Philippa Kaur

AbstractThe rare, vulnerable relict species Pterocarya fraxinifolia is among the last surviving tree species growing in small, scattered populations in the southern Caucasus region; P. fraxinifolia grows up to 1000 m in plain forests and is threatened by habitat loss and environmental changes. Here, we sequenced and annotated the chloroplast genome of P. fraxinifolia from Hyrcanian forests and compared it to the chloroplast genomes of five other Pterocarya species. The evolutionary relationships of P. fraxinifolia were subsequently evaluated using the chloroplast genomes and individual chloroplast loci. The chloroplast genome of P. fraxinifolia was 160,086 bp in length, comprising 128 genes and a typical quadripartite structure. A comparative analysis of the six Pterocarya species revealed limited nucleotide diversity and structural variations in genes. The bulk of the 68 loci identified by SSR analysis comprised A/T repeats. Codon bias analysis revealed strong purifying selection, with the ndhF gene showing the highest Ka/Ks ratio. Our phylogenetic analysis revealed Pterocarya as a sister to the genus Juglans and a distinct subclade within Pterocarya.

IntroductionRelict species have always excited evolutionary biologists and biogeographers who consider these species ‘living fossils’ or relics of prehistoric periods1,2. These species have great value as research models for the geographical distribution of intercontinental rifts and as species that ensure biodiversity and ecosystem balance. Relict species also provide relevant information about the adaptation of species to specific environmental changes, as well as the impact of climate change on the animal and plant kingdoms3.Hyrcanian forests are hotspots for biodiversity and are home to numerous relict species4, including 280 endemic and subendemic species5,6,7. The genus Pterocarya Kunth (Juglandaceae), commonly referred to as wingnuts, has a disjunct distribution in East Asia and the Caucasus region with its most recent common ancestor present 40 Ma8. Pterocarya comprises six species, which are classified into two sections, Pterocarya (P. fraxinifolia, P. hupehensis, P. stenoptera, and P. tonkinensis) and Platyptera (P. macroptera and P. rhoifolia), on the basis of the presence or absence of scales on the terminal buds9. P. fraxinifolia is the only species in western Asia10. The remaining species of Pterocarya occur in eastern Asia, such as China and Japan11,12,13,14,15. Recently, a series of studies have focused on the phylogeny, biogeography, population genetics, and landscape genetics of species in this genus14,15. However, resources regarding the chloroplast genome in this genus are insufficient, and more research is still needed. Pterocarya fraxinifolia is a deciduous tree that can reach 20–25 m in height and 1.8 m in trunk diameter and is wind-pollinated to produce wing-nut fruits12. This species is among the last surviving trees growing in small scattered populations in the southern Caucasus region, which includes northern Iran, Georgia, Armenia, Azerbaijan, and the Anatolian region in Turkey12,15. However, less than two decades ago, small populations were first recorded in western Iran in the provinces of Lorestan and Ilam in the Zagros Mountains16.The chloroplast genome is widely used in phylogenetic studies because of its relatively conserved structure17,18 and uniparental inheritance19,20,. Chloroplast genomes can provide important information about the adaptation of species to different environmental conditions21,22,23. Despite the slow evolutionary rates of chloroplast genomes, coding and noncoding regions are useful for the identification of closely related species24,25,26 and for detecting genome-scale evolutionary patterns. Comparisons of the structure and sequence of these regions across different species within a genus can reveal important evolutionary phenomena such as gene transfer, deletion, or duplication. Recently, with the continuous application of high-throughput sequencing techniques, chloroplast DNA sequences have become readily available13,27,28. However, there is no annotated chloroplast genome available for P. fraxinifolia, which hinders the understanding of the evolution of the chloroplast genome of this species from West Asia14,15.In this study, we aim to (1) assemble and annotate the chloroplast genome of the relict species P. fraxinifolia from Hyrcanian forests; (2) perform comparative genomics of the chloroplast genomes of six Pterocarya species; and (3) assess the systematic affinity of P. fraxinifolia using phylogenetic analysis of the assembled chloroplast genomes.Materials and methodsLeaf material for the P. fraxinifolia sample was collected from a wild population in Mazandaran, Iran (Fig. 1). The voucher samples were deposited at the Herbarium of the Nowshahr Botanical Garden (HNBG) under voucher number 12,876.Fig. 1(a) Fruits in pendulous form; (b) A mature tree; (c) Regeneration under tree canopy; (d) Seedling.Full size imageGenomic DNA was extracted using the CTAB method, and its quality and quantity were checked using a Qubit 2.0 and Agilent 2100 Bioanalyzer. Libraries were created and sequenced at Wuhan Benagen Tech Solutions Company Limited, Wuhan, China, using the DNBSEQ platform (paired-end 150 bp). SOAPnuke v1.3.0 was used to filter the raw data, yielding 20 GB of clean data29.Chloroplast genome assembly and annotationRaw reads were filtered using Trimmomatic v0.3930 with a quality cutoff of 15 in a 4-base sliding window; any reads that were less than 50 bp were removed, and the adapters were filtered out. The quality of the reads before and after trimming was tested using FASTQC v0.12.1. We used GetOrganelle31 v.1.7.7.0 for chloroplast genome assembly, with the embplant_pt database used as a reference and maximum extension rounds of 15 (-R). GetOrganelle produced two isomers of the whole chloroplast genome of P. fraxinifolia, and each genome had a distinct relative orientation for the small single-copy (SSC) region32. A Python script from GetOrganelle was used along with Bowtie2 v2.5.433 to determine the average read coverage throughout the chloroplast genome. GeSeq v2.0334 was used for the initial chloroplast genome annotation of P. fraxinifolia, and the output from GeSeq was imported into Geneious Prime 2025.0.3 for an additional annotation check via the “Transfer Annotation” function. Chloroplot35 was used to produce a circular representation of the plastome.Comparative analyses of the Chloroplast genomesBecause the flanking inverted repeat (IR) regions of the chloroplast genome often vary among species, we used CPJSdraw36 to compare the IR regions of the six species. We used CUSP from EMBOSS v6.6.0.0 to calculate relative synonymous codon usage (RSCU) for protein-coding genes of P. fraxinifolia. To identify simple sequence repetitions (SSR), we used a Perl script from the Microsatellite Identification tool (MISA)37. The settings were adjusted to ten, five, and four repeats for mononucleotides, dinucleotides, and trinucleotides, respectively. Forward, reverse, palindrome, and complementary sequences with a minimum repeat length of eight bp and a maximum computed repeat of 50% were analyzed using REPuter38. The complete chloroplast genome sequences of the six Pterocarya species were aligned with Fast Statistical Alignment v1.15.938 to perform the nucleotide diversity analysis. We used a Perl script (https://github.com/xul962464/perl-Pi-nucleotide-diversity) to estimate the nucleotide diversity (PI) with a sliding window analysis with a step size of 200 bp and a window length of 800 bp.The selection pressure on chloroplast protein-coding genes (CDSs) was evaluated by aligning the nonredundant genes from six species using MAFFT v7.52639. We ran ParaAT.pl v2.040 to compute synonymous substitution rates (Ks), nonsynonymous substitution rates (Ka), and Ka/Ks. Each CDS pair of one-to-one species combinations is used as a homolog with genetic code 11. We estimated Ka, Ks, and Ka/Ks among the six Pterocarya species with KaKs_Calculator v2.041.Phylogenetic analysisWe constructed a maximum likelihood (ML) phylogenetic tree to understand the relationships of Pterocarya species. Chloroplast genome sequences were acquired from GenBank for the other Pterocarya and related genera in the Juglandaceae family. The multiple sequence alignment contained a total of 21 taxa. We performed our phylogenetic analysis using the full chloroplast genome alignment, treating it as a standard coalescent gene41. The chloroplast genomes were aligned using Fast Statistical Alignment v1.15.942 and then trimmed with trimAL v1.543 with the following settings: -automated1 -res overlap 0.7, -seqoverlap 65. To overcome the alignment issues, we also employed TAPER v1.0.047 with the -m N -a N parameters.Using RAxML-NG v1.2.144, we constructed the GTR + G model and the ML tree with 500 bootstrap repetitions. The phylogenetic tree was rooted using Engelhardia roxburghiana Wall. as an outgroup. The tree was drawn using FigTree v1.4.4 (https://github.com/rambaut/figtree). To determine the genetic distance between the six Pterocarya species, the HKY85 model45 was used, and a phylogenetic network was generated using the NeighborNet approach in SplitsTree CE v6.0.046.ResultsChloroplast genome assembly and annotationThe total numbers of raw and trimmed reads for P. fraxinifolia in this study were 143,190,876 and 141,927,817 base pairs (bp), respectively. The number of matched mapped pairs across the chloroplast genome was 393.42 ± 82.15 (Fig. S1). The complete chloroplast genome of P. fraxinifolia has a typical quadripartite structure that is 160,086 bp in length with a large single-copy region (LSC) of 89,582 bp, a small single-copy region (SSC) of 18,398 bp, and a pair of inverted repeat regions (IRs) of 26,053 bp (Fig. 2). A total of 148 genes were annotated in the chloroplast genome of P. fraxinifolia, including 103 protein-coding genes, 37 transfer RNA (tRNA) genes, and eight ribosomal RNA (rRNA) genes (Table 1 and Table S1). The GC content of the chloroplast genome was 36.17%. The annotated complete chloroplast genome of P. fraxinifolia was deposited in GenBank (accession number PV791734).Fig. 2Schematic map of overall features of the chloroplast genome of P. fraxinifolia. From the center outward, the first track shows the small single-copy (SSC), inverted repeat (IRa and IRb), and large single-copy (LSC) regions. The GC content along the genome is plotted on the second track. The genes are shown on the third track. Genes are color-coded by their functional classification. The transcription directions for the inner and outer genes are clockwise and anticlockwise, respectively. The functional classification of the genes is shown in the bottom left corner.Full size imageTable 1 Summary of the genome of Pterocarya species.Full size tableComparative analyses of the Chloroplast genome and nucleotide diversityAccording to a comparative analysis of the chloroplast genomes of Pterocarya species, the locations of eight genes in the chloroplast maps differed among species. The rps19 gene starts at position zero of the LSC region for P. fraxinifolia, but its position has shifted three times into the IRb region in the others. However, in other species of Pterocarya, a small portion of the genes were located in the IRb region. The ndhF gene in P. fraxinifolia, P. stenoptera, P. macroptera, and P. rhoifolia is located inside the SSC and is 2226 bp in length, whereas in P. tonkinensis and P. hupehensis, it spans 69 and 145 bp, respectively, into the IRb region (Fig. 3a).Fig. 3(A) Comparisons of LSC, SSC, and IR region boundaries among six Pterocarya species; (B) Nucleotide diversity (π) of CDS regions.Full size imageThe average nucleotide diversity (π) value was 0.001492, with a range of 0 to 0.00556 (Fig. 3B). The CDSs with the highest π values, which were greater than 0.0031, were ndhF, infA, ycf1, rps15, and matK. The ycf1 gene is found in the SSC area, whereas ndhF, infA, rps15, and matK are found in the LSC region. Nucleotide diversity decreased in both IR zones. Furthermore, 35 CDSs had a π value of zero among the six Pterocarya species, indicating that they were conserved (Table S1).Repeated sequence analysisThe six Pterocarya chloroplast genomes have an average of 72.6 SSR loci (Fig. 4A), with P. rhoifolia having the most SSR loci (85) of the six species (Table S2). A thorough examination of the chloroplast genome of P. fraxinifolia revealed 68 microsatellites, comprising 63 mononucleotides, four dinucleotides, and one trinucleotide simple sequence repeat. The five types of sequence repeat motifs—forward, reverse, complementary, palindromic, and tandem—are summarized in Table S3 and Fig. 4B. The analysis also revealed that the number of repetitive sequences differed across the six Pterocarya chloroplast genomes. Approximately 96.82% of the mononucleotide repeats found in P. fraxinifolia were classified as A/T (61), and 3.18% (2 repeats) were classified as C/G. In contrast, approximately 88.2% of the repeats found in P. rhoifolia were classified as A/T (75), and 3.52% (3 repeats) were classified as C/G (Fig. 4C). Dinucleotide repeats (6) for P. rhoifolia and (4) for P. fraxinifolia were the next most prevalent type of SSR. This investigation revealed no repeats of tetranucleotides, pentanucleotides, or hexanucleotides.Fig. 4Analysis of perfect simple sequence repeats (SSRs) in six Pterocarya chloroplast genomes. (A) The frequency of identified SSRs in large single-copy (LSC), inverted repeat (IR,) and small single-copy (SSC) regions; (B) The number of SSR types detected in the nine sequenced chloroplast genomes; (C) The frequency of identified SSR motifs in different repeat class types.Full size imageKa/Ks ratio and codon bias analysisStrong purifying selection and functional limitations are indicated by the very low Ka/Ks ratios found in most CDS regions among Pterocarya species (Fig. 5A). With the exception of P. tonkinensis and P. stenoptera, the highest Ka/Ks ratio was detected in the chloroplast NADH dehydrogenase F (ndhF) gene. The GC contents for the first, second, and third codon locations were 45.30%, 38.25%, and 30.36%, respectively, whereas the overall coding GC content was 37.97%. The greatest frequencies were 42.361 for the ATT codon and 37.605 for the GAA codon. The only two codons with an RSCU value of 1 were tryptophan (TGG) and methionine (ATG) (Fig. 5B). Every codon ending in A or T had an RSCU value greater than 0.5.Fig. 5Ka/Ks ratios of chloroplast protein-coding sequences across six Pterocarya species. (A) The X-axis is selected CDS with Ka/Ks ratios above 0.001. The Y-axis shows the mean Ka/Ks ratio for each gene. (B) Relative Synonymous Codon Usage (RSCU) value for each codon.Full size imagePhylogenetic analysisThe aligned multiple sequence alignment for the phylogenetic analysis consisted of 158,422 bp across 21 accessions, with 0.21% gaps and 96.19% invariant sites. The phylogenetic tree revealed Pterocarya as a sister genus to Juglans L. with 100% bootstrap support (Fig. 6A). The ML phylogenetic tree confirmed the monophyly of the genus Pterocarya with 100% bootstrap support with two subclades. P. fraxinifolia is a sister to a monophyletic subclade that include P. tonkinensis and P. macroptera and a sister to another subclade that includes P. rhoifolia, P. stenoptera, and P. hupehensis. The network analysis of the six Pterocarya species revealed a topology similar to that of the ML tree, with P. tonkinensis clustering with P. macroptera and P. rhoifolia clustering with P. stenoptera and P. hupehensis, while P. fraxinifolia branched off independently. In this study, the efficiency of two barcode regions, matK and ycf1, in the phylogeny of the genus Pterocarya was evaluated (Fig. 6B and C). The results revealed that the phylogenetic tree based on the matK region was identical to the phylogenetic tree derived from the complete chloroplast genome sequence. Pairwise distance analysis using the HKY85 method revealed that P. fraxinifolia is distantly related to Asiatic Pterocarya species (Fig. S2). The genetic distances between P. macroptera and P. tonkinensis (0.000259) and between P. stenoptera and P. hupehensis (0.000526) were the lowest, whereas the genetic distances between P. fraxinifolia and P. hupehensis (0.002153) and between P. fraxinifolia and P. tonkinensis (0.001928) were more than eightfold greater (Table S4). In the MatK dataset, P. fraxinifolia had three unique character states that differentiated it from other species of Pterocarya (Table S5).Fig. 6Comparison of three phylogenetic trees based on different chloroplast sequences: (a) Whole chloroplast genome, (b) matK gene region, and (c) ndhF gene region.Full size imageDiscussionChloroplast genomes are useful tools for studying the evolutionary relationships among species because of their preserved structure and uniparental inheritance (usually maternal in angiosperms47,48. Considering mechanisms of plant evolution49,50 and that the evolutionary history of chloroplasts is normally different from that of nuclear markers51,52, the use of genetic information from chloroplasts could reflect how seed dispersal affects the genetic makeup of wild populations and species.This study is the first to annotate the chloroplast genome of P. fraxinifolia and compare it to that of other species. We found that the positions of eight markers, namely, rps19, rpl2, ycf1 (IRa and IRb), ndhF, trnN, rpl2, and trnH, varied among the six Pterocarya chloroplast genomes. This implies that the expansion and contraction of the IR, LSC, and SSC areas are the primary sources of fluctuations in chloroplast genome size53,54. Between 68 and 85 SSRs were found among the chloroplast genomes of the six Pterocarya species. While the number of poly(G)/(C) repeats was shown to be greater in other angiosperms, the number of poly(A)/(T) repeats was significantly greater in Pterocarya.Five genes, ndhF, infA, ycf1, rps15 and matK, presented the greatest nucleotide variability (above 0.003). The matK and ycf1 genes have been suggested to function as barcode regions in plants55. The matK gene encodes the maturase protein, which facilitates the splicing of group II introns in several chloroplast genes and is considered a core barcode for land plants50,51. The ycf1 gene, which encodes the TIC214 protein that is essential for plant viability, is the second largest in the chloroplast genome and has recently been assessed for its DNA barcoding potential50,51,52, showing higher variability than the existing chloroplast candidate barcodes (such as rbcL, matK and trnH-psbA). Therefore, the ycf1 gene might be potentially useful as a DNA barcode for the Pterocarya genus56. With the exception of the matK region, none of the seven recommended barcode candidate genes in chloroplast genomes50 have the potential for barcoding of the Pterocarya genus because of a lack of nucleotide variation. Surprisingly, the accuracy of the matK region in resolving the phylogeny of the genus Pterocarya was identical to that of the complete chloroplast genome. Therefore, the matK gene alone is sufficient for reconstructing the phylogenetic relationships within the genus Pterocarya, eliminating the need for the additional time and financial resources required for whole-chloroplast-genome sequencing.The genus Pterocarya consists of six species and is closely related to Juglans in terms of pollen morphology, wood anatomy and molecular phylogenetics8,9. Our phylogenetic results confirm the sister relationship of Pterocarya to Juglans. Two sections for Pterocarya have been proposed on the basis of the presence or absence of scales on the terminal buds9,13,50. P. fraxinifolia, P. hupehensis, P. stenoptera, and P. tonkinensis belong to the section Pterocarya, while P. macroptera and P. rhoifolia belong to the section Platyptera. According to our chloroplast genome-based phylogeny, this suggested morphological classification is not supported, and the Caucasian wingnut (P. fraxinifolia) is in a distant subclade from the Chinese wingnut (P. stenoptera) and the Japanese wingnut (P. rhoifolia).The pairwise genetic distance between the Caucasian wingnut and other Asiatic Pterocarya species is greater. This distance might reflect the prolonged isolation and considerable geographic distance between Caucasian wingnut and East Asian species. Recent divergence time analyses based on fossil calibrations estimated the age of P. fraxinifolia between 9.4 and 18.4 Ma from the Miocene period and suggested the westward dispersal of Pterocarya from East Asia8. Wingnut fruit structure could facilitate the dispersal of these species by wind and water57. In this study, we collected P. fraxinifolia materials from its natural habitat in Hyrcanian forests. Our initial phylogenetic results revealed that the publicly available P. fraxinifolia in GenBank (NC046430) is not a P. fraxinifolia and is most likely a misidentified voucher that could be P. stenoptera (data not shown).Toward conservation of P. fraxinifolia
P. fraxinifolia is classified as a vulnerable relict species on the IUCN Red List12. Our phylogenetic tree, which was constructed on the basis of chloroplast genome analysis, indicates that this species is completely distinct from other species of the genus originating from China and Japan. This distinction might highlight the species’ unique evolutionary path and specialized ecological environments. Recent studies have shown that the potentially suitable ranges of P. fraxinifolia will increase under future climate scenarios8,58, and the rapid loss of its habitat, combined with growing threats such as drought and the destruction of riparian ecosystems in Hyrcanian forests, will result in its conservation an urgent priority.

