Ecology

Sampling strategyAll sampling sites were located in the Eifel National park, situated in the south-western part of Germany close to the Belgian border (Supplementary Fig. 1, Supplementary Table 1).In this study the sampling site comprised a forest conversion gradient from a Norway Spruce (Picea abies) monoculture to a European Beech forest (Fagus sylvatica). To reflect the different stages of conversion from spruce to beech, four forest types were defined: pure beech (PB), old beech (OB), young beech (YB) and pure spruce (PS) (Supplementary Table 1, Supplementary Fig. 2).The forest types differed in tree species composition and tree age. The pure beech and pure spruce forest types were monoculture stands. The pure beech stands were approximately 180 years old and partly under special protection through North-Rhine Westphalia (Naturwaldzelle) (Sampling site 01). The spruce monocultures were substantially younger with ca. 60 years old. Spruces of the same age dominated the young beech sampling sites that had only recently been underplanted with beeches. At the old beech sampling sites, beeches had already reached a height of more than 3 m and actions to remove spruces from the forest were conducted.A total of 12 Townsend Malaise traps (three per forest type) were set up in the Eifel National Park, North-Rhine Westphalia, Germany, during July 2016. To ensure that the orientation of the Malaise traps was consistent and to minimize potential biases caused by wind direction and position of sun, the highest point of each trap was set facing south. The traps were left in the field for the full duration of the experiment until April 2017, ensuring that insects were collected from exactly the same locations. In October 2016, two additional traps (Malaise Trap 13, pure spruce and Malaise Trap 14, old beech) were installed at two further sampling sites (Sample Site 13 and Sample Site 14). All traps were equipped with a bottle filled with approximately one litre of absolute ethanol (99,96%) over a 2-week period in July 2016 (13.07–27.07), October 2016 (13.10–27.10), January 2017 (11.01–25.01) and April 2017 (12.04–26.04) (Supplementary Table 2). The ethanol was replaced every week to ensure that the concentration of the preservative ethanol was stable and to avoid loss of insects a mesh filter was used (MICROFIL V Filter White Gridded 0.45 µm-diameter 47 mm & 100 ml Funnel Sterilized). Due to heavy snow during the winter period, new traps were set at the start of the new sampling season in January 2017.Three soil samples were collected around each Malaise trap, from the organic horizon of the top 10 cm layer (excluding the litter layer). Soil sample sites were located 4 m and 5 m away from the trap, forming a triangle in the centre of which the Malaise trap was located (Supplementary Fig. 3). One corner of the sampling triangle was pointing south, while both remaining corners were pointing north west and north east, respectively.Each sampling site was sampled four times in the course of a 1-year period. Soil sampling and Malaise trapping were synchronized and soil sampling was done on day 14 of each Malaise trapping period, when the last bottles were collected (Supplementary Table 3). Each soil sample consisted of approximately twenty 44 mm × 100 mm cores, taken 5 cm apart. A total of 162 soil samples were collected and stored at − 20 °C until further processing.DNA extractionBulk samples from Malaise trapsNon-destructive DNA extraction was performed by overnight incubation in lysis buffer, using a modified protocol of Aljanabi and Martinez (1997). The arthropods were sieved from the collecting ethanol using a mesh filter (MICROFIL V Filter White Gridded 0.45 µm-diameter 47 mm and 100 ml Funnel Sterilized), which was processed with the specimens. The insects were dried for 10 min at room temperature. Depending on biomass, between 15 and 25 ml of extraction buffer (0.4 M NaCl, 10 mM Tris-HCl pH 8.0, 2 mM EDTA pH 8.0) and 2% Sodium dodecyl sulphate (SDS) were added to each bulk sample. Finally, 400 µg Proteinase K was added per ml of lysis buffer and samples were lysed overnight at 52 °C on an orbital shaker at 30 rpm. The next day, the lysate was poured out of the bottles using the MICROFIL V Filter (White Gridded 0.45 µm-Dia 47 mm and 100 ml Funnel Sterilized) and a 6 M NaCl solution was added to the lysate to a concentration of 4 mmol. The samples were vortexed for 30 s, centrifuged at 4700 rpm for 30 s and the supernatant was transferred to a falcon tube and an equal volume of isopropanol was added. After careful mixing by inversion, the tubes were left at − 20 °C for 1 h and subsequently centrifuged at 4700 rpm for 60 min. The supernatant was discarded and the resulting pellet was washed with 20 ml of ice cold 70% ethanol, by centrifuging at 4700 rpm for 15 min. The remaining ethanol was discarded and the pellet was left to dry at 20 °C overnight. The pellet was then resuspended in 1 ml of sterile H2O and stored at − 20 °C until further processing.Soil samplesDNA extraction from the soil samples was conducted using two different extraction methods: a commercial (lysis-based) DNA extraction kit (Macherey-Nagel NucleoSpin Soil) and a (no lysis) phosphate buffer protocol from Taberlet et al. 201240. Each of the triplicated samples were processed individually. After defrosting the soil overnight at 4 °C, the samples were thoroughly mixed, DNA was extracted from 0.5 g of soil per sample using the Macherey-Nagel NucleoSpin Soil Kit following the manufacturer’s protocol.The second DNA extraction method allowed extracellular DNA to be extracted from larger amounts of starting material using a phosphate buffer and did not include a lysis step. Each of the three samples taken per sample site and season were treated individually. Soil samples were removed from the − 20 °C chamber approximately 12 h before DNA extraction and stored at   4 °C overnight. The next morning, each sample was thoroughly mixed and an equal weight of saturated phosphate buffer solution (Na2HPO4; 0.12 M; pH 8)40 was added. Samples were placed in an orbital shaker at 120 rpm for 15 min. Thereafter, duplicates were processed, where two 2 ml Eppendorf tubes were filled with 1.7 ml of the resulting mixture and centrifuged for 10 min at 10,000 g. Four hundred microliters of the resulting supernatant were transferred to a new 2 ml collection tube and 200 μl of SB binding buffer from the Macherey-Nagel NucleoSpin Soil Kit was added. Duplicate lysates were merged by loading onto a single NucleoSpin Soil Column and centrifuged at 10,000 g for 1 min. From this step onwards, the standard manufacturer’s protocol for the Macherey-Nagel NucleoSpin Kit was followed from step 8. DNA was eluted with 50 μl of SE Buffer (Macherey-Nagel). Ten microliters of the resulting DNA eluate was diluted in 90 μl of pure H2O (Sigma), followed by DNA purification using the PowerClean Pro DNA Clean-Up Kit (MO Bio Laboratories, Inc.) following the manufacturer’s protocol. For the purposes of this study, results from the two types of soil extraction were merged.Choice of primers and amplicon library preparationFor amplicon library preparation a primer pair targeting the 313 bp ‘mini barcode’ region of the mitochondrial Cytochrome c Oxidase subunit I gene (COI) was used41. The ‘mini barcode’ primer pair consisted of the forward primer mlCOIintF 5′-ACACTCTTTCCCTACACGACGCTCTTCCGATCTGGWACWGGWTGAACWGTWTAYCCYCC -3′41 and the reverse primer dgHCO2198 5′- GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTTAAACTTCAGGGTGACCAAARAAYCA-3′41 (Illumina overhang in regular font and primer in bold). Library preparation was carried out using a two-step PCR approach42,43 , whereby PCR1 amplifies the gene region of interest and PCR2 adds the sample index together with the Illumina overhang (indexed primers). For each sample, a unique combination of indexes was chosen.DNA extracts were quantified using the Quantus Fluorometer (Promega). Ten nanograms of template DNA was used for PCR1. PCR1 consisted of 7.5 µl Q5 Hot Start High-Fidelity 2 × Master Mix (New England BioLabs), 1 μl Sigma H2O, 0.5 µl forward Primer (10 µM), 0.5 µl reverse primer (10 µM), 0.5 μl Bovine Serum Albumin (Thermo Scientific) and 1 µl template DNA, making up a total of 15 µl. PCR1 cycling conditions were as follows: 2 min at 98 °C (1 ×); 40 s at 98 °C, 40 s at 45 °C, 30 s at 72 °C (20 ×); 3 min at 72 °C (1 ×). PCR products were purified with 4 µl of HT ExoSAP-IT (Applied Biosystems) to each sample, following the manufacturers’ protocol. For PCR2 (index PCR) the purified PCR products were split into two PCR tubes.For PCR2, each tube contained 12.5 µl Q5 Hot Start High-Fidelity 2X Master Mix (New England BioLabs), 3 µl Sigma H2O, 1.2 µl of index forward primer (10 µM) (AATGATACGGCGACCACCGAGATCTACAC NNNNNNNN ACACTCTTTCCCTACACGACGC TC), 1.2 µl of index reverse primer (10 µM) CAAGCAGAAGACGGCATACGAGAT NNNNNNNN GTGACTGGAGTTCAGACGTGTGCTC) and 8 µl of purified PCR1 product. PCR2 cycling conditions were as follows: 2 min at 98 °C (1 ×); 40 s at 98 °C, 30 s at 55 °C, 30 s at 72 °C (20 ×); 3 min at 72 °C (1 ×). All tagged PCR products were visualised by gel electrophoresis and PCR bands with the expected size were excised and purified using the QIAquick Gel Extraction Kit (Qiagen). Purified PCR products were quantified using the Quantus Fluorometer (Promega) and pooled in equal concentrations. The resulting purified amplicon library pool (3 ng/µl) was sequenced on an Illumina MiSeq (MiSeq Reagent Kit v3, 2 × 300 bp) sequencing platform at Liverpool University’s Centre for Genomic Research (Liverpool, UK). Raw sequence data were deposited in the GenBank short read archive (SRA) under accession numberPRJNA681091 and PRJNA706915.Bioinformatics and data analysisThe raw fastq files were trimmed for the presence of Illumina adapter sequences using Cutadapt version 1.2.1. at the Centre for Genomic Research (Liverpool, UK). Sequences were trimmed using Sickle version 1.200 with a minimum window quality score of 20 and reads shorter than 20 bp were removed after trimming.The fastq sequences were then checked for the presence of the COI primers with Cutadapt version 1.1844 using the following settings: maximum error rate (-e): 0.1, minimum overlap (-O): 20, minimum sequence length (-m): 50. Sequences lacking either the forward or reverse primer were removed and primer pairs were trimmed off from the remaining sequences. Subsequently, paired-end reads were merged with vsearch version 2.7.045. Merged sequences with a length of 293–333 bp were retained for further analysis and filtered with a maxEE threshold of 1.0 using vsearch (version 2.7.0)45 before demultiplexing the fastq sequences using the script split_libraries_fastq.py implemented in QIIME146 using a phred quality threshold of 19. Dereplication, size sorting, denovo chimera detection as well as Operational Taxonomic Unit (OTU) clustering with a 97% cutoff was conducted with vsearch 2.7.045. Finally, an OTU table was built by using the –usearch_global function in vsearch 2.7.045 followed by the python script “uc2otutab.py” (https://drive5.com/python/uc2otutab_py.html). For taxonomy assignment, representative sequences were blasted against the GBOL database (https://www.bolgermany.de/gbol1/identifications downloaded on 2nd of July 2019) using blastn 2.9.0+47.The resulting OTU table was curated with LULU48. Curation started with an initial blasting of OTU representative sequences against each other using blastn (version 2.9.0). The following parameter settings were chosen: ‘query coverage high-scoring sequence pair percent’ (-qcov_hsp_perc) was set to 80, meaning that a sequence was reported as a match when 80% of the query formed an alignment with an entry of the reference file. Secondly, minimum percent identity (-perc_identity) was set to 84, requiring the reference and query sequence to match by at least 84% to be reported as a match. The format of the output file was customized using the –outfmt settings ‘6 qseqid sseqid pident’. The output file included the name of the query sequence and the name of the reference sequence next to the percentage match. The resulting OTU match list was uploaded into R (version 3.5)49 and the R-package ‘lulu’ (version 0.1.0)48 was used to perform post clustering curation using standard settings. The LULU algorithm filters the dataset for artificial OTUs and these were either classified as “daughter OTU” and merged with the corresponding “parent OTU” or were discarded from the dataset.The resulting curated OTU table was loaded into Excel where data was formatted to upload into R (R studio running R version 3.5). Only OTUs with an assignment at species level (blastID ≥ 99%) were used for subsequent analysis. Furthermore, results from the two types of soil extraction were merged.UpsetR plots were prepared using the R package UpSetR (version 1.4.0)50 for visualization of shared arthropod OTUs between sample types in each season. Differences in number of OTU proportions are shown in a Marioko plot prepared with the R package ggplot251. To analyze dissimilarities between communities depending on season and sample type, Permutational Multivariate Analysis of Variance (PERMANOVA) using Jaccard distance matrices for incidence data of detected arthropod species (blastID ≥ 99%) were performed using dplyr (version 0.8.3)52, betapart (version 1.5.1)53 and vegan (version 2.5–6)54. In order to analyse dissimilarity differences in arthropod community composition between the different forests and seasons the Jaccard similarity index (J) was used on a presence-absence matrix based on arthropod species. Calculated Jaccard indices were visualized on a heatmap using the R package ggplot251. Sample completeness curves and sample-size-based R/E curve with extrapolations of Hill numbers for incidence data based on the combined dataset for all forests and seasons were prepared using the R-package iNEXT55 at default settings (40 knots, 95% confidence intervals generated by the bootstrap procedure (50 bootstraps)).To correlate community structure and diversity levels with the different seasons and forest types a Permutational Multivariate Analysis of Variance (PERMANOVA) based on the Jaccard similarity index for a presence-absence matrix of detected arthropod species (blastID ≥ 99%) was performed with the adonis function in R. Differences in arthropod community composition between the different forests and seasons was assessed using the Jaccard similarity index (J), where the higher the index, the more similar the communities. More

