Ecology

Global change is predicted to have tremendous implications for the emergence of infectious diseases. Now, Gibb, Redding et al. report that changes in land use (such as urbanization and agriculturalization) cause major changes in the diversity and taxonomic composition of reservoir hosts for pathogens, with implications for the emergence of zoonotic diseases. The authors analysed 6,801 sites and 376 host species worldwide and determined whether there was evidence of pathogens in these hosts and the potential for zoonotic transmission. As anthropogenic land use increased, so did the frequency of zoonotic hosts, and the intensity of land use correlated with increases in zoonotic host species and the number of individuals. Notably, the frequency of rodents, bats and passerine birds was higher in human-dominated sites, suggesting the need for enhanced surveillance efforts in these sites. More

Appella E, Weber IT, Blasi F (1988) Structure and function of epidermal growth factor-like regions in proteins. FEBS Lett 231:1–4
CAS  PubMed  Google Scholar 

Berteaux D, Réale D, McAdam AG, Boutin S (2004) Keeping pace with fast climate change: can arctic life count on evolution? Integr Comp Biol 44:140–151
PubMed  Google Scholar 

Bi K, Linderoth T, Singhal S, Vanderpool D, Patton JL, Nielsen R et al. (2019) Temporal genomic contrasts reveal rapid evolutionary responses in an alpine mammal during recent climate change. PLoS Genet 15:e1008119
CAS  PubMed  PubMed Central  Google Scholar 

Bivand R, Piras G (2015) Comparing implementations of estimation methods for spatial econometrics. J Stat Softw 63:1–36
Google Scholar 

Brauer CJ, Hammer MP, Beheregaray LB (2016) Riverscape genomics of a threatened fish across a hydroclimatically heterogeneous river basin. Mol Ecol 25:5093–5113
CAS  PubMed  Google Scholar 

Braun CE, Hoffman RW, Rogers GE (1976) Wintering areas and winter ecology of white-tailed ptarmigan in Colorado, Colorado Division of Wildlife, Denver, CO, USA. Special Report 38

Braun CE, Taylor WP, Ebbert SE, Kaler RSA, Sandercock BK (2011) Protocols for successful translocation of ptarmigan. In: Watson RT, Cade TJ, Fuller M, Hunt G, Potapov E (eds) Gyrfalcons and ptarmigan in a changing world. The Peregrine Fund, Boise, ID, USA, p 339–348
Google Scholar 

Braun CE, Williams III SO (2015) History and status of the white-tailed ptarmigan in New Mexico. West Birds 46:233–243
Google Scholar 

Brown RD, Brasnett B (2010) Canadian Meteorological Centre (CMC) Daily Snow Depth Analysis Data, version 1. Colorado USA NASA National Snow and Ice Data Center, Distributed Active Archive Center, Boulder, CO, USA
Google Scholar 

Browning SR, Browning BL (2007) Rapid and accurate haplotype phasing and missing data inference for whole genome association studies by use of localized haplotype clustering. Am J Hum Genet 81:1084–1097
CAS  PubMed  PubMed Central  Google Scholar 

Bryant JP, Kuropat PJ (1980) Selection of winter forage by subarctic browsing vertebrates: the role of plant chemistry. Annu Rev Ecol Syst 11:261–285
CAS  Google Scholar 

Capblancq T, Luu K, Blum MGB, Bazin E (2018a) How to make use of ordination methods to identify local adaptation: a comparison of genome scans based on PCA and RDA. bioRxiv: 258988v2

Capblancq T, Luu K, Blum MGB, Bazin E (2018b) Evaluation of redundancy analysis to identify signatures of local adaptation. Mol Ecol Resour 18:1223–1233
CAS  PubMed  Google Scholar 

Carey C, Martin K (1997) Physiological ecology of incubation of ptarmigan eggs at high and low altitudes. Wildlife Biol 3:211–218
Google Scholar 

Charmantier A, McCleery RH, Cole LR, Perrins C, Kruuk LEB, Sheldon BC (2008) Adaptive phenotypic plasticity in response to climate change in a wild bird population. Science 320:800–803
CAS  PubMed  Google Scholar 

Cingolani P, Platts A, Wang LL, Coon M, Nguyen T, Wang L et al. (2012) A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly 6:80–92
CAS  PubMed  PubMed Central  Google Scholar 

Clarke JA, Johnson RE (1992) The influence of spring snow depth on white-tailed ptarmigan breeding success in the Sierra Nevada. Condor 94:622–627
Google Scholar 

Coop G, Witonsky D, Di Rienzo A, Pritchard JK (2010) Using environmental correlations to identify loci underlying local adaptation. Genetics 185:1411–23
CAS  PubMed  PubMed Central  Google Scholar 

Dalloul RA, Long JA, Zimin AV, Aslam L, Beal K, Blomberg LA et al. (2010) Multi-platform next-generation sequencing of the domestic Turkey (Meleagris gallopavo): genome assembly and analysis. PLoS Biol 8:e1000475
PubMed  PubMed Central  Google Scholar 

Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA et al. (2011) The variant call format and VCFtools. Bioinformatics 27:2156–2158
CAS  PubMed  PubMed Central  Google Scholar 

Dragon S, Carey C, Martin K, Baumann R (1999) Effect of high altitude and in vivo adenosine/beta-adrenergic receptor blockade on ATP and 2,3BPG concentrations in red blood cells of avian embryos. J Exp Biol 202:2787–2795
CAS  PubMed  Google Scholar 

Dray S, Blanchet D, Borcard D, Clappe S, Guenard G, Jombart T et al. (2019) Adespatial: multivariate multiscale spatial analysis. R package version 0.3-4. http://cran.r-project.org/package=adespatial

Erikstad KE, Andersen R (1983) The effect of weather on survival, growth rate, and feeding time in different sized willow grouse broods. Ornis Scand 14:249–252
Google Scholar 

Fabian DK, Kapun M, Nolte V, Kofler R, Schmidt PS, Schlötterer C et al. (2012) Genome-wide patterns of latitudinal differentiation among populations of Drosophila melanogaster from North America. Mol Ecol 21:4748–4769
PubMed  PubMed Central  Google Scholar 

Fedy BC, Martin K (2011) The influence of fine-scale habitat features on regional variation in population performance of alpine white-tailed ptarmigan. Condor 113:306–315
Google Scholar 

Fedy BC, Martin K, Ritland C, Young J (2008) Genetic and ecological data provide incongruent interpretations of population structure and dispersal in naturally subdivided populations of white-tailed ptarmigan (Lagopus leucura). Mol Ecol 17:1905–1917
CAS  PubMed  Google Scholar 

Fick SE, Hijmans RJ (2017) Worldclim 2: new 1-km spatial resolution climate surfaces for global land areas. Int J Climatol 37:4302–4315
Google Scholar 

Forester BR, Lasky JR, Wagner HH, Urban DL (2018) Comparing methods for detecting multilocus adaptation with multivariate genotype-environment associations. Mol Ecol 27:2215–2233
CAS  PubMed  Google Scholar 

Forester BR, Jones MR, Joost S, Landguth EL, Lasky JR (2016) Detecting spatial genetic signatures of local adaptation in heterogenous landscapes. Mol Ecol 24:104–120
Google Scholar 

Frederick GP, Gutiérrez RJ (1992) Habitat use and population characteristics of the white-tailed ptarmigan in the Sierra Nevada, California. Condor 94:889–902
Google Scholar 

Freeman BG, Lee-Yaw JA, Sunday JM, Hargreaves AL (2018) Expanding, shifting and shrinking: the impact of global warming on species’ elevational distributions. Glob Ecol Biogeogr 27:1268–1276
Google Scholar 

Friedl MA, Gray J, Sulla-Menashe D (2009) MCD12Q2 MODIS/Terra+Aqua Land Cover Dynamics Yearly L3 Global 500m SIN Grid V005 [2000–2010]. NASA EOSDIS Land Processes DAAC. Sioux Falls, SD, USA

García-González R, Aldezabal A, Laskurain NA, Margalida A, Novoa C (2016) Influence of snowmelt timing on the diet quality of pyrenean rock ptarmigan (Lagopus muta pyrenaica): implications for reproductive success. PLoS ONE 11:1–21
Google Scholar 

Giesen KM, Braun CE, May TA (1980) Reproduction and nest-site selection by white-tailed ptarmigan in Colorado. Wilson Bull 92:188–199
Google Scholar 

Hakkarainen H, Virtanen R, Honkanen JO, Roininen H (2007) Willow bud and shoot foraging by ptarmigan in relation to snow level in NW Finnish Lapland. Polar Biol 30:619–624
Google Scholar 

Hall D, Salomonson V, Riggs G (2006) MODIS/Terra Snow Cover Daily L3 Global 500m Grid, Version 5. NASA Natl Snow Ice Data Cent Distrib Act Arch Center Boulder, CO, USA

Hannon SJ, Eason PK, Martin K (1998) Willow ptarmigan (Lagopus lagopus), version 2.0. In: Poole AF, Gill FB (eds) The Birds of North America. Cornell Lab of Ornithology, Ithaca, New York, NY, USA
Google Scholar 

Henry P, Sim Z, Russello MA (2012) Genetic evidence for restricted dispersal along continuous altitudinal gradients in a climate change-sensitive mammal: The American Pika. PLoS ONE 7:1–10
Google Scholar 

Hoffman RW, Braun CE (1975) Migration of a wintering population of white-tailed ptarmigan in Colorado. J Wildl Manage 39:485–490
Google Scholar 

Hoffmann AA, Sgró CM (2011) Climate change and evolutionary adaptation. Nature 470:479–485
CAS  PubMed  Google Scholar 

Holsinger LM, Parks SA, Parisien M-A, Miller C, Batllori E, Moritz MA (2019) Climate change likely to reshape vegetation in North America’s largest protected areas. Conserv Sci Pract 1:e50
Google Scholar 

Höst P (1942) Effect of light on the moults and sequences of plumage in the willow ptarmigan. Auk 59:388–403
Google Scholar 

Huang DW, Sherman BT, Tan Q, Kir J, Liu D, Bryant D et al. (2007) DAVID bioinformatics resources: expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids Res 35:169–175
Google Scholar 

Imperio S, Bionda R, Viterbi R, Provenzale A (2013) Climate change and human disturbance can lead to local extinction of alpine rock ptarmigan: new insight from the Western Italian Alps. PLoS ONE 8:e81598
PubMed  PubMed Central  Google Scholar 

Jacobsen EE, White CM, Emison WB (2007) Molting adaptations of rock ptarmigan on Amchitka Island, Alaska. Condor 85:420
Google Scholar 

Kawecki T, Ebert D (2004) Conceptual issues in local adaptation. Ecology Letters 7:1225–1241
Google Scholar 

Ke J, Wang L, Xiao D (2017) Cardiovascular adaptation to high-altitude hypoxia. In: Zheng J, Zhou C (eds) Hypoxia and human diseases. IntechOpen Limited, London, UK, p 117–134
Google Scholar 

Klanderud K, Birks HJB (2003) Recent increases in species richness and shifts in altitudinal distributions of Norwegian mountain plants. Holocene 13:1–6
Google Scholar 

Kohl KD, Varner J, Wilkening JL, Dearing MD (2018) Gut microbial communities of American pikas (Ochotona princeps): evidence for phylosymbiosis and adaptations to novel diets. J Anim Ecol 87:323–330
PubMed  Google Scholar 

Kozma R, Rödin-Mörch P, Höglund J (2019) Genomic regions of speciation and adaptation among three species of grouse. Sci Rep 9:1–8
CAS  Google Scholar 

Laiolo P, Obeso JR (2017) Life-history responses to the altitudinal gradient. In: Catalan J (ed) High mountain conservation in a changing world, Advances in Global Change Research, Vol 62, p. 3–36. Springer, Cham

Langin KM, Aldridge CL, Fike JA, Cornman RS, Martin K, Wann GT et al. (2018) Characterizing range-wide divergence in an alpine-endemic bird: a comparison of genetic and genomic approaches. Conserv Genet 19:1471–1485
CAS  Google Scholar 

Latifovic R, Pouliot D, Olthof I (2017) Circa 2010 land cover of Canada: local optimization methodology and product development. Remote Sens 9:1098
Google Scholar 

Legendre P, Legendre L (2012) Numerical Ecology, 3rd edn. Elsevier, Amsterdam, The Netherlands
Google Scholar 

Mangin B, Siberchicot A, Nicolas S, Doligez A, This P, Cierco-Ayrolles C (2012) Novel measures of linkage disequilibrium that correct the bias due to population structure and relatedness. Heredity 108:285–291
CAS  PubMed  Google Scholar 

Martin K, Brown GA, Young JR (2004) The historic and current distribution of the Vancouver Island white-tailed ptarmigan (Lagopus leucurus saxatilis). J F Ornithol 75:239–256
Google Scholar 

Martin K, Robb LA, Wilson S, Braun CE (2015) White-tailed ptarmigan (Lagopus leucura), version 2.0. In: Rodewald PG (ed) The Birds of North America. Cornell Lab of Ornithology, Ithaca, New York, NY, USA
Google Scholar 

