Ecology

In the present study, we used accelerometer enabled animal-borne biologging tags (recording temperature, pressure and three-axis accelerometry) to describe in high temporal resolution the variability and repeatability of 67 breaches made by three sharks over 41 cumulative days (Fig. 1; shark 5 m length (n = 1) 678 kg estimated mass; and sharks 6 m length (n = 2) 160 kg estimated mass). Approximately half (n = 28) of all breaches were single breaches, but we also recorded 13 double breaches, three triple breaches and one shark that breached four times in 47 s (Fig. 2A-D). Consecutive breaches were 18 s apart (mean value ± 6 s.d, range 12–47 s); i.e. sharks ascend from depth to the surface, propel themselves out of the water and swim to depth before commencing the subsequent ascent. Breaching frequency varied among individuals. Shark 1 breached 0.4 ± 0.9 times per day (mean ± 1 s.d.; range 0–2, n = 2 breaches), shark 3 breached 0.9 per day (± 0.9 s.d, range 0–2, n = 5) and shark 2 breached 1.9 times per day (± 1.8 s.d, range 0–6, N = 60) over 4.8, 4.9 and 31.8 tracking days respectively), during both day and night (peak hour of breaching 04:00 am). Multiple breaches by the same sharks have never been empirically demonstrated before, and breaching has not been described to take place at night.
Figure 1

Basking shark breaching. Breaching recorded by a towed camera tag deployed in 2018. These data are from a shark that was not instrumented with an accelerometer, they are included to aid visualisation of the breaching process from a point-of-view perspective. For sharks instrumented with accelerometers in 2017, tags where attached flush to the surface of the animal at the base of the dorsal fin. (A) Basking shark breaching (photo: Youen Jacob). The timing and depth associated with each image (C–H) are identified on the breaching depth profile (B). (C,D) the shark starts to ascend from 72 m depth at 0.94 m of vertical gain per second, reaching the surface (in view, E) in 77 s. The shark can be seen completely out of the water (F), before descending (G,H) to depth again.

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Figure 2

Characteristics of breaching. (A–D) A quadruple breach by a six-metre basking shark over 47 s showing changes in depth (A), tail beat amplitude (B), VeDBA (C) and speed (D) over the series of breaches. (E) Depth profiles of 16 single breaching events performed by a single shark, with time (in seconds) centred on the breach, overlaid on a common timescale showing repeatability of ascent angle and subsequent descent after breaching. (F) Dubai plot showing tri-axial acceleration data as a 3-dimensional histogram, with time spent by sharks in a particular posture on each facet of the sphere extruded as triangular bars, and colour scaling with the cumulative time in a given facet. Data show a right-handed breach of a single shark, where rapid rolling is indicated by short dark blue bars on the right face of the sphere.

Full size image

At the onset of a breach, sharks switched from slow swimming at 0.3 m.s-1 (mean value ± 0.16 s.d., range 0.17–0.4 m.s-1) at 14.8 m depth (± 5, range 4.6–28 m), to swimming towards the surface (Fig. 1C-E) at an angle of 38.9° (± 13.2, 23.08 to 81.6°), and an average (mean) ascent speed of 2.7 m.s-1 ± 0.5 (1.2–3.8 m.s-1). The peak ascent phase of a breach was observed when the rates of ascent and swimming speed rapidly increased. Breaching metrics were calculated separately for this peak ascent phase, where basking sharks reached the surface in 6 s (± 2.1, 2–17), before breaching near vertically at 76° (± 9, 43.3–87.9°), leaving the water at a mean exit speed of 3.9 m.s-1 ± 0.6. range: 2.2–5.6 m.s-1) (Fig. 1F). To contextualise our observations, an 8 m basking shark breached at 5.1 m.s-1 from 28 m depth10 and oceanic whitetip sharks (Carcharhinus longimanus)11 and great white sharks (Carcharodon carcharias)11,12 ambush-breach their prey at 4 and up to 6.5 m s−1 respectively, but from considerably deeper ( > 100 m11) and are smaller sharks. The peak forces generated by the three tagged basking sharks (which were estimated to weigh up to 1160 kg) were 20 G at the peak of breaching. Breaches could be further characterised by whether sharks exited the water on a particular side of their body. Sharks rolled to their right side in 45 of the 67 breaches (representing 67% of breaches), which may be suggestive of lateralisation (Fig. 2F), the preference for breaching on one side consistently across events13,14. Dynamic body acceleration (VeDBA) (linear mixed effects model; χ2 7.6, p = 0.006) along with tailbeat amplitude (linear mixed effects model; χ2 5.54, p = 0.019) increased with the sharks’ ascent pitch towards the surface. Breaching events were highly repeatable, both among and between sharks, following a similar ascent rate, speed and angle, and from a similar starting depth (Fig. 2E). Breaching was more energetically demanding than routine swimming (breaching VeDBA 7.7 m s−2 ± 4.5, range 0.4–14.7 vs routine swimming VeDBA 0.24 m.s-2 ± 0.04, 0.2–0.27), requiring double the tail beat frequency (breaching 0.49 Hz ± 0.12 vs routine swimming 1.08 Hz ± 0.51) and 15 times the tail beat amplitude (breaching 1.5 ± 1.1 Hz vs. routine swimming 0.1 ± 0.05 Hz). During multiple breaching events, the ascent rate, swimming speed and acceleration were similar for every subsequent breach, although the ascent starting depth was often shallower than for the initial breach. The relatively low field metabolic rate that comes with being ectothermic makes energetically demanding behaviour relatively more expensive for sharks. Therefore, the costs of performing multiple breaches may accumulate more rapidly compared to endothermic whales, such as humpback whales (Megaptera novaeangliae), which have been recorded breaching 17 times in a 6.5 h deployment15. On average, sharks required an estimated 11.5 kJ (range 3–22 kJ) of mechanical energy (({E}_{m})) to perform a breach, and expended the same ({E}_{m}) for each breach, regardless of whether they breached once or several times (Wilcoxon rank sum test, W = 198.5, p = 0.87; ({E}_{m} single) = 11.5 to 11.8 kJ, ({E}_{m} multi) = 9.98 to 10.3 kJ). Comparatively, the energetic cost of breaching for an 8 m basking shark weighing 2700 kg was estimated six times greater (63 to 72 kJ10). These differences may be attributed to the sharks in the present study being smaller, with the cost of breaching found to increase with increasing body mass15. A breach likely constitutes approximately 0.05 to 0.09% of their daily metabolic cost, which ranged from 12.8 to 21.5 MJ per day16. For comparison, the relative cost of performing a single breach in a 7.8 m (7000 kg) humpback whale represents 0.08 to 0.5% of its daily field metabolic rate15.
The question remains what the function of breaching is for basking sharks. We are still far from certain what the function of breaching is for many aquatic species, but spinner dolphins, blacktip sharks and humpback whales are known to breach to dislodge epiparasites17. Gore et al.9 noted that epiparasitic lampreys were not dislodged from basking sharks following breaching, suggesting that it may not function for parasite removal, or may require several breaches to dislodge such parasites. Breaching may be used to visually signal between spinner dolphins, and between humpback whales17. Basking sharks breached during the night-time as well as the daytime, and have small eyes, suggesting that breaching is unlikely to be a visual signal. However, breaching may play a role in acoustic communication between distant groups of sharks. Basking sharks can apparently detect weak electric signals produced by zooplankton18, and some elasmobranchs use electro-sensory cues during courtship19, suggesting that breaching could convey readiness to mate. It thus seems possible that the acoustic signal of breaching could be detectable and useful to basking sharks. We have no information in the present study about the presence of other sharks during breaching, although future work using animal-borne acoustic proximity receivers on large numbers of sharks, or aerial drones, could provide insight into the social networks of basking sharks, and whether they breach in proximity to conspecifics. We propose that in the absence of a better explanation and given the predictable and persistent aggregations of basking sharks in Scottish waters, that breaching may be more likely to be related to intra-specific signalling, than anything else yet described.
We show using repeated direct measurements from three individuals, that the mechanical forces required for basking sharks to breach are considerable, but that basking sharks can breach repeatedly in quick succession. The role of breaching seems most likely to be related to intra-specific signalling and may add to a weight of evidence suggesting that Scottish waters may be an important site for breeding for the species. More

1.
Allen, C. D. et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For. Ecol. Manag. 259, 660–684 (2010).
Article  Google Scholar 
2.
Allen, C. D., Breshears, D. D. & McDowell, N. G. On underestimation of global vulnerability to tree mortality and forest die-off from hotter drought in the Anthropocene. Ecosphere 6, 1–55 (2015).
Article  Google Scholar 

3.
Anderegg, W. R. L., Anderegg, L. D. L., Kerr, K. L. & Trugman, A. T. Widespread drought-induced tree mortality at dry range edges indicates that climate stress exceeds species’ compensating mechanisms. Glob. Change Biol. 25, 3793–3802 (2019).
ADS  Article  Google Scholar 

4.
Breshears, D. D. et al. Regional vegetation die-off in response to global-change-type drought. PNAS 102, 15144–15148 (2005).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

5.
D’Orangeville, L. et al. Drought timing and local climate determine the sensitivity of eastern temperate forests to drought. Glob. Change Biol. 24, 2339–2351 (2018).
ADS  Article  Google Scholar 

6.
Kannenberg, S. A. et al. Drought legacies are dependent on water table depth, wood anatomy and drought timing across the eastern US. Ecol. Lett. 22, 119–127 (2019).
PubMed  Article  PubMed Central  Google Scholar 

7.
Jump, A. S. et al. Structural overshoot of tree growth with climate variability and the global spectrum of drought-induced forest dieback. Glob. Change Biol. 23, 3742–3757 (2017).
ADS  Article  Google Scholar 

8.
Choat, B. et al. Global convergence in the vulnerability of forests to drought. Nature 491, 752–755 (2012).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

9.
Cunningham, S. C., Thomson, J. R., MacNally, R., Read, J. & Baker, P. J. Groundwater change forecasts widespread forest dieback across an extensive floodplain system. Freshw. Biol. 56, 1494–1508 (2011).
Article  Google Scholar 

10.
Tockner, K. & Stanford, J. Riverine flood plains: Present state and future trends. Environ. Conserv. 29, 308–330 (2002).
Article  Google Scholar 

11.
Kløve, B. et al. Groundwater dependent ecosystems. Part II. Ecosystem services and management in Europe under risk of climate change and land use intensification. Environ. Sci. Policy 14, 782–793 (2011).
Article  Google Scholar 

12.
Griebler, C. & Avramov, M. Groundwater ecosystem services: A review. Freshw. Sci. 34, 355–367 (2015).
Article  Google Scholar 

13.
Griebler, C., Avramov, M. & Hose, G. Groundwater ecosystems and their services: Current status and potential risks. In Atlas of Ecosystem Services (ed. Schröter, M.) 197–203 (Springer, 2019).
Google Scholar 

14.
Kløve, B. et al. Groundwater dependent ecosystems part I: Hydroecological status and trends. Environ. Sci. Policy 14, 770–781 (2011).
Article  Google Scholar 

15.
Kløve, B. et al. Climate change impacts on groundwater and dependent ecosystems. J. Hydrol. 518, 250–266 (2014).
ADS  Article  Google Scholar 

16.
Cuthbert, M. O. et al. Global patterns and dynamics of climate–groundwater interactions. Nat. Clim. Change 9, 137–141 (2019).
ADS  Article  Google Scholar 

17.
Taylor, R. G. et al. Ground water and climate change. Nat. Clim. Change 3, 322–329 (2013).
ADS  Article  Google Scholar 

18.
Green, T. R. et al. Beneath the surface of global change: Impacts of climate change on groundwater. J. Hydrol. 405, 532–560 (2011).
ADS  Article  Google Scholar 

19.
Earman, S. & Dettinger, M. Potential impacts of climate change on groundwater resources—A global review. J. Water Clim. Change 2, 213–229 (2011).
Article  Google Scholar 

20.
Skiadaresis, G., Schwarz, J. A. & Bauhus, J. Groundwater extraction in floodplain forests reduces radial growth and increases summer drought sensitivity of pedunculate oak trees (Quercus robur L.). Front. For. Glob. Change 2, 267 (2019).
Article  Google Scholar 

21.
Valladares, F. et al. The effects of phenotypic plasticity and local adaptation on forecasts of species range shifts under climate change. Ecol. Lett. 17, 1351–1364 (2014).
PubMed  Article  Google Scholar 

22.
Nicotra, A. B. et al. Plant phenotypic plasticity in a changing climate. Trends Plant Sci. 15, 684–692 (2010).
CAS  PubMed  Article  Google Scholar 

23.
Martínez-Vilalta, J. The rear window: Structural and functional plasticity in tree responses to climate change inferred from growth rings. Tree physiol. 38, 155–158 (2018).
PubMed  Article  Google Scholar 

24.
Lloret, F., Keeling, E. G. & Sala, A. Components of tree resilience: Effects of successive low-growth episodes in old ponderosa pine forests. Oikos 120, 1909–1920 (2011).
Article  Google Scholar 

25.
McDowell, N. G., Brodribb, T. J. & Nardini, A. Hydraulics in the 21st century. New Phytol. 224, 537–542 (2019).
PubMed  Article  Google Scholar 

26.
Anderegg, W. R. L. & Meinzer, F. C. Wood anatomy and plant hydraulics in a changing climate. In Functional and Ecological Xylem Anatomy (ed. Hacke, U.) 235–253 (Springer, 2015).
Google Scholar 

27.
Fonti, P. et al. Studying global change through investigation of the plastic responses of xylem anatomy in tree rings. New phytol. 185, 42–53 (2010).
PubMed  Article  Google Scholar 

28.
Tulik, M. The anatomical traits of trunk wood and their relevance to oak (Quercus robur L.) vitality. Eur. J. For. Res. 133, 845–855 (2014).
Article  Google Scholar 

29.
Fonti, P. & Jansen, S. Xylem plasticity in response to climate. New Phytol. 195, 734–736 (2012).
PubMed  Article  Google Scholar 

30.
Brodribb, T. J. Xylem hydraulic physiology: The functional backbone of terrestrial plant productivity. Plant Sci. 177, 245–251 (2009).
CAS  Article  Google Scholar 

31.
He, P. et al. Growing-season temperature and precipitation are independent drivers of global variation in xylem hydraulic conductivity. Glob. Change Biol. 26, 1833–1841 (2019).
ADS  Article  Google Scholar 

32.
Barbaroux, C. & Bréda, N. Contrasting distribution and seasonal dynamics of carbohydrate reserves in stem wood of adult ring-porous sessile oak and diffuse-porous beech trees. Tree Physiol. 22, 1201–1210 (2002).
CAS  PubMed  Article  PubMed Central  Google Scholar 

33.
Bréda, N. & Granier, A. Intra- and interannual variations of transpiration, leaf area index and radial growth of a sessile oak stand (Quercus petraea). Ann. For. Sci. 53, 521–536 (1996).
Article  Google Scholar 

34.
Pérez-de-Lis, G., Rozas, V., Vázquez-Ruiz, R. A. & García-González, I. Do ring-porous oaks prioritize earlywood vessel efficiency over safety? Environmental effects on vessel diameter and tyloses formation. Agric. For. Meteorol. 248, 205–214 (2018).
ADS  Article  Google Scholar 

35.
Tumajer, J. & Treml, V. Response of floodplain pedunculate oak (Quercus robur L.) tree-ring width and vessel anatomy to climatic trends and extreme hydroclimatic events. For. Ecol. Manag. 379, 185–194 (2016).
Article  Google Scholar 

36.
Kniesel, B. M., Günther, B., Roloff, A. & von Arx, G. Defining ecologically relevant vessel parameters in Quercus robur L. for use in dendroecology: A pointer year and recovery time case study in Central Germany. Trees 29, 1041–1051 (2015).
Article  Google Scholar 

