Ecology

Mounier, A. et al. Deciphering African late middle Pleistocene hominin diversity and the origin of our species. Nat. Commun. https://doi.org/10.1038/s41467-019-11213-w (2019).Schlebusch, C. M. et al. Southern African ancient genomes estimate modern human divergence to 350,000 to 260,000 years ago. Science 358, 652–655 (2017).CAS 
PubMed 

Google Scholar 
Lombard, M. et al. Ancient human DNA: how sequencing the genome of a boy from Ballito Bay changed human history. S Afr. J. Sci. 114, 1–3 (2018).
Google Scholar 
Grün, R. et al. Direct dating of Florisbad hominid. Nature 382, 500–501 (1996).PubMed 

Google Scholar 
Grine, F. et al. The Middle Stone Age human fossil record from Klasies River Main Site. J. Hum. Evol. 103, 53–78 (2017).PubMed 

Google Scholar 
Henshilwood, C. S. et al. A 100,000-year-old ochre-processing workshop at Blombos Cave, South Africa. Science 33, 219–222 (2011).
Google Scholar 
Lombard, M. et al. Four-field co-evolutionary model for human cognition: variation in the Middle Stone Age/Middle Palaeolithic. J. Archeol. Method Theory 28, 142–177 (2021).
Google Scholar 
Wadley, L. What stimulated rapid, cumulative innovation after 100,000 years ago? J. Archeol. Method Theory 28, 120–141 (2021).
Google Scholar 
Tylen, K. et al. The evolution of early symbolic behavior in Homo sapiens. Proc. Natl Acad. Sci. USA 117, 4578–4584 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Rifkin, R. F. et al. Ancient oncogenesis, infection, and human evolution. Evol. Appl. https://doi.org/10.1111/eva.12497 (2017).Pittman, K. J. et al. The legacy of past pandemics: common human mutations that protect against infectious disease. PLoS Pathog. 12, e1005680 (2016).PubMed 
PubMed Central 

Google Scholar 
Andam, C. P. et al. Microbial genomics of ancient plagues and outbreaks. Trends Microbiol. 24, 978–990 (2016).CAS 
PubMed 

Google Scholar 
Houldcroft, C. J. et al. Migrating microbes: what pathogens can tell us about population movements and human evolution. Ann. Hum. Biol. 44, 397–407 (2017).PubMed 

Google Scholar 
Reyes-Centeno, H. et al. Testing modern human out-of-Africa dispersal models using dental nonmetric data. Curr. Anthropol. 58, 406–417 (2017).
Google Scholar 
Pimenoff, V. N. et al. The role of aDNA in understanding the co-evolutionary patterns of human sexually transmitted infections. Genes https://doi.org/10.3390/genes9070317 (2018).Ferwerda, B. et al. Functional consequences of Toll-like Receptor 4 polymorphisms. Mol. Med. 14, 346–352 (2008).CAS 
PubMed 
PubMed Central 

Google Scholar 
Tanabe, K. et al. Plasmodium falciparum accompanied the human expansion out of Africa. Curr. Biol. 20, 1283–1289 (2010).CAS 
PubMed 

Google Scholar 
Linz, B. et al. An African origin for the intimate association between humans and Helicobacter pylori. Nature 445, 915–918 (2007).PubMed 
PubMed Central 

Google Scholar 
Nédélec, Y. et al. Genetic ancestry and natural selection drive population differences in immune responses to pathogens. Cell 167, 657–669 (2016).PubMed 

Google Scholar 
Owers, K. A. et al. Adaptation to infectious disease exposure in indigenous Southern African populations. Proc. Biol. Sci. https://doi.org/10.1098/rspb.2017.0226 (2017).Schlebusch, C. M. et al. Khoe-San genomes reveal unique variation and confirm the deepest population divergence in Homo sapiens. Mol. Biol. Evol. https://doi.org/10.1093/molbev/msaa140 (2020).Kessler, S. E. et al. Selection to outsmart the germs: the evolution of disease recognition and social cognition. J. Hum. Evol. 108, 92–109 (2017).PubMed 

Google Scholar 
Thornhill, R. et al. The parasite-stress theory of sociality, the behavioral immune system, and human social and cognitive uniqueness. Evol. Behav. Sci. 8, 257–264 (2014).
Google Scholar 
Gurven, M. et al. Longevity among hunter‐gatherers: a cross‐cultural examination. Popul Dev. Rev. 33, 321–365 (2007).
Google Scholar 
Pfeiffer, S. et al. The people behind the samples: biographical features of past hunter-gatherers from KwaZulu-Natal who yielded aDNA. Int. J. Paleopathol. 24, 158–164 (2019).PubMed 

Google Scholar 
Schriefer, M. E. et al. Identification of a novel rickettsial infection in a patient diagnosed with murine typhus. J. Clin. Microbiol. 32, 949–954 (1994).CAS 
PubMed 
PubMed Central 

Google Scholar 
Pages, F. et al. The past and present threat of vector-borne diseases in deployed troops. Clin. Microbiol. Infect. 16, 209–224 (2010).CAS 
PubMed 

Google Scholar 
Wood, D. E. et al. Improved metagenomic analysis with Kraken 2. Genome Biol. https://doi.org/10.1186/s13059-019-1891-0 (2019).Jónsson, H. et al. mapDamage 2.0: fast approximate Bayesian estimates of ancient DNA damage parameters. Bioinformatics 29, 1682–1684 (2013).PubMed 
PubMed Central 

Google Scholar 
Gillespie, J. J. et al. Genomic diversification in strains of Rickettsia felis isolated from different arthropods. Genome Biol. Evol. 7, 35–56 (2015).CAS 

Google Scholar 
Cardwell, M. M. et al. The Sca2 autotransporter protein from Rickettsia conorii is sufficient to mediate adherence to and invasion of cultured mammalian cells. Infect. Immun. 77, 5272–5280 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kay, G. L. et al. Recovery of a Medieval Brucella melitensis genome using shotgun metagenomics. mBio. https://doi.org/10.1128/mBio.01337-14 (2014).Schuenemann, V. J. et al. Genome-wide comparison of medieval and modern Mycobacterium leprae. Science 341, 179–183 (2013).CAS 
PubMed 

Google Scholar 
Müller, R. et al. Genotyping of ancient Mycobacterium tuberculosis strains reveals historic genetic diversity. Proc. Biol. Sci. https://doi.org/10.1098/rspb.2013.3236 (2014).Rasmussen, S. et al. Early divergent strains of Yersinia pestis in Eurasia 5,000 years ago. Cell 163, 571–582 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Vågene, A. J. et al. Salmonella enterica genomes from victims of a major sixteenth-century epidemic in Mexico. Nat. Ecol. Evol. 2, 520–528 (2018).PubMed 

Google Scholar 
Guellil, M. et al. Genomic blueprint of a relapsing fever pathogen in 15th century Scandinavia. Proc. Natl Acad. Sci. USA 115, 10422–10427 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Patterson Ross, Z. et al. The paradox of HBV evolution as revealed from a 16th century mummy. PLoS Pathog. https://doi.org/10.1371/journal.ppat.1006750 (2015).Marciniak, S. et al. Plasmodium falciparum malaria in 1st-2nd century CE southern Italy. Curr. Biol. 26, 1220–1222 (2016).
Google Scholar 
Margaryan, A. et al. Ancient pathogen DNA in human teeth and petrous bones. Ecol. Evol. https://doi.org/10.1002/ece3.3924 (2018).Zhou, Z. et al. Pan-genome analysis of ancient and modern Salmonella enterica demonstrates genomic stability of the invasive Para C lineage for millennia. Curr. Biol. 28, 2420–2428 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Williams, K. M. Update on bone health in paediatric chronic disease. Endocrinol. Metab. Clin. North Am. https://doi.org/10.1016/j.ecl.2016.01.009 (2016).Latham, K.E. et al. DNA recovery and analysis from skeletal material in modern forensic contexts. Forensic Sci. Res. https://doi.org/10.1080/20961790.2018.1515594 (2019).Briggs, H. M. et al. Diagnosis and management of tickborne Rickettsial diseases: rocky mountain spotted fever and other spotted fever group Rickettsioses, Ehrlichioses, and Anaplasmosis – United States. MMWR Recomm. Rep. 65, 1–44 (2016).
Google Scholar 
Jonker, F. A. M. et al. Anaemia, iron deficiency and susceptibility to infection in children in sub‐Saharan Africa, guideline dilemmas. Br. J. Haematol. https://doi.org/10.1111/bjh.14593. (2017).Key, F. M. et al. Emergence of human-adapted Salmonella enterica is linked to the Neolithization process. Nat. Ecol. Evol. 4, 324–333 (2020).
Google Scholar 
Angelakis, E. et al. Rickettsia felis: the complex journey of an emergent human pathogen. Trends Parasitol. https://doi.org/10.1016/j.pt.2016.04.009 (2016).Legendre, K. P. et al. Rickettsia felis: A review of transmission mechanisms of an emerging pathogen. Trop. Med. Infect. Dis. https://doi.org/10.3390/tropicalmed2040064 (2017).Mediannikov, O. et al. Common epidemiology of Rickettsia felis infection and malaria, Africa. Emerg. Infect. Dis. https://doi.org/10.3201/eid1911.130361 (2014).Gonçalves, B. P. et al. Examining the human infectious reservoir for Plasmodium falciparum malaria in areas of differing transmission intensity. Nat. Commun. https://doi.org/10.1038/s41467-017-01270-4 (2017).Snowden, J. et al. Rickettsia rickettsiae (Rocky Mountain Spotted Fever). StatPearls Publishing, available from https://www.ncbi.nlm.nih.gov/books/NBK430881/ (2017).Azad, A. A. Pathogenic Rickettsiae as bioterrorism agents. Ann. N. Y Acad. Sci. 990, 734–738 (2007).
Google Scholar 
Oliveira, R. P. et al. Rickettsia felis in Ctenocephalides spp. fleas, Brazil. Emerg. Infect. Dis. https://doi.org/10.3201/eid0803.010301 (2002).Parola, P. et al. Rickettsia felis: The next mosquito-borne outbreak? Lancet Infect. Dis. https://doi.org/10.1016/S1473-3099(16)30331-0 (2016).Wadley, L. Legacies from the Later Stone Age. S Afr Archaeol Bull. Goodwin Ser. 6, 42–53 (1989).
Google Scholar 
Henn, B. M. et al. The great human expansion. Proc. Natl Acad. Sci. USA 109, 17758–17764 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Yang, D. Y. et al. Technical note: improved DNA extraction from ancient bones using silica-based spin columns. Am. J. Phys. Anthropol. 105, 539–543 (1998).CAS 
PubMed 

