Philippa Kaur

Ai H, Fang X, Yang B, Huang Z, Chen H, Mao L et al. (2015) Adaptation and possible ancient interspecies introgression in pigs identified by whole-genome sequencing. Nat Genet 47:217–225Article 
CAS 

Google Scholar 
Alexander DH, Novembre J, Lange K (2009) Fast model-based estimation of ancestry in unrelated individuals. Genome Res 19:1655–1664Article 
CAS 

Google Scholar 
Alexandri P, Megens HJ, Crooijmans RPMA, Groenen MAM, Goedbloed DJ, Herrero-Medrano JM et al. (2017) Distinguishing migration events of different timing for wild boar in the Balkans. J Biogeogr 44:259–270Article 

Google Scholar 
Alexandri P, Triantafyllidis A, Papakostas S, Chatzinikos E, Platis P, Papageorgiou N et al. (2012) The Balkans and the colonization of Europe: the post-glacial range expansion of the wild boar, Sus scrofa. J Biogeogr 39:713–723Article 

Google Scholar 
Alves PC, Pinheiro I, Godinho R, Vicente JJ, Gortázar C, Scandura M et al. (2010) Genetic diversity of wild boar populations and domestic pig breeds (Sus scrofa) in South-western Europe. Biol J Linn Soc 101:797–822Article 

Google Scholar 
Apollonio M, Andersen R, Putman R (2010) European ungulates and their management in the 21st century (M Apollonio, R Andersen, and R Putman, Eds.) Cambridge University Press: Cambridge, UKAzzaroli A, De Giuli C, Ficcarelli G, Torre D (1988) Late pliocene to early mid-pleistocene mammals in Eurasia: Faunal succession and dispersal events. Palaeogeogr Palaeoclimatol Palaeoecol 66:77–100Article 

Google Scholar 
Bérénos C, Ellis PA, Pilkington JG, Pemberton JM (2016) Genomic analysis reveals depression due to both individual and maternal inbreeding in a free‐living mammal population. Mol Ecol 25:3152–3168Article 

Google Scholar 
Braga RT, Rodrigues JFM, Diniz-Filho JAF, Rangel TF (2019) Genetic population structure and allele surfing during range expansion in dynamic habitats. An da Academia Brasileira de Ciências 91:e20180179Article 

Google Scholar 
Bragina EV, Ives AR, Pidgeon AM, Kuemmerle T, Baskin LM, Gubar YP, Piquer-Rodríguez M, Keuler NS, Petrosyan VG, Radeloff VC (2015) Rapid Declines of Large Mammal Populations after the Collapse of the Soviet Union. Cons Biol 29:844–853Article 

Google Scholar 
Brewer S, Cheddadi R, de Beaulieu JL, Reille M, Allen J, Almqvist-Jacobson H et al. (2002) The spread of deciduous Quercus throughout Europe since the last glacial period. For Ecol Manag 156:27–48Article 

Google Scholar 
Cahill S, Llimona F, Cabañeros L, Calomardo F (2012) Characteristics of wild boar (Sus scrofa) habituation to urban areas in the Collserola Natural Park (Barcelona) and comparison with other locations. Anim Biodivers Conserv 35:221–233Article 

Google Scholar 
Canu A, Costa S, Iacolina L, Piatti P, Apollonio M, Scandura M (2014) Are captive wild boar more introgressed than free-ranging wild boar? Two case studies in Italy. Eur J Wildl Res 60:459–467Article 

Google Scholar 
Canu A, Vilaça STT, Iacolina L, Apollonio M, Bertorelle G, Scandura M (2016) Lack of polymorphism at the MC1R wild-type allele and evidence of domestic allele introgression across European wild boar populations. Mamm Biol 81:477–479Article 

Google Scholar 
Carranza J, Salinas M, de Andrés D, Pérez-González J (2016) Iberian red deer: paraphyletic nature at mtDNA but nuclear markers support its genetic identity. Ecol Evol 6:905–922Article 

Google Scholar 
Chang CC, Chow CC, Tellier LCAM, Vattikuti S, Purcell SM, Lee JJ (2015) Second-generation PLINK: Rising to the challenge of larger and richer datasets. Gigascience 4:1–16Article 

Google Scholar 
Cheddadi R, Bar-Hen A (2009) Spatial gradient of temperature and potential vegetation feedback across Europe during the late Quaternary. Clim Dyn 32:371–379Article 

Google Scholar 
Clark PU, Dyke AS, Shakun JD, Carlson AE, Clark J, Wohlfarth B et al. (2009) The Last Glacial Maximum. Science 325:710–714Article 
CAS 

Google Scholar 
DeGiorgio M, Rosenberg NA (2013) Geographic sampling scheme as a determinant of the major axis of genetic variation in principal components analysis. Mol Biol Evol 30:480–488Article 
CAS 

Google Scholar 
Deinet S, Ieronymidou C, McRae L, Burfield IJ, Foppen RP, Collen B, et al. (2013) Wildlife comeback in Europe. The recovery of selected mammal and bird species. London, UKEckert CG, Samis KE, Lougheed SC (2008) Genetic variation across species’ geographical ranges: the central–marginal hypothesis and beyond. Mol Ecol 17:1170–1188Article 
CAS 

Google Scholar 
Fang M, Berg F, Ducos A, Andersson L (2006) Mitochondrial haplotypes of European wild boars with 2n = 36 are closely related to those of European domestic pigs with 2n = 38. Anim Genet 37:459–464Article 
CAS 

Google Scholar 
Ferenčaković M, Sölkner J, Curik I (2013) Estimating autozygosity from high-throughput information: Effects of SNP density and genotyping errors. Genet Sel Evol 45:42Article 

Google Scholar 
Ferreira E, Souto L, Soares AMVM, Fonseca C (2009) Genetic structure of the wild boar population in Portugal: Evidence of a recent bottleneck. Mamm Biol 74:274–285Article 

Google Scholar 
Franois O, Currat M, Ray N, Han E, Excoffier L, Novembre J (2010) Principal component analysis under population genetic models of range expansion and admixture. Mol Biol Evol 27:1257–1268Article 

Google Scholar 
Frantz AC, Bertouille S, Eloy MC, Licoppe A, Chaumont F, Flamand MC (2012) Comparative landscape genetic analyses show a Belgian motorway to be a gene flow barrier for red deer (Cervus elaphus), but not wild boars (Sus scrofa). Mol Ecol 21:3445–3457Article 
CAS 

Google Scholar 
Fulgione D, Rippa D, Buglione M, Trapanese M, Petrelli S, Maselli V (2016) Unexpected but welcome. Artificially selected traits may increase fitness in wild boar. Evol Appl 9:769–776Article 
CAS 

Google Scholar 
Goedbloed DJ, Megens HJ, van Hooft P, Herrero-Medrano JM, Lutz W, Alexandri P et al. (2013a) Genome-wide single nucleotide polymorphism analysis reveals recent genetic introgression from domestic pigs into Northwest European wild boar populations. Mol Ecol 22:856–866Article 
CAS 

Google Scholar 
Goedbloed DJ, van Hooft P, Megens HJ, Langenbeck K, Lutz W, Crooijmans RPMA et al. (2013b) Reintroductions and genetic introgression from domestic pigs have shaped the genetic population structure of Northwest European wild boar. BMC Genet 14:2–10Article 

Google Scholar 
Groenen MAM, Archibald AL, Uenishi H, Tuggle CK, Takeuchi Y, Rothschild MF et al. (2012) Analyses of pig genomes provide insight into porcine demography and evolution. Nature 491:393–398Article 
CAS 

Google Scholar 
Herrero-Medrano JM, Megens H-J, Groenen MAM, Ramis G, Bosse M, Pérez-Enciso M et al. (2013) Conservation genomic analysis of domestic and wild pig populations from the Iberian Peninsula. BMC Genet 14:1–13Article 

Google Scholar 
Hewitt GM (1999) Post-glacial re-colonization of European biota. Biol J Linn Soc 68:87–112Article 

Google Scholar 
Hewitt GM (2004) Genetic consequences of climatic oscillations in the Quaternary. Philos Trans R Soc Lond Ser B Biol Sci 359:183–195Article 
CAS 

Google Scholar 
Hiemstra PH, Pebesma EJ, Twenhöfel CJW, Heuvelink GBM (2009) Real-time automatic interpolation of ambient gamma dose rates from the Dutch radioactivity monitoring network. Comput Geosci 35:1711–1721Article 
CAS 

Google Scholar 
Howrigan DP, Simonson MA, Keller MC (2011) Detecting autozygosity through runs of homozygosity: a comparison of three autozygosity detection algorithms. BMC Genomics 12:460Article 
CAS 

Google Scholar 
Huisman J, Kruuk LEB, Ellis PA, Clutton-Brock T, Pemberton JM (2016) Inbreeding depression across the lifespan in a wild mammal population. Proc Natl Acad Sci 113:3585–3590Article 
CAS 

Google Scholar 
Iacolina L, Corlatti L, Buzan E, Safner T, Šprem N (2019) Hybridisation in European ungulates: an overview of the current status, causes, and consequences. Mamm Rev 49:45–59Article 

Google Scholar 
Iacolina L, Pertoldi C, Amills M, Kusza S, Megens H-J, Bâlteanu VA et al. (2018) Hotspots of recent hybridization between pigs and wild boars in Europe. Sci Rep. 8:17372Article 
CAS 

Google Scholar 
Iacolina L, Scandura M, Goedbloed DJ, Alexandri P, Crooijmans RPMA, Larson G et al. (2016) Genomic diversity and differentiation of a managed island wild boar population. Heredity 116:60–67Article 
CAS 

Google Scholar 
Jombart T, Ahmed I (2011) adegenet 1.3-1: new tools for the analysis of genome-wide SNP data. Bioinformatics 27:1–2Article 

Google Scholar 
de Jong JF, Hooft van P, Megens HJ, Crooijmans RPMA, Groot de GA, Pemberton JM, Huisman J et al. (2020) Fragmentation and translocation distort the genetic landscape of ungulates: red deer in the Netherlands. Front Ecol Evol 8:535715Article 

Google Scholar 
Kamvar ZN, Tabima JF, Grünwald NJ (2014) Poppr: an R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ 2:e281Article 

Google Scholar 
Kardos M, Åkesson M, Fountain T, Flagstad Ø, Liberg O, Olason P et al. (2018) Genomic consequences of intensive inbreeding in an isolated wolf population. Nat Ecol Evol 2:124–131Article 

Google Scholar 
Kaplan JO, Krumhardt KM, Zimmermann N (2009) The prehistoric and preindustrial deforestation of Europe. Quat Sci Rev 28:3016–3034. https://doi.org/10.1016/j.quascirev.2009.09.028Koemle D, Zinngrebe Y, Yu X (2018) Highway construction and wildlife populations: Evidence from Austria. Land use policy 73:447–457Article 

Google Scholar 
Krže B (1982) Divji prašič: biologija, gojitev, ekologija. Lovska zveza Slovenije, Ljubljana
Google Scholar 
Kusza S, Podgórski T, Scandura M, Borowik T, Jávor A, Sidorovich VE et al. (2014) Contemporary genetic structure, phylogeography and past demographic processes of wild boar Sus scrofa population in central and eastern Europe. PLoS One 9:e91401Article 

Google Scholar 
Lorenzini R, Lovari S, Masseti M (2002) The rediscovery of the Italian roe deer: Genetic differentiation and management implications. Ital J Zool 69(4):367–379Article 

Google Scholar 
Lorenzini R, San José C, Braza F, Aragón S (2003) Genetic differentiation and phylogeography of roe deer in Spain, as suggested by mitochondrial DNA and microsatellite analysis. Ital J Zool 70(1):89–99Article 
CAS 