Host and siteB. ermanii Chamiss, a deciduous broad-leaved tree species, occurs naturally in open spaces within subalpine and boreal forests in Northeast China, Japan and the Russian Far East.40,41,42 B. ermanii is also a typical tree species of the timberline, because it can resist to severe frost, strong wind and low temperature by ecophysiological and ecomorphological flexiblity.43,44,45 In addition, a rich mycobiome of B. ermanii has been reported,16,46,47 which may facilitate the survival and spread of the host plant.On the northern slope of Changbai Mountain, Northeast China, B. ermanii grows over a broad elevation range from ca. 1700 to 2100 m a.s.l., and forms pure stands along >300 m vertical belt below the timberline.42,48 The establishment of Changbai Nature Reserve (CNR) in 1960 gave strict protection to the Erman’s birch (B. ermanii) forests.49 All our sampling was located within the core region of the CNR. The region has a typical continental temperate monsoon climate, with higher elevations experiencing lower temperature and greater precipitation.50 Soils of the Erman’s birch forests are Permi-Gelic Cambosols, whereas the soils beyond the upper and lower limits of B. ermanii zone are Permafrost cold Cambosols and Umbri-Gelic Cambosols, respectively. Climate change, particularly rising temperature, threatens the populations of B. ermanii and the whole Erman’s birch forest ecosystem in Changbai Mountain.50,51Field sample collectionWe sampled fine roots and neighboring soils for B. ermanii individuals at six elevation-related habitats of B. ermanii on 2–8 September, 2018 (Fig. 1a). These six elevation habitats include the upper limit (2069–2116 m), tree islands (1997–2042 m), treeline (1949–1992 m), pure stands (including two sub-sites isolated by over 2 km; 1900–1926 m), ecotone of dark coniferous forests and Erman’s birch forests (1742–1765 m) and lower limit (sparse individuals in coniferous forests; 1688–1706 m), respectively. In each habitat, 14 trees were randomly selected, and all trees were located more than 20 m apart from other sampled trees to ensure independence of each sample. Neighboring soils and fine roots were collected according to the protocols of Yang et al.28 and Lankau and Keymer,52 respectively. Briefly, with the trunk as center and the DBH as distance from the stem, we collected four soil cores (diameter = 3.5 cm, depth = 10 cm) after removal of litter and mixed them as a single composite soil sample (Fig. 1b). We excavated surface soils near the base of trees and traced roots from the base to terminal fine roots in three directions. The fine roots of three directions were combined as a composite fine root sample, and each raw sub-fine-root section was nearly 6 cm wide and 8 cm long (Fig. 1c, d). All the samples were brought back to the laboratory with ice bags within 8 h. Soil was sieved through a 2-mm mesh and divided into two subsamples: one was stored at 4 °C to determine the soil properties, whereas the other was stored at −40 °C for subsequent DNA extraction. Fine roots were rinsed with sterile water and cut into 1.5-cm segments: one subsample (ca. 80%) was stored at 4 °C to determine root biochemical traits, and the other subsample (ca. 20%) was stored at −40 °C for subsequent DNA extraction. In the field, tree height, canopy diameter, DBH, elevation, slope, latitude, and longitude of each sampled tree were recorded. In total, 84 fine roots and 84 neighboring soils were collected.Fig. 1: Sampling map and procedures in this study.a Sampling map in the core region of CNR: the icons with different colors represent the tree individuals of B. ermanii. Contours were fitted in a map of Google Earth. b The sampling procedure of neighboring soils: each red point represent one soil core with depth 0–10 cm and diameter 3.5 cm. c The sampling procedure of fine roots: each red square (nearly 6 × 8 cm) represent a sub-fine-root system (namely, d).Full size imageMeasurement of soil properties and root traitsWe measured 28 soil properties, including soil pH, moisture, conductivity, dissolved organic carbon, dissolved organic nitrogen (DON), ammonium nitrogen, nitrate nitrogen, total carbon, total nitrogen, total phosphate, total potassium, total calcium, total magnesium, total manganese, total iron, total aluminum, available phosphate, available potassium, available calcium, available magnesium, available manganese, available iron, available aluminum, C/N ratio, C/P ratio and the proportions of clay, silt and sand. The measurement methods of soil pH, moisture, ammonium nitrogen, nitrate nitrogen, total carbon, total nitrogen and total content of other elements followed our recent study.28 In addition, soil conductivity was determined with a soil to water ratio of 1:5 by conductivity meter (Mettler Toledo FE30, Shanghai, China). Mehlich 353 and three-acid-system (nitric acid, perchloric acid, and hydrofluoric acid) were used to extract the available and total content of elements, respectively. Total and available content of phosphate, potassium, calcium, magnesium, manganese, iron, and aluminum were measured using an ICP Optima 8000 (Perkin-Elmer, Waltham, MA, USA). The proportions of clay, silt, and sand were measured by Laser Particle Sizer LS13320 (Beckman, Brea, CA, USA).Eighteen root traits, including root total carbon (RTC), root total nitrogen (RTN), root phosphate, root potassium, root calcium, root magnesium, root manganese, root iron, root aluminum, root C/N ratio, root N/P ratio, lignin, cellulose, hemicellulose, soluble sugar, soluble protein, free amino acid (FAA) and free fatty acids (FFA), were also measured. Specifically, RTC and RTN were determined with a carbon–hydrogen–nitrogen (CHN) elemental analyzer (2400 II CHN elemental analyzer; PerkinElmer, Boston, MA, USA). Root phosphate, root potassium, root calcium, root magnesium, root manganese, root iron, and root aluminum were measured in ICP Optima 8000 (Perkin-Elmer, Waltham, MA, USA). Soluble protein was measured by a dye-binding assay.54 FAA was analyzed by the amino acid analyzer L-8800 (Hitachi, Tokyo, Japan) with leucine as the standard sample. FFA was determined by NEFA FS kits (Diasys, Holzheim, Garman) and the automatic biochemical analyzer AU680 (Olympus, Tokyo, Japan). The measurement methods of lignin, cellulose, hemicellulose, and soluble sugar followed that of our previous study.16Calculation of distance to forest edgeThe location of each tree individual was determined by latitude and longitude. A high-resolution map (treecover2000) on global forest cover at a spatial resolution of 30 m was used as a base map.55 In the map, the areas where forest cover was more than 30% were defined as the “mainland” in the IBT framework and shown as the green grids in ArcGIS (Fig. S1). This standard referred to the proposal of Convention on Climate Change Kyoto.56 Then, we calculated the minimum distance of each tree to the neighboring forest edge (i.e., green grids) by using the function Near of the Proximity tool box in ArcGIS 10.0 (ESRI, Redlands, CA, USA).Sequencing and bioinformaticsSoil total DNA was extracted from 0.5 g of soil by using FastDNA® Spin kit for Soil (MP Biomedicals, Solon, Ohio, USA). Total DNA of fine roots was extracted from 0.3 g of plant tissue by using Qiagen Plant DNeasy kits (Qiagen, Hilden, Germany). PCR procedures, including primers (ITS1-F: CTTGGTCATTTAGAGGAAGTAA, ITS2: GCTGCGTTCTTCATCGATGC) and conditions were described in our previous studies.16,28 The PCR products of all samples were normalized to equimolar amounts and sequenced on the Illumina MiSeq PE300 platform of the Majorbio Company, Shanghai, China.We first merged the paired-end reads using FLASH.57 QIIME 1.9.058 and Cutadapt 1.9.159 were applied for quality filtering, trimming, and chimera removal. Altogether 8,238,146 sequences passed quality filtering (parameters: minlength = 240; maxambigs = 0; phred quality threshold = 30). ITSx 1.0.11 was used to remove the flanking small ribosomal subunit (SSU) and 5.8 S genes,60 leaving the ITS1 region for further analyses. The putative chimeric sequences were removed using a combination of de novo and reference-based chimera checking, with the parameter –non_chimeras_rentention = union in QIMME.61 The remaining sequences were then clustered into operational taxonomic units (OTUs) at 97% similarity threshold by using USEARCH.62 Singletons were also removed during the USERCH clustering process. Fungal taxonomy was assigned to each OTU by using the Ribosomal Database Project Classifier with minimum confidence of 0.8.63 The UNITE v.8.0 (http://unite.ut.ee) release for QIIME served as a reference database for fungal taxonomy.64 The OTU table was then curated with LULU, a post-clustering OTU table curation method, to improve diversity estimates.65After removing non-fungal sequences, the final data set included 7,849,126 fungal sequences covering 6663 OTUs in 168 samples (minimum 4662; maximum 70,779; mean 46,721 sequences per sample). The rarefaction curves of the average observed OTU number are shown in Fig. S2. FUNGuild was used to assign each OTU to a putative functional guild, and the assignments with confidence ranking “possible” were assigned as “unknown” as recommended by the authors.66 We further modified the assignment of EcM fungi (the subject in the present study) and their lineages according to.31 For some OTUs that were simultaneously assigned to endophytic, saprotrophic, or pathogenic fungi, we considered these as endophytes in roots and saprotrophs in soil samples.StatisticsAll statistical analyses were conducted in R 3.5.267 and AMOS 21.0 (AMOS IBM, New York, USA). In order to analyze the alpha diversities of soil fungi and the three most dominant guilds (viz., EcM, endophytic and saprotrophic fungi) at the same sequencing depth, the data set was subsampled to 4662 reads with 30 iterations. The mean number of observed OTUs was used to represent the diversities of total fungi, EcM fungi, endophytic fungi, and saprotrophic fungi, as previously implemented in.15,68 Numbers of EcM fungal lineages and saprotrophic genera, families, orders, and classes of each sample were also calculated based on the same subsampling.First, linear and quadratic regression models were used to determine the effect of elevation on diversities of total fungi, EcM fungi, endophytic fungi, and saprotrophic fungi. The model with lowest Akaike’s information criterion (AIC) value was selected. In order to account for spatial effects, linear mixed-effects models (LMMs) were fitted using the lme4 package69 to analyze the variation in diversities of total fungi, EcM fungi, endophytic fungi, and saprotrophic fungi along the elevation gradient with latitude and longitude as random factors. Corrected Akaike Information Criterion (AICc) for small data sets was used to identify the best mixed-effects model from linear and quadratic polynomial models. The significance of each LMM was tested by the function Anova in the car package.70 Marginal (m) and conditional (c) R2 were calculated by the function r.squaredGLMM in the MuMIn package.71 Marginal R2 (R2m) represents the variance explained by fixed effects, whereas conditional R2 (R2c) represents the variance explained by both fixed and random effects.Second, to test the application of IBT on EcM fungal diversity, DBH and RTC of B. ermanii were chosen as proxies of island area and energy, respectively, whereas DFE was chosen as the proxy of island isolation (i.e., island distance to mainland). Linear regression models were used to assess the species-area, species-energy, and species-isolation relationships. In order to account for spatial effects, LMMs were also used for these independent relationships with latitude and longitude as random factors as described above. Classical power-law function models were used to identify the species-area relationship for EcM fungal diversities in roots and soils using z-values in the formula S = CAz 72 to compare EcM fungi with macroorganisms in previous studies. Furthermore, ordinary least squares (OLS) multiple regression models were performed to identify the relative contributions of DBH, RTC, and DFE on pattern of EcM fungal diversity when considering other predictive variables. Here, five spatial vectors (PCNM1-5) with significant positive spatial autocorrelation (Fig. S3) were obtained by the principal coordinates of neighbor matrices (PCNM) method,73 and added into OLS multiple regression models to consider the possible geographic effect. EcM fungal diversities in roots and soils, 28 soil properties, 18 root traits, elevation, slopes, DBH, tree height, canopy diameter, and DFE were standardized (average = 0 and SD = 1) before the OLS multiple regression analysis. AIC was used to identify the best OLS multiple regression model, as implemented in the MASS package.74 Variance inflation factor (VIF) was calculated for each model by the function vif in the car package. We used the criterion VIF  More

1.Oke, T. R. The heat island of the urban boundary layer: characteristics, causes and effects. In Wind climate in cities (eds Cermak, J. E. et al.) 81–107 (Springer, 1995). https://doi.org/10.1007/978-94-017-3686-2_5.
Google Scholar 
2.Wang, C., Wang, Z. H. & Yang, J. Cooling effect of urban trees on the built environment of contiguous United States. Earth’s Future 6, 1066–1081. https://doi.org/10.1029/2018EF000891 (2018).ADS 
Article 

Google Scholar 
3.Pauleit, S. Urban street tree plantings: identifying the key requirements. Proc. Inst. Civ. Eng. Munic. Eng. 156, 43–50. https://doi.org/10.1680/muen.2003.156.1.43 (2003).Article 

Google Scholar 
4.Frantzeskaki, N. et al. Nature-based solutions for urban climate change adaptation: linking science, policy, and practice communities for evidence-based decision-making. BioScience 69, 455–466. https://doi.org/10.1093/biosci/biz042 (2019).Article 

Google Scholar 
5.Armson, D., Stringer, P. & Ennos, A. R. The effect of tree shade and grass on surface and globe temperatures in an urban area. Urban For. Urban Green. 11, 245–255. https://doi.org/10.1016/j.ufug.2012.05.002 (2012).Article 

Google Scholar 
6.Oke, T. R. The micrometeorology of the urban forest. Philos. Trans. R. Soc. Lond. B 324, 335–349. https://doi.org/10.1098/rstb.1989.0051 (1989).ADS 
Article 

Google Scholar 
7.Chow, W. T. & Brazel, A. J. Assessing xeriscaping as a sustainable heat island mitigation approach for a desert city. Build. Environ. 47, 170–181. https://doi.org/10.1016/j.buildenv.2011.07.027 (2012).Article 

Google Scholar 
8.Wang, K., Ma, Q., Wang, X. & Wild, M. Urban impacts on mean and trend of surface incident solar radiation. Geophys. Res. Lett. 41, 4664–4668. https://doi.org/10.1002/2014GL060201 (2014).ADS 
Article 

Google Scholar 
9.Bassuk, N. & Whitlow, T. Environmental stress in street trees. Arboricult. J. 12, 195–201. https://doi.org/10.1080/03071375.1988.9746788 (1988).Article 

Google Scholar 
10.Nowak, D. J., Kuroda, M. & Crane, D. E. Tree mortality rates and tree population projections in Baltimore, Maryland, USA. Urban For. Urban Green. 2, 139–147. https://doi.org/10.1078/1618-8667-00030 (2004).Article 

Google Scholar 
11.Monteith, J. . & Unsworth, M. . Principles of Environmental Physics: Plants, Animals, and the Atmosphere 4th edn. (Elsevier Ltd., 2013).
Google Scholar 
12.Simon, H. et al. Modeling transpiration and leaf temperature of urban trees—a case study evaluating the microclimate model ENVI-met against measurement data. Landsc. Urban Plan. 174, 33–40. https://doi.org/10.1016/j.landurbplan.2018.03.003 (2018).Article 

Google Scholar 
13.González-Dugo, M. P., Moran, M. S., Mateos, L. & Bryant, R. Canopy temperature variability as an indicator of crop water stress severity. Irrig. Sci. 24, 1–8. https://doi.org/10.1007/s00271-005-0023-7 (2006).Article 

Google Scholar 
14.Han, M., Zhang, H., DeJonge, K. C., Comas, L. H. & Trout, T. J. Estimating maize water stress by standard deviation of canopy temperature in thermal imagery. Agricult. Water Manag. 177, 400–409. https://doi.org/10.1016/j.agwat.2016.08.031 (2016).Article 

Google Scholar 
15.Hou, M., Tian, F., Zhang, T. & Huang, M. Evaluation of canopy temperature depression, transpiration, and canopy greenness in relation to yield of soybean at reproductive stage based on remote sensing imagery. Agricult. Water Manag. 222, 182–192. https://doi.org/10.1016/j.agwat.2019.06.005 (2019).Article 

