Philippa Kaur

The framework of Swin MSIWe established the first Swin Transformer-based mosquito species identification (Swin MSI) model, with the help of self-constructed image dataset and multi-adjustment-test. Gradient-weighted class activation mapping was used to visualize the identification process (Fig. 1a). The key Swin Transformer block was described on Fig. 1b. Based on practical needs, Swin MSI was additional designed to identify Culex pipiens Complex on the subspecies level (Fig. 1c) and novel mosquito (which was defined as ones beyond 17 species in our dataset) classification attribution (Fig. 1d). Detailed results are shown in the following sections.Figure 1The Framework of Swin MSI. (a)The basic architecture for mosquito features extraction and identification. Attention visualization generated by filters at each layer are shown. (b) Details for Swin Transformer block. (c) For mosquito within our dataset 17 species, output is the top 5 confidence species. (d) For mosquito beyond 17 species (defined as novel species), whether the output is a species or a genus is decided after comparing with confidence threshold.Full size imageMosquito datasetsWe established the highest-definition and most-balanced mosquito image dataset to date. The mosquito image dataset covers 7 genera and 17 species (including 3 morphologically similar subspecies in the Cx. pipiens Complex), which covers the most common and important disease-transmitting mosquitoes at the global scale, with a total of 9,900 mosquito images. The image resolution was 4464 × 2976 pixels. The specific taxonomic status and corresponding images are shown in Fig. 2. Due to the limitation of field collection, Ae. vexans, Coquillettidia ochracea, Mansonia uniformis, An. vagus and Toxorhynchites splendens only have females or only have males. In addition, each mosquito species included 300 images of both sexes, which was large enough and same number for each species, in order to balance the capacity and variety of training sets.Figure 2Taxonomic status and index of mosquito species included in this study Both male and female mosquitoes were photographed from different angles such as dorsal, left side, right side, ventral side, etc. Except for 5 species, each mosquito includes 300 images of both sexes, and the resolution of mosquito photos were 4464 × 2976. Cx. pipiens quinquefasciatus, Cx. pipiens pallens, and Cx. pipiens molestus (subspecies level, in dark gray background) were 3 subspecies in Cx. pipiens Complex (species level).Full size imageWorkflow for mosquito species identificationA three-stage flowchart of building best deep learning model for identification of mosquito species model was adopted (Fig. 3). The first learning stage was conducted by three CNNs (the Mask R-CNN, DenseNet, and YOLOv5) and three transformer models (the Detection Transformer, Vision Transformer, and Swin Transformer). Based on the performance of the first-stage model and the real mosquito labels, the second learning stage involved adjusting the model parameters of the three Swin Transformer variants (T, B, and L) to compare their performances. The third learning stage involved testing the effects of inputting differently sized images (384 × 384 and 224 × 224) to the Swin Transformer-L model; finally, we proposed a deep learning model for mosquito species identification (Swin MSI) to test the recognition effects of different mosquito species. The model was validated on different mosquito species, with a focus on the identification accuracy of three subspecies within the Cx. pipiens Complex and the detection effect of novel mosquito species.Figure 3Flowchart of testing deep learning model for intelligent identification of mosquito species.Full size imageComparison between the CNN model and Transformer model results (1st round of learning)Figure 4a shows the accuracies obtained for the six different computer vision network models tested on the mosquito picture test set. The test results show that the transformer network model had a higher mosquito species discrimination ability than the CNN.Figure 4Comparison of mosquito recognition effects of computer vision network models and variants. (a) Comparison of mosquito identification accuracy between 3 CNNs and 3 Transformer; (b) The best effect CNN (YOLOv5) training set loss curve(blue), validation set loss curve(green) and validation set accuracy curve(orange); (c) The best effect Transformer (Swin Transformer) training set loss curve, validation set loss curve and validation set accuracy curve. (d) Swin-MSI-T test result confusion matrix; (e) Swin-MSI -B test result confusion matrix; (f) Swin-MSI -L test result confusion matrix. Confusion matrix of mosquito labels in which odd numbers represent females and even numbers represent males. The small squares in the confusion matrix represent the recognition readiness rate, from red to green, the recognition readiness rate is getting higher and higher An. sinensis: 1, 2; Cx. pipiens quinquefasciatus: 3, 4; Cx. pipiens pallens: 5, 6; Cx. pipiens molestus: 7,8 Cx. modestus: 9,10; Ae. albopictus: 11, 12 Ae. aegypti: 13, 14; Cx. pallidothorax: 15, 16 Ae. galloisi: 17,18 Ae. vexans: 19, 20; Ae. koreicus: 21, 22 Armigeres subalbatus: 23, 24; Coquillettidia ochracea: 25, 26 Cx. gelidus: 27, 28 Cx. triraeniorhynchus: 29, 30 Mansonia uniformis: 31, 32 An. vagus: 33, 34 Ae. elsaie: 35,36 Toxorhynchites splendens: 37, 38.Full size imageIn the CNN training process (applied to YOLOv5), the validation accuracy requires more than 110 epochs to grow to 0.9, and the validation loss requires 110 epochs to drop to a flat interval; in contrast, during the training step, these losses represent a continuously decreasing process. These results indicate that the deep learning model derived based on the Swin Transformer algorithm was able to achieve a higher recognition accuracy in less time than the rapid convergence ability of the CNN during the iterative process (Fig. 4b).The Swin Transformer model exhibited the highest test accuracy of 96.3%. During the training process, the loss of this model could stabilize after 30 epochs, and its validation accuracy could grow to 0.9 after 20 epochs; during the validation step, the loss can drop to 0.36 after 20 epochs, after which the loss curve fluctuated but did not produce adverse effects (Fig. 4c). Based on the excellent performance of the Swin Transformer model, this model was used as the baseline to carry out the subsequent analyses.Swin Transformer model variant adjustment (2nd round of learning)Following testing performed to clarify the superior performance of the Swin Transformer algorithm, we chose different Drop_path_rate, Embed_dim and Depths parameter settings and labeled the parameter sets as the Swin Transformer-T, Swin Transformer-B, and Swin Transformer-L variants. Drop_path is an efficient regularization method, and an asymmetric Drop_path_rate is beneficial for supervised representation learning when using image classification tasks and Transformer architectures. The Embed_dim parameter represents the image dimensions obtained after the input red–green–blue (RGB) image is calculated by the Swin Transformer block in stage 1. The Depths parameter is the number of Swin Transformer blocks used in the four stages. The parameter information and test results are shown in Table 1. Due to the increase in the Swin Transformer block and Embed_dim parameters in stage 3, the recognition accuracies of the three variants were found to be 95.8%, 96.3%, and 98.2%, Correspondingly, the f1 score were 96.2%, 96.7% and 98.3%; thus, these variants could effectively improve the mosquito species identification ability in a manner similar to the CNN by increasing the number of convolutional channels to extract more features and improve the overall classification ability. In this study, the Swin Transformer-L variant, which exhibited the highest accuracy, was selected as the baseline for the next work.Table 1 Parameters and test accuracy of three variants of Swin Transformer.Full size tableBy plotting a confusion matrix of the test set results derived using the three Swin Transformer variants, we clearly obtained the proportion of correct and incorrect identifications in each category to visually reflect the mosquito species discrimination ability (Fig. 4d–f). In the matrix, the darker diagonal colors indicate higher identification rate accuracies of the corresponding mosquito categories. Among them, five mosquito species were missing because the Ae. vexans, Coquillettidia ochracea, Mansonia uniformis, An. vagus and Toxorhynchites splendens species were represented in the dataset by only females or only males. The confusion matrix shown in Panel C lists the lowest number of mosquito species identification error points and the lowest accuracy level obtained in each category, suggesting that the Swin Transformer-L model has a better classification performance than the Swin Transformer-T and Swin Transformer-B models.Effect of the input image size on the discrimination ability (3rd round of learning)To investigate the relationship between the input image size and mosquito species identification performance, in this study, we conducted a comparison test between input images with sizes of 224 × 224 and 384 × 384, based on the Swin Transformer-L model, and identified 8 categories of mosquito identification accuracy differences. These test results are shown in Table 2. When using an image size of 224 × 224 pixels, the batch_size parameter was set to 16, and when using an image size of 384 × 384 pixels, the batch_size parameter was set to 4; under these conditions, the proportion of utilized video memory accounted for 67%, as shown in Eq. 4, and this was consistent with the description of the relationship between the size of self-attentive operations during the operation of the Swin Transformer model when 384 × 384 pixels images were used. The time required for the Transformer-L model to complete all the training sessions was excessive, reaching 126 h and even exceeding the 124 h required by the YOLOv5 model, which was found to require the highest computation time during the training process in this work. Long-term training process could more fully reflect the performance differences between models. Fortunately and actually, the response speed of the model will not be affected by the training time. Compared to the accuracy of 98.2% obtained for 224 × 224 inputs, the 384 × 384 input image size derived based on the Swin Transformer-L model provided a higher mosquito species identification accuracy of 99.04%, representing an improvement of 0.84%.$$Omega ({text{W}} – {text{MSA}}) = 4{text{HWC}}^{2} + 2{text{M}}^{2} {text{HWC}}$$
(1)
Table 2 Comparison of recognition accuracy for different input image sizes.Full size tableVisualizing and understanding the Swin MSI modelsTo investigate the differences in the attentional features utilized by the Swin MSI and taxonomists for mosquito species identification, we applied the Grad-CAM method to visualize the Swin MSI attentional areas on mosquitoes at different stages. Because the Swin Transformer has different attentional ranges among its multi-head self-attention steps in different stages, different attentional weights can be found on different mosquito positions. In stage 1, the feature dimension of each patch was 4 × 4 × C, thus enabling the Swin Transformer’s multi-head self-attention mechanism to give more attention to the detailed parts of the mosquitoes, such as their legs, wings, antennae, and pronota. In stage 2, the feature dimension of each patch was 8 × 8 × 2C, enabling the Swin Transformer’s multi-head self-attention mechanism to focus on the bodies of the mosquitoes, such as their heads, thoraces, and abdomens. In stage 3, when the feature dimension of each patch was 16 × 16 × 4C, the Swin Transformer’s multi-head self-attention mechanism could focus on most regions of the mosquito, thus forming a global overall attention mechanism for each mosquito (Fig. 5). This attentional focus process is essentially the same as the process used by taxonomists when classifying mosquito morphology, changing from details to localities to the whole mosquito.Figure 5Attention visualization of representative mosquitoes of the genera Ae., Cx., An., Armigeres, Coquillettidia and Mansonia. This is a visualization for identifying the regions in the image that can explain the classification progress. Images of Toxorhynchites contain only males, with obvious differences in morphological characteristics, are not shown.Full size imageAe. aegypti is widely distributed in tropical and subtropical regions around the world and transmits Zika, dengue and yellow fever. A pair of long-stalked sickle-shaped white spots on both shoulder sides of the mesoscutum, with a pair of longitudinal stripes running through the whole mesotergum, is the most important morphological identification feature of this species. This feature was the deepest section in the attention visualization, indicating that the Swin MSI model also recognized it as the principal distinguishing feature. In addition, the abdominal tergum of A. aegypti is black and segments II-VII have lateral silvery white spots and basal white bands; the model also focused on these areas.Cx. triraeniorhynchus is the main vector of Japanese encephalitis; this mosquito has a small body size, a distinctive white ring on the proboscis (its most distinctive morphological feature), and a peppery color on its whole body. Similarly, the model constructed herein focused on both the head and abdominal regions of this species.An. sinensis is the top vector of malaria in China and has no more than three white spots on its anterior wing margin and a distinct white spot on its marginal V5.2 fringe; this feature was observed in Stage 2, at which time the modelstrongly focused on the corresponding area.The most obvious feature of Armigeres subalbatus is the lateral flattening and slightly downward curving of its proboscis; the observation of the attention visualization revealed that the constructed model focused on these regions from Stage 1 to Stage 3. The mesoscutum and abdominal tergum were not critical and were less important for identification than the proboscis, and the attention visualization results correspondingly show that the neural network focused less on these features.Coquillettidia ochracea belongs to the Coquillettidia genus and is golden yellow all over its body, with the most pronounced abdomen among the analyzed species. The model showed a consistent morphological taxonomic focus on the abdomen of this species.Mansonia uniformis is a vector of Malayan filariasis. The abdominal tergum of this species is dark brown, and its abdominal segments II-VII have yellow terminal bands and lateral white spots, which are more obvious than the dark brown feature on proboscis. Through the attention visualization, we determined that the Swin MSI model was more concerned with the abdominal region features than with the proboscis features.Subspecies-level identification tests of mosquitos in the Culex pipiens ComplexFine-grained image classification has been the focus of extensive research in the field of computer vision25,26. Based on the test set (containing 270 images) constructed herein for three subspecies of the Cx. pipiens Complex, the subspecies and sex identification accuracies were 100% when the Swin MSI model was used.The morphological characteristics of Cx. pipiens quinquefasciatus, Cx. pipiens pallens, and Cx. pipiens molestus within the Cx. pipiens Complex are almost indistinguishable, but their host preferences, self-fertility properties, breeding environments, and stagnation overwintering strategies are very different27. Among the existing features available for morphological classification, the stripes on the abdominal tergum of Cx. pipiens quinquefasciatus are usually inverted triangles and are not connected with the pleurosternums, while those of Cx. pipiens pallens are rectangular and are connected with the pleurosternums. Cx. pipiens molestus is morphologically more similar to Cx. pipiens pallens as an ecological subspecies of the Cx. pipiens Complex. However, taxonomists do not recommend using the unstable feature mentioned above as the main taxonomic feature for differentiation. By analyzing the attention visualization results of these three subspecies (last three rows on Fig. 5), we found that the neural networks of Cx. pipiens quinquefasciatus, Cx. pipiens pallens, and Cx. pipiens molestus still focused on the abdominal regions, as shown in dark red. The area of focus of these neural networks differ from that of the human eye, and the results of this study suggest that the Swin MSI model can detect finely granular features among these three mosquito subspecies that are indistinguishable to the naked human eye.Novel mosquito classification attributionAfter we performed a confidence check on the successfully identified mosquito images in the dataset, the lowest confidence value was found to be 85%. A higher confidence threshold mean stricter evaluation criteria, which can better reflect the powerful performance of the model. Therefore, 0.85 was set as the confidence threshold when judging novel mosquitoes. When identifying 10 unknown mosquito species, the highest derived species confidence level was below 85%; when the results were output to the genus level (Fig. 1d), the average probability of obtaining a correct judgment was 96.26%accuracy and 98.09% F1-score (Table 3). The images tested as novel Ae., Cx. and An. mosquito were from Minakshi and Couret et al.28,29.Table 3 Probability of correct attribution of novel species.Full size table More

