Ecology

AbstractBiodiversity loss threatens ecosystems and human well-being, making accurate, large-scale monitoring crucial. Environmental DNA (eDNA) has enabled species detection from substrates such as water, without the need for direct observation. Lately, airborne eDNA has been showing promise for tracking organisms from insects to mammals in terrestrial ecosystems. Conventional biodiversity assessments are often labor-intensive and limited in scope, leaving gaps in our understanding of ecosystem response to environmental change. Here, we demonstrate that airborne eDNA can detect organisms across the tree of life, quantify changes in abundance congruent with traditional monitoring, and reveal land-use induced regional decline of diversity in a northern boreal ecosystem over more than three decades. By analyzing 34 years of archived aerosol filters, we reconstruct weekly temporal relative abundance data for more than 2700 genera using non-targeted methods. This study provides unified, ecosystem-scale biodiversity surveillance spanning multiple decades, with data collected at weekly intervals on both the individual species and community level. Previously, large scale analyses of ecosystem changes, targeting all types of organisms, has been prohibitively expensive and difficult to attempt. Here, we present a way of holistically doing this type of analysis in a single framework.

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First national survey of terrestrial biodiversity using airborne eDNA

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02 June 2025

Shotgun sequencing of airborne eDNA achieves rapid assessment of whole biomes, population genetics and genomic variation

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03 June 2025

Archived natural DNA samplers reveal four decades of biodiversity change across the tree of life

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01 August 2025

Data availability

The sequencing data generated in this study are deposited in the NCBI Sequence Read Archive (SRA) under accession code PRJNA808200. The processed relative abundance data are available in Supplementary Data 6. External datasets used are land cover data (Swedish National Land Cover Database, www.naturvardsverket.se/en/services-and-permits/maps-and-map-services/national-land-cover-database/), map vector data (Natural Earth, www.naturalearthdata.com/), weather data (Copernicus Climate Change Service, https://doi.org/10.24381/cds.e2161bac, National Centers for Environmental Prediction (NCEP) and National Center for Atmospheric Research (NCAR) Reanalysis project, psl.noaa.gov/data/gridded/reanalysis/, Swedish Meteorological and Hydrological Institute (SMHI), www.smhi.se/data/hitta-data-for-en-plats/ladda-ner-vaderobservationer, Climatology Lab, www.climatologylab.org/terraclimate.html, National Oceanic and Atmospheric Administration (NOAA) – Climate Prediction Center, www.cpc.ncep.noaa.gov, Expert Team on Climate Change Detection and Indices (ETCCDI), etccdi.pacificclimate.org/data.shtml), reference sequence data (National Center for Biotechnology Information (NCBI), www.ncbi.nlm.nih.gov/nucleotide/, accession numbers for all sequences used in the Kraken database are available at https://doi.org/10.5281/zenodo.17778887), species observational data (Swedish Species Observation System database, artportalen.se, Global Biodiversity Information Facility (GBIF), www.gbif.org, Swedish Bird Survey, www.fageltaxering.lu.se, Sámi Parliament of Sweden (Sámediggi), sametinget.se/renstatistik), and forestry data (The Swedish National Forest Inventory (NFI), www.slu.se/en/about-slu/organisation/departments/forest-resource-management/miljoanalys/nfi/, Swedish Forest Agency, www.skogsstyrelsen.se/laddanergeodata).
Code availability

StringMeUp, a computer program developed in-house and used in the classification of the sequence data, and the Kraken 2 fork are both available under DOIs https://doi.org/10.5281/zenodo.17569636 and https://doi.org/10.5281/zenodo.17570001, respectively.
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The computations were enabled by resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS) and the Swedish National Infrastructure for Computing (SNIC) at UPPMAX and HPC2N, partially funded by the Swedish Research Council through grant agreement nos. 2022-06725 and 2018-05973. Thomas Ågren provided the organism illustrations in Figs. 1–3. Modified Copernicus Climate Change Service information 2020 was used for the catchment area analysis. Neither the European Commission nor the European Center for Medium-Range Weather Forecasts (ECMWF) is responsible for any use that may be made of the Copernicus information or data it contains. This study was supported by Formas (grant agreement nos. 2016-01371: PS, MF; 2019-00579: P.S., T.B., and M.F.; 2021-02155: PS, MF; 2024-01990: P.S., T.B., M.F., and N.S.), together with grants from Vetenskapsrådet (2021-06283: P.S. and M.F.), SciLifeLab Biodiversity fund (NP00048: P.S., M.F., and T.B.), Kempe foundation (JCK-1919: P.S., M.F., and T.B.), Umeå University Industrial research school (P.S.) and Swedish Defense Research Agency (M.F.)FundingOpen access funding provided by Umea University.Author informationAuthor notesThese authors contributed equally: Alexis R. Sullivan, Edvin Karlsson.Authors and AffiliationsDepartment of Ecology and Environmental Sciences, Umeå University, Umeå, SwedenAlexis R. Sullivan, Edvin Karlsson, Daniel Svensson, Jose Antonio Villegas, Daniel Bellieny, Abu Bakar Siddique, Per-Anders Esseen & Per StenbergDepartment of Wildlife, Fish and Environmental Studies, Swedish University of Agricultural Sciences, Umeå, SwedenAlexis R. Sullivan, Anita Norman, Navinder J. Singh & Tomas BrodinCBRN Defence and Security, Swedish Defence Research Agency (FOI), Umeå, SwedenEdvin Karlsson, Björn Brindefalk, Håkan Grahn, David Sundell, Andreas Sjödin, Mats Forsman & Per StenbergDepartment of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, Stockholm, SwedenBjörn BrindefalkUmeå Plant Science Centre, Department of Plant Physiology, Umeå University, Umeå, SwedenAmanda MikkoDepartment of Plant Biology, Swedish University of Agricultural Sciences, Uppsala, SwedenAbu Bakar SiddiqueDepartment of Molecular Biology, Umeå University, Umeå, SwedenAnna-Mia JohanssonAuthorsAlexis R. SullivanView author publicationsSearch author on:PubMed Google ScholarEdvin KarlssonView author publicationsSearch author on:PubMed Google ScholarDaniel SvenssonView author publicationsSearch author on:PubMed Google ScholarBjörn BrindefalkView author publicationsSearch author on:PubMed Google ScholarJose Antonio VillegasView author publicationsSearch author on:PubMed Google ScholarAmanda MikkoView author publicationsSearch author on:PubMed Google ScholarDaniel BellienyView author publicationsSearch author on:PubMed Google ScholarAbu Bakar SiddiqueView author publicationsSearch author on:PubMed Google ScholarAnna-Mia JohanssonView author publicationsSearch author on:PubMed Google ScholarHåkan GrahnView author publicationsSearch author on:PubMed Google ScholarDavid SundellView author publicationsSearch author on:PubMed Google ScholarAnita NormanView author publicationsSearch author on:PubMed Google ScholarPer-Anders EsseenView author publicationsSearch author on:PubMed Google ScholarAndreas SjödinView author publicationsSearch author on:PubMed Google ScholarNavinder J. SinghView author publicationsSearch author on:PubMed Google ScholarTomas BrodinView author publicationsSearch author on:PubMed Google ScholarMats ForsmanView author publicationsSearch author on:PubMed Google ScholarPer StenbergView author publicationsSearch author on:PubMed Google ScholarContributionsP.S., M.F., T.B., and E.K. conceived and designed the study; E.K. and A.M.J. extracted DNA; DSv constructed the database and performed read classification; E.K., A.R.S., D.B., D.S.v. pre-processed the data; A.R.S. designed and implemented the machine learning approach; and H.G. constructed the particle models. A.R.S. and E.K. conducted most of the data analysis, with support from D.S.v., D.B., A.B.S., J.A.V., A.M., D.S.u., B.B., A.N., A.S., N.S., and P.A.E. E.K., A.R.S., D.S.v., B.B., P.S., and N.S. wrote the first draft of the manuscript. All authors contributed intellectual input and approved the final versionCorresponding authorCorrespondence to
Per Stenberg.Ethics declarations

