Ecology

Alsdurf J, Anderson C, Siemens DH (2016) Epigenetics of drought-induced trans-generational plasticity: consequences for range limit development. Ann Bot 8:plv146
Google Scholar 

Anderson JT, Willis JH, Mitchell-Olds T (2011) Evolutionary genetics of plant adaptation. Trends Genet 27:258–66
CAS  PubMed  PubMed Central  Article  Google Scholar 

Aragón-Gastélum JL, Flores J, Yáñez-Espinosa L, Badano E, Ramírez-Tobías HM, Rodas-Ortíz JP et al. (2014) Induced climate change impairs photosynthetic performance in Echinocactus platyacanthus, an especially protected Mexican cactus species. Flora—Morphol Distrib Funct Ecol Plants 209:499–503
Article  Google Scholar 

Banerjee A, Roychoudhury A (2017) Epigenetic regulation during salinity and drought stress in plants: histone modifications and DNA methylation. Plant Gene 11:199–204
CAS  Article  Google Scholar 

Bartels A, Han Q, Nair P, Stacey L, Gaynier H, Mosley M et al. (2018) Dynamic DNA methylation in plant growth and development. Int J Mol Sci 19:2144
PubMed Central  Article  CAS  Google Scholar 

Benjamini Y, Hochberg Y (1995) Controlling the False Discovery Rate: a practical and powerful approach to multiple testing. J R Stat Soc 57:289–300
Google Scholar 

Bonasio R, Tu S, Reinberg D (2010) Molecular signals of epigenetic states. Science 330:612–6
CAS  PubMed  PubMed Central  Article  Google Scholar 

Bongers FJ, Olmo M, Lopez-Iglesias B, Anten NPR, Villar R (2017) Drought responses, phenotypic plasticity and survival of Mediterranean species in two different microclimatic sites. Plant Biol 19:386–395
CAS  PubMed  Article  PubMed Central  Google Scholar 

Bossdorf O, Richards CL, Pigliucci M (2008) Epigenetics for ecologists. Ecol Lett 11:106–115
PubMed  PubMed Central  Google Scholar 

Bossdorf O, Zhang Y (2011) A truly ecological epigenetics study. Mol Ecol 20:1572–1574
PubMed  Article  PubMed Central  Google Scholar 

Chapin FS, Autumn K, Pugnaire F (1993) Evolution of suites of traits in response to environmental stress. Am Nat 142:78–92
Article  Google Scholar 

Conrath U, Pieterse CMJ, Mauch-Mani B (2002) Priming in plant-pathogen interactions. Trends Plant Sci 7:210–6
CAS  PubMed  Article  PubMed Central  Google Scholar 

Dabros A, Fyles JW (2010) Effects of open-top chambers and substrate type on biogeochemical processes at disturbed boreal forest sites in northwestern Quebec. Plant Soil 327:465–479
CAS  Article  Google Scholar 

Delgado-Baquerizo M, Maestre FT, Rodríguez JGP, Gallardo A (2013) Biological soil crusts promote N accumulation in response to dew events in dryland soils. Soil Biol Biochem 62:22–27
CAS  Article  Google Scholar 

Diez CM, Meca E, Tenaillon MI, Gaut BS (2014) Three groups of transposable elements with contrasting copy number dynamics and host responses in the maize (Zea mays ssp. mays) genome. PLoS Genet 10:e1004298
PubMed  PubMed Central  Article  CAS  Google Scholar 

Doyle JJ, Doyle JL (1987) A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochem Bull 19:11–15
Google Scholar 

Ewens WJ (2013) Genetic variation. In: Maloy S, Hughes K (eds) Brenner’s encyclopedia of genetics, pp 290–291

Excoffier L, Smouse PE, Quattro JM (1992) Analysis of molecular variance inferred from metric distances among DNA haplotypes. Genetics 131:479–91
CAS  PubMed  PubMed Central  Article  Google Scholar 

Fernández-Pascual E, Jiménez-Alfaro B, Caujapé-Castells J, Jaén-Molina R, Díaz TE (2013) A local dormancy cline is related to the seed maturation environment, population genetic composition and climate. Ann Bot 112:937–45
PubMed  PubMed Central  Article  CAS  Google Scholar 

Forestan C, Aiese Cigliano R, Farinati S, Lunardon A, Sanseverino W, Varotto S (2016) Stress-induced and epigenetic-mediated maize transcriptome regulation study by means of transcriptome reannotation and differential expression analysis. Sci Rep 6:30446
CAS  PubMed  PubMed Central  Article  Google Scholar 

Fotiou C, Damialis A, Krigas N, Vokou D (2007) Hordeum murinum pollen as a contributor to pollinosis: important or trivial aeroallergen? Allergy 62:180
Google Scholar 

Freschet GT, Violle C, Bourget MY, Scherer-Lorenzen M, Fort F (2018) Allocation, morphology, physiology, architecture: the multiple facets of plant above- and below-ground responses to resource stress. N Phytol 219:1338–52
Article  Google Scholar 

Garnier E, Shipley B, Roumet C, Laurent G (2001) A standardized protocol for the determination of specific leaf area and leaf dry matter content. Funct Ecol 15:688–95
Article  Google Scholar 

Gayacharan JA (2013) Epigenetic responses to drought stress in rice (Oryza sativa L.). Physiol Mol Biol Plants 19:379–87
CAS  PubMed  PubMed Central  Article  Google Scholar 

Gómez JM (2004) Importance of microhabitat and acorn burial on Quercus ilex early recruitment: Non-additive effects on multiple demographic processes. Plant Ecol 172:287–297
Article  Google Scholar 

Gower JC (1971) A general coefficient of similarity and some of its properties. Biometrics 27:857–71
Article  Google Scholar 

Grigorova B, Vaseva I, Demirevska K, Feller U (2011) Combined drought and heat stress in wheat: changes in some heat shock proteins. Biol Plant 55:105–111
CAS  Article  Google Scholar 

Gu Z, Eils R, Schlesner M (2016) Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32:2847–9
CAS  PubMed  Article  PubMed Central  Google Scholar 

Herrera CM, Bazaga P (2010) Epigenetic differentiation and relationship to adaptive genetic divergence in discrete populations of the violet Viola cazorlensis. N Phytol 187:867–76
CAS  Article  Google Scholar 

Herrera CM, Pozo MI, Bazaga P (2012) Jack of all nectars, master of most: DNA methylation and the epigenetic basis of niche width in a flower-living yeast. Mol Ecol 21:2602–16
CAS  PubMed  Article  PubMed Central  Google Scholar 

Hulting AG, Haavisto JL (2013) Hare barley (Hordeum murinum ssp. leporinum) biology and management in cool season perennial grass pastures of Western Oregon. J Chem Inform Model 33:1689–99
Google Scholar 

Hussain F, Durrani MJ (2009) Seasonal availability, palatability and animal preferences of forage plants in Harboi arid range land, Kalat, Pakistan. Pak J Bot 41:539–554
Google Scholar 

Ibáñez I, Schupp EW (2001) Positive and negative interactions between environmental conditions affecting Cercocarpus ledifolius seedling survival. Oecologia 129:543–550
PubMed  Article  PubMed Central  Google Scholar 

Jakob SS, Meister A, Blattner FR (2004) The considerable genome size variation of Hordeum species (Poaceae) is linked to phylogeny, life form, ecology, and speciation rates. Mol Biol Evol 21:860–9
CAS  PubMed  Article  PubMed Central  Google Scholar 

Jaskiewicz M, Conrath U, Peterhälnsel C (2011) Chromatin modification acts as a memory for systemic acquired resistance in the plant stress response. EMBO Rep 12:50–55
CAS  PubMed  Article  PubMed Central  Google Scholar 

Jeremias G, Barbosa J, Marques SM, Asselman J, Gonçalves FJM, Pereira JL (2018) Synthesizing the role of epigenetics in the response and adaptation of species to climate change in freshwater ecosystems. Mol Ecol 27:2790–2806
PubMed  Article  PubMed Central  Google Scholar 

Jiang Y, Huang B (2000) Effects of drought or heat stress alone and in combination on Kentucky bluegrass. Crop Sci 40:1358–62
Article  Google Scholar 

Kaur A, Grewal A, Sharma P (2018) Comparative analysis of DNA methylation changes in two contrasting wheat genotypes under water deficit. Biol Plant 62:471–8
CAS  Article  Google Scholar 

