heatwave disrupts associated microbiomes in the sponge Petrosia ficiformis. Environmental Microbiome, 2025, 21 (1), pp.16. 10.1186/s40793-025-00830-2. hal-05513552

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# Too hot for my bugs: mediterranean heatwave disrupts associated microbiomes in the sponge *Petrosia ficiformis*

Laura Núñez-Pons¹,²*† ID, Luigi Maria Cusano¹,²† ID, Antonia Chiarore³ ID, Alice Mirasole³ ID, Núria Teixidó²,³,⁴, Jana Efremova¹ ID and Valerio Mazzella²,³ ID

## Abstract

**Background** Global climate change exacerbates the incidence of marine heatwaves (MHWs), which have increased in intensity and frequency over the past years, causing severe impacts on marine coastal ecosystems. MHWs have already triggered mass mortalities of habitat-forming species, including corals, sponges and gorgonians, in temperate, tropical and polar seas. In the Mediterranean, these high peaks of temperature have been shown to affect several sponge species, and likely, their symbiotic microbial communities. During the summer of 2022, populations of the sponge *Petrosia ficiformis* (Poiret, 1789) were conspicuously observed with signs of thermal stress linked to a MHW around the Gulf of Naples (Tyrrhenian Sea, Italy). These included depigmentation spots and tissue texture alterations, which often evolved in necrotic processes and eventual death. At the peak of the MHW, however, apparently thermoresistant sponges co-occurred with sensitive unhealthy specimens. In order to explore potential microbial drivers correlated with these divergent thermal-stress tolerances, Healthy and Unhealthy individuals were sampled along the coast of Ischia Island in early September 2022.

**Results** Prokaryotic community characterization based on the 16 S rRNA gene revealed dissimilar compositions in Unhealthy versus apparently Healthy sponges. Increased alpha diversity richness and low evenness in thermosensitive sponges were due to an extensive presence of rare taxa, and to the introduction of potentially pathogenic groups (e.g., Vibrio spp.). Major microbial families regularly associated with *P. ficiformis* - SAR202, Caldilineaceae, Poribacteria or TK17, were replaced in thermosensitive specimens by professed opportunistic groups within Lentimicrobiaceae, Rhodobacteraceae or Flavobacteriaceae. In turn, conservancy of hub microbes and thermotolerant symbionts (e.g., Rhodothermaceae, Thermoanaerobaculaceae) in Healthy sponges were observed during this disrupting event. Unhealthy microbiomes reflected lower network stability with respect to Healthy holobionts, due to the inconsistency of functional keystone taxa and prevalence of transient microbes.

**Conclusions** Dysbiotic shifts due to colonization of scavenger groups and opportunistic microbes, and interconnectivity loss characterized thermally stressed sponges. In contrast, resistant specimens retained keystone

<footnote>† Laura Núñez-Pons and Luigi Maria Cusano have contributed equally to this work.</footnote>

<footnote>*Correspondence: Laura Núñez-Pons laura.nunezpons@szn.it</footnote>

<footnote>Full list of author information is available at the end of the article</footnote>
symbionts that could have ensured functional cooperation, and maintenance of prokaryotic community cohesion under thermal stress. The existence of stress-resistant phenotypes in sponge holobionts offers a glimmer of hope for species persistence, and their study may identify potential source populations for ecosystem recovery.

**Keywords** Marine heatwaves, Microbiome, Dysbiosis, Heat stress, Thermal resistance, Opportunist microbes

**Introduction**

Marine ecosystems are menaced by diverse threats evoked by climate changes, adding to anthropogenic local pressures [1]. Ocean acidification, global warming, among other climatic stressors are fastly triggering a variety of ecological impacts, including shifts in species ranges and local extinctions [2]. In the last decades, climatic extreme events, in particular, recurrent high-impacting marine heatwaves (MHWs) have been reported worldwide [3, 4]. MHWs are defined as seawater temperatures exceeding a threshold –usually the 90th percentile of the climatological mean (30-year historical baseline period) for at least five consecutive days, which can last for a variable period of time (weeks to months or even years), and spread across variable geographic extensions (stretching from 500 to 2000 km²) [4, 5]. These events can extend from the surface down to 2000 m [4, 6, 7]. Tropical and Mediterranean coastal communities were historically the most thermally distressed [8, 9]. However, over the entire globe the prevalence of MHWs has doubled since 1982, and the duration has increased by 50% in the last century [10].

MHWs are associated with extensive mortality events, especially affecting benthic sessile and habitat-forming species [5]. Bleaching is one of the most documented phenomena of the kind, concerning the disruption of the partnership between microbial photosymbionts (e.g., microalgae, cyanobacteria) and metazoan hosts (e.g., corals, sponges, giant clams, among others). It implicates depigmentation, and loss of photoprotection and nutrient exchanges, leading to holobiont starvation [11–18]. Thermal stress in marine invertebrates often induces tissue necrosis, accompanied by microbial dysbiosis and deficit of metabolic functionalities, leading to disease states, and eventually death of the organisms [19–21]. “Dysbiosis” refers to a dysfunctional imbalance of the composition of symbiont, commensal and pathogenic microbes affecting host health; as opposed to “normobiosis” describing a beneficial microbial equilibrium [22]. Under warming conditions, certain microbial associates unveil heat-tolerant physiologies, such is the case of certain *Durusdinium* spp. zooxanthellae or *Endozoicomonas* spp. bacteria [23, 24]. The presence of thermoresistant symbionts may be crucial for species persistence, as they may confer holobiont populations with resilient phenotypic capabilities to cope with climate crises [23, 25, 26].

The Mediterranean Sea is one of the most sensitive regions towards global climate change, reporting an

accelerating warming trend of seawater surface temperature of ~0.4 °C/decade since 1982 [5, 27, 28]. Concurrently, the incidence of MHWs has doubled, with 676 mass mortality events between 1979 and 2020 [29], and their severity is forecasted to increase, considering that since 2015 we are experiencing the hottest years since satellite records [27, 29]. In the past MHWs over 93 species from nine major taxa suffered die-offs and/or necrotic processes, of which Cnidaria and Porifera were the most affected [27, 29]. Sponge holobionts play crucial roles in habitat provision, nutrient cycling, space competition and microbial reservoirs [30, 31]. Thus, impacts in their populations may have devastating ecological consequences in benthic communities [31, 32]. Necrotic and mortality outbreaks in Mediterranean Porifera affect several families [15, 17, 33], and frequently microbial unbalances are involved in these afflictions [34–36], as reported in other temperate counterparts [11, 19].