Mueller, R. C. et al. An emerging view of the diversity, ecology, and function of Archaea in alkaline hydrothermal environments. FEMS Microbiol. Ecol. 97, fiaa246 (2020).
Google Scholar 
López-López, O., Cerdán, M.-E. & González-Siso, M.-I. Thermus thermophilus as a source of thermostable lipolytic enzymes. Microorganisms 3, 792–808 (2015).PubMed 
PubMed Central 

Google Scholar 
Sahay, H. et al. Hot springs of Indian Himalayas: Potential sources of microbial diversity and thermostable hydrolytic enzymes. 3 Biotech 7, 118 (2017).PubMed 
PubMed Central 

Google Scholar 
Patel, A. K., Singhania, R. R., Sim, S. J. & Pandey, A. Thermostable cellulases: Current status and perspectives. Bioresour Technol 279, 385–392 (2019).CAS 
PubMed 

Google Scholar 
Decastro, M.-E., Rodríguez-Belmonte, E. & González-Siso, M.-I. Metagenomics of thermophiles with a focus on discovery of novel thermozymes. Front. Microbiol. 7, 1521–1521 (2016).PubMed 
PubMed Central 

Google Scholar 
Meslé, M. M. et al. Isolation and characterization of lignocellulose-degrading geobacillus thermoleovorans from Yellowstone National Park. Appl. Environ. Microbiol. 88, e0095821 (2022).PubMed 

Google Scholar 
Verma, P., Yadav, A. N., Shukla, L., Saxena, A. K. & Suman, A. Hydrolytic enzymes production by thermotolerant Bacillus altitudinis IARI-MB-9 and Gulbenkiania mobilis IARI-MB-18 isolated from Manikaran hot springs. Int. J. Adv. Res. 3, 1241–1250 (2015).CAS 

Google Scholar 
Wu, B. et al. Microbial sulfur metabolism and environmental implications. Sci. Total Environ. 778, 146085 (2021).ADS 
CAS 
PubMed 

Google Scholar 
Lavrentyeva, E. V. et al. Bacterial diversity and functional activity of microbial communities in hot springs of the Baikal Rift Zone. Microbiology 87, 272–281 (2018).CAS 

Google Scholar 
Miller Scott, R., Strong Aaron, L., Jones Kenneth, L. & Ungerer Mark, C. Bar-Coded pyrosequencing reveals shared bacterial community properties along the temperature gradients of two alkaline hot springs in Yellowstone National Park. Appl. Environ. Microbiol. 75, 4565–4572 (2009).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sharp, C. E. et al. Humboldt’s spa: Microbial diversity is controlled by temperature in geothermal environments. ISME J. 8, 1166–1174 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Stefanova, K. et al. Archaeal and bacterial diversity in two hot springs from geothermal regions in Bulgaria as demostrated by 16S rRNA and GH-57 genes. Int. Microbiol. 18, 217–223 (2015).CAS 
PubMed 

Google Scholar 
Hou, W. et al. A comprehensive census of microbial diversity in hot springs of Tengchong, Yunnan Province China using 16S rRNA gene pyrosequencing. PLoS ONE 8, e53350 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sahm, K. et al. High abundance of heterotrophic prokaryotes in hydrothermal springs of the Azores as revealed by a network of 16S rRNA gene-based methods. Extremophiles 17, 649–662 (2013).CAS 
PubMed 

Google Scholar 
Purcell, D. et al. The effects of temperature, pH and sulphide on the community structure of hyperthermophilic streamers in hot springs of northern Thailand. FEMS Microbiol. Ecol. 60, 456–466 (2007).CAS 
PubMed 

Google Scholar 
Meyer-Dombard, D. R. & Amend, J. P. Geochemistry and microbial ecology in alkaline hot springs of Ambitle Island, Papua New Guinea. Extremophiles 18, 763–778 (2014).CAS 
PubMed 

Google Scholar 
de Leon, K. B., Gerlach, R., Peyton, B. M. & Fields, M. W. Archaeal and bacterial communities in three alkaline hot springs in Heart Lake Geyser Basin, Yellowstone National Park. Front. Microbiol. 4, 10 (2013).
Google Scholar 
Boomer, S. M., Noll, K. L., Geesey, G. G. & Dutton, B. E. Formation of multilayered photosynthetic biofilms in an alkaline thermal spring in Yellowstone National Park, Wyoming. Appl. Environ. Microbiol. 75, 2464–2475 (2009).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wang, S. et al. Greater temporal changes of sediment microbial community than its waterborne counterpart in Tengchong hot springs, Yunnan Province, China. Sci. Rep. 4, 7479 (2014).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sun, Y., Liu, Y., Pan, J., Wang, F. & Li, M. Perspectives on cultivation strategies of archaea. Microb. Ecol. 79, 770–784 (2020).PubMed 

Google Scholar 
Brock, T. D. Life at high temperatures. Science 158, 1012 (1967).ADS 
CAS 
PubMed 

Google Scholar 
Christiansen, R. L. The Quaternary and Pliocene Yellowstone Plateau volcanic field of Wyoming, Idaho, and Montana. Professional Paper (2001).Rowe, J. J., Fournier, R. & Morey, G. Chemical analysis of thermal waters in Yellowstone National Park, Wyoming, 1960–65. USGS https://doi.org/10.3133/b1303 (1973).Article 

Google Scholar 
Fournier, R., Thompson, M. J. & Hutchinson, R. A. The geochemistry of hot spring waters at Norris Geyser Basin, Yellowstone National Park. International symposium on water-rock interactions (1992).Podar, P. T., Yang, Z., Björnsdóttir, S. H. & Podar, M. Comparative analysis of microbial diversity across temperature gradients in hot springs from Yellowstone and Iceland. Front. Microbiol. 11, 1625 (2020).PubMed 
PubMed Central 

Google Scholar 
Pala, C. et al. Environmental drivers controlling bacterial and archaeal abundance in the sediments of a Mediterranean lagoon ecosystem. Curr. Microbiol. 75, 1147–1155 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Foyer, C. H., Noctor, G. & Hodges, M. Respiration and nitrogen assimilation: Targeting mitochondria-associated metabolism as a means to enhance nitrogen use efficiency. J. Exp. Bot. 62, 1467–1482 (2011).CAS 
PubMed 

Google Scholar 
Ershanovich, V. N. et al. Nitrogen assimilation enzymes in Bacillus subtilis mutants with hyperproduction of riboflavin. Mol. Gen. Mikrobiol. Virusol. 2005(3), 29–34 (2005).
Google Scholar 
Offre, P., Spang, A. & Schleper, C. Archaea in biogeochemical cycles. Annu Rev Microbiol 67, 437–457 (2013).CAS 
PubMed 

Google Scholar 
Cabello, P., Roldán, M. D. & Moreno-Vivián, C. Nitrate reduction and the nitrogen cycle in archaea. Microbiology 150, 3527–3546 (2004).CAS 
PubMed 

Google Scholar 
Graupner, M., Xu, H. & White, R. H. The pyrimidine nucleotide reductase step in riboflavin and F(420) biosynthesis in archaea proceeds by the eukaryotic route to riboflavin. J. Bacteriol. 184, 1952–1957 (2002).CAS 
PubMed 
PubMed Central 

Google Scholar 
Chernyh, N. A. et al. Dissimilatory sulfate reduction in the archaeon “Candidatus Vulcanisaeta moutnovskia” sheds light on the evolution of sulfur metabolism. Nat. Microbiol. 5, 1428–1438 (2020).CAS 
PubMed 

Google Scholar 
Castelle, C. J. & Banfield, J. F. Major new microbial groups expand diversity and alter our understanding of the tree of life. Cell 172, 1181–1197 (2018).CAS 
PubMed 

Google Scholar 
Williams, T. A. et al. Integrative modeling of gene and genome evolution roots the archaeal tree of life. Proc. Natl. Acad. Sci. U.S.A. 114, E4602–E4611 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Guy, L. & Ettema, T. J. G. The archaeal ‘TACK’ superphylum and the origin of eukaryotes. Trends Microbiol. 19, 580–587 (2011).CAS 
PubMed 

Google Scholar 
Wang, Y., Wegener, G., Hou, J., Wang, F. & Xiao, X. Expanding anaerobic alkane metabolism in the domain of Archaea. Nat. Microbiol. 4, 595–602 (2019).CAS 
PubMed 

Google Scholar 
Hedlund, B. P. et al. Uncultivated thermophiles: Current status and spotlight on ‘Aigarchaeota’. Curr. Opin. Microbiol. 25, 136–145 (2015).CAS 
PubMed 

Google Scholar 
Reichart, N. J. et al. Activity-based cell sorting reveals responses of uncultured archaea and bacteria to substrate amendment. ISME J. 14, 2851–2861 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hua, Z.-S. et al. Genomic inference of the metabolism and evolution of the archaeal phylum Aigarchaeota. Nat. Commun. 9, 2832 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Beam, J. P. et al. Ecophysiology of an uncultivated lineage of Aigarchaeota from an oxic, hot spring filamentous “streamer” community. ISME J. 10, 210–224 (2016).CAS 
PubMed 

Google Scholar 
Gonsior, M. et al. Yellowstone hot springs are organic chemodiversity hot spots. Sci. Rep. 8, 14155 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Gibson, M. L. & Hinman, N. W. Mixing of hydrothermal water and groundwater near hot springs, Yellowstone National Park (USA): Hydrology and geochemistry. Hydrogeol. J. 21, 919–933 (2013).ADS 
CAS 

Google Scholar 
Campbell, K. M. et al. Sulfolobus islandicus meta-populations in Yellowstone National Park hot springs. Environ. Microbiol. 19, 2334–2347 (2017).PubMed 

Google Scholar 
Thiel, V. et al. The dark side of the mushroom spring microbial mat: Life in the shadow of chlorophototrophs. I. Microbial diversity based on 16S rRNA gene amplicons and metagenomic sequencing. Front. Microbiol. 7, 919 (2016).PubMed 
PubMed Central 

Google Scholar 
Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. U.S.A. 108, 4516–4522 (2011).ADS 
CAS 
PubMed 

Google Scholar 
Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: Assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ. Microbiol. 18, 1403–1414 (2016).CAS 
PubMed 

Google Scholar 
Apprill, A., McNally, S., Parsons, R. & Weber, L. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat. Microb. Ecol. 75, 129–137 (2015).
Google Scholar 
Thompson, L. R. et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature 555, 457–463 (2017).ADS 

Google Scholar 
Eloe-Fadrosh, E. A., Ivanova, N. N., Woyke, T. & Kyrpides, N. C. Metagenomics uncovers gaps in amplicon-based detection of microbial diversity. Nat. Microbiol. 1, 15032 (2016).CAS 
PubMed 

Google Scholar 
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).CAS 
PubMed 

Google Scholar 
Edgar, R. C. UNOISE2: Improved error-correction for Illumina 16S and ITS amplicon sequencing. BioRxiv https://doi.org/10.1101/081257 (2016).Article 

Google Scholar 
Murali, A., Bhargava, A. & Wright, E. S. IDTAXA: A novel approach for accurate taxonomic classification of microbiome sequences. Microbiome 6, 140 (2018).PubMed 
PubMed Central 

Google Scholar 
Parks, D. H. et al. A complete domain-to-species taxonomy for Bacteria and Archaea. Nat. Biotechnol. 38, 1079–1086 (2020).CAS 
PubMed 

Google Scholar 
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Stamatakis, A. RAxML version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v5: An online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 49, W293–W296 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Matsen, F. A., Kodner, R. B. & Armbrust, E. V. pplacer: Linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinform. 11, 538 (2010).
Google Scholar 
Chong, J., Liu, P., Zhou, G. & Xia, J. Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data. Nat. Protoc. 15, 799–821 (2020).CAS 
PubMed 

Google Scholar 
Chambers, M. C. et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat. Biotechnol. 30, 918–920 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Pluskal, T., Castillo, S., Villar-Briones, A. & Oresic, M. MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinform. 11, 395 (2010).
Google Scholar 
Patiny, L. & Borel, A. ChemCalc: A building block for tomorrow’s chemical infrastructure. J. Chem. Inf. Model. 53, 1223–1228 (2013).CAS 
PubMed 

Google Scholar 
Chong, J., Wishart, D. S. & Xia, J. Using MetaboAnalyst 4.0 for comprehensive and integrative metabolomics data analysis. Curr. Protoc. Bioinform. 68, e86 (2019).
Google Scholar 
Liu, G., Lee, D. P., Schmidt, E. & Prasad, G. L. Pathway analysis of global metabolomic profiles identified enrichment of caffeine, energy, and arginine metabolism in smokers but not moist snuff consumers. Bioinform. Biol. Insights 13, 1177932219882961–1177932219882961 (2019).PubMed 
PubMed Central 

Google Scholar 
Xia, J. & Wishart, D. S. MetPA: A web-based metabolomics tool for pathway analysis and visualization. Bioinformatics 26, 2342–2344 (2010).CAS 
PubMed 

Google Scholar 
Huber, W. et al. Orchestrating high-throughput genomic analysis with Bioconductor. Nat. Methods 12, 115–121 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Rohart, F., Gautier, B., Singh, A. & Lé Cao, K.-A. mixOmics: An R package for ’omics feature selection and multiple data integration. PLoS Comput. Biol. 13, e1005752–e1005752 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar  More

Hirai, H. et al. Organic micropollutants in marine plastics debris from the open ocean and remote and urban beaches. Mar. Pollut. Bull. 62(8), 1683–1692. https://doi.org/10.1016/j.marpolbul.2011.06.004 (2011).Article 
CAS 
PubMed 

Google Scholar 
Masó, M., Garcés, E., Pagès, F. & Camp, J. Drifting plastic debris as a potential vector for dispersing harmful algal bloom (HAB) species. Sci. Mar. 67(1), 107–111. https://doi.org/10.3989/scimar.2003.67n1107 (2003).Article 

Google Scholar 
Pandey, D., Singh, A., Ramanathan, A. & Kumar, M. The combined exposure of microplastics and toxic contaminants in the floodplains of North India: A review. J. Environ. Manag. 279, 111557. https://doi.org/10.1016/j.jenvman.2020.111557 (2021).Article 
CAS 

Google Scholar 
Peng, L. et al. Micro- and nano-plastics in marine environment: Source, distribution and threats—A review. Sci. Total Environ. 698, 134254. https://doi.org/10.1016/j.scitotenv.2019.134254 (2020).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Rillig, M. C. & Lehmann, A. Microplastic in terrestrial ecosystems and the soil?. Environ. Sci. Technol. 46(12), 6453–6454. https://doi.org/10.1021/es302011r (2012).Article 
CAS 
PubMed 

Google Scholar 
Rochman, C. M. & Hoellein, T. The global odyssey of plastic pollution. Science 368(6496), 1184–1185. https://doi.org/10.1126/science.abc4428 (2020).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Jan Kole, P., Löhr, A. J., van Belleghem, F. G. A. J. & Ragas, A. M. J. Wear and tear of tyres: A stealthy source of microplastics in the environment. Int. J. Environ. Res. Public Health https://doi.org/10.3390/ijerph14101265 (2017).Article 