Kronholm I, Bassett A, Baulcombe D, Collins S (2017) Epigenetic and genetic contributions to adaptation in Chlamydomonas. Mol Biol Evol 34:2285–2306
CAS  PubMed  Article  PubMed Central  Google Scholar 

de la Riva EG, Tosto A, Pérez-Ramos IM, Navarro-Fernández CM, Olmo M, Anten NPR et al. (2016) A plant economics spectrum in Mediterranean forests along environmental gradients: is there coordination among leaf, stem and root traits? J Veg Sci 27:187–199
Article  Google Scholar 

Lamaoui M, Jemo M, Datla R, Bekkaoui F (2018) Heat and drought stresses in crops and approaches for their mitigation. Front Chem 6:26
PubMed  PubMed Central  Article  CAS  Google Scholar 

Lampei C (2019) Multiple simultaneous treatments change plant response from adaptive parental effects to within-generation plasticity, in Arabidopsis thaliana. Oikos 128:368–379
Article  Google Scholar 

Latzel V, Allan E, Bortolini Silveira A, Colot V, Fischer M, Bossdorf O (2013) Epigenetic diversity increases the productivity and stability of plant populations. Nat Commun 4:2875
PubMed  Article  CAS  PubMed Central  Google Scholar 

Laughlin DC, Leppert JJ, Moore MM, Sieg CH (2010) A multi-trait test of the leaf-height-seed plant strategy scheme with 133 species from a pine forest flora. Funct Ecol 24:493–501
Article  Google Scholar 

Li X, Zhu J, Hu F, Ge S, Ye M, Xiang H et al. (2012) Single-base resolution maps of cultivated and wild rice methylomes and regulatory roles of DNA methylation in plant gene expression. BMC Genom 2:300
Article  CAS  Google Scholar 

Lindner M, Maroschek M, Netherer S, Kremer A, Barbati A, Garcia-Gonzalo J et al. (2010) Climate change impacts, adaptive capacity, and vulnerability of European forest ecosystems. Ecol Manag 259:698–709
Article  Google Scholar 

Lira-Medeiros CF, Parisod C, Fernandes RA, Mata CS, Cardoso MA, Ferreira PCG (2010) Epigenetic variation in mangrove plants occurring in contrasting natural environment. PLoS One 5:e10326
PubMed  PubMed Central  Article  CAS  Google Scholar 

Liu J, Feng L, Li J, He Z (2015) Genetic and epigenetic control of plant heat responses. Front Plant Sci 6:267
PubMed  PubMed Central  Google Scholar 

Liu G, Xia Y, Liu T, Dai S, Hou X (2018) The DNA methylome and association of differentially methylated regions with differential gene expression during heat stress in Brassica rapa. Int J Mol Sci 19:1414
PubMed Central  Article  CAS  Google Scholar 

Liu Z, Xin M, Qin J, Peng H, Ni Z, Yao Y et al. (2015) Temporal transcriptome profiling reveals expression partitioning of homoeologous genes contributing to heat and drought acclimation in wheat (Triticum aestivum L.). BMC Plant Biol 15:1
Article  CAS  Google Scholar 

Maestre FT, Escolar C, de Guevara ML, Quero JL, Lázaro R, Delgado-Baquerizo M et al. (2013) Changes in biocrust cover drive carbon cycle responses to climate change in drylands. Glob Chang Biol 19:3835–3847
PubMed  PubMed Central  Article  Google Scholar 

Marion GM, Henry GHR, Freckman DW, Johnstone J, Jones G, Jones MH et al. (1997) Open-top designs for manipulating field temperature in high-latitude ecosystems. Glob Chang Biol 3:20–32
Article  Google Scholar 

Mariotte P, Vandenberghe C, Kardol P, Hagedorn F, Buttler A (2013) Subordinate plant species enhance community resistance against drought in semi‐natural grasslands (S Schwinning, Ed.). J Ecol 101:763–773
Article  Google Scholar 

Mastan SG, Rathore MS, Bhatt VD, Yadav P, Chikara J (2012) Assessment of changes in DNA methylation by methylation-sensitive amplification polymorphism in Jatropha curcas L. subjected to salinity stress. Gene 508:125–9
CAS  PubMed  Article  PubMed Central  Google Scholar 

Matías L, Godoy O, Gómez-Aparicio L, Pérez-Ramos IM (2018) An experimental extreme drought reduces the likelihood of species to coexist despite increasing intransitivity in competitive networks. J Ecol 106:826–837
Article  Google Scholar 

Metzger DCH, Schulte PM (2017) Persistent and plastic effects of temperature on DNA methylation across the genome of threespine stickleback (Gasterosteus aculeatus). Proc R Soc B Biol Sci 284:20171667
Article  CAS  Google Scholar 

Mitchell P, Wardlaw T, Pinkard L (2015) Combined stresses in forests (R Mahalingam, Ed.). Springer International Publishing, Switzerland
Google Scholar 

Moles AT, Westoby M (2004) Seedling survival and seed size: a synthesis of the literature. J Ecol 92:372–383
Article  Google Scholar 

Moore LM, Lauenroth WK (2017) Differential effects of temperature and precipitation on early- vs. late-flowering species. Ecosphere 8:e01819
Article  Google Scholar 

Muller-Landau HC (2010) The tolerance-fecundity trade-off and the maintenance of diversity in seed size. Proc Natl Acad Sci USA 107:4242–4247
PubMed  Article  CAS  PubMed Central  Google Scholar 

Münzbergová Z, Latzel V, Šurinová M, Hadincová V (2019) DNA methylation as a possible mechanism affecting ability of natural populations to adapt to changing climate. Oikos 128:124–34
Article  CAS  Google Scholar 

Nicotra AB, Atkin OK, Bonser SP, Davidson AM, Finnegan EJ, Mathesius U et al. (2010) Plant phenotypic plasticity in a changing climate. Trends Plant Sci 15:684–92
CAS  PubMed  Article  PubMed Central  Google Scholar 

Ogaya R, Peñuelas J, Martínez-Vilalta J, Mangirón M (2003) Effect of drought on diameter increment of Quercus ilex, Phillyrea latifolia, and Arbutus unedo in a holm oak forest of NE Spain. Ecol Manag 180:175–184
Article  Google Scholar 

Olea L, San Miguel A (2006) The Spanish dehesa. A traditional Mediterranean silvopastoral system linking production and nature conservation. In: Sustainable grassland productivity: Proceedings of the 21st General Meeting of the European Grassland Federation

Paun O, Bateman RM, Fay MF, Hedrén M, Civeyrel L, Chase MW (2010) Stable epigenetic effects impact adaptation in allopolyploid orchids (Dactylorhiza: Orchidaceae). Mol Biol Evol 27:2465–73
CAS  PubMed  PubMed Central  Article  Google Scholar 

Pérez-Figueroa A (2013) msap: a tool for the statistical analysis of methylation-sensitive amplified polymorphism data. Mol Ecol Resour 13:522–527
PubMed  Article  PubMed Central  Google Scholar 

Pérez-Ramos IM, Cambrollé J, Hidalgo-Galvez MD, Matías L, Montero-Ramírez A, Santolaya S et al. (2020) Phenological responses to climate change in communities of plants species with contrasting functional strategies. Environ Exp Bot 170:103852
Article  CAS  Google Scholar 

Pérez-Ramos IM, Díaz-Delgado R, de la Riva EG, Villar R, Lloret F, Marañon T (2017) Climate variability and community stability in Mediterranean shrublands: the role of functional diversity and soil environment. J Ecol 105:1335–1346
Article  Google Scholar 

Pérez-Ramos IM, Matías L, Gómez-Aparicio L, Godoy Ó (2019) Functional traits and phenotypic plasticity modulate species coexistence across contrasting climatic conditions. Nat Commun 10:2555
PubMed  PubMed Central  Article  CAS  Google Scholar 

Piikkia K, De Temmerman L, Högy P, Pleijel H (2008) The open-top chamber impact on vapour pressure deficit and its consequences for stomatal ozone uptake. Atmos Environ 42:6513–22
Article  CAS  Google Scholar 

Poorter H, Niinemets Ü, Poorter L, Wright IJ, Villar R (2009) Causes and consequences of variation in leaf mass per area (LMA): A meta-analysis. N Phytol 182:565–588
Article  Google Scholar 

R Core Team (2013) R: a language and environment for statistical computing. 55: 275–286.