37.
Gričar, J., de Luis, M., Hafner, P. & Levanič, T. Anatomical characteristics and hydrologic signals in tree-rings of oaks (Quercus robur L.). Trees 27, 1669–1680 (2013).
Article  Google Scholar 

38.
Castagneri, D., Regev, L., Boaretto, E. & Carrer, M. Xylem anatomical traits reveal different strategies of two Mediterranean oaks to cope with drought and warming. Environ. Exp. Bot. 133, 128–138 (2017).
Article  Google Scholar 

39.
Fonti, P. & García-González, I. Earlywood vessel size of oak as a potential proxy for spring precipitation in mesic sites. J. Biogeogr. 35, 2249–2257 (2008).
Article  Google Scholar 

40.
González, I. G. & Eckstein, D. Climatic signal of earlywood vessels of oak on a maritime site. Tree Physiol. 23, 497–504 (2003).
PubMed  Article  PubMed Central  Google Scholar 

41.
Pérez-de-Lis, G., Rossi, S., Vázquez-Ruiz, R. A., Rozas, V. & García-González, I. Do changes in spring phenology affect earlywood vessels? Perspective from the xylogenesis monitoring of two sympatric ring-porous oaks. New Phytol. 209, 521–530 (2016).
PubMed  Article  PubMed Central  Google Scholar 

42.
García-González, I., Souto-Herrero, M. & Campelo, F. Ring-porosity and earlywood vessels: A review on extracting environmental information through time. IAWA J. 37, 295–314 (2016).
Article  Google Scholar 

43.
Friedrichs, D. A. et al. Species-specific climate sensitivity of tree growth in Central-West Germany. Trees 23, 729–739 (2009).
Article  Google Scholar 

44.
Büntgen, U. et al. Tree-ring indicators of German summer drought over the last millennium. Quat. Sci. Rev. 29, 1005–1016 (2010).
ADS  Article  Google Scholar 

45.
Bhuyan, U., Zang, C. & Menzel, A. Different responses of multispecies tree ring growth to various drought indices across Europe. Dendrochronologia 44, 1–8 (2017).
Article  Google Scholar 

46.
Árvai, M., Morgós, A. & Kern, Z. Growth-climate relations and the enhancement of drought signals in pedunculate oak (Quercus robur L.) tree-ring chronology in Eastern Hungary. iForest 11, 267–274 (2018).
Article  Google Scholar 

47.
Bramer, I. et al. Advances in monitoring and modelling climate at ecologically relevant scales. Adv. Ecol. Res. 58, 101–161 (2018).
Article  Google Scholar 

48.
Sass-Klaassen, U., Sabajo, C. R. & den Ouden, J. Vessel formation in relation to leaf phenology in pedunculate oak and European ash. Dendrochronologia 29, 171–175 (2011).
Article  Google Scholar 

49.
Souto-Herrero, M., Rozas, V. & García-González, I. Earlywood vessels and latewood width explain the role of climate on wood formation of Quercus pyrenaica Willd. across the Atlantic-Mediterranean boundary in NW Iberia. For. Ecol. Manage. 425, 126–137 (2018).
Article  Google Scholar 

50.
Fonti, P. & García-González, I. Suitability of chestnut earlywood vessel chronologies for ecological studies. New Phytol. 163, 77–86 (2004).
Article  Google Scholar 

51.
Jacobsen, A. L., Valdovinos-Ayala, J. & Pratt, R. B. Functional lifespans of xylem vessels: Development, hydraulic function, and post-function of vessels in several species of woody plants. Am. J. Bot. 105, 142–150 (2018).
PubMed  Article  Google Scholar 

52.
Jia, X. et al. Carbon and water exchange over a temperate semi-arid shrubland during three years of contrasting precipitation and soil moisture patterns. Agric. For. Meteorol. 228, 120–129 (2016).
ADS  Article  Google Scholar 

53.
Carter, J. L. & White, D. A. Plasticity in the Huber value contributes to homeostasis in leaf water relations of a mallee Eucalypt with variation to groundwater depth. Tree Physiol. 29, 1407–1418 (2009).
PubMed  Article  Google Scholar 

54.
Zolfaghar, S., Villalobos-Vega, R., Zeppel, M. & Eamus, D. The hydraulic architecture of Eucalyptus trees growing across a gradient of depth-to-groundwater. Funct. Plant Biol. 42, 888–898 (2015).
PubMed  Article  Google Scholar 

55.
Horton, J. L., Kolb, T. E. & Hart, S. C. Responses of riparian trees to interannual variation in ground water depth in a semi-arid river basin. Plant Cell Environ. 24, 293–304 (2001).
Article  Google Scholar 

56.
Gazol, A., Camarero, J. J., Anderegg, W. R. L. & Vicente-Serrano, S. M. Impacts of droughts on the growth resilience of Northern Hemisphere forests. Glob. Ecol. Biogeogr. 26, 166–176 (2017).
Article  Google Scholar 

57.
Garzón, M. B., Alía, R., Robson, T. M. & Zavala, M. A. Intra-specific variability and plasticity influence potential tree species distributions under climate change. Glob. Ecol. Biogeogr. 20, 766–778 (2011).
Article  Google Scholar 

58.
Choat, B. et al. Triggers of tree mortality under drought. Nature 558, 531–539 (2018).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

59.
Sperry, J. S., Meinzer, F. C. & McCulloh, K. A. Safety and efficiency conflicts in hydraulic architecture: Scaling from tissues to trees. Plant Cell Environ. 31, 632–645 (2008).
PubMed  Article  PubMed Central  Google Scholar 

60.
Tyree, M. T. & Ewers, F. W. The hydraulic architecture of trees and other woody plants. New Phytol. 119, 345–360 (1991).
Article  Google Scholar 

61.
Oosterbaan, A. & Nabuurs, G. J. Relationships between oak decline and groundwater class in The Netherlands. Plant Soil 136, 87–93 (1991).
Article  Google Scholar 

62.
Leuschner, C. & Ellenberg, H. Ecology of Central European Forests (Springer, 2017).
Google Scholar 

63.
McDowell, N. et al. Mechanisms of plant survival and mortality during drought: Why do some plants survive while others succumb to drought?. New Phytol. 178, 719–739 (2008).
PubMed  Article  Google Scholar 

64.
McDowell, N. G. et al. The interdependence of mechanisms underlying climate-driven vegetation mortality. Trends Ecol. Evol. 26, 523–532 (2011).
PubMed  Article  Google Scholar 

65.
Pellizzari, E., Camarero, J. J., Gazol, A., Sangüesa-Barreda, G. & Carrer, M. Wood anatomy and carbon-isotope discrimination support long-term hydraulic deterioration as a major cause of drought-induced dieback. Glob. Change Biol. 22, 2125–2137 (2016).
ADS  Article  Google Scholar 

66.
McCarroll, D., Whitney, M., Young, G. H. F., Loader, N. J. & Gagen, M. H. A simple stable carbon isotope method for investigating changes in the use of recent versus old carbon in oak. Tree Physiol. 37, 1021–1027 (2017).
CAS  PubMed  Article  Google Scholar 

67.
Hacke, U. G. Variable plant hydraulic conductance. Tree Physiol. 34, 105–108 (2014).
PubMed  Article  Google Scholar 

68.
Hacke, U. G., Sperry, J. S., Wheeler, J. K. & Castro, L. Scaling of angiosperm xylem structure with safety and efficiency. Tree Physiol. 26, 689–701 (2006).
PubMed  Article  Google Scholar 

69.
Anderegg, W. R. L. et al. Pervasive drought legacies in forest ecosystems and their implications for carbon cycle models. Science 349, 528–532 (2015).
ADS  CAS  PubMed  Article  Google Scholar 

70.
Cailleret, M. et al. A synthesis of radial growth patterns preceding tree mortality. Glob. Change Biol. 23, 1675–1690 (2017).
ADS  Article  Google Scholar 

71.
Ehleringer, J. R. & Dawson, T. E. Water uptake by plants: perspectives from stable isotope composition. Plant Cell Environ. 15, 1073–1082 (1992).
CAS  Article  Google Scholar 

72.
Roloff, A. Baumkronen: Verständnis und praktische Bedeutung eines komplexen Naturphänomens [Tree crowns: comprehension and practical meaning of a complex phenomenon]. Ulmer, Stuttgart [original in German] (2001).

73.
Bunn, A. G. A dendrochronology program library in R (dplR). Dendrochronologia 26, 115–124 (2008).
Article  Google Scholar 

74.
Bunn, A. G. Statistical and visual crossdating in R using the dplR library. Dendrochronologia 28, 251–258 (2010).
Article  Google Scholar 

75.
R Core Team. R: A language and environment for Statistical Computing (R Foundation for Statistical Computing, 2019).

76.
García-González, I. & Fonti, P. Ensuring a representative sample of earlywood vessels for dendroecological studies: An example from two ring-porous species. Trees 22, 237–244 (2008).
Article  Google Scholar 

77.
Kolb, K. J. & Sperry, J. S. Transport constraints on water use by the Great Basin shrub, Artemisia tridentata. Plant Cell Environ. 22, 925–936 (1999).
Article  Google Scholar 

78.
Sterck, F. J., Zweifel, R., Sass-Klaassen, U. & Chowdhury, Q. Persisting soil drought reduces leaf specific conductivity in Scots pine (Pinus sylvestris) and pubescent oak (Quercus pubescens). Tree Physiol. 28, 529–536 (2008).
PubMed  Article  PubMed Central  Google Scholar 

79.
von Arx, G., Crivellaro, A., Prendin, A. L., Čufar, K. & Carrer, M. Quantitative wood anatomy-practical guidelines. Front. Plant Sci. 7, 781 (2016).
Google Scholar 

80.
Speer, J. H. Fundamentals of tree-ring research (University of Arizona Press, 2010).

81.
Cook, E. R. & Peters, K. The smoothing spline: A new approach to standardizing forest interior tree-ring width series for dendroclimatic studies. (1981).

82.
Carrer, M., von Arx, G., Castagneri, D. & Petit, G. Distilling allometric and environmental information from time series of conduit size: the standardization issue and its relationship to tree hydraulic architecture. Tree Physiol. 35, 27–33 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

83.
Vicente-Serrano, S. M., Beguería, S. & López-Moreno, J. I. A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index. J. Clim. 23, 1696–1718 (2010).
ADS  Article  Google Scholar 

84.
Zink, M. et al. The German drought monitor. Environ. Res. Lett. 11, 74002 (2016).
Article  Google Scholar 

85.
Samaniego, L., Kumar, R. & Zink, M. Implications of parameter uncertainty on soil moisture drought analysis in Germany. J. Hydrometeor. 14, 47–68 (2013).
ADS  Article  Google Scholar 

86.
Samaniego, L., Kumar, R. & Attinger, S. Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale. Water Resour. Res. 46, 1–25 (2010).
Google Scholar 

87.
Zang, C. & Biondi, F. treeclim: An R package for the numerical calibration of proxy-climate relationships. Ecography 38, 431–436 (2015).
Article  Google Scholar 

88.
Schwarz, J. et al. Quantifying growth responses of trees to drought—A critique of commonly used resilience indices and recommendations for future studies. Curr. For. Rep. 6, 185–200 (2020).
Google Scholar 

89.
Jacobsen, A. L., Pratt, R. B., Venturas, M. D., & Hacke, U. G. Large volume vessels are vulnerable to water-stress-induced embolism in stems of poplar. IAWA J. 40(1), 4–S4 (2019).

90.
Cai, J., & Tyree, M. T. (2010). The impact of vessel size on vulnerability curves: data and models for within‐species variability in saplings of aspen, Populus tremuloides Michx. Plant Cell Environ. 33(7), 1059–1069. More

Abbott I (2000) Improving the conservation of threatened and rare mammal species through translocation to islands: case study Western Australia. Biol Conserv 93:195–201
Article  Google Scholar 

Adamack AT, Gruber B (2014) PopGenReport: simplifying basic population genetic analyses in R. Methods Ecol Evol 5:384–387
Article  Google Scholar 

Aplin KP (2006) Ten million years of rodent evolution in Australasia: phylogenetic evidence and a speculative historical biogeography. In: Merrick JR (ed) Evolution and biogeography of Australasian vertebrates. Auscipub, Sydney, pp 707–744

Ball IR, Possingham H, Watts M (2009) Marxan and relatives: software for spatial conservation prioritization. In: Moilanen A, Wilson KA, Possingham H (eds) Spatial conservation prioritisation: quantitative methods and computational tools. Oxford University Press, Oxford, p 320

Berger-Tal O, Blumstein DT, Swaisgood RR (2020) Conservation translocations: a review of common difficulties and promising directions. Anim Conserv 23:121–131
Article  Google Scholar 

Blonder B, Harris DJ (2018) hypervolume: High dimensional geometry and set operations using kernel density estimation, support vector machines, and convex hulls. R Package Version 2.0.11. https://CRAN.R-project.org/package=hypervolume

Blonder B, Morrow CB, Maitner B, Harris DJ, Lamanna C, Violle C et al. (2018) New approaches for delineating n-dimensional hypervolumes. Methods Ecol Evol 9:305–319
Article  Google Scholar 

Breed M, Ford F (2007) Native mice and rats. CSIRO Publishing, Collingwood, Victoria
Google Scholar 

Cardoso MJ, Eldridge MDB, Oakwood M, Rankmore B, Sherwin WB, Firestone KB (2009) Effects of founder events on the genetic variation of translocated island populations: implications for conservation management of the northern quoll. Conserv Genet 10:1719–1733
Article  Google Scholar 

Catchen J, Hohenlohe PA, Bassham S, Amores A, Cresko WA (2013) Stacks: an analysis tool set for population genomics. Mol Ecol 22:3124–3140
PubMed  PubMed Central  Article  Google Scholar 

Caughley G (1994) Directions in conservation biology. J Anim Ecol 63:215–244
Article  Google Scholar 

Cramb J, Hocknull S (2010) New Quaternary records of Conilurus (Rodentia: Muridae) from eastern and northern Australia with the description of a new species. Zootaxa 2634:41–56
Article  Google Scholar 

Davies HF, Maier SW, Murphy BP (2020) Feral cats are more abundant under severe disturbance regimes in an Australian tropical savanna. Wildl Res 47:624–632
Article  Google Scholar 

Davies HF, McCarthy MA, Firth RSC, Woinarski JCZ, Gillespie GR, Andersen AN et al. (2017) Top-down control of species distributions: feral cats driving the regional extinction of a threatened rodent in northern Australia (N Roura-Pascual, Ed.). Divers Distrib 23:272–283
Article  Google Scholar 

Davies HF, McCarthy MA, Firth RSC, Woinarski JCZ, Gillespie GR, Andersen AN et al. (2018) Declining populations in one of the last refuges for threatened mammal species in northern Australia. Austral Ecol 43:602–612
Article  Google Scholar 

Davies HF, McCarthy MA, Rioli W, Puruntatameri J, Roberts W, Kerinaiua C et al. (2018) An experimental test of whether pyrodiversity promotes mammal diversity in a northern Australian savanna. J Appl Ecol 55:2124–2134
Article  Google Scholar 

DoE (2015) Threatened species strategy. Australian Government Department of the Environment, Canberra, ACT

Doherty TS, Driscoll DA (2018) Competition in the historical niche: a response to Scheele et al. Trends Ecol Evol 33:147–148
PubMed  Article  PubMed Central  Google Scholar 

Dyer R (2016) gstudio: tools related to the spatial analysis of genetic marker data. R Package Version 1.5.2

