Ecology

1.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. https://doi.org/10.1890/Es15-00203.1 (2015).Article 

Google Scholar 
2.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. https://doi.org/10.1016/j.foreco.2009.09.001 (2010).Article 

Google Scholar 
3.Anderegg, W. R. L., Kane, J. M. & Anderegg, L. D. L. Consequences of widespread tree mortality triggered by drought and temperature stress. Nat Clim Change 3, 30–36 (2013).ADS 
Article 

Google Scholar 
4.Taccoen, A. et al. Background mortality drivers of European tree species: climate change matters. Proc R Soc B-Biol Sci 286, 1–10. https://doi.org/10.1098/rspb.2019.0386 (2019).Article 

Google Scholar 
5.Hartmann, H. et al. Research frontiers for improving our understanding of drought-induced tree and forest mortality. New Phytol. 218, 15–28. https://doi.org/10.1111/nph.15048 (2018).Article 
PubMed 

Google Scholar 
6.Trugman, A. T., Anderegg, L. D. L., Anderegg, W. R. L., Das, A. J. & Stephenson, N. L. Why is tree drought mortality so hard to predict? Trends Ecol. Evol., 1–13. https://doi.org/10.1016/j.tree.2021.02. (2021).7.McDowell, N. G. et al. Evaluating theories of drought-induced vegetation mortality using a multimodel-experiment framework. New Phytol. 200, 304–321. https://doi.org/10.1111/nph.12465 (2013).CAS 
Article 
PubMed 

Google Scholar 
8.Keane, R. E. et al. Tree mortality in gap models: application to climate change. Clim. Change 51, 509–540. https://doi.org/10.1023/A:1012539409854 (2001).Article 

Google Scholar 
9.Bircher, N., Cailleret, M. & Bugmann, H. The agony of choice: different empirical mortality models lead to sharply different future forest dynamics. Ecol. Appl. 25, 1303–1318. https://doi.org/10.1890/14-1462.1 (2015).Article 
PubMed 

Google Scholar 
10.Bugmann, H. et al. Tree mortality submodels drive long term forest dynamics: an assessment across 15 models from the stand to the global scale. Ecosphere 10, 1–22. https://doi.org/10.1002/ecs2.2616 (2019).Article 

Google Scholar 
11.Friend, A. D. et al. Carbon residence time dominates uncertainty in terrestrial vegetation responses to future climate and atmospheric CO2. Proc Natl Acad Sci USA 111, 3280–3285. https://doi.org/10.1073/pnas.1222477110 (2014).ADS 
CAS 
Article 
PubMed 

Google Scholar 
12.Lines, E. R., Coomes, D. A. & Purves, D. W. Influences of forest structure, climate and species composition on tree mortality across the Eastern US. PLoS ONE 5, 1–12. https://doi.org/10.1371/journal.pone.0013212 (2010).CAS 
Article 

Google Scholar 
13.Purves, D. & Pacala, S. Predictive models of forest dynamics. Science 320, 1452–1453. https://doi.org/10.1126/science.1155359 (2008).ADS 
CAS 
Article 
PubMed 

Google Scholar 
14.Cailleret, M., Bircher, N., Hartig, F., Hülsmann, L. & Bugmann, H. Bayesian calibration of a growth-dependent tree mortality model to simulate the dynamics of European temperate forests. Ecol. Appl. 30, 1–17. https://doi.org/10.1002/eap.2021 (2020).Article 

Google Scholar 
15.Rowland, L., Martinez-Vilalta, J. & Mencuccini, M. Hard times for high expectations from hydraulics: predicting drought-induced forest mortality at landscape scales remains a challenge. New Phytol. 230, 1685–1687. https://doi.org/10.1111/nph.17317 (2021).Article 
PubMed 

Google Scholar 
16.Cailleret, M. et al. A synthesis of radial growth patterns preceding tree mortality. Glob. Change Biol. 23, 1675–1690. https://doi.org/10.1111/gcb.13535 (2017).ADS 
Article 

Google Scholar 
17.Bigler, C. & Bugmann, H. Growth-dependent tree mortality models based on tree rings. Can. J. For. Res. 33, 210–221. https://doi.org/10.1139/X02-180 (2003).Article 

Google Scholar 
18.Hülsmann, L., Bugmann, H., Cailleret, M. & Brang, P. How to kill a tree: empirical mortality models for 18 species and their performance in a dynamic forest model. Ecol. Appl. 28, 522–540. https://doi.org/10.1002/eap.1668 (2018).Article 
PubMed 

Google Scholar 
19.Weiskittel, A. R., Hann, D. W., Kershaw, J. A. & Vanclay, J. K. in Forest Growth and Yield Modeling Ch. 8, 139–155 (Wiley, 2011).20.Holzwarth, F., Kahl, A., Bauhus, J. & Wirth, C. Many ways to die – partitioning tree mortality dynamics in a near-natural mixed deciduous forest. J. Ecol. 101, 220–230. https://doi.org/10.1111/1365-2745.12015 (2013).Article 

Google Scholar 
21.Dobbertin, M. Tree growth as indicator of tree vitality and of tree reaction to environmental stress: a review. Eur. J. For. Res. 124, 319–333. https://doi.org/10.1007/s10342-005-0085-3 (2005).Article 

Google Scholar 
22.Thrippleton, T., Hülsmann, L., Cailleret, M. & Bugmann, H. Projecting forest dynamics across Europe: potentials and pitfalls of empirical mortality algorithms. Ecosystems 23, 188–203. https://doi.org/10.1007/s10021-019-00397-3 (2020).Article 

Google Scholar 
23.Adams, H. D. et al. Empirical and process-based approaches to climate-induced forest mortality models. Front Plant Sci 4, 1–5. https://doi.org/10.3389/fpls.2013.00438 (2013).ADS 
CAS 
Article 

Google Scholar 
24.Archambeau, J. et al. Similar patterns of background mortality across Europe are mostly driven by drought in European beech and a combination of drought and competition in Scots pine. Agric. For. Meteorol. 280, 1–12. https://doi.org/10.1016/j.agrformet.2019.107772 (2020).Article 

Google Scholar 
25.Luo, Y. & Chen, H. Y. H. Competition, species interaction and ageing control tree mortality in boreal forests. J. Ecol. 99, 1470–1480. https://doi.org/10.1111/j.1365-2745.2011.01882.x (2011).Article 

Google Scholar 
26.Brzeziecki, B. & Kienast, F. Classifying the life-history strategies of trees on the basis of the grimian model. For. Ecol. Manage. 69, 167–187. https://doi.org/10.1016/0378-1127(94)90227-5 (1994).Article 

Google Scholar 
27.Valladares, F. & Niinemets, U. Shade tolerance, a key plant feature of complex nature and consequences. Annu. Rev. Ecol. Evol. Syst. 39, 237–257. https://doi.org/10.1146/annurev.ecolsys.39.110707.173506 (2008).Article 

Google Scholar 
28.Kobe, R. K. & Coates, K. D. Models of sapling mortality as a function of growth to characterize interspecific variation in shade tolerance of eight tree species of northwestern British Columbia. Can. J. For. Res. 27, 227–236. https://doi.org/10.1139/x96-182 (1997).Article 

Google Scholar 
29.Wyckoff, P. H. & Clark, J. S. The relationship between growth and mortality for seven co-occurring tree species in the southern Appalachian Mountains. J. Ecol. 90, 604–615. https://doi.org/10.1046/j.1365-2745.2002.00691.x (2002).Article 

Google Scholar 
30.Anderegg, L. D. L. & HilleRisLambers, J. Drought stress limits the geographic ranges of two tree species via different physiological mechanisms. Glob. Change Biol. 22, 1029–1045. https://doi.org/10.1111/gcb.13148 (2016).ADS 
Article 

Google Scholar 
31.Clark, J. S. et al. The impacts of increasing drought on forest dynamics, structure, and biodiversity in the United States. Glob. Change Biol. 22, 2329–2352. https://doi.org/10.1111/gcb.13160 (2016).ADS 
Article 

Google Scholar 
32.Etzold, S. et al. One century of forest monitoring data in Switzerland reveals species- and site-specific trends of climate-induced tree mortality. Front Plant Sci 10, 1–19. https://doi.org/10.3389/fpls.2019.00307 (2019).Article 

Google Scholar 
33.Schuldt, B. et al. A first assessment of the impact of the extreme 2018 summer drought on Central European forests. Basic Appl. Ecol. 45, 86–103. https://doi.org/10.1016/j.baae.2020.04.003 (2020).Article 

Google Scholar 
34.Vanoni, M., Cailleret, M., Hülsmann, L., Bugmann, H. & Bigler, C. How do tree mortality models from combined tree-ring and inventory data affect projections of forest succession?. For. Ecol. Manage. 433, 606–617. https://doi.org/10.1016/j.foreco.2018.11.042 (2019).Article 

Google Scholar 
35.Huber, N., Bugmann, H. & Lafond, V. Capturing ecological processes in dynamic forest models: why there is no silver bullet to cope with complexity. Ecosphere 11, 1–34. https://doi.org/10.1002/ecs2.3109 (2020).Article 

Google Scholar 
36.Bugmann, H. A simplified forest model to study species composition along climate gradients. Ecology 77, 2055–2074. https://doi.org/10.2307/2265700 (1996).Article 

Google Scholar 
37.Hülsmann, L., Bugmann, H. & Brang, P. How to predict tree death from inventory data – lessons from a systematic assessment of European tree mortality models. Can. J. For. Res. 47, 890–900. https://doi.org/10.1139/cjfr-2016-0224 (2017).Article 

Google Scholar 
38.Eid, T. & Tuhus, E. Models for individual tree mortality in Norway. For. Ecol. Manag. 154, 69–84. https://doi.org/10.1016/S0378-1127(00)00634-4 (2001).Article 

