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

Purvis, A., Gittleman, J. L., Cowlishaw, G. & Mace, G. M. Predicting extinction risk in declining species. Proc. R. Soc. B 267, 1947–1952 (2000).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Crooks, K. R. Relative sensitivities of mammalian carnivores to habitat fragmentation. Conserv. Biol. 16, 488–502 (2002).Article 

Google Scholar 
Fahrig, L. Non-optimal animal movement in human-altered landscapes. Funct. Ecol. 21, 1003–1015 (2007).Article 

Google Scholar 
Fahrig, L. & Rytwinski, T. Effects of roads on animal abundance: An empirical review and synthesis. Ecol. Soc. 14, 21 (2009).Article 

Google Scholar 
Lowry, H., Lill, A. & Wong, B. B. M. Behavioural responses of wildlife to urban environments. Biol. Rev. 88, 537–549 (2013).Article 
PubMed 

Google Scholar 
Sévêque, A., Gentle, L. K., López-Bao, J. V., Yarnell, R. W. & Uzal, A. Human disturbance has contrasting effects on niche partitioning within carnivore communities. Biol. Rev. 95, 1689–1705 (2020).Article 
PubMed 

Google Scholar 
Woodroffe, R. & Ginsberg, J. R. Edge effects and the extinction of populations inside protected areas. Science 1979(280), 2126–2128 (1998).Article 
ADS 

Google Scholar 
Dressel, S., Sandström, C. & Ericsson, G. A meta-analysis of studies on attitudes toward bears and wolves across Europe 1976–2012. Conserv. Biol. 29, 565–574 (2015).Article 
CAS 
PubMed 

Google Scholar 
Owen, D. & Pemberton, D. Tasmanian Devil: A Unique and Threatened Animal (Allen & Unwin, 2005).
Google Scholar 
Yirga, G. et al. Adaptability of large carnivores to changing anthropogenic food sources: diet change of spotted hyena (Crocuta crocuta) during Christian fasting period in northern Ethiopia. J. Anim. Ecol. 81, 1052–1055 (2012).Article 
PubMed 

Google Scholar 
Knight, R. L. & Kawashima, J. Y. Responses of raven and red-tailed hawk populations to linear right-of-ways. J. Wildl. Manag. 57, 266–271 (1993).Article 

Google Scholar 
Wilmers, C. C., Stahler, D. R., Crabtree, R. L., Smith, D. W. & Getz, W. M. Resource dispersion and consumer dominance: Scavenging at wolf- and hunter-killed carcasses in Greater Yellowstone, USA. Ecol. Lett. 6, 996–1003 (2003).Article 

Google Scholar 
Lambertucci, S. A., Speziale, K. L., Rogers, T. E. & Morales, J. M. How do roads affect the habitat use of an assemblage of scavenging raptors?. Biodivers. Conserv. 18, 2063–2074 (2009).Article 

Google Scholar 
Šálek, M., Kreisinger, J., Sedláček, F. & Albrecht, T. Do prey densities determine preferences of mammalian predators for habitat edges in an agricultural landscape?. Landsc. Urban Plan. 98, 86–91 (2010).Article 

Google Scholar 
Bateman, P. W. & Fleming, P. A. Big city life: Carnivores in urban environments. J. Zool. 287, 1–23 (2012).Article 

Google Scholar 
Auman, H. J., Meathrel, C. E. & Richardson, A. Supersize me: Does anthropogenic food change the body condition of silver gulls? A comparison between urbanized and remote, non-urbanized areas. Waterbirds 31, 122–126 (2008).Article 

Google Scholar 
Coon, C. A. C., Nichols, B. C., McDonald, Z. & Stoner, D. C. Effects of land-use change and prey abundance on the body condition of an obligate carnivore at the wildland-urban interface. Landsc. Urban Plan. 192, 103648 (2019).Article 

Google Scholar 
Beckmann, J. P. & Berger, J. Using black bears to test ideal-free distribution models experimentally. J. Mammal. 84, 594–606 (2003).Article 

Google Scholar 
Fedriani, J. M., Fuller, T. K. & Sauvajot, R. M. Does availability of anthropogenic food enhance densities of omnivorous mammals? An example with coyotes in southern California. Ecography 24, 325–331 (2001).Article 

Google Scholar 
Prange, S., Gehrt, S. D. & Wiggers, E. P. Influences of anthropogenic resources on raccoon (Procyon lotor) movements and spatial distribution. J. Mammal. 85, 483–490 (2004).Article 

Google Scholar 
Tucker, M. A., Santini, L., Carbone, C. & Mueller, T. Mammal population densities at a global scale are higher in human-modified areas. Ecography 44, 1–13 (2021).Article 

Google Scholar 
Blanco, G., Lemus, J. A. & García-Montijano, M. When conservation management becomes contraindicated: Impact of food supplementation on health of endangered wildlife. Ecol. Appl. 21, 2469–2477 (2011).Article 
PubMed 

Google Scholar 
Fischer, J. R., Stallknecht, D. E., Luttrell, M. P., Dhondt, A. A. & Converse, K. A. Mycoplasmal conjunctivitis in wild songbirds: The spread of a new contagious disease in a mobile host population. Emerg. Infect. Dis. 3, 69–72 (1997).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Brittingham, M. C. & Temple, S. A. A survey of avian mortality at winter feeders. Wildl. Soc. Bull. 14, 445–450 (1986).
Google Scholar 
Hivert, L. G. et al. High blood lead concentrations in captive Tasmanian devils (Sarcophilus harrisii): A threat to the conservation of the species?. Aust. Vet. J. 96, 442–449 (2018).Article 
CAS 
PubMed 

Google Scholar 
Carrete, M., Donázar, J. A. & Margalida, A. Density-dependent productivity depression in pyrenean bearded vultures: Implications for conservation. Ecol. Appl. 16, 1674–1682 (2006).Article 
PubMed 

Google Scholar 
Bozek, C. K., Prange, S. & Gehrt, S. D. The influence of anthropogenic resources on multi-scale habitat selection by raccoons. Urban Ecosyst. 10, 413–425 (2007).Article 

Google Scholar 
Jones, J. D. et al. Supplemental feeding alters migration of a temperate ungulate. Ecol. Appl. 24, 1769–1779 (2014).Article 
PubMed 

Google Scholar 
Šálek, M., Drahníková, L. & Tkadlec, E. Changes in home range sizes and population densities of carnivore species along the natural to urban habitat gradient. Mamm. Rev. 45, 1–14 (2015).Article 

Google Scholar 
Newsome, D. & Rodger, K. To feed or not to feed: a contentious issues in wildlife tourism. In Too Close for Comfort: Contentious Issues in Human-Wildlife Encounters (ed. Lunney, D.) 255–270 (Royal Zoological Society of New South Wales, 2008).Chapter 

Google Scholar 
Tucker, M. A. et al. Moving in the anthropocene: Global reductions in terrestrial mammalian movements. Science 1979(359), 466–469 (2018).Article 
ADS 

Google Scholar 
Polis, G. A., Anderson, W. B. & Holt, R. D. Toward an integration of landscape and food web ecology: The dynamics of spatially subsidized food webs. Annu. Rev. Ecol. Syst. 28, 289–316 (1997).Article 

Google Scholar 
Prange, S. & Gehrt, S. D. Changes in mesopredator-community structure in response to urbanization. Can. J. Zool. 82, 1804–1817 (2004).Article 

Google Scholar 
Rodewald, A. D., Kearns, L. J. & Shustack, D. P. Anthropogenic resource subsidies decouple predator–prey relationships. Ecol. Appl. 21, 936–943 (2011).Article 
PubMed 

Google Scholar 
Cortés-Avizanda, A., Jovani, R., Carrete, M. & Donázar, J. A. Resource unpredictability promotes species diversity and coexistence in an avian scavenger guild: A field experiment. Ecology 93, 2570–2579 (2012).Article 
PubMed 

Google Scholar 
Arrondo, E., Cortés-Avizanda, A. & Donázar, J. A. Temporally unpredictable supplementary feeding may benefit endangered scavengers. Ibis 157, 648–651 (2015).Article 

Google Scholar 
Smith, J. A., Thomas, A. C., Levi, T., Wang, Y. & Wilmers, C. C. Human activity reduces niche partitioning among three widespread mesocarnivores. Oikos 127, 890–901 (2018).Article 

Google Scholar 
de León, L. F. et al. Urbanization erodes niche segregation in Darwin’s finches. Evol. Appl. 12, 1329–1343 (2019).Article 
PubMed 

Google Scholar 
Manlick, P. J. & Pauli, J. N. Human disturbance increases trophic niche overlap in terrestrial carnivore communities. PNAS 117, 26842–26848 (2020).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Blair, R. B. Land use and avian species diversity along an urban gradient. Ecol. Appl. 6, 506–519 (1996).Article 