Google Scholar 
Magri D (2013) Early to Middle Pleistocene dynamics of plant and mammal communities in South West Europe. Quat Int 288:63–72Article 

Google Scholar 
Manunza A, Zidi A, Yeghoyan S, Balteanu VA, Carsai TC, Scherbakov O et al. (2013) A high throughput genotyping approach reveals distinctive autosomal genetic signatures for European and Near Eastern wild boar. PLoS One 8:e55891Article 
CAS 

Google Scholar 
Maselli V, Rippa D, De Luca A, Larson G, Wilkens B, Linderholm A et al. (2016) Southern Italian wild boar population, hotspot of genetic diversity. Hystrix 27:137–144
Google Scholar 
McVean G (2009) A genealogical interpretation of principal components analysis. PLoS Genet 5:e1000686Article 

Google Scholar 
Megens H-J, Crooijmans RP, Cristobal M, Hui X, Li N, Groenen MA (2008) Biodiversity of pig breeds from China and Europe estimated from pooled DNA samples: differences in microsatellite variation between two areas of domestication. Genet Sel Evol 40:103
Google Scholar 
Melis C, Szafrańska PA, Jȩdrzejewska B, Bartoń K (2006) Biogeographical variation in the population density of wild boar (Sus scrofa) in western Eurasia. J Biogeogr 33:803–811Article 

Google Scholar 
Mihalik B, Stéger V, Frank K, Szendrei L, Kusza S (2018) Barrier effect of the M3 highway in Hungary on the genetic diversity of wild boar (Sus scrofa) population. Res J Biotechnol 13:32–38
Google Scholar 
NCBI (2018) Genome Organism Overview: Sus scrofa (pig). https://www.ncbi.nlm.nih.gov/genome?term=sus%20scrofa%20%5BOrganism%5D&cmd=DetailsSearch&report=OverviewNikolov IS, Gum B, Markov G, Kuehn R (2009) Population genetic structure of wild boar Sus scrofa in Bulgaria as revealed by microsatellite analysis. Acta Theriol (Warsz) 54:193–205Article 

Google Scholar 
Nykänen M, Rogan E, Foote AD, Kaschner K, Dabin W, Louis M et al. (2019) Postglacial colonization of northern coastal habitat by bottlenose dolphins: a marine leading-edge expansion? J Hered 110:662–674Article 

Google Scholar 
Palombo M, Romana AV-G (2003) Remarks on the biochronology of mammalian faunal complexes from the Pliocene to the Middle Pleistocene in France. Geol Rom: 145–163Paradis E, Claude J, Strimmer K (2004) APE: analysis of phylogenetics and evolution in R language. Bioinformatics 20:289–290Article 
CAS 

Google Scholar 
Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D et al (2007) PLINK: A tool Set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:559–575. www.cog-genomics.org/plink/1.9/Putman R, Apollonio M, Andersen R (2011) Ungulate management in Europe: problems and practices. Cambridge University Press, Cambridge, UKBook 

Google Scholar 
R Core Team (2018) R: A language and environment for statistical computing. Vienna, AustriaRejduch B, Sota E, Ró M, Ko M (2003) Chromosome number polymorphism in a litter of European wild boar (Sus scrofa scrofa L.). Anim Sci Pap Rep. 21:57–62
Google Scholar 
Scandura M, Iacolina L, Apollonio M (2011a) Genetic diversity in the European wild boar Sus scrofa: phylogeography, population structure and wild x domestic hybridization: Genetic variation in European wild boar. Mamm Rev 41:125–137Article 

Google Scholar 
Scandura M, Iacolina L, Cossu A, Apollonio M (2011b) Effects of human perturbation on the genetic make-up of an island population: The case of the Sardinian wild boar. Heredity 106:1012–1020Article 
CAS 

Google Scholar 
Scandura M, Iacolina L, Crestanello B, Pecchioli E, Di Benedetto MF, Russo V et al. (2008) Ancient vs. recent processes as factors shaping the genetic variation of the European wild boar: Are the effects of the last glaciation still detectable? Mol Ecol 17:1745–1762Article 
CAS 

Google Scholar 
Scandura M, Fabbri G, Caniglia R, Iacolina L, Mattucci F, Mengoni C, Pante G, Apollonio M, Mucci N (2022) Resilience to Historical Human Manipulations in the Genomic Variation of Italian Wild Boar Populations. Front Ecol Evol 10:833081Article 

Google Scholar 
Schmitt T, Varga Z (2012) Extra-Mediterranean refugia: the rule and not the exception. Front Zool 9:22Article 

Google Scholar 
Sommer RS, Fahlke JM, Schmölcke U, Benecke N, Zachos FE (2009) Quaternary history of the European roe deer Capreolus capreolus. Mamm Rev 39:1–16Article 

Google Scholar 
Sommer RS, Nadachowski A (2006) Glacial refugia of mammals in Europe: evidence from fossil records. Mamm Rev 36:251–265Article 

Google Scholar 
Sommer RS, Zachos FE (2009) Fossil evidence and phylogeography of temperate species: ‘glacial refugia’ and post-glacial recolonization. J Biogeogr 36:2013–2020Article 

Google Scholar 
Sommer RS, Zachos FE, Street M, Jöris O, Skog A, Benecke N (2008) Late Quaternary distribution dynamics and phylogeography of the red deer (Cervus elaphus) in Europe. Quat Sci Rev 27:714–733Article 

Google Scholar 
Stillfried M, Fickel J, Börner K, Wittstatt U, Heddergott M, Ortmann S et al. (2017) Do cities represent sources, sinks or isolated islands for urban wild boar population structure? J Appl Ecol 54:272–281Article 

Google Scholar 
Taberlet P, Fumagalli L, Wust-Saucy AG, Cossons JF (1998) Comparative phylogeography and post-glacial colonization routes in Europe. Mol Ecol 7:453–461.Article 
CAS 

Google Scholar 
Veličković N, Djan M, Ferreira E, Stergar M, Obreht D, Maletić V et al. (2015) From north to south and back: the role of the Balkans and other southern peninsulas in the recolonization of Europe by wild boar. J Biogeogr 42:716–728Article 

Google Scholar 
Veličković N, Ferreira E, Djan M, Ernst M, Obreht Vidaković D, Monaco A et al. (2016) Demographic history, current expansion and future management challenges of wild boar populations in the Balkans and Europe. Heredity 117:348–357Article 

Google Scholar 
Vernesi C, Crestanello B, Pecchioli E, Tartari D, Caramelli D, Hauffe H et al. (2003) The genetic impact of demographic decline and reintroduction in the wild boar (Sus scrofa): A microsatellite analysis. Mol Ecol 12:585–595Article 
CAS 

Google Scholar 
Vilaça ST, Biosa D, Zachos F, Iacolina L, Kirschning J, Alves PC et al. (2014) Mitochondrial phylogeography of the European wild boar: The effect of climate on genetic diversity and spatial lineage sorting across Europe. J Biogeogr 41:987–998Article 

Google Scholar 
Zachos FE, Frantz AC, Kuehn R, Bertouille S, Colyn M, Niedziałkowska M et al. (2016) Genetic structure and effective population sizes in European red deer (Cervus elaphus) at a continental scale: insights from microsatellite DNA. J Hered 107:318–326 More

Laundré, J. W., Hernández, L. & Altendorf, K. B. Wolves, elk, and bison: reestablishing the “landscape of fear” in Yellowstone National Park, U.S.A.. Can. J. Zool. 79, 1401–1409 (2001).Article 

Google Scholar 
Laundré, J. W., Hernandez, L. & Ripple, W. J. The landscape of fear: Ecological implications of being afraid. Open Ecol. J. 3, 1–7 (2010).Article 

Google Scholar 
Suraci, J. P., Clinchy, M., Zanette, L. Y. & Wilmers, C. C. Fear of humans as apex predators has landscape-scale impacts from mountain lions to mice. Ecol. Lett. 22, 1578–1586 (2019).Article 

Google Scholar 
Miller, S. G., Knight, R. L. & Miller, C. K. Wildlife responses to pedestrians and dogs. Wildl. Soc. B. 29, 124–132 (2001).
Google Scholar 
Larson, C., Reed, S., Merenlender, A. M. & Crooks, K. R. Effects of recreation on animals revealed as widespread through a global systemic review. PLoS ONE 11, 1–21 (2016).Article 

Google Scholar 
Balmford, A. et al. Walk on the wild side: Estimating the global magnitude of visits to protected areas. PLoS Biol 13, 1–6 (2015).Article 

Google Scholar 
Baker, A. D. & Leberg, P. L. Impacts of human recreation on carnivores in protected areas. PLoS Biol 13, 1–21 (2018).
Google Scholar 
Schulze, K. et al. An assessment of threats to terrestrial protected areas. Cons. Lett. 11, 1–10 (2018).Article 

Google Scholar 
Suraci, J. P. et al. Disturbance type and species life history predict mammal responses to humans. Glob. Change Biol. 27, 3718–3731 (2021).Article 
CAS 

Google Scholar 
Reilly, M. L., Tobler, M. W., Sonderegger, D. L. & Beier, P. Spatial and temporal response of wildlife to recreational activities in the San Francisco Bay ecoregion. Biol. Conserv. 207, 117–126 (2017).Article 

Google Scholar 
Naidoo, R. & Burton, A. C. Relative effects of recreational activities on a temperate terrestrial wildlife assemblage. Conserv. Sci. Pract. 2, e271 (2020).
Google Scholar 
Nickel, B. A., Suraci, J. P., Allen, M. L. & Wilmers, C. C. Human presence and human footprint have non-equivalent effects on wildlife spatiotemporal habitat use. Biol. Conserv. 241, 1–11 (2020).Article 

Google Scholar 
Blanchet, F. G., Cazelles, K. & Gravel, D. Co-occurrence is not evidence of ecological interactions. Ecol. Lett. 23, 1050–1063 (2020).Article 

Google Scholar 
Poggiato, G. et al. On the interpretations of joint modeling in community ecology. TREE 36, 391–401 (2021).
Google Scholar 
Bates, A. E., Primack, R. B., Moraga, P. & Duarte, C. M. COVID-19 pandemic and associated lockdown as a “Global Human Confinement Experiment” to investigate biodiversity conservation. Biol. Conserv. 248, 1–6 (2020).Article 

Google Scholar 
Rutz, C. et al. COVID-19 lockdown allows researchers to quantify the effects of human activity on wildlife. Nat. Ecol. Evol. 4, 1156–1159 (2020).Article 

Google Scholar 
Wang, Y., Allen, M. L. & Wilmers, C. C. Mesopredator spatial and temporal responses to large predators and human development in the Santa Cruz Mountains of California. Biol. Conserv. 190, 23–33 (2015).Article 

Google Scholar 
Lewis, J. S. et al. Human activity influences wildlife populations and activity patterns: implications for spatial and temporal refuges. Ecosphere 12, 1–16 (2021).Article 

Google Scholar 
Corradini, A. et al. Effects of cumulated outdoor activity on wildlife habitat use. Biol. Conserv. 253, 108818 (2021).Article 

Google Scholar 
Soule, M. E. et al. Dynamics of rapid extinctions of chaparral-requiring birds in urban habitat islands. Conserv. Biol. 2, 75–92 (1988).Article 

Google Scholar 
Feit, B., Feit, A. & Letnic, M. Apex predators decouple population dynamics between mesopredators and their prey. Ecosystems 22, 1606–1617 (2019).Article 

Google Scholar 
Berger, J. Fear, human shields and the redistribution of prey and predators in protected areas. Biol. Lett. 3, 620–623 (2007).Article 

