More stories

  • in

    Publisher Correction: Future temperature extremes threaten land vertebrates

    Authors and AffiliationsJacob Blaustein Center for Scientific Cooperation, The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Midreshet Ben-Gurion, IsraelGopal MuraliMitrani Department of Desert Ecology, The Swiss Institute for Dryland Environments and Energy Research, The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Midreshet Ben-Gurion, IsraelGopal Murali & Uri RollDepartment F.-A. Forel for Aquatic and Environmental Sciences, Faculty of Science, University of Geneva, Geneva, SwitzerlandTakuya IwamuraDepartment of Forest Ecosystems and Society, College of Forestry, Oregon State University, Corvallis, OR, USATakuya IwamuraSchool of Zoology, Tel Aviv University, Tel Aviv, IsraelShai MeiriThe Steinhardt Museum of Natural History, Tel Aviv University, Tel Aviv, IsraelShai MeiriAuthorsGopal MuraliTakuya IwamuraShai MeiriUri RollCorresponding authorCorrespondence to
    Gopal Murali. More

  • in

    Ocean warming and acidification affect the transitional C:N:P ratio and macromolecular accumulation in the harmful raphidophyte Heterosigma akashiwo

    Pachauri, R. K. et al. Climate change 2014: synthesis report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC, 2014).Fu, F. X., Warner, M. E., Zhang, Y., Feng, Y. & Hutchins, D. A. Effects of Increased temperature and CO2 on photosynthesis, growth, and elemental ratios in marine Synechococcus and Prochlorococcus (cyanobacteria) 1. J. Phycol. 43, 485–496 (2007).
    Google Scholar 
    Schippers, P., Lürling, M. & Scheffer, M. Increase of atmospheric CO2 promotes phytoplankton productivity. Ecol. Lett. 7, 446–451 (2004).
    Google Scholar 
    Raven, J. A., Gobler, C. J. & Hansen, P. J. Dynamic CO2 and pH levels in coastal, estuarine, and inland waters: Theoretical and observed effects on harmful algal blooms. Harmful Algae 91, 101594 (2020).CAS 

    Google Scholar 
    Gobler, C. J. et al. Ocean warming since 1982 has expanded the niche of toxic algal blooms in the North Atlantic and North Pacific oceans. Proc. Natl Acad. Sci. 114, 4975–4980 (2017).CAS 

    Google Scholar 
    Rost, B., Richter, K. U., Riebesell, U. & Hansen, P. J. Inorganic carbon acquisition in red tide dinoflagellates. Plant, Cell Environ. I 29, 810–822 (2006).CAS 

    Google Scholar 
    Honjo, T. Harmful Red Tides of Heterosigma akashiwo. NOAA Technical Report NMFS. 111, 27–32 (1992).Rensel, J. J. & Haigh, N. Fraser river sockeye salmon marine survival decline and harmful blooms of Heterosigma akashiwo. Harmful Algae 10, 98–115 (2010).
    Google Scholar 
    Herndon, J. & Cochlan, W. P. Nitrogen utilization by the raphidophyte Heterosigma akashiwo: growth and uptake kinetics in laboratory cultures. Harmful Algae 6, 260–270 (2007).
    Google Scholar 
    Haley, S. T., Alexander, H., Juhl, A. R. & Dyhrman, S. T. Transcriptional response of the harmful raphidophyte Heterosigma akashiwo to nitrate and phosphate stress. Harmful Algae 68, 258–270 (2017).CAS 

    Google Scholar 
    Wang, Z.-h, Liang, Y. & Kang, W. Utilization of dissolved organic phosphorus by different groups of phytoplankton taxa. Harmful Algae 12, 113–118 (2011).CAS 

    Google Scholar 
    Ji, N. et al. Metatranscriptome analysis reveals environmental and diel regulation of a Heterosigma akashiwo (raphidophyceae) bloom. Environ. Microbiol. 20, 1078–1094 (2018).CAS 

    Google Scholar 
    Zhang, H. et al. Functional differences in the blooming phytoplankton Heterosigma akashiwo and Prorocentrum donghaiense revealed by comparative metaproteomics. Appl. Environ. Microbiol. 85, e01425–01419 (2019).CAS 