Martin K, Wiebe KL (2004) Coping mechanisms of alpine and arctic breeding birds: extreme weather and limitations to reproductive resilience. Integr Comp Biol 44:177–185
PubMed  Google Scholar 

May TA, Braun CE (1972) Seasonal foods of adult white-tailed ptarmigan in Colorado. J Wildl Manage 36:1180–1186
Google Scholar 

McKinnon L, Picotin M, Bolduc E, Juillet C, Bêty J (2012) Timing of breeding, peak food availability, and effects of mismatch on chick growth in birds nesting in the High Arctic. Can J Zool 90:961–971
Google Scholar 

Mills LS, Bragina EV, Kumar AV, Zimova M, Lafferty DJR, Feltner J et al. (2018) Winter color polymorphisms identify global hot spots for evolutionary rescue from climate change. Science 359:1033–1036
CAS  PubMed  Google Scholar 

Montgomerie R, Holder K (2008) Rock ptarmigan (Lagopus muta), version 2.0. In: Poole AF (ed) The Birds of North America. Cornell Lab of Ornithology, Ithaca, New York, NY, USA
Google Scholar 

Morgulis A, Coulouris G, Raytselis Y, Madden TL, Agarwala R, Schäffer AA (2008) Database indexing for production MegaBLAST searches. Bioinformatics 24:1757–1764
CAS  PubMed  PubMed Central  Google Scholar 

Moss R (1973) The digestion and intake of winter foods by wild ptarmigan in Alaska. Condor 75:293–300
Google Scholar 

Moss R (1974) Winter diets, gut lengths, and interspecific competition in Alaskan ptarmigan. Auk 91:737–746
Google Scholar 

Moss R (1983) Gut size, body weight, and digestion of winter foods by grouse and ptarmigan. Condor 85:185–193
Google Scholar 

Nei M, Kumar S (2000) Molecular evolution and phylogenetics. Oxford University Press, New York, NY, USA
Google Scholar 

New Mexico Department of Game and Fish (2016) White-tailed ptarmigan (Lagopus leucura) recovery plan. New Mexico Department of Game and Fish, Wildlife Management Division, Santa Fe, NM, USA

NOAA National Centers for Environmental Prediction (NCEP) (2018) NCEP-NCAR Reanalysis montly zonal and meridional winds at standard pressure levels on a 2.5 lat/lon grid. NOAA National Centers for Environmental Prediction (NCEP), College Park, MD, USA

Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D et al. (2017) vegan: community ecology package version 2.4-3. https://cran.r-project.org/package=vegan

Oyler-McCance SJ, Langin KM, Cornman RS, Fike J, Aldridge CL, Martin KM et al. (2018) Sample collection information, single nucleotide polymorphism, and microsatellite data for white-tailed ptarmigan across the species range generated in the Molecular Ecology Lab during 2016: U.S. Geological Survey data release, https://doi.org/10.5066/F7GM86GZ

Palo RT (1984) Distribution of birch (Betula spp.), willow (Salix spp.), and poplar (Populus spp.) secondary metabolites and their potential role as chemical defense against herbivores. J Chem Ecol 10:499–520
CAS  PubMed  Google Scholar 

Paradis E, Schlier K (2018) ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35:526–528
Google Scholar 

Parmesan C, Yohe G (2003) A globally coherent fingerprint of climate change. Nature 421:37–42
CAS  PubMed  Google Scholar 

Pedersen S, Odden M, Pedersen HC (2017) Climate change induced molting mismatch? Mountain hare abundance reduced by duration of snow cover and predator abundance. Ecosphere 8:e01722
Google Scholar 

Pepin N, Bradley RS, Diaz HF, Baraer M, Caceres EB, Forsythe N et al. (2015) Elevation-dependent warming in mountain regions of the world. Nat Clim Chang 5:424–430
Google Scholar 

Persons NW, Hosner PA, Meiklejohn KA, Braun EL, Kimball RT (2016) Sorting out relationships among the grouse and ptarmigan using intron, mitochondrial, and ultra-conserved element sequences. Mol Phylogenet Evol 98:123–132
CAS  PubMed  Google Scholar 

Peterson BK, Weber JN, Kay EH, Fisher HS, Hoekstra HE (2012) Double digest RADseq: an inexpensive method for de novo SNP discovery and genotyping in model and non-model species. PLoS ONE 7:e37135
CAS  PubMed  PubMed Central  Google Scholar 

Pörtner HO (2002) Climate variations and the physiological basis of temperature dependent biogeography: systemic to molecular hierarchy of thermal tolerance in animals. Comp Biochem Physiol part A 132:739–761
Google Scholar 

Price N, Lopez L, Platts AE, Lasky JR (2020) In the presence of population structure: from genomics to candidate genes underlying local adaptation. Ecol Evol 10:1889–1904
PubMed  PubMed Central  Google Scholar 

Pyle P (2007) Revision of molt and plumage terminology in ptarmigan (Phasianidae: Lagopus spp.) based on evolutionary considerations. Auk 124:508
Google Scholar 

Rellstab C, Gugerli F, Eckert AJ, Hancock AM, Holderegger R (2015) A practical guide to environmental association analysis in landscape genomics. Mol Ecol 24:4348–4370
PubMed  Google Scholar 

Resano-Mayor J, Korner-Nievergelt F, Vignali S, Horrenberger N, Barras AG, Braunisch V et al. (2019) Snow cover phenology is the main driver of foraging habitat selection for a high-alpine passerine during breeding: implications for species persistence in the face of climate change. Biodivers Conserv 28:2669
Google Scholar 

Rolando A, Laiolo P, Formica M (1997) A comparative analysis of the foraging behaviour of the chough Pyrrhocorax pyrrhocorax and the Alpine chough Pyrrhocorax graculus coexisting in the Alps. Ibis 139:461–467
Google Scholar 

Rundel PW, Millar CI (2016) Alpine Ecosystems. In: Zavaleta E, Mooney H (eds) Ecosystems of California. University of California, Berkeley, CA, USA, p 613–634
Google Scholar 

Salomonsen F (1936) On a new race of willow grouse. Bull Br Ornithol Club 56:99–100
Google Scholar 

Singh CP (2008) Alpine ecosystems in relation to climate change. ISG Newsl 14:54–55
Google Scholar 

Slatkin M (2008) Linkage disequilibrium: understanding the genetic past and mapping the medical future. Nat Rev Genet 9:477–485
CAS  PubMed  PubMed Central  Google Scholar 

Somero G (2010) The physiology of climate change: how potentials for acclimatization and genetic adaptation will determine ‘winners’ and ‘losers. J Exp Biol 213:912–920
CAS  Google Scholar 

Spear SL, Aldridge CL, Wann GT, Braun CE (2019) Fine-scale habitat selection by breeding white-tailed ptarmigan in Colorado. J Wildl Manage 84:172–184
Google Scholar 

Stokken K-A (1993) Energetics and adaptations to cold in ptarmigan in winter. Ornis Scand 23:366–370
Google Scholar 

Storz JF (2005) Using genome scans of DNA polymorphism to infer adaptive population divergence. Mol Ecol 14:671–688
CAS  PubMed  Google Scholar 

Swanson DL, King MO, Harmon E (2014) Seasonal variation in pectoralis muscle and heart myostatin and tolloid-like proteinases in small birds: a regulatory role for seasonal phenotypic flexibility? J Comp Physiol B 184:249–258
CAS  PubMed  Google Scholar 

Thornton PE, Running SW, White MA (1997) Generating surfaces of daily meteorological variables over large regions of complex terrain. J Hydrol 190:214–251
Google Scholar 

Thornton PE, Thornton MM, Mayer BW, Wei Y, Devarakonda R, Vose RS et al. (2018) Daymet: daily surface weather data on a 1-km grid for North America, version 3. Oak Ridge, Tennessee, USA

Thuiller W (2004) Patterns and uncertainties of species’ range shifts under climate change. Glob Change Biol 10:2020–2027
Google Scholar 

United States Fish and Wildlife Service (2012) Endangered and threatened wildlife and plants; 90-day finding on a petition to list the southern white-tailed ptarmigan and the Mt. Rainier white-tailed ptarmigan as threatened with critical habitat. Fed Regist 77:33143–33155
Google Scholar 

United States Geological Survey EROS Center (2007) North American elevation 1-kilometer resolution, 3rd edn. National Atlas of the US, Reston, VA
Google Scholar 

Vos PG, Paulo MJ, Voorrips RE, Visser RGF, van Eck HJ, van Eeuwijk FA (2017) Evaluation of LD decay and various LD‑decay estimators in simulated and SNP‑array data of tetraploid potato. Theor Appl Genet 130:123–135
PubMed  Google Scholar 

Walker WP, Aradhya S, Hu C-L, Shen S, Zhang W, Azarani A et al. (2007) Genetic analysis of attractin homologs. Genesis 45:744–756
CAS  PubMed  Google Scholar 

Walker WP, Gunn TM (2010) Shades of meaning: the pigment-type switching system as a tool for discovery. Pigment Cell Melanoma Res 23:485–495
CAS  PubMed  Google Scholar 

Wang G, Hobbs NT, Galbraith H, Giesen KM (2002a) Signatures of large-scale and local climates on the demography of white-tailed ptarmigan in Rocky Mountain National Park, Colorado, USA. Int J Biometeorol 46:197–201
PubMed  Google Scholar 

Wang G, Hobbs NT, Giesen KM, Galbraith H, Ojima DS, Braun CE (2002b) Relationships between climate and population dynamics of white-tailed ptarmigan Lagopus leucurus in Rocky Mountain National Park, Colorado, USA. Clim Res 23:81–87
Google Scholar 

Wann GT, Aldridge CL, Braun CE (2014) Estimates of annual survival, growth, and recruitment of a white-tailed ptarmigan population in Colorado over 43 years. Popul Ecol 56:555–567
Google Scholar 

Wann GT, Aldridge CL, Braun CE (2016) Effects of seasonal weather on breeding phenology and reproductive success of alpine ptarmigan in Colorado. PLoS ONE 11:e0158913
PubMed  PubMed Central  Google Scholar 

Wann GT, Aldridge CL, Seglund AE, Oyler‐McCance SJ, Kondratieff BC, Braun CE (2019) Mismatches between breeding phenology and resource abundance of resident alpine ptarmigan negatively affect chick survival. Ecol Evol 9:7200–7212
PubMed  PubMed Central  Google Scholar 

Weeden RB (1967) Seasonal and geographic variation in the foods of adult white-tailed ptarmigan. Condor 69:303–309
Google Scholar 

Werhahn G, Liu Y, Meng Y, Cheng C, Lu Z, Atzeni L et al. (2020) Himalayan wolf distribution and admixture based on multiple genetic markers J Biogeogr https://doi.org/10.1111/jbi.13824
Article  Google Scholar 

Wiebe KL, Martin K (1998) Costs and benefits of nest cover for ptarmigan: changes within and between years. Anim Behav 56:1137–1144
CAS  PubMed  Google Scholar 

Wilson S, Martin K (2008) Breeding habitat selection of sympatric white-tailed, rock and willow ptarmigan in the southern Yukon Territory, Canada. J Ornithol 149:629–637
Google Scholar 

Wilson S, Martin K (2011) Life-history and demographic variation in an alpine specialist at the latitudinal extremes of the range. Popul Ecol 53:459–471
Google Scholar 

Xin J-W, Chai Z-X, Zhang C-F, Zhang Q, Zhu Y, Cao H-W et al. (2019) Transcriptome profiles revealed the mechanisms underlying the adaptation of yak to high-altitude environments. Sci Rep 9:7558
PubMed  PubMed Central  Google Scholar 

Zimova M, Hackländer K, Good JM, Melo-Ferreira J, Alves PC, Mills LS (2018) Function and underlying mechanisms of seasonal colour moulting in mammals and birds: what keeps them changing in a warming world? Biol Rev 93:1478–1498
PubMed  Google Scholar  More

1.
Burgess, S. D., Bowring, S. & Shen, S.-Z. High-precision timeline for Earth’s most severe extinction. Proc. Natl Acad. Sci. USA 111, 3316–3321 (2014).
Google Scholar 
2.
Wignall, P. B. & Twitchett, R. J. Oceanic anoxia and the end Permian mass extinction. Science 272, 1155–1158 (1996).
Google Scholar 

3.
Cao, C. et al. Biogeochemical evidence for euxinic oceans and ecological disturbance presaging the end-Permian mass extinction event. Earth Planet. Sci. Lett. 281, 188–201 (2009).
Google Scholar 

4.
Nabbefeld, B. et al. An integrated biomarker, isotopic and palaeoenvironmental study through the Late Permian event at Lusitaniadalen, Spitsbergen. Earth Planet. Sci. Lett. 291, 84–96 (2010).
Google Scholar 

5.
Brennecka, G. A., Herrmann, A. D., Anbar, A. D. & Algeo, T. J. Rapid expansion of oceanic anoxia immediately before the end-Permian mass extinction. Proc. Natl Acad. Sci. USA 108, 17631–17634 (2011).
Google Scholar 