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Nature Communications thanks David Schmale III and the other anonymous reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

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AbstractVocalizations play crucial roles in dolphin biological activities. Analysis of dolphin vocalizations provides valuable insights into their behaviors and population status. In this data descriptor, we present a dataset of underwater vocalizations of Indo-Pacific humpback dolphins (Sousa chinensis) recorded in Xiamen Bay, China. The dataset comprises a diverse range of dolphin emissions, including 143 whistles (100 of which were classified as high-quality), and 897 pulse trains, categorized as echolocation clicks, burst pulses, and buzzes. A range of acoustic parameters was measured to characterize these acoustic signals. The presented dataset serves as an essential contribution to addressing existing data gaps regarding vocalizations of the Indo-Pacific humpback dolphin population in Xiamen Bay. It provides an important resource for studying vocalization patterns and temporal variability in the acoustic behaviors of the Indo-Pacific humpback dolphins, offering key insights to inform conservation strategies for this endangered population. Additionally, the dataset holds potential for population connectivity research, enabling acoustic comparisons between dolphin populations across different geographic regions to assess potential isolation or interaction.

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Background & SummaryTo adapt to the complex and dark underwater environment, dolphins have evolved the ability to use sound for sensing their surroundings, navigation, foraging, and communication1. Dolphin vocalizations primarily consist of two main types: pulsed signals and frequency-modulated whistles. Pulsed signals, including echolocation clicks, burst pulses, and buzzes, are characterized by high-frequency, broadband properties and are typically produced in trains2. Among these, echolocation clicks are the most commonly observed acoustic signals. Dolphins emit highly directional echolocation clicks while navigating and detecting prey or other targets of interest2. Burst pulses share similar characteristics with echolocation clicks but generally have lower frequencies and higher repetition rates. These signals are primarily used for intraspecific communication3. Buzzes are associated with close-range echolocation, particularly during the capture phase of hunting, as indicated by a rapid increase in pulse repetition rate. In addition to their role in predation, buzzes also serve as functional signals in social interactions, such as mating behaviors and mother-calf communication4. Whistles, which are narrow-band signals with modulated frequencies, are predominantly used for communication1. This type of emission plays crucial roles in social contexts, including reproductive gathering5, group cohesion6, individual identification7, coordinating group activities8.Dolphin vocalizations, both whistles and pulsed signals, are highly dynamic and flexible. The characteristics of dolphin sound production are known to vary depending on the environmental conditions9,10,11,12,13,14,15. For instance, Jensen et al.9 demonstrated that Irrawaddy dolphins (Orcaella brevirostris) and Ganges river dolphins (Platanista gangetica gangetica) produce echolocation clicks with significantly lower peak frequencies and sound amplitudes in shallow waters compared to deep waters, likely to mitigate reverberation. The minimum, maximum, and peak frequencies of whistles produced by common bottlenose dolphins (Tursiops truncatus) were found to increase with the ambient noise10. Changes in both click and whistle parameters in response to vessel presence have also been reported11,12,13,14. Additionally, alterations in dolphin sound production patterns have been linked to factors such as group size, group composition, and behavioral contexts12,15,16,17. For example, Akiyama and Ohta18 observed that common bottlenose dolphins increase whistle production during feeding, with a preference for upsweep-contour whistles. In dolphin groups containing calves, whistles tend to have lower end and minimum frequencies and longer durations12. Dolphins also exhibit adaptive modifications in their click characteristics during prey perception and target detection. They dynamically adjust their click rate, output amplitude, and acoustic directivity across different phases of target approach to achieve precise detection and recognition19,20,21,22. Recently, research on the adaptive acoustic control of dolphin biosonar during target detection has intensified, driven by the dual objectives of understanding biological systems and inspiring biomimetic applications.In summary, dolphin vocalizations are complex and dynamic yet critical for their survival. The collection and analysis of these acoustic signals can help to better understand their behaviors and population status. As one of the vulnerable dolphin species assessed by the IUCN23, the Indo-Pacific humpback dolphin (Sousa chinensis) in Chinese waters is sporadically distributed along the southeastern coast of China, including Xiamen Bay. The population inhabiting Xiamen Bay is under significant survival pressure due to increasing anthropogenic disturbances, with a notable reduction in population size over the past two decadesSousa chinensis) in Chinese Waters. Aquatic Mammals 30, 149–158 (2004).” href=”https://www.nature.com/articles/s41597-025-06253-5#ref-CR24″ id=”ref-link-section-d279053545e574″>24,25,26,27,28,29,30. Enhanced conservation efforts are urgently needed to mitigate ongoing threats to this endangered population. A comprehensive assessment of population status, including population size and structure, distribution patterns, surface behaviors, and vocalizations, is essential for developing effective conservation strategies and management initiatives. While studies have reported on