    Google Scholar 
    Redfield, A. C. The biological control of chemical factors in the environment. Am. Scientist 46, 230A–221 (1958).
    Google Scholar 
    Liefer, J. D. et al. The macromolecular basis of phytoplankton C: N: P under nitrogen starvation. Front. Microbiol. 10, 763 (2019).
    Google Scholar 
    Matsumoto, K., Tanioka, T. & Rickaby, R. Linkages between dynamic phytoplankton C: N: P and the ocean carbon cycle under climate change. Oceanography 33, 44–52 (2020).
    Google Scholar 
    Thrane, J. E., Hessen, D. O. & Andersen, T. Plasticity in algal stoichiometry: Experimental evidence of a temperature‐induced shift in optimal supply N: P ratio. Limnol. Oceanogr. 62, 1346–1354 (2017).CAS 

    Google Scholar 
    Toseland, A. et al. The impact of temperature on marine phytoplankton resource allocation and metabolism. Nat. Clim. Change 3, 979–984 (2013).CAS 

    Google Scholar 
    Mittler, R., Finka, A. & Goloubinoff, P. How do plants feel the heat? Trends Biochem. Sci. 37, 118–125 (2012).CAS 

    Google Scholar 
    Dingman, J. E. & Lawrence, J. E. Heat-stress-induced programmed cell death in Heterosigma akashiwo (Raphidophyceae). Harmful Algae 16, 108–116 (2012).
    Google Scholar 
    Whitten, S. T., García-Moreno E, B. & Hilser, V. J. Local conformational fluctuations can modulate the coupling between proton binding and global structural transitions in proteins. Proc. Natl Acad. Sci. 102, 4282–4287 (2005).CAS 

    Google Scholar 
    Casey, J. R., Grinstein, S. & Orlowski, J. Sensors and regulators of intracellular pH. Nat. Rev. Mol. Cell Biol. 11, 50–61 (2010).CAS 

    Google Scholar 
    Kim, H., Spivack, A. J. & Menden-Deuer, S. pH alters the swimming behaviors of the raphidophyte Heterosigma akashiwo: implications for bloom formation in an acidified ocean. Harmful Algae 26, 1–11 (2013).CAS 

    Google Scholar 
    Hennon, G. M., Williamson, O. M., Limón, M. D. H., Haley, S. T. & Dyhrman, S. T. Non-linear physiology and gene expression responses of harmful alga Heterosigma akashiwo to rising CO2. Protist 170, 38–51 (2019).CAS 

    Google Scholar 
    Xu, H., Jaynes, J. & Ding, X. Combining two-level and three-level orthogonal arrays for factor screening and response surface exploration. Statistica Sin. 24, 269–289 (2014).
    Google Scholar 
    Boyd, P. W. & Ellwood, M. J. The biogeochemical cycle of iron in the ocean. Nat. Geosci. 3, 675–682 (2010).CAS 

    Google Scholar 
    Sterner, R. W. & Elser, J. J. in Ecological Stoichiometry (Princeton university press, 2002).Liu, H. C., Liao, H. T. & Charng, Y. Y. The role of class A1 heat shock factors (HSFA1s) in response to heat and other stresses in Arabidopsis. Plant Cell Environ. 34, 738–751 (2011).CAS 

    Google Scholar 
    Geider, R. J. & La Roche, J. J. Redfield revisited: variability of C [ratio] N [ratio] P in marine microalgae and its biochemical basis. Eur. J. Phycol. 37, 1–17 (2002).
    Google Scholar 
    Loladze, I. & Elser, J. J. The origins of the Redfield nitrogen‐to‐phosphorus ratio are in a homoeostatic protein‐to‐rRNA ratio. Ecol. Lett. 14, 244–250 (2011).
    Google Scholar 
    Hennige, S. J., Coyne, K. J., MacIntyre, H., Liefer, J. & Warner, M. E. The photobiology of Heterosigma akashiwo. Photoacclimation, diurnal periodicity, and its ability to rapidly exploit exposure to high light. J. Phycol. 49, 349–360 (2013).CAS 

    Google Scholar 
    Collier, J. L. & Grossman, A. A small polypeptide triggers complete degradation of light‐harvesting phycobiliproteins in nutrient‐deprived cyanobacteria. EMBO J. 13, 1039–1047 (1994).CAS 

    Google Scholar 
    Gordillo, F. J., Jimenez, C., Figueroa, F. L. & Niell, F. X. Influence of elevated CO2 and nitrogen supply on the carbon assimilation performance and cell composition of the unicellular alga Dunaliella viridis. Physiologia Plant. 119, 513–518 (2003).CAS 

