Ecology

Study site and animalsWe studied n = 64 flocks of Japanese tits in mixed deciduous-coniferous forests in Nagano and Gumma (36°17-31’N, 138°26-39’E), Japan. Although most of the birds had not been individually colour-ringed, all the experimental trials were conducted at least 400 m apart; previous observations on colour-ringed individuals showed that this distance was enough to ensure the collection of data from different individuals30. In this site, one of the major predators of small birds is the bull-headed shrike, which is often mobbed by small birds including Japanese tits.Playback stimulusTo test whether Japanese tits recognize an alert-recruitment call sequence as a single unit, we prepared four treatments: (i) one-speaker playback of alert-recruitment call sequences, (ii) two-speaker playback of alert-recruitment call sequences with alert and recruitment calls played from different speakers, (iii) one-speaker playback of recruitment-alert call sequences, (iv) two-speaker playback of recruitment-alert call sequences with recruitment and alert calls played from different speakers (Fig. 3). We created sound files for these treatments using the software program Audacity 2.1.3 (http://www.audacityteam.org). For one-speaker treatments, we composed mono sound files where call sequences were repeated onto a single channel, whereas for two-speaker treatments, we composed stereo sound files where either alert or recruitment calls were repeated onto the right or left channels, respectively. All the files contained an equal number of alert calls (30 calls) and recruitment calls (30 calls) at the same rate (one call every 3 s), resulting in 90-s of stimuli (Fig. 3), which corresponds to the range of the natural calling rate of alert-recruitment sequences during mobbing by Japanese tits10. For all stimuli, within-call-sequence intervals between alert and recruitment calls were constant (0.1 s), which is within the range of intervals of these calls in natural call sequences11,17. In contrast, between-call-sequence intervals varied from 1.50 to 1.81 (median = 1.68) due to the difference in call length, but were constant across playback stimuli within the same “block” where the four treatments were created using the same call exemplars (see below). While alert calls are composed of three distinct note types, recruitment calls are strings of the same note type that vary in repetition number. Since the repetition number can vary depending on predator type10, we conducted predator exposure experiments to Japanese tit flocks (n = 12) and recorded call sequences towards a bull-headed shrike life-like specimen. In response to a shrike specimen, tits produced alert-recruitment call sequences with a recruitment note repetition number ranging from 5 to 15. Since the interquartile range of repetition number was 6.75 to 10, we used recruitment calls with 7–10 notes as playback stimuli in this study. In consideration for the possible influence of sound editing procedure, we created all the stimuli in the same manner; we copied alert and recruitment call parts separately from recording files, and pasted them onto background noise files to produce all four types of stimuli. Playback amplitudes were constant across treatments, 70 dB at 1.0 m measured using a sound level meter (SM-325, AS ONE Corporation). Therefore, the differences between treatments only depend on whether these calls are produced as sequences from the same source and how the calls are ordered.We carefully designed experiments to control for the possibility that individual-based acoustic features in alert and recruitment calls might influence tits’ responses. First, we prepared 16 unique sets of alert and recruitment calls using either calls from the same bird (n = 8 source individuals, n = 8 unique call sets) or from two different birds (n = 16 source individuals, n = 8 unique call sets). Then, we created the four types of treatments (i.e., alert-recruitment call sequences from the same speaker, from different speakers, and in reversed order from the same speaker and from different speakers) from each of the alert-recruitment call sets, resulting in 16 blocks of playback stimuli (Supplementary Table 3). This allows us to test the possible influence of individual-based acoustic variation on receivers’ responses.We were also careful to avoid the possible influence of population-level signatures of acoustic features: we only used Japanese tits’ call sequences that had been previously recorded from the same study population. We saved the sound files in .wav format (16-bit accuracy, 48-kHz sampling rate) onto a playback device (iPhone 8, Apple Inc.). We used the default Music app (Apple Inc.) to playback the sound files.ExperimentWe (TNS and YKM) conducted experimental trials from 26 October to 4 December 2020 and during the period of 0800 and 1600 h (Japan Standard Time). We did not conduct trials under wet and windy weather conditions, since these may influence behavioural patterns of forest birds31. First, we searched for and located a flock of Japanese tits. Upon finding a flock, we fixed a taxidermic specimen of bull-headed shrike in a perching posture on the branch at 1.8 ± 0.2 m (mean ± s.d., n = 64) above the ground. Then, we placed either one or two Bluetooth speakers (SoundLink Micro, BOSE) on tree branches at 1.6 ± 0.2 m (mean ± s.d., n = 96) above the ground, and oriented them upwards to control for the possible influence of directionality. We set the distance between the shrike specimen and the speaker(s) at 5 m. For trials with two speakers, we set the distance between speakers at 10 m, mimicking the situation in which two birds are calling (Fig. 3). The shrike specimen was first covered with a black cloth and was exposed by removing the cloth just before each trial.We began playbacks when at least two Japanese tits were present within 15 m from the shrike specimen. During 90-s of playbacks, we recorded (i) whether birds approached within 2-m of the shrike specimen during the playback and (ii) whether birds exhibited wing flicking displays12,13. We counted the number of individuals within 15 m from the shrike during 90-s of playbacks and considered it as flock size. During trials, we sat on the ground at ca. 10 m from the shrike specimen to decrease the influence of the observers’ presence on bird behaviour. To account for the inter-observer reliability32, we calculated intra-class correlation coefficient (ICC; icc function in the R package irr) between us. The lowest ICC was 0.998, indicating high degree of inter-observer reliability for the two behavioural measurements. We also video-recorded the responses of tits using a digital video camera (FDR-AX60, SONY). After completion of each trial, we checked the video recording and made an on-the-spot confirmation of the exact location at which each bird made the closest approach to the shrike specimen during the 90-s of playbacks. Then, using a tape measure, we recorded the minimum approach distance of birds to the shrike specimen. Thus, our final data set consisted of the most reliable observations confirmed by two experimenters and video evidence.The order of trials was randomized within each block (n = 16 blocks), each of which is composed of a unique alert-recruitment call set but includes four treatments differing in the number of speakers and call order. Therefore, responses to all four treatments were observed under largely similar conditions. In a few trials, the first bird to approach the shrike specimen was from a heterospecific species, such as a varied tit (n = 1) or a long-tailed tit (n = 1). To account for the possibility that these birds evoke mobbing behaviour in Japanese tits, we only used the data from instances where the first individual to approach the shrike was a Japanese tit. Otherwise, we repeated the same treatment at a different site.We used 64 unique playbacks created from 16 unique sets of alert-recruitment calls for 64 trials in order to avoid pseudoreplication33. We prepared two specimens of male bull-headed shrikes and used each of them for the equal number of trials. We did not use specimens of female shrikes since females migrate from the study site in late summer and only males were observed during the study period.Statistical analysisWe analyzed the effect of playback treatments on the mobbing behaviours of Japanese tits using generalized linear mixed models in R34,35. We used the proportions of Japanese tits in flocks that (i) approached within 2-m of the shrike specimen and (ii) exhibited wing flicking displays. For the analysis of predator approach, we prepared two vectors (i.e., the number of Japanese tits that approached the shrike specimen and the number of Japanese tits that did not approach the shrike specimen). Then, we created a single response variable by binding together these two vectors using cbind function. Similarly, for the analysis of wing flicking displays, we created a single response variable by binding two vectors (i.e., the number of tits that exhibit wing flicking and the number of tits that did not exhibit wing flicking). We fitted playback treatments as a fixed term, and flock size (maximum number of Japanese tits observed during 90-s of playback) and the way of creating playback stimuli (whether the two call types were recorded from a single individual or two individuals) as covariates. We also included identity of alert-recruitment call sets that were used for creating playback stimuli (i.e., call sets from either one or two source individuals) and identity of shrike specimens as random terms. We used a binomial error distribution and logit-link function (glmer in the R package lme4) for these models. Statistical significance was calculated by log-likelihood ratio tests using anova in the R package stats. We further conducted post-hoc pairwise comparisons between treatments by using estimated marginal means (emmeans in the R package emmeans). When making pairwise comparisons, we adjusted p-values by applying a false discovery rate control for multiple testing36. All tests were two-sided and the significance level was set at α = 0.05. Exact p-values are reported when p ≥ 0.0001.Ethics statementAll protocols were approved by the ethics committee of Kyoto University, the Ministry of the Environment, and the Forestry Agency of Japan, and adhered to Guidelines for the Use of Animals of the Association for the Study of Animal Behaviour/Animal Behavior Society37.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Ethical statementThis research project was ethically reviewed and approved by the Ethics Committee of the Instituto Gulbenkian de Ciência (license reference: A009.2018), and by the Portuguese National Entity that regulates the use of laboratory animals (DGAV – Direção Geral de Alimentação e Veterinária (license reference: 008958). All experiments conducted on animals followed the Portuguese (Decreto-Lei n° 113/2013) and European (Directive 2010/63/EU) legislations, concerning housing, husbandry and animal welfare.Escherichia coli clonesThe ancestral invader E. coli strain expresses a Yellow Fluorescent Protein (YFP), and carries streptomycin and ampicillin resistance markers for easiness of isolation from the mouse feces [galK::amp (pZ12)::PLlacO−1-YFP, strR (rpsl150), ΔlacIZYA::scar]. An E. coli strain used for the in vivo competition experiments is isogenic to the ancestral invader but expresses a Cyan Fluorescent Protein (CFP) and carries streptomycin and chloramphenicol resistance markers [galK::chlor (pZ12)::PLlacO−1-CFP, strR (rpsl150), ΔlacIZYA::scar]. The resident E. coli lineage was isolated from the feces along time using McConkey + 0.4% lactose medium, as previously described9. All the resident clones sampled from each mouse belong to E.coli phylogenetic group B9. The invader E. coli strains (YFP and CFP) derive from the K-12 MG1655 strain (DM08) and exhibit a gat negative phenotype, gatZ::IS112. The resident E. coli clone used for the competition experiments in the mouse gut expresses a mCherry fluorescent protein and a chloramphenicol resistance marker, allowing to distinguish the invader and resident strains in the mice feces.E. coli clones were grown at 37 °C under aeration in liquid media Luria broth (LB) from SIGMA — or McConkey and LB agar plates. Media were supplemented with antibiotics streptomycin (100 µg/mL), ampicillin (100 µg/mL) or chloramphenicol (30 µg/mL) when specified.Serial plating of 1X PBS dilutions of feces in LB agar plates supplemented with the appropriate antibiotics were incubated overnight and YFP, CFP or mCherry-labeled bacterial numbers were assessed by counting the fluorescent colonies using a fluorescent stereoscope (SteREO Lumar, Carl Zeiss). The detection limit for bacterial plating was ~300 CFU/g of feces9.In vivo evolution and competition experimentsAll mice (Mus musculus) used in this study were supplied by the Rodent Facility at Instituto Gulbenkian de Ciência (IGC) and were given ad libitum access to food (Rat and Mouse No.3 Breeding (Special Diets Services) and water. Mice were kept at 20-24 °C and 40-60% humidity with a 12-h light-dark cycle. For the in vivo evolution experiment we used the gut colonization model previously established9. Briefly, mice drank water with streptomycin (5 g/L) only for 24 h before a 4 h starvation period of food and water. The animals were then inoculated by gavage with 100 µL of an E. coli bacterial suspension of ~108 colony-forming units (CFUs). Mice A2, B2, D2, E2, G2, H2 and I2 were successfully colonized with the invader E. coli, while mice C2 and F2 failed to be colonized. Six- to eight-week-old C57BL/6 J non-littermate female mice were kept in individually ventilated cages under specified pathogen-free (SPF) barrier conditions at the IGC animal facility. Fecal pellets were collected during more than one year ( >400 days) and stored in 15% glycerol at −80 °C for later analysis. In the competition experiments between the invader ancestral E. coli and evolved populations, we colonized the mice using a 1:1 ratio of each genotype, with bacterial loads being assessed and frozen on a daily basis after gavage.In vivo competition experiments in which the two modes of selection (directional and diversifying) were acting for a longer time period were performed using evolved invader E. coli populations colonizing mice D2, B2 and A2, H2. Here we used both male (n = 8) and female (n = 8) C57BL/6 J mice aged six- to eight-week-old treated with streptomycin during 3 days before gavage. E. coli populations evolving for short time periods do not allow for strong conclusions on which mode of selection is taking place. Evolved invader populations such as I2 or G2 were therefore not used for in vivo fitness assays. To assess the impact of the mouse resident E. coli in the competitive fitness of dgoR we performed one-to-one competitions between the invader ancestral and dgoR KO clones. We first homogenized the mice microbiotas by co-housing the animals during seven days. The animals (n = 6, female C57BL/6 J mice aged six- to eight-week-old) were then maintained under co-housing and given streptomycin-supplemented (5 g/L) water during seven days to break colonization resistance and eradicate their resident E. coli. At this point, the co-housed mice were removed from the antibiotic-supplemented water for two days. The following day, one group of mice was gavaged with an mCherry-expressing resident E. coli (n = 3 mice) while the other group (n = 3) was not, with all animals being individually caged from this point on and receiving normal water without antibiotic. The day after gavage, all mice were colonized with a mix (1:1) of the invader ancestral and the dgoR KO clones, and the bacterial loads were assessed and frozen on a daily basis.Microbiota analysisFecal DNA was extracted with a QIAamp DNA Stool MiniKit (Qiagen), according to the manufacturer’s instructions and with an additional step of mechanical disruption32. 