6.
Dustira, A. M. et al. Gradual onset of anoxia across the Permian–Triassic boundary in Svalbard, Norway. Palaeogeogr. Palaeoclimatol. Palaeoecol. 374, 303–313 (2013).
Google Scholar 

7.
Schobben, M. et al. Flourishing ocean drives the end-Permian marine mass extinction. Proc. Natl Acad. Sci. USA 112, 10298–10303 (2015).
Google Scholar 

8.
Stanley, S. M. Estimates of the magnitudes of major marine mass extinctions in Earth history. Proc. Natl Acad. Sci. USA 113, E6325–E6334 (2016).
Google Scholar 

9.
Kiehl, J. T. & Shields, C. A. Climate simulation of the latest Permian: implications for mass extinction. Geology 33, 757–760 (2005).
Google Scholar 

10.
Hotinski, R. M., Bice, K. L., Kump, L. R., Najjar, R. G. & Arthur, M. A. Ocean stagnation and end-Permian anoxia. Geology 29, 7–10 (2001).
Google Scholar 

11.
Meyer, K., Kump, L. & Ridgwell, A. Biogeochemical controls on photic-zone euxinia during the end-Permian mass extinction. Geology 36, 747–750 (2008).
Google Scholar 

12.
Algeo, T. J. & Twitchett, R. J. Anomalous Early Triassic sediment fluxes due to elevated weathering rates and their biological consequences. Geology 38, 1023–1026 (2010).
Google Scholar 

13.
Shen, J. et al. Marine productivity changes during the end-Permian crisis and Early Triassic recovery. Earth-Sci. Rev. 149, 136–162 (2015).
Google Scholar 

14.
Tyrrell, T. The relative influences of nitrogen and phosphorus on oceanic primary production. Nature 400, 525–531 (1999).
Google Scholar 

15.
Sephton, M. A. et al. Catastrophic soil erosion during the end-Permian biotic crisis. Geology 33, 941–944 (2005).
Google Scholar 

16.
Sun, H. et al. Rapid enhancement of chemical weathering recorded by extremely light seawater lithium isotopes at the Permian–Triassic boundary. Proc. Natl Acad. Sci. USA 115, 3782–3787 (2018).
Google Scholar 

17.
Visscher, H. et al. Environmental mutagenesis during the end-Permian ecological crisis. Proc. Natl Acad. Sci. USA 101, 12952–12956 (2004).
Google Scholar 

18.
Burgess, S. D., Muirhead, J. D. & Bowring, S. A. Initial pulse of Siberian Traps sills as the trigger of the end-Permian mass extinction. Nat. Commun. 8, 164 (2017).
Google Scholar 

19.
Ward, P. D., Montgomery, D. R. & Smith, R. Altered river morphology in South Africa related to the Permian–Triassic extinction. Science 289, 1740–1743 (2000).
Google Scholar 

20.
Algeo, T. et al. Evidence for a diachronous late Permian marine crisis from the Canadian Arctic region. Geol. Soc. Am. Bull. 124, 1424–1448 (2012).
Google Scholar 

21.
Froelich, P. N. et al. Early oxidation of organic matter in pelagic sediments of the eastern equatorial: suboxic diagenesis. Geochim. Cosmochim. Acta 43, 1075–1090 (1979).
Google Scholar 

22.
Krom, M. D. & Berner, R. A. The diagenesis of phosphorus in a nearshore marine sediment. Geochim. Cosmochim. Acta 45, 207–216 (1981).
Google Scholar 

23.
Slomp, C. P., Van Der Gaast, S. J. & Van Raaphorst, W. Phosphorus binding by poorly crystalline iron oxides in North Sea sediments. Mar. Chem. 52, 55–73 (1996).
Google Scholar 

24.
Schenau, S. J. & De Lange, G. J. A novel chemical method to quantify fish debris in marine sediments. Limnol. Oceanogr. 45, 963–971 (2000).
Google Scholar 

25.
Ruttenberg, K. C. Development of a sequential extraction method for different forms of phosphorus in marine sediments. Limnol. Oceanogr. 37, 1460–1482 (1992).
Google Scholar 

26.
Egger, M., Jilbert, T., Behrends, T., Rivard, C. & Slomp, C. P. Vivianite is a major sink for phosphorus in methanogenic coastal surface sediments. Geochim. Cosmochim. Acta 169, 217–235 (2015).
Google Scholar 

27.
Cappellen, P. V. & Ingall, E. D. Redox stabilization of the atmosphere and oceans by phosphorus-limited marine productivity. Science 271, 493–496 (1996).
Google Scholar 

28.
Algeo, T. J. & Ingall, E. Sedimentary Corg:P ratios, paleocean ventilation, and Phanerozoic atmospheric pO2. Palaeogeogr. Palaeoclimatol. Palaeoecol. 256, 130–155 (2007).
Google Scholar 

29.
Harland, W. The Geology of Svalbard (Geological Society, 1997).

30.
Blomeier, D., Dustira, A. M., Forke, H. & Scheibner, C. Facies analysis and depositional environments of a storm-dominated, temperate to cold, mixed siliceous–carbonate ramp: the Permian Kapp Starostin Formation in NE Svalbard. Nor. J. Geol. 93, 75–93 (2013).
Google Scholar 

31.
Zuchuat, V. et al. A new high-resolution stratigraphic and palaeoenvironmental record spanning the end-Permian mass extinction and its aftermath in central Spitsbergen, Svalbard. Palaeogeogr. Palaeoclimatol. Palaeoecol. 554, 109732 (2020).

32.
Poulton, S. W. & Canfield, D. E. Development of a sequential extraction procedure for iron: implications for iron partitioning in continentally derived particulates. Chem. Geol. 214, 209–221 (2005).
Google Scholar 

33.
Algeo, T. & Tribovillard, N. Environmental analysis of paleoceanographic systems based on molybdenum–uranium covariation. Chem. Geol. 268, 211–225 (2009).
Google Scholar 

34.
Raiswell, R. & Canfield, D. E. Sources of iron for pyrite formation in marine sediments. Am. J. Sci. 298, 219–245 (1998).
Google Scholar 

35.
Poulton, S. W. & Raiswell, R. The low-temperature geochemical cycle of iron: from continental fluxes to marine sediment deposition. Am. J. Sci. 302, 774–805 (2002).
Google Scholar 

36.
Poulton, S. W. & Canfield, D. E. Ferruginous conditions: a dominant feature of the ocean through Earth’s history. Elements 7, 107–112 (2011).
Google Scholar 

37.
Lyons, T. W. & Severmann, S. A critical look at iron paleoredox proxies: new insights from modern euxinic marine basins. Geochim. Cosmochim. Acta 70, 5698–5722 (2006).
Google Scholar 

38.
Poulton, S. W., Fralick, P. W. & Canfield, D. E. Spatial variability in oceanic redox structure 1.8 billion years ago. Nat. Geosci. 3, 486–490 (2010).
Google Scholar 

39.
Doyle, K. A., Poulton, S. W., Newton, R. J., Podkovyrov, V. N. & Bekker, A. Shallow water anoxia in the Mesoproterozoic ocean: evidence from the Bashkir Meganticlinorium, Southern Urals. Precambrian Res. 317, 196–210 (2018).
Google Scholar 

40.
Kendall, B. et al. Pervasive oxygenation along late Archaean ocean margins. Nat. Geosci. 3, 647–652 (2010).
Google Scholar 

41.
Chafetz, H. S. & Reid, A. Syndepositional shallow-water precipitation of glauconitic minerals. Sediment. Geol. 136, 29–42 (2000).
Google Scholar 

42.
Peters, S. E. & Gaines, R. R. Formation of the ‘Great Unconformity’ as a trigger for the Cambrian explosion. Nature 484, 363–366 (2012).
Google Scholar 

43.
Manwell, C. Oxygen equilibrium of brachiopod Lingula hemerythrin. Science 132, 550–551 (1960).
Google Scholar 

44.
Peng, Y., Shi, G. R., Gao, Y., He, W. & Shen, S. How and why did the Lingulidae (Brachiopoda) not only survive the end-Permian mass extinction but also thrive in its aftermath? Palaeogeogr. Palaeoclimatol. Palaeoecol. 252, 118–131 (2007).
Google Scholar 

45.
Scott, C. & Lyons, T. W. Contrasting molybdenum cycling and isotopic properties in euxinic versus non-euxinic sediments and sedimentary rocks: refining the paleoproxies. Chem. Geol. 324-325, 19–27 (2012).
Google Scholar 

46.
Lyons, T. W. Sulfur isotopic trends and pathways of iron sulfide formation in upper Holocene sediments of the anoxic Black Sea. Geochim. Cosmochim. Acta 61, 3367–3382 (1997).
Google Scholar 

47.
Shen, Y., Canfield, D. E. & Knoll, A. H. Middle proterozoic ocean chemistry: evidence from the McArthur Basin, Northern Australia. Am. J. Sci. 302, 81–109 (2002).
Google Scholar 

48.
Borgnino, L., Avena, M. & De Pauli, C. Synthesis and characterization of Fe(III)-montmorillonites for phosphate adsorption. Colloids Surf. A 341, 46–52 (2009).
Google Scholar 

49.
Foster, W. J., Danise, S. & Twitchett, R. J. A silicified Early Triassic marine assemblage from Svalbard. J. Syst. Palaeontol. 15, 851–877 (2017).
Google Scholar 

50.
Barnosky, A. D. et al. Approaching a state shift in Earth’s biosphere. Nature 486, 52–58 (2012).
Google Scholar 

51.
Wedepohl, K. H. in Metals and Their Compounds in the Environment (ed. Merian, E.) 3–17 (Verlag Chemie, 1991).

52.
Thompson, J. et al. Development of a modified SEDEX phosphorus speciation method for ancient rocks and modern iron-rich sediments. Chem. Geol. 524, 383–393 (2019).
Google Scholar 

53.
Canfield, D. E., Raiswell, R., Westrich, J. T., Reaves, C. M. & Berner, R. A. The use of chromium reduction in the analysis of reduced inorganic sulfur in sediments and shales. Chem. Geol. 54, 149–155 (1986).
Google Scholar  More

1.
Sato, T., Ikeda, O., Yamakoshi, Y. & Tsubouchi, M. X-ray tomography for microstructural objects. Appl. Opt. 20, 3880–3883 (1981).
ADS  CAS  PubMed  Google Scholar 
2.
Elliott, J. C. & Dover, S. D. X-ray microtomography. J. Microsc. 126, 211–213 (1982).
CAS  PubMed  Google Scholar 

3.
Elliott, J. C. & Dover, S. D. X-ray microscopy using computerized axial tomography. J. Microsc. 138, 329–331 (1985).
CAS  PubMed  Google Scholar 

4.
Sutton, M., Rahman, I. & Garwood, R. Techniques for Virtual Palaeontology 208 (Wiley-Blackwell, London, 2014).
Google Scholar 

5.
Davies, T. G. et al. Open data and digital morphology. Proc. R. Soc. B 284, 20170194 (2017).
PubMed  Google Scholar 

6.
Gutiérrez, Y., Ott, D., Töpperwien, M., Salditt, T. & Scherber, C. X-ray computed tomography and its potential in ecological research: a review of studies and optimization of specimen preparation. Ecol. Evol. 8, 7717–7732 (2018).
PubMed  PubMed Central  Google Scholar 

7.
Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

8.
Ketcham, R. A. Computational methods for quantitative analysis of three-dimensional features in geological specimens. Geosphere 1, 32–41 (2005).
ADS  Google Scholar 

9.
Page, L. M., MacFadden, B. J., Fortes, J. A., Soltis, P. S. & Riccardi, G. Digitization of biodiversity collections reveals biggest data on biodiversity. Bioscience 65, 841–842 (2015).
Google Scholar 

10.
Faulwetter, S., Vasileiadou, A., Kouratoras, M., Dailianis, T. & Arvanitidis, C. Micro-computed tomography: introducing new dimensions to taxonomy. ZooKeys 263, 1–45 (2013).
Google Scholar 

11.
Akkari, N. et al. New avatars for Myriapods: complete 3D morphology of type specimens transcends conventional species description (Myriapoda, Chilopoda). PLoS ONE 13, 0200158. https://doi.org/10.1371/journal.pone.0200158 (2018).
CAS  Article  Google Scholar 

12.
Fontaine, B., Perrard, A. & Bouchet, P. 21 years of shelf life between discovery and description of new species. Curr. Biol. 22, R943–R944 (2012).
CAS  PubMed  Google Scholar 

13.
Hipsley, C. A. & Sherratt, E. Psychology, not technology, is our biggest challenge to open digital morphology data. Sci. Data. 6, 41 (2019).
PubMed  PubMed Central  Google Scholar 

14.
Blagoderov, V., Kitching, I. J., Livermore, L., Simonsen, T. J. & Smith, V. S. No specimen left behind: industrial scale digitization of natural history collections. Zookeys 209, 133–146 (2012).
Google Scholar 