Reyna-López GE, Simpson J, Ruiz-Herrera J (1997) Differences in DNA methylation patterns are detectable during the dimorphic transition of fungi by amplification of restriction polymorphisms. Mol Gen Genet 253:703–710
PubMed  Article  PubMed Central  Google Scholar 

Richards CL, Verhoeven KJF, Bossdorf O (2012) Evolutionary significance of epigenetic variation. In: Plant Genome Diversity Volume 1: Plant Genomes, their Residents, and their Evolutionary Dynamics, pp 257–274

Rizhsky L (2004) When defense pathways collide. the response of Arabidopsis to a combination of drought and heat stress. Plant Physiol 134:1683–96
CAS  PubMed  PubMed Central  Article  Google Scholar 

Rodríguez-Calcerrada J, Letts MG, Rolo V, Roset S, Rambal S (2013) Multiyear impacts of partial throughfall exclusion on Buxus sempervirens in a Mediterranean forest. Syst 22:202–213
Google Scholar 

Seifan M, Tielbörger K, Kadmon R (2010) Direct and indirect interactions among plants explain counterintuitive positive drought effects on an eastern Mediterranean shrub species. Oikos 119:1601–9
Article  Google Scholar 

Sharifi-Rigi P, Saeidi H, Rahiminejad MR (2014) Genetic diversity and geographic distribution of variation of Hordeum murinum as revealed by retroelement insertional polymorphisms in Iran. Biology 69:469–77
Google Scholar 

Shen X, De Jonge J, Forsberg SKG, Pettersson ME, Sheng Z, Hennig L et al. (2014) Natural CMT2 variation is associated with genome-wide methylation changes and temperature seasonality. PLoS Genet 10:e1004842
PubMed  PubMed Central  Article  CAS  Google Scholar 

Suzuki MM, Bird A (2008) DNA methylation landscapes: Provocative insights from epigenomics. Nat Rev Genet 9:465–76
CAS  PubMed  Article  PubMed Central  Google Scholar 

Tan MP (2010) Analysis of DNA methylation of maize in response to osmotic and salt stress based on methylation-sensitive amplified polymorphism. Plant Physiol Biochem 48:21–6
CAS  PubMed  Article  PubMed Central  Google Scholar 

Tani E, Polidoros AN, Nianiou-Obeidat I, Tsaftaris AS (2005) DNA methylation patterns are differently affected by planting density in maize inbreeds and their hybrids. Maydica 50:19–23
Google Scholar 

Valencia E, Méndez M, Saavedra N, Maestre FT (2016) Plant size and leaf area influence phenological and reproductive responses to warming in semiarid Mediterranean species. Perspect Plant Ecol Evol Syst 21:31–40
PubMed  PubMed Central  Article  Google Scholar 

Verhoeven KJF, Jansen JJ, van Dijk PJ, Biere A (2010) Stress-induced DNA methylation changes and their heritability in asexual dandelions. N Phytol 185:1108–18
CAS  Article  Google Scholar 

Vos P, Hogers R, Bleeker M, Reijans M, Van De Lee T, Hornes M et al. (1995) AFLP: a new technique for DNA fingerprinting. Nucleic Acids Res 23:4407–14
CAS  PubMed  PubMed Central  Article  Google Scholar 

Wang WS, Pan YJ, Zhao XQ, Dwivedi D, Zhu LH, Ali J et al. (2011) rought-induced site-specific DNA methylation and its association with drought tolerance in rice (Oryza sativa) L.). J Exp Bot 62:1951–60
CAS  PubMed  Article  PubMed Central  Google Scholar 

Watson RGA, Baldanzi S, Pérez-Figueroa A, Gouws G, Porri F (2018) Morphological and epigenetic variation in mussels from contrasting environments. Mar Biol 165:50
Article  CAS  Google Scholar 

Westoby M (1998) A Leaf-Height-Seed (LHS) plant ecology strategy scheme. Plant Soil 199:213–227
CAS  Article  Google Scholar 

Whittington HR, Tilman D, Wragg PD, Powers JS, Browning DM (2015) Phenological responses of prairie plants vary among species and year in a three-year experimental warming study. Ecosphere 6:1–15
Article  Google Scholar 

Wolkovich EM, Cleland EE (2014) Phenological niches and the future of invaded ecosystems with climate change. AoB Plants 6:plu013
PubMed  PubMed Central  Article  Google Scholar 

Wright IJ, Reich PB, Westoby M, Ackerly DD, Baruch Z, Bongers F et al. (2004) The worldwide leaf economics spectrum. Nature 428:821–7
CAS  Article  Google Scholar 

Zhang Y-Y, Parepa M, Fischer M, Bossdorf O (2016) Epigenetics of colonizing species? A study of japanese knotweed in central Europe. In: Barrett SC., Colautti RI, Dlugosch KM, Rieseberg LH (eds) Invasion genetics: the baker and Stebbins legacy, John Wiley & Sons, Ltd, pp 328–340

Zhang X, Yazaki J, Sundaresan A, Cokus S, Chan SWL, Chen H et al. (2006) Genome-wide high-resolution mapping and functional analysis of DNA methylation in arabidopsis. Cell 126:1189–201
CAS  PubMed  Article  PubMed Central  Google Scholar 

Zhu JK (2016) Abiotic stress signaling and responses in plants. Cell 167:313–24
CAS  PubMed  PubMed Central  Article  Google Scholar  More

Miniature flight recorders
Honey bees regularly carry a payload of 55–65 mg6, but a mounted flight recorder should consume only a small fraction of this allowable weight to avoid significantly affecting bee behavior. The dimensions of the recorder must also be small, as the available mounting area on the bee thorax is limited. We propose a flight recorder consisting of a (2times 2times 0.3,text {mm}^3) ASIC mounted on a (3times 3times 0.4,text {mm}^3) printed circuit board, which is similar in size to 3 mm diameter, 1.5 mm tall conventional bee tags (“Queen number set,” Betterbee) and is on par with previous studies using honey bee tagging methods30. The ASIC provides most core functionality including signal detection, memory, power harvesting, and communications circuitry, and the PCB provides a magnetic backscatter coil for near-field wireless communication. The combined weight of our chip-PCB assembly is expected to be at most 10 mg, which is a small fraction of the honey bee payload (Fig. 1c). Based on previous studies, we expect this may slightly reduce foraging trip time but not significantly impact flight characteristics, which is the focus of our system31. Furthermore, future iterations of our flight recorder will have smaller size and weight, minimizing the overall impact on honey bees. Power will be harvested from sunlight, which can provide intensity greater than 1 mW/mm(^2). On-chip photovoltaics can offer power conversion efficiency on the order of 5%, supplying 50 μW of electrical power for the chip. This power budget, while low, is sufficient for the chip, since solar angle measurement and storage of data in memory are not energy-intensive operations and need only occur a few times per second. Furthermore, wireless communication for data upload is only used when the recorder is at the base station, thus allowing the base station to fully power the near-field wireless link. Additionally, IC technology is generally robust to the environmental factors likely to be encountered by honey bees. For instance, the variations in humidity level and temperature experienced by honey bees are not expected to affect chip operation. Our proposed design is a fully power-autonomous, environmentally-robust, miniature flight recorder well-suited to the task of recording honey bee activity.
The orientation of a bee during flight can be described by the yaw ((gamma)), which represents the absolute heading relative to the sun, and the angle-of-incidence (AOI) ((psi)), which represents the overhead angle between the sun and the sensor (Fig. 1b). To record flights, the chip uses ASPs to measure the AOI of sunlight and stores these measurements in on-chip memory. ASPs achieve AOI-sensitivity via a pair of diffraction gratings stacked over a photodiode (Fig. 1b), wherein the first grating induces a diffraction pattern that shifts laterally across the second grating as AOI is swept, thus passing a periodically-varying intensity of light to the photodiode. The stored AOI measurements can be downloaded to a base station upon return to the hive, and from these data the heading throughout the flight can be extracted. Assuming a constant speed of 6.5 m/s32, we can use the sequence of recorded headings to reconstruct the honey bee’s trajectory in post-processing.
The data taken by the flight recorder will be subject to measurement errors, and these errors will manifest in the reconstructed trajectory. Here, we identify and explore methods to mitigate the primary sources of error. We posit that errors will stem primarily from finite heading measurement resolution, finite sampling rate, and random fluctuations in sampling rate (jitter). Each of these error sources can be suppressed through careful chip design, but improvements in these performance variables can only be made at the expense of larger chip area. For instance, the heading measurement resolution will increase if more ASPs are used to measure AOI, but each pixel consumes significant silicon area and contributes additional data that must be stored in memory. Increasing the sampling rate and decreasing sampling rate jitter requires that more measurements be stored if flight time is unchanged, thus increasing the chip area required for memory. The size of the chip is thus inversely related to the severity of the expected measurement error, and trade-offs between chip size and achievable precision should be examined. A smaller sensor is feasible if trajectory reconstruction performance requirements are relaxed; more stringent requirements will necessitate a larger chip that will increase the burden on the bee. If the relationship between final uncertainty in the reconstructed position of the bee and the core sensor specifications is understood, then chip-level performance goals can be formed based on trajectory-level precision requirements.
To determine required heading resolution, sampling rate, and sampling rate jitter, we first describe the procedure to reconstruct trajectories. We define the timestep estimate (hat{Delta t}) as the inverse of the sampling rate, and for known flight speed v the sequence of measured heading estimates ({hat{gamma }_0, hat{gamma }_1, …, hat{gamma }_{n-1}}) can be mapped to position estimate (hat{mathbf {p}}_n = [hat{x}_n, , hat{y}_n]^T) as a function of discrete time index n via the motion model