Data availability

The annotated complete chloroplast genome of P. fraxinifolia was deposited in GenBank, under accession number PV791734.1.
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Download referencesAcknowledgementsMost data analysis was performed at the Smithsonian Institution Hydra cluster https://doi.org/10.25572/SIHPC. M.V. thanks the support of Rebecca B. Dikow, Matthew Kweskin, and Eric Schuettpelz.FundingThis work was supported by a grant from the Iranian National Science Foundation (INSF), project No 4024068.Author informationAuthors and AffiliationsFaculty of Natural Sciences, Department of Environment Science, Tarbiat Modares University, Tehran, IranSeyedeh Alemeh SabbaghFaculty of Natural Sciences, Department of Forestry, Tarbiat Modares University, Tehran, IranHamed YousefzadehRoyal Botanic Gardens Kew, Richmond, Surrey, UKMohammad VatanparastDepartment of Animal and Poultry Science, College of Aburaihan, University of Tehran, Tehran, IranMohammad Reza BakhtiarizadehDepartment of Biology and Botanic Garden, University of Fribourg, Chemin du Musée 10, Fribourg, CH-1700, SwitzerlandGregor KozlowskiNatural History Museum Fribourg, Chemin du Musée 6, Fribourg, CH-1700, SwitzerlandGregor KozlowskiEastern China Conservation Centre for Wild Endangered Plant Resources, Shanghai Chenshan Botanical Garden, Shanghai, 201602, ChinaGregor Kozlowski & Yi-Gang SongAuthorsSeyedeh Alemeh SabbaghView author publicationsSearch author on:PubMed Google ScholarHamed YousefzadehView author publicationsSearch author on:PubMed Google ScholarMohammad VatanparastView author publicationsSearch author on:PubMed Google ScholarMohammad Reza BakhtiarizadehView author publicationsSearch author on:PubMed Google ScholarGregor KozlowskiView author publicationsSearch author on:PubMed Google ScholarYi-Gang SongView author publicationsSearch author on:PubMed Google ScholarContributionsH. Y. conceived and designed this study. M. V. and S. A. S conducted a formal analysis. M. B. contributed to the analytical methods. S. A. S, H. Y., and M. V. wrote the original draft. G. K. and Y. G. S. edited the manuscript. All authors have read and agreed to the published version of the manuscript.Corresponding authorsCorrespondence to
Hamed Yousefzadeh, Mohammad Vatanparast or Yi-Gang Song.Ethics declarations

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The authors declare no competing interests.

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The plant material of P. fraxinifolia was collected from natural populations in northern Iran under a PhD research project approved by Tarbiat Modares University, the Ministry of Science, Research and Technology of Iran. According to national regulations, the collection of plant material for academic research within Iran does not require additional permits when conducted as part of an approved university project. All sampling was done in compliance with institutional and national guidelines. We fully acknowledge the importance of adhering to the IUCN Policy Statement on Research Involving Species at Risk of Extinction as well as the Convention on the Trade in Endangered Species of Wild Fauna and Flora (CITES). We are committed to ensuring that our research complies with these guidelines and supports the conservation of endangered species.

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Sci Rep 15, 44153 (2025). https://doi.org/10.1038/s41598-025-23028-5Download citationReceived: 17 June 2025Accepted: 03 October 2025Published: 19 December 2025Version of record: 19 December 2025DOI: https://doi.org/10.1038/s41598-025-23028-5Share 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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KeywordsComparative genomicsConservationHyrcanian forestsPlastome evolution More

AbstractCoyote (Canis latrans) presence in many North American cities evokes fear in some humans, driving demands for management action. With societal values shifting towards non-lethal coexistence practices, many wildlife managers turn to strategies like aversion conditioning, designed to increase coyotes’ fear of humans. Yet, scant knowledge exists about baseline fear behaviors (e.g., vigilance, alertness) in urban coyotes. This has implications for coexistence practices, as the motivation for coyotes’ behavior should underscore how managers respond. To explore urbanization effects on fear and behavior, we used remote cameras to monitor three coyote families during the pup-rearing season in urban, peri-urban, and rural sites in/near Calgary, Canada (2021–2022). We coded behaviors observed in adults and pups using 62 822 images. Rural adult coyotes were observed more around pups, while urban and peri-urban coyotes were observed more around pups that were playing, spent more time den-guarding, and showed higher alertness. This adaptive response in urban and peri-urban coyotes may force some coyotes into a behavioral trade-off (e.g., guarding pups vs. foraging), which could translate into more risky behaviors (e.g., consuming garbage). The elevated baseline fear in coyotes facing urban pressures suggests that coexistence practitioners must consider the risks of increasing fear during aversion conditioning.

