Ecology

Study area
The Source Region of Yangtze River (SRYR for short, Latitude: 32° 25′ E and 35° 53′ E; Longitude: 89° 43′ E–97° 19′ E), located in the western Tibetan plateau, covers an area of 141,398 km2 (Fig. 10a). The elevation ranges from 6456 m in the West to 3512 m in the East, with an average of 4779 m. The SRYR belongs to transition zone from semi-arid to semi-humid alpine area. The annual temperature is − 2 to − 3 °C. Monthly mean temperature in the coldest month is − 13.0 °C and that in the warmest month is 9.7 °C. The annual temperature of the study area is 265 mm. The temperature decreases from southeast to northwest37. The aridity index is 3.67 in the SRYR, which means the climate is very dry. The vegetation types are mainly meadow (84,985 km2) and grassland (33,743 km2), which are 60.1% and 23.9% (Fig. 10b) of the study area respectively. We divided the SRYR into five sub-regions, including Tuotuo River Basin (I), Dam River Basin (II), Qumar River Basin (III), Middle Stream Region (IV) and Downstream Region (V).
Figure 10

The location of Source Region of Yangtze River (a) and vegetation types (b). Map was generated using ArcGIS 10.3 (http://www.esri.com/software/arcgis/arcgis-for-desktop).

Full size image

Datasets
The monthly NDVI data for SRYR was obtained from Resource and Environment Data Cloud Platform (RESDC, http://www.resdc.cn/). It was produced with Maximum Value Composite (MVC) approach based on the SPOT/VEGETATION NDVI data. The effects of cloud cover and non-vegetation were reduced. This dataset was at a spatial resolution of 1 km, covering the period 2000 to 2014.
The gridded meteorological data used are obtained from China Ground Precipitation 0.5° × 0.5° Grid Dataset V2.0 and China Ground Temperature 0.5° × 0.5° Grid Dataset V2.0. These datasets are provided by National Meteorological Information Center (NMIC, http://data.cma.cn/). A total of 102 grids in the SRYR and the surroundings during 2000–2014 are selected. The gridded data has been projected and resampled in order to ensure the same coordinate system and resolution with NDVI data. The NMIC also provides meteorological data of 9 meteorological stations within and around the study area, including parameters such as solar radiation, surface water, pressure, sunshine hours, wind speed and relative humidity. Grid data of the study area was interpolated by ANUSPLINE.
NPP simulation
In this study, the NPP were simulated by CASA (Carnegie–Ames–Stanford Approach) model. The CASA model is based on the plant growing mechanism38,39,40 which can be summarized by Eq. (1).

$$ NPPleft( {x,t} right) = APARleft( {x,t} right) times varepsilon left( {x,t} right) $$
(1)

where x and t are spatial location and time respectively, NPP is simulated value (gC m−2). APAR and ε represent absorbed photosynthetically active radiation and light use efficiency, which can be obtained by Eqs. (2) and (3).

$$ APARleft( {x,t} right) = fPARleft( {x,t} right) times SOLleft( {x,t} right) times R $$
(2)

$$ varepsilon left( {x,t} right) = Tleft( {x,t} right) times Wleft( {x,t} right) times varepsilon_{max } $$
(3)

where fPAR is the fraction of absorbed photosynthetically active radiation, SOL is the total solar radiation (MJ/m2), R is the fraction of solar active radiation that can be used by vegetation. T and W are temperature stress index and moisture stress factor, respectively. εmax is maximum light utilization efficiency. Further details of the above equations can be obtained from previous studies38,39,40.
The NPP calculated by CASA model can be considered as the actual NPP which is influenced by both climate change and human activities. It can be expressed as Eq. (4).

$$ NPP = PNPP – HNPP $$
(4)

where PNPP and HNPP represent potential NPP and human-induced NPP, respectively. PNPP is only determined by climate conditions and without interference from human activities. It can be calculated by Thornthwaite Memorial model41, using the follows formulas:

$$ PNPP = 3000left[ {1 – e^{{ – 0.0009695left( {v – 20} right)}} } right] $$
(5)

$$ v = frac{1.05N}{{sqrt {1 + left( {1.05{N mathord{left/ {vphantom {N L}} right. kern-nulldelimiterspace} L}} right)^{2} } }} $$
(6)

$$ L = 300 + 25t + 0.05t^{3} $$
(7)

where t, L, N and v are average annual temperature (°C), annual maximum evapotranspiration (mm), annual total precipitation (mm) and average annual actual evapotranspiration (mm).
According to Eq. (4), the HNPP can be represented by the difference between PNPP and NPP.
Statistical analysis
To identify the inter-annual trends of temperature (Tem.), precipitation (Pre.) and NPP, the linear regression method was adopted to eliminate the increase or decrease rate42, which can be calculated as follows:

$$ theta_{Slope} = frac{{n times sumnolimits_{i = 1}^{n} {(i times X_{i} ) – sumnolimits_{i = 1}^{n} {isumnolimits_{i = 1}^{n} {X_{i} } } } }}{{n times sumnolimits_{i = 1}^{n} {i^{2} – left( {sumnolimits_{i = 1}^{n} i } right)^{2} } }} $$
(8)