Google Scholar 
Dettori, E. E. et al. Distribution and diet of recovering Eurasian otter (Lutra lutra) along the natural-to-urban habitat gradient (river Segura, SE Spain). Urban Ecosyst. 24, 1221–1230 (2021).Article 

Google Scholar 
McKinney, M. L. Urbanization as a major cause of biotic homogenization. Biol. Conserv. 127, 247–260 (2006).Article 

Google Scholar 
Guiler, E. R. Temporal and spatial distribution of the Tasmanian Devil, Sarcophilus harrisii (Dasyuridae: Marsupialia). Pap. Proc. R. Soc. Tasman 116, 153–163 (1982).
Google Scholar 
Patton, A. H. et al. A transmissible cancer shifts from emergence to endemism in Tasmanian devils. Science (1979) 370, eabb9772 (2020).CAS 

Google Scholar 
Cunningham, C. X. et al. Quantifying 25 years of disease-caused declines in Tasmanian devil populations: Host density drives spatial pathogen spread. Ecol. Lett. 24, 958–969 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Rose, R. K., Pemberton, D. A., Mooney, N. J. & Jones, M. E. Sarcophilus harrisii (Dasyuromorphia: Dasyuridae). Mamm. Species 49, 1–17 (2017).Article 

Google Scholar 
Guiler, E. R. Observations on the Tasmanian devil, Sarcophilus harrisii (Marsupialia: Dasyuridae) I. Numbers, home range, movements and food in two populations. Aust. J. Zool. 18, 49–62 (1970).Article 

Google Scholar 
Jones, M. E. & Barmuta, L. A. Diet overlap and relative abundance of sympatric dasyurid carnivores: A hypothesis of competition. J. Anim. Ecol. 67, 410–421 (1998).Article 

Google Scholar 
Pemberton, D. et al. The diet of the Tasmanian Devil, Sarcophilus harrisii, as determined from analysis of scat and stomach contents. Pap. Proc. R. Soc. Tasman. 142, 13–22 (2008).
Google Scholar 
Rogers, T. L., Fox, S., Pemberton, D. & Wise, P. Sympathy for the devil: Captive-management style did not influence survival, body-mass change or diet of Tasmanian devils 1 year after wild release. Wildl. Res. 43, 544–552 (2016).Article 

Google Scholar 
Andersen, G. E., Johnson, C. N., Barmuta, L. A. & Jones, M. E. Dietary partitioning of Australia’s two marsupial hypercarnivores, the Tasmanian devil and the spotted-tailed quoll, across their shared distributional range. PLoS ONE 12, e0188529 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Department of Primary Industries Parks Water and Environment. Recovery Plan for the Tasmanian devil (Sarcophilus harrisii) (2010).Brown, O. J. F. Tasmanian devil (Sarcophilus harrisii) extinction on the Australian mainland in the mid-Holocene: multicausality and ENSO intensification. Alcheringa Aust. J. Palaeontol. 30, 49–57 (2006).Article 

Google Scholar 
Lewis, A. C., Hughes, C. & Rogers, T. L. Effects of intraspecific competition and body mass on diet specialization in a mammalian scavenger. Ecol. Evol. 12, e8338 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Andersen, G. E., McGregor, H. W., Johnson, C. N. & Jones, M. E. Activity and social interactions in a wide-ranging specialist scavenger, the Tasmanian devil (Sarcophilus harrisii), revealed by animal-borne video collars. PLoS ONE 15, e0230216 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Jones, M. E. Road upgrade, road mortality and remedial measures: Impacts on a population of eastern quolls and Tasmanian devils. Wildl. Res. 27, 289–296 (2000).Article 

Google Scholar 
Jones, M. E. & Barmuta, L. A. Niche differentiation among sympatric australian dasyurid carnivores. J. Mammal. 81, 434–447 (2000).Article 

Google Scholar 
Andersen, G. E., Johnson, C. N., Barmuta, L. A. & Jones, M. E. Use of anthropogenic linear features by two medium-sized carnivores in reserved and agricultural landscapes. Sci. Rep. 7, 11624 (2017).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Hamede, R. K., McCallum, H. & Jones, M. Seasonal, demographic and density-related patterns of contact between Tasmanian devils (Sarcophilus harrisii): Implications for transmission of devil facial tumour disease. Austral. Ecol. 33, 614–622 (2008).Article 

Google Scholar 
Kitchener, A. & Harris, S. From Forest to Fjaeldmark: Descriptions of Tasmania’s Vegetation (Department of Primary Industries, Parks, Water and Environment, Tasmania, 2013).
Google Scholar 
Wiggins, N. L. & Bowman, D. M. J. S. Macropod habitat use and response to management interventions in an agricultural—Forest mosaic in north-eastern Tasmania as inferred by scat surveys. Wildl. Res. 38, 103–113 (2011).Article 

Google Scholar 
Hobday, A. J. & Minstrell, M. L. Distribution and abundance of roadkill on Tasmanian highways: Human management options. Wildl. Res. 35, 712–726 (2008).Article 

Google Scholar 
Hingston, A. B. Impacts of logging on autumn bird populations in the southern forests of Tasmania. Pap. Proc. R. Soc. Tasman. 134, 19–28 (2000).
Google Scholar 
Taylor, R. J. Notes on the diet of the carnivorous mammals of the Upper Henty River Region, western Tasmania. Pap. Proc. R. Soc. Tasman. 120, 7–10 (1986).
Google Scholar 
Hall-Aspland, S., Rogers, T., Canfield, R. & Tripovich, J. Food transit times in captive leopard seals (Hydrurga leptonyx). Polar Biol. 34, 95–99 (2011).Article 

Google Scholar 
Bell, O. et al. Age-related variation in the trophic characteristics of a marsupial carnivore, the Tasmanian devil Sarcophilus harrisii. Ecol. Evol. 10, 7861–7871 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Bell, O. et al. Isotopic niche variation in Tasmanian devils Sarcophilus harrisii with progression of devil facial tumor disease. Ecol. Evol. 11, 8038–8053 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Bearhop, S., Adams, C. E., Waldron, S., Fuller, R. A. & MacLeod, H. Determining trophic niche width: A novel approach using stable isotope analysis. J. Anim. Ecol. 73, 1007–1012 (2004).Article 

Google Scholar 
Layman, C. A. et al. Applying stable isotopes to examine food-web structure: An overview of analytical tools. Biol. Rev. 87, 545–562 (2012).Article 
PubMed 

Google Scholar 
Crawford, K., McDonald, R. A. & Bearhop, S. Applications of stable isotope techniques to the ecology of mammals. Mamm. Rev. 38, 87–107 (2008).Article 

Google Scholar 
Bender, M. M., Rouhani, I., Vines, H. M. & Black, C. C. Jr. 13C/12C ratio changes in crassulacean acid metabolism plants. Plant Physiol. 52, 427–430 (1973).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
O’Leary, M. H. Carbon isotope fractionation in plants. Phytochemistry 20, 553–567 (1981).Article 

Google Scholar 
Farquhar, G. D., O’Leary, M. H. & Berry, J. A. On the relationship between carbon isotope discrimination and the intercellular carbon dioxide concentration in leaves. Aust. J. Plant Physiol. 9, 121–137 (1982).CAS 

Google Scholar 
Cernusak, L. A. et al. Environmental and physiological determinants of carbon isotope discrimination in terrestrial plants. New Phytol. 200, 950–965 (2013).Article 
CAS 
PubMed 

Google Scholar 
NSW Parliamentary Counsel. Animal Research Act 1985 (NSW Parliamentary Counsel, 1985).
Google Scholar 
National Health and Medical Research Council (Australia). Australian Code for the Care and Use of Animals for Scientific Purposes (National Health and Medical Research Council, 2013).
Google Scholar 
du Sert, N. P. et al. Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 2.0. PLoS Biol. 18, e3000411 (2020).Article 

Google Scholar 
Environmental Systems Research Institute. ArcGIS Desktop Version 10.8.1. https://www.esri.com/en-us/arcgis/products/arcgis-desktop/overview (2020).Tasmanian Vegetation Monitoring and Mapping Program. TASVEG 4.0. Natural Values Conservation Branch, Department of Primary Industries, Parks, Water and Environment thelist.tas.gov.au/app/content/data/geo-meta-data-record?detailRecordUID=b5c7a079-14bc-4b3c-af73-db7585d34cdd (2020).Land Tasmania. LIST Land Tenure. Land Tasmania thelist.tas.gov.au/app/content/data/geo-meta-data-record?detailRecordUID=9b8bf099-d668–433d-981b-a0f8f964f827 (2015).Hickey, J. E. & Wilkinson, G. R. The development and current implementation of silvicultural pratices in native forests in Tasmania. Aust. For. 62, 245–254 (1999).Article 