IntroductionGlobal urban expansion has enmeshed many wildlife species in human landscapes. Examples include coyotes in Calgary1, wild boar (Sus scrofa) in Berlin2 and caracals (Caracal caracal) in Cape Town3. It has been argued that several wildlife species now complete their entire lifecycles in cities4, leading to increased reports of human-wildlife conflict5. Coyotes are at the forefront of this conflict in North America.Evidence shows coyotes have a plethora of adaptive responses that enable urban living. For instance, coyotes can alter their population size, prey selection, and temporal use of habitat6,7,8. This resilience however can keep coyotes closer to humans, increasing the risk of negative encounters9 (e.g., a coyote bites a pet), which can cause lasting financial and emotional damage for humans and their pets.Coyote behavior throughout their lifecycle is relatively stable and predictable10, which should make coexistence straightforward. However, coexistence remains challenged by mixed human perceptions and incomplete knowledge about coyote behavior and adaptive responses in cities11. In tandem, while lethal removal of coyotes remains a core response to conflict12, there is a growing societal demand for non-lethal coexistence strategies13. This push for non-lethal approaches stems from a widespread acceptance that lethal approaches are not an effective long-term solution to human-coyote conflict, may be ecologically damaging, and are inhumane when compared to alternative non-lethal methods (e.g., aversion conditioning)14,15. Moreover, without addressing the root cause of the conflict, which is most often human behavior, there is compelling evidence that the conflict will repeat with another coyote or species15,16.In the case of urban coyotes, the pup-rearing season (May to August) is a time when conflict with humans typically spikes10 as coyotes are defensive of their pups, especially toward humans with dogs (Canis lupus familiaris)17. Previous research related to the pup-rearing stage has explored behaviors such as boldness, exploration, and aggression in both captive18 and wild coyotes19. Other work has developed a generic ethogram to measure coyote behaviors20. To our knowledge, no research has compared activity patterns and behaviors specific to pup-rearing in urban coyote families to those in less disturbed settings. Yet, changes in adaptive pup-rearing behaviors that result from urban pressure could impact conflict. For instance, a reduction in play activity amongst pups, or between pups and adults due to a heightened need for adults to be vigilant could result in less socialization of pups. In turn, lower socialization could affect pup survival after dispersal21.Consequently, we believe there exists an opportunity to develop a baseline understanding of how urbanization affects activity and behavior of coyote families. Such understanding is important in and of itself but also may foster best practices in non-lethal conflict management practice. Presently, non-lethal coexistence programs may integrate aversion conditioning (AC) to respond to conflict. Some AC approaches used on carnivores (coyotes especially) are predicated on an assumed need to re-instil or heighten an animal’s fear response to humans12,22. In the case of coyotes, some managers have used these high-intensity AC approaches when coyote behavior is labelled as ‘bold,’ ‘aggressive,’ or a risk to human safety23. Yet, no studies appear to examine fear behavior in coyotes, nor any change in behaviors because of living under urbanization pressure. If such a behavioral study demonstrated that urbanization relates to greater vigilance and fear in adult coyotes, that could be a reason to re-evaluate the use of using fear-evoking AC.To understand whether fear in coyotes is affected by urbanization we must first address the question “What is natural fear in wild animals?” Here, we can borrow from the “landscape of fear” concept, in which prey species will adjust their behavior in response to the threat of predation24. This concept derives from fear ecology work in which the behavioral response of prey was compared between fear-driven systems and direct predation-driven systems25. The concept explains how the knowledge of predation risk impacts prey choices in space use, foraging, and vigilance behavior26. For example, black-tailed jackrabbits (Lepus californicus) and desert cottontails (Sylvilagus audobonii) adjust their movements and behaviors in response to fear of predators27. The landscape of fear concept also has been invoked in situations where humans play the role of the “super-predator,” such as with elk (Cervus canadensis)28 and marsupials29. Importantly though, while fear can be adaptive for reducing predation risk and increasing life expectancy in prey species30, it can come at a cost of foraging31. In turn, this can add food stress, which can increase conflict within the prey populations. Similarly, human activity may drive changes in top predator behavior, impacting the animals’ ability to regulate prey populations. In the case of coyotes – the top predator in many cities – additive fear in a landscape could lead to unchecked populations of small mammal species32 and larger mammals like deer (Odocoileus spp)32. This may lead to spill-over effects for humans, such as rodent infestations or greater deer-vehicle impacts33. Most critically, if coyotes spend more time in behaviors that arise out of fear (e.g., vigilance) at the expense of foraging, this could lead to opportunistic feeding on anthropogenic food sources, which may increase stress and conflict in coyotes34. Fear in urban-adapted wildlife has been previously studied in the foraging behavior of smaller, prey mammals, with lower levels of vigilance in treatments closer to urban areas but higher responses to fear stimuli in treatments in a peri-urban environment35. However, fear in non-foraging coyotes across an urban to rural gradient is not well understood.To explore whether urbanization affected adult coyote activity and behavior during the pup-rearing season, and specifically whether fear was higher in urban coyotes, we narrowed our analysis to the following general questions: (1) Were there changes in adult presence and fear-related behavior (i.e., den-guarding) around pups across rural to urban sites?; (2) Did the percent of time spent by adult coyotes on high alert during captured activity sequences increase across the rural to urban gradient?ResultsWe coded 81 442 images from camera traps (CTs), of which 62 822 captures showed coyotes (including adults and pups), across 923 total trap nights (402 from Campus [the urban site], 188 from Spyhill [the peri-urban site], and 333 from WA Ranches [the rural site]). We used a selection of the coded CT photos that were isolated to the pup-rearing season in 2022, totalling 15 000 captures from Campus, 15 108 captures from Spyhill, and 14 808 captures from WA Ranches. Amongst the latter subset, 21 386 contained adults, 31 663 contained pups, and 8 513 had both pups and adults present. We converted the CT photos into 4 556 sequences of activity across the entire sample area, 2 021 of which were used for proportional analysis of behavior Fig 1.Fig. 1Study site locations within and around the city of Calgary, Alberta. The arrow indicates the direction of the gradient of urban to rural.Full size imageAdult Coyote presence and behavior around pupsIn our first set of comparisons, we examined adult behavior and presence around pups and pup play (Fig. 2). We observed adult attendance to pups in behaviors such as interacting, nursing, and guarding (Table 1). While pups were attended by adults in ~ 27% of photos across all three sites, we found significant differences between adult presence around pups by site (X2 = 37.717, P = 6.454e-09, df = 2), adult presence around pups playing by site (X2 = 137.63, P < 2.2e-16, df = 2), and den guarding around pups by site (X2 = 352.2, P < 2.2e-16, df = 2). Using Pearson residuals, we investigated which frequencies deviated the most from what would be expected if there was no difference between sites. We found significantly more adult presence around pups at WA Ranches (i.e., rural). We observed significantly greater adult presence around pups that were playing and adult guarding at Campus (i.e., urban).Fig. 2Proportions of photos of adult presence and behavior around pups. Proportions were determined as the number of photos withing a subset of photos of pups or of pups playing that display the behavior of interest (i.e. guarding) over the total number of photos within the subset. Stars indicate significant contributions to deviance from independence, as determined by Pearson’s residuals (P < 0.05).Full size imageTable 1 Description of all Coyote behaviors captured. Assessment of behaviors was based on previous behavioral studies20,55,56.Full size tableAdult Coyote alertness by site and conditional variablesWe compared the percent of activity time spent by adults on high alert by site including other conditional variables (e.g., pup presence, time of day, novel object presence). The mean proportion of images showing high alert behavior relative to not high alert behavior was 12.8% across all three sites during the spring/summer sample period. At Campus, the mean proportion of high alert behavior was 16.7%, at Spyhill it was 14.3%, and at WA Ranches it was 4.8%. The results of our zero-inflated binomial mixed-effects model of the percentage of high alert behavior per activity sequence, including all independent variables with significant interactions with each other, are presented in Table 2. Note that for novel object presence, only six out of 2 021 image sequences captured a novel object.Table 2 Results of the zero-inflated binomial mixed-effects model on the proportion of high alert behavior by sequence. Data come from the May-August 2022 sample period (n = 2 021).Full size tableDue to the high number of pairwise interactions between independent variables in the model, the post-hoc Tukey analysis was Sidak-adjusted for the comparison of means36. The variation in these means of the proportions of high alert behavior per sequence by site, time of day, and pup presence is presented in Fig. 3. The highest estimated marginal means for the proportion of high alert behavior per sequence occurred at the Campus site in the daytime with pups present, while the lowest occurrence of high alert behavior occurred at the WA Ranches site at twilight with pups present. Significant differences included higher marginal means for alertness at Campus than WA Ranches at twilight without pups present (Z ratio = 2.638, P = 0.0227, df = inf) and at any time of day with pups present (Day: Z ratio = 4.849, P < 0.0001, df = inf; Night: Z ratio = 3.036, P = 0.0068, df = inf; Twilight: Z ratio = 4.499, P < 0.0001, df = inf), and higher marginal means for alertness at Spyhill than WA Ranches in the daytime with pups present (Z ratio = 2.473, P < 0.0357, df = inf) and at twilight with pups present (Z ratio = 3.182, P < 0.0042, df = inf). Degrees of freedom are labeled as infinite using the emmeans package36 as estimates were compared against the standard normal distribution. Fig. 3Pairwise comparison of marginal means from the zero-inflated binomial mixed effects model on the proportion of high alert behavior per sequence. The interaction term effects are shown for the relationship between adult coyote vigilance and study site, pup presence, and time of day. Boxes indicate the marginal mean while the error bars indicate the 95% confidence interval of the marginal mean. The marginal means were determined using the emmeans package36 while the visual was created using the ggplot2 package63.Full size imageDiscussionCoyotes have a high investment in their pups, as seen by the frequent attendance of them by both parents37 and the contribution of non-breeding helpers to pup-rearing38. At the WA Ranches (i.e., rural) site, we observed slightly more adult presence with the pups, which could be a positive indicator for pup survival, as reported previously by Bekoff and Wells38. However, when engaged in play activities, we observed that the pups were left unsupervised significantly more at WA Ranches and Spyhill (i.e., peri-urban) when compared to Campus (i.e., urban). We also observed that guarding behavior by adults was significantly more common amongst the Campus coyotes. One explanation is that the Campus coyote family perceived this urban homesite to be riskier for pups due to being embedded in high density urban matrix. When pups engaged in play, we observed routinely that they spread quickly outside the visual range of adult coyotes. In an urban site, the higher incidence of potentially dangerous novel objects may demand higher levels of pup supervision and guarding by adults.The significantly higher incidence of guarding pups at Campus could indicate that urban coyotes may experience more concern and fear for themselves and their offspring. To mitigate the risks to pups, one might expect that adult coyotes may have to adjust their activity budgets. Particularly concerning would be if increased time spent in pup-supervision leads to adult coyotes making trade-offs with essential behaviors like hunting. The latter was observed with African wild dogs (Lycaon pictus), where an increase in pup-guarding, though effective in decreasing pup mortality, negatively impacted hunting as the wild dog groups were constrained by the absence of pup-guarders to assist with hunts39. Arguably, we are seeing evidence that suggests urban coyotes (at least in the Campus focal family) may be redirecting time to protect pups.Another potential indicator of a heightened fear response is seen in vigilance behavior. We measured vigilance as high alert behavior and den-guarding. Vigilance has been associated with fear hormonally as it can be paired with the release of fear-response hormones40. Vigilance has been observed to increase in other species around human presence28,32,41, but it has not been studied before in this manner in wild coyotes. In previous studies, vigilance has been measured in behavioral experiments of urban coyotes19 and in understanding the response of captive coyotes to human activity42. We found that adult coyotes spent significantly more time displaying vigilance behaviors (high