Philippa Kaur

1.Moran NA, McCutcheon JP, Nakabachi A. Genomics and evolution of heritable bacterial symbionts. Annu Rev Genet. 2008;42:165–90.CAS 
Article 

Google Scholar 
2.Bourguignon T, Lo N, Dietrich C, Roisin Y, Brune A, Evans TA, et al. Rampant host switching shaped the termite gut microbiome. Curr Biol. 2018;28:649–54.CAS 
Article 

Google Scholar 
3.Matsuura Y, Moriyama M, Łukasik P, Vanderpool D, Tanahashi M, Meng X. Recurrent symbiont recruitment from fungal parasites in cicadas. Proc Natl Acad Sci USA. 2018;115:E5970–9.CAS 
Article 

Google Scholar 
4.Chong RA, Moran NA. Evolutionary loss and replacement of Buchnera, the obligate endosymbiont of aphids. ISME J. 2018;12:898–908.CAS 
Article 

Google Scholar 
5.Bennett GM, Moran NA. Heritable symbiosis: the advantages and perils of an evolutionary rabbit hole. Proc Natl Acad Sci USA. 2015;112:10169–76.CAS 
Article 

Google Scholar 
6.Sudakaran S, Kost C, Kaltenpoth M. Symbiont acquisition and replacement as a source of ecological innovation. Trends Microbiol. 2017;25:1–16.Article 

Google Scholar 
7.Martinson VG, Danforth BN, Minckley RL, Rueppell O, Tingek S, Moran NA. A simple and distinctive microbiota associated with honey bees and bumble bees. Mol Ecol. 2011;20:619–28.Article 

Google Scholar 
8.Kwong WK, Medina LA, Koch H, Sing KW, Soh EJY, Ascher JS, et al. Dynamic microbiome evolution in social bees. Sci Adv. 2017;3:1–17.Article 

Google Scholar 
9.Kwong WK, Moran NA. Gut microbial communities of social bees. Nat Rev Microbiol. 2016;14:374–84.CAS 
Article 

Google Scholar 
10.Rothman JA, Leger L, Graystock P, Russell K, McFrederick QS. The bumble bee microbiome increases survival of bees exposed to selenate toxicity. Environ Microbiol. 2019;21:3417–29.CAS 
Article 

Google Scholar 
11.Koch H, Schmid-Hempel P. Socially transmitted gut microbiota protect bumble bees against an intestinal parasite. Proc Natl Acad Sci USA. 2011;108:19288–92.CAS 
Article 

Google Scholar 
12.Zheng H, Powell JE, Steele MI, Dietrich C, Moran NA. Honeybee gut microbiota promotes host weight gain via bacterial metabolism and hormonal signaling. Proc Natl Acad Sci USA. 2017;114:4775–80.CAS 
Article 

Google Scholar 
13.Mockler BK, Kwong WK, Moran NA, Koch H. Microbiome structure influences infection by the parasite Crithidia bombi in bumble bees. Appl Environ Microbiol. 2018;84:1–11.CAS 
Article 

Google Scholar 
14.Giannini TCG, Boff S, Cordeiro GD, Cartonalo EA Jr, Veiga AK, Imperatriz-Fonseca VL, et al. Crop pollinators in Brazil: a review of reported interactions. Apidologie. 2015;46:209–23.Article 

Google Scholar 
15.Koch H, Abrol DP, Li J, Schmid-Hempel P. Diversity and evolutionary patterns of bacterial gut associates of corbiculate bees. Mol Ecol. 2013;22:2028–44.CAS 
Article 

Google Scholar 
16.Leonhardt SD, Kaltenpoth M. Microbial communities of three sympatric Australian stingless bee species. PLoS One. 2014;9:1–6.Article 

Google Scholar 
17.Díaz S, de Souza Urbano S, Caesar L, Blochtein B, Sattler A, Zuge V, et al. Report on the microbiota of Melipona quadrifasciata affected by a recurrent disease. J Invertebr Pathol. 2017;143:35–39.Article 

Google Scholar 
18.Teixeira ACP, Marini MM, Nicoli JR, Antonini Y, Martins RP, Lachance M-A, et al. Starmerella meliponinorum sp. nov., a novel ascomycetous yeast species associated with stingless bees. Int J Syst Evol Microbiol. 2003;53:339–43.Article 

Google Scholar 
19.Paludo CR, Menezes C, Silva-Junior EA, Vollet-Neto A, Andrade-Dominguez A, Pishchany G, et al. Stingless bee larvae require fungal steroid to pupate. Sci Rep. 2018;8:1–10.CAS 
Article 