where θslope is the linear slope of the time series variable, which can be used to characterize the increase or decrease rate during a given study period; n is the number of years (here n = 15); Xi is the temperature, precipitation and NPP for the ith year (i = 1,2, … n).
A nonparametric test, Mann–Kendall (M–K) trend analysis43,44 was utilized to detect the break points of temperature, precipitation and NPP series in the SRYR. The test statistic UFi is calculated as follows:

$$ begin{array}{*{20}c} {UF_{i} = frac{{S_{i} – Eleft( {S_{i} } right)}}{{sqrt {Varleft( {S_{i} } right)} }}} & {left( {i = 1,2, ldots ,n} right)} \ end{array} $$
(9)

$$ begin{array}{*{20}c} {S_{k} = sumlimits_{i = 1}^{k} {r_{i} } } & {left( {k = 2,3, ldots ,n} right)} \ end{array} $$
(10)

$$ begin{array}{*{20}c} {ri = left{ {begin{array}{*{20}c} { + 1} & {x_{i} > x_{j} } \ 0 & {x_{i} le x_{j} } \ end{array} } right.} & {(j = 1,2, ldots ,i – 1)} \ end{array} $$
(11)

where xi is the variable with the sample of n. E(Sk) and variance Var(Sk) could be estimated as follows:

$$ Eleft( {S_{i} } right) = frac{{ileft( {i – 1} right)}}{4} $$
(12)

$$ Varleft( {S_{i} } right) = frac{{ileft( {i – 1} right)left( {2i + 5} right)}}{72} $$
(13)

Using the same equation but in the reverse data series (xn, xn − 1, …, x1), UFi could be calculated again. Defining UBi = UFi (i = n, n − 1, …, 1), we can get the curve of UFi and UBi. If the intersection of the UFi and UBi curves occurs within the confidence interval, it indicates a change point45.
To assess the effects of temperature and precipitation on NPP in the SRYR, correlation coefficient R was employed to analyze the correlation between two variables (NPP vs. Tem., NPP vs. Pre.), using the following formula:

$$ R_{XY} = frac{{sumnolimits_{i = 1}^{n} {left( {X_{i} – overline{X} } right)left( {Y_{i} – overline{Y} } right)} }}{{sqrt {sumnolimits_{i = 1}^{n} {left( {X_{i} – overline{X} } right)^{2} sqrt {sumnolimits_{i = 1}^{n} {left( {Y_{i} – overline{Y} } right)^{2} } } } } }} $$
(14)

where Y denotes the NPP and X denotes temperature or precipitation.
The results of the statistical analysis above can be got by MATLAB.
Identification of the relative roles of climate change and human activities in NPP
A positive PNPP slope indicates that vegetation growth is promoted by climate change, whereas a negative PNPP slope means that climate change reduced the vegetation NPP. A positive HNPP slope suggests that human activities have negative influence on vegetation growth and create ecological degradation, whereas a negative HNPP slope means that human activities contribute to vegetation growth46. Thus, the determinants for NPP change can be identified according to Table 2.
Table 2 The causes of actual NPPA change.
Full size table More

1.
Geiser, F. Metabolic rate and body temperature reduction during hibernation and daily torpor. Annu. Rev. Physiol. 66, 239–274. https://doi.org/10.1146/annurev.physiol.66.032102.115105 (2004).
ADS  CAS  Article  PubMed  Google Scholar 
2.
Speakman, J. R. & Thomas, D. W. In Bat Ecology (eds T. H. Kunz & B. M. Fenton) 430–490 (The University of Chicago Press, 2003).

3.
Thomas, D. W., Dorais, M. & Bergeron, J.-M. Winter energy budgets and cost of arousals for hibernating little brown bats, myotis lucifugus. J. Mammal. 71, 475–479. https://doi.org/10.2307/1381967 (1990).
Article  Google Scholar 

4.
Thomas, D. W., Cloutier, D. & Gagné, D. Arrhythmic breathing, apnea and non-steady state oxygen uptake in hibernating Little Brown Bats (Myotis lucifugus). J. Exp. Biol. 149, 395–406 (1990).
Google Scholar 

5.
Hock, R. J. The metabolic rates and body temperatures of bats. Biol. Bull. 101, 289–299 (1951).
CAS  Article  Google Scholar 

6.
McNab, B. K. The behavior of temperate cave bats in a subtropical environment. Ecology 55, 943–958 (1974).
Article  Google Scholar 

7.
Belkin, V. V., Panchenko, D. V., Tirronen, K. F., Yakimova, A. E. & Fedorov, F. V. Ecological status of bats (Chiroptera) in winter roosts in eastern Fennoscandia. Russ. J. Ecol. 46, 463–469. https://doi.org/10.1134/s1067413615050045 (2015).
Article  Google Scholar 

8.
Richter, A. R., Humphrey, S. R., Cope, J. B. & Brack, V. Modified cave entrances – thermal effect on body-mass and resulting decline of endangered indiana bats (Myotis sodalis). Conserv. Biol. 7, 407–415. https://doi.org/10.1046/j.1523-1739.1993.07020407.x (1993).
Article  Google Scholar 

9.
Arlettaz, R. et al. Physiological traits affecting the distribution and wintering strategy of the bat Tadarida teniotis. Ecology 81, 1004–1014. https://doi.org/10.1890/0012-9658(2000)081[1004:ptatda]2.0.co;2 (2000).
Article  Google Scholar 