Google Scholar 
Whiteley, S. B. Calculating the sustainable yield of Tasmania’s State forests. Tasforests 11, 23–34 (1999).
Google Scholar 
Pemberton, D. Social Organisation and Behaviour of the Tasmanian devil, Sarcophilus harrisii (University of Tasmania, 1990).
Google Scholar 
Attard, M. R. G., Lewis, A. C., Wroe, S., Hughes, C. & Rogers, T. L. Whisker growth in Tasmanian devils (Sarcophilus harrisii) and applications for stable isotope studies. Ecosphere 12, e03846 (2021).Article 

Google Scholar 
von Bertalanffy, L. Quantitative laws in metabolism and growth. Q. Rev. Biol. 32, 217–231 (1957).Article 

Google Scholar 
Rogers, T. L., Fung, J., Slip, D., Steindler, L. & O’Connell, T. C. Calibrating the time span of longitudinal biomarkers in vertebrate tissues when fine-scale growth records are unavailable. Ecosphere 7, e01449 (2016).Article 

Google Scholar 
Qi, H., Coplen, T. B., Geilmann, H., Brand, W. A. & Böhlke, J. K. Two new organic reference materials for δ13C and δ15N measurements and a new value for the δ13C of NBS 22 oil. Rapid Commun. Mass Spectrom. 17, 2483–2487 (2003).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Qi, H. et al. A new organic reference material, l-glutamic acid, USGS41a, for δ13C and δ15N measurements—A replacement for USGS41. Rapid Commun. Mass Spectrom. 30, 859–866 (2016).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Bond, A. L. & Hobson, K. A. Reporting stable-isotope ratios in ecology: Recommended terminology. Guidel. Best Pract. Waterbirds 35, 324–331 (2012).
Google Scholar 
O’Connell, T. C. & Hedges, R. E. M. Investigations into the effect of diet on modern human hair isotopic values. Am. J. Phys. Anthropol. 108, 409–425 (1999).Article 
PubMed 

Google Scholar 
Jackson, A. L., Inger, R., Parnell, A. C. & Bearhop, S. Comparing isotopic niche widths among and within communities: SIBER—Stable Isotope Bayesian Ellipses in R. J. Anim. Ecol. 80, 595–602 (2011).Article 
PubMed 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing Version 4.2.0. https://www.r-project.org/ (2022).Bartoń, K. MuMIn: Multi-model inference. R Package Version 1.47.1. https://cran.r-project.org/package=MuMIn (2022).Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (Colorado Cooperative Fish and Wildlife Research Unit, 2002).MATH 

Google Scholar 
Stock, B. C. et al. Analyzing mixing systems using a new generation of Bayesian tracer mixing models. PeerJ 6, e5096 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Stock, B. C. & Semmens, B. X. MixSIAR: Bayesian Mixing Models in R. R Package Version 3.1.12. https://doi.org/10.5281/zenodo.1209993 (2022).Plummer, M., Stukalov, A. & Denwood, M. rjags: Bayesian graphical models using MCMC. R Package Version 4-13. https://cran.r-project.org/web/packages/rjags/rjags.pdf (2022).Newsome, S. D. et al. Variation in δ13C and δ15N diet–vibrissae trophic discrimination factors in a wild population of California sea otters. Ecol. Appl. 20, 1744–1752 (2010).Article 
PubMed 

Google Scholar 
Brooks, T. M. et al. Habitat loss and extinction in the hotspots of biodiversity. Conserv. Biol. 16, 909–923 (2002).Article 

Google Scholar 
Fahrig, L. Effects of habitat fragmentation on biodiversity. Annu. Rev. Ecol. Evol. Syst. 34, 487–515 (2003).Article 

Google Scholar 
Pardini, R., Nichols, E. & Püttker, T. Biodiversity response to habitat loss and fragmentation. Encycl. Anthr. 3, 229–239 (2018).Article 

Google Scholar 
Koch, A., Munks, S. & Driscoll, D. The use of hollow-bearing trees by vertebrate fauna in wet and dry Eucalyptus obliqua forest, Tasmania. Wildl. Res. 35, 727–746 (2008).Article 

Google Scholar 
Donázar, J. A., Cortés-Avizanda, A. & Carrete, M. Dietary shifts in two vultures after the demise of supplementary feeding stations: consequences of the EU sanitary legislation. Eur. J. Wildl. Res. 56, 613–621 (2010).Article 

Google Scholar 
Carbone, C., Teacher, A. & Rowcliffe, J. M. The costs of carnivory. PLoS Biol. 5, e22 (2007).Article 
PubMed 
PubMed Central 

Google Scholar 
Tucker, M. A., Ord, T. J. & Rogers, T. L. Revisiting the cost of carnivory in mammals. J. Evol. Biol. 29, 2181–2190 (2016).Article 
CAS 
PubMed 

Google Scholar 
Carbone, C., Mace, G. M., Roberts, S. C. & Macdonald, D. W. Energetic constraints on the diet of terrestrial carnivores. Nature 402, 286–288 (1999).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Fisher, D. O. & Dickman, C. R. Body size-prey relationships in insectivorous marsupials: Tests of three hypotheses. Ecology 74, 1871–1883 (1993).Article 

Google Scholar 
Ruxton, G. D. & Houston, D. C. Obligate vertebrate scavengers must be large soaring fliers. J. Theor. Biol. 228, 431–436 (2004).Article 
ADS 
MathSciNet 
PubMed 
MATH 

Google Scholar 
Pemberton, D. & Renouf, D. A field-study of communication and social-behavior of the Tasmanian devil at feeding sites. Aust. J. Zool. 41, 507–526 (1993).Article 

Google Scholar 
Pye, R. J. et al. A second transmissible cancer in Tasmanian devils. Proc. Natl. Acad. Sci. USA 113, 374–379 (2016).Article 
ADS 
CAS 
PubMed 

Google Scholar 
James, S. et al. Tracing the rise of malignant cell lines: Distribution, epidemiology and evolutionary interactions of two transmissible cancers in Tasmanian devils. Evol. Appl. 12, 1772–1780 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Hawkins, C. E. et al. Emerging disease and population decline of an island endemic, the Tasmanian devil Sarcophilus harrisii. Biol. Conserv. 131, 307–324 (2006).Article 

Google Scholar 
Pearse, A.-M. & Swift, K. Transmission of devil facial-tumour disease. Nature 439, 549 (2006).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Wood, S. W., Hua, Q. & Bowman, D. M. J. S. Fire-patterned vegetation and the development of organic soils in the lowland vegetation mosaics of south-west Tasmania. Aust. J. Bot. 59, 126–136 (2011).Article 

Google Scholar 
Kohn, M. J. Carbon isotope compositions of terrestrial C3 plants as indicators of (paleo)ecology and (paleo)climate. PNAS 107, 19691–19695 (2010).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Mayer, M., Ullmann, W., Sunde, P., Fischer, C. & Blaum, N. Habitat selection by the European hare in arable landscapes: The importance of small-scale habitat structure for conservation. Ecol. Evol. 8, 11619–11633 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Barker, R. & Vestjens, W. Food of Australian Birds 1. Non-Passerines (CSIRO Publishing, 1989).Book 

Google Scholar 
Thomas, D. G. The bird community of Tasmanian temperate rainforest. Ibis 122, 298–306 (1980).Article 

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

Google Scholar 
DPIPWE. Annual Statewide Spotlight Surveys, Tasmania 2020/2021. Nature Conservation Report 21/2. (2021).Nguyen, H. K. D., Fielding, M. W., Buettel, J. C. & Brook, B. W. Habitat suitability, live abundance and their link to road mortality of Tasmanian wildlife. Wildl. Res. 46, 236–246 (2019).Article 

Google Scholar  More

Pianka, E. R. Niche overlap and diffuse competition. Proc. Natl. Acad. Sci. 71, 2141–2145 (1974).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Chesson, P. Mechanisms of maintenance of species diversity. Annu. Rev. Ecol. Syst. 31, 343–366 (2000).Article 

Google Scholar 
Tokeshi, M. Species Coexistence: Ecological and Evolutionary Perspectives. (Wiley-Blackwell, 2009).Grinnell, J. Geography and evolution. Ecology 5, 225–229 (1924).Article 

Google Scholar 
Roughgarden, J. Resource partitioning among competing species—A coevolutionary approach. Theor. Popul. Biol. 9, 388–424 (1976).Article 
MathSciNet 
CAS 
PubMed 
MATH 

Google Scholar 
Syme, J., Kiszka, J. J. & Parra, G. J. Dynamics of cetacean mixed-species groups: A review and conceptual framework for assessing their functional significance. Front. Mar. Sci. 8, 1–19 (2021).Article 

Google Scholar 
Stensland, E., Angerbjörn, A. & Berggren, P. Mixed species groups in mammals. Mamm. Rev. 33, 205–223 (2003).Article 

