Ecology

Occurrence of the target OCs
The following individual OCs were found in fish samples with a detection frequency lower than 50%: i.e. p,p’-methoxychlor, heptachlor, heptachlor exo-epoxide, CB-66, 77, 81, 105, 114, 118, 123, 126, 128, 138, 153, 156, 157,167, 169, 170, and 180. These OCs are not discussed later. The concentrations of seven OC compounds in the muscle samples of 41 Nile tilapia and 34 Redbelly tilapia are shown in Supplementary Table S4. Since there were no significant interspecies differences between Nile tilapia and Redbelly tilapia (t-test, p = 0.16), the results of OCs analysis will be reported by tilapia genus in this study.
Median concentrations of OCPs and PCBs in tilapia samples from the main rivers system in the southern Guangxi are summarized in Table 1. PCBs and OCPs were detected in the muscle of all tilapia samples. DDTs were the predominant contaminant with a median concentration of 15.2 ng/g lw, and endosulfan was the second most common contaminant with a median concentration of 12.2 ng/g lw. PCBs, Drins, HCB, and HCHs concentrations in the fish were relatively low with median concentrations between 1.37 and 9.11 ng/g lw. The concentrations of the various OC compounds measured in the tilapia samples in this study were lower than those in tilapia collected from Guangdong province, China9, Africa10,11,12, Europe, and America13,14,15 (see Supplementary Table S5 online). This study showed that the main rivers in the southern Guangxi have low levels of OCs pollution, and the fish muscle contamination might be related to the low levels of pollution in the water and sediment. According to data from the National Bureau of statistics of China, the gross output value of industry and agriculture in Guangxi has been lower than that of other provinces or regions in China in the past few decades16. Therefore, the low levels of OCs pollution found in this study area are mainly the result of lower pollution input. In addition, most of the study area is located in the tropics, which have a relatively high perennial temperature. A warm climate is very conducive to enhance the metabolism rate of OCs by organisms17. The metabolism of organic pollutants by organisms occurs under the catalysis of a series of enzymes18,19. Factors affecting the enzymatic reaction, such as enzyme concentration and temperature, will affect the metabolism of OCs in organisms. Temperature also affects the air–water partitioning, which influences the volatilization of chemical pollutants from water20. Thus, dissolved chemical concentrations tend to be higher in cooler than in warmer waters21. In alignment with this supposition, Sobek et al. (2010) reported a largely reduced difference in bioaccumulation factor of PCBs between the Arctic and the temperate food webs, after adjustment for temperature effect22.
Table 1 Organochlorine concentration [median (range), ng/g lw] in the wild tilapia from the main rivers in Guangxi, South China.
Full size table

Distribution characteristics
Spatial distribution of OCs
The spatial distributions of seven OC compounds are presented in Fig. 1. The spatial distribution did not show a gradient in selected OCs concentrations. The spatial distribution of OCPs was under the double influence of a global distillation effect and the usage of OCPs23. Human activities can affect the distribution of OCPs in hilly areas24. However, there was no significant correlation between elevation and the residues of OCPs in this study (non-parametric test, p  > 0.05) (Fig. S2). Therefore, the distribution pattern of OCs in this study was hardly affected by global distillation. High levels of OCPs were found in TD and GG, where endosulfan or DDTs were the predominant contributors. Endosulfan is a cyclodiene pesticide extensively used throughout the world to control a wide variety of insects and mites23. Endosulfan levels were remarkably higher (10–411 times) in tilapia samples from TD than in samples from other sites. This observation was consistent with the fact that the local fruit and vegetable farming industry is the primary income source in the TD25. Therefore, we believe that the high levels of endosulfan in this study could be attributed to local pesticide practices specific to pest control needs over a short period26. Similarly, the higher levels of DDTs observed in GG also might be related to local short-term agricultural activities.
Figure 1

Spatial variations of log-transformed concentrations of OC compounds (ng/g lw) residues in wild tilapia from the main rivers in Guangxi, South China. TD: Tiandong County; LA: Longan County; CZ: Chongzuo City; FS: Fusui City; NN: Nanning City; GG: Guigang City; WX: Wuxuan County; PN: Pingnan City; TX: Tengxian County; WZ: Wuzhou City.

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PCBs are ubiquitous in tilapia samples from the study area, with a detection rate of 100%. In contrast to the OCP compounds, the overall trend of the PCBs was fairly homogenous. A relatively high median PCB concentration was detected in tilapia samples from TX, while slightly lower concentrations were detected from PN. There were no significant differences among different sampling locations (t-test, p  > 0.05). The minor differences could be explained by the migration and spread of PCBs in the environment. The limited historical use of PCBs in the present study area is another important factor contributing to this phenomenon25.
Spatial differences in pollutant metabolites
The ratio of parent compounds to their metabolites can provide useful information for the diagnosis of their sources23,24,27. The scatter plots for isomeric ratios of selected OCPs are shown in Fig. 2.
Figure 2

Scatter plots of molecular indices to identify DDTs (a) and endosulfan (b) contamination sources.

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The ratios between p,p’-DDT, p,p’-DDE and p,p’-DDD have been regarded as an indication of increasing or decreasing inputs to the environment. A ratio of (p,p’-DDE + p,p’-DDD)/p,p’-DDT  1.3) were found in two fish samples from FS (1.80) and CZ (1.71) districts, which indicates that Dicofol may be the main contributor to DDTs in these areas. In summary, the DDT residues in wild tilapia from rivers of the southern Guangxi originated mainly from the historical application of Dicofol and technical DDTs, whereas recent application of technical DDTs are indicated in TD.
Technical endosulfan includes two active ingredients: α-endosulfan (70%) and β-endosulfan (30%)23. Because α-endosulfan decomposes more easily than β-endosulfan, a α-/β-endosulfan ratio of  2.33) present in the tilapia samples from TD and FS, indicate continual use of endosulfan in these areas. In the other sites, those ratios are all below 2.33, indicating there was no recent application of technical endosulfan in that area. It is noteworthy that one sample (from TX site) contained β-endosulfan at a level below the limit of detection, but had appreciable levels of α-endosulfan, which may have been transported in from other areas. Because the Henry’s law constant for α-endosulfan is higher than the constant for β-endosulfan, there is a greater tendency for α-endosulfan to evaporate from the surface medium to air23,30.
The concentrations of ten PCB congeners in the present study area are illustrated in Fig. 3. Using degree of chlorination, these congeners can be divided into light PCBs (2–3 chlorines), medium PCBs (4–6 chlorines), and heavy PCBs (7–10 chlorines). The PCB sources of the 75 fish samples can be classified into the same categories since the PCBs in all sampling sites generally exhibited the following order: heavy PCBs (63.3–86.1% of ∑10PCBs)  > medium PCBs (9.72–18.2% of ∑10PCBs)  > light PCBs (4.66–18.3% of ∑10PCBs). The higher residual content of heavy PCBs may be related to historical production and use, or relate to their stably chemical structure31. Tri-CBs and penta-CBs were the major PCB products manufactured in China from 1965 until they were banned in 197431,32. The proportion of these compounds in PCBs was only 2.86–24.7% in this study. On the other hand, light PCBs have higher volatility and a lower octanol–water distribution coefficient than heavy PCBs33. Once absorbed into the organisms, light PCBs are usually more rapidly metabolized than the more highly chlorinated congeners34. Our results also indicated that the proportion of deca-CBs in heavy PCBs and PCBs was 78.3–98.1% and 51.8–88.6%, respectively. And the sampling sites with high deca-CBs ratio were distributed in the main agricultural farming areas (middle and upper reaches of rivers). And China banned the production of deca-CBs as early as 197435. Therefore, we believe that historical heritage was the main source of deca-CBs in the study area.
Figure 3

Composition profiles of PCB congeners in the main rivers from Guangxi, South China.

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Correlation among biological parameters and OC compounds
We studied the effects of biological parameters (including total length, body mass, age, and lipid content) on the bioaccumulation of individual contaminants in tissue samples of wild tilapia (based on dry weight). The loading and scores plot of PCA based on the concentrations of OCs in the tilapia muscle samples are displayed in Fig. 4. The PC1 explained 58.6% of the total variance and PC2 accounted for 23.5% of the variance. Table S7 lists the correlation coefficients between OC compounds and biological parameters, while the correlation coefficients between OC congeners and biological parameters are listed in Table S8. A significant relationship between growth parameters (i.e. total length, age, and body mass) was found in the tilapia samples, but only age and lipid content were significantly correlated (p  More

Environmental parameters of deep ocean seawater and sediment
Challenger Deep seawater and surface sediment samples were taken from its entire ~11,000 depth profile (Fig. 1a and Supplementary Table 1). The clines in temperature (29.8 °C in surface waters, decreasing to ~1.0 °C below 3000 m) and pressure (0.1 MPa in surface waters to ~104 MPa at the bottom of the trench) were recorded. The waters were oxic throughout the water column and the salinity ranged between 34 and 35 Practical Salinity Units (PSUs) (Supplementary Table 1). Seawater total DMSP and DMS concentrations were similar to those in previous studies21,26,27,28 and were positively correlated with Chl-a levels, being highest in the Chl-a maximum layer (10.51 × 10−3 nmol ml−1 DMSP and 4.97 × 10−3 nmol ml−1 DMS) and at lower but relatively stable levels (0.96–2.39 × 10−3 nmol ml−1 DMSP and 0.15–1.06 × 10−3 nmol ml−1 DMS) in the aphotic waters below 200 m (Fig. 1b, c and Supplementary Table 1). It should be noted that a small portion of this ‘background DMSP’ (3 μm) bacteria, which dominated the metagenomes of both these fractions, contained DMSP biosynthesis and catabolic genes (Figs. 2a and 3a, and Table 1), indicating that size fractionation is not a foolproof method of separating DMSP-producing bacteria from phytoplankton. Bacteria with dysB were shown by qPCR and metagenomics to be relatively abundant in the surface waters (dsyB total abundance of 2.61 × 105 copies L−1; 0.78–0.98% of surface water bacteria) representing ~3.49–4.38 × 103 bacteria ml−1 of surface seawater. These numbers are comparable to those predicted from the ocean microbial reference gene catalog metagenomic database (OM-RGC)37 in Williams et al.10, at ~4.8–9.6 × 103 bacteria ml−1. The abundance of these potential DMSP-producing bacteria initially decreased in 1000–2000 m deep seawater samples (3.46 × 104 copies L−1; ~0.43% bacteria at these depths), but then steadily increased with depth to reach maximal levels at 10,500 m (3.95 × 106 copies L−1; 4.03% of bacteria at 10,500 m), which were up to 15-fold higher than in the surface water (Fig. 2b, Table 1, and Supplementary Table 2). All detected dsyB sequences, including 37/162 metagenome assembled genomes (MAGs), were Alphaproteobacterial, mainly Rhodobacterales, Rhizobiales, and Rhodospirillales (Supplementary Data 1). At the genus level, Pseudooceanicola and Roseovarius were the most abundant potential DMSP producers at all depths, with much higher abundances (P  More

1.
Tilman, D. & Downing, J. A. Biodiversity and stability in grasslands. Nature 367, 363 (1994).
ADS  Google Scholar 
2.
Watkinson, A. R. & Ormerod, S. J. Grasslands, grazing and biodiversity: editors’ introduction. J. Appl. Ecol. 38, 233–237 (2001).
Google Scholar 

3.
Dengler, J., Janišová, M., Török, P. & Wellstein, C. Biodiversity of Palaearctic grasslands: a synthesis. Agric. Ecosyst. Environ. 182, 1–14 (2014).
Google Scholar 

4.
Dover, J. W., Spencer, S., Collins, S., Hadjigeorgiou, I. & Rescia, A. Grassland butterflies and low intensity farming in Europe. J. Insect Conserv. 15, 129–137 (2011).
Google Scholar 

5.
Morelli, F. High nature value farmland increases taxonomic diversity, functional richness and evolutionary uniqueness of bird communities. Ecol. Indic. 90, 540–546 (2018).
Google Scholar 