Google Scholar 
16.Heilman, J. L., Brittin, C. L. & Zajicek, J. M. Water use by shrubs as affected by energy exchange with building walls. Agricult. For. Meteorol. 48, 345–357. https://doi.org/10.1016/0168-1923(89)90078-6 (1989).ADS 
Article 

Google Scholar 
17.Perini, K. & Magliocco, A. Effects of vegetation, urban density, building height, and atmospheric conditions on local temperatures and thermal comfort. Urban For. Urban Green. 13, 495–506. https://doi.org/10.1016/j.ufug.2014.03.003 (2014).Article 

Google Scholar 
18.Soer, G. J. Estimation of regional evapotranspiration and soil moisture conditions using remotely sensed crop surface temperatures. Remote Sens. Environ. 9, 27–45. https://doi.org/10.1016/0034-4257(80)90045-0 (1980).ADS 
Article 

Google Scholar 
19.Spronken-Smith, R. A. & Oke, T. R. The thermal regime of urban parks in two cities with different summer climates. Int. J. Remote Sens. 19, 2085–2104. https://doi.org/10.1080/014311698214884 (1998).ADS 
Article 

Google Scholar 
20.Rahman, M. A. et al. Traits of trees for cooling urban heat islands: a meta-analysis. Build. Environ. 170, 106606. https://doi.org/10.1016/j.buildenv.2019.106606 (2020).Article 

Google Scholar 
21.Leuzinger, S., Vogt, R. & Körner, C. Tree surface temperature in an urban environment. Agricult. For. Meteorol. 150, 56–62. https://doi.org/10.1016/j.agrformet.2009.08.006 (2010).ADS 
Article 

Google Scholar 
22.Meier, F. & Scherer, D. Spatial and temporal variability of urban tree canopy temperature during summer 2010 in Berlin, Germany. Theor. Appl. Climatol. 110, 373–384. https://doi.org/10.1007/s00704-012-0631-0 (2012).ADS 
Article 

Google Scholar 
23.Ballester, C., Jiménez-Bello, M. A., Castel, J. R. & Intrigliolo, D. S. Usefulness of thermography for plant water stress detection in citrus and persimmon trees. Agric. For. Meteorol. 168, 120–129. https://doi.org/10.1016/j.agrformet.2012.08.005 (2013).ADS 
Article 

Google Scholar 
24.Jones, H. G. Application of thermal imaging and infrared sensing in plant physiology and ecophysiology. Adv. Botan. Res. 41, 107–163. https://doi.org/10.1016/s0065-2296(04)41003-9 (2004).ADS 
Article 

Google Scholar 
25.Givoni, B. Impact of planted areas on urban environmental quality: a review. Atmos. Environ. Part B Urban Atmos. 25, 289–299. https://doi.org/10.1016/0957-1272(91)90001-U (1991).ADS 
Article 

Google Scholar 
26.Kalnay, E. & Cai, M. Impact of urbanization and land-use change on climate. Nature 423, 528–531. https://doi.org/10.1038/nature01675 (2003).ADS 
CAS 
Article 
PubMed 

Google Scholar 
27.A. Coutts, A. et al. Impacts of water sensitive urban design solutions on human thermal comfort. p. 20 (online link: https://watersensitivecities.org.au/wp-content/uploads/2016/07/TMR_B3-1_WSUD_thermal_comfort_no2.pdf) (2014).28.Coutts1, A. et al. The Impacts of WSUD Solutions on Human Thermal Comfort Green Cities and Micro-climate-B3.1-2-2014 Contributing Authors. Tech. Rep. (1968).29.Gunawardena, K. R., Wells, M. J. & Kershaw, T. Utilising green and bluespace to mitigate urban heat island intensity. Sci. Total Environ. 584–585, 1040–1055. https://doi.org/10.1016/j.scitotenv.2017.01.158 (2017).ADS 
CAS 
Article 
PubMed 

Google Scholar 
30.Völker, S., Baumeister, H., Classen, T., Hornberg, C. & Kistemann, T. Evidence for the temperature-mitigating capacity of urban blue space—a health geographic perspective. Erdkunde 67, 355–371. https://doi.org/10.3112/erdkunde.2013.04.05 (2013).Article 

Google Scholar 
31.Hu, L. & Li, Q. Greenspace, bluespace, and their interactive influence on urban thermal environments. Environ. Res. Lett. 15, 034041. https://doi.org/10.1088/1748-9326/ab6c30 (2020).ADS 
Article 

Google Scholar 
32.Theeuwes, N. E., Solcerová, A. & Steeneveld, G. J. Modeling the influence of open water surfaces on the summertime temperature and thermal comfort in the city. J. Geophys. Res. Atmos. 118, 8881–8896. https://doi.org/10.1002/jgrd.50704 (2013).ADS 
Article 

Google Scholar 
33.Ziter, C. D., Pedersen, E. J., Kucharik, C. J. & Turner, M. G. Scale-dependent interactions between tree canopy cover and impervious surfaces reduce daytime urban heat during summer. Proc. Natl. Acad. Sci. U. S. A. 116, 7575–7580. https://doi.org/10.1073/pnas.1817561116 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
34.Johnson, S., Ross, Z., Kheirbek, I. & Ito, K. Characterization of intra-urban spatial variation in observed summer ambient temperature from the New York City Community Air Survey. Urban Clim. 31, 100583. https://doi.org/10.1016/j.uclim.2020.100583 (2020).Article 

Google Scholar 
35.Wetherley, E. B., McFadden, J. P. & Roberts, D. A. Megacity-scale analysis of urban vegetation temperatures. Remote Sens. Environ. 213, 18–33. https://doi.org/10.1016/j.rse.2018.04.051 (2018).ADS 
Article 

Google Scholar 
36.Zhou, W., Wang, J. & Cadenasso, M. L. Effects of the spatial configuration of trees on urban heat mitigation: a comparative study. Remote Sens. Environ. 195, 1–12. https://doi.org/10.1016/j.rse.2017.03.043 (2017).ADS 
Article 

Google Scholar 
37.Weng, Q., Lu, D. & Schubring, J. Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies. Remote Sens. Environ. 89, 467–483. https://doi.org/10.1016/j.rse.2003.11.005 (2004).ADS 
Article 

Google Scholar 
38.Hulley, G., Hook, S., Fisher, J. & Lee, C. ECOSTRESS, A NASA Earth-Ventures Instrument for studying links between the water cycle and plant health over the diurnal cycle. In International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2017-July, 5494–5496 (Institute of Electrical and Electronics Engineers Inc., 2017). https://doi.org/10.1109/IGARSS.2017.8128248.39.Wood, S. N. Low-rank scale-invariant tensor product smooths for generalized additive mixed models. Biometrics 62, 1025–1036. https://doi.org/10.1111/j.1541-0420.2006.00574.x (2006).MathSciNet 
Article 
PubMed 
MATH 

Google Scholar 
40.Duan, S.-B. et al. Estimation of diurnal cycle of land surface temperature at high temporal and spatial resolution from clear-sky MODIS data. Remote Sens. 6, 3247–3262. https://doi.org/10.3390/rs6043247 (2014).ADS 
Article 

Google Scholar 
41.Hu, L., Sun, Y., Collins, G. & Fu, P. Improved estimates of monthly land surface temperature from MODIS using a diurnal temperature cycle (DTC) model. ISPRS J. Photogramm. Remote Sens. 168, 131–140. https://doi.org/10.1016/j.isprsjprs.2020.08.007 (2020).ADS 
Article 

Google Scholar 
42.New York City Department of Information Technology and Telecommunications (NYC DoITT). Land Cover Raster Data 6-inch Resolution (2017).43.New York City Department of Information Technology and Telecommunications (NYC DoITT). New York City Building Footprint (2017).44.Wood, S. N. Generalized Additive Models: An Introduction with R 2nd edn. (CRC Press, 2017).Book 

Google Scholar 
45.Cârlan, I., Mihai, B. A., Nistor, C. & Große-Stoltenberg, A. Identifying urban vegetation stress factors based on open access remote sensing imagery and field observations. Ecol. Inform. 55, 101032. https://doi.org/10.1016/j.ecoinf.2019.101032 (2020).Article 

Google Scholar 
46.Cregg, B. & Dix, M. E. Tree moisture stress and insect damage in urban areas in relation to heat island effects. J. Arboricult. 27, 8–17 (2001).
Google Scholar 
47.Novem, D. & Falxa, N. United States Department of Agriculture The Urban Forest of New York City. Tech. Rep. https://doi.org/10.2737/NRS-RB-117 (2018).48.Shaker, R. R., Altman, Y., Deng, C., Vaz, E. & Forsythe, K. W. Investigating urban heat island through spatial analysis of New York City streetscapes. J. Clean. Prod. 233, 972–992. https://doi.org/10.1016/j.jclepro.2019.05.389 (2019).Article 

Google Scholar 
49.Meir, T., Orton, P. M., Pullen, J., Holt, T. & Thompson, W. T. Forecasting the New York City urban heat island and sea breeze during extreme heat events. Weather Forecast. 28, 1460–1477. https://doi.org/10.1175/WAF-D-13-00012.1 (2013).ADS 
Article 

Google Scholar 
50.Ramamurthy, P., González, J., Ortiz, L., Arend, M. & Moshary, F. Impact of heatwave on a megacity: an observational analysis of New York City during July 2016. Environ. Res. Lett. 12, 054011. https://doi.org/10.1088/1748-9326/aa6e59 (2017).ADS 
Article 

Google Scholar 
51.Gedzelman, S. D. et al. Mesoscale aspects of the Urban Heat Island around New York City. Theor. Appl. Climatol. 75, 29–42. https://doi.org/10.1007/s00704-002-0724-2 (2003).ADS 
Article 

Google Scholar 
52.Cavender, N. & Donnelly, G. Intersecting urban forestry and botanical gardens to address big challenges for healthier trees, people, and cities. Plants People Planet 1, 315–322. https://doi.org/10.1002/ppp3.38 (2019).Article 

Google Scholar 
53.Marias, D. E., Meinzer, F. C. & Still, C. Impacts of leaf age and heat stress duration on photosynthetic gas exchange and foliar nonstructural carbohydrates in Coffea arabica. Ecol. Evol. 7, 1297–1310. https://doi.org/10.1002/ece3.2681 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
54.Brune, M. Urban trees under climate change. Potential impacts of dry spells and heat waves in three Germany regions in the 1950s. Report 20, Climate Service Center Germany, Hamburg 123 (2016).55.Jim, C. Y. Green-space preservation and allocation for sustainable greening of compact cities. Cities 21, 311–320. https://doi.org/10.1016/j.cities.2004.04.004 (2004).Article 

Google Scholar 
56.Santamouris, M. Cooling the cities—a review of reflective and green roof mitigation technologies to fight heat island and improve comfort in urban environments. Sol. Energy 103, 682–703. https://doi.org/10.1016/j.solener.2012.07.003 (2014).ADS 
Article 

Google Scholar 
57.Drescher, M. Urban heating and canopy cover need to be considered as matters of environmental justice. Proc. Natl. Acad. Sci. U. S. A. 116, 26153–26154. https://doi.org/10.1073/pnas.1917213116 (2019).ADS 
CAS 
Article 
PubMed Central 

Google Scholar 
58.Roloff, A. . Bäume in der Stadt (Ulmer, E, 2013).
Google Scholar 
59.Bruse, M. ENVI-met 3.0: Updated Model Overview. Tech. Rep. (2004).60.US Census Bureau. State and County Quick Facts https://www.census.gov/quickfacts/fact/table/US/PST045219 (2019).61.Shorris, A. Cool Neighborhoods NYC: A Comprehensive Approach to Keep Communities Safe in Extreme Heat. Tech. Rep.62.Hulley, G. ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) Mission Level 2 Product Specification Document. Tech. Rep. (2018).63.Stark, P. & Parker, R. Bounded-Variable Least-squares: An Algorithm and Applications. Tech. Rep. 394, (1995). More

1.Collingsworth, P. D. et al. Climate change as a long-term stressor for the fisheries of the Laurentian Great Lakes of North America. Rev. Fish Biol. Fish. 27, 363–391 (2017).Article 

Google Scholar 
2.Hilborn, R. & Walters, C. J. Quantitative Fisheries Stock Assessment: Choice, Dynamics and the Uncertainty (Routledge, Chapman and Hall Inc., 1992).Book 

Google Scholar 
3.Bean, C. W., Winfield, I. J. & Fletcher, J. M. Stock assessment of the Arctic charr (Salvelinus alpinus) population in Loch Ness, UK in stock assessment. In Inland Fisheries (ed. Cowx, I. G.) 206–223 (Blackwell Scientific Publications, 1996).
Google Scholar 
4.Emmrich, M. et al. Strong correspondence between gillnet catch per effort and hydroacoustically derived fish biomass in stratified lakes. Freshw. Biol. 57, 2436–2448 (2012).Article 

Google Scholar 
5.CEN (European Committee for Standardization). Water quality – sampling of fish with multi-mesh gillnets. European Committee for Standardization, European Standard EN 14757:2015 (Brussels, 2015).6.Murphy, B. & Willis, D. W. Fisheries Techniques 2nd edn. (American Fisheries Society, 1996).
Google Scholar 
7.Kinzelbach, R. Neozoans in European waters—Exemplifying the worldwide process of invasion and species mixing. Cell. Mol. Life Sci. 51, 526–538 (1995).CAS 
Article 

Google Scholar 
8.Cerwenka, A. F. Phenotypic and genetic differentiation of invasive gobies in the upper Danube River. Dissertation (Technische
Universität München, 2014).9.Byström, P. et al. Declining coastal piscivore populations in the Baltic Sea: Where and when do sticklebacks matter?. Ambio 44, 462–471 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
10.Ustups, D. et al. Diet overlap between juvenile flatifish and the invasive round goby in the central Baltic Sea. J. Sea Res. 107, 121–129 (2016).ADS 
Article 

Google Scholar 
11.Jackson, D. A. & Harvey, H. H. Qualitative and quantitative sampling of lake fish communities. Can. J. Fish. Aquat. Sci. 54, 2807–2813 (1997).Article 

Google Scholar 
12.Argyle, R. L. Acoustics as a tool for the assessment of Great Lakes forage fishes. Fish. Res. 14, 179–196 (1992).Article 

Google Scholar 
13.Jurvelius, J., Leinikki, J., Mamylov, V. & Pushkin, S. Stock assessment of pelagic three-spined stickleback (Gasterosteus aculeatus): A simultaneous up- and down-looking echo-sounding study. Fish. Res. 27, 227–241 (1996).Article 

Google Scholar 
14.Horne, J. K. Acoustic approaches to remote species identification: A review. Fish. Oceanogr. 9, 356–371 (2000).Article 