One of the most impacted sponges in the Mediterranean basin due to MHWs is *Petrosia ficiformis* (Poiret, 1789), for which several studies describe its thermal sensitivity, and microbial engagement in stress responses [15, 16, 37]. High temperature expositions readily induce necrotic processes [38], and activate calcium-dependent nitric oxide synthase system as a stress response in *P. ficiformis* [39]. Instead, wild populations from hydrothermal springs were proposed to harbor prokaryotic taxa (e.g., *Caldilineaceae*, *Microtrichaceae*, *Rhodothermaceae*, *Thermoanaerobaculaceae*) that could acclimatize to these thermal environments, revealing a potential microbial contribution to holobiont success under geothermally heated conditions [40]. *Petrosia ficiformis* is distributed in the Atlantic Ocean (Macaronesia) and the Mediterranean Sea, along a depth range from the shallow to the deep mesophotic zones, with microscale patterns of population differentiation [41, 42]. This species is a high microbial abundance sponge (HMA, ca. 10⁸ to 10¹⁰ microbial cells/g tissue), which acquires most its microorganismal load from the environment [43–45]. In Mediterranean populations around the Island of Ischia (Tyrrhenian Sea, Italy) the microbiomes associated with healthy hosts are dominated by phylum *Chloroflexota*, represented by *Caldilineales*, SAR202 clade, and TK17, along with other less abundant taxa, including *Gemmatorimonadota*, *Microtrichales*, *Thermoanaerobaculales* and *Rhodothermales*, and by low abundances of *Cyanobacteria*, overall reporting putative functions of chemoheterotrophy and nitrification [43]. Monitoring microbiome
community shifts under the influence of MHWs can be
crucial for defining fitness status of these holobionts, as
well as to unravel potential acclimatization processes
developing at the population scale [46].

An intense MHW, persisting throughout several weeks,
with a peak of 83 days in 2022 between June and Sep-
tember, hit the Gulf of Napoli, Tyrrhenian Sea (South-
ern Italy), yielding signs of widespread tissue necrosis
in the local populations of *P. ficiformis*. Contemporá-
neously, though, some conspecific individuals did not
exhibit any visual signs of affection or stress. This study
focused on the characterization and comparison of pro-
karyotic microbiomes associated with apparently healthy
and unhealthy individuals of *P. ficiformis*. Our aim was to
enhance our understanding on sponge-microbial interac-
tions under natural conditions of anomalously elevated
summer temperatures and mass mortality events, and
how these symbioses may shape the fate of future ben-
thic communities. We assessed changes in the microbial
community structure, through the application of diver-
sity, dysbiosis indexes, co-occurrence networks and dif-
ferential abundance analyses. We further looked for taxa
indicators of disease and stress states, as well as for part-
nership patterns defining resilient states under elevated
temperatures. Our principal questions sought to address:
(a) was there a microbiome shift in sponge holobionts
from apparently healthy towards unhealthy states?; (b)
if so, which descriptive community indexes could bet-
ter define either normobiotic or dysbiotic conditions?;
and (c) which were the driving taxa mostly defining sus-
ceptible thermally-stressed *versus* healthy and resilient
phenotypes?

**Methods and material**

Study species

*Petrosia ficiformis* reveals slow growth (~ 1% volume increase per month) [47], and oviparous reproduction with restricted dispersion capability [48]. It has preference towards sciaphilous areas, but it can live in the absence of light, displaying dispar growth morphologies (composite-massive and lobate to branched, creeping and cylindrical). This phenotypic flexibility, supported by recent molecular evidence, suggested an incipient speciation process in *P. ficiformis* [42, 49]. Colouration in these sponges can also vary from white to pink or wine-violet, probably determined by cyanobacteria derived pigments' (phycoerythrin) in the cortex, fluctuating according to the amount of irradiation [41, 49, 50] (but see [43]). As HMA species, dense populations of microorganisms are housed in the mesohyl, in specialized cells (bacterio- cytes). For the most part, these microbial associates are likely introduced horizontally from the water column [44, 45], in concomitance with geographically driven microbi- ome signatures [49]. In several locations around the Gulf

of Naples, encompassing Ischia Island, Schiacchettiello
and. Nisida Island, apparently fit individuals with com-
posite-massive and creeping growths were observed co-
occurring with specimens with necrotic processes during
the effects of local a MHW in 2022 (author's personal
observations).

Sponge photographic surveys and sampling

In the peak of the heatwave – in September 2022, the
abundance and proportion of healthy and unhealthy *P.
ficiformis* specimens was estimated applying 19 pho-
toquadrats (25 × 25 cm) positioned on specimens
growing on the cave walls and outer flanks, along four
permanent transects at 2–3 m depth in the study area
of Grotta di Punta Vico (Ischia Island, Gulf of Naples,
Italy; 40°45′32.28″N, 13°52′55.38″E). This site has been
monitored for long term ecosystem functioning under
climate change conditions [51, 52]. Images were collected
using an Olympus TG5 camera with Nauticam housing
and twin Inon strobes. We considered non healthy those
individuals displaying signs of recent mortality, such as,
bleached and opaque colouration patches (white/brown
spots), or totally brown specimens with necrotic tissue
(denuded skeletons, changes in texture with turgidity loss
and tissue detachment and decay). These unhealthy mor-
phologies have been characterized previously in marine
heatwaves driven mortality events [e.g., 15, 17, 52]. The
observed morphological responses were not attributable
to sponge predation, as no bite marks were detected. To
estimate the percentage of individuals in each health cat-
egory, specimens were classified as follows: Healthy (no
visible signs of mortality), Low severity (>10% and < 30%
affected tissue), Moderate severity (>30% and < 60%), and
Severe (>60%).

Sponge samples of ~ 1 cm³ were cut from five speci-
mens with no necrotic signs –hereinafter referred to
as “Healthy”, and from five individuals of the mod-
erate severity category –from now on referred to as
“Unhealthy” (n = 10). Sampling took place in the same
period of the surveys, and was performed by scuba diving
at 3–5 m depth from rocky walls inside a cave, with no
sunlight exposition. Collections were conducted under
authorization from the Marine Protected Area ‘Regno di
Nettuno’ (Island of Ischia). The habitat was typical of a
dim Mediterranean rocky community, with encrusting
calcareous and non-calcareous algae, sponges, ascid-
ians, corals, gorgonians, bryozoans, molluscs, echino-
derms [51]. In order to reduce bias related with different
host genotypes [42, 49], unbalanced presence of photo-
symbionts [49, 50], or consequent fluctuant prooxidant
pressure under different irradiation regimes [37], five
Healthy and five Unhealthy individuals of *P. ficiformis*, all
with creeping growth and similar depigmentation tone
(white), were collected. Small pieces (~ 5 cm³) were cut
sponge DNA for prokaryotic metabarcoding using the
universal bacterial primer pairs Bakt_341F 5'-CCTAC-
GGGNGGCWGCAG-3' and Bakt_805R 5'-GACTACH-
VGGGTATCTAATCC-3' [60], which may potentially
capture more Bacteria over Archaea. Sequencing was
performed at the Station Biologique de Roscoff using a
MiSeq v3 kit (2 × 300 bp, paired-end, 600-cycle) to obtain
~400-450 bp amplicons. For the sequencing a standard
metabarcoding Illumina protocol (Illumina Inc, 2013)
[61, 62] was applied as reported by Mazzella and co-
authors [63].