6.
Morelli, F., Benedetti, Y. & Tryjanowski, P. Introduction. In Birds as Useful Indicators of High Nature Value Farmlands (eds Morelli, F. & Tryjanowski, P.) 1–26 (Springer, Berlin, 2017). https://doi.org/10.1007/978-3-319-50284-7_1.
Google Scholar 

7.
Sutcliffe, L. M. E. et al. Harnessing the biodiversity value of Central and Eastern European farmland. Divers. Distrib. 21, 722–730 (2015).
Google Scholar 

8.
Donald, P. F., Green, R. E. & Heath, M. F. Agricultural intensification and the collapse of Europe’s farmland bird populations. Proc. R. Soc. Lond. Ser. B Biol. Sci. 268, 25–29 (2001).
Google Scholar 

9.
Donald, P. F., Pisano, G., Rayment, M. D. & Pain, D. J. The Common Agricultural Policy, EU enlargement and the conservation of Europe’s farmland birds. Agric. Ecosyst. Environ. 89, 167–182 (2002).
Google Scholar 

10.
Fragoso, R., Marques, C., Lucas, M. R., Martins, M. B. & Jorge, R. The economic effects of common agricultural policy on Mediterranean montado/dehesa ecosystem. J. Policy Model. 33, 311–327 (2011).
Google Scholar 

11.
Ribeiro, P. F. et al. Modelling farming system dynamics in high nature value farmland under policy change. Agric. Ecosyst. Environ. 183, 138–144 (2014).
Google Scholar 

12.
Suárez, F., Naveso, M. A. & De Juana, E. Farming in the drylands of Spain: birds of the pseudosteppes. In Farming and Birds in Europe. The common Agricultural Policy and its Implications for Bird Conservation (eds Pain, D. & Pienkowsky, M.) 297–330 (Academic Press, New York, 1997).
Google Scholar 

13.
Hoogeveen, Y., Petersen, J. E., Balazs, K. & Higuero, I. High Nature Value Farmland: Characteristics, Trends and Policy Challenges. EEA Report. No 1/2004. European Environment Agency, Copenhagen, Denmark (2004).

14.
Moreira, F., Pinto, M. J., Henriques, I. & Marques, T. The importance of low-intensive farming systems for fauna, flora and habitats protected under the european “birds” and “habitats” directives: is agriculture essential for preserving biodiversity in the mediterranean region? In Trends in Biodiversity Research (ed. Burk, A. R.) 117–145 (Nova Science Publishers, Huappauge, 2005).
Google Scholar 

15.
Lomba, A. et al. Mapping and monitoring high nature value farmlands: challenges in European landscapes. J. Environ. Manag. 143, 140–150 (2014).
Google Scholar 

16.
Delgado, A. & Moreira, F. Bird assemblages of an Iberian cereal steppe. Agric. Ecosyst. Environ. 78, 65–76 (2000).
Google Scholar 

17.
Ribeiro, P. F. et al. An applied farming systems approach to infer conservation-relevant agricultural practices for agri-environment policy design. Land Use Policy 58, 165–172 (2016).
Google Scholar 

18.
Stoate, C. et al. Ecological impacts of early 21st century agricultural change in Europe—a review. J. Environ. Manag. 91, 22–46 (2009).
CAS  Google Scholar 

19.
Faria, N., Morales, M. B. & Rabaça, J. E. Exploring nest destruction and bird mortality in mown Mediterranean dry grasslands: an increasing threat to grassland bird conservation. Eur. J. Wildl. Res. 62, 663–671 (2016).
Google Scholar 

20.
Santana, J. et al. Using beta diversity to inform agricultural policies and conservation actions on Mediterranean farmland. J. Appl. Ecol. 54, 1825–1835 (2017).
Google Scholar 

21.
Trombulak, S. C. & Frissell, C. A. Review of ecological effects of roads on terrestrial and aquatic communities. Conserv. Biol. 14, 18–30 (2000).
Google Scholar 

22.
Bernardino, J. et al. Bird collisions with power lines: state of the art and priority areas for research. Biol. Conserv. 222, 1–13 (2018).
Google Scholar 

23.
Loss, S. R., Will, T. & Marra, P. P. Direct mortality of birds from anthropogenic causes. Annu. Rev. Ecol. Evol. Syst. 46, 99–120 (2015).
Google Scholar 

24.
Hernández-Matías, A., Real, J., Parés, F. & Pradel, R. Electrocution threatens the viability of populations of the endangered Bonelli’s eagle (Aquila fasciata) in Southern Europe. Biol. Conserv. 191, 110–116 (2015).
Google Scholar 

25.
Shaw, J. M., Reid, T. A., Schutgens, M., Jenkins, A. R. & Ryan, P. G. High power line collision mortality of threatened bustards at a regional scale in the Karoo, South Africa. Ibis (Lond. 1859) https://doi.org/10.1111/ibi.12553 (2017).
Article  Google Scholar 

26.
Borda-de-Água, L., Grilo, C. & Pereira, H. M. Modeling the impact of road mortality on barn owl (Tyto alba) populations using age-structured models. Ecol. Model. 276, 29–37 (2014).
Google Scholar 

27.
Reijnen, R., Foppen, R. & Meeuwsen, H. The effects of traffic on the density of breeding birds in Dutch agricultural grasslands. Biol. Conserv. 75, 255–260 (1996).
Google Scholar 

28.
Mcnew, L. B., Hunt, L. M., Gregory, A. J., Wisely, S. M. & Sandercock, B. K. Effects of wind energy development on nesting ecology of greater prairie-chickens in fragmented grasslands. Conserv. Biol. 28, 1089–1099 (2014).
PubMed  PubMed Central  Google Scholar 

29.
Wolfe, D. H., Patten, M. A., Shochat, E., Pruett, C. L. & Sherrod, S. K. Causes and patterns of mortality in lesser prairie-chickens Tympanuchus pallidicinctus and implications for management. Wildl. Biol. 13, 95–104 (2007).
Google Scholar 

30.
Shaw, J. M., Jenkins, A. R., Smallie, J. J. & Ryan, P. G. Modelling power-line collision risk for the Blue Crane Anthropoides paradiseus in South Africa. Ibis (Lond. 1859) 152, 590–599 (2010).
Google Scholar 

31.
Birdlife International. The IUCN Red List of Threatened Species 2018 (2018). www.iucnredlist.org. Accessed 2nd August 2019.

32.
Faria, N. Implications of Dry Grassland Management in the Ecology and Conservation of Grassland Birds in South Portugal (Universidad Autónoma de Madrid, Madrid, 2015).
Google Scholar 

33.
Iñigo, A. & Barov, B. Action plan for the Little Bustard Tetrax tetrax in the European Union. Report. SEO| BirdLife and BirdLife International for the European Commission (2010).

34.
Morales, M. B., García, J. T. & Arroyo, B. Can landscape composition changes predict spatial and annual variation of little bustard male abundance?. Anim. Conserv. 8, 167–174 (2005).
Google Scholar 

35.
Moreira, F. et al. Mosaic-level inference of the impact of land cover changes in agricultural landscapes on biodiversity: a case-study with a threatened grassland bird. PLoS ONE 7, e38876 (2012).
ADS  MathSciNet  CAS  PubMed  PubMed Central  Google Scholar 

36.
Silva, J. P., Palmeirim, J. M. & Moreira, F. Higher breeding densities of the threatened little bustard Tetrax tetrax occur in larger grassland fields: implications for conservation. Biol. Conserv. 143, 2553–2558 (2010).
Google Scholar 

37.
Silva, J. P. et al. EU protected area network did not prevent a country wide population decline in a threatened grassland bird. PeerJ 6, e4284 (2018).
PubMed  PubMed Central  Google Scholar 

38.
García de la Morena, Bota, G., Mañosa, S. & Morales, M. B. El sisón común en España. II Censo Nacional (2016). Report (2018).

39.
Traba, J. & Morales, M. B. The decline of farmland birds in Spain is strongly associated to the loss of fallowland. Sci. Rep. 9, 9473 (2019).
ADS  PubMed  PubMed Central  Google Scholar 

40.
Marcelino, J. et al. Tracking data of the Little Bustard Tetrax tetrax in Iberia shows high anthropogenic mortality. Bird Conserv. Int. https://doi.org/10.1017/S095927091700051X (2017).
Article  Google Scholar 

41.
Bevanger, K. Biological and conservation aspects of bird mortality caused by electricity power lines: a review. Biol. Conserv. 86, 67–76 (1998).
Google Scholar 

42.
Janss, G. F. E. Avian mortality from power lines: a morphologic approach of a species-specific mortality. Biol. Conserv. 95, 353–359 (2000).
Google Scholar 

43.
Martin, G. R. Understanding bird collisions with man-made objects: a sensory ecology approach. Ibis. 153, 239–254 (2011).
Google Scholar 

44.
Martin, G. R. & Shaw, J. M. Bird collisions with power lines: failing to see the way ahead?. Biol. Conserv. 143, 2695–2702 (2010).
Google Scholar 

45.
Marques, A. T., Martins, R. C., Silva, J. P., Palmeirim, J. M. & Moreira, F. Power line routing and configuration as major drivers of collision risk in two bustard species. Oryx https://doi.org/10.1017/S0030605319000292 (2020).
Article  Google Scholar 

46.
Silva, J. P. et al. A spatially explicit approach to assess the collision risk between birds and overhead power lines: a case study with the little bustard. Biol. Conserv. 170, 256–263 (2014).
Google Scholar 

47.
García, J., Suárez-Seoane, S., Miguélez, D., Osborne, P. E. & Zumalacárregui, C. Spatial analysis of habitat quality in a fragmented population of little bustard (Tetrax tetrax): implications for conservation. Biol. Conserv. 137, 45–56 (2007).
Google Scholar 

48.
Osborne, P. E. & Suárez-Seoane, S. Identifying core areas in a species’ range using temporal suitability analysis: an example using little bustards Tetrax Tetrax L. in Spain. Biodivers. Conserv. 16, 3505–3518 (2007).
Google Scholar 

49.
Santangeli, A. & Dolman, P. M. Density and habitat preferences of male little bustard across contrasting agro-pastoral landscapes in Sardinia (Italy). Eur. J. Wildl. Res. 57, 805–815 (2011).
Google Scholar 

50.
Santos, M. et al. Impacts of land use and infrastructural changes on threatened Little Bustard Tetrax tetrax breeding populations: quantitative assessments using a recently developed spatially explicit dynamic modelling framework. Bird Conserv. Int. 26, 418–435 (2016).
Google Scholar 

51.
Suárez-Seoane, S., Osborne, P. E. & Alonso, J. C. Large-scale habitat selection by agricultural steppe birds in Spain: identifying species-habitat responses using generalized additive models. J. Appl. Ecol. 39, 755–771 (2002).
Google Scholar 

52.
Silva, J. P. et al. Estimating the influence of overhead transmission power lines and landscape context on the density of little bustard Tetrax tetrax breeding populations. Ecol. Model. 221, 1954–1963 (2010).
Google Scholar 

53.
Morales, M. B., Traba, J., Carriles, E., Delgado, M. P. & de la Morena, E. L. G. Sexual differences in microhabitat selection of breeding little bustards Tetrax tetrax: ecological segregation based on vegetation structure. Acta Oecologica 34, 345–353 (2008).
ADS  Google Scholar 

54.
Faria, N., Rabaça, J. E. & Morales, M. B. The importance of grazing regime in the provision of breeding habitat for grassland birds: the case of the endangered little bustard (Tetrax tetrax). J. Nat. Conserv. 20, 211–218 (2012).
Google Scholar 

55.
Silva, J. P., Estanque, B., Moreira, F. & Palmeirim, J. M. Population density and use of grasslands by female Little Bustards during lek attendance, nesting and brood-rearing. J. Ornithol. 155, 53–63 (2014).
Google Scholar 

56.
INE. Statistical data: Database. (2019). https://www.ine.pt/xportal/xmain?xpid=INE&xpgid=ine_base_dados. Accessed 9th May 2019.