10.
Clawson, R. L., Laval, R. K., Laval, M. L. & Caire, W. Clustering behaviour of hibernating Myotis Sodalis in Missouri. J. Mammal. 61, 245–253. https://doi.org/10.2307/1380045 (1980).
Article  Google Scholar 

11.
McManus, J. J. Activity and thermal preference of the little brown bat, Myotis lucifugus, during hibernation. J. Mammal. 55, 844–846 (1974).
CAS  Article  Google Scholar 

12.
Ingersoll, T. E., Navo, K. W. & de Valpine, P. Microclimate preferences during swarming and hibernation in the Townsend’s big-eared bat, Corynorhinus townsendii. J. Mammal. 91, 1242–1250. https://doi.org/10.1644/09-mamm-a-288.1 (2010).
Article  Google Scholar 

13.
Webb, P. I., Speakman, J. R. & Racey, P. A. How hot is a hibernaculum? A review of the temperatures at which bats hibernate. Can. J. Zool.-Rev. Can. Zool. 74, 761–765. https://doi.org/10.1139/z96-087 (1996).
Article  Google Scholar 

14.
Gaisler, J. Remarks on the thermopreferendum of palearctic bats in their natural habitats. Bijdragen tot de Dierkunde 40, 33–35 (1970).
Article  Google Scholar 

15.
Bogdanowicz, W. & Urbanczyk, Z. Some ecological aspects of bats hibernating in the city of Poznan. Acta Theriologica 28, 371–385 (1983).
Article  Google Scholar 

16.
Lesinski, G. Ecology of bats hibernating underground in Central Poland. Acta Theriologica 31, 507–521 (1986).
Article  Google Scholar 

17.
Nagel, A. & Nagel, R. How do bats choose optimal temperatures for hibernation?. Comp. Biochem. Physiol. A Physiol. 99, 323–326. https://doi.org/10.1016/0300-9629(91)90008-Z (1991).
Article  Google Scholar 

18.
Siivonen, Y. & Wermundsen, T. Characteristics of winter roosts of bat species in southern Finland. Mammalia 72, 50–56. https://doi.org/10.1515/mamm.2008.003 (2008).
Article  Google Scholar 

19.
Brack, V. Jr. Temperatures and locations used by hibernating bats, including Myotis sodalis (Indiana bat), in a limestone mine: Implications for conservation and management. Environ. Manag. 40, 739–746. https://doi.org/10.1007/s00267-006-0274-y (2007).
ADS  MathSciNet  Article  Google Scholar 

20.
Boyles, J. G., Johnson, J. S., Blomberg, A. & Lilley, T. M. Optimal hibernation theory. Mammal Rev. 50, 91–100. https://doi.org/10.1111/mam.12181 (2020).
Article  Google Scholar 

21.
Prendergast, B. J., Freeman, D. A., Zucker, I. & Nelson, R. J. Periodic arousal from hibernation is necessary for initiation of immune responses in ground squirrels. Am. J. Physiol.-Regul. Integr. Comp. Physiol. 282, R1054–R1062. https://doi.org/10.1152/ajpregu.00562.2001 (2002).
CAS  Article  PubMed  Google Scholar 

22.
Burton, R. S. & Reichman, O. J. Does immune challenge affect torpor duration?. Funct. Ecol. 13, 232–237. https://doi.org/10.1046/j.1365-2435.1999.00302.x (1999).
Article  Google Scholar 

23.
Daan, S., Barnes, B. M. & Strijkstra, A. M. Warming up for sleep? Ground-squirrels sleep during arousals from hibernation. Neurosci. Lett. 128, 265–268. https://doi.org/10.1016/0304-3940(91)90276-y (1991).
CAS  Article  PubMed  Google Scholar 

24.
van Breukelen, F. & Martin, S. L. Molecular biology of thermoregulation – Invited review: molecular adaptations in mammalian hibernators: unique adaptations or generalized responses?. J. Appl. Physiol. 92, 2640–2647. https://doi.org/10.1152/japplphysiol.01007.2001 (2002).
Article  PubMed  Google Scholar 

25.
Kokurewicz, T. Sex and age related habitat selection and mass dynamics of Daubenton’s bats Myotis daubentonii (Kuhl, 1817) hibernating in natural conditions. Acta Chiropterologica 6, 121–144 (2004).
Article  Google Scholar 

26.
Czenze, Z. J., Jonasson, K. A. & Willis, C. K. R. Thrifty females, frisky males: winter energetics of hibernating bats from a cold climate. Physiol. Biochem. Zool. 90, 502–511. https://doi.org/10.1086/692623 (2017).
Article  PubMed  Google Scholar 

27.
Boyles, J. G., Dunbar, M. B., Storm, J. J. & Brack, V. Jr. Energy availability influences microclimate selection of hibernating bats. J. Exp. Biol. 210, 4345–4350. https://doi.org/10.1242/jeb.007294 (2007).
Article  PubMed  Google Scholar 

28.
Daan, S. & Wichers, H. J. Habitat selection of bats hibernating in a limestone cave. Z. Fur Saugetierkunde-Int. J. Mammalian Biol. 33, 262–287 (1968).

