Philippa Kaur

Angilletta, M. J. Thermal Adaptation: A Theoretical and Empirical Synthesis (Oxford Univ. Press, 2009).Cossins, A. R. & Bowler, K. Temperature Biology of Animals (Chapman and Hall, 1987).Sunday, J. M. et al. Thermal-safety margins and the necessity of thermoregulatory behavior across latitude and elevation. Proc. Natl Acad. Sci. USA 111, 5610–5615 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Perry, A. L., Low, P. J., Ellis, J. R. & Reynolds, J. D. Climate change and distribution shifts in marine fishes. Science 308, 1912–1915 (2005).CAS 
PubMed 

Google Scholar 
Kellermann, V. et al. Upper thermal limits of Drosophila are linked to species distributions and strongly constrained phylogenetically. Proc. Natl Acad. Sci. USA 109, 16228–16233 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
IPCC. Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).Hofmann, G. E. & Todgham, A. E. Living in the now: physiological mechanisms to tolerate a rapidly changing environment. Annu. Rev. Physiol. 72, 127–145 (2010).CAS 
PubMed 

Google Scholar 
Schulte, P. M. The effects of temperature on aerobic metabolism: towards a mechanistic understanding of the responses of ectotherms to a changing environment. J. Exp. Biol. 218, 1856–1866 (2015).PubMed 

Google Scholar 
Sunday, J. et al. Thermal tolerance patterns across latitude and elevation. Philos. Trans. R. Soc. B 374, 20190036 (2019).
Google Scholar 
Parratt, S. R. et al. Temperatures that sterilize males better match global species distributions than lethal temperatures. Nat. Clim. Change 11, 481–484 (2021).
Google Scholar 
Sunday, J. M., Bates, A. E. & Dulvy, N. K. Thermal tolerance and the global redistribution of animals. Nat. Clim. Change 2, 686–690 (2012).
Google Scholar 
Schmidt-Nielsen, K. Animal physiology: Adaptation and Environment 5th edn (Cambridge Univ. Press, 1997).Dell, A. I., Pawar, S. & Savage, V. M. Systematic variation in the temperature dependence of physiological and ecological traits. Proc. Natl Acad. Sci. USA 108, 10591–10596 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Seebacher, F., White, C. R. & Franklin, C. E. Physiological plasticity increases resilience of ectothermic animals to climate change. Nat. Clim. Change 5, 61–66 (2014).
Google Scholar 
Dillon, M. E., Wang, G. & Huey, R. B. Global metabolic impacts of recent climate warming. Nature 467, 704–706 (2010).CAS 
PubMed 

Google Scholar 
Deutsch, C. A. et al. Increase in crop losses to insect pests in a warming climate. Science 361, 916–919 (2018).CAS 
PubMed 

Google Scholar 
Jørgensen, L. B., Malte, H. & Overgaard, J. How to assess Drosophila heat tolerance: unifying static and dynamic tolerance assays to predict heat distribution limits. Funct. Ecol. 33, 629–642 (2019).
Google Scholar 
Hollingsworth, M. J. Temperature and length of life in Drosophila. Exp. Gerontol. 4, 49–55 (1969).CAS 
PubMed 

Google Scholar 
Fry, F. E. J., Hart, J. S. & Walker, K. F. Lethal Temperature Relations for a Sample of Young Speckled Trout, Salvelinus fontinalis 9–35 (Univ. Toronto, 1946).MacLean, H. J. et al. Evolution and plasticity of thermal performance: an analysis of variation in thermal tolerance and fitness in 22 Drosophila species. Philos. Trans. R. Soc. B 374, 20180548 (2019).
Google Scholar 
Pörtner, H.-O. & Farrell, A. P. Physiology and climate change. Science 322, 690–692 (2008).PubMed 

Google Scholar 
Ørsted, M., Jørgensen, L. B. & Overgaard, J. Finding the right thermal limit: a framework to reconcile ecological, physiological, and methodological aspects of CTmax in ectotherms. J. Exp. Biol. 225, jeb244514 (2022).Brown, J. H., Gillooly, J. F., Alle, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).
Google Scholar 
Munch, S. B. & Salinas, S. Latitudinal variation in lifespan within species is explained by the metabolic theory of ecology. Proc. Natl Acad. Sci. USA 106, 13860–13864 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
Jørgensen, L. B., Malte, H., Ørsted, M., Klahn, N. A. & Overgaard, J. A unifying model to estimate thermal tolerance limits in ectotherms across static, dynamic and fluctuating exposures to thermal stress. Sci. Rep. 11, 12840 (2021).PubMed 
PubMed Central 

Google Scholar 
Rezende, E. L., Castañeda, L. E. & Santos, M. Tolerance landscapes in thermal ecology. Funct. Ecol. 28, 799–809 (2014).
Google Scholar 
Bowler, K. Heat death in poikilotherms: is there a common cause? J. Therm. Biol. 76, 77–79 (2018).PubMed 

Google Scholar 
Somero, G. N. The physiology of climate change: how potentials for acclimatization and genetic adaptation will determine ‘winners’ and ‘losers’. J. Exp. Biol. 213, 912–920 (2010).CAS 
PubMed 

Google Scholar 
Buckley, L. B., Huey, R. B. & Kingsolver, J. G. Asymmetry of thermal sensitivity and the thermal risk of climate change. Glob. Ecol. Biogeogr. 31, 2231–2244 (2022).Overgaard, J., Kearney, M. R. & Hoffmann, A. A. Sensitivity to thermal extremes in Australian Drosophila implies similar impacts of climate change on the distribution of widespread and tropical species. Glob. Change Biol. 20, 1738–1750 (2014).
Google Scholar 
Pinsky, M. L., Eikeset, A. M., McCauley, D. J., Payne, J. L. & Sunday, J. M. Greater vulnerability to warming of marine versus terrestrial ectotherms. Nature 569, 108–111 (2019).CAS 
PubMed 

Google Scholar 
Huey, R. B. et al. Predicting organismal vulnerability to climate warming: roles of behaviour, physiology and adaptation. Philos. Trans. R. Soc. B 367, 1665–1679 (2012).
Google Scholar 
Kearney, M., Shine, R. & Porter, W. P. The potential for behavioral thermoregulation to buffer ‘cold-blooded’ animals against climate warming. Proc. Natl Acad. Sci. USA 106, 3835–3840 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
Woods, H. A., Dillon, M. E. & Pincebourde, S. The roles of microclimatic diversity and of behavior in mediating the responses of ectotherms to climate change. J. Therm. Biol 54, 86–97 (2015).PubMed 

Google Scholar 
Stevenson, R. D. The relative importance of behavioral and physiological adjustments controlling body temperature in terrestrial ectotherms. Am. Nat. 126, 362–386 (1985).
Google Scholar 
Chen, I., Hill, J. K., Ohlemüller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024–1026 (2011).CAS 
PubMed 

Google Scholar 
Buckley, L. B. & Kingsolver, J. G. Functional and phylogenetic approaches to forecasting species’ responses to climate change. Annu. Rev. Ecol. Evol. Syst. 43, 205–226 (2012).
Google Scholar 
Roeder, K. A., Bujan, J., de Beurs, K. M., Weiser, M. D. & Kaspari, M. Thermal traits predict the winners and losers under climate change: an example from North American ant communities. Ecosphere 12, e03645 (2021).
Google Scholar 
Penick, C. A., Diamond, S. E., Sanders, N. J. & Dunn, R. R. Beyond thermal limits: comprehensive metrics of performance identify key axes of thermal adaptation in ants. Funct. Ecol. 31, 1091–1100 (2017).
Google Scholar 
Deutsch, C. A. et al. Impacts of climate warming on terrestrial ectotherms across latitude. Proc. Natl Acad. Sci. USA 105, 6668–6672 (2008).CAS 
PubMed 
PubMed Central 

Google Scholar 
Huey, R. B. & Stevenson, R. D. Integrating thermal physiology and ecology of ectotherms: a discussion of approaches. Integr. Comp. Biol. 19, 357–366 (1979).
Google Scholar 
Sinclair, B. J. et al. Can we predict ectotherm responses to climate change using thermal performance curves and body temperatures? Ecol. Lett. 19, 1372–1385 (2016).PubMed 

Google Scholar 
Tewksbury, J. J., Huey, R. B. & Deutsch, C. A. Putting the heat on tropical animals the scale of prediction. Science 320, 1296–1297 (2008).CAS 
PubMed 

Google Scholar 
Kingsolver, J. G., Diamond, S. E. & Buckley, L. B. Heat stress and the fitness consequences of climate change for terrestrial ectotherms. Funct. Ecol. 27, 1415–1423 (2013).
Google Scholar 
Kingsolver, J. G. & Woods, H. A. Beyond thermal performance curves: modeling time-dependent effects of thermal stress on ectotherm growth rates. Am. Nat. 187, 283–294 (2016).PubMed 

Google Scholar 
Kingsolver, J. G., Higgins, J. K. & Augustine, K. E. Fluctuating temperatures and ectotherm growth: distinguishing non-linear and time-dependent effects. J. Exp. Biol. 218, 2218–2225 (2015).PubMed 

Google Scholar 
Clusella-Trullas, S., Garcia, R. A., Terblanche, J. S. & Hoffmann, A. A. How useful are thermal vulnerability indices? Trends Ecol. Evol. 36, 1000–1010 (2021).PubMed 

Google Scholar 
Pincebourde, S. & Casas, J. Narrow safety margin in the phyllosphere during thermal extremes. Proc. Natl Acad. Sci. USA 116, 5588–5596 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
Google Scholar 
Moss, R. H. et al. The next generation of scenarios for climate change research and assessment. Nature 463, 747–756 (2010).CAS 
PubMed 

Google Scholar 
Hausfather, Z. & Peters, G. P. Emissions—the ‘business as usual’ story is misleading. Nature 577, 618–620 (2020).CAS 
PubMed 

Google Scholar 
Tollefson, J. How hot will Earth get by 2100? Nature 580, 443–445 (2020).CAS 
PubMed 

Google Scholar 
Assis, J. et al. Bio‐ORACLE v2.0: extending marine data layers for bioclimatic modelling. Glob. Ecol. Biogeogr. 27, 277–284 (2018).
Google Scholar 
Tyberghein, L. et al. Bio-ORACLE: a global environmental dataset for marine species distribution modelling. Glob. Ecol. Biogeogr. 21, 272–281 (2012).
Google Scholar 
Jørgensen, L. B., Ørsted, M., Malte, H., Wang, T. & Overgaard, J. Data from: Extreme escalation of heat failure rates in ectotherms with global warming. Zenodo https://doi.org/10.5281/zenodo.6979789 (2022).Grove, T. J., McFadden, L. A., Chase, P. B. & Moerland, T. S. Effects of temperature, ionic strength and pH on the function of skeletal muscle myosin from a eurythermal fish, Fundulus heteroclitus. J. Muscle Res. Cell Motil. 26, 191–197 (2005).CAS 
PubMed 

Google Scholar 
Doudoroff, P. The resistance and acclimatization of marine fishes to temperature changes. II. Experiments with Fundulus and Atherinops. Biol. Bull. 88, 194–206 (1945).
Google Scholar 
Sirikharin, R., Söderhäll, I. & Söderhäll, K. Characterization of a cold-active transglutaminase from a crayfish, Pacifastacus leniusculus. Fish Shellfish Immunol. 80, 546–549 (2018).CAS 
PubMed 

Google Scholar 
Becker, C. D. & Genoway, R. G. Resistance of crayfish to acute thermal shock: preliminary studies. in Proc. Thermal Ecology NTIS Conf. 730505 (eds Gibbons, J. W. & Sharitz, R. R.) 146–150 (NTIS, 1974).Widdows, J. Effect of temperature and food on the heart beat, ventilation rate and oxygen uptake of Mytilus edulis. Mar. Biol. 20, 269–276 (1973).
Google Scholar 
Wallis, R. L. Thermal tolerance of Mytilus edulis of eastern Australia. Mar. Biol. 30, 183–191 (1975).
Google Scholar 
Gray, J. The mechanism of ciliary movement. III. The effect of temperature. Proc. R. Soc. B 95, 6–15 (1923).CAS 