    Google Scholar 
    Satoh, E., Watanabe, M. M. & Fujii, T. Photoperiodic regulation of cell division and chloroplast replication in Heterosigma akashiwo. Plant Cell Physiol. 28, 1093–1099 (1987).
    Google Scholar 
    Ashworth, J. et al. Genome-wide diel growth state transitions in the diatom Thalassiosira pseudonana. Proc. Natl Acad. Sci. 110, 7518–7523 (2013).CAS 

    Google Scholar 
    Thangaraj, S. & Sun, J. J. E. M. Transcriptomic reprogramming of the oceanic diatom Skeletonema dohrnii under warming ocean and acidification. Environ. Microbiol. 23, 980–995 (2021).CAS 

    Google Scholar 
    Nakajima, K., Tanaka, A. & Matsuda, Y. SLC4 family transporters in a marine diatom directly pump bicarbonate from seawater. Proc. Natl Acad. Sci. 110, 1767–1772 (2013).CAS 

    Google Scholar 
    Kranz, S. A. et al. Low temperature reduces the energetic requirement for the CO2 concentrating mechanism in diatoms. N. Phytologist 205, 192–201 (2015).CAS 

    Google Scholar 
    Ralston, A. & Shaw, K. Gene expression regulates cell differentiation. Nat. Educ. 1, 127–131 (2008).
    Google Scholar 
    Lobo, I. Environmental influences on gene expression. Nat. Educ. 1, 39 (2008).
    Google Scholar 
    Suzuki, N. et al. Respiratory burst oxidases: the engines of ROS signaling. Curr. Opin. Plant Biol. 14, 691–699 (2011).CAS 

    Google Scholar 
    Saidi, Y., Finka, A. & Goloubinoff, P. Heat perception and signalling in plants: a tortuous path to thermotolerance. N. Phytologist 190, 556–565 (2011).CAS 

    Google Scholar 
    Saidi, Y. et al. The heat shock response in moss plants is regulated by specific calcium-permeable channels in the plasma membrane. Plant Cell 21, 2829–2843 (2009).CAS 

    Google Scholar 
    Zhang, W. et al. Molecular and genetic evidence for the key role of AtCaM3 in heat-shock signal transduction in Arabidopsis. Plant Physiol. 149, 1773–1784 (2009).CAS 

    Google Scholar 
    Li, S. et al. Functional characterization of Arabidopsis thaliana WRKY39 in heat stress. Mol. Cells 29, 475–483 (2010).CAS 

    Google Scholar 
    Sangwan, V., Örvar, B. L., Beyerly, J., Hirt, H. & Dhindsa, R. S. Opposite changes in membrane fluidity mimic cold and heat stress activation of distinct plant MAP kinase pathways. Plant J. 31, 629–638 (2002).CAS 

    Google Scholar 
    Reddy, A. S., Ali, G. S., Celesnik, H. & Day, I. S. Coping with stresses: roles of calcium-and calcium/calmodulin-regulated gene expression. Plant Cell 23, 2010–2032 (2011).CAS 

    Google Scholar 
    Meiri, D. & Breiman, A. J. Arabidopsis ROF1 (FKBP62) modulates thermotolerance by interacting with HSP90. 1 and affecting the accumulation of HsfA2‐regulated sHSPs. Plant J. 59, 387–399 (2009).CAS 

    Google Scholar 
    Mishkind, M., Vermeer, J. E., Darwish, E. & Munnik, T. J. Heat stress activates phospholipase D and triggers PIP2 accumulation at the plasma membrane and nucleus. Plant J. 60, 10–21 (2009).CAS 

    Google Scholar 
    Zheng, S. Z. et al. Phosphoinositide‐specific phospholipase C9 is involved in the thermotolerance of Arabidopsis. Plant J. 69, 689–700 (2012).CAS 

    Google Scholar 
    Pincus, D. et al. BiP binding to the ER-stress sensor Ire1 tunes the homeostatic behavior of the unfolded protein response. PLoS Biol. 8, e1000415 (2010).
    Google Scholar 
    Sugio, A., Dreos, R., Aparicio, F. & Maule, A. J. The cytosolic protein response as a subcomponent of the wider heat shock response in Arabidopsis. Plant Cell 21, 642–654 (2009).CAS 

    Google Scholar 
    Vasseur, F., Pantin, F. & Vile, D. J. P. Cell & Environment. Changes in light intensity reveal a major role for carbon balance in Arabidopsis responses to high temperature. Plant Cell Environ. 34, 1563–1576 (2011).CAS 