Google Scholar 
39.Monserud, R. A. & Sterba, H. Modeling individual tree mortality for Austrian forest species. For. Ecol. Manag. 113, 109–123. https://doi.org/10.1016/S0378-1127(98)00419-8 (1999).Article 

Google Scholar 
40.Dursky, J. Modellierung der Absterbeprozesse in Rein- und Mischbeständen aus Fichte und Buche. Allg. Forst- u. Jagdztg. 168, 131–134 (1997).
Google Scholar 
41.Trasobares, A., Pukkala, T. & Muna, J. Growth and yield model for uneven-aged mixtures of Pinus sylvestris L. and Pinus nigra Arn. in Catalonia, north-east Spain. Ann. For. Sci. 61, 9–24, doi:https://doi.org/10.1051/forset:2003080 (2004).42.Crecente-Campo, F., Soares, P., Tome, M. & Dieguez-Aranda, U. Modelling annual individual-tree growth and mortality of Scots pine with data obtained at irregular measurement intervals and containing missing observations. For. Ecol. Manage. 260, 1965–1974. https://doi.org/10.1016/j.foreco.2010.08.044 (2010).Article 

Google Scholar 
43.Palahi, M., Pukkala, T., Miina, J. & Montero, G. Individual-tree growth and mortality models for Scots pine (Pinus sylvestris L.) in north-east Spain. Ann. For. Sci. 60, 1–10, https://doi.org/10.1051/forest:2002068 (2003).44.Bravo-Oviedo, A., Sterba, H., del Rio, M. & Bravo, F. Competition-induced mortality for Mediterranean Pinus pinaster Ait. and P-sylvestris L. For. Ecol. Manag. 222, 88–98, doi:https://doi.org/10.1016/j.foreco.2005.10.016 (2006).45.Fridman, J. & Ståhl, G. A three-step approach for modelling tree mortality in Swedish forests. Scand. J. For. Res. 16, 455–466. https://doi.org/10.1080/02827580152632856 (2001).Article 

Google Scholar 
46.Wunder, J. et al. Growth-mortality relationships as indicators of life-history strategies: a comparison of nine tree species in unmanaged European forests. Oikos 117, 815–828. https://doi.org/10.1111/j.0030-1299.2008.16371.x (2008).Article 

Google Scholar 
47.Das, A., Battles, J., Stephenson, N. L. & van Mantgem, P. J. The contribution of competition to tree mortality in old-growth coniferous forests. For. Ecol. Manage. 261, 1203–1213. https://doi.org/10.1016/j.foreco.2010.12.035 (2011).Article 

Google Scholar 
48.Bigler, C. & Bugmann, H. Predicting the time of tree death using dendrochronological data. Ecol. Appl. 14, 902–914. https://doi.org/10.1890/03-5011 (2004).Article 

Google Scholar 
49.Larocque, G. R., Archambault, L. & Delisle, C. Development of the gap model ZELIG-CFS to predict the dynamics of North American mixed forest types with complex structures. Ecol. Model. 222, 2570–2583. https://doi.org/10.1016/j.ecolmodel.2010.08.035 (2011).Article 

Google Scholar 
50.Timofeeva, G. et al. Long-term effects of drought on tree-ring growth and carbon isotope variability in Scots pine in a dry environment. Tree Physiol. 37, 1028–1041. https://doi.org/10.1093/treephys/tpx041 (2017).CAS 
Article 
PubMed 

Google Scholar 
51.Neumann, M., Mues, V., Moreno, A., Hasenauer, H. & Seidl, R. Climate variability drives recent tree mortality in Europe. Glob. Change Biol. 23, 4788–4797. https://doi.org/10.1111/gcb.13724 (2017).ADS 
Article 

Google Scholar 
52.Levesque, M. et al. Drought response of five conifer species under contrasting water availability suggests high vulnerability of Norway spruce and European larch. Glob. Change Biol. 19, 3184–3199. https://doi.org/10.1111/gcb.12268 (2013).ADS 
Article 

Google Scholar 
53.Rigling, A. et al. Driving factors of a vegetation shift from Scots pine to pubescent oak in dry Alpine forests. Glob. Change Biol. 19, 229–240. https://doi.org/10.1111/gcb.12038 (2013).ADS 
Article 

Google Scholar 
54.Eyvindson, K., Repo, A. & Mönkkönen, M. Mitigating forest biodiversity and ecosystem service losses in the era of bio-based economy. Forest Policy Econ 92, 119–127. https://doi.org/10.1016/j.forpol.2018.04.009 (2018).Article 

Google Scholar 
55.Mina, M. et al. Future ecosystem services from European mountain forests under climate change. J. Appl. Ecol. 54, 389–401. https://doi.org/10.1111/1365-2664.12772 (2017).Article 

Google Scholar 
56.Thom, D., Rammer, W. & Seidl, R. The impact of future forest dynamics on climate: interactive effects of changing vegetation and disturbance regimes. Ecol. Monogr. 87, 665–684. https://doi.org/10.1002/ecm.1272 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
57.Blattert, C., Lemm, R., Thees, O., Lexer, M. J. & Hanewinkel, M. Management of ecosystem services in mountain forests: review of indicators and value functions for model based multi-criteria decision analysis. Ecol Indic 79, 391–409. https://doi.org/10.1016/j.ecolind.2017.04.025 (2017).Article 

Google Scholar 
58.Haeler, E. et al. Saproxylic species are linked to the amount and isolation of dead wood across spatial scales in a beech forest. Landscape Ecol. 36, 89–104. https://doi.org/10.1007/s10980-020-01115-4 (2021).Article 

Google Scholar 
59.Das, A. J., Stephenson, N. L. & Davis, K. P. Why do trees die? Characterizing the drivers of background tree mortality. Ecology 97, 2616–2627. https://doi.org/10.1002/ecy.1497 (2016).Article 
PubMed 

Google Scholar 
60.Franklin, J. F., Shugart, H. H. & Harmon, M. E. Tree death as an ecological process. Bioscience 37, 550–556. https://doi.org/10.2307/1310665 (1987).Article 

Google Scholar 
61.Huber, N., Bugmann, H. & Lafond, V. Global sensitivity analysis of a dynamic vegetation model: model sensitivity depends on successional time, climate and competitive interactions. Ecol. Model. 368, 377–390. https://doi.org/10.1016/j.ecolmodel.2017.12.013 (2018).Article 

Google Scholar 
62.Portier, J. et al. “Latent reserves”: a hidden treasure in National Forest Inventories. J. Ecol. 109, 369–383. https://doi.org/10.1111/1365-2745.13487 (2021).Article 

Google Scholar 
63.Kunstler, G. et al. Demographic performance of European tree species at their hot and cold climatic edges. J. Ecol. 109, 1041–1054. https://doi.org/10.1111/1365-2745.13533 (2021).Article 

Google Scholar 
64.Gutierrez, A. G., Snell, R. S. & Bugmann, H. Using a dynamic forest model to predict tree species distributions. Glob. Ecol. Biogeogr. 25, 347–358. https://doi.org/10.1111/geb.12421 (2016).Article 

Google Scholar 
65.Botkin, D. B., Janak, J. F. & Wallis, J. R. Some ecological consequences of a computer model of forest growth. J. Ecol. 60, 849–872. https://doi.org/10.2307/2258570 (1972).Article 

Google Scholar 
66.Bugmann, H. A review of forest gap models. Clim. Change 51, 259–305. https://doi.org/10.1023/A:1012525626267 (2001).Article 

Google Scholar 
67.Watt, A. S. Pattern and process in the plant community. J. Ecol. 35, 1–22. https://doi.org/10.2307/2256497 (1947).Article 

Google Scholar 
68.Shugart, H. H. & Smith, T. M. A review of forest patch models and their application to global change research. Clim. Change 34, 131–153. https://doi.org/10.1007/BF00224626 (1996).ADS 
Article 

Google Scholar 
69.Monserud, R. A. Simulation of forest tree mortality. Forest Science 22, 438–444. https://doi.org/10.1093/forestscience/22.4.438 (1976).Article 

Google Scholar 
70.IPCC. Climate Change 2014: Impacts, adaptation, and vulnerability, Pt A: global and sectoral aspects. Climate Change 2014: Impacts, Adaptation, and Vulnerability, Pt A: Global and Sectoral Aspects, 1-1131, doi:https://doi.org/10.1017/CBO9781107415379 (2014).71.Manusch, C., Bugmann, H., Heiri, C. & Wolf, A. Tree mortality in dynamic vegetation models: a key feature for accurately simulating forest properties. Ecol. Model. 243, 101–111. https://doi.org/10.1016/j.ecolmodel.2012.06.008 (2012).Article 

Google Scholar 
72.R: A language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria, 2020). More

1.Graven, H. D. et al. Enhanced seasonal exchange of CO2 by Northern ecosystems since 1960. Science 341, 1085–1089 (2013).ADS 
CAS 
Article 

Google Scholar 
2.Humphrey, V. et al. Sensitivity of atmospheric CO2 growth rate to observed changes in terrestrial water storage. Nature 560, 628–631 (2018).ADS 
CAS 
Article 

Google Scholar 
3.Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).ADS 
CAS 
Article 

Google Scholar 
4.Schlesinger, W. H. & Jasechko, S. Agricultural and forest meteorology transpiration in the global water cycle. Agric. For. Meteorol. 189–190, 115–117 (2014).ADS 
Article 

Google Scholar 
5.Dawson, T. E. & Pate, J. S. Seasonal water uptake and movement in root systems of Australian phraeatophytic plants of dimorphic root morphology: a stable isotope investigation. Oecologia 107, 13–20 (1996).ADS 
Article 