Google Scholar 
Sommer, F. et al. Tire abrasion as a major source of microplastics in the environment. Aerosol Air Qual. Res. 18(8), 2014–2028. https://doi.org/10.4209/aaqr.2018.03.0099 (2018).Article 
CAS 

Google Scholar 
Beita-Sandí, W., Selbes, M., Ersan, M. S. & Karanfil, T. Release of nitrosamines and nitrosamine precursors from scrap tires. Environ. Sci. Technol. Lett. 6(4), 251–256. https://doi.org/10.1021/acs.estlett.9b00172 (2019).Article 
CAS 

Google Scholar 
Kaminsky, W. & Mennerich, C. Pyrolysis of synthetic tire rubber in a fluidised-bed reactor to yield 1,3-butadiene, styrene and carbon black. J. Anal. Appl. Pyrolysis 58–59, 803–811. https://doi.org/10.1016/S0165-2370(00)00129-7 (2001).Article 

Google Scholar 
Sundt, P., Schulze, P. E. & Syversen, F. Sources of microplastic- pollution to the marine environment. Mepex Nor. Environ. Agency 86, 20 (2014).
Google Scholar 
White, W. C. Butadiene production process overview. Chem. Biol. Interact. 166(1–3), 10–14. https://doi.org/10.1016/j.cbi.2007.01.009 (2007).Article 
CAS 
PubMed 

Google Scholar 
Alimi, O. S., Farner Budarz, J., Hernandez, L. M. & Tufenkji, N. Microplastics and nanoplastics in aquatic environments: Aggregation, deposition, and enhanced contaminant transport. Environ. Sci. Technol. 52(4), 1704–1724. https://doi.org/10.1021/acs.est.7b05559 (2018).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Cooper, D. A. & Corcoran, P. L. Effects of mechanical and chemical processes on the degradation of plastic beach debris on the island of Kauai, Hawaii. Mar. Pollut. Bull. 60(5), 650–654. https://doi.org/10.1016/j.marpolbul.2009.12.026 (2010).Article 
CAS 
PubMed 

Google Scholar 
O’Brine, T. & Thompson, R. C. Degradation of plastic carrier bags in the marine environment. Mar. Pollut. Bull. 60(12), 2279–2283. https://doi.org/10.1016/j.marpolbul.2010.08.005 (2010).Article 
CAS 
PubMed 

Google Scholar 
Song, Y. K. et al. Combined effects of UV exposure duration and mechanical abrasion on microplastic fragmentation by polymer type. Environ. Sci Technol. 51(8), 4368–4376. https://doi.org/10.1021/acs.est.6b06155 (2017).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Adobe Inc. (2019). Adobe illustrator. Retrieved from https://www.adobe.com/Products/Illustrator.Chamas, A. et al. Degradation rates of plastics in the environment. ACS Sustain. Chem. Eng. 8(9), 3494–3511. https://doi.org/10.1021/acssuschemeng.9b06635 (2020).Article 
CAS 

Google Scholar 
Councell, T. B., Duckenfield, K. U., Landa, E. R. & Callender, E. Tire-wear particles as a source of zinc to the environment. Environ. Sci. Technol. 38(15), 4206–4214. https://doi.org/10.1021/es034631f (2004).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Awet, T. T. et al. Effects of polystyrene nanoparticles on the microbiota and functional diversity of enzymes in soil. Environ. Sci. Eur. https://doi.org/10.1186/s12302-018-0140-6 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Chung, H., Son, Y., Yoon, T. K., Kim, S. & Kim, W. The effect of multi-walled carbon nanotubes on soil microbial activity. Ecotoxicol. Environ. Saf. 74(4), 569–575. https://doi.org/10.1016/j.ecoenv.2011.01.004 (2011).Article 
CAS 
PubMed 

Google Scholar 
Huber, M., Welker, A. & Helmreich, B. Critical review of heavy metal pollution of traffic area runoff: Occurrence, influencing factors, and partitioning. Sci. Total Environ. 541, 895–919. https://doi.org/10.1016/j.scitotenv.2015.09.033 (2016).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Miandad, R., Barakat, M. A., Aburiazaiza, A. S., Rehan, M. & Nizami, A. S. Catalytic pyrolysis of plastic waste: A review. Process Saf. Environ. Prot. 102, 822–838. https://doi.org/10.1016/j.psep.2016.06.022 (2016).Article 
CAS 

Google Scholar 
Zhang, X., Li, H., Cao, Q., Jin, L. & Wang, F. Upgrading pyrolytic residue from waste tires to commercial carbon black. Waste Manag. Res. 36(5), 436–444. https://doi.org/10.1177/0734242X18764292 (2018).Article 
CAS 
PubMed 

Google Scholar 
Zhu, D., Li, G., Wang, H. T. & Duan, G. L. Effects of nano- or microplastic exposure combined with arsenic on soil bacterial, fungal, and protistan communities. Chemosphere 281, 130998. https://doi.org/10.1016/j.chemosphere.2021.130998 (2021).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Pathan, S. I. et al. Soil Pollution from micro-and nanoplastic debris: A hidden and unknown biohazard. Sustainability 12(18), 1–31. https://doi.org/10.3390/su12187255 (2020).Article 
CAS 

Google Scholar 
Rillig, M. C. & Bonkowski, M. Microplastic and soil protists: A call for research. Environ. Pollut. 241, 1128–1131. https://doi.org/10.1016/j.envpol.2018.04.147 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zettler, E. R., Mincer, T. J. & Amaral-Zettler, L. A. Life in the “Plastisphere”: Microbial communities on plastic marine debris. Environ. Sci. Technol. 47(13), 7137–7146. https://doi.org/10.1021/es401288x (2013).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Burns, E. E. & Boxall, A. B. A. Microplastics in the aquatic environment: Evidence for or against adverse impacts and major knowledge gaps. Environ. Toxicol. Chem. 37(11), 2776–2796. https://doi.org/10.1002/etc.4268 (2018).Article 
CAS 
PubMed 

Google Scholar 
Bradney, L. et al. Particulate plastics as a vector for toxic trace-element uptake by aquatic and terrestrial organisms and human health risk. Environ. Int. 2019(131), 104937. https://doi.org/10.1016/j.envint.2019.104937 (2018).Article 
CAS 

Google Scholar 
Duis, K. & Coors, A. Microplastics in the Aquatic and Terrestrial Environment: Sources (with a Specific Focus on Personal Care Products), fate and effects. Environ. Sci. Eur. 28(1), 1–25. https://doi.org/10.1186/s12302-015-0069-y (2016).Article 
CAS 

Google Scholar 
Geyer, R., Jambeck, J. R. & Law, K. L. Production, use, and fate of all plastics ever made. Sci. Adv. 3(7), 25–29. https://doi.org/10.1126/sciadv.1700782 (2017).Article 
CAS 

Google Scholar 
Jayasiri, H. B., Purushothaman, C. S. & Vennila, A. Quantitative analysis of plastic debris on recreational beaches in Mumbai, India. Mar. Pollut. Bull. 77(1–2), 107–112. https://doi.org/10.1016/j.marpolbul.2013.10.024 (2013).Article 
CAS 
PubMed 

Google Scholar 
Lassen, C., Hansen, S. F., Magnusson, K., Hartmann, N. B., Rehne Jensen, P., Nielsen, T. G. & Brinch, A. Microplastics occurrence, effects and sources of releases (2015).Weithmann, N. et al. Organic fertilizer as a vehicle for the entry of microplastic into the environment. Sci. Adv. https://doi.org/10.1126/sciadv.aap8060 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Hidalgo-Ruz, V., Gutow, L., Thompson, R. C. & Thiel, M. Microplastics in the marine environment: A review of the methods used for identification and quantification. Environ. Sci. Technol. 46(6), 3060–3075. https://doi.org/10.1021/es2031505 (2012).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Boenigk, J., Matz, C., Jürgens, K. & Arndt, H. Confusing selective feeding with differential digestion in bacterivorous nanoflagellates. J. Eukaryot. Microbiol. 48(4), 425–432. https://doi.org/10.1111/j.1550-7408.2001.tb00175.x (2001).Article 
CAS 
PubMed 

Google Scholar 
Boenigk, J., Matz, C., Jürgens, K. & Arndt, H. Food concentration-dependent regulation of food selectivity of interception-feeding bacterivorous nanoflagellates. Aquat. Microb. Ecol. 27(2), 195–202. https://doi.org/10.3354/ame027195 (2002).Article 

Google Scholar 
Wright, S. L., Thompson, R. C. & Galloway, T. S. The physical impacts of microplastics on marine organisms: A review. Environ. Pollut. 178, 483–492. https://doi.org/10.1016/j.envpol.2013.02.031 (2013).Article 
CAS 
PubMed 

Google Scholar 
Moore, C. J. Synthetic polymers in the marine environment: A rapidly increasing, long-term threat. Environ. Res. 108(2), 131–139. https://doi.org/10.1016/j.envres.2008.07.025 (2008).Article 
CAS 
PubMed 

Google Scholar 
Fu, S. F. et al. Exposure to polystyrene nanoplastic leads to inhibition of anaerobic digestion system. Sci. Total Environ. 625, 64–70. https://doi.org/10.1016/j.scitotenv.2017.12.158 (2018).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Bock, C. et al. Factors shaping community patterns of protists and bacteria on a European scale. Environ. Microbiol. 22(6), 2243–2260. https://doi.org/10.1111/1462-2920.14992 (2020).Article 
PubMed 

Google Scholar 
Besseling, E., Wang, B., Lürling, M. & Koelmans, A. A. Nanoplastic affects growth of S. obliquus and reproduction of D. magna. Environ. Sci. Technol. 48(20), 12336–12343. https://doi.org/10.1021/es503001d (2014).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Brown, D. M., Wilson, M. R., MacNee, W., Stone, V. & Donaldson, K. Size-dependent proinflammatory effects of ultrafine polystyrene particles: A role for surface area and oxidative stress in the enhanced activity of ultrafines. Toxicol. Appl. Pharmacol. 175(3), 191–199. https://doi.org/10.1006/taap.2001.9240 (2001).Article 
CAS 
PubMed 

Google Scholar 
Jeong, C. B. et al. Microplastic size-dependent toxicity, oxidative stress induction, and p-JNK and p-P38 activation in the monogonont rotifer (Brachionus Koreanus). Environ. Sci. Technol. 50(16), 8849–8857. https://doi.org/10.1021/acs.est.6b01441 (2016).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Kang, H. C., Jeong, H. J., Jang, S. H. & Lee, K. H. Feeding by common heterotrophic protists on the phototrophic dinoflagellate Biecheleriopsis adriatica (Suessiaceae) compared to that of other suessioid dinoflagellates. Algae 34(2), 127–140. https://doi.org/10.4490/algae.2019.34.5.29 (2019).Article 
CAS 

Google Scholar 
Sjollema, S. B., Redondo-Hasselerharm, P., Leslie, H. A., Kraak, M. H. S. & Vethaak, A. D. Do plastic particles affect microalgal photosynthesis and growth?. Aquat. Toxicol. 170, 259–261. https://doi.org/10.1016/j.aquatox.2015.12.002 (2016).Article 
CAS 
PubMed 

Google Scholar 
Rossi, G., Barnoud, J. & Monticelli, L. Polystyrene nanoparticles perturb lipid membranes. J. Phys. Chem. Lett. 5(1), 241–246. https://doi.org/10.1021/jz402234c (2014).Article 
CAS 
PubMed 

Google Scholar 
Brandts, I. et al. Effects of nanoplastics on mytilus galloprovincialis after individual and combined exposure with carbamazepine. Sci. Total Environ. 643, 775–784. https://doi.org/10.1016/j.scitotenv.2018.06.257 (2018).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Ciacci, C. et al. Nanoparticle-biological interactions in a marine benthic foraminifer. Sci. Rep. https://doi.org/10.1038/s41598-019-56037-2 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Kim, J. A. et al. Low dose of amino-modified nanoparticles induces cell cycle arrest. ACS Nano 7(9), 7483–7494 (2013).Article 
CAS 
PubMed 

Google Scholar 
Mao, Y. et al. Phytoplankton response to polystyrene microplastics: Perspective from an entire growth period. Chemosphere https://doi.org/10.1016/j.chemosphere.2018.05.170 (2018).Article 
PubMed 

Google Scholar 
Wang, F. et al. Time resolved study of cell death mechanisms induced by amine-modified polystyrene nanoparticles. Nanoscale 5(22), 10868–10876. https://doi.org/10.1039/c3nr03249c (2013).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Xia, T. et al. Comparison of the abilities of ambient and manufactured nanoparticles to induce cellular toxicity according to an oxidative stress paradigm. Nano Lett. 6(8), 1794–1807. https://doi.org/10.1021/nl061025k (2006).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Lagarde, F. et al. Microplastic interactions with freshwater microalgae: Hetero-aggregation and changes in plastic density appear strongly dependent on polymer type. Environ. Pollut. 215, 331–339. https://doi.org/10.1016/j.envpol.2016.05.006 (2016).Article 
CAS 
PubMed 

Google Scholar 
Bhattacharya, P., Lin, S., Turner, J. P. & Ke, P. C. Physical adsorption of charged plastic nanoparticles affects algal photosynthesis. J. Phys. Chem. C 114(39), 16556–16561. https://doi.org/10.1021/jp1054759 (2010).Article 
CAS 

Google Scholar 
Johansen, J. L., Rønn, R. & Ekelund, F. Toxicity of cadmium and zinc to small soil protists. Environ. Pollut. 242, 1510–1517. https://doi.org/10.1016/j.envpol.2018.08.034 (2018).Article 
CAS 
PubMed 

Google Scholar 
Díaz, S., Martín-González, A. & Carlos Gutiérrez, J. Evaluation of heavy metal acute toxicity and bioaccumulation in soil ciliated protozoa. Environ. Int. 32(6), 711–717. https://doi.org/10.1016/j.envint.2006.03.004 (2006).Article 
CAS 
PubMed 

Google Scholar 
Subba, P. et al. Zinc stress induces physiological, ultra-structural and biochemical changes in mandarin orange (Citrus Reticulata Blanco) seedlings. Physiol. Mol. Biol. Plants 20(4), 461–473. https://doi.org/10.1007/s12298-014-0254-2 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Corcoll, N. et al. The effect of metals on photosynthesis processes and diatom metrics of biofilm from a metal-contaminated river: A translocation experiment. Ecol. Indic. 18, 620–631. https://doi.org/10.1016/j.ecolind.2012.01.026 (2012).Article 
CAS 

Google Scholar 
Moffett, B. F. et al. Zinc contamination decreases the bacterial diversity of agricultural soil. FEMS Microbiol. Ecol. 43(1), 13–19. https://doi.org/10.1016/S0168-6496(02)00448-8 (2003).Article 
CAS 
PubMed 