Bioinformatics analysis

Quality control and filtering of sequence reads

Demultiplexed fastq files containing 16 S rRNA V3-V4
gene paired-end reads were loaded into the “Quantitative
Insights Into Microbial Ecology” environment, (QIIME2
v.2020.1) [64]. The “Divisive Amplicon Denoising Algo-
rithm” (DADA2) was used to denoise the sequencing
reads [65]. This step filters out noise and corrects errors
in marginal reads, removes chimeric sequences and sin-
gletons, merges paired-end reads, and dereplicates the
resulting sequences (100% identity threshold), deliver-
ing an Amplicon Sequence Variants (ASVs) feature table
(Supplementary Data1, Table S3). In particular, we used
the qiime2 denoise-paired command, with the trimming
parameters: trunc_len_f: 212, trunc_len_r: 171, and all
other options left as default. ASVs matching mitochon-
dria and chloroplast sequences, as well as singletons
and low abundance features with a minimum overall
frequency of 10% were filtered out. This filtering step
resulted in 638,147 total reads (1137 ASV features dis-
tributed in 10 samples) ranging from 39,010 to 103,246
reads per sample (Supplementary Data1, Table S4). The
sequencing depth ranged 41,458–74,508 over the Healthy
samples and 48,591–102,590 across the Unhealthy, with
rarefaction curves plateauing at ~ 6000 and ~ 30,000
reads respectively. Rarefaction at a sampling depth
of 39,010 reads for diversity comparisons resulted in
390,100 total reads distributed over 1136 ASVs, which
ensured prokaryotic biodiversity saturation, while keep-
ing all the samples (Table S3). Taxonomy was assigned by
blasting sequences against a pre-trained classifier based
on the 16 S SILVA v.138.1 reference database (99% iden-
tity similarity cutoff) [66, 67].

Alpha and beta diversity

Alpha diversity indexes and beta diversity compari-
sons based on relative abundance were calculated on
rarefied data on RStudio version 2023.12.1 + 402 [68]
running with R version 4.3 [69]. For alpha diversity
the indexes observed ASV (classification by presence/
absence of microbes in a sample), Shannon-Weaner and
Pielou (Evenness). were estimated and plotted with the

R package “microbiome” [70]. Faith’s Phylogenetic diversity was also calculated integrating the phylogenetic tree obtained from QIIME2, which was imported into the R environment through the package “qiime2r” [71], and the analyses were run with the R package “phyloseq” [72]. Community distance matrices based on methods of Bray-Curtis, Jaccard, (un)weighted UniFrac, were calculated on rarefied data and visualized as PCoA (Principal Coordinate Analysis) ordination plots. Aitchison beta diversity, which applies a CLR (Centred Log Ratio) transformation and Euclidean distance was calculated on unrarefied data and was plotted as Principal Component Analysis (PCA), to obtain vectorial reference of feature taxa in the ordination space. Ordinations were performed with “microViz” [73].

Core microbiome and shared taxa

Core ASVs were defined at 99% of prevalence and 0.001% of detection within samples using the “microbiome” R package [70]. Detection was conducted both on the whole unrarefied dataset and on the two subsets (Healthy and Unhealthy). The core microbiome was analysed in terms of relative abundance size, and for taxonomic composition. Bullseye plots were used to represent the core sizes, while donut plots were applied to depict the core microbiome composition, using “ggplot2” R package [74]. Shared ASVs were calculated using the R packages “phyloseq” [72] and “microbiome” [70], whereas prevalence and detection were set at 0% and the results were plotted in a Venn’s diagram using “eulerr” [75].

Differential abundance analysis

Compositional differential abundance analysis was per-
formed on R using "ALDEx2" [76] and "microbiome-
Marker" [77]. The unrarefied dataset was filtered for low
prevalence ASVs (10% prevalence threshold across all
samples), CLR transformed and analysed at the genus
and ASV level. Divergent barplots of differentially abun-
dant genera were generated with "ggplot2" [74], and heat-
maps with "microbiomeMarker" [77].

Dysbiosis index examination

Our microbial dataset was assessed for dysbiosis, calcu-
lating indexes based on median community level varia-
tion [78], distance to reference plane [79] and on Random
Forest prediction score [80], applying Aitchison (CLR
transformation and Euclidean distance) dissimilarity
matrix, using the R packages "dysbiosisR" [81], "microbi-
ome" [70] and "dplyr" [82]. Gradient plots were obtained
with "ggplot2" [74] and "patchwork" [83].

Co-occurrence networks analysis

Co-occurrence networks is a quantitative analytical tool
that measures how features (microbes) interact with each
other and as a collective, by examining nodes (taxa) con-
nections and their strengths. It affords a comprehensive
view of potential bacterial functional cooperation, and
how these communities may be structurally disrupted
under stressful conditions [84, 85]. Compositional co-
occurrence networks were constructed with "Sparse
Correlation Network Investigation for Compositional
Data (SCNIC)" based on "SparCC" correlation method,
which included a 10% total prevalence threshold taxa
prefiltering [86]. The analysis was run with the "within"
method and "modules detection" on unrarefied feature
tables of bacterial ASVs counts data, for each Healthy
and Unhealthy datasets. Modules are partitions of highly
connected subcommunities, likely sharing niche/func-
tions, and high modularity generally denotes network
stability [84]. Parameters settings for the analysis were:
cutoff SparCC correlation (R = SparCC correlation coef-
ficient): |R| >0.35 for outcomes with loose connections,
and |R| >0.98 for networks with very strong interac-
tions. Network outputs were visualized and analysed for
topological characteristics on "Cytoscape" [87]. Nodes in
stringent networks with highest degree and betweenness
centrality were topology indicators for putative keystone
bacterial taxa, in conjunction with high ubiquity across
samples, and moderate abundance (as in [84, 88]). Key-
stone taxa sequences were blasted on BLAST * - Basic
Local Alignment Search Tool - (https://blast.ncbi.nlm.n
ih.gov/) for informative support on isolation origin and
taxonomy.

Statistical analyses

Alpha diversity, core microbiome, and differential abundance analyses were statistically evaluated with Welch's Heteroscedastic T-tests, with Benjamin-Hochberg false discovery rate (FDR) corrections for multiple comparisons (significance set to *q*-value < 0.05; i.e., FDR adjusted *p*-value) [89]. Permutational multivariate analysis of variance (PERMANOVA) with 999 permutations was applied for determining inter-group differences in beta diversity measurements according to the central locations (centroids). Moreover, beta dispersion (PERMDISP) with 999 permutations was used to compare the within-group dispersion (spread around centroids), while Welch's Heteroscedastic T-tests were used to compare samples intra-group distance from the centroid. In all tests, significance was set to *q*-value < 0.05 (i.e., FDR adjusted *p*-value). AUC-ROC curve classification was applied to validate dysbiosis indexes [81, 90].

Results

Environmental data

Over 10 years of temperature data at Ischia, mean tem-
peratures ranged from 23.5 °C in 2012 to 25.5 °C in
2022 at 5 m depth from July 1 to September 30 (Table

S1). Summer 2022 presented maximum temperatures of 28.9 °C with 36% of measurements registered above 27 °C, (Table S1). Summer 2022 was the summer presenting the highest number of MHWs (n = 6), with a total duration of 83 days and with a maximum intensity of 2.9 °C (Fig. 1, Table S4).

Morphology and taxonomical confirmation in healthy and  
unhealthy sponges

Healthy sponges showed a continuous texture and colour
pattern [41], and lack of necrotic tissues (Fig. 2A and B).
Unhealthy specimens instead, presented inconsistent
patterns of colouration resulting in white/brown/grey
opaque spots (Fig. 2C) and necrotic areas on the surface
(Fig. 2D and F), yielding morphologies largely reported
to heat stress [52]. Unhealthy individuals represented
89.5% of the surveyed *P. ficiformis*. Most sponges exhib-
ited signs of Moderate (37%) and Severe (37%) severity,
while 16% experienced Low severity, and a small fraction
remained Healthy (10.5%). Depigmented opaque patches
and necrotic tissue patterns were not due to predators,
as no bitten marks were evident (Fig. 2G). In addition,
other taxa were affected including incrusting calcareous
algae (e.g., *Mesophyllum*) and the bryozoan *Myriapora
truncata*.