Google Scholar 
Sarmento, W., Biel, M. & Berger, J. Redistribution, human shields and loss of migratory behavior in the crown of the continent. Intermt. J. Sci. 22, 2016 (2016).
Google Scholar 
Gaynor, K. M., Hojnowski, C. E., Carter, N. H. & Brashares, J. S. The influence of human disturbance on wildlife nocturnality. Science 360, 1232–1235 (2018).Article 
ADS 
CAS 

Google Scholar 
Microsoft. AI for Earth camera trap image processing API (2020).Niedballa, J., Sollmann, R., Courtiol, A. & Wilting, A. camtrapR: an R package for efficient camera trap data management. Methods Ecol. Evol. 7, 1457–1462 (2016).Article 

Google Scholar 
MacKenzie, D. I., Nichols, J. D., Hines, J. E., Knutson, M. G. & Franklin, A. B. Estimating site occupancy, colonization, and local extinction when a species is detected imperfectly. Ecology 84, 2200–2207. https://doi.org/10.1890/02-3090 (2003).Article 

Google Scholar 
MacKenzie, D. I. et al. Occupancy estimation and modeling (Elsevier, 2018).
Google Scholar 
Fiske, I. & Chandler, R. unmarked: An R package for fitting hierarchical models of wildlife occurrence and abundance. J. Stat. Soft. 4, 1–23 (2011).
Google Scholar 
Mazerolle, M. J. AICcmodavg: Model selection and multimodel inference based on (Q)AIC(c). R package version 2.3-1 (2020). https://cran.r-project.org/package=AICcmodavg.Ladle, A., Steenweg, R., Shepherd, B. & Boyce, M. S. The role of human outdoor recreation in shaping patterns of grizzly bear-black bear co-occurrence. PLoS ONE 13, 1–16 (2018).Article 

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 
Ridout, M. S. & Linkie, M. Estimating overlap of daily activity patterns from camera trap data. J. Agric. Biol. Environ. Stat. 14, 322–337 (2009).Article 
MathSciNet 
MATH 

Google Scholar 
Agostinelli, C. & Lund, U. R package ‘circular’: circular statistics (version 0.4-94.1 (2022). https://r-forge.r-project.org/projects/circular/.Santos, F. et al. Prey availability and temporal partitioning modulate felid coexistence in Neotropical forests. PLoS ONE 14, 1–23 (2019).Article 

Google Scholar 
Olea, P. P., Iglesias, N. & Mateo-Tomás, P. Temporal resource partitioning mediates vertebrate coexistence at carcasses: the role of competitive and facilitative interactions. Basic Appl. Ecol. 60, 63–75 (2022).Article 

Google Scholar 
Shilling, F. et al. A reprieve from US wildlife mortality on roads during the COVID-19 pandemic. Biol. Conserv. 256, 109013 (2021).Article 

Google Scholar 
Behera, A. K. et al. The impacts of COVID-19 lockdown on wildlife in Deccan Plateau. India. Sci. Total Environ. 822, 153268 (2022).Article 
ADS 
CAS 

Google Scholar 
Procko, M., Naidoo, R., LeMay, V. & Burton, A. C. Human impacts on mammals in and around a protected area before, during, and after COVID-19 lockdowns. Conserv. Sci. Pract. 4, e12743. https://doi.org/10.1111/csp2.12743 (2022).Article 

Google Scholar 
Sanderfoot, O. V., Kaufman, J. D. & Gardner, B. Drivers of avian habitat use and detection of backyard birds in the Pacific Northwest during COVID-19 pandemic lockdowns. Sci. Rep. 12, 12655 (2022).Article 
ADS 
CAS 

Google Scholar 
Nevin, J. A. & Grace, R. C. Behavioral momentum and the law of effect. Behav. Brain Sci. 23, 73–90 (2000).Article 
CAS 

Google Scholar 
Kautz, T. M. et al. Large carnivore response to human road use suggests a landscape of coexistence. Glob. Ecol. Conserv. 30, e01772 (2021).Article 

Google Scholar 
Frey, S., Volpe, J. P., Heim, N. A., Paczkowski, J. & Fisher, J. T. Move to nocturnality not a universal trend in carnivore species on disturbed landscapes. Oikos 129, 1128–1140 (2020).Article 

Google Scholar 
Schwartz, C. C. et al. Contrasting activity patterns of sympatric and allopatric black and grizzly bears. J. Wildl. Manag. 74, 1628–1638 (2010).Article 

Google Scholar 
Fortin, J. K. et al. Impacts of human recreation on brown bears (Ursus arctos): a review and new management tool. PLoS ONE 11, 1–26 (2016).Article 

Google Scholar 
Kendall, K. C. et al. Grizzly bear density in Glacier National Park. Montana. J. Wildl. Manag. 72, 1693–1705 (2008).Article 

Google Scholar 
Stetz, J. B., Kendall, K. C. & Macleod, A. C. Black bear density in Glacier National Park, Montana. Wildl. Soc. Bull. 38, 60–70 (2014).Article 

Google Scholar 
Sargeant, A. B. & Allen, S. H. Observed interactions between coyotes and red foxes. J. Mamm. 70, 631–633 (1989).Article 

Google Scholar 
Newsome, T. M. & Ripple, W. J. A continental scale trophic cascade from wolves through coyotes to foxes. J. Anim. Ecol. 84, 49–59 (2015).Article 

Google Scholar 
Naylor, L. M., Wisdom, M. J. & Anthony, R. G. Behavioral responses of North American elk to recreational activity. J. Wildl. Manag. 73, 328–338 (2009).Article 

Google Scholar 
Sarmento, W. M. & Berger, J. Human visitation limits the utility of protected areas as ecological baselines. Biol. Conserv. 212, 316–326 (2017).Article 

Google Scholar 
Garber, S. D. & Burger, J. A 20-year study documenting the relationship between turtle decline and human recreation. Ecol. Appl. 5, 1151–1162 (1995).Article 

Google Scholar 
Winter, P. L., Selin, S., Cerveny, L. & Bricker, K. Outdoor recreation, nature-based tourism, and sustainability. Sustainability 12, 1–12 (2020).
Google Scholar 
Geffroy, B., Samia, D. S. M., Bessa, E. & Blumstein, D. T. How nature-based tourism might increase prey vulnerability to predators. TREE 30, 755–765 (2015).
Google Scholar 
Eagles, P. F. J., McCool, S. F. & Haynes, C. D. Sustainable Tourism in Protected Areas: Guidelines for Planning and Management Vol. 8 (IUCN, 2002).Book 