Google Scholar 
Shertzer, R. H., Hart, R. G. & Pavlick, F. M. Thermal acclimation in selected tissues of the leopard frog Rana pipiens. Comp. Biochem. Physiol. A 51, 327–334 (1975).CAS 
PubMed 

Google Scholar 
Orr, P. R. Heat death. II. Differential response of entire animal (Rana pipiens) and several organ systems. Physiol. Zool. 28, 294–302 (1955).
Google Scholar 
Lighton, J. R. B. & Duncan, F. D. Energy cost of locomotion: validation of laboratory data by in situ respirometry. Ecology 83, 3517–3522 (2002).
Google Scholar 
Heatwole, H. & Harrington, S. Heat tolerances of some ants and beetles from the pre-Saharan steppe of Tunisia. J. Arid Environ. 16, 69–77 (1989).
Google Scholar  More

We found a significant positive relationship between pollen- and plant richness regardless of differences in plant diversity, landscape structure and environmental conditions between the two study regions. This finding represents a major step stone towards more accurate paleoecological reconstructions of plant diversity in temperate Central Europe, as previous studies on this topic have mostly been conducted in boreal and boreal-nemoral zones8,11, in high mountain habitats10 or in southern Europe9,12.Methodological differences e.g., in diversity indices, data transformations or sample sizes used make comparison between studies difficult. Nevertheless, the strongest relationships seem to be found when habitats with contrasting patterns of plant diversity are compared, such as forests and alpine vegetation7 or forests, peatlands and grasslands11. Also in our study, we found the strongest correlations when complete datasets combining forested and open habitats were analysed together for both study regions. As it is well known that plant richness is generally lower in forests than in open landscapes across temperate and boreal regions28, this finding may seem rather trivial. However, it is important for paleoecological reconstruction because Holocene changes in diversity in temperate regions were largely driven by changes in the relative abundance of major habitat types (such as forests, grasslands, wetlands and man-made habitats), and not just by changes in species richness within these habitats5,6.Regarding individual habitats, the pollen-plant diversity relationship is often rather strong and significant in grasslands and other open habitats8,11; for example the WCM open-habitat subset in this study. Open habitats are generally richer in species, thus providing a longer gradient of species richness compensating for the taxonomical imprecision of the pollen analysis. In forested sites with less species, we found mostly non-significant relationships. Moreover, two other factors may play a role.First, high pollen productivity of trees biases the diversity relationship according to the studies from northern Europe16. However, a study from an elevational transect in southern Norway showed that the strongest bias in representation occurs only in the boreal forest biome, which is dominated by high pollen producers10. Our dominant vegetation component, Picea and Quercus, have intermediate to high pollen productivity (2–2.5), whereas true high pollen producers such as Alnus and Betula ( > 3) are less abundant in our study area (Supplementary Fig. S2). Adjustment of pollen counts by PPEs led to stronger relationship between pollen and floristic richness only in the WCM open-habitat subset (Supplementary Fig. S4).Second, interception of pollen by the tree canopies29 and subsequent washout to the forest floor affects the diversity relationship of forest sites more than pollen productivity. This noise described also as a vegetation filtering30 can be illustrated in our dataset by pollen of long-distance transport from Ambrosia artemisiifolia-type, which has the closest source populations ca. 50 km south-eastwards from WCM region31; or pollen of Artemisia, growing in open habitats. Both pollen taxa are more abundant in the forest than in open sites (Supplementary Fig. S3).Regarding the application of these results for the interpretation of fossil record, we suggest to consider only marked changes of pollen richness in the past and to avoid overinterpretation of small differences, as the non-significant relationships obtained in both forest datasets suggest some limitations of the method.We showed that the pollen-plant diversity relationship may be at least partly disentangled by knowing the exact spatial position of plant species in broader surroundings of the pollen sampling sites. Changes in the relationship with changing spatial scale are largely driven by the numbers of species newly appearing as the radius of surveyed area increases, especially as new habitats are added (Fig. 5, Supplementary Fig. S5). Remarkably, in the BMH region it increases with distance, whereas the opposite trend was observed in the WCM region. This discrepancy may be explained by non-uniform richness patterns in different habitats and by different landscape structure (i.e. spatial arrangement of different habitats) in the two study regions.At open-habitat sites in the WCM area, most species generally appeared within the first 40 m. This observation is consistent with the knowledge of extremely high fine-scale plant diversity in the local steppic meadows, where a substantial portion of the species pool occurs on a scale of tens of square meters32. Moreover, the grain size of the habitat mosaic in the WCM region is finer than in the BMH region. Therefore, the closest pollen-plant diversity relationship across habitats in the WCM region is achieved over shorter distances. Although habitats such as built-up areas and roads occurring at distances greater than 40 m may be species-rich and compositionally different from the grasslands and forests, it appears that high fine-scale plant diversity (in our case in WCM open-habitat subset) limits the influence of the surrounding landscape on pollen richness and reduces the source area of pollen richness. Several studies of the relevant source area of pollen report analogous results33,34,35. A weakening relationship between pollen diversity and plant diversity with distance has also been observed in the Mediterranean region9, although their interpretations are limited by field survey methodology.The appearance of open habitats within forests led to the increase of species numbers and the local maxima of adjusted R2 in both regions. While in the BMH forest the appearance of forest roads at about 70 m was crucial, meadows and orchards at about 250 m played a similar role in the WCM forest subset. In the WCM open-habitat subset diversity patterns in the first tens of metres were crucial, while in the BMH open-habitat subset increased correlation of floristic and pollen richness appeared only at 400 and 550 m; at this distance many species appeared due to the frequent transition of meadow complexes to shrubby habitats and built-up areas. Also other studies from semi-open landscapes found a high correlation between pollen richness and landscape openness17,26,27.Estimating the source area of pollen variance as a regression of pollen and floristic variance implies that the resulting distance of 100–250 m represents all datasets. Although they differ in species richness, openness and habitats, the relationship between variances is fairly linear. The exception is the WCM open-habitat subset suggesting that the spatial scale at which the pollen variance corresponds to the floristic variance cannot be generalized.The strong effect of high pollen richness in the WCM open-habitat subset is also visible in the comparison of pollen and floristic variance. At 150 m, the WCM open-habitat subset had much lower floristic variance than the other subsets. Floristic variance in this subset corresponding to the pollen variance and the pattern of the other datasets lay at 6 m (Fig. 6b). Again, this may be caused by the high fine-scale diversity of the meadows, which include most pollen types present in the surrounding landscape. Only a few new species appeared in broader surroundings and at 150 m, WCM open habitats are more similar than other analysed habitats. The fact that extremely high alpha diversity is compensated by low beta diversity has already been reported from the open habitats of the White Carpathians36. The linearity and the significance of the variance relationship within the rest of the datasets indicate robustness and possible applicability to a variety of fossil records.The mechanism of establishing the source area of pollen variance was similar to that mentioned for the source area of pollen richness. The appearance of new habitats with new species (Fig. 5) like open habitat for forest sites (WCM forest subset) or built-up areas for open sites (BMH open-habitat subset), caused small to negligible increases of floristic variance. Moreover, the high yet insignificant relationship of the variances at the distance between 250 and 600 m (Fig. 6a) corresponds to the distance of the second range of fit between floristic and pollen richness (Fig. 4a).Beta diversity, understood as directional turnover (temporal or spatial), is becoming more frequently used in pollen analysis22,24 than beta diversity as a non-directional variation. According to Nieto-Lugilde et al.25 pollen-based turnover correlates with forest-inventory-based turnover. We extend this finding from woody taxa to all species and from directional turnover to non-directional variance. Moreover, forest sites with high contributions to pollen beta diversity also show an increased contribution to floristic beta diversity (Fig. 4b).The reference data on plant diversity report 1477 species in 15 mapping squares covered by our survey for the BMH region and 2045 species in 14 squares for the WCM region37. It means that we recorded 54.1 and 53.7%, respectively, of the known regional species pool in the two regions. We consider this as a rather good result and the close agreement in representativeness between the two regions speaks for consistency in data quality between the datasets. We advise that future studies covering wider areas and various biomes should preferentially use high-quality floristic data collected in targeted field surveys rather than database data or data from simplified field surveys. Only then we will be able to understand the pollen-plant diversity relationships more realistically and in a spatially explicit manner.In order to interpret fossil pollen richness in the light of our present results, we need to consider landscape openness, which can be roughly inferred from the ratio of arboreal and non-arboreal pollen. Variation of pollen richness during the forest phases of the records should be interpreted more carefully, especially in cases of low variation. In all other cases, the pollen richness is significantly linked to the plant richness within a distance of ten to several hundreds of meters, depending on the distance of the expected species-rich patches. More

Carothers, J. H. & Jaksić, F. M. Time as a Niche difference: The role of interference competition. Oikos 42, 403–406 (1984).
Google Scholar 
Kronfeld-Schor, N. & Dayan, T. Partitioning of time as an ecological resource. Annu. Rev. Ecol. Evol. Syst. 34, 153–181 (2003).
Google Scholar 
Lesmeister, D. B., Nielsen, C. K., Schauber, E. M. & Hellgren, E. C. Spatial and temporal structure of a mesocarnivore guild in midwestern North America. Wildl. Monogr. 191, 1–61 (2015).
Google Scholar 
Chmura, H. E. et al. Plasticity and repeatability of activity patterns in free-living Arctic ground squirrels. Anim. Behav. 169, 81–91 (2020).
Google Scholar 
Helm, B. et al. Two sides of a coin: Ecological and chronobiological perspectives of timing in the wild. Philos. Trans. R. Soc. B Biol. Sci. 372, 20160246 (2017).
Google Scholar 
Alós, J., Martorell-Barceló, M. & Campos-Candela, A. Repeatability of circadian behavioural variation revealed in free-ranging marine fish. R. Soc. Open Sci. 4, 160791 (2017).PubMed 
PubMed Central 

Google Scholar 
Schlicht, L. & Kempenaers, B. The effects of season, sex, age and weather on population-level variation in the timing of activity in Eurasian Blue Tits Cyanistes caeruleus. Ibis 162, 1146–1162 (2020).
Google Scholar 
Helm, B. & Visser, M. E. Heritable circadian period length in a wild bird population. Proc. R. Soc. B Biol. Sci. 277, 3335–3342 (2010).
Google Scholar 
Nikhil, K. L., Abhilash, L. & Sharma, V. K. Molecular correlates of circadian clocks in fruit fly drosophila melanogaster populations exhibiting early and late emergence chronotypes. J. Biol. Rhythms 31, 125–141 (2016).CAS 
PubMed 

Google Scholar 
Allebrandt, K. V. et al. CLOCK gene variants associate with sleep duration in two independent populations. Biol. Psychiatry 67, 1040–1047 (2010).CAS 
PubMed 

Google Scholar 
Maukonen, M. et al. Genetic associations of chronotype in the finnish general population. J. Biol. Rhythms 35, 501–511 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Roecklein, K. A. et al. Melanopsin gene variations interact with season to predict sleep onset and chronotype. Chronobiol. Int. 29, 1036–1047 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Steinmeyer, C., Kempenaers, B. & Mueller, J. C. Testing for associations between candidate genes for circadian rhythms and individual variation in sleep behaviour in blue tits. Genetica 140, 219–228 (2012).CAS 
PubMed 

Google Scholar 
Stuber, E. F., Baumgartner, C., Dingemanse, N. J., Kempenaers, B. & Mueller, J. C. Genetic correlates of individual differences in sleep behavior of free-living great tits (Parus major). G3 GenesGenomesGenetics 6, 599–607 (2016).CAS 