15.
Rogers, N. Museum drawers go digital. Science 352, 762–765 (2016).
ADS  CAS  PubMed  Google Scholar 

16.
Meineke, E. K., Davies, T. J., Daru, B. H. & Davis, C. C. Biological collections for understanding biodiversity in the Anthropocene. Philos. Trans. R. Soc. B 374, 20170386 (2018).
Google Scholar 

17.
Schmitt, C. J., Cook, J. A., Zamudio, K. R. & Edwards, S. V. Museum specimens of terrestrial vertebrates are sensitive indicators of environmental change in the Anthropocene. Philos. Trans. R. Soc. B 374, 20170387 (2018).
Google Scholar 

18.
Sherratt, E., Gower, D. J., Klingenberg, C. P. & Wilkinson, M. Evolution of cranial shape in caecilians (Amphibia: Gymnophiona). Evol. Biol. 41, 528–545 (2014).
Google Scholar 

19.
Watanabe, A. et al. Ecomorphological diversification in squamates from conserved pattern of cranial integration. Proc. Natl. Acad. Sci. 116, 14688–14697 (2019).
CAS  PubMed  Google Scholar 

20.
Simon, M. N., Machado, F. A. & Marroig, G. High evolutionary constraints limited adaptive responses to past climate changes in toad skulls. Proc. R. Soc. B-Biol. Sci. 283, 20161783 (2016).
Google Scholar 

21.
Sherratt, E., Serb, J. M. & Adams, D. C. Rates of morphological evolution, asymmetry and morphological integration of shell shape in scallops. BMC Evol. Biol. 17, 248 (2017).
PubMed  PubMed Central  Google Scholar 

22.
Chira, A. M. et al. Correlates of rate heterogeneity in avian ecomorphological traits. Ecol. Lett. 21, 1505–1514 (2018).
CAS  PubMed  PubMed Central  Google Scholar 

23.
Percival, C. J. et al. The effect of automated landmark identification on morphometric analyses. J. Anat. 234, 917–935 (2019).
PubMed  PubMed Central  Google Scholar 

24.
Bouxsein, M. L. et al. Guidelines for assessment of bone microstructure in rodents using micro –computed tomography. J. Bone Miner. Res. 25, 1468–1486 (2010).
Google Scholar 

25.
Broeckhoven, C. & du Plessis, A. X-ray microtomography in herpetological research: a review. Amphibia-Reptilia 39, 377–401 (2018).
Google Scholar 

26.
Marcy, A. E., Fruciano, C., Phillips, M. J., Mardon, K. & Weisbecker, V. Low resolution scans can provide a sufficiently accurate, cost- and time-effective alternative to high resolution scans for 3D shape analyses. PeerJ 6, 5032. https://doi.org/10.7717/peerj.5032 (2018).
Article  Google Scholar 

27.
Gray, J. A., McDowell, M. C., Hutchinson, M. N. & Jones, M. E. Geometric morphometrics provides an alternative approach for interpreting the affinity of fossil lizard jaws. J. Herpetol. 51, 375–382 (2017).
Google Scholar 

28.
Thorn, K. M., Hutchinson, M. N., Archer, M. & Lee, M. S. Y. A new scincid lizard from the Miocene of northern Australia, and the evolutionary history of social skinks (Scincidae: Egerniinae). J. Vertebr. Paleontol. 39, 1 (2019).
Google Scholar 

29.
Chaplin, K., Sumner, J., Hipsley, C. A. & Melville, J. An integrative approach using phylogenomics and high-resolution X-ray computed tomography for species delimitation in cryptic taxa. Syst. Biol. 69, syz048. https://doi.org/10.1093/sysbio/syz048 (2019).
Article  Google Scholar 

30.
Melville, J. et al. Integrating phylogeography and high-resolution X-ray CT reveals five new cryptic species and multiple hybrid zones among Australian earless dragons. R. Soc. Open Sci. 6, 191166. https://doi.org/10.1098/rsos.191166 (2019).
ADS  Article  PubMed  PubMed Central  Google Scholar 

31.
Caro, A., Gómez-Moliner, B. J. & Madeira, M. J. Integrating multilocus DNA data and 3D geometric morphometrics to elucidate species boundaries in the case of Pyrenaearia (Pulmonata: Hygromiidae). Mol. Phylogenet. Evol. 132, 194–206 (2019).
CAS  PubMed  Google Scholar 

32.
Winkelmann, C. T. & Wise, L. D. High-throughput micro-computed tomography imaging as a method to evaluate rat and rabbit fetal skeletal abnormalities for developmental toxicity studies. J. Pharmacol. Tox. Met. 59, 156–165 (2009).
CAS  Google Scholar 

33.
Sevilla, R. S. et al. Development and optimization of a high-throughput micro-computed tomography imaging method incorporating a novel analysis technique to evaluate bone mineral density of arthritic joints in a rodent model of collagen induced arthritis. Bone 73, 32–41 (2015).
PubMed  Google Scholar 

34.
Wong, M. D., Maezawa, Y., Lerch, J. P. & Henkelman, R. M. Automated pipeline for anatomical phenotyping of mouse embryos using micro-CT. Development 141, 2533–2541 (2014).
CAS  PubMed  PubMed Central  Google Scholar 

35.
Wu, D. et al. Combining high-throughput micro-CT-RGB phenotyping and genome-wide association study to dissect the genetic architecture of tiller growth in rice. J. Exp. Bot. 70, 545–561 (2019).
CAS  PubMed  Google Scholar 

36.
Ding, Y. et al. Computational 3D histological phenotyping of whole zebrafish by X-ray histotomography. Elife 8, 44898. https://doi.org/10.7554/eLife.44898.001 (2019).
Article  Google Scholar 

37.
Staedtler, Y. M., Masson, D. & Schönenberger, J. Plant tissues in 3D via X-ray tomography: simple contrasting methods allow high resolution imaging. PLoS ONE 8, 75295. https://doi.org/10.1371/journal.pone.0075295 (2013).
ADS  CAS  Article  Google Scholar 

38.
Keklikoglou, K. et al. Micro-computed tomography for natural history specimens: a handbook of best practice protocols. Eur. J. Taxon. 522, 1–55 (2019).
Google Scholar 

39.
Adams, D., Collyer, M. & Kaliontzopoulou, A. Geomorph: Software for geometric morphometric analyses. R package version 3.1.0. https://cran.r-project.org/package=geomorph (2019).

40.
Klingenberg, C. P. MorphoJ: an integrated software package for geometric morphometrics. Mol. Ecol. Resour. 11, 353–357 (2011).
PubMed  Google Scholar 

41.
du Plessis, A., Broeckhoven, C., Guelpa, A. & le Roux, S. G. Laboratory X-ray micro-computed tomography: a user guideline for biological samples. Gigascience 6, 1–11 (2017).
PubMed  PubMed Central  Google Scholar 

42.
Hocknull, S. A., Zhao, J. X., Feng, Y. X. & Webb, G. E. Responses of middle Pleistocene rainforest vertebrates to climate change in Australia. Earth Planet. Sci. Lett. 264, 317–331 (2007).
ADS  CAS  Google Scholar 

43.
Hedrick, B. P. et al. Digitization and the future of natural history collections. Bioscience 70, 243–251 (2020).
Google Scholar 

44.
Lawrence, R. A. & Hocknull, S. Engaging the public with small vertebrate fossils and utilizing citizen science to maximise scientific discovery at Capricorn Caves, Central Eastern Queensland, Australia. J. Vertebr. Paleontol. Program Abstr. 139 (2019).

45.
Long, J. A., Young, G. C., Holland, T., Senden, T. J. & Fitzgerald, E. M. An exceptional Devonian fish from Australia sheds light on tetrapod origins. Nature 444, 199–202 (2006).
ADS  CAS  PubMed  Google Scholar 

46.
Arbour, J. H., Curtis, A. A. & Santana, S. E. Signatures of echolocation and dietary ecology in the adaptive evolution of skull shape in bats. Nat. Commun. 10, 2036 (2019).
ADS  PubMed  PubMed Central  Google Scholar 

47.
Park, T., Fitzgerald, E. M. & Evans, A. R. Ultrasonic hearing and echolocation in the earliest toothed whales. Biol. Lett. 12, 20160060. https://doi.org/10.1098/rsbl.2016.0060 (2016).
Article  PubMed  PubMed Central  Google Scholar 

48.
Müller, J. et al. Eocene lizard from Germany reveals amphisbaenian origins. Nature 473, 364–367 (2011).
ADS  PubMed  Google Scholar 

49.
Miralles, A. et al. Distinct patterns of desynchronized limb regression in Malagasy scincine lizards (Squamata, Scincidae). PLoS ONE 10, 0126074. https://doi.org/10.1371/journal.pone.0126074 (2015).
CAS  Article  Google Scholar 

50.
Weisbecker, V. Monotreme ossification sequences and the riddle of mammalian skeletal development. Evolution 65, 1323–1335 (2011).
PubMed  Google Scholar 

51.
Newton, A. H. et al. Letting the ‘cat’ out of the bag: pouch young development of the extinct Tasmanian tiger revealed by X-ray computed tomography. R. Soc. Open Sci. 5, 171914. https://doi.org/10.1098/rsos.171914 (2018).
ADS  Article  PubMed  PubMed Central  Google Scholar 

52.
Hublin, J. J. et al. New fossils from Jebel Irhoud, Morocco and the pan-African origin of Homo sapiens. Nature 546, 289–292 (2017).
ADS  CAS  PubMed  Google Scholar 

53.
Beaudet, A. & Gilissen, E. Fossil primate endocasts: perspectives from advanced imaging techniques In Digital Endocasts: from Skulls to Brains (eds. Bruner, E., Ogihara, N. & Tanabe, H.) 47–58 (Springer, Berlin, 2018).

54.
Wulff, N. C., Lehmann, A. W., Hipsley, C. A. & Lehmann, G. U. C. Copulatory courtship by bushcricket genital titillators revealed by functional morphology, μCT scanning for 3D reconstruction and female sense structures. Arthropod Struct. Dev. 44, 388–397 (2015).
PubMed  Google Scholar 

55.
Gee, C. T. Applying microCT and 3D visualization to Jurassic silicified conifer seed cones: a virtual advantage over thin-sectioning. Appl. Plant Sci. 1, 1300039. https://doi.org/10.3732/apps.1300039 (2013).
Article  Google Scholar 

56.
Meyer, M. et al. Three-dimensional microCT analysis of the Ediacara fossil Pteridinium simplex sheds new light on its ecology and phylogenetic affinity. Precambrian Res. 249, 79–87 (2014).
ADS  CAS  Google Scholar 

57.
Gooday, A. J., Sykes, D., Goral, T., Zubkov, M. V. & Glover, A. G. Micro-CT 3D imaging reveals the internal structure of three abyssal xenophyophore species (Protista, Foraminifera) from the eastern equatorial Pacific Ocean. Sci. Rep. 8, 12103. https://doi.org/10.1038/s41598-018-30186-2 (2018).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

58.
Dunlop, J. A. et al. Microtomography of the Baltic amber tick Ixodes succineus reveals affinities with the modern Asian disease vector Ixodes ovatus. BMC Evol. Biol. 16, 203 (2016).
PubMed  PubMed Central  Google Scholar  More

1.
Zhang, T., Niinemets, Ü., Sheffield, J. & Lichstein, J. W. Shifts in tree functional composition amplify the response of forest biomass to climate. Nature 556, 99–102 (2018).
CAS  Google Scholar 
2.
Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).
CAS  Google Scholar 

3.
Parmesan, C. & Hanley, M. E. Plants and climate change: complexities and surprises. Ann. Bot. 116, 849–864 (2015).
Google Scholar 

4.
Telwala, Y., Brook, B. W., Manish, K. & Pandit, M. K. Climate-induced elevational range shifts and increase in plant species richness in a Himalayan biodiversity epicentre. PLoS ONE 8, e57103 (2013).
CAS  Google Scholar 

5.
Jump, A. S., Huang, T. J. & Chou, C. H. Rapid altitudinal migration of mountain plants in Taiwan and its implications for high altitude biodiversity. Ecography 35, 204–210 (2012).
Google Scholar 

6.
Angelo, C. L. & Daehler, C. C. Upward expansion of fire‐adapted grasses along a warming tropical elevation gradient. Ecography 36, 551–559 (2013).
Google Scholar 

7.
Morueta-Holme, N. et al. Strong upslope shifts in Chimborazo’s vegetation over two centuries since Humboldt. Proc. Natl Acad. Sci. USA 112, 12741–12745 (2015).
CAS  Google Scholar 

8.
Parolo, G. & Rossi, G. Upward migration of vascular plants following a climate warming trend in the Alps. Basic Appl. Ecol. 9, 100–107 (2008).
Google Scholar 

9.
Chen, I.-C., Hill, J. K., Ohlemüller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024–1026 (2011).
CAS  Google Scholar 