Google Scholar  More

Sequence analysisProtein sequence searches were conducted in the GenBank nr database with BLASTP42 using default parameters, including automatically adjusting parameters for short input sequences (Table S1). Conserved domain searches were run against the GenBank Conserved Domain Database (CDD)43. Sequence alignments were conducted in CLC Main Workbench 20.0.4 (Qiagen) with the fast alignment algorithm, gap open cost = 10, and gap extension cost = 1. Biochemical properties of the fish putative thiaminase I protein sequences were predicted with the Create Sequence Statistics function in CLC Main Workbench 20.0.4 (Qiagen, Hilden, Germany). The molecular weights were calculated from the sum of the amino acids in the sequence, and the isoelectric points (pIs) were calculated from the pKa values for the individual amino acids in the sequence.Bacteria culturePure cultures of P. thiaminolyticus strain 818822 were cultured at 37 °C in Terrific Broth (MO BIO Laboratories, Carlsbad, CA) in either a shaking incubator or in a beveled flask with a stir bar and were harvested after 48–80 h of culture. Upon harvest, cultures were processed immediately or frozen whole in 50 mL Falcon tubes at − 80 °C. Fresh or thawed cultures were spun at 14,000×g, and culture supernatant was concentrated using Amicon-ultra 10 kDa molecular weight cut-off (MWCO) filters (EMD Millipore, Billerica, MA).The zebrafish and alewife candidate thiaminase I genes were cloned and overexpressed in E. coli to determine whether they produced functional thiaminases. The recombinant thiaminase I gene from P. thiaminolyticus was overexpressed in E. coli as a positive control. Candidate and control genes were synthesized (Integrated DNA Technologies, Inc., Coralville, Iowa) and placed into the pET52b vector (EMD Millipore). Insert sequences are provided in Supplementary Figs. S10–S13. The empty pET52b vector was used as a negative control. The plasmid was transformed into E. coli (Rosetta 2(DE3)pLysS Singles Competent Cells, EMD Millipore) according to the manufacturer’s instructions, and expression of candidate genes was induced by the addition of IPTG. Cells were lysed in 1X BugBuster (Millipore) according to the manufacturer’s instructions in the presence of benzonase nuclease, and soluble and insoluble fractions were separated by centrifugation.Tissue collectionsAdult common carp were captured from Lake Erie using short-set gill nets. Adult alewife and quagga mussels (Dreissena bugensis) were collected from Sturgeon Bay, Lake Michigan using bottom trawls. Fish collections were completed during July 2007. Sex of sampled fish was not identified. Upon collection, unanesthetized animals were immediately euthanized by flash freezing between slabs of dry ice and stored at − 80 °C. Fish were harvested by the Great Lakes Science Center, U.S. Geological Survey (USGS). Laboratory use of frozen animal tissues and wild type and recombinant bacteria was in accordance with institutional guidelines and biosafety procedures at Oregon State University and USGS. Animal care and use procedures were approved by the Great Lakes Science Center, USGS. All USGS sampling and handling of fish during research are carried out in accordance with guidelines for the care and use of fishes by the American Fisheries Society44. All methods are reported in accordance with applicable ARRIVE guidelines (https://arriveguidelines.org). Zebrafish from OSU’s zebrafish facility were anesthetized and euthanized by overdose with waterborne 200 ppm ethyl 3-aminobenzoate methanesulfonate (MS-222, Sigma-Aldrich, St. Louis, MO) following protocols approved by the OSU Animal Institutional Care and Use Committee and were frozen at − 80 °C after euthanization. Gills, liver, spleen, and the intestinal tract were dissected, and gill tissue was homogenized separately from liver, spleen, and gut, which were homogenized together and designated “viscera.” Homogenization and protein preparation procedures were the same as that for alewife. Zebrafish from Columbia Environmental Research Center (CERC), USGS cultures were anesthetized and euthanized by overdose with 200 ppm ethyl 3-aminobenzoate methanesulfonate (MS-222, Sigma-Aldrich, St. Louis, MO) in water following protocols approved by CERC Institutional Animal Care and Use Committee (IACUC). Whole fish (0.2–0.6 g) were homogenized in 10 mL cold phosphate buffer, pH 6.5. Whole common carp and alewife were thawed until they could just be dissected. Preliminary trial extractions on alewife stomach and intestines, spleen, and gills revealed similar results and revealed that gills and spleen tissue produced the cleanest protein preparations. Therefore, subsequent extractions for common carp and alewife used gill tissue. Samples were pooled from 3 to 5 individual fish, haphazardly chosen from the sampled fish without exclusions. Quagga mussels were thawed just sufficiently to be husked from their shell and were used whole. Researchers were aware of the species and tissue designation of each sample throughout the experiments. Animal tissues were placed in ice-cold (4 °C) beakers containing cold extraction buffer (16 mM K3HPO4, 84 mM KH2PO4, 100 mM NaCl, pH 6.5 with 1 mM DTT, 2 mM EDTA, 3 mM Pepstatin, 1X Protease inhibitor cocktail (Sigma), and 1 mM AEBSF). All extractions were carried out at 4 °C in pre-chilled glassware. Samples were mechanically homogenized using a rotor–stator tissue grinder. Samples were stirred gently for several hours to overnight at 4 °C, centrifuged at 14,000×g to remove debris, and strained through cheesecloth to remove any insoluble lipids. Extracts were then subjected to 30–75% ammonium sulfate precipitation. Pellets from the precipitation were resuspended in buffer (83 mM KH2PO4, 17 mM K2HPO4, and 100 mM NaCl), centrifuged to remove any remaining debris, and stored in 30% glycerol at − 20 °C.Protein electrophoresisNative PAGE was run using either pre-cast TGX gels (BioRad, Hercules, California) of varying percentage (7.5% to 12% or 8–16% gradient gels) or on hand-cast gels (TGX FastCast, BioRad) made according to the manufacturer’s instructions.Blue-native PAGE was used to estimate the mass of thiaminases in their native conformation. Blue-native PAGE45 gels were run using the NativePage Novex Bis–Tris system (Life Technologies) or hand-cast equivalents46. Light blue cathode buffer was used to facilitate visualization of the activity stain.Standard denaturing SDS-PAGE was used to estimate the molecular mass of thiaminases after denaturation. Denaturing SDS-PAGE was run using one of three relatively equivalent methods: pre-cast TGX gels (BioRad) according to the manufacturer’s instructions, hand-cast Tris–HCl gels using standard Laemmli chemistry47 with an operating pH of approximately 9.5, or hand-cast Bis–Tris gels (MOPS buffer) with an operating pH of approximately 7. For all denaturing and non-denaturing SDS-PAGE applications, standard Laemmli sample buffer was used, and samples were heated to 75 °C for 15 min to facilitate denaturation followed by brief centrifugation to eliminate any precipitated debris.Non-denaturing PAGE was used as an alternative to denaturing PAGE for the common carp thiaminase that could not be renatured (i.e., activity could not be recovered) following a denaturing SDS-PAGE. Non-denaturing PAGE was conducted using any of the three aforementioned gel chemistries with SDS-containing running buffers including reductant (DTT), but samples were not heated prior to application to the gel. Samples for non-denaturing PAGE were allowed to incubate in sample buffer at room temperature for 30 min prior to gel loading. This preserves the charge-shift induced by SDS but does not result in protein denaturation, facilitating in-gel analysis of thiaminase I activity after separation.To visualize proteins following electrophoresis, gels were stained with Coomassie stain (CBR-250 at 1 g/L in methanol/acetic acid/water (4:5:1) and destained with methanol/acetic acid/water (1.7:1:11.5). Mini-gels were run on BioRad’s mini-protean gel rigs. Midi-gels (16 cm length) were run on Hoefer’s SE660, and large-format gels (32 cm length) were run on a BioRad’s Protean Slab Cell. Mini-gels were generally run at room temperature, and midi- and large-format gels were run at 4 °C. Blue-native PAGE was always run at 4 °C.Two-dimensional electrophoresis (2DE) separated proteins in the first dimension based on pI and in the second dimension based on mass (either native or denatured). 2DE was performed by combining in-gel IEF with either denaturing SDS-PAGE, non-denaturing SDS-PAGE, or native PAGE. IPG strips were incubated in TRIS-buffered equilibration solution48 either with 6 M urea, SDS, and iodacetamide (denaturing) or without urea, SDS, and iodacetamide (non-denaturing) for 20 min. Low melting point agarose was used to solidify IGP strips in place. Agarose was cooled to just above the gelling temperature, as hot agarose inactivated thiaminase I activity.Isoelectric focusingIsoelectric focusing (IEF) was conducted both in-gel and in-liquid. In-gel IEF was conducted in immobilized pH gradient (IPG) strips using a Multifor II (GE Healthcare Life Sciences). Prior to rehydration, all protein preparations were desalted in low-salt (~ 5 to 10 mM) sodium or potassium phosphate buffer (pH 6.5) using 10 kDA MWCO filters. All samples were applied using sample volumes and protein concentrations recommended by the manufacturer. For standard denaturing in-gel IEF, rehydration solution consisted of 8 M urea, 2% CHAPS, 2% IPG buffer of the appropriate pH-range, 1% bromophenol blue, and 18 mM DTT. The IEF was conducted at maximum of 2 mA total current and 5 W total power, with an EPS3500 XL power supply in gradient mode. Voltage gradients were based on standard protocols recommended by the manufacturer. In-gel IEF was also performed under native conditions to allow thiaminase I activity staining of IPG strips. Protocols were essentially the same as those for denaturing conditions, with the following exceptions: (1) urea was eliminated and the CHAPS concentration was reduced to 0.5% in the rehydration solution; (2) rehydration was conducted at 14 °C; and (3) the water in the cooling tray was cooled to 4 °C.In-liquid IEF was conducted using a Rotofor (BioRad) according to the manufacturer’s instructions. Non-denaturing in-liquid IEF was also conducted using a focusing solution including no urea, 2% pH 3–10 biolyte, 0.5% CHAPS, 20% glycerol, and 5 mM DTT. The addition of glycerol helped retain activity but also increased focusing times. The Rotofor was run at a constant 15 W with a maximum current of 20 mA and voltage set for a maximum of 2000 V. Samples containing 8 M urea were cooled to 14 °C during focusing to avoid urea precipitation, whereas samples lacking urea were cooled to 4 °C during focusing. Protein extracts in salt solutions greater than 10 mM were desalted directly in focusing solution using a 10 kDA MWCO filter. Focusing runs were allowed to proceed until the voltage stabilized and fractions were harvested with the needle array and vacuum pump. Ampholytes were removed by addition of NaCl to 1 M and then samples were desalted into phosphate buffer using a 10kD MWCO filter.Thiaminase I activity measurementsFor quantitative measurements of thiaminase I activity, we conducted a radiometric assay at CERC as previously described49. Zebrafish homogenates were diluted 1:8, 1:16, or 1:32 in cold phosphate buffer, pH 6.5. Two replicates per dilution were assayed. Activity was calculated from the greatest dilution that gave activity within the linear range of the assay and was reported as pmol thiamine consumed per g tissue (wet weight) per minute (pmol/g/min).Thiaminase I activity stainingAfter electrophoresis, gels were stained for thiaminase I activity using a previously described diazo-coupling reaction19,50. Briefly, gels were washed 3 times in water, twice in 25 mM sodium phosphate buffer with 1 mM DTT, and once in 25 mM sodium phosphate buffer without DTT. Gels were then incubated in 0.89 mM thiamine-HCl and co-substrate (1.45 mM pyridoxine, 24 mM nicotinic acid, or 20 mM pyridine) in 25 mM sodium phosphate buffer for 10 min. Gels were briefly rinsed in water and placed in a lidded container and incubated at 37 °C for 30 min to allow thiamine degradation by any thiaminases in the gel. The diazo stain19,50 was then applied to detect remaining thiamine in the gel for five minutes with gentle agitation. Stained gels were rinsed with water and photographed, and further stained with Coomassie to visualize proteins. More

Crowther, T. W. et al. Mapping tree density at a global scale. Nature 525, 201–205 (2015).Article 
CAS 

Google Scholar 
Asner, G. P. et al. A universal airborne LiDAR approach for tropical forest carbon mapping. Oecologia 168, 1147–1160 (2012).Article 

Google Scholar 
Walker, A. P. et al. Predicting long‐term carbon sequestration in response to CO2 enrichment: How and why do current ecosystem models differ? Glob. Biogeochem. Cy. 29, 476–495 (2015).Article 
CAS 

Google Scholar 
Madrigal-González, J. et al. Climate reverses directionality in the richness–abundance relationship across the World’s main forest biomes. Nat. Commun. 11, 1–7 (2020).Article 

Google Scholar 
Stephenson, N. L. Climatic control of vegetation distribution: the role of the water balance. Am. Nat. 135, 649–670 (1990).Article 

Google Scholar 
Weiskittel, A. R., Maguire, D. A., Monserud, R. A. Development of a hybrid model for intensively managed Douglas-fir in the Pacific Northwest. In Forest Growth and Timber Quality, 49 (USDA, Portland, 2009).Paoli, G. D., Curran, L. M. & Slik, J. W. F. Soil nutrients affect spatial patterns of aboveground biomass and emergent tree density in southwestern Borneo. Oecologia 155, 287–299 (2008).Article 

Google Scholar 
Yoda, K., Kira, T., Ogawa, H. & Hozami, K. Self-thinning in overcrowded pure stands under cultivated and natural conditions. J. Biol. Osaka City Univ. 14, 107–129 (1963).
Google Scholar 
Westoby, M. The self-thinning rule. Adv. Ecol. Res. 14, 167–225 (1984).Article 

Google Scholar 
Weiner, J. & Freckleton, R. P. Constant final yield. Annu. Rev. Ecol. Evol. S. 41, 173–192 (2010).Article 

Google Scholar 
Pillet, M. et al. Disentangling competitive vs. climatic drivers of tropical forest mortality. J. Ecol. 106, 1165–1179 (2018).Article 

Google Scholar 
Schluter, D. Experimental evidence that competition promotes divergence in adaptive radiation. Science 266, 798–801 (1994).Article 
CAS 

Google Scholar 
Pacala, S.W. & Levin, S.A. Biologically generated spatial pattern and the coexistence of competing species. Spatial Ecology: The Role of Space in Population Dynamics and Interspecific Interactions. (Princeton University Press, Princeton, NJ, p. 204-232, 1997).Asefa, M., Cao, M., Zhang, G., Ci, X. & Li, J. Yang Environmental filtering structures tree functional traits combination and lineages across space in tropical tree assemblages. Sci. Rep. 7, 1–10 (2017).Article 
CAS 

Google Scholar 
Pretzsch, H. & Biber, P. Tree species mixing can increase maximum stand density. Can. J. For. Res. 46, 1179–1193 (2016).Article 

Google Scholar 
Escudero, A. et al. Every bit helps: The functional role of individuals in assembling any plant community, from the richest to monospecific ones. J. Veg. Sci. 32, e13059 (2021).Article 

Google Scholar 
Jump, A. S. et al. Structural overshoot of tree growth with climate variability and the global spectrum of drought-induced forest dieback. Glob. Chang. Biol. 23, 3742–3757 (2017).Article 

Google Scholar 
M. Takyu, Y. Kubota, S.I. Aiba, T. Seino, T. Nishimura. Pattern of changes in species diversity, structure and dynamics of forest ecosystems along latitudinal gradients in East Asia. In Forest Ecosystems and Environments (Springer, Tokyo, 2005), pp. 49–58.Rivoire, M. & Le, G. A. Moguedec, generalized self-thinning relationship for multi-species and mixed-size forests. Ann. Sci. 69, 207–219 (2012).Article 

Google Scholar 
Salas‐Eljatib, C. & Weiskittel, A. R. Evaluation of modelling strategies for assessing self‐thinning behaviour and carrying capacity. Ecol. Evol. 8, 10768–10779 (2018).Article 

Google Scholar 
Schietti, J. et al. Forest structure along a 600 km transect of natural disturbances and seasonality gradients in central‐southern Amazonia. J. Ecol. 104, 1335–1346 (2016).Article 

Google Scholar 
Vanclay, J. K. & Sands, P. J. Calibrating the self-thinning frontier. For. Ecol. Manag. 259, 81–85 (2009).Article 

Google Scholar 
Sapijanskas, J., Paquette, A., Potvin, C., Kunert, N. & Loreau, M. Tropical tree diversity enhances light capture through crown plasticity and spatial and temporal niche differences. Ecology 95, 2479–2492 (2014).Article 

Google Scholar 
Lieth, H. Modeling the primary productivity of the world. In H. Lieth & R. H. Whittaker, eds. Primary Productivity of the Biosphere (Springer-Verlag, New York, New York, USA, 1975), pp. 237–264.Grieser, J., Gommes, R., Cofield, S., Bernardi, M. World Maps of Climatological net Primary Production of Biomass, NPP. Food and Agriculture Organization of the United Nations. (GEONETWORK. FAO, Rome, Italy, 2006).Hengl, T. et al. SoilGrids250m: global gridded soil information based on machine learning. PLoS One 12, e0169748 (2017).Article 