Google Scholar 
Kuperman, R. G. & Carreiro, M. M. Soil heavy metal concentrations, microbial biomass and enzyme activities in a contaminated grassland ecosystem. Soil Biol. Biochem. 29(2), 179–190. https://doi.org/10.1016/S0038-0717(96)00297-0 (1997).Article 
CAS 

Google Scholar 
Masmoudi, S. et al. Cadmium, copper, sodium and zinc effects on diatoms: From heaven to hell-a review. Cryptogam Algol 34(2), 185–225. https://doi.org/10.7872/crya.v34.iss2.2013.185 (2013).Article 

Google Scholar 
Gadd, G. M. & de Rome, L. Biosorption of copper by fungal melanin. Appl. Microbiol. Biotechnol. 29(6), 610–617. https://doi.org/10.1007/BF00260993 (1988).Article 
CAS 

Google Scholar 
Khan, M. & Scullion, J. Effects of metal (Cd, Cu, Ni, Pb or Zn) enrichment of sewage-sludge on soil micro-organisms and their activities. Appl. Soil. Ecol. 20(2), 145–155. https://doi.org/10.1016/S0929-1393(02)00018-5 (2002).Article 

Google Scholar 
Guillard, R. R. L. & Lorenzen, C. J. Yellow-green algae with chlorophyllide C12. J. Phycol. 8(1), 10–14. https://doi.org/10.1111/j.1529-8817.1972.tb03995.x (1972).Article 
CAS 

Google Scholar 
Zagata, P., Kopańska, M., Greczek-Stachura, M. & Burnecki, T. Acute toxicity of metals: Nickel and zinc to Paramecium bursaria and its endosymbionts. J. Microbiol. Biotechnol. Food Sci. 04, 128–131. https://doi.org/10.15414/jmbfs.2015.4.special2.128-131 (2015).Article 
CAS 

Google Scholar 
Lenz, R., Enders, K. & Nielsen, T. G. Microplastic exposure studies should be environmentally realistic. Proc. Natl. Acad. Sci. U. S. A. 113(29), E4121–E4122. https://doi.org/10.1073/pnas.1606615113 (2016).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Schertzinger, G., Ruchter, N. & Sures, B. Metal accumulation in sediments and amphipods downstream of combined sewer overflows. Sci. Total Environ. 616–617, 1199–1207. https://doi.org/10.1016/j.scitotenv.2017.10.199 (2018).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Erasmus, J. H. et al. Metal accumulation in riverine macroinvertebrates from a platinum mining region. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2019.134738 (2020).Article 
PubMed 

Google Scholar 
Pradhan, S., Hedberg, J., Blomberg, E., Wold, S. & Odnevall Wallinder, I. Effect of sonication on particle dispersion, administered dose and metal release of non-functionalized, non-inert metal nanoparticles. J. Nanopart. Res. 18(9), 1–14. https://doi.org/10.1007/s11051-016-3597-5 (2016).Article 
CAS 

Google Scholar 
Taurozzi, J. S., Hackley, V. A. & Wiesner, M. R. Preparation of nanoparticle dispersions from powdered material using ultrasonic disruption. NIST Spec. Publ. 1200–2, 1–15 (2012).
Google Scholar 
Graupner, N. et al. Effects of short-term flooding on aquatic and terrestrial microeukaryotic communities: A mesocosm approach. Aquat. Microb. Ecol. 80(3), 257–272. https://doi.org/10.3354/ame01853 (2017).Article 

Google Scholar 
Strasser, R., Srivastava, A. & Tsimilli-Michael, M. The fluorescence transient as a tool to characterize and screen photosynthetic samples. In Probing Photosynthesis Mechanisms, Regulation and Adaption (eds Yanus, M. et al.) (Taylor and Francis, 2020).
Google Scholar 
Thwe, A. & Kasemsap, P. Quantification of OJIP fluorescence transient in tomato plants under acute ozone stress (2015).Amaral-Zettler, L. A., McCliment, E. A., Ducklow, H. W. & Huse, S. M. A method for studying protistan diversity using massively parallel sequencing of V9 hypervariable regions of small-subunit ribosomal RNA genes. PLoS ONE 4(7), 1–9. https://doi.org/10.1371/journal.pone.0006372 (2009).Article 
CAS 

Google Scholar 
Medlin, L., Elwood, H. J., Stickel, S. & Sogin, M. L. The characterization of enzymatically amplified eukaryotic 16S-like RRNA-coding regions. Gene 71(2), 491–499. https://doi.org/10.1016/0378-1119(88)90066-2 (1988).Article 
CAS 
PubMed 

Google Scholar 
Andrews, S. FastQC: A quality control tool for high throughput sequence data (2015).Lange, A. et al. AmpliconDuo: A split-sample filtering protocol for high-throughput amplicon sequencing of microbial communities. PLoS ONE 10(11), 1–22. https://doi.org/10.1371/journal.pone.0141590 (2015).Article 
CAS 

Google Scholar 
Schmieder, R. & Edwards, R. Quality control and preprocessing of metagenomic datasets. Bioinformatics 27(6), 863–864. https://doi.org/10.1093/bioinformatics/btr026 (2011).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Masella, P. A., Bartram, A. K., Truszkowski, J. M., Brow, D. G. & Neufeld, J. D. PANDAseq: Paired-end assembler for illumina sequences. BMC Bioinform. https://doi.org/10.1186/1471-2105-13-31 (2012).Article 

Google Scholar 
Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27(16), 2194–2200. https://doi.org/10.1093/bioinformatics/btr381 (2011).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Mahé, F., Rognes, T., Quince, C., de Vargas, C. & Dunthorn, M. Swarm: Robust and fast clustering method for amplicon-based studies. PeerJ 2014(1), 1–13. https://doi.org/10.7717/peerj.593 (2014).Article 

Google Scholar 
Callahan, B. J. et al. DADA2: High-resolution sample inference from illumina amplicon data. Nat. Methods 13(7), 581–583. https://doi.org/10.1038/nmeth.3869 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Welzel, M. et al. Natrix: A snakemake-based workflow for processing, clustering, and taxonomically assigning amplicon sequencing reads. BMC Bioinform. 21(1), 1–14. https://doi.org/10.1186/s12859-020-03852-4 (2020).Article 
CAS 

Google Scholar 
Oksanen, J. Package “vegan” Title Community Ecology Package (2022).R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.r-project.org/.Chen, W., Simpson, J. & Leveque, C. RAM: R for amplicon-sequencing-based microbial-ecology (2018).Yarza, P. et al. Uniting the classification of cultured and uncultured bacteria and archaea using 16S RRNA gene sequences. Nat. Rev. Microbiol. 12(9), 635–645. https://doi.org/10.1038/nrmicro3330 (2014).Article 
CAS 
PubMed 

Google Scholar 
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15(12), 1–21. https://doi.org/10.1186/s13059-014-0550-8 (2014).Article 
CAS 

Google Scholar 
Palarea-Albaladejo, J. & Martín-Fernández, J. A. ZCompositions—R package for multivariate imputation of left-censored data under a compositional approach. Chemom. Intell. Lab. Syst. 143, 85–96. https://doi.org/10.1016/j.chemolab.2015.02.019 (2015).Article 
CAS 

Google Scholar 
Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: And this is not optional. Front. Microbiol. 8, 1–6. https://doi.org/10.3389/fmicb.2017.02224 (2017).Article 

Google Scholar 
Dusaucy, J., Gateuille, D., Perrette, Y. & Naffrechoux, E. Microplastic pollution of worldwide lakes. Environ. Pollut. 284, 117075. https://doi.org/10.1016/j.envpol.2021.117075 (2021).Article 
CAS 
PubMed 

Google Scholar 
Vardhan, K. H., Kumar, P. S. & Panda, R. C. A review on heavy metal pollution, toxicity and remedial measures: Current trends and future perspectives. J. Mol. Liq. 290, 111197. https://doi.org/10.1016/j.molliq.2019.111197 (2019).Article 
CAS 

Google Scholar 
Damare, V. S. Diversity of thraustochytrid protists isolated from brown alga, Sargassum cinereum using 18S RDNA sequencing and their morphological response to heavy metals. J. Mar. Biol. Assoc. 95(2), 265–276. https://doi.org/10.1017/S0025315414001696 (2015).Article 
CAS 

Google Scholar 
Giongo, A. et al. Adaption of Microbial communities to the hostile environment in the Doce river after the collapse of two iron ore tailing dams. Heliyon https://doi.org/10.1016/j.heliyon.2020.e04778 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Kelly, J. J., Häggblom, M. M. & Tate, R. L. Effects of heavy metal contamination and remediation on soil microbial communities in the vicinity of a zinc smelter as indicated by analysis of microbial community phospholipid fatty acid profiles. Biol. Fertil. Soils 38(2), 65–71. https://doi.org/10.1007/s00374-003-0642-1 (2003).Article 
CAS 

Google Scholar 
Baddar, Z. E., Peck, E. & Xu, X. Temporal deposition of copper and zinc in the sediments of metal removal constructed wetlands. PLoS ONE 16, 1–14. https://doi.org/10.1371/journal.pone.0255527 (2021).Article 
CAS 

Google Scholar 
Li, X., Shen, Z., Wai, O. W. H. & Li, Y. S. Chemical partitioning of heavy metal contaminants in sediments of the Pearl River Estuary. Chem. Speciat. Bioavailab. 12(1), 17–25. https://doi.org/10.3184/095422900782775607 (2000).Article 
CAS 

Google Scholar 
Müller, B. & Sigg, L. Interaction of trace metals with natural particle surfaces: Comparison between adsorption experiments and field measurements—Dedicated to Werner Stumm for his 65th birthday. Aquat. Sci. 52(1), 75–92. https://doi.org/10.1007/BF00878242 (1990).Article 

Google Scholar 
Bradl, H. B. Adsorption of heavy metal ions on soils and soils constituents. J. Colloid Interface Sci. 277(1), 1–18. https://doi.org/10.1016/j.jcis.2004.04.005 (2004).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Siegel, F. R. Environmental Geochemistry of Potentially Toxic Heavy Metals (Springer-Verlag, 2002).Book 

Google Scholar 
Vig, K., Megharaj, M., Sethunathan, N. & Naidu, R. Bioavailability and toxicity of cadmium to microorganisms and their activities in soil: A review. Adv. Environ. Res. 8(1), 121–135. https://doi.org/10.1016/S1093-0191(02)00135-1 (2003).Article 
CAS 

Google Scholar 
Nicolau, A., Mota, M. & Lima, N. Physiological responses of tetrahymena pyriformis to copper, zinc, cycloheximide and triton X-100. FEMS Microbiol. Ecol. 30(3), 209–216. https://doi.org/10.1016/S0168-6496(99)00057-4 (1999).Article 
CAS 
PubMed 

Google Scholar 
Admiraal, W. et al. Short-term toxicity of zinc to microbenthic algae and bacteria in a metal polluted stream. Water Res. 33(9), 1989–1996. https://doi.org/10.1016/S0043-1354(98)00426-6 (1999).Article 
CAS 

Google Scholar 
Bradac, P., Navarro, E., Odzak, N., Behra, R. & Sigg, L. Kinetics of cadmium accumulation in periphyton under freshwater conditions. Environ. Toxicol. Chem. 28(10), 2108–2116. https://doi.org/10.1897/08-511R1.1 (2009).Article 
CAS 
PubMed 

Google Scholar 
Collard, J. & Matagne, R. F. Cd2+ resistance in wild-type and mutant strains of Chlamydomonas reinhardtii. Environ. Exp. Bot. 34(2), 235–244 (1994).Article 
CAS 

Google Scholar 
Wright, R. J., Gibson, M. I. & Christie-Oleza, J. A. Understanding microbial community dynamics to improve optimal microbiome selection. Microbiome 7(1), 1–14. https://doi.org/10.1186/s40168-019-0702-x (2019).Article 

Google Scholar 
Buffle, J. The key role of environmental colloids/nanoparticles for the sustainability of life. Environ. Chem. 3(3), 155–158. https://doi.org/10.1071/ENv3n3_ES (2006).Article 
CAS 

Google Scholar 
Nowack, B. & Bucheli, T. D. Occurrence, behavior and effects of nanoparticles in the environment. Environ. Pollut. 150(1), 5–22. https://doi.org/10.1016/j.envpol.2007.06.006 (2007).Article 
CAS 
PubMed 

Google Scholar 
Fetzer, I. et al. The extent of functional redundancy changes as species’ roles shift in different environments. Proc. Natl. Acad. Sci. U. S. A. 112(48), 14888–14893. https://doi.org/10.1073/pnas.1505587112 (2015).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Biggs, C. R. et al. Does functional redundancy affect ecological stability and resilience? A review and meta-analysis. Ecosphere https://doi.org/10.1002/ecs2.3184 (2020).Article 

Google Scholar 
Fleeger, J. W. How do indirect effects of contaminants inform ecotoxicology? A review. Processes https://doi.org/10.3390/pr8121659 (2020).Article 

Google Scholar 
Oriekhova, O. & Stoll, S. Heteroaggregation of nanoplastic particles in the presence of inorganic colloids and natural organic matter. Environ. Sci. Nano. 5(3), 792–799. https://doi.org/10.1039/c7en01119a (2018).Article 
CAS 

Google Scholar 
Rowenczyk, L. et al. Heteroaggregates of polystyrene nanospheres and organic matter: Preparation, characterization and evaluation of their toxicity to algae in environmentally relevant conditions. Nanomaterials 11(2), 1–15. https://doi.org/10.3390/nano11020482 (2021).Article 
CAS 

Google Scholar 
Saavedra, J., Stoll, S. & Slaveykova, V. I. Influence of nanoplastic surface charge on eco-corona formation, aggregation and toxicity to freshwater zooplankton. Environ. Pollut. 252, 715–722. https://doi.org/10.1016/j.envpol.2019.05.135 (2019).Article 
CAS 
PubMed 

Google Scholar 
Bižic-Ionescu, M., Ionescu, D. & Grossart, H. P. Organic particles: Heterogeneous hubs for microbial interactions in aquatic ecosystems. Front. Microbiol. 9, 1–15. https://doi.org/10.3389/fmicb.2018.02569 (2018).Article 

Google Scholar 
Lespes, G., Faucher, S. & Slaveykova, V. I. natural nanoparticles, anthropogenic nanoparticles, where is the Frontier?. Front. Environ. Sci. 8, 1–5. https://doi.org/10.3389/fenvs.2020.00071 (2020).Article 

Google Scholar 
Stabnikova, O. et al. Microbial life on the surface of microplastics in natural waters. Appl. Sci. 11(24), 1–19. https://doi.org/10.3390/app112411692 (2021).Article 
CAS 