    Google Scholar 
    Paroutis, P., Touret, N. & Grinstein, S. The pH of the secretory pathway: measurement, determinants, and regulation. J. Physiol. 19, 207–215 (2004).CAS 

    Google Scholar 
    Forgac, M. Vacuolar ATPases: rotary proton pumps in physiology and pathophysiology. Nat. Rev. Mol. Cell Biol. 8, 917–929 (2007).CAS 

    Google Scholar 
    Cipriano, D. J. et al. Structure and regulation of the vacuolar ATPases. Biochim. et. Biophys. Acta -Bioenerg. 1777, 599–604 (2008).CAS 

    Google Scholar 
    Abad, M. F. C., Di Benedetto, G., Magalhães, P. J., Filippin, L. & Pozzan, T. Mitochondrial pH monitored by a new engineered green fluorescent protein mutant. J. Biol. Chem. 279, 11521–11529 (2004).CAS 

    Google Scholar 
    McCORMACK, J. G., Halestrap, A. P. & Denton, R. M. Role of calcium ions in regulation of mammalian intramitochondrial metabolism. Physiol. Rev. 70, 391–425 (1990).CAS 

    Google Scholar 
    Garlid, K. D., Sun, X., Paucek, P. & Woldegiorgis, G. in Methods in enzymology Vol. 260 331–348 (Elsevier, 1995).Yamada, E. W. & Huzel, N. J. J. B. Calcium-binding ATPase inhibitor protein of bovine heart mitochondria. Role in ATP synthesis and effect of calcium. Biochemistry 28, 9714–9718 (1989).CAS 

    Google Scholar 
    Moreno-Sánchez, R. Inhibition of oxidative phosphorylation by a Ca2+-induced diminution of the adenine nucleotide translocator. Biochim. et. Biophys. Acta -Bioenerg. 724, 278–285 (1983).
    Google Scholar 
    Matsuyama, S., Llopis, J., Deveraux, Q. L., Tsien, R. Y. & Reed, J. Changes in intramitochondrial and cytosolic pH: early events that modulate caspase activation during apoptosis. Nat. Cell Biol. 2, 318–325 (2000).CAS 

    Google Scholar 
    Sunda, W. G., Price, N. M. & Morel, F. M. Trace metal ion buffers and their use in culture studies. Algal Cultur. Tech. 4, 35–63 (2005).
    Google Scholar 
    Sun, J. et al. Effects of changing pCO2 and phosphate availability on domoic acid production and physiology of the marine harmful bloom diatom Pseudo‐nitzschia multiseries. Limnol. Oceanogr. 56, 829–840 (2011).CAS 

    Google Scholar 
    Pierrot, D., Lewis, E. & Wallace, D. J. MS Excel Program Developed for CO2 System Calculations ORNL/CDIAC‐105 (US Dept. of Energy, Oak Ridge, TN, 2006).Wilbur, K. M. & Anderson, N. G. Electrometric and colorimetric determination of carbonic anhydrase. J. Biol. Chem. 176, 147–154 (1948).CAS 

    Google Scholar 
    Solórzano, L. & Sharp, J. H. Determination of total dissolved phosphorus and particulate phosphorus in natural waters 1. Limnol. Oceanogr. 25, 754–758 (1980).
    Google Scholar 
    Myklestad, S. M., Skånøy, E. & Hestmann, S. J. Sensitive and rapid method for analysis of dissolved mono-and polysaccharides in seawater. Mar. Chem. 56, 279–286 (1997).CAS 

    Google Scholar 
    Pakulski, J. D. & Benner, R. J. An improved method for the hydrolysis and MBTH analysis of dissolved and particulate carbohydrates in seawater. Mar. Chem. 40, 143–160 (1992).CAS 

    Google Scholar 
    Folch, J. & Lees, M. & Sloane Stanley, G. H. A simple method for the isolation and purification of total lipids from animal tissues. J. Biol. Chem. 226, 497–509 (1957).CAS 

    Google Scholar 
    Pande, S., Khan, R. P. & Venkitasubramanian, T. Microdetermination of lipids and serum total fatty acids. Anal. Biochem. 6, 415–423 (1963).CAS 

    Google Scholar 
    Lowry, O., Rosebrough, N., Farr, A. L. & Randall, R. J. Protein measurement with the Folin phenol reagent. J. Biol. Chem. 193, 265–275 (1951).CAS 

