Ecology

de Sousa, L. L., Silva, S. M. & Xavier, R. DNA metabarcoding in diet studies: Unveiling ecological aspects in aquatic and terrestrial ecosystems. Environ. DNA 1, 199–214. https://doi.org/10.1002/edn3.27 (2019).Article 

Google Scholar 
Pompanon, F. et al. Who is eating what: Diet assessment using next generation sequencing. Mol. Ecol. 21, 1931–1950. https://doi.org/10.1111/j.1365-294X.2011.05403.x (2012).Article 
CAS 

Google Scholar 
Liu, M. X., Clarke, L. J., Baker, S. C., Jordan, G. J. & Burridge, C. P. A practical guide to DNA metabarcoding for entomological ecologists. Ecol. Entomol. 45, 373–385. https://doi.org/10.1111/een.12831 (2020).Article 

Google Scholar 
Traugott, M., Thalinger, B., Wallinger, C. & Sint, D. Fish as predators and prey: DNA-based assessment of their role in food webs. J. Fish Biol. 98, 367–382. https://doi.org/10.1111/jfb.14400 (2021).Article 

Google Scholar 
Clare, E. L. Molecular detection of trophic interactions: Emerging trends, distinct advantages, significant considerations and conservation applications. Evol. Appl. 7, 1144–1157. https://doi.org/10.1111/eva.12225 (2014).Article 

Google Scholar 
Deagle, B. E. et al. Counting with DNA in metabarcoding studies: How should we convert sequence reads to dietary data?. Mol. Ecol. 28, 391–406. https://doi.org/10.1111/mec.14734 (2019).Article 

Google Scholar 
Ando, H. et al. Methodological trends and perspectives of animal dietary studies by noninvasive fecal DNA metabarcoding. Environ. DNA 2, 391–406. https://doi.org/10.1002/edn3.117 (2020).Article 

Google Scholar 
Masonick, P., Hernandez, M. & Weirauch, C. No guts, no glory: Gut content metabarcoding unveils the diet of a flower-associated coastal sage scrub predator. Ecosphere. https://doi.org/10.1002/ecs2.2712 (2019).Article 

Google Scholar 
Eitzinger, B. et al. Assessing changes in arthropod predator–prey interactions through DNA-based gut content analysis-variable environment, stable diet. Mol. Ecol. 28, 266–280. https://doi.org/10.1111/mec.14872 (2019).Article 
CAS 

Google Scholar 
Kim, T. N. et al. Using high-throughput amplicon sequencing to determine diet of generalist lady beetles in agricultural landscapes. Biol. Control. https://doi.org/10.1016/j.biocontrol.2022.104920 (2022).Article 

Google Scholar 
Wallinger, C. et al. The effect of plant identity and the level of plant decay on molecular gut content analysis in a herbivorous soil insect. Mol. Ecol. Resour. 13, 75–83. https://doi.org/10.1111/1755-0998.12032 (2013).Article 
CAS 

Google Scholar 
Seabra, S. G. et al. PCR-based detection of prey DNA in the gut contents of the tiger-fly, Coenosia attenuata (Diptera: Muscidae), a biological control agent in Mediterranean greenhouses. Eur. J. Entomol. 118, 335–343. https://doi.org/10.14411/eje.2021.035 (2021).Article 

Google Scholar 
Panni, S. & Pizzolotto, R. Fast molecular assay to detect the rate of decay of Bactrocera oleae (Diptera: Tephritidae) DNA in Pterostichus melas (Coleoptera: Carabidae) gut contents. Appl. Entomol. Zool. 53, 425–431. https://doi.org/10.1007/s13355-018-0564-x (2018).Article 
CAS 

Google Scholar 
Greenstone, M. H., Payton, M. E., Weber, D. C. & Simmons, A. M. The detectability half-life in arthropod predator–prey research: What it is, why we need it, how to measure it, and how to use it. Mol. Ecol. 23, 3799–3813. https://doi.org/10.1111/mec.12552 (2014).Article 

Google Scholar 
Fülöp, D., Szita, E., Gerstenbrand, R., Tholt, G. & Samu, F. Consuming alternative prey does not influence the DNA detectability half-life of pest prey in spider gut contents. PeerJ https://doi.org/10.7717/peerj.7680 (2019).Article 

Google Scholar 
Zhang, G. F., Lu, Z. C., Wan, F. H. & Lovei, G. L. Real-time PCR quantification of Bemisia tabaci (Homoptera: Aleyrodidae) B-biotype remains in predator guts. Mol. Ecol. Notes 7, 947–954. https://doi.org/10.1111/j.1471-8286.2007.01819.x (2007).Article 
CAS 

Google Scholar 
Weber, D. C. & Lundgren, J. G. Detection of predation using qPCR: Effect of prey quantity, elapsed time, chaser diet, and sample preservation on detectable quantity of prey DNA. J. Insect Sci. https://doi.org/10.1673/031.009.4101 (2009).Article 

Google Scholar 
Paula, D. P. et al. Detection and decay rates of prey and prey symbionts in the gut of a predator through metagenomics. Mol. Ecol. Resour. 15, 880–892. https://doi.org/10.1111/1755-0998.12364 (2015).Article 
CAS 

Google Scholar 
Hindson, B. J. et al. High-throughput droplet digital PCR system for absolute quantitation of DNA copy number. Anal. Chem. 83, 8604–8610. https://doi.org/10.1021/ac202028g (2011).Article 
CAS 

Google Scholar 
Wood, S. A. et al. A comparison of droplet digital polymerase chain reaction (PCR), quantitative PCR and metabarcoding for species-specific detection in environmental DNA. Mol. Ecol. Resour. 19, 1407–1419. https://doi.org/10.1111/1755-0998.13055 (2019).Article 
CAS 

Google Scholar 
Nathan, L. M., Simmons, M., Wegleitner, B. J., Jerde, C. L. & Mahon, A. R. Quantifying environmental DNA signals for aquatic invasive species across multiple detection platforms. Environ. Sci. Technol. 48, 12800–12806. https://doi.org/10.1021/es5034052 (2014).Article 
ADS 
CAS 

Google Scholar 
Kim, T. G., Jeong, S. Y. & Cho, K. S. Comparison of droplet digital PCR and quantitative real-time PCR for examining population dynamics of bacteria in soil. Appl. Microbiol. Biotechnol. 98, 6105–6113. https://doi.org/10.1007/s00253-014-5794-4 (2014).Article 
CAS 

Google Scholar 
Thalinger, B., Pütz, Y. & Traugott, M. Endpoint PCR coupled with capillary electrophoresis (celPCR) provides sensitive and quantitative measures of environmental DNA in singleplex and multiplex reactions. PLoS ONE https://doi.org/10.1371/journal.pone.0254356 (2021).Article 

Google Scholar 
Mata, V. A. et al. How much is enough? Effects of technical and biological replication on metabarcoding dietary analysis. Mol. Ecol. 28, 165–175. https://doi.org/10.1111/mec.14779 (2019).Article 
CAS 

Google Scholar 
Sint, D., Guenay, Y., Mayer, R., Traugott, M. & Wallinger, C. The effect of plant identity and mixed feeding on the detection of seed DNA in regurgitates of carabid beetles. Ecol. Evol. 8, 10834–10846. https://doi.org/10.1002/ece3.4536 (2018).Article 

Google Scholar 
Nielsen, J. M., Clare, E. L., Hayden, B., Brett, M. T. & Kratina, P. Diet tracing in ecology: Method comparison and selection. Methods Ecol. Evol. 9, 278–291. https://doi.org/10.1111/2041-210x.12869 (2018).Article 

Google Scholar 
Schrader, C., Schielke, A., Ellerbroek, L. & Johne, R. PCR inhibitors—Occurrence, properties and removal. J. Appl. Microbiol. 113, 1014–1026. https://doi.org/10.1111/j.1365-2672.2012.05384.x (2012).Article 
CAS 

Google Scholar 
Juen, A. & Traugott, M. Amplification facilitators and multiplex PCR: Tools to overcome PCR-inhibition in DNA-gut-content analysis of soil-living invertebrates. Soil Biol. Biochem. 38, 1872–1879. https://doi.org/10.1016/j.soilbio.2005.11.034 (2006).Article 
CAS 

Google Scholar 
Wallinger, C. et al. Evaluation of an automated protocol for efficient and reliable DNA extraction of dietary samples. Ecol. Evol. 7, 6382–6389. https://doi.org/10.1002/ece3.3197 (2017).Article 

Google Scholar 
Marotz, C. et al. DNA extraction for streamlined metagenomics of diverse environmental samples. Biotechniques 62, 290–293. https://doi.org/10.2144/000114559 (2017).Article 
CAS 

Google Scholar 
Dingle, T. C., Sedlak, R. H., Cook, L. & Jerome, K. R. Tolerance of droplet-digital PCR vs real-time quantitative PCR to inhibitory substances. Clin. Chem. 59, 1670–1672. https://doi.org/10.1373/clinchem.2013.211045 (2013).Article 
CAS 

Google Scholar 
Racki, N., Dreo, T., Gutierrez-Aguirre, I., Blejec, A. & Ravnikar, M. Reverse transcriptase droplet digital PCR shows high resilience to PCR inhibitors from plant, soil and water samples. Plant Methods https://doi.org/10.1186/s13007-014-0042-6 (2014).Article 

Google Scholar 
Juen, A. & Traugott, M. Detecting predation and scavenging by DNA gut-content analysis: A case study using a soil insect predator-prey system. Oecologia 142, 344–352. https://doi.org/10.1007/s00442-004-1736-7 (2005).Article 
ADS 

Google Scholar 
Lundgren, J. G. & Lehman, M. Bacterial gut symbionts contribute to seed digestion in an omnivorous beetle. PLoS ONE https://doi.org/10.1371/journal.pone.0010831 (2010).Article 

Google Scholar 
Waldner, T. & Traugott, M. DNA-based analysis of regurgitates: A noninvasive approach to examine the diet of invertebrate consumers. Mol. Ecol. Resour. 12, 669–675. https://doi.org/10.1111/j.1755-0998.2012.03135.x (2012).Article 

Google Scholar 
Kamenova, S. et al. Comparing three types of dietary samples for prey DNA decay in an insect generalist predator. Mol. Ecol. Resour. 18, 966–973. https://doi.org/10.1111/1755-0998.12775 (2018).Article 
CAS 

Google Scholar 
Cheeseman, M. T. & Pritchard, G. Spatial organization of digestive processes in an adult carabid beetle, Scaphinotus marginatus (Coleoptera: Carabidae). Can. J. Zool. 62, 1200–1203. https://doi.org/10.1139/z84-173 (1984).Article 

Google Scholar 
Sunderland, K. D. Diet of some predatory arthropods in cereal crops. J. Appl. Ecol. 12, 507–515. https://doi.org/10.2307/2402171 (1975).Article 

Google Scholar 
Sunderland, K. D., Lovei, G. L. & Fenlon, J. Diets and reproductive phenologies of the introduced ground beetles Harpalus affinis and Clivina australasiae (Coleoptera: Carabidae) in New Zealand. Aust. J. Zool. 43, 39–50. https://doi.org/10.1071/zo9950039 (1995).Article 

Google Scholar 
Deagle, B. E. & Tollit, D. J. Quantitative analysis of prey DNA in pinniped faeces: Potential to estimate diet composition?. Conserv. Genet. 8, 743–747. https://doi.org/10.1007/s10592-006-9197-7 (2007).Article 
CAS 

Google Scholar 
Snider, A. M., Bonisoli-Alquati, A., Perez-Umphrey, A. A., Stouffer, P. C. & Taylor, S. S. Metabarcoding of stomach contents and fecal samples provide similar insights about Seaside Sparrow diet. Ornithol. Appl. https://doi.org/10.1093/ornithapp/duab060 (2022).Article 

Google Scholar 
Paula, D. P., Timbo, R. V., Togawa, R. C., Vogler, A. P. & Andow, D. A. Quantitative prey species detection in predator guts across multiple trophic levels by mapping unassembled shotgun reads. Mol Ecol Resour 23, 64–80. https://doi.org/10.1111/1755-0998.13690 (2023).Article 
CAS 

Google Scholar 
Elbrecht, V. & Leese, F. Can DNA-based ecosystem assessments quantify species abundance? Testing primer bias and biomass-sequence relationships with an innovative metabarcoding protocol. PLoS ONE https://doi.org/10.1371/journal.pone.0130324 (2015).Article 

Google Scholar 
Krehenwinkel, H. et al. Estimating and mitigating amplification bias in qualitative and quantitative arthropod metabarcoding. Sci. Rep. 7, 17668. https://doi.org/10.1038/s41598-017-17333-x (2017).Article 
ADS 
CAS 

Google Scholar 
Piñol, J., San Andrés, V., Clare, E. L., Mir, G. & Symondson, W. O. C. A pragmatic approach to the analysis of diets of generalist predators: The use of next-generation sequencing with no blocking probes. Mol. Ecol. Resour. 14, 18–26. https://doi.org/10.1111/1755-0998.12156 (2014).Article 
CAS 

Google Scholar 
Baksay, S. et al. Experimental quantification of pollen with DNA metabarcoding using ITS1 and trnL. Sci. Rep. https://doi.org/10.1038/s41598-020-61198-6 (2020).Article 

Google Scholar 
Valentini, A. et al. New perspectives in diet analysis based on DNA barcoding and parallel pyrosequencing: The trnL approach. Mol. Ecol. Resour. 9, 51–60. https://doi.org/10.1111/j.1755-0998.2008.02352.x (2009).Article 
CAS 

Google Scholar 
Murray, D. C. et al. DNA-based faecal dietary analysis: A comparison of qPCR and high throughput sequencing approaches. PLoS ONE https://doi.org/10.1371/journal.pone.0025776 (2011).Article 

Google Scholar 
Hansen, B. K. et al. From DNA to biomass: Opportunities and challenges in species quantification of bulk fisheries products. ICES J. Mar. Sci. 77, 2557–2566. https://doi.org/10.1093/icesjms/fsaa115 (2020).Article 

Google Scholar 
Pawluczyk, M. et al. Quantitative evaluation of bias in PCR amplification and next-generation sequencing derived from metabarcoding samples. Anal. Bioanal. Chem. 407, 1841–1848. https://doi.org/10.1007/s00216-014-8435-y (2015).Article 
CAS 

Google Scholar 
Piñol, J., Mir, G., Gomez-Polo, P. & Agusti, N. Universal and blocking primer mismatches limit the use of high-throughput DNA sequencing for the quantitative metabarcoding of arthropods. Mol. Ecol. Resour. 15, 819–830. https://doi.org/10.1111/1755-0998.12355 (2015).Article 
CAS 

Google Scholar 
Czernik, M. et al. Fast and efficient DNA-based method for winter diet analysis from stools of three cervids: Moose, red deer, and roe deer. Acta Theriol. 58, 379–386. https://doi.org/10.1007/s13364-013-0146-9 (2013).Article 

Google Scholar 
Richardson, R. T. et al. Quantitative multi-locus metabarcoding and waggle dance interpretation reveal honey bee spring foraging patterns in Midwest agroecosystems. Mol. Ecol. 28, 686–697. https://doi.org/10.1111/mec.14975 (2019).Article 
CAS 

Google Scholar 
Taberlet, P. et al. Power and limitations of the chloroplast trnL (UAA) intron for plant DNA barcoding. Nucleic Acids Res. https://doi.org/10.1093/nar/gkl938 (2007).Article 

Google Scholar 
Briem, F. et al. Identifying plant DNA in the sponging-feeding insect pest Drosophila suzukii. J. Pest. Sci. 91, 985–994. https://doi.org/10.1007/s10340-018-0963-3 (2018).Article 

Google Scholar 
Frei, B., Guenay, Y., Bohan, D. A., Traugott, M. & Wallinger, C. Molecular analysis indicates high levels of carabid weed seed consumption in cereal fields across Central Europe. J. Pest. Sci. https://doi.org/10.1007/s10340-019-01109-5 (2019).Article 

Google Scholar 
Luff, M. L. The biology of the ground beetle Harpalus rufipes in a strawberry field in Northumberland. Ann. Appl. Biol. 94, 153–164. https://doi.org/10.1111/j.1744-7348.1980.tb03907.x (1980).Article 

Google Scholar 
Illumina. Effects of index Misassignment on multiplexing and downstream analysis. https://www.illumina.com/content/dam/illumina-marketing/documents/products/whitepapers/index-hopping-white-paper-770-2017-004.pdf?linkId=36607862 accessed 2022-11-10 (2018).Guenay-Greunke, Y., Bohan, D. A., Traugott, M. & Wallinger, C. Handling of targeted amplicon sequencing data focusing on index hopping and demultiplexing using a nested metabarcoding approach in ecology. Sci. Rep. https://doi.org/10.1038/s41598-021-98018-4 (2021).Article 

Google Scholar 
Staudacher, K., Wallinger, C., Schallhart, N. & Traugott, M. Detecting ingested plant DNA in soil-living insect larvae. Soil Biol. Biochem. 43, 346–350. https://doi.org/10.1016/j.soilbio.2010.10.022 (2011).Article 
CAS 

Google Scholar 
Espunyes, J. et al. Comparing the accuracy of PCR-capillary electrophoresis and cuticle microhistological analysis for assessing diet composition in ungulates: A case study with Pyrenean chamois. PLoS ONE https://doi.org/10.1371/journal.pone.0216345 (2019).Article 

Google Scholar 
Wallinger, C. et al. Detection of seed DNA in regurgitates of granivorous carabid beetles. Bull. Entomol. Res. 105, 728–735. https://doi.org/10.1017/s000748531500067x (2015).Article 
CAS 

Google Scholar 
Taberlet, P., Gielly, L., Pautou, G. & Bouvet, J. Universal primers for amplification of 3 noncoding regions of chloroplast DNA. Plant Mol. Biol. 17, 1105–1109. https://doi.org/10.1007/bf00037152 (1991).Article 
CAS 

Google Scholar 
FastQC: A Quality Control Tool for High Throughput Sequence Data [Online]. (2010).Danecek, P. et al. Twelve years of SAMtools and BCFtools. Gigascience https://doi.org/10.1093/gigascience/giab008 (2021).Article 

Google Scholar 
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. Next Gener. Seq. Data Anal. https://doi.org/10.14806/ej.17.1.200 (2011).Article 

Google Scholar 
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461. https://doi.org/10.1093/bioinformatics/btq461 (2010).Article 
CAS 