Google Scholar 
6.Voltas, J., Devon, L., Maria Regina, C. & Juan Pedro, F. Intraspecific variation in the use of water sources by the circum-Mediterranean conifer Pinus halepensis. New Phytol. 208, 1031–1041 (2015).Article 

Google Scholar 
7.Grossiord, C. et al. Prolonged warming and drought modify belowground interactions for water among coexisting plants. Tree Physiol. 39, 55–63 (2018).Article 

Google Scholar 
8.Rempe, D. M. & Dietrich, W. E. Direct observations of rock moisture, a hidden component of the hydrologic cycle. Proc. Natl Acad. Sci. USA 115, 2664–2669 (2018).ADS 
CAS 
Article 

Google Scholar 
9.Querejeta, J. I., Estrada-Medina, H., Allen, M. F. & Jiménez-Osornio, J. J. Water source partitioning among trees growing on shallow karst soils in a seasonally dry tropical climate. Oecologia 152, 26–36 (2007).ADS 
Article 

Google Scholar 
10.Evaristo, J. & McDonnell, J. J. Prevalence and magnitude of groundwater use by vegetation: a global stable isotope meta-analysis. Sci Rep. 7, 44110 (2017).ADS 
Article 

Google Scholar 
11.Barbeta, A. & Peñuelas, J. Relative contribution of groundwater to plant transpiration estimated with stable isotopes. Sci Rep. 7, 10580 (2017).ADS 
Article 

Google Scholar 
12.Jobbágy, E. G., Nosetto, M. D., Villagra, P. E. & Jackson, R. B. Water subsidies from mountains to deserts: their role in sustaining groundwater-fed oases in a sandy landscape. Ecol. Appl. 21, 678–694 (2011).Article 

Google Scholar 
13.Fan, Y., Miguez-Macho, G., Jobbágy, E. G., Jackson, R. B. & Otero-Casal, C. Hydrologic regulation of plant rooting depth. Proc. Natl Acad. Sci. USA 114, 10572–10577 (2017).ADS 
CAS 
Article 

Google Scholar 
14.Ellsworth, P. Z. & Sternberg, L. S. L. Seasonal water use by deciduous and evergreen woody species in a scrub community is based on water availability and root distribution. Ecohydrology 551, 538–551 (2015).Article 

Google Scholar 
15.Sohel, S. Spatial and Temporal Variation of Sources of Water Across Multiple Tropical Rainforest Trees. PhD thesis, Univ. Queensland (2019).16.Williams, D. G. & Ehleringer, J. R. Intra- and interspecific variation for summer precipitation use in pinyon-juniper woodlands. Ecol. Monogr. 70, 517–537 (2000).
Google Scholar 
17.Allen, S. T., Kirchner, J. W., Braun, S., Siegwolf, R., T. W. & Goldsmith, G. R. Seasonal origins of soil water used by trees. Hydrol. Earth Syst. Sci. 23, 1199–1210 (2019).ADS 
CAS 
Article 

Google Scholar 
18.David, T. S. et al. Water-use strategies in two co-occurring Mediterranean evergreen oaks: surviving the summer drought. Tree Physiol. 27, 793–803 (2007).CAS 
Article 

Google Scholar 
19.Zencich, S. J., Froend, R. H., Turner, J. V. & Gailitis, V. Influence of groundwater depth on the seasonal sources of water accessed by Banksia tree species on a shallow, sandy coastal aquifer. Oecologia 131, 8–19 (2002).ADS 
Article 

Google Scholar 
20.Naumburg, E., Mata-Gonzalez, R., Hunter, R. G. & Martin, D. W. Phreatophytic vegetation and groundwater fluctuations: a review of current research and application of ecosystem response modeling with an emphasis on Great Basin vegetation. Environ. Manage. 35, 726–740 (2005).Article 

Google Scholar 
21.Snyder, K. A. & Williams, D. G. Water sources used by riparian trees varies among stream types on the San Pedro River, Arizona. Agric. For. Meteorol. 105, 227–240 (2000).ADS 
Article 

Google Scholar 
22.Kottek, M., Grieser, J., Beck, C., Rudolf, B. & Rubel, F. World map of the Köppen–Geiger climate classification updated. Meteorol. Zeitschrift 15, 259–263 (2006).ADS 
Article 

Google Scholar 
23.Eleringer J. R. & Dawson T. Water uptake by plants: perspectives from stable isotope composition. Plant Cell Environ. 1073–1082 (1992).24.Dawson, T. E., Mambelli, S., Plamboeck, A. H., Templer, P. H. & Tu, K. P. Stable isotopes in plant ecology. Annu. Rev. Ecol. Syst. 33, 507–559 (2002).Article 

Google Scholar 
25.Rothfuss, Y. & Javaux, M. Reviews and syntheses: isotopic approaches to quantify root water uptake: a review and comparison of methods. Biogeosciences 14, 2199–2224 (2017).ADS 
CAS 
Article 

Google Scholar 
26.Orlowski, N. et al. Inter-laboratory comparison of cryogenic water extraction systems for stable isotope analysis of soil water. Hydrol. Earth Syst. Sci. 22, 3619–3637 (2018).ADS 
CAS 
Article 

Google Scholar 
27.Chen, Y. et al. Stem water cryogenic extraction biases estimation in deuterium isotope composition of plant source water. Proc. Natl Acad. Sci. USA 117, 33345–33350 (2021).ADS 
Article 

Google Scholar 
28.Pastorello, G., Trotta, C., Canfora, E. & Al., E. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci. Data 7, 225 (2020).Article 

Google Scholar 
29.Zhao, Y. & Wang, L. Plant water use strategy in response to spatial and temporal variation in precipitation patterns in China: a stable isotope analysis. Forests 9, 1–21 (2018).
Google Scholar 
30.Miguez-Macho, G. & Fan, Y. The role of groundwater in the Amazon water cycle: 2. Influence on seasonal soil moisture and evapotranspiration. J. Geophys. Res. Atmos. 117, (2012).31.Poulter, B. et al. Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle. Nature 509, 600–603 (2014).ADS 
CAS 
Article 

Google Scholar 
32.Ahlstrom, A. et al. The dominant role of semi-arid ecosystems in the trend and variability of the land CO2 sink. Science 348, 895–899 (2015).ADS 
Article 

Google Scholar  More

1.Ripple, W. J. et al. Status and ecological effects of the world’s largest carnivores. Science 343, 1241484–1241484 (2014).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
2.Treves, A. & Karanth, K. U. Human–carnivore conflict and perspectives on carnivore management worldwide. Conserv. Biol. 17, 1491–1499 (2003).Article 

Google Scholar 
3.Linnell, J. D. C. & Boitani, L. Building biological realism into wolf management policy: The development of the population approach in Europe. Hystrix Ital. J. Mammal. 23, 80–91 (2011).
Google Scholar 
4.Heurich, M. et al. Illegal hunting as a major driver of the source-sink dynamics of a reintroduced lynx population in Central Europe. Biol. Conserv. 224, 355–365 (2018).Article 

Google Scholar 
5.Breitenmoser-Würsten, C., Vandel, J.-M., Zimmermann, F. & Breitenmoser, U. Demography of lynx Lynx lynx in the Jura Mountains. Wildl. Biol. 13, 381–392 (2007).Article 

Google Scholar 
6.Clutton-Brock, T. & Sheldon, B. C. Individuals and populations: The role of long-term, individual-based studies of animals in ecology and evolutionary biology. Trends Ecol. Evol. 25, 562–573 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
7.O’Connell, A., Nichols, J. D. & Karanth, K. U. Camera Traps in Animal Ecology: Methods and Analyses. (Springer Tokyo, 2011).8.Noss, A. J. et al. A Camera trapping and radio telemetry study of lowland tapir (Tapirus terrestris) in Bolivian Dry Forests. Tapir Cons. 12, 9 (2003).
Google Scholar 
9.Karanth, K. U. & Nichols, J. D. Estimation of tiger densities in India using photographic captures and recaptures. Ecology 79, 11 (1998).Article 

Google Scholar 
10.Satter, C. B., Augustine, B. C., Harmsen, B. J., Foster, R. J. & Kelly, M. J. Sex‐specific population dynamics of ocelots in Belize using open population spatial capture–recapture. Ecosphere 10, e02792 (2019).Article 

Google Scholar 
11.Silver, S. C. et al. The use of camera traps for estimating jaguar Panthera onca abundance and density using capture/recapture analysis. Oryx 38, 148–154 (2004).Article 

Google Scholar 
12.Zimmermann, F., Breitenmoser-Würsten, C., Molinari-Jobin, A. & Breitenmoser, U. Optimizing the size of the area surveyed for monitoring a Eurasian lynx (Lynx lynx) population in the Swiss Alps by means of photographic capture–recapture. Integr. Zool. 8, 232–243 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
13.Royle, J. A., Chandler, R. B., Sollmann, R. & Gardner, B. Spatial Capture–Recapture. (Elsevier, 2014).
Google Scholar 
14.Chandler, R. B. & Clark, J. D. Spatially explicit integrated population models. Methods Ecol. Evol. 5, 1351–1360 (2014).Article 

Google Scholar 
15.Kaczensky, P. et al. Status, management and distribution of large carnivores—Bear, lynx, wolf and wolverine in Europe (EuropeanCommission, 2013).16.Magg, N. et al. Habitat availability is not limiting the distribution of the Bohemian–Bavarian lynx Lynx lynx population. Oryx 50, 742–752 (2016).Article 

Google Scholar 
17.Müller, J. et al. Protected areas shape the spatial distribution of a European lynx population more than 20 years after reintroduction. Biol. Conserv. 177, 210–217 (2014).Article 

