Ecology

1.Ramírez-Llodrà, E. et al. Man and the last great wilderness: Human impact on the deep sea. PLoS ONE 6, e22588 (2011).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
2.Hein, J. R. & Koschinsky, A. Deep-ocean ferromanganese crusts and nodules. in Treatise on Geochemistry (eds. Holland, H. & Turekian, K.) vol. 13 273–291 (Elsevier Ltd., 2014).3.Hein, J. R. Manganese nodules. Encyclop. Mar. Geosci. 1, 408–412 (2016).
Google Scholar 
4.Kuhn, T., Wegorzewski, A. V., Rühlemann, C. & Vink, A. Composition, formation, and occurrence of polymetallic nodules. in Deep-Sea Mining (ed. Sharma, R.) 23–63 (Springer International Publishing, 2017). https://doi.org/10.1007/978-3-319-52557-0_2.5.Amon, D. J. et al. Insights into the abundance and diversity of abyssal megafauna in a polymetallic-nodule region in the eastern Clarion–Clipperton Zone. Sci. Rep. 6, 1–12 (2016).Article 
CAS 

Google Scholar 
6.Purser, A. et al. Association of deep-sea incirrate octopods with manganese crusts and nodule fields in the Pacific Ocean. Curr. Biol. 26, R1268–R1269 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Vanreusel, A., Hilário, A., Ribeiro, P. A., Menot, L. & Martínez Arbizu, P. Threatened by mining, polymetallic nodules are required to preserve abyssal epifauna. Sci. Rep. 6, 26808 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
8.Iken, K., Brey, T., Wand, U., Voigt, J. & Junghans, P. Food web structure of the benthic community at the Porcupine Abyssal Plain (NE Atlantic): A stable isotope analysis. Prog. Oceanogr. 50, 383–405 (2001).ADS 
Article 

Google Scholar 
9.Aberle, N. & Witte, U. Deep-sea macrofauna exposed to a simulated sedimentation event in the abyssal NE Atlantic: In situ pulse-chase experiments using 13C-labelled phytodetritus. Mar. Ecol. Prog. Ser. 251, 37–47 (2003).ADS 
Article 

Google Scholar 
10.Sweetman, A. K. & Witte, U. Response of an abyssal macrofaunal community to a phytodetrital pulse. Mar. Ecol. Prog. Ser. 355, 73–84 (2008).ADS 
Article 

Google Scholar 
11.van Oevelen, D., Soetaert, K. & Heip, C. H. R. Carbon flows in the benthic food web of the Porcupine Abyssal Plain: The (un)importance of labile detritus in supporting microbial and faunal carbon demands. Limnol. Oceanogr. 57, 645–664 (2012).ADS 
Article 
CAS 

Google Scholar 
12.Dunlop, K. M. et al. Carbon cycling in the deep eastern North Pacific benthic food web: Investigating the effect of organic carbon input. Limnol. Oceanogr. 61, 1956–1968 (2016).ADS 
Article 

Google Scholar 
13.de Jonge, D. S. W. et al. Abyssal food-web model indicates faunal carbon flow recovery and impaired microbial loop 26 years after a sediment disturbance experiment. Prog. Oceanogr. 189, 102446 (2020).Article 

Google Scholar 
14.Smith, C. R., De Léo, F. C., Bernardino, A. F., Sweetman, A. K. & Martínez Arbizu, P. Abyssal food limitation, ecosystem structure and climate change. Trends Ecol. Evol. 23, 518–528 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
15.Meysman, F. J. R., Middelburg, J. J. & Heip, C. H. R. Bioturbation: A fresh look at Darwin’s last idea. Trends Ecol. Evol. 21, 688–695 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
16.van der Zee, E. M. et al. How habitat-modifying organisms structure the food web of two coastal ecosystems. Proc. R. Soc. B Biol. Sci. 283, 20152326 (2016).Article 
CAS 

Google Scholar 
17.Giere, O. Meiobenthology: The Microscopic Motile Fauna of Aquatic Sediment (Springer, 2009).
Google Scholar 
18.Hall, S. J. & Raffaelli, D. G. Food webs: Theory and reality. Adv. Ecol. Res. 24, 187–239 (1993).Article 

Google Scholar 
19.Mahatma, R. Meiofauna communities of the Pacific nodule province: Abundance, diversity and community structure. PhD-Thesis (Carl von Ossietzky Universität Oldenburg, 2009).20.McIntyre, A. Ecoloy of marine meiobenthos. Biol. Rev. 44, 245–288 (1969).Article 

Google Scholar 
21.Borowski, C. Physically disturbed deep-sea macrofauna in the Peru Basin, Southeast Pacific, revisited 7 years after the experimental impact. Deep. Res. II(48), 3809–3839 (2001).ADS 

Google Scholar 
22.Kéfi, S. et al. More than a meal… integrating non-feeding interactions into food webs. Ecol. Lett. 15, 291–300 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
23.Roberts, D. & Moore, H. M. Tentacular diversity in deep-sea deposit-feeding holothurians: Implications for biodiversity in the deep sea. Biodivers. Conserv. 6, 1487–1505 (1997).Article 

Google Scholar 
24.Buhl-Mortensen, L. et al. Habitat complexity and bottom fauna composition at different scales on the continental shelf and slope of northern Norway. Hydrobiologia 685, 191–219 (2012).Article 

Google Scholar 
25.Simon-Lledó, E. et al. Ecology of a polymetallic nodule occurrence gradient: Implications for deep-sea mining. Limnol. Oceanogr. 64, 1883–1894 (2019).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
26.Hasemann, C. et al. Effects of dropstone-induced habitat heterogeneity on Arctic deep-sea benthos with special reference to nematode communities. Mar. Biol. Res. 9, 229–245 (2013).Article 

Google Scholar 
27.Riehl, T., Wölfl, A. C., Augustin, N., Devey, C. W. & Brandt, A. Discovery of widely available abyssal rock patches reveals overlooked habitat type and prompts rethinking deep-sea biodiversity. Proc. Natl. Acad. Sci. USA. 117, 15450–15459 (2020).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
28.Kidd, R. B., Huggett, J. & Huggett, Q. J. Rock debris on abyssal plains in the northeast Atlantic: A comparison of epibenthic sledge hauls and photographic surveys. Oceanol. Acta 4, 99–104 (1981).
Google Scholar 
29.Gooday, A. J., Goineau, A. & Voltski, I. Abyssal foraminifera attached to polymetallic nodules from the eastern Clarion-Clipperton Fracture Zone: A preliminary description and comparison with North Atlantic dropstone assemblages. Mar. Biodivers. 45, 391–412 (2015).Article 

Google Scholar 
30.Ziegler, A. F., Smith, C. R., Edwards, K. F. & Vernet, M. Glacial dropstones: Islands enhancing seafloor species richness of benthic megafauna in West Antarctic Peninsula fjords. Mar. Ecol. Prog. Ser. 583, 1–14 (2017).ADS 
Article 

Google Scholar 
31.Schulz, M., Bergmann, M., von Juterzenka, K. & Soltwedel, T. Colonisation of hard substrata along a channel system in the deep Greenland Sea. Polar Biol. 33, 1359–1369 (2010).Article 

Google Scholar 
32.Simon-Lledó, E. et al. Multi-scale variations in invertebrate and fish megafauna in the mid-eastern Clarion Clipperton Zone. Prog. Oceanogr. 187, 102405 (2020).Article 

Google Scholar 
33.Ilan, M., Ben-Eliahu, M. N. & Galil, B. Three deep water sponges from the eastern Mediterranean and their associated Fauna. Ophelia 39, 45–54 (1994).Article 

Google Scholar 
34.Beaulieu, S. E. Colonization of habitat islands in the deep sea: Recruitment to glass sponge stalks. Deep. Res. I(48), 1121–1137 (2001).Article 

Google Scholar 
35.Buhl-Mortensen, L. et al. Biological structures as a source of habitat heterogeneity and biodiversity on the deep ocean margins. Mar. Ecol. 31, 21–50 (2010).ADS 
Article 

Google Scholar 
36.Huston, M. A. Introduction. in Biological Diversity. The coexistence of species on changing landscapes 1–11 (Cambridge University Press, 1994).37.Beaulieu, S. E. Life on glass houses: Sponge stalk communities in the deep sea. Mar. Biol. 138, 803–817 (2001).Article 

Google Scholar 
38.Dunne, J. A., Williams, R. J. & Martinez, N. D. Network structure and biodiversity loss in food webs: robustness increases with connectance. Ecol. Lett. 5, 558–567 (2002).Article 

Google Scholar 
39.Sole, R. V. & Montoya, M. Complexity and fragility in ecological networks. Proc. R. Soc. B Biol. Sci. 268, 2039–2045 (2001).CAS 
Article 

Google Scholar 
40.Warren, P. H. Spatial and temporal variation in the structure of a freshwater food web. Oikos 55, 299–311 (1989).Article 

Google Scholar 
41.Van Dover, C. L. et al. Biodiversity loss from deep-sea mining. Nat. Geosci. 10, 464–465 (2017).ADS 
Article 
CAS 

Google Scholar 
42.Niner, H. J. et al. Deep-sea mining with no net loss of biodiversity: An impossible aim. Front. Mar. Sci. 5, 00195 (2018).Article 

Google Scholar 
43.Washburn, T. W. et al. Ecological risk assessment for deep-sea mining. Ocean Coast. Manag. 176, 24–39 (2019).Article 

Google Scholar 
44.Christodoulou, M. et al. Unexpected high abyssal ophiuroid diversity in polymetallic nodule fields of the northeast Pacific Ocean and implications for conservation. Biogeosciences 17, 1845–1876 (2020).ADS 
CAS 
Article 

Google Scholar 
45.Christodoulou, M., O’Hara, T. D., Hugall, A. F. & Arbizu, P. M. Dark ophiuroid biodiversity in a prospective abyssal mine field. Curr. Biol. 29, 3909-3912.e3 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
46.Ramírez-Llodrà, E. et al. Deep, diverse and definitely different: Unique attributes of the world’s largest ecosystem. Biogeosciences 7, 2851–2899 (2010).ADS 
Article 

Google Scholar 
47.International Seabed Authority. Regulations on prospecting and exploration for polymetallic nodules in the Area. (2000).48.Stratmann, T. et al. Abyssal plain faunal carbon flows remain depressed 26 years after a simulated deep-sea mining disturbance. Biogeosciences 15, 4131–4145 (2018).ADS 
CAS 
Article 

Google Scholar 
49.Soetaert, K. & van Oevelen, D. Modeling food web interactions in benthic deep-sea ecosystems: A practical guide. Oceanography 22, 128–143 (2009).Article 

Google Scholar 
50.van Oevelen, D. et al. Quantifying food web flows using linear inverse models. Ecosystems 13, 32–45 (2010).Article 

Google Scholar 
51.International Seabed Authority. Draft environmental management plan for the Clarion-Clipperton Zone I. 1–18 (International Seabed Authority, 2011).52.Jung, H.-S., Lee, C.-B., Jeong, K.-S. & Kang, J.-K. Geochemical and mineralogical characteristics in two-color core sediments from the Korea Deep Ocean Study (KODOS) area, northeast equatorial Pacific. Mar. Geol. 144, 295–309 (1998).ADS 
CAS 
Article 

Google Scholar 
53.Wedding, L. M. et al. From principles to practice: A spatial approach to systematic conservation planning in the deep sea. Proc. R. Soc. B Biol. Sci. 280, 20131684 (2013).CAS 
Article 

Google Scholar 
54.Hannides, A. K. & Smith, C. R. The Northeast Pacific abyssal plain. in Biogeochemistry of Marine Systems (eds. Black, K. D. & Shimmield, G. B.) 208–237 (Blackwell Publishing, 2003).55.International Seabed Authority. A geological model of polymetallic nodule deposits in the Clarion Clipperton Fracture Zone. ISA technical study No. 6. (2010).56.Schoening, T., Jones, D. O. B. & Greinert, J. Compact-morphology-based polymetallic nodule delineation. Sci. Rep. 7, 13338 (2017).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
57.Anonymous. Google Earth. https://www.google.com/earth/ (2018).58.Klein, H. Near-bottom currents in the deep Peru Basin, DISCOL experimental area. Dtsch. Hydrogr. Z. 45, 31–42 (1993).Article 

Google Scholar 
59.Bharatdwaj, K. Reliefs of the ocean basins. in Physical Geography (Oceanography) 1–53 (Discovery Publishing House, 2006).60.Glasby, G. P. Manganese: Predominant role of nodules and crusts. in Marine Geochemistry (eds. Schulz, H. D. & Zabel, M.) 371–427 (Springer-Verlag, 2006). https://doi.org/10.1007/3-540-32144-6_11.61.Haeckel, M., König, I., Riech, V., Weber, M. E. & Suess, E. Pore water profiles and numerical modelling of biogeochemical processes in Peru Basin deep-sea sediments. Deep. Res. I(48), 3713–3736 (2001).ADS 

Google Scholar 
62.Moher, D., Liberati, A., Tetzlaff, J. & Altman, D. G. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med. 6, e1000097 (2009).PubMed 
PubMed Central 
Article 

Google Scholar 
63.Thiel, H. Use and protection of the deep sea: An introduction. Deep. Res. II(48), 3427–3431 (2001).ADS 