Google Scholar 
15.Godlewska, M., Świerzowski, A. & Winfield, I. J. Hydroacoustics as a tool for studies of fish and their habitat. Ecohydrol. Hydrobiol. 4, 417–427 (2004).
Google Scholar 
16.Muška, M. et al. Real-time distribution of pelagic fish: combining hydroacoustics, GIS and spatial modelling at a fine spatial scale. Sci. Rep. 8, 5381. https://doi.org/10.1038/s41598-018-23762-z (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
17.Berger, L. et al. Acoustic target classification. ICES Coop. Res. Rep. https://doi.org/10.17895/ices.pub.4567 (2018).ADS 
Article 

Google Scholar 
18.Emmrich, M., Helland, I. P., Busch, S., Schiller, S. & Mehner, T. Hydroacoustic estimates of fish densities in comparison with stratified pelagic trawl sampling in two deep, coregonid-dominated lakes. Fish. Res. 105, 178–186 (2010).Article 

Google Scholar 
19.DuFour, M. R., Qian, S. S., Mayer, C. M. & Vandergoot, C. S. Embracing uncertainty to reduce bias in hydroacoustic species apportionment. Fish. Res. https://doi.org/10.1016/j.fishres.2020.105750 (2021).Article 

Google Scholar 
20.Cabreira, A. G., Tripode, M. & Madirolas, A. Artificial neural networks for fish-species identification. ICES J. Mar. Sci. 66, 1119–1129 (2009).Article 

Google Scholar 
21.Robotham, H., Bosch, P., Gutierrez-Estrada, J., Castillo, J. & Pulido-Calvo, I. Acoustic identification of small pelagic fish species in Chile using support vector machines and neural networks. Fish. Res. 102, 115–122 (2010).Article 

Google Scholar 
22.Taylor, J. C. & Maxwell, D. L. Hydroacoustics: lakes and reservoirs. in Salmonid Field Protocols Handbook: Techniques for Assessing Status and Trends in Salmon and Trout Populations (ed. Johnson, D. H. et al.) 153–172 (American Fisheries Society in association with State of the Salmon, 2007).23.Parker-Stetter, S. L., Rudstam, L. G., Sullivan, L. G. & Warner, D. M. Standard operating procedures for fisheries acoustic surveys in the Great Lakes. Great Lakes Fisheries Commission Special Publication 09-01 (2009).24.Guillard, J., Perga, M. E., Colon, M. & Angeli, N. Hydroacoustic assessment of young-of-the-year perch, Perca fluviatilis, population dynamics in an oligotrophic lake (Lake Annecy, France). Fish. Manag. Ecol. 13, 319–327 (2006).Article 

Google Scholar 
25.Winfield, I. J., Fletcher, J. M., James, J. B. & Bean, J. B. Assessment of fish populations in still waters using hydroacoustics and survey gill netting: Experiences with Arctic charr (Salvelinus alpinus) in the UK. Fish. Res. 96, 30–38 (2009).Article 

Google Scholar 
26.Yule, D. L., Lori, M. E., Cachera, S., Colon, M. & Guillard, J. Comparing two fish sampling standards over time: Largely congruent results but with caveats. Freshw. Biol. 58, 2074–2088 (2013).Article 

Google Scholar 
27.DuFour, M. R., Song, S. Q., Mayer, C. M. & Vandergoot, C. S. Evaluating catchability in a large-scale gillnet survey using hydroacoustics: Making the case for coupled surveys. Fish. Res. 211, 309–318 (2019).Article 

Google Scholar 
28.Haralabous, J. & Georgakarakos, S. Artificial neural networks as a tool for species identification of fish schools. ICES J. Mar. Sci. 53, 173–180 (1996).Article 

Google Scholar 
29.Zakharia, M. E., Magand, F., Hetroit, F. & Diner, N. Wideband sounder for fish species identification at sea. ICES J. Mar. Sci. 53, 203–208 (1996).Article 

Google Scholar 
30.Fernandes, P. G. Classification trees for species identification of fish-school echo traces. ICES J. Mar. Sci. 66, 1073–1080 (2009).Article 

Google Scholar 
31.Eckmann, R. A hydroacoustic study of the pelagic spawning behavior of whitefish (Coregonus lavaretus) in lake constance. Can. J. Fish. Aquat. Sci. 48, 995–1002 (1991).Article 

Google Scholar 
32.Eckmann, R. & Engesser, B. Reconstructing the build-up of a pelagic stickleback (Gasterosteus aculeatus) population using hydroacoustics. Fish. Res. 210, 189–192 (2018).Article 

Google Scholar 
33.Peltonen, H., Ruuhijärvi, J., Malinen, T. & Horppila, J. Estimation of roach (Rutilus rutilus (L.)) and smelt (Osmerus eperlanus (L.)) stocks with virtual population analysis. Hydroacoustics and Gillnet CPUE. Fish. Res. 44, 25–36 (1999).Article 

Google Scholar 
34.MacLennan, D. N., Fernandes, P. G. & Dalen, J. A consistent approach to definitions and symbols in fisheries acoustics. ICES J. Mar. Sci. 59, 365–369 (2002).Article 

Google Scholar 
35.Korneliussen, R. J. The acoustic identification of Atlantic mackerel. ICES J. Mar. Sci. 67, 1749–1758 (2010).Article 

Google Scholar 
36.Langkau, M. C., Balk, H., Schmidt, M. B. & Borcherding, J. Can acoustic shadows identify fish species? A novel application of imaging sonar data. Fish. Manag. Ecol. 19, 313–322 (2012).Article 

Google Scholar 
37.Boswell, K. M., Wilson, M. P. & Cowan, J. H. Jr. A semiautomated approach to estimating fish size, abundance, and behavior from dual-frequency identification sonar (DIDSON) data. N. Am. J. Fish. Manag. 28, 799–807 (2008).Article 

Google Scholar 
38.Crossman, J. A., Martel, G., Johnson, P. N. & Bray, K. The use of Dual-Frequency Identification SONar (DIDSON) to document white sturgeon activity in the Columbia River, Canada. J. Appl. Ichthyol. 27, 53–57 (2011).Article 

Google Scholar 
39.Rakowitz, G. et al. Use of high-frequency imaging sonar (DIDSON) to observe fish behavior towards a surface trawl. Fish. Res. 123–124, 37–48 (2012).Article 

Google Scholar 
40.Skowronski, M. D. & Harris, J. G. Automatic detection of microchiroptera echolocation calls from field recordings using machine learning algorithms. J. Acoust. Soc. Am. 119, 1817–1833 (2005).Article 

Google Scholar 
41.Witten, I. H. & Frank, E. Data Mining: Practical Machine Learning Tools and Techniques 4th edn. (Morgan Kaufmann USA, 2017).MATH 

Google Scholar 
42.Jordan, M. I. & Mitchell, T. M. Machine learning: Trends, perspectives, and prospects. Science 349(6245), 255–260 (2015).ADS 
MathSciNet 
CAS 
MATH 
Article 

Google Scholar 
43.Jech, J. M., Lawson, G. L. & Lavery, A. C. Wideband (15–260 kHz) acoustic volume backscattering spectra of Northern krill (Meganyctiphanes norvegica) and butterfish (Peprilus triacanthus). ICES J. Mar. Sc. 74, 2249–2261 (2017).Article 

Google Scholar 
44.Lavery, A. C., Bassett, C., Lawson, G. L. & Jech, J. M. Exploiting signal processing approaches for broadband echosounders. ICES J. of Mar. Sci. 74, 2262–2275 (2017).Article 

Google Scholar 
45.Bassett, C., De Robertis, A. & Wilson, C. D. Broadband echosounder measurements of the frequency response of fishes and euphausiids in the Gulf of Alaska. ICES J. Mar. Sci. 75, 1131–1142 (2018).Article 

Google Scholar 
46.Demer, D. A. et al. 2016 USA–Norway EK80 Workshop Report: Evaluation of a wideband echosounder for fisheries and marine ecosystem science. ICES Coop. Res. Rep. https://doi.org/10.17895/ices.pub.2318 (2017).Article 

Google Scholar 
47.Tuzlukov, V. Signal Processing in Radar Systems 1st edn. (CRC Press Taylor & Francis Group USA, 2013).MATH 

Google Scholar 
48.Baer, J., Eckmann, R., Rösch, R., Arlinghaus, R. & Brinker, A. Managing upper lake constance fishery in a multi-sector policy landscape: Beneficiary and victim of a century of anthropogenic trophic change. In Inter-Sectoral Governance of Inland Fisheries (eds Song, A. M. et al.) 32–47 (TBTI Publication Series, 2017).
Google Scholar 
49.Roch, S., von Ammon, L., Geist, J. & Brinker, A. Foraging habits of invasive three-spined sticklebacks (Gasterosteus aculeatus)—Impacts on fisheries yield in Upper Lake Constance. Fish. Res. 204, 172–180 (2018).Article 

Google Scholar 
50.Rösch, R., Baer, J. & Brinker, A. Impact of the invasive three-spined stickleback (Gasterosteus aculeatus) on relative abundance and growth of native pelagic whitefish (Coregonus wartmanni) in Upper Lake Constance. Hydrobiol. 824, 255–270 (2018).Article 
CAS 

Google Scholar 
51.Balk, H., & Lindem, T. Sonar4 and Sonar5-Pro Post processing systems Operator manual, version 6.0.3. (2018).52.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017).
Google Scholar 
53.Liaw, A. & Wiener, M. Classification and regression by randomForest. R News. 2, 18–22 (2002).
Google Scholar 
54.Degan, D. J. & Wilson, W. Comparison of four hydroacoustic frequencies for sampling pelagic fish populations in Lake Texoma. N. Am. J. Fish. Manag. 15, 924–932 (1995).Article 

Google Scholar 
55.Godlewska, M. et al. Hydroacoustic measurements at two frequencies: 70 and 120 kHz—Consequences for fish stock estimation. Fish. Res. 96, 11–16 (2009).Article 

Google Scholar 
56.Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).Article 
MATH 

Google Scholar 
57.Cutler, D. R. et al. Random forests for classification in ecology. Ecology 88, 2783–2792 (2007).PubMed 
Article 

Google Scholar 
58.Oppel, S. et al. Comparison of five modelling techniques to predict the spatial distribution and abundance of seabirds. Biol. Cons. 156, 94–104 (2012).Article 

Google Scholar 
59.Kuhn, M. Caret: Classification and Regression Training. R package version 6.0-81. https://CRAN.R-project.org/package=caret (2018).60.Archer, K. J. & Kimes, R. V. Empirical characterization of random forest variable importance measures. Comput. Stat. Data Anal. 52, 2249–2260 (2008).MathSciNet 
MATH 
Article 

Google Scholar 
61.Lawson, G. J., Barange, M. & Fréon, P. Species identification of pelagic fish schools on the South African continental shelf using acoustic descriptors and ancillary information. ICES J. Mar. Sci. 58, 275–287 (2001).Article 

Google Scholar 
62.Simmonds, E. J., Armstrong, F. & Copland, P. J. Species identification using wideband backscatter with neural network and discriminant analysis. ICES J. Mar. Sci. 53, 189–195 (1996).Article 

Google Scholar 
63.Bergström, U. et al. Stickleback increase in the Baltic Sea—A thorny issue for coastal predatory fish. Estuar. Coast. Shelf Sci. 163, 134–142 (2015).ADS 
Article 

Google Scholar 
64.Pepin, T. & Shears, T. H. Influence of body size and alternate prey abundance on the risk of predation to fish larvae. Mar. Ecol. Prog. Ser. 128, 279–285 (1995).ADS 
Article 

Google Scholar 
65.Frouzová, J., Kubečka, J., Balk, H. & Frouz, J. Target strength of some European fish species and its dependence onfish body parameters. Fish. Res. 75, 86–96 (2005).Article 

Google Scholar 
66.Marques, D. A., Lucek, K., Sousa, V. C., Excoffier, L. & Seehausen, O. Admixture between old lineages facilitated contemporary ecological speciation in Lake Constance stickleback. Nat. Commun. 10, 4240. https://doi.org/10.1038/s41467-019-12182-w (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar  More

Study site and subject detailsThe study was conducted at the Inkawu Vervet Project (IVP) in a 12,000-hectare private game reserve: Mawana (28° 00.327 S, 031° 12.348 E) in KwaZulu Natal province, South Africa. The vegetation of the study site consisted in a savannah characterized by a mosaic of grasslands and clusters of trees of the typical savannah thornveld, bushveld and thicket patches. We studied two groups of wild vervet monkeys (Chlorocebus pygerythrus): ‘Noha’ (NH) and ‘Kubu’ (KB). NH was composed of 34 individuals (6 adult males; 9 adult females; 6 juvenile males; 7 juvenile females; 5 infant males; 1 infant female) and KB was composed of 19 individuals (1 adult male; 6 adult females; 3 juvenile males; 4 juvenile females; 3 infant males; 2 infant females; Table S1). Males were considered as adults once they dispersed, and females were considered as adults after they gave their first birth. Individuals that did not fulfil these criteria were considered as juveniles28 and infants were aged less than 1 year old. In EWA models, infants and juveniles were lumped in a single category “juveniles”. Each group had been habituated to the presence of human observers: since 2010 for NH and since 2013 for KB. All individuals were identifiable thanks to portrait photographs and specific individual body and face features (scars, colours, shape etc.).This research adhered to the “Guidelines for the use of animals in research” of Association for Study of Animal Behaviour, was approved by the relevant local authority, Ezemvelo KZN Wildlife, South Africa and complied with the ARRIVE guidelines.Hierarchy establishmentAgonistic interactions (aggressor behaviour: stare, chase, attack, hit, bite, take place; victim behaviour: retreat, flee, leave, avoid, jump aside) were collected from May 2018 to October 2018, aside from experiment days, on all the adults and juveniles of both groups via ad libitum sampling50 and food competition tests (i.e. corn provided to the whole group from a plastic box). Data were collected by CC, MBC and different observers from the IVP team. Before beginning data collection, observers had to pass an inter-observer reliability test with 80% reliability for each data category between two observers. Data were collected on tablets (Vodacom Smart Tab 2) equipped with Pendragon version 8.Individual hierarchical ranks were determined by the outcome of dyadic agonistic interactions recorded ad libitum and through food competition tests using Socprog software version 2.751. Hierarchies in both groups were significantly linear (NH: h′ = 0.27; P  More

1.Murphy, P. G. & Lugo, A. E. Ecology of tropical dry forest. Ann. Rev. Ecol. Syst. 17, 67–88. https://doi.org/10.1146/annurev.es.17.110186.000435 (1986).Article 

Google Scholar 
2.Hasselquist, N. J., Allen, M. F. & Santiago, L. S. Water relations of evergreen and drought-deciduous trees along a seasonally dry tropical forest chronosequence. Oecologia 164, 881–890. https://doi.org/10.1007/s00442-010-1725-y (2010).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
3.Maass, M. et al. Long-term (33 years) rainfall and runoff dynamics in a tropical dry forest ecosystem in western Mexico: Management implications under extreme hydrometeorological events. For. Ecol. Manage. 426, 7–17. https://doi.org/10.1016/j.foreco.2017.09.040 (2018).Article 

