Ecology

Distribution, frequency, and functional implications of mutations during laboratory evolution of obligate syntrophy
We evaluated whether the selection of mutations in the same genes (i.e., “parallel evolution” [17]) had contributed to improvements in syntrophic growth of Dv and Mm across independent evolution lines, all of which started with the same ancestral clone of each organism. The goal of this analysis was to focus on generalized strategies for adaptation to syntrophy, irrespective of the culturing condition so we investigated parallelism across both U and H lines. Based on the number of mutations (normalized to gene length and genome size) in Dv and Mm across 13 evolved lines (six lines designated U for “uniform” conditions with continuous shaking and seven H lines for “heterogenous” conditions without shaking), we calculated a G-score [18] (“goodness-of-fit”, see “Methods” section [18]) to assess if the observed parallel evolution rate was higher than expected by chance. The “observed G-score” was calculated as the sum of G-scores for all genes in the genome of each organism; mean and standard deviation of “expected G-scores” were calculated through 1000 simulations of randomizing locations of observed numbers of mutations across the genome of each organism. The observed total G-score for Dv (1092.617) and Mm (805.02) was significantly larger than the expected mean G-score (Dv: 798.19 ± 14.99, Z = 19.63 and Mm: 564.83 ± 15.95, Z = 15.06), demonstrating significant parallel evolution across lines.
With the exception of five high G-score genes (DVU0597, DVU1862, DVU0436, DVU0013, and DVU2394), which were mutated during long term salt adaptation of Dv [19], mutations in other high G-score genes appeared to be putatively specific to syntrophic interactions. Altogether, 24 genes in Dv and 16 genes in Mm associated with core processes had accumulated function modulating mutations across at least 2 or more evolution lines (Fig. 2 and Supplementary Table S1). Signal transduction and regulatory gene mutations (seven in Dv and six in Mm) represented 19.9% and 27.2% of all mutations in Dv and Mm, respectively, similar to long term laboratory evolution of E. coli [18], potentially because their influence on the functions of many genes [20, 21]. We also observed missense and nonsense mutations in outer membrane and transport functions (four genes in Dv and three genes in Mm). For example, the highest G-score gene in Dv, DVU0799—an abundant outer membrane porin for the uptake of sulfate and other solutes in low-sulfate conditions [22], was mutated early across all lines, with at least two missense mutations in UE3 (S223Y) and UA3 (T242P). Notably, the regulator of the archaellum operon (MMP1718) had the highest G-score with frameshift (11 lines) and nonsynonymous coding (2 lines) mutations [23]. Similarly, two motility-associated genes of Dv (DVU1862 and DVU3227) also accumulated frameshift, nonsense and nonsynonymous mutations across 4 H and 3 U lines. Together, these observations were consistent with other laboratory evolution experiments performed in liquid media [24], suggesting that retaining motility has a fitness cost during syntrophy [25, 26].
Fig. 2: Frequency and location of high G-score mutations in Dv (A) and Mm (B) across 13 independent evolution lines.

SnpEff predicted impact of mutations* are indicated as moderate (orange circles) or high (red circles) with the frequency of mutations indicated by node size. Expected number of mutations for each gene was calculated based on the gene length and the total number of mutations in a given evolution line. Genes with parallel changes were ranked by calculating a G (goodness of fit) score between observed and expected values and indicated inside each panel. Mutations for each gene are plotted along their genomic coordinates (vertical axes) across 13 evolution lines (horizontal axes). Total number of mutations for a given gene is shown as horizontal bar plots. [*HIGH impact mutations: gain or loss of start and stop codons and frameshift mutations; MODERATE impact mutations: codon deletion, nonsynonymous in coding sequence, change or insertion of codon; low impact mutations: synonymous coding and nonsynonymous start codon].

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Consistent with our previous observation that obligate mutual interdependence drove the erosion of metabolic independence of Dv [5, 27], mutations in SR genes were among the top contributors to the total G-score in Dv (DVU2776 (74.7), DVU1295 (46.5), DVU0846 (42.9), and DVU0847 (22.3)). However, it was intriguing that DVU2776 (DsrC), which catalyzes the conversion of sulfite to sulfide, the final step in SR, accumulated function modulating but not loss-of-function mutations across six lines. The functional impact of these mutations is not clear but it is possible that these changes might alter previously suggested alternative roles for this gene, including electron confurcation for the oxidation of lactate [28], sulfite reduction, 2-thiouridine biosynthesis and possibly gene regulation [29].
Analysis of temporal appearance and combinations of mutations across evolution lines
Growth characteristics of all evolution lines improved by the 300th generation [4], and in some lines even before the appearance of SR− mutations, indicating that mutations in other genes had also contributed to improvements in syntrophy. Each evolution line had at least 8 and up to 13 out of 24 high G-score mutations in Dv, while Mm had mutations in at least 5 and up to 10 out of 16 high G-score genes. We interrogated the temporal order in which high G-score mutations were selected and the combinations in which they co-existed in each evolution line to uncover evidence for epistatic interactions in improving obligate syntrophy. Indeed, missense mutations in DsrC (DVU2776) were fixed simultaneously with the appearance of loss of function mutations in one of two sigma 54 type regulators (DVU2894, DVU2394) in lines HA2, and UR1 (P = 5.40 × 10−5). In rare instances, we also observed that some high G-score mutations co-occurred across evolution lines, e.g., two U- and one H-line consistently showed for at least two time points a mutation in DVU1283 (GalU) coexisting with mutations in DVU2394 (P = 5.04 × 10−3). More commonly, the combinations of high G-score gene mutations varied across multiple lines. In fact, no two lines possessed identical combination of high G-score gene mutations (Fig. 3A, B), and many high-frequency mutations were uniquely present or absent in different lines (Fig. 3C, D).
Fig. 3: Frequency and time of appearance of mutations through 1 K generations of laboratory evolution lines of Dv and Mm cocultures.

The heat maps display frequency of mutations in genes (rows) in Dv (A) and Mm (B) in each evolution line, ordered from early to later generations (horizontal axis). High G-score genes are shown in red font and their G-score rank is shown to the left in gray shaded box, also in red font. Bar plots above heat maps indicate total number of mutations in each generation and the color indicates impact of mutation. Use “Frequency”, “Generations”, and “Mutation impact” key below the heat maps for interpretation. Mutations that were unique to each evolution line is shown in (C, D) for Dv and Mm, respectively. E The heatmap illustrates a selective sweep across both organisms in line HS3.

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Mutations in high G-score genes appeared consistently in all evolution lines (P 80% EPD-03 vs, More

Chemistry
General
Racemic mixture of cis/trans (35%:65%) isomers of phytol (1) (PYT) (97% purity), N-bromosuccinimide (NBS, 99% purity) and N-chlorosuccinimide (NCS, 98% purity) were purchased from Sigma-Aldrich Chemical Co. (St. Louis, MO, USA), while trimethylortoacetate was purchased from Fluka. Analytical grade acetic acid, sodium hydrogen carbonate, acetone, hexane, diethyl ether, tetrahydrofuran (THF), anhydrous magnesium sulfate, sodium chloride were purchased from Chempur (Poland).
Analytical Thin Layer Chromatography (TLC) was carried out on silica gel coated aluminium plates (DC-Alufolien Kieselgel 60 F254, Merck, Darmstadt, Germany) with a mixture of hexane, acetone and diethyl ether in various ratios as the developing systems. Compounds were visualized by spraying the plates with solution of 1% Ce(SO4)2 and 2% H3[P(Mo3O10)4] (2 g) in 10% H2SO4, followed by heating to 120–200 °C.
The products of chemical synthesis were purified by column chromatography on silica gel (Kieselgel 60, 230–400 mesh ASTM, 40–63 μm, Merck) using a mixture of hexane, acetone, and diethyl ether (in various ratios) as eluents.
Gas chromatography (GC) analysis was carried out on an Agilent Technologies 6890 N Network GC instrument (Santa Clara, CA, USA) equipped with autosampler, split injection (20:1) and FID detector using a DB-5HT column (Agilent, Santa Clara, USA) (polyimide-coated fused silica tubing, 30 m × 0.25 mm × 0.1 µm) with hydrogen as the carrier gas. Products of the chemical reactions were analysed using the following temperature programme: injector 250 °C, detector (FID) 250 °C, initial column temperature: 100 °C, 100–300 °C (rate 30 °C/min), final column temperature 300 °C (hold 2 min).
Nuclear magnetic resonance spectra 1H NMR, 13C NMR, DEPT 135, HSQC, 1H–1H COSY and NOESY were recorded in CDCl3 solutions with signals of residual solvent (δH = 7.26 δC = 77) on a Brüker Avance II 600 MHz (Brüker, Rheinstetten, Germany) spectrometer.
High-resolution mass spectra (HRMS) were recorded using electron spray ionization (ESI) technique on spectrometer Waters ESI-Q-TOF Premier XE (Waters Corp., Milford, MA, USA).
General procedure for the synthesis of compounds (2–7)
The preparation of ester 2 and acid 3 has been illustrated in detail in our previous work39, and so the synthesis method would not be listed here.
To a solution of acid 3 (7.8 mmol) in THF (30 mL) the N-bromosuccinimide (7.8 mmol) or N-chlorosuccinimide (7.8 mmol) was added. The mixture was stirred at room temperature for 48–96 h. When the substrate reacted completely (TLC, GC) the mixture was diluted with diethyl ether and washed with saturated NaHCO3 solution and brine. Organic layer of ether extract was separated and dired over anhydrous magnesium sulfate and evaporated on a rotary evaporator. New δ-halogeno-γ-lactones (4–7) were separated by silica gel column using for elution hexane/diethyl eter in gradient system. Bromo- and chlorolactonization afforded products with the following physical and spectral data presented below:
trans-5-Bromomethyl-4-methyl-4-(4′,8′,12′-trimethyltridecyl)dihydrofuran-2-one ( 4 )
(25% yield); 1H NMR (600 MHz, CDCl3): δ 0.85 (four t, J = 6.4 Hz, 12H, CH3-4′, CH3-8′, (CH3)2–12′), 1.05–1.55 (m, 21H, CH2-1′, CH2-2′, CH2-3′, CH2-5′, CH2-6′, CH2-7′, CH2-9′, CH2-10′, CH2-11′, H-4′, H-8′, H-12′), 1.24 (s, 3H, CH3-4), 2.30 and 2.60 (two d, J = 17.2 Hz, 2H, CH2-3), 3.47 (dd, J = 11.3, 7.3 Hz, 1H, one of CH2-Br), 3.55 (dd, J = 11.3, 4.5 Hz, 1H, one of CH2-Br), 4.39 (dd, J = 7.2, 4.5 Hz, 1H, H-5); 13C NMR (150 MHz, CDCl3): δ 19.61, 19.69 (CH3)2–12′), 22.65, 22.75 (CH3-4′, CH3-8′), 24.50 (CH3-4), 29.21 (CH2-Br), 41.49 (CH2-3), 42.86 (C-4), 22.00, 24.47, 24.82, 34.08, 37.28, 37.41, 37.61, 37.70, 39.38 (CH2-1′, CH2-2′, CH2-3′, CH2-5′, CH2-6′, CH2-7′, CH2-9′, CH2-10′, CH2-11′), 28.00, 32.69, 32.80 (H-4′, H-8′, H-12′), 87.66 (H-5), 174.92 (C-2); HRMS (ESI): m/z calcd. for C22H41BrO2 [M + Na]+ 439.2188; found 439.2182.
cis-5-Bromomethyl-4-methyl-4-(4′,8′,12′-trimethyltridecyl)dihydrofuran-2-one ( 5 )
(46% yield); 1H NMR (600 MHz, CDCl3): δ 0.85 (four t, J = 6.4 Hz, 12H, CH3-4′, CH3-8′, (CH3)2–12′), 1.04–1.55 (m, 21H, CH2-1′, CH2-2′, CH2-3′, CH2-5′, CH2-6′, CH2-7′, CH2-9′, CH2-10′, CH2-11′, H-4′, H-8′, H-12′), 1.08 (s, 3H, CH3-4), 2.38 and 2.49 (two d, J = 17.2 Hz, 2H, CH2-3), 3.48 (m, 2H, one CH2-Br), 4.41 (dd, J = 7.5, 4.5 Hz, 1H, H-5); 13C NMR (150 MHz, CDCl3): δ 18.96 (CH3-4), 19.69, 19.76 (CH3)2–12′), 22.65, 22.75 (CH3-4′, CH3-8′), 29.17 (CH2-Br), 42.70 (CH2-3), 43.09 (C-4), 22.20, 24.46, 24.82, 37.28, 37.39, 37.41, 37.49, 39.38, 39.96 (CH2-1′, CH2-2′, CH2-3′, CH2-5′, CH2-6′, CH2-7′, CH2-9′, CH2-10′, CH2-11′), 28.00, 30.95, 32.72 (H-4′, H-8′, H-12′), 86.46 (H-5), 174.76 (C-2); HRMS (ESI): m/z calcd. for C22H41BrO2 [M + Na]+ 439.2188; found 439.2183.
trans-5-Chloromethyl-4-methyl-4-(4′,8′,12′-trimethyltridecyl)dihydrofuran-2-one ( 6 )
(21% yield); 1H NMR (600 MHz, CDCl3): δ 0.84 (four t, J = 6.4 Hz, 12H, CH3-4′, CH3-8′, (CH3)2–12′), 1.03–1.54 (m, 21H, CH2-1′, CH2-2′, CH2-3′, CH2-5′, CH2-6′, CH2-7′, CH2-9′, CH2-10′, CH2-11′, H-4′, H-8′, H-12′), 1.23 (s, 3H, CH3-4), 2.23 and 2.60 (two d, J = 17.2 Hz, 2H, CH2-3), 3.67 (dd, J = 12.1, 6.1 Hz, 1H, one of CH2-Cl), 3.73 (dd, J = 12.1, 4.6 Hz, 1H, one of CH2-Cl), 4.33 (dd, J = 7.2, 4.6 Hz, 1H, H-5); 13C NMR (150 MHz, CDCl3): δ 19.61, 19.67 (CH3)2–12′), 22.65, 22.75 (CH3-4′, CH3-8′), 24.67 (CH3-4), 42.30 (CH2-Cl), 41.48 (CH2-3), 42.38 (C-4), 22.09, 24.46, 24.82, 34.21, 37.29, 37.39, 37.61, 37.70, 39.38 (CH2-1′, CH2-2′, CH2-3′, CH2-5′, CH2-6′, CH2-7′, CH2-9′, CH2-10′, CH2-11′), 28.00, 32.70, 32.80 (H-4′, H-8′, H-12′), 87.45 (H-5), 175.21 (C-2); HRMS (ESI): m/z calcd. for C22H41ClO2 [M + Na]+ 395.2693; found 395.2698.
cis-5-chloromethyl-4-methyl-4-(4′,8′,12′-trimethyltridecyl)dihydrofuran-2-one ( 7 )
(39% yield); 1H NMR (600 MHz, CDCl3): δ 0.85 (four t, J = 6.6 Hz, 12H, CH3-4′, CH3-8′, (CH3)2–12′), 1.04–1.56 (m, 21H, CH2-1′, CH2-2′, CH2-3′, CH2-5′, CH2-6′, CH2-7′, CH2-9′, CH2-10′, CH2-11′, H-4′, H-8′, H-12′), 1.06 (s, 3H, CH3-4), 2.38 and 2.47 (two d, J = 17.2 Hz, 2H, CH2-3), 3.67 (m, 2H, one CH2-Cl), 4.35 (dd, J = 6.4, 4.9 Hz, 1H, H-5); 13C NMR (150 MHz, CDCl3): δ 19.06 (CH3-4), 19.69, 19.76 (CH3)2–12′), 22.65, 22.74 (CH3-4′, CH3-8′), 42.52 (CH2-Cl), 42.39 (CH2-3), 42.54 (C-4), 22.14, 24.46, 24.83, 37.28, 37.37, 37.41, 37.49, 39.38, 40.16 (CH2-1′, CH2-2′, CH2-3′, CH2-5′, CH2-6′, CH2-7′, CH2-9′, CH2-10′, CH2-11′), 28.00, 32.64, 32.80 (H-4′, H-8′, H-12′), 86.24 (H-5), 175.04 (C-2); HRMS (ESI): m/z calcd. for C22H41ClO2 [M + Na]+ 395.2693; found 395.2697.
Deterrent activity of phytol and its derivatives
Aphids, plants and compound application
The peach potato aphids Myzus persicae (Sulzer) and the Chinese cabbage Brassica rapa subsp. pekinensis (Lour.) Hanelt were reared in laboratory at 20 °C, 65% r.h., and L16:8D photoperiod. One to seven days old apterous females of M. persicae and 3-week old plants with 4–5 fully developed leaves were used for experiments. M. persicae were obtained from the laboratory culture maintained at the Department of Botany and Ecology for many generations since 2000. All experiments were carried out under the same conditions of temperature, relative humidity, and photoperiod. The bioassays were started at 10–11.a.m. Each compound was dissolved in 70% ethanol to obtain the recommended 0.1% solution40. All compounds were applied on the adaxial and abaxial leaf surfaces by immersing a leaf in the ethanolic solution of a given compound for 30 s.20. Control leaves of similar size were immersed in 70% ethanol that was used as a solvent for the studied compounds. Experiments were performed 1 h after the compounds application to allow the evaporation of the solvent. Every plant and aphid were used only once.
Aphid settling (choice test)
This bioassay allows the study of aphid host preferences under semi-natural conditions41. In the present study, aphids were given free choice between control and treated excised leaves that were placed in a Petri dish. Aphids were placed in the dish equidistance from treated and untreated leaves, so that aphids could choose between treated (on one half of a Petri dish) and control leaves (on the other half of the dish). Aphids that settled, i.e. they did not move, and the position of their antennae indicated feeding, on each leaf were counted at 1 h, 2 h, and 24 h intervals after access to the leaf. Each experiment was replicated 8 times (n = 8 replicates, 20 viviparous apterous females/replicate). Aphids that were moving or not on any of the leaves were not counted.
Behavioral responses of aphids Myzus persicae during probing and feeding (no-choice test)
Aphid probing and the phloem sap uptake by M. persicae was monitored using the technique of electronic registration of aphid probing in plant tissues, known as EPG (= Electrical Penetration Graph), that is frequently employed in insect–plant relationship studies considering insects with sucking-piercing mouthparts42,43,44. In this experimental set-up, aphid and plant are connected to electrodes and thus made parts of an electric circuit, which is completed when the aphid inserts its stylets into the plant. Weak voltage is supplied in the circuit, and all changing electric properties are recorded as EPG waveforms that can be correlated with aphid activities and stylet position in plant tissues45,46. The parameters describing aphid behaviour during probing and feeding, such as total time of probing, proportion of phloem patterns E1 and E2, number of probes, etc., are good indicators of plant suitability or interference of probing by chemical or physical factors in individual plant tissues44,45,46. In the present study, aphids were attached to a golden wire electrode with conductive silver paint (epgsystems. eu) and starved for 1 h prior to the experiment. Probing behaviour of 12 apterous females/studied compound and control was monitored for 8 h continuously with Giga-4 and Giga-8 DC EPG with 1 GΩ of input resistance recording equipment (EPG Systems, Wageningen, The Netherlands). Each aphid was given access to a freshly prepared plant and each aphid/plant combination was used only once. Various behavioural phases were labelled manually using the Stylet + software (www.epgsystems.eu). The following aphid behaviours were distinguished: no penetration (waveform ‘np’ – aphid stylets outside the plant), pathway phase—penetration of non-phloem tissues (waveforms ‘ABC’), phloem phase (salivation into sieve elements, waveform ‘E1’ and ingestion of phloem sap, waveform ‘E2’), and xylem phase (ingestion of xylem sap, waveform ‘G’). Waveform ‘G’ occurred rarely irrespective of the treatment. Therefore, in all calculations the xylem phase was added to the pathway phase and termed as probing in non-phloem tissues. The E1/E2 transition patterns were included in E2. Waveform patterns that were not terminated before the end of the experimental period (8 h) were not excluded from the calculations. The parameters derived from EPG recordings were analyzed according to their frequency and duration in configuration related to activities in peripheral and vascular tissues.
Statistical analysis
The data of the choice-test were analyzed using Student’s t-test (STATISTICA 13.1. package). If aphids showed clear preference for the leaf treated with the tested compound (p  More