Google Scholar 
64.Anthropogenic disturbances in the deep sea. (Frontiers Media SA, 2019). https://doi.org/10.3389/978-2-88963-288-6.65.Assessing environmental impacts of deep-sea mining – revisiting decade-old benthic disturbances in Pacific nodule areas. Biogeosciences (2018).66.Martínez Arbizu, P. & Haeckel, M. RV SONNE Fahrtbericht/Cruise Report SO239. EcoResponse assessing the ecology, connectivity and resilience of polymetallic nodule field systems. vol. 25 (2015).67.Boetius, A. RV SONNE SO242/2. Cruise Report/Fahrtbericht. DISCOL revisited. Guayaquil: 28 August 2015: Guayaquil: 1 October 2015. SO242/2: JPI Oceans Ecological Aspects of Deep-Sea Mining. (2015).68.Horton, T. et al. World Register of Marine Species (WoRMS). http://www.marinespecies.org (2018). https://doi.org/10.14284/170.69.Ahnert, A. & Schriever, G. Response of abyssal copepoda Harpacticoida (Crustacea) and other meiobenthos to an artificial disturbance and its bearing on future mining for polymetallic nodules. Deep. Res. II 48, 3779–3794 (2001).ADS 
CAS 
Article 

Google Scholar 
70.Radziejewska, T. Responses of deep-sea meiobenthic communities to sediment disturbance simulating effects of polymetallic nodule mining. Int. Rev. Hydrobiol. 87, 457–477 (2002).Article 

Google Scholar 
71.Borowski, C. & Thiel, H. Deep-sea macrofaunal impacts of a large-scale physical disturbance experiment in the Southeast Pacific. Deep. Res. II 45, 55–81 (1998).ADS 
Article 

Google Scholar 
72.Pimm, S. L., Lawton, J. H. & Cohen, J. E. Food web patterns and their consequences. Nature 350, 669–674 (1991).ADS 
Article 

Google Scholar  More

Our analysis suggests that, over 465 ha of mangrove area, almost 83% of aboveground tree biomass were harvested annually for commercial timber purposes, using a keyhole harvest pattern (Fig. S3b). Yet after 25 years of natural and human-induced regeneration, both field- and satellite-based assessments reveal that biomass carbon stocks and canopy cover had fully recovered. Our approach using space-for-time substitution indicates that manual selective logging did not significantly affect soil carbon stocks and rates of annual carbon burial. While the differences in soil carbon stock between sites may be due to the diverse hydro-geomorphic settings8,14 the mangrove root mass in the top 1-m were not disturbed by manual logging activities. Similar situation was found in Tampa Bay, Florida where peat formation from root mass has enhance carbon sequestration15. These findings reduce uncertainty around the effects of mangrove forest management on the long-term functional capacity of blue carbon storage and provide evidence that managed mangrove ecosystems may deliver nature-based climate solutions.Recovery of forest structure, canopy cover and species diversityAlong carbon stocks, forest structure and species diversity also demonstrated recovery (Fig. 2, Table S1). Seedling densities were significantly higher in 5 year-old mangrove plots than in plots at any other stage (F(5,13) = 28.321, p  More

Genetic diversity of E. chinense based on COIThe partial fragment of COI, 595 bp in length, was sequenced from 113 E. chinense individuals collected from the eleven sites in South Korea (Table 1). The resultant COI sequences were aligned together with 27 COI individual sequences12,13,14 retrieved from the NCBI GenBank (Table 1). The latter consists of 26 from six collection sites in South Korea and one from a Japanese site. Hence, 140 COI sequences of E. chinense were analyzed, representing 18 collection sites at the nine populations in South Korea and Japan (Table 1). Based on the alignment set (no indels) of these 140 COI sequences (Data S1), we obtained a total of 58 COI haplotypes, of which 43 were singleton, appeared in only a single site. The novel 41 out of 58 COI haplotypes obtained were registered under the GenBank accession nos. MW265437–MW265477 (Table S2). According to the sequence alignment of the 58 COI haplotypes (Fig. S2; Data S2), there were 71 polymorphic sites and 31 parsimoniously informative sites (Fig. 1C), among which four adenine/guanine hotspots at 207, 282, 354, and 420 were ascertained to articulately divide the haplotypes of E. chinense into four meaningful phylogenetic groups: (a) A(207)–A(282)–A(354)–A(420), (b) A–A–G–A, (c) G–A–G–A, and (d) G–G–G–G.Table 1 List of collection sites and the number of individuals of Ellobium chinense with genetic markers applied to each of the nine populations in South Korea and Japan.Full size tableBased on the COI haplotype sequence alignment (Fig. S2; Data S2), we reconstructed a ML tree using Ellobium aurisjudae as an outgroup. In the resultant tree topology (Fig. S3), it was confirmed that E. chinense appeared as a monophyletic group, but no distinction between the haplotypes from each geographical population was observed. To define detailed relationships among the COI haplotypes, the outgroup was removed and then an unrooted ML tree (Fig. 1D) was reconstructed. The resultant tree showed two distinctive phylogenetic groups, namely A–A–A–A and the other groups (including at least one G or more in the four positions), regardless of collection localities. The A–A–A–A group included 35 of the 58 COI haplotypes. The others could be divided into the A–A–G–A group (N = 12: ECH11, 12, 15, 16, 18, 23, 27, 28, 32, 33, 36, and 49), the G–A–G–A group (N = 1: ECH35), and the G–G–G–G group (N = 8: ECH01, 07, 19, 29, 41, 45, 48, and 54).As shown in Table S2 and Fig. S3, ECH01 was a dominant member of the G–G–G–G group with the most individuals (27), which appeared across all the South Korean populations examined here. As shown in Fig. 1D, the A–A–A–A group is likely to be an ancestral type because it was most frequently found in the other species within Ellobiidae (unpublished data) and its haplotype diversity was the highest among the four genetic groups. Given that the G–G–G–G group exhibited much lower haplotype diversity than the A–A–A–A group, and was not observed in any other ellobiid species (unpublished data), it is reasonable to suggest that the G–G–G–G group is a derived rather than an ancestral type. Thus, as shown in Fig. 1D, it is conceivable that unidirectional and stepwise A → G transition events from A–A–A–A to G–G–G–G may have been occurred in E. chinense. Within the A–A–A–A and A–A–G–A groups, parsimoniously informative A → G transition events were found at the sites 120 (ECH12, 15, 16, 18, 23, 38, 40, 44, and 49) and 183 (ECH3, 9, 10, 20, 43, 50, and 55), with a few exceptional cases of G → A at the sites 216 (ECH12, 15, 16, 18, and 23), 372 (ECH12, 15, and 23), and 429 (ECH37, 38, 46, and 47; ECH19 found in the G–G–G–G group).As indicated in Table 2, the nucleotide diversity (π) is relatively low among the nine populations of E. chinense, ranging from 0.00749 (population BG) to 0.01042 (SC) with an average of 0.00865, whereas the haplotype diversity was very high across these populations, ranging from 0.924 (YG) to 1.000 (SC and JB) with an average of 0.939). All values of Tajima’s D and Fu’s FS were congruently negative, with averages of − 1.87100 (P  More

1.Hemes, K. S. et al. Assessing the carbon and climate benefit of restoring degraded agricultural peat soils to managed wetlands. Agric. For. Meteorol. 268, 202–214 (2019).ADS 
Article 

Google Scholar 
2.Sun, L. et al. Wetland-atmosphere methane exchange in Northeast China: A comparison of permafrost peatland and freshwater wetlands. Agric. For. Meteorol. 249, 239–249 (2018).ADS 
Article 

Google Scholar 
3.Davidson, N. C. How much wetland has the world lost? Long-term and recent trends in global wetland area. Mar. Freshw. Res. 65(10), 934–941 (2014).Article 

Google Scholar 
4.Havril, T., Tóth, Á., Molson, J. W., Galsa, A. & Mádl-Szőnyi, J. Impacts of predicted climate change on groundwater flow systems: Can wetlands disappear due to recharge reduction?. J. Hydrol. 563, 1169–1180 (2018).ADS 
Article 

Google Scholar 
5.Ye, X. C., Meng, Y. K., Xu, L. G. & Xu, C. Y. Net primary productivity dynamics and associated hydrological driving factors in the floodplain wetland of China’s largest freshwater lake. Sci. Total Environ. 659, 302–313 (2019).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
6.Shen, G., Yang, X., Jin, Y., Xu, B. & Zhou, Q. Remote sensing and evaluation of the wetland ecological degradation process of the Zoige Plateau Wetland in China. Ecol. Ind. 104, 48–58 (2019).Article 

Google Scholar 
7.Jiang, T. T., Pan, J. F., Pu, X. M., Wang, B. & Pan, J. J. Current status of coastal wetlands in China: Degradation, restoration, and future management. Estuar. Coast. Shelf Sci. 164, 265–275 (2015).Article 

Google Scholar 
8.Deng, L., Wang, K. B., Chen, M. L., Shangguan, Z. P. & Sweeney, S. Soil organic carbon storage capacity positively related to forest succession on the Loess Plateau, China. CATENA 110, 1–7 (2013).CAS 
Article 

Google Scholar 
9.Fujisaki, K. et al. From forest to cropland and pasture systems: A critical review of soil organic carbon stocks changes in Amazonia. Glob. Change Biol. 21(7), 2773–2786 (2015).ADS 
Article 

Google Scholar 
10.Gregorich, E. G., Beare, M. H., Mckim, U. F. & Skjemstad, J. O. Chemical and biological characteristics of physically uncomplexed organic matter. Soil. Sci. Soc. Am. J. 70, 975–985 (2006).ADS 
CAS 
Article 

Google Scholar 
11.Jones, D. L., Rousk, J., Edwards-Jones, G., DeLuca, T. H. & Murphy, D. V. Biochar-mediated changes in soil quality and plant growth in a three year field trial. Soil Biol. Biochem. 45, 113–124 (2012).CAS 
Article 

Google Scholar 
12.Paul, E. A. The nature and dynamics of soil organic matter: Plant inputs, microbial transformations, and organic matter stabilization. Soil Biol. Biochem. 98, 109–126 (2016).CAS 
Article 

Google Scholar 
13.Yuan, G. et al. Effects of straw incorporation and potassium fertilizer on crop yields, soil organic carbon, and active carbon in the rice-wheat system. Soil Tillage Res. 209, 104958 (2021).Article 

Google Scholar 
14.Xiao, Y., Huang, Z. & Lu, X. Changes of soil labile organic carbon fractions and their relation to soil microbial characteristics in four typical wetlands of Sanjiang Plain, Northeast China. Ecol. Eng. 82, 381–389 (2015).Article 

Google Scholar 
15.Wang, Y., Fu, B., Lü, Y. & Chen, L. Effects of vegetation restoration on soil organic carbon sequestration at multiple scales in semi-arid Loess Plateau, China. CATENA 85(1), 58–66 (2011).CAS 
Article 

Google Scholar 
16.Wang, G. X., Li, Y. S., Wang, Y. B. & Wu, Q. B. Effects of permafrost thawing on vegetation and soil carbon pool losses on the Qinghai-Tibet Plateau, China. Geoderma 143(1–2), 143–152 (2008).CAS 

Google Scholar 
17.Guo, J., Wang, B., Wang, G., Wu, Y. & Cao, F. Vertical and seasonal variations of soil carbon pools in ginkgo agroforestry systems in eastern China. CATENA 171, 450–459 (2018).CAS 
Article 

Google Scholar 
18.Cheng, X. et al. Assessing the effects of short-term Spartina alterniflora invasion on labile and recalcitrant C and N pools by means of soil fractionation and stable C and N isotopes. Geoderma 145(3–4), 177–184 (2008).ADS 
CAS 
Article 

Google Scholar 
19.Zhou, L. et al. Spartina alterniflora invasion alters carbon exchange and soil organic carbon in eastern salt marsh of China. Clean-Soil Air Water 43(4), 569–576 (2015).CAS 
Article 

Google Scholar 
20.Yang, W., Zhao, H. & Cheng, X. Consequences of short-term C4 plant Spartina alterniflora invasions for soil organic carbon dynamics in a coastal wetland of eastern China. Ecol. Eng. 61(12), 50–57 (2013).Article 

Google Scholar 
21.Shao, X. X., Yang, W. Y. & Wu, M. Seasonal dynamics of soil labile organic carbon and enzyme activities in relation to vegetation types in Hangzhou Bay Tidal Flat Wetland. PLoS ONE 10(11), e0142677 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
22.Zhu, J. et al. Multicriteria decision analysis for monitoring ecosystem service function of the Three-River Headwaters region of the Qinghai-Tibet Plateau, China. Environ. Monit. Assess. 187(6), 355 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
23.Li, Z. et al. Dynamics of soil respiration in alpine wetland meadows exposed to different levels of degradation in the Qinghai-Tibet Plateau, China. Sci. Rep. 9(1), 1–14 (2019).ADS 

Google Scholar 
24.Wang, G., Wang, Y., Li, Y. & Cheng, H. Influences of alpine ecosystem responses to climatic change on soil properties on the Qinghai-Tibet Plateau, China. CATENA 70(3), 506–514 (2007).Article 

Google Scholar 
25.Wu, P. et al. Impacts of alpine wetland degradation on the composition, diversity and trophic structure of soil nematodes on the Qinghai-Tibetan Plateau. Sci. Rep. 7(1), 837 (2017).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
26.Li, B., Dong, S. C., Jiang, X. B. & Li, Z. H. Analysis on the driving factors of grassland desertification in Zoige wetland. J. Soil Water Conserv. 15, 112–115 (2008).
Google Scholar 
27.Peng, F., You, Q., Xue, X., Guo, J. & Wang, T. Effects of rodent-induced land degradation on ecosytem carbon fluxes in alpine meadow in the qinghai-tibet plateau, china. Solid Earth 6(1), 303–310 (2015).ADS 
Article 

Google Scholar 
28.Bai, J. et al. Spatial variability of soil carbon, nitrogen, and phosphorus content and storage in an alpine wetland in the Qinghai-Tibet Plateau, China. Soil Res. 48(8), 730–736 (2010).CAS 
Article 

Google Scholar 
29.Jia, B., Niu, Z., Wu, Y., Kuzyakov, Y. & Li, X. G. Waterlogging increases organic carbon decomposition in grassland soils. Soil Biol. Biochem. 148, 107927 (2020).CAS 
Article 