16 S rRNA gene amplification and sequencing was carried out at the Gene Expression Unit from Instituto Gulbenkian de Ciência, following the service protocol. For each sample, the V4 region of the 16 S rRNA gene was amplified in triplicate, using the primer pair F515/R806, under the following PCR cycling conditions: 94 °C for 3 min, 35 cycles of 94 °C for 60 s, 50 °C for 60 s, and 72 °C for 105 s, with an extension step of 72 °C for 10 min. Samples were then pair-end sequenced on an Illumina MiSeq Benchtop Sequencer, following Illumina recommendations. Sampling for microbiota analysis was performed until the microbiota composition stabilized (~1 year after the antibiotic perturbation).QIIME2 version 2017.1133 was used to analyze the 16 S rRNA sequences by following the authors’ online tutorials (https://docs.qiime2.org/2017.11/tutorials/). Briefly, the demultiplexed sequences were filtered using the “denoise-single” command of DADA2 version 1.1434, and forward and reverse sequences were trimmed in the position in which the 25th percentile’s quality score got below 20. Diversity analysis was performed following the QIIME2 tutorial35. Beta diversity distances were calculated through Unweighted Unifrac36. For taxonomic analysis, OTU were picked by assigning operational taxonomic units at 97% similarity against the Greengenes database version 13 (Greengenes 13_8 99% OTUs (250 bp, V4 region 515 F/806 R))37.Whole-genome sequencing and analysis pipelineDNA was extracted38 from E. coli populations (mixture of  > 1000 clones) or a single clone growing in LB plates supplemented with antibiotic to avoid contamination. DNA concentration and purity were quantified using Qubit and NanoDrop, respectively. The DNA library construction and sequencing were carried out by the IGC genomics facility using the Illumina Miseq platform. Processing of raw reads and variants analysis was based on the previous work39. Briefly, sequencing adapters were removed using fastp version 0.20.040 and raw reads were trimmed bidirectionally by 4 bp window sizes across which an average base quality of 20 was required to be retained. Further retention of reads required a minimum length of 100 bps per read containing at least 50% base pairs with phred scores at or above 20. BBsplit (part of BBMap version 38.9)41 was used to remove likely contaminating reads as explained previously39. Separate reference genomes were used for the alignment of invader (K-12 (substrain MG1655; Accession Number: NC_000913.2)) and resident (Accession Number: SAMN15163749) E. coli genomes. Alignments were performed via three alignment approaches: BWA-sampe version 0.7.1742, MOSAIK version 2.743, and Breseq version 0.35.144,45. Final average alignment depths for invader and resident populations across time points equalled 302 (median = 236) and 253 (median = 235), respectively. While Breseq provides variant analysis in addition to alignment, other variant calling approaches were used to identify putative variation in the sequenced genomes, and to verify data from Breseq. A naïve pipeline39 using the mpileup utility within SAMtools version 1.946 and a custom script written in python was employed. Only reads with a minimum mapping quality of 20 were considered for analysis, and variant calling was limited to bases with call qualities of at least 30. At these positions, a minimum of 5 quality reads had to support a putative variant on both strands (with strand bias, pos. strand / neg. strand, above 0.2 or below 5) for further consideration. Finally, mutations were retained if detected in more than one of the alignment approaches, and if they reached a minimum frequency of 5% at a minimum of one time point sampled. Further simple and complex small variants were considered from freebayes version 0.9.2147 with similar thresholds, while insertion sequence movements and other mobile element activity was inferred via is mapper version 248 and panISa version 0.1.649, as well as Breseq, as previously described39. All putative variants were verified manually in IGV version 2.750,51. Raw sequencing reads were deposited in the sequence read archive under bioproject PRJNA666769. Population dynamics of lineage-specific dynamics and the resulting Muller plots were inferred manually and are meant strictly as a means of presenting the data. In order to generate these plots, mutations were sorted by frequency (descending for each time point at which the population was sampled). The largest frequency mutations were considered major lineages within which minor frequency mutations occurred. Assuming that a mutation, which arises subsequent to a preexisting mutation (an already differentiated lineage) cannot exceed the frequency of that preexisting mutation at any point, and will fluctuate in frequency with the preexisting one, we assigned mutations to the lineages within each population. While this resolved the majority of high frequency and medium frequency mutations, low-frequency mutations within the Muller plots cannot be placed with high confidence, and are only included for completeness.Prophage induction rateTo calculate the maximum prophage induction rate we grew E. coli lysogenic clones, starting with the same initial OD600 values: ~0.1 (Bioscreen C system, Oy Growth Curves Ab Ltd), with agitation at 37 °C in LB medium in the presence or absence of mitomycin C along time (5 µg/mL)9. The OD600 values were normalized by dividing the ones in the presence of mitomycin C by the ones in the absence of mitomycin C (sampling interval: 30 min). The LN of this ratios along time originates a lysis curve, where the maximum slope corresponds to the maximal prophage induction rate for each clone analyzed. We tested evolved clones from mouse A2, H2 and G2 against the ancestral clone which only carries the Nef and the KingRac prophages. We also tested clones of the resident strain that had evolved in the presence of the invader for more than 400 days (these clones were sampled from mouse A2).
E. coli growth rate, growth curves, cell aggregation, biofilm and motility capacityTo calculate the maximum bacterial growth rate, we grew E. coli lysogenic clones, starting with the same initial OD600 values: ~0.1 (Bioscreen C system, Oy Growth Curves Ab Ltd), with agitation at 37 °C in LB medium along time using reading intervals of 30 min. The LN of the OD600 values along time originates a growth curve, where the maximum slope corresponds to the maximum bacterial growth rate for each clone analyzed.To test for metabolic differences of the psuK/fruA mutation, growth curves of evolved lysogenic E. coli clones, bearing the Nef and KingRac prophages, with or without the psuK/fruA mutation were performed with the same initial OD600 value (~0.03) for each clone. The clones were grown in glucose (0.4%) minimal medium (MM9-SIGMA) with or without pseudouridine (80 μM) and absorbance values were obtained using the Bioscreen C apparatus during 12 h.Frozen stocks of E. coli clones were used to seed tubes with 5 mL of liquid LB. These were incubated overnight at 37 °C under static conditions to assess the formation of cell flocks/clumps, observable to the naked eye, in order to evaluate the formation of cell aggregates. Biofilm was tested according a previously published protocol52 and to evaluate the motility capacity we adapted the protocol from Croze and colleagues53. Briefly, overnight E. coli clonal cultures grown with agitation at 37 °C in 5 mL LB medium supplemented with streptomycin (100 ug/mL) were adjusted to the same absorbance and a 3uL volume was dropped on top of soft agar (0.25%). Plates were incubated at 37 °C and photos were taken at day 1, 2 and 5 post-inoculation to assess swarming motility phenotype.Number of E. coli generations during mouse gut colonizationTo estimate the number of generations of E. coli in the mouse gut, we used a previously described protocol to measure the fluorescent intensity of a probe specific to E. coli 23 S rRNA (as a measure of ribosomal content) that correlates with the growth rate of the bacterial cells54. We measured the number of generations of the ancestral E. coli clone while colonizing the gut of 2 mice, treated during 24 h with streptomycin (5 g/L) before gavage, during 25 days.Plasmid DNA extraction and PCR detection of ~69Kb (repA) and ~109Kb (repB) plasmidsPlasmid DNA was extracted from overnight cultures using a Plasmid Mini Kit (Qiagen), according to the manufacturer’s guidelines. Specific primers for the amplification of repA and repB genes, were used to determine the frequency of the 68935 bp (~69 Kb) and 108557 bp (~109 Kb) plasmids, respectively, in the invader E. coli population.The primers used for repA gene were:repA-Forward: 5’-CAGTCCCCTAAAGAATCGCCCC-3’ and repA-Reverse: 5’-TGACCAGGAGCGGCACAATCGC-3’.For repB the primer sequences were:repB-Forward: 5’-GTGGATAAGTCGTCCGGTGAGC-3’ and repB-Reverse: 5’-GTTCAAACAGGCGGGGATCGGC3’.PCR amplification of plasmid-specific genes was performed in 12 isolated random clones from mouse A2 at days 104 and 493. PCR reactions were performed in a total volume of 25 μL, containing 1 μL of plasmid DNA, 1X Taq polymerase buffer, 200 μM dNTPs, 0.2 μM of each primer and 1.25 U Taq polymerase. PCR reaction conditions: 95 °C for 3 min, followed by 35 cycles of 95 °C for 30 s, 65 °C for 30 s and 72 °C for 30 s, finalizing with 5 min at 72 °C. DNA was visualized on a 2% agarose gel stained with GelRed and run at 160 V for 60 min.Construction of the dgoR KO mutantP1 transduction was used to construct a ΔdgoR mutant (dgoR KO). This KO strain was created by replacing the wild-type dgoR in the invader ancestral YFP-expressing genetic background by the respective knock-out from the KEIO collection, strain JW562755, in which the dgoR sequence is replaced by a kanamycin resistance cassette. The presence of the cassette was confirmed by PCR using primers dgoK-F: GCGATGTAGCGAGCTGTC, and yidX-R: GGGAATAAACCGGCAGCC. PCR reactions were performed in a total volume of 25 μL, containing 1 μL of DNA, 1X Taq polymerase buffer, 200 μM dNTPs, 0.2 μM of each primer and 1.25 U Taq polymerase. PCR reaction conditions: 95 °C for 3 min, followed by 35 cycles of 95 °C for 30 s, 65 °C for 30 s and 72 °C for 30 s, finalizing with 5 min at 72 °C. DNA was visualized in a 2% agarose gel stained with GelRed and run at 160 V for 60 min.RNA extraction, DNAse treatment, RT-PCR and qPCRThe Qiagen RNeasy Mini Kit was used for RNA extraction. RNA concentration and quality were evaluated in the Nanodrop 2000 and by gel-electrophoresis. DNase treatment was performed with the RQ1 DNase (Promega) by adding 0.5 μl of DNase to 1 μg of RNA and 1 μl buffer in a final volume of 15 ul, followed by incubation 30 min at 37 °C. Afterwards, 1 ul of stop solution was added and incubation for 15 min at 65 °C was performed to inactivate the DNase. As a control for complete DNA digest a PCR was performed on the reactions including positive controls. Reverse transcription was performed with M-MLV RT[-H] (Promega) by mixing 1 μg of RNA with 0.5 μl random primers (Promega) and nuclease free water to a volume of 15 μl, incubation at 70 °C for 5 min and a quick cool down on ice. Afterwards the reverse transcription was accomplished by adding 5 μl of RT buffer, 0.5 μl RT enzyme and 2 μl dNTP mix, followed by incubation for 10 min at 25 °C, 50 min at 50 °C and 10 min at 70 °C. The resulting cDNA was diluted 100-fold in nuclease free water before changes in gene expression were detected using the The QuantStudio 7Flex (Applied Biosystems) with iTaq Universal SYBR Green Supermix (BioRad) and the following cycling protocol: Hold stage: 2 min at 50 °C, 10 min at 95 °C. PCR stage (40 cycles): 15 s at 95 °C, 30 s at 58 °C, 30 s at 60 °C. Melt curve stage: 15 s at 95 °C, 1 min at 50 °C then increments of 0.05 °C/s until 95 °C. Melt curve analysis was performed to verify product homogeneity. All reactions included six biological and three technical replicates for each sample. A relative quantification method of analysis with normalization against the endogenous control rrsA and employing the primer specific efficiencies was used according to the Pfaffl method (add reference). The primers used were designed with PrimerQuest (idt). The used primer sequences were: psuK – TGCGTTAGCAGCGATTGA, AATTTACGCCTGGTGGAGTAG; arcA – GATTCATGGTACGGGACAGTAG, CCGTGACAACGAAGTCGATAA; yjtD – CGCACATGGATCTGGTGATA, GGCGTGGCGTAGTAATGATA and rrsR – GTCAGCTCGTGTTGTGAAATG, CCCACCTTCCTCCAGTTTATC.Statistics and reproducibilityCorrelation between microbiota diversity measures and E. coli loads (CFU) or persistence (1-presence or 0-absence) was performed in R using the statistical package rmcorr (version 0.5.2)56 and lme4 (version 1.1-10)57, respectively. The rate of accumulation of new ISs in vivo was compared using Wilcoxon paired signed ranked test for expected and observed insertions, while the rate of selective sweeps correlation was performed using the Spearman Correlation test. Selective sweeps were taken to be mutations or HGT events that reached  > 95% frequency in the population and kept high frequency until the end of the colonization. Statistical analysis of prophage induction as well as biofilm levels was performed using the Mann-Whitney test in GraphPad Prism (version 8.4.3). A single sample T-Test was used test if the growth rate of evolved invader clones deviates from the mean of the ancestral. A Wilcoxon rank sum test with continuity correction was used to compare the relative expression levels of the evolved clones with the ancestral. P values of x0 are of order of their inverse selection coefficient (up to logarithmic corrections):$${{{{{rm{G}}}}}}left(xright)approx 1,{{{{{rm{T}}}}}}left(xright)sim frac{1}{s}.$$
(1)