Google Scholar 
18.Bull, J. K. et al. The effect of reintroductions on the genetic variability in Eurasian lynx populations: The cases of Bohemian–Bavarian and Vosges–Palatinian populations. Conserv. Genet. 17, 1229–1234 (2016).Article 

Google Scholar 
19.Walston, J. et al. Bringing the tiger back from the brink—The six percent solution. PLoS Biol. 8, e1000485 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
20.Schmidt, K., Jędrzejewski, W. & Okarma, H. Spatial organization and social relations in the Eurasian lynx population in Bialowieza Primeval Forest, Poland. Acta Theriol. (Warsz.) 42, 289–312 (1997).Article 

Google Scholar 
21.Bunnefeld, N., Linnell, J. D. C., Odden, J., van Duijn, M. A. J. & Andersen, R. Risk taking by Eurasian lynx (Lynx lynx) in a human-dominated landscape: Effects of sex and reproductive status. J. Zool. 270, 31–39 (2006).
Google Scholar 
22.Gaillard, J.-M., Nilsen, E. B., Odden, J., Andrén, H. & Linnell, J. D. C. One size fits all: Eurasian lynx females share a common optimal litter size. J. Anim. Ecol. 83, 107–115 (2014).PubMed 
Article 

Google Scholar 
23.Nilsen, E. B., Linnell, J. D. C., Odden, J., Samelius, G. & Andrén, H. Patterns of variation in reproductive parameters in Eurasian lynx (Lynx lynx). Acta Theriol. (Warsz.) 57, 217–223 (2012).Article 

Google Scholar 
24.O’Brien, T. G., Kinnaird, M. F. & Wibisono, H. T. Crouching tigers, hidden prey: Sumatran tiger and prey populations in a tropical forest landscape. Anim. Conserv. 6, 131–139 (2003).Article 

Google Scholar 
25.Cailleret, M., Heurich, M. & Bugmann, H. Reduction in browsing intensity may not compensate climate change effects on tree species composition in the Bavarian Forest National Park. For. Ecol. Manag. 328, 179–192 (2014).Article 

Google Scholar 
26.Heurich, M. et al. Country, cover or protection: What shapes the distribution of red deer and roe deer in the Bohemian Forest Ecosystem?. PLoS ONE 10, e0120960 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
27.van Beeck Calkoen, S. T. S. et al. The blame game: Using eDNA to identify species-specific tree browsing by red deer (Cervus elaphus) and roe deer (Capreolus capreolus) in a temperate forest. For. Ecol. Manag. 451, 117483 (2019).Article 

Google Scholar 
28.Wölfl, M. et al. Distribution and status of lynx in the border region between Czech Republic, Germany and Austria. Acta Theriol. 46, 181–194 (2001).Article 

Google Scholar 
29.Mináriková, T. et al. Lynx monitoring report for Bohemian–Bavarian–Austrian lynx population for lynx year 2017 (INTERREG Central Europe, 2019).30.Weingarth, K. et al. First estimation of Eurasian lynx (Lynx lynx) abundance and density using digital cameras and capture–recapture techniques in a German national park. Anim. Biodivers. Conserv. 35, 197–207 (2012).Article 

Google Scholar 
31.Belotti, E. et al. Patterns of lynx predation at the interface between protected areas and multi-use landscapes in Central Europe. PLoS ONE 10, e0138139 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
32.Tobler, M. W. & Powell, G. V. N. Estimating jaguar densities with camera traps: Problems with current designs and recommendations for future studies. Biol. Conserv. 159, 109–118 (2013).Article 

Google Scholar 
33.Zimmermann, F., Breitenmoser-Würsten, C. & Breitenmoser, U. Natal dispersal of Eurasian lynx ( Lynx lynx ) in Switzerland. J. Zool. 267, 381 (2005).Article 

Google Scholar 
34.Andrén, H. et al. Survival rates and causes of mortality in Eurasian lynx (Lynx lynx) in multi-use landscapes. Biol. Conserv. 131, 23–32 (2006).Article 

Google Scholar 
35.Gimenez, O. et al. Spatial density estimates of Eurasian lynx (Lynx lynx) in the French Jura and Vosges Mountains. Ecol. Evol. 9, 11707–11715 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
36.Pesenti, E. & Zimmermann, F. Density estimations of the Eurasian lynx (Lynx lynx) in the Swiss Alps. J. Mammal. 94, 73–81 (2013).Article 

Google Scholar 
37.Weingarth, K. et al. Hide and seek: Extended camera-trap session lengths and autumn provide best parameters for estimating lynx densities in mountainous areas. Biodivers. Conserv. 24, 2935–2952 (2015).Article 

Google Scholar 
38.Pollock, K. H. A capture–recapture design robust to unequal probability of capture. J. Wildl. Manag. 46, 752 (1982).Article 

Google Scholar 
39.Augustine, B. benaug/OpenPopSCR. (2019). https://github.com/benaug/OpenPopSCR.40.Ergon, T. & Gardner, B. Separating mortality and emigration: Modelling space use, dispersal and survival with robust-design spatial capture–recapture data. Methods Ecol. Evol. 5, 1327–1336 (2014).Article 

Google Scholar 
41.Schaub, M. & Royle, J. A. Estimating true instead of apparent survival using spatial Cormack–Jolly–Seber models. Methods Ecol. Evol. 5, 1316–1326 (2014).Article 

Google Scholar 
42.Plummer, M., Best, N., Cowles, K. & Vines, K. CODA: Convergence diagnosis and output analysis for MCMC. R News 6, 7–11 (2005).
Google Scholar 
43.Brooks, S. P. & Gelman, A. General methods for monitoring convergence of iterative simulations. J. Comput. Graph. Stat. 7, 434–455 (1998).MathSciNet 

Google Scholar 
44.Efford, M. secr 4.1—Spatially explicit capture–recapture in R. (2019). https://cran.microsoft.com/snapshot/2019-12-24/web/packages/secr/vignettes/secr-overview.pdf.45.Burnham, K. P. & Overton, W. S. Robust estimation of population size when capture probabilities vary among animals. Ecology 60, 927–936 (1979).Article 

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

Google Scholar 
47.O’Brien, T. G. Abundance, density and relative abundance: A conceptual framework. In Camera Traps in Animal Ecology (eds O’Connell, A. F. et al.) 71–96 (Springer Japan, 2011). https://doi.org/10.1007/978-4-431-99495-4_6.Chapter 

Google Scholar 
48.Rovero, F. & Zimmermann, F. Camera Trapping for Wildlife Research (Pelagic Publishing Ltd, 2016).
Google Scholar 
49.Augustine, B. C. et al. Sex-specific population dynamics and demography of capercaillie (Tetrao urogallus L.) in a patchy environment. Popul. Ecol. 62, 80–90 (2020).Article 

Google Scholar 
50.Duľa, M. et al. Multi-seasonal systematic camera-trapping reveals fluctuating densities and high turnover rates of Carpathian lynx on the western edge of its native range. Sci. Rep. 11, 9236 (2021).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
51.Avgan, B., Zimmermann, F., Güntert, M., Arıkan, F. & Breitenmoser, U. The first density estimation of an isolated Eurasian lynx population in southwest Asia. Wildl. Biol. 20, 217–221 (2014).Article 

Google Scholar 
52.Mengüllüoğlu, D., Ambarlı, H., Berger, A. & Hofer, H. Foraging ecology of Eurasian lynx populations in southwest Asia: Conservation implications for a diet specialist. Ecol. Evol. 8, 9451–9463 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
53.Heurich, M. et al. Activity patterns of Eurasian lynx are modulated by light regime and individual traits over a wide latitudinal range. PLoS ONE 9, e114143 (2014).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
54.Jedrzejewski, W. et al. Population dynamics (1869–1994), demography, and home ranges of the lynx in Bialowieza Primeval Forest (Poland and Belarus). Ecography 19, 122–138 (1996).Article 

Google Scholar 
55.Gardner, B., Sollmann, R., Kumar, N. S., Jathanna, D. & Karanth, K. U. State space and movement specification in open population spatial capture–recapture models. Ecol. Evol. 8, 10336–10344 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
56.López-Bao, J. V. et al. Eurasian lynx fitness shows little variation across Scandinavian human-dominated landscapes. Sci. Rep. 9, 8903 (2019).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
57.Engleder, T. et al. First breeding record of a 1-year-old female Eurasian lynx. Eur. J. Wildl. Res. 65, 17 (2019).Article 

Google Scholar 
58.Heurich, M. et al. Selective predation of a stalking predator on ungulate prey. PLoS ONE 11, e0158449 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
59.Andrén, H. & Liberg, O. Large impact of Eurasian lynx predation on roe deer population dynamics. PLoS ONE 10, e0120570 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
60.Elmhagen, B. & Rushton, S. P. Trophic control of mesopredators in terrestrial ecosystems: Top-down or bottom-up?. Ecol. Lett. 10, 197–206 (2007).PubMed 
Article 

Google Scholar 
61.Wikenros, C. et al. Fear or food—Abundance of red fox in relation to occurrence of lynx and wolf. Sci. Rep. 7, 9059 (2017).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
62.Helldin, J. O., Liberg, O. & Glöersen, G. Lynx (Lynx lynx) killing red foxes (Vulpes vulpes) in boreal Sweden? Frequency and population effects. J. Zool. 270, 657–663 (2006).Article 

Google Scholar 
63.Sollmann, R., Mohamed, A., Samejima, H. & Wilting, A. Risky business or simple solution—Relative abundance indices from camera-trapping. Biol. Conserv. 159, 405–412 (2013).Article 

Google Scholar 
64.Linnell, J. D. C., Kaczensky, P., Wotschikowsky, U., Lescureux, N. & Boitani, L. Framing the relationship between people and nature in the context of European conservation: Relationship between people and nature. Conserv. Biol. 29, 978–985 (2015).PubMed 
Article 