Google Scholar 
30.Liu, W. et al. Storage, patterns, and control of soil organic carbon and nitrogen in the northeastern margin of the Qinghai-Tibetan Plateau. Environ. Res. Lett. 7(3), 035401 (2012).ADS 
Article 
CAS 

Google Scholar 
31.Wu, X. et al. Soil organic carbon and its relationship to vegetation communities and soil properties in permafrost areas of the central western Qinghai-Tibet plateau, China. Permafrost Periglac. Process. 23(2), 162–169 (2012).Article 

Google Scholar 
32.Rui, Y. et al. Warming and grazing affect soil labile carbon and nitrogen pools differently in an alpine meadow of the Qinghai-Tibet Plateau in China. J. Soils Sediments 11(6), 903 (2011).CAS 
Article 

Google Scholar 
33.Ma, W., Li, G., Wu, J., Xu, G. & Wu, J. Respiration and CH4 fluxes in Tibetan peatlands are influenced by vegetation degradation. CATENA 195, 104789 (2020).CAS 
Article 

Google Scholar 
34.Ma, W., Li, G., Wu, J., Xu, G. & Wu, J. Response of soil labile organic carbon fractions and carbon-cycle enzyme activities to vegetation degradation in a wet meadow on the Qinghai-Tibet Plateau. Geoderma 377, 114565 (2020).ADS 
CAS 
Article 

Google Scholar 
35.Alhassan, A. R. M., Ma, W. W., Li, G., Wu, J. Q. & Chen, G. P. Response of soil organic carbon to vegetation degradation along a moisture gradient in a wet meadow on the Qinghai-Tibet Plateau. Ecol. Evol. 8(23), 11999–12010 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
36.Wu, J. Q. et al. Vegetation degradation along water gradient leads to soil active organic carbon loss in Gahai wetland. Ecol. Eng. 145, 105666 (2020).Article 

Google Scholar 
37.Butenschoen, O., Scheu, S. & Eisenhauer, N. Interactive effects of warming, soil humidity and plant diversity on litter decomposition and microbial activity. Soil Biol. Biochem. 43(9), 1902–1907 (2011).CAS 
Article 

Google Scholar 
38.Fan, J., Cao, Y., Yan, Y., Lu, X. & Wang, X. Freezingthawing cycles effect on the water soluble organic carbon, nitrogen and microbial biomass of alpine grassland soil in Northern Tibet. Afr. J. Microbiol. Res. 6(3), 562–567 (2012).CAS 

Google Scholar 
39.Wang, J., Song, C., Wang, X. & Song, Y. Changes in labile soil organic carbon fractions in wetland ecosystems along a latitudinal gradient in Northeast China. CATENA 96, 83–89 (2012).CAS 
Article 

Google Scholar 
40.Wu, J. et al. Responses of CH4 flux and microbial diversity to changes in rainfall amount and frequencies in a wet meadow in the Tibetan Plateau. CATENA 202, 105253 (2021).CAS 
Article 

Google Scholar 
41.Ren, J. et al. Shifts in soil bacterial and archaeal communities during freeze-thaw cycles in a seasonal frozen marsh, Northeast China. Sci. Total Environ. 625, 782–791 (2018).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
42.Lu, Y., Si, B., Li, H. & Biswas, A. Elucidating controls of the variability of deep soil bulk density. Geoderma 348, 146–157 (2019).ADS 
Article 

Google Scholar 
43.Mao, J., Nierop, K. G., Rietkerk, M., Damsté, J. S. S. & Dekker, S. C. The influence of vegetation on soil water repellency-markers and soil hydrophobicity. Sci. Total Environ. 566, 608–620 (2016).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
44.Beljkaš, B. et al. Rapid method for determination of protein content in cereals and oilseeds: Validation, measurement uncertainty and comparison with the Kjeldahl method. Accred. Qual. Assur. 15(10), 555–561 (2010).Article 
CAS 

Google Scholar 
45.McKie, V. A. & MccleAry, B. V. A novel and rapid colorimetric method for measuring TP and phytic acid in foods and animal feeds. J. AOAC Int. 99(3), 738–743 (2016).CAS 
Article 

Google Scholar 
46.Wang, H. Y., Wu, J. Q., Li, G. & Yan, L. J. Changes in soil carbon fractions and enzyme activities under different vegetation types of the northern Loess Plateau. Ecol. Evol. 10, 12211–12223 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
47.Li, S. et al. Dynamics of soil labile organic carbon fractions and C-cycle enzyme activities under straw mulch in Chengdu Plain. Soil Tillage Res. 155, 289–297 (2016).Article 

Google Scholar 
48.Nie, X. J., Zhang, J. H., Cheng, J. X., Gao, H. & Guan, Z. M. Effect of soil redistribution on various organic carbons in a water-and tillage-eroded soil. Soil Tillage Res. 155, 1–8 (2016).Article 

Google Scholar 
49.Xu, C. Y. et al. The interplay of labile organic carbon, enzyme activities and microbial communities of two forest soils across seasons. Sci. Rep. 11(1), 1–12 (2021).Article 
CAS 

Google Scholar 
50.dos Reis Ferreira, C. et al. Dynamics of soil aggregation and organic carbon fractions over 23 years of no-till management. Soil Tillage Res. 198, 104533 (2020).Article 

Google Scholar 
51.Luan, J. et al. Different grazing removal exclosures effects on soil C stocks among alpine ecosystems in east Qinghai-Tibet Plateau. Ecol. Eng. 64, 262–268 (2014).Article 

Google Scholar 
52.Li, J. et al. Soil labile organic carbon fractions and soil organic carbon stocks as affected by long-term organic and mineral fertilization regimes in the North China Plain. Soil Tillage Res. 175, 281–290 (2018).Article 

Google Scholar 
53.Davidson, E. A. & Janssens, I. A. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440(7081), 165–173 (2006).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.Wang, J., Bai, J., Zhao, Q., Lu, Q. & Xia, Z. Five-year changes in soil organic carbon and total nitrogen in coastal wetlands affected by flow-sediment regulation in a Chinese delta. Sci. Rep. 6(1), 1–8 (2016).Article 
CAS 

Google Scholar 
55.Huo, L. et al. Effect of wetland reclamation on soil organic carbon stability in peat mire soil around Xingkai Lake in Northeast China. Chin. Geogr. Sci. 28(2), 325–336 (2018).Article 

Google Scholar 
56.Norton, J. B., Olsen, H. R., Jungst, L. J., Legg, D. E. & Horwath, W. R. Soil carbon and nitrogen storage in alluvial wet meadows of the Southern Sierra Nevada Mountains, USA. J. Soils Sediments 14(1), 34–43 (2014).CAS 
Article 

Google Scholar 
57.Chaudhari, P. R., Ahire, D. V., Ahire, V. D., Chkravarty, M. & Maity, S. Soil bulk density as related to soil texture, organic matter content and available total nutrients of Coimbatore soil. Int. J. Sci. Res. Publ. 3(2), 1–8 (2013).CAS 

Google Scholar 
58.Enriquez, A. S., Chimner, R. A., Cremona, M. V., Diehl, P. & Bonvissuto, G. L. Grazing intensity levels influence C reservoirs of wet and mesic meadows along a precipitation gradient in Northern Patagonia. Wetlands Ecol. Manage. 23(3), 439–451 (2015).CAS 
Article 

Google Scholar 
59.Li, X. G., Rengel, Z. & Mapfumo, E. Increase in pH stimulates mineralization of ‘native’ organic carbon and nitrogen in naturally salt-affected sandy soils. Plant Soil 290(1), 269–282 (2007).CAS 
Article 

Google Scholar 
60.Kemmitt, S. J., Wright, D., Goulding, K. W. & Jones, D. L. pH regulation of carbon and nitrogen dynamics in two agricultural soils. Soil Biol. Biochem. 38(5), 898–911 (2006).CAS 
Article 

Google Scholar 
61.Sihi, D., Inglett, P. W., Gerber, S. & Inglett, K. S. Rate of warming affects temperature sensitivity of anaerobic peat decomposition and greenhouse gas production. Glob. Change Biol. 24(1), e259–e274 (2018).ADS 
Article 

Google Scholar 
62.Page, S. E., Rieley, J. O. & Banks, C. J. Global and regional importance of the tropical peatland carbon pool. Glob. Change Biol. 17(2), 798–818 (2011).ADS 
Article 

Google Scholar 
63.Sun, Z. et al. Priming of soil organic carbon decomposition induced by exogenous organic carbon input: a meta-analysis. Plant Soil 443(1–2), 463–471 (2019).CAS 
Article 

Google Scholar 
64.Wang, H. et al. Differential effects of conifer and broadleaf litter inputs on soil organic carbon chemical composition through altered soil microbial community composition. Sci. Rep. 6, 27097 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
65.Fontaine, S. et al. Stability of organic carbon in deep soil layers controlled by fresh carbon supply. Nature 450, 277–280 (2007).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
66.Frey, S. D., Lee, J., Melillo, J. M. & Six, J. The temperature response of soil microbial efficiency and its feedback to climate. Nat. Clim. Chang. 3(4), 395–398 (2013).ADS 
CAS 
Article 

Google Scholar 
67.Zhou, Y., Hartemink, A. E., Shi, Z., Liang, Z. & Lu, Y. Land use and climate change effects on soil organic carbon in North and Northeast China. Sci. Total Environ. 647, 1230–1238 (2019).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
68.Meier, I. C., Finzi, A. C. & Phillips, R. P. Root exudates increase N availability by stimulating microbial turnover of fast-cycling N pools. Soil Biol. Biochem. 106, 119–128 (2017).CAS 
Article 

Google Scholar 
69.Strand, L. T., Abrahamsen, G. & Stuanes, A. O. Leaching from organic matter-rich soils by rain of different qualities: I Concentrations. J. Environ. Qual. 31(2), 547–556 (2002).CAS 
PubMed 
PubMed Central 

Google Scholar 
70.Liu, S. et al. The role of UV-B radiation and precipitation on straw decomposition and topsoil C turnover. Soil Biol. Biochem. 77, 197–202 (2014).CAS 
Article 

Google Scholar 
71.Liu, C. P. & Sheu, B. H. Dissolved organic carbon in precipitation, throughfall, stemflow, soil solution, and stream water at the Guandaushi subtropical forest in Taiwan. For. Ecol. Manage. 172(2–3), 315–325 (2003).Article 

Google Scholar 
72.Biederbeck, V. O., Janzen, H. H., Campbell, C. A. & Zentner, R. P. Labile soil organic matter as influenced by cropping practices in an arid environment. Soil Biol. Biochem. 26(12), 1647–1656 (1994).CAS 
Article 

Google Scholar 
73.García-Díaz, A., Marqués, M. J., Sastre, B. & Bienes, R. Labile and stable soil organic carbon and physical improvements using groundcovers in vineyards from central Spain. Sci. Total Environ. 621, 387–397 (2018).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
74.Yuan, Y., Zhao, Z., Li, X., Wang, Y. & Bai, Z. Characteristics of labile organic carbon fractions in reclaimed mine soils: Evidence from three reclaimed forests in the Pingshuo opencast coal mine, China. Sci. Total Environ. 613, 1196–1206 (2018).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
75.Yang, X. et al. Labile organic carbon fractions and carbon pool management index in a 3-year field study with biochar amendment. J. Soils Sediments 18(4), 1569–1578 (2018).CAS 
Article 

Google Scholar 
76.Soucémarianadin, L. N. et al. Environmental factors controlling soil organic carbon stability in French forest soils. Plant Soil 426(1–2), 267–286 (2018).Article 
CAS 

Google Scholar 
77.Mueller, T., Jensen, L. S., Nielsen, N. E. & Magid, J. Turnover of carbon and nitrogen in a sandy loam soil following incorporation of chopped maize plants, barley straw and blue grass in the field. Soil Biol. Biochem. 30(5), 561–571 (1998).CAS 
Article 

Google Scholar 
78.Oades, J. M., Vassallo, A. M., Waters, A. G. & Wilson, M. A. Characterization of organic matter in particle size and density fractions from a red-brown earth by solid state 13C NMR. Soil Res. 25(1), 71–82 (1987).CAS 
Article 

Google Scholar 
79.Li, Q. et al. Consistent temperature sensitivity of labile soil organic carbon mineralization along an elevation gradient in the Wuyi Mountains, China. Appl. Soil Ecol. 117, 32–37 (2017).ADS 
Article 

Google Scholar 
80.Luo, Z., Feng, W., Luo, Y., Baldock, J. & Wang, E. Soil organic carbon dynamics jointly controlled by climate, carbon inputs, soil properties and soil carbon fractions. Glob. Change Biol. 23(10), 4430–4439 (2017).ADS 
Article 

Google Scholar 
81.Yang, K. & Wang, C. Water storage effect of soil freeze-thaw process and its impacts on soil hydro-thermal regime variations. Agric. For. Meteorol. 265, 280–294 (2019).ADS 
Article 

Google Scholar 
82.Oztas, T. & Fayetorbay, F. Effect of freezing and thawing processes on soil aggregate stability. CATENA 52(1), 1–8 (2003).CAS 
Article 

Google Scholar 
83.Yang, Y. et al. Effects of forest conversion on soil labile organic carbon fractions and aggregate stability in subtropical China. Plant Soil 323(1–2), 153–162 (2009).CAS 
Article 

Google Scholar 
84.Kreyling, J., Beierkuhnlein, C. & Jentsch, A. Effects of soil freeze-thaw cycles differ between experimental plant communities. Basic Appl. Ecol. 11(1), 65–75 (2010).Article 

Google Scholar 
85.Guglielmin, M., Evans, C. J. E. & Cannone, N. Active layer thermal regime under different vegetation conditions in permafrost areas: A case study at Signy Island (Maritime Antarctica). Geoderma 144(1–2), 73–85 (2008).ADS 
Article 