Our sponges were genetically identified as *Petro-
sia ficiformis*, according to COI and 28 S markers. COI
sequences retrieved from all ten sponge samples matched
identically with other *P. ficiformis* references from Gen-
bank. Unhealthy sponges only yielded the I3-M11 parti-
tion (567–643 bp), while three of the Healthy individuals
provided both the Folmer and the I3-M11 partition (639–
1134 bp), and revealed 100% match with Burgsdorf et
al., (2014) [49] Italian sequences of the creeping mor-
photype, and differed by three nucleotides from Italian
composite-massive violet specimens (reference accession
numbers: KM452904–KM452912). Five ITSs (758–796
bp) and two 28 S (544 bp) barcodes were obtained from
Healthy sponges. The 28 S reported 93% and 91% simi-
larities with *P. ficiformis* sponges from Spain (reference
entries: OX345645.1, OX345649.1) and Israel (reference
entries: KX688752) respectively. ITS barcodes from the
present study were the first entries for this marker gene
from *P. ficiformis* in NCBI database.

Microbial community composition

A total of 1136 different ASVs were identified belonging
to 36 Phyla, 75 Classes, 153 Orders, 220 Families and 312
genera (Supplementary Data2, Table S5). Our sponges
were sparse in *Synechococcus* cyanobacteria, in compli-
ance with the white morph, and showed completely dif-
ferent microbiomes when comparing Healthy *versus*
Unhealthy specimens, at all taxonomic ranks – phylum
to genus (Fig. 3; Supplementary File S1, Fig. S1–S4).
<figure><img src="image_3.png" /><figcaption>**Fig. 1** Temperature time series data. Marine heatwaves (MHWs) at 5 m in Ischia from 2012 to 2022. **A** Number of events, **B** Total duration, and **C** Maximum intensity detected during a given MHW over the study period. Sumicum (in °C) shows the yearly sum of the cumulative intensities of MHWs, and is determined by summing the daily degrees above the historical 90th percentile for the studied period (year). **D** Daily mean temperature calculated from hourly measurements from 2012 to 2022. **E** Temperature profile capturing the MHW event of 2022, with the baseline climatology, temperature values, and threshold (the baseline 90th percentile). The climatology period 2012–2021</figcaption></figure>

Prokaryotic community structures, defined by most abundant taxa groups in relative abundance, characterizing Healthy and Unhealthy specimens are described in Table 1 (see also Supplementary Results S1 and Table S5 for detailed information).

Specimens belonging to the Healthy group revealed 244 exclusive ASVs, while 845 were identified as unique of the Unhealthy category, with only 48 being shared between the two groupings (Fig. S5; Supplementary Data S2, Table S6). One ASV belonging to family *Halobacteroidaceae* (phylum *Halanaerobiaeota*) was inconsistently abundant in the Unhealthy specimens, accounting for up to 44% in one sample, whereas in the Healthy specimens this same ASV was present at only ~ 0.01%.

## Alpha diversity

Observed ASVs richness was higher in Unhealthy sponges (448 ASVs in average) with respect to Healthy sponges (200 ASVs in average) (Welch's T-test, *p*-value < 0.05), and a similar pattern was observed with phylogenetic diversity (Welch's T-test, *p*-value < 0.05). For Pielou evenness index instead, Healthy sponges recorded

higher values (Welch's T-test, *p*-value < 0.05), whereas for the Shannon index the difference was not statistically supported (Welch's T-test, *p*-value > 0.05). Intraspecific variability in alpha diversity values was recurrently higher in the Unhealthy specimens compared with the Healthy individuals (Supplementary Data1, Fig. S6), with strong statistical support (Supplementary Data2, Table S7).

## Beta diversity

Healthy and Unhealthy individuals had different prokaryotic community structures, based on classical beta diversity distance measurements (i.e., Bray-Curtiss, Jaccard, unweighted and weighted UniFrac dissimilarities; see Supplementary Data1, Fig. S7), and these differences were statistically supported (PERMANOVA; *p*-value < 0.01; Supplementary Data2, Table S7). Also, beta dispersion (as the average distance from centroids) for the previous distance measurements (Bray-Curtis, Jaccard, unweighted and weighted UniFrac) was found to be higher in Unhealthy sponge microbiomes (0.4328, 0.5578, 0.252, 0.2573, respectively) compared with Healthy ones (0.2354, 0.4447, 0.141, 0.0814, respectively;
<figure><img src="image_4.png" /><figcaption>**Fig. 2** Local *P. ficiformis* population during the heatwave from the photoquadrats survey data. **A** Healthy individual of *P. ficiformis*, composite-massive growth. **B** Healthy individual of *P. ficiformis*, creeping growth form. **C** Spawning of white patches on specimens of *P. ficiformis* in response to thermal stress. **D** Spreading of heat stress signs, with the formation of a completely necrotic part against healthy tissues. **E** A completely necrotized specimen of *P. ficiformis*. **F** Differences among healthy (pink arrows) and necrotic (grey arrows) tissues in a whitish morphotype of *P. ficiformis*. **G** Percentage of individuals of *P. ficiformis* -of either composite-massive coloured or creeping white morphotypes- along the survey transects in each health category, specimens were classified in severity classes according to necrosis extension as follows: Healthy (no visible necrotic signs), Low (>0 – <30%), Moderate (>30 – <60%), and Severe (>60 % affected tissue); number of individuals = 19. Only pictures **B** and **F** allude to sampled sponges for microbiome analysis, the rest **A**, **C**, and **D** depict the effects of thermal stress in the local sponge community, as recorded in the survey. Photographs taken by Núria Teixidó</figcaption></figure>

Welch's T-tests, *p*-value < 0.01 for all distance matrices; Table S7; Supplementary Data1, Fig. S8).

Compositional Aitchison distance also demonstrated different microbial compositions in both sponge groups (PERMANOVA; *p*-value < 0.01; Table S7). The PCA ordination explained 79.5% grouping differences along the first axis, and the most relevant taxa (ASVs) driving this clustering belonged to SAR202 clade, *Caldilineaceae*, TK17 and *Poribacteria* associated with Healthy samples, and *Rhodobacteraceae*, *Flavobacteraceae*, *Pirellulaceae* and *Lentimicrobiaceae* linked to the Unhealthy. Some ASVs in family *Microtrichaceae* were ranked to both groups (Fig. 4). Aitchison beta dispersion displayed larger distance from centroid in the Unhealthy sponges (20.93) with respect to the Healthy ones (9.84) (Welch's T-test; *p*-value < 0.01; Table S7; Supplementary Data1, Fig. S9).