Clonal interference under uniform directional selection. This mode occurs in asexual populations when adaptive mutations become frequent enough to interfere with one another59,60,61. Only a fraction of the established adaptive mutations reaches fixation; sojourn times to intermediate frequencies are set by a global coalescence rate (widetilde{sigma }) that is higher than the typical selection coefficient of individual mutations62:

$${{{{{rm{G}}}}}}left(xright) < 1,{{{{{rm{T}}}}}}left(xright)sim frac{1}{widetilde{sigma}}.$$ (2) Details of these dynamics depend on the spectrum of selection coefficients and on the overall mutation rate, which set the strength of clonal interference. For moderate interference, where a few concurrent beneficial mutations compete for fixation, we expect a roughly exponential drop of the frequency propagator, (Gleft(xright)sim {{exp }}left(-lambda xright)), reflecting the probability that a trajectory reaches frequency x without interference by a stronger competing clade. Moderate interference generates an effective neutrality for weaker beneficial mutations and at higher frequencies63. This regime has been mapped for influenza64. In the asymptotic regime of a travelling fitness wave, where many beneficial mutations are simultaneously present, the fate of a mutation is settled in the range of small frequencies; that is, at the tip of the wave65. In this regime, emergent neutrality affects the vast majority of beneficial mutations and most of the frequency regime66. Hence, the frequency propagator rapidly drops to its asymptotic value (Gleft(x=1right)ll 1.) Adaptation under diversifying selection. More complex selection scenarios involve selection within and between ecotypes, i.e., subpopulations occupying distinct ecological niches67,68. An important factor generating niches and ecotypes is the differential use of food and other environmental resources. In this mode, ecotype-specific, conditionally beneficial mutations reach intermediate frequencies after a time given by their within-ecotype selection coefficients, but fixation can be slowed down or suppressed by diversifying (negative frequency-dependent) cross-ecotype selection18, $${{{{{rm{G}}}}}}left(xright)approx 1,{{{{{rm{T}}}}}}left(xright)sim frac{1}{s},left(xlesssim ,frac{1}{2}right)$$ (3) $${{{{{rm{G}}}}}}left(xright) < 1,{{{{{rm{T}}}}}}left(xright)gg frac{1}{s}left(xto 1right).$$ (4) The details depend on the details of the eco-evolutionary model (synergistic vs. antagonistic interactions, carrying capacities, amount of resource competition vs. explicitly frequency-dependent selection). In a model with directional selection within ecotypes, conditionally beneficial mutations rapidly fix within ecotypes, but lead only to finite shifts of the ecotype frequencies. In the simplest case, the resulting dynamics of ecotype frequencies is diffusive, resulting in an effectively neutral turnover of ecotypes18. Given negative frequency-dependent selection between ecotypes, fixations become even rarer and can be completely suppressed; that is, ecotypes can become stable on the time scales of observation. The separation of time and selection scales between intra- and cross-ecotype frequency changes is expected to be a robust feature of ecotype-dependent selection: sojourn of adaptive alleles to intermediate frequencies is fast, fixation is slower and rarer. In other words, ecotype-dependent selection is characterized by two regimes of coalescence times T(x).Frequency propagators and the coalescence time spectra expected under these evolutionary modes are qualitatively sketched in Supplementary Fig. 11. For periodic sweeps under directional selection (dark green, left column), G(x) depends weakly on x and T(x) is set by rapid sweeps for all x. For clonal interference under directional selection (green, center column), G(x) decreases substantially with increasing x and T(x) becomes uniformly shorter. Under negative frequency-dependent selection (brown, right column), G(x) decreases substantially with increasing x, while T(x) substantially increases for large x and diverges in case of strong frequency-dependent selection generating stable ecotypes (dashed lines). (see Supplementary Fig. 11 for the results of simulations assuming a model of direction selection or assuming a resource competition model where ecotype formation occurs31.The ({{{{{boldsymbol{p}}}}}})-({{{{{boldsymbol{tau }}}}}}) selection testThis test is based on qualitative characteristics of the functions G(x), T(x) and does not depend on details of the evolutionary process. We evaluate G(x) and T(x) for host-specific families of frequency trajectories; sojourn times are counted from an initial frequency x0=0.01. Origination times at this frequency are inferred by backward extrapolation of the first observed trajectory segment; the reported results are robust under variations of the threshold x0 and the extrapolation procedure. We then compute two summary statistics: the probability (p) that a mutation established at an intermediate frequency xm reaches near-fixation at a frequency xf,$$p=frac{{{{{{rm{G}}}}}}({x}_{f})}{{{{{{rm{G}}}}}}({x}_{m})},$$ (5) and the corresponding fraction of sojourn times,$$tau=,frac{{{{{{rm{T}}}}}}({x}_{f})}{{{{{{rm{T}}}}}}({x}_{m})}.$$ (6) Here we use xm=0.3 and xf=0.95 to limit the uncertainties of empirical trajectories at low and high frequency; however, the selection test is robust under variation of these frequencies. We find evidence for different modes of evolution: The long-term frequency trajectories of mice B2, D2 and E2 are consistent with predominantly frequency-dependent selection (Fig. 2, Fig. 4a–c). The propagator G(x) is a strongly decreasing function of x, resulting in fixation probabilities (p) 0.6, as measured by time ratios τ  > 3.

The trajectories of mice A2, G2, and I2 show a signature of recurrent selective sweeps and clonal interference under uniform directional selection (Fig. 4a–c). The propagator G(x) is a decreasing function of x, resulting in fixation probabilities (p=0.2-0.8), depending on the strength of clonal interference. Fixation times are short, giving time ratios (tau lesssim 2).

The shorter trajectory of mouse H2 signals periodic sweeps under uniform directional selection (Fig. 3, Fig. 4a–c). The origination rate of mutations is lower than in the longer trajectories, and G(x) shows a weak decrease with (p=1.) Sojourn times T(x) are short and grow uniformly with x, resulting in a time ratio τ=2.25. This pattern is expected under directional selection in the low mutation regime: (Tleft(xright)={{log }}left[x/(1-x)right]/{s}) for individual mutations with a uniform selection coefficient s, leading to τ=2.0 for xm=0.3 and xf=0.95 (this value is marked as a dashed line in Fig. 4c).

The trajectories of non-mutator lines in the long-term in vitro evolution experiment of Good et al1, evaluated over the first 7500 generations, show an overall signal of clonal interference under uniform directional selection (Fig. 4c, Supplementary Fig. 12). The frequency propagators G(x) are strongly decreasing functions of x and sojourn times T(x) grow uniformly with x. We find (p=0.2-0.8) and (tau lesssim 2), similar to the pattern in mice A2, G2, and I2.