Google Scholar 
Wood, S. N. Stable and efficient multiple smoothing parameter estimation for generalized additive models. J. Am. Stat. Assoc. 99, 673–686 (2004).Article 

Google Scholar 
J.B. Grace. Structural Equation Modeling and Natural Systems. (Cambridge University Press, Cambridge, 2006).Aho, K., Derryberry, D. & Peterson, T. Model selection for ecologists: the worldviews of AIC and BIC. Ecology 95, 631–636 (2014).Article 

Google Scholar 
R Core Team (2021). R: A language and environment for statistical computing. (R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/).Wood, S., Scheipl, F. & Wood, M. S. Package ‘gamm4’. Am. Stat. 45, 339 (2017).
Google Scholar 
Lefcheck, J. S. piecewiseSEM: Piecewise structural equation modelling in r for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2016).Article 

Google Scholar 
Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. R. Core Team nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1-148 (2020).Latham J, Cumani R, Rosati I, Bloise M. FAO Global Land Cover (GLC-SHARE) Database Beta-Release 1.0, Land and Water Division. 2014. http://www.fao.org/uploads/media/glc-share-doc.pdf. More

Study areaWe focused on hiking activity in the four main islands of Japan (Honshu, Hokkaido, Kyushu, and Shikoku) and nearby small islands connected to the main islands by a bridge (Fig. 1a). These islands lie between latitudes 31.0° and 45.5°N, and the total area is 361,000 km2. The islands are generally mountainous and tallest mountains in central Honshu exceed 3000 m a.s.l. (Fig. 1c). In Tokyo, mean monthly temperatures range between 5.2 °C in January and 26.4 °C in August, while they range between − 18.4 °C in January and 6.2 °C in August at the summit of the highest mountain, Mt. Fuji (3776 m a.s.l., Japan Meteorological Agency). In northern Honshu and Hokkaido, snow depth can exceed 1 m even at low elevations and high mountains are covered with snow even in southern Japan.Vegetation excluding farmland and pasture covers 70.9% of the study area and the 93.9% is forest. Plantations of mostly evergreen conifers such as Japanese cedar (Cryptomeria japonica) occupy 37.6% of the vegetation area (National Surveys on the Natural Environment by the Biodiversity Center of Japan 2nd–7th; http://www.biodic.go.jp/trialSystem/top_en.html). Secondary vegetation after past human disturbances occupies 39.4% of the total vegetation and the remaining 23.0% is primary vegetation. The typical primary vegetation types are, from north to south, boreal mixed forest, deciduous broad leaved forest, and evergreen broad leaved forest.Grid squaresRecords of hiking activity were summarized for 4244 secondary grid squares based on Standard Grid Square System, which was defined by the Minister’s Order of Administrative Management Agency in 1973. In the system, the secondary grid was defined as a grid of 5′ in latitude and 7′ 30″ in longitude, which roughly corresponds to a 10 km grid in the study area. This is the standard grid system of the government and we adopted the system for convenience in future application uses and communication with practitioners. The grids, which are defined by latitude and longitude, are different in the area up to 22% between the north and south ends. Therefore, area of each grid was included in a model as an offset term.Hiking activityAccording to a government survey in 2016, (the Survey on Time Use and Leisure Activities by the Statistics Bureau of Japan, http://www.stat.go.jp/english/data/shakai/index.htm), 10.0% (about 10.7 million people) of Japan’s population age 15 or over enjoyed hiking/mountaineering in the last year. The census showed also that hiking is more popular among urban residents in the metropolitan areas. Both multi-day expedition to high mountains and day trek to low mountains in suburban areas are popular. Because of the severe winter climate, unskilled hikers use the high mountains in summer and early autumn only. During a summer vacation, whose peak time in Japan is August, many hikers enjoy multi-day trips to distant mountains. Spring and autumn are also popular seasons because of the mild weather and the scenic beauty of the fresh green or autumn colors.Data collectionIn this study, we used number of hiking records accumulated on the most popular social networking service for hikers in Japan (Yamareco; https://www.yamareco.com) as a surrogate for flow of recreation service. For all the registered destinations in the study area, the number of hiking records for each month and the latitude and longitude of the destination were collected from the service in September 2016 with the rvest28 package in R software29. This service launched in October 2005 hosts records of the hiking route, photos, participants, and impressions of a hiking trip and facilitates communication among users. Although monthly number of records for each destination is always available on the site, the exact date of each hiking record is not always public information for privacy reasons; therefore, all of the records from the almost 11 years since the start of the service were lumped together in our analysis. Hikers may record multiple places in a single trip, so the total number of records must be larger than the number of unique trips. Users of the service sometime record a place that is not a destination, e.g. start points and stations of trails, parking areas, stations of transports, and bus stops. Such records were excluded before analyses as far as it can be judged from the name of the place. As a result, the total number of hiking records was 4,708,229 records for 16,179 destinations. Finally, these records were assigned to the 4244 grids based on the latitude and longitude of each destination and then total number of records for each grid was used as a surrogate of the recreation service flow in our analysis. Not only total number but also monthly number was used in our analysis to examine seasonal changes in associations between the service and vegetation. Total record number of the grids was strongly right-skewed; no record (handled as 0 in our analysis) was found in 2036 grids while mean and maximum record number were 1109 and 350,384, respectively.Explanation variablesFifty ecological, environmental, and social/infrastructural variables (Table S1) were prepared for each grid by using ArcGIS version 10.5 (ESRI, Redlands, CA, USA). For vegetation and land-use attributes, National Surveys on the Natural Environment by the Biodiversity Center of Japan (2nd–7th; http://www.biodic.go.jp/trialSystem/top_en.html) and National Land Numerical Information (http://nlftp.mlit.go.jp/ksj-e/index.html) were used. The proportion of sea, that of total vegetation cover (excluding agricultural land and pasture) to land area, that of agricultural land (including pasture) to land area, that of natural vegetation (vegetation excluding plantations) to total vegetated area, and that of primary vegetation (vegetation with no record or evidence of a disturbance) to natural vegetation were summarized at four spatial scales: a radius of 10 km, 20 km, 50 km, and 100 km from the center of each grid. Spatial patterns of the three vegetation variables in 10 km radius were summarized in Fig. 1d–f.Maximum elevation, minimum elevation, and ruggedness (index of topographic heterogeneity30) were summarized at the four spatial scales based on a digital elevation model (10-m resolution) provided by the Geospatial Information Authority of Japan (https://fgd.gsi.go.jp/download/menu.php). For climatic variables (annual and monthly mean temperature, annual and monthly precipitation, annual and monthly hours of sunshine, and annual maximum snow depth), the National Land Numeric Information provided by the Ministry of Land, Infrastructure, Transport and Tourism of Japan (http://nlftp.mlit.go.jp/ksj-e/index.html) was referenced. Densities of population and roads at the four spatial scales were prepared from population census data from the Statistics Bureau of Japan (http://e-stat.go.jp/SG2/eStatGIS/page/download.html) and the National Land Numeric Information. For calculation of these densities, the sea surface was excluded. In addition, latitude and longitude of center of each grid were also used as explanatory variables to average effects of spatial coordinates.Statistical analysisIn this study, we employed BRT, a machine-learning method based on regression trees31 for modeling the complex relationship between a CES flow and landscape attributes12. BRT is an ensemble learning method where multiple regression trees are sequentially combined to minimize the loss function by means of gradient descent. This technique has advantage in the development of a model with a high predictive performance, in which high-dimensional interactions among explanatory variables and nonlinear responses are fully accounted for. In ecology, BRT has been frequently used for modeling of a species distribution32.Total and monthly numbers of hiking records were modeled as a function of the 50 variables described above under the assumption of a Poisson response. For temperature, precipitation, and hours of sunshine, annual and monthly average were used for the analysis of total and monthly records, respectively. In modeling by BRT, parameters for building of each learner and assembly of the learners must be carefully chosen to maximize generalization ability of a model31. In our case, candidate parameters were 2, 5, and 10 for the maximum depth of variable interactions for each learner; 2, 5, 10, and 20 for the minimum number of observations in the terminal nodes for each learner; 0.5 and 0.75 for the proportion of training data used for building each learner; and 1000, 2000, 4000, 6000, 8000 and 10,000 for the total number of learners (Table S2). In the model assembling process, the value of 0.01 was used as a shrinkage parameter. Ten-fold cross validation was used to obtain the best suites of parameters. R2 based on sum of squares:$${R}^{2}=1-frac{{sum ({y}_{i}-widehat{{y}_{i}})}^{2}}{{sum ({y}_{i}-overline{{y }_{i}})}^{2}}$$
was used for evaluation of the model’s prediction performance. The importance of explanatory variables was evaluated as an increase of mean absolute error after 100-times permutation of a variable33.Effects of each explanatory variable (a landscape attribute) on the response variable (record number) and the context dependence were visually inspected by individual conditional expectation (ICE) plot34. ICE plot visualizes the effect of a given explanatory variable for each observation by connecting outcome of a model for shifting values of the focal explanatory variable throughout the range while keeping other explanatory variables as the original value. Predictions were performed in log-scale and each line was centered to be zero at the left end of the x-axis to show relative effects of explanatory variables (c-ICE plot sensu Goltstein et al.34). Each line in ICE plot can be colored based on value of the second explanatory variable to assist assessment of the interactive effects of the two predictors. Friedman’s H statistic35 was used to detect explanatory variables whose interaction with the vegetation variables are important and therefore should be used for color-coding of an ICE plot. Friedman’s H is defined as a proportion of variance of partial dependence estimates explained by interactive effects for arbitrary suites of explanatory variables.Then, expected impacts of 0.1 decrease in the three local vegetation variables were assessed by the trained model and mapped. Although vegetation variables were sometimes more important at larger spatial scales (see “Results”), we focused on vegetation at a local (10 km radius) scale because most changes in vegetation occur at the scale in Japan (National Surveys on the Natural Environment by the Biodiversity Center of Japan, https://www.biodic.go.jp/kiso/fnd_list_h.html).All statistical analyses were performed using the R software packag29. The gbm36 package was used for BRT, the iml37 package was used for calculation of Friedman’s H statistic, and the cv.models (Oguro, https://github.com/Marchen/cv.models) packages was used for cross validation and parameter tuning. More

Microbial community composition in cave sedimentsThe ITS1 dataset generated 5,793,980 raw sequence reads, resulting in 5,458,895 gene quality-filtered reads, ranging from 1252 up to 540,803 per sample. After singletons and rare taxa ( More

Alphan, H. Land use change and urbanisation of Adana, Turkey. Land Degrad. Dev. 14, 575–586 (2003).Article 

Google Scholar 
Catalàn, B., Sauri, D. & Serra, P. Urban sprawl in the Mediterranean? Patterns of growth and change in the Barcelona Metropolitan Region 1993–2000. Landsc. Urban Plan. 85(3–4), 174–184 (2008).
Google Scholar 
Chen, K., Long, H., Liao, L., Tu, S. & Li, T. Land use transitions and urban-rural integrated development: Theoretical framework and China’s evidence. Land Use Policy 92, 104465 (2020).Article 

Google Scholar 
Bianchini, L. et al. Forest transition and metropolitan transformations in developed countries: Interpreting apparent and latent dynamics with local regression models. Land 11(1), 12 (2021).Article 