1.
Leroux, S. J. & Loreau, M. Subsidy hypothesis and strength of trophic cascades across ecosystems. Ecol. Lett. 11, 1147–1156 (2008).
PubMed  Article  PubMed Central  Google Scholar 
2.
Moore, J. C. et al. Detritus, trophic dynamics and biodiversity. Ecol. Lett. 7, 584–600 (2004).
ADS  Article  Google Scholar 

3.
Nowlin, W. H., Vanni, M. J. & Yang, L. H. Comparing resource pulses in aquatic and terrestrial ecosystems. Ecology 89, 647–659 (2008).
PubMed  Article  PubMed Central  Google Scholar 

4.
Wilson, E. E. & Wolkovich, E. M. Scavenging: how carnivores and carrion structure communities. Trends Ecol. Evol. 26, 129–135 (2011).
PubMed  Article  PubMed Central  Google Scholar 

5.
Margalida, A., Donázar, J. A., Carrete, M. & Sánchez-Zapata, J. A. Sanitary versus environmental policies: fitting together two pieces of the puzzle of European vulture conservation. J. Appl. Ecol. 47, 931–935 (2010).
Article  Google Scholar 

6.
Margalida, A., Colomer, M. À. & Oro, D. Man-induced activities modify demographic parameters in a long-lived species: effects of poisoning and health policies. Ecol. Appl. 24, 436–444 (2014).
PubMed  Article  PubMed Central  Google Scholar 

7.
Moreno-Opo, R. & Margalida, A. Carcasses provide resources not exclusively to scavengers: patterns of carrion exploitation by passerine birds. Ecosphere 4, art105 (2013).

8.
DeVault, T. L., Rhodes, O. E. Jr. & Shivik, J. A. Scavenging by vertebrates: behavioral, ecological, and evolutionary perspectives on an important energy transfer pathway in terrestrial ecosystems. Oikos 102, 225–234 (2003).
Article  Google Scholar 

9.
Barton, P. S., Cunningham, S. A., Lindenmayer, D. B. & Manning, A. D. The role of carrion in maintaining biodiversity and ecological processes in terrestrial ecosystems. Oecologia 171, 761–772 (2013).
ADS  PubMed  Article  PubMed Central  Google Scholar 

10.
Bump, J. K. et al. Ungulate carcasses perforate ecological filters and create biogeochemical hotspots in forest herbaceous layers allowing trees a competitive advantage. Ecosystems 12, 996–1007 (2009).
Article  Google Scholar 

11.
Danell, K., Berteaux, D. & Bråthen, K. A. Effect of muskox carcasses on nitrogen concentration in tundra vegetation. Arctic 55, 389–392 (2002).
Article  Google Scholar 

12.
Klink, R., Laar-Wiersma, J., Vorst, O. & Smit, C. Rewilding with large herbivores: positive direct and delayed effects of carrion on plant and arthropod communities. PLoS ONE 15, e0226946 (2020).
PubMed  PubMed Central  Article  CAS  Google Scholar 

13.
Turner, W. C. et al. Fatal attraction: vegetation responses to nutrient inputs attract herbivores to infectious anthrax carcass sites. Proc. R. Soc. Lond. B Biol. Sci. 281, e20141785 (2014).

14.
Mateo-Tomás, P. et al. From regional to global patterns in vertebrate scavenger communities subsidized by big game hunting. Divers. Distrib. 21, 913–924 (2015).
Article  Google Scholar 

15.
Markandya, A. et al. Counting the cost of vulture decline—an appraisal of the human health and other benefits of vultures in India. Ecol. Econ. 67, 194–204 (2008).
Article  Google Scholar 

16.
Selva, N., Jędrzejewska, B., Jędrzejewski, W. & Wajrak, A. Factors affecting carcass use by a guild of scavengers in European temperate woodland. Can. J. Zool. 83, 1590–1601 (2005).
Article  Google Scholar 

17.
DeVault, T. L., Brisbin, J., Lehr, I., Rhodes, J. & Olin, E. Factors influencing the acquisition of rodent carrion by vertebrate scavengers and decomposers. Can. J. Zool. 82, 502–509 (2004).
Article  Google Scholar 

18.
Arrondo, E. et al. Rewilding traditional grazing areas affects scavenger assemblages and carcass consumption patterns. Basic Appl. Ecol. 41, 56–66 (2019).
Article  Google Scholar 

19.
Morales-Reyes, Z. et al. Scavenging efficiency and red fox abundance in Mediterranean mountains with and without vultures. Acta Oecologica 79, 81–88 (2017).
ADS  Article  Google Scholar 

20.
Ruzicka, R. E. & Conover, M. R. Does weather or site characteristics influence the ability of scavengers to locate food? Ethology 118, 187–196 (2012).
Article  Google Scholar 

21.
Moleón, M., Sánchez-Zapata, J., Sebastián-González, E. & Owen-Smith, N. Carcass size shapes the structure and functioning of an African scavenging assemblage. Oikos 124, 1391–1403 (2015).

22.
Cornwell, W. K. et al. Plant species traits are the predominant control on litter decomposition rates within biomes worldwide. Ecol. Lett. 11, 1065–1071 (2008).
PubMed  Article  PubMed Central  Google Scholar 

23.
Ogada, D. L., Torchin, M. E., Kinnaird, M. F. & Ezenwa, V. O. Effects of vulture declines on facultative scavengers and potential implications for mammalian disease transmission. Conserv. Biol. 26, 453–460 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 

24.
Sekercioglu, Ç. H., Wenny, D. G. & Whelan, C. J. Why Birds Matter: Avian Ecological Function and Ecosystem Services (University of Chicago Press, 2016).

25.
Pereira, L. M., Owen-Smith, N. & Moleón, M. Facultative predation and scavenging by mammalian carnivores: seasonal, regional and intra-guild comparisons. Mammal Rev. 44, 44–55 (2014).
Article  Google Scholar 

26.
Selva, N. & Fortuna, M. A. The nested structure of a scavenger community. Proc. R. Soc. B Biol. Sci. 274, 1101–1108 (2007).
Article  Google Scholar 

27.
Wolf, C. & Ripple, W. J. Range contractions of the world’s large carnivores. R. Soc. Open Sci. 4, 170052 (2017).
ADS  PubMed  PubMed Central  Article  Google Scholar 

28.
Grimm, N. B. et al. The impacts of climate change on ecosystem structure and function. Front. Ecol. Environ. 11, 474–482 (2013).
Article  Google Scholar 

29.
Lauenroth, W. et al. Potential effects of climate change on the temperate zones of North and South America. Rev. Chil. Hist. Nat. 77, 439–453 (2004).
Article  Google Scholar 

30.
Shanley, C. S. et al. Climate change implications in the northern coastal temperate rainforest of North America. Clim. Change 130, 155–170 (2015).
ADS  CAS  Article  Google Scholar 

31.
Wilmers, C. C. & Getz, W. M. Gray wolves as climate change buffers in yellowstone. PLOS Biol. 3, e92 (2005).
PubMed  PubMed Central  Article  CAS  Google Scholar 

32.
Sebastián-González, E. et al. Network structure of vertebrate scavenger assemblages at the global scale: drivers and ecosystem functioning implications. Ecography 43, 1143–1155 (2020).
Article  Google Scholar 

33.
Pardo-Barquín, E., Mateo-Tomás, P. & Olea, P. P. Habitat characteristics from local to landscape scales combine to shape vertebrate scavenging communities. Basic Appl. Ecol. 34, 126–139 (2019).
Article  Google Scholar 

34.
Sebastián-González, E. et al. Scavenging in the Anthropocene: human impact drives vertebrate scavenger species richness at a global scale. Glob. Change Biol. 25, 3005–3017 (2019).
ADS  Article  Google Scholar 

35.
Turner, K. L., Abernethy, E. F., Conner, L. M., Rhodes, O. E. & Beasley, J. C. Abiotic and biotic factors modulate carrion fate and vertebrate scavenging communities. Ecology 98, 2413–2424 (2017).
PubMed  Article  PubMed Central  Google Scholar 

36.
Janßen, F., Treude, T. & Witte, U. Scavenger assemblages under differing trophic conditions: a case study in the deep Arabian Sea. Deep Sea Res. Part II Top. Stud. Oceanogr. 47, 2999–3026 (2000).
ADS  Article  Google Scholar 

37.
Houston, D. C. To the vultures belong the spoils. Nat. Hist. 103, 34–41 (1994).
Google Scholar 

38.
Houston, D. C. Scavenging efficiency of turkey vultures in tropical forest. The Condor 88, 318–323 (1986).
Article  Google Scholar 

39.
Sauer, J. et al. The North American breeding bird survey, results and analysis 1966–2015. (2017).

40.
Hill, J. E., DeVault, T. L., Beasley, J. C., Rhodes, O. E. & Belant, J. L. Effects of vulture exclusion on carrion consumption by facultative scavengers. Ecol. Evol. 8, 2518–2526 (2018).
PubMed  PubMed Central  Article  Google Scholar 

41.
Heinrich, B. Winter foraging at carcasses by three sympatric corvids, with emphasis on recruitment by the raven, Corvus corax. Behav. Ecol. Sociobiol. 23, 141–156 (1988).
Article  Google Scholar 

42.
Bellan, S. E., Turnbull, P. C. B., Beyer, W. & Getz, W. M. Effects of experimental exclusion of scavengers from carcasses of anthrax-infected herbivores on bacillus anthracis sporulation, survival, and distribution. Appl. Environ. Microbiol. 79, 3756–3761 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

43.
The IUCN Red List of Threatened Species. IUCN Red List of Threatened Species https://www.iucnredlist.org/en.

44.
Kiff, L. F. The current status of North American vultures. In Raptors at Risk 175–189 (World Working Group on Birds of Prey/Hancock House, 2000).