Google Scholar 
86.Herrmann, A. & Witter, E. Sources of C and N contributing to the flush in mineralization upon freeze-thaw cycles in soils. Soil Biol. Biochem. 34(10), 1495–1505 (2002).CAS 
Article 

Google Scholar 
87.Zhu, E. et al. Leaching of organic carbon from grassland soils under anaerobiosis. Soil Biol. Biochem. 141, 107684 (2020).CAS 
Article 

Google Scholar 
88.Tian, J., Branfireun, B. A. & Lindo, Z. Global change alters peatland carbon cycling through plant biomass allocation. Plant Soil 455, 1–12 (2020).Article 
CAS 

Google Scholar 
89.Yan, J. et al. Plant litter composition selects different soil microbial structures and in turn drives different litter decomposition pattern and soil carbon sequestration capability. Geoderma 319, 194–203 (2018).ADS 
CAS 
Article 

Google Scholar 
90.Kögel-Knabner, I. The macromolecular organic composition of plant and microbial residues as inputs to soil organic matter: fourteen years on. Soil Biol. Biochem. 105, A3–A8 (2017).Article 
CAS 

Google Scholar 
91.Wiesmeier, M. et al. Soil organic carbon storage as a key function of soils-a review of drivers and indicators at various scales. Geoderma 333, 149–162 (2019).ADS 
CAS 
Article 

Google Scholar 
92.Sun, T., Wang, Y., Hui, D., Jing, X. & Feng, W. Soil properties rather than climate and ecosystem type control the vertical variations of soil organic carbon, microbial carbon, and microbial quotient. Soil Biol. Biochem. 148, 107905 (2020).CAS 
Article 

Google Scholar 
93.Li, X. G., Li, F. M., Zed, R. & Zhan, Z. Y. Soil physical properties and their relations to organic carbon pools as affected by land use in an alpine pastureland. Geoderma 139(1–2), 98–105 (2007).ADS 
CAS 
Article 

Google Scholar 
94.Singh, A. K., Rai, A. & Singh, N. Effect of long term land use systems on fractions of glomalin and soil organic carbon in the Indo-Gangetic plain. Geoderma 277, 41–50 (2016).ADS 
CAS 
Article 

Google Scholar 
95.Ghosh, A. et al. Long-term fertilization effects on soil organic carbon sequestration in an Inceptisol. Soil Tillage Res. 177, 134–144 (2018).Article 

Google Scholar  More

1.Agrawal, A. A. A scale-dependent framework for trade-offs, syndromes, and specialization in organismal biology. Ecology 101, e02924 (2020).PubMed 
Article 

Google Scholar 
2.Agrawal, A. A., Conner, J. K. & Rasmann, S. in Evolution After Darwin: The First 150 Years (eds Bell, M. et al.) 243–268 (Sinauer Associates, 2010).3.Futuyma, D. J. & Moreno, G. The evolution of ecological specialization. Annu. Rev. Ecol. Syst. 19, 207–233 (1988).Article 

Google Scholar 
4.Grime, J. P. & Pierce, S. The Evolutionary Strategies that Shape Ecosystems (John Wiley & Sons, 2012).5.Fry, J. D. Detecting ecological trade-offs using selection experiments. Ecology 84, 1672–1678 (2003).Article 

Google Scholar 
6.Grubb, P. J. Trade-offs in interspecific comparisons in plant ecology and how plants overcome proposed constraints. Plant Ecol. Divers. 9, 3–33 (2016).Article 

Google Scholar 
7.Kneitel, J. M. & Chase, J. M. Trade-offs in community ecology: linking spatial scales and species coexistence. Ecol. Lett. 7, 69–80 (2004).Article 

Google Scholar 
8.Tilman, D. Plant Strategies and the Dynamics and Structure of Plant Communities (Princeton Univ. Press, 1988).9.Lusk, C. H. & Jorgensen, M. A. The whole-plant compensation point as a measure of juvenile tree light requirements. Funct. Ecol. 27, 1286–1294 (2013).Article 

Google Scholar 
10.Ho, M. D., Rosas, J. C., Brown, K. M. & Lynch, J. P. Root architectural tradeoffs for water and phosphorus acquisition. Funct. Plant Biol. 32, 737–748 (2005).CAS 
PubMed 
Article 

Google Scholar 
11.Forister, M. L. & Jenkins, S. H. A neutral model for the evolution of diet breadth. Am. Nat. 190, E40–E54 (2017).PubMed 
Article 

Google Scholar 
12.Laughlin, D. C., Strahan, R. T., Adler, P. B. & Moore, M. M. Survival rates indicate that correlations between community-weighted mean traits and environments can be unreliable estimates of the adaptive value of traits. Ecol. Lett. 21, 411–421 (2018).PubMed 
Article 

Google Scholar 
13.Pollock, L. J., Morris, W. K. & Vesk, P. A. The role of functional traits in species distributions revealed through a hierarchical model. Ecography 35, 716–725 (2012).Article 

Google Scholar 
14.Mason, N. W. H. et al. Changes in coexistence mechanisms along a long-term soil chronosequence revealed by functional trait diversity. J. Ecol. 100, 678–689 (2012).CAS 
Article 

Google Scholar 
15.Gompert, Z. et al. The evolution of novel host use is unlikely to be constrained by trade-offs or a lack of genetic variation. Mol. Ecol. 24, 2777–2793 (2015).PubMed 
Article 

Google Scholar 
16.Laliberté, E. Below-ground frontiers in trait-based plant ecology. New Phytol. 213, 1597–1603 (2017).PubMed 
Article 

Google Scholar 
17.Bergmann, J. et al. The fungal collaboration gradient dominates the root economics space in plants. Sci. Adv. 6, eaba3756 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
18.Tedersoo, L., Bahram, M. & Zobel, M. How mycorrhizal associations drive plant population and community biology. Science 367, eaba1223 (2020).CAS 
PubMed 
Article 

Google Scholar 
19.Kong, D. et al. Leading dimensions in absorptive root trait variation across 96 subtropical forest species. New Phytol. 203, 863–872 (2014).PubMed 
Article 

Google Scholar 
20.Ma, Z. et al. Evolutionary history resolves global organization of root functional traits. Nature 555, 94–97 (2018).CAS 
PubMed 
Article 

Google Scholar 
21.Weemstra, M. et al. Towards a multidimensional root trait framework: a tree root review. New Phytol. 211, 1159–1169 (2016).CAS 
PubMed 
Article 

Google Scholar 
22.Kramer-Walter, K. R. et al. Root traits are multidimensional: specific root length is independent from root tissue density and the plant economic spectrum. J. Ecol. 104, 1299–1310 (2016).Article 

Google Scholar 
23.Díaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2016).PubMed 
Article 
CAS 

Google Scholar 
24.Tedersoo, L. et al. Global diversity and geography of soil fungi. Science 346, 1256688 (2014).PubMed 
Article 
CAS 

Google Scholar 
25.Steidinger, B. S. et al. Climatic controls of decomposition drive the global biogeography of forest–tree symbioses. Nature 569, 404–408 (2019).CAS 
PubMed 
Article 

Google Scholar 
26.Soudzilovskaia, N. A. et al. Global mycorrhizal plant distribution linked to terrestrial carbon stocks. Nat. Commun. 10, 5077 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
27.Kytöviita, M.-M. Asymmetric symbiont adaptation to Arctic conditions could explain why high Arctic plants are non-mycorrhizal. FEMS Microbiol. Ecol. 53, 27–32 (2005).PubMed 
Article 
CAS 

Google Scholar 
28.Augé, R. M., Toler, H. D. & Saxton, A. M. Arbuscular mycorrhizal symbiosis alters stomatal conductance of host plants more under drought than under amply watered conditions: a meta-analysis. Mycorrhiza 25, 13–24 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
29.Gill, R. A. & Jackson, R. B. Global patterns of root turnover for terrestrial ecosystems. New Phytol. 147, 13–31 (2000).Article 

Google Scholar 
30.Butterfield, B. J., Bradford, J. B., Munson, S. M. & Gremer, J. R. Aridity increases below-ground niche breadth in grass communities. Plant Ecol. 218, 385–394 (2017).Article 

Google Scholar 
31.Bruelheide, H. et al. sPlot—a new tool for global vegetation analyses. J. Veg. Sci. 30, 161–186 (2019).Article 

Google Scholar 
32.Guerrero-Ramírez, N. R. et al. Global root traits (GRooT) database. Glob. Ecol. Biogeogr. 30, 25–37 (2021).Article 

Google Scholar 
33.Valverde-Barrantes, O. J., Freschet, G. T., Roumet, C. & Blackwood, C. B. A worldview of root traits: the influence of ancestry, growth form, climate and mycorrhizal association on the functional trait variation of fine-root tissues in seed plants. New Phytol. 215, 1562–1573 (2017).PubMed 
Article 

Google Scholar 
34.Kong, D. et al. Nonlinearity of root trait relationships and the root economics spectrum. Nat. Commun. 10, 2203 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
35.Fort, F. & Freschet, G. T.Plant ecological indicator values as predictors of fine-root trait variations. J. Ecol. 108, 1565–1577 (2020).Article 

Google Scholar 
36.Purcell, A. S. T., Lee, W. G., Tanentzap, A. J. & Laughlin, D. C. Fine root traits are correlated with flooding duration while aboveground traits are related to grazing in an ephemeral wetland. Wetlands 39, 291–302 (2019).Article 

Google Scholar 
37.Laughlin, D. C., Fulé, P. Z., Huffman, D. W., Crouse, J. & Laliberté, E. Climatic constraints on trait-based forest assembly. J. Ecol. 99, 1489–1499 (2011).Article 

Google Scholar 
38.Simpson, A. H., Richardson, S. J. & Laughlin, D. C. Soil–climate interactions explain variation in foliar, stem, root and reproductive traits across temperate forests. Glob. Ecol. Biogeogr. 25, 964–978 (2016).Article 

Google Scholar 
39.Chen, W., Zeng, H., Eissenstat, D. M. & Guo, D. Variation of first-order root traits across climatic gradients and evolutionary trends in geological time. Glob. Ecol. Biogeogr. 22, 846–856 (2013).Article 

Google Scholar 
40.Freschet, G. T. et al. Climate, soil and plant functional types as drivers of global fine-root trait variation. J. Ecol. 105, 1182–1196 (2017).Article 

Google Scholar 
41.Ostonen, I. et al. Adaptive root foraging strategies along a boreal–temperate forest gradient. New Phytol. 215, 977–991 (2017).CAS 
PubMed 
Article 

Google Scholar 
42.Wang, R. et al. Different phylogenetic and environmental controls of first-order root morphological and nutrient traits: evidence of multidimensional root traits. Funct. Ecol. 32, 29–39 (2018).Article 

Google Scholar 
43.Craine, J. M. & Lee, W. G. Covariation in leaf and root traits for native and non-native grasses along an altitudinal gradient in New Zealand. Oecologia 134, 471–478 (2003).CAS 
PubMed 
Article 

Google Scholar 
44.Craine, J. M., Lee, W. G., Bond, W. J., Williams, R. J. & Johnson, L. C. Environmental constraints on a global relationship among leaf and root traits of grasses. Ecology 86, 12–19 (2005).Article 

Google Scholar 
45.Zadworny, M. et al. Patterns of structural and defense investments in fine roots of Scots pine (Pinus sylvestris L.) across a strong temperature and latitudinal gradient in Europe. Glob. Change Biol. 23, 1218–1231 (2017).Article 

Google Scholar 
46.Oliverio, A. M. et al. The global-scale distributions of soil protists and their contributions to belowground systems. Sci. Adv. 6, eaax8787 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
47.Bennett, A. E., Grussu, D., Kam, J., Caul, S. & Halpin, C. Plant lignin content altered by soil microbial community. New Phytol. 206, 166–174 (2015).CAS 
PubMed 
Article 

Google Scholar 
48.Moore, B. D. & Johnson, S. N. Get tough, get toxic, or get a bodyguard: identifying candidate traits conferring belowground resistance to herbivores in grasses. Front. Plant Sci. 7, 1925 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
49.Delgado-Baquerizo, M. et al. The proportion of soil-borne pathogens increases with warming at the global scale. Nat. Clim. Change 10, 550–554 (2020).Article 

Google Scholar 
50.De la Riva, E. G. et al. Root traits across environmental gradients in Mediterranean woody communities: are they aligned along the root economics spectrum? Plant Soil 424, 35–48 (2018).CAS 
Article 

Google Scholar 
51.Hacke, U. G., Sperry, J. S. & Pittermann, J. Drought experience and cavitation resistance in six shrubs from the Great Basin, Utah. Basic Appl. Ecol. 1, 31–41 (2000).Article 

Google Scholar 
52.Wright, I. J., Reich, P. B. & Westoby, M. Strategy shifts in leaf physiology, structure and nutrient content between species of high- and low-rainfall and high- and low-nutrient habitats. Funct. Ecol. 15, 423–434 (2001).Article 

Google Scholar 
53.Wang, B. et al. Presence of three mycorrhizal genes in the common ancestor of land plants suggests a key role of mycorrhizas in the colonization of land by plants. New Phytol. 186, 514–525 (2010).PubMed 
Article 

Google Scholar 
54.Grubb, P. in Handbook of Vegetation Science Vol. 3 (ed. White, J.) 595–621 (Dr. W. Junk Publishers, 1985).55.Laughlin, D. C. et al. Quantifying multimodal trait distributions improves trait-based predictions of species abundances and functional diversity. J. Veg. Sci. 26, 46–57 (2015).Article 

Google Scholar 
56.Pfahl, S., O’Gorman, P. A. & Fischer, E. M. Understanding the regional pattern of projected future changes in extreme precipitation. Nat. Clim. Change 7, 423–427 (2017).Article 

Google Scholar 
57.Read, D. J. Mycorrhizas in ecosystems. Experientia 47, 376–391 (1991).Article 