Google Scholar 
Angel, S., Parent, J., Civco, D. L., Blei, A. & Potere, D. The dimensions of global urban expansion: Estimates and projections for all countries, 2000–2050. Prog. Plan. 75(2), 53–107 (2011).Article 

Google Scholar 
Fischer, A. P. Forest landscapes as social-ecological systems and implications for management. Landsc. Urban Plan. 177, 138–147 (2018).Article 

Google Scholar 
Darvishi, A., Yousefi, M. & Marull, J. Modelling landscape ecological assessments of land use and cover change scenarios. Application to the Bojnourd Metropolitan Area (NE Iran). Land Use Policy 99, 105098 (2020).Article 

Google Scholar 
Cheng, L. L., Tian, C. & Yin, T. T. Identifying driving factors of urban land expansion using Google earth engine and machine-learning approaches in Mentougou District, China. Sci. Rep. 12(1), 1–13 (2022).Article 
CAS 

Google Scholar 
Kasanko, M. et al. Are European Cities becoming dispersed? A comparative analysis of fifteen European urban areas. Landsc. Urban Plan. 77(1–2), 111–130 (2006).Article 

Google Scholar 
Terzi, F. & Bolen, F. Urban sprawl measurement of Istanbul. Eur. Plan. Stud. 17(10), 1559–1570 (2009).Article 

Google Scholar 
Angel, S., Parent, J. & Civco, D. L. Ten compactness properties of circles: measuring shape in geography. Can. Geogr. 54, 441–461 (2010).Article 

Google Scholar 
Salvati, L., Gemmiti, R. & Perini, L. Land degradation in Mediterranean urban areas: An unexplored link with planning?. Area 44(3), 317–325 (2012).Article 

Google Scholar 
Attorre, F., Bruno, M., Francesconi, F., Valenti, R. & Bruno, F. Landscape changes of Rome through tree-lined roads. Landsc. Urban Plan. 49, 115–128 (2000).Article 

Google Scholar 
Turok, I. & Mykhnenko, V. The trajectories of European cities, 1960–2005. Cities 24(3), 165–182 (2007).Article 

Google Scholar 
Ioannidis, C., Psaltis, C. & Potsiou, C. Towards a strategy for control of suburban informal buildings through automatic change detection. Comput. Environ. Urban Syst. 33, 64–74 (2009).Article 

Google Scholar 
Grekousis, G., Manetos, P. & Photis, Y. N. Modeling urban evolution using neural networks, fuzzy logic and GIS: The case of the athens metropolitan area. Cities 30, 193–203 (2013).Article 

Google Scholar 
Salvati, L. Towards a polycentric region? The socioeconomic trajectory of Rome, an ‘Eternally Mediterranean’ city. Tijdschr. Econ. Soc. Geogr. 105(3), 268–284 (2014).Article 

Google Scholar 
Chorianopoulos, I., Pagonis, T., Koukoulas, S. & Drymoniti, S. Planning, competitiveness and sprawl in the Mediterranean city: The case of Athens. Cities 27, 249–259 (2010).Article 

Google Scholar 
Munafò, M., Salvati, L. & Zitti, M. Estimating soil sealing rate at national level—Italy as a case study. Ecol. Ind. 26, 137–140 (2013).Article 

Google Scholar 
Morelli, V. G., Rontos, K. & Salvati, L. Between suburbanisation and re-urbanisation: Revisiting the urban life cycle in a Mediterranean compact city. Urban Res. Pract. 7(1), 74–88 (2014).Article 

Google Scholar 
Basem Ajjur, S. & Al-Ghamdi, S. G. Exploring urban growth–climate change–flood risk nexus in fast growing cities. Sci. Rep. 12, 12265 (2022).Article 
ADS 

Google Scholar 
Li, H. & Wu, J. Use and misuse of landscape indices. Landsc. Ecol. 19, 389–399 (2004).Article 

Google Scholar 
Salvati, L. Agro-forest landscape and the ‘fringe’city: A multivariate assessment of land-use changes in a sprawling region and implications for planning. Sci. Total Environ. 490, 715–723 (2014).Article 
ADS 
CAS 

Google Scholar 
Sang, X. et al. Intensity and stationarity analysis of land use change based on CART algorithm. Sci. Rep. 9(1), 1–12 (2019).Article 
ADS 
CAS 

Google Scholar 
Ettehadi Osgouei, P., Sertel, E. & Kabadayı, M. E. Integrated usage of historical geospatial data and modern satellite images reveal long-term land use/cover changes in Bursa/Turkey, 1858–2020. Sci. Rep. 12(1), 1–17 (2022).Article 

Google Scholar 
He, S., Yu, S., Li, G. & Zhang, J. Exploring the influence of urban form on land-use efficiency from a spatiotemporal heterogeneity perspective: Evidence from 336 Chinese cities. Land Use Policy 95, 104576 (2020).Article 

Google Scholar 
Bockarjova, M., Wouter Botzen, W. J., Bulkeley, H. A. & Toxopeus, H. Estimating the social value of nature-based solutions in European cities. Sci. Rep. 12, 19833 (2022).Article 
ADS 
CAS 

Google Scholar 
Liu, J. & Niyogi, D. Meta-analysis of urbanisation impact on rainfall modification. Sci. Rep. 9(1), 1–14 (2019).ADS 

Google Scholar 
Holland, J. H. Studying complex adaptive systems. J. Syst. Sci. Complex. 19(1), 1–8 (2006).Article 
MathSciNet 
MATH 

Google Scholar 
Salvati, L. & Serra, P. Estimating rapidity of change in complex urban systems: A multidimensional, local-scale approach. Geogr. Anal. 48(2), 132–156 (2016).Article 

Google Scholar 
Bura, S., Guerin-Pace, F., Mathian, H., Pumain, D. & Sanders, L. Multi-agents systems and the dynamics of a settlement system. Geogr. Anal. 28(2), 161–178 (1996).Article 

Google Scholar 
Hasse, J. E. & Lathrop, R. G. Land resource impact indicators of urban sprawl. Appl. Geogr. 23, 159–175 (2003).Article 

Google Scholar 
Grafius, D. R., Corstanje, R. & Harris, J. A. Linking ecosystem services, urban form and green space configuration using multivariate landscape metric analysis. Landsc. Ecol. 33(4), 557–573 (2018).Article 

Google Scholar 
Pumain, D. Hierarchy in Natural and Social Sciences (Kluwer-Springer, 2005).
Google Scholar 
Cabral, P., Augusto, G., Tewolde, M. & Araya, Y. Entropy in urban systems. Entropy 15(12), 5223–5236 (2013).Article 
ADS 

Google Scholar 
Salvati, L. & Carlucci, M. In-between stability and subtle changes: Urban growth, population structure, and the city life cycle in Rome. Popul. Space Place 22(3), 216–227 (2016).Article 

Google Scholar 
Batty, M. & Longley, P. Fractal Cities (Academic Press, 1994).MATH 

Google Scholar 
Berry, B. J. L. Cities as systems within systems of cities. Pap. Reg. Sci. 13, 147–163 (2005).Article 

Google Scholar 
Petrosillo, I. et al. The resilient recurrent behavior of mediterranean semi-arid complex adaptive landscapes. Land 10(3), 296 (2021).Article 

Google Scholar 
Portugali, J. Complexity, Cognition and the City, Understanding Complex Systems (Springer, 2011).Book 

Google Scholar 
Wu, J., Jenerette, G. D., Buyantuyev, A. & Redman, C. L. Quantifying spatiotemporal patterns of urbanisation: The case of the two fastest growing metropolitan regions in the United States. Ecol. Complex. 8(1), 1–8 (2011).Article 

Google Scholar 
Sun, Y., Gao, C., Li, J., Li, W. & Ma, R. Examining urban thermal environment dynamics and relations to biophysical composition and configuration and socioeconomic factors: A case study of the Shanghai metropolitan region. Sustain. Cities Soc. 40, 284–295 (2018).Article 

Google Scholar 
Phillips, M. A. & Ritala, P. A complex adaptive systems agenda for ecosystem research methodology. Technol. Forecast. Soc. Change 148, 119739 (2019).Article 

Google Scholar 
Walker, B., Holling, C. S., Carpenter, S. R. & Kinzig, A. Resilience, adaptability and transformability in social-ecological systems. Ecol. Soc. 9(2), 5 (2004).Article 

Google Scholar 
Kelly, C. et al. Community resilience and land degradation in forest and shrublandsocio-ecological systems: A case study in Gorgoglione, Basilicata regionn, Italy. Land Use Policy 46, 11–20 (2015).Article 

Google Scholar 
Preiser, R., Biggs, R., De Vos, A. & Folke, C. Social-ecological systems as complex adaptive systems. Ecol. Soc. 23(4), 46 (2018).Article 

Google Scholar 
Ferrara, A. et al. Shaping the role of ‘fast’ and ‘slow’ drivers of change in forest-shrubland socio-ecological systems. J. Environ. Manag. 169, 155–166 (2016).Article 

Google Scholar 
Lamy, T., Liss, K. N., Gonzalez, A. & Bennett, E. M. Landscape structure affects the provision of multiple ecosystem services. Environ. Res. Lett. 11(12), 124017 (2016).Article 
ADS 

Google Scholar 
Riitters, K. H., Vogt, P., Soille, P., Kozak, J. & Estreguil, C. Neutral model analysis of landscape patterns from mathematical morphology. Landsc. Ecol. 22(7), 1033–1043 (2007).Article 

Google Scholar 
Riitters, K., Vogt, P., Soille, P. & Estreguil, C. Landscape patterns from mathematical morphology on maps with contagion. Landsc. Ecol. 24(5), 699–709 (2009).Article 

Google Scholar 
Anas, A., Arnott, R. & Small, K. Urban spatial structure. J. Econ. Lit. 36(3), 1426–1464 (1998).
Google Scholar 
Arroyo-Mora, J. P., Sánchez-Azofeifa, G. A., Rivard, B., Calvo, J. C. & Janzen, D. H. Dynamics in landscape structure and composition for the Chorotega region, Costa Rica from 1960 to 2000. Agr. Ecosyst. Environ. 106(1), 27–39 (2005).Article 

Google Scholar 
Siles, G., Charland, A., Voirin, Y. & Bénié, G. B. Integration of landscape and structure indicators into a web-based geoinformation system for assessing wetlands status. Eco. Inform. 52, 166–176 (2019).Article 

Google Scholar 
Soille, P. Morphological Image Analysis: Principles and Applications (Springer, 2003).MATH 

Google Scholar 
Soille, P. & Vogt, P. Morphological segmentation of binary patterns. Pattern Recogn. Lett. 30, 456–459 (2009).Article 
ADS 

Google Scholar 
Vogt, P. et al. Mapping spatial patterns with morphological image processing. Landsc. Ecol. 22(2), 171–177 (2007).Article 

Google Scholar 
Bajocco, S., Ceccarelli, T., Smiraglia, D., Salvati, L. & Ricotta, C. Modeling the ecological niche of long-term land use changes: The role of biophysical factors. Ecol. Ind. 60, 231–236 (2016).Article 

Google Scholar 
Yin, Y., Zhou, K. & Chen, Y. Deconstructing the driving factors of land development intensity from multi-scale in differentiated functional zones. Sci. Rep. 12(1), 1–13 (2022).Article 

Google Scholar 
Duvernoy, I., Zambon, I., Sateriano, A. & Salvati, L. Pictures from the other side of the fringe: Urban growth and peri-urban agriculture in a post-industrial city (Toulouse, France). J. Rural. Stud. 57, 25–35 (2018).Article 

Google Scholar 
Smiraglia, D., Ceccarelli, T., Bajocco, S., Salvati, L. & Perini, L. Linking trajectories of land change, land degradation processes and ecosystem services. Environ. Res. 147, 590–600 (2016).Article 
CAS 