Google Scholar 
20.Ramírez SR, Nieh JC, Quental TB, Roubik DW, Imperatriz-Fonseca VL, Pierce NE. A molecular phylogeny of the stingless bee genus Melipona (Hymenoptera: Apidae). Mol Phylogenet Evol. 2010;56:519–25.Article 

Google Scholar  More

AffiliationsEnergy and Resources Group, UC Berkeley and Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USALara M. KueppersAuthorsLara M. KueppersCorresponding authorCorrespondence to
Lara M. Kueppers. More

This Article is being retracted by the authors as the result of a coding error, correction of which undermines the main conclusions of the study. This was an inadvertent error related to the use of the ‘use=pairwise.complete.obs’ option in the function ‘cor.test’. This function was used to estimate the correlation matrix between all tree-ring series. We had assumed the option pairwise.complete.obs would fully exclude tree-ring series with incomplete records for each time window. Unfortunately, ‘not available’ (NA) values were excluded only on a pairwise base between tree-ring series within each time window. This resulted in shorter time series being retained and inconsistent time windows in recent years and, consequently, a greater chance of higher correlation coefficients. When we excluded all incomplete tree-ring series for each time window in subsequent analyses, as was our original intention, the recent increase in synchrony originally reported in this Article (Figs. 2,3) is, unfortunately, mostly an artefact of this coding error. Because our sensitivity analyses all used the same correlation functions and option, we did not detect this error until S. Klesse, R. Brienen and R. Peters brought it to our attention. In fact, the consistent response in all sensitivity analyses reinforced our original interpretation. The sub-sampling sensitivity analysis (Supplementary Fig. 5b) remains unaffected by this coding error, since samples were selected to maintain a constant sample size and exclude all NAs. However, the increasing synchrony trend in this analysis is of much smaller magnitude and spatial scale than the originally reported trend, and thus would require examination on its own. Because the main conclusion of this paper is now unsupported, all authors agree to this retraction. We thank S. Klesse, R. Brienen and R. Peters for quickly detecting and informing us of this error. More

The above developed intuition is converted into an agent-based evolutionary model in the context of public goods provision. In the proposed evolutionary agent-based model42, all the agents play a linear public goods game by using conditional cooperative strategies, enforcing altruistic punishments based on relative differences in their cooperation tendencies, and imitating successful role models’ social behavior with certain errors. The process is iterated several thousands of generations.Population typeIn the proposed model, the individuals or agents in the population behave like conditional cooperators and the population is heterogeneous in its conditional nature. The agents who are more willing to cooperate are also more willing punish to potential free riders. Each individual is born with an arbitrary conditional cooperative criterion (CCC) and a propensity, β. Both are positive values. The agents with higher CCC donate less frequently than the agents with a higher CCC for the given same amount of past cooperation levels. The same agents can cooperate or enforce altruistic punishments or free ride given the past cooperation levels in the population. β indicates the propensity to implement a conditional cooperative decision and imitate the successful role model’s social behavior. Each agent’s CCC value is drawn from a uniform distribution (0, N), where N is the population size, and β is drawn from a uniform distribution (0, 3). With β = 0 the actions of the individuals are random and with β = 3 the individuals behave like ideal conditional cooperators. With intermediate values the individuals behave like non-ideal conditional cooperators. The consideration is equal to the natural selection designing the conditional cooperative strategies. The combinations of CCC and β create heterogeneous populations with varieties of propensities. The consideration is close to the conditional nature of the population observed in experimental settings12,36.Conditional cooperative decisionThe conditional cooperative decision of the agent is operationalized in the following way43,44. For instance, in the rth round, an agent i (with CCC = CCCi value) donates to the public good with probability, qd,$${q}_{d}= frac{1}{1+mathrm{exp}(-left({n}_{C}-{CCC}_{i}right){upbeta}_{i})}$$
(1)
nC indicates the number of donations in the (r − 1)th round. The parameter βi controls the steepness of the probability function. For the higher βi, the agent is highly sensitive to the (nC-CCCi). For instance, as βi → ∞, the qd is sensitive to the sign of the (nC -CCCi), i.e., if (nC -CCCi)  > 0 then qd = 1 and if (nC -CCCi)  1 and βi  > 2 or with (CCCj-CCCi)  > 2 and βi  > 1, the agent i punishes the agent j with high probability. With (CCCj-CCCi) × βi  2 punishes a higher CCC agent more accurately than a slightly lower CCC agent. A lower CCC agent with β  2 agent i punishes the agent j with a high probability close to one and βi  More

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