Google Scholar 
58.Bruelheide, H. et al. Global trait–environment relationships of plant communities. Nat. Ecol. Evol. 2, 1906–1917 (2018).PubMed 
Article 

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

Google Scholar 
60.Kumordzi, B. B. et al. Geographic scale and disturbance influence intraspecific trait variability in leaves and roots of North American understorey plants. Funct. Ecol. 33, 1771–1784 (2019).Article 

Google Scholar 
61.Velázquez, E., Paine, C. E. T., May, F. & Wiegand, T. Linking trait similarity to interspecific spatial associations in a moist tropical forest. J. Veg. Sci. 26, 1068–1079 (2015).Article 

Google Scholar 
62.Butterfield, B. J. Environmental filtering increases in intensity at both ends of climatic gradients, though driven by different factors, across woody vegetation types of the southwest USA. Oikos 124, 1374–1382 (2015).Article 

Google Scholar 
63.Iversen, C. M. et al. A global fine-root ecology database to address below-ground challenges in plant ecology. New Phytol. 215, 15–26 (2017).PubMed 
Article 

Google Scholar 
64.Kattge, J. et al. TRY plant trait database—enhanced coverage and open access. Glob. Change Biol. 26, 119–188 (2020).Article 

Google Scholar 
65.Pakeman, R. J. & Quested, H. M. Sampling plant functional traits: what proportion of the species need to be measured? Appl. Veg. Sci. 10, 91–96 (2007).Article 

Google Scholar 
66.Karger, D. N. et al. Climatologies at high resolution for the Earth’s land surface areas. Sci. Data 4, 170122 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
67.Zomer, R. J., Trabucco, A., Bossio, D. A. & Verchot, L. V. Climate change mitigation: a spatial analysis of global land suitability for clean development mechanism afforestation and reforestation. Agric. Ecosyst. Environ. 126, 67–80 (2008).Article 

Google Scholar 
68.Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth: a new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. BioScience 51, 933–938 (2001).Article 

Google Scholar 
69.Jamil, T., Ozinga, W. A., Kleyer, M. & Ber Braak, C. J. F. Selecting traits that explain species–environment relationships: a generalized linear mixed model approach. J. Veg. Sci. 24, 988–1000 (2013).Article 

Google Scholar 
70.Miller, J. E. D., Damschen, E. I. & Ives, A. R. Functional traits and community composition: a comparison among community-weighted means, weighted correlations, and multilevel models. Methods Ecol. Evol. 10, 415–425 (2018).
Google Scholar 
71.R Development Core Team R: A language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).72.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).73.Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
74.Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest Package: tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).Article 

Google Scholar 
75.Lüdecke, D., Makowski, D. & Waggoner, P. performance: Assessment of regression models performance. R package version 0.4.2 (2019).76.Stefan, V. & Levin, S. plotbiomes: Plot Whittaker biomes with ggplot2. R package version 0.0.0.9001 (2020).77.Roberts, D. W. labdsv: Ordination and multivariate analysis for ecology. R package version 1.8.0 https://CRAN.R-project.org/package=labdsv (2016).78.Anderson, D. R. Model Based Inference in the Life Sciences: a Primer on Evidence (Springer Science & Business Media, 2008). More

1.Case, T. J. & Gilpin, M. E. Interference competition and niche theory. Proc. Natl. Acad. Sci. 71, 3073–3077 (1974).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.Linnell, J. D. & Strand, O. Interference interactions, co-existence and conservation of mammalian carnivores. Divers. Distrib. 6, 169–176 (2000).Article 

Google Scholar 
3.Prugh, L. R. & Sivy, K. J. Enemies with benefits: Integrating positive and negative interactions among terrestrial carnivores. Ecol. Lett. 23, 902–918 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
4.Polis, G. A., Myers, C. A. & Holt, R. D. The ecology and evolution of intraguild predation: Potential competitors that eat each other. Annu. Rev. Ecol. Syst. 20, 297–330 (1989).Article 

Google Scholar 
5.Belant, J. L., Griffith, B., Zhang, Y., Follmann, E. H. & Adams, L. G. Population-level resource selection by sympatric brown and American black bears in Alaska. Polar Biol. 33, 31–40 (2010).Article 

Google Scholar 
6.Lima, S. L. & Dill, L. M. Behavioral decisions made under the risk of predation: A review and prospectus. Can. J. Zool. 68, 619–640 (1990).Article 

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

Google Scholar 
8.Moll, R. J. et al. The many faces of fear: A synthesis of the methodological variation in characterizing predation risk. J. Anim. Ecol. 86, 749–765 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
9.Kohl, M. T. et al. Diel predator activity drives a dynamic landscape of fear. Ecol. Monogr. 88, 638–652 (2018).Article 

Google Scholar 
10.Kuijper, D. P. J. et al. Landscape of fear in Europe: Wolves affect spatial patterns of ungulate browsing in Białowieża Primeval Forest, Poland. Ecography 36, 1263–1275 (2013).Article 

Google Scholar 
11.Smith, J. A., Donadio, E., Pauli, J. N., Sheriff, M. J. & Middleton, A. D. Integrating temporal refugia into landscapes of fear: Prey exploit predator downtimes to forage in risky places. Oecologia 189, 883–890 (2019).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
12.Flagel, D. G., Belovsky, G. E. & Beyer, D. E. Natural and experimental tests of trophic cascades: Gray wolves and white-tailed deer in a Great Lakes forest. Oecologia 180, 1183–1194 (2016).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
13.Gaynor, K. M., Brown, J. S., Middleton, A. D., Power, M. E. & Brashares, J. S. Landscapes of fear: Spatial patterns of risk perception and response. Trends Ecol. Evol. 34, 355–368 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
14.Prugh, L. R. et al. Designing studies of predation risk for improved inference in carnivore-ungulate systems. Biol. Conserv. 232, 194–207 (2019).Article 

Google Scholar 
15.Fisher, J. T., Anholt, B., Bradbury, S., Wheatley, M. & Volpe, J. P. Spatial segregation of sympatric marten and fishers: The influence of landscapes and species-scapes. Ecography 36, 240–248 (2013).Article 

Google Scholar 
16.Manlick, P. J., Woodford, J. E., Zuckerberg, B. & Pauli, J. N. Niche compression intensifies competition between reintroduced American martens (Martes americana) and fishers (Pekania pennanti). J. Mammal. 98, 690–702 (2017).Article 

Google Scholar 
17.Powell, R. A., Buskirk, S. W., & Zielinski, W. J. Fisher and marten. In Wild Mammals of North America: Biology, Management, and Conservation (eds. Feldhamer, G. A et al.), 635–649 (JHU Press, 2003).18.Krohn, W. B., Elowe, K. D. & Boone, R. B. Relations among fishers, snow, and martens: Development and evaluation of two hypotheses. For. Chron. 71, 97–105 (1995).Article 

Google Scholar 
19.Williams, B. W., Gilbert, J. H., & Zollner, P. A. Historical Perspective on the Reintroduction of the Fisher and American Marten in Wisconsin and Michigan, vol. 5. (US Department of Agriculture, Forest Service, Northern Research Station, 2007).20.McCann, N. P., Zollner, P. A. & Gilbert, J. H. Survival of adult martens in northern Wisconsin. J. Wildl. Manag. 74, 1502–1507 (2010).Article 

Google Scholar 
21.Kupferman, C. A. An Expanding Meso-Carnivore: Fisher (Pekania pennanti) Occupancy and Coexistence with Native Mustelids in Southeast Alaska (University of Idaho, 2019).
Google Scholar 
22.Hall, L. K. et al. Vigilance of kit foxes at water sources: A test of competing hypotheses for a solitary carnivore subject to predation. Behav. Proc. 94, 76–82 (2013).Article 

Google Scholar 
23.Chitwood, M. C., Lashley, M. A., Higdon, S. D., DePerno, C. S. & Moorman, C. E. Raccoon vigilance and activity patterns when sympatric with coyotes. Diversity 12, 341 (2020).Article 

Google Scholar 
24.Vanak, A. T., Thaker, M. & Gompper, M. E. Experimental examination of behavioural interactions between free-ranging wild and domestic canids. Behav. Ecol. Sociobiol. 64, 279–287 (2009).Article 

Google Scholar 
25.Croose, E., Bled, F., Fowler, N. L., Beyer, D. E. Jr. & Belant, J. L. American marten and fisher do not segregate in space and time during winter in a mixed-forest system. Ecol. Evol. 9, 4906–4916 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
26.Gilbert, J. H., Zollner, P. A., Green, A. K., Wright, J. L. & Karasov, W. H. Seasonal field metabolic rates of American martens in Wisconsin. Am. Midl. Nat. 162, 327–334 (2009).Article 

Google Scholar 
27.Hughes, N. K., Price, C. J. & Banks, P. B. Predators are attracted to the olfactory signals of prey. PLoS ONE 5, e13114 (2010).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
28.Bytheway, J. P., Carthey, A. J. & Banks, P. B. Risk vs. reward: How predators and prey respond to aging olfactory cues. Behav. Ecol. Sociobiol. 67, 715–725 (2013).Article 

Google Scholar 
29.Haynes, G. Utilization and skeletal disturbances of North American prey carcasses. Arctic 35, 266–281 (1982).Article 

Google Scholar 
30.Kaufmann, J. H. On the definitions and functions of dominance and territoriality. Biol. Rev. 58, 1–20 (1983).Article 

Google Scholar 
31.Zielinski, W. J., Tucker, J. M. & Rennie, K. M. Niche overlap of competing carnivores across climatic gradients and the conservation implications of climate change at geographic range margins. Biol. Conserv. 209, 533–545 (2017).Article 

Google Scholar 
32.Jensen, P. G. & Humphries, M. M. Abiotic conditions mediate intraguild interactions between mammalian carnivores. J. Anim. Ecol. 88, 1305–1318 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
33.Manlick, P. J., Windels, S. K., Woodford, J. E. & Pauli, J. N. Can landscape heterogeneity promote carnivore coexistence in human-dominated landscapes?. Landsc. Ecol. 35, 2013–2027 (2020).Article 

Google Scholar 
34.Krohn, W., Hoving, C., Harrison, D., Phillips, D., & Frost, H. Martes foot-loading and snowfall patterns in eastern North America. In Martens and Fishers (Martes) in Human-Altered Environments (eds. Harrison, D. J. et al.) 115–131 (Springer, 2005).35.Hiller, T. L., Etter, D. R., Belant, J. L. & Tyre, A. J. Factors affecting harvests of fishers and American martens in northern Michigan. J. Wildl. Manag. 75, 1399–1405 (2011).Article 

Google Scholar 
36.Childress, M. J. & Lung, M. A. Predation risk, gender and the group size effect: Does elk vigilance depend upon the behaviour of conspecifics?. Anim. Behav. 66, 38–398 (2003).Article 

Google Scholar 
37.Gehr, B. et al. Stay home, stay safe—Site familiarity reduces predation risk in a large herbivore in two contrasting study sites. J. Anim. Ecol. 89, 1329–1339 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
38.Bull, E. L. & Heater, T. W. Survival, causes of mortality, and reproduction in the American marten in northeastern Oregon. Northwest. Nat. 82, 1–6 (2001).Article 

Google Scholar 
39.White, K. S., Golden, H. N., Hundertmark, K. J. & Lee, G. R. Predation by wolves, Canis lupus, on wolverines, Gulo gulo, and an American marten, Martes americana, Alaska. Can. Field Nat. 116, 132–134 (2002).
Google Scholar 
40.Erb, J., Sampson, B., & Coy, P. Survival and causes of mortality for fisher and marten in Minnesota. Minnesota Department of Natural Resources Summary of Wildlife Research Findings, 2009, 24–31 (2009).41.Wengert, G. M., Gabriel, M. W., Foley, J. E., Kun, T. & Sacks, B. N. Molecular techniques for identifying intraguild predators of fishers and other North American small carnivores. Wildl. Soc. Bull. 37, 659–663 (2013).
Google Scholar 
42.Stricker, H. K. et al. Use of modified snares to estimate bobcat abundance. Wildl. Soc. Bull. 36, 257–263 (2012).Article 

Google Scholar 
43.Kautz, T. M. et al. Predator densities and white-tailed deer fawn survival. J. Wildl. Manag. 83, 1261–1270 (2019).Article 

Google Scholar 
44.Caravaggi, A. et al. A review of camera trapping for conservation behaviour research. Remote Sens. Ecol. Conserv. 3, 109–122 (2017).Article 

Google Scholar 
45.Berger, K. M. & Gese, E. M. Does interference competition with wolves limit the distribution and abundance of coyotes?. J. Anim. Ecol. 76, 1075–1085 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
46.Merkle, J. A., Stahler, D. R. & Smith, D. W. Interference competition between gray wolves and coyotes in Yellowstone National Park. Can. J. Zool. 87, 56–63 (2009).Article 

Google Scholar 
47.Crimmins, S. M. & Van Deelen, T. R. Limited evidence for mesocarnivore release following wolf recovery in Wisconsin, USA. Wildl. Biol. 2019, 1–7 (2019).Article 

Google Scholar 
48.Petroelje, T. R., Belant, J. L., Beyer, D. E., & Kautz, T. M. Interference competition between wolves and coyotes during variable prey abundance. Ecol. Evol 11, 1413–1431 (2021).
49.Switalski, T. A. Coyote foraging ecology and vigilance in response to gray wolf reintroduction in Yellowstone National Park. Can. J. Zool. 81, 985–993 (2003).Article 

Google Scholar 
50.Hilborn, A. et al. Cheetahs modify their prey handling behavior depending on risks from top predators. Behav. Ecol. Sociobiol. 72, article 74 (2018).Article 