Google Scholar 
Suominen, S., Doorenspleet, K., Sinninghe Damsté, J. S. & Villanueva, L. Microbial community development on model particles in the deep sulfidic waters of the Black Sea. Environ. Microbiol. 23(6), 2729–2746. https://doi.org/10.1111/1462-2920.15024 (2021).Article 
CAS 
PubMed 

Google Scholar 
Wagner, S., Gondikas, A., Neubauer, E., Hofmann, T. & von der Kammer, F. Spot the difference: Engineered and natural nanoparticles in the environment-release, behavior, and fate. Angew. Chem. Int. Ed. 53(46), 12398–12419. https://doi.org/10.1002/anie.201405050 (2014).Article 
CAS 

Google Scholar 
Amelia, T. S. et al. Marine microplastics as vectors of major ocean pollutants and its hazards to the marine ecosystem and humans. Prog. Earth Planet. Sci. https://doi.org/10.1186/s40645-020-00405-4 (2021).Article 

Google Scholar 
Liu, J., Huang, J. & Che, F. Microalgae as feedstocks for biodiesel production. In Biodiesel—Feedstocks and Processing Technologies (ed. Stoytcheva, M.) (InTech, 2011). https://doi.org/10.5772/25600.Chapter 

Google Scholar 
Takamura, N., Kasai, F. & Watanabe, M. M. Effects of Cu, Cd and Zn on photosynthesis of freshwater benthic algae. J. Appl. Phycol. 1(1), 39–52. https://doi.org/10.1007/BF00003534 (1989).Article 
CAS 

Google Scholar 
Brembu, T., Jørstad, M., Winge, P., Valle, K. C. & Bones, A. M. Genome-wide profiling of responses to cadmium in the diatom Phaeodactylum tricornutum. Environ. Sci. Technol. 45(18), 7640–7647. https://doi.org/10.1021/es2002259 (2011).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Fernandez, J. C. & Henriques, F. S. Biochemical, physiological and structural effects of excess copper in plants. Bot. Rev. 57(3), 246–273 (1991).Article 

Google Scholar 
Haq, R. U., Rehman, A. & Shakoori, A. R. Effect of dichromate on population and growth of various protozoa isolated from industrial effluents. Folia Microbiol. 45(3), 275–278. https://doi.org/10.1007/bf02908959 (2000).Article 
CAS 

Google Scholar 
Rehman, A., Shakoori, F. R. & Shakoori, A. R. Heavy metal resistant freshwater ciliate, Euplotes mutabilis, isolated from industrial effluents has potential to decontaminate wastewater of toxic metals. Bioresour. Technol. 99(9), 3890–3895. https://doi.org/10.1016/j.biortech.2007.08.007 (2008).Article 
CAS 
PubMed 

Google Scholar 
Rehman, A., Ashraf, S., Qazi, J. I. & Shakoori, A. R. Uptake of lead by a ciliate, stylonychia mytilus, isolated from industrial effluents: Potential use in bioremediation of wastewater. Bull. Environ. Contam. Toxicol. 75(2), 290–296. https://doi.org/10.1007/s00128-005-0751-7 (2005).Article 
CAS 
PubMed 

Google Scholar 
Shakoori, A. R., Rehman, A. & ul-Haq, R. Multiple metal resistance in the ciliate protozoan, vorticella microstoma, isolated from industrial effluents and its potential in bioremediation of toxic wastes. Bull. Environ. Contam. Toxicol. 72(5), 1046–1051. https://doi.org/10.1007/s00128-004-0349-5 (2004).Article 
CAS 
PubMed 

Google Scholar 
Falasco, E. et al. Morphological abnormalities of diatom silica walls in relation to heavy metal contamination and artificial growth conditions. Water SA 35(5), 595–606. https://doi.org/10.4314/wsa.v35i5.49185 (2009).Article 
CAS 

Google Scholar 
Tadros, M. G., Mbuthia, P. & Smith, W. Differential response of marine diatoms to trace metals. Bull. Environ. Contam. Toxicol. 44(6), 826–831. https://doi.org/10.1007/BF01702170 (1990).Article 
CAS 
PubMed 

Google Scholar 
Wanner, M. et al. Soil testate amoebae and diatoms as bioindicators of an old heavy metal contaminated floodplain in Japan. Microb. Ecol. 79(1), 123–133. https://doi.org/10.1007/s00248-019-01383-x (2020).Article 
CAS 
PubMed 

Google Scholar 
Shi, J., Podola, B. & Melkonian, M. Application of a prototype-scale twin-layer photobioreactor for effective N and P removal from different process stages of municipal wastewater by immobilized microalgae. Bioresour. Technol. 154, 260–266. https://doi.org/10.1016/j.biortech.2013.11.100 (2014).Article 
CAS 
PubMed 

Google Scholar 
Li, T., Lin, G., Podola, B. & Melkonian, M. Continuous removal of zinc from wastewater and mine dump leachate by a microalgal biofilm PSBR. J. Hazard. Mater. 297, 112–118. https://doi.org/10.1016/j.jhazmat.2015.04.080 (2015).Article 
CAS 
PubMed 

Google Scholar 
Bruning, K. Infection of the diatom Asterionella by a chytrid. I. Effects of light on reproduction and infectivity of the parasite. J. Plankton Res. 13(1), 103–117. https://doi.org/10.1093/plankt/13.1.103 (1991).Article 

Google Scholar 
Carney, L. T. & Lane, T. W. Parasites in algae mass culture. Front. Microbiol. 5, 1–8. https://doi.org/10.3389/fmicb.2014.00278 (2014).Article 

Google Scholar 
Hanic, L. A., Sekimoto, S. & Bates, S. S. Oomycete and chytrid infections of the marine diatom Pseudo-nitzschia pungens (Bacillariophyceae) from Prince Edward Island. Botany 87(11), 1096–1105. https://doi.org/10.1139/B09-070 (2009).Article 
CAS 

Google Scholar 
Sun, A. et al. Fertilization alters protistan consumers and parasites in crop-associated microbiomes. Environ. Microbiol. 23(4), 2169–2183. https://doi.org/10.1111/1462-2920.15385 (2021).Article 
CAS 
PubMed 

Google Scholar 
Scholz, B., Guillou, L., Marano, A. V., Neuhauser, S. & Brooke, K. Europe PMC funders group zoosporic parasites infecting marine diatoms—A black box that needs to be opened. Fungal Ecol. https://doi.org/10.1016/j.funeco.2015.09.002.Zoosporic (2017).Article 

Google Scholar 
Peacock, E. E., Olson, R. J. & Sosik, H. M. Parasitic infection of the diatom Guinardia delicatula, a recurrent and ecologically important phenomenon on the New England Shelf. Mar. Ecol. Prog. Ser. 503, 1–10. https://doi.org/10.3354/meps10784 (2014).Article 
ADS 

Google Scholar 
Duarte, S., Pascoal, C. & Cássio, F. Effects of zinc on leaf decomposition by fungi in streams: Studies in microcosms. Microb. Ecol. 48(3), 366–374. https://doi.org/10.1007/s00248-003-2032-5 (2004).Article 
CAS 
PubMed 

Google Scholar 
Kammerlander, B. et al. High diversity of protistan plankton communities in remote high mountain lakes in the European Alps and the Himalayan Mountains. FEMS Microbiol. Ecol. 91(4), 1–10. https://doi.org/10.1093/femsec/fiv010 (2015).Article 
CAS 

Google Scholar 
Sieber, G., Beisser, D., Bock, C. & Boenigk, J. Protistan and fungal diversity in soils and freshwater lakes are substantially different. Sci. Rep. 10(1), 1–11. https://doi.org/10.1038/s41598-020-77045-7 (2020).Article 
CAS 

Google Scholar 
Gunaalan, K., Fabbri, E. & Capolupo, M. The hidden threat of plastic leachates: A critical review on their impacts on aquatic organisms. Water Res. https://doi.org/10.1016/j.watres.2020.116170 (2020).Article 
PubMed 

Google Scholar 
Tetu, S. G., Sarker, I., Schrameyer, V., Pickford, R., Elbourne, L. D., Moore, L.R. & Paulsen, I.T. Plastic leachates impair growth and oxygen production in Prochlorococcus, the ocean’s most abundant photosynthetic bacteria. Commun. Biol. 2(1), 1–9. https://doi.org/10.1038/s42003-019-0410-x (2019).Gouin, T., Roche, N., Lohmann, R. & Hodges, G. A Thermodynamic approach for assessing the environmental exposure of chemicals absorbed to microplastic. Environ. Sci. Technol. 45(4), 1466–1472. https://doi.org/10.1021/es1032025 (2011).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Lohmann, R. Microplastics are not important for the cycling and bioaccumulation of organic pollutants in the oceans—But should microplastics be considered POPs themselves?. Integr. Environ. Assess. Manag. 13(3), 460–465. https://doi.org/10.1002/ieam.1914 (2017).Article 
CAS 
PubMed 

Google Scholar 
Sukkasem, C. & Laehlah, S. An economical upflow bio-filter circuit (UBFC): A biocatalyst microbial fuel cell for sulfate-sulfide rich wastewater treatment. Environ. Sci. 1(2), 161–168. https://doi.org/10.1039/c4ew00028e (2015).Article 
CAS 

Google Scholar 
Abatenh, E., Gizaw, B., Tsegaye, Z. & Wassie, M. The role of microorganisms in bioremediation-A review. Open J. Environ. Biol. 2(1), 38–46. https://doi.org/10.17352/ojeb (2017).Article 

Google Scholar 
Zrimec, J., Kokina, M., Jonasson, S., Zorrilla, F. & Zelezniak, A. Plastic-degrading potential across the global microbiome correlates with recent pollution trends. MBio https://doi.org/10.1128/mBio (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Siver, P. A. Synurophyte algae. In Freshwater Algae of North America. Ecology and classification (eds Wehr, J. D. & Sheath, R. G.) 523–558 (Elsevier, 2003).Chapter 

Google Scholar 
Andersen, R. A. Molecular systematics of the chrysophyceae and synurophyceae. In Unravelling the Algae: The Past, Present, and Future of Algal Systematics (eds Brodie, J. & Lewis, J.) 285–314 (CRC Press, Boca Raton, 2007).Chapter 

Google Scholar 
Engin, I. K., Cekmecelioglu, D., Yücel, A. M. & Oktem, H. A. Evaluation of heterotrophic and mixotrophic cultivation of novel Micractinium Sp. ME05 on vinasse and its scale up for biodiesel production. Bioresour. Technol. 251, 128–134. https://doi.org/10.1016/j.biortech.2017.12.023 (2018).Article 
CAS 
PubMed 

Google Scholar 
Patrick, R. Ecology of freshwater diatoms and diatom communities. In The Biology of Diatoms (ed. Werner, D.) 284–332 (University of California Press, 1977).
Google Scholar 
Findenig, B. M., Chatzinotas, A. & Boenigk, J. Taxonomic and ecological characterization of stomatocysts of spumella-like flagellates (Chrysophyceae). J. Phycol. 46(5), 868–881. https://doi.org/10.1111/j.1529-8817.2010.00892.x (2010).Article 

Google Scholar 
Perez-Garcia, O., Escalante, F. M. E., de-Bashan, L. E. & Bashan, Y. Heterotrophic cultures of microalgae: Metabolism and potential products. Water Res. 45(1), 11–36. https://doi.org/10.1016/j.watres.2010.08.037 (2011).Article 
CAS 
PubMed 

Google Scholar 
Preisig, H. R. & Hibberd, D. J. Ultrastructure and taxonomy of Paraphysomonas (Chrysophyceae) and related genera 3. Nord. J. Bot. 3(6), 695–723. https://doi.org/10.1111/j.1756-1051.1983.tb01481.x (1983).Article 

Google Scholar 
Atkins, M. S. et al. Tolerance of flagellated protists to high sulfide and metal concentrations potentially encountered at deep-sea hydrothermal vents. Mar. Ecol. Prog. Ser. 226, 63–75. https://doi.org/10.3354/meps226063 (2002).Article 
ADS 
CAS 

Google Scholar 
Manru, G., Weisong, F. & Yunfen, S. Ecological study on protozoa in the sediment of the three-gorges area of the Changjiang River. Chin. J. Oceanol. Limnol. 6(3), 272–280. https://doi.org/10.1007/BF02846505 (1988).Article 

Google Scholar 
Tomilina, I. I., Gremyachikh, V. A., Myl’Nikov, A. P. & Komov, V. T. The effect of metal oxide nanoparticles (CeO2, TiO2, and ZnO) on biological parameters of freshwater nanoflagellates and crustaceans. Dokl. Biol. Sci. 436(1), 53–55. https://doi.org/10.1134/S0012496611010169 (2011).Article 
CAS 
PubMed 

Google Scholar 
Schampera, C. et al. Exposure to nanoplastics affects the outcome of infectious disease in phytoplankton. Environ. Pollut. https://doi.org/10.1016/j.envpol.2021.116781 (2021).Article 
PubMed 

Google Scholar 
Gonçalves, J. M., Sousa, V. S., Teixeira, M. R. & Bebianno, M. J. Chronic toxicity of polystyrene nanoparticles in the marine mussel Mytilus galloprovincialis. Chemosphere https://doi.org/10.1016/j.chemosphere.2021.132356 (2021).Article 
PubMed 

Google Scholar 
Kelpsiene, E., Torstensson, O., Ekvall, M. T., Hansson, L. A. & Cedervall, T. Long-term exposure to nanoplastics reduces life-time in Daphnia magna. Sci. Rep. 10(1), 1–7. https://doi.org/10.1038/s41598-020-63028-1 (2020).Article 
CAS 

Google Scholar 
Amin, N. M. Techniques for assessment of heavy metal toxicity using Acanthamoeba Sp, a small, naked and free-living amoeba. Funct. Ecosyst. https://doi.org/10.5772/36008 (2012).Article 

Google Scholar 
Amin, N. M., Azhar, N. & Shazili, M. Cytotoxic effects of mercury, cadmium, lead and zinc on Acanthamoeba Castellanii (2006).Gnecco, I., Berretta, C., Lanza, L. G. & la Barbera, P. Storm water pollution in the urban environment of Genoa, Italy. Atmos. Res. 77, 60–73. https://doi.org/10.1016/j.atmosres.2004.10.017 (2005).Article 
CAS 

Google Scholar 
Heim, R. R. An overview of weather and climate extremes—Products and trends. Weather Clim. Extrem. 10, 1–9. https://doi.org/10.1016/j.wace.2015.11.001 (2015).Article 

Google Scholar 
Saiki, M. K., Castleberry, D. T., May, T. W., Martin, B. A. & Bullard, F. N. Copper, cadmium, and zinc concentrations in aquatic food chains from the upper Sacramento River (California) and selected tributaries. Arch. Environ. Contam. Toxicol. 29(4), 484–491. https://doi.org/10.1007/BF00208378 (1995).Article 
CAS 