Firth RSC, Brook BW, Woinarski JCZ, Fordham DA (2010) Decline and likely extinction of a northern Australian native rodent, the brush-tailed rabbit-rat Conilurus penicillatus. Biol Conserv 143:1193–1201
Article  Google Scholar 

Firth RSC, Jefferys E, Woinarski JCZ, Noske RA (2005) The diet of the brush-tailed rabbit-rat (Conilurus penicillatus) from the monsoonal tropics of the Northern Territory. Aust Wildl Res 32:517–523
Article  Google Scholar 

Firth RSC, Woinarski JCZ, Brennan KG, Hempel C (2006) Environmental relationships of the brush-tailed rabbit-rat, Conilurus penicillatus, and other small mammals on the Tiwi Islands, northern Australia. J Biogeogr 33:1820–1837
Article  Google Scholar 

Frankham R (2005) Genetics and extinction. Biol Conserv 126:131–140
Article  Google Scholar 

Frankham R, Ballou JD, Ralls K, Eldridge MDB, Dudash MR, Fenster CB et al. (2019) Population fragmentation causes inadequate gene flow and increases extinction risk. In: A practical guide for genetic management of fragmented animal and plant populations. Oxford University Press, New York

Frankham R, Bradshaw CJA, Brook BW (2014) Genetics in conservation management: revised recommendations for the 50/500 rules, red list criteria and population viability analyses. Biol Conserv 170:56–63
Article  Google Scholar 

Frichot E, François O (2015) LEA: an R package for landscape and ecological association studies. Methods Ecol Evol 6:925–929
Article  Google Scholar 

Frichot E, Mathieu F, Trouillon T, Bouchard G, François O (2014) Fast and efficient estimation of individual ancestry coefficients. Genetics 196:973–983
PubMed  PubMed Central  Article  Google Scholar 

Goudet J (2005) hierfstat, a package for r to compute and test hierarchical F‐statistics. Mol Ecol Notes 5:184–186
Article  Google Scholar 

Green AW, Correll MD, George TL, Davidson I, Gallagher S, West C et al. (2018) Using structured decision making to prioritize species assemblages for conservation. J Nat Conserv 45:48–57
Article  Google Scholar 

Hanson JO, Schuster R, Morrell N, Strimas-Mackey M, Watts ME, Arcese P et al. (2020) prioritizr: systematic conservation prioritization in R. R Package Version 4.1.5. https://CRAN.R-project.org/package=prioritizr

Hanson JO, Veríssimo A, Velo‐Antón G, Marques A, Camacho‐Sanchez M, Martínez‐Solano Í et al. (2020) Evaluating surrogates of genetic diversity for conservation planning. Conserv Biol

Jolly CJ, Webb JK, Phillips BL (2018) The perils of paradise: an endangered species conserved on an island loses antipredator behaviours within 13 generations. Biol Lett 14:20180222
PubMed  PubMed Central  Article  Google Scholar 

Jombart T (2008) adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 24:1403–1405
CAS  PubMed  Article  PubMed Central  Google Scholar 

Kamvar ZN, Tabima JF, Grünwald NJ (2014) Poppr: an R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ 2:e281
PubMed  PubMed Central  Article  Google Scholar 

Kemper CM (2020) Growth and development of the brush-tailed rabbit-rat (Conilurus penicillatus), a threatened tree-rat from northern Australia. Aust Mammal

Kemper CM, Schmitt LH (1992) Morphological variation between populations of the brush-tailed tree rat (Conilurus penicillatus) in Northern Australia and New-Guinea. Aust J Zool 40:437–452
Article  Google Scholar 

Korneliussen TS, Albrechtsen A, Nielsen R (2014) Open Access ANGSD: analysis of Next Generation Sequencing Data. BMC Bioinforma 15:1–13
Article  Google Scholar 

Kutt A, Felderhof L, Vanderwal J, Stone P, Perkins G (2009) Terrestrial ecosystems of northern Australia. In: Stone P (ed) Northern Australia land and water science review. Department of Infrastructure, Transport, Regional Development and Local Government, Canberra, ACT, pp 1–42

Laurie CC, Nickerson DA, Anderson AD, Weir BS, Livingston RJ, Dean MD et al. (2007) Linkage disequilibrium in wild mice. Plos Genet 3:e144
PubMed  PubMed Central  Article  CAS  Google Scholar 

Legge S, Kennedy MS, Lloyd R, Murphy SA, Fisher A (2011) Rapid recovery of mammal fauna in the central Kimberley, northern Australia, following the removal of introduced herbivores. Austral Ecol 36:791–799
Article  Google Scholar 

Legge S, Woinarski JCZ, Burbidge AA, Palmer R, Ringma J, Radford JQ et al. (2018) Havens for threatened Australian mammals: the contributions of fenced areas and offshore islands to the protection of mammal species susceptible to introduced predators. Wildl Res 45:627–644
Article  Google Scholar 

Li H (2013) Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at https://arxiv.org/abs/1303.3997

Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N et al. (2009) The Sequence Alignment/Map format and SAMtools. Bioinformatics 25:2078–2079
PubMed  PubMed Central  Article  CAS  Google Scholar 

McGregor H, Legge S, Jones ME, Johnson CN (2014) Landscape management of fire and grazing regimes alters the fine-scale habitat utilisation by feral cats. PLoS ONE 9:e109097
PubMed  PubMed Central  Article  CAS  Google Scholar 

McGregor H, Legge S, Jones ME, Johnson CN (2015) Feral cats are better killers in open habitats, revealed by animal-borne video. PLoS ONE 10:e0133915
PubMed  PubMed Central  Article  CAS  Google Scholar 

Moritz C (1999) Conservation units and translocations: strategies for conserving evolutionary processes. Hereditas 130:217–228
Article  Google Scholar 

Orsini L, Vanoverbeke J, Swillen I, Mergeay J, Meester L (2013) Drivers of population genetic differentiation in the wild: isolation by dispersal limitation, isolation by adaptation and isolation by colonization. Mol Ecol 22:5983–5999
PubMed  Article  PubMed Central  Google Scholar 

Ottewell KM, Bickerton DC, Byrne M, Lowe AJ (2016) Bridging the gap: a genetic assessment framework for population-level threatened plant conservation prioritization and decision-making. Divers Distrib 22:174–188
Article  Google Scholar 

Palsbøll PJ, Bérubé M, Allendorf FW (2007) Identification of management units using population genetic data. Trends Ecol Evol 22:11–16
PubMed  Article  PubMed Central  Google Scholar 

Peakall R, Smouse PE (2006) GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Mol Ecol Notes 6:288–295
Article  Google Scholar 

Peakall R, Smouse PE (2012) GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research-an update. Bioinformatics 28:2537–2539
CAS  PubMed  PubMed Central  Article  Google Scholar 

Penton CE, Woolley L-A, Radford IJ, Murphy BP (2020) Overlapping den tree selection by three declining arboreal mammal species in an Australian tropical savanna. J Mammal 101:1165–1176

Pérez I, Anadón JD, Díaz M, Nicola GG, Tella JL, Giménez A (2012) What is wrong with current translocations? A review and a decision-making proposal. Front Ecol Environ 10:494–501
Article  Google Scholar 

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

Petit RJ, Mousadik AE, Pons O (1998) Identifying populations for conservation on the basis of genetic markers. Conserv Biol 12:844–855
Article  Google Scholar 

R Core Team (2020) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria
Google Scholar 

Radford IJ, Woolley L-A, Corey B, Vigilante T, Hatherley E, Fairman R et al. (2020) Prescribed burning benefits threatened mammals in northern Australia. Biodivers Conserv 29:2985–3007
Article  Google Scholar 

Radford IJ, Woolley L-A, Dickman CR, Corey B, Trembath D, Fairman R (2020) Invasive anuran driven trophic cascade: an alternative hypothesis for recent critical weight range mammal collapses across northern Australia. Biol Invasions 22:1967–1982
Article  Google Scholar 

Ralls K, Ballou JD, Dudash MR, Eldridge MDB, Fenster CB, Lacy RC et al. (2018) Call for a paradigm shift in the genetic management of fragmented populations. Conserv Lett 11:e12412
Article  Google Scholar 

Roycroft EJ, Moussalli A, Rowe KC (2020) Phylogenomics uncovers confidence and conflict in the rapid radiation of Australo-Papuan rodents. Syst Biol 69:431–444

Saccheri I, Kuussaari M, Kankare M, Vikman P, Fortelius W, Hanski I (1998) Inbreeding and extinction in a butterfly metapopulation. Nature 392:491–494
CAS  Article  Google Scholar 

Scheele BC, Foster CN, Banks SC, Lindenmayer DB (2017) Niche contractions in declining species: mechanisms and consequences. Trends Ecol Evol 32:346–355
PubMed  Article  PubMed Central  Google Scholar 

Silcock JL, Simmons CL, Monks L, Dillon R, Reiter N, Jusaitis M et al. (2019) Threatened plant translocation in Australia: a review. Biol Conserv 236:211–222
Article  Google Scholar 

Smouse PE, Banks SC, Peakall R (2017) Converting quadratic entropy to diversity: Both animals and alleles are diverse, but some are more diverse than others. PLoS ONE 12:1–19
Article  CAS  Google Scholar 

Soulé ME (1985) What is conservation biology? A new synthetic discipline addresses the dynamics and problems of perturbed species, communities, and ecosystems. BioScience 35:727–734
Article  Google Scholar 

Stobo‐Wilson AM, Stokeld D, Einoder LD, Davies HF, Fisher A, Hill BM et al. (2020) Habitat structural complexity explains patterns of feral cat and dingo occurrence in monsoonal Australia. Divers Distrib 26:832–842
Article  Google Scholar 

Stobo-Wilson AM, Stokeld D, Einoder LD, Davies HF, Fisher A, Hill BM et al. (2020) Bottom-up and top-down processes influence contemporary patterns of mammal species richness in Australia’s monsoonal tropics. Biol Conserv 247:108638
Article  Google Scholar 

Storck-Tonon D, Peres CA (2017) Forest patch isolation drives local extinctions of Amazonian orchid bees in a 26years old archipelago. Biol Conserv 214:270–277
Article  Google Scholar 

Traill LW, Perhans K, Lovelock CE, Prohaska A, McFallan S, Rhodes JR et al. (2011) Managing for change: wetland transitions under sea-level rise and outcomes for threatened species. Divers Distrib 17:1225–1233
Article  Google Scholar 

Van Dyck S, Gynther I, Baker A (eds) (2013) Field companion to the mammals of Australia. New Holland Publishers, London, Sydney

Van Dyck S, Strahan R (2008) The mammals of Australia. 3rd edn. Reed New Holland, Chatswood, Australia

Vavrek M (2011) Fossil: palaeoecological and palaeogeographical analysis tools. Palaeontol Electron 14:16
Google Scholar 

Vladislav K (2018) lpsymphony: symphony integer linear programming solver in R. R Package Version 1.18.0. http://R-Forge.R-project.org/projects/rsymphony

von Takach Dukai B, Peakall R, Lindenmayer DB, Banks SC (2020) The influence of fire and silvicultural practices on the landscape-scale genetic structure of an Australian foundation tree species. Conserv Genet 21:231–246
Article  CAS  Google Scholar 

von Takach B, Scheele BC, Moore H, Murphy BP, Banks SC (2020) Patterns of niche contraction identify vital refuge areas for declining mammals. Divers Distrib 26:1467–1482
Article  Google Scholar 

Watts ME, Ball IR, Stewart RS, Klein CJ, Wilson K, Steinback C et al. (2009) Marxan with Zones: software for optimal conservation based land- and sea-use zoning. Environ Model Softw 24:1513–1521
Article  Google Scholar 

Winter DJ (2012) mmod: an R library for the calculation of population differentiation statistics. Mol Ecol Resour 12:1158–1160
CAS  PubMed  Article  PubMed Central  Google Scholar 

Woinarski JCZ, Braby MF, Burbidge AA, Coates D, Garnett ST, Fensham RJ et al. (2019) Reading the black book: the number, timing, distribution and causes of listed extinctions in Australia. Biol Conserv 239:108261
Article  Google Scholar 

Woinarski JC, Burbidge AA, Harrison P (2014) The action plan for Australian mammals 2012. CSIRO Publishing, Collingwood, Australia
Google Scholar 

Woinarski JCZ, Legge S, Fitzsimons JA, Traill BJ, Burbidge AA, Fisher A et al. (2011) The disappearing mammal fauna of northern Australia: context, cause, and response. Conserv Lett 4:192–201
Article  Google Scholar 

Woinarski JCZ, Milne DJ, Wanganeen G (2001) Changes in mammal populations in relatively intact landscapes of Kakadu National Park, Northern Territory, Australia. Austral Ecol 26:360–370
Article  Google Scholar 

Woinarski JCZ, Palmer C, Fisher A, Brennan K, Southgate R, Masters P (1999) Distributional patterning of mammals on the Wessel and English Company Islands, Arnhem Land, Northern Territory, Australia. Aust J Zool 47:87–111
Article  Google Scholar 

Zheng X, Levine D, Shen J, Gogarten SM, Laurie C, Weir BS (2012) A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics 28:3326–3328
CAS  PubMed  PubMed Central  Article  Google Scholar  More

1.
Darwin, C. On the Origin of Species by Means of Natural Selection Vol. 167 (John Murray, London, 1859).
Google Scholar 
2.
Kahlke, R.-D. The origin of Eurasian mammoth faunas (Mammuthus–Coelodonta faunal complex). Quatern. Sci. Rev. 96, 32–49 (2014).
ADS  Article  Google Scholar 

3.
Ashcroft, M. B. Identifying refugia from climate change. J. Biogeogr. 37, 1407–1413 (2010).
Google Scholar 

4.
Tarkhnishvili, D., Gavashelishvili, A. & Mumladze, L. Palaeoclimatic models help to understand current distribution of Caucasian forest species. Biol. J. Lin. Soc. 105, 231–248 (2012).
Article  Google Scholar 

5.
Hewitt, G. The genetic legacy of the Quaternary ice ages. Nature 405, 907–913 (2000).
ADS  CAS  Article  Google Scholar 

6.
Lumibao, C. Y., Hoban, S. M. & McLachlan, J. Ice ages leave genetic diversity ‘hotspots’ in Europe but not in Eastern North America. Ecol. Lett. 20, 1459–1468 (2017).
Article  Google Scholar 

7.
Williams, E. E. in Evolutionary Biology 47–89 (Springer, 1972).

8.
Peters, G. in Handbuch der Säugetiere Europas: Raubsäuger (Teil 1) (eds M. Stubbe & F. Krapp) 47–106 (AULA-Verlag, 1993).

9.
Leonard, J. A. et al. Megafaunal extinctions and the disappearance of a specialized wolf ecomorph. Curr. Biol. 17, 1146–1150 (2007).
CAS  Article  Google Scholar 

10.
Perri, A. A wolf in dog’s clothing: Initial dog domestication and Pleistocene wolf variation. J. Archaeol. Sci. 68, 1–4. https://doi.org/10.1016/j.jas.2016.02.003 (2016).
Article  Google Scholar 

11.
Weniger, G.-C. in The Pleistocene Old World: Regional Perspectives (ed Olga Soffer) 201–215 (Springer, 1987).

12.
Weniger, G.-C. Magdalenian settlement and subsistence in South-west Germany. Proc. Prehist. Soc. 53, 293–307. https://doi.org/10.1017/S0079497X0000623X (1987).
ADS  Article  Google Scholar 

13.
Taller, A., Bolus, M. & Conard, N. The Magdalenian of Hohle Fels Cave and the resettlement of the Swabian Jura after the LGM. in Modes de Contacts et de Déplacements au Paléolithique Eurasiatique/Modes of Contact and Mobility During the Eurasian Palaeolithic. ERAUL Vol. 140, 383–399 (2014).