Google Scholar 
51.Elgar, M. A. Predator vigilance and group size in mammals and birds: A critical review of the empirical evidence. Biol. Rev. 64, 13–33 (1989).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
52.Bøving, P. S. & Post, E. Vigilance and foraging behaviour of female caribou in relation to predation risk. Rangifer 17, 55–63 (1997).Article 

Google Scholar 
53.Hunter, L. T. B. & Skinner, J. D. Vigilance behaviour in African ungulates: The role of predation pressure. Behaviour 135, 195–211 (1998).Article 

Google Scholar 
54.Liley, S. & Creel, S. What best explains vigilance in elk: Characteristics of prey, predators, or the environment?. Behav. Ecol. 19, 245–254 (2008).Article 

Google Scholar 
55.Makin, D. F., Chamaillé-Jammes, S. & Shrader, A. M. Herbivores employ a suite of antipredator behaviours to minimize risk from ambush and cursorial predators. Anim. Behav. 127, 225–231 (2017).Article 

Google Scholar 
56.Wikenros, C., Ståhlberg, S. & Sand, H. Feeding under high risk of intraguild predation: Vigilance patterns of two medium-sized generalist predators. J. Mammal. 95, 862–870 (2014).Article 

Google Scholar 
57.Welch, R. J., le Roux, A., Petelle, M. B. & Périquet, S. The influence of environmental and social factors on high-and low-cost vigilance in bat-eared foxes. Behav. Ecol. Sociobiol. 72, article 29 (2018).Article 

Google Scholar 
58.Yang, L. et al. A new generation of the United States National Land Cover Database: Requirements, research priorities, design, and implementation strategies. ISPRS J. Photogramm. Remote. Sens. 146, 108–123 (2018).ADS 
Article 

Google Scholar 
59.Lovallo, M. J. & Anderson, E. M. Bobcat (Lynx rufus) home range size and habitat use in northwest Wisconsin. Am. Midl. Nat. 135, 241–252 (1996).Article 

Google Scholar 
60.Burton, A. C. et al. Wildlife camera trapping: A review and recommendations for linking surveys to ecological processes. J. Appl. Ecol. 52, 675–685 (2015).Article 

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

Google Scholar 
62.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/. Accessed Aug 2020.63.National Operational Hydrologic Remote Sensing Center. Snow Data Assimilation System (SNODAS) Data Products at NSIDC, Version 1. Boulder, Colorado USA. https://doi.org/10.7265/N5TB14TC (NSIDC: National Snow and Ice Data Center, 2004).64.Hutchings, M. R. & White, P. C. Mustelid scent-marking in managed ecosystems: Implications for population management. Mammal Rev. 30, 157–169 (2000).Article 

Google Scholar 
65.Mumm, C. A., & Knörnschild, M. Mustelid Communication. In Encyclopedia of Animal Cognition and Behavior (ed. Choe, J.), 1–11 (Springer International, 2018).66.Sullivan, T. P., Nordstrom, L. O. & Sullivan, D. S. Use of predator odors as repellents to reduce feeding damage by herbivores. J. Chem. Ecol. 11, 903–919 (1985).CAS 
PubMed 
Article 

Google Scholar 
67.Rowcliffe, J. M., Kays, R., Kranstauber, B., Carbone, C. & Jansen, P. A. Quantifying levels of animal activity using camera trap data. Methods Ecol. Evol. 5, 1170–1179 (2014).Article 

Google Scholar  More

1.Coristine, L. E. & Kerr, J. T. Habitat loss, climate change, and emerging conservation challenges in Canada. Can. J. Zool. 89, 435–451 (2011).Article 

Google Scholar 
2.Proctor, M. F. et al. Population fragmentation and inter-ecosystem movements of grizzly bears in Western Canada and the Northern United States. Wildl. Monogr. 180, 1–46 (2012).Article 

Google Scholar 
3.Festa-Bianchet, M. Status of the grizzly bear (Ursus arctos) in Alberta: Update 2010. Wildlife Status Report No. 37. (Alberta Sustainable Resource Development, Fish and Wildlife Division, Alberta Conservation Association, Edmonton, Alberta, Canada, 2010).4.Berland, A., Nelson, T., Stenhouse, G., Graham, K. & Cranston, J. The impact of landscape disturbance on grizzly bear habitat use in Foothills Model Forest, Alberta, Canada. For. Ecol. Manag. 256, 1875–1883 (2008).Article 

Google Scholar 
5.Nielsen, S. E., Cranston, J. & Stenhouse, G. B. Identification of priority areas for grizzly bear conservation and recovery in Alberta, Canada. J. Conserv. Plan. 5, 38–60 (2009).
Google Scholar 
6.Boulanger, J. & Stenhouse, G. B. The impact of roads on the demography of grizzly bears in Alberta. PLoS ONE 9, e115535 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
7.Acevedo-Whitehouse, K. & Duffus, A. L. J. Effects of environmental change on wildlife health. Philos. Trans. R. Soc. B Biol. Sci. 364, 3429–3438 (2009).Article 

Google Scholar 
8.Stephen, C. Toward a new definition of animal health: Lessons from the Cohen Commission and the SPS agreement. Optim. Online 43, 1–8 (2013).
Google Scholar 
9.Stephen, C. Toward a modernized definition of wildlife health. J. Wildl. Dis. 50, 427–430 (2014).PubMed 
Article 

Google Scholar 
10.Wittrock, J., Duncan, C. & Stephen, C. A determinants of health conceptual model for fish and wildlife health. J. Wildl. Dis. 55, 285–297 (2019).PubMed 
Article 

Google Scholar 
11.Stephen, C. The Pan-Canadian approach to wildlife health. Can. Vet. J. 60, 145–146 (2019).PubMed 
PubMed Central 

Google Scholar 
12.Ricklefs, R. E. & Wikelski, M. The physiology/life- history nexus. Trends Ecol. Evol. 17, 462–468 (2002).Article 

Google Scholar 
13.Dammhahn, M., Dingemanse, N. J., Niemelä, P. T. & Réale, D. Pace-of-life syndromes: A framework for the adaptive integration of behaviour, physiology and life history. Behav. Ecol. Sociobiol. 72, 62 (2018).Article 

Google Scholar 
14.Réale, D. et al. Personality and the emergence of the pace-of-life syndrome concept at the population level. Philos. Trans. R. Soc. B Biol. Sci. 365, 4051–4063 (2010).Article 

Google Scholar 
15.Lovegrove, B. G. The influence of climate on the basal metabolic rate of small mammals: A slow-fast metabolic continuum. J. Comp. Physiol. B Biochem. Syst. Environ. Physiol. 173, 87–112 (2003).CAS 
Article 

Google Scholar 
16.Garshelis, D., Gibeau, M. & Herrero, S. Grizzly bear demographics in and around Banff National Park and Kananaskis Country, Alberta. J. Wildl. Manag. 69, 277–297 (2005).Article 

Google Scholar 
17.Ferguson, S. H. & Mcloughlin, P. D. Effect of Energy Availability, Seasonality, and Geographic Range on Brown Bear Life History. Ecography (Cop.) 23, 193–200 (2000).Article 

Google Scholar 
18.Brewis, I. A. & Brennan, P. Proteomics Technologies for the Global Identification and Quantification of Proteins. Advances in Protein Chemistry and Structural Biology Vol. 80 (Elsevier, 2010).
Google Scholar 
19.Cox, J. & Mann, M. Is proteomics the new genomics?. Cell 130, 395–398 (2007).CAS 
PubMed 
Article 

Google Scholar 
20.Lu, P., Vogel, C., Wang, R., Yao, X. & Marcotte, E. M. Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation. Nat. Biotechnol. 25, 117–124 (2007).CAS 
PubMed 
Article 

Google Scholar 
21.Vidova, V. & Spacil, Z. A review on mass spectrometry-based quantitative proteomics: Targeted and data independent acquisition. Anal. Chim. Acta 964, 7–23 (2017).CAS 
PubMed 
Article 

Google Scholar 
22.Hoofnagle, A. N. et al. Multiple-reaction monitoring-mass spectrometric assays can accurately measure the relative protein abundance in complex mixtures. Clin. Chem. 58, 777–781 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
23.Addona, T. A. et al. Multi-site assessment of the precision and reproducibility of multiple reaction monitoring-based measurements of proteins in plasma. Nat. Biotechnol. 27, 633–641 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
24.Percy, A. J., Chambers, A. G., Yang, J., Hardie, D. B. & Borchers, C. H. Advances in multiplexed MRM-based protein biomarker quantitation toward clinical utility. Biochim. Biophys. Acta 1844, 917–926 (2014).CAS 
PubMed 
Article 

Google Scholar 
25.Michaud, S. A. et al. Molecular phenotyping of laboratory mouse strains using 500 multiple reaction monitoring mass spectrometry plasma assays. Commun. Biol. 1, 1–9 (2018).CAS 
Article 

Google Scholar 
26.Burke, H. B. Predicting clinical outcomes using molecular biomarkers. Biomark. Cancer 8, BIC.S33380 (2016).Article 

Google Scholar 
27.Zhang, A., Sun, H., Wang, P. & Wang, X. Salivary proteomics in biomedical research. Clin. Chim. Acta 415, 261–265 (2013).CAS 
PubMed 
Article 

Google Scholar 
28.Wilson, A. E. et al. Development and validation of protein biomarkers of health in grizzly bears. Conserv. Physiol. 8, coaa056 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
29.Zmijewski, M. A. & Slominski, A. T. Neuroendocrinology of the skin: An overview and selective analysis. Dermatoendocrinol. 3, 3–10 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
30.Slominski, A. T., Zmijewski, M. A., Plonka, P. M., Szaflarski, J. P. & Paus, R. How UV light touches the brain and endocrine system through skin, and why. Endocrinology 159, 1992–2007 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
31.Slominski, A. T. et al. Sensing the environment: Regulation of local and global homeostasis by the skin neuroendocrine system. Adv. Anat. Embryol. Cell Biol. 212, 1–98 (2012).Article 

Google Scholar 
32.Esmaili, S., Hemmati, M. & Karamian, M. Physiological role of adiponectin in different tissues: A review. Arch. Physiol. Biochem. 126, 67–73 (2018).PubMed 
Article 
CAS 

Google Scholar 
33.Ishaq, S., Kaur, H. & Bhatia, S. Clusterin: It’s implication in health and diseases. Ann. Appl. Bio-Sciences 4, R30–R34 (2017).Article 

Google Scholar 
34.Bali, S. & Utaal, M. S. Serum lipids and lipoproteins: A brief review of the composition, transport and physiological functions. Int. J. Sci. Rep. 5, 309 (2019).Article 

Google Scholar 
35.Linder, M. C. Ceruloplasmin and other copper binding components of blood plasma and their functions: An update. Metallomics 8, 887–905 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
36.Dietzel, E., Floehr, J. & Jahnen-dechent, W. The biological role of fetuin-B in female reproduction. Ann. Reprod. Med. Treat 1(1), 1003 (2016).
Google Scholar 
37.Helliwell, R. J. A., Adams, L. F. & Mitchell, M. D. Prostaglandin synthases: Recent developments and a novel hypothesis. Prostaglandins Leukot. Essent. Fat. Acids 70, 101–113 (2004).CAS 
Article 

Google Scholar 
38.Meyer, E. J., Nenke, M. A., Rankin, W., Lewis, J. G. & Torpy, D. J. Corticosteroid-binding globulin: A review of basic and clinical advances. Horm. Metab. Res. 48, 359–371 (2016).CAS 
PubMed 
Article 

Google Scholar 
39.Hoter, A., El-Sabban, M. E. & Naim, H. Y. The HSP90 family: Structure, regulation, function, and implications in health and disease. Int. J. Mol. Sci. 19, 2560 (2018).PubMed Central 
Article 
CAS 

Google Scholar 
40.Bruschi, M. et al. Annexin a1 and autoimmunity: From basic science to clinical applications. Int. J. Mol. Sci. 19, 1–13 (2018).Article 
CAS 

Google Scholar 
41.Bogdan, A. R., Miyazawa, M., Hashimoto, K. & Tsuji, Y. Regulators of iron homeostasis: New players in metabolism, cell death, and disease. Trends Biochem. Sci. 41, 274–286 (2016).CAS 
PubMed 
Article 

Google Scholar 
42.Dieplinger, H. & Dieplinger, B. Afamin—A pleiotropic glycoprotein involved in various disease states. Clin. Chim. Acta 446, 105–110 (2015).CAS 
PubMed 
Article 

Google Scholar 
43.Ricklin, D., Reis, E. S., Mastellos, D. C., Gros, P. & Lambris, J. D. Complement component C3—The “Swiss Army Knife” of innate immunity and host defense. Immunol. Rev. 274, 33–58 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
44.Bteich, M. An overview of albumin and alpha-1-acid glycoprotein main characteristics: Highlighting the roles of amino acids in binding kinetics and molecular interactions. Heliyon 5, e02879 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
45.Tóthová, C. & Nagy, O. Transthyretin in the evaluation of health and disease in human and veterinary medicine. In Pathophysiology—Altered Physiological States (ed. Gaze, D. C.) (IntechOpen, 2017). https://doi.org/10.5772/57353.Chapter 

Google Scholar 
46.Willis, E. L., Kersey, D. C., Durrant, B. S. & Kouba, A. J. The acute phase protein ceruloplasmin as a non-invasive marker of pseudopregnancy, pregnancy, and pregnancy loss in the giant panda. PLoS One 6, e21159 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
47.Floehr, J. et al. Association of high fetuin-B concentrations in serum with fertilization rate in IVF: A cross-sectional pilot study. Hum. Reprod. 31, 630–637 (2016).CAS 
PubMed 
Article 