Google Scholar 
Cuthill, I. C. & Macdonald, W. A. Experimental manipulation of the dawn and dusk chorus in the blackbird Turdus merula. Behav. Ecol. Sociobiol. 26, 209–216 (1990).
Google Scholar 
Grava, T., Grava, A. & Otter, K. A. Supplemental feeding and dawn singing in black-capped chickadees. Condor 111, 560–564 (2009).
Google Scholar 
Saggese, K., Korner-Nievergelt, F., Slagsvold, T. & Amrhein, V. Wild bird feeding delays start of dawn singing in the great tit. Anim. Behav. 81, 361–365 (2011).
Google Scholar 
Dominoni, D. M. Effects of artificial light at night on daily and seasonal organization of European blackbirds (Turdus merula). https://kops.uni-konstanz.de/handle/123456789/32198 Accessed 23 February 2022 (2013).
Lehmann, M., Spoelstra, K., Visser, M. E. & Helm, B. Effects of temperature on circadian clock and chronotype: An experimental study on a passerine bird. Chronobiol. Int. 29, 1062–1071 (2012).PubMed 

Google Scholar 
Zsebők, S. et al. Short- and long-term repeatability and pseudo-repeatability of bird song: Sensitivity of signals to varying environments. Behav. Ecol. Sociobiol. 71, 154 (2017).
Google Scholar 
Raap, T., Pinxten, R. & Eens, M. Artificial light at night disrupts sleep in female great tits (Parus major) during the nestling period and is followed by a sleep rebound. Environ. Pollut. 215, 125–134 (2016).CAS 
PubMed 

Google Scholar 
Grunst, M. L., Grunst, A. S., Pinxten, R. & Eens, M. Variable and consistent traffic noise negatively affect the sleep behavior of a free-living songbird. Sci. Total Environ. 778, 146338 (2021).CAS 
PubMed 

Google Scholar 
Gaynor, K. M., Hojnowski, C. E., Carter, N. H. & Brashares, J. S. The influence of human disturbance on wildlife nocturnality. Science 360, 1232–1235 (2018).CAS 
PubMed 

Google Scholar 
Stuber, E. F. et al. Perceived predation risk affects sleep behaviour in free-living great tits Parus major. Anim. Behav. 98, 157–165 (2014).
Google Scholar 
Niemelä, P. T. & Dingemanse, N. J. Individual versus pseudo-repeatability in behaviour: Lessons from translocation experiments in a wild insect. J. Anim. Ecol. 86, 1033–1043 (2017).PubMed 

Google Scholar 
Garamszegi, L. Z. & Møller, A. P. Partitioning within-species variance in behaviour to within- and between-population components for understanding evolution. Ecol. Lett. 20, 599–608 (2017).PubMed 

Google Scholar 
Niemelä, P. T. & Dingemanse, N. J. On the usage of single measurements in behavioural ecology research on individual differences. Anim. Behav. 145, 99–105 (2018).
Google Scholar 
Browne, W. J., McCleery, R. H., Sheldon, B. C. & Pettifor, R. A. Using cross-classified multivariate mixed response models with application to life history traits in great tits (Parus major). Stat. Model. 7, 217–238 (2007).MathSciNet 
MATH 

Google Scholar 
Pettifor, R. A., Sheldon, B. C., Browne, W. J., Rasbash, J. & McCleery, R.
H. Partitioning of Phenotypic Variance in Life-history Traits in the Great Tit, Parus major.
https://seis.bristol.ac.uk/~frwjb/materials/phenovar.pdf (2003). Accessed 23 February 2022.Casasole, G. et al. Neither artificial light at night, anthropogenic noise nor distance from roads are associated with oxidative status of nestlings in an urban population of songbirds. Comp. Biochem. Physiol. A 210, 14–21 (2017).CAS 

Google Scholar 
Payevsky, V. A. Mortality rate and population density regulation in the great tit, Parus major L.: A review. Russ. J. Ecol. 37, 180 (2006).
Google Scholar 
Vermeulen, A., Eens, M., Van Dongen, S. & Müller, W. Does baseline innate immunity change with age? A multi-year study in great tits. Exp. Gerontol. 92, 67–73 (2017).CAS 
PubMed 

Google Scholar 
Haftorn, S. Incubation during the egg-laying period in relation to clutch-size and other aspects of reproduction in the great tit Parus major. Ornis Scand. Scand. J. Ornithol. 12, 169–185 (1981).
Google Scholar 
Grunst, M. L., Grunst, A. S., Pinxten, R., Eens, G. & Eens, M. An experimental approach to investigating effects of artificial light at night on free-ranging animals: Implementation, results and directions for future research. J. Vis. Exp. 180, e63381 (2022).

Google Scholar 
Halfwerk, W. et al. Low-frequency songs lose their potency in noisy urban conditions. Proc. Natl. Acad. Sci. 108, 14549–14554 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Specht, R. Avisoft-saslab pro: Sound analysis and synthesis laboratory. Avis. Bioacoustics
http://avisoft.com/SASLab_deutsch.pdf Accessed 23 February 2022 (2002).Iserbyt, A., Griffioen, M., Borremans, B., Eens, M. & Müller, W. How to quantify animal activity from radio-frequency identification (RFID) recordings. Ecol. Evol. 8, 10166–10174 (2018).PubMed 
PubMed Central 

Google Scholar 
Raap, T., Pinxten, R. & Eens, M. Light pollution disrupts sleep in free-living animals. Sci. Rep. 5, 13557 (2015).PubMed 
PubMed Central 

Google Scholar 
Meijdam, M., Müller, W., Thys, B. & Eens, M. No relationship between chronotype and timing of breeding when variation in daily activity patterns across the breeding season is taken into account. Ecol. Evol. 12, e9353 (2022).PubMed 
PubMed Central 

Google Scholar 
R Core Team. R: a language and environment for statistical computing. R Found. Stat. Comput. https://www.R-project.org/ Accessed 23 February 2022 (2013).Rousset, F. & Ferdy, J.-B. Testing environmental and genetic effects in the presence of spatial autocorrelation. Ecography 37, 781–790 (2014).
Google Scholar 
Araya-Ajoy, Y. G., Mathot, K. J. & Dingemanse, N. J. An approach to estimate short-term, long-term and reaction norm repeatability. Methods Ecol. Evol. 6, 1462–1473 (2015).
Google Scholar 
Mitchell, D. J., Dujon, A. M., Beckmann, C. & Biro, P. A. Temporal autocorrelation: A neglected factor in the study of behavioral repeatability and plasticity. Behav. Ecol. 31, 222–231 (2020).
Google Scholar 
Bell, A. M., Hankison, S. J. & Laskowski, K. L. The repeatability of behaviour: A meta-analysis. Anim. Behav. 77, 771–783 (2009).PubMed 
PubMed Central 

Google Scholar 
Graham, J. L., Cook, N. J., Needham, K. B., Hau, M. & Greives, T. J. Early to rise, early to breed: A role for daily rhythms in seasonal reproduction. Behav. Ecol. 28, 1266–1271 (2017).
Google Scholar 
Maury, C., Serota, M. W. & Williams, T. D. Plasticity in diurnal activity and temporal phenotype during parental care in European starlings Sturnus vulgaris. Anim. Behav. 159, 37–45 (2020).
Google Scholar 
Schlicht, L., Valcu, M., Loës, P., Girg, A. & Kempenaers, B. No relationship between female emergence time from the roosting place and extrapair paternity. Behav. Ecol. 25, 650–659 (2014).
Google Scholar 
Steinmeyer, C., Schielzeth, H., Mueller, J. C. & Kempenaers, B. Variation in sleep behaviour in free-living blue tits, Cyanistes caeruleus: Effects of sex, age and environment. Anim. Behav. 80, 853–864 (2010).
Google Scholar 
Stuber, E. F., Dingemanse, N. J., Kempenaers, B. & Mueller, J. C. Sources of intraspecific variation in sleep behaviour of wild great tits. Anim. Behav. 106, 201–221 (2015).
Google Scholar 
Raap, T., Pinxten, R. & Eens, M. Cavities shield birds from effects of artificial light at night on sleep. J. Exp. Zool. Part Ecol. Integr. Physiol. 329, 449–456 (2018).
Google Scholar 
Edelaar, P., Siepielski, A. M. & Clobert, J. Matching habitat choice causes directed gene flow: A neglected dimension in evolution and ecology. Evolution 62, 2462–2472 (2008).PubMed 

Google Scholar 
Gorissen, L. & Eens, M. Interactive communication between male and female great tits (Parus major) during the dawn chorus. Auk 121, 184–191 (2004).
Google Scholar 
Halfwerk, W., Bot, S. & Slabbekoorn, H. Male great tit song perch selection in response to noise-dependent female feedback. Funct. Ecol. 26, 1339–1347 (2012).
Google Scholar 
Steinmeyer, C., Mueller, J. C. & Kempenaers, B. Individual variation in sleep behaviour in blue tits Cyanistes caeruleus: Assortative mating and associations with fitness-related traits. J. Avian Biol. 44, 159–168 (2013).
Google Scholar 
Cain, J. R. & Wilson, W. O. The influence of specific environmental parameters on the circadian rhythms of chickens. Poult. Sci. 53, 1438–1447 (1974).CAS 
PubMed 

Google Scholar 
Zhang, Z. C. et al. Circadian clock genes are rhythmically expressed in specific segments of the hen oviduct. Poult. Sci. 95, 1653–1659 (2016).CAS 
PubMed 

Google Scholar 
Womack, R. J. Clocks in the wild: biological rhythms of great tits and the environment. https://theses.gla.ac.uk/81345/ Accessed 23 February 2022 (2020).Dominoni, D., Smit, J. A. H., Visser, M. E. & Halfwerk, W. Multisensory pollution: Artificial light at night and anthropogenic noise have interactive effects on activity patterns of great tits (Parus major). Environ. Pollut. 256, 113314 (2020).CAS 
PubMed 

Google Scholar 
Matthysen, E., Adriaensen, F. & Dhondt, A. A. Multiple responses to increasing spring temperatures in the breeding cycle of blue and great tits (Cyanistes caeruleus, Parus major). Glob. Change Biol. 17, 1–16 (2011).
Google Scholar  More

Hantson, S., Huxman, T. E., Kimball, S., Randerson, J. T. & Goulden, M. L. Warming as a driver of vegetation loss in the Sonoran Desert of California. J. Geophys. Res. Biogeosci. 126, e2020JG005942. https://doi.org/10.1029/2020JG005942 (2021).Article 
ADS 

Google Scholar 
Lortie, C. J., Filazzola, A., Kelsey, R., Hart, A. K. & Butterfield, H. S. Better late than never: A synthesis of strategic land retirement and restoration in California. Ecosphere 9, e02367. https://doi.org/10.1002/ecs2.2367 (2018).Article 

Google Scholar 
Ye, J.-S., Reynolds, J. F., Sun, G.-J. & Li, F.-M. Impacts of increased variability in precipitation and air temperature on net primary productivity of the Tibetan Plateau: A modeling analysis. Clim. Change 119, 321–332. https://doi.org/10.1007/s10584-013-0719-2 (2013).Article 
ADS 

Google Scholar 
Pendergrass, A. G., Knutti, R., Lehner, F., Deser, C. & Sanderson, B. M. Precipitation variability increases in a warmer climate. Sci. Rep. 7, 17966. https://doi.org/10.1038/s41598-017-17966-y (2017).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhang, W. et al. Increasing precipitation variability on daily-to-multiyear time scales in a warmer world. Sci. Adv. 7, eabf8021. https://doi.org/10.1126/sciadv.abf8021 (2021).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Stahle David, W. Anthropogenic megadrought. Science 368, 238–239. https://doi.org/10.1126/science.abb6902 (2020).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Williams, A. P. et al. Large contribution from anthropogenic warming to an emerging North American megadrought. Science 368, 314–318. https://doi.org/10.1126/science.aaz9600 (2020).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Bryant, B. P. et al. Shaping land use change and ecosystem restoration in a water-stressed agricultural landscape to achieve multiple benefits. Front. Sustain. Food Syst. 4, 138 (2020).Article 

Google Scholar 
Ross, C. W. et al. Woody-biomass projections and drivers of change in sub-Saharan Africa. Nat. Clim. Chang. 11, 449–455. https://doi.org/10.1038/s41558-021-01034-5 (2021).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Scanlon, B. R., Reedy, R. C., Stonestrom, D. A., Prudic, D. E. & Dennehy, K. F. Impact of land use and land cover change on groundwater recharge and quality in the southwestern US. Glob. Change Biol. 11, 1577–1593. https://doi.org/10.1111/j.1365-2486.2005.01026.x (2005).Article 
ADS 