10.
Moret, P., Muriel, P., Jaramillo, R. & Dangles, O. Humboldt’s tableau physique revisited. Proc. Natl Acad. Sci. USA 116, 12889–12894 (2019).
CAS  Google Scholar 

11.
Lenoir, J. & Svenning, J. C. Climate-related range shifts—a global multidimensional synthesis and new research directions. Ecography 38, 15–28 (2015).
Google Scholar 

12.
Lenoir, J., Gegout, J. C., Marquet, P. A., de Ruffray, P. & Brisse, H. A significant upward shift in plant species optimum elevation during the 20th century. Science 320, 1768–1771 (2008).
CAS  Google Scholar 

13.
Feeley, K. J. Distributional migrations, expansions, and contractions of tropical plant species as revealed in dated herbarium records. Glob. Change Biol. 18, 1335–1341 (2012).
Google Scholar 

14.
Fei, S. et al. Divergence of species responses to climate change. Sci. Adv. 3, e1603055 (2017).
Google Scholar 

15.
Zhu, K., Woodall, C. W. & Clark, J. S. Failure to migrate: lack of tree range expansion in response to climate change. Glob. Change Biol. 18, 1042–1052 (2012).
Google Scholar 

16.
Crimmins, S. M., Dobrowski, S. Z., Greenberg, J. A., Abatzoglou, J. T. & Mynsberge, A. R. Changes in climatic water balance drive downhill shifts in plant species’ optimum elevations. Science 331, 324–327 (2011).
CAS  Google Scholar 

17.
Kelly, A. E. & Goulden, M. L. Rapid shifts in plant distribution with recent climate change. Proc. Natl Acad. Sci. USA 105, 11823–11826 (2008).
CAS  Google Scholar 

18.
Wieczynski, D. J. et al. Climate shapes and shifts functional biodiversity in forests worldwide. Proc. Natl Acad. Sci. USA 116, 587–592 (2019).
CAS  Google Scholar 

19.
Bertrand, R. et al. Changes in plant community composition lag behind climate warming in lowland forests. Nature 479, 517–520 (2011).
CAS  Google Scholar 

20.
Blonder, B. et al. Linking environmental filtering and disequilibrium to biogeography with a community climate framework. Ecology 96, 972–985 (2015).
Google Scholar 

21.
Gottfried, M. et al. Continent-wide response of mountain vegetation to climate change. Nat. Clim. Change 2, 111–115 (2012).
Google Scholar 

22.
Duque, A., Stevenson, P. & Feeley, K. J. Thermophilization of adult and juvenile tree communities in the northern tropical Andes. Proc. Natl Acad. Sci. USA 112, 10744–10749 (2015).
CAS  Google Scholar 

23.
Fadrique, B. et al. Widespread but heterogeneous responses of Andean forests to climate change. Nature 564, 207–212 (2018).
CAS  Google Scholar 

24.
Feeley, K. J., Hurtado, J., Saatchi, S., Silman, M. R. & Clark, D. B. Compositional shifts in Costa Rican forests due to climate-driven species migrations. Glob. Change Biol. 19, 3472–3480 (2013).
Google Scholar 

25.
Feeley, K. J. et al. Upslope migration of Andean trees. J. Biogeogr. 38, 783–791 (2011).
Google Scholar 

26.
Esquivel‐Muelbert, A. et al. Compositional response of Amazon forests to climate change. Glob. Change Biol. 25, 39–56 (2019).
Google Scholar 

27.
Feeley, K. J. & Silman, M. R. Biotic attrition from tropical forests correcting for truncated temperature niches. Glob. Change Biol. 16, 1830–1836 (2010).
Google Scholar 

28.
Title, P. O. & Bemmels, J. B. ENVIREM: an expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. Ecography 41, 291–307 (2018).
Google Scholar 

29.
Santiago, L. S. et al. Coordination and trade-offs among hydraulic safety, efficiency and drought avoidance traits in Amazonian rainforest canopy tree species. New Phytol. 218, 1015–1024 (2018).
Google Scholar 

30.
Strzepek, K., Yohe, G., Neumann, J. & Boehlert, B. Characterizing changes in drought risk for the United States from climate change. Environ. Res. Lett. 5, 044012 (2010).
Google Scholar 

31.
Sheffield, J. & Wood, E. F. Projected changes in drought occurrence under future global warming from multi-model, multi-scenario, IPCC AR4 simulations. Clim. Dynam. 31, 79–105 (2008).
Google Scholar 

32.
Duffy, P. B., Brando, P., Asner, G. P. & Field, C. B. Projections of future meteorological drought and wet periods in the Amazon. Proc. Natl Acad. Sci. USA 112, 13172–13177 (2015).
CAS  Google Scholar 

33.
Conradi, T., Van Meerbeek, K., Ordonez, A. & Svenning, J. C. Biogeographic historical legacies in the net primary productivity of Northern Hemisphere forests. Ecol. Lett. 23, 800–810 (2020).

34.
Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. BioScience 67, 534–545 (2017).
Google Scholar 

35.
Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth. BioScience 51, 933–938 (2001).
Google Scholar 

36.
Anderson-Teixeira, K. J. et al. CTFS-ForestGEO: a worldwide network monitoring forests in an era of global change. Glob. Change Biol. 21, 528–549 (2015).
Google Scholar 

37.
Dauby, G. et al. RAINBIO: a mega-database of tropical African vascular plants distributions. PhytoKeys 74, 1–18 (2016).
Google Scholar 

38.
DeWalt, S. J., Bourdy, G., de Michel, L. R. & Quenevo, C. Ethnobotany of the Tacana: quantitative inventories of two permanent plots of Northwestern Bolivia. Econ. Bot. 53, 237–260 (1999).
Google Scholar 

39.
Enquist, B. & Boyle, B. SALVIAS—the SALVIAS vegetation inventory database. Biodivers. Ecol. 4, 288 (2012).
Google Scholar 

40.
Enquist, B. J., Condit, R., Peet, R. K., Schildhauer, M. & Thiers, B. M. Cyberinfrastructure for an integrated botanical information network to investigate the ecological impacts of global climate change on plant biodiversity. Preprint at https://peerj.com/preprints/2615/ (2016).

41.
Fegraus, E. Tropical Ecology Assessment and Monitoring Network (TEAM Network). Biodivers. Ecol. 4, 287 (2012).
Google Scholar 

42.
Maitner, B. S. et al. The BIEN R package: a tool to access the Botanical Information and Ecology Network (BIEN) database. Methods Ecol. Evol. 9, 373–379 (2018).
Google Scholar 

43.
Peet, R. K. et al. Vegetation-plot database of the Carolina Vegetation Survey. Biodivers. Ecol. 4, 243–253 (2012).
Google Scholar 

44.
Peet, R. K., Lee, M. T., Jennings, M. D. & Faber-Langendoen, D. VegBank: a permanent, open-access archive for vegetation plot data. Biodivers. Ecol. 4, 233–241 (2012).
Google Scholar 

45.
Sosef, M. S. M. et al. Exploring the floristic diversity of tropical Africa. BMC Biol. 15, 15 (2017).
Google Scholar 

46.
König, C. et al. Biodiversity data integration—the significance of data resolution and domain. PLoS Biol. 17, e3000183 (2019).
Google Scholar 

47.
Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 170122 (2017).
Google Scholar 

48.
Enquist, B. J. et al. The commonness of rarity: global and future distribution of rarity across land plants. Sci. Adv. 5, eaaz0414 (2019).
Google Scholar 

49.
Feeley, K. J., Davies, S. J., Perez, R., Hubbell, S. P. & Foster, R. B. Directional changes in the species composition of a tropical forest. Ecology 92, 871–882 (2011).
Google Scholar 

50.
Gosselin, F. Putting floristic thermophilization in forests into a conservation biology perspective: beyond mean trait approaches. Ann. For. Sci. 73, 215–218 (2016).
Google Scholar 

51.
De Frenne, P. et al. Microclimate moderates plant responses to macroclimate warming. Proc. Natl Acad. Sci. USA 110, 18561–18565 (2013).
Google Scholar 

52.
Stevens, J. T., Safford, H. D., Harrison, S. & Latimer, A. M. Forest disturbance accelerates thermophilization of understory plant communities. J. Ecol. 103, 1253–1263 (2015).
Google Scholar 

53.
Bush, M. B., Silman, M. R. & Urrego, D. H. 48,000 years of climate and forest change in a biodiversity hot spot. Science 303, 827–829 (2004).
CAS  Google Scholar 

54.
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2014).

55.
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Soft. 67, 1–48 (2015).
Google Scholar 

56.
Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R 2 from generalized linear mixed‐effects models. Methods Ecol. Evol. 4, 133–142 (2013).
Google Scholar  More