### Core microbiome

Core microbiomes were defined as the ASVs shared in 100% of the samples for each health status group. The core microbiome of Healthy sponges represented 55.69 ± 2.37% of the total relative abundance (Fig. 4) and was composed of 66 ASVs. By order of abundance SAR202 (23 ASVs) occupied 27.83 ± 1.4% of the core size, followed by *Caldilineaceae* (four ASVs; 15.91 ± 2.77%), TK17 group (five ASVs; 12.48 ± 2.38%), Sva 0996 group (four ASVs; 10.17 ± 1.58%), Sub10 group (one ASV; 4.15 ± 1.15%), *Dadabacteriales* (one ASV; 3.83 ± 0.86%), Sub9 group (one ASV; 3.16 ± 2.57%), *Vicinamibacterales* (four ASVs; 3.08 ± 0.45%), TK30 group (one ASVs; 3.01 ± 0.46%). Other core taxa altogether representing less than 20% of the core microbiome were reported in Fig. 5 and in Supplementary Data2, Table S8. Unhealthy specimens revealed a core microbiome size of 40.58 ± 12.03%, composed of 20 ASVs. *Lentimicrobiaceae* was the most abundant core taxon (one ASV; 39.66 ± 31.53%), followed by *Halobacteroidaceae* (one ASV; 16.14 ± 31.44%), Sva 0996 group (one ASV; 13.05 ± 14.44%), *Ruegeria* (two ASVs; 5.9 ± 7.28%), *Saprospiraceae* (one ASV; 4.17 ± 3.48%) and *Pir4* lineage (one ASV; 3.05 ± 2.13%). Core taxa occupying less than 20% of the core were reported in Fig. 6 and in Supplementary Data2, Table S9. Core microbiome size was significantly larger in Healthy sponges than in Unhealthy ones (Welch's T-test, *p*-value = 0.047; Tables S10, S11). No core microbiome was detected when considering the whole dataset (Healthy + Unhealthy microbiomes, 0 shared ASVs across 100% of the samples).

### Differential abundance analysis

Differential abundance analysis detected 62 markers (representing differential abundant genera) significantly ranked to either of the two health conditions (Welch's T-test *q*-value < 0.05, Table S12). Differentially
<figure><img src="image_5.png" /><figcaption>**Fig. 3** Prokaryotic community compositions and structures. A Relative abundance of prokaryotic communities at the best annotated taxonomic rank down to Family associated with Healthy (blue) and Unhealthy (red) specimens. The top 40 families across the overall the dataset were considered, while all the taxa whose relative abundance was < 1% (sum of the frequencies) were grouped in "Other". B PCA on CLR transformed data, where each dot represents a sponge sample, and colours code for Healthy (blue) or Unhealthy (red) specimens. C The ten most relevant families influencing sample clustering in the PCA ordination were plotted as vectors (family *Microtrichaceae* was shared for both groupings, resulting in nine labels)</figcaption></figure>

<table><caption>**Table 1** Prokaryotic composition showing relative abundances of the major taxonomic groups characterizing healthy and unhealthy sponges</caption><thead><tr><th colspan="2">Healthy</th><th colspan="2">Unhealthy</th></tr><tr><th>Taxon</th><th>Abundance (%)</th><th>Taxon</th><th>Abundance (%)</th></tr></thead><tbody><tr><td>Chloroflexota</td><td>46.1 ± 2.1</td><td>Bacteroidota</td><td>25.4 ± 12.0</td></tr><tr><td>SAR202 Clade</td><td>24.29 ± 0.89</td><td>Lentimicrobiaceae</td><td>15.36 ± 11.75</td></tr><tr><td>TK 17</td><td>7.19 ± 1.52</td><td>Flavobacteriaceae</td><td>4.87 ± 3.48</td></tr><tr><td>Caldilineaceae</td><td>10.54 ± 0.61</td><td>Proteobacteria</td><td>19.3 ± 7.3</td></tr><tr><td>Acidobacteriota</td><td>11.4 ± 1.6</td><td>Rhodobacteraceae</td><td>7.73 ± 5.25</td></tr><tr><td>Actinobacteriota</td><td>9.1 ± 1.4</td><td>Firmicutes</td><td>13.9 ± 5.2</td></tr><tr><td>Microtrichaceae</td><td>7.95 ± 1.10</td><td>Planctomycetota</td><td>8.5 ± 0.5</td></tr><tr><td>Poribacteria</td><td>7.5 ± 2.6</td><td>Pirellulaceae</td><td>4.12 ± 0.65</td></tr><tr><td>Bacteroidota</td><td>7.6 ± 1.6</td><td>Actinobacteriota</td><td>6.6 ± 7.5</td></tr><tr><td>Rhodothermaceae</td><td>4.70 ± 1.60</td><td>Microtrichaceae</td><td>6.52 ± 7.60</td></tr><tr><td>Proteobacteria</td><td>6.7 ± 1.5</td><td>Cyanobacteria</td><td>6.5 ± 6.1</td></tr><tr><td></td><td></td><td>Gastranaerophilales</td><td>6.38 ± 6.18</td></tr></tbody></table>

abundant taxa strongly associated with Healthy specimens belonged to SAR202 clade, TK17, *Poribacteria*, BD2-11, *Caldilineaceae* and *Rhodothermaceae* together with other 33 taxa. By contrast, Unhealthy specimens were strongly ranked to *Lentimicrobiaceae*, *Gastranaerophilales*, *Flavobacteriaceae*, *Ruegeria* (family *Rhodobacteraceae*), *AQUIBACTER* (family *Flavobacteriaceae*), and *Saprospiraceae* along with other 17 taxa (Fig. 5; Fig. S10; Table S12). Finally, seven ASVs belonging to the genus Sva0996 group (Family *Microtrichaceae*) were

differentially abundant, six associated with Healthy sponges and one strongly enriched in the Unhealthy specimens. Among these seven Sva0996 ASVs, genetic pairwise distances (Kimura) ranged 0.005 ± 0.004–0.111 ± 0.017. The ASV ranked with Unhealthy specimens revealed genetic variation of 0.077 ± 0.014–0.111 ± 0.017 with respect to the other six ASVs correlated with Healthy sponges; and these last six ASVs had a sequence divergence of 0.005 ± 0.004–0.102 ± 0.017 with each other. No phylogenetic pattern seemed to link the association of different Sva0996 ASVs (genetic distances 0.005 ± 0.004–0.111 ± 0.017) to either Healthy or Unhealthy sponges (Fig. S11; Table S13).

### Dysbiosis scores

All the dysbiosis scores highlighted that Unhealthy specimens could be classified as strongly microbially dysbiotic (statistical support by AUC-ROC curve = 1.0; Table S7). With Random Forest prediction score Healthy and Unhealthy samples were clearly classified separately according to an Out Of Bag probability. Median community level variation detected largely divergent samples in the Unhealthy group compared to the reference sample group (Healthy). While in distance to reference plane dysbiotic samples were determined by a large deviation from the Healthy or reference plane, fitted based on Aitchison PCoA-space (Fig. 6). Overall, Unhealthy specimens showed larger dispersion, as mentioned above.
<figure><img src="image_6.png" /><figcaption>**Fig. 4** Core microbiome size and composition. Core microbiome of **A** Healthy (blue) sponges (core 53.69%, non-core 46.31%); and **B** Unhealthy (red) sponges (core 40.58%, 59.42% non-core %). Internal bullseye plots report the core size: proportion between core and non-core microbiome. The external donut plots report the diversity in relative abundance of the core taxa (ASVs) agglomerated at the genus level, or at the lowest taxonomy annotation</figcaption></figure>