Google Scholar 
Scanlon, B. R. et al. Global synthesis of groundwater recharge in semiarid and arid regions. Hydrol. Process. 20, 3335–3370. https://doi.org/10.1002/hyp.6335 (2006).Article 
ADS 
CAS 

Google Scholar 
Kelsey, R., Hart, A., Butterfield, H. S. & Vink, D. Groundwater sustainability in the San Joaquin Valley: Multiple benefits if agricultural lands are retired and restored strategically. Calif. Agric. 2, 151–154 (2018).Article 

Google Scholar 
Capdevila, P. et al. Reconciling resilience across ecological systems, species and subdisciplines. J. Ecol. 109, 3102–3113. https://doi.org/10.1111/1365-2745.13775 (2021).Article 

Google Scholar 
Thebault, A., Mariotte, P., Lortie, C. & MacDougall, A. Land management trumps the effects of climate change and elevated CO2 on grassland functioning. J. Ecol. 102, 896–904. https://doi.org/10.1111/1365-2745.12236 (2014).Article 

Google Scholar 
Turney, C., Ausseil, A.-G. & Broadhurst, L. Urgent need for an integrated policy framework for biodiversity loss and climate change. Nature Ecol. Evol. 4, 996–996. https://doi.org/10.1038/s41559-020-1242-2 (2020).Article 

Google Scholar 
Strassburg, B. B. N. et al. Global priority areas for ecosystem restoration. Nature 586, 724–729. https://doi.org/10.1038/s41586-020-2784-9 (2020).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Ellison, A. M. Foundation species, non-trophic interactions, and the value of being common. iScience 13, 254–268. https://doi.org/10.1016/j.isci.2019.02.020 (2019).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
O’Brien, M. J., Carbonell, E. P., Losapio, G., Schlüter, P. M. & Schöb, C. Foundation species promote local adaptation and fine-scale distribution of herbaceous plants. J. Ecol. 109, 191–203. https://doi.org/10.1111/1365-2745.13461 (2021).Article 
CAS 

Google Scholar 
Bagley, J. E. et al. The influence of land cover on surface energy partitioning and evaporative fraction regimes in the U.S. Southern Great Plains. J. Geophys. Res.: Atmos. 122, 5793–5807. https://doi.org/10.1002/2017JD026740 (2017).Article 
ADS 

Google Scholar 
Norris, C., Hobson, P. & Ibisch, P. L. Microclimate and vegetation function as indicators of forest thermodynamic efficiency. J. Appl. Ecol. 49, 562–570. https://doi.org/10.1111/j.1365-2664.2011.02084.x (2012).Article 

Google Scholar 
Brooker, R. W. et al. Tiny niches and translocations: The challenge of identifying suitable recipient sites for small and immobile species. J. Appl. Ecol. 55, 621–630. https://doi.org/10.1111/1365-2664.13008 (2018).Article 

Google Scholar 
Forzieri, G. et al. Increased control of vegetation on global terrestrial energy fluxes. Nat. Clim. Chang. 10, 356–362. https://doi.org/10.1038/s41558-020-0717-0 (2020).Article 
ADS 

Google Scholar 
Milling, C. R. et al. Habitat structure modifies microclimate: An approach for mapping fine-scale thermal refuge. Methods Ecol. Evol. 9, 1648–1657. https://doi.org/10.1111/2041-210X.13008 (2018).Article 

Google Scholar 
Ghazian, N., Zuliani, M. & Lortie, C. J. Micro-climatic amelioration in a california desert: Artificial shelter versus shrub canopy. J. Ecol. Eng. 21, 216–228. https://doi.org/10.12911/22998993/126875 (2020).Article 

Google Scholar 
Wright, A. J., Barry, K. E., Lortie, C. J. & Callaway, R. M. Biodiversity and ecosystem functioning: Have our experiments and indices been underestimating the role of facilitation?. J. Ecol. 109, 1962–1968. https://doi.org/10.1111/1365-2745.13665 (2021).Article 

Google Scholar 
Germano, D. J. et al. The San Joaquin Desert of California: Ecologically misunderstood and overlooked. Nat. Areas J. 31, 138–147. https://doi.org/10.3375/043.031.0206 (2011).Article 

Google Scholar 
Fairbairn, M., LaChance, J., De Master, K. T. & Ashwood, L. In vino veritas, in aqua lucrum: Farmland investment, environmental uncertainty, and groundwater access in California’s Cuyama Valley. Agric. Hum. Values 38, 285–299. https://doi.org/10.1007/s10460-020-10157-y (2021).Article 

Google Scholar 
Filazzola, A., Lortie, C. J., Westphal, M. F. & Michalet, R. Species-specificity challenges the predictability of facilitation along a regional desert gradient. J. Veg. Sci. 1, 1–12. https://doi.org/10.1111/jvs.12909 (2020).Article 

Google Scholar 
Cutlar, H. C. Monograph of the North American species of the genus Ephedra. Ann. Mo. Bot. Gard. 26, 373–428 (1939).Article 

Google Scholar 
Hollander, J. L., Wall, S. B. V. & Baguley, J. G. Evolution of seed dispersal in North American Ephedra. Evol. Ecol. 24, 333–345. https://doi.org/10.1007/s10682-009-9309-1 (2010).Article 

Google Scholar 
Filazzola, A., Brown, C., Westphal, M. & Lortie, C. J. Establishment of a desert foundation species is limited by exotic plants and light but not herbivory or water. Appl. Veg. Sci. 1, 1–12. https://doi.org/10.1111/avsc.12515 (2020).Article 

Google Scholar 
Lortie, C. J., Gruber, E., Filazzola, A., Noble, T. & Westphal, M. The Groot effect: Plant facilitation and desert shrub regrowth following extensive damage. Ecol. Evol. 8, 706–715. https://doi.org/10.1002/ece3.3671 (2018).Article 
PubMed 

Google Scholar 
Lortie, C. J. et al. Telemetry of the lizard species Gambelia sila at Carrizo plain national monument. Figshare. Dataset. https://doi.org/10.6084/m9.figshare.8239667.v2 (2019).Article 

Google Scholar 
Braun, J., Westphal, M. & Lortie, C. J. The shrub Ephedra californica facilitates arthropod communities along a regional desert climatic gradient. Ecosphere 12, e03760. https://doi.org/10.1002/ecs2.3760 (2021).Article 

Google Scholar 
Terando, A., Youngsteadt, E., Meineke, E. & Prado, S. Accurate near surface air temperature measurements are necessary to gauge large-scale ecological responses to global climate change. Ecol. Evol. 8, 5233–5234. https://doi.org/10.1002/ece3.3972 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Tielborger, K. & Kadmon, R. Indirect effects in a desert plant community: Is competition among annuals more intense under shrub canopies?. Plant Ecol. 150, 53–63 (2000).Article 

Google Scholar 
Holzapfel, C., Tielbörger, K., Parag, H. A., Kigel, J. & Sternberg, M. Annual plant–shrub interactions along an aridity gradient. Basic Appl. Ecol. 7, 268–279. https://doi.org/10.1016/j.baae.2005.08.003 (2006).Article 

Google Scholar 
Jankju, M. Role of nurse shrubs in restoration of an arid rangeland: Effects of microclimate on grass establishment. J. Arid Environ. 89, 103–109. https://doi.org/10.1016/j.jaridenv.2012.09.008 (2013).Article 
ADS 

Google Scholar 
Baldelomar, M., Atala, C. & Molina-Montenegro, M. A. Top-down and Bottom-up effects deployed by a nurse shrub allow facilitating an endemic mediterranean orchid. Front. Ecol. Evol. 7, 466 (2019).Article 

Google Scholar 
Tielborger, K. & Kadmon, R. Temporal environmental variation tips the balance between facilitation and interference in desert plants. Ecology 81, 1544–1553. https://doi.org/10.1890/0012-9658(2000)081[1544:TEVTTB]2.0.CO;2 (2000).Article 

Google Scholar 
Walter, J. Effects of changes in soil moisture and precipitation patterns on plant-mediated biotic interactions in terrestrial ecosystems. Plant Ecol. https://doi.org/10.1007/s11258-018-0893-4 (2018).Article 

Google Scholar 
Schob, C., Armas, C. & Pugnaire, F. Direct and indirect interactions co-determine species composition in nurse plant systems. Oikos 122, 1371–1379. https://doi.org/10.1111/j.1600-0706.2013.00390.x (2013).Article 

Google Scholar 
Eldridge, D. J., Beecham, G. & Grace, J. B. Do shrubs reduce the adverse effects of grazing on soil properties?. Ecohydrology 8, 1503–1513. https://doi.org/10.1002/eco.1600 (2015).Article 

Google Scholar 
Nerlekar, A. N. & Veldman, J. W. High plant diversity and slow assembly of old-growth grasslands. Proc. Natl. Acad. Sci. 117, 18550. https://doi.org/10.1073/pnas.1922266117 (2020).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Tielbörger, K. et al. Middle-Eastern plant communities tolerate 9 years of drought in a multi-site climate manipulation experiment. Nat. Commun. 5, 5102. https://doi.org/10.1038/ncomms6102 (2014).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Griffin, D. & Anchukaitis, K. J. How unusual is the 2012–2014 California drought?. Geophys. Res. Lett. 41, 9017–9023. https://doi.org/10.1002/2014GL062433 (2014).Article 
ADS 

Google Scholar 
Data, U. C. In US Climate Data Product, New Cuyama, vol. 1. https://www.usclimatedata.com (2021).Gherardi, L. A. & Sala, O. E. Effect of interannual precipitation variability on dryland productivity: A global synthesis. Glob. Change Biol. 25, 269–276. https://doi.org/10.1111/gcb.14480 (2019).Article 
ADS 

Google Scholar 
Ding, Y., Li, Z. & Peng, S. Global analysis of time-lag and -accumulation effects of climate on vegetation growth. Int. J. Appl. Earth Obs. Geoinf. 92, 102179. https://doi.org/10.1016/j.jag.2020.102179 (2020).Article 

Google Scholar 
Liu, H. et al. Analysis of the time-lag effects of climate factors on grassland productivity in Inner Mongolia. Glob. Ecol. Conserv. 30, e01751. https://doi.org/10.1016/j.gecco.2021.e01751 (2021).Article 

Google Scholar 
Liancourt, P., Song, X., Macek, M., Santrucek, J. & Dolezal, J. Plant’s-eye view of temperature governs elevational distributions. Glob. Change Biol. 26, 4094–4103. https://doi.org/10.1111/gcb.15129 (2020).Article 
ADS 

Google Scholar 
Ryan, M. J. et al. Too dry for lizards: Short-term rainfall influence on lizard microhabitat use in an experimental rainfall manipulation within a pinon-juniper woodland. Funct. Ecol. https://doi.org/10.1111/1365-2435.12595 (2015).Article 

Google Scholar 
Moore, D., Stow, A. & Kearney, M. R. Under the weather?—The direct effects of climate warming on a threatened desert lizard are mediated by their activity phase and burrow system. J. Anim. Ecol. 87, 660–671. https://doi.org/10.1111/1365-2656.12812 (2018).Article 
PubMed 

Google Scholar 
Gaudenti, N., Nix, E., Maier, P., Westphal, M. F. & Taylor, E. N. Habitat heterogeneity affects the thermal ecology of an endangered lizard. Ecol. Evol. 11, 14843–14856. https://doi.org/10.1002/ece3.8170 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Lortie, C. J., Filazzola, A. & Sotomayor, D. A. Functional assessment of animal interactions with shrub-facilitation complexes: A formal synthesis and conceptual framework. Funct. Ecol. 30, 41–51. https://doi.org/10.1111/1365-2435.12530 (2016).Article 

Google Scholar 
Lortie, C. J. et al. Shrub and vegetation cover predict resource selection use by an endangered species of desert lizard. Sci. Rep. 10, 4884. https://doi.org/10.1038/s41598-020-61880-9 (2020).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
De Frenne, P. et al. Global buffering of temperatures under forest canopies. Nature Ecol. Evol. 3, 744–749. https://doi.org/10.1038/s41559-019-0842-1 (2019).Article 