Data sources
Our dataset includes all collared wolves monitored by telemetry (virtually all VHF radio-transmitters) in Wisconsin (WI) between 1979 and April 2012, published previously in full detail5. The dataset includes 486 wolves fitted with collars by the Wisconsin Department of Natural Resources (WDNR) or its agents, plus 27 collared wolves initially captured in the neighboring state of Michigan, which later migrated to Wisconsin (for a total n = 513 individuals).
Our dataset includes 257 wolves that were reported by the WDNR as ‘lost-to-follow-up’ (LTF) because they were not detected via repeated aerial telemetry. LTF may occur for various reasons: (a) individuals that have moved permanently out of telemetry range (i.e., migrants), (b) collars that stopped transmitting because of battery depletion or mechanical failure, and (c) unreported poaching followed by destruction of the transmitter (cryptic poaching). The WDNR suspended telemetry monitoring and assigned an LTF to a wolf if their personnel were unable to detect the collar signal after several months of statewide aerial or ground telemetry. However, the WDNR did not quantify telemetry effort. Dead wolves (n = 242) were recovered by the mortality signals emitted from their collars, after legal killing by management agents, or after private citizens reported a dead wolf between monitoring flights41,42. Some LTF wolves were subsequently recovered by means other than telemetry, such as reporting by private citizens. For these cases we used the estimated date of LTF for the endpoint (i.e., death from various causes or disappearance). For fuller treatment of disappearances, detection, and causes of individual wolf death see5.
Estimating conditional hazards
Our analyses exploit the survival history of monitored wolves, measured in days from date of collaring until date of endpoint (i.e., date of death, last monitoring date, or end of our analysis period on April 15, 2012) for each monitored individual.
We modeled endpoint-specific hazard and subhazard in a competing risk framework, which are extensions of survival (or ‘time-to-event’) analyses. Survival analyses estimate ‘time-to-event’ functions, which describe the probability of observing a time interval (T) to an endpoint (‘event’) within a specified analysis time (t) that a subject was observed, such that (Sleft(tright)=P(T >t)) . Alternatively, these techniques allow for calculating the hazard function, ({h}_{k}(t)), or the instantaneous rate of occurrence of a particular endpoint k conditional on not experiencing any endpoint until time t30,43,44. We also used the (conditional) hazard functions for all endpoints to estimate the probability of any endpoint up to a particular time T, i.e., the incidence over time for particular endpoints, such as LTF or death by vehicle collision, nonhuman cause, etc.
Semi-parametric, Cox proportional hazard models estimate how the endpoint-specific ({h}_{k}(t)) changes as a function of survival time and a set of hypothetical covariates; (Sleft(tright)={e}^{-{h}_{k}(t,x,beta )}), where x is a vector of covariates acting on the hazard, and β is a vector of their respective parameter estimates. The estimation of covariate effects on the endpoint-specific hazard is modeled as ({h}_{k}left(tright)= {h}_{0k}(t){e}^{({beta }_{1}{x}_{1}+dots +{beta }_{j}{x}_{j})}), where ({h}_{0k}(t)) is an unestimated baseline hazard function (i.e., semi-parametric) and ({beta }_{j}) represent estimates of hazard ratios (HRs) for each covariate ({x}_{j}) (HR  1 an increase in hazard).
The estimated HRs, ({beta }_{j}), are assumed proportional throughout the analysis time, t, (only differ multiplicatively between categorical covariate levels). Furthermore, we include time-varying effects on hazards and incidences by including interactions between covariates and monitoring time (in days) (see “Model covariates” section)43,44,45. These models allow us to estimate covariate effects on the rate of occurrence of an endpoint looking only at those wolves reaching that endpoint (so that the presence of other endpoints would not affect these estimates). Inference from hazards is limited in the presence of other endpoints competing to bring about the end of monitoring because interaction between endpoint hazards is unaccounted for. Interactions between endpoints are crucial for our tests of hypotheses that relate legal killing to poaching (i.e., illegal killing, both reported poaching and cryptic poaching through the LTF endpoint) at an individual level.
Estimating unconditional incidences
Competing risk analyses extend standard survival analysis by considering multiple endpoints simultaneously (e.g.: multiple causes of death or disappearance). These models are useful for estimating the incidence of a particular endpoint while accounting for the potential occurrence of all other competing endpoints (e.g., the incidence of wolf-poaching in the presence of other causes of death or LTF). In a competing risk framework, individuals can potentially experience one of multiple mutually exclusive endpoints at each interval T. Because only one endpoint can occur first, we refer to the endpoints as ‘competing’ over time, and to the respective probabilities over time as ‘competing risks’.
Rather than estimating the endpoint-specific HRs, as in the Cox model explained above, competing risk analyses estimate the cumulative incidence function (CIF) for each endpoint, defined by the failure probability (Prob(Tle t,D=k)); the cumulative probability of endpoint k occurring over time in the presence of other competing endpoints30,31,46. Competing risk analysis accounts for the CIF of any endpoint being a function of all endpoint-specific hazards, ({h}_{k}(t)), reflecting the rate of occurrence of that endpoint as well as how it is influenced by others32.
Although CIFs can be derived by using all endpoint-specific HRs derived from Cox models, such a procedure cannot estimate the magnitude of the relative difference between covariate CIFs for each endpoint. Using Fine-Gray (FG) models instead of Cox models allows us to estimate differences in CIFs for a given endpoint conditional on covariates31,47. FG models are also semi-parametric (i.e., the baseline subhazard function is not estimated) and assume proportionality of subhazard functions, defined as the risk of failure at time t from endpoint k in subjects that have yet to reach an endpoint or have experienced any other endpoint30,31,47. Therefore, FG models estimate the subhazard functions of endpoint-specific CIFs using similar regression techniques as the Cox model (but on the subhazard rather than the hazard thus yielding SHR rather than HR for ratios that compare to a standard), but parameter interpretation changes. Subhazards are interpreted as relative incidence in the presence of other endpoints29,30,31.
In sum, endpoint-specific Cox models and their HRs allow us to test the hypothesis that liberalized wolf-killing affected the rate of occurrence of any endpoint; for example, if liberalized killing increased or decreased the rate of occurrence of reported poaching or LTF. By contrast, the FG models and their SHRs allow us to account for the simultaneous presence of all competing endpoints to test if and how much liberalized killing affected the probability and incidence of reported poaching or LTF, in addition to the potential simultaneous effects of other covariates described after data preparation. CIFs allow us to visualize those effects on incidence while considering the prevalence of each endpoint in the population.
Data preparation
For wolves monitored until death, our endpoints classify the cause of death by 5 mutually exclusive causes of death similar to5: “collision” (trauma caused by vehicles; n = 24, 4.7%), “legal” (lethal control by management agencies; n = 32, 6.2%), “poached” (illegal human-caused killing; n = 88, 17.2%), “nonhuman” (causes unrelated to people, e.g.: other wolves or diseases; n = 77, 15.0%) and “uncertain” (uncertain cause but the wolf carcass was recovered, i.e.: difficult to discern in necropsy; n = 21, 4.1%). We added a sixth distinct category of LTF endpoint (n = 231, 45.0%, and see Supplementary Data S1) and we address 40 collared wolves missing endpoint dates (7.7%) below.
We defined the date of endpoint either as the recorded date of death for wolves monitored by telemetry until death (n = 242, 47.2% of sample) or as the date of last telemetry contact for LTF wolves (n = 231, 45.0%). Some of the LTF wolves were found dead later (n = 51), through means other than telemetry (e.g., visual detection), which might bias to a later date of ‘death’, if carcasses were found long after the actual date of death which was not uncommon5. Given the sensitivity of time-to-event models to the accuracy of endpoint dates and because most (n = 206, 78% of the LTF subsample) were never detected again, our step to restrict the record histories of LTF wolves to the last date of monitoring is an important yet imperfect improvement in measurement precision.
Accounting for all individuals at risk of experiencing an endpoint at any particular time T (the ‘risk set’) is essential for obtaining unbiased estimates of HR, SHR, and CIF43,44,48. Omitting a class of individuals (e.g., LTF) strongly biased risk estimates for four populations of wolves, and in the Wisconsin wolf population specifically, as summarized above5,9.
Model covariates
We included three time-dependent categorical covariates in our models. Time-dependent covariates are variables that change value due to external events at a known date, either for individual wolves or all wolves. For example, we modeled policy period as time-dependent by changing the covariate value at the dates of policy change for a particular individual’s history of monitoring. To assign categorical values of the time-dependent covariates to each monitored wolf, we split each history at each specified date of change in covariate value. We refer to the splits for a monitored wolf as ‘spells’, because they refer to briefer time periods within an individual’s total monitoring time T. So, the time-dependent categorical covariates have a duration that overlaps the monitoring period for collared wolves during that period, but the wolves have individual spells that might be less than or equal to the duration (see example in Supplementary Table S1).
Our main covariate of interest is policy that liberalized wolf-killing (lib_kill where 1 = liberalized killing, 0 = full protection). Gray wolves experienced full protection under the ESA from 1979 to March 31, 2003. From April 1, 2003, wolves in WI and MI were subject to 11 alternating sequential, non-overlapping periods in which wolf-killing policies were first liberalized and then restricted for varied durations (Supplementary Table S2)5,12,28. Although WDNR or its agents occasionally killed a wolf during full protection periods, in capture-related accidents or after verified threats to human safety, these were rare and few. By contrast, liberalized killing periods were characterized by an announcement of policy change that allowed managers or private landowners to kill wolves for perceived or verified losses of domestic animals. Liberalized killing periods included:

‘Downlisting’ to threatened status (one period starting April 1, 2003; 670 days, Supplementary Table S2)—allows for lethal control in defense of human property or safety as well as for population management or conservation purposes under ESA section Rule 4(d).

Issuing of sub-permits for “take” (“to harass, harm, pursue, hunt, shoot, wound, kill, trap, capture, or collect, or to attempt to engage in any such conduct” [ESA]) of wolves by managers and sometimes private landowners (periods within 2005 and 2006; 263 days, Supplementary Table S2) under ESA sections 9 and 10.

‘Delisting’, or removing ESA protections entirely (periods of 2007, 2009 and 2012; 701 days, Supplementary Table S2).

Choosing to end our study on April 14, 2012 presented several advantages. First, the WDNR summarized wolf census data and population reports for the preceding year on April 15th. Second, we could compare our results to prior work12,21,49. Third, the April 2012 passage of Act 169 enacting the first wolf-hunting seasons since wolf bounties were terminated in the 1950s50 was a qualitatively different policy signal than those of the liberalized killing periods (Supplementary Table S2).
Our second binary covariate, winter, produced spells for October–March (‘1’, winter) and April-September (‘0’, summer). Our inclusion of this variable is warranted by robust independent evidence of seasonal differences in both overall and endpoint-specific mortality21,51,52. Most LTF endpoints occurred during winter months (143/231 = 62% of LTF wolves, with n = 40 wolves censored).
Our third covariate had three levels for periods with different methods of censusing wolves (method_change). In the winter of 1994–1995 the wolf census methods changed, and did so again sometime between summer 2000 and winter 2003–2004, with changes in monitoring techniques and protocols for data handling18,23. Those changes affected effort and training of wolf census-takers, so might have affected the detection and monitoring effort for collared wolves also. Although there is some ambiguity in the literature over the exact dates of these changes, we opted for the following splits based on year of endpoint: 1979–1994 (‘1’), 1995–2000 (‘2’) and 2001–2012 (‘3’).
Imputation for 2012 records without endpoint data
We right-censored the interval for individuals that did not experience an endpoint during the analysis period (start of monitoring until April 14, 2012), meaning they are considered as part of the risk set from collaring until the end of the analysis period. Our dataset includes 40 wolves without attributed mortality of disappearance data, because we could not find their endpoint (i.e., cause of death or disappearance) in public records after December 31st, 2011 (see supplementary data files for WDNR monitoring records for 2012 and 2013). Although 14 of those 40 wolves were later found dead in mortality reports between May 2012 and October 2013 (Supplementary Data S2), those reports did not reveal the last date of monitoring but rather a lengthy interval without a record of monitoring followed by discovery of the dead animal. Therefore, we conservatively censored those 14 wolves at April 14, 2012 to consider them as within the risk set (monitored) for the corresponding time intervals, yet without experiencing an endpoint during that time. For the other 26 censored wolves that vanished from public records after December 31st, 2011, our repeated efforts to obtain data were not fulfilled by the WDNR. We submitted four separate requests to the WDNR (1 open records request, 1 state Natural Heritage Inventory request, a personal request to research staff who have published analyses with those data, and we enlisted the aid of the lieutenant governor and governor’s offices to request those data) for all collared wolves monitored in the state in 2012. Therefore, we simulated their endpoints in three scenarios described below.
We imputed either an LTF or censored status to the n = 26 wolves with missing endpoints based on the rationale that if any of these monitored wolves had suffered a death rather than a disappearance, their deaths should have appeared in mortality records spanning January 1, 2012 (when missing records for these wolves begin) to October 31, 2013, as happened with the 14 wolves with missing endpoint but found in subsequent mortality reports and therefore censored. Thus, the two remaining possibilities are that these wolves were either LTF or survived our analysis period and beyond October 31, 2013 which means they must be included in the risk set but be censored for endpoint analyses because they do not fit our 6 categories of endpoint.
For our simulation scenarios, we developed a series of FG imputation models (IMs) with LTF as the endpoint of interest using the above covariates for the full, original dataset (i.e., with all 40 wolves with missing data classified as ‘censored’ on April 14, 2012). We then used the most appropriate FG model (accounting for Akaike’s Information Criterion (AIC), Bayesian Information Criterion (BIC), log-likelihood (LL), parsimony and proportionality assumptions) to predict the probability of LTF incidence by April 14, 2012 for each of the 26 wolves. Because we assumed all 26 wolves were alive on April 14, 2012 (i.e., each is imputed their maximum survival time) for all models, whereas they might actually have disappeared earlier in 2012, our approach is conservative because it likely underestimates the relative incidence of LTF.
To calculate each of the 26 wolves’ probability of LTF, we first calculated the baseline CIF for the best IM and multiplied it by the exponentiated lib_kill and winter coefficients in Model 2 to obtain a probability of LTF for each wolf during winter periods with liberalized killing, as wolves experienced during the period beginning January 28, 2012 until April 14, 2012 (Supplementary Table S2). Then we ran 1,000 simulations for each wolf going LTF, using a Bernoulli distribution with the LTF probability for each wolf as the probability of success (‘LTF’). For our MAIN imputation scenario, each wolf was imputed an LTF endpoint (on April 14, 2012) if the simulated occurrence of the LTF endpoint was higher than the probability of LTF predicted from the FG model (used as an imputation threshold), ({p}_{i,SIM}left(ltfright) >{p}_{i,FG}(ltf)), otherwise we censored that wolf. To analyze sensitivity to the MAIN scenario, we also developed HIGH and LOW scenarios following a similar imputation process (Supplementary Data S3). For the HIGH imputation scenario, we increased the threshold probability for going LTF by half the difference between ({p}_{i,FG}(ltf)) and 1; ({p}_{i,HI}left(ltfright)={p}_{i,FG}left(ltfright)+(1-{p}_{i,FG}left(ltfright)/2). For the LOW imputation scenario, we decreased the threshold probability for going LTF by half of ({p}_{i,FG}(ltf)); ({p}_{i,LO}left(ltfright)={p}_{i,FG}left(ltfright)-{p}_{i,FG}(ltf)/2). The LOW and HIGH scenarios provided bounds on the point estimates of relative hazard and incidence for the simulated LTF process in the MAIN scenario.
Statistical tests
To model all endpoint-specific HRs, we employed Lunn & McNeil’s (1995 Method B) data augmentation method. Namely, we augmented the data by our 6 endpoint categories and employed stratified joint Cox multiple regression (on endpoint) with interactions between covariates and each endpoint. Our initial model included all interactions. We then discarded the weakest first to follow model selection procedures while retaining the policy variable in all models (7 models total, Supplementary Table S5). The approach provides us with covariate HRs for all endpoints and we use those HRs for estimating the CIFs by policy period for each endpoint. We model HR distributions of covariates for our poaching and LTF by exponentiating a normal distribution parameterized with the covariate coefficients and standard deviations obtained from their respective Cox models.
We also ran separate FG univariate and multivariate models, which mirrored the best stratified joint Cox model, to estimate FG CIFs for each endpoint. We compared CIFs visually to identify the most appropriate CIF model estimate (Cox or FG), following53.
Given the complete survival history of each individual wolf was split into multiple spells, we clustered all our regression analyses using a unique identifier for each wolf, following methods in54. Clustering on wolf identity accounts for auto-correlation (e.g., all spells are analyzed within-subjects) and avoids pseudo-replication of observations. We evaluated compliance with the proportionality assumptions for each model through the inclusion of time-varying coefficients (tvc). A tvc is an interaction of each parameter with analysis time which models changes in that parameter’s effect over time; i.e., non-proportionality. Endpoint-specific models with significant non-proportionality in a covariate (tvc) cannot provide predictions of risk or incidence due to computational limitations. We further verified proportionality using Schoenfeld residuals43,44,48, which should show a random pattern against time as evidence of compliance with the PH assumption. We selected the best regression models considering AIC, BIC, LL, parsimony, and compliance with model assumptions. When we set aside a best model because of non-proportionality, we present and discuss the best model but our CIF calculations use parameters from the same Cox or FG model without the tvc. We visually assessed goodness-of-fit for each selected endpoint-specific Cox model by Cox-Snell residual plots, which should show the Nelson-Aalen cumulative hazard closely following the line of Cox-Snell residuals if the model is a good fit. We conducted all statistical analyses in Stata 15 (StatCorp, College Station, TX, 2015; see supplementary materials for statistical code). More

1.
Tollefson, J. The hard truths of climate change—by the numbers. Nature 573, 324–327 (2019).
CAS  Google Scholar 
2.
Rogelj, J. et al. in Special Report on Global Warming of 1.5 °C (eds Masson-Delmotte, V. et al.) Ch. 2 (IPCC, WMO, 2018).