29.
Daan, S. Activity during natural hibernation in three species of vespertilionid bats. Netherlands J. Zool. 23, 1–71 (1973).
Article  Google Scholar 

30.
Kirkpatrick, L., Apoznanski, G., De Bruyn, L., Gyselings, R. & Kokurewicz, T. Bee markers: a novel method for non invasive short term marking of bats. Acta Chiropterologica 21, 465–471. https://doi.org/10.3161/15081109acc2019.21.2.020 (2019).
Article  Google Scholar 

31.
Bagrowska-Urbanczyk, E. & Urbanczyk, Z. Structure and dynamics of a winter colony of bats. Acta Theriologica 28, 183–196 (1983).
Article  Google Scholar 

32.
Boyles, J. G., Boyles, E., Dunlap, R. K., Johnson, S. A. & Brack, V. Long-term microclimate measurements add further evidence that there is no “optimal” temperature for bat hibernation. Mammalian Biol. 86, 9–16. https://doi.org/10.1016/j.mambio.2017.03.003 (2017).
Article  Google Scholar 

33.
Boyles, J. G. & McKechnie, A. E. Energy conservation in hibernating endotherms: why “suboptimal” temperatures are optimal. Ecol. Model. 221, 1644–1647. https://doi.org/10.1016/j.ecolmodel.2010.03.018 (2010).
Article  Google Scholar 

34.
Webb, P. I., Speakman, J. R. & Racey, P. A. Population dynamics of a maternity colony of the pipistrelle bat (Pipistrellus pipistrellus) in north-east Scotland. J. Zool. 240, 777–780. https://doi.org/10.1111/j.1469-7998.1996.tb05323.x (1996).
Article  Google Scholar 

35.
IPCC. Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Eds. Parry, M., Canziani, M., Palutikof, O., van der Linden, J., Hanson, P., Cambridge, C., (Cambridge University Press, 2007).

36.
Lutenbacher, J., Dietrich, D., Xoplaki, E., Grosjean, M. & Wanner, H. European seasonal and annual temperature variability, trends, and extremes since 1500. Science 303, 1499–1503 (2004).
ADS  Article  Google Scholar 

37.
Piniewski, M., Mezghani, A., Szcześniak, M. & Kundzewicz, Z. W. Regional projections of temperature and precipitation changes: robustness and uncertainty aspects. Meteorol. Z. 26, 223–234. https://doi.org/10.1127/metz/2017/0813 (2017).
Article  Google Scholar 

38.
Humphries, M. M., Thomas, D. W. & Speakman, J. R. Climate-mediated energetic constraints on the distribution of hibernating mammals. Nature 418, 313–316. https://doi.org/10.1038/nature00828 (2002).
ADS  CAS  Article  PubMed  Google Scholar 

39.
Day, K. M. & Tomasi, T. E. Winter energetics of female Indiana bats Myotis sodalis. Physiol. Biochem. Zool. 87, 56–64. https://doi.org/10.1086/671563 (2014).
Article  PubMed  Google Scholar 

40.
Rebelo, H., Tarroso, P. & Jones, G. Predicted impact of climate change on European bats in relation to their biogeographic patterns. Glob. Change Biol. 16, 561–576. https://doi.org/10.1111/j.1365-2486.2009.02021.x (2010).
ADS  Article  Google Scholar 

41.
Gottfried, I. et al. Long-term changes in winter abundance of the barbastelle Barbastella barbastellus in Poland and the climate change: are current monitoring schemes still reliable for cryophilic bat species?. PLoS ONE 15, 18. https://doi.org/10.1371/journal.pone.0227912 (2020).
CAS  Article  Google Scholar 

42.
Rydell, J. & Bogdanowicz, W. Barbastella barbastellus. Mammalian Species, 1–8 (1997).

43.
Lesinski, G. et al. The importance of small cellars to bat hibernation in Poland. Mammalia 68, 345–352. https://doi.org/10.1515/mamm.2004.034 (2004).
Article  Google Scholar 

44.
Sachanowicz, K. & Zub, K. Numbers of hibernating Barbastella barbastellus (Schreber, 1774) (Chiroptera, Vespertilionidae) and thermal conditions in military bunkers. Mammalian Biol. 67, 179–184. https://doi.org/10.1078/1616-5047-00026 (2002).
Article  Google Scholar 

45.
Greenaway, F. The barbastelle in Britain. British Wildlife 12, 327–334 (2001).
Google Scholar 

46.
Sherwin, H. A., Montgomery, W. I. & Lundy, M. G. The impact and implications of climate change for bats. Mammal Rev. 43, 171–182. https://doi.org/10.1111/j.1365-2907.2012.00214.x (2013).
Article  Google Scholar 

47.
Dietz, C., Von Helversen, O. & Nill, D. Bats of Britain, Europe & Northwest Africa. (A &C Black Publishers Ltd., 2009).

48.
Hutterer, R., Ivanova, T., Meyer-Cords, C. & Rodrigues, L. Bat migrations in Europe: a review of banding data and literature. Vol. 28 (Federal Agency for Nature Conservation in Germany, 2005).