Google Scholar 
4.NOAA. National Weather Service. Climate Prediction Center. Cold and warm episodes by season. http://www.cpc.noaa.gov/products/analysis_monitoring/ensostuff/ensoyears.shtml. (Accessed 19 October 2019).5.Detto, M., Wright, J., Calderón, O. & Muller-Landau, H. C. Resource acquisition and reproductive strategies of tropical forest in response to the El Niño-Southern Oscillation. Nat. Commun. 9, 9–13. https://doi.org/10.1038/s41467-018-03306-9 (2018).CAS 
Article 

Google Scholar 
6.Bretfeld, M., Ewers, B. E. & Hal, J. S. Plant water use responses along secondary forest succession during the 2015–2016 El Niño drought in Panama. New Phytol. 219, 885–899. https://doi.org/10.1111/nph.15071 (2018).Article 
PubMed 

Google Scholar 
7.Meakem, V. et al. Role of tree size in moist tropical forest carbon cycling and water deficit responses. New Phytol. 219, 947–958. https://doi.org/10.1111/nph.14633 (2018).CAS 
Article 
PubMed 

Google Scholar 
8.Salmon, Y. et al. Drought impacts on tree phloem: From cell-level responses to ecological significance. Tree Physiol. 39, 173–191. https://doi.org/10.1093/treephys/tpy153 (2019).CAS 
Article 
PubMed 

Google Scholar 
9.Brodribb, T. J., Powers, J., Cochard, H. & Choat, B. Hanging by a thread? Forests and drought. Science 368, 261–266. https://doi.org/10.1126/science.aat7631 (2020).ADS 
CAS 
Article 
PubMed 

Google Scholar 
10.Powers, J. S. et al. A catastrophic tropical drought kills hydraulically vulnerable tree species. Glob. Change Biol. 26, 3122–3133. https://doi.org/10.1111/gcb.15037 (2020).ADS 
Article 

Google Scholar 
11.Wigneron, J. P. et al. Tropical forests did not recover from the strong 2015–2016 El Niño event. Sci. Adv. 6, eaay4603. https://doi.org/10.1126/sciadv.aay4603 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
12.Martinez-Vilalta, J. & Lloret, F. Drought-induced vegetation shifts in terrestrial ecosystems: The key role of regeneration dynamics. Glob. Planet. Change 144, 94–108. https://doi.org/10.1016/j.gloplacha.2016.07.009 (2016).ADS 
Article 

Google Scholar 
13.Allen, C. D. et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For. Ecol. Manag. 259, 660–684. https://doi.org/10.1016/j.foreco.2009.09.001 (2010).Article 

Google Scholar 
14.Anderegg, W. R. L. et al. Meta-analysis reveals that hydraulic traits explain cross-species patterns of drought-induced tree mortality across the globe. PNAS 113, 5024–5029. https://doi.org/10.1073/pnas.1525678113 (2016).ADS 
CAS 
Article 
PubMed 

Google Scholar 
15.Greenwood, S. et al. Tree mortality across biomes is promoted by drought intensity, lower wood density and higher specific leaf area. Ecol. Lett. 20, 539–553. https://doi.org/10.1111/ele.12748 (2017).Article 
PubMed 

Google Scholar 
16.Sperry, J. S., Meinzer, F. C. & McCulloh, K. A. Safety and efficiency conflicts in hydraulic architecture: Scaling from tissues to trees. Plant Cell Environ. 31, 632–645. https://doi.org/10.1111/j.1365-3040.2007.01765.x (2008).Article 
PubMed 

Google Scholar 
17.Borchert, R. & Pockman, W. T. Water storage capacitance and xylem tension in isolated branches of temperate and tropical trees. Tree Physiol. 25, 457–466. https://doi.org/10.1093/treephys/25.4.457 (2005).Article 
PubMed 

Google Scholar 
18.Valdez-Hernández, M., Andrade, J. L., Jackson, P. C. & Rebolledo-Vieyra, M. Phenology of five tree species of a tropical dry forest in Yucatán, Mexico: Effects of environmental and physiological factors. Plant Soil 329, 155–171. https://doi.org/10.1007/s11104-009-0142-7 (2010).CAS 
Article 

Google Scholar 
19.Santiago, L. S. et al. Coordination and trade-offs among hydraulic safety, efficiency and drought avoidance traits in Amazonian rainforest canopy tree species. New Phytol. 218, 1015–1024. https://doi.org/10.1111/nph.15058 (2018).Article 
PubMed 

Google Scholar 
20.Bussotti, F., Pollastrini, M., Holland, V. & Bruggemann, W. Functional traits and adaptive capacity of European forests to climate change. Environ. Experim. Bot. 111, 91–113. https://doi.org/10.1016/j.envexpbot.2014.11.006 (2015).Article 

Google Scholar 
21.Reich, P. B. & Borchert, R. Water stress and tree phenology in a tropical dry forest in the lowlands of Costa Rica. J. Ecol. 72(1), 61–74. https://doi.org/10.2307/2260006 (1984).Article 

Google Scholar 
22.Holbrook, N. M., Whitbeck, J. L. & Mooney, H. A. Drought responses of neotropical dry forest trees. In Seasonally Dry Tropical Forests (eds Bullock, S. H. et al.) (Cambridge University Press, 1995). https://doi.org/10.1017/CBO9780511753398.010.
Google Scholar 
23.Wolfe, B. T. & Kursar, T. A. Diverse patterns of stored water use among saplings in seasonally dry tropical forests. Oecologia 179, 925–936. https://doi.org/10.1007/s00442-015-3329-z (2015).ADS 
Article 
PubMed 

Google Scholar 
24.Borchert, R., Rivera, G. & Hagnauer, W. Modification of vegetative phenology in a tropical semi-deciduous forest by abnormal drought and rain. Biotropica 34, 27–39. https://doi.org/10.1111/j.1744-7429.2002.tb00239.x (2002).Article 

Google Scholar 
25.Markesteijn, L., Poorter, L., Paz, H., Sack, L. & Bongers, F. Ecological differentiation in xylem cavitation resistance is associated with stem and leaf structural traits. Plant Cell Environ. 34, 137–148. https://doi.org/10.1111/j.1365-3040.2010.02231.x (2011).Article 
PubMed 

Google Scholar 
26.Aragón-Moreno, A. A., Islebe, G. A., Torrescano-Valle, N. & Arellano-Verdejo, J. Middle and late Holocene mangrove dynamics of the Yucatan Peninsula, Mexico. J. S. Am. Earth Sci. 85, 307–311. https://doi.org/10.1016/j.jsames.2018.05.015 (2018).Article 

Google Scholar 
27.De la Barreda, B., Metcalfe, E. S. & Boyd, D. S. Precipitation regionalization, anomalies and drought occurrence in the Yucatan peninsula, Mexico. Int. J. Climatol. 40(10), 1–15. https://doi.org/10.1002/joc.6474 (2020).Article 

Google Scholar 
28.IPCC. Summary for Policymakers. In: Global warming of 1.5 °C. An IPCC Special Report on the impacts of global warming of 1.5 °C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty (V. Masson-Delmotte, P., Zhai, H. O., Pörtner, D., Roberts, J., Skea, P.R., Shukla, A., Pirani, W., Moufouma-Okia, C., Péan, R., Pidcock, S., Connors, J.B.R., Matthews, Y., Chen, X., Zhou, M. I., Gomis, E., Lonnoy, T., Maycock, M., Tignor, T., Waterfield, eds.). World Meteorological Organization, Geneva, Switzerland. https://www.ipcc.ch/sr15/ (Accessed 15 November 2019).29.Eller, C. B., Rowland, L. & Oliveira, R. S. Modelling tropical forest responses to drought and El Niño with a stomatal optimization model based on xylem hydraulics. Phil. Trans. R. Soc. B 373, 1–12. https://doi.org/10.1098/rstb.2017.0315 (2018).CAS 
Article 

Google Scholar 
30.Feng, X., Porporato, A. & Rodriguez-Iturbe, I. Changes in rainfall seasonality in the tropics. Nat. Clim. Chang. 3(9), 811–815. https://doi.org/10.1038/nclimate1907 (2013).ADS 
Article 

Google Scholar 
31.Goldstein, G. et al. Stem water storage and diurnal patterns of water use in tropical forest canopy trees. Plant Cell Environ. 21, 397–406. https://doi.org/10.1046/j.1365-3040.1998.00273.x (1998).Article 

Google Scholar 
32.Landsberg, J. & Waring, R. Water relations in tree physiology: where to from here?. Tree Physiol. 37, 18–32. https://doi.org/10.1093/treephys/tpw102 (2016).Article 

Google Scholar 
33.Kim, J. S. & Kug, J.-S. Increased atmospheric CO2 growth rate during El Niño driven by reduced terrestrial CO2 capture in the CMIP5 ESMs. J. Clim. 29, 8783–8805. https://doi.org/10.1175/JCLI-D-14-00672.1 (2016).ADS 
Article 

Google Scholar 
34.Kim, J. S., Kug, J.-S. & Jeong, S. Intensification of terrestrial carbon cycle related to El Niño-Southern Oscillation under greenhouse warming. Nat. Commun. 8, 1674. https://doi.org/10.1038/s41467-017-01831-7 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
35.Wang, Q., Cai, W., Zeng, L. & Wang, D. Nonlinear meridional moisture advection and the ENSO-southern China rainfall teleconnection. Geophys. Res. Lett. 45(9), 4353–4360. https://doi.org/10.1029/2018GL077446 (2018).ADS 
Article 

Google Scholar 
36.Wang, Q., Wang, Y., Sui, J., Zhou, W. & Li, D. Effects of weak and strong winter currents on the thermal state of the South China Sea. J. Clim. 34(1), 313–325. https://doi.org/10.1175/JCLI-D-19-0790.1 (2021).ADS 
Article 

Google Scholar 
37.Xie, S.-P. et al. Eastern Pacific ITCZ dipole and ENSO diversity. J. Clim. 31, 4449–4462. https://doi.org/10.1175/JCLI-D-17-0905.1 (2018).ADS 
Article 

Google Scholar 
38.Peng, Q., Xie, S.-P., Wang, D., Zheng, X.-T. & Zhang, H. Coupled ocean–atmosphere dynamics of the 2017 extreme coastal El Niño. Nat. Commun. 10, 298. https://doi.org/10.1038/s41467-018-08258-8 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
39.Peng, Q. et al. Eastern Pacific winds in the evolution of El Niño: implications for ENSO diversity. J. Clim. 33, 3197–3212. https://doi.org/10.1175/JCLI-D-19-0435.1 (2020).ADS 
Article 

Google Scholar 
40.Barkhodarian, A., Saatchi, S. S., Behrangi, A., Loikith, P. C. & Mechoso, C. R. A recent systematic increase in vapor pressure deficit over tropical South America. Sci. Rep. 9, 15331. https://doi.org/10.1038/s41598-019-51857-8 (2019).ADS 
CAS 
Article 

Google Scholar 
41.Meinzer, F. C., James, S. A., Goldstein, G. & Woodruff, D. Whole-tree water transport scales sapwood capacitance in tropical forest canopy trees. Plant Cell Environ. 26, 1147–1155. https://doi.org/10.1046/j.1365-3040.2003.01039.x (2003).Article 

Google Scholar 
42.Luo, Z. et al. Responses of plant water use to a severe summer drought for two subtropical tree species in the central southern China. J. Hydrol. Reg. Stud. 8, 1–9. https://doi.org/10.1016/j.ejrh.2016.08.001 (2016).CAS 
Article 

Google Scholar 
43.Vinya, R., Malhi, Y., Brown, N. & Fisher, J. Functional coordination between branch hydraulic properties and leaf functional traits in miombo woodlands: Implications for water stress management and species habitat preference. Acta Physiol. Plant 34, 1701–1710. https://doi.org/10.1007/s11738-012-0965-3 (2012).CAS 
Article 

Google Scholar 
44.Choat, B., Ball, M. C., Luly, J. G. & Holtum, J. A. M. Hydraulic architecture of deciduous and evergreen dry rainforest tree species from north-eastern Autralia. Trees 19, 305–311. https://doi.org/10.1007/s00468-004-0392-1 (2005).Article 

Google Scholar 
45.Romero, E., González, E. J., Meave, J. A. & Terrazas, T. Wood anatomy of dominant species with contrasting ecological performance in tropical dry forest succession. Plant Biosyst. 154, 524–534. https://doi.org/10.1080/11263504.2019.1651775 (2019).Article 

Google Scholar 
46.Pineda-García, F., Paz, H. & Meinzer, F. C. Drought resistance in early and late secondary successional species from a tropical dry forest: The interplay between xylem resistance to embolism, sapwood water storage and leaf shedding. Plant Cell Environ. 36, 405–418. https://doi.org/10.1111/j.1365-3040.2012.02582.x (2013).Article 
PubMed 

Google Scholar 
47.Choat, B., Sack, L. & Holbrook, M. Diversity of hydraulic traits in nine Cordia species growing in tropical forests with contrasting precipitation. New Phytol. 175, 686–698. https://doi.org/10.1111/j.1469-8137.2007.02137.x (2007).Article 
PubMed 

Google Scholar 
48.Fallas-Cedeño, L., Holbrook, N. M., Rocha, O. J., Vásquez, N. & Gutiérrez-Soto, M. Phenology, lignotubers, and water relations of Cochlospermum vitifolium, a pioneer tropical dry forest tree in Costa Rica. Biotropica 42, 104–111. https://doi.org/10.1111/j.1744-7429.2009.00539.x (2010).Article 

Google Scholar 
49.Quintanar-Isaías, A., Velasquez-Nuñez, M., Solares-Arenas, F., Pérez-Olvera, C. P. & Torre-Blanco, A. Secondary stem anatomy and uses or four drought-deciduous species of a tropical dry forest in Mexico. Rev. Biol. Trop. 53, 29–48. https://doi.org/10.15517/RBT.V53I1-2.14297 (2005).Article 

Google Scholar 
50.Veneklaas, E. J., Santos-Silva, M. P. & den Ouden, F. Determinants of growth rate in Ficus benjamina L. compared to related faster-growing woody and herbaceous species. Sci. Hortic. 93, 75–84. https://doi.org/10.1016/S0304-4238(01)00315-6 (2002).Article 

Google Scholar 
51.Mediavilla, S., Escudero, A. & Heilmeier, H. Internal leaf anatomy and photosynthetic resource-use efficiency: Interspecific and intraspecific comparisons. Tree Physiol. 21, 251–259. https://doi.org/10.1093/treephys/21.4.251 (2001).CAS 
Article 
PubMed 

Google Scholar 
52.Peguero-Pina, J. J., Sancho-Knapik, D. & Gil-Pelegrin, E. Ancientcell structural traits and photosynthesis in today’s environment. J. Exp. Bot. 68, 1389–1392. https://doi.org/10.1093/jxb/erx081 (2017).CAS 
Article 
PubMed Central 