vocalizations of Indo-Pacific humpback dolphins in Chinese waters31,32,33,34,35,36,37,38,39, limited research focused on the Xiamen Bay population, necessitating more targeted bioacoustic investigations.In this paper, we present a dataset of acoustic recordings of Indo-Pacific humpback dolphin vocalizations, collected during our regular surveys of the species’ population status. These data represent an important supplement to the Indo-Pacific humpback dolphin sound library in Chinese waters and, to a large extent, fill the data gaps regarding the dolphin population in Xiamen Bay. In addition to sharing the complete acoustic data, a quantitative analysis was conducted on dolphin vocalizations, both whistles and pulsed signals (classified as echolocation clicks, burst pulses, and buzzes), to determine characteristic parameters of each signal, and the results are incorporated into the dataset. It offers a valuable resource to study the vocalization patterns of the Indo-Pacific humpback dolphin population in Xiamen Bay, as well as to investigate population connectivity through acoustic comparisons across different geographic regions, helping to assess potential population isolation or interaction. Notably, this dataset spans a period of up to three years, allowing for investigations into potential temporal variability in dolphin acoustic behaviors over a long timeline. The Indo-Pacific humpback dolphins inhabit shallow coastal waters where they face significant echolocation challenges due to high reverberation. Dolphins present remarkable biosonar plasticity, adjusting their acoustic signals in response to varying environmental conditions9,10,11,12,13,14,15. This dataset contains echolocation click trains recorded across a wide range of water depths from 2.1 m to 24.3 m, providing valuable acoustic materials for exploring biosonar operational mechanisms— particularly how dolphins adapt their signals in shallow-water environments with strong reverberation. The comprehensive collection of dolphin vocalization signals also serves as an important resource for bionic applications40,41,42,43.MethodsData collectionWe conducted a total of 22 regular vessel-based surveys in Xiamen Bay, China, from June 2022 to September 2024, to investigate the abundance and distribution patterns of the Indo-Pacific humpback dolphin population, as well as their underwater vocalizations (Fig. 1). During the surveys, the vessel navigated at a speed of 5–7 knots, with three observers equipped with Navigator 7 × 50 binoculars (magnification: 7×, objective lens diameter: 50 mm, field of view: 419 ft at 1000 meters, Steiner company, Germany) positioned on the forward deck to search for dolphin presence. Upon sighting dolphins, the vessel approached at a reduced speed and stopped its engine once a distance was reached less than 50 m. During this period, a calibrated underwater acoustic recorder, SoundTrap 300HF (Ocean Instruments, Auckland, New Zealand), was deployed to record the underwater sounds produced by the dolphins. The recorder was equipped with a 16-bit analog-to-digital converter (ADC) and a pre-amplified hydrophone exhibiting a linear frequency response across the frequency range from 20 Hz to 150 kHz, with a sensitivity of −189 ± 3 dB (re 1 V/μPa) in low-gain mode. The recorder was housed in a steel holder and positioned 1.5–2 m underwater using a steel pipe. A sampling rate of 576 kS/s was used, and the recorded sound data were stored as WAV audio files in real time. During acoustic recording, the vessel engine was turned off to minimize low-frequency sound interference. Dolphins were photographed for individual identification, and detailed information about each acoustic recording session was documented, including geographic location, water depth, dolphin group size, and dolphin behavioral state. From our field surveys, a total of 1019 minutes of original sound data were collected. These recordings were subsequently processed to extract communication whistles and high-frequency pulsed signals produced by the Indo-Pacific humpback dolphin.Fig. 1(a) Map of the survey area, with red dots indicating locations where acoustic recordings of dolphin vocalizations were conducted. (b) Aerial photographs of the survey vessel and an Indo-Pacific humpback dolphin captured by an unmanned aerial vehicle. (c) The SoundTrap 300HF underwater acoustic logger used in the survey.Full size imageDetection of whistlesThe original WAV files were displayed in the time-frequency domain using Adobe Audition (Version 2021) and manually reviewed by an experienced observer to identify whistles within consecutive 3-second time windows. A continuous tonal contour without temporal breakpoints on the spectrogram was identified as a single whistle. Additionally, consecutive contours were also considered a single whistle if the gap between them was shorter than 200 ms and less than the duration of the contours, following established methodologies35,44. Each identified whistle signal was subsequently extracted and saved as an individual WAV file, and the number and position of whistles within the original acoustic file were documented. Whistles were visually divided into three grades based on signal-to-noise ratio (SNR), referencing previous studies12,45. Grade 1 includes whistles with weak contour, but is visible in the spectrogram; Grade 2 includes whistles with clear and unambiguous contours; and Grade 3 includes whistles with contour prominent in the spectrogram. Whistles of Grade 2 and 3 were considered to be of high quality and selected for detailed analysis. All whistle contours were visually categorized into six tonal types32,35,36,44 (Fig. 2): (a) constant, (b) upsweep, (c) downsweep, (d) concave, (e) convex, and (f) sinusoidal. For each high-quality whistle, 13 acoustic characteristics were manually measured from the spectrograms: (a) duration (ms), (b) start frequency (kHz), (c) end frequency (kHz), (d) frequency change (kHz), (e) absolute frequency gradient (kHz/s), (f) minimum frequency (kHz), (g) maximum frequency (kHz), (h) delta frequency (kHz), (i) number of extrema, (j) number of inflection points, (k) number of saddle points, (l) number of breaks, and (m) presence/absence of harmonics. Detailed definitions of these acoustic parameters were consistent with those described in Marley et al.45.Fig. 2Example frequency contours illustrating the six whistle types: (a) constant, (b) upsweep, (c) downsweep, (d) concave, (e) convex, (f) sinusoidal.Full size imageDetection of pulsed signalsThe raw acoustic data contained various pulsed sounds produced by the Indo-Pacific humpback dolphins, including echolocation clicks, burst pulses, and buzzes. These pulsed signals have been shown to exhibit a Gabor-type waveform structure46,47, characterized by a distinct Gaussian envelope in the output of the Teager–Kaiser energy operator (TKEO). Based on this feature, a mature processing method proposed by Madhusudhana et al.48 was applied to extract pulsed signals from the recordings. Initially, raw WAV files were split into 30-second segments to reduce the computational load. A Butterworth high-pass filter with a cut-off frequency of 5 kHz was used to remove most low-frequency noise from the acoustic data.Subsequently, the TKEO output was calculated for each filter data segment as follows49:$${Psi }_{d}[{x}_{n}]={x}_{n}^{2}-{x}_{n-1}{x}_{n+1}$$
(1)
where xn represents the sampled points of each 30-second signal segment. A Gaussian-weighted averaging filter (MAF1) was then applied to highlight short-duration energy surges in TKEO outputs. The Gaussian filter was defined as:$$MAF1(n)=frac{{T}_{s}}{{sigma }_{G}sqrt{2pi }}{e}^{-{(n{T}_{s})}^{2}/2{{sigma }_{G}}^{2}}$$
(2)
where n = −N, …, 0, …, N, is the index of the sampled point in the filter, Ts is the sampling interval, and ({sigma }_{G}) is the standard deviation of the Gaussian, given by:$${sigma }_{G}=frac{FWHM}{2sqrt{2,mathrm{ln}(2)}}$$
(3)
where FWHM is the width of the Gaussian at half its peak value, which is taken as 1 × 10−4 s, based on the length of a representative pulsed signal produced by the dolphin.The value of N is determined based on ({sigma }_{G}), given as the following formula:$$N=frac{5{sigma }_{G}}{{T}_{s}}$$
(4)
where Ts is the sampling interval.Convolution of TKEO output with MAF1 can be expressed as:$${h}_{MAF1}(n)=frac{{T}_{s}}{{sigma }_{G}sqrt{2pi }}mathop{sum }limits_{i=-N}^{N}{e}^{-{(i{T}_{s})}^{2}/2{{sigma }_{G}}^{2}}{x}_{n+i}$$
(5)
TKEO outputs were also filtered by a rectangular averaging filter (MAF2) with identical length and filter gain to MAF1:$$MAF2(n)=frac{mathop{sum }limits_{m=-N}^{N}MAF1(m)}{2N+1}$$
(6)
The filter difference ratio (FDR) was then computed to measure the difference in the responses of the two filters:$$FDR(n)=frac{{h}_{MAF1}(n)-{h}_{MAF2}(n)}{{h}_{MAF1}(n)}$$
(7)
The obtained FDR is expected to yield local maximums at locations of Gaussian-like spikes in the TKEO outputs, indicating the presence of pulsed signals in the original recordings (Fig. 3). Since the FDR values remains relatively constant irrespective of signal amplitude, a detection threshold was set at 85% of the peak FDR value (FDRpeak) following Madhusudhana et al.48 Pulsed signals were confirmed only when the FDR value exceeded this threshold and the signal-to-noise ratio (SNR) of the signal was above 10 dB. For all identified pulses, the inter-pulse intervals (IPIs) were measured, which is defined as the time interval between two consecutive pulses.Fig. 3(a) Example data segment of dolphin vocalizations containing clicks, buzzes, and burst pulses. (b) A representative single pulsed signal, along with its outputs from (c) the Teager–Kaiser energy operator (TEKO) and (d) the filter difference ratio (FDR). (e) Positions of dolphin pulsed signals within the data segment as identified by the automated processing procedure.Full size imagePulse trains were identified by using an adaptive IPI threshold50. Consecutive pulses with a gradual change in the IPI were recognized to belong to the same pulse train, and a pulse train was considered terminated when an abrupt IPI increase occurred. Each pulse train detected was visually confirmed, and those containing signals with high reverberation and from more than one animal were removed. Based on IPI thresholds established by Wang et al.37, the identified pulse trains were subsequently classified into echolocation clicks, burst pulses, and buzzes, according to their mean IPI values. Pulse trains with a mean IPI value less than 4.9 ms were recognized as buzzes, those with mean IPIs greater than 15.5 ms were considered echolocation clicks, and pulse trains with intermediate mean IPIs (4.9–15.5 ms) were categorized as burst pulses. Each identified pulse train, along with its constituent individual pulses, were saved as separate WAV files within the dataset. Pulse trains were sequentially numbered based on their chronological order of appearance in the original recordings, and the position and total number of pulse trains in each original recording were documented. For each identified pulse train, six acoustic parameters were measured for the pulsed signals according to previous studies2,51,52: (a) inter-pulse-interval (IPI), (b) sound pressure level (SPLpp), (c) duration, (d) peak frequency (Fpeak), (e) -3dB bandwidth (-3dBBW), and (f) -10dB bandwidth (-10dBBW). Additionally, the mean values of these acoustic parameters were calculated and recorded for each pulse train.Data RecordsThe acoustic dataset contained a total of 143 whistle signals, of which 100 were identified as high-quality based on visual inspection. Additionally, 897 pulse trains were included, comprising 832 echolocation click trains, 15 burst pulse trains, and 50 buzz trains. The complete dataset is publicly accessible via an unrestricted repository at figshare53, consisting of WAV audio files, TXT text document files, Excel data sheets, and Portable Network Graphic (PNG) image files.The acoustic recordings are provided in different folders based on the signal type:

1.

Original Audio File contains the complete original recordings collected from field surveys, including 35 WAV audio files sequentially named according to the recording time (e.g., Ori_Recording_01.wav, Ori_Recording_02.wav, Ori_Recording_03.wav).

2.

Whistles comprises 143 extracted whistle signals saved as separate WAV files (e.g., Whistle_001.wav, Whistle_002.wav, Whistle_003.wav), along with PNG image files illustrating spectrograms of each whistle (e.g., Whistle_001.png, Whistle_002. png, Whistle_003.png).

3.

Click Trains, Burst Pulse Trains, and Buzz Trains contain WAV audio files corresponding to the detected click trains, burst pulse trains, and buzz trains, respectively. Each pulse train, along with its individual pulsed signals, is saved in a separate subfolder. These subfolders are sequentially named according to the serial number of the pulse train (e.g., PulseTrain_001, PulseTrain_002, PulseTrain_003). Each subfolder additionally contains a tab-separated TXT file named “PulseParameters.txt”, documenting acoustic parameters for each pulsed signal.

Results.xlsx is an Excel file containing detailed information on the original acoustic recordings and quantitative data on whistles and pulsed signals detected within each original acoustic file:

1.

recording dates when the original acoustic data were collected;

2.

geographic coordinates (latitude and longitude) of the recording locations;

3.

start and end times of each original acoustic file;

4.

number of pulse trains detected within each original acoustic file;

5.

number of identified whistles, including counts of high-quality whistles (Grade 2 and 3) within each original acoustic file;

6.

additional information about the recordings, including season, tide, water depth, dolphin group size, and behavioral state of dolphins.

Whistles.xlsx is an Excel file that provides detailed descriptions of each whistle signal:

1.

the original acoustic file from which the whistle signal was extracted;

2.

start and end times of each whistle signal within the original acoustic file, relative to the file’s start, expressed in seconds;

3.

contour type classification for each whistle signal (constant, upsweep, downsweep, concave, convex, and sinusoidal);

4.

quality grading for each whistle (Grade 1, Grade 2, and Grade 3);

5.

acoustic parameters of each whistle signal: Duration, Start Frequency, End Frequency, Frequency Change, Absolute Frequency Gradient, Minimum Frequency, Maximum Frequency, Delta Frequency, Number of Extrema, Number of Inflection Points, Number of Saddle Points, Number of Breaks, and Presence/Absence of Harmonics.

ClickTrains.xlsx, BurstPulseTrains.xlsx, and BuzzTrains.xlsx are Excel files containing detailed information on each identified pulse train type:

1.

serial number of pulse trains denoting their order of appearance within original recordings;

2.

original acoustic file from which each pulse train was extracted;

3.

start and end times of each pulse train within the original acoustic file, relative to the file’s start, expressed in seconds;

4.

lengths of each pulse train (Length);

5.

number of pulsed signals within each pulse train (NumP);

6.

mean values of acoustic parameters of the pulsed signals within each pulse train: mean SNR (Mean_SNR), mean IPI (Mean_IPI), mean sound pressure level (Mean_SPLpp), mean duration (Mean_Duration), mean peak frequency (Mean_Fpeak), mean -3dB bandwidth (Mean_-3dBBW), and mean -10dB bandwidth (Mean_-10dB BW).

Technical ValidationTo ensure the reliability of the presented dataset, the signal processing procedures in this study strictly adhered to the methodologies reported in published studies32,35,36,44,45,48,54,55. The identification, quality grading, classification, and subsequent characteristic measurements of the whistles were manually completed and reviewed by trained and experienced operators using the specialized acoustic signal processing software (Adobe Audition, Version 2021). Statistical results for the whistle parameters are summarized in Table 1, including the range (minimum to maximum), mean, and standard deviation for the 12 measured characteristic parameters of the 100 high-quality whistles.Table 1 Summary statistics of the characteristic parameters for the 100 high-quality whistles.Full size tableLocal extrema, inflections, saddles, breaks, and harmonics were observed in 56%, 36%, 30%, 42%, and 65% of all high-quality whistles, respectively. The constant-shaped contour was the most dominant tonal type, accounting for 48.25% of all whistles identified in this dataset (Fig. 4), consistent with previous reported data for Indo-Pacific humpback dolphin populations in Zhanjiang, Sanniang Bay, and Hainan waters32,35,36. This consistency provides support for the reliability of the presented dataset.Fig. 4Proportions of the six contour types in all whistles in the dataset.Full size imageDetection and classification of pulsed signals were conducted following standard procedures and methodologies widely adopted in published research54,55,56,57,58,59,60. Clicks were the most frequent pulse type, comprising approximately 93% of the identified 897 pulse trains, with a total of 832 occurrences. The acoustic characteristics of the three pulse train types are statistically summarized in Table 2, and each parameter was averaged per train. Consistent with previous findings37, the pulsed signals in this dataset were characterized by high-frequency, broadband features, with click trains exhibiting longer train lengths and higher peak frequencies compared to burst pulses and buzzes. Among the three sound types, burst pulses had the lowest peak frequency and bandwidth. These results further support the reliability of the presented dataset.Table 2 Descriptive statistics for acoustic characteristics of pulse trains produced by Indo-Pacific humpback dolphins in Xiamen Bay, China.Full size table

Data availability

The acoustic dataset described in the current paper is publicly accessible via an unrestricted repository at figshare (https://doi.org/10.6084/m9.figshare.29143727).
Code availability