## Co-occurrence microbial networks

Prokaryotic network stability in normobiotic (Healthy) and dysbiotic (Unhealthy) sponge holobionts were explored via compositional co-occurrence network analysis applying loose (R > 0.35) and very strong (R > 0.98) correlation thresholds. Unhealthy specimens displayed higher numbers of nodes and edges (ASV-ASV interactions) in networks with lax correlations and further revealed lower centralization with higher heterogeneity and density, reflecting a less structured but more connected network, with respect to the Healthy individuals. In very strongly correlated networks instead, Healthy microbiomes showed more nodes and edges, along with lower centralization, higher heterogeneity and lower density. The number of nodes displayed a moderate reduction (286 to 222) from the loose to the strongly correlated network in Healthy individuals, while the number of components went from one to two, maintaining a major interconnected component and a secondary satellite component. In contrast, Unhealthy networks experienced a conspicuous reduction in the number of nodes (871 to 77) from loose to strongly correlated outcomes, and the number of components increased from a single component to 19 disconnected components (Fig. 7, Table S14). Modularity was consistently higher in both types of Healthy networks, with 43 and 38 modules in the loose and very strongly correlated networks respectively, as compared to 33 and 28 in the Unhealthy group (Tables S15–18).

A series of potential keystone taxa, with likely relevant roles in microbial co-operative interactions were proposed, corresponding to nodes with elevated degree and betweenness centrality values. Keystone taxa consortium according to network topology was taxonomically consistent in the Healthy sponge microbiomes for networks with loose and strong interactions, and included ASVs in the genera: S085, TK17, TK30, SAR202, PAU26f, BD2-11 terrestrial, order *Vicinamibacterales* and family *Caldilineaceae*. All these sequences coincided with Genbank reference entries isolated from sponges, except for S085 (Table S19). In Unhealthy specimens the only coinciding keystone taxa in both network types were *Lentimicrobiaceae* and *Cohaesibacter*. Other potential keystone groups included ASVs in families *Rhodobacteraceae* and *Flavobacteraceae*, order *Rhizobiales*, and genera: SM1A02, *Rubripirellula*, *Paramaledivibacter*, *Clostridia* vadinBB60, *Haloferula*, *Rhodopirellula*, *Psychoerpens*, 0319-6G20, *Labrenzia* in the loose networks; or chiefly family *Saprospiraceae*, and *Gastrenaeriphilales*, *Ruegeria*, *Pir4* lineage, in the strong networks (Fig. S12). Only one keystone taxon from the Unhealthy strongly correlated network (*Ruegeria* sp.) matched with Genbank sequences isolated from sponges (Fig. S12; Table S19).
<figure><img src="image_7.png" /><figcaption>**Fig. 5** Differential abundance analysis. Heatmap representing log₁₀ abundance of taxa markers enriched in Healthy (blue) and Unhealthy (red) sponge specimens. All of the differentially abundant genera –except for Sva0996 group, were found to be exclusive to either condition, revealing a strong clustering, clearly separating samples by sponge health status. The best annotated taxonomy down to the genus level is provided</figcaption></figure>

# Discussion

## Necrosis in *P. ficiformis* and microbial dysbiosis

In September 2022 sponge populations of *P. ficiformis* around the Island of Ischia (Gulf of Napoli, Italy) exhibited conspicuous incidence of necrosis and/or mortality.

Unhealthy individuals displayed necrotic processes, characterized by scattered depigmentation patches, accompanied by changes in the texture (loss of turgidity) and seeming organic decay. Similar observations were described by [16] in 1999 mass mortality, and
<figure><img src="image_8.png" /><figcaption>**Fig. 6** Dysbiosis indexes gradient plots. Dysbiosis indexes based on: A Random Forest classification based on Out Of Bag predicted probability (RF – OOB) where the dotted white line indicates 0.5 probability for equal classification, any sample above the line can be considered dysbiotic and below the line normobiotic; B Median Community Level Variation (CLV) with the white and black lines indicating the dysbiosis and normobiosis thresholds respectively; and C Distance to Reference Plane (DRP) based on Aitchison distance where deviation from a healthy or reference plane is marked by the black dotted line. All the dysbiosis scores classified Unhealthy microbiomes as dysbiotic</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>**Fig. 7** Co-occurrence networks. Compositional co-occurrence prokaryotic community networks constructed with R threshold > 0.35 for A Healthy and B Unhealthy sponge microbiomes; and networks constructed with R threshold > 0.98 for C Healthy and D Unhealthy sponge microbiomes. The nodes represent interacting ASVs coloured by modularity class, where only the top 43 modules were given a random colour code. The remaining modules and connectors are shown in light grey. The edges are the grey lines connecting the ASVs indicating co-presence/positive co-occurrence interactions. Modules compositions and colour codes are reported in Tables S15, S16, S17, S18</figcaption></figure>
previous events affecting *P. ficiformis* in the Ligurian sea – from the Tuscan Archipelago (Italy) to Marseille (France), reporting ~ 25% necrosis in local populations (in 120 specimens). Necrotic affections were described as sloughing of the superficial pinacodermal layer, and tissue degeneration, which turned sponges vulnerable to polymicrobial infections by bacteria, fungi and pro-tozoans [16, 17]. In their surveys however, authors did not detect signs of conspicuous microorganismal invasions, based on microscopy observations [16]. These approaches focusing solely on anatomical microbial aggregations, seldom disclose holobiont functional unbalances relying on microbiome taxonomy shifts [91]. These processes, so-called microbial dysbiosis, are causative of symbiotic and metabolic impairment, and lead to disease states, and eventually death [22, 91]. In our study, strong microbial shifts were observed between Unhealthy and Healthy sponges, which resulted in completely dissimilar microbiomes in composition and structure. Unhealthy specimens revealed highly variable and inconsistent microbiomes, as compared to Healthy individuals. The microbial incongruity of the Unhealthy necrotic sponges was reflected in increased observed ASVs and phylogenetic alpha diversity, high beta dispersion, and reduced core microbiome size, all together defining an “Anna Karenina Principle” (AKP) situation, by which dysbiosis reflects larger microbial volatility than in Healthy normobiosis states [92]. This principle suggests that more variable dysbiotic microbiomes, hamper holobionts ability to regulate stressful contexts. On this line, increased microbial divergence was reported in Austral temperate sponges with signs of necrosis after natural [11, 19] and experimental thermal stress [13], and in sponge microbiomes from polluted and degraded environments [93]. In general, seldom specific pathogens are identified as sole drivers of mass mortality events or disease, and instead, early microbial imbalances driven by environmental stressors often relay on enhanced taxonomic variability, which end up destabilizing the holobiont [94–97].