Google Scholar 
Wagner, S. et al. Tire wear particles in the aquatic environment—A review on generation, analysis, occurrence, fate and effects. Water Res. 139, 83–100. https://doi.org/10.1016/j.watres.2018.03.051 (2018).Article 
CAS 
PubMed 

Google Scholar 
Zhang, L., Zhao, B., Xu, G. & Guan, Y. Characterizing fluvial heavy metal pollutions under different rainfall conditions: Implication for aquatic environment protection. Sci. Total Environ. 635, 1495–1506. https://doi.org/10.1016/j.scitotenv.2018.04.211 (2018).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Zhao, B. et al. Characterization of nitrosamines and nitrosamine precursors as non-point source pollutants during heavy rainfall events in an urban water environment. J. Hazard. Mater. 424, 127552. https://doi.org/10.1016/j.jhazmat.2021.127552 (2022).Article 
CAS 
PubMed 

Google Scholar 
Hüffer, T., Wagner, S., Reemtsma, T. & Hofmann, T. Sorption of organic substances to tire wear materials: Similarities and differences with other types of microplastic. TrAC Trends Anal. Chem. 113, 392–401. https://doi.org/10.1016/j.trac.2018.11.029 (2019).Article 
CAS 

Google Scholar 
Tamis, J. E. et al. Environmental risks of car tire microplastic particles and other road runoff pollutants. Microplastics Nanoplastics 1(1), 1–17. https://doi.org/10.1186/s43591-021-00008-w (2021).Article 

Google Scholar 
Chèvre, N. et al. Substance flow analysis as a tool for urban water management. Water Sci. Technol. 63(7), 1341–1348. https://doi.org/10.2166/wst.2011.132 (2011).Article 
PubMed 

Google Scholar 
Šourková, M., Adamcová, D. & Vaverková, M. D. The influence of microplastics from ground tyres on the acute, subchronical toxicity and microbial respiration of soil. Environ. MDPI 8(11), 1–14. https://doi.org/10.3390/environments8110128 (2021).Article 

Google Scholar 
Ye, G., Zhang, X., Yan, C., Lin, Y. & Huang, Q. Polystyrene microplastics induce microbial dysbiosis and dysfunction in surrounding seawater. Environ. Int. 156, 106724. https://doi.org/10.1016/j.envint.2021.106724 (2021).Article 
CAS 
PubMed 

Google Scholar  More

Potts, S. G. et al. (eds.). IPBES: The Assessment Report of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services on Pollinators, Pollination and Food Production (Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, Bonn, Germany) (2016).Potts, S. G. et al. Global pollinator declines: Trends, impacts and drivers. Trends Ecol. Evol. 25, 345–353 (2010).PubMed 

Google Scholar 
Goulson, D., Nicholls, E., Botías, C. & Rotheray, E. L. Bee declines driven by combined stress from parasites, pesticides, and lack of flowers. Science 347, 1255957 (2015).PubMed 

Google Scholar 
Majewska, A. A. & Altizer, S. Planting gardens to support insect pollinators. Conserv. Biol. 34, 15–25 (2020).PubMed 

Google Scholar 
Image, M. et al. Does agri-environment scheme participation in England increase pollinator populations and crop pollination services?. Agric. Ecosyst. Environ. 325, 107755 (2022).
Google Scholar 
Vaissière, B., Freitas, B. M. & Gemmill-Herren, B. Protocol to Detect and Assess Pollination Deficits in Crops: A Handbook for Its Use (FAO, 2011).
Google Scholar 
Archer, C. R., Pirk, C. W. W., Carvalheiro, L. G. & Nicolson, S. W. Economic and ecological implications of geographic bias in pollinator ecology in the light of pollinator declines. Oikos 123, 401–407 (2014).
Google Scholar 
M’Gonigle, L. K., Ponisio, L. C., Cutler, K. & Kremen, C. Habitat restoration promotes pollinator persistence and colonization in intensively managed agriculture. Ecol. Appl. 25, 1557–1565 (2015).PubMed 

Google Scholar 
Garbuzov, M. & Ratnieks, F. L. W. Listmania: The strengths and weaknesses of lists of garden plants to help pollinators. Bioscience 64, 1019–1026 (2014).
Google Scholar 
Garbuzov, M. & Ratnieks, F. L. W. Quantifying variation among garden plants in attractiveness to bees and other flower-visiting insects. Funct. Ecol. 28, 364–374 (2014).
Google Scholar 
Garbuzov, M., Alton, K. & Ratnieks, F. L. W. Most ornamental plants on sale in garden centres are unattractive to flower-visiting insects. PeerJ 5, e3066 (2017).PubMed 
PubMed Central 

Google Scholar 
Nichols, R. N., Goulson, D. & Holland, J. M. The best wildflowers for wild bees. J. Insect Conserv. 23, 819–830 (2019).
Google Scholar 
Harmon-Threatt, A. Influence of nesting characteristics on health of wild bee communities. Annu. Rev. Entomol. 65, 39–56 (2020).CAS 
PubMed 

Google Scholar 
Requier, F. & Leonhardt, S. D. Beyond flowers: Including non-floral resources in bee conservation schemes. J. Insect Conserv. 24, 5–16 (2020).
Google Scholar 
Sinu, P. A. & Bronstein, J. L. Foraging preferences of leafcutter bees in three contrasting geographical zones. Divers. Distrib. 24, 621–628 (2018).
Google Scholar 
Cecala, J. M. & Rankin, E. E. Pollinators and plant nurseries: How irrigation and pesticide treatment of native ornamental plants impact solitary bees. Proc. R. Soc. B Biol. Sci. 288, 20211287 (2021).
CAS 

Google Scholar 
Gonzalez, V. H., Gustafson, G. T. & Engel, M. S. Morphological phylogeny of Megachilini and the evolution of leaf-cutter behavior in bees (Hymenoptera: Megachilidae). J. Melittology 85, 1–123 (2019).
Google Scholar 
Kambli̇, S. S. et al. M. S. Aiswarya, K. Manoj, S. Varma, G. Asha, T. P. Rajesh, P. A. Sinu, Leaf foraging sources of leafcutter bees in a tropical environment: Implications for conservation. Apidologie 48, 473–482 (2017).Ascher, J. S. & Pickering, J. Discover Life Bee Species Guide and World Checklist (Hymenoptera: Apoidea: Anthophila) (2019).McCabe, L. M., Aslan, S. E. & Cobb, N. S. Decreased bee emergence along an elevation gradient: implications for climate change revealed by a transplant experiment. Ecology 103, e03598 (2021).PubMed 

Google Scholar 
Pitts-Singer, T. L. & Cane, J. H. The Alfalfa leafcutting bee, Megachile rotundata: The worlds most intensively managed solitary bee. Annu. Rev. Entomol. 56, 221–237 (2011).CAS 
PubMed 

Google Scholar 
MacIvor, J. S. & Packer, L. “Bee hotels” as tools for native pollinator conservation: A premature verdict?. PLoS ONE 10, e0122126 (2015).PubMed 
PubMed Central 

Google Scholar 
Maclvor, J. S. DNA barcoding to identify leaf preference of leafcutting bees. R. Soc. Open Sci. 3, 150623 (2016).ADS 

Google Scholar 
Wissemann, V. & Ritz, C. M. The genus Rosa (Rosoideae, Rosaceae) revisited: Molecular analysis of nrITS-1 and atpB-rbcL intergenic spacer (IGS) versus conventional taxonomy. Botanical J. Linn. Soc. 147, 275–290 (2005).
Google Scholar 
Wang, G. Study on the history of Chinese roses from ancient works and images. Acta Hort. 751, 347–356 (2007).
Google Scholar 
Nybom, H. & Werlemark, G. Realizing the potential of health-promoting rosehips from dogroses (Rosa sect. Caninae). Curr. Bioact. Compd. 13, 3–17 (2016).
Google Scholar 
Chang, Y. Z., Chen, H. M. & Qi, R. S. Ornamental pest—studies on leafcutting bees Megachile subtranquilla Yasumatsu. Acta Agriculturae Universitatis Pekinensis 15, 208–213 (1989).
Google Scholar 
Stroom, K., Fetzer, J. & Krischik, V. Insect Pests of Roses. 1–12 (Minnesota Extension Service, University of Minnesota, 1997).Knox, G. W., Paret, M. & Mizell, R. F. III. Pests of roses in Florida (2008).Hayward, A. et al. The leafcutter bee, Megachile rotundata, is more sensitive to N-cyanoamidine neonicotinoid and butenolide insecticides than other managed bees. Nat. Ecol. Evol. 3, 1521–1524 (2019).PubMed 

Google Scholar 
Fox, J. et al. Package ‘car’, Vol. 16, (R Foundation for Statistical Computing, 2012).K. Barton, Package Multi-Model Inference (MuMIn). https://cran.r-project.org/web/packages/MuMIn/MuMIn.pdf (2013).Hartig, F. & Hartig M. F. Package ‘DHARMa’:R package (2017).R Core Team. R: A Language and Environment for Statistical Computing https://www.R-project.org/ (R Foundation for Statistical Computing, 2021).Boff, S., Raizer, J. & Lupi, D. Environmental display can buffer the effect of pesticides on solitary bees. Insects. 11, 1–15 (2020).
Google Scholar 
Cameron, S. A. et al. Patterns of widespread decline in North American bumble bees. Proc. Natl. Acad. Sci. USA. 108, 662–667 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Cameron, S. A. & Sadd, B. M. Global trends in bumble bee health. Annu. Rev. Entomol. 65, 209–232 (2020).CAS 
PubMed 

Google Scholar 
Kopit, A. M. & Pitts-Singer, T. L. Routes of pesticide exposure in solitary, cavity-nesting bees. Environ. Entomol. 47, 499–510 (2018).CAS 

Google Scholar 
Pitts-Singer, T. L. & Barbour, J. D. Effects of residual novaluron on reproduction in alfalfa leafcutting bees, Megachile rotundata F. (Megachilidae). Pest Manag. Sci. 73, 153–159 (2017).CAS 
PubMed 

Google Scholar 
McKinney, M. L. Urbanization, biodiversity, and conservation. Bioscience 52, 883–890 (2002).
Google Scholar 
Baldock, K. C. R. et al. A systems approach reveals urban pollinator hotspots and conservation opportunities. Nat. Ecol. Evol. 3, 363–373 (2019).PubMed 
PubMed Central 

Google Scholar 
Choate, B. A., Hickman, P. L. & Moretti, E. A. Wild bee species abundance and richness across an urban–rural gradient. J. Insect Conserv. 22, 391–403 (2018).
Google Scholar 
Theodorou, P. et al. Pollination services enhanced with urbanization despite increasing pollinator parasitism. Proc. R. Soc. B Biol. Sci. 283, 20160561 (2016).
Google Scholar 
Theodorou, P. et al. Urban areas as hotspots for bees and pollination but not a panacea for all insects. Nat. Commun. 11, 1–13 (2020).
Google Scholar 
Rocha-Filho, L. C., Martins, A. C. & Marchi, P. Notes on a nest of Megachile (Moureapis) apicipennis Schrottky (Megachilidae) constructed in an abandoned gallery of Xylocopa frontalis (Olivier) (Apidae). Sociobiology 64, 442–450 (2017).
Google Scholar 
Sheffield, C. S. Unusual nesting behavior in Megachile (Eutricharaea) rotundata (Hymenoptera: Megachilidae). J. Melittol. 69, 1–6 (2017).
Google Scholar 
Krischik, V., Rogers, M., Gupta, G. & Varshney, A. Soil-applied imidacloprid translocates to ornamental flowers and reduces survival of adult Coleomegilla maculata, Harmonia axyridis, and Hippodamia convergens lady beetles, and larval Danaus plexippus and Vanessa cardui butterflies. PLoS ONE 10, e0119133 (2015).PubMed 
PubMed Central 

Google Scholar 
Senapathi, D. et al. The impact of over 80 years of land cover changes on bee and wasp pollinator communities in England. Proc. R. Soc. B Biol. Sci. 282, 20150294 (2015).
Google Scholar 
Potts, S. G. et al. Role of nesting resources in organising diverse bee communities in a Mediterranean landscape. Ecol. Entomol. 30, 78–85 (2005).
Google Scholar 
Acar, C., Acar, H. & Eroǧlu, E. Evaluation of ornamental plant resources to urban biodiversity and cultural changing: A case study of residential landscapes in Trabzon city (Turkey). Build. Environ. 42, 218–229 (2007).
Google Scholar 
Wang, H. F., Qureshi, S., Knapp, S., Friedman, C. R. & Hubacek, K. A basic assessment of residential plant diversity and its ecosystem services and disservices in Beijing, China. Appl. Geogr. 64, 121–131 (2015).
Google Scholar 
Pergl, J. et al. Dark side of the fence: ornamental plants as a source of wildgrowing flora in the Czech Republic. Preslia 88, 163–184 (2016).
Google Scholar 
Avolio, M. et al. Urban plant diversity in Los Angeles, California: Species and functional type turnover in cultivated landscapes. Plants People Planet. 2, 144–156 (2020).
Google Scholar 
Orr, M. C. et al. Global patterns and drivers of bee distribution. Curr. Biol. 31, 451–458 (2021).CAS 
PubMed 

Google Scholar 
Sinu, P. A., Kuriakose, G. & Shivanna, K. R. Is the bumblebee (Bombus haemorrhoidalis) the only pollinator of large cardamom in central Himalayas, India?. Apidologie 42, 690–695 (2012).
Google Scholar 
Veereshkumar, V. V. & Gupta, A. Parasitisation of leaf-cutter bees (Megachilidae: Apoidea) by Melittobia. Entomon 40, 105–112 (2015).
Google Scholar 
Cecala, J. M. & Wilson Rankin, E. E. Petals and leaves: Quantifying the use of nest building materials by the world’s most valuable solitary bee. Ecology 103, e03584 (2021).PubMed 

Google Scholar 
Soh, E. J. Y., Soh, Z. W. W., Ascher, J. S. & Tan, H. T. W. Diversity of plants with leaves cut by bees of the genus Megachile in Singapore. Nat. Singap. 12, 63–74 (2019).
Google Scholar 
MacIvor, J. S. & Moore, A. E. Bees collect polyurethane and polyethylene plastics as novel nest materials. Ecosphere 4, 155 (2013).
Google Scholar 
Allasino, M. L., Marrero, H. J., Dorado, J. & Torretta, J. P. Scientific note: First global report of a bee nest built only with plastic. Apidologie 50, 230–233 (2019).
Google Scholar  More