Google Scholar 
Cords, M. & Würsig, B. A Mix of Species: Associations of Heterospecifics Among Primates and Dolphins. in Primates and Cetaceans: Field Research and Conservation of Complex Mammalian Societies (eds. Yamagiwa, J. & Karczmarski, L.) 409–431 (Springer, 2014). doi:https://doi.org/10.1007/978-4-431-54523-1_21.Goodale, E., Beauchamp, G. & Ruxton, G. D. Mixed-Species Groups of Animals: Behavior, Community Structure, and Conservation. (Academic Press, 2017).Krause, J. & Ruxton, G. D. Living in Groups. Oxford Series in Ecology and Evolution (Oxford University Press, 2002).Heymann, E. W. & Buchanan-Smith, H. M. The behavioural ecology of mixed-species troops of callitrichine primates. Biol. Rev. 75, 169–190 (2000).Article 
CAS 
PubMed 

Google Scholar 
Sridhar, H. & Guttal, V. Friendship across species borders: factors that facilitate and constrain heterospecific sociality. Philos. Trans. R. Soc. B Biol. Sci. 373, 1–9 (2018).Greenberg, R. Birds of many feathers: The formation and structure of mixed-species flocks of forest birds. in On the Move: How and Why Animals Travel in groups (eds. Boinski, S. & Gerber, P. A.) 521–558 (University of Chicago Press, 2000).Waser, P. M. ‘Chance’ and mixed-species associations. Behav. Ecol. Sociobiol. 15, 197–202 (1984).Article 

Google Scholar 
Whitesides, G. H. Interspecific associations of Diana monkeys, Cercopithecus diana, in Sierra Leone, West Africa: biological significance or chance?. Anim. Behav. 37, 760–776 (1989).Article 

Google Scholar 
Waser, P. M. Primate polyspecific associations: Do they occur by chance?. Anim. Behav. 30, 1–8 (1982).Article 

Google Scholar 
Alexander, R. D. The evolution of social behavior. Annu. Rev. Ecol. Syst. 5, 325–383 (1974).Article 

Google Scholar 
Kasozi, H. & Montgomery, R. A. Variability in the estimation of ungulate group sizes complicates ecological inference. Ecol. Evol. 10, 6881–6889 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Syme, J., Kiszka, J. J. & Parra, G. J. How to define a dolphin ‘group’? Need for consistency and justification based on objective criteria. Ecol. Evol. 12, 1–18 (2022).Article 

Google Scholar 
Hutchinson, J. M. C. & Waser, P. M. Use, misuse and extensions of ‘ideal gas’ models of animal encounter. Biol. Rev. 82, 335–359 (2007).Article 
PubMed 

Google Scholar 
Gotelli, N. J. Null model analysis of species co-occurrence patterns. Ecology 81, 2606–2621 (2000).Article 

Google Scholar 
Astaras, C., Krause, S., Mattner, L., Rehse, C. & Waltert, M. Associations between the drill (Mandrillus leucophaeus) and sympatric monkeys in Korup National Park. Cameroon. Am. J. Primatol. 73, 127–134 (2011).Article 
PubMed 

Google Scholar 
Mammides, C., Chen, J., Goodale, U. M., Kotagama, S. W. & Goodale, E. Measurement of species associations in mixed-species bird flocks across environmental and human disturbance gradients. Ecosphere 9, 1–14 (2018).Article 

Google Scholar 
Ovaskainen, O., Abrego, N., Halme, P. & Dunson, D. Using latent variable models to identify large networks of species-to-species associations at different spatial scales. Methods Ecol. Evol. 7, 549–555 (2016).Article 

Google Scholar 
Pollock, L. J. et al. Understanding co-occurrence by modelling species simultaneously with a Joint Species Distribution Model (JSDM). Methods Ecol. Evol. 5, 397–406 (2014).Article 

Google Scholar 
Warton, D. I. et al. So Many variables: Joint modeling in community ecology. Trends Ecol. Evol. 30, 766–779 (2015).Article 
PubMed 

Google Scholar 
Ovaskainen, O. et al. How to make more out of community data? A conceptual framework and its implementation as models and software. Ecol. Lett. 20, 561–576 (2017).Article 
PubMed 

Google Scholar 
Ovaskainen, O. & Abrego, N. Joint Species Distribution Modelling. (Cambridge University Press, 2020). https://doi.org/10.1017/9781108591720.Blanchet, F. G., Cazelles, K. & Gravel, D. Co-occurrence is not evidence of ecological interactions. Ecol. Lett. 23, 1050–1063 (2020).Article 
PubMed 

Google Scholar 
Haak, C. R., Hui, F. K., Cowles, G. W. & Danylchuk, A. J. Positive interspecific associations consistent with social information use shape juvenile fish assemblages. Ecology 101, 1–16 (2020).Article 

Google Scholar 
Bastianelli, G., Wintle, B. A., Martin, E. H., Seoane, J. & Laiolo, P. Species partitioning in a temperate mountain chain: Segregation by habitat vs. interspecific competition. Ecol. Evol. 7, 2685–2696 (2017).Aspin, T. & House, A. Alpha and beta diversity and species co-occurrence patterns in headwaters supporting rare intermittent-stream specialists. Freshw. Biol. n/a, (2022).Astarloa, A. et al. Identifying main interactions in marine predator-prey networks of the Bay of Biscay. ICES J. Mar. Sci. 76, 2247–2259 (2019).Article 

Google Scholar 
Parra, G. J. Resource partitioning in sympatric delphinids: space use and habitat preferences of Australian snubfin and Indo-Pacific humpback dolphins. J. Anim. Ecol. 75, 862–874 (2006).Article 
PubMed 

Google Scholar 
Parra, G. J., Wojtkowiak, Z., Peters, K. J. & Cagnazzi, D. Isotopic niche overlap between sympatric Australian snubfin and humpback dolphins. Ecol. Evol. 12, 1–11 (2022).Article 

Google Scholar 
Kiszka, J. J. et al. Ecological niche segregation within a community of sympatric dolphins around a tropical island. Mar. Ecol. Prog. Ser. 433, 273–288 (2011).Article 
ADS 

Google Scholar 
Bearzi, M. Dolphin sympatric ecology. Mar. Biol. Res. 1, 165–175 (2005).Article 

Google Scholar 
Zaeschmar, J. R. et al. Occurrence of false killer whales (Pseudorca crassidens) and their association with common bottlenose dolphins (Tursiops truncatus) off northeastern New Zealand. Mar. Mammal Sci. 30, 594–608 (2014).Article 

Google Scholar 
Elliser, C. R. & Herzing, D. L. Long-term interspecies association patterns of Atlantic bottlenose dolphins, Tursiops truncatus, and Atlantic spotted dolphins, Stenella frontalis, in the Bahamas. Mar. Mammal Sci. 32, 38–56 (2016).Article 

Google Scholar 
Kiszka, J. J., Perrin, W. F., Pusineri, C. & Ridoux, V. What drives island-associated tropical dolphins to form mixed-species associations in the southwest Indian Ocean?. J. Mammal. 92, 1105–1111 (2011).Article 

Google Scholar 
Brown, A. M., Bejder, L., Cagnazzi, D., Parra, G. J. & Allen, S. J. The north west cape, Western Australia: A potential hotspot for Indo-Pacific humpback dolphins Sousa chinensis?. Pacific Conserv. Biol. 18, 240–246 (2012).Article 

Google Scholar 
Allen, S. J., Cagnazzi, D., Hodgson, A. J., Loneragan, N. R. & Bejder, L. Tropical inshore dolphins of north-western Australia: Unknown populations in a rapidly changing region. Pacific Conserv. Biol. 18, 56–63 (2012).Article 

Google Scholar 
Palmer, C., Parra, G. J., Rogers, T. & Woinarski, J. Collation and review of sightings and distribution of three coastal dolphin species in waters of the Northern Territory. Australia. Pacific Conserv. Biol. 20, 116–125 (2014).Article 

Google Scholar 
Corkeron, P. J. Aspects of the Behavioral Ecology of Inshore Dolphins Tursiops truncatus and Sousa chinensis in Moreton Bay, Australia. in The Bottlenose Dolphin (eds. Leatherwood, S. & Reeves, R.) 285–293 (Elsevier, 1990). https://doi.org/10.1016/B978-0-12-440280-5.50018-4.Haughey, R. et al. Distribution and habitat preferences of Indo-Pacific Bottlenose Dolphins (Tursiops aduncus) inhabiting coastal waters with mixed levels of protection. Front. Mar. Sci. 8, 1–20 (2021).Article 

Google Scholar 
Hanf, D., Hodgson, A. J., Kobryn, H., Bejder, L. & Smith, J. N. Dolphin distribution and habitat suitability in North Western Australia: Applications and Implications of a Broad-Scale, Non-targeted Dataset. Front. Mar. Sci. 8, 1–18 (2022).Article 