Google Scholar 
48.Khalkhali-Ellis, Z. Maspin: The new frontier. Clin. Cancer Res. 12, 7279–7283 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
49.Chim, S. S. C. et al. Detection of the placental epigenetic signature of the maspin gene in maternal plasma. Proc. Natl. Acad. Sci. U.S.A. 102, 14753–14758 (2005).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
50.Carillon, J., Rouanet, J. M., Cristol, J. P. & Brion, R. Superoxide dismutase administration, a potential therapy against oxidative stress related diseases: Several routes of supplementation and proposal of an original mechanism of action. Pharm. Res. 30, 2718–2728 (2013).CAS 
PubMed 
Article 

Google Scholar 
51.Demers, N. & Bayne, C. Immediate increase of plasma protein complement C3 in response to an acute stressor. Fish Shellfish Immunol. 107, 411–413 (2020).CAS 
PubMed 
Article 

Google Scholar 
52.Bourbonnais, M. L., Nelson, T. A., Cattet, M. R. L., Darimont, C. T. & Stenhouse, G. B. Spatial analysis of factors influencing long-term stress in the grizzly bear (Ursus arctos) population of alberta, canada. PLoS One 8, e83768 (2013).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
53.Zedrosser, A., Bellemain, E., Taberlet, P. & Swenson, J. E. Genetic estimates of annual reproductive success in male brown bears: The effects of body size, age, internal relatedness and population density. J. Anim. Ecol. 76, 368–375 (2007).PubMed 
Article 

Google Scholar 
54.Pop, M. I., Iosif, R., Miu, I. V., Rozylowicz, L. & Popescu, V. D. Combining resource selection functions and home-range data to identify habitat conservation priorities for brown bears. Anim. Conserv. 21, 352–362 (2018).Article 

Google Scholar 
55.Pagano, A. M. et al. High-energy, high-fat lifestyle challenges an Arctic apex predator, the polar bear. Science 359, 568–572 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
56.Wasser, S. K. et al. Scat detection dogs in wildlife research and management: Application to grizzly and black bears in the Yellowhead Ecosystem, Alberta, Canada. Can. J. Zool. 82, 475–492 (2004).Article 

Google Scholar 
57.Cristescu, B., Stenhouse, G. B., Symbaluk, M., Nielsen, S. E. & Boyce, M. S. Wildlife habitat selection on landscapes with industrial disturbance. Environ. Conserv. 43, 327–336 (2016).Article 

Google Scholar 
58.Naves, J., Wiegand, T., Revilla, E. & Delibes, M. Endangered species constrained by natural and human factors: The case of brown bears in northern Spain. Conserv. Biol. 17, 1276–1289 (2003).Article 

Google Scholar 
59.Munro, R. H. M., Nielsen, S. E., Price, M. H., Stenhouse, G. B. & Boyce, M. S. Seasonal and diel patterns of grizzly bear diet and activity in west-central Alberta. J. Mammal. 87, 1112–1121 (2006).Article 

Google Scholar 
60.Nielsen, S. E., Boyce, M. S. & Stenhouse, G. B. Grizzly bears and forestry: I. Selection of clearcuts by grizzly bears in west-central Alberta, Canada. For. Ecol. Manag. 199, 51–65 (2004).Article 

Google Scholar 
61.Larsen, T. A., Nielsen, S. E., Cranston, J. & Stenhouse, G. B. Do remnant retention patches and forest edges increase grizzly bear food supply?. For. Ecol. Manag. 433, 741–761 (2019).Article 

Google Scholar 
62.Nielsen, S. E., Stenhouse, G. B. & Boyce, M. S. A habitat-based framework for grizzly bear conservation in Alberta. Biol. Conserv. 130, 217–229 (2006).Article 

Google Scholar 
63.Wilson, A. E. et al. Population-level monitoring of stress in grizzly bears between 2004 and 2014. Ecosphere 11, e03181 (2020).Article 

Google Scholar 
64.Graham, K. & Stenhouse, G. B. Home range, movements, and denning chronology of the grizzly bear (Ursus arctos) in west-central Alberta. Can. Field-Nat. 128, 223–234 (2014).Article 

Google Scholar 
65.Blanchard, B. M. & Knight, R. R. Movements of yellowstone grizzly bears. Biol. Conserv. 58, 41–67 (1991).Article 

Google Scholar 
66.McLoughlin, P. D., Case, R. L., Gau, R. J., Ferguson, S. H. & Messier, F. Annual and seasonal movement patterns of barren-ground grizzly bears in the central northwest territories. Ursus 11, 79–86 (1999).
Google Scholar 
67.Kadowaki, T. et al. Adiponectin and adiponectin receptors in insulin resistance, diabetes, and the metabolic syndrome. J. Clin. Investig. 116, 1784–1792 (2006).CAS 
PubMed 
Article 

Google Scholar 
68.Rivet, D. R., Nelson, O. L., Vella, C. A., Jansen, H. T. & Robbins, C. T. Systemic effects of a high saturated fat diet in grizzly bears (Ursus arctos horribilis). Can. J. Zool. 95, 797–807 (2017).CAS 
Article 

Google Scholar 
69.Rigano, K. S. et al. Life in the fat lane: Seasonal regulation of insulin sensitivity, food intake, and adipose biology in brown bears. J. Comp. Physiol. B Biochem. Syst. Environ. Physiol. 187, 649–676 (2017).CAS 
Article 

Google Scholar 
70.Lee, Y. S. et al. Adipocytokine orosomucoid integrates inflammatory and metabolic signals to preserve energy homeostasis by resolving immoderate inflammation. J. Biol. Chem. 285, 22174–22185 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
71.Ráez-bravo, A. et al. Acute phase proteins increase with sarcoptic mange status and severity in Iberian ibex (Capra pyrenaica, Schinz 1838). Parasitol. Res. 114, 4005–4010. https://doi.org/10.1007/s00436-015-4628-3 (2015).Article 
PubMed 

Google Scholar 
72.Agra, R. M. et al. Orosomucoid as prognosis factor associated with inflammation in acute or nutritional status in chronic heart failure. Int. J. Cardiol. 228, 488–494 (2017).PubMed 
Article 

Google Scholar 
73.Mugahid, D. A. et al. Proteomic and transcriptomic changes in hibernating grizzly bears reveal metabolic and signaling pathways that protect against muscle atrophy. Sci. Rep. 9, 1–16 (2019).Article 
CAS 

Google Scholar 
74.Vella, C. A. et al. Regulation of metabolism during hibernation in brown bears (Ursus arctos): Involvement of cortisol, PGC-1α and AMPK in adipose tissue and skeletal muscle. Comp. Biochem. Physiol. Part A Mol. Integr. Physiol. 240, 110591 (2020).CAS 
Article 

Google Scholar 
75.Jansen, H. T. et al. Hibernation induces widespread transcriptional remodeling in metabolic tissues of the grizzly bear. Commun. Biol. 2, 336 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
76.Phoebus, I., Segelbacher, G. & Stenhouse, G. B. Do large carnivores use riparian zones? Ecological implications for forest management. For. Ecol. Manag. 402, 157–165 (2017).Article 

Google Scholar 
77.Nielsen, S. E., McDermid, G., Stenhouse, G. B. & Boyce, M. S. Dynamic wildlife habitat models: Seasonal foods and mortality risk predict occupancy-abundance and habitat selection in grizzly bears. Biol. Conserv. 143, 1623–1634 (2010).Article 

Google Scholar 
78.Bielli, P. & Calabrese, L. Cellular and molecular life sciences structure to function relationships in ceruloplasmin: A ‘moonlighting’ protein. Cell. Mol. Life Sci. 59, 1413–1427 (2002).CAS 
PubMed 
Article 

Google Scholar 
79.Pagano, A. M. et al. Energetic costs of locomotion in bears: Is plantigrade locomotion energetically economical?. J. Exp. Biol. 221, jeb175372 (2018).PubMed 
Article 

Google Scholar 
80.Kurki, S., Nikula, A., Helle, P. & Linden, H. Landscape fragmentation and forest composition effects on grouse breeding success in boreal forests. Ecology 81, 1985–1997 (2000).
Google Scholar 
81.Graham, K., Boulanger, J., Duval, J. & Stenhouse, G. Spatial and temporal use of roads by grizzly bears in west-central Alberta. Ursus 21, 43–56 (2010).Article 

Google Scholar 
82.McLellan, B. N. & Shackleton, D. M. Grizzly bears and resource-extraction industries: Effects of roads on behaviour, habitat use and demography. J. Appl. Ecol. 25, 451–460 (1988).Article 

Google Scholar 
83.Massey, A. J. et al. Relationship between hair and salivary cortisol and pregnancy in women undergoing IVF. Psychoneuroendocrinology 74, 397–405 (2016).CAS 
PubMed 
Article 

Google Scholar 
84.Benn, B. & Herrero, S. Grizzly bear mortality and human access in Banff and Yoho National Parks, 1971–98. Ursus 13, 213–221 (2002).
Google Scholar 
85.Nielsen, S. E. et al. Modelling the spatial distribution of human-caused grizzly bear mortalities in the Central Rockies ecosystem of Canada. Biol. Conserv. 120, 101–113 (2004).Article 

Google Scholar 
86.Pagano, A. M., Peacock, E. & Mckinney, M. A. Remote biopsy darting and marking of polar bears. Mar. Mammal Sci. 30, 169–183 (2014).Article 

Google Scholar 
87.Berland, A., Nelson, T., Stenhouse, G., Graham, K. & Cranston, J. The impact of landscape disturbance on grizzly bear habitat use in the Foothills Model Forest, Alberta, Canada. For. Ecol. Manag. 256, 1875–1883 (2008).Article 

Google Scholar 
88.Stenhouse, G. et al. Grizzly bear associations along the eastern slopes of Alberta. Ursus 16, 31–40 (2005).Article 

Google Scholar 
89.Nielsen, S. E., Munro, R. H. M., Bainbridge, E. L., Stenhouse, G. B. & Boyce, M. S. Grizzly bears and forestry: II. Distribution of grizzly bear foods in clearcuts of west-central Alberta, Canada. For. Ecol. Manag. 199, 67–82 (2004).Article 

Google Scholar 
90.Cattet, M., Boulanger, J., Stenhouse, G., Powell, R. A. & Reynolds-Hogland, M. J. An evaluation of long-term capture effects in ursids: Implications for wildlife welfare and research. J. Mammal. 89, 973–990 (2008).Article 

Google Scholar 
91.McDermid, G. J. Remote Sensing for Large-Area, Multi-Jurisdictional Habitat Mapping. PhD Thesis. University of Waterloo: Canada. 258p (2005).92.Smulders, M. et al. Quantifying spatial-temporal patterns in wildlife ranges using STAMP: A grizzly bear example. Appl. Geogr. 35, 124–131 (2012).Article 

Google Scholar 
93.Sorensen, A. A., Stenhouse, G. B., Bourbonnais, M. L. & Nelson, T. A. Effects of habitat quality and anthropogenic disturbance on grizzly bear (Ursus arctos horribilis) home-range fidelity. Can. J. Zool. 93, 857–865 (2015).Article 

Google Scholar 
94.Franklin, S. E., Peddle, D. R., Dechka, J. A. & Stenhouse, G. B. Evidential reasoning with Landsat TM, DEM and GIS data for landcover classification in support of grizzly bar habitat mapping. Int. J. Remote Sens. 23, 4633–4652 (2002).ADS 
Article 

Google Scholar 
95.Gessler, P. E., Moore, I. D., McKenzie, N. J. & Ryan, P. J. Soil-landscape modelling and spatial prediction of soil attributes. Int. J. Geogr. Inf. Syst. 9, 421–432 (1995).Article 

Google Scholar 
96.Wilson, J. P. & Gallant, J. C. Terrain Analysis: Principles and Applications (Wiley, 2000).
Google Scholar 
97.Riley, S. J., DeGloria, S. D. & Elliot, R. A Terrain ruggedness index that quantifies topographic heterogeneity. Int. J. Sci. 5, 23–27 (1999).
Google Scholar 
98.Stoneberg, R. P. & Jonkel, C. J. Age determination of black bears by cementum layers. J. Wildl. Manag. 30, 411–414 (1966).Article 

Google Scholar 
99.Matson, G. M., Van Daele, L., Goodwin, E., Aumiller, A., Reynolds, H.V. & Hristienko, H. A Laboratory Manual for Cementum Age Determination of Alaskan Brown Bear First Premolar Teeth. 1–52 (Matson’s Laboratory, Milltown, MT, 1993).100.Nielsen, S. E. et al. Environmental, biological and anthropogenic effects on grizzly bear body size: Temporal and spatial considerations. BMC Ecol. 13, 1 (2013).CAS 
Article 

Google Scholar 
101.Bourbonnais, M. L. et al. Environmental factors and habitat use influence body condition of individuals in a species at risk, the grizzly bear. Conserv. Physiol. 2, 1–14 (2014).Article 
CAS 

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

Google Scholar 
103.Cattet, M. et al. The quantification of reproductive hormones in the hair of captive adult brown bears and their application as indicators of sex and reproductive state. Conserv. Physiol. 5, 1–21 (2017).Article 
CAS 

Google Scholar 
104.Cattet, M. et al. Can concentrations of steroid hormones in brown bear hair reveal age class?. Conserv. Physiol. 6, 1–20 (2018).Article 
CAS 

Google Scholar 
105.Carlson, R. et al. Development and application of an antibody-based protein microarray to assess stress in grizzly bears (Ursus arctos). Conserv. Physiol. 4, 1–17 (2016).Article 
CAS 

Google Scholar 
106.Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical information-Theoretic Approach (Springer, 2002).MATH 

Google Scholar 
107.Grueber, C. E., Nakagawa, S., Laws, R. J. & Jamieson, I. G. Multimodel inference in ecology and evolution: Challenges and solutions. J. Evol. Biol. 24, 699–711 (2011).CAS 
PubMed 
Article 

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

Google Scholar 
109.R Core Team. R: A language and environment for statistical computing. https://www.R-project.org/ (R Foundation for Statistical Computing, Vienna, Austria, 2020). More