57.
Gameiro, J., Silva, J. P., Franco, A. M. A. & Palmeirim, J. M. Effectiveness of the European Natura 2000 network at protecting Western Europe’s agro-steppes. Biol. Conserv. 248, 108681 (2020).
Google Scholar 

58.
Beja, P. et al. Predators and livestock reduce bird nest survival in intensive Mediterranean farmland. Eur. J. Wildl. Res. 60, 249–258 (2014).
Google Scholar 

59.
van der Wal, R. & Palmer, S. C. Is breeding of farmland wading birds depressed by a combination of predator abundance and grazing?. Biol. Lett. 4, 256–258 (2008).
PubMed  PubMed Central  Google Scholar 

60.
Lane, S. J., Alonso, J. C. & Martín, C. A. Habitat preferences of great bustard Otis tarda flocks in the arable steppes of central Spain: are potentially suitable areas unoccupied?. J. Appl. Ecol. 38, 193–203 (2001).
Google Scholar 

61.
Ahlering, M. A., Johnson, D. H. & Faaborg, J. Conspecific attraction in a grassland bird, the Baird’s Sparrow. J. Field Ornithol. 77, 365–371 (2006).
Google Scholar 

62.
Tarjuelo, R. et al. Not only habitat but also sex: factors affecting spatial distribution of Little Bustard Tetrax tetrax families. Acta Ornithol. 48, 119–128 (2013).
Google Scholar 

63.
Reino, L. et al. Effects of changed grazing regimes and habitat fragmentation on Mediterranean grassland birds. Agric. Ecosyst. Environ. 138, 27–34 (2010).
Google Scholar 

64.
Walters, K., Kosciuch, K. & Jones, J. Can the effect of tall structures on birds be isolated from other aspects of development?. Wildl. Soc. Bull. 38, 250–256 (2014).
Google Scholar 

65.
Fahrig, L. & Rytwinski, T. Effects of roads on animal abundance: an empirical review and synthesis. Ecol. Soc. 14, 21 (2009).
Google Scholar 

66.
Tryjanowski, P. et al. Conservation of farmland birds faces different challenges in Western and Central-Eastern Europe. Acta Ornithol. 46, 1–12 (2011).
Google Scholar 

67.
Gudka, M., Santos, C. D., Dolman, P. M., Abad-Gómez, J. M. & Silva, J. P. Feeling the heat: elevated temperature affects male display activity of a lekking grassland bird. PLoS ONE 14, e0221999 (2019).
CAS  PubMed  PubMed Central  Google Scholar 

68.
Silva, J. P., Catry, I., Palmeirim, J. M. & Moreira, F. Freezing heat: thermally imposed constraints on the daily activity patterns of a free-ranging grassland bird. Ecosphere 6, art119 (2015).

69.
Alonso, H. et al. Male post-breeding movements and stopover habitat selection of an endangered short-distance migrant, the Little Bustard Tetrax tetrax. Ibis (Lond. 1859) 162, 279–292 (2020).
Google Scholar 

70.
García de la Morena, E. L. et al. Migration patterns of Iberian little bustards Tetrax tetrax. Ardeola 62, 95–112 (2015).
Google Scholar 

71.
Silva, J. P., Faria, N. & Catry, T. Summer habitat selection and abundance of the threatened little bustard in Iberian agricultural landscapes. Biol. Conserv. 139, 186–194 (2007).
Google Scholar 

72.
Equipa Atlas. Atlas das aves nidificantes em Portugal (1999–2005). (Instituto da Conservaçăo da Natureza e da Biodiversidade, Sociedade Portuguesa para o Estudo das Aves, Parque Natural da Madeira e Secretaria Regional do Ambiente e do Mar, 2008).

73.
De Juana, E. & Martínez, C. Distribution and conservation status of Little bustard Tetrax tetrax in the Iberian Peninsula. Ardeola 43, 157–167 (1996).
Google Scholar 

74.
Delgado, A. & Moreira, F. Between-year variations in Little Bustard Tetrax tetrax population densities are influenced by agricultural intensification and rainfall. Ibis (Lond. 1859) 152, 633–642 (2010).
Google Scholar 

75.
DGT. Especificações técnicas da Carta de Uso e Ocupação do Solo de Portugal Continental para 1995, 2007, 2010 e 2015. 103 (2018).

76.
INE. Recenseamento Agrícola 1999—Análise de resultados (2001).

77.
INE. Recenseamento Agrícola 2009—Análise dos principais resultados (2011).

78.
Haklay, M. & Weber, P. Openstreetmap: user-generated street maps. IEEE Pervasive Comput. 7, 12–18 (2008).
Google Scholar 

79.
R Core Team. R: a language and environment for statistical computing (2016).

80.
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Google Scholar 

81.
Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).
Google Scholar 

82.
Wood, S. N. Generalized Additive Models: An Introduction with R 2nd edn. (Chapman and Hall/CRC, Boca Raton, 2017).
Google Scholar 

83.
Zuur, A. F., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer, New York, 2009). https://doi.org/10.1007/978-0-387-87458-6.
Google Scholar 

84.
Wood, S. N. mgcv: mixed GAM computation vehicle with automatic smoothness estimation. R package version 1.8-24. 302 (2018).

85.
Bjørnstad, O. N. & Falck, W. Nonparametric spatial covariance functions: estimation and testing. Environ. Ecol. Stat. 8, 53–70 (2001).
MathSciNet  Google Scholar 

86.
Rhodes, J. R., McAlpine, C. A., Zuur, A. F., Smith, G. M. & Ieno, E. N. Mixed Effects Models and Extensions in Ecology with R 469–492 (Springer, Berlin, 2009). https://doi.org/10.1007/978-0-387-87458-6_21.
Google Scholar 

87.
Bjørnstad, O. N. ncf: spatial nonparametric covariance functions. R package version 1.1–7. (2016).

88.
QGIS Development Team. QGIS Geographic Information System. Open Source Geospatial Foundation Project. https://qgis.osgeo.org (2017). More

1.
Schuster, P. F. et al. Permafrost stores a globally significant amount of mercury. Geophys. Res. Lett. 45, 1463–1471 (2018).
ADS  CAS  Article  Google Scholar 
2.
Smith-Downey, N. V., Sunderland, E. M. & Jacob, D. J. Anthropogenic impacts on global storage and emissions of mercury from terrestrial soils: Insights from a new global model. J. Geophys. Res. 115, G03008 (2010).
ADS  Article  Google Scholar 

3.
Zimov, S. A. et al. Permafrost carbon: Stock and decomposability of a globally significant carbon pool. Geophys. Res. Lett. 33, https://doi.org/10.1029/2006GL027484 (2006).

4.
Romanovsky, V., Grosse, G. & Marchenko, S. Past, present and future of permafrost in a changing world. Geo. Soc. Am. 40, 397 (2008).
Google Scholar 

5.
Biskaborn et al. Permafrost is warming at a global scale. Nat. Comm. 10, 264 (2019).
ADS  Article  Google Scholar 

6.
Koven, C. D., Riley, W. J. & Stern, A. Analysis of permafrost thermal dynamics and response to climate change in the CMIP5 Earth System Models. J. Clim. V26, 1887–1900 (2013).
ADS  Google Scholar 

7.
McGuire, A. D. et al. Dependence of the evolution of carbon dynamics in the northern permafrost region on the trajectory of climate change. Proc. Natl Acad. Sci. USA 115/15, 3882–3887 (2018).
ADS  Article  Google Scholar 

8.
Driscoll, C. T., Mason, R. P., Chan, H. M., Jacob, D. J. & Pirrone, N. Mercury as a global pollutant: sources, pathways, and effects. Environ. Sci. Technol. 47, 4967–4983 (2013).
ADS  CAS  Article  Google Scholar 

9.
Obrist, D. et al. Tundra uptake of atmospheric elemental mercury drives Arctic mercury pollution. Nature 547, 201–204 (2017).
ADS  CAS  Article  Google Scholar 

10.
Skyllberg, U., Bloom, P. R., Qian, J., Lin, C. M. & Bleam, W. F. Complexation of mercury(II) in soil organic matter: EXAFS evidence for linear two-coordination with reduced sulfur groups. Environ. Sci. Technol. 40, 4174–4180 (2006).
ADS  CAS  Article  Google Scholar 

11.
Giesler, R., Clemmensen, K. E., Wardle, D. A., Klaminder, J. & Bindler, R. Boreal forests sequester large amounts of mercury over millennial time scales in the absence of wildfire. Environ. Sci. Technol. 51, 2621–2627 (2017).
ADS  CAS  Article  Google Scholar 

12.
Arnold, J., Gustin, M. S. & Weisberg, P. J. Evidence for nonstomatal uptake of Hg by Aspen and translocation of Hg from foliage to tree rings in Austrian pine. Environ. Sci. Technol. 52, 1174–1182 (2018).
ADS  CAS  Article  Google Scholar 

13.
Clackett, S. P., Porter, T. J. & Lehnherr, I. 400-year record of atmospheric mercury from tree-rings in Northwestern Canada. Environ. Sci. Technol. 52, 9625–9633 (2018).
ADS  CAS  Article  Google Scholar 

14.
Lindberg, S. E., Hanson, P. J., Meyers, T. P. & Kim, K. H. Air/surface exchange of mercury vapor over forests – the need for a reassessment of continental biogenic emissions. Atm. Environ. 32, 895–908 (1998).
CAS  Article  Google Scholar 

15.
Jiskra, M. et al. Mercury deposition and re-emission pathways in boreal forest soils investigated with Hg isotope signatures. Environ. Sci. Technol. 49, 7188–7196 (2015).
ADS  CAS  Article  Google Scholar 

16.
Schuster, P. F. et al. Mercury export from the Yukon River Basin and potential response to a changing climate. Environ. Sci. Technol. 45, 9262–9267 (2011).
ADS  CAS  Article  Google Scholar 

17.
Schaefer, K. et al. Combined Simple Biosphere/Carnegie-Ames-Stanford Approach terrestrial carbon cycle model. J. Geophys. Res. 113, G03034 (2008).
Article  Google Scholar 

18.
Olson, C., Jiskra, M., Biester, H., Chow, J. & Obrist, D. Mercury in active-layer Tundra soils of Alaska: concentrations, pools, origins, and spatial distribution. Glob. Biogeochemical Cycles 32, 1058–1073 (2018).
ADS  CAS  Article  Google Scholar 

19.
Mikan, C. J., Schimel, J. P. & Doyle, A. P. Temperature controls of microbial respiration in arctic tundra soils above and below freezing. Soil Biol. Biochem. 34, 1785–1795 (2002).
CAS  Article  Google Scholar 

20.
Wang, Z. & Roulet, N. Comparison of plant litter and peat decomposition changes with permafrost thaw in a subarctic peatland. Plant Soil 417, 197–216 (2017).
CAS  Article  Google Scholar 

21.
Wickland, K. P. et al. Dissolved organic carbon and nitrogen release from boreal Holocene permafrost and seasonally frozen soils of Alaska. Environ. Res. Lett. 13, 065011 (2018).
ADS  Article  Google Scholar 

22.
Striegl, R. G., Aiken, G. R., Dornblaser, M. M., Raymond, P. A. & Wickland, K. P. A decrease in discharge-normalized DOC export by the Yukon River during summer through autumn. Geophys. Res. Lett. 32, L21413 (2005).
ADS  Article  Google Scholar 

23.
Walvoord, M. A. & Striegl, R. G. Increased groundwater to stream discharge from permafrost thawing in the Yukon River basin: potential impacts on lateral export of carbon and nitrogen. Geophys. Res. Lett. 34, L12402 (2007).
ADS  Article  Google Scholar 

24.
Holmes, C. D. et al. Global atmospheric model for mercury including oxidation by bromine atoms. Atmos. Chem. Phys. 10, 12037–12057 (2010).
ADS  CAS  Article  Google Scholar 

25.
Pacyna, J. M. et al. Current and future levels of mercury atmospheric pollution on a global scale. Atmos. Chem. Phys. 16, 12495–12511 (2016).
ADS  CAS  Article  Google Scholar 

26.
Schaefer, K., Zhang, T., Bruhwiler, L. & Barrett, A. P. Amount and timing of permafrost carbon release in response to climate warming. Tellus Series B Chem. Phys. Met. https://doi.org/10.1111/j1600-0889201100527x (2011).