Google Scholar 
Malmström, E. M. et al. More on contamination: the use of asymmetric molecular behavior to identify authentic ancient human DNA. Mol. Biol. Evol. 24, 998–1004 (2007).PubMed 

Google Scholar 
Dabney, J. et al. Complete mitochondrial genome sequence of a Middle Pleistocene cave bear reconstructed from ultrashort DNA fragments. Proc. Natl Acad. Sci. USA 110, 15758–15763 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Meyer, M. et al. Illumina sequencing library preparation for highly multiplexed target capture and sequencing. Cold Spring Harbor Protoc. https://doi.org/10.1101/pdb.prot5448 (2010).Li, H. et al. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 26, 589–595 (2010).PubMed 
PubMed Central 

Google Scholar 
Briggs, A. W. et al. Patterns of damage in genomic DNA sequences from a Neandertal. Proc. Natl Acad. Sci. USA 104, 14616–14621 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
Borry, M. et al. PyDamage: automated ancient damage identification and estimation for contigs in ancient DNA de novo assembly. PeerJ. https://doi.org/10.7717/peerj.11845 (2021).Schubert, M. et al. AdapterRemoval v2: rapid adapter trimming, identification, and read merging. BMC Res. https://doi.org/10.1186/s13104-016-1900-2 (2016).Langmead, B. et al. Fast gapped-read alignment with Bowtie 2. Nat. Methods. https://doi.org/10.1038/nmeth.1923 (2012).Bankevich, A. et al. SPAdes: A new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. https://doi.org/10.1089/cmb.2012.0021 (2012).Jain, C. et al. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. https://doi.org/10.1038/s41467-018-07641-9 (2018).Gardner, S. H. et al. kSNP3.0: SNP detection and phylogenetic analysis of genomes without genome alignment or reference genome. Bioinformatics. https://doi.org/10.1093/bioinformatics/btv271 (2015).Contreras-Moreira, B. et al. GET_HOMOLOGUES, a versatile software package for scalable and robust microbial pangenome analysis. Appl. Environ. Microbiol. https://doi.org/10.1128/AEM.02411-13 (2013).Krzywinski, M. et al. Circos: an information aesthetic for comparative genomics. Genome Res. https://doi.org/10.1101/gr.092759.109 (2009).Parks, D. H. et al. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes Genome Res. https://doi.org/10.1101/gr.186072.114 (2015).Suyama, M. et al. PAL2NAL: robust conversion of protein sequence alignments into the corresponding codon alignments. Nucleic Acids Res. 34, W609–W612 (2006).CAS 
PubMed 
PubMed Central 

Google Scholar 
Dereeper, A. et al. Phylogeny. fr: Robust phylogenetic analysis for the non-specialist. Nucleic Acids Res. https://doi.org/10.1093/nar/gkn180 (2008).Nguyen, L. T. et al. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. https://doi.org/10.1093/molbev/msu300 (2015).Hoang, D. T. et al. UFBoot2: improving the ultrafast bootstrap approximation. Mol. Biol. Evol. https://doi.org/10.1093/molbev/msx281 (2018).Kalyaanamoorthy, S. et al. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat. Methods. https://doi.org/10.1038/nmeth.4285 (2017).Price, M. N. et al. FastTree 2–approximately maximum-likelihood trees for large alignments. PLoS One. https://doi.org/10.1371/journal.pone.0009490 (2010).Stamatakis, A. RAxML-VI-HPC: Maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics. https://doi.org/10.1093/bioinformatics/btl446 (2006).Kumar, S. et al. MEGA-CC: Computing core of molecular evolutionary genetics analysis program for automated and iterative data analysis. Bioinformatics. https://doi.org/10.1093/bioinformatics/bts507 (2012).Shimodaira, H. An approximately unbiased test of phylogenetic tree selection. Syst. Biol. https://doi.org/10.1080/10635150290069913 (2002).Olm, M. R. et al. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. https://doi.org/10.1038/ismej.2017.126 (2017).Posada, D. jModelTest: phylogenetic model averaging. Mol. Biol. Evol. 7, 1253–1256 (2008).
Google Scholar 
Letunic, I. et al. Interactive Tree Of Life (iTOL): an online tool for phylogenetic tree display and annotation. Bioinformatics 23, 127–128 (2007).CAS 
PubMed 

Google Scholar  More

RESEARCH HIGHLIGHT
03 March 2023

Human hunt at least 19% of bat species worldwide — especially flying foxes, which can have wingspans of 1.5 metres. More

Beer, C. et al. Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. Science 329, 834–838 (2010).Article 
CAS 
PubMed 

Google Scholar 
Badgley, G., Field, C. B. & Berry, J. A. Canopy near-infrared reflectance and terrestrial photosynthesis. Sci. Adv.3, e1602244 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Chapin, F. S. III Effects of plant traits on ecosystem and regional processes: a conceptual framework for predicting the consequences of global change. Ann. Bot. 91, 455–463 (2003).Article 
PubMed 
PubMed Central 

Google Scholar 
Chu, C. et al. Does climate directly influence NPP globally? Global Chan. Biol. 22, 12–24 (2016).Article 

Google Scholar 
Yao, Y. et al. Spatiotemporal pattern of gross primary productivity and its covariation with climate in China over the last thirty years. Global Chan. Biol. 24, 184–196 (2018).Article 

Google Scholar 
Fang, J., Lutz, J. A., Wang, L., Shugart, H. H. & Yan, X. Using climate-driven leaf phenology and growth to improve predictions of gross primary productivity in North American forests. Global Chan. Biol. 26, 6974–6988 (2020).Article 

Google Scholar 
Fernández-Martínez, M. et al. The role of climate, foliar stoichiometry and plant diversity on ecosystem carbon balance. Global Chan. Biol. 26, 7067–7078 (2020).Article 

Google Scholar 
Migliavacca, M. et al. The three major axes of terrestrial ecosystem function. Nature 598, 468–472 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Reichstein, M., Bahn, M., Mahecha, M. D., Kattge, J. & Baldocchi, D. D. Linking plant and ecosystem functional biogeography. Proc. Natl. Acad. Sci. 111, 13697–13702 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Funk, J. L. et al. Revisiting the Holy Grail: using plant functional traits to understand ecological processes. Biol. Rev. 92, 1156–1173 (2017).Article 
PubMed 

Google Scholar 
Lavorel, S. & Garnier, É. Predicting changes in community composition and ecosystem functioning from plant traits: revisiting the Holy Grail. Fun. Ecol. 16, 545–556 (2002).Article 

Google Scholar 
Enquist, B. J. et al. in Advances in Ecological Research 52 (eds Samraat P, Guy W, & Anthony I. D) 249–318 (Academic Press, 2015).Garnier, E. et al. Plant functional markers capture ecosystem properties during secondary succession. Ecology 85, 2630–2637 (2004).Article 

Google Scholar 
Enquist, B. J. et al. Assessing trait-based scaling theory in tropical forests spanning a broad temperature gradient. Global Ecol. Biogeogr. 26, 1357–1373 (2017).Article 

Google Scholar 
Fyllas, N. M. et al. Solar radiation and functional traits explain the decline of forest primary productivity along a tropical elevation gradient. Ecol. Lett. 20, 730–740 (2017).Article 
PubMed 

Google Scholar 
Van der Plas, F. et al. Plant traits alone are poor predictors of ecosystem properties and long-term ecosystem functioning. Nat. Ecol. Evol. 4, 1602–1611 (2020).Article 
PubMed 

Google Scholar 
Ali, A., Yan, E.-R., Chang, S. X., Cheng, J.-Y. & Liu, X.-Y. Community-weighted mean of leaf traits and divergence of wood traits predict aboveground biomass in secondary subtropical forests. Sci. Total Environ. 574, 654–662 (2017).Article 
CAS 
PubMed 