Google Scholar 
Camacho, C. et al. BLAST plus: Architecture and applications. BMC Bioinform. https://doi.org/10.1186/1471-2105-10-421 (2009).Article 

Google Scholar 
R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018).Wickham, H. ggplot2: Elegant Graphics for Data Analysis. (Springer, 2016).Arnold, J. B. ggthemes: Extra Themes, Scales and Geoms for ‘ggplot2’. R package version 4.2.0 https://CRAN.R-project.org/package=ggthemes (2019).Hebbali, A. olsrr: Tools for Building OLS Regression Models. R package version 0.5.3. https://CRAN.R-project.org/package=olsrr (2020).Fox, J. & Weisberg, S. An {R} Companion to Applied Regression. 2nd ed. (Sage, 2011).Zeileis, A. & Hothorn, T. Diagnostic checking in regression relationships. R News 2, 7–10 (2002).
Google Scholar 
Zeileis, A. Econometric computing with HC and HAC covariance matrix estimators. J. Stat. Softw. 11, 1–17. https://doi.org/10.18637/jss.v011.i10 (2004).Article 

Google Scholar 
Zeileis, A., Köll, S. & Graham, N. Various versatile variances: An object-oriented implementation of clustered covariances in {R}. J. Stat. Softw. 95, 1–36. https://doi.org/10.18637/jss.v095.i01 (2020).Article 

Google Scholar 
boot: Bootstrap R (S-Plus) Functions v. R package version 1.3-28 (2021).Davison, A. C. & Hinkley, D. V. Bootstrap Methods and Their Applications. (Cambridge University Press, 1997). More

Analysis of FTIRUnderstanding the functional groups involved in the biosorption of toxic metals is essential to elucidate the mechanism of this process. Groups such as carboxylic, hydroxyl and amine are among the main responsible for the absorption of metals by cellulose34 In the Fig. 1, show the FTIR of ECx.Figure 1FTIR of ECx before and after of adsorptions of Cr (VI).Full size imageAccording to13 the bandwidth at 3000–3600 cm−1 corresponds to bonds related to the -OH group. These hydrogen bonds are useful tools for cation exchange with heavy metals. This evidenced in the color spectrum (dark green) that represents an ECx sample with attached Cr (VI) after the adsorption process, where the stretching of the (OH) group lost part of its extension. The change observed in the peak from 3420 cm−1 of ECx to 3440 cm−1 in ECx-Cr indicates that these groups have a participation in the bond with the Cr (VI) ions. The variation of bands in the peak of the amines after adsorption confirms the participation of these groups in the adsorption process. This result confirmed by the ion exchange evaluation experiment discussed later in section SEM–EDX.The change in peak 3280, after Cr (VI) adsorption, indicates that EC removed Cr (VI) based on interaction with (OH), part of (OH) lost due to formation of vibrations of ascension O–Cr. Also, after Cr (VI) biosorption on ECx, the peak of the EC-S group is shifted to 590. This can be explained by surface complexation or ion exchange35.In general, comparable results reported in the literature for cellulose in the absorption of other toxic metals, as for other cellulose-derived biosorbentes in the removal of Cr (VI) ions36.One way to corroborate the information presented in the FTIR measurements is through SEM images since with these images it is possible to observe the distribution of the reagents in the ECx biomass treatment and subsequently the Cr (VI) adsorption process.SEM–EDXFigure 2 shows the micrographs obtained for the biomass before (a) the adsorption of Cr (VI), in addition to showing the distribution of the different biomass chemical modifications in (b) and in (c) it shows the distribution of chromium around all biomasses.Figure 2Biomass before (a) Cr (VI) adsorption, biomass chemical modifications in (b) and shows the distribution of chromium around the whole biomass (c).Full size imageFrom Fig. 2a, it can see that the biomass has a very irregular rough surface, with macropores and cracks. Many of these irregularities may associated with damage caused by the delignification process of E. crassipes cellulose with NaOH14. In Fig. 2b it is possible to visualize the components of the cellulose xanthogenate, coming from sodium, distributed throughout the biomass, a result like that reported in other studies35 The colored dots represent the elements in the samples, green dots represent carbon, red dots represent oxygen, and yellow dots represent the places where sodium lodged.Table 2 shows that, in addition to carbon and oxygen, the element with the greatest presence in the composition of pure waste is sodium and sulfur from the xanthogenate cellulose transformation process. Table 2 shows the physicochemical characterization of the ECx sample, through EDS.Table 2 Features of sample of ECx.Full size tableCellulose xanthogenate, is one of the cellulose transformations to improve the adsorption performance of heavy metals, this compound produced from dry and ground biomass, mixing with sodium hydroxide (NaOH) to remove lignin, creating alkaline biomass, then disulfide (CS2) added13,14. (CS2) reacts with hydratable hydroxycellulose, forming C-SNa complexes; these are responsible for the cation exchange with heavy metals. Metal ions enter the interior of E. crassipes with (CS2), exchanging with Na36,37.The SEM morphology of ECx and coupled with the high content of sulfides (7.3%) determined by the spectrum in Table 2, it further confirms that xanthate groups are successfully grafted onto the biomass of E. crassipes, and Fig. 3 represents this information based on13,36,37,38.Figure 3Prototype.Full size imageExchange biochemistry is usually identified as the main mechanism for the adsorption of metals in cellulose and its derivatives35 and through the evaluation of EDS this process could verify. Similar observations were made by36 where the adhesion of Cr (VI) in this biomass was observed. Also, in xanthogenate cellulose processes, the adhesion of Pb (II) to this type of biomass verified, concluding that this cellulose is important in the removal of heavy metals from water13.The SEM morphology of ECx with Cr (VI) coupled with the high content of sulfides determined by the spectrum in Table 3, was the determinate for the chemisorption’s of Cr (VI). The mechanism of Cr (VI) sorption by cellulose xanthate is:$$left[ {{4}left( {{text{C}}_{{6}} {text{H}}_{{{12}}} {text{O}}_{{6}} } right)} right]*{text{2CS}}_{{2}} {text{Na }} + {text{ Cr}}_{{2}} {text{O}}_{7}^{ – 2} to left{ {left[ {{4}left( {{text{C}}_{{6}} {text{H}}_{{5}} {text{O}}_{{6}} } right)} right] , *{text{2CS}}_{{2}} } right}*{mathbf{Cr}}_{{mathbf{2}}} + {text{Na}} + {text{7H}}_{{2}} {text{O}}$$where [4(C6H12O6)] *2CS2Na represents the xanthogenate biomass, and Cr2O7–2 represents the Cr (VI), that 4 parts of glucose xanthate react with the dichromate. In the Tables 3 and 4, the relationship between cellulose xanthogenate and Cr (VI), with related weights of 10.4 for Cr (VI).Table 3 Features of sample of ECx with Cr (VI).Full size tableTable 4 Researcher of process of the desorption.Full size tableMass balance in treatmentAdsorption is the phenomenon through which the removal of Cr (VI) achieved in the treatment systems; this quantified by means of the general balance equation of the treatment system as shown in Fig. 3.Adsorption is the phenomenon through which the removal of Cr (VI) achieved in treatment systems, this quantified by mass balance. Equation (1) shows the general balance of matter in the treatment system, together with the accumulation, inputs, and outputs of the system and the chemical process of adsorption.$${text{Acumulation }}upvarepsilon *frac{{partial {text{Cr}}left( {{text{VI}}} right)}}{{partial {text{t}}}} = {text{In}} frac{{partial {text{Cr}}left( {{text{VI}}} right)_{0} }}{{partial {text{t}}}} – {text{Out}}frac{{partial {text{Cr}}left( {{text{VI}}} right)}}{{partial {text{t}}}} – {text{Adsortion}},{rho b}frac{{partial {text{q}}}}{{partial {text{t}}}}$$
(1)
Accumulation represents by Eq. (1), where ∂C(VI) is the contaminant input to the treatment system, (ε) is the porosity of the bed, which calculated as the ratio between the density of the bed of treatment and the density of the microparticle of this biomass. This parameter must be above 0.548 achieved using particle diameters less than 0.212 mm, which favors contact between the contaminant and the particle49. The contaminant input to the treatment system represents by the design speed and the amount of contaminant that the system could treat. The output in the treatment system represents by the same input speed and the amount of contaminant that comes out. With these equations, the general material balance will be complete, summarized in Eq. (2), where it can see that the accumulation is equal to the input to the system, minus the output, and minus the adsorption.$$upvarepsilon *frac{{partial {text{Cr}}left( {{text{VI}}} right)}}{{partial {text{t}}}} = frac{{partial {text{Cr}} left( {{text{VI}}} right)}}{{partial {text{t}}}} – frac{{partial {text{Cr}} left( {{text{VI}}} right)}}{{partial {text{t}}}} – frac{{text{M}}}{{text{V}}}*frac{{partial {text{q}}}}{{partial {text{t}}}}$$
(2)
where V = System volume (ml), ε = Porosity, Co = Initial concentration of Cr (VI) (mg/ml), C = Final concentration Cr (VI) in the treated solution (mg/ml), Q = design flow (ml/min), Tb = Breaking time (Min), M = amount of biomass used (g), q = Adsorption capacity of the biomass used (mg/g).$${text{V}}*upvarepsilon *{text{Co}} = {text{Q}}*{text{Tb}}*{text{Co}} – {text{Q}}*{text{Tb}}*{text{C}} – {text{M}}*{text{q}}$$
(3)
Depending on the most important parameters when building a treatment system, Eq. (3) could use to model and validate the best form of treatment, for example, the necessary amount of biomass to use to treat a certain amount of contaminant, in the present investigation it used to establish the adsorption capacity in these initial treatment conditions. The remaining Eq. (4) determines the adsorption capacity.$${text{q}} = frac{{{text{QTbCo}}}}{{text{M}}} – frac{{{text{QTbCf}}}}{{text{M}}} – frac{{upvarepsilon {text{VCo}}}}{{text{M}}}$$
(4)
Adsorption capacity is generally taken through Eq. (5) for both batch and continuous experiments20,21But unlike Eqs. (5), (4) takes into account design variables such as flow rate (Q), rupture time (Tb), particle bed porosity ε, and vessel design volume (v).$${text{q}} = frac{{{text{v}}left( {{text{Co}} – {text{C}}} right)}}{{text{m }}}$$
(5)
where m: Mass used in the treatment, V: Volume, Co: Initial concentration, C: Final Concentration, Q: adsorption capacity.However, unlike Eqs. (5),  (4) considers the design variables such as flow rate (Q), rupture time (Tb), particle bed porosity ε and vessel design volume (v).When a desorption-elution process is involved for the reuse of biomass, Eq. (4) would be:$${text{q}}_{{text{T}}} = mathop sum limits_{j = 1}^{n} left[ {frac{{{text{QTbjCo}}}}{{text{M}}} – frac{{{text{QTbjCj}}}}{{text{M}}} – frac{{upvarepsilon {text{VCo}}}}{{text{M}}}} right]$$
(6)
where Q = design flow (ml/min), Tbj = Break time of use number j (Min), Co = Initial concentration of Cr (VI) (mg/ml), C = Final concentration Cr (VI) in the treated solution (mg/ml), V = System volume (ml), ε = Porosity, M = amount of biomass used (g), q_T = Total adsorption capacity of the biomass used (mg/g).This model (6) is design to determine the adsorption capacity when different elution processes have conducted, it will used to determine the new adsorption capacity and is one of the contributions of the present investigation.Result process of adsorptionsIn Fig. 4 shows the Cr (VI) adsorption process of the system.Figure 4Percentages of Cr (VI) removal the system for ECx.Full size imageVarious researchers have extensively studied the influence of factors such as bed height, flow rate and metal inlet concentration on rupture (Tb) curves. For example, the influence and similarity of the initial contaminant concentrations should be reflected as in the case of a tannery, with initial concentrations of 600 mg/l. Figure 4 shows the progress curves obtained for the study of Cr (VI) removal by the studied biomasses, reflecting the percentage of Cr (VI) removal in contrast to the treated volume, which is a very important parameter to time to scale the process.Regarding the effect of the input concentration, it can see in Fig. 5 that the breakpoint had a better performance in all the initial concentrations in the ECx biomass. comparing it with the EC-Na biomass (see Fig. 5), always obtaining breakpoints with more treated volume.Figure 5Percentages of Cr (VI) removal the system for EC-Na.Full size imageThe difference between the rupture curves between ECx and EC-Na indicates that the cellulose xanthate modification scheme should completed, although it can also elucidate that the EC-Na biomass has high yields compared to other biomass studied. for example, in Ref.34 investigate the biomass of E. crassipes without modifying, having removals below this alkaline cellulose.Adsorption capacitiesThrough Eq. (3), the adsorption capacity of ECx, using the initial concentration of 600 mg/l, since it was the maximum concentration used.The break point was around 1200 ml according to Fig. 6 and together with the flow rate of 15 ml/min; the break time obtained in 80 min.$${text{q}} = frac{{80{*}15{*}0.6}}{40} – frac{{80{*}15{*}0.04}}{40} – frac{{0.66{*}78{*}0.6}}{40}$$q: Adsorption capacity, Co: 0.6 mg/ml, C: 0.06 mg/ml, M: 40 g, Tb: rupture time 80 min, Q: 15 Flow rate ml/min, ε: 0.6649, V: Occupied volume: 70 ml.Figure 6Adsorption capacities in the different adsorption processes in the biomass ECx.Full size imageA result of 16 mg/g obtained in this continuous study for the biomass ECx. With this same equation it gives the capacity of the biomass EC-Na, with 11 mg/g.Desorption-Elution and reuseThrough Eq. (6), the sum of the Cr (VI) adsorption capacities established, after different biomass reuses due to EDTA elution. In the second treatment process, it yielded the following results under concentrations of 6 g/l of EDTA.$${text{q}}left( {text{T}} right) = frac{{60{*}15{*}0.6}}{40} – frac{{50{*}15{*}0.06}}{40} – frac{{0.66{*}68{*}0.6}}{40}$$Co: 0.6 mg/ml, C: 0.06 mg/ml, M: 45 g Biomass eluted with EDTA, Tb: rupture time: 60 min, Q: 15 Flow ml/min, ε: 0.6649, V: Occupied volume: 68 ml, q: 10 mg/g.Five Cr (VI) adsorption cycles performed using ECx and EC-Na cellulose in a continuous system to evaluate the regeneration and reuse potential. Between each biosorption cycle, a desorption cycle performed using three different concentrations of EDTA eluent.According to Figs. 6 and 7, although the adsorption capacity gradually decreases from the first adsorption process, it could consider that it is a satisfactory biomass recycling process and a design parameter for later stages of this treatment system.Figure 7Adsorption capacities in the different adsorption processes in the biomass EC-Na.Full size imageIn the experiments with concentrations of 6 g/l, five reuse processes obtained, obtaining a final sum of 52 mg/g. In concentrations of 3 g/l of EDTA, final capacities of 51 mg/g obtained lower than concentrations of 6 g/l but with half of this reagent. With concentrations of 1 g/l, final capacities of 33 mg/g obtained.The desorption processes of the EC-Na biomass with initial capacities of 11 mg/g were also evaluated and through desorption processes with EDTA of 3 g/l this biomass recycled on 5 occasions, reaching 32 mg/l in capacities of adsorption and like the EC-Na biomass, the ideal concentration in the process for desorption processes is 3 g/l, due to the considerable increase in reuse processes and low concentration compared to 6 g/l, which, although higher, does not this value is significant in the absorption capacity.Through Eq. (6) and with different bibliographic references, representative data obtained to feed this equation, determining the capacities of each of these biomasses together with the new capacities determining the desorption power of the different eluents shown and summarized in Table 4.For the EDTA eluent and with Eq. (6), satisfactory results evidenced by removing Al (II), reaching almost 150% of its adsorption capacity, corroborating what presented in the present investigation, also the EDTA reagent obtained interesting yields to recycle the cassava biomass increasing up to 40 mg/g. In Ref.39 used the biomass of Phanera vahlii to remove Cr (VI) obtaining results of 30 mg/g and with NaOH they reached capacities in the reuse process of this biomass up to 62 mg/g, reaching almost double of its total capacity41, also used NaOH for desorption processes with green synthesized nanocrystalline chlorapatite biomass, achieving results of 75% more. The eluent HCl is also a good chemical agent to use in desorption processes since it reached more than 100% in the reuse of biochar alginate for Cr (VI) but not so significant with biomass A. barbadensis Miller to remove Ni (II) and in40 significant results were also obtained to remove Pb (II) with pine cone Shell biomass. With the chemical agent HNO3, interesting contaminant recycling processes obtained, since more than 100% of the adsorption capacity of the biomasses used in this process used1,45.Mathematical models of adsorptionIn general, the models presented R2 greater than 0.95 for the adjustment of all the advance curves, which indicates a good adherence to the data, the model that best describes the behavior of the ECx system was the phenomenological model Thomas, which presented all the R2 values above 0.99.This model could use for the extension of the Cr (VI) ion biosorption system using cellulose xanthogenate, in the literature it is possible to observe that this model often tends to better adapt to the experimental data of the adsorption systems that use cellulose for the absorption of toxic metals28,30,31.With qt values remarkably close to the experimental values of Eq. (4) designed and presented in this investigation, indicating the validity of this equation where it reflects the maximum capacity obtained. Table 5 shows the adsorption constant of the Thomas model (Kt), which corresponds to the adsorption rate of Cr (VI) in the biomass49 This value was 0.048 (ml/mg*min) reflecting the speed with which Cr (VI) is chemisorbed in the biomass of ECx, in the EC-Na cellulose there was a Thomas model speed of 0.039 (ml/ mg*min) evidencing a lower adsorption rate than ECx. In the adsorption of Cr (VI) by rice biomass, the Thomas constant is 0.1 (ml/mg*min)47,50 also in the adsorption of Cr (VI) by biomass. Nanocrystalline chlorapatite biomass obtained at the Thomas constant 0.013 (ml/mg*min)49.Table 5 Summary of the experiments obtained with material ECx.Full size tableIn the Table 6, it presents summary of the experiments obtained with material EC-Na.Table 6 Summary of the experiments obtained with material EC-Na.Full size tableThe Cr (VI) adsorption process in the EC-Na biomass represented through the Bohart equation, since the sorption rate is proportional to the biomass capacity, obtaining an adsorption rate of 0.85(ml/mg*min). Having an alkalized biomass represents this model due to the homogeneity of this adsorbent.Mathematical models in desorption processesThe continuous desorption process with its fit to the Thomas model for biomass ECx always shows the fit of this model with significance, because this type of model fits representatively to desorption processes with good performance32,51 It can also verify that with values of qt it is close to the experimental values of Eq. (6) designed and presented in this research, indicating the validity of this equation again, where it reflects the maximum capacity obtained.In the Table 7. Show Summary of the experiments obtained with material ECx in process of desorption’s.Table 7 Summary of the experiments obtained with material ECx in process of desorption’s.Full size tableIn the Table 8 the EC-Na biomass had a different behavior and in its second and third cycle it adjusted to the Yoon model and later to the Bohart model.Table 8 Summary of the experiments obtained with material EC-Na in process of desorption’s.Full size tableThis behavior is due to the alkalinization of the biomass and this process makes the biomass a little more unstable. The values of qt, although a resemblance evidenced, were not so representative due to the little adjustment that there was with respect to the Thomas model. More