27.
St Pierre, K. A. et al. Unprecedented increases in total and methyl mercury concentrations downstream of retrogressive thaw slumps in the western Canadian arctic. Environ. Sci. Technol. 52, 14099–14109 (2018).
ADS  Article  Google Scholar 

28.
EPA. Ambient water quality criteria for mercury. U.S. Environmental Protection Agency, 440/5-84-026 (https://www.epa.gov/sites/production/files/2019-03/documents/ambient-wqc-mercury-1984.pdf) (1984).

29.
Brumbaugh, W. G., Krabbenhoft, D. P., Helsel, D. R., Wiener, J. G., & Echols, K. R. A national pilot study of mercury contamination of aquatic ecosystems along multiple gradients: bioaccumulation in fish, Biological Science Report. USGS/BRD/BSR-2001-0009 (2001).

30.
Scudder, E. et al. Optimizing fish sampling for fish-mercury bioaccumulation factors. Chemosphere 135, 467–473 (2015).
ADS  Article  Google Scholar 

31.
National Research Council. Toxicological effects of methylmercury. https://doi.org/10.17226/9899 (The National Academies Press, Washington, DC, 2000).

32.
Borum, D., Manibusan, M. K., Schoeny, R., Winchester, E. L. Water quality criterion for the protection of human health: methylmercury. EPA-823-R-01-001 (U.S. Environmental Protection Agency, Washington, DC 20460, 2001).

33.
Schaefer, K. et al. Improving simulated soil temperatures and soil freeze/thaw at high-latitude regions in the Simple Biosphere/Carnegie-Ames-Stanford Approach model. J. Geophys. Res. 114, F02021 (2009).
ADS  Article  Google Scholar 

34.
Schaefer, K., Lantuit, H., Romanovsky, V. E., Schuur, E. A. G. & Witt, R. The impact of the permafrost carbon feedback on global climate. Env. Res. Lett. 9, 085003 (2014).
Article  Google Scholar 

35.
Schaefer, K. & Jafarov, E. A parameterization of respiration in frozen soils based on substrate availability. Biogeosciences 13, 1991–2001. www.biogeosciences.net/13/1991/2016/ (2016).

36.
Jafarov, E. & Schaefer, K. The importance of a surface organic layer in simulating permafrost thermal and carbon dynamics. Cryosphere 10, 465–475 (2016).
ADS  Article  Google Scholar 

37.
USGS, United States Geological Survey. Data inventory page for site 15565447-Yukon River at Pilot Station, Alaska. U.S. Geological Survey, https://waterdata.usgs.gov/nwis/inventory/site_no=15565447 (2019).

38.
Agnan, Y., Le Dantec, T., Moore, C. W., Edwards, G. C. & Obrist, D. New constraints on terrestrial surface atmosphere fluxes of gaseous elemental mercury using a global database. Environ. Sci. Technol. 50, 507–524 (2016).
ADS  CAS  Article  Google Scholar  More

Locating and collecting badgers
Members of the public, local authorities and countryside organisations in Wales reported the locations of found dead badgers to the Welsh Government or APHA Field Services who recorded the map reference details. The collection by APHA staff of badger carcases that met pre-defined criteria took place between 1st September 2014 and 31st December 2016. There were instances where the collection of reported carcases was not attempted. Reasons for not attempting collection included safety concerns arising from the specific location of the carcass or the non-availability of staff (or other necessary resource) to undertake the collection (supplementary table online ST1). In some further instances collection was attempted but was unsuccessful because either the reported carcase could not be found or the condition of the carcase made it unsuitable for further investigation (for example viscera were herniated externally through wounds, there was severe myiasis (flystrike), the carcase was distended with gas or it was flattened).
Depending on where they were found, the carcases were delivered to the APHA Veterinary Investigation Centres in Carmarthen and Shrewsbury and to the Wales Veterinary Science Centre in Aberystwyth (from 28/01/2016) where they were stored at between 2 and 8 °C for no more than four days before post-mortem examination.
Post-mortem examination and sampling
Of the 1863 dead badgers reported, 841 were collected and 681 (37% of reported carcasses) were suitable for post-mortem examination. The prevalence of bTB was calculated for suitable carcasses only. The sampling protocol was adapted from the standard and detailed protocols described in a comparison by Crawshaw and others24, so that fewer overall samples were taken than the detailed protocol but that the samples chosen were the ones most likely to yield M. bovis if present.
The initial external examination comprised the following: measuring the length from nose tip to tail base (cm), assessing and recording tooth wear, recording the sex of the animal and whether female animals were lactating, andrecording any evidence of vaccination: To temporarily identify vaccinated badgers, the guard hairs of the dorsal back (usually caudal) were trimmed and a coloured marker applied at the same site at the time of vaccination. During the external examination of the badger carcases, to attempt to detect recent vaccination, the skin and hair of the back was visually examined for guard hair trimming and coloured marker. Furthermore the examination recorded any evidence of trap injury, or of illegal interference with the animal and the presence and location of bite wounds.
A detailed examination of the lungs, pericardial sac, liver and kidneys was conducted at post-mortem examination. The lungs were examined by making multiple longitudinal incisions approximately one centimetre apart. At least four slices were made in the liver and three slices in the kidney24.
Each lymph node of all suitable badgers was incised at least once and a pool of lymph nodes (pool 1) consisting of the retropharyngeal, bronchial, mediastinal and hepatic lymph nodes (or as many as were detectable) was collected for subsequent bacteriological culture. If any gross internal lesions suggestive of tuberculosis were observed or if bite wounds were detected, the lesioned tissue and/or excised bite wounds were added to a separate container (pool 2). The pooled samples for bacteriological culture were preserved in 15 ml of 1% aqueous cetylpyridinium chloride. Samples were sent to the Animal Plant & Health Agency Laboratory inStarcross, Devon on the day of examination, for next day receipt. After taking samples, two or three incisions were made in the muscles of the anterior thigh of both hind legs to look for any potential adverse reactions to BCG vaccination24.
Culture and molecular typing
On receipt at APHA Starcross, the samples were washed in sterile 0.85% saline solution, homogenized by standard methods, inoculated onto six modified Middlebrook 7H11 agar slopes26 and incubated at 37 °C. Pool 1, and Pool 2 (if collected), were cultured separately. The slopes were examined weekly from the end of week 2 for a maximum of 12 weeks. M. bovis isolates were harvested when growth was sufficient for genotyping and sent to APHA Weybridge.
Genotyping was performed using spoligotyping27 and VNTR typing (Exact Tandem Repeat Loci A to F)9,28. Spoligotyping confirmed that the isolates were M. bovis. Genotypes of M. bovis were labelled according to the current APHA convention, using numbers to represent spoligotypes and lower case letters to represent the VNTR pattern within each spoligotype. The same genotyping methods were applied to the cultures of M. bovis from cattle slaughtered as part of the national bTB control programme. The cattle genotype home ranges were determined for 2015 (Data Systems Group, Dept of Epidemiological Sciences, Animal & Plant Health Agency Weybridge); for inclusion a genotype had to have been present for at least three years, on at least two different 5 km × 5 km grid squares, in the last 5 years (with a 10 km buffer applied).
Disease status in cattle herds
The disease status in cattle herds from the five TB regions of Wales has been calculated for all Spatial Units (for definition please refer to introduction) with at least 10 badger submissions and compared with prevalence estimates for the badger populations in these areas. The metric used to describe disease status in cattle is average annual herd prevalence during most of the AWBFD sampling period (2015–2016, number of herds under restriction at any point of year/registered live herds in region). Herd size is a known risk factor for bovine TB29; therefore, direct standardisation was used to adjust for varying herd sizes in the Spatial Units3,30,31. Briefly, annual stratum-specific prevalence was calculated for each Spatial Unit across four strata (reduced from six in the cited studies due to the small herd denominator in Wales Spatial Units) of herd size and then applied to the reference population, which comprised the sum of cattle populations across all Spatial Units. The standardised population was then used for herd-level disease measures, resulting in a standardised herd prevalence (Fig. 4).
Badger prevalence mapping
For the Spatial Unit badger prevalence map each Spatial Unit was labelled with the number of submissions, the number of positive results and the resulting prevalence estimate. When added to ArcMap (Esri ArcGIS 10.2.2) the Spatial Unit layer was symbolised using the prevalence value and six pre-defined range values or classes were applied and colour ramped. Spatial Units with less than 10 AWBFD submissions (“insufficient data”) were not colour ramped. All Spatial Units were labelled with the number of positives/number of submissions.
Statistical analysis
The prevalence of bTB in badgers was estimated among the sampled badgers as in previous studies3,8 with the underlying assumption that the carcasses collected were representative of the overall population.
Analysis was performed to test the null hypotheses (Ho) that:

There are no differences in badger bTB prevalence between the five TB Areas in Wales.

There was no change in overall badger bTB prevalence in Wales between the surveys in 2005–2006 and in 2014–2016.

There was no change in badger bTB prevalence within the five TB Areas between the surveys in 2005–2006 and in 2014–2016.

There was no change in badger bTB prevalence between the surveys in 2005–2006 and in 2014–2016 within the Intensive Action Area (IAA), site of a 2012–2015 badger vaccination trial.

There is no correlation between cattle herd prevalence and estimated bTB prevalence in dead badgers in different geographic regions.

There is no difference in cattle herd prevalence between areas with infected badgers and those with no evidence of bTB in badgers.

For statistical purposes, for the comparisons of prevalence estimates between the TB Areas and over time, both Intermediate TB Areas were combined. All data were tested for normality with the Kolmogorov–Smirnov test (SPSS Version 21 for Windows). Since the dependent variable in these analyses, prevalence, is a rate and fulfilled the criteria for normality, the z-test for comparisons of population proportions was used to test for the statistical significance of differences between prevalence estimates between the TB Areas and over time. A condition of the z-test is that each sample contains at least 10 observations in each category of the dependent variable and for comparisons between samples with less than 10 submissions in at least one category, Fishers Exact test was used32. To explore the correlation between prevalence in badgers and cattle, linear regression analysis was undertaken to calculate Pearson’s coefficient of correlation (SPPS 22 for Windows). Spatial Units with  > 10 badger submissions with one or more positives were compared with those which had none, using the two sample t test with unequal variances. In order to compare genotypes between badgers and cattle, as in the previous Wales badger survey3, the authors calculated the associations between the frequency distribution of the genotypes in badgers and the frequency distribution in cattle for each TB Area. In order to prevent ties and to account for the small number of positive badger submissions, frequencies were adjusted, by replacing them with their deviations from expected values which were calculated as (TB Area subtotal) × (Wales genotype subtotal)/(Wales grand total). A Spearman rank correlation between the ranks of genotypes in badgers and their ranks in cattle was then calculated for each TB Area. More

1.
Whittaker, R. J., Fernández-Palacios, J. M., Matthews, T. J., Borregaard, M. K. & Triantis, K. A. Island biogeography: taking the long view of nature’s laboratories. Science. 357, eaam8326 (2017).
PubMed  Google Scholar 
2.
Lyons, S. K. et al. The changing role of mammal life histories in Late Quaternary extinction vulnerability on continents and islands. Biol. Lett. 12, 20160342 (2016).
PubMed  PubMed Central  Google Scholar 

3.
Mychajliw, A. M. & Harrison, R. G. Genetics reveal the origin and timing of a cryptic insular introduction of muskrats in North America. PLoS ONE 9, e111856 (2014).
ADS  PubMed  PubMed Central  Google Scholar 