Google Scholar 
Yang, J., Cao, M. & Swenson, N. G. Why Functional Traits Do Not Predict Tree Demographic Rates. Trend Ecol. Evol. 33, 326–336 (2018).Article 

Google Scholar 
Šímová, I. et al. The relationship of woody plant size and leaf nutrient content to large-scale productivity for forests across the Americas. J. Ecol. 107, 2278–2290 (2019).Article 

Google Scholar 
Li, Y. et al. Leaf size of woody dicots predicts ecosystem primary productivity. Ecol. Lett. 23, 1003–1013 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
He, N. et al. Ecosystem Traits Linking Functional Traits to Macroecology. Trend. Ecol. Evol. 34, 200–210 (2019).Article 

Google Scholar 
Rubio, V. E., Zambrano, J., Iida, Y., Umaña, M. N. & Swenson, N. G. Improving predictions of tropical tree survival and growth by incorporating measurements of whole leaf allocation. J. Ecol. 109, 1331–1343 (2021).Article 

Google Scholar 
Drake, J. E. et al. Increases in the flux of carbon belowground stimulate nitrogen uptake and sustain the long-term enhancement of forest productivity under elevated CO2. Ecol. Lett. 14, 349–357 (2011).Article 
PubMed 

Google Scholar 
Hilty, J., Muller, B., Pantin, F. & Leuzinger, S. Plant growth: the What, the How, and the Why. New Phytol. 232, 25–41 (2021).Article 
PubMed 

Google Scholar 
Xia, J. et al. Joint control of terrestrial gross primary productivity by plant phenology and physiology. Proc. Natl. Acad. Sci. 112, 2788–2793 (2015).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Suding, K. N. et al. Scaling environmental change through the community-level: A trait-based response-and-effect framework for plants. Global Chan. Biol. 14, 1125–1140 (2008).Article 

Google Scholar 
Liu, C., Li, Y., Yan, P. & He, N. How to Improve the Predictions of Plant Functional Traits on Ecosystem Functioning? Front. Plant Sci. 12, 622260 (2021).Reich, P. B., Walters, M. B. & Ellsworth, D. S. From tropics to tundra: global convergence in plant functioning. Proc. Natl. Acad. of Sci. 94, 13730–13734 (1997).Article 
CAS 

Google Scholar 
Reich, P. B. The world-wide ‘fast–slow’ plant economics spectrum: a traits manifesto. J. Ecol. 102, 275–301 (2014).Article 

Google Scholar 
Monteith, J. L. Climate and the efficiency of crop production in Britain. Philosophical Transactions of the Royal Society of London. B. Biol. Sci. 281, 277–294 (1977).
Google Scholar 
Garnier, E. Resource capture, biomass allocation and growth in herbaceous plants. Trend. Ecol. Evol. 6, 126–131 (1991).Article 
CAS 

Google Scholar 
Farnsworth, K. D., Albantakis, L. & Caruso, T. Unifying concepts of biological function from molecules to ecosystems. Oikos 126, 1367–1376 (2017).Article 

Google Scholar 
Zhang, R. et al. Biodiversity alleviates the decrease of grassland multifunctionality under grazing disturbance: A global meta-analysis. Global Ecol. Biogeogr. 31, 155–167 (2022).Article 

Google Scholar 
Jing, X. et al. The links between ecosystem multifunctionality and above-and belowground biodiversity are mediated by climate. Nat. Commun. 6, 1–8 (2015).Article 

Google Scholar 
Peters, M. K. et al. Climate–land-use interactions shape tropical mountain biodiversity and ecosystem functions. Nature 568, 88–92 (2019).Article 
CAS 
PubMed 

Google Scholar 
Hu, W. et al. Aridity-driven shift in biodiversity–soil multifunctionality relationships. Nat. Commun. 12, 1–15 (2021).Article 

Google Scholar 
Jing, X. et al. The influence of aboveground and belowground species composition on spatial turnover in nutrient pools in alpine grasslands. Global Ecol. Biogeogr. 31, 486–500 (2022).Article 

Google Scholar 
Jing, X. et al. Above-and belowground complementarity rather than selection drives tree diversity-productivity relationships in European forests. Funct Ecol. 35, 1756–1767 (2021).He, N. et al. Predicting ecosystem productivity based on plant community traits. Trend. Plant Sci. 28, 43–53 (2023).Maynard, D. S. et al. Global relationships in tree functional traits. Nat. Commun. 13, 1–12 (2022).Article 

Google Scholar 
Michaletz, S. T., Kerkhoff, A. J. & Enquist, B. J. Drivers of terrestrial plant production across broad geographical gradients. Global Ecol. Biogeogr. 27, 166–174 (2018).Article 

Google Scholar 
Shipley, B. Net assimilation rate, specific leaf area and leaf mass ratio: which is most closely correlated with relative growth rate? A meta-analysis. Funct. Ecol. 20, 565–574 (2006).Article 

Google Scholar 
Violle, C. et al. Let the concept of trait be functional! Oikos 116, 882–892 (2007).Article 

Google Scholar 
Jucker, T., Bouriaud, O. & Coomes, D. A. Crown plasticity enables trees to optimize canopy packing in mixed-species forests. Funct. Ecol. 29, 1078–1086 (2015).Article 

Google Scholar 
McGill, B. J. Matters of Scale. Science 328, 575 (2010).Article 
CAS 
PubMed 

Google Scholar 
Penuelas, J. et al. Increasing atmospheric CO2 concentrations correlate with declining nutritional status of European forests. Communi. Biol. 3, 1–11 (2020).
Google Scholar 
Weemstra, M. et al. Towards a multidimensional root trait framework: a tree root review. New Phytol. 211, 1159–1169 (2016).Article 
CAS 
PubMed 

Google Scholar 
Oehri, J., Schmid, B., Schaepman-Strub, G. & Niklaus, P. A. Biodiversity promotes primary productivity and growing season lengthening at the landscape scale. Proc. Natl. Acad. Sci. 114, 10160–10165 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wright, I. J. et al. The worldwide leaf economics spectrum. Nature 428, 821–827 (2004).Article 
CAS 
PubMed 

Google Scholar 
Diaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2015).Article 
PubMed 

Google Scholar 
Liu, Y. et al. The optimum temperature of soil microbial respiration: Patterns and controls. Soil Biol. Biochem. 121, 35–42 (2018).Article 
CAS 

Google Scholar 
Zhao, N. et al. Coordinated pattern of multi-element variability in leaves and roots across Chinese forest biomes. Global Ecol. Biogeogr. 25, 359–367 (2016).Article 

Google Scholar 
Zhang, J. et al. C: N: P stoichiometry in China’s forests: From organs to ecosystems. Funct. Ecol. 32, 50–60 (2018).Article 

Google Scholar 
Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 1–20 (2017).Article 

Google Scholar 
Dirk Nikolaus, K. et al. Climatologies at high resolution for the earth’s land surface areas. EnviDat. https://doi.org/10.16904/envidat.332 (2021).Kerkhoff, A. J., Enquist, B. J., Elser, J. J. & Fagan, W. F. Plant allometry, stoichiometry and the temperature-dependence of primary productivity. Global Ecol. Biogeogr. 14, 585–598 (2005).Article 

Google Scholar 
Wright, I. J. et al. Global climatic drivers of leaf size. Science 357, 917–921 (2017).Article 
CAS 
PubMed 

Google Scholar 
Zhang, Y. et al. A global moderate resolution dataset of gross primary production of vegetation for 2000-2016. Sci. Data 4, 170165 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Jolliffe, I. T. & Cadima, J. Principal component analysis: a review and recent developments. Philos. Trans. Soc. A Math. Phys. Eng. Sci. 374, 20150202 (2016).Article 

Google Scholar 
Wieczynski, D. J. et al. Climate shapes and shifts functional biodiversity in forests worldwide. Proc. Natl. Acad. Sci. 116, 587–592 (2019).Article 
CAS 
PubMed 

Google Scholar 
Lefcheck, J. S. piecewiseSEM: Piecewise structural equation modelling in r for ecology, evolution, and systematics. Method Ecol. Evol. 7, 573–579 (2016).Article 

Google Scholar 
Bürkner, P.-C. Advanced bayesian multilevel modeling with the R Package brms. R J. 10, 395–411 (2018).Bürkner, P.-C. brms: An R package for Bayesian multilevel models using Stan. J. Stat. Software 80, 1–28 (2017).Article 

Google Scholar 
Vehtari, A., Gelman, A. & Gabry, J. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat. Comput. 27, 1413–1432 (2017).Article 

Google Scholar 
Vehtari, A. et al. loo: Efficient leave-one-out cross-validation and WAIC for Bayesian models. R package version 2, 1003 (2019).
Google Scholar 
Gabry, J. & Mahr, T. bayesplot: Plotting for Bayesian models. R package version 1 (2017).Mac Nally, R. & Walsh, C. J. Hierarchical partitioning public-domain software. Biodivers. Conserv. 13, 659 (2004).Article 

Google Scholar 
Murray, K. & Conner, M. M. Methods to quantify variable importance: implications for the analysis of noisy ecological data. Ecology 90, 348–355 (2009).Article 
PubMed 