alertness, den-guarding) at the urban site compared to the peri-urban and rural sites. For instance, in the rural site (WA Ranches), where coyotes share the landscape with larger predators caught on camera like grizzly bears (Ursus arctos) and cougars (Puma concolor), rural coyotes only spent ~ 5% of their time on high alert, while the urban coyotes spent ~ 17% of their time on high alert (as captured in the data). Given the potential for a trade-off to occur between fear and foraging31, the significant increase in time spent in vigilance (assuming it is fear related) by urban coyotes may drive them to procure opportunistic easier anthropogenic food sources like garbage, which can increase human-coyote conflict34,43. This behavioral shift may in turn be paired with an avoidance of certain high-human use areas, potentially resulting in an increase in prey animal presence, as has been suggested in the “human-shield hypothesis”45.In grouping all three coyote families, we also found that alertness was higher overall in the presence of pups. This would be expected, given demonstrated parental investment by coyote parents in their young37. However, examining WA Ranches on its own, adult alertness was significantly lower in the presence of pups than the absence of pups. This may suggest that these rural coyotes perceive less risk and therefore experience a lower baseline level of fear than their urban cousins. Certainly, the rural site is protected from hunting, trapping, and poisoning (within the ranch) and very few people enter the site, which would create a sense of security from humans.When examining the relationship between time of day and high alertness, we found a significant increase in alertness during the twilight period. We observed more high alertness overall in the twilight period with no significant variation between sites but less high alertness in the presence of pups in the twilight and night periods, suggesting there is heightened concern for pups in the daylight. Previous urban coyote studies have shown a decrease in coyote activity in the twilight period corresponding with higher human activity, suggesting coyotes use an avoidance technique at this time of day45. As the coyotes in this study were viewed at homesites where they can avoid humans, the heightened alertness may be another response to an increase in human activity.Examining the relationship between the length of an activity sequence (as determined by the number of images of coyotes in the sequence) and high alertness, there was a slight but significant negative effect of more photos. This may relate to longer sequences having more behaviors visible, which could offset the overall presence of high alertness; alternatively, a short sequence of photos could have three photos all of which display high alertness making for a much higher proportion.Exploring the relationship between the maximum number of adults in a sequence and high alertness, there was a significant decrease in high alertness with more adults. More adults in the sequence meant a lower proportion of time spent on high alert which corroborates the hypothesis of the negative relationship between vigilance and group size seen among other species46.Finally, in our investigation of how the presence of a novel object related to high alertness, we found a significant negative effect. That said, we rarely captured novel objects in the photos (only six out of 2 021 sequences featured novel objects) and this result may have occurred out of chance as the few observations happened to occur when coyotes were on lower alert. Novel objects in the study were sporadically occurring objects observed to have drifted into view of the cameras. Novel objects also typically occurred in the urban site where coyotes may be more adjusted to the presence of such things and have less of a response. Urban coyotes have also been observed to be more investigative in the presence of novel objects19, so this data could simply be a further indication of this behavior. This will be an important area of future research to understand whether the likelihood of encountering many more novel objects in an urban ecosystem means that adult coyotes spend even more time supervising pups at the expense of other vital behaviors.Our study provides evidence for a heightened state of fear-related behavior among urban relative to peri-urban and rural coyote families. As noted, this can come at a cost of adaptive behaviors, as seen in other species32,39. Because fear is associated with stress and stress can lead to riskier behavior in coyotes and conflict with humans23, understanding that urban coyotes exhibit significantly more fear daily is important to coexistence practices. In particular, non-lethal coyote management strategies that implement fear-based AC methods have not to our knowledge evaluated what baseline levels of fear exist, or whether adding stress or fear may have compounding negative impacts on coexistence. In our opinion, coexistence programs should consider the efficacy and ethics of programs that ‘stack’ fear upon fear47. We observed significantly higher rates of vigilance behavior related to pup guarding, which suggests coyotes may be more fearful during that time. Therefore, there is a risk that using fear-based AC with urban coyotes may create a condition called “trigger stacking”48,49. In domestic dogs, trigger stacking is known to exacerbate reactivity rather than change or de-escalate behavior48. If coyotes might become more reactive due to AC-caused trigger stacking that would be counterintuitive to the goals of coexistence.While urban coyotes have been characterized as bolder and more exploratory, our results highlight fearfulness (i.e., guarding, pup-attendance, vigilance) as another key element motivating their behavior. If more fear leads to restricting mobility and access to natural foods, this could drive consumption of more easily accessible anthropogenic food, which has historically increased human-coyote conflict. While we examined only three families, one per level of urbanization, the magnitude of difference in behaviors between the sites highlights the need to better understand the baseline ecology and behavioral adaptations of coyotes before applying untested invasive coexistence management techniques. Further study could benefit from exploring behaviors outside of the pup-rearing season and look at direct impacts of such management on coyote and other urban wildlife behaviors.MethodsStudy areaOur research was conducted at three sites in and around the City of Calgary, Alberta, Canada (Fig. 1). The study sites rest within the Foothills Parkland Natural Subregions, a hilly area with a mixture of grasslands, shrublands, and forest that lies between the prairies to the east and the foothills to the west49. The region is home to a diverse array of plant species and several mammal species from hares (Lepus spp.) to coyotes to bears (Ursus spp.). The region has a relatively dry and cold climate, with a mean temperature of 4.3 °C ranging from − 30 °C to + 30 °C and a mean precipitation of 417 mm of rain and 100 cm of snow50. The research reported was covered by Animal Care Certificate number AC20-0160 issued by the University of Calgary Life and Environment Sciences Animal Care Committee for the project “UC Campus Coyote Ecology and Coexistence” on February 23, 2021. The experiments reported in this manuscript were minimally invasive and conducted in accordance with Animal Care Committee guidelines. Clinical trial number: not applicable.Each study site represented a unique level of human use, with the Campus site (i.e., urban) located within the City of Calgary and surrounded by residential area, the Spyhill site (i.e., peri-urban) located on the northwest edge of the city, at the juncture of urban residential and agricultural land use, and the WA Ranches (i.e., rural) site located approximately 30 km northwest of Calgary, surrounded by ranch land and natural areas. Each site was the core area of a unique coyote family comprised of a breeding pair, one ‘helper,’ and an annual litter of three to eight pups (SM Alexander, unpublished data). Sites were approximately 30 km apart, which is outside the limits of a resident home range51 and reduced the chances of detecting the same coyote at different sites. Our visual records showed that there was no observed overlap of individuals from different families, even though as Gehrt et al.53 note, this distance can easily allow transient coyotes to cross amongst sites. AC by agencies was only reported to have occurred within the core habitat of Campus coyotes.Camera-Trap methodsWe deployed 27 camera traps (CTs), divided equally by site (21 Reconyx Hyperfire 2 and 6 Cabela’s Outfitter Gen 3). Each camera captured three images at a detected motion, with images continuously captured if motion continued, resulting in a sequence of activity. Images were captured at a rate of one image per second. The resolution of images was 16 megapixels for the Cabela’s cameras and two megapixels for the Reconyx cameras. No differences were noted in the ability of either camera to capture coyote activity, though both would occasionally cease functioning in extremely cold temperatures (i.e., less than − 20 °C). Cameras were set to operate at all times of day and night, only stopping if the batteries died or the SD card storage was filled, but the frequency of camera checks allowed them to run for the most part continuously. The time and date were set on the camera at its placement. Cameras were placed on trees or fences at 30–60 cm off the ground to maximize the field of view for capturing coyotes. Instead of using bait, the cameras were pointed toward known high-coyote-activity areas, such as around the home sites and high-use pathways, as determined from field surveying. The camera locations were purposefully selected as the goal was to capture the highest amount of coyote activity. While a random selection may have captured a more natural range of coyote behaviors throughout their territory, the focus here was specific behaviors at high-use areas within the homesite and comparing these behaviors between sites.All cameras were in place year-round as part of long-term monitoring of coyotes, but for this project we screened focal images from CTs at post-natal homesites only for the period of May/June 2021 and January to August 2022 from Campus (48 290 images) and May to August 2022 from Spyhill (15 893 images) and WA Ranches (17 259). We focused on the pup-rearing season to capture and compare behavior and activity budgets when we were most likely to see interactions amongst coyote family members at the three sites of interest. We divided the CT photos of coyotes into sequences of activity, using a separation of one minute between a coyote disappearing and reappearing on the screen. Within these sequences we could then determine the proportion of time spent displaying any one behavior (i.e., calculated as the number of captures displaying the behavior over the total captures in the sequence). While we used shorter image capture intervals than other studies, such as 5 min in Wooster et al.54 and 10 min in Marion et al.42. Our method suited the resolution of our research question; We had no need to try to separate sequences by unique individuals, as known individuals frequented our same site and our exceptionally large photo counts increased replicates when compared to other noted studies.We developed an ethogram in reference to our CT data and classified fear-related behaviors for coyotes as alertness and pup-rearing behaviors (e.g., den-guarding). The ethogram was founded on previous behavioral research on coyotes54, red foxes55, and felids56 and honed to the study animals during initial reviews of the photos. We describe all behaviors documented and how the behaviors were coded in Table 1. For each CT photo sequence of activity, we documented the following: proportion of time spent in the behavior, site, time of day, pup presence, number of photos, maximum number of adults, novel object presence, date, and camera location. One observer classified all photos into their behavioral categories.Statistical analysesTo determine whether coyote behavior around pups varied by category (type) amongst study sites, we performed multiple chi-squared tests57. For this analysis, we only used images that showed pups to be present (i.e., if photos were adults only, we removed them from this analysis). We compared the frequency of the photos showing the following behaviors across sites: pup photos with and without adult presence, pup play photos with and without adult presence, and pup photos with adults demonstrating a den-guarding posture or not. We used Pearson residuals to determine which behaviors differed significantly by site, visualizing the differences using the vcd package58.To explore whether fear-related behaviors differed across human disturbance categories, we developed a generalized linear model and compared the proportion of photos per sequence demonstrating high alertness. We included variables previously identified to be relevant to behavior: site, pup presence, time of day, sequence length, maximum adult presence, and novel object presence37. We also were interested in how pup presence and time of day might interact with site, so we included an interaction term for those three independent variables. Data were proportional and thus followed a binomial distribution57. Given many sequences of activity had proportions of zero for high alert behavior, we used a zero-inflated model using the glmmTMB package59. Since our data included multiple images coming from the same cameras and from each site, we followed Zuur and Ieno60, using a mixed effects model with a random effect of camera nested within site to account for this level of variation. Following from the previous, our final model was a zero-inflated binomial mixed effects model, described as:proportion of high alert behavior per sequence ~ site*time of day*pup presence + number of photos in the sequence + maximum number of adults in the sequence + novel object presence in the sequence.We performed a post-hoc Tukey analysis of multiple comparisons61 using emmeans package36 to identify pair-wise interactions between model terms. All statistical analyses were performed using R version 3.4.263.