3.
Egli, F. & Stunzi, A. A dynamic climate finance allocation mechanism reflecting the Paris Agreement. Environ. Res. Lett. 14, 114024 (2019).
Google Scholar 

4.
Griscom, B. W. et al. We need both natural and energy solutions to stabilize our climate. Glob. Change Biol. 25, 1889–1890 (2019).
Google Scholar 

5.
Griscom, B. W. et al. Natural climate solutions. Proc. Natl Acad. Sci. USA 114, 11645–11650 (2017).
CAS  Google Scholar 

6.
Smith, P. et al. Biophysical and economic limits to negative CO2 emissions. Nat. Clim. Change 6, 42–50 (2015).
Google Scholar 

7.
Fargione, J. E. et al. Natural climate solutions for the United States. Sci. Adv. 4, eaat1869 (2018).
Google Scholar 

8.
Bastin, J.-F. et al. The global tree restoration potential. Science 365, 76–79 (2019).
CAS  Google Scholar 

9.
Busch, J. et al. Potential for low-cost carbon dioxide removal through tropical reforestation. Nat. Clim. Change 9, 463–466 (2019).
CAS  Google Scholar 

10.
Luedeling, E. et al. Forest restoration: overlooked constraints. Science 366, 315 (2019).
Google Scholar 

11.
Chazdon, R. & Brancalion, P. Restoring forests as a means to many ends. Science 365, 24–25 (2019).
CAS  Google Scholar 

12.
Cohn, A. S. et al. Smallholder agriculture and climate change. Annu. Rev. Environ. Resour. 42, 347–375 (2017).
Google Scholar 

13.
Lazos-Chavero, E. et al. Stakeholders and tropical reforestation: challenges, trade-offs, and strategies in dynamic environments. Biotropica 48, 900–914 (2016).
Google Scholar 

14.
Barr, C. M. & Sayer, J. A. The political economy of reforestation and forest restoration in Asia–Pacific: critical issues for REDD. Biol. Conserv. 154, 9–19 (2012).
Google Scholar 

15.
Wilson, K. A. et al. Optimal restoration: accounting for space, time and uncertainty. J. Appl. Ecol. 48, 715–725 (2011).
Google Scholar 

16.
Kettle, C. J. Ecological considerations for using dipterocarps for restoration of lowland rainforest in Southeast Asia. Biodivers. Conserv. 19, 1137–1151 (2010).
Google Scholar 

17.
Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Classifying drivers of global forest loss. Science 361, 1108–1111 (2018).
CAS  Google Scholar 

18.
Estoque, R. C. et al. The future of Southeast Asia’s forests. Nat. Commun. 10, 1829 (2019).
Google Scholar 

19.
Hengl, T. et al. Global mapping of potential natural vegetation: an assessment of machine learning algorithms for estimating land potential. PeerJ 6, e5457 (2018).
Google Scholar 

20.
Budiharta, S. et al. Restoring degraded tropical forests for carbon and biodiversity. Environ. Res. Lett. 9, 114020 (2014).
Google Scholar 

21.
Oakleaf, J. R. et al. Mapping global development potential for renewable energy, fossil fuels, mining and agriculture sectors. Sci. Data 6, 101 (2019).
Google Scholar 

22.
Lewis, S. L., Wheeler, C. E., Mitchard, E. T. A. & Koch, A. Restoring natural forests is the best way to remove atmospheric carbon. Nature 568, 25–28 (2019).
CAS  Google Scholar 

23.
Löfqvist, S. & Ghazoul, J. Private funding is essential to leverage forest and landscape restoration at global scales. Nat. Ecol. Evol. 3, 1612–1615 (2019).
Google Scholar 

24.
Meyfroidt, P. & Lambin, E. F. The causes of the reforestation in Vietnam. Land Use Policy 25, 182–197 (2008).
Google Scholar 

25.
Chazdon, R. L. & Guariguata, M. R. Natural regeneration as a tool for large-scale forest restoration in the tropics: prospects and challenges. Biotropica 48, 716–730 (2016).
Google Scholar 

26.
Chazdon, R. L. Beyond deforestation: restoring forests and ecosystem services on degraded lands. Science 320, 1458–1460 (2008).
CAS  Google Scholar 

27.
Brancalion, P. H. S. et al. Global restoration opportunities in tropical rainforest landscapes. Sci. Adv. 5, eaav3223 (2019).
Google Scholar 

28.
Sheil, D. et al. Forest restoration: transformative trees. Science 366, 316–317 (2019).
Google Scholar 

29.
Delzeit, R. et al. Forest restoration: expanding agriculture. Science 366, 316–317 (2019).
Google Scholar 

30.
Strassburg, B. B. N. et al. Strategic approaches to restoring ecosystems can triple conservation gains and halve costs. Nat. Ecol. Evol. 3, 62–70 (2019).
Google Scholar 

31.
National Inventory Submissions 2019 (UNFCCC, 2019); https://unfccc.int/process-and-meetings/transparency-and-reporting/reporting-and-review-under-the-convention/greenhouse-gas-inventories-annex-i-parties/national-inventory-submissions-2019

32.
Crouzeilles, R. et al. Achieving cost-effective landscape-scale forest restoration through targeted natural regeneration. Conserv. Lett. 13, e12709 (2020).
Google Scholar 

33.
Financing Emission Reductions for the Future: State of Voluntary Carbon Markets 2019 (Forest Trends’ Ecosystem Marketplace, 2019).

34.
Tobón, W. et al. Restoration planning to guide Aichi targets in a megadiverse country. Conserv. Biol. 31, 1086–1097 (2017).
Google Scholar 

35.
Griggs, D. et al. Sustainable development goals for people and planet. Nature 495, 305–307 (2013).
Google Scholar 

36.
Miettinen, J. & Liew, S. C. Degradation and development of peatlands in Peninsular Malaysia and in the islands of Sumatra and Borneo since 1990. Land Degrad. Dev. 21, 285–296 (2010).
Google Scholar 

37.
Miettinen, J., Shi, C. & Liew, S. C. Land cover distribution in the peatlands of Peninsular Malaysia, Sumatra and Borneo in 2015 with changes since 1990. Glob. Ecol. Conserv. 6, 67–78 (2016).
Google Scholar 

38.
Bunting, P. et al. The global mangrove watch—a new 2010 global baseline of mangrove extent. Remote Sens. 10, 1669 (2018).
Google Scholar 

39.
Land Cover CCI Product User Guide Version 2 (ESA, 2017); http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf

40.
Graham, V., Laurance, S. G., Grech, A. & Venter, O. Spatially explicit estimates of forest carbon emissions, mitigation costs and REDD+ opportunities in Indonesia. Environ. Res. Lett. 12, 044017 (2017).
Google Scholar 

41.
Baccini, A. et al. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nat. Clim. Change 2, 182–185 (2012).
Google Scholar 

42.
Avitabile, V. et al. An integrated pan-tropical biomass map using multiple reference datasets. Glob. Change Biol. 22, 1406–1420 (2016).
Google Scholar 

43.
Worthington, T. & Spalding, M. Mangrove Restoration Potential: A Global Map Highlighting a Critical Opportunity (Univ. Cambridge, 2018); https://doi.org/10.17863/CAM.39153

44.
Miettinen, J., Shi, C. & Liew, S. C. Deforestation rates in insular Southeast Asia between 2000 and 2010. Glob. Change Biol. 17, 2261–2270 (2011).
Google Scholar 

45.
Miettinen, J., Shi, C. & Liew, S. C. 2015 Land cover map of Southeast Asia at 250 m spatial resolution. Remote Sens. Lett. 7, 701–710 (2016).
Google Scholar 

46.
Friedlingstein, P., Allen, M., Canadell, J. G., Peters, G. P. & Seneviratne, S. I. Comment on ‘The global tree restoration potential’. Science 366, eaay8060 (2019).
Google Scholar 

47.
Veldman, J. W. et al. Comment on ‘The global tree restoration potential’. Science 366, eaay7976 (2019).
Google Scholar 

48.
Buendia, C. et al. (eds) 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories Volume 4: Agriculture, Forestry and Other Land Use (IPCC, 2019).

49.
Fritz, S. et al. Mapping global cropland and field size. Glob. Change Biol. 21, 1980–1992 (2015).
Google Scholar 

50.
Requena Suarez, D. et al. Estimating aboveground net biomass change for tropical and subtropical forests: Refinement of IPCC default rates using forest plot data. Glob. Change Biol. 25, 3609–3624 (2019).
Google Scholar 

51.
Cameron, C., Hutley, L. B., Friess, D. A. & Brown, B. High greenhouse gas emissions mitigation benefits from mangrove rehabilitation in Sulawesi, Indonesia. Ecosyst. Serv. 40, 101035 (2019).
Google Scholar 

52.
World Development Report 2013: Jobs (World Bank, 2012).

53.
The World Bank Annual Report 2018 (World Bank, 2018).

54.
FAOSTAT (FAO, 2017); http://www.fao.org/faostat/en/#data

55.
Producer Prices-Annua (FAO, 2017); http://www.fao.org/faostat/en/#data/PP

56.
Global Agro-Ecological Zones: Suitability and Potential Yield — Agro-Climatic Yield (International Institute for Applied Systems Analysis, 2015); http://gaez.fao.org/Main.html#

57.
Employment by Sex and Age—ILO Modelled Estimates (International Labour Organization, 2014); https://ilostat.ilo.org/data

58.
World Development Indicators (The World Bank, 2018); http://data.worldbank.org/data-catalog/world-development-indicators

59.
Naylor, R. L., Higgins, M. M., Edwards, R. B. & Falcon, W. P. Decentralization and the environment: assessing smallholder oil palm development in Indonesia. Ambio 48, 1195–1208 (2019).
Google Scholar 

60.
Hewson, J., Crema, S. C., González-Roglich, M., Tabor, K. & Harvey, C. A. New 1 km resolution datasets of global and regional risks of tree cover loss. Land 8, 14 (2019).
Google Scholar 

61.
The World Database on Protected Areas (WDPA) (UNEP-WCMC and IUCN, 2016); http://protectedplanet.net

62.
Weiss, D. J. et al. A global map of travel time to cities to assess inequalities in accessibility in 2015. Nature 553, 333–336 (2018).
CAS  Google Scholar 

63.
Potapov, P. et al. The last frontiers of wilderness: tracking loss of intact forest landscapes from 2000 to 2013. Sci. Adv. 3, e1600821 (2017).
Google Scholar 

64.
Page, S. E. et al. Review of Peat Surface Greenhouse Gas Emissions from Oil Palm Plantations in Southeast Asia White Paper No. 15 (International Council on Clean Transportation, 2011).

65.
Reijnders, L. & Huijbregts, M. A. J. Palm oil and the emission of carbon-based greenhouse gases. J. Clean. Prod. 16, 477–482 (2008).
Google Scholar 

66.
Saragi-Sasmito, M. F., Murdiyarso, D., June, T. & Sasmito, S. D. Carbon stocks, emissions, and aboveground productivity in restored secondary tropical peat swamp forests. Mitig. Adapt. Strateg. Glob. Change 24, 521–533 (2019).
Google Scholar 

67.
R v.3.6.0 (R Foundation for Statistical Computing, 2019).

68.
Hijmans, R. J. et al. raster: Geographic data analysis and modeling. R package v.2.5-8.

69.
QGIS Geographic Information System Version 2.14 (Open Source Geospatial Foundation Project, 2019); http://qgis.org More

1.
Darwin, C. The Voyage of the Beagle (1839).
2.
Forbes, E. Report on the Mollusca and Radiata of the Aegean Sea: And on Their Distribution, Considered as Bearing on Geology (1843).