49.
Kokurewicz, T. et al. 45 years of bat study and conservation in Nietoperek bat reserve (Western Poland). Nyctalus 19, 252–269 (2019).
Google Scholar 

50.
Cichocki, J. et al. In 23th Polish Chiropterological Conference. (ed W. Grzywinski) 9–10 (2014).

51.
Cichocki, J. et al. In Proceedings of the 24th Polish Chiropterological Conference. (ed W. Grzywinski) 36–37 (2015).

52.
Brack, V. & Twente, J. W. The duration of the period of hibernationof 3 species of Vespertilionid bats. 1. Field studies. Can. J. Zool.-Rev. Can. Zool. 63, 2952–2954 (1985).

53.
Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A. & Smith, G. M. Mixed effects models and extensions in ecology with R. (Springer, 2009).

54.
Onkelinx, T., Devos, K. & Quataert, P. Working with population totals in the presence of missing data comparing imputation methods in terms of bias and precision. J. Ornithol. 158, 603–615. https://doi.org/10.1007/s10336-016-1404-9 (2017).
Article  Google Scholar 

55.
Rubin, D. B. Multiple Imputation for Nonresponse in Surveys. (Wiley, 1987).

56.
Rubin, D. B. Multiple imputation after 18+ years. J. Am. Stat. Assoc. 91, 473–489. https://doi.org/10.1080/01621459.1996.10476908 (1996).
Article  MATH  Google Scholar 

57.
RCoreTeam. in Version 3.6.1 (URL https://www.R-project.org/: R Foundation for Statistical Computing, Vienna, Austria, 2019).

58.
Mixed GAM Computation Vehicle with GCV/AIC/REML smoothness estimation v. 1.8–0 (2014).

59.
Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S. Fourth Edition. (Springer, 2002).

60.
Tuttle, M. D. & Stevenson, D. E. in BCI Bat Conservation and Management Workshop. 19–35 (Bat Conservation International).

61.
Lesinski, G., Fuszara, E., Fuszara, M., Jurczyszyn, M. & Urbanczyk, Z. Long-term changes in the numbers of the barbastelle Barbastella barbastellus in Poland. Folia Zool. 54, 351–358 (2005).
Google Scholar 

62.
Klug-Baerwald, B. J., Lausen, C. L., Willis, C. K. R. & Brigham, R. M. Home is where you hang your bat: winter roost selection by prairie-living big brown bats. J. Mammal. 98, 752–760. https://doi.org/10.1093/jmammal/gyx039 (2017).
Article  Google Scholar 

63.
Martinkova, N., Baird, S. J. E., Kana, V. & Zima, J. Bat population recoveries give insight into clustering strategies during hibernation. Front. Zool. 17, 11. https://doi.org/10.1186/s12983-020-00370-0 (2020).
Article  Google Scholar 

64.
Tuttle, M. D. & Kennedy, J. In BCI Bat Conservation and Management Workshop. 73–82 (Bat Conservation International).

65.
Suggitt, A. J. et al. Extinction risk from climate change is reduced by microclimatic buffering. Nat. Climate Change 8, 713–717. https://doi.org/10.1038/s41558-018-0231-9 (2018).
ADS  Article  Google Scholar 

66.
Thomas, D. W. Hibernating bats are sensitive to nontactile human disturbance. J. Mammal. 76, 940–946. https://doi.org/10.2307/1382764 (1995).
Article  Google Scholar 

67.
Speakman, J. R., Webb, P. I. & Racey, P. A. Effects of disturbance on the energy expenditure of hibernating bats. J. Appl. Ecol. 28, 1087–1104. https://doi.org/10.2307/2404227 (1991).
Article  Google Scholar 

68.
Jurga, R. M. & Kędryna A. M. Festungsfront Oder-Warthe Bogen. Katalog (Wydawnictwo Donjon, 2006). More

1.
Cain, M. L., Milligan, B. G. & Strand, A. E. Long-distance seed dispersal in plant populations. Am. J. Bot. 87, 1217–1227 (2000).
CAS  PubMed  PubMed Central  Article  Google Scholar 
2.
Cousens, R., Dytham, C. & Law, R. Dispersal in Plants: A Population Perspective 1st edn. (Oxford University Press, Oxford, 2008).
Google Scholar 

3.
Jordano, P. Fruits and frugivory. In Seeds: The Ecology of Regeneration in Plant Communities 2nd edn (ed. Fenner, M.) 125–166 (UK CAB International, Wallingford, 2000).
Google Scholar 

4.
Jordano, P., García, C., Godoy, J. A. & García-Castaño, J. L. Differential contribution of frugivores to complex seed dispersal patterns. PNAS 104, 3278–3282 (2007).
CAS  PubMed  Article  ADS  Google Scholar 

5.
Bueno, R. S. et al. Functional redundancy and complementarities of seed dispersal by the last neotropical megafrugivores. PLoS ONE 8, 0056252 (2013).
Article  ADS  CAS  Google Scholar 

6.
Pérez-Méndez, N., Jordano, P., García, C. & Valido, A. The signatures of Anthropocene defaunation: cascading effects of the seed dispersal collapse. Sci. Rep. 6, 24820 (2016).
PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

7.
Hamrick, J. L., Murawski, D. A. & Nason, J. D. The influence of seed dispersal mechanisms on the genetic structure of tropical tree populations. Vegetatio 107, 281–297 (1993).
Google Scholar 

8.
Mueller, T., Lenz, J., Caprano, T., Fiedler, W. & Böhning-Gaese, K. Large frugivorous birds facilitate functional connectivity of fragmented landscapes. J. Appl. Ecol. 51, 684–692 (2014).
Article  Google Scholar 