$$begin{aligned} hat{mathbf {p}}_n = sum _{i=0}^{n-1} v, mathbf {h}(hat{gamma }_i) hat{Delta t} end{aligned}$$
(1)

where h is a unit vector pointing along the heading of the bee. Each sensor estimate of heading and timestep will be subject to errors, and by modeling these errors as additive white noise, we can evaluate a confidence region for each position estimate (hat{mathbf {p}}_n) (Fig. 2a,b). Each analog heading measurement (hat{gamma }_i) must be digitized for storage on-chip and will therefore suffer from quantization error, a form of rounding. We denote this error (epsilon _{gamma ,i}) and model it as a uniform random variable with variance (sigma ^2_gamma) on the interval (pm frac{Delta gamma }{2}), where (Delta gamma) is the heading bin width. Furthermore, the timestep estimate (hat{Delta t}) will be subject to random clock jitter that can be modeled as a Gaussian random variable (epsilon _{Delta t,i}) with variance (sigma ^2_{Delta t}). In this model, we posit for simplicity that the random error in timing scales linearly in proportion to oscillator frequency, thus maintaining a fixed ratio of (sigma _{Delta t}/hat{Delta t}). These measurement errors contribute random error to position estimate ({hat{mathbf {p}}}_n), and thus each position estimate should be viewed as a random variable (mathbf {p}_n). The confidence region surrounding ({hat{mathbf {p}}}_n) depends on the covariance matrix of ({mathbf {p}_n}), and we evaluate these terms by first using the small-angle approximation to linearize the motion model with respect to (epsilon _{gamma ,i}):

$$begin{aligned} mathbf {p}_n = sum _{i=0}^{n-1} v , mathbf {h}(hat{gamma }_i + epsilon _{gamma ,i}) (hat{Delta t}+epsilon _{Delta t, i}) &approx sum _{i=0}^{n-1} v , left( mathbf {h}(hat{gamma }_i) + mathbf {h}^perp (hat{gamma }_i)epsilon _{gamma ,i}right) (hat{Delta t}+epsilon _{Delta t, i}) = sum _{i=0}^{n-1} v , mathbf {h}(hat{gamma }_i)(hat{Delta t}+epsilon _{Delta t,i}) + sum _{i=0}^{n-1} v , mathbf {h}^perp (hat{gamma }_i)epsilon _{gamma ,i} (hat{Delta t} + epsilon _{Delta t, i}) end{aligned}$$
(2)

Figure 2

(a) Sensor measurement errors produce confidence regions surrounding each estimated position shown in an example circular trajectory. (b) Zoomed-in view of confidence region at end of flight, with principle components shown. (c) The two directed standard deviations grow throughout the duration of the trajectory, and are bounded above and below by the directed standard deviations computed from the case of the straight-line trajectory. (d–f) Both directional standard deviations characterizing the final error region will depend on all three of the core sensor specs ({Delta gamma , hat{Delta t}, sigma _{Delta t}}), and the max confidence region dimension will grow if these specs are relaxed. Plots were created in MATLAB33.

Full size image

This approximation is valid if (epsilon _{gamma ,i}) is kept small, which can be guaranteed by keeping heading bin width (Delta gamma) small. The covariance matrix of (mathbf {p}_n) is then given by

$$begin{aligned} mathrm {Cov}(mathbf {p}_n, mathbf {p}_n) = varvec{ Sigma }_n approx sum _{i=0}^{n-1} R(hat{{gamma }}_{i}) begin{bmatrix} v^2sigma ^2_{Delta t} &{} 0 \ 0 &{} v^2sigma ^2_{gamma }(sigma _{Delta t}^2 + (hat{Delta t})^2) end{bmatrix} {R^T({hat{gamma }}_i)} end{aligned}$$
(3)

where R is the standard 2 × 2 rotation matrix (Appendix: Derivation of Trajectory Precision Equation). By the Central Limit Theorem, after a sufficient number of timesteps (mathbf {p}_n) will become Gaussian distributed. Thus, the confidence region will be an ellipse with major and minor axes spanned by the eigenvectors ({mathbf {v}_1, mathbf {v}_2}) of ({varvec{Sigma }}_n). The covariances of the confidence region along each of these axes are given by the eigenvalues ({lambda _1, lambda _2}) of (varvec{Sigma }_n), and directed standard deviations can then be defined as ({sigma _1,sigma _2}). A 99% confidence region for position estimate ({mathbf {hat{p}}}_{n}) is given by an ellipse with major and minor axes lengths ({3sigma _1mathbf {v}_1, 3sigma _2mathbf {v}_2}). We therefore conclude that (3sigma _1) and (3sigma _2) are critical values defining achievable trajectory reconstruction precision.
These (3sigma)-bounds can be computed for any measured sequence of headings, but general upper and lower bounds for (3sigma _1) and (3sigma _2) across all possible trajectories can be derived from the (3sigma _1) and (3sigma _2) given by the case in which the bee flies in a straight line. In the straight-line case, the eigenvalues of (varvec{Sigma }_n) are given by n times the diagonal entries of the diagonal matrix in Eq. (3). The directed (3sigma)-bounds are then

$$begin{aligned} 3sigma _{1,n}^*= & {} 3sqrt{n} v sigma _{Delta t} end{aligned}$$
(4)

$$begin{aligned} 3sigma _{2,n}^*= & {} 3sqrt{n} v sigma _{gamma }sqrt{sigma _{Delta t}^2 + (hat{Delta t})^2} end{aligned}$$
(5)

For some values of ({sigma _{gamma }, hat{Delta t}, sigma _{Delta t}}), (3sigma _{1,n}^*) will be larger than (3sigma _{2,n}^*); for others, the converse will be true. These equations provide simple bounds on achievable reconstruction precision that are valid for any trajectory and can be computed from sensor characteristics. Since heading error (epsilon _{gamma _i}) is uniformly distributed, heading variance (sigma ^2_gamma) is defined by heading bin width (Delta gamma), and thus the reconstruction precision is defined by a core suite of sensor specifications: ({Delta gamma , hat{Delta t}, sigma _{Delta t}}). An illustration of the trajectory reconstruction process, along with confidence regions, is shown in Fig. 2, as well as the relationship between reconstruction precision and each of the core chip specs.
For a maximum specified chip sensing area, trajectory precision should be optimized through balanced allocation of area to solar AOI detection and to memory (Fig. 3). We evaluate the optimal area allocation by defining the standard deviation upper bound (3sigma ^*_{max} = max (3sigma _{1,f}^*, 3sigma _{2,f}^*)), where (3sigma _{1,f}^*) and (3sigma _{2,f}^*) are the directed (3sigma)-values computed from a straight-line trajectory that is long enough to completely fill the memory. If more area is spent on pixels for AOI detection, heading resolution can be increased, thus causing (sigma _gamma) to be reduced and correspondingly lowering (3sigma ^*_{2,f}). Conversely, if more area is spent on memory, measurements can be taken more frequently, and timestep and clock jitter can be reduced, thus reducing (hat{Delta t}) and (sigma _{Delta t}). This will cause (3sigma ^*_{1,f}) to decrease, but may cause an increase in (3sigma ^*_{2,f}) since less area is now available for heading sensors. As shown in Fig. 3, the upper bound (3sigma ^*_{max}) minimizes when the two counteracting variables (3sigma ^*_{1,f}) and (3sigma ^*_{2,f}) are equal, and this intersection point prescribes the optimal allocation of sensor area. Our proposed flight recorder features a 4 (mathrm {mm}^2) chip, which can offer sensing area of approximately 3 (mathrm {mm}^2). When this area is allocated optimally, the heading resolution is (2^circ) and timestep is approximately 240 ms at timestep jitter of (3sigma _{Delta t}/Delta t = 0.03). With these specifications, the maximum recordable trajectory length is approximately 4 km, with (3sigma) uncertainty of (pm 2.4) m.
Figure 3

(a) Area usage is optimized where (3sigma ^*_{1,f}) and (3sigma ^*_{2,f}) cross, as this design point co-minimizes the two directional standard deviations characterizing trajectory precision. (b) This design point specifies the heading resolution and number of data words in memory that minimize trajectory uncertainty given a fixed sensor area constraint. Plots were created in MATLAB33.