Google Scholar 
Hunt, T. N., Allen, S. J., Bejder, L. & Parra, G. J. Identifying priority habitat for conservation and management of Australian humpback dolphins within a marine protected area. Sci. Rep. 10, 1–14 (2020).Article 

Google Scholar 
Hunt, T. N. Demography, habitat use and social structure of Australian humpback dolphins (Sousa sahulensis) around the North West Cape, Western Australia: Implications for conservation and management. PhD Thesis, Flinders University, Adelaide, Australia. (Flinders University, 2018).Cassata, L. & Collins, L. B. Coral reef communities, habitats, and substrates in and near sanctuary zones of Ningaloo marine park. J. Coast. Res. 241, 139–151 (2008).Article 

Google Scholar 
CALM MPRA. Management plan for the Ningaloo Marine Park and Muiron Islands Marine Management Area 2005–2015. (2005).Hunt, T. N. et al. Demographic characteristics of Australian humpback dolphins reveal important habitat toward the southwestern limit of their range. Endanger. Species Res. 32, 71–88 (2017).Article 

Google Scholar 
Mann, J. Behavioral sampling methods for cetaceans: A review and critique. Mar. Mammal Sci. 15, 102–122 (1999).Article 

Google Scholar 
Python Software Foundation. Python Language Reference, version 3.8.0. at https://www.python.org/ (2016).QGIS Development Team. QGIS Geographic Information System, version 3.8.3 Zanzibar. at http://qgis.osgeo.org (2019).Zanardo, N., Parra, G., Passadore, C. & Möller, L. Ensemble modelling of southern Australian bottlenose dolphin Tursiops sp. distribution reveals important habitats and their potential ecological function. Mar. Ecol. Prog. Ser. 569, 253–266 (2017).Hanberry, B. B. Finer grain size increases effects of error and changes influence of environmental predictors on species distribution models. Ecol. Inform. 15, 8–13 (2013).Article 

Google Scholar 
Gottschalk, T. K., Aue, B., Hotes, S. & Ekschmitt, K. Influence of grain size on species–habitat models. Ecol. Modell. 222, 3403–3412 (2011).Article 

Google Scholar 
Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).Article 

Google Scholar 
Passadore, C., Möller, L. M., Diaz-Aguirre, F. & Parra, G. J. Modelling dolphin distribution to inform future spatial conservation decisions in a marine protected area. Sci. Rep. 8, 1–14 (2018).Article 
CAS 

Google Scholar 
Parra, G. J., Schick, R. & Corkeron, P. J. Spatial distribution and environmental correlates of Australian snubfin and Indo-Pacific humpback dolphins. Ecography (Cop.) 29, 396–406 (2006).Article 

Google Scholar 
Conrad, O. et al. System for Automated Geoscientific Analyses (SAGA) v. 2.1.4. Geosci. Model Dev. 8, 1991–2007 (2015).R Core Team. R version 3.6.1. at https://www.r-project.org/ (2019).RStudio Team. RStudio: Integrated Develpment for R. at http://rstudio.com/ (2019).Dormann, C. F. et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography (Cop.) 36, 27–46 (2013).Article 

Google Scholar 
Tikhonov, G. et al. Joint species distribution modelling with the r-package Hmsc. Methods Ecol. Evol. 11, 442–447 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Gelman, A. & Rubin, D. B. Inference from iterative simulation using multiple sequences. Stat. Sci. 7, 457–472 (1992).Article 
MATH 

Google Scholar 
Pearce, J. & Ferrier, S. Evaluating the predictive performance of habitat models developed using logistic regression. Ecol. Modell. 133, 225–245 (2000).Article 

Google Scholar 
Tjur, T. Coefficients of determination in logistic regression models—A new proposal: The coefficient of discrimination. Am. Stat. 63, 366–372 (2009).Article 
MathSciNet 
MATH 

Google Scholar 
Syme, J. The behavioural ecology of mixed-species groups of delphinids. PhD Thesis, Flinders University, Adelaide, Australia. (Flinders University, 2023).Wang, J. Y. Bottlenose Dolphin, Tursiops aduncus, Indo-Pacific Bottlenose Dolphin. in Encyclopedia of Marine Mammals (eds. Würsig, B., Thewissen, J. G. M. & Kovacs, K. M.) 125–130 (Elsevier, 2018). https://doi.org/10.1016/B978-0-12-804327-1.00073-X.Parra, G. J. & Jefferson, T. A. Humpback Dolphins. in Encyclopedia of Marine Mammals (eds. Würsig, B., Thewissen, J. G. M. & Kovacs, K. M.) 483–489 (Elsevier, 2018). https://doi.org/10.1016/B978-0-12-804327-1.00153-9.Dröge, E., Creel, S., Becker, M. S. & M’soka, J. Spatial and temporal avoidance of risk within a large carnivore guild. Ecol. Evol. 7, 189–199 (2017).Article 
PubMed 

Google Scholar 
Browning, N. E., Cockcroft, V. G. & Worthy, G. A. J. Resource partitioning among South African delphinids. J. Exp. Mar. Bio. Ecol. 457, 15–21 (2014).Article 

Google Scholar 
Kiszka, J. J., Méndez-Fernandez, P., Heithaus, M. R. & Ridoux, V. The foraging ecology of coastal bottlenose dolphins based on stable isotope mixing models and behavioural sampling. Mar. Biol. 161, 953–961 (2014).Article 
CAS 

Google Scholar 
Saayman, G. S. & Tayler, C. K. The socioecology of humpback dolphins (Sousa sp.). in Behavior of Marine Animals Current Perspectives in Research Volume 3: Cetaceans (eds. Winn, H. E. & Olla, B. L.) 165–226 (Springer, 1979).Gowans, S. & Whitehead, H. Distribution and habitat partitioning by small odontocetes in the Gully, a submarine canyon on the Scotian Shelf. Can. J. Zool. 73, 1599–1608 (1995).Article 

Google Scholar 
Clua, E. Mixed-species feeding aggregation of dolphins, large tunas and seabirds in the Azores. Aquat. Living Resour. 14, 11–18 (2001).Article 

Google Scholar 
Quérouil, S. et al. Why do dolphins form mixed-species associations in the azores?. Ethology 114, 1183–1194 (2008).Article 

Google Scholar 
Heithaus, M. R. & Dill, L. M. Food availability and tiger shark predation risk influence bottlenose dolphin habitat use. Ecology 83, 480–491 (2002).Article 