Data processing followed established published methodologies, and no custom code was used in this paper. For detailed procedures on pulsed signal detections, refer to Madhusudhana et al.44.
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Reprints and permissionsAbout this articleCite this articleFu, W., Peng, X., Wu, F. et al. Acoustic recordings of underwater vocalizations of Indo-Pacific humpback dolphins in Xiamen Bay, China.
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AbstractThis study investigated the interactive effects of salinity levels (0‰, 18‰, and 36‰) and stocking densities (50, 100, 150, and 200 fish/m3) on water quality, growth performance, physiological responses, and economic returns of red tilapia (Oreochromis spp., initial weight of 12.33 ± 2.51 g/fish) reared in a biofloc technology (BFT) system using saline groundwater. A 3 × 4 factorial design with 36 fiberglass tanks (1 m3 each) was employed for 6 months. Key water quality indicators, fish growth indices, hematological and biochemical markers, antioxidant enzymes, immune parameters, and economic performance metrics were assessed. Results showed that increasing salinity and density significantly reduced dissolved oxygen (DO) levels and increased total ammonia nitrogen (TAN), NH3, NO2, and NO3 concentrations (p < 0.001). Biofloc volume (BFV) increased with stocking density across salinities, peaking at 44.4 ± 1.06 mL/L at 0‰ and 200 fish/m3, while higher salinity (36‰) generally reduced BFV. Variations in biofloc composition (protein 22–33%) and fish muscle composition (protein and lipid reduction at 36‰ and 200 fish/m3) indicated metabolic adjustments under stress. The highest final weight (261 ± 1.69 g/fish) was observed at 36‰ salinity with low stocking density (50 fish/m3), whereas the most favorable combination of growth rate, feed conversion ratio, and protein efficiency ratio occurred at 18‰ salinity and moderate stocking densities (100–150 fish/m3). Growth performance and feed utilization declined markedly at 36‰ with high density (200 fish/m3). Hematological indicators (RBC, Hb, Hct) and immune biomarkers (lysozyme, IgM, complement C3) were suppressed at extreme salinity-density combinations, while oxidative stress (high MDA) and hepatic dysfunction (elevated AST and ALT) were evident. Economic analysis confirmed that 18‰ salinity with 200 fish/m3 yielded the highest profit (1000 ± 54.8 EGP/treatment) and lowest operating ratio, while 150 fish/m3 at the same salinity provided slightly lower profit but better fish welfare indicators and immune responses, whereas high-density and hypersaline conditions reduced profitability due to poor growth and increased feed costs. In conclusion, 18‰ salinity combined with 100–150 fish/m3 provides the optimal balance between biological performance, fish welfare, and economic viability in red tilapia BFT systems. These findings offer evidence-based guidelines for sustainable inland saline aquaculture, supporting enhanced production efficiency and profitability in arid and saline-prone regions.

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Download referencesFundingOpen access funding is provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB).Author informationAuthors and AffiliationsNational Institute of Oceanography and Fisheries (NIOF), Cairo City, EgyptGhada R. SallamDepartment of Animal and Fish Production, Faculty of Agriculture (Saba Basha), Alexandria University, Alexandria City, 21531, EgyptMohamed Hamdy, Samy Y. El-Zaeem, Walied M. Fayed & Akram Ismael ShehataDepartment of Animal Production, Faculty of Agriculture, Tanta University, Tanta City, 31527, EgyptMohammed F. El BasuiniFaculty of Desert Agriculture, King Salman International University, Sinai City, South Sinai, EgyptMohammed F. El BasuiniDepartment of Medical Analysis, Faculty of Applied Science, Tishk International University, Erbil City, IraqYusuf Jibril HabibDepartment of Animal and Poultry Production, Faculty of Agriculture, Damanhour University, Damanhour City, 22516, EgyptEslam TefalAuthorsGhada R. SallamView author publicationsSearch author on:PubMed Google ScholarMohamed HamdyView author publicationsSearch author on:PubMed Google ScholarMohammed F. El BasuiniView author publicationsSearch author on:PubMed Google ScholarSamy Y. El-ZaeemView author publicationsSearch author on:PubMed Google ScholarYusuf Jibril HabibView author publicationsSearch author on:PubMed Google ScholarWalied M. FayedView author publicationsSearch author on:PubMed Google ScholarEslam TefalView author publicationsSearch author on:PubMed Google ScholarAkram Ismael ShehataView author publicationsSearch author on:PubMed Google ScholarContributionsGhada R. Sallam: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data Curation, Visualization, Writing – Review & Editing. Mohamed Hamdy: Methodology, Validation, Formal analysis, Investigation, Resources, Data Curation. Mohammed F. El Basuini: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data Curation, Visualization, Supervision, Writing – Original Draft, Writing – Review & Editing. Samy Y. El-Zaeem: Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data Curation, Visualization. Yusuf Jibril Habib: Formal analysis, Data Curation, Writing – Review & Editing. Walied M. Fayed: Methodology, Formal analysis, Investigation, Visualization. Eslam Tefal: Methodology, Software, Validation, Formal analysis, Data Curation, Original Draft, Writing – Review & Editing. Akram Ismael Shehata: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data Curation, Visualization, Supervision, Writing – Original Draft, Writing – Review & Editing.Corresponding authorsCorrespondence to
Mohammed F. El Basuini or Akram Ismael Shehata.Ethics declarations

Competing interests
The authors declare no competing interests.

Ethical approval
All experimental procedures were reviewed and approved by the Animal Use Ethics Committee of Alexandria University (protocol number AU:19/24/06/11/1/34). The study was conducted following the ARRIVE guidelines v2.0, ensuring compliance with internationally accepted ethical standards for the care and use of animals in research. Fish were handled carefully to minimize stress during all experimental procedures, and no unnecessary harm was inflicted.