Google Scholar 
Yan, P., He, N., Yu, K., Xu, L. & Van Meerbeek, K. Integrating multiple functional traits to predict ecosystem productivity. figshare (2023). Dataset. https://doi.org/10.6084/m9.figshare.22081634.v1. More

We performed data acquisition, processing, analysis and visualization using Python23 version 3.8 with the packages Numpy24, Pandas25, Geopandas26, Matplotlib27, Selenium, Beautiful Soup28, SciPy14 and scikit-learn29. The functions used for specific tasks are explicitly mentioned to allow validation and replication studies.Data acquisition and processingHuman PUUV-incidenceHantavirus disease has been notifiable in Germany since 2001. The Robert Koch Institute collects anonymized data from the local and state public health departments and offers via the SurvStat application2 a freely available, limited version of its database for research and informative purposes. We retrieved the reported laboratory-confirmed human PUUV-infections (({text{n}}=text{11,228}) from 2006 to 2021, status: 2022-02-07). From the attributes available for each case, we retrieved the finest temporal and spatial resolution, i.e., the week and the year of notification, together with the district (named “County” in the English version of the SurvStat interface).To avoid bias through underreporting, our dataset was limited to PUUV-infections since 2006. The years 2006–2021 contain 91.9% of the total cases from 2001 to 2021. Human PUUV-incidence was calculated as the number of infections per 100,000 people, by using population data from Eurostat30. For each year, we used the population reported for the January 1 of that year. The population for 2020 was also used for 2021.In the analysis, we only included districts where the total infections were (ge {20}) and the maximum annual incidence was (ge {2}) in the period 2006–2021. The spatial information about the infections provided by the SurvStat application refers to the district where the infection was reported. Therefore, in most of the cases, the reported district corresponds to the residence of the infected person, which may differ from the district of infection. To compensate partially for differences between the reported place of residence and the place of infection, we combined most of the urban districts with their surrounding rural district. The underlying assumption was that most infections reported in urban districts occurred in the neighboring or surrounding rural district. In addition, some urban and rural districts have the same health department. Supplementary Table 1 lists the combined districts.Weather dataFrom the German Meteorological Service31 we retrieved grids of the following monthly weather parameters over Germany from 2004 to 2021: mean daily air temperature—Tmean, minimum daily air temperature—Tmin, and maximum daily air temperature—Tmax (all temperatures are the monthly averages of the corresponding daily values, in 2 m height above ground, in °C); total precipitation in mm—Pr, total sunshine duration in hours—SD, mean monthly soil temperature in 5 cm depth under uncovered typical soil of location in °C—ST, and soil moisture under grass and sandy loam in percent plant useable water—SM. The dataset version for Tmean, Tmin, Tmax, Pr, and SD was v1.0; for ST and SM the dataset version was 0. × . The spatial resolution was 1 × 1 km2.The data acquisition was performed with the Selenium package. The processing was based on the geopandas package26 using a geospatial vector layer for the district boundaries of Germany32. Each grid was processed to obtain the average value of the parameter over each district. We first used the function within to define a mask based on the grid centers contained in the district; we then applied this mask to the grid. In this method, called “central point rasterizing”33, each rectangle of the grid was assigned to a single district, the one that contained its center. The typical processing error was estimated to be about 1%, which agrees with the rasterizing error reported by Bregt et al.33; we consider that most likely this error is significantly less than the uncertainties of the grids themselves, caused by calculation, interpolation, and erroneous or missing observations.Data structureOur analysis was performed at the district level based on the annual infections, acquired by aggregating the weekly cases. From each monthly weather parameter, we created 24 records, for all months of the two previous years. Each observation in our dataset characterized one district in one year. Its target was acquired by transforming the annual incidence, as described in the following section. Each observation comprised all 168 available predictors from the weather parameters (7 parameters × 24 months), thereafter called “variables”. The notation for the naming of the variables follows the format Vx__, where “Vx” can be V1 or V2 that corresponds to one or two years before, respectively;  is the abbreviation of the weather parameter (see previous subsection: “Weather data”); and  is the numerical value of the month, i.e., from 1 to 12.The observations for combined districts retained the label of the rural district. For their infections and populations, we aggregated the individual values, and recalculated the incidence. For their weather variables, we assigned the mean values weighted by the area of each district.Target transformationTo consider the effects that drive the occurrence of high district-relative incidence, we discretized the incidence at the district level. The incidence scaled at its maximum value for each district showed extreme values for minima and maxima. About 49% of all observations were in the range [0, 0.1) and 8% in the range [0.9, 1] (Fig. 5). Therefore, we specifically selected to discretize the scaled incidence with two bins, i.e., to binarize it.Figure 5Histograms of the annual PUUV incidence from 2006 to 2021, scaled to its maximum value for each of the selected districts. Left: Raw incidence. Right: Log-transformed incidence, according to Eq. (6).Full size imageWe first applied a log-transformation to the incidence values34, described in Eq. (6).$${text{Log – incidence}} = log_{10} left( {{text{incidence}} + 1} right)$$
(6)
The addition of a positive constant ensured a noninfinite value for zero incidence, with 1 selected so that the log-incidence is nonnegative, and a zero incidence was transformed into a zero log-incidence. This transformation aimed to increase the influence of nonzero incidence values; values that are not pronounced, but still hint at a nonzero infection risk. Its effect is demonstrated in the right plot of Fig. 5, where the positive skewness of the original data is reduced, i.e., low incidence values are spread to higher values, resulting to more uniform bin heights in the range [0.05, 0.95] after the transformation. Formally, in this case the log-transformation achieves a more uniform distribution for the non-extreme incidence values.For the binarization, we performed unsupervised clustering of the log-transformed incidence, separately for each district, applying the function KBinsDiscretizer of the scikit-learn package29. Our selected strategy was the k-means clustering with two bins, because it does not require a pre-defined threshold, and it can operate with the same fixed number of bins for every district, by automatically adjusting the cluster centroids accordingly.Classification methodWe concentrated only on those variable combinations that led to a linear decision boundary for the classification of our selected target. We selected support vector machines (SVM)35 with a linear kernel, because they combine high performance with low model complexity, in that they return the decision boundary as a linear equation of the variables. In addition, SVM is geometrically motivated36 and expected to be less prone to outliers and overfitting than other machine-learning classification algorithms, such as the logistic regression. For the complete modelling process, the regularization parameter C was set to 1, that is the default value in the applied SVC method of the scikit-learn package29, and the weights for both risk classes were also set to 1.Feature selectionOur aim was to use the smallest possible number of weather parameters as variables for a classification model with sufficient performance. To identify the optimal variable combination, we first applied an SVM with a linear kernel for all 2-variable combinations of the monthly weather variables from V2 and V1, i.e., 168 variables (7 weather parameters × 2 years × 12 months). Only for this step, the variables were scaled to their minimum and maximum values, which significantly reduced the processing time. For all the following steps, the scaler was omitted, because the unscaled support vectors were required for the final model. From the total 14,028 models for each unique pair ((frac{168!}{2!cdot left(168-2right)!})), we kept the 100 models with the best F1-score, i.e., of the harmonic mean of sensitivity and precision, and counted the occurrences of each year-month combination in the variables. The best F1-score was 0.752 for the pair (V1_Tmean_9 and V2_Tmax_4); and the best sensitivity was 83% for the pair (V2_Tmax_9 and V1_ST_9).The year-month combinations with more than 10% occurrences were: V1_9 (September of the previous year, with 49% occurrences), V2_9 (September of two years before, with 12%) and V2_4 (April of two years before, with 10%). To avoid sets with highly correlated variables, we formed 3-variable combinations, with exactly one variable from each year-month combination (threefold Cartesian product). From the total 343 models (73 combinations, i.e., 7 weather parameters for 3 year-month combinations), we selected the model with the best sensitivity and at least 70% precision, i.e., the variable set (V2_ST_4, V2_SD_9, and V1_ST_9). We consider that the criteria for this selection are not particularly crucial; and we expect comparable performance for most variable sets with a high F1-score, because the variables for each dimension of the Cartesian product were highly correlated. The eight variable sets with at least 70% precision and at least 80% sensitivity are shown in Supplementary Table 2.The SVM classifier has two hyperparameters: the regularization parameter C and the class weights. By decreasing C, the decision boundary becomes softer and more misclassifications are allowed. On the other hand, increasing the high-risk class weight, the misclassifications of high-risk observations are penalized higher, which is expected to increase the sensitivity and decrease the precision. The simultaneous adjustment of both hyperparameters ensures that the resulting model has the optimal performance with respect to the preferred metric. However, in order to avoid overfitting, we considered redundant a further model optimization with these two hyperparameters. For completeness, we examined SVM models for different values of the hyperparameters and found that the global maximum for the F1-score is in the region of 0.001 for C and 1.5 for the high-risk class weight. Our selected values C = 1 and high-risk class weight equal to 1 give the second best F1-score, which is a local maximum with comparable performance, mostly insensitive to the selection of C from the range [0.2, 5.5].The addition of a fourth variable from V1_6 (June of the previous year) resulted in a model with higher sensitivity but lower precision and specificity (for V1_Pr_6). The highest F1-score was achieved for the quadruple (V2_ST_4, V2_SD_9, V1_ST_9, V1_Pr_6). Because of the increased complexity without significant improvement in the performance, we considered unnecessary a further expansion of our variable triplet. More