Seed sampling and germinationSeed from ten trees from each of 21 native Scottish Scots pine populations (Table 1) were collected in March 2007 and germinated at the James Hutton Institute, Aberdeen (latitude 57.133214, longitude −2.158764) in June 2007. Populations were chosen to represent the species’ native range in Scotland and to include three populations from each of the seven seed zones (Fig. 2). There was no selection of seed-trees on the basis of any traits except for the possession of cones on the date of sampling. Ten seed trees were sampled from each population according to a spatial protocol designed to cover a circle of approximately 1 km in diameter located around a central tree. The sampling strategy identified nine points each in a pre-determined random direction from the central point, whilst stratifying the number sampled with increasing distance from the central point in the ratio 1: 3: 5. This strategy avoids over-sampling the areas close to the centre point. For smaller fragments of woodland, or where only a few trees with cones were present, then the directions of the sampled trees from the central tree were maintained to give a wide coverage of the woodland area, but the distances between trees varied but were never closer than 50 m. To break dormancy, seeds were soaked for 24 hours on the benchtop at room temperature, after which they were stored in wet paper towels and refrigerated in darkness at 3–5 °C for approximately 4 weeks. Seeds were kept moist and transferred to room temperature until germination began (approx. 5–7 days), then transplanted to 8 cm × 8 cm × 9 cm, 0.4 L pots filled with Levington’s C2a compost and 1.5 g of Osmocote Exact 16–18 months slow release fertiliser and kept in an unheated glasshouse. The compost was covered with a layer of grit to reduce moss and liverwort growth. Seedlings from the same mother tree are described as a family and are assumed to be half-siblings.Table 1 Locations and basic environmental data for the populations sampled for seed to establish the trial. See the maternal traits dataset15 for precise data for each mother tree sampled.Full size tableExperimental design: nurseriesThe full collection consisted of 210 families (10 families from each of 21 populations) each consisting of 24 half sibling progeny (total 5,040 individuals); needle tissue was sampled from each seedling and preserved for long term storage, one needle on silica gel, 2–5 needles at −20 °C. After transfer into pots, 8 seedlings per family were moved to one of three nurseries (total 1,680 seedlings per nursery): outdoors at Inverewe Gardens in western Scotland (nursery in the west of Scotland: coded NW, latitude 57.775714, longitude −5.597181, Fig. 2); outdoors in a fruit cage (to minimise browsing) at the James Hutton Institute in Aberdeen (nursery in the east of Scotland: NE); in an unheated glasshouse at the James Hutton Institute in Aberdeen (nursery in a glasshouse: NG). Trees were arranged in 40 randomised trays (blocks) in each nursery. Each block contained two trees per population (total 42 trees). Watering was automatic in NG, and manually as required for NE and NW. No artificial light was used in any of the nurseries. In May 2010 the seedlings from NG were moved outdoors to Glensaugh in Aberdeenshire (latitude 56.893567, longitude −2.535736). In 2010 all plants were repotted into 19 cm (3 L) pots containing Levingtons CNSE Ericaceous compost with added Osmocote STD 16–18 month slow release fertilizer.Experimental design: field sitesIn 2012 the trees were transplanted to one of three field sites: Yair in the Scottish Borders (field site in the south of Scotland: FS, latitude 55.603625, longitude −2.893025); Glensaugh (field site in the east of Scotland: FE); and Inverewe (field site in the west of Scotland: FW). All trees transplanted to FS were raised in the NG and all but four of the trees transplanted to FE were raised locally in the NE (the remainder were grown in NG). In contrast, following mortality and ‘beating up’ (filling gaps where saplings had died), the FW experiment ultimately contained cohorts of trees raised in each of the three nurseries as follows: 290 grown locally in the NW; 132 were grown in the NG; and 82 were grown in the NE.Site historiesThe Yair site (FS) had previously been used for growing Noble fir (Abies procera) for Christmas trees and Lodgepole pine (Pinus contorta), a section of the former were felled and chipped to create a clear area prior to planting. The planting site is also adjacent to a large block of commercial Sitka spruce (Picea sitchensis) forestry, and the Glenkinnon Burn Site of Special Scientific Interest (SSSI NatureScot site code 736; EU site code 135445), an area of mixed broadleaf woodland. Prior to planting, major areas of tall weeds were strimmed. The site was protected by a deer fence. The experiment was planted 8–11 October 2012. The Glensaugh site (FE) is in Forestry Compartment 3 of the Glensaugh Research Station, adjacent to Cleek Loch. It is thought to have been cleared of Scots pine and Larch (Larix decidua) around 1917, after which it reverted to rough grazing. An attempt to reseed part of the site in the 1980s was unsuccessful and it quickly reverted to rough grazing for a second time. The whole site (within which the experimental area is embedded) was deer fenced and re-planted under the Scottish Rural Development Programme (SRDP) in 2012. The experimental plot was planted up 7–9 March 2012. The Inverewe site (FW) had previously been a Sitka spruce and Lodgepole pine plantation (50:50 mix) that had been clear-felled in 2010 following substantial windthrow. The experimental site was deer fenced in early 2012, and the experiment was planted 12–16 March 2012, followed by beating up on 27–28 March 2013 and 22–24 October 2013. There had been minimal preparation of the site in line with current practice for restocking sites. The experimental site is included in the Inverewe Forest Plan, which included deer fencing of a larger area (2014) around the experimental site. Planting of this area was completed in early 2015, funded by NTS (National Trust for Scotland), although natural regeneration is also taking place.At each site, trees were planted in randomised blocks at 3 m × 3 m spacing. There are four randomised blocks in both FS and FE and three in FW. A guard row of Scots pine trees was planted around the periphery of the blocks and between blocks B and C at FS. Each block comprised one individual from each of eight (of the 10 sampled) families per 21 populations (168 trees). Although most families (N = 159) were represented at each of the three sites, families with insufficient trees (N = 9) were replaced in one site (FS) with a different family from the same population. Each experimental site was designed with redundancy such that, if thinning becomes necessary as the trees mature, then the systematic removal of trees (i.e. trees 1,3,5,7, etc of row 1, and 2,4,6,8, etc of row 2, 1,3,5,7,etc of row 3) will maintain a balanced design of the experiment, with sufficient family and population representation to provide an ongoing experiment with full geographic coverage.The field sites generally experience different climates, with FW typically warmer and wetter and with more growing degree days per year and a much longer growing season than both FE and FS (Table 2). The coldest site with the shortest growing season is generally FE.Table 2 Average climatic variables at field sites Glensaugh (FE), Inverewe (FW) and Yair (FS) from planting in 2012 until 2020. Climatic variables are derived from data provided by the Met Office (daily mean, minimum and maximum temperatures and monthly rainfall).Full size tablePhenotype assessmentsMaternal traitsFollowing seed collection, a range of traits were measured in the mother trees in order to control for maternal effects in subsequent measurements of their progeny (Table 3). For each mother tree, measurements of height and diameter at breast height (DBH) were taken, and ten cones were collected and assessed in detail. Cone width and length were measured prior to drying the cones (when they were still closed). Cone weight was measured post-drying. Seed removed from each cone was assessed for total weight (after wings had been removed) and for the count and percentage of seeds which were classed as viable (viable seed were those which had both a wing and an obvious seed). No further seed sorting was applied.Table 3 Traits assessed in mother trees, cones, seeds (dataset: Maternal), nursery seedlings (dataset: Nursery) and field trials (dataset: Field). Within the datasets, traits are recorded in a single column for each year using the format Code-Year (e.g. absolute height in 2008 = HA08) except for the maternal traits datasets which were all measured in 2007.Full size tableNursery traitsSeedling phenotype assessments were performed annually from 2007–2010 for three different trait types: phenology (budburst and growth cessation); form (total number of buds, needle length); cumulative growth (stem diameter and height, canopy width). Measurements of tree form and cumulative growth traits were taken after the end of each growing season. Phenology was assessed weekly during the spring and autumn of 2008 for budburst and growth cessation, respectively. Budburst was defined as the number of days from 31 March 2008 to the time when newly emerged green needles were observed (budburst stage 6: Fig. 3). In some seedlings in 2008, a secondary flush of growth occurred from terminal buds that had formed during the summer of that year. Therefore, growth cessation was defined retrospectively as the number of days from 10 September 2008 to the date when a terminal bud had formed on the leading shoot of the seedling, providing no further growth was observed either on the stem below that bud, nor from the bud itself. Canopy width (widest point) was measured at two perpendicular points in the horizontal plane. Needle length was measured for three needles per tree. Mortality was recorded each year from 2007 to 2010.Fig. 3Phenological stages of bud burst in Pinus sylvestris assessed in field trials. Inset numbers correspond to budburst stage. Budburst stage 1: bud dormant; 2: bud swelling and showing signs of linear expansion; 3: scales open at base revealing green tissue. Remaining bud remains encased by smooth bud scales; 4: scales open along length of shoot revealing green tissue and partially visible needles; 5: white tipped needles visible along length of the shoot; 6: green needles emerging away from the shoot (bottle brush appearance) along its entire length; 7: Needles have separated and next year’s terminal bud is usually formed and clearly visible.Full size imageField traitsTree height was measured in the field in the winter after each growing season from 2013 at FE and FW, and from 2014 to 2020 at all sites. Height was taken as the vertical measurement in cm from top bud straight to the ground. Basal stem diameter was measured at the end of the growing season for trees growing at FE and FW from 2014 to 2020 and for FS in 2020.Phenology assessments were performed in spring at each site from 2015 to 2019. Seven distinct stages of budburst (assessed on the terminal bud) were defined (Fig. 3) although only stages 4 to 6 are included in the dataset and considered for analysis due to high proportions of missing data for the early and late stages. Each tree was assessed for budburst stage at weekly intervals from early spring until budburst was complete. In order to allow comparisons within and among sites and years, the date at which each stage of budburst occurred was considered relative to 31 March of that year. For example, 25 May 2019 is recorded as 55 days since 31 March 2019. The duration of budburst (time taken to reach stage 6 from stage 4) was also estimated.When trees progressed through budburst stages rapidly, skipping a stage between assessments, a mean value was taken from the two assessment dates. For example, if a tree was at stage 4 on day 55 and was recorded as stage 6 at the next assessment on day 62, it is assumed to have reached stage 5 at day 58.5. More

SamplesThe intestine and muscle samples from 22 wild boars were collected between September 4 and March 2, 2020, in Namie town in Fukushima prefecture. Furthermore, control intestine samples were collected from three wild boars in Hyogo prefecture. Each location is depicted in Fig. 1. In each case, after the licensed hunters slaughtered the wild boar to be exterminated, only the tissue was transferred to the study.Measurement of radioactivityRadioactivity in the muscle samples was determined by gamma-ray spectrometry using high-purity germanium (HPGe) detectors (Ortec Co., Oak Ridge, TN, USA), as described in our previous report3. Gamma rays from 137Cs were observed.Exposure dose estimationIn order to estimate internal and external dose rates of the wild boars according to the ICRP publication 10826, we supposed the shapes of wild boars as prolate spheroids whose long axis was to be their body lengths. The short axis was given from their weight assuming their specific gravities were the same
as water. The dose rates were calculated from the contribution of 137Cs, not including
natural radionuclides. The energy deposition to the spheroids by beta and gamma rays from radionuclides were calculated by the numerical simulation with the use of the Particle and Heavy Ion Transport code System (PHITS)27. For the sake of simplicity, we supposed the spheroids consisted of only muscle, which would give overestimated values because muscle contains more radio cesium than other organs. The external exposure dose was calculated from the air dose rates which were observed from the monitoring post near the boars captured place. The average values of the air dose rates were obtained from fitting observed data of two years with decay curve. The background due to the natural radionuclides was estimated to be 0.05 µGy/h which was observed before the Fukushima Daiichi accident, and was removed before the fittings. The half-lives of the air dose rates were 2000–3000 days depending on the environment. Assuming the external exposure dose was ascribed to the 137Cs included in the surface of the ground. The amount of the 137Cs was calculated so as to reproduce the observed air does rates. Since the maximum range of the beta ray from 137Cs is a few millimeters, almost all of the beta ray from inside the body should be absorbed in the boar’s body, but the beta ray from outside the body would stop in its fur. The beta rays contribute 100% to internal exposure dose but 0% to external one. Since the linear attenuation coefficient for gamma rays from 137Cs is 0.084 cm−1 = (12 cm)−1, some of the gamma rays cannot stop in the body depending on the size of the body. The numerical simulation suggested that 65–90 percent of the gamma rays from 137Cs inside the body would go out, and 40–65 percent of the gamma rays from 137Cs outside would go through the body.Pathological analysisA piece of the small intestine was fixed in 10% neutral formalin at 4 °C for 24–48 h. Then, paraffin blocks were prepared for pathomorphological examination using hematoxylin and eosin (HE) staining.Gene expression analysisTotal RNA was extracted from the whole tissue of the intestine using TRIzol Reagent (Life Technologies, Inc., Frederic, MD, USA) according to the manufacturer’s instructions. RNA concentration was measured using a NanoDrop spectrophotometer (Thermo Scientific, Wilmington, DE, USA), and cDNA was synthesized using random primers and SuperScript II (Life Technologies, Inc.). Real-time PCR for IFN-γ, TLR3, and CyclinG1 was performed using Brilliant SYBR Green QPCR Master Mix III (Stratagene, La Jolla, CA, USA) with an AriaMx system (Agilent Technologies, Santa Clara, CA, USA). Primer sequences were designed using Primer-BLAST with sequences obtained from GenBank as described in the previous report4. Amplification conditions were 95 °C for 3 min, 40 cycles at 95 °C for 5 s, and 60 °C for 20 s. Fluorescence signals measured during the amplification were analyzed. Ribosomal RNA primers were used as an internal control, and all data were normalized to constitutive rRNA values. Quantitative differences between the groups were analyzed using the AriaMx software (Agilent Technologies).Statistical analysisAll data are presented as mean ± standard error (SE) for each treatment group. Differences in mRNA expression among the groups were determined using the unpaired t-test with Welch’s correction. (Prism: GraphPad Software Inc., La Jolla, CA, USA). Differences were considered to be statistically significant at a P value of  More

Davies EK, Peters AD, Keightley PD (1999) High frequency of cryptic deleterious mutations in Caenorhabditis elegans. Science 285:1748–1751Article 
CAS 
PubMed 

Google Scholar 
Eyre-Walker A, Keightley PD (2007) The distribution of fitness effects of new mutations. Nat Rev Genet 8:610–618Article 
CAS 
PubMed 

Google Scholar 
Eyre-Walker A, Keightley PD (2013) A comparison of models to infer the distribution of fitness effects of new mutations. Genetics 193:1197–1208Article 

Google Scholar 
Fry JD, Keightley PD, Heinsohn SL, Nuzhdi SV (1999) New estimates of the rates and effects of mildly deleterious mutation in Drosophila melanogaster. Proc Natl Acad Sci 96:574–579Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
García-Dorado A (2007) Shortcut predictions for fitness properties at the mutation-selection-drift balance and for its buildup after size reduction under different management strategies. Genetics 176:983–997Article 
PubMed 
PubMed Central 