45.
Prasad, A. M., Iverson, L. R., Peters, M. P. & Matthews, S. N. Climate change tree atlas (Northern Research Station, US Forest Service, Delaware, OH, 2014).
Google Scholar 

46.
Kiff, L. The current status of North American vultures. in 175–189 (2000).

47.
Houston, D. C. Competition for food between Neotropical vultures in forest. Ibis 130, 402–417 (1988).
Article  Google Scholar 

48.
Gomez, L. G., Houston, D. C., Cotton, P. & Tye, A. The role of greater yellow-headed vultures Cathartes melambrotus as scavengers in neotropical forest. Ibis 136, 193–196 (1994).
Article  Google Scholar 

49.
Ripple, W. J. et al. Status and ecological effects of the world’s largest carnivores. Science 343, e1241484 (2014).

50.
Tomberlin, J. K., Barton, B. T., Lashley, M. A. & Jordan, H. R. Mass mortality events and the role of necrophagous invertebrates. Curr. Opin. Insect Sci. 23, 7–12 (2017).
PubMed  Article  PubMed Central  Google Scholar 

51.
Fey, S. B. et al. Recent shifts in the occurrence, cause, and magnitude of animal mass mortality events. Proc. Natl. Acad. Sci. 112, 1083–1088 (2015).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

52.
Wikenros, C., Sand, H., Ahlqvist, P. & Liberg, O. Biomass flow and scavengers use of carcasses after re-colonization of an apex predator. PLoS ONE 8, e77373 (2013).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

53.
Kočárek, P. Decomposition and Coleoptera succession on exposed carrion of small mammal in Opava, the Czech Republic. Eur. J. Soil Biol. 39, 31–45 (2003).
Article  Google Scholar 

54.
Matuszewski, S., Bajerlein, D., Konwerski, S. & Szpila, K. Insect succession and carrion decomposition in selected forests of Central Europe. Part 1: pattern and rate of decomposition. Forensic Sci. Int. 194, 85–93 (2010).
PubMed  Article  PubMed Central  Google Scholar 

55.
Reed, H. B. A study of dog carcass communities in tennessee, with special reference to the insects. Am. Midl. Nat. 59, 213–245 (1958).
Article  Google Scholar 

56.
Bauer, J. W., Logan, K. A., Sweanor, L. L. & Boyce, W. M. Scavenging behavior in Puma. Southwest. Nat. 50, 466–471 (2005).
Article  Google Scholar 

57.
Burkepile, D. E. et al. Chemically mediated competition between microbes and animals: microbes as consumers in food webs. Ecology 87, 2821–2831 (2006).
PubMed  Article  PubMed Central  Google Scholar 

58.
Janzen, D. H. Why fruits rot, seeds mold, and meat spoils. Am. Nat. 111, 691–713 (1977).
CAS  Article  Google Scholar 

59.
DeVault, T. L. & Rhodes, O. E. Identification of vertebrate scavengers of small mammal carcasses in a forested landscape. Acta Theriol. (Warsz.) 47, 185–192 (2002).
Article  Google Scholar 

60.
Parker, K. L., Robbins, C. T. & Hanley, T. A. Energy expenditures for locomotion by Mule Deer and Elk. J. Wildl. Manag. 48, 474–488 (1984).
Article  Google Scholar 

61.
Crête, M. & Larivière, S. Estimating the costs of locomotion in snow for coyotes. Can. J. Zool. 81, 1808–1814 (2003).
Article  Google Scholar 

62.
Droghini, A. & Boutin, S. The calm during the storm: snowfall events decrease the movement rates of grey wolves (Canis lupus). PLoS ONE 13, e0205742 (2018).

63.
Green, G. I., Mattson, D. J. & Peek, J. M. Spring feeding on ungulate carcasses by grizzly bears in Yellowstone National Park. J. Wildl. Manag. 61, 1040–1055 (1997).
Article  Google Scholar 

64.
De Jong, G. D. & Chadwick, J. W. Decomposition and arthropod succession on exposed rabbit carrion during summer at high altitudes in colorado, USA. J. Med. Entomol. 36, 833–845 (1999).
PubMed  Article  PubMed Central  Google Scholar 

65.
Sun, S.-J. et al. Climate-mediated cooperation promotes niche expansion in burying beetles. Elife 3, e02440 (2014).
PubMed  PubMed Central  Article  Google Scholar 

66.
Krofel, M. Monitoring of facultative avian scavengers on large mammal carcasses in Dinaric forest of Slovenia. Acrocephalus 32, 45–51 (2011).
Article  Google Scholar 

67.
DeVault, T. L., Seamans, T. W., Linnell, K. E., Sparks, D. W. & Beasley, J. C. Scavenger removal of bird carcasses at simulated wind turbines: Does carcass type matter?. Ecosphere 8, e01994 (2017).
Article  Google Scholar 

68.
Turner, K. L., Conner, L. M. & Beasley, J. C. Effect of mammalian mesopredator exclusion on vertebrate scavenging communities. Sci. Rep. 10, 2644 (2020).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

69.
Abernethy, E. F., Turner, K. L., Beasley, J. C. & Rhodes, O. E. Scavenging along an ecological interface: utilization of amphibian and reptile carcasses around isolated wetlands. Ecosphere 8, e01989 (2017).
Article  Google Scholar 

70.
Olson, Z. H., Beasley, J. C. & Rhodes, O. E. Carcass type affects local scavenger guilds more than habitat connectivity. PLoS ONE 11, (2016).

71.
Ragg, J., Mackintosh, C. & Moller, H. The scavenging behaviour of ferrets (Mustela furo), feral cats (Felis domesticus), possums (Trichosurus vulpecula), hedgehogs (Erinaceus europaeus) and harrier hawks (Circus approximans) on pastoral farmland in New Zealand: Implications for bovine tuberculosis transmission. N. Z. Vet. J. 48, 166–175 (2001).
Article  Google Scholar 

72.
Laundré, J. W., Hernández, L. & Altendorf, K. B. Wolves, elk, and bison: reestablishing the” landscape of fear” in Yellowstone National Park, USA. Can. J. Zool. 79, 1401–1409 (2001).
Article  Google Scholar 

73.
Ripple, W. J. & Beschta, R. L. Linking wolves to willows via risk-sensitive foraging by ungulates in the northern Yellowstone ecosystem. For. Ecol. Manag. 230, 96–106 (2006).
Article  Google Scholar 

74.
Ripple, W. J. & Beschta, R. L. Trophic cascades in Yellowstone: the first 15 years after wolf reintroduction. Biol. Conserv. 145, 205–213 (2012).
Article  Google Scholar 

75.
Smith, D. W., Peterson, R. O. & Houston, D. B. Yellowstone after wolves. Bioscience 53, 330–340 (2003).
Article  Google Scholar 

76.
White, P. J. & Garrott, R. A. Northern Yellowstone elk after wolf restoration. Wildl. Soc. Bull. 33, 942–955 (2005).
Article  Google Scholar 

77.
Clark, P. J. & Evans, F. C. Distance to nearest neighbor as a measure of spatial relationships in populations. Ecology 35, 445–453 (1954).
Article  Google Scholar 

78.
Cook, R. C., Cook, J. G. & Irwin, L. L. Estimating elk body mass using chest-girth circumference. Wildl. Soc. Bull. 1973-2006 31, 536–543 (2003).
Google Scholar 

79.
Craine, J. M., Towne, E. G. & Elmore, A. Intra-annual bison body mass trajectories in a tallgrass prairie. Mammal Res. 60, 263–270 (2015).
Article  Google Scholar 

80.
Lott, D. F. & Galland, J. C. Body mass as a factor influencing dominance status in American Bison Cows. J. Mammal. 68, 683–685 (1987).
Article  Google Scholar 

81.
Fox, J. & Weisberg, S. An R Companion to Applied Regression. (Sage Publications, 2018).

82.
R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2020).

83.
Pan, Y. & Jackson, R. T. Ethnic difference in the relationship between acute inflammation and serum ferritin in US adult males. Epidemiol. Infect. 136, 421–431 (2008).
CAS  PubMed  Article  PubMed Central  Google Scholar 

84.
Brewer, M. J., Butler, A. & Cooksley, S. L. The relative performance of AIC, AICC and BIC in the presence of unobserved heterogeneity. Methods Ecol. Evol. 679, 692. https://doi.org/10.1111/2041-210X.12541 (2016).
Article  Google Scholar 

85.
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 

86.
Franklin, J. Mapping Species Distributions: Spatial Inference and Prediction (Cambridge University Press, Cambridge, 2010).
Google Scholar 

87.
Kleiber, C. & Zeileis, A. Applied Econometrics with R (Springer, Berlin, 2008).
Google Scholar 

88.
Cameron, A. C. & Trivedi, P. K. Regression-based tests for overdispersion in the Poisson model. J. Econom. 46, 347–364 (1990).
MathSciNet  Article  Google Scholar  More

1.
Fischer, H. et al. Palaeoclimate constraints on the impact of 2 °C anthropogenic warming and beyond. Nat. Geosci. 11, 474–485 (2018).
ADS  CAS  Article  Google Scholar 
2.
Diffenbaugh, N. S., Singh, D. & Mankin, J. S. Unprecedented climate events: historical changes, aspirational targets, and national commitments. Sci. Adv. 4, eaao3354 (2018).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

3.
Kim, J.-S. et al. Reduced North American terrestrial primary productivity linked to anomalous Arctic warming. Nat. Geosci. 10, 572–576 (2017).
ADS  CAS  Article  Google Scholar 

4.
United Nations Framework Convention on Climate Change (UNFCCC). Adoption of the Paris Agreement (2015).

5.
Intergovernmental Panel on Climate Change (IPCC). Fifth Assessment Report: Climate Change (AR5) (2014).

6.
Nabuurs, G. J. et al. Forestry. In Climate Change 2007: Mitigation (Cambridge University Press, 2007).

7.
Smyth, C. E. et al. Quantifying the biophysical climate change mitigation potential of Canada’s forest sector. Biogeosciences 11, 3515–3529 (2014).
ADS  Article  Google Scholar 

8.
Xu, Z., Smyth, C. E., Lemprière, T. C., Rampley, G. J. & Kurz, W. A. Climate change mitigation strategies in the forest sector: biophysical impacts and economic implications in British Columbia. Can. Mitig. Adapt. Strateg. Glob. Change 23, 257–290 (2018).
Article  Google Scholar 

9.
Bastin, J.-F. et al. The global tree restoration potential. Science 365, 76–79 (2019).
ADS  CAS  PubMed  Article  Google Scholar 

10.
Peterson St-Laurent, G., Hagerman, S., Kozak, R. & Hoberg, G. Public perceptions about climate change mitigation in British Columbia’s forest sector. PLoS ONE 13, e0195999 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

11.
Ma, Z. et al. Regional drought-induced reduction in the biomass carbon sink of Canada’s boreal forests. Proc. Natl Acad. Sci. USA 109, 2423–2427 (2012).
ADS  CAS  PubMed  Article  Google Scholar 

12.
Charney, N. D. et al. Observed forest sensitivity to climate implies large changes in 21st century North American forest growth. Ecol. Lett. 19, 1119–1128 (2016).
PubMed  Article  Google Scholar 

13.
Girardin, M. P. et al. No growth stimulation of Canada’s boreal forest under half-century of combined warming and CO2 fertilization. Proc. Natl Acad. Sci. USA 113, E8406–E8414 (2016).
CAS  PubMed  Article  Google Scholar 

14.
Marchand, W. et al. Untangling methodological and scale considerations in growth and productivity trend estimates of Canada’s forests. Environ. Res. Lett. 13, 093001 (2018).
ADS  Article  Google Scholar 

15.
Browne, L., Wright, J. W., Fitz-Gibbon, S., Gugger, P. F. & Sork, V. L. Adaptational lag to temperature in valley oak (Quercus lobata) can be mitigated by genome-informed assisted gene flow. Proc. Natl Acad. Sci. USA. 116, 25179–25185 (2019).
CAS  PubMed  Article  Google Scholar 

16.
Sally, N. A. & Whitlock, M. C. Assisted gene flow to facilitate local adaptation to climate change. Annu Rev. Ecol. Evol. Syst. 44, 367–388 (2013).
Article  Google Scholar 

17.
Lemprière, T. C. et al. Canadian boreal forests and climate change mitigation. Environ. Rev. 21, 293–321 (2013).
Article  Google Scholar 

18.
Winder, R., Nelson, E. & Beardmore, T. Ecological implications for assisted migration in Canadian forests. For. Chron. 87, 731–744 (2011).
Article  Google Scholar 

19.
Teskey, R. et al. Responses of tree species to heat waves and extreme heat events. Plant Cell Environ. 38, 1699–1712 (2015).
PubMed  Article  Google Scholar 