Google Scholar 
Avolio, M. L. et al. Determinants of community compositional change are equally affected by global change. Ecol. Lett. 24, 1892–1904. https://doi.org/10.1111/ele.13824 (2021).Article 
PubMed 

Google Scholar 
Cook-Patton, S. C. et al. Protect, manage and then restore lands for climate mitigation. Nat. Clim. Chang. 11, 1027–1034. https://doi.org/10.1038/s41558-021-01198-0 (2021).Article 
ADS 

Google Scholar 
Hedden-Nicely, D. R. Climate change and the future of western US water governance. Nat. Clim. Chang. https://doi.org/10.1038/s41558-021-01141-3 (2021).Article 

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

Google Scholar 
Hanson, R. T., Flint, L. E., Faunt, C. C., Gibbs, D. R. & Schmid, W. Hydrologic models and analysis of water availability in Cuyama Valley, California. In U.S. Geological Survey Scientific Investigations Report, 2015 1–126 (2015).John, S. In Encyclopedia of World Climatology (ed John, E. O.) 89–94 (Springer Netherlands, 2005).James-Jeremy, J. et al. A systems approach to restoring degraded drylands. J. Appl. Ecol. 50, 730–739. https://doi.org/10.1111/1365-2664.12090 (2013).Article 

Google Scholar 
Upson, J. E. & Worts, G. F. In Ground water in the Cuyama Valley, California. Report No. 1110B 1–82 (1951).Hanson, M. T., Randall, T. & Sweetkind, D. Cuyama Valley, California hydrologic study—an assessment of water availability. In U.S. Geological Survey Scientific Investigations Report 2014 1–4. https://doi.org/10.3133/fs20143075 (2014).Greicius, T. NASA data show California’s San Joaquin Valley Still Sinking. JPL 28, 1–9 (2017).
Google Scholar 
Döll, P. et al. Impact of water withdrawals from groundwater and surface water on continental water storage variations. J. Geodyn. 59–60, 143–156. https://doi.org/10.1016/j.jog.2011.05.001 (2012).Article 

Google Scholar 
Lortie, C. J. & Filazzola, A. US climate data, New Cuyama, CA, 2016–2017. Figshare 1, 2016–2017. https://doi.org/10.6084/m9.figshare.17162600.v1 (2021).Article 

Google Scholar 
Lortie, C. J. & Filazzola, A. Vegetation surveys in Cuyama Valley, CA, USA in 2016 and 2017 at the peak of megadrought. Knowl. Netw. Biocompl. 1, 1–15. https://doi.org/10.5063/F1MG7MZH (2021).Article 

Google Scholar 
Hickman, J. C. The Jepson Manual (University of California Press, 1996).
Google Scholar 
Villanueva-Almanza, L. & Fonseca, R. M. In Taxonomic review and geographic distribution of Ephedra (Ephedraceae) in Mexico. ACTA BOTANICA MEXICANA 96 (2011).Alfieri, F. J. & Mottola, P. M. Seasonal changes in the phloem of Ephedra californica Wats. Bot. Gaz. 144, 240–246 (1983).Article 

Google Scholar 
Hoffman, O., de-Falco, N., Yizhaq, H. & Boeken, B. Annual plant diversity decreases across scales following widespread ecosystem engineer shrub mortality. J. Veg. Sci. https://doi.org/10.1111/jvs.12372 (2016).Article 

Google Scholar 
Ivey, K. N. et al. Thermal ecology of the federally endangered blunt-nosed leopard lizard (Gambelia sila). Conserv. Physiol. 2020, 8. https://doi.org/10.1093/conphys/coaa014 (2020).Article 

Google Scholar 
Grimes, A. J., Corrigan, G., Germano, D. J. & Smith, P. T. Mitochondrial phylogeography of the endangered blunt-nosed leopard lizard, Gambelia sila. Southwestern Natural. 59, 38–46. https://doi.org/10.1894/F06-GC-233.1 (2014).Article 

Google Scholar 
Stewart, J. A. E. et al. Habitat restoration opportunities, climatic niche contraction, and conservation biogeography in California’s San Joaquin Desert. PLoS ONE 14, e0210766. https://doi.org/10.1371/journal.pone.0210766 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Germano, D. J., Rathbun, G. B. & Saslaw, L. R. Effects of grazing and invasive grasses on desert vertebrates in California. J. Wildl. Manag. 76, 670–682. https://doi.org/10.1002/jwmg.316 (2012).Article 

Google Scholar 
Moss, B. The water framework directive: Total environment or political compromise?. Sci. Total Environ. 400, 32–41. https://doi.org/10.1016/j.scitotenv.2008.04.029 (2008).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Denevan, W. M. The “Pristine Myth ” revisited. Geogr. Rev. 101, 576–591. https://doi.org/10.1111/j.1931-0846.2011.00118.x (2011).Article 

Google Scholar 
da Cunha, A. R. Evaluation of measurement errors of temperature and relative humidity from HOBO data logger under different conditions of exposure to solar radiation. Environ. Monit. Assess. 187, 236. https://doi.org/10.1007/s10661-015-4458-x (2015).Article 
PubMed 

Google Scholar 
Terando, A. J., Youngsteadt, E., Meineke, E. K. & Prado, S. G. Ad hoc instrumentation methods in ecological studies produce highly biased temperature measurements. Ecol. Evol. 7, 9890–9904. https://doi.org/10.1002/ece3.3499 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Nature, I. I. U. f. C. o. The IUCN red list of threatened species. IUCN 2019-1 1–142 (2019).Lortie, C. J., Filazzola, A., Butterfield, H. S. & Westphal, M. Cuyama Micronet. Figshare 1, 1–6. https://doi.org/10.6084/m9.figshare.11888199.v2 (2020).Article 

Google Scholar 
Team, R. C. R: A Language and Environment for Statistical Computing. Vol. 4.2.1 (R foundation for Statistical Computing, 2022).Pinheiro, J., Bates, D., DebRoy, S. & Deepayan, S. nlme: Linear and nonlinear mixed effects models. CRAN 3, 1–153 (2021).
Google Scholar 
Pebesma, E. spacetime: Spatio-temporal data in R. J. Stat. Softw. 1(7), 2012. https://doi.org/10.18637/jss.v051.i07 (2012).Article 

Google Scholar 
Bates, D. et al. lme4: Linear mixed-effects models using “Eigen” and S4. CRAN 2020, 1–122 (2020).
Google Scholar 
Lenth, R. V. emmeans: Estimated marginal means. CRAN 1, 1–89 (2022).
Google Scholar  More

.readcube-buybox { display: none !important;}
A roaring trans-European herring trade that began in the Viking Age might have depleted stocks1.

Access options

/* style specs start */
style{display:none!important}.LiveAreaSection-193358632 *{align-content:stretch;align-items:stretch;align-self:auto;animation-delay:0s;animation-direction:normal;animation-duration:0s;animation-fill-mode:none;animation-iteration-count:1;animation-name:none;animation-play-state:running;animation-timing-function:ease;azimuth:center;backface-visibility:visible;background-attachment:scroll;background-blend-mode:normal;background-clip:borderBox;background-color:transparent;background-image:none;background-origin:paddingBox;background-position:0 0;background-repeat:repeat;background-size:auto auto;block-size:auto;border-block-end-color:currentcolor;border-block-end-style:none;border-block-end-width:medium;border-block-start-color:currentcolor;border-block-start-style:none;border-block-start-width:medium;border-bottom-color:currentcolor;border-bottom-left-radius:0;border-bottom-right-radius:0;border-bottom-style:none;border-bottom-width:medium;border-collapse:separate;border-image-outset:0s;border-image-repeat:stretch;border-image-slice:100%;border-image-source:none;border-image-width:1;border-inline-end-color:currentcolor;border-inline-end-style:none;border-inline-end-width:medium;border-inline-start-color:currentcolor;border-inline-start-style:none;border-inline-start-width:medium;border-left-color:currentcolor;border-left-style:none;border-left-width:medium;border-right-color:currentcolor;border-right-style:none;border-right-width:medium;border-spacing:0;border-top-color:currentcolor;border-top-left-radius:0;border-top-right-radius:0;border-top-style:none;border-top-width:medium;bottom:auto;box-decoration-break:slice;box-shadow:none;box-sizing:border-box;break-after:auto;break-before:auto;break-inside:auto;caption-side:top;caret-color:auto;clear:none;clip:auto;clip-path:none;color:initial;column-count:auto;column-fill:balance;column-gap:normal;column-rule-color:currentcolor;column-rule-style:none;column-rule-width:medium;column-span:none;column-width:auto;content:normal;counter-increment:none;counter-reset:none;cursor:auto;display:inline;empty-cells:show;filter:none;flex-basis:auto;flex-direction:row;flex-grow:0;flex-shrink:1;flex-wrap:nowrap;float:none;font-family:initial;font-feature-settings:normal;font-kerning:auto;font-language-override:normal;font-size:medium;font-size-adjust:none;font-stretch:normal;font-style:normal;font-synthesis:weight style;font-variant:normal;font-variant-alternates:normal;font-variant-caps:normal;font-variant-east-asian:normal;font-variant-ligatures:normal;font-variant-numeric:normal;font-variant-position:normal;font-weight:400;grid-auto-columns:auto;grid-auto-flow:row;grid-auto-rows:auto;grid-column-end:auto;grid-column-gap:0;grid-column-start:auto;grid-row-end:auto;grid-row-gap:0;grid-row-start:auto;grid-template-areas:none;grid-template-columns:none;grid-template-rows:none;height:auto;hyphens:manual;image-orientation:0deg;image-rendering:auto;image-resolution:1dppx;ime-mode:auto;inline-size:auto;isolation:auto;justify-content:flexStart;left:auto;letter-spacing:normal;line-break:auto;line-height:normal;list-style-image:none;list-style-position:outside;list-style-type:disc;margin-block-end:0;margin-block-start:0;margin-bottom:0;margin-inline-end:0;margin-inline-start:0;margin-left:0;margin-right:0;margin-top:0;mask-clip:borderBox;mask-composite:add;mask-image:none;mask-mode:matchSource;mask-origin:borderBox;mask-position:0 0;mask-repeat:repeat;mask-size:auto;mask-type:luminance;max-height:none;max-width:none;min-block-size:0;min-height:0;min-inline-size:0;min-width:0;mix-blend-mode:normal;object-fit:fill;object-position:50% 50%;offset-block-end:auto;offset-block-start:auto;offset-inline-end:auto;offset-inline-start:auto;opacity:1;order:0;orphans:2;outline-color:initial;outline-offset:0;outline-style:none;outline-width:medium;overflow:visible;overflow-wrap:normal;overflow-x:visible;overflow-y:visible;padding-block-end:0;padding-block-start:0;padding-bottom:0;padding-inline-end:0;padding-inline-start:0;padding-left:0;padding-right:0;padding-top:0;page-break-after:auto;page-break-before:auto;page-break-inside:auto;perspective:none;perspective-origin:50% 50%;pointer-events:auto;position:static;quotes:initial;resize:none;right:auto;ruby-align:spaceAround;ruby-merge:separate;ruby-position:over;scroll-behavior:auto;scroll-snap-coordinate:none;scroll-snap-destination:0 0;scroll-snap-points-x:none;scroll-snap-points-y:none;scroll-snap-type:none;shape-image-threshold:0;shape-margin:0;shape-outside:none;tab-size:8;table-layout:auto;text-align:initial;text-align-last:auto;text-combine-upright:none;text-decoration-color:currentcolor;text-decoration-line:none;text-decoration-style:solid;text-emphasis-color:currentcolor;text-emphasis-position:over right;text-emphasis-style:none;text-indent:0;text-justify:auto;text-orientation:mixed;text-overflow:clip;text-rendering:auto;text-shadow:none;text-transform:none;text-underline-position:auto;top:auto;touch-action:auto;transform:none;transform-box:borderBox;transform-origin:50% 50%0;transform-style:flat;transition-delay:0s;transition-duration:0s;transition-property:all;transition-timing-function:ease;vertical-align:baseline;visibility:visible;white-space:normal;widows:2;width:auto;will-change:auto;word-break:normal;word-spacing:normal;word-wrap:normal;writing-mode:horizontalTb;z-index:auto;-webkit-appearance:none;-moz-appearance:none;-ms-appearance:none;appearance:none;margin:0}.LiveAreaSection-193358632{width:100%}.LiveAreaSection-193358632 .login-option-buybox{display:block;width:100%;font-size:17px;line-height:30px;color:#222;padding-top:30px;font-family:Harding,Palatino,serif}.LiveAreaSection-193358632 .additional-access-options{display:block;font-weight:700;font-size:17px;line-height:30px;color:#222;font-family:Harding,Palatino,serif}.LiveAreaSection-193358632 .additional-login >li:not(:first-child)::before{transform:translateY(-50%);content:””;height:1rem;position:absolute;top:50%;left:0;border-left:2px solid #999}.LiveAreaSection-193358632 .additional-login >li:not(:first-child){padding-left:10px}.LiveAreaSection-193358632 .additional-login >li{display:inline-block;position:relative;vertical-align:middle;padding-right:10px}.BuyBoxSection-683559780{display:flex;flex-wrap:wrap;flex:1;flex-direction:row-reverse;margin:-30px -15px 0}.BuyBoxSection-683559780 .box-inner{width:100%;height:100%}.BuyBoxSection-683559780 .readcube-buybox{background-color:#f3f3f3;flex-shrink:1;flex-grow:1;flex-basis:255px;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .subscribe-buybox{background-color:#f3f3f3;flex-shrink:1;flex-grow:4;flex-basis:300px;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .subscribe-buybox-nature-plus{background-color:#f3f3f3;flex-shrink:1;flex-grow:4;flex-basis:100%;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .title-readcube{display:block;margin:0;margin-right:20%;margin-left:20%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .title-buybox{display:block;margin:0;margin-right:29%;margin-left:29%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .title-asia-buybox{display:block;margin:0;margin-right:5%;margin-left:5%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .asia-link{color:#069;cursor:pointer;text-decoration:none;font-size:1.05em;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:1.05em6}.BuyBoxSection-683559780 .access-readcube{display:block;margin:0;margin-right:10%;margin-left:10%;font-size:14px;color:#222;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .access-asia-buybox{display:block;margin:0;margin-right:5%;margin-left:5%;font-size:14px;color:#222;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .access-buybox{display:block;margin:0;margin-right:30%;margin-left:30%;font-size:14px;color:#222;opacity:.8px;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .usps-buybox{display:block;margin:0;margin-right:30%;margin-left:30%;font-size:14px;color:#222;opacity:.8px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .price-buybox{display:block;font-size:30px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;padding-top:30px;text-align:center}.BuyBoxSection-683559780 .price-from{font-size:14px;padding-right:10px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .issue-buybox{display:block;font-size:13px;text-align:center;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:19px}.BuyBoxSection-683559780 .no-price-buybox{display:block;font-size:13px;line-height:18px;text-align:center;padding-right:10%;padding-left:10%;padding-bottom:20px;padding-top:30px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif}.BuyBoxSection-683559780 .vat-buybox{display:block;margin-top:5px;margin-right:20%;margin-left:20%;font-size:11px;color:#222;padding-top:10px;padding-bottom:15px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:17px}.BuyBoxSection-683559780 .button-container{display:flex;padding-right:20px;padding-left:20px;justify-content:center}.BuyBoxSection-683559780 .button-container >*{flex:1px}.BuyBoxSection-683559780 .button-container >a:hover,.Button-505204839:hover,.Button-1078489254:hover,.Button-2808614501:hover{text-decoration:none}.BuyBoxSection-683559780 .readcube-button{background:#fff;margin-top:30px}.BuyBoxSection-683559780 .button-asia{background:#069;border:1px solid #069;border-radius:0;cursor:pointer;display:block;padding:9px;outline:0;text-align:center;text-decoration:none;min-width:80px;margin-top:75px}.BuyBoxSection-683559780 .button-label-asia,.ButtonLabel-3869432492,.ButtonLabel-3296148077,.ButtonLabel-1566022830{display:block;color:#fff;font-size:17px;line-height:20px;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;text-align:center;text-decoration:none;cursor:pointer}.Button-505204839,.Button-1078489254,.Button-2808614501{background:#069;border:1px solid #069;border-radius:0;cursor:pointer;display:block;padding:9px;outline:0;text-align:center;text-decoration:none;min-width:80px;max-width:320px;margin-top:10px}.Button-505204839 .readcube-label,.Button-1078489254 .readcube-label,.Button-2808614501 .readcube-label{color:#069}
/* style specs end */Subscribe to Nature+Get immediate online access to Nature and 55 other Nature journal$29.99monthlySubscribe to JournalGet full journal access for 1 year$199.00only $3.90 per issueAll prices are NET prices.VAT will be added later in the checkout.Tax calculation will be finalised during checkout.Buy articleGet time limited or full article access on ReadCube.$32.00All prices are NET prices.