Google Scholar 
Shaker, R. R. Examining sustainable landscape function across the Republic of Moldova. Habitat Int. 72, 77–91 (2018).Article 
ADS 

Google Scholar 
Zheng, H. & Li, H. Spatial–temporal evolution characteristics of land use and habitat quality in Shandong Province, China. Sci. Rep. 12(1), 1–12 (2022).Article 

Google Scholar 
Tombolini, I., Munafò, M. & Salvati, L. Soil sealing footprint as an indicator of dispersed urban growth: A multivariate statistics approach. Urban Res. Pract. 9(1), 1–15 (2016).Article 

Google Scholar 
Salvati, L., Sateriano, A., Grigoriadis, E. & Carlucci, M. New wine in old bottles: The (changing) socioeconomic attributes of sprawl during building boom and stagnation. Ecol. Econ. 131, 361–372 (2017).Article 

Google Scholar 
Zambon, I., Benedetti, A., Ferrara, C. & Salvati, L. Soil matters? A multivariate analysis of socioeconomic constraints to urban expansion in Mediterranean Europe. Ecol. Econ. 146, 173–183 (2018).Article 

Google Scholar 
Paul, V. & Tonts, M. Containing urban sprawl: Trends in land use and spatial planning in the Metropolitan Region of Barcelona. J. Environ. Plann. Manag. 48(1), 7–35 (2005).Article 

Google Scholar 
Serra, P., Vera, A., Tulla, A. F. & Salvati, L. Beyond urban–rural dichotomy: Exploring socioeconomic and land-use processes of change in Spain (1991–2011). Appl. Geogr. 55, 71–81 (2014).Article 

Google Scholar 
Seifollahi-Aghmiuni, S., Kalantari, Z., Egidi, G., Gaburova, L. & Salvati, L. Urbanisation-driven land degradation and socioeconomic challenges in peri-urban areas: Insights from Southern Europe. Ambio 51(6), 1446–1458 (2022).Article 

Google Scholar 
Pili, S., Grigoriadis, E., Carlucci, M., Clemente, M. & Salvati, L. Towards sustainable growth? A multi-criteria assessment of (changing) urban forms. Ecol. Ind. 76, 71–80 (2017).Article 

Google Scholar 
Salvati, L., Sateriano, A. & Grigoriadis, E. Crisis and the city: Profiling urban growth under economic expansion and stagnation. Lett. Spat. Resour. Sci. 9(3), 329–342 (2016).Article 

Google Scholar 
Champion, T. & Hugo, G. New Forms of Urbanisation: Beyond the Urban-Rural Dichotomy (Ashgate, 2004).
Google Scholar 
Frondoni, R., Mollo, B. & Capotorti, G. A landscape analysis of land cover change in the municipality of Rome (Italy): Spatio-temporal characteristics and ecological implications of land cover transitions from 1954 to 2001. Landsc. Urban Plan. 100(1–2), 117–128 (2011).Article 

Google Scholar 
Perrin, C., Nougarèdes, B., Sini, L., Branduini, P. & Salvati, L. Governance changes in peri-urban farmland protection following decentralisation: A comparison between Montpellier (France) and Rome (Italy). Land Use Policy 70, 535–546 (2018).Article 

Google Scholar 
Salvati, L. Monitoring high-quality soil consumption driven by urban pressure in a growing city (Rome, Italy). Cities 31, 349–356 (2013).Article 

Google Scholar 
Salvati, L., Ciommi, M. T., Serra, P. & Chelli, F. M. Exploring the spatial structure of housing prices under economic expansion and stagnation: The role of socio-demographic factors in metropolitan Rome, Italy. Land Use Policy 81, 143–152 (2019).Article 

Google Scholar 
Ferrara, C., Salvati, L. & Tombolini, I. An integrated evaluation of soil resource depletion from diachronic settlement maps and soil cartography in peri-urban Rome, Italy. Geoderma 232, 394–405 (2014).Article 
ADS 

Google Scholar 
Egidi, G. & Salvati, L. Beyond the suburban-urban divide: Convergence in age structures in metropolitan Rome, Italy. J. Popul. Soc. Stud. 28(2), 130–142 (2020).Article 

Google Scholar 
Pili, S., Serra, P. & Salvati, L. Landscape and the city: Agro-forest systems, land fragmentation and the ecological network in Rome, Italy. Urban For. Urban Green. 41, 230–237 (2019).Article 

Google Scholar 
European Environment Agency. Urban Sprawl in Europe – The Ignored Challenge. Copenhagen: EEA Report no. 10 (2006).Park, S., Hepcan, Ç. C., Hepcan, Ş & Cook, E. A. Influence of urban form on landscape pattern and connectivity in metropolitan regions: a comparative case study of Phoenix, AZ, USA, and Izmir, Turkey. Environ. Monit. Assess. 186(10), 6301–6318 (2014).Article 

Google Scholar 
Luo, F., Liu, Y., Peng, J. & Wu, J. Assessing urban landscape ecological risk through an adaptive cycle framework. Landsc. Urban Plan. 180, 125–134 (2018).Article 

Google Scholar 
Ortega, M., Pascual, S., Elena-Rosselló, R. & Rescia, A. J. Land-use and spatial resilience changes in the Spanish olive socio-ecological landscape. Appl. Geogr. 117, 102171 (2020).Article 

Google Scholar 
Parcerisas, L. et al. Land use changes, landscape ecology and their socioeconomic driving forces in the Spanish Mediterranean coast (El Maresme County, 1850–2005). Environ. Sci. Policy 23, 120–132 (2012).Article 

Google Scholar 
Masini, E. et al. Urban growth, land-use efficiency and local socioeconomic context: A comparative analysis of 417 metropolitan regions in Europe. Environ. Manag. 63(3), 322–337 (2019).Article 
ADS 

Google Scholar 
Luck, M. & Wu, J. A gradient analysis of urban landscape pattern: a case study from the Phoenix metropolitan region, Arizona, USA. Landsc. Ecol. 17(4), 327–339 (2002).Article 

Google Scholar 
Pesaresi, M. & Bianchin, A. Recognising settlement structure using mathematical morphology and image texture. Remote Sensing Urban Anal. GISDATA 9, 46–60 (2003).
Google Scholar 
Schneider, A. & Woodcock, C. E. Compact, dispersed, fragmented, extensive? A comparison of urban growth in twenty-five global cities using remotely sensed data, pattern metrics and census information. Urban Stud. 45(3), 659–692 (2008).Article 

Google Scholar 
Mubareka, S., Koomen, E., Estreguil, C. & Lavalle, C. Development of a composite index of urban compactness for land use modelling applications. Landsc. Urban Plan. 103(3–4), 303–317 (2011).Article 

Google Scholar 
Vogt, P. et al. Mapping landscape corridors. Ecol. Ind. 7(2), 481–488 (2007).Article 

Google Scholar 
Daya Sagar, B. S. & Murthy, K. S. R. Generation of a fractal landscape using nonlinear mathematical morphological transformations. Fractals 8(03), 267–272 (2000).Article 

Google Scholar 
Scott, A. J., Carter, C., Reed, M. R., Stonyer, B. & Coles, R. Disintegrated development at the rural-urban fringe: Re-connecting spatial planning theory and practice. Prog. Plan. 83, 1–52 (2013).Article 

Google Scholar 
Zhao, Q., Wen, Z., Chen, S., Ding, S. & Zhang, M. Quantifying land use/land cover and landscape pattern changes and impacts on ecosystem services. Int. J. Environ. Res. Public Health 17(1), 126 (2020).Article 

Google Scholar 
Parr, J. The regional economy, spatial structure and regional urban systems. Reg. Stud. 48(12), 1926–1938 (2014).Article 

Google Scholar 
Salvati, L., Zambon, I., Chelli, F. M. & Serra, P. Do spatial patterns of urbanisation and land consumption reflect different socioeconomic contexts in Europe?. Sci. Total Environ. 625, 722–730 (2018).Article 
ADS 
CAS 

Google Scholar 
Coppi, R. & Bolasco, S. Multiway Data Analysis (Elsevier, 1988).MATH 

Google Scholar 
Kroonenberg, P. M. Applied Multiway Data Analysis (Wiley, 2008).Book 
MATH 

Google Scholar 
Escofier, B. & Pages, J. Multiple factor analysis (AFMULT Package). Comput. Stat. Data Anal. 18, 121–140 (1994).Article 
MATH 

Google Scholar 
De Rosa, S. & Salvati, L. Beyond a ‘side street story’? Naples from spontaneous centrality to entropic polycentricism, towards a ‘crisis city’. Cities 51, 74–83 (2016).Article 

Google Scholar 
Favaro, J.-M. & Pumain, D. Gibrat revisited: An urban growth model incorporating spatial interaction and innovation cycles. Geogr. Anal. 43(3), 261–286 (2011).Article 

Google Scholar 
Walker, B. H., Carpenter, S. R., Rockstrom, J., Crepin, A.-S. & Peterson, G. D. “Drivers, “slow” variables, “fast” variables, shocks, and resilience. Ecol. Soc. 17(3), 30 (2012).Article 

Google Scholar 
Zhang, Z., Su, S., Xiao, R., Jiang, D. & Wu, J. Identifying determinants of urban growth from a multi-scale perspective: A case study of the urban agglomeration around Hangzhou Bay, China. Appl. Geogr. 45, 193–202 (2013).Article 

Google Scholar 
Fratarcangeli, C., Fanelli, G., Franceschini, S., De Sanctis, M. & Travaglini, A. Beyond the urban-rural gradient: Self-organising map detects the nine landscape types of the city of Rome. Urban For. Urban Green. 38, 354–370 (2019).Article 

Google Scholar 
Crisci, M., Benassi, F., Rabiei-Dastjerdi, H., McArdle, G. Spatio-temporal variations and contextual factors of the supply of Airbnb in Rome. An initial investigation. Lett. Spat. Resour. Sci. 1–17 (2022).Lelo, K., Monni, S. & Tomassi, F. Socio-spatial inequalities and urban transformation. The case of Rome districts. Socio-Econ. Plann. Sci. 68, 100696 (2019).Article 

Google Scholar 
Crisci, M. The impact of the real estate crisis on a south european metropolis: From urban diffusion to Reurbanisation. Appl. Spat. Anal. Policy 15(3), 797–820 (2022).Article 

Google Scholar 
Wang, Y. & Zhang, X. A dynamic modeling approach to simulating socioeconomic effects on landscape changes. Ecol. Model. 140(1–2), 141–162 (2001).Article 

Google Scholar 
Voghera, A. The River agreement in Italy. Resilient planning for the co-evolution of communities and landscapes. Land Use Policy 91, 104377 (2020).Article 

Google Scholar 
Chen, A. & Partridge, M. D. When are cities engines of growth in China? Spread and backwash effects across the urban hierarchy. Reg. Stud. 47(8), 1313–1331 (2013).Article 

Google Scholar 
Ciommi, M., Chelli, F. M., Carlucci, M. & Salvati, L. Urban growth and demographic dynamics in southern Europe: Toward a new statistical approach to regional science. Sustainability 10(8), 2765 (2018).Article 

Google Scholar 
Jacobs-Crisioni, C., Rietveld, P. & Koomen, E. The impact of spatial aggregation on urban development analyses. Appl. Geogr. 47, 46–56 (2014).Article 

Google Scholar 
Kourtit, K., Nijkamp, P. & Reid, N. The new urban world: Challenges and policy. Appl. Geogr. 49, 1–3 (2014).Article 

Google Scholar 
Bruegmann, R. Sprawl: A Compact History (University of Chicago Press, 2005).Book 