Code for Selection testsThe code for selection tests from the mutation frequency trajectories can be found in the Supplementary Information file.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Study areasThe study took place in the cities of Basel, Lausanne, Lugano and Zurich, in Switzerland. Basel, Lausanne and Zurich are located north of the Alps, in the geographical region of the Central Plateau (Supplementary Fig. S1). This region stretches from Lake Geneva in the southwest to Lake Constance in the northeast and is the most densely populated region in Switzerland. Zurich is the largest city of Switzerland and encompasses 88 km2 with a total human resident population of 420,21741. Lausanne and Basel are smaller than Zurich, with a surface of 41 and 24 km2 and a total population of 139,408 and 173,232, respectively41. The climate in these three cities is moderately continental, with cold winters often reaching freezing temperatures in January, and warm summers. Lugano is located in Ticino, south of the Alps (Supplementary Fig. S1), where the climate is strongly affected by the Mediterranean Sea, with mild winters and summers warm and humid, sometimes hot. Lugano is the smallest of the four cities with 50,603 residents in 26 km241.Aedes albopictus is well established in Lugano since 2009 and an integrated vector management is constantly implemented to contain the numbers of the mosquito at a manageable level. This consists of an intensive surveillance, with oviposition traps distributed according to a grid system, several control interventions, such as the removal of breeding sites and the systematic application of larvicides in public areas, mainly in catch basins, and extensive public information campaigns24,26. In Basel, two populations of Ae. albopictus are established since 2018: a first population in an area adjacent to the motorway toll on the border with France and a second population in an area near the border with Germany27. The mosquito has also been recorded repeatedly at various locations in the city of Basel and the surveillance indicates that the mosquito is spreading42. Control actions are taken exclusively within the perimeter of repeated detections of the mosquito and include regular treatment of catch basins with larvicides, distribution of flyers and door-to-door information campaigns42. In Zurich, Ae. albopictus was first detected in 2016 in a bus station for international coach services located in the centre of the city, near the main train station. Thanks to immediate surveillance and control actions (i.e., treatment of catch basins in the area with larvicides), to date there is no established population within the perimeter of the bus station despite continuous repeated introductions40. A small population was also detected in 2018 in a suburban neighbourhood in the Wollishofen district of Zurich, approximately 5 km southwest from the international bus station. Also in this case, immediate surveillance and control actions, including larval control and door-to-door information, were taken with success and no adults, eggs or aquatic stages have been found in 2020 and 202140. In Lausanne, no tiger mosquito has been reported to date (Swiss Mosquito Network, http://www.mosquitoes-switzerland.ch (accessed on 17 February 2022)).Microclimate dataBased on a previous investigation we conducted in Ticino, Basel and Zurich20, we focused the microclimate monitoring on ordinary stormwater catch basins positioned on the side of public roads. In each city, we monitored ten catch basins located either in urban context (defined as areas with high-density development, consisting of apartment blocks, commercial or industrial units) or in residential areas consisting mainly of houses with private gardens located in peri-urban area (Supplementary Table S1, Supplementary Fig. S2). The catch basins were usually homogeneous in dimension, in the same city, although we recorded variations in depth. In Basel, we included catch basins located in the urban area near the border with France, in which Ae. albopictus is established. In Zurich, we included catch basins located in the international bus station, where Ae. albopictus was recorded in summer, and in the residential area of Wollishofen, where a small population of Ae. albopictus was detected and then likely eradicated. In Lausanne, some catch basins were selected in potential points of introduction of the mosquito (e.g., near a campsite, the main train station, etc.). In Lugano, Ae. albopictus was established in all the locations selected.A sensor device was installed in each selected catch basin. The sensor devices were built in house. The development of the devices and the Wireless Sensor Network (WSN) has been described in detail by Strigaro et al.29. Briefly, the device consisted of a waterproof plastic box containing a LoPy Micro-Controller Unit (Pycom, Guildford, United Kingdom), a waterproof temperature probe (accuracy of ± 0.5 °C), a light sensor (measuring illuminance arriving at the sensor device, in lux), an SD card, the rechargeable batteries and other parts. The main box, with the light sensor, was hung on the inside wall of the catch basin. The temperature probe was attached to the wall at a depth ranging from 0.3 to 0.5 m, depending on the depth of the catch basin and the level of the water in the catch basin. The probe was placed in direct contact with the inside wall of the catch basin, in order to measure the microclimatic conditions where the mosquito eggs are potentially laid. The data collected was transmitted to a data warehouse based on istSOS, an open-source Python based implementation of the Sensor Observation Service standard (SOS) of the Open Geospatial Consortium (OGC)43. The data was transmitted through the Swisscom Low Power Network (LPN) LoRaWAN (Swisscom Ltd, Ittigen, Switzerland): the data sent by the sensor devices was received by a Swisscom Gateway and then sent to the data warehouse29.In addition to the sensor devices installed in the catch basins, four devices were installed outside four catch basins in each city, except in Lugano, where three devices were installed. These external devices were placed in vegetation representing potential resting habitats for Ae. albopictus adults in the reproductive season, at 1–2 m above the ground and analyzed to confirm the close similarity between measured external temperatures and MeteoSwiss gridded temperature data. However, since the main goal of the data collection was to model the differences between MeteoSwiss gridded temperature data and catch basins’ temperatures, only a small number of external sensors were deployed. Microclimate data were collected from beginning of December 2019 to end of February 2020, a period defined as cold season, with acquisition interval set at one hour. In Lugano, data collection started on the 12th or 13th of December 2019.Local climate dataWe used two types of local climate data. The first type is the momentary hourly free-air temperatures recorded at 2 m above ground level by permanent weather stations. The weather stations belong to SwissMetNet, the automatic monitoring network of MeteoSwiss. For each city, we selected the weather station closest to the study area (Supplementary Table S1, Supplementary Fig. S2) and temperature data were retrieved from https://gate.meteoswiss.ch/idaweb (source: MeteoSwiss, Zurich-Airport, Switzerland; accessed on 12 August 2021).The second type of local climate data is the MeteoSwiss spatial climate daily datasets (source: MeteoSwiss). These temperature datasets are constructed through interpolation of daily minimum, maximum, and mean temperatures from a network of approximately 90 SwissMetNet permanent weather stations to a 1 km resolution grid in the Swiss coordinate system CH190344,45. This results in three temperature datasets describing the km-scale distribution of day-to-day temperature variations in Switzerland. We referred to them as gridded temperature data. Each monitored catch basin and external device was assigned, based on its geographical position, to the corresponding 1 km × 1 km cell of the climate grid. Each cell was identified with its MeteoSwiss (MS) number (Supplementary Table S1).Data analysisThe hourly temperatures were used to compute daily mean, maximum and minimum temperatures and daily temperature ranges, which were calculated as the difference between the maximum and minimum daily temperature. Temperatures of catch basins and external habitats were compared to temperatures of permanent weather stations and to the gridded temperatures both graphically and using the nonparametric Mann–Whitney U-test, for which a P value of  More

Bonan, G. B. Forests and climate change: Forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449. https://doi.org/10.1126/science.1155121 (2008).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Le Quere, C. et al. Global carbon budget 2016. Earth Syst. Sci. Data 8, 605–649. https://doi.org/10.5194/essd-8-605-2016 (2016).ADS 
Article 

Google Scholar 
DavidMorales-Hidalgo, D., Oswalt, S. N. & Somanathan, E. Status and trends in global primary forest, protected areas, and areas designated for conservation of biodiversity from the Global Forest Resources Assessment 2015. Forest Ecol. Manag. 352, 68–77. https://doi.org/10.1016/j.foreco.2015.06.011 (2015).Article 

Google Scholar 
Kauppi, P. E., Sandstrom, V. & Lipponen, A. Forest resources of nations in relation to human well-being. PLoS One 13, e0196248. https://doi.org/10.1371/journal.pone.0196248 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Anderegg, W. R. L. et al. Climate-driven risks to the climate mitigation potential of forests. Science 368, 1327. https://doi.org/10.1126/science.aaz7005 (2020).CAS 
Article 

Google Scholar 
Wilson, M. C. et al. Habitat fragmentation and biodiversity conservation: Key findings and future challenges. Landsc. Ecol. 31, 219–227. https://doi.org/10.1007/s10980-015-0312-3 (2016).Article 

Google Scholar 
Haddad, N. M. et al. Habitat fragmentation and its lasting impact on earth’s ecosystems. Sci. Adv. 1, e1500052. https://doi.org/10.1126/sciadv.1500052 (2015).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Holl, K. D. Restoring tropical forests from the bottom up. Science 355, 455–456. https://doi.org/10.1126/science.aam5432 (2017).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Audino, L. D., Murphy, S. J., Zambaldi, L., Louzada, J. & Comita, L. S. Drivers of community assembly in tropical forest restoration sites: Role of local environment, landscape, and space. Ecol. Appl. 27, 1731–1745. https://doi.org/10.1002/eap.1562 (2017).Article 
PubMed 

Google Scholar 
Temperton, V. M., Hobbs, R. J., Nuttle, T. & Halle, S. in Assembly Rules and Restoration Ecology: Bridging the Gap Between Theory and Practice [Science and Practice of Ecological Restoration]. i–xv, 1–439 (2004).Young, T. P., Chase, J. M. & Huddleston, R. T. Community succession and assembly: Comparing, contrasting and combining paradigms in the context of ecological restoration. Ecol. Restor. 19, 5–18 (2001).Article 

Google Scholar 
Vellend, M. The Theory of Ecological Communities (Princeton University Press, 2016).
Google Scholar 
HilleRisLambers, J., Adler, P. B., Harpole, W. S., Levine, J. M. & Mayfield, M. M. Rethinking community assembly through the lens of coexistence theory. Annu. Rev. Ecol. Evol. Syst. 43(43), 227–248. https://doi.org/10.1146/annurev-ecolsys-110411-160411 (2012).Article 

Google Scholar 
Connell, J. H. On the role of natural enemies in preventing competitive exclusion in some marine animals and in rain forest trees. In Dynamics of Populations (eds Den Boer, P. J. & Gradwell, G. R.) (Centre for Agricultural Publishing and Documentation, 1971).
Google Scholar 
Janzen, D. H. Herbivores and the number of tree species in tropical forests. Am. Nat. 104, 501. https://doi.org/10.1086/282687 (1970).Article 

Google Scholar 
Schmid, J. S., Taubert, F., Wiegand, T., Sun, I. F. & Huth, A. Network science applied to forest megaplots: Tropical tree species coexist in small-world networks. Sci. Rep. https://doi.org/10.1038/s41598-020-70052-8 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Wang, H. X. et al. Prevalence of inter-tree competition and its role in shaping the community structure of a natural Mongolian scots pine (Pinus sylvestris var. mongolica) forest. Forests https://doi.org/10.3390/f8030084 (2017).Article 

Google Scholar 
Hubbell, S. P. et al. Light-gap disturbances, recruitment limitation, and tree diversity in a neotropical forest. Science 283, 554–557. https://doi.org/10.1126/science.283.5401.554 (1999).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Janik, D. et al. Breaking through beech: A three-decade rise of sycamore in old-growth European forest. Forest Ecol. Manag. 366, 106–117. https://doi.org/10.1016/j.foreco.2016.02.003 (2016).Article 

Google Scholar 
Svatek, M., Rejzek, M., Kvasnica, J., Repka, R. & Matula, R. Frequent fires control tree spatial pattern, mortality and regeneration in argentine open woodlands. Forest Ecol. Manag. 408, 129–136. https://doi.org/10.1016/j.foreco.2017.10.048 (2018).Article 

Google Scholar 
Giammarchi, F. et al. Effects of the lack of forest management on spatiotemporal dynamics of a subalpine Pinus cembra forest. Scand. J. Forest Res. 32, 142–153. https://doi.org/10.1080/02827581.2016.1207802 (2017).Article 

Google Scholar 
Janik, D. et al. Patterns of Fraxinus angustifolia in an alluvial old-growth forest after declines in flooding events. Eur. J. Forest Res. 135, 215–228. https://doi.org/10.1007/s10342-015-0925-8 (2016).Article 

Google Scholar 
Bagchi, R. et al. Defaunation increases the spatial clustering of lowland western amazonian tree communities. J. Ecol. 106, 1470–1482. https://doi.org/10.1111/1365-2745.12929 (2018).Article 

Google Scholar 
Zhang, L. Y., Dong, L. B., Liu, Q. & Liu, Z. G. Spatial patterns and interspecific associations during natural regeneration in three types of secondary forest in the central part of the greater Khingan mountains, Heilongjiang province, China. Forests https://doi.org/10.3390/f11020152 (2020).Article 

Google Scholar 
Obiang, N. L. E. et al. Determinants of spatial patterns of canopy tree species in a tropical evergreen forest in Gabon. J. Veg. Sci. 30, 929–939. https://doi.org/10.1111/jvs.12778 (2019).Article 

Google Scholar 
Wiegand, T. et al. Spatially explicit metrics of species diversity, functional diversity, and phylogenetic diversity: Insights into plant community assembly processes. Annu. Rev. Ecol. Evol. Syst. 48(48), 329–351. https://doi.org/10.1146/annurev-ecolsys-110316-022936 (2017).Article 

Google Scholar 
Gabriel, E. Spatial point patterns: Methodology and applications with R. Math. Geosci. 49, 815–817. https://doi.org/10.1007/s11004-016-9670-x (2017).CAS 
Article 
MATH 

Google Scholar 
Baddeley, A., Rubak, R. & Turner, R. Spatial Point Patterns, Methodology and Applications with R (CRC Press, 2016).MATH 

Google Scholar 
Wiegand, T. & Moloney, K. A. Rings, circles, and null-models for point pattern analysis in ecology. Oikos 104, 209–229. https://doi.org/10.1111/j.0030-1299.2004.12497.x (2004).Article 

Google Scholar 
Plotkin, J. B., Chave, J. M. & Ashton, P. S. Cluster analysis of spatial patterns in Malaysian tree species. Am. Nat. 160, 629–644. https://doi.org/10.1086/342823 (2002).Article 
PubMed 

Google Scholar 
Ripley, B. D. Modeling spatial patterns. J. R. Stat. Soc. B 39, 172–212 (1977).
Google Scholar 
He, F. L. & Gaston, K. J. Estimating species abundance from occurrence. Am. Nat. 156, 553–559. https://doi.org/10.1086/303403 (2000).Article 
PubMed 