1.Hoekstra H. Genetics, development and evolution of adaptive pigmentation in vertebrates. Heredity. 2006;97:222–234.CAS 
PubMed 
Article 

Google Scholar 
2.McNamara ME, Rossi V, Slater TS, Rogers CS, Ducrest AL, Dubey S, et al. Decoding the evolution of melanin in vertebrates. Trends Ecol Evol. 2021; https://doi.org/10.1016/j.tree.2020.12.012.3.Roulin A. Melanin-based colour polymorphism responding to climate change. Glob Chang Biol. 2014;20:3344–3350.PubMed 
Article 

Google Scholar 
4.Laurent S, Pfeifer SP, Settles ML, Hunter SS, Hardwick KM, Ormond L, et al. The population genomics of rapid adaptation: disentangling signatures of selection and demography in white sands lizards. Mol Ecol. 2016;25:306–323.CAS 
PubMed 
Article 

Google Scholar 
5.Naranjo-Ortiz MA, Gabaldón T. Fungal evolution: major ecological adaptations and evolutionary transitions. Biol Rev Camb Philos Soc. 2019;94:1443–1476.PubMed 
PubMed Central 
Article 

Google Scholar 
6.Cordero RJ, Casadevall A. Functions of fungal melanin beyond virulence. Fungal Biol Rev. 2017;31:99–112.PubMed 
PubMed Central 
Article 

Google Scholar 
7.Treseder KK, Lennon JT. Fungal traits that drive ecosystem dynamics on land. Microbiol Mol Biol Rev. 2015;79:243–262.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
8.Kejžar A, Gobec S, Plemenitaš A, Lenassi M. Melanin is crucial for growth of the black yeast Hortaea werneckii in its natural hypersaline environment. Fungal Biol. 2013;117:368–379.PubMed 
Article 
CAS 

Google Scholar 
9.Singaravelan N, Grishkan I, Beharav A, Wakamatsu K, Ito S, Nevo E. Adaptive melanin response of the soil fungus Aspergillus niger to UV radiation stress at “Evolution Canyon”, Mount Carmel, Israel. PLoS ONE. 2008;3:e2993.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
10.Krishnan P, Meile L, Plissonneau C, Ma X, Hartmann FE, Croll D, et al. Transposable element insertions shape gene regulation and melanin production in a fungal pathogen of wheat. BMC Biol. 2018;16:78.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
11.Pereira D, Croll D, Brunner PC, McDonald BA. Natural selection drives population divergence for local adaptation in a wheat pathogen. Fungal Genet Biol. 2020;141:103398.CAS 
PubMed 
Article 

Google Scholar 
12.Desjardins CA, Giamberardino C, Sykes SM, Yu CH, Tenor JL, Chen Y, et al. Population genomics and the evolution of virulence in the fungal pathogen Cryptococcus neoformans. Genome Res. 2017;27:1207–1219.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
13.Robertson KL, Mostaghim A, Cuomo CA, Soto CM, Lebedev N, Bailey RF, et al. Adaptation of the black yeast Wangiella dermatitidis to ionizing radiation: molecular and cellular mechanisms. PLoS ONE. 2012;7:e48674.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
14.Knapp DG, Németh JB, Barry K, Hainaut M, Henrissat B, Johnson J, et al. Comparative genomics provides insights into the lifestyle and reveals functional heterogeneity of dark septate endophytic fungi. Sci Rep. 2018;8:6321.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
15.Fernandez CW, Koide RT. The function of melanin in the ectomycorrhizal fungus Cenococcum geophilum under water stress. Fungal Ecol. 2013;6:479–486.Article 

Google Scholar 
16.Redman RS, Sheehan KB, Stout RG, Rodriguez RJ, Henson JM. Thermotolerance generated by plant/fungal symbiosis. Science. 2002;298:1581.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
17.Peay KG, Kennedy PG, Talbot JM. Dimensions of biodiversity in the earth mycobiome. Nat Rev Microbiol. 2016;14:434–447.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Rodriguez RJ, White JF, Arnold AE, Redman RS. Fungal endophytes: diversity and functional roles. N Phytol. 2009;182:314–330.CAS 
Article 

Google Scholar 
19.Yuan ZL, Su ZZ, Zhang CL. Understanding the biodiversity and functions of root fungal endophytes: the ascomycete Harpophora oryzae as a model case. In: Irina S Druzhinina IS, Kubicek CP editors). The mycota Vol. IV: environmental and microbial relationships. 3rd ed. Springer; 2016, pp 205–214.20.Berthelot C, Leyval C, Foulon J, Chalot M, Blaudez D. Plant growth promotion, metabolite production and metal tolerance of dark septate endophytes isolated from metal-polluted poplar phytomanagement sites. FEMS Microbiol Ecol. 2016;92:fiw144.PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
21.Hill PW, Broughton R, Bougoure J, Havelange W, Newsham KK, Grant H, et al. Angiosperm symbioses with non-mycorrhizal fungal partners enhance N acquisition from ancient organic matter in a warming maritime Antarctic. Ecol Lett. 2019;22:2111–2119.PubMed 
PubMed Central 
Article 

Google Scholar 
22.Mateu M, Baldwin A, Maul J, Yarwood S. Dark septate endophyte improves salt tolerance of native and invasive lineages of Phragmites australis. ISME J. 2020;14:1943–1954.Article 
CAS 

Google Scholar 
23.Porras-Alfaro A, Herrera J, Sinsabaugh RL, Odenbach KJ, Lowrey T, Natvig DO. Novel root fungal consortium associated with a dominant desert grass. Appl Environ Microbiol. 2008;74:2805–2813.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
24.Qin Y, Pan XY, Kubicek CP, Druzhinina IS, Chenthamara K, Labbé J, et al. Diverse plant-associated pleosporalean fungi from saline areas: ecological tolerance and nitrogen-status dependent effects on plant growth. Front Microbiol. 2017;8:158.PubMed 
PubMed Central 

Google Scholar 
25.Gostinčar C, Grube M, de Hoog S, Zalar P, Gunde-Cimerman N. Extremotolerance in fungi: evolution on the edge. FEMS Microbiol Ecol. 2010;71:2–11.PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
26.Yuan ZL, Druzhinina IS, Labbé J, Redman R, Qin Y, Rodriguez R, et al. Specialized microbiome of a halophyte and its role in helping non-host plants to withstand salinity. Sci Rep. 2016;6:32467.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
27.Yuan ZL, Druzhinina IS, Wang X, Zhang X, Peng L, Labbé J. Insight into a highly polymorphic endophyte isolated from the roots of the halophytic seepweed suaeda salsa: Laburnicola rhizohalophila sp. nov. (Didymosphaeriaceae, Pleosporales). Fungal Biol. 2020;124:327–337.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
28.Ellison CE, Hall C, Kowbel D, Welch J, Brem RB, Glass NL, et al. Population genomics and local adaptation in wild isolates of a model microbial eukaryote. Proc Natl Acad Sci USA. 2011;108:2831–2836.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
29.Li H, Durbin R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 2010;26:589–595.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
30.Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25:2078–2079.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
31.Van der Auwera GA, Carneiro MO, Hartl C, Poplin R, Del Angel G, Levy-Moonshine A, et al. From FastQ data to high confidence variant calls: the genome analysis toolkit best practices pipeline. Curr Protoc Bioinform. 2013;43:11.10.1–11.10.33.Article 

Google Scholar 
32.Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015;4:7.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
33.Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet. 2011;88:76–82.CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar 
35.Huson DH, Bryant D. Application of phylogenetic networks in evolutionary studies. Mol Biol Evol. 2006;23:254–267.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
36.Wilken M, Steenkamp E, Wingfield M, De Beer ZW, Wingfield B. Which MAT gene? Pezizomycotina (Ascomycota) mating-type gene nomenclature reconsidered. Fungal Biol Rev. 2017;31:199–211.Article 

Google Scholar 
37.Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997;25:3389–3402.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
38.Wright S. The genetical structure of populations. Ann Eugen. 1951;15:323–354.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
39.Nei M (ed). Molecular evolutionary genetics. Columbia University Press; 1987.40.Carlson CS, Thomas DJ, Eberle MA, Swanson JE, Livingston RJ, Rieder MJ, et al. Genomic regions exhibiting positive selection identified from dense genotype data. Genome Res. 2005;15:1553–1565.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
41.Pembleton LW, Cogan NOI, Forster JW. StAMPP: an R package for calculation of genetic differentiation and structure of mixed-ploidy level populations. Mol Ecol Resour. 2013;13:946–952.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
42.Axelsson E, Ratnakumar A, Arendt ML, Maqbool K, Webster MT, Perloski M, et al. The genomic signature of dog domestication reveals adaptation to a starch-rich diet. Nature. 2013;495:360–364.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
43.Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics. 2012;16:284–287.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
44.Zhao S, Gibbons JG. A population genomic characterization of copy number variation in the opportunistic fungal pathogen Aspergillus fumigatus. PLoS ONE. 2018;13:e0201611.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
45.Klambauer G, Schwarzbauer K, Mayr A, Clevert DA, Mitterecker A, Bodenhofer U, et al. cn.MOPS: mixture of Poissons for discovering copy number variations in next-generation sequencing data with a low false discovery rate. Nucleic Acids Res. 2012;40:e69.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
46.Tajima F. Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics. 1989;123:585–595.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
47.Nei M, Li WH. Mathematical model for studying genetic variation in terms of restriction endonucleases. Proc Natl Acad Sci USA. 1979;76:5269–5273.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
48.Hutter S, Vilella AJ, Rozas J. Genome-wide DNA polymorphism analyses using VariScan. BMC Bioinform. 2006;7:409.Article 
CAS 

Google Scholar 
49.Wagner DN, Baris TZ, Dayan DI, Du X, Oleksiak MF, Crawford DL. Fine-scale genetic structure due to adaptive divergence among microhabitats. Heredity. 2017;118:594–604.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
50.Rech GE, Sanz-Martín JM, Anisimova M, Sukno SA, Thon MR. Natural selection on coding and noncoding DNA sequences is associated with virulence genes in a plant pathogenic fungus. Genome Biol Evol. 2014;6:2368–2379.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
51.Sterken R, Kiekens R, Coppens E, Vercauteren I, Zabeau M, Inzé D, et al. A population genomics study of the Arabidopsis core cell cycle genes shows the signature of natural selection. Plant Cell. 2009;21:2987–2998.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
52.Yu F, Keinan A, Chen H, Ferland RJ, Hill RS, Mignault AA, et al. Detecting natural selection by empirical comparison to random regions of the genome. Hum Mol Genet. 2009;18:4853–4867.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
53.Nielsen R, Williamson S, Kim Y, Hubisz MJ, Clark AG, Bustamante C. Genomic scans for selective sweeps using SNP data. Genome Res. 2005;15:1566–1575.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
54.Pavlidis P, Živkovic D, Stamatakis A, Alachiotis N. SweeD: likelihood-based detection of selective sweeps in thousands of genomes. Mol Biol Evol. 2013;30:2224–2234.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
55.Barrett JC, Fry B, Maller J, Daly MJ. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics. 2005;21:263–265.CAS 
PubMed 
Article 

Google Scholar 
56.Zhan F, He Y, Zu Y, Li T, Zhao Z. Characterization of melanin isolated from a dark septate endophyte (DSE), Exophiala pisciphila. World J Microbiol Biotechnol. 2011;27:2483–2489.CAS 
Article 

Google Scholar 
57.Taylor JW, Hann-Soden C, Branco S, Sylvain I, Ellison CE. Clonal reproduction in fungi. Proc Natl Acad Sci USA. 2015;112:8901–8908.CAS 
PubMed 
Article 

Google Scholar 
58.McGuire IC, Davis JE, Double ML, MacDonald WL, Rauscher JT, McCawley S, et al. Heterokaryon formation and parasexual recombination between vegetatively incompatible lineages in a population of the chestnut blight fungus, Cryphonectria parasitica. Mol Ecol. 2005;14:3657–3669.CAS 
PubMed 
Article 

Google Scholar 
59.Szulkin M, Gagnaire PA, Bierne N, Charmantier A. Population genomic footprints of fine-scale differentiation between habitats in Mediterranean blue tits. Mol Ecol. 2016;25:542–558.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
60.Hamilton JA, De la Torre AR, Aitken SN. Fine-scale environmental variation contributes to introgression in a three-species spruce hybrid complex. Tree Genet Genomes. 2015;11:817.Article 

Google Scholar 
61.Yeaman S. Genomic rearrangements and the evolution of clusters of locally adaptive loci. Proc Natl Acad Sci USA. 2013;110:E1743–E1751.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
62.Kroken S, Glass NL, Taylor JW, Yoder OC, Turgeon BG. Phylogenomic analysis of type I polyketide synthase genes in pathogenic and saprobic ascomycetes. Proc Natl Acad Sci USA. 2003;100:15670–15675.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
63.Woo PCY, Tam EW, Chong KT, Cai JJ, Tung ET, Ngan AH, et al. High diversity of polyketide synthase genes and the melanin biosynthesis gene cluster in Penicillium marneffei. FEBS J. 2010;277:3750–3758.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
64.Kameyama K, Montague PM, Hearing VJ. Expression of melanocyte stimulating hormone receptors correlates with mammalian pigmentation, and can be modulated by interferons. J Cell Physiol. 1988;137:35–44.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
65.Upadhyay S, Xu X, Lowry D, Jackson JC, Roberson RW, Lin X. Subcellular compartmentalization and trafficking of the biosynthetic machinery for fungal melanin. Cell Rep. 2016;14:2511–2518.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
66.Coleman JJ, Mylonakis E. Efflux in fungi: la pièce de résistance. PLoS Pathog. 2009;5:e1000486.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
67.Cosgrove DJ. Microbial expansins. Annu Rev Microbiol. 2017;71:479–497.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar  More