Google Scholar 
53.Kitajima, K. & Poorter, L. Tissue-level leaf toughness, but not lamina thickness, predicts sapling leaf lifespan and shade tolerance of tropical tree species. New Phytol. 186, 708–721. https://doi.org/10.1111/j.1469-8137.2010.03212.x (2010).Article 
PubMed 

Google Scholar 
54.Schwedenman, L., Pendall, E., Sanchez-Bragado, R., Kunert, N. & Holscher, D. Tree water uptake in a tropical plantation varying in tree diversity: Interspecific differences, seasonal shifts and complementary. Ecohydrology 8, 1–12. https://doi.org/10.1002/eco.1479 (2015).Article 

Google Scholar 
55.Reyes-García, C., Andrade, J. L., Simá, J. L., Us-Santamaría, R. & Jackson, P. C. Sapwood to heartwood ratio affects whole-tree water use in dry forest legume and non-legume trees. Trees 26, 1317–1330. https://doi.org/10.1007/s00468-012-0708-5 (2012).Article 

Google Scholar 
56.Santiago, L. et al. Leaf photosynthetic traits scale with hydraulic conductivity and wood density in Panamanian forest canopy trees. Oecologia 140, 543–550. https://doi.org/10.1007/s00442-004-1624-1 (2004).ADS 
CAS 
Article 
PubMed 

Google Scholar 
57.Li, X. et al. Tree hydraulic traits are coordinated and strongly linked to climate-of-origin across a rainfall gradient. Plant Cell Environ. 41, 646–660. https://doi.org/10.1111/pce.13129 (2018).CAS 
Article 
PubMed 

Google Scholar 
58.Querejeta, J. I., Estrada-Medina, H., Allen, M. F., Jiménez-Osorio, J. J. & Ruenes, R. Utilization of bedrock water by Brosimum alicastrum trees growing on shallow soil atop limestone in a dry tropical climate. Plant Soil 287, 187–197. https://doi.org/10.1007/s11104-006-9065-8 (2006).CAS 
Article 

Google Scholar 
59.Scholz, F. G., Phillips, N. G., Bucci, S. J., Meinzer, F. C. & Goldstein, G. Hydraulic capacitance: Biophysics and functional significance of internal water sources in relation to tree size. In Size- and Age-Related Changes in Tree Structure and Function (eds Meinzer, F. C. et al.) 341–362 (Springer, 2011). https://doi.org/10.1007/978-94-007-1242-3_13.
Google Scholar 
60.Bennett, A. C., McDowell, N. G., Allen, C. D. & Anderson-Teixeira, K. J. Larger trees suffer most during drought in forests worldwide. Nat. Plants 139, 1–5. https://doi.org/10.1038/nplants.2015.139 (2015).Article 

Google Scholar 
61.Sobrado, M. A. Embolism vulnerability in drought-deciduous and evergreen species of a tropical dry forest. Acta Oecol. 18, 383–391. https://doi.org/10.1016/S1146-609X(97)80030-6 (1997).ADS 
Article 

Google Scholar 
62.Brodribb, T. J., Holbrook, N. M., Edwards, E. J. & Gutierrez, M. V. Relation between stomatal closure, leaf turgor and xylem vulnerability in eight tropical dry forest trees. Plant Cell Environ. 26, 443–450. https://doi.org/10.1046/j.1365-3040.2003.00975.x (2003).Article 

Google Scholar 
63.Orellana, R., Balam, M. & Bañuelos, I. Balance Ombrotérmico, evaluación climática. In Atlas de procesos territoriales de Yucatán (eds de Fuentes, A. G. et al.) 174–175 (Universidad Autónoma de Yucatán, 1999).
Google Scholar 
64.Instituto Nacional de Estadística Geografía e Informática, 2017. Anuario estadístico y geográfico de Quintana Roo. INEGI, México. https://www.datatur.sectur.gob.mx/ITxEF_Docs/QROO_ANUARIO. (Accessed 12 December 2019).65.Espinoza-Avalos, J., Islebe, G. A. & Hernández-Arana, H. A. El sistema ecológico de la bahía de Chetumal/corozal: Costa occidental del mar caribe (El Colegio de la Frontera Sur, 2009).
Google Scholar 
66.McKee, T.B., Doesken, N.J. & Kelist, J. The relationship of drought frequency and duration to time scale. in American Meteorological Society, Proceedings of the Eighth Conference on Applied Climatology, 17–22 January, Anaheim, California 179–184 (1993).67.Cheval, S. The Standardized Precipitation Index—An overview. Rom. J. Meteorol. 12(1–2), 17–64 (2015).
68.Koide, R. T., Robichaux, R. H., Morse, S. R. & Smith, C. M. Plant water status, hydraulic resistance and capacitance. In Plant Physiological Ecology, Field Methods and Instrumentation (eds Pearcy, R. W. et al.) 161–178 (Chapman and Hall, 1991). https://doi.org/10.1007/978-94-010-9013-1_9.
Google Scholar 
69.Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9(7), 671–675. https://doi.org/10.1038/nmeth.2089 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
70.StatSoft, Inc. STATISTICA (data analysis software system), version 12. www.statsoft.com (2013). More

1.Endler, J. A. Signals, signal conditions, and the direction of evolution. Am. Nat. 139, S125–S153 (1992).Article 

Google Scholar 
2.Endler, J. A. Some general comments on the evolution and design of animal communication systems. Philos. Trans. R. Soc. Lond. B 340, 215–225 (1993).ADS 
CAS 
Article 

Google Scholar 
3.Bakker, T. C. & Mundwiler, B. Female mate choice and male red coloration in a natural three-spined stickleback (Gasterosteus aculeatus) population. Behav. Ecol. 5, 74–80 (1994).Article 

Google Scholar 
4.Molnár, O., Bajer, K., Mészáros, B., Török, J. & Herczeg, G. Negative correlation between nuptial throat colour and blood parasite load in male European green lizards supports the Hamilton-Zuk hypothesis. Naturwissenschaften 100, 551–558 (2013).ADS 
PubMed 
Article 
CAS 

Google Scholar 
5.Wolfenbarger, L. L. Red coloration of male northern cardinals correlates with mate quality and territory quality. Behav. Ecol. 10, 80–90 (1999).Article 

Google Scholar 
6.Endler, J. A. Natural-selection on color patterns in Poecilia reticulata. Evolution 34, 76–91 (1980).PubMed 
Article 

Google Scholar 
7.Marcondes, R. S. & Brumfield, R. T. Fifty shades of brown: Macroevolution of plumage brightness in the Furnariida, a large clade of drab Neotropical passerines. Evolution 73, 704–719 (2019).PubMed 
Article 

Google Scholar 
8.Alberts, A. C. Constraints on the design of chemical communication systems in terrestrial vertebrates. Am. Nat. 139, S62–S89 (1992).9.Campos, S. M. et al. Volatile fatty acid and aldehyde abundances evolve with behavior and habitat temperature in Sceloporus lizards. Behav. Ecol. (2020).10.Stuart-Fox, D. M. & Ord, T. J. Sexual selection, natural selection and the evolution of dimorphic coloration and ornamentation in agamid lizards. Proc. R. Soc. B 271, 2249–2255 (2004).PubMed 
Article 

Google Scholar 
11.Karlson, P. & Lüscher, M. ‘Pheromones’: A new term for a class of biologically active substances. Nature 183, 55–56 (1959).ADS 
CAS 
PubMed 
Article 

Google Scholar 
12.Schmidt, H. R. & Benton, R. Molecular mechanisms of olfactory detection in insects: Beyond receptors. Open Biol. 10, 200252 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
13.Symonds, M. R. & Elgar, M. A. The evolution of pheromone diversity. Trends Ecol. Evol. 23, 220–228 (2008).PubMed 
Article 

Google Scholar 
14.Boulet, M., Charpentier, M. J. & Drea, C. M. Decoding an olfactory mechanism of kin recognition and inbreeding avoidance in a primate. BMC Evol. Biol. 9, 281 (2009).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
15.Scordato, E. S., Dubay, G. & Drea, C. M. Chemical composition of scent marks in the ringtailed lemur (Lemur catta): Glandular differences, seasonal variation, and individual signatures. Chem. Senses 32, 493–504 (2007).CAS 
PubMed 
Article 

Google Scholar 
16.Janssenswillen, S. et al. Origin and diversification of a salamander sex pheromone system. Mol. Biol. Evol. 32, 472–480 (2015).PubMed 
Article 

Google Scholar 
17.Kikuyama, S. et al. Sodefrin: A female-attracting peptide pheromone in newt cloacal glands. Science 267, 1643–1645 (1995).ADS 
CAS 
PubMed 
Article 

Google Scholar 
18.Wabnitz, P. A., Bowie, J. H., Tyler, M. J., Wallace, J. C. & Smith, B. P. Aquatic sex pheromone from a male tree frog. Nature 401, 444–445 (1999).ADS 
CAS 
PubMed 
Article 

Google Scholar 
19.Baeckens, S. et al. Environmental conditions shape the chemical signal design of lizards. Funct. Ecol. 32, 566–580 (2018).Article 

Google Scholar 
20.Martín, J. & López, P. Pheromones and chemical communication in lizards. In Reproductive Biology and Phylogeny of Lizards and Tuatara (eds Rheubert, J. L. et al.) 43–75 (CRC Press, Boca Raton, 2014).
Google Scholar 
21.Silva, L. & Antunes, A. Vomeronasal receptors in vertebrates and the evolution of pheromone detection. Ann. Rev. Anim. Biosci. 5, 353–370 (2017).CAS 
Article 

Google Scholar 
22.Bonadonna, F. & Nevitt, G. A. Partner-specific odor recognition in an Antarctic seabird. Science 306, 835–835 (2004).CAS 
PubMed 
Article 

Google Scholar 
23.Bonadonna, F. & Sanz-Aguilar, A. Kin recognition and inbreeding avoidance in wild birds: The first evidence for individual kin-related odour recognition. Anim. Behav. 84, 509–513 (2012).Article 

Google Scholar 
24.Krause, E. T., Krüger, O., Kohlmeier, P. & Caspers, B. A. Olfactory kin recognition in a songbird. Biol. Lett. 8, 327–329 (2012).Article 

Google Scholar 
25.Baeckens, S. et al. Environmental conditions shape the chemical signal design of lizards. Funct. Ecol. 32, 566–580 (2018).Article 

Google Scholar 
26.Wyatt, T. D. Proteins and peptides as pheromone signals and chemical signatures. Anim. Behav. 97, 273–280 (2014).Article 

Google Scholar 
27.Baeckens, S., Edwards, S., Huyghe, K. & Van Damme, R. Chemical signalling in lizards: An interspecific comparison of femoral pore numbers in Lacertidae. Biol. J. Linn. Soc. 114, 44–57 (2015).Article 

Google Scholar 
28.Ossip-Klein, A. G., Fuentes, J. A., Hews, D. K. & Martins, E. P. Information content is more important than sensory system or physical distance in guiding the long-term evolutionary relationships between signaling modalities in Sceloporus lizards. Behav. Ecol. Sociobiol. 67, 1513–1522 (2013).Article 

Google Scholar 
29.Pincheira-Donoso, D., Hodgson, D. J. & Tregenza, T. Comparative evidence for strong phylogenetic inertia in precloacal signalling glands in a species-rich lizard clade. Evol. Ecol. Res. 10, 11–28 (2008).
Google Scholar 
30.Wang, Z. et al. The draft genomes of soft-shell turtle and green sea turtle yield insights into the development and evolution of the turtle-specific body plan. Nat. Genet. 45, 701–706 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
31.Schwenk, K. Comparative anatomy and physiology of chemical senses in nonavian aquatic reptiles. In Sensory Evolution on the Threshold: Adaptations in Secondarily Aquatic Vertebrates (eds Thewissen, J. H. M. & Nummela, S.) 65–81 (University of California Press, Berkeley, 2008).
Google Scholar 
32.Vieyra, M. L. Olfactory receptor genes in terrestrial, freshwater, and sea turtles: Evidence for a reduction in the number of functional genes in aquatic species. Chelon. Conserv. Biol. 10, 181–187 (2011).Article 

Google Scholar 
33.Mason, R. T. & Parker, M. R. Social behavior and pheromonal communication in reptiles. J. Comp. Physiol. A. 196, 729–749 (2010).CAS 
Article 

Google Scholar 
34.Ehrenfeld, J. G. & Ehrenfeld, D. W. Externally secreting glands of freshwater and sea turtles. Copeia 1973, 305–314 (1973).Article 

Google Scholar 
35.Waagen, G. N. Musk glands in recent turtles. Master of Science thesis, Department of Biology, University of Utah (1972).36.Weldon, P. J., Flachsbarth, B. & Schulz, S. Natural products from the integument of nonavian reptiles. Nat. Prod. Rep. 25, 738–756 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
37.Ibáñez, A. et al. The chemistry and histology of sexually dimorphic mental glands in the freshwater turtle, Mauremys leprosa. PeerJ 8, e9047 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
38.Rose, F. L., Drotman, R. & Weaver, W. G. Electrophoresis of chin gland extracts of Gopherus (tortoises). Comp. Biochem. Physiol. 29, 847–851 (1969).CAS 
Article 

Google Scholar 
39.Winokur, R. M. & Legler, J. M. Chelonian mental glands. J. Morphol. 147, 275–291 (1975).PubMed 
Article 

Google Scholar 
40.Alberts, A. C., Rostal, D. C. & Lance, V. A. Studies on the chemistry and social significance of chin gland secretions in the desert tortoise, Gopherus agassizii. Herpetol. Monogr. 8, 116–124 (1994).Article 

Google Scholar 
41.Kelley, M. D. & Mendonça, M. T. Mental gland secretions as a social cue in gopher tortoises (Gopherus polyphemus): Tortoise presence stimulates and maintains social behaviour with chemical cues. Acta Ethol. 24, 1–8 (2020).Article 

Google Scholar 
42.Rose, F. L. Tortoise chin gland fatty acid composition: Behavioral significance. Comp. Biochem. Physiol. 32, 577–580 (1970).CAS 
Article 

Google Scholar 
43.Pereira, A. G., Sterli, J., Moreira, F. R. & Schrago, C. G. Multilocus phylogeny and statistical biogeography clarify the evolutionary history of major lineages of turtles. Mol. Phylogenet. Evol. 113, 59–66 (2017).PubMed 
Article 

Google Scholar 
44.Grosse, A. M., Sterrett, S. C. & Maerz, J. C. Effects of turbidity on the foraging success of the eastern painted turtle. Copeia 2010, 463–467 (2010).Article 

Google Scholar 
45.Vitt, L. J. & Caldwell, J. P. Herpetology: An Introductory Biology of Amphibians and Reptiles (Academic Press, 2013).
Google Scholar 
46.Thomson, R. C., Spinks, P. Q. & Shaffer, H. B. A global phylogeny of turtles reveals a burst of climate-associated diversification on continental margins. Proc. Natl. Acad. Sci. 118, e2012215118 (2021).PubMed 
Article 
CAS 