Data availability

Data is provided within the manuscript or supplementary information files.
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AbstractEradicating invasive species and maintaining their populations at acceptable densities is both costly and challenging in marine environments, primarily due to the open water connectivity between culled and non-culled areas. This research aims to evaluate the short- and long-term effects of culling invasive species, considering the invasive gastropod Drupella rugosa (Born, 1778) from the coral reefs of Koh Tao (Gulf of Thailand) as a case study. Ecological, logistical, and behavioural factors that influenced the removal efforts were identified, highlighting key components that can inform future strategies aimed at managing outbreak events. Specific objectives included: (1) estimating gastropod densities and to study the behaviour of D. rugosa on Acropora-dominated reefs; (2) assessing short-term effects of D. rugosa removal by monitoring the fate of grazed corals; (3) examining the long-term impact of culling by analysing data from a removal campaign spanning over a decade, including an evaluation of the effort in terms of time and diver involvement. The relationship between damselfish and the feeding activity of corallivorous gastropods was also investigated. A key finding of this study is that poorly planned culling is ineffective in controlling outbreaks of invasive species such as those belonging to the genus Drupella. Long-term data from culling campaigns conducted between 2010 and 2024 revealed that the number of removed specimens remained relatively constant, despite significant differences in effort. This disparity underscores the lack of strategic coordination in the implementation of removal activities. Following a critical comparison with cases reported in the literature, common issues and transferable strategies were identified and thoroughly analyzed. Directions for management were provided, with the understanding that future actions should be grounded in a thorough knowledge of the species’ ecological traits, the biotic and abiotic drivers of outbreak events, a quantitative assessment of its impact on Acropora reefs, and integration into with well-established international removal and prevention programs.