Google Scholar  More

Owing to drastic changes in the environment caused by human interference, wild mammals that are reservoirs of Leishmania have invaded residential areas where species of sand flies with eclectic feeding habits are found, and established a transmission cycle that eventually reaches humans23,24,25. In the study area, it was observed that the largest frequency of specimens over the years was captured in the residential environment, which are represented by residential and peridomicile areas. The lowest frequency was captured in the borders of the forest.The municipality of Paraty, located on the southern coast in the state of Rio de Janeiro, where the study was conducted, has many preserved areas of the Atlantic Forest and its climate is wet with no dry season13, which was confirmed during the three years of the present study, where the relative air humidity stayed high every month. The highest average rainfalls occur in summer and fall (autumn). The average temperature during the hottest months of the year was between approximately 25 °C and 26 °C, with a maximum of 31 °C, and in the coldest months, the temperature averaged between 20 and 21 °C, with a minimum of 16 °C, exhibiting an ideal environment for the activity of sand flies throughout the year.Barretto26 noted that atmospheric conditions, such as relative humidity, rainfall, and temperature directly influence the activity of these sand fly species. Migonemyia migonei and Ny. whitmani had lower activity at temperatures below 15 °C, Pi. fischeri below 10 °C, and Ny. intermedia at temperatures below 9.5 °C. The author also reported that heavy rains prevent sand flies from leaving their shelters; however, this can increase their density within residences, especially for species located next to residential areas. Light rain will not impede their activity, but in these conditions, they are not as frequently observed as they usually are. However, during rain periods, especially in the hot and humid summer period, the density of sand flies increases considerably.In the present study, four key vector species of Leishmania braziliensis Vianna, 1911, the etiologic agent of tegumentary leishmaniasis, were captured throughout the year. The most frequent was Ny. intermedia, followed by Pi. fischeri, Mg. migonei, and Ny. whitmani. Carvalho et al.27, in the State of Pernambuco, northeast region of Brazil, reported having found Mg. migonei infected with Leishmania infantum Nicolle, 1908, the etiologic agent of visceral leishmaniasis.According to Forattini28, there are sand fly species that are essentially resistant to climate changes throughout the seasons. Several are found, albeit in lower densities, during the cooler, dry months, while others disappear during this period. However, other factors also influence the incidence of sand flies in the same location, even under the same temperature and humidity conditions. Thus, to study the seasonality of sand fly species, it is important to perform systematized captures, for a period exceeding two years, to minimize the effects of these additional factors, for example, atypical years with a longer period of drought or humidity, more or less high temperatures, months with higher than expected rainfall or control measures applied by the municipality.In studies carried out in the Northeast region of Brazil, in a study carried out in the municipality of Codó, in the State of Maranhão, an inversely proportional correlation of the captured sandflies was observed in relation to relative air humidity, a direct correlation in relation to temperature and precipitation, a correlation directly proportional29. In the municipality of Sobral, State of Ceará, in the first year of the study, observed a negative correlation with temperature and a high positive correlation with humidity and precipitation, however, in the following year, there was no correlation between the density of captured sandflies and climatic variables30. The same occurred in this study, in the municipality of Paraty, in relation to relative air humidity and precipitation, but in relation to temperature, a strong positive correlation was obtained.In the studied area Ny. intermedia occurred in greater numbers in every month of the year, except in June and July, when it was less frequent than Pi. fischeri. The same pattern was observed for these two species, i.e., a gradual increase in abundance beginning in August, peak abundance in summer (January), followed by a decrease until winter (July). Brito et al.31, when researching the northern coast of the state of São Paulo, municipality of São Sebastião, noted the opposite, that Ny. intermedia had the highest abundance peaks during the driest and coldest period of the year, i.e., from May to August. However, the authors also emphasized the presence of this species throughout the year, mainly in the residential environment, and they stressed the importance of seasonal analyses for periods longer than a year.In the São Francisco River region, in the state of Minas Gerais, on the banks of the Rio Velhas, Saraiva et al.32, in a study over a two-year period, observed a different pattern. In the first year of study, after the rainy season from February to May, with high humidity and high temperature, Ny. intermedia was captured in greater numbers than during other months of the year. In the second year, peaks occurred in October, March, and June, with the highest peak in March, when there was elevated rainfall, high humidity, and high temperatures.In the state of Rio de Janeiro, in Serra dos Órgãos National Park, Aguiar and Soucasaux33 analyzed the monthly frequency in human bait and observed that Ny. fischeri was captured in every month except November. In the hot and humid period, from December to February, there was a gradual increase in the average abundances of this species, and then a slight decrease began in March and continued into April. During the cold and dry period of May and June, abundances started to increase, then decreased in July, and peaked in August. During August, Pi. fischeri was the dominant species of wildlife, and in September, abundances began to decline again.Mayo et al.34, studying the southeastern region of the state of São Paulo, observed that there was a seasonal trend in the abundance for species Mg. migonei, Ny. whitmani, Ny. intermedia, and Pi. fischeri, with abundance peaks recorded during the cooler, drier season (April to September) and low abundances during the warmer, wetter season (October to March). The authors revealed that the occurrence of intense fires in the study area in October, which caused severe environmental change, possibly interfered with the population dynamics of the species. In the present study, the opposite trend of seasonality was shown for the four key species, Ny. intermedia, Pi. fischeri, Mg. migonei, and Ny. whitmani, then what was observed by the above authors, the highest abundances occurred during the hottest period, increasing gradually until a maximum peak in January, and lowest abundances were seen during the coldest period, in July for the first three species, and in June for Ny. whitmani.In the neighboring municipality of this study in Angra dos Reis, in the Ilha Grande, Carvalho et al.35 reinforced the epidemiological importance of Ny. intermedia in the State of Rio de Janeiro and highlighted the role of Mg. migonei in the transmission of cutaneous leishmaniasis with its high rate of infection natural by Leishmania. Still in the same region, along the southern coast of the State of Rio de Janeiro, Aguiar et al.8 conducted systematic catches for two years, with the aim being to analyze the monthly frequency of sand flies in residential and forest environments. The authors discovered results like what occurred in this study in Paraty, that the four most important species caught, Ny. intermedia, Pi. fischeri, Mg. migonei, and Ny. whitmani, had higher average numbers during the hot and humid period of the year, i.e., between October and January, with a maximum peak in December for Ny. intermedia and Pi. fischeri, and January for Mg. migonei. The prevalence of Ny. intermedia was evident in every month, both inside the residence and around the residential area. In the colder and drier season, from May to August, there was a balance with Pi. fischeri, but from August, inside the residence, and from September, around the residence, the frequency increased until it reached its peak in December. There was a gradual increase in the frequency of this species in the warmer and wetter period (between October and January), with average temperatures ranging from 26 to 29 °C and relative air humidity between 84 and 87%.Condino et al.36, when studying the southwestern region of the state of São Paulo, observed that Ny. intermedia and Ny. whitmani had the highest frequencies during the months of May, September, and December with temperatures ranging from 21 to 25.7 °C and rainfall between 66.7 and 195.1 mm. In June, the lowest frequency of sand flies was observed, which then increased until a maximum peak in September. Temperature data and rainfall index were not correlated with the density of specimens, especially as the study was carried out over only one year. In this study, the opposite was observed for Ny. intermedia and Ny. whitmani in the month of May, one of the months with the lowest density.In the city of Petrópolis, state of Rio de Janeiro, Souza et al.24 observed a prevalence of Ny. intermedia and Ny. whitmani, with the latter species prevailing around the residence. Migonemyia migonei and Pi. fischeri were also present but to a lesser extent. In the forest, Ny. whitmani was more abundant, followed by Pi. fischeri, while Ny. intermedia was found at lower abundances. However, Ny. intermedia and Pi. fischeri were present during every month of the year. The authors also found a significant correlation between the number of sand flies and environmental changes such as temperature, relative humidity, and rainfall. The same was observed, in this study, in the forest with Ny. intermedia, however, in this environment the number of Pi. fischeri specimens was higher than that of Ny. whitmani.In the north of Espírito Santo, Virgens et al.37 observed that Ny. intermedia was present in almost every month of the study period, with peaks in the warmer and wetter months. The authors highlighted that the low numbers of this species were recorded during and after high rainfall periods, suggesting that heavy rain is unfavorable for the development of immature forms, as breeding sites in altered habitats suffered a greater impact because of extreme weather conditions.In a study carried out by Guimarães et al.38 to observe the competence of Mg. Migonei to Leishmania infantum, concluded that this species is highly susceptible to the development of this parasite and that in addition to its anthropophilia and abundance in areas with an active focus of visceral leishmaniasis, it can act as a vector of this disease in Latin America.In the studied area, Ny. intermedia, one of the main vectors of the etiological agent of tegumentary leishmaniasis in the region2, was present in significant numbers in the home environment throughout all months of the year. The species Pi. fischeri was present over the months in expressive numbers in all types and locations of capture, that is, both in the environment altered by human activity and in the natural environment where leishmaniasis occurs in its natural enzootic cycle. Migonemyia migonei, present throughout the year in the peridomestic environment, showed its association with the dog, where it was prevalent throughout the year in the kennel, being an important vector of the etiological agent of tegumentary leishmaniasis, as well as being suspected in areas of visceral leishmaniasis transmission, where the main vector of this disease is not found. And Ny. whitmani present in the peridomicile, mainly in the hottest months of the year, in addition to the forest and forest margins, it was observed that in this study region the species is emerging through a selective process of adaptation in environments that were negatively affected by the increase of human activity. Thus, despite observing a period of greater frequency of sand flies in the hottest months of the year, a period with high rainfall, the high relative humidity is observed throughout the year, as well as the presence of species of epidemiological importance Ny. intermedia, Pi. fischeri, Mg. migonei and Ny. whitmani, who are involved in the propagation of the etiological agent of tegumentary leishmaniasis to humans and animals, causing greater contact between the region’s inhabitants with these dipterans and thus, a greater risk of contracting the disease. More

Rahaman, A. et al. The increasing hunger concern and current need in the development of sustainable food security in the developing countries. Trends Food Sci. Technol. 113, 423–429. https://doi.org/10.1016/j.tifs.2021.04.048 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Porter, J. R. et al. In Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change 485–533 (Cambridge University Press, 2014).
Google Scholar 
Yan, H. et al. Crop traits enabling yield gains under more frequent extreme climatic events. Sci. Total Environ. 808, 152170. https://doi.org/10.1016/j.scitotenv.2021.152170 (2022).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Lobell, D. et al. The critical role of extreme heat for maize production in the United States. Nat. Clim. Change. 3, 497–501. https://doi.org/10.1038/nclimate1832 (2013).Article 
ADS 

Google Scholar 
Vermeulen, S. J. et al. Addressing uncertainty in adaptation planning for agriculture. Proc. Natl. Acad. Sci. 110, 8357–8362. https://doi.org/10.1073/pnas.1219441110 (2013).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
FAO. Climate Change and Food Security: Risks and Responses (FAO, 2015).
Google Scholar 
Ray, D. K., Gerber, J. S., MacDonald, G. K. & West, P. C. Climate variation explains a third of global crop yield variability. Nat. Commun. 6, 5989. https://doi.org/10.1038/ncomms6989 (2015).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Ding, Z. et al. Modeling the combined impacts of deficit irrigation, rising temperature and compost application on wheat yield and water productivity. Agric. Water Manag. 244, 106626. https://doi.org/10.1016/j.agwat.2020.106626 (2021).Article 