There are cases though, in which dysbiotic bacterial remodulations (in response to stress) do not affect community dispersion, but still, bring about disintegrated functional networks associated with reduced holobiont homeostasis and resilience [98]. Since the definition of dysbiosis can be somewhat imprecise due to inter-individual variability [99], a series of indexes were developed to diagnose microbiome imbalances in humans [78–80, 100]. Some of these indicators have been applied in marine invertebrates, for example in sponges undergoing incipient dysbiosis by ocean acidification [63]. In the present study, dysbiosis indexes indicated a profound altered microbiome status in Unhealthy specimens, which together with the alpha and beta diversity outcomes suggested a concomitant entrance of

exogenous microbes. In general, in either dispersed or non-dispersed dysbiotic configurations, it seems to occur a stochastic colonization of transient (opportunistic, scavenger) taxa with no relevant symbiotic roles [92, 98, 99]; and this has been documented in Porifera experiencing stressful states related with temperature [11, 13, 95, 101], and pH [63]. This footing frequently becomes deleterious on one hand, for introducing potential pathogens, and on the other, for weakening microbial interactome networks inducing the loss of vital functions to maintain fitness [92, 98, 99]. Less modularity, along with reduced size and complexity and component disaggregation were observed under stringent correlation thresholds in cooccurrence networks of Unhealthy microbiomes. This likely reflected lower homeostatic capacity with respect to Healthy holobionts. Seemingly, multi-microbial network instability with complexity loss was observed in thermally stressed corals of susceptible phenotypes after massive bleaching events [23]. It was further stated that analogous patterns of network disruption and interactome destabilization, in soil microbiota under persistent environmental stresses or dysbiotic microbiomes under nutritional restriction, were due to the removal of functional key generalist taxa [98, 102, 103]. In our sponges, keystone taxa were consistent in Healthy networks with loose and very strong correlation thresholds; and these microbes have been previously isolated from sponge hosts, and thus could play symbiotic roles within the sponge holobiont (see below). In lieu, Unhealthy cooccurrence networks shared only two keystone ASV taxa (*Lentimicrobiaceae*, *Cohaesibacter*), being the rest of selected keystone taxa variable, and for the most part representing non-sponge associates, but likely opportunistic and/or transient microbes (see below).

## Microbiome shifts in healthy vs. unhealthy sponges

The microbiome composition and core structure of Healthy sponges was comparable to normobiotic microbiomes described in other studies on *P. ficiformis*, reporting predominance of families SAR202 clade, *Caldilineaceae* (*Chloroflexota*), *Microtrichaceae* (*Actinobacteria*), *Poribacteria* and TK17 (*Chloroflexota*); and accounting with scant inconsistent occurrence of *Cyanobacteria* for being white unpigmented specimens [49, 50, 104] (but see [43]). Anaplerotic carbon assimilation was the principal function assigned to *Acidobacteria*, *Alphaproteobacteria*, and *Chloroflexi* symbionts [105]. In Healthy specimens of the present study, core taxa members may contribute in maintaining homeostasis through the degradation of recalcitrant organic matter (SAR202), nutrient recycling (*Caldilineaceae*), acting as engineering microbes (skeleton forming species) and in the dissolved organic nitrogen cycle (Sva0996 group), and participating in biodegradation processes (Sub 10,
family: *Thermoanaerobaculaceae*) [106–109]. TK17 still retains an unknown functionality, but mechanisms of symbiont recognition have been proposed through correlations with structural lipids [43]. Among the differentially abundant taxa markers most strongly ranked with Healthy sponges, *Rhodothermaceae* (phylum: *Bacteroidota*; class *Rhodothermia*; order *Rhodothermales*) involves sponge symbionts in warm tropical waters [110, 111], that display abundance reduction with depth [112]. Also in experiments of coral-Symbiodiniaceae-bacteria thermal adaptation, *Rhodothermaceae* proliferated disclosing benefits under higher temperatures [113]. Therefore, the preponderance of these halo-thermophile associates, with catalase, cellulase and xylanase activities [114, 115], could contribute, along the above core taxa, to microbiome stability under thermal crises.

Unhealthy specimens were characterized by the loss of microbial groups normally present in *P. ficiformis*, accomplishing heterotrophic and nitrification metabolisms [43, 49, 50, 104]; and their replacement by opportunistic taxa related with pathological events in marine organisms [11, 13, 21, 116]. In particular, *Flavobacteriaceae* and *Clostridia*, have been correlated with thermo-dependent dysbiosis in Porifera [11, 13], and *Vibrio* with necrotic and mortality events [34, 35]. *Gastranaerophilales* are non-photosynthetic cyanobacteria with predatory lifestyle [117], whereas some Sva0996 group strains can be opportunistic colonizers in decaying material [106]. The only keystone taxon in Unhealthy microbiomes previously isolated from Porifera belonged to *Ruegeria* spp., which have been proposed as probiotics under challenging conditions [118]. These microbes were reported in heat stressed sponges [119], and Mediterranean corals during MHWs [118], with some coral strains revealing inhibition towards temperature-dependent *Vibrio coralliilyticus* [120]. Denitrification, sulfate permeases, and phosphorus sequestration genes were recently disclosed in *Ruegeria* associates from the model sponge *Ianthella basta* [121]. *Lentimicrobiaceae* and *Saprospiraceae*, seem to act as scavengers, exerting nutrient removal and macromolecule degradation [122–124], which together with *Halobacteroidaceae* displaying abilities to ferment complex carbohydrates [125], and *Cohaesibacter* exhibiting proteolytic activity [126], could cooperate in the decomposition of necrotic host tissues. It was observed that under temperature increases, thermo-susceptible sponges lost their efficiency to transfer assimilated organic matter for tissue repair and, instead leaked resources towards cell degradation [127]. This consequent detritus accumulation could represent an appealing substrate for microbial opportunistic colonization during dysbiosis.

## Implications for sponge resilience to climate change

Marine sponges are predicted to be winners over corals under global climate change scenarios [128–131]. Despite this generalistic assumption, several sponge species are highly susceptible to extreme events, such as MHWs [16, 17, 29, 36], which disrupt their physiology [37], and bacterial populations [132]. Therefore, if extreme warming events keep being recurrent in the next years or decades [5, 28, 29], this trend may act as a selective force for the evolution of different thermo-tolerant populations in the benthic sponge fauna [16, 17, 52]. Several authors have proposed acclimatizing strategies under anthropogenic, thermal and oxidative stress in marine holobionts, relying on the host, the microbiome and/or their interaction [133, 134]. These may include among others, host organic resources relocation to prioritize somatic growth and homeostasis [127], as well as microbial selection for beneficial stress-tolerant partnerships, e.g., “Probiotic Hypothesis” in coral and sponge holobionts [135–137]. In the case of *P. ficiformis*, specimens of the same morphotype developed necrotic processes, while others remained apparently healthy after undergoing a MHW. Since we ignore the microbiome composition of the two experimental groups prior to this event, we cannot propose a clear contribution of certain prokaryotic groups to withstand thermal stress. However, because Healthy hosts revealed a highly matching microbiome as other *P. ficiformis* in healthful conditions (e.g., [49, 50, 104]), even from the same area [43], we can hypothesize that the most likely scenario is that Unhealthy specimens would have lost the capacity to maintain a “normobiotic microbiome” during the MHW. The incipient cause for this dysbiotic response is an interruption of the molecular interactions between the host and the symbiont community, and the consequent loss of archetypal keystone and hub microbes [21]. The acquisition of specific thermo-resistant taxa in Healthy sponges is less plausible, but still, we cannot discard the co-existence of two populations of *P. ficiformis* with different fitted microbiomes before the MHW. Other possible reasons underlying the differential susceptibility to heat stress may rely on dispar genotypic characteristics of the hosts [42], together with different physiological states, age or nutritional status [30], which cannot be traced with our data. Even if all the specimens here analysed came from the same morphotype, underlying host genotypic and physiological variations not controlled in our analyses, could have driven dissimilar environmental responses [30].