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Reprints and permissionsAbout this articleCite this articleSallam, G.R., Hamdy, M., El Basuini, M.F. et al. Optimizing salinity and stocking density for red tilapia in zero-water-exchange biofloc system: integrated performance, physiological, and economic assessment.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-28812-xDownload citationReceived: 12 August 2025Accepted: 12 November 2025Published: 18 December 2025DOI: https://doi.org/10.1038/s41598-025-28812-xShare this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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KeywordsBiofloc technology (BFT)Red tilapia (Oreochromis spp.)SalinityStocking densityGrowth performanceHematological biomarkersAntioxidant enzymesImmune response More

AbstractSundarbans region is an assemblage several delta formed in Bay of Bengal with largest concentration of mangrove which plays a crucial role in mitigating the impact of climate change with large ecosystem. The mangrove ecosystem demands further investigations to assess the vulnerability of vegetation. In context of present environmental change, the existing vegetation of Sundarbans is threatened by natural and human induced factors. This study incorporated these issues by analysing the vulnerability of mangrove forest in Indian Sundarbans deltaic region. To assess the vegetation condition, various vegetation indices are used including Normalised Difference Vegetation Index (NDVI), Transformed Normalised Difference Vegetation Index (TNDVI), Green Chlorophyll Index (GCI), Chlorophyll Vegetation Index (CVI), Soil Adjusted Vegetation Index (SAVI), and Atmospherically Resistant Vegetation Index (ARVI) etc. These indices are calculated using remote sensing satellite data of 2010 and 2020. Vulnerability has been assessed through vegetation health assessment by spatial modelling with the data from aforesaid vegetation indices. The result shows that specific regions have experienced an increase in stressed vegetation condition accompanied by the problems such as waterlogging and expanding areas under aquaculture. Furthermore the area under healthy vegetation has significantly decreased between 2010 and 2020.

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Data availability

Remote Sensing Satellite data has been used in this study for the year 2010 and 2020. The data for different bands (Blue, Green, Red and NIR) of Landsat 5 TM and Landsat 8 OLI have been collected from USGS (https://earthexplorer.usgs.gov/) for the month of December for both the years with 30 m spatial resolution (Table 1). The satellite imageries of different bands have been analyzed with QGIS Software. The data further has been modified by different vegetation indices and vegetation health assessment with spatial modeling. For the validation of the results from the analysis of satellite imageries, this study has also incorporated Google Earth historical images of specific locations along with some field visits.
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Amlan Ghosh or Padmaja Mondal.Ethics declarations

Competing interests
The authors declare no competing interests.

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Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Reprints and permissionsAbout this articleCite this articleGhosh, A., Mondal, P. Analysing the vulnerability of mangrove forest by vegetation health assessment: a study of Indian sundarbans deltaic region.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-26905-1Download citationReceived: 13 June 2025Accepted: 30 October 2025Published: 18 December 2025DOI: https://doi.org/10.1038/s41598-025-26905-1Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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KeywordsMangrove vulnerabilityVegetation indicesVegetation health condition More

AbstractMonitoring microclimatic conditions in underground environments is crucial for understanding chemical and biological processes occurring in caves and their effect on archaeological, palaeontological, and palaeobotanical records. The Israel Cave Climate Project (ICCP) dataset provides high-resolution microclimatic data from twelve caves across three climate zones — Desert, Steppe, and Mediterranean — measured during 2019–2021 using a uniform protocol. All twelve are natural karstic caves containing diverse, rich, and typically multi-period archaeological records. Within each cave, hourly air temperature and relative humidity measurements were recorded over a year, and these data are presented here in full. The physical and speleological characteristics of the studied caves and the content and nature of their archaeological records are also detailed. The combined high-resolution datasets, incorporating speleological, climatological, and archaeological records, provide unparalleled raw data valuable for studying cave environments, particularly cave archaeology, site formation processes, and preservation and conservation of ancient material and bioarchaeological records.

Data availability

The dataset is available at Zenodo (https://doi.org/10.5281/ZENODO.17505739)51.
Code availability

While the netCDF data file51 can be operated and managed in any suitable software, we also offer the Shiny-based ICCP package built for R v. 4.5.2, that we wrote to overview the data file along with the comparative CHELSA data. The code is open-source and can be found either on Zenodo51, or GitHub repository.
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Micka Ullman or Mitya Kletzerman.Ethics declarations

Competing interests
The authors declare no competing interests.

Additional informationPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Reprints and permissionsAbout this articleCite this articleUllman, M., Kletzerman, M., Oron, A. et al. Year-round hourly temperature and humidity sensor readings from arid caves, Judean Desert, Israel.
Sci Data (2025). https://doi.org/10.1038/s41597-025-06420-8Download citationReceived: 09 June 2025Accepted: 03 December 2025Published: 17 December 2025DOI: https://doi.org/10.1038/s41597-025-06420-8Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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AbstractHigh-resolution data of individual trees are critical for advancing forest monitoring, inventory development, and ecological research. This dataset, BioDiv-3DTrees, comprises 4,952 individual tree point clouds of 19 species, captured using Terrestrial Laser Scanning (TLS) and Unmanned Aerial Vehicle Laser Scanning (ULS), along with 3,386 Quantitative Structure Models (QSMs) and graph representations of the 14 broadleafed species in the dataset. The trees were sampled across the three research areas of the Biodiversity Exploratories in Germany. Each tree is linked to an existing open-access forest inventory dataset, which includes species identity, diameter at breast height (DBH), and tree height. The dataset is suitable for various research applications, including biomass estimation, algorithm development, tree structure analysis, and data fusion with traditional inventory methods. All QSMs were generated using TreeQSM 2.4.1 and have been validated for tree height, diameter at breast height and crown projection area against their underlying point clouds to ensure consistency. The dataset provides a reliable and scalable resource for forest science and remote sensing communities.