Google Scholar 
47.Colston, T. J., Kulkarni, P., Jetz, W. & Pyron, R. A. Phylogenetic and spatial distribution of evolutionary diversification, isolation, and threat in turtles and crocodilians (non-avian archosauromorphs). BMC Evol. Biol. 20, 1–16 (2020).Article 

Google Scholar 
48.Joyce, W. G., Parham, J. F., Lyson, T. R., Warnock, R. C. & Donoghue, P. C. A divergence dating analysis of turtles using fossil calibrations: An example of best practices. J. Paleontol. 87, 612–634 (2013).Article 

Google Scholar 
49.Shaffer, H. B., McCartney-Melstad, E., Near, T. J., Mount, G. G. & Spinks, P. Q. Phylogenomic analyses of 539 highly informative loci dates a fully resolved time tree for the major clades of living turtles (Testudines). Mol. Phylogenet. Evol. 115, 7–15 (2017).PubMed 
Article 

Google Scholar 
50.Beaulieu, J. M., O’Meara, B. C. & Donoghue, M. J. Identifying hidden rate changes in the evolution of a binary morphological character: The evolution of plant habit in campanulid angiosperms. Syst. Biol. 62, 725–737 (2013).PubMed 
Article 

Google Scholar 
51.Joyce, W. G. & Gauthier, J. A. Palaeoecology of Triassic stem turtles sheds new light on turtle origins. Proc. R. Soc. Lond. B 271, 1–5 (2004).Article 

Google Scholar 
52.Quagliata, S., Malentacchi, C., Delfino, C., Brunasso, A. M. & Delfino, G. Adaptive evolution of secretory cell lines in vertebrate skin. Caryologia 59, 187–206 (2006).Article 

Google Scholar 
53.Shi, P. & Zhang, J. Extraordinary diversity of chemosensory receptor gene repertoires among vertebrates. In Chemosensory Systems in Mammals, Fishes, and Insects (eds Meyerhof, W. & Korsching, S.) 1–23 (Springer, Berlin, 2009).
Google Scholar 
54.Swaney, W. T. & Keverne, E. B. The evolution of pheromonal communication. Behav. Brain Res. 200, 239–247 (2009).CAS 
PubMed 
Article 

Google Scholar 
55.Martín, J. & López, P. Effects of global warming on sensory ecology of rock lizards: Increased temperatures alter the efficacy of sexual chemical signals. Funct. Ecol. 27, 1332–1340 (2013).Article 

Google Scholar 
56.Ibáñez, A., López, P. & Martín, J. Discrimination of conspecifics’ chemicals may allow Spanish terrapins to find better partners and avoid competitors. Anim. Behav. 83, 1107–1113 (2012).Article 

Google Scholar 
57.Lewis, C. H., Molloy, S. F., Chambers, R. M. & Davenport, J. Response of common musk turtles (Sternotherus odoratus) to intraspecific chemical cues. J. Herpetol. 41, 349–353 (2007).Article 

Google Scholar 
58.Poschadel, J. R., Meyer-Lucht, Y. & Plath, M. Response to chemical cues from conspecifics reflects male mating preference for large females and avoidance of large competitors in the European pond turtle, Emys orbicularis. Behaviour 143, 569–587 (2006).Article 

Google Scholar 
59.Weaver, W. G. Courtship and combat behavior in Gopherus berlandieri. Bull. Fla. St. Mus. 15, 1–43 (1970).
Google Scholar 
60.Auffenberg, W. On the courtship of Gopherus polyphemus. Herpetologica 22, 113–117 (1966).
Google Scholar 
61.Augustine, L. & Haislip, N. Husbandry and reproduction of the Indochinese box turtle Cuora galbinifrons, Bourret’s box turtle Cuora bourreti and Southern Vietnam box turtle Cuora picturata in North America. Int. Zoo Yearb. 53, 238–249 (2019).Article 

Google Scholar 
62.Liu, Y.-X., Davy, C. M., Shi, H.-T. & Murphy, R. W. Sex in the half-shell: A review of the functions and evolution of courtship behavior in freshwater turtles. Chelon. Conserv. Biol. 12, 84–100 (2013).Article 

Google Scholar 
63.Schilde, M. Beobachtungen zum Fortpflanzungsverhalten von Sacalia bealei und Sacalia quadriocellata. Radiata 14, 30–32 (2005).
Google Scholar 
64.Fritz, U. Courtship behavior and systematics in the subtribe Nectemydina. 2. A comparison above the species level and remarks on the evolution of behaviour elements. Bull. Chicago Herpetol. Soc. 34, 225–236 (1999).
Google Scholar 
65.Martín, J. & López, P. Multimodal sexual signals in male ocellated lizards Lacerta lepida: Vitamin E in scent and green coloration may signal male quality in different sensory channels. Naturwissenschaften 97, 545–553 (2010).ADS 
PubMed 
Article 
CAS 

Google Scholar 
66.Rowe, C. Receiver psychology and the evolution of multicomponent signals. Anim. Behav. 58, 921–931 (1999).CAS 
PubMed 
Article 

Google Scholar 
67.Martins, E. P. et al. Evolving from static to dynamic signals: Evolutionary compensation between two communicative signals. Anim. Behav. 102, 223–229 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
68.Ferrara, C. R., Vogt, R. C. & Sousa-Lima, R. S. Turtle vocalizations as the first evidence of posthatching parental care in chelonians. J. Comp. Psychol. 127, 24 (2013).PubMed 
Article 

Google Scholar 
69.Bulté, G., Germain, R. R., O’Connor, C. M. & Blouin-Demers, G. Sexual dichromatism in the northern map turtle, Graptemys geographica. Chelon. Conserv. Biol. 12, 187–192 (2013).Article 

Google Scholar 
70.Ibáñez, A., Marzal, A., López, P. & Martín, J. Sexually dichromatic coloration reflects size and immunocompetence in female Spanish terrapins, Mauremys leprosa. Naturwissenschaften 100, 1137–1147 (2013).ADS 
PubMed 
Article 
CAS 

Google Scholar 
71.Rowe, J. W., Gradel, J. R., Bunce, C. F. & Clark, D. L. Sexual dimorphism in size and shell shape, and dichromatism of spotted turtles (Clemmys guttata) in southwestern Michigan. Amphibia-Reptilia 33, 443–450 (2013).Article 

Google Scholar 
72.Steffen, J. E., Learn, K. M., Drumheller, J. S., Boback, S. M. & McGraw, K. J. Carotenoid composition of colorful body stripes and patches in the painted turtle (Chrysemys picta) and red-eared slider (Trachemys scripta). Chelon. Conserv. Biol. 14, 56–63 (2015).Article 

Google Scholar 
73.Moll, E. O., Matson, K. E. & Krehbiel, E. B. Sexual and seasonal dichromatism in the Asian river turtle Callagur borneoensis. Herpetologica 37, 181–194 (1981).
Google Scholar 
74.Praschag, P. et al. A new subspecies of Batagur affinis (Cantor, 1847), one of the world’s most critically endangered chelonians (Testudines: Geoemydidae). Zootaxa 2233, 57–68 (2009).Article 

Google Scholar 
75.Praschag, P., Hundsdörfer, A. & Fritz, U. Phylogeny and taxonomy of endangered South and South-east Asian freshwater turtles elucidated by mtDNA sequence variation (Testudines: Geoemydidae: Batagur, Callagur, Hardella, Kachuga, Pangshura). Zool. Scr. 36, 429–442 (2007).Article 

Google Scholar 
76.Fritz, U. Courtship behavior and systematics in the subtribe Nectemydina. 1. The genus Trachemys, especially Trachemys scripta callirostris (Gray, 1855). Bull. Chicago Herpetol. Soc. 33, 225–236 (1998).
Google Scholar 
77.Ferrara, C. R., Vogt, R. C., Eisemberg, C. C. & Doody, J. S. First evidence of the pig-nosed turtle (Carettochelys insculpta) vocalizing underwater. Copeia 105, 29–32 (2017).Article 

Google Scholar 
78.Baeckens, S. & Whiting, M. J. Investment in chemical signalling glands facilitates the evolution of sociality in lizards. Proc. R. Soc. B 288, 20202438 (2021).PubMed 
Article 
PubMed Central 

Google Scholar 
79.Baeckens, S., García-Roa, R., Martín, J. & Van Damme, R. The role of diet in shaping the chemical signal design of lacertid lizards. J. Chem. Ecol. 43, 902–910 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
80.Kopena, R., López, P. & Martín, J. Relative contribution of dietary carotenoids and vitamin E to visual and chemical sexual signals of male Iberian green lizards: An experimental test. Behav. Ecol. Sociobiol. 68, 571–581 (2014).Article 

Google Scholar 
81.Kopena, R., Martín, J., López, P. & Herczeg, G. Vitamin E supplementation increases the attractiveness of males’ scent for female European green lizards. PLoS ONE 6, e19410 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
82.Martin, J., Ortega, J. & Lopez, P. Interpopulational variations in sexual chemical signals of Iberian wall lizards may allow maximizing signal efficiency under different climatic conditions. PLoS ONE 10, e0131492 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
83.Donihue, C. M. et al. Rapid and repeated divergence of animal chemical signals in an island introduction experiment. J. Anim. Ecol. 89, 1458–1467 (2020).PubMed 
Article 

Google Scholar 
84.Novelli, I. A. Estudo morfológico (anatômico e histológico) do sistema tegumentar de Hydromedusa maximiliani (Mikan, 1820) (Testudines, Chelidae) e Phrynops geoffroanus (Schweigger, 1812) (Testudines, Chelidae). Doctoral thesis, Universidade Federal Rural do Rio de Janeiro (2011).85.Crawford, N. G. et al. A phylogenomic analysis of turtles. Mol. Phylogenet. Evol. 83, 250–257 (2015).PubMed 
Article 

Google Scholar 
86.Bonin, F., Devaux, B. & Dupré, A. Turtles of the World (JHU Press, Baltimore, 2006).
Google Scholar 
87.Bour, R. Global diversity of turtles (Chelonii; Reptilia) in freshwater. Hydrobiologia 595, 593–598 (2008).Article 

Google Scholar 
88.Ernst, C. H. & Barbour, R. W. Turtles of the World (Smithsonian Institution Press, Washington DC, 1989).
Google Scholar 
89.Beaulieu, J. M., Oliver, J. C. & O’Meara, B. C. corHMM: Analysis of Binary Character Evolution, https://CRAN.R-project.org/package=corHMM (2017).90.Boyko, J. D. & Beaulieu, J. M. Generalized hidden Markov models for phylogenetic comparative datasets. Methods Ecol. Evol. 12, 468–478 (2021).Article 

Google Scholar 
91.Beaulieu, J. M. & Donoghue, M. J. Fruit evolution and diversification in campanulid angiosperms. Evolution 67, 3132–3144 (2013).PubMed 
Article 

Google Scholar 
92.Burnham, K. P. & Anderson, D. R. Multimodel inference: Understanding AIC and BIC in model selection. Sociol. Methods Res. 33, 261–304 (2004).MathSciNet 
Article 

Google Scholar 
93.Pagel, M. Detecting correlated evolution on phylogenies: A general method for the comparative analysis of discrete characters. Proc. R. Soc. Lond. B 255, 37–45 (1994).ADS 
Article 

Google Scholar 
94.Gray, K. M. & Steidl, R. J. A plant invasion affects condition but not density or population structure of a vulnerable reptile. Biol. Invasions 17, 1979–1988 (2015).Article 

Google Scholar 
95.Edwards, T. et al. The desert tortoise trichotomy: Mexico hosts a third, new sister-species of tortoise in the Gopherus morafkai—G. agassizii group. ZooKeys 562, 131–158 (2016).Article 

Google Scholar  More

1.Hobbs, P. R. & Gupta, R. K. Problems and challenges of no-till farming for the Rice–Wheat systems of the Indo-Gangetic Plains in South Asia. In Sustainable Agriculture and the International Rice–Wheat System (eds Lal, R. et al.) 101–119 (Ohio State University; Marcel Dekker Inc, 2004).
Google Scholar 
2.Saharawat, Y. S. et al. Evaluation of alternative tillage and crop establishment methods in a rice–wheat rotation in north-western IGP. Field Crops Res. 116, 260–267 (2010).Article 

Google Scholar 
3.Jat, R. K. et al. Seven years of conservation agriculture in a rice–wheat rotation of eastern Gangetic Plains of South Asia: Yield trends and economic profitability. Field Crops Res. 164, 199–210 (2014).Article 

Google Scholar 
4.Parihar, C. M. et al. Long-term conservation agriculture and intensified cropping systems: Effects on growth, yield, water, and energy-use efficiency of maize in north-western India. Pedosphere 28(6), 952–963 (2018).Article 

Google Scholar 
5.Shiferaw, B., Prasanna, B. M., Hellin, J. & Banziger, M. Crops that feed the world 6. Past successes and future challenges to the role played by maize in global food security. Food Secur. 3, 30–327 (2011).
Google Scholar 
6.Srinivasan, G., Zaidi, P. H., Prasanna, B. M., Gonzalez, F. & Lesnick, K. eds. Proceedings of the Eighth Asian Regional Maize Workshop: New Technologies for the New Millennium. Bangkok, Thailand, 5–8 August 2002. Mexico, D.F.: CIMMYT (2004).7.FAO. FAOSTAT database 2019. http://www.fao.org/faostat/en/#data/QC (Accessed 17 April 2021). FAO, Rome (2019).8.Aulakh, M. S. & Grant, C. A. Integrated Nutrient Management for Sustainable Crop Production (The Haworth Press, Taylor and Francis Group, 2008).Book 

Google Scholar 
9.Jat, S. L., Parihar, C. M., Singh, A. K., Jat, M. L. & Jat, R. K. Carbon sustainability and productivity of maize based cropping system under conservation agriculture practices in Indo-Gangetic plains. In: Resilient food systems for a changing world, Proceedings of the 5th World Congress of Conservation Agriculture Incorporating 3rd Farming Systems Design Conference, Brisbane Australia, 26-29 September, 2011, p. 110–111 (2011).10.Parihar, C. M. et al. Conservation agriculture in irrigated intensive maize-based systems of north-western India: Effects on crop yields, water productivity and economic profitability. Field Crops Res. 193, 104–116 (2016).Article 

Google Scholar 
11.Jat, M. L. et al. Crop residue management for sustainable production of maize (Zea mays) in dryland ecosystem. Chem. Sci. Rev. Lett. 6(23), 1681–1686 (2017).CAS 

Google Scholar 
12.Pooniya, V. et al. Six years of conservation agriculture and nutrient management in maize–mustard rotation: Impact on soil properties, system productivity and profitability. Field Crops Res. 260, 108002 (2021).Article 