14.
Maier, A. Population and settlement dynamics from the Gravettian to the Magdalenian. Mitteilungen Gesellschaft Urgeschichte 26, 83–101 (2017).
Google Scholar 

15.
Hulme-Beaman, A., Dobney, K., Cucchi, T. & Searle, J. B. An ecological and evolutionary framework for commensalism in anthropogenic environments. Trends Ecol. Evol. 31, 633–645. https://doi.org/10.1016/j.tree.2016.05.001 (2016).
Article  PubMed  Google Scholar 

16.
Baumann, C. et al. Dietary niche partitioning among Magdalenian canids in southwestern Germany and Switzerland. Quatern. Sci. Rev. 227, 106032 (2020).
Article  Google Scholar 

17.
Baumann, C., Bocherens, H., Drucker, D. G. & Conard, N. J. Fox dietary ecology as a tracer of human impact on Pleistocene ecosystems. PLoS ONE 15, e0235692. https://doi.org/10.1371/journal.pone.0235692 (2020).
CAS  Article  PubMed  PubMed Central  Google Scholar 

18.
Smith, B. D. A cultural niche construction theory of initial domestication. Biol. Theory 6, 260–271 (2011).
Article  Google Scholar 

19.
Zeder, M. A. Domestication as a model system for niche construction theory. Evol. Ecol. 30, 325–348 (2016).
Article  Google Scholar 

20.
Belyaev, D. K., Plyusnina, I. Z. & Trut, L. N. Domestication in the silver fox (Vulpes fulvus Desm): Changes in physiological boundaries of the sensitive period of primary socialization. Appl. Anim. Behav. Sci. 13, 359–370. https://doi.org/10.1016/0168-1591(85)90015-2 (1985).
Article  Google Scholar 

21.
Germonpré, M., Láznickova-Galetova, M., Sablin, M. V. & Bocherens, H. in Hybrid Communities, Biosocial Approaches to Domestication and Other Trans-Species Relationships Routledge Studies in Anthropology (eds C. Stépanoff & J.-D. Vigne) (Routledge, 2018).

22.
Thalmann, O. & Perri, A. Population Genetics 1–34 (Springer, Cham, 2018).
Google Scholar 

23.
MacHugh, D. E., Larson, G. & Orlando, L. Taming the past: Ancient DNA and the study of animal domestication. Annu. Rev. Anim. Biosci. 5, 329–351. https://doi.org/10.1146/annurev-animal-022516-022747 (2017).
CAS  Article  PubMed  Google Scholar 

24.
Frantz, L. A. et al. Genomic and archaeological evidence suggest a dual origin of domestic dogs. Science 352, 1228–1231 (2016).
ADS  CAS  Article  Google Scholar 

25.
Thalmann, O. et al. Complete mitochondrial genomes of ancient canids suggest a European origin of domestic dogs. Science 342, 871–874 (2013).
ADS  CAS  Article  Google Scholar 

26.
Bocherens, H. et al. Reconstruction of the Gravettian food-web at Předmostí I using multi-isotopic tracking (13C, 15N, 34S) of bone collagen. Quatern. Int. 359–360, 211–228. https://doi.org/10.1016/j.quaint.2014.09.044 (2015).  
Article  Google Scholar 

27.
Prassack, K. A., DuBois, J., Lázničková-Galetová, M., Germonpré, M. & Ungar, P. S. Dental microwear as a behavioral proxy for distinguishing between canids at the Upper Paleolithic (Gravettian) site of Předmostí, Czech Republic. J. Archaeol. Sci. 115, 105092 (2020).
Article  Google Scholar 

28.
Wilczyński, J. et al. Friend or foe? Large canid remains from Pavlovian sites and their archaeozoological context. J. Anthropol. Archaeol. 59, 101197. https://doi.org/10.1016/j.jaa.2020.101197 (2020).
Article  Google Scholar 

29.
Street, M., Napierala, H. & Janssens, L. The late Paleolithic dog from Bonn-Oberkassel in context. in The Late Glacial Burial from Oberkassel Revisited 253–274. (Verlag Phillip von Zabern, Darmstadt, 2015).

30.
Janssens, L. et al. A new look at an old dog: Bonn-Oberkassel reconsidered. J. Archaeol. Sci. 92, 126–138. https://doi.org/10.1016/j.jas.2018.01.004 (2018).
Article  Google Scholar 

31.
Rütimeyer, L. Die Knochenhöhle von Thayingen bei Schaffhausen. (F. Vieweg & Sohn, 1875).

32.
Napierala, H. & Uerpmann, H.-P. A ‘new’ palaeolithic dog from central Europe. Int. J. Osteoarchaeol. 22, 127–137. https://doi.org/10.1002/oa.1182 (2012).
Article  Google Scholar 

33.
Napierala, H. Die Tierknochen aus dem Kesslerloch: Neubearbeitung der paläolithischen Fauna (Baudepartement des Kantons Schaffhausen, Kantonsarchäologie Schaffhausen, 2008).
Google Scholar 

34.
Albrecht, G., Drautz, D. & Kind, J. Eine Station des Magdalénien in der Gnirshöhle bei Engen- Bittelbrunn im Hegau. Archäol. Korrespondenzblatt 7, 161–179 (1977).
Google Scholar 

35.
Germonpré, M., Lázničková-Galetová, M., Losey, R. J., Räikkönen, J. & Sablin, M. V. Large canids at the Gravettian Předmostí site, the Czech Republic: The mandible. Quatern. Int. 359–360, 261–279. https://doi.org/10.1016/j.quaint.2014.07.012 (2015).
Article  Google Scholar 

36.
Von den Driesch, A. A Guide to the Measurement of Animal Bones from Archaeological Sites: as Developed by the Institut für Palaeoanatomie, Domestikationsforschung und Geschichte der Tiermedizin of the University of Munich. Vol. 1 (Peabody Museum Press, 1976).

37.
Benecke, N. Archäozoologische Studien zur Entwicklung der Haustierhaltung. (Akademie Verlag, 1994).

38.
Ameen, C. et al. A landmark-based approach for assessing the reliability of mandibular tooth crowding as a marker of dog domestication. J. Archaeol. Sci. 85, 41–50. https://doi.org/10.1016/j.jas.2017.06.014 (2017).
Article  Google Scholar 

39.
Bocherens, H., Drucker, D., Billiou, D. & Moussa, I. Une nouvelle approche pour évaluer l’état de conservation de l’os et du collagène pour les mesures isotopiques (datation au radiocarbone, isotopes stables du carbone et de l’azote). l’Anthropologie 109, 557–567 (2005).

40.
Bocherens, H. et al. Isotopic evidence for dietary ecology of cave lion (Panthera spelaea) in North-Western Europe: Prey choice, competition and implications for extinction. Quatern. Int. 245, 249–261. https://doi.org/10.1016/j.quaint.2011.02.023 (2011).
Article  Google Scholar 

41.
Loog, L. et al. Ancient DNA suggests modern wolves trace their origin to a late Pleistocene expansion from Beringia. Mol. Ecol. 1–15 (2019).

42.
Nei, M. & Miller, J. C. A simple method for estimating average number of nucleotide substitutions within and between populations from restriction data. Genetics 125, 873–879 (1990).
CAS  Article  Google Scholar 

43.
Botigué, L. R. et al. Ancient European dog genomes reveal continuity since the Early Neolithic. Nat. Commun. 8, 1–11 (2017).
ADS  Article  Google Scholar 

44.
Nichols, R. Gene trees and species trees are not the same. Trends Ecol. Evol. 16, 358–364 (2001).
CAS  Article  Google Scholar 

45.
Ollivier, M. et al. Dogs accompanied humans during the Neolithic expansion into Europe. Biol. Lett. 14, 20180286 (2018).
Article  Google Scholar 

46.
Bergström, A. et al. Origins and genetic legacy of prehistoric dogs. Science 370, 557–564 (2020).
Article  Google Scholar 

47.
Larson, G. et al. Rethinking dog domestication by integrating genetics, archeology, and biogeography. Proc. Natl. Acad. Sci. 109, 8878 (2012).
ADS  CAS  Article  Google Scholar 

48.
Bocherens, H. et al. Paleobiological implications of the isotopic signatures (13C, 15N) of fossil mammal collagen in Scladina Cave (Sclayn, Belgium). Quatern. Res. 48, 370–380 (1997).
ADS  Article  Google Scholar 

49.
Bocherens, H. & Drucker, D. Trophic level isotopic enrichment of carbon and nitrogen in bone collagen: Case studies from recent and ancient terrestrial ecosystems. Int. J. Osteoarchaeol. 13, 46–53. https://doi.org/10.1002/oa.662 (2003).
Article  Google Scholar 

50.
Krajcarz, M. T., Krajcarz, M. & Bocherens, H. Collagen-to-collagen prey-predator isotopic enrichment ( Δ 13C, Δ 15N) in terrestrial mammals – A case study of a subfossil red fox den. Palaeogeogr. Palaeoclimatol. Palaeoecol. 490, 563–570. https://doi.org/10.1016/j.palaeo.2017.11.044 (2018).
Article  Google Scholar 

51.
von Seth, J., Niemann, J. & Dalén, L. in Paleogenomics 393–418 (Springer, 2018).

52.
Orlando, L. in Paleogenomics 325–351 (Springer, 2018).

53.
Janssens, L. A. A. From Wolf to Dog, Uitgever Niet Vastgesteld (2019).

54.
Zeder, M. A. The domestication of animals. J. Anthropol. Res. 68, 161–190 (2012).
Article  Google Scholar 

55.
Zeder, M. A. Pathways to animal domestication. in Biodiversity in Agriculture: Domestication, Evolution, and Sustainability, 227–259 (2012).

56.
Albrecht, G. Magdalénien-Inventare vom Petersfels: Siedlungsarchäologische Ergebnisse der Ausgrabungen 1974–1976. Vol. 6 (Verlag Archaeologica Venatoria, 1979).

57.
Albrecht, G. in Urgeschichte in Baden-Württemberg (ed Hansjürgen Müller-Beck) 331–353 (Theiss Verlag, 1983).

58.
Albrecht, G. & Berke, H. in De la Loire à l’Oder BAR International Series (ed Marcel Otte) 465–473 (1988).

59.
Albrecht, G., Wong, G. L. & Münzel, S. C. in „All der holden Hügel ist keiner mir fremd…“ Festschrift zum 65. Geburtstag von Claus-Joachim Kind. (eds M. Baales & C. Pasda) 301–310 (Archaeologica Venatoria, 2019).

60.
Nobis, G. Die Wildsäugetiere in der Umwelt des Menschen von Oberkassel bei Bonn und das Domestikationsproblem von Wölfen im Jungpaläolithikum. in Bonner Jahrbücher, 367–376 (1986).

61.
Boessneck, J., von den Driesch, A., Lepiksaar, J., Riek, G. & Storch, G. Das Paläolithikum der Brillenhöhle bei Blaubeuren (Schwäbische Alb) II: Die Jungpleistozänen Tierknochenfunde aus der Brillenhöhle. (Verlag Müller & Gräff, 1973).

62.
Münzel, S. C. in Geißenklösterle: Chronostratigraphie, Paläoumwelt und Subsistenz im Mittel- und Jungpaläolithikum der Schwäbischen Alb (eds Nicholas J. Conard, M. Bolus, & Susanne C. Münzel) 147–327 (Kerns Verlag, 2019).

63.
Team, R. C. R: A Language and Environment for Statistical Computing, https://www.R-project.org.

64.
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. https://doi.org/10.1111/j.1365-2656.2011.01806.x (2011).
Article  PubMed  Google Scholar 

65.
Stock, B. C. & Semmens, B. X. MixSIAR GUI User Manual v3.1. (2016).

66.
Cooper, A. & Poinar, H. N. Ancient DNA: Do it right or not at all. Science 289, 1139–1139 (2000).
CAS  Article  Google Scholar 

67.
Knapp, M. & Hofreiter, M. Next generation sequencing of ancient DNA: Requirements, strategies and perspectives. Genes 1, 227–243 (2010).
CAS  Article  Google Scholar 

68.
Peltzer, A. et al. EAGER: Efficient ancient genome reconstruction. Genome Biol. 17, 60 (2016).
Article  Google Scholar 

69.
Andrews, S. Babraham Bioinformatics (Babraham Institute, Cambridge, 2010).
Google Scholar 

70.
Schubert, M., Lindgreen, S. & Orlando, L. AdapterRemoval v2: Rapid adapter trimming, identification, and read merging. BMC Res. Notes 9, 1–7 (2016).
Article  Google Scholar 

71.
Ginolhac, A., Rasmussen, M., Gilbert, M. T. P., Willerslev, E. & Orlando, L. mapDamage: Testing for damage patterns in ancient DNA sequences. Bioinformatics 27, 2153–2155 (2011).
CAS  Article  Google Scholar 

72.
Skoglund, P. et al. Separating endogenous ancient DNA from modern day contamination in a Siberian Neandertal. Proc. Natl. Acad. Sci. 111, 2229–2234 (2014).
ADS  CAS  Article  Google Scholar 

73.
Skoglund, P., Ersmark, E., Palkopoulou, E. & Dalén, L. Ancient wolf genome reveals an early divergence of domestic dog ancestors and admixture into high-latitude breeds. Curr. Biol. 25, 1515–1519 (2015).
CAS  Article  Google Scholar 

74.
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).
CAS  Article  Google Scholar 

75.
Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K., von Haeseler, A. & Jermiin, L. S. ModelFinder: Fast model selection for accurate phylogenetic estimates. Nat. Methods 14, 587–589 (2017).
CAS  Article  Google Scholar 

76.
Nguyen, L.-T., Schmidt, H. A., Von Haeseler, A. & Minh, B. Q. IQ-TREE: A fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).
CAS  Article  Google Scholar 

77.
Lanfear, R., Frandsen, P. B., Wright, A. M., Senfeld, T. & Calcott, B. PartitionFinder 2: New methods for selecting partitioned models of evolution for molecular and morphological phylogenetic analyses. Mol. Biol. Evol. 34, 772–773 (2017).
CAS  PubMed  Google Scholar 

78.
Suchard, M. A. et al. Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10. Virus Evolut. 4, vey016 (2018).

79.
Gill, M. S. et al. Improving Bayesian population dynamics inference: A coalescent-based model for multiple loci. Mol. Biol. Evol. 30, 713–724 (2013).
CAS  Article  Google Scholar 

80.
Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior summarization in Bayesian phylogenetics using Tracer 1.7. Syst. Biol. 67, 901 (2018).

81.
Rambaut, A. FigTree 1.4. 2 Software. (Institute of Evolutionary Biology, Univ. Edinburgh, 2014).

82.
Librado, P. & Rozas, J. DnaSP v5: A software for comprehensive analysis of DNA polymorphism data. Bioinformatics 25, 1451–1452 (2009).
CAS  Article  Google Scholar 

83.
Nei, M. Molecular Evolutionary Genetics. (Columbia University Press, 1987). More

1.
Montzka SA, Dlugokencky EJ, Butler JH. Non-CO2 greenhouse gases and climate change. Nature. 2011;476:43–50.
CAS  PubMed  Article  Google Scholar 
2.
Ravishankara A, Daniel JS, Portmann RW. Nitrous oxide (N2O): the dominant ozone-depleting substance emitted in the 21st century. Science. 2009;326:123–5.
CAS  PubMed  Article  Google Scholar 

3.
Pachauri RK, Reisinger A, Climate Change 2007. Synthesis report. Contribution of Working Groups I, II and III to the fourth assessment report. Cambridge: Cambridge University Press; 2008.
Google Scholar 

4.
Masson-Delmotte V, Zhai P, Pörtner H-O, Roberts D, Skea J, Shukla PR, et al. An IPCC special report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty. Geneva: World Meteorological Organisation; 2018.