Google Scholar 
Malhi, G. S., Kaur, M. & Kaushik, P. Impact of climate change on agriculture and its mitigation strategies: A review. Sustainability 13, 1318 (2021).Article 
CAS 

Google Scholar 
Persson, T. & Kværnø, S. Impact of projected mid-21st century climate and soil extrapolation on simulated spring wheat grain yield in Southeastern Norway. J. Agric. Sci. 155, 361–377. https://doi.org/10.1017/S0021859616000241 (2017).Article 

Google Scholar 
Zhu, X. & Troy, T. J. Agriculturally relevant climate extremes and their trends in the world’s major growing regions. Earth’s Future 6, 656–672. https://doi.org/10.1002/2017EF000687 (2018).Article 
ADS 

Google Scholar 
Fischer, T. et al. Increase in irrigated wheat yield in north-west Mexico from 1960 to 2019: Unravelling the negative relationship to minimum temperature. Field Crops Res. 275, 108331. https://doi.org/10.1016/j.fcr.2021.108331 (2022).Article 
ADS 

Google Scholar 
Lobell, D. B., Schlenker, W. & Costa-Roberts, J. Climate trends and global crop production since 1980. Science 333, 616–620. https://doi.org/10.1126/science.1204531 (2011).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Harkness, C. et al. Adverse weather conditions for UK wheat production under climate change. Agric. For. Meteorol. 282, 107862. https://doi.org/10.1016/j.agrformet.2019.107862 (2020).Article 
ADS 
PubMed 

Google Scholar 
Seehusen, T. & Uhlen, A. K. Analyses of yield gaps for the production of wheat and barley in Norway, potential to increase yields on existing farmland. Norwegian Institute for Bioeconomics, Report 5/166/2019 (2020).Hakala, K. et al. Sensitivity of barley varieties to weather in Finland. J. Agric. Sci. 150, 145–160. https://doi.org/10.1017/S0021859611000694 (2012).Article 
CAS 
PubMed 

Google Scholar 
Peltonen-Sainio, P., Jauhiainen, L., Hakala, K. & Ojanen, H. Climate change and prolongation of growing season, changes in regional potential for field crop production in Finland. Agric. Food Sci. 18, 171–190. https://doi.org/10.2137/145960609790059479 (2009).Article 

Google Scholar 
Fleisher, D. H. et al. A potato model intercomparison across varying climates and productivity levels. Glob. Change Biol. 23, 1258–1281. https://doi.org/10.1111/gcb.13411 (2017).Article 
ADS 

Google Scholar 
Moen, A. National Atlas of Norway: Vegetation (Hønefoss, 1999).
Google Scholar 
Bakkestuen, V., Erikstad, L. & Halvorsen, R. Step-less models for regional environmental variation in Norway. J. Biogeogr. 35, 1906–1922 (2008).Article 

Google Scholar 
Statistics-Norway. 2020. https://www.ssb.no/jord-skog-jakt-og-fiskeri/statistikker/stjord (Accessed 10 November 2020).Hanssen-Bauer, I. et al. Climate in Norway 2100 – a knowledge base for climate adaptation. Norwegian Centre for Climate Sciences, Report 1/2017 49 (2017).Blandford, D., Gaasland, I., Vårdal, E. & McIntosh, C. Greenhouse gas emissions, land use, and food supply under the paris climate agreement—Policy choice in Norway. Appl. Econ. Perspect. Policy 41, 249–264. https://doi.org/10.1093/aepp/ppy011 (2019).Article 

Google Scholar 
Rötter, R. P. et al. What would happen to barley production in Finland if global warming exceeded 4 °C? A model-based assessment. Eur. J. Agron. 35, 205–214. https://doi.org/10.1016/j.eja.2011.06.003 (2011).Article 

Google Scholar 
Ozturk, I., Sharif, B., Baby, S., Jabloun, M. & Olesen, J. E. The long-term effect of climate change on productivity of winter wheat in Denmark, scenario analysis using three crop models. J. Agric. Sci. 155, 733–750. https://doi.org/10.1017/S0021859616001040 (2017).Article 
CAS 

Google Scholar 
An, H. & Carew, R. Effect of climate change and use of improved varieties on barley and canola yield in Manitoba. Can. J. Plant Sci. 95, 127–139. https://doi.org/10.1139/CJPS-2014-221 (2014).Article 

Google Scholar 
Zhou, Z., Plauborg, F., Kristensen, K. & Andersen, M. Dry matter production, radiation interception and radiation use efficiency of potato in response to temperature and nitrogen application regimes. Agric. For. Meteorol. 232, 595–605. https://doi.org/10.1016/j.agrformet.2016.10.017 (2017).Article 
ADS 

Google Scholar 
Jensen, K. J. S. et al. Yield and development of winter wheat (Triticum aestivum L.) and spring barley (Hordeum vulgare L.) in field experiments with variable weather and drainage conditions. Eur. J. Agron. 122, 126075. https://doi.org/10.1016/j.eja.2020.126075 (2021).Article 
CAS 

Google Scholar 
Lobell, D. B., Cahill, K. N. & Field, C. B. Historical effects of temperature and precipitation on California crop yields. Clim. Change 81, 187–203. https://doi.org/10.1007/s10584-006-9141-3 (2007).Article 
ADS 

Google Scholar 
Skjelvag, A. O. Climatic conditions for crop production in Nordic countries. Agric. Food Sci. Finland 7(2), 149–160 (1998).Article 

Google Scholar 
Norsk-Klimaservicesenter. https://seklima.met.no/ (2020).Erikstad, L. & Bakkestuen, V. Calculating cumulative effects in GIS using a stepless multivariate model. MethodsX 8, 101407. https://doi.org/10.1016/j.mex.2021.101407 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Aune-Lundberg, L. & Strand, G.-H. The content and accuracy of the CORINE land cover dataset for Norway. Int. J. Appl. Earth Observ. Geoinform. 96, 102266. https://doi.org/10.1016/j.jag.2020.102266 (2021).Article 

Google Scholar 
QGIS Geographic Information System (QGIS Association, 2020).R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).
Google Scholar 
Lobell, D. B. & Field, C. B. Global scale climate–crop yield relationships and the impacts of recent warming. Environ. Res. Lett. 2, 014002. https://doi.org/10.1088/1748-9326/2/1/014002 (2007).Article 
ADS 

Google Scholar 
Shumway, R. H. & Stoffer, D. S. Time Series Analysis and its Applications Vol. 560 (Springer, 2016).MATH 

Google Scholar 
Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R. J. 9, 378–400 (2017).Article 

Google Scholar 
Lüdecke, D., Ben Shachar, M., Patil, I., Waggoner, P. & Makowski, D. Performance: An R Package for Assessment, Comparison and Testing of Statistical Models (2021).Hartig, F. DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models. R package version 0.3.3.0 (2020).Friedman, J. H., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33(22), 2010. https://doi.org/10.18637/jss.v033.i01 (2010).Article 

Google Scholar 
Tibshirani, R. Regression shrinkage and selection via the Lasso. J. R. Stat. Soc. Ser. B-Methodol. 58, 267–288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x (1996).Article 
MathSciNet 
MATH 

Google Scholar 
Hastie, T., Tibshirani, R. & Friendman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer, 2009).Book 
MATH 

Google Scholar 
Meinshausen, N. & Bühlmann, P. Stability selection. J. Roy. Stat. Soc. B 72, 417–473. https://doi.org/10.2307/40802220 (2010).Article 
MathSciNet 
MATH 

Google Scholar 
Efron, B. & Stein, C. The jackknife estimate of variance. Ann. Stat. 9, 586–596. https://doi.org/10.1214/aos/1176345462 (1981).Article 
MathSciNet 
MATH 

Google Scholar 
Milborrow, S. plotmo: Plot a Model’s Residuals, Response, and Partial Dependence Plots. R package version 3.5.7 (2020).Liu, H. Xu, X. & Li, J.J. HDCI: High Dimensional Confidence Interval Based on Lasso and Bootstrap. R package version 1.0–2 (2017).. Seehusen, T. & Uhlen, A. K. Analyses of yield gaps for the production of wheat and barley in Norway, potential to increase yields on existing farmland. Norwegian Institute for Bioeconomics, Report 5/166/2019. http://hdl.handle.net/11250/2637490 (2019).Stabbetorp, H. Dyrkingsomfang og avling i kornproduksjonen. Norsk institutt for bioøkonomi, Report 4 (1) (2017).Ebrahimi, E. et al. Assessing the impact of climate change on crop management in winter wheat—A case study for Eastern Austria. J. Agric. Sci. 154, 1153–1170. https://doi.org/10.1017/S0021859616000083 (2016).Article 