Eilers, E. J., Kremen, C., Smith Greenleaf, S., Garber, A. K. & Klein, A. M. Contribution of pollinator-mediated crops to nutrients in the human food supply. PLoS ONE 6, 21363 (2011).ADS 

Google Scholar 
Williams, P. H. The dependence of crop pollination within the European Union on pollination by honey bees. Agric. Zool. Rev. 6, 229–257 (1994).
Google Scholar 
Burd, M. Bateman’s principle and plant reproduction: the role of pollen limitation in fruit and seed set. Bot. Rev. 60, 83–139 (1994).MathSciNet 

Google Scholar 
Aguilar, R., Ashworth, L., Galetto, L. & Aizen, M. A. Plant reproductive susceptibility to habitat fragmentation: Review and synthesis through a meta-analysis. Ecol. Lett. 9, 968–980 (2006).
Google Scholar 
Potts, S. G. et al. Declines of managed honey bees and beekeepers in Europe. J. Apic. Res. 49, 15–22 (2010).
Google Scholar 
van Engelsdorp, D., Hayes, J., Underwood, R. M. & Pettis, J. A survey of honey bee colony losses in the U.S., fall 2007 to spring 2008. PLoS ONE 3, e4071 (2008).ADS 

Google Scholar 
Aizen, M. A. & Harder, L. D. The global stock of domesticated honey bees is growing slower than agricultural demand for pollination. Curr. Biol. 19, 915–918 (2009).CAS 

Google Scholar 
Van Engelsdorp, D. et al. Colony collapse disorder: A descriptive study. PLoS ONE 4, e6481 (2009).ADS 

Google Scholar 
Genersch, E. American foulbrood in honeybees and its causative agent, Paenibacillus larvae. J. Invertebr. Pathol. 103(suppl 1), 10–19 (2010).
Google Scholar 
Graystock, P., Yates, K., Darvill, B., Goulson, D. & Hughes, W. O. H. Emerging dangers: Deadly effects of an emergent parasite in a new pollinator host. J. Invertebr. Pathol. 114, 114–119 (2013).
Google Scholar 
Insolia, L. et al. Honey bee colony loss linked to parasites, pesticides and extreme weather across the United States. Sci. Rep. 12(1), 20787. https://doi.org/10.1038/s41598-022-24946-4 (2022).Article 
ADS 
CAS 

Google Scholar 
Sanchez-Bayo, F. & Goka, K. Pesticide residues and bees: A risk assessment. PLoS ONE 9(4), e94482 (2014).ADS 

Google Scholar 
Bolognesi, C. & Merlo, F. D. Pesticides: Human health effects. Encyclop. Environ. Health 1, 438–453 (2011).
Google Scholar 
Mullin, C. A. et al. High levels of miticides and agrochemicals in North American apiaries: Implications for honey bee health. PLoS ONE 1, e9754 (2015).
Google Scholar 
Calatayud-Vernich, P., Calatayud, F., Simó, E. & Picó, Y. Pesticide residues in honey bees, pollen and beeswax: Assessing beehive exposure. Environ. Pollut. 241, 106–114. https://doi.org/10.1016/j.envpol.2018.05.062 (2018).Article 
CAS 

Google Scholar 
Woodcock, B. A. et al. Impacts of neonicotinoid use on long-term population changes in wild bees in England. Nat. Commun. 2016(7), 12459 (2016).ADS 

Google Scholar 
Zhao, H. et al. Review on effects of some insecticides on honey bee health. Pestic. Biochem. Physiol. 188, 105219. https://doi.org/10.1016/j.pestbp.2022.105219 (2022).Article 
CAS 

Google Scholar 
Ludicke, J. C. & Nieh, J. C. Thiamethoxam impairs honey bee visual learning, alters decision times, and increases abnormal behaviors. Ecotoxicol. Environ. Saf. 193, 110367 (2020).CAS 

Google Scholar 
Tison, L., Duer, A., Púčiková, V., Greggers, U. & Menzel, R. Detrimental effects of clothianidin on foraging and dance communication in honey bees. PLoS ONE 15(10), e0241134 (2020).CAS 

Google Scholar 
Fent, K., Schmid, M. & Christen, V. Global transcriptome analysis reveals relevant effects at environmental concentrations of cypermethrin in honey bees (Apis mellifera). Environ. Pollut. 259, 113715 (2020).CAS 

Google Scholar 
Christen, V., Krebs, J., Bünter, I. & Fent, K. Biopesticide spinosad induces transcriptional alterations in genes associated with energy production in honey bees (Apis mellifera) at sublethal concentrations. J. Hazard. Mater. 378, 120736 (2019).CAS 

Google Scholar 
Christen, V., Krebs, J. & Fent, K. Fungicides chlorothanolin, azoxystrobin and folpet induce transcriptional alterations in genes encoding enzymes involved in oxidative phosphorylation and metabolism in honey bees (Apis mellifera) at sublethal concentrations. J. Hazard. Mater. 377, 215–226 (2019).CAS 

Google Scholar 
Fent, K., Haltiner, T., Kunz, P. & Christen, V. Insecticides cause transcriptional alterations of endocrine related genes in the brain of honey bee foragers. Chemosphere 260, 127542 (2020).ADS 
CAS 

Google Scholar 
Christen, V., Grossar, D., Charrière, J. D., Eyer, M. & Jeker, L. Correlation between increased homing flight duration and altered gene expression in the brain of honey bee foragers after acute oral exposure to thiacloprid and thiamethoxam. Insect Sci. 1, 1–15 (2021).
Google Scholar 
Mao, W., Schuler, M. A. & Berenbaum, M. R. Disruption of quercetin metabolism by fungicide affects energy production in honey bees (Apis mellifera). Proc. Natl. Acad. Sci. USA 114(10), 2538–2543 (2017).ADS 
CAS 

Google Scholar 
Christen, V., Kunz, P. Y. & Fent, K. Endocrine disruption and chronic effects of plant protection products in bees: Can we better protect our pollinators?. Environ. Pollut. 243(Pt B), 1588–1601 (2018).CAS 

Google Scholar 
Testai, E., Buratti, F. & Di Consiglio, E. Chlorpyrifos Hayes’ Handbook of Pesticide Toxicology 1505–1526 (Academic Press, 2010).
Google Scholar 
Eastmond, D. & Balakrishnan, S. Genotoxicity of Pesticides Hayes’ Handbook of Pesticide Toxicology 357–380 (Academic Press, 2010).
Google Scholar 
Urlacher, E. et al. Measurements of chlorpyrifos levels in forager bees and comparison with levels that disrupt honey bee odor-mediated learning under laboratory conditions. J. Chem. Ecol. 42(2), 127–138 (2016).CAS 

Google Scholar 
Li, Z. et al. Effects of sublethal concentrations of chlorpyrifos on olfactory learning and memory performances in two bee species, Apis mellifera and Apis cerana. Sociobiology 64, 174 (2017).
Google Scholar 
DeGrandi-Hoffman, G., Chen, Y. & Simonds, R. The effects of pesticides on queen rearing and virus titers in honey bees (Apis mellifera L.). Insects 4, 71–89 (2013).
Google Scholar 
Cutler, G. C., Purdy, J., Giesy, J. P. & Solomon, K. R. Risk to pollinators from the use of chlorpyrifos in the United States. In Ecological Risk Assessment for Chlorpyrifos in Terrestrial and Aquatic Systems in the United States Reviews of Environmental Contamination and Toxicology (eds Giesy, J. & Solomon, K.) (Springer, 2014).
Google Scholar 
Christen, V. & Fent, K. Exposure of honey bees (Apis mellifera) to different classes of insecticides exhibit distinct molecular effect patterns at concentrations that mimic environmental contamination. Environ. Pollut. 226, 48–59 (2017).CAS 

Google Scholar 
Stevenson, J. H. The acute toxicity of unformulated pesticides to worker honey bees (Apis mellifera L.). Plant Pathol. 27, 38–40 (1978).CAS 

Google Scholar 
Bartlett, D. W. et al. The strobilurin fungicides. Pest. Manag. Sci. 58, 649–662 (2002).CAS 

Google Scholar 
Ostiguy, N. et al. Honey bee exposure to pesticides: A four-year nationwide study. Insects. 10, 13 (2019).
Google Scholar 
Inoue, L. V. B., Domingues, C. E. C., Gregorc, A., Silva-Zacarin, E. C. M. & Malaspina, O. Harmful effects of pyraclostrobin on the fat body and pericardial cells of foragers of africanized honey bee. Toxics 10, 530. https://doi.org/10.3390/toxics10090530 (2022).Article 
CAS 

Google Scholar 
Nicodemo, D. et al. Mitochondrial respiratory inhibition promoted by pyraclostrobin in fungi is also observed in honey bees. Environ. Toxicol. Chem. 39, 1267–1272 (2020).CAS 

Google Scholar 
Domingues, C. E. C., Inoue, L. V. B., Silva-Zacarin, E. C. M. & Malaspina, O. Foragers of Africanized honeybee are more sensitive to fungicide pyraclostrobin than newly emerged bees. Environ. Pollut. 266, 115267 (2020).
Google Scholar 
Tadei, R. et al. Late effect of larval co-exposure to the insecticide clothianidin and fungicide pyraclostrobin in Africanized Apis mellifera. Sci. Rep 9, 3277 (2019).ADS 

Google Scholar 
Zioga, E., Kelly, R., White, B. & Stout, J. C. Plant protection product residues in plant pollen and nectar: A review of current knowledge. Environ. Res. 189, 109873 (2020).CAS 

Google Scholar 
Corona, M. et al. Vitellogenin, juvenile hormone, insulin signaling, and queen honey bee longevity. Proc. Natl. Acad. Sci. USA 104, 7128–7133 (2007).ADS 
CAS 

Google Scholar 
Winston, M. L. The Biology of the Honey Bee (Harvard University Press, 1987).
Google Scholar 
Ueno, T., Nakaoka, T., Takeuchi, H. & Kubo, T. Differential gene expression in the hypopharyngeal glands of worker honeybees (Apis mellifera L.) associated with an age-dependent role change. Zool. Sci. 8, 557–563 (2009).
Google Scholar 
Kubo, T. et al. Change in the expression of hypopharyngealgland proteins of the worker honeybees (Apis mellifera L.) with age and/or role. J. Biochem. 119, 291–295 (1996).CAS 

Google Scholar 
Ohashi, K., Sawata, M., Takeuchi, H., Natori, S. & Kubo, T. Molecular cloning of cDNA and analysis of expression of the gene for alpha-glucosidase from the hypopharyngeal gland of the honeybee Apis mellifera L. Biochem. Biophys. Res. Commun. 221, 380–385 (1996).CAS 

Google Scholar 
Ohashi, K., Natori, S. & Kubo, T. Expression of amylase and glucose oxidase in the hypopharyngeal gland with an age dependent role change of the worker honeybee (Apis mellifera L.). Eur. J. Biochem. 265, 127–133 (1999).CAS 

Google Scholar 
Chanchao, C., Padoongsupalai, R. & Sangvanich, P. Expression and characterization of α-glucosidase III in the dwarf honeybee, Apis florea (Hymenoptera: Apoidea: Apidae). Insect Sci. 14(4), 283–293 (2007).CAS 

Google Scholar 
Corby-Harris, V. & Snyder, L. A. Measuring hypopharyngeal gland acinus size in honey bee (Apis mellifera) Workers. J. Vis. Exp. 139, 58261 (2018).
Google Scholar 
Yamada, T. & Yamada, K. Comparison of long-term changes in size and longevity of bee colonies in mid-west Japan and Maui with and without exposure to pesticide, cold winters, and mites. PeerJ 8, e9505 (2020).
Google Scholar 
Rinkevich, F. D. et al. Genetics, synergists, and age affect insecticide sensitivity of the honey bee, Apis mellifera. PLoS ONE 10(10), e0139841 (2015).
Google Scholar 
Weidenmüller, A. The control of nest climate in bumblebee (Bombus terrestris) colonies: Interindividual variability and self reinforcement in fanning response. Behav. Ecol. 15(1), 120–128 (2004).MathSciNet 

Google Scholar 
Flatt, T., Tu, M. P. & Tatar, M. Hormonal pleiotropy and the juvenile hormone regulation of Drosophila development and life history. BioEssays 27, 999–1010 (2005).CAS 

Google Scholar 
Wu, M. C., Chang, Y. W., Lu, K. H. & Yang, E. C. Gene expression changes in honey bees induced by sublethal imidacloprid exposure during the larval stage. Insect. Biochem. Mol. Biol. 88, 12–20 (2017).CAS 

Google Scholar 
Ament, S. A., Corona, M., Pollock, H. S. & Robinson, G. E. Insulin signaling is involved in the regulation of worker division of labor in honey bee colonies. Proc. Natl. Acad. Sci. USA 105, 4226–4231 (2008).ADS 
CAS 

Google Scholar 
Nicodemo, D. et al. Fipronil and imidacloprid reduce honeybee mitochondrial activity. Environ. Toxicol. Chem. 33(9), 2070–2075 (2014).CAS 

Google Scholar 
Syromyatnikov, M. Y., Lopatin, A. V., Starkov, A. A. & Popov, V. N. Isolation and properties of flight muscle mitochondria of the bumblebee Bombus terrestris (L.). Biochemistry 78(8), 909–914 (2013).CAS 

Google Scholar 
Dayer, F. C. Coadaptation of colony design and worker performance in honeybees. In Diversity in the Genus Apis (ed. Smith, D. R.) 2133–2245 (Westview Press, 1991).
Google Scholar 
Simon-Delso, N., Amaral-Rogers, V. & Belzunces, L. P. Systemic insecticides (neonicotinoids and fipronil): Trends, uses, mode of action and metabolites. Environ. Sci. Pollut. Res. 22, 5–34 (2015).CAS 

Google Scholar 
Evans, J. D. et al. Immune pathways and defence mechanisms in honey bees Apis mellifera. Insect. Mol. Biol. 5, 645–656 (2006).
Google Scholar 
Pankiw, T. & Page, R. E. Response thresholds to sucrose predict foraging division of labor in honeybees. Behav. Ecol. Sociobiol. 47, 265–267 (2000).
Google Scholar  More

Bay, R. A. et al. Genetic coupling of female mate choice with polygenic ecological divergence facilitates stickleback speciation. Curr. Biol. 27, 3344–3349 (2017).CAS 

Google Scholar 
Johannesson, K., Butlin, R. K., Panova, M. & Westram, A. M. Population Genomics: Marine Organisms (eds. Oleksiak, M. F. & Rajora, O. P.) 277–301 (Springer, 2017).Riesch, R. et al. Transitions between phases of genomic differentiation during stick-insect speciation. Nat. Ecol. Evol. 1, 1–13 (2017).
Google Scholar 
Feder, J. L. & Nosil, P. The efficacy of divergence hitchhiking in generating genomic islands during ecological speciation. Evolution 64, 1729–1747 (2010).
Google Scholar 
Rosenblum, E. B., Hickerson, M. J. & Moritz, C. A multilocus perspective on colonization accompanied by selection and gene flow. Evolution 61, 2971–2985 (2007).CAS 

Google Scholar 
Nosil, P., Egan, S. P. & Funk, D. J. Heterogeneous genomic differentiation between walking‐stick ecotypes: “isolation by adaptation” and multiple roles for divergent selection. Evolution 62, 316–336 (2008).
Google Scholar 
Orsini, L., Vanoverbeke, J., Swillen, I., Mergeay, J. & Meester, L. Drivers of population genetic differentiation in the wild: isolation by dispersal limitation, isolation by adaptation and isolation by colonization. Mol. Ecol. 22, 5983–5999 (2013).
Google Scholar 
Sexton, J. P., Hangartner, S. B. & Hoffmann, A. A. Genetic isolation by environment or distance: which pattern of gene flow is most common? Evolution 68, 1–15 (2014).CAS 

Google Scholar 
Roderick, G. K. & Gillespie, R. G. Speciation and phylogeography of Hawaiian terrestrial arthropods. Mol. Ecol. 7, 519–531 (1998).CAS 

Google Scholar 
Juan, C., Emerson, B. C., Oromı́, P. & Hewitt, G. M. Colonization and diversification: towards a phylogeographic synthesis for the Canary Islands. Trends Ecol. Evol. 15, 104–109 (2000).CAS 

Google Scholar 
Brown, R. P., Hoskisson, P. A., Welton, J. H. & Báez, M. Geological history and within‐island diversity: a debris avalanche and the Tenerife lizard Gallotia galloti. Mol. Ecol. 15, 3631–3640 (2006).CAS 

Google Scholar 
O’Connell, K. A., Prates, I., Scheinberg, L. A., Mulder, K. P. & Bell, R. C. Speciation and secondary contact in a fossorial island endemic, the São Tomé caecilian. Mol. Ecol. 30, 2859–2871 (2021).
Google Scholar 
Malhotra, A. & Thorpe, R. S. The dynamics of natural selection and vicariance in the Dominican anole: patterns of within‐island molecular and morphological divergence. Evolution 54, 245–258 (2000).CAS 