    Google Scholar 
    Berdalet, E., Roldán, C., Olivar, M. P. & Lysnes, K. Quantifying RNA and DNA in planktonic organisms with SYBR Green II and nucleases. Part A. Optimisation of the assay. Sci. Mar. 69, 1–16 (2005).CAS 

    Google Scholar 
    Chomoczynski, P. Single-step method of RNA isolation by acid guanidinium thiocyanate-phenol-chloro-form extraction. Anal. Biochem. 162, 156–159 (1987).
    Google Scholar 
    Sañudo-Wilhelmy, S. A. et al. The impact of surface-adsorbed phosphorus on phytoplankton Redfield stoichiometry. Phycol. Res. 432, 897–901 (2004).
    Google Scholar 
    Dyhrman, S. T. Nutrients and their acquisition: phosphorus physiology in microalgae. Physiol. Microalgae 155–183 (2016).Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 

    Google Scholar 
    Waterhouse, R. M. et al. BUSCO applications from quality assessments to gene prediction and phylogenomics. Mol. Biol. Evol. 35, 543–548 (2018).CAS 

    Google Scholar 
    Pruitt, K. D., Tatusova, T. & Maglott, D. R. J. N. A. R. NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 35, D61–D65 (2007).CAS 

    Google Scholar 
    Kanehisa, M. & Goto, S. J. N. A. R. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000).CAS 

    Google Scholar 
    Mitchell, A. L. et al. InterPro in 2019: improving coverage, classification and access to protein sequence annotations. Nucleic Acids Res. 47, D351–D360 (2019).CAS 

    Google Scholar 
    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 1–21 (2014).
    Google Scholar 
    Wang, L., Feng, Z., Wang, X., Wang, X. & Zhang, X. J. B. DEGseq: an R package for identifying differentially expressed genes from RNA-seq data. Bioinformatics 26, 136–138 (2010).
    Google Scholar  More

  • in

    Reconciling oil palm and ecosystems

    Oil palm plantations can supplant once biodiverse tropical forests. As planted areas expand, it is vital to plan landscapes to better balance biodiversity and oil palm production. Strategic ‘set-asides’ offer a key approach.In recent decades, oil palm has expanded spectacularly in some of the most biodiverse areas of the tropics, especially in Indonesia and Malaysia. This expansion has caused extensive deforestation (including loss of more than 2.1 million ha of primary forests in Borneo2, as well as other forests and agroforests), and management of plantations often relies heavily on clearing, herbicides and pesticides. This has generated many direct and indirect impacts on wildlife, ecosystems, climate and human communities3. Further expansion is ongoing, and global demand continues to rise4. More

  • in

    Rapid diversification underlying the global dominance of a cosmopolitan phytoplankton