9.
Pérez-Méndez, N., Jordano, P. & Valido, A. Persisting in defaunated landscapes: reduced plant population connectivity after seed dispersal collapse. J. Ecol. 106, 936–947 (2018).
Article  Google Scholar 

10.
Schupp, E. W. Quantity, quality and the effectiveness of seed dispersal by animals. Vegetatio 107, 15–29 (1993).
Google Scholar 

11.
Schupp, E. W., Jordano, P. & Gómez, J. M. Seed dispersal effectiveness revisited: a conceptual review. New Phytol. 188, 333–353 (2010).
PubMed  Article  Google Scholar 

12.
Traveset, A. & Richardson, D. M. Mutualistic interactions and biological invasions. Annu. Rev. Ecol. Evol. Syst. 45, 89–113 (2014).
Article  Google Scholar 

13.
Herrera, C. M. Seed dispersal by vertebrates. In Plant—animal interactions, an evolutionary approach (eds Herrera, C. & Pellmyr, O.) 185–209 (Wiley, Oxford, 2002).
Google Scholar 

14.
Vidal, M. M., Pires, M. M. & Guimarães, J. P. R. Large vertebrates as the missing components of seed-dispersal networks. Biol. Conserv. 163, 42–48 (2013).
Article  Google Scholar 

15.
Moleón, M. et al. Rethinking megafauna. Proc. R. Soc. B 287, 20192643 (2020).
PubMed  Article  Google Scholar 

16.
Pires, M. M., Guimarães, P. R., Galetti, M. & Jordano, P. Pleistocene megafaunal extinctions and the functional loss of long-distance seed-dispersal services. Ecography 41, 153–163 (2018).
Article  Google Scholar 

17.
Chen, S. C. & Moles, A. T. A mammoth mouthful? A test of the idea that larger animals ingest larger seeds. Glob. Ecol. Biogeogr. 24, 1269–1280 (2015).
Article  Google Scholar 

18.
Dirzo, R. et al. Defaunation of the anthropocene. Science 345, 401–406 (2014).
CAS  PubMed  Article  ADS  Google Scholar 

19.
Galetti, M. et al. Functional extinction of birds drives rapid evolutionary changes in seed size. Science 340, 1086–1090 (2013).
CAS  PubMed  Article  ADS  Google Scholar 

20.
Pasitschniak-Arts, M. Ursus arctos. Mamm. Species 439, 1–10 (1993).
Article  Google Scholar 

21.
Steyaert, S. M. J. G., Endrestøl, A., Hacklaender, K., Swenson, J. E. & Zedrosser, A. The mating system of the brown bear Ursus arctos. Mamm. Rev. 42, 12–34 (2012).
Article  Google Scholar 

22.
Bojarska, K. & Selva, N. Spatial patterns in brown bears Ursus arctos diet: the role of geographical and environmental factors. Mamm. Rev. 42, 120–143 (2012).
Article  Google Scholar 

23.
Blanchard, B. N. Size and growth patterns of the Yellowstone grizzly bear. Bears Their Biol. Manag. 7, 99–107 (1987).
Article  Google Scholar 

24.
Palomero, G., Fernández-Gil, A. & Naves, J. Reproductive rates of brown bears in the Cantabrian Mountains, Spain. Bears Their Biol. Manag. 9, 129–132 (1997).
Article  Google Scholar 

25.
Welch, C. A., Keay, J., Kendall, K. C. & Robbins, C. T. Constraints on frugivory by bears. Ecology 78, 1105–1119 (1997).
Article  Google Scholar 

26.
Hilderbrand, G. V. et al. The importance of meat, particularly salmon, to body size, population productivity, and conservation of North American brown bears. Can. J. Zool. 77, 132–138 (1999).
Article  Google Scholar 

27.
McLoughlin, P. D., Ferguson, S. H. & Messier, F. Intraspecific variation in home range overlap with habitat quality: a comparison among brown bear populations. Evol. Ecol. 14, 39–60 (2000).
Article  Google Scholar 

28.
Nomura, F. & Higashi, S. Effects of food distribution on the habitat usage of a female brown bear Ursus arctos yesoensis in a beech-forest zone of northernmost Japan. Ecol. Res. 15, 209–217 (2000).
Article  Google Scholar 

29.
Hertel, A. G. et al. Berry production drives bottom-up effects on body mass and reproductive success in an omnivore. Oikos 127, 197–207 (2017).
Article  Google Scholar 

30.
Zalewski, A. Geographical and seasonal variation in food habits and prey size of European pine martens. In Gilbert Martens and Fishers (Martes) in Human-Altered Environments (eds Harrison, D. J. & Fuller, A. K. P.) 77–98 (Springer, Boston, 2005).
Google Scholar 

31.
Soe, E. et al. Europe-wide biogeographical patterns in the diet of an ecologically and epidemiologically important mesopredator, the red fox Vulpes vulpes: a quantitative review. Mamm. Rev. 47, 198–211 (2017).
Article  Google Scholar 

32.
Jaroszewicz, B., Pirożnikow, E. & Sondej, I. Endozoochory by the guild of ungulates in Europe’s primeval forest. Forest Ecol. Manag. 305, 21–28 (2013).
Article  Google Scholar 