Google Scholar  More

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References1.Anderson, D. M. et al. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.2107387118 (2021).Article 

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Inverted paradoxNeutral theory can reproduce properties of terrestrial biodiversity observed at local (e.g., an island) or metacommunity (i.e., a set of interacting communities linked by dispersal of species) scales, particularly ranked species abundance curves (i.e., histograms of species abundance ordered along the x-axis from most to least common) [14]. Central to neutral theory is the interplay between ‘stochastic exclusion’ and either immigration or speciation. Stochastic exclusion is the reduction in biodiversity caused by random deaths and abundance-dependent replacement and, if not countered by other processes, ultimately leads to only a single remaining species [14]. Immigration of species into a local community or speciation within the metacommunity offset stochastic exclusion and maintain biodiversity [14]. This relationship is illustrated in Fig. 1 by simulated time-series of phytoplankton diversity for three populations at steady-state with 10,000, 100,000, and 1,000,000 total individuals and an initial condition of 10,000 species each (Fig. 1) (Methods). Subjection of these populations to 50% random mortality per generation and replacement in proportion to the relative abundance of remaining species results in an eventual rate of decrease in diversity that is equivalent across population sizes (Fig. 1; dashed black lines), eventually yielding the expected final equilibrium of a single species. When a small rate of immigration is added to this simulation (here, 0.03% or 0.3% per generation), complete stochastic exclusion is replaced by steady-state diversities that vary in direct proportion to population size and immigration rate (Fig. 1; colored dashed and dotted lines). Similar considerations led Hubbell [14] to earlier propose in his “Unified Neutral Theory” a fundamental biodiversity number, θ, controlling both species richness and relative abundance:$$theta ,=, 2Jupsilon$$
(1)
where J is the total number of individuals in the community and υ the rate of immigration (local) or speciation (metacommunity).Fig. 1: Phytoplankton biodiversity following purely stochastic processes.Red, blue, and green = phytoplankton populations (J) of 10,000, 100,000, and 1,000,000 individuals, respectively (Methods). Colored solid lines = species richness in the absence of immigration (υ). Colored dashed and dotted lines = species richness for υ values of 0.03% and 0.3% per generation. Black dashed line = mean rate of decline for the primary phase of stochastic exclusion (slope of this line is the same for all three populations). Blue and green downturned triangles = threshold for the two larger populations where diversity begins to decline rapidly because a sufficient number of species have been reduced to an abundance where extinction within a generation becomes likely.Full size imageIn addition to illustrating the balance between stochastic exclusion and immigration into a local phytoplankton community, Fig. 1 shows that significant decreases in species richness only ensue after a subpopulation of species within a community has been sufficiently decimated in number that their remaining individuals might be lost through random mortality within a generation. In our simulations, this threshold is demarked by the downturn in species richness for the populations of 100,000 and 1,000,000 individuals (Fig. 1; blue and green triangles). The significance of stochastic exclusion is thus dependent on the relation between extant species number and size of the physically-homogenized community. With respect to the latter property, typical horizontal eddy diffusion values for the upper ocean are O(103 m2 s−1), implying that the length scale for mixing in 1 day is O(1000 m). Typical number concentrations for phytoplankton of different species in the ocean range from More

1.Krebs, C. J. Of lemmings and snowshoe hares: the ecology of northern Canada. Proc. R. Soc. B: Biol. Sci. 278, 481–489 (2011).Article 

Google Scholar 
2.Oli, M. K. Population cycles in voles and lemmings: state of the science and future directions. Mammal Rev. 49, 226–239 (2019).Article 

Google Scholar 
3.Tallian, A. et al. Competition between apex predators? Brown bears decrease wolf kill rate on two continents. Proc. R. Soc. B: Biol. Sci. 284, 20162368 (2017).Article 

Google Scholar 
4.Henden, J.-A., Ims, R. A., Yoccoz, N. G., Hellström, P. & Angerbjörn, A. Strength of asymmetric competition between predators in food webs ruled by fluctuating prey: the case of foxes in tundra. Oikos 119, 27–34 (2010).Article 

Google Scholar 
5.Sih, A., Englund, G. & Wooster, D. Emergent impacts of multiple predators on prey. Trends Ecol. Evol. 13, 350–355 (1998).CAS 
PubMed 
Article 

Google Scholar 
6.Griffen, B. D. Detecting emergent effects of multiple predator species. Oecologia 148, 702–709 (2006).ADS 
PubMed 
Article 

Google Scholar 
7.Jones, H. P. et al. Severity of the effects of invasive rats on seabirds: a global review. Conserv. Biol. 22, 16–26 (2008).PubMed 
Article 

Google Scholar 
8.Dowding, J. E. & Murphy, E. C. The impact of predation by introduced mammals on endemic shorebirds in New Zealand: a conservation perspective. Biol. Conserv. 99, 47–64 (2001).Article 

Google Scholar 
9.Pascal, M., Lorvelec, O., Bretagnolle, V. & Culioli, J.-M. Improving the breeding success of a colonial seabird: a cost-benefit comparison of the eradication and control of its rat predator. Endangered Species Res. 4, 267–276 (2008).Article 

Google Scholar 
10.Ruffino, L. et al. Invasive rats and seabirds after 2,000 years of an unwanted coexistence on Mediterranean islands. Biol. Invasions 11, 1631–1651 (2009).Article 

Google Scholar 
11.Harper, G. A. Detecting predation of a burrow-nesting seabird by two introduced predators, using stable isotopes, dietary analysis and experimental removals. Wildl. Res. 34, 443–453 (2007).Article 

Google Scholar 
12.Gaze, P. The response of a colony of sooty shearwater (Puffinus griseus) and flesh-footed shearwater (P. carneipes) to the cessation of harvesting and the eradication of Norway rats (Rattus norvegicus). N. Z. J. Zool. 27, 375–379 (2000).Article 

Google Scholar 
13.Dowding, J. E. & Murphy, E. C. Ecology of ship rats (Rattus rattus) in a kauri (Agathis australis) forest in Northland, New Zealand. N. Z. J. Ecol., 19–27 (1994).14.Major, H., Bond, A., Jones, I. & Eggleston, C. Stability of a seabird population in the presence of an introduced predator. Avian Conserv. Ecol. 8 (2013).15.Bond, A. L. & Eggleston, C. J. Application of a non-invasive indexing method for introduced Norway rats (Rattus norvegicus) in the Aleutian Islands, Alaska. Biodiversity Conserv. 24, 2551–2563 (2015).Article 

Google Scholar 
16.Major, H. L., Jones, I. L., Byrd, G. V. & Williams, J. C. Assessing the effects of introduced Norway rats (Rattus norvegicus) on survival and productivity of Least Auklets (Aethia pusilla). Auk 123, 681–694 (2006).Article 

Google Scholar 
17.Eaton, M. et al. Birds of conservation concern 4: the population status of birds in the UK, Channel Islands and Isle of Man. British Birds 108, 708–746 (2015).
Google Scholar 
18.JNCC. Manx shearwater (Puffinus puffinus). https://jncc.gov.uk/our-work/manx-shearwater-puffinus-puffinus/ (2021).19.Bell, E. et al. The ground-based eradication of Norway rats (Rattus norvegicus) from the Isle of Canna, Inner Hebrides, Scotland. In Island Invasives: Eradication and Management (eds Veitch C. R. et al.) 269–274 (IUCN, Gland, Switzerland, 2011).20.Luxmoore, R., Swann, R. & Bell, E. Canna seabird recovery project: 10 years on. In Island invasives: scaling up to meet the challenge (eds Veitch C. R. et al.) 576–579 (IUCN, Gland, Switzerland, 2019).21.Mavor, R. A., Parsons, M., Heubeck, M. & Schmitt, S. Seabird numbers and breeding success in Britain and Ireland, 2004. (Peterborough, Joint Nature Conservation Committee (UK Nature Conservation, No. 29), 2005).22.Ratcliffe, N., Mitchell, I., Varnham, K., Verboven, N. & Higson, P. How to prioritize rat management for the benefit of petrels: a case study of the UK, Channel Islands and Isle of Man. Ibis 151, 699–708 (2009).Article 

Google Scholar 
23.Duron, Q., Shiels, A. B. & Vidal, E. Control of invasive rats on islands and priorities for future action. Conserv. Biol. 31, 761–771 (2017).PubMed 
Article 

Google Scholar 
24.Watt, L. & Sargent, I. Management Plan for Rum National Nature Reserve 2016–2026 (Scottish Natural Heritage, 2018).25.Lloyd, C., Tasker, M. L. & Partridge, K. The status of seabirds in Britain and Ireland. (A&C Black, 2010).26.Harris, M. Breeding biology of the Manx Shearwater Puffinus puffinus. Ibis 108, 17–33 (1966).Article 

Google Scholar 
27.Thompson, K. R. The ecology of the Manx Shearwater Puffinus puffinus on Rhum, west Scotland, Ph.D. thesis, University of Glasgow (1987).28.Perrins, C., Harris, M. & Britton, C. Survival of Manx shearwaters Puffinus puffinus. Ibis 115, 535–548 (1973).Article 

Google Scholar 
29.Clutton-Brock, T. H. & Ball, M. Rhum: the natural history of an island. (Edinburgh University Press, 1987).30.Virtanen, R., Edwards, G. R. & Crawley, M. J. Red deer management and vegetation on the Isle of Rum. J. Appl. Ecol. 39, 572–583 (2002).31.Corbet, G. Origin of the British insular races of small mammals and of the ‘Lusitanian’ fauna. Nature 191, 1037–1040 (1961).ADS 
Article 