Google Scholar 
Brown, R. P., Woods, M. & Thorpe, R. S. Historical volcanism and within-island genetic divergence in the Tenerife skink (Chalcides viridanus). Biol. J. Linnean Soc. 122, 166–175 (2017).
Google Scholar 
Losos, J. Lizards in an Evolutionary Tree: Ecology and Adaptive Radiation of Anoles (University of California Press, 2009).Mahler, D. L., Revell, L. J., Glor, R. E. & Losos, J. B. Ecological opportunity and the rate of morphological evolution in the diversification of Greater Antillean anoles. Evolution 64, 2731–2745 (2010).
Google Scholar 
Wang, I. J., Glor, R. E. & Losos, J. B. Quantifying the roles of ecology and geography in spatial genetic divergence. Ecol. Lett. 16, 175–182 (2013).
Google Scholar 
Beerli, P. & Felsenstein, J. Maximum-likelihood estimation of migration rates and effective population numbers in two populations using a coalescent approach. Genetics 152, 763–773 (1999).CAS 

Google Scholar 
Hey, J. & Nielsen, R. Multilocus methods for estimating population sizes, migration rates and divergence time, with applications to the divergence of Drosophila pseudoobscura and D. persimilis. Genetics 167, 747–760 (2004).CAS 

Google Scholar 
Hey, J. Recent advances in assessing gene flow between diverging populations and species. Curr. Opin. Genet. Dev. 16, 592–596 (2006).CAS 

Google Scholar 
Excoffier, L., Dupanloup, I., Huerta-Sánchez, E., Sousa, V. C. & Foll, M. Robust demographic inference from genomic and SNP data. PLoS Genet. 9, 1003905 (2013).
Google Scholar 
Butlin, R. K. et al. Parallel evolution of local adaptation and reproductive isolation in the face of gene flow. Evolution 68, 935–949 (2014).
Google Scholar 
Rosenblum, E. B., Hoekstra, H. E. & Nachman, M. W. Adaptive reptile color variation and the evolution of the MCIR gene. Evolution 58, 1794–1808 (2004).CAS 

Google Scholar 
Rosenblum, E. B. Convergent evolution and divergent selection: lizards at the White Sands ecotone. Am. Nat. 167, 1–15 (2006).
Google Scholar 
Sumner, F. B. An analysis of geographic variation in mice of the Peromyscus polionotus group from Florida and Alabama. J. Mammal. 7, 149–184 (1926).
Google Scholar 
Davenport, J., & Dellinger, T. Melanism and foraging behaviour in an intertidal population of the Madeiran lizard Podarcis (= Lacerta) dugesii (Milne-Edwards, 1829). Herpetol. J. 5, 200–203 (1995).
Google Scholar 
Galán, P. Demography and population dynamics of the lacertid lizard Podarcis bocagei in north-west Spain. J. Zool. 249, 203–218 (1999).
Google Scholar 
Censky, E. J., Hodge, K. & Dudley, J. Over-water dispersal of lizards due to hurricanes. Nature 395, 556 (1998).CAS 

Google Scholar 
Rolán‐Alvarez, E., Erlandsson, J., Johannesson, K. & Cruz, R. Mechanisms of incomplete prezygotic reproductive isolation in an intertidal snail: testing behavioural models in wild populations. J. Evol. Biol. 12, 879–890 (1999).
Google Scholar 
Ludt, W. B. & Rocha, L. A. Shifting seas: the impacts of Pleistocene sea‐level fluctuations on the evolution of tropical marine taxa. J. Biogeogr. 42, 25–38 (2015).
Google Scholar 
Lambeck, K. Late Pleistocene, Holocene and present sea-levels: constraints on future change. Glob. Planet Change 3, 205–217 (1990). & J.
Google Scholar 
Rosenblum, E. B. The role of phenotypic plasticity in color variation of Tularosa Basin lizards. Copeia 2005, 586–596 (2005).
Google Scholar 
Jin, Y. et al. Dorsal pigmentation and its association with functional variation in MC1R in a lizard from different elevations on the Qinghai–Tibetan plateau. Genome Biol. Evol. 12, 2303–2313 (2020).CAS 

Google Scholar 
Corl, A. et al. The genetic basis of adaptation following plastic changes in coloration in a novel environment. Curr. Biol. 28, 2970–2977 (2018).CAS 

Google Scholar 
Sacchi, R. et al. Genetic and phenotypic component in head shape of common wall lizard Podarcis muralis. Amphib.-Reptilia 37, 301–310 (2016).
Google Scholar 
Dice, L. R. Variation of the deer-mouse (Peromyscus maniculatus) on the Sand Hills of Nebraska and adjacent areas. Contrib. Lab Vertebrate Biol. Univ. Mich. 15, 1–19 (1941).
Google Scholar 
Vitt, L. J., Caldwell, J. P., Zani, P. A. & Titus, T. A. The role of habitat shift in the evolution of lizard morphology: evidence from tropical Tropidurus. Proc. Natl Acad. Sci. USA 94, 3828–3832 (1997).CAS 

Google Scholar 
Pfeifer, S. P. et al. The evolutionary history of Nebraska deer mice: local adaptation in the face of strong gene flow. Mol. Biol. Evol. 35, 792–806 (2018).CAS 

Google Scholar 
Scherrer, R., Donihue, C. M., Reynolds, R. G., Losos, J. B. & Geneva, A. J. Dewlap colour variation in Anolis sagrei is maintained among habitats within islands of the West Indies. J. Evol. Biol. 35, 680–692 (2022).
Google Scholar 
Janson, K. Selection and migration in two distinct phenotypes of Littorina saxatilis in Sweden. Oecologia 59, 58–61 (1983).CAS 

Google Scholar 
Richardson, J. L., Urban, M. C., Bolnick, D. I. & Skelly, D. K. Microgeographic adaptation and the spatial scale of evolution. Trends Ecol. Evol. 29, 165–176 (2014).
Google Scholar 
Engelstoft, C., Robinson, J., Fraser, D. & Hanke, G. Recent rapid expansion of common wall lizards (Podarcis muralis) in British Columbia, Canada. Northwest. Naturalist 101, 50–55 (2020).
Google Scholar 
Cascio, P. L. & Pasta, S. Preliminary data on the biometry and the diet of a microinsular population of Podarcis wagleriana (Reptilia: Lacertidae). Acta Herpetol. 1, 147–152 (2006).
Google Scholar 
Janssen, J., Towns, D. R., Duxbury, M. & Heitkönig, I. M. Surviving in a semi-marine habitat: dietary salt exposure and salt secretion of a New Zealand intertidal skink. Comp. Biochem Physiol. A Mol. Integr. Physiol. 189, 21–29 (2015).CAS 

Google Scholar 
Grismer, L. L. Three new species of intertidal side-blotched lizards (genus Uta) from the Gulf of California, Mexico. Herpetologica 50, 451–474 (1994).
Google Scholar 
Sepúlveda, M., Sabat, P., Porter, W. P. & Fariña, J. M. One solution for two challenges: the lizard Microlophus atacamensis avoids overheating by foraging in intertidal shores. PLoS One 9, 97735 (2014).
Google Scholar 
Hobson, E. S. Observations on diving in the Galapagos marine iguana, Amblyrhynchus cristatus (Bell). Copeia 1965, 249–250 (1965).Balakrishna, S., Amdekar, M. S. & Thaker, M. Morphological divergence, tail loss, and predation risk in urban lizards. Urban Ecosyst. 24, 1391–1398 (2021).
Google Scholar 
Falvey, C. H., Aviles-Rodriguez, K. J., Hagey, T. J. & Winchell, K. M. The finer points of urban adaptation: intraspecific variation in lizard claw morphology. Biol. J. Linn. Soc. 131, 304–318 (2020).
Google Scholar 
Marnocha, E., Pollinger, J. & Smith, T. B. Human‐induced morphological shifts in an island lizard. Evol. Appl 4, 388–396 (2011).
Google Scholar 
Rocha, R., Paixão, M. & Gouveia, R. Predation note: Anthus berthelotii madeirensis (Passeriformes: Motacillidae) catches Teira dugesii mauli (Squamata: Lacertidae) in Deserta Grande, Madeira Archipel. Herpetol. Notes 3, 77–78 (2010).
Google Scholar 
Völkl, W. & Brandl, R. Tail break rate in the Madeiran lizard (Podarcis dugesii). Amphibia-Reptilia 9, 213–218 (1988).Malhotra, A. & Thorpe, R. S. Microgeographic variation in Anolis oculatus, on the island of Dominica, West Indies. J. Evol. Biol. 4, 321–335 (1991).
Google Scholar 
Thorpe, R. S. & Brown, R. P. Microgeographic variation in the colour pattern of the lizard Gallotia galloti within the island of Tenerife: distribution, pattern and hypothesis testing. Biol. J. Linn. Soc. 38, 303–322 (1989).
Google Scholar 
Brown, R. P., Thorpe, R. S. & Báez, M. Parallel within-island microevolution of lizards on neighbouring islands. Nature 352, 60–62 (1991).
Google Scholar 
Báez, M. & Brown, R. P. Testing multivariate patterns of within‐island differentiation in Podarcis dugesii from Madeira. J. Evol. Biol. 10, 575–587 (1997).
Google Scholar 
Cook, L. M. Density of lizards in Madeira. Bocagiana (Funchal) 66, 1–3 (1983).
Google Scholar 
Sadek, R. A. The diet of the Madeiran lizard Lacerta dugesii. Zool. J. Linn. Soc. 73, 313–341 (1981).
Google Scholar 
Brehm, A. et al. Phylogeography of the Madeiran endemic lizard Lacerta dugesii inferred from mtDNA sequences. Mol. Phylogenetics Evol. 26, 222–230 (2003).CAS 

Google Scholar 
Suárez, N. M., Pestano, J. & Brown, R. P. Ecological divergence combined with ancient allopatry in lizard populations from a small volcanic island. Mol. Ecol. 23, 4799–4812 (2014).
Google Scholar 
Towns, D. R. Ecology of the black shore skink, Leiolopisma suteri (Lacertilia: Scincidae), in boulder beach habitats. N. Z. J. Zool. 2, 389–407 (1975).
Google Scholar 
Cook, L. M. Variation in the Madeiran lizard Lacerta dugesii. J. Zool. 187, 327–340 (1979).
Google Scholar 
Troscianko, J. & Stevens, M. Image calibration and analysis toolbox–a free software suite for objectively measuring reflectance, colour, and pattern. Methods Ecol. Evol. 6, 1320–1331 (2015).
Google Scholar 
Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).CAS 

Google Scholar 
Rohlf, F. J. The tps series of software. Hystrix, Ital. J. Mammal. 26, 9–12 (2015).
Google Scholar 
Bookstein, F. L. Morphometric Tools for Landmark Data: Geometry and Biology (Cambridge University Press, 1991).Klingenberg, C. P. MorphoJ: an integrated software package for geometric morphometrics. Mol. Ecol. Resour. 11, 353–357 (2011).
Google Scholar 
Rohlf, F. J. & Slice, D. Extensions of the Procrustes method for the optimal superimposition of landmarks. Syst. Biol. 39, 40–59 (1990).
Google Scholar 
Klingenberg, C. P., Barluenga, M. & Meyer, A. Shape analysis of symmetric structures: quantifying variation among individuals and asymmetry. Evolution 56, 1909–1920 (2002).
Google Scholar 
Andrews, S. FastQC: a Quality Control Tool for High Throughput Sequence Data. Babraham Bioinformatics version 0.11.7. https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (2010).Melo, A. T., Bartaula, R. & Hale, I. GBS-SNP-CROP: a reference-optional pipeline for SNP discovery and plant germplasm characterization using variable length, paired-end genotyping-by-sequencing data. BMC Bioinform. 17, 1–15 (2016).
Google Scholar 
Sabadin, F., Carvalho, H. F., Galli, G. & Fritsche-Neto, R. Population-tailored mock genome enables genomic studies in species without a reference genome. Mol. Genet. Genom. 297, 33–46 (2022).CAS 

Google Scholar 
Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).CAS 

Google Scholar 
Pfeifer, B., Wittelsbürger, U., Ramos-Onsins, S. E. & Lercher, M. J. PopGenome: an efficient swiss army knife for population genomic analyses in R. Mol. Biol. Evol. 31, 1929–1936 (2014).CAS 

Google Scholar 
Team, R. C. R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/ (2022).Jombart, T. adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 24, 1403–1405 (2008).CAS 

Google Scholar 
Luu, K., Bazin, E. & Blum, M. G. pcadapt: an R package to perform genome scans for selection based on principal component analysis. Mol. Ecol. Resour. 17, 67–77 (2017).CAS 

Google Scholar 
Günther, T. & Coop, G. Robust identification of local adaptation from allele frequencies. Genetics 195, 205–220 (2013).
Google Scholar 
Dray, S. et al. Package ‘adespatial.’ Available from: https://cran.r-project.org/package=adespatial (2018).Montano, V. & Jombart, T. An eigenvalue test for spatial principal component analysis. BMC Bioinform. 18, 1–7 (2017).
Google Scholar 
Pickrell, J. K. & Pritchard, J. K. Inference of population splits and mixtures from genome-wide allele frequency data. PLoS Genet. 8, e1002967 (2012).CAS 

Google Scholar  More

Muller, Z. et al. Giraffa camelopardalis. The IUCN red list of threatened species 2016:e.T9194A109326950 (2018).Oconnor, D. et al. Updated geographic range maps for giraffe, Giraffa spp., throughout sub-Saharan Africa, and implications of changing distributions for conservation. Mamm. Rev. 49, 285–299. https://doi.org/10.1111/mam.12165 (2019).Article 

Google Scholar 
Brown, M. B. et al. Conservation status of giraffe: Evaluating contemporary distribution and abundance with evolving taxonomic perspectives. Ref. Module Earth Syst. Environ. Sci. https://doi.org/10.1016/B978-0-12-821139-7.00139-2 (2021).Article 

Google Scholar 
Dunn, M. E. et al. Investigating the international and pan-African trade in giraffe parts and derivatives. Conserv. Sci. Pract. 3, e390. https://doi.org/10.1111/csp2.390 (2021).Article 

Google Scholar 
Hassanin, A. et al. Mitochondrial DNA variability in Giraffa camelopardalis: Consequences for taxonomy, phylogeography and conservation of giraffes in West and Central Africa. C.R. Biol. 330, 265–274. https://doi.org/10.1016/j.crvi.2007.02.008 (2007).Article 
CAS 

Google Scholar 
Groves, C. & Grubb, P. Ungulate Taxonomy (Johns Hopkins University Press, 2011).Book 

Google Scholar 
Fennessy, J. et al. Multi-locus analyses reveal four giraffe species instead of one. Curr. Biol. 26, 1–7. https://doi.org/10.1016/j.cub.2016.07.036 (2016).Article 
CAS 

Google Scholar 
Winter, S., Fennessy, J. & Janke, A. Limited introgression supports division of giraffe into four species. Ecol. Evol. 8, 10156–10165. https://doi.org/10.1002/ece3.4490 (2018).Article 

Google Scholar 
Bercovitch, F. B. Giraffe taxonomy, geographic distribution, and conservation. Afr. J. Ecol. 58, 150–158. https://doi.org/10.1111/aje.12741 (2020).Article 

Google Scholar 
Petzold, A. & Hassanin, A. A comparative approach for species delimitation based on multiple methods of multi-locus DNA sequence analysis: A case study of the genus Giraffa (Mammalia, Cetartiodactyla). PLoS ONE 15, e0217956. https://doi.org/10.1371/journal.pone.0217956 (2020).Article 
CAS 

Google Scholar 
Petzold, A. et al. First insights into past biodiversity of giraffes based on mitochondrial sequences from museum specimens. Eur. J. Taxon. 703, L57-63. https://doi.org/10.1371/journal.pone.0217956 (2020).Article 
CAS 

Google Scholar 
Coimbra, R. T. F. et al. Whole-genome analysis of giraffe supports four distinct species. Curr. Biol. 31, 2929-2938.e5. https://doi.org/10.1016/j.cub.2021.04.033 (2021).Article 
CAS 

Google Scholar 
Muneza, A. B. et al. Giraffa camelopardalis ssp. reticulata. The IUCN Red List of Threatened Species 2018:e.T88420717A88420720 (2018).Miller, M. F. Dispersal of Acacia seeds by ungulates and ostriches in an African Savanna. J. Trop. Ecol. 12, 345–356. https://doi.org/10.1017/S0266467400009548 (1996).Article 

Google Scholar 
Palmer, T. M. et al. Breakdown of an ant-plant mutualism follows the loss of large herbivores from an African savanna. Science 319, 192–195. https://doi.org/10.1126/science.1151579 (2008).Article 
ADS 
CAS 

Google Scholar 
Kalema, G. Investigation of a skin disease in giraffe in Murchison Falls National Park. Report Submitted to Uganda National Park. Uganda National Parks. Kampala, Uganda (1996).Muneza, A. B. et al. Regional variation of the manifestation, prevalence, and severity of giraffe skin disease: A review of an emerging disease in wild and captive giraffe populations. Biol. Conserv. 198, 145–156. https://doi.org/10.1016/j.biocon.2016.04.014 (2016).Article 

Google Scholar 
Epaphras, A. M., Karimuribo, E. D., Mpanduji, D. G. & Meing’ataki, G. E. Prevalence, disease description and epidemiological factors of a novel skin disease in giraffes (Giraffa camelopardalis) in Ruaha National Park, Tanzania. Res. Opin. Anim. Vet. Sci. 2, 60–65 (2012).
Google Scholar 
Lee, D. E. & Bond, M. L. The occurrence and prevalence of giraffe skin disease in protected areas of northern Tanzania. J. Wildl. Dis. 52, 753–755. https://doi.org/10.7589/2015-09-24 (2016).Article 

Google Scholar 
Muneza, A. B. et al. Examining disease prevalence for species of conservation concern using non-invasive spatial capture–recapture techniques. J. Appl. Ecol. 54, 709–717. https://doi.org/10.1111/1365-2664.12796 (2017).Article 

Google Scholar 
Brown, M. Murchison falls giraffe project: Field report. Giraffid 9, 5–10 (2015).
Google Scholar 
Muneza, A. B. et al. Quantifying the severity of an emerging skin disease affecting giraffe populations using photogrammetry analysis of camera trap data. J. Wildl. Dis. 55, 770–781. https://doi.org/10.7589/2018-06-149 (2019).Article 

Google Scholar 
Han, S. et al. Giraffe skin disease: Clinicopathologic characterization of cutaneous filariasis in the critically endangered Nubian giraffe (Giraffa camelopardalis camelopardalis). Vet. Pathol. https://doi.org/10.1177/03009858221082606 (2022).Article 

Google Scholar 
Whittier, C. A. et al. Cutaneous filariasis in free-ranging Rothschild’s giraffes (Giraffa Camelopardalis rothschildi) in Uganda. J. Wildl. Dis. 56, 1–5. https://doi.org/10.7589/2018-09-212 (2020).Article 