Data availability

Data available on request by contacting both the correspondent Author ([email protected]) and the New Heaven Reef Conservation Program ([email protected]).
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Download referencesAcknowledgementsWe would like to thank New Heaven Reef Conservation and above all Kirsty Magson, the program manager, for providing part of the data and making this study possible. We are also deeply grateful to all the volunteers who, over the years, have contributed to the collection of data and to the implementation of the research.FundingNo Funding.Author informationAuthors and AffiliationsNew Heaven Reef Conservation Program, 48 Moo 3, Chalok Ban Kao, Koh Tao, 84360, ThailandBaruffaldi MatildeDepartment of Life and Environmental Sciences (DiSVA), Università Politecnica delle Marche, Via Brecce Bianche s.n.c, 60131, Ancona, ItalyBaruffaldi Matilde, Roveta Camilla, Tonolini Rosita, Pulido Mantas Torcuato & Cristina Gioia Di CamilloNational Biodiversity Future Center (NBFC), Piazza Marina 61, 90133, Palermo, ItalyRoveta Camilla, Pulido Mantas Torcuato & Cristina Gioia Di CamilloConsorzio Nazionale Interuniversitario per le Scienze del Mare (CoNISMa), Piazzale Flaminio 9, 00196, Rome, ItalyCristina Gioia Di CamilloAuthorsBaruffaldi MatildeView author publicationsSearch author on:PubMed Google ScholarRoveta CamillaView author publicationsSearch author on:PubMed Google ScholarTonolini RositaView author publicationsSearch author on:PubMed Google ScholarPulido Mantas TorcuatoView author publicationsSearch author on:PubMed Google ScholarCristina Gioia Di CamilloView author publicationsSearch author on:PubMed Google ScholarContributionsDi Camillo CG and Baruffaldi M contributed to the study conception and design. Baruffaldi M and Di Camillo CG wrote the first draft of the paper and provided figures. Baruffaldi M and Tonolini R performed samplings and collected data. Roveta C, Baruffaldi M, Pulido Mantas T analyzed data. All authors contributed to improve and revise the manuscript.Corresponding authorCorrespondence to
Cristina Gioia Di Camillo.Ethics declarations

Competing interests
The authors declare no competing interests.

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Reprints and permissionsAbout this articleCite this articleMatilde, B., Camilla, R., Rosita, T. et al. Short- and long-term effects of culling invasive corallivorous gastropods.
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KeywordsBiological invasionsRemovalMuricidaeControl of pestsSurge More

AbstractFreshwater mussels (Unionidae) depend on specific fish hosts to complete their life cycle. Glochidia, their parasitic larvae, must attach to the gills or fins of suitable fish species to metamorphose. However, non-host fish may intercept glochidia, reducing larval availability for competent hosts—a phenomenon known as the dilution effect. We investigated this mechanism in a natural population of the endangered mussel Unio crassus, focusing on the interaction between the dominating host Phoxinus phoxinus and the non-host Gobio gobio. Field surveys across three separate reaches of the Warkocz River (2015–2016) and a controlled infestation experiment demonstrated that G. gobio removes a substantial proportion of glochidia without supporting their metamorphosis. Co-occurrence analysis showed a negative relation between infestation levels of G. gobio vs. P. phoxinus, with a significant interaction modulated by U. crassus density. At low mussel densities, the impact of G. gobio on parasitic success was strongest. Gobio gobio was recorded at 90% of the known U. crassus localities in Poland, and in all of these sites it formed a dominant component of the fish assemblage. Our findings provide direct evidence of a context-dependent dilution effect and highlight the importance of fish community composition and behaviour in conservation of unionid mussels. The presence of non-host fish in habitats with low mussel abundance may undermine recruitment and increase extinction risk in fragmented populations.

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Tadeusz A. Zając.Ethics declarations

Competing interests
The authors declare no competing interests.

Additional informationPublisher’s noteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Rights and permissions
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Reprints and permissionsAbout this articleCite this articleDołęga, J., Zając, T.A., Ćmiel, A. et al. Evidence for dilution effect by Gobio gobio, a dead-end host in the Unio crassus–Cyprinidae coevolutionary system.
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AbstractPteridium aquilinum is a medicinally important fern with a limited range in northern Iran, increasingly threatened by climate change. Using morphological, genetic, and environmental data, we assessed differentiation, adaptive capacity, and vulnerability across 11 populations. Factor analysis of mixed data (FAMD) identified stipe indument, pinnule shape, and pinnae number as key traits distinguishing populations. Redundancy and association analyses (RDA/CCA) revealed strong links between both morphological and genetic variation and climatic gradients, particularly temperature and humidity, indicating local adaptation. Several SCoT loci were detected as adaptive outliers. Spatial PCA showed that variation is shaped by both global and local spatial factors, forming clines and local variants. Populations varied in sensitivity and adaptive capacity; populations 2, 3, 7, and 8 exhibited the lowest adaptive indices and highest vulnerability. Connectivity modeling suggested that while some populations (e.g., 2, 4, and 6) may maintain or slightly improve connectivity, others risk isolation under future climates. Structural equation modeling (SEM) indicated a positive genetic contribution to adaptation, while differential equation modeling (DEM) predicted logistic growth with temporary instability and genetic decline in vulnerable groups. Overall, findings highlight spatially uneven adaptive responses and recommend targeted conservation through connectivity enhancement, assisted gene flow, and ex-situ preservation of adaptive genotypes.

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Data availability

The datasets used and/ or analyzed during the current study available from the corresponding author on reasonable request.
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Download referencesAuthor informationAuthors and AffiliationsDepartment of Plant Sciences and Biotechnology, Faculty of Life Sciences & Biotechnology, Shahid Beheshti University, Tehran, IranMasoud Sheidai, Maedeh Alaeifar & Fahimeh KoohdarAuthorsMasoud SheidaiView author publicationsSearch author on:PubMed Google ScholarMaedeh AlaeifarView author publicationsSearch author on:PubMed Google ScholarFahimeh KoohdarView author publicationsSearch author on:PubMed Google ScholarContributionsM. Sh. and F. K. Conceptualization of the project, designed the research, analysis and wrote the manuscript and M. A. collected the samples and lab work. All authors reviewed the manuscript.Corresponding authorCorrespondence to
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Reprints and permissionsAbout this articleCite this articleSheidai, M., Alaeifar, M. & Koohdar, F. Computational analysis and modeling of climate impact on Pteridium aquilinum (L.) populations.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-33035-1Download citationReceived: 24 August 2025Accepted: 15 December 2025Published: 19 December 2025DOI: https://doi.org/10.1038/s41598-025-33035-1Share 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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KeywordsMultivariate statisticsStatistical modellingSCoT markersMorphometricGenetic markers More

Abstract

Nitrogen (N) fertilization improves crop productivity. However, the long-term effects of N application on methane (CH4) emissions in drained peat soils, particularly under different hydrological conditions, remain poorly understood. Accurate quantification of CH4 emissions from peatlands is essential for assessing carbon losses and formulating effective climate change mitigation strategies. This study was conducted to investigate the impact of N fertilization on CH4 emissions and identify the main factors influencing CH4 emissions from drained tropical peatlands. This study was conducted on an oil palm plantation in Sarawak, Malaysia, a randomized block design included four N fertilizer treatments: Control (0 kg N ha− 1 yr− 1) (T1); low (31.1 kg N ha⁻¹ yr⁻¹) (T2), moderate (62.2 kg N ha⁻¹ yr⁻¹) (T3), and high (124.3 kg N ha⁻¹ yr⁻¹) (T4). Soil CH4 fluxes showed no statistically significant differences between treatments or across years, with emissions ranging from − 163.6 to 320.7 µg C m− 2 hr− 1 at T1, -86.7 to 285.8 µg C m− 2 hr− 1 at T2, -131.6 to 274.1 µg C m− 2 hr− 1 at T3 and − 125.7 to 185.9 µg C m− 2 hr− 1 at T4 (p > 0.05). Although ammonium sulfate fertilization did not significantly alter CH4 emissions, its pronounced acidifying effect on soil pH, particularly at application rates above 62.2 kg N ha⁻¹ yr⁻¹ along with elevated sulfate (SO42−) inputs and nitrogen pools exceeding the critical threshold (> 400 ppm), likely suppressed methanogenic activity and constrained soil organic matter decomposition. Water-filled pore space (WFPS) influenced CH4 emissions more than groundwater level (GWL), with the low GWL at the site limiting its impact. Increased WFPS (60–80%) reduced nitrate (NO3−) through enhanced denitrification, lowering its inhibition on CH4 production and thus increasing emissions. This study highlights the key role of soil moisture and nitrogen cycling in regulating CH4 emissions in peatland.

Data availability

The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.
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Download referencesAcknowledgementsWe would like to express our sincere gratitude for the generous support from the Sarawak State Government and the Federal Government of Malaysia for making this research possible. We would also like to express our sincere appreciation to the dedicated staff of the Sarawak Tropical Peat Research Institute (TROPI) for their invaluable technical assistance and unwavering support throughout every phase of this study, including the challenging fieldwork. Their expertise and dedication contributed greatly to the successful completion of this study.FundingThis research was funded by the Federal Government of Malaysia and the Sarawak State Government.Author informationAuthors and AffiliationsSarawak Tropical Peat Research Institute, Kuching-Samarahan Expressway, Kota Samarahan, Sarawak, 94300, MalaysiaAuldry Chaddy, Faustina Elfrida Sangok, Sharon Yu Ling Lau & Lulie MellingAuthorsAuldry ChaddyView author publicationsSearch author on:PubMed Google ScholarFaustina Elfrida SangokView author publicationsSearch author on:PubMed Google ScholarSharon Yu Ling LauView author publicationsSearch author on:PubMed Google ScholarLulie MellingView author publicationsSearch author on:PubMed Google ScholarContributionsLiterature collection, data collection and analysis were performed by Auldry Chaddy, Faustina Elfrida Sangok, and Sharon Yu Ling Lau. The first draft of the manuscript was written by Auldry Chaddy. Faustina Elfrida Sangok, Sharon Lau Yu Ling and Lulie Melling revised the draft. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.Corresponding authorCorrespondence to
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AbstractGlobal food production requires a major upheaval to feed a burgeoning human population despite multiple disruptors, ranging from climate change to geopolitical instability. Innovation and a policy shift that focuses on the Bioeconomy could address these challenges. This Perspective highlights plant cellular agriculture, molecular farming, and plant cell culture as a potential “fourth pillar” that could diversify supply and produce high-value compounds associated with regulatory uncertainty, cost, and energy constraints.