Google Scholar 
Diggle, P. Statistical Analysis of Spatial Point Patterns (Academic Press, 1983).MATH 

Google Scholar 
Pielou, E. C. The use of point-to-plant distances in the study of the pattern of plant-populations. J. Ecol. 47, 607–613. https://doi.org/10.2307/2257293 (1959).Article 

Google Scholar 
Losapio, G., Montesinos-Navarro, A. & Saiz, H. Perspectives for ecological networks in plant ecology. Plant Ecol. Divers. 12, 87–102. https://doi.org/10.1080/17550874.2019.1626509 (2019).Article 

Google Scholar 
Fuller, M. M., Wagner, A. & Enquist, B. J. Using network analysis to characterize forest structure. Nat. Resour. Model. 21, 225–247. https://doi.org/10.1111/j.1939-7445.2008.00004.x (2008).MathSciNet 
Article 

Google Scholar 
Montoya, J. M., Pimm, S. L. & Sole, R. V. Ecological networks and their fragility. Nature 442, 259–264. https://doi.org/10.1038/nature04927 (2006).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Proulx, S. R., Promislow, D. E. L. & Phillips, P. C. Network thinking in ecology and evolution. Trends Ecol. Evol. 20, 345–353. https://doi.org/10.1016/j.tree.2005.04.004 (2005).Article 
PubMed 

Google Scholar 
Nakagawa, Y., Yokozaw, M. & Hara, T. Complex network analysis reveals novel essential properties of competition among individuals in an even-aged plant population. Ecol. Complex 26, 95–116. https://doi.org/10.1016/j.ecocom.2016.03.005 (2016).Article 

Google Scholar 
Wiegand, T. & Moloney, K. A. Handbook of Spatial Point Pattern Analysis in Ecology (CRC Press, 2013).Book 

Google Scholar 
Barthelemy, M. Spatial networks. Phys. Rep. Rev. Sect. Phys. Lett. 499, 1–101. https://doi.org/10.1016/j.physrep.2010.11.002 (2011).MathSciNet 
CAS 
Article 

Google Scholar 
Keren, S. Modeling tree species count data in the understory and canopy layer of two mixed old-growth forests in the Dinaric region. Forests https://doi.org/10.3390/f11050531 (2020).Article 

Google Scholar 
Podlaski, R. Models of the fine-scale spatial distributions of trees in managed and unmanaged forest patches with Abies alba Mill. and Fagus sylvatica L. Forest Ecol. Manag. 439, 1–8 (2019).Article 

Google Scholar 
Levin, S. A. Theoretical ecology—Principles and applications, 3rd edition. Science 316, 1699–1700. https://doi.org/10.1126/science.1141870 (2007).CAS 
Article 

Google Scholar 
Martinez-Lopez, V., Garcia, C., Zapata, V., Robledano, F. & De la Rua, P. Intercontinental long-distance seed dispersal across the Mediterranean basin explains population genetic structure of a bird-dispersed shrub. Mol. Ecol. 29, 1408–1420. https://doi.org/10.1111/mec.15413 (2020).Article 
PubMed 

Google Scholar 
Dale, M. R. T. & Fortin, M. J. From graphs to spatial graphs. Annu. Rev. Ecol. Evol. Syst. 41, 21–38. https://doi.org/10.1146/annurev-ecolsys-102209-144718 (2010).Article 

Google Scholar 
Silva, C. A. et al. Treetop: A shiny-based application and R package for extracting forest information from LiDAR data for ecologists and conservationists. Methods Ecol. Evol. 13, 1164–1176. https://doi.org/10.1111/2041-210x.13830 (2022).Article 

Google Scholar 
Tatsumi, S., Yamaguchi, K. & Furuya, N. Forestscanner: A mobile application for measuring and mapping trees with LiDAR-equipped iPhone and iPad. Methods Ecol. Evol. https://doi.org/10.1111/2041-210x.13900 (2022).Article 

Google Scholar 
Ferraz, A., Saatchi, S. S., Longo, M. & Clark, D. B. Tropical tree size-frequency distributions from airborne LiDAR. Ecol. Appl. 30, e02154. https://doi.org/10.1002/eap.2154 (2020).Article 
PubMed 

Google Scholar 
Bianchi, E., Bugmann, H., Hobi, M. L. & Bigler, C. Spatial patterns of living and dead small trees in subalpine Norway spruce forest reserves in Switzerland. Forest Ecol. Manag. 494, 119315. https://doi.org/10.1016/j.foreco.2021.119315 (2021).Article 

Google Scholar 
Tatsumi, S., Owari, T., Yin, M. F. & Ning, L. Z. Neighborhood analysis of underplanted Korean pine demography in larch plantations: Implications for uneven-aged management in northeast china. Forest Ecol. Manag. 322, 10–18. https://doi.org/10.1016/j.foreco.2014.03.022 (2014).Article 

Google Scholar 
Cornett, M. W., Reich, P. B. & Puettmann, K. J. Canopy feedbacks and microtopography regulate conifer seedling distribution in two Minnesota conifer-deciduous forests. Ecoscience 4, 353–364. https://doi.org/10.1080/11956860.1997.11682414 (1997).Article 

Google Scholar 
Wang, X. F., Zheng, G., Yun, Z. X. & Moskal, L. M. Characterizing tree spatial distribution patterns using discrete aerial LiDAR data. Remote Sens. Basel 12, 712. https://doi.org/10.3390/rs12040712 (2020).ADS 
Article 

Google Scholar 
Matérn, B. Spatial variation: Stochastic models and their application to some problems in forest surveys and other sampling investigations. Meddelanden från Statens Skogsforskningsinstitut 49, 1–144 (1960).MathSciNet 

Google Scholar 
Matérn, B. Spatial Variation. Lecture Notes in Statistics Vol. 36 (Springer, 1986).Book 

Google Scholar 
Thomas, M. A generalisation of Poisson’s binomial limit for use in ecology. Biometrika 36, 18–25 (1949).MathSciNet 
CAS 
Article 

Google Scholar 
Lotwick, H. W. Simulation of some spatial hard core models, and the complete packing problem. J. Stat. Comput. Simul. 15, 295–314 (1982).MathSciNet 
Article 

Google Scholar 
Strauss, D. J. A model for clustering. Biometrika 62, 467–475 (1975).MathSciNet 
Article 

Google Scholar 
Cressie Noel, A. C. Statistics for Spatial Data (Wiley-Interscience, 1993).Book 

Google Scholar 
Besag, J. E. Contribution to the discussion of the paper by Ripley. J. R. Stat. Soc. 39, 193–195 (1977).MathSciNet 

Google Scholar  More

Royse DJ. A global perspective on the high five: Agaricus, Pleurotus, Lentinula, Auricularia and Flammulina. In: Singh M, editor. Proceedings of the 8th International Conference on Mushroom Biology and Mushroom Products. New Delhi; 2014. p. 1–6.Vos AM, Heijboer A, Boschker HTS, Bonnet B, Lugones LG, Wosten HAB. Microbial biomass in compost during colonization of Agaricus bisporus. AMB Express. 2017; 7:12.Jurak E, Punt AM, Arts W, Kabel MA, Gruppen H. Fate of carbohydrates and lignin during composting and mycelium growth of Agaricus bisporus on wheat straw based compost. PLoS ONE. 2015;10:e0138909.PubMed 
PubMed Central 
Article 

Google Scholar 
Beyer DM. Basic procedures for Agaricus mushroom growing PennState Extension: the Pennsylvania State University. 2003. https://extension.psu.edu/basic-procedures-for-agaricus-mushroom-growing.Wang L, Mao J, Zhao H, Li M, Wei Q, Zhou Y, et al. Comparison of characterization and microbial communities in rice straw- and wheat straw-based compost for Agaricus bisporus production. J Ind Microbiol Biotechnol. 2016;43:1249–60.CAS 
PubMed 
Article 

Google Scholar 
Adams JDW, Frostick LE. Investigating microbial activities in compost using mushroom (Agaricus bisporus) cultivation as an experimental system. Bioresour Technol. 2008;99:1097–102.CAS 
PubMed 
Article 

Google Scholar 
Liu L, Wang S, Guo X, Zhao T, Zhang B. Succession and diversity of microorganisms and their association with physicochemical properties during green waste thermophilic composting. Waste Manage. 2018;73:101–12.CAS 
Article 

Google Scholar 
Reyes-Torres M, Oviedo-Ocana ER, Dominguez I, Komilis D, Sanchez A. A systematic review on the composting of green waste: feedstock quality and optimization strategies. Waste Manage. 2018;77:486–99.CAS 
Article 

Google Scholar 
Pardo‐Giménez A, González JEP, Zied DC. Casing materials and techniques in Agaricus bisporus cultivation. In: Zied DC, Pardo‐Giménez A, editors. Edible and medicinal mushrooms technology and applications. Chichester, UK: Wiley; 2017. p. 149–74.Baars JJP, Scholtmeijer K, Sonnenberg ASM, van Peer A. Critical factors involved in primordia building in Agaricus bisporus: a review. Molecules. 2020;25:2984.Vieira FR, Pecchia JA. Bacterial community patterns in the Agaricus bisporus cultivation system, from compost raw materials to mushroom caps. Microb Ecol. 2021;84:20–32.PubMed 
Article 

Google Scholar 
Kristensen JB, Thygesen LG, Felby C, Jorgensen H, Elder T. Cell-wall structural changes in wheat straw pretreated for bioethanol production. Biotechnol Biofuels. 2008;1:1–9.Article 

Google Scholar 
Jurak E, Patyshakuliyeva A, de Vries RP, Gruppen H, Kabel MA. Compost grown Agaricus bisporus lacks the ability to degrade and consume highly substituted xylan fragments. PLoS ONE. 2015;10:e0134169.PubMed 
PubMed Central 
Article 

Google Scholar 
Ryckeboer J, Mergaert J, Vaes K, Klammer S, De Clercq D, Coosemans J, et al. A survey of bacteria and fungi occurring during composting and self-heating processes. Ann Microbiol. 2003;53:349–410.
Google Scholar 
Kutzner HJ. Microbiology of composting. In: Rehm H-J, Reed G, editors. Biotechnology. 11c. 2nd ed. Verlag: Wiley-VCH; 2000. p. 35–100.Carrasco J, Garcia-Delgado C, Lavega R, Tello ML, De Toro M, Barba-Vicente V, et al. Holistic assessment of the microbiome dynamics in the substrates used for commercial champignon (Agaricus bisporus) cultivation. Microb Biotechnol. 2020;13:1933–47.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Vieira FR, Pecchia JA. Bacterial community patterns in the Agaricus bisporus cultivation system, from compost raw materials to mushroom caps. Microb Ecol. 2021;82. https://doi.org/10.1007/s00248-021-1833-5.Vieira FR, Pecchia JA. An exploration into the bacterial community under different pasteurization conditions during substrate preparation (composting–Phase II) for Agaricus bisporus cultivation. Microb Ecol. 2018;75:318–30.CAS 
PubMed 
Article 

Google Scholar 
Cao GT, Song TT, Shen YY, Jin QL, Feng WL, Fan LJ, et al. Diversity of bacterial and fungal communities in wheat straw compost for Agaricus bisporus cultivation. Hortscience. 2019;54:100–9.CAS 
Article 

Google Scholar 
Wiegant WM. Growth characteristics of the thermophilic fungus Scytalidium thermophilum in relation to production of mushroom compost. Appl Environ Microbiol. 1992;58:1301–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Fermor T, Randle P, Smith J. Compost as a substrate and its preparation. In: Flegg PB, Spencer DM, Wood D, editors. The biology and technology of the cultivated mushroom. Chichester, UK: John Wiley & Sons, Ltd; 1985. p. 81–109.Straatsma G, Samson RA, Olijnsma TW, Op den Camp HJM, Gerrits JPG, Griensven LJLDV. Ecology of thermophilic fungi in mushroom compost, with emphasis on Scytalidium thermophilum and growth stimulation of Agaricus bisporus mycelium. Appl Environ Microbiol. 1994;60:454–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ross RC, Harris PJ. An investigation into the selective nature of mushroom compost. Sci Hortic. 1983;19:55–64.Article 