Google Scholar 
Pellew, R. Food consumption and energy budgets of the giraffe. J. Appl. Ecol. 21, 141–159. https://doi.org/10.2307/2403043 (1984).Article 

Google Scholar 
Strauss, M. K. L. & Packer, C. Using claw marks to study lion predation on giraffes of the Serengeti. J. Zool. 289, 134–142. https://doi.org/10.1111/j.1469-7998.2012.00972.x (2013).Article 

Google Scholar 
Muneza, A. B. et al. Exploring the connections between giraffe skin disease and lion predation. J. Zool. https://doi.org/10.1111/jzo.12930 (2021).Article 

Google Scholar 
Lindsey, P. A. et al. The bushmeat trade in African savannas: Impacts, drivers, and possible solutions. Biol. Conserv. 160, 80–96. https://doi.org/10.1016/j.biocon.2012.12.020 (2013).Article 

Google Scholar 
Becker, M. et al. Evaluating wire-snare poaching trends and the impacts of by-catch on elephants and large carnivores. Biol. Conserv. 158, 26–36. https://doi.org/10.1016/j.biocon.2012.08.017 (2013).Article 

Google Scholar 
Mudumba, T., Jingo, S., Heit, D. & Montgomery, R. A. The landscape configuration and lethality of snare poaching of sympatric guilds of large carnivores and ungulates. Afr. J. Ecol. 59, 51–62. https://doi.org/10.1111/aje.12781 (2020).Article 

Google Scholar 
Strauss, M. K. L., Kilewo, M., Rentsch, D. & Packer, C. Food supply and poaching limit giraffe abundance in the Serengeti. Popul. Ecol. 57, 505–516. https://doi.org/10.1007/s10144-015-0499-9 (2015).Article 

Google Scholar 
Munn, J. Effects of injury on the locomotion of free-ranging chimpanzees in the Budongo Forest Reserve, Uganda. In Primates of Western Uganda: Developments in Primatology: Progress and Prospects (eds. Newton-Fisher, N. E., Notman, H., Paterson, J. D., & Reynolds, V.) 259–280 (Springer, 2006).Yersin, H., Asiimwe, C., Voordouw, M. J. & Zuberbühler, K. Impact of snare injuries on parasite prevalence in wild chimpanzees (Pan troglodytes). Int. J. Primatol. 38, 21–30. https://doi.org/10.1007/s10764-016-9941-x (2017).Article 

Google Scholar 
Dagg, A. I. Gaits of the giraffe and okapi. J. Mammal. 41, 282–282. https://doi.org/10.2307/1376381 (1960).Article 

Google Scholar 
Dagg, A. I. The role of the neck in the movements of the giraffe. J. Mammal. 43, 88–97. https://doi.org/10.2307/1376883 (1962).Article 

Google Scholar 
Dagg, A. I. & Vos, A. D. The walking gaits of some species of Pecora. J. Zool. 155, 103–110. https://doi.org/10.1111/j.1469-7998.1968.tb03031.x (1968).Article 

Google Scholar 
Alexander, R. M. N., Langman, V. A. & Jayes, A. S. Fast locomotion of some African ungulates. J. Zool. 183, 291–300. https://doi.org/10.1111/j.1469-7998.1977.tb04188.x (1977).Article 

Google Scholar 
Basu, C., Deacon, F., Hutchinson, J. R. & Wilson, A. M. The running kinematics of free-roaming giraffes, measured using a low cost unmanned aerial vehicle (UAV). PeerJ 7, e6312. https://doi.org/10.7717/peerj.6312 (2019).Article 

Google Scholar 
Basu, C., Wilson, A. M. & Hutchinson, J. R. The locomotor kinematics and ground reaction forces of walking giraffes. J. Exp. Biol. 222, jeb159277. https://doi.org/10.1242/jeb.159277 (2019).Article 

Google Scholar 
Hildebrand, M. The adaptive significance of tetrapod gait selection. Am. Zool. 20, 255–267. https://doi.org/10.1093/icb/20.1.255 (1980).Article 

Google Scholar 
Flower, F. C., Sanderson, D. J. & Weary, D. M. Hoof pathologies influence kinematic measures of dairy cow gait. J. Dairy Sci. 88, 3166–3173. https://doi.org/10.3168/jds.s0022-0302(05)73000-9 (2005).Article 
CAS 

Google Scholar 
Brown, M. B., Bolger, D. T. & Fennessy, J. All the eggs in one basket: A countrywide assessment of current and historical giraffe population distribution in Uganda. Glob. Ecol. Conserv. 19, e00612. https://doi.org/10.1016/j.gecco.2019.e00612 (2019).Article 

Google Scholar 
Foster, J. B. The giraffe of Nairobi National Park: Home range, sex ratios, the herd, and food. Afr. J. Ecol. 4, 139–148. https://doi.org/10.1111/j.1365-2028.1966.tb00889.x (1966).Article 

Google Scholar 
Bond, M. L., Strauss, M. K. L. & Lee, D. E. Soil correlates and mortality from giraffe skin disease in Tanzania. J. Wildl. Dis. 52, 953–958. https://doi.org/10.7589/2016-02-047 (2016).Article 

Google Scholar 
Dunham, N. T., McNamara, A., Shapiro, L., Hieronymus, T. & Young, J. W. A user’s guide for the quantitative analysis of substrate characteristics and locomotor kinematics in free-ranging primates. Am. J. Phys. Anthropol. 167, 569–584. https://doi.org/10.1002/ajpa.23686 (2018).Article 

Google Scholar 
Rueden, C. T. et al. Imagej 2: Imagej for the next generation of scientific image data. BMC Bioinform. 18, 529. https://doi.org/10.1186/s12859-017-1934-z (2017).Article 

Google Scholar 
Cartmill, M., Lemelin, P. & Schmitt, D. Support polygons and symmetrical gaits in mammals. Zool. J. Linn. Soc. 136, 401–420. https://doi.org/10.1046/j.1096-3642.2002.00038.x (2002).Article 

Google Scholar 
Hildebrand, M. Analysis of the symmetrical gaits of tetrapods. Folia Biotheoretica 6, 1–22. https://doi.org/10.2307/1379571 (1966).Article 

Google Scholar 
Shapiro, L. J. & Young, J. W. Kinematics of quadrupedal locomotion in sugar gliders (Petaurus breviceps): Effects of age and substrate size. J. Exp. Biol. 215, 480–496. https://doi.org/10.1242/jeb.062588 (2012).Article 

Google Scholar 
Shapiro, L. J., Young, J. W. & VandeBerg, J. L. Body size and the small branch niche: Using marsupial ontogeny to model primate locomotor evolution. J. Hum. Evol. 68, 14–31. https://doi.org/10.1016/j.jhevol.2013.12.006 (2014).Article 

Google Scholar 
Dunham, N. T., McNamara, A., Shapiro, L., Phelps, T. & Young, J. W. Asymmetrical gait kinematics of free-ranging callitrichines in response to changes in substrate diameter, orientation, and displacement. J. Exp. Biol. 223, jeb217562. https://doi.org/10.1242/jeb.217562 (2020).Article 

Google Scholar 
Robinson, R., Herzog, W. & Nigg, B. Use of force platform variables to quantify the effects of chiropractic manipulation on gait symmetry. J. Manipulative Physiol. Ther. 10, 172–176 (1987).CAS 

Google Scholar 
Vanden Hole, C. et al. How innate is locomotion in precocial animals? A study on the early development of spatiotemporal gait variables and gait symmetry in piglets. J. Exp. Biol. 220, 2706–2716. https://doi.org/10.1242/jeb.157693 (2017).Article 

Google Scholar 
Jacobs, B. Y., Kloefkorn, H. E. & Allen, K. D. Gait analysis methods for rodent models of osteoarthritis. Curr. Pain Headache Rep. 18, 456–475. https://doi.org/10.1007/s11916-014-0456-x (2014).Article 

Google Scholar 
Pfau, T., Spence, A., Starke, S., Ferrari, M. & Wilson, A. Modern riding style improves horse racing times. Science 325, 289–289. https://doi.org/10.1126/science.1174605 (2009).Article 
ADS 
CAS 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2019). http://www.R-project.org/.Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. LmerTest package: Tests in linear mixed effects models. J. Stat. Softw. https://doi.org/10.18637/jss.v082.i13 (2017).Article 

Google Scholar 
Length, R. emmeans: Estimated marginal means, aka least‐squares means. R package version 0.9. https://CRAN.R-project.org/package=emmeans (2017).Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B (Methodol.) 57, 289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x (1995).Article 
MathSciNet 
MATH 

Google Scholar 
Merkens, H. W. & Schamhardt, H. C. Evaluation of equine locomotion during different degrees of experimentally induced lameness I: Lameness model and quantification of ground reaction force patterns of the limbs. Equine Vet. J. 20, 99–106. https://doi.org/10.1111/j.2042-3306.1988.tb04655.x (1988).Article 

Google Scholar 
Fanchon, L. & Grandjean, D. Accuracy of asymmetry indices of ground reaction forces for diagnosis of hind limb lameness in dogs. Am. J. Vet. Res. 68, 1089–1094. https://doi.org/10.2460/ajvr.68.10.1089 (2007).Article 

Google Scholar 
Bragança, F. M. S., Rhodin, M. & van Weeren, P. R. On the brink of daily clinical application of objective gait analysis: What evidence do we have so far from studies using an induced lameness model?. Vet. J. 234, 11–23. https://doi.org/10.1016/j.tvjl.2018.01.006 (2018).Article 

Google Scholar 
Brown, M. B. & Bolger, D. T. Male-biased partial migration in a giraffe population. Front. Ecol. Evol. 7, 524. https://doi.org/10.3389/fevo.2019.00524 (2020).Article 

Google Scholar 
Dagg, A. I. Giraffe: Biology, Behaviour and Conservation (Cambridge University Press, 2014).Book 

Google Scholar 
Castles, M. P. et al. Relationships between male giraffes’ colour, age and sociability. Anim. Behav. 157, 13–25. https://doi.org/10.1016/j.anbehav.2019.08.003 (2019).Article 

Google Scholar  More

Anae, J. et al. Recent advances in biochar engineering for soil contaminated with complex chemical mixtures: Remediation strategies and future perspectives. Sci. Total Environ. 767, 144351. https://doi.org/10.1016/j.scitotenv.2020.144351 (2021).Article 
ADS 
CAS 

Google Scholar 
Kiran, B. R. & Prasad, M. N. V. Biochar and rice husk ash assisted phytoremediation potentials of Ricinus communis L. for lead-spiked soils. Ecotoxicol Environ Saf 183, 109574. https://doi.org/10.1016/j.ecoenv.2019.109574 (2019).Article 
CAS 

Google Scholar 
Bolan, N. et al. Remediation of heavy metal(loid)s contaminated soils – To mobilize or to immobilize?. J. Hazard. Mater. 266, 141–166. https://doi.org/10.1016/j.jhazmat.2013.12.018 (2014).Article 
CAS 

Google Scholar 
Burachevskaya, M. et al. The effect of granular activated carbon and biochar on the availability of Cu and Zn to Hordeum sativum distichum in contaminated soil. Plants https://doi.org/10.3390/plants10050841 (2021).Article 

Google Scholar 
Cao, P. et al. Mercapto propyltrimethoxysilane- and ferrous sulfate-modified nano-silica for immobilization of lead and cadmium as well as arsenic in heavy metal-contaminated soil. Environ. Pollut. 266, 115152. https://doi.org/10.1016/j.envpol.2020.115152 (2020).Article 
CAS 

Google Scholar 
Ok, Y. S. et al. Ameliorants to immobilize Cd in rice paddy soils contaminated by abandoned metal mines in Korea. Environ. Geochem. Health 33(Suppl 1), 23–30. https://doi.org/10.1007/s10653-010-9364-0 (2011).Article 
CAS 

Google Scholar 
Qin, Y. et al. Dual-wastes derived biochar with tailored surface features for highly efficient p-nitrophenol adsorption. J. Clean. Prod. 353, 131571. https://doi.org/10.1016/j.jclepro.2022.131571 (2022).Article 
CAS 

Google Scholar 
Rajput, V. D. et al. Nano-biochar: A novel solution for sustainable agriculture and environmental remediation. Environ. Res. 210, 112891. https://doi.org/10.1016/j.envres.2022.112891 (2022).Article 
CAS 

Google Scholar 
Ding, Y. et al. Biochar to improve soil fertility. A review. Agron. Sustain. Dev. 36, 36. https://doi.org/10.1007/s13593-016-0372-z (2016).Article 
CAS 

Google Scholar 
Oni, B. A., Oziegbe, O. & Olawole, O. O. Significance of biochar application to the environment and economy. Ann. Agric. Sci. 64, 222–236. https://doi.org/10.1016/j.aoas.2019.12.006 (2019).Article 

Google Scholar 
He, E. et al. Two years of aging influences the distribution and lability of metal(loid)s in a contaminated soil amended with different biochars. Sci. Total Environ. 673, 245–253. https://doi.org/10.1016/j.scitotenv.2019.04.037 (2019).Article 
ADS 
CAS 

Google Scholar 
Netherway, P. et al. Phosphorus-rich biochars can transform lead in an urban contaminated soil. J. Environ. Qual. 48, 1091–1099. https://doi.org/10.2134/jeq2018.09.0324 (2019).Article 
CAS 

Google Scholar 
O’Connor, D. et al. Biochar application for the remediation of heavy metal polluted land: A review of in situ field trials. Sci. Total Environ. 619–620, 815–826. https://doi.org/10.1016/j.scitotenv.2017.11.132 (2018).Article 
ADS 
CAS 

Google Scholar 
Xu, X. et al. Effect of physicochemical properties of biochar from different feedstock on remediation of heavy metal contaminated soil in mining area. Surf. Interfaces 32, 102058. https://doi.org/10.1016/j.surfin.2022.102058 (2022).Article 
CAS 

Google Scholar 
Melo, L. C. A. et al. Sorption and desorption of cadmium and zinc in two tropical soils amended with sugarcane-straw-derived biochar. J. Soils Sediments 16, 226–234. https://doi.org/10.1007/s11368-015-1199-y (2016).Article 

Google Scholar 
Uchimiya, M., Chang, S. & Klasson, K. T. Screening biochars for heavy metal retention in soil: Role of oxygen functional groups. J. Hazard. Mater. 190, 432–441. https://doi.org/10.1016/j.jhazmat.2011.03.063 (2011).Article 
CAS 

Google Scholar 
Jatav, H. S. et al. Sustainable approach and safe use of biochar and its possible consequences. Sustainability https://doi.org/10.3390/su131810362 (2021).Article 

Google Scholar 
Varalta, F. & Sorvari, J. In Organic Waste Composting through Nexus Thinking: Practices, Policies, and Trends (eds Hettiarachchi, H. et al.) 213–232 (Springer International Publishing, 2020).Chapter 

Google Scholar 
Pinotti, L. et al. Recycling food leftovers in feed as opportunity to increase the sustainability of livestock production. J. Clean. Prod. 294, 126290. https://doi.org/10.1016/j.jclepro.2021.126290 (2021).Article 

Google Scholar 
Jafri, N., Wong, W. Y., Doshi, V., Yoon, L. W. & Cheah, K. H. A review on production and characterization of biochars for application in direct carbon fuel cells. Process Saf. Environ. Prot. 118, 152–166. https://doi.org/10.1016/j.psep.2018.06.036 (2018).Article 
CAS 

Google Scholar 
Jin, Y. et al. Characterization of biochars derived from various spent mushroom substrates and evaluation of their adsorption performance of Cu(II) ions from aqueous solution. Environ. Res. 196, 110323. https://doi.org/10.1016/j.envres.2020.110323 (2021).Article 
CAS 

Google Scholar 
Tomczyk, A., Sokołowska, Z. & Boguta, P. Biomass type effect on biochar surface characteristic and adsorption capacity relative to silver and copper. Fuel 278, 118168. https://doi.org/10.1016/j.fuel.2020.118168 (2020).Article 
CAS 

Google Scholar 
FAO. Food Outlook – Biannual Report on Global Food Markets: November 2020. Rome. Phytoremediation of copper-contaminated soil by Artemisia absinthium: comparative effect of chelating agents. Environmental Geochemistry and Health. (2020). https://doi.org/10.4060/cb1993enRussian-Statistical-Year-Book. Statistical handbook. P76 M., 2020 – 700 p. ISBN 978-5-89476-497-9 (2020).Cheng, C.-H., Lehmann, J., Thies, J. E. & Burton, S. D. Stability of black carbon in soils across a climatic gradient. J. Geophys. Res. Biogeosci. 113, 55. https://doi.org/10.1029/2007JG000642 (2008).Article 
CAS 

Google Scholar 
Singh, B. P., Cowie, A. L. & Smernik, R. J. Biochar carbon stability in a clayey soil as a function of feedstock and pyrolysis temperature. Environ. Sci. Technol. 46, 11770–11778. https://doi.org/10.1021/es302545b (2012).Article 
ADS 
CAS 

Google Scholar 
He, Y. et al. Effects of biochar application on soil greenhouse gas fluxes: A meta-analysis. GCB Bioenergy 9, 743–755. https://doi.org/10.1111/gcbb.12376 (2017).Article 
CAS 

Google Scholar 
Janu, R. et al. Biochar surface functional groups as affected by biomass feedstock, biochar composition and pyrolysis temperature. Carbon Resour. Convers. 4, 36–46. https://doi.org/10.1016/j.crcon.2021.01.003 (2021).Article 
CAS 

Google Scholar 
Tan, X. et al. Application of biochar for the removal of pollutants from aqueous solutions. Chemosphere 125, 70–85. https://doi.org/10.1016/j.chemosphere.2014.12.058 (2015).Article 
ADS 
CAS 

Google Scholar 
Ni, B.-J. et al. Competitive adsorption of heavy metals in aqueous solution onto biochar derived from anaerobically digested sludge. Chemosphere 219, 351–357. https://doi.org/10.1016/j.chemosphere.2018.12.053 (2019).Article 
ADS 
CAS 

Google Scholar 
Park, J.-H. et al. Competitive adsorption of heavy metals onto sesame straw biochar in aqueous solutions. Chemosphere 142, 77–83. https://doi.org/10.1016/j.chemosphere.2015.05.093 (2016).Article 
ADS 
CAS 