4.
Hofman, C. A. & Rick, T. C. Ancient biological invasions and island ecosystems: tracking translocations of wild plants and animals. J. Archaeol. Res. 21, 217–306 (2017).
Google Scholar 

5.
Muhs, D. R. et al. Late Quaternary sea-level history and the antiquity of mammoths (Mammuthus exilis and Mammuthus columbi), Channel Islands National Park, California USA. Q. Res. 83, 502–521 (2015).
ADS  Google Scholar 

6.
Reeder-Myers, L., Erlandson, J., Muhs, D. R. & Rick, T. Sea level, paleogeography, and archaeology on California’s Northern Channel Islands. Quat. Res. 83, 263–272 (2015).
Google Scholar 

7.
Rick, T. C. et al. Ecological change on California’s Channel Islands from the Pleistocene to the Anthropocene. Bioscience 64, 680–692 (2014).
Google Scholar 

8.
Erlandson, J. et al. An archaeological and paleontological chronology for Daisy Cave (CA-SMI-261), San Miguel Island California. Radiocarbon 38, 355–373 (1996).
CAS  Google Scholar 

9.
McLaren, D. et al. Late Pleistocene archaeological discovery models along the Pacific Coast of North America. PaleoAmerica. 6, 43–63 (2020).
Google Scholar 

10.
Collins, P. W., Guthrie, D. A., Whistler, E. L., Vellanoweth, R. L. & Erlandson, J. M. Terminal Pleistocene-Holocene avifauna of San Miguel and Santa Rosa Islands: identifications of previously unidentified avian remains recovered from fossil sites and prehistoric cave deposits. West. North Am. Nat. 78, 370–403 (2018).
Google Scholar 

11.
Rick, T. C., Culleton, B. J., Smith, C. B., Johnson, J. R. & Kennett, D. J. Stable isotope analysis of dog, fox, and human diets at a Late Holocene Chumash village (CA-SRI-2) on Santa Rosa Island California. J. Archaeol. Sci. 38, 1385–1393 (2011).
Google Scholar 

12.
Hofman, C. A. et al. Tracking the origins and diet of an endemic island canid (Urocyon littoralis) across 7300 years of human cultural and environmental change. Quat. Sci. Rev. 146, 147–160 (2016).
ADS  Google Scholar 

13.
Shirazi, S., Rick, T. C., Erlandson, J. M. & Hofman, C. A. A tale of two mice: a trans-Holocene record of Peromyscus nesodytes and Peromyscus maniculatus at Daisy Cave, San Miguel Island California. The Holocene 28, 827–833 (2017).
ADS  Google Scholar 

14.
Kurten, B. Pleistocene bears of North America, Part 2. Genus Arctodus, short-faced bears. Acta Zool. Fennica. 117, 1–60 (1967).
Google Scholar 

15.
Buckley, M. Zooarchaeology by mass spectrometry (ZooMS) collagen fingerprinting for the species identification of archaeological bone fragments. In Zooarchaeology in practice (eds Giovas, C. & LeFebvre, M.) 22–247 (Springer, Cham, 2018).
Google Scholar 

16.
Figueirido, B., Perez-Claros, J. A., Torregrosa, V., Martin-Serra, A. & Palmqvist, P. Demythologizing Arctodus simus, the ‘short-faced’ long-legged and predaceous bear that never was. J. Vertebr. Paleontol. 30, 262–275 (2010).
Google Scholar 

17.
Schubert, B. W. Late Quaternary chronology and extinction of North American giant short-faced bears (Arctodus). Quat. Int. 217, 188–194 (2010).
Google Scholar 

18.
Matheus, P. E. Diet and co-ecology of Pleistocene short-faced bears and brown bears in Eastern Beringia. Quat. Res. 44, 447–453 (1995).
CAS  Google Scholar 

19.
Emslie, S. D. & Czaplewski, N. J. A new record of the giant short-faced bear, Arctodus simus, from western North America with a reevaluation of its paleobiology. Nat Hist. Mus. Los Angel. Cty. Contrib. Sci. 371, 1–12 (1985).
Google Scholar 

20.
Fox-Dobbs, K., Leonard, J. A. & Koch, P. L. Pleistocene megafauna from eastern Beringia: paleoecological and paleoenvironmental interpretations of stable carbon and nitrogen isotope and radiocarbon records. Palaeogeogr. Palaeoclimatol. Palaeoecol. 261, 30–46 (2008).
Google Scholar 

21.
Bocherens, H., Emslie, S. D., Billiou, D. & Mariotti, A. Stable isotopes (13C, 15N) and paleodiet of the giant short-faced bear (Arctodus simus). Comptes Rendus de l’Académie des Sciences, Série II, Paris. 320, 779–784 (1995).
CAS  Google Scholar 

22.
Figueirido, B. et al. Dental caries in the fossil record: a window to the evolution of dietary plasticity in an extinct bear. Sci. Rep. 7, 17813 (2017).
ADS  PubMed  PubMed Central  Google Scholar 

23.
Donohue, S. L., DeSantis, L. R. G., Schubert, B. W. & Ungar, P. S. Was the giant short-faced bear a hyper-scavenger? A new approach to the dietary study of ursids using dental microwear textures. PLoS ONE 8, e77531 (2013).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

24.
Marks, S. A. & Erickson, A. W. Age determination in the black bear. J. Wildl. Manag. 30, 389–410 (1966).
Google Scholar 

25.
Kurten, B. A. Skull of the grizzly bear (Ursus arctos L.) from Pit 10, Rancho La Brea. Nat Hist. Mus. Los Angel. Cty. Sci. Ser. 39, 1–7 (1960).
Google Scholar 

26.
Stock, C. & Harris, J. M. Rancho la Brea: a record of Pleistocene life in California. Nat Hist. Mus. Los Angel. Cty. Sci. Ser 37, 1–113 (1992).
Google Scholar 

27.
Fuller, B. T., Harris, J. M., Farrell, A. B., Takeuchi, G. & Southon, J. R. Sample preparation for radiocarbon dating and isotopic analysis of bone from Rancho La Brea. Nat Hist. Mus. Los Angel. Cty. Sci. Ser. 42, 151–167 (2015).
Google Scholar 

28.
Walker, P. L. Archaeological evidence for the recent extinction of three terrestrial mammals on San Miguel Island. In: The California Islands: proceedings of a multidisciplinary symposium (ed. D. M. Powers). Santa Barbara Museum of Natural History, Santa Barbara, CA, 703–717 (1980).

29.
Buckley, M., Collins, M., Thomas-Oates, J. & Wilson, J. C. Species identification by analysis of bone collagen using matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry. Rapid Commun. Mass Spectr. 23, 3843–3854 (2009).
ADS  CAS  Google Scholar 

30.
Delisle, I. & Strobeck, C. Conserved primers for rapid sequencing of the complete mitochondrial genome from carnivores, applied to three species of bears. Mol. Biol. Evol. 19, 357–361 (2002).
CAS  PubMed  Google Scholar 

31.
Yu, L., Li, Y. W., Ryder, O. A. & Zhang, Y. P. Analysis of complete mitochondrial genome sequences increases phylogenetic resolution of bears (Ursidae), a mammalian family that experienced rapid speciation. BMC Evol. Biol. 7, 198 (2007).
PubMed  PubMed Central  Google Scholar 

32.
Krause, J. et al. Mitochondrial genomes reveal an explosive radiation of extinct and extant bears near the Miocene-Pliocene boundary. BMC Evol. Biol. 8, 220 (2008).
PubMed  PubMed Central  Google Scholar 

33.
Mitchell, K. J. Ancient mitochondrial DNA reveals convergent evolution of giant short-faced bears (Tremarctinae) in North and South America. Biol. Lett. 12, 20160062 (2016).
PubMed  PubMed Central  Google Scholar 

34.
Steffen, M. L. & Harington, C. R. Giant short-faced bear (Arctodus simus) from late Wisconsinan deposits at Cowichan Head, Vancouver Island British Columbia. Can. J. Earth Sci. 47, 1029–1036 (2010).
ADS  Google Scholar 

35.
Parnell, A. C., Inger, R., Bearhop, S. & Jackson, A. L. Source partitioning using stable isotopes: coping with too much variation. PLoS ONE 5, e9672 (2010).
ADS  PubMed  PubMed Central  Google Scholar 

36.
Parnell, A. C. et al. Bayesian stable isotope mixing models. Environmetrics 24, 387–399 (2013).
MathSciNet  Google Scholar 

37.
Tahmasebi, F., Longstaffe, F. J. & Zazula, G. Nitrogen isotopes suggest a change in nitrogen dynamics between the Late Pleistocene and modern time in Yukon Canada. PLoS ONE 13, e0192713 (2018).
PubMed  PubMed Central  Google Scholar 

38.
Long, E. S., Sweitzer, R. A., Diefenbach, D. R. & Ben-David, M. Controlling for anthropogenically induced atmospheric variation in stable carbon isotopes studies. Oecologia 146, 148–156 (2005).
ADS  PubMed  Google Scholar 

39.
Coltrain, J. B. et al. Rancho La Brea stable isotope biogeochemistry and its implications for the paleoecology of late Pleistocene, coastal southern California. Palaeogeogr. Palaeoclimatol. Palaeoecol. 205, 199–219 (2004).
Google Scholar 

40.
Chamberlain, C. P. et al. Pleistocene to recent dietary shifts in California condors. Proc. Natl. Acad. Sci. 102, 16707–16711 (2005).
ADS  CAS  PubMed  Google Scholar 

41.
Newsome, S. D. et al. The shifting baseline of northern fur seal ecology in the northeast Pacific Ocean. Proc. Natl. Acad. Sci. 104, 9709–9714 (2007).
ADS  CAS  PubMed  Google Scholar 

42.
Semprebon, G. M. et al. Dietary reconstruction of pygmy mammoths from Santa Rosa Island of California. Quat. Int. 406, 123–136 (2016).
Google Scholar 

43.
Anderson, R. S., Starratt, S., Jass, R. M. B. & Pinter, N. Fire and vegetation history on Santa Rosa Island, Channel Islands, and long-term environmental change in southern California. J. Quat. Sci. 25, 782–797 (2010).
Google Scholar 

44.
Erlandson, J. et al. Paleoindian seafaring, maritime technologies, and coastal foraging on California’s Channel Islands. Science 331, 1181–1185 (2011).
ADS  CAS  PubMed  Google Scholar 

45.
Bronk Ramsey, C. Bayesian analysis of radiocarbon dates. Radiocarbon 51, 337–360 (2009).
Google Scholar 

46.
Saltré, F. et al. Uncertainties in dating constrain model choice for inferring extinction time from fossil records. Quat. Sci. Rev. 112, 128–137 (2015).
ADS  Google Scholar 

47.
Naito, Y. I. et al. Evidence for herbivorous cave bears (Ursus spelaeus) in Goyet Cave, Belgium: implications for paleodietary reconstruction of fossil bears using amino acid δ15N approaches. J. Quat. Sci. 31, 598–606 (2016).
Google Scholar 

48.
Naito, Y. et al. Heavy reliance on plants for Romanian cave bears evidenced by amino acid nitrogen isotope analysis. Sci. Rep. 10, 6612 (2020).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

49.
Mowat, G. & Heard, D. C. Major components of grizzly bear diet across North America. Can. J. Zool. 84, 473–489 (2011).
Google Scholar 

50.
Trayler, R. B., Dundas, R. G., Fox-Dobbs, K. & Van De Water, P. K. Inland California during the Pleistocene- megafaunal stable isotope records reveal new paleoecological and paleoenvironmental insights. Palaeogeogr. Palaeoclimatol. Palaeoecol. 437, 132–140 (2015).
Google Scholar 

51.
Goldberg, C. F. The application of stable carbon and nitrogen isotope analysis to human dietary reconstruction in prehistoric Southern California. PhD Thesis, University of California, Los Angeles, Los Angeles, California (1993).