Full size image

Sensor calibration and noise modeling
We next examined the output from existing ASP array sensors in order to create a model for future flight recorders. These particular sensors have 96 pixels, or 24 sets of 4 oriented in 90° angles. Specifically, we designed a calibration apparatus that consists of a platform holding the ASP array driven by a custom microcontroller PCB and an arm with a light emitting diode (LED) to imitate the sun. The platform rotates to mimic a change in yaw, while the arm rotates to mimic different AOI solar light at different times of the day (Fig. 4a). Using this apparatus, we measured light input in 0.9° increments across the entire hemisphere and record the ASP array response (Fig. 4a inset) for a total resolution of 40,000 measurements in a single sweep with 200 AOI angles and 200 yaw angles. Measurements sampled by the microcontroller were transmitted to a desktop computer for logging and processing via a custom MATLAB33 script. Each ASP array response consists of a 48-bit sequence. To interpret the output, we created a lookup table from the unique 48-bit sequence that is stored for each yaw-AOI angle pair. Future data was then compared to these stored sequences in parallel using an XOR operation and the pair with the least difference in bit values was returned.
Figure 4

(a) Calibration apparatus with inset example of a measured ASP quadrature response. (b) ASP array repeatability over a single sensor (left) and multiple sensors (right). The magenta lines denote the expected operating region. (c) Expected system operating region (45°–75°) shown in magenta given the peak foraging hours (10 a.m.–4 p.m.) shown in grey and the AOI solar light during the Summer in Ithaca, NY. Plots in (a-inset), (b) and (c) were created in MATLAB33.

Full size image

We characterized sensor repeatability by repeating a sweep three times with a single ASP array and, similarly, characterized precision by comparing sweeps from two additional ASP arrays. A sample curve from the calibration sweep seen in the inset in Fig. 4a shows the characteristic angle dependence. The sensor exhibits poor response uniqueness when the sun approaches zenith and when it nears the horizon; upwards of 100(^circ) error in yaw near 90(^circ) AOI (zenith) and up to 180(^circ) error in yaw at 0°–25° AOI (horizon) (Fig. 4b). The former occurs because the position of a light source directly overhead is ambiguous to the sensor across yaw and consequently, indeterminate. We find that the sensor simply does not operate well in the latter region where light is arriving nearly parallel to the surface of the chip. Furthermore, as is expected, the difference in response is generally greater when comparing different sensors. The horizontal dark bars in the right graph in Fig. 4b are examples of this increased error. We expect our system to operate under favorable foraging conditions. Based on previously published data, we estimate this operating region to be May through September at an example location of the authors’ hometown of Ithaca, New York, USA, with the most active foraging hours being from 10 a.m. to 4 p.m.34. During this time, the AOI spans (45^circ) to (75^circ) (Fig. 4c). Within this region, we see a significantly reduced same-chip error in yaw with a mean and standard deviation of (1.52^circ pm 1.23^circ). We compensate for the remaining error within the operating region as discussed in the following sections.
In order to realistically simulate sensor output, we create a lookup table with an error model for each individual AOI value. Similar to our theoretical model, we fit a normal distribution to error in the yaw angle measured at each AOI. We use this error model to inform reconstruction of recorded bee paths as described in the following sections. For this work, we assume that we have access to calibration data for each particular sensor, however, given the low discrepancy between sensors (Fig. 4b right), we believe that it is possible to avoid individual calibration with more sophisticated data processing. We leave this aspect for future work.
Honey bee foraging simulation
To properly develop our methodology for using instrumented bees to monitor the state of pollination and bloom, we designed a colony foraging simulator with an example apple orchard. Central to our approach is an understanding of the behavior and environmental conditions surrounding honey bee foraging, summarized in Fig. 1c. The following subsections detail orchard, honey bee motion, and colony foraging models.
Orchard model
We modelled the orchard based on common characteristics seen in real orchards (Fig. 5a) as well as those reported by the University of Vermont Cooperative Extension for Growing Fruit Trees35. Specifically, these include a tree trunk radius of 0.15 m, separation between individual trees in a given planted row as 2.4 m, and separation between rows as 5 m. To make the model realistic to a variety of orchards, we add randomness to the trunk radius (0.15–0.30 m) and to the tree locations (up to 0.5 m in any direction). We further use a 60 × 60 m2 area with 200 trees, as is representative of the common grower practice utilizing a single colony per acre36. We account for the fact that trees can be in different stages of bloom by assigning each a randomly generated quality factor between 1 and 10; this quality factor affects the number of feeding events in a flight.
Colony foraging model
A high-quality honey bee colony for commercial apple pollination contains a laying queen, developing brood, and 20,000–40,000 worker bees, of which approximately 25% are “foragers”, or those that leave the hive to collect pollen, nectar, resin, and water29,37,38. Since resin and water foragers are a small proportion of the forager workforce, we expect our flight dataset to be largely from bees that are visiting flowers39. For this work, we assume favorable foraging conditions as previously described and a colony size of 35,000, 25% of which are foragers for a total of 8750 foragers conducting ~ 36,000 flights per day (an average of 4 flights per forager)37,40. In our model, a forager can perform either a learning-, return-, or scout flight. Note that we exclude orientation flights, which are conducted by new foragers, under the assumption that these can be easily classified given their tortuous nature23. A learning flight is when a bee orients to a feeding site it has not previously visited after learning the bearing and distance from one of its sisters in the hive41,42. A return flight occurs when a bee orients to a feeding site it has previously visited, and can be thought of as an optimized version of the learning flight in terms of distance flown43. A scout flight occurs when a forager leaves the hive to search independently for new feeding sites. In our model, we make the assumption based on published behavioral research that 20% of foragers are acting as scout foragers and the remaining 80% perform an initial learning flight followed by return flights to the same source41,42,43.
We incorporate that return flights will frequent the same feeding sites and that neighboring trees are likely to bloom together by randomly assigning initial goal locations (trees that bees advertise in the colony as high quality food sources) to 5 neighboring trees. Bees will randomly choose between these 5, then continue feeding on neighboring trees until they have visited trees with quality factors accumulating to at least 10 before returning to the hive. Scout foragers randomly visit trees in the orchard. Realistically, not all bees will be tagged and some tags will be lost. Here, we consider a conservative estimate that at least 430 or 5% of all foragers will be tagged, leading us to 1750 recorded flights per day, and use accumulated data to overcome the loss of tagged bees, which we expect to occur as a result of predation, senescence, stress, and other factors. Note that honey bees have a pronounced division of labor associated with worker age41, making it easy to tag a cohort and wait for them to become foragers, or to identify foragers and tag them specifically. Tagging 430 bees would take our honey bee technician approximately a day; speeding up this process is an area of future investigation.
Honey bee motion model
To better illustrate the characteristics of foraging flights, we recorded activity between a queenright colony with about 10,000 workers and a nearby feeder station (Fig. 5b–d). Three distinct phases of the bee flights were recorded: an initial orientation flight upon leaving the hive, flights between the hive and the feeder, and search flights near the feeder. Flights near the entrance and the feeder were characterized by rapid turning, whereas flights in between the hive and the feeder were nearly straight “bee lines”. While at the feeder, bees crawl around at a significantly reduced velocity.
Figure 5

(a) Photo of a honey bee in a conventional apple orchard. (b–d) Setup to showcase different types of honey bee flights. Still images from videos recorded at the hive entrance, in between, and at the feeder station, with tracked paths overlaid. (e–f) Recorded heading over the course of a simulated foraging flight and feeding event. Straight line flights are marked in grey, turns in blue, and feeding events in green. Image overlays in (b), (c) and (d) were created in MATLAB33. Plots in (e) and (f) were created in Python 3.7.