Google Scholar 
Methodological-Guidelines. Methodological guidelines for the determination of heavy metals in the soils of agricultural land and crop production – M., TSINAO, 61 (1992)Zhang, A., Li, X., Xing, J. & Xu, G. Adsorption of potentially toxic elements in water by modified biochar: A review. J. Environ. Chem. Eng. 8, 104196. https://doi.org/10.1016/j.jece.2020.104196 (2020).Article 
CAS 

Google Scholar 
Avramiotis, E., Frontistis, Z., Manariotis, I. D., Vakros, J. & Mantzavinos, D. On the performance of a sustainable rice husk biochar for the activation of persulfate and the degradation of antibiotics. Catalysts 11, 1303 (2021).Article 
CAS 

Google Scholar 
Maiti, S., Dey, S., Purakayastha, S. & Ghosh, B. Physical and thermochemical characterization of rice husk char as a potential biomass energy source. Biores. Technol. 97, 2065–2070. https://doi.org/10.1016/j.biortech.2005.10.005 (2006).Article 
CAS 

Google Scholar 
Herrera, K., Morales, L. F., Tarazona, N. A., Aguado, R. & Saldarriaga, J. F. Use of biochar from rice husk pyrolysis: Part A: Recovery as an adsorbent in the removal of emerging compounds. ACS Omega 7, 7625–7637. https://doi.org/10.1021/acsomega.1c06147 (2022).Article 
CAS 

Google Scholar 
Szewczuk-Karpisz, K., Tomczyk, A., Grygorczuk-Płaneta, K. & Naveed, S. Rhizobium leguminosarum bv. trifolii exopolysaccharide and sunflower husk biochar as factors affecting immobilization of both tetracycline and Cd2+ ions on soil solid phase. J. Soils Sediments 22, 2620–2639. https://doi.org/10.1007/s11368-022-03255-3 (2022).Article 
CAS 

Google Scholar 
Hubetska, T. S., Kobylinska, N. G. & García, J. R. Sunflower biomass power plant by-products: Properties and its potential for water purification of organic pollutants. J. Anal. Appl. Pyrolysis 157, 105237. https://doi.org/10.1016/j.jaap.2021.105237 (2021).Article 
CAS 

Google Scholar 
Braghiroli, F. L. et al. The influence of pilot-scale pyro-gasification and activation conditions on porosity development in activated biochars. Biomass Bioenerg. 118, 105–114. https://doi.org/10.1016/j.biombioe.2018.08.016 (2018).Article 
CAS 

Google Scholar 
Braghiroli, F. L. et al. The conversion of wood residues, using pilot-scale technologies, into porous activated biochars for supercapacitors. J. Porous Mater. 27, 537–548. https://doi.org/10.1007/s10934-019-00823-w (2020).Article 
CAS 

Google Scholar 
Boraah, N., Chakma, S. & Kaushal, P. Attributes of wood biochar as an efficient adsorbent for remediating heavy metals and emerging contaminants from water: A critical review and bibliometric analysis. J. Environ. Chem. Eng. 10, 107825. https://doi.org/10.1016/j.jece.2022.107825 (2022).Article 
CAS 

Google Scholar 
Phillips, C. L. et al. Towards predicting biochar impacts on plant-available soil nitrogen content. Biochar 4, 9. https://doi.org/10.1007/s42773-022-00137-2 (2022).Article 
CAS 

Google Scholar 
Sun, L. & Gong, K. Silicon-based materials from rice husks and their applications. Ind. Eng. Chem. Res. 40, 5861–5877. https://doi.org/10.1021/ie010284b (2001).Article 
CAS 

Google Scholar 
Islam, T. et al. Synthesis of rice husk-derived magnetic biochar through liquefaction to adsorb anionic and cationic dyes from aqueous solutions. Arab. J. Sci. Eng. 46, 233–246. https://doi.org/10.1007/s13369-020-04537-z (2021).Article 
CAS 

Google Scholar 
Mohan, D. et al. Biochar production and applications in soil fertility and carbon sequestration – a sustainable solution to crop-residue burning in India. RSC Adv. 8, 508–520. https://doi.org/10.1039/C7RA10353K (2018).Article 
ADS 
CAS 

Google Scholar 
Li, F. et al. Preparation and characterization of biochars from Eichornia crassipes for cadmium removal in aqueous solutions. PLoS ONE 11, e0148132. https://doi.org/10.1371/journal.pone.0148132 (2016).Article 
CAS 

Google Scholar 
Song, H. et al. Potential of novel biochars produced from invasive aquatic species outside food chain in removing ammonium nitrogen: Comparison with conventional biochars and clinoptilolite. Sustainability https://doi.org/10.3390/su11247136 (2019).Article 

Google Scholar 
Yang, G. et al. Effects of pyrolysis temperature on the physicochemical properties of biochar derived from vermicompost and its potential use as an environmental amendment. RSC Adv. 5, 40117–40125. https://doi.org/10.1039/C5RA02836A (2015).Article 
ADS 
CAS 

Google Scholar 
Enders, A., Hanley, K., Whitman, T., Joseph, S. & Lehmann, J. Characterization of biochars to evaluate recalcitrance and agronomic performance. Bioresour. Technol. 114, 644–653. https://doi.org/10.1016/j.biortech.2012.03.022 (2012).Article 
CAS 

Google Scholar 
Zhang, Y., Wang, J. & Feng, Y. The effects of biochar addition on soil physicochemical properties: A review. CATENA 202, 105284. https://doi.org/10.1016/j.catena.2021.105284 (2021).Article 
CAS 

Google Scholar 
Özçimen, D. & Ersoy-Meriçboyu, A. Characterization of biochar and bio-oil samples obtained from carbonization of various biomass materials. Renew. Energy 35, 1319–1324. https://doi.org/10.1016/j.renene.2009.11.042 (2010).Article 
CAS 

Google Scholar 
Lin, Q. et al. Effects of biochar-based materials on the bioavailability of soil organic pollutants and their biological impacts. Sci. Total Environ. 826, 153956. https://doi.org/10.1016/j.scitotenv.2022.153956 (2022).Article 
ADS 
CAS 

Google Scholar 
Yang, H. et al. Thermogravimetric analysis−fourier transform infrared analysis of palm oil waste pyrolysis. Energy Fuels 18, 1814–1821. https://doi.org/10.1021/ef030193m (2004).Article 
CAS 

Google Scholar 
Pasangulapati, V. et al. Effects of cellulose, hemicellulose and lignin on thermochemical conversion characteristics of the selected biomass. Biores. Technol. 114, 663–669. https://doi.org/10.1016/j.biortech.2012.03.036 (2012).Article 
CAS 

Google Scholar 
Kim, P. et al. Surface functionality and carbon structures in lignocellulosic-derived biochars produced by fast pyrolysis. Energy Fuels 25, 4693–4703. https://doi.org/10.1021/ef200915s (2011).Article 
CAS 

Google Scholar 
Keiluweit, M., Nico, P. S., Johnson, M. G. & Kleber, M. dynamic molecular structure of plant biomass-derived black carbon (biochar). Environ. Sci. Technol. 44, 1247–1253. https://doi.org/10.1021/es9031419 (2010).Article 
ADS 
CAS 

Google Scholar 
Wijeyawardana, P. et al. Removal of Cu, Pb and Zn from stormwater using an industrially manufactured sawdust and paddy husk derived biochar. Environ. Technol. Innov. 28, 102640. https://doi.org/10.1016/j.eti.2022.102640 (2022).Article 
CAS 

Google Scholar 
Kołodyńska, D., Krukowska, J. & Thomas, P. Comparison of sorption and desorption studies of heavy metal ions from biochar and commercial active carbon. Chem. Eng. J. 307, 353–363. https://doi.org/10.1016/j.cej.2016.08.088 (2017).Article 
CAS 

Google Scholar 
Uchimiya, M. et al. Immobilization of heavy metal ions (CuII, CdII, NiII, and PbII) by broiler litter-derived biochars in water and soil. J. Agric. Food Chem. 58, 5538–5544. https://doi.org/10.1021/jf9044217 (2010).Article 
CAS 

Google Scholar 
Misono, M., Ochiai, E. I., Saito, Y. & Yoneda, Y. A new dual parameter scale for the strength of lewis acids and bases with the evaluation of their softness. J. Inorg. Nucl. Chem. 29, 2685–2691. https://doi.org/10.1016/0022-1902(67)80006-X (1967).Article 
CAS 

Google Scholar 
McBride, M. B. Environmental Chemistry of Soils (Oxford University Press, 1994).
Google Scholar 
Basta, N. T. & Tabatabai, M. A. Effect of cropping systems on adsorption of metals by soils: III. Competitive adsorption1. Soil Sci. 153, 331–337 (1992).Article 
ADS 
CAS 

Google Scholar 
Sposito, G. The Chemistry of Soils (Oxford University Press, 2016).Bauer, T. V. et al. Application of XAFS and XRD methods for describing the copper and zinc adsorption characteristics in hydromorphic soils. Environ. Geochem. Health 44, 335–347. https://doi.org/10.1007/s10653-020-00773-2 (2022).Article 
CAS 

Google Scholar 
Abd-Elfattah, A. L. Y. & Wada, K. Adsorption of lead, copper, zinc, cobalt, and cadmium by soils that differ in cation-exchange materials. J. Soil Sci. 32, 271–283. https://doi.org/10.1111/j.1365-2389.1981.tb01706.x (1981).Article 
CAS 

Google Scholar 
Etesami, H., Fatemi, H. & Rizwan, M. Interactions of nanoparticles and salinity stress at physiological, biochemical and molecular levels in plants: A review. Ecotoxicol. Environ. Saf. 225, 112769. https://doi.org/10.1016/j.ecoenv.2021.112769 (2021).Article 
CAS 

Google Scholar 
Soria, R. I., Rolfe, S. A., Betancourth, M. P. & Thornton, S. F. The relationship between properties of plant-based biochars and sorption of Cd(II), Pb(II) and Zn(II) in soil model systems. Heliyon 6, e05388. https://doi.org/10.1016/j.heliyon.2020.e05388 (2020).Article 

Google Scholar 
Alfarra, A., Frackowiak, E. & Béguin, F. The HSAB concept as a means to interpret the adsorption of metal ions onto activated carbons. Appl. Surf. Sci. 228, 84–92. https://doi.org/10.1016/j.apsusc.2003.12.033 (2004).Article 
ADS 
CAS 

Google Scholar 
Hu, J., Zhou, X., Shi, Y., Wang, X. & Li, H. Enhancing biochar sorption properties through self-templating strategy and ultrasonic fore-modified pre-treatment: Characteristic, kinetic and mechanism studies. Sci. Total Environ. 769, 144574. https://doi.org/10.1016/j.scitotenv.2020.144574 (2021).Article 
ADS 
CAS 

Google Scholar 
Ward, J., Rasul, M. G. & Bhuiya, M. M. K. Energy recovery from biomass by fast pyrolysis. Proced. Eng. 90, 669–674. https://doi.org/10.1016/j.proeng.2014.11.791 (2014).Article 
CAS 

Google Scholar 
Al-Wabel, M. I., Al-Omran, A., El-Naggar, A. H., Nadeem, M. & Usman, A. R. A. Pyrolysis temperature induced changes in characteristics and chemical composition of biochar produced from conocarpus wastes. Biores. Technol. 131, 374–379. https://doi.org/10.1016/j.biortech.2012.12.165 (2013).Article 
CAS 

Google Scholar 
Calvelo Pereira, R. et al. Contribution to characterisation of biochar to estimate the labile fraction of carbon. Org. Geochem. 42, 1331–1342. https://doi.org/10.1016/j.orggeochem.2011.09.002 (2011).Article 
CAS 

Google Scholar 
Vorob’eva, L. A. Theory and Practice Chemical Analysis of Soils (GEOS Press, Moscow, 2006).
Google Scholar 
Pinskii, D. L. et al. Copper adsorption by chernozem soils and parent rocks in Southern Russia. Geochem. Int. 56, 266–275. https://doi.org/10.1134/S0016702918030072 (2018).Article 
CAS 

Google Scholar 
Wang, Q., Wang, B., Lee, X., Lehmann, J. & Gao, B. Sorption and desorption of Pb(II) to biochar as affected by oxidation and pH. Sci. Total Environ. 634, 188–194. https://doi.org/10.1016/j.scitotenv.2018.03.189 (2018).Article 
ADS 
CAS 

Google Scholar 
Pourret, O. & Houben, D. Characterization of metal binding sites onto biochar using rare earth elements as a fingerprint. Heliyon 4, e00543. https://doi.org/10.1016/j.heliyon.2018.e00543 (2018).Article 

Google Scholar 
Huang, L. et al. High-resolution insight into the competitive adsorption of heavy metals on natural sediment by site energy distribution. Chemosphere 197, 411–419. https://doi.org/10.1016/j.chemosphere.2018.01.056 (2018).Article 
ADS 
MathSciNet 
CAS 

Google Scholar 
Ming, H. et al. Competitive sorption of cadmium and zinc in contrasting soils. Geoderma 268, 60–68. https://doi.org/10.1016/j.geoderma.2016.01.021 (2016).Article 
ADS 
CAS 

Google Scholar 
Musso, T. B., Parolo, M. E., Pettinari, G. & Francisca, F. M. Cu(II) and Zn(II) adsorption capacity of three different clay liner materials. J. Environ. Manag. 146, 50–58. https://doi.org/10.1016/j.jenvman.2014.07.026 (2014).Article 
CAS 

Google Scholar 
Cui, H. et al. Immobilization of Cu and Cd in a contaminated soil: One- and four-year field effects. J. Soils Sediments 14, 1397–1406. https://doi.org/10.1007/s11368-014-0882-8 (2014).Article 
CAS 

Google Scholar 
Elbana, T. A. et al. Freundlich sorption parameters for cadmium, copper, nickel, lead, and zinc for different soils: Influence of kinetics. Geoderma 324, 80–88. https://doi.org/10.1016/j.geoderma.2018.03.019 (2018).Article 
ADS 
CAS 