52.
Newsome, S. D. et al. Pleistocene to historic shifts in bald eagle diets on the Channel Islands California. Proc. Natl. Acad. Sci. 107, 9246–9251 (2010).
ADS  CAS  PubMed  Google Scholar 

53.
Schubert, B. W. & Kaufmann, J. E. A partial short-faced bear skeleton from an Ozark cave with comments on the paleobiology of the species. J. Cave Karst Stud. 65, 101–110 (2003).
Google Scholar 

54.
Steffen, M. L. & Fulton, T. L. On the association of giant short-faced bear (Arctodus simus) and brown bear (Ursus arctos) in late Pleistocene North America. Geobios 51, 61–74 (2018).
Google Scholar 

55.
Pigati, J. S., Muhs, D. R. & McGeehin, J. P. On the importance of stratigraphic control for vertebrate fossil sites in Channel Islands National Park, California, USA: Examples from new Mammuthus finds on San Miguel Island. Quat. Int. 443, 129–139 (2017).
Google Scholar 

56.
Erlandson, J. M. & Moss, M. L. Shellfish eaters, carrion feeders, and the archaeology of aquatic adaptations. Am. Antiquity 66, 413–432 (2001).
Google Scholar 

57.
Richards, R. I., Churcher, C. S. & Turnbull, W. D. Distribution and size variation in North American short-faced bears, Arctodus simus. In Palaeoecology and Palaeoenvironments of Late Cenozoic Mammals (eds Stewart, K. M. & Semour, K. L.) 191–246 (University of Toronto Press, Toronto, 1996).
Google Scholar 

58.
Orr, P. C. Appendix: Additional Bone Artifacts. In California Shell Artifacts by Anthropological Records (ed. Gifford, E. W.) (University of California Press, California, 1947).
Google Scholar 

59.
Fox-Dobbs, K., Dundas, R. G., Trayler, R. B. & Holroyd, P. A. Paleoecological implications of new megafaunal 14C ages from the McKittrick tar seeps California. J. Vertebr. Paleontol. 34, 220–223 (2014).
Google Scholar 

60.
Guthrie, D. A. Analysis of Avifaunal and Bat Remains from Midden Sites on San Miguel Island. In: D. M. Powers (eds) The California Islands: proceedings of a multidisciplinary symposium. Santa Barbara Museum of Natural History, Santa Barbara, 689–702 (1980).

61.
Guthrie, D. A. Fossil vertebrates from Pleistocene terrestrial deposits on the northern Channel Islands, southern California. Contributions to the Geology of the Northern Channel Islands, So. California, 187–192 (1998).

62.
Hofman, C. A. et al. Collagen fingerprinting and the earliest marine mammal hunting in North America. Sci. Rep. 8, 10014 (2018).
ADS  PubMed  PubMed Central  Google Scholar 

63.
Bocherens, H. Isotopic tracking of large carnivore palaeoecology in the mammoth steppe. Quat. Sci. Rev. 117, 42–71 (2015).
ADS  Google Scholar 

64.
van der Sluis, L. G. et al. Combining histology, stable isotope analysis and ZooMS collagen fingerprinting to investigate the taphonomic history and dietary behavior of the extinct giant tortoises from the Mare aux Songes deposit on Mauritius. Palaeogeogr. Palaeoclimatol. Palaeoecol. 416, 80–91 (2014).
Google Scholar 

65.
Buckley, M. & Collins, M. J. Collagen survival and its use for species identification in Holocene-lower Pleistocene bone fragments from British archaeological and paleontological sites. Antiqua 1, e1 (2011).
Google Scholar 

66.
Dabney, J. et al. mtDNA genome from a Middle Pleistocene cave bear. Proc. Natl. Acad. Sci. 110, 15758–15763 (2013).
ADS  CAS  PubMed  Google Scholar 

67.
Rohland, N., Harney, E., Mallick, S., Nordenfelt, S. & Reich, D. Partial uracil DNA glycosylase treatment for screening of ancient DNA. Philos. Trans. R. Soc. Lond Biol. Sci. 370, 624 (2015).
Google Scholar 

68.
Caroe, C. et al. Single-tube library preparation for degraded DNA. Methods Ecol. Evolut. 9, 410–419 (2018).
Google Scholar 

69.
Jonsson, H., Ginolhac, A., Schubert, M., Johnson, P. L. & Orlando, L. mapDamage20: fast approximate Bayesian estimates of ancient DNA damage parameters. Bioinformatics 29, 1682–1684 (2013).
CAS  PubMed  PubMed Central  Google Scholar 

70.
Katoh, K. & Standley, D. MAFFT Multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).
CAS  PubMed  PubMed Central  Google Scholar 

71.
Hasegawa, M., Kishino, H. & Yano, T. A. Dating of the human-ape splitting by a molecular clock of mitochondrial DNA. J. Mol. Evolut. 22, 160–174 (1985).
CAS  Google Scholar 

72.
Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evolut. 35, 1547–1549 (2018).
CAS  Google Scholar 

73.
Stecher, G., Tamura, K. & Kumar, S. Molecular evolutionary genetics analysis (MEGA) for macOS. Mol. Biol. Evolut. 37, 1237–1239 (2020).
Google Scholar 

74.
Felsenstein, J. Confidence limits on phylogenies with a molecular clock. Syst. Zool. 34, 152–161 (1985).
Google Scholar 

75.
Feranec, R. S., Hadly, E. A., Blois, J. L., Barnosky, A. D. & Paytan, A. Radiocarbon dates from the Pleistocene fossil deposits of Samwel Cave, Shasta County, California USA. Radiocarbon 49, 117–121 (2007).
CAS  Google Scholar 

76.
Feranec, R. S. Implications of radiocarbon dates from Potter Creek Cave, Shasta County, California USA. Radiocarbon 51, 931–936 (2009).
CAS  Google Scholar 

77.
Jefferson, G. T. A catalogue of Late Quaternary vertebrates from California part two: mammals. Nat. Hist. Mus. LosAngel. Cty. Tech. Rep. 7, 1–129 (1991).
Google Scholar 

78.
Jefferson, G. T. Stratigraphy and paleontology of the middle to late Pleistocene Manix Formation, and paleoenvironments of the central Mojave River, southern California. In: Paleoenvironments and Paleohydrology of the Mojave and Southern Great Basin Deserts (Y. Enzel, S.G. Wells (Eds.)), Geol. Soc. Amer. Spec. Paper 368. 43–60 (2003).

79.
Springer, K., Scott, E., Sagebiel, J. C. & Murray, L. K. Late Pleistocene large mammal faunal dynamics from inland southern California: the Diamond Valley Lake local fauna. Quat. Int. 217, 256–265 (2010).
Google Scholar 

80.
Buckley, M. Species identification of bovine, ovine and porcine type 1 collagen; comparing peptide mass fingerprinting and LC-based proteomics methods. Int. J. Mol. Sci. 17, 445 (2016).
PubMed  PubMed Central  Google Scholar 

81.
Johnson, J. R., Stafford Jr, T. W., Ajie, H. O., & Morris, D. P. Arlington springs revisited. In Proceedings of the fifth California Islands symposium (Vol. 5, pp. 541–545). Santa Barbara, CA: Santa Barbara Museum of Natural History (2002).

82.
Reimer, P. J. et al. IntCal13 and Marine13 radiocarbon age calibration curves 0-50,000 years cal bp. Radiocarbon 55, 1869–1887 (2013).   More

1.
Violle, C., Reich, P. B., Pacala, S. W., Enquist, B. J. & Kattge, J. The emergence and promise of functional biogeography. Proc. Natl Acad. Sci. USA 111, 13690–13696 (2014).
ADS  PubMed  Google Scholar 
2.
Angilletta, M. J. Thermal Adaptation: A Theoretical and Empirical Synthesis (Oxford Univ. Press, 2009).

3.
Sunday, J. M., Bates, A. E. & Dulvy, N. K. Global analysis of thermal tolerance and latitude in ectotherms. Proc. R. Soc. B 278, 1823–1830 (2011).
PubMed  Google Scholar 

4.
Deutsch, C., Ferrel, A., Seibel, B., Pörtner, H.-O. & Huey, R. B. Ecophysiology. Climate change tightens a metabolic constraint on marine habitats. Science 348, 1132–1135 (2015).
ADS  PubMed  Google Scholar 

5.
Mandic, M., Todgham, A. E. & Richards, J. G. Mechanisms and evolution of hypoxia tolerance in fish. Proc. R. Soc. B 276, 735–744 (2009).
PubMed  Google Scholar 

6.
Seibel, B. A. & Drazen, J. C. The rate of metabolism in marine animals: environmental constraints, ecological demands and energetic opportunities. Phil. Trans. R. Soc. Lond. B 362, 2061–2078 (2007).
Google Scholar 

7.
Brey, T. An empirical model for estimating aquatic invertebrate respiration. Methods Ecol. Evol. 1, 92–101 (2010).
Google Scholar 

8.
Peterson, C. C., Nagy, K. A. & Diamond, J. Sustained metabolic scope. Proc. Natl Acad. Sci. USA 87, 2324–2328 (1990).
ADS  PubMed  Google Scholar 

9.
Hammond, K. A. & Diamond, J. Maximal sustained energy budgets in humans and animals. Nature 386, 457–462 (1997).
ADS  PubMed  Google Scholar 

10.
Fry, F. E. J. Effect of the environment on animal activity. Univ. Tor. Stud. Biol. Ser. 55, 1–62 (1947).
Google Scholar 

11.
Brett, J. R. Energetic responses of salmon to temperature. A study of some thermal relations in the physiology and freshwater ecology of sockeye salmon (Oncorhynchus nerka). Am. Zool. 11, 99–113 (1971).
Google Scholar 

12.
Pörtner, H.-O. & Farrell, A. P. Physiology and climate change. Science 322, 690–692 (2008).
PubMed  Google Scholar 

13.
Piiper, J., Dejours, P., Haab, P. & Rahn, H. Concepts and basic quantities in gas exchange physiology. Respir. Physiol. 13, 292–304 (1971).
PubMed  Google Scholar 

14.
Chan, F. et al. Emergence of anoxia in the California current large marine ecosystem. Science 319, 920 (2008).
ADS  PubMed  Google Scholar 

15.
Diaz, R. J. & Rosenberg, R. Spreading dead zones and consequences for marine ecosystems. Science 321, 926–929 (2008).
ADS  PubMed  Google Scholar 

16.
Wishner, K. F. et al. Ocean deoxygenation and zooplankton: very small oxygen differences matter. Sci. Adv. 4, eaau5180 (2018).
ADS  PubMed  PubMed Central  Google Scholar 

17.
Howard, E. M. et al. Climate-driven aerobic habitat loss in the California current system. Sci. Adv. 6, eaay3188 (2020).
ADS  PubMed  PubMed Central  Google Scholar 

18.
Nilsson, G. E. & Östlund-Nilsson, S. Does size matter for hypoxia tolerance in fish? Biol. Rev. Camb. Philos. Soc. 83, 173–189 (2008).
PubMed  Google Scholar 

19.
DeLong, J. P., Okie, J. G., Moses, M. E., Sibly, R. M. & Brown, J. H. Shifts in metabolic scaling, production, and efficiency across major evolutionary transitions of life. Proc. Natl Acad. Sci. USA 107, 12941–12945 (2010).
ADS  PubMed  Google Scholar 

20.
Deutsch, C., Brix, H., Ito, T., Frenzel, H. & Thompson, L. Climate-forced variability of ocean hypoxia. Science 333, 336–339 (2011).
ADS  PubMed  Google Scholar 

21.
Dell, A. I., Pawar, S. & Savage, V. M. Systematic variation in the temperature dependence of physiological and ecological traits. Proc. Natl Acad. Sci. USA 108, 10591–10596 (2011).
ADS  PubMed  Google Scholar 

22.
Verberk, W. C. E. P., Bilton, D. T., Calosi, P. & Spicer, J. I. Oxygen supply in aquatic ectotherms: partial pressure and solubility together explain biodiversity and size patterns. Ecology 92, 1565–1572 (2011).
PubMed  Google Scholar 

23.
Emerson, S. & Hedges, J. Chemical Oceanography and the Marine Carbon Cycle (Cambridge Univ. Press, 2008).

24.
Kristensen, E. Ventilation and oxygen uptake by three species of Nereis (Annelida: Polychaeta). II. Effects of temperature and salinity changes.  Mar. Ecol. Prog. Ser. 12, 229–306 (1983).