IntroductionEvery person deserves appropriate nutrition. Our world approaches a human population of 10 billion within the next 30 years, with global food demand increasing by more than 50% during this time period1.By 2050, global food demand is projected to increase by 50–60% compared to 2010 levels, with protein demand expected to double in some regions2. This growing demand encompasses diverse nutritional needs, including high-energy staples such as rice, wheat, and maize to ensure calorie sufficiency; high-quality proteins from sources like meat, dairy, plant-based alternatives, and novel proteins to support nutrition security3; and high-value foods such as functional ingredients and specialty crops that contribute to economic diversification. Global food systems are undergoing an upheaval, with vulnerabilities such as economic shocks due to tariff changes, the risk of zoonotic infectious diseases such as bovine influenza in the US, and geopolitical conflict such as grain shortages due to the Russian- Ukraine war. Collectively, these disruptions have greatly affected food prices and availability4. However, scaling up supply across these categories is challenged by the impacts of climate change, dietary shifts driven by urbanization and rising affluence, as well as policy and trade uncertainties5.In response, and due to concerns about global food security issues, many nations such as the US are trying to change the way they produce food, to mitigate global shocks of all nature, and to decentralize yet strengthen food supply chains to reduce their vulnerabilities6. The most noteworthy way this is taking place is by investing in novel strategies to produce alternative proteins. Alternative proteins, then, refer to those made that are equally as nutritious as conventional animal proteins, but are cheaper, require fewer inputs, have a lower carbon footprint, and are resilient to climate shocks7.Alternative protein production should reduce the load of zoonotic diseases as well as agricultural pest pressures and other exacerbating problems associated with livestock production, ranging from antimicrobial resistance to animal cruelty, from fair trade to bioterrorism8. Decentralizing our food production to an abundance of smaller locations would mitigate these problems substantially. The overall effect will be a shift in trade relations from one that is fixed due to geography, to one that is fluid and unconstrained.Alternative protein technologies for food are often placed into three main categories: cultivated meat, plant-based protein, and precision fermentation9. Cultivated, or cell-based meat, refers to the production of meat cells in culture to produce a food product such as hamburger, sausage or chicken nuggets. Plant-based proteins can be defined as proteins which have been processed in such a way that they resemble animal sourced products, such as oat milk. Precision fermentation covers the use of microbial fermentation systems to produce individual animal protein in a manner that more closely resembles the technology used in the past to produce pharmaceutical proteins. This synthetic biology approach includes the incorporation of a gene encoding an animal protein into the genome of a bacterial or fungal strain, which is then cultivated in a bioreactor to produce large amounts of target proteins, such as casein and whey. These three pillars represent the fundamentals of alternative protein production.A fourth ‘pillar’ has been defined as a facet of cellular agriculture based on plant molecular farming and plant cell culture technologies. Plant molecular farming refers to the use of plants themselves to replace microbial bioreactors, in such a way that a gene of interest is expressed and extracted from plants instead of from microbes10. Plant cellular agriculture, on the other hand, makes use of plant cell culture to produce large amounts of plant biomass which can be processed into food products, analogous to some of the cultivated meat production technologies11. Plant molecular farming and plant cell culture have been proposed as a potential “fourth pillar” of alternative proteins, though their precise definition and boundaries remain debated within the field.The following Perspective presents various examples of this fourth pillar of alternative plant cell-based technologies and describes the advantages that it has over the others. The Perspective concludes with a prediction of the prospects of plant cellular agriculture to address the widening cracks found within our current food system.Plant molecular farmingPlant molecular farming can be defined as the use of plants as a production platform to express a target protein12. Originally a production platform for pharmaceutical proteins (molecular pharming) that was developed over a quarter of a century ago, the technology has matured to such an extent that animal food proteins found in dairy, meat and eggs have become a more recent series of products under development. A great advantage of plant molecular farming is that in place of costly bioreactors, greenhouses or farm fields can be used to produce the protein of interest, thus mitigating the economic and environmental costs associated with farming livestock13. Plant molecular farming thus does not encounter scaling challenges the way other protein production platforms, such as precision fermentation, must face. Plants can perform post translational modifications that more resemble their animal counterparts, thus enabling them to follow a form and functionality that is superior to proteins produced in many microbial systems14. Animal proteins can be produced and stored in a wide diversity of plant tissues, such as potato tubers, rice grains, and legumes such as peas and soybeans15. Since these are edible tissues, it is feasible that partial purification of the protein in question may be sufficient, or, depending on the circumstances, completely unnecessary. Originally, this technology was adapted by companies such as Medicago, iBio and Kentucky Bioprocessing Co, to produce vaccines, monoclonal antibodies and other biologics16. Today, over 30 molecular farming companies can be found which produce different animal food proteins. Examples include Argentinian company Moolec (recently merged with Bioceres group limited), which produces the heme protein myoglobin in soybean and pea that can be processed into iron loaded products such as textured vegetable protein (valorasoy.com). Alpine Bio (formally Nobell Foods), based in San Francisco produces dairy proteins such as casein for cheese in soybean (alpbio.com). PoloPo is an Israeli company which produces the egg protein ovalbumin in potato tubers (PoLopo.tech). In Europe, molecular farming company Nambawan Spain produces and purifies sweet proteins such as thaumatin in transgenic tobacco seed (namba-wan.com).The key steps to plant molecular farming include determining the appropriate mode of animal gene delivery to crops, then optimizing expression levels, scaling-up to produce the desired amount of protein and finally, purification of protein, if required. Animal genes can be introduced via stable transformation to produce transgenic plants, or transiently, using replicating constructs based on virus expression vectors17. To date, largely transgenic plants have been created which express the target protein; these crops can be produced in the field or greenhouse and the protein extracted using standard agricultural techniques. Limitations for these processes include regulatory issues for GMOs (for plants grown in the open field) and scale up limitations (for plants grown in the greenhouse). Transient expression performed in the greenhouse using virus expression vectors can increase yield considerably and can be introduced to field crops using novel spray technologies, which are currently under development18.Expression levels can vary depending on the type of protein being produced (this problem exists for precision fermentation as well) and the tissue that it is expressed in, as well as environmental factors such as temperature and humidity. Oilseed crops, like soy, for example, have been shown to express myoglobin at 26.6% of the total soluble protein in the legume19; this can be easily stored at ambient temperatures and extracted later, whereas the level of protein expressed in a leafy crop like lettuce or tobacco may be considerably lower, but may not require extensive purification, depending on its future use. Existing agricultural infrastructure can be used whether the plants are produced in the greenhouse or in open field, and both farming practices can support local rural economies in a fashion that is more environmentally sustainable than livestock agriculture10.A comparison between plant molecular farming and precision fermentation indicates that on average, plant molecular farming requires a much lower initial investment, Capex and scaleup costs than precision fermentation. Precision fermentation, on the other hand, has lower land use requirements but also relies on sugar and other carbon sources, as well as continuous power to run the bioreactors13. These limitations make it more challenging to scale up to global demand, due to the inhibitory costs of bioreactors and in fact sufficient access to global steel to produce them20. While transgenic plants in the open field remain subject to GMO concerns (although protein purified from such sources is not considered to be a GMO), plants do not harbor mammalian pathogens and thus contain lower safety concerns than some microbial expression systems.Artificial intelligence (AI) and machine learning (ML) are now accelerating breakthroughs in plant molecular farming by enabling high-throughput strain optimization, metabolic pathway prediction, and the identification of gene-editing targets21,22. AI-driven algorithms are increasingly used to analyze large-scale omics datasets, predict optimal gene regulatory networks, and guide the design of synthetic constructs for enhanced metabolite production23. For instance, deep learning frameworks can assist in optimizing codon usage, protein folding stability, and promoter strength for cell factory development in plants or plant cells. When combined with CRISPR-based genome editing, these tools can significantly reduce the trial-and-error cycle in engineering high-yielding production strains, paving the way for scalable and cost-effective plant-based biofactories. Integrating AI into strain design thus not only enhances precision and efficiency but also supports predictive modeling for sustainable and economically viable molecular farming systems24.Plant cell-based productsCellular agriculture, a rising field focused on producing a plant-based product directly from a single cell rather than using whole organisms in their natural habitat, offers a transformative approach for the sustainable production of ingredients used in food, cosmetics, and nutraceuticals11. Within this framework, plant cell culture serves as a powerful platform for generating high-value bioactive compounds, flavors, pigments, and even staple ingredients through controlled, in vitro methods. Techniques such as micropropagation, adventitious shoot or root formation, and somatic embryogenesis are widely applied for the regeneration of whole plants and the production of targeted compounds from cultured cells25. The commercialization of these processes using bioreactor systems helps overcome major limitations of conventional methods, which are often labor-intensive and difficult to scale. Bioreactors enable precise control of physical and chemical conditions, improve nutrient distribution, reduce physiological disorders such as hyperhydricity, and support automation, making large-scale production more efficient and economically viable26.Thus, plant-based cellular agriculture not only reduces reliance on land, water, and traditional farming practices, but also supports global efforts toward a circular and sustainable bioeconomy, where biologically derived, renewable resources drive industrial innovation, environmental sustainability, and inclusive economic growth27.Plant tissue culture involves the sterile cultivation of plant parts under controlled conditions, first conceptualized by Gottlieb Haberlandt in 1902, and based on his pioneering work with single-cell cultures28. Initially developed at the beginning of the 20th Century, plant tissue culture has come a long way since then, and includes technologies that make use of root cultures, embryonic cultures, and many others29. Plant cell culture can assist in the production of a plethora of secondary metabolites, and their yields can be vastly improved using genome editing technologies for an increasing number of plant species30. Resembling a cross between cell-based meat and precision fermentation in terms of technology, plant cell culture will facilitate the production of ingredients which would reduce supply chain disruptions. Today, plant cell culture can be produced in bioreactors as great as 100,000 L31.The number of food products that can be produced in plant cell culture has exploded and will continue to expand as concerns about supply chain disruptions grow. For example, cocoa production in cell culture is now being explored as a viable option by several different cellular agriculture companies. Current cocoa production is restricted to tropical regions and is under pressure in terms of loss of land, human rights issues, pest pressures, and is not particularly environmentally friendly32. While these issues, when combined with predictive models of climate change, will undoubtedly reduce our future global cocoa supplies, the demand for cocoa is increasing at a rate that cannot be met using traditional manufacturing processes.Plant cell culture technology is emerging as a transformative platform for the sustainable production of high-value food ingredients. Cultivation of specific plant tissues or cells in a controlled system bypasses traditional agricultural constraints such as seasonal variation, climate vulnerability, and ethical concerns related to labor practices.A notable example is California Cultured (cacultured.com), a U.S.-based biotechnology company that is producing cocoa from cell cultures. Cocoa bean cell cultivation, rapid cell growth and maturation are all possible as well as scalable. This method also minimizes the use of water and labor. It avoids environmental and social issues commonly associated with cocoa farming in West Africa, where most global cocoa is sourced.Due to increasing cocoa demand and the vulnerability of the supply chain, cell culture-based cocoa offers a scalable and ethical alternative, providing substantial reductions in land use, water consumption, and labor requirements compared to conventional cultivation. To truly understand whether plant-based or cell-culture cocoa is more sustainable, the industry needs to apply life-cycle assessment (LCA) more widely. Future LCA studies on chocolate should clearly define their system boundaries, select functional units that are relevant to the purpose, and, where possible, combine both established and newer assessment methods. Adding steps such as uncertainty and sensitivity analysis can help ensure that the results are not only accurate but also reliable for guiding decisions33.Beyond cocoa, similar cellular agriculture technologies are also being applied to coffee production. Arabica coffee is the most widely consumed variety, and is threatened by climate-induced stress and fungal pathogens34. Pluri Biotech (pluri-biotech.com), an Israeli company, is developing coffee from plant cell cultures. Using bioreactors designed to support structured cell growth, the company cultivates coffee cells capable of synthesizing key bioactive compounds such as caffeine. The resulting biomass is harvested, dried, and roasted, yielding a product that visually and sensorial resembles conventional ground coffee.In Europe, the French startup Stem (s-tem.fr) is also working with coffee cell cultures. The cultured coffee powder with natural flavor extracts derived from coffee processing byproducts creates a final product that maintains the sensory characteristics of traditionally harvested beans35.Like cocoa and coffee, cellular agriculture is now an attractive alternative for the production of other bioactive and commercially valuable compounds, including vanillin, saffron, natural colorants, flavor compounds, and dietary supplements30. Growing consumer demand for traceable, sustainable and ethically produced food sources worldwide has fueled the development of plant cell-cultured products. Plant cell culture offers a promising platform for localized, scalable, and clean-label production of essential ingredients for food, cosmetics, and nutraceuticals, addressing both environmental challenges and evolving consumer expectations. Recent advances in plant cell culture and molecular farming are driving a growing number of startups to translate the science into commercial progress. These companies illustrate the technology’s potential through measurable funding rounds, strategic partnerships, and scale-up milestones (Table 1).Table 1 Key startups in plant cell culture and molecular farming with funding and progress metricsFull size tablePlant cell culture offers reduced land use and zero exposure to pests compared to open-field agriculture; however, it requires substantial energy, high-purity water, and refined media components, including sucrose and hormones, enabling the development of heterotrophic cultures. Life-cycle assessments indicate that although emissions per biomass unit may be lower, energy consumption remains a key barrier to economic scalability without renewable energy and media recycling15,36. Techno-economic analyses further emphasize electricity and sugar sourcing as critical factors that need optimization for commercial viability37.Regulatory and ethical considerations in molecular farmingRegulatory frameworks remain a critical consideration for the deployment of products derived from plant biotechnology. While open-field genetically modified (GM) crops typically undergo approval through distinct regulatory pathways, such as the Novel Food Regulation of European Union (EU 2015/2283) and the U.S. FDA approved Generally Recognized as Safe (GRAS) process, plant cell culture–derived products from controlled environments may follow different routes with unique timelines, transparency requirements, and public consultations38. Moreover, societal concerns regarding “laboratory-grown” or “genetically modified” ingredients could impact consumer acceptance and market adoption, highlighting the importance of proactive engagement and clear communication strategies to address public perception and ethical considerations39. Specifically, the molecular farming of animal proteins in plants raises additional public health, stewardship, religious, and ethical questions, underscoring the need for collaborative dialog among scientists, regulators, industry, and religious leaders to ensure responsible development and societal acceptance40.ConclusionsThe rise of cellular agriculture and plant molecular farming has the promise to transform global food systems by producing high-quality alternative proteins and novel ingredients with reduced land and water demands. The success of this growth is hindered by the cost, scalability, consumer acceptance, technical, regulatory, and societal hurdles. Life-cycle assessments and policy frameworks can facilitate the adoption of these technologies, which can complement alternative protein, fermentation, and conventional agriculture to form a resilient and diversified landscape of plant-based products. Strategic innovation, integrating advanced breeding, AI-driven optimization, genome editing, or other breakthrough modern technologies, helps scientists select better cell lines, tweak metabolic processes, and automate production steps, along with supportive policy, to accelerate their path to scale. Combining these advances as part of sustainable food production will ensure they complement, rather than compete with, other alternative protein pillars, positioning them to play a decisive role in meeting the nutritional and environmental challenges of the coming decades for both people and the planet.