Massey, J. H. & Wittkopp, P. J. The genetic basis of pigmentation differences within and between Drosophila species. Curr. Top. Dev. Biol. 119, 27–61 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Yassin, A. et al. The pdm3 locus is a hotspot for recurrent evolution of female-limited color dimorphism in Drosophila. Curr. Biol. 26, 2412–2422 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Williams, T. M. et al. The regulation and evolution of a genetic switch controlling sexually dimorphic traits in Drosophila. Cell 134, 610–623 (2008).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bastide, H. et al. A genome-wide, fine-scale map of natural pigmentation variation in Drosophila melanogaster. PLoS Genet. 9, e1003534 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Pool, J. E. & Aquadro, C. F. The genetic basis of adaptive pigmentation variation in Drosophila melanogaster. Mol. Ecol. 16, 2844–2851 (2007).Article 
PubMed 
PubMed Central 

Google Scholar 
Wittkopp, P. J. et al. Intraspecific polymorphism to interspecific divergence: genetics of pigmentation in Drosophila. Science 326, 540–544 (2009).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Jeong, S. et al. The evolution of gene regulation underlies a morphological difference between two Drosophila sister species. Cell 132, 783–793 (2008).Article 
CAS 
PubMed 

Google Scholar 
Rajpurohit, S. et al. Pigmentation and fitness trade-offs through the lens of artificial selection. Biol. Lett. 12, (2016).Massey, J. H. et al. Pleiotropic effects of ebony and tan on pigmentation and cuticular hydrocarbon composition in Drosophila melanogaster. Front. Physiol. 10, 518 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Parkash, R., Rajpurohit, S. & Ramniwas, S. Impact of darker, intermediate and lighter phenotypes of body melanization on desiccation resistance in Drosophila melanogaster. J. Insect Sci. 9, 1–10 (2009).Article 
PubMed 

Google Scholar 
Dombeck, I. & Jaenike, J. Ecological genetics of abdominal pigmentation in Drosophila falleni: A pleiotropic link to nematode parasitism. Evolution 58, 587–596 (2004).PubMed 

Google Scholar 
Kutch, I. C., Sevgili, H., Wittman, T. & Fedorka, K. M. Thermoregulatory strategy may shape immune investment in Drosophila melanogaster. J. Exp. Biol. 217, 3664–3669 (2014).PubMed 

Google Scholar 
Wittkopp, P. J. & Beldade, P. Development and evolution of insect pigmentation: Genetic mechanisms and the potential consequences of pleiotropy. Semin. Cell Dev. Biol. 20, 65–71 (2009).Article 
CAS 
PubMed 

Google Scholar 
Bastide, H., Yassin, A., Johanning, E. J. & Pool, J. E. Pigmentation in Drosophila melanogaster reaches its maximum in Ethiopia and correlates most strongly with ultra-violet radiation in sub-Saharan Africa. BMC Evol. Biol. 14, 179 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Arnold, S. J. Morphology, performance and fitness. Am. Zool. 23, 347–361 (1983).Article 

Google Scholar 
Gibert, P., Moreteau, B. & David, J. R. Developmental constraints on an adaptive plasticity: Reaction norms of pigmentation in adult segments of Drosophila melanogaster. Evol. Dev. 2, 249–260 (2000).Article 
CAS 
PubMed 

Google Scholar 
Parkash, R., Rajpurohit, S. & Ramniwas, S. Changes in body melanisation and desiccation resistance in highland vs. lowland populations of D. melanogaster. J. Insect Physiol. 54, 1050–1056 (2008).Article 
CAS 
PubMed 

Google Scholar 
Telonis-Scott, M., Hoffmann, A. A. & Sgro, C. M. The molecular genetics of clinal variation: A case study of ebony and thoracic trident pigmentation in Drosophila melanogaster from eastern Australia. Mol. Ecol. 20, 2100–2110 (2011).Article 
PubMed 

Google Scholar 
Munjal, A. K. et al. Thoracic trident pigmentation in Drosophila melanogaster: latitudinal and altitudinal clines in Indian populations. Genet. Sel. Evol. 29, 601–610 (1997).Article 
PubMed Central 

Google Scholar 
David, J. R., Capy, P., Payant, V. & Tsakas, S. Thoracic trident pigmentation in Drosophila melanogaster: Differentiation of geographical populations. Genet. Sel. Evol. 17, 211–224 (1985).Article 
CAS 

Google Scholar 
Clusella Trullas, S., van Wyk, J. H. & Spotila, J. R. Thermal melanism in ectotherms. J. Therm. Biol. 32, 235–245 (2007).Cordero, R. J. B. et al. Impact of yeast pigmentation on heat capture and latitudinal distribution. Curr. Biol. 28, 2657-2664.e3 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sibilia, C. D. et al. Thermal Physiology and Developmental Plasticity of Pigmentation in the Harlequin Bug (Hemiptera: Pentatomidae). J. Insect Sci. 18, (2018).Jong, P., Gussekloo, S. & Brakefield, P. Differences in thermal balance, body temperature and activity between non-melanic and melanic two-spot ladybird beetles (Adalia bipunctata) under controlled conditions. J. Exp. Biol. 199, 2655–2666 (1996).Article 
CAS 
PubMed 

Google Scholar 
Zverev, V., Kozlov, M. V., Forsman, A. & Zvereva, E. L. Ambient temperatures differently influence colour morphs of the leaf beetle Chrysomela lapponica: Roles of thermal melanism and developmental plasticity. J. Therm. Biol 74, 100–109 (2018).Article 
PubMed 

Google Scholar 
Watt, W. B. Adaptive significance of pigment polymorphisms in Colias butterflies, II. Thermoregulation and photoperiodically controlled melanin variation in Colias eurytheme. Proc. Natl. Acad. Sci. USA 63, 767–74 (1969).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kuyucu, A. C., Sahin, M. K. & Caglar, S. S. The relation between melanism and thermal biology in a colour polymorphic bush cricket, Isophya rizeensis. J. Therm. Biol. 71, 212–220 (2018).Article 
PubMed 

Google Scholar 
Köhler, G. & Schielzeth, H. Green-brown polymorphism in alpine grasshoppers affects body temperature. Ecol. Evol. 10, 441–450 (2020).Article 
PubMed 

Google Scholar 
Willmer, P. G. & Unwin, D. M. Field analyses of insect heat budgets: Reflectance, size and heating rates. Oecologia 50, 250–255 (1981).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Pecsenye, K., Bokor, K., Lefkovitch, L. P., Giles, B. E. & Saura, A. Enzymatic responses of Drosophila melanogaster to long- and short-term exposures to ethanol. Mol. Gen. Genet. 255, 258–268 (1997).Article 
CAS 
PubMed 

Google Scholar 
De Castro, S., Peronnet, F., Gilles, J.-F., Mouchel-Vielh, E. & Gibert, J.-M. bric à brac (bab), a central player in the gene regulatory network that mediates thermal plasticity of pigmentation in Drosophila melanogaster. PLoS Genet. 14, e1007573 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Cooley, A. M., Shefner, L., McLaughlin, W. N., Stewart, E. E. & Wittkopp, P. J. The ontogeny of color: Developmental origins of divergent pigmentation in Drosophila americana and D. novamexicana. Evol. Dev. 14, 317–25 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
John, A. V., Sramkoski, L. L., Walker, E. A., Cooley, A. M. & Wittkopp, P. J. Sensitivity of allelic divergence to genomic position: Lessons from the Drosophila tan Gene. G3 (Bethesda) (2016) doi:https://doi.org/10.1534/g3.116.032029.Liu, Y. et al. Changes throughout a genetic network mask the contribution of hox gene evolution. Curr. Biol. 29, 2157-2166.e6 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
David, J. R. et al. Evolution of assortative mating following selective introgression of pigmentation genes between two Drosophila species. Ecol. Evol. 12, e8821 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Wittkopp, P. J., True, J. R. & Carroll, S. B. Reciprocal functions of the Drosophila yellow and ebony proteins in the development and evolution of pigment patterns. Development 129, 1849–1858 (2002).Article 
CAS 
PubMed 

Google Scholar 
Davis, J. S. & Moyle, L. C. Desiccation resistance and pigmentation variation reflects bioclimatic differences in the Drosophila americana species complex. BMC Evol. Biol. 19, 204 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Nagy, O. et al. Correlated evolution of two copulatory organs via a single cis-regulatory nucleotide change. Curr. Biol. 28, 3450-3457.e13 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lachaise, D. et al. Evolutionary novelties in islands: Drosophila santomea, a new melanogaster sister species from São Tomé. Proc. Biol. Sci. 267, 1487–1495 (2000).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Haldane, J. B. S. Sex ratio and unisexual sterility in hybrid animals. J. Gen. 12, 101–109 (1922).Article 

Google Scholar 
Turissini, D. A. & Matute, D. R. Fine scale mapping of genomic introgressions within the Drosophila yakuba clade. PLoS Genet. 13, e1006971 (2017).Article 
PubMed 
PubMed Central 

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

Google Scholar 
Sunaga, S., Akiyama, N., Miyagi, R. & Takahashi, A. Factors underlying natural variation in body pigmentation of Drosophila melanogaster. Genes Genet. Syst. 91, 127–137 (2016).Article 
CAS 
PubMed 