20.
Isaac-Renton, M. et al. Northern forest tree populations are physiologically maladapted to drought. Nat. Commun. 9, 5254 (2018).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

21.
Field, E., Schönrogge, K., Barsoum, N., Hector, A. & Gibbs, M. Individual tree traits shape insect and disease damage on oak in a climate‐matching tree diversity experiment. Ecol. Evol. 9, 8524–8540 (2019).
PubMed  PubMed Central  Article  Google Scholar 

22.
Depardieu, C. et al. Adaptive genetic variation to drought in a widely distributed conifer suggests a potential for increasing forest resilience in a drying climate. N. Phytol. 227, 427–439 (2020).
CAS  Article  Google Scholar 

23.
Montwé, D., Isaac-Renton, M., Hamann, A. & Spiecker, H. Cold adaptation recorded in tree rings highlights risks associated with climate change and assisted migration. Nat. Commun. 9, 1574 (2018).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

24.
Girardin, M. P. et al. Negative impacts of high temperatures on growth of black spruce forests intensify with the anticipated climate warming. Glob. Change Biol. 22, 627–643 (2016).
ADS  Article  Google Scholar 

25.
Hember, R. A., Kurz, W. A. & Coops, N. C. Increasing net ecosystem biomass production of Canada’s boreal and temperate forests despite decline in dry climates. Glob. Biogeochem. Cycles 31, 134–158 (2017).
CAS  Article  Google Scholar 

26.
Boucher, D. et al. Current and projected cumulative impacts of fire, drought, and insects on timber volumes across Canada. Ecol. Appl. 28, 1245–1259 (2018).
PubMed  Article  Google Scholar 

27.
Klein, R. J. T., Schipper, E. L. F. & Dessai, S. Integrating mitigation and adaptation into climate and development policy: three research questions. Environ. Sci. Policy 8, 579–588 (2005).
Article  Google Scholar 

28.
Zamudio, K. R., Bell, R. C. & Mason, N. A. Phenotypes in phylogeography: species’ traits, environmental variation, and vertebrate diversification. Proc. Natl Acad. Sci. USA 113, 8041–8048 (2016).
CAS  PubMed  Article  Google Scholar 

29.
Massatti, R. et al. Population history provides foundational knowledge for utilizing and developing native plant restoration materials. Evol. Appl. 11, 2025–2039 (2018).
PubMed  PubMed Central  Article  Google Scholar 

30.
Sork, V. L. et al. Putting the landscape into the genomics of trees: approaches for understanding local adaptation and population responses to changing climate. Tree Genet. Genomes 9, 901–911 (2013).
Article  Google Scholar 

31.
Morgenstern, E. K. & Mullin, T. J. Growth and survival of black spruce in the range-wide provenance study. Can. J. Res. 20, 130–143 (1990).
Article  Google Scholar 

32.
Rehfeldt, G. E., Wykoff, W. R. & Ying, C. C. Physiologic plasticity, evolution, and impacts of a changing climate on Pinus contorta. Clim. Change 50, 355–376 (2001).
Article  Google Scholar 

33.
Morgenstern, E. K. Range-wide genetic variation of black spruce. Can. J. Res. 8, 463–473 (1978).
Article  Google Scholar 

34.
Thomson, A. M., Riddell, C. L. & Parker, W. H. Boreal forest provenance tests used to predict optimal growth and response to climate change: 2. Black spruce. Can. J. Res. 39, 143–153 (2009).
Article  Google Scholar 

35.
Pedlar, J. H. & McKenney, D. W. Assessing the anticipated growth response of northern conifer populations to a warming climate. Sci. Rep. 7, 43881 (2017).
ADS  PubMed  PubMed Central  Article  Google Scholar 

36.
Mahony, C. R. et al. Evaluating genomic data for management of local adaptation in a changing climate: a lodgepole pine case study. Evol. Appl. 13, 116–131 (2020).
PubMed  Article  Google Scholar 

37.
Housset, J. M. et al. Tree rings provide a new class of phenotypes for genetic associations that foster insights into adaptation of conifers to climate change. N. Phytol. 218, 630–645 (2018).
Article  Google Scholar 

38.
Heer, K. et al. Linking dendroecology and association genetics in natural populations: stress responses archived in tree rings associate with SNP genotypes in silver fir (Abies alba Mill.). Mol. Ecol. 27, 1428–1438 (2018).
CAS  PubMed  Article  Google Scholar 

39.
Bouriaud, O., Teodosiu, M., Kirdyanov, A. V. & Wirth, C. Influence of wood density in tree-ring-based annual productivity assessments and its errors in Norway spruce. Biogeosciences 12, 6205–6217 (2015).
ADS  CAS  Article  Google Scholar 

40.
Babst, F. et al. When tree rings go global: challenges and opportunities for retro- and prospective insight. Quat. Sci. Rev. 197, 1–20 (2018).
ADS  Article  Google Scholar 

41.
Jaramillo-Correa, J. P., Beaulieu, J. & Bousquet, J. Variation in mitochondrial DNA reveals multiple distant glacial refugia in black spruce (Picea mariana), a transcontinental North American conifer. Mol. Ecol. 13, 2735–2747 (2004).
CAS  PubMed  Article  Google Scholar 

42.
Gérardi, S., Jaramillo-Correa, J. P., Beaulieu, J. & Bousquet, J. From glacial refugia to modern populations: new assemblages of organelle genomes generated by differential cytoplasmic gene flow in transcontinental black spruce: assemblages of organelle genomes. Mol. Ecol. 19, 5265–5280 (2010).
PubMed  Article  CAS  Google Scholar 

43.
Rehfeldt, G. E., Leites, L. P., Joyce, D. G. & Weiskittel, A. R. Role of population genetics in guiding ecological responses to climate. Glob. Change Biol. 24, 858–868 (2018).
ADS  Article  Google Scholar 

44.
Beaulieu, J., Corriveau, A. & Daoust, G. Phenotypic Stability and Delineation of Black Spruce Breeding Zones in Quebec. Vol. LAU-X-85E (Forestry Canada, Quebec Region, Sainte-Foy, Quebec, 1989).

45.
Perrin, M., Rossi, S. & Isabel, N. Synchronisms between bud and cambium phenology in black spruce: early-flushing provenances exhibit early xylem formation. Tree Physiol. 37, 593–603 (2017).
PubMed  Article  Google Scholar 

46.
Sniderhan, A. E., McNickle, G. G. & Baltzer, J. L. Assessing local adaptation vs. plasticity under different resource conditions in seedlings of a dominant boreal tree species. AoB Plants 10, ply004 (2018).

47.
Newton, P. F. Systematic review of yield responses of four North American conifers to forest tree improvement practices. Ecol. Manag. 172, 29–51 (2003).
Article  Google Scholar 

48.
Marchand, W. et al. Strong overestimation of water‐use efficiency responses to rising CO2 in tree‐ring studies. Glob. Change Biol. https://doi.org/10.1111/gcb.15166 (2020).

49.
Metsaranta, J. M. Long-term tree-ring derived carbon dynamics of an experimental plantation in relation to species and density in Northwestern Ontario. Can. Ecol. Manag. 441, 229–241 (2019).
Article  Google Scholar 

50.
Büntgen, U. et al. Limited capacity of tree growth to mitigate the global greenhouse effect under predicted warming. Nat. Commun. 10, 2171 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

51.
DOrangeville, L. et al. Northeastern North America as a potential refugium for boreal forests in a warming climate. Science 352, 1452–1455 (2016).
ADS  CAS  Article  Google Scholar 

52.
Babst, F. et al. Twentieth century redistribution in climatic drivers of global tree growth. Sci. Adv. 5, eaat4313 (2019).
ADS  PubMed  PubMed Central  Article  Google Scholar 

53.
Rossi, S. Bud break responds more strongly to daytime than night‐time temperature under asymmetric experimental warming. Glob. Change Biol. 9 (2016).

54.
Frechette, E., Ensminger, I., Bergeron, Y., Gessler, A. & Berninger, F. Will changes in root-zone temperature in boreal spring affect recovery of photosynthesis in Picea mariana and Populus tremuloides in a future climate? Tree Physiol. 31, 1204–1216 (2011).
CAS  PubMed  Article  Google Scholar 

55.
Verbyla, D. Remote sensing of interannual boreal forest NDVI in relation to climatic conditions in interior Alaska. Environ. Res. Lett. 10, 125016 (2015).
ADS  Article  Google Scholar 

56.
Trujillo, E. Elevation-dependent influence of snow accumulation on forest greening. Nat. Geosci. 5, 5 (2012).
Article  CAS  Google Scholar 

57.
Vaganov, E. A., Hughes, M. K., Kirdyanov, A. V., Schweingruber, F. H. & Silkin, P. P. Influence of snowfall and melt timing on tree growth in subarctic Eurasia. Nature 400, 149–151 (1999).
ADS  CAS  Article  Google Scholar 

58.
Ols, C., Girardin, M. P., Hofgaard, A., Bergeron, Y. & Drobyshev, I. Monitoring climate sensitivity shifts in tree-rings of eastern boreal North America using model-data comparison: shifts in tree growth sensivity to climate. Ecosystems 21, 1042–1057 (2018).
CAS  Article  Google Scholar 

59.
Prunier, J., Gérardi, S., Laroche, J., Beaulieu, J. & Bousquet, J. Parallel and lineage-specific molecular adaptation to climate in boreal black spruce. Mol. Ecol. 21, 4270–4286 (2012).
CAS  PubMed  Article  Google Scholar 

60.
Capblancq, T. et al. Climate-associated genetic variation in Fagus sylvatica and potential responses to climate change in the French Alps. J. Evol. Biol. https://doi.org/10.1111/jeb.13610 (2020).

61.
Liepe, K. J., Hamann, A., Smets, P., Fitzpatrick, C. R. & Aitken, S. N. Adaptation of lodgepole pine and interior spruce to climate: implications for reforestation in a warming world. Evol. Appl. 9, 409–419 (2016).
PubMed  PubMed Central  Article  Google Scholar 

62.
Fitzpatrick, M. C. & Keller, S. R. Ecological genomics meets community-level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation. Ecol. Lett. 18, 1–16 (2015).
PubMed  Article  Google Scholar 

63.
Wegrzyn, J. L. et al. Cyberinfrastructure and resources to enable an integrative approach to studying forest trees. Evol. Appl. 13, 228–241 (2020).
PubMed  Article  Google Scholar 

64.
Kurz, W. A. et al. Carbon in Canada’s boreal forest —a synthesis. Environ. Rev. 21, 260–292 (2013).
CAS  Article  Google Scholar 

65.
Alberto, F. J. et al. Potential for evolutionary responses to climate change—evidence from tree populations. Glob. Change Biol. 19, 1645–1661 (2013).
ADS  Article  Google Scholar 

66.
Splawinski, T. B., Cyr, D., Gauthier, S., Jetté, J.-P. & Bergeron, Y. Analyzing risk of regeneration failure in the managed boreal forest of northwestern Quebec. Can. J. Res. 49, 680–691 (2019).
Article  Google Scholar 

67.
Chaste, E., Girardin, M. P., Kaplan, J. O., Bergeron, Y. & Hély, C. Increases in heat-induced tree mortality could drive reductions of biomass resources in Canada’s managed boreal forest. Landsc. Ecol. https://doi.org/10.1007/s10980-019-00780-4 (2019).

68.
McLane, S. C., Daniels, L. D. & Aitken, S. N. Climate impacts on lodgepole pine (Pinus contorta) radial growth in a provenance experiment. Ecol. Manag. 262, 115–123 (2011).
Article  Google Scholar 

69.
Larsson, L. CooRecorder and Cdendro Programs of the CooRecorder/Cdendro Package (Version 7.6) (Cybis Elektronik, 2013).

70.
Holmes, R. L. Computer-assisted quality control in tree-ring dating and measurement. Tree Ring Bull. 43, 69–78 (1983).

71.
de Lafontaine, G., Prunier, J., Gérardi, S. & Bousquet, J. Tracking the progression of speciation: variable patterns of introgression across the genome provide insights on the species delimitation between progenitor-derivative spruces (Picea mariana × P. rubens). Mol. Ecol. 24, 5229–5247 (2015).
PubMed  Article  Google Scholar 

72.
Ehrich, M. et al. Quantitative high-throughput analysis of DNA methylation patterns by base-specific cleavage and mass spectrometry. Proc. Natl Acad. Sci. USA. 102, 15785–15790 (2005).
ADS  CAS  PubMed  Article  Google Scholar 

73.
Ung, C.-H., Jing Guo, X. & Fortin, M. Canadian national taper models. For. Chron. 89, 211–224 (2013).
Article  Google Scholar 

74.
Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).
CAS  PubMed  PubMed Central  Google Scholar 

75.
Wang, J. The computer program structure for assigning individuals to populations: easy to use but easier to misuse. Mol. Ecol. Resour. 17, 981–990 (2017).
CAS  PubMed  Article  Google Scholar 

76.
Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software structure: a simulation study. Mol. Ecol. 14, 2611–2620 (2005).
CAS  PubMed  Article  Google Scholar 

77.
Excoffier, L. Evolution of human mitochondrial DNA: evidence for departure from a pure neutral model of populations at equilibrium. J. Mol. Evol. 30, 125–139 (1990).
ADS  CAS  PubMed  Article  Google Scholar 

78.
Meirmans, P. G. genodive version 3.0: easy‐to‐use software for the analysis of genetic data of diploids and polyploids. Mol. Ecol. Resour. 20, 1126–1131 (2020).
CAS  PubMed  PubMed Central  Article  Google Scholar 

79.
Regniere, J. & Bolstad, P. Statistical simulation of daily air temperature patterns Eastern North America to forecast seasonal events in insect pest management. Environ. Entomol. 23, 1368–1380 (1994).
Article  Google Scholar 

80.
Hogg, E. H., Barr, A. G. & Black, T. A. A simple soil moisture index for representing multi-year drought impacts on aspen productivity in the western Canadian interior. Agric. Meteorol. 178–179, 173–182 (2013).
Article  Google Scholar 

81.
Wood, S. N. Thin plate regression splines. J. R. Stat. Soc. Ser. B Stat. Methodol. 65, 95–114 (2003).
MathSciNet  MATH  Article  Google Scholar 

82.
Rossi, S., Morin, H. & Deslauriers, A. Causes and correlations in cambium phenology: towards an integrated framework of xylogenesis. J. Exp. Bot. 63, 2117–2126 (2012).
CAS  PubMed  Article  Google Scholar 

83.
Wood, S. Generalized Additive Models: An Introduction with R. 2nd edn Vol. 66 (Chapman and Hall/CRC, 2006).

84.
R Development Core Team. R: A Language and Environment for Statistical Computing (2013).

85.
Dray, S. & Dufour, A.-B. The ade4 package: implementing the duality diagram for ecologists. J. Stat. Softw. 22, https://doi.org/10.18637/jss.v022.i04 (2007).