Google Scholar  More

Now consider a generalized model in which the species interactions are heterogeneous. A natural way of introducing heterogeneity in the system is by having a species diversify into subpopulations with different interaction strengths12,13,14,15. This way of modeling heterogeneity is useful as it can describe different kinds of heterogeneity. For example, the subpopulations could represent polymorphic traits that are genetically determined or result from plastic response to heterogeneous environments. A population could also be divided into local subpopulations in different spatial patches, which can migrate between patches and may face different local predators. We can also model different behavioral modes as subpopulations that, for instance, spend more time foraging for food or hiding from predators. We study several kinds of heterogeneity after we introduce a common mathematical framework. By studying these different scenarios using variants of the model, we show that our main results are not sensitive to the details of the model.We focus on the simple case where only the prey species splits into two types, (C_1) and (C_2), as illustrated in Fig. 1b. The situation is interesting when predator A consumes (C_1) more readily than predator B and B consumes (C_2) more readily than A (i.e., (a_1 / a_0 > b_1 / b_0) and (b_2 / b_0 > a_2 / a_0), which is equivalent to the condition that the nullclines of A and B cross, see section “Resources competition and nullcline analysis”). The arrows between (C_1) and (C_2) in Fig. 1b represent the exchange of individuals between the two subpopulations, which can happen by various mechanisms considered below. Such exchange as well as intraspecific competition between (C_1) and (C_2) result from the fact that the two prey types remain a single species.The system is now described by an enlarged Lotka-Volterra system with four variables, A, B, (C_1), and (C_2): $$begin{aligned} dot{A}&= varepsilon _A ,alpha _{A1} , A , C_1 + alpha _{A2} , A , C_2 – beta _A , A end{aligned}$$
(3a)
$$begin{aligned} dot{B}&= varepsilon _B , alpha _{B1} , B , C_1 + alpha _{B2} , B , C_2 – beta _B , B end{aligned}$$
(3b)
$$begin{aligned} dot{C_1}&= C_1 , (beta _C – alpha _{CC} , C)-alpha _{A1} , C_1 A-alpha _{B1} , C_1 B – sigma _1 , C_1 + sigma _2 , C_2 end{aligned}$$
(3c)
$$begin{aligned} dot{C_2}&= C_2 , (beta _C – alpha _{CC} , C) -alpha _{A2} , C_2 A -alpha _{B2} , C_2 B + sigma _1 , C_1 – sigma _2 , C_2 end{aligned}$$
(3d)
The parameters in these equations and their meanings are listed in Table 1. Here we assume that the prey types (C_1) and (C_2) have the same birth rate and intraspecific competition strength, but different interaction strengths with A and B. Note that (C_1) and (C_2) are connected by the (sigma _i) terms, which represent the exchange of individuals between these subpopulations through mechanisms studied below; these terms indicate a major difference between our model of a prey with intraspecific heterogeneity and other models of two prey species. For the convenience of analysis, we transform the variables (C_1) and (C_2) to another pair of variables C and (lambda), where (C equiv C_1 + C_2) is the total population of C as before, and (lambda equiv C_2 / (C_1 + C_2)) represents the composition of the population (Fig. 1c). After this transformation and rescaling of variables (described in “Methods”), the new dynamical system can be written as: $$begin{aligned} dot{A}&= A , big ( C , (a_1 (1-lambda ) + a_2 lambda ) – a_0 big ) end{aligned}$$
(4a)
$$begin{aligned} dot{B}&= B , big ( C , (b_1 (1-lambda ) + b_2 lambda ) – b_0 big ) end{aligned}$$
(4b)
$$begin{aligned} dot{C}&= C , big ( 1 – C – A (a_1 (1-lambda ) + a_2 lambda ) – B (b_1 (1-lambda ) + b_2 lambda ) big ) end{aligned}$$
(4c)
$$begin{aligned} dot{lambda }&= lambda (1-lambda ) , big ( A (a_1 – a_2) + B (b_1 – b_2) big ) + eta _1 (1-lambda ) – eta _2 lambda end{aligned}$$
(4d)
Here, (a_i) and (b_i) are the (rescaled) feeding rates of the predators on the prey type (C_i); (a_0) and (b_0) are the death rates of the predators as before; (eta _1) and (eta _2) are the exchange rates of the prey types (Table 1). The latter can be functions of other variables, representing different kinds of heterogeneous interactions that we study below. Notice that Eqs. (4a–4c) are equivalent to the homogeneous Eqs. (2a–2c) but with effective interaction strengths (a_text {eff} = (1-lambda ) , a_1 + lambda , a_2) and (b_text {eff} = (1-lambda ) , b_1 + lambda , b_2) that both depend on the prey composition (lambda) (Fig. 1c).Table 1 Model parameters (before/after rescaling) and their meanings.Full size tableThe variable (lambda) can be considered an internal degree of freedom within the C population. In all of the models we study below, (lambda) dynamically stabilizes to a special value (lambda ^*) (a bifurcation point), as shown in Fig. 3a. Accordingly, a new equilibrium point (P_N) appears (on the line (mathscr {L}) in Fig. 2), at which all three species coexist. For comparison, Fig. 3b shows the equilibrium points if (lambda) is held fixed at any other values, which all result in the exclusion of one of the predators. Thus, heterogeneous interactions give rise to a new coexistence phase (see Fig. 4 below) by bringing the prey composition (lambda) to the value (lambda ^*), instead of having to fine-tune the interaction strengths. The exact conditions for this new equilibrium to be stable are detailed in “Methods”.Figure 3(a) Time series of (lambda) for systems with each kind of heterogeneity. All three systems stabilize at the same (lambda ^*) value, which is the bifurcation point in panel (b). (b) Equilibrium population of each species (X = A), B, or C, with (lambda) held fixed at different values. Solid curves represent stable equilibria and dashed curves represent unstable equilibria (see Eq. (9) in “Methods”). The vertical dashed line is where (lambda = lambda ^*), which is also the bifurcation point. Notice that the equilibrium population of C is maximized at this point (for (a_1 > a_2) and (b_2 > b_1)). Parameters used here are ((a_0, a_1, a_2, b_0, b_1, b_2, rho , eta _1, eta _2, kappa ) = (0.25, 0.5, 0.2, 0.4, 0.2, 0.6, 0.5, 0.05, 0.05, 50)).Full size imageInherent heterogeneityWe first consider a scenario where individuals of the prey species are born as one of two types with a fixed ratio, such that a fraction (rho) of the newborns are (C_2) and ((1-rho )) are (C_1). This could describe dimorphic traits, such as the winged and wingless morphs in aphids12 or the horned and hornless morphs in beetles13. We call this “inherent” heterogeneity, because individuals are born with a certain type and cannot change in later stages of life. The prey type given at birth determines the individual’s interaction strength with the predators. This kind of heterogeneity can be described by Eq. (4d) with (eta _1 = rho (1-C)) and (eta _2 = (1-rho ) (1-C)) (see “Methods”).Figure 4Phase diagrams showing regions of parameter space identified by the stable equilibrium points. Yellow region represents (P_C) (predators A, B both extinct), red represents (P_A) (A excludes B), blue represents (P_B) (B excludes A), and green represents (P_N) (A, B coexist). The middle point (black dot) is where the preferences of the two predators are identical, (a_2/a_0=b_2/b_0) and (b_1/b_0=a_1/a_0). The coexistence phase appears in all three kinds of heterogeneity modeled here. (a–d) Inherent heterogeneity: Individuals of the prey population are born in two types with a fixed composition (rho). In the extreme cases of (rho = 0) and 1, the prey is homogeneous and there is no coexistence of the predators. (e–h) Reversible heterogeneity: Individual prey can switch types with fixed switching rates (eta _1) and (eta _2). As the switching rates increase, the coexistence region shrinks because the prey population becomes effectively homogeneous (the occasional green spots are numerical artifacts because the time to reach the equilibrium becomes long in this limit). (i–l) Adaptive heterogeneity: The switching rates (eta _i) dynamically adapt to the predator densities, so as to maximize the growth rate of the prey. As the sharpness (kappa) of the sigmoidal decision function is increased, the prey adapts more optimally and the region of coexistence expands. Parameters used here are ((a_0, a_1, b_0, b_2) = (0.3, 0.5, 0.4, 0.6)).Full size imageThe stable equilibrium of the system can be represented by phase diagrams that show the identities of the species at equilibrium. We plot these phase diagrams by varying the parameters (a_2) and (b_1) while keeping (a_1) and (b_2) constant. As shown in Fig. 4a–d, the equilibrium state depends on the parameter (rho). In the limit (rho = 0) or 1, we recover the homogeneous case because only one type of C is produced. The corresponding phase diagrams (Fig. 4a, d) contain only two phases where either of the predators is excluded, illustrating the competitive exclusion principle. For intermediate values of (rho), however, there is a new phase of coexistence that separates the two exclusion phases (Fig. 4b, c). There are two such regions of coexistence, which touch at a middle point and open toward the bottom left and upper right, respectively. The middle point is at ((a_2/a_0 = b_2/b_0, b_1/b_0 = a_1/a_0)), where the feeding preferences of the two predators are identical (hence their niches fully overlap). Towards the origin and the far upper right, the predators consume one type of C each (hence their niches separate). The coexistence region in the bottom left is where the feeding rates of the predators are the lowest overall. There can be a region (yellow) where both predators go extinct, if their primary prey type alone is not enough to sustain each predator. Increasing the productivity of the system by increasing the birth rate ((beta _C)) of the prey eliminates this extinction region, whereas lowering productivity causes the extinction region to take over the lower coexistence region. Because the existence and identity of the phases is determined by the configuration of the equilibrium points (Fig. 2, see also section “Mathematical methods”), the qualitative shape of the phase diagram is not sensitive to changes of parameter values.The new equilibrium is not only where the predators A and B can coexist, but also where the prey species C grows to a larger density than what is possible for a homogeneous population. This is illustrated in Fig. 3b, which shows the equilibrium population of C if we hold (lambda) fixed at different values. The point (lambda = lambda ^*) is where the system with a dynamic (lambda) is stable, and also where the population of C is maximized (when A and B prefer different prey types). That means the population automatically stabilizes at the optimal composition of prey types. Moreover, the value of (C^*) at this coexistence point can even be larger than the equilibrium population of C when there is only one predator A or B. This is discussed further in section “Multiple-predator effects and emergent promotion of prey”. These results suggest that heterogeneity in interaction strengths can potentially be a strategy for the prey population to leverage the effects of multiple predators against each other to improve survival.Reversible heterogeneityWe next consider a scenario where individual prey can switch their types. This kind of heterogeneity can model reversible changes of phenotypes, i.e., trait changes that affect the prey’s interaction with predators but are not permanent. For example, changes in coat color or camouflage14,16,17, physiological changes such as defense15, and biomass allocation among tissues18,19. One could also think of the prey types as subpopulations within different spatial patches, if each predator hunts at a preferred patch and the prey migrate between the patches20,21. With some generalization, one could even consider heterogeneity in resources, such as nutrients located in different places, that can be reached by primary consumers, such as swimming phytoplankton22. We can model this “reversible” kind of heterogeneity by introducing switching rates from one prey type to the other. In Eq. (4d), (eta _1) and (eta _2) now represent the switching rates per capita from (C_1) to (C_2) and from (C_2) to (C_1), respectively. Here we study the simplest case where both rates are fixed.In the absence of the predators, the natural composition of the prey species given by the switching rates would be (rho equiv eta _1 / (eta _1 + eta _2)), and the rate at which (lambda) relaxes to this natural composition is (gamma equiv eta _1 + eta _2). Compared to the previous scenario where we had only one parameter (rho), here we have an additional parameter (gamma) that modifies the behavior of the system. Fig. 4e–h shows phase diagrams for the system as (rho) is fixed and (gamma) varies. We again find the new equilibrium (P_N) where all three species coexist. When (gamma) is small, the system has a large region of coexistence. As (gamma) is increased, this region is squeezed into a border between the two regions of exclusion, where the slope of the border is given by (eta _1/eta _2) as determined by the parameter (rho). However, this is different from the exclusion we see in the case of inherent heterogeneity, which happens only for (rho rightarrow 0) or 1, where the borders are horizontal or vertical (Fig. 4a,d). Here the predators exclude each other despite having a mixture of prey types in the population.This special limit can be understood as follows. For a large (gamma), (lambda) is effectively set to a constant value equal to (rho), because it has a very fast relaxation rate. In other words, the prey types exchange so often that the population always maintains a constant composition. In this limit, the system behaves as if it were a homogeneous system with effective interaction strengths (a_text {eff} = (1-rho ) , a_1 + rho , a_2) and (b_text {eff} = (1-rho ) , b_1 + rho , b_2). As in a homogeneous system, there is competitive exclusion between the predators instead of coexistence. This demonstrates that having a constant level of heterogeneity is not sufficient to cause coexistence. The overall composition of the population must be able to change dynamically as a result of population growth and consumption by predators.An interesting behavior is seen when we examine a point inside the shrinking coexistence region as (gamma) is increased. Typical trajectories of the system for such parameter values are shown in Fig. 5. As (gamma) increases, the system relaxes to the line (mathscr {L}) quickly, then slowly crawls along it towards the final equilibrium point (P_N). This is because increasing (gamma) increases the speed that (lambda) relaxes to (lambda ^*), and when (lambda rightarrow lambda ^*), (mathscr {L}) becomes marginally stable. Therefore, the attraction to (mathscr {L}) in the perpendicular direction is strong, but the attraction towards the equilibrium point along (mathscr {L}) is weak. This leads to a long transient behavior that makes the system appear to reach no equilibrium in a limited time23,24. It is especially true when there is noise in the dynamics, which causes the system to diffuse along (mathscr {L}) with only a weak drift towards the final equilibrium (Fig. 5). Thus, the introduction of a fast timescale (quick relaxation of (lambda) due to a large (gamma)) actually results in a long transient.Figure 5Trajectories of the system projected in the A-B plane, with parameters inside the coexistence region (by holding the position of (P_N) fixed). As (gamma) increases, the system tends to approach the line (mathscr {L}) quickly and then crawl along it. The grey trajectory is with independent Gaussian white noise ((sim mathscr {N}(0,0.5))) added to each variable’s dynamics. Noise causes the system to diffuse along (mathscr {L}) for a long transient period before coming to the equilibrium point (P_N). Parameters used here are ((a_0, a_1, a_2, b_0, b_1, b_2) = (0.2, 0.8, 0.5, 0.2, 0.6, 0.9)), chosen to place (P_N) away from the middle of (mathscr {L}) to show the trajectory drifting toward the equilibrium.Full size imageAdaptive heterogeneityA third kind of heterogeneity we consider is the change of interactions in time. By this we mean an individual can actively change its interaction strength with others in response to certain conditions. This kind of response is often invoked in models of adaptive foraging behavior, where individuals choose appropriate actions to maximize some form of fitness25,26. For example, we may consider two behaviors, resting and foraging, as our prey types. Different predators may prefer to strike when the prey is doing different things. In response, the prey may choose to do one thing or the other depending on the current abundances of different predators. Such behavioral modulation is seen, for example, in systems of predatory spiders and grasshoppers27. Phenotypic plasticity is also seen in plant tissues in response to consumers28,29,30.This kind of “adaptive” heterogeneity can be modeled by having switching rates (eta _1) and (eta _2) that are time-dependent. Let us assume that the prey species tries to maximize its population growth rate by switching to the more favorable type. From Eq. (4c), we see that the growth rate of C depends linearly on the composition (lambda) with a coefficient (u(A,B) equiv (a_1 – a_2) A + (b_1 – b_2) B). Therefore, when this coefficient is positive, it is favorable for C to increase (lambda) by switching to type (C_2). This can be achieved by having a positive switching rate (eta _2) whenever (u(A,B) > 0). Similarly, whenever (u(A,B) < 0), it is favorable for C to switch to type (C_1) by having a positive (eta _1). In this way, the heterogeneity of the prey population constantly adapts to the predator densities. We model such adaptive switching by making (eta _1) and (eta _2) functions of the coefficient u(A, B), e.g., (eta _1(u) = 1/(1+mathrm {e}^{kappa u})) and (eta _2(u) = 1/(1+mathrm {e}^{-kappa u})). The sigmoidal form of the functions means that the switching rate in the favorable direction for C is turned on quickly, while the other direction is turned off. The parameter (kappa) controls the sharpness of this transition.Phase diagrams for the system with different values of (kappa) are shown in Fig. 4i–l. A larger (kappa) means the prey adapts its composition faster and more optimally, which causes the coexistence region to expand. In the extreme limit, the system changes its dynamics instantaneously whenever it crosses the boundary where (u(A,B) = 0), like in a hybrid system31. Such a system can still reach a stable equilibrium that lies on the boundary, if the flow on each side of the boundary points towards the other side32. This is what happens in our system and, interestingly, the equilibrium is the same three-species coexistence point (P_N) as in the previous scenarios. The region of coexistence turns out to be largest in this limit (Fig. 4l).Our results suggest that the coexistence of the predators can be viewed as a by-product of the prey’s strategy to maximize its own benefit. The time-dependent case studied here represents a strategy that involves the prey evaluating the risk posed by different predators. This is in contrast to the scenarios studied above, where the prey population passively creates phenotypic heterogeneity regardless of the presence of the predators. These two types of behavior are analogous to the two strategies studied for adaptation in varying environments, i.e., sensing and bet-hedging33,34. The former requires accessing information about the current environment to make optimal decisions, whereas the latter relies on maintaining a diverse population to reduce detrimental effects caused by environmental changes. Here the varying abundances of the predators play a similar role as the varying environment. From this point of view, the heterogeneous interactions studied here can be a strategy of the prey species that is evolutionarily favorable. More

Ren, W. et al. Changes of periphyton abundance and biomass driven by factors specific to flooding inflow in a river inlet area in Erhai Lake, China. Front. Environ. Sci. 9, 680718. https://doi.org/10.3389/fenvs.2021.680718 (2021).Article 

Google Scholar 
Woodruff, S. L. et al. The effects of a developing biofilm on chemical changes across the sediment-water interface in a freshwater environment. Int. Rev. Hydrobiol. 84(5), 509–532 (1999).CAS 

Google Scholar 
Muñoz, I., Real, M., Guasch, H., Navarro, E. & Sabater, S. Effects of atrazine on periphyton under grazing pressure. Aquat. Toxicol. 55(3–4), 239–249 (2001).
Google Scholar 
Hoagland, K. D., Roemer, S. C. & Rosowski, J. R. Colonization and community structure of two periphyton assemblages, with emphasis on the diatoms (Bacillariophyceae). Am. J. Bot. 69, 188–213. https://doi.org/10.2307/2443006 (1982).Article 

Google Scholar 
Steinman, A. D. & McIntire, C. D. Effects of current velocity and light energy on the structure of periphyton assemblages in laboratory streams. J. Phycol. 22, 352–361. https://doi.org/10.1111/J.1529-8817.1986.TB00035.X (1986).Article 

Google Scholar 
Tonkin, J. D., Death, R. G. & Barquín, J. Periphyton control on stream invertebrate diversity: Is periphyton architecture more important than biomass?. Mar. Freshw. Res. 65(9), 818–829 (2014).
Google Scholar 
Beck, W. S., Markman, D. W., Oleksy, I. A., Lafferty, M. H. & Poff, N. L. Seasonal shifts in the importance of bottom-up and top-down factors on stream periphyton community structure. Oikos 128, 680–691. https://doi.org/10.1111/oik.05844 (2018).Article 
CAS 

Google Scholar 
Hogsden, K. L. & Harding, J. S. Consequences of acid mine drainage for the structure and function of benthic stream communities: A review. Freshw. Sci. 31, 108–120. https://doi.org/10.1899/11-091.1 (2012).Article 

Google Scholar 
Sofi, M. S., Bhat, S. U., Rashid, I. & Kuniyal, J. C. The natural flow regime: A master variable for maintaining river ecosystem health. Ecohydrology 13(8), e2247. https://doi.org/10.1002/eco.2247 (2020).Article 

Google Scholar 
Biggs, B. J. F. Eutrophication of streams and rivers: Dissolved nutrient-chlorophyllrelationship for benthic algae. J. N. Am. Benthol. Soc. 19, 17–31 (2000).
Google Scholar 
Ormerod, S. J., Dobson, M., Hildrew, A. G. & Townsend, C. Multiple stressors in freshwater ecosystems. Freshw. Biol. 55, 1–4 (2010).
Google Scholar 
Poff, et al. The natural flow regime: A paradigm for river conservation and restoration. Bioscience 47, 769–784 (1997).
Google Scholar 
Naiman, R. J., Décamps, H., & McClain, M. E. Riparia: Ecology, Conservation and Management of Streamside Communities, (Elsevier/Academic Press, 2005).Gleick, P. H. Water use. Annu. Rev. Environ. Resour. 28, 275–314 (2003).
Google Scholar 
Jenkins, K. M. & Boulton, A. J. Connectivity in a dryland river: Short-term aquatic macroinvertebrate recruitment following floodplain inundation. Ecology 84(10), 2708–2723 (2003).
Google Scholar 
Biggs, B. J. F. Patterns in benthic algae of streams. In Algal Ecology in Freshwater Benthic Ecosystems (eds. Stevenson, R. J., Bothwell, M. L., & Lowe, R. L.) 31–56 (Academic Press, 1996).Smolar-Žvanut, N. & Mikoš, M. The impact of flow regulation by hydropower dams on the periphyton community in the Soča River, Slovenia. Hydrol. Sci. J. 59(5), 1032–1045. https://doi.org/10.1080/02626667.2013.834339 (2014).Article 
CAS 

Google Scholar 
Curry, C. J. & Baird, D. J. Habitat type and dispersal ability influence spatial structuring of larval Odonata and Trichoptera assemblages. Freshw. Biol. 60, 2142–2152 (2015).
Google Scholar 
Wu, N., Cai, Q. & Fohrer, N. Contribution of microspatial factors to benthic diatom communities. Hydrobiologia 732, 49–60. https://doi.org/10.1007/s10750-014-1843-3 (2014).Article 
CAS 

Google Scholar 
Mueller, M., Pander, J. & Geist, J. The effects of weirs on structural stream habitat and biological communities. J. Appl. Ecol 48(6), 1450–1461. https://doi.org/10.1111/j.1365-2664.2011.02035.x (2011).Article 

Google Scholar 
Davies, P. M. et al. Flow–ecology relationships: closing the loop on effective environmental flows. Mar. Freshw. Res. 65(2), 133–141 (2013).
Google Scholar 
Jun, Y. C. et al. Spatial distribution of benthic macroinvertebrate assemblages in relation to environmental variables in Korean nationwide streams. Water 8(1), 27. https://doi.org/10.3390/w8010027 (2016).Article 