Google Scholar 
Rajpurohit, S. & Schmidt, P. S. Latitudinal pigmentation variation contradicts ultraviolet radiation exposure: A case study in Tropical Indian Drosophila melanogaster. Front. Physiol. 10, 84 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Bergland, A. O., Behrman, E. L., O’Brien, K. R., Schmidt, P. S. & Petrov, D. A. Genomic evidence of rapid and stable adaptive oscillations over seasonal time scales in Drosophila. PLoS Genet. 10, e1004775 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Rudman, S. M. et al. Direct observation of adaptive tracking on ecological time scales in Drosophila. Science 375, eabj7484 (2022).Fabian, D. K. et al. Genome-wide patterns of latitudinal differentiation among populations of Drosophila melanogaster from North America. Mol. Ecol. 21, 4748–4769 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Zeuss, D., Brandl, R., Brändle, M., Rahbek, C. & Brunzel, S. Global warming favours light-coloured insects in Europe. Nat. Commun. 5, 3874 (2014).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Brakefield, P. M. & de Jong, P. W. A steep cline in ladybird melanism has decayed over 25 years: A genetic response to climate change?. Heredity (Edinb) 107, 574–578 (2011).Article 
CAS 
PubMed 

Google Scholar 
Zvereva, E. L., Hunter, M. D., Zverev, V., Kruglova, O. Y. & Kozlov, M. V. Climate warming leads to decline in frequencies of melanic individuals in subarctic leaf beetle populations. Sci. Total Environ. 673, 237–244 (2019).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Balanyá, J., Oller, J. M., Huey, R. B., Gilchrist, G. W. & Serra, L. Global genetic change tracks global climate warming in Drosophila subobscura. Science 313, 1773–1775 (2006).Article 
ADS 
PubMed 

Google Scholar  More

Hui, C. & Richardson, D. M. Invasion Dynamics (Oxford University Press, 2017).Book 
MATH 

Google Scholar 
Clobert, J., Baguette, M., Benton, T. G. & Bullock, J. M. Dispersal Ecology and Evolution (Oxford University Press, 2012).Book 

Google Scholar 
Shigesada, N., Kawasaki, K. & Takeda, Y. Modeling stratified diffusion in biological invasions. Am. Nat. 146, 229–251 (1995).Article 

Google Scholar 
Chuang, A. & Peterson, C. R. Expanding population edges: Theories, traits, and trade-offs. Glob. Change Biol. 22, 494–512 (2016).Article 
ADS 

Google Scholar 
Cayuela, H. et al. Determinants and consequences of dispersal in vertebrates with complex life cycles: A review of pond-breeding amphibians. Q. Rev. Biol. 95, 36 (2020).Article 

Google Scholar 
Measey, G. J. et al. A global assessment of alien amphibian impacts in a formal framework. Divers. Distrib. 22, 970–981 (2016).Article 

Google Scholar 
Antonelli, A., Smith, R. J., Perrigo, A. L. & Crottini, A. Madagascar’s extraordinary biodiversity: Evolution, distribution, and use. Science 378, eabf0869 (2022).
Article 
CAS 
PubMed 

Google Scholar 
Marshall, B. M. et al. Widespread vulnerability of Malagasy predators to the toxins of an introduced toad. Curr. Biol. 28, R654–R655 (2018).Article 
CAS 
PubMed 

Google Scholar 
Licata, F. et al. Toad invasion of Malagasy forests triggers severe mortality of a predatory snake. Biol. Inv. 24, 1189–1198 (2022).Article 

Google Scholar 
Licata, F. et al. Abundance, distribution and spread of the invasive Asian toad Duttaphrynus melanostictus in eastern Madagascar. Biol. Inv. 21, 1615–1626 (2019).Article 

Google Scholar 
McClelland, P., Reardon, J. T., Kraus, F., Raxworthy, C. J. & Randrianantoandro, C. Asian toad eradication feasibility report for Madagascar (Te Anau, 2015).Smith, M. A. & Green, D. M. Dispersal and the metapopulation paradigm in amphibian ecology and conservation: Are all amphibian populations metapopulations?. Ecography 28, 110–128 (2005).Article 

Google Scholar 
Shine, R. et al. Increased rates of dispersal of free-ranging cane toads (Rhinella marina) during their global invasion. Sci. Rep. 11, 23574 (2021).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Myles-Gonzalez, E., Burness, G., Yavno, S., Rooke, A. & Fox, M. G. To boldly go where no goby has gone before: Boldness, dispersal tendency, and metabolism at the invasion front. Behav. Ecol. 26, 1083–1090 (2015).Article 

Google Scholar 
Van Petegem, K. H. P. et al. Empirically simulated spatial sorting points at fast epigenetic changes in dispersal behaviour. Evol. Ecol. 29, 299–310 (2015).Article 

Google Scholar 
Stuart, Y. E. et al. Rapid evolution of a native species following invasion by a congener. Science 346, 463–466 (2014).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Licata, F., Andreone, F., Crottini, A., Harison, R. F. & Ficetola, G. F. Does spatial sorting occur in the invasive Asian toad in Madagascar? Insights into the invasion unveiled by morphological analyses. JZSER 2021, 1–9 (2021).
Google Scholar 
Schwarzkopf, L. & Alford, R. A. Nomadic movement in tropical toads. Oikos 96, 492–506 (2002).Article 

Google Scholar 
Brown, G. P., Kelehear, C. & Shine, R. Effects of seasonal aridity on the ecology and behaviour of invasive cane toads in the Australian wet–dry tropics. Funct. Ecol. 25, 1339–1347 (2011).Article 

Google Scholar 
Duellman, W. E. & Trueb, L. Biology of Amphibians (JHU Press, 1994).Book 

Google Scholar 
Wells, K. D. The Ecology and Behavior of Amphibians (University of Chicago Press, 2010). https://doi.org/10.7208/9780226893334.Book 

Google Scholar 
Shaw, A. K., Kokko, H. & Neubert, M. G. Sex difference and Allee effects shape the dynamics of sex-structured invasions. J. Anim. Ecol. 87, 36–46 (2018).Article 
PubMed 

Google Scholar 
Schwarzkopf, L. & Alford, R. A. Desiccation and shelter-site use in a tropical amphibian: Comparing toads with physical models. Funct. Ecol. 10, 193–200 (1996).Article 

Google Scholar 
Wogan, G. O. U., Stuart, B. L., Iskandar, D. T. & McGuire, J. A. Deep genetic structure and ecological divergence in a widespread human commensal toad. Biol. Lett. 12, 20150807 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Licata, F. Exploring the invasion dynamics and impacts of the invasive Asian common toad in Madagascar (University of Porto, 2022).
Google Scholar 
Reilly, S. B. et al. Toxic toad invasion of Wallacea: A biodiversity hotspot characterized by extraordinary endemism. Glob. Change Biol. 23, 5029–5031 (2017).Article 
ADS 

Google Scholar 
Jørgensen, C. B., Shakuntala, K. & Vijayakumar, S. Body size, reproduction and growth in a tropical toad, Bufo melanostictus, with a comparison of ovarian cycles in tropical and temperate zone anurans. Oikos 46, 379 (1986).Article 

Google Scholar 
Vences, M. et al. Tracing a toad invasion: Lack of mitochondrial DNA variation, haplotype origins, and potential distribution of introduced Duttaphrynus melanostictus in Madagascar. Amphib. Reptilia 38, 197–207 (2017).Article 

Google Scholar 
Ngo, B. V. & Ngo, C. D. Reproductive activity and advertisement calls of the Asian common toad Duttaphrynus melanostictus (Amphibia, Anura, Bufonidae) from Bach Ma National Park, Vietnam. Zool. Stud. 52, 12 (2013).Article 

Google Scholar 
Licata, F. et al. The Asian toad (Duttaphrynus melanostictus) in Madagascar: A report of an ongoing invasion. In Problematic Wildlife II: New Conservation and Management Challenges in the Human-Wildlife Interactions (eds Angelici, F. M. & Rossi, L.) 617–638 (Springer, 2020). https://doi.org/10.1007/978-3-030-42335-3_21.Chapter 

Google Scholar 
Moore, M., Solofo Niaina Fidy, J. F. & Edmonds, D. The new toad in town: Distribution of the Asian toad, Duttaphrynus melanostictus, in the Toamasina area of eastern Madagascar. Trop. Conserv. Sci. 8, 440–455 (2015).Article 

Google Scholar 
Licata, F. et al. Using public surveys to rapidly profile biological invasions in hard-to-monitor areas. Anim. Conserv. https://doi.org/10.1111/acv.12835 (2023).Article 

Google Scholar 
Zhang, M. et al. Automatic high-resolution land cover production in madagascar using sentinel-2 time series, tile-based image classification and google earth engine. Remote Sensing 12, 3663 (2020).Article 
ADS 

Google Scholar 
Peel, M. C., Finlayson, B. L. & Mcmahon, T. A. Updated world map of the Köppen-Geiger climate classification. Hydrol. Earth Syst. Sci. 4, 439–473 (2007).
Google Scholar 
Merkel, A. Toamasina Climate (Madagascar). Accessed 20 July 2022. https://en.climate-data.org/africa/madagascar/toamasina/toamasina-4029/
(2021).Gordon, A. Secondary sexual characters of Bufo melanostictus schneider. Copeia 1933, 204–207 (1933).Article 