86.
Fisher R. A. Statistical Methods for Research Workers 4th edn (Oliver and Boyd, London, 1932).

87.
Legendre, P. & Legendre, L. Numerical Ecology Vol. 24, 3rd edn (Elsevier Science BV, Amsterdam, 2012).

88.
Reiss, P. T. & Ogden, R. T. Smoothing parameter selection for a class of semiparametric linear models. J. R. Stat. Soc. Ser. B Stat. Methodol. 71, 505–523 (2009).
MathSciNet  MATH  Article  Google Scholar 

89.
Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. Ser. B Stat. Methodol. 73, 3–36 (2011).
MathSciNet  MATH  Article  Google Scholar 

90.
Beaudoin, A. et al. Mapping attributes of Canada’s forests at moderate resolution through k NN and MODIS imagery. Can. J. Res. 44, 521–532 (2014).
Article  Google Scholar  More

1.
Garibaldi, L. A. et al. Wild pollinators enhance fruit set of crops regardless of honey bee abundance. Science 339, 1608–1611. https://doi.org/10.1126/science.1230200 (2013).
ADS  CAS  Article  PubMed  Google Scholar 
2.
Potts, S. G. et al. Safeguarding pollinators and their values to human well-being. Nature 540, 220–229. https://doi.org/10.1038/nature20588 (2016).
ADS  CAS  Article  PubMed  Google Scholar 

3.
Rader, R. et al. Non-bee insects are important contributors to global crop pollination. Proc. Natl. Acad. Sci. U.S.A. 113, 146–151. https://doi.org/10.1073/pnas.1517092112 (2016).
ADS  CAS  Article  PubMed  Google Scholar 

4.
Aizen, M. A., Garibaldi, L. A., Cunningham, S. A. & Klein, A. M. Long-term global trends in crop yield and production reveal no current pollination shortage but increasing pollinator dependency. Curr. Biol. 18, 1572–1575. https://doi.org/10.1016/j.cub.2008.08.066 (2008).
CAS  Article  PubMed  Google Scholar 

5.
Aizen, M. A. & Harder, L. D. The global stock of domesticated honey bees is growing slower than the agricultural demand for pollination. Curr. Biol. 19, 915–918. https://doi.org/10.1016/j.cub.2009.03.071 (2009).
CAS  Article  PubMed  Google Scholar 

6.
Vanbergen, A. J. & Initiative, I. P. Threats to an ecosystem service: Pressures on pollinators. Front. Ecol. Environ. 11, 251–259. https://doi.org/10.1890/120126 (2013).
Article  Google Scholar 

7.
Whitaker, T. & Davis, G. Cucurbits: Botany, Cultivation & Utilization (Biotech Books, Delhi, 2012).
Google Scholar 

8.
Hurd, P. D. Jr., Linsley, E. G. & Whitaker, T. Squash and gourd bees (Peponapis, Xenoglossa) and the origin of the cultivated Cucurbita. Evolution 25, 218–234. https://doi.org/10.2307/2406514 (1971).
Article  PubMed  Google Scholar 

9.
Artz, D. R. & Nault, B. A. Performance of Apis mellifera, Bombus impatiens, and Peponapis pruinosa (Hymenoptera: Apidae) as pollinators of pumpkin. J. Econ. Entomol. 104, 1153–1161. https://doi.org/10.1603/EC10431 (2011).
Article  PubMed  Google Scholar 

10.
Cane, J. H., Sampson, B. J. & Miller, S. Pollination value of male bees: the specialist bee Peponapis pruinosa (Apidae) at summer squash (Cucurbita pepo). Environ. Entomol. 40, 614–620. https://doi.org/10.1603/EN10084 (2011).
Article  PubMed  Google Scholar 

11.
Hurd, P. D. Jr. & Linsley, E. G. The squash and gourd bees-genera Peponapis Robertson and Xenoglossa Smith-inhabiting America north of Mexico (Hymenoptera: Apoidea). Hilgardia 35, 375–453. https://doi.org/10.3733/hilg.v35n15p375 (1964).
Article  Google Scholar 

12.
López-Uribe, M. M., Cane, J. H., Minckley, R. L. & Danforth, B. N. Crop domestication facilitated rapid geographical expansion of a specialist pollinator, the squash bee Peponapis pruinosa. Proc. R. Soc. B-Biol. Sci. 283, 20160443. https://doi.org/10.1098/rspb.2016.0443 (2016).
Article  Google Scholar 

13.
Tepedino, V. J. The pollination efficiency of the squash bee (Peponapis pruinosa) and the honey bee (Apis mellifera) on summer squash (Cucurbita pepo). J. Kansas Entomol. Soc. 54, 359–377. Retrieved from https://www.jstor.org/stable/25084168 (1981).

14.
Patton, W. Generic arrangement of the bees allied to Melissodes and Anthophora. Bull. U. S. Geolog. Surv. 5, 471–479. Retrieved from https://books.google.ca/books?hl=en&lr=&id=R38uAAAAYAAJ&oi=fnd&pg=PA469&ots=LVcsvi2gE5&sig=xlz2XhDKuN5qMenv47JIRhYfy_8&redir_esc=y#v=onepage&q&f=false (1879).

15.
Willis, D. S. & Kevan, P. G. Foraging dynamics of Peponapis pruinosa (Hymenoptera: Anthophoridae) on pumpkin (Cucurbita pepo) in Southern Ontario. Can. Entomol. 127, 167–175 (1995).
Article  Google Scholar 

16.
Hurd, P. D. Jr., Linsley, E. G. & Michelbacher, A. E. Ecology of the squash and gourd bee, Peponapis pruinosa, on cultivated cucurbits in California (Hymenoptera: Apoidea). Smiths. Contrib. Zool. 168, 1–17. Smithsonian Institution Press. Retrieved from https://repository.si.edu/bitstream/handle/10088/5347/SCtZ-0168-Lo_res.pdf?sequence=2 (1974).

17.
Mathewson, J. A. Nest construction and life history of the eastern cucurbit bee, Peponapis pruinosa (Hymenoptera: Apoidea). J. Kansas Entomol. Soc. 41, 255–261. Retrieved from https://www.jstor.org/stable/25083703 (1968).

18.
Julier, H. E. & Roulston, T. H. Wild bee abundance and pollination service in cultivated pumpkins: Farm management, nesting landscape effects. J. Econ. Entomol. 102, 563–573. https://doi.org/10.1603/029.102.0214 (2009).
Article  PubMed  Google Scholar 

19.
Willis Chan, D. S., Prosser, R. S., Rodríguez-Gil, J. L. & Raine, N. E. Risks of exposure to systemic insecticides in agricultural soil in Ontario, Canada for the hoary squash bee (Peponapis pruinosa) and other ground-nesting bee species. Sci. Rep. 9, 11870. https://doi.org/10.1038/s41598-019-47805-1 (2019).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

20.
Sgolastra, F. et al. Pesticide exposure assessment paradign for solitary bees. Environ. Entomol. 48, 22–35. https://doi.org/10.1093/ee/nvy105 (2019).
Article  PubMed  Google Scholar 

21.
Franklin, E. L. & Raine, N. E. Moving beyond honey bee-centric pesticide risk assessments to protect all pollinators. Nat. Ecol. Evol. 3, 1373–1375. https://doi.org/10.1038/s41559-019-0987-y (2019).
Article  PubMed  Google Scholar 

22.
Blacquière, T., Smagghe, G., van Gestel, C. A. M. & Mommaerts, V. Neonicotinoids in bees: A review on concentrations, side-effects and risk assessment. Ecotoxicology 24, 73–92. https://doi.org/10.1007/s10646-012-0863-x (2012).
CAS  Article  Google Scholar 

23.
Godfray, H. C. J. et al. A restatement of the natural science evidence base concerning neonicotinoid insecticides and insect pollinators. Proc. R. Soc. B Biol. Sci. 281, 20140558. https://doi.org/10.1098/rspb.2014.0558 (2014).
Article  Google Scholar 

24.
Godfray, H. C. J. et al. A restatement of recent advances the natural science evidence base concerning neonicotinoid insecticides and insect pollinators. Proc. R. Soc. B Biol. Sci. 281, 20151821. https://doi.org/10.1098/rspb.2015.1821 (2015).
CAS  Article  Google Scholar 

25.
Samuelson, E. E. W., Chen-Wishart, Z. P., Gill, R. J. & Leadbeater, E. Effect of acute pesticide exposure on bee spatial working memory using an analogue of the radial-arm maze. Sci. Rep. 6, 38957. https://doi.org/10.1038/srep38957 (2016).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

26.
Stanley, D. A., Smith, K. E. & Raine, N. E. Bumblebee learning and memory is impaired by chronic exposure to a neonicotinoid pesticide. Sci. Rep. 5, 16508. https://doi.org/10.1038/srep16508 (2015).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

27.
Gill, R. J., Ramos-Rodríguez, O. & Raine, N. E. Combined pesticide exposure severely affects individual- and colony-level traits in bees. Nature 491, 105–108 https://doi.org/10.1038/nature11585 (2012).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

28.
Gill, R. J. & Raine, N. E. Chronic impairment of bumblebee natural foraging behaviour induced by sublethal pesticide exposure. Funct. Ecol. 28, 1459–1471. https://doi.org/10.1111/1365-2435.12292 (2014).
Article  Google Scholar 

29.
Feltham, H., Park, K. & Goulson, D. Field realistic doses of pesticide imidacloprid reduce bumblebee pollen foraging efficiency. Ecotoxicology 23, 317–323. https://doi.org/10.1007/s10646-014-1189-7 (2014).
CAS  Article  PubMed  Google Scholar 

30.
Stanley, D. A. & Raine, N. E. Chronic exposure to a neonicotinoid pesticide alters the interactions between bumblebees and wild plants. Funct. Ecol. 30, 1132–1139. https://doi.org/10.1111/1365-2435.12644 (2016).
Article  PubMed  PubMed Central  Google Scholar 

31.
Stanley, D. A., Russell, A. L., Morrison, S. J., Rogers, C. & Raine, N. E. Investigating the impacts of field-realistic exposure to a neonicotinoid pesticide on bumblebee foraging, homing ability and colony growth. J. Appl. Ecol. 53, 1440–1449. https://doi.org/10.1111/1365-2664.12689 (2016).
CAS  Article  PubMed  PubMed Central  Google Scholar 

32.
Muth, F. & Leonard, A. S. A neonicotinoid pesticide impairs foraging, but not learning, in free-flying bumblebees. Sci. Rep. 9, 4764. https://doi.org/10.1038/s41598-019-39701-5 (2019).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

33.
Baron, G. L., Jansen, V. A. A., Brown, M. J. F. & Raine, N. E. Pesticide reduces bumblebee colony initiation and increases probability of population extinction. Nat. Ecol. Evol. 1, 1308–1316. https://doi.org/10.1038/s41559-017-0260-1 (2017).
Article  PubMed  PubMed Central  Google Scholar 

34.
Wu-Smart, J. & Spivak, M. Effects of neonicotinoid imidacloprid exposure on bumble bee (Hymenoptera: Apidae) queen survival and nest initiation. Environ. Entomol. 47, 55–62. https://doi.org/10.1093/ee/nvx175 (2018).
CAS  Article  PubMed  Google Scholar 

35.
Whitehorn, P. R., O’Connor, S., Wackers, F. L. & Goulson, D. Neonicotinoid pesticide reduces bumble bee colony growth and queen production. Science 336, 351–352. https://doi.org/10.1126/science.1215025 (2012).
ADS  CAS  Article  PubMed  Google Scholar 

36.
Woodcock, B. A. et al. Country-specific effects of neonicotinoid pesticides on honey bees and wild bees. Science 356, 1393–1395. https://doi.org/10.1126/science.aaa1190 (2017).
ADS  CAS  Article  PubMed  Google Scholar 

37.
Rundlöf, M. et al. Seed coating with a neonicotinoid insecticide negatively affects wild bees. Nature 571, 77–80. https://doi.org/10.1038/nature14420 (2015).
ADS  CAS  Article  Google Scholar 

38.
Ellis, C., Park, K. J., Whitehorn, P., David, A. & Goulson, D. The neonicotinoid insecticide thiacloprid impacts upon bumblebee colony development under field conditions. Environ. Sci. Technol. 51, 1727–1732. https://doi.org/10.1021/acs.est.6b04791 (2017).
ADS  CAS  Article  PubMed  Google Scholar 

39.
Switzer, C. M. & Combes, S. A. The neonicotinoid pesticide, imidacloprid, affects Bombus impatiens (bumblebee) sonication behavior when consumed at doses below the LD50. Ecotoxicology 25, 1150–1159. https://doi.org/10.1007/s10646-016-1669-z (2016).
CAS  Article  PubMed  Google Scholar 

40.
Stanley, D. A. et al. Neonicotinoid pesticide exposure impairs crop pollination services provided by bumblebees. Nature 528, 548–550. https://doi.org/10.1038/nature16167 (2015).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

41.
Jin, N., Klein, S., Leimig, F., Bischoff, G. & Menzel, R. The neonicotinoid clothianidin interferes with navigation of the solitary bee Osmia cornuta in a laboratory test. J. Exp. Biol. 218, 2821–2825. https://doi.org/10.1242/jeb.123612 (2015).
Article  PubMed  Google Scholar 

42.
Sandrock, C. et al. Sublethal neonicotinoid insecticide exposure reduces solitary bee reproductive success. Agric. For. Entomol. 16, 119–128. https://doi.org/10.1111/afe.12041 (2014).
Article  Google Scholar 

43.
Anderson, N. L. & Harmon-Threatt, A. N. Chronic contact with realistic soil concentrations of imidacloprid affects the mass, immature development speed, and adult longevity of solitary bees. Sci. Rep. 9, 3724. https://doi.org/10.1038/s41598-019-40031-9 (2019).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

44.
Danforth, B. N., Minckley, R. L. & Neff, J. L. The Solitary Bees: Biology, Evolution, Conservation (Princeton University Press, Princeton, 2019).