Data availability

The complete dataset is available on GROdata (https://doi.org/10.25625/8PB1IF).
Code availability

The code of the used histogram-based outlier removal algorithm, as well as the code to reverse the coordinate normalization is available at the dataset’s GitLab repository (https://gitlab.gwdg.de/griese1/biodiv-3dtrees/). The repository also includes the code that creates and cleans up the graph representation.
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Download referencesAcknowledgementsWe thank Tim Geis, Muluken Bazezew, Tao Jiang and Hans Fuchs for their help during the data acquisition and point cloud post processing. We thank the authors of the forest inventory dataset Peter Schall, Christian Ammer, as well the data collector Andreas Parth for their work, which made it possible to add tree species labels to this dataset. We thank the managers of the three Exploratories, Max Müller, Robert Künast, Franca Marian, and all former managers for their work in maintaining the plot and project infrastructure; Victoria Grießmeier for giving support through the central office, Andreas Ostrowski for managing the central data base, and Markus Fischer, Eduard Linsenmair, Dominik Hessenmöller, Daniel Prati, Ingo Schöning, François Buscot, Ernst-Detlef Schulze, Wolfgang W. Weisser, and the late Elisabeth Kalko for their role in setting up the Biodiversity Exploratories project. We thank the administration of the Hainich national park, the UNESCO Biosphere Reserve Swabian Alb, and the UNESCO Biosphere Reserve Schorfheide-Chorin as well as all land owners for the excellent collaboration. Field work permits were issued by the responsible state environmental offices of Baden-Württemberg, Thüringen, and Brandenburg. The work has been partly funded by the DFG Priority Program 1374 “Biodiversity-Exploratories” (DFG project numbers 433273584 and 193957772). Funding for Nils Griese was provided by the German Research Foundation (DFG project number 496533645). This project has received funding from the European Research Council (ERC) under the European Union’s Horizon Europe research and innovation program (Grant agreement No. 101041669).FundingOpen Access funding enabled and organized by Projekt DEAL.Author informationAuthors and AffiliationsDepartment of Forest Inventory and Remote Sensing, University of Göttingen, Göttingen, GermanyNils Griese & Nils NölkeInstitute of Computer Science and Campus Institute Data Science, University of Göttingen, Göttingen, GermanyMartin RitzertAuthorsNils GrieseView author publicationsSearch author on:PubMed Google ScholarMartin RitzertView author publicationsSearch author on:PubMed Google ScholarNils NölkeView author publicationsSearch author on:PubMed Google ScholarContributionsN.G. collected the data. N.G. developed the methods described apart from the Graph derivation. M.R. derived the Graphs from the data and contributed to the data validation. N.N. supervised the project. All authors discussed the results and contributed to the final manuscript.Corresponding authorCorrespondence to
Nils Griese.Ethics declarations

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Reprints and permissionsAbout this articleCite this articleGriese, N., Ritzert, M. & Nölke, N. A large dataset of labelled single tree point clouds, QSMs and tree graphs.
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AbstractUrban rewilding is increasingly recognized as a nature-based solution for restoring biodiversity, mitigating climate risks, and strengthening urban resilience. Yet, empirical evidence on how rewilding is perceived and supported by both policymakers and the public—particularly in post-socialist contexts—remains scarce. This study investigates expert and community perspectives on urban rewilding in Poland through a mixed-method design: a nationwide survey of 32 municipal environmental officials and a visual preference survey with 1,000 residents of the coastal city of Sopot. Expert responses highlight strong conceptual support for rewilding’s ecological and social benefits, but also identify persistent concerns about institutional feasibility, funding, and integration into existing planning frameworks. Community results reveal consistent public endorsement of moderate rewilding, with more cautious acceptance of intensive ecological designs in highly symbolic civic spaces. Taken together, the findings suggest that urban rewilding in Central and Eastern Europe is both socially viable and ecologically desirable, but its successful implementation will depend on adaptive governance, participatory planning, and the strategic use of visual engagement tools to bridge policy ambition with public expectations.

Data availability

The data that support the findings of this study are openly available in the Figshare repository at [https://doi.org/10.6084/m9.figshare.27089560](https:/doi.org/10.6084/m9.figshare.27089560) .
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Giuseppe T. Cirella.Ethics declarations

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The authors declare no competing interests.

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Reprints and permissionsAbout this articleCite this articleCirella, G.T., Kempa, J., Paczoski, A. et al. Stakeholder perceptions and planning implications for urban rewilding as a nature-based solution in Poland.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-32655-xDownload citationReceived: 12 August 2025Accepted: 11 December 2025Published: 17 December 2025DOI: https://doi.org/10.1038/s41598-025-32655-xShare this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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KeywordsCircular land useCommunity perceptionsEnvironmental governancePublic spaceRewilding policySustainable urban planning More

AbstractThis study characterized a green-waste-derived biochar from date palms and ghaf trees and investigated its potential as a soil amendment with halotolerant Bacillus spp. to improve tomato seedling quality under dual stress of salinity and nutrient deficiency. Biochar was produced through pyrolysis at 450 °C and then characterized for yield, pH, electrical conductivity, proximate analysis, surface morphology, energy-dispersive X-ray spectroscopy, and heavy-metal content. Its effectiveness was tested both alone and in combination with a Bacillus sp. mix, using a completely randomized design with varying NPK fertilizer levels and saline irrigation. Tomato seedlings were evaluated 45 days after planting for various vegetative, morphological, physiological, and nutrient content indicators. Under normal conditions, applying biochar combined with a Bacillus mix at 0% NPK greatly enhanced all measured parameters, often exceeding values observed with 100% NPK fertilization. This approach was especially effective under saline irrigation, resulting in significant increases in morphological parameters (40–150%), physiological parameters (51–94%), and nutrient content (34–63%) compared to control plants that received 100% NPK. Additionally, this treatment resulted in a 42% decrease in sodium accumulation. Using the biochar with the Bacillus mix effectively replaces chemical fertilizers and enhances salinity tolerance, supporting sustainable farming through waste recycling and less dependence on fertilizers.

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The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.
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Munawwar Ali Khan.Ethics declarations

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KeywordsTomato seedlingsSustainabilityChemical fertilization
Bacillus mixSalinity More