Full size image

We use this study to inform our foraging simulation. We assume generally straight paths in obstacle-free environments, slow turns when avoiding obstacles, and rapid turns in AOI and yaw when nearing and crawling on a food source. We further base our flight model on the following assumptions, summarized in Fig. 1c. (1) We assume the starting location is well known since the flight path will always originate and terminate at the hive entrance. (2) Based on past studies and the fact that our simulation takes place in a dense apple orchard, we assume most flights will be within the 4 km range of our flight recorder44,45,46. When leaving the hive, bees will fly an average of ~ 7.5 m/s, but once loaded with nectar, flight speed is reduced to ~ 6.5 m/s32. Here, we assume a constant velocity of ~ 6.5 m/s. (3) Based on prior honey bee tracking studies23,47, we represent flight in only two dimensions. The apple trees in the orchards we model are not tall and bees will therefore experience much greater motion in the horizontal plane than the vertical. Regardless of whether a bee in reality will fly over or under the canopy, we can model this issue in two dimensions. Turns around tree trunks represent the largest source of error for flight reconstruction, therefore by modeling flight under the canopy, we model the “worst case” scenario. (4) We estimate that the AOI of sunlight with respect to the orchard will remain within a quantifiable margin throughout the duration of the simulated flights, as bees have been found to spend an average of 20–45 min on foraging flights48. (5) We model the yaw of a bee as constant during bee-line flights, i.e. given no nearby obstacles. When the bee changes its heading to circumvent obstacles, this causes a change in yaw. (6) Once implemented on bees, we expect to be able to add sensors near the hive which would help us acquire current temperature and weather patterns as well as other dynamic factors specific to a particular environment for calibrating our model.
To simulate scouting, learning, and return foraging flights, we combine the honey bee motion model previously described, with the Bug2 algorithm and grid-based path planning49. Grid based path planning uses a discrete grid of points over which an agent searches to find obstacle-free path segments. We compute scout and learning flights as follows. Using the Bug2 algorithm, honey bee paths are generated by first assuming direct flight along a known heading from the hive. Once an obstacle is encountered, the bee searches for a path around it, until it can once more move unhindered toward the goal. The process is repeated until all goals are reached and the bee has returned to the hive. Since no two paths are identical in nature, we plan obstacle navigation with a randomly generated grid. Return flights are found by forming a graph of all the points visited during a learning flight and using a Dijkstra’s search algorithm49 to find the shortest path through these points from the hive to the goal. Once paths are generated, we compute the sequence of headings given the 240 ms sensor sampling frequency reported earlier. An example flight is shown in Fig. 5e,f. These headings are then discretized based on the ASP calibration data discussed earlier, and noise is added given the error shown in Fig. 4c left.
When bees land at a feeding site, they tend to crawl on and among flowers to gather nectar and pollen. We simulate this by generating random motion centered around the feeding site. The average feeding time was reported to be 1–2 min per feeding site6. To make the simulation more realistic, we randomly generate a feeding time between 60 and 120 s for foragers, and between 20 and 130 s for scouts. We furthermore assume that the AOI of sunlight changes as the honey bee tilts up and down while crawling on flowers.
Path reconstruction and generation of foraging activity maps
Path reconstruction inherently depends on the accuracy with which our sensor is able to describe the motion of an instrumented bee. Beyond limited angle resolution, errors related to the sampling rate accumulate when turns occur, at worst (v_{loaded} Delta t) = 1.6 m. To increase the accuracy of our foraging activity maps, we use models of sensor noise and flight speed, and leverage all recorded flights. The full workflow is shown in Fig. 6, where the foraging simulation portion generates the data we expect if our sensors are placed on actual bees, and the remaining flow is the data processing portion of our methodology.
Figure 6

Flow chart combining the colony foraging simulation, simulated flight recorder, path reconstruction, and data processing to output the final foraging activity map. Recorded heading plot made in Python 3.7. Other plots were created in MATLAB33.

Full size image

We first identify feeding and turn features in our path data that stand out above the noise floor. Feeding features consist of crawling behavior in which the bee is moving at much lower speed compared to flying, but with rapidly varying yaw and AOI, inducing an elevated rate of change in measured AOI. A turn is marked by a significant change in yaw. Given the time of day and location, we can find the expected AOI on the orchard and use this to find the yaw from our lookup table. Our algorithm then indexes the sensor noise lookup table to find the related mean and standard deviation and uses this to estimate turns and feeding status. Our method marks a turn for a change greater than three standard deviations in yaw ((6.06^circ)). A feeding site is marked if the detected AOI deviates more than three standard deviations ((3.72^circ)) from the one expected, or if two consecutive AOI samples deviate more than 3 standard deviations, adjustable depending on the total flight time. The average detection accuracy of a turn is 99% with a standard deviation of 0.28% and average detection accuracy of a feeding site is 99% with a standard deviation of 0.29%.
We explore three methods to generate activity maps: from the raw data, we classify feeding features using the aforementioned statistical approach and produce maps based on accumulated path reconstructions; in “iteration 1” and “iteration 4” we take a particle filter approach to improve path localization based on knowledge of the hive and frequented feeding sites respectively. Particle filters are used to track a variable of interest over time by creating many representative particles, generating predictions according to dynamics and error models, and then updating them according to observation models50. In this case, each particle forms a candidate trajectory and predictions are based on speed, sensor readings, and the sensor error model. Assuming that we start without knowledge of the orchard, we build up an observation model by reconstructing and accumulating the raw flight paths (Fig. 6, raw data). To account for the fact that bees may turn at any point between sensor readings, we then upsample our sensor readings by a factor of 8, essentially producing 8 guesses for where the bee actually turned. Based on the artificially upsampled data and a random sample from the sensor noise distribution at a given AOI, we then generate the displacement in each path segment as follows:

$$begin{aligned} begin{bmatrix} Delta x_{t} \ Delta y_{t} end{bmatrix} = v Delta t begin{bmatrix} cos {(gamma _{t} + gamma _{noise})} \ sin {(gamma _{t} + gamma _{noise})} end{bmatrix} end{aligned}$$
(6)

The final path is found as a cumulative sum of these displacements. We repeat this process to generate 5000 particles for each flight. The choice of 5000 is guided by our variable dimensions; the upsampling rate of 8 was the highest we could handle on a quadcore desktop computer with 16 GB RAM—to truly represent all potential turns we would need a number of particles equal to 8 to the power of the number of turns per flight. For reference, the average number of turns per flight is 13, thus the true representation in our sampling approach would require (8^{13}) particles.
In iteration 1, we choose ten of these particles according to proximity to the hive upon return, and use the feeding features from these to form an initial foraging activity map, represented by a discrete grid with computed visit numbers. Note that in a typical localization approach a single particle, or average of several particles, is chosen as the final reconstruction. In our approach, we retain 10 different reconstructions for each individual path in order to better represent the distribution of points in a path due to sensor noise.
After this first pass, we repeat the process, but now filter particles by using the initial activity map as an observation model for the particle filter (Fig. 6, iteration 2–4). Specifically, we update particle probability at each detected feeding site by assigning the probability of the nearest grid cell in the map to the particle, and then sampling the particles by weight. At the end of the process, the ten particles with the highest probability product are used to construct an updated activity map. 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