45.
Wheelock, M. J., Rey, K. P. & O’Neal, M. E. Defining the insect pollinator community found in Iowa corn and soybean fields: Implications for pollinator conservation. Environ. Entomol. 4, 1099–1106. https://doi.org/10.1093/ee/nvw1087 (2016).
Article  Google Scholar 

46.
USDA. Attractiveness of agricultural crops to pollinating bees for the collection of nectar and/or pollen. Retrieved from https://www.ars.usda.gov/ARSUserFiles/OPMP/Attractiveness%20of%20Agriculture%20Crops%20to%20Pollinating%20Bees%20Report-FINAL_Web%20Version_Jan%203_2018.pdf (2017).

47.
OMAFRA. Vegetable Crop Protection Guide, 82–83. Government of Ontario (2014).

48.
Leza, M., Watrous, K. M., Bratu, J. & Woodard, S. H. Effects of neonicotinoid insecticide exposure and monofloral diet on nest-founding bumblebee queens. Proc. R. Soc. B Biol. Sci. 285, 20180761. https://doi.org/10.1098/rspb.2018.0761 (2018).
CAS  Article  Google Scholar 

49.
Baron, G. L., Raine, N. E. & Brown, M. J. F. General and species-specific impacts of a neonicotinoid insecticide on the ovary development and feeding of wild bumblebee queens. Proc. R. Soc. B Biol. Sci. 284, 20170123. https://doi.org/10.1098/rspb.2017.0123 (2017).
CAS  Article  Google Scholar 

50.
Roulston, T. H. & Cane, J. H. The effect of diet breadth and nesting ecology on body size variation in bees (Apiformes). J. Kansas Entomol. Soc. 73, 129–142. Retrieved from https://www.jstor.org/stable/25085957 (2000).

51.
Klostermeyer, E., Mech, S. J. & Rasmussen, W. Sex and weight of Megachile rotundata (Hymenoptera: Megachilidae) progeny associated with provision weights. J. Kansas Entomol. Soc. 46, 536–548. Retrieved from https://www.jstor.org/stable/25082604 (1973).

52.
Bosch, J. & Vicens, N. Relationship between body size, provisioning rate, longevity and reproductive success in females of the solitary bee Osmia cornuta. Behav. Ecol. Sociobiol. 60, 26–33. https://doi.org/10.1007/s00265-005-0134-4 (2006).
Article  Google Scholar 

53.
Bonmatin, J. M. et al. Environmental fate and exposure: Neonicotinoids and fipronil. Environ. Sci. Pollut. Res. 22, 35–67. https://doi.org/10.1007/s11356-014-3332-7 (2015).
CAS  Article  Google Scholar 

54.
Hilton, M., Jarvis, T. & Ricketts, D. The degradation rate of thiamethoxam in European field studies. Pest Manag. Sci. 72, 388–397. https://doi.org/10.1002/ps.4024 (2016).
CAS  Article  PubMed  Google Scholar 

55.
Scott-Dupree, C. D., Conroy, L. & Harris, C. R. Impact of currently used or potentially useful insecticides for canola agroecosystems on Bombus impatiens (Hymenoptera: Apidae), Megachile rotundata (Hymenoptera: Megachildidae), and Osmia lignaria (Hymenoptera: Megachilidae). J. Econ. Entomol. 102, 177–182. https://doi.org/10.1603/029.102.0125 (2009).
CAS  Article  PubMed  Google Scholar 

56.
Stephen, W. P., Bohart, G. E. & Torchio, P. F. The biology and external morphology of bees with a synopsis of the genera of northwestern America. Corvallis: Oregon State University. Retrieved from https://www.jstor.org/stable/25082339 (1969).

57.
Seidelmann, K. & Ulbrich, K. M. Conditional sex allocation in the Red Mason bee Osmia rufa. Behav. Ecol. Sociobiol. 64, 337–347. https://doi.org/10.1007/s00265-009-0850-2 (2010).
Article  Google Scholar 

58.
Dively, G. P. & Kamel, A. Insecticide residues in pollen and nectar of a cucurbit crop and their potential exposure to pollinators. J. Agric. Food Chem. 60, 4449–4456. https://doi.org/10.1021/jf205393x (2012).
CAS  Article  PubMed  Google Scholar 

59.
Stoner, K. A. & Eitzer, B. D. Movement of soil-applied imidacloprid and thiamethoxam into nectar and pollen of squash (Cucurbita pepo). PLoS ONE 7, e39114. https://doi.org/10.1371/journal.pone.0039114 (2012).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

60.
Goulson, D. An overview of the environmental risks posed by neonicotinoid insecticides. J. Appl. Ecol. 50, 977–987. https://doi.org/10.1111/1365-2664.12111 (2013).
Article  Google Scholar 

61.
Wang, T. T. et al. Suppression of chlorantraniliprole sorption on biochar in soil–biochar systems. Bull. Environ. Contam. Toxicol. 95, 401–406. https://doi.org/10.1007/s00128-015-1541-5 (2015).
ADS  CAS  Article  PubMed  Google Scholar 

62.
Winsor, J. A., Davis, L. E. & Stephenson, A. G. The relationship between pollen load and fruit maturation and the effect of pollen load on offspring vigor in Cucurbita pepo. Am. Nat. 129, 643–656. https://doi.org/10.1086/284664 (1987).
Article  Google Scholar 

63.
Aizen, M. A., Garibaldi, L. A., Cunningham, S. A. & Klein, A. M. How much does agriculture depend on pollinators? Lessons from long-term trends in crop production. Ann. Bot. 103, 1579–1588. https://doi.org/10.1093/aob/mcp076 (2009).
Article  PubMed  PubMed Central  Google Scholar 

64.
McGrady, C. M., Troyer, R. & Fleischer, S. J. Wild bee visitation rates exceed pollination thresholds in commercial Cucurbita agroecosystems. J. Econ. Entomol. 113, 562–574. https://doi.org/10.1093/jee/toz295 (2020).
CAS  Article  PubMed  Google Scholar 

65.
Pes, M. et al. Translocation of chlorantraniliprole and cyantraniliprole applied to corn as seed treatment and foliar spraying to control Spodoptera frugiperda (Lepidoptera: Noctuidae). PLoS ONE 15, e0229151–e0229151. https://doi.org/10.1371/journal.pone.0229151 (2020).
CAS  Article  PubMed  PubMed Central  Google Scholar 

66.
Dinter, A., Brugger, K. E., Frost, N.-M. & Woodward, M. D. Chlorantraniliprole (Rynaxypyr): A novel DuPont insecticide with low toxicity and low risk for honey bees (Apis mellifera) and bumble bees (Bombus terrestris) providing excellent tools for uses in integrated pest management. Julius-Kühn-Arch. 423, 84–96 (2009).
Google Scholar 

67.
Gradish, A. E., Scott-Dupree, C. D., Shipp, L., Harris, C. R. & Ferguson, G. Effect of reduced risk pesticides for use in greenhouse vegetable production on Bombus impatiens (Hymenoptera: Apidae). Pest Manag. Sci. 66, 142–146. https://doi.org/10.1002/ps.1846 (2010).
CAS  Article  PubMed  Google Scholar 

68.
Tomé, H. V. V. et al. Reduced-risk insecticides in neotropical stingless bee species: impact on survival and activity. Ann. Appl. Biol. 167, 186–196. https://doi.org/10.1111/aab.12217 (2015).
CAS  Article  Google Scholar 

69.
Williams, J. R., Swale, D. R. & Anderson, T. D. Comparative effects of technical-grade and formulated chlorantraniliprole to the survivorship and locomotor activity of the honey bee, Apis mellifera (L.). Pest Manag. Sci. 76, 2582–2588. https://doi.org/10.1002/ps.5832 (2020).
CAS  Article  PubMed  Google Scholar 

70.
Larson, J. L., Redmond, C. T. & Potter, D. A. Assessing insecticide hazard to bumble bees foraging on flowering weeds in treated lawns. PLoS ONE 8, e66375. https://doi.org/10.1371/journal.pone.0066375 (2013).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

71.
Brugger, K. E. et al. Selectivity of chlorantraniliprole to parasitoid wasps. Pest Manag. Sci. 66, 1075–1081. https://doi.org/10.1002/ps.1977 (2010).
CAS  Article  PubMed  Google Scholar 

72.
Wang, J. et al. Molecular characterization of a ryanodine receptor gene in the rice leaf folder, Cnaphalocrocis medinalis (Guenée). PLoS ONE 7, e36623. https://doi.org/10.1371/journal.pone.0036623 (2012).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

73.
Willis, D. S. The pollination system of Cucurbita pepo and Peponapis pruinosa in southern Ontario. MSc Thesis. University of Guelph, Guelph, Ontario, Canada (1991).

74.
Kiernan, K. Insights into using the GLIMMIX procedure to model categorical outcomes with random effects. SAS Institute Inc. Retrieved from https://blogs.sas.com/con60tent/iml/2019/04/03/g-matrix-is-not-positive-definite.html (2018). More

1.
Darwin, C. The Descent of Man and the Selection in Relation to Sex (John Murray, London, 1871).
Google Scholar 
2.
Miller, E. J., Eldridge, M. D. B., Cooper, D. W. & Herbert, C. A. Dominance, body size and internal relatedness influence male reproductive success in eastern grey kangaroos (Macropus giganteus). Reprod. Fertil. Dev. 22, 539–549 (2010).
PubMed  Article  PubMed Central  Google Scholar 

3.
Hirsch, B. T. & Maldonado, J. E. Familiarity breeds progeny: Sociality increases reproductive success in adult male ring-tailed coatis (Nasua nasua). Mol. Ecol. 20, 409–419 (2011).
PubMed  Article  PubMed Central  Google Scholar 

4.
Natoli, E., Schmid, M., Say, L. & Pontier, D. Male reproductive success in a social group of urban feral cats (Felis catus L.). Ethology 113, 283–289 (2007).
Article  Google Scholar 

5.
Clutton-Brock, T. & Isvaran, K. Paternity loss in contrasting mammalian societies. Biol. Lett. 2, 513–516 (2006).
CAS  PubMed  PubMed Central  Article  Google Scholar 

6.
Altmann, S. A. A field study of the sociobiology of rhesus monkeys, Macaca mulatta. Ann. N. Y. Acad. Sci. 102, 338–435 (1962).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

7.
Kutsukake, N. & Nunn, C. L. Comparative tests of reproductive skew in male primates: The roles of demographic factors and incomplete control. Behav. Ecol. Sociobiol. 60, 695–706 (2006).
Article  Google Scholar 

8.
Ostner, J., Nunn, C. L. & Schülke, O. Female reproductive synchrony predicts skewed paternity across primates. Behav Ecol 19, 1150–1158 (2008).
PubMed  PubMed Central  Article  Google Scholar 

9.
Janson, C. & Verdolin, J. Seasonality of primate births in relation to climate. In Seasonality in Primates—Studies of Living and Extinct Human and Non-human Primates (eds Brockmann, D. K. & Van Schaik, C.) 308–351 (Cambridge University Press, Cambridge, 2005).
Google Scholar 

10.
Gogarten, J. F. & Koenig, A. Reproductive seasonality is a poor predictor of receptive synchrony and male reproductive skew among nonhuman primates. Behav. Ecol. Sociobiol. 67, 123–134 (2012).
Article  Google Scholar 

11.
Brockmann, D. K. & Van Schaik, C. P. Seasonality and reproductive function. In Seasonality in Primates: Studies of Living and Extinct Human and Non-human Primates (eds Brockmann, D. K. & Van Schaik, C. P.) 269–306 (Cambridge University Press, Cambridge, 2005).
Google Scholar 

12.
Sterck, E. H. M., Watts, D. P. & van Schaik, C. P. The evolution of female social relationships in nonhuman primates. Behav. Ecol. Sociobiol. 41, 291–309 (1997).
Article  Google Scholar 

13.
Nunn, C. L. The number of males in primate social groups: A comparative test of the socioecological model. Behav. Ecol. Sociobiol. 46, 1–13 (1999).
Article  Google Scholar 

14.
Carnes, L. M., Nunn, C. L. & Lewis, R. J. Effects of the distribution of female primates on the number of males. PLoS One 6, 20 (2011).
Google Scholar 

15.
Manson, J. H. Primate consortships: A critical review. Curr. Anthropol. 38(3), 353–374 (1997).
Article  Google Scholar 

16.
Andersson, M. B. Sexual Selection (Princeton University Press, Princeton, 1994).
Google Scholar 

17.
Fürtbauer, I., Heistermann, M., Schülke, O. & Ostner, J. Concealed fertility and extended female sexuality in a non-human primate (Macaca assamensis). PLoS One 6, e23105 (2011).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

18.
Plavcan, J. M. Understanding dimorphism as a function of changes in male and female traits. Evol. Anthropol. Issues News Rev. 20, 143–155 (2011).
Article  Google Scholar 

19.
Setchell, J. M., Charpentier, M. & Wickings, E. J. Mate guarding and paternity in mandrills: Factors influencing alpha male monopoly. Anim. Behav. 70, 1105–1120 (2005).
Article  Google Scholar 

20.
Bradley, B. J. et al. Mountain gorilla tug-of-war: Silverbacks have limited control over reproduction in multimale groups. Proc. Natl. Acad. Sci. USA 102, 9418–9423 (2005).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

21.
Nunn, C. L. The evolution of exaggerated sexual swellings in primates and the graded-signal hypothesis. Anim. Behav. 58(2), 229–246 (1999).
CAS  PubMed  PubMed Central  Article  Google Scholar 

22.
Rodriguez-Llanes, J. M., Verbeke, G. & Finlayson, C. Reproductive benefits of high social status in male macaques (Macaca). Anim. Behav. 78, 643–649 (2009).
Article  Google Scholar 

23.
Paul, A., Kuester, J., Timme, A. & Arnemann, J. The association between rank, mating effort and reproductive success in male Barbary macaques (Macaca sylvanus). Primates 34, 491–502 (1993).
Article  Google Scholar 