Google Scholar 
13.Gathala, M. K. et al. Effect of tillage and crop establishment methods on physical properties of a medium-textured soil under a seven-year rice–wheat rotation. Soil Sci. Soc. Am. J. 75, 1851–1862 (2011).ADS 
CAS 
Article 

Google Scholar 
14.Ladha, J. K. et al. Integrating crop and resource management technologies for enhanced productivity, profitability and sustainability of the rice-wheat system in South Asia. p. 69–108. In J.K. Ladha et al. (ed.) Integrated crop and resource management in the rice-wheat system of South Asia. IRRI, Los Baños, Philippines (2009).15.Corsi, S. & Muminjanov, H. Conservation Agriculture: Training guide for extension agents and farmers in Eastern Europe and Central Asia. Rome – ISBN 978-92-5-131456-2 (FAO, 2019).16.Balwinder, S., Humphreys, E., Gaydon, D. S. & Yadav, S. Options for increasing the productivity of the rice–wheat system of north-west India while reducing groundwater depletion Part 2. Is conservation agriculture the answer?. Field Crops Res. 173, 81–94 (2015).Article 

Google Scholar 
17.Laik, R. et al. Integration of conservation agriculture with best management practices for improving system performance of the rice–wheat rotation in eastern Indo-Gangetic plains of India. Agric. Ecosyst. Environ. 195, 68–82 (2014).ADS 
Article 

Google Scholar 
18.Govaerts, B. et al. Conservation agriculture as a sustainable option for the central Mexican highlands. Soil Till. Res. 103, 222–230 (2009).Article 

Google Scholar 
19.Jat, M. L. et al. Double no-till and permanent raised beds in maize–wheat rotation of north western Indo-Gangetic plains of India: Effects on crop yields, water productivity, profitability and soil physical properties. Field Crop Res. 149, 291–299 (2013).Article 

Google Scholar 
20.Ludwig, B. et al. Effects of fertilization and soil management on crop yields and carbon stabilization in soils. A review. Agron. Sustain. Dev. 31, 361–372 (2011).Article 

Google Scholar 
21.Powlson, D. S., Stirling, C. M., Thierfelder, C., White, R. P. & Jat, M. L. Does conservation agriculture deliver climate change mitigation through soil carbon sequestration in tropical agro- ecosystems?. Agric. Ecosyst. Environ. 220, 164–174 (2016).CAS 
Article 

Google Scholar 
22.Kandeler, E. et al. Response of soil microbial biomass and enzyme activities to the transient elevation of carbon dioxide in semi-arid grassland. Soil Biol. Biochem. 38, 2448–2460 (2006).CAS 
Article 

Google Scholar 
23.Witt, C., Pasuquin, J. M., Pampolino, M.F., Buresh, R. J. & Dobermann, A. A manual for the development and participatory evaluation of site-specific nutrient management for maize in tropical, favorable environments. International Plant Nutrition Institute, Penang, Malaysia. http://seap.ipni.net (2009).24.Pampolino, M. et al. Development and evaluation of nutrient expert for wheat in South Asia (special issue): Nutrient management for wheat. Better Crops 96(3), 29–31 (2012).
Google Scholar 
25.Johnston, A. M. Nutrient expert-going global with improved fertilizer recommendations. Better Crops.Plant Food. 99(3), 20, (2015).26.Sapkota, T. B. et al. Precision nutrient management in conservation agriculture based wheat production system of north–west India: Profitability, nutrient use efficiency and environmental footprint. Field Crop Res. 155, 233–244 (2014).Article 

Google Scholar 
27.Pooniya, V. et al. ‘Nutrient expert’ assisted site-specific-nutrient-management: An alternative precision fertilization technology for maize–wheat cropping system in South-Asian Indo-Gangetic Plains. Indian J. Agric. Sci. 85(8), 996–1002 (2015).CAS 

Google Scholar 
28.Budhathoki, S. et al. Assessing growth, productivity and profitability of drought tolerant rice using nutrient expert—rice and other precision fertilizer management practices in Lamjung, Nepal. Acta. Sci. Agric. 2(8), 153–158 (2018).
Google Scholar 
29.Satyanarayana, T. et al. Economics of nitrogen fertilizer application in rice, wheat and maize grown in the Indo-Gangetic Plains. Indian J. of Fert. 8(8), 62–71 (2012).
Google Scholar 
30.Satyanarayana, T., Majumdar, K., Pampolino, M., Johnston, A. M. & Jat, M. L. Nutrient ExpertTM: A tool to optimize nutrient use and improve productivity of maize. Better Crops. South Asia 97(1), 21–24 (2013).
Google Scholar 
31.Anonymous. Annual Progress Report Kharif Maize. All India Coordinated Research Project on Maize. Eds. V. Mahajan et al. Indian Institute of Maize Research, PAU Campus, Ludhiana-141 004, India. pp. 1082 (2016).32.Abrol, I. P. & Sangar, S. Sustaining Indian agriculture-conservation agriculture the way forward. Curr. Sci. 91, 1020–2015 (2006).
Google Scholar 
33.Ghosh, P. K. et al. Conservation agriculture towards achieving food security in north-east India. Curr. Sci. 99, 915–921 (2010).
Google Scholar 
34.Parihar, C. M. et al. Soil quality and carbon sequestration under conservation agriculture with balanced nutrition in intensive cereal-based system. Soil Tillage Res. 202, 104653 (2020).Article 

Google Scholar 
35.Mahajan, G., Singh, K. & Gill, M. S. Scope for enhancing and sustaining rice productivity in Punjab (food bowl of India). Afr. J. Agric. Res. 7, 5611–5620 (2012).Article 

Google Scholar 
36.Jat, H. S. et al. Effects of tillage, crop establishment and diversification on soil organic carbon, aggregation, aggregate associated carbon and productivity in cereal systems of semi-arid Northwest India. Soil Till. Res. 190, 128–138 (2019).CAS 
Article 

Google Scholar 
37.Jat, H. S. et al. Assessing soil properties and nutrient availability under conservation agriculture practices in a reclaimed sodic soil in cereal-based systems of North-West India. Arch. Agron. Soil Sci. 64, 531–545 (2018).CAS 
Article 

Google Scholar 
38.Kaschuk, G., Alberton, O. & Hungria, M. Three decades of soil microbial biomass studies in Brazilian ecosystems: Lessons learned about soil quality and indications for improving sustainability. Soil Biol. Biochem. 42, 1–13 (2010).CAS 
Article 

Google Scholar 
39.Sarkar, D. et al. Can sustainability of maize–mustard cropping system be achieved through balanced nutrient management?. Field Crops Res. 225, 9–21 (2018).Article 

Google Scholar 
40.Pooniya, V., Palta, J. A., Chen, Y., Delhaize, E. & Siddique, K. H. M. Impact of the TaMATE1B gene on above and below-ground growth of durum wheat grown on an acid and Al3+-toxic soil. Plant Soil 447, 73–84 (2020).CAS 
Article 

Google Scholar 
41.Pasuquin, J. M. et al. Closing yield gaps in maize production in Southeast Asia through site-specific nutrient management. Field Crops Res. 156, 219–230 (2014).Article 

Google Scholar 
42.Choudhary, K. M. et al. Evaluating alternatives to rice–wheat system in western Indo-Gangetic Plains: Crop yields, water productivity and economic profitability. Field Crop Res. 218, 1–10 (2018).Article 

Google Scholar 
43.Jat, R. K. et al. Ten years of conservation agriculture in a rice-maize rotation of Eastern Gangetic Plains of India: Yield trends, water productivity and economic profitability. Field Crop Res. 232, 1–10 (2019).Article 

Google Scholar 
44.Jat, H. S. et al. Designing profitable, resource use efficient and environmentally sound cereal based systems for the Western Indo-Gangetic plains. Sci. Rep. 10(1), 1–16 (2020).Article 
CAS 

Google Scholar 
45.Thierfelder, C., Amezquita, E. & Stahr, K. Effects of intensifying organic manuring and tillage practices on penetration resistance and infiltration rate. Soil Till. Res. 82, 211–226 (2005).Article 

Google Scholar 
46.Bescansa, P., Imaz, M. J., Virto, I., Enrique, A. & Hoogmoed, W. B. Soil water retention as affected by tillage and residue management in semiarid Spain. Soil Till. Res. 87, 19–27 (2006).Article 

Google Scholar 
47.Lal, R. Sequestering carbon and increasing productivity by conservation agriculture. JSWC. 70, 55A-62A (2015).
Google Scholar 
48.Page, K. L., Dang, Y. P. & Dalal, R. C. The ability of conservation agriculture to conserve soil organic carbon and the subsequent impact on soil physical, chemical, and biological properties and yield. Front. Sustain. Food Syst. 4, 31 (2020).Article 

Google Scholar 
49.Six, J., Elliott, E. T. & Paustian, K. Soil macroaggregate turnover and microaggregate formation: A mechanism for C sequestration under no-tillage agriculture. Soil Bio. Biochem. 32, 2099–2103 (2000).CAS 
Article 

Google Scholar 
50.Tan, Z., Lal, R., Owens, L. & Izaurralde, R. C. Distribution of light and heavy fractions of soil organic carbon as related to land use and tillage practice. Soil Till. Res. 92, 53–59 (2007).Article 

Google Scholar 
51.Blanco-Canqui, H. & Ruis, S. J. No-tillage and soil physical environment. Geoderma 326, 164–200 (2018).ADS 
Article 

Google Scholar 
52.Somasundaram, J. et al. Conservation agriculture effects on soil properties and crop productivity in a semiarid region of India. Soil Res. 57, 187–199 (2019).Article 

Google Scholar 
53.Hansen, N. C., Allen, B. L., Baumhardt, R. L. & Lyon, D. J. Research achievements and adoption of no-till, dryland cropping in the semi-arid US Great Plains. Field Crops Res. 132, 196–203 (2012).Article 

Google Scholar 
54.Dalal, R. C., Wang, W., Allen, D. E., Reeves, S. & Menzies, N. W. Soil nitrogen and nitrogen-use efficiency under long-term no-till practice. Soil Sci. Society Am. J. 75, 2251–2261 (2011).ADS 
CAS 
Article 

Google Scholar 
55.Zhang, H. L., Lal, R., Zhao, X., Xue, J. F. & Chen, F. Opportunities and challenges of soil carbon sequestration by conservation agriculture in China. Adv. Agron. 124, 1–36 (2014).CAS 
Article 

Google Scholar 
56.Mandal, A., Patra, A. K., Singh, D., Swarup, A. & Masto, R. E. Effect of long-term application of manure and fertilizer on biological and biochemical activities in soil during crop development stages. Biores. Tech. 98, 3585–3592 (2007).CAS 
Article 

Google Scholar 
57.Singh, G., Kumar, D., Marwaha, T. S., Singh, A. K. & Srinivasmurthy, K. Conservation tillage and integrated nitrogen management stimulates soil microbial properties under varying water regimes in maize–wheat cropping system in northern India. Arch. Agro. Soil Sci. 57(5), 507–521 (2011).Article 

Google Scholar 
58.Manning, D. A. C. & Renforth, P. Passive sequestration of atmospheric CO2 through coupled plant mineral reactions in urban soils. Environ. Sci. Technol. 47, 135–141 (2013).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
59.Mullen, M., Melhorn, C., Tyler, C. & Duck, D. Biological and biochemical soil properties in no–till corn with different cover crops. J. Soil Water Conserv. 5, 219–224 (1998).
Google Scholar 
60.Pooniya, V. & Shivay, Y. S. Summer green-manuring crops and zinc fertilization on productivity and economics of basmati rice (Oryza sativa L.). Arch. Agron. Soil. Sci. 58(6), 593–616 (2012).Article 

Google Scholar 
61.Pooniya, et al. Influence of summer legume residue–recycling and varietal diversification on productivity, energetics and nutrient dynamics in basmati rice–wheat cropping system of western Indo-Gangetic Plains. J. Plant Nutr. 41(12), 1491–1506 (2018).CAS 
Article 

Google Scholar 
62.Pooniya, V., Shivay, Y. S., Rana, A., Nain, L. & Prasanna, R. Enhancing soil nutrient dynamics and productivity of Basmati rice through residue incorporation and zinc fertilization. Eur. J. Agron. 41, 28–37 (2012).CAS 
Article 

Google Scholar 
63.Walkley, A. J. & Black, I. A. An examination of the Degtjareff method for determination of soil organic matter and a proposed modification of the chromic acid titration method. Soil Sci. 37, 29–38 (1934).ADS 
CAS 
Article 

Google Scholar 
64.Subbiah, B. V. & Asija, G. L. A rapid procedure for estimation of the available nitrogen in soil. Curr. Sci. 25, 259–260 (1956).CAS 

Google Scholar 
65.Olsen, S. R., Cole, C. V., Watanabe, F. S. & Dean, L. Estimation of Available Phosphorus in Soil by Extraction with Sodium Carbonate (USDA, 1954).
Google Scholar 
66.Hanway, J. J. & Heidel, H. Soil Analysis Methods as used in Iowa State College Soil Testing Laboratory, Bulletin 57 (Iowa State College of Agriculture, 1952).
Google Scholar 
67.Babu, S. et al. Impact of land configuration and organic nutrient management on productivity, quality and soil properties under baby corn in Eastern Himalayas. Sci. Rep. 10(1), 1–14 (2020).Article 
CAS 

Google Scholar 
68.Dey, A. et al. Effect of conservation agriculture on soil organic and inorganic carbon sequestration and lability: A study from a rice–wheat cropping system on a calcareous soil of the eastern Indo-Gangetic Plains. Soil Use Manage. 36, 429–438 (2018).Article 

Google Scholar 
69.Ellert, B. H. & Bettany, J. R. Calculation of organic matter and nutrients stored in soils under contrasting management regimes. Can. J. Soil Sci. 75, 529–538 (1995).CAS 
Article 

Google Scholar 
70.Vance, E. D., Brookes, P. C. & Jenkinson, D. S. An extraction method for measuring soil microbial biomass carbon. Soil Biol. Biochem. 19, 703–704 (1987).CAS 
Article 

Google Scholar 
71.Bhushan, L. et al. Saving of water and labor in a rice–wheat system with no-tillage and direct seeding technologies. Agron. J. 99, 1288–1296 (2007).Article 

Google Scholar 
72.Singh, R. P., Das, S. K., Rao, U. M. B. & Reddy, N. Towards Sustainable Dryland Agricultural Practices (CRIDA, 1990).
Google Scholar 
73.Wanjari, R. H., Singh, M. V. & Ghosh, P. K. Sustainable yield index: An approach to evaluate the sustainability of long-term intensive cropping systems in India. J. Sustain. Agric. 24(4), 39–56 (2004).Article 

Google Scholar 
74.Gomez, K. A. & Gomez, A. A. Statistical Procedures for Agricultural Research 2nd edn, 180–209 (Wiley, 1984).
Google Scholar  More