Google Scholar 
32.Berry, R. History in the evolution of Apodemus sylvaticus (Mammalia) at one edge of its range. J. Zool. 159, 311–328 (1969).Article 

Google Scholar 
33.Meehan, A. P. Rats and mice: Their biology and control. (Rentokil Ltd, 1984).34.Carlisle, S. B. Rum rats: ecology and behaviour, Ph.D. thesis, Anglia Ruskin University (2019).35.Berry, R., Evans, I. & Sennitt, B. The relationships and ecology of Apodemus sylvaticus from the Small Isles of the Inner Hebrides, Scotland. J. Zool. 152, 333–346 (1967).Article 

Google Scholar 
36.SNH. Rum SSSI Citation, https://apps.snh.gov.uk/sitelink-api/v1/sites/1396/documents/1 (2010).37.Ambagis, J. A comparison of census and monitoring techniques for Leach’s storm petrel. Waterbirds 27, 211–215 (2004).Article 

Google Scholar 
38.Bicknell, T. W., Reid, J. B. & Votier, S. C. Probable predation of Leach’s Storm-petrel Oceanodroma leucorhoa eggs by St Kilda Field Mice Apodemus sylvaticus hirtensis. Bird Study 56, 419–422 (2009).Article 

Google Scholar 
39.Rodríguez, B., De León, L., Martín, A., Alonso, J. & Nogales, M. Status and distribution of breeding seabirds in the northern islets of Lanzarote, Canary Islands. Atlantic Seabirds 5, 41–56 (2003).
Google Scholar 
40.Seto, N. W. H. & Conant, S. The effects of rat (Rattus rattus) predation on the reproductive success of the Bonin Petrel (Pterodroma hypoleuca) on Midway Atoll. Colon. Waterbirds 19, 171–185 (1996).Article 

Google Scholar 
41.Walsh, P. et al. Seabird monitoring handbook for Britain and Ireland: a compilation of methods for survey and monitoring of breeding seabirds. (JNCC/RSPB/ITE/Seabird Group, 1995).42.Quy, R. J., Cowan, D. P. & Swinney, T. Tracking as an activity index to measure gross changes in Norway rat populations. Wildl. Soc. Bull. 21, 122–127 (1993).
Google Scholar 
43.Martin, A. & Richardson, M. Rodent eradication scaled up: clearing rats and mice from South Georgia. Oryx 53, 27–35 (2019).Article 

Google Scholar 
44.European Chemicals Agency. Technical Notes for Guidance on Product Evaluation Appendices to Chapter 7 Product Type 14 Efficacy Evaluation of Rodenticidal Biocidal Products. https://echa.europa.eu/documents/10162/16960215/bpd_guid_revised_appendix_chapter_7_pt14_2009_en.pdf (2009).45.European Chemicals Agency. Transitional Guidance on Efficacy Assessment for Product Type 14 Rodenticides, Helsinki, Finland. https://echa.europa.eu/documents/10162/23492134/tg_efficacy_pt14_superseded_en.pdf/f82f63de-6ca6-abed-53fe-8b7f11b53493 (2016).46.CRRU. CRRU UK Code of Best Practice. Campaign for Responsible Rodenticide Use, Leeds, UK. https://bpca.org.uk/write/MediaUploads/Documents/Codes%20of%20Best%20Practice/COBP-CRRU-Rodent-Control-and-Safe-Use-of-Rodenticides-2015.PDF (2015).47.Pelz, H.-J. et al. The genetic basis of resistance to anticoagulants in rodents. Genetics 170, 1839–1847 (2005).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
48.Rost, S. et al. Novel mutations in the VKORC1 gene of wild rats and mice – a response to 50 years of selection pressure by warfarin? BMC Genet. 10, 1–9 (2009).Article 
CAS 

Google Scholar 
49.Berny, P., Esther, A., Jacob, J. & Prescott, C. Development of resistance to anticoagulant rodenticides in rodents. In Anticoagulant rodenticides and wildlife. Emerging Topics in Ecotoxicology (Principles, Approaches and Perspectives), Vol. 5 (eds van den Brink, N. et al.) 259–286 (Springer, 2018).50.Fisher, P. Review of house mouse (Mus musculus) susceptibility to anticoagulant poisons. (Department of Conservation, Wellington, 2005).51.Lambert, M. S., Quy, R. J., Smith, R. H. & Cowan, D. P. The effect of habitat management on home-range size and survival of rural Norway rat populations. J. Appl. Ecol. 45, 1753–1761. https://doi.org/10.1111/j.1365-2664.2008.01543.x (2008).Article 

Google Scholar 
52.Taylor, K. D. & Quy, R. J. Long distance movements of a Common rat (Rattus norvegicus ) revealed by radio-tracking. Mammalia 42, 63–71 (1978).Article 

Google Scholar 
53.Schall, R. Estimation in generalized linear models with random effects. Biometrika 78, 719–727 (1991).MATH 
Article 

Google Scholar 
54.Engeman, R. M. Indexing principles and a widely applicable paradigm for indexing animal populations. Wildl. Res. 32, 203–210 (2005).Article 

Google Scholar 
55.Saad, S. M., Sanderson, R., Robertson, P. & Lambert, M. Effects of supplementary feed for game birds on activity of brown rats Rattus norvegicus on arable farms. Mammal Res. 66, 163–171 (2021).56.O’Hara, R. B. & Kotze, D. J. Do not log-transform count data. Methods Ecol. Evol. 1, 118–122 (2010).Article 

Google Scholar 
57.Watts, C. H. The foods eaten by wood mice (Apodemus sylvaticus) and bank voles (Clethrionomys glareolus) in Wytham Woods, Berkshire. J. Anim. Ecol. 37, 25–41 (1968).58.Zubaid, A. & Gorman, M. The diet of wood mice Apodemus sylvaticus living in a sand dune habitat in north-east Scotland. J. Zool. 225, 227–232 (1991).Article 

Google Scholar 
59.Bicknell, A. W. et al. Stable isotopes reveal the importance of seabirds and marine foods in the diet of St Kilda field mice. Sci. Rep. 10, 1–12 (2020).Article 
CAS 

Google Scholar 
60.Haarsma, A.-J. & Kaal, R. Predation of wood mice (Apodemus sylvaticus) on hibernating bats. Popul. Ecol. 58, 567–576 (2016).Article 

Google Scholar 
61.Taylor, K. D., Quy, R. J. & Gurnell, J. Comparison of three methods for estimating the numbers of common rats (Rattus norvegicus). Mammalia 45, 403–413 (1981).Article 

Google Scholar 
62.Lambert, M. et al. Validating activity indices from camera traps for commensal rodents and other wildlife in and around farm buildings. Pest Manag. Sci. 74, 70–77 (2018).CAS 
PubMed 
Article 

Google Scholar 
63.Pankhurst, S., Conlan, H., Rogers, M., Smith, A. Survey of Wood Mice (Apodemus sylvaticus) on the Isle of Rum, Inner Hebrides. (http://insight.cumbria.ac.uk/id/eprint/2191/1/Pankhurst_SurveyOfWoodmice.pdf, 2010).64.Harris, D. B. & Macdonald, D. W. Interference competition between introduced black rats and endemic Galápagos rice rats. Ecology 88, 2330–2344 (2007).PubMed 
Article 

Google Scholar 
65.Rayner, M. J., Hauber, M. E., Imber, M. J., Stamp, R. K. & Clout, M. N. Spatial heterogeneity of mesopredator release within an oceanic island system. Proc. Natl. Acad. Sci. 104, 20862–20865 (2007).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
66.MacKay, J. W., Russell, J. C. & Murphy, E. C. Eradicating house mice from islands: successes, failures and the way forward. In Managing Vertebrate Invasive Species: Proceedings of an International Symposium (eds Witmer, G. W. et al.) 294–304 (USDA/APHIS/WS, National Wildlife Research Center, Fort Collins, CO., 2007).67.Capizzi, D. A review of mammal eradications on Mediterranean Islands. Mammal Rev. 50, 124–135 (2020).Article 

Google Scholar 
68.Ballari, S. A., Kuebbing, S. E. & Nuñez, M. A. Potential problems of removing one invasive species at a time: a meta-analysis of the interactions between invasive vertebrates and unexpected effects of removal programs. PeerJ 4, e2029 (2016).69.Anon. No UK products authorised for control of wood mice, https://www.pestcontrolnews.com/rodenticide-alert-prompts-reminder-of-permitted-target-species/ (2020).70.Furness, R. Predation on ground-nesting seabirds by island populations of red deer Cervus elaphus and sheep Ovis. J. Zool. 216, 565–573 (1988).Article 

Google Scholar 
71.Duff, A. Population ecology of the Manx shearwater Puffinus puffinus on the island of Rum, Scotland, MSc thesis, Edinburgh Napier University (2011).72.Towns, D.R., West, C.J. & Broome, K.G. Purposes, outcomes and challenges of eradicating invasive mammals from New Zealand islands: an historical perspective. Wildl. Res. 40, 94–107 (2012). Article 