24.
Kümmerli, R. & Martin, R. D. Male and female reproductive success in Macaca sylvanus in Gibraltar: No evidence for rank dependence. Int. J. Primatol. 26, 1229–1249 (2005).
ADS  Article  Google Scholar 

25.
Brauch, K. et al. Sex-specific reproductive behaviours and paternity in free-ranging Barbary macaques (Macaca sylvanus). Behav. Ecol. Sociobiol. 62, 1453–1466 (2008).
Article  Google Scholar 

26.
Berard, J. D., Nurnberg, P., Epplen, J. T. & Schmidtke, J. Alternative reproductive tactics and reproductive success in male rhesus macaques. Behaviour 129, 177–201 (1994).
Article  Google Scholar 

27.
Widdig, A. et al. A longitudinal analysis of reproductive skew in male rhesus macaques. Proc. Biol. Sci. 271, 819–826 (2004).
PubMed  PubMed Central  Article  Google Scholar 

28.
Dubuc, C., Muniz, L., Heistermann, M., Engelhardt, A. & Widdig, A. Testing the priority-of-access model in a seasonally breeding primate species. Behav. Ecol. Sociobiol. 65, 1615–1627 (2011).
PubMed  PubMed Central  Article  Google Scholar 

29.
de Ruiter, J. R., van Hooff, J. A. R. A. M. & Scheffrahn, W. Social and genetic aspects of paternity in wild long-tailed macaques (Macaca fascicularis). Behaviour 129, 204–224 (1994).
Article  Google Scholar 

30.
Engelhardt, A., Heistermann, M., Hodges, J. K., Nuernberg, P. & Niemitz, C. Determinants of male reproductive success in wild long-tailed macaques (Macaca fascicularis)—male monopolisation, female mate choice or post-copulatory mechanisms?. Behav. Ecol. Sociobiol. 59, 740–752 (2006).
Article  Google Scholar 

31.
Plavcan, J. M. & van Schaik, C. P. Intrasexual competition and body weight dimorphism in anthropoid primates. Am. J. Phys. Anthropol. 103, 37–68 (1997).
CAS  PubMed  Article  Google Scholar 

32.
Plavcan, J. M., van Schaik, C. P. & Kappeler, P. M. Competition, coalitions and canine size in primates. J. Hum. Evol. 28, 245–276 (1995).
Article  Google Scholar 

33.
Groves, C. Primate Taxonomy (Smithsonian Books, Washington, 2001).
Google Scholar 

34.
Thierry, B., Iwaniuk, A. N. & Pellis, S. M. The influence of phylogeny on the social behaviour of macaques (Primates: Cercopithecidae, genus Macaca). Ethology 106, 713–728 (2000).
Article  Google Scholar 

35.
Duboscq, J. et al. Social tolerance in wild female crested macaques (Macaca nigra) in Tangkoko-Batuangus Nature Reserve, Sulawesi, Indonesia. Am. J. Primatol. 75, 361–375 (2013).
PubMed  PubMed Central  Article  Google Scholar 

36.
Plavcan, J. M., van Schaik, C. P. & McGraw, W. S. Seasonality, social organization, and sexual dimorphism in primates. In Seasonality in Primates: Studies of Living and Extinct Human and Non-Human Primates (eds van Schaik, C. P. & Brockman, D. K.) 401–442 (Cambridge University Press, Cambridge, 2005). https://doi.org/10.1017/CBO9780511542343.015.
Google Scholar 

37.
Marty, P. R., Hodges, K., Agil, M. & Engelhardt, A. Alpha male replacements and delayed dispersal in crested macaques (Macaca nigra). Am. J. Primatol. 79, e22448 (2017).
Article  Google Scholar 

38.
Kerhoas, D., Perwitasari-Farajallah, D., Agil, M., Widdig, A. & Engelhardt, A. Social and ecological factors influencing offspring survival in wild macaques. Behav. Ecol. 25, 1164–1172 (2014).
PubMed  PubMed Central  Article  Google Scholar 

39.
Neumann, C., Assahad, G., Hammerschmidt, K., Perwitasari-Farajallah, D. & Engelhardt, A. Loud calls in male crested macaques, Macaca nigra: A signal of dominance in a tolerant species. Anim. Behav. 79, 187–193 (2010).
Article  Google Scholar 

40.
Martinez-Iñigoa, L., Agil, M., Engelhardt, A., Pilot, M. & Majolo, B. Resource and mate defence influence the outcome of intergroup encounters in wild crested macaques (Macaca nigra). Primate Eye 123, 48–49 (2017).
Google Scholar 

41.
Higham, J. P. et al. Sexual signalling in female crested macaques and the evolution of primate fertility signals. BMC Evol. Biol. 12, 89–99 (2012).
PubMed  PubMed Central  Article  Google Scholar 

42.
Engelhardt, A. & Perwitasari-Farajallah, D. Reproductive biology of Sulawesi crested black macaques (Macaca nigra). Folia Primatol. (Basel) 79, 326 (2008).
Google Scholar 

43.
Marty, P. R., Hodges, K., Agil, M. & Engelhardt, A. Determinants of immigration strategies in male crested macaques (Macaca nigra). Sci. Rep. 6, 32028 (2016).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

44.
Wigby, S. & Chapman, T. Sperm competition. Curr. Biol. 14, R100–R103 (2004).
CAS  PubMed  Article  PubMed Central  Google Scholar 

45.
Tregenza, T. & Wedell, N. Benefits of multiple mates in the cricket gryllus bimaculatus. Evolution 52, 1726–1730 (1998).
PubMed  Article  PubMed Central  Google Scholar 

46.
Clutton-Brock, T. H. Reproductive skew, concessions and limited control. Trends Ecol. Evol. 13, 288–292 (1998).
CAS  PubMed  Article  PubMed Central  Google Scholar 

47.
Alberts, S. C., Buchan, J. C. & Altmann, J. Sexual selection in wild baboons: From mating opportunities to paternity success. Anim. Behav. 72, 1177–1196 (2006).
Article  Google Scholar 

48.
Boesch, C., Kohou, G., Néné, H. & Vigilant, L. Male competition and paternity in wild chimpanzees of the Taï forest. Am. J. Phys. Anthropol. 130, 103–115 (2006).
PubMed  Article  PubMed Central  Google Scholar 

49.
Higham, J. P., Heistermann, M. & Maestripieri, D. The energetics of male-male endurance rivalry in free-ranging rhesus macaques, Macaca mulatta. Anim. Behav. 81, 1001–1007 (2011).
Article  Google Scholar 

50.
Muniz, L. et al. Male dominance and reproductive success in wild white-faced capuchins (Cebus capucinus) at Lomas Barbudal, Costa Rica. Am. J. Primatol. 72, 1118–1130 (2010).
PubMed  Article  PubMed Central  Google Scholar 

51.
Strier, K. B., Chaves, P. B., Mendes, S. L., Fagundes, V. & Di Fiore, A. Low paternity skew and the influence of maternal kin in an egalitarian, patrilocal primate. Proc. Natl. Acad. Sci. 108, 18915–18919 (2011).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

52.
Daspre, A., Heistermann, M., Hodges, J. K., Lee, P. C. & Rosetta, L. Signals of female reproductive quality and fertility in colony-living baboons (Papio hanubis) in relation to ensuring paternal investment. Am. J. Primatol. 71, 529–538 (2009).
PubMed  Article  PubMed Central  Google Scholar 

53.
Weingrill, T., Lycett, J. E., Barrett, L., Hill, R. A. & Henzi, S. P. Male consortship behaviour in chacma baboons: The role of demographic factors and female conceptive probabilities. Behaviour 140, 405–427 (2003).
Article  Google Scholar 

54.
Engelhardt, A. et al. Assessment of female reproductive status by male longtailed macaques, Macaca fascicularis, under natural conditions. Anim. Behav. 67, 915–924 (2004).
Article  Google Scholar 

55.
Higham, J. P., Semple, S., MacLarnon, A., Heistermann, M. & Ross, C. Female reproductive signaling, and male mating behavior, in the olive baboon. Horm. Behav. 55, 60–67 (2009).
CAS  PubMed  Article  PubMed Central  Google Scholar 

56.
Schülke, O. & Ostner, J. Male reproductive skew, paternal relatedness, and female social relationships. Am. J. Primatol. 70, 695–698 (2008).
PubMed  Article  PubMed Central  Google Scholar 

57.
Schülke, O. & Ostner, J. Ecological and social influences on sociality. In The evolution of Primate Societies (eds Mitani, J. C. et al.) 193–219 (University of Chicago Press, Chicago, 2012).
Google Scholar 

58.
Higham, J. P. et al. Female fertile phase synchrony, and male mating and reproductive skew, in the crested macaque. Dryad, Dataset. https://doi.org/10.5061/dryad.rfj6q578x. (2021).

59.
Rosenbaum, B., O’Brien, T. G., Kinnaird, M. & Supriatna, J. Population densities of Sulawesi crested black macaques (Macaca nigra) on Bacan and Sulawesi, Indonesia: Effects of habitat disturbance and hunting. Am. J. Primatol. 44, 89–106 (1998).
CAS  PubMed  Article  PubMed Central  Google Scholar 

60.
Collins, N. M. The Conservation Atlas of Tropical Forests: Asia and the Pacifics (Springer, Berlin, 1991).
Google Scholar 

61.
O’Brien, T. G. & Kinnaird, M. F. Behavior, diet, and movements of the Sulawesi crested black macaque (Macaca nigra). Int. J. Primatol. 18, 321–351 (1997).
Article  Google Scholar 

62.
Kinnaird, M. F. & O’Brien, T. G. A contextual analysis of the loud call of the Sulawesi crested black macaque, Macaca nigra. Trop. Biodivers. 20, 37–42 (1999).
Google Scholar 

63.
Neumann, C. et al. Assessing dominance hierarchies: Validation and advantages of progressive evaluation with Elo-rating. Anim. Behav. 82, 911–921 (2011).
Article  Google Scholar 

64.
Hadidian, J. & Bernstein, I. S. Female reproductive cycles and birth data from an Old World monkey colony. Primates 20, 429–442 (1979).
Article  Google Scholar 

65.
Altmann, J. Observational study of behavior: Sampling methods. Behaviour 49, 227–267 (1974).
CAS  Article  Google Scholar 

66.
Danish, L. M. & Palombit, R. A. “Following”, an alternative mating strategy used by male olive baboons (Papio hamadryas anubis): Quantitative behavioral and functional description. Int. J. Primatol. 35, 394–410 (2014).
Article  Google Scholar 

67.
Hodges, J. K. & Heistermann, M. Field Endocrinology: Monitoring Hormonal Changes in Free-Ranging Primates 353–370 (Cambridge University Press, Cambridge, 2011).
Google Scholar 

68.
Heistermann, M. et al. Loss of oestrus, concealed ovulation and paternity confusion in free-ranging Hanuman langurs. Proc. Biol. Sci. 268, 2445–2451 (2001).
CAS  PubMed  PubMed Central  Article  Google Scholar 

69.
Engelhardt, A., Hodges, J. K., Niemitz, C. & Heistermann, M. Female sexual behavior, but not sex skin swelling, reliably indicates the timing of the fertile phase in wild long-tailed macaques (Macaca fascicularis). Horm. Behav. 47, 195–204 (2005).
CAS  PubMed  Article  PubMed Central  Google Scholar 

70.
Nsubuga, A. M. et al. Factors affecting the amount of genomic DNA extracted from ape faeces and the identification of an improved sample storage method. Mol. Ecol. 13, 2089–2094 (2004).
CAS  PubMed  Article  PubMed Central  Google Scholar 

71.
Engelhardt, A., Muniz, L., Perwitasari-Farajallah, D. & Widdig, A. Highly polymorphic microsatellite markers for the assessment of male reproductive skew and genetic variation in Critically Endangered crested macaques (Macaca nigra). Int. J. Primatol. 38, 672–691 (2017).
PubMed  PubMed Central  Article  Google Scholar 

72.
Taberlet, P. et al. Reliable genotyping of samples with very low DNA quantities using PCR. Nucleic Acids Res. 24, 3189–3194 (1996).
CAS  PubMed  PubMed Central  Article  Google Scholar 

73.
Taberlet, P. & Luikart, G. Non-invasive genetic sampling and individual identification. Biol. J. Linn. Soc. 68, 41–55 (1999).
Article  Google Scholar 

74.
Arandjelovic, M. et al. Two-step multiplex polymerase chain reaction improves the speed and accuracy of genotyping using DNA from noninvasive and museum samples. Mol. Ecol. Resour. 9, 28–36 (2009).
CAS  PubMed  Article  PubMed Central  Google Scholar 

75.
Kalinowski, S. T., Taper, M. L. & Marshall, T. C. Revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment. Mol. Ecol. 16, 1099–1106 (2007).
PubMed  Article  Google Scholar 

76.
Hadfield, J. D. MCMC methods for multi-response generalized linear mixed models: The MCMCglmm package. J. Stat. Softw. 33, 1–25 (2010).
Article  Google Scholar 

77.
Nonacs, P. Measuring the reliability of skew indices: Is there one best index? Anim. Behav. 65, 615–627 (2003).
Article  Google Scholar  More

1.
Webb, T. J. Marine and terrestrial ecology: unifying concepts, revealing differences. Trends Ecol. Evol. 27, 535–541 (2012).
PubMed  Article  Google Scholar 
2.
Calosi, P., Bilton, D. T., Spicer, J. I., Votier, S. C. & Atfield, A. What determines a species’ geographical range? Thermal biology and latitudinal range size relationships in European diving beetles (Coleoptera: Dytiscidae). J. Anim. Ecol. 79, 194–204 (2010).
PubMed  Article  Google Scholar 

3.
Sunday, J. M., Bates, A. E. & Dulvy, N. K. Thermal tolerance and the global redistribution of animals. Nat. Clim. Chang. 2, 686–690 (2012).
ADS  Article  Google Scholar 

4.
Wiens, J. J. et al. Niche conservatism as an emerging principle in ecology and conservation biology. Ecol. Lett. 13, 1310–1324 (2010).
PubMed  Article  Google Scholar 

5.
Huey, R. B. et al. Predicting organismal vulnerability to climate warming: roles of behaviour, physiology and adaptation. Philos. Trans. R. Soc. B 367, 1665–1679 (2012).
Article  Google Scholar 

6.
Wake, D. B., Roth, G. & Wake, M. H. On the problem of stasis in organismal evolution. J. Theor. Biol. 101, 211–224 (1983).
Article  Google Scholar 

7.
Hoffmann, A. A., Chown, S. L. & Clusella-Trullas, S. Upper thermal limits in terrestrial ectotherms: how constrained are they? Funct. Ecol. 27, 934–949 (2013).
Article  Google Scholar 

8.
Storch, D., Menzel, L., Frickenhaus, S. & Pörtner, H. Climate sensitivity across marine domains of life: limits to evolutionary adaptation shape species interactions. Glob. Chang. Biol. 20, 3059–3067 (2014).
ADS  PubMed  Article  Google Scholar 

9.
Addo-Bediako, A., Chown, S. L. & Gaston, K. J. Thermal tolerance, climatic variability and latitude. Proc. R. Soc. Lond. B 267, 739–745 (2000).
CAS  Article  Google Scholar 

10.
Sunday, J. M., Bates, A. E. & Dulvy, N. K. Global analysis of thermal tolerance and latitude in ectotherms. Proc. R. Soc. Lond. B 278, 1823–1830 (2011).
Google Scholar 

11.
van Berkum, F. H. Latitudinal patterns of the thermal sensitivity of sprint speed in lizards. Am. Nat. 132, 327–343 (1988).