    Genetic and morphological delineation between G. huxleyi strainsWe first assessed genetic variability through analysis of genomic polymorphism to determine whether distinct genetic lineages exist in G. huxleyi and to test whether these relate to morphotypes. We used 2,086,643 high-quality biallelic single nucleotide polymorphisms (SNPs) retrieved from the 47 clonal culture strains with the best genome sequence coverage ( >20×). A principal component analysis (PCA) and a discriminant analysis in principal component (DAPC) both delineate three well-defined genetic groups, with the distribution of strains being unequal and with no overlap on the principal components (Fig. 1a; Supplementary Fig. S3a,b). With regards to population structure, the DAPC analysis suggested that 3 clusters (K = 3) can be used to depict a genotype membership matrix for each strain (Fig. 1b; Supplementary Fig. S4). As such, it confirmed the three-lineage delineation proposed by the PCA, while illustrating no admixture between lineages.Fig. 1: Relationship between genetic structure and morphotypes in G. huxleyi.a Principal component analysis (PCA) based on 2,086,643 SNPs recovered from 47 G. huxleyi genomes; b Relationship between coalescent species phylogeny (ASTRAL tree based on 1000 supergenes) and DAPC clustering; c Correspondence between morphotypes and lineages within G. huxleyi, and sub-lineages within A1 (scale bar = 4 μm). Variable elements in relation to genotypes are highlighted in the schematics under the SEM pictures; d Distribution of coccolith length for 5 randomly chosen strains representing each clade and sub-clade, with a jittered box-plot on the left and a half-violin plot on the right for each group; e Matrix plot of Bonferroni corrected p-value corresponding to the Dunn-test for the comparison of coccolith length measurements between groups.Full size imagePhylogenetic inference based on alignments with higher mapping coverage only (47 strains) or including sequences with lower mapping coverage (59 strains) all supported segregation of strains into three main lineages, which we term clades A1, A2 and B, with A1 and A2 being more closely related to each other than to B (Fig. 1b; Supplementary Fig. S5a, b). This delineation is congruent with previous studies on the phylogeny of the Gephyrocapsa genus [17, 46, 65]. These clades also correspond to differences in morphotypes (Fig. 1b, c). All strains in clade A1 produce unambiguous A-group coccolith morphotypes (type A and type R). Similarly, all strains in clade B produce unambiguous B-group coccolith morphotypes (type B and type O). Clade A2 is less distinctive, with strains producing lightly calcified type A coccoliths. Some of these strains could be classified as type B/C [66] or C (both regarded as B-group morphotypes), but distinctive by the lower elevation of distal shield elements and by greater degree of calcification of the central area grid (which is reduced and sometimes absent in morphotypes B/C and C). At a finer level, clade A1 is composed of four sub-clades, which we term A1a, A1b, A1c, and A1d. Strains in sub-clades A1a, A1c and A1d all produce coccoliths with type A morphologies and distinctive degrees of calcification: strains in the sub-clade A1a form relatively lightly calcified coccoliths with regular elements, while strains in sub-clades A1c and A1d produce similar moderately calcified coccoliths, sometimes with conspicuous irregularities (inner tube elements overlapping into the central area). Strains in clade A1b produce distinct coccoliths exhibiting A-group morphology but with heavy calcification, including forms with heavily calcified shields which have been termed type R and also forms with heavily calcified central areas which have been referred to as “type A overcalcified”. Some clade A2 strains produce coccoliths with a similar morphology to strains in A1a, indicative of partially cryptic lineages (Supplementary Fig. S2; Supplementary Table S4).The congruence between morphotypes and clades is also supported by significant differences in the length of coccoliths measured between some of the clades (Fig. 1d, e). The morphogroups A and B differ significantly, and insignificant comparison relates to the comparison of sub-clades against the clade A2, which reinforces the closest relationship between A1 and A2. We denote also that the case of A1a and A2 demonstrating no significant difference in coccolith length concurs with the cryptic delineation mentioned above.Based on the clustering analyses and the phylogenetic reconstructions, we tested whether different groupings are distinct species with regards to the null hypothesis “G. huxleyi is a single species”, which correspond to the current state of taxonomy. Species delimitation based on comparison of Marginal Likelihood Estimators (MLE) with Bayes Factors (BF) supported the hypothesis that the three lineages depicted by ordination and phylogenetic reconstructions are distinct species as the best model (Table 1).Table 1 Species delimitation based on Bayes Factor Delimitation (BDF).Full size tableD-statistics calculated to estimate gene flow reveal a non-significant excess of alleles shared between the three lineages (Fig. 2a; Supplementary Table S5). Fbranch statistics, (fb) revealed significant signatures of gene-flow between sub-lineages within A1 associated with correlated estimates in relation to A1a, A2 and B (Fig. 2a) [60]. Signatures on the basal branch of diversification in A1 may correspond to genetic exchanges between A1 and B, with gene-flow signatures attributed to A2 corresponding to correlated estimates due to common ancestry. Recent signatures of gene-flow throughout the evolution of A1 are thus likely associated to the common ancestry between A1a, A2 and B during gene-flow events between the sub-lineages, as supported by the non-significant D statistics between the three lineages. Moreover, the phylogenetic network revealed similar convolutions between A1 sub-lineages but clear separation of the main lineages and longer branches in the A2 lineage (Fig. 2b).Fig. 2: Excess of allele sharing and differentiation in G. huxleyi.a f-branch (fb) statistics between lineages and sub-lineages. The gradient represents the fb score, grey blocks represents tests not consistent with the species tree (for each branch on the topology of the y axis, having itself or a sister taxon as donor on the topology of the x axis); asterisks denote block jack-knifing significance at p  More

  • in

    Different effects of pesticides on transcripts of the endocrine regulation and energy metabolism in honeybee foragers from different colonies

    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

  • in

    Evaluating the effects of giraffe skin disease and wire snare wounds on the gaits of free-ranging Nubian giraffe

    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

  • in

    Fabrication of biochar derived from different types of feedstocks as an efficient adsorbent for soil heavy metal removal

    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

  • in

    Life on a beach leads to phenotypic divergence despite gene flow for an island lizard

    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