Google Scholar 
Kristensen, K., Schelde, K. & Olesen, J. Winter wheat yield response to climate variability in Denmark. J. Agric. Sci. 148, 1–15. https://doi.org/10.1017/S0021859610000675 (2010).Article 

Google Scholar 
Thaler, S., Eitzinger, J., Trnka, M. & Dubrovsky, M. Impacts of climate change and alternative adaptation options on winter wheat yield and water productivity in a dry climate in Central Europe. J. Agric. Sci. 150, 537–555. https://doi.org/10.1017/S0021859612000093 (2012).Article 
CAS 

Google Scholar 
Ortiz, R. et al. Climate change, can wheat beat the heat?. Agr. Ecosyst. Environ. 126, 46–58. https://doi.org/10.1016/j.agee.2008.01.019 (2008).Article 

Google Scholar 
Semenov, M., Stratonovitch, P., Alghabari, F. & Gooding, M. Adapting wheat in Europe for climate change. J. Cereal Sci. 59, 245–256. https://doi.org/10.1016/j.jcs.2014.01.006 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Roberts, M. J., Braun, N. O., Sinclair, T. R., Lobell, D. B. & Schlenker, W. Comparing and combining process-based crop models and statistical models with some implications for climate change. Environ. Res. Lett. 12, 095010. https://doi.org/10.1088/1748-9326/aa7f33 (2017).Article 
ADS 

Google Scholar 
Zhu, X., Troy, T. & Devineni, N. Stochastically modeling the projected impacts of climate change on rainfed and irrigated US crop yields. Environ. Res. Lett. 14, 074021. https://doi.org/10.1088/1748-9326/ab25a1 (2019).Article 
ADS 

Google Scholar 
Lobell, D. & Asseng, S. Comparing estimates of climate change impacts from process-based and statistical crop models. Environ. Res. Lett. 12, 015001. https://doi.org/10.1088/1748-9326/aa518a (2017).Article 
ADS 
CAS 

Google Scholar 
Flø, S. et al. Rom for bruk av Norsk korn. Felleskjøpet, Report 49 (2017).Lillemo, M., Reitan, L. & Bjornstad, A. Increasing impact of plant breeding on barley yields in central Norway from 1946 to 2008. Plant Breeding 129, 484–490. https://doi.org/10.1111/j.1439-0523.2009.01710.x (2010).Article 

Google Scholar 
Wonneberger, R., Ficke, A. & Lillemo, M. Mapping of quantitative trait loci associated with resistance to net form net blotch (Pyrenophora teres f. teres) in a doubled haploid Norwegian barley population. PLoS One 12, e0175773. https://doi.org/10.1371/journal.pone.0175773 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Moore, F. C. & Lobell, D. B. The fingerprint of climate trends on European crop yields. Proc. Natl. Acad. Sci. 112, 2670–2675. https://doi.org/10.1073/pnas.1409606112 (2015).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Martin, P. et al. Recent warming across the North Atlantic region may be contributing to an expansion in barley cultivation. Clim. Change 145, 351–365. https://doi.org/10.1007/s10584-017-2093-y (2017).Article 
ADS 

Google Scholar 
Martin, P., Wishart, J., Dalmannsdottir, S., Halland, H. & Thomsen, a. M. Recent warming and the thermal requirement of barley in coastal Norway. Norwegian Institute for Bioeconomics, Report T2.4.3 ii (2018).Cattivelli, L., Ceccarelli, S., Romagosa, I. & Stanca, M. Abiotic stresses in Barley: Problems and solutions. In Barley: Production, Improvement, and Uses Vol. 4 (ed. Ullrich, S.) 282–306 (Blackwell UP, 2011).
Google Scholar 
Hura, T. Wheat and barley acclimatization to abiotic and biotic stress. Int. J. Mol. Sci. 21, 7423. https://doi.org/10.3390/ijms21197423 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Kolberg, D., Persson, T., Mangerud, K. & Riley, H. Impact of projected climate change on workability, attainable yield, profitability and farm mechanization in Norwegian spring cereals. Soil Till. Res. 185, 122–138. https://doi.org/10.1016/j.still.2018.09.002 (2019).Article 

Google Scholar 
Olesen, J. E. et al. Impacts and adaptation of European crop production systems to climate change. Eur. J. Agron. 34, 96–112. https://doi.org/10.1016/j.eja.2010.11.003 (2011).Article 

Google Scholar 
Gammans, M., Mérel, P. & Ortiz-Bobea, A. Negative impacts of climate change on cereal yields: Statistical evidence from France. Environ. Res. Lett. 12, 054007. https://doi.org/10.1088/1748-9326/aa6b0c (2017).Article 
ADS 
CAS 

Google Scholar 
Ahmed, I., Harrison, M., Meinke, H. & Zhou, M. Barley phenology: physiological and molecular mechanisms for heading date and modelling of genotype-environment- management interactions. Plant Growth InTech 8, 175–202. https://doi.org/10.5772/64827 (2016).Article 
CAS 

Google Scholar 
Hossain, A., da Silva, J. A. T., Lozovskaya, M. V. & Zvolinsky, V. P. High temperature combined with drought affect rainfed spring wheat and barley in South-Eastern Russia. Saudi J. Biol. Sci. 19, 473–487. https://doi.org/10.1016/j.sjbs.2012.07.005 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Møllerhagen, P. Norsk potetproduksjon 2011. Bioforsk, Report 7(1) (2012).Hermansen, A., Lu, D. & Forbes, G. Potato production in China and Norway, similarities, differences and future challenges. Potato Res. 55, 197–203. https://doi.org/10.1007/s11540-012-9224-7 (2012).Article 

Google Scholar 
Hermansen, A., Nærstad, R., Le, V. & Nordskog, B. In Proceedings of the Eleventh EuroBlight Workshop (The Norwegian Institute for Agricultural and Environmental Research, Hamar, 2018).Raymundo, R. et al. Climate change impact on global potato production. Eur. J. Agron. 100, 87–98. https://doi.org/10.1016/j.eja.2017.11.008 (2018).Article 

Google Scholar 
Rabia, A., Yacout, D., Shahin, S., Mohamed, A. & Abdelaty, E. Towards sustainable production of potato under climate change conditions. Curr. J. Appl. Sci. Technol. 18, 200–207. https://doi.org/10.14456/cast.2018.15 (2018).Article 

Google Scholar 
Haverkort, A. J., Franke, A. C., Engelbrecht, F. A. & Steyn, J. M. Climate change and potato production in contrasting South African agro-ecosystems. Potato Res. 56, 67–84. https://doi.org/10.1007/s11540-013-9230-4 (2013).Article 

Google Scholar 
Martinelli, F. et al. Advanced methods of plant disease detection A review. Agron. Sustain. Dev. 35, 1–25. https://doi.org/10.1007/s13593-014-0246-1 (2015).Article 

Google Scholar 
Borus, D. Impacts of Climate Change on the Potato (Solanum Tuberosum L.) Productivity in Tasmania, Australia and Kenya (University of Tasmania, 2017).
Google Scholar 
Fageria, N., Baligar, V. & Jones, C. Growth and Mineral Nutrition of Field Crops Vol. 5, 586 (CRC Press, 2010).Book 

Google Scholar 
Fleisher, D. H. et al. Effects of elevated CO2 and cyclic drought on potato under varying radiation regimes. Agric. For. Meteorol. 171, 270–280. https://doi.org/10.1016/j.agrformet.2012.12.011 (2013).Article 
ADS 

Google Scholar 
Haverkort, A. J. & Struik, P. C. Yield levels of potato crops: Recent achievements and future prospects. Field Crop Res. 182, 76–85. https://doi.org/10.1016/j.fcr.2015.06.002 (2015).Article 

Google Scholar 
Van Oort, P. A. J., Timmermans, B. G. H., Meinke, H. & Van Ittersum, M. K. Key weather extremes affecting potato production in the Netherlands. Eur. J. Agron. 37, 11–22. https://doi.org/10.1016/j.eja.2011.09.002 (2012).Article 

Google Scholar 
Najafi, E., Devineni, N., Khanbilvardi, R. & Kogan, F. Understanding the changes in global crop yields through changes in climate and technology. Earth’s Future 6, 410–427. https://doi.org/10.1002/2017EF000690 (2018).Article 
ADS 
CAS 

Google Scholar 
Pulatov, B., Anna Maria, J. N., Karin, H. & Maj-Lena, L. Modeling climate change impact on potato crop phenology, and risk of frost damage and heat stress in northern Europe. Agric. For. Meteorol. 214, 281–292. https://doi.org/10.1016/j.agrformet.2015.08.266 (2015).Article 
ADS 

Google Scholar  More