3.
De La Beche, H. T. Researches in Theoretical Geology (FJ Huntington & co., New York, 1837).
Google Scholar 

4.
Darwin, C. On the Origin of Species by Means of Natural Selection, or the Preservation of Favoured Races in the Struggle for Life (John Murray, Hachette, 1859).
Google Scholar 

5.
Caccone, A. et al. Phylogeography and history of giant Galápagos tortoises. Evolution 56, 2052–2066 (2002).
PubMed  Google Scholar 

6.
Grant, P. R. Ecology and Evolution of Darwin’s Finches (Princeton University Press, Princeton, 1999).
Google Scholar 

7.
Wikelski, M. & Thom, C. Marine iguanas shrink to survive El Niño. Nature 403, 37–38 (2000).
ADS  CAS  PubMed  Google Scholar 

8.
Glynn, P. W. & Wellington, G. M. Corals and Coral Reefs of the Galapagos Islands (University of California Press, California, 1983).
Google Scholar 

9.
McCosker, J. E. & Rosenblatt, R. H. The fishes of the Galápagos Archipelago: an update. Proc. Calif. Acad. Sci. 61, 167–195 (2010).
Google Scholar 

10.
Salinas de León, P. et al. Largest global shark biomass found in the northern Galápagos Islands of Darwin and Wolf. PeerJ 4, e1911 (2016).
PubMed  PubMed Central  Google Scholar 

11.
Wellington, G. M. The Galápagos coastal marine environment (1975).

12.
Witman, J. D. & Smith, F. Rapid community change at a tropical upwelling site in the Galápagos Marine Reserve. Biodivers. Conserv. 12, 25–45 (2003).
Google Scholar 

13.
Corliss, J. B., Dymond, J., Gordon, L. I. & Edmond, J. M. on the Galapagos Rift. Science 203, 16 (1979).
Google Scholar 

14.
Hessler, R. R. & Smithey Jr, W. M. The distribution and community structure of megafauna at the Galapagos Rift hydrothermal vents. In Hydrothermal Processes at Seafloor Spreading Centers 735–770 (Springer, 1983).

15.
Harpp, K. S. & White, W. M. Tracing a mantle plume: isotopic and trace element variations of Galápagos seamounts. Geochem. Geophys. Geosystems 2 (2001).

16.
Lubetkin, M. et al. Nontronite-bearing tubular hydrothermal deposits from a Galapagos seamount. Deep Sea Res. Part II Top. Stud. Oceanogr. https://doi.org/10.1016/j.dsr2.2017.09.017 (2017).
Article  Google Scholar 

17.
Karl, D. M., Wirsen, C. & Jannasch, H. Deep-sea primary production at the Galapagos hydrothermal vents. Sci. States 207, 1345–1347 (1980).
CAS  Google Scholar 

18.
Rhoads, D. C., Lutz, R. A., Revelas, E. C. & Cerrato, R. M. Growth of bivalves at deep-sea hydrothermal vents along the Galapagos Rift. Science 214, 911–913 (1981).
ADS  CAS  PubMed  Google Scholar 

19.
Van Dover, C. L., Berg Jr, C. J. & Turner, R. D. Recruitment of marine invertebrates to hard substrates at deep-sea hydrothermal vents on the East Pacific Rise and Galapagos spreading center. Deep Sea Res. Part Oceanogr. 35, 1833–1849 (1988).
ADS  Google Scholar 

20.
Iwamoto, T. & McCosker, J. E. Notes on Galápagos grenadiers (Pisces, Gadiformes, Macrouridae), with the description of a new species of Coryphaenoides. Rev. Biol. Trop. 49, 21–27 (2001).
PubMed  Google Scholar 

21.
Long, D. J., McCosker, J. E., Blum, S. & Klapfer, A. Tropical Eastern Pacific records of the prickly shark, Echinorhinus cookei (Chondrichthyes: Echinorhinidae). Pac. Sci. 65, 433–440 (2011).
Google Scholar 

22.
McCosker, J. E., Long, D. J. & Baldwin, C. C. Description of a new species of deepwater catshark, Bythaelurus giddingsi sp. Nov., from the Galápagos Islands (Chondrichthyes: Carcharhiniformes: Scyliorhinidae). Zootaxa 59, 48–59 (2012).
Google Scholar 

23.
Cerutti-Pereyra, F., Yanez, A., Ebert, D. A., Arnés-Urgellés, C. & Salinas-De-León, P. New record and range extension of the deepsea skate, Bathyraja Abyssicola (Chondrichthyes: Arhynchobatidae). The Galapagos Islands. https://doi.org/10.5281/zenodo.1400829 (2018).
Article  Google Scholar 

24.
Cairns, S. D. Deep-water octocorals (Cnidaria, Anthozoa) from the Galápagos and Cocos Islands. Part 1: Suborder Calcaxonia. ZooKeys 729, 1–46 (2018).
Google Scholar 

25.
Cairns, S. D. New records of Stylasteridae (Hydrozoa: Hydroida) from the Galápagos and Cocos Islands (1991).

26.
Faxon, W. Reports on an exploration off the west coast of Mexico, Central and South America, and off the Galapagos Islands by the US Fish Commission steamer «Albatross» during 1891…. XV. Mem. Mus. Comp. Zool. 18, 1–292 (1895).
Google Scholar 

27.
Ramirez-Llodra, E. et al. Deep, diverse and definitely different: unique attributes of the world’s largest ecosystem. Biogeosciences 7, 2851–2899 (2010).
ADS  Google Scholar 

28.
Appeltans, W. et al. The magnitude of global marine species diversity. Curr. Biol. 22, 2189–2202 (2012).
CAS  PubMed  Google Scholar 

29.
Archer, S. K. et al. Pyrosome consumption by benthic organisms during blooms in the northeast Pacific and Gulf of Mexico. Ecology 99, 981–984 (2018).
PubMed  Google Scholar 

30.
Gates, A. R., Morris, K. J., Jones, D. O. & Sulak, K. J. An association between a cusk eel (Bassozetus sp.) and a black coral (Schizopathes sp.) in the deep western Indian Ocean. Mar. Biodivers. 47, 971–977 (2017).
Google Scholar 

31.
Salinas-de-León, P. et al. Deep-sea hydrothermal vents as natural egg-case incubators at the Galapagos Rift. Sci. Rep. 8, 1788 (2018).
ADS  PubMed  PubMed Central  Google Scholar 

32.
Harris, P., Macmillan-Lawler, M., Rupp, J. & Baker, E. Geomorphology of the oceans. Mar. Geol. 352, 4–24 (2014).
ADS  Google Scholar 

33.
Geist, D. J., Snell, H., Snell, H., Goddard, C. & Kurz, M. D. A paleogeographic model of the Galápagos Islands and biogeographical and evolutionary implications. Galápagos Nat. Lab. Earth Sci. Am. Geophys. Union Wash. DC USA 145–166 (2014).

34.
Morato, T., Hoyle, S. D., Allain, V. & Nicol, S. J. Seamounts are hotspots of pelagic biodiversity in the open ocean. Proc. Natl. Acad. Sci. 107, 9707–9711 (2010).
ADS  CAS  PubMed  Google Scholar 

35.
Pitcher, T. J. et al. Seamounts: Ecology, Fisheries & Conservation (Wiley, Hoboken, 2008).
Google Scholar 

36.
Rogers, A. The biology of seamounts. Adv. Mar. Biol. 30, 305–350 (1994).
Google Scholar 

37.
Genin, A., Dayton, P. K., Lonsdale, P. F. & Spiess, F. N. Corals on seamount peaks provide evidence of current acceleration over deep-sea topography. Nature 322, 59 (1986).
ADS  Google Scholar 

38.
Palacios, D. M. Seasonal patterns of sea-surface temperature and ocean color around the Galápagos: regional and local influences. Deep-Sea Res. Part II Top. Stud. Oceanogr. 51, 43–57 (2004).
ADS  Google Scholar 

39.
Edgar, G. J., Banks, S., Fariña, J. M., Calvopiña, M. & Martínez, C. Regional biogeography of shallow reef fish and macro-invertebrate communities in the Galapagos archipelago. J. Biogeogr. 31, 1107–1124 (2004).
Google Scholar 

40.
Koslow, J. et al. Continental slope and deep-sea fisheries: implications for a fragile ecosystem. ICES J. Mar. Sci. 57, 548–557 (2000).
Google Scholar 

41.
Watling, L. & Norse, E. A. Disturbance of the seabed by mobile fishing gear: a comparison to forest clearcutting. Conserv. Biol. 12, 1180–1197 (1998).
Google Scholar 

42.
Breedy, O., van Ofwegen, L. P. & Vargas, S. A new family of soft corals (Anthozoa, Octocorallia, Alcyonacea) from the aphotic tropical eastern Pacific waters revealed by integrative taxonomy. Syst. Biodivers. 10, 351–359 (2012).
Google Scholar 

43.
Ardron, J. A. et al. A systematic approach towards the identification and protection of vulnerable marine ecosystems. Mar. Policy 49, 146–154 (2014).
Google Scholar 

44.
Halpern, B. S., Selkoe, K. A., Micheli, F. & Kappel, C. V. Evaluating and ranking the vulnerability of global marine ecosystems to anthropogenic threats. Conserv. Biol. 21, 1301–1315 (2007).
PubMed  Google Scholar 

45.
Miloslavich, P. et al. Marine biodiversity in the atlantic and pacific coasts of south america: knowledge and gaps. PLoS ONE 6, e14631 (2011).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

46.
Clark, M. R., Schlacher, T. A., Rowden, A. A., Stocks, K. I. & Consalvey, M. Science priorities for seamounts: research links to conservation and management. PLoS ONE 7, e29232 (2012).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

47.
Sinton, C. W., Christie, D. M. & Duncan, R. A. Geochronology of Galápagos seamounts. J. Geophys. Res. Solid Earth 101, 13689–13700 (1996).
CAS  Google Scholar 

48.
Christie, D. et al. Drowned islands downstream from the Galapagos hotspot imply extended speciation times. Nature 355, 246 (1992).
ADS  Google Scholar 

49.
Watling, L., Guinotte, J., Clark, M. R. & Smith, C. R. A proposed biogeography of the deep ocean floor. Prog. Oceanogr. 111, 91–112 (2013).
ADS  Google Scholar 

50.
Dirección del Parque Nacional Galápagos. Plan de Manejo de las Areas Protegidas de Galápagos par el Buen Vivir (2014).

51.
Carey, S. et al. Exploring the undersea world of the Galápagos Islands. Ocean. Mag 29, 32–34 (2016).
Google Scholar 

52.
Salinas-De-León, P., Acuña-Marrero, D., Carrión-Tacuri, J. & Sala, E. Valor ecológico de los ecosistemas marinos de Darwin y Wolf, Reserva Marina de Galápagos. 15 (2015).

53.
Acuña-Marrero, D. et al. Spatial patterns of distribution and relative abundance of coastal shark species in the Galapagos Marine Reserve. Mar. Ecol. Prog. Ser. 593, 73–95 (2018).
ADS  Google Scholar 

54.
Acuña-Marrero, D. et al. Whale shark (Rhincodon typus) seasonal presence, residence time and habitat use at Darwin Island Galapagos Marine Reserve. PLoS ONE 9, e115946 (2014).
ADS  PubMed  PubMed Central  Google Scholar 

55.
Ministerio del Ambiente del Ecuador. Acuerdo Ministerial 076/2018. (2018).

56.
Ramirez-Llodra, E. et al. Man and the last great wilderness: human impact on the deep sea. PLoS ONE 6, e22588 (2011).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

57.
Roberts, C. M. Deep impact: the rising toll of fishing in the deep sea. Trends Ecol. Evol. 17, 242–245 (2002).
MathSciNet  Google Scholar 

58.
Hoegh-Guldberg, O. & Bruno, J. F. The impact of climate change on the world’s marine ecosystems. Science 328, 1523–1528 (2010).
ADS  CAS  PubMed  Google Scholar 

59.
Danovaro, R., Dell’Anno, A. & Pusceddu, A. Biodiversity response to climate change in a warm deep sea: biodiversity and climate change in the deep sea. Ecol. Lett. 7, 821–828 (2004).
Google Scholar 

60.
Danovaro, R., Dell’Anno, A., Fabiano, M., Pusceddu, A. & Tselepides, A. Deep-sea ecosystem response to climate changes: the eastern Mediterranean case study. Trends Ecol. Evol. 16, 505–510 (2001).
Google Scholar 

61.
Sweetman, A. K. et al. Major impacts of climate change on deep-sea benthic ecosystems. Elem. Sci. Anthr. 5, 4 (2017).
Google Scholar 

62.
Etnoyer, P. et al. Deep-sea coral collection protocols. NOAA Tech. Memo. NMFS-OPR 28 (2006).

63.
Folmer, O., Black, M., Hoeh, W., Lutz, R. & Vrijenhoek, R. DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Mol. Mar. Biol. Biotechnol. 3, 294–299 (1994).
CAS  PubMed  Google Scholar 

64.
Lemmon, A. R., Emme, S. A. & Lemmon, E. M. Anchored hybrid enrichment for massively high-throughput phylogenomics. Syst. Biol. 61, 727–744 (2012).
CAS  PubMed  Google Scholar 

65.
Wieczorek, J. et al. Darwin core: an evolving community-developed biodiversity data standard. PLoS ONE 7, e29715 (2012).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

66.
Ontrup, J., Ehnert, N., Bergmann, M. & Nattkemper, T. W. BIIGLE-Web 2.0 Enabled Labelling and Exploring of Images from the Arctic Deep-Sea Observatory HAUSGARTEN. 1–7 (IEEE, 2009). More