Additional access options:

doi: https://doi.org/10.1038/d41586-022-03431-y

References

Subjects

Latest on: More

Lal, R. Soil carbon sequestration impacts on global climate change and food security. Science 304, 1623–1627 (2004).ADS 
PubMed 

Google Scholar 
Torn, M. S., Vitousek, P. M. & Trumbore, S. E. The influence of nutrient availability on soil organic matter turnover estimated by incubations and radiocarbon modeling. Ecosystems 8, 352–372 (2005).
Google Scholar 
Schimel, J. P. & Schaeffer, S. M. Microbial control over carbon cycling in soil. Front. Microbiol. 3, 348 (2012).PubMed 
PubMed Central 

Google Scholar 
Ding, X., Liang, C., Zhang, B., Yuan, Y. & Han, X. Higher rates of manure application lead to greater accumulation of both fungal and bacterial residues in macroaggregates of a clay soil. Soil Biol. Biochem. 84, 137–146 (2015).
Google Scholar 
Liang, C., Schimel, J. P. & Jastrow, J. D. The importance of anabolism in microbial control over soil carbon storage. Nat. Microbiol. 2, 17105 (2017).PubMed 

Google Scholar 
Kögel-Knabner, I. The macromolecular organic composition of plant and microbial residues as inputs to soil organic matter: fourteen years on. Soil Biol. Biochem. 105, A3–A8 (2017).
Google Scholar 
Miltner, A., Bombach, P., Schmidt-Brücken, B. & Kästner, M. SOM genesis: microbial biomass as a significant source. Biogeochemistry 111, 41–55 (2012).
Google Scholar 
Cotrufo, M. F., Wallenstein, M. D., Boot, C. M., Denef, K. & Paul, E. The Microbial Efficiency-Matrix Stabilization (MEMS) framework integrates plant litter decomposition with soil organic matter stabilization: Do labile plant inputs form stable soil organic matter?. Global Change Biol. 19, 988–995 (2013).ADS 

Google Scholar 
Sokol, N. W. & Bradford, M. A. Microbial formation of stable soil carbon is more efficient from belowground than aboveground input. Nat. Geosci. 12, 46–53 (2019).ADS 

Google Scholar 
Six, J., Frey, S. D., Thiet, R. K. & Batten, K. M. Bacterial and fungal contributions to carbon sequestration in agroecosystems. Soil Sci. Soc. Am. J. 70, 555–569 (2006).ADS 

Google Scholar 
Simpson, A. J., Simpson, M. J., Smith, E. & Kelleher, B. P. Microbially derived inputs to soil organic matter: Are current estimates too low?. Environ. Sci. Technol. 41, 8070–8076 (2007).ADS 
PubMed 

Google Scholar 
Liang, C., Fujinuma, R. & Balser, T. C. Comparing PLFA and amino sugars for microbial analysis in an Upper Michigan old growth forest. Soil Biol. Biochem. 40, 2063–2065 (2008).
Google Scholar 
Shao, P., Liang, C., Lynch, L., Xie, H. & Bao, X. Reforestation accelerates soil organic carbon accumulation: Evidence from microbial biomarkers. Soil Biol. Biochem. 131, 182–190 (2019).
Google Scholar 
Ma, S. et al. Effects of seven-year nitrogen and phosphorus additions on soil microbial community structures and residues in a tropical forest in Hainan Island, China. Geoderma 361, 114034 (2020).ADS 

Google Scholar 
Zelles, L. Fatty acid patterns of phospholipids and lipopolysaccharides in the characterisation of microbial communities in soil: A review. Biol. Fertil. Soils 29, 111–129 (1999).
Google Scholar 
Kong, A. Y. Y. et al. Microbial community composition and carbon cycling within soil microenvironments of conventional, low-input, and organic cropping systems. Soil Biol. Biochem. 43, 20–30 (2011).PubMed 
PubMed Central 

Google Scholar 
Müller, K., Marhan, S., Kandeler, E. & Poll, C. Carbon flow from litter through soil microorganisms: From incorporation rates to mean residence times in bacteria and fungi. Soil Biol. Biochem. 115, 187–196 (2017).
Google Scholar 
Amelung, W. Syntax of Referencing in Assessment Methods for Soil Carbon (Lewis Publishers, 2001).
Google Scholar 
Joergensen, R. & Wichern, F. Quantitative assessment of the fungal contribution to microbial tissue in soil. Soil Biol. Biochem. 40, 2977–2991 (2008).
Google Scholar 
Joergensen, R. G. Amino sugars as specific indices for fungal and bacterial residues in soil. Biol. Fert. Soils 54, 559–568 (2018).
Google Scholar 
Wang, X. et al. Distinct regulation of microbial processes in the immobilization of labile carbon in different soils. Soil Biol. Biochem. 142, 107723 (2020).
Google Scholar 
Wang, J., Chapman, S. J. & Yao, H. Incorporation of 13C-labelled rice rhizodeposition into soil microbial communities under different fertilizer applications. Appl. Soil Ecol. 101, 11–19 (2016).ADS 

Google Scholar 
Cui, S. et al. Long-term fertilization management affects the C utilization from crop residues by the soil micro-food web. Plant Soil 429, 335–348 (2018).
Google Scholar 
Liu, X., Zhang, X. & Herbert, S. Feeding China’s growing needs for grain. Nature 465, 420 (2010).ADS 
PubMed 

Google Scholar 
Edmeades, D. C. The long-term effects of manures and fertilisers on soil productivity and quality: A review. Nutr. Cycl. Agroecosys. 66, 165–180 (2003).
Google Scholar 
Chaparro, J., Sheflin, A., Manter, D. & Vivanco, J. Manipulating the soil microbiome to increase soil health and plant fertility. Biol. Fertil. Soils 48, 489–499 (2012).
Google Scholar 
Jin, X. et al. Enhanced conversion of newly-added maize straw to soil microbial biomass C under plastic film mulching and organic manure management. Geoderma 313, 154–162 (2018).ADS 

Google Scholar 
Chen, X., Li, Z., Liu, M., Jiang, C. & Che, Y. Microbial community and functional diversity associated with different aggregate fractions of a paddy soil fertilized with organic manure and/or NPK fertilizer for 20 years. J. Soil Sediment. 15, 292–301 (2014).
Google Scholar 
Wang, Y. et al. Soil aggregation regulates distributions of carbon, microbial community and enzyme activities after 23-year manure amendment. Appl. Soil Ecol. 111, 65–72 (2017).
Google Scholar 
Joergensen, R. G., Mäder, P. & Fließbach, A. Long-term effects of organic farming on fungal and bacterial residues in relation to microbial energy metabolism. Biol. Fert. Soils 46, 303–307 (2010).
Google Scholar 
Sun, H. et al. Soil microbial community and microbial residues respond positively to minimum tillage under organic farming in Southern Germany. Appl. Soil Ecol. 108, 16–24 (2016).
Google Scholar 
Heijboer, A. et al. Plant biomass, soil microbial community structure and nitrogen cycling under different organic amendment regimes; A 15N tracer-based approach. Appl. Soil Ecol. 107, 251–260 (2016).
Google Scholar 
Six, J., Elliott, E. T. & Paustian, K. Soil macroaggregate turnover and microaggregate formation: A mechanism for C sequestration under no-tillage agriculture. Soil Biol. Biochem. 32, 2099–2103 (2000).
Google Scholar 
Wall, D. et al. Soil Ecology and Ecosystem Services (Oxford University Press, 2012).
Google Scholar 
Helgason, B. L., Walley, F. L. & Germida, J. J. No-till soil management increases microbial biomass and alters community profiles in soil aggregates. Appl. Soil Ecol. 46, 390–397 (2010).
Google Scholar 
Blaud, A. et al. Dynamics of bacterial communities in relation to soil aggregate formation during the decomposition of 13C-labelled rice straw. Appl. Soil Ecol. 53, 1–9 (2012).
Google Scholar 
Tisdall, J. M. & Oades, J. M. Organic matter and water stable aggregates in soils. Eur. J. Soil Sci. 33, 141–163 (1982).
Google Scholar 
Bronick, C. J. & Lal, R. Soil structure and management: A review. Geoderma 124, 3–22 (2005).ADS 