Google Scholar 
Biggs, B. J. F. & Close, M. E. Periphyton biomass dynamics in gravel bed rivers: The relative effects of flows and nutrients. Freshw. Biol. 22, 209–231 (1989).CAS 

Google Scholar 
Jowett, I. & Biggs, B. J. F. Flood and velocity effects on periphyton and silt accumulation in two New Zealand rivers. N. Zeal. J. Mar. Freshw. Res. 31, 287–300 (1997).
Google Scholar 
Biggs, B. J. F., Goring, D. G. & Nikora, V. I. Subsidy and stress responses of stream periphyton to gradients in water velocity as a function of community growth form. J. Phycol. 34, 598–607 (1998).
Google Scholar 
Malmqvist, B. & Englund, G. Effects of hydropower-induced flow perturbations on mayfly (Ephemeroptera) richness and abundance in north Swedish river rapids. Hydrobiologia 341(2), 145–158 (1996).
Google Scholar 
Poff, N. L. & Ward, J. V. Herbivory under different flow regimes: A field experiment and test of a model with a benthic stream insect. Oikos 72, 179–188 (1995).
Google Scholar 
Poff, L. N., Wellnitz, T. & Monroe, J. B. Redundancy among three herbivorous insects across an experimental current velocity gradient. Oecologia 134, 262–269. https://doi.org/10.1007/s00442-002-1086-2 (2003).Article 

Google Scholar 
Vaughn, C. C. The role of periphyton abundance and quality in the microdistribution of a stream grazer, Helicopsyche borealis (Trichoptera: Helicopsychidae). Freshw. Biol. 16, 485–493 (1986).
Google Scholar 
Francoeur, S. N. Meta-analysis of lotic nutrient amendment experiments: Detecting and quantifying subtle responses. J. N. Am. Benthol. Soc. 20, 358–368 (2001).
Google Scholar 
Elser, J. J. et al. Global analysis of nitrogen and phosphorus limitation of primary producers in freshwater, marine and terrestrial ecosystems. Ecol. Lett. 10, 1135–1142 (2007).
Google Scholar 
Hillebrand, H. Meta-analysis of grazer control of periphyton biomass across aquatic ecosystems. J. Phycol. 45, 798–806 (2009).
Google Scholar 
Lamberti, G. A. The role of periphyton in benthic food webs. In Algal Ecology—Freshwater Benthic Ecosystems, 533–572 (eds. Stevenson, R. J., Bothwell, M. L. & Lowe, R. L.) (Academic Press, 1996).Lamberti, G. A. et al. Influence of grazer type and abundance on plant–herbivore interactions in streams. Hydrobiologia 306, 179–188 (1995).
Google Scholar 
Gregory, S. V. Plant–herbivore interactions in stream systems. In Stream Ecology (eds. Barnes, J. R. & Minshall, G. W.) 157–189 (Plenum, 1983).Lamberti, G. A. & Moore, J. W. Aquatic insects as primary consumers. In The Ecology of Aquatic Insects (eds Resh, V. H. & Rosenberg, D. M.) 164–195 (Praeger, 1984).
Google Scholar 
Sterner, R. W., Elser, J. J. & Hessen, D. O. Stoichiometric relationships among producers, consumers and nutrient cycling in pelagic ecosystems. Biogeochemistry 17, 49–67 (1992).CAS 

Google Scholar 
Kahlert, M. & Baunsgaard, M. T. Nutrient recycling—A strategy of a grazer community to overcome nutrient limitation. J. N. Am. Benthol. Soc. 18, 363–369 (1999).
Google Scholar 
Burkholder, J. M., Wetzel, R. G. & Klomparens, K. L. Direct comparison of phosphate uptake by adnate and loosely attached microalgae within and intact biofilm matrix. Appl. Environ. Microbiol. 56, 2882–2890 (1990).CAS 

Google Scholar 
Steinman, A. D. Effects of grazers on freshwater benthic algae. In Algal Ecology: Freshwater Benthic Ecosystems (eds. Stevenson, R. J., Bothwell & Lowe, R. L.) 341–366 (Academic Press, 1996).Smucker, N. J. & Vis, M. L. Spatial factors contribute to benthic diatom structure in streams across spatial scales: Considerations for biomonitoring. Ecol. Indic. 11, 1191–1203 (2011).
Google Scholar 
Myers, et al. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).Article 
CAS 

Google Scholar 
Wang, J., Pan, F., Soininen, J., Heino, J. & Shen, J. Nutrient enrichment modifies temperature-biodiversity relationship in large scale field experiments. Nat. Commun. 7, 13 (2016).
Google Scholar 
Wu, et al. Flow regimes filter species traits of benthic diatom communities and modify the functional features of lowland streams-a nationwide scale study. Sci. Total Environ. 651, 357–366 (2019).CAS 

Google Scholar 
Nisar, M. A. Geospatial approach to study environmental characterization of a Kashmir wetland (Anchar) catchment with special reference to land use/land cover and changing climate. Ph.D Thesis, Sher-e-Kashmir University of Agricultural Sciences and Technology, Kashmir. Weblink. http://krishikosh.egranth.ac.in/handle/1/91309 (2012).Bhat, S. U., Sofi, A. H., Yaseen, T., Pandit, A. K. & Yousuf, A. R. Macro invertebrate community from Sonamarg streams of Kashmir Himalaya. Pak. J. Biol. Sci. 14(3), 182–194. https://doi.org/10.3923/pjbs.2011.182.194 (2011).Article 
CAS 

Google Scholar 
Baba, A. I., Sofi, A. H., Bhat, S. U., & Pandit, A. K. Periphytic algae of river Sindh in the Sonamarg area of Kashmir valley. J. Phytol. 3(6) (2011).Sofi, M. S., Rautela, K. S., Bhat, S. U., Rashid, I. & Kuniyal, J. C. Application of geomorphometric approach for the estimation of hydro-sedimentological flows and cation weathering rate: Towards understanding the sustainable land use policy for the Sindh Basin, Kashmir Himalaya. Water Air Soil Pollut. 232(7), 1–11. https://doi.org/10.1007/s11270-021-05217-w (2021).Article 
CAS 

Google Scholar 
Romshoo, S. A., & Fayaz, M. Use of high resolution remote sensing for improving environmental Friendly tourism master planning in the Alpine Himalaya: A case study of Sonamarg tourist resort, Kashmir. J. Himalayan Ecol. Sustain. Dev. 14 (2019).Biggs, B. J. F. & Kilroy, C. Stream periphyton monitoring manual. Published by NIWA for Ministry for the Environment, 226 Christchurch, New Zealand: NIWA (2000).APHA. Standard Methods for Examination of Water and Wastewater, 22nd edn. (American Public Health Association, 2012).Cox, E. J. Identification of Freshwater Diatoms from Live Material. (Chapman and Hall, 1996). https://doi.org/10.1017/S0025315400041023.Krammer, K., & Lange-Bertalot, H. Bacillariophyceae, Part 5. English and French Translation of the Keys. (VEB Gustav Fisher Verlag, 2000).Reichardt, E. A remarkable association of diatoms in a spring habitat in the Grazer Bergland, Austria. In Iconographia Diatomologica (ed. Lange-Bertalot, H.) 419–479 (2004).Żelazna-Wieczorek, J. Diatom flora in springs of Lódz Hills (Central Poland). Biodiversity, taxonomy and temporal changes of epipsammic diatom assemblages in springs affected by human impact, 419. Volume 13 of Diatom monographs. Gantner. https://books.google.co.in/books?id=bdxeewAACAAJ (2011).Stark, J. D., Boothroyd, I. K. G., Harding, J. S., Maxted, J. R. & Scarsbrook, M. R. Protocols for sampling macroinvertebrates in wadeable streams. In New Zealand Macroinvertebrate Working Group Report no. 1. Prepared for the Ministry for the Environment. Sustainable Management Fund Project, 5103 (2001).Winterbourn, M. J. Sampling stream invertebrates. In Biological Monitoring of Freshwaters. Proceedings of the Seminar. Water and Soil Miscellaneous Publication No. 83 (eds. Pridmore, R. D., Cooper, A. B.) 241–258. (National Water and Soil Conservation Authority, 1985).Barbour, M. T., Gerritsen, J., Snyder, B. D., Stribling, J. B. Rapid bioassessment protocols for use in streams and wadeable rivers: periphyton, benthic macroinvertebrates and fish, 339. (United States Environmental Protection Agency, Office of Water, 1999).Malmqvist, B. & Hoffsten, P. O. Macroinvertebrate taxonomic richness, community structure and nestedness in Swedish streams. Fundam. Appl. Limnol. 150(1), 29–54. https://doi.org/10.1127/archiv-hydrobiol/150/2000/29 (2000).Article 

Google Scholar 
Ilmonen, J. & Paasivirta, L. Benthic macrocrustacean and insect assemblages in relation to spring habitat characteristics: Patterns in abundance and diversity. Hydrobiologia 533(1–3), 99–113. https://doi.org/10.1007/s10750-004-2399-4 (2005).Article 

Google Scholar 
Munasinghe, D. S. N., Najim, M. M. M., Quadroni, S. & Musthafa, M. M. Impacts of streamflow alteration on benthic macroinvertebrates by mini-hydro diversion in Sri Lanka. Sci. Rep. 11(1), 546. https://doi.org/10.1038/s41598-020-79576-5 (2021).Article 
CAS 

Google Scholar 
Edmondson, W. T. Fresh-Water Biology, 2nd ed. 1050–1056 (Wiley, 1959).Pennak, R. W. Freshwater Invertebrates of United States. (Wiley, 1978).McCafferty, W. P., Provonsha, A. V. Aquatic entomology: The fishermen’s and ecologists’ Illustrated Guide to Insects and their Relatives. (Jones and Bartlett Publishers, 1983).Borror, D., Triplehorn, C., Johnson, N. An Introduction to the Study of Insects, 6th ed. (Saunders College Publishing, 1989).Ward, J. V. Aquatic Insect Ecology, Biology and Habitat. (Wiley, 1992).Engblom, E. & Lingdell, P.E. Analyses of Benthic Invertebrates (ed. Nyman, L.) (1999).Bouchard, R. W. Guide to Aquatic Invertebrates of the Upper Midwest: Identification Manual for Students (University of Minnesota, 2004).
Google Scholar 
Subramanian, K. A. & Sivaramakrishnan, K. G. Aquatic Insects for Biomonitoring Freshwater Ecosystems—A Methodology Manual. (Ashoka Trust for Ecology and Environment (ATREE), 2007).Thorp, J. H., & Covich, A. P. (eds.) Ecology and Classification of North American Freshwater Invertebrates. (Academic Press, 2009).Allan, J. D. & Castillo, M.M. An introduction to fluvial ecosystems. In Stream Ecology: Structure and Function of Running Waters, 1–12 (2007).Oksanen, et al. Vegan: Community ecology package. In: R package version 2.4-3.McCune, B. & Grace, B. Analysis of Ecological Communities (MjM Software Design, 2016).Clarke, K. R. & Gorley, R. N. Primer v6 Permanova+ (Primer-E Ltd., 2006).
Google Scholar 
Salazar, G. EcolUtils: Utilities for Community Ecology Analysis. R package version 0.1 software (2018).Anderson, M. J., Ellingsen, K. E. & McArdle, B. H. Multivariate dispersion as a measure of beta diversity. Ecol. Lett. 9(6), 683–693 (2006).
Google Scholar 
Gardener, M. Community Ecology: Analytical Methods in Using R and Excel. (Pelagic Publishing, 2014).Chao, A. & Bunge, J. Estimating the number of species in a stochastic abundance model. Biometrics 58, 531–539. https://doi.org/10.1111/j.0006-341X.2002.00531.x (2002).Article 
MATH 

Google Scholar 
Peres-Neto, P. R., Legendre, P., Dray, S. & Borcard, D. Variation partitioning of species data matrices: estimation and comparison of fractions. Ecology 87, 2614–2625 (2006).
Google Scholar 
Meng, X. L. et al. Responses of macroinvertebrates and local environment to short-term commercial sand dredging practices in a flood-plain lake. Sci. Total Environ. 631, 1350–1359 (2018).
Google Scholar 
Core Team, R. R: A Language and Environmental for Statistical Computing. (R Foundation for Statistical Computing, 2017).Wood, P. J. & Armitage, P. D. Biological effects of fine sediment in the lotic environment. Environ. Manag. 21, 203–217 (1997).CAS 

Google Scholar 
Marchant, R. Changes in the benthic invertebrate communities of the Thomson River, southeastern Australia, after dam construction. Regul. Rivers Res. Manag. 4, 71–89 (1989).
Google Scholar 
Gray, L. J. & Ward, J. V. Effects of sediment releases from a reservoir on stream macroinvertebrates. Hydrobiologia 96, 177–184 (1982).
Google Scholar 
Sand-Jensen, K., Moller, J. & Olesen, B. H. Biomass regulation of microbenthic algae in Danish lowland streams. Oikos 53, 332–340 (1988).
Google Scholar 
Lewis, M. A., Weber, D. E., Stanley, R. S. & Moore, J. C. Dredging impact on an urbanized Florida bayou: Effects on benthos and algal-periphyton. Environ. Pollut. 115(2), 161–171 (2001).CAS 

Google Scholar 
Biggs, B. J. Algal ecology in freshwater benthic ecosystems geology and landuse to the habitat template of periphyton in stream ecosystems. Freshw. Biol. 33, 419–438 (1995).
Google Scholar 
Taylor, et al. Can diatom-based pollution indices be used for biomonitoring in South Africa? A case study of the Crocodile West and Marico water management area. Hydrobiologia 592, 455–464 (2007).
Google Scholar 
Porter, et al. Efficacy of algal metrics for assessing nutrient and organic enrichment in flowing waters. Freshw. Biol. 53, 1036–1054 (2008).
Google Scholar 
Wetzel, R. G. & Likens, G. E. Limnological analyses, 3rd ed. In Nitrogen, Phosphorus, and Other Nutrients, 85–113. (Springer, 2000). https://doi.org/10.1007/978-1-4757-3250-4.Wetzel, R. G. Attached algal-substrata interactions: Fact or myth, and when and how? vol. 17. In Periphyton of Freshwater Ecosystems (ed. Wetzel, R.) 207–215 (Springer, 1983). https://doi.org/10.1007/978-94-009-7293-3_28.Krajenbrink, H. J. et al. Diatoms as indicators of the effects of river impoundment at multiple spatial scales. PeerJ 7, e8092. https://doi.org/10.7717/peerj.8092 (2019).Article 

Google Scholar 
Poff, N. L., Voelz, N. J., Ward, J. V. & Lee, R. E. Algal colonization under four experimentally-controlled current regimes in a high mountain stream. J. N. Am. Benthol. Soc. 9, 303–318 (1990).
Google Scholar 
Dodds, W. K. & Marra, J. L. Behaviors of the midge, Cricotopus (Diptera; Chironomidae) related to mutualism with Nostoc parmeloides (Cyanobacteria). Aquat. Insects 11, 201–208 (1989).
Google Scholar 
Tang, T., Niu, S. Q. & Dudgeon, D. Responses of epibenthic algal assemblages to water abstraction in Hong Kong streams. Hydrobiologia 703(1), 225–237. https://doi.org/10.1007/s10750-012-1362-z (2013).Article 
CAS 

Google Scholar 
Maheshwari, K., Vashistha, J., Paulose, P. V. & Agarwal, T. Seasonal changes in phytoplankton community of lake Ramgarh, India. Int. J. Curr. Microbiol. Appl. Sci. 4(11), 318–330 (2015).CAS 

Google Scholar 
Luttenton, M. R., & Baisden, C. The relationships among disturbance, substratum size and periphyton community structure. In Advances in Algal Biology: A Commemoration of the Work of Rex Lowe 111–117. (Springer, 2006).Uehlinger, U. Spatial and temporal variability of periphyton biomass in a prealpine river (Necker, Switzerland). Arch. Fur. Hydrobiol. 123, 219–237 (1991).
Google Scholar 
Hill, W. R. Effects of light. In Algal Ecology in Freshwater Benthic Ecosystems. 121–148 (eds. Stevenson, R. J., Bothwell, M. L., Lowe, R. L.) (Academic Press, 1996).DeNichola, D. M. Periphyton responses to temperature at different ecological levels. In Algal Ecology in Freshwater Benthic Ecosystems. (eds. Stevenson, R. J., Bothwell, M. L., Lowe, R. L.) 149–181 (Academic Press, 1996).O’Reilly, C. M. Seasonal dynamics of periphyton in a large tropical lake. Hydrobiologia 553, 293–301. https://doi.org/10.1007/s10750-005-0878-x (2006).Article 

Google Scholar 
Borduqui, M. & Ferragut, C. Factors determining periphytic algae succession in a tropical hypereutrophic reservoir. Hydrobiologia 683, 109–122. https://doi.org/10.1007/s10750-011-0943-6 (2012).Article 
CAS 

Google Scholar 
De Souza, M. L., Pellegrini, B. G. & Ferragut, C. Periphytic algal community structure in relation to seasonal variation and macrophyte richness in a shallow tropical reservoir. Hydrobiologia 755, 183–196. https://doi.org/10.1007/s10750-015-2232-2 (2015).Article 

Google Scholar 
Prowse, T. D. River-ice hydrology. In Encyclopedia of Hydrological Sciences, vol. 4 (ed. Anderson, M. G.). (Wiley, 2005).Rusanov, A. G., Stanislavskaya, E. V. & Ács, É. Periphytic algal assemblages along environmental gradients in the rivers of the Lake Ladoga basin, Northwestern Russia: Implication for the water quality assessment. Hydrobiologia 695(1), 305–327 (2012).CAS 

Google Scholar 
Sofi, M. S., Hamid, A., Bhat, S. U., Rashid, I. & Kuniyal, J. C. Impact evaluation of the run-of-river hydropower projects on the water quality dynamics of the Sindh River in the Northwestern Himalayas. Environ. Monit. Assess. 194(9), 1–6 (2022).
Google Scholar 
MCCormick, P. V. Resource competition and species coexistence in freshwater algal assemblages. In Algal ecology—Freshwater Benthic Ecosystems (eds. Stevenson, R. J., Bothwell, M. L., Lowe, R. L.) 229–252 (Academic, 1996).Hillebrand, H., Worm, B. & Lotze, H. K. Marine microbenthic community structure regulated by nitrogen loading and grazing pressure. Mar. Ecol. Prog. Ser. 204, 27–38 (2000).CAS 

Google Scholar  More