25.
Gehrke, P. C. Response surface analysis of teleost cardio-respiratory responses to temperature and dissolved oxygen. Comp. Biochem. Physiol. A 89, 587–592 (1988).
Google Scholar 

26.
Spitzer, K. W., Marvin, D. E. Jr & Heath, A. G. The effect of temperature on the respiratory and cardiac response of the bluegill sunfish to hypoxia. Comp. Biochem. Physiol. 30, 83–90 (1969).
PubMed  Google Scholar 

27.
Kielland, Ø. N., Bech, C. & Einum, S. Warm and out of breath: thermal phenotypic plasticity in oxygen supply. Funct. Ecol. 33, 2142–2149 (2019).
Google Scholar 

28.
Chung, M.-T., Trueman, C. N., Godiksen, J. A., Holmstrup, M. E. & Grønkjær, P. Field metabolic rates of teleost fishes are recorded in otolith carbonate. Commun. Biol. 2, 24 (2019).
PubMed  PubMed Central  Google Scholar 

29.
Pörtner, H.-O. Oxygen- and capacity-limitation of thermal tolerance: a matrix for integrating climate-related stressor effects in marine ecosystems. J. Exp. Biol. 213, 881–893 (2010).
PubMed  Google Scholar 

30.
Verberk, W. C. E. P., Durance, I., Vaughan, I. P. & Ormerod, S. J. Field and laboratory studies reveal interacting effects of stream oxygenation and warming on aquatic ectotherms. Glob. Change Biol. 22, 1769–1778 (2016).
ADS  Google Scholar 

31.
Verberk, W. C. E. P., Leuven, R. S. E. W., van der Velde, G. & Gabel, F. Thermal limits in native and alien freshwater peracarid Crustacea: the role of habitat use and oxygen limitation. Funct. Ecol. 32, 926–936 (2018).
PubMed  PubMed Central  Google Scholar 

32.
Sunday, J. M., Bates, A. E. & Dulvy, N. K. Thermal tolerance and the global redistribution of animals. Nat. Clim. Change 2, 686–690 (2012).
ADS  Google Scholar 

33.
Verberk, W. C. E. P. et al. Does oxygen limit thermal tolerance in arthropods? A critical review of current evidence. Comp. Biochem. Physiol. A 192, 64–78 (2016).
Google Scholar 

34.
Lefevre, S. Are global warming and ocean acidification conspiring against marine ectotherms? A meta-analysis of the respiratory effects of elevated temperature, high CO2 and their interaction. Conserv. Physiol. 4, cow009 (2016).
PubMed  PubMed Central  Google Scholar 

35.
Jutfelt, F. et al. Oxygen- and capacity-limited thermal tolerance: blurring ecology and physiology. J. Exp. Biol. 221, jeb169615 (2018).
PubMed  Google Scholar 

36.
Ern, R., Norin, T., Gamperl, A. K. & Esbaugh, A. J. Oxygen dependence of upper thermal limits in fishes. J. Exp. Biol. 219, 3376–3383 (2016).
PubMed  Google Scholar 

37.
Deutsch, C. A. et al. Impacts of climate warming on terrestrial ectotherms across latitude. Proc. Natl Acad. Sci. USA 105, 6668–6672 (2008).
ADS  PubMed  Google Scholar 

38.
Sunday, J. M. et al. Thermal-safety margins and the necessity of thermoregulatory behavior across latitude and elevation. Proc. Natl Acad. Sci. USA 111, 5610–5615 (2014).
ADS  PubMed  Google Scholar 

39.
Rummer, J. L. et al. Life on the edge: thermal optima for aerobic scope of equatorial reef fishes are close to current day temperatures. Glob. Change Biol. 20, 1055–1066 (2014).
ADS  Google Scholar 

40.
Penn, J. L., Deutsch, C., Payne, J. L. & Sperling, E. A. Temperature-dependent hypoxia explains biogeography and severity of end-Permian marine mass extinction. Science 362, eaat1327 (2018).
ADS  PubMed  Google Scholar 

41.
Killen, S. S. et al. Ecological influences and morphological correlates of resting and maximal metabolic rates across teleost fish species. Am. Nat. 187, 592–606 (2016).
PubMed  Google Scholar 

42.
Gillooly, J. F., Brown, J. H., West, G. B., Savage, V. M. & Charnov, E. L. Effects of size and temperature on metabolic rate. Science 293, 2248–2251 (2001).
ADS  PubMed  Google Scholar 

43.
Malte, H. & Weber, R. E. A mathematical model for gas exchange in the fish gill based on non-linear blood gas equilibrium curves. Respir. Physiol. 62, 359–374 (1985).
PubMed  Google Scholar 

44.
Rogers, N. J., Urbina, M. A., Reardon, E. E., McKenzie, D. J. & Wilson, R. W. A new analysis of hypoxia tolerance in fishes using a database of critical oxygen level (P crit). Conserv. Physiol. 4, cow012 (2016).
PubMed  PubMed Central  Google Scholar 

45.
Locarnini, R. A. et al. World Ocean Atlas 2013. Volume 1: Temperature (National Centers for Environmental Information, National Oceanic and Atmospheric Administration, 2013).

46.
Garcia, H. E. et al. World Ocean Atlas 2013, Volume 3: Dissolved Oxygen, Apparent Oxygen Utilization, and Oxygen Saturation (National Centers for Environmental Information, National Oceanic and Atmospheric Administration, 2013).

47.
Amante, C. & Eakins, B. W. ETOPO1 1 Arc-Minute Global Relief Model: Procedures, Data Sources and Analysis. NOAA Technical Memorandum NESDIS NGDC-24 https://doi.org/10.7289/V5C8276M (2009)

48.
Shawe-Taylor, J. & Cristianini, N. Kernel Methods for Pattern Analysis (Cambridge Univ. Press, 2004).

49.
Liu, C., White, M. & Newell, G. Measuring and comparing the accuracy of species distribution models with presence–absence data. Ecography 34, 232–243 (2011).
Google Scholar 

50.
Schurmann, H. & Steffensen, J. F. Effects of temperature, hypoxia and activity on the metabolism of juvenile Atlantic cod. J. Fish Biol. 50, 1166–1180 (1997).
Google Scholar 

51.
Heath, A. G. & Hughes, G. M. Cardiovascular and respiratory changes during heat stress in rainbow trout (Salmo gairdneri). J. Exp. Biol. 59, 323–338 (1973).
PubMed  Google Scholar  More

1.
Reid, K. & Croxall, J. P. Environmental response of upper trophic-level predators reveals a system change in an Antarctic marine ecosystem. Proc. R. Soc. B Biol. Sci. 268, 377–384 (2001).
CAS  Google Scholar 
2.
Constable, A. J. et al. Climate change and Southern Ocean ecosystems I: How changes in physical habitats directly affect marine biota. Glob. Chang. Biol. 20, 3004–3025 (2014).
ADS  PubMed  Google Scholar 

3.
Rintoul, S. R. et al. Choosing the future of Antarctica. Nature 558, 233–241 (2018).
ADS  CAS  PubMed  Google Scholar 

4.
Gutt, J. et al. Cross-disciplinarity in the advance of Antarctic ecosystem research. Mar. Genom. 37, 1–17 (2018).
CAS  Google Scholar 

5.
IPCC (2019) Meredith, M. et al. Special report on the ocean and cryosphere in a changing climate: Polar regions. In press.

6.
Kwok, R. & Comiso, J. C. Spatial patterns of variability in Antarctic surface temperature: Connections to the southern hemisphere annular mode and the southern oscillation. Geophys. Res. Lett. 29, 1–4 (2002).
Google Scholar 

7.
Thompson, D. W. J. & Solomon, S. Interpretation of recent Southern Hemisphere climate change. Science 296, 895–899 (2002).
ADS  CAS  PubMed  Google Scholar 

8.
IPCC (2013) Stocker, T. F. et al. Climate Change: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK, Cambridge Univ. Press.

9.
Meredith, M. P., Murphy, E. J., Hawker, E. J., King, J. C. & Wallace, M. I. On the interannual variability of ocean temperatures around South Georgia, Southern Ocean: Forcing by El Niño/Southern Oscillation and the Southern Annular Mode. Deep Res. Part II Top. Stud. Oceanogr. 55, 2007–2022 (2008).
ADS  Google Scholar 

10.
Turner, J. The El Niño-Southern oscillation and Antarctica. Int. J. Climatol. 24, 1–31 (2004).
Google Scholar 

11.
Pardo, D. et al. Additive effects of climate and fisheries drive ongoing declines in multiple albatross species. Proc. Natl. Acad. Sci. USA. 114, e10829–e10837 (2017).
CAS  PubMed  Google Scholar 

12.
Trathan, P. N. & Murphy, E. J. Sea surface temperature anomalies near South Georgia: Relationships with the Pacific el niño regions. J. Geophys. Res. C. Ocean. 108, 1–10 (2002).
Google Scholar 

13.
Forcada, J. & Trathan, P. N. Penguin responses to climate change in the Southern Ocean. Glob. Chang. Biol. 15, 1618–1630 (2009).
ADS  Google Scholar 

14.
Horswill, C. et al. Unravelling the relative roles of top-down and bottom-up forces driving population change in an oceanic predator. Ecology 97, 1919–1928 (2016).
CAS  PubMed  PubMed Central  Google Scholar 

15.
Gillett, N. P. & Thompson, D. W. J. Simulation of recent Southern Hemisphere climate change. Science 302, 273–275 (2003).
ADS  CAS  PubMed  Google Scholar 

16.
Lovenduski, N. S. & Gruber, N. Impact of the Southern annular mode on Southern Ocean circulation and biology. Geophys. Res. Lett. 32, 1–4 (2005).
Google Scholar 

17.
Inchausti, P. et al. Inter-annual variability in the breeding performance of seabirds in relation to oceanographic anomalies that affect the Crozet and the Kerguelen sectors of the Southern Ocean. J. Avian Biol. 2, 170–176 (2003).
Google Scholar 

18.
Siniff, D. B., Garrott, R. A., Rotella, J. J., Fraser, W. R. & Ainley, D. G. Opinion: Projecting the effects of environmental change on Antarctic seals. Antarct. Sci. 20, 425–435 (2008).
ADS  Google Scholar 

19.
Ito, M., Minami, H., Tanaka, Y. & Watanuki, Y. Seasonal and inter-annual oceanographic changes induce diet switching in a piscivorous seabird. Mar. Ecol. Prog. Ser. 393, 273–284 (2009).
ADS  Google Scholar 

20.
Xavier, J. C. et al. Seasonal changes in the diet and feeding behaviour of a top predator indicate a flexible response to deteriorating oceanographic conditions. Mar. Biol. 160, 1597–1606 (2013).
Google Scholar 

21.
Xavier, J. C. et al. A review on the biodiversity, distribution and trophic role of cephalopods in the Arctic and Antarctic marine ecosystems under a changing ocean. Mar. Biol. 165, 1–26 (2018).
Google Scholar 

22.
Hill, S. L., Phillips, T. & Atkinson, A. Potential climate change effects on the habitat of Antarctic krill in the Weddell quadrant of the Southern Ocean. PLoS ONE 8, e72246 (2013).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

23.
Freer, J. J., Tarling, G. A., Collins, M. A., Partridge, J. C. & Genner, M. J. Predicting future distributions of lanternfish, significant ecological resource within the Southern Ocean. Diver. Distr. 25, 1259–1272 (2019).
Google Scholar 

24.
Xavier, J. C., Croxall, J. P., Trathan, P. & Wood, A. G. Feeding strategies and diets of breeding grey-headed and wandering albatrosses at South Georgia. Mar. Biol. 143, 221–232 (2003).
Google Scholar 