Present study aimed to develop a risk model to identify the risk localities in the dengue high risk areas. Kernel density and Euclidean distance based approaches are widely used in raster development of GIS modelling. Kernel density was used to fit a smoothly tapered surface to point layers while Euclidean distance was used to identify close exposures of polygon layers17. The risk values were ranked for each layer depending on their contribution to the transmission of dengue incidences. Based on the ILWIS Applications Guide18, the maximum risk value for the developed model was assigned as 10. Previous study conducted on mathematical modelling of dengue incidences in the Gampaha District have stated exponential influence of previous month cases on current month disease transmission in the district19. Further, investigation on adult and immature stages of dengue vector mosquitoes indicated that DENV are present in adult dengue vector mosquitoes and significant correlations of entomological indices with patient cases in the same district20,21. Therefore, patient locations and positive breeding container layers were selected as maximum risk variables and assigned the risk value of 10 as these variables are directly involved in disease transmission. In the modelling, dispersed risk distance patient cases and breeding places were selected less than average flight distance of dengue vector mosquitoes which is 400m22. Further, these study areas are considered to be highly congested areas and therefore, total building and home garden layers were given second highest ranking. Previous study conducted in Indonesia reported that consistent high number of dengue cases in larger areas of buildings even though the correlation is weak23. Further, higher dengue vector population densities were reported around home gardens from many countries24,25,26. Therefore, moderate risk level, risk value of 6, was assigned to total buildings and home garden layers. Recent study conducted in the Gampaha District demonstrated the contribution of daily commutes of people for transmission of dengue in the district27. When people visited to urban areas, there is higher probability of acquiring of dengue as these urban and suburban areas may act as dengue hot spots and artificial reservoirs which has been documented previously in Sri Lanka as well as other countries28,29,30,31. Therefore, land use layer for urban areas was given third highest ranking in the risk model. Another study conducted in Sri Lanka reported roads are important aspects for transmission of dengue25 and households in the present study areas located along main roads or have access roads. Further, previous study in Sri Lanka reported the potentials of public places play as artificial reservoirs dengue32 because of higher prevalence of breeding places around public places. It is a well-known fact that distribution of dengue vector mosquitoes varies with the elevation depending on geographical areas. Therefore, roads, public places and elevation layers were ranked in the third position with risk level of 4. However, the lower risk distances were assigned to road and contour layers as the layers are not related directly for transmission of the disease even though they play important role. Previous study conducted in Kenya and Uganda has reported higher dengue vector mosquito populations close to vegetation and marshy lands33 which may provide resting places of dengue vectors, especially for male mosquitos. When considering the study areas, with the exception of the 3rd Kurana study area, all other study areas have close proximity to marshy areas and therefore these areas were included as a variable in the present study. Since it is not directly involving in mosquito population increase or disease transmission, the lowest rank was assigned to marshy areas of land use layers.
When comparing the generated risk maps with satellite imageries, vegetation covers were observed in high risk localities in all study areas. The reason could be the vegetation covers make better resting places for dengue vector mosquitoes. Even though the Ae. aegypti mosquitoes, the main vector of DENV, rest indoors34, previous studies conducted in Malaysia and Kenya reported the preference of Ae. aegypti to rest and breed outdoors due to increased breeding opportunities without affecting lifespan or gonotrophic activity35,36. Meanwhile, it is well-known fact that Ae. albopictus, the subsidiary vector of DENV, prefers vegetation to rest and breeding in both natural and man-made containers37,38.
When comparing the intensity maps generated from the Poisson point process model with generated risk maps, differences in localization of intensities were observed specially in Eriyawetiya and Welikadamulla study areas. In the risk map of Eriyawetiya study area, risk localities were located mainly along the roads in the area and this observation was even statistically significant in Pearson correlation analysis. However, when considering the intensity map from the Poisson point process model, lower predicted intensity was observed in most of the locations in the study area and high intensities were observed around the southern border along the Devasumithrarama road and in central area. When considering the Welikadamulla study area, even though risk map indicates that dengue is high virtually all over the area, the predicted intensity map illustrates that dengue may high in central and northern border of the area along the Welikadamulla road. Interestingly, while the dengue high intensity localities in both Eriyawetiya and Welikadamulla study areas are mainly used as home gardens, these localities have close proximity to crowded public places, such as schools, temples, community halls, etc. Perhaps, these public places may have acted as artificial reservoirs of dengue. This is further observation in the high density localities in Akbar Town and 3rd Kurana study areas. In the Akbar Town area, high intensities were observed around mosques. In the 3rd Kurana study area, many public places, such as schools and churches, are located in the central and southern area where intensities were high. However, the lowest dengue intensities were observed from the 3rd Kurana study area.
In the Poisson point process model, highest intensity range was observed in the Eriyawetiya study area while the lowest was observed from 3rd Kurana. Eriyawetiya study area is located close to the northern border of Colombo, the commercial capital in Sri Lanka, where highest number of dengue cases are reported in the country39. Recent study reported that human commutes to risk areas in Colombo and transportations may play significant role transmission of dengue in the nearby areas, such as Eriyawetiya study area, leading to higher intensities21. However, the overall lowest intensities reported from 3rd Kurana study area may be due to continuous encouragement of dwellers in the area to remove dengue vector mosquito breeding places and use of protective measures by the churches and clergies.
The results of Pearson correlation analysis and Poisson multivariate point process model were also different especially with respect to positive breeding locations and roads layers. Positive correlation was observed between breeding places and patient locations in Pearson correlation analysis, which can be expected as dengue vector mosquitoes are anthropophilic mosquitoes with low flying ranges, were different from the results of Poisson point process model. In the model, no or negative correlation was observed between patient locations and breeding places. In a multivariate model, all explanatory variables are modelled to capture the true variation of the response variable while in Pearson correlation only one explanatory variable is considered at a time. The negative correlation in Poisson model with breeding places may be due to the hidden breeding places. These breeding places may be unidentified due to level of personal expertise, restrictions of accessibility to household, limitations due to inadequate resources, etc. which lead to differences between actual adult population and larval indices21. Further, even though road layers were shown similar behaviours for 3rd Kurana and Welikadamulla study areas both in Pearson correlations and Poisson modelling, differences were observed in Eriyawetiya and Akbar Town. The positive correlations observed between patient locations and road layers could probably be because of high congestion of households alongside the roads and therefore, even single DENV infected mosquitos can spread the disease to all households as these mosquitoes probe many humans during blood feeding. Similar observation has been reported in previous study conducted in West Indies40. The study further states that more dengue cases being found within 1–3 km away from various types of roads. This may be the reason for the observed negative estimates from multivariate Poisson model in Eriyawetiya and Akbar Town study areas as the patient locations are very close to access roads.
When analysing the observed (K) -functions of the developed Poisson multivariate models for the study areas, both clustering and dispersions were observed for Eriyawetiya and 3rd Kurana study areas while only clustering was observed in the Akbar Town and Welikadamulla areas. Interestingly, in Eriyawetiya and 3rd Kurana study areas, clustering was observed a radius of approximately 150 m. This is comparable to the general flying range of dengue vector mosquitoes, especially with regards to the Ae. aegypti41, the main dengue vector mosquito. Further, this may be an indicative of that patients in a small areal cluster are prompted due to a single infected dengue vector mosquito. During the analysis, both isotropic42 and translation43 edge correction methods were considered, therefore, edge effects arising from the unobserved patient locations outside study area can be hampered when estimating the (K)-functions. The estimations of (K)-functions were within the upper and lower envelopes of simulated functions in Akbar Town, 3rd Kurana and Welikadamulla study areas, that is, given particular distance, the data and simulated patterns were statistically equivalent. This indicates that dengue patient locations in the study areas were undergone a complete random pattern or CSR except for Eriyawetiya study area. This observation is further confirmed by the results of Maximum Absolute Deviation (MAD) and the Diggle-Cressie-Loosmore-Ford (DCLF) non-graphical tests44.
Among four monsoon seasons, the first inter-monsoon season occurs during March and April months. The Southwest monsoon period starts in May and it lasts till September. During the October and November, the second inter-monsoon period occurs and the Northeast monsoon lasts for three months from December to February. When analysing the distribution of dengue incidences in the monsoon periods, the highest number of dengue incidences were reported from the Southwest monsoon period in all study areas. The Gampaha District is located in the western part of Sri Lanka and during the monsoon period, the district experiences a rainfall of 750–2000 mm. In other monsoon periods, rainfall of the Gampaha District is less than 1000 mm45. The reason for higher precipitation in the Southwest monsoon period includes the presence of abundant water bodies, such as Arabian Sea and Indian Ocean, leading to higher accumulation of moisture in Southwest monsoon winds46. The higher rainfalls increase not only the availability of the breeding containers for dengue vector mosquitoes, but also favourable environmental conditions, viz. humidity and temperature, for its development. This will lead to increased disease transmission during the Southwest monsoon season compared to other monsoon seasons.
The developed models can be used to identify risk localities easily for healthcare workers and decision makers. The Poisson point process models can be developed using freely available software and packages. Further, road maps can be easily obtained for freely available sources and modified easily using freely available GIS software. With the advantages of technology, correct GPS locations of positive dengue vector mosquito breeding places and patients can be easily obtained using mobile devices with minimum wage during vector control programmes and export directly into GIS software. Since roads, land use, buildings and contour being not changing frequently in a particular area, with the aid of available data on patient locations as well as positive breeding places, it is possible to develop risk maps monthly or biannually to assess the risk levels of high risk areas. Further, when health authorities have risk map of particular area over few years, then it is possible to identify risk localities and transmission of dengue in an area in advance. This is particularly important in outbreaks and epidemic progression, so that they can have a better scenario of undergone situation to use scarce health resources effectively to control disease transmission. Meantime, the model can be further enhanced by incorporating serotype data which may lead identify index cases and initial clusters. A combined approach of predictive mathematical models19 and genetic approaches to identify the virulence of circulating dengue viruses21 will provide sufficient information for health authorities to take timely actions, such as intensive source reduction programmes, targeted intervention programmes or deploy vector reduction tools such as ovitraps9, to manage the situation to prevent propagation of outbreaks and epidemics. More