Google Scholar 
Alford, R. & Rowley, J. Techniques for tracking amphibians: The effects of tag attachment, and harmonic direction finding versus radio telemetry. Amphib. Reptilia 28, 367–376 (2007).Article 

Google Scholar 
Lassueur, T., Joost, S. & Randin, C. F. Very high resolution digital elevation models: Do they improve models of plant species distribution?. Ecol. Modell. 198, 139–153 (2006).Article 

Google Scholar 
Abrams, M., Crippen, R. & Fujisada, H. ASTER global digital elevation model (GDEM) and ASTER global water body dataset (ASTWBD). Remote Sensing 12, 1156 (2020).Article 
ADS 

Google Scholar 
Brown, G. P., Phillips, B. L., Webb, J. K. & Shine, R. Toad on the road: Use of roads as dispersal corridors by cane toads (Bufo marinus) at an invasion front in tropical Australia. Biol. Conserv. 133, 88–94 (2006).Article 

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

Google Scholar 
Hijmans, R. J. et al. raster: Geographic data analysis and modeling. https://CRAN.R-project.org/package=raster (2021).Yagi, K. T. & Green, D. M. Performance and movement in relation to postmetamorphic body size in a pond-breeding amphibian. J. Herpetol. 51, 482–489 (2017).Article 

Google Scholar 
Labocha, M. K., Schutz, H. & Hayes, J. P. Which body condition index is best?. Oikos 123, 111–119 (2014).Article 

Google Scholar 
Tingley, R. & Shine, R. Desiccation risk drives the spatial ecology of an invasive anuran (Rhinella marina) in the australian semi-desert. PLoS ONE 6, e25979 (2011).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Richards, S. J., Sinsch, U. & Alford, R. A. Radio Tracking. In Measuring and Monitoring Biological Diversity: Standard Methods for Amphibians (eds Heyer, R. et al.) 155–158 (Smithsonian Institution, 1994).
Google Scholar 
Altobelli, J. T., Dickinson, K. J. M., Godfrey, S. S. & Bishop, P. J. Methods in amphibian biotelemetry: Two decades in review. Austral. Ecol. 47, 1382–1395 (2022).Article 

Google Scholar 
Dormann, C. F. et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46 (2013).Article 

Google Scholar 
Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (Springer, 2002). https://doi.org/10.1007/978-1-4757-2917-7_3.Book 
MATH 

Google Scholar 
Richards, S. A., Whittingham, M. J. & Stephens, P. A. Model selection and model averaging in behavioural ecology: The utility of the IT-AIC framework. Behav. Ecol. Sociobiol. 65, 77–89 (2011).Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. (2021).Bates, D. et al. lme4: Linear Mixed-Effects Models using ‘Eigen’ and S4. (2020).Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: Tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).Article 

Google Scholar 
Barton, K. MuMIn: Multi-Model Inference. (2022).Hodges, C. W., Marshall, B. M., Hill, J. G. & Strine, C. T. Malayan kraits (Bungarus candidus) show affinity to anthropogenic structures in a human dominated landscape. bioRxiv https://doi.org/10.1101/2021.09.08.459477 (2021).Article 

Google Scholar 
Muller, B. J., Cade, B. S. & Schwarzkopf, L. Effects of environmental variables on invasive amphibian activity: Using model selection on quantiles for counts. Ecosphere 9, e02067 (2018).Article 

Google Scholar 
Linsenmair, K. E. & Spieler, M. Migration patterns and diurnal use of shelter in a ranid frog of a West African savannah: A telemetric study. Amphib. Reptilia 19, 43–64 (1998).Article 

Google Scholar 
Clobert, J., Le Galliard, J.-F., Cote, J., Meylan, S. & Massot, M. Informed dispersal, heterogeneity in animal dispersal syndromes and the dynamics of spatially structured populations. Ecol. Lett. 12, 197–209 (2009).Article 
PubMed 

Google Scholar 
Ward-Fear, G., Greenlees, M. J. & Shine, R. Toads on lava: spatial ecology and habitat use of invasive cane yoads (Rhinella marina) in Hawai’i. PLoS ONE 11, e0151700 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Huang, W.-S., Lin, J.-Y. & Yu, J.Y.-L. Male reproductive cycle of the toad Bufo melanostictus in Taiwan. Zool. Sci. 14, 497–503 (1997).Article 

Google Scholar 
Brown, G. P., Phillips, B. L. & Shine, R. The straight and narrow path: the evolution of straight-line dispersal at a cane toad invasion front. Proc. R. Soc. B 281, 20141385 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Perkins, T. A., Phillips, B. L., Baskett, M. L. & Hastings, A. Evolution of dispersal and life history interact to drive accelerating spread of an invasive species. Ecol. Lett. 16, 1079–1087 (2013).Article 
PubMed 

Google Scholar 
Ochocki, B. M. & Miller, T. E. X. Rapid evolution of dispersal ability makes biological invasions faster and more variable. Nat. Commun. 8, 14315 (2017).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Phillips, B. L., Brown, G. P., Travis, J. M. J. & Shine, R. Reid’s paradox revisited: The evolution of dispersal kernels during range expansion. Am. Nat. 172, S34–S48 (2008).Article 
PubMed 

Google Scholar 
Kot, M., Lewis, M. A. & van den Driessche, P. Dispersal data and the spread of invading organisms. Ecology 77, 2027–2042 (1996).Article 

Google Scholar 
Deguise, I. & Richardson, J. S. Movement behaviour of adult western toads in a fragmented, forest landscape. Can. J. Zool. 87, 1184–1194 (2009).Article 

Google Scholar 
Mitrovich, M. J., Gallegos, E. A., Lyren, L. M., Lovich, R. E. & Fisher, R. N. Habitat use and movement of the endangered Arroyo toad (Anaxyrus californicus) in coastal southern California. J. Herpetol. 45, 319–328 (2011).Article 

Google Scholar 
Urban, M. C., Phillips, B. L., Skelly, D. K. & Shine, R. A toad more traveled: The heterogeneous invasion dynamics of cane toads in Australia. Am. Nat. 171, E134–E148 (2008).Article 
PubMed 

Google Scholar 
Enriquez-Urzelai, U., Montori, A., Llorente, G. A. & Kaliontzopoulou, A. Locomotor mode and the evolution of the hindlimb in western mediterranean anurans. Evol. Biol. 42, 199–209 (2015).Article 

Google Scholar 
Junior, B. T. & Gomes, F. R. Relation between water balance and climatic variables associated with the geographical distribution of anurans. PLoS ONE 10, e0140761 (2015).Article 

Google Scholar 
Klockmann, M., Günter, F. & Fischer, K. Heat resistance throughout ontogeny: Body size constrains thermal tolerance. Glob. Change Biol. 23, 686–696 (2017).Article 
ADS 

Google Scholar 
Petrovskii, S., Mashanova, A. & Jansen, V. A. A. Variation in individual walking behavior creates the impression of a Lévy flight. PNAS 108, 8704–8707 (2011).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lindström, T., Brown, G. P., Sisson, S. A., Phillips, B. L. & Shine, R. Rapid shifts in dispersal behavior on an expanding range edge. PNAS 110, 13452–13456 (2013).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Tingley, R. et al. New weapons in the toad toolkit: A review of methods to control and mitigate the biodiversity impacts of invasive Cane toads (Rhinella marina). Q. Rev. Biol. 92, 123–149 (2017).Article 
PubMed 

Google Scholar 
Novoa, A. et al. Invasion syndromes: A systematic approach for predicting biological invasions and facilitating effective management. Biol. Invasions 22, 1801–1820 (2020).Article 

Google Scholar 
DeVore, J. L., Crossland, M. R., Shine, R. & Ducatez, S. The evolution of targeted cannibalism and cannibal-induced defenses in invasive populations of cane toads. Proc. Natl. Acad. Sci. 118, e2100765118 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Muller, B. J. & Schwarzkopf, L. Relative effectiveness of trapping and hand-capture for controlling invasive cane toads (Rhinella marina). Int. J. Pest Manag. 64, 185–192 (2018).Article 
CAS 

Google Scholar  More

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.This is a summary of: Zhu, L. et al. Comparable biophysical and biogeochemical feedbacks on warming from tropical moist forest degradation. Nat. Geosci. https://doi.org/10.1038/s41561-023-01137-y (2023). More