In some species thermal cumulative damages result in higher mortality rates [33, 36], while in others, repeated exposition and recovery from dysbiosis to normobiosis states may promote tolerance in the face of climate warming [127, 133]. Experimentally, heat stressed sponges produced a better adapted offspring, increasing
also the presence of endosymbiotic bacteria [13]. Therefore, in some instances recovered holobionts may develop less thermo-competent progeny [138], or on the contrary, may yield offspring better adapted/acclimatized to confront future MHWs [13, 139]. *Petrosia ficiformis* has been observed to recover from necrotic stages in a healing period of five months, remaining chronic scars over longer periods [16, 17]. In this research, longitudinal tracking of the same individuals would have been required to demonstrate whether Unhealthy sponges could recover a “healthy” microbiome state. It has been often postulated that marine holobionts –e.g., corals [133, 134], sponges [19] display species-specific trends after experiencing heat stress; but there are examples in scleractinian corals [23], and now in the case of *P. ficiformis*, where it has been demonstrated an individual-specific response, with dispar thermos-tolerant capabilities.

## Conclusions

Although thermo-resistance in marine holobionts has often been described as to be species-specific [119], in *Petrosia ficiformis* the response towards MHWs has proved to be individual-specific. Despite its susceptibility to thermal stress, this species has demonstrated recovery capabilities from extreme events, and ability to thrive under challenging conditions [16, 17, 140]. Different co-occurring genotypic morphotypes (composite-massive, creeping), have colonized diverse habitats [49], ranging from dimly lit environments to marine caves [43]. However, recently strong population differentiation was unveiled [42] and hypothetically correlated with mortality events [15]. Such low genetic connectivity could unwarrantedly drive to bottleneck processes in *P. ficiformis* around areas influenced by environmental catastrophes [42]. Under these constraints, phenotypic plasticity in response to challenging conditions may be attained through unknown intrinsic factors, that may involve host capacity to retain hub and keystone symbionts [119], and/or horizontal acquisition of beneficial microbial strains [44]. These symbiotic partnerships should ensure metabolic redundancy, with multiple microbial taxa covering essential ecological functions under fluctuating conditions, enhancing holobiont resilience [141–144]. The acquisition of microbes from the water column though, may increase the chance of introducing opportunistic strains, which under stressful contexts may promote dysbiosis [11, 19, 29]. This microbial dichotomy was observed between our Healthy normobiotic and Unhealthy dysbiotic sponge holobionts. Therefore, future studies should correlate host's genotypic and physiological characteristics, and simultaneously focus on the divergent selective permeability of microbial acquisition, which may define thermo-tolerant *versus* thermosusceptible phenotypes. While marine sponges are often

predicted to be defeater survivors in global change scenarios [13, 128–130], it is important to note that the Mediterranean Sea hosts over 650 sponge species [145], and our predictive capabilities currently extend to only a limited number of them. Further research is crucial to better understand and anticipate the responses of these diverse and ecologically significant organisms to ongoing environmental changes.

## Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s40793-025-00830-2.

Supplementary Material 1

Supplementary Material 2

## Acknowledgements

Thanks are due to Bruno Iacono for fieldwork support on board. We thank the marine protected area: "Regno di Nettuno" for the authorization to collect samples for this study. We acknowledge the financial support received from the EuroMarine-EMBRC 2020 call for collaborative research with EMBRC marine research facilities, to conduct next-generation sequencing at the Station Biologique de Roscoff in France. This study was partially funded under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.4 - Call for tender No. 3138 of 16 December 2021, rectified by Decree n.3175 of 18 December 2021 of Italian Ministry of University and Research funded by the European Union— NextGenerationEU: Award Number: Project code CN_00000033, Concession Decree No. 1034 of 17 June 2022 adopted by the Italian Ministry of University and Research, CUP C63C22000520001, Project title "National Biodiversity Future Center - NBFC".

## Author contributions

**Laura Núñez-Pons**\*\* : Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. \*\*Luigi Maria Cusano\*\* : Formal analysis, Software, Validation, Visualization, Writing – original draft. \*\*Antonia Chiarore\*\* : Methodology, Visualization, Writing – review & editing. \*\*Alice Mirasole\*\* : Data curation, Methodology, Writing – review & editing. \*\*Núria Teixidó\*\* : Formal Analysis, Data Curation, Methodology, Resources, Validation, Visualization, Writing – original draft, Writing – review & editing. \*\*Jana Efremova\*\* : Funding acquisition, Writing – review & editing. \*\*Valerio Mazzella\*\* : Conceptualization, Methodology, Supervision, Visualization, Writing – original draft, Writing – review & editing.

## Funding

Financial support was received from the EuroMarine-EMBRC 2020 call for collaborative research with EMBRC marine research facilities, to conduct next-generation sequencing at the Station Biologique de Roscoff in France. This study was partially funded under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.4 - Call for tender No. 3138 of 16 December 2021, rectified by Decree n.3175 of 18 December 2021 of Italian Ministry of University and Research funded by the European Union— NextGenerationEU: Award Number: Project code CN_00000033, Concession Decree No. 1034 of 17 June 2022 adopted by the Italian Ministry of University and Research, CUP C63C22000520001, Project title "National Biodiversity Future Center - NBFC". N. Teixidó was supported by the French government through the National Research Agency Investments for the Future ("4Oceans-Make Our Planet Great Again" grant, ANR-17-MOPGA-0001). Boat services and laboratory material was covered by internal institutional personal funds L. Núñez-Pons (Stazione Zoologica Anton Dohrn, EMI Department).

## Data availability

Amplicon sequencing data of the 16 S rRNA gene were deposited at the National Center for Biotechnology Information (NCBI) database Genbank under the bioproject PRJNA1187510, with the accession numbers SRX26780023 - SRX26780032) at: [https://www.ncbi.nlm.nih.gov/bioproje
ct/PRJNA1187510](https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1187510)Data for the *in situ* temperature data analysis are accessible from the T-MEDNet network at: [https://t-mednet.org/visualize-data/temperature?view=tfigure&type=1&project\_id=93](https://t-mednet.org/visualize-data/temperature?view=tfigure&type=1&project_id=93)Sponge host DNA barcodes are available at NCBI under the accession numbers: PV981945 – PV981954 for COI, PV995703 – PV995707 for ITS, and PX057281 – PX057282 for 28 S.

## Declarations

### Ethics approval and consent for publication

All research was conducted under permission from national and regional authorities, from the Marine Protected Area 'Regno di Nettuno' (Island of Ischia). Not applicable.

### Consent for publication

Not applicable.

### Competing interests

Authors declare no competing interests.

### Author details

¹Department of Integrative Marine Ecology (EMI), Stazione Zoologica Anton Dohrn, Villa Comunale, 80121 Naples, Italy

²NBFC, National Biodiversity Future Center, 90133 Palermo, Italy

³Stazione Zoologica Anton Dohrn National Institute of Marine Biology, Ecology and Biotechnology, Ischia Marine Center, Ischia, Naples, Italy

⁴Laboratoire d'Océanographie de Villefranche, Sorbonne Université, CNRS, Villefranche-sur-mer, France

Received: 11 August 2025 / Accepted: 24 November 2025

Published online: 02 December 2025

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