5.
Reay DS, Davidson EA, Smith KA, Smith P, Melillo JM, Dentener F, et al. Global agriculture and nitrous oxide emissions. Nat Clim Change. 2012;2:410–6.
CAS  Article  Google Scholar 

6.
Itakura M, Uchida Y, Akiyama H, Hoshino YT, Shimomura Y, Morimoto S, et al. Mitigation of nitrous oxide emissions from soils by Bradyrhizobium japonicum inoculation. Nat Clim Change. 2013;3:208–12.
CAS  Article  Google Scholar 

7.
Akiyama H, Yan X, Yagi K. Evaluation of effectiveness of enhanced‐efficiency fertilizers as mitigation options for N2O and NO emissions from agricultural soils: meta‐analysis. Glob Change Biol. 2010;16:1837–46.
Article  Google Scholar 

8.
Hu HW, Trivedi P, He JZ, Singh BK. Microbial nitrous oxide emissions in dryland ecosystems: mechanisms, microbiome and mitigation. Environ Microbiol. 2017;19:4808–28.
CAS  PubMed  Article  Google Scholar 

9.
Bakken LR, Frostegård Å. Sources and sinks for N2O, can microbiologist help to mitigate N2O emissions? Environ Microbiol. 2017;19:4801–5.
CAS  PubMed  Article  Google Scholar 

10.
Paustian K, Lehmann J, Ogle S, Reay D, Robertson GP, Smith P. Climate-smart soils. Nature. 2016;532:49–57.
CAS  PubMed  Article  Google Scholar 

11.
Wrage N, Velthof G, Van Beusichem M, Oenema O. Role of nitrifier denitrification in the production of nitrous oxide. Soil Biol Biochem. 2001;33:1723–32.
CAS  Article  Google Scholar 

12.
Shoun H, Kim D-H, Uchiyama H, Sugiyama J. Denitrification by fungi. FEMS Microbiol Lett. 1992;94:277–81.
CAS  Article  Google Scholar 

13.
Maeda K, Spor A, Edel-Hermann V, Heraud C, Breuil M-C, Bizouard F, et al. N2O production, a widespread trait in fungi. Sci Rep. 2015;5:1–7.
Article  CAS  Google Scholar 

14.
Mothapo NV, Chen H, Cubeta MA, Shi W. Nitrous oxide producing activity of diverse fungi from distinct agroecosystems. Soil Biol Biochem. 2013;66:94–101.
CAS  Article  Google Scholar 

15.
Wei W, Isobe K, Shiratori Y, Nishizawa T, Ohte N, Otsuka S, et al. N2O emission from cropland field soil through fungal denitrification after surface applications of organic fertilizer. Soil Biol Biochem. 2014;69:157–67.
CAS  Article  Google Scholar 

16.
Jirout J, Šimek M, Elhottová D. Fungal contribution to nitrous oxide emissions from cattle impacted soils. Chemosphere. 2013;90:565–72.
CAS  PubMed  Article  Google Scholar 

17.
Laughlin RJ, Stevens RJ. Evidence for fungal dominance of denitrification and codenitrification in a grassland soil. Soil Sci Soc Am J. 2002;66:1540–8.
CAS  Article  Google Scholar 

18.
Blagodatskaya E, Dannenmann M, Gasche R, Butterbach-Bahl K. Microclimate and forest management alter fungal-to-bacterial ratio and N2O-emission during rewetting in the forest floor and mineral soil of mountainous beech forests. Biogeochemistry. 2010;97:55–70.
CAS  Article  Google Scholar 

19.
Wankel SD, Ziebis W, Buchwald C, Charoenpong C, de Beer D, Dentinger J, et al. Evidence for fungal and chemodenitrification based N2O flux from nitrogen impacted coastal sediments. Nat Commun. 2017;8:1–11.
Article  CAS  Google Scholar 

20.
Fujimura R, Azegami Y, Wei W, Kakuta H, Shiratori Y, Ohte N, et al. Distinct community composition of previously uncharacterized denitrifying bacteria and fungi across different land-use types. Microbes Environ. 2020;35:ME19064.
PubMed Central  Article  PubMed  Google Scholar 

21.
Scheu S, Ruess L, Bonkowski M. Interactions between microorganisms and soil micro-and mesofauna. Microorganisms in soils: roles in genesis and functions. Berlin, Germany: Springer; 2005. p. 253–75.
Google Scholar 

22.
Coleman DC, Callaham MA, Crossley Jr DA. In: Fundamentals of soil ecology. 3rd edn. Burlington, USA: Academic Press; 2017. p. 106–21.

23.
Whitford W, Sobhy H. Effects of repeated drought on soil microarthropod communities in the northern Chihuahuan Desert. Biol Fertil Soils. 1999;28:117–20.
Article  Google Scholar 

24.
Benckiser G. Fauna in soil ecosystems: recycling processes, nutrient fluxes, and agricultural production. New York, USE: CRC Press; 1997. p. 225–45.

25.
Crossley D Jr, Mueller BR, Perdue JC. Biodiversity of microarthropods in agricultural soils: relations to processes. Agric Ecosyst Environ. 1992;40:37–46.
Article  Google Scholar 

26.
Behan-Pelletier VM. Oribatid mite biodiversity in agroecosystems: role for bioindication. Agric Ecosyst Environ. 1999;74:411–23.
Article  Google Scholar 

27.
Lindberg N, Engtsson JB, Persson T. Effects of experimental irrigation and drought on the composition and diversity of soil fauna in a coniferous stand. J Appl Ecol. 2002;39:924–36.
Article  Google Scholar 

28.
De Ruiter P, Moore J, Zwart K, Bouwman L, Hassink J, Bloem J, et al. Simulation of nitrogen mineralization in the below-ground food webs of two winter wheat fields. J Appl Ecol. 1993;30:95–106.
Article  Google Scholar 

29.
Thakur MP, van Groenigen JW, Kuiper I, De Deyn GB. Interactions between microbial-feeding and predatory soil fauna trigger N2O emissions. Soil Biol Biochem. 2014;70:256–62.
CAS  Article  Google Scholar 

30.
Porre RJ, van Groenigen JW, De Deyn GB, de Goede RG, Lubbers IM. Exploring the relationship between soil mesofauna, soil structure and N2O emissions. Soil Biol Biochem. 2016;96:55–64.
CAS  Article  Google Scholar 

31.
Johari K, Saman N, Song ST, Chin CS, Kong H, Mat H. Adsorption enhancement of elemental mercury by various surface modified coconut husk as eco-friendly low-cost adsorbents. Int Biodeterior Biodegradation 2016;109:45–52.
CAS  Article  Google Scholar 

32.
Cardoen D, Joshi P, Diels L, Sarma PM, Pant D. Agriculture biomass in India: part 2. Post-harvest losses, cost and environmental impacts. Resour Conserv Recycl. 2015;101:143–53.
Article  Google Scholar 

33.
Crowther TW, Boddy L, Jones TH. Functional and ecological consequences of saprotrophic fungus–grazer interactions. ISME J. 2012;6:1992–2001.
CAS  PubMed  PubMed Central  Article  Google Scholar 

34.
Chu H, Hosen Y, Yagi KNO. N2O, CH4 and CO2 fluxes in winter barley field of Japanese Andisol as affected by N fertilizer management. Soil Biol Biochem. 2007;39:330–9.
CAS  Article  Google Scholar 

35.
Wei W, Isobe K, Shiratori Y, Nishizawa T, Ohte N, Ise Y, et al. Development of PCR primers targeting fungal nirK to study fungal denitrification in the environment. Soil Biol Biochem. 2015;81:282–6.
CAS  Article  Google Scholar 

36.
Wei W, Isobe K, Nishizawa T, Zhu L, Shiratori Y, Ohte N, et al. Higher diversity and abundance of denitrifying microorganisms in environments than considered previously. ISME J. 2015;9:1954–65.
CAS  PubMed  PubMed Central  Article  Google Scholar 

37.
Gao N, Shen W, Camargo E, Shiratori Y, Nishizawa T, Isobe K, et al. Nitrous oxide (N2O)-reducing denitrifier-inoculated organic fertilizer mitigates N2O emissions from agricultural soils. Biol Fertil Soils. 2017;53:885–98.
CAS  Article  Google Scholar 

38.
Shen W, Xue H, Gao N, Shiratori Y, Kamiya T, Fujiwara T, et al. Effects of copper on nitrous oxide (N2O) reduction in denitrifiers and N2O emissions from agricultural soils. Biol Fertil Soils. 2020;56:39–51.
CAS  Article  Google Scholar 

39.
Haarløv N. A new modification of the Tullgren apparatus. J Anim Ecol. 1947;16:115–21.
Article  Google Scholar 

40.
Krantz GW, Walter DE. A manual of acarology. 3rd edn. Lubbock, TX: Texas Tech University Press; 2009.
Google Scholar 

41.
Liyanage MD, Jayasekara KS, Fernandopulle MN. Effects of application of coconut husk and coir dust on the yield of coconut. Cocos. 1993;9:15–22.
Article  Google Scholar 

42.
Romero GQ, Benson WW. Biotic interactions of mites, plants and leaf domatia. Curr Opin Plant Biol. 2005;8:436–40.
CAS  PubMed  Article  Google Scholar 

43.
Grostal R, O’Dowd DJ. Plants, mites and mutualism: leaf domatia and the abundance and reproduction of mites on Viburnum tinus (Caprifoliaceae). Oecologia. 1994;97:308–15.
PubMed  Article  Google Scholar 

44.
Norton AP, English-Loeb G, Belden E. Host plant manipulation of natural enemies: leaf domatia protect beneficial mites from insect predators. Oecologia. 2001;126:535–42.
PubMed  Article  Google Scholar 

45.
Norton AP, English-Loeb G, Gadoury D, Seem RC. Mycophagous mites and foliar pathogens: leaf domatia mediate tritrophic interactions in grapes. Ecology. 2000;81:490–9.
Article  Google Scholar 

46.
Behan VM, Hill S. Feeding habits and spore dispersal of oribatid mites in the North American arctic. Rev Ecol Biol Sol. 1978;15:497–516.
Google Scholar 

47.
Seastedt T. The role of microarthropods in decomposition and mineralization processes. Annu Rev Entomol. 1984;29:25–46.
Article  Google Scholar 

48.
Singh BK, Trivedi P. Microbiome and the future for food and nutrient security. Microb Biotechnol. 2017;10:50–3.
PubMed  PubMed Central  Article  Google Scholar  More

1.
Avtar, R., Tripathi, S., Aggarwal, A. K. & Kumar, P. Population–Urbanization–Energy nexus: A review. Resources 8(3), 136 (2019).
Article  Google Scholar 
2.
Fahrig, L. Effects of habitat fragmentation on biodiversity. Annu. Rev. Ecol. Evol. S. 34, 487–515 (2003).
Article  Google Scholar 

3.
Macarthur, R. H. & Wilson, E. O. The theory of island biogeography (Princeton Univ Press, Princeton, 1967).
Google Scholar 

4.
Levins, R. Some demographic and genetic consequences of environmental heterogeneity for biological control. Ann. Entomol. Soc. Am. 15, 237–240 (1969).
Google Scholar 

5.
Hobbs, R. J. The role of corridor in the conservation, solution or bandwagon?. Tree 7(11), 389–392 (1992).
CAS  PubMed  Google Scholar 

6.
Badola, R. Economic assessment of human–forest interrelationship in the forest corridor connecting the Rajaji and Corbett national parks. Ph.D. Thesis, Jiwaji Univ, Gwalior (1997).

7.
Badola, R. Attitudes of local people towards conservation and alternatives to forest resources: A case study from the lower Himalayas. Biodivers. Conserv. 7, 1245–1259 (1998).
Article  Google Scholar 

8.
MacDonald, M. A. The role of corridors in biodiversity conservation in production forest landscapes: A literature review. Tasforest 14, 41–52 (2003).
Google Scholar 

9.
Ament, R., Callahan, R., McClure, M., Reuling, M. & Tabor, G. Wildlife Connectivity: Fundamentals for Conservation Action (Centre for large landscape conservation, Bozeman, 2014).
Google Scholar 

10.
Thompson, I., Mackey, B., McNulty, S. & Mosseler, A. Forest resilience, biodiversity, and climate change. A synthesis of the biodiversity/resilience/stability relationship in forest ecosystems. Technical Series No. 43, Secretariat of the Convention on Biological Diversity, Montreal. https://www.cbd.int/doc/publications/cbd-ts-43-en.pdf (2009).

11.
Qureshi, Q., Saini, S., Basu, P., Gopal, R., Raza, R. & Jhala, Y. Connecting tiger populations for long-term conservation. National Tiger Conservation Authority & Wildlife Institute of India, Dehradun. https://wii.gov.in/images/images/documents/connecting_tiger.pdf (2014).

12.
Menon, V. et al. Right of Passage: Elephant Corridors of India (Wildlife Trust of India, New Delhi, 2017).
Google Scholar 

13.
Kumar, A., Bargali, H. S., David, A. & Edgaonkar, A. Pattern of crop raiding by wild ungulates and elephants in Ramnagar Forest Division, Uttarakhand. Hum.-Wildl. Interact. 11(1), 41–49 (2017).
CAS  Google Scholar 

14.
Bargali, H. S. & Ahmed, T. Pattern of livestock depredation by tiger (Panthera tigris) and leopard (Panthera pardus) in and around Corbett Tiger Reserve, Uttarakhand, India. PLoS ONE 13(5), e0195612 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

15.
Nyhus, P. J. Human–wildlife conflict and coexistence. Annu. Rev. Environ. Resour. 41, 143–171 (2016).
Article  Google Scholar 

16.
Webber, A. D., Hill, C. M. & Reynolds, V. Assessing the failure of a community-based human–wildlife conflict mitigation project in Budongo Forest Reserve, Uganda. Oryx 41(2), 177–184 (2007).
Article  Google Scholar 

17.
Athreya, V., Odden, M., Linnell, J. D. C. & Karanth, K. U. Translocation as a tool for mitigating conflict with leopards in human-dominated landscapes of India. Conserv. Biol. 25(1), 133–141 (2010).
PubMed  Article  Google Scholar 

18.
Ogra, M. & Badola, R. Compensating human–wildlife conflict in protected area communities: Ground-level perspectives from Uttarakhand, India. Hum. Ecol. 36(5), 717–729 (2008).
Article  Google Scholar 

19.
Madhusudan, M. D. Living amidst large wildlife: Livestock and crop depredation by large mammals in the interior villages of Bhadra Tiger Reserve, South India. Environ. Manage. 31(4), 466–475 (2003).
CAS  PubMed  Article  Google Scholar 

20.
Barua, M., Bhagwat, S. A. & Jadhav, S. The hidden dimensions of human–wildlife conflict: Health impacts, opportunity and transaction costs. Biol. Conserv. 157, 309–316 (2013).
Article  Google Scholar 

21.
Mishra, C. et al. The role of incentive programs in conserving the snow leopard. Conserv. Biol. 17(6), 1512–1520 (2003).
Article  Google Scholar 

22.
Pagiola, S. Payments for environmental services in Costa Rica. Ecol. Econ. 65(4), 712–724 (2008).
Article  Google Scholar 

23.
Naeem, B. S. et al. Get the science right when paying for nature’s services. Science 347(6227), 1206–1207 (2015).
CAS  PubMed  Article  Google Scholar 

24.
Dickman, A. J., Macdonald, E. & Macdonald, D. W. A review of financial instruments to pay for predator conservation and encourage human–carnivore coexistence. Proc. Natl. Acad. Sci. USA 108(34), 13937–13944 (2011).
CAS  PubMed  Article  Google Scholar 

25.
Greiber, T. Payments for ecosystem services: Legal and institutional frameworks. IUCN Environmental Policy and Law Paper No. 78, Gland, Switzerland. https://www.iucn.org/content/payments-ecosystem-services-legal-and-institutional-frameworks-2 (2009).