All procedures involving animals were conducted in accordance with the guidelines and regulations from Institutional Animal Care and Use Committee (IACUC) of the University of Florida (protocol #201509019). Tis manuscript is reported in accordance with ARRIVE guidelines.Site descriptionThis study was carried out at the North Florida Research and Education Center, in Marianna, FL (30°46′35″N 85°14′17″W, 51 m.a.s.l). The trial was performed in two experimental years (2019 and 2020) in a greenhouse.The soil used was collected from a pasture of rhizoma peanut (Arachis glabrata Benth.) and Argentine bahiagrass (Paspalum notatum Flügge) as the main forages. Without plant and root material, only soil was placed into buckets, as described below in the bucket assemblage section. Soil was classified as Orangeburg loamy sand (fine-loamy-kaolinitic, thermic Typic Kandiudults), with a pHwater of 6.7, Mehlich-1-extratable P, K, Mg and Ca concentrations of 41, 59, 63, 368 mg kg−1, respectively. Average of minimum and maximum daily temperature and relative humidity in the greenhouse for September and November (September for beetle trial due seasonal appearance of beetles, and October and November to the Pear Millet trial) in 2019 and 2020 were 11 and 33 °C, 81%; 10 and 35 °C, 77%, respectively.Biological material determinationTo select the species of beetles, a previous dung beetle sampling was performed in the grazing experiment in the same area (grass and legume forage mixture) to determine the number of dung beetle species according to the functional groups as described by Conover et al.44. Beetles were pre-sampled from March 2017 to June 2018, where Tunnelers group were dominant and represented by Onthophagus taurus (Schreber), Digitonthophagus gazella (Fabricius), Phanaeus vindex (MacLeay), Onthophagus oklahomensis (Brown), and Euniticellus intermedius (Reiche). Other species were present but not abundant, including Aphodius psudolividus (Linnaeus), Aphodius carolinus (Linnaeus), and Canthon pilularius (Linnaeus) identified as Dweller and Roller groups, respectively. The pre-sampling indicated three species from the Tunneler group were more abundant, and thereby, were chosen to compose the experimental treatments (Fig. 4).Figure 4Most abundant dung beetle species in Marianna, FL used in the current study. Credits: Carlos C.V. García.Full size imageBeetles collection and experimental treatmentsThree species of common communal dung beetles were used: O. taurus (1), D. gazella (2), and P. vindex (3). Treatments included two treatments containing only soil and soil + dung without beetles were considered as Control 1 (T1) and Control 2 (T2), respectively. Isolated species T3 = 1, T4 = 2, T5 = 3 and their combinations T6 = 1 + 2 and T7 = 1 + 2 + 3. Dung beetles were trapped in the pasture with grazing animals using the standard cattle-dung-baited pitfall traps, as described by Bertone et al.41. To avoid losing samples due to cattle trampling, 18 traps were randomized in nine paddocks (two traps per paddock) and installed protected by metal cages, and after a 24-h period, beetles were collected, and the traps removed. Table 1 shows the number of dung beetles, their total mass (used to standardize treatments) per treatment, and the average mass per species. To keep uniformity across treatments we kept beetle biomass constant across species at roughly 1.7 to 1.8 g per assemblage (Table 1). Twenty-four hours after retrieving the beetles from the field traps, they were separated using an insect rearing cage, classified, and thereafter stored in small glass bottles provided with a stopper and linked to a mesh to keep the ventilation and maintaining the beetles alive.Table 1 Total number and biomass of dung beetles per treatment.Full size tableBuckets assemblageThe soil used in the buckets was collected from the grazing trial in two experimental years (August 2019 and August 2020) across nine paddocks (0.9 ha each). The 21 plastic buckets had a 23-cm diameter and 30-cm (0.034 m2) and each received 10 kg of soil (Fig. 5). At the bottom of the recipient, seven holes were made for water drainage using a metallic mesh with 1-mm diameter above the surface of the holes to prevent dung beetles from escaping. Water was added every four days to maintain the natural soil conditions at 60% of the soil (i.e., bucket) field capacity (measured with the soil weight and water holding capacity of the soil). Because soil from the three paddocks had a slightly different texture (sandy clay and sandy clay loam), we used them as the blocking factor.Figure 5Bucket plastic bucket details for dung beetle trial.Full size imageThe fresh dung amount used in the trial was determined based on the average area covered by dung and dung weight (0.05 to 0.09 m2 and 1.5 to 2.7 kg) from cattle in grazing systems, as suggested by Carpinelli et al.45. Fresh dung was collected from Angus steers grazing warm-season grass (bahiagrass) pastures and stored in fridge for 24 h, prior to start the experiment. A total of 16.2 kg of fresh dung was collected, in which 0.9 kg were used in each bucket. After the dung application, dung beetles were added to the bucket. To prevent dung beetles from escaping, a mobile plastic mesh with 0.5 mm diameter was placed covering the buckets before and after each evaluation. The experiment lasted for 24 days in each experimental year (2019 and 2020), with average temperature 28 °C and relative humidity of 79%, acquired information from the Florida Automated Weather Network (FAWN).Chamber measurementsThe gas fluxes from treatments were evaluated using the static chamber technique46. The chambers were circular, with a radius of 10.5 cm (0.034 m2). Chamber bases and lids were made of polyvinyl chloride (PVC), and the lid were lined with an acrylic sheet to avoid any reactions of gases of interest with chamber material (Fig. 6). The chamber lids were covered with reflective tape to provide insulation, and equipped with a rubber septum for sampling47. The lid was fitted with a 6-mm diameter, 10-cm length copper venting tube to ensure adequate air pressure inside the chamber during measurements, considering an average wind speed of 1.7 m s−148,49. During measurements, chamber lids and bases were kept sealed by fitting bicycle tire inner tubes tightly over the area separating the lid and the base. Bases of chambers were installed on top of the buckets to an 8-cm depth, with 5 cm extending above ground level. Bases were removed in the last evaluation day (24th) of each experimental year.Figure 6Static chamber details and instruments for GHG collection in the dung beetle trial.Full size imageGas fluxes measurementsThe gas fluxes were measured at 1000 h following sampling recommendations by Parkin & Venterea50, on seven occasions from August 28th to September 22nd in both years (2019 and 2020), being days 0, 1, 2, 3, 6, 12, and 24 after dung application. For each chamber, gas samples were taken using a 60-mL syringe at 15-min intervals (t0, t15, and t30). The gas was immediately flushed into pre-evacuated 30-mL glass vials equipped with a butyl rubber stopper sealed with an aluminium septum (this procedure was made twice per vial and per collection time). Time zero (t0) represented the gas collected out of the buckets (before closing the chamber). Immediately thereafter, the bucket lid was tightly closed by fitting the lid to the base with the bicycle inner tube, followed by the next sample deployment times.Gas sample analyses were conducted using a gas chromatograph (Trace 1310 Gas Chromatograph, Thermo Scientific, Waltham, MA). For N2O, an electron capture detector (350 °C) and a capillary column (J&W GC packed column in stainless steel tubing, length 6.56 ft (2 M), 1/8 in. OD, 2 mm ID, Hayesep D packing, mesh size 80/100, pre-conditioned, Agilent Technologies) were used. Temperature of the injector and columns were 80 and 200 °C, respectively. Daily flux of N2O-N (g ha−1 day−1) was calculated as described in Eq. (1):$${text{F}}, = ,{text{A}}*{text{dC}}/{text{dt}}$$
(1)
where F is flux of N2O (g ha−1 day−1), A is the area of the chamber, and dC/dt is the change of concentration in time calculated using a linear method of integration by Venterea et al.49.Ammonia volatilization measurementAmmonia volatilization was measured using the open chamber technique, as described by Araújo et al.51. The ammonia chamber was made of a 2-L volume polyethylene terephthalate (PET) bottle. The bottom of the bottle was removed and used as a cap above the top opening to keep the environment controlled, free of insects and other sources of contamination. An iron wire was used to support the plastic jar. A strip of polyfoam (250 mm in length, 25 mm wide, and 3 mm thick) was soaked in 20 ml of acid solution (H2SO4 1 mol dm−3 + glycerine 2% v/v) and fastened to the top, with the bottom end of the foam remaining inside the plastic jar. Inside each chamber there was a 250-mm long wire designed with a hook to support it from the top of the bottle, and wire basket at the bottom end to support a plastic jar (25 mL) that contained the acid solution to keep the foam strip moist during sampling periods (Fig. 7). The ammonia chambers were placed installed in the bucket located in the middle of each experimental block after the last gas sampling of the day and removed before the start of the next gas sampling.Figure 7Mobile ammonia chamber details for ammonia measurement in dung beetle trial. Adapted from Araújo et al.51.Full size imageNutrient cyclingPhotographs of the soil and dung portion of each bucket were taken twenty-four hours after the last day of gas flux measurement sampling to determine the dung removal from single beetle species and their combination. In the section on statistical analysis, the programming and statistical procedures are described. After this procedure, seeds of pearl millet were planted in each bucket. After 5 days of seed germination plants were thinned, maintaining four plants per bucket. Additionally, plants were clipped twice in a five-week interval, with the first cut occurring on October 23rd and the second cut occurring on November 24th, in both experimental years. Before each harvest, plant height was measured twice in the last week. In the harvest day all plants were clipped 10 cm above the ground level. Samples were dried at 55 °C in a forced-air oven until constant weight and ball-milled using a Mixer Mill MM 400 (Retsch, Newton, PA, USA) for 9 min at 25 Hz, and analyzed for total N concentration using a C, H, N, and S analyzer by the Dumas dry combustion method (Vario Micro Cube; Elementar, Hanau, Germany).Statistical analysisTreatments were distributed in a randomized complete block design (RCBD), with three replications. Data were analyzed using the Mixed Procedure from SAS (ver. 9.4., SAS Inst., Cary, NC) and LSMEANS compared using PDIFF adjusted by the t-test (P  More