Study area
The study took place in the south-western Kalahari region of Botswana, known as the Bakgalagadi Schwelle (S 24.35°, E 20.62°), including the Botswana side of the Kgalagadi Transfrontier Park. The vegetation forms an open savanna, overlying deep sandy substrate with limited free-standing water. There is an intermittent river, Nossob river, in the south, ~ 80 km from the centre of the study area. A characteristic of this area is the highly mineralized, clay-rich depressions called pans, which retain water for variable periods after rain6. Air temperatures exceed 40 °C in summer and fall below 1 °C in winter6. Rainfall is seasonal but erratic, falling primarily during short-duration, high-intensity thunderstorms between November and April6. Mean annual rainfall in the Schwelle region ranges between 250 and 350 mm13.
Climatic variables
A free-standing miniature black globe thermometer (“miniglobe”), identical to the collar miniglobe thermometer, was placed within the area used by the animals in direct sun, 1 m aboveground, and recorded temperature (°C) every hour (S 24.307°, E 20.745°; reference miniglobe). Dry-bulb air temperature (°C), wind speed (ms−1), and solar radiation (Wm−2) data were obtained from the Agricultural Research Council (ARC) weather station located at the Nossob campsite (S 25.4°, E 20.6°). Normalised Difference Vegetation Index (NDVI) (MODIS Terra 16-day) and local rainfall (mm; CHIRPS) data covering the study area (S 24.434°, E 20.293°) were obtained from Google Earth Engine14.
Study species and data collection
In August 2013, eight individual female gemsbok and eight individual female wildebeest, each from separate herds, were darted by a veterinarian from a helicopter. Each dart consisted of Thiafentanil (gemsbok: 7–8 mg, wildebeest: 4–6 mg, Thianil, Kyron Laboratories, Johannesburg, South Africa), medetomidine hydrochloride (gemsbok: 3–6 mg, wildebeest: 2–4 mg, Kyron Laboratories, Johannesburg, South Africa) and ketamine (gemsbok: 75–150 mg, wildebeest: 50–150 mg Pfizer Animal Health, Sandton, South Africa). Each individual was fitted with a GPS collar (African Wildlife Tracking, Pretoria, South Africa) that supported a miniglobe attached to the top to record the thermal environment that the individual bearing it occupied15. Miniglobe temperatures and GPS locations were recorded hourly. In addition, each individual underwent surgery to implant miniature temperature-sensitive data loggers in the retroperitoneal space and had a motion-sensitive data logger tethered to the abdominal muscle wall (see7 for details). The data loggers were covered with biologically and chemically inert wax (Sasol, South Africa) and sterilised in instant sterilant (F10 Sterilant with rust inhibitor, Health and Hygiene (Pty) Ltd., Roodepoort, South Africa) before implantation. Once the individual animal was immobile, it was placed in sternal recumbency with its head elevated and supported by sandbags. Following intubation, anaesthesia was maintained with 2–5% isoflurane (Aerrane, Astra Zeneca, Johannesburg, South Africa), administered in 100% oxygen. Incision sites were shaved and sterilised with chlorhexidine gluconate (Hibitane, Zeneca, Johannesburg, South Africa). A local anaesthetic (3 ml subcutaneously (S.C.); lignocaine hydrochloride, Bayer Animal Health (Pty) Ltd., Isando, South Africa) was administered to the incision site. After placement of the loggers, the incision was sutured closed. Respiratory rate, heart rate, arterial oxygen saturation, and rectal temperature were monitored throughout the surgery, which lasted approximately 30–45 min. Each individual animal also received an antibiotic (~ 0.04 ml kg−1, intra muscularly (I.M.), Duplocillin, Schering-Plough Animal Health Ltd., New Zealand), and anti-inflammatory (~ 0.5 mg kg−1 I.M., Metacam, Meloxicam injectable solution, Boehringer Ingelheim Vetmedica, Inc, St. Joseph, U.S.A.) medication. Following surgery and termination of inhalation anaesthesia, the immobilization drugs were completely reversed by a combination of naltrexone (gemsbok: 75–120 mg, wildebeest: 60–100 mg, I.M. Naltrexone, Kyron Laboratories, Johannesburg, South Africa) and atipamezole (gemsbok: 10–20 mg; wildebeest: 10–15 mg, I.M. Antisedan, Orion Corporation, Orion Pharma, Finland).
The temperature-sensitive data loggers (DST centi-T, Star-Oddi, Iceland) recorded body temperature at 10-min intervals (Fig. 1a,b) and the motion-sensitive data logger recorded whole body movements (i.e., motion changes) as activity counts within the first 10 s of each 5-min interval. The motion-sensitive logger had a triaxial accelerometer (ADXL345, Sigma Delta Technologies, Australia) with equal sensitivity across three planes (resolution one-fourth 4 mg/least significant bit). We adjusted the activity units to be relative to the maximum activity count for the entire study period per logger, to account for differences in the sensitivity of the individual motion-sensitive loggers. The data loggers and the collar weighed less than 1% of the individual animal’s body mass and is unlikely to have adversely affected their behaviour.
Figure 1

Ten-min recordings of body temperature from a representative female wildebeest (a) and female gemsbok (b) over the study period (September 2013 to November 2014); and the monthly dry-bulb air temperature (solid black line), rainfall (grey bars) and monthly composited vegetation greenness (NDVI; dashed grey line) over two years (c) highlighting drought conditions in the first year. The light grey boxes represent the two hot-dry seasons compared in the current study.

Full size image

Two wildebeest were never relocated, possibly as a result of collar failure or predation. Three gemsbok died in October 2013. The remaining 11 animals were recaptured in May 2015, and data loggers and collars were removed. Thereafter the animals were released. Because of the inability to relocate all animals, animal deaths, and technological failures, we recovered a sample of 11 internal body temperature loggers (five gemsbok and six wildebeest); eight internal motion-sensitive loggers (four gemsboks and four wildebeest); nine GPS units (five gemsboks and four wildebeest) and nine miniglobe temperature sensors from the collars (five gemsboks and four wildebeest).
All procedures were approved by the Animal Ethics Screening Committee of the University of the Witwatersrand (protocol no. 2012/24/04) and all experiment procedures were performed in accordance with relevant guidelines and regulations as well as the ARRIVE guidelines (https://arriveguidelines.org/). The Government of Botswana via the Ministry of Environment, Wildlife and Tourism and Department of Wildlife and National Parks granted approvals and permits [numbers EWT 8/36/4 XX (32), EWT 8/36/4 XXVII (15), EWT 8/36/4 XXIV (102)] to conduct the study.
Data analysis
During the study period, the first hot-dry season (September to November 2013, ‘drought’) occurred at the end of a prolonged dry period, whereas the second hot-dry season (September to November 2014, ‘non-drought’) followed more typical rainfall conditions (Fig. 1c). Miniglobe temperature (24 h mean, minimum and maximum) and dry-bulb air temperature (24 h minimum and maximum), as well as mean 24 h wind speed and solar radiation were similar between the two hot-dry periods (Table 1). We averaged 16-day NDVI composites per season as an index of vegetation greenness in response to prior rainfall. Rainfall during the wet season prior to the commencement of the study (December to May 2013) was less than 40% ( More