26.
Wunder, S. Are direct payments for environmental services spelling doom for sustainable forest management in the tropics?. Ecol. Soc. 11(2), 23 (2006).
Article  Google Scholar 

27.
Bishop, J. & Huberman, D. Ecosystems, economics and payment for ecosystem services in Global Biodiversity Finance: The case for international payments for ecosystem services (eds. Bishop, J. & Hill, C.) 13-33. (Edward Elgar Publishing 2014).

28.
Badola, R., Barthwal, S. & Hussain, S. A. Attitudes of local communities towards conservation of mangrove forests, a case study from the east coast of India. Estuar. Coast. Shelf. S. 96, 188–196 (2012).
Article  Google Scholar 

29.
Ogra, M. V. Human–wildlife conflict and gender in protected area borderlands: A case study of costs, perceptions, and vulnerabilities from Uttarakhand (Uttaranchal), India. Geoforum 39(3), 1408–1422 (2008).
Article  Google Scholar 

30.
Mackenzie, C. A. & Ahabyona, P. Elephants in the garden: Financial and social costs of crop raiding. Ecol. Econom. 75, 72–82 (2012).
Article  Google Scholar 

31.
Semwal, R. L. The Terai Arc Landscape in India: Securing protected areas in the face of Global Change. Forest and biodiversity conservation programme, World Wide Fund for Nature- India, New Delhi. https://www.iucn.org/content/terai-arc-landscape-india-securing-protected-areas-face-global-change (2005).

32.
Kolipakam, V., Singh, S., Pant, B., Qureshi, Q. & Jhala, Y. V. Genetic structure of tigers (Panthera tigris tigris) in India and its implications for conservation. Glob. Ecol. Conserv. 20, e00710 (2019).
Article  Google Scholar 

33.
Johnsingh, A. J. T. et al. Conservation Status of Tiger and Associated Species in the Terai Arc Landscape, India (Wildlife Institute of India, Dehradun, 2004).
Google Scholar 

34.
Santiapillai, C. & Widodo, S. R. Why do elephants raid crops in Sumatra. Gajah 11, 55–58 (1993).
Google Scholar 

35.
Barnes, R. F. W. The conflict between humans and elephants in central African forests. Mamm. Rev. 26, 67–80 (1996).
Article  Google Scholar 

36.
Chaudhry, S., Veeraswami, G. G., Mazumdar, K. & Samal, P. K. Conflict identification and prioritization in proposed Tsangyang Gyatso Biosphere Reserve, eastern Himalaya, India. J. Bombay Nat. Hist. Soc. 107(3), 189–197 (2010).
Google Scholar 

37.
Sitati, N. W., Walpole, M. J. & Leader-Williams, N. Factors affecting susceptibility of farms to crop raiding by African elephants, using a predictive model to mitigate conflict. J. Appl. Ecol. 42(6), 1175–1182 (2005).
Article  Google Scholar 

38.
Joel, M., Edward, A., Doreen, R. & Biryahwaho, B. Management of conservation based conflicts in South western Uganda. Draft report prepared by Koalition on behalf of Eastern and Central Africa Programme for Agricultural Policy Analysis, Association for Strengthening Agricultural Research in Eastern and Central Africa. https://idl-bnc-idrc.dspacedirect.org/handle/10625/42466 (2005).

39.
Roy, P. B. & Sah, R. Economic loss analysis of crop yield due to elephant raiding, a case study of Buxa Tiger Reserve (West), West Bengal. J. Econ. Sustain. Dev. 3(10), 83–88 (2012).
Google Scholar 

40.
Lahm, S. A nationwide survey of crop raiding by elephants and other species in Gabon. Pachyderm 21, 69–77 (1996).
Google Scholar 

41.
Sangay, T. & Vernes, K. Human–wildlife conflict in Kingdom of Bhutan: Pattern of livestock predation by large mammalian carnivores. Biol. Conserv. 141, 1272–1282 (2008).
Article  Google Scholar 

42.
Patterson, B. D., Kasiki, S. M., Selempo, E. & Kays, R. W. Livestock predation by Lion (Panthera leo) and other carnivores on ranches neighbouring Tsavo National Park, Kenya. Biol. Conserv. 119, 297–310 (2004).
Article  Google Scholar 

43.
Seidensticker, J. On the ecological separation between tiger and leopard. Biotropica 8, 225–234 (1976).
Article  Google Scholar 

44.
Johnsingh, A. J. T. Prey selection in three large sympatric carnivores in Bandipur. Mammalia 56, 517–526 (1992).
Article  Google Scholar 

45.
Karanth, K. K., Gopalaswamy, A. M., DeFries, R. & Ballal, N. Assessing patterns of human–wildlife conflicts and compensation around a central Indian protected area. PLoS ONE 7(12), e50433 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

46.
Karanth, K. K., Naughton-Treves, L., DeFries, R. & Gopalaswamy, A. M. Living with wildlife and mitigating conflicts around three Indian protected areas. Environ. Manage. 52(6), 1320–1332 (2013).
PubMed  Article  Google Scholar 

47.
Gore, M. L., Knuth, B. A., Scherer, C. W. & Curtis, P. D. Evaluating a conservation investment designed to reduce human–wildlife conflict. Conserv. Lett. 1, 136–145 (2008).
Article  Google Scholar 

48.
Karanth, K. K., Gupta, S. & Vanamamalai, A. Compensation payments, procedures and policies towards human-wildlife conflict management: Insights from India. Biol. Conserv. 227, 383–389 (2018).
Article  Google Scholar 

49.
Nyhus, P. J., Osofsky, S. A., Ferraro, P., Madden, F., & Fischer, H. Bearing the costs of human-wildlife conflict: The challenges of compensation schemes in People and Wildlife, Conflict or Co-existence? (eds. Woofroffe, R., Thirgood, S. & Rabinowitz, A.) 107–121 (Cambridge University Press, 2005).

50.
Grima, N., Singh, S. J., Smetschka, B. & Ringhofer, L. Payment for Ecosystem Services (PES) in Latin America: Analysing the performance of 40 case studies. Ecosyst. Serv. 17, 24–32 (2016).
Article  Google Scholar 

51.
Ruggiero, P. G., Metzger, J. P., Tambosi, L. R. & Nichols, E. Payment for ecosystem services programs in the Brazilian Atlantic Forest: Effective but not enough. Land Use Policy 82, 283–291 (2019).
Article  Google Scholar 

52.
Xian, J., Xia, C. & Cao, S. Cost–benefit analysis for China’s Grain for Green Program. Ecol. Eng. 151, 105850 (2020).
Article  Google Scholar 

53.
Tuanmu, M. N. et al. Effects of payments for ecosystem services on wildlife habitat recovery. Conserv. Biol. 30(4), 827–835 (2016).
PubMed  Article  Google Scholar 

54.
Uchida, E., Rozelle, S. & Xu, J. Conservation payments, liquidity constraints, and off-farm labor: Impact of the Grain-for-Green Program on rural households in China. Am. J. Agric. 91(1), 70–86 (2009).
Article  Google Scholar 

55.
Osano, P. M. et al. Why keep lions instead of livestock? Assessing wildlife tourism-based payment for ecosystem services involving herders in the Maasai Mara, Kenya. Nat. Resour. Forum 37(4), 242–256 (2013).
Article  Google Scholar 

56.
Ogra, M. Attitudes toward resolution of human–wildlife conflict among forest-dependent agriculturalists near Rajaji National Park, India. Hum. Ecol. 37(2), 161–177 (2009).
Article  Google Scholar 

57.
Ghazoul, J., Butler, R. A., Mateo-Vega, J. & Koh, L. P. REDD: A reckoning of environment and development implications. Trends Ecol. Evol. 25(7), 396–402 (2010).
PubMed  Article  Google Scholar 

58.
Rajasekharan Pillai, K. & Suchintha, B. Women empowerment for biodiversity conservation through self help groups: A case from Periyar Tiger Reserve, Kerala, India. Int. J. Agric. Resour. Gov. Ecol. 5(4), 338–355 (2006).
Google Scholar 

59.
Planning Commission. Eleventh Five Year Plan 2007-12. Agriculture, Rural Development, Industry, Services and Physical Infrastructure. Volume-III. Planning Commission, Government of India (Oxford University Press, 2008).

60.
Bulte, E. H., van Kooten, G. C., & Swanson, T. Economic incentives and wildlife conservation. 1-3 December. CITES, Geneva, Switzerland. https://cites.org/sites/default/files/eng/prog/economics/CITES-draft6-final.pdf (2008).

61.
Poudel, J., Munn, I. A. & Henderson, J. E. Economic contributions of wildlife watching recreation expenditures (2006 & 2011) across the US south: An input–output analysis. J. Outdoor Recreat. Tour. 17, 93–99 (2017).
Article  Google Scholar 

62.
Andrade, G. S. M. & Rhodes, J. R. Protected areas and local communities: An inevitable partnership toward successful conservation strategies?. Ecol. Soc. 17, 14 (2012).
Article  Google Scholar 

63.
Babu, S., Singh, S., Goyal, S. P. & Shruti, M. Dynamics of Asian elephant habitat in Shivalik landscape and environs of Kalesar–Rajaji–Corbett protected area network. Int. J. Ecol. Environ. Sci. 45(2), 191–203 (2019).
Google Scholar 

64.
Lee, W. S., Jin, W. H. & Chung, H. A study of categorization and development strategy formulation for rural village districts in the greenbelt released area. Int. J. Urban Sci. 15(2), 93–106 (2011).
Article  Google Scholar 

65.
Varian, H. R. Intermediate Microeconomics, A Modern Approach 8th edn. (W.W. Norton & Company Inc., New York, 2010).
Google Scholar 

66.
Chen, Z. M. et al. Net ecosystem services value of wetland, environmental economic account. Commun. Nonlinear Sci. 14(6), 2837–2843 (2009).
Article  Google Scholar 

67.
Asian Development Bank. Cost-Benefit Analysis for Development: A Practical Guide. (Asian Development Bank, 2013). https://www.adb.org/documents/cost-benefit-analysis-development-practical-guide. More

1.
Baldrian, P. et al. Enzyme activities and microbial biomass in topsoil layer during spontaneous succession in spoil heaps after brown coal mining. Soil Biol. Biochem. 40, 2107–2215. https://doi.org/10.1016/j.soilbio.2008.02.019 (2008).
CAS  Article  Google Scholar 
2.
Gómez-Sagasti, M. T. et al. Microbial monitoring of the recovery of soil quality during heavy metal phytoremediation. Water Air Soil Poll. 223, 3249–3262. https://doi.org/10.1007/s11270-012-1106-8 (2012).
ADS  CAS  Article  Google Scholar 

3.
Yang, Y. Z., Liu, S., Zheng, D. & Feng, S. Effect of cadmium, zinc and lead on soil enzymes activities. J. Environ. Sci. 18, 1135–1141. https://doi.org/10.1016/S1001-0742(06)60051-X (2006).
Article  Google Scholar 

4.
Sheoran, V., Sheoran, A. S. & Poonia, P. Soil reclamation of abandoned mine land by revegetation: A review. Int. J. Soil Sedim. Water 3, 1–20 (2010).
Google Scholar 

5.
Vahed, H., Shahinrokhsar, P. & Rezaei, M. Influence of some soil properties and temperature on urease activity in wetland rice soils. Am.-Euras. J. Agric. Environ. Sci. 11, 310–313 (2011).
Google Scholar 

6.
Błońska, E. & Januszek, K. Usability of enzyme activity in the estimation of forest soil quality. Folia For. Polonica Ser. A 55, 18–26. https://doi.org/10.2478/ffp-2013-0003 (2013).
Article  Google Scholar 

7.
Zhang, T., Wan, S., Kang, Y. & Feng, H. Urease activity and its relationships to soil physiochemical soil properties in a highly saline-sodic soil. J. Soil Sci. Plant. Nut. 14, 304–315. https://doi.org/10.4067/s0718-95162014005000025 (2014).
Article  Google Scholar 

8.
Zhang, Y. et al. Soil bacterial and fungal diversity differently correlated with soil biochemistry in alpine grassland ecosystems in response to environmental changes. Sci. Rep. 7, 43077. https://doi.org/10.1038/srep43077 (2017).
ADS  Article  PubMed  PubMed Central  Google Scholar 

9.
Zhang, Q. M. et al. Effects of fomesafen on soil enzyme activity, microbial population, and bacterial community composition. Environ. Monit. Assess. 186, 2801–2812. https://doi.org/10.1007/s10661-013-3581-9 (2014).
CAS  Article  PubMed  Google Scholar 

10.
Sarathchandra, S., Perrott, K. & Upsdell, M. Microbiological and biochemical characteristics of a range of New Zealand soils under established pasture. Soil Biol. Biochem. 16, 177–183. https://doi.org/10.1016/0038-0717(84)90109-3 (1984).
CAS  Article  Google Scholar 

11.
Fernández-Calviño, D. et al. Enzyme activities in vineyard soils long-term treated with copper-based fungicides. Soil Biol. Biochem. 42, 2119–2127. https://doi.org/10.1016/j.soilbio.2010.08.007 (2010).
CAS  Article  Google Scholar 

12.
Bartelt-Ryser, J., Joshi, J., Schmid, B., Brandl, H. & Balser, T. Soil feedbacks of plant diversity on soil microbial communities and subsequent plant growth. Perspect. Plant. Ecol. 7, 27–49. https://doi.org/10.1016/j.ppees.2004.11.002 (2005).
Article  Google Scholar 

13.
Kao-Kniffin, J. T. & Balser, T. C. Elevated CO2 differentially alters belowground plant and soil microbial community structure in reed canary grass-invaded experimental wetlands. Soil Biol. Biochem. 39, 517–525. https://doi.org/10.1016/j.soilbio.2006.08.024 (2007).
CAS  Article  Google Scholar 

14.
Eisenhauer, N. et al. Root biomass and exudates link plant diversity with soil bacterial and fungal biomass. Sci. Rep. 7, 44641. https://doi.org/10.1038/srep44641 (2017).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

15.
Broughton, L. C. & Gross, K. L. Patterns of diversity in plant and soil microbial communities along a productivity gradient in a Michigan old-field. Oecologia 125, 420–427. https://doi.org/10.1007/s004420000456 (2000).
ADS  CAS  Article  PubMed  Google Scholar 

16.
Stephan, A., Meyer, A. H. & Schmid, B. Plant diversity affects culturable soil bacteria in experimental grassland communities. J. Ecol. 86, 988–998. https://doi.org/10.1046/j.1365-2745.2000.00510.x (2000).
Article  Google Scholar 

17.
Zak, D. R., Holmes, W. E., White, D. C., Peacock, A. D. & Tilman, D. Plant diversity, soil microbial communities, and ecosystem function: Are there any links?. Ecology 84, 2042–2050. https://doi.org/10.1890/02-0433 (2003).
Article  Google Scholar 

18.
Eisenhauer, N. et al. Plant diversity effects on soil microorganisms support the singular hypothesis. Ecology 91, 485–496. https://doi.org/10.1890/08-2338.1 (2010).
CAS  Article  PubMed  Google Scholar 

19.
Lange, M. et al. Biotic and abiotic properties mediating plant diversity effects on soil microbial communities in an experimental grassland. PLoS ONE 9, e96182. https://doi.org/10.1371/journal.pone.0096182 (2014).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

20.
Wu, J. et al. Vegetation degradation impacts soil nutrients and enzyme activities in wet meadow on the Qinghai-Tibet Plateau. Sci. Rep. 10, 21271. https://doi.org/10.1038/s41598-020-78182-9 (2020).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