33.
Lundgren, E. J., Ramp, D., Ripple, W. J. & Wallach, A. D. Introduced megafauna are rewilding the Anthropocene. Ecography 41, 857–866 (2018).
Article  Google Scholar 

34.
Kowalczyk, R. et al. Foraging plasticity allows a large herbivore to persist in a sheltering forest habitat: DNA metabarcoding diet analysis of the European bison. Forest Ecol. Manag. 449, 117474 (2019).
Article  Google Scholar 

35.
Gebert, C. & Verheyden-Tixier, H. Variation of diet composition of red deer (Cervus elaphus L.) in Europe. Mamm. Rev. 31, 189–201 (2008).
Article  Google Scholar 

36.
Cosyns, E., Delporte, A., Lens, L. & Hoffmann, M. Germination success of temperate grassland species after gut passage through ungulate and rabbit guts. J. Ecol. 93, 353–361 (2005).
Article  Google Scholar 

37.
Albrecht, J. et al. Humans and climate change drove the Holocene decline of the brown bear. Sci. Rep. 7, 1–11 (2017).
CAS  Article  Google Scholar 

38.
Hertel, A. G. et al. Bears and berries: species-specific selective foraging on a patchily distributed food resource in a human-altered landscape. Behav. Ecol. Sociobiol. 70, 831–842 (2016).
PubMed  PubMed Central  Article  Google Scholar 

39.
Valido, A., Schaefer, H. M. & Jordano, P. Colour, design and reward: phenotypic integration of fleshy fruit displays. J. Evol. Biol. 24, 751–760 (2011).
CAS  PubMed  Article  Google Scholar 

40.
MacHutchon, A. G. & Wellwood, D. W. Grizzly bear food habits in the northern Yukon, Canada. Ursus 14, 225–235 (2003).
Google Scholar 

41.
Sato, Y., Mano, T. & Takatsuki, S. Stomach contents of brown bears Ursus arctos in Hokkaido, Japan. Wildl. Biol. 11, 133–144 (2005).
Article  Google Scholar 

42.
Lalleroni, A., Quenette, P.-Y., Daufresne, T., Pellerin, M. & Baltzinger, C. Exploring the potential of brown bear (Ursus arctos) as a long-distance seed disperser: a pilot study in South-Western Europe. Mammalia 81, 1–9 (2017).
Article  Google Scholar 

43.
Baldwin, R. A. & Bender, L. C. Foods and nutritional components of diets of black bear in Rocky Mountain National Park, Colorado. Can. J. Zool. 87, 1000–1008 (2009).
CAS  Article  Google Scholar 

44.
Koike, S. Long-term trends in food habits of Asiatic black bears in the Misaka Mountains on the Pacific coast of central Japan. Mamm. Biol. 75, 17–28 (2010).
Article  Google Scholar 

45.
Campos-Arceiz, A. & Blake, S. Megagardeners of the forest—the role of elephants in seed dispersal. Acta Oecol. 37, 542–553 (2011).
Article  ADS  Google Scholar 

46.
Willson, M. F. & Gende, S. M. Seed dispersal by brown bears, Ursus arctos, in southeastern Alaska. Can. Field-Nat. 118, 499–503 (2004).
Article  Google Scholar 

47.
Naoe, S. et al. Mountain-climbing bears protect cherry species from global warming through vertical seed dispersal. Curr. Biol. 26, 315–316 (2016).
Article  CAS  Google Scholar 

48.
Naoe, S. et al. Downhill seed dispersal by temperate mammals: a potential threat to plant escape from global warming. Sci. Rep. 9, 1–11 (2019).
CAS  Article  Google Scholar 

49.
McConkey, K. R. & O’Farrill, G. Loss of seed dispersal before the loss of seed dispersers. Biol. Conserv. 201, 38–49 (2016).
Article  Google Scholar 

50.
Skuban, M., Finďo, S. & Kajba, M. Human impacts on bear feeding habits and habitat selection in the Poľana Mountains, Slovakia. Eur. J. Wildl. Res. 62, 353–364 (2016).
Article  Google Scholar 

51.
Štofík, J., Merganič, J., Merganičová, K., Bučko, J. & Saniga, M. Brown bear winter feeding ecology in the area with supplementary feeding—Eastern Carpathians (Slovakia). Pol. J. Ecol. 64, 277–288 (2016).
Article  Google Scholar 

52.
Selva, N. et al. Supplementary ungulate feeding affects movement behavior of brown bears. Basic Appl. Ecol. 24, 68–76 (2017).
Article  Google Scholar 

53.
López-Bao, J. V. & González-Varo, J. P. Frugivory and spatial patterns of seed deposition by carnivorous mammals in anthropogenic landscapes: a multi-scale approach. PLoS ONE 6, e14569 (2011).
PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

54.
Traveset, A. & Willson, M. F. Effect of birds and bears on seed germination of fleshy-fruited plants in temperate rainforests of southeast Alaska. Oikos 80, 89–95 (1997).
Article  Google Scholar 

55.
Nowak, J. & Crone, E. E. It is good to be eaten by a bear: effects of ingestion on seed germination. Am. Midl. Nat. 167, 205–209 (2012).
Article  Google Scholar 

56.
Steyaert, S. M. J. G., Hertel, A. G. & Swenson, J. E. Endozoochory by brown bears stimulates germination in bilberry. Wildl. Biol. 2019, wlb.00573 (2019).
Article  Google Scholar 