12.
Munoz, M. M. et al. Evolutionary stasis and lability in thermal physiology in a group of tropical lizards. Proc. R. Soc. Lond. B 281, 20132433 (2014).
Google Scholar 

13.
Araújo, M. B. et al. Heat freezes niche evolution. Ecol. Lett. 16, 1206–1219 (2013).
PubMed  Article  Google Scholar 

14.
Kellermann, V. et al. Upper thermal limits of Drosophila are linked to species distributions and strongly constrained phylogenetically. Proc. Natl Acad. Sci. USA 109, 16228–16233 (2012).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

15.
Bogert, C. M. Thermoregulation in reptiles, a factor in evolution. Evolution 3, 195–211 (1949).
CAS  PubMed  Article  PubMed Central  Google Scholar 

16.
Ruddiman, W. F. Earth’s Climate: Past and Future (Macmillan, 2001).

17.
Romdal, T. S., Araújo, M. B. & Rahbek, C. Life on a tropical planet: niche conservatism and the global diversity gradient. Glob. Ecol. Biogeogr. 22, 344–350 (2013).
Article  Google Scholar 

18.
Hedges, S. B., Marin, J., Suleski, M., Paymer, M. & Kumar, S. Tree of life reveals clock-like speciation and diversification. Mol. Biol. Evol. 32, 835–845 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

19.
Herrando-Pérez, S. et al. Heat tolerance is more variable than cold tolerance across species of Iberian lizards after controlling for intraspecific variation. Funct. Ecol. 34, 631–645 (2020).
Article  Google Scholar 

20.
Hamilton, W. J. Life’s Color Code (New York: McGraw-Hill, 1973).

21.
Cooper, N., Thomas, G. H., Venditti, C., Meade, A. & Freckleton, R. P. A cautionary note on the use of Ornstein Uhlenbeck models in macroevolutionary studies. Biol. J. Linn. Soc. 118, 64–77 (2016).
Article  Google Scholar 

22.
Münkemüller, T., Boucher, F. C., Thuiller, W. & Lavergne, S. Phylogenetic niche conservatism—common pitfalls and ways forward. Funct. Ecol. 29, 627–639 (2015).
PubMed  PubMed Central  Article  Google Scholar 

23.
Buckley, L. B. & Huey, R. B. Temperature extremes: geographic patterns, recent changes, and implications for organismal vulnerabilities. Glob. Chang. Biol. 22, 3829–3842 (2016).
ADS  PubMed  Article  Google Scholar 

24.
Hoffmann, A. A. Physiological climatic limits in Drosophila: patterns and implications. J. Exp. Biol. 213, 870–880 (2010).
CAS  PubMed  Article  Google Scholar 

25.
Bennett, J. M. et al. GlobTherm a global database on thermal tolerances for aquatic and terrestrial organisms. Sci. Data 5, 180022 (2018).
PubMed  PubMed Central  Article  Google Scholar 

26.
Rangel, T. F. et al. Modeling the ecology and evolution of biodiversity: biogeographical cradles, museums, and graves. Science (80-.) 361, eaar5452 (2018).
Article  CAS  Google Scholar 

27.
Stephens, P. R. & Wiens, J. J. Explaining species richness from continents to communities: the time-for-speciation effect in emydid turtles. Am. Nat. 161, 112–128 (2003).
PubMed  Article  Google Scholar 

28.
Grosberg, R. K., Vermeij, G. J. & Wainwright, P. C. Biodiversity in water and on land. Curr. Biol. 22, R900–R903 (2012).
CAS  PubMed  Article  Google Scholar 

29.
Cutler, D. R. et al. Random forests for classification in ecology. Ecology 88, 2783–2792 (2007).
Article  Google Scholar 

30.
Pörtner, H. Climate change and temperature-dependent biogeography: oxygen limitation of thermal tolerance in animals. Naturwissenschaften 88, 137–146 (2001).
ADS  PubMed  Article  Google Scholar 

31.
Colwell, R. K., Brehm, G., Cardelús, C. L., Gilman, A. C. & Longino, J. T. Global warming, elevational range shifts, and lowland biotic attrition in the wet tropics. Science (80-.) 322, 258–261 (2008).
ADS  CAS  Article  Google Scholar 

32.
Tewksbury, J. J., Huey, R. B. & Deutsch, C. A. Putting the heat on tropical animals. Science (80-.) 320, 1296–1297 (2008).
CAS  Article  Google Scholar 

33.
Sinervo, B. et al. Erosion of lizard diversity by climate change and altered thermal niches. Science (80-.) 328, 894–899 (2010).
ADS  CAS  Article  Google Scholar 

34.
Barnosky, A. D. et al. Has the Earth’s sixth mass extinction already arrived? Nature 471, 51–57 (2011).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

35.
Gavrilets, S. & Vose, A. Dynamic patterns of adaptive radiation. Proc. Natl Acad. Sci. USA 102, 18040–18045 (2005).
ADS  CAS  PubMed  Article  Google Scholar 

36.
Schluter, D. & Pennell, M. W. Speciation gradients and the distribution of biodiversity. Nature 546, 48–55 (2017).
ADS  CAS  PubMed  Article  Google Scholar 

37.
Porter, W. P. & Kearney, M. Size, shape, and the thermal niche of endotherms. Proc. Natl Acad. Sci. USA 106, 19666–19672 (2009).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

38.
Rubalcaba, J. G. & Olalla‐Tárraga, M. Á. The biogeography of thermal risk for terrestrial ectotherms: scaling of thermal tolerance with body size and latitude. J. Anim. Ecol. 89, 1277–1285 (2020).

39.
Hochachka, P. W. & Somero, G. N. Biochemical Adaptation: Mechanism and Process in Physiological Evolution (Oxford University Press, 2002).

40.
Wiens, J. J. & Graham, C. H. Niche conservatism: integrating evolution, ecology, and conservation biology. Annu. Rev. Ecol. Evol. Syst. 36, 519–539 (2005).

41.
IUCN. The IUCN Red List of Threatened Species http://www.iucnredlist.org (2015).

42.
Horton, T. et al. World Register of Marine Species (WoRMS) http://www.marinespecies.org (2017).

43.
Guiry, M. D. & Guiry, G. M. AlgaeBase. World-wide electronic publication http://www.algaebase.org (2016).

44.
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).

45.
Assis, J. et al. Bio‐ORACLE v2. 0: extending marine data layers for bioclimatic modelling. Glob. Ecol. Biogeogr. 27, 277–284 (2018).
Article  Google Scholar 

46.
Tyberghein, L. et al. Bio‐ORACLE: a global environmental dataset for marine species distribution modelling. Glob. Ecol. Biogeogr. 21, 272–281 (2012).
Article  Google Scholar 

47.
Caspermeyer, J. New grand tree of life study shows a clock-like trend in the emergence of new species and diversity. Mol. Biol. Evol. 32, 1113 (2015).
CAS  PubMed  Article  Google Scholar 

48.
Holt, B. G. & Jønsson, K. A. Reconciling hierarchical taxonomy with molecular phylogenies. Syst. Biol. 63, 1010–1017 (2014).
PubMed  Article  Google Scholar 

49.
Ruggiero, M. A. et al. A higher level classification of all living organisms. PLoS ONE 10, e0119248 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

50.
Cooper, N. & Purvis, A. Body size evolution in mammals: complexity in tempo and mode. Am. Nat. 175, 727–738 (2010).
PubMed  Article  Google Scholar 

51.
Felsenstein, J. Maximum-likelihood estimation of evolutionary trees from continuous characters. Am. J. Hum. Genet. 25, 471 (1973).
CAS  PubMed  PubMed Central  Google Scholar 

52.
Zanne, A. E. et al. Three keys to the radiation of angiosperms into freezing environments. Nature 506, 89–92 (2014).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

53.
Alexander Pyron, R. & Wiens, J. J. A large-scale phylogeny of Amphibia including over 2800 species, and a revised classification of extant frogs, salamanders, and caecilians. Mol. Phylogenet. Evol. 61, 543–583 (2011).
PubMed  Article  Google Scholar 

54.
Pyron, R. A., Burbrink, F. T. & Wiens, J. J. A phylogeny and revised classification of Squamata, including 4161 species of lizards and snakes. BMC Evol. Biol. 13, 93 (2013).
PubMed  PubMed Central  Article  CAS  Google Scholar 

55.
Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444 (2012).
ADS  CAS  PubMed  Article  Google Scholar 

56.
Faurby, S. & Svenning, J.-C. A species-level phylogeny of all extant and late Quaternary extinct mammals using a novel heuristic-hierarchical Bayesian approach. Mol. Phylogenet. Evol. 84, 14–26 (2015).
PubMed  Article  Google Scholar 

57.
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).
MATH  Article  Google Scholar 

58.
R Development Core Team. R: A Language and Environment for Statistical Computing (R Development Core Team, 2020).

59.
Hedges, S. B., Dudley, J. & Kumar, S. TimeTree: a public knowledge-base of divergence times among organisms. Bioinformatics 22, 2971–2972 (2006).
CAS  PubMed  Article  PubMed Central  Google Scholar 

60.
Zanne, A. E. et al. Data from: three keys to the radiation of angiosperms into freezing environments. Dryad Digit. Repos. 10, https://doi.org/10.5061/dryad.63q27 (2014).

61.
Pyron, R. A. & Wiens, J. J. Data from: a large-scale phylogeny of Amphibia including over 2800 species, and a revised classification of extant frogs, salamanders, and caecilians. https://doi.org/10.5061/dryad.vd0m7 (2011).

62.
Pyron, R. Alexander, Burbrink, Frank T., Wiens, J. J. Data from: a phylogeny and revised classification of Squamata, including 4161 species of lizards and snakes. Dryad Digit. Repos. https://doi.org/10.5061/dryad.82h0me (2013).

63.
Morales-Castilla, I. MoralesCastilla/ThermalEvolution: ThermalEvolution (Version v1.0). Zenodo https://doi.org/10.5281/zenodo.4311705 (2020). More

In most of the forest soil samples used in this survey, specimens belonging to the genus Hantzschia are quite common. Based molecular as well as on light microscopy (LM) and scanning electron microscopy (SEM) observations of 25 strains, seven different taxa were recognized. Figure 1 contains the locations of the strain’s habitats. In anticipation of the nomenclatural consequences, we are using the new names already here but will describe them formally later.
Figure 1

Map with the habitat locations of the studied strains.

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Molecular data
The obtained phylogenetic tree for representatives of the different strains Hantzschia contains several large clades, some of which are monophyletic, while others contain several different species names (Fig. 2). In the analyzed tree, the largest clade is represented by different strains of H. amphioxys, the structure of which is described in the corresponding molecular analysis section. At the same time, the most significant is that in the same clade there is strain H. amphioxys D27_008, which has been designated as epitype20. One of the largest is the clade with H. abundans, which, in addition to our strains, and some that have already been published, includes the group of strains referred to as “Hantzschia sp. 3” (Sterre6)e, (Sterre6)f from Souffreau et al.16. We propose to refer to all of these strains as H. abundans. The next clade consists of the new species of H. attractiva and three strains of Hantzschia sp. 2 (Mo1)a, (Mo1)e, (Mo1)m from Souffreau et al.16, the latter we propose to merge into the new species named H. pseudomongolica, which is sister to H. attractiva. Given the topology of the tree and the morphological features of the representatives, we can conclude that there is a close relation between H. abundans and H. attractiva plus H. pseudomongolica. A separate group consists of two clades with sufficient statistical support (likelihood bootstrap, LB 76; posterior probability, PP 100), one of which is represented by two strains of H. parva, and the other with strains of H. cf. amphioxys (Sterre1)f, (Sterre1)h. Another large clade represents a set of strains of Hantzschia sp. 1 and Hantzschia sp. 2 (Mo1)h, (Mo1)l from Souffreau et al.16, among which there are both large cells (86–89 µm length) and smaller ones (37–39 µm length); strains also differ by striation – from 18–20 striae in 10 μm (strain (Mo1)h) to 21–22 in 10 μm (strain (Ban1)h). It is possible that Hantzschia sp. 1 and Hantzschia sp. 2 (Mo1)h, (Mo1)l may be several closely related species. Besides the large clades, there are a number of separate branches in the tree, representing separate strains: Hantzschia sp. 1 (Ban1)d, and the new species H. belgica (H. cf. amphioxys (Sterre3)a from Souffreaua et al.16) and H. stepposa. Interesting is the position of the H. abundans (Tor3)c strain, which is very distant from other representatives of H. abundans and probably is a cryptic taxon, whose taxonomic status needs to be revised.
Figure 2

Bayesian tree for representatives of the different strains Hantzschia, from an alignment with 40 sequences and 1785 characters (partial rbcL gene and 28S rDNA fragments). Type strains indicated in bold. The epitype of Hantzschia amphioxys is underlined. Values above the horizontal lines (on the left of slash) are bootstrap support from RAxML analyses ( More