Google Scholar  More

Temporal variation and dynamic analysis of eucalyptus forestsData from the 1st-9th NFIs suggested that the total area of eucalyptus plantations had started to increase since 1973 (Table 1). In 1973–1976, Eucalyptus plantations only existed in Guangxi and Guangdong (including Hainan) in China with a total area of 23.0 × 104 hectares, taking up 0.38% of total forest area in China. The stock volume was 372.0 × 104 m3, about 0.04% of that in China. In 2014–2018, the eucalyptus plantation area increased to 546.74 × 104 hectares, about 24 times of that in 1973–1976. The growing stock has increased to 21,562.90 × 104 m3 in 2014–2018 which increased about 58 times from 1973–1976.Table 1 Eucalyptus plantation area and stand volume in different time periods by province.Full size tableThe stock volume per unit area of eucalyptus plantations did not increase significantly from 1973–2008, ranging from 14–30 m3/hectares, but it increased rapidly from 2009 to 2018, reaching 39.43 m3/hectares. This increase occurred because China started to focus and value the development of eucalyptus plantations. As a. result, plantations expanded rapidly, and the need for eucalyptus with greater trunk radius increased. The extended harvest cycle of eucalyptus plantations, not the rise of eucalyptus productivity, caused the increase in stock volume per unit area29,30,31.Based on CFLDM, the distribution of eucalyptus plantations in 2003 and 2016 are mapped (Fig. 1a,b). It suggests that the distribution of eucalyptus plantations extended from Leizhou Peninsula, Guangdong and Hainan Province to the north (Guangxi, Hunan, and Guizhou provinces), east (Fujian and Jiangxi provinces), and west (Yunan and Sichuan provinces). This is consistent with data from the NFIs. The widespread expansion of eucalyptus also leads to several regions with clustered plantations.Figure 1Distribution of eucalyptus in the south of China [(a) 2003; (b) 2016]. This figure was created by spatially overlaying spatial sample plots data from National Forest Inventory (NFI) and patch vectors data from China Forest-Land Database Map (CFLDM), (a) shows that the point data are from 6th NFIs eucalyptus sample plots and the polygon vector data are from the 2003 CFLDM; (b) shows that the point data are from 9th NFIs eucalyptus sample plots and the polygon vector data are from the 2016 CFLDM. The extents of eucalyptus plantations is mainly concerned with 11 provinces (e.g. Zhejiang, Fujian, Guangdong, Guangxi) in Southern China.Full size imageChanges in spatial distributionBased on the database of sample plots (including climate and elevation data) and sampled eucalyptus, we analyze the distribution of eucalyptus plantations, and how it is affected by elevation and climate conditions. It is found that most eucalyptus plantations are within the region of 110°23′–120°5′E and 18°21′–30°39′N. The annual mean temperature within this region ranges from 11 to 25 °C with an average of 19.5 °C, and the annual precipitation ranges from 600 to 2000 mm with an average of 1455 mm. Elevation in this region is 0–2500 m with an average of 338 m.To find out the most suitable conditions for eucalyptus growth and its plantation management, we classify this region based on their elevation and climate conditions. The classification is done separately and independently for each factor (i.e., elevation, temperature, and precipitation). In terms of elevation, the region is assigned to seven grades from below 300 m to 2100 m with an interval of 300 m in between (i.e., below 300 m, 300–600 m, 600–900 m, 900–1200 m, 1200–1500 m, 1500–1800 m, 1800–2100 m). Land with elevation above 2100 mm has limited eucalyptus plantations, and thus is not taken into consideration. Similar criterion is applied to the classification based on annual mean temperature and annual precipitation. The grades are from 11 to 25 °C within an interval of 2 °C for temperature and 600–2000 mm with an interval of 200 mm for precipitation.We examine how the eucalyptus forest area changes with these factors. This is done by plotting the eucalyptus plantation area within a certain group against the corresponding grade number (Fig. 2). Eucalyptus is mostly distributed below 300 m, reaching an area of 301.1 × 104 hectares and counting for 67.58% of the total eucalyptus plantation area in China. Eucalyptus occurs rarely in areas with elevation above 900 m.Figure 2Area of eucalyptus plantations in China based on grades defined in text.Full size imageEucalyptus is sensitive to temperature, and its distribution is limited within areas with annual mean temperature below 19 °C. Eucalyptus is mostly distributed within areas with annual mean temperature of 19–21 °C. Areas with annual mean temperature within this range have a total of 291.49 × 104 hectares eucalyptus plantations, approximately 65.43% of all eucalyptus plantations in China. This result is slightly different from previous studies3,5, which suggests that eucalyptus prefers areas with mean annual temperature above 20 °C.Eucalyptus has a high tolerance to annual precipitation. Eucalyptus plantations can be found in areas with annual precipitation ranging from 600–2000 mm. It should be noted that this is related to irrigation conditions in production and management. However, in areas with annual precipitation below 600 mm, Eucalyptus plantations are extremely rare. Areas with annual precipitation of 1400–1600 mm (and without considering other factors) have the largest portion of eucalyptus plantations, whose total area reaches 146.49 × 104 hectares. This accounts for about 32.94% of total eucalyptus plantations in China.Productivity analysis of eucalyptusVariability in mean productivityEucalyptus annual productivity for each province based on the 5th to 9th NFIs (no digitized data for the 1th to 4th NFIs) is calculated, which includes 3564 sample plots (Table 2). Among which, 769 sample plots had been harvested at the time of the survey, and to remove the influence of these 769 sample plots, their data were removed during the productivity of the eucalyptus age-productivity relationship graph (see Fig. 3), which shows that the period of maximum productivity for eucalyptus lasts for approximately 2–3 years. Its productivity declines rapidly after 10 years of growth. Therefore, the harvest cycle of eucalyptus is normally 4–5 years. After coppicing and growing for another 4–5 years, Eucalyptus will be harvested again, which will be followed by its replanting.Table 2 Basic sample plot statistics (quantity, mean and maximum annual productivity) of eucalyptus plantations by province from the 5th to 9th NFIs.Full size tableFigure 3Relationship between age and productivity of eucalyptus sample plots.Full size imageVariability in eucalyptus productivityEucalyptus productivity for each province based on the 5th to 9th NFIs is calculated and shown in Table 2. It can be seen that from 1994 to 2018, mean and maximum productivities of eucalyptus plantations have increased. This is especially show for Guangxi and Fujian Provinces during 2009–2018. The averaged productivity of eucalyptus plantations in China increased from 4.14 to 8.57 m3 hm−2 a−1 from 1994–1998 to 2014–2018, which can be explained by the improved management of eucalyptus plantations (e.g., high soil fertility for newly cultivated lands and improved ability for irrigation) and their expansion.Data from the 5th to 9th NFIs suggest that a lot of sampled plots were no longer used for growing eucalyptus before the next inventory (Table 3). There were 226, 273, 687, and 109 eucalyptus plots in the 5th, 6th, 7th, 8th inventories, respectively, and in the corresponding next NFI (6th, 7th, 8th, 9th), only 150, 179, 544, and 848 of these sample plots were left unabandoned. This suggests that 33.63%, 34.43%, 20.82%, and 22.63% of the plots were abandoned before the next inventory. New plots have been included in each inventory, but large portions of these plots were abandoned as well. There are 123, 508, and 552 new eucclyptus plots in the 6th, 7th, 8th inventories, and 76, 413, and 433 of them were left unabandoned in the next inventories. The land abandonment rates for them are 38.21%, 18.70%, 21.56%, respectively.Table 3 Quantity of newly-cultivated, retained, and abandoned sample plots during different NFIs.Full size tableWe examine how the productivity changes with time for eucalyptus plantations that have been operated for more than 20 years. From the 5th to 9th NFIs, we find 55 and 38 such (operating for more than 25 and 20 years, respectively; Table 4). It is found that the productivity of eucalyptus is relatively low in the first 5 years of its growing. The productivity increases in the 5th–10th years, and reaches its peak after 10–15 years of growing eucalyptus. For land that have been continuously growing eucalytptus for 15–25 years or more, the productivity decreases significantly. This is due to the decrease in soil fertility.Table 4 Relationship between Continuous planting time and Mean productivity of reserved and newly-cultivated eucalyptus sample plots during different NFIs (unit: m3 hm−2 a−1).Full size tableMost (~ 90%) of the sample plots have mean annual productivity below 10 m3 hm−2 a−1 (Fig. 4). The productivity reaches its peak after 10–15 years of growing eucalyptus (mean annual average: 7.0175 m3 hm−2 a−1), and starts to decrease afterwards. Statistical model (Table 5) is established between productivity and age of eucalyptus plots. The results suggest that eucalyptus productivity follows a consistent pattern: it increases with time until a peak and then decrease (Fig. 5), and this applies to the old, newly-included plots and their average. The statistical model also agrees to the observed data, suggesting that the productivity peak is reached 10–15 years after the planting of eucalyptus, and the productivity reaches its minimum or even zero after 50 years of growing eucalyptus continuously.Figure 4Distribution of mean annual productivity for sample plot from different NFIs.Full size imageTable 5 Productivity prediction model of multi-stage reserved and increase eucalyptus sample plots.Full size tableFigure 5Statistical model showing how mean annual productivity of eucalyptus sample plots changes with time.Full size imageSoil fertility variation of eucalyptus plantationsHow eucalyptus affects soil fertility is not well-studied. Here, based on 948 sample points from Tang32, which includes monitoring of soil fertility of eucalyptus plantations from 1993 to 2018, we report and study the temporal soil fertility variation for eucalyptus plantations. After 25 years of growing eucalyptus, acidification of the corresponding lands persists. The pH value changed to 4.63 in 2018, a 4.14% decrease compared to that in1993. The organic content within the soil reached its minimum of 17.98 g/kg in 2018, a decrease of 23.19% compared to 1993. Total nitrogen content of the investigated samples changed from 2.11 to 1.98 g/kg, and total phosphorus content decreased from 1.12 to 0.75 g/kg. The temporal variation of potassium does not change in a consistent pattern with time. Alkaline hydrolysis of nitrogen and available potassium content in 2018 are significantly lower than