Google Scholar 
Li, N. et al. Separation of soil microbial community structure by aggregate size to a large extent under agricultural practices during early pedogenesis of a Mollisol. Appl. Soil Ecol. 88, 9–20 (2015).
Google Scholar 
Bidisha, M., Joerg, R. & Yakov, K. Effects of aggregation processes on distribution of aggregate size fractions and organic C content of a long-term fertilized soil. Eur. J. Soil Biol. 46, 365–370 (2010).
Google Scholar 
Xiang, X. et al. Divergence in fungal abundance and community structure between soils under long-term mineral and organic fertilization. Soil Till. Res. 196, 104491 (2020).
Google Scholar 
Jin, X. et al. Long-term plastic film mulching and fertilization treatments changed the annual distribution of residual maize straw C in soil aggregates under field conditions: Characterization by 13C tracing. J. Soils Sediment. 18, 169–178 (2018).
Google Scholar 
Kemper, W. & Rosenau, R. Syntax of referencing. In Methods of Soil Analysis (ed. Klute, A.) (ASA and SSSA, 1986).
Google Scholar 
Bossio, D. A. & Scow, K. M. Impact of carbon and flooding on the metabolic diversity of microbial communities in soils. Appl. Environ. Microbiol. 61, 4043–4050 (1995).ADS 
PubMed 
PubMed Central 

Google Scholar 
Denef, K. et al. Community shifts and carbon translocation within metabolically-active rhizosphere microorganisms in grasslands under elevated CO2. Biogeosciences 4, 769–779 (2007).ADS 

Google Scholar 
Tavi, N. M. et al. Linking microbial community structure and allocation of plant-derived carbon in an organic agricultural soil using 13CO2 pulse-chase labelling combined with 13C-PLFA profiling. Soil Biol. Biochem. 58, 207–215 (2013).
Google Scholar 
Bach, E. M., Baer, S. G., Meyer, C. K. & Six, J. Soil texture affects soil microbial and structural recovery during grassland restoration. Soil Biol. Biochem. 42, 2182–2191 (2010).
Google Scholar 
Pan, F., Li, Y., Chapman, S. J., Khan, S. & Yao, H. Microbial utilization of rice straw and its derived biochar in a paddy soil. Sci. Total Environ. 559, 15–23 (2016).ADS 
PubMed 

Google Scholar 
Olsson, P. A. Signature fatty acids provide tools for determination of the distribution and interactions of mycorrhizal fungi in soil. FEMS Microbial Ecol. 29, 303–310 (1999).
Google Scholar 
Zhang, X. & Amelung, W. Gas Chromatographic determination of muramic acid, glucosamine, mannosamine, and galactosamine in soils. Soil Biol. Biochem. 28, 1201–1206 (1996).
Google Scholar 
Zhang, X. et al. Land-use effects on amino sugars in particle size fractions of an Argiudoll. Appl. Soil Ecol. 11, 271–275 (1999).
Google Scholar 
van Groenigen, K.-J. et al. Abundance, production and stabilization of microbial biomass under conventional and reduced tillage. Soil Biol. Biochem. 42, 48–55 (2010).
Google Scholar 
Liang, C., Amelung, W., Lehmann, J. & Kastner, M. Quantitative assessment of microbial necromass contribution to soil organic matter. Global Change Biol. 25, 3578–3590 (2019).ADS 

Google Scholar 
Engelking, B., Flessa, H. & Joergensen, R. G. Shifts in amino sugar and ergosterol contents after addition of sucrose and cellulose to soil. Soil Biol. Biochem. 39, 2111–2118 (2007).
Google Scholar 
Chander, K. & Joergensen, R. G. Decomposition of 14C glucose in two soils with different amounts of heavy metal contamination. Soil Biol. Biochem. 33, 1811–1816 (2001).
Google Scholar 
Zhu, Z. et al. Fate of rice shoot and root residues, rhizodeposits, and microbial assimilated carbon in paddy soil – part 2: turnover and microbial utilization. Plant Soil. 416, 243–257 (2017).
Google Scholar 
Appuhn, A. & Joergensen, R. Microbial colonisation of roots as a function of plant species. Soil Biol. Biochem. 38, 1040–1051 (2006).
Google Scholar 
Huang, Y., Liang, C., Duan, X., Chen, H. & Li, D. Variation of microbial residue contribution to soil organic carbon sequestration following land use change in a subtropical karst region. Geoderma 353, 340–346 (2019).ADS 

Google Scholar 
Liang, C. et al. Microorganisms and their residues under restored perennial grassland communities of varying diversity. Soil Biol. Biochem. 103, 192–200 (2016).
Google Scholar 
Kallenbach, C. M., Frey, S. D. & Grandy, A. S. Direct evidence for microbial-derived soil organic matter formation and its ecophysiological controls. Nat. Commun. 7, 13630 (2016).ADS 
PubMed 
PubMed Central 

Google Scholar 
Veresoglou, S. D., Chen, B. & Rillig, M. C. Arbuscular mycorrhiza and soil nitrogen cycling. Soil Biol. Biochem. 46, 53–62 (2012).
Google Scholar 
Treseder, K. K. A meta-analysis of mycorrhizal responses to nitrogen, phosphorus, and atmospheric CO2 in field studies. New Phytol. 164, 347–355 (2004).PubMed 

Google Scholar 
Xu, Y. et al. Microbial assimilation dynamics differs but total mineralization from added root and shoot residues is similar in agricultural Alfsols. Soil Biol. Biochem. 148, 107901 (2020).
Google Scholar 
Chenu, C. & Stotzky, G. Syntax of referencing in Interactions between soil particles and microorganisms (eds. Huang, P., Bollag, J. & Senesi, N.) 3–39 (Wiley-VCH, 2002).Chantigny, M., Angers, D., Prévost, D., Vézina, L.-P. & Chalifour, F. Soil aggregation and fungal and bacterial biomass under annual and perennial cropping systems. Soil Sci. Soc. Am. J. 61, 262–267 (1997).ADS 

Google Scholar 
Liang, C., Duncan, D., Balser, T., Tiedje, J. & Jackson, R. Soil microbial residue storage linked to soil legacy under biofuel cropping systems in southern Wisconsin, USA. Soil Biol. Biochem. 57, 939–942 (2013).
Google Scholar 
Feng, Y. et al. Temperature thresholds drive the global distribution of soil fungal decomposers. Glaobal Change Biol. 28, 2779–2789 (2022).
Google Scholar 
An, T. et al. Carbon fluxes from plants to soil and dynamics of microbial immobilization under plastic film mulching and fertilizer application using 13C pulse-labeling. Soil Biol. Biochem. 80, 53–61 (2015).
Google Scholar 
Lauer, F., Kösters, R., du Preez, C. C. & Amelung, W. Microbial residues as indicators of soil restoration in South African secondary pastures. Soil Biol. Biochem. 43, 787–794 (2011).
Google Scholar  More

Roff, D. A. The Evolution of Life Histories: Theory and Analysis (Chapman and Hall, 1992).
Google Scholar 
Sæther, B. E. & Bakke, O. Avian life history variation and contribution of demographic traits to the population growth rate. Ecology 81, 642–653 (2000).
Google Scholar 
Koons, D. N., Pavard, S., Baudisch, A. & Metcalf, J. E. C. Is life-history buffering or lability adaptive in stochastic environments?. Oikos 118, 972–980 (2009).
Google Scholar 
Boyce, M. S., Haridas, C. V. & Lee, C. T. Demography in an increasingly variable world. Trends Ecol. Evol. 21, 141–148 (2006).PubMed 

Google Scholar 
Morris, W. F. & Doak, D. F. Buffering of life histories against environmental stochasticity: Accounting for a spurious correlation between the variabilities of vital rates and their contributions to fitness. Am. Nat. 163, 579–590 (2004).PubMed 

Google Scholar 
Ripple, W. J. et al. Saving the world’s terrestrial megafauna. Bioscience 66(10), 807–812 (2016).PubMed 
PubMed Central 

Google Scholar 
He, F. et al. Disappearing giants: A review of threats to freshwater megafauna. WIREs Water 4, e1208 (2017).
Google Scholar 
Blackburn, T. M., Cassey, P., Duncan, R. P., Evans, K. L. & Gaston, K. J. Avian extinction and mammalian introductions on oceanic islands. Science 305(5692), 1955–1958 (2004).ADS 
CAS 
PubMed 

Google Scholar 
Courchamp, F., Langlais, M. & Sugihara, G. Rabbits killing birds: Modelling the hyperpredation process. J. Anim. Ecol. 69, 154–164 (2000).
Google Scholar 
Roemer, G. W., Coonan, T. J., Garcelon, D. K., Bascompte, J. & Laughrin, L. Feral pigs facilitate hyperpredation by golden eagles and indirectly cause the decline of the island fox. Anim. Conserv. 4, 307–318 (2001).
Google Scholar 
Kristan, W. B. & Boarman, W. I. Spatial patterns of risk of common raven predation on desert tortoises. Ecology 84, 2432–2443 (2003).
Google Scholar 
Whelan, C. J., Brown, J. S. & Maina, G. Search biases, frequency-dependent predation and species co-existence. Evol. Ecol. Res. 5, 329–343 (2003).
Google Scholar 
Moleón, M., Almaraz, P. & Sánchez-Zapata, J. A. An emerging infectious disease triggering large-scale hyperpredation. PLoS ONE 3, e2307 (2008).ADS 
PubMed 
PubMed Central 

Google Scholar 
Moleón, M., Almaraz, P. & Sánchez-Zapata, J. A. Inferring ecological mechanisms from hunting bag data in wildlife management: A reply to blanco-aguiar et al. 2012. Eur. J. Wildl. Res. 59, 599–608 (2013).
Google Scholar 
Bate, A. M. & Hilker, F. M. Rabbits protecting birds: Hypopredation and limitations of hyperpredation. J. Theor. Biol. 297, 103–115 (2012).ADS 
MathSciNet 
PubMed 
MATH 