Google Scholar 
Coello-Castillo MM, Sanchez JE, Royse DJ. Production of Agaricus bisporus on substrates pre-colonized by Scytalidium thermophilum and supplemented at casing with protein-rich supplements. Bioresour Technol. 2009;100:4488–92.CAS 
PubMed 
Article 

Google Scholar 
Szekely A, Sipos R, Berta B, Vajna B, Hajdu C, Marialigeti K. DGGE and T-RFLP analysis of bacterial succession during mushroom compost production and sequence-aided T-RFLP profile of mature compost. Microb Ecol. 2009;57:522–33.PubMed 
Article 

Google Scholar 
Kertesz M, Safianowicz K, Bell TL. New insights into the microbial communities and biological activities that define mushroom compost. Sci Cultiv Edible Fungi. 2016;19:161–5.
Google Scholar 
McGee CF, Byrne H, Irvine A, Wilson J. Diversity and dynamics of the DNA and cDNA-derived bacterial compost communities throughout the Agaricus bisporus mushroom cropping process. Ann Microbiol. 2017;67:751–61.CAS 
Article 

Google Scholar 
McGee CF, Byrne H, Irvine A, Wilson J. Diversity and dynamics of the DNA- and cDNA-derived compost fungal communities throughout the commercial cultivation process for Agaricus bisporus. Mycologia. 2017;109:475–84.CAS 
PubMed 
Article 

Google Scholar 
Yeates C, Gillings MR. Rapid purification of DNA from soil for molecular biodiversity analysis. Lett Appl Microbiol. 1998;27:49–53.CAS 
Article 

Google Scholar 
Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 2012;6:1621–4.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
White TJ, Bruns T, Lee S, Taylor JW. Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. In: Innis MA, Gelfand DH, Sninsky JJ, White TJ, editors. PCR protocols: a guide to methods and applications. New York: Academic Press; 1990. p. 315–22.
Google Scholar 
Lever MA, Torti A, Eickenbusch P, Michaud AB, Santl-Temkiv T, Jorgensen BB. A modular method for the extraction of DNA and RNA, and the separation of DNA pools from diverse environmental sample types. Front Microbiol. 2015;6:476.Muyzer G, Waal ECD, Uitterlinden AG. Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA. Appl Environ Microbiol. 1993;59:695–700.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Lozupone CA, Turnbaugh PJ, et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc Natl Acad Sci USA. 2011;108:4516–22.CAS 
PubMed 
Article 

Google Scholar 
R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation For Statistical Computing; 2019.Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from Illumina aplicon data. Nat Meth. 2016;13:581–3.CAS 
Article 

Google Scholar 
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucl Acids Res. 2012;41:D590–6.PubMed 
PubMed Central 
Article 

Google Scholar 
Schliep KP. phangorn: phylogenetic analysis in R. Bioinformatics. 2010;27:592–3.PubMed 
PubMed Central 
Article 

Google Scholar 
Wright ES. Using DECIPHER v2.0 to analyze big biological sequence data in R. R J. 2016;8:352–9.Article 

Google Scholar 
McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:e61217.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Dixon P. VEGAN, a package of R functions for community ecology. J Veget Sci. 2003;14:927–30.Article 

Google Scholar 
Wickham H. ggplot2: elegant graphics for data analysis. New York: Springer; 2016.Sharma HS, Kilpatrick M. Mushroom (Agaricus bisporus) compost quality factors for predicting potential yield of fruiting bodies. Can J Microbiol. 2000;46:515–9.CAS 
PubMed 
Article 

Google Scholar 
Seaby DA. Mushroom (Agaricus bisporus) yield modelling for the bag method of mushroom production using commercial yields and from micro plots. Sci Cultiv Edible Fungi. 1995;14:409–16.
Google Scholar 
O’Donoghue DC. Relationship between some compost factors and their effects on yield of Agaricus. Mushroom Sci. 1965;6:245–54.
Google Scholar 
Andersen B, Sorensen JL, Nielsen KF, van den Ende BG, de Hoog S. A polyphasic approach to the taxonomy of the Alternaria infectoria species-group. Fungal Genet Biol. 2009;46:642–56.CAS 
PubMed 
Article 

Google Scholar 
van den Brink J, Samson RA, Hagen F, Boekhout T, de Vries RP. Phylogeny of the industrial relevant, thermophilic genera Myceliophthora and Corynascus. Fungal Divers. 2012;52:197–207.Article 

Google Scholar 
Souza TP, Marques SC, Santos D, Dias ES. Analysis of thermophilic fungal populations during phase II of composting for the cultivation of Agaricus subrufescens. World J Microbiol Biotechnol. 2014;30:2419–25.PubMed 
Article 

Google Scholar 
Vajna B, Szili D, Nagy A, Márialigeti K. An improved sequence-aided T-RFLP analysis of bacterial succession during oyster mushroom substrate preparation. Microb Ecol. 2012;64:702–13.CAS 
PubMed 
Article 

Google Scholar 
Du R, Yan J, Li S, Zhang L, Zhang S, Li J, et al. Cellulosic ethanol production by natural bacterial consortia is enhanced by Pseudoxanthomonas taiwanensis. Biotechnol Biofuels. 2015;8:10.Kato S, Haruta S, Cui ZJ, Ishii M, Igarashi Y. Stable coexistence of five bacterial strains as a cellulose-degrading community. Appl Environ Microbiol. 2005;71:7099–106.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Haruta S, Cui Z, Huang Z, Li M, Ishii M, Igarashi Y. Construction of a stable microbial community with high cellulose-degradation ability. Appl Microbiol Biotechnol. 2002;59:529–34.CAS 
PubMed 
Article 

Google Scholar 
Vajna B, Adrienn N, Sajben-Nagy E, Manczinger L, Szijártó N, Kádár Z, et al. Microbial community structure changes during oyster mushroom substrate preparation. Appl Microbiol Biotechnol. 2010;86:367–75.CAS 
PubMed 
Article 

Google Scholar 
Karadag D, Özkaya B, Ölmez E, Nissilä ME, Çakmakçı M, Yıldız Ş, et al. Profiling of bacterial community in a full-scale aerobic composting plant. Int Biodeter Biodeg. 2013;77:85–90.CAS 
Article 

Google Scholar 
Rathinam NK, Gorky, Bibra M, Salem DR, Sani RK. Bioelectrochemical approach for enhancing lignocellulose degradation and biofilm formation in Geobacillus strain WSUCF1. Bioresour Technol. 2020;295:122271.Song TT, Shen YY, Jin QL, Feng WL, Fan LJ, Cao GT, et al. Bacterial community diversity, lignocellulose components, and histological changes in composting using agricultural straws for Agaricus bisporus production. PeerJ. 2021;9:e10452.PubMed 
PubMed Central 
Article 

Google Scholar 
Zhang X, Zhong Y, Yang S, Zhang W, Xu M, Ma A, et al. Diversity and dynamics of the microbial community on decomposing wheat straw during mushroom compost production. Bioresour Technol. 2014;170:183–95.CAS 
PubMed 
Article 

Google Scholar 
Goodfellow M, Maldonado LA, Quintana ET. Reclassification of Nonomuraea flexuosa (Meyer 1989) Zhang et al. 1998 as Thermopolyspora flexuosa gen. nov., comb. nov., nom. rev. Int J Syst Evol Microbiol. 2005;55:1979–83.CAS 
PubMed 
Article 

Google Scholar 
Lin SB, Stutzenberger FJ. Purification and characterization of the major beta-1,4-endoglucanase from Thermomonospora curvata. J Appl Bacteriol. 1995;79:447–53.CAS 
PubMed 
Article 

Google Scholar 
Kukolya J, Nagy I, Láday M, Tóth E, Oravecz O, Márialigeti K, et al. Thermobifida cellulolytica sp. nov., a novel lignocellulose-decomposing actinomycete. Int J Syst Evol Microbiol. 2002;52:1193–9.CAS 
PubMed 

Google Scholar 
Weon H-Y, Lee S-Y, Kim B-Y, Noh H-J, Schumann P, Kim J-S, et al. Ureibacillus composti sp. nov. and Ureibacillus thermophilus sp. nov., isolated from livestock-manure composts. Int J Syst Evol Microbiol. 2007;57:2908–11.CAS 
PubMed 
Article 

Google Scholar 
Poli A, Laezza G, Gul-Guven R, Orlando P, Nicolaus B. Geobacillus galactosidasius sp. nov., a new thermophilic galactosidase-producing bacterium isolated from compost. Syst Appl Microbiol. 2011;34:419–23.CAS 
PubMed 
Article 

Google Scholar 
Gavande PV, Basak A, Sen S, Lepcha K, Murmu N, Rai V, et al. Functional characterization of thermotolerant microbial consortium for lignocellulolytic enzymes with central role of Firmicutes in rice straw depolymerization. Sci Rep. 2021;11:3032.Xu JQ, Lu YY, Shan GC, He XS, Huang JH, Li QL. Inoculation with compost-born thermophilic complex microbial consortium induced organic matters degradation while reduced nitrogen loss during co-composting of dairy manure and sugarcane leaves. Waste Biomass Valor. 2019;10:2467–77.CAS 
Article 

Google Scholar 
Yoon JH, Kang SJ, Im WT, Lee ST, Oh TK. Chelatococcus daeguensis sp nov., isolated from wastewater of a textile dye works, and emended description of the genus Chelatococcus. Int J Syst Evol Microbiol. 2008;58:2224–8.CAS 
PubMed 
Article 

Google Scholar 
Zhou C, Liu Z, Huang Z-L, Dong M, Yu X-L, Ning P. A new strategy for co-composting dairy manure with rice straw: addition of different inocula at three stages of composting. Waste Manage. 2015;40:38–43.CAS 
Article 

Google Scholar 
Gómez A. New technology in Agaricus bisporus cultivation. In: Zied DC, Pardo-Giménez A, editors. Edible and medicinal mushrooms. Chichester, UK: John Wiley & Sons; 2017. p. 211–20.von Minnigerode HF, editor. Method for controlling and regulating the composting process. Proceedings of the Eleventh International Scientific Congress on the Cultivation of Edible Fungi. Sydney, Australia: The International Society for Mushroom Science; 1981.Jurak E, Gruppen H, Kabel MA, Eggink G, Meyer AS, van der Maarel MJEC, et al. How mushrooms feed on compost: conversion of carbohydrates and lignin in industrial wheat straw based compost enabling the growth of Agaricus bisporus. Wageningen University—Graduate School VLAG; 2015.Miller FC, Macauley BJ, Harper ER. Investigation of various gases, pH and redox potential in mushroom composting Phase-I stacks. Aust J Exper Agric. 1991;31:415–25.Article 

Google Scholar 
Miller FC, Harper ER, Macauley BJ, Gulliver A. Composting based on moderately thermophilic and aerobic conditions for the production of commercial growing compost. Aust J Exper Agric. 1990;30:287–96.Article 

Google Scholar 
Carrasco J, Preston GM. Growing edible mushrooms: a conversation between bacteria and fungi. Environ Microbiol. 2020;22:858–72.PubMed 
Article 