57.
Samuels, I. A. & Levey, D. J. Effects of gut passage on seed germination: do experiments answer the questions they ask?. Funct. Ecol. 19, 365–368 (2005).
Article  Google Scholar 

58.
Valido, A. & Olesen, J. M. The importance of lizards as frugivores and seed dispersers. In Seed Dispersal: Theory and its Application in a Changing World (eds Dennis, A. J. et al.) 124–147 (CAB International, Wallingford, 2007).
Google Scholar 

59.
Traveset, A. Effect of seed passage through vertebrate frugivores’ guts on germination: a review. Perspect. Plant. Ecol. Syst. 1, 151–190 (1998).
Article  Google Scholar 

60.
Eriksson, O. & Fröborg, H. “Windows of opportunity” for recruitment in long-lived clonal plants: experimental studies of seedling establishment in Vaccinium shrubs. Can J. Bot. 74, 1369–1374 (1996).
Article  Google Scholar 

61.
Jansen, P. A. et al. Thieving rodents as substitute dispersers of megafaunal seeds. PNAS 109, 12610–12615 (2012).
CAS  PubMed  Article  ADS  PubMed Central  Google Scholar 

62.
Koike, S. et al. Seed removal and survival in Asiatic black bears Ursus thibetanus scats: effect of rodents as secondary seed dispersers. Wildlife Biol. 18, 24–34 (2012).
Article  Google Scholar 

63.
Bartoń, K. A., Zwijacz-Kozica, T., Zięba, F., Sergiel, A. & Selva, N. Bears without borders: long-distance movement in human-dominated landscapes. Glob. Ecol. Conserv. 17, e00541 (2019).
Article  Google Scholar 

64.
Willson, M. F. & Traveset, A. The ecology of seed dispersal. In Seeds: The Ecology of Regeneration in Plant Communities 2nd edn (ed. Fenner, M.) 85–111 (CAB International, Wallingford, 2000).
Google Scholar 

65.
Elfström, M., Støen, O.-G., Zedrosser, A., Warrington, I. & Swenson, J. E. Gut retention times in captive brown bears Ursus arctos. Wildl. Biol. 19, 317–324 (2013).
Article  Google Scholar 

66.
Koike, S. et al. Estimate of the seed shadow created by the Asiatic black bear Ursus thibetanus and its characteristics as a seed disperser in Japanese cool-temperate forest. Oikos 120, 280–290 (2010).
Article  Google Scholar 

67.
Hickey, J. R., Flynn, R. W., Buskirk, S. W., Gerow, K. G. & Willson, M. F. An evaluation of a mammalian predator, Martes americana, as a disperser of seeds. Oikos 87, 499–508 (1999).
Article  Google Scholar 

68.
Terakawa, M., Isagi, Y., Matsui, K. & Yumoto, T. Microsatellite analysis of the maternal origin of Myrica rubra seeds in the feces of Japanese macaques. Ecol. Res. 24, 663–670 (2009).
CAS  Article  Google Scholar 

69.
González-Varo, J. P., López-Bao, J. V. & Guitián, J. Functional diversity among seed dispersal kernels generated by carnivorous mammals. J. Anim. Ecol. 82, 562–571 (2013).
PubMed  Article  Google Scholar 

70.
Tsuji, Y., Okumura, T., Kitahara, M. & Jiang, Z. Estimated seed shadow generated by Japanese martens (Martes melampus): comparison with forest-dwelling animals in Japan. Zool. Sci. 33, 352–357 (2016).
Article  Google Scholar 

71.
Santini, L. et al. Ecological correlates of dispersal distance in terrestrial mammals. Hystrix 24, 181–186 (2013).
Google Scholar 

72.
Bunney, K., Bond, W. J. & Henley, M. Seed dispersal kernel of the largest surviving megaherbivore—the African savanna elephant. Biotropica 49, 395–401 (2017).
Article  Google Scholar 

73.
Galetti, et al. Ecological and evolutionary legacy of megafauna extinctions. Biol. Rev. 93, 845–862 (2018).
PubMed  Article  Google Scholar 

74.
Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on earth: a new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. Bioscience 51, 933–938 (2001).
Article  Google Scholar 

75.
Nin, S., Petrucci, W. A., Del Bubba, M., Ancillotti, C. & Giordani, E. Effects of environmental factors on seed germination and seedling establishment in bilberry (Vaccinium myrtillus L.). Sci. Hortic. 226, 241–249 (2017).
Article  Google Scholar 

76.
Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Article  Google Scholar 

77.
Oksanen, J. et al. Vegan package: community ecology package. R package version 2.5–6 (2019).

78.
Silva, L. J. D. & Medeiros, A. D. D. SeedCalc, a new automated R software tool for germination and seedling length data processing. J. Seed. Sci. 41, 250–257 (2019).
Article  Google Scholar 

79.
R Development Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2017).

80.
South, A. rworldmap: a new R package for mapping global data. R J. 3, 35–43 (2011).
Article  Google Scholar 

81.
IUCN SSC Bear Specialist Group. Ursus arctos. The IUCN Red List of Threatened Species. Version 2017-3 (2017). http://www.iucnredlist.org (Downloaded in May 2020). More