Google Scholar 
Neuman, M. & Hull, A. The Futures of the City Region. Reg. Stud. 43(6), 777–787 (2009).Article 

Google Scholar 
Couch, C., Petschel-held, G. & Leontidou, L. Urban Sprawl In Europe: Landscapes, Land-use Change and Policy (Blackwell, 2007).Book 

Google Scholar 
Longhi, C. & Musolesi, A. European cities in the process of economic integration: towards structural convergence. Ann. Reg. Sci. 41, 333–351 (2007).Article 

Google Scholar 
Tian, G., Ouyang, Y., Quan, Q. & Wu, J. Simulating spatiotemporal dynamics of urbanisation with multi-agent systems—A case study of the Phoenix metropolitan region, USA. Ecol. Model. 222(5), 1129–1138 (2011).Article 

Google Scholar 
Tian, L., Chen, J. & Yu, S. X. Coupled dynamics of urban landscape pattern and socioeconomic drivers in Shenzhen, China. Landsc. Ecol. 29(4), 715–727 (2014).Article 

Google Scholar 
Fielding, A. J. Counterurbanization in Western Europe. Prog. Plan. 17, 1–52 (1982).Article 

Google Scholar 
Oueslati, W., Alvanides, S. & Garrod, G. Determinants of urban sprawl in European cities. Urban Stud. 52(9), 1594–1614 (2015).Article 

Google Scholar 
Tress, B., Tress, G., Décamps, H. & d’Hauteserre, A. M. Bridging human and natural sciences in landscape research. Landsc. Urban Plan. 57(3–4), 137–141 (2001).Article 

Google Scholar 
Xu, Z., Lv, Z., Li, J., Sun, H. & Sheng, Z. A Novel perspective on travel demand prediction considering natural environmental and socioeconomic factors. IEEE Intell. Transp. Syst. Mag. https://doi.org/10.1109/MITS.2022.3162901 (2022).Article 

Google Scholar 
Xu, Z., Lv, Z., Li, J. & Shi, A. A novel approach for predicting water demand with complex patterns based on ensemble learning. Water Resour. Manag. 36(11), 4293–4312 (2022).Article 

Google Scholar 
Lv, Z., Li, J., Dong, C., Li, H. & Xu, Z. Deep learning in the COVID-19 epidemic: A deep model for urban traffic revitalisation index. Data Knowl. Eng. 135, 101912 (2021).Article 

Google Scholar  More

Evapotranspiration measurements and above-ground biomassFigure 1 shows daily evapotranspiration (ET, mm day−1) of each CC tested before mowing (DOY, day of the year, 184) and at 2, 8, 17 and 25 days after mowing (DOY 190, 196, 205 and 213); bare soil was also included as a reference. Before mowing, ET rates showed significant differences between and within the three groups. CR plants had a mean ET of 8.1 mm day−1, which was lower, compared to the other two groups (10.6 and 18.6 mm day−1 for GR and LE, respectively) and the bare soil control (8.5 mm day−1). On DOY 184, values as high as 9.4 (Glechoma hederacea L., GH) and 9.8 mm day−1 (Trifolium subterraneum L. cv. Denmark, TS) were found (Fig. 1), while ranging around 7 mm day-1, Dichondra repens J.R.Forst. & G.Forst. (DR), Hieracium pilosella L. (HP), and Sagina subulata (Swartz) C. Presl (SS) ET were lower than soil evaporation itself.Figure 1Vertical bars represent the daily water use as referred to unit of soil (ET, mm day−1) for the bare soil (yellow) and all the cover crop species as divided into creeping plants (shades of blue), legumes (shades of green) and grasses (shades of orange). Evapotranspiration was measured though a gravimetric method before (i.e. − 4) and at 2, 8, 17 and 25 days after mowing. ET data are mean values ± SE (n = 4).Full size imageOn the same day, a large ET variation was recorded within the GR group as Festuca arundinacea Schreb. cv. Thor (FA) scored the highest daily ET values (13.4 mm day−1), whereas in Festuca ovina L. cv. Ridu (FO), water loss was reduced by 45% (7.5 mm day−1). Within the 15 CCs, LE registered the highest pre-mowing ET with Trifolium michelianum Savi cv. Bolta (TM) peaking at 22.6 mm day−1. However, within LE, Medicago polymorpha L. cv. Scimitar (MP) showed ET values as low as 12.1 mm day−1 (Fig. 1).Two days after mowing, all tested CCs recorded ET values lower than 9 mm day−1 (Fig. 1). Moreover, water use reduction among LE ranged between 56% (M. polymorpha, MP) and 73% (T. michelianum, TM), such that T. michelianum (TM, 6.1 mm day−1), Medicago truncatula Gaertn. cv. Paraggio (MT, 5.6 mm day−1) and M. polymorpha (MP, 5.2 mm day−1) registered ET values lower than the bare soil (7.0 mm day−1). Even though registering a consistent ET reduction after mowing, GR retained ET rates slightly higher than bare soil, except for F. ovina (FO), which recorded the lowest at 6.3 mm day−1. Subsequent samplings showed that most of the CCs had a progressive recovery in water use (Fig. 1) and data taken 17 days after mowing confirmed that Lotus corniculatus L. cv. Leo (LC) and all GR fetched pre-mowing ET rates. Medicago lupulina L. cv. Virgo (ML) registered a partial recovery with similar rates (about 13 mm day−1) at 17 and 25 days after the mowing event. F. ovina and all remaining LE stayed below 10 mm day−1 with ET values close to the control until the end of the trial. At 17 days from grass cutting, under a quite high exceeding-the-pot biomass, both G. hederacea (GH) and T. subterraneum (TS) reached ET values as high as 12.0 and 11.4 mm day−1, respectively. On the other hand, D. repens (DR), H. pilosella (HP), and S. subulata (SS) even though with slightly higher ET values than those registered at the beginning of the trial (DOY 184), remained close to the soil evaporation rates until DOY 213.Aboveground dry clipped biomass at the first mowing date (ADW_MW1, DOY 188) showed large differences among groups, as represented in Table 1. ADW_MW1 within LE was quite variable, as values ranged between 274.3 g m−2 (M. polymorpha, MP) and 750.0 g m−2 (T. michelianum, TM). With a mean value of 565.9 g m−2, LE aboveground biomass was 80% higher than the mean GR ADW_MW1 (110.2 g m-2). F. ovina (FO) scored the lowest value at 48.4 g m−2 among grasses, while within the creeping group, G. hederacea (GH) and T. subterraneum (TS) had biomass development outside the pot edges totalling 89.6 g m−2 and 23.2 g m−2, respectively.Table 1 Aboveground dry biomass clipped at the first mowing event (ADW _MW1), the corresponding leaf area surface index (LAI) and water use per leaf area unit (ETLEAF) of all cover crops tested.Full size tableLeaf area index (LAI, m2 m−2) at mowing showed the highest values in LE with LAI peaking at 12.4 (Table 1). Among GR, LAI did not show significant differences, being around 1.2. Concerning CR, LAI was assessed at 0.2 and 0.8 for T. subterraneum (TS) and G. hederacea (GH) respectively, while LAI estimated through photo analysis ranged between 1.3 (D. repens, DR) and 3.6 (T. subterraneum TS).Evapotranspiration per leaf area unit (ETLEAF) was notably higher in GR, ranging between 7.75 (F. ovina, FO) and 9.22 (Lolium perenne L. cv. Playfast, LP) mm m−2 day−1 (Table 1). In descending order, ETLEAF was the highest in D. repens (DR, 5.46 mm m−2 day−1). Similar ETLEAF was found when comparing some LE and CR species such as M. truncatula (MT, 3.40 mm m−2 day−1), M. lupulina (ML, 4.05 mm m−2 day−1), G. hederacea (GH, 3.68 mm m−2 day−1), H. pilosella (HP, 3.86 mm m-2 day-1) and T. subterraneum (TS, 2.74 mm m−2 day−1). T. michelianum (TM), with 1.81 mm m-2 day-1 scored the lowest ETLEAF of all species (Table 1).Plotting LAI versus the before-mowing ET yielded a significant quadratic relationship (R2  > 0.76) (Fig. 2a) which helped to distinguish two different data clouds. Till LAI values of about 6, the model was linear, having at its lower end all GR and CR species with the inclusion of M. polymorpha (MP) as a legume, while, at the other end, M. truncatula (MT), L. corniculatus (LC) and M. lupulina (ML) were grouped together. T. michelianum (TM) was isolated from all CCs at 22.56 mm day−1.Figure 2Panel (a): quadratic regression of leaf area index (LAI, m2 m−2) vs cover crop evapotranspiration per unit of soil (ET, mm day−1). Each data point is mean value ± SE (n = 4). The quadratic model equation is y = − 0.128×2 + 2.9968x + 5.4716, R2 = 0.76. Panel (b): the quadratic regression between LAI corresponding to the clipped biomass (m2 m−2) and cover crop ET reduction (%). Each data point is mean value ± SE (n = 4). Quadratic model equation is y = − 0.8985×2 + 16.503x + 5.1491, R2 = 0.94.Full size imageWhen regressing the fraction of ET reduction, compared to pre-mowing values vs LAI (Fig. 2b), the same quadratic model achieved a very close fit (R2 = 0.94, p  1 mm) root diameters as affected by soil cover.Full size tableThe highest values of diameter class length (DCL, mm cm−3) for very fine roots (DCL_VF,  1.0 mm) roots although, most notably, L. corniculatus roots showed the highest abundance for both DCL_M (23.08 cm cm−3) and DCL_C (0.54 cm cm−3).At the 10–20 cm soil depth, GR confirmed the highest values for both very fine and fine roots, with F. arundinacea reaching maximum DCL of 2.269 and 5.215 cm cm-3, respectively (Table 2). L. corniculatus largely outscored any other species for both medium and coarse root diameter (6.173 and 0.037 cm cm−3, respectively), with F. arundinacea ranking second (3.157 and 0.016 cm cm−3, respectively).The highest root dry weight (RDW, mg cm-3) within the topsoil layer was reached by L. corniculatus (8.7 mg cm−3) and F. arundinacea (7.6 mg cm-3). Notably, such values were significantly higher than those recorded on the remaining species, except for the F. arundinacea vs F. rubra commutata comparison (Table 2). At 10–20 depth, scant variation was recorded in RDW measured in grasses, whereas L. corniculatus held its supremacy within legumes (4.5 mg cm−3). Within the creeping type, D. repens (DR) and G. hederacea (GH) scored RDW values as high as those determined for grass species (namely F. arundinacea , P. pratensis and F. rubra commutata), whereas S. subulata (SS) essentially had no root development.Soil aggregates and mean weight diameter (MWD)Table 3 reports the proportional aggregate weight (g kg−1) for both 0–10 and 10–20 cm soil depths. Compared to bare soil, the largest increase in large macroaggregates (LM,  > 2000 µm) in the top 10 cm of soil was achieved by L. corniculatus with 461 g kg−1. L. corniculatus differed from the rest of the LE group, whose grand mean (90 g kg−1) was the lowest of the three tested groups. As a legume, T. subterraneum (TS, 122 g kg−1) recorded the lowest values compared to fellow CR species, ranging between 211 (D. repens, DR) and 316 g kg−1 (G. hederacea, GH). GR recorded LM values slightly lower than those of CR, with a mean value of 217 vs 224 g kg-1.Table 3 Proportional aggregate weight (g kg−1) of sand-free aggregate-size fractions acquired from wet sieving as affected by soil cover and mean weight diameter (MWD). Aggregate-size fraction divided as macroaggregates with large size ( > 2 mm, LM) and small size (2 mm—250 μm, sM), microaggregates (250 μm—53 μm, m), and silt and clay ( More