21.
Mocek-Płóciniak, A. Utilisation of enzymatic activity for the evaluation of the impact of anthropogenic changes caused by heavy metals in soil environment. Nauka Przyr. Technol. 4, 1–10 (2010).
Google Scholar 

22.
Kot, A. & Frąc, M. Methods used in the evaluation of the organic wastes influence on soil microbial activity. Post. Mikrobiol. 53, 183–193 (2014).
Google Scholar 

23.
Kuziemska, B. Enzymatic activity of nickel contaminated soils. Teka. Kom. Ochr. Kszt. Środ. Przyr. 11, 77–89 (2014).
Google Scholar 

24.
Kourtev, P. S., Ehrenfeld, J. G. & Haggblom, M. Exotic plant species alter the microbial community structure and function in the soil. Ecology 83, 3152–3166. https://doi.org/10.1890/0012-9658(2002)083(3152:epsatm)2.0.co;2 (2002).
Article  Google Scholar 

25.
Pei, S. F., Fu, H. & Wan, C. G. Changes in soil properties and vegetation following exclosure and grazing in degraded Alxa desert steppe of Inner Mongolia, China. Agric. Ecosyst. Environ. 124, 33–39. https://doi.org/10.1016/j.agee.2007.08.008 (2008).
Article  Google Scholar 

26.
Wang, C., Wang, G., Liu, W. & Wu, P. The effect of plant-soil-enzyme interaction on plant composition, biomass, diversity of alpine meadows in the Qinghai-Tibetan Plateau. Int. J. Ecol. 2011, 1–10. https://doi.org/10.1155/2011/180926 (2011).
Article  Google Scholar 

27.
Wyszkowska, J. Effect of soil contamination with Treflan 480 EC on biochemical properties of soil. Pol. J. Environ. Stud. 11, 71–78 (2002).
CAS  Google Scholar 

28.
Aon, M. A. & Colaneri, A. C. II. Temporal and spatial evolution of enzymatic activities and physico-chemical properties in an agricultural soil. Appl. Soil. Ecol. 18, 255–270. https://doi.org/10.1016/s0929-1393(01)00161-5 (2001).
Article  Google Scholar 

29.
Li, J. J., Zheng, Y. M., Yan, J. X., Li, H. J. & He, J. Z. Succession of plant and soil microbial communities with restoration of abandoned land in the Loess Plateau, China. J. Soil. Sediment 13, 760–769. https://doi.org/10.1007/s11368-013-0652-z (2013).
Article  Google Scholar 

30.
Heděnec, P. et al. Enzyme activity of topsoil layer on reclaimed and unreclaimed post-mining sites. Biol. Commun. 62, 19–25. https://doi.org/10.21638/11701/spbu03.2017.103 (2017).
Article  Google Scholar 

31.
Lia, J., Zhoub, X., Yan, J., Lia, H. & He, J. Effects of regenerating vegetation on soil enzyme activity and microbial structure in reclaimed soils on a surface coalmine site. Appl. Soil Ecol. 87, 56–62. https://doi.org/10.1016/j.apsoil.2014.11.010 (2015).
Article  Google Scholar 

32.
Kompała-Bąba, A. et al. Do the dominant plant species impact the substrate and vegetation composition of post-coal mining spoil heaps?. Ecol. Eng. 143, 105685. https://doi.org/10.1016/j.ecoleng.2019.105685 (2020).
Article  Google Scholar 

33.
Kowarik, I. Novel urban ecosystems, biodiversity, and conservation. Environ. Pollut. 159, 1974–1983. https://doi.org/10.1016/j.envpol.2011.02.022 (2011).
CAS  Article  PubMed  Google Scholar 

34.
Frouz, J. & Nováková, A. Development of soil microbial properties in topsoil layer during spontaneous succession in heaps after brown coal mining in relation to humus microstructure development. Geoderma 129, 54–64. https://doi.org/10.1016/j.geoderma.2004.12.033 (2005).
ADS  Article  Google Scholar 

35.
Frouz, J. et al. The effect of topsoil removal in restored heathland on soil fauna, topsoil microstructure and cellulose: Implication for ecosystem restoration. Biodivers. Conserv. 18, 3963–3978. https://doi.org/10.1007/s10531-009-9692-5 (2008).
Article  Google Scholar 

36.
Cabała, J. M., Cmiel, S. R. & Idziak, A. F. Environmental impact of mining activity inthe Upper Silesian Coal Basin (Poland). Geol. Belg. 7, 225–229 (2004).
Google Scholar 

37.
Kompała-Bąba, A. et al. Vegetation diversity on coal mine spoil heaps—How important is the texture of the soil substrate?. Biologia 74, 419–436. https://doi.org/10.2478/s11756-019-00218-x (2019).
CAS  Article  Google Scholar 

38.
Woźniak, G. Diversity of vegetation on coal-mine heaps of the Upper Silesia (Poland) (Instytut Botaniki im. W. Szafera, 2010).
Google Scholar 

39.
Bąba, W. et al. Arbuscular mycorrhizal fungi (AMF) root colonization dynamics of Molinia caerulea (L.) Moench. in grasslands and post-industrial sites. Ecol. Eng. 95, 817–827. https://doi.org/10.1016/j.ecoleng.2016.07.013 (2016).
Article  Google Scholar 

40.
Pietrzykowski, M., Socha, J. & van Doorn, N. S. Linking heavy metal bioavailability (Cd, Cu, Zn and Pb) in Scots pine needles to soil properties in reclaimed mine areas. Sci. Total Environ. 470, 501–510. https://doi.org/10.1016/j.scitotenv.2013.10.008 (2014).
ADS  CAS  Article  PubMed  Google Scholar 

41.
Braun-Blanquet, J. Grundzüge der Vegetationskunde 3rd edn. (Springer, 1964).
Google Scholar 

42.
Bonham, C. D. Measurements for Terrestrial Vegetation 2nd edn. (Wiley, 2013).
Google Scholar 

43.
Tichý, L. & Holt, J. JUICE Program for Management, Analysis and Classifications of Ecological Data (Masaryk University, 2006).
Google Scholar 

44.
Ge, G. F. et al. Geographical and climatic differences in long-term effect of organic and inorganic amendments on soil enzymatic activities and respiration in field experimental stations of China. Ecol. Complex. 6, 421–431. https://doi.org/10.1016/j.ecocom.2009.02.001 (2009).
Article  Google Scholar 

45.
Wolińska, A. & Stępniewska, Z. Dehydrogenase Activity in the Soil Environment in Dehydrogenases 183–210 (Intech Open, 2012).
Google Scholar 

46.
Lemanowicz, J. Dynamics of phosphorus content and the activity of phosphatase in forest soil in the sustained nitrogen compounds emissions zone. Environ. Sci. Pollut. R. 25, 33773–33782. https://doi.org/10.1007/s11356-018-3348-5 (2018).
CAS  Article  Google Scholar 

47.
Haroni, N. N., Zarafshar, M., Badehian, Z., Sharma, A. & Bader, M. K. F. Tree seedlings suffer oxidative stress but stimulate soil enzyme activity in oil sludge-contaminated soil in a species-specific manner. Trees 34, 1267–1279. https://doi.org/10.1007/s00468-020-01996-7 (2020).
CAS  Article  Google Scholar 

48.
Banks, M. et al. The Effect of Plants on the Degradation and Toxicity of Petroleum Contaminants in Soil: A Field Assessment in Phytoremediation 75–96 (Springer, 2003).
Google Scholar 

49.
Schinner, F., Öhlinger, R., Kandeler, E. & Margasin, R. Methods in Soil Biology (Springer-Verlag, 1996).
Google Scholar 

50.
Alef, K. & Nannipieri, P. Methods in Applied Soil Microbiology and Biochemistry (Academic Press, 1995).
Google Scholar 

51.
Wyszkowska, J. & Wyszkowski, M. Effect of cadmium and magnesium on enzymatic activity in soil. Pol. J. Environ. Stud. 12, 473–479 (2003).
CAS  Google Scholar 

52.
Bending, G. D., Turner, M. K., Rayns, F., Marie-Claude Marx, M.-C. & Wood, M. Microbial and biochemical soil quality indicators and their potential for differentiating areas under contrasting agricultural management regimes. Soil Biol. Biochem. 36, 1785–1792. https://doi.org/10.1016/j.soilbio.2004.04.035 (2004).
CAS  Article  Google Scholar 

53.
Rodriguez-Loinaz, G., Onaindia, M., Amezaga, I., Mijangos, I. & Garbisu, C. Relationship between vegetation diversity and soil functional diversity in native mixed-oak forests. Soil Biol. Biochem. 40, 49–60. https://doi.org/10.1016/j.soilbio.2007.04.015 (2008).
CAS  Article  Google Scholar 

54.
Bednarek, R., Dziadowiec, H., Pokojska, U. & Prusinkiewicz, Z. Ecological and Soil Studies (PWN, 2005).
Google Scholar 

55.
Saetre, P. Decomposition, microbial community structure, and earthworm effects along a birch–spruce soil gradient. Ecology 79, 834–846. https://doi.org/10.1890/0012-9658(1998)079(0834:DMCSAE)2.0.CO;2 (1998).
Article  Google Scholar 

56.
Orwin, K. H., Wardle, D. A. & Greenfield, L. G. Context-dependent changes in the resistance and resilience of soil microbes to an experimental disturbance for three primary plant chronosequences. Oikos 112, 196–208. https://doi.org/10.1111/j.0030-1299.2006.13813.x (2006).
Article  Google Scholar 

57.
ter Braak, C. J. F. & Šmilauer, P. Canoco Reference Manual and User’s Guide: Software for Ordination, Version 5.0 (Microcomputer Power, 2012).
Google Scholar 

58.
R Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, 2018).

59.
Dell Inc. Dell Statistica (data analysis software system), version 13. software.dell.com.

60.
Fontaine, S., Mariotti, A. & Abbadie, L. The priming effect of organic matter: A question of microbial competition?. Soil Biol. Biochem. 35, 837–843. https://doi.org/10.1016/s0038-0717(03)00123-8 (2003).
CAS  Article  Google Scholar 

61.
Chodak, M. & Niklińska, M. Effect of texture and tree species on microbial properties of mine soils. Appl. Soil Ecol. 46, 268–275. https://doi.org/10.1016/j.apsoil.2010.08.002 (2010).
Article  Google Scholar 

62.
Chodak, M. & Niklińska, M. The effect of different tree species on the chemical and microbial properties of reclaimed mine soils. Biol. Fertil. Soils 46, 555–566. https://doi.org/10.1007/s00374-010-0462-z (2010).
CAS  Article  Google Scholar 

63.
Ciarkowska, K., Solek-Podwika, K. & Wieczorek, J. Enzyme activity as an indicator of soil-rehabilitation processes at a zinc and lead ore mining and processing area. J. Environ. Manage. 132, 250–256. https://doi.org/10.1016/j.jenvman.2013.10.022 (2014).
CAS  Article  PubMed  Google Scholar 

64.
Zhang, W. Y., Yao, D. X., Zhang, Z. G., Yang, Q. & An, S. K. Characteristics of soil enzymes and the dominant species of repair trees in the reclamation of coal mine area. J. Coal Sci. Eng. 19, 256–261. https://doi.org/10.1007/s12404-013-0223-3 (2013).
CAS  Article  Google Scholar 

65.
Šantrůčková, H., Jaroslav, V., Picek, T. & Kopáček, J. Soil biochemical activity and phosphorus transformations and losses from acidified forest soils. Soil Biol. Biochem. 36, 1569–1576. https://doi.org/10.1016/j.soilbio.2004.07.015 (2004).
CAS  Article  Google Scholar 

66.
Abakumov, E. & Frouz, J. Humus accumulation and humification during soil development in post-mining soil. In Soil Biota and Ecosystem Development in Post Mining Sites (ed. Frouz, J.) (CRC Press, 2013).
Google Scholar 

67.
Stefanowicz, A. M., Kapusta, P., Błońska, A., Kompała-Bąba, A. & Woźniak, G. Effects of Calamagrostis epigejos, Chamaenerion palustre and Tussilago farfara on nutrient availability and microbial activity in the surface layer of spoil heaps after hard coal mining. Ecol. Eng. 83, 328–337. https://doi.org/10.1016/j.ecoleng.2015.06.034 (2015).
Article  Google Scholar 

68.
Markowicz, A., Woźniak, G., Borymski, S., Piotrowska-Seget, Z. & Chmura, D. Links in the functional diversity between soil microorganisms and plant communities during natural succession in coal mine spoil heaps. Ecol. Res. 30, 1005–1014. https://doi.org/10.1007/s11284-015-1301-3 (2015).
CAS  Article  Google Scholar 

69.
Lauber, C. L., Hamady, M., Knight, R. & Fierer, N. Pyrosequencing-based assessment of soil pH as a predictor of soil bacterial community composition at the continental scale. Appl. Environ. Microbiol. 75, 5111–5120. https://doi.org/10.1128/aem.00335-09 (2009).
CAS  Article  PubMed  PubMed Central  Google Scholar 

70.
Wang, A. S., Angle, J. S., Chaney, R. L., Delorme, T. A. & McIntosh, M. Changes in soil biological activities under reduced soil pH during Thlaspi caerulescens phytoextraction. Soil Biol. Biochem. 38, 1451–1461. https://doi.org/10.1016/j.soilbio.2005.11.001 (2006).
CAS  Article  Google Scholar 

71.
Lee, S. S. et al. Heavy metal immobilization in soil near abandoned mines using eggshell waste and rapeseed residue. Environ. Sci. Pollut. R. 20, 1719–1726. https://doi.org/10.1007/s11356-012-1104-9 (2013).
CAS  Article  Google Scholar 

72.
Olander, L. P. & Vitousek, P. M. Regulation of soil phosphatase and chitinase activity by N and P availability. Biogeochemistry 49, 175–190 (2000).
CAS  Article  Google Scholar 

73.
Geisseler, D., Horwath, W. & Scow, K. Soil moisture and plant residue addition interact in their effect on extracellular enzyme activity. Pedobiologia 54, 71–78. https://doi.org/10.1016/j.pedobi.2010.10.001 (2011).
Article  Google Scholar 

74.
Patrzałek, A. The properties of initial soil arising at the dumping site of carboniferous waste. Zesz. Nauk. Polit. Śl. Górnictwo 248, 151–156 (2001).
Google Scholar 

75.
Šourková, M. et al. Soil development and properties of microbial biomass succession in reclaimed postmining sites near Sokolov (Czech Republic) and near Cottbus (Germany). Geoderma 129, 73–80. https://doi.org/10.1016/j.geoderma.2004.12.032 (2005).
ADS  CAS  Article  Google Scholar 

76.
Ellhottová, D., Krištůlek, V., Malý, S. & Frouz, J. Rhizosphere effect of colonizer plant species on the development of soil microbial community during primary succession on postmining sites. Commun. Soil Sci. Plant Anal. 40, 758–770. https://doi.org/10.1080/00103620802693193 (2009).
CAS  Article  Google Scholar 

77.
Finkenbein, P., Kretschmer, K., Kuka, K., Klotz, S. & Heilmeier, H. Soil enzymes activities as bioindicators for substrate quality in revegetation of a subtropical coal mining dump. Soil Biol. Biochem. 56, 87–89. https://doi.org/10.1016/j.soilbio.2012.02.012 (2013).
CAS  Article  Google Scholar 

78.
Tischer, A., Blagodatskaya, E. & Harmer, U. Extracellular enzyme activities in a tropical mountain rainforest region of southern Ecuador affected by low soil P status and land-use change. Appl. Soil Ecol. 74, 1–11. https://doi.org/10.1016/j.apsoil.2013.09.007 (2014).
Article  Google Scholar 