Google Scholar  More

Occurrence of pesticides in small mammals: general patternsA total of 112 different compounds were detected over the 140 parent pesticides and metabolites screened in hair samples (80% of the compounds screened). The full lists of compounds with their acronyms, the details of their full names and chemical families are provided in Tables 1 and 2.Table 1 Concentrations of banned and restricted pesticides (BRPs) in small mammal hair samples, classified by decreasing number of detection.Full size tableTable 2 Concentrations of currently used pesticides (CUPs) in small mammal hair samples, ordered by decreasing number of detection.Full size tableAs a whole, 51 BRPs over 67 analyzed (76%) were detected in small mammal hair, with 27 parent chemicals detected out of 39 screened (67%) and 25 metabolites detected out of 28 (89%) (Table 1). Thirteen compounds were present in more than 75% of individuals: DMP, PNP, 1-(3,4-dichlorophenyl)urea, DEP, PCP, 3Me4NP, 1-(3,4-dichlorophenyl)-3-methylurea, DETP, fipronil, fipronil sulfone, trifluralin, DMTP and HCB. Most of them are transformation products of organochlorine, organophosphorous, urea and phenylpyrazole pesticides. Then, the proportion of detection rapidly dropped under 25% of the samples. Only three compounds were detected in 50–75% of the individuals (Table 1: lindane γ-HCH (organochlorine insecticide), terbutryn (triazine/triazinone herbicide) and fenuron (urea herbicide). Five substances were found in 25–50% of the animals: DMST (metabolite of tolylfluanide, an amide fungicide), flusilazole (azole fungicide), α-endosulfan (organochlorine insecticide), DMDTP (organophosphorous insecticide metabolite) and diuron (urea herbicide). The 10 highest measured concentrations ranged between 30 and 118 ng/g, and were mostly represented by DMP (seven of the 10 values) together with PNP and 1-(3,4-dichlorophenyl)urea. Seven compounds exhibited concentrations higher than 10 ng/g, which were the same as the most frequent: DMP, PNP, 1-(3,4-dichlorophenyl)urea, DEP, PCP, 3Me4NP, plus DEDTP (organophosphorous metabolite, 6% of individuals). Considering the 16 BRPs that have never been detected, 13 were parent pesticides and three were metabolites, distributed in one fungicide, three herbicides, and 12 insecticides/biocides. The non-detected compounds belong to several chemical families including organochlorines, organophosphorous, carbamate, and urea pesticides.A total of 61 CUPs out of 73 analyzed were detected in small mammal hair, with 54 parent pesticides out of 66 tested (82%) and seven metabolites detected out of seven screened (100%) (Table 2). Many of the detected CUPs were found in a large proportion of individuals: 25 compounds were detected in more than 75% of the individuals, which means that 41% of the 61 detected CUPs were present in 75–100% of individuals. These 25 most frequently detected compounds belonged to various chemical families and all uses of CUPs (Table 2). The herbicides belonged to the families of organochlorines (metolachlor and metazachlor), acid herbicides (MCPA, 2,4-d,dichlorprop and mecoprop), thiocarbamates (prosulfocarb), amide pesticides (dimethachlor), uracils (lenacil), and dinitroaniline (pendimethalin). The fungicides were of the main families strobilurines (azoxystrobin and pyraclostrobin), azoles (tebuconazole, epoxiconazole, thiabendazole, prochloraz, and propiconazole; cyproconazole in 73% of individuals), carbamates (carbendazim) and carboxamides (boscalid). The most frequently detected insecticides were mainly metabolites of pyrethroids (3-PBA, Cl2CA, and ClCF3CA), as well as neonicotinoids (thiacloprid and imidacloprid) and the specific metabolite of chlorpyrifos TCPy (3,5,6-trichloro-2-pyridinol; organophosphorous pesticide). Noticeably, the five herbicides isoproturon (urea), propyzamide (benzamide), chlortoluron (urea), oxadiazon (oxadiazin) and diflufenican (carboxamide), as well as the fungicide trifloxystrobin (strobilurin) and the insecticide cypermethrine (pyrethroid), were detected in at least 50% of the samples (Table 2). Five more compounds were detected in 25–50% of animals: zoxamide (benzamide), difenoconazole (azole), cyhalothrin and Br2CA (pyrethroids), and 2,4-DB (acid herbicide). The 10 highest measured concentrations ranged from 200 to 500 ng/g, which were far higher than for BRPs. These high concentrations were found for the fungicides boscalid, carbendazim, and prochloraz and the herbicides dichlorprop, MCPA, and propyzamide. A greater number of compounds exhibited higher concentrations than observed for BRPs, since 29 compounds presented concentrations higher than 10 ng/g. Moreover, 16 compounds were quantified at higher levels than 50 ng/g, and 10 compounds at higher levels than 100 ng/g (Table 2). The 10 compounds that had the highest concentrations were the herbicides propyzamide, MCPA, dichlorprop, diflufenican, mecoprop, and metolachlor, and the fungicides boscalid, epoxiconazole, carbendazim, and prochloraz. They were not all among the most detected compounds (Table 2). Six compounds exhibited concentrations ranging from 50 to 100 ng/g: the insecticide imidacloprid, the herbicides aclonifen and isoproturon, and the fungicides cyproconazole, propiconazole and tebuconazole. Various chemical families are represented among the CUPs exhibiting high concentrations in small mammals, including carbamates, carboxamids and benzamids, acid and urea herbicids, azoles and neonicotinoids (Table 2). The insecticides showed concentrations overall lower than herbicides and fungicides, since no value above 50 ng/g was measured within insecticides except for imidacloprid. Besides the neonicotinoid imidacloprid, the insecticides showing the highest values ( > 10 ppb) were all pyrethroids, either parents or their metabolites (cyfluthrine, cyhalothrine, permethrine, 3-PBA, Br2CA, Cl2CA). Among the 12 CUPs that have never been detected, only parent compounds were present, with six fungicides, two herbicides and four insecticides belonging to various chemical families such as azole, carbamate, organophosphorous, triazine, neonicotinoid, strobilurine, oxadiazine and urea pesticides.A significant positive relationship was found between detections of CUPs in small mammal hair samples and the quantities of pesticides sold in 2016 in the Region were the ZAPVS is located (i.e. Deux-Sèvre, where most of small mammals in this study were captured and analyzed) (Spearman’s rho = 0.66, p-value More

Yuan, F. & Miyamoto, S. Dominant processes controlling water chemistry of the Pecos River in American Southwest. Geophys. Res. Lett. 32(17), L17406. https://doi.org/10.1029/2005GL023359 (2005).ADS 
CAS 
Article 

Google Scholar 
Yuan, F., Miyamoto, S. & Anand, S. Changes in major element hydrochemistry of the Pecos River in the American Southwest since 1935. Appl. Geochem. 22(8), 1798–1813. https://doi.org/10.1016/j.apgeochem.2007.03.036 (2007).ADS 
CAS 
Article 

Google Scholar 
Harley, G. L. & Maxwell, J. T. Current declines of Pecos River (New Mexico, USA) streamflow in a 700-year context. Holocene 28(5), 766–777. https://doi.org/10.1177/0959683617744263 (2018).ADS 
Article 

Google Scholar 
Jensen, R., Hatler, W., Mecke, M. & Hart, C. The Influences of Human Activities on the Water of the Pecos River Basin of Texas: A Brief Overview. Technical Report. SR-2006-03. Texas Water Resources Institute (2006).Hoagstrom, C. W. Causes and impacts of salinization in the lower Pecos River. Gt. Plains Res. 19(1), 27–44 (2009).
Google Scholar 
Williams, A. P., Cook, B. I. & Smerdon, J. E. Rapid intensification of the emerging North American megadrought in 2020–2021. Nat. Clim. Change 12(3), 232–234. https://doi.org/10.1038/s41558-022-01290-z (2022).ADS 
Article 

Google Scholar 
Cheek, C. A. & Taylor, C. M. Salinity and geomorphology drive long-term changes to local and regional fish assemblage attributes in the lower Pecos River, Texas. Ecol. Freshw. Fish 25(3), 340–351. https://doi.org/10.1111/eff.12214 (2015).Article 

Google Scholar 
Pease, A. A. & Delaune, K. D. Dried and salted: cumulative impacts of diminished flows and salinization on the lower Pecos River food webs. In Proceedings of the Desert Fishes Council Special Publication. Vol. 2021, 2–19. https://doi.org/10.26153/tsw/12364 (2021)Linam, G. W. & Kleinsasser, L. J. Relationships Between Fishes and Water Quality in the Pecos River, Texas. River Studies Report. No. 9. Texas Parks and Wildlife Department (1996).Hoagstrom, C. W., Zymonas, N. D., Davenport, S. R., Propst, D. L. & Brooks, J. E. Rapid species replacements between fishes of the North American plains: A case history from the Pecos River. Aquat. Invasions 5(2), 141–153. https://doi.org/10.3391/ai.2010.5.2.03 (2010).Article 

Google Scholar 
Randklev, C. R. et al. A semi-arid river in distress: Contributing factors and recovery solutions for three imperiled freshwater mussels (Family Unionidae) endemic to the Rio Grande Basin in North America. Sci. Total Environ. 631–632, 733–744. https://doi.org/10.1016/j.scitotenv.2018.03.032 (2018).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Kimmons, J. B. & Moll, D. Seed dispersal by Red-eared sliders (Trachemys scripta elegans) and Common snapping turtles (Chelydra serpentina). Chelonian Conserv. Biol. 9(2), 289–294. https://doi.org/10.2744/CCB-0797.1 (2010).Article 

Google Scholar 
Lazar, B. et al. Loggerhead sea turtles (Caretta caretta) as bioturbators in neritic habitats: An insight through the analysis of benthic molluscs in the diet. Mar. Ecol. 32(1), 65–74. https://doi.org/10.1111/j.1439-0485.2010.00402.x (2011).ADS 
Article 

Google Scholar 
Lovich, J. E., Ennen, J. R., Agha, M. & Gibbons, J. W. Where have all the turtles gone, and why does it matter?. Bioscience 68(10), 771–781. https://doi.org/10.1093/biosci/biy095 (2018).Article 

Google Scholar 
de Solla, S. R., Fernie, K. J. & Ashpole, S. Snapping turtles (Chelydra serpentina) as bioindicators in Canadian areas of concern in the Great Lakes Basin. II. Changes in hatching success and hatchling deformities in relation to persistent organic pollutants. Environ. Pollut. 153(3), 529–536. https://doi.org/10.1016/j.envpol.2007.09.017 (2008).CAS 
Article 
PubMed 

Google Scholar 
Adams, C. I. M., Baker, J. E. & Kjellerup, B. V. Toxicological effects of polychlorinated biphenyls (PCBs) on freshwater turtles in the United States. Chemosphere 154, 148–154. https://doi.org/10.1016/j.chemosphere.2016.03.102 (2016).ADS 
CAS 
Article 

Google Scholar 
Beau, F., Bustamante, P., Michaud, B. & Brischoux, F. Environmental causes and reproductive correlates of mercury contamination in European pond turtles (Emys orbicularis). Environ. Res. 172(4), 338–344. https://doi.org/10.1016/j.envres.2019.01.043 (2019).CAS 
Article 
PubMed 