those in 1993. From more to less, the rank of soil fertility indicator affiliation polygon area is 1993  > 1998  > 2003  > 2013  > 2018  > 2008. The rank of soil fertility index is 1993  > 1998  > 2018  > 2003  > 2013  > 2008. It decreased first, and then increased. The minimum soil fertility (0.475) was reached in 2008 (22.51), which is smaller than that in 1993. The soil fertility decreases at the greatest rate after 15 years of growing eucalyptus. This argument from Tang32 is consistent with this work (Table 6). Soil fertility generally decreases with the age of eucalyptus plantations.Table 6 Evolutionary characteristics of soil chemical indicators in eucalyptus plantation forests.Full size tableIn addition, Parfitt et al.33 studied the variation of soil fertility of pine plantations in New Zealand for a period of 20 years, and found that long-term successive rotations lead to an increase of the soil C/N ratio. Carbon is lost at a speed much greater than nitrogen. Successive rotations of eucalyptus lead to environmental issues such as decrease in soil fertility and ecological diversity and soil erosion. These would limit the sustainable management of eucalyptus plantations34,35,36,37,38,39,40.Abandonment of sample plotsWe find that many sample plots were not used for growing eucalyptus anymore after each inventory. The abandonment rate is high, ranging from 18.7 to 38.21%. The 226 eucalyptus sample plots in the 5th inventory decreased to 103 (the others are abandoned) during the 7th inventory, and the land abandonment rate was 31.33%. In the 8th and 9th inventories, the abandonment rates are 30.10% and 23.61%, respectively. The cumulative land abandonment rates are 33.63%, 54.43%, 68.15%, and 75.66% after 5, 10, 15, and 20 years of growing eucalyptus, respectively (Table 7).Table 7 Quantity (rates) of retained and abandoned sample plots after certain periods of plantation management.Full size tableThere are a total of 1843 eucalyptus plots from the 5th to 9th NFIs. In the last NFI, there are 1282 sampled plots still growing eucalyptus, and the rest 561 plots are abandoned. The averaged land abandonment rates of these plots every 5 years are 23.92%, 24.26% (43.52% cumulatively), 32.10% (68.48% cumulatively), and 23.61% (75.66% cumulatively) over 5, 10, 15, and 20 years, respectively.These data suggest that the abandonment rate of eucalyptus plantations reaches its peak (about one third) after 15 years of operation. For other time intervals (i.e., 5, 10, and 20 years), the rate remains at around 25%. This is related to the management of eucalyptus plantations in the south of China: the first eucalyptus harvest cycle is about 6 years. The second generation of eucalyptus reproduces by division propagation (sprout naturally) with 4 years of harvest cycle, and the third generation follows the same pattern. These amount to 15–16 years-long period for plantation management. Eucalyptus requires stubble-cleaning after twice of division propagations (sprout reproduction), and needs to be re-planted. This is consistent with the timing of abandonment rate peak as stated above. It is highly likely that the eucalyptus plantations are abandoned due to the low soil fertility, and plantation managers or land owners decide to stop growing eucalyptus as a result.A simple statistical model (second-order polynomial) is established between eucalyptus plantations abandonment rate and time (Fig. 6), which suggests that all plantations will stop growing eucalyptus after 50 years, and the corresponding lands will be used for other purposes. The expansion of eucalyptus plantations relies on sustained cultivation of new lands (land reclamation). The total area of eucalyptus plantations reached 5,647,400 hectares in the 9th NFI, but only 4.29% of them (that) have been continuously growing eucalyptus since the 5th NFL (i.e., 24.34% of the plots from the 5th NFI are kept).Figure 6Statistical model showing how abandonment and replanting rates of eucalyptus plantations change with time.Full size imageThere are two main reasons to explain the loss of eucalyptus plantations. The land might be taken over for non-agricultural use (e.g., infrastructure and building construction), or they could be used for growing other crops. The latter is help for soil fertility restoration and soil microorganism readjustment. As most eucalyptus plantations in China are cultivated on lands with poor growing conditions, most of them were abandoned voluntarily by the land owner or plantation manager as stated earlier.After harvest, eucalyptus plantations could be reused for the continuation of eucalyptus growing or used for other purposes (e.g., growing other crops). The plots that were temporarily not used for growing eucalyptus could be used for re-growing it under certain conditions after a certain time period. We investigate the replanting rate of the 561 abandoned eucalyptus plantations, and study whether the abandoned plantations are used for growing other outcrops, and, if so, the corresponding tree species (Tables 8, 9, 10, 11). The 6th NFI data suggests that there are 76 plots abandoned after the 5th NFI. Their replanting rates are 2.63%, 7.89% (10.53% cumulatively), 0.00% (10.53% cumulatively), and 5.26% (15.79% cumulatively) within every 5 years, and after 5, 10, and 15 years of abandonment. For all the 561 abandoned plots, replanting rates are 9.09%, 5.53% (cumulatively 14.62% within 10 years), 0.53% (cumulatively 15.15% within 15 years), 5.26% (cumulatively 15.86% within 20 years) within every 5 years, and after 5, 10, and 15 years of abandonment. These suggest that about five sixth of the abandoned plots had not replanted eucalyptus for at least 20 years since abandonment.Table 8 Land use of eucalyptus plantation sample plots during different NFIs.Full size tableTable 9 Temporal change of tree species planted in sample plots.Full size tableTable 10 Replanting rate of eucalyptus plantations.Full size tableTable 11 Productivity of plantations that have replanted eucalyptus.Full size tableA simple statistical model is established between replanting rate and time (second-order polynomial) based on the current data (Fig. 5). It suggests that the replanting rates after 30 and 50 years are around 20% and 30%, respectively. These suggest that if the plantation management does not improve significantly, it would be difficult to maintain the current supply of eucalyptus and areal distribution of its plantations in the long term. It is necessary to rely on both land rotation and cultivation of new lands to maintain the current supply of eucalyptus.The NFI data suggests that very few eucalyputs plots are turned to non-plantation purposes. The exception is from the 6th NFI in which 34.21% of plots have been used for other purposes after harvest. This rate is below 20% for all other inventory data. A lot of abandoned eucalyptus plantations are still used as plantations, and they are for growing eucalyptus, and the rate of regrowing eucalyptus tends to remain low for a long period of time (below one sixth after 20 years based on current data). This is because eucalyptus grows fast with high productivity, and it has high demand for soil fertility and water. Land rotation is necessary after a few harvest cycles to restore the soil fertility, which would take relatively long period of time before the land becomes suitable to regrow euccalyptus. Among the 561 abandoned eucalyptus plots, broad-leaf and economic tree species are the most commonly planted species after stop growing eucalyptus (e.g., rubber tree and Lychee; 18.54% and 18.36%; Table 9). The greater variability of land use for the abandoned plots suggests greater management intensity. Afforestation with eucalyptus is dominated by short rotation period (harvest cycle). Frequently modifying tree species planted within plantations helps maintain a high productivity of the land.
Carbon storage and fixation of eucalyptus plantationsVariability in carbon storageBased on the 9th NFI data and Eqs. (2–8), we calculate the BEF of eucalyptus in each province (Table 12). The results suggest that the BEF ranges from 0.982–1.652 with a weighted average of 1.236 (weight determined by stock volume).Table 12 BEF of eucalyptus by province.Full size tableCalculation from Eqs. (9) and (10) suggests that the total carbon storage (excluding harvest volume) of eucalyptus in China is 2.40 TgC (1973–1976, 1Tg = 1012 g), 4.14 TgC (1977–1981), 2.73 TgC (1984–1988), 5.42 TgC (1989–1993), 9.73 TgC (1994–1998), 12.58 TgC (1999–2003), 28.90 TgC (2004–2008), 98.61 TgC (2009–2013), and 133.00 TgC (2014–2018) in different time periods in the past 45 years (Table 13).Table 13 Eucalyptus carbon density and storage by province.Full size tableThe carbon storage of eucalyptus increased rapidly in the past 45 years especially since the end of last century. This is due to the rapid expansion of eucalyptus plantations in China, and its carbon storage in 2014–2018 is 55.42 times of that in 1973–1976. The carbon density per square hectometer also increases from 5.22 MgC (1 Mg = 106 g) in 1973–1976 to 12.16 MgC in 2014–2018, about 2.33 times of the former.Carbon fixationThe mean annual productivity of eucalyptus is 8.57 m3 hm−2 a−1 in 2014–2018 based on the 9th NFI. This is a lot greater compared to other species widely planted in the same areas (Pinus massoniana Lamb.: 2.91 m3 hm−2 a−1; Cunninghamia lanceolata Lamb.: 3.93 m3 hm−2 a−1). Using the stock volume biomass method with BEF being 1.2336 (from previous calculation) and carbon storage coefficient of 0.5, the mean annual carbon fixed by eucalyptus is 5.29 t hm−2 a−1, which are about 2.95 and 2.18 times that of Pinus massoniana Lamb. (1.79 t hm−2 a−1) and Cunninghamia lanceolata Lamb. (2.42 t hm−2 a−1), respectively. This shows that eucalyptus is characterized by high biomass productivity and high carbon fixation capability. It thus plays an important role in maintaining the carbon balance in China. More

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05 October 2021

Fund natural-history museums, not de-extinction

Corrie S. Moreau

 ORCID: http://orcid.org/0000-0003-1139-5792

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Jessica L. Ware

 ORCID: http://orcid.org/0000-0002-4066-7681

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Corrie S. Moreau

Cornell University, Ithaca, New York, USA.

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Jessica L. Ware

American Museum of Natural History, New York City, New York, USA.

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The only way to study extinct species is by leveraging the irreplaceable collections of natural-history museums. It is unfortunate, then, that instead of supporting these often imperilled institutions, private investors are spending millions on attempts to resurrect species. For example, the US start-up firm Colossal Laboratories and Biosciences, co-founded by synthetic biologist George Church, is exploring such feats.

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Nature 598, 32 (2021)
doi: https://doi.org/10.1038/d41586-021-02710-4

Competing Interests
The authors declare no competing interests.

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