Google Scholar 
Turner, F. B., Medica, P. A. & Lyons, C. L. Reproduction and survival of the desert tortoise (Scaptochelys agassizii) in Ivanpah Valley California. Copeia 1984(4), 811–820 (1984).
Google Scholar 
Graciá, E. et al. Assessment of the key evolutionary traits that prevent extinctions in human altered habitats using a spatially explicit individual-based model. Ecol. Model. 415, 108823 (2020).
Google Scholar 
Segura, A., Jiménez, J. & Acevedo, P. Predation of young tortoises by rabbits: The effect of habitat structure on tortoise detectability and abundance. Sci. Rep. 10, 1–9 (2020).
Google Scholar 
Watson, J. The golden eagle (Bloomsbury Publishing, 2010).
Google Scholar 
Fischer, W., Zenker, D. & Baumgart, W. Ein beitrag zum bestand und zur ernährung des steinadlers Aquila chrysaetos af der balkanhalbinsel. Beiträge zur Vogelskunde 21, 275–287 (1975).
Google Scholar 
Delibes, M., Calderón, J. & Hiraldo, F. Selección de presa y alimentación en españa del águila real (Aquila chrysaetos). Ardeola 21, 285–303 (1975).
Google Scholar 
Handrinos, G. The Golden Eagle in Greece. Actes 1er Coll. Intern. Aigle Royal en Europe, Arvieux, 1986: 18–22 (1987).Bautista, J., Gil-Sánchez, J. M. & Moleón, M. Dieta del águila real en el sur de españa. Quercus 364, 17–23 (2016).
Google Scholar 
Bautista, J., Castillo, S., Paz, J. L., Llamas, J. & Ellis, D. H. Golden eagles (Aquila chrysaetos) as potential predators of barbary macaques (Macaca sylvanus) in northern Morocco: Evidences of predation. Go-South Bull. 15, 172–179 (2018).
Google Scholar 
Kouzmanov, G., Stoyanov, R. & Todorov, V. Sur la biologie et la Protection de l`Aigle royal Aquila chrysaetos en Bulgarie. In Eagle studies (eds Meyburg, B. & Chancellor, R.) 505–516 (World Working Group on Birds of Prey, 1996).
Google Scholar 
Capper, S. The predation of Testudo spp. By Golden Eagles Aquila chrysaetos in Dadia Forest Reserve, NE Greece. University of Reading (1998).Karyakin, I. V., Kovalenko, A. V., Levin, A. S. & Pazhenkov, A. S. Eagles of the Aral-Caspian region Kazakhstan. Raptors Conserv. 22, 92–152 (2011).
Google Scholar 
Papageorgiou, N., Vlachos, C., Bakaloudis, D. E., Kazaklis, A., Birtsas, P. Study on the biology and management of raptors in Dadia forest–Evros. Thessaloniki, GR (1995).Sidiropoulos, L. et al. Pronounced seasonal diet diversity expansion of golden eagles (Aquila chrysaetos) in Northern Greece during the non-breeding season: The role of tortoises. Diversity 14(2), 135 (2022).
Google Scholar 
IUCN. The IUCN red list of threatened species. Version 2020–3 (2020).Graciá, E. et al. Expansion after expansion: dissecting the phylogeography of the widely distributed spur-thighed tortoise, Testudo graeca (Testudines: Testudinidae). Biol. J. Linn. Soc. 121, 641–654 (2017).
Google Scholar 
Graciá, E. et al. Genetic patterns of a range expansion: The spur-thighed tortoise Testudo graeca graeca in southeastern Spain. Amphib. Reptil. 32, 49–61 (2011).
Google Scholar 
Graciá, E. et al. The uncertainty of late pleistocene range expansions in the western Mediterranean: A case study of the colonization of south-eastern Spain by the spur-thighed tortoise, Testudo graeca.. J. Biogeogr 40, 323–334 (2013).
Google Scholar 
Anadón, J. D., Giménez, A., Perez, I., Martinez, M. & Esteve-Selma, M. A. Habitat selection by the spur-thighed tortoise Testudo graeca in a multisuccessional landscape: implications for habitat management. Biodivers. Conserv. 15, 2287–2299 (2006).
Google Scholar 
Rodríguez-Caro, R. C. et al. Low tortoise abundances in pine forest plantations in forest-shrubland transition areas. PLoS ONE 12, e0173485 (2017).PubMed 
PubMed Central 

Google Scholar 
Rodríguez-Caro, R. C. et al. The limits of demographic buffering in coping with environmental variation. Oikos 130(8), 1346–1358 (2021).
Google Scholar 
Rodríguez-Caro, R. C., Lima, M., Anadón, J. D., Graciá, E. & Giménez, A. Density dependence, climate and fires determine population fluctuations of the spur-thighed tortoise, Testudo graeca. J. Zool. 300, 265–273 (2016).
Google Scholar 
Rodríguez-Caro, R. C. et al. A low cost approach to estimate demographic rates using inverse modeling. Biol. Conserv. 237, 358–365 (2019).
Google Scholar 
Jiménez-Franco, M. V. et al. Sperm storage reduces the strength of the mate-finding allee effect. Ecol. Evol. 10(4), 1938–1948 (2020).PubMed 
PubMed Central 

Google Scholar 
Graciá, E. et al. From troubles to solutions: Conservation of mediterranean tortoises under global change. Basic Appl. Herpetol. 34, 5–16 (2020).
Google Scholar 
Pérez, I. et al. Exurban sprawl increases the extinction probability of a threatened tortoise due to pet collections. Ecol. Model. 245, 19–30 (2012).
Google Scholar 
Del Moral, J. C. El águila real en España. Población reproductora en 2008 y método de censo. SEO/BirdLife. Madrid. pp. 30–50 (2009).Virgós, E., Cabezas-Díaz, S. & Lozano, J. Is the wild rabbit (Oryctolagus cuniculus) a threatened species in Spain? Sociological constraints in the conservation of species. Biodivers. Conserv. 16, 3489–3504 (2007).
Google Scholar 
Fernández, C. Effect of the viral haemorrhagic pneumonia of the wild rabbit on the diet and breeding success of the golden eagle Aquila chrysaetos (L.). Rev. Ecol. Terre et Vie 48, 323–329 (1993).
Google Scholar 
Villafuerte, R., Luco, D. F., Gortázar, C. & Blanco, J. C. Effect on red fox litter size and diet after rabbit haemorrhagic disease in northeastern Spain. J. Zool. 240, 764–767 (1996).
Google Scholar 
Martínez, J. A. & Zuberogoitia, I. The response of eagle owl (Bubo bubo) to an outbreak of the rabbit haemorrhagic disease. J. Ornithol. 142, 204–211 (2001).
Google Scholar 
Moleón, M. et al. Large-scale spatiotemporal shifts in the diet of a predator mediated by an emerging infectious disease of its main prey. J. Biogeogr. 36, 1502–1515 (2009).
Google Scholar 
Adamakopoulos, T., Gatzoyannis, S., Poirazidis, K. Study on the assessment, the enhancement of the legal infrastructure and the management of the protected area in the forest of Dadia. Specific Environmental Study, WWF-Greece, Athens (1995).Delibes, M., Hiraldo, F. The rabbit as prey in the Iberian Mediterranean ecosystem. In Proceedings of the World Lagomorph Conference. Guelph: University of Guelph. 1979: 614–622 (1979).Futuyma, D. J. & Moreno, G. The evolution of ecological specialization. Annu. Rev. Ecol. Syst. 19, 207–233 (1988).
Google Scholar 
Moleón, M. et al. Predator–prey relationships in a mediterranean vertebrate system: Bonelli’s eagles, rabbits and partridges. Oecologia 168, 679–689 (2012).ADS 
PubMed 

Google Scholar 
Fedriani, J. M., Ferreras, P. & Delibes, M. Dietary response of the Eurasian badger, Meles meles, to a decline of its main prey in the Doñana national park. J. Zool. 245, 214–218 (1998).
Google Scholar 
Ferrer, M. & Negro, J. J. The near extinction of two large European predators: Super specialists pay a price. Conserv. Biol. 18, 344–349 (2004).
Google Scholar 
Lozano, J., Moleón, M. & Virgós, E. Biogeographical patterns in the diet of the wildcat, Felis silvestris Schreber, in Eurasia: Factors affecting the trophic diversity. J. Biogeogr. 33, 1076–1085 (2006).
Google Scholar 
Burgos, T. et al. Prey density determines the faecal-marking behaviour of a solitary predator, the Iberian lynx (Lynx pardinus). Ethol. Ecol. Evol. 31, 219–230 (2019).
Google Scholar 
Ontiveros, D. & Pleguezuelos, J. M. Influence of prey densities in the distribution and breeding success of Bonelli’s eagle (Hieraaetus fasciatus): Management implications. Biol. Conserv. 93, 19–25 (2000).
Google Scholar 
Araújo, M. S. & Gonzaga, M. O. Individual specialization in the hunting wasp Trypoxylon (Trypargilum) albonigrum (Hymenoptera, Crabronidae). Behav. Ecol. Sociobiol. 61, 1855–1863 (2007).
Google Scholar 
Stephens, D. W. & Krebs, J. R. Foraging Theory 1st edn. (Monographs in Behavior and Ecology. Princeton University Press, 1986).
Google Scholar 
Heath, J. A. et al. Golden Eagle dietary shifts following wildfire and shrub loss have negative consequences for nestling survivorship. Ornithol. Appl. 123(4), duabo34 (2021).
Google Scholar 
Anadón, J. D., Wiegand, T. & Giménez, A. Individual-based movement models reveal sex-biased effects of landscape fragmentation on animal movement. Ecosphere 3, 1–32 (2012).
Google Scholar 
Sanz-Aguilar, A., Anadón, J. D., Giménez, A., Ballestar, R. & Oro, D. Coexisting with fire: The case of the terrestrial tortoise Testudo graeca in mediterranean shrublands. Biol. Conserv. 144, 1040–1049 (2011).
Google Scholar 
Arroyo, B. Águila real – Aquila chrysaetos. In: Enciclopedia Virtual de los Vertebrados Españoles. Salvador, A., Morales, M. B. (Eds.). Museo Nacional de Ciencias Naturales, Madrid. http://www.vertebradosibericos.org/ (2017).Arroyo, B., Ferreiro, E., Garza, V. El águila real (Aquila chrysaetos) en España. Censo, distribución, reproducción y conservación. Serie Técnica, ICONA. Madrid (1990).Bautista, J., Gil-Sánchez, J. M., González Miras, E., Gómez, G. J. & Sánchez Balsera, J. L. Increase in the population of golden eagle in andalusian baetic system mountain ranges (southern of Spain): evidences of competition with the Bonelli’s eagle. Quercus 332, 16–22 (2013).
Google Scholar 
Rodríguez-Caro, R. C., Graciá, E., Anadón, J. D. & Giménez, A. Maintained effects of fire on individual growth and survival rates in a spur-thighed tortoise population. Eur. J. Wildl. Res. 59, 911–913 (2013).
Google Scholar 
Beissinger, S. R. & McCullough, D. R. Population viability analysis (University of Chicago Press, 2002).
Google Scholar 
Tylianakis, J. M., Didham, R. K., Bascompte, J. & Wardle, D. A. Global change and species interactions in terrestrial ecosystems. Ecol. Lett. 11, 1351–1363 (2008).PubMed 

Google Scholar 
Real, J. Biases in diet study methods in the Bonelli’s eagle. J. Wildl. Manag. 60(3), 632–638 (1996).
Google Scholar 
Moleón, M. et al. Laying the foundations for a human-predator conflict solution: Assessing the impact of Bonelli’s eagle on rabbits and partridges. PLoS ONE 6, e22851 (2011).ADS 
PubMed 
PubMed Central 

Google Scholar 
Esteve-Selma, M. A., et al. Effects of climate change on the potential distribution of Testudo graeca in southeastern Iberian Peninsula. In Graciá E, Rodríguez-Caro RC and Giménez A. Conservation of Mediterranean tortoises under global change. Madrid. Asociación Herpetológica Española. ISBN: 978-84-921999-6-9.Anadón, J. D., Giménez, A., Ballestar, R. & Pérez, I. Evaluation of local ecological knowledge as a method for collecting extensive data on animal abundance. Conserv. Biol. 23, 617–625 (2009).PubMed 

Google Scholar 
Abad, V. Variaciones del Índice corporal en una población de tortuga mora (Testudo graeca) del Sureste Ibérico. MSc thesis, Universidad Miguel Hernández de Elche, Spain (2007).Linden, H., Wikman, M. Goshawk predation on tetraonids: Availability of prey and diet of the predator in the breeding season. J. Anim. Ecol., 953–968 (1983).Fevold, H. R. & Craighead, J. J. Food requirements of the golden eagle. Auk 75, 312–317 (1958).
Google Scholar 
Collopy, M. W. Food consumption and growth energetics of nestling golden eagles. Wilson Bull. 445–458 (1986).Blanco, J. C., Villafuerte, R. Factores ecológicos que influyen sobre las poblaciones de conejos. Efectos de la enfermedad hemorrágico vírica. TRAGSA, Madrid Spain (1993). More

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript. More