Data availability

No datasets were generated or analysed during the current study.
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Download referencesAuthor informationAuthors and AffiliationsDepartment of Microbiology, Cornell University, Ithaca, NY, USAKathleen HefferonSchool of Integrative Plant Sciences, Cornell University, Ithaca, NY, USAAdam GannonDepartment of Biotechnology and Genetic Engineering, Jahangirnagar University, Dhaka, BangladeshAbdullah Mohammad ShohaelAuthorsKathleen HefferonView author publicationsSearch author on:PubMed Google ScholarAdam GannonView author publicationsSearch author on:PubMed Google ScholarAbdullah Mohammad ShohaelView author publicationsSearch author on:PubMed Google ScholarContributionsK.H. and A.S. wrote the main manuscript. A.G. revised and updated. All authors reviewed the manuscript.Corresponding authorCorrespondence to
Abdullah Mohammad Shohael.Ethics declarations

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Reprints and permissionsAbout this articleCite this articleHefferon, K., Gannon, A. & Shohael, A.M. Bridging agriculture, health and industry through plant molecular farming in the bioeconomic era.
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Abstract

Skeletochronology combined with growth curve reconstruction is routinely used to assess the age and growth dynamics of extinct and extant vertebrates. Here we performed in vivo labelling studies of the bone histology of four 2 years-old Crocodylus niloticus individuals. We found that all the crocodiles have more growth marks in their compacta than expected for their age, i.e., they deposited stochastic growth marks in their bones. Using the fluorochrome markers we determined that these stochastic growth marks were deposited during their favourable season of growth. The variable preservation of growth marks in the crocodile bones highlights developmental plasticity in their growth, which can be extrapolated to extinct archosaurs, and other reptiles. We caution the use of growth marks in fossil bones as a reliable estimator of age and discuss the far-reaching implications this has for growth curve reconstruction and life history assessments of extinct vertebrates, such as nonavian dinosaurs.

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High resolution images will be uploaded onto Morphobank. All thin sections will be deposited in the Vertebrate Comparative Collections of Iziko Museums of Cape Town.
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Download referencesAcknowledgementsWe are grateful to Le Bonheur Reptiles and Adventures for permitting access to the crocodiles investigated here. Aurore Canoville and Andrea Plos are warmly thanked for assisting with fieldwork. Vidushi Dabee is acknowledged for having prepared some of the thin sections. We thank Viantha Naidoo and Dirk Lang at the Confocal and Light Microscope Imaging Facility of the Faculty of Health Sciences at UCT. Shafi M. Bhat of the Department of Geosciences at Auburn University, Alabama, is acknowledged for having read an earlier draft of this manuscript. Devin Hoffman and two additional anonymous reviewers are thanked for their comments. The University of Cape Town Research Committee (URC) is thanked for the postdoctoral fellowship awarded to the second author.Author informationAuthors and AffiliationsDepartment of Biological Sciences, University of Cape Town, Private Bag, Rhodes Gift, Rondebosch, 7700, South AfricaAnusuya Chinsamy & Maria-Eugenia PereyraAuthorsAnusuya ChinsamyView author publicationsSearch author on:PubMed Google ScholarMaria-Eugenia PereyraView author publicationsSearch author on:PubMed Google ScholarContributionsAC conceived and designed the project and administered the fluorochrome labelling to the crocodiles. M-EP and AC analysed the histological thin sections, and both contributed to the data interpretation and analysis.  M-EP did the confocal and petrographic micrographs and figures for the manuscript. AC wrote the first draft, and M-EP contributed to the write up and made important suggestions. Both authors approved the final version of the manuscript.Corresponding authorCorrespondence to
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Reprints and permissionsAbout this articleCite this articleChinsamy, A., Pereyra, ME. Stochastic growth marks in Crocodylus niloticus.
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