Google Scholar 
García-Dorado A (2012) Understanding and predicting the fitness decline of shrunk populations: inbreeding, purging, mutation, and standard selection. Genetics 190:1461–1476Article 
PubMed 
PubMed Central 

Google Scholar 
García-Dorado A (2015) On the consequences of ignoring purging on genetic recommendations for minimum viable population rules. Heredity 115:185–187Article 
PubMed 
PubMed Central 

Google Scholar 
García-Dorado A, Caballero A (2021) Neutral genetic diversity as a useful tool for conservation biology. Conserv Genet 22:541–545Article 

Google Scholar 
Garner BA, Hoban S, Luikart G (2020) IUCN Red List and the value of integrating genetics. Conserv Genet 21:795–801Article 

Google Scholar 
Hedrick PW, García-Dorado A (2016) Understanding inbreeding depression, purging, and genetic rescue. Trends Ecol Evol 31:940–952Article 
PubMed 

Google Scholar 
Kardos M, Armstrong EE, Fitzpatrick SW, Hauser S, Hedrick PW, Miller J et al. (2021) The crucial role of genome-wide genetic variation in conservation. Proc Natl Acad Sci USA 118:e2104642118Khan A, Patel A, Shukla H, Viswanathan A, van der Valk T, Borthakur U, … & Ramakrishnan U (2021) Genomic evidence for inbreeding depression and purging of deleterious genetic variation in Indian tigers. Proc. Natl. Acad. Sci. 118Kimura M, Maruyama T, Crow JF (1963) The mutation load in small populations. Genetics 48:1303–1312Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kimura M (1980) Average time until fixation of a mutant allele in a finite population under continued mutation pressure: Studies by analytical, numerical, and pseudo-sampling methods. Proc Natl Acad Sci 77:522–526Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Morin PA, Archer FI, Avila CD, Balacco JR, Bukhman YV, Chow, W, … & Jarvis ED (2021) Reference genome and demographic history of the most endangered marine mammal, the vaquita. Mol Ecol Resour 21:1008–1020Mukai T (1964) The genetic structure of natural populations of Drosophila melanogaster. I. Spontaneous mutation rate of polygenes controlling viability. Genetics 50:1–19Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Nietlisbach P, Muff S, Reid JM, Whitlock MC, Keller LF (2019) Nonequivalent lethal equivalents: Models and inbreeding metrics for unbiased estimation of inbreeding load. Evol Applic 12:266–279Article 

Google Scholar 
O’Grady JJ, Brook BW, Reed DH, Ballou JD, Tonkyn DW, Frankham R (2006) Realistic levels of inbreeding depression strongly affect extinction risk in wild populations. Biol Conserv 133:42–51Article 

Google Scholar 
Pérez-Pereira N, Caballero A, García-Dorado A (2021) Reviewing the consequences of genetic purging on the success of rescue programs. Conserv Gen 23:1–17Article 

Google Scholar 
Pérez-Pereira N, Wang J, Quesada H, Caballero A (2022). Prediction of the minimum effective size of a population viable in the long term. Biodivers Conserv https://doi.org/10.1007/s10531-022-02456-zRobinson JA, Kyriazis CC, Nigenda-Morales SF, Beichman AC, Rojas-Bracho L, Robertson KM et al. (2022) The critically endangered vaquita is not doomed to extinction by inbreeding depression. Science 376:635–639Article 
CAS 
PubMed 

Google Scholar 
Teixeira JC, Huber CD (2021) The inflated significance of neutral genetic diversity in conservation genetics. Proc Natl Acad Sci USA 118:e2015096118Wade EE, Kyriazis C, Cavassim MIA, Lohmueller KE (2022) Quantifying the fraction of new mutations that are recessive lethal. bioRxiv 1–24, https://www.biorxiv.org/content/10.1101/2022.04.22.489225v1 More

Most records of N-tiger cats were from savanna environments, and it was not surprising that this vegetative formation has a key influence on the N-tiger cat range in the Amazon. The bulk of the L. t. tigrinus distribution lies in the savannas, dry forests and shrublands of the Cerrado and Caatinga biomes. These are also the areas with the vast majority of records for this lowland subspecies (Supplemental Fig. S5). Hence, L. t. tigrinus is more associated with savannas and savanna-like environments than with rainforests. In fact, more than 80% of the records in the Amazon were within 100 km of a savanna patch. Colonization of the northern savanna formations of the Amazon by the N-tiger cat likely occurred during the forest-savanna shifts of the glacial period18, and the cat currently shows a patchy distribution. Strong evidence of established biogeographic corridor connections between the savannas of the Cerrado and those of the Amazon exists, suggesting northward expansion of the former during glacial periods, perhaps predating the Last Glacial Maximum19,20,21. Further corroborating this evidence, tiger cat ‘gene flow’ niche modelling showed prior connectivity between the Guiana population and that of Central Brazil and no connectivity with the Andean population22. Additionally, Guianan tiger cat skin patterns are found in savanna and transitional savanna/Amazon areas and in the semiarid shrub-woodland of Brazil and are very distinct from the patterns of the tiger cats from the Andes of northwestern South America and Central America (Supplementary Information Fig. S6).The bioclimatic variables in the best model also supported the cat’s preference for savanna areas. The best model indicated a positive effect of precipitation in the driest month on the probability of the presence of the N-tiger cat, likely indicating the Aw/As climates of tropical savannas23. These climates are marked by seasonal variation in rainfall, with a pronounced dry season. Higher rainfall during the dry season favors the growth of vegetation, which results in some tree cover within the savannas. Thus, our results agree with previous research suggesting that tiger cats avoid open savanna formations24. Similarly, the species had a significant negative response to net primary productivity. This also supports the species’ avoidance of dense lowland rainforests, which are the most productive habitats. In the Amazon biome, the least productive areas are found in more open landscapes25.The N-tiger cat’s range considered from an ecoregion perspective12 could biogeographically explain its distribution in the Amazon. All records but 2 fell within Guiana savannas, Guiana highland forest, Guiana rainforest, part of the Uatumã-Trombetas rainforest bordering the Guianas or all of it connecting to Gurupá and Monte Alegre varzea forests, as well as Marajó varzeas, the interfluve Tocantins-Araguaia/Maranhão, and the southern block of the interfluve Xingu/Tocantins-Araguaia. There were two records from the Negro-Branco moist forest, which also includes savanna-like “campinarana” formations. The range also reaches the transitional babaçu palm forests of Maranhão and the Mato Grosso seasonal forests (Supplementary Information Fig. S7, Table S3). The N-tiger cat’s range in the Amazon was determined by combining records with species distribution modeling, also matching the ecoregion perspective.Outside the Guiana Shield and likely the savanna patches of the region of the Upper Negro River, in other parts of the Amazon, the N-tiger cat seems to be restricted to the forests of the eastern Amazon, along the arc of deforestation and to transitional areas with savanna formations. The presence and absence points at camera-trapping sites could explain the N-tiger cat’s range in the Amazon and define its distribution range in the biome. Absence points, for instance, were usually located in dense rainforest habitats throughout the Amazon biome.The species may occasionally occupy rainforests, such as those of the Guianas, where it tends to be very rare. At a site in central Suriname, after an enormous trapping effort of  > 20,000 trap days in four years by cat specialists, over an area  > 1100 km2, no records of the N-tiger cat were found (Supplementary Information Table S2), although its presence is expected in that area26. This finding attests to the inherent rarity of this felid in its limited range within the Amazon. However, could its association with the arc of deforestation be related to the replacement of forest by bushy savanna-like vegetation that succeeds abandoned pastures? The other currently recognized subspecies, L. t. pardinoides (the Andean tiger cat) and L. t. oncilla (the oncilla), and the recently split southern tiger cat L. guttulus are all associated with forested areas. Conversely, L. t. tigrinus has higher abundance and is mostly found in the nonforested habitats of the Cerrado and Caatinga domains of Brazil and only rarely in rainforests. Thus, L. t. tigrinus may be an open-habitat (sub)species. However, within savannas, N-tiger cats are restricted to denser savanna formations, with open savannas deemed unsuitable24. In the semiarid Caatinga, the N-tiger cat also prefers denser formations27,28.One of the most interesting findings was the clear relationship between the ranges of the dominant mesopredator and subordinate species. The ranges of ocelots and N-tiger cats in the Amazon were diametrically opposite (Fig. 1), a finding never recorded for felids. The reported ocelot densities and relative abundance indexes (RAIs) in the Amazon range from 0.29 to 0.95 ind/km2 and 0.07–13.2 ind/100 trap-days, respectively7,29. Thus, the expected ocelot density found using modeling that allows for N-tiger cat presence is very low (Fig. 2A). In the Rupununi, the ocelot:N-tiger cat RAI ratio was roughly 10:1, with a very low RAI and expected density for N-tiger cats (see Supplementary Material). The only other relative abundance estimate of tiger cats presented for the Amazon30 was not confirmed as an estimate of tiger cats following inspection of the original records by the authors but as an estimate of margays or ocelots. This antagonistic relationship between ocelots and all other small cat species in their area of sympatry is quite impressive. It is density-dependent, as it seems to take effect only above an ocelot density threshold of 0.12 ind./km231. The influence can range from patterns of density, distribution, and occupancy to spatial and temporal use. Conversely, such an impact was not detected when either the small cats or ocelots were compared to the larger cats31,32,33,34,35.In view of the Red List assessments and applying the limited estimates presented, the expected total population size for N-tiger cats in the Amazon would be approximately 150 and 1622 individuals, considering their AOO or EOO, respectively. Applying the IUCN’s formula for mature individuals8, these numbers would be 45 and 487 individuals for the AOO and EOO, respectively.The ocelot’s preference for very dense rainforests may explain the low probability of N-tiger cat occurrence within the Amazon biome. Notably, most tiger cat records from rainforests and all those from premontane forests came from the Guiana Shield, a region where tropical grasslands and savannas dot more forested landscapes. The Guiana Highlands and Pantepui ecoregions, which make up a considerable portion of the shield, tend to have low ocelot densities (below 0.30 ind/km2), although they do contain some rainforest. Ocelot densities reach some of their lowest values in the Guianan savanna ecoregion (mean ocelot density of 0.029 in the savanna formations), where the N-tiger cat probability of occurrence was highest. At the Karanambu site in the Rupununi, all ocelot records came from either gallery forests or forest patches embedded in the savanna. Although the data did not allow us to test further hypotheses, it is likely that spatial partitioning occurs in the Guiana Shield, with N-tiger cats favoring habitats that are more open. Conversely, areas farther west in the Amazon biome, other than the predicted area, do not have any major savanna patches and are covered mostly by lowland tropical rainforest formations, where ocelots can potentially reach densities in excess of 0.7 ind/km2. Of all Amazonian records of N-tiger cats, only one came from west of the 68th meridian: a preserved specimen from Puerto Leguizamo on the Putumayo River in Colombia. The specimen was identified as L. t. pardinoides by its collector, so it most likely represents an individual that came down from the foothills of the Andes. Alternatively, it could have been caught in the Andean foothills but labeled generally as from Puerto Leguizamo, as museum records do not always present precise locations, like most of those from our dataset; thus, they could represent a broader region, not a single collection location.The records of L. t. tigrinus in the Monte-Alegre Várzea ecoregion and Tapajós-Xingu Moist Forest ecoregion (which shares a border with the Amazon River) are actually from the small savanna patches of Terra Santa and Alter do Chão, respectively, which are imbedded within the forests of these ecoregions. Similarly, the Negro-Branco Moist Forest ecoregion includes open-canopy white sand forests with savanna-like vegetation, known as ‘campinaranas’36.Although our model predicted a high probability of N-tiger cat presence in the Marajó Várzea ecoregion, the records from the island came from savanna patches and not from flooded forests and mangroves. Hence, we did not include such large areas in the AOO for the subspecies. It is likely that the highly predicted probability of presence there is an artifact of low predicted ocelot density. Nevertheless, the environment there is not suitable for either cat. Our ocelot density model was highly significant and explained almost 50% of the variation in ocelot density. The remaining variation was related to either other variables that could not be measured via satellite imagery (such as prey availability) or the sampling design of the different studies. Nonetheless, ocelot densities predicted from our model across the Amazon were within the expected range for the species29.Why are N-tiger cats absent in camera-trapping studies in Amazonian forests throughout the biome? The most straightforward answer seems to be because they simply are not there (central and western Amazon) or, where present, their numbers are extremely low (Guianas and eastern Amazon). The lack of surveys cannot be cited as a potential reason for their apparent absence because the studies that did not detect the species were conducted throughout the Amazon biome, in all nine Amazonian countries. Some of the areas have been surveyed for several years—or decades in some cases—and have failed to record a single individual (Supplementary Information Table S2). Typically, N-tiger cats appear, even prominently, on cameras in other biomes, such as in the savannas of the Cerrado and semiarid scrub of the Caatinga domain in Brazil, including sites where ocelots are present24,27,37. Clouded tiger cats (L. t. pardinoides) have also been frequently recorded on cameras in the Andes, higher than 1500 m above sea level34,38, but not in lowland Amazonian forests. This finding indicates that the N-tiger cat is not camera-shy. In northern Brazilian savannas, its density can reach 0.25 ind/km2 24. Coincidentally, this highest density estimate of the N-tiger cat is the same as the lowest ocelot density estimate for Amazonian forests24,29.Tiger cats and margays show high similarity, making misidentifications relatively common39. However, the evaluation of  > 3000 camera trap images of small-medium felids in the Amazon revealed that only one mildly resembled a tiger cat, a finding that supports the species being absent there and does not represent a case of mistaken identity with margays or even ocelots7.The Amazonian range of L. tigrinus is very limited, and populations are expected to be very small. With the upcoming split of L. t. tigrinus and L. t. pardinoides into two different species40, this situation would have serious implications for the conservation of the former. Thus, L. t. tigrinus conservation lies outside the “Amazonian safe haven” of most other carnivore species found there7. The Brazilian drylands Cerrado and Caatinga represent such places for L. t. tigrinus populations. Unfortunately, these biomes have had  > 50% of their cover completely removed41. Very importantly, besides being extremely rare in the Amazonian savannas, this rather limited vegetative formation is also considered highly threatened and of conservation priority42. Therefore, the tiger cat could become an emblematic flagship species representing the uniqueness of this vegetative formation in dire need of protection.In short, the picture that emerges is that although the N-tiger cat uses both rainforests and deciduous forests in the Amazon, it seems to be mostly associated with savanna formations and that its distribution in the Amazon is highly influenced by the ocelot, the dominant mesopredator. The N-tiger cat’s inherent rarity, expected population size, and restricted range in the Amazon suggest that this biome does not in fact represent a safe haven for the global conservation of this small felid. In addition to shedding light on and refining the N-tiger cat distribution in the Amazon, this paper highlights the importance of including biological variables, such as the potential impacts of competitors and predators on species presence and distribution, in SDMs. More