79.
Liu, Y. et al. Long-term forest succession improves plant diversity and soil quality but not significantly increase soil microbial diversity: Evidence from the Loess Plateau. Ecol. Eng. 142, 105631. https://doi.org/10.1016/j.ecoleng.2019.105631 (2020).
Article  Google Scholar 

80.
Tscherko, D., Hammesfahr, U., Marx, M. C. & Kandeler, E. Shifts in rhizosphere microbial communities and enzyme activity of Poa alpina across an alpine chronosequence. Soil Biol. Biochem. 36, 1685–1698. https://doi.org/10.1016/j.soilbio.2004.07.004 (2004).
CAS  Article  Google Scholar 

81.
Orwin, K. H. et al. Linkages of plant traits to soil properties and the functioning of temperate grassland. J. Ecol. 98, 1074–1083. https://doi.org/10.1111/j.1365-2745.2010.01679.x (2010).
Article  Google Scholar 

82.
Han, X. M. et al. Effects of vegetation type on soil microbial community structure and catabolic diversity assessed by polyphasic methods in North China. J. Environ. Sci. China 19, 1228–1234. https://doi.org/10.1016/s1001-0742(07)60200-9 (2007).
Article  PubMed  Google Scholar 

83.
Lambers, H., Mougel, C., Jaillard, B. & Hinsinger, P. Plant-microbe-soil interactions in the rhizosphere: An evolutionary perspective. Plant Soil 321, 83–115. https://doi.org/10.1007/s11104-009-0042-x (2009).
CAS  Article  Google Scholar 

84.
De Baets, S., Poesen, J., Gyssels, G. & Knapen, A. Effects of grass roots on the erodibility of topsoil during concentrated flow. Geomorphology 76, 54–67. https://doi.org/10.1016/j.geomorph.2005.10.002 (2006).
ADS  Article  Google Scholar 

85.
Mueller, K., Tilman, D., Fornara, D. & Hobbie, S. Root depth distribution and the diversity-productivity relationship in a long-term grassland experiment. Ecology 94, 787–793. https://doi.org/10.1890/12-1399.1 (2013).
Article  Google Scholar 

86.
Ravenek, J. M. et al. Long-term study of root biomass in a biodiversity experiment reveals shifts in diversity effects over time. Oikos 123, 1528–1536. https://doi.org/10.1111/oik.01502 (2014).
Article  Google Scholar 

87.
Gil-Sotres, F., Trasar-Cepeda, C., Leirós, M. C. & Seoane, S. Different approaches to evaluating soil quality using biochemical properties. Soil Biol. Biochem. 37, 877–887. https://doi.org/10.1016/j.soilbio.2004.10.003 (2005).
CAS  Article  Google Scholar 

88.
Tan, X., Chang, S. X. & Kabzems, R. Soil compaction and forest floor removal reduced microbial biomass and enzyme activities in a boreal aspen forest soil. Biol. Fertil. Soils 44, 471–479. https://doi.org/10.1007/s00374-007-0229-3 (2008).
Article  Google Scholar 

89.
Nanippieri, P., Giagnoni, L., Landi, L. & Renella, G. Role of phosphatase enzymes in soil. In Phosphorus in Action (eds Bünemann, E. K. et al.) 215–244 (Springer, 2011).
Google Scholar 

90.
Sakurai, M., Wasaki, J., Tomizawa, Y., Shinano, T. & Osaki, M. Analysis of bacterial communities on alkaline phosphatase genes in soil supplied with organic matter. Soil Sci. Plant Nutr. 54, 62–71. https://doi.org/10.1111/j.1747-0765.2007.00210.x (2008).
CAS  Article  Google Scholar 

91.
Mariotte, P. et al. Subordinate plant species impact on soil microbial communities and ecosystem functioning in grasslands: Findings from a removal experiment. Perspect. Plant. Ecol. 15, 77–85. https://doi.org/10.1016/j.ppees.2012.12.003 (2013).
Article  Google Scholar 

92.
Zhang, C. B. et al. Effects of plant diversity on nutrient retention and enzyme activities in a full-scale constructed wetland. Bioresour. Technol. 101, 1686–1692. https://doi.org/10.1016/j.biortech.2009.10.001 (2010).
CAS  Article  PubMed  Google Scholar 

93.
Allison, V. J., Condron, L. M., Peltzer, D. A., Richardson, S. J. & Turner, B. L. Changes in enzyme activities and soil microbial community composition along carbon and nutrient gradients at the Franz Josef chronosequence, New Zealand. Soil Biol. Biochem. 39, 1770–1781. https://doi.org/10.1016/j.soilbio.2007.02.006 (2007).
CAS  Article  Google Scholar  More

Extensive hydrodynamic model validation
To provide a comprehensive validation of the modeled surge-tide-wave dynamics during Sandy, this study made use of all available field data from an extensive network of coastal water level gauges and wave buoys in the study region, as well as hundreds of HWMs and many rapid deployment surge sensors (SI Fig. S1). The good agreements between simulated and observed water levels and waves were quantified in terms of the root-mean-square error (RMSE) and correlation coefficient (CORR).
Storm tide
Available data included those from hundreds of permanent and temporary surge sensors from NOAA (SI Fig. S2), Hudson River Environmental Conditions Observing System (HRECOS), and USGS. Statistics (SI Table S1) showed excellent agreement between the time series of simulated and measured storm tide data. The averaged RMSE at USGS temporary sites was 0.20 m and the averaged CORR was 0.94. CH3D-SSMS successfully reproduced the surge and tides with high confidence in not only the open ocean but also the complex estuarine system during Sandy (SI Fig. S3) with a maximum coastal storm tide at NY Bight of 3.36 m and NJ coast of 3.27 m.
High water marks and inundation
A total of 526 out of 653 independent HWM locations were located within the study region, most of which clustered in NJ and NY coastal areas. 83.7% (440 out of 526) of HWMs were captured by the CH3D-SSMS model grid and compared with the surveyed values. The model results had 0.33 m RMSE and 0.87 CORR (SI Fig. S4). When only “good” and “excellent” HWMs (rated by USGS) were used, the RMSE dropped to 0.30 m, and CORR increased to 0.90. The noticeable data disagreements (Fig. SI 4) were caused by the inconsistency of surveyed data.
Wave
During Sandy, four wave-buoys within the domain recorded the wave data: two at the apex of NY Bight, and another two in Long Island Sound (LIS). The significant wave height (H_{sig}) and peak wave period (T_{p}) simulated by the Simulating Waves Nearshore (SWAN) model in the CH3D-SSMS were compared with measured data and Wave Watch III (WW3) operational run results (SI Fig. S5)25,26. SWAN more accurately captured the evolution of (H_{sig}) in NY Bight and LIS. Maximum significant wave height over land was as high as 2.17 m at NY Bight and 2.60 m along NJ coast.
Impact of wetland on surge and wave during super storm sandy
To estimate the value of the coastal wetlands in reducing flood, we calculated the following four metrics for inundation: the Average Inundation Height ((AIH)), the Maximum Inundation Height ((MIH)), the Total Inundation Area ((TIA)), and the Total Inundation Volume ((TIV)) with and without wetlands. The (TIA) and (TIV) are defined as

$$ TIA = iint_{{{text{Landward}};{text{area}}}} {dxdy} , $$
(1)

$$ TIV = iint_{{{text{Landward}};{text{area}}}} [H_{max} left( {x,y} right) – H_{0} left( {x,y} right)]dxdy, $$
(2)

where (H_{max} left( {x,y} right)) and (H_{0} left( {x,y} right)) are the maximum water level and the land elevation at land cells (left( {x,y} right)), respectively11,12. The wave analysis was carried out by calculating the average wave height ((AWH)), the maximum wave height ((MWH)), and the total wave energy ((TWE)) which is defined as

$$ TWE = iint_{{{text{Landward}};{text{area}}}} [frac{1}{8}rho_{w} g{ }left( {H_{{rms,max{ }}} } right)^{2} ]dxdy, $$
(3)

where (H_{rms max}) is the maximum root-mean-square wave height over the flooded land. The wave energy, instead of wave height, is more directly related to the wave-induced structure loss.
We define the relative inundation reduction ((RIR)) as the difference in (TIV) (value without wetland minus value with wetland), divided by the (TIV) with wetland. The relative wave reduction ((RWR)) is defined accordingly using the (TWE). The inundation and wave analysis were carried out at the regional level (SI Table S2) and zip-code resolution (Figs. 1, S6, S7).
Figure 1

Zip-code resolution wetland’s effect on (TIV) and (TWE) during Sandy in 2012. Map showing zip-code resolution avoidance in (A) (TIV) and (B) (TWE) during Sandy without wetlands, as a percentage of those for the with-wetland scenario. Dark red values show zip-code with the most wetland benefit while dark blue areas have the least wetland benefit. Negative values indicate that the presence of wetland would increase (TIV)/(TWE) and positive values indicate that wetland would lower (TIV)/(TWE). The map is produced using ESRI ArcGIS Pro 2.7 (https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview).

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If the wetlands were absent during Sandy, the (RIR) of the entire model domain would have been 4% and the (RWR) would have been 19%. Breaking the entire model domain into 6 regions (Table S2): South NJ (SNJ), Central NJ (CNJ), North NJ (NNJ), CT, Long Island (LI), and Mainland NY (MNY), it was found that the (RIR) during sandy was low ( 50%) (RWR), while LI. CNJ and SNJ had significant (~ 25–50%) (RWR). The regions with the highest (RIR) and (RWR) were closest to the storm landfall location.
Benefit of wetland on surge and wave for the 1% annual chance flood
The maximum 1% annual chance maximum flood elevation (Fig. S9) was 5.15 m for NY Bight while 5.34 m for the NJ coast. The 1% annual chance maximum wave height (Fig. S9) overland is 2.24 m at NY Bight and 1.90 m at NJ coast. All the regions had moderate (RIR) except for SNJ which had a significant (RIR) due to highest (25.4%) wetland cover (see Table S2) and NNJ experienced significant (RWR) while the remaining regions had moderate values. CT ranked 2nd in (RIR ) although it has less wetland cover than CNJ because the mostly woody wetlands in CT are more effective in buffering storm surge than the marshes in NJ and NY. On the other hand, CT ranked 3rd in (RWR) due to the blocking of offshore wave energy by LI. (RIR) and (RWR) are found to be functions of storm characteristics, wetland type, and cover, and local conditions. Fig. S10 shows the percent wetland cover, RIR (relative TIV reduction), and RWR (relative wave energy reduction) in six regions (New York, North New Jersey, Long Island, Connecticut, Central New Jersey, and South New Jersey) during 1% annual chance events. As the wetland cover increases from less than 5% (NY, NNJ, and LI) to more than 10% in CNJ and SNJ, RIR and RWR generally increase, showing the increasing role of wetland in reducing inundation and wave. Relative reduction in inundation and wave energy are modest, between 10 and 30%. NNJ has properties behind the relatively sparse marsh, followed by woody wetland which protects properties behind them. Connecticut has less wetland than Central Jersey, but the mostly woody wetland is more effective in reducing flood and wave.
Benefit of wetlands on reducing residential structure loss
The monetary loss of residential structures was estimated using the simulated inundation and wave results while employing damage functions from the United States Army Corps of Engineer (USACE) North Atlantic Comprehensive Coastal Study (NACCS) and was validated using the NFIP building loss payouts aggregated by zip-code21,24. Direct simulation of wave-induced damage requires understanding and calculation of wave loads on structures using a depth- and phase-resolving model, which is beyond the capability of the models used in this study. Therefore, in this paper, we did not directly simulate wave-induced damage, but are accounting for wave-induced change in total water level which results in increase in estimated damage based on depth-damage functions. Overall, 96 coastal zip-codes in the state of NJ were used to validate the estimated loss. The model showed a correlation coefficient (CORR = 0.69) between simulated structure losses and NFIP payouts (Fig. 2). In NJ, as of 2019, the total NFIP payout was $3.9 billion USD, in comparison to the estimated total structure loss of $3.6 billion USD (SI Table S3), with an absolute error of 7.7%. This good agreement, plus the good agreement between the simulated and observed surge and wave reported earlier, confirms the validity of our “dynamics-based” loss assessment.
Figure 2

Economic model validation at zip-code resolution. Simulated losses during Sandy in NJ using the USACE damage functions versus FEMA NFIP payouts. Results were aggregated by zip-code and the corresponding correlation coefficient is 0.69 (R2 = 0.47). Validation use transformed structure loss ((PL_{T})) instead of the structure loss ((PL)). The figure is produced using MATLAB R2020 (https://www.mathworks.com/).

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We define the structural loss reduction ((SLR)) as the structural loss without wetland minus the structural loss with wetland, and the relative structural loss reduction ((RSLR)) as the ratio between (SLR) and the loss with wetland. A state-level analysis of structure loss in NJ showed a (RSLR) of 8.5%, 26.0%, and 52.3% for Sandy, BS storm, and 1% chance flood/wave, respectively (Table S3). Analysis of losses due to flood and wave indicated that for Sandy and the 1% event, most of the loss came from flood, while most of the loss in the BS storm came from waves. Avoided wave-induced loss was comparable to the avoided flood-induced loss during the BS storm and the 1% event, but much higher during Sandy, suggesting that NJ wetlands are more effective in reducing wave-induced loss vs. flood-induced loss. Results from the zip-code scale analysis during Sandy (Fig. 3) showed less variability: for most north NJ, (RSLR) ranged from 10 to 100% except for those along the Hudson River that had small increased loss (negative avoided loss). Most zip-codes in SNJ had (RSLR) between 5% and more than 100%. (RSLR) during the BS storm (Fig. S11) was more notable: ~ 50–100% for north NJ, 0–100% for central NJ. The 1% flood event (Fig. 4) showed an average RSLR  > 25% for NJ.
Figure 3

Percent structural loss reduction during Sandy. Map showing zip-code resolution avoided loss (difference in loss without wetlands and loss with wetlands) during Sandy, as a percentage of the loss of the with-wetland scenario. Dark red values show zip-code with the highest wetland benefit, while dark blue areas have the least benefit. Negative values indicate that the presence of wetland would increase structural losses and positive values indicate that wetland would lower the structural losses. The results are shown for the NJ coastal zip-codes affected by Sandy. This study shows that the percent avoided loss in this figure does not always represent the actual wetland value for loss reduction because areas with few structures and losses could give misleadingly high values of percent avoided loss, as shown in south NJ. The primary purpose is for comparison with a similar figure in NAR17. The map is produced using ESRI ArcGIS Pro 2.7 (https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview).

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Figure 4

Effect of wetlands on structural losses over zip code scale during the 1% annual event. Map showing zip code resolution difference in losses if the wetlands were absent, as a percentage of the wetland present scenario. Dark red values show zip code with the highest benefit of having wetlands while dark blue areas show the least benefited area. Negative values indicate that the presence of wetland would increase structural losses and positive values indicate that wetland would lower the structural losses. The map is produced using ESRI ArcGIS Pro 2.7 (https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview).

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The above results showed that the value of coastal wetlands for flood/wave protection varies significantly with the storm. While wetlands may be more effective in reducing wave loss in some storms but flood loss in other storms, they may be ineffective in extreme storms. The 1% annual chance flood and wave event, which resulted from an ensemble of many less extreme but more frequent storms28, provides a more reasonable integrated scenario for the loss analysis. This is similar to the preferred use of the 1% flood map, instead of the flood map associated with a single design storm, for assessing the flood risk in any coastal region.
As shown in Fig. 5, in zip-codes with larger wetland coverage area in SNJ, wetlands could only prevent  $60,000 annual loss. On the other hand, wetlands in north NJ zip-codes with smaller wetland coverage area would increase the loss. The annual loss in most of the zip-codes is  More