25.
Forcada, J., Trathan, P. N., Reid, K. & Murphy, E. J. The effects of global climate variability in pup production of Antarctic fur seals. Ecology 86, 2408–2417 (2005).
Google Scholar 

26.
Arthur, B. et al. Return Customers : Foraging site fidelity and the effect of environmental variability in wide-ranging Antarctic Fur Seals. PLoS ONE 10, 1–19 (2015).
Google Scholar 

27.
Mills, W. F. et al. Long-term trends in albatross diets in relation to prey availability and breeding success. Mar. Biol. 167, 29 (2020).
Google Scholar 

28.
Atkinson, A., Siegel, V., Pakhomov, E. & Rothery, P. Long-term decline in krill stock and increase in salps within the Southern Ocean. Nature 432, 100–103 (2004).
ADS  CAS  PubMed  Google Scholar 

29.
Atkinson, A. et al. Krill (Euphausia superba) distribution contracts southward during rapid regional warming. Nat. Clim. Chan. 9, 142–147 (2019).
ADS  Google Scholar 

30.
Clarke, M. R. Cephalopod biomass—estimates from predation. Memoir. Natl. Museum Victoria. 44, 95–107 (1983).
Google Scholar 

31.
Rodhouse, P. G. et al. Environmental effects on cephalopod population dynamics: Implications for management of fisheries. Adv. Mar. Biol. 67, 99–233 (2014).
PubMed  Google Scholar 

32.
Xavier, J. C., Raymond, B., Jones, D. C. & Griffiths, H. Biogeography of cephalopods in the Southern Ocean using habitat suitability prediction models. Ecosystems 19, 220–247 (2016).
CAS  Google Scholar 

33.
Saunders, R. A., Tarling, G. A., Hill, S. & Murphy, E. J. Myctophid fish (Family Myctophidae) are central consumers in the food web of the Scotia Sea (Southern Ocean). Front. Mar. Sci. 6, 530 (2019).
Google Scholar 

34.
Rodhouse, P. G. Role of squid in the Southern Ocean pelagic ecosystem and the possible consequences of climate change. Deep. Res. Part II Top. Stud. Oceanogr. 95, 129–138 (2013).
ADS  CAS  Google Scholar 

35.
Doubleday, Z. A. et al. Global proliferation of cephalopods. Curr. Biol. 26, 406–407 (2016).
Google Scholar 

36.
Boyle, P. & Rodhouse, P. G. Cephalopods ecology and fisheries (Blackell Science, Oxford, 2005).
Google Scholar 

37.
Cherel, Y. & Hobson, K. A. Stable isotopes, beaks and predators: A new tool to study the trophic ecology of cephalopods, including giant and colossal squids. Proc. R. Soc. B. Biol. Sci. 272, 1601–1607 (2005).
Google Scholar 

38.
Ruiz-Cooley, R. I., Villa, E. C. & Gould, W. R. Ontogenetic variation of δ13C and δ15N recorded in the gladius of the jumbo squid Dosidicus gigas: geographic differences. Mar. Ecol. Prog. Ser. 399, 187–198 (2010).
ADS  CAS  Google Scholar 

39.
Xavier, J. C. Foraging ecology and interactions with fisheries of wandering albatrosses (Diomedea exulans) breeding at South Georgia. Fish. Oceanogr. 13, 324–344 (2004).
Google Scholar 

40.
Cherel, Y., Xavier, J. C., Grissac, S., Trouvé, C. & Weimerskirch, H. Feeding ecology, isotopic niche, and ingestion of fishery-related items of the wandering albatross Diomedea exulans at Kerguelen and Crozet Islands. Mar. Ecol. Prog. Ser. 565, 197–215 (2017).
ADS  CAS  Google Scholar 

41.
Xavier, J. C., Croxall, J. P. & Cresswell, K. A. Boluses: an effective method for assessing the proportions of cephalopods in the diet of albatrosses. Auk. 122, 403–413 (2005).
Google Scholar 

42.
Cherel, Y., Fontaine, C., Jackson, G. D., Jackson, C. H. & Richard, P. Tissue, ontogenic and sex-related differences in δ13C and δ15N values of the oceanic squid Todarodes filippovae (Cephalopoda: Ommastrephidae). Mar. Biol. 156, 699–708 (2009).
Google Scholar 

43.
Guerreiro, M. et al. Habitat and trophic ecology of Southern Ocean cephalopods from stable isotope analyses. Mar. Ecol. Prog. Ser. 530, 119–134 (2015).
ADS  CAS  Google Scholar 

44.
Arkhipkin, A. I. et al. World squid fisheries. Rev. Fish. Sci. Aquac. 8249, 2 (2015).
Google Scholar 

45.
Xavier, J. C. et al. Future challenges in Southern Ocean ecology research. Front. Mar. Sci. 3, 1–9 (2016).
Google Scholar 

46.
Froy, H. et al. Age-related variation in foraging behaviour in the wandering albatross at South Georgia: No evidence for senescence. PLoS ONE 10, e0116415 (2015).
PubMed  PubMed Central  Google Scholar 

47.
Pecl, G. T. & Jackson, G. D. The potential impacts of climate change on inshore squid: Biology, ecology and fisheries. Rev. Fish. Biol. Fish. 18, 373–385 (2008).
Google Scholar 

48.
Rogers, A. D. et al. Antarctic futures: An assessment of climate-driven changes in ecosystem structure, function, and service provisioning in the Southern Ocean. Ann. Rev. Mar. Sci. 12, 87–120 (2019).
PubMed  Google Scholar 

49.
Griffiths, H. J. Antarctic marine biodiversity—what do we know about the distribution of life in the Southern Ocean?. PLoS ONE 5, e11683 (2010).
ADS  PubMed  PubMed Central  Google Scholar 

50.
Turner, J. et al. Antarctic climate change and the environment : an update. Polar. Rec. 50, 237–259 (2014).
Google Scholar 

51.
Rodhouse, P. G., Griffiths, H. J., & Xavier, J. C. Southern Ocean squid. In: The Biogeographic Atlas of the Southern Ocean. Cambridge, SCAR, 284–289 (2014a).

52.
Weimerskirch, H., Louzao, M., de Grissac, S. & Delord, K. Changes in wind pattern alter albatross distribution and life-history traits. Science 335, 211–214 (2012).
ADS  CAS  PubMed  Google Scholar 

53.
Golikov, A. V., Sabirov, R. M., Lubin, P. A. & Jørgensen, L. L. Changes in distribution and range structure of Arctic cephalopods due to climatic changes of the last decades. Biodiversity 14, 28–35 (2013).
Google Scholar 

54.
Golikov, A. V., Sabirov, R. M., Lubin, P. A., Jørgensen, L. L. & Beck, I. M. The northernmost record of Sepietta oweniana (Cephalopoda: Sepiolidae) and comments on boreo-subtropical cephalopod species occurrence in the Arctic. Mar. Biodivers. Rec. 7, e58 (2014).
Google Scholar 

55.
Guerra, A., Gonzalez, A. F. & Rocha, F. Appearance of the common paper nautilus Argonauta argo related to the increase of the sea surface temperature in the north-eastern Atlantic. J. Mar. Biol. Assoc. UK 82, 855–858 (2002).
Google Scholar 

56.
Stowasser, G. et al. Food web dynamics in the Scotia Sea in summer: a stable isotope study. Deep. Sea Res. Part II Top. Stud. Oceanogr. 59–60, 208–221 (2012).
ADS  Google Scholar 

57.
Cherel, Y., Bustamante, P. & Richard, P. Amino acid δ13C and δ15N from sclerotized beaks: A new tool to investigate the foraging ecology of cephalopods, including giant and colossal squids. Mar. Ecol. Prog. Ser. 624, 89–102 (2019).
ADS  CAS  Google Scholar 

58.
Steffan, S. A. et al. Trophic hierarchies illuminated via amino acid isotopic analysis. PLoS ONE 8, e76152 (2013).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

59.
Collins, M. A. & Rodhouse, P. G. Southern ocean cephalopods. Adv. Mar. Biol. 50, 191–265 (2006).
PubMed  Google Scholar 

60.
Clarke, M. R., Croxall, J. P. & Prince, P. A. Cephalopods remains in regurgitation of the wandering albatross Diomedea exulans L. at South Georgia. Br. Antarct. Surv. Bull. 54, 9–21 (1981).
Google Scholar 

61.
Rodhouse, P. G., Clarke, M. R. & Murray, A. W. Cephalopod prey of the wandering albatross Diomedea exulans. Mar. Biol. 10, 1–10 (1987).
Google Scholar 

62.
Xavier, J. C., Croxall, J. P., Trathan, P. N. & Rodhouse, P. G. Inter-annual variation in the cephalopod component of the diet of the wandering albatross, Diomedea exulans, breeding at Bird Island, South Georgia. Mar. Biol. 142, 611–622 (2003).
Google Scholar 

63.
Kennicutt, M., Chown, S. & Cassano, J. Six priorities for Antarctic science. Nature 512, 23–25 (2014).
ADS  CAS  PubMed  Google Scholar 

64.
Gutt, J. et al. The Southern Ocean ecosystem under multiple climate change stresses—an integrated circumpolar assessment. Glob. Chang. Biol. 21, 1434–1453 (2015).
ADS  PubMed  Google Scholar 

65.
Polito, M. J. et al. Contrasting specialist and generalist patterns facilitate foraging niche partitioning in sympatric populations of Pygoscelis penguins. Mar. Ecol. Prog. Ser. 519, 221–237 (2015).
ADS  CAS  Google Scholar 

66.
Xavier, J. C., & Cherel, Y. Cephalopod beak guide for Southern Ocean. (2009).

67.
Jaeger, A., Lecomte, V. J., Weimerskirch, H., Richard, P. & Cherel, Y. Seabird satellite tracking validates the use of latitudinal isoscapes to depict predators’ foraging areas in the Southern Ocean. Rapid. Commun. Mass. Spectrom. 24, 1457–1466 (2010).
Google Scholar 

68.
Cherel, Y. & Hobson, K. A. Geographical variation in carbon stable isotope signatures of marine predators: A tool to investigate their foraging areas in the Southern Ocean. Mar. Ecol. Prog. Ser. 329, 281–287 (2007).
ADS  CAS  Google Scholar 

69.
Hobson, K. A., Piatt, J. F. & Pitocchelli, J. Using stable isotopes to determine seabird trophic relationships. J. Anim. Ecol. 63, 786–798 (1994).
Google Scholar 

70.
Brault, E. K. et al. Carbon and nitrogen zooplankton isoscapes in West Antarctica reflect oceanographic transitions. Mar. Ecol. Prog. Ser 593, 29–45 (2018).
ADS  CAS  Google Scholar 

71.
Seco, J. et al. Distribution, habitat and trophic ecology of Antarctic squid Kondakovia longimana and Moroteuthis knipovitchi: inferences from predators and stable isotopes. Polar Biol. 39, 167–175 (2016).
Google Scholar 

72.
Keeling, C. D. The Suess effect: 13Carbon-14Carbon interrelations. Environ. Int. 2, 229–300 (1979).
CAS  Google Scholar 

73.
Hilton, G. M. et al. A stable isotopic investigation into the causes of decline in a sub-Antarctic predator, the rockhopper penguin. Glob. Chang. Biol. 12, 611–625 (2006).
ADS  Google Scholar 

74.
Jaeger, A. & Cherel, Y. Isotopic investigation of contemporary and historic changes in penguin trophic niches and carrying capacity of the Southern Indian Ocean. PLoS ONE 6, e16484 (2011).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

75.
Jackson, A. L., Inger, R., Parnell, A. C. & Bearhop, S. Comparing isotopic niche widths among and within communities: SIBER—Stable Isotope Bayesian Ellipses in R. J. Anim. Ecol. 80, 595–602 (2011).
PubMed  Google Scholar 

76.
R Development Core Team (2018) R: a language and environment for statistical computing Vienna More