Google Scholar 
van Dijk, P. P. Pseudemys gorzugi (errata version published in 2016). The IUCN Red List of Threatened Species Vol. 2011, e.T18459A97. (2011).NMDGF [New Mexico Department of Game and Fish]. Threatened and Endangered Species of New Mexico, 2020 Biennial Review. Management and Fisheries Management Divisions (2020).SEMARNAT [Secretaríade Medio Ambiente y Recursos Naturales]. NORMA Oficial Mexicana NOM-059-SEMARNAT-2010, Protección ambiental–Especies nativas de México de flora y fauna silvestres–Categorías de riesgo y especificaciones para su inclusión, exclusión o cambio–Lista de especies en riesgo. Diario Oficial de la Federación Vol. 2 (2010).TPWD [Texas Parks & Wildlife Department]. Species Account: the Rio Grande River Cooter (Pseudemys gorzugi). In Texas Comprehensive Wildlife Conservation Strategy 2005–2010 (eds Bender, S., Shelton, S., Bender, K. & Kalmbach, A.). Nongame Division, 1075–7076 (2012).Pierce, L. J. S., Stuart, J. N., Ward, J. P. & Painter, C. W. Pseudemys gorzugi Ward 1984–Rio Grande Cooter, Western River Cooter, Tortuga de Oreja Amarilla, Jicotéa del Rio Bravo In Conservation Biology of Freshwater Turtles and Tortoises: A Compilation Project of the IUCN/SSC Tortoise and Freshwater Turtle Specialist Group (eds. Rhodin, A. G. J. et al.). Chelonian Res. Monog. Vol. 5, No. 9, 100.1–100.12. https://doi.org/10.3854/crm.5.100.gorzugi.v1.2016 (2016).Endangered and Threatened Wildlife and Plants. Endangered and Threatened Wildlife and Plants; three species not warranted for listing as endangered or threatened species. Fed. Reg. 87(49), 14227–14228 (2022).
Google Scholar 
Bailey, L. A., Forstner, M. R. J., Dixon, J. R. & Hudson, R. Contemporary status of the Rio Grande Cooter (Testudines: Emydidae: Pseudemys gorzugi) in Texas: phylogenetic, ecological and conservation consideration. In Proceedings of the Sixth Symposium on the Natural Resources of the Chihuahuan Desert Region (eds. Hoyt, C. A. & Karges, J.) 320–324. (Chihuahuan Desert Research Institute, 2014).Suriyamongkol, T., Waldon, K. J. & Mali, I. Trachemys scripta (Red-eared Slider) and Pseudemys gorzugi (Rio Grande Cooter). Fish hook ingestion and shooting. Herpetol. Rev. 50(4), 776–777 (2019).
Google Scholar 
Degenhardt, W. G., Painter, C. W. & Price, A. H. Amphibians and Reptiles of New Mexico (University of New Mexico Press, 1996).
Google Scholar 
Ernst, C. H. Turtles of the United States and Canada 2nd edn. (Johns Hopkins University Press, 2009).
Google Scholar 
Dixon, J. R. Amphibians and Reptiles of Texas: With Keys, Taxonomic Synopses, Bibliography, and Distribution Maps 3rd edn. (Texas A&M University Press, 2013).
Google Scholar 
Suriyamongkol, T. et al. Geographic distribution. Pseudemys gorzugi (Rio Grande Cooter). Herpetol. Rev. 51(3), 536–537 (2020).
Google Scholar 
Christman, B. L. & Kamees, L. K. Current Distribution of the Blotched Watersnake (Nerodia erythrogaster) and the Rio Grande Cooter (Pseudemys gorzugi) in the Lower Pecos River System Eddy County, New Mexico 2006–2007. Final Report. New Mexico Department of Game and Fish (2007).Bogolin, A. P., Davis, D. R., Ruppert, K. M., Kline, R. J. & Rahmann, A. F. Geographic distribution. Pseudemys gorzugi (Rio Grande Cooter). Herpetol. Rev. 50(4), 745 (2019).
Google Scholar 
Congdon, J. D., Dunham, A. E. & Van Loben Sels, R. C. Demographics of common snapping turtles (Chelydra serpentina): Implications for conservation and management of long-lived organisms. Am. Zool. 34, 397–408. https://doi.org/10.1093/icb/34.3.397 (1994).Article 

Google Scholar 
Brooks, R. J., Brown, G. P. & Galbraith, D. A. Effects of a sudden increase in natural mortality of adults on a population of the common snapping turtle (Chelydra serpentina). Can. J. Zool. 69, 1314–1320. https://doi.org/10.1139/z91-185 (1991).Article 

Google Scholar 
Congdon, J. D., Dunham, A. E. & Van Loben Sels, R. C. Delayed sexual maturity and demographics of Blanding’s turtles (Emydoidea blandingii): Implications for conservation and management of long-lived organisms. Conserv. Biol. 7(4), 826–833. https://doi.org/10.1046/j.1523-1739.1993.740826.x (1993).Article 

Google Scholar 
Suriyamongkol, M. & Mali, I. Aspects of the reproductive biology of the Rio Grande Cooter (Pseudemys gorzugi) on the Black River, New Mexico. Chelonian Conserv. Biol. https://doi.org/10.2744/CCB-1385.1 (2019).Article 

Google Scholar 
Bailey, L. A., Dixon, J. R., Hudson, R. & Forstner, M. R. J. Minimal genetic structure of the Rio Grande Cooter (Pseudemys gorzugi). Southwest. Nat. 53(3), 406–411. https://doi.org/10.1894/GC-179.1 (2008).Article 

Google Scholar 
Mali, I., Duarte, A. & Forstner, M. R. J. Comparison of hoop-net trapping and visual surveys to monitor abundance of the Rio Grande Cooter (Pseudemys gorzugi). PeerJ 6, e4677:1-16. https://doi.org/10.7717/peerj.4677 (2018).Article 

Google Scholar 
Hart, C. R., McDonald, A. & Hatler, W. Pecos River Ecosystem Monitoring Project. Technical Report. Texas Cooperative Extension: The Texas A&M University System. (2005).Hong, M., Zhang, K., Shu, C., Xie, D. & Shi, H. Effect of salinity on the survival, ions, and urea modulation in Red-eared Slider (Trachemys scripta elegans). Asian Herpetol. Res. 5(2), 128–136. https://doi.org/10.3724/SP.J.1245.2014.00128 (2014).Article 

Google Scholar 
Hintz, W. D. et al. Salinization triggers a trophic cascade in experimental freshwater communities with varying food-chain length. Ecol. Appl. 27(3), 833–844. https://doi.org/10.1002/eap.1487 (2017).Article 
PubMed 

Google Scholar 
Letter, A. W., Waldon, K. J., Pollock, D. A. & Mali, I. Dietary habits of Rio Grande Cooters (Pseudemys gorzugi) from two sites within the Black River, Eddy County, New Mexico, USA. J. Herpetol. 53(3), 204–208. https://doi.org/10.1670/18-057 (2019).Article 

Google Scholar 
Suriyamongkol, T., Ortega-Berno, V., Mahan, L. B. & Mali, I. Using stable isotopes to study resource partitioning between Red-eared Slider and Rio Grande Cooter in the Pecos River watershed. Ichthyol. Herpetol. 110(1), 96–105. https://doi.org/10.1643/h2021023 (2022).Article 

Google Scholar 
Bassett, L. G., Mali, I., Nowlin, W. H., Foley, D. H. & Forstner, M. R. J. Diet and isotopic niche of the Rio Grande Cooter (Pseudemys gorzugi) and syntopic Red-eared Slider (Trachemys scripta elegans) in San Felipe Creek, Texas, USA. Chelonian Conserv. Biol. (in Press).Bárcenas-García, A. et al. Impacts of dams on freshwater turtles: A global review to identify conservation solutions. Trop. Conserv. Sci. 15(4), 1–21. https://doi.org/10.1177/194008292211037098 (2021).Article 

Google Scholar 
Smith, M. J. et al. Association between anuran tadpoles and salinity in a landscape mosaic of wetlands impacted by secondary salinisation. Freshw. Biol. 52(1), 75–84. https://doi.org/10.1111/j.1365-2427.2006.01672.x (2007).Article 

Google Scholar 
Wohner, P. J. et al. Integrating monitoring and optimization modeling to inform flow decisions for Chinook salmon smolts. Ecol. Model. 471(2022), 110058. https://doi.org/10.1016/j.ecolmodel.2022.110058 (2022).Article 

Google Scholar 
Suriyamongkol, T., Tian, W. & Mali, I. Monitoring the basking behavior of Rio Grande Cooter (Pseudemys gorzugi) through game camerias in southeastern New Mexico, USA. West. N. Am. Nat. 81(3), 361–371. https://doi.org/10.3398/064.081.0305 (2021).Article 

Google Scholar 
Painter, C. W. Preliminary Investigations of the Distribution and Natural History of the Rio Grande River Cooter (Pseudemys gorzugi) in New Mexico. Preliminary Report. (United States Department of the Interior–Bureau of Land Management, 1993).Hak, J. C. & Comer, P. J. Modeling landscape condition for biodiversity assessment—Application in temperate North America. Ecol. Indic. 82, 206–216. https://doi.org/10.1016/j.ecolind.2017.06.049 (2017).Article 

Google Scholar 
ESRI. ArcGIS Desktop. Ver. 10.8 (Environmental System Research Institute, 2020).MacKenzie, D. I. et al. Estimating site occupancy rates when detection probabilities are less than one. Ecology 83(8), 2248–2255. https://doi.org/10.1890/0012-9658(2002)083[2248:ESORWD]2.0.CO;2 (2002).Article 

Google Scholar 
Tyre, A. J. et al. Improving precision and reducing bias in biological surveys: Estimating false-negative error rates. Ecol. Appl. 13(6), 1790–1801. https://doi.org/10.1890/02-5078 (2003).Article 

Google Scholar 
Mackenzie, D. I. et al. Occupancy Estimation and Modeling: Inferring Dynamics of Species Occurrence 2nd edn. (Elsevier, 2017).
Google Scholar 
Duarte, A., Whitlock, S. L. & Peterson, J. T. Species distribution modeling. In Encyclopedia of Ecology 2nd edn (ed. Fath, B. D.) (Elsevier, 2019).
Google Scholar 
MacLaren, A. R., Foley, D. H., Sirsi, S. & Forstner, M. R. J. Updating methods of satellite transmitter attachment for long-term monitoring of the Rio Grande Cooter (Pseudemys gorzugi). Herpetol. Rev. 48(1), 48–52 (2017).
Google Scholar 
MacLaren, A. R., Sirsi, S., Foley, D. H. & Forstner, M. R. J. Pseudemys gorzugi (Rio Grande Cooter). Long distance dispersal. Herpetol. Rev. 48(1), 180–181 (2017).
Google Scholar 
Fiske, I. & Chandler, R. unmarked: An R package for fitting hierarchical models of wildlife occurrence and abundance. J. Stat. Softw. 43(10), 1–23. https://doi.org/10.18637/jss.v043.i10 (2011).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (Foundation For Statistical Computing, 2021).
Google Scholar 
Morin, D. J. et al. Is your ad hoc model selection strategy affecting your multimodel inference?. Ecosphere 11(1), e02997. https://doi.org/10.1002/ecs2.2997 (2020).Article 

Google Scholar 
Burnham, K. P. & Anderson, D. R. Model Selection and Inference: A Practical Information-Theoretic Approach 1st edn. (Springer, XXX, 1998).Book 

Google Scholar 
Hosmer, D. W., Lemeshow, S. & Sturdivant, R. X. Applied Logistic Regression 3rd edn. (Wiley, 2013).Book 

Google Scholar 
Gasparrini, A., Armstrong, B. & Kenward, M. G. Multivariate meta-analysis for non-linear and other multi-parameter associations. Stat. Med. 31(29), 3821–3839. https://doi.org/10.1002/sim.5471 (2012).MathSciNet 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Jackson, D., White, I. R. & Riley, R. D. A matrix-based method of moments for fitting the multivariate random effects model for meta-analysis and meta-regression. Biom. J. 55(2), 231–245. https://doi.org/10.1002/bimj.201200152 (2013).MathSciNet 
Article 
PubMed 
PubMed Central 
MATH 

Google Scholar  More

Predator abundanceThe number of predators on the aphid colonies varied spatiotemporally (Fig. 2). In particular, the number of predators in population A was significantly larger than that in population B in August but not in September (August, t20 = 3.93, P  0.05). In population A, we found predators on the aphid colonies in August and September, but not in June and July. In August, the only predators found were A. ignipicta larvae (0.76 ± 0.19 individuals per aphid colony), whereas in September the predators comprised both A. ignipicta larvae (0.033 ± 0.033 individuals per aphid colony) and T. hamada larvae (0.033 ± 0.033 individuals per aphid colony). In population B, we found no predators in any of the months.Figure 2Temporal and between-population variation in the number of predators per aphid colony. The number of predators represents the sum of the numbers of A. ignipicta and T. hamada larvae. Error bars denote s.e. Asterisks indicate a significant difference between populations (***P  More